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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into develop

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fix-typo
zchen0211 8 years ago
parent 31187e7e72 0885de47eb
commit 7e34b8e366
3 changed files with 287 additions and 1 deletions
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154
paddle/operators/rnn_memory_helper_op.cc
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@ -0,0 +1,154 @@
/* 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/framework/op_registry.h"
#include "paddle/framework/operator.h"
namespace paddle {
namespace operators {
class RNNMemoryHelperOp : public framework::OperatorBase {
public:
RNNMemoryHelperOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
const platform::DeviceContext &dev_ctx) const override {
auto mem_var_name = Input("X");
auto *mem_var = scope.FindVar(mem_var_name);
PADDLE_ENFORCE(mem_var != nullptr,
"Cannot find mem_var in scope, mem_var_name is %s",
mem_var_name);
auto out_name = this->Output("Out");
auto *out_var = scope.FindVar(out_name);
PADDLE_ENFORCE(out_var != nullptr,
"Cannot find out_var in scope, out_var_name is %s",
out_name);
auto *out_tensor = out_var->GetMutable<framework::LoDTensor>();
auto &mem_tensor = mem_var->Get<framework::LoDTensor>();
out_tensor->ShareDataWith(mem_tensor);
out_tensor->set_lod(mem_tensor.lod());
}
};
class RNNMemoryHelperOpShapeInference : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "");
PADDLE_ENFORCE(ctx->HasOutput("Out"), "");
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->ShareLoD("X", /*->*/ "Out");
}
};
class RNNMemoryHelperOpInfoMaker : public framework::OpProtoAndCheckerMaker {
public:
RNNMemoryHelperOpInfoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "");
AddOutput("Out", "");
AddAttr<int>("data_type",
"(int, default 5 (FP32)) "
"Output data type")
.SetDefault(framework::DataType::FP32);
AddComment("");
}
};
class RNNMemoryHelperGradOp : public framework::OperatorBase {
public:
RNNMemoryHelperGradOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
const platform::DeviceContext &dev_ctx) const override {
auto out_grad_var_name = Input(framework::GradVarName("Out"));
auto *out_grad_var = scope.FindVar(out_grad_var_name);
auto in_grad_var_name = Output(framework::GradVarName("X"));
auto *in_grad_var = scope.FindVar(in_grad_var_name);
PADDLE_ENFORCE(in_grad_var != nullptr,
"Cannot find in_grad_var in scope, name is %s",
in_grad_var_name);
if (out_grad_var == nullptr) {
VLOG(5) << "Using fill constant 0 as starting gradient";
auto in_var_name = Input("X");
auto *in_var = scope.FindVar(in_var_name);
auto &in_var_tensor = in_var->Get<framework::LoDTensor>();
framework::AttributeMap attrs;
attrs["data_type"] = framework::ToDataType(in_var_tensor.type());
attrs["shape"] = framework::vectorize2int(in_var_tensor.dims());
attrs["value"] = 0.0f;
auto zero_op = framework::OpRegistry::CreateOp(
"fill_constant", {}, {{"Out", {in_grad_var_name}}}, attrs);
zero_op->Run(scope, dev_ctx);
} else {
auto &out_grad_tensor = out_grad_var->Get<framework::LoDTensor>();
auto *in_grad_tensor = in_grad_var->GetMutable<framework::LoDTensor>();
in_grad_tensor->ShareDataWith(out_grad_tensor);
in_grad_tensor->set_lod(out_grad_tensor.lod());
}
}
};
class RNNMemoryHelperGradOpInfoMaker
: public framework::OpProtoAndCheckerMaker {
public:
RNNMemoryHelperGradOpInfoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(framework::GradVarName("Out"), "");
AddInput("X", "");
AddInput("Out", "");
AddOutput(framework::GradVarName("X"), "");
AddAttr<int>("data_type",
"(int, default 5 (FP32)) "
"Output data type")
.SetDefault(framework::DataType::FP32);
AddComment("");
}
};
class RNNMemoryHelperGradOpShapeInference : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *ctx) const override {
auto x_grad_name = framework::GradVarName("X");
auto out_grad_name = framework::GradVarName("Out");
PADDLE_ENFORCE(ctx->HasInput(out_grad_name), "");
PADDLE_ENFORCE(ctx->HasOutput(x_grad_name), "");
ctx->SetOutputDim(x_grad_name, ctx->GetInputDim(out_grad_name));
ctx->ShareLoD(out_grad_name, /*->*/ x_grad_name);
}
};
} // namespace operators
} // namespace paddle
REGISTER_OPERATOR(rnn_memory_helper, paddle::operators::RNNMemoryHelperOp,
paddle::operators::RNNMemoryHelperOpInfoMaker,
paddle::operators::RNNMemoryHelperOpShapeInference,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(rnn_memory_helper_grad,
paddle::operators::RNNMemoryHelperGradOp,
paddle::operators::RNNMemoryHelperGradOpInfoMaker,
paddle::operators::RNNMemoryHelperGradOpShapeInference);

4
python/paddle/v2/framework/framework.py
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@ -264,7 +264,9 @@ class Operator(object):
self.desc.set_attr(attr_name, attrs[attr_name])
self.desc.check_attrs()
no_kernel_op_set = {'feed', 'fetch', 'save', 'load'}
no_kernel_op_set = {
'feed', 'fetch', 'save', 'load', 'rnn_memory_helper_grad'
}
if type not in no_kernel_op_set:
self.desc.infer_var_type(self.block.desc)
self.desc.infer_shape(self.block.desc)

130
python/paddle/v2/framework/tests/test_rnn_memory_helper_op.py
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import unittest
from paddle.v2.framework.framework import Program
from paddle.v2.framework.executor import Executor
from paddle.v2.framework.backward import append_backward_ops
import numpy as np
import paddle.v2.framework.core as core
def create_tensor(np_data, place):
tensor = core.LoDTensor()
tensor.set(np_data, place)
return tensor
class RNNMemoryHelperOpTest(unittest.TestCase):
def setUp(self):
self.program = Program()
self.place = core.CPUPlace()
self.X = self.program.global_block().create_var(
name='X', shape=[2, 3], dtype='float32')
self.Out = self.program.global_block().create_var(
name='Out', shape=[2, 3], dtype='float32')
self.program.global_block().append_op(
type='rnn_memory_helper',
inputs={"X": self.X},
outputs={"Out": self.Out},
attrs={})
def test_forward(self):
x_np = np.random.normal(size=(2, 3)).astype("float32")
self.feed_map = {'X': create_tensor(x_np, self.place)}
self.fetch_list = [self.Out]
exe = Executor(self.place)
out = exe.run(self.program,
feed=self.feed_map,
fetch_list=self.fetch_list)
np.isclose(np.array(out[0]), x_np, rtol=1e-5)
class RNNMemoryHelperGradOpTest(unittest.TestCase):
def setUp(self):
self.program = Program()
self.place = core.CPUPlace()
self.input_names = ['X', 'Out', 'Out@GRAD']
self.input_vars = {
name: self.program.global_block().create_var(
name=name, shape=[2, 3], dtype='float32')
for name in self.input_names
}
self.output_names = ['X@GRAD']
self.output_vars = {
name: self.program.global_block().create_var(
name=name, shape=[2, 3], dtype='float32')
for name in self.output_names
}
self.program.global_block().append_op(
type='rnn_memory_helper_grad',
inputs=self.input_vars,
outputs=self.output_vars,
attrs={})
def test_backward(self):
self.feed_map = {
name: create_tensor(
np.random.normal(size=(2, 3)).astype("float32"), self.place)
for name in self.input_names
}
self.fetch_list = [self.output_vars['X@GRAD']]
exe = Executor(self.place)
out = exe.run(self.program,
feed=self.feed_map,
fetch_list=self.fetch_list)
np.isclose(np.array(out[0]), self.feed_map['Out@GRAD'], rtol=1e-5)
class RNNMemoryHelperGradOpWithoutInputTest(unittest.TestCase):
def setUp(self):
self.program = Program()
self.fake_program = Program()
self.place = core.CPUPlace()
self.input_names = ['X', 'Out']
self.input_vars = {
name: self.program.global_block().create_var(
name=name, shape=[2, 3], dtype='float32')
for name in self.input_names
}
self.input_vars["Out@GRAD"] = \
self.fake_program.global_block().create_var(
name="Out@GRAD", shape=[2, 3], dtype='float32')
self.output_names = ['X@GRAD']
self.output_vars = {
name: self.program.global_block().create_var(
name=name, shape=[2, 3], dtype='float32')
for name in self.output_names
}
self.program.global_block().append_op(
type='rnn_memory_helper_grad',
inputs=self.input_vars,
outputs=self.output_vars,
attrs={})
def test_backward(self):
self.feed_map = {
name: create_tensor(
np.random.normal(size=(2, 3)).astype("float32"), self.place)
for name in ['X', 'Out']
}
self.fetch_list = [self.output_vars['X@GRAD']]
exe = Executor(self.place)
out = exe.run(self.program,
feed=self.feed_map,
fetch_list=self.fetch_list)
np.isclose(
np.array(out[0]),
np.zeros(shape=(2, 3)).astype("float32"),
rtol=1e-5)
if __name__ == '__main__':
unittest.main()
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