From 62251f71792c06dbe4c9d1985816ba15bcad14e4 Mon Sep 17 00:00:00 2001 From: Gian Marco Iodice Date: Mon, 11 Mar 2019 16:07:12 +0000 Subject: COMPMID-2002: Implement CLGEMMLowpMatrixMultiplyReshapedOnlyRHS - Transposed Change-Id: I3907d151107766dc34749fe5710d7219e810b39f Signed-off-by: Gian Marco Iodice Reviewed-on: https://review.mlplatform.org/c/875 Comments-Addressed: Arm Jenkins Reviewed-by: Giuseppe Rossini Tested-by: Arm Jenkins Reviewed-by: Georgios Pinitas --- tests/validation/fixtures/GEMMLowpFixture.h | 208 ++++++++++++++++++++++++++++ 1 file changed, 208 insertions(+) (limited to 'tests/validation/fixtures') diff --git a/tests/validation/fixtures/GEMMLowpFixture.h b/tests/validation/fixtures/GEMMLowpFixture.h index 90a4b5cf40..5793ebdd2d 100644 --- a/tests/validation/fixtures/GEMMLowpFixture.h +++ b/tests/validation/fixtures/GEMMLowpFixture.h @@ -611,6 +611,214 @@ protected: TensorType _target{}; SimpleTensor _reference{}; }; + +template +class GEMMLowpMatrixMultiplyReshapedOnlyRHSValidationFixture : 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, unsigned int h0, bool interleave_rhs, bool transpose_rhs) + { + GEMMLHSMatrixInfo lhs_info; + lhs_info.m0 = m0; + lhs_info.k0 = k0; + + GEMMRHSMatrixInfo rhs_info; + rhs_info.n0 = n0; + rhs_info.k0 = k0; + rhs_info.h0 = h0; + rhs_info.interleave = interleave_rhs; + rhs_info.transpose = transpose_rhs; + + // 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); + _reference = compute_reference(lhs_shape, rhs_shape); + } + +protected: + template + void fill(U &&tensor, int i) + { + // Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path + std::uniform_int_distribution<> distribution(1, 254); + library->fill(tensor, distribution, i); + } + + TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info) + { + // Create tensors + TensorType lhs = create_tensor(lhs_shape, DataType::QASYMM8, 1); + TensorType rhs = create_tensor(rhs_shape, DataType::QASYMM8, 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 + ReshapeRHSFunctionType reshape_rhs; + GEMMFunctionType gemm; + reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info); + gemm.configure(&lhs, &rhs_reshaped, &dst, 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(); + 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 + reshape_rhs.run(); + gemm.run(); + + return dst; + } + + SimpleTensor compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape) + { + TensorShape dst_shape = lhs_shape; + dst_shape[0] = rhs_shape[0]; + dst_shape[1] = lhs_shape[1]; + + // Create reference + SimpleTensor lhs{ lhs_shape, DataType::QASYMM8, 1 }; + SimpleTensor rhs{ rhs_shape, DataType::QASYMM8, 1 }; + + // Fill reference + fill(lhs, 0); + fill(rhs, 1); + + return reference::gemmlowp_matrix_multiply_core(lhs, rhs, dst_shape, 0, 0); + } + + TensorType _target{}; + SimpleTensor _reference{}; +}; + +template +class GEMMLowpMatrixMultiplyReshapedOnlyRHS3DValidationFixture : 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, unsigned int h0, + bool interleave_rhs, bool transpose_rhs) + { + GEMMLHSMatrixInfo lhs_info; + lhs_info.m0 = m0; + lhs_info.k0 = k0; + + GEMMRHSMatrixInfo rhs_info; + rhs_info.n0 = n0; + rhs_info.k0 = k0; + rhs_info.h0 = h0; + rhs_info.interleave = interleave_rhs; + rhs_info.transpose = transpose_rhs; + + // 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, m_h); + _reference = compute_reference(lhs_shape, rhs_shape, m_h); + } + +protected: + template + void fill(U &&tensor, int i) + { + // Between 1 and 254 in order to avoid having -128 and 128 for the DOT product path + std::uniform_int_distribution<> distribution(1, 254); + library->fill(tensor, distribution, i); + } + + TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, unsigned int m_h) + { + // Create tensors + TensorType lhs = create_tensor(lhs_shape, DataType::QASYMM8, 1); + TensorType rhs = create_tensor(rhs_shape, DataType::QASYMM8, 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 + ReshapeRHSFunctionType reshape_rhs; + GEMMFunctionType gemm; + reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info); + gemm.configure(&lhs, &rhs_reshaped, &dst, 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 + reshape_rhs.run(); + gemm.run(); + + return dst; + } + + SimpleTensor compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, 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, DataType::QASYMM8, 1 }; + SimpleTensor rhs{ rhs_shape, DataType::QASYMM8, 1 }; + + // Fill reference + fill(lhs, 0); + fill(rhs, 1); + + return reference::gemmlowp_matrix_multiply_core(lhs, rhs, dst_shape, 0, 0); + } + + TensorType _target{}; + SimpleTensor _reference{}; +}; } // namespace validation } // namespace test } // namespace arm_compute -- cgit v1.2.1