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Merged
jacquesqiao merged 4 commits into from
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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62 changes: 28 additions & 34 deletions paddle/operators/recurrent_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -30,21 +30,23 @@ 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*/);

for (size_t step_id = 0; step_id < seq_len_; step_id++) {
// create output alias variables
if (step_id > 0) {
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);
seq_len_ = input0->GetMutable<LoDTensor>()->dims()[0];
PADDLE_ENFORCE_GT(seq_len_, 0);

CreateScopes(scope);
auto& step_scopes = GetStepScopes(scope);
rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_);
InitMemories(step_scopes[0]);

for (size_t i = 0; i < seq_len_; i++) {
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@Superjomn Superjomn Oct 2, 2017

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i -> stepid

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@jacquesqiao jacquesqiao Oct 2, 2017

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done

if (i > 0) {
rnn::LinkMemories(step_scopes, arg_->memories, i, -1);
}
(*stepnet_)->Run(*step_scopes[step_id], dev_ctx);
(*stepnet_)->Run(*step_scopes[i], 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 {
Expand Down Expand Up @@ -82,21 +84,17 @@ 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 +144,22 @@ class RecurrentAlgorithmProtoAndCheckerMaker

void RecurrentGradientAlgorithm::Run(
const Scope& scope, const platform::DeviceContext& dev_ctx) const {
seq_len_ =
scope.FindVar(arg_->inlinks[0])->GetMutable<LoDTensor>()->dims()[0];
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enforce not null scope.FindVar

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@jacquesqiao jacquesqiao Oct 2, 2017

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done

auto step_scopes = GetStepScopes(scope);
rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
false /*infer_shape_mode*/);
rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_);
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*/);
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 +168,8 @@ 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
6 changes: 3 additions & 3 deletions paddle/operators/recurrent_op.h
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Original file line number Diff line number Diff line change
Expand Up @@ -56,7 +56,7 @@ 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_;
Expand Down Expand Up @@ -86,8 +86,7 @@ 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 Down Expand Up @@ -123,6 +122,7 @@ 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
55 changes: 23 additions & 32 deletions paddle/operators/rnn/recurrent_op_utils.cc
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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]);
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]);
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auto*

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done

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 @@ -100,11 +94,8 @@ void LinkMemories(const std::vector<Scope*>& scopes,
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);
}
mem->Resize(linked_mem->dims());
mem->ShareDataWith<float>(*linked_mem);
}
}

Expand Down
6 changes: 3 additions & 3 deletions paddle/operators/rnn/recurrent_op_utils.h
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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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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
29 changes: 15 additions & 14 deletions python/paddle/v2/framework/tests/test_recurrent_op.py
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Expand Up @@ -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):
Expand All @@ -36,7 +39,7 @@ def segment_inputs(self):
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):
'''
Expand All @@ -47,8 +50,8 @@ def step(self, step_id, x):
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)
Expand Down Expand Up @@ -102,7 +105,8 @@ def forward(self):
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
Expand Down Expand Up @@ -142,7 +146,7 @@ def create_step_net(self):
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]:
Expand Down Expand Up @@ -179,7 +183,7 @@ def create_forward_op(self):
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]:
Expand All @@ -197,7 +201,4 @@ def test_grad(self):


if __name__ == '__main__':
exit(
0
) # FIXME(yuyang18): InferShape has been removed, this unittest may error
unittest.main()