From 944170e1591ff23c9e6ede2201f0f6aba0f3439b Mon Sep 17 00:00:00 2001 From: Gian Marco Iodice Date: Mon, 24 Jun 2019 14:40:30 +0100 Subject: COMPMID-2172: Fuse bias addition with CLGEMMMatrixMultiplyNativeKernel Change-Id: I714b92ec001fc71172719b67fb66d490538b6948 Signed-off-by: Gian Marco Iodice Reviewed-on: https://review.mlplatform.org/c/1399 Reviewed-by: Giuseppe Rossini Comments-Addressed: Arm Jenkins Tested-by: Arm Jenkins --- tests/validation/CL/GEMMMatrixMultiplyNative.cpp | 74 +++++++++++++-------- tests/validation/fixtures/GEMMFixture.h | 85 +++++++++++++++++------- 2 files changed, 109 insertions(+), 50 deletions(-) (limited to 'tests') diff --git a/tests/validation/CL/GEMMMatrixMultiplyNative.cpp b/tests/validation/CL/GEMMMatrixMultiplyNative.cpp index c7c390353a..b0d1fd2ad1 100644 --- a/tests/validation/CL/GEMMMatrixMultiplyNative.cpp +++ b/tests/validation/CL/GEMMMatrixMultiplyNative.cpp @@ -68,6 +68,9 @@ constexpr float tolerance_num_f16 = 0.02f; /** Alpha values to test - Precommit */ const auto a_values = framework::dataset::make("alpha", {1.0f, -0.75f} ); +/** Beta values to test - Precommit */ +const auto beta_values = framework::dataset::make("beta", {-0.75f, 0.0f} ); + /** M values to test */ const auto m_values = framework::dataset::make("M", 37); @@ -107,8 +110,11 @@ const auto n0_values_nightly = framework::dataset::make("N0", { 2, 3, 4, 8 }); /** K0 values to test - Nightly */ const auto k0_values_nightly = framework::dataset::make("K0", { 2, 3, 4, 8 }); +/** Broadcast bias from vector to matrix */ +const auto broadcast_bias_values = framework::dataset::make("broadcast_bias", {false, true} ); + /** Configuration test */ -void validate_configuration(unsigned int m_value, unsigned int n_value, unsigned int k_value, unsigned int b_value, unsigned int m0_value, unsigned int n0_value, unsigned int k0_value, DataType data_type) +void validate_configuration(unsigned int m_value, unsigned int n_value, unsigned int k_value, unsigned int b_value, unsigned int m0_value, unsigned int n0_value, unsigned int k0_value, bool broadcast_bias, DataType data_type) { const unsigned int M = m_value; const unsigned int N = n_value; @@ -122,27 +128,30 @@ void validate_configuration(unsigned int m_value, unsigned int n_value, unsigned rhs_info.n0 = n0_value; rhs_info.k0 = k0_value; - GEMMReshapeInfo gemm_info(M, N, K); + GEMMReshapeInfo gemm_info(M, N, K, false, false, 0, false, broadcast_bias); const TensorShape lhs_shape(K, M, b_value); const TensorShape rhs_shape(N, K, b_value); - + const TensorShape bias_shape(N, + broadcast_bias? 1 : M, + broadcast_bias? 1 : b_value); const TensorShape dst_shape = compute_mm_shape(TensorInfo(lhs_shape, 1, data_type), TensorInfo(rhs_shape, 1, data_type), gemm_info); - // Create tensors - CLTensor lhs = create_tensor(lhs_shape, data_type); + CLTensor lhs = create_tensor(lhs_shape, data_type); CLTensor rhs = create_tensor(rhs_shape, data_type); - CLTensor dst = create_tensor(dst_shape, data_type); + CLTensor bias = create_tensor(bias_shape, data_type); + CLTensor dst = create_tensor(dst_shape, data_type); ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); // Create and configure function CLGEMMMatrixMultiplyNative gemm; - gemm.configure(&lhs, &rhs, &dst, 1.0f, lhs_info, rhs_info, gemm_info); + gemm.configure(&lhs, &rhs, &bias, &dst, 1.0f, 1.0f, lhs_info, rhs_info, gemm_info); } } // namespace @@ -150,7 +159,7 @@ TEST_SUITE(CL) TEST_SUITE(GEMMMatrixMultiplyNative) TEST_SUITE(Float) TEST_SUITE(FP32) -DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine( +DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine( m_values, n_values), k_values), @@ -158,13 +167,14 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(combi m0_values_precommit), n0_values_precommit), k0_values_precommit), -m_value, n_value, k_value, b_value, m0_value, n0_value, k0_value) + broadcast_bias_values), +m_value, n_value, k_value, b_value, m0_value, n0_value, k0_value, broadcast_bias) { - validate_configuration(m_value, n_value, k_value, b_value, m0_value, n0_value, k0_value, DataType::F32); + validate_configuration(m_value, n_value, k_value, b_value, m0_value, n0_value, k0_value, broadcast_bias, DataType::F32); } FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyNativeFixture, framework::DatasetMode::ALL, - combine(combine(combine(combine(combine(combine(combine(combine( + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_values, n_values), k_values), @@ -173,14 +183,16 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyNativeFixture, frame n0_values_precommit), k0_values_precommit), framework::dataset::make("DataType", DataType::F32)), - a_values)) + a_values), + beta_values), + broadcast_bias_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMMatrixMultiplyNativeFixture, framework::DatasetMode::NIGHTLY, - combine(combine(combine(combine(combine(combine(combine(combine( + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_values, n_values), k_values), @@ -189,14 +201,16 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMMatrixMultiplyNativeFixture, frame n0_values_nightly), k0_values_nightly), framework::dataset::make("DataType", DataType::F32)), - a_values)) + a_values), + beta_values), + broadcast_bias_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMMatrixMultiplyNative3DFixture, framework::DatasetMode::ALL, - combine(combine(combine(combine(combine(combine(combine(combine(combine( + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_w_values, m_h_values), n_values), @@ -206,14 +220,15 @@ FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMMatrixMultiplyNative3DFixture, f n0_values_precommit), k0_values_precommit), framework::dataset::make("DataType", DataType::F32)), - a_values)) + a_values), + beta_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyNative3DFixture, framework::DatasetMode::NIGHTLY, - combine(combine(combine(combine(combine(combine(combine(combine(combine( + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_w_values, m_h_values), n_values), @@ -223,7 +238,8 @@ FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyNative3DFixture, f n0_values_nightly), k0_values_nightly), framework::dataset::make("DataType", DataType::F32)), - a_values)) + a_values), + beta_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); @@ -232,7 +248,7 @@ TEST_SUITE_END() // FP32 TEST_SUITE(FP16) FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyNativeFixture, framework::DatasetMode::ALL, - combine(combine(combine(combine(combine(combine(combine(combine( + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_values, n_values), k_values), @@ -241,14 +257,16 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyNativeFixture, framew n0_values_precommit), k0_values_precommit), framework::dataset::make("DataType", DataType::F16)), - a_values)) + a_values), + beta_values), + broadcast_bias_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16); } FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMMatrixMultiplyNativeFixture, framework::DatasetMode::NIGHTLY, - combine(combine(combine(combine(combine(combine(combine(combine( + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_values, n_values), k_values), @@ -257,14 +275,16 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMMatrixMultiplyNativeFixture, framew n0_values_nightly), k0_values_nightly), framework::dataset::make("DataType", DataType::F16)), - a_values)) + a_values), + beta_values), + broadcast_bias_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16); } FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMMatrixMultiplyNative3DFixture, framework::DatasetMode::ALL, - combine(combine(combine(combine(combine(combine(combine(combine(combine( + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_w_values, m_h_values), n_values), @@ -274,14 +294,15 @@ FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMMatrixMultiplyNative3DFixture, fr n0_values_precommit), k0_values_precommit), framework::dataset::make("DataType", DataType::F16)), - a_values)) + a_values), + beta_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16); } FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyNative3DFixture, framework::DatasetMode::NIGHTLY, - combine(combine(combine(combine(combine(combine(combine(combine(combine( + combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_w_values, m_h_values), n_values), @@ -291,7 +312,8 @@ FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyNative3DFixture, fr n0_values_nightly), k0_values_nightly), framework::dataset::make("DataType", DataType::F16)), - a_values)) + a_values), + beta_values)) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16); diff --git a/tests/validation/fixtures/GEMMFixture.h b/tests/validation/fixtures/GEMMFixture.h index fcb41bb0ba..b721d841f7 100644 --- a/tests/validation/fixtures/GEMMFixture.h +++ b/tests/validation/fixtures/GEMMFixture.h @@ -684,7 +684,7 @@ class GEMMMatrixMultiplyNativeValidationFixture : public framework::Fixture { public: template - void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, DataType data_type, float alpha) + void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, DataType data_type, float alpha, float beta, bool broadcast_bias) { GEMMLHSMatrixInfo lhs_info; lhs_info.m0 = m0; @@ -697,9 +697,12 @@ public: // Set the tensor shapes for LHS and RHS matrices const TensorShape lhs_shape(k, m, batch_size); const TensorShape rhs_shape(n, k, batch_size); + const TensorShape bias_shape(n, + broadcast_bias ? 1 : m, + broadcast_bias ? 1 : batch_size); - _target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, data_type, alpha); - _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha); + _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, broadcast_bias); + _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, broadcast_bias); } protected: @@ -714,11 +717,13 @@ protected: library->fill_borders_with_garbage(tensor, distribution_inf, i); } - TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, DataType data_type, float alpha) + TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, + DataType data_type, float alpha, float beta, bool broadcast_bias) { // Create tensors - TensorType lhs = create_tensor(lhs_shape, data_type, 1); - TensorType rhs = create_tensor(rhs_shape, data_type, 1); + TensorType lhs = create_tensor(lhs_shape, data_type, 1); + TensorType rhs = create_tensor(rhs_shape, data_type, 1); + TensorType bias = create_tensor(bias_shape, data_type, 1); TensorType dst; const unsigned int M = lhs_shape[1]; @@ -727,23 +732,27 @@ protected: // Create and configure function GEMMFunctionType gemm; - gemm.configure(&lhs, &rhs, &dst, alpha, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K)); + gemm.configure(&lhs, &rhs, &bias, &dst, alpha, beta, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, 0, false, broadcast_bias)); ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); // Allocate tensors lhs.allocator()->allocate(); rhs.allocator()->allocate(); + bias.allocator()->allocate(); dst.allocator()->allocate(); ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); // Fill tensors fill(AccessorType(lhs), 0); fill(AccessorType(rhs), 1); + fill(AccessorType(bias), 2); // Compute GEMM gemm.run(); @@ -751,7 +760,7 @@ protected: return dst; } - SimpleTensor compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha) + SimpleTensor compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, bool broadcast_bias) { TensorShape dst_shape = lhs_shape; dst_shape[0] = rhs_shape[0]; @@ -760,13 +769,27 @@ protected: // Create reference SimpleTensor lhs{ lhs_shape, data_type, 1 }; SimpleTensor rhs{ rhs_shape, data_type, 1 }; - SimpleTensor c{ dst_shape, data_type, 1 }; + SimpleTensor bias{ dst_shape, data_type, 1 }; + + const int n = rhs_shape[0]; + const int m = lhs_shape[1]; + const int batch_size = lhs_shape[2]; // Fill reference fill(lhs, 0); fill(rhs, 1); + fill(bias, 2); - return reference::gemm(lhs, rhs, c, alpha, 0.0f); + if(broadcast_bias) + { + // In case of broadcast, we need simply copy the first into the following "M" ones + for(int i = 1; i < m * batch_size; i++) + { + memcpy(bias.data() + i * n, bias.data(), n * sizeof(T)); + } + } + + return reference::gemm(lhs, rhs, bias, alpha, beta); } TensorType _target{}; @@ -778,7 +801,7 @@ class GEMMMatrixMultiplyNative3DValidationFixture : public framework::Fixture { public: template - void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, DataType data_type, float alpha) + void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, DataType data_type, float alpha, float beta) { GEMMLHSMatrixInfo lhs_info; lhs_info.m0 = m0; @@ -794,9 +817,10 @@ public: // Set the tensor shapes for LHS and RHS matrices const TensorShape lhs_shape(k, m, batch_size); const TensorShape rhs_shape(n, k, batch_size); + const TensorShape bias_shape(n, 1, 1); - _target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, data_type, alpha, m_h); - _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha, m_h); + _target = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, m_h); + _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, m_h); } protected: @@ -807,13 +831,13 @@ protected: library->fill(tensor, distribution, i); } - TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, DataType data_type, float alpha, - unsigned int m_h) + TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, + DataType data_type, float alpha, float beta, unsigned int m_h) { // Create tensors - TensorType lhs = create_tensor(lhs_shape, data_type, 1); - TensorType rhs = create_tensor(rhs_shape, data_type, 1); - TensorType rhs_reshaped; + TensorType lhs = create_tensor(lhs_shape, data_type, 1); + TensorType rhs = create_tensor(rhs_shape, data_type, 1); + TensorType bias = create_tensor(bias_shape, data_type, 1); TensorType dst; const unsigned int M = lhs_shape[1]; @@ -824,25 +848,27 @@ protected: // Create and configure function GEMMFunctionType gemm; - gemm.configure(&lhs, &rhs, &dst, alpha, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, m_h)); + gemm.configure(&lhs, &rhs, &bias, &dst, alpha, beta, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, m_h, false, true)); ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); // Allocate tensors lhs.allocator()->allocate(); rhs.allocator()->allocate(); - rhs_reshaped.allocator()->allocate(); + bias.allocator()->allocate(); dst.allocator()->allocate(); ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS); - ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); // Fill tensors fill(AccessorType(lhs), 0); fill(AccessorType(rhs), 1); + fill(AccessorType(bias), 2); // Compute GEMM gemm.run(); @@ -850,7 +876,7 @@ protected: return dst; } - SimpleTensor compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha, unsigned int m_h) + SimpleTensor compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, unsigned int m_h) { TensorShape dst_shape = lhs_shape; dst_shape.set(0, rhs_shape[0]); @@ -861,13 +887,24 @@ protected: // Create reference SimpleTensor lhs{ lhs_shape, data_type, 1 }; SimpleTensor rhs{ rhs_shape, data_type, 1 }; - SimpleTensor c{ dst_shape, data_type, 1 }; + SimpleTensor bias{ dst_shape, data_type, 1 }; + + const int n = rhs_shape[0]; + const int m = lhs_shape[1]; + const int batch_size = lhs_shape[2]; // Fill reference fill(lhs, 0); fill(rhs, 1); + fill(bias, 2); - return reference::gemm(lhs, rhs, c, alpha, 0.0f); + // In case of broadcast, we need simply copy the first into the following "M" ones + for(int i = 1; i < m * batch_size; i++) + { + memcpy(bias.data() + i * n, bias.data(), n * sizeof(T)); + } + + return reference::gemm(lhs, rhs, bias, alpha, beta); } TensorType _target{}; -- cgit v1.2.1