Merge pull request #15304 from tensor-tang/fuse/second_order_mul_sub
Fuse/second order mul sub and fuse repeated fc relurecover_files
tensor-tang
6 years ago
committed by
GitHub
commit
a7fc3d42a0
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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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#pragma once
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#include <string>
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#include "paddle/fluid/framework/ir/fuse_pass_base.h"
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#include "paddle/fluid/framework/ir/graph.h"
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#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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/**
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* Fuse Repeated FC Relu
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*/
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class RepeatedFCReluFusePass : public FusePassBase {
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public:
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virtual ~RepeatedFCReluFusePass() {}
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protected:
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std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
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const std::string name_scope_{"repeated_fc_relu_fuse"};
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};
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} // namespace ir
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} // namespace framework
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} // namespace paddle
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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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#pragma once
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#include <string>
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#include "paddle/fluid/framework/ir/fuse_pass_base.h"
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#include "paddle/fluid/framework/ir/graph.h"
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#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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/**
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* Fuse ( (A.^2 * B.^2) - (A * B).^2 ) .* scalar
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*/
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class SquaredMatSubFusePass : public FusePassBase {
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public:
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virtual ~SquaredMatSubFusePass() {}
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protected:
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std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
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const std::string name_scope_{"squared_mat_sub_fuse"};
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};
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} // namespace ir
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} // namespace framework
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} // namespace paddle
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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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#include "paddle/fluid/operators/fused/fusion_repeated_fc_relu_op.h"
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#include <string>
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#include <vector>
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#include "paddle/fluid/operators/jit/kernels.h"
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namespace paddle {
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namespace operators {
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void FusionRepeatedFCReluOp::InferShape(
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framework::InferShapeContext* ctx) const {
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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of FusionRepeatedFCReluOp should not be null.");
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auto sz = ctx->Inputs("W").size();
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PADDLE_ENFORCE_GT(
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sz, 1UL, "Inputs(W) of FusionRepeatedFCReluOp should larger than 1.");
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PADDLE_ENFORCE_EQ(ctx->Inputs("Bias").size(), sz,
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"Size of inputs(Bias) of FusionRepeatedFCReluOp should be "
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"equal to inputs size.");
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PADDLE_ENFORCE_EQ(ctx->Outputs("ReluOut").size(), sz - 1,
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"Size of output(ReluOut) of FusionRepeatedFCReluOp should "
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"be equal to inputs size -1.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of FusionRepeatedFCReluOp should not be null.");
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auto i_dims = ctx->GetInputDim("X");
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PADDLE_ENFORCE_EQ(i_dims.size(), 2UL, "Input shape size should be 2");
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auto w_dims = ctx->GetInputsDim("W");
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auto b_dims = ctx->GetInputsDim("Bias");
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PADDLE_ENFORCE_EQ(w_dims.size(), b_dims.size(),
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"Shape size of weight and bias should be equal");
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PADDLE_ENFORCE_EQ(w_dims.size(), sz,
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"Shape size of weight and bias should be equal");
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PADDLE_ENFORCE_EQ(i_dims[1], w_dims[0][0],
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"inpute width should be equal with weight height");
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for (size_t i = 1; i < sz; ++i) {
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PADDLE_ENFORCE_EQ(w_dims[i].size(), 2UL,
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"Every weight shape size should be 2.");
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PADDLE_ENFORCE_EQ(framework::product(b_dims[i]), w_dims[i][1],
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"The length of Bias must be equal with w_dims[1].");
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}
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ctx->SetOutputDim("Out", {i_dims[0], w_dims[sz - 1][1]});
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ctx->ShareLoD("X", /*->*/ "Out");
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}
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framework::OpKernelType FusionRepeatedFCReluOp::GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const {
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return framework::OpKernelType(framework::GetDataTypeOfVar(ctx.InputVar("X")),
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ctx.GetPlace());
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}
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void FusionRepeatedFCReluOpMaker::Make() {
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AddInput("X", "(LoDTensor) Input tensors of this operator.");
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AddInput("W", "(Tensor) The weight tensors of this operator.").AsDuplicable();
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AddInput("Bias", "(Tensor) The bias tensors of this operator.")
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.AsDuplicable();
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AddOutput("ReluOut", "(Tensor) The output tensor of each relu operator.")
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.AsDuplicable()
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.AsIntermediate();
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AddOutput("Out", "(LoDTensor) Output tensor of this operator.");
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AddComment(R"DOC(
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Fusion Repeated FC with Relu Operator.
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)DOC");
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}
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template <typename T>
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static void fc_relu(const T* x, const T* w, const T* b, T* y, int m, int n,
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int k) {
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auto matmul =
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jit::Get<jit::kMatMul, jit::MatMulTuples<T>, platform::CPUPlace>(k);
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auto addbias_relu =
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jit::Get<jit::kVAddRelu, jit::XYZNTuples<T>, platform::CPUPlace>(n);
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matmul(x, w, y, m, n, k);
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T* dst = y;
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for (int i = 0; i < m; ++i) {
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addbias_relu(b, dst, dst, n);
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dst += n;
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}
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}
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template <typename T>
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class FusionRepeatedFCReluKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto in = ctx.Input<Tensor>("X");
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auto weights = ctx.MultiInput<Tensor>("W");
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auto biases = ctx.MultiInput<Tensor>("Bias");
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auto relus = ctx.MultiOutput<Tensor>("ReluOut");
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auto* out = ctx.Output<Tensor>("Out");
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auto place = ctx.GetPlace();
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int weight_sz = static_cast<int>(weights.size());
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auto i_dims = in->dims();
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auto w_dims = weights[0]->dims();
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int m = i_dims[0];
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int n = w_dims[1];
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int k = w_dims[0];
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relus[0]->Resize({m, n});
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fc_relu(in->data<T>(), weights[0]->data<T>(), biases[0]->data<T>(),
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relus[0]->mutable_data<T>(place), m, n, k);
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for (int i = 1; i < weight_sz - 1; ++i) {
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auto i_dims = relus[i - 1]->dims();
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auto w_dims = weights[i]->dims();
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int m = i_dims[0];
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int n = w_dims[1];
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int k = w_dims[0];
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relus[i]->Resize({m, n});
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fc_relu(relus[i - 1]->data<T>(), weights[i]->data<T>(),
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biases[i]->data<T>(), relus[i]->mutable_data<T>(place), m, n, k);
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}
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auto i_dims_last = relus[weight_sz - 2]->dims();
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auto w_dims_last = weights[weight_sz - 1]->dims();
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m = i_dims_last[0];
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n = w_dims_last[1];
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k = w_dims_last[0];
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fc_relu(relus[weight_sz - 2]->data<T>(), weights[weight_sz - 1]->data<T>(),
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biases[weight_sz - 1]->data<T>(), out->mutable_data<T>(place), m, n,
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k);
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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_OPERATOR(fusion_repeated_fc_relu, ops::FusionRepeatedFCReluOp,
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ops::FusionRepeatedFCReluOpMaker,
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paddle::framework::DefaultGradOpDescMaker<true>);
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REGISTER_OP_CPU_KERNEL(fusion_repeated_fc_relu,
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ops::FusionRepeatedFCReluKernel<float>,
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ops::FusionRepeatedFCReluKernel<double>);
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@ -0,0 +1,41 @@
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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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#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 LoDTensor = framework::LoDTensor;
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using Tensor = framework::Tensor;
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class FusionRepeatedFCReluOp : 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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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override;
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};
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class FusionRepeatedFCReluOpMaker : 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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@ -0,0 +1,137 @@
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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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#include "paddle/fluid/operators/fused/fusion_squared_mat_sub_op.h"
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#include <string>
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#include <vector>
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#include "paddle/fluid/operators/jit/kernels.h"
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namespace paddle {
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namespace operators {
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void FusionSquaredMatSubOp::InferShape(
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framework::InferShapeContext* ctx) const {
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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of FusionSquaredMatSubOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Y"),
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"Input(Y) of FusionSquaredMatSubOp should not be null.");
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PADDLE_ENFORCE(
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ctx->HasOutput("SquaredX"),
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"Output(SquaredX) of FusionSquaredMatSubOp should not be null.");
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PADDLE_ENFORCE(
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ctx->HasOutput("SquaredY"),
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"Output(SquaredY) of FusionSquaredMatSubOp should not be null.");
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PADDLE_ENFORCE(
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ctx->HasOutput("SquaredXY"),
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"Output(SquaredXY) of FusionSquaredMatSubOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of FusionSquaredMatSubOp should not be null.");
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auto x_dims = ctx->GetInputDim("X");
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auto y_dims = ctx->GetInputDim("Y");
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PADDLE_ENFORCE_EQ(x_dims.size(), y_dims.size(),
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"Input tensors dims size should be equal.");
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PADDLE_ENFORCE_EQ(x_dims.size(), 2UL, "Input tensors should be a Matrix.");
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PADDLE_ENFORCE_EQ(x_dims[1], y_dims[0], "Inputs Matrix should be multiply.");
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ctx->SetOutputDim("SquaredX", x_dims);
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ctx->SetOutputDim("SquaredY", y_dims);
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ctx->SetOutputDim("SquaredXY", {x_dims[0], y_dims[1]});
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ctx->SetOutputDim("Out", {x_dims[0], y_dims[1]});
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}
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framework::OpKernelType FusionSquaredMatSubOp::GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const {
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return framework::OpKernelType(framework::GetDataTypeOfVar(ctx.InputVar("X")),
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ctx.GetPlace());
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}
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void FusionSquaredMatSubOpMaker::Make() {
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AddInput("X", "(Tensor) Input Mat A of this operator.");
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AddInput("Y", "(Tensor) Input Mat B of this operator.");
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AddOutput("SquaredX", "(Tensor) Squared X.").AsIntermediate();
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AddOutput("SquaredY", "(Tensor) Squared Y.").AsIntermediate();
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AddOutput("SquaredXY", "(Tensor) Squared X*Y.").AsIntermediate();
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AddOutput("Out", "(Tensor) Output tensor of concat operator.");
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AddAttr<float>("scalar", "The scalar on output matrix.").SetDefault(1.f);
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AddComment(R"DOC(
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Fusion Squared Matrix and substrct operator.
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( (X * Y).^2 - (X.^2 * Y.^2) ) .* scalar
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)DOC");
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}
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template <typename T>
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class FusionSquaredMatSubKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto x = ctx.Input<Tensor>("X");
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auto y = ctx.Input<Tensor>("Y");
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auto* squared_x = ctx.Output<Tensor>("SquaredX");
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auto* squared_y = ctx.Output<Tensor>("SquaredY");
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auto* squared_xy = ctx.Output<Tensor>("SquaredXY");
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auto* out = ctx.Output<Tensor>("Out");
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auto place = ctx.GetPlace();
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T scalar = static_cast<T>(ctx.Attr<float>("scalar"));
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auto x_dims = x->dims();
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auto y_dims = y->dims();
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int m = x_dims[0];
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int k = x_dims[1];
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int n = y_dims[1];
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int o_numel = m * n;
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auto vsquare_x =
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jit::Get<jit::kVSquare, jit::XYNTuples<T>, platform::CPUPlace>(m * k);
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auto vsquare_y =
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jit::Get<jit::kVSquare, jit::XYNTuples<T>, platform::CPUPlace>(k * n);
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auto vsquare_xy =
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jit::Get<jit::kVSquare, jit::XYNTuples<T>, platform::CPUPlace>(o_numel);
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auto vsub =
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jit::Get<jit::kVSub, jit::XYZNTuples<T>, platform::CPUPlace>(o_numel);
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auto vscal =
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jit::Get<jit::kVScal, jit::AXYNTuples<T>, platform::CPUPlace>(o_numel);
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auto matmul =
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jit::Get<jit::kMatMul, jit::MatMulTuples<T>, platform::CPUPlace>(k);
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const T* x_data = x->data<T>();
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const T* y_data = y->data<T>();
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T* squared_x_data = squared_x->mutable_data<T>(place);
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T* squared_y_data = squared_y->mutable_data<T>(place);
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T* squared_xy_data = squared_xy->mutable_data<T>(place);
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T* o_data = out->mutable_data<T>(place);
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matmul(x_data, y_data, squared_xy_data, m, n, k);
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vsquare_xy(squared_xy_data, squared_xy_data, o_numel);
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vsquare_x(x_data, squared_x_data, m * k);
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vsquare_y(y_data, squared_y_data, k * n);
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matmul(squared_x_data, squared_y_data, o_data, m, n, k);
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vsub(squared_xy_data, o_data, o_data, o_numel);
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vscal(&scalar, o_data, o_data, o_numel);
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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_OPERATOR(fusion_squared_mat_sub, ops::FusionSquaredMatSubOp,
|
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ops::FusionSquaredMatSubOpMaker,
|
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paddle::framework::DefaultGradOpDescMaker<true>);
|
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|
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REGISTER_OP_CPU_KERNEL(fusion_squared_mat_sub,
|
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ops::FusionSquaredMatSubKernel<float>,
|
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ops::FusionSquaredMatSubKernel<double>);
|
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@ -0,0 +1,42 @@
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/* Copyright (c) 2018 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. */
|
||||
|
||||
#pragma once
|
||||
#include "paddle/fluid/framework/op_registry.h"
|
||||
|
||||
namespace paddle {
|
||||
namespace operators {
|
||||
|
||||
using LoDTensor = framework::LoDTensor;
|
||||
using Tensor = framework::Tensor;
|
||||
|
||||
// ( (A.^2 * B.^2) - (A * B).^2 ) .* scalar
|
||||
class FusionSquaredMatSubOp : public framework::OperatorWithKernel {
|
||||
public:
|
||||
using framework::OperatorWithKernel::OperatorWithKernel;
|
||||
|
||||
void InferShape(framework::InferShapeContext* ctx) const override;
|
||||
|
||||
protected:
|
||||
framework::OpKernelType GetExpectedKernelType(
|
||||
const framework::ExecutionContext& ctx) const override;
|
||||
};
|
||||
|
||||
class FusionSquaredMatSubOpMaker : public framework::OpProtoAndCheckerMaker {
|
||||
public:
|
||||
void Make() override;
|
||||
};
|
||||
|
||||
} // namespace operators
|
||||
} // namespace paddle
|
||||
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Reference in new issue