From bb7f5558034e6d28da096d91d6341f829591d9a2 Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Fri, 29 Sep 2017 16:56:27 +0800 Subject: [PATCH] remove rowwise_add_op --- paddle/operators/fc_op.cc | 2 +- paddle/operators/rowwise_add_op.cc | 109 ------------------ paddle/operators/rowwise_add_op.cu | 23 ---- paddle/operators/rowwise_add_op.h | 80 ------------- .../v2/framework/tests/test_rowwise_add_op.py | 51 -------- 5 files changed, 1 insertion(+), 264 deletions(-) delete mode 100644 paddle/operators/rowwise_add_op.cc delete mode 100644 paddle/operators/rowwise_add_op.cu delete mode 100644 paddle/operators/rowwise_add_op.h delete mode 100644 python/paddle/v2/framework/tests/test_rowwise_add_op.py diff --git a/paddle/operators/fc_op.cc b/paddle/operators/fc_op.cc index 5ac0e8cc45..7c422c81fc 100644 --- a/paddle/operators/fc_op.cc +++ b/paddle/operators/fc_op.cc @@ -100,7 +100,7 @@ class FCOp : public NetOp { add_out = Output("AddOut"); AppendOp(framework::OpRegistry::CreateOp( - "rowwise_add", {{"X", {sum_out}}, {"b", {Input("B")}}}, + "elementwise_add", {{"X", {sum_out}}, {"Y", {Input("B")}}}, {{"Out", {add_out}}}, {})); } else { if (Output("AddOut") != framework::kEmptyVarName) { diff --git a/paddle/operators/rowwise_add_op.cc b/paddle/operators/rowwise_add_op.cc deleted file mode 100644 index 1fcf0959df..0000000000 --- a/paddle/operators/rowwise_add_op.cc +++ /dev/null @@ -1,109 +0,0 @@ -/* 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/rowwise_add_op.h" - -namespace paddle { -namespace operators { - -using framework::Tensor; - -class RowwiseAddOp : public framework::OperatorWithKernel { - public: - using framework::OperatorWithKernel::OperatorWithKernel; - - protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("X"), - "Input(X) of RowwiseAddOp should not be null."); - PADDLE_ENFORCE(ctx->HasInput("b"), - "Input(b) of RowwiseAddOp should not be null."); - PADDLE_ENFORCE(ctx->HasOutput("Out"), - "Output(Out) of RowwiseAddOp should not be null."); - - auto x_dims = ctx->GetInputDim("X"); - auto b_dims = ctx->GetInputDim("b"); - PADDLE_ENFORCE_GT( - x_dims.size(), b_dims.size(), - "The rank of input `X` must be larger than the one of input `b`."); - - int num_col_dims = x_dims.size() - b_dims.size(); - - PADDLE_ENFORCE_EQ( - framework::slice_ddim(x_dims, num_col_dims, x_dims.size()), b_dims, - "The width of two operands must be same"); - PADDLE_ENFORCE_EQ(ctx->Outputs("Out").size(), 1, - "The output size must be 1"); - ctx->SetOutputDim("Out", x_dims); - ctx->ShareLoD("X", /*->*/ "Out"); - } -}; - -class RowwiseAddOpMaker : public framework::OpProtoAndCheckerMaker { - public: - RowwiseAddOpMaker(framework::OpProto* proto, - framework::OpAttrChecker* op_checker) - : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("X", "The left input of row-wise add op, must be matrix"); - AddInput("b", "The right input of row-wise add op, must be vector"); - AddOutput("Out", "The output of row-wise add op"); - AddComment(R"DOC(Row-wise Add operator - -for i in xrange(X.shape[0]): - Out = X[i] + b -)DOC"); - } -}; -class RowwiseAddGradOp : public framework::OperatorWithKernel { - public: - using framework::OperatorWithKernel::OperatorWithKernel; - - protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("X"), "X should not be null"); - PADDLE_ENFORCE(ctx->HasInput("b"), "b should not be null"); - PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), - "Input(Out@GRAD) should not be null"); - auto x_dims = ctx->GetInputDim("X"); - auto b_dims = ctx->GetInputDim("b"); - PADDLE_ENFORCE_GT( - x_dims.size(), b_dims.size(), - "The rank of input `X` must be larger than the one of input `b`."); - - int64_t num_col_dims = x_dims.size() - b_dims.size(); - PADDLE_ENFORCE_EQ( - framework::slice_ddim(x_dims, num_col_dims, x_dims.size()), b_dims, - "The width of two operands must be same"); - auto x_grad_name = framework::GradVarName("X"); - auto b_grad_name = framework::GradVarName("b"); - if (ctx->HasOutput(x_grad_name)) { - ctx->SetOutputDim(x_grad_name, x_dims); - } - if (ctx->HasOutput(b_grad_name)) { - ctx->SetOutputDim(b_grad_name, b_dims); - } - } -}; - -} // namespace operators -} // namespace paddle - -namespace ops = paddle::operators; -REGISTER_OP(rowwise_add, ops::RowwiseAddOp, ops::RowwiseAddOpMaker, - rowwise_add_grad, ops::RowwiseAddGradOp); -REGISTER_OP_CPU_KERNEL( - rowwise_add, ops::RowwiseAddKernel); -REGISTER_OP_CPU_KERNEL( - rowwise_add_grad, - ops::RowwiseAddGradKernel); diff --git a/paddle/operators/rowwise_add_op.cu b/paddle/operators/rowwise_add_op.cu deleted file mode 100644 index 4a57f64c89..0000000000 --- a/paddle/operators/rowwise_add_op.cu +++ /dev/null @@ -1,23 +0,0 @@ -/* 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/rowwise_add_op.h" - -namespace ops = paddle::operators; -REGISTER_OP_GPU_KERNEL( - rowwise_add, ops::RowwiseAddKernel); -REGISTER_OP_GPU_KERNEL( - rowwise_add_grad, - ops::RowwiseAddGradKernel); diff --git a/paddle/operators/rowwise_add_op.h b/paddle/operators/rowwise_add_op.h deleted file mode 100644 index b43e5d868b..0000000000 --- a/paddle/operators/rowwise_add_op.h +++ /dev/null @@ -1,80 +0,0 @@ -/* 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 { - -using Tensor = framework::Tensor; -template -using EigenVector = framework::EigenVector; -template -using EigenMatrix = framework::EigenMatrix; - -template -class RowwiseAddKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& context) const override { - auto out = context.Output("Out"); - out->mutable_data(context.GetPlace()); - int num_col_dims = context.Input("X")->dims().size() - - context.Input("b")->dims().size(); - auto input = - EigenMatrix::Reshape(*context.Input("X"), num_col_dims); - auto bias = EigenVector::Flatten(*context.Input("b")); - auto output = EigenMatrix::Reshape(*out, num_col_dims); - - const int bias_size = bias.dimension(0); - const int rest_size = input.size() / bias_size; - Eigen::DSizes one_d(input.size()); - Eigen::DSizes bcast(rest_size); - output.reshape(one_d).device(context.GetEigenDevice()) = - input.reshape(one_d) + bias.broadcast(bcast).reshape(one_d); - } -}; - -template -class RowwiseAddGradKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& context) const override { - auto* dout = context.Input(framework::GradVarName("Out")); - auto* dx = context.Output(framework::GradVarName("X")); - auto* db = context.Output(framework::GradVarName("b")); - int num_col_dims = context.Input("X")->dims().size() - - context.Input("b")->dims().size(); - - auto out_grad = EigenMatrix::Reshape(*dout, num_col_dims); - auto place = context.GetEigenDevice(); - - if (dx) { - dx->mutable_data(context.GetPlace()); - EigenMatrix::Reshape(*dx, num_col_dims).device(place) = out_grad; - } - - if (db) { - db->mutable_data(context.GetPlace()); - // https://eigen.tuxfamily.org/dox/unsupported/TensorBase_8h_source.html - // colwise add - Eigen::array dims{{0}}; /* dimension to reduce */ - EigenVector::Flatten(*db).device(place) = out_grad.sum(dims); - } - } -}; -} // namespace operators -} // namespace paddle diff --git a/python/paddle/v2/framework/tests/test_rowwise_add_op.py b/python/paddle/v2/framework/tests/test_rowwise_add_op.py deleted file mode 100644 index 336645bd99..0000000000 --- a/python/paddle/v2/framework/tests/test_rowwise_add_op.py +++ /dev/null @@ -1,51 +0,0 @@ -import unittest -import numpy as np -from op_test import OpTest - - -class TestRowwiseAddOp(OpTest): - def setUp(self): - self.op_type = "rowwise_add" - self.inputs = { - 'X': np.random.uniform(0.1, 1, [5, 10]).astype("float32"), - 'b': np.random.uniform(0.1, 1, [10]).astype("float32") - } - self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])} - - def test_check_output(self): - self.check_output() - - def test_check_grad_normal(self): - self.check_grad(['X', 'b'], 'Out') - - def test_check_grad_ingore_b(self): - self.check_grad(['X'], 'Out', no_grad_set=set('b')) - - def test_check_grad_ingore_x(self): - self.check_grad(['b'], 'Out', no_grad_set=set('X')) - - -class TestRowwiseAddOp2(OpTest): - def setUp(self): - self.op_type = "rowwise_add" - self.inputs = { - 'X': np.random.uniform(0.1, 1, [2, 3, 2, 5]).astype("float32"), - 'b': np.random.uniform(0.1, 1, [2, 5]).astype("float32") - } - self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])} - - def test_check_output(self): - self.check_output() - - def test_check_grad_normal(self): - self.check_grad(['X', 'b'], 'Out') - - def test_check_grad_ignore_b(self): - self.check_grad(['X'], 'Out', no_grad_set=set('b')) - - def test_check_grad_ignore_x(self): - self.check_grad(['b'], 'Out', no_grad_set=set('X')) - - -if __name__ == "__main__": - unittest.main() -- GitLab