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Add paddle.linalg.solve OP (PaddlePaddle#35715)
* Add linalg.solve op, test=develop * Fix a bug caused by accidental deletion * updated description and fix a bug: missing a comma * Add linalg.solve op, test=develop * updated solve op backward logic * updated solve op backward logic again * Add linalg.solve Op, test=develop * Updated and modified to fit CI requirements * Fix a bug * 1)Add more test cases; 2)Fix a wrong usage in reduces operation; 3)Remove redundant code * Remove redundant comments * 1)Removed redundant code; 2)Updated to enhance code robustness * Removed redundant code * Updated API documents
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| /* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. */ | ||
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| #include "paddle/fluid/operators/math/matrix_solve.h" | ||
| #include "Eigen/Core" | ||
| #include "Eigen/LU" | ||
| #include "paddle/fluid/operators/math/blas.h" | ||
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| namespace paddle { | ||
| namespace operators { | ||
| namespace math { | ||
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| template <typename T> | ||
| class MatrixSolveFunctor<platform::CPUDeviceContext, T> { | ||
| public: | ||
| void operator()(const platform::CPUDeviceContext& dev_ctx, | ||
| const framework::Tensor& a, const framework::Tensor& b, | ||
| framework::Tensor* out) { | ||
| compute_solve_eigen<platform::CPUDeviceContext, T>(dev_ctx, a, b, out); | ||
| } | ||
| }; | ||
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| template class MatrixSolveFunctor<platform::CPUDeviceContext, float>; | ||
| template class MatrixSolveFunctor<platform::CPUDeviceContext, double>; | ||
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| } // namespace math | ||
| } // namespace operators | ||
| } // namespace paddle |
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| /* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. */ | ||
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| #include "paddle/fluid/operators/math/matrix_solve.h" | ||
| #include "paddle/fluid/framework/tensor_util.h" | ||
| #include "paddle/fluid/operators/math/blas.h" | ||
| #include "paddle/fluid/operators/math/math_function.h" | ||
| #include "paddle/fluid/operators/solve_op.h" | ||
| #include "paddle/fluid/platform/device_context.h" | ||
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| namespace paddle { | ||
| namespace platform { | ||
| class CUDADeviceContext; | ||
| } // namespace platform | ||
| } // namespace paddle | ||
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| namespace paddle { | ||
| namespace operators { | ||
| namespace math { | ||
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| template <typename DeviceContext, typename T> | ||
| class MatrixSolveFunctor; | ||
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| template <typename T> | ||
| class MatrixSolveFunctor<platform::CUDADeviceContext, T> { | ||
| public: | ||
| void operator()(const platform::CUDADeviceContext& context, | ||
| const framework::Tensor& a, const framework::Tensor& b, | ||
| framework::Tensor* out) { | ||
| #ifndef PADDLE_WITH_HIP | ||
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| // solve the equation: Ax = B, | ||
| // use cuBlas cublas<S/D>getrfBatched funcion to performs the LU | ||
| // factorization of each matrix A, | ||
| // and then use cuBlas cublas<S/D>getriBatched function to solve the | ||
| // equation after LU factorization. | ||
| // ref: | ||
| // https://docs.nvidia.com/cuda/cublas/index.html#cublas-lt-t-gt-getrfbatched | ||
| const auto& a_dims = a.dims(); | ||
| const int a_rank = a_dims.size(); | ||
| int n = a_dims[a_rank - 1]; | ||
| int lda = n; | ||
| int batch_size = a_rank > 2 ? a.numel() / (n * n) : 1; | ||
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| const auto& b_dims = b.dims(); | ||
| const int b_rank = b_dims.size(); | ||
| int nrhs = b_dims[b_rank - 1]; | ||
| int ldb = b_dims[b_rank - 2]; | ||
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| // make sure the out dims is right | ||
| out->Resize(b_dims); | ||
| out->mutable_data<T>(context.GetPlace()); | ||
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| // copy input A to a temporary tensor tmp_a, | ||
| // LU factorization, written back to original matrix A, so in the beginning, | ||
| // it's necessary to create a temporary tensor tmp_a. | ||
| Tensor tmp_a(a.type()); | ||
| tmp_a.Resize(a.dims()); | ||
| tmp_a.mutable_data<T>(context.GetPlace()); | ||
| TensorCopy(a, context.GetPlace(), &tmp_a); | ||
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| // copy input B to a temporary tensor tmp_b, and transpose tmp_b, | ||
| // because cuBlas assumes column-major while Paddle uses row-majar. | ||
| Tensor tmp_b(b.type()); | ||
| const auto& new_dims_vec = getNewDimsVec(b_dims); | ||
| tmp_b.Resize(framework::make_ddim(new_dims_vec)); | ||
| tmp_b.mutable_data<T>(context.GetPlace()); | ||
| math::TransposeNormal<platform::CUDADeviceContext, T> trans; | ||
| std::vector<int> new_axis = getNewAxis(b_rank); | ||
| trans(context, b, &tmp_b, new_axis); | ||
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| const T* a_data_in_gpu = tmp_a.data<T>(); | ||
| const T* b_data_in_gpu = tmp_b.data<T>(); | ||
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| std::vector<const T*> cpu_ptrs(batch_size * 2); | ||
| for (int i = 0; i < batch_size; ++i) { | ||
| cpu_ptrs[i] = a_data_in_gpu + i * n * n; | ||
| cpu_ptrs[i + batch_size] = b_data_in_gpu + i * n * nrhs; | ||
| } | ||
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| // Copy the addresses of A and tmp_b from host to device. | ||
| memory::allocation::AllocationPtr tmp_gpu_ptrs_data = | ||
| memory::Alloc(context, cpu_ptrs.size() * sizeof(T*)); | ||
| memory::Copy(BOOST_GET_CONST(platform::CUDAPlace, context.GetPlace()), | ||
| tmp_gpu_ptrs_data->ptr(), platform::CPUPlace(), | ||
| static_cast<void*>(cpu_ptrs.data()), | ||
| cpu_ptrs.size() * sizeof(T*), context.stream()); | ||
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| T** gpu_tmp_b_ptrs = | ||
| reinterpret_cast<T**>(tmp_gpu_ptrs_data->ptr()) + batch_size; | ||
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| // Allocate device memory for BatchedGETRF's info and pivots. | ||
| int num_ints = n < 32 ? batch_size : batch_size * (n + 1); | ||
| memory::allocation::AllocationPtr tmp_gpu_info_data = | ||
| memory::Alloc(context, num_ints * sizeof(int)); | ||
| int* gpu_info_ptr = reinterpret_cast<int*>(tmp_gpu_info_data->ptr()); | ||
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| auto blas = math::GetBlas<platform::CUDADeviceContext, T>(context); | ||
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| // only for singular checking | ||
| std::vector<int> info; | ||
| info.resize(batch_size); | ||
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| int* gpu_pivot_ptr = | ||
| reinterpret_cast<int*>(tmp_gpu_info_data->ptr()) + batch_size; | ||
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| // This function performs the LU factorization of each matrix A by the | ||
| // equation A = L * U. L and U are written back to original matrix A, | ||
| // and diagonal elements of L are discarded. | ||
| blas.BatchedGETRF(n, reinterpret_cast<T**>(tmp_gpu_ptrs_data->ptr()), | ||
| gpu_pivot_ptr, gpu_info_ptr, batch_size); | ||
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| // check whether BatchedGETRF is executed successfully or not | ||
| memory::Copy(platform::CPUPlace(), info.data(), | ||
| BOOST_GET_CONST(platform::CUDAPlace, context.GetPlace()), | ||
| gpu_info_ptr, sizeof(int) * batch_size, context.stream()); | ||
| for (int i = 0; i < batch_size; ++i) { | ||
| PADDLE_ENFORCE_EQ(info[i], 0, | ||
| platform::errors::PreconditionNotMet( | ||
| "For batch [%d]: U(%d, %d) is zero, singular U. " | ||
| "Please check the matrix value and change it to a " | ||
| "non-singular matrix", | ||
| i, info[i], info[i])); | ||
| } | ||
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| // hold the result code from BatchedGETRS | ||
| int host_info = 0; | ||
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| // to solve the equation after LU factorization | ||
| CBLAS_TRANSPOSE transA = CblasTrans; | ||
| blas.BatchedGETRS( | ||
| transA, n, nrhs, reinterpret_cast<const T**>(tmp_gpu_ptrs_data->ptr()), | ||
| lda, gpu_pivot_ptr, gpu_tmp_b_ptrs, ldb, &host_info, batch_size); | ||
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| // check whether BatchedGETRS is executed successfully or not | ||
| PADDLE_ENFORCE_EQ(host_info, 0, | ||
| platform::errors::InvalidArgument( | ||
| "The [%d]'th argument to cublas*getrsBatched had " | ||
| "an illegal value.", | ||
| -host_info)); | ||
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| // transpose tmp_b to get the final result in row-major form. | ||
| math::TransposeNormal<platform::CUDADeviceContext, T> trans2; | ||
| trans2(context, tmp_b, out, new_axis); | ||
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| #else | ||
| compute_solve_eigen<platform::CUDADeviceContext, T>(context, a, b, out); | ||
| #endif | ||
| } | ||
| }; | ||
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| template class MatrixSolveFunctor<platform::CUDADeviceContext, float>; | ||
| template class MatrixSolveFunctor<platform::CUDADeviceContext, double>; | ||
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| } // namespace math | ||
| } // namespace operators | ||
| } // namespace paddle |
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