chengduoZH
7 years ago
parent
e593113a84
commit
1e60c9b2e8
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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.
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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/sequence_project_op.h"
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namespace paddle {
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namespace operators {
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class SequenceProjectOp : 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* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of SequenceProjectOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of SequenceProjectOp should not be null.");
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auto in_dims = ctx->GetInputDim("X");
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PADDLE_ENFORCE(in_dims.size() == 2, "Input(X) should be 2-D tensor.");
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int context_length = ctx->Attrs().Get<int>("context_length");
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bool padding_trainable = ctx->Attrs().Get<bool>("padding_trainable");
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int context_start = ctx->Attrs().Get<int>("context_start");
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if (padding_trainable) {
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PADDLE_ENFORCE(
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ctx->HasInput("PaddingData"),
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"Output(PaddingData) of SequenceProjectOp should not be null.");
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framework::DDim padding_dim = ctx->GetOutputDim("PaddingData");
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int up_pad = std::max(0, -context_start);
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int down_pad = std::max(0, context_start + context_length - 1);
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int total_pad = up_pad + down_pad;
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int input_width = static_cast<int>(in_dims[1]);
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PADDLE_ENFORCE(padding_dim.size() == 2,
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"Input(PaddingData) should be 2-D tensor.");
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PADDLE_ENFORCE(
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padding_dim[0] == total_pad && padding_dim[1] == input_width,
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"Input(PaddingData)'s shape is not consistent with 'context_start' "
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"and 'context_length'.");
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if (context_start == 0 && context_length == 1) {
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PADDLE_THROW(
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"if context_start == 0 && context_length == 1, padding_trainable "
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"should be false.");
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}
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}
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in_dims[1] = in_dims[1] * context_length;
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ctx->SetOutputDim("Out", in_dims);
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}
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};
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class SequenceProjectGradOp : 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* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
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"Gradient of Out should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("X"), "The input X should not be null.");
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if (ctx->Attrs().Get<bool>("padding_trainable")) {
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PADDLE_ENFORCE(
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ctx->HasOutput("PaddingData"),
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"Output(PaddingData) of SequenceProjectOp should not be null.");
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}
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ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
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}
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};
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class SequenceProjectOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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SequenceProjectOpMaker(framework::OpProto* proto,
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framework::OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput(
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"X",
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"A float LoDTensor, the variable-length input of SequenceProjectOp");
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AddOutput(
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"Out",
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"A float LoDTensor, the variable-length output of SequenceProjectOp.");
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AddOutput("PaddingData",
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"A float LoDTensor, the padding data of SequenceProjectOp.");
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AddAttr<bool>("padding_trainable",
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"(bool, default false) the padding data of SequenceProjectOp "
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"is trainable or not.")
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.SetDefault(false);
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AddAttr<int>("context_length",
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"(int, default 3) the stride of SequenceProjectOp.")
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.SetDefault(3)
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.GreaterThan(0);
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AddAttr<int>("context_start",
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"(int, default 0) the xx of SequenceProjectOp.")
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.SetDefault(0);
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AddAttr<int>("context_stride",
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"(int, default 1) the xx of SequenceProjectOp.")
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.SetDefault(1)
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.GreaterThan(0);
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AddComment(R"DOC(
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SequenceProjectOp projects features of context_length time-steps of each instance.
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For a mini-batch of 2 variable lengths sentences, containing 3, and 1 time-steps:
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Assumed input (X) is a [4, M, N] float LoDTensor, and X->lod()[0] = [0, 3, 4].
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Besides, for the sake of simplicity, we assume M=1 and N=2.
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X = [[a1, a2,
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b1, b2.
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c1, c2]
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[d1, d2]]
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This is to say that input (X) has 4 words and the dimension of each word
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representation is 2.
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- Case1:
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If we use zero to pad instead of learned weight to pad,
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and the context_lenth is 3, the output (Out) is:
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Out = [0, 0, a1, a2, b1, b2;
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a1, a2, b1, b2, c1, c2;
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b1, b2, c1, c2, 0, 0;
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0, 0, d1, d2, 0, 0]
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- Case2:
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// If we use zero to pad instead of learned weight to pad,
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// and the context_lenth is 3, the output (Out) is:
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//
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// Out = [0, 0, a1, a2, b1, b2;
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// a1, a2, b1, b2, c1, c2;
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// b1, b2, c1, c2, 0, 0;
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// 0, 0, d1, d2, 0, 0]
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)DOC");
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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(sequence_project, ops::SequenceProjectOp,
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ops::SequenceProjectOpMaker, sequence_project_grad,
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ops::SequenceProjectGradOp);
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REGISTER_OP_CPU_KERNEL(
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sequence_project,
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ops::SequenceProjectKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(
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sequence_project_grad,
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ops::SequenceProjectGradKernel<paddle::platform::CPUPlace, float>);
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@ -0,0 +1,25 @@
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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.
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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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#define EIGEN_USE_GPU
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#include "paddle/operators/sequence_project_op.h"
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namespace ops = paddle::operators;
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REGISTER_OP_GPU_KERNEL(
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sequence_project,
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ops::SequenceProjectKernel<paddle::platform::GPUPlace, float>);
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REGISTER_OP_GPU_KERNEL(
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sequence_project_grad,
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ops::SequenceProjectGradKernel<paddle::platform::GPUPlace, float>);
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File diff suppressed because it is too large
Load Diff
@ -0,0 +1,96 @@
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import unittest
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import numpy as np
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from op_test import OpTest
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class TestSeqProject(OpTest):
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def setUp(self):
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self.init_test_case()
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self.op_type = 'sequence_project'
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# one level, batch size
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x = np.random.uniform(
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0.1, 1, [self.input_size[0], self.input_size[1]]).astype('float32')
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lod = [[0, 4, 5, 8, self.input_size[0]]]
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self.begin_pad = np.max([0, -self.context_start])
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self.end_pad = np.max([0, self.context_start + self.context_length - 1])
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self.total_pad = self.begin_pad + self.end_pad
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w = np.ones((self.total_pad, self.input_size[1])) * 100
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self.inputs = {'X': (x, lod), 'PaddingData': w}
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self.attrs = {
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'context_start': self.context_start,
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'context_length': self.context_length,
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'padding_trainable': self.padding_trainable
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}
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out = np.zeros((self.input_size[0], self.input_size[1] *
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self.context_length)).astype('float32')
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self.outputs = {'Out': out}
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self.compute()
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def compute(self):
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x, lod = self.inputs['X']
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w = self.inputs['PaddingData']
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out = self.outputs['Out']
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lod = lod[0]
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for i in range(len(lod) - 1):
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for j in range(self.context_length):
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in_begin = lod[i] + self.context_start + j
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in_end = lod[i + 1] + self.context_start + j
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out_begin = lod[i]
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out_end = lod[i + 1]
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if in_begin < lod[i]:
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pad_size = np.min([lod[i] - in_begin, lod[i + 1] - lod[i]])
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if self.padding_trainable:
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sub_w = w[j:pad_size, :]
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out[lod[i]:lod[i] + pad_size, j * self.input_size[1]:(
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j + 1) * self.input_size[1]] = sub_w
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# pass
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out_begin = lod[i] + pad_size
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in_begin = lod[i]
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if in_end > lod[i + 1]:
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pad_size = np.min(
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[in_end - lod[i + 1], lod[i + 1] - lod[i]])
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out_sub = out[lod[i + 1] - pad_size:lod[i + 1], :]
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if self.padding_trainable:
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sub_w = w[j - pad_size:j, :]
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out[lod[i + 1] - pad_size:lod[i + 1], j * self.
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input_size[1]:(j + 1) * self.input_size[1]] = sub_w
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# pass
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in_end = lod[i + 1]
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out_end = lod[i + 1] - pad_size
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if in_end <= in_begin:
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continue
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in_sub = x[in_begin:in_end, :]
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out[out_begin:out_end, j * self.input_size[1]:(j + 1) *
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self.input_size[1]] += in_sub
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def init_test_case(self):
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self.input_size = [11, 23]
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self.op_type = "sequence_project"
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self.context_start = -1
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self.context_length = 3
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self.padding_trainable = False
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def test_check_output(self):
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self.check_output()
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# def test_check_grad(self):
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# self.check_grad(["X"], "Out")
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# class TestSeqAvgPool2D(TestSeqProject):
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# def init_test_case(self):
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# self.input_size = [11, 23]
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# self.op_type = "sequence_project"
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#
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# self.context_start = -1
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# self.context_length = 3
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# self.padding_trainable = True
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if __name__ == '__main__':
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unittest.main()
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