diff options
332 files changed, 5918 insertions, 3309 deletions
diff --git a/tensorflow/compiler/aot/codegen.cc b/tensorflow/compiler/aot/codegen.cc index 2b1ce34b37..b17bc658fa 100644 --- a/tensorflow/compiler/aot/codegen.cc +++ b/tensorflow/compiler/aot/codegen.cc @@ -20,6 +20,7 @@ limitations under the License. #include <vector> #include "absl/memory/memory.h" +#include "absl/strings/str_cat.h" #include "absl/strings/str_join.h" #include "absl/strings/str_replace.h" #include "absl/types/span.h" @@ -31,7 +32,6 @@ limitations under the License. #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/lib/core/errors.h" -#include "tensorflow/core/lib/strings/strcat.h" namespace tensorflow { namespace tfcompile { @@ -135,12 +135,12 @@ Status AddRewritesForShape(int i, const xla::Shape& shape, indices = "[0]"; } else { for (int dim = 0; dim < shape.dimensions_size(); ++dim) { - dim_vars.push_back(strings::StrCat("size_t dim", dim)); - dim_sizes += strings::StrCat("[", shape.dimensions(dim), "]"); - indices += strings::StrCat("[dim", dim, "]"); + dim_vars.push_back(absl::StrCat("size_t dim", dim)); + dim_sizes += absl::StrCat("[", shape.dimensions(dim), "]"); + indices += absl::StrCat("[dim", dim, "]"); } } - rewrites->push_back({"{{I}}", strings::StrCat(i)}); + rewrites->push_back({"{{I}}", absl::StrCat(i)}); rewrites->push_back({"{{TYPE}}", type}); rewrites->push_back({"{{DIM_VARS}}", absl::StrJoin(dim_vars, ", ")}); rewrites->push_back({"{{DIM_SIZES}}", dim_sizes}); @@ -194,7 +194,7 @@ Status GenArgMethods(const tf2xla::Config& config, const xla::ProgramShape& ps, arg_data({{I}}))){{INDICES}}; } )"; - *methods += RewriteWithName(strings::StrCat(i), code, rewrites); + *methods += RewriteWithName(absl::StrCat(i), code, rewrites); if (!config.feed(i).name().empty()) { *methods += RewriteWithName("_" + config.feed(i).name(), code, rewrites); } @@ -235,7 +235,7 @@ Status GenResultMethods(const tf2xla::Config& config, result_data({{I}}))){{INDICES}}; } )"; - *methods += RewriteWithName(strings::StrCat(i), code, rewrites); + *methods += RewriteWithName(absl::StrCat(i), code, rewrites); if (!config.fetch(i).name().empty()) { *methods += RewriteWithName("_" + config.fetch(i).name(), code, rewrites); } @@ -304,8 +304,8 @@ std::vector<string> BufferInfosToCppExpression( string encoded_second_as_str = encoded.second == ~0ULL ? "~0ULL" - : strings::StrCat(encoded.second, "ULL"); - return strings::StrCat( + : absl::StrCat(encoded.second, "ULL"); + return absl::StrCat( "::tensorflow::cpu_function_runtime::BufferInfo({", encoded.first, "ULL, ", encoded_second_as_str, "})"); }); @@ -352,13 +352,13 @@ Status GenerateHeader(const CodegenOpts& opts, const tf2xla::Config& config, // Create rewrite strings for namespace start and end. string ns_start; for (const string& n : opts.namespaces) { - ns_start += strings::StrCat("namespace ", n, " {\n"); + ns_start += absl::StrCat("namespace ", n, " {\n"); } ns_start += "\n"; string ns_end("\n"); for (int i = opts.namespaces.size() - 1; i >= 0; --i) { const string& n = opts.namespaces[i]; - ns_end += strings::StrCat("} // end namespace ", n, "\n"); + ns_end += absl::StrCat("} // end namespace ", n, "\n"); } // Generate metadata. @@ -568,10 +568,10 @@ class {{CLASS}} : public tensorflow::XlaCompiledCpuFunction { )"; // The replacement strategy is naive, but good enough for our purposes. const std::vector<std::pair<string, string>> rewrites = { - {"{{ARG_BYTES_ALIGNED}}", strings::StrCat(arg_bytes_aligned)}, - {"{{ARG_BYTES_TOTAL}}", strings::StrCat(arg_bytes_total)}, + {"{{ARG_BYTES_ALIGNED}}", absl::StrCat(arg_bytes_aligned)}, + {"{{ARG_BYTES_TOTAL}}", absl::StrCat(arg_bytes_total)}, {"{{ARG_NAMES_CODE}}", arg_names_code}, - {"{{ARG_NUM}}", strings::StrCat(arg_index_table.size())}, + {"{{ARG_NUM}}", absl::StrCat(arg_index_table.size())}, {"{{ARG_INDEX_TABLE}}", absl::StrJoin(arg_index_table, ", ")}, {"{{ASSIGN_PROFILE_COUNTERS_SIZE}}", assign_profile_counters_size}, {"{{CLASS}}", opts.class_name}, @@ -590,11 +590,11 @@ class {{CLASS}} : public tensorflow::XlaCompiledCpuFunction { {"{{PROGRAM_SHAPE}}", xla::ShapeUtil::HumanString(ps)}, {"{{PROGRAM_SHAPE_SHIM_EXPRESSION}}", metadata_result.program_shape_access_shim}, - {"{{RESULT_INDEX}}", strings::StrCat(result_index)}, + {"{{RESULT_INDEX}}", absl::StrCat(result_index)}, {"{{RESULT_NAMES_CODE}}", result_names_code}, - {"{{TEMP_BYTES_ALIGNED}}", strings::StrCat(temp_bytes_aligned)}, - {"{{TEMP_BYTES_TOTAL}}", strings::StrCat(temp_bytes_total)}, - {"{{NUM_BUFFERS}}", strings::StrCat(buffer_infos.size())}, + {"{{TEMP_BYTES_ALIGNED}}", absl::StrCat(temp_bytes_aligned)}, + {"{{TEMP_BYTES_TOTAL}}", absl::StrCat(temp_bytes_total)}, + {"{{NUM_BUFFERS}}", absl::StrCat(buffer_infos.size())}, {"{{BUFFER_INFOS_AS_STRING}}", absl::StrJoin(buffer_infos_as_strings, ",\n")}}; absl::StrReplaceAll(rewrites, header); @@ -602,13 +602,13 @@ class {{CLASS}} : public tensorflow::XlaCompiledCpuFunction { } static string CreateUniqueIdentifier(const CodegenOpts& opts, - StringPiece suffix) { + absl::string_view suffix) { string result = "__tfcompile"; for (const string& n : opts.namespaces) { - strings::StrAppend(&result, "_", n); + absl::StrAppend(&result, "_", n); } - strings::StrAppend(&result, "_", opts.class_name, "_", suffix); + absl::StrAppend(&result, "_", opts.class_name, "_", suffix); return result; } @@ -678,7 +678,7 @@ Status ParseCppClass(const string& cpp_class, string* class_name, return Status::OK(); } -Status ValidateCppIdent(StringPiece ident, StringPiece msg) { +Status ValidateCppIdent(absl::string_view ident, absl::string_view msg) { if (ident.empty()) { return errors::InvalidArgument("empty identifier: ", msg); } diff --git a/tensorflow/compiler/aot/codegen.h b/tensorflow/compiler/aot/codegen.h index 83f2d3ee11..90410c46a8 100644 --- a/tensorflow/compiler/aot/codegen.h +++ b/tensorflow/compiler/aot/codegen.h @@ -19,9 +19,9 @@ limitations under the License. #include <string> #include <vector> +#include "absl/strings/string_view.h" #include "tensorflow/compiler/aot/compile.h" #include "tensorflow/compiler/tf2xla/tf2xla.pb.h" -#include "tensorflow/core/lib/core/stringpiece.h" namespace tensorflow { namespace tfcompile { @@ -96,7 +96,7 @@ Status ParseCppClass(const string& cpp_class, string* class_name, // ValidateCppIdent returns OK iff ident is a valid C++ identifier. The msg is // appended to error messages. -Status ValidateCppIdent(StringPiece ident, StringPiece msg); +Status ValidateCppIdent(absl::string_view ident, absl::string_view msg); } // namespace tfcompile } // namespace tensorflow diff --git a/tensorflow/compiler/aot/codegen_test.cc b/tensorflow/compiler/aot/codegen_test.cc index e3a53edb73..bb288d2300 100644 --- a/tensorflow/compiler/aot/codegen_test.cc +++ b/tensorflow/compiler/aot/codegen_test.cc @@ -19,11 +19,11 @@ limitations under the License. #include <vector> #include "absl/strings/match.h" +#include "absl/strings/string_view.h" #include "llvm/Support/TargetSelect.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/lib/core/status_test_util.h" -#include "tensorflow/core/lib/core/stringpiece.h" #include "tensorflow/core/lib/io/path.h" #include "tensorflow/core/platform/env.h" #include "tensorflow/core/platform/test.h" diff --git a/tensorflow/compiler/aot/embedded_protocol_buffers.cc b/tensorflow/compiler/aot/embedded_protocol_buffers.cc index f1e8e5c084..3c32d533f6 100644 --- a/tensorflow/compiler/aot/embedded_protocol_buffers.cc +++ b/tensorflow/compiler/aot/embedded_protocol_buffers.cc @@ -38,11 +38,11 @@ using xla::llvm_ir::AsStringRef; static void AddEmbeddedProtocolBufferToLlvmModule( llvm::Module* module, const ::tensorflow::protobuf::MessageLite& proto, - StringPiece unique_identifier, string* protobuf_array_symbol_name, + absl::string_view unique_identifier, string* protobuf_array_symbol_name, int64* protobuf_array_size) { string protobuf_array_contents = proto.SerializeAsString(); *protobuf_array_symbol_name = - strings::StrCat(unique_identifier, "_protobuf_array_contents"); + absl::StrCat(unique_identifier, "_protobuf_array_contents"); *protobuf_array_size = protobuf_array_contents.size(); llvm::Constant* protobuf_array_initializer = @@ -55,9 +55,9 @@ static void AddEmbeddedProtocolBufferToLlvmModule( protobuf_array_initializer, AsStringRef(*protobuf_array_symbol_name)); } -static string CreateCPPShimExpression(StringPiece qualified_cpp_protobuf_name, - StringPiece protobuf_array_symbol_name, - int64 protobuf_array_size) { +static string CreateCPPShimExpression( + absl::string_view qualified_cpp_protobuf_name, + absl::string_view protobuf_array_symbol_name, int64 protobuf_array_size) { string code = "[]() {\n" " {{PROTOBUF_NAME}}* proto = new {{PROTOBUF_NAME}};\n" @@ -68,9 +68,9 @@ static string CreateCPPShimExpression(StringPiece qualified_cpp_protobuf_name, return absl::StrReplaceAll( code, { - {"{{ARRAY_SYMBOL}}", strings::StrCat(protobuf_array_symbol_name)}, - {"{{ARRAY_SIZE}}", strings::StrCat(protobuf_array_size)}, - {"{{PROTOBUF_NAME}}", strings::StrCat(qualified_cpp_protobuf_name)}, + {"{{ARRAY_SYMBOL}}", absl::StrCat(protobuf_array_symbol_name)}, + {"{{ARRAY_SIZE}}", absl::StrCat(protobuf_array_size)}, + {"{{PROTOBUF_NAME}}", absl::StrCat(qualified_cpp_protobuf_name)}, }); } @@ -93,7 +93,7 @@ static StatusOr<string> CodegenModule(llvm::TargetMachine* target_machine, } static StatusOr<std::unique_ptr<llvm::TargetMachine>> -GetTargetMachineFromTriple(StringPiece target_triple) { +GetTargetMachineFromTriple(absl::string_view target_triple) { std::string error; std::string normalized_triple = llvm::Triple::normalize(AsStringRef(absl::string_view(target_triple))); @@ -110,7 +110,7 @@ GetTargetMachineFromTriple(StringPiece target_triple) { } StatusOr<EmbeddedProtocolBuffers> CreateEmbeddedProtocolBuffers( - StringPiece target_triple, + absl::string_view target_triple, absl::Span<const ProtobufToEmbed> protobufs_to_embed) { TF_ASSIGN_OR_RETURN(std::unique_ptr<llvm::TargetMachine> target_machine, GetTargetMachineFromTriple(target_triple)); @@ -135,8 +135,8 @@ StatusOr<EmbeddedProtocolBuffers> CreateEmbeddedProtocolBuffers( protobuf_to_embed.qualified_cpp_protobuf_name, protobuf_array_symbol_name, protobuf_array_size); - cpp_variable_decl = strings::StrCat("extern \"C\" char ", - protobuf_array_symbol_name, "[];"); + cpp_variable_decl = + absl::StrCat("extern \"C\" char ", protobuf_array_symbol_name, "[];"); } else { cpp_shim = "nullptr"; } diff --git a/tensorflow/compiler/aot/embedded_protocol_buffers.h b/tensorflow/compiler/aot/embedded_protocol_buffers.h index 4f940c0197..cf5c04ac4b 100644 --- a/tensorflow/compiler/aot/embedded_protocol_buffers.h +++ b/tensorflow/compiler/aot/embedded_protocol_buffers.h @@ -83,7 +83,7 @@ struct ProtobufToEmbed { // is stored in the object_file_data field in the returned // EmbeddedProtocolBuffers instance. StatusOr<EmbeddedProtocolBuffers> CreateEmbeddedProtocolBuffers( - StringPiece target_triple, + absl::string_view target_triple, absl::Span<const ProtobufToEmbed> protobufs_to_embed); } // namespace tfcompile diff --git a/tensorflow/compiler/aot/tests/BUILD b/tensorflow/compiler/aot/tests/BUILD index 723e9bec8a..8d94f5495c 100644 --- a/tensorflow/compiler/aot/tests/BUILD +++ b/tensorflow/compiler/aot/tests/BUILD @@ -67,7 +67,12 @@ genrule( "test_graph_tfmatmulandadd.pb", "test_graph_tfsplits.pb", ], - cmd = "$(location :make_test_graphs) --out_dir $(@D)", + # Set CUDA_VISIBLE_DEVICES='' to prevent the code we launch from using any + # GPUs which might be present. This is important because builds may run + # concurrently with tests, and tests need to be able to assume that they + # have control of the full GPU. + cmd = "CUDA_VISIBLE_DEVICES='' " + + "$(location :make_test_graphs) --out_dir $(@D)", tags = ["manual"], tools = [":make_test_graphs"], ) diff --git a/tensorflow/compiler/aot/tfcompile.bzl b/tensorflow/compiler/aot/tfcompile.bzl index 326f73b975..792b7fe14a 100644 --- a/tensorflow/compiler/aot/tfcompile.bzl +++ b/tensorflow/compiler/aot/tfcompile.bzl @@ -105,12 +105,18 @@ def tf_library( freeze_file = freeze_name + ".pb" # First run tfcompile to generate the list of out_nodes. + # + # Here and below, we set CUDA_VISIBLE_DEVICES='' to prevent the code we + # launch from using any GPUs which might be present. This is important + # because builds may run concurrently with tests, and tests need to be + # able to assume that they have control of the full GPU. out_nodes_file = "out_nodes_" + freeze_name native.genrule( name = ("gen_" + out_nodes_file), srcs = [config], outs = [out_nodes_file], - cmd = ("$(location " + tfcompile_tool + ")" + + cmd = ("CUDA_VISIBLE_DEVICES='' " + + "$(location " + tfcompile_tool + ")" + " --config=$(location " + config + ")" + " --dump_fetch_nodes > $@"), tools = [tfcompile_tool], @@ -142,9 +148,12 @@ def tf_library( out_nodes_file, ] + freeze_saver_srcs, outs = [freeze_file], - cmd = ("$(location " + - "//tensorflow/python/tools:freeze_graph)" + - freeze_args), + cmd = ( + "CUDA_VISIBLE_DEVICES='' " + + "$(location " + + "//tensorflow/python/tools:freeze_graph)" + + freeze_args + ), tools = ["//tensorflow/python/tools:freeze_graph"], tags = tags, ) @@ -177,16 +186,19 @@ def tf_library( metadata_object_file, function_object_file, ], - cmd = ("$(location " + tfcompile_tool + ")" + - " --graph=$(location " + tfcompile_graph + ")" + - " --config=$(location " + config + ")" + - " --entry_point=" + ep + - " --cpp_class=" + cpp_class + - " --target_triple=" + target_llvm_triple() + - " --out_header=$(@D)/" + header_file + - " --out_metadata_object=$(@D)/" + metadata_object_file + - " --out_function_object=$(@D)/" + function_object_file + - " " + flags + " " + profiling_flag), + cmd = ( + "CUDA_VISIBLE_DEVICES='' " + + "$(location " + tfcompile_tool + ")" + + " --graph=$(location " + tfcompile_graph + ")" + + " --config=$(location " + config + ")" + + " --entry_point=" + ep + + " --cpp_class=" + cpp_class + + " --target_triple=" + target_llvm_triple() + + " --out_header=$(@D)/" + header_file + + " --out_metadata_object=$(@D)/" + metadata_object_file + + " --out_function_object=$(@D)/" + function_object_file + + " " + flags + " " + profiling_flag + ), tools = [tfcompile_tool], visibility = visibility, testonly = testonly, @@ -216,14 +228,17 @@ def tf_library( outs = [ session_module_pb, ], - cmd = ("$(location " + tfcompile_tool + ")" + - " --graph=$(location " + tfcompile_graph + ")" + - " --config=$(location " + config + ")" + - " --entry_point=" + ep + - " --cpp_class=" + cpp_class + - " --target_triple=" + target_llvm_triple() + - " --out_session_module=$(@D)/" + session_module_pb + - " " + flags), + cmd = ( + "CUDA_VISIBLE_DEVICES='' " + + "$(location " + tfcompile_tool + ")" + + " --graph=$(location " + tfcompile_graph + ")" + + " --config=$(location " + config + ")" + + " --entry_point=" + ep + + " --cpp_class=" + cpp_class + + " --target_triple=" + target_llvm_triple() + + " --out_session_module=$(@D)/" + session_module_pb + + " " + flags + ), tools = [tfcompile_tool], visibility = visibility, testonly = testonly, diff --git a/tensorflow/compiler/aot/tfcompile_main.cc b/tensorflow/compiler/aot/tfcompile_main.cc index f3c44e9dda..b95b063348 100644 --- a/tensorflow/compiler/aot/tfcompile_main.cc +++ b/tensorflow/compiler/aot/tfcompile_main.cc @@ -20,6 +20,7 @@ limitations under the License. #include "absl/strings/match.h" #include "absl/strings/str_join.h" +#include "absl/strings/string_view.h" #include "tensorflow/compiler/aot/codegen.h" #include "tensorflow/compiler/aot/compile.h" #include "tensorflow/compiler/aot/flags.h" @@ -34,7 +35,6 @@ limitations under the License. #include "tensorflow/core/graph/graph.h" #include "tensorflow/core/graph/tensor_id.h" #include "tensorflow/core/lib/core/errors.h" -#include "tensorflow/core/lib/core/stringpiece.h" #include "tensorflow/core/lib/strings/numbers.h" #include "tensorflow/core/platform/env.h" #include "tensorflow/core/platform/init_main.h" @@ -92,8 +92,9 @@ Status Main(const MainFlags& flags) { // Write output files. Env* env = Env::Default(); const std::vector<char>& obj = compile_result.aot->object_file_data(); - TF_RETURN_IF_ERROR(WriteStringToFile(env, flags.out_function_object, - StringPiece(obj.data(), obj.size()))); + TF_RETURN_IF_ERROR( + WriteStringToFile(env, flags.out_function_object, + absl::string_view(obj.data(), obj.size()))); CodegenOpts codegen_opts; codegen_opts.gen_name_to_index = flags.gen_name_to_index; codegen_opts.gen_program_shape = flags.gen_program_shape; diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD index df81f3c23e..de7cd26d1d 100644 --- a/tensorflow/compiler/jit/BUILD +++ b/tensorflow/compiler/jit/BUILD @@ -410,6 +410,7 @@ cc_library( "//tensorflow/core:graph", "//tensorflow/core:protos_all_cc", "//tensorflow/core/kernels:bounds_check", + "@com_google_absl//absl/strings", "@com_google_absl//absl/types:optional", ], ) @@ -566,6 +567,7 @@ cc_library( "//tensorflow/core/grappler:grappler_item", "//tensorflow/core/grappler/optimizers:custom_graph_optimizer", "//tensorflow/core/grappler/optimizers:custom_graph_optimizer_registry", + "@com_google_absl//absl/strings", ], ) diff --git a/tensorflow/compiler/jit/deadness_analysis.cc b/tensorflow/compiler/jit/deadness_analysis.cc index 82aa03810b..9128b48da3 100644 --- a/tensorflow/compiler/jit/deadness_analysis.cc +++ b/tensorflow/compiler/jit/deadness_analysis.cc @@ -154,7 +154,7 @@ class AndPredicate : public Predicate { std::back_inserter(operands_str), [](Predicate* pred) { return pred->ToString(); }); - return strings::StrCat("(", absl::StrJoin(operands_str, " & "), ")"); + return absl::StrCat("(", absl::StrJoin(operands_str, " & "), ")"); } Kind kind() const override { return Kind::kAnd; } @@ -185,7 +185,7 @@ class OrPredicate : public Predicate { std::back_inserter(operands_str), [](Predicate* pred) { return pred->ToString(); }); - return strings::StrCat("(", absl::StrJoin(operands_str, " | "), ")"); + return absl::StrCat("(", absl::StrJoin(operands_str, " | "), ")"); } Kind kind() const override { return Kind::kOr; } @@ -206,7 +206,7 @@ class NotPredicate : public Predicate { operands_({operand}) {} string ToString() const override { - return strings::StrCat("~", operand()->ToString()); + return absl::StrCat("~", operand()->ToString()); } Kind kind() const override { return Kind::kNot; } @@ -240,8 +240,8 @@ class AndRecurrencePredicate : public Predicate { Predicate* step() const { return operands_[1]; } string ToString() const override { - return strings::StrCat("{", start()->ToString(), ",&,", step()->ToString(), - "}"); + return absl::StrCat("{", start()->ToString(), ",&,", step()->ToString(), + "}"); } Kind kind() const override { return Kind::kAndRecurrence; } @@ -267,7 +267,7 @@ class SymbolPredicate : public Predicate { must_be_true_(must_be_true) {} string ToString() const override { - return must_be_true() ? strings::StrCat("*", tensor_id_.ToString()) + return must_be_true() ? absl::StrCat("*", tensor_id_.ToString()) : tensor_id_.ToString(); } diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc index 2788102620..ae7a22f451 100644 --- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc +++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc @@ -22,6 +22,7 @@ limitations under the License. #include <unordered_map> #include <vector> +#include "absl/strings/str_cat.h" #include "tensorflow/compiler/jit/graphcycles/graphcycles.h" #include "tensorflow/compiler/jit/mark_for_compilation_pass.h" #include "tensorflow/compiler/jit/shape_inference_helpers.h" @@ -45,7 +46,6 @@ limitations under the License. #include "tensorflow/core/lib/gtl/flatset.h" #include "tensorflow/core/lib/gtl/map_util.h" #include "tensorflow/core/lib/hash/hash.h" -#include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/public/session_options.h" #include "tensorflow/core/public/version.h" #include "tensorflow/core/util/device_name_utils.h" @@ -755,7 +755,7 @@ Status Encapsulator::Subgraph::RecordArg( if (inserted) { NodeDef arg_def; NodeDefBuilder builder( - strings::StrCat(src_node->name(), "_", src_slot, "_arg"), kArgOp); + absl::StrCat(src_node->name(), "_", src_slot, "_arg"), kArgOp); DataType dtype = edge->dst()->input_type(edge->dst_input()); builder.Attr("T", dtype); builder.Attr("index", arg_index); @@ -790,7 +790,7 @@ Status Encapsulator::Subgraph::RecordResult( if (inserted) { NodeDef ret_def; NodeDefBuilder builder( - strings::StrCat(src_node->name(), "_", src_slot, "_retval"), kRetValOp); + absl::StrCat(src_node->name(), "_", src_slot, "_retval"), kRetValOp); DataType dtype = src_node->output_type(src_slot); builder.Attr("T", dtype); builder.Attr("index", ret_index); @@ -950,16 +950,15 @@ Status Encapsulator::Subgraph::AddHostComputes( } NodeDef host_compute_def; - NodeDefBuilder builder(strings::StrCat("outside_compilation_", - oc_subgraph_name, "_host_compute"), + NodeDefBuilder builder(absl::StrCat("outside_compilation_", + oc_subgraph_name, "_host_compute"), kHostComputeOp); builder.Input(inputs); builder.Attr("Tinputs", input_dtypes); builder.Attr("Toutputs", output_dtypes); builder.Attr("ancestors", host_compute_ancestors); - builder.Attr("key", - strings::StrCat("host_compute_channel_", subgraph_name, "_", - oc_subgraph_name)); + builder.Attr("key", absl::StrCat("host_compute_channel_", subgraph_name, + "_", oc_subgraph_name)); builder.Attr("_outside_compilation_subgraph", oc_subgraph_name); Status s = builder.Finalize(&host_compute_def); if (!s.ok()) return s; @@ -1017,8 +1016,7 @@ Status Encapsulator::Subgraph::MakeSequencingNode(const string& subgraph_name, Graph* graph_out) { if (sequencer_ == nullptr) { NodeDef seq_def; - NodeDefBuilder builder(strings::StrCat(subgraph_name, "_sequencer"), - "NoOp"); + NodeDefBuilder builder(absl::StrCat(subgraph_name, "_sequencer"), "NoOp"); builder.Attr(kXlaHostTransferSequencerAttr, subgraph_name); builder.Device(device_); Status s = builder.Finalize(&seq_def); @@ -1091,10 +1089,10 @@ Status Encapsulator::Subgraph::BuildFunctionDef( if (VLOG_IS_ON(1)) { VLOG(2) << "Build function def " << name; - dump_graph::DumpGraphToFile( - strings::StrCat("encapsulate_fdef_graph_", name), *graph_, library); - dump_graph::DumpFunctionDefToFile( - strings::StrCat("encapsulate_fdef_", name), fdef); + dump_graph::DumpGraphToFile(absl::StrCat("encapsulate_fdef_graph_", name), + *graph_, library); + dump_graph::DumpFunctionDefToFile(absl::StrCat("encapsulate_fdef_", name), + fdef); } if (!reuse_existing_functions || library->Find(name) == nullptr) { @@ -1130,8 +1128,8 @@ Status Encapsulator::Subgraph::AddShapeInferenceInfo( host_compute->AddAttr("shapes", shapes); } else { string inference_graph_name = - strings::StrCat("_outside_compilation_shape_inference_", subgraph_name, - "_", outside_compilation_subgraph_name); + absl::StrCat("_outside_compilation_shape_inference_", subgraph_name, + "_", outside_compilation_subgraph_name); FunctionDef fdef; TF_RETURN_IF_ERROR( GraphToFunctionDef(*inference_graph, inference_graph_name, &fdef)); @@ -1155,10 +1153,10 @@ Status Encapsulator::Subgraph::ReplaceFunctionDef( if (VLOG_IS_ON(1)) { VLOG(2) << "Replace function def " << name; dump_graph::DumpGraphToFile( - strings::StrCat("replace_encapsulate_fdef_graph_", name), *graph_, + absl::StrCat("replace_encapsulate_fdef_graph_", name), *graph_, library); dump_graph::DumpFunctionDefToFile( - strings::StrCat("replace_encapsulate_fdef_", name), fdef); + absl::StrCat("replace_encapsulate_fdef_", name), fdef); } TF_RETURN_IF_ERROR(library->ReplaceFunction(name, fdef)); @@ -1186,8 +1184,7 @@ Status Encapsulator::Subgraph::AddHostComputeKeyPlaceholder( GraphDefBuilder::Options options(graph_out, /*status=*/nullptr); NodeDef key_def; NodeDefBuilder builder( - strings::StrCat(call_node_def_.name(), "_key_placeholder"), - "Placeholder"); + absl::StrCat(call_node_def_.name(), "_key_placeholder"), "Placeholder"); builder.Attr("dtype", DT_STRING); builder.Attr("shape", shape_proto); builder.Attr("_host_compute_call_node", call_node_def_.name()); @@ -1221,16 +1218,16 @@ Status Encapsulator::Subgraph::AddRecvAtHostNode( } NodeDef recv_def; - NodeDefBuilder builder(strings::StrCat("outside_compilation_", subgraph_name, - "_", oc_subgraph_name, "_recv"), + NodeDefBuilder builder(absl::StrCat("outside_compilation_", subgraph_name, + "_", oc_subgraph_name, "_recv"), kRecvAtHostOp); builder.Device(device_); builder.Attr("Toutputs", dtypes); // The correct device_ordinal will be inserted during replication in a // subsequent rewrite. builder.Attr("device_ordinal", 0); - builder.Attr("key", strings::StrCat("host_compute_channel_", subgraph_name, - "_", oc_subgraph_name)); + builder.Attr("key", absl::StrCat("host_compute_channel_", subgraph_name, "_", + oc_subgraph_name)); builder.Attr(group_attribute, subgraph_name); builder.Attr(outside_compilation_attribute, oc_subgraph_name); builder.Input(host_compute_key_placeholder_->name(), 0, DT_STRING); @@ -1276,13 +1273,13 @@ Status Encapsulator::Subgraph::AddSendFromHostNode( } NodeDef send_def; - NodeDefBuilder builder(strings::StrCat("outside_compilation_", subgraph_name, - "_", oc_subgraph_name, "_send"), + NodeDefBuilder builder(absl::StrCat("outside_compilation_", subgraph_name, + "_", oc_subgraph_name, "_send"), kSendFromHostOp); builder.Device(device_); builder.Attr("Tinputs", dtypes); - builder.Attr("key", strings::StrCat("host_compute_channel_", subgraph_name, - "_", oc_subgraph_name)); + builder.Attr("key", absl::StrCat("host_compute_channel_", subgraph_name, "_", + oc_subgraph_name)); // The correct device_ordinal will be inserted during replication in a // subsequent rewrite. builder.Attr("device_ordinal", 0); @@ -1516,7 +1513,7 @@ Status Encapsulator::SplitIntoSubgraphs(FunctionLibraryDefinition* library) { // Dump subgraphs. for (auto& entry : subgraphs_) { dump_graph::DumpGraphToFile( - strings::StrCat("encapsulate_subgraphs_subgraph_", entry.first), + absl::StrCat("encapsulate_subgraphs_subgraph_", entry.first), *entry.second.GetGraph(), library); } } @@ -2052,7 +2049,7 @@ struct PathDetails { struct SubgraphAndClusterHash { inline std::size_t operator()(const SubgraphAndCluster& v) const { return hash<string>()( - strings::StrCat(v.subgraph, v.outside_compilation_cluster)); + absl::StrCat(v.subgraph, v.outside_compilation_cluster)); } }; diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc b/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc index 7bc0ef0303..49958093b8 100644 --- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc +++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc @@ -16,6 +16,7 @@ limitations under the License. #include <memory> #include <utility> +#include "absl/strings/str_cat.h" #include "tensorflow/compiler/jit/encapsulate_subgraphs_pass.h" #include "absl/strings/match.h" @@ -48,7 +49,7 @@ Status AddGraphDefToFunctionLibrary(const GraphDefBuilder& graphdef_builder, FunctionDef* fdef = library->add_function(); TF_RETURN_IF_ERROR(GraphToFunctionDef( *graph, - strings::StrCat("_outside_compilation_shape_inference_", name_suffix), + absl::StrCat("_outside_compilation_shape_inference_", name_suffix), fdef)); return Status::OK(); } @@ -65,18 +66,18 @@ bool EqualProtoMap(const ::tensorflow::protobuf::Map<Tkey, Tvalue>& a, const auto iter = b.find(elt_a.first); if (iter == b.end()) { if (diff) { - *diff = strings::StrCat( - map_name, " expected: contains element with key '", - key_to_string(elt_a.first), "' got: map has no such element"); + *diff = absl::StrCat(map_name, " expected: contains element with key '", + key_to_string(elt_a.first), + "' got: map has no such element"); } return false; } if (!compare(elt_a.first, elt_a.second, iter->second)) { if (diff) { - *diff = strings::StrCat(map_name, " expected: element with key '", - key_to_string(elt_a.first), "' has value '", - value_to_string(elt_a.second), "' got: '", - value_to_string(iter->second), "'"); + *diff = absl::StrCat(map_name, " expected: element with key '", + key_to_string(elt_a.first), "' has value '", + value_to_string(elt_a.second), "' got: '", + value_to_string(iter->second), "'"); } return false; } @@ -85,9 +86,9 @@ bool EqualProtoMap(const ::tensorflow::protobuf::Map<Tkey, Tvalue>& a, const auto iter = a.find(elt_b.first); if (iter == a.end()) { if (diff) { - *diff = strings::StrCat(map_name, " got: contains element with key '", - key_to_string(elt_b.first), - "' expected: map has no such element"); + *diff = absl::StrCat(map_name, " got: contains element with key '", + key_to_string(elt_b.first), + "' expected: map has no such element"); } return false; } @@ -99,25 +100,25 @@ bool EqualFunctionNodeDef(const NodeDef& a, const NodeDef& b, const string& diff_preamble, string* diff) { if (a.op() != b.op()) { if (diff) { - *diff = strings::StrCat(diff_preamble, " mismatch for node ", a.name(), - ", expected op '", a.op(), "' got '", b.op()); + *diff = absl::StrCat(diff_preamble, " mismatch for node ", a.name(), + ", expected op '", a.op(), "' got '", b.op()); } return false; } if (a.device() != b.device()) { if (diff) { - *diff = strings::StrCat(diff_preamble, " mismatch for node ", a.name(), - ", expected device '", a.device(), "' got '", - b.device()); + *diff = absl::StrCat(diff_preamble, " mismatch for node ", a.name(), + ", expected device '", a.device(), "' got '", + b.device()); } return false; } if (a.input_size() != b.input_size()) { if (diff) { - *diff = strings::StrCat(diff_preamble, " mismatch for node ", a.name(), - ", expected ", a.input_size(), " inputs got ", - b.input_size(), " expected:\n", a.DebugString(), - "\ngot:\n", b.DebugString()); + *diff = absl::StrCat(diff_preamble, " mismatch for node ", a.name(), + ", expected ", a.input_size(), " inputs got ", + b.input_size(), " expected:\n", a.DebugString(), + "\ngot:\n", b.DebugString()); } return false; } @@ -127,10 +128,10 @@ bool EqualFunctionNodeDef(const NodeDef& a, const NodeDef& b, if (absl::StartsWith(a.input(i), "^")) { if (!absl::StartsWith(b.input(i), "^")) { if (diff) { - *diff = strings::StrCat( - diff_preamble, " mismatch for node ", a.name(), " input ", i, - ", expected control input ", a.input(i), " got ", b.input(i), - " expected:\n", a.DebugString(), "\ngot:\n", b.DebugString()); + *diff = absl::StrCat(diff_preamble, " mismatch for node ", a.name(), + " input ", i, ", expected control input ", + a.input(i), " got ", b.input(i), " expected:\n", + a.DebugString(), "\ngot:\n", b.DebugString()); } return false; } @@ -138,19 +139,19 @@ bool EqualFunctionNodeDef(const NodeDef& a, const NodeDef& b, control_input_b.insert(b.input(i)); } else if (a.input(i) != b.input(i)) { if (diff) { - *diff = strings::StrCat(diff_preamble, " mismatch for node ", a.name(), - " input ", i, ", expected ", a.input(i), - " got ", b.input(i), " expected:\n", - a.DebugString(), "\ngot:\n", b.DebugString()); + *diff = absl::StrCat(diff_preamble, " mismatch for node ", a.name(), + " input ", i, ", expected ", a.input(i), " got ", + b.input(i), " expected:\n", a.DebugString(), + "\ngot:\n", b.DebugString()); } return false; } } if (control_input_a != control_input_b) { if (diff) { - *diff = strings::StrCat(diff_preamble, " mismatch for node ", a.name(), - " control inputs differ expected:\n", - a.DebugString(), "\ngot:\n", b.DebugString()); + *diff = absl::StrCat(diff_preamble, " mismatch for node ", a.name(), + " control inputs differ expected:\n", + a.DebugString(), "\ngot:\n", b.DebugString()); } return false; } @@ -170,18 +171,17 @@ bool EqualFunctionNodeDef(const NodeDef& a, const NodeDef& b, return av.DebugString() == bv.DebugString(); } }, - strings::StrCat(diff_preamble, " attr mismatch for node ", a.name()), - diff); + absl::StrCat(diff_preamble, " attr mismatch for node ", a.name()), diff); } bool EqualFunctionDef(const FunctionDef& a, const FunctionDef& b, string* diff) { if (a.signature().DebugString() != b.signature().DebugString()) { if (diff) { - *diff = strings::StrCat("Signature mismatch for function ", - a.signature().name(), ", expected:\n", - a.signature().DebugString(), "\ngot:\n", - b.signature().DebugString()); + *diff = + absl::StrCat("Signature mismatch for function ", a.signature().name(), + ", expected:\n", a.signature().DebugString(), "\ngot:\n", + b.signature().DebugString()); } return false; } @@ -191,7 +191,7 @@ bool EqualFunctionDef(const FunctionDef& a, const FunctionDef& b, [](const string& key, const AttrValue& av, const AttrValue& bv) { return av.DebugString() == bv.DebugString(); }, - strings::StrCat("attr mismatch for function ", a.signature().name()), + absl::StrCat("attr mismatch for function ", a.signature().name()), diff)) { return false; } @@ -201,7 +201,7 @@ bool EqualFunctionDef(const FunctionDef& a, const FunctionDef& b, [](const string& key, const string& av, const string& bv) { return av == bv; }, - strings::StrCat("ret mismatch for function ", a.signature().name()), + absl::StrCat("ret mismatch for function ", a.signature().name()), diff)) { return false; } @@ -211,7 +211,7 @@ bool EqualFunctionDef(const FunctionDef& a, const FunctionDef& b, if (a.node_def(i).name() == b.node_def(j).name()) { if (!EqualFunctionNodeDef( a.node_def(i), b.node_def(j), - strings::StrCat("Function ", a.signature().name()), diff)) { + absl::StrCat("Function ", a.signature().name()), diff)) { return false; } found = true; @@ -220,9 +220,9 @@ bool EqualFunctionDef(const FunctionDef& a, const FunctionDef& b, } if (!found) { if (diff) { - *diff = strings::StrCat("Function ", a.signature().name(), - ", expected: has node '", a.node_def(i).name(), - "' got: no node of that name"); + *diff = absl::StrCat("Function ", a.signature().name(), + ", expected: has node '", a.node_def(i).name(), + "' got: no node of that name"); } return false; } @@ -237,9 +237,9 @@ bool EqualFunctionDef(const FunctionDef& a, const FunctionDef& b, } if (!found) { if (diff) { - *diff = strings::StrCat("Function ", a.signature().name(), - ", got: has node '", b.node_def(i).name(), - "' expected: no node of that name"); + *diff = absl::StrCat("Function ", a.signature().name(), + ", got: has node '", b.node_def(i).name(), + "' expected: no node of that name"); } return false; } @@ -258,8 +258,8 @@ bool EqualFunctionDefLibrary(const FunctionDefLibrary& expected, auto it = actual_index.find(expected_function.signature().name()); if (it == actual_index.end()) { if (diff) { - *diff = strings::StrCat("Did not find expected function '", - expected_function.signature().name(), "'"); + *diff = absl::StrCat("Did not find expected function '", + expected_function.signature().name(), "'"); } return false; } @@ -269,9 +269,9 @@ bool EqualFunctionDefLibrary(const FunctionDefLibrary& expected, if (!actual_index.empty()) { if (diff != nullptr) { - *diff = strings::StrCat("Found unexpected function '", - actual_index.begin()->second->signature().name(), - "'"); + *diff = + absl::StrCat("Found unexpected function '", + actual_index.begin()->second->signature().name(), "'"); } return false; } @@ -420,10 +420,9 @@ Node* RecvAtHost(ops::NodeOut key_input, const string& cluster, const string& oc_cluster, absl::Span<const DataType> dtypes, const GraphDefBuilder::Options& opts) { if (opts.HaveError()) return nullptr; - string key = - strings::StrCat("host_compute_channel_", cluster, "_", oc_cluster); - string name = strings::StrCat("outside_compilation_", cluster, "_", - oc_cluster, "_recv"); + string key = absl::StrCat("host_compute_channel_", cluster, "_", oc_cluster); + string name = + absl::StrCat("outside_compilation_", cluster, "_", oc_cluster, "_recv"); NodeBuilder node_builder(opts.WithName(name).GetNameForOp("_XlaRecvAtHost"), "_XlaRecvAtHost", opts.op_registry()); node_builder.Input(std::move(key_input)); @@ -440,10 +439,9 @@ Node* SendFromHost(ops::NodeOut key_input, const string& cluster, const std::vector<ops::NodeOut>& inputs, const GraphDefBuilder::Options& opts) { if (opts.HaveError()) return nullptr; - string key = - strings::StrCat("host_compute_channel_", cluster, "_", oc_cluster); - string name = strings::StrCat("outside_compilation_", cluster, "_", - oc_cluster, "_send"); + string key = absl::StrCat("host_compute_channel_", cluster, "_", oc_cluster); + string name = + absl::StrCat("outside_compilation_", cluster, "_", oc_cluster, "_send"); NodeBuilder node_builder(opts.WithName(name).GetNameForOp("_XlaSendFromHost"), "_XlaSendFromHost", opts.op_registry()); node_builder.Input(inputs); @@ -682,8 +680,8 @@ std::vector<std::pair<string, string>> GraphEdges(const Graph& graph) { for (const Edge* edge : graph.edges()) { if (edge->src()->IsSource() || edge->dst()->IsSink()) continue; edges.emplace_back( - strings::StrCat(edge->src()->name(), ":", edge->src_output()), - strings::StrCat(edge->dst()->name(), ":", edge->dst_input())); + absl::StrCat(edge->src()->name(), ":", edge->src_output()), + absl::StrCat(edge->dst()->name(), ":", edge->dst_input())); } std::sort(edges.begin(), edges.end()); return edges; diff --git a/tensorflow/compiler/jit/graphcycles/BUILD b/tensorflow/compiler/jit/graphcycles/BUILD index 676f71a75a..8212956adf 100644 --- a/tensorflow/compiler/jit/graphcycles/BUILD +++ b/tensorflow/compiler/jit/graphcycles/BUILD @@ -14,6 +14,7 @@ cc_library( hdrs = ["graphcycles.h"], deps = [ "//tensorflow/core:lib", + "@com_google_absl//absl/container:inlined_vector", ], ) diff --git a/tensorflow/compiler/jit/graphcycles/graphcycles.cc b/tensorflow/compiler/jit/graphcycles/graphcycles.cc index 805bbc62c1..756377bd95 100644 --- a/tensorflow/compiler/jit/graphcycles/graphcycles.cc +++ b/tensorflow/compiler/jit/graphcycles/graphcycles.cc @@ -34,7 +34,7 @@ limitations under the License. #include <algorithm> #include <unordered_set> -#include "tensorflow/core/lib/gtl/inlined_vector.h" +#include "absl/container/inlined_vector.h" #include "tensorflow/core/platform/logging.h" namespace tensorflow { @@ -44,7 +44,7 @@ namespace { typedef std::unordered_set<int32> NodeSet; template <typename T> struct VecStruct { - typedef gtl::InlinedVector<T, 4> type; + typedef absl::InlinedVector<T, 4> type; }; template <typename T> using Vec = typename VecStruct<T>::type; diff --git a/tensorflow/compiler/jit/mark_for_compilation_pass.cc b/tensorflow/compiler/jit/mark_for_compilation_pass.cc index 4e4abade32..44caf0be52 100644 --- a/tensorflow/compiler/jit/mark_for_compilation_pass.cc +++ b/tensorflow/compiler/jit/mark_for_compilation_pass.cc @@ -43,7 +43,6 @@ limitations under the License. #include "tensorflow/core/kernels/bounds_check.h" #include "tensorflow/core/lib/gtl/cleanup.h" #include "tensorflow/core/lib/gtl/flatset.h" -#include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/public/version.h" @@ -617,7 +616,7 @@ Status MarkForCompilationPass::Run( } static string RatioToString(int numerator, int denominator) { - return strings::Printf("%d / %d (%.2f%%)", numerator, denominator, + return absl::StrFormat("%d / %d (%.2f%%)", numerator, denominator, (100.0 * numerator) / denominator); } @@ -626,14 +625,14 @@ static void VLogClusteringSummary(const Graph& g) { return; } - std::map<StringPiece, int> cluster_name_to_size; - std::map<StringPiece, std::map<StringPiece, int>> + std::map<absl::string_view, int> cluster_name_to_size; + std::map<absl::string_view, std::map<absl::string_view, int>> cluster_name_to_op_histogram; - std::map<StringPiece, int> unclustered_op_histogram; + std::map<absl::string_view, int> unclustered_op_histogram; int clustered_node_count = 0; for (Node* n : g.nodes()) { - absl::optional<StringPiece> cluster_name = GetXlaClusterForNode(*n); + absl::optional<absl::string_view> cluster_name = GetXlaClusterForNode(*n); if (cluster_name) { clustered_node_count++; cluster_name_to_size[*cluster_name]++; @@ -650,7 +649,7 @@ static void VLogClusteringSummary(const Graph& g) { << RatioToString(clustered_node_count, g.num_nodes()); for (const auto& cluster_name_size_pair : cluster_name_to_size) { - StringPiece cluster_name = cluster_name_size_pair.first; + absl::string_view cluster_name = cluster_name_size_pair.first; int size = cluster_name_size_pair.second; VLOG(2) << " " << cluster_name << " " << RatioToString(size, g.num_nodes()); @@ -670,14 +669,15 @@ static void VLogClusteringSummary(const Graph& g) { } struct EdgeInfo { - StringPiece node_name; - absl::optional<StringPiece> cluster_name; + absl::string_view node_name; + absl::optional<absl::string_view> cluster_name; - StringPiece GetClusterName() const { + absl::string_view GetClusterName() const { return cluster_name ? *cluster_name : "[none]"; } - std::pair<StringPiece, absl::optional<StringPiece>> AsPair() const { + std::pair<absl::string_view, absl::optional<absl::string_view>> AsPair() + const { return {node_name, cluster_name}; } @@ -686,19 +686,21 @@ static void VLogClusteringSummary(const Graph& g) { } }; - using EdgeInfoMap = std::map<StringPiece, std::map<EdgeInfo, int64>>; + using EdgeInfoMap = std::map<absl::string_view, std::map<EdgeInfo, int64>>; EdgeInfoMap incoming_edge_infos; EdgeInfoMap outgoing_edge_infos; - std::set<StringPiece> cluster_names_to_print; + std::set<absl::string_view> cluster_names_to_print; for (const Edge* e : g.edges()) { const Node* from = e->src(); - absl::optional<StringPiece> from_cluster_name = GetXlaClusterForNode(*from); + absl::optional<absl::string_view> from_cluster_name = + GetXlaClusterForNode(*from); const Node* to = e->dst(); - absl::optional<StringPiece> to_cluster_name = GetXlaClusterForNode(*to); + absl::optional<absl::string_view> to_cluster_name = + GetXlaClusterForNode(*to); if (to_cluster_name == from_cluster_name) { continue; @@ -721,9 +723,9 @@ static void VLogClusteringSummary(const Graph& g) { VLOG(2) << " [none]"; } - auto print_edge_info_set_for_cluster = [&](StringPiece cluster_name, + auto print_edge_info_set_for_cluster = [&](absl::string_view cluster_name, const EdgeInfoMap& edge_info_map, - StringPiece desc) { + absl::string_view desc) { auto it = edge_info_map.find(cluster_name); if (it != edge_info_map.end()) { VLOG(2) << " " << it->second.size() << " " << desc << " edges"; @@ -737,7 +739,7 @@ static void VLogClusteringSummary(const Graph& g) { } }; - for (StringPiece cluster_name : cluster_names_to_print) { + for (absl::string_view cluster_name : cluster_names_to_print) { VLOG(2) << " ** Cluster " << cluster_name; print_edge_info_set_for_cluster(cluster_name, incoming_edge_infos, "incoming"); @@ -966,7 +968,7 @@ Status MarkForCompilationPass::RunImpl( string& name = cluster_names[cluster]; if (name.empty()) { - name = strings::StrCat("cluster_", cluster_sequence_num++); + name = absl::StrCat("cluster_", cluster_sequence_num++); } n->AddAttr(kXlaClusterAttr, name); VLOG(3) << "Assigning node " << n->name() << " to cluster " << name; diff --git a/tensorflow/compiler/jit/mark_for_compilation_pass_test.cc b/tensorflow/compiler/jit/mark_for_compilation_pass_test.cc index 807ab51fd3..9473ac0a4c 100644 --- a/tensorflow/compiler/jit/mark_for_compilation_pass_test.cc +++ b/tensorflow/compiler/jit/mark_for_compilation_pass_test.cc @@ -633,7 +633,7 @@ TEST(XlaCompilationTest, IllegalCycle_UsefulErrorMessage) { std::unique_ptr<Graph> graph(new Graph(OpRegistry::Global())); Scope root = Scope::NewRootScope().ExitOnError(); { - auto BuildNoopNode = [](StringPiece name, Graph* graph) { + auto BuildNoopNode = [](absl::string_view name, Graph* graph) { NodeDefBuilder builder(name, "NoOp"); NodeDef def; TF_CHECK_OK(builder.Finalize(&def)); diff --git a/tensorflow/compiler/jit/partially_decluster_pass.cc b/tensorflow/compiler/jit/partially_decluster_pass.cc index a8f09bfa50..584c963f71 100644 --- a/tensorflow/compiler/jit/partially_decluster_pass.cc +++ b/tensorflow/compiler/jit/partially_decluster_pass.cc @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/jit/partially_decluster_pass.h" +#include "absl/strings/str_cat.h" #include "tensorflow/compiler/jit/xla_cluster_util.h" #include "tensorflow/core/framework/memory_types.h" #include "tensorflow/core/framework/node_def.pb.h" @@ -30,7 +31,7 @@ Status FindNodesToDecluster(const Graph& graph, gtl::FlatSet<Node*>* result, MemoryTypeVector input_mtypes, output_mtypes; for (Node* n : post_order) { - absl::optional<StringPiece> from_cluster = GetXlaClusterForNode(*n); + absl::optional<absl::string_view> from_cluster = GetXlaClusterForNode(*n); if (!from_cluster) { continue; } @@ -79,7 +80,7 @@ Status FindNodesToDecluster(const Graph& graph, gtl::FlatSet<Node*>* result, // Check if `dst` is in a different cluster, unclustered, or about to be // partially declustered (here we rely on the post-order traversal order). // If yes, decluster `n` to avoid the device-to-host memcpy. - absl::optional<StringPiece> dst_cluster = + absl::optional<absl::string_view> dst_cluster = result->count(dst) ? absl::nullopt : GetXlaClusterForNode(*dst); if (from_cluster != dst_cluster) { CHECK(result->insert(n).second); @@ -91,15 +92,16 @@ Status FindNodesToDecluster(const Graph& graph, gtl::FlatSet<Node*>* result, } Status PartiallyDeclusterNode(Graph* graph, Node* n) { - StringPiece cluster_name = *GetXlaClusterForNode(*n); - gtl::InlinedVector<const Edge*, 6> out_edges_to_clone; + absl::string_view cluster_name = *GetXlaClusterForNode(*n); + absl::InlinedVector<const Edge*, 6> out_edges_to_clone; for (const Edge* out_edge : n->out_edges()) { if (out_edge->IsControlEdge()) { continue; } Node* dst = out_edge->dst(); - absl::optional<StringPiece> dst_cluster_name = GetXlaClusterForNode(*dst); + absl::optional<absl::string_view> dst_cluster_name = + GetXlaClusterForNode(*dst); if (dst_cluster_name != cluster_name) { out_edges_to_clone.push_back(out_edge); } @@ -108,7 +110,7 @@ Status PartiallyDeclusterNode(Graph* graph, Node* n) { CHECK(!out_edges_to_clone.empty()) << n->DebugString(); NodeDef ndef = n->def(); - ndef.set_name(strings::StrCat(n->name(), "/declustered")); + ndef.set_name(absl::StrCat(n->name(), "/declustered")); RemoveFromXlaCluster(&ndef); Status s; Node* cloned_node = graph->AddNode(ndef, &s); diff --git a/tensorflow/compiler/jit/resource_operation_safety_analysis.cc b/tensorflow/compiler/jit/resource_operation_safety_analysis.cc index 1ba4a5ef73..56e35c0059 100644 --- a/tensorflow/compiler/jit/resource_operation_safety_analysis.cc +++ b/tensorflow/compiler/jit/resource_operation_safety_analysis.cc @@ -165,7 +165,7 @@ bool IsEdgeSafe(XlaResourceOpKind from, XlaResourceOpKind to) { using ResourceOp = std::pair<int, XlaResourceOpKind>; string ResourceOpToString(const ResourceOp& resource_op) { - return strings::StrCat( + return absl::StrCat( resource_op.first, ": ", XlaResourceOpInfo::XlaResourceOpKindToString(resource_op.second)); } @@ -257,11 +257,11 @@ string ResourceOpSetToString(const ResourceOpSet& resource_op_set) { std::vector<string> elements_debug_string; std::transform(resource_op_set.begin(), resource_op_set.end(), std::back_inserter(elements_debug_string), ResourceOpToString); - return strings::StrCat("{", absl::StrJoin(elements_debug_string, ","), "}"); + return absl::StrCat("{", absl::StrJoin(elements_debug_string, ","), "}"); } string NodeToString(const Node& n, XlaResourceOpKind resource_op_kind) { - return strings::StrCat( + return absl::StrCat( "[", n.name(), ": ", n.type_string(), "(", XlaResourceOpInfo::XlaResourceOpKindToString(resource_op_kind), ")", "]"); } diff --git a/tensorflow/compiler/jit/xla_cluster_util.cc b/tensorflow/compiler/jit/xla_cluster_util.cc index 4f2fabd658..03380e9406 100644 --- a/tensorflow/compiler/jit/xla_cluster_util.cc +++ b/tensorflow/compiler/jit/xla_cluster_util.cc @@ -17,6 +17,7 @@ limitations under the License. #include <unordered_map> +#include "absl/strings/str_cat.h" #include "tensorflow/compiler/jit/resource_operation_safety_analysis.h" #include "tensorflow/core/framework/node_def.pb.h" #include "tensorflow/core/graph/control_flow.h" @@ -52,8 +53,8 @@ string DescribeCycle(const GraphCycles* cycles, const Graph& graph, int src, }; string description; - strings::StrAppend(&description, "Edge from ", node_name(src), " to ", - node_name(dst), " would create a cycle.\n"); + absl::StrAppend(&description, "Edge from ", node_name(src), " to ", + node_name(dst), " would create a cycle.\n"); path.resize(path_size); for (int32 node_id : path) { string ascii_art; @@ -64,7 +65,7 @@ string DescribeCycle(const GraphCycles* cycles, const Graph& graph, int src, } else { ascii_art = "+-- "; } - strings::StrAppend(&description, ascii_art, node_name(node_id), "\n"); + absl::StrAppend(&description, ascii_art, node_name(node_id), "\n"); } return description; } @@ -186,7 +187,7 @@ Status CreateCycleDetectionGraph(const Graph* graph, GraphCycles* cycles) { return Status::OK(); } -absl::optional<StringPiece> GetXlaClusterForNode(const Node& node) { +absl::optional<absl::string_view> GetXlaClusterForNode(const Node& node) { const AttrValue* attr_value = node.attrs().Find(kXlaClusterAttr); if (attr_value == nullptr) { return absl::nullopt; diff --git a/tensorflow/compiler/jit/xla_cluster_util.h b/tensorflow/compiler/jit/xla_cluster_util.h index b0439a63ca..17ae510a0e 100644 --- a/tensorflow/compiler/jit/xla_cluster_util.h +++ b/tensorflow/compiler/jit/xla_cluster_util.h @@ -47,7 +47,7 @@ Status CreateCycleDetectionGraph(const Graph* graph, GraphCycles* cycles); // Returns the XLA cluster in which `node` is placed if it is in an XLA cluster, // otherwise returns nullopt. -absl::optional<StringPiece> GetXlaClusterForNode(const Node& node); +absl::optional<absl::string_view> GetXlaClusterForNode(const Node& node); // Removes `node_def` its XLA cluster (by clearing its _XlaCluster attribute). void RemoveFromXlaCluster(NodeDef* node_def); diff --git a/tensorflow/compiler/jit/xla_compilation_cache.cc b/tensorflow/compiler/jit/xla_compilation_cache.cc index ef6b0e67d3..3aa9e9c7ed 100644 --- a/tensorflow/compiler/jit/xla_compilation_cache.cc +++ b/tensorflow/compiler/jit/xla_compilation_cache.cc @@ -67,12 +67,12 @@ string XlaCompilationCache::DebugString() { string XlaCompilationCache::SignatureDebugString(const Signature& sig) { string result = sig.name; for (const auto& a : sig.arg_types) { - strings::StrAppend(&result, ",", DataTypeString(a.first), - a.second.DebugString()); + absl::StrAppend(&result, ",", DataTypeString(a.first), + a.second.DebugString()); } for (const auto& v : sig.arg_values) { - strings::StrAppend(&result, "; ", v.DebugString()); + absl::StrAppend(&result, "; ", v.DebugString()); } return result; } @@ -259,7 +259,7 @@ Status XlaCompilationCache::CompileImpl( const XlaCompiler::CompileOptions& compile_options, bool compile_single_op) { CHECK_NE(executable, nullptr); - VLOG(1) << "XlaCompilationCache::Compile " << DebugString(); + VLOG(2) << "XlaCompilationCache::Compile " << DebugString(); if (VLOG_IS_ON(2)) { VLOG(2) << "num_inputs=" << ctx->num_inputs() @@ -310,7 +310,7 @@ Status XlaCompilationCache::CompileImpl( // cache eviction. mutex_lock entry_lock(entry->mu); if (!entry->compiled) { - VLOG(1) << "Compilation cache miss for signature: " + VLOG(2) << "Compilation cache miss for signature: " << SignatureDebugString(signature); tensorflow::Env* env = tensorflow::Env::Default(); const uint64 compile_start_us = env->NowMicros(); diff --git a/tensorflow/compiler/jit/xla_device.cc b/tensorflow/compiler/jit/xla_device.cc index f31879a2bc..51797def04 100644 --- a/tensorflow/compiler/jit/xla_device.cc +++ b/tensorflow/compiler/jit/xla_device.cc @@ -148,10 +148,9 @@ Status DefaultPaddedShapeFn(const Tensor& tensor, xla::Shape* shape) { } const DeviceAttributes attrs = Device::BuildDeviceAttributes( - strings::StrCat(name_prefix, "/device:", device_name, ":", - device_ordinal), + absl::StrCat(name_prefix, "/device:", device_name, ":", device_ordinal), DeviceType(device_name), Bytes(16ULL << 30), DeviceLocality(), - strings::StrCat("device: ", device_name, " device")); + absl::StrCat("device: ", device_name, " device")); device->reset( new XlaDevice(options, attrs, device_ordinal, DeviceType(jit_device_name), diff --git a/tensorflow/compiler/jit/xla_device_context.cc b/tensorflow/compiler/jit/xla_device_context.cc index ee07c5c964..af83c792e5 100644 --- a/tensorflow/compiler/jit/xla_device_context.cc +++ b/tensorflow/compiler/jit/xla_device_context.cc @@ -203,7 +203,7 @@ void XlaTransferManager::CopyCPUTensorToDevice(const Tensor* cpu_tensor, } void XlaTransferManager::CopyDeviceTensorToCPU(const Tensor* device_tensor, - StringPiece tensor_name, + absl::string_view tensor_name, Device* device, Tensor* cpu_tensor, StatusCallback done) { @@ -339,7 +339,7 @@ void XlaDeviceContext::CopyCPUTensorToDevice(const Tensor* cpu_tensor, } void XlaDeviceContext::CopyDeviceTensorToCPU(const Tensor* device_tensor, - StringPiece tensor_name, + absl::string_view tensor_name, Device* device, Tensor* cpu_tensor, StatusCallback done) { manager_.CopyDeviceTensorToCPU(device_tensor, tensor_name, device, cpu_tensor, diff --git a/tensorflow/compiler/jit/xla_device_context.h b/tensorflow/compiler/jit/xla_device_context.h index 2e7445340c..df82421294 100644 --- a/tensorflow/compiler/jit/xla_device_context.h +++ b/tensorflow/compiler/jit/xla_device_context.h @@ -57,7 +57,7 @@ class XlaTransferManager { void CopyCPUTensorToDevice(const Tensor* cpu_tensor, Device* device, Tensor* device_tensor, StatusCallback done) const; void CopyDeviceTensorToCPU(const Tensor* device_tensor, - StringPiece tensor_name, Device* device, + absl::string_view tensor_name, Device* device, Tensor* cpu_tensor, StatusCallback done); void CopyDeviceTensorToDevice(const Tensor& src_tensor, Tensor* dst_tensor, @@ -111,7 +111,7 @@ class XlaDeviceContext : public DeviceContext { Tensor* device_tensor, StatusCallback done) const override; void CopyDeviceTensorToCPU(const Tensor* device_tensor, - StringPiece tensor_name, Device* device, + absl::string_view tensor_name, Device* device, Tensor* cpu_tensor, StatusCallback done) override; void CopyDeviceTensorToDevice(const Tensor& src_tensor, Tensor* dst_tensor, const StatusCallback& done); diff --git a/tensorflow/compiler/jit/xla_device_ops.h b/tensorflow/compiler/jit/xla_device_ops.h index 13da5d2f94..49c8582682 100644 --- a/tensorflow/compiler/jit/xla_device_ops.h +++ b/tensorflow/compiler/jit/xla_device_ops.h @@ -198,33 +198,33 @@ class XlaAssignVariableOp : public AsyncOpKernel { \ REGISTER_KERNEL_BUILDER( \ Name("GeneratorDataset").Device(DEVICE).HostMemory("handle"), \ - GeneratorDatasetOp); \ + data::GeneratorDatasetOp); \ REGISTER_KERNEL_BUILDER(Name("PrefetchDataset") \ .Device(DEVICE) \ .HostMemory("buffer_size") \ .HostMemory("input_dataset") \ .HostMemory("handle"), \ - PrefetchDatasetOp); \ + data::PrefetchDatasetOp); \ \ REGISTER_KERNEL_BUILDER(Name("IteratorV2").Device(DEVICE), \ - IteratorHandleOp); \ + data::IteratorHandleOp); \ REGISTER_KERNEL_BUILDER( \ Name("MakeIterator").Device(DEVICE).HostMemory("dataset"), \ - MakeIteratorOp); \ + data::MakeIteratorOp); \ REGISTER_KERNEL_BUILDER(Name("AnonymousIterator").Device(DEVICE), \ - AnonymousIteratorHandleOp); \ + data::AnonymousIteratorHandleOp); \ REGISTER_KERNEL_BUILDER(Name("IteratorGetNext").Device(DEVICE), \ - IteratorGetNextOp); \ + data::IteratorGetNextOp); \ REGISTER_KERNEL_BUILDER(Name("IteratorGetNextSync").Device(DEVICE), \ - IteratorGetNextSyncOp); \ + data::IteratorGetNextSyncOp); \ REGISTER_KERNEL_BUILDER(Name("IteratorToStringHandle") \ .Device(DEVICE) \ .HostMemory("string_handle"), \ - IteratorToStringHandleOp); \ + data::IteratorToStringHandleOp); \ REGISTER_KERNEL_BUILDER(Name("IteratorFromStringHandleV2") \ .Device(DEVICE) \ .HostMemory("string_handle"), \ - IteratorFromStringHandleOp); \ + data::IteratorFromStringHandleOp); \ REGISTER_KERNEL_BUILDER(Name(FunctionLibraryDefinition::kArgOp) \ .Device(DEVICE) \ .HostMemory("output") \ diff --git a/tensorflow/compiler/jit/xla_fusion_optimizer.cc b/tensorflow/compiler/jit/xla_fusion_optimizer.cc index 07cfab6151..bc0db558d8 100644 --- a/tensorflow/compiler/jit/xla_fusion_optimizer.cc +++ b/tensorflow/compiler/jit/xla_fusion_optimizer.cc @@ -20,6 +20,7 @@ limitations under the License. #include <unordered_map> #include <unordered_set> +#include "absl/strings/str_cat.h" #include "tensorflow/compiler/jit/deadness_analysis.h" #include "tensorflow/compiler/jit/defs.h" #include "tensorflow/compiler/jit/graphcycles/graphcycles.h" @@ -326,7 +327,7 @@ Status XlaFusionOptimizer::Optimize(grappler::Cluster* cluster, string& name = cluster_names[cluster]; if (name.empty()) { - name = strings::StrCat("cluster_", cluster_sequence_num++); + name = absl::StrCat("cluster_", cluster_sequence_num++); } n->AddAttr(kXlaClusterAttr, name); VLOG(3) << "Assigning node " << n->name() << " to cluster " << name; diff --git a/tensorflow/compiler/jit/xla_tensor.h b/tensorflow/compiler/jit/xla_tensor.h index 4c9bb2e27b..d95da63405 100644 --- a/tensorflow/compiler/jit/xla_tensor.h +++ b/tensorflow/compiler/jit/xla_tensor.h @@ -122,7 +122,7 @@ class XlaTensor { std::shared_ptr<se::Event> definition_event_; // A list of all streams for which the tensor's content is defined for any // newly enqueued command. - gtl::InlinedVector<se::Stream*, 2> streams_defined_on_ GUARDED_BY(mu_); + absl::InlinedVector<se::Stream*, 2> streams_defined_on_ GUARDED_BY(mu_); mutex mu_; }; diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD index 34defe1c7a..050d827a09 100644 --- a/tensorflow/compiler/tests/BUILD +++ b/tensorflow/compiler/tests/BUILD @@ -1103,6 +1103,7 @@ cc_library( "//tensorflow/core:test", "//tensorflow/core:testlib", "//tensorflow/core/kernels:ops_util", + "@com_google_absl//absl/strings", ], ) diff --git a/tensorflow/compiler/tests/randomized_tests.cc b/tensorflow/compiler/tests/randomized_tests.cc index 0faf0fd8ed..bddda6f302 100644 --- a/tensorflow/compiler/tests/randomized_tests.cc +++ b/tensorflow/compiler/tests/randomized_tests.cc @@ -45,6 +45,8 @@ limitations under the License. #include <random> #include <unordered_map> +#include "absl/strings/str_cat.h" +#include "absl/strings/string_view.h" #include "tensorflow/compiler/jit/defs.h" #include "tensorflow/compiler/tf2xla/type_util.h" #include "tensorflow/core/common_runtime/device.h" @@ -61,7 +63,6 @@ limitations under the License. #include "tensorflow/core/kernels/ops_util.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/lib/core/status_test_util.h" -#include "tensorflow/core/lib/core/stringpiece.h" #include "tensorflow/core/lib/gtl/flatset.h" #include "tensorflow/core/platform/test.h" #include "tensorflow/core/public/session.h" @@ -81,7 +82,7 @@ string* tf_xla_test_device_ptr; // initial value set in main() bool tf_xla_test_use_jit = true; string LocalDeviceToFullDeviceName(const string& device) { - return strings::StrCat("/job:localhost/replica:0/task:0/device:", device); + return absl::StrCat("/job:localhost/replica:0/task:0/device:", device); } constexpr std::array<DataType, 5> kAllXlaTypes = { @@ -107,11 +108,12 @@ class OpTestBuilder { // Sets an attribute. template <class T> - OpTestBuilder& Attr(StringPiece attr_name, T&& value); + OpTestBuilder& Attr(absl::string_view attr_name, T&& value); // Overload needed to allow {...} expressions for value. template <class T> - OpTestBuilder& Attr(StringPiece attr_name, std::initializer_list<T> value); + OpTestBuilder& Attr(absl::string_view attr_name, + std::initializer_list<T> value); // Adds nodes that executes the operator under test on 'device' to 'graphdef'. // If 'use_jit' is true, marks the operator under test to be compiled by XLA. @@ -185,13 +187,13 @@ OpTestBuilder& OpTestBuilder::RandomUniqueInput(DataType type, } template <class T> -OpTestBuilder& OpTestBuilder::Attr(StringPiece attr_name, T&& value) { +OpTestBuilder& OpTestBuilder::Attr(absl::string_view attr_name, T&& value) { AddNodeAttr(attr_name, std::forward<T>(value), &node_def_); return *this; } template <class T> -OpTestBuilder& OpTestBuilder::Attr(StringPiece attr_name, +OpTestBuilder& OpTestBuilder::Attr(absl::string_view attr_name, std::initializer_list<T> value) { Attr<std::initializer_list<T>>(attr_name, std::move(value)); return *this; @@ -209,7 +211,7 @@ Status OpTestBuilder::BuildGraph(const string& name_prefix, NodeDef* test_def = graphdef->add_node(); *test_def = node_def_; - test_def->set_name(strings::StrCat(name_prefix, "_op_under_test")); + test_def->set_name(absl::StrCat(name_prefix, "_op_under_test")); test_def->set_device(device); AddDefaultsToNodeDef(*op_def, test_def); if (use_jit) { @@ -224,7 +226,7 @@ Status OpTestBuilder::BuildGraph(const string& name_prefix, // Build feed and fetch nodes. for (int i = 0; i < input_types.size(); ++i) { NodeDef* def = graphdef->add_node(); - string name = strings::StrCat(name_prefix, "_input_", i); + string name = absl::StrCat(name_prefix, "_input_", i); TF_RETURN_IF_ERROR(NodeDefBuilder(name, "Placeholder") .Device(device) .Attr("dtype", input_types[i]) @@ -235,7 +237,7 @@ Status OpTestBuilder::BuildGraph(const string& name_prefix, for (int i = 0; i < output_types.size(); ++i) { NodeDef* def = graphdef->add_node(); - string name = strings::StrCat(name_prefix, "_output_", i); + string name = absl::StrCat(name_prefix, "_output_", i); TF_RETURN_IF_ERROR(NodeDefBuilder(name, "Identity") .Device(device) .Attr("T", output_types[i]) @@ -726,11 +728,11 @@ bool IsClose<complex64>(const complex64& x, const complex64& y, double atol, template <typename T> string Str(T x) { - return strings::StrCat(x); + return absl::StrCat(x); } template <> string Str<complex64>(complex64 x) { - return strings::StrCat("(", x.real(), ", ", x.imag(), ")"); + return absl::StrCat("(", x.real(), ", ", x.imag(), ")"); } template <typename T> @@ -740,11 +742,11 @@ Status TensorsAreCloseImpl(const Tensor& x, const Tensor& y, double atol, auto Ty = y.flat<T>(); for (int i = 0; i < Tx.size(); ++i) { if (!IsClose(Tx(i), Ty(i), atol, rtol)) { - return errors::InvalidArgument(strings::StrCat( - i, "-th tensor element isn't close: ", Str(Tx(i)), " vs. ", - Str(Ty(i)), ". x = ", x.DebugString(), "y = ", y.DebugString(), - "atol = ", atol, " rtol = ", rtol, - " tol = ", atol + rtol * Abs(Tx(i)))); + return errors::InvalidArgument( + absl::StrCat(i, "-th tensor element isn't close: ", Str(Tx(i)), + " vs. ", Str(Ty(i)), ". x = ", x.DebugString(), + "y = ", y.DebugString(), "atol = ", atol, + " rtol = ", rtol, " tol = ", atol + rtol * Abs(Tx(i)))); } } return Status::OK(); @@ -756,7 +758,7 @@ Status TensorsAreEqualImpl(const Tensor& x, const Tensor& y) { auto Ty = y.flat<T>(); for (int i = 0; i < Tx.size(); ++i) { if (Tx(i) != Ty(i)) { - return errors::InvalidArgument(strings::StrCat( + return errors::InvalidArgument(absl::StrCat( i, "-th tensor element isn't equal: ", Tx(i), " vs. ", Ty(i), ". x = ", x.DebugString(), "y = ", y.DebugString())); } @@ -771,14 +773,14 @@ Status TensorsAreEqualImpl(const Tensor& x, const Tensor& y) { Status TensorsAreClose(const Tensor& a, const Tensor& b, double atol, double rtol) { if (a.dtype() != b.dtype()) { - return errors::InvalidArgument(strings::StrCat( + return errors::InvalidArgument(absl::StrCat( "Tensors have different types: ", DataTypeString(a.dtype()), " and ", DataTypeString(b.dtype()))); } if (!a.IsSameSize(b)) { - return errors::InvalidArgument(strings::StrCat( - "Tensors have different shapes: ", a.shape().DebugString(), " and ", - b.shape().DebugString())); + return errors::InvalidArgument( + absl::StrCat("Tensors have different shapes: ", a.shape().DebugString(), + " and ", b.shape().DebugString())); } switch (a.dtype()) { @@ -827,7 +829,7 @@ OpTest::TestResult OpTest::ExpectTfAndXlaOutputsAreClose( } string cpu_device = - LocalDeviceToFullDeviceName(strings::StrCat(DEVICE_CPU, ":0")); + LocalDeviceToFullDeviceName(absl::StrCat(DEVICE_CPU, ":0")); string test_device = LocalDeviceToFullDeviceName(*tf_xla_test_device_ptr); DeviceNameUtils::ParsedName parsed_name; @@ -842,7 +844,7 @@ OpTest::TestResult OpTest::ExpectTfAndXlaOutputsAreClose( std::vector<string> expected_inputs, test_inputs; std::vector<string> expected_fetches, test_fetches; Status status = builder.BuildGraph( - strings::StrCat("test", num_tests_, "_expected"), cpu_device, + absl::StrCat("test", num_tests_, "_expected"), cpu_device, /* use_jit= */ false, &graph, /* test_node_def= */ nullptr, &expected_inputs, &expected_fetches); if (!status.ok()) { @@ -851,7 +853,7 @@ OpTest::TestResult OpTest::ExpectTfAndXlaOutputsAreClose( } NodeDef* node_def; - status = builder.BuildGraph(strings::StrCat("test", num_tests_, "_test"), + status = builder.BuildGraph(absl::StrCat("test", num_tests_, "_test"), test_device, tf_xla_test_use_jit, &graph, &node_def, &test_inputs, &test_fetches); if (!status.ok()) { diff --git a/tensorflow/compiler/tests/xla_ops_test.py b/tensorflow/compiler/tests/xla_ops_test.py index b2f026df6c..3f928a1bea 100644 --- a/tensorflow/compiler/tests/xla_ops_test.py +++ b/tensorflow/compiler/tests/xla_ops_test.py @@ -97,9 +97,9 @@ class XlaOpsTest(xla_test.XLATestCase, parameterized.TestCase): args=(np.array([0xFFFFFFFF, 16], dtype=np.uint32), np.uint32(4)), expected=np.array([0xFFFFFFFF, 1], dtype=np.uint32)) - PRECISION_VALUES = (None, xla_data_pb2.PrecisionConfigProto.DEFAULT, - xla_data_pb2.PrecisionConfigProto.HIGH, - xla_data_pb2.PrecisionConfigProto.HIGHEST) + PRECISION_VALUES = (None, xla_data_pb2.PrecisionConfig.DEFAULT, + xla_data_pb2.PrecisionConfig.HIGH, + xla_data_pb2.PrecisionConfig.HIGHEST) @parameterized.parameters(*PRECISION_VALUES) def testConv(self, precision): @@ -120,7 +120,7 @@ class XlaOpsTest(xla_test.XLATestCase, parameterized.TestCase): dnums.output_spatial_dimensions.extend(range(2, 2 + num_spatial_dims)) precision_config = None if precision: - precision_config = xla_data_pb2.PrecisionConfigProto() + precision_config = xla_data_pb2.PrecisionConfig() precision_config.operand_precision.extend([precision, precision]) return xla.conv( lhs, @@ -151,7 +151,7 @@ class XlaOpsTest(xla_test.XLATestCase, parameterized.TestCase): dnums.rhs_batch_dimensions.append(0) precision_config = None if precision: - precision_config = xla_data_pb2.PrecisionConfigProto() + precision_config = xla_data_pb2.PrecisionConfig() precision_config.operand_precision.extend([precision, precision]) return xla.dot_general( lhs, diff --git a/tensorflow/compiler/tf2xla/BUILD b/tensorflow/compiler/tf2xla/BUILD index 0797b2cb17..22be7f048f 100644 --- a/tensorflow/compiler/tf2xla/BUILD +++ b/tensorflow/compiler/tf2xla/BUILD @@ -291,6 +291,7 @@ cc_library( "//tensorflow/core:graph", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", + "@com_google_absl//absl/strings", "@com_google_absl//absl/types:optional", ], ) @@ -433,6 +434,7 @@ cc_library( "//tensorflow/core:framework_internal", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", + "@com_google_absl//absl/strings", ], ) @@ -609,11 +611,10 @@ cc_library( srcs = ["resource_operation_table.cc"], hdrs = ["resource_operation_table.h"], deps = [ - "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core:ops", - "//tensorflow/core:protos_all_cc", "@com_google_absl//absl/algorithm:container", + "@com_google_absl//absl/strings", ], ) diff --git a/tensorflow/compiler/tf2xla/dump_graph.cc b/tensorflow/compiler/tf2xla/dump_graph.cc index 24616c01c7..380c6a7e23 100644 --- a/tensorflow/compiler/tf2xla/dump_graph.cc +++ b/tensorflow/compiler/tf2xla/dump_graph.cc @@ -18,8 +18,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/dump_graph.h" +#include "absl/strings/str_cat.h" #include "tensorflow/compiler/tf2xla/dump_graph_flags.h" -#include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/platform/env.h" #include "tensorflow/core/platform/mutex.h" @@ -52,9 +52,9 @@ string MakeUniqueFilename(string name) { string filename = name; if (count > 0) { - strings::StrAppend(&filename, "_", count); + absl::StrAppend(&filename, "_", count); } - strings::StrAppend(&filename, ".pbtxt"); + absl::StrAppend(&filename, ".pbtxt"); return filename; } @@ -69,7 +69,7 @@ string WriteTextProtoToUniqueFile( << proto_type << ": " << status; return "(unavailable)"; } - string filepath = strings::StrCat(dirname, "/", MakeUniqueFilename(name)); + string filepath = absl::StrCat(dirname, "/", MakeUniqueFilename(name)); status = WriteTextProto(Env::Default(), filepath, proto); if (!status.ok()) { LOG(WARNING) << "Failed to dump " << proto_type << " to file: " << filepath diff --git a/tensorflow/compiler/tf2xla/functionalize_cond.cc b/tensorflow/compiler/tf2xla/functionalize_cond.cc index e2affee51f..0911550f1f 100644 --- a/tensorflow/compiler/tf2xla/functionalize_cond.cc +++ b/tensorflow/compiler/tf2xla/functionalize_cond.cc @@ -42,7 +42,7 @@ namespace functionalize_cond { // TODO(jpienaar): Move to OutputTensor. string DebugString(const OutputTensor& tensor) { - return strings::StrCat(tensor.node->name(), ":", tensor.index); + return absl::StrCat(tensor.node->name(), ":", tensor.index); } string Branch_Name(BranchType b) { @@ -61,17 +61,17 @@ string Branch_Name(BranchType b) { string DebugString(StateMap::CondId cond_state) { if (cond_state == nullptr || cond_state->empty()) return "{}"; using value_type = StateMap::CondState::value_type; - return strings::StrCat( + return absl::StrCat( "{", absl::StrJoin(*cond_state, ", ", [](string* output, const value_type& pred_branch) { const OutputTensor& pred = pred_branch.first; const BranchType& branch = pred_branch.second; if (branch == BranchType::kNeither) - strings::StrAppend(output, "d"); + absl::StrAppend(output, "d"); else - strings::StrAppend(output, "s(", DebugString(pred), ",", - Branch_Name(branch), ")"); + absl::StrAppend(output, "s(", DebugString(pred), ",", + Branch_Name(branch), ")"); }), "}"); } @@ -159,8 +159,8 @@ struct CondArgNode { : src(src), src_output(src_output) {} string ToString() const { - return strings::StrCat("src=", src->name(), ":", src_output, - " switches=", NodesToString(switches)); + return absl::StrCat("src=", src->name(), ":", src_output, + " switches=", NodesToString(switches)); } Node* src; @@ -171,11 +171,11 @@ struct CondArgNode { using CondArgNodes = std::vector<CondArgNode>; string DebugString(const CondArgNodes& nodes) { - return strings::StrCat( + return absl::StrCat( "[", absl::StrJoin(nodes, ", ", [](string* output, const CondArgNode& node) { - strings::StrAppend(output, node.ToString()); + absl::StrAppend(output, node.ToString()); }), "]"); } @@ -373,7 +373,7 @@ Status Conditional::BuildArgumentNodes() { for (auto branch : {BranchType::kElseBranch, BranchType::kThenBranch}) { int branch_index = static_cast<int>(branch); TF_RETURN_IF_ERROR( - NodeBuilder(strings::StrCat("_Arg", arg_count), + NodeBuilder(absl::StrCat("_Arg", arg_count), FunctionLibraryDefinition::kArgOp) .Attr("T", dtype) .Attr("index", arg_count) @@ -441,7 +441,7 @@ Status Conditional::AddSwitchNodeAlongEdge(const Edge* edge, BranchType branch, Node* src = edge->src(); int src_output = edge->src_output(); TF_RETURN_IF_ERROR( - NodeBuilder(graph->NewName(strings::StrCat(src->name(), "_added_switch")), + NodeBuilder(graph->NewName(absl::StrCat(src->name(), "_added_switch")), "Switch") .Input(src, src_output) .Input(const_cast<Node*>(predicate_.node), predicate_.index) @@ -650,8 +650,8 @@ Status Conditional::BuildIfNode(Graph* graph, int64 id = ++sequence_num; NameAttrList body_name; - body_name.set_name(strings::StrCat("_functionalize_if_", - branch_name[branch_index], "_", id)); + body_name.set_name( + absl::StrCat("_functionalize_if_", branch_name[branch_index], "_", id)); VLOG(3) << "FunctionalizeControlFlow (" << branch_name[branch_index] << "): " @@ -804,7 +804,7 @@ Status Conditional::BuildAndReplace(Graph* graph, string Conditional::name() const { CHECK(!merges_.empty()); - return strings::StrCat((*merges_.begin())->name(), "_if"); + return absl::StrCat((*merges_.begin())->name(), "_if"); } Status FunctionalizeCond::AddIdentityNode(const Node* replacee, Node* if_node, @@ -1327,12 +1327,12 @@ void FunctionalizeCond::DumpGraphWithCondState(const string& name) { for (Node* n : graph_->nodes()) { n->ClearAttr(kCondGroupDebugAttr); n->AddAttr(kCondGroupDebugAttr, - strings::StrCat(state_map_.CondStateToString(n), "_", - state_map_.AncestorStateToString(n))); + absl::StrCat(state_map_.CondStateToString(n), "_", + state_map_.AncestorStateToString(n))); } LOG(INFO) << "FunctionalizeControlFlow (" << name << "): " - << dump_graph::DumpGraphToFile( - strings::StrCat("functionalize_", name), *graph_, library_); + << dump_graph::DumpGraphToFile(absl::StrCat("functionalize_", name), + *graph_, library_); } Status FunctionalizeCond::Functionalize(Graph* graph, diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow_util.cc b/tensorflow/compiler/tf2xla/functionalize_control_flow_util.cc index 924fcdd9cd..54cebc6177 100644 --- a/tensorflow/compiler/tf2xla/functionalize_control_flow_util.cc +++ b/tensorflow/compiler/tf2xla/functionalize_control_flow_util.cc @@ -42,7 +42,7 @@ xla::StatusOr<Node*> BuildRetvalNode(Graph* graph, DataType type, int index) { const char* const kRetValOp = "_Retval"; NodeDef ret_def; ret_def.set_op(kRetValOp); - ret_def.set_name(strings::StrCat(kRetValOp, index)); + ret_def.set_name(absl::StrCat(kRetValOp, index)); AddNodeAttr("T", type, &ret_def); AddNodeAttr("index", index, &ret_def); return AddNodeDefToGraph(ret_def, graph); diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow_util.h b/tensorflow/compiler/tf2xla/functionalize_control_flow_util.h index 61940e3586..582b49d511 100644 --- a/tensorflow/compiler/tf2xla/functionalize_control_flow_util.h +++ b/tensorflow/compiler/tf2xla/functionalize_control_flow_util.h @@ -43,13 +43,12 @@ xla::StatusOr<Node*> BuildRetvalNode(Graph* graph, DataType type, int index); // Returns a textual representation of the names of the nodes in the input. template <typename T> string NodesToString(const T& nodes) { - return strings::StrCat("{", - absl::StrJoin(nodes, ",", - [](string* output, const Node* node) { - strings::StrAppend(output, - node->name()); - }), - "}"); + return absl::StrCat("{", + absl::StrJoin(nodes, ",", + [](string* output, const Node* node) { + absl::StrAppend(output, node->name()); + }), + "}"); } } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/functionalize_while.cc b/tensorflow/compiler/tf2xla/functionalize_while.cc index 6e3c4b0e0f..7f45e3bffa 100644 --- a/tensorflow/compiler/tf2xla/functionalize_while.cc +++ b/tensorflow/compiler/tf2xla/functionalize_while.cc @@ -132,7 +132,7 @@ Status CopySubgraph(const Graph& graph, const Frame* frame, StatusOr<Node*> BuildArgNode(Graph* graph, DataType type, int index) { const char* const kArgOp = "_Arg"; NodeDef arg_def; - NodeDefBuilder builder(strings::StrCat(kArgOp, index), kArgOp); + NodeDefBuilder builder(absl::StrCat(kArgOp, index), kArgOp); builder.Attr("T", type); builder.Attr("index", index); TF_RETURN_IF_ERROR(builder.Finalize(&arg_def)); @@ -487,9 +487,9 @@ Status FunctionalizeLoop(const FunctionLibraryDefinition* lookup_library, static std::atomic<int64> sequence_num(0LL); int64 id = ++sequence_num; NameAttrList cond_name; - cond_name.set_name(strings::StrCat("_functionalize_cond_", id)); + cond_name.set_name(absl::StrCat("_functionalize_cond_", id)); NameAttrList body_name; - body_name.set_name(strings::StrCat("_functionalize_body_", id)); + body_name.set_name(absl::StrCat("_functionalize_body_", id)); FunctionDef cond_fdef; TF_RETURN_IF_ERROR( GraphToFunctionDef(*cond_graph, cond_name.name(), &cond_fdef)); diff --git a/tensorflow/compiler/tf2xla/graph_compiler.cc b/tensorflow/compiler/tf2xla/graph_compiler.cc index 1ed1fb3b02..bc2e640559 100644 --- a/tensorflow/compiler/tf2xla/graph_compiler.cc +++ b/tensorflow/compiler/tf2xla/graph_compiler.cc @@ -127,7 +127,7 @@ Status GraphCompiler::Compile() { TF_RET_CHECK(!n->IsRecv() && !n->IsSend() && !n->IsSwitch()) << "Not supported node: " << n->DebugString(); params.op_kernel = op_kernel.get(); - gtl::InlinedVector<AllocatorAttributes, 4> output_attr(n->num_outputs()); + absl::InlinedVector<AllocatorAttributes, 4> output_attr(n->num_outputs()); params.output_attr_array = output_attr.data(); // tensor_inputs_ is a buffer reused across graph traversal. We clean up and diff --git a/tensorflow/compiler/tf2xla/graph_compiler.h b/tensorflow/compiler/tf2xla/graph_compiler.h index 127562eb23..ab7cac7100 100644 --- a/tensorflow/compiler/tf2xla/graph_compiler.h +++ b/tensorflow/compiler/tf2xla/graph_compiler.h @@ -89,7 +89,7 @@ class GraphCompiler { ScopedStepContainer* step_container_; // A buffer to hold tensor inputs to a node, this is reused across the graph // traversal. - gtl::InlinedVector<TensorValue, 4> tensor_inputs_; + absl::InlinedVector<TensorValue, 4> tensor_inputs_; }; } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc b/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc index edced6bc0e..a18e04995b 100644 --- a/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc @@ -26,7 +26,7 @@ void BatchToSpace(XlaOpKernelContext* ctx, const xla::XlaOp& input, absl::Span<const int64> block_shape, const xla::Literal& crops) { const int input_rank = input_tensor_shape.dims(); - const gtl::InlinedVector<int64, 4> input_shape = + const absl::InlinedVector<int64, 4> input_shape = input_tensor_shape.dim_sizes(); const int block_rank = block_shape.size(); diff --git a/tensorflow/compiler/tf2xla/kernels/bcast_ops.cc b/tensorflow/compiler/tf2xla/kernels/bcast_ops.cc index 2e383b1473..182f7c9934 100644 --- a/tensorflow/compiler/tf2xla/kernels/bcast_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/bcast_ops.cc @@ -39,7 +39,7 @@ class BCastArgsOp : public XlaOpKernel { OP_REQUIRES( ctx, ctx->num_inputs() == 2, errors::Unimplemented("Broadcast for n-ary operations (n > 2)")); - gtl::InlinedVector<BCast::Vec, 2> shapes; + absl::InlinedVector<BCast::Vec, 2> shapes; for (int i = 0; i < ctx->num_inputs(); ++i) { const TensorShape in_shape = ctx->InputShape(i); OP_REQUIRES(ctx, TensorShapeUtils::IsVector(in_shape), @@ -88,7 +88,7 @@ class BCastGradArgsOp : public XlaOpKernel { ctx, ctx->num_inputs() == 2, errors::Unimplemented("Broadcast for n-ary operations (n > 2)")); - gtl::InlinedVector<BCast::Vec, 4> shapes; + absl::InlinedVector<BCast::Vec, 4> shapes; for (int i = 0; i < ctx->num_inputs(); ++i) { const TensorShape in_shape = ctx->InputShape(i); OP_REQUIRES(ctx, TensorShapeUtils::IsVector(in_shape), diff --git a/tensorflow/compiler/tf2xla/kernels/depthtospace_op.cc b/tensorflow/compiler/tf2xla/kernels/depthtospace_op.cc index 12b0e38288..e96a1adce4 100644 --- a/tensorflow/compiler/tf2xla/kernels/depthtospace_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/depthtospace_op.cc @@ -48,7 +48,7 @@ class DepthToSpaceOp : public XlaOpKernel { OP_REQUIRES(ctx, kRequiredDims == input_rank, errors::InvalidArgument("Input rank should be ", kRequiredDims, "; got: ", input_rank)); - const gtl::InlinedVector<int64, 4> input_shape = + const absl::InlinedVector<int64, 4> input_shape = input_tensor_shape.dim_sizes(); xla::XlaOp input = ctx->Input(0); diff --git a/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc b/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc index f6f158a73b..27690c156e 100644 --- a/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc @@ -138,7 +138,7 @@ xla::TensorFormat XlaTensorFormat(tensorflow::TensorFormat data_format, int num_dims = num_spatial_dims + 2; int batch_dimension = GetTensorBatchDimIndex(num_dims, data_format); int feature_dimension = GetTensorFeatureDimIndex(num_dims, data_format); - gtl::InlinedVector<int64, 4> spatial_dimensions(num_spatial_dims); + absl::InlinedVector<int64, 4> spatial_dimensions(num_spatial_dims); for (int spatial_dim = 0; spatial_dim < num_spatial_dims; ++spatial_dim) { spatial_dimensions[spatial_dim] = GetTensorSpatialDimIndex(num_dims, data_format, spatial_dim); diff --git a/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc b/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc index 598248563b..118f2798d5 100644 --- a/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc +++ b/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc @@ -69,7 +69,7 @@ void XlaReductionOp::Compile(XlaOpKernelContext* ctx) { VLOG(1) << "data shape: " << data_shape.DebugString(); VLOG(1) << "axes : " << absl::StrJoin(axes, ","); - gtl::InlinedVector<bool, 4> bitmap(data_shape.dims(), false); + absl::InlinedVector<bool, 4> bitmap(data_shape.dims(), false); std::vector<int64> xla_axes; int64 num_elements_reduced = 1LL; for (int64 i = 0; i < axes_tensor_shape.num_elements(); ++i) { @@ -103,7 +103,7 @@ void XlaReductionOp::Compile(XlaOpKernelContext* ctx) { xla::XlaBuilder* const b = ctx->builder(); // Construct the builder for the reduction lambda. - xla::XlaBuilder r(strings::StrCat(desc, "-reduction")); + xla::XlaBuilder r(absl::StrCat(desc, "-reduction")); xla::PrimitiveType type; TF_CHECK_OK(DataTypeToPrimitiveType(reduction_type_, &type)); diff --git a/tensorflow/compiler/tf2xla/kernels/reverse_op.cc b/tensorflow/compiler/tf2xla/kernels/reverse_op.cc index c0afccaa5b..8494864b33 100644 --- a/tensorflow/compiler/tf2xla/kernels/reverse_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/reverse_op.cc @@ -97,7 +97,7 @@ class ReverseV2Op : public XlaOpKernel { // witnessed_axes is used to ensure that the same axis is not marked to be // reversed multiple times. - gtl::InlinedVector<bool, 8> witnessed_axes(x_shape.dims(), false); + absl::InlinedVector<bool, 8> witnessed_axes(x_shape.dims(), false); for (int d = 0; d < axes.size(); ++d) { OP_REQUIRES( diff --git a/tensorflow/compiler/tf2xla/kernels/shape_op.cc b/tensorflow/compiler/tf2xla/kernels/shape_op.cc index 4e0cf99d8e..2e0a69b70e 100644 --- a/tensorflow/compiler/tf2xla/kernels/shape_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/shape_op.cc @@ -115,7 +115,7 @@ class ExpandDimsOp : public XlaOpKernel { // accept legacy scalars, even when they should be forbidden by the graphdef // version. OP_REQUIRES(ctx, dim_shape.num_elements() == 1, - errors::InvalidArgument(strings::StrCat( + errors::InvalidArgument(absl::StrCat( "dim input to ExpandDims must be a scalar; got ", dim_shape.DebugString()))); diff --git a/tensorflow/compiler/tf2xla/kernels/spacetobatch_op.cc b/tensorflow/compiler/tf2xla/kernels/spacetobatch_op.cc index b7b4f3a546..76b79be6f6 100644 --- a/tensorflow/compiler/tf2xla/kernels/spacetobatch_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/spacetobatch_op.cc @@ -26,7 +26,7 @@ void SpaceToBatch(XlaOpKernelContext* ctx, const xla::XlaOp& input, absl::Span<const int64> block_shape, const xla::Literal& paddings) { const int input_rank = input_tensor_shape.dims(); - const gtl::InlinedVector<int64, 4> input_shape = + const absl::InlinedVector<int64, 4> input_shape = input_tensor_shape.dim_sizes(); const int block_rank = block_shape.size(); diff --git a/tensorflow/compiler/tf2xla/kernels/spacetodepth_op.cc b/tensorflow/compiler/tf2xla/kernels/spacetodepth_op.cc index 4493539fe3..3293c13b21 100644 --- a/tensorflow/compiler/tf2xla/kernels/spacetodepth_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/spacetodepth_op.cc @@ -48,7 +48,7 @@ class SpaceToDepthOp : public XlaOpKernel { OP_REQUIRES(ctx, kRequiredDims == input_rank, errors::InvalidArgument("Input rank should be ", kRequiredDims, "; got ", input_rank)); - const gtl::InlinedVector<int64, 4> input_shape = + const absl::InlinedVector<int64, 4> input_shape = input_tensor_shape.dim_sizes(); xla::XlaOp input = ctx->Input(0); diff --git a/tensorflow/compiler/tf2xla/kernels/stack_ops.cc b/tensorflow/compiler/tf2xla/kernels/stack_ops.cc index df91900570..ee70f508a9 100644 --- a/tensorflow/compiler/tf2xla/kernels/stack_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/stack_ops.cc @@ -111,7 +111,7 @@ class StackOp : public XlaOpKernel { xla::XlaOp value; XlaContext& xc = XlaContext::Get(ctx); XlaResource* resource; - string name = strings::StrCat("Stack: ", stack_name_); + string name = absl::StrCat("Stack: ", stack_name_); OP_REQUIRES_OK( ctx, xc.CreateResource(XlaResource::kStack, -1, std::move(name), dtype_, TensorShape(), value, /*tensor_array_size=*/size, diff --git a/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc b/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc index 472d4744d7..2b2e3de64f 100644 --- a/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc @@ -46,9 +46,9 @@ class StridedSliceOp : public XlaOpKernel { const TensorShape input_shape = ctx->InputShape(0); TensorShape final_shape; - gtl::InlinedVector<int64, 4> begin; - gtl::InlinedVector<int64, 4> end; - gtl::InlinedVector<int64, 4> strides; + absl::InlinedVector<int64, 4> begin; + absl::InlinedVector<int64, 4> end; + absl::InlinedVector<int64, 4> strides; xla::Literal begin_literal, end_literal, strides_literal; OP_REQUIRES_OK(ctx, ctx->ConstantInput(1, &begin_literal)); @@ -72,8 +72,8 @@ class StridedSliceOp : public XlaOpKernel { shrink_axis_mask_, &dummy_processing_shape, &final_shape, &dummy, &dummy, &dummy, &begin, &end, &strides)); - gtl::InlinedVector<int64, 4> dimensions_to_reverse; - gtl::InlinedVector<int64, 4> slice_begin, slice_end, slice_strides; + absl::InlinedVector<int64, 4> dimensions_to_reverse; + absl::InlinedVector<int64, 4> slice_begin, slice_end, slice_strides; for (int i = 0; i < begin.size(); ++i) { if (strides[i] > 0) { @@ -127,9 +127,9 @@ class StridedSliceGradOp : public XlaOpKernel { void Compile(XlaOpKernelContext* ctx) override { TensorShape processing_shape, final_shape; - gtl::InlinedVector<int64, 4> begin; - gtl::InlinedVector<int64, 4> end; - gtl::InlinedVector<int64, 4> strides; + absl::InlinedVector<int64, 4> begin; + absl::InlinedVector<int64, 4> end; + absl::InlinedVector<int64, 4> strides; TensorShape input_shape; OP_REQUIRES_OK(ctx, ctx->ConstantInputAsShape(0, &input_shape)); @@ -175,7 +175,7 @@ class StridedSliceGradOp : public XlaOpKernel { grad = xla::Reshape(grad, processing_shape.dim_sizes()); // Pad the input gradients. - gtl::InlinedVector<int64, 4> dimensions_to_reverse; + absl::InlinedVector<int64, 4> dimensions_to_reverse; xla::PaddingConfig padding_config; for (int i = 0; i < processing_shape.dims(); ++i) { @@ -238,9 +238,9 @@ class StridedSliceAssignOp : public XlaOpKernel { void Compile(XlaOpKernelContext* ctx) override { TensorShape final_shape; - gtl::InlinedVector<int64, 4> begin; - gtl::InlinedVector<int64, 4> end; - gtl::InlinedVector<int64, 4> strides; + absl::InlinedVector<int64, 4> begin; + absl::InlinedVector<int64, 4> end; + absl::InlinedVector<int64, 4> strides; xla::Literal begin_literal, end_literal, strides_literal; OP_REQUIRES_OK(ctx, ctx->ConstantInput(1, &begin_literal)); @@ -287,8 +287,8 @@ class StridedSliceAssignOp : public XlaOpKernel { xla::XlaOp rhs = ctx->Input(4); - gtl::InlinedVector<int64, 4> dimensions_to_reverse; - gtl::InlinedVector<int64, 4> slice_begin, slice_dims; + absl::InlinedVector<int64, 4> dimensions_to_reverse; + absl::InlinedVector<int64, 4> slice_begin, slice_dims; for (int i = 0; i < begin.size(); ++i) { // TODO(phawkins): implement strides != 1 OP_REQUIRES( diff --git a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc index bb114d1aed..94108b764f 100644 --- a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc @@ -167,7 +167,7 @@ class TensorArrayOp : public XlaOpKernel { XlaContext& xc = XlaContext::Get(ctx); XlaResource* var; - string name = strings::StrCat("TensorArray: ", tensor_array_name_); + string name = absl::StrCat("TensorArray: ", tensor_array_name_); OP_REQUIRES_OK( ctx, xc.CreateResource(XlaResource::kTensorArray, -1, std::move(name), dtype_, shape, value, /*tensor_array_size=*/size, diff --git a/tensorflow/compiler/tf2xla/kernels/transpose_op.cc b/tensorflow/compiler/tf2xla/kernels/transpose_op.cc index f9148b3942..6b303b31d4 100644 --- a/tensorflow/compiler/tf2xla/kernels/transpose_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/transpose_op.cc @@ -61,7 +61,7 @@ class TransposeOp : public XlaOpKernel { std::vector<int64> transposed_order; // Check whether permutation is a permutation of integers of [0 .. dims). - gtl::InlinedVector<bool, 8> bits(dims); + absl::InlinedVector<bool, 8> bits(dims); bool is_identity = true; for (int i = 0; i < dims; ++i) { const int32 d = perm[i]; diff --git a/tensorflow/compiler/tf2xla/kernels/xla_conv_op.cc b/tensorflow/compiler/tf2xla/kernels/xla_conv_op.cc index 8848623868..fecc7c556e 100644 --- a/tensorflow/compiler/tf2xla/kernels/xla_conv_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/xla_conv_op.cc @@ -84,7 +84,7 @@ class XlaConvOp : public XlaOpKernel { private: xla::ConvolutionDimensionNumbers dnums_; - xla::PrecisionConfigProto precision_config_; + xla::PrecisionConfig precision_config_; TF_DISALLOW_COPY_AND_ASSIGN(XlaConvOp); }; diff --git a/tensorflow/compiler/tf2xla/kernels/xla_dot_op.cc b/tensorflow/compiler/tf2xla/kernels/xla_dot_op.cc index 2fed53e5c0..40b15b5579 100644 --- a/tensorflow/compiler/tf2xla/kernels/xla_dot_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/xla_dot_op.cc @@ -54,7 +54,7 @@ class XlaDotOp : public XlaOpKernel { private: xla::DotDimensionNumbers dnums_; - xla::PrecisionConfigProto precision_config_; + xla::PrecisionConfig precision_config_; TF_DISALLOW_COPY_AND_ASSIGN(XlaDotOp); }; diff --git a/tensorflow/compiler/tf2xla/lib/BUILD b/tensorflow/compiler/tf2xla/lib/BUILD index 9365d203f0..8597e7f139 100644 --- a/tensorflow/compiler/tf2xla/lib/BUILD +++ b/tensorflow/compiler/tf2xla/lib/BUILD @@ -205,7 +205,7 @@ cc_library( "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/core:lib", + "@com_google_absl//absl/strings", "@com_google_absl//absl/types:span", ], ) diff --git a/tensorflow/compiler/tf2xla/lib/batch_dot.cc b/tensorflow/compiler/tf2xla/lib/batch_dot.cc index d8c050d09e..64f2d781a6 100644 --- a/tensorflow/compiler/tf2xla/lib/batch_dot.cc +++ b/tensorflow/compiler/tf2xla/lib/batch_dot.cc @@ -28,7 +28,7 @@ namespace tensorflow { xla::XlaOp BatchDot(xla::XlaOp x, xla::XlaOp y, bool transpose_x, bool transpose_y, bool conjugate_x, bool conjugate_y, - xla::PrecisionConfigProto::Precision precision) { + xla::PrecisionConfig::Precision precision) { xla::XlaBuilder* builder = x.builder(); return builder->ReportErrorOrReturn([&]() -> xla::StatusOr<xla::XlaOp> { TF_ASSIGN_OR_RETURN(xla::Shape x_shape, builder->GetShape(x)); @@ -96,7 +96,7 @@ xla::XlaOp BatchDot(xla::XlaOp x, xla::XlaOp y, bool transpose_x, y = xla::Conj(y); } - xla::PrecisionConfigProto precision_proto; + xla::PrecisionConfig precision_proto; precision_proto.add_operand_precision(precision); precision_proto.add_operand_precision(precision); diff --git a/tensorflow/compiler/tf2xla/lib/batch_dot.h b/tensorflow/compiler/tf2xla/lib/batch_dot.h index 6cfccd5553..6edd63a4d3 100644 --- a/tensorflow/compiler/tf2xla/lib/batch_dot.h +++ b/tensorflow/compiler/tf2xla/lib/batch_dot.h @@ -43,11 +43,11 @@ namespace tensorflow { // It is computed as: // // output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :]) -xla::XlaOp BatchDot(xla::XlaOp x, xla::XlaOp y, bool transpose_x = false, - bool transpose_y = false, bool conjugate_x = false, - bool conjugate_y = false, - xla::PrecisionConfigProto::Precision precision = - xla::PrecisionConfigProto::DEFAULT); +xla::XlaOp BatchDot( + xla::XlaOp x, xla::XlaOp y, bool transpose_x = false, + bool transpose_y = false, bool conjugate_x = false, + bool conjugate_y = false, + xla::PrecisionConfig::Precision precision = xla::PrecisionConfig::DEFAULT); } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/cholesky.cc b/tensorflow/compiler/tf2xla/lib/cholesky.cc index c50a8de33e..ab3d0a5668 100644 --- a/tensorflow/compiler/tf2xla/lib/cholesky.cc +++ b/tensorflow/compiler/tf2xla/lib/cholesky.cc @@ -50,7 +50,7 @@ namespace { // l[..., j, j] // return l xla::XlaOp CholeskyUnblocked(xla::XlaOp a, - xla::PrecisionConfigProto::Precision precision) { + xla::PrecisionConfig::Precision precision) { xla::XlaBuilder* builder = a.builder(); return builder->ReportErrorOrReturn([&]() -> xla::StatusOr<xla::XlaOp> { TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); @@ -150,7 +150,7 @@ xla::XlaOp CholeskyUnblocked(xla::XlaOp a, } // namespace xla::XlaOp Cholesky(xla::XlaOp a, int64 block_size, - xla::PrecisionConfigProto::Precision precision) { + xla::PrecisionConfig::Precision precision) { xla::XlaBuilder* builder = a.builder(); return builder->ReportErrorOrReturn([&]() -> xla::StatusOr<xla::XlaOp> { TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); diff --git a/tensorflow/compiler/tf2xla/lib/cholesky.h b/tensorflow/compiler/tf2xla/lib/cholesky.h index 60cd7ded53..9a561c34b9 100644 --- a/tensorflow/compiler/tf2xla/lib/cholesky.h +++ b/tensorflow/compiler/tf2xla/lib/cholesky.h @@ -30,9 +30,9 @@ namespace tensorflow { // TODO(phawkins): check for negative values on the diagonal and return an // error, instead of silently yielding NaNs. // TODO(znado): handle the complex Hermitian case -xla::XlaOp Cholesky(xla::XlaOp a, int64 block_size = 256, - xla::PrecisionConfigProto::Precision precision = - xla::PrecisionConfigProto::HIGHEST); +xla::XlaOp Cholesky( + xla::XlaOp a, int64 block_size = 256, + xla::PrecisionConfig::Precision precision = xla::PrecisionConfig::HIGHEST); } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/qr.cc b/tensorflow/compiler/tf2xla/lib/qr.cc index 0a140fa93c..6b3f2b6e06 100644 --- a/tensorflow/compiler/tf2xla/lib/qr.cc +++ b/tensorflow/compiler/tf2xla/lib/qr.cc @@ -150,7 +150,7 @@ struct QRBlockResult { xla::XlaOp vs; // Shape: [..., m, n] }; xla::StatusOr<QRBlockResult> QRBlock( - xla::XlaOp a, xla::PrecisionConfigProto::Precision precision) { + xla::XlaOp a, xla::PrecisionConfig::Precision precision) { xla::XlaBuilder* builder = a.builder(); TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); const int num_dims = xla::ShapeUtil::Rank(a_shape); @@ -257,7 +257,7 @@ xla::StatusOr<QRBlockResult> QRBlock( xla::StatusOr<xla::XlaOp> ComputeWYRepresentation( xla::PrimitiveType type, absl::Span<const int64> batch_dims, xla::XlaOp vs, xla::XlaOp taus, int64 m, int64 n, - xla::PrecisionConfigProto::Precision precision) { + xla::PrecisionConfig::Precision precision) { std::vector<int64> batch_dim_indices(batch_dims.size()); std::iota(batch_dim_indices.begin(), batch_dim_indices.end(), 0); int64 n_index = batch_dims.size() + 1; @@ -332,7 +332,7 @@ xla::StatusOr<xla::XlaOp> ComputeWYRepresentation( // rather than WY transformations. xla::StatusOr<QRDecompositionResult> QRDecomposition( xla::XlaOp a, bool full_matrices, int64 block_size, - xla::PrecisionConfigProto::Precision precision) { + xla::PrecisionConfig::Precision precision) { xla::XlaBuilder* builder = a.builder(); TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); const int num_dims = xla::ShapeUtil::Rank(a_shape); diff --git a/tensorflow/compiler/tf2xla/lib/qr.h b/tensorflow/compiler/tf2xla/lib/qr.h index 8a389fb7b0..24b537ac8b 100644 --- a/tensorflow/compiler/tf2xla/lib/qr.h +++ b/tensorflow/compiler/tf2xla/lib/qr.h @@ -35,8 +35,7 @@ struct QRDecompositionResult { xla::StatusOr<QRDecompositionResult> QRDecomposition( xla::XlaOp a, bool full_matrices, int64 block_size = 128, - xla::PrecisionConfigProto::Precision precision = - xla::PrecisionConfigProto::HIGHEST); + xla::PrecisionConfig::Precision precision = xla::PrecisionConfig::HIGHEST); } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve.cc b/tensorflow/compiler/tf2xla/lib/triangular_solve.cc index 37b2240b45..6524c2a9b1 100644 --- a/tensorflow/compiler/tf2xla/lib/triangular_solve.cc +++ b/tensorflow/compiler/tf2xla/lib/triangular_solve.cc @@ -110,9 +110,9 @@ xla::XlaOp DiagonalBlocks(xla::XlaOp a, int64 block_size) { }); } -xla::XlaOp InvertDiagonalBlocks( - xla::XlaOp diag_blocks, bool lower, bool transpose_a, bool conjugate_a, - xla::PrecisionConfigProto::Precision precision) { +xla::XlaOp InvertDiagonalBlocks(xla::XlaOp diag_blocks, bool lower, + bool transpose_a, bool conjugate_a, + xla::PrecisionConfig::Precision precision) { xla::XlaBuilder* builder = diag_blocks.builder(); return builder->ReportErrorOrReturn([&]() -> xla::StatusOr<xla::XlaOp> { // Input is a batch of square lower triangular square matrices. Its shape is @@ -216,7 +216,7 @@ xla::XlaOp InvertDiagonalBlocks( dnums.add_rhs_batch_dimensions(0); dnums.add_lhs_contracting_dimensions(2); dnums.add_rhs_contracting_dimensions(1); - xla::PrecisionConfigProto precision_proto; + xla::PrecisionConfig precision_proto; precision_proto.add_operand_precision(precision); precision_proto.add_operand_precision(precision); auto update = -DotGeneral(input_row, body_out, dnums, &precision_proto); @@ -245,7 +245,7 @@ xla::XlaOp InvertDiagonalBlocks( xla::XlaOp SolveWithInvertedDiagonalBlocks( xla::XlaOp a, xla::XlaOp b, xla::XlaOp inv_diag_blocks, bool left_side, bool lower, bool transpose_a, bool conjugate_a, - xla::PrecisionConfigProto::Precision precision) { + xla::PrecisionConfig::Precision precision) { xla::XlaBuilder* builder = a.builder(); return builder->ReportErrorOrReturn([&]() -> xla::StatusOr<xla::XlaOp> { TF_ASSIGN_OR_RETURN(xla::Shape blocks_shape, @@ -346,7 +346,7 @@ xla::XlaOp SolveWithInvertedDiagonalBlocks( xla::XlaOp TriangularSolve(xla::XlaOp a, xla::XlaOp b, bool left_side, bool lower, bool transpose_a, bool conjugate_a, int64 block_size, - xla::PrecisionConfigProto::Precision precision) { + xla::PrecisionConfig::Precision precision) { xla::XlaBuilder* builder = a.builder(); return builder->ReportErrorOrReturn([&]() -> xla::StatusOr<xla::XlaOp> { TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve.h b/tensorflow/compiler/tf2xla/lib/triangular_solve.h index ac42a48352..2303234f36 100644 --- a/tensorflow/compiler/tf2xla/lib/triangular_solve.h +++ b/tensorflow/compiler/tf2xla/lib/triangular_solve.h @@ -57,11 +57,10 @@ namespace tensorflow { // // Uses a blocked algorithm if `block_size` is > 1; if block_size == 1 then no // blocking is used. -xla::XlaOp TriangularSolve(xla::XlaOp a, xla::XlaOp b, bool left_side, - bool lower, bool transpose_a, bool conjugate_a, - int64 block_size = 128, - xla::PrecisionConfigProto::Precision precision = - xla::PrecisionConfigProto::HIGHEST); +xla::XlaOp TriangularSolve( + xla::XlaOp a, xla::XlaOp b, bool left_side, bool lower, bool transpose_a, + bool conjugate_a, int64 block_size = 128, + xla::PrecisionConfig::Precision precision = xla::PrecisionConfig::HIGHEST); } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/while_loop.cc b/tensorflow/compiler/tf2xla/lib/while_loop.cc index 5300e2c878..594ab1dfd0 100644 --- a/tensorflow/compiler/tf2xla/lib/while_loop.cc +++ b/tensorflow/compiler/tf2xla/lib/while_loop.cc @@ -24,7 +24,7 @@ namespace tensorflow { xla::StatusOr<std::vector<xla::XlaOp>> XlaWhileLoop( const LoopConditionFunction& condition_function, const LoopBodyFunction& body_function, - absl::Span<const xla::XlaOp> initial_values, StringPiece name, + absl::Span<const xla::XlaOp> initial_values, absl::string_view name, xla::XlaBuilder* builder) { int arity = initial_values.size(); std::vector<xla::Shape> var_shapes; @@ -47,7 +47,7 @@ xla::StatusOr<std::vector<xla::XlaOp>> XlaWhileLoop( // Build the condition. std::unique_ptr<xla::XlaBuilder> cond_builder = - builder->CreateSubBuilder(strings::StrCat(name, "_condition")); + builder->CreateSubBuilder(absl::StrCat(name, "_condition")); { auto parameter = xla::Parameter(cond_builder.get(), 0, tuple_shape, "parameter"); @@ -61,7 +61,7 @@ xla::StatusOr<std::vector<xla::XlaOp>> XlaWhileLoop( // Build the body. std::unique_ptr<xla::XlaBuilder> body_builder = - builder->CreateSubBuilder(strings::StrCat(name, "_body")); + builder->CreateSubBuilder(absl::StrCat(name, "_body")); { auto parameter = xla::Parameter(body_builder.get(), 0, tuple_shape, "parameter"); @@ -84,7 +84,7 @@ xla::StatusOr<std::vector<xla::XlaOp>> XlaWhileLoop( xla::StatusOr<std::vector<xla::XlaOp>> XlaForEachIndex( int64 num_iterations, xla::PrimitiveType num_iterations_type, const ForEachIndexBodyFunction& body_function, - absl::Span<const xla::XlaOp> initial_values, StringPiece name, + absl::Span<const xla::XlaOp> initial_values, absl::string_view name, xla::XlaBuilder* builder) { auto while_cond_fn = [&](absl::Span<const xla::XlaOp> values, diff --git a/tensorflow/compiler/tf2xla/lib/while_loop.h b/tensorflow/compiler/tf2xla/lib/while_loop.h index 115ebf390d..f2134bb449 100644 --- a/tensorflow/compiler/tf2xla/lib/while_loop.h +++ b/tensorflow/compiler/tf2xla/lib/while_loop.h @@ -19,11 +19,11 @@ limitations under the License. #include <functional> #include <vector> +#include "absl/strings/string_view.h" #include "absl/types/span.h" #include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/statusor.h" -#include "tensorflow/core/lib/core/stringpiece.h" namespace tensorflow { @@ -50,7 +50,7 @@ typedef std::function<xla::StatusOr<std::vector<xla::XlaOp>>( xla::StatusOr<std::vector<xla::XlaOp>> XlaWhileLoop( const LoopConditionFunction& condition_function, const LoopBodyFunction& body_function, - absl::Span<const xla::XlaOp> initial_values, StringPiece name, + absl::Span<const xla::XlaOp> initial_values, absl::string_view name, xla::XlaBuilder* builder); // Builds an XLA loop that repeats a computation `num_iterations` times. @@ -65,7 +65,7 @@ typedef std::function<xla::StatusOr<std::vector<xla::XlaOp>>( xla::StatusOr<std::vector<xla::XlaOp>> XlaForEachIndex( int64 num_iterations, xla::PrimitiveType num_iterations_type, const ForEachIndexBodyFunction& body_function, - absl::Span<const xla::XlaOp> initial_values, StringPiece name, + absl::Span<const xla::XlaOp> initial_values, absl::string_view name, xla::XlaBuilder* builder); } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/ops/xla_ops.cc b/tensorflow/compiler/tf2xla/ops/xla_ops.cc index 2cd9ae799f..68cfdc1785 100644 --- a/tensorflow/compiler/tf2xla/ops/xla_ops.cc +++ b/tensorflow/compiler/tf2xla/ops/xla_ops.cc @@ -83,7 +83,7 @@ lhs_dilation: dilation to apply between input elements rhs_dilation: dilation to apply between kernel elements feature_group_count: number of feature groups for grouped convolution. dimension_numbers: a serialized xla::ConvolutionDimensionNumbers proto. -precision_config: a serialized xla::PrecisionConfigProto proto. +precision_config: a serialized xla::PrecisionConfig proto. )doc"); REGISTER_OP("XlaDot") @@ -102,7 +102,7 @@ Wraps the XLA ConvGeneralDilated operator, documented at lhs: the LHS tensor rhs: the RHS tensor dimension_numbers: a serialized xla::DotDimensionNumbers proto. -precision_config: a serialized xla::PrecisionConfigProto proto. +precision_config: a serialized xla::PrecisionConfig proto. )doc"); REGISTER_OP("XlaDynamicUpdateSlice") diff --git a/tensorflow/compiler/tf2xla/resource_operation_table.cc b/tensorflow/compiler/tf2xla/resource_operation_table.cc index 32ba6df2e6..20f2ce2919 100644 --- a/tensorflow/compiler/tf2xla/resource_operation_table.cc +++ b/tensorflow/compiler/tf2xla/resource_operation_table.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/core/lib/gtl/flatmap.h" namespace tensorflow { -/*static*/ StringPiece XlaResourceOpInfo::XlaResourceOpKindToString( +/*static*/ absl::string_view XlaResourceOpInfo::XlaResourceOpKindToString( XlaResourceOpKind op_kind) { switch (op_kind) { case XlaResourceOpKind::kRead: @@ -30,11 +30,11 @@ namespace tensorflow { } } -static gtl::FlatMap<StringPiece, XlaResourceOpInfo>* CreateResourceOpInfoMap() { - gtl::FlatMap<StringPiece, XlaResourceOpInfo>* result = - new gtl::FlatMap<StringPiece, XlaResourceOpInfo>; +static gtl::FlatMap<absl::string_view, XlaResourceOpInfo>* +CreateResourceOpInfoMap() { + auto* result = new gtl::FlatMap<absl::string_view, XlaResourceOpInfo>; - auto add = [&](StringPiece op, XlaResourceOpKind op_kind, + auto add = [&](absl::string_view op, XlaResourceOpKind op_kind, XlaResourceKind resource_kind) { auto insert_result = result->insert({op, XlaResourceOpInfo(op_kind, resource_kind)}); @@ -103,23 +103,23 @@ static gtl::FlatMap<StringPiece, XlaResourceOpInfo>* CreateResourceOpInfoMap() { return result; } -static const gtl::FlatMap<StringPiece, XlaResourceOpInfo>& +static const gtl::FlatMap<absl::string_view, XlaResourceOpInfo>& GetStaticResourceOpInfoMap() { - static gtl::FlatMap<StringPiece, XlaResourceOpInfo>* op_info_map = + static gtl::FlatMap<absl::string_view, XlaResourceOpInfo>* op_info_map = CreateResourceOpInfoMap(); return *op_info_map; } -const XlaResourceOpInfo* GetResourceOpInfoForOp(StringPiece op) { - const gtl::FlatMap<StringPiece, XlaResourceOpInfo>& op_infos = +const XlaResourceOpInfo* GetResourceOpInfoForOp(absl::string_view op) { + const gtl::FlatMap<absl::string_view, XlaResourceOpInfo>& op_infos = GetStaticResourceOpInfoMap(); auto it = op_infos.find(op); return it == op_infos.end() ? nullptr : &it->second; } namespace resource_op_table_internal { -std::vector<StringPiece> GetKnownResourceOps() { - std::vector<StringPiece> result; +std::vector<absl::string_view> GetKnownResourceOps() { + std::vector<absl::string_view> result; for (const auto& p : GetStaticResourceOpInfoMap()) { result.push_back(p.first); } diff --git a/tensorflow/compiler/tf2xla/resource_operation_table.h b/tensorflow/compiler/tf2xla/resource_operation_table.h index 7f627a64c6..61c7a56ff0 100644 --- a/tensorflow/compiler/tf2xla/resource_operation_table.h +++ b/tensorflow/compiler/tf2xla/resource_operation_table.h @@ -19,7 +19,7 @@ limitations under the License. #include <string> #include <vector> -#include "tensorflow/core/lib/core/stringpiece.h" +#include "absl/strings/string_view.h" #include "tensorflow/core/platform/logging.h" // Exposes information about the resource operations supported by tf2xla in a @@ -47,7 +47,7 @@ class XlaResourceOpInfo { XlaResourceOpKind kind() const { return op_kind_; } XlaResourceKind resource_kind() const { return resource_kind_; } - static StringPiece XlaResourceOpKindToString(XlaResourceOpKind op_kind); + static absl::string_view XlaResourceOpKindToString(XlaResourceOpKind op_kind); private: XlaResourceOpKind op_kind_; @@ -57,13 +57,13 @@ class XlaResourceOpInfo { // Returns a XlaResourceOpInfo describing `op` if it is a resource operation // supported by tf2xla, otherwise returns null (i.e. if this returns null then // `op` is either not a resource operation or is unsupported by XLA). -const XlaResourceOpInfo* GetResourceOpInfoForOp(StringPiece op); +const XlaResourceOpInfo* GetResourceOpInfoForOp(absl::string_view op); namespace resource_op_table_internal { // NB! Implementation detail exposed for unit testing, do not use. // // Returns the set of resource operations known by this module. -std::vector<StringPiece> GetKnownResourceOps(); +std::vector<absl::string_view> GetKnownResourceOps(); } // namespace resource_op_table_internal } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/resource_operation_table_test.cc b/tensorflow/compiler/tf2xla/resource_operation_table_test.cc index 0343f80de9..a85ef040a7 100644 --- a/tensorflow/compiler/tf2xla/resource_operation_table_test.cc +++ b/tensorflow/compiler/tf2xla/resource_operation_table_test.cc @@ -34,7 +34,7 @@ bool HasResourceInputOrOutput(const OpDef& op_def) { TEST(ResourceOperationTableTest, HaveAllResourceOps) { gtl::FlatMap<string, bool> known_resource_ops; - for (StringPiece known_resource_op : + for (absl::string_view known_resource_op : resource_op_table_internal::GetKnownResourceOps()) { ASSERT_TRUE( known_resource_ops.insert({string(known_resource_op), false}).second); diff --git a/tensorflow/compiler/tf2xla/sharding_util.cc b/tensorflow/compiler/tf2xla/sharding_util.cc index 2d7eb8b915..8aae498be1 100644 --- a/tensorflow/compiler/tf2xla/sharding_util.cc +++ b/tensorflow/compiler/tf2xla/sharding_util.cc @@ -17,7 +17,6 @@ limitations under the License. #include "absl/strings/match.h" #include "tensorflow/core/framework/node_def.pb.h" #include "tensorflow/core/lib/core/errors.h" -#include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/util/device_name_utils.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/tf2xla.cc b/tensorflow/compiler/tf2xla/tf2xla.cc index f34af2d67d..7dbe3a0b58 100644 --- a/tensorflow/compiler/tf2xla/tf2xla.cc +++ b/tensorflow/compiler/tf2xla/tf2xla.cc @@ -22,6 +22,7 @@ limitations under the License. #include <utility> #include <vector> +#include "absl/strings/str_cat.h" #include "absl/strings/str_join.h" #include "tensorflow/compiler/tf2xla/dump_graph.h" #include "tensorflow/compiler/tf2xla/shape_util.h" @@ -41,7 +42,6 @@ limitations under the License. #include "tensorflow/core/graph/graph_constructor.h" #include "tensorflow/core/graph/node_builder.h" #include "tensorflow/core/lib/core/errors.h" -#include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/types.h" @@ -75,7 +75,7 @@ Status AddArgNodes(Graph* graph, const NodeMap& node_map, auto node_it = node_map.find(remap_it->second); if (node_it == node_map.end()) { // Strip off the aot_feed_#/ prefix. - StringPiece name(remap_it->second); + absl::string_view name(remap_it->second); const auto index = name.find('/'); if (index > 0) name.remove_prefix(index + 1); return errors::InvalidArgument( @@ -89,7 +89,7 @@ Status AddArgNodes(Graph* graph, const NodeMap& node_map, // explicitly specify or override them. Node* arg_node = nullptr; TF_RETURN_IF_ERROR( - NodeBuilder(strings::StrCat("_arg_", arg_index), kArgOp) + NodeBuilder(absl::StrCat("_arg_", arg_index), kArgOp) .Attr("T", BaseType(feed_node->output_type(output_index))) .Attr("index", arg_index) .Attr(kFeedIdAttr, TensorIdToString(feed.id())) @@ -136,7 +136,7 @@ Status AddRetvalNodes(Graph* graph, const NodeMap& node_map, // Connects fetch_node -> retval_node. Node* retval_node = nullptr; TF_RETURN_IF_ERROR( - NodeBuilder(strings::StrCat("_retval_", ret_index), kRetvalOp) + NodeBuilder(absl::StrCat("_retval_", ret_index), kRetvalOp) .Input(fetch_node, id.output_index()) .Attr("T", BaseType(fetch_node->output_type(id.output_index()))) .Attr("index", ret_index) @@ -256,7 +256,7 @@ Status ConvertGraphToXla(std::unique_ptr<Graph> graph, xla::Client* client, XlaOpRegistry::RegisterCompilationKernels(); for (Node* node : graph->nodes()) { node->set_assigned_device_name( - strings::StrCat("/device:", DEVICE_CPU_XLA_JIT)); + absl::StrCat("/device:", DEVICE_CPU_XLA_JIT)); } std::vector<XlaCompiler::Argument> xla_args; TF_RETURN_IF_ERROR(CreateXlaArgs(*graph, &xla_args)); diff --git a/tensorflow/compiler/tf2xla/tf2xla_util.cc b/tensorflow/compiler/tf2xla/tf2xla_util.cc index e284e0b191..211caf8736 100644 --- a/tensorflow/compiler/tf2xla/tf2xla_util.cc +++ b/tensorflow/compiler/tf2xla/tf2xla_util.cc @@ -20,6 +20,7 @@ limitations under the License. #include <set> #include <unordered_map> +#include "absl/strings/str_cat.h" #include "absl/types/optional.h" #include "tensorflow/compiler/tf2xla/sharding_util.h" #include "tensorflow/compiler/tf2xla/tf2xla.pb.h" @@ -33,7 +34,6 @@ limitations under the License. #include "tensorflow/core/graph/tensor_id.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/core/status.h" -#include "tensorflow/core/lib/strings/strcat.h" namespace tensorflow { @@ -112,8 +112,8 @@ Status AddPlaceholdersForFeeds( const string name_port = TensorIdToString(feed->id()); PlaceholderInfo& info = placeholder_info[name_port]; info.feed = feed; - info.placeholder_name = strings::StrCat( - "aot_feed_", feed->id().output_index(), "/", feed->id().node_name()); + info.placeholder_name = absl::StrCat("aot_feed_", feed->id().output_index(), + "/", feed->id().node_name()); (*feed_remapping)[name_port] = info.placeholder_name; } @@ -258,7 +258,7 @@ Status PruneGraphDefInto(const tf2xla::Config& config, const GraphDef& in, } string TensorIdToString(const tf2xla::TensorId& id) { - return strings::StrCat(id.node_name(), ":", id.output_index()); + return absl::StrCat(id.node_name(), ":", id.output_index()); } Status SetNodeShardingFromNeighbors(Node* n, bool out_edges) { @@ -289,7 +289,7 @@ Status SetNodeShardingFromNeighbors(Node* n, bool out_edges) { return Status::OK(); } -void AddDtypeToKernalDefConstraint(StringPiece name, DataType dtype, +void AddDtypeToKernalDefConstraint(absl::string_view name, DataType dtype, KernelDef* kdef) { for (KernelDef::AttrConstraint& constraint : *kdef->mutable_constraint()) { if (constraint.name() == name) { diff --git a/tensorflow/compiler/tf2xla/tf2xla_util.h b/tensorflow/compiler/tf2xla/tf2xla_util.h index 33620ef810..a29e764466 100644 --- a/tensorflow/compiler/tf2xla/tf2xla_util.h +++ b/tensorflow/compiler/tf2xla/tf2xla_util.h @@ -53,7 +53,7 @@ string TensorIdToString(const tf2xla::TensorId& id); Status SetNodeShardingFromNeighbors(Node* n, bool out_edges); // Add an allowed data type to the AttrConstraint with the given name. -void AddDtypeToKernalDefConstraint(StringPiece name, DataType dtype, +void AddDtypeToKernalDefConstraint(absl::string_view name, DataType dtype, KernelDef* kdef); // Returns the next random seed to use for seeding xla rng. diff --git a/tensorflow/compiler/tf2xla/tf2xla_util_test.cc b/tensorflow/compiler/tf2xla/tf2xla_util_test.cc index 2b1f724dc7..68441b3d47 100644 --- a/tensorflow/compiler/tf2xla/tf2xla_util_test.cc +++ b/tensorflow/compiler/tf2xla/tf2xla_util_test.cc @@ -16,6 +16,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/tf2xla_util.h" #include "absl/strings/match.h" +#include "absl/strings/str_cat.h" +#include "absl/strings/string_view.h" #include "tensorflow/cc/framework/ops.h" #include "tensorflow/cc/ops/data_flow_ops.h" #include "tensorflow/cc/ops/function_ops.h" @@ -25,8 +27,6 @@ limitations under the License. #include "tensorflow/core/graph/graph.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/lib/core/status_test_util.h" -#include "tensorflow/core/lib/core/stringpiece.h" -#include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/platform/test.h" namespace tensorflow { @@ -153,7 +153,7 @@ static tf2xla::Config FetchesConfig(std::vector<string> fetches) { tf2xla::Config config; for (const auto& fetch_node_name : fetches) { auto* fetch = config.add_fetch(); - fetch->set_name(strings::StrCat("fetch_", fetch_node_name)); + fetch->set_name(absl::StrCat("fetch_", fetch_node_name)); fetch->mutable_id()->set_node_name(fetch_node_name); } return config; diff --git a/tensorflow/compiler/tf2xla/xla_compilation_device.cc b/tensorflow/compiler/tf2xla/xla_compilation_device.cc index d98237bd5c..7f860500c7 100644 --- a/tensorflow/compiler/tf2xla/xla_compilation_device.cc +++ b/tensorflow/compiler/tf2xla/xla_compilation_device.cc @@ -76,12 +76,11 @@ class XlaCompilationAllocator : public Allocator { XlaCompilationDevice::XlaCompilationDevice(const SessionOptions& options, DeviceType type) - : LocalDevice( - options, - Device::BuildDeviceAttributes( - strings::StrCat("/device:", type.type(), ":0"), type, - Bytes(256 << 20), DeviceLocality(), - strings::StrCat("device: XLA compilation device ", type.type()))), + : LocalDevice(options, Device::BuildDeviceAttributes( + absl::StrCat("/device:", type.type(), ":0"), + type, Bytes(256 << 20), DeviceLocality(), + absl::StrCat("device: XLA compilation device ", + type.type()))), allocator_(new XlaCompilationAllocator()) {} XlaCompilationDevice::~XlaCompilationDevice() {} diff --git a/tensorflow/compiler/tf2xla/xla_compiler.cc b/tensorflow/compiler/tf2xla/xla_compiler.cc index 0c300c282e..41d305d461 100644 --- a/tensorflow/compiler/tf2xla/xla_compiler.cc +++ b/tensorflow/compiler/tf2xla/xla_compiler.cc @@ -198,14 +198,14 @@ Status XlaCompiler::CompileFunction(const XlaCompiler::CompileOptions& options, // lowest-numbered core that consumes the argument. We choose the // lowest-numbered core so the assignment is deterministic. for (Node* n : graph->nodes()) { - if (StringPiece(n->type_string()) == "_Arg") { + if (absl::string_view(n->type_string()) == "_Arg") { TF_RETURN_IF_ERROR(SetNodeShardingFromNeighbors(n, /*out_edges=*/true)); } } // Do _Retval as a second loop, in case the retval's input is an _Arg (which // may have gotten a device assignment from the first loop). for (Node* n : graph->nodes()) { - if (StringPiece(n->type_string()) == "_Retval") { + if (absl::string_view(n->type_string()) == "_Retval") { TF_RETURN_IF_ERROR(SetNodeShardingFromNeighbors(n, /*out_edges=*/false)); } } @@ -213,8 +213,7 @@ Status XlaCompiler::CompileFunction(const XlaCompiler::CompileOptions& options, if (VLOG_IS_ON(2)) { VLOG(2) << "XlaCompiler::CompileFunction: " << dump_graph::DumpGraphToFile( - strings::StrCat("xla_compile_function_", function_id), - *graph); + absl::StrCat("xla_compile_function_", function_id), *graph); } VLOG(1) << "===================================================="; @@ -522,7 +521,7 @@ Status XlaCompiler::BuildArguments( // Use the _Arg nodes in the graph to resolve core assignments. for (const Node* n : graph.nodes()) { - if (StringPiece(n->type_string()) != "_Arg") continue; + if (absl::string_view(n->type_string()) != "_Arg") continue; int index; TF_RETURN_IF_ERROR(GetNodeAttr(n->attrs(), "index", &index)); TF_RET_CHECK(index >= 0 && index < args.size()) @@ -581,7 +580,7 @@ Status XlaCompiler::BuildArguments( builder, core == -1 ? absl::optional<xla::OpSharding>() : xla::sharding_builder::AssignDevice(core)); arg_handles[i] = xla::Parameter(builder, i, (*input_shapes)[i], - strings::StrCat("arg", i)); + absl::StrCat("arg", i)); } } @@ -644,7 +643,7 @@ Status XlaCompiler::CompileSingleOp( // dependency edge to the _SOURCE node. for (int64 i = 0; i < ctx->num_inputs(); ++i) { Node* node; - string name = strings::StrCat(ctx->op_kernel().name(), "_", i, "_arg"); + string name = absl::StrCat(ctx->op_kernel().name(), "_", i, "_arg"); Status status = NodeBuilder(name, "_Arg") .ControlInput(graph->source_node()) .Attr("T", ctx->input_dtype(i)) @@ -657,7 +656,7 @@ Status XlaCompiler::CompileSingleOp( // Similarly with return values, create dummy _Retval nodes fed by `node`. for (int64 i = 0; i < ctx->num_outputs(); ++i) { Node* node; - string name = strings::StrCat(ctx->op_kernel().name(), "_", i, "_retval"); + string name = absl::StrCat(ctx->op_kernel().name(), "_", i, "_retval"); Status status = NodeBuilder(name, "_Retval") .Input(main_node, i) .Attr("T", ctx->expected_output_dtype(i)) @@ -693,7 +692,7 @@ Status ValidateGraph(const Graph* graph, const DeviceType& device_type, const string& name) { auto maybe_error = [&](const Node* node, const Status& s) -> Status { if (!s.ok()) { - return errors::InvalidArgument(strings::StrCat( + return errors::InvalidArgument(absl::StrCat( "Detected unsupported operations when trying to compile graph ", name, " on ", device_type.type_string(), ": ", node->def().op(), " (", s.error_message(), ")", FormatNodeForError(*node))); @@ -734,7 +733,7 @@ Status XlaCompiler::CompileGraph(const XlaCompiler::CompileOptions& options, if (VLOG_IS_ON(2)) { VLOG(2) << "XlaCompiler::CompileGraph: " << dump_graph::DumpGraphToFile( - strings::StrCat("xla_compile_graph_", name), *graph); + absl::StrCat("xla_compile_graph_", name), *graph); } // Report the error here if initialization failed. diff --git a/tensorflow/compiler/tf2xla/xla_context.cc b/tensorflow/compiler/tf2xla/xla_context.cc index 24a4b92b45..e8b4b0eb36 100644 --- a/tensorflow/compiler/tf2xla/xla_context.cc +++ b/tensorflow/compiler/tf2xla/xla_context.cc @@ -32,7 +32,6 @@ limitations under the License. #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/core/common_runtime/dma_helper.h" -#include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/platform/logging.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/xla_op_kernel.cc b/tensorflow/compiler/tf2xla/xla_op_kernel.cc index 1499c99ed1..d67e50375b 100644 --- a/tensorflow/compiler/tf2xla/xla_op_kernel.cc +++ b/tensorflow/compiler/tf2xla/xla_op_kernel.cc @@ -67,7 +67,7 @@ const xla::XlaOp& XlaOpKernelContext::Input(int index) { return GetComputationFromTensor(context_->input(index)); } -const xla::XlaOp& XlaOpKernelContext::Input(StringPiece name) { +const xla::XlaOp& XlaOpKernelContext::Input(absl::string_view name) { return GetComputationFromTensor(GetInputTensorByName(name)); } @@ -75,7 +75,7 @@ TensorShape XlaOpKernelContext::InputShape(int index) { return context_->input(index).shape(); } -TensorShape XlaOpKernelContext::InputShape(StringPiece name) { +TensorShape XlaOpKernelContext::InputShape(absl::string_view name) { return GetInputTensorByName(name).shape(); } @@ -100,7 +100,7 @@ Status XlaOpKernelContext::ConstantInput(int index, } static xla::StatusOr<int> InputIndex(XlaOpKernelContext* context, - StringPiece name) { + absl::string_view name) { int start, stop; TF_RETURN_IF_ERROR(context->op_kernel().InputRange(name, &start, &stop)); if (stop != start + 1) { @@ -112,7 +112,7 @@ static xla::StatusOr<int> InputIndex(XlaOpKernelContext* context, return start; } -Status XlaOpKernelContext::ConstantInput(StringPiece name, +Status XlaOpKernelContext::ConstantInput(absl::string_view name, xla::Literal* constant_literal) { TF_ASSIGN_OR_RETURN(int index, InputIndex(this, name)); return ConstantInput(index, constant_literal); @@ -265,7 +265,7 @@ Status XlaOpKernelContext::ConstantInputAsIntScalar(int index, int64* out) { return LiteralToInt64Scalar(literal, out); } -Status XlaOpKernelContext::ConstantInputAsIntScalar(StringPiece name, +Status XlaOpKernelContext::ConstantInputAsIntScalar(absl::string_view name, int64* out) { TF_ASSIGN_OR_RETURN(int index, InputIndex(this, name)); return ConstantInputAsIntScalar(index, out); @@ -305,7 +305,7 @@ Status XlaOpKernelContext::ConstantInputAsIntVector(int index, return LiteralToInt64Vector(literal, out); } -Status XlaOpKernelContext::ConstantInputAsIntVector(StringPiece name, +Status XlaOpKernelContext::ConstantInputAsIntVector(absl::string_view name, std::vector<int64>* out) { TF_ASSIGN_OR_RETURN(int index, InputIndex(this, name)); return ConstantInputAsIntVector(index, out); @@ -344,7 +344,7 @@ Status XlaOpKernelContext::ConstantInputAsInt64Literal(int index, } } -Status XlaOpKernelContext::ConstantInputAsInt64Literal(StringPiece name, +Status XlaOpKernelContext::ConstantInputAsInt64Literal(absl::string_view name, xla::Literal* out) { TF_ASSIGN_OR_RETURN(int index, InputIndex(this, name)); return ConstantInputAsInt64Literal(index, out); @@ -361,7 +361,7 @@ Status XlaOpKernelContext::ConstantInputAsShape(int index, TensorShape* shape) { return Status::OK(); } -Status XlaOpKernelContext::InputList(StringPiece name, +Status XlaOpKernelContext::InputList(absl::string_view name, std::vector<xla::XlaOp>* handles, std::vector<TensorShape>* shapes) { OpInputList inputs; @@ -376,7 +376,7 @@ Status XlaOpKernelContext::InputList(StringPiece name, } Status XlaOpKernelContext::ConstantInputList( - StringPiece name, std::vector<xla::Literal>* outputs) { + absl::string_view name, std::vector<xla::Literal>* outputs) { int start, stop; TF_RETURN_IF_ERROR(op_kernel().InputRange(name, &start, &stop)); outputs->resize(stop - start); @@ -429,8 +429,8 @@ Status XlaOpKernelContext::ReadVariableInput(int index, DataType type, value); } -Status XlaOpKernelContext::ReadVariableInput(StringPiece name, DataType type, - TensorShape* shape, +Status XlaOpKernelContext::ReadVariableInput(absl::string_view name, + DataType type, TensorShape* shape, xla::XlaOp* value) { return ReadVariableInputTensor(GetInputTensorByName(name), type, context_, shape, value); @@ -564,7 +564,7 @@ Status XlaOpKernelContext::AssignVariable(int input_index, DataType type, handle, builder()); } -Status XlaOpKernelContext::AssignVariable(StringPiece name, DataType type, +Status XlaOpKernelContext::AssignVariable(absl::string_view name, DataType type, xla::XlaOp handle) { TF_RET_CHECK(handle.valid()); return AssignVariableTensor(GetInputTensorByName(name), type, context_, @@ -610,7 +610,7 @@ const xla::XlaComputation* XlaOpKernelContext::GetOrCreateMul( return XlaContext::Get(context_).GetOrCreateMul(type); } -const Tensor& XlaOpKernelContext::GetInputTensorByName(StringPiece name) { +const Tensor& XlaOpKernelContext::GetInputTensorByName(absl::string_view name) { const Tensor* tensor; CHECK(context_->input(name, &tensor).ok()); return *tensor; diff --git a/tensorflow/compiler/tf2xla/xla_op_kernel.h b/tensorflow/compiler/tf2xla/xla_op_kernel.h index 45cfa7da74..962c86d3a5 100644 --- a/tensorflow/compiler/tf2xla/xla_op_kernel.h +++ b/tensorflow/compiler/tf2xla/xla_op_kernel.h @@ -80,14 +80,14 @@ class XlaOpKernelContext { TensorShape InputShape(int index); // Returns the shape of input `name`. - TensorShape InputShape(StringPiece name); + TensorShape InputShape(absl::string_view name); // Returns input `index` as a XlaOp. Unlike // OpKernelContext::Input returns a symbolic value rather than a concrete // Tensor. const xla::XlaOp& Input(int index); // Returns input `name` as a XlaOp. - const xla::XlaOp& Input(StringPiece name); + const xla::XlaOp& Input(absl::string_view name); // Returns true if all inputs are the same shape, otherwise sets the // status to a non-OK value and returns false. @@ -97,7 +97,7 @@ class XlaOpKernelContext { // Returns the named list-valued immutable input in "list", as // defined in the OpDef. If the named output is not list-valued, // returns a one-element list. - Status InputList(StringPiece name, std::vector<xla::XlaOp>* handles, + Status InputList(absl::string_view name, std::vector<xla::XlaOp>* handles, std::vector<TensorShape>* shapes); // Helper methods for constant inputs. @@ -106,7 +106,7 @@ class XlaOpKernelContext { // expression cannot be evaluated, e.g., because it depends on unbound // parameters, returns a non-OK status. Status ConstantInput(int index, xla::Literal* constant_literal); - Status ConstantInput(StringPiece name, xla::Literal* constant_literal); + Status ConstantInput(absl::string_view name, xla::Literal* constant_literal); // Evaluates input `index`, reshapes it to `new_shape` if new_shape != // InputShape(index), and stores it in `*constant_literal`. If the input @@ -118,14 +118,15 @@ class XlaOpKernelContext { // Converts a constant scalar int32 or int64 tensor into an int64. Status ConstantInputAsIntScalar(int index, int64* out); - Status ConstantInputAsIntScalar(StringPiece name, int64* out); + Status ConstantInputAsIntScalar(absl::string_view name, int64* out); // Converts a constant scalar float32 or float64 tensor into a float64. Status ConstantInputAsFloatScalar(int index, double* out); // Converts a constant 1D int32 or int64 tensor into a vector of int64s. Status ConstantInputAsIntVector(int index, std::vector<int64>* out); - Status ConstantInputAsIntVector(StringPiece name, std::vector<int64>* out); + Status ConstantInputAsIntVector(absl::string_view name, + std::vector<int64>* out); // Reshapes and converts a constant int32 or int64 tensor into a vector of // int64s. @@ -133,7 +134,7 @@ class XlaOpKernelContext { // Converts a constant int32 or int64 Tensor into an xla int64 Literal. Status ConstantInputAsInt64Literal(int index, xla::Literal* out); - Status ConstantInputAsInt64Literal(StringPiece name, xla::Literal* out); + Status ConstantInputAsInt64Literal(absl::string_view name, xla::Literal* out); // Converts a constant 1D int32 or int64 tensor into a TensorShape. Status ConstantInputAsShape(int index, TensorShape* shape); @@ -141,7 +142,7 @@ class XlaOpKernelContext { // Returns the named list-valued immutable input in "list", as // defined in the OpDef. If the named output is not list-valued, // returns a one-element list. - Status ConstantInputList(StringPiece name, + Status ConstantInputList(absl::string_view name, std::vector<xla::Literal>* literals); // Outputs @@ -190,8 +191,8 @@ class XlaOpKernelContext { xla::XlaOp* value); // Reads the current value of the resouce variable referred to by input // `name`. - Status ReadVariableInput(StringPiece name, DataType type, TensorShape* shape, - xla::XlaOp* value); + Status ReadVariableInput(absl::string_view name, DataType type, + TensorShape* shape, xla::XlaOp* value); // Assigns the value `handle` to the variable referenced by input // `input_index`. The variable must be of `type`. Returns an error if the @@ -199,7 +200,8 @@ class XlaOpKernelContext { // different shape. Status AssignVariable(int input_index, DataType type, xla::XlaOp handle); // Assigns the value `handle` to the variable referenced by input `name`. - Status AssignVariable(StringPiece name, DataType type, xla::XlaOp handle); + Status AssignVariable(absl::string_view name, DataType type, + xla::XlaOp handle); // Helper routines for the OP_REQUIRES macros void CtxFailure(const Status& s); @@ -248,7 +250,7 @@ class XlaOpKernelContext { private: // Returns the tensor of input `name`. - const Tensor& GetInputTensorByName(StringPiece name); + const Tensor& GetInputTensorByName(absl::string_view name); OpKernelContext* const context_; }; diff --git a/tensorflow/compiler/tf2xla/xla_op_registry.cc b/tensorflow/compiler/tf2xla/xla_op_registry.cc index dae2d956ca..b0eeee3174 100644 --- a/tensorflow/compiler/tf2xla/xla_op_registry.cc +++ b/tensorflow/compiler/tf2xla/xla_op_registry.cc @@ -371,26 +371,28 @@ XlaOpRegistry& XlaOpRegistry::Instance() { return *r; } -XlaOpRegistrationBuilder::XlaOpRegistrationBuilder(StringPiece name) { +XlaOpRegistrationBuilder::XlaOpRegistrationBuilder(absl::string_view name) { registration_.reset(new XlaOpRegistry::OpRegistration); registration_->name = string(name); } -XlaOpRegistrationBuilder XlaOpRegistrationBuilder::Name(StringPiece name) { +XlaOpRegistrationBuilder XlaOpRegistrationBuilder::Name( + absl::string_view name) { XlaOpRegistrationBuilder registration(name); return registration; } XlaOpRegistrationBuilder& XlaOpRegistrationBuilder::Device( - absl::Span<const StringPiece> devices) { + absl::Span<const absl::string_view> devices) { registration_->has_device_whitelist = true; - for (StringPiece device : devices) { + for (absl::string_view device : devices) { registration_->device_whitelist.emplace(device); } return *this; } -XlaOpRegistrationBuilder& XlaOpRegistrationBuilder::Device(StringPiece device) { +XlaOpRegistrationBuilder& XlaOpRegistrationBuilder::Device( + absl::string_view device) { registration_->has_device_whitelist = true; registration_->device_whitelist.emplace(device); return *this; @@ -407,7 +409,7 @@ XlaOpRegistrationBuilder& XlaOpRegistrationBuilder::AllowResourceTypes() { } XlaOpRegistrationBuilder& XlaOpRegistrationBuilder::TypeConstraint( - StringPiece attr_name, DataType allowed) { + absl::string_view attr_name, DataType allowed) { std::set<DataType>& types = registration_->type_constraints[string(attr_name)]; types.insert(allowed); @@ -415,7 +417,7 @@ XlaOpRegistrationBuilder& XlaOpRegistrationBuilder::TypeConstraint( } XlaOpRegistrationBuilder& XlaOpRegistrationBuilder::TypeConstraint( - StringPiece attr_name, absl::Span<const DataType> allowed) { + absl::string_view attr_name, absl::Span<const DataType> allowed) { std::set<DataType>& types = registration_->type_constraints[string(attr_name)]; for (DataType t : allowed) { @@ -425,7 +427,7 @@ XlaOpRegistrationBuilder& XlaOpRegistrationBuilder::TypeConstraint( } XlaOpRegistrationBuilder& XlaOpRegistrationBuilder::CompileTimeConstInput( - StringPiece input_name) { + absl::string_view input_name) { registration_->compile_time_constant_inputs.emplace(input_name); return *this; } @@ -452,7 +454,7 @@ XlaOpRegistrar::XlaOpRegistrar( } XlaBackendRegistrar::XlaBackendRegistrar( - StringPiece name, absl::Span<const DataType> types, + absl::string_view name, absl::Span<const DataType> types, XlaOpRegistry::BackendOpFilter op_filter) { XlaOpRegistry& registry = XlaOpRegistry::Instance(); registry.RegisterBackend(string(name), types, op_filter); diff --git a/tensorflow/compiler/tf2xla/xla_op_registry.h b/tensorflow/compiler/tf2xla/xla_op_registry.h index c640842dc0..74a4885f1f 100644 --- a/tensorflow/compiler/tf2xla/xla_op_registry.h +++ b/tensorflow/compiler/tf2xla/xla_op_registry.h @@ -232,18 +232,18 @@ class XlaOpRegistry { class XlaOpRegistrationBuilder { public: // Starts an operator registration chain. - static XlaOpRegistrationBuilder Name(StringPiece name); + static XlaOpRegistrationBuilder Name(absl::string_view name); // Specifies a whitelist of devices on which the operator may run. - XlaOpRegistrationBuilder& Device(StringPiece devices); - XlaOpRegistrationBuilder& Device(absl::Span<const StringPiece> devices); + XlaOpRegistrationBuilder& Device(absl::string_view devices); + XlaOpRegistrationBuilder& Device(absl::Span<const absl::string_view> devices); // Specifies a type constraint for a type variable attribute. Each constraint // specifies the set of types that the type variable may assume. - XlaOpRegistrationBuilder& TypeConstraint(StringPiece attr_name, + XlaOpRegistrationBuilder& TypeConstraint(absl::string_view attr_name, DataType allowed); - XlaOpRegistrationBuilder& TypeConstraint(StringPiece attr_name, + XlaOpRegistrationBuilder& TypeConstraint(absl::string_view attr_name, absl::Span<const DataType> allowed); // Specifies that a dummy copy of this operator should not be registered on @@ -254,13 +254,13 @@ class XlaOpRegistrationBuilder { XlaOpRegistrationBuilder& AllowResourceTypes(); // Mark 'input_name' as an argument whose value must be known at compile-time. - XlaOpRegistrationBuilder& CompileTimeConstInput(StringPiece input_name); + XlaOpRegistrationBuilder& CompileTimeConstInput(absl::string_view input_name); std::unique_ptr<XlaOpRegistry::OpRegistration> Build( XlaOpRegistry::Factory factory); private: - XlaOpRegistrationBuilder(StringPiece name); + XlaOpRegistrationBuilder(absl::string_view name); std::unique_ptr<XlaOpRegistry::OpRegistration> registration_; }; @@ -288,7 +288,7 @@ class XlaOpRegistrar { class XlaBackendRegistrar { public: - XlaBackendRegistrar(StringPiece name, absl::Span<const DataType> types, + XlaBackendRegistrar(absl::string_view name, absl::Span<const DataType> types, XlaOpRegistry::BackendOpFilter op_filter = nullptr); }; diff --git a/tensorflow/compiler/tf2xla/xla_resource.cc b/tensorflow/compiler/tf2xla/xla_resource.cc index 7928fa0347..56c2e01055 100644 --- a/tensorflow/compiler/tf2xla/xla_resource.cc +++ b/tensorflow/compiler/tf2xla/xla_resource.cc @@ -43,7 +43,7 @@ XlaResource::XlaResource(Kind kind, int arg_num, string name, DataType type, for (const string& gradient : tensor_array_gradients) { tensor_array_gradients_[gradient].reset(new XlaResource( /*kind=*/kTensorArray, /*arg_num=*/-1, - /*name=*/strings::StrCat("TensorArrayGrad: ", name_), type_, shape_, + /*name=*/absl::StrCat("TensorArrayGrad: ", name_), type_, shape_, xla::XlaOp(), tensor_array_size_, /*tensor_array_gradients=*/{})); } } @@ -135,7 +135,7 @@ Status XlaResource::GetOrCreateTensorArrayGradient(const string& source, xla::Broadcast(XlaHelpers::Zero(builder, type_), ta_shape.dim_sizes()); gradient.reset( new XlaResource(/*kind=*/kTensorArray, /*arg_num=*/-1, - /*name=*/strings::StrCat("TensorArrayGrad: ", name_), + /*name=*/absl::StrCat("TensorArrayGrad: ", name_), type_, shape_, gradient_value, tensor_array_size_, /*tensor_array_gradients=*/{})); } diff --git a/tensorflow/compiler/xla/client/xla_builder.cc b/tensorflow/compiler/xla/client/xla_builder.cc index 7f2125f74c..887b970661 100644 --- a/tensorflow/compiler/xla/client/xla_builder.cc +++ b/tensorflow/compiler/xla/client/xla_builder.cc @@ -820,7 +820,7 @@ XlaOp XlaBuilder::Lt(const XlaOp& lhs, const XlaOp& rhs, } XlaOp XlaBuilder::Dot(const XlaOp& lhs, const XlaOp& rhs, - const PrecisionConfigProto* precision_config_proto) { + const PrecisionConfig* precision_config) { return ReportErrorOrReturn([&]() -> StatusOr<XlaOp> { TF_ASSIGN_OR_RETURN(const Shape& lhs_shape, GetShape(lhs)); @@ -828,14 +828,13 @@ XlaOp XlaBuilder::Dot(const XlaOp& lhs, const XlaOp& rhs, dimension_numbers.add_lhs_contracting_dimensions( lhs_shape.dimensions_size() == 1 ? 0 : 1); dimension_numbers.add_rhs_contracting_dimensions(0); - return DotGeneral(lhs, rhs, dimension_numbers, precision_config_proto); + return DotGeneral(lhs, rhs, dimension_numbers, precision_config); }); } -XlaOp XlaBuilder::DotGeneral( - const XlaOp& lhs, const XlaOp& rhs, - const DotDimensionNumbers& dimension_numbers, - const PrecisionConfigProto* precision_config_proto) { +XlaOp XlaBuilder::DotGeneral(const XlaOp& lhs, const XlaOp& rhs, + const DotDimensionNumbers& dimension_numbers, + const PrecisionConfig* precision_config) { return ReportErrorOrReturn([&]() -> StatusOr<XlaOp> { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& lhs_shape, GetShape(lhs)); @@ -844,8 +843,8 @@ XlaOp XlaBuilder::DotGeneral( ShapeInference::InferDotOpShape(lhs_shape, rhs_shape, dimension_numbers)); *instr.mutable_dot_dimension_numbers() = dimension_numbers; - if (precision_config_proto != nullptr) { - *instr.mutable_precision_config() = *precision_config_proto; + if (precision_config != nullptr) { + *instr.mutable_precision_config() = *precision_config; } return AddInstruction(std::move(instr), HloOpcode::kDot, {lhs, rhs}); }); @@ -899,28 +898,26 @@ Status XlaBuilder::VerifyConvolution( XlaOp XlaBuilder::Conv(const XlaOp& lhs, const XlaOp& rhs, absl::Span<const int64> window_strides, Padding padding, int64 feature_group_count, - const PrecisionConfigProto* precision_config_proto) { + const PrecisionConfig* precision_config) { return ConvWithGeneralDimensions( lhs, rhs, window_strides, padding, CreateDefaultConvDimensionNumbers(window_strides.size()), - feature_group_count, precision_config_proto); + feature_group_count, precision_config); } XlaOp XlaBuilder::ConvWithGeneralPadding( const XlaOp& lhs, const XlaOp& rhs, absl::Span<const int64> window_strides, absl::Span<const std::pair<int64, int64>> padding, - int64 feature_group_count, - const PrecisionConfigProto* precision_config_proto) { + int64 feature_group_count, const PrecisionConfig* precision_config) { return ConvGeneral(lhs, rhs, window_strides, padding, CreateDefaultConvDimensionNumbers(window_strides.size()), - feature_group_count, precision_config_proto); + feature_group_count, precision_config); } XlaOp XlaBuilder::ConvWithGeneralDimensions( const XlaOp& lhs, const XlaOp& rhs, absl::Span<const int64> window_strides, Padding padding, const ConvolutionDimensionNumbers& dimension_numbers, - int64 feature_group_count, - const PrecisionConfigProto* precision_config_proto) { + int64 feature_group_count, const PrecisionConfig* precision_config) { return ReportErrorOrReturn([&]() -> StatusOr<XlaOp> { TF_ASSIGN_OR_RETURN(const Shape& lhs_shape, GetShape(lhs)); TF_ASSIGN_OR_RETURN(const Shape& rhs_shape, GetShape(rhs)); @@ -948,7 +945,7 @@ XlaOp XlaBuilder::ConvWithGeneralDimensions( MakePadding(base_area_dimensions, window_dimensions, window_strides, padding), dimension_numbers, feature_group_count, - precision_config_proto); + precision_config); }); } @@ -956,11 +953,10 @@ XlaOp XlaBuilder::ConvGeneral( const XlaOp& lhs, const XlaOp& rhs, absl::Span<const int64> window_strides, absl::Span<const std::pair<int64, int64>> padding, const ConvolutionDimensionNumbers& dimension_numbers, - int64 feature_group_count, - const PrecisionConfigProto* precision_config_proto) { + int64 feature_group_count, const PrecisionConfig* precision_config) { return ConvGeneralDilated(lhs, rhs, window_strides, padding, {}, {}, dimension_numbers, feature_group_count, - precision_config_proto); + precision_config); } XlaOp XlaBuilder::ConvGeneralDilated( @@ -968,8 +964,7 @@ XlaOp XlaBuilder::ConvGeneralDilated( absl::Span<const std::pair<int64, int64>> padding, absl::Span<const int64> lhs_dilation, absl::Span<const int64> rhs_dilation, const ConvolutionDimensionNumbers& dimension_numbers, - int64 feature_group_count, - const PrecisionConfigProto* precision_config_proto) { + int64 feature_group_count, const PrecisionConfig* precision_config) { return ReportErrorOrReturn([&]() -> StatusOr<XlaOp> { HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& lhs_shape, GetShape(lhs)); @@ -996,8 +991,8 @@ XlaOp XlaBuilder::ConvGeneralDilated( *instr.mutable_convolution_dimension_numbers() = dimension_numbers; instr.set_feature_group_count(feature_group_count); - if (precision_config_proto != nullptr) { - *instr.mutable_precision_config() = *precision_config_proto; + if (precision_config != nullptr) { + *instr.mutable_precision_config() = *precision_config; } return AddInstruction(std::move(instr), HloOpcode::kConvolution, @@ -2594,43 +2589,40 @@ XlaOp Le(const XlaOp& lhs, const XlaOp& rhs, } XlaOp Dot(const XlaOp& lhs, const XlaOp& rhs, - const PrecisionConfigProto* precision_config_proto) { - return lhs.builder()->Dot(lhs, rhs, precision_config_proto); + const PrecisionConfig* precision_config) { + return lhs.builder()->Dot(lhs, rhs, precision_config); } XlaOp DotGeneral(const XlaOp& lhs, const XlaOp& rhs, const DotDimensionNumbers& dimension_numbers, - const PrecisionConfigProto* precision_config_proto) { + const PrecisionConfig* precision_config) { return lhs.builder()->DotGeneral(lhs, rhs, dimension_numbers, - precision_config_proto); + precision_config); } XlaOp Conv(const XlaOp& lhs, const XlaOp& rhs, absl::Span<const int64> window_strides, Padding padding, - int64 feature_group_count, - const PrecisionConfigProto* precision_config_proto) { + int64 feature_group_count, const PrecisionConfig* precision_config) { return lhs.builder()->Conv(lhs, rhs, window_strides, padding, - feature_group_count, precision_config_proto); + feature_group_count, precision_config); } -XlaOp ConvWithGeneralPadding( - const XlaOp& lhs, const XlaOp& rhs, absl::Span<const int64> window_strides, - absl::Span<const std::pair<int64, int64>> padding, - int64 feature_group_count, - const PrecisionConfigProto* precision_config_proto) { - return lhs.builder()->ConvWithGeneralPadding(lhs, rhs, window_strides, - padding, feature_group_count, - precision_config_proto); +XlaOp ConvWithGeneralPadding(const XlaOp& lhs, const XlaOp& rhs, + absl::Span<const int64> window_strides, + absl::Span<const std::pair<int64, int64>> padding, + int64 feature_group_count, + const PrecisionConfig* precision_config) { + return lhs.builder()->ConvWithGeneralPadding( + lhs, rhs, window_strides, padding, feature_group_count, precision_config); } XlaOp ConvWithGeneralDimensions( const XlaOp& lhs, const XlaOp& rhs, absl::Span<const int64> window_strides, Padding padding, const ConvolutionDimensionNumbers& dimension_numbers, - int64 feature_group_count, - const PrecisionConfigProto* precision_config_proto) { + int64 feature_group_count, const PrecisionConfig* precision_config) { return lhs.builder()->ConvWithGeneralDimensions( lhs, rhs, window_strides, padding, dimension_numbers, feature_group_count, - precision_config_proto); + precision_config); } XlaOp ConvGeneral(const XlaOp& lhs, const XlaOp& rhs, @@ -2638,10 +2630,10 @@ XlaOp ConvGeneral(const XlaOp& lhs, const XlaOp& rhs, absl::Span<const std::pair<int64, int64>> padding, const ConvolutionDimensionNumbers& dimension_numbers, int64 feature_group_count, - const PrecisionConfigProto* precision_config_proto) { + const PrecisionConfig* precision_config) { return lhs.builder()->ConvGeneral(lhs, rhs, window_strides, padding, dimension_numbers, feature_group_count, - precision_config_proto); + precision_config); } XlaOp ConvGeneralDilated(const XlaOp& lhs, const XlaOp& rhs, @@ -2651,10 +2643,10 @@ XlaOp ConvGeneralDilated(const XlaOp& lhs, const XlaOp& rhs, absl::Span<const int64> rhs_dilation, const ConvolutionDimensionNumbers& dimension_numbers, int64 feature_group_count, - const PrecisionConfigProto* precision_config_proto) { + const PrecisionConfig* precision_config) { return lhs.builder()->ConvGeneralDilated( lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation, - dimension_numbers, feature_group_count, precision_config_proto); + dimension_numbers, feature_group_count, precision_config); } XlaOp Fft(const XlaOp& operand, FftType fft_type, diff --git a/tensorflow/compiler/xla/client/xla_builder.h b/tensorflow/compiler/xla/client/xla_builder.h index 59fbc664f2..58e8f4e7fa 100644 --- a/tensorflow/compiler/xla/client/xla_builder.h +++ b/tensorflow/compiler/xla/client/xla_builder.h @@ -496,20 +496,19 @@ class XlaBuilder { // Enqueues a dot instruction onto the computation. XlaOp Dot(const XlaOp& lhs, const XlaOp& rhs, - const PrecisionConfigProto* precision_config_proto = nullptr); + const PrecisionConfig* precision_config = nullptr); // Enqueues a general dot instruction onto the computation. - XlaOp DotGeneral( - const XlaOp& lhs, const XlaOp& rhs, - const DotDimensionNumbers& dimension_numbers, - const PrecisionConfigProto* precision_config_proto = nullptr); + XlaOp DotGeneral(const XlaOp& lhs, const XlaOp& rhs, + const DotDimensionNumbers& dimension_numbers, + const PrecisionConfig* precision_config = nullptr); // Enqueues a convolution instruction onto the computation, which uses the // default convolution dimension numbers. XlaOp Conv(const XlaOp& lhs, const XlaOp& rhs, absl::Span<const int64> window_strides, Padding padding, int64 feature_group_count = 1, - const PrecisionConfigProto* precision_config_proto = nullptr); + const PrecisionConfig* precision_config = nullptr); // Enqueues a convolution instruction onto the computation, with the caller // provided padding configuration in the format returned by MakePadding(). @@ -518,7 +517,7 @@ class XlaBuilder { absl::Span<const int64> window_strides, absl::Span<const std::pair<int64, int64>> padding, int64 feature_group_count = 1, - const PrecisionConfigProto* precision_config_proto = nullptr); + const PrecisionConfig* precision_config = nullptr); // Enqueues a convolution instruction onto the computation, with the caller // provided dimension numbers configuration. @@ -527,29 +526,27 @@ class XlaBuilder { absl::Span<const int64> window_strides, Padding padding, const ConvolutionDimensionNumbers& dimension_numbers, int64 feature_group_count = 1, - const PrecisionConfigProto* precision_config_proto = nullptr); + const PrecisionConfig* precision_config = nullptr); // Enqueues a convolution instruction onto the computation, with the caller // provided padding configuration as well as the dimension numbers. - XlaOp ConvGeneral( - const XlaOp& lhs, const XlaOp& rhs, - absl::Span<const int64> window_strides, - absl::Span<const std::pair<int64, int64>> padding, - const ConvolutionDimensionNumbers& dimension_numbers, - int64 feature_group_count = 1, - const PrecisionConfigProto* precision_config_proto = nullptr); + XlaOp ConvGeneral(const XlaOp& lhs, const XlaOp& rhs, + absl::Span<const int64> window_strides, + absl::Span<const std::pair<int64, int64>> padding, + const ConvolutionDimensionNumbers& dimension_numbers, + int64 feature_group_count = 1, + const PrecisionConfig* precision_config = nullptr); // Enqueues a convolution instruction onto the computation, with the caller // provided padding configuration, dilation factors and dimension numbers. - XlaOp ConvGeneralDilated( - const XlaOp& lhs, const XlaOp& rhs, - absl::Span<const int64> window_strides, - absl::Span<const std::pair<int64, int64>> padding, - absl::Span<const int64> lhs_dilation, - absl::Span<const int64> rhs_dilation, - const ConvolutionDimensionNumbers& dimension_numbers, - int64 feature_group_count = 1, - const PrecisionConfigProto* precision_config_proto = nullptr); + XlaOp ConvGeneralDilated(const XlaOp& lhs, const XlaOp& rhs, + absl::Span<const int64> window_strides, + absl::Span<const std::pair<int64, int64>> padding, + absl::Span<const int64> lhs_dilation, + absl::Span<const int64> rhs_dilation, + const ConvolutionDimensionNumbers& dimension_numbers, + int64 feature_group_count = 1, + const PrecisionConfig* precision_config = nullptr); // Enqueues an FFT instruction onto the computation, of the given type and // with the given FFT length. @@ -1150,32 +1147,30 @@ class XlaBuilder { friend XlaOp Le(const XlaOp& lhs, const XlaOp& rhs, absl::Span<const int64> broadcast_dimensions); friend XlaOp Dot(const XlaOp& lhs, const XlaOp& rhs, - const PrecisionConfigProto* precision_config_proto); + const PrecisionConfig* precision_config); friend XlaOp DotGeneral(const XlaOp& lhs, const XlaOp& rhs, const DotDimensionNumbers& dimension_number, - const PrecisionConfigProto* precision_config_proto); + const PrecisionConfig* precision_config); friend XlaOp Conv(const XlaOp& lhs, const XlaOp& rhs, absl::Span<const int64> window_strides, Padding padding, int64 feature_group_count, - const PrecisionConfigProto* precision_config_proto); + const PrecisionConfig* precision_config); friend XlaOp ConvWithGeneralPadding( const XlaOp& lhs, const XlaOp& rhs, absl::Span<const int64> window_strides, absl::Span<const std::pair<int64, int64>> padding, - int64 feature_group_count, - const PrecisionConfigProto* precision_config_proto); + int64 feature_group_count, const PrecisionConfig* precision_config); friend XlaOp ConvWithGeneralDimensions( const XlaOp& lhs, const XlaOp& rhs, absl::Span<const int64> window_strides, Padding padding, const ConvolutionDimensionNumbers& dimension_numbers, - int64 feature_group_count, - const PrecisionConfigProto* precision_config_proto); + int64 feature_group_count, const PrecisionConfig* precision_config); friend XlaOp ConvGeneral(const XlaOp& lhs, const XlaOp& rhs, absl::Span<const int64> window_strides, absl::Span<const std::pair<int64, int64>> padding, const ConvolutionDimensionNumbers& dimension_numbers, int64 feature_group_count, - const PrecisionConfigProto* precision_config_proto); + const PrecisionConfig* precision_config); friend XlaOp ConvGeneralDilated( const XlaOp& lhs, const XlaOp& rhs, absl::Span<const int64> window_strides, @@ -1183,8 +1178,7 @@ class XlaBuilder { absl::Span<const int64> lhs_dilation, absl::Span<const int64> rhs_dilation, const ConvolutionDimensionNumbers& dimension_numbers, - int64 feature_group_count, - const PrecisionConfigProto* precision_config_proto); + int64 feature_group_count, const PrecisionConfig* precision_config); friend XlaOp Fft(const XlaOp& operand, FftType fft_type, absl::Span<const int64> fft_length); friend XlaOp Infeed(XlaBuilder* builder, const Shape& shape, @@ -1629,27 +1623,27 @@ XlaOp Le(const XlaOp& lhs, const XlaOp& rhs, // Enqueues a dot instruction onto the computation. XlaOp Dot(const XlaOp& lhs, const XlaOp& rhs, - const PrecisionConfigProto* precision_config_proto = nullptr); + const PrecisionConfig* precision_config = nullptr); // Enqueues a general dot instruction onto the computation. XlaOp DotGeneral(const XlaOp& lhs, const XlaOp& rhs, const DotDimensionNumbers& dimension_numbers, - const PrecisionConfigProto* precision_config_proto = nullptr); + const PrecisionConfig* precision_config = nullptr); // Enqueues a convolution instruction onto the computation, which uses the // default convolution dimension numbers. XlaOp Conv(const XlaOp& lhs, const XlaOp& rhs, absl::Span<const int64> window_strides, Padding padding, int64 feature_group_count = 1, - const PrecisionConfigProto* precision_config_proto = nullptr); + const PrecisionConfig* precision_config = nullptr); // Enqueues a convolution instruction onto the computation, with the caller // provided padding configuration in the format returned by MakePadding(). -XlaOp ConvWithGeneralPadding( - const XlaOp& lhs, const XlaOp& rhs, absl::Span<const int64> window_strides, - absl::Span<const std::pair<int64, int64>> padding, - int64 feature_group_count = 1, - const PrecisionConfigProto* precision_config_proto = nullptr); +XlaOp ConvWithGeneralPadding(const XlaOp& lhs, const XlaOp& rhs, + absl::Span<const int64> window_strides, + absl::Span<const std::pair<int64, int64>> padding, + int64 feature_group_count = 1, + const PrecisionConfig* precision_config = nullptr); // Enqueues a convolution instruction onto the computation, with the caller // provided dimension numbers configuration. @@ -1657,7 +1651,7 @@ XlaOp ConvWithGeneralDimensions( const XlaOp& lhs, const XlaOp& rhs, absl::Span<const int64> window_strides, Padding padding, const ConvolutionDimensionNumbers& dimension_numbers, int64 feature_group_count = 1, - const PrecisionConfigProto* precision_config_proto = nullptr); + const PrecisionConfig* precision_config = nullptr); // Enqueues a convolution instruction onto the computation, with the caller // provided padding configuration as well as the dimension numbers. @@ -1666,17 +1660,18 @@ XlaOp ConvGeneral(const XlaOp& lhs, const XlaOp& rhs, absl::Span<const std::pair<int64, int64>> padding, const ConvolutionDimensionNumbers& dimension_numbers, int64 feature_group_count = 1, - const PrecisionConfigProto* precision_config_proto = nullptr); + const PrecisionConfig* precision_config = nullptr); // Enqueues a convolution instruction onto the computation, with the caller // provided padding configuration, dilation factors and dimension numbers. -XlaOp ConvGeneralDilated( - const XlaOp& lhs, const XlaOp& rhs, absl::Span<const int64> window_strides, - absl::Span<const std::pair<int64, int64>> padding, - absl::Span<const int64> lhs_dilation, absl::Span<const int64> rhs_dilation, - const ConvolutionDimensionNumbers& dimension_numbers, - int64 feature_group_count = 1, - const PrecisionConfigProto* precision_config_proto = nullptr); +XlaOp ConvGeneralDilated(const XlaOp& lhs, const XlaOp& rhs, + absl::Span<const int64> window_strides, + absl::Span<const std::pair<int64, int64>> padding, + absl::Span<const int64> lhs_dilation, + absl::Span<const int64> rhs_dilation, + const ConvolutionDimensionNumbers& dimension_numbers, + int64 feature_group_count = 1, + const PrecisionConfig* precision_config = nullptr); // Enqueues an FFT instruction onto the computation, of the given type and // with the given FFT length. diff --git a/tensorflow/compiler/xla/reference_util.cc b/tensorflow/compiler/xla/reference_util.cc index 8a05d1b0d7..9f1afa2671 100644 --- a/tensorflow/compiler/xla/reference_util.cc +++ b/tensorflow/compiler/xla/reference_util.cc @@ -574,9 +574,9 @@ ReferenceUtil::ConvArray4DGeneralDimensionsDilated( HloInstruction* rhs_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(rhs_literal))); - PrecisionConfigProto precision_config; + PrecisionConfig precision_config; precision_config.mutable_operand_precision()->Resize( - /*new_size=*/2, PrecisionConfigProto::DEFAULT); + /*new_size=*/2, PrecisionConfig::DEFAULT); b.AddInstruction(HloInstruction::CreateConvolve( shape, lhs_instruction, rhs_instruction, /*feature_group_count=*/1, window, dnums, precision_config)); diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index f6cfac6537..ab86dce510 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -989,6 +989,7 @@ tf_cc_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/core:test", "@com_google_absl//absl/memory", ], ) @@ -1036,6 +1037,7 @@ tf_cc_test( ":flatten_call_graph", ":hlo", ":hlo_ordering", + ":hlo_schedule", ":hlo_scheduling", "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", @@ -1049,6 +1051,7 @@ tf_cc_test( "//tensorflow/compiler/xla/tests:hlo_verified_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:lib", + "//tensorflow/core:test", "@com_google_absl//absl/memory", ], ) @@ -1062,6 +1065,7 @@ cc_library( ":hlo", ":hlo_dataflow_analysis", ":hlo_proto", + ":hlo_schedule", ":hlo_value", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", @@ -1082,6 +1086,7 @@ tf_cc_test( ":hlo", ":hlo_dataflow_analysis", ":hlo_ordering", + ":hlo_schedule", ":hlo_scheduling", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:types", @@ -1089,6 +1094,7 @@ tf_cc_test( "//tensorflow/compiler/xla/service:hlo_parser", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/core:test", ], ) @@ -1102,6 +1108,7 @@ cc_library( ":hlo", ":hlo_ordering", ":hlo_proto", + ":hlo_schedule", ":tuple_points_to_analysis", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:util", @@ -1125,6 +1132,7 @@ tf_cc_test( "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:lib", + "//tensorflow/core:test", "@com_google_absl//absl/memory", ], ) @@ -1170,6 +1178,43 @@ cc_library( ) cc_library( + name = "hlo_schedule", + srcs = ["hlo_schedule.cc"], + hdrs = ["hlo_schedule.h"], + deps = [ + ":hlo", + "//tensorflow/compiler/xla:status", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:util", + "//tensorflow/core:lib_internal", + "@com_google_absl//absl/strings", + "@com_google_absl//absl/strings:str_format", + "@com_google_absl//absl/types:span", + ], +) + +tf_cc_test( + name = "hlo_schedule_test", + srcs = ["hlo_schedule_test.cc"], + deps = [ + ":heap_simulator", + ":hlo", + ":hlo_dce", + ":hlo_ordering", + ":hlo_parser", + ":hlo_schedule", + ":hlo_scheduling", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/tests:hlo_test_base", + "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/core:test", + "@com_google_absl//absl/algorithm:container", + ], +) + +cc_library( name = "hlo_scheduling", srcs = ["hlo_scheduling.cc"], hdrs = ["hlo_scheduling.h"], @@ -1177,6 +1222,7 @@ cc_library( ":heap_simulator", ":hlo", ":hlo_ordering", + ":hlo_schedule", ":logical_buffer", ":tuple_points_to_analysis", "//tensorflow/compiler/xla:shape_util", @@ -1205,6 +1251,7 @@ tf_cc_test( "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:test", + "@com_google_absl//absl/algorithm:container", ], ) @@ -2366,6 +2413,7 @@ cc_library( ":hlo", ":hlo_dce", ":hlo_ordering", + ":hlo_schedule", ":hlo_scheduling", ":logical_buffer", ":tuple_points_to_analysis", @@ -2520,6 +2568,7 @@ cc_library( "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/core:lib", + "@com_google_absl//absl/container:inlined_vector", ], ) @@ -3187,6 +3236,7 @@ cc_library( "//tensorflow/compiler/xla:util", "//tensorflow/core:lib", "@com_google_absl//absl/algorithm:container", + "@com_google_absl//absl/container:inlined_vector", ], ) diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc index 019840b476..aa40fba9bb 100644 --- a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc +++ b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc @@ -1013,13 +1013,6 @@ TEST_F(AlgebraicSimplifierTest, PowNegative1) { 1); } -PrecisionConfigProto DefaultPrecisionConfig(int operands) { - PrecisionConfigProto precision_config; - precision_config.mutable_operand_precision()->Resize( - operands, PrecisionConfigProto::DEFAULT); - return precision_config; -} - TEST_F(AlgebraicSimplifierTest, ZeroSizedConvolution) { auto builder = HloComputation::Builder(TestName()); HloInstruction* lhs = builder.AddInstruction(HloInstruction::CreateParameter( @@ -2386,9 +2379,9 @@ TEST_P(ConvFilterPaddingTest, DoIt) { // Add a PrecisionConfig and check that AlgebraicSimplifier keeps it in place // after the transformation. - PrecisionConfigProto precision_config; - precision_config.add_operand_precision(PrecisionConfigProto::HIGH); - precision_config.add_operand_precision(PrecisionConfigProto::HIGHEST); + PrecisionConfig precision_config; + precision_config.add_operand_precision(PrecisionConfig::HIGH); + precision_config.add_operand_precision(PrecisionConfig::HIGHEST); orig_conv->set_precision_config(precision_config); auto module = CreateNewModule(); @@ -2408,9 +2401,8 @@ TEST_P(ConvFilterPaddingTest, DoIt) { conv->operand(1)->shape().dimensions(2), conv->operand(1)->shape().dimensions(3), testcase.expected_conv_window)); - EXPECT_THAT( - conv->precision_config().operand_precision(), - ElementsAre(PrecisionConfigProto::HIGH, PrecisionConfigProto::HIGHEST)); + EXPECT_THAT(conv->precision_config().operand_precision(), + ElementsAre(PrecisionConfig::HIGH, PrecisionConfig::HIGHEST)); } } diff --git a/tensorflow/compiler/xla/service/bfloat16_normalization_test.cc b/tensorflow/compiler/xla/service/bfloat16_normalization_test.cc index d480d72297..933cf873e0 100644 --- a/tensorflow/compiler/xla/service/bfloat16_normalization_test.cc +++ b/tensorflow/compiler/xla/service/bfloat16_normalization_test.cc @@ -308,9 +308,9 @@ TEST_F(BFloat16NormalizationTest, DoNotAddUnsupportedMixedPrecision) { DotDimensionNumbers dot_dnums; dot_dnums.add_lhs_contracting_dimensions(1); dot_dnums.add_rhs_contracting_dimensions(0); - PrecisionConfigProto precision_config; + PrecisionConfig precision_config; precision_config.mutable_operand_precision()->Resize( - 2, PrecisionConfigProto::DEFAULT); + 2, PrecisionConfig::DEFAULT); HloInstruction* dot = builder.AddInstruction( HloInstruction::CreateDot(bf16_shape, a, b, dot_dnums, precision_config)); diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc index 8b8c6bfd26..0f0af57626 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment.cc +++ b/tensorflow/compiler/xla/service/buffer_assignment.cc @@ -617,18 +617,24 @@ Status BufferAssignment::ComputeSummaryStats() { } // Only compute total fragmentation if all computations have schedules. - SequentialHloOrdering::HloModuleSequence module_sequence; + HloSchedule schedule(module_); + bool schedule_complete = true; for (const auto& computation : module_->computations()) { - const std::vector<const HloInstruction*>* sequence = - liveness_->hlo_ordering().SequentialOrder(*computation); - if (sequence != nullptr) { - module_sequence.emplace(computation, *sequence); + if (!computation->IsFusionComputation()) { + const std::vector<const HloInstruction*>* sequence = + liveness_->hlo_ordering().SequentialOrder(*computation); + if (sequence == nullptr) { + schedule_complete = false; + } else { + schedule.set_sequence(computation, *sequence); + } } } - if (module_sequence.size() == module_->computation_count()) { + if (schedule_complete) { + TF_RETURN_IF_ERROR(schedule.Verify()); TF_ASSIGN_OR_RETURN( const int64 min_size, - HeapSimulator::MinimumMemoryForModule(module_sequence, buffer_size_)); + HeapSimulator::MinimumMemoryForModule(schedule, buffer_size_)); stats_.total_fragmentation_bytes = stats_.total_allocation_bytes - min_size; } @@ -1064,7 +1070,7 @@ Status BufferAssigner::AssignBuffersWithSequentialOrdering( // since buffers for kCall, kWhile, and kConditional sub-computations are // only live for the duration of their calling instructions. VLOG(1) << "Running whole-module heap simulation"; - SequentialHloOrdering::HloModuleSequence module_sequence; + HloSchedule schedule(&assignment->module()); FlatSet<const LogicalBuffer*> all_buffers_to_assign; for (const auto& pair : buffers_to_assign_sequentially) { const HloComputation* computation = pair.first; @@ -1072,7 +1078,7 @@ Status BufferAssigner::AssignBuffersWithSequentialOrdering( const std::vector<const HloInstruction*>* instruction_sequence = hlo_ordering.SequentialOrder(*computation); CHECK(instruction_sequence != nullptr) << computation->name(); - module_sequence[computation] = *instruction_sequence; + schedule.set_sequence(computation, *instruction_sequence); all_buffers_to_assign.insert(buffers_to_assign.begin(), buffers_to_assign.end()); } @@ -1090,7 +1096,7 @@ Status BufferAssigner::AssignBuffersWithSequentialOrdering( const HeapSimulator::Result result, HeapSimulator::Run(absl::make_unique<DecreasingSizeRunsHeap>( absl::make_unique<LazyBestFitHeap>(alignment)), - assignment->module(), module_sequence, + assignment->module(), schedule, assignment->points_to_analysis(), assignment->buffer_size_, options)); AssignBuffersFromHeapSimulator(result, assignment, @@ -1121,7 +1127,7 @@ Status BufferAssigner::AssignBuffersWithSequentialOrdering( HeapSimulator::Run( absl::make_unique<DecreasingSizeRunsHeap>( absl::make_unique<LazyBestFitHeap>(alignment)), - *computation, *instruction_sequence, + *computation, HloInstructionSequence(*instruction_sequence), assignment->points_to_analysis(), assignment->buffer_size_, options)); AssignBuffersFromHeapSimulator(result, assignment, diff --git a/tensorflow/compiler/xla/service/buffer_assignment_test.cc b/tensorflow/compiler/xla/service/buffer_assignment_test.cc index 7398f105a0..5a231c173d 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment_test.cc +++ b/tensorflow/compiler/xla/service/buffer_assignment_test.cc @@ -33,6 +33,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_opcode.h" #include "tensorflow/compiler/xla/service/hlo_ordering.h" #include "tensorflow/compiler/xla/service/hlo_parser.h" +#include "tensorflow/compiler/xla/service/hlo_schedule.h" #include "tensorflow/compiler/xla/service/hlo_scheduling.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/test.h" @@ -40,6 +41,7 @@ limitations under the License. #include "tensorflow/compiler/xla/tests/hlo_verified_test_base.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/core/status_test_util.h" #include "tensorflow/core/platform/macros.h" namespace xla { @@ -120,14 +122,10 @@ class BufferAssignmentTest : public HloVerifiedTestBase { HloModule* module, absl::Span<const HloInstruction* const> instruction_sequence, int64 alignment = 1) { - SequentialHloOrdering::HloModuleSequence module_sequence; - module_sequence[module->entry_computation()] = - std::vector<const HloInstruction*>(instruction_sequence.begin(), - instruction_sequence.end()); + HloSchedule schedule(module); + schedule.set_sequence(module->entry_computation(), instruction_sequence); return BufferAssigner::Run( - module, - absl::make_unique<SequentialHloOrdering>(module, - module_sequence), + module, absl::make_unique<SequentialHloOrdering>(schedule), backend().compiler()->BufferSizeBytesFunction(), [alignment](LogicalBuffer::Color) { return alignment; }, /*allow_input_output_aliasing=*/false, @@ -1490,9 +1488,9 @@ TEST_F(BufferAssignmentTest, OneTempAllocation) { DotDimensionNumbers dot_dnums; dot_dnums.add_lhs_contracting_dimensions(1); dot_dnums.add_rhs_contracting_dimensions(0); - PrecisionConfigProto precision_config; + PrecisionConfig precision_config; precision_config.mutable_operand_precision()->Resize( - 2, PrecisionConfigProto::DEFAULT); + 2, PrecisionConfig::DEFAULT); auto dot_ab = builder.AddInstruction(HloInstruction::CreateDot( shape_2x4, param_a, param_b, dot_dnums, precision_config)); auto dot_bc = builder.AddInstruction(HloInstruction::CreateDot( @@ -1785,11 +1783,10 @@ class WhileBufferAssignmentTest : public HloVerifiedTestBase { std::unique_ptr<BufferAssignment> RunBufferAssignment(HloModule* module, int64 alignment = 1) { - auto sequence = - ScheduleComputationsInModule(*module, ByteSizeOf).ConsumeValueOrDie(); + HloSchedule schedule = + ScheduleModule(*module, ByteSizeOf).ConsumeValueOrDie(); return BufferAssigner::Run( - module, - absl::make_unique<SequentialHloOrdering>(module, sequence), + module, absl::make_unique<SequentialHloOrdering>(schedule), ByteSizeOf, [alignment](LogicalBuffer::Color) { return alignment; }, /*allow_input_output_aliasing=*/false, @@ -2096,17 +2093,25 @@ TEST_F(WhileBufferAssignmentTest, ColocatedBuffers) { // Create a sequential order among all the instructions in the entry // computation, since the issue this test stresses depends on the order the // nodes are traversed during BufferAssignment. - SequentialHloOrdering::HloModuleSequence sequence; - sequence[module->entry_computation()] = { - token, infeed, infeed_data, while0, while1, zero, add, while2, tuple}; + TF_ASSERT_OK_AND_ASSIGN( + HloSchedule schedule, + ScheduleModule(*module, [](const BufferValue& buffer) { + return ShapeUtil::ByteSizeOf(buffer.shape(), + /*pointer_size=*/sizeof(void*)); + })); + schedule.set_sequence( + module->entry_computation(), + {token, infeed, infeed_data, while0, while1, zero, add, while2, tuple}); + TF_ASSERT_OK(schedule.Verify()); + TF_ASSERT_OK_AND_ASSIGN( auto assignment, - BufferAssigner::Run( - module, absl::make_unique<SequentialHloOrdering>(module, sequence), - backend().compiler()->BufferSizeBytesFunction(), - [](LogicalBuffer::Color) { return 1; }, - /*allow_input_output_aliasing=*/false, - /*allocate_buffers_for_constants=*/true)); + BufferAssigner::Run(module, + absl::make_unique<SequentialHloOrdering>(schedule), + backend().compiler()->BufferSizeBytesFunction(), + [](LogicalBuffer::Color) { return 1; }, + /*allow_input_output_aliasing=*/false, + /*allocate_buffers_for_constants=*/true)); // The result tuple elements must be assigned with different buffers. TF_ASSERT_OK_AND_ASSIGN(auto slice0, assignment->GetUniqueSlice(tuple, {0})); @@ -2263,29 +2268,6 @@ ENTRY Main { GetAllocation(*buffers, param0, {1, 1})); } -static bool IsPostOrderTraversal( - const std::vector<const HloInstruction*>& sequence) { - tensorflow::gtl::FlatSet<const HloInstruction*> seen_so_far; - auto has_not_been_seen_yet = [&](const HloInstruction* instruction) { - return seen_so_far.count(instruction) == 0; - }; - - for (auto instruction : sequence) { - if (std::any_of(instruction->operands().begin(), - instruction->operands().end(), has_not_been_seen_yet) || - std::any_of(instruction->control_predecessors().begin(), - instruction->control_predecessors().end(), - has_not_been_seen_yet)) { - return false; // Not a post order. - } - if (!seen_so_far.insert(instruction).second) { - return false; // Not a "traversal". - } - } - - return true; -} - TEST_F(WhileBufferAssignmentTest, WhileLoopsInterferingResultRange) { auto module = CreateNewModule(); auto builder = HloComputation::Builder(TestName()); @@ -2340,27 +2322,27 @@ TEST_F(WhileBufferAssignmentTest, WhileLoopsInterferingResultRange) { RunCopyInsertion(module); - auto sequence = - ScheduleComputationsInModule(*module, ByteSizeOf).ConsumeValueOrDie(); + HloSchedule schedule = + ScheduleModule(*module, ByteSizeOf).ConsumeValueOrDie(); - // To trigger b/38494731, we want a specific Hlo sequence for the + // To trigger b/38494731, we want a specific Hlo schedule for the // root computation, so we overwrite that entry with a manually // crafted sequence. - sequence[module->entry_computation()] = { - input1, weights1, one, output1, while1->operand(0), while1, - input0, weights0, zero, output0, while0->operand(0), while0, - gte0, gte1, root_add}; + schedule.set_sequence(module->entry_computation(), + {input1, weights1, one, output1, while1->operand(0), + while1, input0, weights0, zero, output0, + while0->operand(0), while0, gte0, gte1, root_add}); - // If this ASSERT_TRUE fails, we constructed a bogus sequence above - // and this test itself is buggy. - ASSERT_TRUE(IsPostOrderTraversal(sequence[module->entry_computation()])); + // If this ASSERT fails, we constructed a bogus sequence above and this test + // itself is buggy. + TF_ASSERT_OK(schedule.Verify()); auto assignment = - BufferAssigner::Run( - module, absl::make_unique<SequentialHloOrdering>(module, sequence), - ByteSizeOf, [](LogicalBuffer::Color) { return 1; }, - /*allow_input_output_aliasing=*/false, - /*allocate_buffers_for_constants=*/true) + BufferAssigner::Run(module, + absl::make_unique<SequentialHloOrdering>(schedule), + ByteSizeOf, [](LogicalBuffer::Color) { return 1; }, + /*allow_input_output_aliasing=*/false, + /*allocate_buffers_for_constants=*/true) .ConsumeValueOrDie(); EXPECT_TRUE(BuffersDistinct({while0}, {while1}, *assignment)); diff --git a/tensorflow/compiler/xla/service/buffer_liveness_test.cc b/tensorflow/compiler/xla/service/buffer_liveness_test.cc index 26e26e316d..414bfe7999 100644 --- a/tensorflow/compiler/xla/service/buffer_liveness_test.cc +++ b/tensorflow/compiler/xla/service/buffer_liveness_test.cc @@ -27,6 +27,7 @@ limitations under the License. #include "tensorflow/compiler/xla/tests/hlo_test_base.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/core/status_test_util.h" namespace xla { namespace { @@ -166,12 +167,12 @@ TEST_F(BufferLivenessTest, MultipleEntryParameters_Sequential) { auto module = CreateNewModule(); HloComputation* entry = module->AddEntryComputation(builder.Build()); - SequentialHloOrdering::HloModuleSequence sequence; - sequence.insert({entry, {param0, negate, param1, exp, add}}); - auto liveness = BufferLiveness::Run(module.get(), - absl::make_unique<SequentialHloOrdering>( - module.get(), sequence)) - .ConsumeValueOrDie(); + HloSchedule schedule(module.get()); + schedule.set_sequence(entry, {param0, negate, param1, exp, add}); + auto liveness = + BufferLiveness::Run(module.get(), + absl::make_unique<SequentialHloOrdering>(schedule)) + .ConsumeValueOrDie(); // Entry parameters interfere as if they are defined simultaneously at // the very beginning. @@ -291,13 +292,12 @@ TEST_F(BufferLivenessTest, OverlappedBuffersSequentialOrder) { auto module = CreateNewModule(); auto computation = module->AddEntryComputation(builder.Build()); - SequentialHloOrdering::HloModuleSequence module_sequence; - std::vector<const HloInstruction*> order = {param, negate, exp, add}; - module_sequence.emplace(computation, order); - auto liveness = BufferLiveness::Run(module.get(), - absl::make_unique<SequentialHloOrdering>( - module.get(), module_sequence)) - .ConsumeValueOrDie(); + HloSchedule schedule(module.get()); + schedule.set_sequence(computation, {param, negate, exp, add}); + auto liveness = + BufferLiveness::Run(module.get(), + absl::make_unique<SequentialHloOrdering>(schedule)) + .ConsumeValueOrDie(); EXPECT_TRUE(InstructionsMayInterfere(*liveness, param, negate)); EXPECT_FALSE(InstructionsMayInterfere(*liveness, param, exp)); @@ -339,14 +339,14 @@ TEST_F(BufferLivenessTest, RootInstructionIsNotLastInSequentialOrder) { auto module = CreateNewModule(); auto computation = module->AddEntryComputation(builder.Build(add)); - SequentialHloOrdering::HloModuleSequence module_sequence; - std::vector<const HloInstruction*> order = {param, add, recv, - recv_done, send, send_done}; - module_sequence.emplace(computation, order); - auto liveness = BufferLiveness::Run(module.get(), - absl::make_unique<SequentialHloOrdering>( - module.get(), module_sequence)) - .ConsumeValueOrDie(); + HloSchedule schedule(module.get()); + schedule.set_sequence(computation, + {param, add, token, recv, recv_done, send, send_done}); + TF_ASSERT_OK(schedule.Verify()); + auto liveness = + BufferLiveness::Run(module.get(), + absl::make_unique<SequentialHloOrdering>(schedule)) + .ConsumeValueOrDie(); EXPECT_FALSE(InstructionsMayInterfere(*liveness, param, add)); // Check the root instruction (add) buffer interferes with the recv buffer. diff --git a/tensorflow/compiler/xla/service/cpu/conv_canonicalization_test.cc b/tensorflow/compiler/xla/service/cpu/conv_canonicalization_test.cc index 616c453750..05792795a1 100644 --- a/tensorflow/compiler/xla/service/cpu/conv_canonicalization_test.cc +++ b/tensorflow/compiler/xla/service/cpu/conv_canonicalization_test.cc @@ -56,13 +56,6 @@ class ConvCanonicalizationTest : public HloTestBase { static constexpr int kOutputFeatureCount = 64; }; -PrecisionConfigProto DefaultPrecisionConfig(int operands) { - PrecisionConfigProto precision_config; - precision_config.mutable_operand_precision()->Resize( - operands, PrecisionConfigProto::DEFAULT); - return precision_config; -} - TEST_F(ConvCanonicalizationTest, NonCanonicalToCanonical) { auto builder = HloComputation::Builder(TestName()); // The input dimensions are in CNHW order. diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc index 796f36510e..e7b6075994 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc @@ -584,16 +584,14 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::RunBackend( // computation. Using this sequence enables tighter buffer liveness analysis // and reduced memory usage (as compared to using DependencyHloOrdering). TF_ASSIGN_OR_RETURN( - SequentialHloOrdering::HloModuleSequence module_sequence, - ScheduleComputationsInModule(*module, BufferSizeBytesFunction(), - DFSMemoryScheduler)); + HloSchedule schedule, + ScheduleModule(*module, BufferSizeBytesFunction(), DFSMemoryScheduler)); // Run buffer allocation on the HLO graph. TF_ASSIGN_OR_RETURN( std::unique_ptr<BufferAssignment> assignment, BufferAssigner::Run(module.get(), - absl::make_unique<SequentialHloOrdering>( - module.get(), module_sequence), + absl::make_unique<SequentialHloOrdering>(schedule), BufferSizeBytesFunction(), memory_alignment, /*allow_input_output_aliasing=*/false, /*allocate_buffers_for_constants=*/true)); @@ -627,9 +625,10 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::RunBackend( } TF_RETURN_IF_ERROR( ir_emitter - .EmitComputation(embedded_computation, embedded_computation->name(), - /*is_top_level_computation=*/false, - &module_sequence.at(embedded_computation)) + .EmitComputation( + embedded_computation, embedded_computation->name(), + /*is_top_level_computation=*/false, + &schedule.sequence(embedded_computation).instructions()) .status()); } string function_name_prefix = entry_computation->name().empty() @@ -637,9 +636,10 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::RunBackend( : entry_computation->name(); TF_ASSIGN_OR_RETURN( llvm::Function * entry_function, - ir_emitter.EmitComputation(entry_computation, function_name_prefix, - /*is_top_level_computation=*/true, - &module_sequence.at(entry_computation))); + ir_emitter.EmitComputation( + entry_computation, function_name_prefix, + /*is_top_level_computation=*/true, + &schedule.sequence(entry_computation).instructions())); string function_name = [&]() { llvm::SmallVector<char, 40> function_name_vector; @@ -771,20 +771,18 @@ CpuCompiler::CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules, VLOG(2) << "After optimization:"; XLA_VLOG_LINES(2, module->ToString()); - TF_ASSIGN_OR_RETURN( - SequentialHloOrdering::HloModuleSequence module_sequence, - ScheduleComputationsInModule(*module, BufferSizeBytesFunction())); + TF_ASSIGN_OR_RETURN(HloSchedule schedule, + ScheduleModule(*module, BufferSizeBytesFunction())); // Run buffer analysis on the HLO graph. This analysis figures out which // temporary buffers are required to run the computation. TF_ASSIGN_OR_RETURN( std::unique_ptr<BufferAssignment> assignment, - BufferAssigner::Run( - module, - absl::make_unique<SequentialHloOrdering>(module, module_sequence), - BufferSizeBytesFunction(), memory_alignment, - /*allow_input_output_aliasing=*/false, - /*allocate_buffers_for_constants=*/true)); + BufferAssigner::Run(module, + absl::make_unique<SequentialHloOrdering>(schedule), + BufferSizeBytesFunction(), memory_alignment, + /*allow_input_output_aliasing=*/false, + /*allocate_buffers_for_constants=*/true)); // BufferAssignment::ToString() includes a header, so no need for us to // print one ourselves. XLA_VLOG_LINES(2, assignment->ToString()); @@ -824,18 +822,18 @@ CpuCompiler::CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules, } TF_RETURN_IF_ERROR( ir_emitter - .EmitComputation(embedded_computation, - embedded_computation->name(), - /*is_top_level_computation=*/false, - &module_sequence.at(embedded_computation)) + .EmitComputation( + embedded_computation, embedded_computation->name(), + /*is_top_level_computation=*/false, + &schedule.sequence(embedded_computation).instructions()) .status()); } const string& entry_point_name = options.entry_point_name(); - TF_ASSIGN_OR_RETURN( - llvm::Function * entry_function, - ir_emitter.EmitComputation(computation, entry_point_name, - /*is_top_level_computation=*/true, - &module_sequence.at(computation))); + TF_ASSIGN_OR_RETURN(llvm::Function * entry_function, + ir_emitter.EmitComputation( + computation, entry_point_name, + /*is_top_level_computation=*/true, + &schedule.sequence(computation).instructions())); CHECK(entry_function->getName() == llvm_ir::AsStringRef(entry_point_name)); diff --git a/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc b/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc index 6bd0a2dd90..0fea462c85 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc @@ -38,9 +38,9 @@ std::unique_ptr<HloInstruction> MakeDot(const Shape& shape, HloInstruction* lhs, DotDimensionNumbers dot_dnums; dot_dnums.add_lhs_contracting_dimensions(1); dot_dnums.add_rhs_contracting_dimensions(0); - PrecisionConfigProto precision_config; + PrecisionConfig precision_config; precision_config.mutable_operand_precision()->Resize( - 2, PrecisionConfigProto::DEFAULT); + 2, PrecisionConfig::DEFAULT); return HloInstruction::CreateDot(shape, lhs, rhs, dot_dnums, precision_config); } diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc index e5cf15c686..df8c2a636b 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc @@ -110,7 +110,7 @@ IrEmitter::IrEmitter( StatusOr<llvm::Function*> IrEmitter::EmitComputation( HloComputation* computation, const string& function_name_prefix, bool is_top_level_computation, - std::vector<const HloInstruction*>* instruction_order) { + const std::vector<const HloInstruction*>* instruction_order) { string function_name = name_uniquer_.GetUniqueName(function_name_prefix); VLOG(2) << "Emitting IR for CPU function [" << function_name_prefix << "]; ordered? " << (instruction_order != nullptr); diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.h b/tensorflow/compiler/xla/service/cpu/ir_emitter.h index 58a333b8fb..3df99464ba 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.h +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.h @@ -98,7 +98,7 @@ class IrEmitter : public DfsHloVisitorWithDefault, StatusOr<llvm::Function*> EmitComputation( HloComputation* computation, const string& function_name_prefix, bool is_top_level_computation, - std::vector<const HloInstruction*>* instruction_order); + const std::vector<const HloInstruction*>* instruction_order); llvm::IRBuilder<>* b() { return &b_; } diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD index a68b7a1bef..13ccff35f8 100644 --- a/tensorflow/compiler/xla/service/gpu/BUILD +++ b/tensorflow/compiler/xla/service/gpu/BUILD @@ -813,6 +813,7 @@ cc_library( "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_ordering", "//tensorflow/compiler/xla/service:hlo_reachability", + "//tensorflow/compiler/xla/service:hlo_schedule", "//tensorflow/compiler/xla/service:hlo_scheduling", "@com_google_absl//absl/memory", ], diff --git a/tensorflow/compiler/xla/service/gpu/cudnn_convolution_rewriter_test.cc b/tensorflow/compiler/xla/service/gpu/cudnn_convolution_rewriter_test.cc index 9b46bfc098..bda8ebe579 100644 --- a/tensorflow/compiler/xla/service/gpu/cudnn_convolution_rewriter_test.cc +++ b/tensorflow/compiler/xla/service/gpu/cudnn_convolution_rewriter_test.cc @@ -95,13 +95,6 @@ class CudnnConvolutionRewriterTest : public HloVerifiedTestBase { ConvolutionDimensionNumbers tf_default_dnums_for_backward_input_; }; -PrecisionConfigProto DefaultPrecisionConfig(int operands) { - PrecisionConfigProto precision_config; - precision_config.mutable_operand_precision()->Resize( - operands, PrecisionConfigProto::DEFAULT); - return precision_config; -} - TEST_F(CudnnConvolutionRewriterTest, BackwardFilterConvolve) { HloComputation::Builder builder(TestName()); HloInstruction* activations = diff --git a/tensorflow/compiler/xla/service/gpu/gpu_hlo_schedule.cc b/tensorflow/compiler/xla/service/gpu/gpu_hlo_schedule.cc index 743035a84e..ea9376e101 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_hlo_schedule.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_hlo_schedule.cc @@ -22,6 +22,7 @@ limitations under the License. #include "absl/memory/memory.h" #include "tensorflow/compiler/xla/service/buffer_value.h" #include "tensorflow/compiler/xla/service/hlo_reachability.h" +#include "tensorflow/compiler/xla/service/hlo_schedule.h" #include "tensorflow/compiler/xla/service/hlo_scheduling.h" #include "tensorflow/compiler/xla/types.h" @@ -198,11 +199,12 @@ StatusOr<std::unique_ptr<GpuHloSchedule>> GpuHloSchedule::Build( // All kernels are launched on a single stream, so there's no loss of // concurrency by optimizing for minimal memory usage. TF_ASSIGN_OR_RETURN( - schedule->thunk_launch_order_, - ScheduleOneComputation( + HloInstructionSequence sequence, + ScheduleComputation( *entry_computation, [pointer_size](const BufferValue& buffer) { return ShapeUtil::ByteSizeOf(buffer.shape(), pointer_size); })); + schedule->thunk_launch_order_ = sequence.instructions(); } else { // BFS tends to increase concurrency, but also increases memory usage. BFSLaunchOrder(entry_computation, &schedule->thunk_launch_order_); diff --git a/tensorflow/compiler/xla/service/gpu/gpu_hlo_schedule.h b/tensorflow/compiler/xla/service/gpu/gpu_hlo_schedule.h index 30a0e7cecd..07a7fc67aa 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_hlo_schedule.h +++ b/tensorflow/compiler/xla/service/gpu/gpu_hlo_schedule.h @@ -33,7 +33,9 @@ namespace gpu { // launches, because thunks may be scheduled onto concurrent streams. This // schedule is used by BufferAssigner to determine buffer liveness (i.e. to // minimize allocations), and also by ThunkSchedule to determine the thunk -// launch order. +// launch order. This class differs from xla::HloSchedule in that HloSchedule +// represents a total order of all instructions in the module for backends which +// execute HLO instructions strictly sequentially. class GpuHloSchedule { public: // Constructs an GpuHloSchedule for the given module, based on the given diff --git a/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc b/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc index c822c94f1b..8a6e5327e0 100644 --- a/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc +++ b/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc @@ -259,7 +259,7 @@ TEST_F(MultiOutputFusionTest, MultiOutputFusionTwoLoops) { TEST_F(MultiOutputFusionTest, MultiOutputFusionLoopReduceToInputFusion) { // Fusing a reduce into a loop fusion would require changing the fusion kind. // That's not supported yet. - auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( + auto module = ParseHloString(absl::StrCat(kModulePrefix, R"( fused_computation_1 { p0.1 = f32[6400]{0} parameter(0) ROOT mul = f32[6400]{0} multiply(p0.1, p0.1) @@ -277,7 +277,7 @@ TEST_F(MultiOutputFusionTest, MultiOutputFusionLoopReduceToInputFusion) { } TEST_F(MultiOutputFusionTest, MultiOutputFusionLoopElementwise) { - auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( + auto module = ParseHloString(absl::StrCat(kModulePrefix, R"( fused_computation_1 { p0.1 = f32[6400]{0} parameter(0) ROOT mul = f32[6400]{0} multiply(p0.1, p0.1) @@ -301,7 +301,7 @@ TEST_F(MultiOutputFusionTest, MultiOutputFusionLoopElementwise) { } TEST_F(MultiOutputFusionTest, MultiOutputFusionSiblingLoopsDifferentShapes) { - auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( + auto module = ParseHloString(absl::StrCat(kModulePrefix, R"( fused_computation_1 { p0.1 = f32[8,1,5,16,1,1]{5,4,3,2,1,0} parameter(0) ROOT mul = f32[8,1,5,16,1,1]{5,4,3,2,1,0} multiply(p0.1, p0.1) @@ -324,7 +324,7 @@ TEST_F(MultiOutputFusionTest, MultiOutputFusionSiblingLoopsDifferentShapes) { } TEST_F(MultiOutputFusionTest, MultiOutputFusionSiblingLoopAndMultiOutputLoop) { - auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( + auto module = ParseHloString(absl::StrCat(kModulePrefix, R"( fused_computation_1 { p0.1 = f32[8,1,5,16,1,1]{5,4,3,2,1,0} parameter(0) mul = f32[8,1,5,16,1,1]{5,4,3,2,1,0} multiply(p0.1, p0.1) @@ -358,7 +358,7 @@ TEST_F(MultiOutputFusionTest, MultiOutputFusionSiblingLoopAndMultiOutputLoop) { TEST_F(MultiOutputFusionTest, MultiOutputFusionSiblingLoopAndMultiOutputLoopDifferentShapes) { - auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( + auto module = ParseHloString(absl::StrCat(kModulePrefix, R"( fused_computation_1 { p0.1 = f32[8,1,5,16,1,1]{5,4,3,2,1,0} parameter(0) mul = f32[8,1,5,16,1,1]{5,4,3,2,1,0} multiply(p0.1, p0.1) diff --git a/tensorflow/compiler/xla/service/graphviz_example.cc b/tensorflow/compiler/xla/service/graphviz_example.cc index 0a49d85c6d..ef70b68877 100644 --- a/tensorflow/compiler/xla/service/graphviz_example.cc +++ b/tensorflow/compiler/xla/service/graphviz_example.cc @@ -112,9 +112,9 @@ std::unique_ptr<HloModule> MakeBigGraph() { DotDimensionNumbers dot_dnums; dot_dnums.add_lhs_contracting_dimensions(1); dot_dnums.add_rhs_contracting_dimensions(0); - PrecisionConfigProto precision_config; + PrecisionConfig precision_config; precision_config.mutable_operand_precision()->Resize( - /*new_size=*/2, PrecisionConfigProto::DEFAULT); + /*new_size=*/2, PrecisionConfig::DEFAULT); auto dot = builder.AddInstruction(HloInstruction::CreateDot( vshape, clamp, param_v0, dot_dnums, precision_config)); auto tuple = builder.AddInstruction( diff --git a/tensorflow/compiler/xla/service/heap_simulator.cc b/tensorflow/compiler/xla/service/heap_simulator.cc index 38c3982ebf..e0f3a7e0e2 100644 --- a/tensorflow/compiler/xla/service/heap_simulator.cc +++ b/tensorflow/compiler/xla/service/heap_simulator.cc @@ -29,13 +29,13 @@ using tensorflow::gtl::FlatSet; /*static*/ StatusOr<int64> HeapSimulator::MinimumMemoryForModule( - const SequentialHloOrdering::HloModuleSequence& module_sequence, + const HloSchedule& schedule, const LogicalBuffer::SizeFunction& size_function) { - if (module_sequence.empty()) { + if (schedule.empty()) { return 0; } - const HloModule* module = module_sequence.begin()->first->parent(); + const HloModule* module = schedule.module(); TF_ASSIGN_OR_RETURN(std::unique_ptr<TuplePointsToAnalysis> points_to_analysis, TuplePointsToAnalysis::Run(module)); @@ -47,14 +47,13 @@ StatusOr<int64> HeapSimulator::MinimumMemoryForModule( TF_ASSIGN_OR_RETURN( HeapSimulator::Result result, HeapSimulator::Run(absl::make_unique<NoFragmentationStatsHeap>(), *module, - module_sequence, *points_to_analysis, size_function)); + schedule, *points_to_analysis, size_function)); return result.heap_size; } /*static*/ StatusOr<int64> HeapSimulator::MinimumMemoryForComputation( - const HloComputation& computation, - const std::vector<const HloInstruction*>& sequence, + const HloComputation& computation, const HloInstructionSequence& sequence, const TuplePointsToAnalysis& points_to_analysis, const LogicalBuffer::SizeFunction& size_function, const tensorflow::gtl::FlatMap<const HloComputation*, int64>* @@ -71,13 +70,13 @@ StatusOr<int64> HeapSimulator::MinimumMemoryForComputation( /*static*/ StatusOr<HeapSimulator::Result> HeapSimulator::Run( std::unique_ptr<HeapAlgorithm> algorithm, const HloModule& module, - const SequentialHloOrdering::HloModuleSequence& module_sequence, + const HloSchedule& schedule, const TuplePointsToAnalysis& points_to_analysis, const BufferValue::SizeFunction& size_fn, const Options& options) { - HeapSimulator heap(std::move(algorithm), size_fn, options, &module_sequence); + HeapSimulator heap(std::move(algorithm), size_fn, options, &schedule); const HloComputation* entry_computation = module.entry_computation(); - const std::vector<const HloInstruction*>& instruction_sequence = - FindOrDie(module_sequence, entry_computation); + const HloInstructionSequence& instruction_sequence = + schedule.sequence(entry_computation); TF_RETURN_IF_ERROR(heap.RunComputation( *entry_computation, instruction_sequence, points_to_analysis)); return heap.Finish(); @@ -86,13 +85,13 @@ StatusOr<HeapSimulator::Result> HeapSimulator::Run( /*static*/ StatusOr<HeapSimulator::Result> HeapSimulator::Run( std::unique_ptr<HeapAlgorithm> algorithm, const HloComputation& computation, - const std::vector<const HloInstruction*>& instruction_sequence, + const HloInstructionSequence& instruction_sequence, const TuplePointsToAnalysis& points_to_analysis, const BufferValue::SizeFunction& size_fn, const Options& options, const tensorflow::gtl::FlatMap<const HloComputation*, int64>* memory_by_computation) { HeapSimulator heap(std::move(algorithm), size_fn, options, - /*module_sequence=*/nullptr, memory_by_computation); + /*schedule=*/nullptr, memory_by_computation); TF_RETURN_IF_ERROR(heap.RunComputation(computation, instruction_sequence, points_to_analysis)); return heap.Finish(); @@ -102,7 +101,7 @@ StatusOr<HeapSimulator::Result> HeapSimulator::Run( // 'instruction_sequence'. Status HeapSimulator::RunComputation( const HloComputation& computation, - const std::vector<const HloInstruction*>& instruction_sequence, + const HloInstructionSequence& instruction_sequence, const TuplePointsToAnalysis& points_to_analysis) { VLOG(3) << "Computation:\n" << computation.ToString(); // The goal here is to minimize memory usage, assuming the given sequential @@ -133,7 +132,8 @@ Status HeapSimulator::RunComputation( // set of instructions that need to be visited contains all users of all // aliases, that is, all users of all instructions that have the buffer // contained in their points-to set. - for (const HloInstruction* instruction : instruction_sequence) { + for (const HloInstruction* instruction : + instruction_sequence.instructions()) { const PointsToSet& points_to = points_to_analysis.GetPointsToSet(instruction); const PointsToSet::BufferSet& buffer_set = points_to.CreateFlattenedSet(); @@ -166,7 +166,8 @@ Status HeapSimulator::RunComputation( std::vector<const BufferValue*> dead_buffers_to_free; std::vector<const BufferValue*> operand_buffers_to_free; - for (const HloInstruction* instruction : instruction_sequence) { + for (const HloInstruction* instruction : + instruction_sequence.instructions()) { const TuplePointsToAnalysis::BufferDefinitionVector& buffers_defined_by_instruction = points_to_analysis.GetBuffersDefinedByInstruction(instruction); @@ -285,14 +286,14 @@ Status HeapSimulator::RunComputation( // The order that the sub-computations are simulated does not affect // correctness; since the whole module has been scheduled, we know that the // sub-computations will never be run concurrently. - if (module_sequence_ != nullptr) { + if (schedule_ != nullptr) { if (instruction->opcode() == HloOpcode::kCall || instruction->opcode() == HloOpcode::kConditional || instruction->opcode() == HloOpcode::kWhile) { for (const HloComputation* called_computation : instruction->called_computations()) { - const std::vector<const HloInstruction*>& called_sequence = - FindOrDie(*module_sequence_, called_computation); + const HloInstructionSequence& called_sequence = + schedule_->sequence(called_computation); TF_RETURN_IF_ERROR(RunComputation( *called_computation, called_sequence, points_to_analysis)); } @@ -343,16 +344,16 @@ Status HeapSimulator::RunComputation( HeapSimulator::HeapSimulator( std::unique_ptr<HeapAlgorithm> algorithm, const BufferValue::SizeFunction& size_fn, const Options& options, - const SequentialHloOrdering::HloModuleSequence* module_sequence, + const HloSchedule* schedule, const tensorflow::gtl::FlatMap<const HloComputation*, int64>* memory_by_computation) : no_fragmentation_stats_(absl::make_unique<NoFragmentationStatsHeap>()), algorithm_(std::move(algorithm)), size_fn_(size_fn), options_(options), - module_sequence_(module_sequence), + schedule_(schedule), memory_by_computation_(memory_by_computation) { - debug_trace_.set_whole_module_simulation(module_sequence_ != nullptr); + debug_trace_.set_whole_module_simulation(schedule_ != nullptr); } HeapSimulator::~HeapSimulator() {} diff --git a/tensorflow/compiler/xla/service/heap_simulator.h b/tensorflow/compiler/xla/service/heap_simulator.h index af05bedee7..ffbf947d5a 100644 --- a/tensorflow/compiler/xla/service/heap_simulator.h +++ b/tensorflow/compiler/xla/service/heap_simulator.h @@ -27,6 +27,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_ordering.h" +#include "tensorflow/compiler/xla/service/hlo_schedule.h" #include "tensorflow/compiler/xla/service/tuple_points_to_analysis.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/core/lib/gtl/flatmap.h" @@ -88,23 +89,22 @@ class HeapSimulator { // Returns the minimum memory required to compute an HLO module where all // computations have been scheduled (represented by the given - // module_sequence), assuming no fragmentation. + // schedule), assuming no fragmentation. static StatusOr<int64> MinimumMemoryForModule( - const SequentialHloOrdering::HloModuleSequence& module_sequence, + const HloSchedule& schedule, const LogicalBuffer::SizeFunction& size_function); // Returns the minimum memory required to compute the given computation, // assuming no fragmentation. static StatusOr<int64> MinimumMemoryForComputation( - const HloComputation& computation, - const std::vector<const HloInstruction*>& sequence, + const HloComputation& computation, const HloInstructionSequence& sequence, const TuplePointsToAnalysis& points_to_analysis, const LogicalBuffer::SizeFunction& size_function, const tensorflow::gtl::FlatMap<const HloComputation*, int64>* memory_by_computation = nullptr); // Run the heap simulation with the given algorithm, assuming the given - // module_sequence, which must contain a topologically-consistent total + // schedule, which must contain a topologically-consistent total // ordering of all instructions within each computation. The result is invalid // if instructions are not run in exactly this sequence. // @@ -112,12 +112,12 @@ class HeapSimulator { // to running on a per-computation basis, since we can re-use buffer space for // called sub-computations. // - static StatusOr<Result> Run( - std::unique_ptr<HeapAlgorithm> algorithm, const HloModule& module, - const SequentialHloOrdering::HloModuleSequence& module_sequence, - const TuplePointsToAnalysis& points_to_analysis, - const BufferValue::SizeFunction& size_fn, - const Options& options = Options()); + static StatusOr<Result> Run(std::unique_ptr<HeapAlgorithm> algorithm, + const HloModule& module, + const HloSchedule& schedule, + const TuplePointsToAnalysis& points_to_analysis, + const BufferValue::SizeFunction& size_fn, + const Options& options = Options()); // Same as above, but runs on a single computation. The 'instruction_sequence' // must contain a topologically-consistent total ordering of all instructions @@ -126,7 +126,7 @@ class HeapSimulator { static StatusOr<Result> Run( std::unique_ptr<HeapAlgorithm> algorithm, const HloComputation& computation, - const std::vector<const HloInstruction*>& instruction_sequence, + const HloInstructionSequence& instruction_sequence, const TuplePointsToAnalysis& points_to_analysis, const BufferValue::SizeFunction& size_fn, const Options& options = Options(), @@ -134,21 +134,19 @@ class HeapSimulator { memory_by_computation = nullptr); private: - // If 'module_sequence' is non-null, it is used to find kCall and kWhile + // If 'schedule' is non-null, it is used to find kCall and kWhile // sub-computations, and the heap simulation for those sub-computations will // be run recursively. I.e. the simulation is run over the whole module. - HeapSimulator( - std::unique_ptr<HeapAlgorithm> algorithm, - const BufferValue::SizeFunction& size_fn, const Options& options, - const SequentialHloOrdering::HloModuleSequence* module_sequence = nullptr, - const tensorflow::gtl::FlatMap<const HloComputation*, int64>* - memory_by_computation = nullptr); + HeapSimulator(std::unique_ptr<HeapAlgorithm> algorithm, + const BufferValue::SizeFunction& size_fn, + const Options& options, const HloSchedule* schedule = nullptr, + const tensorflow::gtl::FlatMap<const HloComputation*, int64>* + memory_by_computation = nullptr); ~HeapSimulator(); - Status RunComputation( - const HloComputation& computation, - const std::vector<const HloInstruction*>& instruction_sequence, - const TuplePointsToAnalysis& points_to_analysis); + Status RunComputation(const HloComputation& computation, + const HloInstructionSequence& instruction_sequence, + const TuplePointsToAnalysis& points_to_analysis); bool IgnoreBuffer(const BufferValue* buffer) const; void Alloc(const BufferValue* buffer, const HloInstruction* instruction); @@ -169,11 +167,11 @@ class HeapSimulator { const std::unique_ptr<HeapAlgorithm> algorithm_; const BufferValue::SizeFunction size_fn_; const Options options_; - // module_sequence_ is set by buffer assignment, and memory_by_computation_ is + // schedule_ is set by buffer assignment, and memory_by_computation_ is // set by hlo scheduling. Then, in RunComputation, we check both in order to // handle subcomputations. It would be good to unify the handling of // subcomputations, but it's not clear how. - const SequentialHloOrdering::HloModuleSequence* module_sequence_; + const HloSchedule* schedule_; const tensorflow::gtl::FlatMap<const HloComputation*, int64>* memory_by_computation_; diff --git a/tensorflow/compiler/xla/service/heap_simulator_test.cc b/tensorflow/compiler/xla/service/heap_simulator_test.cc index 576c5ff7a4..00a25db467 100644 --- a/tensorflow/compiler/xla/service/heap_simulator_test.cc +++ b/tensorflow/compiler/xla/service/heap_simulator_test.cc @@ -30,6 +30,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/tuple_points_to_analysis.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/core/lib/core/status_test_util.h" #include "tensorflow/core/lib/gtl/flatmap.h" namespace xla { @@ -85,13 +86,16 @@ TEST_F(MinimumMemoryForSequenceTest, MultiComputation) { return ShapeUtil::ByteSizeOf(buffer.shape(), /*pointer_size=*/8); }; - SequentialHloOrdering::HloModuleSequence module_sequence; - module_sequence[cond_computation] = {cond_param, cond_iter, cond_data, - cond_lt}; - module_sequence[body_computation] = {body_param}; - module_sequence[entry_computation] = {iter, data, tuple, while_op}; - EXPECT_EQ(56, HeapSimulator::MinimumMemoryForModule(module_sequence, size_fn) - .ValueOrDie()); + HloSchedule schedule(module.get()); + schedule.set_sequence(cond_computation, + {cond_param, cond_iter, cond_data, cond_lt}); + schedule.set_sequence(body_computation, {body_param}); + schedule.set_sequence(entry_computation, {iter, data, tuple, while_op}); + TF_ASSERT_OK(schedule.Verify()); + + EXPECT_EQ( + 56, + HeapSimulator::MinimumMemoryForModule(schedule, size_fn).ValueOrDie()); } const char kAlloc[] = "Alloc"; @@ -149,10 +153,11 @@ class HeapSimulatorTracker { auto zero_size = [](const BufferValue& buffer) { return 0; }; auto algorithm = absl::make_unique<DecreasingSizeRunsHeap>( absl::make_unique<HeapCallRecorder>(&actual_calls_)); - result_ = HeapSimulator::Run( - std::move(algorithm), *module_->entry_computation(), - instruction_sequence, *points_to_analysis_, zero_size) - .ConsumeValueOrDie(); + result_ = + HeapSimulator::Run(std::move(algorithm), *module_->entry_computation(), + HloInstructionSequence(instruction_sequence), + *points_to_analysis_, zero_size) + .ConsumeValueOrDie(); } explicit HeapSimulatorTracker(const string& name) { @@ -168,11 +173,12 @@ class HeapSimulatorTracker { TuplePointsToAnalysis::Run(module_.get()).ConsumeValueOrDie(); // Construct the module sequence grouped by computation. - SequentialHloOrdering::HloModuleSequence module_sequence; + HloSchedule schedule(module_.get()); tensorflow::gtl::FlatMap<const HloInstruction*, int> reverse_position; for (int i = 0; i < full_module_sequence.size(); ++i) { const HloInstruction* instruction = full_module_sequence[i]; - module_sequence[instruction->parent()].push_back(instruction); + schedule.GetOrCreateSequence(instruction->parent()) + .push_back(instruction); reverse_position[instruction] = full_module_sequence.size() - i; } @@ -185,8 +191,8 @@ class HeapSimulatorTracker { }; auto algorithm = absl::make_unique<DecreasingSizeRunsHeap>( absl::make_unique<HeapCallRecorder>(&actual_calls_)); - result_ = HeapSimulator::Run(std::move(algorithm), *module_, - module_sequence, *points_to_analysis_, size_fn) + result_ = HeapSimulator::Run(std::move(algorithm), *module_, schedule, + *points_to_analysis_, size_fn) .ConsumeValueOrDie(); } @@ -353,13 +359,6 @@ TEST_F(HeapSimulatorTest, BufferReusedOnce) { (neg_buffer == output_buffer_1)); } -PrecisionConfigProto DefaultPrecisionConfig(int operands) { - PrecisionConfigProto precision_config; - precision_config.mutable_operand_precision()->Resize( - operands, PrecisionConfigProto::DEFAULT); - return precision_config; -} - TEST_F(HeapSimulatorTest, MultiplyDot) { auto builder = HloComputation::Builder(TestName()); auto paramA = builder.AddInstruction( diff --git a/tensorflow/compiler/xla/service/hlo.proto b/tensorflow/compiler/xla/service/hlo.proto index 58b7af93eb..99d0cf50ca 100644 --- a/tensorflow/compiler/xla/service/hlo.proto +++ b/tensorflow/compiler/xla/service/hlo.proto @@ -172,7 +172,7 @@ message HloInstructionProto { xla.ScatterDimensionNumbers scatter_dimension_numbers = 48; // Precision configuration for the instruction. Has backend-specific meaning. - xla.PrecisionConfigProto precision_config = 51; + xla.PrecisionConfig precision_config = 51; // Collective permute field. repeated SourceTarget source_target_pairs = 52; diff --git a/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc index 54abe3345d..0cd0ab36fc 100644 --- a/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc +++ b/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc @@ -885,18 +885,20 @@ TEST_F(HloAliasAnalysisTest, WhileInterference) { // For a sequential order, if there is interference iff the negate is after // the while. - SequentialHloOrdering::HloModuleSequence sequence; - sequence[body] = {body_param, body_root}; - sequence[condition] = {cond_param, cond_root}; + HloSchedule schedule(module_); + schedule.set_sequence(body, {body_param, body_root}); + schedule.set_sequence(condition, {cond_param, cond_root}); { - sequence[entry] = {init, xla_while, negate, entry_root}; - SequentialHloOrdering ordering(module_, sequence); + schedule.set_sequence(entry, {init, xla_while, negate, entry_root}); + TF_ASSERT_OK(schedule.Verify()); + SequentialHloOrdering ordering(schedule); EXPECT_TRUE(analysis.HasLiveRangeInterference(ordering)); } { - sequence[entry] = {init, negate, xla_while, entry_root}; - SequentialHloOrdering ordering(module_, sequence); + schedule.set_sequence(entry, {init, negate, xla_while, entry_root}); + TF_ASSERT_OK(schedule.Verify()); + SequentialHloOrdering ordering(schedule); EXPECT_FALSE(analysis.HasLiveRangeInterference(ordering)); } } diff --git a/tensorflow/compiler/xla/service/hlo_computation_test.cc b/tensorflow/compiler/xla/service/hlo_computation_test.cc index a2c1ce34c6..2aaaef1d36 100644 --- a/tensorflow/compiler/xla/service/hlo_computation_test.cc +++ b/tensorflow/compiler/xla/service/hlo_computation_test.cc @@ -601,9 +601,9 @@ TEST_F(HloComputationTest, Stringification) { DotDimensionNumbers dot_dnums; dot_dnums.add_lhs_contracting_dimensions(1); dot_dnums.add_rhs_contracting_dimensions(0); - PrecisionConfigProto precision_config; + PrecisionConfig precision_config; precision_config.mutable_operand_precision()->Resize( - 2, PrecisionConfigProto::DEFAULT); + 2, PrecisionConfig::DEFAULT); builder.AddInstruction( HloInstruction::CreateDot(sout, x, reshape, dot_dnums, precision_config)); auto module = CreateNewModule(); @@ -636,9 +636,9 @@ TEST_F(HloComputationTest, StringificationIndent) { DotDimensionNumbers dot_dnums; dot_dnums.add_lhs_contracting_dimensions(1); dot_dnums.add_rhs_contracting_dimensions(0); - PrecisionConfigProto precision_config; + PrecisionConfig precision_config; precision_config.mutable_operand_precision()->Resize( - 2, PrecisionConfigProto::DEFAULT); + 2, PrecisionConfig::DEFAULT); builder.AddInstruction( HloInstruction::CreateDot(sout, x, reshape, dot_dnums, precision_config)); auto module = CreateNewModule(); @@ -672,9 +672,9 @@ TEST_F(HloComputationTest, StringificationCanonical) { DotDimensionNumbers dot_dnums; dot_dnums.add_lhs_contracting_dimensions(1); dot_dnums.add_rhs_contracting_dimensions(0); - PrecisionConfigProto precision_config; + PrecisionConfig precision_config; precision_config.mutable_operand_precision()->Resize( - 2, PrecisionConfigProto::DEFAULT); + 2, PrecisionConfig::DEFAULT); builder.AddInstruction( HloInstruction::CreateDot(sout, x, reshape, dot_dnums, precision_config)); auto module = CreateNewModule(); diff --git a/tensorflow/compiler/xla/service/hlo_creation_utils.cc b/tensorflow/compiler/xla/service/hlo_creation_utils.cc index a6ae0337a5..a3fcc0fefa 100644 --- a/tensorflow/compiler/xla/service/hlo_creation_utils.cc +++ b/tensorflow/compiler/xla/service/hlo_creation_utils.cc @@ -63,7 +63,7 @@ StatusOr<HloInstruction*> MakeSliceHlo(HloInstruction* operand, StatusOr<HloInstruction*> MakeConvolveHlo( HloInstruction* lhs, HloInstruction* rhs, int64 feature_group_count, const Window& window, const ConvolutionDimensionNumbers& dimension_numbers, - const PrecisionConfigProto& precision_config) { + const PrecisionConfig& precision_config) { HloComputation* computation = lhs->parent(); CHECK_EQ(computation, rhs->parent()); TF_ASSIGN_OR_RETURN(Shape convolve_shape, @@ -167,10 +167,9 @@ StatusOr<HloInstruction*> MakeConcatHlo( HloInstruction::CreateConcatenate(concat_shape, operands, dimension)); } -StatusOr<HloInstruction*> MakeDotHlo( - HloInstruction* lhs, HloInstruction* rhs, - const DotDimensionNumbers& dim_numbers, - const PrecisionConfigProto& precision_config) { +StatusOr<HloInstruction*> MakeDotHlo(HloInstruction* lhs, HloInstruction* rhs, + const DotDimensionNumbers& dim_numbers, + const PrecisionConfig& precision_config) { HloComputation* computation = lhs->parent(); CHECK_EQ(computation, rhs->parent()); TF_ASSIGN_OR_RETURN( diff --git a/tensorflow/compiler/xla/service/hlo_creation_utils.h b/tensorflow/compiler/xla/service/hlo_creation_utils.h index 1c82956907..b22058abb4 100644 --- a/tensorflow/compiler/xla/service/hlo_creation_utils.h +++ b/tensorflow/compiler/xla/service/hlo_creation_utils.h @@ -50,7 +50,7 @@ StatusOr<HloInstruction*> MakeSliceHlo(HloInstruction* operand, StatusOr<HloInstruction*> MakeConvolveHlo( HloInstruction* lhs, HloInstruction* rhs, int64 feature_group_count, const Window& window, const ConvolutionDimensionNumbers& dimension_numbers, - const PrecisionConfigProto& precision_config); + const PrecisionConfig& precision_config); // Creates a transpose HLO instruction and adds it to the computation containing // `operand`. @@ -98,10 +98,9 @@ StatusOr<HloInstruction*> MakeConcatHlo( // Creates a Dot HLO instruction and adds it to the computation containing `lhs` // and `rhs` (both must be in the same computation). -StatusOr<HloInstruction*> MakeDotHlo( - HloInstruction* lhs, HloInstruction* rhs, - const DotDimensionNumbers& dim_numbers, - const PrecisionConfigProto& precision_config); +StatusOr<HloInstruction*> MakeDotHlo(HloInstruction* lhs, HloInstruction* rhs, + const DotDimensionNumbers& dim_numbers, + const PrecisionConfig& precision_config); // Creates a Map HLO instruction and adds it to the computation containing the // operands. All operands must be in the same computation. diff --git a/tensorflow/compiler/xla/service/hlo_cse.cc b/tensorflow/compiler/xla/service/hlo_cse.cc index cb367adf5e..b59c9ba3ed 100644 --- a/tensorflow/compiler/xla/service/hlo_cse.cc +++ b/tensorflow/compiler/xla/service/hlo_cse.cc @@ -23,6 +23,7 @@ limitations under the License. #include <utility> #include <vector> +#include "absl/container/inlined_vector.h" #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" @@ -34,7 +35,6 @@ limitations under the License. #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/gtl/flatset.h" -#include "tensorflow/core/lib/gtl/inlined_vector.h" #include "tensorflow/core/lib/hash/hash.h" namespace xla { diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc index 62eea2b06c..510d6360a1 100644 --- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc +++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc @@ -28,6 +28,7 @@ limitations under the License. #include "tensorflow/compiler/xla/test_helpers.h" #include "tensorflow/compiler/xla/tests/hlo_test_base.h" #include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/core/status_test_util.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/test.h" @@ -1261,9 +1262,10 @@ TEST_P(HloDataflowAnalysisTest, MultipleEntryParameters_Sequential) { auto entry = module_->AddEntryComputation(builder.Build()); RunAnalysis(GetParam()); - SequentialHloOrdering::HloModuleSequence sequence; - sequence.insert({entry, {param0, negate, param1, exp, add}}); - SequentialHloOrdering ordering(module_.get(), sequence); + HloSchedule schedule(module_.get()); + schedule.set_sequence(entry, {param0, negate, param1, exp, add}); + TF_ASSERT_OK(schedule.Verify()); + SequentialHloOrdering ordering(schedule); // Entry parameters interfere as if they are defined simultaneously at // the very beginning. @@ -1339,14 +1341,16 @@ TEST_P(HloDataflowAnalysisTest, WhileParameters_Sequential) { bool ssa_form = GetParam(); RunAnalysis(ssa_form); - SequentialHloOrdering::HloModuleSequence sequence; - sequence.insert({entry, {param, xla_while}}); - sequence.insert({condition, {cond_param, cond_constant}}); + HloSchedule schedule(module_.get()); + schedule.set_sequence(entry, {param, xla_while}); + schedule.set_sequence(condition, {cond_param, cond_constant}); // Construct the order such that 'constant' and its use 'exp' are before // body_param. - sequence.insert({body, {constant, exp, body_param, add}}); + schedule.set_sequence( + body, {constant, exp, body_param, add, dead_constant, dead_negate}); + TF_ASSERT_OK(schedule.Verify()); - SequentialHloOrdering ordering(module_.get(), sequence); + SequentialHloOrdering ordering(schedule); // 'add' is live out of the body and will interfere with an later instructions // such as 'dead_constant' and 'dead_negate'. @@ -1476,11 +1480,10 @@ TEST_P(HloDataflowAnalysisTest, OverlappedValuesSequentialOrder) { auto entry = module_->AddEntryComputation(builder.Build()); RunAnalysis(GetParam()); - SequentialHloOrdering::HloModuleSequence sequence; - std::vector<const HloInstruction*> order = {param, negate, exp, add}; - sequence.emplace(entry, order); - - SequentialHloOrdering ordering(module_.get(), sequence); + HloSchedule schedule(module_.get()); + schedule.set_sequence(entry, {param, negate, exp, add}); + TF_ASSERT_OK(schedule.Verify()); + SequentialHloOrdering ordering(schedule); EXPECT_TRUE(InstructionsMayInterfere(ordering, param, negate)); EXPECT_FALSE(InstructionsMayInterfere(ordering, param, exp)); @@ -2334,9 +2337,9 @@ TEST_F(CanShareOperandBufferWithUserTest, FusedDotAdd) { DotDimensionNumbers dot_dnums; dot_dnums.add_lhs_contracting_dimensions(1); dot_dnums.add_rhs_contracting_dimensions(0); - PrecisionConfigProto precision_config; + PrecisionConfig precision_config; precision_config.mutable_operand_precision()->Resize( - 2, PrecisionConfigProto::DEFAULT); + 2, PrecisionConfig::DEFAULT); auto dot = builder.AddInstruction( HloInstruction::CreateDot(data_shape, a, b, dot_dnums, precision_config)); diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc index ffb3451164..d0d955fea8 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator.cc +++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc @@ -345,7 +345,7 @@ StatusOr<std::unique_ptr<Literal>> HloEvaluator::EvaluateElementwiseUnaryOp( StatusOr<std::unique_ptr<Literal>> HloEvaluator::EvaluateDotOp( const DotDimensionNumbers& dim_numbers, - const PrecisionConfigProto& precision_config, const Literal& lhs, + const PrecisionConfig& precision_config, const Literal& lhs, const Literal& rhs) { std::unique_ptr<HloInstruction> lhs_instr = HloInstruction::CreateConstant(lhs.CloneToUnique()); diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.h b/tensorflow/compiler/xla/service/hlo_evaluator.h index e13af8e999..72252bafc7 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator.h +++ b/tensorflow/compiler/xla/service/hlo_evaluator.h @@ -116,7 +116,7 @@ class HloEvaluator : public DfsHloVisitorWithDefault { StatusOr<std::unique_ptr<Literal>> EvaluateDotOp( const DotDimensionNumbers& dim_numbers, - const PrecisionConfigProto& precision_config, const Literal& lhs, + const PrecisionConfig& precision_config, const Literal& lhs, const Literal& rhs); protected: diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc index f586f253da..abd4bb1f73 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc +++ b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc @@ -622,13 +622,6 @@ TEST_P(HloEvaluatorTest, NegativeAndInteriorPadding2D) { EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result)); } -PrecisionConfigProto DefaultPrecisionConfig(int operands) { - PrecisionConfigProto precision_config; - precision_config.mutable_operand_precision()->Resize( - operands, PrecisionConfigProto::DEFAULT); - return precision_config; -} - TEST_P(HloEvaluatorTest, DotRank2AndRank1) { HloComputation::Builder b(TestName()); diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc index f25761ac70..471a12d6aa 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction.cc @@ -347,9 +347,9 @@ StatusOr<std::unique_ptr<HloInstruction>> HloInstruction::CreateFromProto( << proto.operand_ids_size(); TF_RET_CHECK(proto.has_window()); TF_RET_CHECK(proto.has_convolution_dimension_numbers()); - PrecisionConfigProto precision_config = proto.precision_config(); + PrecisionConfig precision_config = proto.precision_config(); precision_config.mutable_operand_precision()->Resize( - proto.operand_ids_size(), PrecisionConfigProto::DEFAULT); + proto.operand_ids_size(), PrecisionConfig::DEFAULT); instruction = CreateConvolve( proto.shape(), operands(0), operands(1), std::max<int64>(proto.feature_group_count(), 1), proto.window(), @@ -475,7 +475,7 @@ StatusOr<std::unique_ptr<HloInstruction>> HloInstruction::CreateFromProto( if (instruction->opcode() == HloOpcode::kDot) { instruction->precision_config_ = proto.precision_config(); instruction->precision_config_.mutable_operand_precision()->Resize( - instruction->operand_count(), PrecisionConfigProto::DEFAULT); + instruction->operand_count(), PrecisionConfig::DEFAULT); TF_RET_CHECK(proto.has_dot_dimension_numbers()); instruction->dot_dimension_numbers_ = absl::make_unique<DotDimensionNumbers>( @@ -657,7 +657,7 @@ HloInstruction::CreateGetTupleElement(const Shape& shape, const Shape& shape, HloInstruction* lhs, HloInstruction* rhs, int64 feature_group_count, const Window& window, const ConvolutionDimensionNumbers& dimension_numbers, - const PrecisionConfigProto& precision_config) { + const PrecisionConfig& precision_config) { return absl::make_unique<HloConvolutionInstruction>( shape, lhs, rhs, feature_group_count, window, dimension_numbers, precision_config); @@ -673,7 +673,7 @@ HloInstruction::CreateGetTupleElement(const Shape& shape, /* static */ std::unique_ptr<HloInstruction> HloInstruction::CreateDot( const Shape& shape, HloInstruction* lhs, HloInstruction* rhs, const DotDimensionNumbers& dimension_numbers, - const PrecisionConfigProto& precision_config) { + const PrecisionConfig& precision_config) { auto instruction = absl::WrapUnique(new HloInstruction(HloOpcode::kDot, shape)); instruction->AppendOperand(lhs); @@ -2888,8 +2888,8 @@ string RandomDistributionToString(const RandomDistribution& distribution) { return absl::AsciiStrToLower(RandomDistribution_Name(distribution)); } -string PrecisionToString(const PrecisionConfigProto::Precision& precision) { - return absl::AsciiStrToLower(PrecisionConfigProto::Precision_Name(precision)); +string PrecisionToString(const PrecisionConfig::Precision& precision) { + return absl::AsciiStrToLower(PrecisionConfig::Precision_Name(precision)); } string ConvolutionDimensionNumbersToString( @@ -2967,32 +2967,31 @@ StatusOr<RandomDistribution> StringToRandomDistribution(const string& name) { string HloInstruction::PrecisionConfigToString() const { if (absl::c_all_of( precision_config_.operand_precision(), [](int32 precision) { - return static_cast<PrecisionConfigProto::Precision>(precision) == - PrecisionConfigProto::DEFAULT; + return static_cast<PrecisionConfig::Precision>(precision) == + PrecisionConfig::DEFAULT; })) { return ""; } return StrCat( "operand_precision={", - StrJoin(precision_config_.operand_precision(), ",", - [](string* out, int32 precision) { - CHECK(PrecisionConfigProto::Precision_IsValid(precision)) - << precision; - StrAppend(out, PrecisionToString( - static_cast<PrecisionConfigProto::Precision>( - precision))); - }), + StrJoin( + precision_config_.operand_precision(), ",", + [](string* out, int32 precision) { + CHECK(PrecisionConfig::Precision_IsValid(precision)) << precision; + StrAppend(out, + PrecisionToString( + static_cast<PrecisionConfig::Precision>(precision))); + }), "}"); } -StatusOr<PrecisionConfigProto::Precision> StringToPrecision( - const string& name) { - static std::unordered_map<string, PrecisionConfigProto::Precision>* map = [] { +StatusOr<PrecisionConfig::Precision> StringToPrecision(const string& name) { + static std::unordered_map<string, PrecisionConfig::Precision>* map = [] { static auto* map = - new std::unordered_map<string, PrecisionConfigProto::Precision>; - for (int i = 0; i < PrecisionConfigProto::Precision_ARRAYSIZE; i++) { - if (PrecisionConfigProto::Precision_IsValid(i)) { - auto value = static_cast<PrecisionConfigProto::Precision>(i); + new std::unordered_map<string, PrecisionConfig::Precision>; + for (int i = 0; i < PrecisionConfig::Precision_ARRAYSIZE; i++) { + if (PrecisionConfig::Precision_IsValid(i)) { + auto value = static_cast<PrecisionConfig::Precision>(i); (*map)[PrecisionToString(value)] = value; } } diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h index 55d592ff94..691f8155f9 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.h +++ b/tensorflow/compiler/xla/service/hlo_instruction.h @@ -407,7 +407,7 @@ class HloInstruction { const Shape& shape, HloInstruction* lhs, HloInstruction* rhs, int64 feature_group_count, const Window& window, const ConvolutionDimensionNumbers& dimension_numbers, - const PrecisionConfigProto& precision_config); + const PrecisionConfig& precision_config); // Creates an FFT op, of the type indicated by fft_type. static std::unique_ptr<HloInstruction> CreateFft( @@ -419,7 +419,7 @@ class HloInstruction { static std::unique_ptr<HloInstruction> CreateDot( const Shape& shape, HloInstruction* lhs, HloInstruction* rhs, const DotDimensionNumbers& dimension_numbers, - const PrecisionConfigProto& precision_config); + const PrecisionConfig& precision_config); // Creates a dot op with operands 'lhs' and 'rhs' that contracts dimension 1 // of the LHS with dimension 0 of the RHS with no batch dimensions. Both LHS @@ -1262,10 +1262,8 @@ class HloInstruction { // information. Transformations to other HLOs will not preserve this // information but it is presumed that the alternate lowering is strictly // superior. - const PrecisionConfigProto& precision_config() const { - return precision_config_; - } - void set_precision_config(const PrecisionConfigProto& precision_config) { + const PrecisionConfig& precision_config() const { return precision_config_; } + void set_precision_config(const PrecisionConfig& precision_config) { precision_config_ = precision_config; } @@ -1680,7 +1678,7 @@ class HloInstruction { // Information used to communicate to the implementation about the algorithm // used to produce results. See the documentation on precision_config(). - PrecisionConfigProto precision_config_; + PrecisionConfig precision_config_; // String identifier for instruction. string name_; @@ -1704,12 +1702,12 @@ StatusOr<HloInstruction::FusionKind> StringToFusionKind( string PaddingConfigToString(const PaddingConfig& padding); string OpMetadataToString(const OpMetadata& metadata); string RandomDistributionToString(const RandomDistribution& distribution); -string PrecisionToString(const PrecisionConfigProto::Precision& precision); +string PrecisionToString(const PrecisionConfig::Precision& precision); string ConvolutionDimensionNumbersToString( const ConvolutionDimensionNumbers& dnums); StatusOr<RandomDistribution> StringToRandomDistribution(const string& name); -StatusOr<PrecisionConfigProto::Precision> StringToPrecision(const string& name); +StatusOr<PrecisionConfig::Precision> StringToPrecision(const string& name); std::ostream& operator<<(std::ostream& os, HloInstruction::FusionKind kind); diff --git a/tensorflow/compiler/xla/service/hlo_instruction_test.cc b/tensorflow/compiler/xla/service/hlo_instruction_test.cc index b4e302e832..c1b7c3832b 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction_test.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction_test.cc @@ -1122,13 +1122,6 @@ TEST_F(HloInstructionTest, PartiallyElementwiseWithReuse) { } } -PrecisionConfigProto DefaultPrecisionConfig(int operands) { - PrecisionConfigProto precision_config; - precision_config.mutable_operand_precision()->Resize( - operands, PrecisionConfigProto::DEFAULT); - return precision_config; -} - TEST_F(HloInstructionTest, CloneOfFusionPreservesShape) { // Fused expression: // @@ -1759,9 +1752,9 @@ TEST_F(HloInstructionTest, PreserveOperandPrecisionOnCloneConv) { auto* conv = module->entry_computation()->root_instruction(); auto clone = conv->Clone(); - EXPECT_THAT(clone->precision_config().operand_precision(), - ::testing::ElementsAre(PrecisionConfigProto::HIGH, - PrecisionConfigProto::DEFAULT)); + EXPECT_THAT( + clone->precision_config().operand_precision(), + ::testing::ElementsAre(PrecisionConfig::HIGH, PrecisionConfig::DEFAULT)); } } // namespace diff --git a/tensorflow/compiler/xla/service/hlo_instructions.cc b/tensorflow/compiler/xla/service/hlo_instructions.cc index e3683aaec9..ad87aa1123 100644 --- a/tensorflow/compiler/xla/service/hlo_instructions.cc +++ b/tensorflow/compiler/xla/service/hlo_instructions.cc @@ -1630,7 +1630,7 @@ HloConvolutionInstruction::HloConvolutionInstruction( const Shape& shape, HloInstruction* lhs, HloInstruction* rhs, int64 feature_group_count, const Window& window, const ConvolutionDimensionNumbers& dimension_numbers, - const PrecisionConfigProto& precision_config) + const PrecisionConfig& precision_config) : HloInstruction(HloOpcode::kConvolution, shape), feature_group_count_(feature_group_count), window_(window), diff --git a/tensorflow/compiler/xla/service/hlo_instructions.h b/tensorflow/compiler/xla/service/hlo_instructions.h index 1c85aa4681..e1215a7566 100644 --- a/tensorflow/compiler/xla/service/hlo_instructions.h +++ b/tensorflow/compiler/xla/service/hlo_instructions.h @@ -944,7 +944,7 @@ class HloConvolutionInstruction : public HloInstruction { const Shape& shape, HloInstruction* lhs, HloInstruction* rhs, int64 feature_group_count, const Window& window, const ConvolutionDimensionNumbers& dimension_numbers, - const PrecisionConfigProto& precision_config); + const PrecisionConfig& precision_config); const Window& window() const override { return window_; } void set_window(const Window& window) override { window_ = window; } const ConvolutionDimensionNumbers& convolution_dimension_numbers() const { diff --git a/tensorflow/compiler/xla/service/hlo_ordering.cc b/tensorflow/compiler/xla/service/hlo_ordering.cc index 0581d5c404..2105f7a349 100644 --- a/tensorflow/compiler/xla/service/hlo_ordering.cc +++ b/tensorflow/compiler/xla/service/hlo_ordering.cc @@ -18,6 +18,7 @@ limitations under the License. #include <utility> #include <vector> +#include "absl/strings/str_cat.h" #include "absl/strings/str_format.h" #include "absl/strings/str_join.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" @@ -252,6 +253,12 @@ bool HloOrdering::LiveRangeStrictlyBefore( VLOG(4) << a << " not defined before " << b; return false; } + + if (a.live_out_of_module()) { + VLOG(4) << a << " is live out of module and defined before " << b; + return false; + } + // All uses of 'a' must be before 'b' is defined. for (const HloUse& use : a.uses()) { if (dataflow.DoesNotUseOperandBuffer(a.instruction(), a.index(), @@ -264,6 +271,18 @@ bool HloOrdering::LiveRangeStrictlyBefore( return false; } } + + if (a.instruction()->parent() == b.instruction()->parent()) { + for (const HloPosition& position : a.positions()) { + if (position.instruction == + a.instruction()->parent()->root_instruction()) { + VLOG(4) << a << " is live out of computation and defined before " << b + << " which is in same computation"; + return false; + } + } + } + return true; } @@ -336,15 +355,24 @@ string DependencyHloOrdering::ToString() const { return ToStringHelper("DependencyHloOrdering"); } -SequentialHloOrdering::SequentialHloOrdering( - const HloModule* module, const HloModuleSequence& module_sequence) - : HloOrdering(module), module_sequence_(module_sequence) { +SequentialHloOrdering::SequentialHloOrdering(const HloSchedule& schedule) + : HloOrdering(schedule.module()), schedule_(schedule) { + Initialize(); +} + +SequentialHloOrdering::SequentialHloOrdering(HloSchedule&& schedule) + : HloOrdering(schedule.module()), schedule_(std::move(schedule)) { + Initialize(); +} + +void SequentialHloOrdering::Initialize() { // Create a map from instruction to its order position. - for (auto computation_order : module_sequence_) { - const std::vector<const HloInstruction*>& order = computation_order.second; + TF_DCHECK_OK(schedule_.Verify()); + for (const auto& computation_sequence : schedule_.sequences()) { + const std::vector<const HloInstruction*>& order = + computation_sequence.second.instructions(); for (int i = 0; i < order.size(); ++i) { - DCHECK_EQ(0, order_position_.count(order[i])); - order_position_.emplace(order[i], i); + InsertOrDie(&order_position_, order[i], i); } } } @@ -362,49 +390,13 @@ bool SequentialHloOrdering::ExecutesBeforeInSameComputation( const std::vector<const HloInstruction*>* SequentialHloOrdering::SequentialOrder( const HloComputation& computation) const { - auto find_it = module_sequence_.find(&computation); - return find_it == module_sequence_.end() ? nullptr : &find_it->second; + return schedule_.is_computation_scheduled(&computation) + ? &schedule_.sequence(&computation).instructions() + : nullptr; } string SequentialHloOrdering::ToString() const { - std::vector<string> pieces; - pieces.push_back("SequentialHloOrdering"); - for (auto* computation : module_->computations()) { - pieces.push_back( - absl::StrFormat("computation %s order:", computation->name())); - // Gather all instructions in the module sequence for this computation and - // sort them by their position. - std::vector<const HloInstruction*> instructions; - for (auto& instruction_position : order_position_) { - const HloInstruction* instruction = instruction_position.first; - if (instruction->parent() == computation) { - instructions.push_back(instruction); - } - } - std::sort(instructions.begin(), instructions.end(), - [this](const HloInstruction* a, const HloInstruction* b) { - return order_position_.at(a) < order_position_.at(b); - }); - for (auto instruction : instructions) { - pieces.push_back(absl::StrFormat(" %s", instruction->name())); - } - } - return absl::StrJoin(pieces, "\n"); -} - -std::ostream& operator<<( - std::ostream& out, - const SequentialHloOrdering::HloModuleSequence& module_sequence) { - for (auto computation_pair : module_sequence) { - const HloComputation* computation = computation_pair.first; - const std::vector<const HloInstruction*>& computation_sequence = - computation_pair.second; - out << "Computation " << computation->name() << ":\n"; - for (auto* instruction : computation_sequence) { - out << " " << instruction->name() << "\n"; - } - } - return out; + return absl::StrCat("SequentialHloOrdering\n", schedule_.ToString()); } } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_ordering.h b/tensorflow/compiler/xla/service/hlo_ordering.h index 985f3fa64d..b21071c4b2 100644 --- a/tensorflow/compiler/xla/service/hlo_ordering.h +++ b/tensorflow/compiler/xla/service/hlo_ordering.h @@ -25,6 +25,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_dataflow_analysis.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/service/hlo_schedule.h" #include "tensorflow/compiler/xla/service/hlo_value.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/core/lib/gtl/flatmap.h" @@ -183,17 +184,8 @@ class DependencyHloOrdering : public PredecessorHloOrdering { // interference is reduced relative to DependencyHloOrdering. class SequentialHloOrdering : public HloOrdering { public: - // TODO(dimvar): HloModuleSequence is not a good name because it sounds like - // a sequence of modules, instead of a map of schedules for all computations - // in a module. We should change it at some point. - // - // A sequence of instructions for each computation in the module. - using HloModuleSequence = - tensorflow::gtl::FlatMap<const HloComputation*, - std::vector<const HloInstruction*>>; - - SequentialHloOrdering(const HloModule* module, - const HloModuleSequence& module_sequence); + SequentialHloOrdering(const HloSchedule& schedule); + SequentialHloOrdering(HloSchedule&& schedule); ~SequentialHloOrdering() override = default; // Returns the sequential instruction order for the given computation. @@ -203,10 +195,12 @@ class SequentialHloOrdering : public HloOrdering { string ToString() const override; protected: + void Initialize(); + bool ExecutesBeforeInSameComputation(const HloInstruction* a, const HloInstruction* b) const override; - const HloModuleSequence module_sequence_; + const HloSchedule schedule_; // The position of every instruction in the HLO module in its respective // computation sequence (a value of zero indicates the instruction is first in @@ -217,10 +211,6 @@ class SequentialHloOrdering : public HloOrdering { tensorflow::gtl::FlatMap<const HloInstruction*, int> order_position_; }; -std::ostream& operator<<( - std::ostream& out, - const SequentialHloOrdering::HloModuleSequence& module_sequence); - } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_ORDERING_H_ diff --git a/tensorflow/compiler/xla/service/hlo_ordering_test.cc b/tensorflow/compiler/xla/service/hlo_ordering_test.cc index 126d3a2d9c..6b6005e7a5 100644 --- a/tensorflow/compiler/xla/service/hlo_ordering_test.cc +++ b/tensorflow/compiler/xla/service/hlo_ordering_test.cc @@ -23,11 +23,13 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" #include "tensorflow/compiler/xla/service/hlo_parser.h" +#include "tensorflow/compiler/xla/service/hlo_schedule.h" #include "tensorflow/compiler/xla/service/hlo_scheduling.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/tests/hlo_test_base.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/core/status_test_util.h" namespace xla { namespace { @@ -376,5 +378,104 @@ ENTRY root { dataflow->GetValueDefinedAt(add_3))); } +TEST_F(HloOrderingTest, + ValuesLiveOutOfModuleInterfereWithInstructionsAfterRoot) { + // Tests that values live out of the module should interfere with values + // defined after the root instruction. That is: + // + // %param = param(0) + // ROOT %root = negate(%param) + // %dead = Constant(123.0) + // + // %root should interfere with %dead. + auto module = CreateNewModule(); + const Shape scalar_shape = ShapeUtil::MakeShape(xla::F32, {}); + + auto builder = HloComputation::Builder(TestName()); + HloInstruction* param = builder.AddInstruction( + HloInstruction::CreateParameter(0, scalar_shape, "param")); + HloInstruction* root = builder.AddInstruction( + HloInstruction::CreateUnary(scalar_shape, HloOpcode::kNegate, param)); + HloInstruction* dead = builder.AddInstruction( + HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(123.0f))); + HloComputation* entry = + module->AddEntryComputation(builder.Build(/*root_instruction=*/root)); + + HloSchedule schedule(module.get()); + schedule.set_sequence(entry, {param, root, dead}); + TF_ASSERT_OK(schedule.Verify()); + SequentialHloOrdering ordering(schedule); + + TF_ASSERT_OK_AND_ASSIGN(auto dataflow, + HloDataflowAnalysis::Run(*module, /*ssa_form=*/true)); + + EXPECT_TRUE(ordering.ExecutesBefore(root, dead)); + EXPECT_FALSE(ordering.ExecutesBefore(dead, root)); + + EXPECT_FALSE(ordering.LiveRangeStrictlyBefore( + dataflow->GetValueDefinedAt(root), dataflow->GetValueDefinedAt(dead), + *dataflow)); + + EXPECT_TRUE(ordering.MayInterfere(dataflow->GetValueDefinedAt(root), + dataflow->GetValueDefinedAt(dead), + *dataflow)); +} + +TEST_F(HloOrderingTest, + ValuesLiveOutOfComputationInterfereWithInstructionsAfterRoot) { + // Tests that values live out of a computation should interfere with values + // defined after the root instruction of the computation. That is: + // + // subcomputation: + // %param = param(0) + // ROOT %root = negate(%param) + // %dead = Constant(123.0) + // + // entry computation: + // %c = constant(42.0) + // ROOT %call = call({%c}), subcomputation + // + // %root should interfere with %dead. + auto module = CreateNewModule(); + const Shape scalar_shape = ShapeUtil::MakeShape(xla::F32, {}); + + auto subbuilder = HloComputation::Builder(TestName() + ".sub"); + HloInstruction* param = subbuilder.AddInstruction( + HloInstruction::CreateParameter(0, scalar_shape, "param")); + HloInstruction* root = subbuilder.AddInstruction( + HloInstruction::CreateUnary(scalar_shape, HloOpcode::kNegate, param)); + HloInstruction* dead = subbuilder.AddInstruction( + HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(123.0f))); + HloComputation* subcomputation = module->AddEmbeddedComputation( + subbuilder.Build(/*root_instruction=*/root)); + + auto builder = HloComputation::Builder(TestName()); + HloInstruction* c = builder.AddInstruction( + HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0f))); + HloInstruction* call = builder.AddInstruction( + HloInstruction::CreateCall(scalar_shape, {c}, subcomputation)); + HloComputation* entry = module->AddEntryComputation(builder.Build()); + + HloSchedule schedule(module.get()); + schedule.set_sequence(subcomputation, {param, root, dead}); + schedule.set_sequence(entry, {c, call}); + TF_ASSERT_OK(schedule.Verify()); + SequentialHloOrdering ordering(schedule); + + TF_ASSERT_OK_AND_ASSIGN(auto dataflow, + HloDataflowAnalysis::Run(*module, /*ssa_form=*/true)); + + EXPECT_TRUE(ordering.ExecutesBefore(root, dead)); + EXPECT_FALSE(ordering.ExecutesBefore(dead, root)); + + EXPECT_FALSE(ordering.LiveRangeStrictlyBefore( + dataflow->GetValueDefinedAt(root), dataflow->GetValueDefinedAt(dead), + *dataflow)); + + EXPECT_TRUE(ordering.MayInterfere(dataflow->GetValueDefinedAt(root), + dataflow->GetValueDefinedAt(dead), + *dataflow)); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_parser.cc b/tensorflow/compiler/xla/service/hlo_parser.cc index 62f01c4adb..0f26ed4235 100644 --- a/tensorflow/compiler/xla/service/hlo_parser.cc +++ b/tensorflow/compiler/xla/service/hlo_parser.cc @@ -221,7 +221,7 @@ class HloParser { bool ParseWindowPad(std::vector<std::vector<tensorflow::int64>>* pad); bool ParseSliceRanges(SliceRanges* result); - bool ParsePrecisionList(std::vector<PrecisionConfigProto::Precision>* result); + bool ParsePrecisionList(std::vector<PrecisionConfig::Precision>* result); bool ParseInt64List(const TokKind start, const TokKind end, const TokKind delim, std::vector<tensorflow::int64>* result); @@ -240,7 +240,7 @@ class HloParser { bool ParseFftType(FftType* result); bool ParseFusionKind(HloInstruction::FusionKind* result); bool ParseRandomDistribution(RandomDistribution* result); - bool ParsePrecision(PrecisionConfigProto::Precision* result); + bool ParsePrecision(PrecisionConfig::Precision* result); bool ParseInt64(tensorflow::int64* result); bool ParseDouble(double* result); bool ParseBool(bool* result); @@ -909,7 +909,7 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, AttrTy::kConvolutionDimensionNumbers, &dnums}; attrs["feature_group_count"] = {/*required=*/false, AttrTy::kInt64, &feature_group_count}; - optional<std::vector<PrecisionConfigProto::Precision>> operand_precision; + optional<std::vector<PrecisionConfig::Precision>> operand_precision; attrs["operand_precision"] = {/*required=*/false, AttrTy::kPrecisionList, &operand_precision}; if (!ParseOperands(&operands, /*expected_size=*/2) || @@ -922,13 +922,13 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, if (!feature_group_count) { feature_group_count = 1; } - PrecisionConfigProto precision_config; + PrecisionConfig precision_config; if (operand_precision) { *precision_config.mutable_operand_precision() = { operand_precision->begin(), operand_precision->end()}; } else { precision_config.mutable_operand_precision()->Resize( - operands.size(), PrecisionConfigProto::DEFAULT); + operands.size(), PrecisionConfig::DEFAULT); } instruction = builder->AddInstruction(HloInstruction::CreateConvolve( shape, /*lhs=*/operands[0], /*rhs=*/operands[1], @@ -1279,7 +1279,7 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, optional<std::vector<tensorflow::int64>> rhs_batch_dims; attrs["rhs_batch_dims"] = {/*required=*/false, AttrTy::kBracedInt64List, &rhs_batch_dims}; - optional<std::vector<PrecisionConfigProto::Precision>> operand_precision; + optional<std::vector<PrecisionConfig::Precision>> operand_precision; attrs["operand_precision"] = {/*required=*/false, AttrTy::kPrecisionList, &operand_precision}; @@ -1306,13 +1306,13 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, rhs_batch_dims->end()}; } - PrecisionConfigProto precision_config; + PrecisionConfig precision_config; if (operand_precision) { *precision_config.mutable_operand_precision() = { operand_precision->begin(), operand_precision->end()}; } else { precision_config.mutable_operand_precision()->Resize( - operands.size(), PrecisionConfigProto::DEFAULT); + operands.size(), PrecisionConfig::DEFAULT); } instruction = builder->AddInstruction(HloInstruction::CreateDot( @@ -2410,11 +2410,11 @@ bool HloParser::ParseAttributeHelper( return ParseDomain(static_cast<DomainData*>(attr_out_ptr)); } case AttrTy::kPrecisionList: { - std::vector<PrecisionConfigProto::Precision> result; + std::vector<PrecisionConfig::Precision> result; if (!ParsePrecisionList(&result)) { return false; } - static_cast<optional<std::vector<PrecisionConfigProto::Precision>>*>( + static_cast<optional<std::vector<PrecisionConfig::Precision>>*>( attr_out_ptr) ->emplace(result); return true; @@ -2698,9 +2698,9 @@ bool HloParser::ParseSliceRanges(SliceRanges* result) { // ::= /*empty*/ // ::= precision_val (delim precision_val)* bool HloParser::ParsePrecisionList( - std::vector<PrecisionConfigProto::Precision>* result) { + std::vector<PrecisionConfig::Precision>* result) { auto parse_and_add_item = [&]() { - PrecisionConfigProto::Precision item; + PrecisionConfig::Precision item; if (!ParsePrecision(&item)) { return false; } @@ -3032,7 +3032,7 @@ bool HloParser::ParseRandomDistribution(RandomDistribution* result) { return true; } -bool HloParser::ParsePrecision(PrecisionConfigProto::Precision* result) { +bool HloParser::ParsePrecision(PrecisionConfig::Precision* result) { VLOG(1) << "ParsePrecision"; if (lexer_.GetKind() != TokKind::kIdent) { return TokenError("expects random distribution"); diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization.cc b/tensorflow/compiler/xla/service/hlo_rematerialization.cc index c9629926ea..0a0a6a323e 100644 --- a/tensorflow/compiler/xla/service/hlo_rematerialization.cc +++ b/tensorflow/compiler/xla/service/hlo_rematerialization.cc @@ -962,8 +962,7 @@ StatusOr<int64> HloRematerialization::CalledComputationsMemoryUsage( } StatusOr<bool> HloRematerialization::RematerializeComputation( - HloComputation* computation, - SequentialHloOrdering::HloModuleSequence* sequence, + HloComputation* computation, HloSchedule* schedule, int64 memory_limit_bytes) { VLOG(1) << "Rematerializing computation " << computation->name() << " with limit " << HumanReadableNumBytes(memory_limit_bytes); @@ -971,7 +970,8 @@ StatusOr<bool> HloRematerialization::RematerializeComputation( << HumanReadableNumBytes(computation_peak_memory_.at(computation)); CHECK(!ContainsKey(rematerialized_computations_, computation)); - InstructionList instruction_list(sequence->at(computation)); + InstructionList instruction_list( + schedule->sequence(computation).instructions()); MemoryUsageTracker memory_tracker(computation, size_function_, *points_to_analysis_, instruction_list); bool changed = false; @@ -1145,7 +1145,7 @@ StatusOr<bool> HloRematerialization::RematerializeComputation( 0, memory_limit_bytes - memory_tracker.memory_usage()); TF_ASSIGN_OR_RETURN( bool subcomputation_changed, - RematerializeComputation(called_computation, sequence, + RematerializeComputation(called_computation, schedule, subcomputation_memory_limit_bytes)); changed |= subcomputation_changed; } @@ -1179,12 +1179,12 @@ StatusOr<bool> HloRematerialization::RematerializeComputation( computation_peak_memory_.at(computation) = peak_memory; // Update order to include rematerialized instructions. - auto& dst = sequence->at(computation); - dst.clear(); + HloInstructionSequence& sequence = schedule->GetOrCreateSequence(computation); + sequence.clear(); for (auto* item = instruction_list.first(); item != nullptr; item = instruction_list.next(item)) { const HloInstruction* instruction = item->instruction; - dst.push_back(instruction); + sequence.push_back(instruction); } rematerialized_computations_.insert(computation); @@ -1194,20 +1194,21 @@ StatusOr<bool> HloRematerialization::RematerializeComputation( return changed; } -StatusOr<bool> HloRematerialization::Run( - HloModule* module, SequentialHloOrdering::HloModuleSequence* sequence, - int64 memory_limit_bytes, RematerializationSizes* sizes, - CopyInsertion* copy_insertion) { - // The sequence is constructed entirely by this method. - TF_RET_CHECK(sequence->empty()); +StatusOr<bool> HloRematerialization::Run(HloModule* module, + HloSchedule* schedule, + int64 memory_limit_bytes, + RematerializationSizes* sizes, + CopyInsertion* copy_insertion) { + // The schedule is constructed entirely by this method. + TF_RET_CHECK(schedule->empty()); VLOG(1) << "HloRematerialization() with memory limit of " << HumanReadableNumBytes(memory_limit_bytes); XLA_VLOG_LINES(3, "Before HloRematerialization:\n" + module->ToString()); - // Create initial sequence of HLO instructions. - TF_ASSIGN_OR_RETURN(*sequence, ScheduleComputationsInModule( - *module, + // Create initial schedule of HLO instructions. + TF_ASSIGN_OR_RETURN(*schedule, + ScheduleModule(*module, [this](const BufferValue& buffer) { return size_function_(buffer.shape()); }, @@ -1217,16 +1218,7 @@ StatusOr<bool> HloRematerialization::Run( // ordering from the HLO schedule allows for more copies to be eliminated. // TODO(b/80249101): Instead of a separate copy elision pass, use the // ordering from the HLO schedule directly for copy insertion. - - // First create a copy of the schedule which contains HloInstruction unique - // ids instead of HloInstruction*. This is necessary for updating the - // schedule below. - // TODO(b/113175018): Remove this when the HLO schedule is self-contained - // and can update itself. - tensorflow::gtl::FlatMap<const HloComputation*, std::vector<int>> - id_sequence = ComputeIdSchedule(*sequence); - - SequentialHloOrdering ordering(module, *sequence); + SequentialHloOrdering ordering(*schedule); TF_RETURN_IF_ERROR( copy_insertion->RemoveUnnecessaryCopies(ordering, module)); @@ -1241,10 +1233,10 @@ StatusOr<bool> HloRematerialization::Run( // The passes above can add and remove copies, update the schedule to // account for these transformations. Newly added instructions will be // placed ASAP in the schedule. - TF_RETURN_IF_ERROR(UpdateSchedule(*module, id_sequence, sequence)); + TF_RETURN_IF_ERROR(schedule->Update()); TF_DCHECK_OK(copy_insertion->VerifyNoLiveRangeInterference( - SequentialHloOrdering(module, *sequence), module)); + SequentialHloOrdering(*schedule), module)); } TF_ASSIGN_OR_RETURN(points_to_analysis_, TuplePointsToAnalysis::Run(module)); @@ -1271,12 +1263,13 @@ StatusOr<bool> HloRematerialization::Run( // sequential context. call_graph_ = CallGraph::Build(module); TF_RETURN_IF_ERROR(call_graph_->VisitNodes( - [this, sequence](const CallGraphNode& node) -> Status { + [this, schedule](const CallGraphNode& node) -> Status { if (node.context() == CallContext::kSequential) { TF_ASSIGN_OR_RETURN( computation_peak_memory_[node.computation()], - ComputePeakMemory(node.computation(), - sequence->at(node.computation()))); + ComputePeakMemory( + node.computation(), + schedule->sequence(node.computation()).instructions())); } return Status::OK(); }, @@ -1295,7 +1288,7 @@ StatusOr<bool> HloRematerialization::Run( // Subcomputations called by the entry computation will also be // rematerialized. TF_ASSIGN_OR_RETURN(bool changed, RematerializeComputation( - module->entry_computation(), sequence, + module->entry_computation(), schedule, adjusted_memory_limit_bytes)); // Rematerialization can introduce dead code. This occurs if all uses of an @@ -1305,30 +1298,7 @@ StatusOr<bool> HloRematerialization::Run( // After DCE, the module sequence may include instructions which no longer // exist. - for (const auto* computation : module->MakeNonfusionComputations()) { - if (sequence->at(computation).size() != computation->instruction_count()) { - // A size mismatch between the computation instruction count and the size - // of the ordering of instructions can only be caused by DCE. Rebuild the - // order by removing the deleted instructions from the order. - tensorflow::gtl::FlatSet<const HloInstruction*> instruction_set; - for (const auto& instruction : computation->instructions()) { - instruction_set.insert(instruction); - } - // Move the old order into a temporary vector, then build new order - // inplace. - std::vector<const HloInstruction*>& order = sequence->at(computation); - std::vector<const HloInstruction*> old_order; - using std::swap; - swap(order, old_order); - std::copy_if(old_order.begin(), old_order.end(), - std::back_inserter(order), - [&instruction_set](const HloInstruction* instruction) { - return ContainsKey(instruction_set, instruction); - }); - TF_RET_CHECK(sequence->at(computation).size() == - computation->instruction_count()); - } - } + TF_RETURN_IF_ERROR(schedule->Update()); VLOG(1) << "Rematerialized " << instructions_rematerialized_ << " instructions in module " << module->name() << "; " << net_instructions_added_ << " net instructions added"; @@ -1366,11 +1336,10 @@ StatusOr<bool> HloRematerialization::Run( /* static */ StatusOr<bool> HloRematerialization::RematerializeAndSchedule( const HloRematerialization::ShapeSizeFunction& size_function, int64 memory_limit_bytes, HloModule* hlo_module, - MemorySchedulerAlgorithm scheduler_algorithm, - SequentialHloOrdering::HloModuleSequence* sequence, + MemorySchedulerAlgorithm scheduler_algorithm, HloSchedule* schedule, RematerializationSizes* sizes, CopyInsertion* copy_insertion) { HloRematerialization remat(scheduler_algorithm, size_function); - return remat.Run(hlo_module, sequence, memory_limit_bytes, sizes, + return remat.Run(hlo_module, schedule, memory_limit_bytes, sizes, copy_insertion); } diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization.h b/tensorflow/compiler/xla/service/hlo_rematerialization.h index 2ec004350a..fa0414b472 100644 --- a/tensorflow/compiler/xla/service/hlo_rematerialization.h +++ b/tensorflow/compiler/xla/service/hlo_rematerialization.h @@ -21,6 +21,7 @@ #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/service/hlo_schedule.h" #include "tensorflow/compiler/xla/service/hlo_scheduling.h" #include "tensorflow/compiler/xla/service/tuple_points_to_analysis.h" @@ -50,7 +51,7 @@ class HloRematerialization { // // hlo_module: HLO module to rematerialize instructions in. // - // sequence: Should point to an empty HloModuleSequence. Upon return + // schedule: Should point to an empty HloSchedule. Upon return // contains the HLO instruction order which was used for // rematerialization. This is the order in which HLO instructions should // be emitted to minimize memory use. @@ -75,8 +76,8 @@ class HloRematerialization { static StatusOr<bool> RematerializeAndSchedule( const ShapeSizeFunction& size_function, int64 memory_limit_bytes, HloModule* hlo_module, MemorySchedulerAlgorithm scheduler_algorithm, - SequentialHloOrdering::HloModuleSequence* sequence, - RematerializationSizes* sizes, CopyInsertion* copy_insertion = nullptr); + HloSchedule* schedule, RematerializationSizes* sizes, + CopyInsertion* copy_insertion = nullptr); protected: HloRematerialization(MemorySchedulerAlgorithm scheduler_algorithm, @@ -87,10 +88,9 @@ class HloRematerialization { // Runs rematerialization on the given module. Returns whether the module was // changed. memory_limit is the target maximum peak memory usage by the - // module. sequence should be an empty HloModuleSequence. Upon return sequence + // module. schedule should be an empty HloSchedule. Upon return sequence // contains the memory-minimizing order in which to emit the HLO instructions. - StatusOr<bool> Run(HloModule* module, - SequentialHloOrdering::HloModuleSequence* sequence, + StatusOr<bool> Run(HloModule* module, HloSchedule* schedule, int64 memory_limit, RematerializationSizes* sizes, CopyInsertion* copy_insertion); @@ -98,10 +98,9 @@ class HloRematerialization { // order in which the computation's instructions will be emitted in the // backend. Rematerialized instructions will be added to the HLO computation // and inserted into 'order'. - StatusOr<bool> RematerializeComputation( - HloComputation* computation, - SequentialHloOrdering::HloModuleSequence* sequence, - int64 computation_memory_limit); + StatusOr<bool> RematerializeComputation(HloComputation* computation, + HloSchedule* schedule, + int64 memory_limit_bytes); // Computes and returns the peak memory used by the given computation. The // peak memory is the maximum total size of all live HLO instruction values at diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc b/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc index ac8c97d380..83cb113bfb 100644 --- a/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc +++ b/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc @@ -141,13 +141,13 @@ class HloRematerializationTest : public HloTestBase { return ShapeUtil::ByteSizeOf(shape, sizeof(void*)); } - StatusOr<bool> RunHloRematerialization( - int64 memory_limit_bytes, HloModule* module, - SequentialHloOrdering::HloModuleSequence* sequence) { + StatusOr<bool> RunHloRematerialization(int64 memory_limit_bytes, + HloModule* module, + HloSchedule* schedule) { TF_EXPECT_OK(verifier().Run(module).status()); return HloRematerialization::RematerializeAndSchedule( ByteSizeOf, memory_limit_bytes, module, DefaultMemoryScheduler, - sequence, /*sizes=*/nullptr); + schedule, /*sizes=*/nullptr); } // Various shapes used in the canned computations. @@ -170,12 +170,12 @@ TEST_F(HloRematerializationTest, SingleComputation) { const HloInstruction* concat = slice->operand(0); const HloInstruction* bcast = concat->operand(0); - SequentialHloOrdering::HloModuleSequence sequence; + HloSchedule schedule(module.get()); // Computation requires 16KB without rematerialization, but uses only 12KB // with rematerialization so pick a memory limit between these values (14KB). TF_ASSERT_OK_AND_ASSIGN(bool changed, RunHloRematerialization( /*memory_limit_bytes=*/14 * 1024, - module.get(), &sequence)); + module.get(), &schedule)); EXPECT_TRUE(changed); // Root should not have changed. @@ -187,9 +187,11 @@ TEST_F(HloRematerializationTest, SingleComputation) { // The rematerialized broadcast should be immediate before the concat in the // sequence. - EXPECT_EQ(sequence.at(computation)[computation->instruction_count() - 2], + EXPECT_EQ(schedule.sequence(computation) + .instructions()[computation->instruction_count() - 2], concat); - EXPECT_EQ(sequence.at(computation)[computation->instruction_count() - 3], + EXPECT_EQ(schedule.sequence(computation) + .instructions()[computation->instruction_count() - 3], remat_bcast); } @@ -203,10 +205,10 @@ TEST_F(HloRematerializationTest, SingleComputationNoRematerialization) { EXPECT_EQ(computation->instruction_count(), 8); - SequentialHloOrdering::HloModuleSequence sequence; + HloSchedule schedule(module.get()); TF_ASSERT_OK_AND_ASSIGN(bool changed, RunHloRematerialization( /*memory_limit_bytes=*/20 * 1024, - module.get(), &sequence)); + module.get(), &schedule)); // No instructions should have been materialized. EXPECT_FALSE(changed); @@ -242,10 +244,10 @@ TEST_F(HloRematerializationTest, RematerializeAroundWhile) { // The body computation uses 16KB and the entry computation uses 2KB at the // while so the peak memory use of the module is 18KB. Set the memory limit a // bit lower (17KB) to force rematerialization of the entry computation. - SequentialHloOrdering::HloModuleSequence sequence; + HloSchedule schedule(module.get()); TF_ASSERT_OK_AND_ASSIGN(bool changed, RunHloRematerialization( /*memory_limit_bytes=*/17 * 1024, - module.get(), &sequence)); + module.get(), &schedule)); EXPECT_TRUE(changed); // Only the entry computation should have a rematerialized instruction added. @@ -276,10 +278,10 @@ TEST_F(HloRematerializationTest, RematerializeEntryAndWhileBody) { EXPECT_EQ(entry_computation->instruction_count(), 7); EXPECT_EQ(body_computation->instruction_count(), 8); - SequentialHloOrdering::HloModuleSequence sequence; + HloSchedule schedule(module.get()); TF_ASSERT_OK_AND_ASSIGN(bool changed, RunHloRematerialization( /*memory_limit_bytes=*/15 * 1024, - module.get(), &sequence)); + module.get(), &schedule)); EXPECT_TRUE(changed); // Both computations should have rematerialized instructions added. @@ -316,10 +318,10 @@ TEST_F(HloRematerializationTest, RematerializeNestedComputations) { // If all computations are maximally rematerialized then peak memory usage is // ~12K so pick something slightly larger. - SequentialHloOrdering::HloModuleSequence sequence; + HloSchedule schedule(module.get()); TF_ASSERT_OK_AND_ASSIGN(bool changed, RunHloRematerialization( /*memory_limit_bytes=*/13 * 1024, - module.get(), &sequence)); + module.get(), &schedule)); EXPECT_TRUE(changed); // All computations should have rematerialized instructions added. @@ -382,14 +384,14 @@ TEST_F(HloRematerializationTest, RngNotRematerialized) { ASSERT_EQ(count_rngs(entry_computation), 1); const int64 original_instruction_count = entry_computation->instruction_count(); - SequentialHloOrdering::HloModuleSequence sequence; + HloSchedule schedule(module.get()); // Pick a memory limit some where between 24KB (initial peak memory including // parameter and output) and 20KB (peak memory possible with // rematerialization). TF_ASSERT_OK_AND_ASSIGN( bool changed, RunHloRematerialization( /*memory_limit_bytes=*/4 * ByteSizeOf(vec1024_shape_), - module.get(), &sequence)); + module.get(), &schedule)); EXPECT_TRUE(changed); // The rng should not have been rematerialized. EXPECT_EQ(count_rngs(entry_computation), 1); @@ -476,13 +478,13 @@ TEST_F(HloRematerializationTest, InstructionRematerializedMultipleTimes) { EXPECT_EQ(add_3->operand(0), bcast); EXPECT_EQ(add_4->operand(0), bcast); - SequentialHloOrdering::HloModuleSequence sequence; + HloSchedule schedule(module.get()); // Pick a memory limit some where between 24KB (initial peak memory including // parameter and output) and 20KB (peak memory possible with // rematerialization). TF_ASSERT_OK_AND_ASSIGN(bool changed, RunHloRematerialization( /*memory_limit_bytes=*/22 * 1024, - module.get(), &sequence)); + module.get(), &schedule)); EXPECT_TRUE(changed); // The broadcast should have been rematerialized 3 times. @@ -571,13 +573,13 @@ TEST_P(IndirectUseTest, IndirectUseNotRematerialized) { EXPECT_EQ(entry_computation->instruction_count(), 8); - SequentialHloOrdering::HloModuleSequence sequence; + HloSchedule schedule(module.get()); // Pick a memory limit some where between 24KB (initial peak memory including // parameter and output) and 20KB (peak memory possible with // rematerialization). TF_ASSERT_OK_AND_ASSIGN(bool changed, RunHloRematerialization( /*memory_limit_bytes=*/22 * 1024, - module.get(), &sequence)); + module.get(), &schedule)); // Rematerialization should only occur if the rematerializable instruction has // no indirect uses. if (indirectly_used) { diff --git a/tensorflow/compiler/xla/service/hlo_schedule.cc b/tensorflow/compiler/xla/service/hlo_schedule.cc new file mode 100644 index 0000000000..a65b33bf40 --- /dev/null +++ b/tensorflow/compiler/xla/service/hlo_schedule.cc @@ -0,0 +1,291 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/hlo_schedule.h" + +#include <queue> +#include <vector> + +#include "absl/strings/str_format.h" +#include "absl/strings/str_join.h" +#include "tensorflow/compiler/xla/map_util.h" +#include "tensorflow/compiler/xla/status_macros.h" +#include "tensorflow/compiler/xla/util.h" +#include "tensorflow/core/lib/gtl/map_util.h" + +namespace xla { + +void HloSchedule::set_sequence( + const HloComputation* computation, + absl::Span<const HloInstruction* const> sequence) { + set_sequence(computation, HloInstructionSequence(sequence)); +} + +void HloSchedule::set_sequence(const HloComputation* computation, + HloInstructionSequence sequence) { + CHECK(computation->parent() == module_); + sequences_[computation->unique_id()] = std::move(sequence); +} + +HloInstructionSequence& HloSchedule::GetOrCreateSequence( + const HloComputation* computation) { + auto it = sequences_.find(computation->unique_id()); + if (it == sequences_.end()) { + // No sequence found for computation. Create and return an empty one. + CHECK(computation->parent() == module_); + return sequences_[computation->unique_id()]; + } else { + return it->second; + } +} + +const HloInstructionSequence& HloSchedule::sequence( + const HloComputation* computation) const { + return sequences_.at(computation->unique_id()); +} + +Status HloSchedule::UpdateComputationSchedule( + const HloComputation* computation) { + // Map from unique ID to HloInstruction pointer for instructions in the + // computation. + tensorflow::gtl::FlatMap<int, const HloInstruction*> id_to_instruction; + for (const HloInstruction* instruction : computation->instructions()) { + InsertOrDie(&id_to_instruction, instruction->unique_id(), instruction); + } + + // Set of all HloInstructions in the schedule. + tensorflow::gtl::FlatSet<int> ids_in_schedule; + for (int id : sequences_.at(computation->unique_id()).ids()) { + InsertOrDie(&ids_in_schedule, id); + } + + // Map from HloInstruction X to newly added instructions (instruction is in + // computation, but not in schedule) which use X. If an instruction is not in + // the map, then it has no users which are newly added instructions. + tensorflow::gtl::FlatMap<const HloInstruction*, + std::vector<const HloInstruction*>> + new_instruction_uses; + + // For each newly added instruction, this is the count of the instruction's + // operands that have not yet been scheduled. When this value reaches zero, + // then the instruction may be placed in the schedule. + tensorflow::gtl::FlatMap<const HloInstruction*, int> + unscheduled_operand_count; + + // Create a worklist of newly added instructions which are ready to be added + // to the schedule. Initialize worklist with those that have zero operands. + std::queue<const HloInstruction*> worklist; + + for (const HloInstruction* instruction : computation->instructions()) { + if (ids_in_schedule.count(instruction->unique_id()) == 0) { + // This is a newly added instruction which is not in the schedule. + if (instruction->operands().empty()) { + worklist.push(instruction); + } else { + for (const HloInstruction* operand : instruction->operands()) { + new_instruction_uses[operand].push_back(instruction); + } + unscheduled_operand_count[instruction] = instruction->operand_count(); + } + } + } + + // Update the schedule with the newly added instructions, and remove any + // instructions no longer in the graph. + HloInstructionSequence new_sequence; + + // Lambda which schedules all instructions on the worklist. + auto schedule_worklist = [&]() { + while (!worklist.empty()) { + const HloInstruction* instruction = worklist.front(); + worklist.pop(); + new_sequence.push_back(instruction); + std::vector<const HloInstruction*>* new_users = + tensorflow::gtl::FindOrNull(new_instruction_uses, instruction); + if (new_users != nullptr) { + // This just-scheduled instruction has users which are newly added to + // the module. Update the number of unscheduled operands and push the + // newly added instruction to the worklist if it is ready to + // schedule. + for (const HloInstruction* new_user : *new_users) { + unscheduled_operand_count.at(new_user)--; + CHECK_GE(unscheduled_operand_count.at(new_user), 0); + if (unscheduled_operand_count.at(new_user) == 0) { + worklist.push(new_user); + } + } + } + } + }; + + schedule_worklist(); + for (int id : sequences_.at(computation->unique_id()).ids()) { + auto it = id_to_instruction.find(id); + if (it == id_to_instruction.end()) { + // This instruction in the schedule is no longer in the module. Do not add + // it to the new schedule. + continue; + } + worklist.push(it->second); + schedule_worklist(); + } + + set_sequence(computation, std::move(new_sequence)); + return Status::OK(); +} + +Status HloSchedule::Update() { + // The schedule must contain a sequence for every non-fusion computation in + // the module, but can have sequences for computations which no longer exist + // (these are removed). + std::vector<HloComputation*> nonfusion_computations = + module_->MakeNonfusionComputations(); + for (const HloComputation* computation : nonfusion_computations) { + TF_RET_CHECK(sequences_.count(computation->unique_id()) == 1) + << "Computation " << computation->name() << " not in HloSchedule."; + } + if (sequences_.size() > nonfusion_computations.size()) { + // Schedule contains some computations which have been removed from the + // HloModule. Remove them from the schedule as well. + tensorflow::gtl::FlatSet<int64> nonfusion_computations_ids; + for (const HloComputation* computation : nonfusion_computations) { + nonfusion_computations_ids.insert(computation->unique_id()); + } + for (auto it = sequences_.begin(); it != sequences_.end();) { + if (nonfusion_computations_ids.count(it->first) == 0) { + it = sequences_.erase(it); + } else { + it++; + } + } + } + CHECK_EQ(sequences_.size(), nonfusion_computations.size()); + + for (const HloComputation* computation : nonfusion_computations) { + TF_RETURN_IF_ERROR(UpdateComputationSchedule(computation)); + } + + TF_RETURN_IF_ERROR(Verify()); + return Status::OK(); +} + +Status HloSchedule::Verify() const { + VLOG(2) << "VerifySchedule()"; + XLA_VLOG_LINES(3, module_->ToString()); + XLA_VLOG_LINES(2, ToString()); + + // Verify schedule contains exactly the same set of non-fusion computations as + // module currently does. + std::vector<HloComputation*> nonfusion_computations = + module_->MakeNonfusionComputations(); + TF_RET_CHECK(nonfusion_computations.size() == sequences_.size()) + << "Schedule has " << sequences_.size() << " sequences, but module has " + << nonfusion_computations.size() << " non-fusion computations"; + for (const HloComputation* computation : nonfusion_computations) { + TF_RET_CHECK(sequences_.count(computation->unique_id()) == 1) + << "Computation " << computation->name() + << " missing from HLO schedule."; + } + + // For each computation verify the set of instructions is the same and that + // each dependency and control edge is honored. + for (const HloComputation* computation : nonfusion_computations) { + tensorflow::gtl::FlatMap<const HloInstruction*, int> instruction_position; + int pos = 0; + for (const HloInstruction* instruction : + sequence(computation).instructions()) { + TF_RET_CHECK(instruction_position.insert({instruction, pos}).second) + << "Instruction " << instruction->name() + << " appears more than once in the schedule"; + pos++; + } + + TF_RET_CHECK(instruction_position.size() == + computation->instruction_count()); + for (const HloInstruction* instruction : computation->instructions()) { + TF_RET_CHECK(instruction_position.count(instruction) == 1) + << "Instruction " << instruction->name() << " is not in schedule"; + } + + for (const HloInstruction* instruction : computation->instructions()) { + for (const HloInstruction* operand : instruction->operands()) { + TF_RET_CHECK(instruction_position.at(operand) < + instruction_position.at(instruction)) + << "Instruction " << instruction->name() + << " is not scheduled after its operand " << operand->name(); + } + + for (const HloInstruction* pred : instruction->control_predecessors()) { + TF_RET_CHECK(instruction_position.at(pred) < + instruction_position.at(instruction)) + << "Instruction " << instruction->name() + << " is not scheduled after its control predecessor " + << pred->name(); + } + } + } + + return Status::OK(); +} + +namespace { + +// Returns the computation in the given module with the given unique ID. Returns +// nullptr if no such computation exists. +const HloComputation* IdToComputation(const HloModule* module, int64 id) { + for (const HloComputation* computation : module->computations()) { + if (computation->unique_id() == id) { + return computation; + } + } + return nullptr; +} + +} // namespace + +string HloSchedule::ToString() const { + std::vector<string> pieces; + + pieces.push_back("HloSchedule"); + for (const auto& id_sequence : sequences_) { + const HloComputation* computation = + IdToComputation(module_, id_sequence.first); + if (computation == nullptr) { + // The computation is not in the module and may have been deleted so it is + // not safe to dereference any HLO pointers. Just use the HLO unique ids + // stored in this object. + pieces.push_back( + absl::StrFormat("computation with id %d (no longer in HLO module):", + id_sequence.first)); + for (int id : id_sequence.second.ids()) { + pieces.push_back(absl::StrCat(" ", id)); + } + } else { + pieces.push_back(absl::StrFormat("computation %s:", computation->name())); + for (const HloInstruction* instruction : + id_sequence.second.instructions()) { + pieces.push_back(absl::StrCat(" ", instruction->name())); + } + } + } + return absl::StrJoin(pieces, "\n"); +} + +std::ostream& operator<<(std::ostream& out, const HloSchedule& schedule) { + out << schedule.ToString(); + return out; +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_schedule.h b/tensorflow/compiler/xla/service/hlo_schedule.h new file mode 100644 index 0000000000..21c6988638 --- /dev/null +++ b/tensorflow/compiler/xla/service/hlo_schedule.h @@ -0,0 +1,151 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_SCHEDULE_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_SCHEDULE_H_ + +#include <vector> + +#include "absl/types/span.h" +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/service/hlo_schedule.h" +#include "tensorflow/compiler/xla/status.h" + +namespace xla { + +// Class representing a sequence of HLO instructions such as the sequential +// execution order of an HLO computation. +class HloInstructionSequence { + public: + HloInstructionSequence() = default; + HloInstructionSequence(absl::Span<const HloInstruction* const> instructions) { + for (const HloInstruction* instruction : instructions) { + push_back(instruction); + } + } + + // Adds the instruction to the end of the sequence. + void push_back(const HloInstruction* instruction) { + instruction_sequence_.push_back(instruction); + id_sequence_.push_back(instruction->unique_id()); + } + + // Clears the sequence of all instructions. + void clear() { + instruction_sequence_.clear(); + id_sequence_.clear(); + } + + int64 size() const { return instruction_sequence_.size(); } + + // Returns the sequence of HLO instructions. + const std::vector<const HloInstruction*>& instructions() const { + return instruction_sequence_; + } + + // Returns the unique IDs of the instructions in the sequence (in order). + const std::vector<int>& ids() const { return id_sequence_; } + + private: + // The sequence as HloInstructions. + std::vector<const HloInstruction*> instruction_sequence_; + + // The sequence of HLO instructions, represented by their unique IDs. The + // sequence is stored as both HloInstructions and unique IDs because the + // sequence may be referenced after transformations to the HLO graph and HLO + // pointers can be invalidated or recycled in this process (see + // HloSchedule::Update). + std::vector<int> id_sequence_; +}; + +// A class representing a sequential schedule of instructions for an HLO +// module. A complete HLO schedule contains an instruction sequence for every +// non-fusion computation in the HLO module. +class HloSchedule { + public: + HloSchedule(const HloModule* module) : module_(module) {} + + // Returns a reference to the sequence for the given computation. + const HloInstructionSequence& sequence( + const HloComputation* computation) const; + + // Returns the sequence for the given computation. An empty sequence is + // created if none exists for the computation. + HloInstructionSequence& GetOrCreateSequence( + const HloComputation* computation); + + // Sets the sequence for the given computation to the given sequence. + void set_sequence(const HloComputation* computation, + absl::Span<const HloInstruction* const> sequence); + void set_sequence(const HloComputation* computation, + HloInstructionSequence sequence); + + // Returns a map from HloComputation unique ID to instruction sequence. The + // map contains all sequences in the schedule. + const tensorflow::gtl::FlatMap<int64, HloInstructionSequence>& sequences() + const { + return sequences_; + } + + // Returns true if the schedule has a sequence for the given computation. + bool is_computation_scheduled(const HloComputation* computation) const { + return sequences_.count(computation->unique_id()) == 1; + } + + // Updates the schedule such that it is (again) a valid schedule for the + // module. This is used to update a schedule after the HLO module has been + // transformed in some way. In general, the only transformations to the module + // for which a schedule can be updated is the addition or removal of + // instructions and removal of computations. Updating the schedule after new + // dependencies between existing instructions in the module is not supported + // and may result in an error status returned. + // + // Instructions in the module which also exist in the given schedule will + // remain in the same order in the updated schedule. Instructions which exist + // in the module but not in the given schedule will be placed as early as + // possible in the updated schedule. + Status Update(); + + // Verifies that the given schedule is valid for the given module. + // Specifically, the schedule contains exactly the instructions in the + // non-fusion computations in the module and every dependency in the module is + // satisfied in the schedule. + Status Verify() const; + + string ToString() const; + + bool empty() const { return sequences_.empty(); } + + const HloModule* module() const { return module_; } + + private: + // Updates the instruction sequence for the given computation. + Status UpdateComputationSchedule(const HloComputation* computation); + + const HloModule* module_; + + // A map from computation unique ID to instruction sequence. Unique IDs are + // used rather than HloComputation pointers because HLO pointers are not + // unique across HLO transformations because pointers may be recycled. + tensorflow::gtl::FlatMap<int64, HloInstructionSequence> sequences_; +}; + +std::ostream& operator<<(std::ostream& out, const HloSchedule& schedule); + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_SCHEDULE_H_ diff --git a/tensorflow/compiler/xla/service/hlo_schedule_test.cc b/tensorflow/compiler/xla/service/hlo_schedule_test.cc new file mode 100644 index 0000000000..eb52582bb5 --- /dev/null +++ b/tensorflow/compiler/xla/service/hlo_schedule_test.cc @@ -0,0 +1,341 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/hlo_schedule.h" + +#include <memory> +#include <string> + +#include "absl/algorithm/container.h" +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_dce.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/service/hlo_ordering.h" +#include "tensorflow/compiler/xla/service/hlo_parser.h" +#include "tensorflow/compiler/xla/service/hlo_scheduling.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/core/status_test_util.h" + +namespace xla { +namespace { + +class HloScheduleTest : public HloTestBase {}; + +TEST_F(HloScheduleTest, UpdateScheduleUnchangedModule) { + // Updating the schedule of an unchanged HLO module should not affect the + // schedule at all. + const string module_str = R"( +HloModule UpdateScheduleUnchanged + +ENTRY main { + a = f32[] parameter(0) + b = f32[] parameter(1) + c = f32[] constant(42.0) + sum = f32[] add(a, b) + neg = f32[] negate(c) + ROOT root = f32[] multiply(sum, neg) +} +)"; + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr<HloModule> module, + ParseHloString(module_str)); + TF_ASSERT_OK_AND_ASSIGN( + HloSchedule schedule, + ScheduleModule(*module, [](const BufferValue& buffer) { + return ShapeUtil::ByteSizeOf(buffer.shape()); + })); + const std::vector<const HloInstruction*>& entry_schedule = + schedule.sequence(module->entry_computation()).instructions(); + + EXPECT_EQ(entry_schedule.size(), 6); + + TF_ASSERT_OK(schedule.Update()); + TF_ASSERT_OK(schedule.Verify()); + + EXPECT_EQ(entry_schedule, + schedule.sequence(module->entry_computation()).instructions()); +} + +TEST_F(HloScheduleTest, UpdateScheduleWithNewInstructions) { + // Add some additional instructions to a module and verify the schedule can be + // updated. + const string module_str = R"( +HloModule UpdateScheduleWithNewInstructions + +ENTRY main { + a = f32[] parameter(0) + b = f32[] parameter(1) + c = f32[] constant(42.0) + sum = f32[] add(a, b) + neg = f32[] negate(c) + ROOT root = f32[] multiply(sum, neg) +} +)"; + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr<HloModule> module, + ParseHloString(module_str)); + TF_ASSERT_OK_AND_ASSIGN( + HloSchedule schedule, + ScheduleModule(*module, [](const BufferValue& buffer) { + return ShapeUtil::ByteSizeOf(buffer.shape()); + })); + + HloComputation* entry = module->entry_computation(); + const Shape shape = entry->root_instruction()->shape(); + HloInstruction* constant = entry->AddInstruction( + HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0))); + HloInstruction* sub = entry->AddInstruction(HloInstruction::CreateBinary( + shape, HloOpcode::kSubtract, constant, entry->root_instruction())); + entry->set_root_instruction(sub); + + auto in_schedule = [&](const HloInstruction* hlo) { + return absl::c_linear_search(schedule.sequence(entry).instructions(), hlo); + }; + + EXPECT_EQ(schedule.sequence(entry).size(), 6); + EXPECT_FALSE(in_schedule(constant)); + EXPECT_FALSE(in_schedule(sub)); + + ASSERT_IS_NOT_OK(schedule.Verify()); + TF_ASSERT_OK(schedule.Update()); + TF_ASSERT_OK(schedule.Verify()); + + EXPECT_EQ(schedule.sequence(entry).size(), 8); + EXPECT_TRUE(in_schedule(constant)); + EXPECT_TRUE(in_schedule(sub)); +} + +TEST_F(HloScheduleTest, UpdateScheduleWithAddedAndDeletedInstruction) { + // Add and delete some instructions from a module and verify that the schedule + // can be updated successfully. + const string module_str = R"( +HloModule UpdateScheduleWithAddedAndDeletedInstruction + +ENTRY main { + a = f32[] parameter(0) + b = f32[] parameter(1) + c = f32[] constant(42.0) + sum = f32[] add(a, b) + neg = f32[] negate(c) + ROOT root = f32[] multiply(sum, neg) +} +)"; + + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr<HloModule> module, + ParseHloString(module_str)); + TF_ASSERT_OK_AND_ASSIGN( + HloSchedule schedule, + ScheduleModule(*module, [](const BufferValue& buffer) { + return ShapeUtil::ByteSizeOf(buffer.shape()); + })); + + // Set the entry root to some expression containing just a parameter and a + // constant. + HloComputation* entry = module->entry_computation(); + HloInstruction* constant = entry->AddInstruction( + HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0))); + HloInstruction* new_root = entry->AddInstruction( + HloInstruction::CreateBinary(constant->shape(), HloOpcode::kSubtract, + constant, entry->parameter_instruction(0))); + entry->set_root_instruction(new_root); + + // DCE should remove everything but the parameters and the newly added code. + HloDCE dce; + TF_ASSERT_OK(dce.Run(module.get()).status()); + + EXPECT_EQ(schedule.sequence(entry).size(), 6); + + ASSERT_IS_NOT_OK(schedule.Verify()); + TF_ASSERT_OK(schedule.Update()); + TF_ASSERT_OK(schedule.Verify()); + + EXPECT_EQ(schedule.sequence(entry).size(), 4); +} + +TEST_F(HloScheduleTest, UpdateScheduleWithCompletelyReplacedModule) { + // Completely replace a module with an entirely new set of instructions and + // verify that the schedule can be updated successfully. + const string module_str = R"( +HloModule UpdateScheduleWithCompletelyReplacedModule + +ENTRY main { + a = f32[] constant(42.0) + b = f32[] constant(123.0) + ROOT sum = f32[] add(a, b) +} +)"; + + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr<HloModule> module, + ParseHloString(module_str)); + TF_ASSERT_OK_AND_ASSIGN( + HloSchedule schedule, + ScheduleModule(*module, [](const BufferValue& buffer) { + return ShapeUtil::ByteSizeOf(buffer.shape()); + })); + + // Replace the entry computation with the negation of a constant. + HloComputation* entry = module->entry_computation(); + HloInstruction* constant = entry->AddInstruction( + HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0))); + HloInstruction* new_root = entry->AddInstruction(HloInstruction::CreateUnary( + constant->shape(), HloOpcode::kNegate, constant)); + entry->set_root_instruction(new_root); + + // DCE the old instructions. + HloDCE dce; + TF_ASSERT_OK(dce.Run(module.get()).status()); + + EXPECT_EQ(schedule.sequence(entry).size(), 3); + + ASSERT_IS_NOT_OK(schedule.Verify()); + TF_ASSERT_OK(schedule.Update()); + TF_ASSERT_OK(schedule.Verify()); + + EXPECT_EQ(schedule.sequence(entry).size(), 2); +} + +TEST_F(HloScheduleTest, UpdateScheduleWithMultipleComputations) { + // Create changes to more than one computation in an HLO module and verify + // that the schedule can be updated. + const string module_str = R"( +HloModule UpdateScheduleWithMultipleComputations + +%Body (param.1: (s32[], token[])) -> (s32[], token[]) { + %param.1 = (s32[], token[]) parameter(0) + %get-tuple-element.1 = s32[] get-tuple-element((s32[], token[]) %param.1), index=0 + %constant.1 = s32[] constant(1) + %add = s32[] add(s32[] %get-tuple-element.1, s32[] %constant.1) + %get-tuple-element.2 = token[] get-tuple-element((s32[], token[]) %param.1), index=1 + %after-all = token[] after-all(token[] %get-tuple-element.2) + ROOT %tuple = (s32[], token[]) tuple(s32[] %add, token[] %after-all) +} + +%Cond (param: (s32[], token[])) -> pred[] { + %param = (s32[], token[]) parameter(0) + %get-tuple-element = s32[] get-tuple-element((s32[], token[]) %param), index=0 + %constant = s32[] constant(42) + ROOT %less-than = pred[] less-than(s32[] %get-tuple-element, s32[] %constant) +} + +ENTRY %WhileLoop () -> s32[] { + %zero = s32[] constant(0) + %init_token = token[] after-all() + %init_tuple = (s32[], token[]) tuple(s32[] %zero, token[] %init_token) + %while = (s32[], token[]) while((s32[], token[]) %init_tuple), condition=%Cond, body=%Body + ROOT %root = s32[] get-tuple-element((s32[], token[]) %while), index=0 +} +)"; + + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr<HloModule> module, + ParseHloString(module_str)); + TF_ASSERT_OK_AND_ASSIGN( + HloSchedule schedule, + ScheduleModule(*module, [](const BufferValue& buffer) { + return ShapeUtil::ByteSizeOf(buffer.shape(), + /*pointer_size=*/sizeof(void*)); + })); + + const HloInstruction* xla_while = + module->entry_computation()->root_instruction()->operand(0); + HloComputation* body = xla_while->while_body(); + HloComputation* cond = xla_while->while_condition(); + + // Negate the root of the cond. + cond->set_root_instruction(cond->AddInstruction( + HloInstruction::CreateUnary(ShapeUtil::MakeShape(PRED, {}), + HloOpcode::kNot, cond->root_instruction()))); + + // Replace the body with a computation which just passes through its + // parameter. + body->set_root_instruction(body->parameter_instruction(0)); + + // DCE the dead code in the body. + HloDCE dce; + TF_ASSERT_OK(dce.Run(module.get()).status()); + + EXPECT_EQ(schedule.sequence(body).size(), 7); + EXPECT_EQ(schedule.sequence(cond).size(), 4); + + ASSERT_IS_NOT_OK(schedule.Verify()); + TF_ASSERT_OK(schedule.Update()); + TF_ASSERT_OK(schedule.Verify()); + + EXPECT_EQ(schedule.sequence(body).size(), 1); + EXPECT_EQ(schedule.sequence(cond).size(), 5); +} + +TEST_F(HloScheduleTest, UpdateScheduleComputationRemoved) { + // Remove computations from a module and verify the schedule can be updated. + const string module_str = R"( +HloModule UpdateScheduleWithMultipleComputations + +%Body (param.1: (s32[], token[])) -> (s32[], token[]) { + %param.1 = (s32[], token[]) parameter(0) + %get-tuple-element.1 = s32[] get-tuple-element((s32[], token[]) %param.1), index=0 + %constant.1 = s32[] constant(1) + %add = s32[] add(s32[] %get-tuple-element.1, s32[] %constant.1) + %get-tuple-element.2 = token[] get-tuple-element((s32[], token[]) %param.1), index=1 + %after-all = token[] after-all(token[] %get-tuple-element.2) + ROOT %tuple = (s32[], token[]) tuple(s32[] %add, token[] %after-all) +} + +%Cond (param: (s32[], token[])) -> pred[] { + %param = (s32[], token[]) parameter(0) + %get-tuple-element = s32[] get-tuple-element((s32[], token[]) %param), index=0 + %constant = s32[] constant(42) + ROOT %less-than = pred[] less-than(s32[] %get-tuple-element, s32[] %constant) +} + +ENTRY %WhileLoop () -> s32[] { + %zero = s32[] constant(0) + %init_token = token[] after-all() + %init_tuple = (s32[], token[]) tuple(s32[] %zero, token[] %init_token) + %while = (s32[], token[]) while((s32[], token[]) %init_tuple), condition=%Cond, body=%Body + ROOT %root = s32[] get-tuple-element((s32[], token[]) %while), index=0 +} +)"; + + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr<HloModule> module, + ParseHloString(module_str)); + TF_ASSERT_OK_AND_ASSIGN( + HloSchedule schedule, + ScheduleModule(*module, [](const BufferValue& buffer) { + return ShapeUtil::ByteSizeOf(buffer.shape(), + /*pointer_size=*/sizeof(void*)); + })); + + HloInstruction* xla_while = + module->entry_computation()->root_instruction()->mutable_operand(0); + HloInstruction* init = xla_while->mutable_operand(0); + + // Replace the while with its init value. The conditional and body + // computations should then be dead. + TF_ASSERT_OK(xla_while->ReplaceAllUsesWith(init)); + + // DCE the dead code in the body. + HloDCE dce; + ASSERT_EQ(module->computation_count(), 3); + TF_ASSERT_OK(dce.Run(module.get()).status()); + ASSERT_EQ(module->computation_count(), 1); + + ASSERT_IS_NOT_OK(schedule.Verify()); + TF_ASSERT_OK(schedule.Update()); + TF_ASSERT_OK(schedule.Verify()); +} + +} // namespace +} // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_scheduling.cc b/tensorflow/compiler/xla/service/hlo_scheduling.cc index 0fc3b268c0..9bfb0af96c 100644 --- a/tensorflow/compiler/xla/service/hlo_scheduling.cc +++ b/tensorflow/compiler/xla/service/hlo_scheduling.cc @@ -70,7 +70,7 @@ class ListScheduler { public: // Construct and return a memory-minimizing sequence of HLO instructions // containing the given HLO computation. - static StatusOr<std::vector<const HloInstruction*>> Run( + static StatusOr<HloInstructionSequence> Run( const HloComputation& computation, const TuplePointsToAnalysis& points_to_analysis, const LogicalBuffer::SizeFunction& size_function, @@ -229,8 +229,8 @@ class ListScheduler { return {BytesFreedIfScheduled(entry), entry.instruction->user_count()}; } - std::vector<const HloInstruction*> CreateSchedule() { - std::vector<const HloInstruction*> schedule; + HloInstructionSequence CreateSchedule() { + HloInstructionSequence schedule; // Populate the ready list with instructions which have no operands or // control predecessors. @@ -374,7 +374,7 @@ int64 SumLogicalBufferSizes( return size; } -StatusOr<std::vector<const HloInstruction*>> ScheduleComputationHelper( +StatusOr<HloInstructionSequence> ScheduleComputationHelper( const HloComputation& computation, const TuplePointsToAnalysis& points_to_analysis, const LogicalBuffer::SizeFunction& size_function, @@ -392,7 +392,7 @@ StatusOr<std::vector<const HloInstruction*>> ScheduleComputationHelper( } // namespace -StatusOr<std::vector<const HloInstruction*>> DFSMemoryScheduler( +StatusOr<HloInstructionSequence> DFSMemoryScheduler( const HloComputation& computation, const TuplePointsToAnalysis& points_to_analysis, const LogicalBuffer::SizeFunction& size_function, @@ -443,7 +443,7 @@ StatusOr<std::vector<const HloInstruction*>> DFSMemoryScheduler( // Construct a total order based on DFS post-order, visiting operands in // decreasing cumulative extra user order, and next by cumulative size, with a // tiebreaker by name for determinism. - std::vector<const HloInstruction*> sequence; + HloInstructionSequence sequence; FunctionVisitor visitor([&sequence](HloInstruction* hlo) { sequence.push_back(hlo); return Status::OK(); @@ -463,7 +463,7 @@ StatusOr<std::vector<const HloInstruction*>> DFSMemoryScheduler( return sequence; } // namespace xla -StatusOr<std::vector<const HloInstruction*>> ListMemoryScheduler( +StatusOr<HloInstructionSequence> ListMemoryScheduler( const HloComputation& computation, const TuplePointsToAnalysis& points_to_analysis, const LogicalBuffer::SizeFunction& size_function, @@ -473,18 +473,16 @@ StatusOr<std::vector<const HloInstruction*>> ListMemoryScheduler( memory_by_computation); } -StatusOr<std::vector<const HloInstruction*>> PostOrderMemoryScheduler( +StatusOr<HloInstructionSequence> PostOrderMemoryScheduler( const HloComputation& computation, const TuplePointsToAnalysis& points_to_analysis, const LogicalBuffer::SizeFunction& size_function, const tensorflow::gtl::FlatMap<const HloComputation*, int64>& memory_by_computation) { - const auto& post_order = computation.MakeInstructionPostOrder(); - return std::vector<const HloInstruction*>{post_order.begin(), - post_order.end()}; + return HloInstructionSequence(computation.MakeInstructionPostOrder()); } -StatusOr<std::vector<const HloInstruction*>> DefaultMemoryScheduler( +StatusOr<HloInstructionSequence> DefaultMemoryScheduler( const HloComputation& computation, const TuplePointsToAnalysis& points_to_analysis, const LogicalBuffer::SizeFunction& size_function, @@ -499,7 +497,7 @@ StatusOr<std::vector<const HloInstruction*>> DefaultMemoryScheduler( // List wins for most of our benchmarks; postorder-based schedulers win for // some RNNs. TF_ASSIGN_OR_RETURN( - std::vector<const HloInstruction*> list_sequence, + HloInstructionSequence list_sequence, ListMemoryScheduler(computation, points_to_analysis, size_function, memory_by_computation)); TF_ASSIGN_OR_RETURN(const int64 list_memory, @@ -508,7 +506,7 @@ StatusOr<std::vector<const HloInstruction*>> DefaultMemoryScheduler( size_function, &memory_by_computation)); VLOG(2) << "Min-memory list sequence: " << HumanReadableNumBytes(list_memory); - TF_ASSIGN_OR_RETURN(std::vector<const HloInstruction*> dfs_sequence, + TF_ASSIGN_OR_RETURN(HloInstructionSequence dfs_sequence, DFSMemoryScheduler(computation, points_to_analysis, size_function, memory_by_computation)); TF_ASSIGN_OR_RETURN(const int64 dfs_memory, @@ -518,7 +516,7 @@ StatusOr<std::vector<const HloInstruction*>> DefaultMemoryScheduler( VLOG(2) << "Min-memory dfs sequence: " << HumanReadableNumBytes(dfs_memory); TF_ASSIGN_OR_RETURN( - std::vector<const HloInstruction*> post_order_sequence, + HloInstructionSequence post_order_sequence, PostOrderMemoryScheduler(computation, points_to_analysis, size_function, memory_by_computation)); TF_ASSIGN_OR_RETURN(const int64 post_order_memory, @@ -545,32 +543,35 @@ StatusOr<std::vector<const HloInstruction*>> DefaultMemoryScheduler( } } -StatusOr<SequentialHloOrdering::HloModuleSequence> ScheduleComputationsInModule( +StatusOr<HloSchedule> ScheduleModule( const HloModule& module, const LogicalBuffer::SizeFunction& size_function, const MemorySchedulerAlgorithm& algorithm) { - SequentialHloOrdering::HloModuleSequence sequence; + HloSchedule schedule(&module); TF_ASSIGN_OR_RETURN(std::unique_ptr<TuplePointsToAnalysis> points_to_analysis, TuplePointsToAnalysis::Run(&module)); tensorflow::gtl::FlatMap<const HloComputation*, int64> memory_by_computation; for (const auto* computation : module.MakeComputationPostOrder()) { if (!computation->IsFusionComputation()) { - TF_ASSIGN_OR_RETURN(auto one_computation_sequence, + TF_ASSIGN_OR_RETURN(HloInstructionSequence computation_sequence, ScheduleComputationHelper( *computation, *points_to_analysis, size_function, algorithm, memory_by_computation)); memory_by_computation[computation] = HeapSimulator::MinimumMemoryForComputation( - *computation, one_computation_sequence, *points_to_analysis, + *computation, computation_sequence, *points_to_analysis, size_function, &memory_by_computation) .ValueOrDie(); - sequence[computation] = std::move(one_computation_sequence); + schedule.set_sequence(computation, std::move(computation_sequence)); } } - VLOG(1) << "Module schedule:\n" << sequence; - return sequence; + VLOG(1) << "Module schedule:\n" << schedule; + + TF_RETURN_IF_ERROR(schedule.Verify()); + + return std::move(schedule); } -StatusOr<std::vector<const HloInstruction*>> ScheduleOneComputation( +StatusOr<HloInstructionSequence> ScheduleComputation( const HloComputation& computation, const LogicalBuffer::SizeFunction& size_function) { CHECK(!computation.IsFusionComputation()); @@ -581,187 +582,4 @@ StatusOr<std::vector<const HloInstruction*>> ScheduleOneComputation( size_function, nullptr, empty_map); } -tensorflow::gtl::FlatMap<const HloComputation*, std::vector<int>> -ComputeIdSchedule(const SequentialHloOrdering::HloModuleSequence& sequence) { - tensorflow::gtl::FlatMap<const HloComputation*, std::vector<int>> id_sequence; - for (const auto& computation_sequence : sequence) { - for (const HloInstruction* instruction : computation_sequence.second) { - id_sequence[computation_sequence.first].push_back( - instruction->unique_id()); - } - } - return id_sequence; -} - -Status UpdateSchedule( - const HloModule& module, - const tensorflow::gtl::FlatMap<const HloComputation*, std::vector<int>>& - id_sequence, - SequentialHloOrdering::HloModuleSequence* sequence) { - // Map from unique ID to HloInstruction pointer for instructions in the - // module. - tensorflow::gtl::FlatMap<int, const HloInstruction*> id_to_instruction; - // Set of all HloInstructions in the schedule. - tensorflow::gtl::FlatSet<int> ids_in_schedule; - std::vector<HloComputation*> nonfusion_computations = - module.MakeNonfusionComputations(); - for (const HloComputation* computation : nonfusion_computations) { - for (const HloInstruction* instruction : computation->instructions()) { - TF_RET_CHECK( - id_to_instruction.insert({instruction->unique_id(), instruction}) - .second); - } - for (int id : id_sequence.at(computation)) { - ids_in_schedule.insert(id); - } - } - - // Map from HloInstruction X to newly added instructions (instruction is in - // module, but not in schedule) which use X. If an instruction is not in the - // map, then it has no users which are newly added instructions. - tensorflow::gtl::FlatMap<const HloInstruction*, - std::vector<const HloInstruction*>> - new_instruction_uses; - - // For each newly added instruction, this is the count of the instruction's - // operands that have not yet been scheduled. When this value reaches zero, - // then the instruction may be placed in the schedule. - tensorflow::gtl::FlatMap<const HloInstruction*, int> - unscheduled_operand_count; - // For each computation, this is the set of newly added instructions which - // have no operands. These must be handled specially and are added to the - // beginning of the schedule. - tensorflow::gtl::FlatMap<const HloComputation*, - std::vector<const HloInstruction*>> - new_zero_operand_instructions; - for (const HloComputation* computation : nonfusion_computations) { - new_zero_operand_instructions[computation] = {}; - for (const HloInstruction* instruction : computation->instructions()) { - if (ids_in_schedule.count(instruction->unique_id()) == 0) { - // This is a newly added instruction which is not in the schedule. - for (const HloInstruction* operand : instruction->operands()) { - new_instruction_uses[operand].push_back(instruction); - } - if (instruction->operands().empty()) { - new_zero_operand_instructions[computation].push_back(instruction); - } - unscheduled_operand_count[instruction] = instruction->operand_count(); - } - } - } - - // Update the schedule with the newly added instructions, and remove any - // instructions no longer in the graph. - for (const HloComputation* computation : nonfusion_computations) { - std::vector<const HloInstruction*> old_computation_sequence = - std::move(sequence->at(computation)); - sequence->at(computation).clear(); - - // Create a worklist of newly added instructions which are ready to be added - // to the schedule. Initialize worklist with those that have zero operands. - std::queue<const HloInstruction*> worklist; - for (const HloInstruction* instruction : - new_zero_operand_instructions.at(computation)) { - worklist.push(instruction); - } - - // Lambda which schedules all instructions on the worklist. - auto schedule_worklist = [&]() { - while (!worklist.empty()) { - const HloInstruction* instruction = worklist.front(); - worklist.pop(); - sequence->at(computation).push_back(instruction); - std::vector<const HloInstruction*>* new_users = - tensorflow::gtl::FindOrNull(new_instruction_uses, instruction); - if (new_users != nullptr) { - // This just-scheduled instruction has users which are newly added to - // the module. Update the number of unscheduled operands and push the - // newly added instruction to the worklist if it is ready to - // schedule. - for (const HloInstruction* new_user : *new_users) { - unscheduled_operand_count.at(new_user)--; - CHECK_GE(unscheduled_operand_count.at(new_user), 0); - if (unscheduled_operand_count.at(new_user) == 0) { - worklist.push(new_user); - } - } - } - } - }; - - schedule_worklist(); - for (int id : id_sequence.at(computation)) { - auto it = id_to_instruction.find(id); - if (it == id_to_instruction.end()) { - // This instruction in the schedule is no longer in the module. - continue; - } - const HloInstruction* instruction = it->second; - worklist.push(instruction); - schedule_worklist(); - } - } - - TF_RETURN_IF_ERROR(VerifySchedule(module, *sequence)); - return Status::OK(); -} - -Status VerifySchedule( - const HloModule& module, - const SequentialHloOrdering::HloModuleSequence& sequence) { - VLOG(2) << "VerifySchedule()"; - XLA_VLOG_LINES(2, module.ToString()); - VLOG(2) << sequence; - - // Verify the set of computations in the sequence is exactly the set of - // computations in the module. - std::vector<HloComputation*> nonfusion_computations = - module.MakeNonfusionComputations(); - TF_RET_CHECK(nonfusion_computations.size() == sequence.size()); - tensorflow::gtl::FlatSet<const HloComputation*> computations_in_module( - module.computations().begin(), module.computations().end()); - for (const auto& computation_sequence : sequence) { - TF_RET_CHECK(computations_in_module.count(computation_sequence.first) == 1); - } - - // For each computation verify the set of instructions is the same and that - // each dependency and control edge is honored. - for (const HloComputation* computation : nonfusion_computations) { - tensorflow::gtl::FlatMap<const HloInstruction*, int> instruction_position; - int pos = 0; - for (const HloInstruction* instruction : sequence.at(computation)) { - TF_RET_CHECK(instruction_position.insert({instruction, pos}).second) - << "Instruction " << instruction->name() - << " appears more than once in the schedule"; - pos++; - } - - TF_RET_CHECK(instruction_position.size() == - computation->instruction_count()); - for (const HloInstruction* instruction : computation->instructions()) { - TF_RET_CHECK(instruction_position.count(instruction) == 1) - << "Instruction " << instruction->name() << " is not in schedule"; - } - - for (const HloInstruction* instruction : computation->instructions()) { - for (const HloInstruction* operand : instruction->operands()) { - TF_RET_CHECK(instruction_position.at(operand) < - instruction_position.at(instruction)) - << "Instruction " << instruction->name() - << " is not scheduled after its operand " << operand->name(); - } - - for (const HloInstruction* pred : instruction->control_predecessors()) { - TF_RET_CHECK(instruction_position.at(pred) < - instruction_position.at(instruction)) - << "Instruction " << instruction->name() - << " is not scheduled after its control predecessor " - << pred->name(); - } - } - } - - return Status::OK(); -} - } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_scheduling.h b/tensorflow/compiler/xla/service/hlo_scheduling.h index d06b8d9a5c..54e32340ba 100644 --- a/tensorflow/compiler/xla/service/hlo_scheduling.h +++ b/tensorflow/compiler/xla/service/hlo_scheduling.h @@ -21,6 +21,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_module.h" #include "tensorflow/compiler/xla/service/hlo_ordering.h" +#include "tensorflow/compiler/xla/service/hlo_schedule.h" #include "tensorflow/compiler/xla/service/logical_buffer.h" #include "tensorflow/compiler/xla/service/tuple_points_to_analysis.h" #include "tensorflow/compiler/xla/statusor.h" @@ -32,14 +33,14 @@ namespace xla { // 'computation' that minimizes peak memory, given a points-to analysis result // that describes buffer aliasing, together with a target-specific size function // that maps a tensor's logical size to its padded size. -typedef std::function<StatusOr<std::vector<const HloInstruction*>>( +typedef std::function<StatusOr<HloInstructionSequence>( const HloComputation&, const TuplePointsToAnalysis&, const LogicalBuffer::SizeFunction&, const tensorflow::gtl::FlatMap<const HloComputation*, int64>&)> MemorySchedulerAlgorithm; // List scheduler -StatusOr<std::vector<const HloInstruction*>> ListMemoryScheduler( +StatusOr<HloInstructionSequence> ListMemoryScheduler( const HloComputation& computation, const TuplePointsToAnalysis& points_to_analysis, const LogicalBuffer::SizeFunction& size_function, @@ -47,7 +48,7 @@ StatusOr<std::vector<const HloInstruction*>> ListMemoryScheduler( memory_by_computation); // DFS-order scheduler -StatusOr<std::vector<const HloInstruction*>> DFSMemoryScheduler( +StatusOr<HloInstructionSequence> DFSMemoryScheduler( const HloComputation& computation, const TuplePointsToAnalysis& points_to_analysis, const LogicalBuffer::SizeFunction& size_function, @@ -55,7 +56,7 @@ StatusOr<std::vector<const HloInstruction*>> DFSMemoryScheduler( memory_by_computation); // Naive Post Order scheduler -StatusOr<std::vector<const HloInstruction*>> PostOrderMemoryScheduler( +StatusOr<HloInstructionSequence> PostOrderMemoryScheduler( const HloComputation& computation, const TuplePointsToAnalysis& points_to_analysis, const LogicalBuffer::SizeFunction& size_function, @@ -65,63 +66,26 @@ StatusOr<std::vector<const HloInstruction*>> PostOrderMemoryScheduler( // The default scheduling algorithm. Runs both the list scheduler // and the DFS scheduler, and chooses whichever returns a lower min-memory, // not accounting for fragmentation. -StatusOr<std::vector<const HloInstruction*>> DefaultMemoryScheduler( +StatusOr<HloInstructionSequence> DefaultMemoryScheduler( const HloComputation& computation, const TuplePointsToAnalysis& points_to_analysis, const LogicalBuffer::SizeFunction& size_function, const tensorflow::gtl::FlatMap<const HloComputation*, int64>& memory_by_computation); -// Returns an HloModuleSequence which seeks to minimize the memory required for +// Returns an HloSchedule which seeks to minimize the memory required for // the computation. size_function is the function returning the number of bytes // required for a LogicalBuffer. -StatusOr<SequentialHloOrdering::HloModuleSequence> ScheduleComputationsInModule( +StatusOr<HloSchedule> ScheduleModule( const HloModule& module, const LogicalBuffer::SizeFunction& size_function, const MemorySchedulerAlgorithm& algorithm = {}); // Computes the schedule for a single computation. // Currently only used by the GPU backend. -StatusOr<std::vector<const HloInstruction*>> ScheduleOneComputation( +StatusOr<HloInstructionSequence> ScheduleComputation( const HloComputation& computation, const LogicalBuffer::SizeFunction& size_function); -// Transforms the given schedule such that it is (again) a valid schedule for -// the module. This is used to update a schedule after the HLO module has been -// transformed in some way. In general, the only transformations to the module -// for which a schedule can be updated is the addition or removal of -// instructions to/from the module. Updating the schedule after new dependencies -// between existing instructions in the module is not supported and may result -// in an error status returned. -// -// Instructions in the module which also exist in the given schedule will remain -// in the same order in the updated schedule. Instructions which exist in the -// module but not in the given schedule will be placed as early as possible in -// the updated schedule. -// -// 'id_sequence' is a mirror of the given schedule 'sequence' but with -// HloInstruction ids rather than HloInstruction pointers. This should be -// constructed using ComputeIdSchedule below after the schedule is constructed -// but before the HLO module is transformed. -Status UpdateSchedule( - const HloModule& module, - const tensorflow::gtl::FlatMap<const HloComputation*, std::vector<int>>& - id_sequence, - SequentialHloOrdering::HloModuleSequence* sequence); - -// Constructs a copy of the given schedule but with HloInstruction unique ids -// rather than HloInstruction pointers. This is necessary for updating a -// schedule as HloInstruction points in the schedule may become invalid if -// instructions are removed from the module. Used by UpdateSchedule above.. -// TODO(b/113175018): Remove this function when HLO schedule is its own class. -tensorflow::gtl::FlatMap<const HloComputation*, std::vector<int>> -ComputeIdSchedule(const SequentialHloOrdering::HloModuleSequence& sequence); - -// Verifies that the given schedule is valid for the given module. Specifically, -// the schedule contains exactly the instructions in the module and every -// dependency in the module is satisfied in the schedule. -Status VerifySchedule(const HloModule& module, - const SequentialHloOrdering::HloModuleSequence& sequence); - } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_SCHEDULING_H_ diff --git a/tensorflow/compiler/xla/service/hlo_scheduling_test.cc b/tensorflow/compiler/xla/service/hlo_scheduling_test.cc index d49d09d459..6afe51997e 100644 --- a/tensorflow/compiler/xla/service/hlo_scheduling_test.cc +++ b/tensorflow/compiler/xla/service/hlo_scheduling_test.cc @@ -18,6 +18,7 @@ limitations under the License. #include <memory> #include <string> +#include "absl/algorithm/container.h" #include "tensorflow/compiler/xla/service/heap_simulator.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_dce.h" @@ -67,19 +68,20 @@ TEST_F(HloSchedulingTest, LastUseScheduledFirst) { module->AddEntryComputation(builder.Build()); TF_ASSERT_OK_AND_ASSIGN( - SequentialHloOrdering::HloModuleSequence sequence, - ScheduleComputationsInModule(*module, [](const BufferValue& buffer) { + HloSchedule schedule, + ScheduleModule(*module, [](const BufferValue& buffer) { return ShapeUtil::ByteSizeOf(buffer.shape()); })); // Verify that all instructions are in the sequence. - EXPECT_EQ(module->entry_computation()->instruction_count(), - sequence.at(module->entry_computation()).size()); + const std::vector<const HloInstruction*>& sequence = + schedule.sequence(module->entry_computation()).instructions(); + EXPECT_EQ(module->entry_computation()->instruction_count(), sequence.size()); // The first instruction should be the parameter and the last the root "sub". - EXPECT_EQ(param, sequence.at(module->entry_computation()).front()); - EXPECT_EQ(sub, sequence.at(module->entry_computation()).back()); + EXPECT_EQ(param, sequence.front()); + EXPECT_EQ(sub, sequence.back()); - SequentialHloOrdering ordering(module.get(), sequence); + SequentialHloOrdering ordering(schedule); EXPECT_TRUE(ordering.ExecutesBefore(add, negate)); } @@ -108,28 +110,26 @@ ENTRY root { return ShapeUtil::ByteSizeOf(buffer.shape(), /*pointer_size=*/8); }; TF_ASSERT_OK_AND_ASSIGN( - SequentialHloOrdering::HloModuleSequence sequence, - ScheduleComputationsInModule(*module, size_fn, ListMemoryScheduler)); + HloSchedule schedule, + ScheduleModule(*module, size_fn, ListMemoryScheduler)); // Verify that all instructions are in the sequence. - EXPECT_EQ(module->entry_computation()->instruction_count(), - sequence.at(module->entry_computation()).size()); + const std::vector<const HloInstruction*>& sequence = + schedule.sequence(module->entry_computation()).instructions(); + EXPECT_EQ(module->entry_computation()->instruction_count(), sequence.size()); std::unordered_map<string, const HloInstruction*> instructions_by_name; - for (const HloInstruction* instruction : - sequence.at(module->entry_computation())) { + for (const HloInstruction* instruction : sequence) { instructions_by_name[instruction->name()] = instruction; } // The first instruction should be the parameter and the last the root. - EXPECT_EQ(instructions_by_name.at("param"), - sequence.at(module->entry_computation()).front()); - EXPECT_EQ(instructions_by_name.at("result"), - sequence.at(module->entry_computation()).back()); + EXPECT_EQ(instructions_by_name.at("param"), sequence.front()); + EXPECT_EQ(instructions_by_name.at("result"), sequence.back()); // Instructions "d" and "e" will both be schedulable at the same time, but // instruction "d" allows us to free the buffer of "p1", so the list scheduler // should prefer it. - SequentialHloOrdering ordering(module.get(), sequence); + SequentialHloOrdering ordering(schedule); EXPECT_TRUE(ordering.ExecutesBefore(instructions_by_name.at("d"), instructions_by_name.at("e"))); } @@ -220,13 +220,13 @@ TEST_F(HloSchedulingTest, ListAccountsForSubcomputations) { return ShapeUtil::ByteSizeOf(buffer.shape()); }; TF_ASSERT_OK_AND_ASSIGN( - SequentialHloOrdering::HloModuleSequence sequence, - ScheduleComputationsInModule(*module, size_fn, ListMemoryScheduler)); + HloSchedule schedule, + ScheduleModule(*module, size_fn, ListMemoryScheduler)); // Verify that all instructions are in the sequence. auto entry_computation = module->entry_computation(); EXPECT_EQ(entry_computation->instruction_count(), - sequence.at(entry_computation).size()); - SequentialHloOrdering ordering(module.get(), sequence); + schedule.sequence(entry_computation).size()); + SequentialHloOrdering ordering(schedule); // This schedule is an example of List's greedy heuristics being suboptimal. // The while_loop is more expensive than transpose, so it would have been // better to schedule it first, instead of during the busy time. @@ -243,13 +243,13 @@ TEST_F(HloSchedulingTest, ListAccountsForSubcomputations) { // HeapSimulator doesn't account for subcomputations EXPECT_EQ(80, HeapSimulator::MinimumMemoryForComputation( - *entry_computation, sequence.at(entry_computation), + *entry_computation, schedule.sequence(entry_computation), *points_to_analysis, size_fn) .ValueOrDie()); // HeapSimulator accounts for subcomputations. The output buffer is aliased, // so we don't double count. EXPECT_EQ(64, HeapSimulator::MinimumMemoryForComputation( - *entry_computation, sequence.at(entry_computation), + *entry_computation, schedule.sequence(entry_computation), *points_to_analysis, size_fn, &memory_by_computation) .ValueOrDie()); } @@ -281,19 +281,18 @@ TEST_F(HloSchedulingTest, TuplesAreAccountedCorrectly) { auto module = CreateNewModule(); module->AddEntryComputation(builder.Build()); - TF_ASSERT_OK_AND_ASSIGN( - SequentialHloOrdering::HloModuleSequence sequence, - ScheduleComputationsInModule(*module, - [](const BufferValue& buffer) { - return ShapeUtil::ByteSizeOf( - buffer.shape(), TUPLE_SIZE); - }, - ListMemoryScheduler)); + TF_ASSERT_OK_AND_ASSIGN(HloSchedule schedule, + ScheduleModule(*module, + [](const BufferValue& buffer) { + return ShapeUtil::ByteSizeOf( + buffer.shape(), TUPLE_SIZE); + }, + ListMemoryScheduler)); // Verify that all instructions are in the sequence. EXPECT_EQ(module->entry_computation()->instruction_count(), - sequence.at(module->entry_computation()).size()); - SequentialHloOrdering ordering(module.get(), sequence); + schedule.sequence(module->entry_computation()).size()); + SequentialHloOrdering ordering(schedule); // tuple allocates the tuple buffer and doesn't free anything. // abs_abs2 uses the same buffer for input/output, so its bytes-freed is 0. // abs_abs2 should be scheduled before tuple by List. @@ -332,18 +331,18 @@ TEST_F(HloSchedulingTest, MultiOutputFusionAccountedCorrectly) { auto fusion = computation->CreateFusionInstruction( {tuple, mul, add}, HloInstruction::FusionKind::kLoop); - TF_ASSERT_OK_AND_ASSIGN(SequentialHloOrdering::HloModuleSequence sequence, - ScheduleComputationsInModule( - *module, - [](const BufferValue& buffer) { - return ShapeUtil::ByteSizeOf(buffer.shape(), 2); - }, - ListMemoryScheduler)); + TF_ASSERT_OK_AND_ASSIGN(HloSchedule schedule, + ScheduleModule(*module, + [](const BufferValue& buffer) { + return ShapeUtil::ByteSizeOf( + buffer.shape(), 2); + }, + ListMemoryScheduler)); // Verify that all instructions are in the sequence. EXPECT_EQ(module->entry_computation()->instruction_count(), - sequence.at(module->entry_computation()).size()); - SequentialHloOrdering ordering(module.get(), sequence); + schedule.sequence(module->entry_computation()).size()); + SequentialHloOrdering ordering(schedule); // fusion allocates memory for the tuple elements and doesn't free anything, // so it's more expensive than exp. EXPECT_TRUE(ordering.ExecutesBefore(exp, fusion)); @@ -391,12 +390,12 @@ TEST_F(HloSchedulingTest, HeapSimulatorAccountsForSubcomputations) { return ShapeUtil::ByteSizeOf(buffer.shape()); }; TF_ASSERT_OK_AND_ASSIGN( - SequentialHloOrdering::HloModuleSequence sequence, - ScheduleComputationsInModule(*module, size_fn, ListMemoryScheduler)); + HloSchedule schedule, + ScheduleModule(*module, size_fn, ListMemoryScheduler)); // Verify that all instructions are in the sequence. auto entry_computation = module->entry_computation(); - EXPECT_EQ(entry_computation->instruction_count(), - sequence.at(entry_computation).size()); + EXPECT_EQ(module->entry_computation()->instruction_count(), + schedule.sequence(module->entry_computation()).size()); tensorflow::gtl::FlatMap<const HloComputation*, int64> memory_by_computation; memory_by_computation[cond_computation] = 17; @@ -406,262 +405,16 @@ TEST_F(HloSchedulingTest, HeapSimulatorAccountsForSubcomputations) { // HeapSimulator doesn't account for subcomputations EXPECT_EQ(16, HeapSimulator::MinimumMemoryForComputation( - *entry_computation, sequence.at(entry_computation), + *entry_computation, schedule.sequence(entry_computation), *points_to_analysis, size_fn) .ValueOrDie()); // HeapSimulator accounts for subcomputations. Cond is the largest one. // The output buffer of the while is aliased. EXPECT_EQ(17, HeapSimulator::MinimumMemoryForComputation( - *entry_computation, sequence.at(entry_computation), + *entry_computation, schedule.sequence(entry_computation), *points_to_analysis, size_fn, &memory_by_computation) .ValueOrDie()); } -TEST_F(HloSchedulingTest, UpdateScheduleUnchangedModule) { - // Updating the schedule of an unchanged HLO module should not affect the - // schedule at all. - const string module_str = R"( -HloModule UpdateScheduleUnchanged - -ENTRY main { - a = f32[] parameter(0) - b = f32[] parameter(1) - c = f32[] constant(42.0) - sum = f32[] add(a, b) - neg = f32[] negate(c) - ROOT root = f32[] multiply(sum, neg) -} -)"; - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr<HloModule> module, - ParseHloString(module_str)); - TF_ASSERT_OK_AND_ASSIGN( - SequentialHloOrdering::HloModuleSequence sequence, - ScheduleComputationsInModule(*module, [](const BufferValue& buffer) { - return ShapeUtil::ByteSizeOf(buffer.shape()); - })); - tensorflow::gtl::FlatMap<const HloComputation*, std::vector<int>> - id_sequence = ComputeIdSchedule(sequence); - std::vector<const HloInstruction*> entry_schedule = sequence.begin()->second; - - EXPECT_EQ(entry_schedule.size(), 6); - - TF_ASSERT_OK(UpdateSchedule(*module, id_sequence, &sequence)); - TF_ASSERT_OK(VerifySchedule(*module, sequence)); - - EXPECT_EQ(entry_schedule, sequence.begin()->second); -} - -TEST_F(HloSchedulingTest, UpdateScheduleWithNewInstructions) { - // Add some additional instructions to a module and verify the schedule can be - // updated. - const string module_str = R"( -HloModule UpdateScheduleWithNewInstructions - -ENTRY main { - a = f32[] parameter(0) - b = f32[] parameter(1) - c = f32[] constant(42.0) - sum = f32[] add(a, b) - neg = f32[] negate(c) - ROOT root = f32[] multiply(sum, neg) -} -)"; - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr<HloModule> module, - ParseHloString(module_str)); - TF_ASSERT_OK_AND_ASSIGN( - SequentialHloOrdering::HloModuleSequence sequence, - ScheduleComputationsInModule(*module, [](const BufferValue& buffer) { - return ShapeUtil::ByteSizeOf(buffer.shape()); - })); - tensorflow::gtl::FlatMap<const HloComputation*, std::vector<int>> - id_sequence = ComputeIdSchedule(sequence); - - HloComputation* entry = module->entry_computation(); - const Shape shape = entry->root_instruction()->shape(); - HloInstruction* constant = entry->AddInstruction( - HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0))); - HloInstruction* sub = entry->AddInstruction(HloInstruction::CreateBinary( - shape, HloOpcode::kSubtract, constant, entry->root_instruction())); - entry->set_root_instruction(sub); - - auto in_schedule = [&](const HloInstruction* hlo) { - return std::find(sequence.at(entry).begin(), sequence.at(entry).end(), - hlo) != sequence.at(entry).end(); - }; - - EXPECT_EQ(sequence.at(entry).size(), 6); - EXPECT_FALSE(in_schedule(constant)); - EXPECT_FALSE(in_schedule(sub)); - - TF_ASSERT_OK(UpdateSchedule(*module, id_sequence, &sequence)); - TF_ASSERT_OK(VerifySchedule(*module, sequence)); - - EXPECT_EQ(sequence.at(entry).size(), 8); - EXPECT_TRUE(in_schedule(constant)); - EXPECT_TRUE(in_schedule(sub)); -} - -TEST_F(HloSchedulingTest, UpdateScheduleWithAddedAndDeletedInstruction) { - // Add and delete some instructions from a module and verify that the schedule - // can be updated successfully. - const string module_str = R"( -HloModule UpdateScheduleWithAddedAndDeletedInstruction - -ENTRY main { - a = f32[] parameter(0) - b = f32[] parameter(1) - c = f32[] constant(42.0) - sum = f32[] add(a, b) - neg = f32[] negate(c) - ROOT root = f32[] multiply(sum, neg) -} -)"; - - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr<HloModule> module, - ParseHloString(module_str)); - TF_ASSERT_OK_AND_ASSIGN( - SequentialHloOrdering::HloModuleSequence sequence, - ScheduleComputationsInModule(*module, [](const BufferValue& buffer) { - return ShapeUtil::ByteSizeOf(buffer.shape()); - })); - tensorflow::gtl::FlatMap<const HloComputation*, std::vector<int>> - id_sequence = ComputeIdSchedule(sequence); - - // Set the entry root to some expression containing just a parameter and a - // constant. - HloComputation* entry = module->entry_computation(); - HloInstruction* constant = entry->AddInstruction( - HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(42.0))); - HloInstruction* new_root = entry->AddInstruction( - HloInstruction::CreateBinary(constant->shape(), HloOpcode::kSubtract, - constant, entry->parameter_instruction(0))); - entry->set_root_instruction(new_root); - - // DCE should remove everything but the parameters and the newly added code. - HloDCE dce; - TF_ASSERT_OK(dce.Run(module.get()).status()); - - EXPECT_EQ(sequence.at(entry).size(), 6); - - TF_ASSERT_OK(UpdateSchedule(*module, id_sequence, &sequence)); - TF_ASSERT_OK(VerifySchedule(*module, sequence)); - - EXPECT_EQ(sequence.at(entry).size(), 4); -} - -TEST_F(HloSchedulingTest, UpdateScheduleWithCompletelyReplacedModule) { - // Completely replace a module with an entirely new set of instructions and - // verify that the schedule can be updated successfully. - const string module_str = R"( -HloModule UpdateScheduleWithCompletelyReplacedModule - -ENTRY main { - a = f32[] constant(42.0) - b = f32[] constant(123.0) - ROOT sum = f32[] add(a, b) -} -)"; - - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr<HloModule> module, - ParseHloString(module_str)); - TF_ASSERT_OK_AND_ASSIGN( - SequentialHloOrdering::HloModuleSequence sequence, - ScheduleComputationsInModule(*module, [](const BufferValue& buffer) { - return ShapeUtil::ByteSizeOf(buffer.shape()); - })); - tensorflow::gtl::FlatMap<const HloComputation*, std::vector<int>> - id_sequence = ComputeIdSchedule(sequence); - - // Replace the entry computation with the negation of a constant. - HloComputation* entry = module->entry_computation(); - HloInstruction* constant = entry->AddInstruction( - HloInstruction::CreateConstant(LiteralUtil::CreateR0<float>(1.0))); - HloInstruction* new_root = entry->AddInstruction(HloInstruction::CreateUnary( - constant->shape(), HloOpcode::kNegate, constant)); - entry->set_root_instruction(new_root); - - // DCE the old instructions. - HloDCE dce; - TF_ASSERT_OK(dce.Run(module.get()).status()); - - EXPECT_EQ(sequence.at(entry).size(), 3); - - TF_ASSERT_OK(UpdateSchedule(*module, id_sequence, &sequence)); - TF_ASSERT_OK(VerifySchedule(*module, sequence)); - - EXPECT_EQ(sequence.at(entry).size(), 2); -} - -TEST_F(HloSchedulingTest, UpdateScheduleWithMultipleComputations) { - // Create changes to more than one computation in an HLO module and verify - // that the schedule can be updated. - const string module_str = R"( -HloModule UpdateScheduleWithMultipleComputations - -%Body (param.1: (s32[], token[])) -> (s32[], token[]) { - %param.1 = (s32[], token[]) parameter(0) - %get-tuple-element.1 = s32[] get-tuple-element((s32[], token[]) %param.1), index=0 - %constant.1 = s32[] constant(1) - %add = s32[] add(s32[] %get-tuple-element.1, s32[] %constant.1) - %get-tuple-element.2 = token[] get-tuple-element((s32[], token[]) %param.1), index=1 - %after-all = token[] after-all(token[] %get-tuple-element.2) - ROOT %tuple = (s32[], token[]) tuple(s32[] %add, token[] %after-all) -} - -%Cond (param: (s32[], token[])) -> pred[] { - %param = (s32[], token[]) parameter(0) - %get-tuple-element = s32[] get-tuple-element((s32[], token[]) %param), index=0 - %constant = s32[] constant(42) - ROOT %less-than = pred[] less-than(s32[] %get-tuple-element, s32[] %constant) -} - -ENTRY %WhileLoop () -> s32[] { - %zero = s32[] constant(0) - %init_token = token[] after-all() - %init_tuple = (s32[], token[]) tuple(s32[] %zero, token[] %init_token) - %while = (s32[], token[]) while((s32[], token[]) %init_tuple), condition=%Cond, body=%Body - ROOT %root = s32[] get-tuple-element((s32[], token[]) %while), index=0 -} -)"; - - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr<HloModule> module, - ParseHloString(module_str)); - TF_ASSERT_OK_AND_ASSIGN( - SequentialHloOrdering::HloModuleSequence sequence, - ScheduleComputationsInModule(*module, [](const BufferValue& buffer) { - return ShapeUtil::ByteSizeOf(buffer.shape(), - /*pointer_size=*/sizeof(void*)); - })); - tensorflow::gtl::FlatMap<const HloComputation*, std::vector<int>> - id_sequence = ComputeIdSchedule(sequence); - - const HloInstruction* xla_while = - module->entry_computation()->root_instruction()->operand(0); - HloComputation* body = xla_while->while_body(); - HloComputation* cond = xla_while->while_condition(); - - // Negate the root of the cond. - cond->set_root_instruction(cond->AddInstruction( - HloInstruction::CreateUnary(ShapeUtil::MakeShape(PRED, {}), - HloOpcode::kNot, cond->root_instruction()))); - - // Replace the body with a computation which just passes through its - // parameter. - body->set_root_instruction(body->parameter_instruction(0)); - - // DCE the dead code in the body. - HloDCE dce; - TF_ASSERT_OK(dce.Run(module.get()).status()); - - EXPECT_EQ(sequence.at(body).size(), 7); - EXPECT_EQ(sequence.at(cond).size(), 4); - - TF_ASSERT_OK(UpdateSchedule(*module, id_sequence, &sequence)); - TF_ASSERT_OK(VerifySchedule(*module, sequence)); - - EXPECT_EQ(sequence.at(body).size(), 1); - EXPECT_EQ(sequence.at(cond).size(), 5); -} - } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/service/indexed_array_analysis.cc b/tensorflow/compiler/xla/service/indexed_array_analysis.cc index 4a71ee909b..37b774b8a5 100644 --- a/tensorflow/compiler/xla/service/indexed_array_analysis.cc +++ b/tensorflow/compiler/xla/service/indexed_array_analysis.cc @@ -1031,8 +1031,8 @@ bool CanFoldDotIntoIndexedArray( StatusOr<Analysis::Array*> IndexedArrayAnalysis::ComputeArrayForDotWithIndexedLhs( const Shape& shape, const DotDimensionNumbers& dim_numbers, - const PrecisionConfigProto& precision_config, - ScalarIndexedConstantArray* lhs, ConstantArray* rhs) { + const PrecisionConfig& precision_config, ScalarIndexedConstantArray* lhs, + ConstantArray* rhs) { VLOG(3) << "ComputeArrayForDotWithIndexedLhs(" << ToString(lhs) << " " << ToString(rhs); if (!CanFoldDotIntoIndexedArray( @@ -1066,7 +1066,7 @@ IndexedArrayAnalysis::ComputeArrayForDotWithIndexedLhs( StatusOr<Analysis::Array*> IndexedArrayAnalysis::ComputeArrayForDotWithIndexedRhs( const Shape& shape, const DotDimensionNumbers& dim_numbers, - const PrecisionConfigProto& precision_config, ConstantArray* lhs, + const PrecisionConfig& precision_config, ConstantArray* lhs, ScalarIndexedConstantArray* rhs) { VLOG(3) << "ComputeArrayForDotWithIndexedRhs(" << ToString(lhs) << " " << ToString(rhs); @@ -1101,7 +1101,7 @@ IndexedArrayAnalysis::ComputeArrayForDotWithIndexedRhs( StatusOr<Analysis::Array*> IndexedArrayAnalysis::ComputeArrayForDot( const Shape& shape, const DotDimensionNumbers& dim_numbers, - const PrecisionConfigProto& precision_config, Array* lhs, Array* rhs) { + const PrecisionConfig& precision_config, Array* lhs, Array* rhs) { // Intuitively, if // // - The LHS of a dot product is a gathered sequence of rows from a constant diff --git a/tensorflow/compiler/xla/service/indexed_array_analysis.h b/tensorflow/compiler/xla/service/indexed_array_analysis.h index f21e784a4d..9746d176cc 100644 --- a/tensorflow/compiler/xla/service/indexed_array_analysis.h +++ b/tensorflow/compiler/xla/service/indexed_array_analysis.h @@ -267,17 +267,18 @@ class IndexedArrayAnalysis { StatusOr<Array*> ComputeArrayForDotWithIndexedLhs( const Shape& shape, const DotDimensionNumbers& dim_numbers, - const PrecisionConfigProto& precision_config, - ScalarIndexedConstantArray* lhs, ConstantArray* rhs); + const PrecisionConfig& precision_config, ScalarIndexedConstantArray* lhs, + ConstantArray* rhs); StatusOr<Array*> ComputeArrayForDotWithIndexedRhs( const Shape& shape, const DotDimensionNumbers& dim_numbers, - const PrecisionConfigProto& precision_config, ConstantArray* lhs, + const PrecisionConfig& precision_config, ConstantArray* lhs, ScalarIndexedConstantArray* rhs); - StatusOr<Array*> ComputeArrayForDot( - const Shape& shape, const DotDimensionNumbers& dim_numbers, - const PrecisionConfigProto& precision_config, Array* lhs, Array* rhs); + StatusOr<Array*> ComputeArrayForDot(const Shape& shape, + const DotDimensionNumbers& dim_numbers, + const PrecisionConfig& precision_config, + Array* lhs, Array* rhs); // This tries to fold a ScalarIndexedArray which has another // ScalarIndexedArray as a source into a ScalarIndexedArray that instead has a diff --git a/tensorflow/compiler/xla/service/transpose_folding_test.cc b/tensorflow/compiler/xla/service/transpose_folding_test.cc index e486a00e53..79b5c09abb 100644 --- a/tensorflow/compiler/xla/service/transpose_folding_test.cc +++ b/tensorflow/compiler/xla/service/transpose_folding_test.cc @@ -215,13 +215,6 @@ ENTRY entry_computation { /*lhs_contracting_dim=*/1, /*rhs_contracting_dim=*/1)); } -PrecisionConfigProto DefaultPrecisionConfig(int operands) { - PrecisionConfigProto precision_config; - precision_config.mutable_operand_precision()->Resize( - operands, PrecisionConfigProto::DEFAULT); - return precision_config; -} - // Test that a two dimension swap of the kernel gets folded into convolution. TEST_F(TransposeFoldingTest, FoldConvDimSwapTransposeRhs) { auto builder = HloComputation::Builder("entry_computation"); diff --git a/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc b/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc index e3328203a6..2b2a2eb42a 100644 --- a/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc +++ b/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc @@ -1064,9 +1064,9 @@ TEST_F(CanShareOperandBufferWithUserTest, FusedDotAdd) { DotDimensionNumbers dot_dnums; dot_dnums.add_lhs_contracting_dimensions(1); dot_dnums.add_rhs_contracting_dimensions(0); - PrecisionConfigProto precision_config; + PrecisionConfig precision_config; precision_config.mutable_operand_precision()->Resize( - /*new_size=*/2, PrecisionConfigProto::DEFAULT); + /*new_size=*/2, PrecisionConfig::DEFAULT); auto dot = builder.AddInstruction( HloInstruction::CreateDot(data_shape, a, b, dot_dnums, precision_config)); diff --git a/tensorflow/compiler/xla/service/while_loop_constant_sinking.cc b/tensorflow/compiler/xla/service/while_loop_constant_sinking.cc index aab1180662..56145822be 100644 --- a/tensorflow/compiler/xla/service/while_loop_constant_sinking.cc +++ b/tensorflow/compiler/xla/service/while_loop_constant_sinking.cc @@ -15,10 +15,10 @@ limitations under the License. #include "tensorflow/compiler/xla/service/while_loop_constant_sinking.h" #include "absl/algorithm/container.h" +#include "absl/container/inlined_vector.h" #include "tensorflow/compiler/xla/service/while_util.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/lib/gtl/flatmap.h" -#include "tensorflow/core/lib/gtl/inlined_vector.h" namespace xla { diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.cc b/tensorflow/compiler/xla/tests/hlo_test_base.cc index fc4c68246e..3df99aac7d 100644 --- a/tensorflow/compiler/xla/tests/hlo_test_base.cc +++ b/tensorflow/compiler/xla/tests/hlo_test_base.cc @@ -120,6 +120,14 @@ StatusOr<bool> HloTestBase::RunHloPass(HloPassInterface* hlo_pass, return status_or; } +/* static */ +PrecisionConfig HloTestBase::DefaultPrecisionConfig(int operands) { + PrecisionConfig precision_config; + precision_config.mutable_operand_precision()->Resize( + operands, PrecisionConfig::DEFAULT); + return precision_config; +} + DebugOptions HloTestBase::GetDebugOptionsForTest() { auto debug_options = legacy_flags::GetDebugOptionsFromFlags(); // TODO(b/38354253): Change tests to use Parameters instead of Constants. diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.h b/tensorflow/compiler/xla/tests/hlo_test_base.h index 4c88257bb2..21d77c0cc4 100644 --- a/tensorflow/compiler/xla/tests/hlo_test_base.h +++ b/tensorflow/compiler/xla/tests/hlo_test_base.h @@ -80,6 +80,8 @@ class HloTestBase : public ::testing::Test { static StatusOr<bool> RunHloPass(HloPassInterface* hlo_pass, HloModule* module); + static PrecisionConfig DefaultPrecisionConfig(int operands); + protected: // This uses the interpreter backend as the reference backend and // automatically finds another supported backend as the test backend. If the diff --git a/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc b/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc index 53b5e933b6..c5e0b9b097 100644 --- a/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc +++ b/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc @@ -47,13 +47,6 @@ limitations under the License. namespace xla { namespace { -PrecisionConfigProto DefaultPrecisionConfig(int operands) { - PrecisionConfigProto precision_config; - precision_config.mutable_operand_precision()->Resize( - operands, PrecisionConfigProto::DEFAULT); - return precision_config; -} - class MultiOutputFusionTest : public HloTestBase { protected: MultiOutputFusionTest() { error_spec_ = ErrorSpec{0.0001, 1e-2}; } diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto index 8e43f275e1..dd329f1181 100644 --- a/tensorflow/compiler/xla/xla_data.proto +++ b/tensorflow/compiler/xla/xla_data.proto @@ -580,7 +580,7 @@ message SourceTarget { // Used to indicate the precision configuration. It has backend specific // meaning. -message PrecisionConfigProto { +message PrecisionConfig { enum Precision { DEFAULT = 0; HIGH = 1; diff --git a/tensorflow/compiler/xrt/BUILD b/tensorflow/compiler/xrt/BUILD index efbe980278..2ff97914f8 100644 --- a/tensorflow/compiler/xrt/BUILD +++ b/tensorflow/compiler/xrt/BUILD @@ -56,6 +56,7 @@ cc_library( "//tensorflow/core:lib_internal", "//tensorflow/stream_executor", "@com_google_absl//absl/memory", + "@com_google_absl//absl/strings", "@com_google_absl//absl/synchronization", ], ) diff --git a/tensorflow/compiler/xrt/kernels/BUILD b/tensorflow/compiler/xrt/kernels/BUILD index 68ba17a424..9e3d2454d1 100644 --- a/tensorflow/compiler/xrt/kernels/BUILD +++ b/tensorflow/compiler/xrt/kernels/BUILD @@ -46,19 +46,15 @@ cc_library( deps = [ ":xrt_state_ops", "//tensorflow/compiler/tf2xla:xla_compiler", - "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", - "//tensorflow/compiler/xla/client:compile_only_client", "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/legacy_flags:debug_options_flags", "//tensorflow/compiler/xla/service:compiler", "//tensorflow/compiler/xla/service:computation_placer", - "//tensorflow/compiler/xla/service:hlo_proto", "//tensorflow/compiler/xrt:xrt_proto", "//tensorflow/compiler/xrt:xrt_utils", "//tensorflow/core:core_cpu_internal", @@ -67,6 +63,7 @@ cc_library( "//tensorflow/core:lib_internal", "//tensorflow/core:protos_all_cc", "//tensorflow/stream_executor:stream_executor_headers_lib", + "@com_google_absl//absl/strings", ], alwayslink = 1, ) diff --git a/tensorflow/compiler/xrt/kernels/xrt_compile_ops.cc b/tensorflow/compiler/xrt/kernels/xrt_compile_ops.cc index 5cf2bc8861..1d4f8d97f2 100644 --- a/tensorflow/compiler/xrt/kernels/xrt_compile_ops.cc +++ b/tensorflow/compiler/xrt/kernels/xrt_compile_ops.cc @@ -22,6 +22,7 @@ limitations under the License. #include <utility> #include <vector> +#include "absl/strings/str_cat.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/xla_computation.h" @@ -40,7 +41,6 @@ limitations under the License. #include "tensorflow/core/lib/core/refcount.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/lib/strings/proto_serialization.h" -#include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/platform/fingerprint.h" #include "tensorflow/core/platform/types.h" @@ -70,7 +70,7 @@ Status CompilationCacheKey(const xrt::XLAComputation& computation, string serialized; TF_RET_CHECK(SerializeToStringDeterministic(computation, &serialized)); uint64 fingerprint = Fingerprint64(serialized); - *key = strings::StrCat(fingerprint); + *key = absl::StrCat(fingerprint); return Status::OK(); } diff --git a/tensorflow/compiler/xrt/xrt_state.cc b/tensorflow/compiler/xrt/xrt_state.cc index 911ac9a78b..2c3b07da58 100644 --- a/tensorflow/compiler/xrt/xrt_state.cc +++ b/tensorflow/compiler/xrt/xrt_state.cc @@ -24,6 +24,7 @@ limitations under the License. #include <utility> #include "absl/memory/memory.h" +#include "absl/strings/str_cat.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/backend.h" #include "tensorflow/compiler/xla/shape_util.h" @@ -32,7 +33,6 @@ limitations under the License. #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/framework/resource_mgr.h" #include "tensorflow/core/lib/core/status.h" -#include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/platform/types.h" #include "tensorflow/stream_executor/stream_executor.h" @@ -201,14 +201,14 @@ const se::DeviceMemoryBase& XRTTupleAllocation::root_allocation() { /*static*/ Status XRTTupleAllocation::Lookup(ResourceMgr* rm, int64 key, XRTTupleAllocation** allocation) { - string key_string = strings::StrCat(key); + string key_string = absl::StrCat(key); TF_RETURN_IF_ERROR(rm->Lookup(kTupleContainer, key_string, allocation)); return Status::OK(); } /*static*/ Status XRTTupleAllocation::DeleteFromResourceManager(ResourceMgr* rm, int64 key) { - string key_string = strings::StrCat(key); + string key_string = absl::StrCat(key); return rm->Delete<XRTTupleAllocation>(kTupleContainer, key_string); } @@ -410,7 +410,7 @@ typedef XRTBufferAllocation* XRTBufferAllocationPtr; Status XRTTupleAllocation::Intern(ResourceMgr* rm, int64* key) { *key = get_uid(); - string key_string = strings::StrCat(*key); + string key_string = absl::StrCat(*key); return rm->Create(kTupleContainer, key_string, this); } diff --git a/tensorflow/contrib/__init__.py b/tensorflow/contrib/__init__.py index 5f477a79a3..9478e42b46 100644 --- a/tensorflow/contrib/__init__.py +++ b/tensorflow/contrib/__init__.py @@ -21,6 +21,14 @@ from __future__ import print_function import os +from tensorflow.python.tools import component_api_helper +component_api_helper.package_hook( + parent_package_str=( + "tensorflow.contrib"), + child_package_str=( + "tensorflow_estimator.contrib.estimator")) +del component_api_helper + # Add projects here, they will show up under tf.contrib. from tensorflow.contrib import autograph from tensorflow.contrib import batching diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_kernels.cc b/tensorflow/contrib/bigtable/kernels/bigtable_kernels.cc index a25a641cdb..6138d79126 100644 --- a/tensorflow/contrib/bigtable/kernels/bigtable_kernels.cc +++ b/tensorflow/contrib/bigtable/kernels/bigtable_kernels.cc @@ -172,6 +172,11 @@ class BigtableTableOp : public OpKernel { REGISTER_KERNEL_BUILDER(Name("BigtableTable").Device(DEVICE_CPU), BigtableTableOp); +} // namespace + +namespace data { +namespace { + class ToBigtableOp : public AsyncOpKernel { public: explicit ToBigtableOp(OpKernelConstruction* ctx) @@ -354,5 +359,6 @@ REGISTER_KERNEL_BUILDER(Name("DatasetToBigtable").Device(DEVICE_CPU), ToBigtableOp); } // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_lib.h b/tensorflow/contrib/bigtable/kernels/bigtable_lib.h index a2a5df1037..4652021fec 100644 --- a/tensorflow/contrib/bigtable/kernels/bigtable_lib.h +++ b/tensorflow/contrib/bigtable/kernels/bigtable_lib.h @@ -79,6 +79,8 @@ class BigtableTableResource : public ResourceBase { ::google::cloud::bigtable::noex::Table table_; }; +namespace data { + // BigtableReaderDatasetIterator is an abstract class for iterators from // datasets that are "readers" (source datasets, not transformation datasets) // that read from Bigtable. @@ -138,6 +140,8 @@ class BigtableReaderDatasetIterator : public DatasetIterator<Dataset> { ::google::cloud::bigtable::RowReader::iterator iterator_ GUARDED_BY(mu_); }; +} // namespace data + } // namespace tensorflow #endif // TENSORFLOW_CONTRIB_BIGTABLE_KERNELS_BIGTABLE_LIB_H_ diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_lookup_dataset_op.cc b/tensorflow/contrib/bigtable/kernels/bigtable_lookup_dataset_op.cc index bd32672aa9..11f530e82a 100644 --- a/tensorflow/contrib/bigtable/kernels/bigtable_lookup_dataset_op.cc +++ b/tensorflow/contrib/bigtable/kernels/bigtable_lookup_dataset_op.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/core/framework/op_kernel.h" namespace tensorflow { +namespace data { namespace { class BigtableLookupDatasetOp : public UnaryDatasetOpKernel { @@ -226,4 +227,5 @@ REGISTER_KERNEL_BUILDER(Name("BigtableLookupDataset").Device(DEVICE_CPU), BigtableLookupDatasetOp); } // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_prefix_key_dataset_op.cc b/tensorflow/contrib/bigtable/kernels/bigtable_prefix_key_dataset_op.cc index a803fdcb49..5cab729d9c 100644 --- a/tensorflow/contrib/bigtable/kernels/bigtable_prefix_key_dataset_op.cc +++ b/tensorflow/contrib/bigtable/kernels/bigtable_prefix_key_dataset_op.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/core/framework/op_kernel.h" namespace tensorflow { +namespace data { namespace { class BigtablePrefixKeyDatasetOp : public DatasetOpKernel { @@ -111,4 +112,5 @@ REGISTER_KERNEL_BUILDER(Name("BigtablePrefixKeyDataset").Device(DEVICE_CPU), BigtablePrefixKeyDatasetOp); } // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_range_key_dataset_op.cc b/tensorflow/contrib/bigtable/kernels/bigtable_range_key_dataset_op.cc index 5cd0371c79..4dc4647bd2 100644 --- a/tensorflow/contrib/bigtable/kernels/bigtable_range_key_dataset_op.cc +++ b/tensorflow/contrib/bigtable/kernels/bigtable_range_key_dataset_op.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/core/framework/op_kernel.h" namespace tensorflow { +namespace data { namespace { class BigtableRangeKeyDatasetOp : public DatasetOpKernel { @@ -117,4 +118,5 @@ class BigtableRangeKeyDatasetOp : public DatasetOpKernel { REGISTER_KERNEL_BUILDER(Name("BigtableRangeKeyDataset").Device(DEVICE_CPU), BigtableRangeKeyDatasetOp); } // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_sample_key_pairs_dataset_op.cc b/tensorflow/contrib/bigtable/kernels/bigtable_sample_key_pairs_dataset_op.cc index 6928d9423c..736775bdac 100644 --- a/tensorflow/contrib/bigtable/kernels/bigtable_sample_key_pairs_dataset_op.cc +++ b/tensorflow/contrib/bigtable/kernels/bigtable_sample_key_pairs_dataset_op.cc @@ -18,6 +18,7 @@ limitations under the License. #include "tensorflow/core/framework/op_kernel.h" namespace tensorflow { +namespace data { namespace { class BigtableSampleKeyPairsDatasetOp : public DatasetOpKernel { @@ -205,4 +206,5 @@ REGISTER_KERNEL_BUILDER( BigtableSampleKeyPairsDatasetOp); } // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_sample_keys_dataset_op.cc b/tensorflow/contrib/bigtable/kernels/bigtable_sample_keys_dataset_op.cc index a759fb5063..208b7b3e08 100644 --- a/tensorflow/contrib/bigtable/kernels/bigtable_sample_keys_dataset_op.cc +++ b/tensorflow/contrib/bigtable/kernels/bigtable_sample_keys_dataset_op.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/core/framework/op_kernel.h" namespace tensorflow { +namespace data { namespace { class BigtableSampleKeysDatasetOp : public DatasetOpKernel { @@ -118,4 +119,5 @@ REGISTER_KERNEL_BUILDER(Name("BigtableSampleKeysDataset").Device(DEVICE_CPU), BigtableSampleKeysDatasetOp); } // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_scan_dataset_op.cc b/tensorflow/contrib/bigtable/kernels/bigtable_scan_dataset_op.cc index 78a920b077..9407855fe8 100644 --- a/tensorflow/contrib/bigtable/kernels/bigtable_scan_dataset_op.cc +++ b/tensorflow/contrib/bigtable/kernels/bigtable_scan_dataset_op.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/core/framework/op_kernel.h" namespace tensorflow { +namespace data { namespace { class BigtableScanDatasetOp : public DatasetOpKernel { @@ -224,4 +225,5 @@ REGISTER_KERNEL_BUILDER(Name("BigtableScanDataset").Device(DEVICE_CPU), BigtableScanDatasetOp); } // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/contrib/data/kernels/assert_next_dataset_op.cc b/tensorflow/contrib/data/kernels/assert_next_dataset_op.cc index e36c9c0634..c19a609780 100644 --- a/tensorflow/contrib/data/kernels/assert_next_dataset_op.cc +++ b/tensorflow/contrib/data/kernels/assert_next_dataset_op.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/core/framework/tensor.h" namespace tensorflow { +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -150,4 +151,5 @@ REGISTER_KERNEL_BUILDER(Name("AssertNextDataset").Device(DEVICE_CPU), AssertNextDatasetOp); } // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/contrib/data/kernels/csv_dataset_op.cc b/tensorflow/contrib/data/kernels/csv_dataset_op.cc index 0ba905b92e..74107d5242 100644 --- a/tensorflow/contrib/data/kernels/csv_dataset_op.cc +++ b/tensorflow/contrib/data/kernels/csv_dataset_op.cc @@ -24,6 +24,7 @@ limitations under the License. #include "tensorflow/core/lib/io/zlib_inputstream.h" namespace tensorflow { +namespace data { namespace { class CSVDatasetOp : public DatasetOpKernel { @@ -851,4 +852,5 @@ class CSVDatasetOp : public DatasetOpKernel { REGISTER_KERNEL_BUILDER(Name("CSVDataset").Device(DEVICE_CPU), CSVDatasetOp); } // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/contrib/data/kernels/directed_interleave_dataset_op.cc b/tensorflow/contrib/data/kernels/directed_interleave_dataset_op.cc index ccf7ec1f84..a5321620bf 100644 --- a/tensorflow/contrib/data/kernels/directed_interleave_dataset_op.cc +++ b/tensorflow/contrib/data/kernels/directed_interleave_dataset_op.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/core/lib/hash/hash.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -276,5 +276,5 @@ REGISTER_KERNEL_BUILDER(Name("DirectedInterleaveDataset").Device(DEVICE_CPU), DirectedInterleaveDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/contrib/data/kernels/identity_indexed_dataset.cc b/tensorflow/contrib/data/kernels/identity_indexed_dataset.cc index 4718c1c8b9..c3cb45dbf7 100644 --- a/tensorflow/contrib/data/kernels/identity_indexed_dataset.cc +++ b/tensorflow/contrib/data/kernels/identity_indexed_dataset.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/core/lib/core/errors.h" namespace tensorflow { +namespace data { namespace { class IdentityIndexedDatasetOp : public IndexedDatasetOpKernel { @@ -150,4 +151,5 @@ REGISTER_KERNEL_BUILDER(Name("IdentityIndexedDataset").Device(DEVICE_CPU), IdentityIndexedDatasetOp); } // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/contrib/data/kernels/ignore_errors_dataset_op.cc b/tensorflow/contrib/data/kernels/ignore_errors_dataset_op.cc index db24e60846..beec344534 100644 --- a/tensorflow/contrib/data/kernels/ignore_errors_dataset_op.cc +++ b/tensorflow/contrib/data/kernels/ignore_errors_dataset_op.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/core/lib/random/random.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -137,5 +137,5 @@ REGISTER_KERNEL_BUILDER(Name("IgnoreErrorsDataset").Device(DEVICE_CPU), IgnoreErrorsDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/contrib/data/kernels/indexed_dataset.cc b/tensorflow/contrib/data/kernels/indexed_dataset.cc index c69564a31b..ced8ab0d60 100644 --- a/tensorflow/contrib/data/kernels/indexed_dataset.cc +++ b/tensorflow/contrib/data/kernels/indexed_dataset.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/core/lib/gtl/cleanup.h" namespace tensorflow { - +namespace data { namespace { Status VerifyTypesMatch(const DataTypeVector& expected, @@ -367,6 +367,7 @@ REGISTER_KERNEL_BUILDER(Name("IndexedDatasetMaterialize").Device(DEVICE_CPU), MaterializeDatasetOp); REGISTER_KERNEL_BUILDER(Name("IndexedDatasetGet").Device(DEVICE_CPU), IndexedDatasetGet); -} // namespace +} // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/contrib/data/kernels/indexed_dataset.h b/tensorflow/contrib/data/kernels/indexed_dataset.h index 6149de888c..7aa2d3fdbc 100644 --- a/tensorflow/contrib/data/kernels/indexed_dataset.h +++ b/tensorflow/contrib/data/kernels/indexed_dataset.h @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/core/framework/op_kernel.h" namespace tensorflow { +namespace data { // TODO(saeta): Urgh, this is ugly. class MaterializedIndexedDataset { @@ -112,6 +113,7 @@ Status GetIndexedDatasetFromVariantTensor(const Tensor& tensor, Status StoreIndexedDatasetInVariantTensor(IndexedDataset* dataset, Tensor* tensor); +} // namespace data } // namespace tensorflow #endif // TENSORFLOW_CONTRIB_DATA_KERNELS_INDEXED_DATASET_H_ diff --git a/tensorflow/contrib/data/kernels/lmdb_dataset_op.cc b/tensorflow/contrib/data/kernels/lmdb_dataset_op.cc index 80f39992fb..d233c1f8ec 100644 --- a/tensorflow/contrib/data/kernels/lmdb_dataset_op.cc +++ b/tensorflow/contrib/data/kernels/lmdb_dataset_op.cc @@ -22,6 +22,7 @@ limitations under the License. #include "lmdb.h" // NOLINT(build/include) namespace tensorflow { +namespace data { namespace { class LMDBDatasetOp : public DatasetOpKernel { @@ -212,4 +213,5 @@ class LMDBDatasetOp : public DatasetOpKernel { REGISTER_KERNEL_BUILDER(Name("LMDBDataset").Device(DEVICE_CPU), LMDBDatasetOp); } // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/contrib/data/kernels/prefetching_kernels.cc b/tensorflow/contrib/data/kernels/prefetching_kernels.cc index 725f8933c9..078de717e0 100644 --- a/tensorflow/contrib/data/kernels/prefetching_kernels.cc +++ b/tensorflow/contrib/data/kernels/prefetching_kernels.cc @@ -24,6 +24,7 @@ limitations under the License. #include "tensorflow/core/util/device_name_utils.h" namespace tensorflow { +namespace data { namespace { struct BufferElement { @@ -1114,5 +1115,6 @@ REGISTER_KERNEL_BUILDER( Name("MultiDeviceIteratorFromStringHandle").Device(DEVICE_CPU), MultiDeviceIteratorFromStringHandleOp); -} // anonymous namespace +} // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc b/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc index ab584504a0..30fa97a636 100644 --- a/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc +++ b/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/core/util/work_sharder.h" namespace tensorflow { +namespace data { namespace { class ThreadPoolResource : public ResourceBase { @@ -214,4 +215,5 @@ REGISTER_KERNEL_BUILDER(Name("ThreadPoolDataset").Device(DEVICE_CPU), ThreadPoolDatasetOp); } // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/contrib/data/kernels/unique_dataset_op.cc b/tensorflow/contrib/data/kernels/unique_dataset_op.cc index 6fbf5d2ebb..57fc5697a4 100644 --- a/tensorflow/contrib/data/kernels/unique_dataset_op.cc +++ b/tensorflow/contrib/data/kernels/unique_dataset_op.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/core/lib/hash/hash.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -219,5 +219,5 @@ REGISTER_KERNEL_BUILDER(Name("UniqueDataset").Device(DEVICE_CPU), UniqueDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/contrib/data/python/kernel_tests/map_defun_op_test.py b/tensorflow/contrib/data/python/kernel_tests/map_defun_op_test.py index 73cde40305..091eb5ce37 100644 --- a/tensorflow/contrib/data/python/kernel_tests/map_defun_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/map_defun_op_test.py @@ -130,6 +130,22 @@ class MapDefunTest(test.TestCase): with self.assertRaises(errors.InvalidArgumentError): self.evaluate(result) + def testMapDefunCancelledCorrectly(self): + + @function.Defun(dtypes.int64) + def defun(x): + # x has leading dimension 5, this will raise an error + return array_ops.gather(x, 10) + + c = array_ops.tile( + array_ops.expand_dims( + constant_op.constant([1, 2, 3, 4, 5], dtype=dtypes.int64), 0), + [100, 1]) + map_defun_op = map_defun.map_defun(defun, [c], [dtypes.int64], [()])[0] + with self.assertRaisesRegexp(errors.InvalidArgumentError, + r"indices = 10 is not in \[0, 5\)"): + self.evaluate(map_defun_op) + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/ops/batching.py b/tensorflow/contrib/data/python/ops/batching.py index 9c2001c34f..367c159dc5 100644 --- a/tensorflow/contrib/data/python/ops/batching.py +++ b/tensorflow/contrib/data/python/ops/batching.py @@ -272,9 +272,9 @@ def _padded_batch_dense_window(dataset, padded_shape, padding_value=None): padding_value = 0 def batch_init_fn(_): - return array_ops.fill( - array_ops.concat([np.array([0], dtype=np.int32), padded_shape], 0), - constant_op.constant(padding_value, dtype=dataset.output_types)) + batch_shape = array_ops.concat( + [np.array([0], dtype=np.int32), padded_shape], 0) + return gen_array_ops.empty(batch_shape, dtype=dataset.output_types) def batch_reduce_fn(state, value): return array_ops.concat([state, [value]], 0) diff --git a/tensorflow/contrib/distribute/python/examples/keras_mnist.py b/tensorflow/contrib/distribute/python/examples/keras_mnist.py index a20069c4fe..0495134636 100644 --- a/tensorflow/contrib/distribute/python/examples/keras_mnist.py +++ b/tensorflow/contrib/distribute/python/examples/keras_mnist.py @@ -58,13 +58,13 @@ def get_input_datasets(): train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)) train_ds = train_ds.repeat() train_ds = train_ds.shuffle(100) - train_ds = train_ds.batch(64) + train_ds = train_ds.batch(64, drop_remainder=True) # eval dataset eval_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)) eval_ds = eval_ds.repeat() eval_ds = eval_ds.shuffle(100) - eval_ds = eval_ds.batch(64) + eval_ds = eval_ds.batch(64, drop_remainder=True) return train_ds, eval_ds, input_shape diff --git a/tensorflow/contrib/hadoop/kernels/hadoop_dataset_ops.cc b/tensorflow/contrib/hadoop/kernels/hadoop_dataset_ops.cc index 80b2d3e08b..2bf6097d01 100644 --- a/tensorflow/contrib/hadoop/kernels/hadoop_dataset_ops.cc +++ b/tensorflow/contrib/hadoop/kernels/hadoop_dataset_ops.cc @@ -18,6 +18,7 @@ limitations under the License. #include "tensorflow/core/platform/file_system.h" namespace tensorflow { +namespace data { namespace { static const size_t kSyncMarkerSize = 16; @@ -332,9 +333,10 @@ class SequenceFileDatasetOp : public DatasetOpKernel { }; DataTypeVector output_types_; }; -} // namespace REGISTER_KERNEL_BUILDER(Name("SequenceFileDataset").Device(DEVICE_CPU), SequenceFileDatasetOp); +} // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/contrib/lite/builtin_op_data.h b/tensorflow/contrib/lite/builtin_op_data.h index e81f9e4f51..aecd71910c 100644 --- a/tensorflow/contrib/lite/builtin_op_data.h +++ b/tensorflow/contrib/lite/builtin_op_data.h @@ -25,6 +25,11 @@ extern "C" { // TODO(aselle): Consider using "if this then that" for testing. +// Useful placeholder to put in otherwise empty structs to avoid size warnings. +typedef struct { + char dummy_; +} EmptyStructPlaceholder; + // Possible padding types (for convolutions) typedef enum { kTfLitePaddingUnknown = 0, @@ -129,9 +134,11 @@ typedef struct { } TfLiteAddParams; typedef struct { + EmptyStructPlaceholder placeholder_; } TfLiteSpaceToBatchNDParams; typedef struct { + EmptyStructPlaceholder placeholder_; } TfLiteBatchToSpaceNDParams; typedef struct { @@ -178,9 +185,11 @@ typedef struct { } TfLiteResizeBilinearParams; typedef struct { + EmptyStructPlaceholder placeholder_; } TfLitePadParams; typedef struct { + EmptyStructPlaceholder placeholder_; } TfLitePadV2Params; typedef struct { @@ -220,6 +229,7 @@ typedef struct { } TfLiteGatherParams; typedef struct { + EmptyStructPlaceholder placeholder_; } TfLiteTransposeParams; typedef struct { diff --git a/tensorflow/contrib/lite/context.h b/tensorflow/contrib/lite/context.h index c7f4df3cdc..b23183b743 100644 --- a/tensorflow/contrib/lite/context.h +++ b/tensorflow/contrib/lite/context.h @@ -39,6 +39,12 @@ extern "C" { typedef enum { kTfLiteOk = 0, kTfLiteError = 1 } TfLiteStatus; +// Forward declarations for use with dependent types. +struct TfLiteContext; +struct TfLiteNode; +struct _TfLiteRegistration; +struct _TfLiteDelegate; + // The list of external context types known to TF Lite. This list exists solely // to avoid conflicts and to ensure ops can share the external contexts they // need. Access to the external contexts is controled by one of the @@ -60,10 +66,6 @@ typedef struct { TfLiteStatus (*Refresh)(struct TfLiteContext* context); } TfLiteExternalContext; -// Forward declare so GetNode can use this is in Context. -typedef struct _TfLiteRegistration TfLiteRegistration; -typedef struct _TfLiteDelegate TfLiteDelegate; - #define kOptionalTensor (-1) // Fixed size list of integers. Used for dimensions and inputs/outputs tensor @@ -240,7 +242,7 @@ typedef struct { // The delegate which knows how to handle `buffer_handle`. // WARNING: This is an experimental interface that is subject to change. - TfLiteDelegate* delegate; + struct _TfLiteDelegate* delegate; // An integer buffer handle that can be handled by `delegate`. // The value is valid only when delegate is not null. @@ -278,7 +280,7 @@ void TfLiteTensorRealloc(size_t num_bytes, TfLiteTensor* tensor); // A structure representing an instance of a node. // This structure only exhibits the inputs, outputs and user defined data, not // other features like the type. -typedef struct { +typedef struct TfLiteNode { // Inputs to this node expressed as indices into the simulator's tensors. TfLiteIntArray* inputs; @@ -305,7 +307,7 @@ typedef struct { // The pointer to the delegate. This is non-null only when the node is // created by calling `interpreter.ModifyGraphWithDelegate`. // WARNING: This is an experimental interface that is subject to change. - TfLiteDelegate* delegate; + struct _TfLiteDelegate* delegate; } TfLiteNode; typedef struct TfLiteContext { @@ -351,15 +353,15 @@ typedef struct TfLiteContext { // Get a Tensor node by node_index. // WARNING: This is an experimental interface that is subject to change. - TfLiteStatus (*GetNodeAndRegistration)(struct TfLiteContext*, int node_index, - TfLiteNode** node, - TfLiteRegistration** registration); + TfLiteStatus (*GetNodeAndRegistration)( + struct TfLiteContext*, int node_index, struct TfLiteNode** node, + struct _TfLiteRegistration** registration); // Replace ops with one or more stub delegate operations. This function // does not take ownership of `nodes_to_replace`. TfLiteStatus (*ReplaceSubgraphsWithDelegateKernels)( - struct TfLiteContext*, TfLiteRegistration registration, - const TfLiteIntArray* nodes_to_replace, TfLiteDelegate* delegate); + struct TfLiteContext*, struct _TfLiteRegistration registration, + const TfLiteIntArray* nodes_to_replace, struct _TfLiteDelegate* delegate); // Number of threads that are recommended to subsystems like gemmlowp and // eigen. @@ -447,19 +449,20 @@ typedef struct _TfLiteDelegate { // will look at the nodes and call ReplaceSubgraphsWithDelegateKernels() // to ask the TensorFlow lite runtime to create macro-nodes to represent // delegated subgraphs of the original graph. - TfLiteStatus (*Prepare)(TfLiteContext* context, TfLiteDelegate* delegate); + TfLiteStatus (*Prepare)(struct TfLiteContext* context, + struct _TfLiteDelegate* delegate); // Copy the data from delegate buffer handle to raw memory. // This can be null if the delegate doesn't use its own buffer. - TfLiteStatus (*CopyFromBufferHandle)(TfLiteContext* context, - TfLiteDelegate* delegate, + TfLiteStatus (*CopyFromBufferHandle)(struct TfLiteContext* context, + struct _TfLiteDelegate* delegate, TfLiteBufferHandle buffer_handle, void* data, size_t size); // Copy the data from raw memory to delegate buffer handle. // This can be null if the delegate doesn't use its own buffer. - TfLiteStatus (*CopyToBufferHandle)(TfLiteContext* context, - TfLiteDelegate* delegate, + TfLiteStatus (*CopyToBufferHandle)(struct TfLiteContext* context, + struct _TfLiteDelegate* delegate, TfLiteBufferHandle buffer_handle, void* data, size_t size); @@ -467,7 +470,8 @@ typedef struct _TfLiteDelegate { // this doesn't release the underlying resource (e.g. textures). The // resources are either owned by application layer or the delegate. // This can be null if the delegate doesn't use its own buffer. - void (*FreeBufferHandle)(TfLiteContext* context, TfLiteDelegate* delegate, + void (*FreeBufferHandle)(struct TfLiteContext* context, + struct _TfLiteDelegate* delegate, TfLiteBufferHandle* handle); } TfLiteDelegate; diff --git a/tensorflow/contrib/lite/delegates/eager/delegate_test.cc b/tensorflow/contrib/lite/delegates/eager/delegate_test.cc index eb47f46c0b..984f8bbc98 100644 --- a/tensorflow/contrib/lite/delegates/eager/delegate_test.cc +++ b/tensorflow/contrib/lite/delegates/eager/delegate_test.cc @@ -72,6 +72,26 @@ TEST_F(DelegateTest, FullGraph) { ASSERT_THAT(GetShape(8), ElementsAre(2, 1)); ASSERT_THAT(GetValues(8), ElementsAre(14.52f, 38.72f)); + ASSERT_EQ(GetType(8), kTfLiteFloat32); +} + +TEST_F(DelegateTest, NonFloatTypeInference) { + AddTensors(3, {0, 1}, {2}, kTfLiteInt32, {2}); + + AddTfOp(testing::kAdd, {0, 1}, {2}); + + ConfigureDelegate(); + + SetShape(0, {2, 2}); + SetTypedValues<int>(0, {1, 2, 3, 4}); + SetShape(1, {2, 2}); + SetTypedValues<int>(1, {4, 3, 2, 1}); + + ASSERT_TRUE(Invoke()); + + ASSERT_THAT(GetShape(2), ElementsAre(2, 2)); + ASSERT_THAT(GetTypedValues<int>(2), ElementsAre(5, 5, 5, 5)); + ASSERT_EQ(GetType(2), kTfLiteInt32); } TEST_F(DelegateTest, MixedGraph) { diff --git a/tensorflow/contrib/lite/delegates/eager/kernel.cc b/tensorflow/contrib/lite/delegates/eager/kernel.cc index f8467c7cb2..0ee4db1ffb 100644 --- a/tensorflow/contrib/lite/delegates/eager/kernel.cc +++ b/tensorflow/contrib/lite/delegates/eager/kernel.cc @@ -278,7 +278,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { TfLiteTensor* tensor = &context->tensors[tensor_index]; TF_LITE_ENSURE_OK( context, - CopyShape(context, buffer_map->GetTensor(tensor_index), tensor)); + CopyShapeAndType(context, buffer_map->GetTensor(tensor_index), tensor)); tensor->buffer_handle = tensor_index; tensor->data_is_stale = true; } diff --git a/tensorflow/contrib/lite/delegates/eager/test_util.cc b/tensorflow/contrib/lite/delegates/eager/test_util.cc index b8c9e2652a..8584999ace 100644 --- a/tensorflow/contrib/lite/delegates/eager/test_util.cc +++ b/tensorflow/contrib/lite/delegates/eager/test_util.cc @@ -25,19 +25,6 @@ namespace testing { bool EagerModelTest::Invoke() { return interpreter_->Invoke() == kTfLiteOk; } -void EagerModelTest::SetValues(int tensor_index, - const std::vector<float>& values) { - float* v = interpreter_->typed_tensor<float>(tensor_index); - for (float f : values) { - *v++ = f; - } -} - -std::vector<float> EagerModelTest::GetValues(int tensor_index) { - TfLiteTensor* o = interpreter_->tensor(tensor_index); - return std::vector<float>(o->data.f, o->data.f + o->bytes / sizeof(float)); -} - void EagerModelTest::SetShape(int tensor_index, const std::vector<int>& values) { ASSERT_EQ(interpreter_->ResizeInputTensor(tensor_index, values), kTfLiteOk); @@ -54,13 +41,21 @@ std::vector<int> EagerModelTest::GetShape(int tensor_index) { return result; } +TfLiteType EagerModelTest::GetType(int tensor_index) { + return interpreter_->tensor(tensor_index)->type; +} + void EagerModelTest::AddTensors(int num_tensors, const std::vector<int>& inputs, const std::vector<int>& outputs, - const TfLiteType& type, - const std::vector<int>& dims) { + TfLiteType type, const std::vector<int>& dims) { interpreter_->AddTensors(num_tensors); for (int i = 0; i < num_tensors; ++i) { TfLiteQuantizationParams quant; + // Suppress explicit output type specification to ensure type inference + // works properly. + if (std::find(outputs.begin(), outputs.end(), i) != outputs.end()) { + type = kTfLiteFloat32; + } CHECK_EQ(interpreter_->SetTensorParametersReadWrite(i, type, /*name=*/"", /*dims=*/dims, quant), @@ -101,18 +96,26 @@ void EagerModelTest::AddTfOp(TfOpType op, const std::vector<int>& inputs, return " attr{ key: '" + key + "' value {" + value + "}}"; }; + // Crude type attribution, will need fleshing out as more tests are added. + // TODO(b/113613439): Use nodedef string utilities to properly handle + // all types. + string type_attribute = attr("T", "type: DT_FLOAT"); + if (interpreter_->tensor(inputs[0])->type == kTfLiteInt32) { + type_attribute = attr("T", "type: DT_INT32"); + } + if (op == kUnpack) { - string attributes = attr("T", "type: DT_FLOAT") + attr("num", "i: 2") + - attr("axis", "i: 0"); + string attributes = + type_attribute + attr("num", "i: 2") + attr("axis", "i: 0"); AddTfOp("EagerUnpack", "Unpack", attributes, inputs, outputs); } else if (op == kIdentity) { - string attributes = attr("T", "type: DT_FLOAT"); + string attributes = type_attribute; AddTfOp("EagerIdentity", "Identity", attributes, inputs, outputs); } else if (op == kAdd) { - string attributes = attr("T", "type: DT_FLOAT"); + string attributes = type_attribute; AddTfOp("EagerAdd", "Add", attributes, inputs, outputs); } else if (op == kMul) { - string attributes = attr("T", "type: DT_FLOAT"); + string attributes = type_attribute; AddTfOp("EagerMul", "Mul", attributes, inputs, outputs); } else if (op == kNonExistent) { AddTfOp("NonExistentOp", "NonExistentOp", "", inputs, outputs); diff --git a/tensorflow/contrib/lite/delegates/eager/test_util.h b/tensorflow/contrib/lite/delegates/eager/test_util.h index 0eab9e1135..816db41931 100644 --- a/tensorflow/contrib/lite/delegates/eager/test_util.h +++ b/tensorflow/contrib/lite/delegates/eager/test_util.h @@ -44,11 +44,30 @@ class EagerModelTest : public ::testing::Test { bool Invoke(); + // Sets the (typed) tensor's values at the given index. + template <typename T> + void SetTypedValues(int tensor_index, const std::vector<T>& values) { + memcpy(interpreter_->typed_tensor<T>(tensor_index), values.data(), + values.size() * sizeof(T)); + } + + // Returns the (typed) tensor's values at the given index. + template <typename T> + std::vector<T> GetTypedValues(int tensor_index) { + const TfLiteTensor* t = interpreter_->tensor(tensor_index); + const T* tdata = interpreter_->typed_tensor<T>(tensor_index); + return std::vector<T>(tdata, tdata + t->bytes / sizeof(T)); + } + // Sets the tensor's values at the given index. - void SetValues(int tensor_index, const std::vector<float>& values); + void SetValues(int tensor_index, const std::vector<float>& values) { + SetTypedValues<float>(tensor_index, values); + } // Returns the tensor's values at the given index. - std::vector<float> GetValues(int tensor_index); + std::vector<float> GetValues(int tensor_index) { + return GetTypedValues<float>(tensor_index); + } // Sets the tensor's shape at the given index. void SetShape(int tensor_index, const std::vector<int>& values); @@ -56,13 +75,16 @@ class EagerModelTest : public ::testing::Test { // Returns the tensor's shape at the given index. std::vector<int> GetShape(int tensor_index); + // Returns the tensor's type at the given index. + TfLiteType GetType(int tensor_index); + const TestErrorReporter& error_reporter() const { return error_reporter_; } // Adds `num_tensor` tensors to the model. `inputs` contains the indices of // the input tensors and `outputs` contains the indices of the output // tensors. All tensors are set to have `type` and `dims`. void AddTensors(int num_tensors, const std::vector<int>& inputs, - const std::vector<int>& outputs, const TfLiteType& type, + const std::vector<int>& outputs, TfLiteType type, const std::vector<int>& dims); // Adds a TFLite Mul op. `inputs` contains the indices of the input tensors diff --git a/tensorflow/contrib/lite/delegates/eager/util.cc b/tensorflow/contrib/lite/delegates/eager/util.cc index 4426c653e6..051246bf86 100644 --- a/tensorflow/contrib/lite/delegates/eager/util.cc +++ b/tensorflow/contrib/lite/delegates/eager/util.cc @@ -26,8 +26,17 @@ TfLiteStatus ConvertStatus(TfLiteContext* context, return kTfLiteOk; } -TfLiteStatus CopyShape(TfLiteContext* context, const tensorflow::Tensor& src, - TfLiteTensor* tensor) { +TfLiteStatus CopyShapeAndType(TfLiteContext* context, + const tensorflow::Tensor& src, + TfLiteTensor* tensor) { + tensor->type = GetTensorFlowLiteType(static_cast<TF_DataType>(src.dtype())); + if (tensor->type == kTfLiteNoType) { + context->ReportError(context, + "TF Lite does not support TensorFlow data type: %s", + DataTypeString(src.dtype()).c_str()); + return kTfLiteError; + } + int num_dims = src.dims(); TfLiteIntArray* shape = TfLiteIntArrayCreate(num_dims); for (int j = 0; j < num_dims; ++j) { @@ -68,5 +77,28 @@ TF_DataType GetTensorFlowDataType(TfLiteType type) { } } +TfLiteType GetTensorFlowLiteType(TF_DataType type) { + switch (type) { + case TF_FLOAT: + return kTfLiteFloat32; + case TF_INT16: + return kTfLiteInt16; + case TF_INT32: + return kTfLiteInt32; + case TF_UINT8: + return kTfLiteUInt8; + case TF_INT64: + return kTfLiteInt64; + case TF_COMPLEX64: + return kTfLiteComplex64; + case TF_STRING: + return kTfLiteString; + case TF_BOOL: + return kTfLiteBool; + default: + return kTfLiteNoType; + } +} + } // namespace eager } // namespace tflite diff --git a/tensorflow/contrib/lite/delegates/eager/util.h b/tensorflow/contrib/lite/delegates/eager/util.h index a9407be071..ff500d18f3 100644 --- a/tensorflow/contrib/lite/delegates/eager/util.h +++ b/tensorflow/contrib/lite/delegates/eager/util.h @@ -28,14 +28,19 @@ namespace eager { TfLiteStatus ConvertStatus(TfLiteContext* context, const tensorflow::Status& status); -// Copies the given shape of the given 'src' into a TF Lite 'tensor'. Logs an -// error and returns kTfLiteError if the shape can't be converted. -TfLiteStatus CopyShape(TfLiteContext* context, const tensorflow::Tensor& src, - TfLiteTensor* tensor); +// Copies the given shape and type of the TensorFlow 'src' tensor into a TF Lite +// 'tensor'. Logs an error and returns kTfLiteError if the shape or type can't +// be converted. +TfLiteStatus CopyShapeAndType(TfLiteContext* context, + const tensorflow::Tensor& src, + TfLiteTensor* tensor); // Returns the TF C API Data type that corresponds to the given TfLiteType. TF_DataType GetTensorFlowDataType(TfLiteType type); +// Returns the TfLiteType that corresponds to the given TF C API Data type. +TfLiteType GetTensorFlowLiteType(TF_DataType); + } // namespace eager } // namespace tflite diff --git a/tensorflow/contrib/lite/delegates/eager/util_test.cc b/tensorflow/contrib/lite/delegates/eager/util_test.cc index 53378a1eaf..aebc91149c 100644 --- a/tensorflow/contrib/lite/delegates/eager/util_test.cc +++ b/tensorflow/contrib/lite/delegates/eager/util_test.cc @@ -26,6 +26,7 @@ namespace eager { namespace { using tensorflow::DT_FLOAT; +using tensorflow::DT_INT32; using tensorflow::Tensor; using ::testing::ElementsAre; @@ -71,27 +72,41 @@ TEST(UtilTest, ConvertStatus) { EXPECT_TRUE(context.error.empty()); } -TEST(UtilTest, CopyShape) { +TEST(UtilTest, CopyShapeAndType) { TestContext context; context.ReportError = ReportError; context.ResizeTensor = ResizeTensor; TfLiteTensor dst; - EXPECT_EQ(CopyShape(&context, Tensor(), &dst), kTfLiteOk); + EXPECT_EQ(CopyShapeAndType(&context, Tensor(), &dst), kTfLiteOk); EXPECT_THAT(context.new_size, ElementsAre(0)); + EXPECT_EQ(dst.type, kTfLiteFloat32); - EXPECT_EQ(CopyShape(&context, Tensor(DT_FLOAT, {1, 2}), &dst), kTfLiteOk); + EXPECT_EQ(CopyShapeAndType(&context, Tensor(DT_FLOAT, {1, 2}), &dst), + kTfLiteOk); EXPECT_THAT(context.new_size, ElementsAre(1, 2)); + EXPECT_EQ(dst.type, kTfLiteFloat32); - EXPECT_EQ(CopyShape(&context, Tensor(DT_FLOAT, {1LL << 44, 2}), &dst), + EXPECT_EQ(CopyShapeAndType(&context, Tensor(DT_INT32, {1, 2}), &dst), + kTfLiteOk); + EXPECT_THAT(context.new_size, ElementsAre(1, 2)); + EXPECT_EQ(dst.type, kTfLiteInt32); + + EXPECT_EQ(CopyShapeAndType(&context, Tensor(DT_FLOAT, {1LL << 44, 2}), &dst), kTfLiteError); EXPECT_EQ(context.error, "Dimension value in TensorFlow shape is larger than supported by " "TF Lite"); + + EXPECT_EQ( + CopyShapeAndType(&context, Tensor(tensorflow::DT_HALF, {1, 2}), &dst), + kTfLiteError); + EXPECT_EQ(context.error, + "TF Lite does not support TensorFlow data type: half"); } -TEST(UtilTest, TypeConversions) { +TEST(UtilTest, TypeConversionsFromTFLite) { EXPECT_EQ(TF_FLOAT, GetTensorFlowDataType(kTfLiteNoType)); EXPECT_EQ(TF_FLOAT, GetTensorFlowDataType(kTfLiteFloat32)); EXPECT_EQ(TF_INT16, GetTensorFlowDataType(kTfLiteInt16)); @@ -103,6 +118,19 @@ TEST(UtilTest, TypeConversions) { EXPECT_EQ(TF_BOOL, GetTensorFlowDataType(kTfLiteBool)); } +TEST(UtilTest, TypeConversionsFromTensorFlow) { + EXPECT_EQ(kTfLiteFloat32, GetTensorFlowLiteType(TF_FLOAT)); + EXPECT_EQ(kTfLiteInt16, GetTensorFlowLiteType(TF_INT16)); + EXPECT_EQ(kTfLiteInt32, GetTensorFlowLiteType(TF_INT32)); + EXPECT_EQ(kTfLiteUInt8, GetTensorFlowLiteType(TF_UINT8)); + EXPECT_EQ(kTfLiteInt64, GetTensorFlowLiteType(TF_INT64)); + EXPECT_EQ(kTfLiteComplex64, GetTensorFlowLiteType(TF_COMPLEX64)); + EXPECT_EQ(kTfLiteString, GetTensorFlowLiteType(TF_STRING)); + EXPECT_EQ(kTfLiteBool, GetTensorFlowLiteType(TF_BOOL)); + EXPECT_EQ(kTfLiteNoType, GetTensorFlowLiteType(TF_RESOURCE)); + EXPECT_EQ(kTfLiteNoType, GetTensorFlowLiteType(TF_VARIANT)); +} + } // namespace } // namespace eager } // namespace tflite diff --git a/tensorflow/contrib/lite/g3doc/models.md b/tensorflow/contrib/lite/g3doc/models.md index 0f9d016e6d..88f6cda420 100644 --- a/tensorflow/contrib/lite/g3doc/models.md +++ b/tensorflow/contrib/lite/g3doc/models.md @@ -3,33 +3,34 @@ ## Image classification (Float Models) -Model Name | Paper_Model_Files^ | Model_Size | Top-1 Accuracy | Top-5 Accuracy | TF Lite Performance^^ | Tensorflow Performance -------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | ---------: | -------------: | -------------: | --------------------: | ---------------------: -DenseNet | [paper](https://arxiv.org/abs/1608.06993), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/densenet_2018_04_27.tgz) | 43.6 Mb | 64.2% | 85.6% | 894 ms | 1262 ms -SqueezeNet | [paper](https://arxiv.org/abs/1602.07360), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/squeezenet_2018_04_27.tgz) | 5.0 Mb | 49.0% | 72.9% | 224 ms | 255 ms -NASNet mobile | [paper](https://arxiv.org/abs/1707.07012), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/nasnet_mobile_2018_04_27.tgz) | 21.4 Mb | 74.2% | 91.7% | 261 ms | 389 ms -NASNet large | [paper](https://arxiv.org/abs/1707.07012), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/nasnet_large_2018_04_27.tgz) | 355.3 Mb | 82.8% | 96.2% | 6697 ms | 7940 ms -ResNet_V2_50 | [paper](https://arxiv.org/abs/1603.05027), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/resnet_v2_50_2018_04_27.tgz) | 102.3 Mb | 68.1% | 88.4% | 942 ms | 1008 ms -ResNet_V2_101 | [paper](https://arxiv.org/abs/1603.05027), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/resnet_v2_101_2018_04_27.tgz) | 178.3 Mb | 70.4% | 89.6% | 1880 ms | 1970 ms -Inception_V3 | [paper](http://arxiv.org/abs/1512.00567), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v3_2018_04_27.tgz) | 95.3 Mb | 78.2% | 94.0% | 1433 ms | 1522 ms -Inception_V4 | [paper](http://arxiv.org/abs/1602.07261), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v4_2018_04_27.tgz) | 170.7 Mb | 80.4% | 95.2% | 2986 ms | 3139 ms -Inception_ResNet_V2 | [paper](https://arxiv.org/abs/1602.07261), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_resnet_v2_2018_04_27.tgz) | 121.0 Mb | 77.8% | 94.1% | 2731 ms | 2926 ms -Mobilenet_V1_0.25_128 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_128.tgz) | 1.9 Mb | 41.6% | 66.6% | 6.2 ms | 13.0 ms -Mobilenet_V1_0.25_160 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_160.tgz) | 1.9 Mb | 45.7% | 70.6% | 8.6 ms | 19.5 ms -Mobilenet_V1_0.25_192 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_192.tgz) | 1.9 Mb | 47.5% | 72.4% | 12.1 ms | 27.8 ms -Mobilenet_V1_0.25_224 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_224.tgz) | 1.9 Mb | 50.0% | 74.4% | 16.2 ms | 37.3 ms -Mobilenet_V1_0.50_128 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_128.tgz) | 5.3 Mb | 56.5% | 79.5% | 18.1 ms | 29.9 ms -Mobilenet_V1_0.50_160 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_160.tgz) | 5.3 Mb | 59.3% | 82.1% | 26.8 ms | 45.9 ms -Mobilenet_V1_0.50_192 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_192.tgz) | 5.3 Mb | 62.0% | 83.7% | 35.6 ms | 65.3 ms -Mobilenet_V1_0.50_224 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_224.tgz) | 5.3 Mb | 63.5% | 85.0% | 47.6 ms | 164.2 ms -Mobilenet_V1_0.75_128 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_128.tgz) | 10.3 Mb | 62.3% | 84.1% | 34.6 ms | 48.7 ms -Mobilenet_V1_0.75_160 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_160.tgz) | 10.3 Mb | 65.5% | 86.1% | 51.3 ms | 75.2 ms -Mobilenet_V1_0.75_192 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_192.tgz) | 10.3 Mb | 67.4% | 87.4% | 71.7 ms | 107.0 ms -Mobilenet_V1_0.75_224 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_224.tgz) | 10.3 Mb | 68.6% | 88.3% | 95.7 ms | 143.4 ms -Mobilenet_V1_1.0_128 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_128.tgz) | 16.9 Mb | 65.5% | 85.9% | 57.4 ms | 76.8 ms -Mobilenet_V1_1.0_160 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_160.tgz) | 16.9 Mb | 68.3% | 87.8% | 86.0 ms | 117.7 ms -Mobilenet_V1_1.0_192 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_192.tgz) | 16.9 Mb | 70.2% | 89.3% | 118.6 ms | 167.3 ms -Mobilenet_V1_1.0_224 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz) | 16.9 Mb | 71.3% | 90.1% | 160.1 ms | 224.3 ms +Model Name | Paper_Model_Files^ | Model_Size | Top-1 Accuracy | Top-5 Accuracy | TF Lite Performance^^ | Tensorflow Performance +--------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | ---------: | -------------: | -------------: | --------------------: | ---------------------: +DenseNet | [paper](https://arxiv.org/abs/1608.06993), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/densenet_2018_04_27.tgz) | 43.6 Mb | 64.2% | 85.6% | 894 ms | 1262 ms +SqueezeNet | [paper](https://arxiv.org/abs/1602.07360), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/squeezenet_2018_04_27.tgz) | 5.0 Mb | 49.0% | 72.9% | 224 ms | 255 ms +NASNet mobile | [paper](https://arxiv.org/abs/1707.07012), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/nasnet_mobile_2018_04_27.tgz) | 21.4 Mb | 74.2% | 91.7% | 261 ms | 389 ms +NASNet large | [paper](https://arxiv.org/abs/1707.07012), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/nasnet_large_2018_04_27.tgz) | 355.3 Mb | 82.8% | 96.2% | 6697 ms | 7940 ms +ResNet_V2_50 | [paper](https://arxiv.org/abs/1603.05027), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/resnet_v2_50_2018_04_27.tgz) | 102.3 Mb | 68.1% | 88.4% | 942 ms | 1008 ms +ResNet_V2_101 | [paper](https://arxiv.org/abs/1603.05027), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite_11_05_08/resnet_v2_101.tgz) | 178.3 Mb | 70.4% | 89.6% | 1880 ms | 1970 ms +Inception_V3 | [paper](http://arxiv.org/abs/1512.00567), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v3_2018_04_27.tgz) | 95.3 Mb | 78.2% | 94.0% | 1433 ms | 1522 ms +Inception_V4 | [paper](http://arxiv.org/abs/1602.07261), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v4_2018_04_27.tgz) | 170.7 Mb | 80.4% | 95.2% | 2986 ms | 3139 ms +Inception_ResNet_V2 | [paper](https://arxiv.org/abs/1602.07261), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_resnet_v2_2018_04_27.tgz) | 121.0 Mb | 77.8% | 94.1% | 2731 ms | 2926 ms +Mobilenet_V1_0.25_128 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_128.tgz) | 1.9 Mb | 41.6% | 66.6% | 6.2 ms | 13.0 ms +Mobilenet_V1_0.25_160 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_160.tgz) | 1.9 Mb | 45.7% | 70.6% | 8.6 ms | 19.5 ms +Mobilenet_V1_0.25_192 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_192.tgz) | 1.9 Mb | 47.5% | 72.4% | 12.1 ms | 27.8 ms +Mobilenet_V1_0.25_224 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_224.tgz) | 1.9 Mb | 50.0% | 74.4% | 16.2 ms | 37.3 ms +Mobilenet_V1_0.50_128 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_128.tgz) | 5.3 Mb | 56.5% | 79.5% | 18.1 ms | 29.9 ms +Mobilenet_V1_0.50_160 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_160.tgz) | 5.3 Mb | 59.3% | 82.1% | 26.8 ms | 45.9 ms +Mobilenet_V1_0.50_192 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_192.tgz) | 5.3 Mb | 62.0% | 83.7% | 35.6 ms | 65.3 ms +Mobilenet_V1_0.50_224 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_224.tgz) | 5.3 Mb | 63.5% | 85.0% | 47.6 ms | 164.2 ms +Mobilenet_V1_0.75_128 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_128.tgz) | 10.3 Mb | 62.3% | 84.1% | 34.6 ms | 48.7 ms +Mobilenet_V1_0.75_160 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_160.tgz) | 10.3 Mb | 65.5% | 86.1% | 51.3 ms | 75.2 ms +Mobilenet_V1_0.75_192 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_192.tgz) | 10.3 Mb | 67.4% | 87.4% | 71.7 ms | 107.0 ms +Mobilenet_V1_0.75_224 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_224.tgz) | 10.3 Mb | 68.6% | 88.3% | 95.7 ms | 143.4 ms +Mobilenet_V1_1.0_128 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_128.tgz) | 16.9 Mb | 65.5% | 85.9% | 57.4 ms | 76.8 ms +Mobilenet_V1_1.0_160 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_160.tgz) | 16.9 Mb | 68.3% | 87.8% | 86.0 ms | 117.7 ms +Mobilenet_V1_1.0_192 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_192.tgz) | 16.9 Mb | 70.2% | 89.3% | 118.6 ms | 167.3 ms +Mobilenet_V1_1.0_224 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz) | 16.9 Mb | 71.3% | 90.1% | 160.1 ms | 224.3 ms +Mobilenet_V2_1.0_224 | [paper](https://arxiv.org/pdf/1801.04381.pdf), [tflite&pb](http://download.tensorflow.org/models/tflite_11_05_08/mobilenet_v2_1.0_224.tgz) | 14.0 Mb | 71.9% | 90.1% | 117 ms | ^ The model files include both TF Lite FlatBuffer and Tensorflow frozen Graph. @@ -41,8 +42,8 @@ after excluding blacklisted images. ## Image classification (Quantized Models) -Model Name | Paper_Model_Files | Model_Size | Top-1 Accuracy | Top-5 Accuracy | TF Lite Performance ------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | ---------: | -------------: | -------------: | ------------------: +Model Name | Paper_Model_Files | Model_Size | Top-1 Accuracy | Top-5 Accuracy | TF Lite Performance +--------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | ---------: | -------------: | -------------: | ------------------: Mobilenet_V1_0.25_128_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.25_128_quant.tgz) | 0.5 Mb | 39.8% | 64.8% | 3.7 ms Mobilenet_V1_0.25_160_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.25_160_quant.tgz) | 0.5 Mb | 43.0% | 68.4% | 5.5 ms Mobilenet_V1_0.25_192_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.25_192_quant.tgz) | 0.5 Mb | 46.0% | 71.2% | 7.9 ms @@ -59,9 +60,12 @@ Mobilenet_V1_1.0_128_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tf Mobilenet_V1_1.0_160_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_160_quant.tgz) | 4.3 Mb | 67.2% | 86.9% | 37.4 ms Mobilenet_V1_1.0_192_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_192_quant.tgz) | 4.3 Mb | 69.4% | 88.3% | 51.9 ms Mobilenet_V1_1.0_224_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz) | 4.3 Mb | 70.2% | 89.1% | 70.2 ms +Mobilenet_v2_1.0_224_quant | [paper](https://arxiv.org/abs/1806.08342), [tflite&pb](http://download.tensorflow.org/models/tflite_11_05_08/mobilenet_v2_1.0_224_quant.tgz) | 3.4 Mb | 71.1% | 90.1% | 80.3 ms +Inception_v3_quant | [paper](https://arxiv.org/abs/1806.08342),[tflite&pb](http://download.tensorflow.org/models/tflite_11_05_08/inception_v3_quant.tgz) | 23 Mb | 77.5% | 93.6% | 637 ms ## Other models -Model | TF Lite FlatBuffer ------------------------ | :----------------: -Smart Reply 1.0 Android | [reference](https://research.googleblog.com/2017/11/on-device-conversational-modeling-with.html), [tflite](https://storage.googleapis.com/download.tensorflow.org/models/smartreply_1.0_2017_11_01.zip) +Lite FlatBuffer ----------------------- | :----------------: Smart Reply 1.0 +Android | +[reference](https://research.googleblog.com/2017/11/on-device-conversational-modeling-with.html), +[tflite](https://storage.googleapis.com/download.tensorflow.org/models/smartreply_1.0_2017_11_01.zip) diff --git a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc index cde4f55a16..6b8ecdd5c3 100644 --- a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc +++ b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc @@ -104,6 +104,19 @@ constexpr int kBwInputActivationStateTensor = 37; // Cell state tensors of size {n_batch, n_cell} constexpr int kBwInputCellStateTensor = 38; +// Auxiliary input and weights when stacking. +constexpr int kAuxInputTensor = 39; // Optional +// Forward weights. +constexpr int kFwAuxInputToInputWeightsTensor = 40; // Optional +constexpr int kFwAuxInputToForgetWeightsTensor = 41; // Optional +constexpr int kFwAuxInputToCellWeightsTensor = 42; // Optional +constexpr int kFwAuxInputToOutputWeightsTensor = 43; // Optional +// Backward weights. +constexpr int kBwAuxInputToInputWeightsTensor = 44; // Optional +constexpr int kBwAuxInputToForgetWeightsTensor = 45; // Optional +constexpr int kBwAuxInputToCellWeightsTensor = 46; // Optional +constexpr int kBwAuxInputToOutputWeightsTensor = 47; // Optional + // Output tensors. constexpr int kFwOutputTensor = 0; constexpr int kBwOutputTensor = 1; @@ -115,14 +128,15 @@ enum TemporaryTensor { kBwScratchBuffer = 1, // Quantized tensors needed for the hybrid kernel. kInputQuantized = 2, - kFwActivationStateQuantized = 3, - kBwActivationStateQuantized = 4, - kFwCellStateQuantized = 5, - kBwCellStateQuantized = 6, - kScalingFactors = 7, - kProductScalingFactors = 8, - kRecoveredCellWeights = 9, - kNumTemporaryTensors = 10 + kAuxInputQuantized = 3, // Quantized tensor needed for auxiliary input. + kFwActivationStateQuantized = 4, + kBwActivationStateQuantized = 5, + kFwCellStateQuantized = 6, + kBwCellStateQuantized = 7, + kScalingFactors = 8, + kProductScalingFactors = 9, + kRecoveredCellWeights = 10, + kNumTemporaryTensors = 11 }; void* Init(TfLiteContext* context, const char* buffer, size_t length) { @@ -335,7 +349,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { int* scratch_tensor_index = reinterpret_cast<int*>(node->user_data); // Check we have all the inputs and outputs we need. - TF_LITE_ENSURE_EQ(context, node->inputs->size, 39); + TF_LITE_ENSURE_EQ(context, node->inputs->size, 48); TF_LITE_ENSURE_EQ(context, node->outputs->size, 2); // Inferring batch size, number of outputs and sequence length and @@ -366,6 +380,48 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { context, CheckInputTensorDimensions(context, node, n_input, n_fw_output, n_fw_cell)); + // Get (optional) auxiliary inputs and weights. + const TfLiteTensor* aux_input = + GetOptionalInputTensor(context, node, kAuxInputTensor); + const TfLiteTensor* fw_aux_input_to_input_weights = + GetOptionalInputTensor(context, node, kFwAuxInputToInputWeightsTensor); + const TfLiteTensor* fw_aux_input_to_forget_weights = + GetOptionalInputTensor(context, node, kFwAuxInputToForgetWeightsTensor); + const TfLiteTensor* fw_aux_input_to_cell_weights = + GetOptionalInputTensor(context, node, kFwAuxInputToCellWeightsTensor); + const TfLiteTensor* fw_aux_input_to_output_weights = + GetOptionalInputTensor(context, node, kFwAuxInputToOutputWeightsTensor); + const TfLiteTensor* bw_aux_input_to_input_weights = + GetOptionalInputTensor(context, node, kBwAuxInputToInputWeightsTensor); + const TfLiteTensor* bw_aux_input_to_forget_weights = + GetOptionalInputTensor(context, node, kBwAuxInputToForgetWeightsTensor); + const TfLiteTensor* bw_aux_input_to_cell_weights = + GetOptionalInputTensor(context, node, kBwAuxInputToCellWeightsTensor); + const TfLiteTensor* bw_aux_input_to_output_weights = + GetOptionalInputTensor(context, node, kBwAuxInputToOutputWeightsTensor); + + const bool aux_inputs_all_or_none = + ((aux_input != nullptr) && (fw_aux_input_to_cell_weights != nullptr) && + (fw_aux_input_to_forget_weights != nullptr) && + (fw_aux_input_to_output_weights != nullptr) && + (bw_aux_input_to_cell_weights != nullptr) && + (bw_aux_input_to_forget_weights != nullptr) && + (bw_aux_input_to_output_weights != nullptr)) || + ((fw_aux_input_to_cell_weights == nullptr) && + (fw_aux_input_to_forget_weights == nullptr) && + (fw_aux_input_to_output_weights == nullptr) && + (bw_aux_input_to_cell_weights == nullptr) && + (bw_aux_input_to_forget_weights == nullptr) && + (bw_aux_input_to_output_weights == nullptr)); + TF_LITE_ENSURE(context, aux_inputs_all_or_none); + const bool has_aux_input = (aux_input != nullptr); + + if (has_aux_input) { + // Check that aux_input has the same dimensions (except last) as the input. + TF_LITE_ASSERT_EQ(aux_input->dims->data[0], input->dims->data[0]); + TF_LITE_ASSERT_EQ(aux_input->dims->data[1], input->dims->data[1]); + } + // Get the pointer to output, activation_state and cell_state buffer tensors. TfLiteTensor* fw_output = GetOutput(context, node, kFwOutputTensor); TfLiteTensor* fw_activation_state = @@ -406,6 +462,10 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { const TfLiteTensor* fw_input_to_input_weights = GetOptionalInputTensor(context, node, kFwInputToInputWeightsTensor); + if (has_aux_input) { + TF_LITE_ENSURE_EQ(context, fw_aux_input_to_input_weights->dims->data[0], + fw_input_to_input_weights->dims->data[0]); + } const bool fw_use_cifg = (fw_input_to_input_weights == nullptr); TfLiteIntArray* fw_scratch_buffer_size = TfLiteIntArrayCreate(2); fw_scratch_buffer_size->data[0] = n_batch; @@ -470,6 +530,10 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { const TfLiteTensor* bw_input_to_input_weights = GetOptionalInputTensor(context, node, kBwInputToInputWeightsTensor); + if (has_aux_input) { + TF_LITE_ENSURE_EQ(context, bw_aux_input_to_input_weights->dims->data[0], + bw_input_to_input_weights->dims->data[0]); + } const bool bw_use_cifg = (bw_input_to_input_weights == nullptr); TfLiteIntArray* bw_scratch_buffer_size = TfLiteIntArrayCreate(2); bw_scratch_buffer_size->data[0] = n_batch; @@ -483,8 +547,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, bw_scratch_buffer, bw_scratch_buffer_size)); if (is_hybrid_op) { - // Allocate temporary tensors to store quantized values of input, - // output_state and cell_state tensors. + // Allocate temporary tensors to store quantized values of input, aux_input + // (if present), activation_state and cell_state tensors. node->temporaries->data[kInputQuantized] = *scratch_tensor_index + kInputQuantized; TfLiteTensor* input_quantized = @@ -497,6 +561,22 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { input_quantized_size)); } + if (has_aux_input) { + node->temporaries->data[kAuxInputQuantized] = + *scratch_tensor_index + kAuxInputQuantized; + TfLiteTensor* aux_input_quantized = + GetTemporary(context, node, kAuxInputQuantized); + aux_input_quantized->type = kTfLiteUInt8; + aux_input_quantized->allocation_type = kTfLiteArenaRw; + if (!TfLiteIntArrayEqual(aux_input_quantized->dims, aux_input->dims)) { + TfLiteIntArray* aux_input_quantized_size = + TfLiteIntArrayCopy(aux_input->dims); + TF_LITE_ENSURE_OK(context, + context->ResizeTensor(context, aux_input_quantized, + aux_input_quantized_size)); + } + } + node->temporaries->data[kFwActivationStateQuantized] = *scratch_tensor_index + kFwActivationStateQuantized; TfLiteTensor* fw_activation_state_quantized = @@ -617,7 +697,11 @@ TfLiteStatus EvalFloat( const TfLiteTensor* recurrent_to_output_weights, const TfLiteTensor* cell_to_input_weights, const TfLiteTensor* cell_to_forget_weights, - const TfLiteTensor* cell_to_output_weights, + const TfLiteTensor* cell_to_output_weights, const TfLiteTensor* aux_input, + const TfLiteTensor* aux_input_to_input_weights, + const TfLiteTensor* aux_input_to_forget_weights, + const TfLiteTensor* aux_input_to_cell_weights, + const TfLiteTensor* aux_input_to_output_weights, const TfLiteTensor* input_gate_bias, const TfLiteTensor* forget_gate_bias, const TfLiteTensor* cell_bias, const TfLiteTensor* output_gate_bias, const TfLiteTensor* projection_weights, const TfLiteTensor* projection_bias, @@ -627,6 +711,7 @@ TfLiteStatus EvalFloat( const int max_time = input->dims->data[0]; const int n_batch = input->dims->data[1]; const int n_input = input->dims->data[2]; + const int aux_input_size = (aux_input) ? aux_input->dims->data[2] : 0; // n_cell and n_output will be the same size when there is no projection. const int n_cell = input_to_output_weights->dims->data[0]; @@ -671,25 +756,41 @@ TfLiteStatus EvalFloat( const float* projection_bias_ptr = (projection_bias == nullptr) ? nullptr : projection_bias->data.f; + float* aux_input_ptr = nullptr; + float* aux_input_to_input_weights_ptr = nullptr; + float* aux_input_to_forget_weights_ptr = nullptr; + float* aux_input_to_cell_weights_ptr = nullptr; + float* aux_input_to_output_weights_ptr = nullptr; + if (aux_input_size > 0) { + aux_input_ptr = aux_input->data.f; + aux_input_to_input_weights_ptr = aux_input_to_input_weights->data.f; + aux_input_to_forget_weights_ptr = aux_input_to_forget_weights->data.f; + aux_input_to_cell_weights_ptr = aux_input_to_cell_weights->data.f; + aux_input_to_output_weights_ptr = aux_input_to_output_weights->data.f; + } + // Loop through the sequence. if (forward_sequence) { for (int t = 0; t < max_time; t++) { const float* input_ptr = input->data.f + t * n_batch * n_input; float* output_ptr_time = output->data.f + t * n_batch * n_output; - kernel_utils::LstmStep( + kernel_utils::LstmStepWithAuxInput( input_ptr, input_to_input_weights_ptr, input_to_forget_weights->data.f, input_to_cell_weights->data.f, - input_to_output_weights->data.f, recurrent_to_input_weights_ptr, - recurrent_to_forget_weights->data.f, + input_to_output_weights->data.f, aux_input_ptr, + aux_input_to_input_weights_ptr, aux_input_to_forget_weights_ptr, + aux_input_to_cell_weights_ptr, aux_input_to_output_weights_ptr, + recurrent_to_input_weights_ptr, recurrent_to_forget_weights->data.f, recurrent_to_cell_weights->data.f, recurrent_to_output_weights->data.f, cell_to_input_weights_ptr, cell_to_forget_weights_ptr, cell_to_output_weights_ptr, input_gate_bias_ptr, forget_gate_bias->data.f, cell_bias->data.f, output_gate_bias->data.f, projection_weights_ptr, projection_bias_ptr, - params, n_batch, n_cell, n_input, n_output, activation_state->data.f, - cell_state->data.f, input_gate_scratch, forget_gate_scratch, - cell_scratch, output_gate_scratch, output_ptr_time); + params, n_batch, n_cell, n_input, aux_input_size, n_output, + activation_state->data.f, cell_state->data.f, input_gate_scratch, + forget_gate_scratch, cell_scratch, output_gate_scratch, + output_ptr_time); } } else { // Loop through the sequence backwards. @@ -697,19 +798,22 @@ TfLiteStatus EvalFloat( const float* input_ptr = input->data.f + t * n_batch * n_input; float* output_ptr_time = output->data.f + t * n_batch * n_output; - kernel_utils::LstmStep( + kernel_utils::LstmStepWithAuxInput( input_ptr, input_to_input_weights_ptr, input_to_forget_weights->data.f, input_to_cell_weights->data.f, - input_to_output_weights->data.f, recurrent_to_input_weights_ptr, - recurrent_to_forget_weights->data.f, + input_to_output_weights->data.f, aux_input_ptr, + aux_input_to_input_weights_ptr, aux_input_to_forget_weights_ptr, + aux_input_to_cell_weights_ptr, aux_input_to_output_weights_ptr, + recurrent_to_input_weights_ptr, recurrent_to_forget_weights->data.f, recurrent_to_cell_weights->data.f, recurrent_to_output_weights->data.f, cell_to_input_weights_ptr, cell_to_forget_weights_ptr, cell_to_output_weights_ptr, input_gate_bias_ptr, forget_gate_bias->data.f, cell_bias->data.f, output_gate_bias->data.f, projection_weights_ptr, projection_bias_ptr, - params, n_batch, n_cell, n_input, n_output, activation_state->data.f, - cell_state->data.f, input_gate_scratch, forget_gate_scratch, - cell_scratch, output_gate_scratch, output_ptr_time); + params, n_batch, n_cell, n_input, aux_input_size, n_output, + activation_state->data.f, cell_state->data.f, input_gate_scratch, + forget_gate_scratch, cell_scratch, output_gate_scratch, + output_ptr_time); } } return kTfLiteOk; @@ -726,19 +830,25 @@ TfLiteStatus EvalHybrid( const TfLiteTensor* recurrent_to_output_weights, const TfLiteTensor* cell_to_input_weights, const TfLiteTensor* cell_to_forget_weights, - const TfLiteTensor* cell_to_output_weights, + const TfLiteTensor* cell_to_output_weights, const TfLiteTensor* aux_input, + const TfLiteTensor* aux_input_to_input_weights, + const TfLiteTensor* aux_input_to_forget_weights, + const TfLiteTensor* aux_input_to_cell_weights, + const TfLiteTensor* aux_input_to_output_weights, const TfLiteTensor* input_gate_bias, const TfLiteTensor* forget_gate_bias, const TfLiteTensor* cell_bias, const TfLiteTensor* output_gate_bias, const TfLiteTensor* projection_weights, const TfLiteTensor* projection_bias, const TfLiteLSTMParams* params, bool forward_sequence, TfLiteTensor* scratch_buffer, TfLiteTensor* scaling_factors, TfLiteTensor* prod_scaling_factors, TfLiteTensor* recovered_cell_weights, - TfLiteTensor* input_quantized, TfLiteTensor* output_state_quantized, - TfLiteTensor* cell_state_quantized, TfLiteTensor* output_state, - TfLiteTensor* cell_state, TfLiteTensor* output) { + TfLiteTensor* input_quantized, TfLiteTensor* aux_input_quantized, + TfLiteTensor* output_state_quantized, TfLiteTensor* cell_state_quantized, + TfLiteTensor* output_state, TfLiteTensor* cell_state, + TfLiteTensor* output) { const int max_time = input->dims->data[0]; const int n_batch = input->dims->data[1]; const int n_input = input->dims->data[2]; + const int aux_input_size = (aux_input) ? aux_input->dims->data[2] : 0; // n_cell and n_output will be the same size when there is no projection. const int n_cell = input_to_output_weights->dims->data[0]; const int n_output = recurrent_to_output_weights->dims->data[1]; @@ -842,6 +952,10 @@ TfLiteStatus EvalHybrid( // Temporary storage for quantized values and scaling factors. int8_t* quantized_input_ptr = reinterpret_cast<int8_t*>(input_quantized->data.uint8); + int8_t* quantized_aux_input_ptr = + (aux_input_quantized == nullptr) + ? nullptr + : reinterpret_cast<int8_t*>(aux_input_quantized->data.uint8); int8_t* quantized_output_state_ptr = reinterpret_cast<int8_t*>(output_state_quantized->data.uint8); int8_t* quantized_cell_state_ptr = @@ -850,31 +964,63 @@ TfLiteStatus EvalHybrid( float* prod_scaling_factors_ptr = prod_scaling_factors->data.f; float* recovered_cell_weights_ptr = recovered_cell_weights->data.f; + // Auxiliary input and weights. + float* aux_input_ptr = nullptr; + int8_t* aux_input_to_input_weights_ptr = nullptr; + int8_t* aux_input_to_forget_weights_ptr = nullptr; + int8_t* aux_input_to_cell_weights_ptr = nullptr; + int8_t* aux_input_to_output_weights_ptr = nullptr; + float aux_input_to_input_weights_scale = 0.0f; + float aux_input_to_forget_weights_scale = 0.0f; + float aux_input_to_cell_weights_scale = 0.0f; + float aux_input_to_output_weights_scale = 0.0f; + if (aux_input_size > 0) { + aux_input_ptr = aux_input->data.f; + aux_input_to_input_weights_ptr = + reinterpret_cast<int8_t*>(aux_input_to_input_weights->data.uint8); + aux_input_to_forget_weights_ptr = + reinterpret_cast<int8_t*>(aux_input_to_forget_weights->data.uint8); + aux_input_to_cell_weights_ptr = + reinterpret_cast<int8_t*>(aux_input_to_cell_weights->data.uint8); + aux_input_to_output_weights_ptr = + reinterpret_cast<int8_t*>(aux_input_to_output_weights->data.uint8); + aux_input_to_input_weights_scale = aux_input_to_input_weights->params.scale; + aux_input_to_forget_weights_scale = + aux_input_to_forget_weights->params.scale; + aux_input_to_cell_weights_scale = aux_input_to_cell_weights->params.scale; + aux_input_to_output_weights_scale = + aux_input_to_output_weights->params.scale; + } if (forward_sequence) { // Feed the sequence into the LSTM step-by-step. for (int t = 0; t < max_time; t++) { const float* input_ptr = input->data.f + t * n_batch * n_input; float* output_ptr = output->data.f + t * n_batch * n_output; - kernel_utils::LstmStep( + kernel_utils::LstmStepWithAuxInput( input_ptr, input_to_input_weights_ptr, input_to_input_weights_scale, input_to_forget_weights_ptr, input_to_forget_weights_scale, input_to_cell_weights_ptr, input_to_cell_weights_scale, input_to_output_weights_ptr, input_to_output_weights_scale, - recurrent_to_input_weights_ptr, recurrent_to_input_weights_scale, - recurrent_to_forget_weights_ptr, recurrent_to_forget_weights_scale, - recurrent_to_cell_weights_ptr, recurrent_to_cell_weights_scale, - recurrent_to_output_weights_ptr, recurrent_to_output_weights_scale, - cell_to_input_weights_ptr, cell_to_input_weights_scale, - cell_to_forget_weights_ptr, cell_to_forget_weights_scale, - cell_to_output_weights_ptr, cell_to_output_weights_scale, - input_gate_bias_ptr, forget_gate_bias_ptr, cell_bias_ptr, - output_gate_bias_ptr, projection_weights_ptr, - projection_weights_scale, projection_bias_ptr, params, n_batch, - n_cell, n_input, n_output, input_gate_scratch, forget_gate_scratch, - cell_scratch, output_gate_scratch, scaling_factors_ptr, - prod_scaling_factors_ptr, recovered_cell_weights_ptr, - quantized_input_ptr, quantized_output_state_ptr, + aux_input_ptr, aux_input_to_input_weights_ptr, + aux_input_to_input_weights_scale, aux_input_to_forget_weights_ptr, + aux_input_to_forget_weights_scale, aux_input_to_cell_weights_ptr, + aux_input_to_cell_weights_scale, aux_input_to_output_weights_ptr, + aux_input_to_output_weights_scale, recurrent_to_input_weights_ptr, + recurrent_to_input_weights_scale, recurrent_to_forget_weights_ptr, + recurrent_to_forget_weights_scale, recurrent_to_cell_weights_ptr, + recurrent_to_cell_weights_scale, recurrent_to_output_weights_ptr, + recurrent_to_output_weights_scale, cell_to_input_weights_ptr, + cell_to_input_weights_scale, cell_to_forget_weights_ptr, + cell_to_forget_weights_scale, cell_to_output_weights_ptr, + cell_to_output_weights_scale, input_gate_bias_ptr, + forget_gate_bias_ptr, cell_bias_ptr, output_gate_bias_ptr, + projection_weights_ptr, projection_weights_scale, projection_bias_ptr, + params, n_batch, n_cell, n_input, aux_input_size, n_output, + input_gate_scratch, forget_gate_scratch, cell_scratch, + output_gate_scratch, scaling_factors_ptr, prod_scaling_factors_ptr, + recovered_cell_weights_ptr, quantized_input_ptr, + quantized_aux_input_ptr, quantized_output_state_ptr, quantized_cell_state_ptr, output_state_ptr, cell_state_ptr, output_ptr); } @@ -884,25 +1030,30 @@ TfLiteStatus EvalHybrid( const float* input_ptr = input->data.f + t * n_batch * n_input; float* output_ptr = output->data.f + t * n_batch * n_output; - kernel_utils::LstmStep( + kernel_utils::LstmStepWithAuxInput( input_ptr, input_to_input_weights_ptr, input_to_input_weights_scale, input_to_forget_weights_ptr, input_to_forget_weights_scale, input_to_cell_weights_ptr, input_to_cell_weights_scale, input_to_output_weights_ptr, input_to_output_weights_scale, - recurrent_to_input_weights_ptr, recurrent_to_input_weights_scale, - recurrent_to_forget_weights_ptr, recurrent_to_forget_weights_scale, - recurrent_to_cell_weights_ptr, recurrent_to_cell_weights_scale, - recurrent_to_output_weights_ptr, recurrent_to_output_weights_scale, - cell_to_input_weights_ptr, cell_to_input_weights_scale, - cell_to_forget_weights_ptr, cell_to_forget_weights_scale, - cell_to_output_weights_ptr, cell_to_output_weights_scale, - input_gate_bias_ptr, forget_gate_bias_ptr, cell_bias_ptr, - output_gate_bias_ptr, projection_weights_ptr, - projection_weights_scale, projection_bias_ptr, params, n_batch, - n_cell, n_input, n_output, input_gate_scratch, forget_gate_scratch, - cell_scratch, output_gate_scratch, scaling_factors_ptr, - prod_scaling_factors_ptr, recovered_cell_weights_ptr, - quantized_input_ptr, quantized_output_state_ptr, + aux_input_ptr, aux_input_to_input_weights_ptr, + aux_input_to_input_weights_scale, aux_input_to_forget_weights_ptr, + aux_input_to_forget_weights_scale, aux_input_to_cell_weights_ptr, + aux_input_to_cell_weights_scale, aux_input_to_output_weights_ptr, + aux_input_to_output_weights_scale, recurrent_to_input_weights_ptr, + recurrent_to_input_weights_scale, recurrent_to_forget_weights_ptr, + recurrent_to_forget_weights_scale, recurrent_to_cell_weights_ptr, + recurrent_to_cell_weights_scale, recurrent_to_output_weights_ptr, + recurrent_to_output_weights_scale, cell_to_input_weights_ptr, + cell_to_input_weights_scale, cell_to_forget_weights_ptr, + cell_to_forget_weights_scale, cell_to_output_weights_ptr, + cell_to_output_weights_scale, input_gate_bias_ptr, + forget_gate_bias_ptr, cell_bias_ptr, output_gate_bias_ptr, + projection_weights_ptr, projection_weights_scale, projection_bias_ptr, + params, n_batch, n_cell, n_input, aux_input_size, n_output, + input_gate_scratch, forget_gate_scratch, cell_scratch, + output_gate_scratch, scaling_factors_ptr, prod_scaling_factors_ptr, + recovered_cell_weights_ptr, quantized_input_ptr, + quantized_aux_input_ptr, quantized_output_state_ptr, quantized_cell_state_ptr, output_state_ptr, cell_state_ptr, output_ptr); } @@ -1004,17 +1155,39 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { const TfLiteTensor* bw_projection_bias = GetOptionalInputTensor(context, node, kBwProjectionBiasTensor); + // State tensors. TfLiteTensor* bw_activation_state = GetVariableInput(context, node, kBwInputActivationStateTensor); TfLiteTensor* bw_cell_state = GetVariableInput(context, node, kBwInputCellStateTensor); TfLiteTensor* bw_output = GetOutput(context, node, kBwOutputTensor); + // Temporary tensors. TfLiteTensor* fw_scratch_buffer = GetTemporary(context, node, kFwScratchBuffer); TfLiteTensor* bw_scratch_buffer = GetTemporary(context, node, kBwScratchBuffer); + // (Optional) auxiliary inputs. + const TfLiteTensor* aux_input = + GetOptionalInputTensor(context, node, kAuxInputTensor); + const TfLiteTensor* fw_aux_input_to_input_weights = + GetOptionalInputTensor(context, node, kFwAuxInputToInputWeightsTensor); + const TfLiteTensor* fw_aux_input_to_forget_weights = + GetOptionalInputTensor(context, node, kFwAuxInputToForgetWeightsTensor); + const TfLiteTensor* fw_aux_input_to_cell_weights = + GetOptionalInputTensor(context, node, kFwAuxInputToCellWeightsTensor); + const TfLiteTensor* fw_aux_input_to_output_weights = + GetOptionalInputTensor(context, node, kFwAuxInputToOutputWeightsTensor); + const TfLiteTensor* bw_aux_input_to_input_weights = + GetOptionalInputTensor(context, node, kBwAuxInputToInputWeightsTensor); + const TfLiteTensor* bw_aux_input_to_forget_weights = + GetOptionalInputTensor(context, node, kBwAuxInputToForgetWeightsTensor); + const TfLiteTensor* bw_aux_input_to_cell_weights = + GetOptionalInputTensor(context, node, kBwAuxInputToCellWeightsTensor); + const TfLiteTensor* bw_aux_input_to_output_weights = + GetOptionalInputTensor(context, node, kBwAuxInputToOutputWeightsTensor); + switch (fw_input_to_output_weights->type) { case kTfLiteFloat32: { TfLiteStatus fw_pass_status = EvalFloat( @@ -1023,10 +1196,13 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { fw_recurrent_to_input_weights, fw_recurrent_to_forget_weights, fw_recurrent_to_cell_weights, fw_recurrent_to_output_weights, fw_cell_to_input_weights, fw_cell_to_forget_weights, - fw_cell_to_output_weights, fw_input_gate_bias, fw_forget_gate_bias, - fw_cell_bias, fw_output_gate_bias, fw_projection_weights, - fw_projection_bias, params, /*forward_sequence=*/true, - fw_scratch_buffer, fw_activation_state, fw_cell_state, fw_output); + fw_cell_to_output_weights, aux_input, fw_aux_input_to_input_weights, + fw_aux_input_to_forget_weights, fw_aux_input_to_cell_weights, + fw_aux_input_to_output_weights, fw_input_gate_bias, + fw_forget_gate_bias, fw_cell_bias, fw_output_gate_bias, + fw_projection_weights, fw_projection_bias, params, + /*forward_sequence=*/true, fw_scratch_buffer, fw_activation_state, + fw_cell_state, fw_output); TF_LITE_ENSURE_OK(context, fw_pass_status); TfLiteStatus bw_pass_status = EvalFloat( @@ -1035,16 +1211,21 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { bw_recurrent_to_input_weights, bw_recurrent_to_forget_weights, bw_recurrent_to_cell_weights, bw_recurrent_to_output_weights, bw_cell_to_input_weights, bw_cell_to_forget_weights, - bw_cell_to_output_weights, bw_input_gate_bias, bw_forget_gate_bias, - bw_cell_bias, bw_output_gate_bias, bw_projection_weights, - bw_projection_bias, params, /*forward_sequence=*/false, - bw_scratch_buffer, bw_activation_state, bw_cell_state, bw_output); + bw_cell_to_output_weights, aux_input, bw_aux_input_to_input_weights, + bw_aux_input_to_forget_weights, bw_aux_input_to_cell_weights, + bw_aux_input_to_output_weights, bw_input_gate_bias, + bw_forget_gate_bias, bw_cell_bias, bw_output_gate_bias, + bw_projection_weights, bw_projection_bias, params, + /*forward_sequence=*/false, bw_scratch_buffer, bw_activation_state, + bw_cell_state, bw_output); TF_LITE_ENSURE_OK(context, bw_pass_status); return kTfLiteOk; } case kTfLiteUInt8: { TfLiteTensor* input_quantized = GetTemporary(context, node, kInputQuantized); + TfLiteTensor* aux_input_quantized = + GetTemporary(context, node, kAuxInputQuantized); TfLiteTensor* fw_activation_state_quantized = GetTemporary(context, node, kFwActivationStateQuantized); TfLiteTensor* bw_activation_state_quantized = @@ -1059,19 +1240,23 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { GetTemporary(context, node, kProductScalingFactors); TfLiteTensor* recovered_cell_weights = GetTemporary(context, node, kRecoveredCellWeights); + TfLiteStatus fw_pass_status = EvalHybrid( input, fw_input_to_input_weights, fw_input_to_forget_weights, fw_input_to_cell_weights, fw_input_to_output_weights, fw_recurrent_to_input_weights, fw_recurrent_to_forget_weights, fw_recurrent_to_cell_weights, fw_recurrent_to_output_weights, fw_cell_to_input_weights, fw_cell_to_forget_weights, - fw_cell_to_output_weights, fw_input_gate_bias, fw_forget_gate_bias, - fw_cell_bias, fw_output_gate_bias, fw_projection_weights, - fw_projection_bias, params, /*forward_sequence=*/true, - fw_scratch_buffer, scaling_factors, prod_scaling_factors, - recovered_cell_weights, input_quantized, - fw_activation_state_quantized, fw_cell_state_quantized, - fw_activation_state, fw_cell_state, fw_output); + fw_cell_to_output_weights, aux_input, fw_aux_input_to_input_weights, + fw_aux_input_to_forget_weights, fw_aux_input_to_cell_weights, + fw_aux_input_to_output_weights, fw_input_gate_bias, + fw_forget_gate_bias, fw_cell_bias, fw_output_gate_bias, + fw_projection_weights, fw_projection_bias, params, + /*forward_sequence=*/true, fw_scratch_buffer, scaling_factors, + prod_scaling_factors, recovered_cell_weights, input_quantized, + aux_input_quantized, fw_activation_state_quantized, + fw_cell_state_quantized, fw_activation_state, fw_cell_state, + fw_output); TF_LITE_ENSURE_OK(context, fw_pass_status); TfLiteStatus bw_pass_status = EvalHybrid( @@ -1080,13 +1265,16 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { bw_recurrent_to_input_weights, bw_recurrent_to_forget_weights, bw_recurrent_to_cell_weights, bw_recurrent_to_output_weights, bw_cell_to_input_weights, bw_cell_to_forget_weights, - bw_cell_to_output_weights, bw_input_gate_bias, bw_forget_gate_bias, - bw_cell_bias, bw_output_gate_bias, bw_projection_weights, - bw_projection_bias, params, /*forward_sequence=*/false, - bw_scratch_buffer, scaling_factors, prod_scaling_factors, - recovered_cell_weights, input_quantized, - bw_activation_state_quantized, bw_cell_state_quantized, - bw_activation_state, bw_cell_state, bw_output); + bw_cell_to_output_weights, aux_input, fw_aux_input_to_input_weights, + fw_aux_input_to_forget_weights, fw_aux_input_to_cell_weights, + fw_aux_input_to_output_weights, bw_input_gate_bias, + bw_forget_gate_bias, bw_cell_bias, bw_output_gate_bias, + bw_projection_weights, bw_projection_bias, params, + /*forward_sequence=*/false, bw_scratch_buffer, scaling_factors, + prod_scaling_factors, recovered_cell_weights, input_quantized, + aux_input_quantized, bw_activation_state_quantized, + bw_cell_state_quantized, bw_activation_state, bw_cell_state, + bw_output); TF_LITE_ENSURE_OK(context, bw_pass_status); return kTfLiteOk; } diff --git a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm_test.cc b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm_test.cc index d058fab529..74ba8021c2 100644 --- a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm_test.cc +++ b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm_test.cc @@ -177,6 +177,16 @@ class BidirectionalLSTMOpModel : public SingleOpModel { bw_output_ = AddOutput(TensorType_FLOAT32); + aux_input_ = AddNullInput(); + fw_aux_input_to_input_weights_ = AddNullInput(); + fw_aux_input_to_forget_weights_ = AddNullInput(); + fw_aux_input_to_cell_weights_ = AddNullInput(); + fw_aux_input_to_output_weights_ = AddNullInput(); + bw_aux_input_to_input_weights_ = AddNullInput(); + bw_aux_input_to_forget_weights_ = AddNullInput(); + bw_aux_input_to_cell_weights_ = AddNullInput(); + bw_aux_input_to_output_weights_ = AddNullInput(); + SetBuiltinOp(BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM, BuiltinOptions_LSTMOptions, CreateLSTMOptions(builder_, ActivationFunctionType_TANH, @@ -340,6 +350,16 @@ class BidirectionalLSTMOpModel : public SingleOpModel { int fw_output_; int bw_output_; + int aux_input_; + int fw_aux_input_to_input_weights_; + int fw_aux_input_to_forget_weights_; + int fw_aux_input_to_cell_weights_; + int fw_aux_input_to_output_weights_; + int bw_aux_input_to_input_weights_; + int bw_aux_input_to_forget_weights_; + int bw_aux_input_to_cell_weights_; + int bw_aux_input_to_output_weights_; + int n_batch_; int n_input_; int n_fw_cell_; @@ -415,6 +435,16 @@ TEST(LSTMOpTest, BlackBoxTestNoCifgNoPeepholeNoProjectionNoClipping) { {n_batch, n_output}, // activation_state tensor {n_batch, n_cell}, // cell_state tensor + + {n_batch, sequence_length, 0}, // aux_input tensor + {n_cell, 0}, // aux_fw_input_to_input tensor + {n_cell, 0}, // aux_fw_input_to_forget tensor + {n_cell, 0}, // aux_fw_input_to_cell tensor + {n_cell, 0}, // aux_fw_input_to_output tensor + {n_cell, 0}, // aux_bw_input_to_input tensor + {n_cell, 0}, // aux_bw_input_to_forget tensor + {n_cell, 0}, // aux_bw_input_to_cell tensor + {n_cell, 0}, // aux_bw_input_to_output tensor }); lstm.SetInputToInputWeights({-0.45018822, -0.02338299, -0.0870589, @@ -562,6 +592,16 @@ TEST(LSTMOpTest, BlackBoxTestNoCifgNoPeepholeNoProjectionNoClippingReverse) { {n_batch, n_output}, // activation_state tensor {n_batch, n_cell}, // cell_state tensor + + {n_batch, sequence_length, 0}, // aux_input tensor + {n_cell, 0}, // aux_fw_input_to_input tensor + {n_cell, 0}, // aux_fw_input_to_forget tensor + {n_cell, 0}, // aux_fw_input_to_cell tensor + {n_cell, 0}, // aux_fw_input_to_output tensor + {n_cell, 0}, // aux_bw_input_to_input tensor + {n_cell, 0}, // aux_bw_input_to_forget tensor + {n_cell, 0}, // aux_bw_input_to_cell tensor + {n_cell, 0}, // aux_bw_input_to_output tensor }); lstm.SetInputToInputWeights({-0.45018822, -0.02338299, -0.0870589, @@ -709,6 +749,16 @@ TEST(LSTMOpTest, BlackBoxTestWithCifgWithPeepholeNoProjectionNoClipping) { {n_batch, n_output}, // activation_state tensor {n_batch, n_cell}, // cell_state tensor + + {n_batch, sequence_length, 0}, // aux_input tensor + {n_cell, 0}, // aux_fw_input_to_input tensor + {n_cell, 0}, // aux_fw_input_to_forget tensor + {n_cell, 0}, // aux_fw_input_to_cell tensor + {n_cell, 0}, // aux_fw_input_to_output tensor + {n_cell, 0}, // aux_bw_input_to_input tensor + {n_cell, 0}, // aux_bw_input_to_forget tensor + {n_cell, 0}, // aux_bw_input_to_cell tensor + {n_cell, 0}, // aux_bw_input_to_output tensor }); lstm.SetInputToCellWeights({-0.49770179, -0.27711356, -0.09624726, 0.05100781, @@ -848,6 +898,16 @@ TEST(LSTMOpTest, {n_batch, n_output}, // activation_state tensor {n_batch, n_cell}, // cell_state tensor + + {n_batch, sequence_length, 0}, // aux_input tensor + {n_cell, 0}, // aux_fw_input_to_input tensor + {n_cell, 0}, // aux_fw_input_to_forget tensor + {n_cell, 0}, // aux_fw_input_to_cell tensor + {n_cell, 0}, // aux_fw_input_to_output tensor + {n_cell, 0}, // aux_bw_input_to_input tensor + {n_cell, 0}, // aux_bw_input_to_forget tensor + {n_cell, 0}, // aux_bw_input_to_cell tensor + {n_cell, 0}, // aux_bw_input_to_output tensor }); lstm.SetInputToCellWeights({-0.49770179, -0.27711356, -0.09624726, 0.05100781, @@ -987,6 +1047,16 @@ TEST(LSTMOpTest, BlackBoxTestWithPeepholeWithProjectionNoClipping) { {n_batch, n_output}, // activation_state tensor {n_batch, n_cell}, // cell_state tensor + + {n_batch, sequence_length, 0}, // aux_input tensor + {n_cell, 0}, // aux_fw_input_to_input tensor + {n_cell, 0}, // aux_fw_input_to_forget tensor + {n_cell, 0}, // aux_fw_input_to_cell tensor + {n_cell, 0}, // aux_fw_input_to_output tensor + {n_cell, 0}, // aux_bw_input_to_input tensor + {n_cell, 0}, // aux_bw_input_to_forget tensor + {n_cell, 0}, // aux_bw_input_to_cell tensor + {n_cell, 0}, // aux_bw_input_to_output tensor }); lstm.SetInputToInputWeights( diff --git a/tensorflow/contrib/lite/kernels/eigen_support.h b/tensorflow/contrib/lite/kernels/eigen_support.h index ec77856b10..b235829642 100644 --- a/tensorflow/contrib/lite/kernels/eigen_support.h +++ b/tensorflow/contrib/lite/kernels/eigen_support.h @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/contrib/lite/context.h" namespace EigenForTFLite { -class ThreadPoolDevice; +struct ThreadPoolDevice; } namespace tflite { diff --git a/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc b/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc index 360b472c45..b9dd40ddf9 100644 --- a/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc +++ b/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc @@ -203,9 +203,9 @@ void LstmStep( cell_to_input_weights_ptr, cell_to_forget_weights_ptr, cell_to_output_weights_ptr, input_gate_bias_ptr, forget_gate_bias_ptr, cell_bias_ptr, output_gate_bias_ptr, projection_weights_ptr, - projection_bias_ptr, params, n_batch, n_cell, n_input, n_output, - output_state_ptr, cell_state_ptr, input_gate_scratch, forget_gate_scratch, - cell_scratch, output_gate_scratch, output_ptr_batch); + projection_bias_ptr, params, n_batch, n_cell, n_input, /*n_aux_input=*/0, + n_output, output_state_ptr, cell_state_ptr, input_gate_scratch, + forget_gate_scratch, cell_scratch, output_gate_scratch, output_ptr_batch); } void LstmStepWithAuxInput( @@ -227,8 +227,8 @@ void LstmStepWithAuxInput( const float* forget_gate_bias_ptr, const float* cell_bias_ptr, const float* output_gate_bias_ptr, const float* projection_weights_ptr, const float* projection_bias_ptr, const TfLiteLSTMParams* params, - int n_batch, int n_cell, int n_input, int n_output, float* output_state_ptr, - float* cell_state_ptr, float* input_gate_scratch, + int n_batch, int n_cell, int n_input, int n_aux_input, int n_output, + float* output_state_ptr, float* cell_state_ptr, float* input_gate_scratch, float* forget_gate_scratch, float* cell_scratch, float* output_gate_scratch, float* output_ptr_batch) { // Since we have already checked that weights are all there or none, we can @@ -268,19 +268,20 @@ void LstmStepWithAuxInput( if (aux_input_ptr_batch != nullptr) { if (!use_cifg) { tensor_utils::MatrixBatchVectorMultiplyAccumulate( - aux_input_to_input_weights_ptr, n_cell, n_input, aux_input_ptr_batch, - n_batch, input_gate_scratch, /*result_stride=*/1); + aux_input_to_input_weights_ptr, n_cell, n_aux_input, + aux_input_ptr_batch, n_batch, input_gate_scratch, + /*result_stride=*/1); } tensor_utils::MatrixBatchVectorMultiplyAccumulate( - aux_input_to_forget_weights_ptr, n_cell, n_input, aux_input_ptr_batch, - n_batch, forget_gate_scratch, /*result_stride=*/1); + aux_input_to_forget_weights_ptr, n_cell, n_aux_input, + aux_input_ptr_batch, n_batch, forget_gate_scratch, /*result_stride=*/1); tensor_utils::MatrixBatchVectorMultiplyAccumulate( - aux_input_to_cell_weights_ptr, n_cell, n_input, aux_input_ptr_batch, + aux_input_to_cell_weights_ptr, n_cell, n_aux_input, aux_input_ptr_batch, n_batch, cell_scratch, /*result_stride=*/1); tensor_utils::MatrixBatchVectorMultiplyAccumulate( - aux_input_to_output_weights_ptr, n_cell, n_input, aux_input_ptr_batch, - n_batch, output_gate_scratch, /*result_stride=*/1); + aux_input_to_output_weights_ptr, n_cell, n_aux_input, + aux_input_ptr_batch, n_batch, output_gate_scratch, /*result_stride=*/1); } // For each batch and cell: compute recurrent_weight * output_state. @@ -432,10 +433,11 @@ void LstmStep( cell_to_output_weights_ptr, cell_to_output_weights_scale, input_gate_bias_ptr, forget_gate_bias_ptr, cell_bias_ptr, output_gate_bias_ptr, projection_weights_ptr, projection_weights_scale, - projection_bias_ptr, params, n_batch, n_cell, n_input, n_output, - input_gate_scratch, forget_gate_scratch, cell_scratch, - output_gate_scratch, scaling_factors, product_scaling_factors, - recovered_cell_weights, quantized_input_ptr_batch, + projection_bias_ptr, params, n_batch, n_cell, n_input, + /*n_aux_input=*/0, n_output, input_gate_scratch, forget_gate_scratch, + cell_scratch, output_gate_scratch, scaling_factors, + product_scaling_factors, recovered_cell_weights, + quantized_input_ptr_batch, /*quantized_aux_input_ptr_batch=*/nullptr, quantized_output_state_ptr, quantized_cell_state_ptr, output_state_ptr, cell_state_ptr, output_ptr_batch); @@ -476,8 +478,9 @@ void LstmStep( const float* output_gate_bias_ptr, const int8_t* projection_weights_ptr, float projection_weights_scale, const float* projection_bias_ptr, const TfLiteLSTMParams* params, int n_batch, int n_cell, int n_input, - int n_output, float* input_gate_scratch, float* forget_gate_scratch, - float* cell_scratch, float* output_gate_scratch, float* scaling_factors, + int n_aux_input, int n_output, float* input_gate_scratch, + float* forget_gate_scratch, float* cell_scratch, + float* output_gate_scratch, float* scaling_factors, float* product_scaling_factors, float* recovered_cell_weights, int8_t* quantized_input_ptr_batch, int8_t* quantized_aux_input_ptr_batch, diff --git a/tensorflow/contrib/lite/kernels/internal/kernel_utils.h b/tensorflow/contrib/lite/kernels/internal/kernel_utils.h index 38436c1382..215ad04add 100644 --- a/tensorflow/contrib/lite/kernels/internal/kernel_utils.h +++ b/tensorflow/contrib/lite/kernels/internal/kernel_utils.h @@ -131,8 +131,8 @@ void LstmStepWithAuxInput( const float* forget_gate_bias_ptr, const float* cell_bias_ptr, const float* output_gate_bias_ptr, const float* projection_weights_ptr, const float* projection_bias_ptr, const TfLiteLSTMParams* params, - int n_batch, int n_cell, int n_input, int n_output, float* output_state_ptr, - float* cell_state_ptr, float* input_gate_scratch, + int n_batch, int n_cell, int n_input, int n_aux_input, int n_output, + float* output_state_ptr, float* cell_state_ptr, float* input_gate_scratch, float* forget_gate_scratch, float* cell_scratch, float* output_gate_scratch, float* output_ptr_batch); @@ -252,12 +252,13 @@ void LstmStepWithAuxInput( const float* output_gate_bias_ptr, const int8_t* projection_weights_ptr, float projection_weights_scale, const float* projection_bias_ptr, const TfLiteLSTMParams* params, int n_batch, int n_cell, int n_input, - int n_output, float* input_gate_scratch, float* forget_gate_scratch, - float* cell_scratch, float* output_gate_scratch, float* scaling_factors, - float* product_scaling_factors, float* recovered_cell_weights, - int8_t* quantized_input_ptr_batch, int8_t* quantized_aux_input_ptr_batch, - int8_t* quantized_output_state_ptr, int8_t* quantized_cell_state_ptr, - float* output_state_ptr, float* cell_state_ptr, float* output_ptr_batch); + int n_aux_input, int n_output, float* input_gate_scratch, + float* forget_gate_scratch, float* cell_scratch, float* output_gate_scratch, + float* scaling_factors, float* product_scaling_factors, + float* recovered_cell_weights, int8_t* quantized_input_ptr_batch, + int8_t* quantized_aux_input_ptr_batch, int8_t* quantized_output_state_ptr, + int8_t* quantized_cell_state_ptr, float* output_state_ptr, + float* cell_state_ptr, float* output_ptr_batch); } // namespace kernel_utils } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index 00f9616cc2..a027a47726 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -3398,10 +3398,12 @@ inline void Tanh(const int16* input_data, const RuntimeShape& input_shape, } } -inline void Dequantize(const uint8* input_data, const Dims<4>& input_dims, - int32 zero_point, double scale, float* output_data, - const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(output_dims, input_dims); +inline void Dequantize(const tflite::DequantizationParams& op_params, + const RuntimeShape& input_shape, const uint8* input_data, + const RuntimeShape& output_shape, float* output_data) { + int32 zero_point = op_params.zero_point; + double scale = op_params.scale; + const int flat_size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < flat_size; i++) { int32 val = input_data[i]; @@ -3410,9 +3412,25 @@ inline void Dequantize(const uint8* input_data, const Dims<4>& input_dims, } } -inline void FakeQuant(const float* input_data, const Dims<4>& input_dims, - float rmin, float rmax, int num_bits, float* output_data, - const Dims<4>& output_dims) { +// TODO(b/80418076): Move to legacy ops file, update invocations. +// Legacy Dims<4>. +inline void Dequantize(const uint8* input_data, const Dims<4>& input_dims, + int32 zero_point, double scale, float* output_data, + const Dims<4>& output_dims) { + tflite::DequantizationParams op_params; + op_params.zero_point = zero_point; + op_params.scale = scale; + + Dequantize(op_params, DimsToShape(input_dims), input_data, + DimsToShape(output_dims), output_data); +} + +inline void FakeQuant(const tflite::FakeQuantParams& op_params, + const RuntimeShape& input_shape, const float* input_data, + const RuntimeShape& output_shape, float* output_data) { + float rmin = op_params.minmax.min; + float rmax = op_params.minmax.max; + int num_bits = op_params.num_bits; // 0 should always be a representable value. Let's assume that the initial // min,max range contains 0. TFLITE_DCHECK_LE(rmin, 0.0f); @@ -3425,11 +3443,25 @@ inline void FakeQuant(const float* input_data, const Dims<4>& input_dims, float nudged_min, nudged_max, nudged_scale; NudgeQuantizationRange(rmin, rmax, quant_min, quant_max, &nudged_min, &nudged_max, &nudged_scale); - const int flat_size = MatchingFlatSize(output_dims, input_dims); + const int flat_size = MatchingFlatSize(input_shape, output_shape); FakeQuantizeArray(nudged_scale, nudged_min, nudged_max, input_data, output_data, flat_size); } +// TODO(b/80418076): Move to legacy ops file, update invocations. +// Legacy Dims<4>. +inline void FakeQuant(const float* input_data, const Dims<4>& input_dims, + float rmin, float rmax, int num_bits, float* output_data, + const Dims<4>& output_dims) { + tflite::FakeQuantParams op_params; + op_params.num_bits = num_bits; + op_params.minmax.min = rmin; + op_params.minmax.max = rmax; + + FakeQuant(op_params, DimsToShape(input_dims), input_data, + DimsToShape(output_dims), output_data); +} + template <typename SrcT, typename DstT> inline void Cast(const RuntimeShape& input_shape, const SrcT* input_data, const RuntimeShape& output_shape, DstT* output_data) { @@ -4050,22 +4082,32 @@ inline bool Mean(const T* input_data, const int* input_dims, } template <typename T> -inline void Mean(const T* input_data, const Dims<4>& input_dims, - const std::vector<int>& reduction_indices, T* output_data, - const Dims<4>& output_dims) { - const int output_batch = ArraySize(output_dims, 3); - const int output_height = ArraySize(output_dims, 2); - const int output_width = ArraySize(output_dims, 1); - const int output_depth = ArraySize(output_dims, 0); +inline void Mean(const tflite::MeanParams& op_params, + const RuntimeShape& unextended_input_shape, + const T* input_data, + const RuntimeShape& unextended_output_shape, T* output_data) { + gemmlowp::ScopedProfilingLabel label("Mean"); - const int input_height = ArraySize(input_dims, 2); - const int input_width = ArraySize(input_dims, 1); + TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4); + RuntimeShape input_shape = + RuntimeShape::ExtendedShape(4, unextended_input_shape); + RuntimeShape output_shape = + RuntimeShape::ExtendedShape(4, unextended_output_shape); + + const int output_batch = output_shape.Dims(0); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + const int output_depth = output_shape.Dims(3); + + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); // The current implementation only supports simultaneous reduction over // width and height. - TFLITE_DCHECK_EQ(reduction_indices.size(), 2); - TFLITE_DCHECK((reduction_indices[0] == 1 && reduction_indices[1] == 2) || - (reduction_indices[0] == 2 && reduction_indices[1] == 1)); + TFLITE_DCHECK_EQ(op_params.axis_count, 2); + TFLITE_DCHECK((op_params.axis[0] == 1 && op_params.axis[1] == 2) || + (op_params.axis[0] == 2 && op_params.axis[1] == 1)); TFLITE_DCHECK_EQ(output_height, 1); TFLITE_DCHECK_EQ(output_width, 1); @@ -4074,15 +4116,31 @@ inline void Mean(const T* input_data, const Dims<4>& input_dims, float value = 0; for (int in_h = 0; in_h < input_height; ++in_h) { for (int in_w = 0; in_w < input_width; ++in_w) { - value += input_data[Offset(input_dims, out_d, in_w, in_h, out_b)]; + value += input_data[Offset(input_shape, out_b, in_h, in_w, out_d)]; } } - output_data[Offset(output_dims, out_d, 0, 0, out_b)] = + output_data[Offset(output_shape, out_b, 0, 0, out_d)] = value / (input_width * input_height); } } } +// TODO(b/80418076): Move to legacy ops file, update invocations. +// Legacy Dims<4>. +template <typename T> +inline void Mean(const T* input_data, const Dims<4>& input_dims, + const std::vector<int>& reduction_indices, T* output_data, + const Dims<4>& output_dims) { + tflite::MeanParams op_params; + op_params.axis_count = reduction_indices.size(); + for (int i = 0; i < op_params.axis_count; ++i) { + op_params.axis[i] = reduction_indices[op_params.axis_count - 1 - i]; + } + + Mean(op_params, DimsToShape(input_dims), input_data, DimsToShape(output_dims), + output_data); +} + // Computes the mean of elements across dimensions given in axis. // It does so in two stages, first calculates the sum of elements along the axis // then divides it by the number of element in axis for quantized values. diff --git a/tensorflow/contrib/lite/kernels/internal/types.h b/tensorflow/contrib/lite/kernels/internal/types.h index 9f6e74a267..c4c7cf3842 100644 --- a/tensorflow/contrib/lite/kernels/internal/types.h +++ b/tensorflow/contrib/lite/kernels/internal/types.h @@ -769,6 +769,11 @@ struct DepthwiseParams { int32 output_activation_max; }; +struct DequantizationParams { + double scale; + int32 zero_point; +}; + struct FakeQuantParams { MinMax minmax; int32 num_bits; diff --git a/tensorflow/contrib/lite/nnapi_delegate.cc b/tensorflow/contrib/lite/nnapi_delegate.cc index 602f3ee5d2..484842713d 100644 --- a/tensorflow/contrib/lite/nnapi_delegate.cc +++ b/tensorflow/contrib/lite/nnapi_delegate.cc @@ -64,6 +64,14 @@ void logError(const char* format, ...) { __LINE__); \ } +#define RETURN_ERROR_IF_TFLITE_FAILED(x) \ + if (x != kTfLiteOk) { \ + logError( \ + "Returning error since TFLite returned failure nnapi_delegate.cc:%d.", \ + __LINE__); \ + return kTfLiteError; \ + } + #define RETURN_ERROR_IF_NN_FAILED(x) \ if (x != ANEURALNETWORKS_NO_ERROR) { \ logError( \ @@ -299,17 +307,21 @@ TfLiteStatus AddOpsAndParams( }; auto check_and_add_activation = [&add_scalar_int32](int activation) { if (activation > kTfLiteActRelu6) { - FATAL("NNAPI only supports RELU, RELU1 and RELU6 activations"); + logError("NNAPI only supports RELU, RELU1 and RELU6 activations"); + return kTfLiteError; } add_scalar_int32(activation); + return kTfLiteOk; }; auto add_add_params = [&add_scalar_int32](void* data) { auto* builtin = reinterpret_cast<TfLiteAddParams*>(data); if (builtin->activation > kTfLiteActRelu6) { - FATAL("NNAPI only supports RELU, RELU1 and RELU6 activations"); + logError("NNAPI only supports RELU, RELU1 and RELU6 activations"); + return kTfLiteError; } add_scalar_int32(builtin->activation); + return kTfLiteOk; }; auto add_pooling_params = [&add_scalar_int32, @@ -320,7 +332,7 @@ TfLiteStatus AddOpsAndParams( add_scalar_int32(builtin->stride_height); add_scalar_int32(builtin->filter_width); add_scalar_int32(builtin->filter_height); - check_and_add_activation(builtin->activation); + return check_and_add_activation(builtin->activation); }; auto add_convolution_params = [&add_scalar_int32, @@ -329,7 +341,7 @@ TfLiteStatus AddOpsAndParams( add_scalar_int32(builtin->padding); add_scalar_int32(builtin->stride_width); add_scalar_int32(builtin->stride_height); - check_and_add_activation(builtin->activation); + return check_and_add_activation(builtin->activation); }; auto add_depthwise_conv_params = [&add_scalar_int32, @@ -339,20 +351,22 @@ TfLiteStatus AddOpsAndParams( add_scalar_int32(builtin->stride_width); add_scalar_int32(builtin->stride_height); add_scalar_int32(builtin->depth_multiplier); - check_and_add_activation(builtin->activation); + return check_and_add_activation(builtin->activation); }; auto add_fully_connected_params = [&check_and_add_activation](void* data) { auto builtin = reinterpret_cast<TfLiteFullyConnectedParams*>(data); - check_and_add_activation(builtin->activation); + return check_and_add_activation(builtin->activation); }; auto add_concatenation_params = [&add_scalar_int32](void* data) { auto builtin = reinterpret_cast<TfLiteConcatenationParams*>(data); add_scalar_int32(builtin->axis); if (builtin->activation != kTfLiteActNone) { - FATAL("Concatenation does not support fused activation in NNAPI"); + logError("Concatenation does not support fused activation in NNAPI"); + return kTfLiteError; } + return kTfLiteOk; }; auto add_softmax_params = [&add_scalar_float32](void* data) { @@ -433,22 +447,22 @@ TfLiteStatus AddOpsAndParams( switch (builtin) { case tflite::BuiltinOperator_ADD: nn_op_type = ANEURALNETWORKS_ADD; - add_add_params(node.builtin_data); + RETURN_ERROR_IF_TFLITE_FAILED(add_add_params(node.builtin_data)); break; case tflite::BuiltinOperator_MUL: nn_op_type = ANEURALNETWORKS_MUL; - add_add_params(node.builtin_data); + RETURN_ERROR_IF_TFLITE_FAILED(add_add_params(node.builtin_data)); break; case tflite::BuiltinOperator_AVERAGE_POOL_2D: - add_pooling_params(node.builtin_data); + RETURN_ERROR_IF_TFLITE_FAILED(add_pooling_params(node.builtin_data)); nn_op_type = ANEURALNETWORKS_AVERAGE_POOL_2D; break; case tflite::BuiltinOperator_MAX_POOL_2D: - add_pooling_params(node.builtin_data); + RETURN_ERROR_IF_TFLITE_FAILED(add_pooling_params(node.builtin_data)); nn_op_type = ANEURALNETWORKS_MAX_POOL_2D; break; case tflite::BuiltinOperator_L2_POOL_2D: - add_pooling_params(node.builtin_data); + RETURN_ERROR_IF_TFLITE_FAILED(add_pooling_params(node.builtin_data)); nn_op_type = ANEURALNETWORKS_L2_POOL_2D; break; case tflite::BuiltinOperator_CONV_2D: { @@ -459,7 +473,8 @@ TfLiteStatus AddOpsAndParams( return kTfLiteError; } } - add_convolution_params(node.builtin_data); + RETURN_ERROR_IF_TFLITE_FAILED( + add_convolution_params(node.builtin_data)); nn_op_type = ANEURALNETWORKS_CONV_2D; break; case tflite::BuiltinOperator_RELU: @@ -478,11 +493,13 @@ TfLiteStatus AddOpsAndParams( nn_op_type = ANEURALNETWORKS_LOGISTIC; break; case tflite::BuiltinOperator_DEPTHWISE_CONV_2D: - add_depthwise_conv_params(node.builtin_data); + RETURN_ERROR_IF_TFLITE_FAILED( + add_depthwise_conv_params(node.builtin_data)); nn_op_type = ANEURALNETWORKS_DEPTHWISE_CONV_2D; break; case tflite::BuiltinOperator_CONCATENATION: - add_concatenation_params(node.builtin_data); + RETURN_ERROR_IF_TFLITE_FAILED( + add_concatenation_params(node.builtin_data)); nn_op_type = ANEURALNETWORKS_CONCATENATION; break; case tflite::BuiltinOperator_SOFTMAX: @@ -490,7 +507,8 @@ TfLiteStatus AddOpsAndParams( nn_op_type = ANEURALNETWORKS_SOFTMAX; break; case tflite::BuiltinOperator_FULLY_CONNECTED: - add_fully_connected_params(node.builtin_data); + RETURN_ERROR_IF_TFLITE_FAILED( + add_fully_connected_params(node.builtin_data)); nn_op_type = ANEURALNETWORKS_FULLY_CONNECTED; break; case tflite::BuiltinOperator_RESHAPE: @@ -544,14 +562,14 @@ TfLiteStatus AddOpsAndParams( case tflite::BuiltinOperator_DIV: nnapi_version = 11; // require NNAPI 1.1 nn_op_type = ANEURALNETWORKS_DIV; - check_and_add_activation( - reinterpret_cast<TfLiteDivParams*>(node.builtin_data)->activation); + RETURN_ERROR_IF_TFLITE_FAILED(check_and_add_activation( + reinterpret_cast<TfLiteDivParams*>(node.builtin_data)->activation)); break; case tflite::BuiltinOperator_SUB: nnapi_version = 11; // require NNAPI 1.1 nn_op_type = ANEURALNETWORKS_SUB; - check_and_add_activation( - reinterpret_cast<TfLiteSubParams*>(node.builtin_data)->activation); + RETURN_ERROR_IF_TFLITE_FAILED(check_and_add_activation( + reinterpret_cast<TfLiteSubParams*>(node.builtin_data)->activation)); break; case tflite::BuiltinOperator_SQUEEZE: nnapi_version = 11; // requires NNAPI 1.1 @@ -664,7 +682,8 @@ TfLiteStatus AddOpsAndParams( } if (nnapi_version == 11 && GetAndroidSdkVersionCached() < 28) { - FATAL("Op %d needs NNAPI1.1", builtin); + logError("Op %d needs NNAPI1.1", builtin); + return kTfLiteError; } // Add the operation. @@ -712,9 +731,9 @@ TfLiteStatus NNAPIDelegate::BuildGraph(Interpreter* interpreter) { interpreter->outputs().size()); uint32_t next_id = 0; - RETURN_ERROR_IF_NN_FAILED(addTensorOperands( + RETURN_ERROR_IF_TFLITE_FAILED(addTensorOperands( interpreter, nn_model_, &next_id, &tensor_id_to_nnapi_id)); - RETURN_ERROR_IF_NN_FAILED( + RETURN_ERROR_IF_TFLITE_FAILED( AddOpsAndParams(interpreter, nn_model_, next_id, &model_states_inputs_, &model_states_outputs_, tensor_id_to_nnapi_id)); diff --git a/tensorflow/contrib/lite/nnapi_delegate_disabled.cc b/tensorflow/contrib/lite/nnapi_delegate_disabled.cc index efde72b1a7..e3536d3db6 100644 --- a/tensorflow/contrib/lite/nnapi_delegate_disabled.cc +++ b/tensorflow/contrib/lite/nnapi_delegate_disabled.cc @@ -27,7 +27,13 @@ NNAPIAllocation::NNAPIAllocation(const char* filename, NNAPIAllocation::~NNAPIAllocation() {} -NNAPIDelegate::~NNAPIDelegate() {} +NNAPIDelegate::~NNAPIDelegate() { +#define UNUSED_MEMBER(x) (void)(x) + UNUSED_MEMBER(nn_model_); + UNUSED_MEMBER(nn_compiled_model_); + UNUSED_MEMBER(model_status_); +#undef UNUSED_MEMBER +} TfLiteStatus NNAPIDelegate::BuildGraph(Interpreter* interpreter) { return kTfLiteError; diff --git a/tensorflow/contrib/lite/python/BUILD b/tensorflow/contrib/lite/python/BUILD index 6e30251eff..57e1290e07 100644 --- a/tensorflow/contrib/lite/python/BUILD +++ b/tensorflow/contrib/lite/python/BUILD @@ -70,7 +70,7 @@ py_library( py_test( name = "lite_test", srcs = ["lite_test.py"], - data = ["@tflite_mobilenet_ssd_quant_protobuf//:tflite_graph.pbtxt"], + data = ["@tflite_mobilenet_ssd_quant_protobuf//:tflite_graph.pb"], srcs_version = "PY2AND3", tags = [ "no_oss", diff --git a/tensorflow/contrib/lite/python/lite.py b/tensorflow/contrib/lite/python/lite.py index 2de97fec86..44dfb97b84 100644 --- a/tensorflow/contrib/lite/python/lite.py +++ b/tensorflow/contrib/lite/python/lite.py @@ -58,6 +58,7 @@ from tensorflow.python.framework import graph_util as _tf_graph_util from tensorflow.python.framework import ops as _ops from tensorflow.python.framework.errors_impl import NotFoundError as _NotFoundError from tensorflow.python.framework.importer import import_graph_def as _import_graph_def +from tensorflow.python.lib.io import file_io as _file_io from tensorflow.python.saved_model import signature_constants as _signature_constants from tensorflow.python.saved_model import tag_constants as _tag_constants @@ -225,8 +226,10 @@ class TocoConverter(object): TocoConverter class. Raises: - ValueError: + IOError: + File not found. Unable to parse input file. + ValueError: The graph is not frozen. input_arrays or output_arrays contains an invalid tensor name. input_shapes is not correctly defined when required @@ -234,10 +237,13 @@ class TocoConverter(object): with _ops.Graph().as_default(): with _session.Session() as sess: # Read GraphDef from file. - graph_def = _graph_pb2.GraphDef() - with open(graph_def_file, "rb") as f: + if not _file_io.file_exists(graph_def_file): + raise IOError("File '{0}' does not exist.".format(graph_def_file)) + with _file_io.FileIO(graph_def_file, "rb") as f: file_content = f.read() + try: + graph_def = _graph_pb2.GraphDef() graph_def.ParseFromString(file_content) except (_text_format.ParseError, DecodeError): try: @@ -248,9 +254,10 @@ class TocoConverter(object): file_content = file_content.decode("utf-8") else: file_content = file_content.encode("utf-8") + graph_def = _graph_pb2.GraphDef() _text_format.Merge(file_content, graph_def) except (_text_format.ParseError, DecodeError): - raise ValueError( + raise IOError( "Unable to parse input file '{}'.".format(graph_def_file)) # Handles models with custom TFLite ops that cannot be resolved in diff --git a/tensorflow/contrib/lite/python/lite_test.py b/tensorflow/contrib/lite/python/lite_test.py index 1c94ba605a..3f8ea433ff 100644 --- a/tensorflow/contrib/lite/python/lite_test.py +++ b/tensorflow/contrib/lite/python/lite_test.py @@ -521,14 +521,21 @@ class FromFrozenGraphFile(test_util.TensorFlowTestCase): self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all()) self.assertEqual((0., 0.), output_details[0]['quantization']) - def testInvalidFile(self): + def testInvalidFileNotFound(self): + with self.assertRaises(IOError) as error: + lite.TocoConverter.from_frozen_graph('invalid_file', ['Placeholder'], + ['add']) + self.assertEqual('File \'invalid_file\' does not exist.', + str(error.exception)) + + def testInvalidFileBadData(self): graph_def_file = os.path.join(self.get_temp_dir(), 'invalid_file') with gfile.Open(graph_def_file, 'wb') as temp_file: temp_file.write('bad data') temp_file.flush() # Attempts to convert the invalid model. - with self.assertRaises(ValueError) as error: + with self.assertRaises(IOError) as error: lite.TocoConverter.from_frozen_graph(graph_def_file, ['Placeholder'], ['add']) self.assertEqual( @@ -539,7 +546,7 @@ class FromFrozenGraphFile(test_util.TensorFlowTestCase): def _initObjectDetectionArgs(self): # Initializes the arguments required for the object detection model. self._graph_def_file = resource_loader.get_path_to_datafile( - 'testdata/tflite_graph.pbtxt') + 'testdata/tflite_graph.pb') self._input_arrays = ['normalized_input_image_tensor'] self._output_arrays = [ 'TFLite_Detection_PostProcess', 'TFLite_Detection_PostProcess:1', @@ -586,7 +593,7 @@ class FromFrozenGraphFile(test_util.TensorFlowTestCase): output_details[3]['name']) self.assertTrue(([1] == output_details[3]['shape']).all()) - def testTFLiteGraphDefInvalid(self): + def testTFLiteGraphDefMissingShape(self): # Tests invalid cases for the model that cannot be loaded in TensorFlow. self._initObjectDetectionArgs() @@ -597,6 +604,10 @@ class FromFrozenGraphFile(test_util.TensorFlowTestCase): self.assertEqual('input_shapes must be defined for this model.', str(error.exception)) + def testTFLiteGraphDefInvalidShape(self): + # Tests invalid cases for the model that cannot be loaded in TensorFlow. + self._initObjectDetectionArgs() + # `input_shapes` does not contain the names in `input_arrays`. with self.assertRaises(ValueError) as error: lite.TocoConverter.from_frozen_graph( diff --git a/tensorflow/contrib/lite/testing/BUILD b/tensorflow/contrib/lite/testing/BUILD index 89912fd116..0b3a97d4f5 100644 --- a/tensorflow/contrib/lite/testing/BUILD +++ b/tensorflow/contrib/lite/testing/BUILD @@ -173,6 +173,7 @@ tf_cc_test( srcs = ["tflite_driver_test.cc"], data = ["//tensorflow/contrib/lite:testdata/multi_add.bin"], tags = [ + "no_oss", # b/112769036 "tflite_not_portable_android", "tflite_not_portable_ios", ], diff --git a/tensorflow/contrib/lite/toco/g3doc/toco_landscape.svg b/tensorflow/contrib/lite/toco/g3doc/toco_landscape.svg index 262e13a591..335debde57 100644 --- a/tensorflow/contrib/lite/toco/g3doc/toco_landscape.svg +++ b/tensorflow/contrib/lite/toco/g3doc/toco_landscape.svg @@ -1 +1 @@ -<svg version="1.1" viewBox="0.0 0.0 720.0 540.0" fill="none" stroke="none" stroke-linecap="square" stroke-miterlimit="10" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"><clipPath id="p.0"><path d="m0 0l720.0 0l0 540.0l-720.0 0l0 -540.0z" clip-rule="nonzero"/></clipPath><g clip-path="url(#p.0)"><path fill="#000000" fill-opacity="0.0" d="m0 0l720.0 0l0 540.0l-720.0 0z" fill-rule="evenodd"/><path fill="#f3f3f3" d="m19.375328 28.750656l361.6378 0l0 358.01575l-361.6378 0z" fill-rule="evenodd"/><path stroke="#cccccc" stroke-width="1.0" stroke-linejoin="round" stroke-linecap="butt" d="m19.375328 28.750656l361.6378 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\ No newline at end of file diff --git a/tensorflow/contrib/lite/tools/optimize/quantize_weights.cc b/tensorflow/contrib/lite/tools/optimize/quantize_weights.cc index e5bb3c990a..692efb9029 100644 --- a/tensorflow/contrib/lite/tools/optimize/quantize_weights.cc +++ b/tensorflow/contrib/lite/tools/optimize/quantize_weights.cc @@ -168,11 +168,16 @@ std::vector<TensorInfo> GetQuantizableTensorsFromOperator( bool eval_hybrid = use_hybrid_evaluation && IsHybridEvaluationOp(op, op_code); - bool skipped_tensor = false; std::vector<int32_t> op_input_indices = GetWeightInputIndices(op_code); for (const int32_t op_input_idx : op_input_indices) { int32_t tensor_idx = op->inputs[op_input_idx]; + if (tensor_idx == -1) { + LOG(INFO) << "Skipping optional tensor input " << op_input_idx + << " of operation " << EnumNameBuiltinOperator(op_code); + continue; + } + TensorT* tensor = subgraph->tensors[tensor_idx].get(); // TODO(suharshs): Support shared weights, i.e. If two tensors share the // same weight array, things may break. (i.e. SSD object detection) @@ -180,14 +185,12 @@ std::vector<TensorInfo> GetQuantizableTensorsFromOperator( CountTensorConsumers(model, subgraph, tensor_idx) != 1) { LOG(INFO) << "Skipping quantization of tensor " << tensor->name << " that is shared between multiple multiple operations."; - skipped_tensor = true; continue; } if (tensor->type != TensorType_FLOAT32) { LOG(INFO) << "Skipping quantization of tensor " << tensor->name << " that is not type float."; - skipped_tensor = true; continue; } @@ -196,7 +199,9 @@ std::vector<TensorInfo> GetQuantizableTensorsFromOperator( LOG(INFO) << "Skipping quantization of tensor " << tensor->name << " because it has fewer than " << weights_min_num_elements << " elements (" << num_elements << ")."; - skipped_tensor = true; + // If one of the weights isn't quantized, then we cannot use the hybrid + // kernel for this operation, since it expects everything to be quantized. + eval_hybrid = false; continue; } @@ -209,12 +214,6 @@ std::vector<TensorInfo> GetQuantizableTensorsFromOperator( tensor_infos.push_back(tensor_info); } - // For hybrid operations we either need to quantize all tensors or none. So - // if we skipped any tensors we need to return no quantized tensors. - if (eval_hybrid && skipped_tensor) { - return {}; - } - return tensor_infos; } diff --git a/tensorflow/contrib/tpu/ops/cross_replica_ops.cc b/tensorflow/contrib/tpu/ops/cross_replica_ops.cc index 9ee5ecb123..ea8e0e00ed 100644 --- a/tensorflow/contrib/tpu/ops/cross_replica_ops.cc +++ b/tensorflow/contrib/tpu/ops/cross_replica_ops.cc @@ -18,6 +18,89 @@ limitations under the License. #include "tensorflow/core/framework/shape_inference.h" namespace tensorflow { +using shape_inference::DimensionHandle; +using shape_inference::InferenceContext; +using shape_inference::ShapeHandle; + +REGISTER_OP("AllToAll") + .Input("input: T") + .Input("group_assignment: int32") + .Output("output: T") + .Attr("T: {bfloat16, float}") + .Attr("concat_dimension: int") + .Attr("split_dimension: int") + .Attr("split_count: int") + .SetShapeFn([](InferenceContext* c) { + ShapeHandle input = c->input(0); + int64 rank; + if (c->RankKnown(input)) { + rank = c->Rank(input); + } else { + return errors::InvalidArgument("input's rank is unknown."); + } + int concat_dimension; + int split_dimension; + + TF_RETURN_IF_ERROR(c->GetAttr("concat_dimension", &concat_dimension)); + + if (concat_dimension < 0 || concat_dimension >= rank) { + return errors::InvalidArgument("concat_dimension ", concat_dimension, + " is out of range of input rank ", rank); + } + + TF_RETURN_IF_ERROR(c->GetAttr("split_dimension", &split_dimension)); + if (split_dimension < 0 || split_dimension >= rank) { + return errors::InvalidArgument("split_dimension ", split_dimension, + " is out of range of input rank ", rank); + } + + std::vector<DimensionHandle> dims; + dims.resize(rank); + + for (int32 i = 0; i < rank; ++i) { + int64 in_idx = i; + if (i == concat_dimension) { + in_idx = split_dimension; + } else if (i == split_dimension) { + in_idx = concat_dimension; + } + + dims[i] = c->Dim(input, in_idx); + } + + c->set_output(0, c->MakeShape(dims)); + return Status::OK(); + }) + .Doc(R"doc( +An Op to exchange data across TPU replicas. On each replica, the input is +split into `split_count` blocks along `split_dimension` and send to the other +replicas given group_assignment. After receiving `split_count` - 1 blocks from +other replicas, we concatenate the blocks along `concat_dimension` as the +output. + +For example, suppose there are 2 TPU replicas: +replica 0 receives input: `[[A, B]]` +replica 1 receives input: `[[C, D]]` + +group_assignment=`[[0, 1]]` +concat_dimension=0 +split_dimension=1 +split_count=2 + +replica 0's output: `[[A], [C]]` +replica 1's output: `[[B], [D]]` + +input: The local input to the sum. +group_assignment: An int32 tensor with shape + [num_groups, num_replicas_per_group]. `group_assignment[i]` represents the + replica ids in the ith subgroup. +concat_dimension: The dimension number to concatenate. +split_dimension: The dimension number to split. +split_count: The number of splits, this number must equal to the sub-group + size(group_assignment.get_shape()[1]) +output: The exchanged result. +T: The type of elements to be exchanged. +)doc"); REGISTER_OP("CrossReplicaSum") .Input("input: T") @@ -26,10 +109,8 @@ REGISTER_OP("CrossReplicaSum") .Attr("T: {bfloat16, float}") .SetShapeFn(shape_inference::UnchangedShape) .Doc(R"doc( -An Op to sum inputs across replicated TPU instances. Each -instance supplies its own input. If group_assignment is empty, the output of -each is the sum of all the inputs, otherwise the output of each is the sum of -the inputs belonging to the same group. +An Op to sum inputs across replicated TPU instances. Each instance supplies its +own input. For example, suppose there are 8 TPU instances: `[A, B, C, D, E, F, G, H]`. Passing group_assignment=`[[0,2,4,6],[1,3,5,7]]` sets `A, C, E, G` as group 0, diff --git a/tensorflow/contrib/tpu/proto/optimization_parameters.proto b/tensorflow/contrib/tpu/proto/optimization_parameters.proto index cbf6809257..fc1320501b 100644 --- a/tensorflow/contrib/tpu/proto/optimization_parameters.proto +++ b/tensorflow/contrib/tpu/proto/optimization_parameters.proto @@ -9,8 +9,8 @@ message ClippingLimits { google.protobuf.FloatValue upper = 2; // +inf if not set } -// Get the learning rate from a <yet to be determined> source that can change -// dynamically. +// Get the learning rate from the parameters of the SendTPUEmbeddingGradients +// op. message DynamicLearningRate { } @@ -18,10 +18,8 @@ message DynamicLearningRate { message LearningRate { oneof learning_rate { float constant = 1; - // DynamicLearningRate dynamic = 2; -- disabled while code is being - // rewritten. + DynamicLearningRate dynamic = 2; } - reserved 2; } message AdagradParameters { diff --git a/tensorflow/contrib/tpu/python/ops/tpu_ops.py b/tensorflow/contrib/tpu/python/ops/tpu_ops.py index 3ed571aff9..d92a0652bb 100644 --- a/tensorflow/contrib/tpu/python/ops/tpu_ops.py +++ b/tensorflow/contrib/tpu/python/ops/tpu_ops.py @@ -38,6 +38,62 @@ if platform.system() != "Windows": _tpu_ops = loader.load_op_library( resource_loader.get_path_to_datafile("_tpu_ops.so")) + def _create_default_group_assignment(): + num_shards = tpu_function.get_tpu_context().number_of_shards + if num_shards is None: + logging.warning( + "cross_replica_sum should be used within a tpu_shard_context, but " + "got unset number_of_shards. Assuming 1.") + num_shards = 1 + group_assignment = [list(range(num_shards))] + return group_assignment + + def all_to_all(x, + concat_dimension, + split_dimension, + split_count, + group_assignment=None, + name=None): + """Exchange data across TPU replicas. + + Args: + x: The local tensor. + concat_dimension: The dimension number to concatenate. + split_dimension: The dimension number to split. + split_count: The number of splits, this number must equal to the sub-group + size(group_assignment.get_shape()[1]) + group_assignment: Optional 2d int32 lists with shape [num_groups, + num_replicas_per_group]. `group_assignment[i]` represents the replica + ids in the ith subgroup. + name: Optional op name. + + Returns: + A `Tensor` which is concatenated by data from different replicas. + """ + if group_assignment is None: + group_assignment = _create_default_group_assignment() + return gen_tpu_ops.all_to_all( + x, + group_assignment, + concat_dimension=concat_dimension, + split_dimension=split_dimension, + split_count=split_count, + name=name) + + @ops.RegisterGradient("AllToAll") + def _all_to_all_grad(op, grad): + # The gradient of a all-to-all is also a all-to-all but the + # split_dimension and concat_dimension is swapped. + # The graident with respect to group_assignment is None. + return [ + gen_tpu_ops.all_to_all( + grad, + op.inputs[1], + concat_dimension=op.get_attr("split_dimension"), + split_dimension=op.get_attr("concat_dimension"), + split_count=op.get_attr("split_count")), None + ] + def cross_replica_sum(x, group_assignment=None, name=None): """Sum the input tensor accorss replicas according to group_assignment. @@ -52,13 +108,7 @@ if platform.system() != "Windows": A `Tensor` which is summed across replicas. """ if group_assignment is None: - num_shards = tpu_function.get_tpu_context().number_of_shards - if num_shards is None: - logging.warning( - "cross_replica_sum should be used within a tpu_shard_context, but " - "got unset number_of_shards. Assuming 1.") - num_shards = 1 - group_assignment = [list(range(num_shards))] + group_assignment = _create_default_group_assignment() return gen_tpu_ops.cross_replica_sum(x, group_assignment, name=name) diff --git a/tensorflow/contrib/tpu/python/tpu/keras_support.py b/tensorflow/contrib/tpu/python/tpu/keras_support.py index dd7f8b678f..08e0465b71 100644 --- a/tensorflow/contrib/tpu/python/tpu/keras_support.py +++ b/tensorflow/contrib/tpu/python/tpu/keras_support.py @@ -1657,7 +1657,7 @@ class KerasTPUModel(models.Model): 'make sure your paths are correct and you have ' 'permissions to read the files. Skipping validation') - for step_index in range(steps_per_epoch - 1): + for step_index in range(steps_per_epoch): batch_logs = {'batch': step_index, 'size': 1} callbacks.on_batch_begin(step_index, batch_logs) try: diff --git a/tensorflow/contrib/tpu/python/tpu/keras_tpu_variables.py b/tensorflow/contrib/tpu/python/tpu/keras_tpu_variables.py index a423aeace7..170977d8ab 100644 --- a/tensorflow/contrib/tpu/python/tpu/keras_tpu_variables.py +++ b/tensorflow/contrib/tpu/python/tpu/keras_tpu_variables.py @@ -30,7 +30,6 @@ from tensorflow.python.framework import ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import gen_resource_variable_ops from tensorflow.python.ops import variable_scope -from tensorflow.python.platform import tf_logging as logging @contextlib.contextmanager @@ -258,7 +257,6 @@ def replicated_scope(num_replicas): collections = [ops.GraphKeys.GLOBAL_VARIABLES] kwargs["collections"] = [] - logging.info("Constructing replicated variable %s", name) variables = [] index = {} for i in range(num_replicas): diff --git a/tensorflow/core/framework/dataset.cc b/tensorflow/core/framework/dataset.cc index 9ffd8e1ee0..5281c56f04 100644 --- a/tensorflow/core/framework/dataset.cc +++ b/tensorflow/core/framework/dataset.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/core/graph/node_builder.h" namespace tensorflow { +namespace data { namespace { @@ -329,4 +330,5 @@ void BackgroundWorker::WorkerLoop() { } } +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/framework/dataset.h b/tensorflow/core/framework/dataset.h index 04865a1d4f..4e51fba048 100644 --- a/tensorflow/core/framework/dataset.h +++ b/tensorflow/core/framework/dataset.h @@ -40,6 +40,13 @@ limitations under the License. namespace tensorflow { +// Forward declarations to avoid introducing a dependency on headers in +// "tensorflow/core/graph/...". +class GraphDefBuilder; +class Node; + +namespace data { + class DatasetBase; class SerializationContext; @@ -66,11 +73,6 @@ class IteratorStateWriter { virtual ~IteratorStateWriter() {} }; -// Forward declarations to avoid introducing a dependency on headers in -// "tensorflow/core/graph/...". -class GraphDefBuilder; -class Node; - // Wrapper around GraphDefBuilder. Used to serialize Dataset graph. class GraphDefBuilderWrapper { public: @@ -222,8 +224,7 @@ class GraphDefBuilderWrapper { return (str_util::EndsWith(op_def->name(), "Dataset") && op_def->output_arg_size() == 1 && op_def->output_arg(0).type() == DT_VARIANT) || - dataset::WhitelistedStatefulOpRegistry::Global()->Contains( - op_def->name()); + WhitelistedStatefulOpRegistry::Global()->Contains(op_def->name()); } bool HasAttr(const string& op_type_name, const string& attr_name) const; @@ -751,6 +752,21 @@ class BackgroundWorker { std::deque<std::function<void()>> work_queue_ GUARDED_BY(mu_); }; +} // namespace data + +// TODO(b/114112161): Remove these aliases when all users have moved over to the +// `tensorflow::data` namespace. +using data::DatasetBase; +using data::DatasetContext; +using data::DatasetIterator; +using data::DatasetOpKernel; +using data::IteratorBase; +using data::IteratorContext; +using data::IteratorStateReader; +using data::IteratorStateWriter; +using data::SerializationContext; +using data::UnaryDatasetOpKernel; + } // namespace tensorflow #endif // TENSORFLOW_CORE_FRAMEWORK_DATASET_H_ diff --git a/tensorflow/core/framework/dataset_stateful_op_whitelist.h b/tensorflow/core/framework/dataset_stateful_op_whitelist.h index 3b48999edb..21c21723d0 100644 --- a/tensorflow/core/framework/dataset_stateful_op_whitelist.h +++ b/tensorflow/core/framework/dataset_stateful_op_whitelist.h @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/core/lib/core/status.h" namespace tensorflow { -namespace dataset { +namespace data { // Registry for stateful ops that need to be used in dataset functions. // See below macro for usage details. class WhitelistedStatefulOpRegistry { @@ -47,7 +47,7 @@ class WhitelistedStatefulOpRegistry { std::set<StringPiece> op_names_; }; -} // namespace dataset +} // namespace data // Use this macro to whitelist an op that is marked stateful but needs to be // used inside a map_fn in an input pipeline. This is only needed if you wish @@ -67,10 +67,9 @@ class WhitelistedStatefulOpRegistry { WHITELIST_STATEFUL_OP_FOR_DATASET_FUNCTIONS_UNIQ_HELPER(__COUNTER__, name) #define WHITELIST_STATEFUL_OP_FOR_DATASET_FUNCTIONS_UNIQ_HELPER(ctr, name) \ WHITELIST_STATEFUL_OP_FOR_DATASET_FUNCTIONS_UNIQ(ctr, name) -#define WHITELIST_STATEFUL_OP_FOR_DATASET_FUNCTIONS_UNIQ(ctr, name) \ - static ::tensorflow::Status whitelist_op##ctr TF_ATTRIBUTE_UNUSED = \ - ::tensorflow::dataset::WhitelistedStatefulOpRegistry::Global()->Add( \ - name) +#define WHITELIST_STATEFUL_OP_FOR_DATASET_FUNCTIONS_UNIQ(ctr, name) \ + static ::tensorflow::Status whitelist_op##ctr TF_ATTRIBUTE_UNUSED = \ + ::tensorflow::data::WhitelistedStatefulOpRegistry::Global()->Add(name) } // namespace tensorflow diff --git a/tensorflow/core/framework/stats_aggregator.h b/tensorflow/core/framework/stats_aggregator.h index 4a18efc940..af53ed0a3c 100644 --- a/tensorflow/core/framework/stats_aggregator.h +++ b/tensorflow/core/framework/stats_aggregator.h @@ -25,6 +25,8 @@ namespace tensorflow { class Summary; +namespace data { + // A `StatsAggregator` accumulates statistics incrementally. A // `StatsAggregator` can accumulate multiple different statistics, distinguished // by a string name. @@ -87,6 +89,7 @@ class StatsAggregatorResource : public ResourceBase { const std::shared_ptr<StatsAggregator> stats_aggregator_; }; +} // namespace data } // namespace tensorflow #endif // TENSORFLOW_CORE_FRAMEWORK_STATS_AGGREGATOR_H_ diff --git a/tensorflow/core/grappler/graph_analyzer/graph_analyzer.h b/tensorflow/core/grappler/graph_analyzer/graph_analyzer.h index 26d38a4931..97626346c7 100644 --- a/tensorflow/core/grappler/graph_analyzer/graph_analyzer.h +++ b/tensorflow/core/grappler/graph_analyzer/graph_analyzer.h @@ -138,7 +138,7 @@ class GraphAnalyzer { // The entries are owned by collation_map_, so must be removed from // ordered_collation_ before removing them from collation_map_. struct ReverseLessByCount { - bool operator()(CollationEntry* left, CollationEntry* right) { + bool operator()(CollationEntry* left, CollationEntry* right) const { return left->count > right->count; // Reverse order. } }; diff --git a/tensorflow/core/kernels/data/batch_dataset_op.cc b/tensorflow/core/kernels/data/batch_dataset_op.cc index f9b5353724..a25f78c6f1 100644 --- a/tensorflow/core/kernels/data/batch_dataset_op.cc +++ b/tensorflow/core/kernels/data/batch_dataset_op.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/core/util/batch_util.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -241,5 +241,5 @@ REGISTER_KERNEL_BUILDER(Name("BatchDatasetV2").Device(DEVICE_CPU), BatchDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/cache_dataset_ops.cc b/tensorflow/core/kernels/data/cache_dataset_ops.cc index 6ca0bcd37d..221b5ad835 100644 --- a/tensorflow/core/kernels/data/cache_dataset_ops.cc +++ b/tensorflow/core/kernels/data/cache_dataset_ops.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/core/util/tensor_bundle/tensor_bundle.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level description of @@ -891,5 +891,5 @@ REGISTER_KERNEL_BUILDER(Name("CacheDataset").Device(DEVICE_CPU), CacheDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/captured_function.cc b/tensorflow/core/kernels/data/captured_function.cc index 186740c2ac..ad2365b25b 100644 --- a/tensorflow/core/kernels/data/captured_function.cc +++ b/tensorflow/core/kernels/data/captured_function.cc @@ -23,6 +23,7 @@ limitations under the License. #include "tensorflow/core/platform/notification.h" namespace tensorflow { +namespace data { /* static */ Status CapturedFunction::Create( @@ -418,4 +419,5 @@ CapturedFunction::CapturedFunction(const NameAttrList& func, captured_inputs_(std::move(captured_inputs)), use_inter_op_parallelism_(use_inter_op_parallelism) {} +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/captured_function.h b/tensorflow/core/kernels/data/captured_function.h index ae6bdfc2a0..e44bc78b1c 100644 --- a/tensorflow/core/kernels/data/captured_function.h +++ b/tensorflow/core/kernels/data/captured_function.h @@ -32,6 +32,8 @@ class Device; class OpKernelContext; class ResourceMgr; +namespace data { + // A `CapturedFunction` encapsulates a TensorFlow function and all of // the runtime support required to execute it. // @@ -50,8 +52,8 @@ class CapturedFunction { // Creates a new instance from a list of named attributes and captured inputs. // - // If `low_latency_hint` is true, the runtime may use an executor that is - // optimized for small functions. + // If `use_inter_op_parallelism` is false, the runtime may use an executor + // that is optimized for small functions. static Status Create(const NameAttrList& func, std::vector<Tensor> captured_inputs, bool use_inter_op_parallelism, @@ -141,6 +143,12 @@ class CapturedFunction { TF_DISALLOW_COPY_AND_ASSIGN(CapturedFunction); }; +} // namespace data + +// TODO(b/114112161): Remove these aliases when all users have moved over to the +// `tensorflow::data` namespace. +using data::CapturedFunction; + } // namespace tensorflow #endif // TENSORFLOW_CORE_KERNELS_DATA_CAPTURED_FUNCTION_H_ diff --git a/tensorflow/core/kernels/data/concatenate_dataset_op.cc b/tensorflow/core/kernels/data/concatenate_dataset_op.cc index c361a9adcb..a04f150e71 100644 --- a/tensorflow/core/kernels/data/concatenate_dataset_op.cc +++ b/tensorflow/core/kernels/data/concatenate_dataset_op.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/core/kernels/data/dataset.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -195,5 +195,5 @@ REGISTER_KERNEL_BUILDER(Name("ConcatenateDataset").Device(DEVICE_CPU), ConcatenateDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/dataset_ops.cc b/tensorflow/core/kernels/data/dataset_ops.cc index c71d027f23..bd1ccd5b5d 100644 --- a/tensorflow/core/kernels/data/dataset_ops.cc +++ b/tensorflow/core/kernels/data/dataset_ops.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/core/kernels/data/dataset.h" namespace tensorflow { +namespace data { // See documentation in ../ops/dataset_ops.cc for a high-level // description of the following op. @@ -48,4 +49,5 @@ class DatasetToGraphOp : public OpKernel { REGISTER_KERNEL_BUILDER(Name("DatasetToGraph").Device(DEVICE_CPU), DatasetToGraphOp); +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/dataset_utils.cc b/tensorflow/core/kernels/data/dataset_utils.cc index d85ef1cbab..e7ac368ae3 100644 --- a/tensorflow/core/kernels/data/dataset_utils.cc +++ b/tensorflow/core/kernels/data/dataset_utils.cc @@ -17,8 +17,7 @@ limitations under the License. #include "tensorflow/core/common_runtime/device.h" namespace tensorflow { - -namespace dataset { +namespace data { Status MakeIteratorFromInputElement( IteratorContext* ctx, const std::vector<Tensor>& input_element, @@ -45,6 +44,5 @@ Status MakeIteratorFromInputElement( ctx, strings::StrCat(prefix, "[", thread_index, "]"), out_iterator); } -} // namespace dataset - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/dataset_utils.h b/tensorflow/core/kernels/data/dataset_utils.h index 6c4191c2be..234856ea39 100644 --- a/tensorflow/core/kernels/data/dataset_utils.h +++ b/tensorflow/core/kernels/data/dataset_utils.h @@ -20,16 +20,14 @@ limitations under the License. #include "tensorflow/core/kernels/data/dataset.h" namespace tensorflow { - -namespace dataset { +namespace data { Status MakeIteratorFromInputElement( IteratorContext* ctx, const std::vector<Tensor>& input_element, int64 thread_index, CapturedFunction* captured_func, StringPiece prefix, std::unique_ptr<IteratorBase>* out_iterator); -} // namespace dataset - +} // namespace data } // namespace tensorflow #endif // TENSORFLOW_CORE_KERNELS_DATA_DATASET_UTILS_H_ diff --git a/tensorflow/core/kernels/data/dense_to_sparse_batch_dataset_op.cc b/tensorflow/core/kernels/data/dense_to_sparse_batch_dataset_op.cc index 9770bc025d..237511a07d 100644 --- a/tensorflow/core/kernels/data/dense_to_sparse_batch_dataset_op.cc +++ b/tensorflow/core/kernels/data/dense_to_sparse_batch_dataset_op.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/core/kernels/data/dataset.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -301,5 +301,5 @@ REGISTER_KERNEL_BUILDER(Name("DenseToSparseBatchDataset").Device(DEVICE_CPU), DenseToSparseBatchDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/filter_by_component_dataset_op.cc b/tensorflow/core/kernels/data/filter_by_component_dataset_op.cc index ce577397c5..a7e3a56727 100644 --- a/tensorflow/core/kernels/data/filter_by_component_dataset_op.cc +++ b/tensorflow/core/kernels/data/filter_by_component_dataset_op.cc @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/core/lib/random/random.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -166,5 +166,5 @@ REGISTER_KERNEL_BUILDER(Name("FilterByLastComponentDataset").Device(DEVICE_CPU), FilterByLastComponentDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/filter_dataset_op.cc b/tensorflow/core/kernels/data/filter_dataset_op.cc index bbce001eaf..bf0aecaf3c 100644 --- a/tensorflow/core/kernels/data/filter_dataset_op.cc +++ b/tensorflow/core/kernels/data/filter_dataset_op.cc @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/core/lib/random/random.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -280,5 +280,5 @@ REGISTER_KERNEL_BUILDER(Name("FilterDataset").Device(DEVICE_CPU), FilterDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/flat_map_dataset_op.cc b/tensorflow/core/kernels/data/flat_map_dataset_op.cc index b1eb2fd849..e3c45ef86c 100644 --- a/tensorflow/core/kernels/data/flat_map_dataset_op.cc +++ b/tensorflow/core/kernels/data/flat_map_dataset_op.cc @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/core/lib/random/random.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -245,7 +245,7 @@ class FlatMapDatasetOp : public UnaryDatasetOpKernel { private: Status BuildCurrentElementIteratorLocked(IteratorContext* ctx) EXCLUSIVE_LOCKS_REQUIRED(mu_) { - return dataset::MakeIteratorFromInputElement( + return MakeIteratorFromInputElement( ctx, captured_func_inputs_, element_index_++, dataset()->captured_func_.get(), prefix(), ¤t_element_iterator_); @@ -285,5 +285,5 @@ REGISTER_KERNEL_BUILDER(Name("FlatMapDataset").Device(DEVICE_CPU), FlatMapDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/generator_dataset_op.cc b/tensorflow/core/kernels/data/generator_dataset_op.cc index ccee690d7e..ac5cc1b2c1 100644 --- a/tensorflow/core/kernels/data/generator_dataset_op.cc +++ b/tensorflow/core/kernels/data/generator_dataset_op.cc @@ -23,6 +23,7 @@ limitations under the License. #include "tensorflow/core/lib/random/random.h" namespace tensorflow { +namespace data { // See documentation in ../ops/dataset_ops.cc for a high-level // description of the following op. @@ -188,10 +189,13 @@ void GeneratorDatasetOp::MakeDataset(OpKernelContext* ctx, std::move(finalize_func), output_types_, output_shapes_); } +namespace { REGISTER_KERNEL_BUILDER(Name("GeneratorDataset").Device(DEVICE_CPU), GeneratorDatasetOp); REGISTER_KERNEL_BUILDER( Name("GeneratorDataset").Device(DEVICE_GPU).HostMemory("handle"), GeneratorDatasetOp); +} // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/generator_dataset_op.h b/tensorflow/core/kernels/data/generator_dataset_op.h index 8407543136..d23ed97ec3 100644 --- a/tensorflow/core/kernels/data/generator_dataset_op.h +++ b/tensorflow/core/kernels/data/generator_dataset_op.h @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/core/framework/dataset.h" namespace tensorflow { +namespace data { class GeneratorDatasetOp : public DatasetOpKernel { public: @@ -36,5 +37,6 @@ class GeneratorDatasetOp : public DatasetOpKernel { NameAttrList finalize_func_; }; +} // namespace data } // namespace tensorflow #endif // TENSORFLOW_CORE_KERNELS_DATA_GENERATOR_DATASET_OP_H_ diff --git a/tensorflow/core/kernels/data/group_by_reducer_dataset_op.cc b/tensorflow/core/kernels/data/group_by_reducer_dataset_op.cc index 130f04da3e..d6ee42a7c6 100644 --- a/tensorflow/core/kernels/data/group_by_reducer_dataset_op.cc +++ b/tensorflow/core/kernels/data/group_by_reducer_dataset_op.cc @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/core/lib/random/random.h" namespace tensorflow { +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -433,4 +434,5 @@ REGISTER_KERNEL_BUILDER(Name("GroupByReducerDataset").Device(DEVICE_CPU), GroupByReducerDatasetOp); } // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/group_by_window_dataset_op.cc b/tensorflow/core/kernels/data/group_by_window_dataset_op.cc index 46a3185b49..e4fa557598 100644 --- a/tensorflow/core/kernels/data/group_by_window_dataset_op.cc +++ b/tensorflow/core/kernels/data/group_by_window_dataset_op.cc @@ -23,6 +23,7 @@ limitations under the License. #include "tensorflow/core/lib/random/random.h" namespace tensorflow { +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -549,4 +550,5 @@ REGISTER_KERNEL_BUILDER(Name("GroupByWindowDataset").Device(DEVICE_CPU), GroupByWindowDatasetOp); } // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/interleave_dataset_op.cc b/tensorflow/core/kernels/data/interleave_dataset_op.cc index 716e040277..0768f46665 100644 --- a/tensorflow/core/kernels/data/interleave_dataset_op.cc +++ b/tensorflow/core/kernels/data/interleave_dataset_op.cc @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/core/lib/random/random.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -201,7 +201,7 @@ class InterleaveDatasetOp : public UnaryDatasetOpKernel { TF_RETURN_IF_ERROR(input_impl_->GetNext( ctx, &args_list_[cycle_index_], &end_of_input_)); if (!end_of_input_) { - TF_RETURN_IF_ERROR(dataset::MakeIteratorFromInputElement( + TF_RETURN_IF_ERROR(MakeIteratorFromInputElement( ctx, args_list_[cycle_index_], cycle_index_, dataset()->captured_func_.get(), prefix(), ¤t_elements_[cycle_index_])); @@ -288,7 +288,7 @@ class InterleaveDatasetOp : public UnaryDatasetOpKernel { full_name(strings::StrCat("args_list_[", idx, "][", i, "]")), &args_list_[idx][i])); } - TF_RETURN_IF_ERROR(dataset::MakeIteratorFromInputElement( + TF_RETURN_IF_ERROR(MakeIteratorFromInputElement( ctx, args_list_[idx], idx, dataset()->captured_func_.get(), prefix(), ¤t_elements_[idx])); TF_RETURN_IF_ERROR( @@ -330,5 +330,5 @@ REGISTER_KERNEL_BUILDER(Name("InterleaveDataset").Device(DEVICE_CPU), InterleaveDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/iterator_ops.cc b/tensorflow/core/kernels/data/iterator_ops.cc index 4e9b280968..fe6d705eab 100644 --- a/tensorflow/core/kernels/data/iterator_ops.cc +++ b/tensorflow/core/kernels/data/iterator_ops.cc @@ -36,7 +36,7 @@ limitations under the License. #include "tensorflow/core/public/session_options.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -236,6 +236,8 @@ class IteratorResource : public ResourceBase { const std::vector<PartialTensorShape> output_shapes_; }; +namespace { + // Helper class for reading data from a VariantTensorData object. class VariantTensorDataReader : public IteratorStateReader { public: @@ -443,6 +445,8 @@ class IteratorStateVariant { REGISTER_UNARY_VARIANT_DECODE_FUNCTION(IteratorStateVariant, kIteratorVariantTypeName); +} // namespace + // Note that IteratorHandleOp holds a reference to the resource it creates. If // cleaning up resources with DestroyResourceOp is important, consider creating // resource containers with AnonymousIteratorHandleOp instead. @@ -622,6 +626,8 @@ void MakeIteratorOp::Compute(OpKernelContext* ctx) { OP_REQUIRES_OK(ctx, iterator_resource->set_iterator(std::move(iterator))); } +namespace { + class ToSingleElementOp : public AsyncOpKernel { public: explicit ToSingleElementOp(OpKernelConstruction* ctx) @@ -887,6 +893,8 @@ class OneShotIteratorOp : public AsyncOpKernel { const int graph_def_version_; }; +} // namespace + void IteratorGetNextOp::ComputeAsync(OpKernelContext* ctx, DoneCallback done) { IteratorResource* iterator; OP_REQUIRES_OK_ASYNC( @@ -957,6 +965,8 @@ void IteratorGetNextSyncOp::Compute(OpKernelContext* ctx) { } } +namespace { + class IteratorGetNextAsOptionalOp : public AsyncOpKernel { public: explicit IteratorGetNextAsOptionalOp(OpKernelConstruction* ctx) @@ -1037,6 +1047,8 @@ class IteratorGetNextAsOptionalOp : public AsyncOpKernel { std::vector<PartialTensorShape> output_shapes_; }; +} // namespace + void IteratorToStringHandleOp::Compute(OpKernelContext* ctx) { const Tensor& resource_handle_t = ctx->input(0); OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(resource_handle_t.shape()), @@ -1108,6 +1120,8 @@ void IteratorFromStringHandleOp::Compute(OpKernelContext* ctx) { resource_handle_t->scalar<ResourceHandle>()() = resource_handle; } +namespace { + class SerializeIteratorOp : public OpKernel { public: explicit SerializeIteratorOp(OpKernelConstruction* ctx) : OpKernel(ctx) {} @@ -1202,4 +1216,7 @@ REGISTER_KERNEL_BUILDER(Name("SerializeIterator").Device(DEVICE_CPU), REGISTER_KERNEL_BUILDER(Name("DeserializeIterator").Device(DEVICE_CPU), DeserializeIteratorOp); +} // namespace + +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/iterator_ops.h b/tensorflow/core/kernels/data/iterator_ops.h index 723564286c..8a2b2639a7 100644 --- a/tensorflow/core/kernels/data/iterator_ops.h +++ b/tensorflow/core/kernels/data/iterator_ops.h @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/core/kernels/ops_util.h" namespace tensorflow { +namespace data { class IteratorResource; @@ -142,6 +143,7 @@ class IteratorFromStringHandleOp : public OpKernel { std::vector<PartialTensorShape> output_shapes_; }; +} // namespace data } // namespace tensorflow #endif // TENSORFLOW_CORE_KERNELS_DATA_ITERATOR_OPS_H_ diff --git a/tensorflow/core/kernels/data/map_and_batch_dataset_op.cc b/tensorflow/core/kernels/data/map_and_batch_dataset_op.cc index 8b0c9ad6b2..27c89b3661 100644 --- a/tensorflow/core/kernels/data/map_and_batch_dataset_op.cc +++ b/tensorflow/core/kernels/data/map_and_batch_dataset_op.cc @@ -29,7 +29,7 @@ limitations under the License. #include "tensorflow/core/platform/tracing.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -675,5 +675,5 @@ REGISTER_KERNEL_BUILDER(Name("MapAndBatchDatasetV2").Device(DEVICE_CPU), MapAndBatchDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/map_dataset_op.cc b/tensorflow/core/kernels/data/map_dataset_op.cc index 6c45fcafcc..306486b96a 100644 --- a/tensorflow/core/kernels/data/map_dataset_op.cc +++ b/tensorflow/core/kernels/data/map_dataset_op.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/core/lib/random/random.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -196,5 +196,5 @@ class MapDatasetOp : public UnaryDatasetOpKernel { REGISTER_KERNEL_BUILDER(Name("MapDataset").Device(DEVICE_CPU), MapDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/map_defun_op.cc b/tensorflow/core/kernels/data/map_defun_op.cc index 607d0ca028..3c562fc7f3 100644 --- a/tensorflow/core/kernels/data/map_defun_op.cc +++ b/tensorflow/core/kernels/data/map_defun_op.cc @@ -23,13 +23,13 @@ limitations under the License. #include "tensorflow/core/util/reffed_status_callback.h" namespace tensorflow { +namespace data { namespace { void SetRunOptions(OpKernelContext* ctx, FunctionLibraryRuntime::Options* opts, bool always_collect_stats) { opts->step_id = ctx->step_id(); opts->rendezvous = ctx->rendezvous(); - opts->cancellation_manager = ctx->cancellation_manager(); if (always_collect_stats) { opts->stats_collector = ctx->stats_collector(); } @@ -117,10 +117,13 @@ class MapDefunOp : public AsyncOpKernel { for (size_t i = 0; i < static_cast<size_t>(batch_size); ++i) { auto* call_frame = new MapFunctionCallFrame(*args, *arg_shapes, output, this, i); + CancellationManager* c_mgr = new CancellationManager; + opts_.cancellation_manager = c_mgr; ctx->function_library()->Run( opts_, func_handle_, call_frame, - [call_frame, refcounted](const Status& func_status) { + [call_frame, refcounted, c_mgr](const Status& func_status) { delete call_frame; + delete c_mgr; refcounted->UpdateStatus(func_status); refcounted->Unref(); }); @@ -189,8 +192,9 @@ class MapDefunOp : public AsyncOpKernel { const OpKernel* kernel_; const size_t iter_; }; -}; // namespace +}; REGISTER_KERNEL_BUILDER(Name("MapDefun").Device(DEVICE_CPU), MapDefunOp); } // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/optimize_dataset_op.cc b/tensorflow/core/kernels/data/optimize_dataset_op.cc index 6263dc3cf8..d5b725eac9 100644 --- a/tensorflow/core/kernels/data/optimize_dataset_op.cc +++ b/tensorflow/core/kernels/data/optimize_dataset_op.cc @@ -33,6 +33,7 @@ limitations under the License. #include "tensorflow/core/protobuf/rewriter_config.pb.h" namespace tensorflow { +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -270,4 +271,5 @@ REGISTER_KERNEL_BUILDER(Name("OptimizeDataset").Device(DEVICE_CPU), OptimizeDatasetOp); } // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/optional_ops.cc b/tensorflow/core/kernels/data/optional_ops.cc index cfac45dbc7..b372d31a93 100644 --- a/tensorflow/core/kernels/data/optional_ops.cc +++ b/tensorflow/core/kernels/data/optional_ops.cc @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/core/framework/variant_op_registry.h" namespace tensorflow { +namespace data { namespace { const char kOptionalVariantTypeName[] = "tensorflow::data::Optional"; @@ -267,4 +268,5 @@ Status WriteOptionalNoneToOutput(OpKernelContext* ctx, int output_index) { return Status::OK(); } +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/optional_ops.h b/tensorflow/core/kernels/data/optional_ops.h index 6f25567678..2cbf2933f5 100644 --- a/tensorflow/core/kernels/data/optional_ops.h +++ b/tensorflow/core/kernels/data/optional_ops.h @@ -21,6 +21,7 @@ limitations under the License. #include "tensorflow/core/framework/variant_tensor_data.h" namespace tensorflow { +namespace data { // Stores a DT_VARIANT value representing an Optional with the given value // in the `output_index`^th output of the given kernel execution context. @@ -31,6 +32,7 @@ Status WriteOptionalWithValueToOutput(OpKernelContext* ctx, int output_index, // in the `output_index`^th output of the given kernel execution context. Status WriteOptionalNoneToOutput(OpKernelContext* ctx, int output_index); +} // namespace data } // namespace tensorflow #endif // TENSORFLOW_CORE_KERNELS_DATA_OPTIONAL_OPS_H_ diff --git a/tensorflow/core/kernels/data/padded_batch_dataset_op.cc b/tensorflow/core/kernels/data/padded_batch_dataset_op.cc index be45eac46e..fd0e6c4cd0 100644 --- a/tensorflow/core/kernels/data/padded_batch_dataset_op.cc +++ b/tensorflow/core/kernels/data/padded_batch_dataset_op.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/core/util/batch_util.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -382,5 +382,5 @@ REGISTER_KERNEL_BUILDER(Name("PaddedBatchDatasetV2").Device(DEVICE_CPU), PaddedBatchDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/parallel_interleave_dataset_op.cc b/tensorflow/core/kernels/data/parallel_interleave_dataset_op.cc index f6b3fd97e3..f8287cf0e3 100644 --- a/tensorflow/core/kernels/data/parallel_interleave_dataset_op.cc +++ b/tensorflow/core/kernels/data/parallel_interleave_dataset_op.cc @@ -25,7 +25,7 @@ limitations under the License. #include "tensorflow/core/lib/random/random.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -684,7 +684,7 @@ class ParallelInterleaveDatasetOp : public UnaryDatasetOpKernel { { tf_shared_lock l(ckpt_mu_); worker_thread_states_[thread_index].iterator_creation_status = - dataset::MakeIteratorFromInputElement( + MakeIteratorFromInputElement( ctx.get(), worker_thread_states_[thread_index].input, thread_index, dataset()->captured_func_.get(), prefix(), &worker_thread_states_[thread_index].iterator); @@ -914,7 +914,7 @@ class ParallelInterleaveDatasetOp : public UnaryDatasetOpKernel { worker_thread_states_[index].iterator.reset(); } else { std::unique_ptr<IteratorBase> iterator; - Status s = dataset::MakeIteratorFromInputElement( + Status s = MakeIteratorFromInputElement( ctx, worker_thread_states_[index].input, index, dataset()->captured_func_.get(), prefix(), &iterator); TF_RETURN_IF_ERROR(RestoreInput(ctx, reader, iterator)); @@ -1068,5 +1068,5 @@ REGISTER_KERNEL_BUILDER(Name("ParallelInterleaveDataset").Device(DEVICE_CPU), ParallelInterleaveDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/parallel_map_dataset_op.cc b/tensorflow/core/kernels/data/parallel_map_dataset_op.cc index bff54813d6..ac5ed286ee 100644 --- a/tensorflow/core/kernels/data/parallel_map_dataset_op.cc +++ b/tensorflow/core/kernels/data/parallel_map_dataset_op.cc @@ -24,7 +24,7 @@ limitations under the License. #include "tensorflow/core/lib/random/random.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -180,5 +180,5 @@ REGISTER_KERNEL_BUILDER(Name("ParallelMapDataset").Device(DEVICE_CPU), ParallelMapDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/parallel_map_iterator.cc b/tensorflow/core/kernels/data/parallel_map_iterator.cc index 61f8139b9e..4ae742aaaf 100644 --- a/tensorflow/core/kernels/data/parallel_map_iterator.cc +++ b/tensorflow/core/kernels/data/parallel_map_iterator.cc @@ -20,6 +20,7 @@ limitations under the License. #include <vector> namespace tensorflow { +namespace data { namespace { class ParallelMapIterator : public DatasetBaseIterator { @@ -333,4 +334,5 @@ std::unique_ptr<IteratorBase> NewParallelMapIterator( std::move(map_func), num_parallel_calls)); } +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/parallel_map_iterator.h b/tensorflow/core/kernels/data/parallel_map_iterator.h index 7e6cc586f3..dc26c5cf25 100644 --- a/tensorflow/core/kernels/data/parallel_map_iterator.h +++ b/tensorflow/core/kernels/data/parallel_map_iterator.h @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/core/framework/dataset.h" namespace tensorflow { +namespace data { // A function that transforms elements of one dataset into another // asynchronously. The arguments are: @@ -47,6 +48,7 @@ std::unique_ptr<IteratorBase> NewParallelMapIterator( const DatasetBase* input_dataset, ParallelMapIteratorFunction map_func, int32 num_parallel_calls); +} // namespace data } // namespace tensorflow #endif // TENSORFLOW_CORE_KERNELS_DATA_PARALLEL_MAP_ITERATOR_H_ diff --git a/tensorflow/core/kernels/data/parse_example_dataset_op.cc b/tensorflow/core/kernels/data/parse_example_dataset_op.cc index 9057800d94..0cf5db017b 100644 --- a/tensorflow/core/kernels/data/parse_example_dataset_op.cc +++ b/tensorflow/core/kernels/data/parse_example_dataset_op.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/core/util/example_proto_fast_parsing.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -368,5 +368,5 @@ REGISTER_KERNEL_BUILDER(Name("ParseExampleDataset").Device(DEVICE_CPU), ParseExampleDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/prefetch_autotuner.cc b/tensorflow/core/kernels/data/prefetch_autotuner.cc index b3272f6bcd..533d0bd5d2 100644 --- a/tensorflow/core/kernels/data/prefetch_autotuner.cc +++ b/tensorflow/core/kernels/data/prefetch_autotuner.cc @@ -16,6 +16,7 @@ limitations under the License. #include "tensorflow/core/kernels/data/prefetch_autotuner.h" namespace tensorflow { +namespace data { PrefetchAutotuner::PrefetchAutotuner(int64 initial_buffer_size) : buffer_limit_(initial_buffer_size) { @@ -43,4 +44,5 @@ void PrefetchAutotuner::RecordConsumption(size_t current_buffer_size) { } } +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/prefetch_autotuner.h b/tensorflow/core/kernels/data/prefetch_autotuner.h index fa8a184072..8693205512 100644 --- a/tensorflow/core/kernels/data/prefetch_autotuner.h +++ b/tensorflow/core/kernels/data/prefetch_autotuner.h @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/core/platform/types.h" namespace tensorflow { +namespace data { // PrefetchAutotuner dynamically adjusts the buffer size of a prefetch iterator. // @@ -66,6 +67,7 @@ class PrefetchAutotuner { Mode mode_ = Mode::kDisabled; }; +} // namespace data } // namespace tensorflow #endif // TENSORFLOW_CORE_KERNELS_DATA_PREFETCH_AUTOTUNER_H_ diff --git a/tensorflow/core/kernels/data/prefetch_autotuner_test.cc b/tensorflow/core/kernels/data/prefetch_autotuner_test.cc index 29a8cc50cd..cfc324fc7e 100644 --- a/tensorflow/core/kernels/data/prefetch_autotuner_test.cc +++ b/tensorflow/core/kernels/data/prefetch_autotuner_test.cc @@ -18,6 +18,7 @@ limitations under the License. #include "tensorflow/core/platform/test.h" namespace tensorflow { +namespace data { namespace { TEST(PrefetchAutotuner, Disabled) { @@ -79,4 +80,5 @@ TEST(PrefetchAutotuner, EnabledSteady) { } } // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/prefetch_dataset_op.cc b/tensorflow/core/kernels/data/prefetch_dataset_op.cc index 50efbcbe2a..a7a2935195 100644 --- a/tensorflow/core/kernels/data/prefetch_dataset_op.cc +++ b/tensorflow/core/kernels/data/prefetch_dataset_op.cc @@ -21,6 +21,7 @@ limitations under the License. #include "tensorflow/core/lib/core/error_codes.pb.h" namespace tensorflow { +namespace data { // See documentation in ../ops/dataset_ops.cc for a high-level // description of the following op. @@ -346,6 +347,7 @@ void PrefetchDatasetOp::MakeDataset(OpKernelContext* ctx, DatasetBase* input, *output = new Dataset(ctx, input, buffer_size); } +namespace { REGISTER_KERNEL_BUILDER(Name("PrefetchDataset").Device(DEVICE_CPU), PrefetchDatasetOp); REGISTER_KERNEL_BUILDER(Name("PrefetchDataset") @@ -354,4 +356,7 @@ REGISTER_KERNEL_BUILDER(Name("PrefetchDataset") .HostMemory("input_dataset") .HostMemory("handle"), PrefetchDatasetOp); +} // namespace + +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/prefetch_dataset_op.h b/tensorflow/core/kernels/data/prefetch_dataset_op.h index c40c4b00da..588fb25a06 100644 --- a/tensorflow/core/kernels/data/prefetch_dataset_op.h +++ b/tensorflow/core/kernels/data/prefetch_dataset_op.h @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/core/kernels/data/prefetch_autotuner.h" namespace tensorflow { +namespace data { class PrefetchDatasetOp : public UnaryDatasetOpKernel { public: @@ -34,6 +35,7 @@ class PrefetchDatasetOp : public UnaryDatasetOpKernel { class Dataset; }; +} // namespace data } // namespace tensorflow #endif // TENSORFLOW_CORE_KERNELS_DATA_PREFETCH_DATASET_OP_H_ diff --git a/tensorflow/core/kernels/data/random_dataset_op.cc b/tensorflow/core/kernels/data/random_dataset_op.cc index 7817170e73..044a791a3f 100644 --- a/tensorflow/core/kernels/data/random_dataset_op.cc +++ b/tensorflow/core/kernels/data/random_dataset_op.cc @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/core/lib/random/random_distributions.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -151,5 +151,5 @@ REGISTER_KERNEL_BUILDER(Name("RandomDataset").Device(DEVICE_CPU), RandomDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/range_dataset_op.cc b/tensorflow/core/kernels/data/range_dataset_op.cc index aa38775125..89fbaae369 100644 --- a/tensorflow/core/kernels/data/range_dataset_op.cc +++ b/tensorflow/core/kernels/data/range_dataset_op.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/core/kernels/data/dataset.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -142,5 +142,5 @@ REGISTER_KERNEL_BUILDER(Name("RangeDataset").Device(DEVICE_CPU), RangeDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/reader_dataset_ops.cc b/tensorflow/core/kernels/data/reader_dataset_ops.cc index 086b552936..c474cb4773 100644 --- a/tensorflow/core/kernels/data/reader_dataset_ops.cc +++ b/tensorflow/core/kernels/data/reader_dataset_ops.cc @@ -23,7 +23,7 @@ limitations under the License. #include "tensorflow/core/lib/io/zlib_inputstream.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -691,5 +691,5 @@ REGISTER_KERNEL_BUILDER(Name("TFRecordDataset").Device(DEVICE_CPU), TFRecordDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/repeat_dataset_op.cc b/tensorflow/core/kernels/data/repeat_dataset_op.cc index 299949b99f..94e96635ab 100644 --- a/tensorflow/core/kernels/data/repeat_dataset_op.cc +++ b/tensorflow/core/kernels/data/repeat_dataset_op.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/core/kernels/data/dataset.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -250,5 +250,5 @@ REGISTER_KERNEL_BUILDER(Name("RepeatDataset").Device(DEVICE_CPU), RepeatDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/scan_dataset_op.cc b/tensorflow/core/kernels/data/scan_dataset_op.cc index fccad933d0..6e515d6cc8 100644 --- a/tensorflow/core/kernels/data/scan_dataset_op.cc +++ b/tensorflow/core/kernels/data/scan_dataset_op.cc @@ -23,7 +23,7 @@ limitations under the License. #include "tensorflow/core/lib/random/random.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -279,5 +279,5 @@ class ScanDatasetOp : public UnaryDatasetOpKernel { REGISTER_KERNEL_BUILDER(Name("ScanDataset").Device(DEVICE_CPU), ScanDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/shuffle_dataset_op.cc b/tensorflow/core/kernels/data/shuffle_dataset_op.cc index 93a4376836..66466d6a36 100644 --- a/tensorflow/core/kernels/data/shuffle_dataset_op.cc +++ b/tensorflow/core/kernels/data/shuffle_dataset_op.cc @@ -25,7 +25,7 @@ limitations under the License. #include "tensorflow/core/util/ptr_util.h" namespace tensorflow { - +namespace data { namespace { const int64 kLogIntervalMicros = 10 * 1000000; // 10 seconds. @@ -620,5 +620,5 @@ REGISTER_KERNEL_BUILDER(Name("ShuffleAndRepeatDataset").Device(DEVICE_CPU), ShuffleAndRepeatDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/single_threaded_executor.cc b/tensorflow/core/kernels/data/single_threaded_executor.cc index e785b8b4d5..5b084a16f0 100644 --- a/tensorflow/core/kernels/data/single_threaded_executor.cc +++ b/tensorflow/core/kernels/data/single_threaded_executor.cc @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/core/lib/core/status.h" namespace tensorflow { +namespace data { namespace { typedef gtl::InlinedVector<TensorValue, 4> TensorValueVec; @@ -375,4 +376,5 @@ Status NewSingleThreadedExecutor(const LocalExecutorParams& params, return Status::OK(); } +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/single_threaded_executor.h b/tensorflow/core/kernels/data/single_threaded_executor.h index 15836b24c9..e934352a1d 100644 --- a/tensorflow/core/kernels/data/single_threaded_executor.h +++ b/tensorflow/core/kernels/data/single_threaded_executor.h @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/core/common_runtime/executor.h" namespace tensorflow { +namespace data { // Creates a new `Executor` for executing `graph` synchronously on the caller // thread. @@ -55,6 +56,7 @@ Status NewSingleThreadedExecutor(const LocalExecutorParams& params, std::unique_ptr<const Graph> graph, Executor** executor); +} // namespace data } // namespace tensorflow #endif // TENSORFLOW_CORE_KERNELS_DATA_SINGLE_THREADED_EXECUTOR_H_ diff --git a/tensorflow/core/kernels/data/single_threaded_executor_test.cc b/tensorflow/core/kernels/data/single_threaded_executor_test.cc index f8b5769197..6244e287bb 100644 --- a/tensorflow/core/kernels/data/single_threaded_executor_test.cc +++ b/tensorflow/core/kernels/data/single_threaded_executor_test.cc @@ -37,6 +37,7 @@ limitations under the License. #include "tensorflow/core/public/session_options.h" namespace tensorflow { +namespace data { namespace { class ExecutorTest : public ::testing::Test { @@ -327,4 +328,5 @@ BENCHMARK(BM_FeedInputFetchOutput); #endif } // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/skip_dataset_op.cc b/tensorflow/core/kernels/data/skip_dataset_op.cc index fe7ef38d5f..b8c7fb15f4 100644 --- a/tensorflow/core/kernels/data/skip_dataset_op.cc +++ b/tensorflow/core/kernels/data/skip_dataset_op.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/core/kernels/data/dataset.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -187,5 +187,5 @@ class SkipDatasetOp : public UnaryDatasetOpKernel { REGISTER_KERNEL_BUILDER(Name("SkipDataset").Device(DEVICE_CPU), SkipDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/slide_dataset_op.cc b/tensorflow/core/kernels/data/slide_dataset_op.cc index 14df3a6801..1e73cfc753 100644 --- a/tensorflow/core/kernels/data/slide_dataset_op.cc +++ b/tensorflow/core/kernels/data/slide_dataset_op.cc @@ -23,7 +23,7 @@ limitations under the License. #include "tensorflow/core/util/batch_util.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -293,5 +293,5 @@ REGISTER_KERNEL_BUILDER(Name("SlideDataset").Device(DEVICE_CPU), SlideDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc b/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc index e526578701..85b1e50695 100644 --- a/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc +++ b/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/core/util/sparse/sparse_tensor.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -274,5 +274,5 @@ TF_CALL_DATASET_TYPES(REGISTER_DATASET_KERNEL); #undef REGISTER_DATASET_KERNEL } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/sql/driver_manager.cc b/tensorflow/core/kernels/data/sql/driver_manager.cc index ffabda1a8a..783d1e6cb2 100644 --- a/tensorflow/core/kernels/data/sql/driver_manager.cc +++ b/tensorflow/core/kernels/data/sql/driver_manager.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/core/kernels/data/sql/sqlite_query_connection.h" namespace tensorflow { - +namespace data { namespace sql { std::unique_ptr<QueryConnection> DriverManager::CreateQueryConnection( @@ -30,5 +30,5 @@ std::unique_ptr<QueryConnection> DriverManager::CreateQueryConnection( } } // namespace sql - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/sql/driver_manager.h b/tensorflow/core/kernels/data/sql/driver_manager.h index a34691b5a2..c5428f396b 100644 --- a/tensorflow/core/kernels/data/sql/driver_manager.h +++ b/tensorflow/core/kernels/data/sql/driver_manager.h @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/core/kernels/data/sql/query_connection.h" namespace tensorflow { - +namespace data { namespace sql { // A factory class for creating `QueryConnection` instances. @@ -35,7 +35,7 @@ class DriverManager { }; } // namespace sql - +} // namespace data } // namespace tensorflow #endif // TENSORFLOW_CORE_KERNELS_DATA_SQL_DRIVER_MANAGER_H_ diff --git a/tensorflow/core/kernels/data/sql/query_connection.h b/tensorflow/core/kernels/data/sql/query_connection.h index e9ffca202f..2fd229a9bf 100644 --- a/tensorflow/core/kernels/data/sql/query_connection.h +++ b/tensorflow/core/kernels/data/sql/query_connection.h @@ -18,6 +18,7 @@ limitations under the License. #include "tensorflow/core/framework/tensor.h" namespace tensorflow { +namespace data { class IteratorContext; @@ -63,7 +64,7 @@ class QueryConnection { }; } // namespace sql - +} // namespace data } // namespace tensorflow #endif // TENSORFLOW_CORE_KERNELS_DATA_SQL_QUERY_CONNECTION_H_ diff --git a/tensorflow/core/kernels/data/sql/sqlite_query_connection.cc b/tensorflow/core/kernels/data/sql/sqlite_query_connection.cc index 7cd07bd8ec..5108e83976 100644 --- a/tensorflow/core/kernels/data/sql/sqlite_query_connection.cc +++ b/tensorflow/core/kernels/data/sql/sqlite_query_connection.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/core/lib/strings/stringprintf.h" namespace tensorflow { - +namespace data { namespace sql { SqliteQueryConnection::SqliteQueryConnection() {} @@ -115,5 +115,5 @@ void SqliteQueryConnection::FillTensorWithResultSetEntry( } } // namespace sql - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/sql/sqlite_query_connection.h b/tensorflow/core/kernels/data/sql/sqlite_query_connection.h index 81b19530b7..175492c49d 100644 --- a/tensorflow/core/kernels/data/sql/sqlite_query_connection.h +++ b/tensorflow/core/kernels/data/sql/sqlite_query_connection.h @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/core/platform/types.h" namespace tensorflow { - +namespace data { namespace sql { class SqliteQueryConnection : public QueryConnection { @@ -50,7 +50,7 @@ class SqliteQueryConnection : public QueryConnection { }; } // namespace sql - +} // namespace data } // namespace tensorflow #endif // TENSORFLOW_CORE_KERNELS_DATA_SQL_SQLITE_QUERY_CONNECTION_H_ diff --git a/tensorflow/core/kernels/data/sql_dataset_ops.cc b/tensorflow/core/kernels/data/sql_dataset_ops.cc index 2aa153fcfa..6bbe459332 100644 --- a/tensorflow/core/kernels/data/sql_dataset_ops.cc +++ b/tensorflow/core/kernels/data/sql_dataset_ops.cc @@ -24,8 +24,9 @@ limitations under the License. #include "tensorflow/core/lib/strings/stringprintf.h" namespace tensorflow { - +namespace data { namespace { + // See documentation in ../ops/dataset_ops.cc for a high-level // description of the following ops. @@ -211,5 +212,5 @@ class SqlDatasetOp : public DatasetOpKernel { REGISTER_KERNEL_BUILDER(Name("SqlDataset").Device(DEVICE_CPU), SqlDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/stats_aggregator_dataset_op.cc b/tensorflow/core/kernels/data/stats_aggregator_dataset_op.cc index 75af73df54..f5314f7a75 100644 --- a/tensorflow/core/kernels/data/stats_aggregator_dataset_op.cc +++ b/tensorflow/core/kernels/data/stats_aggregator_dataset_op.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/core/lib/random/random.h" namespace tensorflow { +namespace data { namespace { class SetStatsAggregatorDatasetOp : public UnaryDatasetOpKernel { @@ -135,4 +136,5 @@ class SetStatsAggregatorDatasetOp : public UnaryDatasetOpKernel { REGISTER_KERNEL_BUILDER(Name("SetStatsAggregatorDataset").Device(DEVICE_CPU), SetStatsAggregatorDatasetOp); } // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/stats_aggregator_ops.cc b/tensorflow/core/kernels/data/stats_aggregator_ops.cc index b133cfab54..a7ded67876 100644 --- a/tensorflow/core/kernels/data/stats_aggregator_ops.cc +++ b/tensorflow/core/kernels/data/stats_aggregator_ops.cc @@ -26,6 +26,7 @@ limitations under the License. #include "tensorflow/core/platform/macros.h" namespace tensorflow { +namespace data { namespace { static mutex* get_counters_map_lock() { @@ -145,4 +146,5 @@ REGISTER_KERNEL_BUILDER(Name("StatsAggregatorSummary").Device(DEVICE_CPU), StatsAggregatorSummaryOp); } // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/stats_dataset_ops.cc b/tensorflow/core/kernels/data/stats_dataset_ops.cc index 8957f5d997..e9e42f05a1 100644 --- a/tensorflow/core/kernels/data/stats_dataset_ops.cc +++ b/tensorflow/core/kernels/data/stats_dataset_ops.cc @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/core/lib/random/random.h" namespace tensorflow { +namespace data { namespace { // This op defines a `Dataset` that passes through its input elements and @@ -248,4 +249,5 @@ REGISTER_KERNEL_BUILDER(Name("BytesProducedStatsDataset").Device(DEVICE_CPU), BytesProducedStatsDatasetOp); } // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/take_dataset_op.cc b/tensorflow/core/kernels/data/take_dataset_op.cc index e5c237dfaa..e5cdfdd732 100644 --- a/tensorflow/core/kernels/data/take_dataset_op.cc +++ b/tensorflow/core/kernels/data/take_dataset_op.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/core/kernels/data/dataset.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -174,5 +174,5 @@ class TakeDatasetOp : public UnaryDatasetOpKernel { REGISTER_KERNEL_BUILDER(Name("TakeDataset").Device(DEVICE_CPU), TakeDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/tensor_dataset_op.cc b/tensorflow/core/kernels/data/tensor_dataset_op.cc index 1192fafc4c..e1cefd23d8 100644 --- a/tensorflow/core/kernels/data/tensor_dataset_op.cc +++ b/tensorflow/core/kernels/data/tensor_dataset_op.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/core/kernels/data/dataset.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -140,5 +140,5 @@ REGISTER_KERNEL_BUILDER(Name("TensorDataset").Device(DEVICE_CPU), TensorDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/tensor_queue_dataset_op.cc b/tensorflow/core/kernels/data/tensor_queue_dataset_op.cc index ccd5e60acc..2ed636a400 100644 --- a/tensorflow/core/kernels/data/tensor_queue_dataset_op.cc +++ b/tensorflow/core/kernels/data/tensor_queue_dataset_op.cc @@ -24,7 +24,7 @@ limitations under the License. #include "tensorflow/core/util/batch_util.h" namespace tensorflow { - +namespace data { namespace { bool IsGreaterEqualToOrCompatibleWith(const PartialTensorShape& a, @@ -648,5 +648,5 @@ REGISTER_KERNEL_BUILDER(Name("EnqueueInQueueDataset").Device(DEVICE_CPU), EnqueueInQueueDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/tensor_slice_dataset_op.cc b/tensorflow/core/kernels/data/tensor_slice_dataset_op.cc index dc32cd23e5..7dc64b0a75 100644 --- a/tensorflow/core/kernels/data/tensor_slice_dataset_op.cc +++ b/tensorflow/core/kernels/data/tensor_slice_dataset_op.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/core/util/batch_util.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -168,5 +168,5 @@ REGISTER_KERNEL_BUILDER(Name("TensorSliceDataset").Device(DEVICE_CPU), TensorSliceDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/unbatch_dataset_op.cc b/tensorflow/core/kernels/data/unbatch_dataset_op.cc index 1a79f72b28..81c432b938 100644 --- a/tensorflow/core/kernels/data/unbatch_dataset_op.cc +++ b/tensorflow/core/kernels/data/unbatch_dataset_op.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/core/util/batch_util.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -204,5 +204,5 @@ REGISTER_KERNEL_BUILDER(Name("UnbatchDataset").Device(DEVICE_CPU), UnbatchDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/window_dataset.cc b/tensorflow/core/kernels/data/window_dataset.cc index 0ab6beabfc..2ad4711aab 100644 --- a/tensorflow/core/kernels/data/window_dataset.cc +++ b/tensorflow/core/kernels/data/window_dataset.cc @@ -16,6 +16,7 @@ limitations under the License. #include "tensorflow/core/lib/core/errors.h" namespace tensorflow { +namespace data { namespace { class WindowDataset : public DatasetBase { @@ -107,4 +108,5 @@ Status NewWindowDataset(std::vector<std::vector<Tensor>> elements, return Status::OK(); } +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/window_dataset.h b/tensorflow/core/kernels/data/window_dataset.h index 7bd31a0bc7..84cb3c7860 100644 --- a/tensorflow/core/kernels/data/window_dataset.h +++ b/tensorflow/core/kernels/data/window_dataset.h @@ -23,6 +23,7 @@ limitations under the License. #include "tensorflow/core/kernels/data/dataset.h" namespace tensorflow { +namespace data { // Creates a dataset representing an eagerly-collected window of elements. // @@ -43,6 +44,7 @@ Status NewWindowDataset(std::vector<std::vector<Tensor>> elements, std::vector<PartialTensorShape> output_shapes, DatasetBase** out_dataset); +} // namespace data } // namespace tensorflow #endif // TENSORFLOW_CORE_KERNELS_DATA_WINDOW_DATASET_H_ diff --git a/tensorflow/core/kernels/data/window_dataset_op.cc b/tensorflow/core/kernels/data/window_dataset_op.cc index 41bf9d43fe..3975086841 100644 --- a/tensorflow/core/kernels/data/window_dataset_op.cc +++ b/tensorflow/core/kernels/data/window_dataset_op.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/core/kernels/data/window_dataset.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -195,5 +195,5 @@ REGISTER_KERNEL_BUILDER(Name("WindowDataset").Device(DEVICE_CPU), WindowDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/writer_ops.cc b/tensorflow/core/kernels/data/writer_ops.cc index 1c49874a6a..3f76695bb1 100644 --- a/tensorflow/core/kernels/data/writer_ops.cc +++ b/tensorflow/core/kernels/data/writer_ops.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/core/platform/file_system.h" namespace tensorflow { - +namespace data { namespace { class ToTFRecordOp : public AsyncOpKernel { @@ -104,4 +104,5 @@ REGISTER_KERNEL_BUILDER(Name("DatasetToTFRecord").Device(DEVICE_CPU), ToTFRecordOp); } // namespace +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/data/zip_dataset_op.cc b/tensorflow/core/kernels/data/zip_dataset_op.cc index e4306579ed..61a2078f46 100644 --- a/tensorflow/core/kernels/data/zip_dataset_op.cc +++ b/tensorflow/core/kernels/data/zip_dataset_op.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/core/kernels/data/dataset.h" namespace tensorflow { - +namespace data { namespace { // See documentation in ../ops/dataset_ops.cc for a high-level @@ -175,5 +175,5 @@ class ZipDatasetOp : public DatasetOpKernel { REGISTER_KERNEL_BUILDER(Name("ZipDataset").Device(DEVICE_CPU), ZipDatasetOp); } // namespace - +} // namespace data } // namespace tensorflow diff --git a/tensorflow/core/kernels/eigen_backward_cuboid_convolutions.h b/tensorflow/core/kernels/eigen_backward_cuboid_convolutions.h index 3ebeb7be2b..27918b410b 100644 --- a/tensorflow/core/kernels/eigen_backward_cuboid_convolutions.h +++ b/tensorflow/core/kernels/eigen_backward_cuboid_convolutions.h @@ -51,14 +51,18 @@ EIGEN_ALWAYS_INLINE static const typename internal::conditional< internal::traits<OutputBackward>::NumDimensions>, const TensorContractionOp< const array< - IndexPair<typename internal::traits<OutputBackward>::Index>, 2>, - const TensorReshapingOp< + IndexPair<typename internal::traits<OutputBackward>::Index>, 1>, + const Eigen::TensorForcedEvalOp<const TensorReshapingOp< const DSizes<typename internal::traits<OutputBackward>::Index, - 3>, - const TensorReverseOp<const array<bool, 5>, const Kernel> >, + 2>, + const TensorShufflingOp< + const array< + typename internal::traits<OutputBackward>::Index, 5>, + const TensorReverseOp<const Eigen::array<bool, 5>, + const Kernel> > > >, const TensorReshapingOp< const DSizes<typename internal::traits<OutputBackward>::Index, - 3>, + 2>, const TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const OutputBackward> > > >, TensorReshapingOp< @@ -66,24 +70,27 @@ EIGEN_ALWAYS_INLINE static const typename internal::conditional< internal::traits<OutputBackward>::NumDimensions>, const TensorContractionOp< const array< - IndexPair<typename internal::traits<OutputBackward>::Index>, 2>, + IndexPair<typename internal::traits<OutputBackward>::Index>, 1>, const TensorReshapingOp< const DSizes<typename internal::traits<OutputBackward>::Index, - 3>, + 2>, const TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const OutputBackward> >, - const TensorReshapingOp< + const Eigen::TensorForcedEvalOp<const TensorReshapingOp< const DSizes<typename internal::traits<OutputBackward>::Index, - 3>, - const TensorReverseOp<const array<bool, 5>, - const Kernel> > > > >::type + 2>, + const TensorShufflingOp< + const array< + typename internal::traits<OutputBackward>::Index, 5>, + const TensorReverseOp<const Eigen::array<bool, 5>, + const Kernel> > > > > > >::type CuboidConvolutionBackwardInput( const Kernel& kernel, const OutputBackward& output_backward, typename internal::traits<OutputBackward>::Index inputPlanes, typename internal::traits<OutputBackward>::Index inputRows, typename internal::traits<OutputBackward>::Index inputCols, - const DenseIndex stridePlanes = 1, const DenseIndex strideRows = 1, - const DenseIndex strideCols = 1) { + const DenseIndex plane_stride = 1, const DenseIndex row_stride = 1, + const DenseIndex col_stride = 1) { typedef typename internal::traits<OutputBackward>::Index TensorIndex; const TensorRef<const Tensor<typename internal::traits<Kernel>::Scalar, internal::traits<Kernel>::NumDimensions, @@ -125,58 +132,45 @@ CuboidConvolutionBackwardInput( const TensorIndex outputCols = isColMajor ? out.dimensions()[3] : out.dimensions()[NumDims - 4]; - TensorIndex forward_pad_z, forward_pad_y, forward_pad_x; - const TensorIndex size_z = - Eigen::divup(inputPlanes, static_cast<TensorIndex>(stridePlanes)); - const TensorIndex size_y = - Eigen::divup(inputRows, static_cast<TensorIndex>(strideRows)); - const TensorIndex size_x = - Eigen::divup(inputCols, static_cast<TensorIndex>(strideCols)); - - // Infer padding type. - if (size_z == outputPlanes && size_y == outputRows && size_x == outputCols) { - // SAME padding. - const TensorIndex dz = numext::maxi<TensorIndex>( - 0, (size_z - 1) * stridePlanes + kernelPlanes - inputPlanes); - const TensorIndex dy = numext::maxi<TensorIndex>( - 0, (size_y - 1) * strideRows + kernelRows - inputRows); - const TensorIndex dx = numext::maxi<TensorIndex>( - 0, (size_x - 1) * strideCols + kernelCols - inputCols); - - forward_pad_z = dz / 2; - forward_pad_y = dy / 2; - forward_pad_x = dx / 2; - } else { - // VALID padding. - forward_pad_z = 0; - forward_pad_y = 0; - forward_pad_x = 0; - } - const TensorIndex padding_ztop = kernelPlanes - 1 - forward_pad_z; - const TensorIndex padding_top = kernelRows - 1 - forward_pad_y; - const TensorIndex padding_left = kernelCols - 1 - forward_pad_x; - - const TensorIndex padding_zbottom = inputPlanes + kernelPlanes - 1 - - (outputPlanes - 1) * stridePlanes - 1 - - padding_ztop; - const TensorIndex padding_bottom = inputRows + kernelRows - 1 - - (outputRows - 1) * strideRows - 1 - - padding_top; - const TensorIndex padding_right = inputCols + kernelCols - 1 - - (outputCols - 1) * strideCols - 1 - - padding_left; - - eigen_assert(padding_ztop >= 0); - eigen_assert(padding_zbottom >= 0); + // TODO(ezhulenev): Add support for inflated strides. Without inflated strides + // effective kernel planes/rows/cols are always the same as the kernel itself + // (see eigen_spatial_convolutions for details). + const TensorIndex kernelPlanesEff = kernelPlanes; + const TensorIndex kernelRowsEff = kernelRows; + const TensorIndex kernelColsEff = kernelCols; + + // Computing the forward padding. + const TensorIndex forward_pad_top_z = numext::maxi<Index>( + 0, + ((outputPlanes - 1) * plane_stride + kernelPlanesEff - inputPlanes) / 2); + const TensorIndex forward_pad_top = numext::maxi<Index>( + 0, ((outputRows - 1) * row_stride + kernelRowsEff - inputRows) / 2); + const TensorIndex forward_pad_left = numext::maxi<Index>( + 0, ((outputCols - 1) * col_stride + kernelColsEff - inputCols) / 2); + + const TensorIndex padding_top_z = kernelPlanesEff - 1 - forward_pad_top_z; + const TensorIndex padding_top = kernelRowsEff - 1 - forward_pad_top; + const TensorIndex padding_left = kernelColsEff - 1 - forward_pad_left; + + const TensorIndex padding_bottom_z = inputPlanes - + (outputPlanes - 1) * plane_stride - 2 - + padding_top_z + kernelPlanesEff; + const TensorIndex padding_bottom = inputRows - (outputRows - 1) * row_stride - + 2 - padding_top + kernelRowsEff; + const TensorIndex padding_right = inputCols - (outputCols - 1) * col_stride - + 2 - padding_left + kernelColsEff; + + eigen_assert(padding_top_z >= 0); eigen_assert(padding_top >= 0); eigen_assert(padding_left >= 0); + eigen_assert(padding_bottom_z >= 0); eigen_assert(padding_bottom >= 0); eigen_assert(padding_right >= 0); - // The kernel has dimensions filters X channels X patch_planes X patch_rows X - // patch_cols. + // The kernel has dimensions : + // filters x channels x patch_planes x patch_rows x patch_cols. // We need to reverse the kernel along the spatial dimensions. - array<bool, 5> kernel_reverse; + Eigen::array<bool, 5> kernel_reverse; if (isColMajor) { kernel_reverse[0] = false; kernel_reverse[1] = false; @@ -191,15 +185,35 @@ CuboidConvolutionBackwardInput( kernel_reverse[4] = false; } - DSizes<TensorIndex, 3> kernel_dims; + // Reorder the dimensions to: + // filters x patch_planes x patch_rows x patch_cols x channels + array<TensorIndex, 5> kernel_shuffle; if (isColMajor) { - kernel_dims[0] = kernelFilters; - kernel_dims[1] = kernelChannels; - kernel_dims[2] = kernelRows * kernelCols * kernelPlanes; + // From: filters x channels x planes x rows x cols + // To: filters x planes x rows x cols x channels + kernel_shuffle[0] = 0; + kernel_shuffle[1] = 2; + kernel_shuffle[2] = 3; + kernel_shuffle[3] = 4; + kernel_shuffle[4] = 1; } else { - kernel_dims[0] = kernelRows * kernelCols * kernelPlanes; + // From: cols x rows x planes x channels x filters + // To: channels x cols x rows x planes x filters + kernel_shuffle[0] = 3; + kernel_shuffle[1] = 0; + kernel_shuffle[2] = 1; + kernel_shuffle[3] = 2; + kernel_shuffle[4] = 4; + } + + // Collapse the dims + DSizes<TensorIndex, 2> kernel_dims; + if (isColMajor) { + kernel_dims[0] = kernelFilters * kernelPlanes * kernelRows * kernelCols; kernel_dims[1] = kernelChannels; - kernel_dims[2] = kernelFilters; + } else { + kernel_dims[1] = kernelFilters * kernelPlanes * kernelRows * kernelCols; + kernel_dims[0] = kernelChannels; } // The output_backward has dimensions out_depth X out_planes X out_rows X @@ -208,36 +222,32 @@ CuboidConvolutionBackwardInput( // dimensions: // out_depth X (patch_planes * patch_rows * patch_cols) X (input_planes * // input_rows * input_cols * OTHERS) - DSizes<TensorIndex, 3> pre_contract_dims; + DSizes<TensorIndex, 2> pre_contract_dims; if (isColMajor) { - pre_contract_dims[0] = kernelFilters; - pre_contract_dims[1] = kernelRows * kernelCols * kernelPlanes; - pre_contract_dims[2] = inputRows * inputCols * inputPlanes; + pre_contract_dims[0] = + kernelFilters * kernelPlanes * kernelRows * kernelCols; + pre_contract_dims[1] = inputPlanes * inputRows * inputCols; for (int i = 4; i < NumDims; ++i) { - pre_contract_dims[2] *= out.dimension(i); + pre_contract_dims[1] *= out.dimension(i); } } else { - pre_contract_dims[2] = kernelFilters; - pre_contract_dims[1] = kernelRows * kernelCols * kernelPlanes; - pre_contract_dims[0] = inputRows * inputCols * inputPlanes; + pre_contract_dims[1] = + kernelFilters * kernelPlanes * kernelRows * kernelCols; + pre_contract_dims[0] = inputPlanes * inputRows * inputCols; for (int i = 0; i < NumDims - 4; ++i) { pre_contract_dims[0] *= out.dimension(i); } } - // We will contract along dimensions (0, 2) in kernel and (0, 1) in - // output_backward, if this is col-major, and - // dimensions (0, 2) in kernel and (1, 2) in output_backward, if this - // row-major. - array<IndexPair<TensorIndex>, 2> contract_dims; + // We will contract along the fused dimension that contains the kernelFilters, + // kernelPlanes, kernelRows and kernelCols. + array<IndexPair<TensorIndex>, 1> contract_dims; if (isColMajor) { // col-major: kernel.contract(output.patches) contract_dims[0] = IndexPair<TensorIndex>(0, 0); - contract_dims[1] = IndexPair<TensorIndex>(2, 1); } else { // row-major: output.patches.contract(kernel) - contract_dims[0] = IndexPair<TensorIndex>(1, 0); - contract_dims[1] = IndexPair<TensorIndex>(2, 2); + contract_dims[0] = IndexPair<TensorIndex>(1, 1); } // Post contraction, the dimensions of the input_backprop is @@ -261,40 +271,31 @@ CuboidConvolutionBackwardInput( } } - DSizes<TensorIndex, NumDims> strides; - for (int i = 0; i < NumDims; i++) { - strides[i] = 1; - } - if (isColMajor) { - strides[1] = stridePlanes; - strides[2] = strideRows; - strides[3] = strideCols; - } else { - strides[NumDims - 2] = stridePlanes; - strides[NumDims - 3] = strideRows; - strides[NumDims - 4] = strideCols; - } - return choose( Cond<internal::traits<OutputBackward>::Layout == ColMajor>(), kernel.reverse(kernel_reverse) + .shuffle(kernel_shuffle) .reshape(kernel_dims) + .eval() .contract(output_backward .extract_volume_patches( kernelPlanes, kernelRows, kernelCols, 1, 1, 1, - stridePlanes, strideRows, strideCols, padding_ztop, - padding_zbottom, padding_top, padding_bottom, + plane_stride, row_stride, col_stride, padding_top_z, + padding_bottom_z, padding_top, padding_bottom, padding_left, padding_right) .reshape(pre_contract_dims), contract_dims) .reshape(post_contract_dims), output_backward .extract_volume_patches(kernelPlanes, kernelRows, kernelCols, 1, 1, 1, - stridePlanes, strideRows, strideCols, - padding_ztop, padding_zbottom, padding_top, + plane_stride, row_stride, col_stride, + padding_top_z, padding_bottom_z, padding_top, padding_bottom, padding_left, padding_right) .reshape(pre_contract_dims) - .contract(kernel.reverse(kernel_reverse).reshape(kernel_dims), + .contract(kernel.reverse(kernel_reverse) + .shuffle(kernel_shuffle) + .reshape(kernel_dims) + .eval(), contract_dims) .reshape(post_contract_dims)); } diff --git a/tensorflow/core/kernels/eigen_backward_spatial_convolutions.h b/tensorflow/core/kernels/eigen_backward_spatial_convolutions.h index cb0a76dac4..8d06107553 100644 --- a/tensorflow/core/kernels/eigen_backward_spatial_convolutions.h +++ b/tensorflow/core/kernels/eigen_backward_spatial_convolutions.h @@ -189,14 +189,19 @@ SpatialConvolutionBackwardInput( } #endif - // Reorder the dimensions to filters X patch_rows X patch_cols X channels + // Reorder the dimensions to: + // filters x patch_rows x patch_cols x channels array<TensorIndex, 4> kernel_shuffle; if (isColMajor) { + // From: filters x channels x rows x cols + // To: filters x rows x cols x channels kernel_shuffle[0] = 0; kernel_shuffle[1] = 2; kernel_shuffle[2] = 3; kernel_shuffle[3] = 1; } else { + // From: cols x rows x channels x filters + // To: channels x cols x rows x filters kernel_shuffle[0] = 2; kernel_shuffle[1] = 0; kernel_shuffle[2] = 1; diff --git a/tensorflow/core/kernels/eigen_benchmark_cpu_test.cc b/tensorflow/core/kernels/eigen_benchmark_cpu_test.cc index 7c2bbb8148..3b34f650b6 100644 --- a/tensorflow/core/kernels/eigen_benchmark_cpu_test.cc +++ b/tensorflow/core/kernels/eigen_benchmark_cpu_test.cc @@ -403,9 +403,15 @@ BM_CuboidConvolutions(8, // batch size 16, 5, 5, 5, // filter: count, height, width, panes "conv3d_depth4"); BM_CuboidConvolutions(8, 25, 25, 25, 8, 16, 5, 5, 5, "conv3d_depth8"); +BM_CuboidConvolutions(2, 9, 31, 31, 64, 64, 5, 5, 5, "b2_conv3d_1"); +BM_CuboidConvolutions(2, 5, 27, 27, 64, 64, 5, 5, 5, "b2_conv3d_2"); BM_CuboidConvolutionsBwdInput(8, 25, 25, 25, 4, 16, 5, 5, 5, "conv3d_depth4"); BM_CuboidConvolutionsBwdInput(8, 25, 25, 25, 8, 16, 5, 5, 5, "conv3d_depth8"); +BM_CuboidConvolutionsBwdInput(2, 9, 31, 31, 64, 64, 5, 5, 5, "b2_conv3d_1"); +BM_CuboidConvolutionsBwdInput(2, 5, 27, 27, 64, 64, 5, 5, 5, "b2_conv3d_2"); BM_CuboidConvolutionsBwdKernel(8, 25, 25, 25, 4, 16, 5, 5, 5, "conv3d_depth4"); BM_CuboidConvolutionsBwdKernel(8, 25, 25, 25, 8, 16, 5, 5, 5, "conv3d_depth8"); +BM_CuboidConvolutionsBwdKernel(2, 9, 31, 31, 64, 64, 5, 5, 5, "b2_conv3d_1"); +BM_CuboidConvolutionsBwdKernel(2, 5, 27, 27, 64, 64, 5, 5, 5, "b2_conv3d_2"); diff --git a/tensorflow/core/kernels/gather_nd_op_cpu_impl.h b/tensorflow/core/kernels/gather_nd_op_cpu_impl.h index 66ae7f0894..277ee2be02 100644 --- a/tensorflow/core/kernels/gather_nd_op_cpu_impl.h +++ b/tensorflow/core/kernels/gather_nd_op_cpu_impl.h @@ -123,10 +123,10 @@ struct GatherNdSlice<CPUDevice, T, Index, IXDIM> { // is considerably more efficient. #pragma omp parallel for for (Eigen::DenseIndex i = 0; i < batch_size; i++) { - const Eigen::array<Eigen::DenseIndex, 1> loc = i; + const Eigen::array<Eigen::DenseIndex, 1> loc{i}; gather_nd_generator(loc); } -#else +#else // INTEL_MKL Tscratch.device(d) = Tscratch.reshape(reshape_dims) .broadcast(broadcast_dims) .generate(gather_nd_generator) diff --git a/tensorflow/core/util/ctc/ctc_beam_entry.h b/tensorflow/core/util/ctc/ctc_beam_entry.h index 973e315f09..24002e72a0 100644 --- a/tensorflow/core/util/ctc/ctc_beam_entry.h +++ b/tensorflow/core/util/ctc/ctc_beam_entry.h @@ -1,4 +1,3 @@ -// LINT.IfChange /* Copyright 2016 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); @@ -13,6 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +// LINT.IfChange #ifndef TENSORFLOW_CORE_UTIL_CTC_CTC_BEAM_ENTRY_H_ #define TENSORFLOW_CORE_UTIL_CTC_CTC_BEAM_ENTRY_H_ diff --git a/tensorflow/core/util/ctc/ctc_beam_scorer.h b/tensorflow/core/util/ctc/ctc_beam_scorer.h index 1a622babe1..1e45a8abd3 100644 --- a/tensorflow/core/util/ctc/ctc_beam_scorer.h +++ b/tensorflow/core/util/ctc/ctc_beam_scorer.h @@ -1,4 +1,3 @@ -// LINT.IfChange /* Copyright 2016 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); @@ -13,6 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +// LINT.IfChange // Collection of scoring classes that can be extended and provided to the // CTCBeamSearchDecoder to incorporate additional scoring logic (such as a diff --git a/tensorflow/core/util/ctc/ctc_beam_search.h b/tensorflow/core/util/ctc/ctc_beam_search.h index 5e2aeb7830..6fbb1ed0da 100644 --- a/tensorflow/core/util/ctc/ctc_beam_search.h +++ b/tensorflow/core/util/ctc/ctc_beam_search.h @@ -12,6 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +// LINT.IfChange #ifndef TENSORFLOW_CORE_UTIL_CTC_CTC_BEAM_SEARCH_H_ #define TENSORFLOW_CORE_UTIL_CTC_CTC_BEAM_SEARCH_H_ diff --git a/tensorflow/core/util/ctc/ctc_decoder.h b/tensorflow/core/util/ctc/ctc_decoder.h index 3be36822e5..b55d7d77ac 100644 --- a/tensorflow/core/util/ctc/ctc_decoder.h +++ b/tensorflow/core/util/ctc/ctc_decoder.h @@ -1,4 +1,3 @@ -// LINT.IfChange /* Copyright 2016 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); @@ -13,6 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +// LINT.IfChange #ifndef TENSORFLOW_CORE_UTIL_CTC_CTC_DECODER_H_ #define TENSORFLOW_CORE_UTIL_CTC_CTC_DECODER_H_ diff --git a/tensorflow/core/util/ctc/ctc_loss_util.h b/tensorflow/core/util/ctc/ctc_loss_util.h index 36be9e92ef..054412d388 100644 --- a/tensorflow/core/util/ctc/ctc_loss_util.h +++ b/tensorflow/core/util/ctc/ctc_loss_util.h @@ -1,4 +1,3 @@ -// LINT.IfChange /* Copyright 2016 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); @@ -13,6 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +// LINT.IfChange #ifndef TENSORFLOW_CORE_UTIL_CTC_CTC_LOSS_UTIL_H_ #define TENSORFLOW_CORE_UTIL_CTC_CTC_LOSS_UTIL_H_ diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index 5af6437c56..ba9c6a2320 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -2090,6 +2090,18 @@ py_library( srcs = [ "ops/custom_gradient.py", "ops/gradients.py", + ], + srcs_version = "PY2AND3", + deps = [ + ":gradients_impl", + "//tensorflow/python/eager:function", + "//tensorflow/python/eager:tape", + ], +) + +py_library( + name = "gradients_impl", + srcs = [ "ops/gradients_impl.py", ], srcs_version = "PY2AND3", @@ -4381,6 +4393,7 @@ cuda_py_tests( "training/ftrl_test.py", "training/gradient_descent_test.py", "training/learning_rate_decay_test.py", + "training/learning_rate_decay_v2_test.py", "training/momentum_test.py", "training/optimizer_test.py", "training/proximal_adagrad_test.py", diff --git a/tensorflow/python/__init__.py b/tensorflow/python/__init__.py index a2ab63bb48..4921ecc43c 100644 --- a/tensorflow/python/__init__.py +++ b/tensorflow/python/__init__.py @@ -48,6 +48,13 @@ import numpy as np from tensorflow.python import pywrap_tensorflow +from tensorflow.python.tools import component_api_helper +component_api_helper.package_hook( + parent_package_str='tensorflow.python', + child_package_str=( + 'tensorflow_estimator.python.estimator')) +del component_api_helper + # Protocol buffers from tensorflow.core.framework.graph_pb2 import * from tensorflow.core.framework.node_def_pb2 import * diff --git a/tensorflow/python/client/session_test.py b/tensorflow/python/client/session_test.py index 052be68385..f87a96e547 100644 --- a/tensorflow/python/client/session_test.py +++ b/tensorflow/python/client/session_test.py @@ -49,6 +49,8 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import data_flow_ops from tensorflow.python.ops import gen_control_flow_ops +# Import gradients to resolve circular imports +from tensorflow.python.ops import gradients # pylint: disable=unused-import from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import math_ops # Import resource_variable_ops for the variables-to-tensor implicit conversion. diff --git a/tensorflow/python/eager/BUILD b/tensorflow/python/eager/BUILD index 6f48d38b58..85da1baaf0 100644 --- a/tensorflow/python/eager/BUILD +++ b/tensorflow/python/eager/BUILD @@ -241,7 +241,7 @@ py_library( "//tensorflow/python:dtypes", "//tensorflow/python:errors", "//tensorflow/python:framework_ops", - "//tensorflow/python:gradients", + "//tensorflow/python:gradients_impl", "//tensorflow/python:graph_to_function_def", "//tensorflow/python:util", "//tensorflow/python/eager:context", diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py index 6c87dccaf1..d56c1457e0 100644 --- a/tensorflow/python/eager/function.py +++ b/tensorflow/python/eager/function.py @@ -55,8 +55,11 @@ from tensorflow.python.util import tf_inspect # (function -> gradients_impl -> control_flow_ops -> cond_v2_impl). cond_v2_impl._function = sys.modules[__name__] # pylint: disable=protected-access +# This is to avoid a circular dependency with gradients_impl +gradients_impl._function = sys.modules[__name__] # pylint: disable=protected-access -def create_substitute_placeholder(value, name, dtype=None): + +def _create_substitute_placeholder(value, name, dtype=None): """Creates a placeholder for `value` and propagates shape info to it.""" # Note: setting ops.control_dependencies(None) ensures we always put # capturing placeholders outside of any control flow context. @@ -88,100 +91,6 @@ def create_substitute_placeholder(value, name, dtype=None): return placeholder -def capture_value(tensor_map, value, dtype, name): - """Capture a value from outside the function, to pass in as an extra arg.""" - captured_value = tensor_map.get(value, None) - if captured_value is None: - captured_value = create_substitute_placeholder(value, name=name, - dtype=dtype) - tensor_map[value] = captured_value - tape.record_operation("captured_value", [captured_value], [value], - lambda x: [x]) - return captured_value - - -class CapturingGraph(ops.Graph): - """Graph that can capture tensors from other graphs. - - Attributes: - captures: Maps external tensor -> internal tensor (e.g. input placeholder). - The entries are in the order they were captured. - """ - - def __init__(self): - super(CapturingGraph, self).__init__() - - self.captures = collections.OrderedDict() - self._building_function = True - - # Map from resource tensor name to last op (in program order) which uses - # this tensor. Used to enforce that execution order matches program order - # for resource tensors. - self._last_op_using_resource_tensor = {} - - def clear_resource_control_flow_state(self): - self._last_op_using_resource_tensor = {} - - # TODO(skyewm): get rid of name and use the name of `tensor`. - def capture(self, tensor, name=None): - """Capture `tensor` if it's external to this graph. - - If `tensor` is from a different graph, returns a placeholder for it. - `tensor` and the placeholder will also appears in self.captures. Multiple - calls to this method with the same `tensor` argument will return the same - placeholder. If `tensor` is from this graph, returns `tensor`. - - Args: - tensor: Tensor. May be from this FuncGraph or a different graph. - name: Optional name if a placeholder is created. - - Returns: - Tensor from this FuncGraph. - """ - if isinstance(tensor, ops.EagerTensor): - if name is None: - name = str(ops.uid()) - return capture_value(self.captures, tensor, tensor.dtype, name) - if tensor.graph is not self: - if name is None: - name = tensor.op.name - return capture_value(self.captures, tensor, tensor.dtype, name) - return tensor - - def create_op( - self, - op_type, - inputs, - dtypes, # pylint: disable=redefined-outer-name - input_types=None, - name=None, - attrs=None, - op_def=None, - compute_shapes=True, - compute_device=True): - """Captures an external inputs before calling Graph.capture_op.""" - # This capturing logic interacts poorly with control flow contexts which - # want to replace inputs of ops far too late in the process. This can lead - # the context to get confused and try to create an Enter for an Enter. We - # can detect this here and skip the additional Enter which can confuse loop - # validation logic. - if op_type == "Enter" and inputs[0].op.type == "Enter": - if inputs[0].op.get_attr("frame_name") == attrs["frame_name"].s: - return inputs[0].op - # Calling AddValue on the control flow contexts to force creation of the - # backward accumulators in the original graph before we create placeholders - # to capture the inputs. - ctxt = ops.get_default_graph()._control_flow_context # pylint: disable=protected-access - for i, inp in enumerate(inputs): - if ctxt is not None and hasattr(ctxt, "AddValue"): - inp = ctxt.AddValue(inp) - inp = self.capture(inp) - inputs[i] = inp - return super(CapturingGraph, self).create_op( - op_type, inputs, dtypes, input_types, name, attrs, op_def, - compute_device=compute_device) - - def _get_device_functions(ctx, graph): """Returns a tuple of device functions representing the device stack.""" if ctx.executing_eagerly(): @@ -190,7 +99,7 @@ def _get_device_functions(ctx, graph): return tuple(graph._device_functions_outer_to_inner) # pylint: disable=protected-access -class FuncGraph(CapturingGraph): +class FuncGraph(ops.Graph): """Graph representing a function body. Attributes: @@ -207,6 +116,8 @@ class FuncGraph(CapturingGraph): variables: Variables that should be watched during function execution. outer_graph: The graph this function is defined in. May be another FuncGraph or the global default Graph. + captures: Maps external tensor -> internal tensor (i.e. input placeholder). + The entries are in the order they were captured. seed: The graph-level random seed. """ @@ -227,6 +138,13 @@ class FuncGraph(CapturingGraph): self.structured_outputs = None self.variables = [] self.outer_graph = ops.get_default_graph() + self.captures = collections.OrderedDict() + + self._building_function = True + # Map from resource tensor name to last op (in program order) which uses + # this tensor. Used to enforce that execution order matches program order + # for resource tensors. + self._last_op_using_resource_tensor = {} graph = self.outer_graph @@ -255,15 +173,107 @@ class FuncGraph(CapturingGraph): self._graph_key = graph._graph_key # pylint: enable=protected-access + def create_op( + self, + op_type, + inputs, + dtypes, + input_types=None, + name=None, + attrs=None, + op_def=None, + compute_shapes=True, + compute_device=True): + """Like Graph.create_op, except handles external input tensors. + + This overload adds functionality to create_op to "capture" any external + input tensors, i.e. tensors from the eager context or outer function graphs + if this is a nested function. See `capture` for more information. + + Args: + op_type: The `Operation` type to create. This corresponds to the + `OpDef.name` field for the proto that defines the operation. + inputs: A list of `Tensor` objects that will be inputs to the `Operation`. + dtypes: A list of `DType` objects that will be the types of the tensors + that the operation produces. + input_types: (Optional.) A list of `DType`s that will be the types of + the tensors that the operation consumes. By default, uses the base + `DType` of each input in `inputs`. Operations that expect + reference-typed inputs must specify `input_types` explicitly. + name: (Optional.) A string name for the operation. If not specified, a + name is generated based on `op_type`. + attrs: (Optional.) A dictionary where the key is the attribute name (a + string) and the value is the respective `attr` attribute of the + `NodeDef` proto that will represent the operation (an `AttrValue` + proto). + op_def: (Optional.) The `OpDef` proto that describes the `op_type` that + the operation will have. + compute_shapes: (Optional.) Deprecated. Has no effect (shapes are always + computed). + compute_device: (Optional.) If True, device functions will be executed + to compute the device property of the Operation. + + Returns: + An `Operation` object. + """ + # This capturing logic interacts poorly with control flow contexts which + # want to replace inputs of ops far too late in the process. This can lead + # the context to get confused and try to create an Enter for an Enter. We + # can detect this here and skip the additional Enter which can confuse loop + # validation logic. + if op_type == "Enter" and inputs[0].op.type == "Enter": + if inputs[0].op.get_attr("frame_name") == attrs["frame_name"].s: + return inputs[0].op + # Calling AddValue on the control flow contexts to force creation of the + # backward accumulators in the original graph before we create placeholders + # to capture the inputs. + ctxt = ops.get_default_graph()._control_flow_context # pylint: disable=protected-access + for i, inp in enumerate(inputs): + # TPU Estimator defines a control flow context with no AddValue method. + if ctxt is not None and hasattr(ctxt, "AddValue"): + inp = ctxt.AddValue(inp) + inp = self.capture(inp) + inputs[i] = inp + return super(FuncGraph, self).create_op( + op_type, inputs, dtypes, input_types, name, attrs, op_def, + compute_device=compute_device) + def capture(self, tensor, name=None): - """Calls CapturingGraph.capture and updates self.inputs if necessary.""" - new_capture = tensor not in self.captures - internal_tensor = super(FuncGraph, self).capture(tensor, name) + """Captures `tensor` if it's external to this graph. - if new_capture and tensor is not internal_tensor: - self.inputs.append(internal_tensor) + If `tensor` is from a different graph, returns a placeholder for it. + `tensor` and the placeholder will appear in self.captures, and the + placeholder will appear in self.inputs. Multiple calls to this method with + the same `tensor` argument will return the same placeholder. If `tensor` is + from this graph, returns `tensor`. + + Args: + tensor: Tensor. May be from this FuncGraph or a different graph. + name: Optional name if a placeholder is created. + + Returns: + Tensor from this FuncGraph. + """ + if isinstance(tensor, ops.EagerTensor): + if name is None: + name = str(ops.uid()) + return self._capture_helper(tensor, name) + if tensor.graph is not self: + if name is None: + name = tensor.op.name + return self._capture_helper(tensor, name) + return tensor - return internal_tensor + def _capture_helper(self, tensor, name): + captured_tensor = self.captures.get(tensor, None) + if captured_tensor is None: + captured_tensor = _create_substitute_placeholder(tensor, name=name, + dtype=tensor.dtype) + self.captures[tensor] = captured_tensor + self.inputs.append(captured_tensor) + tape.record_operation("captured_value", [captured_tensor], [tensor], + lambda x: [x]) + return captured_tensor @property def external_captures(self): diff --git a/tensorflow/python/eager/tensor_test.py b/tensorflow/python/eager/tensor_test.py index 871136e2c8..32742a9b96 100644 --- a/tensorflow/python/eager/tensor_test.py +++ b/tensorflow/python/eager/tensor_test.py @@ -295,6 +295,7 @@ class TFETensorUtilTest(test_util.TensorFlowTestCase): def testFloatTensor(self): self.assertEqual(dtypes.float64, _create_tensor(np.float64()).dtype) self.assertEqual(dtypes.float32, _create_tensor(np.float32()).dtype) + self.assertEqual(dtypes.float16, _create_tensor(np.float16()).dtype) self.assertEqual(dtypes.float32, _create_tensor(0.0).dtype) def testSliceDimOutOfRange(self): diff --git a/tensorflow/python/estimator/BUILD b/tensorflow/python/estimator/BUILD index 9fce172bee..cf8e18b216 100644 --- a/tensorflow/python/estimator/BUILD +++ b/tensorflow/python/estimator/BUILD @@ -684,8 +684,11 @@ py_test( shard_count = 4, srcs_version = "PY2AND3", tags = [ + "manual", # b/112769036, b/113907597 + "no_oss", # b/112769036, b/113907597 "no_windows", - "notsan", + "nomsan", + "notsan", # b/67510291 ], deps = [ ":keras", diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py index e44a69b374..0f20acefdf 100644 --- a/tensorflow/python/estimator/estimator.py +++ b/tensorflow/python/estimator/estimator.py @@ -2056,7 +2056,7 @@ class WarmStartSettings( var_name_to_vocab_info: [Optional] Dict of variable names (strings) to `tf.estimator.VocabInfo`. The variable names should be "full" variables, not the names of the partitions. If not explicitly provided, the variable - is assumed to have no vocabulary. + is assumed to have no (changes to) vocabulary. var_name_to_prev_var_name: [Optional] Dict of variable names (strings) to name of the previously-trained variable in `ckpt_to_initialize_from`. If not explicitly provided, the name of the variable is assumed to be same diff --git a/tensorflow/python/estimator/run_config.py b/tensorflow/python/estimator/run_config.py index b1ca207b62..3773810a04 100644 --- a/tensorflow/python/estimator/run_config.py +++ b/tensorflow/python/estimator/run_config.py @@ -521,7 +521,12 @@ class RunConfig(object): eval_distribute=eval_distribute, experimental_distribute=experimental_distribute) - if train_distribute or eval_distribute or experimental_distribute: + # TODO(frankchn,priyag): Eventually use distributed coordinator for TPUs. + if ((train_distribute and + train_distribute.__class__.__name__ != 'TPUStrategy') or + (eval_distribute and + eval_distribute.__class__.__name__ != 'TPUStrategy') or + experimental_distribute): logging.info('Initializing RunConfig with distribution strategies.') distribute_coordinator_training.init_run_config(self, tf_config) else: diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py index 4cfd639bf9..9401309c19 100644 --- a/tensorflow/python/framework/ops.py +++ b/tensorflow/python/framework/ops.py @@ -55,6 +55,7 @@ from tensorflow.python.platform import app from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import compat from tensorflow.python.util import decorator_utils +from tensorflow.python.util import deprecation from tensorflow.python.util import function_utils from tensorflow.python.util import lock_util from tensorflow.python.util import tf_contextlib @@ -5807,11 +5808,8 @@ class GraphKeys(object): _STREAMING_MODEL_PORTS = "streaming_model_ports" @decorator_utils.classproperty + @deprecation.deprecated(None, "Use `tf.GraphKeys.GLOBAL_VARIABLES` instead.") def VARIABLES(cls): # pylint: disable=no-self-argument - logging.log_first_n(logging.WARN, - "VARIABLES collection name is deprecated, please use " - "GLOBAL_VARIABLES instead; VARIABLES will be removed " - "after 2017-03-02.", 1) return cls.GLOBAL_VARIABLES diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py index 3b63e49a84..0925598e33 100644 --- a/tensorflow/python/framework/test_util.py +++ b/tensorflow/python/framework/test_util.py @@ -1073,13 +1073,9 @@ class TensorFlowTestCase(googletest.TestCase): if context.executing_eagerly(): yield None else: - sess = self._create_session(graph, config, use_gpu, force_gpu) - with self._constrain_devices_and_set_default( - sess, use_gpu, force_gpu) as constrained_sess: - # We need to do this to make sure the session closes, otherwise, even - # if the user does with self.session():, it will not close the session. - with constrained_sess: - yield constrained_sess + with self._create_session(graph, config, force_gpu) as sess: + with self._constrain_devices_and_set_default(sess, use_gpu, force_gpu): + yield sess @contextlib.contextmanager def cached_session(self, @@ -1127,10 +1123,11 @@ class TensorFlowTestCase(googletest.TestCase): if context.executing_eagerly(): yield None else: - with self._get_cached_session( - graph, config, use_gpu, force_gpu, - crash_if_inconsistent_args=True) as sess: - yield sess + sess = self._get_cached_session( + graph, config, force_gpu, crash_if_inconsistent_args=True) + with self._constrain_devices_and_set_default(sess, use_gpu, + force_gpu) as cached: + yield cached @contextlib.contextmanager def test_session(self, @@ -1146,10 +1143,11 @@ class TensorFlowTestCase(googletest.TestCase): yield None else: if graph is None: - with self._get_cached_session( - graph, config, use_gpu, force_gpu, - crash_if_inconsistent_args=False) as sess: - yield sess + sess = self._get_cached_session( + graph, config, force_gpu, crash_if_inconsistent_args=False) + with self._constrain_devices_and_set_default(sess, use_gpu, + force_gpu) as cached: + yield cached else: with self.session(graph, config, use_gpu, force_gpu) as sess: yield sess @@ -1835,91 +1833,69 @@ class TensorFlowTestCase(googletest.TestCase): with sess.graph.device("/cpu:0"): yield sess - def _create_session(self, graph, config, use_gpu, force_gpu): + def _create_session(self, graph, config, force_gpu): """See session() for details.""" - if context.executing_eagerly(): - return None - else: + def prepare_config(config): + """Returns a config for sessions. - def prepare_config(config): - """Returns a config for sessions. - - Args: - config: An optional config_pb2.ConfigProto to use to configure the - session. - Returns: - A config_pb2.ConfigProto object. - """ - if config is None: - config = config_pb2.ConfigProto() - config.allow_soft_placement = not force_gpu - config.gpu_options.per_process_gpu_memory_fraction = 0.3 - elif force_gpu and config.allow_soft_placement: - config = config_pb2.ConfigProto().CopyFrom(config) - config.allow_soft_placement = False - # Don't perform optimizations for tests so we don't inadvertently run - # gpu ops on cpu - config.graph_options.optimizer_options.opt_level = -1 - config.graph_options.rewrite_options.constant_folding = ( - rewriter_config_pb2.RewriterConfig.OFF) - config.graph_options.rewrite_options.arithmetic_optimization = ( - rewriter_config_pb2.RewriterConfig.OFF) - return config - - return ErrorLoggingSession(graph=graph, config=prepare_config(config)) + Args: + config: An optional config_pb2.ConfigProto to use to configure the + session. + + Returns: + A config_pb2.ConfigProto object. + """ + if config is None: + config = config_pb2.ConfigProto() + config.allow_soft_placement = not force_gpu + config.gpu_options.per_process_gpu_memory_fraction = 0.3 + elif force_gpu and config.allow_soft_placement: + config = config_pb2.ConfigProto().CopyFrom(config) + config.allow_soft_placement = False + # Don't perform optimizations for tests so we don't inadvertently run + # gpu ops on cpu + config.graph_options.optimizer_options.opt_level = -1 + config.graph_options.rewrite_options.constant_folding = ( + rewriter_config_pb2.RewriterConfig.OFF) + config.graph_options.rewrite_options.arithmetic_optimization = ( + rewriter_config_pb2.RewriterConfig.OFF) + return config + + return ErrorLoggingSession(graph=graph, config=prepare_config(config)) - @contextlib.contextmanager def _get_cached_session(self, graph=None, config=None, - use_gpu=False, force_gpu=False, crash_if_inconsistent_args=True): """See cached_session() for documentation.""" - if context.executing_eagerly(): - yield None + if self._cached_session is None: + sess = self._create_session( + graph=graph, config=config, force_gpu=force_gpu) + self._cached_session = sess + self._cached_graph = graph + self._cached_config = config + self._cached_force_gpu = force_gpu + return sess else: - if self._cached_session is None: - sess = self._create_session( - graph=graph, config=config, use_gpu=use_gpu, force_gpu=force_gpu) - self._cached_session = sess - self._cached_graph = graph - self._cached_config = config - self._cached_use_gpu = use_gpu - self._cached_force_gpu = force_gpu - with self._constrain_devices_and_set_default( - sess, use_gpu, force_gpu) as constrained_sess: - yield constrained_sess - else: - if crash_if_inconsistent_args and self._cached_graph is not graph: - raise ValueError("The graph used to get the cached session is " - "different than the one that was used to create the " - "session. Maybe create a new session with " - "self.session()") - if crash_if_inconsistent_args and self._cached_config is not config: - raise ValueError("The config used to get the cached session is " - "different than the one that was used to create the " - "session. Maybe create a new session with " - "self.session()") - if crash_if_inconsistent_args and self._cached_use_gpu is not use_gpu: - raise ValueError( - "The use_gpu value used to get the cached session is " - "different than the one that was used to create the " - "session. Maybe create a new session with " - "self.session()") - if crash_if_inconsistent_args and (self._cached_force_gpu is - not force_gpu): - raise ValueError( - "The force_gpu value used to get the cached session is " - "different than the one that was used to create the " - "session. Maybe create a new session with " - "self.session()") - # If you modify this logic, make sure to modify it in _create_session - # as well. - sess = self._cached_session - with self._constrain_devices_and_set_default( - sess, use_gpu, force_gpu) as constrained_sess: - yield constrained_sess + if crash_if_inconsistent_args and self._cached_graph is not graph: + raise ValueError("The graph used to get the cached session is " + "different than the one that was used to create the " + "session. Maybe create a new session with " + "self.session()") + if crash_if_inconsistent_args and self._cached_config is not config: + raise ValueError("The config used to get the cached session is " + "different than the one that was used to create the " + "session. Maybe create a new session with " + "self.session()") + if crash_if_inconsistent_args and (self._cached_force_gpu is + not force_gpu): + raise ValueError( + "The force_gpu value used to get the cached session is " + "different than the one that was used to create the " + "session. Maybe create a new session with " + "self.session()") + return self._cached_session @tf_export("test.create_local_cluster") diff --git a/tensorflow/python/framework/test_util_test.py b/tensorflow/python/framework/test_util_test.py index a0939f98b2..c4f8fa9108 100644 --- a/tensorflow/python/framework/test_util_test.py +++ b/tensorflow/python/framework/test_util_test.py @@ -71,9 +71,6 @@ class TestUtilTest(test_util.TensorFlowTestCase): with self.cached_session(graph=ops.Graph()) as sess2: pass with self.assertRaises(ValueError): - with self.cached_session(use_gpu=True) as sess2: - pass - with self.assertRaises(ValueError): with self.cached_session(force_gpu=True) as sess2: pass # We make sure that test_session will cache the session even after the diff --git a/tensorflow/python/keras/engine/base_layer.py b/tensorflow/python/keras/engine/base_layer.py index b6b05c0311..cb19a412a2 100644 --- a/tensorflow/python/keras/engine/base_layer.py +++ b/tensorflow/python/keras/engine/base_layer.py @@ -1001,7 +1001,7 @@ class Layer(checkpointable.CheckpointableBase): self.build(input_shape) with context.graph_mode(): - graph = eager_function.CapturingGraph() + graph = eager_function.FuncGraph('graph') with graph.as_default(): if isinstance(input_shape, list): inputs = [generate_placeholders_from_shape(shape) diff --git a/tensorflow/python/keras/engine/network.py b/tensorflow/python/keras/engine/network.py index f8c23ed124..10dd70cf23 100644 --- a/tensorflow/python/keras/engine/network.py +++ b/tensorflow/python/keras/engine/network.py @@ -770,7 +770,7 @@ class Network(base_layer.Layer): # and graph building, the variables created after building the model in # a Graph are still valid when executing eagerly. with context.graph_mode(): - graph = eager_function.CapturingGraph() + graph = eager_function.FuncGraph('graph') with graph.as_default(): if isinstance(input_shape, list): x = [base_layer.generate_placeholders_from_shape(shape) diff --git a/tensorflow/python/keras/engine/training_distributed.py b/tensorflow/python/keras/engine/training_distributed.py index a7bb1f8177..e440e02bfb 100644 --- a/tensorflow/python/keras/engine/training_distributed.py +++ b/tensorflow/python/keras/engine/training_distributed.py @@ -19,13 +19,16 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np +from tensorflow.python.framework import constant_op from tensorflow.python.framework import errors from tensorflow.python.keras import backend as K from tensorflow.python.keras import callbacks as cbks from tensorflow.python.keras import optimizers from tensorflow.python.keras.engine import distributed_training_utils from tensorflow.python.keras.utils.generic_utils import Progbar +from tensorflow.python.ops import array_ops from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.training import distribute as distribute_lib def fit_loop( @@ -64,6 +67,11 @@ def fit_loop( """ current_strategy = model._distribution_strategy + # TODO(priyag, sourabhbajaj): Remove this when the codepaths are merged. + if current_strategy.__class__.__name__ == 'TPUStrategy': + return _experimental_fit_loop( + model, iterator, epochs, initial_epoch, steps_per_epoch) + clone_model_on_towers( model, current_strategy, make_callback_model=True) @@ -116,11 +124,6 @@ def fit_loop( do_validation = False if validation_steps: do_validation = True - if steps_per_epoch is None: - raise ValueError('Can only use `validation_steps` ' - 'when doing step-wise ' - 'training, i.e. `steps_per_epoch` ' - 'must be set.') # Copy the weights from the original model to each of the replicated models. orig_model_weights = model.get_weights() @@ -140,44 +143,46 @@ def fit_loop( verbose=verbose) out_labels = model.metrics_names or [] callbacks.on_train_begin() + + assert steps_per_epoch is not None + for epoch in range(initial_epoch, epochs): callbacks.on_epoch_begin(epoch) - if steps_per_epoch is not None: - epoch_logs = {} - for step_index in range(steps_per_epoch): - batch_logs = {'batch': step_index, 'size': 1} - callbacks.on_batch_begin(step_index, batch_logs) - try: - outs = distributed_train_function(ins) - except errors.OutOfRangeError: - logging.warning('Your dataset iterator ran out of data; ' - 'interrupting training. Make sure that your dataset ' - 'can generate at least `steps_per_epoch * epochs` ' - 'batches (in this case, %d batches).' % - steps_per_epoch * epochs) - break - - if not isinstance(outs, list): - outs = [outs] - - outs = _aggregate_metrics_across_towers( - current_strategy.num_towers, out_labels, outs) - for l, o in zip(out_labels, outs): - batch_logs[l] = o - callbacks.on_batch_end(step_index, batch_logs) - if callbacks.model.stop_training: - break - if do_validation: - val_outs = test_loop( - model, - val_iterator, - steps=validation_steps, - verbose=0) - if not isinstance(val_outs, list): - val_outs = [val_outs] - # Same labels assumed. - for l, o in zip(out_labels, val_outs): - epoch_logs['val_' + l] = o + epoch_logs = {} + for step_index in range(steps_per_epoch): + batch_logs = {'batch': step_index, 'size': 1} + callbacks.on_batch_begin(step_index, batch_logs) + try: + outs = distributed_train_function(ins) + except errors.OutOfRangeError: + logging.warning('Your dataset iterator ran out of data; ' + 'interrupting training. Make sure that your dataset ' + 'can generate at least `steps_per_epoch * epochs` ' + 'batches (in this case, %d batches).' % + steps_per_epoch * epochs) + break + + if not isinstance(outs, list): + outs = [outs] + + outs = _aggregate_metrics_across_towers( + current_strategy.num_towers, out_labels, outs) + for l, o in zip(out_labels, outs): + batch_logs[l] = o + callbacks.on_batch_end(step_index, batch_logs) + if callbacks.model.stop_training: + break + if do_validation: + val_outs = test_loop( + model, + val_iterator, + steps=validation_steps, + verbose=0) + if not isinstance(val_outs, list): + val_outs = [val_outs] + # Same labels assumed. + for l, o in zip(out_labels, val_outs): + epoch_logs['val_' + l] = o callbacks.on_epoch_end(epoch, epoch_logs) if callbacks.model.stop_training: @@ -192,6 +197,139 @@ def fit_loop( return model.history +def _experimental_fit_loop( + model, + iterator, + epochs=100, + initial_epoch=0, + steps_per_epoch=None): + """fit function when using TPU DistributionStrategy for training. + + Arguments: + model: Keras Model instance. + iterator: Iterator that returns inputs and targets + epochs: Number of times to iterate over the data + initial_epoch: Epoch at which to start training + (useful for resuming a previous training run) + steps_per_epoch: Total number of steps (batches of samples) + before declaring one epoch finished and starting the + next epoch. Ignored with the default value of `None`. + + Returns: + Returns `None`. + + Raises: + ValueError: in case of invalid arguments. + """ + current_strategy = model._distribution_strategy + + # TODO(priyag): Add validation that shapes are fully defined for TPU case. + + # TODO(priyag, sourabhbajaj): This should be moved into a callback instead. + K.get_session().run(current_strategy.initialize()) + + def _per_device_train_function(model): + model._make_train_function() + return (model.train_function.inputs, + model.train_function.outputs, + model.train_function.updates_op, + model.train_function.session_kwargs) + + # TODO(priyag, sourabhbajaj): This should likely not be hardcoded here. + K.set_learning_phase(1) + + def step_fn(ctx, inputs, targets): + """Clones the model and calls make_train_function.""" + # TODO(priyag, sourabhbajaj): Should cache this keyed on input shapes. + clone_model_on_towers( + model, + current_strategy, + make_callback_model=True, + inputs=inputs, + targets=targets) + + (grouped_inputs, grouped_outputs, grouped_updates, + grouped_session_args) = current_strategy.call_for_each_tower( + _per_device_train_function, model._grouped_model) + (all_inputs, all_outputs, all_updates, + all_session_args) = distributed_training_utils.unwrap_values( + current_strategy, grouped_inputs, grouped_outputs, + grouped_updates, grouped_session_args, with_loss_tensor=True) + combined_fn = K.Function( + all_inputs, all_outputs, + updates=all_updates, + name='distributed_train_function', + **all_session_args) + + # TODO(priyag, sourabhbajaj): Perhaps the aggregation type needs to be + # something else for different outputs. + out_labels = model.metrics_names or [] + for label, output in zip(out_labels, combined_fn.outputs): + ctx.set_last_step_output(label, output, + aggregation=distribute_lib.get_loss_reduction()) + + # TODO(priyag, sourabhbajaj): Ignoring these things from the combined_fn: + # feed_dict, session kwargs, run options, run_metadata for now. These should + # be handled appropriately + return combined_fn.updates_op + + # Add initial dummy values for loss and other metric tensors. + initial_loop_values = {} + initial_loop_values['loss'] = constant_op.constant(1e7) + for name, tensor in zip(model.metrics_names[1:], model.metrics_tensors): + initial_loop_values[name] = array_ops.zeros(tensor.shape, tensor.dtype) + + with current_strategy.scope(): + # TODO(priyag, sourabhbajaj): Adjust steps_per_run appropriately based on + # steps_per_epoch and number of epochs. + ctx = current_strategy.run_steps_on_dataset( + step_fn, iterator, iterations=current_strategy.steps_per_run, + initial_loop_values=initial_loop_values) + + train_op = ctx.run_op + output_tensors = ctx.last_step_outputs + + # Copy the weights from the original model to each of the replicated models. + orig_model_weights = model.get_weights() + with current_strategy.scope(): + distributed_model = current_strategy.unwrap(model._grouped_model)[0] + distributed_training_utils.set_weights( + current_strategy, distributed_model, orig_model_weights) + + assert steps_per_epoch is not None + + # TODO(priyag, sourabhbajaj): Add callbacks support. + # TODO(priyag, sourabhbajaj): Add validation. + for epoch in range(initial_epoch, epochs): + for step_index in range( + 0, steps_per_epoch, current_strategy.steps_per_run): + try: + _, outs = K.get_session().run([train_op, output_tensors]) + # TODO(priyag, sourabhbajaj): Remove this logging in favor of proper + # summaries through callbacks. + print('Epoch: {}, step_index: {}, loss: {}'.format( + epoch, step_index, outs['loss'])) + for label, out in outs.items(): + print(label, ': ', out) + except errors.OutOfRangeError: + logging.warning('Your dataset iterator ran out of data; ' + 'interrupting training. Make sure that your dataset ' + 'can generate at least `steps_per_epoch * epochs` ' + 'batches (in this case, %d batches).' % + steps_per_epoch * epochs) + break + + # Copy the weights back from the replicated model to the original model. + with current_strategy.scope(): + updated_weights = current_strategy.unwrap( + model._grouped_model)[0].get_weights() + model.set_weights(updated_weights) + + K.get_session().run(current_strategy.finalize()) + + # TODO(priyag, sourabhbajaj): Return history. + + def test_loop(model, iterator, verbose=0, steps=None): """evaluate method to validate a model that uses DistributionStrategy. @@ -373,12 +511,12 @@ def predict_loop(model, iterator, verbose=0, steps=None): ] -def _clone_and_build_model(model): +def _clone_and_build_model(model, inputs=None, targets=None): """Clone and build the given keras_model.""" # We need to set the import here since we run into a circular dependency # error. from tensorflow.python.keras import models # pylint: disable=g-import-not-at-top - cloned_model = models.clone_model(model, input_tensors=None) + cloned_model = models.clone_model(model, input_tensors=inputs) # Compile and build model. if isinstance(model.optimizer, optimizers.TFOptimizer): @@ -387,22 +525,29 @@ def _clone_and_build_model(model): optimizer_config = model.optimizer.get_config() optimizer = model.optimizer.__class__.from_config(optimizer_config) + # TODO(priyag): Is there a cleaner way to do this? The API doc suggests a + # single tensor should be OK but it throws an error in that case. + if (targets is not None and not isinstance(targets, list) and + not isinstance(targets, dict)): + targets = [targets] cloned_model.compile( optimizer, model.loss, metrics=model.metrics, loss_weights=model.loss_weights, sample_weight_mode=model.sample_weight_mode, - weighted_metrics=model.weighted_metrics) + weighted_metrics=model.weighted_metrics, + target_tensors=targets) return cloned_model -def clone_model_on_towers(model, strategy, make_callback_model=False): +def clone_model_on_towers( + model, strategy, make_callback_model=False, inputs=None, targets=None): """Create a cloned model on each tower, unless already created.""" if not model._grouped_model: with strategy.scope(): model._grouped_model = strategy.call_for_each_tower( - _clone_and_build_model, model) + _clone_and_build_model, model, inputs, targets) if make_callback_model: model._make_callback_model() diff --git a/tensorflow/python/kernel_tests/py_func_test.py b/tensorflow/python/kernel_tests/py_func_test.py index 79fcbaad43..5f5e24bd63 100644 --- a/tensorflow/python/kernel_tests/py_func_test.py +++ b/tensorflow/python/kernel_tests/py_func_test.py @@ -566,6 +566,18 @@ class PyFuncTest(test.TestCase): dy_dx = gradients_impl.gradients(y, x)[0] self.assertEqual(self.evaluate(dy_dx), 6.0) + def testEagerGradientGraphTwoOutputs(self): + + def f(x, y): + return x * y, x / y + + x = constant_op.constant(3.0) + y = constant_op.constant(2.0) + fa, fb = script_ops.eager_py_func(f, inp=[x, y], + Tout=[dtypes.float32, dtypes.float32]) + dy_dx = gradients_impl.gradients(fa + fb, x)[0] + self.assertEqual(self.evaluate(dy_dx), 2.5) + @test_util.run_in_graph_and_eager_modes def testEagerGradientTapeMultipleArgs(self): diff --git a/tensorflow/python/lib/core/py_seq_tensor.cc b/tensorflow/python/lib/core/py_seq_tensor.cc index 3b4f12ae31..269142a7c2 100644 --- a/tensorflow/python/lib/core/py_seq_tensor.cc +++ b/tensorflow/python/lib/core/py_seq_tensor.cc @@ -55,6 +55,10 @@ bool IsPyDouble(PyObject* obj) { return PyIsInstance(obj, &PyDoubleArrType_Type); // NumPy double type. } +bool IsNumpyHalf(PyObject* obj) { + return PyIsInstance(obj, &PyHalfArrType_Type); +} + bool IsPyFloat(PyObject* obj) { return PyFloat_Check(obj) || PyIsInstance(obj, &PyFloatingArrType_Type); // NumPy float types @@ -156,6 +160,8 @@ Status InferShapeAndType(PyObject* obj, TensorShape* shape, DataType* dtype) { } } else if (IsPyDouble(obj)) { *dtype = DT_DOUBLE; + } else if (IsNumpyHalf(obj)) { + *dtype = DT_HALF; } else if (IsPyFloat(obj)) { *dtype = DT_FLOAT; } else if (PyBool_Check(obj) || PyIsInstance(obj, &PyBoolArrType_Type)) { @@ -357,6 +363,17 @@ const char* ConvertOneFloat(PyObject* v, T* out) { DEFINE_HELPER(ConvertDouble, double, DT_DOUBLE, ConvertOneFloat<double>); DEFINE_HELPER(ConvertFloat, float, DT_FLOAT, ConvertOneFloat<float>); +const char* ConvertOneNumpyHalf(PyObject* v, Eigen::half* out) { + // NOTE(nareshmodi): Is there a way to convert to C double without the + // intermediate Python double? This will help with ConvertOneFloat as well. + Safe_PyObjectPtr as_float = make_safe(PyNumber_Float(v)); + double v_double = PyFloat_AS_DOUBLE(as_float.get()); + *out = Eigen::half(v_double); + + return nullptr; +} +DEFINE_HELPER(ConvertNumpyHalf, Eigen::half, DT_HALF, ConvertOneNumpyHalf); + // String support const char* ConvertOneString(PyObject* v, string* out) { @@ -452,6 +469,9 @@ Status PySeqToTensor(PyObject* obj, PyObject* dtype, Tensor* ret) { if (ConvertDouble(obj, shape, ret) == nullptr) return Status::OK(); break; + case DT_HALF: + RETURN_STRING_AS_STATUS(ConvertNumpyHalf(obj, shape, ret)); + case DT_INT64: if (ConvertInt64(obj, shape, ret) == nullptr) return Status::OK(); break; @@ -489,8 +509,13 @@ Status PySeqToTensor(PyObject* obj, PyObject* dtype, Tensor* ret) { // final type. RETURN_STRING_AS_STATUS(ConvertDouble(obj, shape, ret)); } + case DT_DOUBLE: RETURN_STRING_AS_STATUS(ConvertDouble(obj, shape, ret)); + + case DT_HALF: + RETURN_STRING_AS_STATUS(ConvertNumpyHalf(obj, shape, ret)); + case DT_INT64: if (requested_dtype == DT_INVALID) { const char* error = ConvertInt32(obj, shape, ret); diff --git a/tensorflow/python/ops/gradients.py b/tensorflow/python/ops/gradients.py index 9fa8e27d5c..1dc666e78b 100644 --- a/tensorflow/python/ops/gradients.py +++ b/tensorflow/python/ops/gradients.py @@ -19,10 +19,10 @@ from __future__ import division from __future__ import print_function # pylint: disable=unused-import +from tensorflow.python.eager import function from tensorflow.python.eager.backprop import GradientTape from tensorflow.python.ops.custom_gradient import custom_gradient from tensorflow.python.ops.gradients_impl import AggregationMethod from tensorflow.python.ops.gradients_impl import gradients from tensorflow.python.ops.gradients_impl import hessians # pylint: enable=unused-import - diff --git a/tensorflow/python/ops/gradients_impl.py b/tensorflow/python/ops/gradients_impl.py index a68f680224..3268b38b86 100644 --- a/tensorflow/python/ops/gradients_impl.py +++ b/tensorflow/python/ops/gradients_impl.py @@ -31,7 +31,7 @@ from tensorflow.core.framework import attr_value_pb2 from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes -from tensorflow.python.framework import function +from tensorflow.python.framework import function as framework_function from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_util @@ -58,6 +58,10 @@ from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import compat from tensorflow.python.util.tf_export import tf_export +# This is to avoid a circular dependency (eager.function depends on +# gradients_impl). This is set in eager/function.py. +_function = None + # This is to avoid a circular dependency with cond_v2_impl. cond_v2_impl._gradients_impl = sys.modules[__name__] # pylint: disable=protected-access @@ -121,7 +125,7 @@ def _MarkReachedOps(from_ops, reached_ops, func_graphs): Args: from_ops: list of Operations. reached_ops: set of Operations. - func_graphs: list of function._FuncGraphs. This method will traverse through + func_graphs: list of _function.FuncGraphs. This method will traverse through these functions if they capture from_ops or any reachable ops. """ queue = collections.deque() @@ -146,7 +150,7 @@ def _PendingCount(to_ops, from_ops, colocate_gradients_with_ops, func_graphs, to_ops: list of Operations. from_ops: list of Operations. colocate_gradients_with_ops: Python bool. See docstring of gradients(). - func_graphs: list of function._FuncGraphs. This method will traverse through + func_graphs: list of _function.FuncGraphs. This method will traverse through these functions if they capture from_ops or any reachable ops. This is useful if to_ops occur in a function and from_ops are in an outer function or graph. @@ -441,6 +445,19 @@ def _RaiseNoGradWrtInitialLoopValError(op, from_ops, xs): % target_op.name) +def _IsFunction(graph): + return (isinstance(graph, _function.FuncGraph) or + isinstance(graph, framework_function._FuncGraph)) # pylint: disable=protected-access + + +def _Captures(func_graph): + if isinstance(func_graph, _function.FuncGraph): + return func_graph.captures + else: + assert isinstance(func_graph, framework_function._FuncGraph) # pylint: disable=protected-access + return func_graph._captured # pylint: disable=protected-access + + def _MaybeCaptured(t): """If t is a captured value placeholder, returns the original captured value. @@ -448,11 +465,11 @@ def _MaybeCaptured(t): t: Tensor Returns: - A tensor, potentially from a different Graph/function._FuncGraph. + A tensor, potentially from a different Graph/_function.FuncGraph. """ # pylint: disable=protected-access - if isinstance(t.op.graph, function._FuncGraph) and t.op.type == "Placeholder": - for input_t, placeholder_t in t.op.graph._captured.items(): + if _IsFunction(t.op.graph) and t.op.type == "Placeholder": + for input_t, placeholder_t in _Captures(t.op.graph).items(): if t == placeholder_t: return _MaybeCaptured(input_t) # pylint: enable=protected-access @@ -470,10 +487,10 @@ def _Inputs(op, xs): Returns: A list of tensors. The tensors may be from multiple - Graph/function._FuncGraphs if op is in a function._FuncGraph and has + Graph/_function.FuncGraphs if op is in a _function.FuncGraph and has captured inputs. """ - if isinstance(op.graph, function._FuncGraph): # pylint: disable=protected-access + if _IsFunction(op.graph): # pylint: disable=protected-access # If we're differentiating w.r.t. `t`, do not attempt to traverse through it # to a captured value. The algorithm needs to "see" `t` in this case, even # if it's a function input for a captured value, whereas usually we'd like @@ -489,7 +506,7 @@ def _Consumers(t, func_graphs): Args: t: Tensor - func_graphs: a list of function._FuncGraphs that may have captured t. + func_graphs: a list of _function.FuncGraphs that may have captured t. Returns: A list of tensors. The tensors will be from the current graph and/or @@ -497,7 +514,7 @@ def _Consumers(t, func_graphs): """ consumers = t.consumers() for func in func_graphs: - for input_t, placeholder in func._captured.items(): # pylint: disable=protected-access + for input_t, placeholder in _Captures(func).items(): if input_t == t: consumers.extend(_Consumers(placeholder, func_graphs)) return consumers @@ -616,9 +633,13 @@ def _GradientsHelper(ys, # ancestor graphs. This is necessary for correctly handling captured values. func_graphs = [] curr_graph = src_graph - while isinstance(curr_graph, function._FuncGraph): # pylint: disable=protected-access + while _IsFunction(curr_graph): func_graphs.append(curr_graph) - curr_graph = curr_graph._outer_graph # pylint: disable=protected-access + if isinstance(curr_graph, _function.FuncGraph): + curr_graph = curr_graph.outer_graph + else: + assert isinstance(curr_graph, framework_function._FuncGraph) # pylint: disable=protected-access + curr_graph = curr_graph._outer_graph # pylint: disable=protected-access ys = _AsList(ys) xs = _AsList(xs) diff --git a/tensorflow/python/ops/gradients_test.py b/tensorflow/python/ops/gradients_test.py index fa9910b351..3759d8a543 100644 --- a/tensorflow/python/ops/gradients_test.py +++ b/tensorflow/python/ops/gradients_test.py @@ -26,9 +26,10 @@ import numpy as np from tensorflow.python.client import session from tensorflow.python.eager import backprop from tensorflow.python.eager import context +from tensorflow.python.eager import function from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes -from tensorflow.python.framework import function +from tensorflow.python.framework import function as framework_function from tensorflow.python.framework import ops from tensorflow.python.framework import test_ops from tensorflow.python.framework import test_util @@ -369,8 +370,8 @@ class FunctionGradientsTest(test_util.TensorFlowTestCase): @classmethod def _GetFunc(cls, **kwargs): - return function.Defun(dtypes.float32, dtypes.float32, ** - kwargs)(cls.XSquarePlusB) + return framework_function.Defun(dtypes.float32, dtypes.float32, ** + kwargs)(cls.XSquarePlusB) def _GetFuncGradients(self, f, x_value, b_value): x = constant_op.constant(x_value, name="x") @@ -408,8 +409,9 @@ class FunctionGradientsTest(test_util.TensorFlowTestCase): def testFunctionGradientsWithGradFunc(self): g = ops.Graph() with g.as_default(): - grad_func = function.Defun(dtypes.float32, dtypes.float32, - dtypes.float32)(self.XSquarePlusBGradient) + grad_func = framework_function.Defun(dtypes.float32, dtypes.float32, + dtypes.float32)( + self.XSquarePlusBGradient) f = self._GetFunc(grad_func=grad_func) # Get gradients (should add SymbolicGradient node for function, which # uses the grad_func above, which multiplies all gradients by 2). @@ -430,8 +432,9 @@ class FunctionGradientsTest(test_util.TensorFlowTestCase): def testFunctionGradientWithGradFuncAndRegistration(self): g = ops.Graph() with g.as_default(): - grad_func = function.Defun(dtypes.float32, dtypes.float32, - dtypes.float32)(self.XSquarePlusBGradient) + grad_func = framework_function.Defun(dtypes.float32, dtypes.float32, + dtypes.float32)( + self.XSquarePlusBGradient) with self.assertRaisesRegexp(ValueError, "Gradient defined twice"): f = self._GetFunc( grad_func=grad_func, python_grad_func=self._PythonGradient) @@ -441,7 +444,7 @@ class FunctionGradientsTest(test_util.TensorFlowTestCase): with ops.Graph().as_default(): x = constant_op.constant(1.0, name="x") - @function.Defun() + @function.defun() def Foo(): y = math_ops.multiply(x, 2.0, name="y") g = gradients_impl.gradients(y, x) @@ -456,7 +459,7 @@ class FunctionGradientsTest(test_util.TensorFlowTestCase): x = constant_op.constant(1.0, name="x") y = math_ops.multiply(x, 2.0, name="y") - @function.Defun() + @framework_function.Defun() def Foo(): g = gradients_impl.gradients(y, x) return g[0] @@ -469,7 +472,7 @@ class FunctionGradientsTest(test_util.TensorFlowTestCase): with ops.Graph().as_default(): var = resource_variable_ops.ResourceVariable(1.0, name="var") - @function.Defun() + @function.defun() def Foo(): y = math_ops.multiply(var, 2.0, name="y") g = gradients_impl.gradients(y, var) @@ -486,11 +489,11 @@ class FunctionGradientsTest(test_util.TensorFlowTestCase): x2 = constant_op.constant(2.0, name="x2") x3 = math_ops.multiply(x1, x2, name="x3") - @function.Defun() + @function.defun() def Outer(): outer1 = array_ops.identity(x1, name="outer1") - @function.Defun() + @function.defun() def Inner(): inner1 = array_ops.identity(outer1, name="inner1") inner2 = array_ops.identity(x2, name="inner2") @@ -511,11 +514,11 @@ class FunctionGradientsTest(test_util.TensorFlowTestCase): with ops.Graph().as_default(): x = constant_op.constant(1.0, name="x") - @function.Defun() + @function.defun() def Outer(): y = math_ops.multiply(x, 2.0, name="y") - @function.Defun() + @function.defun() def Inner(): z = math_ops.multiply(y, 3.0, name="z") g = gradients_impl.gradients(z, y) diff --git a/tensorflow/python/ops/io_ops.py b/tensorflow/python/ops/io_ops.py index fbc1350c61..f84785df2c 100644 --- a/tensorflow/python/ops/io_ops.py +++ b/tensorflow/python/ops/io_ops.py @@ -33,8 +33,9 @@ from tensorflow.python.ops import gen_io_ops # go/tf-wildcard-import # pylint: disable=wildcard-import from tensorflow.python.ops.gen_io_ops import * -from tensorflow.python.util.tf_export import tf_export # pylint: enable=wildcard-import +from tensorflow.python.util import deprecation +from tensorflow.python.util.tf_export import tf_export # pylint: disable=protected-access @@ -95,7 +96,7 @@ def _restore_slice(file_pattern, tensor_name, shape_and_slice, tensor_type, preferred_shard, name=name) -@tf_export("ReaderBase") +@tf_export(v1=["ReaderBase"]) class ReaderBase(object): """Base class for different Reader types, that produce a record every step. @@ -309,7 +310,7 @@ ops.NotDifferentiable("ReaderRestoreState") ops.NotDifferentiable("ReaderReset") -@tf_export("WholeFileReader") +@tf_export(v1=["WholeFileReader"]) class WholeFileReader(ReaderBase): """A Reader that outputs the entire contents of a file as a value. @@ -324,6 +325,9 @@ class WholeFileReader(ReaderBase): @end_compatibility """ + @deprecation.deprecated( + None, "Queue-based input pipelines have been replaced by `tf.data`. Use " + "`tf.data.Dataset.map(tf.read_file)`.") def __init__(self, name=None): """Create a WholeFileReader. @@ -337,7 +341,7 @@ class WholeFileReader(ReaderBase): ops.NotDifferentiable("WholeFileReader") -@tf_export("TextLineReader") +@tf_export(v1=["TextLineReader"]) class TextLineReader(ReaderBase): """A Reader that outputs the lines of a file delimited by newlines. @@ -351,6 +355,9 @@ class TextLineReader(ReaderBase): """ # TODO(josh11b): Support serializing and restoring state. + @deprecation.deprecated( + None, "Queue-based input pipelines have been replaced by `tf.data`. Use " + "`tf.data.TextLineDataset`.") def __init__(self, skip_header_lines=None, name=None): """Create a TextLineReader. @@ -367,7 +374,7 @@ class TextLineReader(ReaderBase): ops.NotDifferentiable("TextLineReader") -@tf_export("FixedLengthRecordReader") +@tf_export(v1=["FixedLengthRecordReader"]) class FixedLengthRecordReader(ReaderBase): """A Reader that outputs fixed-length records from a file. @@ -380,6 +387,9 @@ class FixedLengthRecordReader(ReaderBase): """ # TODO(josh11b): Support serializing and restoring state. + @deprecation.deprecated( + None, "Queue-based input pipelines have been replaced by `tf.data`. Use " + "`tf.data.FixedLengthRecordDataset`.") def __init__(self, record_bytes, header_bytes=None, @@ -410,7 +420,7 @@ class FixedLengthRecordReader(ReaderBase): ops.NotDifferentiable("FixedLengthRecordReader") -@tf_export("TFRecordReader") +@tf_export(v1=["TFRecordReader"]) class TFRecordReader(ReaderBase): """A Reader that outputs the records from a TFRecords file. @@ -423,6 +433,9 @@ class TFRecordReader(ReaderBase): """ # TODO(josh11b): Support serializing and restoring state. + @deprecation.deprecated( + None, "Queue-based input pipelines have been replaced by `tf.data`. Use " + "`tf.data.TFRecordDataset`.") def __init__(self, name=None, options=None): """Create a TFRecordReader. @@ -441,7 +454,7 @@ class TFRecordReader(ReaderBase): ops.NotDifferentiable("TFRecordReader") -@tf_export("LMDBReader") +@tf_export(v1=["LMDBReader"]) class LMDBReader(ReaderBase): """A Reader that outputs the records from a LMDB file. @@ -452,6 +465,10 @@ class LMDBReader(ReaderBase): use `tf.data` to get data into your model. @end_compatibility """ + + @deprecation.deprecated( + None, "Queue-based input pipelines have been replaced by `tf.data`. Use " + "`tf.contrib.data.LMDBDataset`.") def __init__(self, name=None, options=None): """Create a LMDBReader. @@ -459,6 +476,7 @@ class LMDBReader(ReaderBase): name: A name for the operation (optional). options: A LMDBRecordOptions object (optional). """ + del options rr = gen_io_ops.lmdb_reader(name=name) super(LMDBReader, self).__init__(rr) @@ -466,7 +484,7 @@ class LMDBReader(ReaderBase): ops.NotDifferentiable("LMDBReader") -@tf_export("IdentityReader") +@tf_export(v1=["IdentityReader"]) class IdentityReader(ReaderBase): """A Reader that outputs the queued work as both the key and value. @@ -481,6 +499,9 @@ class IdentityReader(ReaderBase): @end_compatibility """ + @deprecation.deprecated( + None, "Queue-based input pipelines have been replaced by `tf.data`. Use " + "`tf.data.Dataset.map(...)`.") def __init__(self, name=None): """Create a IdentityReader. diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py index 474e0bb295..ef9afd9e8e 100644 --- a/tensorflow/python/ops/nn_ops.py +++ b/tensorflow/python/ops/nn_ops.py @@ -2454,7 +2454,7 @@ def conv1d(value, returned to the caller. Args: - value: A 3D `Tensor`. Must be of type `float16` or `float32`. + value: A 3D `Tensor`. Must be of type `float16`, `float32`, or `float64`. filters: A 3D `Tensor`. Must have the same type as `value`. stride: An `integer`. The number of entries by which the filter is moved right at each step. diff --git a/tensorflow/python/ops/parallel_for/pfor.py b/tensorflow/python/ops/parallel_for/pfor.py index 3c914f6ff6..f9153b6d7d 100644 --- a/tensorflow/python/ops/parallel_for/pfor.py +++ b/tensorflow/python/ops/parallel_for/pfor.py @@ -21,8 +21,6 @@ from __future__ import print_function import collections -from absl import flags - from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -41,6 +39,7 @@ from tensorflow.python.ops import nn_ops from tensorflow.python.ops import parsing_ops from tensorflow.python.ops import sparse_ops from tensorflow.python.ops import tensor_array_ops +from tensorflow.python.platform import flags from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import nest @@ -2013,6 +2012,7 @@ def _convert_biasaddgrad(pfor_input): @RegisterPForWithArgs("ReluGrad") @RegisterPForWithArgs("TanhGrad") @RegisterPForWithArgs("SigmoidGrad") +@RegisterPForWithArgs("SoftplusGrad") def _convert_grads(pfor_input, op_type, *args, **kw_args): del args del kw_args diff --git a/tensorflow/python/ops/script_ops.py b/tensorflow/python/ops/script_ops.py index 8d66de6b20..2ec4b540fb 100644 --- a/tensorflow/python/ops/script_ops.py +++ b/tensorflow/python/ops/script_ops.py @@ -287,19 +287,19 @@ def _internal_py_func(func, # TODO(akshayka): Implement higher-order derivatives. @ops.RegisterGradient("EagerPyFunc") -def _EagerPyFuncGrad(op, dy): +def _EagerPyFuncGrad(op, *dy): """Computes the gradient of an EagerPyFunc.""" token = op.get_attr("token") - def eagerly_executed_grad(dy): + def eagerly_executed_grad(*dy): tape, eager_inputs, eager_outputs = tape_cache.pop(compat.as_bytes(token)) return tape.gradient(eager_outputs, eager_inputs, output_gradients=dy) with ops.control_dependencies(op.outputs): return _internal_py_func( func=eagerly_executed_grad, - inp=[dy] if isinstance(dy, ops.Tensor) else dy, + inp=dy, Tout=[tensor.dtype for tensor in op.inputs], eager=True, is_grad_func=True) diff --git a/tensorflow/python/tools/component_api_helper.py b/tensorflow/python/tools/component_api_helper.py index 988ecc61f0..97f46719e5 100644 --- a/tensorflow/python/tools/component_api_helper.py +++ b/tensorflow/python/tools/component_api_helper.py @@ -65,9 +65,10 @@ def package_hook(parent_package_str, child_package_str, error_msg=None): Will allow the following import statement to work. >>> import parent.child """ - child_pkg_path = [os.path.join(os.path.dirname(child_pkg.__file__), "..")] + child_pkg_path = [os.path.abspath( + os.path.join(os.path.dirname(child_pkg.__file__), ".."))] try: - parent_pkg.__path__ += child_pkg_path + parent_pkg.__path__ = child_pkg_path + parent_pkg.__path__ except AttributeError: parent_pkg.__path__ = child_pkg_path diff --git a/tensorflow/python/tools/print_selective_registration_header_test.py b/tensorflow/python/tools/print_selective_registration_header_test.py index 4b3d98242c..cce8060fb9 100644 --- a/tensorflow/python/tools/print_selective_registration_header_test.py +++ b/tensorflow/python/tools/print_selective_registration_header_test.py @@ -59,6 +59,9 @@ GRAPH_DEF_TXT = """ } """ +# AccumulateNV2 is included because it should be included in the header despite +# lacking a kernel (it's rewritten by AccumulateNV2RemovePass; see +# core/common_runtime/accumulate_n_optimizer.cc. GRAPH_DEF_TXT_2 = """ node: { name: "node_4" @@ -67,6 +70,12 @@ GRAPH_DEF_TXT_2 = """ device: "/cpu:0" attr: { key: "T" value: { type: DT_FLOAT } } } + node: { + name: "node_5" + op: "AccumulateNV2" + attr: { key: "T" value: { type: DT_INT32 } } + attr: { key : "N" value: { i: 3 } } + } """ @@ -100,6 +109,7 @@ class PrintOpFilegroupTest(test.TestCase): self.assertListEqual( [ + ('AccumulateNV2', None), # ('BiasAdd', 'BiasOp<CPUDevice, float>'), # ('MatMul', matmul_prefix + 'MatMulOp<CPUDevice, double, false >'), # @@ -117,6 +127,7 @@ class PrintOpFilegroupTest(test.TestCase): 'rawproto', self.WriteGraphFiles(graphs), default_ops) self.assertListEqual( [ + ('AccumulateNV2', None), # ('BiasAdd', 'BiasOp<CPUDevice, float>'), # ('MatMul', matmul_prefix + 'MatMulOp<CPUDevice, double, false >'), # @@ -196,6 +207,7 @@ class PrintOpFilegroupTest(test.TestCase): constexpr inline bool ShouldRegisterOp(const char op[]) { return false + || isequal(op, "AccumulateNV2") || isequal(op, "BiasAdd") ; } diff --git a/tensorflow/python/tools/selective_registration_header_lib.py b/tensorflow/python/tools/selective_registration_header_lib.py index dc0612bb3f..b99c632c3e 100644 --- a/tensorflow/python/tools/selective_registration_header_lib.py +++ b/tensorflow/python/tools/selective_registration_header_lib.py @@ -32,6 +32,16 @@ from tensorflow.python import pywrap_tensorflow from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging +# Usually, we use each graph node to induce registration of an op and +# corresponding kernel; nodes without a corresponding kernel (perhaps due to +# attr types) generate a warning but are otherwise ignored. Ops in this set are +# registered even if there's no corresponding kernel. +OPS_WITHOUT_KERNEL_WHITELIST = frozenset([ + # AccumulateNV2 is rewritten away by AccumulateNV2RemovePass; see + # core/common_runtime/accumulate_n_optimizer.cc. + 'AccumulateNV2' +]) + def get_ops_and_kernels(proto_fileformat, proto_files, default_ops_str): """Gets the ops and kernels needed from the model files.""" @@ -53,8 +63,10 @@ def get_ops_and_kernels(proto_fileformat, proto_files, default_ops_str): node_def.device = '/cpu:0' kernel_class = pywrap_tensorflow.TryFindKernelClass( node_def.SerializeToString()) - if kernel_class: - op_and_kernel = (str(node_def.op), str(kernel_class.decode('utf-8'))) + op = str(node_def.op) + if kernel_class or op in OPS_WITHOUT_KERNEL_WHITELIST: + op_and_kernel = (op, str(kernel_class.decode('utf-8')) + if kernel_class else None) if op_and_kernel not in ops: ops.add(op_and_kernel) else: @@ -129,6 +141,7 @@ def get_header_from_ops_and_kernels(ops_and_kernels, ''' line += 'constexpr const char* kNecessaryOpKernelClasses[] = {\n' for _, kernel_class in ops_and_kernels: + if kernel_class is None: continue line += '"%s",\n' % kernel_class line += '};' append(line) diff --git a/tensorflow/python/training/checkpoint_ops.py b/tensorflow/python/training/checkpoint_ops.py index a6e9662b73..cfd9b39ddc 100644 --- a/tensorflow/python/training/checkpoint_ops.py +++ b/tensorflow/python/training/checkpoint_ops.py @@ -268,7 +268,8 @@ def _load_and_remap_matrix_initializer(ckpt_path, vocab files are the same, and no column remapping is done. The returned initializer only supports div-partitioning along the row axis. It - does not support partitioning along the column axis or mod-partitioning. + does not support partitioning along the column axis (as this is not common in + practice) or mod-partitioning. NOTE: When this is used to warm-start variables, client code should use `tf.lookup.index_table_from_tensor()` like diff --git a/tensorflow/python/training/input.py b/tensorflow/python/training/input.py index 0d6207f8c4..9d9db70890 100644 --- a/tensorflow/python/training/input.py +++ b/tensorflow/python/training/input.py @@ -45,6 +45,7 @@ from tensorflow.python.ops import sparse_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.summary import summary from tensorflow.python.training import queue_runner +from tensorflow.python.util import deprecation from tensorflow.python.util.tf_export import tf_export @@ -75,7 +76,10 @@ def match_filenames_once(pattern, name=None): collections=[ops.GraphKeys.LOCAL_VARIABLES]) -@tf_export("train.limit_epochs") +@tf_export(v1=["train.limit_epochs"]) +@deprecation.deprecated( + None, "Queue-based input pipelines have been replaced by `tf.data`. Use " + "`tf.data.Dataset.from_tensors(tensor).repeat(num_epochs)`.") def limit_epochs(tensor, num_epochs=None, name=None): """Returns tensor `num_epochs` times and then raises an `OutOfRange` error. @@ -108,7 +112,12 @@ def limit_epochs(tensor, num_epochs=None, name=None): return array_ops.identity(tensor, name=name) -@tf_export("train.input_producer") +@tf_export(v1=["train.input_producer"]) +@deprecation.deprecated( + None, "Queue-based input pipelines have been replaced by `tf.data`. Use " + "`tf.data.Dataset.from_tensor_slices(input_tensor).shuffle" + "(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If " + "`shuffle=False`, omit the `.shuffle(...)`.") def input_producer(input_tensor, element_shape=None, num_epochs=None, @@ -191,7 +200,12 @@ def input_producer(input_tensor, return q -@tf_export("train.string_input_producer") +@tf_export(v1=["train.string_input_producer"]) +@deprecation.deprecated( + None, "Queue-based input pipelines have been replaced by `tf.data`. Use " + "`tf.data.Dataset.from_tensor_slices(string_tensor).shuffle" + "(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If " + "`shuffle=False`, omit the `.shuffle(...)`.") def string_input_producer(string_tensor, num_epochs=None, shuffle=True, @@ -261,7 +275,11 @@ def string_input_producer(string_tensor, cancel_op=cancel_op) -@tf_export("train.range_input_producer") +@tf_export(v1=["train.range_input_producer"]) +@deprecation.deprecated( + None, "Queue-based input pipelines have been replaced by `tf.data`. Use " + "`tf.data.Dataset.range(limit).shuffle(limit).repeat(num_epochs)`. If " + "`shuffle=False`, omit the `.shuffle(...)`.") def range_input_producer(limit, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, name=None): """Produces the integers from 0 to limit-1 in a queue. @@ -299,7 +317,12 @@ def range_input_producer(limit, num_epochs=None, shuffle=True, seed=None, shared_name, "fraction_of_%d_full" % capacity, name) -@tf_export("train.slice_input_producer") +@tf_export(v1=["train.slice_input_producer"]) +@deprecation.deprecated( + None, "Queue-based input pipelines have been replaced by `tf.data`. Use " + "`tf.data.Dataset.from_tensor_slices(tuple(tensor_list)).shuffle" + "(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If " + "`shuffle=False`, omit the `.shuffle(...)`.") def slice_input_producer(tensor_list, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, name=None): """Produces a slice of each `Tensor` in `tensor_list`. @@ -894,7 +917,11 @@ def _shuffle_batch_join(tensors_list, batch_size, capacity, # Batching functions ---------------------------------------------------------- -@tf_export("train.batch") +@tf_export(v1=["train.batch"]) +@deprecation.deprecated( + None, "Queue-based input pipelines have been replaced by `tf.data`. Use " + "`tf.data.Dataset.batch(batch_size)` (or `padded_batch(...)` if " + "`dynamic_pad=True`).") def batch(tensors, batch_size, num_threads=1, capacity=32, enqueue_many=False, shapes=None, dynamic_pad=False, allow_smaller_final_batch=False, shared_name=None, name=None): @@ -989,7 +1016,11 @@ def batch(tensors, batch_size, num_threads=1, capacity=32, name=name) -@tf_export("train.maybe_batch") +@tf_export(v1=["train.maybe_batch"]) +@deprecation.deprecated( + None, "Queue-based input pipelines have been replaced by `tf.data`. Use " + "`tf.data.Dataset.filter(...).batch(batch_size)` (or `padded_batch(...)`" + " if `dynamic_pad=True`).") def maybe_batch(tensors, keep_input, batch_size, num_threads=1, capacity=32, enqueue_many=False, shapes=None, dynamic_pad=False, allow_smaller_final_batch=False, shared_name=None, name=None): @@ -1042,7 +1073,11 @@ def maybe_batch(tensors, keep_input, batch_size, num_threads=1, capacity=32, name=name) -@tf_export("train.batch_join") +@tf_export(v1=["train.batch_join"]) +@deprecation.deprecated( + None, "Queue-based input pipelines have been replaced by `tf.data`. Use " + "`tf.data.Dataset.interleave(...).batch(batch_size)` (or " + "`padded_batch(...)` if `dynamic_pad=True`).") def batch_join(tensors_list, batch_size, capacity=32, enqueue_many=False, shapes=None, dynamic_pad=False, allow_smaller_final_batch=False, shared_name=None, name=None): @@ -1148,7 +1183,11 @@ def batch_join(tensors_list, batch_size, capacity=32, enqueue_many=False, name=name) -@tf_export("train.maybe_batch_join") +@tf_export(v1=["train.maybe_batch_join"]) +@deprecation.deprecated( + None, "Queue-based input pipelines have been replaced by `tf.data`. Use " + "`tf.data.Dataset.interleave(...).filter(...).batch(batch_size)` (or " + "`padded_batch(...)` if `dynamic_pad=True`).") def maybe_batch_join(tensors_list, keep_input, batch_size, capacity=32, enqueue_many=False, shapes=None, dynamic_pad=False, allow_smaller_final_batch=False, shared_name=None, @@ -1201,7 +1240,10 @@ def maybe_batch_join(tensors_list, keep_input, batch_size, capacity=32, name=name) -@tf_export("train.shuffle_batch") +@tf_export(v1=["train.shuffle_batch"]) +@deprecation.deprecated( + None, "Queue-based input pipelines have been replaced by `tf.data`. Use " + "`tf.data.Dataset.shuffle(min_after_dequeue).batch(batch_size)`.") def shuffle_batch(tensors, batch_size, capacity, min_after_dequeue, num_threads=1, seed=None, enqueue_many=False, shapes=None, allow_smaller_final_batch=False, shared_name=None, name=None): @@ -1301,7 +1343,11 @@ def shuffle_batch(tensors, batch_size, capacity, min_after_dequeue, name=name) -@tf_export("train.maybe_shuffle_batch") +@tf_export(v1=["train.maybe_shuffle_batch"]) +@deprecation.deprecated( + None, "Queue-based input pipelines have been replaced by `tf.data`. Use " + "`tf.data.Dataset.filter(...).shuffle(min_after_dequeue).batch(batch_size)`" + ".") def maybe_shuffle_batch(tensors, batch_size, capacity, min_after_dequeue, keep_input, num_threads=1, seed=None, enqueue_many=False, shapes=None, @@ -1361,7 +1407,11 @@ def maybe_shuffle_batch(tensors, batch_size, capacity, min_after_dequeue, name=name) -@tf_export("train.shuffle_batch_join") +@tf_export(v1=["train.shuffle_batch_join"]) +@deprecation.deprecated( + None, "Queue-based input pipelines have been replaced by `tf.data`. Use " + "`tf.data.Dataset.interleave(...).shuffle(min_after_dequeue).batch" + "(batch_size)`.") def shuffle_batch_join(tensors_list, batch_size, capacity, min_after_dequeue, seed=None, enqueue_many=False, shapes=None, allow_smaller_final_batch=False, @@ -1455,7 +1505,11 @@ def shuffle_batch_join(tensors_list, batch_size, capacity, name=name) -@tf_export("train.maybe_shuffle_batch_join") +@tf_export(v1=["train.maybe_shuffle_batch_join"]) +@deprecation.deprecated( + None, "Queue-based input pipelines have been replaced by `tf.data`. Use " + "`tf.data.Dataset.interleave(...).filter(...).shuffle(min_after_dequeue)" + ".batch(batch_size)`.") def maybe_shuffle_batch_join(tensors_list, batch_size, capacity, min_after_dequeue, keep_input, seed=None, enqueue_many=False, shapes=None, diff --git a/tensorflow/python/training/learning_rate_decay.py b/tensorflow/python/training/learning_rate_decay.py index fd195a7965..29b5465321 100644 --- a/tensorflow/python/training/learning_rate_decay.py +++ b/tensorflow/python/training/learning_rate_decay.py @@ -17,19 +17,12 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import math - from tensorflow.python.eager import context -from tensorflow.python.framework import constant_op -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import ops -from tensorflow.python.ops import control_flow_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops import random_ops +from tensorflow.python.training import learning_rate_decay_v2 from tensorflow.python.util.tf_export import tf_export -@tf_export("train.exponential_decay") +@tf_export(v1=["train.exponential_decay"]) def exponential_decay(learning_rate, global_step, decay_steps, @@ -95,32 +88,19 @@ def exponential_decay(learning_rate, the learning rate value across different invocations of optimizer functions. @end_compatibility """ - if global_step is None: - raise ValueError("global_step is required for exponential_decay.") - with ops.name_scope( - name, "ExponentialDecay", - [learning_rate, global_step, decay_steps, decay_rate]) as name: - learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") - dtype = learning_rate.dtype - decay_steps = math_ops.cast(decay_steps, dtype) - decay_rate = math_ops.cast(decay_rate, dtype) - - def decayed_lr(): - """Helper to recompute learning rate; most helpful in eager-mode.""" - global_step_recomp = math_ops.cast(global_step, dtype) - p = global_step_recomp / decay_steps - if staircase: - p = math_ops.floor(p) - return math_ops.multiply( - learning_rate, math_ops.pow(decay_rate, p), name=name) - - if not context.executing_eagerly(): - decayed_lr = decayed_lr() - - return decayed_lr - - -@tf_export("train.piecewise_constant") + decayed_lr = learning_rate_decay_v2.exponential_decay(learning_rate, + global_step, + decay_steps, + decay_rate, + staircase=staircase, + name=name) + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr + + +@tf_export(v1=["train.piecewise_constant"]) def piecewise_constant(x, boundaries, values, name=None): """Piecewise constant from boundaries and interval values. @@ -163,58 +143,15 @@ def piecewise_constant(x, boundaries, values, name=None): the learning rate value across different invocations of optimizer functions. @end_compatibility """ - if len(boundaries) != len(values) - 1: - raise ValueError( - "The length of boundaries should be 1 less than the length of values") - with ops.name_scope(name, "PiecewiseConstant", - [x, boundaries, values, name]) as name: - boundaries = ops.convert_n_to_tensor(boundaries) - values = ops.convert_n_to_tensor(values) - - def decayed_lr(): - """Helper to recompute learning rate; most helpful in eager-mode.""" - x_recomp = ops.convert_to_tensor(x) - # Avoid explicit conversion to x's dtype. This could result in faulty - # comparisons, for example if floats are converted to integers. - for i, b in enumerate(boundaries): - if b.dtype.base_dtype != x_recomp.dtype.base_dtype: - # We can promote int32 boundaries to int64 without loss of precision. - # This covers the most common case where the user passes in boundaries - # as an array of Python integers. - if (b.dtype.base_dtype == dtypes.int32 and - x_recomp.dtype.base_dtype == dtypes.int64): - b = math_ops.cast(b, x_recomp.dtype.base_dtype) - boundaries[i] = b - else: - raise ValueError( - "Boundaries (%s) must have the same dtype as x (%s)." % - (b.dtype.base_dtype, x_recomp.dtype.base_dtype)) - # TODO(rdipietro): Ensure that boundaries' elements strictly increases. - for v in values[1:]: - if v.dtype.base_dtype != values[0].dtype.base_dtype: - raise ValueError( - "Values must have elements all with the same dtype (%s vs %s)." % - (values[0].dtype.base_dtype, v.dtype.base_dtype)) - pred_fn_pairs = [] - pred_fn_pairs.append((x_recomp <= boundaries[0], lambda: values[0])) - pred_fn_pairs.append((x_recomp > boundaries[-1], lambda: values[-1])) - for low, high, v in zip(boundaries[:-1], boundaries[1:], values[1:-1]): - # Need to bind v here; can do this with lambda v=v: ... - pred = (x_recomp > low) & (x_recomp <= high) - pred_fn_pairs.append((pred, lambda v=v: v)) - - # The default isn't needed here because our conditions are mutually - # exclusive and exhaustive, but tf.case requires it. - default = lambda: values[0] - return control_flow_ops.case(pred_fn_pairs, default, exclusive=True) - - if not context.executing_eagerly(): - decayed_lr = decayed_lr() - - return decayed_lr - - -@tf_export("train.polynomial_decay") + decayed_lr = learning_rate_decay_v2.piecewise_constant(x, boundaries, values, + name=name) + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr + + +@tf_export(v1=["train.polynomial_decay"]) def polynomial_decay(learning_rate, global_step, decay_steps, @@ -299,46 +236,22 @@ def polynomial_decay(learning_rate, the learning rate value across different invocations of optimizer functions. @end_compatibility """ - if global_step is None: - raise ValueError("global_step is required for polynomial_decay.") - with ops.name_scope( - name, "PolynomialDecay", - [learning_rate, global_step, decay_steps, end_learning_rate, power - ]) as name: - learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") - dtype = learning_rate.dtype - end_learning_rate = math_ops.cast(end_learning_rate, dtype) - power = math_ops.cast(power, dtype) - - def decayed_lr(): - """Helper to recompute learning rate; most helpful in eager-mode.""" - global_step_recomp = math_ops.cast(global_step, dtype) - decay_steps_recomp = math_ops.cast(decay_steps, dtype) - if cycle: - # Find the first multiple of decay_steps that is bigger than - # global_step. If global_step is zero set the multiplier to 1 - multiplier = control_flow_ops.cond( - math_ops.equal(global_step_recomp, 0), lambda: 1.0, - lambda: math_ops.ceil(global_step_recomp / decay_steps)) - decay_steps_recomp = math_ops.multiply(decay_steps_recomp, multiplier) - else: - # Make sure that the global_step used is not bigger than decay_steps. - global_step_recomp = math_ops.minimum(global_step_recomp, decay_steps) - - p = math_ops.div(global_step_recomp, decay_steps_recomp) - return math_ops.add( - math_ops.multiply(learning_rate - end_learning_rate, - math_ops.pow(1 - p, power)), - end_learning_rate, - name=name) - - if not context.executing_eagerly(): - decayed_lr = decayed_lr() - - return decayed_lr - - -@tf_export("train.natural_exp_decay") + decayed_lr = learning_rate_decay_v2.polynomial_decay( + learning_rate, + global_step, + decay_steps, + end_learning_rate=end_learning_rate, + power=power, + cycle=cycle, + name=name) + + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr + + +@tf_export(v1=["train.natural_exp_decay"]) def natural_exp_decay(learning_rate, global_step, decay_steps, @@ -410,32 +323,17 @@ def natural_exp_decay(learning_rate, the learning rate value across different invocations of optimizer functions. @end_compatibility """ - if global_step is None: - raise ValueError("global_step is required for natural_exp_decay.") - with ops.name_scope(name, "NaturalExpDecay", - [learning_rate, global_step, decay_rate]) as name: - learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") - dtype = learning_rate.dtype - decay_steps = math_ops.cast(decay_steps, dtype) - decay_rate = math_ops.cast(decay_rate, dtype) - - def decayed_lr(): - """Helper to recompute learning rate; most helpful in eager-mode.""" - global_step_recomp = math_ops.cast(global_step, dtype) - p = global_step_recomp / decay_steps - if staircase: - p = math_ops.floor(p) - exponent = math_ops.exp( - math_ops.multiply(math_ops.negative(decay_rate), p)) - return math_ops.multiply(learning_rate, exponent, name=name) - - if not context.executing_eagerly(): - decayed_lr = decayed_lr() - - return decayed_lr - - -@tf_export("train.inverse_time_decay") + decayed_lr = learning_rate_decay_v2.natural_exp_decay( + learning_rate, global_step, decay_steps, decay_rate, staircase=staircase, + name=name) + + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr + + +@tf_export(v1=["train.inverse_time_decay"]) def inverse_time_decay(learning_rate, global_step, decay_steps, @@ -507,32 +405,21 @@ def inverse_time_decay(learning_rate, the learning rate value across different invocations of optimizer functions. @end_compatibility """ - if global_step is None: - raise ValueError("global_step is required for inverse_time_decay.") - with ops.name_scope(name, "InverseTimeDecay", - [learning_rate, global_step, decay_rate]) as name: - learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") - dtype = learning_rate.dtype - decay_steps = math_ops.cast(decay_steps, dtype) - decay_rate = math_ops.cast(decay_rate, dtype) - - def decayed_lr(): - """Helper to recompute learning rate; most helpful in eager-mode.""" - global_step_recomp = math_ops.cast(global_step, dtype) - p = global_step_recomp / decay_steps - if staircase: - p = math_ops.floor(p) - const = math_ops.cast(constant_op.constant(1), dtype) - denom = math_ops.add(const, math_ops.multiply(decay_rate, p)) - return math_ops.div(learning_rate, denom, name=name) - - if not context.executing_eagerly(): - decayed_lr = decayed_lr() - - return decayed_lr - - -@tf_export("train.cosine_decay") + decayed_lr = learning_rate_decay_v2.inverse_time_decay( + learning_rate, + global_step, + decay_steps, + decay_rate, + staircase=staircase, + name=name) + + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr + + +@tf_export(v1=["train.cosine_decay"]) def cosine_decay(learning_rate, global_step, decay_steps, alpha=0.0, name=None): """Applies cosine decay to the learning rate. @@ -581,32 +468,16 @@ def cosine_decay(learning_rate, global_step, decay_steps, alpha=0.0, name=None): the learning rate value across different invocations of optimizer functions. @end_compatibility """ - if global_step is None: - raise ValueError("cosine decay requires global_step") - with ops.name_scope(name, "CosineDecay", - [learning_rate, global_step]) as name: - learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") - dtype = learning_rate.dtype - decay_steps = math_ops.cast(decay_steps, dtype) - - def decayed_lr(): - """Helper to recompute learning rate; most helpful in eager-mode.""" - global_step_recomp = math_ops.cast(global_step, dtype) - global_step_recomp = math_ops.minimum(global_step_recomp, decay_steps) - completed_fraction = global_step_recomp / decay_steps - cosine_decayed = 0.5 * (1.0 + math_ops.cos( - constant_op.constant(math.pi) * completed_fraction)) - - decayed = (1 - alpha) * cosine_decayed + alpha - return math_ops.multiply(learning_rate, decayed) + decayed_lr = learning_rate_decay_v2.cosine_decay( + learning_rate, global_step, decay_steps, alpha=alpha, name=name) - if not context.executing_eagerly(): - decayed_lr = decayed_lr() + if not context.executing_eagerly(): + decayed_lr = decayed_lr() - return decayed_lr + return decayed_lr -@tf_export("train.cosine_decay_restarts") +@tf_export(v1=["train.cosine_decay_restarts"]) def cosine_decay_restarts(learning_rate, global_step, first_decay_steps, @@ -664,57 +535,22 @@ def cosine_decay_restarts(learning_rate, the learning rate value across different invocations of optimizer functions. @end_compatibility """ - if global_step is None: - raise ValueError("cosine decay restarts requires global_step") - with ops.name_scope(name, "SGDRDecay", [learning_rate, global_step]) as name: - learning_rate = ops.convert_to_tensor( - learning_rate, name="initial_learning_rate") - dtype = learning_rate.dtype - first_decay_steps = math_ops.cast(first_decay_steps, dtype) - alpha = math_ops.cast(alpha, dtype) - t_mul = math_ops.cast(t_mul, dtype) - m_mul = math_ops.cast(m_mul, dtype) - - def decayed_lr(): - """Helper to recompute learning rate; most helpful in eager-mode.""" - global_step_recomp = math_ops.cast(global_step, dtype) - completed_fraction = global_step_recomp / first_decay_steps - - def compute_step(completed_fraction, geometric=False): - """Helper for `cond` operation.""" - if geometric: - i_restart = math_ops.floor( - math_ops.log(1.0 - completed_fraction * (1.0 - t_mul)) / - math_ops.log(t_mul)) - - sum_r = (1.0 - t_mul**i_restart) / (1.0 - t_mul) - completed_fraction = (completed_fraction - sum_r) / t_mul**i_restart - - else: - i_restart = math_ops.floor(completed_fraction) - completed_fraction -= i_restart + decayed_lr = learning_rate_decay_v2.cosine_decay_restarts( + learning_rate, + global_step, + first_decay_steps, + t_mul=t_mul, + m_mul=m_mul, + alpha=alpha, + name=name) - return i_restart, completed_fraction + if not context.executing_eagerly(): + decayed_lr = decayed_lr() - i_restart, completed_fraction = control_flow_ops.cond( - math_ops.equal(t_mul, 1.0), - lambda: compute_step(completed_fraction, geometric=False), - lambda: compute_step(completed_fraction, geometric=True)) + return decayed_lr - m_fac = m_mul**i_restart - cosine_decayed = 0.5 * m_fac * (1.0 + math_ops.cos( - constant_op.constant(math.pi) * completed_fraction)) - decayed = (1 - alpha) * cosine_decayed + alpha - return math_ops.multiply(learning_rate, decayed, name=name) - - if not context.executing_eagerly(): - decayed_lr = decayed_lr() - - return decayed_lr - - -@tf_export("train.linear_cosine_decay") +@tf_export(v1=["train.linear_cosine_decay"]) def linear_cosine_decay(learning_rate, global_step, decay_steps, @@ -781,37 +617,22 @@ def linear_cosine_decay(learning_rate, the learning rate value across different invocations of optimizer functions. @end_compatibility """ - if global_step is None: - raise ValueError("linear cosine decay requires global_step") - with ops.name_scope(name, "LinearCosineDecay", - [learning_rate, global_step]) as name: - learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") - dtype = learning_rate.dtype - decay_steps = math_ops.cast(decay_steps, dtype) - num_periods = math_ops.cast(num_periods, dtype) - alpha = math_ops.cast(alpha, dtype) - beta = math_ops.cast(beta, dtype) - - def decayed_lr(): - """Helper to recompute learning rate; most helpful in eager-mode.""" - global_step_recomp = math_ops.cast(global_step, dtype) - global_step_recomp = math_ops.minimum(global_step_recomp, decay_steps) - linear_decayed = (decay_steps - global_step_recomp) / decay_steps - completed_fraction = global_step_recomp / decay_steps - fraction = 2.0 * num_periods * completed_fraction - cosine_decayed = 0.5 * ( - 1.0 + math_ops.cos(constant_op.constant(math.pi) * fraction)) - - linear_cosine_decayed = (alpha + linear_decayed) * cosine_decayed + beta - return math_ops.multiply(learning_rate, linear_cosine_decayed, name=name) - - if not context.executing_eagerly(): - decayed_lr = decayed_lr() - - return decayed_lr - - -@tf_export("train.noisy_linear_cosine_decay") + decayed_lr = learning_rate_decay_v2.linear_cosine_decay( + learning_rate, + global_step, + decay_steps, + num_periods=num_periods, + alpha=alpha, + beta=beta, + name=name) + + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr + + +@tf_export(v1=["train.noisy_linear_cosine_decay"]) def noisy_linear_cosine_decay(learning_rate, global_step, decay_steps, @@ -886,42 +707,17 @@ def noisy_linear_cosine_decay(learning_rate, the learning rate value across different invocations of optimizer functions. @end_compatibility """ - if global_step is None: - raise ValueError("noisy linear cosine decay requires global_step") - with ops.name_scope(name, "NoisyLinearCosineDecay", - [learning_rate, global_step]) as name: - learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") - dtype = learning_rate.dtype - decay_steps = math_ops.cast(decay_steps, dtype) - initial_variance = math_ops.cast(initial_variance, dtype) - variance_decay = math_ops.cast(variance_decay, dtype) - num_periods = math_ops.cast(num_periods, dtype) - alpha = math_ops.cast(alpha, dtype) - beta = math_ops.cast(beta, dtype) - - def decayed_lr(): - """Helper to recompute learning rate; most helpful in eager-mode.""" - global_step_recomp = math_ops.cast(global_step, dtype) - global_step_recomp = math_ops.minimum(global_step_recomp, decay_steps) - linear_decayed = (decay_steps - global_step_recomp) / decay_steps - variance = initial_variance / ( - math_ops.pow(1.0 + global_step_recomp, variance_decay)) - std = math_ops.sqrt(variance) - noisy_linear_decayed = ( - linear_decayed + random_ops.random_normal( - linear_decayed.shape, stddev=std)) - - completed_fraction = global_step_recomp / decay_steps - fraction = 2.0 * num_periods * completed_fraction - cosine_decayed = 0.5 * ( - 1.0 + math_ops.cos(constant_op.constant(math.pi) * fraction)) - noisy_linear_cosine_decayed = ( - (alpha + noisy_linear_decayed) * cosine_decayed + beta) - - return math_ops.multiply( - learning_rate, noisy_linear_cosine_decayed, name=name) - - if not context.executing_eagerly(): - decayed_lr = decayed_lr() - - return decayed_lr + decayed_lr = learning_rate_decay_v2.noisy_linear_cosine_decay( + learning_rate, global_step, + decay_steps, + initial_variance=initial_variance, + variance_decay=variance_decay, + num_periods=num_periods, + alpha=alpha, + beta=beta, + name=name) + + if not context.executing_eagerly(): + decayed_lr = decayed_lr() + + return decayed_lr diff --git a/tensorflow/python/training/learning_rate_decay_v2.py b/tensorflow/python/training/learning_rate_decay_v2.py new file mode 100644 index 0000000000..9c5e144be6 --- /dev/null +++ b/tensorflow/python/training/learning_rate_decay_v2.py @@ -0,0 +1,898 @@ +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Various learning rate decay functions.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import functools +import math + +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import random_ops +from tensorflow.python.util.tf_export import tf_export + + +@tf_export("train.exponential_decay", v1=[]) +def exponential_decay(learning_rate, + global_step, + decay_steps, + decay_rate, + staircase=False, + name=None): + """Applies exponential decay to the learning rate. + + When training a model, it is often recommended to lower the learning rate as + the training progresses. This function applies an exponential decay function + to a provided initial learning rate. It requires a `global_step` value to + compute the decayed learning rate. You can just pass a TensorFlow variable + that you increment at each training step. + + The function returns a no-arg function that produces the decayed learning + rate. This can be useful for changing the learning rate value across + different invocations of optimizer functions. + It is computed as: + + ```python + decayed_learning_rate = learning_rate * + decay_rate ^ (global_step / decay_steps) + ``` + + If the argument `staircase` is `True`, then `global_step / decay_steps` is an + integer division and the decayed learning rate follows a staircase function. + + Example: decay every 100000 steps with a base of 0.96: + + ```python + ... + global_step = tf.Variable(0, trainable=False) + starter_learning_rate = 0.1 + learning_rate_fn = tf.train.exponential_decay(starter_learning_rate, + global_step, 100000, 0.96, + staircase=True) + # Passing global_step to minimize() will increment it at each step. + learning_step = ( + tf.train.GradientDescentOptimizer(learning_rate_fn) + .minimize(...my loss..., global_step=global_step) + ) + ``` + + Args: + learning_rate: A scalar `float32` or `float64` `Tensor` or a + Python number. The initial learning rate. + global_step: A scalar `int32` or `int64` `Tensor` or a Python number. + Global step to use for the decay computation. Must not be negative. + decay_steps: A scalar `int32` or `int64` `Tensor` or a Python number. + Must be positive. See the decay computation above. + decay_rate: A scalar `float32` or `float64` `Tensor` or a + Python number. The decay rate. + staircase: Boolean. If `True` decay the learning rate at discrete intervals + name: String. Optional name of the operation. Defaults to + 'ExponentialDecay'. + + Returns: + A no-arg function that outputs the decayed learning rate, a scalar `Tensor` + of the same type as `learning_rate`. + + Raises: + ValueError: if `global_step` is not supplied. + """ + if global_step is None: + raise ValueError("global_step is required for exponential_decay.") + def decayed_lr(learning_rate, global_step, decay_steps, decay_rate, + staircase, name): + """Helper to recompute learning rate; most helpful in eager-mode.""" + with ops.name_scope( + name, "ExponentialDecay", + [learning_rate, global_step, decay_steps, decay_rate]) as name: + learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") + dtype = learning_rate.dtype + decay_steps = math_ops.cast(decay_steps, dtype) + decay_rate = math_ops.cast(decay_rate, dtype) + + global_step_recomp = math_ops.cast(global_step, dtype) + p = global_step_recomp / decay_steps + if staircase: + p = math_ops.floor(p) + return math_ops.multiply( + learning_rate, math_ops.pow(decay_rate, p), name=name) + + return functools.partial(decayed_lr, learning_rate, global_step, decay_steps, + decay_rate, staircase, name) + + +@tf_export("train.piecewise_constant", v1=[]) +def piecewise_constant(x, boundaries, values, name=None): + """Piecewise constant from boundaries and interval values. + + This function returns a no-arg callable to compute the piecewise constant. + This can be useful for changing the learning rate value across + different invocations of optimizer functions. + + Example: use a learning rate that's 1.0 for the first 100001 steps, 0.5 + for the next 10000 steps, and 0.1 for any additional steps. + + ```python + global_step = tf.Variable(0, trainable=False) + boundaries = [100000, 110000] + values = [1.0, 0.5, 0.1] + learning_rate_fn = tf.train.piecewise_constant(global_step, boundaries, + values) + learning_rate = learning_rate_fn() + + # Later, whenever we perform an optimization step, we increment global_step. + ``` + + Args: + x: A 0-D scalar `Tensor`. Must be one of the following types: `float32`, + `float64`, `uint8`, `int8`, `int16`, `int32`, `int64`. + boundaries: A list of `Tensor`s or `int`s or `float`s with strictly + increasing entries, and with all elements having the same type as `x`. + values: A list of `Tensor`s or `float`s or `int`s that specifies the values + for the intervals defined by `boundaries`. It should have one more element + than `boundaries`, and all elements should have the same type. + name: A string. Optional name of the operation. Defaults to + 'PiecewiseConstant'. + + Returns: + A no-arg function that outputs a 0-D Tensor. The output of the no-arg + function is `values[0]` when `x <= boundaries[0]`, + `values[1]` when `x > boundaries[0]` and `x <= boundaries[1]`, ..., + and values[-1] when `x > boundaries[-1]`. + + Raises: + ValueError: if types of `x` and `boundaries` do not match, or types of all + `values` do not match or + the number of elements in the lists does not match. + """ + if len(boundaries) != len(values) - 1: + raise ValueError( + "The length of boundaries should be 1 less than the length of values") + def decayed_lr(x, boundaries, values, name): + """Helper to recompute learning rate; most helpful in eager-mode.""" + with ops.name_scope(name, "PiecewiseConstant", + [x, boundaries, values, name]) as name: + boundaries = ops.convert_n_to_tensor(boundaries) + values = ops.convert_n_to_tensor(values) + x_recomp = ops.convert_to_tensor(x) + # Avoid explicit conversion to x's dtype. This could result in faulty + # comparisons, for example if floats are converted to integers. + for i, b in enumerate(boundaries): + if b.dtype.base_dtype != x_recomp.dtype.base_dtype: + # We can promote int32 boundaries to int64 without loss of precision. + # This covers the most common case where the user passes in boundaries + # as an array of Python integers. + if (b.dtype.base_dtype == dtypes.int32 and + x_recomp.dtype.base_dtype == dtypes.int64): + b = math_ops.cast(b, x_recomp.dtype.base_dtype) + boundaries[i] = b + else: + raise ValueError( + "Boundaries (%s) must have the same dtype as x (%s)." % + (b.dtype.base_dtype, x_recomp.dtype.base_dtype)) + # TODO(rdipietro): Ensure that boundaries' elements strictly increases. + for v in values[1:]: + if v.dtype.base_dtype != values[0].dtype.base_dtype: + raise ValueError( + "Values must have elements all with the same dtype (%s vs %s)." % + (values[0].dtype.base_dtype, v.dtype.base_dtype)) + pred_fn_pairs = [] + pred_fn_pairs.append((x_recomp <= boundaries[0], lambda: values[0])) + pred_fn_pairs.append((x_recomp > boundaries[-1], lambda: values[-1])) + for low, high, v in zip(boundaries[:-1], boundaries[1:], values[1:-1]): + # Need to bind v here; can do this with lambda v=v: ... + pred = (x_recomp > low) & (x_recomp <= high) + pred_fn_pairs.append((pred, lambda v=v: v)) + + # The default isn't needed here because our conditions are mutually + # exclusive and exhaustive, but tf.case requires it. + default = lambda: values[0] + return control_flow_ops.case(pred_fn_pairs, default, exclusive=True) + + return functools.partial(decayed_lr, x, boundaries, values, name) + + +@tf_export("train.polynomial_decay", v1=[]) +def polynomial_decay(learning_rate, + global_step, + decay_steps, + end_learning_rate=0.0001, + power=1.0, + cycle=False, + name=None): + """Applies a polynomial decay to the learning rate. + + It is commonly observed that a monotonically decreasing learning rate, whose + degree of change is carefully chosen, results in a better performing model. + This function applies a polynomial decay function to a provided initial + `learning_rate` to reach an `end_learning_rate` in the given `decay_steps`. + + It requires a `global_step` value to compute the decayed learning rate. You + can just pass a TensorFlow variable that you increment at each training step. + + The function returns a no-arg callable that outputs the decayed learning + rate. This can be useful for changing the learning rate value across + different invocations of optimizer functions. It is computed as: + + ```python + global_step = min(global_step, decay_steps) + decayed_learning_rate = (learning_rate - end_learning_rate) * + (1 - global_step / decay_steps) ^ (power) + + end_learning_rate + + ``` + + If `cycle` is True then a multiple of `decay_steps` is used, the first one + that is bigger than `global_steps`. + + ```python + decay_steps = decay_steps * ceil(global_step / decay_steps) + decayed_learning_rate_fn = (learning_rate - end_learning_rate) * + (1 - global_step / decay_steps) ^ (power) + + end_learning_rate + decayed_learning_rate = decayed_learning_rate_fn() + + ``` + + Example: decay from 0.1 to 0.01 in 10000 steps using sqrt (i.e. power=0.5): + + ```python + ... + global_step = tf.Variable(0, trainable=False) + starter_learning_rate = 0.1 + end_learning_rate = 0.01 + decay_steps = 10000 + learning_rate_fn = tf.train.polynomial_decay(starter_learning_rate, + global_step, decay_steps, + end_learning_rate, + power=0.5) + # Passing global_step to minimize() will increment it at each step. + learning_step = ( + tf.train.GradientDescentOptimizer(learning_rate_fn) + .minimize(...my loss..., global_step=global_step) + ) + ``` + + Args: + learning_rate: A scalar `float32` or `float64` `Tensor` or a + Python number. The initial learning rate. + global_step: A scalar `int32` or `int64` `Tensor` or a Python number. + Global step to use for the decay computation. Must not be negative. + decay_steps: A scalar `int32` or `int64` `Tensor` or a Python number. + Must be positive. See the decay computation above. + end_learning_rate: A scalar `float32` or `float64` `Tensor` or a + Python number. The minimal end learning rate. + power: A scalar `float32` or `float64` `Tensor` or a + Python number. The power of the polynomial. Defaults to linear, 1.0. + cycle: A boolean, whether or not it should cycle beyond decay_steps. + name: String. Optional name of the operation. Defaults to + 'PolynomialDecay'. + + Returns: + A no-arg function that outputs the decayed learning rate, a scalar `Tensor` + of the same type as `learning_rate`. + + Raises: + ValueError: if `global_step` is not supplied. + """ + if global_step is None: + raise ValueError("global_step is required for polynomial_decay.") + def decayed_lr(learning_rate, global_step, decay_steps, end_learning_rate, + power, cycle, name): + """Helper to recompute learning rate; most helpful in eager-mode.""" + with ops.name_scope( + name, "PolynomialDecay", + [learning_rate, global_step, decay_steps, end_learning_rate, power] + ) as name: + learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") + dtype = learning_rate.dtype + end_learning_rate = math_ops.cast(end_learning_rate, dtype) + power = math_ops.cast(power, dtype) + + global_step_recomp = math_ops.cast(global_step, dtype) + decay_steps_recomp = math_ops.cast(decay_steps, dtype) + if cycle: + # Find the first multiple of decay_steps that is bigger than + # global_step. If global_step is zero set the multiplier to 1 + multiplier = control_flow_ops.cond( + math_ops.equal(global_step_recomp, 0), lambda: 1.0, + lambda: math_ops.ceil(global_step_recomp / decay_steps)) + decay_steps_recomp = math_ops.multiply(decay_steps_recomp, multiplier) + else: + # Make sure that the global_step used is not bigger than decay_steps. + global_step_recomp = math_ops.minimum(global_step_recomp, decay_steps) + + p = math_ops.div(global_step_recomp, decay_steps_recomp) + return math_ops.add( + math_ops.multiply(learning_rate - end_learning_rate, + math_ops.pow(1 - p, power)), + end_learning_rate, + name=name) + + return functools.partial( + decayed_lr, learning_rate, global_step, decay_steps, end_learning_rate, + power, cycle, name) + + +@tf_export("train.natural_exp_decay", v1=[]) +def natural_exp_decay(learning_rate, + global_step, + decay_steps, + decay_rate, + staircase=False, + name=None): + """Applies natural exponential decay to the initial learning rate. + + When training a model, it is often recommended to lower the learning rate as + the training progresses. This function applies an exponential decay function + to a provided initial learning rate. It requires an `global_step` value to + compute the decayed learning rate. You can just pass a TensorFlow variable + that you increment at each training step. + + The function returns a no-arg callable that produces the decayed learning + rate. This can be useful for changing the learning rate value across + different invocations of optimizer functions. It is computed as: + + ```python + decayed_learning_rate = learning_rate * exp(-decay_rate * global_step / + decay_step) + ``` + + or, if `staircase` is `True`, as: + + ```python + decayed_learning_rate = learning_rate * exp(-decay_rate * floor(global_step / + decay_step)) + ``` + + Example: decay exponentially with a base of 0.96: + + ```python + ... + global_step = tf.Variable(0, trainable=False) + learning_rate = 0.1 + decay_steps = 5 + k = 0.5 + learning_rate_fn = tf.train.natural_exp_decay(learning_rate, global_step, + decay_steps, k) + + # Passing global_step to minimize() will increment it at each step. + learning_step = ( + tf.train.GradientDescentOptimizer(learning_rate_fn) + .minimize(...my loss..., global_step=global_step) + ) + ``` + + Args: + learning_rate: A scalar `float32` or `float64` `Tensor` or a + Python number. The initial learning rate. + global_step: A Python number. + Global step to use for the decay computation. Must not be negative. + decay_steps: How often to apply decay. + decay_rate: A Python number. The decay rate. + staircase: Whether to apply decay in a discrete staircase, as opposed to + continuous, fashion. + name: String. Optional name of the operation. Defaults to + 'ExponentialTimeDecay'. + + Returns: + A no-arg function that outputs the decayed learning rate, a scalar `Tensor` + of the same type as `learning_rate`. + + Raises: + ValueError: if `global_step` is not supplied. + """ + if global_step is None: + raise ValueError("global_step is required for natural_exp_decay.") + def decayed_lr(learning_rate, global_step, decay_steps, decay_rate, staircase, + name): + """Helper to recompute learning rate; most helpful in eager-mode.""" + with ops.name_scope(name, "NaturalExpDecay", + [learning_rate, global_step, decay_rate]) as name: + learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") + dtype = learning_rate.dtype + decay_steps = math_ops.cast(decay_steps, dtype) + decay_rate = math_ops.cast(decay_rate, dtype) + + global_step_recomp = math_ops.cast(global_step, dtype) + p = global_step_recomp / decay_steps + if staircase: + p = math_ops.floor(p) + exponent = math_ops.exp( + math_ops.multiply(math_ops.negative(decay_rate), p)) + return math_ops.multiply(learning_rate, exponent, name=name) + + return functools.partial(decayed_lr, learning_rate, global_step, decay_steps, + decay_rate, staircase, name) + + +@tf_export("train.inverse_time_decay", v1=[]) +def inverse_time_decay(learning_rate, + global_step, + decay_steps, + decay_rate, + staircase=False, + name=None): + """Applies inverse time decay to the initial learning rate. + + When training a model, it is often recommended to lower the learning rate as + the training progresses. This function applies an inverse decay function + to a provided initial learning rate. It requires an `global_step` value to + compute the decayed learning rate. You can just pass a TensorFlow variable + that you increment at each training step. + + The function returns a no-arg callable that produces the decayed learning + rate. This can be useful for changing the learning rate value across + different invocations of optimizer functions. It is computed as: + + ```python + decayed_learning_rate = learning_rate / (1 + decay_rate * global_step / + decay_step) + ``` + + or, if `staircase` is `True`, as: + + ```python + decayed_learning_rate = learning_rate / (1 + decay_rate * floor(global_step / + decay_step)) + ``` + + Example: decay 1/t with a rate of 0.5: + + ```python + ... + global_step = tf.Variable(0, trainable=False) + learning_rate = 0.1 + decay_steps = 1.0 + decay_rate = 0.5 + learning_rate_fn = tf.train.inverse_time_decay(learning_rate, global_step, + decay_steps, decay_rate) + + # Passing global_step to minimize() will increment it at each step. + learning_step = ( + tf.train.GradientDescentOptimizer(learning_rate_fn) + .minimize(...my loss..., global_step=global_step) + ) + ``` + + Args: + learning_rate: A scalar `float32` or `float64` `Tensor` or a + Python number. The initial learning rate. + global_step: A Python number. + Global step to use for the decay computation. Must not be negative. + decay_steps: How often to apply decay. + decay_rate: A Python number. The decay rate. + staircase: Whether to apply decay in a discrete staircase, as opposed to + continuous, fashion. + name: String. Optional name of the operation. Defaults to + 'InverseTimeDecay'. + + Returns: + A no-arg function that outputs the decayed learning rate, a scalar `Tensor` + of the same type as `learning_rate`. + + Raises: + ValueError: if `global_step` is not supplied. + """ + if global_step is None: + raise ValueError("global_step is required for inverse_time_decay.") + def decayed_lr(learning_rate, global_step, decay_steps, decay_rate, staircase, + name): + """Helper to recompute learning rate; most helpful in eager-mode.""" + with ops.name_scope(name, "InverseTimeDecay", + [learning_rate, global_step, decay_rate]) as name: + learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") + dtype = learning_rate.dtype + decay_steps = math_ops.cast(decay_steps, dtype) + decay_rate = math_ops.cast(decay_rate, dtype) + + global_step_recomp = math_ops.cast(global_step, dtype) + p = global_step_recomp / decay_steps + if staircase: + p = math_ops.floor(p) + const = math_ops.cast(constant_op.constant(1), dtype) + denom = math_ops.add(const, math_ops.multiply(decay_rate, p)) + return math_ops.div(learning_rate, denom, name=name) + + return functools.partial(decayed_lr, learning_rate, global_step, decay_steps, + decay_rate, staircase, name) + + +@tf_export("train.cosine_decay", v1=[]) +def cosine_decay(learning_rate, global_step, decay_steps, alpha=0.0, + name=None): + """Applies cosine decay to the learning rate. + + See [Loshchilov & Hutter, ICLR2016], SGDR: Stochastic Gradient Descent + with Warm Restarts. https://arxiv.org/abs/1608.03983 + + When training a model, it is often recommended to lower the learning rate as + the training progresses. This function applies a cosine decay function + to a provided initial learning rate. It requires a `global_step` value to + compute the decayed learning rate. You can just pass a TensorFlow variable + that you increment at each training step. + + The function returns a no-arg callable that produces the decayed learning + rate. This can be useful for changing the learning rate value across + different invocations of optimizer functions. It is computed as: + + ```python + global_step = min(global_step, decay_steps) + cosine_decay = 0.5 * (1 + cos(pi * global_step / decay_steps)) + decayed = (1 - alpha) * cosine_decay + alpha + decayed_learning_rate = learning_rate * decayed + ``` + + Example usage: + ```python + decay_steps = 1000 + lr_decayed_fn = tf.train.cosine_decay(learning_rate, global_step, decay_steps) + ``` + + Args: + learning_rate: A scalar `float32` or `float64` Tensor or a Python number. + The initial learning rate. + global_step: A scalar `int32` or `int64` `Tensor` or a Python number. + Global step to use for the decay computation. + decay_steps: A scalar `int32` or `int64` `Tensor` or a Python number. + Number of steps to decay over. + alpha: A scalar `float32` or `float64` Tensor or a Python number. + Minimum learning rate value as a fraction of learning_rate. + name: String. Optional name of the operation. Defaults to 'CosineDecay'. + Returns: + A no-arg function that outputs the decayed learning rate, a scalar `Tensor` + of the same type as `learning_rate`. + Raises: + ValueError: if `global_step` is not supplied. + """ + if global_step is None: + raise ValueError("cosine decay requires global_step") + def decayed_lr(learning_rate, global_step, decay_steps, alpha, name): + """Helper to recompute learning rate; most helpful in eager-mode.""" + with ops.name_scope(name, "CosineDecay", + [learning_rate, global_step]) as name: + learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") + dtype = learning_rate.dtype + decay_steps = math_ops.cast(decay_steps, dtype) + + global_step_recomp = math_ops.cast(global_step, dtype) + global_step_recomp = math_ops.minimum(global_step_recomp, decay_steps) + completed_fraction = global_step_recomp / decay_steps + cosine_decayed = 0.5 * (1.0 + math_ops.cos( + constant_op.constant(math.pi) * completed_fraction)) + + decayed = (1 - alpha) * cosine_decayed + alpha + return math_ops.multiply(learning_rate, decayed) + + return functools.partial(decayed_lr, learning_rate, global_step, decay_steps, + alpha, name) + + +@tf_export("train.cosine_decay_restarts", v1=[]) +def cosine_decay_restarts(learning_rate, + global_step, + first_decay_steps, + t_mul=2.0, + m_mul=1.0, + alpha=0.0, + name=None): + """Applies cosine decay with restarts to the learning rate. + + See [Loshchilov & Hutter, ICLR2016], SGDR: Stochastic Gradient Descent + with Warm Restarts. https://arxiv.org/abs/1608.03983 + + When training a model, it is often recommended to lower the learning rate as + the training progresses. This function applies a cosine decay function with + restarts to a provided initial learning rate. It requires a `global_step` + value to compute the decayed learning rate. You can just pass a TensorFlow + variable that you increment at each training step. + + The function returns a no-arg callable that produces the decayed learning + rate while taking into account possible warm restarts. This can be useful for + changing the learning rate value across different invocations of optimizer + functions. + + The learning rate multiplier first decays + from 1 to `alpha` for `first_decay_steps` steps. Then, a warm + restart is performed. Each new warm restart runs for `t_mul` times more steps + and with `m_mul` times smaller initial learning rate. + + Example usage: + ```python + first_decay_steps = 1000 + lr_decayed_fn = tf.train.cosine_decay_restarts(learning_rate, global_step, + first_decay_steps) + ``` + + Args: + learning_rate: A scalar `float32` or `float64` Tensor or a Python number. + The initial learning rate. + global_step: A scalar `int32` or `int64` `Tensor` or a Python number. + Global step to use for the decay computation. + first_decay_steps: A scalar `int32` or `int64` `Tensor` or a Python number. + Number of steps to decay over. + t_mul: A scalar `float32` or `float64` `Tensor` or a Python number. + Used to derive the number of iterations in the i-th period + m_mul: A scalar `float32` or `float64` `Tensor` or a Python number. + Used to derive the initial learning rate of the i-th period: + alpha: A scalar `float32` or `float64` Tensor or a Python number. + Minimum learning rate value as a fraction of the learning_rate. + name: String. Optional name of the operation. Defaults to 'SGDRDecay'. + Returns: + A no-arg function that outputs the decayed learning rate, a scalar `Tensor` + of the same type as `learning_rate`. + + Raises: + ValueError: if `global_step` is not supplied. + """ + if global_step is None: + raise ValueError("cosine decay restarts requires global_step") + def decayed_lr(learning_rate, global_step, first_decay_steps, t_mul, m_mul, + alpha, name): + """Helper to recompute learning rate; most helpful in eager-mode.""" + with ops.name_scope(name, "SGDRDecay", [learning_rate, global_step] + ) as name: + learning_rate = ops.convert_to_tensor( + learning_rate, name="initial_learning_rate") + dtype = learning_rate.dtype + first_decay_steps = math_ops.cast(first_decay_steps, dtype) + alpha = math_ops.cast(alpha, dtype) + t_mul = math_ops.cast(t_mul, dtype) + m_mul = math_ops.cast(m_mul, dtype) + + global_step_recomp = math_ops.cast(global_step, dtype) + completed_fraction = global_step_recomp / first_decay_steps + + def compute_step(completed_fraction, geometric=False): + """Helper for `cond` operation.""" + if geometric: + i_restart = math_ops.floor( + math_ops.log(1.0 - completed_fraction * (1.0 - t_mul)) / + math_ops.log(t_mul)) + + sum_r = (1.0 - t_mul**i_restart) / (1.0 - t_mul) + completed_fraction = (completed_fraction - sum_r) / t_mul**i_restart + + else: + i_restart = math_ops.floor(completed_fraction) + completed_fraction -= i_restart + + return i_restart, completed_fraction + + i_restart, completed_fraction = control_flow_ops.cond( + math_ops.equal(t_mul, 1.0), + lambda: compute_step(completed_fraction, geometric=False), + lambda: compute_step(completed_fraction, geometric=True)) + + m_fac = m_mul**i_restart + cosine_decayed = 0.5 * m_fac * (1.0 + math_ops.cos( + constant_op.constant(math.pi) * completed_fraction)) + decayed = (1 - alpha) * cosine_decayed + alpha + + return math_ops.multiply(learning_rate, decayed, name=name) + + return functools.partial(decayed_lr, learning_rate, global_step, + first_decay_steps, t_mul, m_mul, alpha, name) + + +@tf_export("train.linear_cosine_decay", v1=[]) +def linear_cosine_decay(learning_rate, + global_step, + decay_steps, + num_periods=0.5, + alpha=0.0, + beta=0.001, + name=None): + """Applies linear cosine decay to the learning rate. + + See [Bello et al., ICML2017] Neural Optimizer Search with RL. + https://arxiv.org/abs/1709.07417 + + For the idea of warm starts here controlled by `num_periods`, + see [Loshchilov & Hutter, ICLR2016] SGDR: Stochastic Gradient Descent + with Warm Restarts. https://arxiv.org/abs/1608.03983 + + Note that linear cosine decay is more aggressive than cosine decay and + larger initial learning rates can typically be used. + + When training a model, it is often recommended to lower the learning rate as + the training progresses. This function applies a linear cosine decay function + to a provided initial learning rate. It requires a `global_step` value to + compute the decayed learning rate. You can just pass a TensorFlow variable + that you increment at each training step. + + The function returns a no-arg callable that produces the decayed learning + rate. This can be useful for changing the learning rate value across + different invocations of optimizer functions. It is computed as: + + ```python + global_step = min(global_step, decay_steps) + linear_decay = (decay_steps - global_step) / decay_steps) + cosine_decay = 0.5 * ( + 1 + cos(pi * 2 * num_periods * global_step / decay_steps)) + decayed = (alpha + linear_decay) * cosine_decay + beta + decayed_learning_rate = learning_rate * decayed + ``` + + Example usage: + ```python + decay_steps = 1000 + lr_decayed_fn = tf.train.linear_cosine_decay(learning_rate, global_step, + decay_steps) + ``` + + Args: + learning_rate: A scalar `float32` or `float64` Tensor or a Python number. + The initial learning rate. + global_step: A scalar `int32` or `int64` `Tensor` or a Python number. + Global step to use for the decay computation. + decay_steps: A scalar `int32` or `int64` `Tensor` or a Python number. + Number of steps to decay over. + num_periods: Number of periods in the cosine part of the decay. + See computation above. + alpha: See computation above. + beta: See computation above. + name: String. Optional name of the operation. Defaults to + 'LinearCosineDecay'. + Returns: + A no-arg function that outputs the decayed learning rate, a scalar `Tensor` + of the same type as `learning_rate`. + Raises: + ValueError: if `global_step` is not supplied. + """ + if global_step is None: + raise ValueError("linear cosine decay requires global_step") + def decayed_lr(learning_rate, global_step, decay_steps, num_periods, alpha, + beta, name): + """Helper to recompute learning rate; most helpful in eager-mode.""" + with ops.name_scope(name, "LinearCosineDecay", + [learning_rate, global_step]) as name: + learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") + dtype = learning_rate.dtype + decay_steps = math_ops.cast(decay_steps, dtype) + num_periods = math_ops.cast(num_periods, dtype) + alpha = math_ops.cast(alpha, dtype) + beta = math_ops.cast(beta, dtype) + + global_step_recomp = math_ops.cast(global_step, dtype) + global_step_recomp = math_ops.minimum(global_step_recomp, decay_steps) + linear_decayed = (decay_steps - global_step_recomp) / decay_steps + completed_fraction = global_step_recomp / decay_steps + fraction = 2.0 * num_periods * completed_fraction + cosine_decayed = 0.5 * ( + 1.0 + math_ops.cos(constant_op.constant(math.pi) * fraction)) + + linear_cosine_decayed = (alpha + linear_decayed) * cosine_decayed + beta + return math_ops.multiply(learning_rate, linear_cosine_decayed, name=name) + + return functools.partial(decayed_lr, learning_rate, global_step, decay_steps, + num_periods, alpha, beta, name) + + +@tf_export("train.noisy_linear_cosine_decay", v1=[]) +def noisy_linear_cosine_decay(learning_rate, + global_step, + decay_steps, + initial_variance=1.0, + variance_decay=0.55, + num_periods=0.5, + alpha=0.0, + beta=0.001, + name=None): + """Applies noisy linear cosine decay to the learning rate. + + See [Bello et al., ICML2017] Neural Optimizer Search with RL. + https://arxiv.org/abs/1709.07417 + + For the idea of warm starts here controlled by `num_periods`, + see [Loshchilov & Hutter, ICLR2016] SGDR: Stochastic Gradient Descent + with Warm Restarts. https://arxiv.org/abs/1608.03983 + + Note that linear cosine decay is more aggressive than cosine decay and + larger initial learning rates can typically be used. + + When training a model, it is often recommended to lower the learning rate as + the training progresses. This function applies a noisy linear + cosine decay function to a provided initial learning rate. + It requires a `global_step` value to compute the decayed learning rate. + You can just pass a TensorFlow variable that you increment at each + training step. + + The function returns a no-arg callable that produces the decayed learning + rate. This can be useful for changing the learning rate value across + different invocations of optimizer functions. It is computed as: + + ```python + global_step = min(global_step, decay_steps) + linear_decay = (decay_steps - global_step) / decay_steps) + cosine_decay = 0.5 * ( + 1 + cos(pi * 2 * num_periods * global_step / decay_steps)) + decayed = (alpha + linear_decay + eps_t) * cosine_decay + beta + decayed_learning_rate = learning_rate * decayed + ``` + where eps_t is 0-centered gaussian noise with variance + initial_variance / (1 + global_step) ** variance_decay + + Example usage: + ```python + decay_steps = 1000 + lr_decayed_fn = tf.train.noisy_linear_cosine_decay(learning_rate, global_step, + decay_steps) + ``` + + Args: + learning_rate: A scalar `float32` or `float64` Tensor or a Python number. + The initial learning rate. + global_step: A scalar `int32` or `int64` `Tensor` or a Python number. + Global step to use for the decay computation. + decay_steps: A scalar `int32` or `int64` `Tensor` or a Python number. + Number of steps to decay over. + initial_variance: initial variance for the noise. See computation above. + variance_decay: decay for the noise's variance. See computation above. + num_periods: Number of periods in the cosine part of the decay. + See computation above. + alpha: See computation above. + beta: See computation above. + name: String. Optional name of the operation. Defaults to + 'NoisyLinearCosineDecay'. + Returns: + A no-arg function that outputs the decayed learning rate, a scalar `Tensor` + of the same type as `learning_rate`. + Raises: + ValueError: if `global_step` is not supplied. + """ + if global_step is None: + raise ValueError("noisy linear cosine decay requires global_step") + def decayed_lr(learning_rate, global_step, decay_steps, initial_variance, + variance_decay, num_periods, alpha, beta, name): + """Helper to recompute learning rate; most helpful in eager-mode.""" + with ops.name_scope(name, "NoisyLinearCosineDecay", + [learning_rate, global_step]) as name: + learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate") + dtype = learning_rate.dtype + decay_steps = math_ops.cast(decay_steps, dtype) + initial_variance = math_ops.cast(initial_variance, dtype) + variance_decay = math_ops.cast(variance_decay, dtype) + num_periods = math_ops.cast(num_periods, dtype) + alpha = math_ops.cast(alpha, dtype) + beta = math_ops.cast(beta, dtype) + + global_step_recomp = math_ops.cast(global_step, dtype) + global_step_recomp = math_ops.minimum(global_step_recomp, decay_steps) + linear_decayed = (decay_steps - global_step_recomp) / decay_steps + variance = initial_variance / ( + math_ops.pow(1.0 + global_step_recomp, variance_decay)) + std = math_ops.sqrt(variance) + noisy_linear_decayed = ( + linear_decayed + random_ops.random_normal( + linear_decayed.shape, stddev=std)) + + completed_fraction = global_step_recomp / decay_steps + fraction = 2.0 * num_periods * completed_fraction + cosine_decayed = 0.5 * ( + 1.0 + math_ops.cos(constant_op.constant(math.pi) * fraction)) + noisy_linear_cosine_decayed = ( + (alpha + noisy_linear_decayed) * cosine_decayed + beta) + + return math_ops.multiply( + learning_rate, noisy_linear_cosine_decayed, name=name) + + return functools.partial(decayed_lr, learning_rate, global_step, decay_steps, + initial_variance, variance_decay, num_periods, alpha, + beta, name) diff --git a/tensorflow/python/training/learning_rate_decay_v2_test.py b/tensorflow/python/training/learning_rate_decay_v2_test.py new file mode 100644 index 0000000000..0f2d60dafc --- /dev/null +++ b/tensorflow/python/training/learning_rate_decay_v2_test.py @@ -0,0 +1,497 @@ +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +"""Functional test for learning rate decay.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math + +from tensorflow.python.eager import context +from tensorflow.python.framework import test_util +# Import resource_variable_ops for the variables-to-tensor implicit conversion. +from tensorflow.python.ops import resource_variable_ops # pylint: disable=unused-import +from tensorflow.python.ops import variables +from tensorflow.python.platform import googletest +from tensorflow.python.training import learning_rate_decay_v2 + + +class LRDecayTestV2(test_util.TensorFlowTestCase): + + @test_util.run_in_graph_and_eager_modes + def testContinuous(self): + self.evaluate(variables.global_variables_initializer()) + step = 5 + decayed_lr = learning_rate_decay_v2.exponential_decay(0.05, step, 10, 0.96) + expected = .05 * 0.96**(5.0 / 10.0) + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + + @test_util.run_in_graph_and_eager_modes + def testStaircase(self): + if context.executing_eagerly(): + step = resource_variable_ops.ResourceVariable(0) + self.evaluate(variables.global_variables_initializer()) + decayed_lr = learning_rate_decay_v2.exponential_decay( + .1, step, 3, 0.96, staircase=True) + + # No change to learning rate due to staircase + expected = .1 + self.evaluate(step.assign(1)) + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + + expected = .1 + self.evaluate(step.assign(2)) + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + + # Decayed learning rate + expected = .1 * 0.96 ** (100 // 3) + self.evaluate(step.assign(100)) + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + + def testVariables(self): + with self.test_session(): + step = variables.Variable(1) + assign_1 = step.assign(1) + assign_2 = step.assign(2) + assign_100 = step.assign(100) + decayed_lr = learning_rate_decay_v2.exponential_decay(.1, step, 3, 0.96, + staircase=True) + variables.global_variables_initializer().run() + # No change to learning rate + assign_1.op.run() + self.assertAllClose(decayed_lr().eval(), .1, 1e-6) + assign_2.op.run() + self.assertAllClose(decayed_lr().eval(), .1, 1e-6) + # Decayed learning rate + assign_100.op.run() + expected = .1 * 0.96 ** (100 // 3) + self.assertAllClose(decayed_lr().eval(), expected, 1e-6) + + @test_util.run_in_graph_and_eager_modes + def testPiecewiseConstant(self): + x = resource_variable_ops.ResourceVariable(-999) + decayed_lr = learning_rate_decay_v2.piecewise_constant( + x, [100, 110, 120], [1.0, 0.1, 0.01, 0.001]) + + self.evaluate(variables.global_variables_initializer()) + + self.assertAllClose(self.evaluate(decayed_lr()), 1.0, 1e-6) + self.evaluate(x.assign(100)) + self.assertAllClose(self.evaluate(decayed_lr()), 1.0, 1e-6) + self.evaluate(x.assign(105)) + self.assertAllClose(self.evaluate(decayed_lr()), 0.1, 1e-6) + self.evaluate(x.assign(110)) + self.assertAllClose(self.evaluate(decayed_lr()), 0.1, 1e-6) + self.evaluate(x.assign(120)) + self.assertAllClose(self.evaluate(decayed_lr()), 0.01, 1e-6) + self.evaluate(x.assign(999)) + self.assertAllClose(self.evaluate(decayed_lr()), 0.001, 1e-6) + + @test_util.run_in_graph_and_eager_modes + def testPiecewiseConstantEdgeCases(self): + x_int = resource_variable_ops.ResourceVariable( + 0, dtype=variables.dtypes.int32) + boundaries, values = [-1.0, 1.0], [1, 2, 3] + with self.assertRaises(ValueError): + decayed_lr = learning_rate_decay_v2.piecewise_constant( + x_int, boundaries, values) + decayed_lr() + + x = resource_variable_ops.ResourceVariable(0.0) + boundaries, values = [-1.0, 1.0], [1.0, 2, 3] + with self.assertRaises(ValueError): + decayed_lr = learning_rate_decay_v2.piecewise_constant( + x, boundaries, values)() + decayed_lr() + + # Test that ref types are valid. + if not context.executing_eagerly(): + x = variables.Variable(0.0) + x_ref = x.op.outputs[0] # float32_ref tensor should be accepted + boundaries, values = [1.0, 2.0], [1, 2, 3] + learning_rate_decay_v2.piecewise_constant(x_ref, boundaries, values) + + # Test casting boundaries from int32 to int64. + x_int64 = resource_variable_ops.ResourceVariable( + 0, dtype=variables.dtypes.int64) + boundaries, values = [1, 2, 3], [0.4, 0.5, 0.6, 0.7] + decayed_lr = learning_rate_decay_v2.piecewise_constant( + x_int64, boundaries, values) + + self.evaluate(variables.global_variables_initializer()) + self.assertAllClose(self.evaluate(decayed_lr()), 0.4, 1e-6) + self.evaluate(x_int64.assign(1)) + self.assertAllClose(self.evaluate(decayed_lr()), 0.4, 1e-6) + self.evaluate(x_int64.assign(2)) + self.assertAllClose(self.evaluate(decayed_lr()), 0.5, 1e-6) + self.evaluate(x_int64.assign(3)) + self.assertAllClose(self.evaluate(decayed_lr()), 0.6, 1e-6) + self.evaluate(x_int64.assign(4)) + self.assertAllClose(self.evaluate(decayed_lr()), 0.7, 1e-6) + + +class LinearDecayTestV2(test_util.TensorFlowTestCase): + + @test_util.run_in_graph_and_eager_modes + def testHalfWay(self): + step = 5 + lr = 0.05 + end_lr = 0.0 + decayed_lr = learning_rate_decay_v2.polynomial_decay(lr, step, 10, end_lr) + expected = lr * 0.5 + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + + @test_util.run_in_graph_and_eager_modes + def testEnd(self): + step = 10 + lr = 0.05 + end_lr = 0.001 + decayed_lr = learning_rate_decay_v2.polynomial_decay(lr, step, 10, end_lr) + expected = end_lr + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + + @test_util.run_in_graph_and_eager_modes + def testHalfWayWithEnd(self): + step = 5 + lr = 0.05 + end_lr = 0.001 + decayed_lr = learning_rate_decay_v2.polynomial_decay(lr, step, 10, end_lr) + expected = (lr + end_lr) * 0.5 + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + + @test_util.run_in_graph_and_eager_modes + def testBeyondEnd(self): + step = 15 + lr = 0.05 + end_lr = 0.001 + decayed_lr = learning_rate_decay_v2.polynomial_decay(lr, step, 10, end_lr) + expected = end_lr + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + + @test_util.run_in_graph_and_eager_modes + def testBeyondEndWithCycle(self): + step = 15 + lr = 0.05 + end_lr = 0.001 + decayed_lr = learning_rate_decay_v2.polynomial_decay( + lr, step, 10, end_lr, cycle=True) + expected = (lr - end_lr) * 0.25 + end_lr + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + + +class SqrtDecayTestV2(test_util.TensorFlowTestCase): + + @test_util.run_in_graph_and_eager_modes + def testHalfWay(self): + step = 5 + lr = 0.05 + end_lr = 0.0 + power = 0.5 + decayed_lr = learning_rate_decay_v2.polynomial_decay( + lr, step, 10, end_lr, power=power) + expected = lr * 0.5**power + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + + @test_util.run_in_graph_and_eager_modes + def testEnd(self): + step = 10 + lr = 0.05 + end_lr = 0.001 + power = 0.5 + decayed_lr = learning_rate_decay_v2.polynomial_decay( + lr, step, 10, end_lr, power=power) + expected = end_lr + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + + @test_util.run_in_graph_and_eager_modes + def testHalfWayWithEnd(self): + step = 5 + lr = 0.05 + end_lr = 0.001 + power = 0.5 + decayed_lr = learning_rate_decay_v2.polynomial_decay( + lr, step, 10, end_lr, power=power) + expected = (lr - end_lr) * 0.5**power + end_lr + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + + @test_util.run_in_graph_and_eager_modes + def testBeyondEnd(self): + step = 15 + lr = 0.05 + end_lr = 0.001 + power = 0.5 + decayed_lr = learning_rate_decay_v2.polynomial_decay( + lr, step, 10, end_lr, power=power) + expected = end_lr + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + + @test_util.run_in_graph_and_eager_modes + def testBeyondEndWithCycle(self): + step = 15 + lr = 0.05 + end_lr = 0.001 + power = 0.5 + decayed_lr = learning_rate_decay_v2.polynomial_decay( + lr, step, 10, end_lr, power=power, cycle=True) + expected = (lr - end_lr) * 0.25**power + end_lr + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + + +class PolynomialDecayTestV2(test_util.TensorFlowTestCase): + + @test_util.run_in_graph_and_eager_modes + def testBeginWithCycle(self): + lr = 0.001 + decay_steps = 10 + step = 0 + decayed_lr = learning_rate_decay_v2.polynomial_decay( + lr, step, decay_steps, cycle=True) + expected = lr + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + + +class ExponentialDecayTestV2(test_util.TensorFlowTestCase): + + @test_util.run_in_graph_and_eager_modes + def testDecay(self): + initial_lr = 0.1 + k = 10 + decay_rate = 0.96 + step = resource_variable_ops.ResourceVariable(0) + decayed_lr = learning_rate_decay_v2.natural_exp_decay(initial_lr, step, k, + decay_rate) + + self.evaluate(variables.global_variables_initializer()) + for i in range(k + 1): + expected = initial_lr * math.exp(-i / k * decay_rate) + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + self.evaluate(step.assign_add(1)) + + @test_util.run_in_graph_and_eager_modes + def testStaircase(self): + initial_lr = 0.1 + k = 10 + decay_rate = 0.96 + step = resource_variable_ops.ResourceVariable(0) + decayed_lr = learning_rate_decay_v2.natural_exp_decay( + initial_lr, step, k, decay_rate, staircase=True) + + self.evaluate(variables.global_variables_initializer()) + for i in range(k + 1): + expected = initial_lr * math.exp(-decay_rate * (i // k)) + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + self.evaluate(step.assign_add(1)) + + +class InverseDecayTestV2(test_util.TensorFlowTestCase): + + @test_util.run_in_graph_and_eager_modes + def testDecay(self): + initial_lr = 0.1 + k = 10 + decay_rate = 0.96 + step = resource_variable_ops.ResourceVariable(0) + decayed_lr = learning_rate_decay_v2.inverse_time_decay(initial_lr, step, k, + decay_rate) + + self.evaluate(variables.global_variables_initializer()) + for i in range(k + 1): + expected = initial_lr / (1 + i / k * decay_rate) + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + self.evaluate(step.assign_add(1)) + + @test_util.run_in_graph_and_eager_modes + def testStaircase(self): + initial_lr = 0.1 + k = 10 + decay_rate = 0.96 + step = resource_variable_ops.ResourceVariable(0) + decayed_lr = learning_rate_decay_v2.inverse_time_decay( + initial_lr, step, k, decay_rate, staircase=True) + + self.evaluate(variables.global_variables_initializer()) + for i in range(k + 1): + expected = initial_lr / (1 + decay_rate * (i // k)) + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + self.evaluate(step.assign_add(1)) + + +class CosineDecayTestV2(test_util.TensorFlowTestCase): + + def np_cosine_decay(self, step, decay_steps, alpha=0.0): + step = min(step, decay_steps) + completed_fraction = step / decay_steps + decay = 0.5 * (1.0 + math.cos(math.pi * completed_fraction)) + return (1.0 - alpha) * decay + alpha + + @test_util.run_in_graph_and_eager_modes + def testDecay(self): + num_training_steps = 1000 + initial_lr = 1.0 + for step in range(0, 1500, 250): + decayed_lr = learning_rate_decay_v2.cosine_decay(initial_lr, step, + num_training_steps) + expected = self.np_cosine_decay(step, num_training_steps) + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + + @test_util.run_in_graph_and_eager_modes + def testAlpha(self): + num_training_steps = 1000 + initial_lr = 1.0 + alpha = 0.1 + for step in range(0, 1500, 250): + decayed_lr = learning_rate_decay_v2.cosine_decay(initial_lr, step, + num_training_steps, + alpha) + expected = self.np_cosine_decay(step, num_training_steps, alpha) + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + + +class CosineDecayRestartsTestV2(test_util.TensorFlowTestCase): + + def np_cosine_decay_restarts(self, step, decay_steps, t_mul=2.0, m_mul=1.0, + alpha=0.0): + fac = 1.0 + while step >= decay_steps: + step -= decay_steps + decay_steps *= t_mul + fac *= m_mul + + completed_fraction = step / decay_steps + decay = fac * 0.5 * (1.0 + math.cos(math.pi * completed_fraction)) + return (1.0 - alpha) * decay + alpha + + @test_util.run_in_graph_and_eager_modes + def testDecay(self): + num_training_steps = 1000 + initial_lr = 1.0 + for step in range(0, 1500, 250): + decayed_lr = learning_rate_decay_v2.cosine_decay_restarts( + initial_lr, step, num_training_steps) + expected = self.np_cosine_decay_restarts(step, num_training_steps) + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + + @test_util.run_in_graph_and_eager_modes + def testAlpha(self): + num_training_steps = 1000 + initial_lr = 1.0 + alpha = 0.1 + for step in range(0, 1500, 250): + decayed_lr = learning_rate_decay_v2.cosine_decay_restarts( + initial_lr, step, num_training_steps, alpha=alpha) + expected = self.np_cosine_decay_restarts( + step, num_training_steps, alpha=alpha) + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + + @test_util.run_in_graph_and_eager_modes + def testMMul(self): + num_training_steps = 1000 + initial_lr = 1.0 + m_mul = 0.9 + for step in range(0, 1500, 250): + decayed_lr = learning_rate_decay_v2.cosine_decay_restarts( + initial_lr, step, num_training_steps, m_mul=m_mul) + expected = self.np_cosine_decay_restarts( + step, num_training_steps, m_mul=m_mul) + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + + @test_util.run_in_graph_and_eager_modes + def testTMul(self): + num_training_steps = 1000 + initial_lr = 1.0 + t_mul = 1.0 + for step in range(0, 1500, 250): + decayed_lr = learning_rate_decay_v2.cosine_decay_restarts( + initial_lr, step, num_training_steps, t_mul=t_mul) + expected = self.np_cosine_decay_restarts( + step, num_training_steps, t_mul=t_mul) + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + + +class LinearCosineDecayTestV2(test_util.TensorFlowTestCase): + + def np_linear_cosine_decay(self, + step, + decay_steps, + alpha=0.0, + beta=0.001, + num_periods=0.5): + step = min(step, decay_steps) + linear_decayed = float(decay_steps - step) / decay_steps + fraction = 2.0 * num_periods * step / float(decay_steps) + cosine_decayed = 0.5 * (1.0 + math.cos(math.pi * fraction)) + return (alpha + linear_decayed) * cosine_decayed + beta + + @test_util.run_in_graph_and_eager_modes + def testDefaultDecay(self): + num_training_steps = 1000 + initial_lr = 1.0 + for step in range(0, 1500, 250): + decayed_lr = learning_rate_decay_v2.linear_cosine_decay( + initial_lr, step, num_training_steps) + expected = self.np_linear_cosine_decay(step, num_training_steps) + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + + @test_util.run_in_graph_and_eager_modes + def testNonDefaultDecay(self): + num_training_steps = 1000 + initial_lr = 1.0 + for step in range(0, 1500, 250): + decayed_lr = learning_rate_decay_v2.linear_cosine_decay( + initial_lr, + step, + num_training_steps, + alpha=0.1, + beta=1e-4, + num_periods=5) + expected = self.np_linear_cosine_decay( + step, num_training_steps, alpha=0.1, beta=1e-4, num_periods=5) + self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6) + + +class NoisyLinearCosineDecayTestV2(test_util.TensorFlowTestCase): + + @test_util.run_in_graph_and_eager_modes + def testDefaultNoisyLinearCosine(self): + num_training_steps = 1000 + initial_lr = 1.0 + for step in range(0, 1500, 250): + # No numerical check because of noise + decayed_lr = learning_rate_decay_v2.noisy_linear_cosine_decay( + initial_lr, step, num_training_steps) + # Cannot be deterministically tested + self.evaluate(decayed_lr()) + + @test_util.run_in_graph_and_eager_modes + def testNonDefaultNoisyLinearCosine(self): + num_training_steps = 1000 + initial_lr = 1.0 + for step in range(0, 1500, 250): + # No numerical check because of noise + decayed_lr = learning_rate_decay_v2.noisy_linear_cosine_decay( + initial_lr, + step, + num_training_steps, + initial_variance=0.5, + variance_decay=0.1, + alpha=0.1, + beta=1e-4, + num_periods=5) + # Cannot be deterministically tested + self.evaluate(decayed_lr()) + +if __name__ == "__main__": + googletest.main() diff --git a/tensorflow/python/training/warm_starting_util.py b/tensorflow/python/training/warm_starting_util.py index c0dd46bfa5..bea9bb6dff 100644 --- a/tensorflow/python/training/warm_starting_util.py +++ b/tensorflow/python/training/warm_starting_util.py @@ -41,6 +41,7 @@ class VocabInfo( "old_vocab", "old_vocab_size", "backup_initializer", + "axis", ])): """Vocabulary information for warm-starting. @@ -62,6 +63,42 @@ class VocabInfo( backup_initializer: [Optional] A variable initializer used for variables corresponding to new vocabulary entries and OOV. If not provided, these entries will be zero-initialized. + axis: [Optional] Denotes what axis the vocabulary corresponds to. The + default, 0, corresponds to the most common use case (embeddings or + linear weights for binary classification / regression). An axis of 1 + could be used for warm-starting output layers with class vocabularies. + + For example: + + embeddings_vocab_info = tf.VocabInfo( + new_vocab='embeddings_vocab', + new_vocab_size=100, + num_oov_buckets=1, + old_vocab='pretrained_embeddings_vocab', + old_vocab_size=10000, + backup_initializer=tf.truncated_normal_initializer( + mean=0.0, stddev=(1 / math.sqrt(embedding_dim))), + axis=0) + + softmax_output_layer_kernel_vocab_info = tf.VocabInfo( + new_vocab='class_vocab', + new_vocab_size=5, + num_oov_buckets=0, # No OOV for classes. + old_vocab='old_class_vocab', + old_vocab_size=8, + backup_initializer=tf.glorot_uniform_initializer(), + axis=1) + + softmax_output_layer_bias_vocab_info = tf.VocabInfo( + new_vocab='class_vocab', + new_vocab_size=5, + num_oov_buckets=0, # No OOV for classes. + old_vocab='old_class_vocab', + old_vocab_size=8, + backup_initializer=tf.zeros_initializer(), + axis=0) + + Currently, only axis=0 and axis=1 are supported. """ def __new__(cls, @@ -70,7 +107,12 @@ class VocabInfo( num_oov_buckets, old_vocab, old_vocab_size=-1, - backup_initializer=None): + backup_initializer=None, + axis=0): + if axis != 0 and axis != 1: + raise ValueError("The only supported values for the axis argument are 0 " + "and 1. Provided axis: {}".format(axis)) + return super(VocabInfo, cls).__new__( cls, new_vocab, @@ -79,6 +121,7 @@ class VocabInfo( old_vocab, old_vocab_size, backup_initializer, + axis, ) @@ -149,7 +192,8 @@ def _warm_start_var_with_vocab(var, previous_vocab_size=-1, current_oov_buckets=0, prev_tensor_name=None, - initializer=None): + initializer=None, + axis=0): """Warm-starts given variable from `prev_tensor_name` tensor in `prev_ckpt`. Use this method when the `var` is backed by vocabulary. This method stitches @@ -180,6 +224,7 @@ def _warm_start_var_with_vocab(var, None, we lookup tensor with same name as given `var`. initializer: Variable initializer to be used for missing entries. If None, missing entries will be zero-initialized. + axis: Axis of the variable that the provided vocabulary corresponds to. Raises: ValueError: If required args are not provided. @@ -204,6 +249,8 @@ def _warm_start_var_with_vocab(var, # Assume tensor name remains the same. prev_tensor_name = _infer_var_name(var) + # TODO(eddz): Fix functionality for rank-1 Variables (like FC biases). + total_v_first_axis = sum([v.get_shape().as_list()[0] for v in var]) for v in var: v_shape = v.get_shape().as_list() slice_info = v._get_save_slice_info() @@ -213,19 +260,45 @@ def _warm_start_var_with_vocab(var, full_shape=slice_info.full_shape, var_offset=slice_info.var_offset) - # TODO(eddz): Support cases where class vocabularies need remapping too. + if axis == 0: + new_row_vocab_size = current_vocab_size + new_col_vocab_size = v_shape[1] + old_row_vocab_size = previous_vocab_size + old_row_vocab_file = prev_vocab_path + new_row_vocab_file = current_vocab_path + old_col_vocab_file = None + new_col_vocab_file = None + num_row_oov_buckets = current_oov_buckets + num_col_oov_buckets = 0 + elif axis == 1: + # Note that we must compute this value across all partitions, whereas + # in the axis = 0 case, we can simply use v_shape[1] because we don't + # allow partitioning across axis = 1. + new_row_vocab_size = total_v_first_axis + new_col_vocab_size = current_vocab_size + old_row_vocab_size = -1 + old_row_vocab_file = None + new_row_vocab_file = None + old_col_vocab_file = prev_vocab_path + new_col_vocab_file = current_vocab_path + num_row_oov_buckets = 0 + num_col_oov_buckets = current_oov_buckets + else: + raise ValueError("The only supported values for the axis argument are 0 " + "and 1. Provided axis: {}".format(axis)) + init = checkpoint_ops._load_and_remap_matrix_initializer( ckpt_path=checkpoint_utils._get_checkpoint_filename(prev_ckpt), old_tensor_name=prev_tensor_name, - new_row_vocab_size=current_vocab_size, - new_col_vocab_size=v_shape[1], - old_row_vocab_size=previous_vocab_size, - old_row_vocab_file=prev_vocab_path, - new_row_vocab_file=current_vocab_path, - old_col_vocab_file=None, - new_col_vocab_file=None, - num_row_oov_buckets=current_oov_buckets, - num_col_oov_buckets=0, + new_row_vocab_size=new_row_vocab_size, + new_col_vocab_size=new_col_vocab_size, + old_row_vocab_size=old_row_vocab_size, + old_row_vocab_file=old_row_vocab_file, + new_row_vocab_file=new_row_vocab_file, + old_col_vocab_file=old_col_vocab_file, + new_col_vocab_file=new_col_vocab_file, + num_row_oov_buckets=num_row_oov_buckets, + num_col_oov_buckets=num_col_oov_buckets, initializer=initializer) new_init_val = ops.convert_to_tensor( init(shape=v_shape, partition_info=partition_info)) @@ -374,7 +447,8 @@ def warm_start(ckpt_to_initialize_from, previous_vocab_size=vocab_info.old_vocab_size, current_oov_buckets=vocab_info.num_oov_buckets, prev_tensor_name=prev_var_name, - initializer=vocab_info.backup_initializer) + initializer=vocab_info.backup_initializer, + axis=vocab_info.axis) else: # For the special value of vars_to_warm_start = None, # we only warm-start variables with explicitly specified vocabularies. diff --git a/tensorflow/python/training/warm_starting_util_test.py b/tensorflow/python/training/warm_starting_util_test.py index 70a84bc3f6..3ee0f6aaa2 100644 --- a/tensorflow/python/training/warm_starting_util_test.py +++ b/tensorflow/python/training/warm_starting_util_test.py @@ -107,7 +107,7 @@ class WarmStartingUtilTest(test.TestCase): "fruit_weights", initializer=[[0.], [0.], [0.], [0.]]) ws_util._warm_start_var(fruit_weights, self.get_temp_dir()) sess.run(variables.global_variables_initializer()) - self.assertAllEqual(prev_val, fruit_weights.eval(sess)) + self.assertAllClose(prev_val, fruit_weights.eval(sess)) def testWarmStartVarPrevVarPartitioned(self): _, weights = self._create_prev_run_var( @@ -123,7 +123,7 @@ class WarmStartingUtilTest(test.TestCase): "fruit_weights", initializer=[[0.], [0.], [0.], [0.]]) ws_util._warm_start_var(fruit_weights, self.get_temp_dir()) sess.run(variables.global_variables_initializer()) - self.assertAllEqual(prev_val, fruit_weights.eval(sess)) + self.assertAllClose(prev_val, fruit_weights.eval(sess)) def testWarmStartVarCurrentVarPartitioned(self): _, prev_val = self._create_prev_run_var( @@ -143,7 +143,7 @@ class WarmStartingUtilTest(test.TestCase): fruit_weights = fruit_weights._get_variable_list() new_val = np.concatenate( [fruit_weights[0].eval(sess), fruit_weights[1].eval(sess)], axis=0) - self.assertAllEqual(prev_val, new_val) + self.assertAllClose(prev_val, new_val) def testWarmStartVarBothVarsPartitioned(self): _, weights = self._create_prev_run_var( @@ -170,7 +170,7 @@ class WarmStartingUtilTest(test.TestCase): fruit_weights = fruit_weights._get_variable_list() new_val = np.concatenate( [fruit_weights[0].eval(sess), fruit_weights[1].eval(sess)], axis=0) - self.assertAllEqual(prev_val, new_val) + self.assertAllClose(prev_val, new_val) def testWarmStartVarWithVocab(self): prev_vocab_path = self._write_vocab(["apple", "banana", "guava", "orange"], @@ -189,9 +189,34 @@ class WarmStartingUtilTest(test.TestCase): ws_util._warm_start_var_with_vocab(fruit_weights, new_vocab_path, 5, self.get_temp_dir(), prev_vocab_path) sess.run(variables.global_variables_initializer()) - self.assertAllEqual([[2.], [1.5], [1.], [0.5], [0.]], + self.assertAllClose([[2.], [1.5], [1.], [0.5], [0.]], fruit_weights.eval(sess)) + def testWarmStartVarWithColumnVocab(self): + prev_vocab_path = self._write_vocab(["apple", "orange"], "old_vocab") + self._create_prev_run_var( + "fruit_output_layer", + initializer=[[0.5, 0.3], [1., 0.8], [1.5, 1.2], [2., 2.3]]) + + # New vocab with elements in reverse order and one new element. + new_vocab_path = self._write_vocab(["orange", "apple", "banana"], + "new_vocab") + # New session and new graph. + with ops.Graph().as_default() as g: + with self.test_session(graph=g) as sess: + fruit_output_layer = variable_scope.get_variable( + "fruit_output_layer", + initializer=[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], + [0., 0., 0.]]) + ws_util._warm_start_var_with_vocab(fruit_output_layer, new_vocab_path, + current_vocab_size=3, + prev_ckpt=self.get_temp_dir(), + prev_vocab_path=prev_vocab_path, + axis=1) + sess.run(variables.global_variables_initializer()) + self.assertAllClose([[0.3, 0.5, 0.], [0.8, 1.0, 0.], [1.2, 1.5, 0.], + [2.3, 2., 0.]], fruit_output_layer.eval(sess)) + def testWarmStartVarWithVocabConstrainedOldVocabSize(self): prev_vocab_path = self._write_vocab(["apple", "banana", "guava", "orange"], "old_vocab") @@ -215,7 +240,7 @@ class WarmStartingUtilTest(test.TestCase): previous_vocab_size=2) sess.run(variables.global_variables_initializer()) # Old vocabulary limited to ['apple', 'banana']. - self.assertAllEqual([[0.], [0.], [1.], [0.5], [0.]], + self.assertAllClose([[0.], [0.], [1.], [0.5], [0.]], fruit_weights.eval(sess)) def testWarmStartVarWithVocabPrevVarPartitioned(self): @@ -238,9 +263,36 @@ class WarmStartingUtilTest(test.TestCase): ws_util._warm_start_var_with_vocab(fruit_weights, new_vocab_path, 5, self.get_temp_dir(), prev_vocab_path) sess.run(variables.global_variables_initializer()) - self.assertAllEqual([[2.], [1.5], [1.], [0.5], [0.]], + self.assertAllClose([[2.], [1.5], [1.], [0.5], [0.]], fruit_weights.eval(sess)) + def testWarmStartVarWithColumnVocabPrevVarPartitioned(self): + prev_vocab_path = self._write_vocab(["apple", "orange"], "old_vocab") + self._create_prev_run_var( + "fruit_output_layer", + shape=[4, 2], + initializer=[[0.5, 0.3], [1., 0.8], [1.5, 1.2], [2., 2.3]], + partitioner=lambda shape, dtype: [2, 1]) + + # New vocab with elements in reverse order and one new element. + new_vocab_path = self._write_vocab(["orange", "apple", "banana"], + "new_vocab") + # New session and new graph. + with ops.Graph().as_default() as g: + with self.test_session(graph=g) as sess: + fruit_output_layer = variable_scope.get_variable( + "fruit_output_layer", + initializer=[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], + [0., 0., 0.]]) + ws_util._warm_start_var_with_vocab(fruit_output_layer, new_vocab_path, + current_vocab_size=3, + prev_ckpt=self.get_temp_dir(), + prev_vocab_path=prev_vocab_path, + axis=1) + sess.run(variables.global_variables_initializer()) + self.assertAllClose([[0.3, 0.5, 0.], [0.8, 1.0, 0.], [1.2, 1.5, 0.], + [2.3, 2., 0.]], fruit_output_layer.eval(sess)) + def testWarmStartVarWithVocabCurrentVarPartitioned(self): prev_vocab_path = self._write_vocab(["apple", "banana", "guava", "orange"], "old_vocab") @@ -269,11 +321,43 @@ class WarmStartingUtilTest(test.TestCase): self.assertTrue( isinstance(fruit_weights, variables.PartitionedVariable)) fruit_weights_vars = fruit_weights._get_variable_list() - self.assertAllEqual([[2.], [1.5], [1.]], + self.assertAllClose([[2.], [1.5], [1.]], fruit_weights_vars[0].eval(sess)) - self.assertAllEqual([[0.5], [0.], [0.]], + self.assertAllClose([[0.5], [0.], [0.]], fruit_weights_vars[1].eval(sess)) + def testWarmStartVarWithColumnVocabCurrentVarPartitioned(self): + prev_vocab_path = self._write_vocab(["apple", "orange"], "old_vocab") + self._create_prev_run_var( + "fruit_output_layer", + initializer=[[0.5, 0.3], [1., 0.8], [1.5, 1.2], [2., 2.3]]) + + # New vocab with elements in reverse order and one new element. + new_vocab_path = self._write_vocab(["orange", "apple", "banana"], + "new_vocab") + # New session and new graph. + with ops.Graph().as_default() as g: + with self.test_session(graph=g) as sess: + fruit_output_layer = variable_scope.get_variable( + "fruit_output_layer", + shape=[4, 3], + initializer=[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], + [0., 0., 0.]], + partitioner=lambda shape, dtype: [2, 1]) + ws_util._warm_start_var_with_vocab(fruit_output_layer, new_vocab_path, + current_vocab_size=3, + prev_ckpt=self.get_temp_dir(), + prev_vocab_path=prev_vocab_path, + axis=1) + sess.run(variables.global_variables_initializer()) + self.assertTrue( + isinstance(fruit_output_layer, variables.PartitionedVariable)) + fruit_output_layer_vars = fruit_output_layer._get_variable_list() + self.assertAllClose([[0.3, 0.5, 0.], [0.8, 1.0, 0.]], + fruit_output_layer_vars[0].eval(sess)) + self.assertAllClose([[1.2, 1.5, 0.], [2.3, 2., 0.]], + fruit_output_layer_vars[1].eval(sess)) + def testWarmStartVarWithVocabBothVarsPartitioned(self): prev_vocab_path = self._write_vocab(["apple", "banana", "guava", "orange"], "old_vocab") @@ -301,11 +385,45 @@ class WarmStartingUtilTest(test.TestCase): self.assertTrue( isinstance(fruit_weights, variables.PartitionedVariable)) fruit_weights_vars = fruit_weights._get_variable_list() - self.assertAllEqual([[2.], [1.5], [1.]], + self.assertAllClose([[2.], [1.5], [1.]], fruit_weights_vars[0].eval(sess)) - self.assertAllEqual([[0.5], [0.], [0.]], + self.assertAllClose([[0.5], [0.], [0.]], fruit_weights_vars[1].eval(sess)) + def testWarmStartVarWithColumnVocabBothVarsPartitioned(self): + prev_vocab_path = self._write_vocab(["apple", "orange"], "old_vocab") + self._create_prev_run_var( + "fruit_output_layer", + shape=[4, 2], + initializer=[[0.5, 0.3], [1., 0.8], [1.5, 1.2], [2., 2.3]], + partitioner=lambda shape, dtype: [2, 1]) + + # New vocab with elements in reverse order and one new element. + new_vocab_path = self._write_vocab(["orange", "apple", "banana"], + "new_vocab") + # New session and new graph. + with ops.Graph().as_default() as g: + with self.test_session(graph=g) as sess: + fruit_output_layer = variable_scope.get_variable( + "fruit_output_layer", + shape=[4, 3], + initializer=[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], + [0., 0., 0.]], + partitioner=lambda shape, dtype: [2, 1]) + ws_util._warm_start_var_with_vocab(fruit_output_layer, new_vocab_path, + current_vocab_size=3, + prev_ckpt=self.get_temp_dir(), + prev_vocab_path=prev_vocab_path, + axis=1) + sess.run(variables.global_variables_initializer()) + self.assertTrue( + isinstance(fruit_output_layer, variables.PartitionedVariable)) + fruit_output_layer_vars = fruit_output_layer._get_variable_list() + self.assertAllClose([[0.3, 0.5, 0.], [0.8, 1.0, 0.]], + fruit_output_layer_vars[0].eval(sess)) + self.assertAllClose([[1.2, 1.5, 0.], [2.3, 2., 0.]], + fruit_output_layer_vars[1].eval(sess)) + def testWarmStart_ListOfVariables(self): # Save checkpoint from which to warm-start. _, prev_int_val = self._create_prev_run_var("v1", shape=[10, 1], diff --git a/tensorflow/tools/api/golden/v1/tensorflow.estimator.-vocab-info.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-vocab-info.pbtxt index 5301b94eb3..b6942cb7ed 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.estimator.-vocab-info.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-vocab-info.pbtxt @@ -4,6 +4,10 @@ tf_class { is_instance: "<class \'tensorflow.python.training.warm_starting_util.VocabInfo\'>" is_instance: "<type \'tuple\'>" member { + name: "axis" + mtype: "<type \'property\'>" + } + member { name: "backup_initializer" mtype: "<type \'property\'>" } diff --git a/tensorflow/tools/api/golden/v1/tensorflow.train.-vocab-info.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.train.-vocab-info.pbtxt index 4ce7cb1111..39b946b82f 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.train.-vocab-info.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.train.-vocab-info.pbtxt @@ -4,6 +4,10 @@ tf_class { is_instance: "<class \'tensorflow.python.training.warm_starting_util.VocabInfo\'>" is_instance: "<type \'tuple\'>" member { + name: "axis" + mtype: "<type \'property\'>" + } + member { name: "backup_initializer" mtype: "<type \'property\'>" } diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-fixed-length-record-reader.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-fixed-length-record-reader.pbtxt deleted file mode 100644 index 260c796fd6..0000000000 --- a/tensorflow/tools/api/golden/v2/tensorflow.-fixed-length-record-reader.pbtxt +++ /dev/null @@ -1,46 +0,0 @@ -path: "tensorflow.FixedLengthRecordReader" -tf_class { - is_instance: "<class \'tensorflow.python.ops.io_ops.FixedLengthRecordReader\'>" - is_instance: "<class \'tensorflow.python.ops.io_ops.ReaderBase\'>" - is_instance: "<type \'object\'>" - member { - name: "reader_ref" - mtype: "<type \'property\'>" - } - member { - name: "supports_serialize" - mtype: "<type \'property\'>" - } - member_method { - name: "__init__" - argspec: "args=[\'self\', \'record_bytes\', \'header_bytes\', \'footer_bytes\', \'hop_bytes\', \'name\', \'encoding\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\'], " - } - member_method { - name: "num_records_produced" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "num_work_units_completed" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "read" - argspec: "args=[\'self\', \'queue\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "read_up_to" - argspec: "args=[\'self\', \'queue\', \'num_records\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "reset" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "restore_state" - argspec: "args=[\'self\', \'state\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "serialize_state" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } -} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-identity-reader.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-identity-reader.pbtxt deleted file mode 100644 index 2eda320d63..0000000000 --- a/tensorflow/tools/api/golden/v2/tensorflow.-identity-reader.pbtxt +++ /dev/null @@ -1,46 +0,0 @@ -path: "tensorflow.IdentityReader" -tf_class { - is_instance: "<class \'tensorflow.python.ops.io_ops.IdentityReader\'>" - is_instance: "<class \'tensorflow.python.ops.io_ops.ReaderBase\'>" - is_instance: "<type \'object\'>" - member { - name: "reader_ref" - mtype: "<type \'property\'>" - } - member { - name: "supports_serialize" - mtype: "<type \'property\'>" - } - member_method { - name: "__init__" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "num_records_produced" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "num_work_units_completed" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "read" - argspec: "args=[\'self\', \'queue\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "read_up_to" - argspec: "args=[\'self\', \'queue\', \'num_records\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "reset" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "restore_state" - argspec: "args=[\'self\', \'state\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "serialize_state" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } -} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-l-m-d-b-reader.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-l-m-d-b-reader.pbtxt deleted file mode 100644 index f9b7e9bbca..0000000000 --- a/tensorflow/tools/api/golden/v2/tensorflow.-l-m-d-b-reader.pbtxt +++ /dev/null @@ -1,46 +0,0 @@ -path: "tensorflow.LMDBReader" -tf_class { - is_instance: "<class \'tensorflow.python.ops.io_ops.LMDBReader\'>" - is_instance: "<class \'tensorflow.python.ops.io_ops.ReaderBase\'>" - is_instance: "<type \'object\'>" - member { - name: "reader_ref" - mtype: "<type \'property\'>" - } - member { - name: "supports_serialize" - mtype: "<type \'property\'>" - } - member_method { - name: "__init__" - argspec: "args=[\'self\', \'name\', \'options\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " - } - member_method { - name: "num_records_produced" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "num_work_units_completed" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "read" - argspec: "args=[\'self\', \'queue\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "read_up_to" - argspec: "args=[\'self\', \'queue\', \'num_records\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "reset" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "restore_state" - argspec: "args=[\'self\', \'state\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "serialize_state" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } -} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-reader-base.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-reader-base.pbtxt deleted file mode 100644 index f6a3ce76a1..0000000000 --- a/tensorflow/tools/api/golden/v2/tensorflow.-reader-base.pbtxt +++ /dev/null @@ -1,45 +0,0 @@ -path: "tensorflow.ReaderBase" -tf_class { - is_instance: "<class \'tensorflow.python.ops.io_ops.ReaderBase\'>" - is_instance: "<type \'object\'>" - member { - name: "reader_ref" - mtype: "<type \'property\'>" - } - member { - name: "supports_serialize" - mtype: "<type \'property\'>" - } - member_method { - name: "__init__" - argspec: "args=[\'self\', \'reader_ref\', \'supports_serialize\'], varargs=None, keywords=None, defaults=[\'False\'], " - } - member_method { - name: "num_records_produced" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "num_work_units_completed" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "read" - argspec: "args=[\'self\', \'queue\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "read_up_to" - argspec: "args=[\'self\', \'queue\', \'num_records\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "reset" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "restore_state" - argspec: "args=[\'self\', \'state\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "serialize_state" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } -} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-t-f-record-reader.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-t-f-record-reader.pbtxt deleted file mode 100644 index cdf7937391..0000000000 --- a/tensorflow/tools/api/golden/v2/tensorflow.-t-f-record-reader.pbtxt +++ /dev/null @@ -1,46 +0,0 @@ -path: "tensorflow.TFRecordReader" -tf_class { - is_instance: "<class \'tensorflow.python.ops.io_ops.TFRecordReader\'>" - is_instance: "<class \'tensorflow.python.ops.io_ops.ReaderBase\'>" - is_instance: "<type \'object\'>" - member { - name: "reader_ref" - mtype: "<type \'property\'>" - } - member { - name: "supports_serialize" - mtype: "<type \'property\'>" - } - member_method { - name: "__init__" - argspec: "args=[\'self\', \'name\', \'options\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " - } - member_method { - name: "num_records_produced" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "num_work_units_completed" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "read" - argspec: "args=[\'self\', \'queue\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "read_up_to" - argspec: "args=[\'self\', \'queue\', \'num_records\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "reset" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "restore_state" - argspec: "args=[\'self\', \'state\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "serialize_state" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } -} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-text-line-reader.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-text-line-reader.pbtxt deleted file mode 100644 index e9779f0762..0000000000 --- a/tensorflow/tools/api/golden/v2/tensorflow.-text-line-reader.pbtxt +++ /dev/null @@ -1,46 +0,0 @@ -path: "tensorflow.TextLineReader" -tf_class { - is_instance: "<class \'tensorflow.python.ops.io_ops.TextLineReader\'>" - is_instance: "<class \'tensorflow.python.ops.io_ops.ReaderBase\'>" - is_instance: "<type \'object\'>" - member { - name: "reader_ref" - mtype: "<type \'property\'>" - } - member { - name: "supports_serialize" - mtype: "<type \'property\'>" - } - member_method { - name: "__init__" - argspec: "args=[\'self\', \'skip_header_lines\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " - } - member_method { - name: "num_records_produced" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "num_work_units_completed" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "read" - argspec: "args=[\'self\', \'queue\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "read_up_to" - argspec: "args=[\'self\', \'queue\', \'num_records\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "reset" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "restore_state" - argspec: "args=[\'self\', \'state\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "serialize_state" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } -} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-whole-file-reader.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-whole-file-reader.pbtxt deleted file mode 100644 index 4ac759891c..0000000000 --- a/tensorflow/tools/api/golden/v2/tensorflow.-whole-file-reader.pbtxt +++ /dev/null @@ -1,46 +0,0 @@ -path: "tensorflow.WholeFileReader" -tf_class { - is_instance: "<class \'tensorflow.python.ops.io_ops.WholeFileReader\'>" - is_instance: "<class \'tensorflow.python.ops.io_ops.ReaderBase\'>" - is_instance: "<type \'object\'>" - member { - name: "reader_ref" - mtype: "<type \'property\'>" - } - member { - name: "supports_serialize" - mtype: "<type \'property\'>" - } - member_method { - name: "__init__" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "num_records_produced" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "num_work_units_completed" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "read" - argspec: "args=[\'self\', \'queue\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "read_up_to" - argspec: "args=[\'self\', \'queue\', \'num_records\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "reset" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "restore_state" - argspec: "args=[\'self\', \'state\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } - member_method { - name: "serialize_state" - argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " - } -} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-vocab-info.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-vocab-info.pbtxt index 5301b94eb3..b6942cb7ed 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-vocab-info.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-vocab-info.pbtxt @@ -4,6 +4,10 @@ tf_class { is_instance: "<class \'tensorflow.python.training.warm_starting_util.VocabInfo\'>" is_instance: "<type \'tuple\'>" member { + name: "axis" + mtype: "<type \'property\'>" + } + member { name: "backup_initializer" mtype: "<type \'property\'>" } diff --git a/tensorflow/tools/api/golden/v2/tensorflow.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.pbtxt index 7d45ea22c8..9332e16bf6 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.pbtxt @@ -61,10 +61,6 @@ tf_module { mtype: "<type \'type\'>" } member { - name: "FixedLengthRecordReader" - mtype: "<type \'type\'>" - } - member { name: "GIT_VERSION" mtype: "<type \'str\'>" } @@ -109,10 +105,6 @@ tf_module { mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>" } member { - name: "IdentityReader" - mtype: "<type \'type\'>" - } - member { name: "IndexedSlices" mtype: "<type \'type\'>" } @@ -121,10 +113,6 @@ tf_module { mtype: "<type \'type\'>" } member { - name: "LMDBReader" - mtype: "<type \'type\'>" - } - member { name: "LogMessage" mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>" } @@ -177,10 +165,6 @@ tf_module { mtype: "<type \'type\'>" } member { - name: "ReaderBase" - mtype: "<type \'type\'>" - } - member { name: "RegisterGradient" mtype: "<type \'type\'>" } @@ -225,10 +209,6 @@ tf_module { mtype: "<class \'google.protobuf.pyext.cpp_message.GeneratedProtocolMessageType\'>" } member { - name: "TFRecordReader" - mtype: "<type \'type\'>" - } - member { name: "Tensor" mtype: "<type \'type\'>" } @@ -245,10 +225,6 @@ tf_module { mtype: "<type \'type\'>" } member { - name: "TextLineReader" - mtype: "<type \'type\'>" - } - member { name: "VERSION" mtype: "<type \'str\'>" } @@ -273,10 +249,6 @@ tf_module { mtype: "<class \'enum.EnumMeta\'>" } member { - name: "WholeFileReader" - mtype: "<type \'type\'>" - } - member { name: "app" mtype: "<type \'module\'>" } diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-vocab-info.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-vocab-info.pbtxt index 4ce7cb1111..39b946b82f 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.train.-vocab-info.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-vocab-info.pbtxt @@ -4,6 +4,10 @@ tf_class { is_instance: "<class \'tensorflow.python.training.warm_starting_util.VocabInfo\'>" is_instance: "<type \'tuple\'>" member { + name: "axis" + mtype: "<type \'property\'>" + } + member { name: "backup_initializer" mtype: "<type \'property\'>" } diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.pbtxt index c35e254843..b21dabbde7 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.train.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.pbtxt @@ -249,14 +249,6 @@ tf_module { argspec: "args=[\'supervisor\', \'train_step_fn\', \'args\', \'kwargs\', \'master\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'\'], " } member_method { - name: "batch" - argspec: "args=[\'tensors\', \'batch_size\', \'num_threads\', \'capacity\', \'enqueue_many\', \'shapes\', \'dynamic_pad\', \'allow_smaller_final_batch\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'1\', \'32\', \'False\', \'None\', \'False\', \'False\', \'None\', \'None\'], " - } - member_method { - name: "batch_join" - argspec: "args=[\'tensors_list\', \'batch_size\', \'capacity\', \'enqueue_many\', \'shapes\', \'dynamic_pad\', \'allow_smaller_final_batch\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'32\', \'False\', \'None\', \'False\', \'False\', \'None\', \'None\'], " - } - member_method { name: "checkpoint_exists" argspec: "args=[\'checkpoint_prefix\'], varargs=None, keywords=None, defaults=None" } @@ -317,10 +309,6 @@ tf_module { argspec: "args=[\'ckpt_dir_or_file\', \'assignment_map\'], varargs=None, keywords=None, defaults=None" } member_method { - name: "input_producer" - argspec: "args=[\'input_tensor\', \'element_shape\', \'num_epochs\', \'shuffle\', \'seed\', \'capacity\', \'shared_name\', \'summary_name\', \'name\', \'cancel_op\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'True\', \'None\', \'32\', \'None\', \'None\', \'None\', \'None\'], " - } - member_method { name: "inverse_time_decay" argspec: "args=[\'learning_rate\', \'global_step\', \'decay_steps\', \'decay_rate\', \'staircase\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " } @@ -329,10 +317,6 @@ tf_module { argspec: "args=[\'checkpoint_dir\', \'latest_filename\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { - name: "limit_epochs" - argspec: "args=[\'tensor\', \'num_epochs\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " - } - member_method { name: "linear_cosine_decay" argspec: "args=[\'learning_rate\', \'global_step\', \'decay_steps\', \'num_periods\', \'alpha\', \'beta\', \'name\'], varargs=None, keywords=None, defaults=[\'0.5\', \'0.0\', \'0.001\', \'None\'], " } @@ -353,22 +337,6 @@ tf_module { argspec: "args=[\'pattern\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { - name: "maybe_batch" - argspec: "args=[\'tensors\', \'keep_input\', \'batch_size\', \'num_threads\', \'capacity\', \'enqueue_many\', \'shapes\', \'dynamic_pad\', \'allow_smaller_final_batch\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'1\', \'32\', \'False\', \'None\', \'False\', \'False\', \'None\', \'None\'], " - } - member_method { - name: "maybe_batch_join" - argspec: "args=[\'tensors_list\', \'keep_input\', \'batch_size\', \'capacity\', \'enqueue_many\', \'shapes\', \'dynamic_pad\', \'allow_smaller_final_batch\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'32\', \'False\', \'None\', \'False\', \'False\', \'None\', \'None\'], " - } - member_method { - name: "maybe_shuffle_batch" - argspec: "args=[\'tensors\', \'batch_size\', \'capacity\', \'min_after_dequeue\', \'keep_input\', \'num_threads\', \'seed\', \'enqueue_many\', \'shapes\', \'allow_smaller_final_batch\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'1\', \'None\', \'False\', \'None\', \'False\', \'None\', \'None\'], " - } - member_method { - name: "maybe_shuffle_batch_join" - argspec: "args=[\'tensors_list\', \'batch_size\', \'capacity\', \'min_after_dequeue\', \'keep_input\', \'seed\', \'enqueue_many\', \'shapes\', \'allow_smaller_final_batch\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\', \'False\', \'None\', \'None\'], " - } - member_method { name: "natural_exp_decay" argspec: "args=[\'learning_rate\', \'global_step\', \'decay_steps\', \'decay_rate\', \'staircase\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " } @@ -385,10 +353,6 @@ tf_module { argspec: "args=[\'learning_rate\', \'global_step\', \'decay_steps\', \'end_learning_rate\', \'power\', \'cycle\', \'name\'], varargs=None, keywords=None, defaults=[\'0.0001\', \'1.0\', \'False\', \'None\'], " } member_method { - name: "range_input_producer" - argspec: "args=[\'limit\', \'num_epochs\', \'shuffle\', \'seed\', \'capacity\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'None\', \'32\', \'None\', \'None\'], " - } - member_method { name: "remove_checkpoint" argspec: "args=[\'checkpoint_prefix\', \'checkpoint_format_version\', \'meta_graph_suffix\'], varargs=None, keywords=None, defaults=[\'2\', \'meta\'], " } @@ -409,22 +373,6 @@ tf_module { argspec: "args=[\'weights\', \'l1\', \'l2\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { - name: "shuffle_batch" - argspec: "args=[\'tensors\', \'batch_size\', \'capacity\', \'min_after_dequeue\', \'num_threads\', \'seed\', \'enqueue_many\', \'shapes\', \'allow_smaller_final_batch\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'1\', \'None\', \'False\', \'None\', \'False\', \'None\', \'None\'], " - } - member_method { - name: "shuffle_batch_join" - argspec: "args=[\'tensors_list\', \'batch_size\', \'capacity\', \'min_after_dequeue\', \'seed\', \'enqueue_many\', \'shapes\', \'allow_smaller_final_batch\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\', \'False\', \'None\', \'None\'], " - } - member_method { - name: "slice_input_producer" - argspec: "args=[\'tensor_list\', \'num_epochs\', \'shuffle\', \'seed\', \'capacity\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'None\', \'32\', \'None\', \'None\'], " - } - member_method { - name: "string_input_producer" - argspec: "args=[\'string_tensor\', \'num_epochs\', \'shuffle\', \'seed\', \'capacity\', \'shared_name\', \'name\', \'cancel_op\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'None\', \'32\', \'None\', \'None\', \'None\'], " - } - member_method { name: "summary_iterator" argspec: "args=[\'path\'], varargs=None, keywords=None, defaults=None" } diff --git a/tensorflow/tools/compatibility/renames_v2.py b/tensorflow/tools/compatibility/renames_v2.py index 216aa41b60..7e66ad816a 100644 --- a/tensorflow/tools/compatibility/renames_v2.py +++ b/tensorflow/tools/compatibility/renames_v2.py @@ -65,6 +65,7 @@ renames = { 'tf.fft': 'tf.spectral.fft', 'tf.floor': 'tf.math.floor', 'tf.gather_nd': 'tf.manip.gather_nd', + 'tf.GraphKeys.VARIABLES': 'tf.GraphKeys.GLOBAL_VARIABLES', 'tf.greater': 'tf.math.greater', 'tf.greater_equal': 'tf.math.greater_equal', 'tf.ifft': 'tf.spectral.ifft', diff --git a/tensorflow/tools/compatibility/tf_upgrade_v2.py b/tensorflow/tools/compatibility/tf_upgrade_v2.py index 9702430a12..38216ce9b1 100644 --- a/tensorflow/tools/compatibility/tf_upgrade_v2.py +++ b/tensorflow/tools/compatibility/tf_upgrade_v2.py @@ -19,6 +19,7 @@ from __future__ import division from __future__ import print_function import argparse +import functools from tensorflow.tools.compatibility import ast_edits from tensorflow.tools.compatibility import renames_v2 @@ -45,6 +46,29 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec): # Specially handled functions. self.function_handle = {} + for decay in ["tf.train.exponential_decay", "tf.train.piecewise_constant", + "tf.train.polynomial_decay", "tf.train.natural_exp_decay", + "tf.train.inverse_time_decay", "tf.train.cosine_decay", + "tf.train.cosine_decay_restarts", + "tf.train.linear_cosine_decay", + "tf.train.noisy_linear_cosine_decay"]: + self.function_handle[decay] = functools.partial( + self._learning_rate_decay_handler, decay_name=decay) + + @staticmethod + def _learning_rate_decay_handler(file_edit_recorder, node, decay_name): + comment = ("ERROR: %s has been changed to return a callable instead of a " + "tensor when graph building, but its functionality remains " + "unchanged during eager execution (returns a callable like " + "before). The converter cannot detect and fix this reliably, so " + "you need to inspect this usage manually.\n") % decay_name + file_edit_recorder.add( + comment, + node.lineno, + node.col_offset, + decay_name, + decay_name, + error="%s requires manual check." % decay_name) if __name__ == "__main__": diff --git a/tensorflow/tools/compatibility/tf_upgrade_v2_test.py b/tensorflow/tools/compatibility/tf_upgrade_v2_test.py index 57ac04de06..3886c1e8b9 100644 --- a/tensorflow/tools/compatibility/tf_upgrade_v2_test.py +++ b/tensorflow/tools/compatibility/tf_upgrade_v2_test.py @@ -63,6 +63,19 @@ class TestUpgrade(test_util.TensorFlowTestCase): _, unused_report, unused_errors, new_text = self._upgrade(text) self.assertEqual(new_text, "tf.math.rsqrt(tf.math.log(3.8))\n") + def testLearningRateDecay(self): + for decay in ["tf.train.exponential_decay", "tf.train.piecewise_constant", + "tf.train.polynomial_decay", "tf.train.natural_exp_decay", + "tf.train.inverse_time_decay", "tf.train.cosine_decay", + "tf.train.cosine_decay_restarts", + "tf.train.linear_cosine_decay", + "tf.train.noisy_linear_cosine_decay"]: + + text = "%s(a, b)\n" % decay + _, unused_report, errors, new_text = self._upgrade(text) + self.assertEqual(text, new_text) + self.assertEqual(errors, ["test.py:1: %s requires manual check." % decay]) + class TestUpgradeFiles(test_util.TensorFlowTestCase): diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 742f33f68e..2bf867c7e1 100755 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -240,11 +240,11 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""): tf_http_archive( name = "jpeg", urls = [ - "https://mirror.bazel.build/github.com/libjpeg-turbo/libjpeg-turbo/archive/1.5.3.tar.gz", - "https://github.com/libjpeg-turbo/libjpeg-turbo/archive/1.5.3.tar.gz", + "https://mirror.bazel.build/github.com/libjpeg-turbo/libjpeg-turbo/archive/2.0.0.tar.gz", + "https://github.com/libjpeg-turbo/libjpeg-turbo/archive/2.0.0.tar.gz", ], - sha256 = "1a17020f859cb12711175a67eab5c71fc1904e04b587046218e36106e07eabde", - strip_prefix = "libjpeg-turbo-1.5.3", + sha256 = "f892fff427ab3adffc289363eac26d197ce3ccacefe5f5822377348a8166069b", + strip_prefix = "libjpeg-turbo-2.0.0", build_file = clean_dep("//third_party/jpeg:jpeg.BUILD"), system_build_file = clean_dep("//third_party/systemlibs:jpeg.BUILD"), ) diff --git a/third_party/jpeg/jpeg.BUILD b/third_party/jpeg/jpeg.BUILD index 96e7ac061c..5edf4f8120 100644 --- a/third_party/jpeg/jpeg.BUILD +++ b/third_party/jpeg/jpeg.BUILD @@ -144,27 +144,27 @@ cc_library( "jpeglib.h", "jsimd.h", "jsimddct.h", - "simd/jccolor-altivec.c", - "simd/jcgray-altivec.c", - "simd/jcsample.h", - "simd/jcsample-altivec.c", - "simd/jdcolor-altivec.c", - "simd/jdmerge-altivec.c", - "simd/jdsample-altivec.c", - "simd/jfdctfst-altivec.c", - "simd/jfdctint-altivec.c", - "simd/jidctfst-altivec.c", - "simd/jidctint-altivec.c", - "simd/jquanti-altivec.c", "simd/jsimd.h", - "simd/jsimd_altivec.h", - "simd/jsimd_powerpc.c", + "simd/powerpc/jccolor-altivec.c", + "simd/powerpc/jcgray-altivec.c", + "simd/powerpc/jcsample-altivec.c", + "simd/powerpc/jdcolor-altivec.c", + "simd/powerpc/jdmerge-altivec.c", + "simd/powerpc/jdsample-altivec.c", + "simd/powerpc/jfdctfst-altivec.c", + "simd/powerpc/jfdctint-altivec.c", + "simd/powerpc/jidctfst-altivec.c", + "simd/powerpc/jidctint-altivec.c", + "simd/powerpc/jquanti-altivec.c", + "simd/powerpc/jsimd.c", ], hdrs = [ - "simd/jccolext-altivec.c", # should have been named .inc - "simd/jcgryext-altivec.c", # should have been named .inc - "simd/jdcolext-altivec.c", # should have been named .inc - "simd/jdmrgext-altivec.c", # should have been named .inc + "simd/powerpc/jccolext-altivec.c", + "simd/powerpc/jcgryext-altivec.c", + "simd/powerpc/jdcolext-altivec.c", + "simd/powerpc/jdmrgext-altivec.c", + "simd/powerpc/jcsample.h", + "simd/powerpc/jsimd_altivec.h", ], copts = libjpegturbo_copts, nocopts = libjpegturbo_nocopts, @@ -175,6 +175,7 @@ cc_library( srcs = [ "jchuff.h", "jconfig.h", + "jconfigint.h", "jdct.h", "jerror.h", "jinclude.h", @@ -183,24 +184,35 @@ cc_library( "jpeglib.h", "jsimd.h", "jsimddct.h", - "simd/jccolor-sse2-64.o", - "simd/jcgray-sse2-64.o", - "simd/jchuff-sse2-64.o", - "simd/jcsample-sse2-64.o", - "simd/jdcolor-sse2-64.o", - "simd/jdmerge-sse2-64.o", - "simd/jdsample-sse2-64.o", - "simd/jfdctflt-sse-64.o", - "simd/jfdctfst-sse2-64.o", - "simd/jfdctint-sse2-64.o", - "simd/jidctflt-sse2-64.o", - "simd/jidctfst-sse2-64.o", - "simd/jidctint-sse2-64.o", - "simd/jidctred-sse2-64.o", - "simd/jquantf-sse2-64.o", - "simd/jquanti-sse2-64.o", "simd/jsimd.h", - "simd/jsimd_x86_64.c", + "simd/x86_64/jsimd.c", + "simd/x86_64/jccolor-avx2.o", + "simd/x86_64/jccolor-sse2.o", + "simd/x86_64/jcgray-avx2.o", + "simd/x86_64/jcgray-sse2.o", + "simd/x86_64/jchuff-sse2.o", + "simd/x86_64/jcphuff-sse2.o", + "simd/x86_64/jcsample-avx2.o", + "simd/x86_64/jcsample-sse2.o", + "simd/x86_64/jdcolor-avx2.o", + "simd/x86_64/jdcolor-sse2.o", + "simd/x86_64/jdmerge-avx2.o", + "simd/x86_64/jdmerge-sse2.o", + "simd/x86_64/jdsample-avx2.o", + "simd/x86_64/jdsample-sse2.o", + "simd/x86_64/jfdctflt-sse.o", + "simd/x86_64/jfdctfst-sse2.o", + "simd/x86_64/jfdctint-avx2.o", + "simd/x86_64/jfdctint-sse2.o", + "simd/x86_64/jidctflt-sse2.o", + "simd/x86_64/jidctfst-sse2.o", + "simd/x86_64/jidctint-avx2.o", + "simd/x86_64/jidctint-sse2.o", + "simd/x86_64/jidctred-sse2.o", + "simd/x86_64/jquantf-sse2.o", + "simd/x86_64/jquanti-avx2.o", + "simd/x86_64/jquanti-sse2.o", + "simd/x86_64/jsimdcpu.o", ], copts = libjpegturbo_copts, linkstatic = 1, @@ -210,57 +222,88 @@ cc_library( genrule( name = "simd_x86_64_assemblage23", srcs = [ - "simd/jccolext-sse2-64.asm", - "simd/jccolor-sse2-64.asm", - "simd/jcgray-sse2-64.asm", - "simd/jcgryext-sse2-64.asm", - "simd/jchuff-sse2-64.asm", - "simd/jcolsamp.inc", - "simd/jcsample-sse2-64.asm", - "simd/jdcolext-sse2-64.asm", - "simd/jdcolor-sse2-64.asm", - "simd/jdct.inc", - "simd/jdmerge-sse2-64.asm", - "simd/jdmrgext-sse2-64.asm", - "simd/jdsample-sse2-64.asm", - "simd/jfdctflt-sse-64.asm", - "simd/jfdctfst-sse2-64.asm", - "simd/jfdctint-sse2-64.asm", - "simd/jidctflt-sse2-64.asm", - "simd/jidctfst-sse2-64.asm", - "simd/jidctint-sse2-64.asm", - "simd/jidctred-sse2-64.asm", - "simd/jpeg_nbits_table.inc", - "simd/jquantf-sse2-64.asm", - "simd/jquanti-sse2-64.asm", - "simd/jsimdcfg.inc", - "simd/jsimdext.inc", + "jconfig.h", + "jconfigint.h", + "simd/x86_64/jccolext-avx2.asm", + "simd/x86_64/jccolext-sse2.asm", + "simd/x86_64/jccolor-avx2.asm", + "simd/x86_64/jccolor-sse2.asm", + "simd/x86_64/jcgray-avx2.asm", + "simd/x86_64/jcgray-sse2.asm", + "simd/x86_64/jcgryext-avx2.asm", + "simd/x86_64/jcgryext-sse2.asm", + "simd/x86_64/jchuff-sse2.asm", + "simd/x86_64/jcphuff-sse2.asm", + "simd/x86_64/jcsample-avx2.asm", + "simd/x86_64/jcsample-sse2.asm", + "simd/x86_64/jdcolext-avx2.asm", + "simd/x86_64/jdcolext-sse2.asm", + "simd/x86_64/jdcolor-avx2.asm", + "simd/x86_64/jdcolor-sse2.asm", + "simd/x86_64/jdmerge-avx2.asm", + "simd/x86_64/jdmerge-sse2.asm", + "simd/x86_64/jdmrgext-avx2.asm", + "simd/x86_64/jdmrgext-sse2.asm", + "simd/x86_64/jdsample-avx2.asm", + "simd/x86_64/jdsample-sse2.asm", + "simd/x86_64/jfdctflt-sse.asm", + "simd/x86_64/jfdctfst-sse2.asm", + "simd/x86_64/jfdctint-avx2.asm", + "simd/x86_64/jfdctint-sse2.asm", + "simd/x86_64/jidctflt-sse2.asm", + "simd/x86_64/jidctfst-sse2.asm", + "simd/x86_64/jidctint-avx2.asm", + "simd/x86_64/jidctint-sse2.asm", + "simd/x86_64/jidctred-sse2.asm", + "simd/x86_64/jquantf-sse2.asm", + "simd/x86_64/jquanti-avx2.asm", + "simd/x86_64/jquanti-sse2.asm", + "simd/x86_64/jsimdcpu.asm", + "simd/nasm/jcolsamp.inc", + "simd/nasm/jdct.inc", + "simd/nasm/jpeg_nbits_table.inc", + "simd/nasm/jsimdcfg.inc", + "simd/nasm/jsimdcfg.inc.h", + "simd/nasm/jsimdext.inc", ], outs = [ - "simd/jccolor-sse2-64.o", - "simd/jcgray-sse2-64.o", - "simd/jchuff-sse2-64.o", - "simd/jcsample-sse2-64.o", - "simd/jdcolor-sse2-64.o", - "simd/jdmerge-sse2-64.o", - "simd/jdsample-sse2-64.o", - "simd/jfdctflt-sse-64.o", - "simd/jfdctfst-sse2-64.o", - "simd/jfdctint-sse2-64.o", - "simd/jidctflt-sse2-64.o", - "simd/jidctfst-sse2-64.o", - "simd/jidctint-sse2-64.o", - "simd/jidctred-sse2-64.o", - "simd/jquantf-sse2-64.o", - "simd/jquanti-sse2-64.o", + "simd/x86_64/jccolor-avx2.o", + "simd/x86_64/jccolor-sse2.o", + "simd/x86_64/jcgray-avx2.o", + "simd/x86_64/jcgray-sse2.o", + "simd/x86_64/jchuff-sse2.o", + "simd/x86_64/jcphuff-sse2.o", + "simd/x86_64/jcsample-avx2.o", + "simd/x86_64/jcsample-sse2.o", + "simd/x86_64/jdcolor-avx2.o", + "simd/x86_64/jdcolor-sse2.o", + "simd/x86_64/jdmerge-avx2.o", + "simd/x86_64/jdmerge-sse2.o", + "simd/x86_64/jdsample-avx2.o", + "simd/x86_64/jdsample-sse2.o", + "simd/x86_64/jfdctflt-sse.o", + "simd/x86_64/jfdctfst-sse2.o", + "simd/x86_64/jfdctint-avx2.o", + "simd/x86_64/jfdctint-sse2.o", + "simd/x86_64/jidctflt-sse2.o", + "simd/x86_64/jidctfst-sse2.o", + "simd/x86_64/jidctint-avx2.o", + "simd/x86_64/jidctint-sse2.o", + "simd/x86_64/jidctred-sse2.o", + "simd/x86_64/jquantf-sse2.o", + "simd/x86_64/jquanti-avx2.o", + "simd/x86_64/jquanti-sse2.o", + "simd/x86_64/jsimdcpu.o", ], cmd = "for out in $(OUTS); do\n" + " $(location @nasm//:nasm) -f elf64" + - " -DELF -DPIC -DRGBX_FILLER_0XFF -D__x86_64__ -DARCH_X86_64" + - " -I $$(dirname $(location simd/jdct.inc))/" + - " -I $$(dirname $(location simd/jsimdcfg.inc))/" + + " -DELF -DPIC -D__x86_64__" + + " -I $$(dirname $(location jconfig.h))/" + + " -I $$(dirname $(location jconfigint.h))/" + + " -I $$(dirname $(location simd/nasm/jsimdcfg.inc.h))/" + + " -I $$(dirname $(location simd/x86_64/jccolext-sse2.asm))/" + " -o $$out" + - " $$(dirname $(location simd/jdct.inc))/$$(basename $${out%.o}.asm)\n" + + " $$(dirname $(location simd/x86_64/jccolext-sse2.asm))/$$(basename $${out%.o}.asm)\n" + "done", tools = ["@nasm"], ) @@ -279,8 +322,8 @@ cc_library( "jsimd.h", "jsimddct.h", "simd/jsimd.h", - "simd/jsimd_arm.c", - "simd/jsimd_arm_neon.S", + "simd/arm/jsimd.c", + "simd/arm/jsimd_neon.S", ], copts = libjpegturbo_copts, nocopts = libjpegturbo_nocopts, @@ -300,8 +343,8 @@ cc_library( "jsimd.h", "jsimddct.h", "simd/jsimd.h", - "simd/jsimd_arm64.c", - "simd/jsimd_arm64_neon.S", + "simd/arm64/jsimd.c", + "simd/arm64/jsimd_neon.S", ], copts = libjpegturbo_copts, nocopts = libjpegturbo_nocopts, @@ -332,50 +375,44 @@ template_rule( out = "jconfig_win.h", substitutions = { "@JPEG_LIB_VERSION@": "62", - "@VERSION@": "1.5.1", - "@LIBJPEG_TURBO_VERSION_NUMBER@": "1005001", - "cmakedefine": "define", + "@VERSION@": "2.0.0", + "@LIBJPEG_TURBO_VERSION_NUMBER@": "2000000", "@BITS_IN_JSAMPLE@": "8", - }, -) - -template_rule( - name = "jconfigint_win", - src = "win/jconfigint.h.in", - out = "jconfigint_win.h", - substitutions = { - "@VERSION@": "1.5.1", - "@BUILD@": "20161115", - "@CMAKE_PROJECT_NAME@": "libjpeg-turbo", + "#cmakedefine C_ARITH_CODING_SUPPORTED": "#define C_ARITH_CODING_SUPPORTED", + "#cmakedefine D_ARITH_CODING_SUPPORTED": "#define D_ARITH_CODING_SUPPORTED", + "#cmakedefine MEM_SRCDST_SUPPORTED": "#define MEM_SRCDST_SUPPORTED", + "#cmakedefine WITH_SIMD": "", }, ) JCONFIG_NOWIN_COMMON_SUBSTITUTIONS = { - "LIBJPEG_TURBO_VERSION 0": "LIBJPEG_TURBO_VERSION 1.5.1", - "LIBJPEG_TURBO_VERSION_NUMBER 0": "LIBJPEG_TURBO_VERSION_NUMBER 1005001", - "#undef C_ARITH_CODING_SUPPORTED": "#define C_ARITH_CODING_SUPPORTED 1", - "#undef D_ARITH_CODING_SUPPORTED": "#define D_ARITH_CODING_SUPPORTED 1", - "#undef HAVE_LOCALE_H": "#define HAVE_LOCALE_H 1", - "#undef HAVE_STDDEF_H": "#define HAVE_STDDEF_H 1", - "#undef HAVE_STDLIB_H": "#define HAVE_STDLIB_H 1", - "#undef HAVE_UNSIGNED_CHAR": "#define HAVE_UNSIGNED_CHAR 1", - "#undef HAVE_UNSIGNED_SHORT": "#define HAVE_UNSIGNED_SHORT 1", - "#undef INCOMPLETE_TYPES_BROKEN": "", - "#undef MEM_SRCDST_SUPPORTED": "#define MEM_SRCDST_SUPPORTED 1", - "#undef NEED_BSD_STRINGS": "", - "#undef NEED_SYS_TYPES_H": "#define NEED_SYS_TYPES_H 1", - "#undef __CHAR_UNSIGNED__": "", + "@JPEG_LIB_VERSION@": "62", + "@VERSION@": "2.0.0", + "@LIBJPEG_TURBO_VERSION_NUMBER@": "2000000", + "#cmakedefine C_ARITH_CODING_SUPPORTED": "#define C_ARITH_CODING_SUPPORTED", + "#cmakedefine D_ARITH_CODING_SUPPORTED": "#define D_ARITH_CODING_SUPPORTED", + "#cmakedefine MEM_SRCDST_SUPPORTED": "#define MEM_SRCDST_SUPPORTED", + "@BITS_IN_JSAMPLE@": "8", + "#cmakedefine HAVE_LOCALE_H": "#define HAVE_LOCALE_H 1", + "#cmakedefine HAVE_STDDEF_H": "#define HAVE_STDDEF_H 1", + "#cmakedefine HAVE_STDLIB_H": "#define HAVE_STDLIB_H 1", + "#cmakedefine NEED_SYS_TYPES_H": "#define NEED_SYS_TYPES_H", + "#cmakedefine NEED_BSD_STRINGS": "", + "#cmakedefine HAVE_UNSIGNED_CHAR": "#define HAVE_UNSIGNED_CHAR 1", + "#cmakedefine HAVE_UNSIGNED_SHORT": "#define HAVE_UNSIGNED_SHORT 1", + "#cmakedefine INCOMPLETE_TYPES_BROKEN": "", + "#cmakedefine RIGHT_SHIFT_IS_UNSIGNED": "", + "#cmakedefine __CHAR_UNSIGNED__": "", "#undef const": "", "#undef size_t": "", - "#undef RIGHT_SHIFT_IS_UNSIGNED": "", } JCONFIG_NOWIN_SIMD_SUBSTITUTIONS = { - "#undef WITH_SIMD": "#define WITH_SIMD 1", + "#cmakedefine WITH_SIMD": "#define WITH_SIMD", } JCONFIG_NOWIN_NOSIMD_SUBSTITUTIONS = { - "#undef WITH_SIMD": "", + "#cmakedefine WITH_SIMD": "", } JCONFIG_NOWIN_SIMD_SUBSTITUTIONS.update(JCONFIG_NOWIN_COMMON_SUBSTITUTIONS) @@ -396,22 +433,55 @@ template_rule( substitutions = JCONFIG_NOWIN_SIMD_SUBSTITUTIONS, ) +JCONFIGINT_COMMON_SUBSTITUTIONS = { + "@BUILD@": "20180831", + "@VERSION@": "2.0.0", + "@CMAKE_PROJECT_NAME@": "libjpeg-turbo", + "#undef inline": "", + "#cmakedefine HAVE_INTRIN_H": "", +} + +JCONFIGINT_NOWIN_SUBSTITUTIONS = { + "#cmakedefine HAVE_BUILTIN_CTZL": "#define HAVE_BUILTIN_CTZL", + "@INLINE@": "inline __attribute__((always_inline))", + "#define SIZEOF_SIZE_T @SIZE_T@": "#if (__WORDSIZE==64 && !defined(__native_client__))\n" + + "#define SIZEOF_SIZE_T 8\n" + + "#else\n" + + "#define SIZEOF_SIZE_T 4\n" + + "#endif\n", +} + +JCONFIGINT_WIN_SUBSTITUTIONS = { + "#cmakedefine HAVE_BUILTIN_CTZL": "", + "#define INLINE @INLINE@": "#if defined(__GNUC__)\n" + + "#define INLINE inline __attribute__((always_inline))\n" + + "#elif defined(_MSC_VER)\n" + + "#define INLINE __forceinline\n" + + "#else\n" + + "#define INLINE\n" + + "#endif\n", + "#define SIZEOF_SIZE_T @SIZE_T@": "#if (__WORDSIZE==64)\n" + + "#define SIZEOF_SIZE_T 8\n" + + "#else\n" + + "#define SIZEOF_SIZE_T 4\n" + + "#endif\n", +} + +JCONFIGINT_NOWIN_SUBSTITUTIONS.update(JCONFIGINT_COMMON_SUBSTITUTIONS) +JCONFIGINT_WIN_SUBSTITUTIONS.update(JCONFIGINT_COMMON_SUBSTITUTIONS) + template_rule( name = "jconfigint_nowin", src = "jconfigint.h.in", out = "jconfigint_nowin.h", - substitutions = { - "#undef BUILD": "#define BUILD \"20161115\"", - "#undef inline": "", - "#undef INLINE": "#define INLINE inline __attribute__((always_inline))", - "#undef PACKAGE_NAME": "#define PACKAGE_NAME \"libjpeg-turbo\"", - "#undef VERSION": "#define VERSION \"1.5.1\"", - "#undef SIZEOF_SIZE_T": "#if (__WORDSIZE==64 && !defined(__native_client__))\n" + - "#define SIZEOF_SIZE_T 8\n" + - "#else\n" + - "#define SIZEOF_SIZE_T 4\n" + - "#endif\n", - }, + substitutions = JCONFIGINT_NOWIN_SUBSTITUTIONS, +) + +template_rule( + name = "jconfigint_win", + src = "jconfigint.h.in", + out = "jconfigint_win.h", + substitutions = JCONFIGINT_WIN_SUBSTITUTIONS, ) genrule( |
