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refactor rnn infershape #4553

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Oct 2, 2017
Merged

refactor rnn infershape #4553

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7163dd0
revert code
jacquesqiao Oct 1, 2017
32f5c9d
recurrent_op pass the unit test
jacquesqiao Oct 2, 2017
6b051b6
optimize code
jacquesqiao Oct 2, 2017
cde542e
optimize auto
jacquesqiao Oct 2, 2017
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74 changes: 35 additions & 39 deletions paddle/operators/recurrent_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand All @@ -82,21 +85,17 @@ 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,
"memory [%s]'s boot variable [%s] not exists", attr.var,
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);
}
}

Expand Down Expand Up @@ -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);
Expand All @@ -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);
}
}

Expand Down
10 changes: 4 additions & 6 deletions paddle/operators/recurrent_op.h
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Original file line number Diff line number Diff line change
Expand Up @@ -48,20 +48,19 @@ 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 {
return *scope.FindVar(arg_->step_scopes)
->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 {
Expand All @@ -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(
Expand All @@ -98,7 +96,6 @@ class RecurrentGradientAlgorithm {

private:
rnn::Argument* arg_;
mutable size_t seq_len_;
std::unique_ptr<framework::OperatorBase>* stepnet_;
};

Expand All @@ -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;
Expand Down
71 changes: 31 additions & 40 deletions paddle/operators/rnn/recurrent_op_utils.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand All @@ -41,51 +41,45 @@ 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);
}
}
}

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());
Expand All @@ -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);
}
}

Expand All @@ -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",
Expand Down
6 changes: 3 additions & 3 deletions paddle/operators/rnn/recurrent_op_utils.h
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Original file line number Diff line number Diff line change
Expand Up @@ -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);
Expand Down
5 changes: 3 additions & 2 deletions paddle/operators/sum_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -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");
Expand Down
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