Add match_matrix_tensor op (#18525)
* add matrch_matrix_tensor op test=develop * fix ignore unittest if with_mkl=off test=develop * clean code and rm is_test param test=develop * modify API.spec test=develop * rm useless code in search_compute.h test=develop * modify api.spec test=develop * modify default_grad.spec test=develop * Add API test code test=develop * clean code in search_computer.h * modify PADDLE_ENFORCE and clean search_compute.h test=develop * fix code style test=developpadding_in_crf
Aurelius84
6 years ago
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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class MatchMatrixTensorOP : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override;
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};
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class MatchMatrixTensorOpGrad : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override;
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};
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class MatchMatrixTensorOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override;
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};
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} // namespace operators
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} // namespace paddle
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/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include <immintrin.h>
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#include <cfloat>
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#include <cmath>
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#include <cstring>
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#include "paddle/fluid/operators/math/blas.h"
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#include "paddle/fluid/operators/math/math_function.h"
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#include "paddle/fluid/platform/dynload/mklml.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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using LoDTensor = framework::LoDTensor;
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using LoD = framework::LoD;
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template <typename DeviceContext, typename T>
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void call_gemm(const math::BlasT<DeviceContext, T>& blas,
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const CBLAS_TRANSPOSE TransA, const CBLAS_TRANSPOSE TransB,
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const int M, const int N, const int K, const T alpha, const T* A,
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const T* B, const T beta, T* C) {
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int lda = (TransA == CblasNoTrans) ? K : M;
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int ldb = (TransB == CblasNoTrans) ? N : K;
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blas.GEMM(TransA, TransB, M, N, K, alpha, A, lda, B, ldb, beta, C, N);
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}
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template <typename T>
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void call_gemm(const framework::ExecutionContext& ctx,
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const CBLAS_TRANSPOSE TransA, const CBLAS_TRANSPOSE TransB,
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const int M, const int N, const int K, const T alpha, const T* A,
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const T* B, const T beta, T* C) {
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int lda = (TransA == CblasNoTrans) ? K : M;
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int ldb = (TransB == CblasNoTrans) ? N : K;
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auto blas = math::GetBlas<platform::CPUDeviceContext, T>(ctx);
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blas.GEMM(TransA, TransB, M, N, K, alpha, A, lda, B, ldb, beta, C, N);
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}
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template <typename DeviceContext, typename T>
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void call_gemm_with_lda(const math::BlasT<DeviceContext, T>& blas,
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const CBLAS_TRANSPOSE TransA,
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const CBLAS_TRANSPOSE TransB, const int M, const int N,
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const int K, const T alpha, const T* A, const T* B,
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const T beta, T* C, int lda) {
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int ldb = (TransB == CblasNoTrans) ? N : K;
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blas.GEMM(TransA, TransB, M, N, K, alpha, A, lda, B, ldb, beta, C, N);
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}
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template <typename T>
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void call_gemm_batched(const framework::ExecutionContext& ctx,
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const CBLAS_TRANSPOSE TransA,
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const CBLAS_TRANSPOSE TransB, const int M, const int N,
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const int K, const T alpha, const T** A, const T** B,
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const T beta, T** C, const int batch) {
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for (int i = 0; i < batch; ++i) {
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call_gemm(ctx, TransA, TransB, M, N, K, alpha, A[i], B[i], beta, C[i]);
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}
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}
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#ifndef TYPE_USE_FLOAT
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#define TYPE_USE_FLOAT
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#endif
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#ifndef USE_SSE
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#define USE_SSE
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#endif
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#if defined(TYPE_USE_FLOAT)
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#define __m256x __m256
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#define __m128x __m128
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static const unsigned int AVX_STEP_SIZE = 8;
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static const unsigned int SSE_STEP_SIZE = 4;
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static const unsigned int AVX_CUT_LEN_MASK = 7U;
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static const unsigned int SSE_CUT_LEN_MASK = 3U;
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#define _mm256_mul_px _mm256_mul_ps
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#define _mm256_add_px _mm256_add_ps
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#define _mm256_load_px _mm256_loadu_ps
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#define _mm256_store_px _mm256_storeu_ps
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#define _mm256_broadcast_sx _mm256_broadcast_ss
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#define _mm_add_px _mm_add_ps
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#define _mm_mul_px _mm_mul_ps
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#define _mm_load_px _mm_loadu_ps
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#define _mm_store_px _mm_storeu_ps
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#define _mm_load1_px _mm_load1_ps
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#endif
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template <typename T>
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inline void sse_axpy(const T* x, T* y, size_t len, const T alpha) {
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unsigned int jjj, lll;
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jjj = lll = 0;
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#if defined(USE_AVX)
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lll = len & ~AVX_CUT_LEN_MASK;
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__m256x mm_alpha = _mm256_broadcast_sx(&alpha);
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for (jjj = 0; jjj < lll; jjj += AVX_STEP_SIZE) {
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_mm256_store_px(
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y + jjj,
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_mm256_add_px(_mm256_load_px(y + jjj),
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_mm256_mul_px(mm_alpha, _mm256_load_px(x + jjj))));
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}
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#elif defined(USE_SSE)
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lll = len & ~SSE_CUT_LEN_MASK;
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__m128x mm_alpha = _mm_load1_px(&alpha);
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for (jjj = 0; jjj < lll; jjj += SSE_STEP_SIZE) {
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_mm_store_px(y + jjj,
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_mm_add_px(_mm_load_px(y + jjj),
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_mm_mul_px(mm_alpha, _mm_load_px(x + jjj))));
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}
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#endif
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for (; jjj < len; jjj++) {
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y[jjj] += alpha * x[jjj];
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}
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}
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} // namespace operators
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} // namespace paddle
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@ -0,0 +1,132 @@
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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import numpy as np
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from op_test import OpTest
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import paddle.fluid as fluid
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class TestMatchMatrixTensorOp(OpTest):
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def setUp(self):
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self.init_op_type()
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self.set_data()
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self.compute()
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def init_op_type(self):
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self.op_type = "match_matrix_tensor"
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def set_data(self):
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ix, iy, h, dim_t = [5, 8, 3, 4]
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x_lod = [[1, 2, 2]]
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y_lod = [[3, 1, 4]]
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self.init_data(ix, x_lod, iy, y_lod, h, dim_t)
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def init_data(self, ix, x_lod, iy, y_lod, h, dim_t):
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x_data = np.random.random((ix, h)).astype('float32')
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y_data = np.random.random((iy, h)).astype('float32')
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w_data = np.random.random((h, dim_t, h)).astype('float32')
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self.inputs = {'X': (x_data, x_lod), 'Y': (y_data, y_lod), 'W': w_data}
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self.attrs = {'dim_t': dim_t}
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def compute(self):
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x_data, x_lod = self.inputs['X']
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y_data, y_lod = self.inputs['Y']
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# [k, dim_t, k] -> [dim_t, k, k]
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w_data = self.inputs['W'].transpose(1, 0, 2)
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out = np.zeros((0, 1), dtype=x_data.dtype)
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# for x*w
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tmp = np.zeros((0, 1), dtype=x_data.dtype)
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out_lod = [[]]
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tmp_lod = [[]]
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x_offset, y_offset = 0, 0
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for idx in range(len(x_lod[0])):
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x_len = x_lod[0][idx]
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y_len = y_lod[0][idx]
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x_sub = x_data[x_offset:(x_offset + x_len), :]
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y_sub = y_data[y_offset:(y_offset + y_len), :]
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tmp_sub = np.dot(x_sub, w_data)
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tmp = np.vstack((tmp, tmp_sub.reshape(tmp_sub.size, 1)))
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out_sub = np.dot(tmp_sub, y_sub.T).transpose(1, 0, 2)
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out_lod[0].append(out_sub.size)
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out = np.vstack((out, out_sub.reshape(out_sub.size, 1)))
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x_offset += x_len
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y_offset += y_len
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self.outputs = {'Out': (out, out_lod), 'Tmp': tmp}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.005)
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class TestMatchMatrixTensorOpCase1(TestMatchMatrixTensorOp):
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def set_data(self):
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ix, iy, h, dim_t = [5, 8, 16, 4]
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x_lod = [[5]]
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y_lod = [[8]]
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self.init_data(ix, x_lod, iy, y_lod, h, dim_t)
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class TestMatchMatrixTensorOpCase2(TestMatchMatrixTensorOp):
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def set_data(self):
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ix, iy, h, dim_t = [7, 8, 1, 4]
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x_lod = [[2, 3, 2]]
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y_lod = [[3, 1, 4]]
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self.init_data(ix, x_lod, iy, y_lod, h, dim_t)
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class TestMatchMatrixTensorOpCase3(TestMatchMatrixTensorOp):
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def set_data(self):
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ix, iy, h, dim_t = [5, 9, 32, 1]
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x_lod = [[1, 2, 2]]
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y_lod = [[3, 2, 4]]
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self.init_data(ix, x_lod, iy, y_lod, h, dim_t)
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class TestMatchMatrixTensorOpCase4(TestMatchMatrixTensorOp):
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def set_data(self):
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ix, iy, h, dim_t = [8, 12, 16, 5]
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x_lod = [[1, 2, 3, 1, 1]]
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y_lod = [[3, 2, 4, 1, 2]]
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self.init_data(ix, x_lod, iy, y_lod, h, dim_t)
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def test_api(self):
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x_lod_tensor = fluid.layers.data(name='x', shape=[10], lod_level=1)
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y_lod_tensor = fluid.layers.data(name='y', shape=[10], lod_level=1)
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out, out_tmp = fluid.layers.match_matrix_tensor(
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x=x_lod_tensor, y=y_lod_tensor, channel_num=3)
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place = fluid.CPUPlace()
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x_data = np.random.rand(7, 10).astype('float32')
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y_data = np.random.rand(9, 10).astype('float32')
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x = fluid.create_lod_tensor(x_data, [[2, 5]], place)
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y = fluid.create_lod_tensor(y_data, [[3, 6]], place)
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exe = fluid.Executor(place=place)
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exe.run(fluid.default_startup_program())
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ret = exe.run(feed={'x': x,
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'y': y},
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fetch_list=[out],
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return_numpy=False)
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if __name__ == '__main__':
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unittest.main()
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