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/fixtures/GEMMFixture.h | 85 +++++++++++++++++++++++---------- 1 file changed, 61 insertions(+), 24 deletions(-) (limited to 'tests/validation/fixtures/GEMMFixture.h') 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