提交 b943874f 编写于 作者: Y Yan Chunwei 提交者: GitHub

move namespace rnn to a directory (#3261)

* move namespace rnn to a directory
上级 03a38b3d
......@@ -63,5 +63,6 @@ op_library(sgd_op SRCS sgd_op.cc sgd_op.cu)
op_library(fc_op
SRCS fc_op.cc
DEPS mul_op rowwise_add_op sigmoid_op softmax_op net_op)
op_library(recurrent_op SRCS recurrent_op.cc DEPS op_desc tensor op_registry operator net_op)
op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
DEPS op_desc tensor op_registry operator net_op)
cc_test(recurrent_op_test SRCS recurrent_op_test.cc DEPS recurrent_op gtest mul_op add_op)
......@@ -25,141 +25,6 @@
namespace paddle {
namespace operators {
namespace rnn {
void SegmentInputs(const std::vector<Scope*>& step_scopes,
const std::vector<Link>& inlinks, const size_t seq_len,
bool infer_shape_mode) {
PADDLE_ENFORCE(!inlinks.empty(), "no in links are provided.");
for (size_t i = 0; i < inlinks.size(); ++i) {
auto input_var = step_scopes[0]->FindVar(inlinks[i].external);
PADDLE_ENFORCE(input_var != nullptr, "input link [%s] is not in scope.",
inlinks[i].external);
Tensor* input = input_var->GetMutable<Tensor>();
framework::DDim dims = input->dims();
PADDLE_ENFORCE(static_cast<size_t>(dims[0]) == seq_len,
"all the inlinks must have same length");
framework::DDim step_dims = slice_ddim(dims, 1, dims.size());
for (size_t j = 0; j < seq_len; j++) {
Tensor* step_input =
step_scopes[j]->NewVar(inlinks[i].internal)->GetMutable<Tensor>();
if (!infer_shape_mode) {
*step_input = input->Slice<float>(j, j + 1);
}
step_input->Resize(step_dims);
}
}
}
void ConcatOutputs(const std::vector<Scope*>& step_scopes,
const std::vector<Link>& outlinks, const size_t seq_len,
bool infer_shape_mode) {
for (size_t i = 0; i < outlinks.size(); i++) {
auto output_var = step_scopes[0]->FindVar(outlinks[i].external);
PADDLE_ENFORCE(output_var != nullptr, "output link [%s] is not in scope.",
outlinks[i].external);
Tensor* output = output_var->GetMutable<Tensor>();
if (infer_shape_mode) {
framework::DDim step_dims = step_scopes[0]
->FindVar(outlinks[i].internal)
->GetMutable<Tensor>()
->dims();
std::vector<int> dims_vec = vectorize(step_dims);
dims_vec.insert(dims_vec.begin(), seq_len);
output->Resize(framework::make_ddim(dims_vec));
} else {
output->mutable_data<float>(platform::CPUPlace());
for (size_t j = 0; j < seq_len; j++) {
Tensor* step_output =
step_scopes[j]->FindVar(outlinks[i].internal)->GetMutable<Tensor>();
// TODO(luotao02) data type and platform::DeviceContext() should set
// correctly
(output->Slice<float>(j, j + 1))
.CopyFrom<float>(*step_output, platform::CPUPlace());
}
}
}
}
void LinkMemories(const std::vector<Scope*>& scopes,
const std::vector<rnn::MemoryAttr>& memories,
const size_t step_id, const int offset,
bool infer_shape_mode) {
PADDLE_ENFORCE(step_id < scopes.size(),
"step [%d] is out of range of step scopes' size [%d]", step_id,
scopes.size());
PADDLE_ENFORCE(static_cast<int>(step_id) + offset >= 0,
"offset [%d] must be large than -[%d]", offset, step_id);
PADDLE_ENFORCE(step_id + offset < scopes.size(),
"offset [%d] is out of range, it must be less than (%d - %d)",
offset, scopes.size(), step_id);
auto scope = scopes[step_id];
auto linked_scope = scopes[step_id + offset];
for (auto& attr : memories) {
auto mem = scope->FindVar(attr.pre_var)->GetMutable<Tensor>();
auto linked_mem = linked_scope->FindVar(attr.var)->GetMutable<Tensor>();
if (infer_shape_mode) {
mem->Resize(linked_mem->dims());
} else {
mem->ShareDataWith<float>(*linked_mem);
}
}
}
void InitArgument(const ArgumentName& name, Argument* arg,
const OperatorBase& op) {
arg->step_net = op.Input(name.step_net);
arg->step_scopes = op.Output(name.step_scopes);
auto inlinks = op.Inputs(name.inlinks);
auto inlink_alias = op.GetAttr<std::vector<std::string>>(name.inlink_alias);
PADDLE_ENFORCE(inlinks.size() == inlink_alias.size(),
"the size of inlinks and inlink_alias don't match:%d,%d",
inlinks.size(), inlink_alias.size());
for (size_t i = 0; i < inlinks.size(); ++i) {
rnn::Link link;
link.external = inlinks[i];
link.internal = inlink_alias[i];
(arg->inlinks).push_back(link);
}
auto outlinks = op.Outputs(name.outlinks);
auto outlink_alias = op.GetAttr<std::vector<std::string>>(name.outlink_alias);
PADDLE_ENFORCE(outlinks.size() == outlink_alias.size(),
"the size of outlinks and outlink_alias don't match:%d,%d",
outlinks.size(), outlink_alias.size());
for (size_t i = 0; i < outlinks.size(); ++i) {
rnn::Link link;
link.external = outlinks[i];
link.internal = outlink_alias[i];
(arg->outlinks).push_back(link);
}
auto boot_memories = op.Inputs(name.boot_memories);
// attributes
auto memories = op.GetAttr<std::vector<std::string>>(name.memories);
auto pre_memories = op.GetAttr<std::vector<std::string>>(name.pre_memories);
PADDLE_ENFORCE(memories.size() == boot_memories.size(),
"the size of memories, boot_memories don't match:%d,%d",
memories.size(), boot_memories.size());
PADDLE_ENFORCE(pre_memories.size() == boot_memories.size(),
"the size of pre_memories, boot_memories don't match:%d,%d",
pre_memories.size(), boot_memories.size());
PADDLE_ENFORCE(memories.size() > 0, "more than 1 memories should be set");
for (size_t i = 0; i < memories.size(); ++i) {
rnn::MemoryAttr mem_attr;
mem_attr.var = memories[i];
mem_attr.pre_var = pre_memories[i];
mem_attr.boot_var = boot_memories[i];
(arg->memories).push_back(mem_attr);
}
}
} // namespace rnn
void RecurrentAlgorithm::InferShape(const Scope& scope) const {
seq_len_ = scope.FindVar((arg_->inlinks[0]).external)
->GetMutable<Tensor>()
......
......@@ -15,78 +15,11 @@
#pragma once
#include "paddle/framework/operator.h"
#include "paddle/operators/rnn/recurrent_op_utils.h"
namespace paddle {
namespace operators {
namespace rnn {
/**
* Memory of a RNN (same as the role of `Momory` in PaddlePaddle).
*
* Memory attributes cached by this op, dims will be infered from
* boot memories in father scope. Other attributes are copied from Op's proto
* attributes.
*/
struct MemoryAttr {
// name of current state variable
std::string var;
// name of previous step's state variable
std::string pre_var;
// name of the variables to init this memory (same role of `boot_layer` in
// PaddlePaddle), which is store in father's scope.
std::string boot_var;
};
struct Link {
// input or output links name.
std::string internal;
// alias to avoid duplicate keys in scopes.
std::string external;
};
struct Argument {
std::string step_net;
std::string step_scopes;
std::vector<Link> inlinks;
std::vector<Link> outlinks;
std::vector<rnn::MemoryAttr> memories;
};
struct ArgumentName {
std::string step_net;
std::string step_scopes;
std::string inlinks;
std::string outlinks;
std::string inlink_alias; // the alias of inlinks in step net.
std::string outlink_alias; // the alias of outlinks in step net.
std::string memories; // the memory name
std::string pre_memories; // the previous memory name
std::string boot_memories; // the boot memory name
};
/**
* Prepare inputs for each step net.
*/
void SegmentInputs(const std::vector<framework::Scope*>& step_scopes,
const std::vector<Link>& inlinks, const size_t seq_len,
bool infer_shape_mode);
/**
* Process outputs of step nets and merge to variables.
*/
void ConcatOutputs(const std::vector<framework::Scope*>& step_scopes,
const std::vector<Link>& outlinks, const size_t seq_len,
bool infer_shape_mode);
void LinkMemories(const std::vector<framework::Scope*>& step_scopes,
const std::vector<MemoryAttr>& memories, const size_t step_id,
const int offset, bool infer_shape_mode);
void InitArgument(const ArgumentName& name, Argument* arg);
}; // namespace rnn
// The sequence format in RecurrentOp is Tensor<seq_len, batch_size, dim> now.
// TODO(Yan Chunwei):
// 1. No-padding computing for sequences with indifinite length in one batch.
......
......@@ -391,3 +391,4 @@ TEST(RecurrentOp, LinkMemories) {
USE_OP(add_two);
USE_OP(mul);
USE_OP_WITHOUT_KERNEL(recurrent_op);
/* 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/rnn/recurrent_op_utils.h"
namespace paddle {
namespace operators {
namespace rnn {
namespace fmw = paddle::framework;
void SegmentInputs(const std::vector<Scope*>& step_scopes,
const std::vector<Link>& inlinks, const size_t seq_len,
bool infer_shape_mode) {
PADDLE_ENFORCE(!inlinks.empty(), "no in links are provided.");
for (size_t i = 0; i < inlinks.size(); ++i) {
auto input_var = step_scopes[0]->FindVar(inlinks[i].external);
PADDLE_ENFORCE(input_var != nullptr, "input link [%s] is not in scope.",
inlinks[i].external);
Tensor* input = input_var->GetMutable<Tensor>();
fmw::DDim dims = input->dims();
PADDLE_ENFORCE(static_cast<size_t>(dims[0]) == seq_len,
"all the inlinks must have same length");
fmw::DDim step_dims = slice_ddim(dims, 1, dims.size());
for (size_t j = 0; j < seq_len; j++) {
Tensor* step_input =
step_scopes[j]->NewVar(inlinks[i].internal)->GetMutable<Tensor>();
if (!infer_shape_mode) {
*step_input = input->Slice<float>(j, j + 1);
}
step_input->Resize(step_dims);
}
}
}
void ConcatOutputs(const std::vector<Scope*>& step_scopes,
const std::vector<Link>& outlinks, const size_t seq_len,
bool infer_shape_mode) {
for (size_t i = 0; i < outlinks.size(); i++) {
auto output_var = step_scopes[0]->FindVar(outlinks[i].external);
PADDLE_ENFORCE(output_var != nullptr, "output link [%s] is not in scope.",
outlinks[i].external);
Tensor* output = output_var->GetMutable<Tensor>();
if (infer_shape_mode) {
fmw::DDim step_dims = step_scopes[0]
->FindVar(outlinks[i].internal)
->GetMutable<Tensor>()
->dims();
std::vector<int> dims_vec = vectorize(step_dims);
dims_vec.insert(dims_vec.begin(), seq_len);
output->Resize(fmw::make_ddim(dims_vec));
} else {
output->mutable_data<float>(platform::CPUPlace());
for (size_t j = 0; j < seq_len; j++) {
Tensor* step_output =
step_scopes[j]->FindVar(outlinks[i].internal)->GetMutable<Tensor>();
// TODO(luotao02) data type and platform::DeviceContext() should set
// correctly
(output->Slice<float>(j, j + 1))
.CopyFrom<float>(*step_output, platform::CPUPlace());
}
}
}
}
void LinkMemories(const std::vector<Scope*>& scopes,
const std::vector<rnn::MemoryAttr>& memories,
const size_t step_id, const int offset,
bool infer_shape_mode) {
PADDLE_ENFORCE(step_id < scopes.size(),
"step [%d] is out of range of step scopes' size [%d]", step_id,
scopes.size());
PADDLE_ENFORCE(static_cast<int>(step_id) + offset >= 0,
"offset [%d] must be large than -[%d]", offset, step_id);
PADDLE_ENFORCE(step_id + offset < scopes.size(),
"offset [%d] is out of range, it must be less than (%d - %d)",
offset, scopes.size(), step_id);
auto scope = scopes[step_id];
auto linked_scope = scopes[step_id + offset];
for (auto& attr : memories) {
auto mem = scope->FindVar(attr.pre_var)->GetMutable<Tensor>();
auto linked_mem = linked_scope->FindVar(attr.var)->GetMutable<Tensor>();
if (infer_shape_mode) {
mem->Resize(linked_mem->dims());
} else {
mem->ShareDataWith<float>(*linked_mem);
}
}
}
void InitArgument(const ArgumentName& name, Argument* arg,
const OperatorBase& op) {
arg->step_net = op.Input(name.step_net);
arg->step_scopes = op.Output(name.step_scopes);
auto inlinks = op.Inputs(name.inlinks);
auto inlink_alias = op.GetAttr<std::vector<std::string>>(name.inlink_alias);
PADDLE_ENFORCE(inlinks.size() == inlink_alias.size(),
"the size of inlinks and inlink_alias don't match:%d,%d",
inlinks.size(), inlink_alias.size());
for (size_t i = 0; i < inlinks.size(); ++i) {
rnn::Link link;
link.external = inlinks[i];
link.internal = inlink_alias[i];
(arg->inlinks).push_back(link);
}
auto outlinks = op.Outputs(name.outlinks);
auto outlink_alias = op.GetAttr<std::vector<std::string>>(name.outlink_alias);
PADDLE_ENFORCE(outlinks.size() == outlink_alias.size(),
"the size of outlinks and outlink_alias don't match:%d,%d",
outlinks.size(), outlink_alias.size());
for (size_t i = 0; i < outlinks.size(); ++i) {
rnn::Link link;
link.external = outlinks[i];
link.internal = outlink_alias[i];
(arg->outlinks).push_back(link);
}
auto boot_memories = op.Inputs(name.boot_memories);
// attributes
auto memories = op.GetAttr<std::vector<std::string>>(name.memories);
auto pre_memories = op.GetAttr<std::vector<std::string>>(name.pre_memories);
PADDLE_ENFORCE(memories.size() == boot_memories.size(),
"the size of memories, boot_memories don't match:%d,%d",
memories.size(), boot_memories.size());
PADDLE_ENFORCE(pre_memories.size() == boot_memories.size(),
"the size of pre_memories, boot_memories don't match:%d,%d",
pre_memories.size(), boot_memories.size());
PADDLE_ENFORCE(memories.size() > 0, "more than 1 memories should be set");
for (size_t i = 0; i < memories.size(); ++i) {
rnn::MemoryAttr mem_attr;
mem_attr.var = memories[i];
mem_attr.pre_var = pre_memories[i];
mem_attr.boot_var = boot_memories[i];
(arg->memories).push_back(mem_attr);
}
}
} // namespace rnn
} // namespace operators
} // namespace paddle
/* 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 <string>
#include "paddle/framework/operator.h"
#include "paddle/operators/type_alias.h"
namespace paddle {
namespace operators {
namespace rnn {
/**
* Memory of a RNN (same as the role of `Momory` in PaddlePaddle).
*
* Memory attributes cached by this op, dims will be infered from
* boot memories in father scope. Other attributes are copied from Op's proto
* attributes.
*/
struct MemoryAttr {
// name of current state variable
std::string var;
// name of previous step's state variable
std::string pre_var;
// name of the variables to init this memory (same role of `boot_layer` in
// PaddlePaddle), which is store in father's scope.
std::string boot_var;
};
struct Link {
// input or output links name.
std::string internal;
// alias to avoid duplicate keys in scopes.
std::string external;
};
struct Argument {
std::string step_net;
std::string step_scopes;
std::vector<Link> inlinks;
std::vector<Link> outlinks;
std::vector<rnn::MemoryAttr> memories;
};
struct ArgumentName {
std::string step_net;
std::string step_scopes;
std::string inlinks;
std::string outlinks;
std::string inlink_alias; // the alias of inlinks in step net.
std::string outlink_alias; // the alias of outlinks in step net.
std::string memories; // the memory name
std::string pre_memories; // the previous memory name
std::string boot_memories; // the boot memory name
};
/**
* Prepare inputs for each step net.
*/
void SegmentInputs(const std::vector<Scope*>& step_scopes,
const std::vector<Link>& inlinks, const size_t seq_len,
bool infer_shape_mode);
/**
* Process outputs of step nets and merge to variables.
*/
void ConcatOutputs(const std::vector<Scope*>& step_scopes,
const std::vector<Link>& outlinks, const size_t seq_len,
bool infer_shape_mode);
void LinkMemories(const std::vector<Scope*>& step_scopes,
const std::vector<MemoryAttr>& memories, const size_t step_id,
const int offset, bool infer_shape_mode);
void InitArgument(const ArgumentName& name, Argument* arg,
const OperatorBase& op);
} // namespace rnn
} // namespace operators
} // namespace paddle
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