Skip to content

P
Paddle

Crayon鑫 / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
提交 479e4a50 编写于 作者: Y Yu Yang
浏览文件
操作

Merge branch 'feature/simplify_sum_op' into feature/grad_reg_mechanism_cont2

上级 ff8766e9 297ba1a3
隐藏空白更改
内联 并排
Showing with 656 addition and 161 deletion
paddle/framework/CMakeLists.txt
浏览文件 @ 479e4a50
......@@ -41,3 +41,6 @@ add_custom_command(TARGET framework_py_proto POST_BUILD
cc_library(backward SRCS backward.cc DEPS net_op)
cc_test(backward_test SRCS backward_test.cc DEPS backward recurrent_op device_context)
cc_library(tensor_array SRCS tensor_array.cc DEPS lod_tensor)
cc_test(tensor_array_test SRCS tensor_array_test.cc DEPS tensor_array place)
paddle/framework/block_desc.h
浏览文件 @ 479e4a50
......@@ -19,6 +19,7 @@ limitations under the License. */
#include <vector>
#include "paddle/framework/op_desc.h"
#include "paddle/framework/var_desc.h"
#include "paddle/platform/macros.h"
namespace paddle {
namespace framework {
......@@ -34,9 +35,6 @@ class BlockDescBind {
BlockDescBind(ProgramDescBind *prog, BlockDesc *desc)
: prog_(prog), desc_(desc), need_update_(false) {}
BlockDescBind(const BlockDescBind &o) = delete;
BlockDescBind &operator=(const BlockDescBind &o) = delete;
int32_t ID() const { return desc_->idx(); }
int32_t Parent() const { return desc_->parent_idx(); }
......@@ -66,6 +64,8 @@ class BlockDescBind {
std::deque<std::unique_ptr<OpDescBind>> ops_;
std::unordered_map<std::string, std::unique_ptr<VarDescBind>> vars_;
DISABLE_COPY_AND_ASSIGN(BlockDescBind);
};
} // namespace framework
} // namespace paddle
paddle/framework/op_info.h
浏览文件 @ 479e4a50
......@@ -20,6 +20,7 @@
#include "paddle/framework/attribute.h"
#include "paddle/framework/op_desc.h"
#include "paddle/framework/type_defs.h"
#include "paddle/platform/macros.h"
namespace paddle {
namespace framework {
......@@ -53,11 +54,6 @@ class OpInfoMap {
public:
static OpInfoMap& Instance();
OpInfoMap(const OpInfoMap& o) = delete;
OpInfoMap(OpInfoMap&& o) = delete;
OpInfoMap& operator=(const OpInfoMap& o) = delete;
OpInfoMap& operator=(OpInfoMap&& o) = delete;
bool Has(const std::string& op_type) const {
return map_.find(op_type) != map_.end();
}
......@@ -93,6 +89,8 @@ class OpInfoMap {
private:
OpInfoMap() = default;
std::unordered_map<std::string, const OpInfo> map_;
DISABLE_COPY_AND_ASSIGN(OpInfoMap);
};
} // namespace framework
......
paddle/framework/program_desc.h
浏览文件 @ 479e4a50
......@@ -16,6 +16,7 @@ limitations under the License. */
#include <vector>
#include "paddle/framework/framework.pb.h"
#include "paddle/platform/macros.h"
namespace paddle {
namespace framework {
......@@ -26,9 +27,6 @@ class ProgramDescBind {
public:
static ProgramDescBind &Instance(ProgramDesc *prog);
ProgramDescBind(const ProgramDescBind &o) = delete;
ProgramDescBind &operator=(const ProgramDescBind &o) = delete;
BlockDescBind *AppendBlock(const BlockDescBind &parent);
BlockDescBind *Block(size_t idx) { return blocks_[idx].get(); }
......@@ -46,6 +44,8 @@ class ProgramDescBind {
ProgramDesc *prog_;
std::vector<std::unique_ptr<BlockDescBind>> blocks_;
DISABLE_COPY_AND_ASSIGN(ProgramDescBind);
};
} // namespace framework
} // namespace paddle
paddle/framework/scope.h
浏览文件 @ 479e4a50
......@@ -19,6 +19,7 @@ limitations under the License. */
#include <unordered_map>
#include "paddle/framework/variable.h"
#include "paddle/platform/macros.h"
namespace paddle {
namespace framework {
......@@ -38,11 +39,6 @@ class Scope {
Scope() {}
~Scope();
// Disable Copy, Assign, Move.
Scope(const Scope& other) = delete;
Scope& operator=(const Scope& other) = delete;
Scope(Scope&& other) = delete;
/// Create a sub-scope. Returns a reference other than a pointer so
/// to prevent from manual deletion.
/// Mark it to const because that new kid scope cannot change parent scope.
......@@ -73,6 +69,8 @@ class Scope {
std::unordered_map<std::string, Variable*> vars_;
mutable std::list<Scope*> kids_;
Scope const* parent_{nullptr};
DISABLE_COPY_AND_ASSIGN(Scope);
};
} // namespace framework
......
paddle/framework/tensor_array.cc 0 → 100644
浏览文件 @ 479e4a50
/* 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/tensor_array.h"
#include <glog/logging.h>
#include <algorithm>
#include <limits>
namespace paddle {
namespace framework {
namespace detail {
/*
* Offer an iterator over the length-sorted lod-tensor's top level. The top
* level of a lod-tensor stores batch-size of sequences, each top-level sequence
* may contains several lower-level sequences, sort top-level lod by the numbers
* of lower-level sequences in descending order, so that during RNN's running,
* the batch-size will keep decreasing, the short sentences will end at the tail
* of each batch.
*
* Let's take a simple lod-tensor for example
*
* |(0) |(1) top-level has two instances
* ||| ||||| lower-level
*
* sort by lower-level's length
*
* |(1) |(0)
* ||||| |||
*
* when RNN runs, it get 5 batches (equals the number of elements the longest
* sequence has)
*
* |||||
* |||
*
* the first three batches has two elements, the last two elements just has 1
* element each.
*/
struct DynamicBatchUnpacker {
using value_type = float;
DynamicBatchUnpacker(const LoDTensor& source, size_t level,
bool descend = true)
: source(&source), level(level) {
BuildLengthSortedMeta(descend);
}
LoDTensor GetBatch(size_t index);
std::vector<DySeqMeta> meta;
LoDTensor const* source;
size_t level;
protected:
void BuildLengthSortedMeta(bool descend);
};
LoDTensor PackDynamicBatch(const std::vector<LoDTensor>& source,
const std::vector<DySeqMeta>& meta, const LoD& lod,
size_t level);
} // namespace detail
const LoDTensor& TensorArray::Read(size_t index) const {
PADDLE_ENFORCE_LE(index, MAX_SIZE, "index[%d] too large", index);
if (index >= size()) {
values_.resize(index + 1);
}
return values_[index];
}
void TensorArray::Write(size_t index, const LoDTensor& value) {
PADDLE_ENFORCE_LE(index, MAX_SIZE, "index[%d] too large", index);
if (index >= size()) {
values_.resize(index + 1);
}
values_[index].Resize(value.dims());
values_[index].mutable_data<value_type>(platform::CPUPlace());
values_[index].CopyFrom<value_type>(value, platform::CPUPlace());
}
void TensorArray::WriteShared(size_t index, const LoDTensor& value) {
PADDLE_ENFORCE_LE(index, MAX_SIZE, "index[%d] too large", index);
if (index >= size()) {
values_.resize(index + 1);
}
values_[index].ShareDataWith<value_type>(value);
}
LoDTensor TensorArray::Pack(size_t level, const std::vector<DySeqMeta>& meta,
const LoD& lod) const {
return detail::PackDynamicBatch(values_, meta, lod, level);
}
std::vector<DySeqMeta> TensorArray::Unpack(const LoDTensor& source, int level,
bool length_desend) {
detail::DynamicBatchUnpacker unpacker(source, level,
length_desend /*descend*/);
// find max length of all the sequences
size_t max_length = 0;
for (const auto& seq : unpacker.meta) {
max_length = std::max(max_length, seq.end - seq.begin);
}
// write batches to values
for (size_t batch_id = 0; batch_id < max_length; batch_id++) {
Write(batch_id, unpacker.GetBatch(batch_id));
}
return unpacker.meta;
}
LoDTensor TensorArray::Stack() const {
LoDTensor result;
if (size() == 0) return result;
const auto& first_dims = values_.front().dims();
// check all the values have the same shape
// TODO(superjom) check the same dtypes
for (size_t idx = 1; idx < size(); idx++) {
const auto& value_dims = values_[idx].dims();
PADDLE_ENFORCE_EQ(first_dims, value_dims);
}
// copy
auto result_dims = vectorize(first_dims);
result_dims.insert(result_dims.begin(), size());
result.Resize(make_ddim(result_dims));
result.mutable_data<value_type>(platform::CPUPlace());
for (size_t idx = 0; idx < size(); idx++) {
result.Slice<value_type>(idx, idx + 1)
.CopyFrom<value_type>(Read(idx), platform::CPUPlace());
}
return result;
}
void TensorArray::Unstack(const LoDTensor& source) const {
Unstack(source, false /*data_shared*/);
}
void TensorArray::UnstackShared(const LoDTensor& source) const {
Unstack(source, true /*data_shared*/);
}
void TensorArray::Unstack(const LoDTensor& source, bool data_shared) const {
size_t first_dim = source.dims()[0];
DDim value_dims = slice_ddim(source.dims(), 1, source.dims().size());
PADDLE_ENFORCE_GT(first_dim, 0,
"source should have some data to be unstacked");
values_.resize(first_dim);
for (size_t elem = 0; elem < first_dim; elem++) {
// create a new value
auto& value = values_[elem];
if (data_shared) {
// share memory
value.ShareDataWith<value_type>(source.Slice<value_type>(elem, elem + 1));
} else {
// copy
value.Resize(value_dims);
value.CopyFrom<value_type>(source.Slice<value_type>(elem, elem + 1),
platform::CPUPlace());
}
}
}
size_t TensorArray::size() const { return values_.size(); }
namespace detail {
void DynamicBatchUnpacker::BuildLengthSortedMeta(bool descend) {
PADDLE_ENFORCE(meta.empty(), "duplicate build meta");
// collect meta for each sequence in some level
auto lod = SliceLevels(source->lod(), level, level + 1)[0];
for (size_t seq_id = 0; seq_id < lod.size() - 1; seq_id++) {
DySeqMeta seq_meta({lod[seq_id], lod[seq_id + 1], seq_id});
meta.push_back(seq_meta);
}
PADDLE_ENFORCE_GT(meta.size(), 0, "meta is empty");
// sort by length
sort(meta.begin(), meta.end(),
[descend](const DySeqMeta& a, const DySeqMeta& b) {
bool a_ge_b = (a.end - a.begin) > (b.end - b.begin);
return descend ? a_ge_b : !a_ge_b;
});
}
LoDTensor DynamicBatchUnpacker::GetBatch(size_t index) {
PADDLE_ENFORCE(!meta.empty(), "should build meta first");
LoDTensor result;
// collect indice need to copy to the batch
std::vector<size_t> indice;
for (size_t seq_id = 0; seq_id < meta.size(); seq_id++) {
const auto& seq_meta = meta[seq_id];
if (index >= seq_meta.end) break;
indice.push_back(seq_meta.begin + index);
}
PADDLE_ENFORCE(!indice.empty(), "invalid batch at %d", index);
// copy the indice of records in LoDTensor
auto record_dims = slice_ddim(source->dims(), 1, source->dims().size());
auto record_dims_vec = vectorize(record_dims);
record_dims_vec.insert(record_dims_vec.begin(), indice.size());
result.Resize(make_ddim(record_dims_vec));
result.mutable_data<value_type>(platform::CPUPlace());
for (size_t i = 0; i < indice.size() - 1; i++) {
auto index = indice[i];
auto target = result.Slice<value_type>(i, i + 1);
auto source_ = source->Slice<value_type>(index, index + 1);
target.CopyFrom<value_type>(source_, platform::CPUPlace());
}
return result;
}
LoDTensor PackDynamicBatch(const std::vector<LoDTensor>& source,
const std::vector<DySeqMeta>& meta, const LoD& lod,
size_t level) {
PADDLE_ENFORCE(!source.empty());
PADDLE_ENFORCE(!meta.empty());
PADDLE_ENFORCE(!lod.empty());
LoDTensor result;
// init result space
auto record_dims = slice_ddim(source[0].dims(), 1, source[0].dims().size());
auto record_dims_vec = vectorize(record_dims);
auto height = lod[level].back();
record_dims_vec.insert(record_dims_vec.begin(), height);
result.Resize(make_ddim(record_dims_vec));
result.mutable_data<float>(platform::CPUPlace());
for (size_t batch_id = 0; batch_id < source.size(); batch_id++) {
for (size_t seq_id = 0; seq_id < meta.size(); seq_id++) {
const auto& seq_meta = meta[seq_id];
// source is source[batch_id][seq_id]
// target is result[index]
auto index = seq_meta.begin + batch_id;
if (index >= seq_meta.end) break;
auto source_ = source[batch_id].Slice<float>(seq_id, seq_id + 1);
auto target = result.Slice<float>(index, index + 1);
target.CopyFrom<float>(source_, platform::CPUPlace());
}
}
result.set_lod(lod);
return result;
}
} // namespace detail
} // namespace framework
} // namespace paddle
paddle/framework/tensor_array.h 0 → 100644
浏览文件 @ 479e4a50
/* 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 <vector>
#include "paddle/framework/lod_tensor.h"
namespace paddle {
namespace framework {
/*
* DyBatchSeqPosition stores indices of the basic element in tensor. It is used
* after lod-tensor's re-assembling, its info can be used to recover the order
* in original lod-tensor.
*/
struct DySeqMeta {
size_t begin;
size_t end; // not included
size_t ori_idx;
};
/*
* TensorArray is a C-array-like array of tensors, it is meant to be used with
* dynamic iteration primitives such as while_loop. It is used to segment inputs
* and store states in all time steps.
*
* By providing some methods similar to a C++ array, the difinition of some
* state-based dynamic models such as RNN cound be more natural and highly
* flexible.
*/
class TensorArray {
public:
using value_type = float;
// max number of values allowed to store.
const size_t MAX_SIZE{100000};
/*
* Read the value at location `index` in the `TensorArray`.
*/
const LoDTensor &Read(size_t index) const;
/*
* Write value into the index of the TensorArray.
*/
void Write(size_t index, const LoDTensor &value);
/*
* Write value into the index of the TensorArray, with memory shared.
*/
void WriteShared(size_t index, const LoDTensor &value);
/*
* Recover the original LoD-arranged LoDTensor with the `values`, `level` and
* `indice_map`.
*/
LoDTensor Pack(size_t level, const std::vector<DySeqMeta> &meta,
const LoD &lod) const;
/*
* Split LoDTensor in some `level` and write the generated batches to
* `values`, if set `desend`, will sort by length in descending order else in
* ascending order.
*/
std::vector<DySeqMeta> Unpack(const LoDTensor &source, int level,
bool length_desend);
/*
* Pack the values into a tensor with rank one higher than each tensor in
* values.
*/
LoDTensor Stack() const;
/*
* Unpacks the given division of a rank-`R` tensor into rank-`(R-1)` tensors.
*/
void Unstack(const LoDTensor &source) const;
/*
* Unpacks the given division of a rank-`R` tensor into rank-`(R-1)` tensors,
* with memory of tensors shared.
*/
void UnstackShared(const LoDTensor &source) const;
/*
* Return the number of values.
*/
size_t size() const;
protected:
void Unstack(const LoDTensor &source, bool data_shared) const;
private:
mutable std::vector<LoDTensor> values_;
}; // class TensorArray
} // namespace framework
} // namespace paddle
paddle/framework/tensor_array_test.cc 0 → 100644
浏览文件 @ 479e4a50
/* 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/tensor_array.h"
#include <gtest/gtest.h>
namespace paddle {
namespace framework {
class TensorArrayTester : public ::testing::Test {
protected:
void SetUp() override {
LoDTensor source;
source.Resize(make_ddim({batch_size, dim}));
int* data = source.mutable_data<int>(platform::CPUPlace());
for (int i = 0; i < 16 * 32; i++) {
data[i] = i;
}
ta.Unstack(source);
}
TensorArray ta;
const int batch_size = 16;
const int dim = 32;
};
TEST_F(TensorArrayTester, Read) {
for (int i = 0; i < batch_size; i++) {
const auto& tensor = ta.Read(i);
ASSERT_EQ(tensor.dims()[0], 1);
ASSERT_EQ(tensor.dims()[1], dim);
}
}
TEST_F(TensorArrayTester, Write) {
LoDTensor source;
source.Resize(make_ddim({1, dim}));
for (int i = 0; i < dim; i++) {
*(source.mutable_data<int>(platform::CPUPlace()) + i) = i;
}
ta.Write(2, source);
const auto& tensor = ta.Read(2);
for (int i = 0; i < dim; i++) {
EXPECT_EQ(*(tensor.data<int>() + i), *(source.data<int>() + i));
}
}
TEST_F(TensorArrayTester, WriteShared) {
LoDTensor source;
source.Resize(make_ddim({1, dim}));
for (int i = 0; i < dim; i++) {
*(source.mutable_data<int>(platform::CPUPlace()) + i) = i;
}
ta.WriteShared(2, source);
const auto& tensor = ta.Read(2);
for (int i = 0; i < dim; i++) {
EXPECT_EQ(*(tensor.data<int>() + i), *(source.data<int>() + i));
}
EXPECT_EQ(source.data<int>(), tensor.data<int>());
}
class TensorArrayPackTester : public ::testing::Test {
protected:
virtual void SetUp() override {
lod.push_back(std::vector<size_t>{0, 2, 9, 13});
source.set_lod(lod);
source.Resize(make_ddim({13, 128}));
source.mutable_data<int>(platform::CPUPlace());
// content of each setence: 0 1 2 3 4
const auto& level = lod.front();
for (size_t i = 0; i < level.size() - 1; i++) {
size_t begin = level[i];
size_t end = level[i + 1];
for (size_t j = begin; j < end; j++) {
auto record = source.Slice<int>(j, j + 1);
for (int dim = 0; dim < 128; dim++) {
record.mutable_data<int>(platform::CPUPlace())[dim] = j - begin;
}
}
}
// unpack
meta = ta.Unpack(source, 0, true);
}
LoD lod;
TensorArray ta;
LoDTensor source;
std::vector<DySeqMeta> meta;
};
TEST_F(TensorArrayPackTester, Unpack) {
ASSERT_EQ(ta.size(), 7UL);
const auto& t0 = ta.Read(0);
const auto& t1 = ta.Read(1);
ASSERT_EQ(t0.data<int>()[0], int(0));
ASSERT_EQ(t1.data<int>()[0], int(1));
}
TEST_F(TensorArrayPackTester, Pack) {
LoDTensor packed = ta.Pack(0, meta, lod);
}
TEST_F(TensorArrayTester, size) {
ASSERT_EQ(ta.size(), static_cast<size_t>(batch_size));
}
} // namespace framework
} // namespace paddle
paddle/operators/CMakeLists.txt
浏览文件 @ 479e4a50
......@@ -103,12 +103,16 @@ set(DEPS_OPS
recurrent_op
cond_op
cross_entropy_op
softmax_with_cross_entropy_op)
softmax_with_cross_entropy_op
sum_op)
op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
DEPS framework_proto tensor net_op)
op_library(cond_op SRCS cond_op.cc DEPS framework_proto tensor operator net_op)
op_library(cross_entropy_op DEPS cross_entropy)
op_library(softmax_with_cross_entropy_op DEPS cross_entropy softmax)
op_library(sum_op DEPS net_op)
list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
foreach(src ${GENERAL_OPS})
......
paddle/operators/recurrent_op.cc
浏览文件 @ 479e4a50
......@@ -30,36 +30,39 @@ using LoDTensor = framework::LoDTensor;
void RecurrentAlgorithm::Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const {
auto step_scopes = GetStepScopes(scope);
rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
false /*infer_shape_mode*/);
InitMemories(step_scopes[0], false /*infer_shape_mode*/);
auto* input0 = scope.FindVar(arg_->inlinks[0]);
PADDLE_ENFORCE_NOT_NULL(input0);
size_t seq_len = input0->GetMutable<LoDTensor>()->dims()[0];
PADDLE_ENFORCE_GT(seq_len, 0);
for (size_t step_id = 0; step_id < seq_len_; step_id++) {
// create output alias variables
CreateScopes(scope, seq_len);
auto& step_scopes = GetStepScopes(scope);
rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len);
InitMemories(step_scopes[0]);
for (size_t step_id = 0; step_id < seq_len; step_id++) {
if (step_id > 0) {
rnn::LinkMemories(step_scopes, arg_->memories, step_id, -1,
false /*infer_shape_mode*/);
rnn::LinkMemories(step_scopes, arg_->memories, step_id, -1);
}
(*stepnet_)->Run(*step_scopes[step_id], dev_ctx);
}
rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_,
false /*infer_shape_mode*/);
rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len);
}
void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
void RecurrentAlgorithm::CreateScopes(const Scope& scope,
size_t seq_len) const {
// TODO(superjom) Only two scopes are needed for inference, this case will be
// supported later.
auto step_scopes_var = scope.FindVar(arg_->step_scopes);
auto* step_scopes_var = scope.FindVar(arg_->step_scopes);
PADDLE_ENFORCE(step_scopes_var != nullptr, "");
auto step_scopes = step_scopes_var->GetMutable<std::vector<Scope*>>();
auto* step_scopes = step_scopes_var->GetMutable<std::vector<Scope*>>();
// Now all variables in scope must be created outside of op.
PADDLE_ENFORCE_NOT_NULL(stepnet_);
PADDLE_ENFORCE(!(*stepnet_)->Outputs().empty(), "stepnet_ op has no outputs");
if (seq_len_ > step_scopes->size()) {
for (size_t i = step_scopes->size(); i < seq_len_; ++i) {
if (seq_len > step_scopes->size()) {
for (size_t i = step_scopes->size(); i < seq_len; ++i) {
auto& step_scope = scope.NewScope();
// create step net's temp inputs
......@@ -82,8 +85,7 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
}
}
void RecurrentAlgorithm::InitMemories(Scope* step_scope,
bool infer_shape_mode) const {
void RecurrentAlgorithm::InitMemories(Scope* step_scope) const {
for (auto& attr : arg_->memories) {
auto* pre_mem = step_scope->NewVar(attr.pre_var)->GetMutable<LoDTensor>();
PADDLE_ENFORCE(step_scope->FindVar(attr.boot_var) != nullptr,
......@@ -91,12 +93,9 @@ void RecurrentAlgorithm::InitMemories(Scope* step_scope,
attr.boot_var);
auto* boot_mem =
step_scope->FindVar(attr.boot_var)->GetMutable<LoDTensor>();
if (infer_shape_mode) {
pre_mem->Resize(boot_mem->dims());
PADDLE_ENFORCE_EQ(pre_mem->dims().size(), 2);
} else {
pre_mem->ShareDataWith<float>(*boot_mem);
}
pre_mem->Resize(boot_mem->dims());
PADDLE_ENFORCE_EQ(pre_mem->dims().size(), 2);
pre_mem->ShareDataWith<float>(*boot_mem);
}
}
......@@ -146,23 +145,23 @@ class RecurrentAlgorithmProtoAndCheckerMaker
void RecurrentGradientAlgorithm::Run(
const Scope& scope, const platform::DeviceContext& dev_ctx) const {
auto step_scopes = GetStepScopes(scope);
rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
false /*infer_shape_mode*/);
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,
false /*infer_shape_mode*/);
auto* input0 = scope.FindVar(arg_->inlinks[0]);
PADDLE_ENFORCE_NOT_NULL(input0);
size_t seq_len = input0->GetMutable<LoDTensor>()->dims()[0];
auto& step_scopes = GetStepScopes(scope);
rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len);
for (int step_id = seq_len - 1; step_id >= 0; --step_id) {
if (step_id != seq_len - 1) {
rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1);
}
(*stepnet_)->Run(*step_scopes[step_id], dev_ctx);
}
LinkBootMemoryGradients(step_scopes[0], false);
rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_,
false /*infer_shape_mode*/);
rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len);
LinkBootMemoryGradients(step_scopes[0]);
}
void RecurrentGradientAlgorithm::LinkBootMemoryGradients(
Scope* step_scope, bool infer_shape_mode) const {
Scope* step_scope) const {
for (auto& attr : arg_->memories) {
PADDLE_ENFORCE(step_scope->FindVar(attr.var) != nullptr,
"memory variable [%s] does not exists", attr.var);
......@@ -171,11 +170,8 @@ void RecurrentGradientAlgorithm::LinkBootMemoryGradients(
auto* mem_grad = step_scope->NewVar(attr.var)->GetMutable<LoDTensor>();
auto* boot_mem_grad =
step_scope->NewVar(attr.boot_var)->GetMutable<LoDTensor>();
if (infer_shape_mode) {
boot_mem_grad->Resize(mem_grad->dims());
} else {
boot_mem_grad->ShareDataWith<float>(*mem_grad);
}
boot_mem_grad->Resize(mem_grad->dims());
boot_mem_grad->ShareDataWith<float>(*mem_grad);
}
}
......
paddle/operators/recurrent_op.h
浏览文件 @ 479e4a50
......@@ -48,7 +48,7 @@ class RecurrentAlgorithm {
* 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(const framework::Scope& scope) const;
void CreateScopes(const framework::Scope& scope, size_t seq_len) const;
const std::vector<framework::Scope*>& GetStepScopes(
const framework::Scope& scope) const {
......@@ -56,12 +56,11 @@ class RecurrentAlgorithm {
->GetMutable<std::vector<framework::Scope*>>();
}
void InitMemories(framework::Scope* step_scopes, bool infer_shape_mode) const;
void InitMemories(framework::Scope* step_scopes) const;
private:
std::unique_ptr<framework::OperatorBase>* stepnet_;
rnn::Argument* arg_;
mutable size_t seq_len_;
};
class RecurrentGradientAlgorithm {
......@@ -86,8 +85,7 @@ class RecurrentGradientAlgorithm {
void Run(const framework::Scope& scope,
const platform::DeviceContext& dev_ctx) const;
void LinkBootMemoryGradients(framework::Scope* step_scopes,
bool infer_shape_mode) const;
void LinkBootMemoryGradients(framework::Scope* step_scopes) const;
protected:
inline const std::vector<framework::Scope*>& GetStepScopes(
......@@ -98,7 +96,6 @@ class RecurrentGradientAlgorithm {
private:
rnn::Argument* arg_;
mutable size_t seq_len_;
std::unique_ptr<framework::OperatorBase>* stepnet_;
};
......@@ -123,6 +120,7 @@ class RecurrentOp : public framework::OperatorBase {
void set_stepnet(std::unique_ptr<OperatorBase> net) {
stepnet_ = std::move(net);
}
const OperatorBase& stepnet() const { return *stepnet_; }
static const rnn::ArgumentName kArgName;
......
paddle/operators/rnn/recurrent_op_utils.cc
浏览文件 @ 479e4a50
......@@ -25,7 +25,7 @@ using LoDTensor = framework::LoDTensor;
void SegmentInputs(const std::vector<Scope*>& step_scopes,
const std::vector<std::string>& inlinks,
const size_t seq_len, bool infer_shape_mode) {
const size_t seq_len) {
PADDLE_ENFORCE(!inlinks.empty(), "no in links are provided.");
for (size_t i = 0; i < inlinks.size(); ++i) {
// global inputs
......@@ -41,11 +41,9 @@ void SegmentInputs(const std::vector<Scope*>& step_scopes,
for (size_t j = 0; j < seq_len; j++) {
Tensor* step_input =
step_scopes[j]->NewVar(inlinks[i])->GetMutable<Tensor>();
if (!infer_shape_mode) {
// The input of operators of each step is Tensor here.
// Maybe need to modify Slice function.
*step_input = input->Slice<float>(j, j + 1);
}
// The input of operators of each step is Tensor here.
// Maybe need to modify Slice function.
*step_input = input->Slice<float>(j, j + 1);
step_input->Resize(step_dims);
}
}
......@@ -53,39 +51,35 @@ void SegmentInputs(const std::vector<Scope*>& step_scopes,
void ConcatOutputs(const std::vector<Scope*>& step_scopes,
const std::vector<std::string>& outlinks,
const size_t seq_len, bool infer_shape_mode) {
const size_t seq_len) {
for (size_t i = 0; i < outlinks.size(); i++) {
auto output_var = step_scopes[0]->parent().FindVar(outlinks[i]);
auto* output_var = step_scopes[0]->parent().FindVar(outlinks[i]);
PADDLE_ENFORCE_NOT_NULL(output_var, "output link [%s] is not in scope.",
outlinks[i]);
LoDTensor* output = output_var->GetMutable<LoDTensor>();
if (infer_shape_mode) {
auto step_scope_var = step_scopes[0]->FindVar(outlinks[i]);
PADDLE_ENFORCE_NOT_NULL(step_scope_var, "%s not in scope", outlinks[i]);
f::DDim step_dims =
step_scope_var->template GetMutable<LoDTensor>()->dims();
std::vector<int64_t> dims_vec = vectorize(step_dims);
dims_vec.insert(dims_vec.begin(), seq_len);
output->Resize(f::make_ddim(dims_vec));
} else {
output->mutable_data<float>(platform::CPUPlace());
for (size_t j = 0; j < seq_len; j++) {
LoDTensor* step_output =
step_scopes[j]->FindVar(outlinks[i])->GetMutable<LoDTensor>();
// TODO(luotao02) data type and platform::DeviceContext() should set
// correctly
(output->Slice<float>(j, j + 1))
.CopyFrom<float>(*step_output, platform::CPUPlace());
}
auto* step_scope_var = step_scopes[0]->FindVar(outlinks[i]);
PADDLE_ENFORCE_NOT_NULL(step_scope_var, "%s not in scope", outlinks[i]);
f::DDim step_dims =
step_scope_var->template GetMutable<LoDTensor>()->dims();
std::vector<int64_t> dims_vec = vectorize(step_dims);
dims_vec.insert(dims_vec.begin(), seq_len);
output->Resize(f::make_ddim(dims_vec));
output->mutable_data<float>(platform::CPUPlace());
for (size_t j = 0; j < seq_len; j++) {
LoDTensor* step_output =
step_scopes[j]->FindVar(outlinks[i])->GetMutable<LoDTensor>();
// 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) {
const size_t step_id, const int offset) {
PADDLE_ENFORCE_LT(step_id, scopes.size(),
"step [%d] is out of range of step scopes' size [%d]",
step_id, scopes.size());
......@@ -95,16 +89,13 @@ void LinkMemories(const std::vector<Scope*>& scopes,
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];
auto* scope = scopes[step_id];
auto* linked_scope = scopes[step_id + offset];
for (auto& attr : memories) {
auto mem = scope->FindVar(attr.pre_var)->GetMutable<LoDTensor>();
auto linked_mem = linked_scope->FindVar(attr.var)->GetMutable<LoDTensor>();
if (infer_shape_mode) {
mem->Resize(linked_mem->dims());
} else {
mem->ShareDataWith<float>(*linked_mem);
}
auto* mem = scope->FindVar(attr.pre_var)->GetMutable<LoDTensor>();
auto* linked_mem = linked_scope->FindVar(attr.var)->GetMutable<LoDTensor>();
mem->Resize(linked_mem->dims());
mem->ShareDataWith<float>(*linked_mem);
}
}
......@@ -115,11 +106,11 @@ void InitArgument(const ArgumentName& name, Argument* arg,
arg->inlinks = op.Inputs(name.inlinks);
arg->outlinks = op.Outputs(name.outlinks);
auto boot_memories =
auto& boot_memories =
is_grad ? op.Outputs(name.boot_memories) : op.Inputs(name.boot_memories);
// attributes
auto memories = op.Attr<std::vector<std::string>>(name.memories);
auto pre_memories = op.Attr<std::vector<std::string>>(name.pre_memories);
auto& memories = op.Attr<std::vector<std::string>>(name.memories);
auto& pre_memories = op.Attr<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",
......
paddle/operators/rnn/recurrent_op_utils.h
浏览文件 @ 479e4a50
......@@ -64,18 +64,18 @@ struct ArgumentName {
*/
void SegmentInputs(const std::vector<Scope*>& step_scopes,
const std::vector<std::string>& inlinks,
const size_t seq_len, bool infer_shape_mode);
const size_t seq_len);
/**
* Process outputs of step nets and merge to variables.
*/
void ConcatOutputs(const std::vector<Scope*>& step_scopes,
const std::vector<std::string>& outlinks,
const size_t seq_len, bool infer_shape_mode);
const size_t seq_len);
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);
const int offset);
void InitArgument(const ArgumentName& name, Argument* arg,
const framework::OperatorBase& op, bool is_grad = false);
......
paddle/operators/sum_op.cc
浏览文件 @ 479e4a50
......@@ -11,6 +11,7 @@ limitations under the License. */
#include "paddle/operators/sum_op.h"
#include <vector>
#include "paddle/operators/net_op.h"
namespace paddle {
namespace operators {
......@@ -22,14 +23,15 @@ class SumOp : public framework::OperatorWithKernel {
protected:
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInputs("X"), "Inputs(X) should not be null");
auto x_dims = ctx->GetInputsDim("X");
PADDLE_ENFORCE(!x_dims.empty(), "Input(X) of SumOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of SumOp should not be null.");
auto in_dim = x_dims[0];
size_t N = x_dims.size();
PADDLE_ENFORCE_GT(N, 1, "Input tensors count should > 1.");
auto in_dim = x_dims[0];
for (size_t i = 1; i < N; i++) {
auto dim = x_dims[i];
PADDLE_ENFORCE(in_dim == dim, "Input tensors must have same shape");
......@@ -56,21 +58,23 @@ or not. But the output only shares the LoD with the first input.
}
};
class SumGradOp : public framework::OperatorWithKernel {
class SumGradOp : public NetOp {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
SumGradOp(const std::string& type, const framework::VariableNameMap& inputs,
const framework::VariableNameMap& outputs,
const framework::AttributeMap& attrs)
: NetOp(type, inputs, outputs, attrs) {
auto& x_grad_names = Outputs(framework::GradVarName("X"));
auto out_grad_name = this->Input(framework::GradVarName("Out"));
protected:
void InferShape(framework::InferShapeContextBase* ctx) const override {
auto out_grad_dims = ctx->GetInputDim(framework::GradVarName("Out"));
auto x_grad_names = ctx->Outputs(framework::GradVarName("X"));
size_t x_length = x_grad_names.size();
std::vector<framework::DDim> x_grad_dims;
x_grad_dims.reserve(x_length);
for (size_t i = 0; i < x_length; ++i) {
x_grad_dims.push_back(out_grad_dims);
framework::AttributeMap grad_attrs;
grad_attrs["scale"] = 1.0f;
for (auto& x_grad_name : x_grad_names) {
AppendOp(framework::OpRegistry::CreateOp(
"scale", {{"X", {out_grad_name}}}, {{"Out", {x_grad_name}}},
grad_attrs));
}
ctx->SetOutputsDim(framework::GradVarName("X"), x_grad_dims);
CompleteAddOp(false);
}
};
......@@ -80,5 +84,3 @@ class SumGradOp : public framework::OperatorWithKernel {
namespace ops = paddle::operators;
REGISTER_OP(sum, ops::SumOp, ops::SumOpMaker, sum_grad, ops::SumGradOp);
REGISTER_OP_CPU_KERNEL(sum, ops::SumKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(sum_grad,
ops::SumGradKernel<paddle::platform::CPUPlace, float>);
paddle/operators/sum_op.cu
浏览文件 @ 479e4a50
......@@ -13,6 +13,4 @@ limitations under the License. */
#include "paddle/operators/sum_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(sum, ops::SumKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(sum_grad,
ops::SumGradKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(sum, ops::SumKernel<paddle::platform::GPUPlace, float>);
\ No newline at end of file
paddle/operators/sum_op.h
浏览文件 @ 479e4a50
......@@ -42,24 +42,5 @@ class SumKernel : public framework::OpKernel<T> {
}
};
template <typename Place, typename T>
class SumGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* input = context.Input<Tensor>(framework::GradVarName("Out"));
auto outs = context.MultiOutput<Tensor>(framework::GradVarName("X"));
for (auto out : outs) {
out->mutable_data<T>(context.GetPlace());
}
auto place = context.GetEigenDevice<Place>();
auto in = EigenVector<T>::Flatten(*input);
for (auto out : outs) {
auto result = EigenVector<T>::Flatten(*out);
result.device(place) = in;
}
}
};
} // namespace operators
} // namespace paddle
paddle/platform/macros.h
浏览文件 @ 479e4a50
......@@ -16,8 +16,10 @@ limitations under the License. */
// Disable the copy and assignment operator for a class.
#ifndef DISABLE_COPY_AND_ASSIGN
#define DISABLE_COPY_AND_ASSIGN(classname) \
private: \
classname(const classname&) = delete; \
classname& operator=(const classname&) = delete
#define DISABLE_COPY_AND_ASSIGN(classname) \
private: \
classname(const classname&) = delete; \
classname(const classname&&) = delete; \
classname& operator=(const classname&) = delete; \
classname& operator=(const classname&&) = delete
#endif
python/paddle/v2/framework/tests/test_recurrent_op.py
浏览文件 @ 479e4a50
......@@ -16,14 +16,17 @@ class PySimpleRNN(object):
'''
def __init__(self, input_dim=30, batch_size=50, weight_dim=15, sent_len=11):
self.x = np.random.normal(size=(sent_len, batch_size, input_dim))
self.W = np.random.normal(size=(input_dim, input_dim))
self.U = np.random.normal(size=(input_dim, input_dim))
self.h_boot = np.random.normal(size=(batch_size, input_dim))
self.x = np.random.normal(size=(sent_len, batch_size,
input_dim)).astype("float32")
self.W = np.random.normal(size=(input_dim, input_dim)).astype("float32")
self.U = np.random.normal(size=(input_dim, input_dim)).astype("float32")
self.h_boot = np.random.normal(size=(batch_size,
input_dim)).astype("float32")
# memories
self.mems = [
np.zeros(shape=(batch_size, input_dim)) for i in range(sent_len)
np.zeros(shape=(batch_size, input_dim)).astype("float32")
for i in range(sent_len)
]
def forward(self):
......@@ -36,7 +39,7 @@ class PySimpleRNN(object):
return [self.x[i] for i in range(self.x.shape[0])]
def concat_outputs(self):
return np.array(self.mems)
return np.array(self.mems).astype("float32")
def step(self, step_id, x):
'''
......@@ -47,8 +50,8 @@ class PySimpleRNN(object):
pre_mem = self.mems[step_id - 1]
else:
pre_mem = self.h_boot
xW = np.matmul(x, self.W)
hU = np.matmul(pre_mem, self.U)
xW = np.matmul(x, self.W).astype("float32")
hU = np.matmul(pre_mem, self.U).astype("float32")
sum = xW + hU
self.mems[step_id] = py_sigmoid(sum)
......@@ -102,7 +105,8 @@ class RecurrentOpTest(unittest.TestCase):
self.create_step_net()
ctx = core.DeviceContext.create(core.CPUPlace())
self.rnnop.run(self.scope, ctx)
return np.array(self.scope.find_var("h@mem").get_tensor())
return np.array(self.scope.find_var("h@mem").get_tensor()).astype(
"float32")
def create_global_variables(self):
# create inlink
......@@ -142,7 +146,7 @@ class RecurrentOpTest(unittest.TestCase):
stepnet = core.Net.create()
x_fc_op = Operator("mul", X="x", Y="W", Out="Wx")
h_fc_op = Operator("mul", X="h@pre", Y="U", Out="Uh")
sum_op = Operator("add", X="Wx", Y="Uh", Out="sum")
sum_op = Operator("sum", X=["Wx", "Uh"], Out="sum")
sig_op = Operator("sigmoid", X="sum", Y="h@mem")
for op in [x_fc_op, h_fc_op, sum_op, sig_op]:
......@@ -179,7 +183,7 @@ class RecurrentGradientOpTest(unittest.TestCase):
stepnet = core.Net.create()
x_fc_op = Operator("mul", X="x@alias", Y="W", Out="Wx")
h_fc_op = Operator("mul", X="h@pre", Y="U", Out="Uh")
sum_op = Operator("add", X="Wx", Y="Uh", Out="sum")
sum_op = Operator("sum", X=["Wx", "Uh"], Out="sum")
sig_op = Operator("sigmoid", X="sum", Y="h@alias")
for op in [x_fc_op, h_fc_op, sum_op, sig_op]:
......@@ -197,7 +201,4 @@ class RecurrentGradientOpTest(unittest.TestCase):
if __name__ == '__main__':
exit(
0
) # FIXME(yuyang18): InferShape has been removed, this unittest may error
unittest.main()
Markdown is supported
0% 或 .
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 或