wangmeng28
8 years ago
commit
a8526f105d
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/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
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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/operators/math/math_function.h"
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namespace paddle {
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namespace operators {
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namespace math {
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// Implements the logic of numpy matmul:
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// https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html
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//
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// but allowing also for a, b to be transposed
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//
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// Both a & b can be 1- to 3-dimensional. Higher rank tensors are not supported
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// yet.
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template <typename Place, typename T>
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class MatMulFunctor {
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public:
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void operator()(const platform::DeviceContext& context,
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const framework::Tensor& a, bool trans_a,
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const framework::Tensor& b, bool trans_b, T alpha,
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framework::Tensor* out, T beta) {
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auto dim_a = a.dims();
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auto dim_b = b.dims();
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PADDLE_ENFORCE(a.place() == b.place() && b.place() == out->place(),
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"Tensors must all be in the same place.");
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PADDLE_ENFORCE_GE(dim_a.size(), 1,
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"Input tensor a must be at least 1-dimensional.");
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PADDLE_ENFORCE_GE(dim_b.size(), 1,
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"Input tensor b must be at least 1-dimensional.");
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PADDLE_ENFORCE_LE(dim_a.size(), 3,
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"Input tensor a must be at most 3-dimensional.");
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PADDLE_ENFORCE_LE(dim_b.size(), 3,
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"Input tensor b must be at most 3-dimensional.");
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int M = 0, N = 0, kA = 0, kB = 0, batchCountA = 0, batchCountB = 0,
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strideA = 0, strideB = 0;
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switch (dim_a.size()) {
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case 1:
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// similar to np.matmul:
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// prepend dimension 1 (no transpose) or append dimension 1 (transpose)
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M = trans_a ? dim_a[0] : 1;
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kA = trans_a ? 1 : dim_a[0];
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break;
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case 2:
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M = trans_a ? dim_a[1] : dim_a[0];
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kA = trans_a ? dim_a[0] : dim_a[1];
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break;
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case 3:
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batchCountA = dim_a[0];
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M = trans_a ? dim_a[2] : dim_a[1];
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kA = trans_a ? dim_a[1] : dim_a[2];
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strideA = M * kA;
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break;
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default:
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assert(false);
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}
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switch (dim_b.size()) {
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case 1:
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// similar to np.matmul:
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// append dimension 1 (no transpose) or prepend dimension 1 (transpose)
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kB = trans_b ? 1 : dim_b[0];
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N = trans_b ? dim_b[0] : 1;
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break;
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case 2:
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kB = trans_b ? dim_b[1] : dim_b[0];
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N = trans_b ? dim_b[0] : dim_b[1];
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break;
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case 3:
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batchCountB = dim_b[0];
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kB = trans_b ? dim_b[2] : dim_b[1];
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N = trans_b ? dim_b[1] : dim_b[2];
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strideB = kB * N;
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break;
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default:
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assert(false);
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}
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PADDLE_ENFORCE_EQ(
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kA, kB,
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"First matrix's width must be equal with second matrix's height.");
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if (batchCountA && batchCountB) {
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PADDLE_ENFORCE_EQ(
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batchCountA, batchCountB,
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"When input tensors a and b are both batched, they must have the "
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"same batch dimension.");
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}
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int batchCount = std::max(batchCountA, batchCountB);
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CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
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CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
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if (!batchCount) {
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// regular matrix multiplication
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gemm<Place, T>(context, transA, transB, M, N, kA, alpha, a.data<T>(),
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b.data<T>(), beta, out->data<T>());
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} else {
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// batched matrix multiplication
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batched_gemm<Place, T>(context, transA, transB, M, N, kA, alpha,
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a.data<T>(), b.data<T>(), beta, out->data<T>(),
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batchCount, strideA, strideB);
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}
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}
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};
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} // namespace math
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} // namespace operators
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} // namespace paddle
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@ -0,0 +1,208 @@
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/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
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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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#include "paddle/operators/matmul_op.h"
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namespace paddle {
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namespace operators {
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using framework::Tensor;
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class MatMulOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(framework::InferShapeContext* context) const override {
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PADDLE_ENFORCE(context->HasInput("X"),
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"Input(X) of MatMulOp should not be null.");
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PADDLE_ENFORCE(context->HasInput("Y"),
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"Input(Y) of MatMulOp should not be null.");
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PADDLE_ENFORCE(context->HasOutput("Out"),
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"Output(Out) of MatMulOp should not be null.");
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auto dim_x = context->GetInputDim("X");
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auto dim_y = context->GetInputDim("Y");
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bool transpose_x = context->Attrs().Get<bool>("transpose_X");
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bool transpose_y = context->Attrs().Get<bool>("transpose_Y");
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PADDLE_ENFORCE_GE(dim_x.size(), 1,
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"Input tensor X must be at least 1-dimensional.");
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PADDLE_ENFORCE_GE(dim_y.size(), 1,
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"Input tensor Y must be at least 1-dimensional.");
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PADDLE_ENFORCE_LE(dim_x.size(), 3,
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"Input tensor X must be at most 3-dimensional.");
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PADDLE_ENFORCE_LE(dim_y.size(), 3,
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"Input tensor Y must be at most 3-dimensional.");
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int M = 0, N = 0, KX = 0, KY = 0, batchCountX = 0, batchCountY = 0;
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bool remove_initial_dim = false, remove_final_dim = false;
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switch (dim_x.size()) {
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case 1:
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if (transpose_x) {
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M = dim_x[0];
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KX = 1;
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} else {
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M = 1;
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KX = dim_x[0];
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remove_initial_dim = true;
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}
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break;
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case 2:
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M = transpose_x ? dim_x[1] : dim_x[0];
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KX = transpose_x ? dim_x[0] : dim_x[1];
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break;
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case 3:
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batchCountX = dim_x[0];
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M = transpose_x ? dim_x[2] : dim_x[1];
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KX = transpose_x ? dim_x[1] : dim_x[2];
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break;
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default:
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assert(false);
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}
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switch (dim_y.size()) {
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case 1:
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if (transpose_y) {
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N = dim_y[0];
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KY = 1;
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} else {
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N = 1;
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KY = dim_y[0];
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remove_final_dim = true;
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}
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break;
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case 2:
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KY = transpose_y ? dim_y[1] : dim_y[0];
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N = transpose_y ? dim_y[0] : dim_y[1];
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break;
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case 3:
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batchCountY = dim_y[0];
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KY = transpose_y ? dim_y[2] : dim_y[1];
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N = transpose_y ? dim_y[1] : dim_y[2];
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break;
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default:
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assert(false);
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}
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PADDLE_ENFORCE_EQ(
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KX, KY,
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"First matrix's width must be equal with second matrix's height.");
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if (batchCountX && batchCountY) {
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PADDLE_ENFORCE_EQ(
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batchCountX, batchCountY,
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"When Input(X) and Input(Y) are both three dimensional, they "
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"must have the same batch dimension.");
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}
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int batchCount = std::max(batchCountX, batchCountY);
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std::vector<int64_t> dim_out;
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if (batchCount) {
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dim_out.push_back(batchCount);
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}
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if (!remove_initial_dim) {
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dim_out.push_back(M);
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}
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if (!remove_final_dim) {
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dim_out.push_back(N);
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}
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if (dim_out.size() == 0) {
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// We don't support 0-dimensional Tensors (scalars), so instead
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// treat the output as a Tensor of shape (1, ) in this case.
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dim_out.push_back(1);
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}
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context->SetOutputDim("Out", framework::make_ddim(dim_out));
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context->ShareLoD("X", /*->*/ "Out");
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}
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};
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class MatMulOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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MatMulOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X", "The first input of MatMul op");
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AddInput("Y", "The second input of MatMul op");
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AddOutput("Out", "The output of MatMul op");
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AddAttr<bool>("transpose_X",
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R"DOC(If true, use the transpose of `X`.
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)DOC")
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.SetDefault(false);
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AddAttr<bool>("transpose_Y",
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R"DOC(If true, use the transpose of `Y`.
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)DOC")
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.SetDefault(false);
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AddComment(R"DOC(
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The MatMul operator is used to perform (batched) matrix multiplication
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over the last two dimensions of the input tensors `X` and `Y`.
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If a transpose flag is specified, the last two dimensions of the
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tensor are transposed. If the tensor is rank-1 of shape [D], then
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for `X` it is treated as [1, D] in nontransposed form and as [D, 1]
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in transposed form, whereas for `Y` it is the opposite: It is treated
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as [D, 1] in nontransposed form and as [1, D] in transposed form.
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Examples without transpose:
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- X: [K], Y: [K] => Out: [1]
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- X: [K], Y: [K, N] => Out: [N]
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- X: [B, M, K], Y: [K] => Out: [B, M]
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- X: [M, K], Y: [B, K, N] => Out: [B, M, N]
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- X: [B, M, K], Y: [B, K, N] => Out: [B, M, N]
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The behavior is designed to be similar to the `numpy.matmul` function.
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The differences are:
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- Currently only rank 1 to rank 3 input tensors are supported.
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- We add `transpose_X` and `transpose_Y` flags.
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Both the input `X` and `Y` can carry the LoD (Level of Details) information,
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or not. But the output only shares the LoD with input `X`.
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)DOC");
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}
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};
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class MatMulOpGrad : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(framework::InferShapeContext* context) const override {
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PADDLE_ENFORCE(context->HasInput("X"), "Input(X) should not be null");
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PADDLE_ENFORCE(context->HasInput("Y"), "Input(Y) should not be null");
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PADDLE_ENFORCE(context->HasInput(framework::GradVarName("Out")),
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"Input(Out@GRAD) should not be null");
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auto x_dims = context->GetInputDim("X");
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auto y_dims = context->GetInputDim("Y");
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auto x_grad_name = framework::GradVarName("X");
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auto y_grad_name = framework::GradVarName("Y");
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if (context->HasOutput(x_grad_name)) {
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context->SetOutputDim(x_grad_name, x_dims);
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}
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if (context->HasOutput(y_grad_name)) {
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context->SetOutputDim(y_grad_name, y_dims);
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP(matmul, ops::MatMulOp, ops::MatMulOpMaker, matmul_grad,
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ops::MatMulOpGrad);
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REGISTER_OP_CPU_KERNEL(matmul,
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ops::MatMulKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(
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matmul_grad, ops::MatMulGradKernel<paddle::platform::CPUPlace, float>);
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@ -0,0 +1,21 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
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|
||||
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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|
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#include "paddle/operators/matmul_op.h"
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namespace ops = paddle::operators;
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REGISTER_OP_GPU_KERNEL(matmul,
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ops::MatMulKernel<paddle::platform::GPUPlace, float>);
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REGISTER_OP_GPU_KERNEL(
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matmul_grad, ops::MatMulGradKernel<paddle::platform::GPUPlace, float>);
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@ -0,0 +1,228 @@
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/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
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|
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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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#pragma once
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#include "paddle/framework/op_registry.h"
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#include "paddle/operators/math/matmul.h"
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#include "paddle/operators/transpose_op.h"
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namespace paddle {
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namespace operators {
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namespace matmul_detail {
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using Tensor = framework::Tensor;
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using DDim = framework::DDim;
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using framework::make_ddim;
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using framework::vectorize;
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template <typename Place, typename T>
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class MatMulKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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const Tensor& x = *context.Input<Tensor>("X");
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const Tensor& y = *context.Input<Tensor>("Y");
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Tensor* out = context.Output<Tensor>("Out");
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out->mutable_data<T>(context.GetPlace());
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bool transpose_x = context.Attr<bool>("transpose_X");
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bool transpose_y = context.Attr<bool>("transpose_Y");
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math::MatMulFunctor<Place, T>()(context.device_context(), x, transpose_x, y,
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transpose_y, T(1), out, T(0));
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}
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};
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template <typename T>
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inline Tensor Reshape(const Tensor& input, const DDim& dims) {
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Tensor output;
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output.ShareDataWith<T>(input);
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output.Resize(dims);
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return output;
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}
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// Reshape a rank-3 tensor from P x M x N to (P * M) x N.
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// Identity op if the tensor is not of rank 3.
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template <typename T>
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Tensor CombineBatchAndM(const Tensor& input) {
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Tensor output;
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output.ShareDataWith<T>(input);
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auto in_dims = input.dims();
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if (in_dims.size() == 3) {
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std::vector<int64_t> out_dims = {in_dims[0] * in_dims[1], in_dims[2]};
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output.Resize(make_ddim(out_dims));
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}
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return output;
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}
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// Reshape a rank-3 tensor from P x M x N to M x (P * N).
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// (Warning: This requires transposing data and writes into new memory.)
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// Identity op if the tensor is not of rank 3.
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template <typename Place, typename T>
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Tensor CombineBatchAndN(const framework::ExecutionContext& context,
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const Tensor& input) {
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Tensor output;
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auto in_dims = input.dims();
|
||||
if (in_dims.size() == 3) {
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output.Resize(in_dims);
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output.mutable_data<T>(context.GetPlace());
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EigenTranspose<Place, T, 3>(context, input, output, {1, 0, 2});
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std::vector<int64_t> out_dims = {in_dims[1], in_dims[0] * in_dims[2]};
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output.Resize(make_ddim(out_dims));
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||||
} else {
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output.ShareDataWith<T>(input);
|
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}
|
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return output;
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||||
}
|
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|
||||
// Using dimensional constraints on matrix multiplication, it is
|
||||
// straight-forward to check the following table for when X and Y
|
||||
// are both matrices.
|
||||
//
|
||||
// transpose_X | False | True | False | True
|
||||
// transpose_Y | False | False | True | True
|
||||
// -----------+----------+----------+----------+-----------
|
||||
// dX = | dOut Y^T | Y dOut^T | dOut Y | Y^T dOut^T
|
||||
// dY = | X^T dOut | X dOut | dOut^T X | dOut^T X^T
|
||||
//
|
||||
// When X is a vector of size K, we treat it instead as a matrix of shape
|
||||
// (1, K). Similarly, when Y is a vector of size K, we treat it instead as
|
||||
// a matrix of shape (K, 1).
|
||||
//
|
||||
// When X and Y are both 3-dimensional tensors, then the first dimension
|
||||
// the batch dimension can be ignored and the exact same formulas apply
|
||||
// as for two matrices.
|
||||
//
|
||||
// Finally, when, e.g., X is a 3-dimensional tensor but Y is a matrix, we end
|
||||
// up with formulas like
|
||||
//
|
||||
// dY_{ij} = \sum_{p, m} X_{pmi} dOut_{pmj}
|
||||
//
|
||||
// To handle this sort of scenario, we reshape X : P x M x K, dOut: P x M x N
|
||||
// to X: (P * M) x K, dOut: (P * M) x N.
|
||||
template <typename Place, typename T>
|
||||
class MatMulGradKernel : public framework::OpKernel<T> {
|
||||
public:
|
||||
void Compute(const framework::ExecutionContext& context) const override {
|
||||
const Tensor& x = *context.Input<Tensor>("X");
|
||||
const Tensor& y = *context.Input<Tensor>("Y");
|
||||
const Tensor& dout = *context.Input<Tensor>(framework::GradVarName("Out"));
|
||||
Tensor* dx = context.Output<Tensor>(framework::GradVarName("X"));
|
||||
Tensor* dy = context.Output<Tensor>(framework::GradVarName("Y"));
|
||||
bool transpose_x = context.Attr<bool>("transpose_X");
|
||||
bool transpose_y = context.Attr<bool>("transpose_Y");
|
||||
|
||||
std::vector<int64_t> x_dims = vectorize(x.dims());
|
||||
std::vector<int64_t> y_dims = vectorize(y.dims());
|
||||
|
||||
// If X is a vector, reshape it to a matrix.
|
||||
if (x_dims.size() == 1) {
|
||||
x_dims.insert(x_dims.begin(), 1);
|
||||
}
|
||||
|
||||
// If Y is a vector, reshape it to a matrix.
|
||||
if (y_dims.size() == 1) {
|
||||
y_dims.push_back(1);
|
||||
}
|
||||
|
||||
// Fix the dOut dimensions.
|
||||
int M = 0, N = 0, batchCountX = 0, batchCountY = 0;
|
||||
|
||||
switch (x_dims.size()) {
|
||||
case 2:
|
||||
M = transpose_x ? x_dims[1] : x_dims[0];
|
||||
break;
|
||||
case 3:
|
||||
batchCountX = x_dims[0];
|
||||
M = transpose_x ? x_dims[2] : x_dims[1];
|
||||
break;
|
||||
default:
|
||||
assert(false);
|
||||
}
|
||||
|
||||
switch (y_dims.size()) {
|
||||
case 2:
|
||||
N = transpose_y ? y_dims[0] : y_dims[1];
|
||||
break;
|
||||
case 3:
|
||||
batchCountY = y_dims[0];
|
||||
N = transpose_y ? y_dims[1] : y_dims[2];
|
||||
break;
|
||||
default:
|
||||
assert(false);
|
||||
}
|
||||
if (batchCountX && batchCountY) {
|
||||
PADDLE_ENFORCE_EQ(
|
||||
batchCountX, batchCountY,
|
||||
"When Input(X) and Input(Y) are both three dimensional, they "
|
||||
"must have the same batch dimension.");
|
||||
}
|
||||
int batchCount = std::max(batchCountX, batchCountY);
|
||||
std::vector<int64_t> dout_dims = {M, N};
|
||||
if (batchCount) {
|
||||
dout_dims.insert(dout_dims.begin(), batchCount);
|
||||
}
|
||||
Tensor X = Reshape<T>(x, make_ddim(x_dims));
|
||||
Tensor Y = Reshape<T>(y, make_ddim(y_dims));
|
||||
Tensor dOut = Reshape<T>(dout, make_ddim(dout_dims));
|
||||
|
||||
if (dx) {
|
||||
dx->mutable_data<T>(context.GetPlace());
|
||||
const Tensor& dOut_for_dX =
|
||||
(x_dims.size() == 2 && y_dims.size() == 3)
|
||||
? CombineBatchAndN<Place, T>(context, dOut)
|
||||
: dOut;
|
||||
if (x_dims.size() == 2 && y_dims.size() == 3) {
|
||||
Y = transpose_y ? CombineBatchAndM<T>(Y)
|
||||
: CombineBatchAndN<Place, T>(context, Y);
|
||||
}
|
||||
if (transpose_x) {
|
||||
math::MatMulFunctor<Place, T>()(context.device_context(), Y,
|
||||
transpose_y, dOut_for_dX, transpose_x,
|
||||
T(1), dx, T(0));
|
||||
} else {
|
||||
math::MatMulFunctor<Place, T>()(context.device_context(), dOut_for_dX,
|
||||
transpose_x, Y, !transpose_y, T(1), dx,
|
||||
T(0));
|
||||
}
|
||||
}
|
||||
|
||||
if (dy) {
|
||||
dy->mutable_data<T>(context.GetPlace());
|
||||
const Tensor& dOut_for_dY = (y_dims.size() == 2 && x_dims.size() == 3)
|
||||
? CombineBatchAndM<T>(dOut)
|
||||
: dOut;
|
||||
if (y_dims.size() == 2 && x_dims.size() == 3) {
|
||||
X = transpose_x ? CombineBatchAndN<Place, T>(context, X)
|
||||
: CombineBatchAndM<T>(X);
|
||||
dOut = CombineBatchAndM<T>(dOut);
|
||||
}
|
||||
if (transpose_y) {
|
||||
math::MatMulFunctor<Place, T>()(context.device_context(), dOut_for_dY,
|
||||
transpose_y, X, transpose_x, T(1), dy,
|
||||
T(0));
|
||||
} else {
|
||||
math::MatMulFunctor<Place, T>()(context.device_context(), X,
|
||||
!transpose_x, dOut_for_dY, transpose_y,
|
||||
T(1), dy, T(0));
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
} // namespace matmul_detail
|
||||
|
||||
using matmul_detail::MatMulKernel;
|
||||
using matmul_detail::MatMulGradKernel;
|
||||
|
||||
} // namespace operators
|
||||
} // namespace paddle
|
||||
@ -0,0 +1,94 @@
|
||||
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
|
||||
|
||||
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. */
|
||||
|
||||
#include "paddle/operators/momentum_op.h"
|
||||
|
||||
namespace paddle {
|
||||
namespace operators {
|
||||
|
||||
class MomentumOp : public framework::OperatorWithKernel {
|
||||
public:
|
||||
using framework::OperatorWithKernel::OperatorWithKernel;
|
||||
|
||||
protected:
|
||||
void InferShape(framework::InferShapeContext *ctx) const override {
|
||||
PADDLE_ENFORCE(ctx->HasInput("Param"),
|
||||
"Input(param) of Momentum should not be null.");
|
||||
PADDLE_ENFORCE(ctx->HasInput("Grad"),
|
||||
"Input(grad) of Momentum should not be null.");
|
||||
PADDLE_ENFORCE(ctx->HasInput("Velocity"),
|
||||
"Input(velocity) of Momentum should not be null.");
|
||||
PADDLE_ENFORCE(ctx->HasInput("LearningRate"),
|
||||
"Input(LearningRate) of Momentum should not be null.");
|
||||
|
||||
PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
|
||||
"Output(ParamOut) of Momentum should not be null.");
|
||||
PADDLE_ENFORCE(ctx->HasOutput("VelocityOut"),
|
||||
"Output(VelocityOut) of Momentum should not be null.");
|
||||
|
||||
auto param_dim = ctx->GetInputDim("Param");
|
||||
PADDLE_ENFORCE_EQ(
|
||||
param_dim, ctx->GetInputDim("Grad"),
|
||||
"Param and Grad input of MomentumOp should have the same dimension.");
|
||||
PADDLE_ENFORCE_EQ(
|
||||
param_dim, ctx->GetInputDim("Velocity"),
|
||||
"Param and Velocity of MomentumOp should have the same dimension.");
|
||||
PADDLE_ENFORCE_EQ(framework::product(ctx->GetInputDim("LearningRate")), 1,
|
||||
"Learning_rate should be a scalar");
|
||||
|
||||
ctx->SetOutputDim("ParamOut", param_dim);
|
||||
ctx->SetOutputDim("VelocityOut", param_dim);
|
||||
}
|
||||
};
|
||||
|
||||
class MomentumOpMaker : public framework::OpProtoAndCheckerMaker {
|
||||
public:
|
||||
MomentumOpMaker(framework::OpProto *proto,
|
||||
framework::OpAttrChecker *op_checker)
|
||||
: OpProtoAndCheckerMaker(proto, op_checker) {
|
||||
AddInput("Param",
|
||||
"(Tensor, default Tensor<float>) "
|
||||
"Input parameter that has to be updated");
|
||||
AddInput("Grad",
|
||||
"(Tensor, default Tensor<float>) "
|
||||
"Input gradient of the parameter");
|
||||
AddInput("Velocity",
|
||||
"(Tensor, default Tensor<float>) "
|
||||
"Input velocity (corresponding to the parameter) "
|
||||
"that has to be updated");
|
||||
AddInput("LearningRate",
|
||||
"(Tensor, default Tensor<float>) "
|
||||
"Input learning rate");
|
||||
|
||||
AddOutput("ParamOut", "(Tensor) Output updated parameter");
|
||||
AddOutput("VelocityOut", "(Tensor) Output updated velocity");
|
||||
|
||||
AddAttr<float>("mu", "(float) Momentum coefficient");
|
||||
AddComment(R"DOC(
|
||||
|
||||
Momentum Algorithm (momentum).
|
||||
|
||||
velocity = mu * velocity + gradient
|
||||
param = param - learning_rate * velocity
|
||||
|
||||
)DOC");
|
||||
}
|
||||
};
|
||||
} // namespace operators
|
||||
} // namespace paddle
|
||||
|
||||
namespace ops = paddle::operators;
|
||||
REGISTER_OP_WITHOUT_GRADIENT(momentum, ops::MomentumOp, ops::MomentumOpMaker);
|
||||
REGISTER_OP_CPU_KERNEL(
|
||||
momentum, ops::MomentumOpKernel<paddle::platform::CPUPlace, float>);
|
||||
@ -0,0 +1,20 @@
|
||||
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
|
||||
|
||||
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. */
|
||||
|
||||
#define EIGEN_USE_GPU
|
||||
#include "paddle/operators/momentum_op.h"
|
||||
|
||||
namespace ops = paddle::operators;
|
||||
REGISTER_OP_GPU_KERNEL(
|
||||
momentum, ops::MomentumOpKernel<paddle::platform::GPUPlace, float>);
|
||||
@ -0,0 +1,55 @@
|
||||
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
|
||||
|
||||
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. */
|
||||
|
||||
#pragma once
|
||||
#include "paddle/framework/eigen.h"
|
||||
#include "paddle/framework/op_registry.h"
|
||||
|
||||
namespace paddle {
|
||||
namespace operators {
|
||||
|
||||
template <typename Place, typename T>
|
||||
class MomentumOpKernel : public framework::OpKernel<T> {
|
||||
public:
|
||||
void Compute(const framework::ExecutionContext& ctx) const override {
|
||||
auto param_out = ctx.Output<framework::Tensor>("ParamOut");
|
||||
auto velocity_out = ctx.Output<framework::Tensor>("VelocityOut");
|
||||
auto param = ctx.Input<framework::Tensor>("Param");
|
||||
auto velocity = ctx.Input<framework::Tensor>("Velocity");
|
||||
auto grad = ctx.Input<framework::Tensor>("Grad");
|
||||
auto learning_rate = ctx.Input<framework::Tensor>("LearningRate");
|
||||
|
||||
param_out->mutable_data<T>(ctx.GetPlace());
|
||||
velocity_out->mutable_data<T>(ctx.GetPlace());
|
||||
|
||||
float mu = ctx.Attr<float>("mu");
|
||||
|
||||
auto p_out = framework::EigenVector<T>::Flatten(*param_out);
|
||||
auto v_out = framework::EigenVector<T>::Flatten(*velocity_out);
|
||||
|
||||
auto p = framework::EigenVector<T>::Flatten(*param);
|
||||
auto v = framework::EigenVector<T>::Flatten(*velocity);
|
||||
auto g = framework::EigenVector<T>::Flatten(*grad);
|
||||
auto lr = framework::EigenVector<T>::Flatten(*learning_rate);
|
||||
|
||||
auto place = ctx.GetEigenDevice<Place>();
|
||||
|
||||
Eigen::DSizes<int, 1> grad_dsize(grad->numel());
|
||||
v_out.device(place) = v * mu + g;
|
||||
p_out.device(place) = p - lr.broadcast(grad_dsize) * v_out;
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace operators
|
||||
} // namespace paddle
|
||||
@ -0,0 +1,124 @@
|
||||
import paddle.v2.framework.framework as framework
|
||||
|
||||
__all__ = ['SGDOptimizer']
|
||||
|
||||
|
||||
class Optimizer(object):
|
||||
"""Optimizer Base class.
|
||||
|
||||
Define the common interface of an optimizer.
|
||||
User should not use this class directly, but need to use one of it's implementation.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def _append_optimize_op(self, block, param_and_grad):
|
||||
""" append optimize operator to block and return all the added optimize_op
|
||||
"""
|
||||
raise NotImplementedError()
|
||||
|
||||
def create_backward_pass(self, loss, parameter_list=None, no_grad_set=None):
|
||||
"""
|
||||
create and add gradient Operators in BlockDesc to Compute gradients of `loss`
|
||||
for parameters in parameter_list
|
||||
|
||||
Args:
|
||||
loss: an variable generated by cost function.
|
||||
no_grad_set: variable that should not create gradient
|
||||
parameter_list: parameters that need to compute gradient and update to optimize the lost.
|
||||
|
||||
Returns:
|
||||
list of (parameters, gradients) pair.
|
||||
"""
|
||||
assert isinstance(loss, framework.Variable)
|
||||
param_grad_map = loss.block.program.append_backward(loss, no_grad_set or
|
||||
set())
|
||||
if parameter_list is not None:
|
||||
parameters = parameter_list
|
||||
else:
|
||||
params = loss.block.program.global_block().all_parameters()
|
||||
parameters = [param.name for param in params]
|
||||
params_and_grads = []
|
||||
for param in parameters:
|
||||
if param not in param_grad_map:
|
||||
raise Exception("param %s is not in map" % param)
|
||||
grad_info = param_grad_map[param]
|
||||
grad_block = loss.block.program.block(grad_info[1])
|
||||
if not grad_block.has_var(grad_info[0]):
|
||||
raise Exception("grad block[%d] did not have grad var %s" %
|
||||
grad_info[1], grad_info[0])
|
||||
param_var = loss.block.var(param)
|
||||
grad_var = grad_block.var(grad_info[0])
|
||||
if loss.block.has_var(grad_info[0]):
|
||||
params_and_grads.append((param_var, grad_var))
|
||||
else:
|
||||
params_and_grads.append((param_var, None))
|
||||
return params_and_grads
|
||||
|
||||
def create_optimization_pass(self, parameters_and_grads, loss):
|
||||
"""Add optimization operators to update gradients to variables.
|
||||
|
||||
Args:
|
||||
loss: the target that this optimization is for.
|
||||
parameters_and_grads: a list of (variable, gradient) pair to update.
|
||||
|
||||
Returns:
|
||||
optmization_op_list: a list of optimization operator that will update parameter using gradient.
|
||||
"""
|
||||
optimize_ops = []
|
||||
for param_and_grad in parameters_and_grads:
|
||||
if param_and_grad[1] is not None:
|
||||
optimize_op = self._append_optimize_op(loss.block,
|
||||
param_and_grad)
|
||||
optimize_ops.append(optimize_op)
|
||||
return optimize_ops
|
||||
|
||||
def minimize(self, loss, parameter_list=None, no_grad_set=None):
|
||||
"""Add operations to minimize `loss` by updating `parameter_list`.
|
||||
|
||||
This method combines interface `create_backward_pass()` and
|
||||
`create_optimization_pass()` into one.
|
||||
"""
|
||||
params_grads = self.create_backward_pass(loss, parameter_list,
|
||||
no_grad_set or set())
|
||||
optimize_ops = self.create_optimization_pass(params_grads, loss)
|
||||
return optimize_ops
|
||||
|
||||
|
||||
class SGDOptimizer(Optimizer):
|
||||
""" Simple SGD optimizer without any state.
|
||||
"""
|
||||
|
||||
def __init__(self, learning_rate):
|
||||
assert learning_rate is not None
|
||||
super(Optimizer, self).__init__()
|
||||
self.type = "sgd"
|
||||
self._learning_rate = learning_rate
|
||||
|
||||
def _append_optimize_op(self, block, param_and_grad):
|
||||
assert isinstance(block, framework.Block)
|
||||
lr_shape = [1]
|
||||
# create a var for learning_rate
|
||||
lr = block.create_var(dtype="float32", shape=lr_shape, lod_level=0)
|
||||
|
||||
# create an op to init the learning_rate
|
||||
init_op = block.append_op(
|
||||
type="fill_constant",
|
||||
outputs={"Out": lr},
|
||||
attrs={"shape": lr_shape,
|
||||
"value": self._learning_rate})
|
||||
|
||||
# create the optimize op
|
||||
sgd_op = block.append_op(
|
||||
type=self.type,
|
||||
inputs={
|
||||
"Param": param_and_grad[0],
|
||||
"Grad": param_and_grad[1],
|
||||
"LearningRate": lr
|
||||
},
|
||||
outputs={"ParamOut": param_and_grad[0]},
|
||||
attrs={"shape": [1],
|
||||
"value": self._learning_rate})
|
||||
|
||||
return sgd_op
|
||||
@ -0,0 +1,119 @@
|
||||
import unittest
|
||||
import numpy as np
|
||||
from op_test import OpTest
|
||||
|
||||
|
||||
def generate_compatible_shapes(dim_X, dim_Y, transpose_X, transpose_Y):
|
||||
BATCH_SIZE = 2
|
||||
M = 3
|
||||
N = 4
|
||||
K = 5
|
||||
if (dim_X == 1 and transpose_X) or (dim_Y == 1 and transpose_Y):
|
||||
K = 1
|
||||
if dim_X == 1:
|
||||
if transpose_X:
|
||||
shape_X = [M]
|
||||
else:
|
||||
shape_X = [K]
|
||||
if dim_Y == 1:
|
||||
if transpose_Y:
|
||||
shape_Y = [N]
|
||||
else:
|
||||
shape_Y = [K]
|
||||
if dim_X >= 2:
|
||||
if transpose_X:
|
||||
shape_X = [K, M]
|
||||
else:
|
||||
shape_X = [M, K]
|
||||
if dim_X == 3:
|
||||
shape_X = [BATCH_SIZE] + shape_X
|
||||
if dim_Y >= 2:
|
||||
if transpose_Y:
|
||||
shape_Y = [N, K]
|
||||
else:
|
||||
shape_Y = [K, N]
|
||||
if dim_Y == 3:
|
||||
shape_Y = [BATCH_SIZE] + shape_Y
|
||||
return shape_X, shape_Y
|
||||
|
||||
|
||||
def reference_matmul(X, Y, transpose_X=False, transpose_Y=False):
|
||||
"""Reference forward implementation using np.matmul."""
|
||||
# np.matmul does not support the transpose flags, so we manually
|
||||
# transpose X and Y appropriately.
|
||||
if transpose_X:
|
||||
if X.ndim == 1:
|
||||
X = X.reshape((X.size, 1))
|
||||
elif X.ndim == 2:
|
||||
X = X.T
|
||||
elif X.ndim == 3:
|
||||
X = np.transpose(X, (0, 2, 1))
|
||||
else:
|
||||
raise ValueError('X must have between 1 and 3 dimensions')
|
||||
if transpose_Y:
|
||||
if Y.ndim == 1:
|
||||
Y = Y.reshape((1, Y.size))
|
||||
elif Y.ndim == 2:
|
||||
Y = Y.T
|
||||
elif Y.ndim == 3:
|
||||
Y = np.transpose(Y, (0, 2, 1))
|
||||
else:
|
||||
raise ValueError('Y must have between 1 and 3 dimensions')
|
||||
Out = np.matmul(X, Y)
|
||||
if not Out.shape:
|
||||
# We do not support 0-dimensional Tensors (scalars). So where
|
||||
# np.matmul outputs a scalar, we must convert to a Tensor of
|
||||
# shape (1, ) instead.
|
||||
# Everywhere else, we are compatible with np.matmul.
|
||||
Out = np.array([Out], dtype="float32")
|
||||
return Out
|
||||
|
||||
|
||||
class Generator(object):
|
||||
def setUp(self):
|
||||
self.op_type = "matmul"
|
||||
X = np.random.random(self.shape_X).astype("float32")
|
||||
Y = np.random.random(self.shape_Y).astype("float32")
|
||||
Out = reference_matmul(X, Y, self.transpose_X, self.transpose_Y)
|
||||
self.inputs = {'X': X, 'Y': Y}
|
||||
self.attrs = {
|
||||
'transpose_X': self.transpose_X,
|
||||
'transpose_Y': self.transpose_Y
|
||||
}
|
||||
self.outputs = {'Out': Out}
|
||||
|
||||
def test_check_output(self):
|
||||
self.check_output(atol=1e-2)
|
||||
|
||||
def test_check_grad_normal(self):
|
||||
self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
|
||||
|
||||
def test_check_grad_ignore_x(self):
|
||||
self.check_grad(
|
||||
['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X"))
|
||||
|
||||
def test_check_grad_ignore_y(self):
|
||||
self.check_grad(
|
||||
['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
|
||||
|
||||
|
||||
# Generate test cases for all possibilities
|
||||
for dim_X in [1, 2, 3]:
|
||||
for dim_Y in [1, 2, 3]:
|
||||
for transpose_X in [False, True]:
|
||||
for transpose_Y in [False, True]:
|
||||
test_name = (
|
||||
'TestMatMulOp_dimX_{}_dim_Y_{}_transX_{}_transY_{}'.format(
|
||||
dim_X, dim_Y, transpose_X, transpose_Y))
|
||||
shape_X, shape_Y = generate_compatible_shapes(
|
||||
dim_X, dim_Y, transpose_X, transpose_Y)
|
||||
test_class = type(test_name, (Generator, OpTest), {
|
||||
'shape_X': shape_X,
|
||||
'shape_Y': shape_Y,
|
||||
'transpose_X': transpose_X,
|
||||
'transpose_Y': transpose_Y,
|
||||
})
|
||||
globals()[test_name] = test_class
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
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Reference in new issue