From b3204e76712b8f66218affdd4ad44ec221c6dcb6 Mon Sep 17 00:00:00 2001 From: giuros01 Date: Mon, 1 Apr 2019 13:50:22 +0100 Subject: COMPMID-2093: Implement CLGEMMNative Change-Id: I347130f6b5ae8d08b7c5c101b523b158565874a1 Signed-off-by: giuros01 Reviewed-on: https://review.mlplatform.org/c/1114 Comments-Addressed: Arm Jenkins Reviewed-by: Gian Marco Iodice Tested-by: Arm Jenkins --- tests/validation/fixtures/GEMMFixture.h | 196 ++++++++++++++++++++++++++++++++ 1 file changed, 196 insertions(+) (limited to 'tests/validation/fixtures/GEMMFixture.h') diff --git a/tests/validation/fixtures/GEMMFixture.h b/tests/validation/fixtures/GEMMFixture.h index 77d2ca61fb..b7976104aa 100644 --- a/tests/validation/fixtures/GEMMFixture.h +++ b/tests/validation/fixtures/GEMMFixture.h @@ -490,6 +490,100 @@ protected: SimpleTensor _reference{}; }; +template +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) + { + GEMMLHSMatrixInfo lhs_info; + lhs_info.m0 = m0; + lhs_info.k0 = k0; + + GEMMRHSMatrixInfo rhs_info; + rhs_info.n0 = n0; + rhs_info.k0 = k0; + + // 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); + + _target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, data_type, alpha); + _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha); + } + +protected: + template + void fill(U &&tensor, int i) + { + std::uniform_real_distribution<> distribution(-1.0f, 1.0f); + library->fill(tensor, distribution, i); + + // Fill border with infinity in order to check the presence of NaN values (i.e. inf * 0) + std::uniform_real_distribution<> distribution_inf(std::numeric_limits::infinity(), std::numeric_limits::infinity()); + 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) + { + // Create tensors + TensorType lhs = create_tensor(lhs_shape, data_type, 1); + TensorType rhs = create_tensor(rhs_shape, data_type, 1); + TensorType dst; + + const unsigned int M = lhs_shape[1]; + const unsigned int N = rhs_shape[0]; + const unsigned int K = lhs_shape[0]; + + // Create and configure function + GEMMFunctionType gemm; + gemm.configure(&lhs, &rhs, &dst, alpha, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K)); + + ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + lhs.allocator()->allocate(); + rhs.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(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensors + fill(AccessorType(lhs), 0); + fill(AccessorType(rhs), 1); + + // Compute GEMM + gemm.run(); + + return dst; + } + + SimpleTensor compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha) + { + TensorShape dst_shape = lhs_shape; + dst_shape[0] = rhs_shape[0]; + dst_shape[1] = lhs_shape[1]; + + // Create reference + SimpleTensor lhs{ lhs_shape, data_type, 1 }; + SimpleTensor rhs{ rhs_shape, data_type, 1 }; + SimpleTensor c{ dst_shape, data_type, 1 }; + + // Fill reference + fill(lhs, 0); + fill(rhs, 1); + + return reference::gemm(lhs, rhs, c, alpha, 0.0f); + } + + TensorType _target{}; + SimpleTensor _reference{}; +}; + template class GEMMMatrixMultiplyReshapedOnlyRHS3DValidationFixture : public framework::Fixture { @@ -597,6 +691,108 @@ protected: TensorType _target{}; SimpleTensor _reference{}; }; + +template +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) + { + GEMMLHSMatrixInfo lhs_info; + lhs_info.m0 = m0; + lhs_info.k0 = k0; + + GEMMRHSMatrixInfo rhs_info; + rhs_info.n0 = n0; + rhs_info.k0 = k0; + + // In case of GEMM3D, m is the product between m_w and m_h + const unsigned int m = m_w * m_h; + + // 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); + + _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); + } + +protected: + template + void fill(U &&tensor, int i) + { + std::uniform_real_distribution<> distribution(-1.0f, 1.0f); + 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) + { + // Create tensors + TensorType lhs = create_tensor(lhs_shape, data_type, 1); + TensorType rhs = create_tensor(rhs_shape, data_type, 1); + TensorType rhs_reshaped; + TensorType dst; + + const unsigned int M = lhs_shape[1]; + const unsigned int N = rhs_shape[0]; + const unsigned int K = lhs_shape[0]; + + // The output tensor will be auto-initialized within the function + + // Create and configure function + GEMMFunctionType gemm; + gemm.configure(&lhs, &rhs, &dst, alpha, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, m_h)); + + ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + lhs.allocator()->allocate(); + rhs.allocator()->allocate(); + rhs_reshaped.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(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensors + fill(AccessorType(lhs), 0); + fill(AccessorType(rhs), 1); + + // Compute GEMM + gemm.run(); + + return dst; + } + + SimpleTensor compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha, unsigned int m_h) + { + TensorShape dst_shape = lhs_shape; + dst_shape.set(0, rhs_shape[0]); + dst_shape.set(1, lhs_shape[1] / m_h); + dst_shape.set(2, m_h); + dst_shape.set(3, lhs_shape[2]); + + // Create reference + SimpleTensor lhs{ lhs_shape, data_type, 1 }; + SimpleTensor rhs{ rhs_shape, data_type, 1 }; + SimpleTensor c{ dst_shape, data_type, 1 }; + + // Fill reference + fill(lhs, 0); + fill(rhs, 1); + + return reference::gemm(lhs, rhs, c, alpha, 0.0f); + } + + TensorType _target{}; + SimpleTensor _reference{}; +}; + } // namespace validation } // namespace test } // namespace arm_compute -- cgit v1.2.1