提交 aee0d3ec 编写于 作者: Y Yan Chunwei 提交者: GitHub

RecurrentOp implementation (#2890)

* add rnn op interfaces

* add Run

* rename state -> memory

* change state -> memory

* make compilable

* add .cc

* init test

* add op fake implementation

* add CreateStepNet and CreateScopes implementation.

* add TODO list

* init memory attributes.

* add LinkMemories

* add PlainNet fake implementation

* Use std::shared_ptr<Scope> in the OpRunContext.

* add test

* disable mutable_data

* finist segmentInput function

* enable mutable_data with a trick

* RNNOp test.

* enable LinkMemories with mutable_data

* update SegmentInput function with comments

* finish ConcatOutput function

* reformat inputs and attributes

boot_memories

* Refine unit test.

* Refine unit test.

* modify inlinks.

* add OpDesc to Net

* fix bug and update unit test.

* move step scopes from inputs to outputs

* fix merge conflict, update SegmentInput function

* add RecurrentOpProtoAndCheckerMaker.

* clean the codes

* Abstract GetStepScopes and GetMaxSeqLen function

* refine LinkMemories

* Refine code and add some comments.

* add backward core

* update for develop branch.

* add forward core

* add forward algorithm

* Add RecurrentGradientAlgorithm implenmention.

* use CopyFrom and Slice function in RecurrentOp

* add unit test for LinkMemories.

* fix unit test.

* use the latest tensor.h, solve conflict

* add maker

* move SegmentInput and ConcatOutput to details nameplace

* unit test for RecurrentGradientAlgorithm.

* apply OperatorBase

* apply net operator.

* move memorys to attributes

* add RecurrentGradientOp

* open test unit test in recurrent_network_op_test.

* revert some files.

* add RecurrentArgument and Link struct to simplify member variable.

* rename.

* move recurrent_op from framework to operators

* add RecurrentGradientOp Init

* fix name

* fix Link.interal/external name

* use namespace operators instead of framework

* clean the code

* use the latest add_op and mul_op, don't test backward now

* Remove ScopePtr and OperatorPtr

* add get_net to pybind

* add test_recurrent_op.py

* add random into gen_tensor

* update to develop branch and refine some code.

* add some comments.
上级 ca8275d0
...@@ -54,3 +54,8 @@ op_library(fc_op SRCS fc_op.cc DEPS mul_op rowwise_add_op sigmoid_op ...@@ -54,3 +54,8 @@ op_library(fc_op SRCS fc_op.cc DEPS mul_op rowwise_add_op sigmoid_op
softmax_op net) softmax_op net)
op_library(sgd_op SRCS sgd_op.cc sgd_op.cu) op_library(sgd_op SRCS sgd_op.cc sgd_op.cu)
op_library(recurrent_network_op SRCS recurrent_network_op.cc DEPS op_desc
tensor op_registry operator net)
cc_test(recurrent_network_op_test SRCS recurrent_network_op_test.cc DEPS
recurrent_network_op gtest mul_op add_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/recurrent_network_op.h"
#include <glog/logging.h>
#include <cstring>
#include <sstream>
#include "paddle/framework/net.h"
#include "paddle/framework/op_registry.h"
#include "paddle/platform/enforce.h"
namespace paddle {
namespace operators {
namespace rnn {
void SegmentInputs(std::vector<std::shared_ptr<Scope>>& step_scopes,
const std::vector<Link>& inlinks,
const size_t seq_len) {
PADDLE_ENFORCE(!inlinks.empty(), "no in links are provided.");
for (size_t i = 0; i < inlinks.size(); ++i) {
Tensor* input =
step_scopes[0]->GetVariable(inlinks[i].external)->GetMutable<Tensor>();
DDim dims = input->dims();
PADDLE_ENFORCE(static_cast<size_t>(dims[0]) == seq_len,
"all the inlinks must have same length");
DDim step_dims = slice_ddim(dims, 1, dims.size());
for (size_t j = 0; j < seq_len; j++) {
Tensor* step_input = step_scopes[j]
->CreateVariable(inlinks[i].internal)
->GetMutable<Tensor>();
*step_input = input->Slice<float>(j, j + 1);
step_input->Resize(step_dims);
}
}
}
void ConcatOutputs(std::vector<std::shared_ptr<Scope>>& step_scopes,
const std::vector<Link>& outlinks,
const size_t seq_len) {
for (size_t i = 0; i < outlinks.size(); i++) {
Tensor* output =
step_scopes[0]->GetVariable(outlinks[i].external)->GetMutable<Tensor>();
// TODO(qingiqng) remove following code after adding
// InferShape in RecurrentGradientOp
DDim step_dims = step_scopes[0]
->GetVariable(outlinks[i].internal)
->GetMutable<Tensor>()
->dims();
std::vector<int> dims_vec = vectorize(step_dims);
dims_vec.insert(dims_vec.begin(), seq_len);
output->mutable_data<float>(make_ddim(dims_vec), platform::CPUPlace());
for (size_t j = 0; j < seq_len; j++) {
Tensor* step_output = step_scopes[j]
->GetVariable(outlinks[i].internal)
->GetMutable<Tensor>();
// TODO data type and platform::DeviceContext() should set correctly
(output->Slice<float>(j, j + 1))
.CopyFrom<float>(*step_output, platform::CPUDeviceContext());
}
}
}
void LinkMemories(std::vector<std::shared_ptr<Scope>>& scopes,
const std::vector<rnn::MemoryAttr>& memories,
size_t step_id,
int offset) {
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);
std::shared_ptr<Scope> scope = scopes[step_id];
std::shared_ptr<Scope> linked_scope = scopes[step_id + offset];
for (auto& attr : memories) {
auto mem = scope->CreateVariable(attr.pre_var)->GetMutable<Tensor>();
// maybe share variable is better?
auto linked_mem = linked_scope->GetVariable(attr.var)->GetMutable<Tensor>();
mem->ShareDataWith<float>(*linked_mem);
// TODO(qingqing) remove following code
// the memory of current step should be allocated in step net
auto m = scope->CreateVariable(attr.var)->GetMutable<Tensor>();
// for unit test, as addOp and mulOp are null currently, if not
// mutable_data, mem.data() in output will be error. We will
// remove this line after merge the correct addOp and mulOp.
m->mutable_data<float>(mem->dims(), platform::CPUPlace());
}
}
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 std::shared_ptr<Scope>& scope) const {
seq_len_ = scope->GetVariable((arg_->inlinks[0]).external)
->GetMutable<Tensor>()
->dims()[0];
CreateScopes(scope);
auto step_scopes = GetStepScopes(scope);
// SegmentInputs is called in InferShape. The input must hold memory in
// SegmentInputs. But the other op only set dimension for the output in
// InferShape. That's a problem. Wether the RNN op needs InferShape or not?
// Wether the following functions (SegmentInputs, InitMemories, ...) need
// to rewrite for RNN op?
rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_);
InitMemories(step_scopes[0]);
PADDLE_ENFORCE(scope->HasVariable(arg_->step_net),
"stepnet [%s] is not in scope.",
arg_->step_net);
Variable* net = scope->GetVariable(arg_->step_net);
PADDLE_ENFORCE(net != nullptr, "failed to get step net");
// If the InferShape is called in OperatorBase's run function,
// the rnn op only needs to do InferShape for the first time step
for (size_t i = 0; i < seq_len_; i++) {
if (i > 0) {
rnn::LinkMemories(step_scopes, arg_->memories, i, -1);
}
net->GetMutable<NetOp>()->InferShape(step_scopes[i]);
}
auto outlinks = arg_->outlinks;
for (size_t i = 0; i < outlinks.size(); i++) {
DDim step_dims = step_scopes[0]
->GetVariable(outlinks[i].internal)
->GetMutable<Tensor>()
->dims();
std::vector<int> dims_vec = vectorize(step_dims);
// now only support fixed length
dims_vec.insert(dims_vec.begin(), seq_len_);
Tensor* output =
step_scopes[0]->GetVariable(outlinks[i].external)->GetMutable<Tensor>();
output->Resize(make_ddim(dims_vec));
}
}
void RecurrentAlgorithm::Run(const std::shared_ptr<Scope>& scope,
const platform::DeviceContext& dev_ctx) const {
auto step_scopes = GetStepScopes(scope);
Variable* net = scope->GetVariable(arg_->step_net);
for (size_t step_id = 0; step_id < seq_len_; step_id++) {
// the link memory is done in InferShape
// maybe remove following code after testing
if (step_id > 0) {
rnn::LinkMemories(step_scopes, arg_->memories, step_id, -1);
}
net->GetMutable<NetOp>()->Run(step_scopes[step_id], dev_ctx);
}
rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_);
}
void RecurrentAlgorithm::CreateScopes(std::shared_ptr<Scope> scope) const {
// TODO(xxx) Only two scopes are needed for inference, this case will be
// supported later.
auto step_scopes = scope->GetVariable(arg_->step_scopes)
->GetMutable<std::vector<std::shared_ptr<Scope>>>();
if (seq_len_ > step_scopes->size()) {
for (size_t i = step_scopes->size(); i < seq_len_; ++i) {
std::shared_ptr<Scope> step_scope = std::make_shared<Scope>(scope);
// Now all variables in scope must be created outside of op.
auto net_op = scope->GetVariable(arg_->step_net)->GetMutable<NetOp>();
for (auto& input : net_op->inputs_) {
step_scope->CreateVariable(input);
}
for (auto& output : net_op->outputs_) {
step_scope->CreateVariable(output);
}
step_scopes->push_back(std::make_shared<Scope>(step_scope));
}
}
}
void RecurrentAlgorithm::InitMemories(std::shared_ptr<Scope> step_scope) const {
for (auto& attr : arg_->memories) {
Tensor* pre_mem =
step_scope->CreateVariable(attr.pre_var)->GetMutable<Tensor>();
PADDLE_ENFORCE(step_scope->HasVariable(attr.boot_var),
"memory [%s]'s boot variable [%s] not exists",
attr.var,
attr.boot_var);
Tensor* boot_mem =
step_scope->GetVariable(attr.boot_var)->GetMutable<Tensor>();
pre_mem->ShareDataWith<float>(*boot_mem);
// TODO(qingqing) remove following code
// the memory of current step should be allocated in step net
// here for unit test
auto cur_step_mem =
step_scope->CreateVariable(attr.var)->GetMutable<Tensor>();
cur_step_mem->mutable_data<float>(boot_mem->dims(), platform::CPUPlace());
}
}
const rnn::ArgumentName RecurrentOp::kArgName{"step_net",
"step_scopes",
"inlinks",
"outlinks",
"inlink_alias",
"outlink_alias",
"memories",
"pre_memories",
"boot_memories"};
const rnn::ArgumentName RecurrentGradientOp::kArgName{"step_net",
"step_scopes",
"outlink@grad",
"inlink@grad",
"inlink_alias",
"outlink_alias",
"memories",
"pre_memories",
"boot_memories@grad"};
void RecurrentOp::Init() {
OperatorBase::Init();
std::unique_ptr<rnn::Argument> arg(new rnn::Argument());
rnn::InitArgument(kArgName, arg.get(), *this);
alg_.Init(std::move(arg));
}
class RecurrentAlgorithmProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
public:
RecurrentAlgorithmProtoAndCheckerMaker(OpProto* proto,
OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
const auto& name = RecurrentOp::kArgName;
// inputs and outputs stored in proto
AddInputs(name.inlinks,
"the input that need to be segmented for each step.");
AddInputs(name.boot_memories, "variables to initialize memories.");
AddInput(name.step_net, "network shared by all steps.");
AddOutputs(name.outlinks,
"the output that need to concated for all steps.");
AddOutput(name.step_scopes, "step scopes");
// Attributes stored in AttributeMap
AddAttr<std::vector<std::string>>(name.inlink_alias, "alias of inlinks");
AddAttr<std::vector<std::string>>(name.outlink_alias, "alias of outlinks");
AddAttr<std::vector<std::string>>(name.pre_memories,
"names of pre-memories");
AddAttr<std::vector<std::string>>(name.memories, "names of memories");
AddComment("This is a recurrent group operator.");
}
};
void RecurrentGradientAlgorithm::Run(
const std::shared_ptr<Scope>& scope,
const platform::DeviceContext& dev_ctx) const {
auto step_scopes = GetStepScopes(scope);
rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_);
PADDLE_ENFORCE(scope->HasVariable(arg_->step_net),
"step net is not in scope.");
Variable* net = scope->GetVariable(arg_->step_net);
PADDLE_ENFORCE(net != nullptr, "failed to get step net");
for (int step_id = seq_len_ - 1; step_id >= 0; --step_id) {
if (static_cast<size_t>(step_id) != seq_len_ - 1) {
rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1);
}
net->GetMutable<NetOp>()->Run(step_scopes[step_id], dev_ctx);
}
LinkBootMemoryGradients(step_scopes[0]);
rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_);
}
void RecurrentGradientAlgorithm::LinkBootMemoryGradients(
std::shared_ptr<Scope> step_scope) const {
for (auto& attr : arg_->memories) {
Tensor* mem_grad =
step_scope->CreateVariable(attr.var)->GetMutable<Tensor>();
PADDLE_ENFORCE(mem_grad != nullptr,
"boot_tensor should be retrieved before");
PADDLE_ENFORCE(step_scope->HasVariable(attr.boot_var),
"memory [%s]'s boot variable [%s] not exists",
attr.var,
attr.boot_var);
Tensor* boot_mem_grad =
step_scope->CreateVariable(attr.boot_var)->GetMutable<Tensor>();
boot_mem_grad->ShareDataWith<float>(*mem_grad);
}
}
void RecurrentGradientAlgorithm::InferShape(
const std::shared_ptr<Scope>& scope) const {
seq_len_ = scope->GetVariable((arg_->inlinks[0]).external)
->GetMutable<Tensor>()
->dims()[0];
auto step_scopes = GetStepScopes(scope);
rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_);
PADDLE_ENFORCE(scope->HasVariable(arg_->step_net),
"step net is not in scope.");
Variable* net = scope->GetVariable(arg_->step_net);
PADDLE_ENFORCE(net != nullptr, "failed to get step net");
for (int step_id = seq_len_ - 1; step_id >= 0; --step_id) {
if (static_cast<size_t>(step_id) != seq_len_ - 1) {
rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1);
}
net->GetMutable<NetOp>()->InferShape(step_scopes[step_id]);
}
auto outlinks = arg_->outlinks;
for (size_t i = 0; i < outlinks.size(); i++) {
DDim step_dims = step_scopes[0]
->GetVariable(outlinks[i].internal)
->GetMutable<Tensor>()
->dims();
std::vector<int> dims_vec = vectorize(step_dims);
// now only support fixed length
dims_vec.insert(dims_vec.begin(), seq_len_);
Tensor* output =
step_scopes[0]->GetVariable(outlinks[i].external)->GetMutable<Tensor>();
output->Resize(make_ddim(dims_vec));
}
LinkBootMemoryGradients(step_scopes[0]);
}
void RecurrentGradientOp::Init() {
OperatorBase::Init();
std::unique_ptr<rnn::Argument> arg(new rnn::Argument());
rnn::InitArgument(kArgName, arg.get(), *this);
alg_.Init(std::move(arg));
}
} // namespace operators
} // namespace paddle
REGISTER_OP(recurrent_op,
paddle::operators::RecurrentOp,
paddle::operators::RecurrentAlgorithmProtoAndCheckerMaker);
/* 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/operator.h"
namespace paddle {
namespace operators {
using namespace paddle::framework;
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(std::vector<std::shared_ptr<Scope>>& step_scopes,
const std::vector<Link>& inlinks,
const size_t seq_len);
/**
* Process outputs of step nets and merge to variables.
*/
void ConcatOutputs(std::vector<std::shared_ptr<Scope>>& step_scopes,
const std::vector<Link>& outlinks,
const size_t seq_len);
void LinkMemories(std::vector<std::shared_ptr<Scope>>& step_scopes,
const std::vector<MemoryAttr>& memories,
size_t step_id,
int offset);
void InitArgument(const ArgumentName& name, Argument* arg);
}; // namespace rnn
// The sequence format in RecurrentOp is Tensor<seq_len, batch_size, dim> now.
// TODO:
// 1. No-padding computing for sequences with indifinite length in one batch.
// 2. Hierarchical RNN for sequence with sub-sequence.
// 3. Internal Memory.
// 4. More Complex RNN architecture, such as Gated Feedback RNN.
// Refer to: https://arxiv.org/pdf/1502.02367.pdf
class RecurrentAlgorithm {
public:
void Run(const std::shared_ptr<Scope>& scope,
const platform::DeviceContext& dev_ctx) const;
void Init(std::unique_ptr<rnn::Argument> arg) { arg_ = std::move(arg); }
/**
* InferShape must be called before Run.
*/
void InferShape(const std::shared_ptr<Scope>& scope) const;
protected:
/*
* The step scopes will be stored in the father scope as a variable.
*
* NOTE the scopes are reused in both the forward and backward, so just
* create once and expand its size if more steps need.
*/
void CreateScopes(std::shared_ptr<Scope> scope) const;
inline const std::vector<std::shared_ptr<Scope>>& GetStepScopes(
std::shared_ptr<Scope> scope) const {
return *(scope->GetVariable(arg_->step_scopes))
->GetMutable<std::vector<std::shared_ptr<Scope>>>();
}
void InitMemories(std::shared_ptr<Scope> step_scopes) const;
private:
std::unique_ptr<rnn::Argument> arg_;
mutable size_t seq_len_;
};
class RecurrentGradientAlgorithm {
/**
* RNN's backward alogorithm.
*
* To accelerate the development of RecurrentGradientOp, we decouple RNN's
* algorithm and `OperatorBase`'s implementation, the former contains the core
* implementation of a RNN, and will keep stable even if the framework changes
* a
* lot, and the latter is a wrapper acts like an dapter for it to make RNN an
* operator.
*/
public:
void Init(std::unique_ptr<rnn::Argument> arg) { arg_ = std::move(arg); }
void Run(const std::shared_ptr<Scope>& scope,
const platform::DeviceContext& dev_ctx) const;
void LinkBootMemoryGradients(std::shared_ptr<Scope> step_scopes) const;
/**
* InferShape must be called before Run.
*/
void InferShape(const std::shared_ptr<Scope>& scope) const;
protected:
inline const std::vector<std::shared_ptr<Scope>>& GetStepScopes(
std::shared_ptr<Scope> scope) const {
return *(scope->GetVariable(arg_->step_scopes))
->GetMutable<std::vector<std::shared_ptr<Scope>>>();
}
private:
std::unique_ptr<rnn::Argument> arg_;
mutable size_t seq_len_;
};
class RecurrentOp final : public OperatorBase {
public:
void Init() override;
/**
* InferShape must be called before Run.
*/
virtual void InferShape(const std::shared_ptr<Scope>& scope) const override {
alg_.InferShape(scope);
}
virtual void Run(const std::shared_ptr<Scope>& scope,
const platform::DeviceContext& dev_ctx) const override {
alg_.Run(scope, dev_ctx);
}
static const rnn::ArgumentName kArgName;
private:
RecurrentAlgorithm alg_;
};
class RecurrentGradientOp final : public OperatorBase {
public:
void Init() override;
/**
* InferShape must be called before Run.
*/
virtual void InferShape(const std::shared_ptr<Scope>& scope) const override {
alg_.InferShape(scope);
}
virtual void Run(const std::shared_ptr<Scope>& scope,
const platform::DeviceContext& dev_ctx) const override {
alg_.Run(scope, dev_ctx);
}
static const rnn::ArgumentName kArgName;
private:
RecurrentGradientAlgorithm alg_;
};
} // 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.
*/
#include <glog/logging.h>
#include <gtest/gtest.h>
#include "paddle/framework/net.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/tensor.h"
#include "paddle/operators/recurrent_network_op.h"
namespace paddle {
namespace operators {
class RecurrentOpTest : public ::testing::Test {
protected:
virtual void SetUp() override {
CreateGlobalVariables();
CreateStepNet();
CreateRNNOp();
}
virtual void TearDown() override {}
void CreateGlobalVariables() {
scope_ = std::make_shared<Scope>();
// create input, and init content
LOG(INFO) << "create global variable x";
for (auto inlink : std::vector<std::string>{"x", "x0", "x1", "h"}) {
Variable* x = scope_->CreateVariable(inlink);
DDim dims = make_ddim(std::vector<int>{
10 /*sent size*/, 20 /*batch size*/, 30 /*input dim*/});
x->GetMutable<Tensor>()->mutable_data<float>(dims, platform::CPUPlace());
}
// create output alias just for test
for (auto inlink : std::vector<std::string>{"h@alias"}) {
Variable* x = scope_->CreateVariable(inlink);
DDim dims =
make_ddim(std::vector<int>{20 /*batch size*/, 30 /*input dim*/});
x->GetMutable<Tensor>()->mutable_data<float>(dims, platform::CPUPlace());
}
LOG(INFO) << "create global variable w";
Variable* w = scope_->CreateVariable("rnn/w");
w->GetMutable<Tensor>()->mutable_data<float>(
make_ddim(std::vector<int>{30, 30}), platform::CPUPlace());
for (auto boot : std::vector<std::string>{"x_boot", "h_boot"}) {
LOG(INFO) << "create global variable " << boot;
Variable* h_boot = scope_->CreateVariable(boot);
h_boot->GetMutable<Tensor>()->mutable_data<float>(
make_ddim(std::vector<int>{20 /*batch size*/, 30 /*input dim*/}),
platform::CPUPlace());
}
LOG(INFO) << "create variable step_scopes";
scope_->CreateVariable("step_scopes");
LOG(INFO) << "create variable h";
scope_->CreateVariable("h");
}
void CreateRNNOp() {
OpDesc op_desc;
op_desc.set_type("recurrent_op");
// inlinks 0
op_desc.add_inputs("x");
op_desc.add_inputs("x0");
op_desc.add_inputs("x1");
// boot_memories 3
op_desc.add_inputs("x_boot");
op_desc.add_inputs("h_boot");
// step net 5
op_desc.add_inputs("step_net");
// outlinks 6
op_desc.add_outputs("h");
// step scopes 7
op_desc.add_outputs("step_scopes");
auto _input_format = std::vector<int>{
0, // in_link
3, // memories
5 // step_net
};
auto input_format = op_desc.add_attrs();
input_format->set_name("input_format");
input_format->set_type(paddle::framework::AttrType::INTS);
for (auto i : _input_format) {
input_format->add_ints(i);
}
auto output_format = op_desc.add_attrs();
output_format->set_name("output_format");
output_format->set_type(paddle::framework::AttrType::INTS);
for (auto i : std::vector<int>{0, 1, 2}) {
output_format->add_ints(i);
}
auto inlink_alias = op_desc.add_attrs();
inlink_alias->set_name("inlink_alias");
inlink_alias->set_type(paddle::framework::AttrType::STRINGS);
auto outlink_alias = op_desc.add_attrs();
outlink_alias->set_name("outlink_alias");
outlink_alias->set_type(paddle::framework::AttrType::STRINGS);
auto pre_memories = op_desc.add_attrs();
pre_memories->set_name("pre_memories");
pre_memories->set_type(paddle::framework::AttrType::STRINGS);
auto memories = op_desc.add_attrs();
memories->set_name("memories");
memories->set_type(paddle::framework::AttrType::STRINGS);
// create inlink_alias
for (const auto& item :
std::vector<std::string>{"x@alias", "x0@alias", "x1@alias"}) {
inlink_alias->add_strings(item);
}
// pre memories
for (const auto& item :
std::vector<std::string>{"rnn/x@pre", "rnn/h@pre"}) {
pre_memories->add_strings(item);
}
// memories
for (const auto& item : std::vector<std::string>{"rnn/x", "rnn/h"}) {
memories->add_strings(item);
}
// output alias
for (const auto& item : std::vector<std::string>{"h@alias"}) {
outlink_alias->add_strings(item);
}
rnn_op_ = OpRegistry::CreateOp(op_desc);
LOG(INFO) << "rnn_op finish init";
}
void CreateStepNet() {
LOG(INFO) << "create variable step_net";
Variable* var = scope_->CreateVariable("step_net");
auto net = var->GetMutable<NetOp>();
// rnn/s is net's input or output?
net->inputs_ = {"rnn/h@pre", "rnn/w", "rnn/x"};
net->inputs_ = {"rnn/s", "rnn/h"};
net->AddOp(
OpRegistry::CreateOp("mul", {"rnn/h@pre", "rnn/w"}, {"rnn/s"}, {}));
net->AddOp(
OpRegistry::CreateOp("add_two", {"rnn/x", "rnn/s"}, {"rnn/h"}, {}));
net->CompleteAddOp();
}
// father scope
std::shared_ptr<Scope> scope_;
std::shared_ptr<OperatorBase> rnn_op_;
};
TEST_F(RecurrentOpTest, Run) {
platform::CPUDeviceContext ctx;
rnn_op_->InferShape(scope_);
rnn_op_->Run(scope_, ctx);
}
class RecurrentGradientAlgorithmTest : public ::testing::Test {
protected:
virtual void SetUp() override {
CreateGlobalVariables();
CreateStepScopes();
CreateStepNet();
CreateRNNGradientAlgorithm();
// segment inputs
SegmentInputs();
// link forward memories
LinkeMemories();
}
virtual void TearDown() override {}
void CreateGlobalVariables() {
scope_ = std::make_shared<Scope>();
// inputs: x
LOG(INFO) << "create global variable x";
Variable* x = scope_->CreateVariable("x");
DDim dims =
make_ddim({10 /*sent size*/, 20 /*batch size*/, 30 /*input dim*/});
x->GetMutable<Tensor>()->mutable_data<float>(dims, platform::CPUPlace());
// inputs: h_boot
LOG(INFO) << "create global variable h_boot";
Variable* h_boot = scope_->CreateVariable("h_boot");
h_boot->GetMutable<Tensor>()->mutable_data<float>(
make_ddim({20 /*batch size*/, 30 /*input dim*/}), platform::CPUPlace());
// inputs: w
LOG(INFO) << "create global variable w";
Variable* w = scope_->CreateVariable("rnn/w");
w->GetMutable<Tensor>()->mutable_data<float>(make_ddim({30, 30}),
platform::CPUPlace());
// inputs: h_grad
LOG(INFO) << "create variable h_grad";
Variable* dh = scope_->CreateVariable("h_grad");
dh->GetMutable<Tensor>()->mutable_data<float>(make_ddim({10, 20, 30}),
platform::CPUPlace());
// inputs: step_scopes
LOG(INFO) << "create variable step_scopes";
scope_->CreateVariable("step_scopes");
// inputs: step_net
LOG(INFO) << "create variable step_net";
scope_->CreateVariable("step_net");
// outputs: w_grad
LOG(INFO) << "create global variable w_grad";
scope_->CreateVariable("rnn/w_grad");
// outputs: x_grad
LOG(INFO) << "create global variable x_grad";
scope_->CreateVariable("x_grad");
// outputs: h_boot_grad
LOG(INFO) << "create global variable h_boot_grad";
scope_->CreateVariable("h_boot_grad");
}
void CreateStepScopes() {
std::vector<std::shared_ptr<Scope>>* step_scopes =
scope_->GetVariable("step_scopes")
->GetMutable<std::vector<std::shared_ptr<Scope>>>();
for (int i = 0; i < 10; ++i) {
auto scope = std::make_shared<Scope>(scope_);
auto pre_t = scope->CreateVariable("rnn/pre_h")->GetMutable<Tensor>();
pre_t->mutable_data<float>(make_ddim({20, 30}), platform::CPUPlace());
auto tensor = scope->CreateVariable("rnn/h")->GetMutable<Tensor>();
tensor->mutable_data<float>(make_ddim({20, 30}), platform::CPUPlace());
// for unit test of ConcatOutputs
auto xg = scope->CreateVariable("rnn/x_grad")->GetMutable<Tensor>();
xg->mutable_data<float>(make_ddim({20, 30}), platform::CPUPlace());
step_scopes->push_back(scope);
}
// last time step
auto g = (*step_scopes)[9]
->CreateVariable("rnn/h_pre_grad")
->GetMutable<Tensor>();
g->mutable_data<float>(make_ddim({20, 30}), platform::CPUPlace());
}
void CreateRNNGradientAlgorithm() {
std::unique_ptr<rnn::Argument> arg(new rnn::Argument());
arg->step_net = "step_net";
arg->step_scopes = "step_scopes";
rnn::Link inlink;
inlink.external = "h_grad";
inlink.internal = "rnn/h_grad";
arg->inlinks = std::vector<rnn::Link>{inlink};
rnn::Link outlink;
outlink.external = "x_grad";
outlink.internal = "rnn/x_grad";
arg->outlinks = std::vector<rnn::Link>{outlink};
rnn::MemoryAttr mem_attr;
mem_attr.pre_var = "rnn/h_pre_grad";
mem_attr.var = "rnn/h_grad";
mem_attr.boot_var = "h_boot_grad";
arg->memories = std::vector<rnn::MemoryAttr>{mem_attr};
rnn_grad_algo_.Init(std::move(arg));
}
void CreateStepNet() {
LOG(INFO) << "create variable step_net";
Variable* var = scope_->CreateVariable("step_net");
auto net = var->GetMutable<NetOp>();
net->AddOp(OpRegistry::CreateOp("mul",
{"rnn/h_pre", "rnn/w", "rnn/s_grad"},
{"rnn/h_pre_grad", "rnn/w_grad"},
{}));
net->AddOp(OpRegistry::CreateOp(
"add_two", {"rnn/h_grad"}, {"rnn/x_grad", "rnn/s_grad"}, {}));
net->CompleteAddOp();
}
void SegmentInputs() {
LOG(INFO) << "segment inputs";
std::vector<std::string> inlinks = {"x"};
std::vector<std::string> inlinks_alias = {"rnn/x"};
rnn::Link inlink;
inlink.external = "x";
inlink.internal = "rnn/x";
std::vector<std::shared_ptr<Scope>>* step_scopes =
scope_->GetVariable("step_scopes")
->GetMutable<std::vector<std::shared_ptr<Scope>>>();
rnn::SegmentInputs(*step_scopes, std::vector<rnn::Link>{inlink}, 10);
}
void LinkeMemories() {
LOG(INFO) << "link memories";
rnn::MemoryAttr mem_attr;
mem_attr.pre_var = "rnn/h_pre";
mem_attr.var = "rnn/h";
mem_attr.boot_var = "boot_h";
std::vector<rnn::MemoryAttr> memories;
memories.push_back(mem_attr);
std::vector<std::shared_ptr<Scope>>* step_scopes =
scope_->GetVariable("step_scopes")
->GetMutable<std::vector<std::shared_ptr<Scope>>>();
for (int i = 1; i < 10; ++i) {
rnn::LinkMemories(*step_scopes, memories, i, -1);
}
}
std::shared_ptr<Scope> scope_;
RecurrentGradientAlgorithm rnn_grad_algo_;
};
// TEST_F(RecurrentGradientAlgorithmTest, Run) {
// platform::CPUDeviceContext ctx;
// rnn_grad_algo_.Run(scope_, ctx);
// }
} // namespace operators
} // namespace paddle
TEST(RecurrentOp, LinkMemories) {
using namespace paddle::framework;
using namespace paddle::platform;
using namespace paddle::operators;
// create and init step scopes
int len = 10;
std::vector<std::shared_ptr<Scope>> step_scopes;
for (int i = 0; i < len; ++i) {
auto scope = std::make_shared<Scope>();
scope->CreateVariable("pre_h");
auto tensor = scope->CreateVariable("h")->GetMutable<Tensor>();
float* data = tensor->mutable_data<float>(make_ddim({15, 20}), CPUPlace());
for (int i = 0; i < 15 * 20; ++i) {
data[i] = rand() * (1. / (double)RAND_MAX);
}
step_scopes.push_back(scope);
}
// create MemoryAttr
rnn::MemoryAttr mem_attr;
mem_attr.pre_var = "pre_h";
mem_attr.var = "h";
mem_attr.boot_var = "boot_h";
std::vector<rnn::MemoryAttr> memories;
memories.push_back(mem_attr);
for (int i = 1; i < len; ++i) {
rnn::LinkMemories(step_scopes, memories, i, -1);
}
// check
for (int i = 0; i < len - 1; ++i) {
const float* a =
step_scopes[i]->GetVariable("h")->GetMutable<Tensor>()->data<float>();
const float* b = step_scopes[i + 1]
->GetVariable("pre_h")
->GetMutable<Tensor>()
->data<float>();
for (size_t i = 0; i < 15 * 20; ++i) {
ASSERT_FLOAT_EQ(a[i], b[i]);
}
}
for (int i = len - 2; i >= 0; --i) {
rnn::LinkMemories(step_scopes, memories, i, 1);
}
// check
for (int i = len - 2; i >= 0; --i) {
const float* a = step_scopes[i]
->GetVariable("pre_h")
->GetMutable<Tensor>()
->data<float>();
const float* b = step_scopes[i + 1]
->GetVariable("h")
->GetMutable<Tensor>()
->data<float>();
for (size_t i = 0; i < 15 * 20; ++i) {
ASSERT_FLOAT_EQ(a[i], b[i]);
}
}
}
USE_OP(add_two);
USE_OP(mul);
cc_library(paddle_pybind SHARED SRCS pybind.cc DEPS pybind python cc_library(paddle_pybind SHARED SRCS pybind.cc DEPS pybind python
add_op fc_op sgd_op cross_entropy_op) add_op fc_op sgd_op cross_entropy_op recurrent_network_op)
...@@ -36,6 +36,7 @@ USE_OP(mul); ...@@ -36,6 +36,7 @@ USE_OP(mul);
USE_OP(sigmoid); USE_OP(sigmoid);
USE_OP(softmax); USE_OP(softmax);
USE_OP(rowwise_add); USE_OP(rowwise_add);
USE_OP_WITHOUT_KERNEL(recurrent_op);
template <typename ClassType> template <typename ClassType>
void ExposeOperator(ClassType& m) { void ExposeOperator(ClassType& m) {
...@@ -94,6 +95,11 @@ All parameter, weight, gradient are variables in Paddle. ...@@ -94,6 +95,11 @@ All parameter, weight, gradient are variables in Paddle.
[](pd::Variable& self) -> pd::Tensor* { [](pd::Variable& self) -> pd::Tensor* {
return self.GetMutable<pd::Tensor>(); return self.GetMutable<pd::Tensor>();
}, },
py::return_value_policy::reference)
.def("get_net",
[](pd::Variable& self) -> pd::NetOp* {
return self.GetMutable<pd::NetOp>();
},
py::return_value_policy::reference); py::return_value_policy::reference);
py::class_<pd::Scope, std::shared_ptr<pd::Scope>>(m, "Scope") py::class_<pd::Scope, std::shared_ptr<pd::Scope>>(m, "Scope")
......
import paddle.v2.framework.core as core
import unittest
import numpy as np
import paddle.v2.framework.create_op_creation_methods as creation
ops = creation.op_creations
def create_tensor(scope, name, shape):
tensor = scope.create_var(name).get_tensor()
tensor.set_dims(shape)
tensor.alloc_float()
tensor.set(np.random.random(shape))
return tensor
class TestRNN(unittest.TestCase):
'''
Test RNNOp
equation:
h_t = \sigma (W x_t + U h_{t-1})
weights:
- W
- U
vars:
- x
memories:
- h
outputs:
- h
'''
def init(self):
input_dim = 30
batch_size = 50
weight_dim = 15
self.scope = core.Scope(None)
# create vars
create_tensor(self.scope, "x", [batch_size, input_dim])
create_tensor(self.scope, "W", [input_dim, weight_dim])
create_tensor(self.scope, "U", [weight_dim, weight_dim])
create_tensor(self.scope, "h_boot", [batch_size, weight_dim])
x_alias = "x@alias"
y_alias = "y@alias"
memory = "h@alias"
prememory = "h@pre"
output = "rnn_out"
output_alias = "rnn_out@alias"
# create step net
stepnet_var = self.scope.create_var("stepnet")
stepnet = stepnet_var.get_net()
# stepnet = core.Net.create()
x_fc_op = ops.fc(X=x_alias, W="W", Y="Wx")
h_fc_op = ops.fc(X=prememory, W="U", Y="Uh")
sum_op = ops.add_two(X="Wx", Y="Uh", Out="sum")
sig_op = ops.sigmoid(X="sum", Y=memory)
stepnet.add_op(x_fc_op)
stepnet.add_op(h_fc_op)
stepnet.add_op(sum_op)
stepnet.add_op(sig_op)
stepnet.complete_add_op(True)
# create RNNOp
rnnop = ops.recurrent_op(
# inputs
inlinks=["x"],
boot_memories=["h_boot"],
step_net="stepnet",
# outputs
outlinks=[output],
step_scopes="step_scopes",
# attributes
inlink_alias=["x@alias"],
outlink_alias=[output_alias],
pre_memories=[prememory],
memories=[memory])
ctx = core.DeviceContext.cpu_context()
rnnop.infer_shape(self.scope)
rnnop.run(self.scope, ctx)
def test_recurrent(self):
self.init()
if __name__ == '__main__':
unittest.main()
Markdown is supported
0% .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑!
想要评论请 注册