提交 e0c8cd8a 编写于 作者: Q qiaolongfei

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

......@@ -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(); }
......@@ -68,6 +66,8 @@ 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
......@@ -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 {
......@@ -67,11 +68,6 @@ 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();
}
......@@ -107,6 +103,8 @@ class OpInfoMap {
private:
OpInfoMap() = default;
std::unordered_map<std::string, const OpInfo> map_;
DISABLE_COPY_AND_ASSIGN(OpInfoMap);
};
} // namespace framework
......
......@@ -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
......@@ -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
......
......@@ -47,13 +47,6 @@ class TensorArray {
// max number of values allowed to store.
const size_t MAX_SIZE{100000};
/*
* Inputs:
* - value_shared: share memory between tensors.
*/
explicit TensorArray(bool values_shared = true)
: values_shared_(values_shared) {}
/*
* Read the value at location `index` in the `TensorArray`.
*/
......@@ -111,7 +104,6 @@ class TensorArray {
private:
mutable std::vector<LoDTensor> values_;
bool values_shared_;
}; // class TensorArray
} // namespace framework
......
......@@ -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);
}
}
......
......@@ -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;
......
......@@ -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",
......
......@@ -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);
......
......@@ -22,14 +22,15 @@ 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");
......
......@@ -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
......@@ -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()
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