diff options
| author |  Justin Lebar <jlebar@google.com> | 2018-08-27 14:50:49 -0700 |
|---|---|---|
| committer |  TensorFlower Gardener <gardener@tensorflow.org> | 2018-08-27 14:55:29 -0700 |
| commit | d57f5a82025702d573d478091dc9c385adf53c09 (patch) | |
| tree | e0a9f6b00cd6846cf71a79e02779e1e1f1f9cfc5 /tensorflow/compiler/xla/service | |
| parent | 91f33732cb15f51eaf6ec86c82e42a74e351e061 (diff) | |
[XLA] Switch to absl::StrFormat.
Unlike Printf, StrFormat does not require type-length qualifiers, e.g
%z, %ll. Nor does it require that you call c_str() to print strings.
So these are fixed up here as well.
PiperOrigin-RevId: 210435915
Diffstat (limited to 'tensorflow/compiler/xla/service')
90 files changed, 944 insertions, 1011 deletions
diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index 47d376c8ac..f164a614f1 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -178,6 +178,7 @@ cc_library( "//tensorflow/core:lib", "@com_google_absl//absl/algorithm:container", "@com_google_absl//absl/strings", + "@com_google_absl//absl/strings:str_format", ], ) @@ -465,6 +466,7 @@ cc_library( "//tensorflow/core:lib", "@com_google_absl//absl/memory", "@com_google_absl//absl/strings", + "@com_google_absl//absl/strings:str_format", ], ) @@ -637,6 +639,7 @@ cc_library( "//tensorflow/core:stream_executor_no_cuda", "@com_google_absl//absl/memory", "@com_google_absl//absl/strings", + "@com_google_absl//absl/strings:str_format", ], alwayslink = 1, ) @@ -671,6 +674,7 @@ cc_library( "//tensorflow/core:stream_executor_no_cuda", "@com_google_absl//absl/memory", "@com_google_absl//absl/strings", + "@com_google_absl//absl/strings:str_format", ], ) @@ -746,6 +750,7 @@ cc_library( "//tensorflow/core:stream_executor_no_cuda", "@com_google_absl//absl/memory", "@com_google_absl//absl/strings", + "@com_google_absl//absl/strings:str_format", ], ) @@ -795,6 +800,7 @@ cc_library( "//tensorflow/core:stream_executor_no_cuda", "//tensorflow/stream_executor", "@com_google_absl//absl/memory", + "@com_google_absl//absl/strings:str_format", ], ) @@ -946,6 +952,7 @@ cc_library( "//tensorflow/compiler/xla:util", "//tensorflow/core:lib", "@com_google_absl//absl/strings", + "@com_google_absl//absl/strings:str_format", ], ) @@ -992,6 +999,7 @@ cc_library( "//tensorflow/core:lib_internal", "@com_google_absl//absl/memory", "@com_google_absl//absl/strings", + "@com_google_absl//absl/strings:str_format", ], ) @@ -1040,6 +1048,7 @@ cc_library( "//tensorflow/compiler/xla:util", "//tensorflow/core:lib", "@com_google_absl//absl/strings", + "@com_google_absl//absl/strings:str_format", ], ) @@ -1746,6 +1755,7 @@ cc_library( "//tensorflow/compiler/xla:util", "//tensorflow/core:lib", "@com_google_absl//absl/strings", + "@com_google_absl//absl/strings:str_format", ], ) @@ -2135,6 +2145,7 @@ cc_library( "@com_google_absl//absl/container:inlined_vector", "@com_google_absl//absl/memory", "@com_google_absl//absl/strings", + "@com_google_absl//absl/strings:str_format", ], ) @@ -2187,6 +2198,7 @@ cc_library( "//tensorflow/core:lib", "@com_google_absl//absl/memory", "@com_google_absl//absl/strings", + "@com_google_absl//absl/strings:str_format", ], ) @@ -2325,6 +2337,7 @@ cc_library( "//tensorflow/core:lib_internal", "@com_google_absl//absl/container:inlined_vector", "@com_google_absl//absl/strings", + "@com_google_absl//absl/strings:str_format", ], ) @@ -2448,6 +2461,7 @@ cc_library( "//tensorflow/core:lib", "@com_google_absl//absl/memory", "@com_google_absl//absl/strings", + "@com_google_absl//absl/strings:str_format", ], ) @@ -2803,6 +2817,7 @@ cc_library( "//tensorflow/core:lib_internal", "//tensorflow/core:regexp_internal", "@com_google_absl//absl/strings", + "@com_google_absl//absl/strings:str_format", "@com_google_absl//absl/types:optional", ], alwayslink = 1, @@ -3143,13 +3158,13 @@ cc_library( cc_library( name = "source_map_util", - srcs = ["source_map_util.cc"], + srcs = [], hdrs = ["source_map_util.h"], deps = [ ":executable", "//tensorflow/compiler/xla:status", - "//tensorflow/compiler/xla:util", "//tensorflow/core:lib", + "@com_google_absl//absl/strings:str_format", ], ) @@ -3199,11 +3214,11 @@ cc_library( "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/core:lib", "//tensorflow/core:lib_internal", "@com_google_absl//absl/algorithm:container", "@com_google_absl//absl/memory", "@com_google_absl//absl/strings", + "@com_google_absl//absl/strings:str_format", ], ) diff --git a/tensorflow/compiler/xla/service/allocation_tracker.cc b/tensorflow/compiler/xla/service/allocation_tracker.cc index 5115a14df0..1ed6142dce 100644 --- a/tensorflow/compiler/xla/service/allocation_tracker.cc +++ b/tensorflow/compiler/xla/service/allocation_tracker.cc @@ -69,8 +69,7 @@ StatusOr<GlobalDataHandle> AllocationTracker::RegisterInternal( return InvalidArgument( "AllocationTracker for platform %s cannot register buffer from " "platform %s", - backend_->platform()->Name().c_str(), - shaped_buffer.platform()->Name().c_str()); + backend_->platform()->Name(), shaped_buffer.platform()->Name()); } } @@ -125,7 +124,7 @@ Status AllocationTracker::Unregister(const GlobalDataHandle& data) { // "handle does not exist". auto it = handle_to_shaped_buffers_.find(data.handle()); if (it == handle_to_shaped_buffers_.end()) { - return NotFound("no allocation record for global data handle: %lld", + return NotFound("no allocation record for global data handle: %d", data.handle()); } for (auto& shaped_buffer : it->second) { @@ -144,7 +143,7 @@ StatusOr<std::vector<GlobalDataHandle>> AllocationTracker::DeconstructTuple( // the same for all buffers across replicas. const ShapedBuffer* shaped_buffer = replicated_buffers[0]; if (!ShapeUtil::IsTuple(shaped_buffer->on_host_shape())) { - return InvalidArgument("global data handle %lld is not a tuple", + return InvalidArgument("global data handle %d is not a tuple", data.handle()); } // If the on-host representation is a tuple, then the on-device one should be @@ -201,14 +200,14 @@ StatusOr<std::vector<const ShapedBuffer*>> AllocationTracker::ResolveInternal( VLOG(2) << "resolve:" << data.handle(); auto it = handle_to_shaped_buffers_.find(data.handle()); if (it == handle_to_shaped_buffers_.end()) { - return NotFound("no allocation record for global data handle: %lld", + return NotFound("no allocation record for global data handle: %d", data.handle()); } std::vector<const ShapedBuffer*> replicated_buffers; for (const auto& shaped_buffer : it->second) { if (shaped_buffer == nullptr) { - return InvalidArgument( - "global data handle %lld was previously deallocated", data.handle()); + return InvalidArgument("global data handle %d was previously deallocated", + data.handle()); } replicated_buffers.push_back(shaped_buffer.get()); } diff --git a/tensorflow/compiler/xla/service/backend.cc b/tensorflow/compiler/xla/service/backend.cc index 841d0fa85b..a6889cb171 100644 --- a/tensorflow/compiler/xla/service/backend.cc +++ b/tensorflow/compiler/xla/service/backend.cc @@ -177,7 +177,7 @@ StatusOr<se::StreamExecutor*> Backend::stream_executor( } } return InvalidArgument("device %s not supported by XLA service", - device_name(device_ordinal).c_str()); + device_name(device_ordinal)); } StatusOr<bool> Backend::devices_equivalent(int device_ordinal_a, diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc index c8c36ae60e..b11f15ec7b 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment.cc +++ b/tensorflow/compiler/xla/service/buffer_assignment.cc @@ -24,6 +24,7 @@ limitations under the License. #include "absl/memory/memory.h" #include "absl/strings/str_cat.h" +#include "absl/strings/str_format.h" #include "tensorflow/compiler/xla/map_util.h" #include "tensorflow/compiler/xla/service/buffer_value_containers.h" #include "tensorflow/compiler/xla/service/heap_simulator.h" @@ -37,17 +38,15 @@ limitations under the License. #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/hash/hash.h" #include "tensorflow/core/lib/strings/numbers.h" -#include "tensorflow/core/lib/strings/stringprintf.h" namespace xla { namespace { using absl::StrAppend; +using absl::StrAppendFormat; using ::tensorflow::gtl::FlatMap; using ::tensorflow::gtl::FlatSet; -using ::tensorflow::strings::Appendf; using ::tensorflow::strings::HumanReadableNumBytes; -using ::tensorflow::strings::Printf; template <typename T> string ColocatedBufferSetsToString(const T& container, const char* title) { @@ -105,7 +104,7 @@ Status GatherComputationsByAllocationType( return InvalidArgument( "computation %s has conflicting allocation requirements (global " "and thread-local)", - computation->name().c_str()); + computation->name()); } if (is_thread_local) { @@ -128,7 +127,7 @@ Status GatherComputationsByAllocationType( return InvalidArgument( "computation %s cannot contain call/while op because it " "requires thread-local buffer allocations", - computation->name().c_str()); + computation->name()); } worklist.push_back(std::make_pair(subcomputation, false)); // Not thread local. @@ -145,9 +144,8 @@ Status GatherComputationsByAllocationType( true)); // Thread local. break; default: - return InternalError( - "Unexpected calling opcode: %s", - HloOpcodeString(instruction->opcode()).c_str()); + return InternalError("Unexpected calling opcode: %s", + HloOpcodeString(instruction->opcode())); } } } @@ -296,7 +294,7 @@ BufferAllocationProto BufferAllocation::ToProto() const { string BufferAllocation::ToString() const { string output; - Appendf(&output, "allocation %lld: %p, size %lld", index_, this, size()); + StrAppendFormat(&output, "allocation %d: %p, size %d", index_, this, size()); if (color().value() != 0) { StrAppend(&output, ", color ", color().value()); } @@ -328,11 +326,10 @@ string BufferAllocation::ToString() const { }); for (const LogicalBuffer* buffer : sorted_buffers) { const OffsetSize& offset_size = FindOrDie(assigned_buffers_, buffer); - StrAppend(&output, - tensorflow::strings::Printf( - " %s [%lld,%lld]: %s\n", buffer->ToString().c_str(), - offset_size.offset, offset_size.size, - ShapeUtil::HumanStringWithLayout(buffer->shape()).c_str())); + StrAppend(&output, absl::StrFormat( + " %s [%d,%d]: %s\n", buffer->ToString(), + offset_size.offset, offset_size.size, + ShapeUtil::HumanStringWithLayout(buffer->shape()))); } return output; } @@ -425,7 +422,7 @@ StatusOr<BufferAllocation::Slice> BufferAssignment::GetUniqueSlice( return FailedPrecondition( "BufferAllocation::Slice for instruction %s at index %s cannot " "be determined at compile-time.", - instruction->name().c_str(), index.ToString().c_str()); + instruction->name(), index.ToString()); } } else { VLOG(3) << "No allocation"; @@ -434,7 +431,7 @@ StatusOr<BufferAllocation::Slice> BufferAssignment::GetUniqueSlice( if (result.allocation() == nullptr) { return FailedPrecondition( "BufferAllocation::Slice not assigned for instruction %s at index %s", - instruction->name().c_str(), index.ToString().c_str()); + instruction->name(), index.ToString()); } return result; } @@ -646,30 +643,29 @@ Status BufferAssignment::ComputeSummaryStats() { string BufferAssignment::Stats::ToString() const { string s; - Appendf(&s, "BufferAssignment stats:\n"); - Appendf(&s, " parameter allocation: %10s\n", - HumanReadableNumBytes(parameter_allocation_bytes).c_str()); - Appendf(&s, " constant allocation: %10s\n", - HumanReadableNumBytes(constant_allocation_bytes).c_str()); - Appendf(&s, " maybe_live_out allocation: %10s\n", - HumanReadableNumBytes(maybe_live_out_allocation_bytes).c_str()); - Appendf(&s, " preallocated temp allocation: %10s\n", - HumanReadableNumBytes(preallocated_temp_allocation_bytes).c_str()); + StrAppendFormat(&s, "BufferAssignment stats:\n"); + StrAppendFormat(&s, " parameter allocation: %10s\n", + HumanReadableNumBytes(parameter_allocation_bytes)); + StrAppendFormat(&s, " constant allocation: %10s\n", + HumanReadableNumBytes(constant_allocation_bytes)); + StrAppendFormat(&s, " maybe_live_out allocation: %10s\n", + HumanReadableNumBytes(maybe_live_out_allocation_bytes)); + StrAppendFormat(&s, " preallocated temp allocation: %10s\n", + HumanReadableNumBytes(preallocated_temp_allocation_bytes)); if (preallocated_temp_fragmentation_bytes >= 0) { const double percent = 100. * preallocated_temp_fragmentation_bytes / preallocated_temp_allocation_bytes; - Appendf( + StrAppendFormat( &s, " preallocated temp fragmentation: %10s (%.2f%%)\n", - HumanReadableNumBytes(preallocated_temp_fragmentation_bytes).c_str(), - percent); + HumanReadableNumBytes(preallocated_temp_fragmentation_bytes), percent); } - Appendf(&s, " total allocation: %10s\n", - HumanReadableNumBytes(total_allocation_bytes).c_str()); + StrAppendFormat(&s, " total allocation: %10s\n", + HumanReadableNumBytes(total_allocation_bytes)); if (total_fragmentation_bytes >= 0) { const double percent = 100. * total_fragmentation_bytes / total_allocation_bytes; - Appendf(&s, " total fragmentation: %10s (%.2f%%)\n", - HumanReadableNumBytes(total_fragmentation_bytes).c_str(), percent); + StrAppendFormat(&s, " total fragmentation: %10s (%.2f%%)\n", + HumanReadableNumBytes(total_fragmentation_bytes), percent); } return s; } diff --git a/tensorflow/compiler/xla/service/buffer_liveness.cc b/tensorflow/compiler/xla/service/buffer_liveness.cc index 8d0ac3b84a..9b2783a214 100644 --- a/tensorflow/compiler/xla/service/buffer_liveness.cc +++ b/tensorflow/compiler/xla/service/buffer_liveness.cc @@ -20,6 +20,7 @@ limitations under the License. #include <utility> #include <vector> +#include "absl/strings/str_format.h" #include "absl/strings/str_join.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/logical_buffer.h" @@ -29,7 +30,6 @@ limitations under the License. #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/lib/core/errors.h" -#include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/logging.h" namespace xla { @@ -75,19 +75,17 @@ Status BufferLiveness::Analyze() { string BufferLiveness::ToString() const { std::vector<string> pieces; - pieces.push_back(tensorflow::strings::Printf("BufferLiveness(module=%s):", - module_->name().c_str())); + pieces.push_back( + absl::StrFormat("BufferLiveness(module=%s):", module_->name())); pieces.push_back("HloOrdering:"); pieces.push_back(hlo_ordering_->ToString()); - pieces.push_back(tensorflow::strings::Printf("Aliased buffers:")); + pieces.push_back("Aliased buffers:"); for (const LogicalBuffer* buffer : aliased_buffers_) { - pieces.push_back( - tensorflow::strings::Printf(" %s", buffer->ToString().c_str())); + pieces.push_back(absl::StrFormat(" %s", buffer->ToString())); } - pieces.push_back(tensorflow::strings::Printf("Live out buffers:")); + pieces.push_back("Live out buffers:"); for (const LogicalBuffer* buffer : maybe_live_out_buffers_) { - pieces.push_back( - tensorflow::strings::Printf(" %s", buffer->ToString().c_str())); + pieces.push_back(absl::StrFormat(" %s", buffer->ToString())); } return absl::StrJoin(pieces, "\n"); } diff --git a/tensorflow/compiler/xla/service/call_graph.cc b/tensorflow/compiler/xla/service/call_graph.cc index 37523a73ff..23b2a32709 100644 --- a/tensorflow/compiler/xla/service/call_graph.cc +++ b/tensorflow/compiler/xla/service/call_graph.cc @@ -19,19 +19,19 @@ limitations under the License. #include "absl/memory/memory.h" #include "absl/strings/str_cat.h" +#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/core/errors.h" #include "tensorflow/core/lib/core/status.h" -#include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/types.h" namespace xla { +using absl::StrAppendFormat; using absl::StrCat; -using ::tensorflow::strings::Appendf; string CallContextToString(CallContext context) { switch (context) { @@ -356,20 +356,20 @@ CallGraph::NearestAncestorsInSameComputation(HloInstruction* a, string CallGraph::ToString() const { string out; - Appendf(&out, "Call graph for module %s:\n", module_->name().c_str()); + StrAppendFormat(&out, "Call graph for module %s:\n", module_->name()); for (const CallGraphNode& node : nodes()) { - Appendf(&out, "Computation %s:\n", node.computation()->name().c_str()); - Appendf(&out, " calls:\n"); + StrAppendFormat(&out, "Computation %s:\n", node.computation()->name()); + StrAppendFormat(&out, " calls:\n"); for (const HloComputation* callee : node.callees()) { - Appendf(&out, " %s\n", callee->name().c_str()); + StrAppendFormat(&out, " %s\n", callee->name()); } - Appendf(&out, " called by:\n"); + StrAppendFormat(&out, " called by:\n"); for (const HloComputation* caller : node.callers()) { - Appendf(&out, " %s\n", caller->name().c_str()); + StrAppendFormat(&out, " %s\n", caller->name()); } - Appendf(&out, " callsites:\n"); + StrAppendFormat(&out, " callsites:\n"); for (const CallSite& callsite : node.callsites()) { - Appendf(&out, " %s\n", callsite.ToString().c_str()); + StrAppendFormat(&out, " %s\n", callsite.ToString()); } } return out; diff --git a/tensorflow/compiler/xla/service/call_inliner.cc b/tensorflow/compiler/xla/service/call_inliner.cc index 256d05a73e..1d42140444 100644 --- a/tensorflow/compiler/xla/service/call_inliner.cc +++ b/tensorflow/compiler/xla/service/call_inliner.cc @@ -96,7 +96,7 @@ class SubcomputationInsertionVisitor : public DfsHloVisitorWithDefault { if (it == subcomputation_hlo_to_new_hlo_.end()) { return NotFound( "Could not find mapping from subcomputation HLO %s to a cloned HLO.", - subcomputation_hlo->ToString().c_str()); + subcomputation_hlo->ToString()); } return it->second; } diff --git a/tensorflow/compiler/xla/service/channel_tracker.cc b/tensorflow/compiler/xla/service/channel_tracker.cc index 601a3e9a01..3c2d1ae6d8 100644 --- a/tensorflow/compiler/xla/service/channel_tracker.cc +++ b/tensorflow/compiler/xla/service/channel_tracker.cc @@ -73,20 +73,20 @@ ChannelHandle ChannelTracker::AllocateHandle(ChannelHandle::ChannelType type) { Status ChannelTracker::RegisterSendInternal(const ChannelHandle& handle) { if (opaque_to_channel_.count(handle.handle()) == 0) { - return NotFound("channel handle not found: %lld", handle.handle()); + return NotFound("channel handle not found: %d", handle.handle()); } Channel& channel = opaque_to_channel_[handle.handle()]; if (channel.type == ChannelHandle::HOST_TO_DEVICE) { return FailedPrecondition( "host-to-device channels cannot be used with a Send operation; " - "channel handle: %lld", + "channel handle: %d", handle.handle()); } if (channel.has_sender) { return FailedPrecondition( "when registering send, passed a channel handle that is already used " - "by a sender: %lld", + "by a sender: %d", handle.handle()); } channel.has_sender = true; @@ -95,13 +95,13 @@ Status ChannelTracker::RegisterSendInternal(const ChannelHandle& handle) { Status ChannelTracker::RegisterRecvInternal(const ChannelHandle& handle) { if (opaque_to_channel_.count(handle.handle()) == 0) { - return NotFound("channel handle not found: %lld", handle.handle()); + return NotFound("channel handle not found: %d", handle.handle()); } Channel& channel = opaque_to_channel_[handle.handle()]; if (channel.type == ChannelHandle::DEVICE_TO_HOST) { return FailedPrecondition( "device-to-host channels cannot be used with a Recv operation; " - "channel handle: %lld", + "channel handle: %d", handle.handle()); } @@ -109,7 +109,7 @@ Status ChannelTracker::RegisterRecvInternal(const ChannelHandle& handle) { if (channel.receiver_count >= 1) { return FailedPrecondition( "when registering recv, passed a channel handle that is already used " - "by a receiver: %lld", + "by a receiver: %d", handle.handle()); } channel.receiver_count += 1; diff --git a/tensorflow/compiler/xla/service/compiler.cc b/tensorflow/compiler/xla/service/compiler.cc index 6b3b9820f0..687ecafe0c 100644 --- a/tensorflow/compiler/xla/service/compiler.cc +++ b/tensorflow/compiler/xla/service/compiler.cc @@ -101,7 +101,7 @@ Compiler::GetPlatformCompilers() { return NotFound( "could not find registered compiler for platform %s -- check " "target linkage", - platform->Name().c_str()); + platform->Name()); } // And then we invoke the factory, placing the result into the mapping. diff --git a/tensorflow/compiler/xla/service/computation_placer.cc b/tensorflow/compiler/xla/service/computation_placer.cc index 61b1dba6c9..2210a8578a 100644 --- a/tensorflow/compiler/xla/service/computation_placer.cc +++ b/tensorflow/compiler/xla/service/computation_placer.cc @@ -132,7 +132,7 @@ StatusOr<DeviceAssignment> ComputationPlacer::AssignDevices( return NotFound( "could not find registered computation placer for platform %s -- check " "target linkage", - platform->Name().c_str()); + platform->Name()); } if (it->second.placer == nullptr) { diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD index e01fecffd0..f0adfc5d45 100644 --- a/tensorflow/compiler/xla/service/cpu/BUILD +++ b/tensorflow/compiler/xla/service/cpu/BUILD @@ -235,6 +235,7 @@ cc_library( "//tensorflow/core:lib", "//tensorflow/core:stream_executor_no_cuda", "@com_google_absl//absl/strings", + "@com_google_absl//absl/strings:str_format", "@llvm//:orc_jit", ], ) @@ -283,6 +284,7 @@ cc_library( "//tensorflow/compiler/xla/service/llvm_ir:tuple_ops", "//tensorflow/core:lib", "@com_google_absl//absl/strings", + "@com_google_absl//absl/strings:str_format", "@llvm//:code_gen", "@llvm//:core", "@llvm//:support", @@ -338,12 +340,12 @@ cc_library( hdrs = ["parallel_loop_emitter.h"], deps = [ ":ir_emission_utils", - "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/service/llvm_ir:ir_array", "//tensorflow/compiler/xla/service/llvm_ir:llvm_loop", "//tensorflow/compiler/xla/service/llvm_ir:llvm_util", "//tensorflow/compiler/xla/service/llvm_ir:loop_emitter", "//tensorflow/core:lib", + "@com_google_absl//absl/strings:str_format", "@llvm//:core", ], ) @@ -391,6 +393,7 @@ tf_cc_binary( "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/core:lib", + "@com_google_absl//absl/strings:str_format", ], ) @@ -404,6 +407,7 @@ cc_library( "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:util", "//tensorflow/core:lib", + "@com_google_absl//absl/strings:str_format", "@llvm//:mc", "@llvm//:mc_disassembler", "@llvm//:object", @@ -645,6 +649,7 @@ tf_cc_test( "//tensorflow/core:test", "//third_party/eigen3", "@com_google_absl//absl/memory", + "@com_google_absl//absl/strings:str_format", ], ) diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc index 279aa42fe2..6420180b13 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc @@ -705,8 +705,7 @@ CpuCompiler::CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules, const llvm::Target* target = llvm::TargetRegistry::lookupTarget(triple.getTriple(), error); if (target == nullptr) { - return InternalError("TargetRegistry::lookupTarget failed: %s", - error.c_str()); + return InternalError("TargetRegistry::lookupTarget failed: %s", error); } llvm::Reloc::Model reloc_model = llvm::Reloc::Static; diff --git a/tensorflow/compiler/xla/service/cpu/cpu_executable.cc b/tensorflow/compiler/xla/service/cpu/cpu_executable.cc index fbcbbbd200..08773693fb 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_executable.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_executable.cc @@ -23,6 +23,7 @@ limitations under the License. #include <vector> #include "absl/strings/str_cat.h" +#include "absl/strings/str_format.h" #include "absl/strings/str_join.h" #include "llvm/ExecutionEngine/Orc/IRCompileLayer.h" #include "tensorflow/compiler/xla/service/buffer_assignment.h" @@ -37,7 +38,6 @@ limitations under the License. #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/compiler/xla/xla_data.pb.h" -#include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/env.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/mem.h" @@ -171,20 +171,18 @@ Status CpuExecutable::ExecuteComputeFunction( void* result_buffer = buffer_pointers[result_slice.index()]; if (VLOG_IS_ON(3)) { VLOG(3) << "Executing compute function:"; - VLOG(3) << tensorflow::strings::Printf( - " func(void* result, void* params[null], void* temps[%zu], " - "uint64 profile_counters[%zu])", + VLOG(3) << absl::StrFormat( + " func(void* result, void* params[null], void* temps[%u], " + "uint64 profile_counters[%u])", buffer_pointers.size(), profile_counters_size); - VLOG(3) << tensorflow::strings::Printf(" result = %p", result_buffer); + VLOG(3) << absl::StrFormat(" result = %p", result_buffer); auto ptr_printer = [](string* out, const void* p) { - absl::StrAppend(out, tensorflow::strings::Printf("%p", p)); + absl::StrAppend(out, absl::StrFormat("%p", p)); }; VLOG(3) << " params = nullptr"; - VLOG(3) << tensorflow::strings::Printf( - " temps = [%s]", - absl::StrJoin(buffer_pointers, ", ", ptr_printer).c_str()); - VLOG(3) << tensorflow::strings::Printf(" profile_counters = %p", - profile_counters); + VLOG(3) << absl::StrFormat( + " temps = [%s]", absl::StrJoin(buffer_pointers, ", ", ptr_printer)); + VLOG(3) << absl::StrFormat(" profile_counters = %p", profile_counters); } compute_function_(result_buffer, run_options, nullptr, buffer_pointers.data(), diff --git a/tensorflow/compiler/xla/service/cpu/cpu_hlo_support_checker.cc b/tensorflow/compiler/xla/service/cpu/cpu_hlo_support_checker.cc index 7bd4741a04..7fbe0fa157 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_hlo_support_checker.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_hlo_support_checker.cc @@ -34,9 +34,8 @@ StatusOr<bool> CpuHloSupportChecker::Run(HloModule* module) { return xla::Unimplemented( "CPU backend does not support HLO instruction %s with shape " "containing a sparse layout: %s", - instruction->ToString().c_str(), - ShapeUtil::HumanStringWithLayout(instruction->shape()) - .c_str()); + instruction->ToString(), + ShapeUtil::HumanStringWithLayout(instruction->shape())); } return Status::OK(); })); diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime_test.cc b/tensorflow/compiler/xla/service/cpu/cpu_runtime_test.cc index bc4cfc0999..1ae3aa5711 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_runtime_test.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime_test.cc @@ -20,6 +20,7 @@ limitations under the License. #include <tuple> #include "absl/memory/memory.h" +#include "absl/strings/str_format.h" #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/local_client.h" @@ -28,7 +29,6 @@ limitations under the License. #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/core/common_runtime/eigen_thread_pool.h" -#include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/env.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/test.h" @@ -142,10 +142,10 @@ class EigenMatMulTest : public CpuRuntimeTest, bool transpose_rhs = std::get<2>(info.param); bool single_threaded = std::get<3>(info.param); - return tensorflow::strings::Printf( - "EigenMatMul_%lld_%lld_%lld_%s%s%s_threaded", shape.m, shape.k, shape.n, - transpose_lhs ? "Tlhs_" : "", transpose_rhs ? "Trhs_" : "", - single_threaded ? "single" : "multi"); + return absl::StrFormat("EigenMatMul_%d_%d_%d_%s%s%s_threaded", shape.m, + shape.k, shape.n, transpose_lhs ? "Tlhs_" : "", + transpose_rhs ? "Trhs_" : "", + single_threaded ? "single" : "multi"); } }; @@ -178,10 +178,10 @@ class MKLMatMulTest : public CpuRuntimeTest, bool transpose_rhs = std::get<2>(info.param); bool single_threaded = std::get<3>(info.param); - return tensorflow::strings::Printf( - "MKLMatMul_%lld_%lld_%lld_%s%s%s_threaded", shape.m, shape.k, shape.n, - transpose_lhs ? "Tlhs_" : "", transpose_rhs ? "Trhs_" : "", - single_threaded ? "single" : "multi"); + return absl::StrFormat("MKLMatMul_%d_%d_%d_%s%s%s_threaded", shape.m, + shape.k, shape.n, transpose_lhs ? "Tlhs_" : "", + transpose_rhs ? "Trhs_" : "", + single_threaded ? "single" : "multi"); } }; diff --git a/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.cc b/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.cc index b07cd675ff..0df2abf001 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.cc @@ -104,7 +104,7 @@ Status CpuTransferManager::TransferLiteralToInfeed( if (ShapeUtil::IsNestedTuple(shape)) { return Unimplemented( "Infeed with a nested tuple shape is not supported: %s", - ShapeUtil::HumanString(literal.shape()).c_str()); + ShapeUtil::HumanString(literal.shape())); } // For a tuple, we transfer each of its elements to the device and @@ -152,11 +152,11 @@ CpuTransferManager::TransferBufferToInfeedInternal(se::StreamExecutor* executor, int64 size, const void* source) { if (size > std::numeric_limits<int32>::max()) { - return InvalidArgument("Infeed shape is too large: needs %lld bytes", size); + return InvalidArgument("Infeed shape is too large: needs %d bytes", size); } if (size <= 0) { - return InvalidArgument("Infeed shape must have positive size; got %lld", + return InvalidArgument("Infeed shape must have positive size; got %d", size); } @@ -244,12 +244,12 @@ StatusOr<Shape> CpuTransferManager::TransferBuffersFromOutfeedInternal( for (auto b : buffer_data) { int64 size = b.second; if (size > std::numeric_limits<int32>::max()) { - return InvalidArgument("Outfeed shape is too large: needs %lld bytes", + return InvalidArgument("Outfeed shape is too large: needs %d bytes", size); } if (size <= 0) { - return InvalidArgument("Outfeed shape must have positive size; got %lld", + return InvalidArgument("Outfeed shape must have positive size; got %d", size); } diff --git a/tensorflow/compiler/xla/service/cpu/disassembler.cc b/tensorflow/compiler/xla/service/cpu/disassembler.cc index e4c674e227..3ae64142cd 100644 --- a/tensorflow/compiler/xla/service/cpu/disassembler.cc +++ b/tensorflow/compiler/xla/service/cpu/disassembler.cc @@ -21,13 +21,13 @@ limitations under the License. #include <type_traits> #include <vector> +#include "absl/strings/str_format.h" #include "llvm/MC/MCInst.h" #include "llvm/Support/TargetRegistry.h" #include "llvm/Support/raw_ostream.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/util.h" -#include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/types.h" @@ -151,7 +151,7 @@ StatusOr<DisassemblerResult> Disassembler::DisassembleObjectFile( size = 1; } - ostream << tensorflow::strings::Printf("0x%08lx", index) << " "; + ostream << absl::StrFormat("0x%08lx", index) << " "; if (decode_status == llvm::MCDisassembler::Success) { // For branches, try to determine the actual address and emit it as an @@ -163,7 +163,7 @@ StatusOr<DisassemblerResult> Disassembler::DisassembleObjectFile( uint64_t target; if (inst_analysis_->evaluateBranch( instruction, section_address + index, size, target)) { - annotation = tensorflow::strings::Printf("[0x%08lx]", target); + annotation = absl::StrFormat("[0x%08lx]", target); } } inst_printer_->printInst(&instruction, ostream, annotation.c_str(), diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc index 4af16f4fa0..dd060f54a2 100644 --- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc @@ -1467,7 +1467,7 @@ Status DotOpEmitter::EmitCallToRuntime() { break; default: return Unimplemented("Invalid type %s for dot operation", - PrimitiveType_Name(type).c_str()); + PrimitiveType_Name(type)); } llvm::Type* float_ptr_type = float_type->getPointerTo(); diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc index 417a1dba1f..321c2e9896 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc @@ -28,6 +28,7 @@ limitations under the License. #include "tensorflow/core/platform/logging.h" // IWYU pragma: no_include "llvm/IR/Intrinsics.gen.inc" #include "absl/strings/str_cat.h" +#include "absl/strings/str_format.h" #include "llvm/CodeGen/TargetRegisterInfo.h" #include "llvm/CodeGen/TargetSubtargetInfo.h" #include "llvm/IR/BasicBlock.h" @@ -68,7 +69,6 @@ limitations under the License. #include "tensorflow/core/lib/gtl/array_slice.h" #include "tensorflow/core/lib/gtl/flatmap.h" #include "tensorflow/core/lib/gtl/flatset.h" -#include "tensorflow/core/lib/strings/stringprintf.h" namespace xla { @@ -230,9 +230,8 @@ Status IrEmitter::HandleCopy(HloInstruction* copy) { // Use the elemental emitter for array shapes. return DefaultAction(copy); } - return Unimplemented( - "unsupported operand type %s for copy instruction", - PrimitiveType_Name(copy->shape().element_type()).c_str()); + return Unimplemented("unsupported operand type %s for copy instruction", + PrimitiveType_Name(copy->shape().element_type())); } // Calculate the alignment of a buffer allocated for a given primitive type. @@ -389,7 +388,7 @@ Status IrEmitter::EmitXfeedTransfer(XfeedKind kind, const Shape& shape, int64 length = ByteSizeOf(shape); if (length <= 0 || length > std::numeric_limits<int32>::max()) { return InvalidArgument( - "xfeed (infeed or outfeed) buffer length %lld is outside the valid " + "xfeed (infeed or outfeed) buffer length %d is outside the valid " "size range", length); } @@ -1620,9 +1619,8 @@ StatusOr<bool> IrEmitter::EmitVectorizedReduce( int64 dimension = LayoutUtil::Minor(reduce->shape().layout(), i); int64 start_index = 0; int64 end_index = reduce->shape().dimensions(dimension); - std::unique_ptr<llvm_ir::ForLoop> loop = - loop_nest.AddLoop(start_index, end_index, - tensorflow::strings::Printf("dim.%lld", dimension)); + std::unique_ptr<llvm_ir::ForLoop> loop = loop_nest.AddLoop( + start_index, end_index, absl::StrFormat("dim.%d", dimension)); array_index[dimension] = loop->GetIndVarValue(); } @@ -1641,9 +1639,9 @@ StatusOr<bool> IrEmitter::EmitVectorizedReduce( int64 start_index = 0; int64 end_index = (innermost_dimension_size / vectorization_factor) * vectorization_factor; - std::unique_ptr<llvm_ir::ForLoop> loop = loop_nest.AddLoop( - start_index, end_index, vectorization_factor, - tensorflow::strings::Printf("dim.%lld", innermost_dimension)); + std::unique_ptr<llvm_ir::ForLoop> loop = + loop_nest.AddLoop(start_index, end_index, vectorization_factor, + absl::StrFormat("dim.%d", innermost_dimension)); array_index[innermost_dimension] = loop->GetIndVarValue(); SetToFirstInsertPoint(loop->GetBodyBasicBlock(), &b_); @@ -2170,8 +2168,8 @@ Status IrEmitter::HandleWhile(HloInstruction* xla_while) { return InternalError( "instruction %s %s does not share slice with " "instruction %s %s", - a->ToString().c_str(), slice_a.ToString().c_str(), - b->ToString().c_str(), slice_b.ToString().c_str()); + a->ToString(), slice_a.ToString(), b->ToString(), + slice_b.ToString()); } return Status::OK(); }; @@ -2826,8 +2824,8 @@ Status IrEmitter::ElementTypesSameAndSupported( if (std::find(supported_types.begin(), supported_types.end(), primitive_type) == supported_types.end()) { return Unimplemented("unsupported operand type %s in op %s", - PrimitiveType_Name(primitive_type).c_str(), - HloOpcodeString(instruction.opcode()).c_str()); + PrimitiveType_Name(primitive_type), + HloOpcodeString(instruction.opcode())); } return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.cc b/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.cc index aedb069dce..f8441c3e34 100644 --- a/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.cc @@ -15,9 +15,9 @@ limitations under the License. #include "tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.h" +#include "absl/strings/str_format.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" -#include "tensorflow/core/lib/strings/stringprintf.h" namespace xla { namespace cpu { @@ -52,15 +52,15 @@ ParallelLoopEmitter::EmitIndexAndSetExitBasicBlock(absl::string_view loop_name, llvm::Value* end_index = (*dynamic_loop_bounds_)[bounds_index].second; std::unique_ptr<llvm_ir::ForLoop> loop = loop_nest.AddLoop( - /*suffix=*/tensorflow::strings::Printf("dim.%lld", dimension), - start_index, end_index); + /*suffix=*/absl::StrFormat("dim.%d", dimension), start_index, + end_index); array_index[dimension] = loop->GetIndVarValue(); } else { // Emit static loop bounds for this dimension. std::unique_ptr<llvm_ir::ForLoop> loop = loop_nest.AddLoop( /*start_index=*/0, /*end_index=*/shape_.dimensions(dimension), - /*suffix=*/tensorflow::strings::Printf("dim.%lld", dimension)); + /*suffix=*/absl::StrFormat("dim.%d", dimension)); array_index[dimension] = loop->GetIndVarValue(); } } diff --git a/tensorflow/compiler/xla/service/cpu/sample_harness.cc b/tensorflow/compiler/xla/service/cpu/sample_harness.cc index f227e4ae13..942e2ddd39 100644 --- a/tensorflow/compiler/xla/service/cpu/sample_harness.cc +++ b/tensorflow/compiler/xla/service/cpu/sample_harness.cc @@ -16,6 +16,7 @@ limitations under the License. #include <memory> #include <string> +#include "absl/strings/str_format.h" #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/client.h" #include "tensorflow/compiler/xla/client/client_library.h" @@ -27,7 +28,6 @@ limitations under the License. #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" -#include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/init_main.h" #include "tensorflow/core/platform/logging.h" @@ -67,8 +67,8 @@ int main(int argc, char** argv) { /*execution_profile=*/&profile); std::unique_ptr<xla::Literal> actual = result.ConsumeValueOrDie(); - LOG(INFO) << tensorflow::strings::Printf("computation took %lldns", - profile.compute_time_ns()); + LOG(INFO) << absl::StrFormat("computation took %dns", + profile.compute_time_ns()); LOG(INFO) << actual->ToString(); return 0; diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_intrinsic_test.cc b/tensorflow/compiler/xla/service/cpu/tests/cpu_intrinsic_test.cc index 9457e57d7b..a434c04a98 100644 --- a/tensorflow/compiler/xla/service/cpu/tests/cpu_intrinsic_test.cc +++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_intrinsic_test.cc @@ -65,8 +65,8 @@ class CpuUnaryIntrinsicTest features = ""; } - return absl::StrCat(opcode.c_str(), "_On_", triple.c_str(), - features.empty() ? "" : "_With", features.c_str()); + return absl::StrCat(opcode, "_On_", triple, + (features.empty() ? "" : "_With"), features); } }; diff --git a/tensorflow/compiler/xla/service/device_memory_allocator.cc b/tensorflow/compiler/xla/service/device_memory_allocator.cc index e228bb56bc..1d0297cfbf 100644 --- a/tensorflow/compiler/xla/service/device_memory_allocator.cc +++ b/tensorflow/compiler/xla/service/device_memory_allocator.cc @@ -36,9 +36,8 @@ StatusOr<OwningDeviceMemory> StreamExecutorMemoryAllocator::Allocate( se::DeviceMemoryBase result = stream_executor->AllocateArray<uint8>(size); if (size > 0 && result == nullptr) { return ResourceExhausted( - "Failed to allocate request for %s (%lluB) on device ordinal %d", - tensorflow::strings::HumanReadableNumBytes(size).c_str(), size, - device_ordinal); + "Failed to allocate request for %s (%uB) on device ordinal %d", + tensorflow::strings::HumanReadableNumBytes(size), size, device_ordinal); } return OwningDeviceMemory(result, device_ordinal, this); } @@ -61,12 +60,12 @@ StatusOr<se::StreamExecutor*> StreamExecutorMemoryAllocator::GetStreamExecutor( } if (device_ordinal >= stream_executors_.size()) { return InvalidArgument( - "device ordinal value (%d) >= number of devices (%zu)", device_ordinal, + "device ordinal value (%d) >= number of devices (%u)", device_ordinal, stream_executors_.size()); } if (stream_executors_[device_ordinal] == nullptr) { return NotFound("Device %s:%d present but not supported", - platform()->Name().c_str(), device_ordinal); + platform()->Name(), device_ordinal); } return stream_executors_[device_ordinal]; } diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor.cc b/tensorflow/compiler/xla/service/dfs_hlo_visitor.cc index 2172ae0a29..3e7373adc5 100644 --- a/tensorflow/compiler/xla/service/dfs_hlo_visitor.cc +++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor.cc @@ -28,14 +28,14 @@ template <typename HloInstructionPtr> Status DfsHloVisitorBase<HloInstructionPtr>::HandleElementwiseUnary( HloInstructionPtr hlo) { return Unimplemented("DfsHloVisitor::HandleElementwiseUnary: %s", - HloOpcodeString(hlo->opcode()).c_str()); + HloOpcodeString(hlo->opcode())); } template <typename HloInstructionPtr> Status DfsHloVisitorBase<HloInstructionPtr>::HandleElementwiseBinary( HloInstructionPtr hlo) { return Unimplemented("DfsHloVisitor::HandleElementwiseBinary: %s", - HloOpcodeString(hlo->opcode()).c_str()); + HloOpcodeString(hlo->opcode())); } template <typename HloInstructionPtr> diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc index 26af67cc1c..2e5930fb70 100644 --- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc @@ -264,8 +264,8 @@ StatusOr<llvm::Value*> ElementalIrEmitter::EmitIntegerUnaryOp( } } return Unimplemented("conversion from primitive type %s to %s", - PrimitiveType_Name(from_type).c_str(), - PrimitiveType_Name(to_type).c_str()); + PrimitiveType_Name(from_type), + PrimitiveType_Name(to_type)); } case HloOpcode::kBitcastConvert: { PrimitiveType from_type = op->operand(0)->shape().element_type(); @@ -282,8 +282,7 @@ StatusOr<llvm::Value*> ElementalIrEmitter::EmitIntegerUnaryOp( return InvalidArgument( "bitcast conversion from primitive type %s to %s with unequal " "bit-widths (%u versus %u) ", - PrimitiveType_Name(from_type).c_str(), - PrimitiveType_Name(to_type).c_str(), + PrimitiveType_Name(from_type), PrimitiveType_Name(to_type), primitive_util::BitWidth(from_type), primitive_util::BitWidth(to_type)); } @@ -332,7 +331,7 @@ StatusOr<llvm::Value*> ElementalIrEmitter::EmitIntegerUnaryOp( } default: return Unimplemented("unary integer op '%s'", - HloOpcodeString(op->opcode()).c_str()); + HloOpcodeString(op->opcode())); } } @@ -389,8 +388,8 @@ StatusOr<llvm::Value*> ElementalIrEmitter::EmitFloatUnaryOp( operand_value, llvm_ir::PrimitiveTypeToIrType(to_type, module_)); } return Unimplemented("unhandled conversion operation: %s => %s", - PrimitiveType_Name(from_type).c_str(), - PrimitiveType_Name(to_type).c_str()); + PrimitiveType_Name(from_type), + PrimitiveType_Name(to_type)); } case HloOpcode::kBitcastConvert: { PrimitiveType from_type = op->operand(0)->shape().element_type(); @@ -407,8 +406,7 @@ StatusOr<llvm::Value*> ElementalIrEmitter::EmitFloatUnaryOp( return InvalidArgument( "bitcast conversion from primitive type %s to %s with unequal " "bit-widths (%u versus %u) ", - PrimitiveType_Name(from_type).c_str(), - PrimitiveType_Name(to_type).c_str(), + PrimitiveType_Name(from_type), PrimitiveType_Name(to_type), primitive_util::BitWidth(from_type), primitive_util::BitWidth(to_type)); } @@ -471,7 +469,7 @@ StatusOr<llvm::Value*> ElementalIrEmitter::EmitFloatUnaryOp( return llvm::ConstantFP::get(operand_value->getType(), 0.0); default: return Unimplemented("unary floating-point op '%s'", - HloOpcodeString(op->opcode()).c_str()); + HloOpcodeString(op->opcode())); } } @@ -683,7 +681,7 @@ StatusOr<llvm::Value*> ElementalIrEmitter::EmitComplexUnaryOp( return EmitExtractImag(operand_value); default: return Unimplemented("unary complex op '%s'", - HloOpcodeString(op->opcode()).c_str()); + HloOpcodeString(op->opcode())); } } @@ -755,7 +753,7 @@ StatusOr<llvm::Value*> ElementalIrEmitter::EmitFloatBinaryOp( return EmitAtan2(op->shape().element_type(), lhs_value, rhs_value); default: return Unimplemented("binary floating point op '%s'", - HloOpcodeString(op->opcode()).c_str()); + HloOpcodeString(op->opcode())); } } @@ -873,7 +871,7 @@ StatusOr<llvm::Value*> ElementalIrEmitter::EmitComplexBinaryOp( } default: return Unimplemented("binary complex op '%s'", - HloOpcodeString(op->opcode()).c_str()); + HloOpcodeString(op->opcode())); } } @@ -1247,7 +1245,7 @@ StatusOr<llvm::Value*> ElementalIrEmitter::EmitIntegerBinaryOp( /*saturate_to_sign_bit=*/false); default: return Unimplemented("binary integer op '%s'", - HloOpcodeString(op->opcode()).c_str()); + HloOpcodeString(op->opcode())); } } @@ -1378,7 +1376,7 @@ StatusOr<llvm::Value*> ElementalIrEmitter::ConvertValueForDistribution( default: return InvalidArgument( "unhandled distribution %s", - RandomDistribution_Name(hlo->random_distribution()).c_str()); + RandomDistribution_Name(hlo->random_distribution())); } } @@ -1610,7 +1608,7 @@ StatusOr<llvm::Value*> ElementalIrEmitter::EmitElementalClamp( max_value, EmitIntegralMax(min_value, arg_value, is_signed), is_signed); } else { return Unimplemented("Clamp unimplemented for %s", - PrimitiveType_Name(prim_type).c_str()); + PrimitiveType_Name(prim_type)); } } @@ -2232,7 +2230,7 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator( default: return [hlo](const IrArray::Index& index) { return Unimplemented("Unhandled opcode for elemental IR emission: %s", - HloOpcodeString(hlo->opcode()).c_str()); + HloOpcodeString(hlo->opcode())); }; } } diff --git a/tensorflow/compiler/xla/service/executable.cc b/tensorflow/compiler/xla/service/executable.cc index 1c9f396b68..78edf918a4 100644 --- a/tensorflow/compiler/xla/service/executable.cc +++ b/tensorflow/compiler/xla/service/executable.cc @@ -16,6 +16,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/executable.h" #include "absl/memory/memory.h" +#include "absl/strings/str_format.h" #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h" #include "tensorflow/compiler/xla/service/hlo_graph_dumper.h" #include "tensorflow/compiler/xla/status.h" @@ -23,7 +24,6 @@ limitations under the License. #include "tensorflow/core/lib/hash/hash.h" #include "tensorflow/core/lib/io/path.h" #include "tensorflow/core/lib/strings/proto_serialization.h" -#include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/env.h" using tensorflow::gtl::ArraySlice; @@ -155,9 +155,9 @@ Status Executable::DumpHloSnapshot() { const string& directory_path = module_config().debug_options().xla_dump_executions_to(); const auto& module = hlo_snapshot_->hlo().hlo_module(); - string filename = tensorflow::strings::Printf( - "computation_%lld__%s__execution_%lld", module.id(), - module.entry_computation_name().c_str(), ++execution_count_); + string filename = + absl::StrFormat("computation_%d__%s__execution_%d", module.id(), + module.entry_computation_name(), ++execution_count_); return Executable::DumpToDirectory(directory_path, filename, *hlo_snapshot_); } diff --git a/tensorflow/compiler/xla/service/execution_tracker.cc b/tensorflow/compiler/xla/service/execution_tracker.cc index 70a78c8a2b..997db7c058 100644 --- a/tensorflow/compiler/xla/service/execution_tracker.cc +++ b/tensorflow/compiler/xla/service/execution_tracker.cc @@ -66,7 +66,7 @@ Status ExecutionTracker::Unregister(const ExecutionHandle& handle) { tensorflow::mutex_lock lock(execution_mutex_); auto it = handle_to_execution_.find(handle.handle()); if (it == handle_to_execution_.end()) { - return NotFound("no execution record for execution handle: %lld", + return NotFound("no execution record for execution handle: %d", handle.handle()); } handle_to_execution_.erase(handle.handle()); @@ -78,7 +78,7 @@ StatusOr<const AsyncExecution*> ExecutionTracker::Resolve( tensorflow::mutex_lock lock(execution_mutex_); auto it = handle_to_execution_.find(handle.handle()); if (it == handle_to_execution_.end()) { - return NotFound("no execution record for execution handle: %lld", + return NotFound("no execution record for execution handle: %d", handle.handle()); } return it->second.get(); diff --git a/tensorflow/compiler/xla/service/gather_expander.cc b/tensorflow/compiler/xla/service/gather_expander.cc index d889fd8e88..3f1a881372 100644 --- a/tensorflow/compiler/xla/service/gather_expander.cc +++ b/tensorflow/compiler/xla/service/gather_expander.cc @@ -323,7 +323,7 @@ StatusOr<HloInstruction*> GatherExpander::ExpandGather( return Unimplemented( "Gather operations with more than 2147483647 gather indices are not " "supported. This error occurred for %s.", - gather_instr->ToString().c_str()); + gather_instr->ToString()); } TF_ASSIGN_OR_RETURN( diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD index e53f525517..87b799e78e 100644 --- a/tensorflow/compiler/xla/service/gpu/BUILD +++ b/tensorflow/compiler/xla/service/gpu/BUILD @@ -57,6 +57,7 @@ cc_library( "//tensorflow/core:lib", "//tensorflow/core:stream_executor_no_cuda", "@com_google_absl//absl/memory", + "@com_google_absl//absl/strings:str_format", ], ) @@ -110,6 +111,7 @@ tf_cc_test( "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:lib", "@com_google_absl//absl/memory", + "@com_google_absl//absl/strings:str_format", ], ) @@ -351,6 +353,7 @@ cc_library( "//tensorflow/stream_executor", "@com_google_absl//absl/memory", "@com_google_absl//absl/strings", + "@com_google_absl//absl/strings:str_format", "@com_google_absl//absl/types:optional", ], ) @@ -389,6 +392,7 @@ cc_library( "//tensorflow/core:lib", "//tensorflow/core:stream_executor_no_cuda", "@com_google_absl//absl/strings", + "@com_google_absl//absl/strings:str_format", "@com_google_absl//absl/types:optional", ], ) @@ -819,6 +823,7 @@ tf_cc_test( "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "@com_google_absl//absl/memory", + "@com_google_absl//absl/strings:str_format", ], ) diff --git a/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc b/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc index e208ad61e3..86af83b6b9 100644 --- a/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc +++ b/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc @@ -62,7 +62,7 @@ StatusOr<std::unique_ptr<BufferAllocations>> BufferAllocations::Builder::Build( if (reinterpret_cast<uintptr_t>(address.opaque()) % expected_alignment != 0) { return InternalError( - "Address of registered buffer %lld must be a multiple of %llx, but " + "Address of registered buffer %d must be a multiple of %x, but " "was %p", i, kEntryParameterAlignBytes, address.opaque()); } @@ -83,7 +83,7 @@ StatusOr<std::unique_ptr<BufferAllocations>> BufferAllocations::Builder::Build( 0) { return InternalError( "Address returned by memory_allocator->Allocate must be a " - "multiple of %llx, but was %p", + "multiple of %x, but was %p", kXlaAllocatedBufferAlignBytes, buffer.opaque()); } // We do manual memory management within BufferAllocations. Be sure not diff --git a/tensorflow/compiler/xla/service/gpu/buffer_comparator.cc b/tensorflow/compiler/xla/service/gpu/buffer_comparator.cc index f22c2a8add..13c83c9199 100644 --- a/tensorflow/compiler/xla/service/gpu/buffer_comparator.cc +++ b/tensorflow/compiler/xla/service/gpu/buffer_comparator.cc @@ -124,7 +124,7 @@ StatusOr<F16BufferComparator> F16BufferComparator::Create( StatusOr<bool> F16BufferComparator::CompareEqualImpl( se::DeviceMemory<Eigen::half> test_buffer) { if (ref_buffer_.root_buffer().size() != test_buffer.size()) { - return InternalError("Mismatched buffer size: %lld vs %lld", + return InternalError("Mismatched buffer size: %d vs %d", ref_buffer_.root_buffer().size(), test_buffer.size()); } diff --git a/tensorflow/compiler/xla/service/gpu/conditional_thunk.cc b/tensorflow/compiler/xla/service/gpu/conditional_thunk.cc index 8b0426aa27..9ed523998b 100644 --- a/tensorflow/compiler/xla/service/gpu/conditional_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/conditional_thunk.cc @@ -59,7 +59,7 @@ Status ConditionalThunk::ExecuteOnStream( Status block_status = stream->BlockHostUntilDone(); if (!block_status.ok()) { return InternalError("Failed to retrieve predicate value on stream %p: %s.", - stream, block_status.error_message().c_str()); + stream, block_status.error_message()); } // Execute the true or the false computation depending on the value of the diff --git a/tensorflow/compiler/xla/service/gpu/convolution_thunk.cc b/tensorflow/compiler/xla/service/gpu/convolution_thunk.cc index 854a2f50b2..eea31f3de1 100644 --- a/tensorflow/compiler/xla/service/gpu/convolution_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/convolution_thunk.cc @@ -22,7 +22,6 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/util.h" -#include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" diff --git a/tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_thunk.cc b/tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_thunk.cc index 18a76e8c26..bc3c6f72f6 100644 --- a/tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_thunk.cc @@ -22,7 +22,6 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/ir_emission_utils.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/util.h" -#include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" diff --git a/tensorflow/compiler/xla/service/gpu/cudnn_convolution_algorithm_picker.cc b/tensorflow/compiler/xla/service/gpu/cudnn_convolution_algorithm_picker.cc index 3d421ebb69..dbdf8e7a0e 100644 --- a/tensorflow/compiler/xla/service/gpu/cudnn_convolution_algorithm_picker.cc +++ b/tensorflow/compiler/xla/service/gpu/cudnn_convolution_algorithm_picker.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/cudnn_convolution_algorithm_picker.h" #include "absl/strings/str_cat.h" +#include "absl/strings/str_format.h" #include "absl/types/optional.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/service/gpu/backend_configs.pb.h" @@ -59,8 +60,8 @@ StatusOr<se::DeviceMemory<uint8>> ScratchAllocator::AllocateBytes( if (byte_size > GetMemoryLimitInBytes(stream)) { return se::port::Status( se::port::error::RESOURCE_EXHAUSTED, - tensorflow::strings::Printf( - "Allocating %lld bytes exceeds the memory limit of %lld bytes.", + absl::StrFormat( + "Allocating %d bytes exceeds the memory limit of %d bytes.", byte_size, GetMemoryLimitInBytes(stream))); } @@ -361,7 +362,7 @@ CudnnConvolutionAlgorithmPicker::PickBestAlgorithm( return InternalError( "All algorithms tried for convolution %s failed. Falling back to " "default algorithm.", - instr->ToString().c_str()); + instr->ToString()); } StatusOr<bool> CudnnConvolutionAlgorithmPicker::RunOnInstruction( diff --git a/tensorflow/compiler/xla/service/gpu/cudnn_convolution_runner.cc b/tensorflow/compiler/xla/service/gpu/cudnn_convolution_runner.cc index 68086c86e9..07b96fbd3f 100644 --- a/tensorflow/compiler/xla/service/gpu/cudnn_convolution_runner.cc +++ b/tensorflow/compiler/xla/service/gpu/cudnn_convolution_runner.cc @@ -197,8 +197,8 @@ Status RunCudnnConvolution( if (!stream->ok()) { return InternalError( - "Unable to launch convolution with type %s and algorithm (%lld, %lld)", - CudnnConvKindToString(kind).c_str(), algorithm.algorithm().algo_id(), + "Unable to launch convolution with type %s and algorithm (%d, %d)", + CudnnConvKindToString(kind), algorithm.algorithm().algo_id(), algorithm.algorithm_no_scratch().algo_id()); } return Status::OK(); diff --git a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc index 2460d951bd..afcf9fa2ea 100644 --- a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc @@ -107,7 +107,7 @@ StatusOr<llvm::Value*> GpuElementalIrEmitter::EmitLibdeviceMathCall( break; default: return Unimplemented("Bad type for libdevice math call: %s", - PrimitiveType_Name(output_type).c_str()); + PrimitiveType_Name(output_type)); } llvm::Value* result = EmitMathCall(munged_callee, converted_operands, converted_input_types, output_type) @@ -138,7 +138,7 @@ StatusOr<llvm::Value*> GpuElementalIrEmitter::EmitLlvmIntrinsicMathCall( break; default: return Unimplemented("Bad type for llvm intrinsic math call: %s", - PrimitiveType_Name(output_type).c_str()); + PrimitiveType_Name(output_type)); } return EmitMathCall(munged_callee, operands, input_types, output_type); } @@ -152,8 +152,8 @@ StatusOr<llvm::Value*> GpuElementalIrEmitter::EmitMathCall( for (PrimitiveType input_type : input_types) { if (output_type != input_type) { return Unimplemented("Input type ≠output type: %s ≠%s", - PrimitiveType_Name(input_type).c_str(), - PrimitiveType_Name(output_type).c_str()); + PrimitiveType_Name(input_type), + PrimitiveType_Name(output_type)); } } diff --git a/tensorflow/compiler/xla/service/gpu/fft_thunk.cc b/tensorflow/compiler/xla/service/gpu/fft_thunk.cc index def595d217..11549cdac5 100644 --- a/tensorflow/compiler/xla/service/gpu/fft_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/fft_thunk.cc @@ -18,10 +18,10 @@ limitations under the License. #include <string> #include "absl/strings/str_cat.h" +#include "absl/strings/str_format.h" #include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/util.h" -#include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" @@ -43,8 +43,8 @@ StatusOr<se::DeviceMemory<uint8>> FftScratchAllocator::AllocateBytes( if (byte_size > GetMemoryLimitInBytes(stream)) { return se::port::Status( se::port::error::RESOURCE_EXHAUSTED, - tensorflow::strings::Printf( - "Allocating %lld bytes exceeds the memory limit of %lld bytes.", + absl::StrFormat( + "Allocating %d bytes exceeds the memory limit of %d bytes.", byte_size, GetMemoryLimitInBytes(stream))); } @@ -213,7 +213,7 @@ Status FftThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, return Status::OK(); } return InternalError("Unable to launch fft for thunk %p with type %s", this, - FftTypeToString(fft_type_).c_str()); + FftTypeToString(fft_type_)); } } // namespace gpu diff --git a/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc b/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc index 2c02ec2584..9c4a490366 100644 --- a/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc @@ -186,7 +186,7 @@ StatusOr<se::blas::AlgorithmType> DoGemmAutotune( } return InternalError( - "Unable to autotune cuBLAS gemm on stream %p; none of the %zu algorithms " + "Unable to autotune cuBLAS gemm on stream %p; none of the %u algorithms " "ran successfully", stream, algorithms.size()); } diff --git a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc index 88be63e267..71a02e70df 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc @@ -160,7 +160,7 @@ Status GpuExecutable::ExecuteThunks( if (!block_status.ok()) { return InternalError( "Failed to complete all kernels launched on stream %p: %s", - main_stream, block_status.error_message().c_str()); + main_stream, block_status.error_message()); } } @@ -260,10 +260,9 @@ StatusOr<ScopedShapedBuffer> GpuExecutable::ExecuteOnStream( if (buffer.is_null() && buffer.size() > 0) { return FailedPrecondition( "Cannot run XLA computation because pointer to (sub-)buffer at " - "index %s of parameter %lld was null. All pointers to " - "(sub-)buffers must not be null, unless the (sub-)buffer has zero " - "elements.", - allocation.param_shape_index().ToString().c_str(), param_no); + "index %s of parameter %d was null. All pointers to (sub-)buffers " + "must not be null, unless the (sub-)buffer has zero elements.", + allocation.param_shape_index().ToString(), param_no); } buffer_allocations_builder.RegisterBuffer(i, buffer); diff --git a/tensorflow/compiler/xla/service/gpu/gpu_hlo_support_checker.cc b/tensorflow/compiler/xla/service/gpu/gpu_hlo_support_checker.cc index 4944c41f7d..4268fb2c7a 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_hlo_support_checker.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_hlo_support_checker.cc @@ -34,9 +34,8 @@ StatusOr<bool> GpuHloSupportChecker::Run(HloModule* module) { return xla::Unimplemented( "GPU backend does not support HLO instruction %s with shape " "containing a sparse layout: %s", - instruction->ToString().c_str(), - ShapeUtil::HumanStringWithLayout(instruction->shape()) - .c_str()); + instruction->ToString(), + ShapeUtil::HumanStringWithLayout(instruction->shape())); } return Status::OK(); })); diff --git a/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.cc b/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.cc index 44303724bb..f3c2744292 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.cc @@ -84,7 +84,7 @@ Status GpuTransferManager::EnqueueBuffersToInfeed( Status block_status = stream->BlockHostUntilDone(); if (!block_status.ok()) { return InternalError("Failed to complete data transfer on stream %p: %s", - stream, block_status.error_message().c_str()); + stream, block_status.error_message()); } infeed_manager->EnqueueDestination(std::move(buffers)); @@ -97,7 +97,7 @@ Status GpuTransferManager::EnqueueBuffersToInfeed( StatusOr<InfeedBuffer> GpuTransferManager::TransferBufferToInfeedInternal( se::StreamExecutor* executor, int64 size, const void* source) { if (size > std::numeric_limits<int32>::max()) { - return InvalidArgument("Infeed shape is too large: needs %lld bytes", size); + return InvalidArgument("Infeed shape is too large: needs %d bytes", size); } if (size == 0) { diff --git a/tensorflow/compiler/xla/service/gpu/hlo_schedule_test.cc b/tensorflow/compiler/xla/service/gpu/hlo_schedule_test.cc index d4a96cd5b3..bb147c8d98 100644 --- a/tensorflow/compiler/xla/service/gpu/hlo_schedule_test.cc +++ b/tensorflow/compiler/xla/service/gpu/hlo_schedule_test.cc @@ -19,6 +19,7 @@ limitations under the License. #include <unordered_set> #include "absl/memory/memory.h" +#include "absl/strings/str_format.h" #include "tensorflow/compiler/xla/service/gpu/stream_assignment.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" @@ -266,7 +267,7 @@ TEST_F(HloScheduleTest, LatticeMatMul) { params.reserve(6); for (int i = 0; i < 6; ++i) { params.push_back(builder.AddInstruction(HloInstruction::CreateParameter( - i, f32_2x2_, /*name=*/tensorflow::strings::Printf("param%d", i)))); + i, f32_2x2_, /*name=*/absl::StrFormat("param%d", i)))); } HloInstruction* d00 = builder.AddInstruction( HloInstruction::CreateCanonicalDot(f32_2x2_, params[2], params[3])); diff --git a/tensorflow/compiler/xla/service/gpu/infeed_thunk.cc b/tensorflow/compiler/xla/service/gpu/infeed_thunk.cc index fee6d2af3b..8c3a026740 100644 --- a/tensorflow/compiler/xla/service/gpu/infeed_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/infeed_thunk.cc @@ -96,7 +96,7 @@ Status InfeedThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, Status block_status = stream->BlockHostUntilDone(); if (!block_status.ok()) { return InternalError("Failed to complete data transfer on stream %p: %s", - stream, block_status.error_message().c_str()); + stream, block_status.error_message()); } VLOG(2) << "Infeeding to GPU complete"; diff --git a/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc b/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc index 8d0522bd8f..f53dfaee3d 100644 --- a/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc +++ b/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc @@ -365,7 +365,7 @@ static StatusOr<const HloInstruction*> FindHloInstruction( } return NotFound( "Computation '%s' does not contain an instruction with op code '%s'.", - computation.name().c_str(), HloOpcodeString(op).c_str()); + computation.name(), HloOpcodeString(op)); } TEST_F(InstructionFusionTest, MultiOutputFusion) { diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc index 7111b53944..4cbb6d75a8 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc @@ -384,8 +384,8 @@ Status IrEmitter::EmitAtomicOperationForNestedComputation( // TODO(b/30258929): We only accept binary computations so far. return Unimplemented( "We only support atomic functions with exactly two parameters, but " - "computation %s has %lld.", - computation.name().c_str(), computation.num_parameters()); + "computation %s has %d.", + computation.name(), computation.num_parameters()); } if (MaybeEmitDirectAtomicOperation(computation, output_address, diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index 9c7b508e10..4d98955c58 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -2674,8 +2674,7 @@ Status CheckHloBuffersShareAllocation( if (slice_a != slice_b) { return InternalError( "instruction %s %s does not share allocation with instruction %s %s", - a->ToString().c_str(), slice_a.ToString().c_str(), - b->ToString().c_str(), slice_b.ToString().c_str()); + a->ToString(), slice_a.ToString(), b->ToString(), slice_b.ToString()); } return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/gpu/kernel_thunk.cc b/tensorflow/compiler/xla/service/gpu/kernel_thunk.cc index d856299889..3259eaa2a2 100644 --- a/tensorflow/compiler/xla/service/gpu/kernel_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/kernel_thunk.cc @@ -63,7 +63,7 @@ Status KernelThunk::Initialize(const GpuExecutable& executable, if (kernel_cache_.end() == it) { it = kernel_cache_.emplace(executor, se::KernelBase(executor)).first; if (!executor->GetKernel(*loader_spec_, &it->second)) { - return InternalError("Unable to load kernel %s", kernel_name_.c_str()); + return InternalError("Unable to load kernel %s", kernel_name_); } } @@ -107,7 +107,7 @@ Status KernelThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, stream, se::ThreadDim(launch_dimensions.threads_per_block()), se::BlockDim(launch_dimensions.block_count()), *kernel, *kernel_args)) { - return InternalError("Unable to launch kernel %s", kernel_name_.c_str()); + return InternalError("Unable to launch kernel %s", kernel_name_); } return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/BUILD b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/BUILD index ccf082c4c6..698d2d51cc 100644 --- a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/BUILD +++ b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/BUILD @@ -36,6 +36,7 @@ cc_library( "//tensorflow/core:lib_internal", "@com_google_absl//absl/memory", "@com_google_absl//absl/strings", + "@com_google_absl//absl/strings:str_format", "@llvm//:amdgpu_code_gen", "@llvm//:analysis", "@llvm//:bit_reader", diff --git a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/dump_ir_pass.cc b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/dump_ir_pass.cc index a3c74507dd..85bc58cb44 100644 --- a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/dump_ir_pass.cc +++ b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/dump_ir_pass.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/dump_ir_pass.h" +#include "absl/strings/str_format.h" #include "absl/strings/string_view.h" #include "llvm/IR/Module.h" #include "llvm/Support/FileSystem.h" @@ -22,7 +23,6 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/utils.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/core/lib/io/path.h" -#include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/logging.h" namespace xla { @@ -87,9 +87,10 @@ void IrDumpingPassManager::run(llvm::Module &module) { llvm::PassRegistry::getPassRegistry()->getPassInfo(P->getPassID()); const string basename = ReplaceFilenameExtension( absl::string_view(tensorflow::io::Basename(input_filename_)), - tensorflow::strings::Printf( + absl::StrFormat( "pass-%02d.before.%s.ll", i, - (PI == nullptr ? "unknown" : PI->getPassArgument().data()))); + absl::string_view(PI == nullptr ? "unknown" + : PI->getPassArgument().data()))); llvm::legacy::PassManager::add( new DumpIrPass(tensorflow::io::JoinPath(output_dir_, basename))); } diff --git a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/nvptx_backend_lib.cc b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/nvptx_backend_lib.cc index e18d7e764a..8751e3a9c2 100644 --- a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/nvptx_backend_lib.cc +++ b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/nvptx_backend_lib.cc @@ -57,7 +57,6 @@ limitations under the License. #include "llvm/Transforms/Scalar.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/core/lib/io/path.h" -#include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/env.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/tracing.h" diff --git a/tensorflow/compiler/xla/service/gpu/outfeed_thunk.cc b/tensorflow/compiler/xla/service/gpu/outfeed_thunk.cc index b99d998c4d..e0f3e84a4c 100644 --- a/tensorflow/compiler/xla/service/gpu/outfeed_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/outfeed_thunk.cc @@ -96,7 +96,7 @@ Status OutfeedThunk::ExecuteOnStream( Status block_status = stream->BlockHostUntilDone(); if (!block_status.ok()) { return InternalError("Failed to complete data transfer on stream %p: %s", - stream, block_status.error_message().c_str()); + stream, block_status.error_message()); } VLOG(2) << "Outfeeding from GPU complete"; diff --git a/tensorflow/compiler/xla/service/gpu/partition_assignment.cc b/tensorflow/compiler/xla/service/gpu/partition_assignment.cc index c927c5ee16..cf9f102d31 100644 --- a/tensorflow/compiler/xla/service/gpu/partition_assignment.cc +++ b/tensorflow/compiler/xla/service/gpu/partition_assignment.cc @@ -19,6 +19,7 @@ limitations under the License. #include <string> #include "absl/memory/memory.h" +#include "absl/strings/str_format.h" #include "tensorflow/compiler/xla/map_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" @@ -26,7 +27,6 @@ limitations under the License. #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/lib/core/bits.h" -#include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/logging.h" namespace xla { @@ -34,9 +34,8 @@ namespace gpu { std::ostream& operator<<(std::ostream& out, const LaunchDimensions& launch_dims) { - out << tensorflow::strings::Printf("[block: %lld, thread: %lld]", - launch_dims.block_count(), - launch_dims.threads_per_block()); + out << absl::StrFormat("[block: %d, thread: %d]", launch_dims.block_count(), + launch_dims.threads_per_block()); return out; } @@ -91,9 +90,9 @@ LaunchDimensions CalculateLaunchDimensions( } int64 block_count = CeilOfRatio(num_elements, threads_per_block); - VLOG(2) << tensorflow::strings::Printf( + VLOG(2) << absl::StrFormat( "Initialized the block count to ceil(# of elements / threads per " - "block) = ceil(%lld/%lld) = %lld", + "block) = ceil(%d/%d) = %d", num_elements, threads_per_block, block_count); return LaunchDimensions(block_count, threads_per_block); diff --git a/tensorflow/compiler/xla/service/gpu/stream_assignment_test.cc b/tensorflow/compiler/xla/service/gpu/stream_assignment_test.cc index 3f75d8b559..091aca23e5 100644 --- a/tensorflow/compiler/xla/service/gpu/stream_assignment_test.cc +++ b/tensorflow/compiler/xla/service/gpu/stream_assignment_test.cc @@ -16,13 +16,13 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/stream_assignment.h" #include "absl/memory/memory.h" +#include "absl/strings/str_format.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" #include "tensorflow/compiler/xla/test_helpers.h" #include "tensorflow/compiler/xla/tests/hlo_test_base.h" #include "tensorflow/compiler/xla/types.h" -#include "tensorflow/core/lib/strings/stringprintf.h" namespace xla { namespace gpu { @@ -98,7 +98,7 @@ TEST_F(StreamAssignmentTest, LatticeMatMul) { params.reserve(6); for (int i = 0; i < 6; ++i) { params.push_back(builder.AddInstruction(HloInstruction::CreateParameter( - i, f32_2x2_, /*name=*/tensorflow::strings::Printf("param%d", i)))); + i, f32_2x2_, /*name=*/absl::StrFormat("param%d", i)))); } HloInstruction* d00 = builder.AddInstruction( HloInstruction::CreateCanonicalDot(f32_2x2_, params[2], params[3])); diff --git a/tensorflow/compiler/xla/service/gpu/while_thunk.cc b/tensorflow/compiler/xla/service/gpu/while_thunk.cc index 828fc2884b..c4754fe378 100644 --- a/tensorflow/compiler/xla/service/gpu/while_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/while_thunk.cc @@ -70,7 +70,7 @@ Status WhileThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, if (!block_status.ok()) { return InternalError( "Failed to complete all kernels launched on stream %p: %s", stream, - block_status.error_message().c_str()); + block_status.error_message()); } if (!condition_result) { diff --git a/tensorflow/compiler/xla/service/hlo_computation.cc b/tensorflow/compiler/xla/service/hlo_computation.cc index cf95b112d7..4a59380ed9 100644 --- a/tensorflow/compiler/xla/service/hlo_computation.cc +++ b/tensorflow/compiler/xla/service/hlo_computation.cc @@ -625,16 +625,15 @@ StatusOr<HloInstruction*> HloComputation::DeepCopyInstruction( if (instruction->parent() != this) { return FailedPrecondition( "Can't deep copy instruction %s: instruction is not in computation %s", - instruction->name().c_str(), name().c_str()); + instruction->name(), name()); } if (indices_to_copy != nullptr && !ShapeUtil::Compatible(instruction->shape(), indices_to_copy->shape())) { return FailedPrecondition( "Can't deep copy instruction %s: given shape tree of indices to copy " "has incompatible shapes: %s vs. %s", - instruction->name().c_str(), - ShapeUtil::HumanString(instruction->shape()).c_str(), - ShapeUtil::HumanString(indices_to_copy->shape()).c_str()); + instruction->name(), ShapeUtil::HumanString(instruction->shape()), + ShapeUtil::HumanString(indices_to_copy->shape())); } ShapeIndex index; @@ -664,7 +663,7 @@ StatusOr<HloInstruction*> HloComputation::DeepCopyInstructionWithCustomCopier( if (instruction->parent() != this) { return FailedPrecondition( "Can't deep copy instruction %s: instruction is not in computation %s", - instruction->name().c_str(), name().c_str()); + instruction->name(), name()); } ShapeIndex index; return DeepCopyHelper(instruction, &index, copy_leaf); diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc index 1d35757b42..3376d170e6 100644 --- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc +++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc @@ -837,7 +837,7 @@ Status HloDataflowAnalysis::InitializeInstructionValueSets() { return Unimplemented( "Computation %s is called in both a parallel (eg, kMap) and " "sequential (eg, kCall) context", - computation->name().c_str()); + computation->name()); } if (call_graph_node.caller_callsites().empty() || call_graph_node.context() == CallContext::kParallel) { diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc index ca1c4dd0e9..71f91fde93 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator.cc +++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc @@ -435,7 +435,7 @@ Status HloEvaluator::HandleIsFinite(HloInstruction* is_finite) { if (!ShapeUtil::ElementIsFloating(operand->shape())) { return InvalidArgument( "expected element type in shape to be float for IsFinite op, got: %s", - PrimitiveType_Name(operand->shape().element_type()).c_str()); + PrimitiveType_Name(operand->shape().element_type())); } switch (operand->shape().element_type()) { @@ -476,9 +476,9 @@ Status HloEvaluator::HandleCompare(HloInstruction* compare) { return Unimplemented( "Implicit broadcasting is currently unsupported in HLO evaluator " "Shape Mismatch: %s vs %s vs %s", - ShapeUtil::HumanString(compare->shape()).c_str(), - ShapeUtil::HumanString(lhs->shape()).c_str(), - ShapeUtil::HumanString(rhs->shape()).c_str()); + ShapeUtil::HumanString(compare->shape()), + ShapeUtil::HumanString(lhs->shape()), + ShapeUtil::HumanString(rhs->shape())); } TF_RET_CHECK(lhs->shape().element_type() == rhs->shape().element_type()); @@ -1105,8 +1105,8 @@ Status HloEvaluator::HandleWhile(HloInstruction* while_hlo) { HloEvaluator loop_body_evaluator(max_loop_iterations_); while (keep_going) { if (max_loop_iterations_ >= 0 && iteration_count++ > max_loop_iterations_) { - return InvalidArgument("Loop %s exceeded loop iteration limit (%lld).", - while_hlo->name().c_str(), max_loop_iterations_); + return InvalidArgument("Loop %s exceeded loop iteration limit (%d).", + while_hlo->name(), max_loop_iterations_); } TF_ASSIGN_OR_RETURN(auto cond_val, cond_evaluator.Evaluate<Literal*>( *cond_comp, {lcv.get()})); @@ -1262,7 +1262,7 @@ Status HloEvaluator::HandleSort(HloInstruction* sort) { const int64 rank = ShapeUtil::Rank(sort->operand(0)->shape()); if (sort_dim != rank - 1) { return Unimplemented( - "Trying to support along dimension %lld, which is not the last " + "Trying to support along dimension %d, which is not the last " "dimension", sort_dim); } diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.h b/tensorflow/compiler/xla/service/hlo_evaluator.h index 7588916de5..0ea7089552 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator.h +++ b/tensorflow/compiler/xla/service/hlo_evaluator.h @@ -222,8 +222,8 @@ class HloEvaluator : public DfsHloVisitorWithDefault { return Unimplemented( "Implicit broadcasting is currently unsupported in HLO evaluator " "Shape Mismatch: %s vs %s", - ShapeUtil::HumanString(shape).c_str(), - ShapeUtil::HumanString(operand->shape()).c_str()); + ShapeUtil::HumanString(shape), + ShapeUtil::HumanString(operand->shape())); } auto result = absl::make_unique<Literal>(shape); diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h index 2da2cc2d71..b6566ebefe 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h +++ b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h @@ -143,7 +143,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { Status DefaultAction(HloInstruction* hlo_instruction) override { return Unimplemented("unhandled HLO ops for HloEvaluator: %s.", - HloOpcodeString(hlo_instruction->opcode()).c_str()); + HloOpcodeString(hlo_instruction->opcode())); } // TODO(b/35950897): many of the stl functions used in the handlers are not @@ -2654,9 +2654,8 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { return Unimplemented( "Implicit broadcasting is currently unsupported in HLO evaluator " "Shape Mismatch: %s vs %s vs %s: ", - ShapeUtil::HumanString(shape).c_str(), - ShapeUtil::HumanString(lhs->shape()).c_str(), - ShapeUtil::HumanString(rhs->shape()).c_str()); + ShapeUtil::HumanString(shape), ShapeUtil::HumanString(lhs->shape()), + ShapeUtil::HumanString(rhs->shape())); } const Literal& lhs_literal = parent_->GetEvaluatedLiteralFor(lhs); @@ -2690,10 +2689,9 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { return Unimplemented( "Implicit broadcasting is currently unsupported in HLO evaluator " "Shape Mismatch: %s vs %s vs %s vs %s: ", - ShapeUtil::HumanString(shape).c_str(), - ShapeUtil::HumanString(lhs->shape()).c_str(), - ShapeUtil::HumanString(rhs->shape()).c_str(), - ShapeUtil::HumanString(ehs->shape()).c_str()); + ShapeUtil::HumanString(shape), ShapeUtil::HumanString(lhs->shape()), + ShapeUtil::HumanString(rhs->shape()), + ShapeUtil::HumanString(ehs->shape())); } const Literal& lhs_literal = parent_->GetEvaluatedLiteralFor(lhs); diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc index 59c628e945..6cf7730fdc 100644 --- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc +++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc @@ -28,6 +28,7 @@ limitations under the License. #include "absl/strings/match.h" #include "absl/strings/str_cat.h" +#include "absl/strings/str_format.h" #include "absl/strings/str_join.h" #include "absl/strings/str_replace.h" #include "absl/types/optional.h" @@ -44,7 +45,6 @@ limitations under the License. #include "tensorflow/core/lib/gtl/map_util.h" #include "tensorflow/core/lib/io/path.h" #include "tensorflow/core/lib/strings/numbers.h" -#include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/env.h" #include "tensorflow/core/platform/protobuf.h" #include "tensorflow/core/platform/regexp.h" @@ -57,32 +57,12 @@ using absl::nullopt; using absl::optional; using absl::StrAppend; using absl::StrCat; +using absl::StrFormat; using absl::StrJoin; using tensorflow::Env; using tensorflow::WriteStringToFile; using tensorflow::io::JoinPath; -// Helpers for Printf and Appendf. -template <typename T> -struct PrintfConvert { - const T& operator()(const T& t) const { return t; } -}; -template <> -struct PrintfConvert<string> { - const char* operator()(const string& s) const { return s.c_str(); } -}; - -// Like tensorflow::strings::Printf/Appendf, but you don't need to call c_str() -// on strings. -template <typename... Ts> -string Printf(const char* fmt, const Ts&... ts) { - return tensorflow::strings::Printf(fmt, PrintfConvert<Ts>()(ts)...); -} -template <typename... Ts> -void Appendf(string* s, const char* fmt, const Ts&... ts) { - tensorflow::strings::Appendf(s, fmt, PrintfConvert<Ts>()(ts)...); -} - // Used to indicate how we should treat a given HLOInstruction in the graph. // should we treat it like normal, hide it, and so on? enum NodeFilterResult { @@ -210,10 +190,9 @@ NodeColors NodeColorsForScheme(ColorScheme color) { string NodeColorAttributes(ColorScheme color) { NodeColors node_colors = NodeColorsForScheme(color); - return Printf( - R"(style="%s", fontcolor="%s", color="%s", fillcolor="%s")", - node_colors.style, node_colors.font_color, node_colors.stroke_color, - node_colors.fill_color); + return StrFormat(R"(style="%s", fontcolor="%s", color="%s", fillcolor="%s")", + node_colors.style, node_colors.font_color, + node_colors.stroke_color, node_colors.fill_color); } // Replaces <> with <>, so that this string is safe(er) for use in a @@ -448,7 +427,7 @@ string HloDotDumper::Dump() { } string HloDotDumper::Header() { - const char* fmt = R"(digraph G { + constexpr char fmt[] = R"(digraph G { rankdir = TB; compound = true; label = <<b>%s</b>>; @@ -481,8 +460,8 @@ stylesheet=< } if (profile_ != nullptr) { auto cycles = profile_->total_cycles_executed(*computation_); - Appendf(&graph_label, "<br/>total cycles = %lld (%s)", cycles, - tensorflow::strings::HumanReadableNum(cycles)); + absl::StrAppendFormat(&graph_label, "<br/>total cycles = %d (%s)", cycles, + tensorflow::strings::HumanReadableNum(cycles)); } // Create CSS rules that say, when you hover over the given node or cluster, @@ -509,14 +488,14 @@ stylesheet=< // One could imagine other ways of writing this CSS rule that involve // less duplication, but this way seems to be relatively performant. edge_css_rules.push_back( - Printf(" #%s%d:hover ~ #edge%lld text { fill: %s; }\n" - " #%s%d:hover ~ #edge%lld path { " - "stroke: %s; stroke-width: .2em; }\n" - " #%s%d:hover ~ #edge%lld polygon { " - "fill: %s; stroke: %s; stroke-width: .2em; }\n", - elem_type, elem_id, edge_id, color, // - elem_type, elem_id, edge_id, color, // - elem_type, elem_id, edge_id, color, color)); + StrFormat(" #%s%d:hover ~ #edge%d text { fill: %s; }\n" + " #%s%d:hover ~ #edge%d path { " + "stroke: %s; stroke-width: .2em; }\n" + " #%s%d:hover ~ #edge%d polygon { " + "fill: %s; stroke: %s; stroke-width: .2em; }\n", + elem_type, elem_id, edge_id, color, // + elem_type, elem_id, edge_id, color, // + elem_type, elem_id, edge_id, color, color)); }; // The "to_node" value may be a NULL, indicating that this points to the @@ -559,7 +538,7 @@ stylesheet=< } } - return Printf(fmt, graph_label, StrJoin(edge_css_rules, "\n")); + return StrFormat(fmt, graph_label, StrJoin(edge_css_rules, "\n")); } string HloDotDumper::Footer() { return StrCat(StrJoin(edges_, "\n"), "\n}"); } @@ -600,9 +579,9 @@ string HloDotDumper::DumpSubcomputation(const HloComputation* subcomp, VLOG(2) << "Edge: from " << from->name() << " to " << parent_instr->name() << " as " << next_edge_id_; edge_ids_.insert({{from, parent_instr}, next_edge_id_++}); - const char* edge_fmt = + constexpr char edge_fmt[] = R"(%s -> %s [ltail="%s", style="dashed" tooltip="%s -> %s"];)"; - edges_.push_back(Printf( + edges_.push_back(StrFormat( edge_fmt, InstructionId(from), InstructionId(parent_instr), SubcomputationId(subcomp), subcomp->name(), parent_instr->name())); } @@ -619,9 +598,10 @@ string HloDotDumper::DumpSubcomputation(const HloComputation* subcomp, string subcomp_label, style; if (parent_instr->opcode() == HloOpcode::kFusion) { - subcomp_label = Printf("Fused expression for <b>%s</b><br/>%s", - HtmlLikeStringSanitize(parent_instr->name()), - HtmlLikeStringSanitize(parent_instr->ToCategory())); + subcomp_label = + StrFormat("Fused expression for <b>%s</b><br/>%s", + HtmlLikeStringSanitize(parent_instr->name()), + HtmlLikeStringSanitize(parent_instr->ToCategory())); string extra_info = GetInstructionNodeExtraInfo(parent_instr); if (!extra_info.empty()) { StrAppend(&subcomp_label, "<br/>", extra_info); @@ -647,18 +627,18 @@ string HloDotDumper::DumpSubcomputation(const HloComputation* subcomp, strokecolor = highlight ? "#b71c1c" : "#c2c2c2"; } style = - Printf(R"(style="rounded,filled,bold"; fillcolor="%s"; color="%s;")", - fillcolor, strokecolor); + StrFormat(R"(style="rounded,filled,bold"; fillcolor="%s"; color="%s;")", + fillcolor, strokecolor); } else { - subcomp_label = Printf("Subcomputation for <b>%s</b><br/>%s", - HtmlLikeStringSanitize(parent_instr->name()), - HtmlLikeStringSanitize(subcomp->name())); + subcomp_label = StrFormat("Subcomputation for <b>%s</b><br/>%s", + HtmlLikeStringSanitize(parent_instr->name()), + HtmlLikeStringSanitize(subcomp->name())); style = "style=rounded; color=black;"; } string comp_body = DumpComputation(subcomp); - const char* computation_fmt = R"(subgraph %s { + constexpr char computation_fmt[] = R"(subgraph %s { %s label = <%s>; labelloc = t; @@ -667,7 +647,7 @@ tooltip = " "; } // %s )"; - return Printf(computation_fmt, id, style, subcomp_label, comp_body, id); + return StrFormat(computation_fmt, id, style, subcomp_label, comp_body, id); } string HloDotDumper::DumpComputation(const HloComputation* comp) { @@ -718,11 +698,11 @@ string HloDotDumper::DumpRootTag() { VLOG(2) << "Adding edge from " << from->name() << " to root tag as " << next_edge_id_; edge_ids_.insert({{from, to}, next_edge_id_++}); - edges_.push_back(Printf(R"(%s -> %s [tooltip=" "];)", from_id, to_id)); + edges_.push_back(StrFormat(R"(%s -> %s [tooltip=" "];)", from_id, to_id)); - return Printf(R"(%s [label=<%s>, shape=%s, tooltip=" ", %s];)" - "\n", - to_id, node_body, node_shape, NodeColorAttributes(color)); + return StrFormat(R"(%s [label=<%s>, shape=%s, tooltip=" ", %s];)" + "\n", + to_id, node_body, node_shape, NodeColorAttributes(color)); } static const HloConstantInstruction* TryGetFusionParameterConstant( @@ -817,10 +797,10 @@ string HloDotDumper::DumpInstruction(const HloInstruction* instr) { } } - return Printf(R"(%s [label=<%s>, shape=%s, tooltip="%s", %s];)" - "\n", - InstructionId(instr), node_body, node_shape, node_metadata, - NodeColorAttributes(color)); + return StrFormat(R"(%s [label=<%s>, shape=%s, tooltip="%s", %s];)" + "\n", + InstructionId(instr), node_body, node_shape, node_metadata, + NodeColorAttributes(color)); } string HloDotDumper::GetInstructionNodeInlinedOperands( @@ -833,7 +813,7 @@ string HloDotDumper::GetInstructionNodeInlinedOperands( // enumerates all of its empty dimensions (e.g. "{ { {}, {} }, ..."), which // is just noise. if (ShapeUtil::IsZeroElementArray(shape)) { - return Printf("{} (%s)", ShapeUtil::HumanString(constant->shape())); + return StrFormat("{} (%s)", ShapeUtil::HumanString(constant->shape())); } // Print the literal value of constants with <= K elements. @@ -848,8 +828,8 @@ string HloDotDumper::GetInstructionNodeInlinedOperands( // collected from profiling tools. Those constants may not have a valid // literal. if (elem_count.has_value() && *elem_count <= 8 && constant->HasLiteral()) { - return Printf("%s (%s)", constant->literal().ToString(), - ShapeUtil::HumanString(constant->shape())); + return StrFormat("%s (%s)", constant->literal().ToString(), + ShapeUtil::HumanString(constant->shape())); } // Otherwise, print e.g. "%constant.42 (s32[100])". @@ -859,8 +839,8 @@ string HloDotDumper::GetInstructionNodeInlinedOperands( } else { constant_name = StrCat("constant ", constant->name()); } - return Printf("%s %s", constant_name, - ShapeUtil::HumanString(constant->shape())); + return StrFormat("%s %s", constant_name, + ShapeUtil::HumanString(constant->shape())); }; std::vector<string> lines; @@ -881,7 +861,7 @@ string HloDotDumper::GetInstructionNodeInlinedOperands( TryGetFusionParameterConstant(operand)) { operand_str = stringify_constant(constant); } else { - operand_str = Printf("Parameter %lld", operand->parameter_number()); + operand_str = StrFormat("Parameter %d", operand->parameter_number()); } } else { operand_str = operand->name(); @@ -890,9 +870,9 @@ string HloDotDumper::GetInstructionNodeInlinedOperands( if (operand_str) { if (instr->operand_count() > 1) { - lines.push_back(Printf("<b>operand %lld</b> = %s", i, *operand_str)); + lines.push_back(StrFormat("<b>operand %d</b> = %s", i, *operand_str)); } else { - lines.push_back(Printf("<b>operand</b> = %s", *operand_str)); + lines.push_back(StrFormat("<b>operand</b> = %s", *operand_str)); } } } @@ -1079,13 +1059,13 @@ string HloDotDumper::GetInstructionNodeShape(const HloInstruction* instr) { string HloDotDumper::GetInstructionNodeLabel(const HloInstruction* instr) { // If we have a parameter, put the param number in the name. if (instr->opcode() == HloOpcode::kParameter) { - return Printf("<b>Parameter %lld</b>", instr->parameter_number()); + return StrFormat("<b>Parameter %d</b>", instr->parameter_number()); } // The HLO instruction name contains usually the opcode, e.g. "%add.42" is // an add instruction. In this case we render just the name. if (absl::StartsWith(instr->name(), HloOpcodeString(instr->opcode()))) { - return Printf("<b>%s</b>", HtmlLikeStringSanitize(instr->name())); + return StrFormat("<b>%s</b>", HtmlLikeStringSanitize(instr->name())); } string extended_opcode = StrCat(HloOpcodeString(instr->opcode()), @@ -1093,8 +1073,8 @@ string HloDotDumper::GetInstructionNodeLabel(const HloInstruction* instr) { ? "" : StrCat(":", xla::ToString(instr->fusion_kind()))); // If the name does not contain the opcode, render both. - return Printf("<b>%s</b><br/>%s", HtmlLikeStringSanitize(extended_opcode), - HtmlLikeStringSanitize(instr->name())); + return StrFormat("<b>%s</b><br/>%s", HtmlLikeStringSanitize(extended_opcode), + HtmlLikeStringSanitize(instr->name())); } string HloDotDumper::GetInstructionNodeMetadata(const HloInstruction* instr) { @@ -1103,13 +1083,13 @@ string HloDotDumper::GetInstructionNodeMetadata(const HloInstruction* instr) { lines.push_back(HtmlLikeStringSanitize(instr->metadata().op_name())); } if (!instr->metadata().op_type().empty()) { - lines.push_back(Printf( + lines.push_back(StrFormat( "op_type: %s", HtmlLikeStringSanitize(instr->metadata().op_type()))); } if (!instr->metadata().source_file().empty() && instr->metadata().source_line() != 0) { - lines.push_back(Printf("op_type: %s", instr->metadata().source_file(), - instr->metadata().source_line())); + lines.push_back(StrFormat("op_type: %s:%d", instr->metadata().source_file(), + instr->metadata().source_line())); } return StrJoin(lines, "<br/>"); @@ -1164,7 +1144,7 @@ string HloDotDumper::GetInstructionNodeExtraInfo(const HloInstruction* instr) { lines.push_back(instr_shape); } if (debug_options_.xla_hlo_graph_addresses()) { - lines.push_back(Printf("[%p]", instr)); + lines.push_back(StrFormat("[%p]", instr)); } if (profile_ != nullptr) { double hlo_cycles_executed = profile_->GetCyclesTakenBy(*instr); @@ -1172,8 +1152,8 @@ string HloDotDumper::GetInstructionNodeExtraInfo(const HloInstruction* instr) { profile_->total_cycles_executed(*instr->parent()); if (hlo_cycles_executed > 0 && total_cycles_executed > 0) { lines.push_back( - Printf("%% of cycles executed=%.2f", - 100 * hlo_cycles_executed / total_cycles_executed)); + StrFormat("%% of cycles executed=%.2f", + 100 * hlo_cycles_executed / total_cycles_executed)); } } return StrJoin(lines, "<br/>"); @@ -1208,7 +1188,8 @@ void HloDotDumper::AddInstructionIncomingEdges(const HloInstruction* instr) { string edge_label; if (instr->operand_count() > 1 && !control_edge) { - edge_label = Printf(R"( headlabel="%lld", labeldistance=2)", operand_num); + edge_label = + StrFormat(R"( headlabel="%d", labeldistance=2)", operand_num); } else if (control_edge) { edge_label = "style=\"dotted\" color=\"gray\" label=\"ctrl\""; } @@ -1218,10 +1199,11 @@ void HloDotDumper::AddInstructionIncomingEdges(const HloInstruction* instr) { // means. bool is_big_array = TotalElementsInShape(from->shape()) >= 4096; - const char* kEdgeFmt = R"(%s -> %s [arrowhead=%s tooltip="%s -> %s" %s];)"; - edges_.push_back(Printf(kEdgeFmt, InstructionId(from), InstructionId(to), - (is_big_array ? "normal" : "empty"), from->name(), - to->name(), edge_label)); + constexpr char kEdgeFmt[] = + R"(%s -> %s [arrowhead=%s tooltip="%s -> %s" %s];)"; + edges_.push_back(StrFormat(kEdgeFmt, InstructionId(from), InstructionId(to), + (is_big_array ? "normal" : "empty"), + from->name(), to->name(), edge_label)); }; // Add edges from instr's operands to instr. Parameters within fusion @@ -1262,11 +1244,11 @@ string HloDotDumper::GetInstructionTrivialComputationStr( continue; } if (instr->called_computations().size() == 1) { - lines.push_back(Printf("Subcomputation: <b>%s</b>", - HtmlLikeStringSanitize(*computation_type))); + lines.push_back(StrFormat("Subcomputation: <b>%s</b>", + HtmlLikeStringSanitize(*computation_type))); } else { - lines.push_back(Printf("Subcomputation %lld: <b>%s</b>", i, - HtmlLikeStringSanitize(*computation_type))); + lines.push_back(StrFormat("Subcomputation %d: <b>%s</b>", i, + HtmlLikeStringSanitize(*computation_type))); } } return StrJoin(lines, "<br/>"); diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc index 3e077d8aec..6b4f3c4eb8 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction.cc @@ -2381,7 +2381,7 @@ Status HloInstruction::Visit(DfsHloVisitorBase<HloInstructionPtr>* visitor) { return InternalError( "Unhandled HloOpcode for DfsHloVisitor: %s. This should not happen - " "please file a bug for XLA.", - HloOpcodeString(opcode_).c_str()); + HloOpcodeString(opcode_)); } // Explicit instantiations. @@ -2464,7 +2464,7 @@ static Status PostOrderDFS(HloInstruction* root, Visitor* visitor, if (!TF_PREDICT_TRUE(PushDFSChild(visitor, &dfs_stack, child))) { return FailedPrecondition( "A cycle is detected while visiting instruction %s", - current_node->ToString().c_str()); + current_node->ToString()); } } @@ -2473,7 +2473,7 @@ static Status PostOrderDFS(HloInstruction* root, Visitor* visitor, if (!TF_PREDICT_TRUE(PushDFSChild(visitor, &dfs_stack, child))) { return FailedPrecondition( "A cycle is detected while visiting instruction %s", - current_node->ToString().c_str()); + current_node->ToString()); } } } @@ -2789,7 +2789,7 @@ StatusOr<HloInstruction::FusionKind> StringToFusionKind( if (kind_name == "kCustom") { return HloInstruction::FusionKind::kCustom; } - return InvalidArgument("Unknown fusion kind: %s", kind_name.c_str()); + return InvalidArgument("Unknown fusion kind: %s", kind_name); } string PaddingConfigToString(const PaddingConfig& padding) { diff --git a/tensorflow/compiler/xla/service/hlo_instruction_test.cc b/tensorflow/compiler/xla/service/hlo_instruction_test.cc index 504b13043f..8b0b90dfb3 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction_test.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction_test.cc @@ -53,7 +53,7 @@ class OpAndUserCollectingVisitor : public DfsHloVisitorWithDefault { public: Status DefaultAction(HloInstruction* hlo_instruction) override { return Unimplemented("not implemented %s", - HloOpcodeString(hlo_instruction->opcode()).c_str()); + HloOpcodeString(hlo_instruction->opcode())); } Status HandleParameter(HloInstruction* parameter) override { diff --git a/tensorflow/compiler/xla/service/hlo_lexer.cc b/tensorflow/compiler/xla/service/hlo_lexer.cc index 0e49d343d6..5b23ee7d00 100644 --- a/tensorflow/compiler/xla/service/hlo_lexer.cc +++ b/tensorflow/compiler/xla/service/hlo_lexer.cc @@ -306,8 +306,7 @@ TokKind HloLexer::LexNumberOrPattern() { R"([-]?((\d+|\d+[.]\d*|\d*[.]\d+)([eE][+-]?\d+))|[-]?(\d+[.]\d*|\d*[.]\d+))"}; if (RE2::Consume(&consumable, *float_pattern)) { current_ptr_ = consumable.begin(); - CHECK(absl::SimpleAtod(string(token_start_, current_ptr_).c_str(), - &decimal_val_)); + CHECK(absl::SimpleAtod(string(token_start_, current_ptr_), &decimal_val_)); return TokKind::kDecimal; } diff --git a/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc b/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc index f52a37bc74..a9c5d48983 100644 --- a/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc +++ b/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc @@ -163,7 +163,7 @@ Status HloModuleGroupMetadata::VerifyCompanionSets() const { ss << " " << hlo->name() << std::endl; } ss << "has multiple instructions on the same device"; - return FailedPrecondition("%s", ss.str().c_str()); + return FailedPrecondition("%s", ss.str()); } } } @@ -411,16 +411,16 @@ Status HloModuleGroupMetadata::AddCompanion(HloInstruction* instruction1, Status HloModuleGroupMetadata::VerifyChannelInstructions() { for (const Channel& channel : channels_) { if (channel.send == nullptr) { - return FailedPrecondition("missing send for id : %lld", channel.id); + return FailedPrecondition("missing send for id : %d", channel.id); } if (channel.recv == nullptr) { - return FailedPrecondition("missing recv for id : %lld", channel.id); + return FailedPrecondition("missing recv for id : %d", channel.id); } if (channel.send_done == nullptr) { - return FailedPrecondition("missing send-done for id : %lld", channel.id); + return FailedPrecondition("missing send-done for id : %d", channel.id); } if (channel.recv_done == nullptr) { - return FailedPrecondition("missing recv-done for id : %lld", channel.id); + return FailedPrecondition("missing recv-done for id : %d", channel.id); } } @@ -436,33 +436,33 @@ Status HloModuleGroupMetadata::VerifyChannelInstructions() { auto send_done_device = GetInstructionDevice(*channel.send_done); if (!send_device) { return FailedPrecondition("send instruction must have a device: %s", - channel.send->ToString().c_str()); + channel.send->ToString()); } if (!send_done_device) { return FailedPrecondition("send_done instruction must have a device: %s", - channel.send_done->ToString().c_str()); + channel.send_done->ToString()); } if (*send_device != *send_done_device) { return FailedPrecondition( - "send and send-done (channel=%lld) must be on the same device: %lld " - "vs. %lld", + "send and send-done (channel=%d) must be on the same device: %d " + "vs. %d", channel.id, *send_device, *send_done_device); } auto recv_device = GetInstructionDevice(*channel.recv); auto recv_done_device = GetInstructionDevice(*channel.recv_done); if (!recv_done_device) { return FailedPrecondition("recv_done instruction must have a device: %s", - channel.recv_done->ToString().c_str()); + channel.recv_done->ToString()); } if (*recv_device != *recv_done_device) { return FailedPrecondition( - "recv and recv-done (channel=%lld) must be on the same device: %lld " - "vs. %lld", + "recv and recv-done (channel=%d) must be on the same device: %d " + "vs. %d", channel.id, *recv_device, *recv_done_device); } if (*send_device == *recv_device) { return FailedPrecondition( - "send and recv (channel=%lld) must be on different devices: %lld", + "send and recv (channel=%d) must be on different devices: %d", channel.id, *send_device); } } @@ -483,7 +483,7 @@ Status HloModuleGroupMetadata::VerifyChannelInstructions() { !CheckCompanionPathsCompatibility( path, GetCompanionsPath(channel.recv_done))) { return FailedPrecondition( - "Nest companion paths do not match for channel %lld", channel.id); + "Nest companion paths do not match for channel %d", channel.id); } } return Status::OK(); diff --git a/tensorflow/compiler/xla/service/hlo_module_group_util.cc b/tensorflow/compiler/xla/service/hlo_module_group_util.cc index b5c7681edd..d70328c8a3 100644 --- a/tensorflow/compiler/xla/service/hlo_module_group_util.cc +++ b/tensorflow/compiler/xla/service/hlo_module_group_util.cc @@ -282,7 +282,7 @@ Status HloModuleGroupUtil::VisitTopologicalOrder( "following nodes. Note that the order of the nodes is arbitrary " "and that the list may include nodes that are not part of the " "cycle.\n%s", - predecessor->ToString().c_str(), cyclic_instructions.c_str()); + predecessor->ToString(), cyclic_instructions); } stack.push(predecessor); } diff --git a/tensorflow/compiler/xla/service/hlo_opcode.cc b/tensorflow/compiler/xla/service/hlo_opcode.cc index d1eaf35785..2d4e38589f 100644 --- a/tensorflow/compiler/xla/service/hlo_opcode.cc +++ b/tensorflow/compiler/xla/service/hlo_opcode.cc @@ -39,7 +39,7 @@ StatusOr<HloOpcode> StringToHloOpcode(const string& opcode_name) { }); auto it = opcode_map->find(opcode_name); if (it == opcode_map->end()) { - return InvalidArgument("Unknown opcode: %s", opcode_name.c_str()); + return InvalidArgument("Unknown opcode: %s", opcode_name); } return it->second; } diff --git a/tensorflow/compiler/xla/service/hlo_ordering.cc b/tensorflow/compiler/xla/service/hlo_ordering.cc index 8fe91c7278..0581d5c404 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_format.h" #include "absl/strings/str_join.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/shape_util.h" @@ -26,7 +27,6 @@ limitations under the License. #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/lib/core/errors.h" -#include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/logging.h" namespace xla { @@ -306,17 +306,15 @@ string PredecessorHloOrdering::ToStringHelper(const string& name) const { std::vector<string> pieces; pieces.push_back(name); for (auto* computation : module_->MakeNonfusionComputations()) { - pieces.push_back(tensorflow::strings::Printf("computation %s:", - computation->name().c_str())); + pieces.push_back(absl::StrFormat("computation %s:", computation->name())); const auto all = computation->MakeInstructionPostOrder(); for (auto instruction : all) { - pieces.push_back(tensorflow::strings::Printf( - " %s predecessors:", instruction->name().c_str())); + pieces.push_back( + absl::StrFormat(" %s predecessors:", instruction->name())); for (auto predecessor : all) { if (predecessors_.at(computation) ->IsReachable(predecessor, instruction)) { - pieces.push_back( - tensorflow::strings::Printf(" %s", predecessor->name().c_str())); + pieces.push_back(absl::StrFormat(" %s", predecessor->name())); } } } @@ -372,8 +370,8 @@ string SequentialHloOrdering::ToString() const { std::vector<string> pieces; pieces.push_back("SequentialHloOrdering"); for (auto* computation : module_->computations()) { - pieces.push_back(tensorflow::strings::Printf("computation %s order:", - computation->name().c_str())); + 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; @@ -388,8 +386,7 @@ string SequentialHloOrdering::ToString() const { return order_position_.at(a) < order_position_.at(b); }); for (auto instruction : instructions) { - pieces.push_back( - tensorflow::strings::Printf(" %s", instruction->name().c_str())); + pieces.push_back(absl::StrFormat(" %s", instruction->name())); } } return absl::StrJoin(pieces, "\n"); diff --git a/tensorflow/compiler/xla/service/hlo_parser.cc b/tensorflow/compiler/xla/service/hlo_parser.cc index df789e6222..ba0f07dd14 100644 --- a/tensorflow/compiler/xla/service/hlo_parser.cc +++ b/tensorflow/compiler/xla/service/hlo_parser.cc @@ -18,6 +18,7 @@ limitations under the License. #include "absl/algorithm/container.h" #include "absl/memory/memory.h" #include "absl/strings/str_cat.h" +#include "absl/strings/str_format.h" #include "absl/strings/str_join.h" #include "absl/strings/str_split.h" #include "tensorflow/compiler/xla/literal.h" @@ -29,7 +30,6 @@ limitations under the License. #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/lib/gtl/map_util.h" -#include "tensorflow/core/lib/strings/stringprintf.h" namespace xla { @@ -39,8 +39,8 @@ using absl::nullopt; using absl::optional; using absl::StrAppend; using absl::StrCat; +using absl::StrFormat; using absl::StrJoin; -using ::tensorflow::strings::Printf; const double kF16max = 65504; @@ -1586,8 +1586,7 @@ bool HloParser::ParseInstructionNames( } std::pair<HloInstruction*, LocTy>* instr = FindInstruction(name); if (!instr) { - return TokenError( - Printf("instruction '%s' is not defined", name.c_str())); + return TokenError(StrFormat("instruction '%s' is not defined", name)); } instructions->push_back(instr->first); } while (EatIfPresent(TokKind::kComma)); @@ -1829,17 +1828,17 @@ bool HloParser::ParseDenseLiteral(std::unique_ptr<Literal>* literal, case TokKind::kLbrace: { nest_level++; if (nest_level > rank) { - return TokenError(Printf( - "expects nested array in rank %lld, but sees larger", rank)); + return TokenError(absl::StrFormat( + "expects nested array in rank %d, but sees larger", rank)); } if (nest_level > 1) { elems_seen_per_dim[nest_level - 2]++; if (elems_seen_per_dim[nest_level - 2] > shape.dimensions(nest_level - 2)) { - return TokenError(Printf( - "expects %lld elements in the %sth element, but sees more", + return TokenError(absl::StrFormat( + "expects %d elements in the %sth element, but sees more", shape.dimensions(nest_level - 2), - get_index_str(nest_level - 2).c_str())); + get_index_str(nest_level - 2))); } } lexer_.Lex(); @@ -1848,9 +1847,9 @@ bool HloParser::ParseDenseLiteral(std::unique_ptr<Literal>* literal, case TokKind::kRbrace: { nest_level--; if (elems_seen_per_dim[nest_level] != shape.dimensions(nest_level)) { - return TokenError(Printf( - "expects %lld elements in the %sth element, but sees %lld", - shape.dimensions(nest_level), get_index_str(nest_level).c_str(), + return TokenError(absl::StrFormat( + "expects %d elements in the %sth element, but sees %d", + shape.dimensions(nest_level), get_index_str(nest_level), elems_seen_per_dim[nest_level])); } elems_seen_per_dim[nest_level] = 0; @@ -1871,15 +1870,15 @@ bool HloParser::ParseDenseLiteral(std::unique_ptr<Literal>* literal, if (rank > 0) { if (nest_level != rank) { return TokenError( - Printf("expects nested array in rank %lld, but sees %lld", rank, - nest_level)); + absl::StrFormat("expects nested array in rank %d, but sees %d", + rank, nest_level)); } elems_seen_per_dim[rank - 1]++; if (elems_seen_per_dim[rank - 1] > shape.dimensions(rank - 1)) { - return TokenError( - Printf("expects %lld elements on the minor-most dimension, but " - "sees more", - shape.dimensions(rank - 1))); + return TokenError(absl::StrFormat( + "expects %d elements on the minor-most dimension, but " + "sees more", + shape.dimensions(rank - 1))); } } if (lexer_.GetKind() == TokKind::kw_true || @@ -2135,8 +2134,8 @@ bool HloParser::ParseSubAttributes( for (const auto& attr_it : attrs) { if (attr_it.second.required && seen_attrs.find(attr_it.first) == seen_attrs.end()) { - return Error(loc, Printf("sub-attribute %s is expected but not seen", - attr_it.first.c_str())); + return Error(loc, StrFormat("sub-attribute %s is expected but not seen", + attr_it.first)); } } return ParseToken(TokKind::kRbrace, "expects '}' to end sub attributes"); @@ -2156,8 +2155,8 @@ bool HloParser::ParseAttributes( for (const auto& attr_it : attrs) { if (attr_it.second.required && seen_attrs.find(attr_it.first) == seen_attrs.end()) { - return Error(loc, Printf("attribute %s is expected but not seen", - attr_it.first.c_str())); + return Error(loc, StrFormat("attribute %s is expected but not seen", + attr_it.first)); } } return true; @@ -2173,7 +2172,7 @@ bool HloParser::ParseAttributeHelper( } VLOG(1) << "Parsing attribute " << name; if (!seen_attrs->insert(name).second) { - return Error(loc, Printf("attribute %s already exists", name.c_str())); + return Error(loc, StrFormat("attribute %s already exists", name)); } auto attr_it = attrs.find(name); if (attr_it == attrs.end()) { @@ -2188,8 +2187,8 @@ bool HloParser::ParseAttributeHelper( StrAppend(out, kv.first); })); } - return Error(loc, Printf("unexpected attribute \"%s\". %s", name.c_str(), - allowed_attrs.c_str())); + return Error(loc, StrFormat("unexpected attribute \"%s\". %s", name, + allowed_attrs)); } AttrTy attr_type = attr_it->second.attr_type; void* attr_out_ptr = attr_it->second.result; @@ -2384,7 +2383,7 @@ bool HloParser::ParseAttributeHelper( } }(); if (!success) { - return Error(loc, Printf("error parsing attribute %s", name.c_str())); + return Error(loc, StrFormat("error parsing attribute %s", name)); } return true; } @@ -2548,7 +2547,7 @@ bool HloParser::ParseConvolutionDimensionNumbers( dnums->set_input_spatial_dimensions(c - '0', i); } else { return TokenError( - Printf("expects [0-%lldbf] in lhs dimension numbers", rank - 1)); + StrFormat("expects [0-%dbf] in lhs dimension numbers", rank - 1)); } } } @@ -2571,7 +2570,7 @@ bool HloParser::ParseConvolutionDimensionNumbers( dnums->set_kernel_spatial_dimensions(c - '0', i); } else { return TokenError( - Printf("expects [0-%lldio] in rhs dimension numbers", rank - 1)); + StrFormat("expects [0-%dio] in rhs dimension numbers", rank - 1)); } } } @@ -2593,8 +2592,8 @@ bool HloParser::ParseConvolutionDimensionNumbers( } else if (c < '0' + rank && c >= '0') { dnums->set_output_spatial_dimensions(c - '0', i); } else { - return TokenError( - Printf("expects [0-%lldbf] in output dimension numbers", rank - 1)); + return TokenError(StrFormat( + "expects [0-%dbf] in output dimension numbers", rank - 1)); } } } @@ -2640,9 +2639,10 @@ bool HloParser::ParseSliceRanges(SliceRanges* result) { } const auto& range = ranges.back(); if (range.size() != 2 && range.size() != 3) { - return Error(loc, Printf("expects [start:limit:step] or [start:limit], " - "but sees %ld elements.", - range.size())); + return Error(loc, + StrFormat("expects [start:limit:step] or [start:limit], " + "but sees %d elements.", + range.size())); } } while (EatIfPresent(TokKind::kComma)); @@ -2828,14 +2828,13 @@ bool HloParser::ParseDxD(const string& name, std::vector<tensorflow::int64>* result) { LocTy loc = lexer_.GetLoc(); if (!result->empty()) { - return Error(loc, - Printf("sub-attribute '%s=' already exists", name.c_str())); + return Error(loc, StrFormat("sub-attribute '%s=' already exists", name)); } // 1D if (lexer_.GetKind() == TokKind::kInt) { tensorflow::int64 number; if (!ParseInt64(&number)) { - return Error(loc, Printf("expects sub-attribute '%s=i'", name.c_str())); + return Error(loc, StrFormat("expects sub-attribute '%s=i'", name)); } result->push_back(number); return true; @@ -2844,8 +2843,7 @@ bool HloParser::ParseDxD(const string& name, if (lexer_.GetKind() == TokKind::kDxD) { string str = lexer_.GetStrVal(); if (!SplitToInt64s(str, 'x', result)) { - return Error(loc, - Printf("expects sub-attribute '%s=ixj...'", name.c_str())); + return Error(loc, StrFormat("expects sub-attribute '%s=ixj...'", name)); } lexer_.Lex(); return true; @@ -2940,9 +2938,8 @@ bool HloParser::ParseOpcode(HloOpcode* result) { string val = lexer_.GetStrVal(); auto status_or_result = StringToHloOpcode(val); if (!status_or_result.ok()) { - return TokenError( - Printf("expects opcode but sees: %s, error: %s", val.c_str(), - status_or_result.status().error_message().c_str())); + return TokenError(StrFormat("expects opcode but sees: %s, error: %s", val, + status_or_result.status().error_message())); } *result = status_or_result.ValueOrDie(); lexer_.Lex(); @@ -2956,7 +2953,7 @@ bool HloParser::ParseFftType(FftType* result) { } string val = lexer_.GetStrVal(); if (!FftType_Parse(val, result) || !FftType_IsValid(*result)) { - return TokenError(Printf("expects fft type but sees: %s", val.c_str())); + return TokenError(StrFormat("expects fft type but sees: %s", val)); } lexer_.Lex(); return true; @@ -2970,9 +2967,9 @@ bool HloParser::ParseFusionKind(HloInstruction::FusionKind* result) { string val = lexer_.GetStrVal(); auto status_or_result = StringToFusionKind(val); if (!status_or_result.ok()) { - return TokenError( - Printf("expects fusion kind but sees: %s, error: %s", val.c_str(), - status_or_result.status().error_message().c_str())); + return TokenError(StrFormat("expects fusion kind but sees: %s, error: %s", + val, + status_or_result.status().error_message())); } *result = status_or_result.ValueOrDie(); lexer_.Lex(); @@ -2988,8 +2985,8 @@ bool HloParser::ParseRandomDistribution(RandomDistribution* result) { auto status_or_result = StringToRandomDistribution(val); if (!status_or_result.ok()) { return TokenError( - Printf("expects random distribution but sees: %s, error: %s", - val.c_str(), status_or_result.status().error_message().c_str())); + StrFormat("expects random distribution but sees: %s, error: %s", val, + status_or_result.status().error_message())); } *result = status_or_result.ValueOrDie(); lexer_.Lex(); @@ -3004,9 +3001,9 @@ bool HloParser::ParsePrecision(PrecisionConfigProto::Precision* result) { string val = lexer_.GetStrVal(); auto status_or_result = StringToPrecision(val); if (!status_or_result.ok()) { - return TokenError( - Printf("expects precision but sees: %s, error: %s", val.c_str(), - status_or_result.status().error_message().c_str())); + return TokenError(StrFormat("expects precision but sees: %s, error: %s", + val, + status_or_result.status().error_message())); } *result = status_or_result.ValueOrDie(); lexer_.Lex(); @@ -3100,7 +3097,7 @@ StatusOr<HloSharding> HloParser::ParseShardingOnly() { lexer_.Lex(); OpSharding op_sharding; if (!ParseSharding(&op_sharding)) { - return InvalidArgument("Syntax error:\n%s", GetError().c_str()); + return InvalidArgument("Syntax error:\n%s", GetError()); } if (lexer_.GetKind() != TokKind::kEof) { return InvalidArgument("Syntax error:\nExtra content after sharding"); @@ -3112,7 +3109,7 @@ StatusOr<Window> HloParser::ParseWindowOnly() { lexer_.Lex(); Window window; if (!ParseWindow(&window, /*expect_outer_curlies=*/false)) { - return InvalidArgument("Syntax error:\n%s", GetError().c_str()); + return InvalidArgument("Syntax error:\n%s", GetError()); } if (lexer_.GetKind() != TokKind::kEof) { return InvalidArgument("Syntax error:\nExtra content after window"); @@ -3125,7 +3122,7 @@ HloParser::ParseConvolutionDimensionNumbersOnly() { lexer_.Lex(); ConvolutionDimensionNumbers dnums; if (!ParseConvolutionDimensionNumbers(&dnums)) { - return InvalidArgument("Syntax error:\n%s", GetError().c_str()); + return InvalidArgument("Syntax error:\n%s", GetError()); } if (lexer_.GetKind() != TokKind::kEof) { return InvalidArgument( @@ -3163,7 +3160,7 @@ Status HloParser::ParseSingleInstruction(HloComputation::Builder* builder, // Parse the instruction with the registered hook. if (!ParseInstruction(builder, root_name)) { - return InvalidArgument("Syntax error:\n%s", GetError().c_str()); + return InvalidArgument("Syntax error:\n%s", GetError()); } return Status::OK(); } @@ -3174,7 +3171,7 @@ StatusOr<std::unique_ptr<HloModule>> ParseHloString( absl::string_view str, const HloModuleConfig& config) { HloParser parser(str, config); if (!parser.Run()) { - return InvalidArgument("Syntax error:\n%s", parser.GetError().c_str()); + return InvalidArgument("Syntax error:\n%s", parser.GetError()); } return parser.ConsumeHloModule(); } diff --git a/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc b/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc index df99e131d8..de7ad6d209 100644 --- a/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc +++ b/tensorflow/compiler/xla/service/hlo_pass_pipeline.cc @@ -18,6 +18,7 @@ limitations under the License. #include <functional> #include "absl/strings/str_cat.h" +#include "absl/strings/str_format.h" #include "absl/strings/str_join.h" #include "tensorflow/compiler/xla/service/hlo_graph_dumper.h" #include "tensorflow/compiler/xla/service/hlo_proto_util.h" @@ -48,9 +49,9 @@ void DumpModuleProto(const HloModule& module, const string& dump_to, tensorflow::mutex_lock lock(mu); const int64 pass_number = (*module_id_to_pass_number)[module.unique_id()]++; - const string mod_name = SanitizeFileName(tensorflow::strings::Printf( - "module_%04d.%04lld.%s.after_%s", module.unique_id(), pass_number, - pipeline_name.c_str(), pass_name.c_str())); + const string mod_name = SanitizeFileName( + absl::StrFormat("module_%04d.%04d.%s.after_%s", module.unique_id(), + pass_number, pipeline_name, pass_name)); TF_QCHECK_OK(protobuf_util::DumpProtoToDirectory(MakeHloProto(module), dump_to, mod_name)); diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization.cc b/tensorflow/compiler/xla/service/hlo_rematerialization.cc index 6c6e7c6fec..569d2e5d2d 100644 --- a/tensorflow/compiler/xla/service/hlo_rematerialization.cc +++ b/tensorflow/compiler/xla/service/hlo_rematerialization.cc @@ -22,6 +22,7 @@ limitations under the License. #include "absl/container/inlined_vector.h" #include "absl/strings/str_cat.h" +#include "absl/strings/str_format.h" #include "absl/strings/str_join.h" #include "tensorflow/compiler/xla/map_util.h" #include "tensorflow/compiler/xla/primitive_util.h" @@ -40,7 +41,6 @@ limitations under the License. #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/util.h" -#include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/logging.h" namespace xla { @@ -1353,12 +1353,11 @@ StatusOr<bool> HloRematerialization::Run( XLA_VLOG_LINES(3, "After HloRematerialization:\n" + module->ToString()); if (current_peak_memory > memory_limit_bytes) { - LOG(WARNING) << tensorflow::strings::Printf( - "Can't reduce memory use below %s (%lld bytes) by rematerialization; " - "only reduced to %s (%lld bytes)", - HumanReadableNumBytes(memory_limit_bytes).c_str(), memory_limit_bytes, - HumanReadableNumBytes(current_peak_memory).c_str(), - current_peak_memory); + LOG(WARNING) << absl::StrFormat( + "Can't reduce memory use below %s (%d bytes) by rematerialization; " + "only reduced to %s (%d bytes)", + HumanReadableNumBytes(memory_limit_bytes), memory_limit_bytes, + HumanReadableNumBytes(current_peak_memory), current_peak_memory); } return changed; diff --git a/tensorflow/compiler/xla/service/hlo_scheduling.cc b/tensorflow/compiler/xla/service/hlo_scheduling.cc index 56b14f9fef..0fc3b268c0 100644 --- a/tensorflow/compiler/xla/service/hlo_scheduling.cc +++ b/tensorflow/compiler/xla/service/hlo_scheduling.cc @@ -30,7 +30,6 @@ limitations under the License. #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/gtl/map_util.h" -#include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/logging.h" namespace xla { diff --git a/tensorflow/compiler/xla/service/hlo_verifier.cc b/tensorflow/compiler/xla/service/hlo_verifier.cc index f60c4eab42..81ffb5ac43 100644 --- a/tensorflow/compiler/xla/service/hlo_verifier.cc +++ b/tensorflow/compiler/xla/service/hlo_verifier.cc @@ -128,10 +128,9 @@ Status ShapeVerifier::CheckIsTokenOperand(const HloInstruction* instruction, const HloInstruction* token = instruction->operand(operand_no); if (!ShapeUtil::Equal(token->shape(), ShapeUtil::MakeTokenShape())) { return InternalError( - "Expected operand %lld to be token-shaped, actual shape is " + "Expected operand %d to be token-shaped, actual shape is " "%s:\n%s", - operand_no, StringifyShape(token->shape()).c_str(), - instruction->ToString().c_str()); + operand_no, StringifyShape(token->shape()), instruction->ToString()); } return Status::OK(); } @@ -144,9 +143,8 @@ Status ShapeVerifier::CheckOperandAndParameter( computation->parameter_instruction(parameter_number); if (!ShapesSame(operand->shape(), parameter->shape())) { return InternalError("Operand %s shape does not match parameter's %s in %s", - operand->ToString().c_str(), - parameter->ToString().c_str(), - instruction->ToString().c_str()); + operand->ToString(), parameter->ToString(), + instruction->ToString()); } return Status::OK(); } @@ -171,9 +169,8 @@ Status ShapeVerifier::HandleOutfeed(HloInstruction* instruction) { return InternalError( "Expected outfeed shape to be equal to operand's shape %s, " "actual shape is %s:\n%s", - StringifyShape(outfeed->operand(0)->shape()).c_str(), - StringifyShape(outfeed->outfeed_shape()).c_str(), - outfeed->ToString().c_str()); + StringifyShape(outfeed->operand(0)->shape()), + StringifyShape(outfeed->outfeed_shape()), outfeed->ToString()); } return CheckShape(outfeed, ShapeUtil::MakeTokenShape()); } @@ -191,7 +188,7 @@ bool ShapeVerifier::HasCompatibleElementTypes(const Shape& shape_0, Status ShapeVerifier::HandleRng(HloInstruction* instruction) { if (instruction->operand_count() != 2) { return InternalError("Expected two operands for Rng instruction: %s", - instruction->ToString().c_str()); + instruction->ToString()); } const Shape& shape_0 = instruction->operand(0)->shape(); @@ -199,14 +196,14 @@ Status ShapeVerifier::HandleRng(HloInstruction* instruction) { if (!ShapeUtil::IsScalar(shape_0) || !ShapeUtil::IsScalar(shape_1)) { return InternalError( "Expected scalar types for the two operands of Rng instruction: %s", - instruction->ToString().c_str()); + instruction->ToString()); } if (!HasCompatibleElementTypes(shape_0, shape_1, instruction->shape())) { return InternalError( "Expected compatible element types for the result and the two operands" " of Rng instruction: %s", - instruction->ToString().c_str()); + instruction->ToString()); } PrimitiveType element_type = shape_0.element_type(); @@ -219,7 +216,7 @@ Status ShapeVerifier::HandleRng(HloInstruction* instruction) { "Element type not supported." " Expected element to be of floating point type, integral type or" " predicate type for RngUniform: %s", - instruction->ToString().c_str()); + instruction->ToString()); } break; @@ -228,13 +225,13 @@ Status ShapeVerifier::HandleRng(HloInstruction* instruction) { return InternalError( "Element type not supported." " Expected element to be FloatingPointType for RngNormal: %s", - instruction->ToString().c_str()); + instruction->ToString()); } break; default: return InternalError( "Invalid Rng distribution %s", - RandomDistribution_Name(instruction->random_distribution()).c_str()); + RandomDistribution_Name(instruction->random_distribution())); } return Status::OK(); @@ -253,8 +250,8 @@ Status ShapeVerifier::HandleSort(HloInstruction* sort) { return InternalError( "Expected sort to have to have the same dimensions for the keys and " "the values. Keys shape is: %s\n, Values shape is: %s", - StringifyShape(sort->operand(0)->shape()).c_str(), - StringifyShape(sort->operand(1)->shape()).c_str()); + StringifyShape(sort->operand(0)->shape()), + StringifyShape(sort->operand(1)->shape())); } return CheckVariadicShape(sort); } @@ -333,7 +330,7 @@ Status ShapeVerifier::HandleFusion(HloInstruction* fusion) { int64 param_no = fused_param->parameter_number(); if (!ShapesSame(fused_param->shape(), fusion->operand(param_no)->shape())) { return InternalError( - "Shape mismatch between parameter number %lld and its operand in " + "Shape mismatch between parameter number %d and its operand in " "%s.", param_no, fusion->ToString().c_str()); } @@ -425,7 +422,7 @@ Status ShapeVerifier::HandleWhile(HloInstruction* xla_while) { return InternalError( "Conditional computation shape does not lead to a scalar predicate " "shape: %s", - StringifyShape(conditional_shape).c_str()); + StringifyShape(conditional_shape)); } // The shape of kWhile should match the shape of the body computation it // calls. @@ -556,7 +553,7 @@ Status CheckMixedPrecisionOperands(const HloInstruction* instruction) { return InternalError( "Seen floating point types of different precisions in " "%s, but mixed precision is disallowed.", - instruction->ToString().c_str()); + instruction->ToString()); } return Status::OK(); })); @@ -646,9 +643,8 @@ Status ShapeVerifier::CheckShape(const HloInstruction* instruction, return InternalError( "Expected instruction to have shape equal to %s, actual " "shape is %s:\n%s", - StringifyShape(inferred_shape).c_str(), - StringifyShape(instruction->shape()).c_str(), - instruction->ToString().c_str()); + StringifyShape(inferred_shape), StringifyShape(instruction->shape()), + instruction->ToString()); } return Status::OK(); } @@ -713,23 +709,23 @@ Status VerifyHloStructure(HloModule* module) { for (const HloComputation* computation : module->computations()) { if (computation->parent() == nullptr) { return InternalError("Computation %s has a null parent pointer", - computation->name().c_str()); + computation->name()); } if (computation->parent() != module) { return InternalError( "Computation %s parent() does not point to parent module", - computation->name().c_str()); + computation->name()); } for (const HloInstruction* instruction : computation->instructions()) { if (instruction->parent() == nullptr) { return InternalError("Instruction %s has a null parent pointer", - instruction->name().c_str()); + instruction->name()); } if (instruction->parent() != computation) { return InternalError( "Instruction %s parent() does not point to parent computation", - instruction->name().c_str()); + instruction->name()); } } } @@ -746,9 +742,8 @@ Status VerifyHloStructure(HloModule* module) { return InternalError( "Operand %d (%s) of instruction %s is in a different " "computation: %s vs %s", - i, operand->name().c_str(), instruction->name().c_str(), - operand->parent()->name().c_str(), - instruction->parent()->name().c_str()); + i, operand->name(), instruction->name(), + operand->parent()->name(), instruction->parent()->name()); } } } @@ -764,7 +759,7 @@ Status HloVerifier::CheckFusionInstruction(HloInstruction* fusion) const { "Instruction of fused computation does not match expected " "instruction " "%s.", - fusion->ToString().c_str()); + fusion->ToString()); } // Fused root instruction and fused parameters must all be owned by the @@ -778,7 +773,7 @@ Status HloVerifier::CheckFusionInstruction(HloInstruction* fusion) const { if (fused_root == instruction) { if (root_owned) { return InternalError("Root appears more than once in %s.", - fusion->ToString().c_str()); + fusion->ToString()); } root_owned = true; } @@ -786,7 +781,7 @@ Status HloVerifier::CheckFusionInstruction(HloInstruction* fusion) const { if (fused_parameters[i] == instruction) { if (parameter_owned[i]) { return InternalError("Parameter appears more than once in %s.", - fusion->ToString().c_str()); + fusion->ToString()); } parameter_owned[i] = true; } @@ -794,20 +789,19 @@ Status HloVerifier::CheckFusionInstruction(HloInstruction* fusion) const { } if (!root_owned) { return InternalError("Root not found in computation of %s.", - fusion->ToString().c_str()); + fusion->ToString()); } // Make sure all the parameter_owned entries are set for (int i = 0; i < parameter_owned.size(); i++) { if (!parameter_owned[i]) { return InternalError("Parameter %d not found in computation of %s.", i, - fusion->ToString().c_str()); + fusion->ToString()); } } // Fused root must have no users. if (fused_root->user_count() != 0) { - return InternalError("Root of %s may not have users.", - fusion->ToString().c_str()); + return InternalError("Root of %s may not have users.", fusion->ToString()); } // All uses of fused instructions must be in the fusion computation, and @@ -817,14 +811,13 @@ Status HloVerifier::CheckFusionInstruction(HloInstruction* fusion) const { if (instruction != fused_root) { if (instruction->user_count() == 0) { return InternalError("Non-root instruction %s in %s must have users.", - instruction->ToString().c_str(), - fusion->ToString().c_str()); + instruction->ToString(), fusion->ToString()); } for (auto& user : instruction->users()) { if (fused_computation != user->parent()) { return InternalError( "Non-root instruction %s in %s may not have external users.", - instruction->ToString().c_str(), fusion->ToString().c_str()); + instruction->ToString(), fusion->ToString()); } } } @@ -837,19 +830,19 @@ Status HloVerifier::CheckFusionInstruction(HloInstruction* fusion) const { for (auto fused_param : fused_parameters) { int64 param_no = fused_param->parameter_number(); if (param_no < 0) { - return InternalError("Unexpected negative parameter number %lld in %s.", - param_no, fusion->ToString().c_str()); + return InternalError("Unexpected negative parameter number %d in %s.", + param_no, fusion->ToString()); } if (param_no >= fused_parameters.size()) { return InternalError( - "Unexpected parameter number %lld in %s: higher then number of " + "Unexpected parameter number %d in %s: higher then number of " "parameters %lu.", - param_no, fusion->ToString().c_str(), fused_parameters.size()); + param_no, fusion->ToString(), fused_parameters.size()); } if (parameter_numbers[param_no]) { return InternalError( - "Did not expect parameter number %lld more than once in %s.", - param_no, fusion->ToString().c_str()); + "Did not expect parameter number %d more than once in %s.", param_no, + fusion->ToString()); } parameter_numbers[param_no] = true; } @@ -857,7 +850,7 @@ Status HloVerifier::CheckFusionInstruction(HloInstruction* fusion) const { for (int i = 0; i < parameter_numbers.size(); i++) { if (!parameter_numbers[i]) { return InternalError("Did not see parameter number %d in %s.", i, - fusion->ToString().c_str()); + fusion->ToString()); } } @@ -872,18 +865,18 @@ Status HloVerifier::CheckWhileInstruction(HloInstruction* instruction) { auto* while_body = instruction->while_body(); if (while_cond->num_parameters() != 1) { return FailedPrecondition( - "While condition must have exactly 1 parameter; had %lld : %s", - while_cond->num_parameters(), while_cond->ToString().c_str()); + "While condition must have exactly 1 parameter; had %d : %s", + while_cond->num_parameters(), while_cond->ToString()); } if (while_body->num_parameters() != 1) { return FailedPrecondition( - "While body must have exactly 1 parameter; had %lld : %s", - while_body->num_parameters(), while_body->ToString().c_str()); + "While body must have exactly 1 parameter; had %d : %s", + while_body->num_parameters(), while_body->ToString()); } if (instruction->operand_count() != 1) { return FailedPrecondition( - "While loop must have exactly one operand; had %lld : %s", - instruction->operand_count(), instruction->ToString().c_str()); + "While loop must have exactly one operand; had %d : %s", + instruction->operand_count(), instruction->ToString()); } return Status::OK(); } @@ -891,16 +884,14 @@ Status HloVerifier::CheckWhileInstruction(HloInstruction* instruction) { Status HloVerifier::CheckConditionalInstruction(HloInstruction* instruction) { if (instruction->true_computation()->num_parameters() != 1) { return FailedPrecondition( - "True computation %s of %s must have 1 parameter insted of %lld", - instruction->true_computation()->name().c_str(), - instruction->ToString().c_str(), + "True computation %s of %s must have 1 parameter insted of %d", + instruction->true_computation()->name(), instruction->ToString(), instruction->true_computation()->num_parameters()); } if (instruction->false_computation()->num_parameters() != 1) { return FailedPrecondition( - "False computation %s of %s must have 1 parameter insted of %lld", - instruction->false_computation()->name().c_str(), - instruction->ToString().c_str(), + "False computation %s of %s must have 1 parameter insted of %d", + instruction->false_computation()->name(), instruction->ToString(), instruction->false_computation()->num_parameters()); } return Status::OK(); @@ -915,9 +906,9 @@ Status HloVerifier::CheckElementwiseInstruction(HloInstruction* instruction) { "Implicit broadcast is not allowed in HLO." "Found different shapes for instruction %s.\n" "output: %s\noperand: %s\n", - HloOpcodeString(instruction->opcode()).c_str(), - ShapeUtil::HumanString(out_shape).c_str(), - ShapeUtil::HumanString(operand_shape).c_str()); + HloOpcodeString(instruction->opcode()), + ShapeUtil::HumanString(out_shape), + ShapeUtil::HumanString(operand_shape)); } } return Status::OK(); @@ -948,7 +939,7 @@ Status VerifyEntryAndExitShapes(const HloModule& module) { if (ShapeContainsToken(param->shape())) { return InternalError( "Entry parameter %d is or contains a token shape: %s", i, - ShapeUtil::HumanString(param->shape()).c_str()); + ShapeUtil::HumanString(param->shape())); } } return Status::OK(); @@ -960,9 +951,9 @@ Status CheckSameChannel(const HloInstruction* instr1, if (instr1->channel_id() != instr2->channel_id()) { return InternalError( "Expected to have the same channel id, actual channel ids are: %s " - "(%lld), %s (%lld)", - instr1->ToString().c_str(), instr1->channel_id(), - instr2->ToString().c_str(), instr2->channel_id()); + "(%d), %s (%d)", + instr1->ToString(), instr1->channel_id(), instr2->ToString(), + instr2->channel_id()); } return Status::OK(); } @@ -983,7 +974,7 @@ Status CheckSameIsHostTransfer(const HloInstruction* instr1, "Expected instructions to have the same is-host-transfer property: " "%s, " "%s ", - instr1->ToString().c_str(), instr2->ToString().c_str()); + instr1->ToString(), instr2->ToString()); } return Status::OK(); } @@ -1000,12 +991,12 @@ Status VerifySendsAndRecvs(const HloModule& module) { host_channels.insert({sendrecv->channel_id(), sendrecv}); if (!it_inserted.second) { return FailedPrecondition( - "Channel %lld is used for multiple host send/recv instructions: " + "Channel %d is used for multiple host send/recv instructions: " "%s " "and " "%s", - sendrecv->channel_id(), sendrecv->ToString().c_str(), - it_inserted.first->second->ToString().c_str()); + sendrecv->channel_id(), sendrecv->ToString(), + it_inserted.first->second->ToString()); } } diff --git a/tensorflow/compiler/xla/service/human_readable_profile_builder.cc b/tensorflow/compiler/xla/service/human_readable_profile_builder.cc index 581b3ce1e0..e76b93107c 100644 --- a/tensorflow/compiler/xla/service/human_readable_profile_builder.cc +++ b/tensorflow/compiler/xla/service/human_readable_profile_builder.cc @@ -15,26 +15,26 @@ limitations under the License. #include "tensorflow/compiler/xla/service/human_readable_profile_builder.h" #include "absl/strings/str_cat.h" +#include "absl/strings/str_format.h" #include "tensorflow/compiler/xla/metric_table_report.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/lib/strings/numbers.h" -#include "tensorflow/core/lib/strings/stringprintf.h" namespace xla { using absl::StrAppend; +using absl::StrAppendFormat; using absl::StrCat; -using tensorflow::strings::Appendf; +using absl::StrFormat; using tensorflow::strings::HumanReadableElapsedTime; using tensorflow::strings::HumanReadableNumBytes; -using tensorflow::strings::Printf; string HumanReadableProfileBuilder::ToString() const { string s; - Appendf(&s, "Execution profile for %s: (%s @ f_nom)\n", - computation_name_.c_str(), - HumanReadableElapsedTime(CyclesToSeconds(total_cycles_)).c_str()); + StrAppendFormat(&s, "Execution profile for %s: (%s @ f_nom)\n", + computation_name_, + HumanReadableElapsedTime(CyclesToSeconds(total_cycles_))); int64 cumulative_cycles = 0; auto print_op = [&](const OpInfo& op, bool is_total = false) { @@ -56,7 +56,7 @@ string HumanReadableProfileBuilder::ToString() const { if (op.bytes_accessed > op.cycles) { bytes_per_cycle = StrCat(HumanReadableNumBytes(bpc), "/cycle"); } else { - bytes_per_cycle = Printf("%.3fB/cycle", bpc); + bytes_per_cycle = StrFormat("%.3fB/cycle", bpc); } } @@ -77,27 +77,24 @@ string HumanReadableProfileBuilder::ToString() const { // columns in the output. cycles_percent_str = "100.% 100Σ"; } else { - cycles_percent_str = - Printf("%5.2f%% %2.0fΣ", cycles_percent, cumulative_cycles_percent); + cycles_percent_str = StrFormat("%5.2f%% %2.0fΣ", cycles_percent, + cumulative_cycles_percent); } double nsecs = op.cycles / clock_rate_ghz_; - Appendf( + StrAppendFormat( &s, - "%15lld cycles (%s) :: %12.1f usec %22s :: %18s :: %18s :: %14s :: " + "%15d cycles (%s) :: %12.1f usec %22s :: %18s :: %18s :: %14s :: " "%16s :: %s\n", - op.cycles, cycles_percent_str.c_str(), CyclesToMicroseconds(op.cycles), + op.cycles, cycles_percent_str, CyclesToMicroseconds(op.cycles), op.optimal_seconds < 0 ? "" - : Printf("(%12.1f optimal)", op.optimal_seconds * 1e6).c_str(), - op.flop_count <= 0 - ? "" - : HumanReadableNumFlops(op.flop_count, nsecs).c_str(), + : StrFormat("(%12.1f optimal)", op.optimal_seconds * 1e6), + op.flop_count <= 0 ? "" : HumanReadableNumFlops(op.flop_count, nsecs), op.transcendental_count <= 0 ? "" - : HumanReadableNumTranscendentalOps(op.transcendental_count, nsecs) - .c_str(), - bytes_per_sec.c_str(), bytes_per_cycle.c_str(), op.name.c_str()); + : HumanReadableNumTranscendentalOps(op.transcendental_count, nsecs), + bytes_per_sec, bytes_per_cycle, op.name); }; float optimal_seconds_sum = 0.0; diff --git a/tensorflow/compiler/xla/service/interpreter/platform.cc b/tensorflow/compiler/xla/service/interpreter/platform.cc index e57a9b3672..c9b40d3c61 100644 --- a/tensorflow/compiler/xla/service/interpreter/platform.cc +++ b/tensorflow/compiler/xla/service/interpreter/platform.cc @@ -18,13 +18,13 @@ limitations under the License. #include <utility> #include "absl/memory/memory.h" +#include "absl/strings/str_format.h" #include "tensorflow/compiler/xla/service/interpreter/executor.h" #include "tensorflow/stream_executor/device_options.h" #include "tensorflow/stream_executor/lib/initialize.h" #include "tensorflow/stream_executor/lib/ptr_util.h" #include "tensorflow/stream_executor/lib/status.h" #include "tensorflow/stream_executor/lib/status_macros.h" -#include "tensorflow/stream_executor/lib/stringprintf.h" #include "tensorflow/stream_executor/multi_platform_manager.h" #include "tensorflow/stream_executor/platform.h" @@ -77,9 +77,9 @@ XlaInterpreterPlatform::GetUncachedExecutor( if (!init_status.ok()) { return port::Status{ port::error::INTERNAL, - port::Printf( + absl::StrFormat( "failed initializing StreamExecutor for device ordinal %d: %s", - config.ordinal, init_status.ToString().c_str())}; + config.ordinal, init_status.ToString())}; } return std::move(executor); diff --git a/tensorflow/compiler/xla/service/layout_assignment.cc b/tensorflow/compiler/xla/service/layout_assignment.cc index 5741864282..75d6d22a48 100644 --- a/tensorflow/compiler/xla/service/layout_assignment.cc +++ b/tensorflow/compiler/xla/service/layout_assignment.cc @@ -28,6 +28,7 @@ limitations under the License. #include "absl/memory/memory.h" #include "absl/strings/str_cat.h" +#include "absl/strings/str_format.h" #include "absl/strings/str_join.h" #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/map_util.h" @@ -51,7 +52,6 @@ limitations under the License. #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/lib/gtl/array_slice.h" -#include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/protobuf.h" @@ -71,9 +71,8 @@ BufferLayoutConstraint::BufferLayoutConstraint(const Layout& layout, } string BufferLayoutConstraint::ToString() const { - return tensorflow::strings::Printf("BufferLayoutConstraint %s: %s", - buffer_->ToString().c_str(), - LayoutUtil::HumanString(layout_).c_str()); + return absl::StrFormat("BufferLayoutConstraint %s: %s", buffer_->ToString(), + LayoutUtil::HumanString(layout_)); } OperandLayoutConstraint::OperandLayoutConstraint( @@ -92,15 +91,14 @@ OperandLayoutConstraint::OperandLayoutConstraint( } string OperandLayoutConstraint::ToString() const { - return tensorflow::strings::Printf( - "OperandLayoutConstraint %s, operand %lld: %s", - instruction_->name().c_str(), operand_no_, - shape_layout_.ToString().c_str()); + return absl::StrFormat("OperandLayoutConstraint %s, operand %d: %s", + instruction_->name(), operand_no_, + shape_layout_.ToString()); } string ResultLayoutConstraint::ToString() const { - return tensorflow::strings::Printf("ResultLayoutConstraint: %s", - shape_layout_.ToString().c_str()); + return absl::StrFormat("ResultLayoutConstraint: %s", + shape_layout_.ToString()); } LayoutConstraints::LayoutConstraints( @@ -168,8 +166,7 @@ Status LayoutConstraints::SetBufferLayout(const Layout& layout, return FailedPrecondition( "Layout of buffer %s cannot be constrained because buffer is not " "array-shaped, has shape: %s", - buffer.ToString().c_str(), - ShapeUtil::HumanString(buffer.shape()).c_str()); + buffer.ToString(), ShapeUtil::HumanString(buffer.shape())); } TF_RETURN_IF_ERROR( LayoutUtil::ValidateLayoutForShape(layout, buffer.shape())); @@ -185,9 +182,8 @@ Status LayoutConstraints::SetBufferLayout(const Layout& layout, return FailedPrecondition( "Buffer %s already has the layout constraint %s, cannot add " "incompatible constraint %s", - buffer.ToString().c_str(), - LayoutUtil::HumanString(curr_constraint.layout()).c_str(), - LayoutUtil::HumanString(layout).c_str()); + buffer.ToString(), LayoutUtil::HumanString(curr_constraint.layout()), + LayoutUtil::HumanString(layout)); } iter->second = BufferLayoutConstraint(layout, buffer, mandatory, dfs); } else { @@ -221,11 +217,11 @@ Status LayoutConstraints::SetOperandLayout(const Shape& shape_with_layout, } if (curr_shape_layout->mandatory()) { return FailedPrecondition( - "Operand %lld of instruction %s already has a layout constraint " + "Operand %d of instruction %s already has a layout constraint " "%s, cannot add incompatible constraint %s", - operand_no, instruction->name().c_str(), - curr_shape_layout->shape_layout().ToString().c_str(), - ShapeUtil::HumanStringWithLayout(shape_with_layout).c_str()); + operand_no, instruction->name(), + curr_shape_layout->shape_layout().ToString(), + ShapeUtil::HumanStringWithLayout(shape_with_layout)); } } @@ -234,9 +230,9 @@ Status LayoutConstraints::SetOperandLayout(const Shape& shape_with_layout, // layouts beyond this immediate use and is complicated to handle. if (OperandBufferForwarded(instruction, operand_no)) { return FailedPrecondition( - "Cannot constraint layout of operand %lld of instruction %s " + "Cannot constraint layout of operand %d of instruction %s " "because instruction forwards operand's LogicalBuffer(s)", - operand_no, instruction->name().c_str()); + operand_no, instruction->name()); } auto key = std::make_pair(instruction, operand_no); @@ -278,8 +274,8 @@ Status LayoutConstraints::SetResultLayout(const Shape& shape_with_layout, return FailedPrecondition( "Result of computation %s already has the layout constraint %s, " "cannot add incompatible constraint %s", - computation_->name().c_str(), curr_shape_layout->ToString().c_str(), - ShapeUtil::HumanStringWithLayout(shape_with_layout).c_str()); + computation_->name(), curr_shape_layout->ToString(), + ShapeUtil::HumanStringWithLayout(shape_with_layout)); } // New constraint matches existing constraint. Nothing to do. return Status::OK(); @@ -301,9 +297,8 @@ Status LayoutConstraints::SetInstructionLayout( if (!ShapeUtil::Compatible(shape_with_layout, instruction->shape())) { return FailedPrecondition( "Instruction %s of shape %s cannot be assigned incompatible layout %s", - instruction->name().c_str(), - ShapeUtil::HumanString(instruction->shape()).c_str(), - ShapeUtil::HumanStringWithLayout(shape_with_layout).c_str()); + instruction->name(), ShapeUtil::HumanString(instruction->shape()), + ShapeUtil::HumanStringWithLayout(shape_with_layout)); } // Create a BufferLayoutConstraint for each array shape in the output of the @@ -753,7 +748,7 @@ Status CheckParameterLayout(HloInstruction* parameter, return InternalError( "parameter instruction %s does not match layout of computation " "shape: %s", - parameter->ToString().c_str(), parameter_layout.ToString().c_str()); + parameter->ToString(), parameter_layout.ToString()); } return Status::OK(); } @@ -764,8 +759,8 @@ Status CheckConstantLayout(HloInstruction* constant) { constant->shape())) { return InternalError( "constant instruction %s does not match the layout of its literal %s", - constant->ToString().c_str(), - ShapeUtil::HumanStringWithLayout(constant->literal().shape()).c_str()); + constant->ToString(), + ShapeUtil::HumanStringWithLayout(constant->literal().shape())); } return Status::OK(); } @@ -898,13 +893,10 @@ Status LayoutAssignment::CheckLayouts(HloModule* module) { return InternalError( "Layout of instruction %s at index {%s} does not match " "source LogicalBuffer %s: %s vs %s", - instruction->name().c_str(), - absl::StrJoin(index, ",").c_str(), - buffer->ToString().c_str(), - ShapeUtil::HumanStringWithLayout(instruction_subshape) - .c_str(), - ShapeUtil::HumanStringWithLayout(buffer->shape()) - .c_str()); + instruction->name(), absl::StrJoin(index, ","), + buffer->ToString(), + ShapeUtil::HumanStringWithLayout(instruction_subshape), + ShapeUtil::HumanStringWithLayout(buffer->shape())); } } } @@ -1375,7 +1367,7 @@ StatusOr<Layout> InferArrayLayout( // This should not happen because we've assigned layouts to all // instructions preceding this one. return InternalError("LogicalBuffer %s does not have a layout", - source_buffer->ToString().c_str()); + source_buffer->ToString()); } if (first_buffer_layout == nullptr) { @@ -1390,9 +1382,8 @@ StatusOr<Layout> InferArrayLayout( return FailedPrecondition( "Array at index {%s} in instruction %s aliases buffers %s " "and %s which have different layouts", - absl::StrJoin(index, ",").c_str(), instruction->name().c_str(), - source_buffers[0]->ToString().c_str(), - source_buffer->ToString().c_str()); + absl::StrJoin(index, ","), instruction->name(), + source_buffers[0]->ToString(), source_buffer->ToString()); } } @@ -1560,7 +1551,7 @@ Status LayoutAssignment::ClearComputationLayouts(HloComputation* computation) { // present in the IR before layout assignment is a bug. return InternalError( "Unexpected bitcast operation seen during layout assignment: %s.", - instruction->ToString().c_str()); + instruction->ToString()); } if (instruction->opcode() != HloOpcode::kInfeed) { LayoutUtil::ClearLayout(instruction->mutable_shape()); diff --git a/tensorflow/compiler/xla/service/llvm_ir/BUILD b/tensorflow/compiler/xla/service/llvm_ir/BUILD index fc3289f30d..786448ea76 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/BUILD +++ b/tensorflow/compiler/xla/service/llvm_ir/BUILD @@ -125,6 +125,7 @@ cc_library( "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/core:lib", + "@com_google_absl//absl/strings:str_format", "@llvm//:core", ], ) diff --git a/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.cc b/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.cc index 72ede377e1..6d637cad6d 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.cc @@ -98,7 +98,7 @@ Status FusedIrEmitter::HandleGetTupleElement( return Unimplemented( "GetTupleElement fusion currently only supports" " parameter operands, but found operand: %s", - operand->name().c_str()); + operand->name()); } // Emit code to lookup tuple element pointer, and store it in 'gte_values_'. llvm::Value* tuple_element_ptr = llvm_ir::EmitGetTupleElement( diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc index 978fa5b453..2f6720b042 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc @@ -26,7 +26,6 @@ limitations under the License. #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" -#include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/logging.h" namespace xla { diff --git a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc index cf7445804c..1553b4fc91 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc @@ -18,13 +18,13 @@ limitations under the License. #include <memory> #include <utility> +#include "absl/strings/str_format.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/lib/core/errors.h" -#include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/protobuf.h" #include "tensorflow/core/platform/types.h" @@ -105,7 +105,7 @@ std::vector<IrArray::Index> LoopEmitter::EmitIndexAndSetExitBasicBlock( std::unique_ptr<ForLoop> loop = loop_nest.AddLoop( /*start_index=*/0, /*end_index=*/shape_.dimensions(dimension), - /*suffix=*/tensorflow::strings::Printf("dim.%lld", dimension)); + /*suffix=*/absl::StrFormat("dim.%d", dimension)); array_index[dimension] = loop->GetIndVarValue(); } diff --git a/tensorflow/compiler/xla/service/local_service.cc b/tensorflow/compiler/xla/service/local_service.cc index ea59adadea..768105d9e1 100644 --- a/tensorflow/compiler/xla/service/local_service.cc +++ b/tensorflow/compiler/xla/service/local_service.cc @@ -21,6 +21,7 @@ limitations under the License. #include "absl/memory/memory.h" #include "absl/strings/str_cat.h" +#include "absl/strings/str_format.h" #include "tensorflow/compiler/xla/client/executable_build_options.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/execution_options_util.h" @@ -149,7 +150,7 @@ StatusOr<std::unique_ptr<Executable>> LocalService::CompileExecutable( // Validate incoming layouts. if (argument_layouts.size() != program_shape.parameters_size()) { return InvalidArgument( - "Invalid number of arguments for computation: expected %d, got %zu.", + "Invalid number of arguments for computation: expected %d, got %u.", program_shape.parameters_size(), argument_layouts.size()); } @@ -167,16 +168,15 @@ StatusOr<std::unique_ptr<Executable>> LocalService::CompileExecutable( CHECK(metadata.value() != nullptr); const OpMetadata& m = *metadata.value(); if (!m.source_file().empty()) { - return tensorflow::strings::Printf( - " (%s:%d)", m.source_file().c_str(), m.source_line()); + return absl::StrFormat(" (%s:%d)", m.source_file(), m.source_line()); } return ""; }; return InvalidArgument( "Invalid argument shape for argument %d%s, expected %s, got %s.", i, - metadata_string().c_str(), - ShapeUtil::HumanString(program_shape.parameters(i)).c_str(), - ShapeUtil::HumanString(argument_shape).c_str()); + metadata_string(), + ShapeUtil::HumanString(program_shape.parameters(i)), + ShapeUtil::HumanString(argument_shape)); } } if (build_options.result_layout() != nullptr) { @@ -214,7 +214,7 @@ StatusOr<const ShapedBuffer*> LocalService::GlobalDataToShapedBuffer( TF_ASSIGN_OR_RETURN(auto buffers, allocation_tracker_.Resolve(data)); if (replica_number >= buffers.size()) { return InvalidArgument( - "replica_number %d out of range; must be less than num_replicas = %zu.", + "replica_number %d out of range; must be less than num_replicas = %u.", replica_number, buffers.size()); } return buffers[replica_number]; diff --git a/tensorflow/compiler/xla/service/platform_util.cc b/tensorflow/compiler/xla/service/platform_util.cc index 150af0cd93..ae1e13d8a6 100644 --- a/tensorflow/compiler/xla/service/platform_util.cc +++ b/tensorflow/compiler/xla/service/platform_util.cc @@ -98,7 +98,7 @@ PlatformUtil::GetSupportedPlatforms() { [](string* out, const se::Platform* p) { out->append(p->Name()); }); return InvalidArgument( "must specify platform because more than one platform found: %s", - platforms_string.c_str()); + platforms_string); } /* static */ StatusOr<se::Platform*> PlatformUtil::GetDefaultPlatform() { @@ -123,7 +123,7 @@ PlatformUtil::GetSupportedPlatforms() { return InvalidArgument( "must specify platform because more than one platform (except for the " "interpreter platform) found: %s", - platforms_string.c_str()); + platforms_string); } /*static*/ StatusOr<se::Platform*> PlatformUtil::GetPlatform( @@ -135,7 +135,7 @@ PlatformUtil::GetSupportedPlatforms() { return platform; } } - return InvalidArgument("platform %s not found", platform_name.c_str()); + return InvalidArgument("platform %s not found", platform_name); } /*static*/ StatusOr<se::Platform*> PlatformUtil::GetPlatformExceptFor( @@ -151,7 +151,7 @@ PlatformUtil::GetSupportedPlatforms() { } if (matched.empty()) { return InvalidArgument("unable to find platform that is not %s", - platform_name.c_str()); + platform_name); } if (matched.size() == 1) { return matched[0]; @@ -161,7 +161,7 @@ PlatformUtil::GetSupportedPlatforms() { [](string* out, const se::Platform* p) { out->append(p->Name()); }); return InvalidArgument( "found multiple platforms %s, but expected one platform except for %s", - matched_string.c_str(), platform_name.c_str()); + matched_string, platform_name); } // Returns whether the device underlying the given StreamExecutor is supported @@ -192,7 +192,7 @@ static bool IsDeviceSupported(se::StreamExecutor* executor) { PlatformUtil::GetStreamExecutors(se::Platform* platform) { int device_count = platform->VisibleDeviceCount(); if (device_count <= 0) { - return NotFound("no %s devices found", platform->Name().c_str()); + return NotFound("no %s devices found", platform->Name()); } if (platform->id() == se::host::kHostPlatformId) { // On host "devices", StreamExecutor exports a device for each hardware @@ -231,7 +231,7 @@ PlatformUtil::GetStreamExecutors(se::Platform* platform) { if (std::all_of(stream_executors.begin(), stream_executors.end(), [](se::StreamExecutor* s) { return s == nullptr; })) { return InternalError("no supported devices found for platform %s", - platform->Name().c_str()); + platform->Name()); } return stream_executors; } diff --git a/tensorflow/compiler/xla/service/scatter_expander.cc b/tensorflow/compiler/xla/service/scatter_expander.cc index 338f0c09e9..2077b57c05 100644 --- a/tensorflow/compiler/xla/service/scatter_expander.cc +++ b/tensorflow/compiler/xla/service/scatter_expander.cc @@ -291,7 +291,7 @@ StatusOr<HloInstruction*> ScatterExpander::ExpandScatter( return Unimplemented( "Scatter operations with more than 2147483647 scatter indices are not " "supported. This error occurred for %s.", - scatter->ToString().c_str()); + scatter->ToString()); } // Canonicalize the scatter_indices, after which the size of its most-major diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc index d39a5191b8..e10c1d9927 100644 --- a/tensorflow/compiler/xla/service/service.cc +++ b/tensorflow/compiler/xla/service/service.cc @@ -22,6 +22,7 @@ limitations under the License. #include "absl/memory/memory.h" #include "absl/strings/str_cat.h" +#include "absl/strings/str_format.h" #include "tensorflow/compiler/xla/execution_options_util.h" #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h" @@ -47,7 +48,6 @@ limitations under the License. #include "tensorflow/compiler/xla/util.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/lib/gtl/cleanup.h" -#include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/env.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/protobuf.h" @@ -55,13 +55,12 @@ limitations under the License. #include "tensorflow/core/platform/types.h" #include "tensorflow/core/util/ptr_util.h" -using absl::StrCat; -using ::tensorflow::strings::Printf; - namespace xla { - namespace { +using absl::StrCat; +using absl::StrFormat; + // Records the arguments used to invoke a computation in an HloSnapshot proto. Status RecordArguments( const tensorflow::gtl::ArraySlice<const ShapedBuffer*> arguments, @@ -148,19 +147,19 @@ Service::Service(const ServiceOptions& options, CHECK_GE(execute_backend_->device_count(), options_.number_of_replicas()) << "Requested more replicas than there are devices."; } - LOG(INFO) << Printf( + LOG(INFO) << StrFormat( "XLA service %p executing computations on platform %s. Devices:", this, - execute_backend_->platform()->Name().c_str()); + execute_backend_->platform()->Name()); for (int i = 0; i < execute_backend_->device_count(); ++i) { if (execute_backend_->device_ordinal_supported(i)) { se::StreamExecutor* executor = execute_backend_->stream_executor(i).ValueOrDie(); const auto& description = executor->GetDeviceDescription(); - LOG(INFO) << Printf(" StreamExecutor device (%d): %s, %s", i, - description.name().c_str(), - description.platform_version().c_str()); + LOG(INFO) << StrFormat(" StreamExecutor device (%d): %s, %s", i, + description.name(), + description.platform_version()); } else { - LOG(INFO) << Printf(" StreamExecutor device (%d) not supported", i); + LOG(INFO) << StrFormat(" StreamExecutor device (%d) not supported", i); } } } else { @@ -200,8 +199,8 @@ Status Service::ValidateResultShape(const Shape& client_shape, return InvalidArgument( "Shape used to set computation result layout %s is not compatible " "with result shape %s", - ShapeUtil::HumanStringWithLayout(client_shape).c_str(), - ShapeUtil::HumanString(result_shape).c_str()); + ShapeUtil::HumanStringWithLayout(client_shape), + ShapeUtil::HumanString(result_shape)); } return Status::OK(); } @@ -231,9 +230,9 @@ Service::ResolveAndValidateArguments( return InvalidArgument( "argument %lu is on device %s:%d but computation will be executed " "on device %s", - i, shaped_buffer->platform()->Name().c_str(), + i, shaped_buffer->platform()->Name(), shaped_buffer->device_ordinal(), - execute_backend_->device_name(replica_device_ordinal).c_str()); + execute_backend_->device_name(replica_device_ordinal)); } replicated_arguments[replica].push_back(shaped_buffer); } @@ -249,7 +248,7 @@ StatusOr<std::unique_ptr<HloModuleConfig>> Service::CreateModuleConfig( ComputationLayout* computation_layout = config->mutable_entry_computation_layout(); if (program_shape.parameters_size() != argument_shapes.size()) { - return InvalidArgument("computation takes %d parameters, but %zu given", + return InvalidArgument("computation takes %d parameters, but %u given", program_shape.parameters_size(), argument_shapes.size()); } @@ -261,8 +260,8 @@ StatusOr<std::unique_ptr<HloModuleConfig>> Service::CreateModuleConfig( return InvalidArgument( "Argument does not match shape of computation parameter %d: want " "%s, got %s", - i, ShapeUtil::HumanString(program_shape.parameters(i)).c_str(), - ShapeUtil::HumanString(*argument_shapes[i]).c_str()); + i, ShapeUtil::HumanString(program_shape.parameters(i)), + ShapeUtil::HumanString(*argument_shapes[i])); } TF_RETURN_IF_ERROR( computation_layout->mutable_parameter_layout(i)->CopyLayoutFromShape( @@ -314,7 +313,7 @@ StatusOr<std::vector<std::unique_ptr<Executable>>> Service::BuildExecutables( std::vector<std::unique_ptr<HloModuleConfig>> module_configs, Backend* backend, std::vector<std::vector<se::StreamExecutor*>> executors, DeviceMemoryAllocator* device_allocator) { - VLOG(1) << Printf("BuildExecutable on service %p", this); + VLOG(1) << StrFormat("BuildExecutable on service %p", this); // Dump computation proto state if flag is set. std::vector<std::unique_ptr<HloSnapshot>> hlo_snapshots; @@ -329,9 +328,8 @@ StatusOr<std::vector<std::unique_ptr<Executable>>> Service::BuildExecutables( auto hlo_snapshot = absl::make_unique<HloSnapshot>(); *hlo_snapshot->mutable_hlo()->mutable_hlo_module() = *module_protos[i]; if (!directory_path.empty()) { - string filename = - Printf("computation_%lld__%s", module_protos[i]->id(), - module_protos[i]->entry_computation_name().c_str()); + string filename = StrFormat("computation_%d__%s", module_protos[i]->id(), + module_protos[i]->entry_computation_name()); TF_RETURN_IF_ERROR( Executable::DumpToDirectory(directory_path, filename, *hlo_snapshot)); } @@ -454,8 +452,8 @@ Service::ExecuteParallelAndRegisterResult( for (int64 i = 0; i < streams.size(); ++i) { Status block_status = streams[i]->BlockHostUntilDone(); if (!block_status.ok()) { - return InternalError("failed to complete execution for stream %lld: %s", - i, block_status.error_message().c_str()); + return InternalError("failed to complete execution for stream %d: %s", i, + block_status.error_message()); } } @@ -580,7 +578,7 @@ StatusOr<std::vector<se::StreamExecutor*>> Service::GetExecutors( if (requests_size > 1 && execution_options.device_handles_size() > 1) { return InvalidArgument( "Parallel requests with multiple device handles is not supported. " - "Found %lld parallel requests, with request %lld containing %d device " + "Found %d parallel requests, with request %d containing %d device " "handles.", requests_size, request_index, execution_options.device_handles_size()); } @@ -745,8 +743,8 @@ Status Service::GetDeviceHandles(const GetDeviceHandlesRequest* arg, } if (available_device_count < arg->device_count() * replica_count) { return ResourceExhausted( - "Requested device count (%lld) exceeds the number of available devices " - "on the target (%lld)", + "Requested device count (%d) exceeds the number of available devices " + "on the target (%d)", arg->device_count(), available_device_count); } @@ -796,9 +794,9 @@ StatusOr<std::unique_ptr<Executable>> Service::BuildExecutable( const HloModuleProto& module_proto, std::unique_ptr<HloModuleConfig> module_config, Backend* backend, se::StreamExecutor* executor, DeviceMemoryAllocator* device_allocator) { - VLOG(1) << Printf( + VLOG(1) << StrFormat( "BuildExecutable on service %p with serialized module proto: %s", this, - module_proto.name().c_str()); + module_proto.name()); // Dump computation proto state if flag is set. auto hlo_snapshot = absl::make_unique<HloSnapshot>(); @@ -809,8 +807,8 @@ StatusOr<std::unique_ptr<Executable>> Service::BuildExecutable( if (!directory_path.empty() || !execution_directory_path.empty()) { *hlo_snapshot->mutable_hlo()->mutable_hlo_module() = module_proto; if (!directory_path.empty()) { - string filename = Printf("computation_%lld__%s", module_proto.id(), - module_proto.entry_computation_name().c_str()); + string filename = StrFormat("computation_%d__%s", module_proto.id(), + module_proto.entry_computation_name()); TF_RETURN_IF_ERROR( Executable::DumpToDirectory(directory_path, filename, *hlo_snapshot)); } @@ -1010,8 +1008,7 @@ Status Service::TransferToInfeed(const TransferToInfeedRequest* arg, "%s", StrCat("The replica_id=", arg->replica_id(), " on TransferToInfeedRequest not in range [0, replica_count=", - replica_count, ").") - .c_str()); + replica_count, ").")); } se::StreamExecutor* executor; @@ -1037,8 +1034,7 @@ Status Service::TransferFromOutfeed(const TransferFromOutfeedRequest* arg, const int64 replica_count = options_.number_of_replicas(); if (arg->replica_id() < 0 || arg->replica_id() >= replica_count) { return FailedPrecondition( - "The replica_id=%lld on TransferFromOutfeedRequest not in range [0, " - "%lld)", + "The replica_id=%d on TransferFromOutfeedRequest not in range [0, %d)", arg->replica_id(), replica_count); } diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc index 6a22f8bef4..ae6a366d25 100644 --- a/tensorflow/compiler/xla/service/shape_inference.cc +++ b/tensorflow/compiler/xla/service/shape_inference.cc @@ -23,6 +23,7 @@ limitations under the License. #include "absl/algorithm/container.h" #include "absl/strings/str_cat.h" +#include "absl/strings/str_format.h" #include "absl/strings/str_join.h" #include "absl/strings/string_view.h" #include "tensorflow/compiler/xla/shape_util.h" @@ -34,15 +35,14 @@ limitations under the License. #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/gtl/flatset.h" #include "tensorflow/core/lib/math/math_util.h" -#include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/protobuf.h" namespace xla { namespace { +using absl::StrFormat; using absl::StrJoin; -using tensorflow::strings::Printf; // Returns true if no element is present in slice more than once. bool AllUnique(tensorflow::gtl::ArraySlice<int64> slice) { @@ -52,8 +52,7 @@ bool AllUnique(tensorflow::gtl::ArraySlice<int64> slice) { Status ExpectArray(const Shape& shape, absl::string_view op_type) { if (!ShapeUtil::IsArray(shape)) { return InvalidArgument("Expected array argument for %s, but got %s.", - std::string(op_type).c_str(), - ShapeUtil::HumanString(shape).c_str()); + std::string(op_type), ShapeUtil::HumanString(shape)); } return Status::OK(); } @@ -65,7 +64,7 @@ Status VerifyReducerShape( int64 inputs) { if (reducer_shape.parameters_size() != inputs * 2) { return InvalidArgument( - "Reduction function must take %lld parameters, but " + "Reduction function must take %d parameters, but " "takes %d parameter(s).", inputs * 2, reducer_shape.parameters_size()); } @@ -75,7 +74,7 @@ Status VerifyReducerShape( if (ShapeUtil::IsArray(accumulator_shape)) { if (inputs != 1) { return InvalidArgument( - "Reduction function must produce a tuple with %lld elements, but " + "Reduction function must produce a tuple with %d elements, but " "produces a scalar", inputs); } @@ -83,8 +82,8 @@ Status VerifyReducerShape( } else if (ShapeUtil::IsTuple(accumulator_shape)) { if (ShapeUtil::TupleElementCount(accumulator_shape) != inputs) { return InvalidArgument( - "Reduction function must produce a tuple with %lld elements, but has " - "%lld elements", + "Reduction function must produce a tuple with %d elements, but has " + "%d elements", inputs, ShapeUtil::TupleElementCount(accumulator_shape)); } for (const Shape& element_shape : accumulator_shape.tuple_shapes()) { @@ -94,7 +93,7 @@ Status VerifyReducerShape( return InvalidArgument( "Reduction function must produce a scalar or tuple of scalars, but has " "shape: %s", - ShapeUtil::HumanString(accumulator_shape).c_str()); + ShapeUtil::HumanString(accumulator_shape)); } for (const Shape* element_shape : accumulator_subshapes) { @@ -102,7 +101,7 @@ Status VerifyReducerShape( return InvalidArgument( "Reduction function must return a scalar or tuple of scalars but " "returns shape: %s", - ShapeUtil::HumanString(accumulator_shape).c_str()); + ShapeUtil::HumanString(accumulator_shape)); } } @@ -113,19 +112,19 @@ Status VerifyReducerShape( if (!ShapeUtil::Compatible(*accumulator_subshapes[i], reducer_shape.parameters(i))) { return InvalidArgument( - "Reduction function's %lld-th parameter shape differs from the " + "Reduction function's %d-th parameter shape differs from the " "result shape: %s vs %s", - i, ShapeUtil::HumanString(reducer_shape.parameters(i)).c_str(), - ShapeUtil::HumanString(*accumulator_subshapes[i]).c_str()); + i, ShapeUtil::HumanString(reducer_shape.parameters(i)), + ShapeUtil::HumanString(*accumulator_subshapes[i])); } // Check that init_value's shapes are suitable for reducer_shape. if (!ShapeUtil::CompatibleIgnoringFpPrecision(*accumulator_subshapes[i], *init_value_shapes[i])) { return InvalidArgument( - "Reduction function's accumulator shape at index %lld differs from " + "Reduction function's accumulator shape at index %d differs from " "the init_value shape: %s vs %s", - i, ShapeUtil::HumanString(*accumulator_subshapes[i]).c_str(), - ShapeUtil::HumanString(*init_value_shapes[i]).c_str()); + i, ShapeUtil::HumanString(*accumulator_subshapes[i]), + ShapeUtil::HumanString(*init_value_shapes[i])); } // Check that the inputs can be passed in as the non-accumulator arguments. const Shape input_element_shape = @@ -133,11 +132,11 @@ Status VerifyReducerShape( if (!ShapeUtil::CompatibleIgnoringFpPrecision( input_element_shape, reducer_shape.parameters(inputs + i))) { return InvalidArgument( - "Reduction function's %lld-th parameter shape differs from the " + "Reduction function's %d-th parameter shape differs from the " "input type element type: %s vs %s", inputs + i, - ShapeUtil::HumanString(reducer_shape.parameters(inputs + i)).c_str(), - ShapeUtil::HumanString(input_element_shape).c_str()); + ShapeUtil::HumanString(reducer_shape.parameters(inputs + i)), + ShapeUtil::HumanString(input_element_shape)); } // Check that the accumulator and inputs to the reducer function match. // If the accumulator is scalar, it must have the same type as the inputs @@ -147,11 +146,11 @@ Status VerifyReducerShape( if (!ShapeUtil::CompatibleIgnoringFpPrecision( *accumulator_subshapes[i], reducer_shape.parameters(inputs + i))) { return InvalidArgument( - "Reduction function's %lld-th parameter shape must " + "Reduction function's %d-th parameter shape must " "match the result shape, but got %s vs %s.", inputs + i, - ShapeUtil::HumanString(reducer_shape.parameters(inputs + i)).c_str(), - ShapeUtil::HumanString(*accumulator_subshapes[i]).c_str()); + ShapeUtil::HumanString(reducer_shape.parameters(inputs + i)), + ShapeUtil::HumanString(*accumulator_subshapes[i])); } } @@ -164,7 +163,7 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape, bool allow_negative_padding) { if (window.dimensions_size() != ShapeUtil::Rank(base_shape)) { return InvalidArgument( - "Window has dimension %d but base shape has dimension %lld.", + "Window has dimension %d but base shape has dimension %d.", window.dimensions_size(), ShapeUtil::Rank(base_shape)); } @@ -173,29 +172,29 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape, const auto& dim = window.dimensions(i); if (dim.size() <= 0) { return InvalidArgument("Window %s has a non-positive dimension.", - window.DebugString().c_str()); + window.DebugString()); } if (dim.stride() <= 0) { return InvalidArgument("Window %s has a non-positive stride.", - window.DebugString().c_str()); + window.DebugString()); } if (!allow_negative_padding && dim.padding_low() < 0) { return InvalidArgument("Window %s has a negative low padding.", - window.DebugString().c_str()); + window.DebugString()); } if (!allow_negative_padding && dim.padding_high() < 0) { return InvalidArgument("Window %s has a negative high padding.", - window.DebugString().c_str()); + window.DebugString()); } if (dim.base_dilation() < 1) { return InvalidArgument( "Window %s has a non-positive base area dilation factor.", - window.DebugString().c_str()); + window.DebugString()); } if (dim.window_dilation() < 1) { return InvalidArgument( "Window %s has a non-positive window dilation factor.", - window.DebugString().c_str()); + window.DebugString()); } const int64 dilated_base = window_util::DilatedBound( @@ -238,8 +237,7 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape, return InvalidArgument( "Expected element type in shape to be floating for %s operation; " "got %s.", - HloOpcodeString(opcode).c_str(), - PrimitiveType_Name(shape.element_type()).c_str()); + HloOpcodeString(opcode), PrimitiveType_Name(shape.element_type())); } return shape; case HloOpcode::kCos: @@ -254,8 +252,7 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape, return InvalidArgument( "Expected element type in shape to be floating or complex for %s " "operation; got %s.", - HloOpcodeString(opcode).c_str(), - PrimitiveType_Name(shape.element_type()).c_str()); + HloOpcodeString(opcode), PrimitiveType_Name(shape.element_type())); } return shape; case HloOpcode::kReal: @@ -268,8 +265,7 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape, return InvalidArgument( "Expected element type in shape to be floating or complex for " "%s operation; got %s.", - HloOpcodeString(opcode).c_str(), - PrimitiveType_Name(shape.element_type()).c_str()); + HloOpcodeString(opcode), PrimitiveType_Name(shape.element_type())); } case HloOpcode::kAbs: if (ShapeUtil::ElementIsComplex(shape)) { @@ -281,15 +277,14 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape, return InvalidArgument( "Expected element type in shape to be floating or complex for " "%s operation; got %s.", - HloOpcodeString(opcode).c_str(), - PrimitiveType_Name(shape.element_type()).c_str()); + HloOpcodeString(opcode), PrimitiveType_Name(shape.element_type())); } case HloOpcode::kClz: if (!ShapeUtil::ElementIsIntegral(shape)) { return InvalidArgument( "Expected an integral element type in argument to Clz " "operation; got %s.", - PrimitiveType_Name(shape.element_type()).c_str()); + PrimitiveType_Name(shape.element_type())); } return shape; case HloOpcode::kNegate: @@ -299,8 +294,7 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape, return InvalidArgument( "Expected element type in shape to be integral, floating or " "complex for %s operation; got %s.", - HloOpcodeString(opcode).c_str(), - PrimitiveType_Name(shape.element_type()).c_str()); + HloOpcodeString(opcode), PrimitiveType_Name(shape.element_type())); } return shape; case HloOpcode::kSign: @@ -309,8 +303,7 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape, return InvalidArgument( "Expected element type in shape to be signed or complex for " "%s operation; got %s.", - HloOpcodeString(opcode).c_str(), - PrimitiveType_Name(shape.element_type()).c_str()); + HloOpcodeString(opcode), PrimitiveType_Name(shape.element_type())); } return shape; @@ -320,7 +313,7 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape, return InvalidArgument( "Expected pred or an integral element type in argument to Not " "operation; got %s.", - PrimitiveType_Name(shape.element_type()).c_str()); + PrimitiveType_Name(shape.element_type())); } return shape; @@ -330,14 +323,14 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape, "Expected element type in shape to be floating " "point for IsFinite " "operation; got %s.", - PrimitiveType_Name(shape.element_type()).c_str()); + PrimitiveType_Name(shape.element_type())); } return ShapeUtil::ChangeElementType(shape, PRED); default: return InvalidArgument( "Unknown operation for unary shape inference: \"%s\".", - HloOpcodeString(opcode).c_str()); + HloOpcodeString(opcode)); } } @@ -348,7 +341,7 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape, return InvalidArgument("Concatenate expects at least one argument."); } if (dimension < 0 || dimension >= ShapeUtil::Rank(*arg_shapes[0])) { - return InvalidArgument("Concatenate dimension out of bounds: %lld.", + return InvalidArgument("Concatenate dimension out of bounds: %d.", dimension); } const Shape* arg_shape = nullptr; @@ -362,17 +355,16 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape, } if (ShapeUtil::Rank(*arg_shape) != ShapeUtil::Rank(*shape)) { return InvalidArgument( - "Cannot concatenate arrays with different ranks: %lld (%s) vs %lld " + "Cannot concatenate arrays with different ranks: %d (%s) vs %d " "(%s).", - ShapeUtil::Rank(*arg_shape), - ShapeUtil::HumanString(*arg_shape).c_str(), ShapeUtil::Rank(*shape), - ShapeUtil::HumanString(*shape).c_str()); + ShapeUtil::Rank(*arg_shape), ShapeUtil::HumanString(*arg_shape), + ShapeUtil::Rank(*shape), ShapeUtil::HumanString(*shape)); } if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(*arg_shape, *shape)) { return InvalidArgument( "Cannot concatenate arrays with different element types: %s vs %s.", - PrimitiveType_Name(arg_shape->element_type()).c_str(), - PrimitiveType_Name(shape->element_type()).c_str()); + PrimitiveType_Name(arg_shape->element_type()), + PrimitiveType_Name(shape->element_type())); } for (int64 dimension_number = 0; dimension_number < ShapeUtil::Rank(*arg_shape); ++dimension_number) { @@ -385,9 +377,9 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape, return InvalidArgument( "Cannot concatenate arrays that differ in dimensions other than " "the one being concatenated (the other array dimensions must be " - "the same): %s vs %s in dimension %lld.", - ShapeUtil::HumanString(*arg_shape).c_str(), - ShapeUtil::HumanString(*shape).c_str(), dimension); + "the same): %s vs %s in dimension %d.", + ShapeUtil::HumanString(*arg_shape), ShapeUtil::HumanString(*shape), + dimension); } } element_type = ShapeUtil::HigherPrecisionElementType(*shape, *arg_shape); @@ -419,8 +411,8 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape, !primitive_util::IsComplexType(new_element_type)) { return Unimplemented( "Conversion from complex to real type %s => %s is not implemented.", - ShapeUtil::HumanString(operand_shape).c_str(), - PrimitiveType_Name(new_element_type).c_str()); + ShapeUtil::HumanString(operand_shape), + PrimitiveType_Name(new_element_type)); } if (!ShapeUtil::IsArray(operand_shape) || !primitive_util::IsArrayType(new_element_type)) { @@ -429,8 +421,8 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape, // are valid. For now we just reject them, though. return InvalidArgument( "Convert does not allow non-arrays, so cannot convert from %s to %s.", - ShapeUtil::HumanString(operand_shape).c_str(), - PrimitiveType_Name(new_element_type).c_str()); + ShapeUtil::HumanString(operand_shape), + PrimitiveType_Name(new_element_type)); } return ShapeUtil::ChangeElementType(operand_shape, new_element_type); @@ -442,8 +434,8 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape, if (primitive_util::IsComplexType(old_element_type) != primitive_util::IsComplexType(new_element_type)) { return InvalidArgument("Conversion from complex to real type %s => %s.", - ShapeUtil::HumanString(operand_shape).c_str(), - PrimitiveType_Name(new_element_type).c_str()); + ShapeUtil::HumanString(operand_shape), + PrimitiveType_Name(new_element_type)); } if (!ShapeUtil::IsArray(operand_shape) || !primitive_util::IsArrayType(new_element_type)) { @@ -452,15 +444,15 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape, // are valid. For now we just reject them, though. return InvalidArgument( "Cannot convert from or to tuple type; requested conversion: %s => %s.", - ShapeUtil::HumanString(operand_shape).c_str(), - PrimitiveType_Name(new_element_type).c_str()); + ShapeUtil::HumanString(operand_shape), + PrimitiveType_Name(new_element_type)); } if (primitive_util::BitWidth(old_element_type) != primitive_util::BitWidth(new_element_type)) { return InvalidArgument( "Cannot bitcast types with different bit-widths: %s => %s.", - PrimitiveType_Name(old_element_type).c_str(), - PrimitiveType_Name(new_element_type).c_str()); + PrimitiveType_Name(old_element_type), + PrimitiveType_Name(new_element_type)); } return ShapeUtil::ChangeElementType(operand_shape, new_element_type); @@ -473,7 +465,7 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape, return InvalidArgument( "Expected element type in shape to be floating point for " "ReducePrecision operation; got %s.", - PrimitiveType_Name(operand_shape.element_type()).c_str()); + PrimitiveType_Name(operand_shape.element_type())); } if (exponent_bits < 1) { // One exponent bit is necessary to distinguish 0 from infinity. Having @@ -505,8 +497,8 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape, return InvalidArgument( "The rank of the operand and the padding configuration do not match: " "%s vs %s.", - ShapeUtil::HumanString(operand_shape).c_str(), - padding_config.ShortDebugString().c_str()); + ShapeUtil::HumanString(operand_shape), + padding_config.ShortDebugString()); } if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(operand_shape, padding_value_shape)) { @@ -573,7 +565,7 @@ Status ValidateDotDimensionNumbers( !dims_in_range(ShapeUtil::Rank(rhs), rhs_contracting_dimensions, rhs_batch_dimensions)) { return InvalidArgument("A dimension number is out of range in Dot: %s.", - dimension_numbers.DebugString().c_str()); + dimension_numbers.DebugString()); } // Check that dimension numbers are unique. @@ -591,7 +583,7 @@ Status ValidateDotDimensionNumbers( if (!dims_unique(lhs_contracting_dimensions, lhs_batch_dimensions) || !dims_unique(rhs_contracting_dimensions, rhs_batch_dimensions)) { return InvalidArgument("A dimension number is not unique in Dot: %s.", - dimension_numbers.DebugString().c_str()); + dimension_numbers.DebugString()); } // Check that the count of non-contracting-non-batch dimensions is in {0, 1}. @@ -636,14 +628,13 @@ Status ValidateDotDimensionNumbers( TF_RETURN_IF_ERROR(ExpectArray(rhs, "rhs of dot")); auto fail = [lhs, rhs](const string& addendum) -> Status { - string message = tensorflow::strings::Printf( - "Cannot infer shape for dot operation: %s <dot> %s.", - ShapeUtil::HumanString(lhs).c_str(), - ShapeUtil::HumanString(rhs).c_str()); + string message = + StrFormat("Cannot infer shape for dot operation: %s <dot> %s.", + ShapeUtil::HumanString(lhs), ShapeUtil::HumanString(rhs)); if (!addendum.empty()) { message += " " + addendum; } - return InvalidArgument("%s", message.c_str()); + return InvalidArgument("%s", message); }; // Check if both element types are the same. @@ -739,9 +730,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, } else { return InvalidArgument( "Binary op %s with incompatible shapes: %s and %s.", - HloOpcodeString(operation).c_str(), - ShapeUtil::HumanString(lhs).c_str(), - ShapeUtil::HumanString(rhs).c_str()); + HloOpcodeString(operation), ShapeUtil::HumanString(lhs), + ShapeUtil::HumanString(rhs)); } } return ShapeUtil::MakeShape(ShapeUtil::HigherPrecisionElementType(lhs, rhs), @@ -756,14 +746,14 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, // the user to provide an explicit broadcast dimension in this case. // See b/25177275 for more details. return InvalidArgument("Automatic shape inference not supported: %s and %s", - ShapeUtil::HumanString(smaller_shape).c_str(), - ShapeUtil::HumanString(larger_shape).c_str()); + ShapeUtil::HumanString(smaller_shape), + ShapeUtil::HumanString(larger_shape)); } else if (broadcast_dimensions.size() != ShapeUtil::Rank(smaller_shape)) { return InvalidArgument( "Size of broadcast_dimensions has to match lower-rank operand's " "rank; " - " lower-rank operand's rank is %lld, size of broadcast_dimensions is " - "%zu.", + " lower-rank operand's rank is %d, size of broadcast_dimensions is " + "%u.", ShapeUtil::Rank(smaller_shape), broadcast_dimensions.size()); } @@ -813,12 +803,12 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, int64 dimension_to_match = broadcast_dimensions.at(i); if (dimension_to_match < 0) { return InvalidArgument( - "Broadcast dimension number (%lld) cannot be negative.", + "Broadcast dimension number (%d) cannot be negative.", dimension_to_match); } if (dimension_to_match >= larger_shape.dimensions_size()) { return InvalidArgument( - "Broadcast dimension number (%lld) too large; higher-rank " + "Broadcast dimension number (%d) too large; higher-rank " "operand has rank %d.", dimension_to_match, larger_shape.dimensions_size()); } @@ -830,16 +820,16 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, if (small_dimension_size != large_dimension_size && small_dimension_size != 1 && large_dimension_size != 1) { return InvalidArgument( - "Broadcast dimension %d mismatch: %lld != %lld; %s and %s.", i, + "Broadcast dimension %d mismatch: %d != %d; %s and %s.", i, small_dimension_size, large_dimension_size, - ShapeUtil::HumanString(smaller_shape).c_str(), - ShapeUtil::HumanString(larger_shape).c_str()); + ShapeUtil::HumanString(smaller_shape), + ShapeUtil::HumanString(larger_shape)); } // Make sure the broadcast dimensions are listed in a strictly increasing // order. if (i > 0 && broadcast_dimensions.at(i - 1) >= dimension_to_match) { return InvalidArgument( - "Broadcast dimensions order is wrong: %lld comes after %lld.", + "Broadcast dimensions order is wrong: %d comes after %d.", dimension_to_match, broadcast_dimensions.at(i - 1)); } @@ -858,8 +848,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(lhs, rhs)) { return InvalidArgument( "Binary op %s with different element types: %s and %s.", - HloOpcodeString(operation).c_str(), ShapeUtil::HumanString(lhs).c_str(), - ShapeUtil::HumanString(rhs).c_str()); + HloOpcodeString(operation), ShapeUtil::HumanString(lhs), + ShapeUtil::HumanString(rhs)); } if (ShapeUtil::Rank(lhs) == ShapeUtil::Rank(rhs)) { @@ -909,11 +899,10 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, /* static */ StatusOr<Shape> ShapeInference::InferBinaryOpShape( HloOpcode opcode, const Shape& lhs, const Shape& rhs, tensorflow::gtl::ArraySlice<int64> broadcast_dimensions) { - VLOG(2) << tensorflow::strings::Printf( + VLOG(2) << StrFormat( "inferring shape for <%s>(%s, %s) with broadcast_dimensions={%s}", - HloOpcodeString(opcode).c_str(), ShapeUtil::HumanString(lhs).c_str(), - ShapeUtil::HumanString(rhs).c_str(), - StrJoin(broadcast_dimensions, ", ").c_str()); + HloOpcodeString(opcode), ShapeUtil::HumanString(lhs), + ShapeUtil::HumanString(rhs), StrJoin(broadcast_dimensions, ", ")); TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(lhs)); TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(rhs)); @@ -942,7 +931,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, return InvalidArgument( "Expected element type in shape to be floating for complex compose " "operation; got %s.", - PrimitiveType_Name(lhs.element_type()).c_str()); + PrimitiveType_Name(lhs.element_type())); } TF_ASSIGN_OR_RETURN(const Shape& shape, InferElementwiseBinaryOpShape(opcode, lhs, rhs, @@ -961,7 +950,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, return InvalidArgument( "Expected pred or integral type in argument to and/or operation; " "got %s.", - PrimitiveType_Name(lhs.element_type()).c_str()); + PrimitiveType_Name(lhs.element_type())); } return InferElementwiseBinaryOpShape(opcode, lhs, rhs, broadcast_dimensions); @@ -979,8 +968,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, default: return Unimplemented( "Binary op shape inference: %s; lhs: %s; rhs: %s is not implemented.", - HloOpcodeString(opcode).c_str(), lhs.ShortDebugString().c_str(), - rhs.ShortDebugString().c_str()); + HloOpcodeString(opcode), lhs.ShortDebugString(), + rhs.ShortDebugString()); } } @@ -1003,8 +992,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, case HloOpcode::kTupleSelect: return InferTupleSelectShape(lhs, rhs, ehs); default: - return InvalidArgument("Unknown operation %s.", - HloOpcodeString(opcode).c_str()); + return InvalidArgument("Unknown operation %s.", HloOpcodeString(opcode)); } } @@ -1043,8 +1031,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, return InvalidArgument( "Sort keys and values dimensions must match. " "Keys shape is: %s\n, Values shape is: %s", - ShapeUtil::HumanString(*operand_shapes[0]).c_str(), - ShapeUtil::HumanString(*operand_shapes[1]).c_str()); + ShapeUtil::HumanString(*operand_shapes[0]), + ShapeUtil::HumanString(*operand_shapes[1])); } return ShapeUtil::MakeTupleShape( {*operand_shapes[0], *operand_shapes[1]}); @@ -1052,8 +1040,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, return InvalidArgument("Unexpected number of operands for sort"); } default: - return InvalidArgument("Unknown operation %s.", - HloOpcodeString(opcode).c_str()); + return InvalidArgument("Unknown operation %s.", HloOpcodeString(opcode)); } } @@ -1091,7 +1078,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, return InvalidArgument( "Map operation requires all operands to have the same shape; got: " "%s.", - StrJoin(pieces, ", ").c_str()); + StrJoin(pieces, ", ")); } // Check that dimensions.size == arg_shape.dimensions_size() (we currently @@ -1099,7 +1086,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, if (dimensions.size() != arg_shape->dimensions_size()) { return InvalidArgument( "Map applied to a subset of dimensions currently not supported: " - "arg_dimension_size: %d, requested_map_dimensions_size: %zu.", + "arg_dimension_size: %d, requested_map_dimensions_size: %u.", arg_shape->dimensions_size(), dimensions.size()); } @@ -1108,7 +1095,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, if (dimensions[i] != i) { return InvalidArgument( "Map requires monotonically increasing dimension numbers; got: %s.", - StrJoin(dimensions, ", ").c_str()); + StrJoin(dimensions, ", ")); } } @@ -1116,7 +1103,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, if (arg_shapes.size() != to_apply.parameters_size()) { return InvalidArgument( "Map applied function arity must match number of arguments; got: " - "arity: %d, arguments: %zu.", + "arity: %d, arguments: %u.", to_apply.parameters_size(), arg_shapes.size()); } @@ -1125,7 +1112,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, if (!ShapeUtil::IsScalar(output_shape)) { return InvalidArgument( "Mapped computation's result has to be a scalar; got: %s.", - ShapeUtil::HumanString(output_shape).c_str()); + ShapeUtil::HumanString(output_shape)); } for (int i = 0; i < to_apply.parameters_size(); ++i) { @@ -1135,7 +1122,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, return InvalidArgument( "Mapped computation's parameter has to be a scalar; " "got parameter %d shape: %s.", - i, ShapeUtil::HumanString(parameter_shape).c_str()); + i, ShapeUtil::HumanString(parameter_shape)); } if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(parameter_shape, @@ -1143,8 +1130,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, return InvalidArgument( "Mapped computation's parameter type has to match argument element " "type; got parameter %d shape: %s, argument shape: %s.", - i, ShapeUtil::HumanString(parameter_shape).c_str(), - ShapeUtil::HumanString(*arg_shape).c_str()); + i, ShapeUtil::HumanString(parameter_shape), + ShapeUtil::HumanString(*arg_shape)); } } @@ -1173,35 +1160,35 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, return InvalidArgument( "Expected feature_index of batch-norm-training to be " "smaller than the rank of operand_shape; " - "got feature_index %lld, and rank %lld.", + "got feature_index %d, and rank %d.", feature_index, ShapeUtil::Rank(operand_shape)); } if (feature_index < 0) { return InvalidArgument( "Expected feature_index of batch-norm-training to " - "be a non-negative number, got %lld.", + "be a non-negative number, got %d.", feature_index); } if (ShapeUtil::Rank(operand_shape) < 1) { return InvalidArgument( "Expected the rank of operand to " - "batch-norm-training to be at least 1; got %lld.", + "batch-norm-training to be at least 1; got %d.", ShapeUtil::Rank(operand_shape)); } if (ShapeUtil::Rank(offset_shape) != 1) { return InvalidArgument( "Offset input of batch-norm-training must have" - " rank 1, but has rank %lld.", + " rank 1, but has rank %d.", ShapeUtil::Rank(offset_shape)); } if (ShapeUtil::Rank(scale_shape) != 1) { return InvalidArgument( "Scale input of batch-norm-training must have" - " rank 1, but has rank %lld.", + " rank 1, but has rank %d.", ShapeUtil::Rank(scale_shape)); } @@ -1209,7 +1196,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, return InvalidArgument( "The operand to batch-norm-training must have a floating point " "element type, but the shape is %s.", - PrimitiveType_Name(operand_shape.element_type()).c_str()); + PrimitiveType_Name(operand_shape.element_type())); } if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(offset_shape, @@ -1218,8 +1205,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, "The inputs should have the same element type for batch-norm-training, " "but the shape of offset factor is %s " "and the shape of operand is %s.", - PrimitiveType_Name(offset_shape.element_type()).c_str(), - PrimitiveType_Name(operand_shape.element_type()).c_str()); + PrimitiveType_Name(offset_shape.element_type()), + PrimitiveType_Name(operand_shape.element_type())); } if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(scale_shape, @@ -1228,8 +1215,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, "The inputs should have the same element type for batch-norm-training, " "but the shape of scale factor is %s " "and the shape of operand is %s.", - PrimitiveType_Name(scale_shape.element_type()).c_str(), - PrimitiveType_Name(operand_shape.element_type()).c_str()); + PrimitiveType_Name(scale_shape.element_type()), + PrimitiveType_Name(operand_shape.element_type())); } const int64 feature_count = operand_shape.dimensions(feature_index); @@ -1239,16 +1226,16 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, if (ShapeUtil::GetDimension(offset_shape, 0) != feature_count) { return InvalidArgument( "The size of offset factor should be the same as feature count," - "but the size of offset factor is %lld " - "and the feature count is %lld.", + "but the size of offset factor is %d " + "and the feature count is %d.", ShapeUtil::GetDimension(offset_shape, 0), feature_count); } if (ShapeUtil::GetDimension(scale_shape, 0) != feature_count) { return InvalidArgument( "The size of scale factor should be the same as feature count," - "but the size of scale factor is %lld " - "and the feature count is %lld.", + "but the size of scale factor is %d " + "and the feature count is %d.", ShapeUtil::GetDimension(scale_shape, 0), feature_count); } @@ -1283,35 +1270,35 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, return InvalidArgument( "Expected feature_index of batch-norm-inference to be " "smaller than the rank of operand_shape; " - "got feature_index %lld, and rank %lld.", + "got feature_index %d, and rank %d.", feature_index, ShapeUtil::Rank(operand_shape)); } if (feature_index < 0) { return InvalidArgument( "Expected feature_index of batch-norm-inference to " - "be a non-negative number, got %lld.", + "be a non-negative number, got %d.", feature_index); } if (ShapeUtil::Rank(operand_shape) < 1) { return InvalidArgument( "Expected the rank of operand to " - "batch-norm-inference to be at least 1; got %lld.", + "batch-norm-inference to be at least 1; got %d.", ShapeUtil::Rank(operand_shape)); } if (ShapeUtil::Rank(offset_shape) != 1) { return InvalidArgument( "Offset input of batch-norm-inference must have" - " rank 1, but has rank %lld.", + " rank 1, but has rank %d.", ShapeUtil::Rank(offset_shape)); } if (ShapeUtil::Rank(scale_shape) != 1) { return InvalidArgument( "Scale input of batch-norm-inference must have" - " rank 1, but has rank %lld.", + " rank 1, but has rank %d.", ShapeUtil::Rank(scale_shape)); } @@ -1319,7 +1306,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, return InvalidArgument( "The operand to batch-norm-inference must have a floating point " "element type, but the shape is %s.", - PrimitiveType_Name(operand_shape.element_type()).c_str()); + PrimitiveType_Name(operand_shape.element_type())); } if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(offset_shape, @@ -1329,8 +1316,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, "batch-norm-inference, " "but the shape of offset factor is %s " "and the shape of operand is %s.", - PrimitiveType_Name(offset_shape.element_type()).c_str(), - PrimitiveType_Name(operand_shape.element_type()).c_str()); + PrimitiveType_Name(offset_shape.element_type()), + PrimitiveType_Name(operand_shape.element_type())); } if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(scale_shape, @@ -1340,8 +1327,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, "batch-norm-inference, " "but the shape of scale factor is %s " "and the shape of operand is %s.", - PrimitiveType_Name(scale_shape.element_type()).c_str(), - PrimitiveType_Name(operand_shape.element_type()).c_str()); + PrimitiveType_Name(scale_shape.element_type()), + PrimitiveType_Name(operand_shape.element_type())); } if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(mean_shape, @@ -1351,8 +1338,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, "batch-norm-inference, " "but the shape of mean is %s " "and the shape of operand is %s.", - PrimitiveType_Name(mean_shape.element_type()).c_str(), - PrimitiveType_Name(operand_shape.element_type()).c_str()); + PrimitiveType_Name(mean_shape.element_type()), + PrimitiveType_Name(operand_shape.element_type())); } if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(variance_shape, @@ -1362,8 +1349,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, "batch-norm-inference, " "but the shape of variance is %s " "and the shape of operand is %s.", - PrimitiveType_Name(mean_shape.element_type()).c_str(), - PrimitiveType_Name(variance_shape.element_type()).c_str()); + PrimitiveType_Name(mean_shape.element_type()), + PrimitiveType_Name(variance_shape.element_type())); } const int64 feature_count = operand_shape.dimensions(feature_index); @@ -1373,32 +1360,32 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, if (ShapeUtil::GetDimension(offset_shape, 0) != feature_count) { return InvalidArgument( "The size of offset factor should be the same as feature count," - "but the size of offset factor is %lld " - "and the feature count is %lld.", + "but the size of offset factor is %d " + "and the feature count is %d.", ShapeUtil::GetDimension(offset_shape, 0), feature_count); } if (ShapeUtil::GetDimension(scale_shape, 0) != feature_count) { return InvalidArgument( "The size of scale factor should be the same as feature count," - "but the size of scale factor is %lld " - "and the feature count is %lld.", + "but the size of scale factor is %d " + "and the feature count is %d.", ShapeUtil::GetDimension(scale_shape, 0), feature_count); } if (ShapeUtil::GetDimension(mean_shape, 0) != feature_count) { return InvalidArgument( "The size of mean should be the same as feature count," - "but the size of mean is %lld " - "and the feature count is %lld.", + "but the size of mean is %d " + "and the feature count is %d.", ShapeUtil::GetDimension(mean_shape, 0), feature_count); } if (ShapeUtil::GetDimension(variance_shape, 0) != feature_count) { return InvalidArgument( "The size of variance should be the same as feature count," - "but the size of variance is %lld " - "and the feature count is %lld.", + "but the size of variance is %d " + "and the feature count is %d.", ShapeUtil::GetDimension(variance_shape, 0), feature_count); } @@ -1428,36 +1415,36 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, return InvalidArgument( "Expected feature_index of batch-norm-grad to be " "smaller than the rank of operand_shape; " - "got feature_index %lld, and rank %lld.", + "got feature_index %d, and rank %d.", feature_index, ShapeUtil::Rank(operand_shape)); } if (ShapeUtil::Rank(operand_shape) != ShapeUtil::Rank(output_grad_shape)) { return InvalidArgument( "Expected operand_shape of batch-norm-grad to have the same rank as" - " output_grad_shape; got rank(oprand_shape) %lld, and" - " rank(output_grad_shape) %lld.", + " output_grad_shape; got rank(oprand_shape) %d, and" + " rank(output_grad_shape) %d.", ShapeUtil::Rank(operand_shape), ShapeUtil::Rank(output_grad_shape)); } if (ShapeUtil::Rank(mean_shape) != 1) { return InvalidArgument( "Mean input of batch-norm-grad must have" - " rank 1, but has rank %lld.", + " rank 1, but has rank %d.", ShapeUtil::Rank(mean_shape)); } if (ShapeUtil::Rank(scale_shape) != 1) { return InvalidArgument( "Scale input of batch-norm-grad must have" - " rank 1, but has rank %lld.", + " rank 1, but has rank %d.", ShapeUtil::Rank(scale_shape)); } if (ShapeUtil::Rank(var_shape) != 1) { return InvalidArgument( "Var input of batch-norm-grad must have" - " rank 1, but has rank %lld.", + " rank 1, but has rank %d.", ShapeUtil::Rank(var_shape)); } @@ -1465,14 +1452,14 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, return InvalidArgument( "The operand to batch-norm-grad must have a floating point " "element type, but the shape is %s.", - PrimitiveType_Name(operand_shape.element_type()).c_str()); + PrimitiveType_Name(operand_shape.element_type())); } if (!ShapeUtil::ElementIsFloating(output_grad_shape)) { return InvalidArgument( "The output_grad to batch-norm-grad must have a floating point " "element type, but the shape is %s.", - PrimitiveType_Name(output_grad_shape.element_type()).c_str()); + PrimitiveType_Name(output_grad_shape.element_type())); } if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(output_grad_shape, @@ -1481,8 +1468,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, "The inputs should have the same element type for batch-norm-grad, " "but the element type of output_grad is %s " "and the element type of operand is %s.", - PrimitiveType_Name(output_grad_shape.element_type()).c_str(), - PrimitiveType_Name(operand_shape.element_type()).c_str()); + PrimitiveType_Name(output_grad_shape.element_type()), + PrimitiveType_Name(operand_shape.element_type())); } if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(scale_shape, @@ -1491,8 +1478,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, "The inputs should have the same element type for batch-norm-grad, " "but the element type of scale factor is %s " "and the element type of operand is %s.", - PrimitiveType_Name(scale_shape.element_type()).c_str(), - PrimitiveType_Name(operand_shape.element_type()).c_str()); + PrimitiveType_Name(scale_shape.element_type()), + PrimitiveType_Name(operand_shape.element_type())); } if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(mean_shape, @@ -1501,8 +1488,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, "The inputs should have the same element type for batch-norm-grad, " "but the element type of mean is %s " "and the element type of operand is %s.", - PrimitiveType_Name(mean_shape.element_type()).c_str(), - PrimitiveType_Name(operand_shape.element_type()).c_str()); + PrimitiveType_Name(mean_shape.element_type()), + PrimitiveType_Name(operand_shape.element_type())); } if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(var_shape, @@ -1511,8 +1498,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, "The inputs should have the same element type for batch-norm-grad, " "but the element type of mean is %s " "and the element type of operand is %s.", - PrimitiveType_Name(mean_shape.element_type()).c_str(), - PrimitiveType_Name(operand_shape.element_type()).c_str()); + PrimitiveType_Name(mean_shape.element_type()), + PrimitiveType_Name(operand_shape.element_type())); } const int64 feature_count = operand_shape.dimensions(feature_index); @@ -1523,24 +1510,24 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, if (ShapeUtil::GetDimension(mean_shape, 0) != feature_count) { return InvalidArgument( "The size of mean should be the same as feature count," - "but the size of offset factor is %lld " - "and the feature count is %lld.", + "but the size of offset factor is %d " + "and the feature count is %d.", ShapeUtil::GetDimension(mean_shape, 0), feature_count); } if (ShapeUtil::GetDimension(scale_shape, 0) != feature_count) { return InvalidArgument( "The size of scale factor should be the same as feature count," - "but the size of scale factor is %lld " - "and the feature count is %lld.", + "but the size of scale factor is %d " + "and the feature count is %d.", ShapeUtil::GetDimension(scale_shape, 0), feature_count); } if (ShapeUtil::GetDimension(var_shape, 0) != feature_count) { return InvalidArgument( "The size of variance should be the same as feature count," - "but the size of variance is %lld " - "and the feature count is %lld.", + "but the size of variance is %d " + "and the feature count is %d.", ShapeUtil::GetDimension(var_shape, 0), feature_count); } @@ -1550,8 +1537,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, ShapeUtil::GetDimension(output_grad_shape, i)) { return InvalidArgument( "The bounds of operand shape should be the same as output_grad's," - "but the bound of operand_shape at dimension %lld is %lld " - "and the bound of output_grad_shape is %lld.", + "but the bound of operand_shape at dimension %d is %d " + "and the bound of output_grad_shape is %d.", i, ShapeUtil::GetDimension(operand_shape, i), ShapeUtil::GetDimension(output_grad_shape, i)); } @@ -1570,15 +1557,14 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(lhs, rhs)) { return InvalidArgument( "Convolution with different element types: %s and %s.", - ShapeUtil::HumanString(lhs).c_str(), - ShapeUtil::HumanString(rhs).c_str()); + ShapeUtil::HumanString(lhs), ShapeUtil::HumanString(rhs)); } if (dnums.input_spatial_dimensions_size() != dnums.kernel_spatial_dimensions_size()) { return InvalidArgument( "Both arguments to convolution must have same number of dimensions.\n" "Window: %s", - window.DebugString().c_str()); + window.DebugString()); } const int num_spatial_dims = dnums.input_spatial_dimensions_size(); @@ -1586,19 +1572,19 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, return InvalidArgument( "Window must have same number of dimensions as dimension numbers.\n" "Window: %s\nDimension numbers: %s.", - window.DebugString().c_str(), dnums.DebugString().c_str()); + window.DebugString(), dnums.DebugString()); } const int num_dims = num_spatial_dims + 2; if (ShapeUtil::Rank(lhs) != num_dims) { return InvalidArgument( "The LHS argument to a convolution should have rank %d; lhs: %s.", - num_dims, ShapeUtil::HumanString(lhs).c_str()); + num_dims, ShapeUtil::HumanString(lhs)); } if (ShapeUtil::Rank(rhs) != num_dims) { return InvalidArgument( "The RHS argument to a convolution should have rank %d; lhs: %s.", - num_dims, ShapeUtil::HumanString(lhs).c_str()); + num_dims, ShapeUtil::HumanString(lhs)); } TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(lhs)); TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(rhs)); @@ -1635,26 +1621,26 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, !std::all_of(output_dnums.begin(), output_dnums.end(), in_range)) { return InvalidArgument( "A dimension number is out of range in convolution: %s.", - dnums.DebugString().c_str()); + dnums.DebugString()); } if (input_dnums != expected_dnums) { return InvalidArgument( "Input dimensions of convolution must contain each dimension exactly " "once: %s.", - dnums.DebugString().c_str()); + dnums.DebugString()); } if (window_dnums != expected_dnums) { return InvalidArgument( "Window dimensions of convolution must contain each dimension exactly " "once: %s.", - dnums.DebugString().c_str()); + dnums.DebugString()); } if (output_dnums != expected_dnums) { return InvalidArgument( "Output dimensions of convolution must contain each dimension exactly " "once: %s.", - dnums.DebugString().c_str()); + dnums.DebugString()); } std::vector<int64> input_spatial_dims(num_spatial_dims); @@ -1675,13 +1661,13 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, if (input_features != kernel_input_features * feature_group_count) { return InvalidArgument( - "Expected LHS feature dimension (value %lld) to match RHS " - "input feature dimension * feature_group_count (value %lld); " + "Expected LHS feature dimension (value %d) to match RHS " + "input feature dimension * feature_group_count (value %d); " "got <conv>(%s, %s)\n" "Dimension numbers: {%s}.", input_features, kernel_input_features * feature_group_count, - ShapeUtil::HumanString(lhs).c_str(), - ShapeUtil::HumanString(rhs).c_str(), dnums.DebugString().c_str()); + ShapeUtil::HumanString(lhs), ShapeUtil::HumanString(rhs), + dnums.DebugString()); } std::vector<int64> window_dims(num_spatial_dims); for (int i = 0; i < num_spatial_dims; ++i) { @@ -1693,8 +1679,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, "RHS shape: %s\n\t" "Window: {%s}\n\t" "Dimension numbers: {%s}.", - ShapeUtil::HumanString(rhs).c_str(), window.ShortDebugString().c_str(), - dnums.ShortDebugString().c_str()); + ShapeUtil::HumanString(rhs), window.ShortDebugString(), + dnums.ShortDebugString()); } Shape base_shape = @@ -1720,29 +1706,29 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, const tensorflow::gtl::ArraySlice<int64> fft_length) { const int64 fft_rank = fft_length.size(); if (fft_rank < 1 || fft_rank > 3) { - return InvalidArgument("FFT only supports ranks 1-3; got %lld.", fft_rank); + return InvalidArgument("FFT only supports ranks 1-3; got %d.", fft_rank); } -#define RET_CHECK_RANK(x) \ - if (x.dimensions_size() < fft_rank) { \ - return InvalidArgument( \ - "FFT of rank %lld requires input of at least " \ - "same rank; got input of rank %d", \ - fft_rank, x.dimensions_size()); \ +#define RET_CHECK_RANK(x) \ + if (x.dimensions_size() < fft_rank) { \ + return InvalidArgument( \ + "FFT of rank %d requires input of at least " \ + "same rank; got input of rank %d", \ + fft_rank, x.dimensions_size()); \ } switch (fft_type) { case FFT: case IFFT: if (in.element_type() != C64) { return InvalidArgument("%s requires C64 input type, found %s.", - FftType_Name(fft_type).c_str(), - PrimitiveType_Name(in.element_type()).c_str()); + FftType_Name(fft_type), + PrimitiveType_Name(in.element_type())); } RET_CHECK_RANK(in); return in; case RFFT: { if (in.element_type() != F32) { return InvalidArgument("RFFT requires F32 input type, found %s.", - PrimitiveType_Name(in.element_type()).c_str()); + PrimitiveType_Name(in.element_type())); } RET_CHECK_RANK(in); for (int i = 0; i < fft_rank; i++) { @@ -1750,7 +1736,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, fft_length[i]) { return InvalidArgument( "RFFT requires innermost dimensions match fft_length but " - "dimension %lld is %lld and should be %lld.", + "dimension %d is %d and should be %d.", in.dimensions_size() - fft_rank + i, in.dimensions(in.dimensions_size() - fft_rank + i), fft_length[i]); @@ -1764,7 +1750,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, case IRFFT: { if (in.element_type() != C64) { return InvalidArgument("IRFFT requires C64 input type, found %s.", - PrimitiveType_Name(in.element_type()).c_str()); + PrimitiveType_Name(in.element_type())); } RET_CHECK_RANK(in); Shape result = ShapeUtil::ComplexComponentShape(in); @@ -1773,7 +1759,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, fft_length[i]) { return InvalidArgument( "IRFFT requires all but one innermost dimensions match " - "fft_length, but dimension %lld is %lld and should be %lld.", + "fft_length, but dimension %d is %d and should be %d.", in.dimensions_size() - fft_rank + i, in.dimensions(in.dimensions_size() - fft_rank + i), fft_length[i]); @@ -1783,7 +1769,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, fft_length[fft_rank - 1] / 2 + 1) { return InvalidArgument( "IRFFT requires innermost dimension matches fft_length/2+1, but " - "dimension %d is %lld and should be %lld.", + "dimension %d is %d and should be %d.", in.dimensions_size() - 1, in.dimensions(in.dimensions_size() - 1), fft_length[fft_rank - 1] / 2 + 1); } @@ -1819,18 +1805,18 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, TF_RET_CHECK(split_count > 0); if (split_dimension >= ShapeUtil::Rank(shape) || split_dimension < 0) { return InvalidArgument( - "AllToAll split_dimension %lld is out-of-bounds in shape %s.", - split_dimension, ShapeUtil::HumanString(shape).c_str()); + "AllToAll split_dimension %d is out-of-bounds in shape %s.", + split_dimension, ShapeUtil::HumanString(shape)); } if (concat_dimension >= ShapeUtil::Rank(shape) || concat_dimension < 0) { return InvalidArgument( - "AllToAll concat_dimension %lld is out-of-bounds in shape %s.", - concat_dimension, ShapeUtil::HumanString(shape).c_str()); + "AllToAll concat_dimension %d is out-of-bounds in shape %s.", + concat_dimension, ShapeUtil::HumanString(shape)); } if (shape.dimensions(split_dimension) % split_count != 0) { return InvalidArgument( - "AllToAll split dimension size %lld must be dividable by split_count " - "%lld.", + "AllToAll split dimension size %d must be dividable by split_count " + "%d.", shape.dimensions(split_dimension), split_count); } std::vector<int64> new_dimensions(shape.dimensions().begin(), @@ -1850,8 +1836,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, return InvalidArgument( "HLO all-to-all has operands with different shapes: the 0th " "operand shape %s, but the %dth operand has shape %s.", - ShapeUtil::HumanString(*operand_shapes[0]).c_str(), i, - ShapeUtil::HumanString(*operand_shapes[i]).c_str()); + ShapeUtil::HumanString(*operand_shapes[0]), i, + ShapeUtil::HumanString(*operand_shapes[i])); } } @@ -1880,9 +1866,9 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, if (!ShapeUtil::SameDimensions(*reduced_args[0], *reduced_args[i])) { return InvalidArgument( "All reduced tensors must have the sime dimension. Tensor 0 has " - "shape %s, Tensor %lld has shape %s", - ShapeUtil::HumanString(*reduced_args[0]).c_str(), i, - ShapeUtil::HumanString(*reduced_args[i]).c_str()); + "shape %s, Tensor %d has shape %s", + ShapeUtil::HumanString(*reduced_args[0]), i, + ShapeUtil::HumanString(*reduced_args[i])); } } @@ -1892,9 +1878,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, const Shape& arg = *reduced_args[0]; for (int64 dimension : dimensions_to_reduce) { if (dimension >= ShapeUtil::Rank(arg) || dimension < 0) { - return InvalidArgument( - "Reducing out-of-bounds dimension %lld in shape %s.", dimension, - ShapeUtil::HumanString(arg).c_str()); + return InvalidArgument("Reducing out-of-bounds dimension %d in shape %s.", + dimension, ShapeUtil::HumanString(arg)); } } @@ -1967,16 +1952,16 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, return InvalidArgument( "Select function's first parameter shape currently must " "match the operand element shape, but got %s vs %s.", - ShapeUtil::HumanString(select_shape.parameters(0)).c_str(), - ShapeUtil::HumanString(operand_element_shape).c_str()); + ShapeUtil::HumanString(select_shape.parameters(0)), + ShapeUtil::HumanString(operand_element_shape)); } if (!ShapeUtil::CompatibleIgnoringFpPrecision(operand_element_shape, select_shape.parameters(1))) { return InvalidArgument( "Select function's second parameter shape currently must " "match the operand element shape, but got %s vs %s.", - ShapeUtil::HumanString(select_shape.parameters(1)).c_str(), - ShapeUtil::HumanString(operand_element_shape).c_str()); + ShapeUtil::HumanString(select_shape.parameters(1)), + ShapeUtil::HumanString(operand_element_shape)); } // Check if the scatter function has a proper shape as a reduction. @@ -1994,8 +1979,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, return InvalidArgument( "Source shape does not match the shape of window-reduced operand: " "source(%s), window-reduced operand(%s).", - ShapeUtil::HumanString(source_shape).c_str(), - ShapeUtil::HumanString(window_result_shape).c_str()); + ShapeUtil::HumanString(source_shape), + ShapeUtil::HumanString(window_result_shape)); } return operand_shape; } @@ -2008,29 +1993,27 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, return InvalidArgument( "%s in slice operation; argument shape: %s; starts: {%s}; limits: " "{%s}; strides: {%s}.", - message.c_str(), ShapeUtil::HumanString(arg).c_str(), - StrJoin(starts, ",").c_str(), StrJoin(limits, ",").c_str(), - StrJoin(strides, ",").c_str()); + message, ShapeUtil::HumanString(arg), StrJoin(starts, ","), + StrJoin(limits, ","), StrJoin(strides, ",")); }; TF_RETURN_IF_ERROR(ExpectArray(arg, "operand of slice")); - VLOG(2) << tensorflow::strings::Printf( - "slicing shape %s starts={%s} limits={%s}", - ShapeUtil::HumanString(arg).c_str(), StrJoin(starts, ", ").c_str(), - StrJoin(limits, ", ").c_str()); + VLOG(2) << StrFormat("slicing shape %s starts={%s} limits={%s}", + ShapeUtil::HumanString(arg), StrJoin(starts, ", "), + StrJoin(limits, ", ")); if (starts.size() != limits.size()) { - return error(Printf("slice start and limit sizes differ: %zu vs %zu", - starts.size(), limits.size())); + return error(StrFormat("slice start and limit sizes differ: %u vs %u", + starts.size(), limits.size())); } if (starts.size() != strides.size()) { - return error(Printf("slice start and strides sizes differ: %zu vs %zu", - starts.size(), strides.size())); + return error(StrFormat("slice start and strides sizes differ: %u vs %u", + starts.size(), strides.size())); } if (starts.size() != ShapeUtil::Rank(arg)) { return InvalidArgument( - "Slice index count does not match argument rank: %zu vs %lld.", + "Slice index count does not match argument rank: %u vs %d.", starts.size(), ShapeUtil::Rank(arg)); } @@ -2040,27 +2023,24 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, int64 limit_index = limits[dimension]; int64 stride = strides[dimension]; if (start_index < 0) { - return InvalidArgument("Negative start index to slice: %lld.", - start_index); + return InvalidArgument("Negative start index to slice: %d.", start_index); } if (limit_index > arg.dimensions(dimension)) { return error( - Printf("limit index (%lld) must be less than or equal to dimension " - "size (%lld)", - limit_index, arg.dimensions(dimension))); - } - VLOG(2) << tensorflow::strings::Printf("starts[%lld] = %lld", dimension, - start_index); - VLOG(2) << tensorflow::strings::Printf("limits[%lld] = %lld", dimension, - limit_index); + StrFormat("limit index (%d) must be less than or equal to dimension " + "size (%d)", + limit_index, arg.dimensions(dimension))); + } + VLOG(2) << StrFormat("starts[%d] = %d", dimension, start_index); + VLOG(2) << StrFormat("limits[%d] = %d", dimension, limit_index); if (start_index > limit_index) { return error( - Printf("limit index (%lld) must be greater or equal to " - "start index (%lld) in slice with positive stride", - limit_index, start_index)); + StrFormat("limit index (%d) must be greater or equal to " + "start index (%d) in slice with positive stride", + limit_index, start_index)); } if (stride <= 0) { - return InvalidArgument("Stride (%lld) must be positive.", stride); + return InvalidArgument("Stride (%d) must be positive.", stride); } sizes.push_back((limit_index - start_index + stride - 1) / stride); } @@ -2075,15 +2055,14 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, TF_RETURN_IF_ERROR( ExpectArray(start_indices_shape, "start indices of dynamic slice")); - VLOG(2) << tensorflow::strings::Printf( + VLOG(2) << StrFormat( "slicing shape %s at dynamic start_indices %s with slice_sizes={%s}", - ShapeUtil::HumanString(operand_shape).c_str(), - ShapeUtil::HumanString(start_indices_shape).c_str(), - StrJoin(slice_sizes, ", ").c_str()); + ShapeUtil::HumanString(operand_shape), + ShapeUtil::HumanString(start_indices_shape), StrJoin(slice_sizes, ", ")); if (ShapeUtil::Rank(start_indices_shape) != 1) { return InvalidArgument( - "Dynamic slice start indices of rank %lld must be rank1.", + "Dynamic slice start indices of rank %d must be rank1.", ShapeUtil::Rank(start_indices_shape)); } @@ -2095,16 +2074,15 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, const int64 start_num_dims = start_indices_shape.dimensions(0); if (ShapeUtil::Rank(operand_shape) != start_num_dims) { return InvalidArgument( - "Dynamic slice start number of dimensions %lld (%s) must match rank " - "%lld of slice input (%s).", - start_num_dims, ShapeUtil::HumanString(start_indices_shape).c_str(), - ShapeUtil::Rank(operand_shape), - ShapeUtil::HumanString(operand_shape).c_str()); + "Dynamic slice start number of dimensions %d (%s) must match rank " + "%d of slice input (%s).", + start_num_dims, ShapeUtil::HumanString(start_indices_shape), + ShapeUtil::Rank(operand_shape), ShapeUtil::HumanString(operand_shape)); } if (slice_sizes.size() != ShapeUtil::Rank(operand_shape)) { return InvalidArgument( - "Dynamic slice index count does not match argument rank: %zu vs %lld.", + "Dynamic slice index count does not match argument rank: %u vs %d.", slice_sizes.size(), ShapeUtil::Rank(operand_shape)); } @@ -2112,16 +2090,15 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, const int64 input_dim_size = operand_shape.dimensions(dim); const int64 slice_dim_size = slice_sizes[dim]; if (slice_dim_size < 0) { - return InvalidArgument("Negative size index to dynamic slice: %lld.", + return InvalidArgument("Negative size index to dynamic slice: %d.", slice_dim_size); } if (slice_dim_size > input_dim_size) { return InvalidArgument( - "Slice dim size %lld greater than dynamic slice dimension: %lld.", + "Slice dim size %d greater than dynamic slice dimension: %d.", slice_dim_size, input_dim_size); } - VLOG(2) << tensorflow::strings::Printf("slice_sizes[%lld] = %lld", dim, - slice_dim_size); + VLOG(2) << StrFormat("slice_sizes[%d] = %d", dim, slice_dim_size); } return ShapeUtil::MakeShape(operand_shape.element_type(), slice_sizes); @@ -2137,16 +2114,16 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, TF_RETURN_IF_ERROR(ExpectArray(start_indices_shape, "start indices of dynamic update slice")); - VLOG(2) << tensorflow::strings::Printf( + VLOG(2) << StrFormat( "updating slice of shape %s at dynamic start_indices %s with update " "shape %s", - ShapeUtil::HumanString(operand_shape).c_str(), - ShapeUtil::HumanString(start_indices_shape).c_str(), - ShapeUtil::HumanString(update_shape).c_str()); + ShapeUtil::HumanString(operand_shape), + ShapeUtil::HumanString(start_indices_shape), + ShapeUtil::HumanString(update_shape)); if (ShapeUtil::Rank(start_indices_shape) != 1) { return InvalidArgument( - "Dynamic update slice start indices of rank %lld must be rank1.", + "Dynamic update slice start indices of rank %d must be rank1.", ShapeUtil::Rank(start_indices_shape)); } @@ -2158,17 +2135,16 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, const int64 start_num_dims = start_indices_shape.dimensions(0); if (ShapeUtil::Rank(operand_shape) != start_num_dims) { return InvalidArgument( - "Dynamic update slice start number of dimensions %lld (%s) must match " - "rank %lld of slice input (%s).", - start_num_dims, ShapeUtil::HumanString(start_indices_shape).c_str(), - ShapeUtil::Rank(operand_shape), - ShapeUtil::HumanString(operand_shape).c_str()); + "Dynamic update slice start number of dimensions %d (%s) must match " + "rank %d of slice input (%s).", + start_num_dims, ShapeUtil::HumanString(start_indices_shape), + ShapeUtil::Rank(operand_shape), ShapeUtil::HumanString(operand_shape)); } if (ShapeUtil::Rank(update_shape) != ShapeUtil::Rank(operand_shape)) { return InvalidArgument( "Dynamic update slice update rank does not match argument rank: " - "%lld vs %lld.", + "%d vs %d.", ShapeUtil::Rank(update_shape), ShapeUtil::Rank(operand_shape)); } @@ -2177,8 +2153,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, return InvalidArgument( "Dynamic update slice update element type does not match argument. " "operand.element_type: %s vs update.element_type: %s.", - PrimitiveType_Name(operand_shape.element_type()).c_str(), - PrimitiveType_Name(update_shape.element_type()).c_str()); + PrimitiveType_Name(operand_shape.element_type()), + PrimitiveType_Name(update_shape.element_type())); } for (int64 dim = 0; dim < ShapeUtil::Rank(operand_shape); ++dim) { @@ -2186,16 +2162,15 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, const int64 update_dim_size = update_shape.dimensions(dim); if (update_dim_size < 0) { return InvalidArgument( - "Size index %lld to dynamic update slice must be >= 0.", + "Size index %d to dynamic update slice must be >= 0.", update_dim_size); } if (update_dim_size > input_dim_size) { return InvalidArgument( - "Update dim size %lld greater than dynamic slice dimension: %lld.", + "Update dim size %d greater than dynamic slice dimension: %d.", update_dim_size, input_dim_size); } - VLOG(2) << tensorflow::strings::Printf("update_sizes[%lld] = %lld", dim, - update_dim_size); + VLOG(2) << StrFormat("update_sizes[%d] = %d", dim, update_dim_size); } return operand_shape; @@ -2210,8 +2185,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, for (int64 dimension : dimensions) { if (dimension >= ShapeUtil::Rank(operand_shape) || dimension < 0) { return InvalidArgument( - "One of the reverse dimensions (%lld) is out-of-bounds in shape %s.", - dimension, ShapeUtil::HumanString(operand_shape).c_str()); + "One of the reverse dimensions (%d) is out-of-bounds in shape %s.", + dimension, ShapeUtil::HumanString(operand_shape)); } } return operand_shape; @@ -2222,14 +2197,14 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, if (!ShapeUtil::IsTuple(arg)) { return InvalidArgument( "Cannot infer shape: attempting to index into non-tuple: %s.", - ShapeUtil::HumanString(arg).c_str()); + ShapeUtil::HumanString(arg)); } if (index >= arg.tuple_shapes_size()) { return InvalidArgument( - "Cannot infer shape: attempt to index out of tuple bounds: %lld " + "Cannot infer shape: attempt to index out of tuple bounds: %d " ">= %d in shape %s.", - index, arg.tuple_shapes_size(), ShapeUtil::HumanString(arg).c_str()); + index, arg.tuple_shapes_size(), ShapeUtil::HumanString(arg)); } return arg.tuple_shapes(index); @@ -2249,17 +2224,15 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, } auto shape_string = [&]() { - return tensorflow::strings::Printf( - "Condition: %s; body: %s; init: %s.", - ShapeUtil::HumanString(condition).c_str(), - ShapeUtil::HumanString(body).c_str(), - ShapeUtil::HumanString(init).c_str()); + return StrFormat( + "Condition: %s; body: %s; init: %s.", ShapeUtil::HumanString(condition), + ShapeUtil::HumanString(body), ShapeUtil::HumanString(init)); }; // Check the shapes of computation parameters and return types. if (!ShapeUtil::ShapeIs(condition.result(), PRED, {})) { return InvalidArgument("Condition must return a boolean; got %s.", - shape_string().c_str()); + shape_string()); } if (!ShapeUtil::Compatible(body.result(), condition.parameters(0)) || !ShapeUtil::Compatible(body.result(), body.parameters(0)) || @@ -2267,7 +2240,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, return InvalidArgument( "The parameter of condition and body, the result of the body, and init " "must all have the same shape; got %s.", - shape_string().c_str()); + shape_string()); } return init; @@ -2279,7 +2252,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, const ProgramShape& false_computation) { if (!ShapeUtil::ShapeIs(predicate, PRED, {})) { return InvalidArgument("Predicate must be a boolean; got %s.", - ShapeUtil::HumanString(predicate).c_str()); + ShapeUtil::HumanString(predicate)); } if (true_computation.parameters_size() != 1) { @@ -2288,15 +2261,14 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, } if (!ShapeUtil::Compatible(true_computation.parameters(0), true_operand)) { auto true_shape_string = [&]() { - return tensorflow::strings::Printf( - "true_operand: %s; true_computation: %s", - ShapeUtil::HumanString(true_operand).c_str(), - ShapeUtil::HumanString(true_computation).c_str()); + return StrFormat("true_operand: %s; true_computation: %s", + ShapeUtil::HumanString(true_operand), + ShapeUtil::HumanString(true_computation)); }; return InvalidArgument( "true_operand must match the shape of the only parameter of " "true_computation: got %s.", - true_shape_string().c_str()); + true_shape_string()); } if (false_computation.parameters_size() != 1) { @@ -2305,28 +2277,27 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, } if (!ShapeUtil::Compatible(false_computation.parameters(0), false_operand)) { auto false_shape_string = [&]() { - return tensorflow::strings::Printf( - "false_operand: %s; false_computation: %s", - ShapeUtil::HumanString(false_operand).c_str(), - ShapeUtil::HumanString(false_computation).c_str()); + return StrFormat("false_operand: %s; false_computation: %s", + ShapeUtil::HumanString(false_operand), + ShapeUtil::HumanString(false_computation)); }; return InvalidArgument( "false_operand must match the shape of the only parameter of " "false_computation: got %s.", - false_shape_string().c_str()); + false_shape_string()); } if (!ShapeUtil::Compatible(true_computation.result(), false_computation.result())) { auto shape_string = [&]() { - return tensorflow::strings::Printf( + return StrFormat( "true_computation result: %s; false_computation result: %s.", - ShapeUtil::HumanString(true_computation.result()).c_str(), - ShapeUtil::HumanString(false_computation.result()).c_str()); + ShapeUtil::HumanString(true_computation.result()), + ShapeUtil::HumanString(false_computation.result())); }; return InvalidArgument( "the result of true_computation and false_computation must have the " "same shape: got %s.", - shape_string().c_str()); + shape_string()); } return true_computation.result(); } @@ -2336,7 +2307,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, TF_RETURN_IF_ERROR(ExpectArray(operand, "operand of broadcast")); for (int64 size : broadcast_sizes) { if (size < 0) { - return InvalidArgument("Broadcast with negative dimension size %lld.", + return InvalidArgument("Broadcast with negative dimension size %d.", size); } } @@ -2361,11 +2332,11 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, if (ShapeUtil::ElementsIn(operand) != ShapeUtil::ElementsIn(inferred_shape)) { return InvalidArgument( - "Reshape operation has mismatched element counts: from=%lld (%s) " - "to=%lld (%s).", - ShapeUtil::ElementsIn(operand), ShapeUtil::HumanString(operand).c_str(), + "Reshape operation has mismatched element counts: from=%d (%s) " + "to=%d (%s).", + ShapeUtil::ElementsIn(operand), ShapeUtil::HumanString(operand), ShapeUtil::ElementsIn(inferred_shape), - ShapeUtil::HumanString(inferred_shape).c_str()); + ShapeUtil::HumanString(inferred_shape)); } std::vector<int64> indices(ShapeUtil::Rank(operand)); @@ -2376,8 +2347,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, return InvalidArgument( "Reshape dimensions [%s] are not a permutation of the operand " "dimensions (operand shape is %s).", - StrJoin(dimensions, ",").c_str(), - ShapeUtil::HumanString(operand).c_str()); + StrJoin(dimensions, ","), ShapeUtil::HumanString(operand)); } return inferred_shape; @@ -2412,9 +2382,9 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(min, operand) || !ShapeUtil::SameElementTypeIgnoringFpPrecision(max, operand)) { return InvalidArgument("Clamp with different operand types: %s, %s, %s.", - ShapeUtil::HumanString(min).c_str(), - ShapeUtil::HumanString(operand).c_str(), - ShapeUtil::HumanString(max).c_str()); + ShapeUtil::HumanString(min), + ShapeUtil::HumanString(operand), + ShapeUtil::HumanString(max)); } if (((ShapeUtil::CompatibleIgnoringFpPrecision(min, operand) || ShapeUtil::IsScalar(min)) && @@ -2431,9 +2401,9 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, return ShapeUtil::ChangeElementType(min, operand.element_type()); } } - return Unimplemented( - "%s, %s <clamp> %s is not implemented.", min.ShortDebugString().c_str(), - max.ShortDebugString().c_str(), operand.ShortDebugString().c_str()); + return Unimplemented("%s, %s <clamp> %s is not implemented.", + min.ShortDebugString(), max.ShortDebugString(), + operand.ShortDebugString()); } // TODO(b/36794510): Make broadcast semantics more consistent, by supporting @@ -2444,13 +2414,12 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, if (!ShapeUtil::CompatibleIgnoringFpPrecision(on_true, on_false)) { return InvalidArgument( "Operands to select must be the same shape; got %s and %s.", - ShapeUtil::HumanString(on_true).c_str(), - ShapeUtil::HumanString(on_false).c_str()); + ShapeUtil::HumanString(on_true), ShapeUtil::HumanString(on_false)); } if (pred.element_type() != PRED) { return InvalidArgument( "Select's pred operand must have PRED element type; got %s.", - ShapeUtil::HumanString(pred).c_str()); + ShapeUtil::HumanString(pred)); } if (ShapeUtil::CompatibleIgnoringElementType(pred, on_true) || ShapeUtil::IsScalar(pred)) { @@ -2463,7 +2432,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, return InvalidArgument( "Select operation with non-scalar predicate with dimensionality " " different from the other operands: %s.", - ShapeUtil::HumanString(pred).c_str()); + ShapeUtil::HumanString(pred)); } } @@ -2474,18 +2443,17 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, if (!ShapeUtil::Compatible(on_true, on_false)) { return InvalidArgument( "Operands to tuple-select must be the same shape; got %s and %s.", - ShapeUtil::HumanString(on_true).c_str(), - ShapeUtil::HumanString(on_false).c_str()); + ShapeUtil::HumanString(on_true), ShapeUtil::HumanString(on_false)); } if (pred.element_type() != PRED) { return InvalidArgument( "TupleSelect's pred operand must have PRED element type; got %s.", - ShapeUtil::HumanString(pred).c_str()); + ShapeUtil::HumanString(pred)); } if (!ShapeUtil::IsScalar(pred)) { return InvalidArgument( "TupleSelect operation with non-scalar predicate: %s.", - ShapeUtil::HumanString(pred).c_str()); + ShapeUtil::HumanString(pred)); } return on_true; } @@ -2502,10 +2470,10 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, }); return InvalidArgument( "Call applied function arity must match number of arguments; got: " - "arity: %d, arguments: %zu; computation signature: %s; argument " + "arity: %d, arguments: %u; computation signature: %s; argument " "shapes: [%s].", - to_apply.parameters_size(), arg_shapes.size(), - computation_signature.c_str(), argument_shapes.c_str()); + to_apply.parameters_size(), arg_shapes.size(), computation_signature, + argument_shapes); } // All arguments must be compatible with the program shape. @@ -2516,8 +2484,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, return InvalidArgument( "Call parameter must match argument; got parameter %d shape: %s, " "argument shape: %s.", - i, ShapeUtil::HumanString(param_shape).c_str(), - ShapeUtil::HumanString(arg_shape).c_str()); + i, ShapeUtil::HumanString(param_shape), + ShapeUtil::HumanString(arg_shape)); } } @@ -2531,14 +2499,14 @@ static Status ValidateGatherDimensionNumbers( if (!absl::c_is_sorted(dim_numbers.offset_dims())) { return InvalidArgument( "Output window dimensions in gather op must be ascending; got: %s.", - StrJoin(dim_numbers.offset_dims(), ", ").c_str()); + StrJoin(dim_numbers.offset_dims(), ", ")); } if (absl::c_adjacent_find(dim_numbers.offset_dims()) != dim_numbers.offset_dims().end()) { return InvalidArgument( "Output window dimensions in gather op must not repeat; got: %s.", - StrJoin(dim_numbers.offset_dims(), ", ").c_str()); + StrJoin(dim_numbers.offset_dims(), ", ")); } const int64 output_offset_dim_count = dim_numbers.offset_dims_size(); @@ -2549,9 +2517,9 @@ static Status ValidateGatherDimensionNumbers( int64 offset_dim = dim_numbers.offset_dims(i); if (offset_dim < 0 || offset_dim >= output_shape_rank) { return InvalidArgument( - "Offset dimension %d in gather op is out of bounds; got %lld, but " + "Offset dimension %d in gather op is out of bounds; got %d, but " "should " - "have been in [0,%lld).", + "have been in [0,%d).", i, offset_dim, output_shape_rank); } } @@ -2560,8 +2528,8 @@ static Status ValidateGatherDimensionNumbers( start_indices_shape[dim_numbers.index_vector_dim()]) { return InvalidArgument( "Gather op has %d elements in start_index_map and the " - "bound of dimension index_vector_dim=%lld of start_indices is " - "%lld. These two numbers must be equal.", + "bound of dimension index_vector_dim=%d of start_indices is " + "%d. These two numbers must be equal.", dim_numbers.start_index_map_size(), dim_numbers.index_vector_dim(), start_indices_shape[dim_numbers.index_vector_dim()]); } @@ -2571,7 +2539,7 @@ static Status ValidateGatherDimensionNumbers( if (operand_dim_for_start_index_i < 0 || operand_dim_for_start_index_i >= input_shape.dimensions_size()) { return InvalidArgument( - "Invalid start_index_map; domain is [0, %d), got: %d->%lld.", + "Invalid start_index_map; domain is [0, %d), got: %d->%d.", input_shape.dimensions_size(), i, operand_dim_for_start_index_i); } } @@ -2587,14 +2555,14 @@ static Status ValidateGatherDimensionNumbers( return InvalidArgument( "Repeated dimensions are not allowed in start_index_map; " "got: %s.", - StrJoin(dim_numbers.start_index_map(), ", ").c_str()); + StrJoin(dim_numbers.start_index_map(), ", ")); } for (int64 collapsed_dim : dim_numbers.collapsed_slice_dims()) { if (collapsed_dim < 0 || collapsed_dim >= input_shape.dimensions_size()) { return InvalidArgument( "Invalid collapsed_slice_dims set in gather op; valid range is [0, " - "%d), got: %lld.", + "%d), got: %d.", input_shape.dimensions_size(), collapsed_dim); } } @@ -2602,7 +2570,7 @@ static Status ValidateGatherDimensionNumbers( if (!absl::c_is_sorted(dim_numbers.collapsed_slice_dims())) { return InvalidArgument( "collapsed_slice_dims in gather op must be sorted; got: %s", - StrJoin(dim_numbers.collapsed_slice_dims(), ", ").c_str()); + StrJoin(dim_numbers.collapsed_slice_dims(), ", ")); } if (absl::c_adjacent_find(dim_numbers.collapsed_slice_dims()) != @@ -2610,7 +2578,7 @@ static Status ValidateGatherDimensionNumbers( return InvalidArgument( "Repeated dimensions not allowed in collapsed_slice_dims in gather op; " "got: %s.", - StrJoin(dim_numbers.collapsed_slice_dims(), ", ").c_str()); + StrJoin(dim_numbers.collapsed_slice_dims(), ", ")); } return Status::OK(); @@ -2628,7 +2596,7 @@ static Status ValidateGatherDimensionNumbers( if (!ShapeUtil::ElementIsIntegral(start_indices_shape)) { return InvalidArgument( "Gather indices parameter must be an integral tensor; got %s.", - ShapeUtil::HumanString(start_indices_shape).c_str()); + ShapeUtil::HumanString(start_indices_shape)); } // We implicitly reshape gather indices of shape P[A,B,C] to P[A,B,C,1] if @@ -2641,7 +2609,7 @@ static Status ValidateGatherDimensionNumbers( return InvalidArgument( "Gather index leaf dimension must be within [0, rank(start_indices) + " "1). rank(start_indices) is %d and gather index leaf dimension is " - "%lld.", + "%d.", start_indices_shape.dimensions_size(), gather_dim_numbers.index_vector_dim()); } @@ -2672,9 +2640,8 @@ static Status ValidateGatherDimensionNumbers( "All components of the offset index in a gather op must either be a " "offset dimension or explicitly collapsed; got len(slice_sizes)=%lu, " "output_slice_sizes=%s, collapsed_slice_dims=%s.", - slice_sizes.size(), - StrJoin(gather_dim_numbers.offset_dims(), ",").c_str(), - StrJoin(gather_dim_numbers.collapsed_slice_dims(), ",").c_str()); + slice_sizes.size(), StrJoin(gather_dim_numbers.offset_dims(), ","), + StrJoin(gather_dim_numbers.collapsed_slice_dims(), ",")); } for (int i = 0; i < slice_sizes.size(); i++) { @@ -2683,7 +2650,7 @@ static Status ValidateGatherDimensionNumbers( if (slice_size < 0 || slice_size > corresponding_input_size) { return InvalidArgument( "Slice size at index %d in gather op is out of range, must be " - "within [0, %lld), got %lld.", + "within [0, %d), got %d.", i, corresponding_input_size + 1, slice_size); } } @@ -2692,7 +2659,7 @@ static Status ValidateGatherDimensionNumbers( if (slice_sizes[gather_dim_numbers.collapsed_slice_dims(i)] != 1) { return InvalidArgument( "Gather op can only collapse slice dims with bound 1, but bound is " - "%lld for index %lld at position %d.", + "%d for index %d at position %d.", slice_sizes[gather_dim_numbers.collapsed_slice_dims(i)], gather_dim_numbers.collapsed_slice_dims(i), i); } @@ -2737,20 +2704,20 @@ Status ValidateScatterDimensionNumbers( if (!absl::c_is_sorted(dim_numbers.update_window_dims())) { return InvalidArgument( "update_window_dims in scatter op must be sorted; got: %s.", - StrJoin(dim_numbers.update_window_dims(), ", ").c_str()); + StrJoin(dim_numbers.update_window_dims(), ", ")); } if (absl::c_adjacent_find(dim_numbers.update_window_dims()) != dim_numbers.update_window_dims().end()) { return InvalidArgument( "update_window_dims in scatter op must not repeat; got: %s.", - StrJoin(dim_numbers.update_window_dims(), ", ").c_str()); + StrJoin(dim_numbers.update_window_dims(), ", ")); } const int64 updates_rank = ShapeUtil::Rank(updates_shape); for (int64 window_dim : dim_numbers.update_window_dims()) { if (window_dim < 0 || window_dim >= updates_rank) { return InvalidArgument( "Invalid update_window_dims set in scatter op; valid range is [0, " - "%lld). got: %lld.", + "%d). got: %d.", updates_rank, window_dim); } } @@ -2759,19 +2726,19 @@ Status ValidateScatterDimensionNumbers( if (!absl::c_is_sorted(dim_numbers.inserted_window_dims())) { return InvalidArgument( "inserted_window_dims in scatter op must be sorted; got: %s.", - StrJoin(dim_numbers.inserted_window_dims(), ", ").c_str()); + StrJoin(dim_numbers.inserted_window_dims(), ", ")); } if (absl::c_adjacent_find(dim_numbers.inserted_window_dims()) != dim_numbers.inserted_window_dims().end()) { return InvalidArgument( "inserted_window_dims in scatter op must not repeat; got: %s.", - StrJoin(dim_numbers.inserted_window_dims(), ", ").c_str()); + StrJoin(dim_numbers.inserted_window_dims(), ", ")); } for (int64 inserted_dim : dim_numbers.inserted_window_dims()) { if (inserted_dim < 0 || inserted_dim >= operand_shape.dimensions_size()) { return InvalidArgument( "Invalid inserted_window_dims set in scatter op; valid range is [0, " - "%d), got: %lld.", + "%d), got: %d.", operand_shape.dimensions_size(), inserted_dim); } } @@ -2781,7 +2748,7 @@ Status ValidateScatterDimensionNumbers( scatter_indices_shape[dim_numbers.index_vector_dim()]) { return InvalidArgument( "Scatter op has %d elements in scatter_dims_to_operand_dims and the " - "bound of dimension index_vector_dim=%lld of scatter_indices is %lld. " + "bound of dimension index_vector_dim=%d of scatter_indices is %d. " "These two numbers must be equal.", dim_numbers.scatter_dims_to_operand_dims_size(), dim_numbers.index_vector_dim(), @@ -2794,7 +2761,7 @@ Status ValidateScatterDimensionNumbers( scatter_dim_to_operand_dim >= operand_shape.dimensions_size()) { return InvalidArgument( "Invalid scatter_dims_to_operand_dims mapping; domain is [0, %d), " - "got: %d->%lld.", + "got: %d->%d.", operand_shape.dimensions_size(), i, scatter_dim_to_operand_dim); } } @@ -2807,7 +2774,7 @@ Status ValidateScatterDimensionNumbers( return InvalidArgument( "Repeated dimensions not allowed in scatter_dims_to_operand_dims; " "got: %s.", - StrJoin(dim_numbers.scatter_dims_to_operand_dims(), ", ").c_str()); + StrJoin(dim_numbers.scatter_dims_to_operand_dims(), ", ")); } return Status::OK(); @@ -2828,7 +2795,7 @@ Status ValidateScatterDimensionNumbers( if (!ShapeUtil::ElementIsIntegral(scatter_indices_shape)) { return InvalidArgument( "Scatter indices parameter must be an integral tensor; got %s.", - ShapeUtil::HumanString(scatter_indices_shape).c_str()); + ShapeUtil::HumanString(scatter_indices_shape)); } if (scatter_indices_shape.dimensions_size() < @@ -2837,7 +2804,7 @@ Status ValidateScatterDimensionNumbers( return InvalidArgument( "Scatter index leaf dimension must be within [0, rank(scatter_indices)" " + 1). rank(scatter_indices) is %d and scatter index leaf dimension " - "is %lld.", + "is %d.", scatter_indices_shape.dimensions_size(), scatter_dim_numbers.index_vector_dim()); } @@ -2859,7 +2826,7 @@ Status ValidateScatterDimensionNumbers( int64 expected_updates_rank = expanded_scatter_indices_shape.size() - 1 + scatter_dim_numbers.update_window_dims_size(); if (ShapeUtil::Rank(updates_shape) != expected_updates_rank) { - return InvalidArgument("Updates tensor must be of rank %lld; got %lld.", + return InvalidArgument("Updates tensor must be of rank %d; got %d.", expected_updates_rank, ShapeUtil::Rank(updates_shape)); } @@ -2885,7 +2852,7 @@ Status ValidateScatterDimensionNumbers( return InvalidArgument( "Bounds of the window dimensions of updates must not exceed the " "bounds of the corresponding dimensions of operand. For dimension " - "%lld, updates bound is %lld, operand bound is %lld.", + "%d, updates bound is %d, operand bound is %d.", update_window_dim, updates_shape.dimensions(update_window_dim), max_update_slice_sizes[i]); } @@ -2906,8 +2873,8 @@ Status ValidateScatterDimensionNumbers( return InvalidArgument( "Bounds of the scatter dimensions of updates must be same as the " "bounds of the corresponding dimensions of scatter indices. For " - "scatter dimension %lld, updates bound is %lld, scatter_indices " - "bound is %lld.", + "scatter dimension %d, updates bound is %d, scatter_indices " + "bound is %d.", i, updates_shape.dimensions(i), expanded_scatter_indices_shape[scatter_dims_seen]); } diff --git a/tensorflow/compiler/xla/service/shaped_buffer.cc b/tensorflow/compiler/xla/service/shaped_buffer.cc index 5c12dc37b7..921a984589 100644 --- a/tensorflow/compiler/xla/service/shaped_buffer.cc +++ b/tensorflow/compiler/xla/service/shaped_buffer.cc @@ -20,19 +20,17 @@ limitations under the License. #include "absl/memory/memory.h" #include "absl/strings/str_cat.h" +#include "absl/strings/str_format.h" #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/lib/gtl/flatset.h" -#include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/logging.h" namespace xla { -using ::tensorflow::strings::Appendf; - ShapedBuffer::ShapedBuffer(const Shape& on_host_shape, const Shape& on_device_shape, const se::Platform* platform, int device_ordinal) @@ -93,9 +91,9 @@ string ShapedBuffer::ToString() const { shape_str = ShapeUtil::HumanStringWithLayout(subshape); } const se::DeviceMemoryBase& memory = buffer(index); - Appendf(&s, " %s%p (%lld bytes) : %s\n", - string(index.size() * 2, ' ').c_str(), memory.opaque(), - memory.size(), shape_str.c_str()); + absl::StrAppendFormat(&s, " %s%p (%d bytes) : %s\n", + string(index.size() * 2, ' '), memory.opaque(), + memory.size(), shape_str); }); return s; } diff --git a/tensorflow/compiler/xla/service/source_map_util.cc b/tensorflow/compiler/xla/service/source_map_util.cc index 8cbaac7b37..dd53c7531b 100644 --- a/tensorflow/compiler/xla/service/source_map_util.cc +++ b/tensorflow/compiler/xla/service/source_map_util.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/source_map_util.h" +#include "absl/strings/str_format.h" #include "tensorflow/compiler/xla/util.h" namespace xla { @@ -26,11 +27,10 @@ Status InvalidParameterArgumentV(const OpMetadata& op_metadata, string message; tensorflow::strings::Appendv(&message, format, args); if (!op_metadata.source_file().empty()) { - tensorflow::strings::Appendf(&message, " (%s:%d)", - op_metadata.source_file().c_str(), - op_metadata.source_line()); + absl::StrAppendFormat(&message, " (%s:%d)", op_metadata.source_file(), + op_metadata.source_line()); } - return InvalidArgument("%s", message.c_str()); + return InvalidArgument("%s", message); } } // namespace diff --git a/tensorflow/compiler/xla/service/source_map_util.h b/tensorflow/compiler/xla/service/source_map_util.h index 84607cd012..c5a7e17cb4 100644 --- a/tensorflow/compiler/xla/service/source_map_util.h +++ b/tensorflow/compiler/xla/service/source_map_util.h @@ -16,6 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_SOURCE_MAP_UTIL_H_ #define TENSORFLOW_COMPILER_XLA_SERVICE_SOURCE_MAP_UTIL_H_ +#include "absl/strings/str_format.h" #include "tensorflow/compiler/xla/service/executable.h" #include "tensorflow/compiler/xla/status.h" #include "tensorflow/core/platform/macros.h" @@ -24,21 +25,38 @@ namespace xla { namespace source_map_util { // Creates an INVALID_ARGUMENT status with the given format string. +template <typename... Args> +Status InvalidParameterArgument(const OpMetadata& op_metadata, + const absl::FormatSpec<Args...>& format, + const Args&... args) { + string message = absl::StrFormat(format, args...); + if (!op_metadata.source_file().empty()) { + absl::StrAppendFormat(&message, " (%s:%d)", op_metadata.source_file(), + op_metadata.source_line()); + } + return InvalidArgument("%s", message); +} + +// Creates an INVALID_ARGUMENT status with the given format string. // // Also, attempts to extract the OpMetadata for parameter_number on executable // and append it to the status message for source mapping to user code. // // executable may be nullptr, but parameter_number should not be out of bounds // or a CHECK-failure may occur. +template <typename... Args> Status InvalidParameterArgument(Executable* executable, int parameter_number, - const char* format, ...) - TF_PRINTF_ATTRIBUTE(3, 4); - -// As above, but takes the parameter metadata directly instead of extracting it -// from the executable. -Status InvalidParameterArgument(const OpMetadata& op_metadata, - const char* format, ...) - TF_PRINTF_ATTRIBUTE(2, 3); + const absl::FormatSpec<Args...>& format, + const Args&... args) { + if (executable != nullptr && executable->has_module()) { + const HloModule& module = executable->module(); + const HloComputation& computation = *module.entry_computation(); + HloInstruction* param = computation.parameter_instruction(parameter_number); + const OpMetadata& metadata = param->metadata(); + return InvalidParameterArgument(metadata, format, args...); + } + return InvalidArgument(format, args...); +} } // namespace source_map_util } // namespace xla diff --git a/tensorflow/compiler/xla/service/transfer_manager.cc b/tensorflow/compiler/xla/service/transfer_manager.cc index 0c577ec67a..b8d2d546e5 100644 --- a/tensorflow/compiler/xla/service/transfer_manager.cc +++ b/tensorflow/compiler/xla/service/transfer_manager.cc @@ -149,7 +149,7 @@ Status TransferManager::TransferArrayToDeviceAsync( if (dest.size() < GetByteSizeRequirement(on_device_shape)) { return FailedPrecondition( "Allocation on device not large enough for array: " - "%lld < %lld", + "%d < %d", dest.size(), GetByteSizeRequirement(on_device_shape)); } ShapedBuffer shaped_buffer(/*on_host_shape=*/literal.shape(), on_device_shape, @@ -166,12 +166,12 @@ void TransferManager::TransferArrayFromDevice( auto error = StrCat("Shape ", ShapeUtil::HumanString(shape), " has a differently shaped representation on-device: ", ShapeUtil::HumanString(HostShapeToDeviceShape(shape))); - return done(FailedPrecondition("%s", error.c_str())); + return done(FailedPrecondition("%s", error)); } if (source.size() < GetByteSizeRequirement(shape)) { return done( FailedPrecondition("Allocation on device not large enough for array: " - "%lld < %lld", + "%d < %d", source.size(), GetByteSizeRequirement(shape))); } ShapedBuffer shaped_buffer(/*on_host_shape=*/shape, shape, @@ -203,7 +203,7 @@ void TransferManager::TransferArrayFromDevice( return NotFound( "could not find registered transfer manager for platform %s -- check " "target linkage", - platform->Name().c_str()); + platform->Name()); } if (it->second.manager == nullptr) { @@ -254,7 +254,7 @@ Status TransferManager::TransferBufferFromDevice( if (source.size() < size) { return FailedPrecondition( "Source allocation on device not large enough for data tranfer: " - "%lld < %lld", + "%d < %d", source.size(), size); } stream->ThenMemcpy(destination, source, size); @@ -267,7 +267,7 @@ Status TransferManager::TransferBufferToDevice( if (destination->size() < size) { return FailedPrecondition( "Destination allocation on device not large enough for data tranfer: " - "%lld < %lld", + "%d < %d", destination->size(), size); } stream->ThenMemcpy(destination, source, size); @@ -278,9 +278,8 @@ StatusOr<ScopedShapedBuffer> TransferManager::AllocateScopedShapedBuffer( const Shape& on_host_shape, DeviceMemoryAllocator* allocator, int device_ordinal) { if (!LayoutUtil::HasLayout(on_host_shape)) { - return InvalidArgument( - "Shape must have a layout: %s", - ShapeUtil::HumanStringWithLayout(on_host_shape).c_str()); + return InvalidArgument("Shape must have a layout: %s", + ShapeUtil::HumanStringWithLayout(on_host_shape)); } TF_RETURN_IF_ERROR(ShapeUtil::ValidateShape(on_host_shape)); const Shape on_device_shape = HostShapeToDeviceShape(on_host_shape); diff --git a/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc b/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc index cb07b8d4d3..cf00ca102b 100644 --- a/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc +++ b/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc @@ -21,6 +21,7 @@ limitations under the License. #include "absl/memory/memory.h" #include "absl/strings/str_cat.h" +#include "absl/strings/str_format.h" #include "absl/strings/str_join.h" #include "tensorflow/compiler/xla/map_util.h" #include "tensorflow/compiler/xla/service/hlo_dataflow_analysis.h" @@ -29,7 +30,6 @@ limitations under the License. #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/lib/core/errors.h" -#include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/logging.h" namespace xla { @@ -462,21 +462,20 @@ Status TuplePointsToAnalysis::VerifyBuffer(const LogicalBuffer& buffer) const { return FailedPrecondition( "LogicalBuffer %s is ill-defined: instruction %s does not define a " "buffer at that index", - buffer.ToString().c_str(), buffer.instruction()->name().c_str()); + buffer.ToString(), buffer.instruction()->name()); } } if (buffer.id() < 0 || buffer.id() >= logical_buffer_analysis_->num_logical_buffers()) { - return FailedPrecondition( - "LogicalBuffer %s is ill-defined: invalid id %lld", - buffer.ToString().c_str(), buffer.id()); + return FailedPrecondition("LogicalBuffer %s is ill-defined: invalid id %d", + buffer.ToString(), buffer.id()); } if (GetBuffer(buffer.id()).instruction() != buffer.instruction() || GetBuffer(buffer.id()).index() != buffer.index()) { return FailedPrecondition( "LogicalBuffer %s is ill-defined: buffer with same id differs: %s", - buffer.ToString().c_str(), GetBuffer(buffer.id()).ToString().c_str()); + buffer.ToString(), GetBuffer(buffer.id()).ToString()); } return Status::OK(); @@ -495,7 +494,7 @@ StatusOr<const LogicalBuffer*> TuplePointsToAnalysis::GetBufferDefinedAt( if (buffers.size() != 1 || buffers[0]->instruction() != instruction) { return FailedPrecondition( "instruction %s does not define buffer at index {%s}", - instruction->name().c_str(), absl::StrJoin(index, ",").c_str()); + instruction->name(), absl::StrJoin(index, ",")); } return buffers[0]; } @@ -556,8 +555,8 @@ PointsToSet& TuplePointsToAnalysis::CreateCopiedPointsToSet( } string TuplePointsToAnalysis::ToString() const { - string output = tensorflow::strings::Printf( - "TuplePointsToSet for module %s:\n", module_->name().c_str()); + string output = + absl::StrFormat("TuplePointsToSet for module %s:\n", module_->name()); for (const auto* computation : module_->MakeNonfusionComputations()) { const char* entry = computation == module_->entry_computation() ? "entry " : ""; |