diff options
| author |  Akshay Modi <nareshmodi@google.com> | 2018-06-18 11:48:36 -0700 |
|---|---|---|
| committer |  TensorFlower Gardener <gardener@tensorflow.org> | 2018-06-18 11:55:03 -0700 |
| commit | 148b4381fd0259cae441e459ec8ebe2c5d557722 (patch) | |
| tree | c66c96ea6c60c63385b528dce195af802b8acf3b /tensorflow | |
| parent | fc03fbff3dd7a58fa4f16226df4ada1f21f8b53f (diff) | |
Automated g4 rollback of changelist 201011811
PiperOrigin-RevId: 201033171
Diffstat (limited to 'tensorflow')
221 files changed, 888 insertions, 3235 deletions
diff --git a/tensorflow/BUILD b/tensorflow/BUILD index 6d134dbb80..a73c4ca3aa 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -475,7 +475,7 @@ tf_cc_shared_object( # excludes all but a subset of function names. # On MacOS, the linker does not support version_script, but has an # an "-exported_symbols_list" command. -z defs disallows undefined -# symbols in object files. +# symbols in object files and -s strips the output. tf_cc_shared_object( name = "libtensorflow.so", @@ -489,6 +489,7 @@ tf_cc_shared_object( "//tensorflow:windows_msvc": [], "//conditions:default": [ "-z defs", + "-s", "-Wl,--version-script", # This line must be directly followed by the version_script.lds file "$(location //tensorflow/c:version_script.lds)", ], @@ -514,6 +515,7 @@ tf_cc_shared_object( "//tensorflow:windows_msvc": [], "//conditions:default": [ "-z defs", + "-s", "-Wl,--version-script", # This line must be directly followed by the version_script.lds file "$(location //tensorflow:tf_version_script.lds)", ], diff --git a/tensorflow/c/generate-pc.sh b/tensorflow/c/generate-pc.sh index 7184ad68fb..02a6a58b61 100755 --- a/tensorflow/c/generate-pc.sh +++ b/tensorflow/c/generate-pc.sh @@ -15,12 +15,10 @@ # ============================================================================== TF_PREFIX='/usr/local' -LIBDIR='lib' usage() { echo "Usage: $0 OPTIONS" echo -e "-p, --prefix\tset installation prefix (default: /usr/local)" - echo -e "-l, --libdir\tset lib directory (default: lib)" echo -e "-v, --version\tset TensorFlow version" echo -e "-h, --help\tdisplay this message" } @@ -28,7 +26,7 @@ usage() { [ $# == 0 ] && usage && exit 0 # read the options -ARGS=$(getopt -o p:l:v:h --long prefix:,libdir:,version:,help -n $0 -- "$@") +ARGS=$(getopt -o p:v:h --long prefix:,version:,help -n $0 -- "$@") eval set -- "$ARGS" # extract options and their arguments into variables. @@ -40,11 +38,6 @@ while true ; do "") shift 2 ;; *) TF_PREFIX=$2 ; shift 2 ;; esac ;; - -l|--libdir) - case "$2" in - "") shift 2 ;; - *) LIBDIR=$2 ; shift 2 ;; - esac ;; -v|--version) case "$2" in "") shift 2 ;; @@ -62,7 +55,7 @@ echo "Generating pkgconfig file for TensorFlow $TF_VERSION in $TF_PREFIX" cat << EOF > tensorflow.pc prefix=${TF_PREFIX} exec_prefix=\${prefix} -libdir=\${exec_prefix}/${LIBDIR} +libdir=\${exec_prefix}/lib includedir=\${prefix}/include Name: TensorFlow diff --git a/tensorflow/cc/gradients/math_grad.cc b/tensorflow/cc/gradients/math_grad.cc index 35a01e0341..52c177212a 100644 --- a/tensorflow/cc/gradients/math_grad.cc +++ b/tensorflow/cc/gradients/math_grad.cc @@ -38,7 +38,6 @@ REGISTER_NO_GRADIENT_OP("NotEqual"); REGISTER_NO_GRADIENT_OP("LogicalAnd"); REGISTER_NO_GRADIENT_OP("LogicalOr"); REGISTER_NO_GRADIENT_OP("LogicalNot"); -REGISTER_NO_GRADIENT_OP("Floor"); // Conjugate helper function returns the conjugate of an Output if it // is complex valued. diff --git a/tensorflow/cc/gradients/nn_grad.cc b/tensorflow/cc/gradients/nn_grad.cc index c73482d5f4..0cb3132e94 100644 --- a/tensorflow/cc/gradients/nn_grad.cc +++ b/tensorflow/cc/gradients/nn_grad.cc @@ -255,53 +255,6 @@ Status LRNGradHelper(const Scope& scope, const Operation& op, } REGISTER_GRADIENT_OP("LRN", LRNGradHelper); -Status SoftplusGradHelper(const Scope& scope, const Operation& op, - const std::vector<Output>& grad_inputs, - std::vector<Output>* grad_outputs) { - auto dx = internal::SoftplusGrad(scope, grad_inputs[0], op.input(0)); - grad_outputs->push_back(dx); - return scope.status(); -} -REGISTER_GRADIENT_OP("Softplus", SoftplusGradHelper); - -Status SoftsignGradHelper(const Scope& scope, const Operation& op, - const std::vector<Output>& grad_inputs, - std::vector<Output>* grad_outputs) { - auto dx = internal::SoftsignGrad(scope, grad_inputs[0], op.input(0)); - grad_outputs->push_back(dx); - return scope.status(); -} -REGISTER_GRADIENT_OP("Softsign", SoftsignGradHelper); - -Status FractionalAvgPoolGradHelper(const Scope& scope, const Operation& op, - const std::vector<Output>& grad_inputs, - std::vector<Output>* grad_outputs) { - bool overlapping; - TF_RETURN_IF_ERROR( - GetNodeAttr(op.output(0).node()->attrs(), "overlapping", &overlapping)); - auto dx = internal::FractionalAvgPoolGrad( - scope, Shape(scope, op.input(0), Shape::OutType(DT_INT64)), - grad_inputs[0], op.output(1), op.output(2), - internal::FractionalAvgPoolGrad::Overlapping(overlapping)); - grad_outputs->push_back(dx); - return scope.status(); -} -REGISTER_GRADIENT_OP("FractionalAvgPool", FractionalAvgPoolGradHelper); - -Status FractionalMaxPoolGradHelper(const Scope& scope, const Operation& op, - const std::vector<Output>& grad_inputs, - std::vector<Output>* grad_outputs) { - bool overlapping; - TF_RETURN_IF_ERROR( - GetNodeAttr(op.output(0).node()->attrs(), "overlapping", &overlapping)); - auto dx = internal::FractionalMaxPoolGrad( - scope, op.input(0), op.output(0), grad_inputs[0], op.output(1), - op.output(2), internal::FractionalMaxPoolGrad::Overlapping(overlapping)); - grad_outputs->push_back(dx); - return scope.status(); -} -REGISTER_GRADIENT_OP("FractionalMaxPool", FractionalMaxPoolGradHelper); - } // anonymous namespace } // namespace ops } // namespace tensorflow diff --git a/tensorflow/cc/gradients/nn_grad_test.cc b/tensorflow/cc/gradients/nn_grad_test.cc index b4d457a9d1..c4eba7ecb0 100644 --- a/tensorflow/cc/gradients/nn_grad_test.cc +++ b/tensorflow/cc/gradients/nn_grad_test.cc @@ -28,8 +28,6 @@ namespace { using ops::BiasAdd; using ops::Conv2D; using ops::Elu; -using ops::FractionalAvgPool; -using ops::FractionalMaxPool; using ops::L2Loss; using ops::LogSoftmax; using ops::LRN; @@ -43,8 +41,6 @@ using ops::Relu; using ops::Relu6; using ops::Selu; using ops::Softmax; -using ops::Softplus; -using ops::Softsign; class NNGradTest : public ::testing::Test { protected: @@ -75,30 +71,22 @@ class NNGradTest : public ::testing::Test { EXPECT_LT(max_error, 1e-3); } - // Sets tensor with random values, ensuring that every pair of elements are at - // least a reasonable amount apart. - // This is an issue for max pooling operations, in which perturbations by the - // numeric gradient computation in the gradient checker can change the max - // value if a pool has values that are too close together. + // Sets tensor with random values, ensuring that the max value is largest by + // a reasonable amount. + // This is an issue for MaxPool, MaxPoolV2 and MaxPool3D, in which + // perturbations by the numeric gradient computation in the gradient checker + // can change the max value if values are too close together. template <typename T> - void SetRandomValuesForMaxPooling(Tensor* tensor) { + void SetRandomValuesWithBumpedMax(Tensor* tensor) { auto tensor_flat = tensor->flat<T>(); - // First set the array to an increasing sequence of values spaced - // a reasonable amount apart - T cur = 0; - for (size_t i = 0; i < tensor->NumElements(); i++) { - tensor_flat(i) = cur; - cur += 5e-2; - } - // Fischer-Yates shuffle the array - for (size_t i = tensor->NumElements() - 1; i >= 1; i--) { - // j <- random integer 0 <= j <= i - size_t j = random::New64() % (i + 1); - // swap values at i, j - T tmp = tensor_flat(i); - tensor_flat(i) = tensor_flat(j); - tensor_flat(j) = tmp; + tensor_flat.setRandom(); + int32 max_index = 0; + for (size_t i = 1; i < tensor->NumElements(); i++) { + if (tensor_flat(i) > tensor_flat(max_index)) { + max_index = i; + } } + tensor_flat(max_index) += 1e-2; } Scope scope_; @@ -201,7 +189,7 @@ TEST_F(NNGradTest, MaxPoolGradHelper) { const std::vector<int> strides{1, 2, 2, 1}; auto y = MaxPool(scope_, x, ksize, strides, "VALID"); Tensor x_init_value = Tensor(DT_FLOAT, x_shape); - SetRandomValuesForMaxPooling<float>(&x_init_value); + SetRandomValuesWithBumpedMax<float>(&x_init_value); RunTest(x, x_init_value, y, y_shape); } @@ -214,7 +202,7 @@ TEST_F(NNGradTest, MaxPoolGradV2Helper) { Tensor strides = test::AsTensor<int>({1, 2, 2, 1}, {4}); auto y = MaxPoolV2(scope_, x, ksize, strides, "VALID"); Tensor x_init_value = Tensor(DT_FLOAT, x_shape); - SetRandomValuesForMaxPooling<float>(&x_init_value); + SetRandomValuesWithBumpedMax<float>(&x_init_value); RunTest(x, x_init_value, y, y_shape); } @@ -227,7 +215,7 @@ TEST_F(NNGradTest, MaxPool3DGradHelper) { const std::vector<int> strides{1, 3, 3, 3, 1}; auto y = MaxPool3D(scope_, x, ksize, strides, "VALID"); Tensor x_init_value = Tensor(DT_FLOAT, x_shape); - SetRandomValuesForMaxPooling<float>(&x_init_value); + SetRandomValuesWithBumpedMax<float>(&x_init_value); RunTest(x, x_init_value, y, y_shape); } @@ -260,45 +248,5 @@ TEST_F(NNGradTest, LRN){ RunTest(x, x_shape, y, x_shape); } -TEST_F(NNGradTest, SoftplusGrad) { - TensorShape shape({3, 7}); - auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape)); - auto y = Softplus(scope_, x); - RunTest(x, shape, y, shape); -} - -TEST_F(NNGradTest, SoftsignGrad) { - TensorShape shape({3, 7}); - auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape)); - auto y = Softsign(scope_, x); - RunTest(x, shape, y, shape); -} - -TEST_F(NNGradTest, FractionalAvgPoolGradHelper) { - TensorShape x_shape({1, 3, 7, 1}); - auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); - // Force consistent pooling regions for unit testing. - auto y = FractionalAvgPool( - scope_, x, {1, 1.2, 1.9, 1}, - FractionalAvgPool::Deterministic(true).Overlapping(true).Seed(1).Seed2( - 2)); - TensorShape y_shape({1, 2, 3, 1}); - RunTest(x, x_shape, y.output, y_shape); -} - -TEST_F(NNGradTest, FractionalMaxPoolGradHelper) { - TensorShape x_shape({1, 3, 7, 1}); - auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); - // Force consistent pooling regions for unit testing. - auto y = FractionalMaxPool( - scope_, x, {1, 1.2, 1.9, 1}, - FractionalMaxPool::Deterministic(true).Overlapping(true).Seed(1).Seed2( - 2)); - Tensor x_init_value = Tensor(DT_FLOAT, x_shape); - SetRandomValuesForMaxPooling<float>(&x_init_value); - TensorShape y_shape({1, 2, 3, 1}); - RunTest(x, x_init_value, y.output, y_shape); -} - } // namespace } // namespace tensorflow diff --git a/tensorflow/compiler/aot/codegen_test_h.golden b/tensorflow/compiler/aot/codegen_test_h.golden index 6641d45e83..6e050cf564 100644 --- a/tensorflow/compiler/aot/codegen_test_h.golden +++ b/tensorflow/compiler/aot/codegen_test_h.golden @@ -56,9 +56,9 @@ namespace bar { // // Memory stats: // arg bytes total: 104 -// arg bytes aligned: 192 +// arg bytes aligned: 128 // temp bytes total: 126 -// temp bytes aligned: 320 +// temp bytes aligned: 224 class MyClass : public tensorflow::XlaCompiledCpuFunction { public: // Number of input arguments for the compiled computation. diff --git a/tensorflow/compiler/aot/embedded_protocol_buffers.h b/tensorflow/compiler/aot/embedded_protocol_buffers.h index 4e194a6aba..ebfe4806c2 100644 --- a/tensorflow/compiler/aot/embedded_protocol_buffers.h +++ b/tensorflow/compiler/aot/embedded_protocol_buffers.h @@ -71,7 +71,7 @@ struct ProtobufToEmbed { const ::tensorflow::protobuf::MessageLite* message; }; -// Embeds a sequence of protocol buffers into an object file. +// Embeds a a sequence of protocol buffers into an object file. // // `target_triple` is the target triple for the target architecture for the // generated object file. diff --git a/tensorflow/compiler/aot/runtime.h b/tensorflow/compiler/aot/runtime.h index d1a669ceb1..d085864f00 100644 --- a/tensorflow/compiler/aot/runtime.h +++ b/tensorflow/compiler/aot/runtime.h @@ -25,8 +25,8 @@ namespace tensorflow { namespace tfcompile { namespace runtime { -// Align to 64-bytes, to mimic tensorflow::Allocator::kAllocatorAlignment. -static constexpr size_t kAlign = 64; +// Align to 32-bytes, to mimic tensorflow::Allocator::kAllocatorAlignment. +static constexpr size_t kAlign = 32; // aligned_buffer_bytes returns the sum of each size in `sizes`, skipping -1 // values. There are `n` entries in `sizes`. Each buffer is aligned to kAlign diff --git a/tensorflow/compiler/aot/runtime_test.cc b/tensorflow/compiler/aot/runtime_test.cc index 06ec623eb2..6d603a02eb 100644 --- a/tensorflow/compiler/aot/runtime_test.cc +++ b/tensorflow/compiler/aot/runtime_test.cc @@ -24,7 +24,7 @@ namespace runtime { namespace { TEST(Runtime, AlignmentValue) { - // We've chosen 64 byte alignment for the tfcompile runtime to mimic the + // We've chosen 32 byte alignment for the tfcompile runtime to mimic the // regular tensorflow allocator, which was chosen to play nicely with Eigen. // The tfcompile runtime also has a requirement that comes from the xla // generated code, on the relation: buffer_size >= 16 ? 2 * sizeof(void*) : 8 @@ -39,13 +39,13 @@ TEST(Runtime, AlignedBufferBytes) { EXPECT_EQ(aligned_buffer_bytes(sizesA, 1), 0); static constexpr intptr_t sizesB[1] = {3}; - EXPECT_EQ(aligned_buffer_bytes(sizesB, 1), 64); + EXPECT_EQ(aligned_buffer_bytes(sizesB, 1), 32); static constexpr intptr_t sizesC[1] = {32}; - EXPECT_EQ(aligned_buffer_bytes(sizesC, 1), 64); + EXPECT_EQ(aligned_buffer_bytes(sizesC, 1), 32); static constexpr intptr_t sizesD[7] = {1, -1, 32, -1, 64, 2, 3}; - EXPECT_EQ(aligned_buffer_bytes(sizesD, 7), 320); + EXPECT_EQ(aligned_buffer_bytes(sizesD, 7), 192); } void* add_ptr(void* base, uintptr_t delta) { @@ -101,11 +101,11 @@ TEST(Runtime, MallocFreeContiguousBuffers) { EXPECT_NE(base, nullptr); EXPECT_EQ(bufD[0], add_ptr(base, 0)); EXPECT_EQ(bufD[1], nullptr); - EXPECT_EQ(bufD[2], add_ptr(base, 64)); + EXPECT_EQ(bufD[2], add_ptr(base, 32)); EXPECT_EQ(bufD[3], nullptr); - EXPECT_EQ(bufD[4], add_ptr(base, 128)); - EXPECT_EQ(bufD[5], add_ptr(base, 192)); - EXPECT_EQ(bufD[6], add_ptr(base, 256)); + EXPECT_EQ(bufD[4], add_ptr(base, 64)); + EXPECT_EQ(bufD[5], add_ptr(base, 128)); + EXPECT_EQ(bufD[6], add_ptr(base, 160)); for (int i = 0; i < 7; ++i) { const intptr_t size = sizesD[i]; if (size != -1) { diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD index 1067b38f93..d82922a359 100644 --- a/tensorflow/compiler/xla/service/cpu/BUILD +++ b/tensorflow/compiler/xla/service/cpu/BUILD @@ -178,7 +178,6 @@ cc_library( ":runtime_matmul", ":runtime_matmul_mkl", ":runtime_single_threaded_conv2d", - ":runtime_single_threaded_fft", ":runtime_single_threaded_matmul", "@llvm//:execution_engine", "@llvm//:core", @@ -517,6 +516,7 @@ cc_library( deps = [ "//tensorflow/compiler/xla:executable_run_options", "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/core:framework", "//tensorflow/core:framework_lite", "//third_party/eigen3", ], @@ -579,22 +579,6 @@ cc_library( ) cc_library( - name = "runtime_single_threaded_fft", - srcs = [ - "runtime_fft_impl.h", - "runtime_single_threaded_fft.cc", - ], - hdrs = ["runtime_single_threaded_fft.h"], - copts = runtime_copts(), - visibility = ["//visibility:public"], - deps = [ - "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/core:framework_lite", - "//third_party/eigen3", - ], -) - -cc_library( name = "runtime_single_threaded_matmul", srcs = ["runtime_single_threaded_matmul.cc"], hdrs = ["runtime_single_threaded_matmul.h"], diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc index 54c52bc08f..215405f680 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc @@ -51,8 +51,6 @@ extern const char* const kEigenConvF16SymbolName = extern const char* const kEigenConvF32SymbolName = "__xla_cpu_runtime_EigenConvF32"; extern const char* const kEigenFftSymbolName = "__xla_cpu_runtime_EigenFft"; -extern const char* const kEigenSingleThreadedFftSymbolName = - "__xla_cpu_runtime_EigenSingleThreadedFft"; extern const char* const kEigenSingleThreadedMatMulF16SymbolName = "__xla_cpu_runtime_EigenSingleThreadedMatMulF16"; extern const char* const kEigenSingleThreadedMatMulF32SymbolName = diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h index aa0e967123..1dce6efa5c 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h +++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h @@ -52,7 +52,6 @@ extern const char* const kMKLSingleThreadedMatMulF64SymbolName; extern const char* const kEigenConvF16SymbolName; extern const char* const kEigenConvF32SymbolName; extern const char* const kEigenFftSymbolName; -extern const char* const kEigenSingleThreadedFftSymbolName; extern const char* const kEigenSingleThreadedMatMulF16SymbolName; extern const char* const kEigenSingleThreadedMatMulF32SymbolName; extern const char* const kEigenSingleThreadedMatMulF64SymbolName; diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc index 758b8c62b4..2c20be155f 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc @@ -1172,13 +1172,7 @@ Status IrEmitter::HandleFft(HloInstruction* fft) { {int8_ptr_type, int8_ptr_type, int8_ptr_type, int32_type, int32_type, int64_type, int64_type, int64_type, int64_type}, /*isVarArg=*/false); - - bool multi_threaded_eigen = - hlo_module_config_.debug_options().xla_cpu_multi_thread_eigen(); - const char* fn_name = multi_threaded_eigen - ? runtime::kEigenFftSymbolName - : runtime::kEigenSingleThreadedFftSymbolName; - + const char* fn_name = runtime::kEigenFftSymbolName; llvm::Function* fft_func = llvm::cast<llvm::Function>( module_->getOrInsertFunction(fn_name, fft_type)); fft_func->setCallingConv(llvm::CallingConv::C); diff --git a/tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h b/tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h index 0bf693edd0..984cb0616e 100644 --- a/tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h +++ b/tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h @@ -21,6 +21,8 @@ limitations under the License. #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/framework/numeric_types.h" +#include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/platform/types.h" // 'tensorflow' namespace is used so that int64 and other types don't require @@ -69,9 +71,11 @@ void EigenFftR2C(const EigenDevice& device, complex64* out, float* operand, in_dims[0] = input_batch; Eigen::DSizes<Eigen::DenseIndex, FFTRank + 1> out_dims; out_dims[0] = input_batch; + TensorShape temp_shape{input_batch}; for (int i = 0; i < FFTRank; i++) { in_dims[i + 1] = fft_shape[i]; out_dims[i + 1] = i == FFTRank - 1 ? fft_shape[i] / 2 + 1 : fft_shape[i]; + temp_shape.AddDim(fft_shape[i]); } const Eigen::TensorMap<Eigen::Tensor<float, FFTRank + 1, Eigen::RowMajor>, Eigen::Aligned> @@ -84,8 +88,8 @@ void EigenFftR2C(const EigenDevice& device, complex64* out, float* operand, const auto axes = Eigen::ArrayXi::LinSpaced(FFTRank, 1, FFTRank); // Compute the full FFT using a temporary tensor. - Eigen::Tensor<complex64, FFTRank + 1, Eigen::RowMajor> full_fft(in_dims); - + Tensor temp(DataTypeToEnum<complex64>::v(), temp_shape); + auto full_fft = temp.flat_inner_dims<complex64, FFTRank + 1>(); const Eigen::DSizes<Eigen::DenseIndex, FFTRank + 1> zero_start_indices; full_fft.device(device) = input.template fft<Eigen::BothParts, Eigen::FFT_FORWARD>(axes); @@ -108,9 +112,11 @@ void EigenFftC2R(const EigenDevice& device, float* out, complex64* operand, in_dims[0] = input_batch; Eigen::DSizes<Eigen::DenseIndex, FFTRank + 1> out_dims; out_dims[0] = input_batch; + TensorShape temp_shape{input_batch}; for (int i = 0; i < FFTRank; i++) { in_dims[i + 1] = i == FFTRank - 1 ? fft_shape[i] / 2 + 1 : fft_shape[i]; out_dims[i + 1] = fft_shape[i]; + temp_shape.AddDim(fft_shape[i]); } const Eigen::TensorMap<Eigen::Tensor<complex64, FFTRank + 1, Eigen::RowMajor>, Eigen::Aligned> @@ -123,7 +129,8 @@ void EigenFftC2R(const EigenDevice& device, float* out, complex64* operand, // region we will slice from input given fft_shape. We slice input to // fft_shape on its inner-most dimensions, except the last (which we // slice to fft_shape[-1] / 2 + 1). - Eigen::Tensor<complex64, FFTRank + 1, Eigen::RowMajor> full_fft(out_dims); + Tensor temp(DataTypeToEnum<complex64>::v(), temp_shape); + auto full_fft = temp.flat_inner_dims<complex64, FFTRank + 1>(); // Calculate the starting point and range of the source of // negative frequency part. @@ -172,6 +179,7 @@ template <int FFTRank, typename EigenDevice> void EigenFftWithRank(const EigenDevice& device, void* out, void* operand, int32 fft_type, int64 input_batch, int64 fft_length0, int64 fft_length1, int64 fft_length2) { + CHECK(::xla::FftType_IsValid(fft_type)) << fft_type; switch (fft_type) { case ::xla::FftType::FFT: EigenFftC2C<true, FFTRank, EigenDevice>( @@ -196,8 +204,7 @@ void EigenFftWithRank(const EigenDevice& device, void* out, void* operand, input_batch, fft_length0, fft_length1, fft_length2); break; default: - // Unsupported FFT type - abort(); + LOG(FATAL) << "Unsupported FFT type: " << fft_type; } } @@ -223,8 +230,7 @@ void EigenFftImpl(const EigenDevice& device, void* out, void* operand, fft_length1, fft_length2); break; default: - // Unsupported FFT rank - abort(); + LOG(FATAL) << "Unsupported FFT rank " << fft_rank; } } diff --git a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.cc b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.cc deleted file mode 100644 index 2613ddb127..0000000000 --- a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.cc +++ /dev/null @@ -1,32 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h" - -#include "tensorflow/compiler/xla/service/cpu/runtime_fft_impl.h" -#include "tensorflow/core/platform/dynamic_annotations.h" -#include "tensorflow/core/platform/types.h" - -using tensorflow::int32; -using tensorflow::int64; - -TF_ATTRIBUTE_NO_SANITIZE_MEMORY void __xla_cpu_runtime_EigenSingleThreadedFft( - const void* run_options_ptr, void* out, void* operand, int32 fft_type, - int32 fft_rank, int64 input_batch, int64 fft_length0, int64 fft_length1, - int64 fft_length2) { - tensorflow::xla::EigenFftImpl(Eigen::DefaultDevice(), out, operand, fft_type, - fft_rank, input_batch, fft_length0, fft_length1, - fft_length2); -} diff --git a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h deleted file mode 100644 index dcd133d012..0000000000 --- a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h +++ /dev/null @@ -1,31 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_SINGLE_THREADED_FFT_H_ -#define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_SINGLE_THREADED_FFT_H_ - -#include "tensorflow/core/platform/types.h" - -extern "C" { - -extern void __xla_cpu_runtime_EigenSingleThreadedFft( - const void* /* xla::ExecutableRunOptions* */ run_options_ptr, void* out, - void* operand, tensorflow::int32 fft_type, tensorflow::int32 fft_rank, - tensorflow::int64 input_batch, tensorflow::int64 fft_length0, - tensorflow::int64 fft_length1, tensorflow::int64 fft_length2); - -} // extern "C" - -#endif // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_SINGLE_THREADED_FFT_H_ diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc index c4c90515ac..8d8c5e4c44 100644 --- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc +++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc @@ -38,7 +38,6 @@ limitations under the License. #include "tensorflow/compiler/xla/service/cpu/runtime_matmul.h" #include "tensorflow/compiler/xla/service/cpu/runtime_matmul_mkl.h" #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.h" -#include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_fft.h" #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h" #include "tensorflow/compiler/xla/service/cpu/windows_compatibility.h" #include "tensorflow/compiler/xla/types.h" @@ -203,7 +202,6 @@ bool RegisterKnownJITSymbols() { REGISTER_CPU_RUNTIME_SYMBOL(MKLSingleThreadedMatMulF64); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedConvF16); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedConvF32); - REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedFft); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedMatMulF16); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedMatMulF32); REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedMatMulF64); diff --git a/tensorflow/compiler/xla/service/pattern_matcher.h b/tensorflow/compiler/xla/service/pattern_matcher.h index 2515222cf2..d3bc47e61e 100644 --- a/tensorflow/compiler/xla/service/pattern_matcher.h +++ b/tensorflow/compiler/xla/service/pattern_matcher.h @@ -204,7 +204,7 @@ class LayoutPattern { // Modifies the pattern to match only if the layout equals the given proto. // The layout must outlive the returned pattern. constexpr LayoutPattern<LayoutType, LayoutPatternEqualImpl<Impl>> EqualTo( - const ::xla::Layout* layout) const { + const Layout* layout) const { return LayoutPattern<LayoutType, LayoutPatternEqualImpl<Impl>>( LayoutPatternEqualImpl<Impl>(impl_, layout), matched_layout_); } diff --git a/tensorflow/compiler/xla/service/tuple_simplifier.cc b/tensorflow/compiler/xla/service/tuple_simplifier.cc index 77bdcc9de0..e536c8afbf 100644 --- a/tensorflow/compiler/xla/service/tuple_simplifier.cc +++ b/tensorflow/compiler/xla/service/tuple_simplifier.cc @@ -30,17 +30,10 @@ limitations under the License. namespace xla { -TupleSimplifier::TupleSimplifier(bool exclude_entry_computation) : - exclude_entry_computation_(exclude_entry_computation) {} - StatusOr<bool> TupleSimplifier::Run(HloModule* module) { // Initially add all GTE and Tuple instructions to the worklist. std::queue<HloInstruction*> worklist; for (auto* computation : module->computations()) { - if (exclude_entry_computation_ && - computation == module->entry_computation()) { - continue; - } for (auto* instruction : computation->instructions()) { if (instruction->opcode() == HloOpcode::kTuple || instruction->opcode() == HloOpcode::kGetTupleElement) { diff --git a/tensorflow/compiler/xla/service/tuple_simplifier.h b/tensorflow/compiler/xla/service/tuple_simplifier.h index 7509501883..e5e9b10b5b 100644 --- a/tensorflow/compiler/xla/service/tuple_simplifier.h +++ b/tensorflow/compiler/xla/service/tuple_simplifier.h @@ -27,20 +27,13 @@ namespace xla { // the module. class TupleSimplifier : public HloPassInterface { public: - TupleSimplifier() : TupleSimplifier(/*exclude_entry_computation=*/false) {} - explicit TupleSimplifier(bool exclude_entry_computation); + TupleSimplifier() {} ~TupleSimplifier() override {} tensorflow::StringPiece name() const override { return "tuple-simplifier"; } // Run tuple simplification on the given computation. Returns whether the // computation was changed. StatusOr<bool> Run(HloModule* module) override; - - private: - // When set, this pipeline stage will perform optimization of all computations - // apart from the module's entry computation. This is used by Graphcore's - // backend. - bool exclude_entry_computation_; }; } // namespace xla diff --git a/tensorflow/compiler/xla/service/tuple_simplifier_test.cc b/tensorflow/compiler/xla/service/tuple_simplifier_test.cc index d3635eae81..ca9ae91281 100644 --- a/tensorflow/compiler/xla/service/tuple_simplifier_test.cc +++ b/tensorflow/compiler/xla/service/tuple_simplifier_test.cc @@ -42,12 +42,6 @@ class TupleSimplifierTest : public HloTestBase { TF_ASSERT_OK(changed_status.status()); EXPECT_EQ(change_expected, changed_status.ValueOrDie()); } - void Run(HloModule* module, bool change_expected, bool exclude_entry) { - TupleSimplifier simplifier(exclude_entry); - auto changed_status = simplifier.Run(module); - TF_ASSERT_OK(changed_status.status()); - EXPECT_EQ(change_expected, changed_status.ValueOrDie()); - } const Shape scalar_shape_ = ShapeUtil::MakeShape(F32, {}); const Shape tuple_shape_ = ShapeUtil::MakeTupleShape( @@ -217,76 +211,5 @@ TEST_F(TupleSimplifierTest, IncompatibleTuples) { EXPECT_THAT(computation->root_instruction(), tuple); } -TEST_F(TupleSimplifierTest, CanExcludeEntryComputation) { - // Verify that the root computation can be excluded - auto module = CreateNewModule(); - - HloInstruction* p0; - HloInstruction* p1; - HloComputation* c0; - HloComputation* c1; - HloComputation* entry; - - { - HloComputation::Builder builder(TestName() + "_1"); - p0 = builder.AddInstruction( - HloInstruction::CreateParameter(0, tuple_shape_, "param")); - HloInstruction* gte0 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_shape_, p0, 0)); - HloInstruction* gte1 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_shape_, p0, 1)); - HloInstruction* gte2 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_shape_, p0, 2)); - - builder.AddInstruction(HloInstruction::CreateTuple({gte0, gte1, gte2})); - - c0 = module->AddEmbeddedComputation(builder.Build()); - } - { - HloComputation::Builder builder(TestName() + "_2"); - p1 = builder.AddInstruction( - HloInstruction::CreateParameter(0, tuple_shape_, "param")); - HloInstruction* gte0 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_shape_, p1, 0)); - HloInstruction* gte1 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_shape_, p1, 1)); - HloInstruction* gte2 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_shape_, p1, 2)); - - builder.AddInstruction(HloInstruction::CreateTuple({gte0, gte1, gte2})); - - c1 = module->AddEmbeddedComputation(builder.Build()); - } - { - HloComputation::Builder builder(TestName() + "_Entry"); - HloInstruction* tuple_param = builder.AddInstruction( - HloInstruction::CreateParameter(0, tuple_shape_, "param")); - HloInstruction* call0 = builder.AddInstruction( - HloInstruction::CreateCall(tuple_shape_, {tuple_param}, c0)); - HloInstruction* call1 = builder.AddInstruction( - HloInstruction::CreateCall(tuple_shape_, {tuple_param}, c1)); - HloInstruction* gte0 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_shape_, call0, 0)); - HloInstruction* gte1 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_shape_, call1, 1)); - HloInstruction* tuple0 = - builder.AddInstruction(HloInstruction::CreateTuple({gte0, gte1})); - HloInstruction* gte2 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_shape_, tuple0, 0)); - HloInstruction* gte3 = builder.AddInstruction( - HloInstruction::CreateGetTupleElement(scalar_shape_, tuple0, 1)); - - builder.AddInstruction(HloInstruction::CreateTuple({gte2, gte3})); - - entry = module->AddEntryComputation(builder.Build()); - } - - Run(module.get(), /*change_expected=*/true, /*exclude_entry=*/ true); - - EXPECT_THAT(c0->root_instruction(), p0); - EXPECT_THAT(c1->root_instruction(), p1); - EXPECT_THAT(entry->instruction_count(), 9); -} - } // namespace } // namespace xla diff --git a/tensorflow/contrib/autograph/__init__.py b/tensorflow/contrib/autograph/__init__.py index dbdbad8f4c..637e49c082 100644 --- a/tensorflow/contrib/autograph/__init__.py +++ b/tensorflow/contrib/autograph/__init__.py @@ -23,7 +23,6 @@ from __future__ import print_function # TODO(mdan): Bring only the relevant symbols to the top level. from tensorflow.contrib.autograph import utils -from tensorflow.contrib.autograph import operators from tensorflow.contrib.autograph.impl.api import convert from tensorflow.contrib.autograph.impl.api import converted_call from tensorflow.contrib.autograph.impl.api import do_not_convert @@ -44,8 +43,6 @@ _allowed_symbols = [ 'do_not_convert', 'to_code', 'to_graph', - # Overloaded operators - 'operators', # Special functions and directives 'set_element_type', 'set_loop_options', diff --git a/tensorflow/contrib/cmake/tf_c.cmake b/tensorflow/contrib/cmake/tf_c.cmake index 2e0a2fcef4..bda5e26f43 100644 --- a/tensorflow/contrib/cmake/tf_c.cmake +++ b/tensorflow/contrib/cmake/tf_c.cmake @@ -37,15 +37,13 @@ add_dependencies( tf_core_lib tf_protos_cc) -if(tensorflow_BUILD_PYTHON_BINDINGS) - add_library(tf_c_python_api OBJECT - "${tensorflow_source_dir}/tensorflow/c/python_api.cc" - "${tensorflow_source_dir}/tensorflow/c/python_api.h" - ) - add_dependencies( - tf_c_python_api - tf_c - tf_core_lib - tf_core_framework - tf_protos_cc) -endif() +add_library(tf_c_python_api OBJECT + "${tensorflow_source_dir}/tensorflow/c/python_api.cc" + "${tensorflow_source_dir}/tensorflow/c/python_api.h" +) +add_dependencies( + tf_c_python_api + tf_c + tf_core_lib + tf_core_framework + tf_protos_cc) diff --git a/tensorflow/contrib/cmake/tf_cc_ops.cmake b/tensorflow/contrib/cmake/tf_cc_ops.cmake index 6c90cf398c..f73da0b8ab 100644 --- a/tensorflow/contrib/cmake/tf_cc_ops.cmake +++ b/tensorflow/contrib/cmake/tf_cc_ops.cmake @@ -155,7 +155,7 @@ if (WIN32) set (pywrap_tensorflow_lib "${CMAKE_CURRENT_BINARY_DIR}/pywrap_tensorflow_internal.lib") endif() else (WIN32) - set (pywrap_tensorflow_lib "${CMAKE_CURRENT_BINARY_DIR}/libpywrap_tensorflow_internal${CMAKE_SHARED_LIBRARY_SUFFIX}") + set (pywrap_tensorflow_lib "${CMAKE_CURRENT_BINARY_DIR}/libpywrap_tensorflow_internal.so") endif (WIN32) add_custom_target(tf_extension_ops) diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake index 9244604489..a0c3ddd28b 100755 --- a/tensorflow/contrib/cmake/tf_python.cmake +++ b/tensorflow/contrib/cmake/tf_python.cmake @@ -715,7 +715,7 @@ if(WIN32) endif() else() add_custom_command(TARGET pywrap_tensorflow_internal POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_BINARY_DIR}/libpywrap_tensorflow_internal${CMAKE_SHARED_LIBRARY_SUFFIX} + COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_BINARY_DIR}/libpywrap_tensorflow_internal.so ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/_pywrap_tensorflow_internal.so) endif() @@ -832,6 +832,7 @@ add_custom_command(TARGET tf_python_build_pip_package POST_BUILD add_custom_command(TARGET tf_python_copy_scripts_to_destination PRE_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${tensorflow_source_dir}/tensorflow/contrib/testing/python/framework/util_test.py ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/testing/python/framework/) + add_custom_command(TARGET tf_python_build_pip_package POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${tensorflow_source_dir}/tensorflow/tools/pip_package/README ${CMAKE_CURRENT_BINARY_DIR}/tf_python/) diff --git a/tensorflow/contrib/cmake/tools/create_def_file.py b/tensorflow/contrib/cmake/tools/create_def_file.py index 4f957f1e0b..cffe069aa3 100644 --- a/tensorflow/contrib/cmake/tools/create_def_file.py +++ b/tensorflow/contrib/cmake/tools/create_def_file.py @@ -44,8 +44,7 @@ UNDNAME = "undname.exe" DUMPBIN = "dumpbin.exe" # Exclude if matched -EXCLUDE_RE = re.compile(r"RTTI|deleting destructor|::internal::|Internal|" - r"python_op_gen_internal|grappler") +EXCLUDE_RE = re.compile(r"RTTI|deleting destructor|::internal::") # Include if matched before exclude INCLUDEPRE_RE = re.compile(r"google::protobuf::internal::ExplicitlyConstructed|" @@ -57,10 +56,6 @@ INCLUDEPRE_RE = re.compile(r"google::protobuf::internal::ExplicitlyConstructed|" r"tensorflow::ops::internal::Enter|" r"tensorflow::strings::internal::AppendPieces|" r"tensorflow::strings::internal::CatPieces|" - r"tensorflow::errors::Internal|" - r"tensorflow::Tensor::CopyFromInternal|" - r"tensorflow::kernel_factory::" - r"OpKernelRegistrar::InitInternal|" r"tensorflow::io::internal::JoinPathImpl") # Include if matched after exclude @@ -69,7 +64,7 @@ INCLUDE_RE = re.compile(r"^(TF_\w*)$|" r"tensorflow::|" r"functor::|" r"\?nsync_|" - r"stream_executor::") + r"perftools::gputools") # We want to identify data members explicitly in the DEF file, so that no one # can implicitly link against the DLL if they use one of the variables exported diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_bijector_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_bijector_test.py index 795f1993ba..45760a29ee 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_bijector_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sinh_arcsinh_bijector_test.py @@ -151,24 +151,16 @@ class SinhArcsinhBijectorTest(test.TestCase): self.assertAllClose(y, bijector.forward(x).eval(), rtol=1e-4, atol=0.) self.assertAllClose(x, bijector.inverse(y).eval(), rtol=1e-4, atol=0.) - # On IBM PPC systems, longdouble (np.float128) is same as double except that it can have more precision. - # Type double being of 8 bytes, can't hold square of max of float64 (which is also 8 bytes) and - # below test fails due to overflow error giving inf. So this check avoids that error by skipping square - # calculation and corresponding assert. - - if np.amax(y) <= np.sqrt(np.finfo(np.float128).max) and \ - np.fabs(np.amin(y)) <= np.sqrt(np.fabs(np.finfo(np.float128).min)): - - # Do the numpy calculation in float128 to avoid inf/nan. - y_float128 = np.float128(y) - self.assertAllClose( - np.log(np.cosh( - np.arcsinh(y_float128) / tailweight - skewness) / np.sqrt( - y_float128**2 + 1)) - - np.log(tailweight), - bijector.inverse_log_det_jacobian(y, event_ndims=0).eval(), - rtol=1e-4, - atol=0.) + # Do the numpy calculation in float128 to avoid inf/nan. + y_float128 = np.float128(y) + self.assertAllClose( + np.log(np.cosh( + np.arcsinh(y_float128) / tailweight - skewness) / np.sqrt( + y_float128**2 + 1)) - + np.log(tailweight), + bijector.inverse_log_det_jacobian(y, event_ndims=0).eval(), + rtol=1e-4, + atol=0.) self.assertAllClose( -bijector.inverse_log_det_jacobian(y, event_ndims=0).eval(), bijector.forward_log_det_jacobian(x, event_ndims=0).eval(), diff --git a/tensorflow/contrib/eager/python/datasets.py b/tensorflow/contrib/eager/python/datasets.py index adf92c27ea..d7909dd5a2 100644 --- a/tensorflow/contrib/eager/python/datasets.py +++ b/tensorflow/contrib/eager/python/datasets.py @@ -106,8 +106,7 @@ class Iterator(iterator_ops.EagerIterator, checkpointable.CheckpointableBase): target_device=target, buffer_size=10, container="", - shared_name=_generate_shared_name( - "contrib_eager_iterator_function_buffer_resource")) + shared_name=_generate_shared_name("function_buffer_resource")) self._buffer_resource_deleter = resource_variable_ops.EagerResourceDeleter( # pylint: disable=line-too-long handle=self._buffer_resource_handle, handle_device=self._device) diff --git a/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb index 5749f22ac5..4fe3a0e3f3 100644 --- a/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb +++ b/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb @@ -68,7 +68,7 @@ "# simply construct the object. Most layers take as a first argument the number\n", "# of output dimensions / channels.\n", "layer = tf.keras.layers.Dense(100)\n", - "# The number of input dimensions is often unnecessary, as it can be inferred\n", + "# The number of input dimensionss is often unnecessary, as it can be inferred\n", "# the first time the layer is used, but it can be provided if you want to \n", "# specify it manually, which is useful in some complex models.\n", "layer = tf.keras.layers.Dense(10, input_shape=(None, 5))" @@ -267,7 +267,7 @@ " * `build`, where you know the shapes of the input tensors and can do the rest of the initialization\n", " * `call`, where you do the forward computation\n", "\n", - "Note that you don't have to wait until `build` is called to create your variables, you can also create them in `__init__`. However, the advantage of creating them in `build` is that it enables late variable creation based on the shape of the inputs the layer will operate on. On the other hand, creating variables in `__init__` would mean that shapes required to create the variables will need to be explicitly specified." + "Note that you don't have to wait until `build` is called to create your variables, you can also create them in `__init__`. However, the advantage of creating them in `build` is that it enables late variable creation based on the shape of the inputs the layer will operate on. On the other hand, creating variables in `__init__` would mean that shapes requires to create the variables will need to be explicitly specified." ] }, { diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py index 05bcdac2ca..84a413c791 100644 --- a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py +++ b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py @@ -346,8 +346,7 @@ def sequence_numeric_column( key, shape=(1,), default_value=0., - dtype=dtypes.float32, - normalizer_fn=None): + dtype=dtypes.float32): """Returns a feature column that represents sequences of numeric data. Example: @@ -371,12 +370,6 @@ def sequence_numeric_column( default_value: A single value compatible with `dtype` that is used for padding the sparse data into a dense `Tensor`. dtype: The type of values. - normalizer_fn: If not `None`, a function that can be used to normalize the - value of the tensor after `default_value` is applied for parsing. - Normalizer function takes the input `Tensor` as its argument, and returns - the output `Tensor`. (e.g. lambda x: (x - 3.0) / 4.2). Please note that - even though the most common use case of this function is normalization, it - can be used for any kind of Tensorflow transformations. Returns: A `_SequenceNumericColumn`. @@ -390,16 +383,12 @@ def sequence_numeric_column( if not (dtype.is_integer or dtype.is_floating): raise ValueError('dtype must be convertible to float. ' 'dtype: {}, key: {}'.format(dtype, key)) - if normalizer_fn is not None and not callable(normalizer_fn): - raise TypeError( - 'normalizer_fn must be a callable. Given: {}'.format(normalizer_fn)) return _SequenceNumericColumn( key, shape=shape, default_value=default_value, - dtype=dtype, - normalizer_fn=normalizer_fn) + dtype=dtype) def _assert_all_equal_and_return(tensors, name=None): @@ -418,7 +407,7 @@ class _SequenceNumericColumn( fc._SequenceDenseColumn, collections.namedtuple( '_SequenceNumericColumn', - ['key', 'shape', 'default_value', 'dtype', 'normalizer_fn'])): + ['key', 'shape', 'default_value', 'dtype'])): """Represents sequences of numeric data.""" @property @@ -430,10 +419,7 @@ class _SequenceNumericColumn( return {self.key: parsing_ops.VarLenFeature(self.dtype)} def _transform_feature(self, inputs): - input_tensor = inputs.get(self.key) - if self.normalizer_fn is not None: - input_tensor = self.normalizer_fn(input_tensor) - return input_tensor + return inputs.get(self.key) @property def _variable_shape(self): diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py index 45d7b74046..ee74cf56dc 100644 --- a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py +++ b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py @@ -28,7 +28,6 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor -from tensorflow.python.ops import sparse_ops from tensorflow.python.platform import test from tensorflow.python.training import monitored_session @@ -948,7 +947,6 @@ class SequenceNumericColumnTest(test.TestCase): self.assertEqual((1,), a.shape) self.assertEqual(0., a.default_value) self.assertEqual(dtypes.float32, a.dtype) - self.assertIsNone(a.normalizer_fn) def test_shape_saved_as_tuple(self): a = sfc.sequence_numeric_column('aaa', shape=[1, 2]) @@ -967,10 +965,6 @@ class SequenceNumericColumnTest(test.TestCase): ValueError, 'dtype must be convertible to float'): sfc.sequence_numeric_column('aaa', dtype=dtypes.string) - def test_normalizer_fn_must_be_callable(self): - with self.assertRaisesRegexp(TypeError, 'must be a callable'): - sfc.sequence_numeric_column('aaa', normalizer_fn='NotACallable') - def test_get_sequence_dense_tensor(self): sparse_input = sparse_tensor.SparseTensorValue( # example 0, values [[0.], [1]] @@ -991,41 +985,6 @@ class SequenceNumericColumnTest(test.TestCase): self.assertAllEqual( expected_dense_tensor, dense_tensor.eval(session=sess)) - def test_get_sequence_dense_tensor_with_normalizer_fn(self): - - def _increment_two(input_sparse_tensor): - return sparse_ops.sparse_add( - input_sparse_tensor, - sparse_tensor.SparseTensor(((0, 0), (1, 1)), (2.0, 2.0), (2, 2)) - ) - - sparse_input = sparse_tensor.SparseTensorValue( - # example 0, values [[0.], [1]] - # example 1, [[10.]] - indices=((0, 0), (0, 1), (1, 0)), - values=(0., 1., 10.), - dense_shape=(2, 2)) - - # Before _increment_two: - # [[0.], [1.]], - # [[10.], [0.]], - # After _increment_two: - # [[2.], [1.]], - # [[10.], [2.]], - expected_dense_tensor = [ - [[2.], [1.]], - [[10.], [2.]], - ] - numeric_column = sfc.sequence_numeric_column( - 'aaa', normalizer_fn=_increment_two) - - dense_tensor, _ = numeric_column._get_sequence_dense_tensor( - _LazyBuilder({'aaa': sparse_input})) - - with monitored_session.MonitoredSession() as sess: - self.assertAllEqual( - expected_dense_tensor, dense_tensor.eval(session=sess)) - def test_get_sequence_dense_tensor_with_shape(self): """Tests get_sequence_dense_tensor with shape !=(1,).""" sparse_input = sparse_tensor.SparseTensorValue( diff --git a/tensorflow/contrib/ffmpeg/__init__.py b/tensorflow/contrib/ffmpeg/__init__.py index 484ffee3e7..daba965a98 100644 --- a/tensorflow/contrib/ffmpeg/__init__.py +++ b/tensorflow/contrib/ffmpeg/__init__.py @@ -28,6 +28,7 @@ from __future__ import print_function from tensorflow.contrib.ffmpeg.ffmpeg_ops import decode_audio from tensorflow.contrib.ffmpeg.ffmpeg_ops import decode_video from tensorflow.contrib.ffmpeg.ffmpeg_ops import encode_audio +from tensorflow.contrib.ffmpeg.ffmpeg_ops import decode_video from tensorflow.python.util.all_util import remove_undocumented diff --git a/tensorflow/contrib/ffmpeg/ffmpeg_ops.py b/tensorflow/contrib/ffmpeg/ffmpeg_ops.py index b1b5126d9e..020b5c99c6 100644 --- a/tensorflow/contrib/ffmpeg/ffmpeg_ops.py +++ b/tensorflow/contrib/ffmpeg/ffmpeg_ops.py @@ -21,6 +21,7 @@ from __future__ import print_function from tensorflow.contrib.ffmpeg.ops import gen_decode_audio_op_py from tensorflow.contrib.ffmpeg.ops import gen_decode_video_op_py from tensorflow.contrib.ffmpeg.ops import gen_encode_audio_op_py +from tensorflow.contrib.ffmpeg.ops import gen_decode_video_op_py from tensorflow.contrib.util import loader from tensorflow.python.framework import ops from tensorflow.python.platform import resource_loader diff --git a/tensorflow/contrib/framework/__init__.py b/tensorflow/contrib/framework/__init__.py index dc49383c5c..10d1ecc738 100644 --- a/tensorflow/contrib/framework/__init__.py +++ b/tensorflow/contrib/framework/__init__.py @@ -119,13 +119,14 @@ from tensorflow.python.framework.smart_cond import smart_cond from tensorflow.python.framework.smart_cond import smart_constant_value from tensorflow.python.framework.tensor_spec import BoundedTensorSpec from tensorflow.python.framework.tensor_spec import TensorSpec +from tensorflow.python.ops.array_ops import broadcast_to from tensorflow.python.ops.init_ops import convolutional_delta_orthogonal from tensorflow.python.ops.init_ops import convolutional_orthogonal_1d from tensorflow.python.ops.init_ops import convolutional_orthogonal_2d from tensorflow.python.ops.init_ops import convolutional_orthogonal_3d from tensorflow.python.util.all_util import remove_undocumented -_allowed_symbols = ['nest'] +_allowed_symbols = ['nest', 'broadcast_to'] _nest_allowed_symbols = [ 'assert_same_structure', 'is_sequence', diff --git a/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py index a955e21b72..65cb94b5a4 100644 --- a/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py +++ b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py @@ -301,8 +301,8 @@ class FusedConv2DBiasActivationTest(test.TestCase): conv = tensors[i] value = values[i] ref_value = ref_values[i] - tf_logging.info("expected = ", ref_value) - tf_logging.info("actual = ", value) + print("expected = ", ref_value) + print("actual = ", value) tol = 1e-5 if value.dtype == np.float16: tol = 1e-3 @@ -843,8 +843,7 @@ class FusedConvInt8Tests(test.TestCase): vertical_stride, padding_type) output_width = CalculateConvolvedOutputDim(input_width, filter_width, horizontal_stride, padding_type) - tf_logging.info("output_height=", output_height, ", output_width=", - output_width) + print("output_height=", output_height, ", output_width=", output_width) side_input, _, _ = gen_array_ops.quantize_v2( random_ops.random_uniform( @@ -881,8 +880,8 @@ class FusedConvInt8Tests(test.TestCase): with self.test_session( use_gpu=True, config=NoMemoryOptimizationConfig()) as sess: actual_y, expected_y = sess.run([actual, expected]) - tf_logging.info("actual_y = ", actual_y) - tf_logging.info("expected_y = ", expected_y) + print("actual_y = ", actual_y) + print("expected_y = ", expected_y) self.assertTrue(np.array_equal(actual_y, expected_y)) def testFusedConvInt8(self): diff --git a/tensorflow/contrib/hvx/hexagon_controller/src_impl/hexagon_controller.c b/tensorflow/contrib/hvx/hexagon_controller/src_impl/hexagon_controller.c index 2e5c84704f..6a5d982dc8 100644 --- a/tensorflow/contrib/hvx/hexagon_controller/src_impl/hexagon_controller.c +++ b/tensorflow/contrib/hvx/hexagon_controller/src_impl/hexagon_controller.c @@ -19,7 +19,7 @@ limitations under the License. #include "hexagon_controller.h" -#include <stdlib.h> +#include <malloc.h> #include <stdio.h> #include "adspmsgd.h" diff --git a/tensorflow/contrib/lite/download_dependencies.sh b/tensorflow/contrib/lite/download_dependencies.sh index 840015a7fa..436c3e1d4c 100755 --- a/tensorflow/contrib/lite/download_dependencies.sh +++ b/tensorflow/contrib/lite/download_dependencies.sh @@ -30,7 +30,9 @@ if [ ! -f $BZL_FILE_PATH ]; then fi EIGEN_URL="$(grep -o 'http.*bitbucket.org/eigen/eigen/get/.*tar\.gz' "${BZL_FILE_PATH}" | grep -v mirror.bazel | head -n1)" -GEMMLOWP_URL="$(grep -o 'https://mirror.bazel.build/github.com/google/gemmlowp/.*zip' "${BZL_FILE_PATH}" | head -n1)" +# TODO (yongtang): Replace the following with 'https://mirror.bazel.build/github.com/google/gemmlowp/.*zip' once +# the archive has been propagated in mirror.bazel.build. +GEMMLOWP_URL="$(grep -o 'https://github.com/google/gemmlowp/.*zip' "${BZL_FILE_PATH}" | head -n1)" GOOGLETEST_URL="https://github.com/google/googletest/archive/release-1.8.0.tar.gz" ABSL_URL="$(grep -o 'https://github.com/abseil/abseil-cpp/.*tar.gz' "${BZL_FILE_PATH}" | head -n1)" NEON_2_SSE_URL="https://github.com/intel/ARM_NEON_2_x86_SSE/archive/master.zip" diff --git a/tensorflow/contrib/lite/examples/minimal/minimal.cc b/tensorflow/contrib/lite/examples/minimal/minimal.cc index 8b0ace96cc..106e3b0270 100644 --- a/tensorflow/contrib/lite/examples/minimal/minimal.cc +++ b/tensorflow/contrib/lite/examples/minimal/minimal.cc @@ -38,7 +38,7 @@ using namespace tflite; int main(int argc, char *argv[]) { if(argc != 2) { - fprintf(stderr, "minimal <tflite model>\n"); + fprintf(stderr, "Usage: %s <model>\n"); return 1; } const char* filename = argv[1]; diff --git a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md index 965273f0f0..bb2e615eac 100644 --- a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md +++ b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md @@ -128,6 +128,7 @@ TensorFlow operation not listed above are likely unsupported. Notably, the following common ops are not supported at the moment: * [tf.depth_to_space](https://www.tensorflow.org/api_docs/python/tf/depth_to_space) +* [tf.gather](https://www.tensorflow.org/api_docs/python/tf/gather) * [tf.image.resize_bilinear](https://www.tensorflow.org/api_docs/python/tf/image/resize_bilinear) * [tf.tanh](https://www.tensorflow.org/api_docs/python/tf/tanh) @@ -305,19 +306,6 @@ Options { } ``` -**GATHER** - -``` -Inputs { - 0: params tensor - 1: indices tensor - 2: axis tensor (optional) -} -Outputs { - 0: a tensor with same type as the params tensor. -} -``` - **GREATER** ``` diff --git a/tensorflow/contrib/lite/java/ovic/README.md b/tensorflow/contrib/lite/java/ovic/README.md index 26349347fa..5efa70987e 100644 --- a/tensorflow/contrib/lite/java/ovic/README.md +++ b/tensorflow/contrib/lite/java/ovic/README.md @@ -2,7 +2,7 @@ This folder contains building code for track one of the [Low Power ImageNet Recognition Challenge workshop at CVPR 2018.](https://rebootingcomputing.ieee.org/home/sitemap/14-lpirc/80-low-power-image-recognition-challenge-lpirc-2018) -## Pre-requisite +## Pre-requesits Follow the steps [here](https://www.tensorflow.org/mobile/tflite/demo_android) to install Tensorflow, Bazel, and the Android NDK and SDK. @@ -49,7 +49,7 @@ Once you have a submission that follows the instructions from the [competition s You can call the validator binary below to verify that your model fits the format requirements. This often helps you to catch size mismatches (e.g. output should be [1, 1001] instead of [1,1,1,1001]). Let say the submission file is located at `/path/to/my_model.lite`, then call: ```sh -bazel build --cxxopt=--std=c++11 //tensorflow/contrib/lite/java/ovic:ovic_validator --cxxopt=-Wno-all +bazel build --cxxopt--std=c++11 //tensorflow/contrib/lite/java/ovic:ovic_validator --cxxopt=-Wno-all bazel-bin/tensorflow/contrib/lite/java/ovic/ovic_validator /path/to/my_model.lite ``` diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index 1908f7fa6c..a2f192bbc2 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -1934,7 +1934,7 @@ inline void LstmCell(const float* input_data, const Dims<4>& input_dims, // The quantization of the input, output arrays is as follows: // - The input activations are quantized as uint8 on the interval // [-1, 127/128]. -// The rationale for that is that is the natural interval for output +// The rationale for that is that that is the natural interval for output // activations (see next point) and these need to be concatenated together. // We could accommodate different ranges by re-scaling, but we empirically // found that setting the input activations range to be [-1, 127/128] in the @@ -1999,7 +1999,7 @@ inline void LstmCell(const float* input_data, const Dims<4>& input_dims, // However, for a fixed-point implementation in 16-bit integers, using 5 // integer bits to represent the [-16, 16] range would leave only 11 // fractional bits, giving an increment of 2^-11 = 4.9e-4 between consecutive -// representable values. Notice that is higher than the +// representable values. Notice that that is higher than the // worst-case clamping error with clamping to [-8, 8]: 3.4e-4 for Logistic. // Using [-8, 8] thus seems like the better compromise overall, enjoying // an increment of 2.4e-4 between representable values and a worst-case diff --git a/tensorflow/contrib/lite/python/interpreter.py b/tensorflow/contrib/lite/python/interpreter.py index fd90823425..9400e757b9 100644 --- a/tensorflow/contrib/lite/python/interpreter.py +++ b/tensorflow/contrib/lite/python/interpreter.py @@ -55,7 +55,7 @@ class Interpreter(object): elif model_content and not model_path: self._interpreter = ( _interpreter_wrapper.InterpreterWrapper_CreateWrapperCPPFromBuffer( - model_content)) + model_content, len(model_content))) if not self._interpreter: raise ValueError( 'Failed to create model from {} bytes'.format(len(model_content))) diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc index b283551c45..f705551fcb 100644 --- a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc @@ -397,14 +397,9 @@ InterpreterWrapper* InterpreterWrapper::CreateWrapperCPPFromFile( } InterpreterWrapper* InterpreterWrapper::CreateWrapperCPPFromBuffer( - PyObject* data) { - char * buf = nullptr; - Py_ssize_t length; - if (PY_TO_CPPSTRING(data, &buf, &length) == -1) { - return nullptr; - } + const char* data, size_t len) { std::unique_ptr<tflite::FlatBufferModel> model = - tflite::FlatBufferModel::BuildFromBuffer(buf, length); + tflite::FlatBufferModel::BuildFromBuffer(data, len); return model ? new InterpreterWrapper(std::move(model)) : nullptr; } diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h index cbeb53bee7..b0ed7c4559 100644 --- a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h @@ -40,7 +40,8 @@ class InterpreterWrapper { static InterpreterWrapper* CreateWrapperCPPFromFile(const char* model_path); // SWIG caller takes ownership of pointer. - static InterpreterWrapper* CreateWrapperCPPFromBuffer(PyObject* data); + static InterpreterWrapper* CreateWrapperCPPFromBuffer(const char* data, + size_t len); ~InterpreterWrapper(); bool AllocateTensors(); diff --git a/tensorflow/contrib/lite/python/lite.py b/tensorflow/contrib/lite/python/lite.py index 88dda7290b..0913cd2c5c 100644 --- a/tensorflow/contrib/lite/python/lite.py +++ b/tensorflow/contrib/lite/python/lite.py @@ -34,8 +34,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from six import PY3 - from google.protobuf import text_format as _text_format from google.protobuf.message import DecodeError from tensorflow.contrib.lite.python import lite_constants as constants @@ -56,7 +54,6 @@ from tensorflow.python.framework.importer import import_graph_def from tensorflow.python.ops.variables import global_variables_initializer from tensorflow.python.saved_model import signature_constants from tensorflow.python.saved_model import tag_constants -# from tensorflow.python.util.all_util import remove_undocumented class TocoConverter(object): @@ -206,12 +203,6 @@ class TocoConverter(object): except (_text_format.ParseError, DecodeError): try: print("Ignore 'tcmalloc: large alloc' warnings.") - - if not isinstance(file_content, str): - if PY3: - file_content = file_content.decode('utf-8') - else: - file_content = file_content.encode('utf-8') _text_format.Merge(file_content, graph_def) except (_text_format.ParseError, DecodeError): raise ValueError( @@ -391,5 +382,3 @@ def _freeze_graph(sess, output_tensors): output_arrays) else: return sess.graph_def - -# remove_undocumented(__name__) diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc index 5c7fa09891..e33b430937 100644 --- a/tensorflow/contrib/lite/toco/import_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc @@ -178,7 +178,7 @@ ArrayDataType ConvertDataType(tensorflow::DataType dtype) { else if (dtype == DT_STRING) return ArrayDataType::kString; else - LOG(INFO) << "Unsupported data type in placeholder op: " << dtype; + LOG(INFO) << "Unsupported data type in placehoder op: " << dtype; return ArrayDataType::kNone; } diff --git a/tensorflow/contrib/lite/toco/toco_port.cc b/tensorflow/contrib/lite/toco/toco_port.cc index de76fd4032..1b21c8bc60 100644 --- a/tensorflow/contrib/lite/toco/toco_port.cc +++ b/tensorflow/contrib/lite/toco/toco_port.cc @@ -20,12 +20,6 @@ limitations under the License. #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/platform/logging.h" -#if defined(__ANDROID__) && defined(__ARM_ARCH_7A__) -namespace std { -double round(double x) { return ::round(x); } -} // namespace std -#endif - namespace toco { namespace port { void CopyToBuffer(const string& src, char* dest) { diff --git a/tensorflow/contrib/lite/toco/toco_port.h b/tensorflow/contrib/lite/toco/toco_port.h index 17f82b9dd7..5c019cb2bf 100644 --- a/tensorflow/contrib/lite/toco/toco_port.h +++ b/tensorflow/contrib/lite/toco/toco_port.h @@ -34,24 +34,6 @@ limitations under the License. #define TFLITE_PROTO_NS google::protobuf #endif -#ifdef __ANDROID__ -#include <sstream> -namespace std { - -template <typename T> -std::string to_string(T value) -{ - std::ostringstream os ; - os << value ; - return os.str() ; -} - -#ifdef __ARM_ARCH_7A__ -double round(double x); -#endif -} -#endif - namespace toco { namespace port { diff --git a/tensorflow/contrib/makefile/compile_nsync.sh b/tensorflow/contrib/makefile/compile_nsync.sh index a28fc3a87f..e8c6edd7ba 100755 --- a/tensorflow/contrib/makefile/compile_nsync.sh +++ b/tensorflow/contrib/makefile/compile_nsync.sh @@ -270,7 +270,7 @@ for arch in $archs; do PLATFORM_LDFLAGS=-pthread MKDEP=${CC} -M -std=c++11 PLATFORM_C=../../platform/c++11/src/nsync_semaphore_mutex.cc \ - ../../platform/posix/src/per_thread_waiter.c \ + ../../platform/c++11/src/per_thread_waiter.cc \ ../../platform/c++11/src/yield.cc \ ../../platform/c++11/src/time_rep_timespec.cc \ ../../platform/c++11/src/nsync_panic.cc diff --git a/tensorflow/contrib/makefile/download_dependencies.sh b/tensorflow/contrib/makefile/download_dependencies.sh index 48953e2e38..eff9081e35 100755 --- a/tensorflow/contrib/makefile/download_dependencies.sh +++ b/tensorflow/contrib/makefile/download_dependencies.sh @@ -27,7 +27,9 @@ if [ ! -f $BZL_FILE_PATH ]; then fi EIGEN_URL="$(grep -o 'http.*bitbucket.org/eigen/eigen/get/.*tar\.gz' "${BZL_FILE_PATH}" | grep -v mirror.bazel | head -n1)" -GEMMLOWP_URL="$(grep -o 'https://mirror.bazel.build/github.com/google/gemmlowp/.*zip' "${BZL_FILE_PATH}" | head -n1)" +# TODO (yongtang): Replace the following with 'https://mirror.bazel.build/github.com/google/gemmlowp/.*zip' once +# the archive has been propagated in mirror.bazel.build. +GEMMLOWP_URL="$(grep -o 'https://github.com/google/gemmlowp/.*zip' "${BZL_FILE_PATH}" | head -n1)" GOOGLETEST_URL="https://github.com/google/googletest/archive/release-1.8.0.tar.gz" NSYNC_URL="$(grep -o 'https://mirror.bazel.build/github.com/google/nsync/.*tar\.gz' "${BZL_FILE_PATH}" | head -n1)" PROTOBUF_URL="$(grep -o 'https://mirror.bazel.build/github.com/google/protobuf/.*tar\.gz' "${BZL_FILE_PATH}" | head -n1)" diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops.py b/tensorflow/contrib/metrics/python/ops/metric_ops.py index a6be2084aa..2ed99d50a4 100644 --- a/tensorflow/contrib/metrics/python/ops/metric_ops.py +++ b/tensorflow/contrib/metrics/python/ops/metric_ops.py @@ -2503,7 +2503,7 @@ def _compute_recall_at_precision(tp, fp, fn, precision, name): name: An optional variable_scope name. Returns: - The recall at a given `precision`. + The recall at a the given `precision`. """ precisions = math_ops.div(tp, tp + fp + _EPSILON) tf_index = math_ops.argmin( diff --git a/tensorflow/contrib/mpi_collectives/kernels/ring.h b/tensorflow/contrib/mpi_collectives/kernels/ring.h index c001615d3f..1d56d588bc 100644 --- a/tensorflow/contrib/mpi_collectives/kernels/ring.h +++ b/tensorflow/contrib/mpi_collectives/kernels/ring.h @@ -129,7 +129,7 @@ cudaStream_t CudaStreamForMPI(); * has the fully accumulated Segment 1; and so on. The scatter-reduce is * complete. * - * Next, the allgather distributes these fully accumulated chunks across all + * Next, the allgather distributes these fully accumululated chunks across all * nodes. Communication proceeds in the same ring, once again in N-1 steps. At * the ith step, node j will send chunk (j - i + 1) and receive chunk (j - i). * For example, at the first iteration, the following transfers will occur: diff --git a/tensorflow/contrib/opt/python/training/adamax_test.py b/tensorflow/contrib/opt/python/training/adamax_test.py index 915e6504e1..21bf3f5313 100644 --- a/tensorflow/contrib/opt/python/training/adamax_test.py +++ b/tensorflow/contrib/opt/python/training/adamax_test.py @@ -224,10 +224,8 @@ class AdaMaxOptimizerTest(test.TestCase): var1_np, m1, v1 = adamax_update_numpy(var1_np, grads1_np, t, m1, v1) # Validate updated params - self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0), - rtol=1e-2) - self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1), - rtol=1e-2) + self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0)) + self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1)) if use_resource: self.assertEqual("var0_%d/AdaMax:0" % (i,), opt.get_slot(var=var0, name="m").name) diff --git a/tensorflow/contrib/opt/python/training/model_average_optimizer.py b/tensorflow/contrib/opt/python/training/model_average_optimizer.py index b6b10e500b..a7c97a1da2 100644 --- a/tensorflow/contrib/opt/python/training/model_average_optimizer.py +++ b/tensorflow/contrib/opt/python/training/model_average_optimizer.py @@ -62,7 +62,7 @@ class ModelAverageCustomGetter(object): """ def __init__(self, worker_device): - """Create a new `ModelAverageCustomGetter`. + """Create a new `ElasticAverageCustomGetter`. Args: worker_device: String. Name of the `worker` job. diff --git a/tensorflow/contrib/periodic_resample/BUILD b/tensorflow/contrib/periodic_resample/BUILD index aad1ca04c5..6ca7fe8b6e 100644 --- a/tensorflow/contrib/periodic_resample/BUILD +++ b/tensorflow/contrib/periodic_resample/BUILD @@ -6,13 +6,12 @@ exports_files(["LICENSE"]) load( "//tensorflow:tensorflow.bzl", - "tf_cc_test", + "py_test", "tf_gen_op_libs", "tf_custom_op_library", "tf_custom_op_py_library", "tf_gen_op_wrapper_py", ) -load("//tensorflow:tensorflow.bzl", "py_test") cc_library( name = "all_ops", @@ -85,23 +84,6 @@ py_test( ":init_py", "//tensorflow/contrib/util:util_py", "//tensorflow/python:framework_test_lib", - "//tensorflow/python:gradient_checker", - ], -) - -tf_cc_test( - name = "periodic_resample_op_cc_test", - size = "small", - srcs = [ - "ops/array_ops_test.cc", - ], - deps = [ - ":all_ops", - "//tensorflow/core:framework", - "//tensorflow/core:protos_all_proto", - "//tensorflow/core:test", - "//tensorflow/core:test_main", - "//tensorflow/core:testlib", ], ) diff --git a/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.cc b/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.cc index 514689cf45..e18923c8aa 100644 --- a/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.cc +++ b/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.cc @@ -22,9 +22,4 @@ namespace tensorflow { REGISTER_KERNEL_BUILDER(Name("PeriodicResample").Device(DEVICE_CPU), PeriodicResampleOp); - -REGISTER_KERNEL_BUILDER(Name("PeriodicResampleOpGrad") - .Device(DEVICE_CPU), - PeriodicResampleOpGrad); - } // namespace tensorflow diff --git a/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.h b/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.h index 42fba81a5c..3ab588c458 100644 --- a/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.h +++ b/tensorflow/contrib/periodic_resample/kernels/periodic_resample_op.h @@ -25,202 +25,92 @@ #include "tensorflow/core/framework/shape_inference.h" #include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/lib/core/status.h" -#include "tensorflow/core/util/work_sharder.h" namespace { -// Computes input tensor index for given output index during forward -// propagation through periodic_resample operation. -class InputIndexer { - public: - InputIndexer(const std::vector<tensorflow::int64>& output_dimensions, - const tensorflow::TensorShape& input_shape, - int adjustable_dimension) - : output_dimensions_(output_dimensions), - adjustable_dimension_(adjustable_dimension), - rank_(input_shape.dims()), - linear_output_index_(0), - linear_input_index_(0), - adjustable_dimension_carriage_sum_(0) { - auto input_dimensions = TensorShapeToVector(input_shape); - // factors by which input_dimensions increases/decreases w.r.t. - // output_dimensions - dimension_ceiling_ = - ComputeDimensionCeiling(output_dimensions, input_dimensions); - cumulative_dimensions_ = ComputeCumulativeDimensions(); - - output_indices_.resize(output_dimensions_.size()); - input_indices_.resize(output_dimensions_.size()); - - // Compute index_factors - index_factors_.resize(rank_); - tensorflow::int64 last_index_factor = 1; - for (auto r = rank_ - 1; r >= 0; --r) { - index_factors_[r] = last_index_factor; - last_index_factor *= input_dimensions[r]; - } - } - - tensorflow::int64 linear_input_index() const { return linear_input_index_; } - - void MoveToOutputIndex(tensorflow::int64 output_index); - void IncrementOutputIndex(); - - private: - void RecomputeInputAdjustableDimensionIndex() { - tensorflow::int64 index = adjustable_dimension_carriage_sum_; - index *= output_dimensions_[adjustable_dimension_]; - index += output_indices_[adjustable_dimension_]; - input_indices_[adjustable_dimension_] = index; - } - - std::vector<tensorflow::int64> TensorShapeToVector( - const tensorflow::TensorShape& tensor_shape); - - std::vector<tensorflow::int64> ComputeDimensionCeiling( - const std::vector<tensorflow::int64>& output_dimensions, - const std::vector<tensorflow::int64>& input_dimensions); - - std::vector<tensorflow::int64> ComputeCumulativeDimensions(); - - const std::vector<tensorflow::int64> output_dimensions_; - std::vector<tensorflow::int64> dimension_ceiling_; - std::vector<tensorflow::int64> index_factors_; - std::vector<tensorflow::int64> cumulative_dimensions_; - std::vector<tensorflow::int64> output_indices_; - std::vector<tensorflow::int64> input_indices_; - - const int adjustable_dimension_; - const int rank_; - tensorflow::int64 linear_output_index_; - tensorflow::int64 linear_input_index_; - tensorflow::int64 adjustable_dimension_carriage_sum_; -}; - -void InputIndexer::MoveToOutputIndex(tensorflow::int64 output_index) { - linear_output_index_ = output_index; - linear_input_index_ = 0; +template <class IndexVecT, class IndexT> +IndexT compute_input_index( + IndexVecT* target_dimensions, const IndexT& output_index, + const IndexVecT& original_dimensions, const int& adjustable_dimension, + const std::vector<tensorflow::int64>& dimension_ceiling, + const std::vector<tensorflow::int64>& cumulative_dimensions, IndexT* result, + std::vector<IndexT>* output_indices, const int& rank) { + *result = 0; + output_indices->clear(); // un-rasterize the output index auto last_reduced_i = output_index; - for (auto r = rank_ - 1; r >= 0; --r) { - output_indices_[r] = last_reduced_i % output_dimensions_[r]; + for (auto r = rank - 1; r >= 0; --r) { + (*output_indices)[r] = last_reduced_i % (*target_dimensions)[r]; last_reduced_i = - (last_reduced_i - output_indices_[r]) / output_dimensions_[r]; + (last_reduced_i - (*output_indices)[r]) / (*target_dimensions)[r]; } - tensorflow::int64 carriage_sum = 0; - for (int qi = 0; qi < rank_; ++qi) { - if (qi == adjustable_dimension_) continue; - carriage_sum += cumulative_dimensions_[qi] * - (output_indices_[qi] % dimension_ceiling_[qi]); - } - adjustable_dimension_carriage_sum_ = carriage_sum; - // rasterize the input index - for (auto r = rank_ - 1; r >= 0; --r) { - if (r != adjustable_dimension_) { - input_indices_[r] = output_indices_[r] / dimension_ceiling_[r]; - } else { - RecomputeInputAdjustableDimensionIndex(); - } - } - for (auto r = rank_ - 1; r >= 0; --r) { - linear_input_index_ += index_factors_[r] * input_indices_[r]; - } -} - -void InputIndexer::IncrementOutputIndex() { - linear_output_index_++; - for (auto r = rank_ - 1; r >= 0; --r) { - auto old_carriage_sum_increment = - cumulative_dimensions_[r] * - (output_indices_[r] % dimension_ceiling_[r]); - output_indices_[r] = (output_indices_[r] + 1) % output_dimensions_[r]; - if (r != adjustable_dimension_) { - auto new_input_index = output_indices_[r] / dimension_ceiling_[r]; - linear_input_index_ += - (new_input_index - input_indices_[r]) * index_factors_[r]; - - input_indices_[r] = new_input_index; - - auto new_carriage_sum_increment = - cumulative_dimensions_[r] * - (output_indices_[r] % dimension_ceiling_[r]); - - adjustable_dimension_carriage_sum_ = adjustable_dimension_carriage_sum_ - - old_carriage_sum_increment + - new_carriage_sum_increment; - } - - if (output_indices_[r] != 0) { - // No more carries to higher indices. - break; + IndexT last_index_factor = 1; + for (auto r = rank - 1; r >= 0; --r) { + IndexT index = 0; + if (r != adjustable_dimension) + index = (*output_indices)[r] / dimension_ceiling[r]; + else { + for (int qi = 0; qi < rank; ++qi) { + if (qi == adjustable_dimension) continue; + index += cumulative_dimensions[qi] * + ((*output_indices)[qi] % dimension_ceiling[qi]); + } + index *= (*target_dimensions)[adjustable_dimension]; + index += (*output_indices)[r]; } + *result += last_index_factor * index; + last_index_factor *= original_dimensions[r]; } - auto old_adjustable_dimension_input_index = - input_indices_[adjustable_dimension_]; - RecomputeInputAdjustableDimensionIndex(); - linear_input_index_ += (input_indices_[adjustable_dimension_] - - old_adjustable_dimension_input_index) * - index_factors_[adjustable_dimension_]; -} -std::vector<tensorflow::int64> InputIndexer::TensorShapeToVector( - const tensorflow::TensorShape& tensor_shape) { - std::vector<tensorflow::int64> result(tensor_shape.dims()); - int count = 0; - for (const auto dim_info : tensor_shape) { - result[count] = dim_info.size; - ++count; - } - return result; + return *result; } -std::vector<tensorflow::int64> InputIndexer::ComputeDimensionCeiling( - const std::vector<tensorflow::int64>& output_dimensions, - const std::vector<tensorflow::int64>& input_dimensions) { - std::vector<tensorflow::int64> dimension_ceiling(input_dimensions.size()); - for (size_t i = 0; i < input_dimensions.size(); ++i) { - dimension_ceiling[i] = (output_dimensions[i] + input_dimensions[i] - 1) / - input_dimensions[i]; - } - return dimension_ceiling; -} +template <class InputDataT, + class IndexVecT> // both types are needed here b/c IndexVecT and + // InputDataT are not related + void + fill_periodic_tensor( + tensorflow::OpKernelContext* context, + const IndexVecT& desired_shape, + const tensorflow::Tensor& input_tensor) { + // input is a strided array (last index is fastest, C-ordered) + auto input = input_tensor.flat<InputDataT>(); + const int rank = input_tensor.dims(); + // original and target dimensions + std::vector<tensorflow::int64> original_dimensions(rank), + target_dimensions(rank); + tensorflow::int64 total_size(input_tensor.NumElements()), new_sliced_size(1); + // factors by which original_dimensions increases/decreases w.r.t. + // target_dimensions + std::vector<tensorflow::int64> dimension_ceiling(rank), + cumulative_dimensions(rank); + // index of adjustable dimension + int adjustable_dimension; + tensorflow::TensorShape output_shape; -std::vector<tensorflow::int64> InputIndexer::ComputeCumulativeDimensions() { - std::vector<tensorflow::int64> cumulative_dimensions(rank_); - int count = 0; - for (int i = 0; i < rank_; ++i) { - if (count == 0) { - cumulative_dimensions[count] = 1; - } else { - cumulative_dimensions[count] = - cumulative_dimensions[count - 1] * dimension_ceiling_[count - 1]; - } - ++count; - } - return cumulative_dimensions; -} + // requires that the rank of the input tensor and length of the desired shape + // are equal + OP_REQUIRES(context, rank == desired_shape.size(), + tensorflow::errors::InvalidArgument( + "periodic_resample expects the rank of the input tensor, ", + rank, ", to be the same as the length of the desired shape, ", + desired_shape.size(), ".")); -template <typename IndexVecT> -void process_desired_shape(tensorflow::OpKernelContext* context, - const tensorflow::TensorShape& input_tensor_shape, - const IndexVecT& desired_shape, - int* adjustable_dimension, - std::vector<tensorflow::int64>* target_dimensions, - tensorflow::int64* output_size) { - tensorflow::int64 new_sliced_size = 1; bool found = false; - const int rank = input_tensor_shape.dims(); + const auto& input_tensor_shape = input_tensor.shape(); + for (int i = 0; i < rank; ++i) { + // if (desired_shape(i) < 1) { if (desired_shape[i] < 1) { // only one index can be adjustable OP_REQUIRES(context, !found, tensorflow::errors::InvalidArgument( "periodic_resample expects only " "one index to be marked as adjustable.")); - *adjustable_dimension = i; + adjustable_dimension = i; found = true; } else { OP_REQUIRES( @@ -232,8 +122,9 @@ void process_desired_shape(tensorflow::OpKernelContext* context, i, " input tensor has size ", input_tensor_shape.dim_size(i), ", desired shape has size ", desired_shape[i], ".")); - (*target_dimensions)[i] = desired_shape[i]; - new_sliced_size *= (*target_dimensions)[i]; + // target_dimensions[i] = desired_shape(i); + target_dimensions[i] = desired_shape[i]; + new_sliced_size *= target_dimensions[i]; } } // at least one index needs to be adjustable @@ -241,50 +132,26 @@ void process_desired_shape(tensorflow::OpKernelContext* context, tensorflow::errors::InvalidArgument( "periodic_resample expects at least " "one index to be marked as adjustable.")); - (*target_dimensions)[*adjustable_dimension] = - input_tensor_shape.num_elements() / new_sliced_size; - - *output_size = new_sliced_size * (*target_dimensions)[*adjustable_dimension]; -} - -// Heuristic number based on measurements on -// Intel(R) Core(TM) i7-4930K CPU @ 3.40GHz -const tensorflow::int64 costPerFillIndex = 35; -enum class Mode { - kForward, - kGradient -}; - -// Computes either periodic_resample operation output or gradients for it, -// depending on |mode|. -// |original_shape| is always shape of input to periodic_resample operation. -// |source_tensor| is either source for periodic_resample (for forward mode) -// or gradients tensor. -// |desired_shape| is always shape, provided by user, to which forward -// propagation attempts resample input tensor. -template <class InputDataT, Mode mode> -void -do_periodic_resample_op(tensorflow::OpKernelContext* context, - const tensorflow::TensorShape& original_shape, - const tensorflow::PartialTensorShape& desired_shape, - const tensorflow::Tensor& source_tensor) { - const int rank = source_tensor.dims(); + int count = 0; + for (const auto dim_info : input_tensor.shape()) { + original_dimensions[count] = dim_info.size; + ++count; + } - // requires that the rank of the input tensor and length of the desired shape - // are equal - OP_REQUIRES(context, rank == desired_shape.dims(), - tensorflow::errors::InvalidArgument( - "periodic_resample expects the rank of the input tensor, ", - rank, ", to be the same as the length of the desired shape, ", - desired_shape.dims(), ".")); + target_dimensions[adjustable_dimension] = total_size / new_sliced_size; - std::vector<tensorflow::int64> target_dimensions(rank); - tensorflow::int64 new_size = 0; - // index of adjustable dimension - int adjustable_dimension = 0; - process_desired_shape(context, original_shape, desired_shape.dim_sizes(), - &adjustable_dimension, &target_dimensions, &new_size); + count = 0; + for (int i = 0; i < input_tensor.shape().dims(); ++i) { + dimension_ceiling[count] = tensorflow::int64(std::ceil( + float(target_dimensions[count]) / float(original_dimensions[count]))); + if (count == 0) + cumulative_dimensions[count] = 1; + else + cumulative_dimensions[count] = + cumulative_dimensions[count - 1] * dimension_ceiling[count - 1]; + ++count; + } // ensure that the new dimension is greater than zero OP_REQUIRES(context, target_dimensions[adjustable_dimension] > 0, @@ -293,14 +160,11 @@ do_periodic_resample_op(tensorflow::OpKernelContext* context, "adjustable dimension, ", adjustable_dimension, ", isn't greater than zero, ", target_dimensions[adjustable_dimension], ".")); - tensorflow::TensorShape output_shape; - if (mode == Mode::kForward) { - for (int i = 0; i < rank; ++i) { - output_shape.AddDim(target_dimensions[i]); - } - } else { - output_shape = original_shape; + for (int i = 0; i < rank; ++i) { + output_shape.AddDim(target_dimensions[i]); } + const auto new_size = + new_sliced_size * target_dimensions[adjustable_dimension]; // Create an output tensor and attach it to the current context tensorflow::Tensor* output_tensor = nullptr; @@ -308,73 +172,47 @@ do_periodic_resample_op(tensorflow::OpKernelContext* context, context->allocate_output(0, output_shape, &output_tensor)); auto output = output_tensor->flat<InputDataT>(); - // input is a strided array (last index is fastest, C-ordered) - auto input = source_tensor.flat<InputDataT>(); + // memory is allocated for these variables outside the inner loop for + // efficiency (although, I could create a separate class scope for + // this purpose instead) + tensorflow::int64 result = 0; + std::vector<tensorflow::int64> output_indices(target_dimensions.size()); // Fill output tensor with periodically resampled input tensor values - InputIndexer input_indexer(target_dimensions, original_shape, - adjustable_dimension); - - auto worker_threads = *(context->device()->tensorflow_cpu_worker_threads()); - auto fill_output_tensor = [&input_indexer, &output, &input]( - tensorflow::int64 start, tensorflow::int64 limit) { - InputIndexer local_indexer(input_indexer); - local_indexer.MoveToOutputIndex(start); - for (tensorflow::int64 output_index = start; output_index < limit; - ++output_index) { - if (mode == Mode::kForward) { - output(output_index) = input(local_indexer.linear_input_index()); - } else { - output(local_indexer.linear_input_index()) = input(output_index); - } - local_indexer.IncrementOutputIndex(); - } - }; - ::tensorflow::Shard(worker_threads.num_threads, worker_threads.workers, - new_size, costPerFillIndex, fill_output_tensor); -} - -#define DATA_TYPE_SWITCH(data_type, context, CASE) \ - switch (data_type) { \ - CASE(float) \ - CASE(double) \ - CASE(tensorflow::int32) \ - CASE(tensorflow::int64) \ - default: \ - context->CtxFailure(__FILE__, __LINE__, \ - tensorflow::errors::InvalidArgument( \ - "Unsuppored tensor elements type")); \ - break; \ + for (tensorflow::int64 output_index = 0; output_index < new_size; + ++output_index) { + output(output_index) = input(compute_input_index( + &target_dimensions, output_index, original_dimensions, + adjustable_dimension, dimension_ceiling, cumulative_dimensions, &result, + &output_indices, rank)); } +} void create_output_tensor( tensorflow::OpKernelContext* context, const tensorflow::Tensor& input_tensor, const tensorflow::DataType& input_tensor_type, - const tensorflow::PartialTensorShape& desired_shape) { -#define CASE(type) \ - case tensorflow::DataTypeToEnum<type>::value: \ - do_periodic_resample_op<type, Mode::kForward>( \ - context, input_tensor.shape(), desired_shape, input_tensor); \ - break; + const tensorflow::PartialTensorShape& desired_shape_tensor) { + auto desired_shape = desired_shape_tensor.dim_sizes(); - DATA_TYPE_SWITCH(input_tensor_type, context, CASE); -#undef CASE -} - -void create_grad_tensor(tensorflow::OpKernelContext* context, - const tensorflow::Tensor& grad_tensor, - const tensorflow::DataType& grad_tensor_type, - const tensorflow::TensorShape& original_shape, - const tensorflow::PartialTensorShape& desired_shape) { -#define CASE(type) \ - case tensorflow::DataTypeToEnum<type>::value: \ - do_periodic_resample_op<type, Mode::kGradient>( \ - context, original_shape, desired_shape, grad_tensor); \ + // obligatory type switch + switch (input_tensor_type) { + case tensorflow::DataTypeToEnum<float>::value: + fill_periodic_tensor<float>(context, desired_shape, input_tensor); break; - - DATA_TYPE_SWITCH(grad_tensor_type, context, CASE); -#undef CASE + case tensorflow::DataTypeToEnum<double>::value: + fill_periodic_tensor<double>(context, desired_shape, input_tensor); + break; + case tensorflow::DataTypeToEnum<tensorflow::int32>::value: + fill_periodic_tensor<tensorflow::int32>(context, desired_shape, + input_tensor); + break; + case tensorflow::DataTypeToEnum<tensorflow::int64>::value: + fill_periodic_tensor<tensorflow::int64>(context, desired_shape, + input_tensor); + break; + default:; + } } } // namespace @@ -400,25 +238,4 @@ class PeriodicResampleOp : public tensorflow::OpKernel { tensorflow::PartialTensorShape desired_shape; }; -class PeriodicResampleOpGrad : public tensorflow::OpKernel { - public: - explicit PeriodicResampleOpGrad(tensorflow::OpKernelConstruction* context) - : tensorflow::OpKernel(context) { - OP_REQUIRES_OK(context, - context->GetAttr("original_shape", &original_shape)); - OP_REQUIRES_OK(context, context->GetAttr("desired_shape", &desired_shape)); - } - - void Compute(tensorflow::OpKernelContext* context) override { - const tensorflow::Tensor& grad_tensor = context->input(0); - const tensorflow::DataType grad_tensor_type = context->input_dtype(0); - create_grad_tensor(context, grad_tensor, grad_tensor_type, original_shape, - desired_shape); - } - - private: - tensorflow::TensorShape original_shape; - tensorflow::PartialTensorShape desired_shape; -}; - #endif // TENSORFLOW_KERNELS_PERIODICRESAMPLE_OP_H_ diff --git a/tensorflow/contrib/periodic_resample/ops/array_ops.cc b/tensorflow/contrib/periodic_resample/ops/array_ops.cc index fd38cd09b4..82bd796956 100644 --- a/tensorflow/contrib/periodic_resample/ops/array_ops.cc +++ b/tensorflow/contrib/periodic_resample/ops/array_ops.cc @@ -26,42 +26,7 @@ REGISTER_OP("PeriodicResample") .Input("values: T") .Attr("shape: shape") .Output("output: T") - .SetShapeFn([](shape_inference::InferenceContext* c) { - tensorflow::PartialTensorShape desired_shape; - TF_RETURN_IF_ERROR(c->GetAttr("shape", &desired_shape)); - shape_inference::ShapeHandle input_tensor_shape = c->input(0); - shape_inference::DimensionHandle num_input_elements = - c->NumElements(input_tensor_shape); - shape_inference::ShapeHandle result_shape_handle; - if (!shape_inference::InferenceContext::ValueKnown(num_input_elements)) { - TF_RETURN_IF_ERROR(c->MakeShapeFromPartialTensorShape( - desired_shape, &result_shape_handle)); - } else { - const int rank = c->Rank(input_tensor_shape); - std::vector<tensorflow::int64> target_dimensions(rank); - tensorflow::int64 new_sliced_size = 1; - int adjustable_dimension = 0; - for (int i = 0; i < rank; ++i) { - if (desired_shape.dim_size(i) < 1) { - adjustable_dimension = i; - } else { - target_dimensions[i] = desired_shape.dim_size(i); - new_sliced_size *= target_dimensions[i]; - } - } - target_dimensions[adjustable_dimension] = - shape_inference::InferenceContext::Value( - num_input_elements) / new_sliced_size; - tensorflow::TensorShape result_shape; - for (int i = 0; i < rank; ++i) { - result_shape.AddDim(target_dimensions[i]); - } - TF_RETURN_IF_ERROR(c->MakeShapeFromTensorShape( - result_shape, &result_shape_handle)); - } - c->set_output(0, result_shape_handle); - return Status::OK(); - }) + .SetShapeFn(shape_inference::ExplicitShape) .Doc(R"doc( Periodically resample elements of a tensor to conform to `shape`. @@ -136,20 +101,4 @@ output: Periodically resampled tensor that has dimensions specified as in )doc"); - -REGISTER_OP("PeriodicResampleOpGrad") - .Attr("T: numbertype") - .Input("grad: T") - .Attr("original_shape: shape") - .Attr("desired_shape: shape") - .Output("grad_values: T") - .SetShapeFn([](shape_inference::InferenceContext* c) { - tensorflow::TensorShape original_shape; - TF_RETURN_IF_ERROR(c->GetAttr("original_shape", &original_shape)); - shape_inference::ShapeHandle s; - TF_RETURN_IF_ERROR(c->MakeShapeFromTensorShape(original_shape, &s)); - c->set_output(0, s); - return Status::OK(); -}); - } // namespace tensorflow diff --git a/tensorflow/contrib/periodic_resample/ops/array_ops_test.cc b/tensorflow/contrib/periodic_resample/ops/array_ops_test.cc deleted file mode 100644 index 43b7c1799f..0000000000 --- a/tensorflow/contrib/periodic_resample/ops/array_ops_test.cc +++ /dev/null @@ -1,41 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/core/framework/node_def_builder.h" -#include "tensorflow/core/framework/shape_inference_testutil.h" -#include "tensorflow/core/framework/tensor_shape.pb.h" -#include "tensorflow/core/framework/tensor_testutil.h" -#include "tensorflow/core/lib/core/status_test_util.h" -#include "tensorflow/core/platform/test.h" - -namespace tensorflow { - -TEST(ArrayOpsTest, PeriodicResample_ShapeFn) { - ShapeInferenceTestOp op("PeriodicResample"); - // Case 1: output shape can be fully inferreed. - PartialTensorShape shape({4, 4, -1}); - TensorShapeProto shape_proto; - shape.AsProto(&shape_proto); - - TF_ASSERT_OK(NodeDefBuilder("test", "PeriodicResample") - .Input({"values", 0, DT_INT32}) - .Attr("shape", shape_proto) - .Finalize(&op.node_def)); - INFER_OK(op, "[2,2,4]", "[4,4,1]"); - // Case 2: output shape can not be inferred - report desired shape. - INFER_OK(op, "[2,2,?]", "[4,4,?]"); -} - -} // end namespace tensorflow diff --git a/tensorflow/contrib/periodic_resample/python/kernel_tests/periodic_resample_op_test.py b/tensorflow/contrib/periodic_resample/python/kernel_tests/periodic_resample_op_test.py index 31a6fe1d94..a25de55e18 100644 --- a/tensorflow/contrib/periodic_resample/python/kernel_tests/periodic_resample_op_test.py +++ b/tensorflow/contrib/periodic_resample/python/kernel_tests/periodic_resample_op_test.py @@ -21,11 +21,8 @@ from __future__ import print_function import numpy from tensorflow.contrib.periodic_resample import periodic_resample -from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl from tensorflow.python.framework import test_util -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import gradient_checker from tensorflow.python.ops import variables from tensorflow.python.platform import googletest @@ -96,6 +93,7 @@ class PeriodicResampleTest(test_util.TensorFlowTestCase): def testPeriodicResampleErrors(self): input_tensor = numpy.zeros(shape=[1, 2, 2, 4]) with self.test_session(): + variables.global_variables_initializer().run() with self.assertRaisesWithPredicateMatch( errors_impl.InvalidArgumentError, 'Dimension 3 input tensor has size 4, desired shape has size 1'): @@ -105,29 +103,6 @@ class PeriodicResampleTest(test_util.TensorFlowTestCase): '4, to be the same as the length of the desired shape, 3'): periodic_resample(input_tensor, [None, 4, 4]).eval() - def testPeriodicResampleGradient(self): - desired_shape = numpy.array([4, 4, None]) - result_shape = (4, 4, 1) - input_shape = (2, 2, 4) - with self.test_session() as sess: - x = array_ops.placeholder(dtypes.float32, shape=input_shape) - output = periodic_resample(x, desired_shape) - error = gradient_checker.compute_gradient_error( - x, input_shape, output, result_shape) - self.assertLess(error, 1e-4) - - def testPeriodicResampleShapeInference(self): - with self.test_session() as sess: - # Case 1: output shape can be fully inferreed. - x = array_ops.placeholder(dtypes.float32, shape=(2, 2, 4)) - output = periodic_resample(x, [4, 4, None]) - self.assertEqual(output.shape, [4, 4, 1]) - # Case 2: output shape can not be inferred - report desired shape. - x = array_ops.placeholder(dtypes.float32, shape=(2, 2, None)) - output = periodic_resample(x, [4, 4, None]) - self.assertTrue(output.shape.is_compatible_with([4, 4, None])) - self.assertEqual(output.shape[2].value, None) - if __name__ == '__main__': googletest.main() diff --git a/tensorflow/contrib/periodic_resample/python/ops/periodic_resample_op.py b/tensorflow/contrib/periodic_resample/python/ops/periodic_resample_op.py index 470e300ccb..348623d8f8 100644 --- a/tensorflow/contrib/periodic_resample/python/ops/periodic_resample_op.py +++ b/tensorflow/contrib/periodic_resample/python/ops/periodic_resample_op.py @@ -21,17 +21,11 @@ from __future__ import print_function # pylint: disable=unused-import from tensorflow.contrib.periodic_resample.python.ops import gen_periodic_resample_op -from tensorflow.contrib.periodic_resample.python.ops.gen_periodic_resample_op import periodic_resample, periodic_resample_op_grad +from tensorflow.contrib.periodic_resample.python.ops.gen_periodic_resample_op import periodic_resample from tensorflow.contrib.util import loader -from tensorflow.python.framework import ops from tensorflow.python.platform import resource_loader # pylint: enable=unused-import _periodic_resample_op = loader.load_op_library( resource_loader.get_path_to_datafile('_periodic_resample_op.so')) - -@ops.RegisterGradient("PeriodicResample") -def _periodic_resample_grad_cc(op, grad): - return periodic_resample_op_grad( - grad, op.inputs[0].shape, op.get_attr('shape')) diff --git a/tensorflow/contrib/predictor/contrib_estimator_predictor.py b/tensorflow/contrib/predictor/contrib_estimator_predictor.py index af3b2ad1b5..b7a98c68e2 100644 --- a/tensorflow/contrib/predictor/contrib_estimator_predictor.py +++ b/tensorflow/contrib/predictor/contrib_estimator_predictor.py @@ -34,8 +34,7 @@ class ContribEstimatorPredictor(predictor.Predictor): prediction_input_fn, input_alternative_key=None, output_alternative_key=None, - graph=None, - config=None): + graph=None): """Initialize a `ContribEstimatorPredictor`. Args: @@ -49,7 +48,6 @@ class ContribEstimatorPredictor(predictor.Predictor): multi-headed models. graph: Optional. The Tensorflow `graph` in which prediction should be done. - config: `ConfigProto` proto used to configure the session. """ self._graph = graph or ops.Graph() with self._graph.as_default(): @@ -60,7 +58,6 @@ class ContribEstimatorPredictor(predictor.Predictor): checkpoint_path = saver.latest_checkpoint(estimator.model_dir) self._session = monitored_session.MonitoredSession( session_creator=monitored_session.ChiefSessionCreator( - config=config, checkpoint_filename_with_path=checkpoint_path)) input_alternative_key = ( diff --git a/tensorflow/contrib/predictor/core_estimator_predictor.py b/tensorflow/contrib/predictor/core_estimator_predictor.py index a725072e72..d78d94c269 100644 --- a/tensorflow/contrib/predictor/core_estimator_predictor.py +++ b/tensorflow/contrib/predictor/core_estimator_predictor.py @@ -51,8 +51,7 @@ class CoreEstimatorPredictor(predictor.Predictor): estimator, serving_input_receiver_fn, output_key=None, - graph=None, - config=None): + graph=None): """Initialize a `CoreEstimatorPredictor`. Args: @@ -63,7 +62,6 @@ class CoreEstimatorPredictor(predictor.Predictor): `None`, then `DEFAULT_SERVING_SIGNATURE_DEF_KEY` is used. graph: Optional. The Tensorflow `graph` in which prediction should be done. - config: `ConfigProto` proto used to configure the session. """ self._graph = graph or ops.Graph() with self._graph.as_default(): @@ -73,7 +71,6 @@ class CoreEstimatorPredictor(predictor.Predictor): checkpoint_dir = estimator.model_dir self._session = monitored_session.MonitoredSession( session_creator=monitored_session.ChiefSessionCreator( - config=config, checkpoint_dir=checkpoint_dir)) feed_tensor_info = signature_def.inputs diff --git a/tensorflow/contrib/predictor/predictor_factories.py b/tensorflow/contrib/predictor/predictor_factories.py index f275bc15ad..6e77e934fe 100644 --- a/tensorflow/contrib/predictor/predictor_factories.py +++ b/tensorflow/contrib/predictor/predictor_factories.py @@ -30,8 +30,7 @@ def from_contrib_estimator(estimator, prediction_input_fn, input_alternative_key=None, output_alternative_key=None, - graph=None, - config=None): + graph=None): """Constructs a `Predictor` from a `tf.contrib.learn.Estimator`. Args: @@ -45,7 +44,6 @@ def from_contrib_estimator(estimator, multi-headed models. graph: Optional. The Tensorflow `graph` in which prediction should be done. - config: `ConfigProto` proto used to configure the session. Returns: An initialized `Predictor`. @@ -64,15 +62,13 @@ def from_contrib_estimator(estimator, prediction_input_fn, input_alternative_key=input_alternative_key, output_alternative_key=output_alternative_key, - graph=graph, - config=config) + graph=graph) def from_estimator(estimator, serving_input_receiver_fn, output_key=None, - graph=None, - config=None): + graph=None): """Constructs a `Predictor` from a `tf.python.estimator.Estimator`. Args: @@ -83,7 +79,6 @@ def from_estimator(estimator, `None`, then `DEFAULT_SERVING_SIGNATURE_DEF_KEY` is used. graph: Optional. The Tensorflow `graph` in which prediction should be done. - config: `ConfigProto` proto used to configure the session. Returns: An initialized `Predictor`. @@ -98,19 +93,14 @@ def from_estimator(estimator, 'tf.contrib.learn.Estimator. You likely want to call ' 'from_contrib_estimator.') return core_estimator_predictor.CoreEstimatorPredictor( - estimator, - serving_input_receiver_fn, - output_key=output_key, - graph=graph, - config=config) + estimator, serving_input_receiver_fn, output_key=output_key, graph=graph) def from_saved_model(export_dir, signature_def_key=None, signature_def=None, tags=None, - graph=None, - config=None): + graph=None): """Constructs a `Predictor` from a `SavedModel` on disk. Args: @@ -125,7 +115,6 @@ def from_saved_model(export_dir, `SignatureDef`. Defaults to `DEFAULT_TAGS`. graph: Optional. The Tensorflow `graph` in which prediction should be done. - config: `ConfigProto` proto used to configure the session. Returns: An initialized `Predictor`. @@ -139,5 +128,4 @@ def from_saved_model(export_dir, signature_def_key=signature_def_key, signature_def=signature_def, tags=tags, - graph=graph, - config=config) + graph=graph) diff --git a/tensorflow/contrib/predictor/predictor_factories_test.py b/tensorflow/contrib/predictor/predictor_factories_test.py index a2ef1dc3af..578d9424b2 100644 --- a/tensorflow/contrib/predictor/predictor_factories_test.py +++ b/tensorflow/contrib/predictor/predictor_factories_test.py @@ -20,7 +20,6 @@ from __future__ import print_function from tensorflow.contrib.predictor import predictor_factories from tensorflow.contrib.predictor import testing_common -from tensorflow.core.protobuf import config_pb2 from tensorflow.python.platform import test MODEL_DIR_NAME = 'contrib/predictor/test_export_dir' @@ -42,11 +41,6 @@ class PredictorFactoriesTest(test.TestCase): """Test loading from_saved_model with tags.""" predictor_factories.from_saved_model(self._export_dir, tags='serve') - def testFromSavedModelWithSessionConfig(self): - """Test loading from_saved_model with session config.""" - predictor_factories.from_saved_model( - self._export_dir, config=config_pb2.ConfigProto()) - def testFromSavedModelWithBadTags(self): """Test that loading fails for bad tags.""" bad_tags_regex = ('.*? could not be found in SavedModel') @@ -59,13 +53,6 @@ class PredictorFactoriesTest(test.TestCase): predictor_factories.from_contrib_estimator( estimator, input_fn, output_alternative_key='sum') - def testFromContribEstimatorWithSessionConfig(self): - estimator = testing_common.get_arithmetic_estimator(core=False) - input_fn = testing_common.get_arithmetic_input_fn(core=False) - predictor_factories.from_contrib_estimator( - estimator, input_fn, output_alternative_key='sum', - config=config_pb2.ConfigProto()) - def testFromContribEstimatorWithCoreEstimatorRaises(self): estimator = testing_common.get_arithmetic_estimator(core=True) input_fn = testing_common.get_arithmetic_input_fn(core=True) @@ -77,12 +64,6 @@ class PredictorFactoriesTest(test.TestCase): input_fn = testing_common.get_arithmetic_input_fn(core=True) predictor_factories.from_estimator(estimator, input_fn) - def testFromCoreEstimatorWithSessionConfig(self): - estimator = testing_common.get_arithmetic_estimator(core=True) - input_fn = testing_common.get_arithmetic_input_fn(core=True) - predictor_factories.from_estimator( - estimator, input_fn, config=config_pb2.ConfigProto()) - def testFromCoreEstimatorWithContribEstimatorRaises(self): estimator = testing_common.get_arithmetic_estimator(core=False) input_fn = testing_common.get_arithmetic_input_fn(core=False) diff --git a/tensorflow/contrib/predictor/saved_model_predictor.py b/tensorflow/contrib/predictor/saved_model_predictor.py index 95da6d04ed..0dbca0f813 100644 --- a/tensorflow/contrib/predictor/saved_model_predictor.py +++ b/tensorflow/contrib/predictor/saved_model_predictor.py @@ -121,8 +121,7 @@ class SavedModelPredictor(predictor.Predictor): input_names=None, output_names=None, tags=None, - graph=None, - config=None): + graph=None): """Initialize a `CoreEstimatorPredictor`. Args: @@ -143,7 +142,6 @@ class SavedModelPredictor(predictor.Predictor): the correct `SignatureDef`. Defaults to `DEFAULT_TAGS`. graph: Optional. The Tensorflow `graph` in which prediction should be done. - config: `ConfigProto` proto used to configure the session. Raises: ValueError: If more than one of signature_def_key OR signature_def OR (input_names AND output_names) is specified. @@ -154,7 +152,7 @@ class SavedModelPredictor(predictor.Predictor): self._graph = graph or ops.Graph() with self._graph.as_default(): - self._session = session.Session(config=config) + self._session = session.Session() loader.load(self._session, tags.split(','), export_dir) if input_names is None: diff --git a/tensorflow/contrib/quantize/README.md b/tensorflow/contrib/quantize/README.md index 27a933c0f9..c83623ec94 100644 --- a/tensorflow/contrib/quantize/README.md +++ b/tensorflow/contrib/quantize/README.md @@ -6,7 +6,7 @@ inference. The details of the transformation implemented in this package is described here [1]. This is done using the -[fake quantization op](https://www.tensorflow.org/api_guides/python/array_ops#Fake_quantization). +[fake quantization op](https://www.tensorflow.org/versions/r0.12/api_docs/python/array_ops/fake_quantization). Literature has shown that fixed point networks provide comparable performance to floating point networks [2]. This is achieved by modeling the quantization diff --git a/tensorflow/contrib/slim/python/slim/evaluation_test.py b/tensorflow/contrib/slim/python/slim/evaluation_test.py index 3d0308aaf3..94fc12ca81 100644 --- a/tensorflow/contrib/slim/python/slim/evaluation_test.py +++ b/tensorflow/contrib/slim/python/slim/evaluation_test.py @@ -26,6 +26,7 @@ import time import numpy as np from tensorflow.contrib.framework.python.ops import variables as variables_lib +from tensorflow.contrib.metrics.python.ops import metric_ops from tensorflow.contrib.slim.python.slim import evaluation from tensorflow.contrib.training.python.training import evaluation as evaluation_lib from tensorflow.core.protobuf import saver_pb2 @@ -36,7 +37,6 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops -from tensorflow.python.ops import metrics from tensorflow.python.ops import variables from tensorflow.python.platform import flags from tensorflow.python.platform import gfile @@ -89,8 +89,8 @@ class EvaluationTest(test.TestCase): self._predictions, self._scale = TestModel(self._inputs) def testFinalOpsOnEvaluationLoop(self): - value_op, update_op = metrics.accuracy( - labels=self._labels, predictions=self._predictions) + value_op, update_op = metric_ops.streaming_accuracy(self._predictions, + self._labels) init_op = control_flow_ops.group(variables.global_variables_initializer(), variables.local_variables_initializer()) # Create checkpoint and log directories: @@ -136,10 +136,9 @@ class EvaluationTest(test.TestCase): self.assertTrue(obj.hook_was_run) def _create_names_to_metrics(self, predictions, labels): - accuracy0, update_op0 = metrics.accuracy( - labels=labels, predictions=predictions) - accuracy1, update_op1 = metrics.accuracy( - labels=labels, predictions=predictions + 1) + accuracy0, update_op0 = metric_ops.streaming_accuracy(predictions, labels) + accuracy1, update_op1 = metric_ops.streaming_accuracy(predictions + 1, + labels) names_to_values = {'Accuracy': accuracy0, 'Another_accuracy': accuracy1} names_to_updates = {'Accuracy': update_op0, 'Another_accuracy': update_op1} @@ -199,8 +198,8 @@ class EvaluationTest(test.TestCase): predictions_limited = input.limit_epochs(self._predictions, num_epochs=1) labels_limited = input.limit_epochs(self._labels, num_epochs=1) - value_op, update_op = metrics.accuracy( - labels=labels_limited, predictions=predictions_limited) + value_op, update_op = metric_ops.streaming_accuracy( + predictions_limited, labels_limited) init_op = control_flow_ops.group(variables.global_variables_initializer(), variables.local_variables_initializer()) @@ -261,8 +260,8 @@ class SingleEvaluationTest(test.TestCase): self._prepareCheckpoint(checkpoint_path) # Next, determine the metric to evaluate: - value_op, update_op = metrics.accuracy( - labels=self._labels, predictions=self._predictions) + value_op, update_op = metric_ops.streaming_accuracy(self._predictions, + self._labels) # Run the evaluation and verify the results: accuracy_value = evaluation.evaluate_once( @@ -277,8 +276,8 @@ class SingleEvaluationTest(test.TestCase): self._prepareCheckpoint(checkpoint_path) # Next, determine the metric to evaluate: - value_op, update_op = metrics.accuracy( - labels=self._labels, predictions=self._predictions) + value_op, update_op = metric_ops.streaming_accuracy(self._predictions, + self._labels) dumping_root = os.path.join(self.get_temp_dir(), 'tfdbg_dump_dir') dumping_hook = hooks.DumpingDebugHook(dumping_root, log_usage=False) diff --git a/tensorflow/contrib/summary/summary.py b/tensorflow/contrib/summary/summary.py index d22b80ac88..99ced53e11 100644 --- a/tensorflow/contrib/summary/summary.py +++ b/tensorflow/contrib/summary/summary.py @@ -21,7 +21,6 @@ from @{tf.summary.merge_all} to @{tf.summary.FileWriter}. To use with eager execution enabled, write your code as follows: -```python global_step = tf.train.get_or_create_global_step() summary_writer = tf.contrib.summary.create_file_writer( train_dir, flush_millis=10000) @@ -31,11 +30,9 @@ with summary_writer.as_default(), tf.contrib.summary.always_record_summaries(): tf.contrib.summary.scalar("loss", my_loss) # In this case every call to tf.contrib.summary.scalar will generate a record # ... -``` To use it with graph execution, write your code as follows: -```python global_step = tf.train.get_or_create_global_step() summary_writer = tf.contrib.summary.create_file_writer( train_dir, flush_millis=10000) @@ -56,7 +53,7 @@ with tf.Session(...) as sess: while not_done_training: sess.run([train_op, tf.contrib.summary.all_summary_ops()]) # ... -``` + """ from __future__ import absolute_import diff --git a/tensorflow/contrib/tensor_forest/client/eval_metrics.py b/tensorflow/contrib/tensor_forest/client/eval_metrics.py index d8236a0a6f..e893e1d1c8 100644 --- a/tensorflow/contrib/tensor_forest/client/eval_metrics.py +++ b/tensorflow/contrib/tensor_forest/client/eval_metrics.py @@ -21,10 +21,10 @@ import numpy as np from tensorflow.contrib import losses from tensorflow.contrib.learn.python.learn.estimators import prediction_key +from tensorflow.contrib.metrics.python.ops import metric_ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops -from tensorflow.python.ops import metrics from tensorflow.python.ops import nn INFERENCE_PROB_NAME = prediction_key.PredictionKey.PROBABILITIES @@ -38,13 +38,12 @@ def _top_k_generator(k): targets = math_ops.to_int32(targets) if targets.get_shape().ndims > 1: targets = array_ops.squeeze(targets, axis=[1]) - return metrics.mean(nn.in_top_k(probabilities, targets, k)) + return metric_ops.streaming_mean(nn.in_top_k(probabilities, targets, k)) return _top_k def _accuracy(predictions, targets, weights=None): - return metrics.accuracy( - labels=targets, predictions=predictions, weights=weights) + return metric_ops.streaming_accuracy(predictions, targets, weights=weights) def _r2(probabilities, targets, weights=None): @@ -54,7 +53,7 @@ def _r2(probabilities, targets, weights=None): squares_residuals = math_ops.reduce_sum( math_ops.square(targets - probabilities), 0) score = 1 - math_ops.reduce_sum(squares_residuals / squares_total) - return metrics.mean(score, weights=weights) + return metric_ops.streaming_mean(score, weights=weights) def _squeeze_and_onehot(targets, depth): @@ -63,7 +62,7 @@ def _squeeze_and_onehot(targets, depth): def _sigmoid_entropy(probabilities, targets, weights=None): - return metrics.mean( + return metric_ops.streaming_mean( losses.sigmoid_cross_entropy(probabilities, _squeeze_and_onehot( targets, @@ -72,7 +71,7 @@ def _sigmoid_entropy(probabilities, targets, weights=None): def _softmax_entropy(probabilities, targets, weights=None): - return metrics.mean( + return metric_ops.streaming_mean( losses.sparse_softmax_cross_entropy(probabilities, math_ops.to_int32(targets)), weights=weights) @@ -83,7 +82,7 @@ def _predictions(predictions, unused_targets, **unused_kwargs): def _class_log_loss(probabilities, targets, weights=None): - return metrics.mean( + return metric_ops.streaming_mean( losses.log_loss(probabilities, _squeeze_and_onehot(targets, array_ops.shape(probabilities)[1])), @@ -91,36 +90,34 @@ def _class_log_loss(probabilities, targets, weights=None): def _precision(predictions, targets, weights=None): - return metrics.precision( - labels=targets, predictions=predictions, weights=weights) + return metric_ops.streaming_precision(predictions, targets, weights=weights) def _precision_at_thresholds(predictions, targets, weights=None): - return metrics.precision_at_thresholds( - labels=targets, - predictions=array_ops.slice(predictions, [0, 1], [-1, 1]), - thresholds=np.arange(0, 1, 0.01, dtype=np.float32), + return metric_ops.streaming_precision_at_thresholds( + array_ops.slice(predictions, [0, 1], [-1, 1]), + targets, + np.arange( + 0, 1, 0.01, dtype=np.float32), weights=weights) def _recall(predictions, targets, weights=None): - return metrics.recall( - labels=targets, predictions=predictions, weights=weights) + return metric_ops.streaming_recall(predictions, targets, weights=weights) def _recall_at_thresholds(predictions, targets, weights=None): - return metrics.recall_at_thresholds( - labels=targets, - predictions=array_ops.slice(predictions, [0, 1], [-1, 1]), - thresholds=np.arange(0, 1, 0.01, dtype=np.float32), + return metric_ops.streaming_recall_at_thresholds( + array_ops.slice(predictions, [0, 1], [-1, 1]), + targets, + np.arange( + 0, 1, 0.01, dtype=np.float32), weights=weights) def _auc(probs, targets, weights=None): - return metrics.auc( - labels=targets, - predictions=array_ops.slice(probs, [0, 1], [-1, 1]), - weights=weights) + return metric_ops.streaming_auc(array_ops.slice(probs, [0, 1], [-1, 1]), + targets, weights=weights) _EVAL_METRICS = { diff --git a/tensorflow/contrib/tensor_forest/python/tensor_forest.py b/tensorflow/contrib/tensor_forest/python/tensor_forest.py index 6f62cd11a9..7a35a70bbe 100644 --- a/tensorflow/contrib/tensor_forest/python/tensor_forest.py +++ b/tensorflow/contrib/tensor_forest/python/tensor_forest.py @@ -295,7 +295,7 @@ def get_epoch_variable(): # A simple container to hold the training variables for a single tree. -class TreeVariables(object): +class TreeTrainingVariables(object): """Stores tf.Variables for training a single random tree. Uses tf.get_variable to get tree-specific names so that this can be used @@ -303,7 +303,7 @@ class TreeVariables(object): then relies on restoring that model to evaluate). """ - def __init__(self, params, tree_num, training, tree_config='', tree_stat=''): + def __init__(self, params, tree_num, training): if (not hasattr(params, 'params_proto') or not isinstance(params.params_proto, _params_proto.TensorForestParams)): @@ -315,28 +315,27 @@ class TreeVariables(object): # TODO(gilberth): Manually shard this to be able to fit it on # multiple machines. self.stats = stats_ops.fertile_stats_variable( - params, tree_stat, self.get_tree_name('stats', tree_num)) + params, '', self.get_tree_name('stats', tree_num)) self.tree = model_ops.tree_variable( - params, tree_config, self.stats, self.get_tree_name('tree', tree_num)) + params, '', self.stats, self.get_tree_name('tree', tree_num)) def get_tree_name(self, name, num): return '{0}-{1}'.format(name, num) -class ForestVariables(object): +class ForestTrainingVariables(object): """A container for a forests training data, consisting of multiple trees. - Instantiates a TreeVariables object for each tree. We override the + Instantiates a TreeTrainingVariables object for each tree. We override the __getitem__ and __setitem__ function so that usage looks like this: - forest_variables = ForestVariables(params) + forest_variables = ForestTrainingVariables(params) ... forest_variables.tree ... """ def __init__(self, params, device_assigner, training=True, - tree_variables_class=TreeVariables, - tree_configs=None, tree_stats=None): + tree_variables_class=TreeTrainingVariables): self.variables = [] # Set up some scalar variables to run through the device assigner, then # we can use those to colocate everything related to a tree. @@ -348,13 +347,7 @@ class ForestVariables(object): for i in range(params.num_trees): with ops.device(self.device_dummies[i].device): - kwargs = {} - if tree_configs is not None: - kwargs.update(dict(tree_config=tree_configs[i])) - if tree_stats is not None: - kwargs.update(dict(tree_stat=tree_stats[i])) - self.variables.append(tree_variables_class( - params, i, training, **kwargs)) + self.variables.append(tree_variables_class(params, i, training)) def __setitem__(self, t, val): self.variables[t] = val @@ -368,11 +361,9 @@ class RandomForestGraphs(object): def __init__(self, params, - tree_configs=None, - tree_stats=None, device_assigner=None, variables=None, - tree_variables_class=TreeVariables, + tree_variables_class=TreeTrainingVariables, tree_graphs=None, training=True): self.params = params @@ -380,10 +371,9 @@ class RandomForestGraphs(object): device_assigner or framework_variables.VariableDeviceChooser()) logging.info('Constructing forest with params = ') logging.info(self.params.__dict__) - self.variables = variables or ForestVariables( + self.variables = variables or ForestTrainingVariables( self.params, device_assigner=self.device_assigner, training=training, - tree_variables_class=tree_variables_class, - tree_configs=tree_configs, tree_stats=tree_stats) + tree_variables_class=tree_variables_class) tree_graph_class = tree_graphs or RandomTreeGraphs self.trees = [ tree_graph_class(self.variables[i], self.params, i) diff --git a/tensorflow/contrib/tensor_forest/python/tensor_forest_test.py b/tensorflow/contrib/tensor_forest/python/tensor_forest_test.py index 1c9c81827e..bbe627b157 100644 --- a/tensorflow/contrib/tensor_forest/python/tensor_forest_test.py +++ b/tensorflow/contrib/tensor_forest/python/tensor_forest_test.py @@ -18,14 +18,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from google.protobuf.json_format import ParseDict -from tensorflow.contrib.decision_trees.proto import generic_tree_model_pb2 as _tree_proto from tensorflow.contrib.tensor_forest.python import tensor_forest from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import test_util -from tensorflow.python.ops import resources -from tensorflow.python.ops import variables from tensorflow.python.platform import googletest @@ -114,47 +110,6 @@ class TensorForestTest(test_util.TensorFlowTestCase): self.assertTrue(isinstance(paths, ops.Tensor)) self.assertTrue(isinstance(var, ops.Tensor)) - def testInfrenceFromRestoredModel(self): - input_data = [[-1., 0.], [-1., 2.], # node 1 - [1., 0.], [1., -2.]] # node 2 - expected_prediction = [[0.0, 1.0], [0.0, 1.0], - [0.0, 1.0], [0.0, 1.0]] - hparams = tensor_forest.ForestHParams( - num_classes=2, - num_features=2, - num_trees=1, - max_nodes=1000, - split_after_samples=25).fill() - tree_weight = {'decisionTree': - {'nodes': - [{'binaryNode': - {'rightChildId': 2, - 'leftChildId': 1, - 'inequalityLeftChildTest': - {'featureId': {'id': '0'}, - 'threshold': {'floatValue': 0}}}}, - {'leaf': {'vector': - {'value': [{'floatValue': 0.0}, - {'floatValue': 1.0}]}}, - 'nodeId': 1}, - {'leaf': {'vector': - {'value': [{'floatValue': 0.0}, - {'floatValue': 1.0}]}}, - 'nodeId': 2}]}} - restored_tree_param = ParseDict(tree_weight, - _tree_proto.Model()).SerializeToString() - graph_builder = tensor_forest.RandomForestGraphs(hparams, - [restored_tree_param]) - probs, paths, var = graph_builder.inference_graph(input_data) - self.assertTrue(isinstance(probs, ops.Tensor)) - self.assertTrue(isinstance(paths, ops.Tensor)) - self.assertTrue(isinstance(var, ops.Tensor)) - with self.test_session(): - variables.global_variables_initializer().run() - resources.initialize_resources(resources.shared_resources()).run() - self.assertEquals(probs.eval().shape, (4, 2)) - self.assertEquals(probs.eval().tolist(), expected_prediction) - def testTrainingConstructionClassificationSparse(self): input_data = sparse_tensor.SparseTensor( indices=[[0, 0], [0, 3], [1, 0], [1, 7], [2, 1], [3, 9]], diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index da4dd5a14c..b7b26cfb1c 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -91,11 +91,8 @@ void GetSubGraphIncomingEdges(const tensorflow::Graph& graph, if (!subgraph_node_ids.count(edge->src()->id()) && !edge->src()->IsSource() && !edge->IsControlEdge()) { incoming_edges->insert(edge); - VLOG(2) << "INCOMING " << edge->src()->name() << " -> " << node->name() - << " Y, "; } else { - VLOG(2) << "INCOMING " << edge->src()->name() << " -> " << node->name() - << " N, "; + VLOG(2) << node->name() << " -> " << edge->src()->name() << " N, "; } } } @@ -109,12 +106,10 @@ void GetSubGraphOutgoingEdges(const tensorflow::Graph& graph, for (const tensorflow::Edge* edge : node->out_edges()) { if (!subgraph_node_ids.count(edge->dst()->id()) && !edge->dst()->IsSink() && !edge->IsControlEdge()) { - VLOG(2) << "OUTGOING " << node->name() << " -> " << edge->dst()->name() - << " Y, "; + VLOG(2) << node->name() << " -> " << edge->dst()->name() << " Y, "; outgoing_edges->insert(edge); } else { - VLOG(2) << "OUTGOING " << node->name() << " -> " << edge->dst()->name() - << " N, "; + VLOG(2) << node->name() << " -> " << edge->dst()->name() << " N, "; } } } @@ -186,27 +181,29 @@ struct ConvertGraphParams { static tensorflow::Status FillSubGraphEdgeSets(ConvertGraphParams* p) { GetSubGraphIncomingEdges(p->graph, p->subgraph_node_ids, &p->subgraph_incoming_edges); - - std::set<std::pair<int, int>> unique_tensors; - // Add only unique input source nodes. If output of an outside node is shared - // between multiple nodes inside the engine, only one edge should be created for (const tensorflow::Edge* edge : p->subgraph_incoming_edges) { - unique_tensors.insert({edge->src()->id(), edge->src_output()}); + p->subgraph_inputs.push_back({edge->src()->id(), edge->src_output()}); + } + auto output_name_to_index_map = BuildTensorNameMap(p->output_names); + std::set<std::pair<int, int>> subgraph_outputs_set; + // Collect outputs referenced from output_names + for (int node_id : p->subgraph_node_ids) { + tensorflow::Node* node = p->graph.FindNodeId(node_id); + if (output_name_to_index_map.count(node->name())) { + for (int index : output_name_to_index_map.at(node->name())) { + subgraph_outputs_set.insert({node_id, index}); + } + } } - p->subgraph_inputs.insert(p->subgraph_inputs.begin(), unique_tensors.begin(), - unique_tensors.end()); GetSubGraphOutgoingEdges(p->graph, p->subgraph_node_ids, &p->subgraph_outgoing_edges); - unique_tensors.clear(); - // Similar to above, if multiple ouside nodes are sharing the output of an - // internal node only one output port should be created and shared between - // outputs for (const tensorflow::Edge* edge : p->subgraph_outgoing_edges) { - unique_tensors.insert({edge->src()->id(), edge->src_output()}); + subgraph_outputs_set.insert({edge->src()->id(), edge->src_output()}); } - p->subgraph_outputs.reserve(unique_tensors.size()); + p->subgraph_outputs.reserve(subgraph_outputs_set.size()); p->subgraph_outputs.insert(p->subgraph_outputs.begin(), - unique_tensors.begin(), unique_tensors.end()); + subgraph_outputs_set.begin(), + subgraph_outputs_set.end()); return tensorflow::Status::OK(); } @@ -228,6 +225,7 @@ tensorflow::Status GetCalibNode(ConvertGraphParams* params) { for (auto in_edge : params->subgraph_incoming_edges) { // loop over incoming edges and // attach them to calib node + // tensorflow::Node* src_node = in_edge->src(); auto src_output = in_edge->src_output(); auto dst_node = in_edge->dst(); auto dst_input = in_edge->dst_input(); @@ -259,24 +257,19 @@ tensorflow::Status ConvertSubGraphToTensorRT(ConvertGraphParams* params) { for (size_t i = 0; i < params->subgraph_inputs.size(); ++i) { subgraph_edge_to_input_map.insert({params->subgraph_inputs.at(i), i}); } - std::set<std::pair<int, int>> unique_tensors; for (const tensorflow::Edge* edge : params->subgraph_incoming_edges) { std::pair<int, int> old_src = {edge->src()->id(), edge->src_output()}; - if (unique_tensors.count(old_src)) continue; - unique_tensors.insert(old_src); int new_src_output = subgraph_edge_to_input_map.at(old_src); params->graph.AddEdge(edge->src(), edge->src_output(), trt_node, new_src_output); - VLOG(1) << "Wire " << edge->src()->name() << ":" << edge->src_output() - << " -> " << trt_node->name() << ":" << new_src_output; params->graph.RemoveEdge(edge); } - if (VLOG_IS_ON(2)) { - VLOG(2) << "new edge count: " << trt_node->in_edges().size(); - for (const tensorflow::Edge* edge : trt_node->in_edges()) { - VLOG(2) << edge->src()->name() << " port: " << edge->src_output(); - } + + VLOG(2) << "new wiring edges: " << trt_node->in_edges().size(); + for (const tensorflow::Edge* edge : trt_node->in_edges()) { + VLOG(2) << edge->src()->name() << " port: " << edge->src_output(); } + TF_RETURN_IF_ERROR(status); // Re-map outgoing edges to use the new TRT node instead of the orig subgraph @@ -290,8 +283,6 @@ tensorflow::Status ConvertSubGraphToTensorRT(ConvertGraphParams* params) { int new_src_output = subgraph_edge_to_output_map.at(old_src); TF_RETURN_IF_ERROR(params->graph.UpdateEdge( trt_node, new_src_output, edge->dst(), edge->dst_input())); - VLOG(1) << "Wire " << trt_node->name() << ":" << new_src_output << " -> " - << edge->dst()->name() << ":" << edge->dst_input(); } // Remove the original subgraph for (int node_id : params->subgraph_node_ids) { @@ -326,12 +317,9 @@ tensorflow::Status ConvertCalibGraphToInferGraph( tensorflow::GraphConstructorOptions(), graph_def, &graph)); // get calib nodes std::vector<tensorflow::Node*> calib_nodes; - std::vector<tensorflow::Node*> topo_order; - tensorflow::GetPostOrder(graph, &topo_order); - for (auto rit = topo_order.rbegin(); rit != topo_order.rend(); ++rit) { - auto node = *rit; + for (auto node : graph.op_nodes()) { if (node->type_string() == "TRTCalibOp") { - VLOG(1) << "Found Calib Node " << node->name(); + VLOG(1) << "Found Calib Node"; calib_nodes.push_back(node); } } diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 4e4d295538..96e0700862 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -362,11 +362,10 @@ void ReorderCKtoKC(const TRT_ShapedWeights& iweights, break; } case tensorflow::DataType::DT_HALF: { - Reorder2( - {k, c}, static_cast<Eigen::half const*>(iweights.GetValues()), - istrides, - static_cast<Eigen::half*>(const_cast<void*>(oweights->GetValues())), - ostrides); + Reorder2({k, c}, static_cast<Eigen::half const*>(iweights.GetValues()), + istrides, static_cast<Eigen::half*>( + const_cast<void*>(oweights->GetValues())), + ostrides); break; } default: @@ -1180,9 +1179,9 @@ tensorflow::Status BinaryTensorOpTensor( CHECK_EQ_TYPE(tensor_r->getType(), dtype); auto op_pair = ops.find(node_def.op()); if (op_pair == ops.end()) - return tensorflow::errors::Unimplemented( - "binary op: " + node_def.op() + - " not supported at: " + node_def.name()); + return tensorflow::errors::Unimplemented("binary op: " + node_def.op() + + " not supported at: " + + node_def.name()); nvinfer1::IElementWiseLayer* layer = ctx.network()->addElementWise( *const_cast<nvinfer1::ITensor*>(tensor_l), @@ -2139,7 +2138,9 @@ void Converter::register_op_converters() { } } // namespace - +tensorflow::Status GetTensorRTGraph(tensorrt::convert::SubGraphParams& s) { + return tensorflow::errors::Unimplemented("Not implemented yet"); +} tensorflow::Status ConvertCalibrationNodeToEngineNode( tensorflow::Graph& graph, tensorflow::Node* c_node) { const auto ndef = c_node->def(); @@ -2163,23 +2164,9 @@ tensorflow::Status ConvertCalibrationNodeToEngineNode( for (auto n : graph.op_nodes()) { node_maps.insert({n->name(), n}); } - std::set<int> subgraph_ids; - for (const auto internal_node : segment_nodes) { - subgraph_ids.insert(node_maps.at(internal_node)->id()); - } - if (VLOG_IS_ON(2)) { - string node_names = StrCat(c_node->name(), " segment nodes= "); - - for (const auto& node_name : segment_nodes) { - StrAppend(&node_names, node_name, ", "); - } - VLOG(2) << node_names; - } - VLOG(1) << "Output Nodes:"; std::vector<tensorflow::DataType> out_types; std::vector<const tensorflow::Edge*> out_edges; - for (auto& i : output_nodes) { auto node_port = tensorflow::str_util::Split(i, ":"); VLOG(1) << " " << i << " in graph " << node_maps.count(i); @@ -2199,24 +2186,18 @@ tensorflow::Status ConvertCalibrationNodeToEngineNode( out_types.push_back(out_node->output_type(0)); } for (auto out_edge : out_node->out_edges()) { - if (subgraph_ids.count(out_edge->dst()->id())) - continue; // skip internal edges; if (out_edge->src_output() == port) { out_edges.push_back(out_edge); - VLOG(1) << "OUTPUT EDGE " << out_edge->src()->name() << ":" - << out_edge->src_output() << " -> " << out_edge->dst()->name() - << ":" << out_edge->dst_input(); + break; } } } else { LOG(WARNING) << " couldn't find output node " << out_node_name; } } - if (VLOG_IS_ON(1)) { - VLOG(1) << c_node->name() << " Input Nodes:"; - for (auto& i : input_names) { - VLOG(1) << " Input " << i << " in graph " << node_maps.count(i); - } + VLOG(1) << "Input Nodes:"; + for (auto& i : input_names) { + VLOG(1) << " " << i << " in graph " << node_maps.count(i); } auto trt_rm = tensorflow::tensorrt::TRTResourceManager::instance(); auto resmgr = trt_rm->getManager("TRTCalibOps"); @@ -2250,24 +2231,14 @@ tensorflow::Status ConvertCalibrationNodeToEngineNode( calib_res->builder_ = nullptr; tensorflow::NodeDefBuilder op_builder(engine_name, "TRTEngineOp"); std::vector<tensorflow::NodeDefBuilder::NodeOut> income_edges; - income_edges.resize(c_node->num_inputs()); for (const auto in_edge : c_node->in_edges()) { auto src = in_edge->src(); int dest_port = in_edge->dst_input(); - VLOG(1) << "Incoming connection " << src->name() << ":" - << in_edge->src_output() << " -> " << c_node->name() << ":" - << dest_port; - income_edges.at(dest_port) = {src->name(), in_edge->src_output(), - c_node->input_type(dest_port)}; + income_edges.emplace_back(src->name(), in_edge->src_output(), + c_node->input_type(dest_port)); } tensorflow::gtl::ArraySlice<tensorflow::NodeDefBuilder::NodeOut> input_list( income_edges); - if (VLOG_IS_ON(2)) { - for (const auto& inp : input_list) { - VLOG(2) << " Input from inputlist " << inp.node << ":" << inp.index << " " - << tensorflow::DataTypeString(inp.data_type); - } - } op_builder.Input(input_list); tensorflow::NodeDef engine_node; const char* engine_plan_data = static_cast<const char*>(engine_plan->data()); @@ -2284,26 +2255,13 @@ tensorflow::Status ConvertCalibrationNodeToEngineNode( } auto trt_engine_node = graph.AddNode(engine_node, &status); TF_RETURN_IF_ERROR(status); - std::map<string, int> port_map; - for (size_t t = 0; t < output_nodes.size(); t++) { - port_map.insert({output_nodes.at(t), t}); - } - for (auto& i : out_edges) { - string s(i->src()->name()); - if (i->src_output()) StrAppend(&s, ":", i->src_output()); - int out_port = port_map.at(s); - VLOG(1) << "Connecting " << trt_engine_node->name() << ":" << out_port - << " -> " << i->dst()->name() << ":" << i->dst_input(); - TF_RETURN_IF_ERROR( - graph.UpdateEdge(trt_engine_node, out_port, i->dst(), i->dst_input())); - } - for (const auto ed : trt_engine_node->in_edges()) { - VLOG(1) << "In Edge " << ed->src()->name() << ":" << ed->src_output() - << " -> " << ed->dst()->name() << ":" << ed->dst_input(); - } - for (const auto ed : trt_engine_node->out_edges()) { - VLOG(1) << "Out Edge " << ed->src()->name() << ":" << ed->src_output() - << " -> " << ed->dst()->name() << ":" << ed->dst_input(); + for (size_t i = 0; i < out_edges.size(); i++) { + VLOG(1) << "Connecting trt_engine_node output " << i << " with " + << out_edges.at(i)->dst()->name() << " port " + << out_edges.at(i)->dst_input(); + TF_RETURN_IF_ERROR(graph.UpdateEdge(trt_engine_node, i, + out_edges.at(i)->dst(), + out_edges.at(i)->dst_input())); } VLOG(1) << "Segment nodes:"; for (auto& i : segment_nodes) { @@ -2374,7 +2332,6 @@ tensorflow::Status ConvertSubgraph( std::vector<string>* output_names, std::vector<tensorflow::DataType>* output_dtypes, const string& engine_name) { - std::set<string> added_tensors; for (const std::pair<int, int>& input : s.input_inds) { VLOG(2) << "parsing input. Node id= " << input.first; int node_id = input.first; @@ -2417,6 +2374,7 @@ tensorflow::Status ConvertSubgraph( auto op_info = op_info_vec.at(shape_inference_output_idx); tensorflow::DataType tf_dtype = op_info.dtype(); + input_dtypes->push_back(tf_dtype); nvinfer1::DataType dtype(nvinfer1::DataType::kFLOAT); auto type_status = ConvertDType(tf_dtype, &dtype); @@ -2452,10 +2410,8 @@ tensorflow::Status ConvertSubgraph( if (output_idx != 0) { input_tensor_name = StrCat(node_name, ":", output_idx); } - if (added_tensors.count(input_tensor_name)) continue; - added_tensors.insert(input_tensor_name); + input_names->push_back(input_tensor_name); - input_dtypes->push_back(tf_dtype); nvinfer1::ITensor* input_tensor = converter.network()->addInput( input_tensor_name.c_str(), dtype, input_dim_pseudo_chw); @@ -2479,7 +2435,6 @@ tensorflow::Status ConvertSubgraph( // Gather output metadata int trt_engine_op_output_idx = 0; - added_tensors.clear(); for (const std::pair<int, int>& output : s.output_inds) { int node_id = output.first; int output_idx = output.second; @@ -2496,8 +2451,6 @@ tensorflow::Status ConvertSubgraph( if (output_idx != 0) tensorflow::strings::StrAppend(&tensor_name, ":", output_idx); VLOG(2) << "Output tensor name: " << tensor_name; - if (added_tensors.count(tensor_name)) continue; - added_tensors.insert(tensor_name); output_names->push_back(tensor_name); auto tensor_or_weights = converter.get_tensor(tensor_name); if (!tensor_or_weights.is_tensor()) { diff --git a/tensorflow/contrib/tpu/python/tpu/datasets.py b/tensorflow/contrib/tpu/python/tpu/datasets.py index d879170b68..2e472a2805 100644 --- a/tensorflow/contrib/tpu/python/tpu/datasets.py +++ b/tensorflow/contrib/tpu/python/tpu/datasets.py @@ -166,21 +166,11 @@ def StreamingFilesDataset(files, return remote_iterator.get_next() def MapFn(unused_input): - if isinstance(source_dataset.output_types, dtypes.DType): - output_types = [source_dataset.output_types] - elif isinstance(source_dataset.output_types, (list, tuple)): - output_types = source_dataset.output_types - else: - raise ValueError('source dataset has invalid output types') - remote_calls = functional_ops.remote_call( + return functional_ops.remote_call( args=[source_handle], - Tout=output_types, + Tout=[dtypes.string], f=LoadingFunc, - target='/job:%s/replica:0/task:0/cpu:0' % file_reader_job) - if len(remote_calls) == 1: - return remote_calls[0] - else: - return remote_calls + target='/job:%s/replica:0/task:0/cpu:0' % file_reader_job)[0] with ops.device('/job:%s' % worker_job): output_dataset = dataset_ops.Dataset.range(2).repeat().map( diff --git a/tensorflow/contrib/tpu/python/tpu/datasets_test.py b/tensorflow/contrib/tpu/python/tpu/datasets_test.py index b58d05eac5..918cf0ed8e 100644 --- a/tensorflow/contrib/tpu/python/tpu/datasets_test.py +++ b/tensorflow/contrib/tpu/python/tpu/datasets_test.py @@ -26,8 +26,6 @@ from tensorflow.core.protobuf import config_pb2 from tensorflow.python.client import session from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import readers -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import tensor_shape from tensorflow.python.lib.io import python_io from tensorflow.python.platform import test from tensorflow.python.training import server_lib @@ -164,30 +162,6 @@ class DatasetsTest(test.TestCase): self.assertEqual(set(all_contents), set(retrieved_values)) - def testArbitraryReaderFuncFromDatasetGenerator(self): - - def my_generator(): - yield (1, [1] * 10) - - def gen_dataset(dummy): - return dataset_ops.Dataset.from_generator( - my_generator, (dtypes.int64, dtypes.int64), - (tensor_shape.TensorShape([]), tensor_shape.TensorShape([10]))) - - dataset = datasets.StreamingFilesDataset( - dataset_ops.Dataset.range(10), filetype=gen_dataset) - - iterator = dataset.make_initializable_iterator() - self._sess.run(iterator.initializer) - get_next = iterator.get_next() - - retrieved_values = self._sess.run(get_next) - - self.assertIsInstance(retrieved_values, (list, tuple)) - self.assertEqual(len(retrieved_values), 2) - self.assertEqual(retrieved_values[0], 1) - self.assertItemsEqual(retrieved_values[1], [1] * 10) - def testUnexpectedFiletypeString(self): with self.assertRaises(ValueError): datasets.StreamingFilesDataset( diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index b1c224a345..d89633199d 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -699,9 +699,7 @@ cc_library( srcs = ["platform/stacktrace_handler.cc"], hdrs = ["platform/stacktrace_handler.h"], deps = [ - ":abi", ":lib_platform", - ":stacktrace", ], ) @@ -3091,8 +3089,6 @@ cc_library( # we now need at least "str_util". ":lib", ":lib_platform", - ":stacktrace_handler", - ":test_lite", "//tensorflow/core/platform/default/build_config:test_lite_main", ], alwayslink = 1, @@ -3573,10 +3569,7 @@ tf_cc_tests_gpu( tf_cc_test_mkl( name = "mkl_runtime_tests", size = "small", - srcs = [ - "common_runtime/mkl_cpu_allocator_test.cc", - "common_runtime/mkl_threadpool_device_test.cc", - ], + srcs = ["common_runtime/mkl_cpu_allocator_test.cc"], linkstatic = 1, deps = [ ":core", diff --git a/tensorflow/core/api_def/base_api/api_def_Selu.pbtxt b/tensorflow/core/api_def/base_api/api_def_Selu.pbtxt index 985f09312f..cbe76de415 100644 --- a/tensorflow/core/api_def/base_api/api_def_Selu.pbtxt +++ b/tensorflow/core/api_def/base_api/api_def_Selu.pbtxt @@ -4,10 +4,6 @@ op { description: <<END if < 0, `scale * features` otherwise. -To be used together with -`initializer = tf.variance_scaling_initializer(factor=1.0, mode='FAN_IN')`. -For correct dropout, use `tf.contrib.nn.alpha_dropout`. - See [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515) END } diff --git a/tensorflow/core/api_def/base_api/api_def_StringSplitV2.pbtxt b/tensorflow/core/api_def/base_api/api_def_StringSplitV2.pbtxt deleted file mode 100644 index 6e13d0d049..0000000000 --- a/tensorflow/core/api_def/base_api/api_def_StringSplitV2.pbtxt +++ /dev/null @@ -1,48 +0,0 @@ -op { - graph_op_name: "StringSplitV2" - in_arg { - name: "input" - description: <<END -`1-D` string `Tensor`, the strings to split. -END - } - in_arg { - name: "sep" - description: <<END -`0-D` string `Tensor`, the delimiter character. -END - } - attr { - name: "maxsplit" - description: <<END -An `int`. If `maxsplit > 0`, limit of the split of the result. -END - } - summary: "Split elements of `source` based on `sep` into a `SparseTensor`." - description: <<END -Let N be the size of source (typically N will be the batch size). Split each -element of `source` based on `sep` and return a `SparseTensor` -containing the split tokens. Empty tokens are ignored. - -For example, N = 2, source[0] is 'hello world' and source[1] is 'a b c', -then the output will be -``` -st.indices = [0, 0; - 0, 1; - 1, 0; - 1, 1; - 1, 2] -st.shape = [2, 3] -st.values = ['hello', 'world', 'a', 'b', 'c'] -``` - -If `sep` is given, consecutive delimiters are not grouped together and are -deemed to delimit empty strings. For example, source of `"1<>2<><>3"` and -sep of `"<>"` returns `["1", "2", "", "3"]`. If `sep` is None or an empty -string, consecutive whitespace are regarded as a single separator, and the -result will contain no empty strings at the startor end if the string has -leading or trailing whitespace. - -Note that the above mentioned behavior matches python's str.split. -END -} diff --git a/tensorflow/core/api_def/python_api/api_def_StringSplitV2.pbtxt b/tensorflow/core/api_def/python_api/api_def_StringSplitV2.pbtxt deleted file mode 100644 index 0e8576fb01..0000000000 --- a/tensorflow/core/api_def/python_api/api_def_StringSplitV2.pbtxt +++ /dev/null @@ -1,4 +0,0 @@ -op { - graph_op_name: "StringSplitV2" - visibility: HIDDEN -} diff --git a/tensorflow/core/common_runtime/bfc_allocator.cc b/tensorflow/core/common_runtime/bfc_allocator.cc index 9cda17867b..8f2a419756 100644 --- a/tensorflow/core/common_runtime/bfc_allocator.cc +++ b/tensorflow/core/common_runtime/bfc_allocator.cc @@ -86,7 +86,7 @@ BFCAllocator::Chunk* BFCAllocator::ChunkFromHandle(ChunkHandle h) { return &(chunks_[h]); } -bool BFCAllocator::Extend(size_t alignment, size_t rounded_bytes) { +bool BFCAllocator::Extend(size_t rounded_bytes) { size_t available_bytes = memory_limit_ - total_region_allocated_bytes_; // Rounds available_bytes down to the nearest multiple of kMinAllocationSize. available_bytes = (available_bytes / kMinAllocationSize) * kMinAllocationSize; @@ -108,7 +108,7 @@ bool BFCAllocator::Extend(size_t alignment, size_t rounded_bytes) { // Try allocating. size_t bytes = std::min(curr_region_allocation_bytes_, available_bytes); - void* mem_addr = suballocator_->Alloc(alignment, bytes); + void* mem_addr = suballocator_->Alloc(32, bytes); if (mem_addr == nullptr && !started_backpedal_) { // Only backpedal once. started_backpedal_ = true; @@ -119,7 +119,7 @@ bool BFCAllocator::Extend(size_t alignment, size_t rounded_bytes) { while (mem_addr == nullptr) { bytes = RoundedBytes(bytes * kBackpedalFactor); if (bytes < rounded_bytes) break; - mem_addr = suballocator_->Alloc(alignment, bytes); + mem_addr = suballocator_->Alloc(32, bytes); } } @@ -261,7 +261,7 @@ void* BFCAllocator::AllocateRawInternal(size_t unused_alignment, } // Try to extend - if (Extend(unused_alignment, rounded_bytes)) { + if (Extend(rounded_bytes)) { ptr = FindChunkPtr(bin_num, rounded_bytes, num_bytes); if (ptr != nullptr) { return ptr; diff --git a/tensorflow/core/common_runtime/bfc_allocator.h b/tensorflow/core/common_runtime/bfc_allocator.h index 52aedb1e9c..ba5a3eea3a 100644 --- a/tensorflow/core/common_runtime/bfc_allocator.h +++ b/tensorflow/core/common_runtime/bfc_allocator.h @@ -305,8 +305,7 @@ class BFCAllocator : public VisitableAllocator { // Try to add a new memory region that can satisfy an allocation of // 'rounded_bytes' bytes. Returns true on success and false on // failure. - bool Extend(size_t alignment, size_t rounded_bytes) - EXCLUSIVE_LOCKS_REQUIRED(lock_); + bool Extend(size_t rounded_bytes) EXCLUSIVE_LOCKS_REQUIRED(lock_); // Returns a pointer to an underlying allocated chunk of size // 'rounded_bytes'. diff --git a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc index 9028e6298c..c21a1ea9f2 100644 --- a/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc +++ b/tensorflow/core/common_runtime/direct_session_with_tracking_alloc_test.cc @@ -102,25 +102,9 @@ TEST(DirectSessionWithTrackingAllocTest, CostModelTest) { EXPECT_EQ(2, shape.dim(0).size()); EXPECT_EQ(1, shape.dim(1).size()); if (node->name() == y->name()) { -#ifdef INTEL_MKL - // if MKL is used, it goes through various additional - // graph rewrite pass. In TF, everytime a graph pass - // happens, "constant" nodes are allocated - // and deallocated. Each allocation calls the - // (FindChunkPtr of BFCAllocator), - // which increments the value of AllocationId. - // Thus AllocationId becomes more than 3 and 4 if - // MKL is used. Now they are 9 and 10 for MKL. - EXPECT_EQ(19, cm->AllocationId(node, 0)); -#else EXPECT_EQ(21, cm->AllocationId(node, 0)); -#endif } else { -#ifdef INTEL_MKL - EXPECT_EQ(20, cm->AllocationId(node, 0)); -#else EXPECT_EQ(22, cm->AllocationId(node, 0)); -#endif } } EXPECT_LE(0, cm->MaxExecutionTime(node)); diff --git a/tensorflow/core/common_runtime/mkl_threadpool_device_test.cc b/tensorflow/core/common_runtime/mkl_threadpool_device_test.cc deleted file mode 100644 index 5d583a8360..0000000000 --- a/tensorflow/core/common_runtime/mkl_threadpool_device_test.cc +++ /dev/null @@ -1,53 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifdef INTEL_MKL - -#include "tensorflow/core/common_runtime/threadpool_device.h" - -#include "tensorflow/core/lib/core/status_test_util.h" -#include "tensorflow/core/platform/cpu_info.h" -#include "tensorflow/core/platform/logging.h" -#include "tensorflow/core/platform/test.h" -#include "tensorflow/core/public/session_options.h" - -namespace tensorflow { - -#ifdef _OPENMP -TEST(MKLThreadPoolDeviceTest, TestOmpDefaults) { - SessionOptions options; - unsetenv("OMP_NUM_THREADS"); - - ThreadPoolDevice* tp = new ThreadPoolDevice( - options, "/device:CPU:0", Bytes(256), DeviceLocality(), cpu_allocator()); - - const int ht = port::NumHyperthreadsPerCore(); - EXPECT_EQ(omp_get_max_threads(), (port::NumSchedulableCPUs() + ht - 1) / ht); -} - -TEST(MKLThreadPoolDeviceTest, TestOmpPreSets) { - SessionOptions options; - setenv("OMP_NUM_THREADS", "314", 1); - - ThreadPoolDevice* tp = new ThreadPoolDevice( - options, "/device:CPU:0", Bytes(256), DeviceLocality(), cpu_allocator()); - - EXPECT_EQ(omp_get_max_threads(), 314); -} -#endif // _OPENMP - -} // namespace tensorflow - -#endif // INTEL_MKL diff --git a/tensorflow/core/common_runtime/process_util.cc b/tensorflow/core/common_runtime/process_util.cc index a5d31b75c7..21912236d0 100644 --- a/tensorflow/core/common_runtime/process_util.cc +++ b/tensorflow/core/common_runtime/process_util.cc @@ -16,10 +16,8 @@ limitations under the License. #include "tensorflow/core/common_runtime/process_util.h" #ifdef INTEL_MKL -#ifdef _OPENMP #include <omp.h> -#endif // _OPENMP -#endif // INTEL_MKL +#endif #include <string.h> #include "tensorflow/core/lib/core/threadpool.h" @@ -59,10 +57,7 @@ int32 NumInterOpThreadsFromSessionOptions(const SessionOptions& options) { // MKL library executes ops in parallel using OMP threads // Set inter_op conservatively to avoid thread oversubscription that could // lead to severe perf degradations and OMP resource exhaustion - int mkl_intra_op = 1; -#ifdef _OPENMP - mkl_intra_op = omp_get_max_threads(); -#endif // _OPENMP + const int mkl_intra_op = omp_get_max_threads(); CHECK_GE(mkl_intra_op, 1); const int32 mkl_inter_op = std::max( (port::NumSchedulableCPUs() + mkl_intra_op - 1) / mkl_intra_op, 2); @@ -73,7 +68,7 @@ int32 NumInterOpThreadsFromSessionOptions(const SessionOptions& options) { #else // Default to using the number of cores available in the process. return port::NumSchedulableCPUs(); -#endif // INTEL_MKL +#endif } thread::ThreadPool* NewThreadPoolFromSessionOptions( diff --git a/tensorflow/core/common_runtime/threadpool_device.cc b/tensorflow/core/common_runtime/threadpool_device.cc index 74a87215e1..f7a07fe503 100644 --- a/tensorflow/core/common_runtime/threadpool_device.cc +++ b/tensorflow/core/common_runtime/threadpool_device.cc @@ -31,11 +31,7 @@ limitations under the License. #include "tensorflow/core/public/session_options.h" #ifdef INTEL_MKL -#ifdef _OPENMP -#include <omp.h> -#endif #include "tensorflow/core/common_runtime/mkl_cpu_allocator.h" -#include "tensorflow/core/platform/cpu_info.h" #endif namespace tensorflow { @@ -47,26 +43,7 @@ ThreadPoolDevice::ThreadPoolDevice(const SessionOptions& options, : LocalDevice(options, Device::BuildDeviceAttributes( name, DEVICE_CPU, memory_limit, locality)), allocator_(allocator), - scoped_allocator_mgr_(new ScopedAllocatorMgr(name)) { -#ifdef INTEL_MKL -#ifdef _OPENMP - const char* user_omp_threads = getenv("OMP_NUM_THREADS"); - if (user_omp_threads == nullptr) { - // OMP_NUM_THREADS controls MKL's intra-op parallelization - // Default to available physical cores - const int mkl_intra_op = port::NumSchedulableCPUs(); - const int ht = port::NumHyperthreadsPerCore(); - omp_set_num_threads((mkl_intra_op + ht - 1) / ht); - } else { - uint64 user_val = 0; - if (strings::safe_strtou64(user_omp_threads, &user_val)) { - // Superflous but triggers OpenMP loading - omp_set_num_threads(user_val); - } - } -#endif // _OPENMP -#endif // INTEL_MKL -} + scoped_allocator_mgr_(new ScopedAllocatorMgr(name)) {} ThreadPoolDevice::~ThreadPoolDevice() {} diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.cc b/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.cc index 770a0fcf14..1cea1b1462 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_master_service_impl.cc @@ -147,9 +147,7 @@ MasterService::Stub::Stub( } MasterService::AsyncService::AsyncService() { - int method_len = sizeof(grpcMasterService_method_names) / - sizeof(grpcMasterService_method_names[0]); - for (int i = 0; i < method_len; ++i) { + for (int i = 0; i < 10; ++i) { AddMethod(new ::grpc::internal::RpcServiceMethod( grpcMasterService_method_names[i], ::grpc::internal::RpcMethod::NORMAL_RPC, nullptr)); diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_testlib.cc b/tensorflow/core/distributed_runtime/rpc/grpc_testlib.cc index a8508d2d4f..89f83f9f24 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_testlib.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_testlib.cc @@ -17,7 +17,6 @@ limitations under the License. #include "tensorflow/core/distributed_runtime/rpc/grpc_session.h" #include "tensorflow/core/lib/strings/str_util.h" -#include "tensorflow/core/platform/env.h" #include "tensorflow/core/util/device_name_utils.h" namespace tensorflow { @@ -51,14 +50,9 @@ Status TestCluster::MakeTestCluster(const SessionOptions& options, int n, } for (int i = 0; i < n; ++i) { - string server_file = - strings::StrCat(testing::TensorFlowSrcRoot(), - "/core/distributed_runtime/rpc/grpc_testlib_server"); - if (!options.env->FileExists(server_file).ok()) { - return errors::Internal("Could not find grpc_testlib_server"); - } const std::vector<string> argv( - {server_file, + {strings::StrCat(testing::TensorFlowSrcRoot(), + "/core/distributed_runtime/rpc/grpc_testlib_server"), /* see grpc_testlib_server.cc for flags */ tf_jobs, "--tf_job=localhost", strings::StrCat("--tf_task=", i), strings::StrCat("--num_cpus=", num_cpus), diff --git a/tensorflow/core/framework/allocator.h b/tensorflow/core/framework/allocator.h index 2bb4d32d57..2c87156dca 100644 --- a/tensorflow/core/framework/allocator.h +++ b/tensorflow/core/framework/allocator.h @@ -67,8 +67,13 @@ struct AllocatorStats { // device memory. class Allocator { public: +#ifdef EIGEN_VECTORIZE_AVX512 // Align to 64 byte boundary. static constexpr size_t kAllocatorAlignment = 64; +#else + // Align to 32 byte boundary. + static constexpr size_t kAllocatorAlignment = 32; +#endif virtual ~Allocator(); diff --git a/tensorflow/core/framework/op_gen_lib.cc b/tensorflow/core/framework/op_gen_lib.cc index 4b56d807df..3d7920a6e2 100644 --- a/tensorflow/core/framework/op_gen_lib.cc +++ b/tensorflow/core/framework/op_gen_lib.cc @@ -15,7 +15,6 @@ limitations under the License. #include "tensorflow/core/framework/op_gen_lib.h" -#include <algorithm> #include <vector> #include "tensorflow/core/framework/attr_value.pb.h" #include "tensorflow/core/lib/core/errors.h" diff --git a/tensorflow/core/framework/remote_fused_graph_execute_info.proto b/tensorflow/core/framework/remote_fused_graph_execute_info.proto index 10072724d2..eb689ec1e6 100644 --- a/tensorflow/core/framework/remote_fused_graph_execute_info.proto +++ b/tensorflow/core/framework/remote_fused_graph_execute_info.proto @@ -5,7 +5,7 @@ option cc_enable_arenas = true; option java_outer_classname = "RemoteFusedGraphExecuteInfoProto"; option java_multiple_files = true; option java_package = "org.tensorflow.framework"; -option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework"; +//add go_package externally import "tensorflow/core/framework/graph.proto"; import "tensorflow/core/framework/tensor_shape.proto"; import "tensorflow/core/framework/types.proto"; diff --git a/tensorflow/core/framework/tensor_test.cc b/tensorflow/core/framework/tensor_test.cc index 80e168df97..b613effd18 100644 --- a/tensorflow/core/framework/tensor_test.cc +++ b/tensorflow/core/framework/tensor_test.cc @@ -1147,29 +1147,29 @@ TEST(Tensor, FailureToAllocate) { // On the alignment. // -// As of 2018/5, tensorflow::Tensor allocates its buffer with 64-byte +// As of 2015/8, tensorflow::Tensor allocates its buffer with 32-byte // alignment. Tensor::tensor/flat/vec/matrix methods requires the // buffer satisfies Eigen::Aligned (e.g., 16-bytes aligned usually, -// 32-bytes for AVX, and 64-bytes for AVX512). Tensor::Slice requires -// the caller to ensure its result is aligned if the caller intends -// to use those methods. In this test case, we simply make sure each -// slice is 64-byte aligned: sizeof(float) * 4 * 36 = 576. 576 % 64 = 0. +// and 32-bytes for AVX). Tensor::Slice requires the caller to ensure +// its result is aligned if the caller intends to use those methods. +// In this test case, we simply make sure each slice is 32-byte +// aligned: sizeof(float) * 4 * 2 = 32. TEST(Tensor, Slice_Basic) { Tensor saved; { // General - Tensor x(DT_FLOAT, TensorShape({10, 4, 36})); + Tensor x(DT_FLOAT, TensorShape({10, 4, 34})); // Fills in known values. for (int i = 0; i < 10; ++i) { x.Slice(i, i + 1).flat<float>().setConstant(i * 1.f); } // A simple slice along dim0. Tensor y = x.Slice(4, 8); - EXPECT_TRUE(y.shape().IsSameSize(TensorShape({4, 4, 36}))); + EXPECT_TRUE(y.shape().IsSameSize(TensorShape({4, 4, 34}))); auto tx = x.tensor<float, 3>(); auto ty = y.tensor<float, 3>(); for (int i = 0; i < 4; ++i) { for (int j = 0; j < 4; ++j) { - for (int k = 0; k < 36; ++k) { + for (int k = 0; k < 34; ++k) { EXPECT_EQ(ty(i, j, k), 4.0 + i); EXPECT_EQ(&tx(4 + i, j, k), &ty(i, j, k)); } @@ -1186,7 +1186,7 @@ TEST(Tensor, Slice_Basic) { auto tz = z.tensor<float, 3>(); EXPECT_EQ(1, z.dim_size(0)); for (int j = 0; j < 4; ++j) { - for (int k = 0; k < 36; ++k) { + for (int k = 0; k < 34; ++k) { EXPECT_EQ(tz(0, j, k), 6.0); } } @@ -1198,16 +1198,16 @@ TEST(Tensor, Slice_Basic) { EXPECT_EQ(1, saved.dim_size(0)); auto tsaved = saved.tensor<float, 3>(); for (int j = 0; j < 4; ++j) { - for (int k = 0; k < 36; ++k) { + for (int k = 0; k < 34; ++k) { EXPECT_EQ(tsaved(0, j, k), 6.0); } } } { // Empty - Tensor x(DT_FLOAT, TensorShape({10, 0, 36})); + Tensor x(DT_FLOAT, TensorShape({10, 0, 34})); x.flat<float>().setRandom(); Tensor y = x.Slice(4, 8); - EXPECT_TRUE(y.shape().IsSameSize(TensorShape({4, 0, 36}))); + EXPECT_TRUE(y.shape().IsSameSize(TensorShape({4, 0, 34}))); } { diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc index b9667998d6..72a13d4da7 100644 --- a/tensorflow/core/graph/mkl_layout_pass.cc +++ b/tensorflow/core/graph/mkl_layout_pass.cc @@ -2691,14 +2691,14 @@ class MklLayoutRewritePass : public GraphOptimizationPass { // If Op has been specifically assigned to a non-CPU device, then No. if (!n->assigned_device_name().empty() && - !str_util::StrContains(n->assigned_device_name(), kCPUDeviceSubStr)) { + !str_util::StrContains(n->assigned_device_name(),kCPUDeviceSubStr)) { result = false; reason = "Op has been assigned a runtime device that is not CPU."; } // If user has specifically assigned this op to a non-CPU device, then No. if (!n->def().device().empty() && - !str_util::StrContains(n->def().device(), kCPUDeviceSubStr)) { + !str_util::StrContains(n->def().device(),kCPUDeviceSubStr)) { result = false; reason = "User has assigned a device that is not CPU."; } @@ -2865,9 +2865,9 @@ class MklLayoutRewritePass : public GraphOptimizationPass { return false; } - // If the depth_radius of LRN is not 2, then MKL DNN takes unoptimized - // path. The unoptimized path is slow. Thus we dont rewrite the node - // and use default Eigen. But for depth_radius=2, MKL DNN optimized + // If the depth_radius of LRN is not 2, then MKL DNN takes unoptimized + // path. The unoptimized path is slow. Thus we dont rewrite the node + // and use default Eigen. But for depth_radius=2, MKL DNN optimized // path is taken, i.e., eigen node is rewritten by MKl DNN node. static bool LrnRewrite(const Node* n) { CHECK_NOTNULL(n); @@ -2876,13 +2876,13 @@ class MklLayoutRewritePass : public GraphOptimizationPass { CHECK_EQ(GetNodeAttr(n->def(), "depth_radius", &depth_radius).ok(), true); // if the depth_radius of LRN is not 2, don't rewrite the node by MKL DNN - // and use eigen node instead + // and use eigen node instead if (depth_radius == 2) { return true; } VLOG(1) << "LrnRewrite: The model sets depth_radius as not 2 which" << "case is not optimized by Intel MKL, thus using Eigen op" - << "for LRN "; + << "for LRN " ; return false; } @@ -3015,35 +3015,6 @@ class MklLayoutRewritePass : public GraphOptimizationPass { std::vector<NodeBuilder::NodeOut>* ws_tensors, bool* are_ws_tensors_added); - // Helper function used by FixMklMetaDataEdges. Fixes the metadata edge - // pointed by 'e_metadata' corresponding to the data edge 'e_data' in graph - // 'g'. Returns true is fixup was done; otherwise, it returns false. - bool FixMklMetaDataEdgeIfNeeded(std::unique_ptr<Graph>* g, - const Edge* e_data, const Edge* e_metadata); - - // Are the input Mkl metadata edges for node 'n' in graph 'g' correctly - // connected? If not, then fix them. This is needed because a graph may have - // some input Mkl metadata edges incorrectly setup after node merge and - // rewrite passes. This could happen because GetReversePostOrder function may - // not provide topologically sorted order if a graph contains cycles. The - // function returns true if at least one Mkl metadata edge for node 'n' was - // fixed. Otherwise, it returns false. - // - // Example: - // - // X = MklConv2D(_, _, _) - // Y = MklConv2DWithBias(_, _, _, _, _, _) - // Z = MklAdd(X, Y, DummyMklTensor, Y:1) - // - // For a graph such as shown above, note that 3rd argument of MklAdd contains - // DummyMklTensor. Actually, it should be getting the Mkl metadata from - // MklConv2D op (specifically, X:2). This incorrect plumbing could be possible - // (although rare) if the Mkl NodeMerge + NodeRewrite passes visit Z before X - // (possible if X, Y, Z are part of a loop.) This function fixes the Mkl - // metadata edges only - it does not rewrite nodes nor does it modify the Mkl - // data edges (1st and 2nd arguments of MklAdd). - bool FixMklMetaDataEdges(std::unique_ptr<Graph>* g, Node* n); - // Functions specific to operators to copy attributes // We need operator-specific function to copy attributes because the framework // does not provide any generic function for it. @@ -4271,92 +4242,6 @@ MklLayoutRewritePass::CheckForNodeRewrite(const Node* n) const { } /////////////////////////////////////////////////////////////////////////////// -// Post-rewrite Mkl metadata fixup pass -/////////////////////////////////////////////////////////////////////////////// -bool MklLayoutRewritePass::FixMklMetaDataEdgeIfNeeded(std::unique_ptr<Graph>* g, - const Edge* e_data, const Edge* e_metadata) { - if (g == nullptr || e_data == nullptr || e_metadata == nullptr) { - return false; - } - - Node* n_data = e_data->src(); - int n_data_op_slot = e_data->src_output(); - int n_metadata_op_slot = GetTensorMetaDataIndex(n_data_op_slot, - n_data->num_outputs()); - - // If the source of meta edge is a constant node (producing dummy Mkl metadata - // tensor), then we will need to fix. - if (IsConstant(e_metadata->src())) { - Node* e_metadata_dst = e_metadata->dst(); - int e_metadata_in_slot = e_metadata->dst_input(); - CHECK_NOTNULL((*g)->AddEdge(n_data, n_metadata_op_slot, - e_metadata_dst, e_metadata_in_slot)); - - (*g)->RemoveEdge(e_metadata); - return true; - } - - return false; -} - -bool MklLayoutRewritePass::FixMklMetaDataEdges(std::unique_ptr<Graph>* g, - Node* n) { - bool result = false; - - // If graph node is not Mkl node, then return. - DataType T = DT_INVALID; - if (!GetNodeAttr(n->def(), "T", &T).ok() || - !mkl_op_registry::IsMklOp(n->type_string(), T)) { - return result; - } - - // If it is Mkl node, then check if the input edges to this node that carry - // Mkl metadata are linked up correctly with the source node. - - // For Mkl nodes, we generate twice the number of input tensors (n for Mkl - // data tensors + n for Mkl metadata tensors). We need to check for correct - // connection of n metadata tensors only. - int num_data_inputs = n->num_inputs() / 2; - for (int idx = 0; idx < num_data_inputs; idx++) { - // Get the edge connecting input slot with index (idx). - const Edge* e = nullptr; - TF_CHECK_OK(n->input_edge(idx, &e)); - - // If e is control edge, then skip. - if (e->IsControlEdge()) { - continue; - } - - // Check that the source node for edge 'e' is Mkl node. If it is not an Mkl - // node, then we don't need to do anything. - Node* e_src = e->src(); - if (GetNodeAttr(e_src->def(), "T", &T).ok() && - mkl_op_registry::IsMklOp(e_src->type_string(), T)) { - // Source node for edge 'e' is Mkl node. - // Destination node and destination input slot of e is node 'n' and 'idx' - // resp. - CHECK_EQ(e->dst(), n); - CHECK_EQ(e->dst_input(), idx); - - // Let's get edge that carries Mkl metadata corresponding to Mkl data edge - // 'e'. For that, let's first get the input slot of 'n' where the meta - // edge will feed the value. - int e_meta_in_slot = GetTensorMetaDataIndex(e->dst_input(), - n->num_inputs()); - const Edge* e_meta = nullptr; - TF_CHECK_OK(n->input_edge(e_meta_in_slot, &e_meta)); - - // Let's check if we need to fix this meta edge. - if (FixMklMetaDataEdgeIfNeeded(g, e, e_meta)) { - result = true; - } - } - } - - return result; -} - -/////////////////////////////////////////////////////////////////////////////// // Run function for the pass /////////////////////////////////////////////////////////////////////////////// @@ -4422,25 +4307,6 @@ bool MklLayoutRewritePass::RunPass(std::unique_ptr<Graph>* g) { DumpGraph("After running MklLayoutRewritePass(NodeMerge+Rewrite)", &**g); - order.clear(); - GetReversePostOrder(**g, &order); // This will give us topological sort. - for (Node* n : order) { - // If node is not an op or it cannot run on CPU device, then skip. - if (!n->IsOp() || !CanOpRunOnCPUDevice(n)) { - continue; - } - if (FixMklMetaDataEdges(g, n)) { - string node_name = n->name(); - string op_name = n->type_string(); - - VLOG(1) << "MklLayoutRewritePass: fixed metadata edges for node " - << node_name << " with op " << op_name; - result = true; - } - } - DumpGraph("After running MklLayoutRewritePass(NodeMerge+Rewrite+Fixup)", - &**g); - return result; } diff --git a/tensorflow/core/graph/mkl_layout_pass_test.cc b/tensorflow/core/graph/mkl_layout_pass_test.cc index 7645b4a7f0..029cdcf94a 100644 --- a/tensorflow/core/graph/mkl_layout_pass_test.cc +++ b/tensorflow/core/graph/mkl_layout_pass_test.cc @@ -3519,37 +3519,6 @@ TEST_F(MklLayoutPassTest, NodeMerge_Conv2DWithBias_DeviceTest) { } ///////////////////////////////////////////////////////////////////// -// Post-rewrite fixup pass test - -TEST_F(MklLayoutPassTest, PostRewriteFixUpPass) { - InitGraph( - "node { name: 'A' op: 'Input'}" - "node { name: 'B' op: 'Input'}" - "node { name: 'M' op: '_MklInput'}" - "node { name: 'N' op: '_MklInput'}" - "node { name: 'C' op: '_MklConv2D'" - " attr { key: 'T' value { type: DT_FLOAT } }" - " attr { key: 'data_format' value { s: 'NCHW' } }" - " attr { key: 'use_cudnn_on_gpu' value { b: false } }" - " attr { key: 'strides' value { list: {i: 1, i:1, i:1, i:1} } }" - " attr { key: 'padding' value { s: 'SAME' } }" - " attr { key: 'dilations' value { list: {i: 1, i:1, i:1, i:1} } }" - " input: ['A', 'B', 'M', 'N']}" - "node { name: 'D' op: 'Const' " - " attr { key: 'dtype' value { type: DT_UINT8 } }" - " attr { key: 'value' value { " - " tensor { dtype: DT_UINT8 tensor_shape { dim { size: 1 } } " - " int_val: 0 } } } }" - "node { name: 'E' op: '_MklAdd'" - " attr {key: 'T' value { type: DT_FLOAT } }" - " input: ['C', 'A', 'D', 'D']}"); - EXPECT_EQ(DoMklLayoutOptimizationPass(), - "A(Input);B(Input);C(_MklConv2D);D(Const);E(_MklAdd);" - "M(_MklInput);N(_MklInput)|A->C;A->E:1;B->C:1;C->E;C:2->E:2;" - "D->E:3;M->C:2;N->C:3"); -} - -///////////////////////////////////////////////////////////////////// static void BM_MklLayoutRewritePass(int iters, int op_nodes) { testing::StopTiming(); diff --git a/tensorflow/core/grappler/costs/graph_properties.cc b/tensorflow/core/grappler/costs/graph_properties.cc index 0c02876ac5..6749a7c571 100644 --- a/tensorflow/core/grappler/costs/graph_properties.cc +++ b/tensorflow/core/grappler/costs/graph_properties.cc @@ -610,6 +610,7 @@ class SymbolicShapeRefiner { } }; + // Compute the shape of the tensors outputed by node 'node' at output port // 'port_index' as the union of shape1 and shape2. ShapeHandle OutputAsUnion(const NodeDef* node, int port_index, ShapeHandle shape1, ShapeHandle shape2) { diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD index 8ca726df0b..1b18087cdf 100644 --- a/tensorflow/core/grappler/optimizers/BUILD +++ b/tensorflow/core/grappler/optimizers/BUILD @@ -679,7 +679,6 @@ cc_library( deps = [ ":constant_folding", ":graph_optimizer", - "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", "//tensorflow/core/grappler:graph_view", "//tensorflow/core/grappler:grappler_item", @@ -781,6 +780,7 @@ cc_library( "//tensorflow/core:lib", "//tensorflow/core:lib_internal", "//tensorflow/core:protos_all_cc", + "//tensorflow/core:scoped_allocator_ops_op_lib", "//tensorflow/core/grappler:grappler_item", "//tensorflow/core/grappler:op_types", "//tensorflow/core/grappler:utils", diff --git a/tensorflow/core/grappler/optimizers/remapper.cc b/tensorflow/core/grappler/optimizers/remapper.cc index 03e36a7b9c..4dde7ed1b4 100644 --- a/tensorflow/core/grappler/optimizers/remapper.cc +++ b/tensorflow/core/grappler/optimizers/remapper.cc @@ -22,7 +22,6 @@ limitations under the License. #include "tensorflow/core/grappler/op_types.h" #include "tensorflow/core/grappler/optimizers/constant_folding.h" #include "tensorflow/core/grappler/utils.h" -#include "tensorflow/core/platform/logging.h" namespace tensorflow { namespace grappler { @@ -201,7 +200,8 @@ Status Remapper::Optimize(Cluster* /*cluster*/, const GrapplerItem& item, } } if (optimizable) { - VLOG(1) << "Optimizing fused batch norm node " << node.DebugString(); + VLOG(2) << "Optimizing fused batch norm node " << node.DebugString() + << std::endl; AddBatchNormNodes(optimized_graph, node); continue; } diff --git a/tensorflow/core/kernels/as_string_op.cc b/tensorflow/core/kernels/as_string_op.cc index a7757d1361..66c4aff3e3 100644 --- a/tensorflow/core/kernels/as_string_op.cc +++ b/tensorflow/core/kernels/as_string_op.cc @@ -73,7 +73,6 @@ class AsStringOp : public OpKernel { } switch (dtype) { case DT_INT8: - case DT_INT16: case DT_INT32: strings::Appendf(&format_, "d"); break; @@ -130,7 +129,6 @@ class AsStringOp : public OpKernel { ENCODE_TYPE(DT_FLOAT, float, format_); ENCODE_TYPE(DT_DOUBLE, double, format_); ENCODE_TYPE(DT_INT8, int8, format_); - ENCODE_TYPE(DT_INT16, int16, format_); case (DT_BOOL): { const auto& input_flat = input_tensor->flat<bool>(); for (int i = 0; i < input_flat.size(); ++i) { diff --git a/tensorflow/core/kernels/cwise_op_clip.cc b/tensorflow/core/kernels/cwise_op_clip.cc index 49b90e855b..14d889e8e3 100644 --- a/tensorflow/core/kernels/cwise_op_clip.cc +++ b/tensorflow/core/kernels/cwise_op_clip.cc @@ -33,41 +33,52 @@ class ClipOp : public OpKernel { const Tensor& in0 = ctx->input(0); const Tensor& in1 = ctx->input(1); const Tensor& in2 = ctx->input(2); - OP_REQUIRES(ctx, (in0.shape() == in1.shape() || - TensorShapeUtils::IsScalar(in1.shape())) && - (in0.shape() == in2.shape() || - TensorShapeUtils::IsScalar(in2.shape())), - errors::InvalidArgument( - "clip_value_min and clip_value_max must be either of " - "the same shape as input, or a scalar. ", - "input shape: ", in0.shape().DebugString(), - "clip_value_min shape: ", in1.shape().DebugString(), - "clip_value_max shape: ", in2.shape().DebugString())); - - Tensor* out = nullptr; - OP_REQUIRES_OK( - ctx, ctx->forward_input_or_allocate_output({0}, 0, in0.shape(), &out)); - if (out->NumElements() == 0) return; // Nothing to do for empty output auto in0_flat = in0.flat<T>(); auto in1_flat = in1.flat<T>(); auto in2_flat = in2.flat<T>(); - auto out_flat = out->flat<T>(); const Device& d = ctx->eigen_device<Device>(); + Tensor* out = nullptr; + OP_REQUIRES_OK( + ctx, ctx->forward_input_or_allocate_output({0}, 0, in0.shape(), &out)); + auto out_flat = out->flat<T>(); if (in1.shape() == in2.shape()) { if (in0.shape() == in1.shape()) { functor::TernaryClipOp<Device, T>()(d, in0_flat, in1_flat, in2_flat, out_flat); } else { + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(in1.shape()), + errors::InvalidArgument( + "clip_value_min and clip_value_max must be either of " + "the same shape as input, or a scalar. ", + "input shape: ", in0.shape().DebugString(), + "clip_value_min shape: ", in1.shape().DebugString(), + "clip_value_max shape: ", in2.shape().DebugString())); functor::UnaryClipOp<Device, T>()(d, in0_flat, in1_flat, in2_flat, out_flat); } } else { if (in0.shape() == in1.shape()) { + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(in2.shape()), + errors::InvalidArgument( + "clip_value_min and clip_value_max must be either of " + "the same shape as input, or a scalar. ", + "input shape: ", in0.shape().DebugString(), + "clip_value_min shape: ", in1.shape().DebugString(), + "clip_value_max shape: ", in2.shape().DebugString())); functor::BinaryLeftClipOp<Device, T>()(d, in0_flat, in1_flat, in2_flat, out_flat); } else { + OP_REQUIRES(ctx, + (in0.shape() == in2.shape() && + TensorShapeUtils::IsScalar(in1.shape())), + errors::InvalidArgument( + "clip_value_min and clip_value_max must be either of " + "the same shape as input, or a scalar. ", + "input shape: ", in0.shape().DebugString(), + "clip_value_min shape: ", in1.shape().DebugString(), + "clip_value_max shape: ", in2.shape().DebugString())); functor::BinaryRightClipOp<Device, T>()(d, in0_flat, in1_flat, in2_flat, out_flat); } diff --git a/tensorflow/core/kernels/dense_update_functor_gpu.cu.cc b/tensorflow/core/kernels/dense_update_functor_gpu.cu.cc index 17a85d9773..9a3b2303a3 100644 --- a/tensorflow/core/kernels/dense_update_functor_gpu.cu.cc +++ b/tensorflow/core/kernels/dense_update_functor_gpu.cu.cc @@ -57,7 +57,6 @@ struct DenseUpdate<GPUDevice, T, SUB> { template struct functor::DenseUpdate<GPUDevice, T, ADD>; \ template struct functor::DenseUpdate<GPUDevice, T, SUB>; TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_KERNELS); -TF_CALL_int32(DEFINE_GPU_KERNELS); TF_CALL_int64(DEFINE_GPU_KERNELS); #undef DEFINE_GPU_KERNELS diff --git a/tensorflow/core/kernels/gather_functor.cc b/tensorflow/core/kernels/gather_functor.cc index 5cd8e04927..e6fefe643b 100644 --- a/tensorflow/core/kernels/gather_functor.cc +++ b/tensorflow/core/kernels/gather_functor.cc @@ -37,7 +37,6 @@ namespace functor { DECLARE_GPU_SPECS_INDEX(T, int32); \ DECLARE_GPU_SPECS_INDEX(T, int64) -TF_CALL_int64(DECLARE_GPU_SPECS); TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPECS); TF_CALL_complex64(DECLARE_GPU_SPECS); TF_CALL_complex128(DECLARE_GPU_SPECS); diff --git a/tensorflow/core/kernels/gather_functor_gpu.cu.cc b/tensorflow/core/kernels/gather_functor_gpu.cu.cc index 4563fc6353..39b6924d74 100644 --- a/tensorflow/core/kernels/gather_functor_gpu.cu.cc +++ b/tensorflow/core/kernels/gather_functor_gpu.cu.cc @@ -31,7 +31,6 @@ typedef Eigen::GpuDevice GPUDevice; DEFINE_GPU_SPECS_INDEX(T, int32); \ DEFINE_GPU_SPECS_INDEX(T, int64); -TF_CALL_int64(DEFINE_GPU_SPECS); TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_SPECS); TF_CALL_complex64(DEFINE_GPU_SPECS); TF_CALL_complex128(DEFINE_GPU_SPECS); diff --git a/tensorflow/core/kernels/gather_nd_op.cc b/tensorflow/core/kernels/gather_nd_op.cc index 4e53291b7f..7e5a9e1ec5 100644 --- a/tensorflow/core/kernels/gather_nd_op.cc +++ b/tensorflow/core/kernels/gather_nd_op.cc @@ -228,8 +228,6 @@ namespace functor { DECLARE_GPU_SPECS_INDEX(T, int32); \ DECLARE_GPU_SPECS_INDEX(T, int64) -TF_CALL_int32(DECLARE_GPU_SPECS); -TF_CALL_int64(DECLARE_GPU_SPECS); TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPECS); TF_CALL_complex64(DECLARE_GPU_SPECS); TF_CALL_complex128(DECLARE_GPU_SPECS); @@ -241,8 +239,6 @@ TF_CALL_complex128(DECLARE_GPU_SPECS); // Registration of the GPU implementations. #define REGISTER_GATHER_ND_GPU(type) REGISTER_GATHER_ND_ALL_INDICES(GPU, type) -TF_CALL_int32(REGISTER_GATHER_ND_GPU); -TF_CALL_int64(REGISTER_GATHER_ND_GPU); TF_CALL_GPU_NUMBER_TYPES(REGISTER_GATHER_ND_GPU); TF_CALL_complex64(REGISTER_GATHER_ND_GPU); TF_CALL_complex128(REGISTER_GATHER_ND_GPU); diff --git a/tensorflow/core/kernels/gather_nd_op_gpu.cu.cc b/tensorflow/core/kernels/gather_nd_op_gpu.cu.cc index da8d2e9e3c..b03efc684f 100644 --- a/tensorflow/core/kernels/gather_nd_op_gpu.cu.cc +++ b/tensorflow/core/kernels/gather_nd_op_gpu.cu.cc @@ -119,8 +119,6 @@ struct GatherNdSlice<GPUDevice, T, Index, IXDIM> { DEFINE_GPU_SPECS_INDEX(T, int32); \ DEFINE_GPU_SPECS_INDEX(T, int64); -TF_CALL_int32(DEFINE_GPU_SPECS); -TF_CALL_int64(DEFINE_GPU_SPECS); TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_SPECS); TF_CALL_complex64(DEFINE_GPU_SPECS); TF_CALL_complex128(DEFINE_GPU_SPECS); diff --git a/tensorflow/core/kernels/gather_op.cc b/tensorflow/core/kernels/gather_op.cc index 094504d6b9..ef332ebee3 100644 --- a/tensorflow/core/kernels/gather_op.cc +++ b/tensorflow/core/kernels/gather_op.cc @@ -153,7 +153,6 @@ TF_CALL_uint64(REGISTER_GATHER_CPU); // Registration of the GPU implementations. #define REGISTER_GATHER_GPU(type) REGISTER_GATHER_ALL_INDICES(GPU, type) -TF_CALL_int64(REGISTER_GATHER_GPU); TF_CALL_GPU_NUMBER_TYPES(REGISTER_GATHER_GPU); TF_CALL_complex64(REGISTER_GATHER_GPU); TF_CALL_complex128(REGISTER_GATHER_GPU); diff --git a/tensorflow/core/kernels/mkl_concat_op.cc b/tensorflow/core/kernels/mkl_concat_op.cc index 31d1b949ef..5eeb23d810 100644 --- a/tensorflow/core/kernels/mkl_concat_op.cc +++ b/tensorflow/core/kernels/mkl_concat_op.cc @@ -14,7 +14,6 @@ limitations under the License. #include <limits> #include <vector> -#include <unordered_map> #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/framework/op_kernel.h" @@ -591,8 +590,8 @@ class MklConcatOp : public OpKernel { const int N = input_tensors.size(); // Get Tensor shapes. - std::vector<MklDnnShape> mkl_input_shapes(N); - GetMklShapeList(context, "values", &mkl_input_shapes); + std::vector<MklDnnShape> input_shapes(N); + GetMklShapeList(context, "values", &input_shapes); const Tensor& concat_dim_tensor = (AxisArgName == NAME_IS_CONCAT_DIM) ? MklGetInput(context, 0) @@ -611,14 +610,19 @@ class MklConcatOp : public OpKernel { int i = 0; bool invoke_eigen = false; bool are_all_mkl_inputs = true, are_all_tf_inputs = true; - const TensorShape expected_shape = mkl_input_shapes[0].IsMklTensor() - ? mkl_input_shapes[0].GetTfShape() - : input_tensors[0].shape(); + const TensorShape expected_shape = input_shapes[0].IsMklTensor() + ? input_shapes[0].GetTfShape() + : input_tensors[0].shape(); size_t expected_dims = expected_shape.dims(); if (concat_dim < 0) concat_dim = expected_dims + concat_dim; - for (auto& s : mkl_input_shapes) { + for (auto& s : input_shapes) { + if (s == expected_shape) { + ++i; + continue; + } + TensorShape s_shape = s.IsMklTensor() ? s.GetTfShape() : input_tensors[i].shape(); size_t s_dims = s_shape.dims(); @@ -661,14 +665,21 @@ class MklConcatOp : public OpKernel { // Call Eigen library if (invoke_eigen) { - CallEigenVersion(context, input_tensors, mkl_input_shapes); + TensorShapeList tf_input_shapes; + i = 0; + for (auto& s : input_shapes) { + TensorShape s_shape = + s.IsMklTensor() ? s.GetTfShape() : input_tensors[i].shape(); + tf_input_shapes.push_back(s_shape); + ++i; + } + CallEigenVersion(context, input_tensors, tf_input_shapes); return; } memory::dims dst_dims; - if (are_all_mkl_inputs) - dst_dims = TFShapeToMklDnnDims(mkl_input_shapes[0].GetTfShape()); + dst_dims = TFShapeToMklDnnDims(input_shapes[0].GetTfShape()); else // When all the inputs are in Tensorflow format, we don't know // what is the input data format. In that case, we just use @@ -678,61 +689,26 @@ class MklConcatOp : public OpKernel { std::vector<memory::primitive_desc> srcs_pd; std::vector<MklDnnData<T>> srcs(N, MklDnnData<T>(&cpu_engine)); int64 dst_concat_dim_size = 0; - - bool isMklReorderNeeded = false; - memory::format mkl_common_format = memory::format::any; - if (are_all_mkl_inputs) { - mkl_common_format = - FindMklCommonFormat(mkl_input_shapes, concat_dim, - &isMklReorderNeeded, &dst_concat_dim_size); - - if (!isMklReorderNeeded) { - // All MKL tensors have a same format. Reorder is not needed. - for (int k = 0; k < N; k++) { - if (input_tensors[k].NumElements() == 0) - continue; - - auto src_md = mkl_input_shapes[k].GetMklLayout(); - srcs[k].SetUsrMem(src_md, &input_tensors[k]); - auto src_mpd = srcs[k].GetUsrMemPrimDesc(); - srcs_pd.push_back(src_mpd); - } - } else { - // MKL tensors have different formats. - // Reorder them to most common format. - for (int k = 0; k < N; k++) { - if (input_tensors[k].NumElements() == 0) - continue; - - auto src_dims = TFShapeToMklDnnDims( - mkl_input_shapes[k].GetTfShape()); - auto src_md = mkl_input_shapes[k].GetMklLayout(); - srcs[k].SetUsrMem(src_md, &input_tensors[k]); - - if (src_md.data.format != mkl_common_format) - src_md = memory::desc(src_dims, MklDnnType<T>(), - mkl_common_format); - - srcs_pd.push_back(memory::primitive_desc(src_md, cpu_engine)); - } - } - } else { // All TF inputs - for (int k = 0; k < N; k++) { - if (input_tensors[k].NumElements() == 0) - continue; - - memory::dims src_dims = TFShapeToMklDnnDims(input_tensors[k].shape()); - dst_concat_dim_size += src_dims[concat_dim]; - - // It does not matter what data format to be used (NHWC versus NCHW). - // We just need to ensure that output uses same data format as inputs. - auto src_md = - memory::desc(src_dims, MklDnnType<T>(), memory::format::nchw); - - srcs[k].SetUsrMem(src_md, &input_tensors[k]); - auto src_mpd = srcs[k].GetUsrMemPrimDesc(); - srcs_pd.push_back(src_mpd); - } + for (int k = 0; k < N; k++) { + bool is_mkl_tensor = input_shapes[k].IsMklTensor(); + memory::dims src_dims; + + // Same comment as dst_dims for src_dims. + src_dims = (is_mkl_tensor) + ? TFShapeToMklDnnDims(input_shapes[k].GetTfShape()) + : TFShapeToMklDnnDims(input_tensors[k].shape()); + + dst_concat_dim_size += src_dims[concat_dim]; + auto src_md = + is_mkl_tensor ? input_shapes[k].GetMklLayout() : + // It does not matter what data format we use here + // (NHWC or NCHW). We just need to ensure that output + // of Concat uses same data format as input. + memory::desc(src_dims, MklDnnType<T>(), memory::format::nchw); + + srcs[k].SetUsrMem(src_md, &input_tensors[k]); + auto src_mpd = srcs[k].GetUsrMemPrimDesc(); + srcs_pd.push_back(src_mpd); } dst_dims[concat_dim] = dst_concat_dim_size; @@ -742,33 +718,25 @@ class MklConcatOp : public OpKernel { if (are_all_mkl_inputs) { // Since we are passing a specific format for destination, // we need to have dst_dims in MklDnn order (NCHW). - auto orig_tf_format = mkl_input_shapes[0].GetTfDataFormat(); + auto orig_tf_format = input_shapes[0].GetTfDataFormat(); dst_dims_in_nchw = MklDnnDimsInNCHW( dst_dims, MklDnnDataFormatToTFDataFormat(orig_tf_format)); - // Set the output format same as the most common format of inputs - // to avoid layout conversions. + // We will set the output in the same format as input to avoid layout + // conversions. + // Currently we are setting dst format same as input format. + // See if we can make this choice in a better way. dst_md = memory::desc( - dst_dims_in_nchw, MklDnnType<T>(), mkl_common_format); + dst_dims_in_nchw, MklDnnType<T>(), + (memory::format)input_shapes[0].GetMklLayout().data.format); } else { - // All inputs are TF tensors. - // Set the output format same as input format (nchw). + // Again, format does not matter here. We just need to make it same as + // input format. dst_md = memory::desc(dst_dims, MklDnnType<T>(), memory::format::nchw); } std::vector<primitive::at> inputs; - std::vector<primitive> net; - if (isMklReorderNeeded) { - for (int k = 0; k < input_tensors.size(); k++) { - if (input_tensors[k].NumElements() > 0) { - srcs[k].CheckReorderToOpMem(srcs_pd[k], &net); - } - } - } - for (int k = 0; k < input_tensors.size(); k++) { - if (input_tensors[k].NumElements() > 0) { - inputs.push_back(srcs[k].GetOpMem()); - } - } + for (int k = 0; k < input_tensors.size(); k++) + inputs.push_back(srcs[k].GetOpMem()); // If all inputs are in MKL format, then meaning of concat_dim needs to // change. Value of concat_dim is tied to input Tensorflow data format @@ -777,8 +745,7 @@ class MklConcatOp : public OpKernel { // But ifinput tensors are in NHWC order, then semantics need to change. // E.g., if we are concatinating over Channel (dimension 3 for NHWC), // then since MklDnn order is NCHW, concat_dim needs to be 1. - if (are_all_mkl_inputs) - concat_dim = mkl_input_shapes[0].TfDimIdx(concat_dim); + if (are_all_mkl_inputs) concat_dim = input_shapes[0].TfDimIdx(concat_dim); auto concat_pd = concat::primitive_desc(dst_md, concat_dim, srcs_pd); @@ -791,7 +758,7 @@ class MklConcatOp : public OpKernel { dnn_shape_dst.SetMklLayout(&dst_pd); dnn_shape_dst.SetElemType(MklDnnType<T>()); dnn_shape_dst.SetTfLayout(dst_dims.size(), dst_dims_in_nchw, - mkl_input_shapes[0].GetTfDataFormat()); + input_shapes[0].GetTfDataFormat()); tf_shape_dst.AddDim((dst_pd.get_size() / sizeof(T))); } else { dnn_shape_dst.SetMklTensor(false); @@ -806,6 +773,7 @@ class MklConcatOp : public OpKernel { dst.SetUsrMem(dst_md, dst_tensor); auto concat_op = concat(concat_pd, inputs, dst.GetOpMem()); + std::vector<primitive> net; net.push_back(concat_op); stream(stream::kind::eager).submit(net).wait(); } catch (mkldnn::error& e) { @@ -819,27 +787,15 @@ class MklConcatOp : public OpKernel { } void CallEigenVersion(OpKernelContext* context, const OpInputList& values, - const MklDnnShapeList& mkl_input_shapes) { - CHECK_EQ(values.size(), mkl_input_shapes.size()); + const TensorShapeList& input_shapes) { + CHECK_EQ(values.size(), input_shapes.size()); std::vector<Tensor> converted_values; - TensorShapeList tf_input_shapes; - for (int i = 0; i < mkl_input_shapes.size(); i++) { - if (mkl_input_shapes[i].IsMklTensor()) { - // do conversion from MKL to TF - Tensor tmp_tensor = - ConvertMklToTF<T>(context, values[i], mkl_input_shapes[i]); - converted_values.push_back(tmp_tensor); - tf_input_shapes.push_back(mkl_input_shapes[i].GetTfShape()); - } else { - // no conversion since it is TF tensor already - converted_values.push_back(values[i]); - tf_input_shapes.push_back(values[i].shape()); - } - } + for (int i = 0; i < input_shapes.size(); i++) + converted_values.push_back(values[i]); // Call Eigen concat. - eigen_concat_op_.Compute(context, converted_values, tf_input_shapes); + eigen_concat_op_.Compute(context, converted_values, input_shapes); // Set output Mkl tensor for this op. MklDnnShape dnn_shape_output; @@ -856,55 +812,6 @@ class MklConcatOp : public OpKernel { output_tensor->flat<uint8>().data(), output_tensor->flat<uint8>().size() * sizeof(uint8)); } - - // This method finds the most commom format accross all MKL inputs - // Inputs: - // 1. input_shapes: shapes of input (MKL) tensors. - // 2. concat_dim: concat dimension. - // Outputs: - // 1. is_reorder_needed is set to true if inputs have difference formats - // It is set to false otherwise. - // 2. concat_dim_size is the size of concat_dim. - // Return: - // return the common MKL format. - memory::format FindMklCommonFormat(const MklDnnShapeList& input_shapes, - int concat_dim, bool* is_reorder_needed, int64* concat_dim_size) { - *is_reorder_needed = false; - *concat_dim_size = 0; - std::unordered_map<int, int> occurrence_map; - if (input_shapes.size() == 0) - return memory::format::any; - - // Compute ocurrences of each format of all inputs. - for (int k=0; k <input_shapes.size(); k++) { - auto src_dims = TFShapeToMklDnnDims(input_shapes[k].GetTfShape()); - *concat_dim_size += src_dims[concat_dim]; - int fmt = static_cast<int>( - input_shapes[k].GetMklLayout().data.format); - occurrence_map[fmt] += 1; - } - - if (occurrence_map.size() == 1) { - // this means that all inputs have a same format - // return it with is_reorder_needed set false. - return static_cast<memory::format>( - input_shapes[0].GetMklLayout().data.format); - } - - // Input tensors have different formats. Thus, reorder is needed. - // We pick up the most common format to minimize the total - // number of input reorder. - memory::format commonest_format = memory::format::any; - int max_occurrence = 0; - *is_reorder_needed = true; - for (auto item : occurrence_map) { - if (item.second > max_occurrence) { - commonest_format = static_cast<memory::format>(item.first); - max_occurrence = item.second; - } - } - return commonest_format; - } }; #endif diff --git a/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc b/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc index f857be6c32..c1da0ded1d 100644 --- a/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc @@ -18,7 +18,6 @@ limitations under the License. // bias. #ifdef INTEL_MKL -#ifdef INTEL_MKL_ML #define USE_EIGEN_TENSOR #define EIGEN_USE_THREADS @@ -265,5 +264,4 @@ class MklConv2DCustomBackpropBiasOp : public OpKernel { TF_CALL_float(REGISTER_CPU_KERNELS); #undef REGISTER_CPU_KERNELS } /* namespace tensorflow */ -#endif /* INTEL_MKL_ML */ #endif /* INTEL_MKL */ diff --git a/tensorflow/core/kernels/mkl_pooling_ops_common.h b/tensorflow/core/kernels/mkl_pooling_ops_common.h index c0dfed7d7d..279167aba2 100644 --- a/tensorflow/core/kernels/mkl_pooling_ops_common.h +++ b/tensorflow/core/kernels/mkl_pooling_ops_common.h @@ -199,15 +199,13 @@ class MklPoolingForwardOpBase : public MklPoolingOpBase<T> { CHECK_NOTNULL(pool_params); CHECK_NOTNULL(dnn_data_input); TensorShape input_tensor_shape = input_tensor.shape(); - if (input_tensor.NumElements() != 0) { - memory::desc input_md = + memory::desc input_md = input_mkl_shape.IsMklTensor() ? input_mkl_shape.GetMklLayout() : memory::desc(TFShapeToMklDnnDimsInNCHW(input_tensor_shape, this->data_format_tf_), MklDnnType<T>(), this->data_format_mkldnn_); - dnn_data_input->SetUsrMem(input_md, &input_tensor); - } + dnn_data_input->SetUsrMem(input_md, &input_tensor); this->InitMklPoolParameters(context, pool_params, input_mkl_shape, input_tensor_shape); } diff --git a/tensorflow/core/kernels/scatter_nd_op.cc b/tensorflow/core/kernels/scatter_nd_op.cc index e1fc2ea128..43c5b29509 100644 --- a/tensorflow/core/kernels/scatter_nd_op.cc +++ b/tensorflow/core/kernels/scatter_nd_op.cc @@ -292,7 +292,6 @@ TF_CALL_string(REGISTER_SCATTER_ND_CPU); REGISTER_SCATTER_ND_UPDATE_GPU(type); \ REGISTER_SCATTER_ND_GPU(type); -TF_CALL_int32(REGISTER_SCATTER_ND_ALL_GPU); // TODO(b/66916790): Support half types in ScatterNd. TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_ALL_GPU); TF_CALL_complex64(REGISTER_SCATTER_ND_ALL_GPU); @@ -307,8 +306,6 @@ TF_CALL_complex128(REGISTER_SCATTER_ND_ALL_GPU); #define REGISTER_SCATTER_ND_UPDATE_SYCL(type) \ REGISTER_SCATTER_ND_UPDATE(type, SYCL); -TF_CALL_int32(REGISTER_SCATTER_ND_ADD_SUB_SYCL); -TF_CALL_int32(REGISTER_SCATTER_ND_UPDATE_SYCL); TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_ADD_SUB_SYCL); TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_UPDATE_SYCL); #undef REGISTER_SCATTER_ND_ADD_SUB_SYCL @@ -579,7 +576,6 @@ namespace functor { DECLARE_GPU_SPECS_INDEX(T, int32); \ DECLARE_GPU_SPECS_INDEX(T, int64) -TF_CALL_int32(DECLARE_GPU_SPECS); // TODO(b/66916790): Support half types in ScatterNd. TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPECS); TF_CALL_complex64(DECLARE_GPU_SPECS); diff --git a/tensorflow/core/kernels/scatter_nd_op_gpu.cu.cc b/tensorflow/core/kernels/scatter_nd_op_gpu.cu.cc index 08b657f4c3..a3c21edc15 100644 --- a/tensorflow/core/kernels/scatter_nd_op_gpu.cu.cc +++ b/tensorflow/core/kernels/scatter_nd_op_gpu.cu.cc @@ -170,7 +170,6 @@ struct ScatterNdFunctor<GPUDevice, T, Index, op, IXDIM> { DECLARE_GPU_SPECS_INDEX(T, int32); \ DECLARE_GPU_SPECS_INDEX(T, int64) -TF_CALL_int32(DECLARE_GPU_SPECS); TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPECS); TF_CALL_complex64(DECLARE_GPU_SPECS); TF_CALL_complex128(DECLARE_GPU_SPECS); diff --git a/tensorflow/core/kernels/scoped_allocator_ops_test.cc b/tensorflow/core/kernels/scoped_allocator_ops_test.cc index 634f9ba887..bb0129fa6f 100644 --- a/tensorflow/core/kernels/scoped_allocator_ops_test.cc +++ b/tensorflow/core/kernels/scoped_allocator_ops_test.cc @@ -216,13 +216,8 @@ TEST_F(ScopedAllocatorConcatOpTest, Success3) { } TEST_F(ScopedAllocatorConcatOpTest, Reshape) { - MakeOp({2, 2, 4}, DT_DOUBLE, true, "test", 120, 2); - - // The elements of the third parameter to ExecOp must be multiples of - // Allocator::kAllocatorAlignment in size. If they are not, the backing - // tensor allocated by PrepOp will have too many elements and reshaping - // will fail. - ExecOp(DT_DOUBLE, 120, {{2, 4}, {2, 4}}); + MakeOp({2, 2, 2}, DT_DOUBLE, true, "test", 120, 2); + ExecOp(DT_DOUBLE, 120, {{2, 2}, {2, 2}}); } TEST_F(ScopedAllocatorConcatOpTest, NoReshapeAttr) { diff --git a/tensorflow/core/kernels/segment_reduction_ops.h b/tensorflow/core/kernels/segment_reduction_ops.h index d65692a552..7796bf3587 100644 --- a/tensorflow/core/kernels/segment_reduction_ops.h +++ b/tensorflow/core/kernels/segment_reduction_ops.h @@ -16,14 +16,6 @@ limitations under the License. #ifndef TENSORFLOW_CORE_KERNELS_SEGMENT_REDUCTION_OPS_H_ #define TENSORFLOW_CORE_KERNELS_SEGMENT_REDUCTION_OPS_H_ - -// This file requires the following include because it uses CudaAtomicMax: -// #include "tensorflow/core/util/cuda_kernel_helper.h" - -// Unfortunately we can't add the #include, since it breaks compilation for -// non-GPU targets. This only breaks in clang, because it's more strict for -// template code and CudaAtomicMax is used in template context. - // This file requires the following include because it uses CudaAtomicMax: // #include "tensorflow/core/util/cuda_kernel_helper.h" @@ -138,4 +130,4 @@ struct Highest { } // namespace functor } // namespace tensorflow -#endif // TENSORFLOW_CORE_KERNELS_SEGMENT_REDUCTION_OPS_H_ +#endif // THIRD_PARTY_TENSORFLOW_CORE_KERNELS_SEGMENT_REDUCTION_OPS_H_ diff --git a/tensorflow/core/kernels/sparse_matmul_op.cc b/tensorflow/core/kernels/sparse_matmul_op.cc index 866c5dcd52..a1f9667b78 100644 --- a/tensorflow/core/kernels/sparse_matmul_op.cc +++ b/tensorflow/core/kernels/sparse_matmul_op.cc @@ -1490,7 +1490,7 @@ inline void LibxsmmSparseMatMul<TL, TR>::Compute( #endif // TENSORFLOW_USE_LIBXSMM -// Here is an overview of the SparseMatMul code. Note that we assume that the +// Here is a an overview of the SparseMatMul code. Note that we assume that the // left matrix is sparse. // // The matrix "left" is divided into a grid with blocksize of (M, KL). Each diff --git a/tensorflow/core/kernels/string_split_op.cc b/tensorflow/core/kernels/string_split_op.cc index 26ab72f12e..4c2b312c34 100644 --- a/tensorflow/core/kernels/string_split_op.cc +++ b/tensorflow/core/kernels/string_split_op.cc @@ -22,7 +22,6 @@ limitations under the License. #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/core/status.h" -#include "tensorflow/core/lib/core/stringpiece.h" #include "tensorflow/core/lib/strings/str_util.h" namespace tensorflow { @@ -44,63 +43,6 @@ std::vector<string> Split(const string& str, const string& delimiter, return char_vector; } -std::vector<string> SplitV2(const string& str, StringPiece sep, int maxsplit) { - // This SplitV2 method matches the behavior of python's str.split: - // If sep is given, consecutive delimiters are not grouped together - // and are deemed to delimit empty strings (for example, '1,,2'.split(',') - // returns ['1', '', '2']). The sep argument may consist of multiple - // characters (for example, '1<>2<>3'.split('<>') returns ['1', '2', '3']). - // Splitting an empty string with a specified separator returns ['']. - // - // If sep is not specified or is None, a different splitting algorithm is - // applied: runs of consecutive whitespace are regarded as a single - // separator, and the result will contain no empty strings at the start or - // end if the string has leading or trailing whitespace. Consequently, - // splitting an empty string or a string consisting of just whitespace - // with a None separator returns []. - - std::vector<string> result; - - StringPiece text(str); - if (maxsplit == 0) { - result.emplace_back(std::string(text)); - return result; - } - - if (sep.empty()) { - StringPiece token; - // Remove leading whitespaces. - str_util::RemoveLeadingWhitespace(&text); - int split = 0; - while (str_util::ConsumeNonWhitespace(&text, &token)) { - result.emplace_back(std::string(token)); - str_util::RemoveLeadingWhitespace(&text); - ++split; - if (maxsplit > 0 && split == maxsplit) { - result.emplace_back(std::string(text)); - return result; - } - } - return result; - } - auto p = std::search(text.begin(), text.end(), sep.begin(), sep.end()); - int split = 0; - while (p != text.end()) { - StringPiece token = text.substr(0, p - text.begin()); - result.emplace_back(std::string(token)); - text.remove_prefix(token.size()); - text.remove_prefix(sep.size()); - ++split; - if (maxsplit > 0 && split == maxsplit) { - result.emplace_back(std::string(text)); - return result; - } - p = std::search(text.begin(), text.end(), sep.begin(), sep.end()); - } - result.emplace_back(std::string(text)); - return result; -} - } // namespace class StringSplitOp : public OpKernel { @@ -180,78 +122,6 @@ class StringSplitOp : public OpKernel { bool skip_empty_; }; -class StringSplitV2Op : public OpKernel { - public: - explicit StringSplitV2Op(OpKernelConstruction* context) - : OpKernel(context), maxsplit_(-1) { - OP_REQUIRES_OK(context, context->GetAttr("maxsplit", &maxsplit_)); - } - - void Compute(OpKernelContext* ctx) override { - const Tensor* input_tensor; - OP_REQUIRES_OK(ctx, ctx->input("input", &input_tensor)); - OP_REQUIRES(ctx, TensorShapeUtils::IsVector(input_tensor->shape()), - errors::InvalidArgument("input must be a vector, got shape: ", - input_tensor->shape().DebugString())); - - const auto input_vec = input_tensor->vec<string>(); - const int64 batch_size = input_vec.dimension(0); - - const Tensor* sep_tensor; - OP_REQUIRES_OK(ctx, ctx->input("sep", &sep_tensor)); - OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(sep_tensor->shape()), - errors::InvalidArgument("sep must be a scalar, got shape: ", - sep_tensor->shape().DebugString())); - const auto sep_vec = sep_tensor->flat<string>(); - StringPiece sep(sep_vec(0)); - std::vector<string> tokens; - // Guess that we'll be unpacking a handful of tokens per example. - static constexpr int kReserveSize = 4; - tokens.reserve(batch_size * kReserveSize); - - int64 output_size = 0; - int64 max_num_entries = 0; - std::vector<int64> num_indices(batch_size); - for (int64 i = 0; i < batch_size; ++i) { - std::vector<string> parts = SplitV2(input_vec(i), sep, maxsplit_); - int64 n_entries = parts.size(); - num_indices[i] = n_entries; - output_size += n_entries; - max_num_entries = std::max(max_num_entries, n_entries); - tokens.insert(tokens.end(), parts.begin(), parts.end()); - } - - Tensor* sp_indices_t; - OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({output_size, 2}), - &sp_indices_t)); - Tensor* sp_tokens_t; - OP_REQUIRES_OK( - ctx, ctx->allocate_output(1, TensorShape({output_size}), &sp_tokens_t)); - Tensor* sp_shape_t; - OP_REQUIRES_OK(ctx, ctx->allocate_output(2, TensorShape({2}), &sp_shape_t)); - - auto sp_indices = sp_indices_t->matrix<int64>(); - auto sp_tokens = sp_tokens_t->vec<string>(); - auto sp_shape = sp_shape_t->vec<int64>(); - sp_shape(0) = batch_size; - sp_shape(1) = max_num_entries; - size_t c = 0; - for (size_t i = 0; i < batch_size; ++i) { - for (size_t j = 0; j < num_indices[i]; ++j) { - sp_indices(c, 0) = i; - sp_indices(c, 1) = j; - sp_tokens(c) = tokens[c]; - ++c; - } - } - } - - private: - int maxsplit_; -}; - REGISTER_KERNEL_BUILDER(Name("StringSplit").Device(DEVICE_CPU), StringSplitOp); -REGISTER_KERNEL_BUILDER(Name("StringSplitV2").Device(DEVICE_CPU), - StringSplitV2Op); } // namespace tensorflow diff --git a/tensorflow/core/ops/candidate_sampling_ops.cc b/tensorflow/core/ops/candidate_sampling_ops.cc index 6e589c8d1c..6e4d100b04 100644 --- a/tensorflow/core/ops/candidate_sampling_ops.cc +++ b/tensorflow/core/ops/candidate_sampling_ops.cc @@ -145,15 +145,12 @@ REGISTER_OP("ComputeAccidentalHits") int64 num_true; TF_RETURN_IF_ERROR(c->GetAttr("num_true", &num_true)); - // Validate true_classes, must be a matrix. + // Validate true_classes. ShapeHandle true_classes; TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &true_classes)); DimensionHandle unused; TF_RETURN_IF_ERROR( c->WithValue(c->Dim(true_classes, 1), num_true, &unused)); - // Validate sampled_candidates, must be a vector. - ShapeHandle sampled_candidates; - TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &sampled_candidates)); // All three outputs are the same shape. ShapeHandle v = c->Vector(InferenceContext::kUnknownDim); diff --git a/tensorflow/core/ops/dataset_ops.cc b/tensorflow/core/ops/dataset_ops.cc index 9dca5f53ce..15e0ca8af9 100644 --- a/tensorflow/core/ops/dataset_ops.cc +++ b/tensorflow/core/ops/dataset_ops.cc @@ -218,17 +218,7 @@ REGISTER_OP("MapAndBatchDataset") .Attr("Targuments: list(type) >= 0") .Attr("output_types: list(type) >= 1") .Attr("output_shapes: list(shape) >= 1") - .SetShapeFn([](shape_inference::InferenceContext* c) { - // Use index from the end to retrieve the Input shapes, - // so that to avoid guessing the length of "other_arguments". - // batch_size, num_parallel_batches, and drop_remainder are 0-D scalars. - shape_inference::ShapeHandle unused; - TF_RETURN_IF_ERROR(c->WithRank(c->input(c->num_inputs() - 3), 0, &unused)); - TF_RETURN_IF_ERROR(c->WithRank(c->input(c->num_inputs() - 2), 0, &unused)); - TF_RETURN_IF_ERROR(c->WithRank(c->input(c->num_inputs() - 1), 0, &unused)); - - return shape_inference::ScalarShape(c); - }); + .SetShapeFn(shape_inference::ScalarShape); REGISTER_OP("MapAndBatchDatasetV2") .Input("input_dataset: variant") @@ -241,17 +231,7 @@ REGISTER_OP("MapAndBatchDatasetV2") .Attr("Targuments: list(type) >= 0") .Attr("output_types: list(type) >= 1") .Attr("output_shapes: list(shape) >= 1") - .SetShapeFn([](shape_inference::InferenceContext* c) { - // Use index from the end to retrieve the Input shapes, - // so that to avoid guessing the length of "other_arguments". - // batch_size, num_parallel_calls, and drop_remainder are 0-D scalars. - shape_inference::ShapeHandle unused; - TF_RETURN_IF_ERROR(c->WithRank(c->input(c->num_inputs() - 3), 0, &unused)); - TF_RETURN_IF_ERROR(c->WithRank(c->input(c->num_inputs() - 2), 0, &unused)); - TF_RETURN_IF_ERROR(c->WithRank(c->input(c->num_inputs() - 1), 0, &unused)); - - return shape_inference::ScalarShape(c); - }); + .SetShapeFn(shape_inference::ScalarShape); REGISTER_OP("PrefetchDataset") .Input("input_dataset: variant") diff --git a/tensorflow/core/ops/image_ops.cc b/tensorflow/core/ops/image_ops.cc index 87f4991134..d949e70c66 100644 --- a/tensorflow/core/ops/image_ops.cc +++ b/tensorflow/core/ops/image_ops.cc @@ -454,9 +454,7 @@ REGISTER_OP("DrawBoundingBoxes") DimensionHandle unused; TF_RETURN_IF_ERROR(c->WithValue(c->Dim(boxes, 2), 4, &unused)); - // The rank of the input image (rank = 4) has already been restricted - // above, and the output is of the same shape as the input. - return shape_inference::UnchangedShape(c); + return shape_inference::UnchangedShapeWithRankAtLeast(c, 3); }); // -------------------------------------------------------------------------- diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc index b3487122e2..1740fa152c 100644 --- a/tensorflow/core/ops/math_ops.cc +++ b/tensorflow/core/ops/math_ops.cc @@ -1084,7 +1084,7 @@ REGISTER_OP("UnsortedSegmentProd") .Input("segment_ids: Tindices") .Input("num_segments: Tnumsegments") .Output("output: T") - .Attr("T: numbertype") + .Attr("T: realnumbertype") .Attr("Tindices: {int32,int64}") .Attr("Tnumsegments: {int32,int64} = DT_INT32") .SetShapeFn(UnsortedSegmentReductionShapeFn); diff --git a/tensorflow/core/ops/nn_ops.cc b/tensorflow/core/ops/nn_ops.cc index 41efa49ce3..fc60e807b9 100644 --- a/tensorflow/core/ops/nn_ops.cc +++ b/tensorflow/core/ops/nn_ops.cc @@ -1453,7 +1453,6 @@ REGISTER_OP("QuantizedReluX") ShapeHandle unused; TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused)); TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused)); - TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused)); c->set_output(1, c->Scalar()); c->set_output(2, c->Scalar()); return Status::OK(); diff --git a/tensorflow/core/ops/string_ops.cc b/tensorflow/core/ops/string_ops.cc index 4423062362..1d5c743a56 100644 --- a/tensorflow/core/ops/string_ops.cc +++ b/tensorflow/core/ops/string_ops.cc @@ -78,7 +78,7 @@ REGISTER_OP("ReduceJoin") REGISTER_OP("AsString") .Input("input: T") .Output("output: string") - .Attr("T: {int8, int16, int32, int64, complex64, float, double, bool}") + .Attr("T: {int32, int64, complex64, float, double, bool, int8}") .Attr("precision: int = -1") .Attr("scientific: bool = false") .Attr("shortest: bool = false") @@ -134,24 +134,6 @@ REGISTER_OP("StringSplit") return Status::OK(); }); -REGISTER_OP("StringSplitV2") - .Input("input: string") - .Input("sep: string") - .Output("indices: int64") - .Output("values: string") - .Output("shape: int64") - .Attr("maxsplit: int = -1") - .SetShapeFn([](InferenceContext* c) { - ShapeHandle unused; - TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &unused)); - TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused)); - - c->set_output(0, c->Matrix(InferenceContext::kUnknownDim, 2)); - c->set_output(1, c->Vector(InferenceContext::kUnknownDim)); - c->set_output(2, c->Vector(2)); - return Status::OK(); - }); - REGISTER_OP("StringStrip") .Input("input: string") .Output("output: string") diff --git a/tensorflow/core/platform/cpu_info.cc b/tensorflow/core/platform/cpu_info.cc index e9da3d8e32..99de364042 100644 --- a/tensorflow/core/platform/cpu_info.cc +++ b/tensorflow/core/platform/cpu_info.cc @@ -344,28 +344,5 @@ int CPUModelNum() { #endif } -int CPUIDNumSMT() { -#ifdef PLATFORM_IS_X86 - // https://software.intel.com/en-us/articles/intel-64-architecture-processor-topology-enumeration - // https://software.intel.com/en-us/articles/intel-sdm (Vol 3A) - // Section: Detecting Hardware Multi-threads Support and Topology - // Uses CPUID Leaf 11 to enumerate system topology on Intel x86 architectures - // Other cases not supported - uint32 eax, ebx, ecx, edx; - // Check if system supports Leaf 11 - GETCPUID(eax, ebx, ecx, edx, 0, 0); - if (eax >= 11) { - // 1) Leaf 11 available? CPUID.(EAX=11, ECX=0):EBX != 0 - // 2) SMT_Mask_Width = CPUID.(EAX=11, ECX=0):EAX[4:0] if CPUID.(EAX=11, - // ECX=0):ECX[15:8] is 1 - GETCPUID(eax, ebx, ecx, edx, 11, 0); - if (ebx != 0 && ((ecx & 0xff00) >> 8) == 1) { - return 1 << (eax & 0x1f); // 2 ^ SMT_Mask_Width - } - } -#endif // PLATFORM_IS_X86 - return 0; -} - } // namespace port } // namespace tensorflow diff --git a/tensorflow/core/platform/cpu_info.h b/tensorflow/core/platform/cpu_info.h index 175c9ae8b1..b5be7e8b54 100644 --- a/tensorflow/core/platform/cpu_info.h +++ b/tensorflow/core/platform/cpu_info.h @@ -35,10 +35,6 @@ namespace port { // software can change it dynamically. int NumSchedulableCPUs(); -// Returns an estimate of the number of hyperthreads per physical core -// on the CPU -int NumHyperthreadsPerCore(); - // Mostly ISA related features that we care about enum CPUFeature { // Do not change numeric assignments. @@ -111,9 +107,6 @@ int CPUModelNum(); // Returns nominal core processor cycles per second of each processor. double NominalCPUFrequency(); -// Returns num of hyperthreads per physical core -int CPUIDNumSMT(); - } // namespace port } // namespace tensorflow diff --git a/tensorflow/core/platform/default/build_config.bzl b/tensorflow/core/platform/default/build_config.bzl index a319ccbdbe..ae81f9b5b3 100644 --- a/tensorflow/core/platform/default/build_config.bzl +++ b/tensorflow/core/platform/default/build_config.bzl @@ -71,8 +71,6 @@ def pyx_library( name = filename + "_cython_translation", srcs = [filename], outs = [filename.split(".")[0] + ".cpp"], - # Optionally use PYTHON_BIN_PATH on Linux platforms so that python 3 - # works. Windows has issues with cython_binary so skip PYTHON_BIN_PATH. cmd = "PYTHONHASHSEED=0 $(location @cython//:cython_binary) --cplus $(SRCS) --output-file $(OUTS)", tools = ["@cython//:cython_binary"] + pxd_srcs, ) diff --git a/tensorflow/core/platform/hadoop/hadoop_file_system.cc b/tensorflow/core/platform/hadoop/hadoop_file_system.cc index ff4b4436bb..72c12318ca 100644 --- a/tensorflow/core/platform/hadoop/hadoop_file_system.cc +++ b/tensorflow/core/platform/hadoop/hadoop_file_system.cc @@ -115,17 +115,18 @@ class LibHDFS { const char* kLibHdfsDso = "libhdfs.so"; #endif char* hdfs_home = getenv("HADOOP_HDFS_HOME"); - if (hdfs_home != nullptr) { - string path = io::JoinPath(hdfs_home, "lib", "native", kLibHdfsDso); - status_ = TryLoadAndBind(path.c_str(), &handle_); - if (status_.ok()) { - return; - } + if (hdfs_home == nullptr) { + status_ = errors::FailedPrecondition( + "Environment variable HADOOP_HDFS_HOME not set"); + return; + } + string path = io::JoinPath(hdfs_home, "lib", "native", kLibHdfsDso); + status_ = TryLoadAndBind(path.c_str(), &handle_); + if (!status_.ok()) { + // try load libhdfs.so using dynamic loader's search path in case + // libhdfs.so is installed in non-standard location + status_ = TryLoadAndBind(kLibHdfsDso, &handle_); } - - // Try to load the library dynamically in case it has been installed - // to a in non-standard location. - status_ = TryLoadAndBind(kLibHdfsDso, &handle_); } Status status_; diff --git a/tensorflow/core/platform/posix/port.cc b/tensorflow/core/platform/posix/port.cc index 708f32ba80..8e316472fe 100644 --- a/tensorflow/core/platform/posix/port.cc +++ b/tensorflow/core/platform/posix/port.cc @@ -74,11 +74,6 @@ int NumSchedulableCPUs() { return kDefaultCores; } -int NumHyperthreadsPerCore() { - static const int ht_per_core = tensorflow::port::CPUIDNumSMT(); - return (ht_per_core > 0) ? ht_per_core : 1; -} - void* AlignedMalloc(size_t size, int minimum_alignment) { #if defined(__ANDROID__) return memalign(minimum_alignment, size); diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h index cb1fd09dbb..522a9d84fd 100644 --- a/tensorflow/core/public/version.h +++ b/tensorflow/core/public/version.h @@ -19,12 +19,12 @@ limitations under the License. // TensorFlow uses semantic versioning, see http://semver.org/. #define TF_MAJOR_VERSION 1 -#define TF_MINOR_VERSION 9 +#define TF_MINOR_VERSION 8 #define TF_PATCH_VERSION 0 // TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1", // "-beta", "-rc", "-rc.1") -#define TF_VERSION_SUFFIX "-rc0" +#define TF_VERSION_SUFFIX "" #define TF_STR_HELPER(x) #x #define TF_STR(x) TF_STR_HELPER(x) diff --git a/tensorflow/core/util/mkl_util.h b/tensorflow/core/util/mkl_util.h index 90b6533690..dffc965b14 100644 --- a/tensorflow/core/util/mkl_util.h +++ b/tensorflow/core/util/mkl_util.h @@ -42,7 +42,6 @@ limitations under the License. #ifndef INTEL_MKL_ML #include "mkldnn.hpp" -#include "tensorflow/core/lib/core/stringpiece.h" using mkldnn::engine; using mkldnn::memory; @@ -713,48 +712,15 @@ inline Tensor ConvertMklToTF(OpKernelContext* context, const Tensor& mkl_tensor, return output_tensor; } #else -using mkldnn::stream; -template <typename T> class MklDnnData; - template <typename T> inline Tensor ConvertMklToTF(OpKernelContext* context, const Tensor& mkl_tensor, const MklDnnShape& mkl_shape) { Tensor output_tensor; - try { - if (!mkl_shape.IsMklTensor()) - return mkl_tensor; // return input since it is already TF tensor - - TensorShape output_shape = mkl_shape.GetTfShape();; - - // Allocate output tensor. - context->allocate_temp(DataTypeToEnum<T>::v(), - output_shape, &output_tensor); - - auto cpu_engine = engine(engine::cpu, 0); - MklDnnData<T> input(&cpu_engine); - - // Get Mkl layout of input tensor. - auto input_mkl_md = mkl_shape.GetMklLayout(); - auto output_tf_md = mkl_shape.GetTfLayout(); - auto output_tf_pd = memory::primitive_desc(output_tf_md, cpu_engine); - input.SetUsrMem(input_mkl_md, &mkl_tensor); - - // reorder - if (input.IsReorderNeeded(output_tf_pd)) { - std::vector<primitive> net; - CHECK_EQ(input.CheckReorderToOpMem(output_tf_pd, &output_tensor, &net), - true); - stream(stream::kind::eager).submit(net).wait(); - } else { - // If not, just forward input tensor to output tensor. - CHECK(output_tensor.CopyFrom(mkl_tensor, output_shape)); - } - } catch (mkldnn::error& e) { - string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + string(e.message) + ", in file " + - string(__FILE__) + ":" + std::to_string(__LINE__); - LOG(FATAL) << "Operation received an exception: " << error_msg; - } + TensorShape output_shape; + + TF_CHECK_OK( + Status(error::Code::UNIMPLEMENTED, "Unimplemented conversion function")); + return output_tensor; } #endif @@ -1877,7 +1843,7 @@ class FactoryKeyCreator { template <typename T> void AddAsKey(const T data) { auto buffer = reinterpret_cast<const char *>(&data); - Append(StringPiece(buffer, sizeof(T))); + Append(absl::string_view(buffer, sizeof(T))); } std::string GetKey() { @@ -1888,8 +1854,8 @@ class FactoryKeyCreator { string key_; const char delimiter = 'x'; const int kMaxKeyLength = 256; - void Append(StringPiece s) { - key_.append(s.ToString()); + void Append(absl::string_view s) { + key_.append(string(s)); key_.append(1, delimiter); } }; diff --git a/tensorflow/docs_src/community/groups.md b/tensorflow/docs_src/community/groups.md index 0b07d413da..d92f5775fa 100644 --- a/tensorflow/docs_src/community/groups.md +++ b/tensorflow/docs_src/community/groups.md @@ -1,38 +1,17 @@ # User Groups -TensorFlow has communities around the world. [Submit your community!](https://docs.google.com/forms/d/e/1FAIpQLSc_RQIUYtVgLLihzATaO_WUXkEyBDE_OoRoOXYDPmBEvHuEBA/viewform) +TensorFlow has communities around the world. ## Asia -* [TensorFlow China community](https://www.tensorflowers.cn) -* [TensorFlow Korea (TF-KR) User Group](https://www.facebook.com/groups/TensorFlowKR/) -* [TensorFlow User Group Tokyo](https://tfug-tokyo.connpass.com/) -* [Soleil Data Dojo](https://soleildatadojo.connpass.com/) +* [TensorFlow Korea (TF-KR) User Group](https://www.facebook.com/groups/TensorFlowKR/) _(Korean language)_ +* [TensorFlow User Group Tokyo](https://tfug-tokyo.connpass.com/) _(Japanese Language)_ +* [Soleil Data Dojo](https://soleildatadojo.connpass.com/) _(Japanese language)_ * [TensorFlow User Group Utsunomiya](https://tfug-utsunomiya.connpass.com/) -* [TensorFlow Philippines Community](https://www.facebook.com/groups/TensorFlowPH/) -* [TensorFlow and Deep Learning Singapore](https://www.meetup.com/TensorFlow-and-Deep-Learning-Singapore/) -* [TensorFlow India](https://www.facebook.com/tensorflowindia) ## Europe * [TensorFlow Barcelona](https://www.meetup.com/Barcelona-Machine-Learning-Meetup/) * [TensorFlow Madrid](https://www.meetup.com/TensorFlow-Madrid/) -* [Tensorflow Belgium](https://www.meetup.com/TensorFlow-Belgium) -* [TensorFlow x Rome Meetup](https://www.meetup.com/it-IT/TensorFlow-x-Rome-Meetup) -* [TensorFlow London](https://www.meetup.com/TensorFlow-London/) -* [TensorFlow Edinburgh](https://www.meetup.com/tensorflow-edinburgh/) - -## America - -* [TensorFlow Buenos Aires](https://www.meetup.com/TensorFlow-Buenos-Aires/) - - -## Oceania -* [Melbourne TensorFlow Meetup](https://www.meetup.com/Melbourne-TensorFlow-Meetup) - - -## Africa - -* [TensorFlow Tunis Meetup](https://www.meetup.com/fr-FR/TensorFlow-Tunis-Meetup/) diff --git a/tensorflow/docs_src/get_started/eager.md b/tensorflow/docs_src/get_started/eager.md index bbb25e20c6..f08ac74425 100644 --- a/tensorflow/docs_src/get_started/eager.md +++ b/tensorflow/docs_src/get_started/eager.md @@ -1,3 +1,3 @@ # Get Started with Eager Execution -[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/r1.9.0/samples/core/get_started/eager.ipynb) +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/r1.8.0/samples/core/get_started/eager.ipynb) diff --git a/tensorflow/docs_src/get_started/index.md b/tensorflow/docs_src/get_started/index.md index 232d2f1547..55579d52fb 100644 --- a/tensorflow/docs_src/get_started/index.md +++ b/tensorflow/docs_src/get_started/index.md @@ -10,9 +10,9 @@ course prior to diving into TensorFlow documentation: TensorFlow is a tool for machine learning. While it contains a wide range of functionality, TensorFlow is mainly designed for deep neural network models. -The easiest way to get started with TensorFlow is by using Eager Execution. +The easiest way to get started with TensorFlow is using Eager Execution. - * @{$get_started/eager}, is for anyone new to machine learning or TensorFlow. + * @{$get_started/eager}, is for anyone new to machine learning or TensorFlow. TensorFlow provides many APIs. The remainder of this section focuses on the Estimator API which provide scalable, high-performance models. See the diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md index 2901848745..1abd840ab3 100644 --- a/tensorflow/docs_src/install/install_c.md +++ b/tensorflow/docs_src/install/install_c.md @@ -38,7 +38,7 @@ enable TensorFlow for C: OS="linux" # Change to "darwin" for macOS TARGET_DIRECTORY="/usr/local" curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.9.0-rc0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.8.0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md index 55bc0f64e7..52a2a3f8a6 100644 --- a/tensorflow/docs_src/install/install_go.md +++ b/tensorflow/docs_src/install/install_go.md @@ -38,7 +38,7 @@ steps to install this library and enable TensorFlow for Go: TF_TYPE="cpu" # Change to "gpu" for GPU support TARGET_DIRECTORY='/usr/local' curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.9.0-rc0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.8.0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md index 637231da12..1256fb99c4 100644 --- a/tensorflow/docs_src/install/install_java.md +++ b/tensorflow/docs_src/install/install_java.md @@ -36,7 +36,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs: <dependency> <groupId>org.tensorflow</groupId> <artifactId>tensorflow</artifactId> - <version>1.9.0-rc0</version> + <version>1.8.0</version> </dependency> ``` @@ -65,7 +65,7 @@ As an example, these steps will create a Maven project that uses TensorFlow: <dependency> <groupId>org.tensorflow</groupId> <artifactId>tensorflow</artifactId> - <version>1.9.0-rc0</version> + <version>1.8.0</version> </dependency> </dependencies> </project> @@ -124,12 +124,12 @@ instead: <dependency> <groupId>org.tensorflow</groupId> <artifactId>libtensorflow</artifactId> - <version>1.9.0-rc0</version> + <version>1.8.0</version> </dependency> <dependency> <groupId>org.tensorflow</groupId> <artifactId>libtensorflow_jni_gpu</artifactId> - <version>1.9.0-rc0</version> + <version>1.8.0</version> </dependency> ``` @@ -148,7 +148,7 @@ refer to the simpler instructions above instead. Take the following steps to install TensorFlow for Java on Linux or macOS: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0-rc0.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.8.0.jar), which is the TensorFlow Java Archive (JAR). 2. Decide whether you will run TensorFlow for Java on CPU(s) only or with @@ -167,7 +167,7 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: OS=$(uname -s | tr '[:upper:]' '[:lower:]') mkdir -p ./jni curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.9.0-rc0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.8.0.tar.gz" | tar -xz -C ./jni ### Install on Windows @@ -175,13 +175,13 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: Take the following steps to install TensorFlow for Java on Windows: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0-rc0.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.8.0.jar), which is the TensorFlow Java Archive (JAR). 2. Download the following Java Native Interface (JNI) file appropriate for - [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.9.0-rc0.zip). + [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.8.0.zip). 3. Extract this .zip file. -__Note__: The native library (`tensorflow_jni.dll`) requires `msvcp140.dll` at runtime, which is included in the [Visual C++ 2015 Redistributable](https://www.microsoft.com/en-us/download/details.aspx?id=48145) package. + ### Validate the installation @@ -227,7 +227,7 @@ must be part of your `classpath`. For example, you can include the downloaded `.jar` in your `classpath` by using the `-cp` compilation flag as follows: -<pre><b>javac -cp libtensorflow-1.9.0-rc0.jar HelloTF.java</b></pre> +<pre><b>javac -cp libtensorflow-1.8.0.jar HelloTF.java</b></pre> ### Running @@ -241,11 +241,11 @@ two files are available to the JVM: For example, the following command line executes the `HelloTF` program on Linux and macOS X: -<pre><b>java -cp libtensorflow-1.9.0-rc0.jar:. -Djava.library.path=./jni HelloTF</b></pre> +<pre><b>java -cp libtensorflow-1.8.0.jar:. -Djava.library.path=./jni HelloTF</b></pre> And the following command line executes the `HelloTF` program on Windows: -<pre><b>java -cp libtensorflow-1.9.0-rc0.jar;. -Djava.library.path=jni HelloTF</b></pre> +<pre><b>java -cp libtensorflow-1.8.0.jar;. -Djava.library.path=jni HelloTF</b></pre> If the program prints <tt>Hello from <i>version</i></tt>, you've successfully installed TensorFlow for Java and are ready to use the API. If the program diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md index c8d706cf3c..0ed8160027 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -339,7 +339,9 @@ Docker will download the TensorFlow binary image the first time you launch it. #### GPU support -To launch a Docker container with NVidia GPU support, enter a command of the following format (this [does not require any local CUDA installation](https://github.com/nvidia/nvidia-docker/wiki/CUDA#requirements)): +Prior to installing TensorFlow with GPU support, ensure that your system meets all +[NVIDIA software requirements](#NVIDIARequirements). To launch a Docker container +with NVidia GPU support, enter a command of the following format: <pre> $ <b>nvidia-docker run -it</b> <i>-p hostPort:containerPort TensorFlowGPUImage</i> @@ -436,7 +438,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: <pre> (tensorflow)$ <b>pip install --ignore-installed --upgrade \ - https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp34-cp34m-linux_x86_64.whl</b></pre> + https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp34-cp34m-linux_x86_64.whl</b></pre> <a name="ValidateYourInstallation"></a> ## Validate your installation @@ -515,7 +517,7 @@ on your system: from source. To use the TensorFlow binaries, version 3.5 or higher is required. See the [NVIDIA documentation](https://developer.nvidia.com/cuda-gpus) for a list of supported GPU cards. -* [GPU drivers](http://nvidia.com/drivers) that support your version of the CUDA +* [GPU drivers](http://nvidia.com/driver) that support your version of the CUDA Toolkit. * The `libcupti-dev` library is the NVIDIA CUDA Profile Tools Interface. This library provides advanced profiling support. To install this library, @@ -682,14 +684,14 @@ This section documents the relevant values for Linux installations. CPU only: <pre> -https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp27-none-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp27-none-linux_x86_64.whl </pre> GPU support: <pre> -https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp27-none-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp27-none-linux_x86_64.whl </pre> Note that GPU support requires the NVIDIA hardware and software described in @@ -701,14 +703,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: <pre> -https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp34-cp34m-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp34-cp34m-linux_x86_64.whl </pre> GPU support: <pre> -https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp34-cp34m-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp34-cp34m-linux_x86_64.whl </pre> Note that GPU support requires the NVIDIA hardware and software described in @@ -720,14 +722,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: <pre> -https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp35-cp35m-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp35-cp35m-linux_x86_64.whl </pre> GPU support: <pre> -https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp35-cp35m-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp35-cp35m-linux_x86_64.whl </pre> @@ -739,14 +741,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: <pre> -https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp36-cp36m-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp36-cp36m-linux_x86_64.whl </pre> GPU support: <pre> -https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp36-cp36m-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp36-cp36m-linux_x86_64.whl </pre> diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md index 9d01271c5a..29a867a9e3 100644 --- a/tensorflow/docs_src/install/install_mac.md +++ b/tensorflow/docs_src/install/install_mac.md @@ -119,7 +119,7 @@ Take the following steps to install TensorFlow with Virtualenv: TensorFlow in the active Virtualenv is as follows: <pre> $ <b>pip3 install --upgrade \ - https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl</b></pre> + https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py3-none-any.whl</b></pre> If you encounter installation problems, see [Common Installation Problems](#common-installation-problems). @@ -242,7 +242,7 @@ take the following steps: issue the following command: <pre> $ <b>sudo pip3 install --upgrade \ - https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl</b> </pre> + https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py3-none-any.whl</b> </pre> If the preceding command fails, see [installation problems](#common-installation-problems). @@ -350,7 +350,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: TensorFlow for Python 2.7: <pre> (<i>targetDirectory</i>)$ <b>pip install --ignore-installed --upgrade \ - https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-any.whl</b></pre> + https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py2-none-any.whl</b></pre> <a name="ValidateYourInstallation"></a> @@ -522,7 +522,7 @@ The value you specify depends on your Python version. <pre> -https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-any.whl +https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py2-none-any.whl </pre> @@ -530,5 +530,5 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-a <pre> -https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl +https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py3-none-any.whl </pre> diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index dc6c1e36fc..5ba522b436 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -81,7 +81,7 @@ or [macOS](#PrepareMac) -<a name="PrepareLinux"></a> +<a name="#PrepareLinux"></a> ## Prepare environment for Linux Before building TensorFlow on Linux, install the following build @@ -328,10 +328,10 @@ Invoke `pip install` to install that pip package. The filename of the `.whl` file depends on your platform. For example, the following command will install the pip package -for TensorFlow 1.9.0rc0 on Linux: +for TensorFlow 1.8.0 on Linux: <pre> -$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.9.0rc0-py2-none-any.whl</b> +$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.8.0-py2-none-any.whl</b> </pre> ## Validate your installation @@ -373,9 +373,9 @@ The build and installation problems you encounter typically depend on the operating system. See the "Common installation problems" section of one of the following guides: - * @{$install_linux#common_installation_problems$Installing TensorFlow on Linux} - * @{$install_mac#common_installation_problems$Installing TensorFlow on Mac OS} - * @{$install_windows#common_installation_problems$Installing TensorFlow on Windows} + * @{$install_linux#CommonInstallationProblems$Installing TensorFlow on Linux} + * @{$install_mac#CommonInstallationProblems$Installing TensorFlow on Mac OS} + * @{$install_windows#CommonInstallationProblems$Installing TensorFlow on Windows} Beyond the errors documented in those two guides, the following table notes additional errors specific to building TensorFlow. Note that we @@ -433,8 +433,6 @@ Stack Overflow and specify the `tensorflow` tag. **Linux** <table> <tr><th>Version:</th><th>CPU/GPU:</th><th>Python Version:</th><th>Compiler:</th><th>Build Tools:</th><th>cuDNN:</th><th>CUDA:</th></tr> -<tr><td>tensorflow-1.9.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.11.0</td><td>N/A</td><td>N/A</td></tr> -<tr><td>tensorflow_gpu-1.9.0</td><td>GPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.11.0</td><td>7</td><td>9</td></tr> <tr><td>tensorflow-1.8.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.10.0</td><td>N/A</td><td>N/A</td></tr> <tr><td>tensorflow_gpu-1.8.0</td><td>GPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.9.0</td><td>7</td><td>9</td></tr> <tr><td>tensorflow-1.7.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.10.0</td><td>N/A</td><td>N/A</td></tr> @@ -458,7 +456,6 @@ Stack Overflow and specify the `tensorflow` tag. **Mac** <table> <tr><th>Version:</th><th>CPU/GPU:</th><th>Python Version:</th><th>Compiler:</th><th>Build Tools:</th><th>cuDNN:</th><th>CUDA:</th></tr> -<tr><td>tensorflow-1.9.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.11.0</td><td>N/A</td><td>N/A</td></tr> <tr><td>tensorflow-1.8.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.10.1</td><td>N/A</td><td>N/A</td></tr> <tr><td>tensorflow-1.7.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.10.1</td><td>N/A</td><td>N/A</td></tr> <tr><td>tensorflow-1.6.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.8.1</td><td>N/A</td><td>N/A</td></tr> @@ -475,8 +472,6 @@ Stack Overflow and specify the `tensorflow` tag. **Windows** <table> <tr><th>Version:</th><th>CPU/GPU:</th><th>Python Version:</th><th>Compiler:</th><th>Build Tools:</th><th>cuDNN:</th><th>CUDA:</th></tr> -<tr><td>tensorflow-1.9.0</td><td>CPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>N/A</td><td>N/A</td></tr> -<tr><td>tensorflow_gpu-1.9.0</td><td>GPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>7</td><td>9</td></tr> <tr><td>tensorflow-1.8.0</td><td>CPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>N/A</td><td>N/A</td></tr> <tr><td>tensorflow_gpu-1.8.0</td><td>GPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>7</td><td>9</td></tr> <tr><td>tensorflow-1.7.0</td><td>CPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>N/A</td><td>N/A</td></tr> diff --git a/tensorflow/docs_src/mobile/linking_libs.md b/tensorflow/docs_src/mobile/linking_libs.md index efef5dd0da..cf0db59021 100644 --- a/tensorflow/docs_src/mobile/linking_libs.md +++ b/tensorflow/docs_src/mobile/linking_libs.md @@ -27,7 +27,7 @@ called `libandroid_tensorflow_inference_java.jar`. There are three ways to include this functionality in your program: 1. Include the jcenter AAR which contains it, as in this - [example app](https://github.com/googlecodelabs/tensorflow-for-poets-2/blob/master/android/tfmobile/build.gradle#L59-L65) + [example app](https://github.com/googlecodelabs/tensorflow-for-poets-2/blob/master/android/build.gradle#L59-L65) 2. Download the nightly precompiled version from [ci.tensorflow.org](http://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/). diff --git a/tensorflow/docs_src/mobile/prepare_models.md b/tensorflow/docs_src/mobile/prepare_models.md index 2b84dbb973..8b22c04d87 100644 --- a/tensorflow/docs_src/mobile/prepare_models.md +++ b/tensorflow/docs_src/mobile/prepare_models.md @@ -105,8 +105,8 @@ inline constants so everything’s in one file. To handle the conversion, you†need the `freeze_graph.py` script, that’s held in [`tensorflow/python/tools/freeze_graph.py`](https://www.tensorflow.org/code/tensorflow/python/tools/freeze_graph.py). You’ll run it like this: - bazel build tensorflow/python/tools:freeze_graph - bazel-bin/tensorflow/python/tools/freeze_graph \ + bazel build tensorflow/tools:freeze_graph + bazel-bin/tensorflow/tools/freeze_graph \ --input_graph=/tmp/model/my_graph.pb \ --input_checkpoint=/tmp/model/model.ckpt-1000 \ --output_graph=/tmp/frozen_graph.pb \ diff --git a/tensorflow/docs_src/performance/quantization.md b/tensorflow/docs_src/performance/quantization.md index c97f74139c..2fea02d861 100644 --- a/tensorflow/docs_src/performance/quantization.md +++ b/tensorflow/docs_src/performance/quantization.md @@ -227,8 +227,8 @@ of 30.0f, and an 8-bit array, the quantized values represent the following: <table> <tr><th>Quantized</th><th>Float</th></tr> <tr><td>0</td><td>-10.0</td></tr> - <tr><td>128</td><td>10.0</td></tr> <tr><td>255</td><td>30.0</td></tr> + <tr><td>128</td><td>10.0</td></tr> </table> <figcaption> <b>Table 2</b>: Example quantized value range diff --git a/tensorflow/docs_src/programmers_guide/estimators.md b/tensorflow/docs_src/programmers_guide/estimators.md index b13b47184d..c4aae1d9d6 100644 --- a/tensorflow/docs_src/programmers_guide/estimators.md +++ b/tensorflow/docs_src/programmers_guide/estimators.md @@ -21,17 +21,18 @@ Note: TensorFlow also includes a deprecated `Estimator` class at Estimators provide the following benefits: -* You can run Estimator-based models on a local host or on a +* You can run Estimators-based models on a local host or on a distributed multi-server environment without changing your model. - Furthermore, you can run Estimator-based models on CPUs, GPUs, + Furthermore, you can run Estimators-based models on CPUs, GPUs, or TPUs without recoding your model. * Estimators simplify sharing implementations between model developers. -* You can develop a state of the art model with high-level intuitive code. +* You can develop a state of the art model with high-level intuitive code, In short, it is generally much easier to create models with Estimators than with the low-level TensorFlow APIs. -* Estimators are themselves built on @{tf.layers}, which +* Estimators are themselves built on tf.layers, which simplifies customization. -* Estimators build the graph for you. +* Estimators build the graph for you. In other words, you don't have to + build the graph. * Estimators provide a safe distributed training loop that controls how and when to: * build the graph @@ -56,7 +57,7 @@ the "plumbing" for you. That is, pre-made Estimators create and manage pre-made Estimators let you experiment with different model architectures by making only minimal code changes. @{tf.estimator.DNNClassifier$`DNNClassifier`}, for example, is a pre-made Estimator class that trains classification models -based on dense, feed-forward neural networks. +through dense, feed-forward neural networks. ### Structure of a pre-made Estimators program @@ -78,7 +79,7 @@ of the following four steps: an input function: def input_fn(dataset): - ... # manipulate dataset, extracting the feature dict and the label + ... # manipulate dataset, extracting feature names and the label return feature_dict, label (See @{$programmers_guide/datasets} for full details.) @@ -95,13 +96,13 @@ of the following four steps: population = tf.feature_column.numeric_column('population') crime_rate = tf.feature_column.numeric_column('crime_rate') median_education = tf.feature_column.numeric_column('median_education', - normalizer_fn=lambda x: x - global_education_mean) + normalizer_fn='lambda x: x - global_education_mean') 3. **Instantiate the relevant pre-made Estimator.** For example, here's a sample instantiation of a pre-made Estimator named `LinearClassifier`: # Instantiate an estimator, passing the feature columns. - estimator = tf.estimator.LinearClassifier( + estimator = tf.estimator.Estimator.LinearClassifier( feature_columns=[population, crime_rate, median_education], ) diff --git a/tensorflow/docs_src/programmers_guide/feature_columns.md b/tensorflow/docs_src/programmers_guide/feature_columns.md index 90f5c53a17..845194fe0e 100644 --- a/tensorflow/docs_src/programmers_guide/feature_columns.md +++ b/tensorflow/docs_src/programmers_guide/feature_columns.md @@ -528,10 +528,10 @@ suggested by the following snippet: categorical_column = ... # Create any categorical column # Represent the categorical column as an embedding column. -# This means creating an embedding vector lookup table with one element for each category. +# This means creating a one-hot vector with one element for each category. embedding_column = tf.feature_column.embedding_column( categorical_column=categorical_column, - dimension=embedding_dimensions) + dimension=dimension_of_embedding_vector) ``` @{$programmers_guide/embedding$Embeddings} is a significant topic within machine diff --git a/tensorflow/examples/learn/iris.py b/tensorflow/examples/learn/iris.py index 86f5204ec3..03e60972aa 100644 --- a/tensorflow/examples/learn/iris.py +++ b/tensorflow/examples/learn/iris.py @@ -21,8 +21,7 @@ from __future__ import division from __future__ import print_function import os - -from six.moves.urllib.request import urlretrieve +import urllib import tensorflow as tf @@ -39,7 +38,9 @@ FEATURE_KEYS = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'] def maybe_download_iris_data(file_name, download_url): """Downloads the file and returns the number of data.""" if not os.path.exists(file_name): - urlretrieve(download_url, file_name) + raw = urllib.urlopen(download_url).read() + with open(file_name, 'w') as f: + f.write(raw) # The first line is a comma-separated string. The first one is the number of # total data in the file. diff --git a/tensorflow/java/src/gen/cc/op_generator.cc b/tensorflow/java/src/gen/cc/op_generator.cc index 9b171f66ec..debd95fc62 100644 --- a/tensorflow/java/src/gen/cc/op_generator.cc +++ b/tensorflow/java/src/gen/cc/op_generator.cc @@ -376,6 +376,9 @@ void GenerateOp(const OpSpec& op, const EndpointSpec& endpoint, } } // op annotations + op_class.add_annotation( + Annotation::Create("Generated", "javax.annotation") + .attributes("value = \"TensorFlow Java Op Generator\"")); if (endpoint.deprecated()) { op_class.add_annotation(Annotation::Create("Deprecated")); string explanation; @@ -412,12 +415,8 @@ void GenerateOp(const OpSpec& op, const EndpointSpec& endpoint, SourceFileWriter writer(op_file.get()); std::list<Type> dependencies; CollectOpDependencies(op, mode, &dependencies); - writer.Write(kLicense) - .EndLine() - .Write("// This class has been generated, DO NOT EDIT!") - .EndLine() - .EndLine() - .BeginType(op_class, PUBLIC | FINAL, &dependencies, &op_javadoc); + writer.Write(kLicense).EndLine().BeginType(op_class, PUBLIC | FINAL, + &dependencies, &op_javadoc); if (!op.optional_attributes().empty()) { RenderOptionsClass(op, op_class, &writer); } diff --git a/tensorflow/java/src/gen/cc/op_specs.cc b/tensorflow/java/src/gen/cc/op_specs.cc index 941ab2699c..181fd4c5e3 100644 --- a/tensorflow/java/src/gen/cc/op_specs.cc +++ b/tensorflow/java/src/gen/cc/op_specs.cc @@ -96,7 +96,6 @@ Type TypeResolver::TypeOf(const OpDef_ArgDef& arg_def, bool* iterable_out) { *iterable_out = true; visited_attrs_.insert(std::make_pair(arg_def.number_attr(), Type::Int())); } - Type type = Type::Wildcard(); if (arg_def.type() != DataType::DT_INVALID) { // resolve type from DataType diff --git a/tensorflow/python/eager/backprop.py b/tensorflow/python/eager/backprop.py index bd97b181ff..b2e6c60021 100644 --- a/tensorflow/python/eager/backprop.py +++ b/tensorflow/python/eager/backprop.py @@ -196,11 +196,11 @@ def implicit_val_and_grad(f): # TODO(cais): Remove calls to tf.constant() once the gradients functions # accept lists and np.ndarrays. - def grad_fn(*args, **kwds): + def grad_fn(*args): """Computes the gradient of the wrapped function.""" this_tape = tape.push_new_tape() try: - end_node = f(*args, **kwds) + end_node = f(*args) if end_node is None: raise ValueError("Cannot differentiate a function that returns None; " "did you forget to return a value from {}?".format( diff --git a/tensorflow/python/estimator/BUILD b/tensorflow/python/estimator/BUILD index 20522098b0..9cd17e0407 100644 --- a/tensorflow/python/estimator/BUILD +++ b/tensorflow/python/estimator/BUILD @@ -978,10 +978,7 @@ py_test( size = "large", srcs = ["keras_test.py"], srcs_version = "PY2AND3", - tags = [ - "no_windows", - "notsan", - ], + tags = ["notsan"], deps = [ ":keras", "//tensorflow/core:protos_all_py", diff --git a/tensorflow/python/estimator/exporter.py b/tensorflow/python/estimator/exporter.py index b18212cfcd..7cdf840c97 100644 --- a/tensorflow/python/estimator/exporter.py +++ b/tensorflow/python/estimator/exporter.py @@ -156,7 +156,7 @@ def _loss_smaller(best_eval_result, current_eval_result): return best_eval_result[default_key] > current_eval_result[default_key] -def _verify_compare_fn_args(compare_fn): +def _verify_compre_fn_args(compare_fn): """Verifies compare_fn arguments.""" args = set(util.fn_args(compare_fn)) if 'best_eval_result' not in args: @@ -265,7 +265,7 @@ class BestExporter(Exporter): self._compare_fn = compare_fn if self._compare_fn is None: raise ValueError('`compare_fn` must not be None.') - _verify_compare_fn_args(self._compare_fn) + _verify_compre_fn_args(self._compare_fn) self._saved_model_exporter = _SavedModelExporter( name, serving_input_receiver_fn, assets_extra, as_text) diff --git a/tensorflow/python/estimator/inputs/numpy_io.py b/tensorflow/python/estimator/inputs/numpy_io.py index a6cefdece2..035c7c148c 100644 --- a/tensorflow/python/estimator/inputs/numpy_io.py +++ b/tensorflow/python/estimator/inputs/numpy_io.py @@ -136,13 +136,11 @@ def numpy_input_fn(x, values in `x` have same shape). ValueError: if duplicate keys are in both `x` and `y` when `y` is a dict. ValueError: if x or y is an empty dict. - TypeError: `x` is not a dict or array. - ValueError: if 'shuffle' is not provided or a bool. + TypeError: `x` is not a dict or array, or if `shuffle` is not bool. """ if not isinstance(shuffle, bool): - raise ValueError('shuffle must be provided and explicitly set as boolean ' - '(it is recommended to set it as True for training); ' - 'got {}'.format(shuffle)) + raise TypeError('shuffle must be explicitly set as boolean; ' + 'got {}'.format(shuffle)) def input_fn(): """Numpy input function.""" diff --git a/tensorflow/python/estimator/inputs/numpy_io_test.py b/tensorflow/python/estimator/inputs/numpy_io_test.py index 81b201cc5c..92d057e25d 100644 --- a/tensorflow/python/estimator/inputs/numpy_io_test.py +++ b/tensorflow/python/estimator/inputs/numpy_io_test.py @@ -286,9 +286,8 @@ class NumpyIoTest(test.TestCase): x = np.arange(32, 36) y = np.arange(4) with self.test_session(): - with self.assertRaisesRegexp(ValueError, - 'shuffle must be provided and explicitly ' - 'set as boolean'): + with self.assertRaisesRegexp(TypeError, + 'shuffle must be explicitly set as boolean'): # Default shuffle is None. numpy_io.numpy_input_fn(x, y) diff --git a/tensorflow/python/estimator/inputs/pandas_io.py b/tensorflow/python/estimator/inputs/pandas_io.py index 57f8e5fd6a..938e244fb3 100644 --- a/tensorflow/python/estimator/inputs/pandas_io.py +++ b/tensorflow/python/estimator/inputs/pandas_io.py @@ -68,16 +68,15 @@ def pandas_input_fn(x, Raises: ValueError: if `x` already contains a column with the same name as `y`, or if the indexes of `x` and `y` don't match. - ValueError: if 'shuffle' is not provided or a bool. + TypeError: `shuffle` is not bool. """ if not HAS_PANDAS: raise TypeError( 'pandas_input_fn should not be called without pandas installed') if not isinstance(shuffle, bool): - raise ValueError('shuffle must be provided and explicitly set as boolean ' - '(it is recommended to set it as True for training); ' - 'got {}'.format(shuffle)) + raise TypeError('shuffle must be explicitly set as boolean; ' + 'got {}'.format(shuffle)) x = x.copy() if y is not None: diff --git a/tensorflow/python/estimator/inputs/pandas_io_test.py b/tensorflow/python/estimator/inputs/pandas_io_test.py index dcecf6dd61..e5912a3b28 100644 --- a/tensorflow/python/estimator/inputs/pandas_io_test.py +++ b/tensorflow/python/estimator/inputs/pandas_io_test.py @@ -70,9 +70,8 @@ class PandasIoTest(test.TestCase): return x, _ = self.makeTestDataFrame() y_noindex = pd.Series(np.arange(-32, -28)) - with self.assertRaisesRegexp(ValueError, - 'shuffle must be provided and explicitly ' - 'set as boolean'): + with self.assertRaisesRegexp(TypeError, + 'shuffle must be explicitly set as boolean'): # Default shuffle is None pandas_io.pandas_input_fn(x, y_noindex) diff --git a/tensorflow/python/estimator/inputs/queues/feeding_functions.py b/tensorflow/python/estimator/inputs/queues/feeding_functions.py index 51a61adb21..8e2ec83020 100644 --- a/tensorflow/python/estimator/inputs/queues/feeding_functions.py +++ b/tensorflow/python/estimator/inputs/queues/feeding_functions.py @@ -250,7 +250,7 @@ class _PandasFeedFn(object): num_epochs=None): if len(placeholders) != len(dataframe.columns) + 1: raise ValueError("Expected {} placeholders; got {}.".format( - len(dataframe.columns) + 1, len(placeholders))) + len(dataframe.columns), len(placeholders))) self._index_placeholder = placeholders[0] self._col_placeholders = placeholders[1:] self._dataframe = dataframe diff --git a/tensorflow/python/estimator/keras.py b/tensorflow/python/estimator/keras.py index 2f439f765e..c80af08fba 100644 --- a/tensorflow/python/estimator/keras.py +++ b/tensorflow/python/estimator/keras.py @@ -70,7 +70,7 @@ def _convert_tensor(x): return x -def _any_variable_initialized(): +def _any_variable_initalized(): """Check if any variable has been initialized in the Keras model. Returns: @@ -511,7 +511,7 @@ def model_to_estimator(keras_model=None, keras_model_fn, model_dir=model_dir, config=config) # Check if we need to call get_weights: - if _any_variable_initialized(): + if _any_variable_initalized(): keras_weights = keras_model.get_weights() # Warn if config passed to estimator tries to update GPUOptions. If a # session has already been created, the GPUOptions passed to the first diff --git a/tensorflow/python/estimator/keras_test.py b/tensorflow/python/estimator/keras_test.py index 5e094ae92b..6688a84130 100644 --- a/tensorflow/python/estimator/keras_test.py +++ b/tensorflow/python/estimator/keras_test.py @@ -31,10 +31,10 @@ from tensorflow.python.estimator import run_config as run_config_lib from tensorflow.python.estimator.inputs import numpy_io from tensorflow.python.framework import ops from tensorflow.python.framework import test_util +from tensorflow.python.keras import backend as K from tensorflow.python.keras import testing_utils from tensorflow.python.keras.applications import mobilenet from tensorflow.python.keras.optimizers import SGD -from tensorflow.python.ops.parsing_ops import gen_parsing_ops from tensorflow.python.platform import gfile from tensorflow.python.platform import test from tensorflow.python.summary.writer import writer_cache @@ -146,13 +146,13 @@ def randomize_io_type(array, name): def multi_inputs_multi_outputs_model(): a = keras.layers.Input(shape=(16,), name='input_a') b = keras.layers.Input(shape=(16,), name='input_b') - m = keras.layers.Input(shape=(8,), dtype='string', name='input_m') + m = keras.layers.Input(shape=(8,), dtype='bool', name='input_m') dense = keras.layers.Dense(8, name='dense_1') a_2 = dense(a) - # Read m - m_2 = keras.layers.Lambda(gen_parsing_ops.string_to_number)(m) - s_2 = keras.layers.Lambda(lambda k: k[0] * k[1])([m_2, a_2]) + # Apply a mask + s_2 = keras.layers.Lambda(lambda k: + K.switch(k[0], k[1], K.zeros_like(k[1])))([m, a_2]) b_2 = dense(b) merged = keras.layers.concatenate([s_2, b_2], name='merge') c = keras.layers.Dense(3, activation='softmax', name='dense_2')(merged) @@ -372,13 +372,13 @@ class TestKerasEstimator(test_util.TensorFlowTestCase): def train_input_fn(): input_dict = {'input_a': a_train, 'input_b': b_train, - 'input_m': input_m_train.astype(np.str)} + 'input_m': input_m_train > 0} output_dict = {'dense_2': c_train, 'dense_3': d_train} return input_dict, output_dict def eval_input_fn(): input_dict = {'input_a': a_test, 'input_b': b_test, - 'input_m': input_m_test.astype(np.str)} + 'input_m': input_m_test > 0} output_dict = {'dense_2': c_test, 'dense_3': d_test} return input_dict, output_dict diff --git a/tensorflow/python/grappler/layout_optimizer_test.py b/tensorflow/python/grappler/layout_optimizer_test.py index af5d709f7e..2d6925d1a8 100644 --- a/tensorflow/python/grappler/layout_optimizer_test.py +++ b/tensorflow/python/grappler/layout_optimizer_test.py @@ -1389,7 +1389,7 @@ class LayoutOptimizerTest(test.TestCase): expected_num_transposes = 3 self.assertEqual(expected_num_transposes, num_transposes) self._assert_trans_nhwc_to_nchw('map/while/Conv2D-0', nodes) - self._assert_trans_nchw_to_nhwc('map/while/Add_1-0-2', nodes) + self._assert_trans_nchw_to_nhwc('map/while/Add-0-2', nodes) self.assertAllClose(output_val_ref, output_val, atol=1e-3) def testLoopWithVecAnd4D(self): @@ -1413,7 +1413,7 @@ class LayoutOptimizerTest(test.TestCase): expected_num_transposes = 2 self.assertEqual(expected_num_transposes, num_transposes) self._assert_trans_nhwc_to_nchw('map/while/Conv2D-0', nodes) - self._assert_trans_nchw_to_nhwc('map/while/Add_1-0-2', nodes) + self._assert_trans_nchw_to_nhwc('map/while/Add-0-2', nodes) self.assertAllClose(output_val_ref, output_val, atol=1e-3) def testBinaryOpSecondPort(self): diff --git a/tensorflow/python/keras/activations.py b/tensorflow/python/keras/activations.py index f608dea430..e487f583be 100644 --- a/tensorflow/python/keras/activations.py +++ b/tensorflow/python/keras/activations.py @@ -93,8 +93,6 @@ def selu(x): - To be used together with the initialization "lecun_normal". - To be used together with the dropout variant "AlphaDropout". - References: - - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515) """ alpha = 1.6732632423543772848170429916717 scale = 1.0507009873554804934193349852946 diff --git a/tensorflow/python/keras/callbacks.py b/tensorflow/python/keras/callbacks.py index 9f91368e5b..70b6a8431a 100644 --- a/tensorflow/python/keras/callbacks.py +++ b/tensorflow/python/keras/callbacks.py @@ -724,6 +724,15 @@ class TensorBoard(Callback): for weight in layer.weights: mapped_weight_name = weight.name.replace(':', '_') tf_summary.histogram(mapped_weight_name, weight) + if self.write_grads: + grads = model.optimizer.get_gradients(model.total_loss, weight) + + def is_indexed_slices(grad): + return type(grad).__name__ == 'IndexedSlices' + + grads = [grad.values if is_indexed_slices(grad) else grad + for grad in grads] + tf_summary.histogram('{}_grad'.format(mapped_weight_name), grads) if self.write_images: w_img = array_ops.squeeze(weight) shape = K.int_shape(w_img) @@ -750,18 +759,6 @@ class TensorBoard(Callback): assert len(shape) == 4 and shape[-1] in [1, 3, 4] tf_summary.image(mapped_weight_name, w_img) - if self.write_grads: - for weight in layer.trainable_weights: - mapped_weight_name = weight.name.replace(':', '_') - grads = model.optimizer.get_gradients(model.total_loss, weight) - - def is_indexed_slices(grad): - return type(grad).__name__ == 'IndexedSlices' - - grads = [grad.values if is_indexed_slices(grad) else grad - for grad in grads] - tf_summary.histogram('{}_grad'.format(mapped_weight_name), grads) - if hasattr(layer, 'output'): tf_summary.histogram('{}_out'.format(layer.name), layer.output) self.merged = tf_summary.merge_all() diff --git a/tensorflow/python/keras/callbacks_test.py b/tensorflow/python/keras/callbacks_test.py index 5062a26580..b355f4a269 100644 --- a/tensorflow/python/keras/callbacks_test.py +++ b/tensorflow/python/keras/callbacks_test.py @@ -653,8 +653,6 @@ class KerasCallbacksTest(test.TestCase): model.add( keras.layers.Dense( NUM_HIDDEN, input_dim=INPUT_DIM, activation='relu')) - # non_trainable_weights: moving_variance, moving_mean - model.add(keras.layers.BatchNormalization()) model.add(keras.layers.Dense(NUM_CLASSES, activation='softmax')) model.compile( loss='categorical_crossentropy', diff --git a/tensorflow/python/keras/engine/network.py b/tensorflow/python/keras/engine/network.py index 1c9135982e..a4cd017d60 100644 --- a/tensorflow/python/keras/engine/network.py +++ b/tensorflow/python/keras/engine/network.py @@ -123,7 +123,7 @@ class Network(base_layer.Layer): # Entries are unique. Includes input and output layers. self._layers = [] - # Used in symbolic mode only, only in conjunction with graph-networks + # Used in symbolic mode only, only in conjonction with graph-networks self._outbound_nodes = [] self._inbound_nodes = [] diff --git a/tensorflow/python/keras/engine/saving_test.py b/tensorflow/python/keras/engine/saving_test.py index 7e82db028b..6a94986b9c 100644 --- a/tensorflow/python/keras/engine/saving_test.py +++ b/tensorflow/python/keras/engine/saving_test.py @@ -482,7 +482,7 @@ class TestWholeModelSaving(test.TestCase): with h5py.File(fname, 'r') as h5file: num_names_arrays = len([attr for attr in h5file['model_weights'].attrs if attr.startswith('layer_names')]) - # The chunking of layer names array should have happened. + # The chunking of layer names array should have happend. self.assertGreater(num_names_arrays, 0) out2 = model.predict(x) self.assertAllClose(out, out2, atol=1e-05) @@ -527,7 +527,7 @@ class TestWholeModelSaving(test.TestCase): num_weight_arrays = len( [attr for attr in h5file['model_weights']['nested_model'].attrs if attr.startswith('weight_names')]) - # The chunking of layer names array should have happened. + # The chunking of layer names array should have happend. self.assertGreater(num_weight_arrays, 0) out2 = model.predict(x) self.assertAllClose(out, out2, atol=1e-05) diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py index fce6cbdb7a..89c1f1a40f 100644 --- a/tensorflow/python/keras/engine/training.py +++ b/tensorflow/python/keras/engine/training.py @@ -24,7 +24,6 @@ import numpy as np from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import iterator_ops from tensorflow.python.eager import context -from tensorflow.python.framework import constant_op from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util @@ -410,13 +409,11 @@ class Model(Network): else: if sample_weight_mode == 'temporal': sample_weights.append(array_ops.placeholder_with_default( - constant_op.constant([[1.]], dtype=K.floatx()), - shape=[None, None], name=name + '_sample_weights')) + [[1.]], shape=[None, None], name=name + '_sample_weights')) sample_weight_modes.append('temporal') else: sample_weights.append(array_ops.placeholder_with_default( - constant_op.constant([1.], dtype=K.floatx()), - shape=[None], name=name + '_sample_weights')) + [1.], shape=[None], name=name + '_sample_weights')) sample_weight_modes.append(None) self.sample_weight_modes = sample_weight_modes self._feed_sample_weight_modes = [] diff --git a/tensorflow/python/keras/engine/training_eager.py b/tensorflow/python/keras/engine/training_eager.py index e8838cd3bc..2ecbff3a1c 100644 --- a/tensorflow/python/keras/engine/training_eager.py +++ b/tensorflow/python/keras/engine/training_eager.py @@ -732,7 +732,7 @@ def slice_arrays(arrays, indices, contiguous=True): """Slices batches out of provided arrays (workaround for eager tensors). Unfortunately eager tensors don't have the same slicing behavior as - Numpy arrays (they follow the same slicing behavior as symbolic TF tensors), + Numpy arrays (they folow the same slicing behavior as symbolic TF tensors), hence we cannot use `generic_utils.slice_arrays` directly and we have to implement this workaround based on `concat`. This has a performance cost. diff --git a/tensorflow/python/keras/initializers_test.py b/tensorflow/python/keras/initializers_test.py index c519e194bd..a54d6da839 100644 --- a/tensorflow/python/keras/initializers_test.py +++ b/tensorflow/python/keras/initializers_test.py @@ -71,7 +71,7 @@ class KerasInitializersTest(test.TestCase): stddev=1, seed=126), tensor_shape, - target_mean=0., target_max=2, target_min=-2) + target_mean=0., target_std=None, target_max=2) def test_constant(self): tensor_shape = (5, 6, 4) @@ -83,49 +83,49 @@ class KerasInitializersTest(test.TestCase): tensor_shape = (5, 6, 4, 2) with self.test_session(): fan_in, _ = init_ops._compute_fans(tensor_shape) - std = np.sqrt(1. / fan_in) + scale = np.sqrt(3. / fan_in) self._runner(keras.initializers.lecun_uniform(seed=123), tensor_shape, - target_mean=0., target_std=std) + target_mean=0., target_max=scale, target_min=-scale) def test_glorot_uniform(self): tensor_shape = (5, 6, 4, 2) with self.test_session(): fan_in, fan_out = init_ops._compute_fans(tensor_shape) - std = np.sqrt(2. / (fan_in + fan_out)) + scale = np.sqrt(6. / (fan_in + fan_out)) self._runner(keras.initializers.glorot_uniform(seed=123), tensor_shape, - target_mean=0., target_std=std) + target_mean=0., target_max=scale, target_min=-scale) def test_he_uniform(self): tensor_shape = (5, 6, 4, 2) with self.test_session(): fan_in, _ = init_ops._compute_fans(tensor_shape) - std = np.sqrt(2. / fan_in) + scale = np.sqrt(6. / fan_in) self._runner(keras.initializers.he_uniform(seed=123), tensor_shape, - target_mean=0., target_std=std) + target_mean=0., target_max=scale, target_min=-scale) def test_lecun_normal(self): tensor_shape = (5, 6, 4, 2) with self.test_session(): fan_in, _ = init_ops._compute_fans(tensor_shape) - std = np.sqrt(1. / fan_in) + scale = np.sqrt(1. / fan_in) self._runner(keras.initializers.lecun_normal(seed=123), tensor_shape, - target_mean=0., target_std=std) + target_mean=0., target_std=None, target_max=2 * scale) def test_glorot_normal(self): tensor_shape = (5, 6, 4, 2) with self.test_session(): fan_in, fan_out = init_ops._compute_fans(tensor_shape) - std = np.sqrt(2. / (fan_in + fan_out)) + scale = np.sqrt(2. / (fan_in + fan_out)) self._runner(keras.initializers.glorot_normal(seed=123), tensor_shape, - target_mean=0., target_std=std) + target_mean=0., target_std=None, target_max=2 * scale) def test_he_normal(self): tensor_shape = (5, 6, 4, 2) with self.test_session(): fan_in, _ = init_ops._compute_fans(tensor_shape) - std = np.sqrt(2. / fan_in) + scale = np.sqrt(2. / fan_in) self._runner(keras.initializers.he_normal(seed=123), tensor_shape, - target_mean=0., target_std=std) + target_mean=0., target_std=None, target_max=2 * scale) def test_orthogonal(self): tensor_shape = (20, 20) diff --git a/tensorflow/python/keras/layers/core.py b/tensorflow/python/keras/layers/core.py index f60064ed63..5061825d38 100644 --- a/tensorflow/python/keras/layers/core.py +++ b/tensorflow/python/keras/layers/core.py @@ -19,9 +19,7 @@ from __future__ import division from __future__ import print_function import copy -import sys import types as python_types -import warnings import numpy as np @@ -716,7 +714,6 @@ class Lambda(Layer): return self.mask def get_config(self): - module = self.function.__module__ if isinstance(self.function, python_types.LambdaType): function = generic_utils.func_dump(self.function) function_type = 'lambda' @@ -724,26 +721,21 @@ class Lambda(Layer): function = self.function.__name__ function_type = 'function' - output_shape_module = None if isinstance(self._output_shape, python_types.LambdaType): output_shape = generic_utils.func_dump(self._output_shape) output_shape_type = 'lambda' - output_shape_module = self._output_shape.__module__ elif callable(self._output_shape): output_shape = self._output_shape.__name__ output_shape_type = 'function' - output_shape_module = self._output_shape.__module__ else: output_shape = self._output_shape output_shape_type = 'raw' config = { 'function': function, - 'module': module, 'function_type': function_type, 'output_shape': output_shape, 'output_shape_type': output_shape_type, - 'output_shape_module': output_shape_module, 'arguments': self.arguments } base_config = super(Lambda, self).get_config() @@ -753,16 +745,8 @@ class Lambda(Layer): def from_config(cls, config, custom_objects=None): config = config.copy() globs = globals() - module = config.pop('module', None) - if module in sys.modules: - globs.update(sys.modules[module].__dict__) - elif module is not None: - # Note: we don't know the name of the function if it's a lambda. - warnings.warn('{} is not loaded, but a Lambda layer uses it. ' - 'It may cause errors.'.format(module) - , UserWarning) if custom_objects: - globs.update(custom_objects) + globs = dict(list(globs.items()) + list(custom_objects.items())) function_type = config.pop('function_type') if function_type == 'function': # Simple lookup in custom objects @@ -776,14 +760,6 @@ class Lambda(Layer): else: raise TypeError('Unknown function type:', function_type) - output_shape_module = config.pop('output_shape_module', None) - if output_shape_module in sys.modules: - globs.update(sys.modules[output_shape_module].__dict__) - elif output_shape_module is not None: - # Note: we don't know the name of the function if it's a lambda. - warnings.warn('{} is not loaded, but a Lambda layer uses it. ' - 'It may cause errors.'.format(output_shape_module) - , UserWarning) output_shape_type = config.pop('output_shape_type') if output_shape_type == 'function': # Simple lookup in custom objects diff --git a/tensorflow/python/keras/models_test.py b/tensorflow/python/keras/models_test.py index e6e45902a8..c616d8f24f 100644 --- a/tensorflow/python/keras/models_test.py +++ b/tensorflow/python/keras/models_test.py @@ -144,19 +144,5 @@ class CheckpointingTests(test.TestCase): model.load_weights(save_prefix) self.assertEqual(12., self.evaluate(beta1_power)) -class TestModelBackend(test.TestCase): - - def test_model_backend_float64_use_cases(self): - # Test case for GitHub issue 19318 - floatx = keras.backend.floatx() - keras.backend.set_floatx('float64') - - x = keras.Input((5,)) - y = keras.layers.Dense(1)(x) - model = keras.models.Model(x, y) - model.compile('rmsprop', 'mse') - - keras.backend.set_floatx(floatx) - if __name__ == '__main__': test.main() diff --git a/tensorflow/python/kernel_tests/as_string_op_test.py b/tensorflow/python/kernel_tests/as_string_op_test.py index 94ed8ebd31..9d54add264 100644 --- a/tensorflow/python/kernel_tests/as_string_op_test.py +++ b/tensorflow/python/kernel_tests/as_string_op_test.py @@ -130,16 +130,6 @@ class AsStringOpTest(test.TestCase): result = output.eval(feed_dict={input_: int_inputs_}) self.assertAllEqual(s(result), ["%d" % x for x in int_inputs_]) - def testHalfInt(self): - s = lambda strs: [x.decode("ascii") for x in strs] - - with self.test_session(): - input_ = array_ops.placeholder(dtypes.int16) - int_inputs_ = [np.iinfo(np.int16).min, np.iinfo(np.int16).max] - output = string_ops.as_string(input_) - result = output.eval(feed_dict={input_: int_inputs_}) - self.assertAllEqual(s(result), ["%d" % x for x in int_inputs_]) - def testBool(self): bool_inputs_ = [False, True] s = lambda strs: [x.decode("ascii") for x in strs] diff --git a/tensorflow/python/kernel_tests/betainc_op_test.py b/tensorflow/python/kernel_tests/betainc_op_test.py index 16fdedac41..08b03f8518 100644 --- a/tensorflow/python/kernel_tests/betainc_op_test.py +++ b/tensorflow/python/kernel_tests/betainc_op_test.py @@ -172,7 +172,7 @@ class BetaincTest(test.TestCase): tf_gout_t = math_ops.betainc(tf_ga_s, tf_gb_s, tf_gx_s) err = gradient_checker.compute_gradient_error( [tf_gx_s], [gx_s.shape], tf_gout_t, gx_s.shape) - tf_logging.info("betainc gradient err = %g " % err) + print("betainc gradient err = %g " % err) self.assertLess(err, err_tolerance) # Test broadcast gradient @@ -181,7 +181,7 @@ class BetaincTest(test.TestCase): tf_gout_t = math_ops.betainc(tf_ga_s, tf_gb_s, tf_gx_s) err = gradient_checker.compute_gradient_error( [tf_gx_s], [()], tf_gout_t, ga_s.shape) - tf_logging.info("betainc gradient err = %g " % err) + print("betainc gradient err = %g " % err) self.assertLess(err, err_tolerance) diff --git a/tensorflow/python/kernel_tests/clip_ops_test.py b/tensorflow/python/kernel_tests/clip_ops_test.py index fb52d10475..e08123b041 100644 --- a/tensorflow/python/kernel_tests/clip_ops_test.py +++ b/tensorflow/python/kernel_tests/clip_ops_test.py @@ -18,12 +18,9 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import numpy as np - from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops -from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import gradient_checker from tensorflow.python.platform import test @@ -417,16 +414,6 @@ class ClipTest(test.TestCase): self.assertAllClose(np_ans, tf_ans) - def testClipByValueEmptyTensor(self): - # Test case for GitHub issue 19337 - zero = array_ops.placeholder(dtype=dtypes.float32, shape=None) - x = clip_ops.clip_by_value(zero, zero, zero) - y = clip_ops.clip_by_value(zero, 1.0, 1.0) - z = clip_ops.clip_by_value(zero, zero, 1.0) - w = clip_ops.clip_by_value(zero, 1.0, zero) - with self.test_session(use_gpu=True) as sess: - sess.run([x, y, z, w], feed_dict={zero: np.zeros((7, 0))}) - if __name__ == '__main__': test.main() diff --git a/tensorflow/python/kernel_tests/conv_ops_test.py b/tensorflow/python/kernel_tests/conv_ops_test.py index 80ba7dafc9..8699fd5b25 100644 --- a/tensorflow/python/kernel_tests/conv_ops_test.py +++ b/tensorflow/python/kernel_tests/conv_ops_test.py @@ -312,8 +312,8 @@ class Conv2DTest(test.TestCase): expected_values = self.evaluate(expected_results) computed_values = self.evaluate(computed_results) for e_value, c_value in zip(expected_values, computed_values): - tf_logging.info("expected = ", e_value) - tf_logging.info("actual = ", c_value) + print("expected = ", e_value) + print("actual = ", c_value) self.assertAllClose( e_value.flatten(), c_value.flatten(), atol=tolerance, rtol=1e-4) @@ -337,8 +337,8 @@ class Conv2DTest(test.TestCase): for i in range(len(tensors)): conv = tensors[i] value = values[i] - tf_logging.info("expected = ", expected) - tf_logging.info("actual = ", value) + print("expected = ", expected) + print("actual = ", value) tol = 1e-5 if value.dtype == np.float16: tol = 1e-3 @@ -547,8 +547,8 @@ class Conv2DTest(test.TestCase): # "values" consists of two tensors for two backprops value = self.evaluate(conv) self.assertShapeEqual(value, conv) - tf_logging.info("expected = ", expected) - tf_logging.info("actual = ", value) + print("expected = ", expected) + print("actual = ", value) self.assertArrayNear(expected, value.flatten(), err) def _CompareBackpropInput(self, input_sizes, filter_sizes, output_sizes, @@ -723,8 +723,8 @@ class Conv2DTest(test.TestCase): data_format=data_format) value = self.evaluate(conv) self.assertShapeEqual(value, conv) - tf_logging.info("expected = ", expected) - tf_logging.info("actual = ", value) + print("expected = ", expected) + print("actual = ", value) self.assertArrayNear(expected, value.flatten(), 1e-5) def _CompareBackFilter(self, input_sizes, filter_sizes, output_sizes, @@ -912,8 +912,8 @@ class Conv2DTest(test.TestCase): value_2 = sess.run(conv_2) self.assertShapeEqual(value, conv) self.assertShapeEqual(value_2, conv_2) - tf_logging.info("expected = ", value_2) - tf_logging.info("actual = ", value) + print("expected = ", value_2) + print("actual = ", value) self.assertArrayNear(value_2.flatten(), value.flatten(), err) # Testing for backprops @@ -965,8 +965,8 @@ class Conv2DTest(test.TestCase): value_2 = sess.run(conv_2) self.assertShapeEqual(value, conv) self.assertShapeEqual(value_2, conv_2) - tf_logging.info("expected = ", value_2) - tf_logging.info("actual = ", value) + print("expected = ", value_2) + print("actual = ", value) self.assertArrayNear(value_2.flatten(), value.flatten(), err) def testConv2D2x2Depth3ValidBackpropFilterStride1x1Dilation2x1(self): @@ -1178,7 +1178,7 @@ class Conv2DTest(test.TestCase): # since fp16 numerical gradients are too imprecise. err = np.fabs(jacob_t - reference_jacob_t).max() - tf_logging.info("conv_2d gradient error = ", err) + print("conv_2d gradient error = ", err) self.assertLess(err, 0.002) def testInputGradientValidPaddingStrideOne(self): @@ -1546,7 +1546,7 @@ class DepthwiseConv2DTest(test.TestCase): conv = nn_impl.depthwise_conv2d( t1, t2, strides=[1, stride, stride, 1], padding=padding) value = sess.run(conv) - tf_logging.info("value = ", value) + print("value = ", value) self.assertArrayNear(expected, np.ravel(value), 1e-5) self.assertShapeEqual(value, conv) @@ -1668,7 +1668,7 @@ class SeparableConv2DTest(test.TestCase): conv = array_ops.transpose(conv, [0, 2, 3, 1]) value = sess.run(conv) - tf_logging.info("value = ", value) + print("value = ", value) self.assertArrayNear(expected, np.ravel(value), 1e-5) self.assertShapeEqual(value, conv) @@ -1826,7 +1826,7 @@ class Conv2DBenchmark(test.Benchmark): wall_time = time.time() - start self.report_benchmark( name="conv_stack_iter_%d" % iter_index, wall_time=wall_time) - tf_logging.info("conv_stack_iter_%d: %.4f" % (iter_index, wall_time)) + print("conv_stack_iter_%d: %.4f" % (iter_index, wall_time)) def GetInceptionFwdTest(input_size, filter_size, stride, padding, diff --git a/tensorflow/python/kernel_tests/gather_nd_op_test.py b/tensorflow/python/kernel_tests/gather_nd_op_test.py index 58e2a8ac2a..91ebe8de99 100644 --- a/tensorflow/python/kernel_tests/gather_nd_op_test.py +++ b/tensorflow/python/kernel_tests/gather_nd_op_test.py @@ -197,21 +197,7 @@ class GatherNdTest(test.TestCase): self.assertEqual(None, shape.ndims) self.assertEqual(None, shape[0].value) - def testBadIndicesCPU(self): - with self.test_session(use_gpu=False): - params = [0, 1, 2] - indices = [[[0], [7]]] # Make this one higher rank - gather_nd = array_ops.gather_nd(params, indices) - with self.assertRaisesOpError( - r"flat indices\[1, :\] = \[7\] does not index into param " - r"\(shape: \[3\]\)"): - gather_nd.eval() - - def _disabledTestBadIndicesGPU(self): - # TODO disabled due to different behavior on GPU and CPU - # On GPU the bad indices do not raise error but fetch 0 values - if not test.is_gpu_available(): - return + def testBadIndices(self): with self.test_session(use_gpu=True): params = [0, 1, 2] indices = [[[0], [7]]] # Make this one higher rank @@ -221,21 +207,7 @@ class GatherNdTest(test.TestCase): r"\(shape: \[3\]\)"): gather_nd.eval() - def testBadIndicesWithSlicesCPU(self): - with self.test_session(use_gpu=False): - params = [[0, 1, 2]] - indices = [[[0], [0], [1]]] # Make this one higher rank - gather_nd = array_ops.gather_nd(params, indices) - with self.assertRaisesOpError( - r"flat indices\[2, :\] = \[1\] does not index into param " - r"\(shape: \[1,3\]\)"): - gather_nd.eval() - - def _disabledTestBadIndicesWithSlicesGPU(self): - # TODO disabled due to different behavior on GPU and CPU - # On GPU the bad indices do not raise error but fetch 0 values - if not test.is_gpu_available(): - return + def testBadIndicesWithSlices(self): with self.test_session(use_gpu=True): params = [[0, 1, 2]] indices = [[[0], [0], [1]]] # Make this one higher rank diff --git a/tensorflow/python/kernel_tests/gather_op_test.py b/tensorflow/python/kernel_tests/gather_op_test.py index 033fa95935..a2fcd751df 100644 --- a/tensorflow/python/kernel_tests/gather_op_test.py +++ b/tensorflow/python/kernel_tests/gather_op_test.py @@ -27,8 +27,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import gradients_impl from tensorflow.python.platform import test -_TEST_TYPES = (dtypes.int64, dtypes.float32, - dtypes.complex64, dtypes.complex128) +_TEST_TYPES = (dtypes.float32, dtypes.complex64, dtypes.complex128) class GatherTest(test.TestCase): @@ -123,9 +122,6 @@ class GatherTest(test.TestCase): gather, [tf_params, tf_indices, tf_axis], gather_grad) self.assertEqual(indices_grad, None) self.assertEqual(axis_grad, None) - if dtype.is_integer: - self.assertEqual(params_grad, None) - continue # For axis 0, we are able to create an efficient IndexedSlices for # the gradient. if axis == 0: @@ -181,19 +177,7 @@ class GatherTest(test.TestCase): gather_t = array_ops.gather(params, indices, axis=axis) self.assertEqual(None, gather_t.shape) - def testBadIndicesCPU(self): - with self.test_session(use_gpu=False): - params = [[0, 1, 2], [3, 4, 5]] - with self.assertRaisesOpError(r"indices\[0,0\] = 7 is not in \[0, 2\)"): - array_ops.gather(params, [[7]], axis=0).eval() - with self.assertRaisesOpError(r"indices\[0,0\] = 7 is not in \[0, 3\)"): - array_ops.gather(params, [[7]], axis=1).eval() - - def _disabledTestBadIndicesGPU(self): - # TODO disabled due to different behavior on GPU and CPU - # On GPU the bad indices do not raise error but fetch 0 values - if not test.is_gpu_available(): - return + def testBadIndices(self): with self.test_session(use_gpu=True): params = [[0, 1, 2], [3, 4, 5]] with self.assertRaisesOpError(r"indices\[0,0\] = 7 is not in \[0, 2\)"): diff --git a/tensorflow/python/kernel_tests/init_ops_test.py b/tensorflow/python/kernel_tests/init_ops_test.py index 795aa67248..a9b55854f1 100644 --- a/tensorflow/python/kernel_tests/init_ops_test.py +++ b/tensorflow/python/kernel_tests/init_ops_test.py @@ -362,33 +362,6 @@ class UniformUnitScalingInitializationTest(test.TestCase): dtype=dtypes.string) -class VarianceScalingInitializationTest(test.TestCase): - - def testNormalDistribution(self): - shape = [100, 100] - expect_mean = 0. - expect_var = 1. / shape[0] - init = init_ops.variance_scaling_initializer(distribution='normal') - - with self.test_session(use_gpu=True): - x = init(shape).eval() - - self.assertNear(np.mean(x), expect_mean, err=1e-2) - self.assertNear(np.var(x), expect_var, err=1e-2) - - def testUniformDistribution(self): - shape = [100, 100] - expect_mean = 0. - expect_var = 1. / shape[0] - init = init_ops.variance_scaling_initializer(distribution='uniform') - - with self.test_session(use_gpu=True): - x = init(shape).eval() - - self.assertNear(np.mean(x), expect_mean, err=1e-2) - self.assertNear(np.var(x), expect_var, err=1e-2) - - # TODO(vrv): move to sequence_ops_test? class RangeTest(test.TestCase): diff --git a/tensorflow/python/kernel_tests/pooling_ops_test.py b/tensorflow/python/kernel_tests/pooling_ops_test.py index e95c729715..a0c372db7d 100644 --- a/tensorflow/python/kernel_tests/pooling_ops_test.py +++ b/tensorflow/python/kernel_tests/pooling_ops_test.py @@ -947,7 +947,7 @@ class PoolingTest(test.TestCase): output_sizes, x_init_value=x_init_value, delta=1e-2) - tf_logging.info("%s gradient error = " % func_name, err) + print("%s gradient error = " % func_name, err) self.assertLess(err, err_tolerance) def _ConstructAndTestSecondGradient(self, @@ -1024,7 +1024,7 @@ class PoolingTest(test.TestCase): input_sizes, x_init_value=x_init_value, delta=1e-2) - tf_logging.info("%s second-order gradient error = " % func_name, err) + print("%s second-order gradient error = " % func_name, err) self.assertLess(err, err_tolerance) def _testMaxPoolGradValidPadding1_1(self, data_format, use_gpu): diff --git a/tensorflow/python/kernel_tests/py_func_test.py b/tensorflow/python/kernel_tests/py_func_test.py index 253e43920b..677253946e 100644 --- a/tensorflow/python/kernel_tests/py_func_test.py +++ b/tensorflow/python/kernel_tests/py_func_test.py @@ -19,7 +19,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import gc import re import numpy as np @@ -435,29 +434,13 @@ class PyFuncTest(test.TestCase): # ----- Tests shared by py_func and eager_py_func ----- def testCleanup(self): - # Delete everything created by previous tests to avoid side effects. - ops.reset_default_graph() - gc.collect() - initial_size = script_ops._py_funcs.size() - # Encapsulate the graph generation, so locals can be deleted. - def make_graphs(): - for _ in xrange(1000): - g = ops.Graph() - with g.as_default(): - c = constant_op.constant([1.], dtypes.float32) - _ = script_ops.py_func(lambda x: x + 1, [c], [dtypes.float32]) - _ = script_ops.eager_py_func(lambda x: x + 1, [c], [dtypes.float32]) - # These ops have a reference to 'c' which has a reference to the graph. - # Checks if the functions are being deleted though the graph is referenced from them. - # (see #18292) - _ = script_ops.py_func(lambda x: x + c.shape[0], [c], [dtypes.float32]) - _ = script_ops.eager_py_func(lambda x: x + c.shape[0], [c], [dtypes.float32]) - - # Call garbage collector to enforce deletion. - make_graphs() - ops.reset_default_graph() - gc.collect() - self.assertEqual(initial_size, script_ops._py_funcs.size()) + for _ in xrange(1000): + g = ops.Graph() + with g.as_default(): + c = constant_op.constant([1.], dtypes.float32) + _ = script_ops.py_func(lambda x: x + 1, [c], [dtypes.float32]) + _ = script_ops.eager_py_func(lambda x: x + 1, [c], [dtypes.float32]) + self.assertLess(script_ops._py_funcs.size(), 100) # ----- Tests for eager_py_func ----- @test_util.run_in_graph_and_eager_modes() diff --git a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py index faa4b49a8d..79fe927b8a 100644 --- a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py +++ b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py @@ -144,9 +144,7 @@ class StatefulScatterNdTest(test.TestCase): self.assertAllClose(new, ref_var.eval()) def _VariableRankTests(self, np_scatter, tf_scatter): - for vtype in (np.int32, - np.float32, np.float64, - np.complex64, np.complex128): + for vtype in (np.float32, np.float64, np.complex64, np.complex128): for itype in (np.int32, np.int64): self._VariableRankTest(np_scatter, tf_scatter, vtype, itype) @@ -223,7 +221,7 @@ class StatefulScatterNdTest(test.TestCase): # self._VariableRankTests(_NumpyDiv, state_ops.scatter_nd_div) def _ScatterRepeatIndicesTest(self, np_scatter, tf_scatter): - for vtype in (np.int32, np.float32, np.float64): + for vtype in (np.float32, np.float64): for itype in (np.int32, np.int64): self._VariableRankTest( np_scatter, tf_scatter, vtype, itype, repeat_indices=True) diff --git a/tensorflow/python/kernel_tests/scatter_ops_test.py b/tensorflow/python/kernel_tests/scatter_ops_test.py index 1a0fa744ae..c70a4ffce7 100644 --- a/tensorflow/python/kernel_tests/scatter_ops_test.py +++ b/tensorflow/python/kernel_tests/scatter_ops_test.py @@ -159,13 +159,7 @@ class ScatterTest(test.TestCase): # Clips small values to avoid division by zero. def clip_small_values(x): - threshold = 1e-4 - sign = np.sign(x) - - if isinstance(x, np.int32): - threshold = 1 - sign = np.random.choice([-1, 1]) - return threshold * sign if np.abs(x) < threshold else x + return 1e-4 * np.sign(x) if np.abs(x) < 1e-4 else x updates = np.vectorize(clip_small_values)(updates) old = _AsType(np.random.randn(*((first_dim,) + extra_shape)), vtype) @@ -187,11 +181,7 @@ class ScatterTest(test.TestCase): tf_scatter, repeat_indices=False, updates_are_scalar=False): - vtypes = [np.float32, np.float64] - if tf_scatter != state_ops.scatter_div: - vtypes.append(np.int32) - - for vtype in vtypes: + for vtype in (np.float32, np.float64): for itype in (np.int32, np.int64): self._VariableRankTest(tf_scatter, vtype, itype, repeat_indices, updates_are_scalar) diff --git a/tensorflow/python/kernel_tests/segment_reduction_ops_test.py b/tensorflow/python/kernel_tests/segment_reduction_ops_test.py index a82855dfeb..794be096b7 100644 --- a/tensorflow/python/kernel_tests/segment_reduction_ops_test.py +++ b/tensorflow/python/kernel_tests/segment_reduction_ops_test.py @@ -264,9 +264,7 @@ class UnsortedSegmentTest(SegmentReductionHelper): # A subset of ops has been enabled for complex numbers self.complex_ops_list = [(np.add, None, - math_ops.unsorted_segment_sum, lambda t: 0), - (np.ndarray.__mul__, None, - math_ops.unsorted_segment_prod, lambda t: 1)] + math_ops.unsorted_segment_sum, lambda t: 0)] self.differentiable_dtypes = [dtypes_lib.float16, dtypes_lib.float32, dtypes_lib.float64] self.all_dtypes = (self.differentiable_dtypes + diff --git a/tensorflow/python/kernel_tests/string_split_op_test.py b/tensorflow/python/kernel_tests/string_split_op_test.py index e20daccb28..a5bd1b6ee0 100644 --- a/tensorflow/python/kernel_tests/string_split_op_test.py +++ b/tensorflow/python/kernel_tests/string_split_op_test.py @@ -146,101 +146,5 @@ class StringSplitOpTest(test.TestCase): self.assertAllEqual(shape, [3, 1]) -class StringSplitV2OpTest(test.TestCase): - - def testSplitV2(self): - strings = ["pigs on the wing", "animals"] - - with self.test_session() as sess: - tokens = string_ops.string_split_v2(strings) - indices, values, shape = sess.run(tokens) - self.assertAllEqual(indices, [[0, 0], [0, 1], [0, 2], [0, 3], [1, 0]]) - self.assertAllEqual(values, [b"pigs", b"on", b"the", b"wing", b"animals"]) - self.assertAllEqual(shape, [2, 4]) - - def testSplitV2MultiCharSeparator(self): - # Match Python behavior: - # >>> '1<>2<>3'.split('<>') - # ['1', '2', '3'] - # >>> "<><>4<>5<><>6<>".split("<>") - # ['', '', '4', '5', '', '6', ''] - strings = ["1<>2<>3", "<><>4<>5<><>6<>"] - - with self.test_session() as sess: - tokens = string_ops.string_split_v2(strings, sep="<>") - indices, values, shape = sess.run(tokens) - self.assertAllEqual( - indices, [[0, 0], [0, 1], [0, 2], - [1, 0], [1, 1], [1, 2], [1, 3], [1, 4], [1, 5], [1, 6]]) - self.assertAllEqual(values, [b"1", b"2", b"3", - b"", b"", b"4", b"5", b"", b"6", b""]) - self.assertAllEqual(shape, [2, 7]) - - def testSplitV2SimpleSeparator(self): - # Match Python behavior: - # >>> '1,2,3'.split(',') - # ['1', '2', '3'] - # >>> '1,2,,3,'.split(',') - # ['1', '2', '', '3', ''] - strings = ["1,2,3", "4,5,,6,"] - - with self.test_session() as sess: - tokens = string_ops.string_split_v2(strings, sep=',') - indices, values, shape = sess.run(tokens) - self.assertAllEqual(indices, [[0, 0], [0, 1], [0, 2], - [1, 0], [1, 1], [1, 2], [1, 3], [1, 4]]) - self.assertAllEqual(values, [b"1", b"2", b"3", - b"4", b"5", b"", b"6", b""]) - self.assertAllEqual(shape, [2, 5]) - - def testSplitV2EmptySeparator(self): - # Match Python behavior: - # >>> '1 2 3'.split() - # ['1', '2', '3'] - #>>> ' 1 2 3 '.split() - #['1', '2', '3'] - strings = ["1 2 3", " 4 5 6 "] - - with self.test_session() as sess: - tokens = string_ops.string_split_v2(strings) - indices, values, shape = sess.run(tokens) - self.assertAllEqual(indices, [[0, 0], [0, 1], [0, 2], - [1, 0], [1, 1], [1, 2]]) - self.assertAllEqual(values, [b"1", b"2", b"3", b"4", b"5", b"6"]) - self.assertAllEqual(shape, [2, 3]) - - def testSplitV2SimpleSeparatorMaxSplit(self): - # Match Python behavior: - # >>> '1,2,3'.split(',', maxsplit=1) - # ['1', '2,3'] - # >>> '4,5,,6,'.split(',', maxsplit=1) - # ['4', '5,,6,'] - strings = ["1,2,3", "4,5,,6,"] - - with self.test_session() as sess: - tokens = string_ops.string_split_v2(strings, sep=',', maxsplit=1) - indices, values, shape = sess.run(tokens) - self.assertAllEqual(indices, [[0, 0], [0, 1], - [1, 0], [1, 1]]) - self.assertAllEqual(values, [b"1", b"2,3", b"4", b"5,,6,"]) - self.assertAllEqual(shape, [2, 2]) - - def testSplitV2EmptySeparatorMaxSplit(self): - # Match Python behavior: - # '1 2 3'.split(maxsplit=1) - # ['1', '2 3'] - # >>> " 4 5 6 ".split(maxsplit=1) - # ['4', '5 6 '] - strings = ["1 2 3", " 4 5 6 "] - - with self.test_session() as sess: - tokens = string_ops.string_split_v2(strings, maxsplit=1) - indices, values, shape = sess.run(tokens) - self.assertAllEqual(indices, [[0, 0], [0, 1], - [1, 0], [1, 1]]) - self.assertAllEqual(values, [b"1", b"2 3", b"4", b"5 6 "]) - self.assertAllEqual(shape, [2, 2]) - - if __name__ == "__main__": test.main() diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py index fae63b1132..8129334703 100644 --- a/tensorflow/python/ops/array_ops.py +++ b/tensorflow/python/ops/array_ops.py @@ -2619,10 +2619,6 @@ reverse.__doc__ = gen_array_ops.reverse_v2.__doc__ # pylint: disable=redefined-builtin @tf_export("reverse_sequence") -@deprecation.deprecated_args( - None, "seq_dim is deprecated, use seq_axis instead", "seq_dim") -@deprecation.deprecated_args( - None, "batch_dim is deprecated, use batch_axis instead", "batch_dim") def reverse_sequence(input, seq_lengths, seq_axis=None, diff --git a/tensorflow/python/ops/gradient_checker.py b/tensorflow/python/ops/gradient_checker.py index 94c8d79335..12afcd0b51 100644 --- a/tensorflow/python/ops/gradient_checker.py +++ b/tensorflow/python/ops/gradient_checker.py @@ -283,10 +283,10 @@ def compute_gradient(x, numbers. For example, if `x` is complex with shape `[m]` and `y` is complex with shape `[n]`, each Jacobian `J` will have shape `[m * 2, n * 2]` with - J[::2, ::2] = d(Re y)/d(Re x) - J[::2, 1::2] = d(Im y)/d(Re x) - J[1::2, ::2] = d(Re y)/d(Im x) - J[1::2, 1::2] = d(Im y)/d(Im x) + J[:m, :n] = d(Re y)/d(Re x) + J[:m, n:] = d(Im y)/d(Re x) + J[m:, :n] = d(Re y)/d(Im x) + J[m:, n:] = d(Im y)/d(Im x) Args: x: a tensor or list of tensors diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py index f27d9224c1..bdcf420980 100644 --- a/tensorflow/python/ops/image_ops_impl.py +++ b/tensorflow/python/ops/image_ops_impl.py @@ -28,7 +28,6 @@ from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops -from tensorflow.python.ops import functional_ops from tensorflow.python.ops import gen_image_ops from tensorflow.python.ops import gen_nn_ops from tensorflow.python.ops import math_ops @@ -259,14 +258,14 @@ def random_flip_up_down(image, seed=None): dimension, which is `height`. Otherwise output the image as-is. Args: - image: 4-D Tensor of shape `[batch, height, width, channels]` or - 3-D Tensor of shape `[height, width, channels]`. + image: A 3-D tensor of shape `[height, width, channels].` seed: A Python integer. Used to create a random seed. See @{tf.set_random_seed} for behavior. Returns: - A tensor of the same type and shape as `image`. + A 3-D tensor of the same type and shape as `image`. + Raises: ValueError: if the shape of `image` not supported. """ @@ -281,14 +280,13 @@ def random_flip_left_right(image, seed=None): second dimension, which is `width`. Otherwise output the image as-is. Args: - image: 4-D Tensor of shape `[batch, height, width, channels]` or - 3-D Tensor of shape `[height, width, channels]`. + image: A 3-D tensor of shape `[height, width, channels].` seed: A Python integer. Used to create a random seed. See @{tf.set_random_seed} for behavior. Returns: - A tensor of the same type and shape as `image`. + A 3-D tensor of the same type and shape as `image`. Raises: ValueError: if the shape of `image` not supported. @@ -299,8 +297,7 @@ def random_flip_left_right(image, seed=None): def _random_flip(image, flip_index, seed, scope_name): """Randomly (50% chance) flip an image along axis `flip_index`. Args: - image: 4-D Tensor of shape `[batch, height, width, channels]` or - 3-D Tensor of shape `[height, width, channels]`. + image: A 3-D tensor of shape `[height, width, channels].` flip_index: The dimension along which to flip the image. Vertical: 0, Horizontal: 1 seed: A Python integer. Used to create a random seed. See @@ -309,37 +306,22 @@ def _random_flip(image, flip_index, seed, scope_name): scope_name: Name of the scope in which the ops are added. Returns: - A tensor of the same type and shape as `image`. + A 3-D tensor of the same type and shape as `image`. Raises: ValueError: if the shape of `image` not supported. """ with ops.name_scope(None, scope_name, [image]) as scope: image = ops.convert_to_tensor(image, name='image') - image = _AssertAtLeast3DImage(image) - shape = image.get_shape() - if shape.ndims == 3 or shape.ndims is None: - uniform_random = random_ops.random_uniform([], 0, 1.0, seed=seed) - mirror_cond = math_ops.less(uniform_random, .5) - result = control_flow_ops.cond( - mirror_cond, - lambda: array_ops.reverse(image, [flip_index]), - lambda: image, - name=scope - ) - return fix_image_flip_shape(image, result) - elif shape.ndims == 4: - uniform_random = random_ops.random_uniform( - [array_ops.shape(image)[0]], 0, 1.0, seed=seed - ) - mirror_cond = math_ops.less(uniform_random, .5) - return array_ops.where( - mirror_cond, - image, - functional_ops.map_fn(lambda x: array_ops.reverse(x, [flip_index]), image, dtype=image.dtype) - ) - else: - raise ValueError('\'image\' must have either 3 or 4 dimensions.') + image = _Assert3DImage(image) + uniform_random = random_ops.random_uniform([], 0, 1.0, seed=seed) + mirror_cond = math_ops.less(uniform_random, .5) + result = control_flow_ops.cond( + mirror_cond, + lambda: array_ops.reverse(image, [flip_index]), + lambda: image, + name=scope) + return fix_image_flip_shape(image, result) @tf_export('image.flip_left_right') @@ -1652,13 +1634,13 @@ def is_jpeg(contents, name=None): @tf_export('image.decode_image') -def decode_image(contents, channels=None, dtype=dtypes.uint8, name=None): +def decode_image(contents, channels=None, name=None): """Convenience function for `decode_bmp`, `decode_gif`, `decode_jpeg`, and `decode_png`. Detects whether an image is a BMP, GIF, JPEG, or PNG, and performs the - appropriate operation to convert the input bytes `string` into a `Tensor` - of type `dtype`. + appropriate operation to convert the input bytes `string` into a `Tensor` of + type `uint8`. Note: `decode_gif` returns a 4-D array `[num_frames, height, width, 3]`, as opposed to `decode_bmp`, `decode_jpeg` and `decode_png`, which return 3-D @@ -1670,11 +1652,10 @@ def decode_image(contents, channels=None, dtype=dtypes.uint8, name=None): contents: 0-D `string`. The encoded image bytes. channels: An optional `int`. Defaults to `0`. Number of color channels for the decoded image. - dtype: The desired DType of the returned `Tensor`. name: A name for the operation (optional) Returns: - `Tensor` with type `dtype` and shape `[height, width, num_channels]` for + `Tensor` with type `uint8` with shape `[height, width, num_channels]` for BMP, JPEG, and PNG images and shape `[num_frames, height, width, 3]` for GIF images. @@ -1698,7 +1679,7 @@ def decode_image(contents, channels=None, dtype=dtypes.uint8, name=None): channels_msg = 'Channels must be in (None, 0, 3) when decoding BMP images' assert_channels = control_flow_ops.Assert(good_channels, [channels_msg]) with ops.control_dependencies([assert_decode, assert_channels]): - return convert_image_dtype(gen_image_ops.decode_bmp(contents), dtype) + return gen_image_ops.decode_bmp(contents) def _gif(): # Create assert to make sure that channels is not set to 1 @@ -1711,7 +1692,7 @@ def decode_image(contents, channels=None, dtype=dtypes.uint8, name=None): channels_msg = 'Channels must be in (None, 0, 3) when decoding GIF images' assert_channels = control_flow_ops.Assert(good_channels, [channels_msg]) with ops.control_dependencies([assert_channels]): - return convert_image_dtype(gen_image_ops.decode_gif(contents), dtype) + return gen_image_ops.decode_gif(contents) def check_gif(): # Create assert op to check that bytes are GIF decodable @@ -1720,11 +1701,7 @@ def decode_image(contents, channels=None, dtype=dtypes.uint8, name=None): def _png(): """Decodes a PNG image.""" - return convert_image_dtype( - gen_image_ops.decode_png(contents, channels, - dtype=dtypes.uint8 - if dtype == dtypes.uint8 - else dtypes.uint16), dtype) + return gen_image_ops.decode_png(contents, channels) def check_png(): """Checks if an image is PNG.""" @@ -1740,8 +1717,7 @@ def decode_image(contents, channels=None, dtype=dtypes.uint8, name=None): 'images') assert_channels = control_flow_ops.Assert(good_channels, [channels_msg]) with ops.control_dependencies([assert_channels]): - return convert_image_dtype( - gen_image_ops.decode_jpeg(contents, channels), dtype) + return gen_image_ops.decode_jpeg(contents, channels) # Decode normal JPEG images (start with \xff\xd8\xff\xe0) # as well as JPEG images with EXIF data (start with \xff\xd8\xff\xe1). @@ -1902,7 +1878,7 @@ def sample_distorted_bounding_box(image_size, width / height within this range. area_range: An optional list of `floats`. Defaults to `[0.05, 1]`. The cropped area of the image must contain a fraction of the - supplied image within this range. + supplied image within in this range. max_attempts: An optional `int`. Defaults to `100`. Number of attempts at generating a cropped region of the image of the specified constraints. After `max_attempts` failures, return the diff --git a/tensorflow/python/ops/image_ops_test.py b/tensorflow/python/ops/image_ops_test.py index 2a6ab26e96..45499dcce0 100644 --- a/tensorflow/python/ops/image_ops_test.py +++ b/tensorflow/python/ops/image_ops_test.py @@ -533,37 +533,6 @@ class FlipImageBenchmark(test.Benchmark): iters=benchmark_rounds, wall_time=step_time) - def _benchmarkBatchedRandomFlipLeftRight(self, device, cpu_count): - image_shape = [16, 299, 299, 3] - warmup_rounds = 100 - benchmark_rounds = 1000 - config = config_pb2.ConfigProto() - if cpu_count is not None: - config.inter_op_parallelism_threads = 1 - config.intra_op_parallelism_threads = cpu_count - with session.Session("", graph=ops.Graph(), config=config) as sess: - with ops.device(device): - inputs = variables.Variable( - random_ops.random_uniform(image_shape, dtype=dtypes.float32) * 255, - trainable=False, - dtype=dtypes.float32) - run_op = image_ops.random_flip_left_right(inputs) - sess.run(variables.global_variables_initializer()) - for i in xrange(warmup_rounds + benchmark_rounds): - if i == warmup_rounds: - start = time.time() - sess.run(run_op) - end = time.time() - step_time = (end - start) / benchmark_rounds - tag = device + "_%s" % (cpu_count if cpu_count is not None else "_all") - print("benchmarkBatchedRandomFlipLeftRight_16_299_299_3_%s step_time: " - "%.2f us" % - (tag, step_time * 1e6)) - self.report_benchmark( - name="benchmarkBatchedRandomFlipLeftRight_16_299_299_3_%s" % (tag), - iters=benchmark_rounds, - wall_time=step_time) - def benchmarkFlipLeftRightCpu1(self): self._benchmarkFlipLeftRight("/cpu:0", 1) @@ -582,15 +551,6 @@ class FlipImageBenchmark(test.Benchmark): def benchmarkRandomFlipLeftRightGpu(self): self._benchmarkRandomFlipLeftRight(test.gpu_device_name(), None) - def benchmarkBatchedRandomFlipLeftRightCpu1(self): - self._benchmarkBatchedRandomFlipLeftRight("/cpu:0", 1) - - def benchmarkBatchedRandomFlipLeftRightCpuAll(self): - self._benchmarkBatchedRandomFlipLeftRight("/cpu:0", None) - - def benchmarkBatchedRandomFlipLeftRightGpu(self): - self._benchmarkBatchedRandomFlipLeftRight(test.gpu_device_name(), None) - class AdjustHueBenchmark(test.Benchmark): @@ -1027,7 +987,7 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): with self.test_session(use_gpu=True): x_tf = constant_op.constant(x_np, shape=x_np.shape) - y = image_ops.random_flip_left_right(x_tf, seed=seed) + y = image_ops.random_flip_left_right(x_tf) self.assertTrue(y.op.name.startswith("random_flip_left_right")) count_flipped = 0 @@ -1048,50 +1008,6 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): self.assertGreaterEqual(count_flipped, 20) self.assertGreaterEqual(count_unflipped, 20) - def testRandomFlipLeftRightWithBatch(self): - batch_size = 16 - seed = 42 - - # create single item of test data - x_np_raw = np.array( - [[1, 2, 3], [1, 2, 3]], dtype=np.uint8 - ).reshape([1, 2, 3, 1]) - y_np_raw = np.array( - [[3, 2, 1], [3, 2, 1]], dtype=np.uint8 - ).reshape([1, 2, 3, 1]) - - # create batched test data - x_np = np.vstack([x_np_raw for _ in range(batch_size)]) - y_np = np.vstack([y_np_raw for _ in range(batch_size)]) - - with self.test_session(use_gpu=True): - x_tf = constant_op.constant(x_np, shape=x_np.shape) - y = image_ops.random_flip_left_right(x_tf, seed=seed) - self.assertTrue(y.op.name.startswith("random_flip_left_right")) - - count_flipped = 0 - count_unflipped = 0 - for _ in range(100): - y_tf = y.eval() - - # check every element of the batch - for i in range(batch_size): - if y_tf[i][0][0] == 1: - self.assertAllEqual(y_tf[i], x_np[i]) - count_unflipped += 1 - else: - self.assertAllEqual(y_tf[i], y_np[i]) - count_flipped += 1 - - # 100 trials, each containing batch_size elements - # Mean: 50 * batch_size - # Std Dev: ~5 * sqrt(batch_size) - # Six Sigma: 50 * batch_size - (5 * 6 * sqrt(batch_size)) - # = 50 * batch_size - 30 * sqrt(batch_size) = 800 - 30 * 4 = 680 - six_sigma = 50 * batch_size - 30 * np.sqrt(batch_size) - self.assertGreaterEqual(count_flipped, six_sigma) - self.assertGreaterEqual(count_unflipped, six_sigma) - def testInvolutionUpDown(self): x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8).reshape([2, 3, 1]) @@ -1141,11 +1057,9 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8).reshape([2, 3, 1]) y_np = np.array([[4, 5, 6], [1, 2, 3]], dtype=np.uint8).reshape([2, 3, 1]) - seed = 42 - with self.test_session(use_gpu=True): x_tf = constant_op.constant(x_np, shape=x_np.shape) - y = image_ops.random_flip_up_down(x_tf, seed=seed) + y = image_ops.random_flip_up_down(x_tf, seed=42) self.assertTrue(y.op.name.startswith("random_flip_up_down")) count_flipped = 0 count_unflipped = 0 @@ -1165,50 +1079,6 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): self.assertGreaterEqual(count_flipped, 20) self.assertGreaterEqual(count_unflipped, 20) - def testRandomFlipUpDownWithBatch(self): - batch_size = 16 - seed = 42 - - # create single item of test data - x_np_raw = np.array( - [[1, 2, 3], [4, 5, 6]], dtype=np.uint8 - ).reshape([1, 2, 3, 1]) - y_np_raw = np.array( - [[4, 5, 6], [1, 2, 3]], dtype=np.uint8 - ).reshape([1, 2, 3, 1]) - - # create batched test data - x_np = np.vstack([x_np_raw for _ in range(batch_size)]) - y_np = np.vstack([y_np_raw for _ in range(batch_size)]) - - with self.test_session(use_gpu=True): - x_tf = constant_op.constant(x_np, shape=x_np.shape) - y = image_ops.random_flip_up_down(x_tf, seed=seed) - self.assertTrue(y.op.name.startswith("random_flip_up_down")) - - count_flipped = 0 - count_unflipped = 0 - for _ in range(100): - y_tf = y.eval() - - # check every element of the batch - for i in range(batch_size): - if y_tf[i][0][0] == 1: - self.assertAllEqual(y_tf[i], x_np[i]) - count_unflipped += 1 - else: - self.assertAllEqual(y_tf[i], y_np[i]) - count_flipped += 1 - - # 100 trials, each containing batch_size elements - # Mean: 50 * batch_size - # Std Dev: ~5 * sqrt(batch_size) - # Six Sigma: 50 * batch_size - (5 * 6 * sqrt(batch_size)) - # = 50 * batch_size - 30 * sqrt(batch_size) = 800 - 30 * 4 = 680 - six_sigma = 50 * batch_size - 30 * np.sqrt(batch_size) - self.assertGreaterEqual(count_flipped, six_sigma) - self.assertGreaterEqual(count_unflipped, six_sigma) - def testInvolutionTranspose(self): x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8).reshape([2, 3, 1]) @@ -1286,7 +1156,6 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): #Ops that support 4D input for op in [ image_ops.flip_left_right, image_ops.flip_up_down, - image_ops.random_flip_left_right, image_ops.random_flip_up_down, image_ops.transpose_image, image_ops.rot90 ]: transformed_unknown_dims_4 = op(p_unknown_dims_4) @@ -1297,6 +1166,14 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): "must be at least three-dimensional"): op(p_wrong_rank) + for op in [ + image_ops.random_flip_left_right, + image_ops.random_flip_up_down, + ]: + with self.assertRaisesRegexp(ValueError, "must be three-dimensional"): + op(p_wrong_rank) + + def testRot90GroupOrder(self): image = np.arange(24, dtype=np.uint8).reshape([2, 4, 3]) with self.test_session(use_gpu=True): @@ -1331,6 +1208,41 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase): y_np = np.rot90(image, k=k, axes=(1, 2)) self.assertAllEqual(y_np, y_tf.eval({k_placeholder: k})) +class RandomFlipTest(test_util.TensorFlowTestCase): + + def testRandomLeftRight(self): + x_np = np.array([0, 1], dtype=np.uint8).reshape([1, 2, 1]) + num_iterations = 500 + + hist = [0, 0] + with self.test_session(use_gpu=True): + x_tf = constant_op.constant(x_np, shape=x_np.shape) + y = image_ops.random_flip_left_right(x_tf) + for _ in xrange(num_iterations): + y_np = y.eval().flatten()[0] + hist[y_np] += 1 + + # Ensure that each entry is observed within 4 standard deviations. + four_stddev = 4.0 * np.sqrt(num_iterations / 2.0) + self.assertAllClose(hist, [num_iterations / 2.0] * 2, atol=four_stddev) + + def testRandomUpDown(self): + x_np = np.array([0, 1], dtype=np.uint8).reshape([2, 1, 1]) + num_iterations = 500 + + hist = [0, 0] + with self.test_session(use_gpu=True): + x_tf = constant_op.constant(x_np, shape=x_np.shape) + y = image_ops.random_flip_up_down(x_tf) + for _ in xrange(num_iterations): + y_np = y.eval().flatten()[0] + hist[y_np] += 1 + + # Ensure that each entry is observed within 4 standard deviations. + four_stddev = 4.0 * np.sqrt(num_iterations / 2.0) + self.assertAllClose(hist, [num_iterations / 2.0] * 2, atol=four_stddev) + + class AdjustContrastTest(test_util.TensorFlowTestCase): def _testContrast(self, x_np, y_np, contrast_factor): @@ -3968,88 +3880,5 @@ class SobelEdgesTest(test_util.TensorFlowTestCase): self.assertAllClose(expected_batch, actual_sobel) -class DecodeImageTest(test_util.TensorFlowTestCase): - - def testJpegUint16(self): - with self.test_session(use_gpu=True) as sess: - base = "tensorflow/core/lib/jpeg/testdata" - jpeg0 = io_ops.read_file(os.path.join(base, "jpeg_merge_test1.jpg")) - image0 = image_ops.decode_image(jpeg0, dtype=dtypes.uint16) - image1 = image_ops.convert_image_dtype(image_ops.decode_jpeg(jpeg0), - dtypes.uint16) - image0, image1 = sess.run([image0, image1]) - self.assertAllEqual(image0, image1) - - def testPngUint16(self): - with self.test_session(use_gpu=True) as sess: - base = "tensorflow/core/lib/png/testdata" - png0 = io_ops.read_file(os.path.join(base, "lena_rgba.png")) - image0 = image_ops.decode_image(png0, dtype=dtypes.uint16) - image1 = image_ops.convert_image_dtype( - image_ops.decode_png(png0, dtype=dtypes.uint16), dtypes.uint16) - image0, image1 = sess.run([image0, image1]) - self.assertAllEqual(image0, image1) - - def testGifUint16(self): - with self.test_session(use_gpu=True) as sess: - base = "tensorflow/core/lib/gif/testdata" - gif0 = io_ops.read_file(os.path.join(base, "scan.gif")) - image0 = image_ops.decode_image(gif0, dtype=dtypes.uint16) - image1 = image_ops.convert_image_dtype(image_ops.decode_gif(gif0), - dtypes.uint16) - image0, image1 = sess.run([image0, image1]) - self.assertAllEqual(image0, image1) - - def testBmpUint16(self): - with self.test_session(use_gpu=True) as sess: - base = "tensorflow/core/lib/bmp/testdata" - bmp0 = io_ops.read_file(os.path.join(base, "lena.bmp")) - image0 = image_ops.decode_image(bmp0, dtype=dtypes.uint16) - image1 = image_ops.convert_image_dtype(image_ops.decode_bmp(bmp0), - dtypes.uint16) - image0, image1 = sess.run([image0, image1]) - self.assertAllEqual(image0, image1) - - def testJpegFloat32(self): - with self.test_session(use_gpu=True) as sess: - base = "tensorflow/core/lib/jpeg/testdata" - jpeg0 = io_ops.read_file(os.path.join(base, "jpeg_merge_test1.jpg")) - image0 = image_ops.decode_image(jpeg0, dtype=dtypes.float32) - image1 = image_ops.convert_image_dtype(image_ops.decode_jpeg(jpeg0), - dtypes.float32) - image0, image1 = sess.run([image0, image1]) - self.assertAllEqual(image0, image1) - - def testPngFloat32(self): - with self.test_session(use_gpu=True) as sess: - base = "tensorflow/core/lib/png/testdata" - png0 = io_ops.read_file(os.path.join(base, "lena_rgba.png")) - image0 = image_ops.decode_image(png0, dtype=dtypes.float32) - image1 = image_ops.convert_image_dtype( - image_ops.decode_png(png0, dtype=dtypes.uint16), dtypes.float32) - image0, image1 = sess.run([image0, image1]) - self.assertAllEqual(image0, image1) - - def testGifFloat32(self): - with self.test_session(use_gpu=True) as sess: - base = "tensorflow/core/lib/gif/testdata" - gif0 = io_ops.read_file(os.path.join(base, "scan.gif")) - image0 = image_ops.decode_image(gif0, dtype=dtypes.float32) - image1 = image_ops.convert_image_dtype(image_ops.decode_gif(gif0), - dtypes.float32) - image0, image1 = sess.run([image0, image1]) - self.assertAllEqual(image0, image1) - - def testBmpFloat32(self): - with self.test_session(use_gpu=True) as sess: - base = "tensorflow/core/lib/bmp/testdata" - bmp0 = io_ops.read_file(os.path.join(base, "lena.bmp")) - image0 = image_ops.decode_image(bmp0, dtype=dtypes.float32) - image1 = image_ops.convert_image_dtype(image_ops.decode_bmp(bmp0), - dtypes.float32) - image0, image1 = sess.run([image0, image1]) - self.assertAllEqual(image0, image1) - - if __name__ == "__main__": googletest.main() diff --git a/tensorflow/python/ops/init_ops.py b/tensorflow/python/ops/init_ops.py index 724fcc39cd..2df230d470 100644 --- a/tensorflow/python/ops/init_ops.py +++ b/tensorflow/python/ops/init_ops.py @@ -467,8 +467,7 @@ class VarianceScaling(Initializer): else: scale /= max(1., (fan_in + fan_out) / 2.) if self.distribution == "normal": - # constant taken from scipy.stats.truncnorm.std(a=-2, b=2, loc=0., scale=1.) - stddev = math.sqrt(scale) / .87962566103423978 + stddev = math.sqrt(scale) return random_ops.truncated_normal( shape, 0.0, stddev, dtype, seed=self.seed) else: diff --git a/tensorflow/python/ops/logging_ops.py b/tensorflow/python/ops/logging_ops.py index 8276047cb6..222b8ebc9d 100644 --- a/tensorflow/python/ops/logging_ops.py +++ b/tensorflow/python/ops/logging_ops.py @@ -35,9 +35,8 @@ from tensorflow.python.util.tf_export import tf_export # Assert and Print are special symbols in python, so we must -# have an upper-case version of them. For users with Python 3 or Python 2.7 -# with `from __future__ import print_function`, we also allow lowercase. -@tf_export("Print", "print") +# use an upper-case version of them. +@tf_export("Print") def Print(input_, data, message=None, first_n=None, summarize=None, name=None): """Prints a list of tensors. diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py index 466d0dadc8..e40481f3a7 100644 --- a/tensorflow/python/ops/math_ops.py +++ b/tensorflow/python/ops/math_ops.py @@ -125,8 +125,8 @@ def abs(x, name=None): # pylint: disable=redefined-builtin ``` Args: - x: A `Tensor` or `SparseTensor` of type `float16`, `float32`, `float64`, - `int32`, `int64`, `complex64` or `complex128`. + x: A `Tensor` or `SparseTensor` of type `float32`, `float64`, `int32`, + `int64`, `complex64` or `complex128`. name: A name for the operation (optional). Returns: @@ -430,10 +430,10 @@ def pow(x, y, name=None): # pylint: disable=redefined-builtin ``` Args: - x: A `Tensor` of type `float16`, `float32`, `float64`, `int32`, `int64`, - `complex64`, or `complex128`. - y: A `Tensor` of type `float16`, `float32`, `float64`, `int32`, `int64`, - `complex64`, or `complex128`. + x: A `Tensor` of type `float32`, `float64`, `int32`, `int64`, `complex64`, + or `complex128`. + y: A `Tensor` of type `float32`, `float64`, `int32`, `int64`, `complex64`, + or `complex128`. name: A name for the operation (optional). Returns: @@ -600,7 +600,7 @@ def round(x, name=None): # pylint: disable=redefined-builtin ``` Args: - x: A `Tensor` of type `float16`, `float32`, `float64`, `int32`, or `int64`. + x: A `Tensor` of type `float32` or `float64`. name: A name for the operation (optional). Returns: @@ -1257,7 +1257,7 @@ def reduce_sum(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` is None, all dimensions are reduced, and a + If `axis` has no entries, all dimensions are reduced, and a tensor with a single element is returned. For example: @@ -1397,7 +1397,7 @@ def reduce_mean(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` is None, all dimensions are reduced, and a + If `axis` has no entries, all dimensions are reduced, and a tensor with a single element is returned. For example: @@ -1469,7 +1469,7 @@ def reduce_prod(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` is None, all dimensions are reduced, and a + If `axis` has no entries, all dimensions are reduced, and a tensor with a single element is returned. Args: @@ -1519,7 +1519,7 @@ def reduce_min(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` is None, all dimensions are reduced, and a + If `axis` has no entries, all dimensions are reduced, and a tensor with a single element is returned. Args: @@ -1568,7 +1568,7 @@ def reduce_max(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` is None, all dimensions are reduced, and a + If `axis` has no entries, all dimensions are reduced, and a tensor with a single element is returned. Args: @@ -1617,7 +1617,7 @@ def reduce_all(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` is None, all dimensions are reduced, and a + If `axis` has no entries, all dimensions are reduced, and a tensor with a single element is returned. For example: @@ -1675,7 +1675,7 @@ def reduce_any(input_tensor, entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with length 1. - If `axis` is None, all dimensions are reduced, and a + If `axis` has no entries, all dimensions are reduced, and a tensor with a single element is returned. For example: diff --git a/tensorflow/python/ops/nn_impl.py b/tensorflow/python/ops/nn_impl.py index f47f38e29e..783d485892 100644 --- a/tensorflow/python/ops/nn_impl.py +++ b/tensorflow/python/ops/nn_impl.py @@ -621,7 +621,7 @@ def normalize_moments(counts, mean_ss, variance_ss, shift, name=None): """Calculate the mean and variance of based on the sufficient statistics. Args: - counts: A `Tensor` containing the total count of the data (one value). + counts: A `Tensor` containing a the total count of the data (one value). mean_ss: A `Tensor` containing the mean sufficient statistics: the (possibly shifted) sum of the elements to average over. variance_ss: A `Tensor` containing the variance sufficient statistics: the @@ -689,9 +689,6 @@ def moments( # Compute true mean while keeping the dims for proper broadcasting. mean = math_ops.reduce_mean(y, axes, keepdims=True, name="mean") # sample variance, not unbiased variance - # Note: stop_gradient does not change the gradient that gets - # backpropagated to the mean from the variance calculation, - # because that gradient is zero variance = math_ops.reduce_mean( math_ops.squared_difference(y, array_ops.stop_gradient(mean)), axes, diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py index 0c2f5b06c4..a0b55eb077 100644 --- a/tensorflow/python/ops/nn_ops.py +++ b/tensorflow/python/ops/nn_ops.py @@ -1596,12 +1596,12 @@ def leaky_relu(features, alpha=0.2, name=None): Returns: The activation value. """ - with ops.name_scope(name, "LeakyRelu", [features, alpha]) as name: + with ops.name_scope(name, "LeakyRelu", [features, alpha]): features = ops.convert_to_tensor(features, name="features") if features.dtype.is_integer: features = math_ops.to_float(features) alpha = ops.convert_to_tensor(alpha, dtype=features.dtype, name="alpha") - return math_ops.maximum(alpha * features, features, name=name) + return math_ops.maximum(alpha * features, features) def _flatten_outer_dims(logits): diff --git a/tensorflow/python/ops/nn_test.py b/tensorflow/python/ops/nn_test.py index 035b4735af..46a5f4fae6 100644 --- a/tensorflow/python/ops/nn_test.py +++ b/tensorflow/python/ops/nn_test.py @@ -962,16 +962,6 @@ class LeakyReluTest(test_lib.TestCase): self.assertAllClose( outputs, [-0.4, -0.2, 0.0, 1.0, 2.0], rtol=tol, atol=tol) - def testName(self): - np_values = np.array([-2, -1, 0, 1, 2], dtype=np.float64) - outputs_with_name_set = nn_ops.leaky_relu( - constant_op.constant(np_values), - name='test_relu_op') - self.assertEqual(outputs_with_name_set.name, 'test_relu_op:0') - outputs_without_name_set = nn_ops.leaky_relu( - constant_op.constant(np_values)) - self.assertEqual(outputs_without_name_set.name, 'LeakyRelu:0') - class SwishTest(test_lib.TestCase): diff --git a/tensorflow/python/ops/script_ops.py b/tensorflow/python/ops/script_ops.py index 219562de5d..f8676ccb5f 100644 --- a/tensorflow/python/ops/script_ops.py +++ b/tensorflow/python/ops/script_ops.py @@ -23,7 +23,6 @@ import threading # Used by py_util.cc to get tracebacks. import traceback # pylint: disable=unused-import -import weakref import numpy as np import six @@ -130,14 +129,11 @@ class FuncRegistry(object): def __init__(self): self._lock = threading.Lock() self._unique_id = 0 # GUARDED_BY(self._lock) - # Only store weakrefs to the funtions. The strong reference is stored in - # the graph. - self._funcs = weakref.WeakValueDictionary() + self._funcs = {} def insert(self, func): """Registers `func` and returns a unique token for this entry.""" token = self._next_unique_token() - # Store a weakref to the function self._funcs[token] = func return token @@ -190,7 +186,7 @@ class FuncRegistry(object): Raises: ValueError: if no function is registered for `token`. """ - func = self._funcs.get(token, None) + func = self._funcs[token] if func is None: raise ValueError("callback %s is not found" % token) if isinstance(func, EagerFunc): @@ -232,6 +228,19 @@ _py_funcs = FuncRegistry() pywrap_tensorflow.InitializePyTrampoline(_py_funcs) +class CleanupFunc(object): + """A helper class to remove a registered function from _py_funcs.""" + + def __init__(self, token): + self._token = token + + def __del__(self): + if _py_funcs is not None: + # If _py_funcs is None, the program is most likely in shutdown, and the + # _py_funcs object has been destroyed already. + _py_funcs.remove(self._token) + + def _internal_py_func(func, inp, Tout, @@ -261,15 +270,17 @@ def _internal_py_func(func, # bound to that of the outer graph instead. graph = graph._outer_graph + cleanup = CleanupFunc(token) + # TODO(zhifengc): Consider adding a Graph method to collect # `cleanup` objects in one of its member. - if not hasattr(graph, "_py_funcs_used_in_graph"): - graph._py_funcs_used_in_graph = [] + if not hasattr(graph, "_cleanup_py_funcs_used_in_graph"): + graph._cleanup_py_funcs_used_in_graph = [] - # Store a reference to the function in the graph to ensure it stays alive - # as long as the graph lives. When the graph is destroyed, the function - # is left to the garbage collector for destruction as well. - graph._py_funcs_used_in_graph.append(func) + # When `graph` is destroyed, elements in _cleanup_py_funcs_used_in_graph + # will be destroyed and their __del__ will remove the 'token' from + # the funcs registry. + graph._cleanup_py_funcs_used_in_graph.append(cleanup) # pylint: enable=protected-access if eager: diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py index c3b16a7bd5..0130233746 100644 --- a/tensorflow/python/ops/sparse_ops.py +++ b/tensorflow/python/ops/sparse_ops.py @@ -84,8 +84,6 @@ def _convert_to_sparse_tensors(sp_inputs): # pylint: disable=protected-access @tf_export("sparse_concat") -@deprecation.deprecated_args( - None, "concat_dim is deprecated, use axis instead", "concat_dim") def sparse_concat(axis, sp_inputs, name=None, @@ -599,8 +597,6 @@ class KeywordRequired(object): @tf_export("sparse_split") -@deprecation.deprecated_args( - None, "split_dim is deprecated, use axis instead", "split_dim") def sparse_split(keyword_required=KeywordRequired(), sp_input=None, num_split=None, diff --git a/tensorflow/python/ops/string_ops.py b/tensorflow/python/ops/string_ops.py index 0280c89c10..ae79c01949 100644 --- a/tensorflow/python/ops/string_ops.py +++ b/tensorflow/python/ops/string_ops.py @@ -91,59 +91,6 @@ def string_split(source, delimiter=" ", skip_empty=True): # pylint: disable=inv shape.set_shape([2]) return sparse_tensor.SparseTensor(indices, values, shape) -@tf_export("strings.split") -def string_split_v2(source, sep=None, maxsplit=-1): - """Split elements of `source` based on `sep` into a `SparseTensor`. - - Let N be the size of source (typically N will be the batch size). Split each - element of `source` based on `sep` and return a `SparseTensor` - containing the split tokens. Empty tokens are ignored. - - For example, N = 2, source[0] is 'hello world' and source[1] is 'a b c', - then the output will be - - st.indices = [0, 0; - 0, 1; - 1, 0; - 1, 1; - 1, 2] - st.shape = [2, 3] - st.values = ['hello', 'world', 'a', 'b', 'c'] - - If `sep` is given, consecutive delimiters are not grouped together and are - deemed to delimit empty strings. For example, source of `"1<>2<><>3"` and - sep of `"<>"` returns `["1", "2", "", "3"]`. If `sep` is None or an empty - string, consecutive whitespace are regarded as a single separator, and the - result will contain no empty strings at the startor end if the string has - leading or trailing whitespace. - - Note that the above mentioned behavior matches python's str.split. - - Args: - source: `1-D` string `Tensor`, the strings to split. - sep: `0-D` string `Tensor`, the delimiter character. - maxsplit: An `int`. If `maxsplit > 0`, limit of the split of the result. - - Raises: - ValueError: If sep is not a string. - - Returns: - A `SparseTensor` of rank `2`, the strings split according to the delimiter. - The first column of the indices corresponds to the row in `source` and the - second column corresponds to the index of the split component in this row. - """ - if sep is None: - sep = '' - sep = ops.convert_to_tensor(sep, dtype=dtypes.string) - source = ops.convert_to_tensor(source, dtype=dtypes.string) - - indices, values, shape = gen_string_ops.string_split_v2( - source, sep=sep, maxsplit=maxsplit) - indices.set_shape([None, 2]) - values.set_shape([None]) - shape.set_shape([2]) - return sparse_tensor.SparseTensor(indices, values, shape) - def _reduce_join_reduction_dims(x, axis, reduction_indices): """Returns range(rank(x) - 1, 0, -1) if reduction_indices is None.""" diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py index 47414c28af..f49e2d314d 100644 --- a/tensorflow/python/ops/variable_scope.py +++ b/tensorflow/python/ops/variable_scope.py @@ -1786,23 +1786,6 @@ class variable_scope(object): assert v.name == "foo/bar/v:0" ``` - Simple example of how to reenter a premade variable scope safely: - - ```python - with tf.variable_scope("foo") as vs: - pass - - # Re-enter the variable scope. - with tf.variable_scope(vs, - auxiliary_name_scope=False) as vs1: - # Restore the original name_scope. - with tf.name_scope(vs1.original_name_scope): - v = tf.get_variable("v", [1]) - assert v.name == "foo/v:0" - c = tf.constant([1], name="c") - assert c.name == "foo/c:0" - ``` - Basic example of sharing a variable AUTO_REUSE: ```python @@ -1941,9 +1924,7 @@ class variable_scope(object): (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. auxiliary_name_scope: If `True`, we create an auxiliary name scope with - the scope. If `False`, we don't create it. Note that the argument is - not inherited, and it only takes effect for once when creating. You - should only use it for re-entering a premade variable scope. + the scope. If `False`, we don't touch name scope. Returns: A scope that can be captured and reused. diff --git a/tensorflow/python/tools/import_pb_to_tensorboard.py b/tensorflow/python/tools/import_pb_to_tensorboard.py index 00de044505..00de044505 100644..100755 --- a/tensorflow/python/tools/import_pb_to_tensorboard.py +++ b/tensorflow/python/tools/import_pb_to_tensorboard.py diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl index b59f8e1f98..522965990b 100644 --- a/tensorflow/tensorflow.bzl +++ b/tensorflow/tensorflow.bzl @@ -1719,7 +1719,7 @@ def tf_py_build_info_genrule(): name="py_build_info_gen", outs=["platform/build_info.py"], cmd= - "$(location //tensorflow/tools/build_info:gen_build_info.py) --raw_generate \"$@\" --build_config " + if_cuda("cuda", "cpu"), + "$(location //tensorflow/tools/build_info:gen_build_info.py) --raw_generate \"$@\" --build_config " + if_cuda("cuda", "cpu"), local=1, tools=[clean_dep("//tensorflow/tools/build_info:gen_build_info.py")],) diff --git a/tensorflow/tools/api/generator/create_python_api.py b/tensorflow/tools/api/generator/create_python_api.py index 671b7e387e..bca9fa49eb 100644 --- a/tensorflow/tools/api/generator/create_python_api.py +++ b/tensorflow/tools/api/generator/create_python_api.py @@ -41,11 +41,7 @@ _GENERATED_FILE_HEADER = """# This file is MACHINE GENERATED! Do not edit. # Generated by: tensorflow/tools/api/generator/create_python_api.py script. \"\"\"%s \"\"\" - -from __future__ import print_function - """ -_GENERATED_FILE_FOOTER = "\n\ndel print_function\n" class SymbolExposedTwiceError(Exception): @@ -153,7 +149,6 @@ class _ModuleInitCodeBuilder(object): _names_with_underscore = [%s] __all__ = [_s for _s in dir() if not _s.startswith('_')] __all__.extend([_s for _s in _names_with_underscore]) -__all__.remove('print_function') ''' % underscore_names_str return module_text_map @@ -338,8 +333,7 @@ def create_api_files( if module or not root_init_template: contents = ( _GENERATED_FILE_HEADER % - get_module_docstring(module, package, api_name) + - text + _GENERATED_FILE_FOOTER) + get_module_docstring(module, package, api_name) + text) else: # Read base init file with open(root_init_template, 'r') as root_init_template_file: diff --git a/tensorflow/tools/api/golden/tensorflow.image.pbtxt b/tensorflow/tools/api/golden/tensorflow.image.pbtxt index 10171b3d60..5bb3b3c444 100644 --- a/tensorflow/tools/api/golden/tensorflow.image.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.image.pbtxt @@ -58,7 +58,7 @@ tf_module { } member_method { name: "decode_image" - argspec: "args=[\'contents\', \'channels\', \'dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \"<dtype: \'uint8\'>\", \'None\'], " + argspec: "args=[\'contents\', \'channels\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " } member_method { name: "decode_jpeg" diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt index 3051c4437e..dc2bd40096 100644 --- a/tensorflow/tools/api/golden/tensorflow.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.pbtxt @@ -1533,10 +1533,6 @@ tf_module { argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { - name: "print" - argspec: "args=[\'input_\', \'data\', \'message\', \'first_n\', \'summarize\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " - } - member_method { name: "py_func" argspec: "args=[\'func\', \'inp\', \'Tout\', \'stateful\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], " } diff --git a/tensorflow/tools/api/golden/tensorflow.strings.pbtxt b/tensorflow/tools/api/golden/tensorflow.strings.pbtxt index b641c39feb..a3fbe95bba 100644 --- a/tensorflow/tools/api/golden/tensorflow.strings.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.strings.pbtxt @@ -4,8 +4,4 @@ tf_module { name: "regex_full_match" argspec: "args=[\'input\', \'pattern\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } - member_method { - name: "split" - argspec: "args=[\'source\', \'sep\', \'maxsplit\'], varargs=None, keywords=None, defaults=[\'None\', \'-1\'], " - } } diff --git a/tensorflow/tools/ci_build/builds/pip.sh b/tensorflow/tools/ci_build/builds/pip.sh index 883bb93647..5fa75e1d61 100755 --- a/tensorflow/tools/ci_build/builds/pip.sh +++ b/tensorflow/tools/ci_build/builds/pip.sh @@ -322,10 +322,6 @@ create_activate_virtualenv_and_install_tensorflow() { pip install -v ${PIP_FLAGS} ${WHL_PATH} || \ die "pip install (forcing to reinstall tensorflow) FAILED" echo "Successfully installed pip package ${TF_WHEEL_PATH}" - - # Force downgrade setuptools. - pip install --upgrade setuptools==39.1.0 - } ################################################################################ diff --git a/tensorflow/tools/ci_build/builds/with_the_same_user b/tensorflow/tools/ci_build/builds/with_the_same_user index b216e3549f..d4bf546d40 100755 --- a/tensorflow/tools/ci_build/builds/with_the_same_user +++ b/tensorflow/tools/ci_build/builds/with_the_same_user @@ -40,7 +40,7 @@ if [ -n "${CI_BUILD_USER_FORCE_BADNAME}" ]; then ADDUSER_OPTS="--force-badname" fi -getent group "${CI_BUILD_GID}" || addgroup ${ADDUSER_OPTS} --gid "${CI_BUILD_GID}" "${CI_BUILD_GROUP}" +getent group "${CI_BUILD_GID}" || addgroup --gid "${CI_BUILD_GID}" "${CI_BUILD_GROUP}" getent passwd "${CI_BUILD_UID}" || adduser ${ADDUSER_OPTS} \ --gid "${CI_BUILD_GID}" --uid "${CI_BUILD_UID}" \ --gecos "${CI_BUILD_USER} (generated by with_the_same_user script)" \ diff --git a/tensorflow/tools/ci_build/ci_build.sh b/tensorflow/tools/ci_build/ci_build.sh index 1f0fd0387a..072dd6ab99 100755 --- a/tensorflow/tools/ci_build/ci_build.sh +++ b/tensorflow/tools/ci_build/ci_build.sh @@ -134,12 +134,6 @@ if [[ $? != "0" ]]; then die "ERROR: docker build failed. Dockerfile is at ${DOCKERFILE_PATH}" fi -# If caller wants the with_the_same_user script to allow bad usernames, -# pass the var to the docker environment -if [ -n "${CI_BUILD_USER_FORCE_BADNAME}" ]; then - CI_BUILD_USER_FORCE_BADNAME_ENV="-e CI_BUILD_USER_FORCE_BADNAME=yes" -fi - # Run the command inside the container. echo "Running '${COMMAND[*]}' inside ${DOCKER_IMG_NAME}..." mkdir -p ${WORKSPACE}/bazel-ci_build-cache @@ -154,7 +148,6 @@ ${DOCKER_BINARY} run --rm --pid=host \ -e "CI_BUILD_GROUP=$(id -g -n)" \ -e "CI_BUILD_GID=$(id -g)" \ -e "CI_TENSORFLOW_SUBMODULE_PATH=${CI_TENSORFLOW_SUBMODULE_PATH}" \ - ${CI_BUILD_USER_FORCE_BADNAME_ENV} \ -v ${WORKSPACE}:/workspace \ -w /workspace \ ${GPU_EXTRA_PARAMS} \ diff --git a/tensorflow/tools/ci_build/copy_binary.py b/tensorflow/tools/ci_build/copy_binary.py index 148526492d..420d390d2b 100755 --- a/tensorflow/tools/ci_build/copy_binary.py +++ b/tensorflow/tools/ci_build/copy_binary.py @@ -32,8 +32,7 @@ import shutil import tempfile import zipfile -TF_NIGHTLY_REGEX = (r"(.+)tf_nightly(|_gpu)-(\d\.[\d]{1,2}" - "\.\d.dev[\d]{0,8})-(.+)\.whl") +TF_NIGHTLY_REGEX = r"(.+)tf_nightly(|_gpu)-(\d\.\d\.\d.dev[\d]{0,8})-(.+)\.whl" BINARY_STRING_TEMPLATE = "%s-%s-%s.whl" diff --git a/tensorflow/tools/ci_build/install/install_pip_packages.sh b/tensorflow/tools/ci_build/install/install_pip_packages.sh index 88f1d04193..60290df833 100755 --- a/tensorflow/tools/ci_build/install/install_pip_packages.sh +++ b/tensorflow/tools/ci_build/install/install_pip_packages.sh @@ -115,7 +115,3 @@ pip2 install keras_applications==1.0.2 pip3 install keras_applications==1.0.2 pip2 install keras_preprocessing==1.0.1 pip3 install keras_preprocessing==1.0.1 - -# Install last working version of setuptools. -pip2 install --upgrade setuptools==39.1.0 -pip3 install --upgrade setuptools==39.1.0 diff --git a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh index acd69ef346..edb9d4b929 100755 --- a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh +++ b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh @@ -39,6 +39,7 @@ if [[ -z $pip35_version ]]; then fi set -e +pip3.5 install --upgrade setuptools pip3.5 install --upgrade pip pip3.5 install --upgrade virtualenv @@ -85,7 +86,4 @@ pip3.5 install --upgrade termcolor pip3.5 install keras_applications==1.0.2 pip3.5 install keras_preprocessing==1.0.1 -# Install last working version of setuptools. -pip3.5 install --upgrade setuptools==39.1.0 - # LINT.ThenChange(//tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh) diff --git a/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh index 323b30f48e..5635977731 100755 --- a/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh +++ b/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh @@ -49,6 +49,7 @@ cd Python-3.6.1 make altinstall ln -s /usr/local/bin/pip3.6 /usr/local/bin/pip3 +pip3 install --upgrade setuptools pip3 install --upgrade pip pip3 install --upgrade virtualenv @@ -100,8 +101,4 @@ pip3 install --upgrade termcolor # Keras pip3.5 install keras_applications==1.0.2 pip3.5 install keras_preprocessing==1.0.1 - -# Install last working version of setuptools. -pip3 install --upgrade setuptools==39.1.0 - # LINT.ThenChange(//tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh) diff --git a/tensorflow/tools/ci_build/linux/mkl/basic-mkl-test.sh b/tensorflow/tools/ci_build/linux/mkl/basic-mkl-test.sh deleted file mode 100755 index 10a09a415a..0000000000 --- a/tensorflow/tools/ci_build/linux/mkl/basic-mkl-test.sh +++ /dev/null @@ -1,29 +0,0 @@ -#!/usr/bin/env bash -# Copyright 2015 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -# -# Usage: basic_mkl_test.sh - -# Helper function to traverse directories up until given file is found. -function upsearch () { - test / == "$PWD" && return || \ - test -e "$1" && echo "$PWD" && return || \ - cd .. && upsearch "$1" -} - -# Set up WORKSPACE. -WORKSPACE="${WORKSPACE:-$(upsearch WORKSPACE)}" - -BUILD_TAG=mkl-ci-test CI_BUILD_USER_FORCE_BADNAME=yes ${WORKSPACE}/tensorflow/tools/ci_build/ci_build.sh cpu tensorflow/tools/ci_build/linux/cpu/run_mkl.sh diff --git a/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh b/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh index b8bce57c87..1bd1852ffc 100755 --- a/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh +++ b/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh @@ -79,7 +79,6 @@ if [[ $1 == "PI_ONE" ]]; then --linkopt=-L${OPENBLAS_INSTALL_PATH}/lib/ --linkopt=-l:libopenblas.a" echo "Building for the Pi One/Zero, with no NEON support" - WHEEL_ARCH=linux_armv6l else PI_COPTS='--copt=-march=armv7-a --copt=-mfpu=neon-vfpv4 --copt=-std=gnu11 --copt=-DS_IREAD=S_IRUSR --copt=-DS_IWRITE=S_IWUSR @@ -87,7 +86,6 @@ else --copt=-U__GCC_HAVE_SYNC_COMPARE_AND_SWAP_1 --copt=-U__GCC_HAVE_SYNC_COMPARE_AND_SWAP_2 --copt=-U__GCC_HAVE_SYNC_COMPARE_AND_SWAP_8' - WHEEL_ARCH=linux_armv7l echo "Building for the Pi Two/Three, with NEON acceleration" fi @@ -102,8 +100,6 @@ bazel build -c opt ${PI_COPTS} \ --copt=-fomit-frame-pointer --cpu=armeabi \ --crosstool_top=@local_config_arm_compiler//:toolchain \ --verbose_failures \ - //tensorflow:libtensorflow.so \ - //tensorflow:libtensorflow_framework.so \ //tensorflow/tools/benchmark:benchmark_model \ //tensorflow/tools/pip_package:build_pip_package @@ -116,12 +112,10 @@ BDIST_OPTS="--universal" \ bazel-bin/tensorflow/tools/pip_package/build_pip_package "${OUTDIR}" OLD_FN=$(ls "${OUTDIR}" | grep -m 1 \.whl) -SUB='s/tensorflow-([^-]+)-([^-]+)-.*/tensorflow-\1-\2-none-'${WHEEL_ARCH}'.whl/; print' +SUB='s/tensorflow-([^-]+)-([^-]+)-.*/tensorflow-\1-\2-none-any.whl/; print' NEW_FN=$(echo "${OLD_FN}" | perl -ne "${SUB}") mv "${OUTDIR}/${OLD_FN}" "${OUTDIR}/${NEW_FN}" cp bazel-bin/tensorflow/tools/benchmark/benchmark_model "${OUTDIR}" -cp bazel-bin/tensorflow/libtensorflow.so "${OUTDIR}" -cp bazel-bin/tensorflow/libtensorflow_framework.so "${OUTDIR}" echo "Output can be found here:" find "${OUTDIR}" diff --git a/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl b/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl index f8f63e276c..47539b2423 100644 --- a/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl +++ b/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl @@ -31,11 +31,7 @@ def _def_file_filter_configure_impl(repository_ctx): vc_path = find_vc_path(repository_ctx) if vc_path == "visual-studio-not-found": auto_configure_fail("Visual C++ build tools not found on your machine") - - undname = find_msvc_tool(repository_ctx, vc_path, "undname.exe") - if undname == None: - auto_configure_fail("Couldn't find undname.exe under %s, please check your VC installation and set BAZEL_VC environment variable correctly." % vc_path) - undname_bin_path = undname.replace("\\", "\\\\") + undname_bin_path = find_msvc_tool(repository_ctx, vc_path, "undname.exe").replace("\\", "\\\\") repository_ctx.template( "def_file_filter.py", diff --git a/tensorflow/tools/dist_test/local_test.sh b/tensorflow/tools/dist_test/local_test.sh index b0114721bd..06c2b997cb 100755 --- a/tensorflow/tools/dist_test/local_test.sh +++ b/tensorflow/tools/dist_test/local_test.sh @@ -64,6 +64,9 @@ die() { # Configurations DOCKER_IMG_NAME="tensorflow/tf-dist-test-local-cluster" +# Use TensorFlow v1.5.0 for Python 2.7 and CPU only as we set num_gpus to 0 in the below +DEFAULT_WHL_FILE_LOCATION="https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp27-none-linux_x86_64.whl" + # Parse input arguments LEAVE_CONTAINER_RUNNING=0 MODEL_NAME="" @@ -74,7 +77,8 @@ SYNC_REPLICAS_FLAG="" WHL_FILE_LOCATION=${1} if [[ -z "${WHL_FILE_LOCATION}" ]]; then - echo "WARNING: No wheel url passed. Will use latest tf-nightly cpu p2 wheel." + WHL_FILE_LOCATION=${DEFAULT_WHL_FILE_LOCATION} + echo "use default whl file location" fi while true; do @@ -127,11 +131,7 @@ echo "Building in temporary directory: ${BUILD_DIR}" cp -r ${DIR}/* "${BUILD_DIR}"/ || \ die "Failed to copy files to ${BUILD_DIR}" -# Download whl file into the build context directory. -if [[ -z "${WHL_FILE_LOCATION}" ]]; then - pip2 download --no-deps tf-nightly - cp tf-nightly-*.whl "${BUILD_DIR}"/tensorflow-none-any.whl -elif [[ $WHL_FILE_LOCATION =~ 'http://' || $WHL_FILE_LOCATION =~ 'https://' ]]; then +if [[ $WHL_FILE_LOCATION =~ 'http://' || $WHL_FILE_LOCATION =~ 'https://' ]]; then # Download whl file into the build context directory. wget -P "${BUILD_DIR}" "${WHL_FILE_LOCATION}" || \ die "Failed to download tensorflow whl file from URL: ${WHL_FILE_LOCATION}" diff --git a/tensorflow/tools/dist_test/remote_test.sh b/tensorflow/tools/dist_test/remote_test.sh index e188c88c8f..935535312d 100755 --- a/tensorflow/tools/dist_test/remote_test.sh +++ b/tensorflow/tools/dist_test/remote_test.sh @@ -108,7 +108,7 @@ fi # Parse command-line arguments. WHL_URL=${1} if [[ -z "${WHL_URL}" ]]; then - echo "WARNING: No wheel url passed. Will use latest tf-nightly cpu p2 wheel." + die "whl URL is not specified" fi # Create docker build context directory. @@ -121,13 +121,8 @@ cp -r ${DIR}/* ${BUILD_DIR}/ || \ die "Failed to copy files to ${BUILD_DIR}" # Download whl file into the build context directory. -if [[ -z "${WHL_URL}" ]]; then - pip2 download --no-deps tf-nightly - cp tf-nightly-*.whl "${BUILD_DIR}"/tensorflow-none-any.whl -else - wget -P "${BUILD_DIR}" ${WHL_URL} || \ - die "Failed to download tensorflow whl file from URL: ${WHL_URL}" -fi +wget -P "${BUILD_DIR}" ${WHL_URL} || \ + die "Failed to download tensorflow whl file from URL: ${WHL_URL}" # Build docker image for test. docker build ${NO_CACHE_FLAG} \ diff --git a/tensorflow/tools/docker/Dockerfile.devel b/tensorflow/tools/docker/Dockerfile.devel index 57a491255e..406d134699 100644 --- a/tensorflow/tools/docker/Dockerfile.devel +++ b/tensorflow/tools/docker/Dockerfile.devel @@ -76,7 +76,7 @@ RUN mkdir /bazel && \ # Download and build TensorFlow. WORKDIR /tensorflow -RUN git clone --branch=r1.9 --depth=1 https://github.com/tensorflow/tensorflow.git . +RUN git clone --branch=r1.8 --depth=1 https://github.com/tensorflow/tensorflow.git . # TODO(craigcitro): Don't install the pip package, since it makes it # more difficult to experiment with local changes. Instead, just add diff --git a/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl b/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl index 6796ad70e5..a6cd44ced1 100644 --- a/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl +++ b/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl @@ -3,7 +3,7 @@ FROM tensorflow/tensorflow:latest-devel LABEL maintainer="Clayne Robison<clayne.b.robison@intel.com>" # These arguments are parameterized. Use --build-args to override. -ARG TF_BRANCH=r1.9 +ARG TF_BRANCH=r1.8 ARG WHL_DIR=/whl RUN apt-get update && apt-get install -y --no-install-recommends \ diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu b/tensorflow/tools/docker/Dockerfile.devel-gpu index 204b5b4dba..2fe47f3356 100644 --- a/tensorflow/tools/docker/Dockerfile.devel-gpu +++ b/tensorflow/tools/docker/Dockerfile.devel-gpu @@ -13,8 +13,8 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ cuda-cusparse-dev-9-0 \ curl \ git \ - libcudnn7=7.1.4.18-1+cuda9.0 \ - libcudnn7-dev=7.1.4.18-1+cuda9.0 \ + libcudnn7=7.0.5.15-1+cuda9.0 \ + libcudnn7-dev=7.0.5.15-1+cuda9.0 \ libcurl3-dev \ libfreetype6-dev \ libhdf5-serial-dev \ @@ -85,7 +85,7 @@ RUN mkdir /bazel && \ # Download and build TensorFlow. WORKDIR /tensorflow -RUN git clone --branch=r1.9 --depth=1 https://github.com/tensorflow/tensorflow.git . +RUN git clone --branch=r1.8 --depth=1 https://github.com/tensorflow/tensorflow.git . # Configure the build for our CUDA configuration. ENV CI_BUILD_PYTHON python diff --git a/tensorflow/tools/docker/Dockerfile.gpu b/tensorflow/tools/docker/Dockerfile.gpu index 9197651ff4..bff4a20392 100644 --- a/tensorflow/tools/docker/Dockerfile.gpu +++ b/tensorflow/tools/docker/Dockerfile.gpu @@ -12,7 +12,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ cuda-cusolver-9-0 \ cuda-cusparse-9-0 \ curl \ - libcudnn7=7.1.4.18-1+cuda9.0 \ + libcudnn7=7.0.5.15-1+cuda9.0 \ libfreetype6-dev \ libhdf5-serial-dev \ libpng12-dev \ diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD index 620fef9363..5910f0625e 100644 --- a/tensorflow/tools/pip_package/BUILD +++ b/tensorflow/tools/pip_package/BUILD @@ -61,7 +61,6 @@ COMMON_PIP_DEPS = [ "//tensorflow/contrib/autograph/core:core", "//tensorflow/contrib/autograph/impl:impl", "//tensorflow/contrib/autograph/lang:lang", - "//tensorflow/contrib/autograph/operators:operators", "//tensorflow/contrib/autograph/pyct:pyct", "//tensorflow/contrib/autograph/pyct/static_analysis:static_analysis", "//tensorflow/contrib/boosted_trees:boosted_trees_pip", diff --git a/tensorflow/tools/pip_package/build_pip_package.sh b/tensorflow/tools/pip_package/build_pip_package.sh index f7e42ce536..0c4065bc77 100755 --- a/tensorflow/tools/pip_package/build_pip_package.sh +++ b/tensorflow/tools/pip_package/build_pip_package.sh @@ -41,15 +41,51 @@ function is_windows() { fi } -function prepare_src() { +function main() { if [ $# -lt 1 ] ; then echo "No destination dir provided" exit 1 fi - TMPDIR="$1" - mkdir -p "$TMPDIR" - echo $(date) : "=== Preparing sources in dir: ${TMPDIR}" + DEST=$(real_path $1) + TMPDIR=$(mktemp -d -t tmp.XXXXXXXXXX) + + PKG_NAME_FLAG="" + GPU_BUILD=0 + NIGHTLY_BUILD=0 + PROJECT_NAME="" + while true; do + if [[ "$1" == "--nightly_flag" ]]; then + NIGHTLY_BUILD=1 + elif [[ "$1" == "--gpu" ]]; then + GPU_BUILD=1 + elif [[ "$1" == "--gpudirect" ]]; then + PKG_NAME_FLAG="--project_name tensorflow_gpudirect" + elif [[ "$1" == "--project_name" ]]; then + shift + if [[ -z "$1" ]]; then + break + fi + PROJECT_NAME="$1" + fi + shift + + if [[ -z "$1" ]]; then + break + fi + done + + if [[ -n ${PROJECT_NAME} ]]; then + PKG_NAME_FLAG="--project_name ${PROJECT_NAME}" + elif [[ ${NIGHTLY_BUILD} == "1" && ${GPU_BUILD} == "1" ]]; then + PKG_NAME_FLAG="--project_name tf_nightly_gpu" + elif [[ ${NIGHTLY_BUILD} == "1" ]]; then + PKG_NAME_FLAG="--project_name tf_nightly" + elif [[ ${GPU_BUILD} == "1" ]]; then + PKG_NAME_FLAG="--project_name tensorflow_gpu" + fi + + echo $(date) : "=== Using tmpdir: ${TMPDIR}" if [ ! -d bazel-bin/tensorflow ]; then echo "Could not find bazel-bin. Did you run from the root of the build tree?" @@ -119,28 +155,17 @@ function prepare_src() { # over so user defined ops can be compiled. mkdir -p ${TMPDIR}/google mkdir -p ${TMPDIR}/third_party - pushd ${RUNFILES%org_tensorflow} > /dev/null + pushd ${RUNFILES%org_tensorflow} for header in $(find protobuf_archive -name \*.h); do mkdir -p "${TMPDIR}/google/$(dirname ${header})" cp "$header" "${TMPDIR}/google/$(dirname ${header})/" done - popd > /dev/null + popd cp -R $RUNFILES/third_party/eigen3 ${TMPDIR}/third_party cp tensorflow/tools/pip_package/MANIFEST.in ${TMPDIR} cp tensorflow/tools/pip_package/README ${TMPDIR} cp tensorflow/tools/pip_package/setup.py ${TMPDIR} -} - -function build_wheel() { - if [ $# -lt 2 ] ; then - echo "No src and dest dir provided" - exit 1 - fi - - TMPDIR="$1" - DEST="$2" - PKG_NAME_FLAG="$3" # Before we leave the top-level directory, make sure we know how to # call python. @@ -148,110 +173,15 @@ function build_wheel() { source tools/python_bin_path.sh fi - pushd ${TMPDIR} > /dev/null + pushd ${TMPDIR} rm -f MANIFEST echo $(date) : "=== Building wheel" "${PYTHON_BIN_PATH:-python}" setup.py bdist_wheel ${PKG_NAME_FLAG} >/dev/null mkdir -p ${DEST} cp dist/* ${DEST} - popd > /dev/null + popd + rm -rf ${TMPDIR} echo $(date) : "=== Output wheel file is in: ${DEST}" } -function usage() { - echo "Usage:" - echo "$0 [--src srcdir] [--dst dstdir] [options]" - echo "$0 dstdir [options]" - echo "" - echo " --src prepare sources in srcdir" - echo " will use temporary dir if not specified" - echo "" - echo " --dst build wheel in dstdir" - echo " if dstdir is not set do not build, only prepare sources" - echo "" - echo " Options:" - echo " --project_name <name> set project name to name" - echo " --gpu build tensorflow_gpu" - echo " --gpudirect build tensorflow_gpudirect" - echo " --nightly_flag build tensorflow nightly" - echo "" - exit 1 -} - -function main() { - PKG_NAME_FLAG="" - PROJECT_NAME="" - GPU_BUILD=0 - NIGHTLY_BUILD=0 - SRCDIR="" - DSTDIR="" - CLEANSRC=1 - while true; do - if [[ "$1" == "--help" ]]; then - usage - exit 1 - elif [[ "$1" == "--nightly_flag" ]]; then - NIGHTLY_BUILD=1 - elif [[ "$1" == "--gpu" ]]; then - GPU_BUILD=1 - elif [[ "$1" == "--gpudirect" ]]; then - PKG_NAME_FLAG="--project_name tensorflow_gpudirect" - elif [[ "$1" == "--project_name" ]]; then - shift - if [[ -z "$1" ]]; then - break - fi - PROJECT_NAME="$1" - elif [[ "$1" == "--src" ]]; then - shift - SRCDIR="$(real_path $1)" - CLEANSRC=0 - elif [[ "$1" == "--dst" ]]; then - shift - DSTDIR="$(real_path $1)" - else - DSTDIR="$(real_path $1)" - fi - shift - - if [[ -z "$1" ]]; then - break - fi - done - - if [[ -z "$DSTDIR" ]] && [[ -z "$SRCDIR" ]]; then - echo "No destination dir provided" - usage - exit 1 - fi - - if [[ -z "$SRCDIR" ]]; then - # make temp srcdir if none set - SRCDIR="$(mktemp -d -t tmp.XXXXXXXXXX)" - fi - - prepare_src "$SRCDIR" - - if [[ -z "$DSTDIR" ]]; then - # only want to prepare sources - exit - fi - - if [[ -n ${PROJECT_NAME} ]]; then - PKG_NAME_FLAG="--project_name ${PROJECT_NAME}" - elif [[ ${NIGHTLY_BUILD} == "1" && ${GPU_BUILD} == "1" ]]; then - PKG_NAME_FLAG="--project_name tf_nightly_gpu" - elif [[ ${NIGHTLY_BUILD} == "1" ]]; then - PKG_NAME_FLAG="--project_name tf_nightly" - elif [[ ${GPU_BUILD} == "1" ]]; then - PKG_NAME_FLAG="--project_name tensorflow_gpu" - fi - - build_wheel "$SRCDIR" "$DSTDIR" "$PKG_NAME_FLAG" - - if [[ $CLEANSRC -ne 0 ]]; then - rm -rf "${TMPDIR}" - fi -} - main "$@" diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index 97f625e7e9..d25a9e77b1 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -45,7 +45,7 @@ DOCLINES = __doc__.split('\n') # This version string is semver compatible, but incompatible with pip. # For pip, we will remove all '-' characters from this string, and use the # result for pip. -_VERSION = '1.9.0-rc0' +_VERSION = '1.8.0' REQUIRED_PACKAGES = [ 'absl-py >= 0.1.6', @@ -54,7 +54,6 @@ REQUIRED_PACKAGES = [ 'numpy >= 1.13.3', 'six >= 1.10.0', 'protobuf >= 3.4.0', - 'setuptools <= 39.1.0', 'tensorboard >= 1.8.0, < 1.9.0', 'termcolor >= 1.1.0', ] diff --git a/tensorflow/tools/proto_text/gen_proto_text_functions_lib.cc b/tensorflow/tools/proto_text/gen_proto_text_functions_lib.cc index 15d7c70281..29add6d5ea 100644 --- a/tensorflow/tools/proto_text/gen_proto_text_functions_lib.cc +++ b/tensorflow/tools/proto_text/gen_proto_text_functions_lib.cc @@ -814,9 +814,6 @@ void Generator::Generate(const FileDescriptor& fd) { // Add header to cc file. SetOutput(&cc_); Print("// GENERATED FILE - DO NOT MODIFY"); - Print(); - Print("#include <algorithm>"); // for `std::stable_sort()` - Print(); headers = {GetProtoTextHeaderName(fd, true /* impl */)}; AddHeadersToCurrentSection(headers); Print(); diff --git a/tensorflow/tools/quantization/quantize_graph_test.py b/tensorflow/tools/quantization/quantize_graph_test.py index 92bb5127da..df71840b64 100644 --- a/tensorflow/tools/quantization/quantize_graph_test.py +++ b/tensorflow/tools/quantization/quantize_graph_test.py @@ -119,8 +119,8 @@ def are_tensors_near(a, b, tolerance): flat_a = a.flatten() flat_b = b.flatten() if len(flat_a) != len(flat_b): - tf_logging.info("Tensors are different sizes: " + str(len(flat_a)) + " vs " - + str(len(flat_b))) + print("Tensors are different sizes: " + str(len(flat_a)) + " vs " + str( + len(flat_b))) return False value_count = len(flat_a) how_many_different = 0 @@ -140,10 +140,10 @@ def are_tensors_near(a, b, tolerance): if how_many_different == 0: return True else: - tf_logging.info("Tensors have {0} different values ({1}%), with mean" - " difference {2} and mean absolute difference {3}".format( - how_many_different, proportion_different * 100, - mean_difference, mean_abs_difference)) + print("Tensors have {0} different values ({1}%), with mean difference" + " {2} and mean absolute difference {3}".format( + how_many_different, proportion_different * 100, mean_difference, + mean_abs_difference)) return False diff --git a/tensorflow/tools/test/upload_test_benchmarks.py b/tensorflow/tools/test/upload_test_benchmarks.py index c030575109..9c45359ee1 100644 --- a/tensorflow/tools/test/upload_test_benchmarks.py +++ b/tensorflow/tools/test/upload_test_benchmarks.py @@ -89,6 +89,7 @@ import shutil from six import text_type from google.cloud import datastore +from six import text_type def is_real_file(dirpath, fname): diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 4f3df570a5..dbec66216a 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -50,31 +50,31 @@ def tf_workspace(path_prefix="", tf_repo_name=""): mkl_repository( name = "mkl_linux", urls = [ - "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.14/mklml_lnx_2018.0.3.20180406.tgz", - "https://github.com/intel/mkl-dnn/releases/download/v0.14/mklml_lnx_2018.0.3.20180406.tgz" + "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.13/mklml_lnx_2018.0.2.20180127.tgz", + "https://github.com/intel/mkl-dnn/releases/download/v0.13/mklml_lnx_2018.0.2.20180127.tgz", ], - sha256 = "d2305244fdc9b87db7426ed4496e87a4b3977ad3374d73b8000e8b7a5b7aa725", - strip_prefix = "mklml_lnx_2018.0.3.20180406", + sha256 = "74844bd77294742bf2396ff040369d1aa4cdd9e826fcd38cf8398ae83564d146", + strip_prefix = "mklml_lnx_2018.0.2.20180127", build_file = clean_dep("//third_party/mkl:mkl.BUILD") ) mkl_repository( name = "mkl_windows", urls = [ - "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.14/mklml_win_2018.0.3.20180406.zip", - "https://github.com/intel/mkl-dnn/releases/download/v0.14/mklml_win_2018.0.3.20180406.zip" + "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.13/mklml_win_2018.0.2.20180127.zip", + "https://github.com/intel/mkl-dnn/releases/download/v0.13/mklml_win_2018.0.2.20180127.zip" ], - sha256 = "a584a5bf1c8d2ad70b90d12b52652030e9a338217719064fdb84b7ad0d693694", - strip_prefix = "mklml_win_2018.0.3.20180406", + sha256 = "d8fbf0faa0684bffa3548005d05fe5cfe56ff9dbc0e15e7612d7ac01055a6ded", + strip_prefix = "mklml_win_2018.0.2.20180127", build_file = clean_dep("//third_party/mkl:mkl.BUILD") ) mkl_repository( name = "mkl_darwin", urls = [ - "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.14/mklml_mac_2018.0.3.20180406.tgz", - "https://github.com/intel/mkl-dnn/releases/download/v0.14/mklml_mac_2018.0.3.20180406.tgz" + "https://mirror.bazel.build/github.com/intel/mkl-dnn/releases/download/v0.13/mklml_mac_2018.0.2.20180127.tgz", + "https://github.com/intel/mkl-dnn/releases/download/v0.13/mklml_mac_2018.0.2.20180127.tgz" ], - sha256 = "094e3dfd61c816136dc8d12a45cc611ce26c5f4828176a3644cd0b0efa15a25b", - strip_prefix = "mklml_mac_2018.0.3.20180406", + sha256 = "aa740d71e14562bfea56e6829e6dc186e7487cbcf6748a88dec73826b7ec1943", + strip_prefix = "mklml_mac_2018.0.2.20180127", build_file = clean_dep("//third_party/mkl:mkl.BUILD") ) @@ -85,11 +85,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "mkl_dnn", urls = [ - "https://mirror.bazel.build/github.com/intel/mkl-dnn/archive/v0.14.tar.gz", - "https://github.com/intel/mkl-dnn/archive/v0.14.tar.gz", + "https://mirror.bazel.build/github.com/intel/mkl-dnn/archive/v0.13.tar.gz", + "https://github.com/intel/mkl-dnn/archive/v0.13.tar.gz", ], - sha256 = "efebc53882856afec86457a2da644693f5d59c68772d41d640d6b60a8efc4eb0", - strip_prefix = "mkl-dnn-0.14", + sha256 = "d2cfd93a70cfe86ebe054477c530c9b5c1218b70f75856eb6d1956c68ee89e8f", + strip_prefix = "mkl-dnn-0.13", build_file = clean_dep("//third_party/mkl_dnn:mkldnn.BUILD"), ) @@ -187,11 +187,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "highwayhash", urls = [ - "http://mirror.bazel.build/github.com/google/highwayhash/archive/fd3d9af80465e4383162e4a7c5e2f406e82dd968.tar.gz", - "https://github.com/google/highwayhash/archive/fd3d9af80465e4383162e4a7c5e2f406e82dd968.tar.gz", + "https://mirror.bazel.build/github.com/google/highwayhash/archive/dfcb97ca4fe9277bf9dc1802dd979b071896453b.tar.gz", + "https://github.com/google/highwayhash/archive/dfcb97ca4fe9277bf9dc1802dd979b071896453b.tar.gz", ], - sha256 = "9c3e0e87d581feeb0c18d814d98f170ff23e62967a2bd6855847f0b2fe598a37", - strip_prefix = "highwayhash-fd3d9af80465e4383162e4a7c5e2f406e82dd968", + sha256 = "0f30a15b1566d93f146c8d149878a06e91d9bb7ec2cfd76906df62a82be4aac9", + strip_prefix = "highwayhash-dfcb97ca4fe9277bf9dc1802dd979b071896453b", build_file = clean_dep("//third_party:highwayhash.BUILD"), ) |