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author | Michele Di Giorgio <michele.digiorgio@arm.com> | 2019-06-04 12:41:45 +0100 |
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committer | Michele Di Giorgio <michele.digiorgio@arm.com> | 2019-06-13 13:06:49 +0000 |
commit | 39438b427b293c6d2e7066c68d3c3d3cb6d98a15 (patch) | |
tree | d5de918ca90dfe5641c7e0c3c854724f7de746d4 /tests | |
parent | c86633eb8865d8d2292cc44a8c30d09aee091ece (diff) | |
download | ComputeLibrary-39438b427b293c6d2e7066c68d3c3d3cb6d98a15.tar.gz |
COMPMID-2342: Add layer normalization support in CLLSTMLayer
Change-Id: I25d974aa94e69c5f79a0bd99d5869a351d6d954d
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1324
Reviewed-by: Manuel Bottini <manuel.bottini@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>
Diffstat (limited to 'tests')
-rw-r--r-- | tests/validation/CL/LSTMLayer.cpp | 15 | ||||
-rw-r--r-- | tests/validation/NEON/LSTMLayer.cpp | 15 | ||||
-rw-r--r-- | tests/validation/fixtures/LSTMLayerFixture.h | 131 |
3 files changed, 135 insertions, 26 deletions
diff --git a/tests/validation/CL/LSTMLayer.cpp b/tests/validation/CL/LSTMLayer.cpp index 71a9383d93..69ac61dcf4 100644 --- a/tests/validation/CL/LSTMLayer.cpp +++ b/tests/validation/CL/LSTMLayer.cpp @@ -153,10 +153,11 @@ template <typename T> using CLLSTMLayerFixture = LSTMLayerValidationFixture<CLTensor, CLAccessor, CLLSTMLayer, LSTMParams<ICLTensor>, T>; TEST_SUITE(FP32) -FIXTURE_DATA_TEST_CASE(RunSmall, CLLSTMLayerFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallLSTMLayerDataset(), framework::dataset::make("DataType", +FIXTURE_DATA_TEST_CASE(RunSmall, CLLSTMLayerFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallLSTMLayerDataset(), framework::dataset::make("DataType", DataType::F32)), - framework::dataset::make("ProjectionOpt", { true, false })), - framework::dataset::make("PeepholeOpt", { true, false }))) + framework::dataset::make("ProjectionOpt", { true, false })), + framework::dataset::make("PeepholeOpt", { true, false })), + framework::dataset::make("UseLayerNorm", { true, false }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f32); @@ -165,9 +166,11 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLLSTMLayerFixture<float>, framework::DatasetMo TEST_SUITE_END() // FP32 TEST_SUITE(FP16) -FIXTURE_DATA_TEST_CASE(RunSmall, CLLSTMLayerFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallLSTMLayerDataset(), framework::dataset::make("DataType", DataType::F16)), - framework::dataset::make("ProjectionOpt", { true, false })), - framework::dataset::make("PeepholeOpt", { true, false }))) +FIXTURE_DATA_TEST_CASE(RunSmall, CLLSTMLayerFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallLSTMLayerDataset(), framework::dataset::make("DataType", + DataType::F16)), + framework::dataset::make("ProjectionOpt", { true, false })), + framework::dataset::make("PeepholeOpt", { true, false })), + framework::dataset::make("UseLayerNorm", { true, false }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f16); diff --git a/tests/validation/NEON/LSTMLayer.cpp b/tests/validation/NEON/LSTMLayer.cpp index b27dfae8fa..c503972ba9 100644 --- a/tests/validation/NEON/LSTMLayer.cpp +++ b/tests/validation/NEON/LSTMLayer.cpp @@ -153,10 +153,11 @@ template <typename T> using NELSTMLayerFixture = LSTMLayerValidationFixture<Tensor, Accessor, NELSTMLayer, LSTMParams<ITensor>, T>; TEST_SUITE(FP32) -FIXTURE_DATA_TEST_CASE(RunSmall, NELSTMLayerFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallLSTMLayerDataset(), framework::dataset::make("DataType", +FIXTURE_DATA_TEST_CASE(RunSmall, NELSTMLayerFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallLSTMLayerDataset(), framework::dataset::make("DataType", DataType::F32)), - framework::dataset::make("ProjectionOpt", { true, false })), - framework::dataset::make("PeepholeOpt", { true, false }))) + framework::dataset::make("ProjectionOpt", { true, false })), + framework::dataset::make("PeepholeOpt", { true, false })), + framework::dataset::make("UseLayerNorm", { false }))) { // Validate output validate(Accessor(_target), _reference, tolerance_f32); @@ -166,9 +167,11 @@ TEST_SUITE_END() // FP32 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC TEST_SUITE(FP16) -FIXTURE_DATA_TEST_CASE(RunSmall, NELSTMLayerFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallLSTMLayerDataset(), framework::dataset::make("DataType", DataType::F16)), - framework::dataset::make("ProjectionOpt", { true, false })), - framework::dataset::make("PeepholeOpt", { true, false }))) +FIXTURE_DATA_TEST_CASE(RunSmall, NELSTMLayerFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallLSTMLayerDataset(), framework::dataset::make("DataType", + DataType::F16)), + framework::dataset::make("ProjectionOpt", { true, false })), + framework::dataset::make("PeepholeOpt", { true, false })), + framework::dataset::make("UseLayerNorm", { false }))) { // Validate output validate(Accessor(_target), _reference, tolerance_f16); diff --git a/tests/validation/fixtures/LSTMLayerFixture.h b/tests/validation/fixtures/LSTMLayerFixture.h index 2cf83b8b3d..9260686d56 100644 --- a/tests/validation/fixtures/LSTMLayerFixture.h +++ b/tests/validation/fixtures/LSTMLayerFixture.h @@ -32,6 +32,7 @@ #include "tests/validation/reference/ConcatenateLayer.h" #include "tests/validation/reference/FullyConnectedLayer.h" #include "tests/validation/reference/GEMM.h" +#include "tests/validation/reference/MeanStdDevNormalizationLayer.h" #include "tests/validation/reference/PixelWiseMultiplication.h" #include "tests/validation/reference/Transpose.h" @@ -47,12 +48,13 @@ class LSTMLayerValidationFixture : public framework::Fixture public: template <typename...> void setup(TensorShape input_shape, TensorShape input_weights_shape, TensorShape recurrent_weights_shape, TensorShape cell_bias_shape, TensorShape output_cell_shape, TensorShape output_shape, - TensorShape scratch_shape, ActivationLayerInfo info, float cell_threshold, float projection_threshold, DataType data_type, bool projection_opt, bool peephole_opt) + TensorShape scratch_shape, ActivationLayerInfo info, float cell_threshold, float projection_threshold, DataType data_type, bool projection_opt, bool peephole_opt, + bool use_layer_norm) { _target = compute_target(input_shape, input_weights_shape, recurrent_weights_shape, cell_bias_shape, output_cell_shape, output_shape, scratch_shape, info, cell_threshold, projection_threshold, - data_type, projection_opt, peephole_opt); + data_type, projection_opt, peephole_opt, use_layer_norm); _reference = compute_reference(input_shape, input_weights_shape, recurrent_weights_shape, cell_bias_shape, output_cell_shape, output_shape, scratch_shape, info, cell_threshold, projection_threshold, - data_type, projection_opt, peephole_opt); + data_type, projection_opt, peephole_opt, use_layer_norm); } protected: @@ -70,7 +72,7 @@ protected: } TensorType compute_target(const TensorShape &input_shape, const TensorShape &input_weights_shape, const TensorShape &recurrent_weights_shape, const TensorShape &cell_bias_shape, const TensorShape &output_cell_shape, const TensorShape &output_shape, const TensorShape &scratch_shape, ActivationLayerInfo info, float cell_threshold, - float projection_threshold, DataType data_type, bool projection_opt, bool peephole_opt) + float projection_threshold, DataType data_type, bool projection_opt, bool peephole_opt, bool use_layer_norm) { const unsigned int num_cells = input_weights_shape.y(); const unsigned int num_outputs = recurrent_weights_shape.x(); @@ -100,6 +102,10 @@ protected: TensorType cell_to_output_w; TensorType projection_w; TensorType projection_bias; + TensorType input_layer_norm_w; + TensorType forget_layer_norm_w; + TensorType cell_layer_norm_w; + TensorType output_layer_norm_w; bool cifg_opt = scratch_shape.x() == cell_bias_shape.x() * 4 ? false : true; @@ -131,6 +137,22 @@ protected: lstm_params.set_projection_params(&projection_w, &projection_bias); } + if(use_layer_norm) + { + forget_layer_norm_w = create_tensor<TensorType>(TensorShape(num_cells), data_type); + cell_layer_norm_w = create_tensor<TensorType>(TensorShape(num_cells), data_type); + output_layer_norm_w = create_tensor<TensorType>(TensorShape(num_cells), data_type); + if(!cifg_opt) + { + input_layer_norm_w = create_tensor<TensorType>(TensorShape(num_cells), data_type); + lstm_params.set_layer_normalization_params(&input_layer_norm_w, &forget_layer_norm_w, &cell_layer_norm_w, &output_layer_norm_w); + } + else + { + lstm_params.set_layer_normalization_params(nullptr, &forget_layer_norm_w, &cell_layer_norm_w, &output_layer_norm_w); + } + } + // Create and configure function FunctionType lstm; lstm.configure(&input, &input_to_forget_w, &input_to_cell_w, &input_to_output_w, &recurrent_to_forget_w, @@ -257,6 +279,35 @@ protected: fill(AccessorType(projection_bias), 21); } + if(use_layer_norm) + { + if(!cifg_opt) + { + ARM_COMPUTE_EXPECT(input_layer_norm_w.info()->is_resizable(), framework::LogLevel::ERRORS); + + input_layer_norm_w.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!input_layer_norm_w.info()->is_resizable(), framework::LogLevel::ERRORS); + + fill(AccessorType(input_layer_norm_w), 22); + } + ARM_COMPUTE_EXPECT(forget_layer_norm_w.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(cell_layer_norm_w.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(output_layer_norm_w.info()->is_resizable(), framework::LogLevel::ERRORS); + + forget_layer_norm_w.allocator()->allocate(); + cell_layer_norm_w.allocator()->allocate(); + output_layer_norm_w.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!forget_layer_norm_w.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!cell_layer_norm_w.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!output_layer_norm_w.info()->is_resizable(), framework::LogLevel::ERRORS); + + fill(AccessorType(forget_layer_norm_w), 23); + fill(AccessorType(cell_layer_norm_w), 24); + fill(AccessorType(output_layer_norm_w), 25); + } + // Compute function lstm.run(); @@ -266,7 +317,7 @@ protected: SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &input_weights_shape, const TensorShape &recurrent_weights_shape, const TensorShape &cell_bias_shape, const TensorShape &output_cell_shape, const TensorShape &output_shape, const TensorShape &scratch_shape, ActivationLayerInfo info, float cell_threshold, - float projection_threshold, DataType data_type, bool projection_opt, bool peephole_opt) + float projection_threshold, DataType data_type, bool projection_opt, bool peephole_opt, bool use_layer_norm) { const unsigned int num_cells = input_weights_shape.y(); const unsigned int num_outputs = recurrent_weights_shape.x(); @@ -306,6 +357,8 @@ protected: SimpleTensor<T> cell_state_out{ output_cell_shape, data_type }; SimpleTensor<T> output{ output_shape, data_type }; + bool cifg_opt = scratch_shape.x() == cell_bias_shape.x() * 4 ? false : true; + // Fill reference fill(input, 0); fill(input_to_forget_w, 1); @@ -314,9 +367,18 @@ protected: fill(recurrent_to_forget_w, 4); fill(recurrent_to_cell_w, 5); fill(recurrent_to_output_w, 6); - fill(forget_gate_bias, 7); - fill(cell_bias, 8); - fill(output_gate_bias, 9); + if(use_layer_norm) + { + fill_custom_val(forget_gate_bias, 0.f, 7); + fill_custom_val(cell_bias, 0.f, 8); + fill_custom_val(output_gate_bias, 0.f, 9); + } + else + { + fill(forget_gate_bias, 7); + fill(cell_bias, 8); + fill(output_gate_bias, 9); + } fill(output_state_in, 10); fill(cell_state_in, 11); fill(scratch, 12); @@ -324,14 +386,19 @@ protected: fill(recurrent_to_input_w, 14); fill(cell_to_input_w, 15); fill(recurrent_to_input_w, 16); - fill(input_gate_bias, 17); + if(!cifg_opt && use_layer_norm) + { + fill_custom_val(input_gate_bias, 0.f, 17); + } + else + { + fill(input_gate_bias, 17); + } fill(cell_to_forget_w, 18); fill(cell_to_output_w, 19); fill(projection_w, 20); fill(projection_bias, 21); - bool cifg_opt = scratch_shape.x() == cell_bias_shape.x() * 4 ? false : true; - // Compute forget_gate SimpleTensor<T> fully_connected_forget = reference::fully_connected_layer(input, input_to_forget_w, forget_gate_bias, output_cell_shape); SimpleTensor<T> transposed_weights = reference::transpose(recurrent_to_forget_w); @@ -344,6 +411,15 @@ protected: forget_gate = reference::arithmetic_operation(reference::ArithmeticOperation::ADD, forget_gate, pixelwise_mul_forget_gate, data_type, ConvertPolicy::SATURATE); } + if(use_layer_norm) + { + SimpleTensor<T> forget_layer_norm_w{ cell_bias_shape, data_type }; + fill(forget_layer_norm_w, 23); + forget_gate = reference::mean_std_normalization_layer(forget_gate); + forget_gate = reference::pixel_wise_multiplication(forget_gate, forget_layer_norm_w, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); + fill(forget_gate_bias, 7); + forget_gate = reference::arithmetic_operation(reference::ArithmeticOperation::ADD, forget_gate, forget_gate_bias, data_type, ConvertPolicy::SATURATE); + } forget_gate = reference::activation_layer(forget_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); // Compute input_gate @@ -365,6 +441,15 @@ protected: SimpleTensor<T> pixelwise_mul_input_gate = reference::pixel_wise_multiplication(cell_state_in, cell_to_input_w, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); input_gate = reference::arithmetic_operation(reference::ArithmeticOperation::ADD, input_gate, pixelwise_mul_input_gate, data_type, ConvertPolicy::SATURATE); } + if(use_layer_norm) + { + SimpleTensor<T> input_layer_norm_w{ cell_bias_shape, data_type }; + fill(input_layer_norm_w, 22); + input_gate = reference::mean_std_normalization_layer(input_gate); + input_gate = reference::pixel_wise_multiplication(input_gate, input_layer_norm_w, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); + fill(input_gate_bias, 17); + input_gate = reference::arithmetic_operation(reference::ArithmeticOperation::ADD, input_gate, input_gate_bias, data_type, ConvertPolicy::SATURATE); + } input_gate = reference::activation_layer(input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); } @@ -374,9 +459,18 @@ protected: gemm = reference::gemm(output_state_in, transposed_weights, cell_state_out, 1.f, 0.f); SimpleTensor<T> pixelwise_mul = reference::pixel_wise_multiplication(cell_state_in, forget_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); cell_state_out = reference::arithmetic_operation(reference::ArithmeticOperation::ADD, fully_connected_cell_state, gemm, data_type, ConvertPolicy::SATURATE); - cell_state_out = reference::activation_layer(cell_state_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); - cell_state_out = reference::pixel_wise_multiplication(cell_state_out, input_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); - cell_state_out = reference::arithmetic_operation(reference::ArithmeticOperation::ADD, cell_state_out, pixelwise_mul, data_type, ConvertPolicy::SATURATE); + if(use_layer_norm) + { + SimpleTensor<T> cell_layer_norm_w{ cell_bias_shape, data_type }; + fill(cell_layer_norm_w, 24); + cell_state_out = reference::mean_std_normalization_layer(cell_state_out); + cell_state_out = reference::pixel_wise_multiplication(cell_state_out, cell_layer_norm_w, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); + fill(cell_bias, 8); + cell_state_out = reference::arithmetic_operation(reference::ArithmeticOperation::ADD, cell_state_out, cell_bias, data_type, ConvertPolicy::SATURATE); + } + cell_state_out = reference::activation_layer(cell_state_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); + cell_state_out = reference::pixel_wise_multiplication(cell_state_out, input_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); + cell_state_out = reference::arithmetic_operation(reference::ArithmeticOperation::ADD, cell_state_out, pixelwise_mul, data_type, ConvertPolicy::SATURATE); if(cell_threshold != 0.f) { cell_state_out = reference::activation_layer(cell_state_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -cell_threshold, cell_threshold)); @@ -392,6 +486,15 @@ protected: pixelwise_mul = reference::pixel_wise_multiplication(cell_state_out, cell_to_output_w, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); output = reference::arithmetic_operation(reference::ArithmeticOperation::ADD, output, pixelwise_mul, data_type, ConvertPolicy::SATURATE); } + if(use_layer_norm) + { + SimpleTensor<T> output_layer_norm_w{ cell_bias_shape, data_type }; + fill(output_layer_norm_w, 25); + output = reference::mean_std_normalization_layer(output); + output = reference::pixel_wise_multiplication(output, output_layer_norm_w, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); + fill(output_gate_bias, 9); + output = reference::arithmetic_operation(reference::ArithmeticOperation::ADD, output, output_gate_bias, data_type, ConvertPolicy::SATURATE); + } output = reference::activation_layer(output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)); // Compute output state |