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RNNOp remove alias #4274

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merged 4 commits into from
Sep 21, 2017
Merged

RNNOp remove alias #4274

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0d7e429
remove alias
Superjomn Sep 19, 2017
b818e64
Merge branch 'develop' of github.com:PaddlePaddle/Paddle into rnn_rem…
Superjomn Sep 19, 2017
3c29224
remove alias
Superjomn Sep 20, 2017
075e0e3
Merge branch 'develop' of github.com:PaddlePaddle/Paddle into rnn_rem…
Superjomn Sep 20, 2017
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2 changes: 2 additions & 0 deletions paddle/framework/scope.h
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Original file line number Diff line number Diff line change
Expand Up @@ -58,6 +58,8 @@ class Scope {
/// nullptr if cannot find.
Variable* FindVar(const std::string& name) const;

const Scope& parent() const { return *parent_; }

/// Find the scope or an ancestor scope that contains the given variable.
const Scope* FindScope(const Variable* var) const;

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23 changes: 10 additions & 13 deletions paddle/operators/recurrent_op.cc
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Expand Up @@ -29,9 +29,11 @@ using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;

void RecurrentAlgorithm::InferShape(const Scope& scope) const {
seq_len_ = scope.FindVar((arg_->inlinks[0]).external)
->GetMutable<LoDTensor>()
->dims()[0];
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_,
Expand Down Expand Up @@ -123,14 +125,12 @@ void RecurrentAlgorithm::InitMemories(Scope* step_scope,
}

const rnn::ArgumentName RecurrentOp::kArgName{
"step_net", "step_scopes", "inlinks",
"outlinks", "inlink_alias", "outlink_alias",
"step_net", "step_scopes", "inlinks", "outlinks",
"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"};
"step_net", "step_scopes", "outlink@grad", "inlink@grad",
"memories", "pre_memories", "boot_memories@grad"};

RecurrentOp::RecurrentOp(const std::string& type,
const framework::VariableNameMap& inputs,
Expand Down Expand Up @@ -160,8 +160,6 @@ class RecurrentAlgorithmProtoAndCheckerMaker
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");
Expand Down Expand Up @@ -206,9 +204,8 @@ void RecurrentGradientAlgorithm::LinkBootMemoryGradients(
}

void RecurrentGradientAlgorithm::InferShape(const Scope& scope) const {
seq_len_ = scope.FindVar((arg_->inlinks[0]).external)
->GetMutable<LoDTensor>()
->dims()[0];
seq_len_ =
scope.FindVar(arg_->inlinks[0])->GetMutable<LoDTensor>()->dims()[0];
auto step_scopes = GetStepScopes(scope);
rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
true /*infer_shape_mode*/);
Expand Down
61 changes: 20 additions & 41 deletions paddle/operators/rnn/recurrent_op_utils.cc
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Expand Up @@ -24,22 +24,23 @@ using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;

void SegmentInputs(const std::vector<Scope*>& step_scopes,
const std::vector<Link>& inlinks, const size_t seq_len,
bool infer_shape_mode) {
const std::vector<std::string>& inlinks,
const size_t seq_len, bool infer_shape_mode) {
PADDLE_ENFORCE(!inlinks.empty(), "no in links are provided.");
for (size_t i = 0; i < inlinks.size(); ++i) {
auto input_var = step_scopes[0]->FindVar(inlinks[i].external);
PADDLE_ENFORCE(input_var != nullptr, "input link [%s] is not in scope.",
inlinks[i].external);
// global inputs
auto input_var = step_scopes[0]->parent().FindVar(inlinks[i]);
PADDLE_ENFORCE_NOT_NULL(input_var, "input link [%s] is not in scope.",
inlinks[i]);

LoDTensor* input = input_var->GetMutable<LoDTensor>();
f::DDim dims = input->dims();
PADDLE_ENFORCE(static_cast<size_t>(dims[0]) == seq_len,
"all the inlinks must have same length");
PADDLE_ENFORCE_EQ(static_cast<size_t>(dims[0]), seq_len,
"all the inlinks be the same length");
f::DDim step_dims = slice_ddim(dims, 1, dims.size());
for (size_t j = 0; j < seq_len; j++) {
Tensor* step_input =
step_scopes[j]->NewVar(inlinks[i].internal)->GetMutable<Tensor>();
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.
Expand All @@ -51,18 +52,17 @@ void SegmentInputs(const std::vector<Scope*>& step_scopes,
}

void ConcatOutputs(const std::vector<Scope*>& step_scopes,
const std::vector<Link>& outlinks, const size_t seq_len,
bool infer_shape_mode) {
const std::vector<std::string>& outlinks,
const size_t seq_len, bool infer_shape_mode) {
for (size_t i = 0; i < outlinks.size(); i++) {
auto output_var = step_scopes[0]->FindVar(outlinks[i].external);
PADDLE_ENFORCE(output_var != nullptr, "output link [%s] is not in scope.",
outlinks[i].external);
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].internal);
PADDLE_ENFORCE(step_scope_var != nullptr, "%s not in scope",
outlinks[i].internal);
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);
Expand All @@ -71,9 +71,8 @@ void ConcatOutputs(const std::vector<Scope*>& step_scopes,
} 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].internal)
->GetMutable<LoDTensor>();
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))
Expand Down Expand Up @@ -113,29 +112,9 @@ void InitArgument(const ArgumentName& name, Argument* arg,
const framework::OperatorBase& op) {
arg->step_scopes = op.Output(name.step_scopes);

auto inlinks = op.Inputs(name.inlinks);
auto inlink_alias = op.Attr<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);
}
arg->inlinks = op.Inputs(name.inlinks);

auto outlinks = op.Outputs(name.outlinks);
auto outlink_alias = op.Attr<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);
}
arg->outlinks = op.Outputs(name.outlinks);

auto boot_memories = op.Inputs(name.boot_memories);

Expand Down
21 changes: 6 additions & 15 deletions paddle/operators/rnn/recurrent_op_utils.h
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Original file line number Diff line number Diff line change
Expand Up @@ -41,18 +41,11 @@ struct MemoryAttr {
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<std::string> inlinks;
std::vector<std::string> outlinks;
std::vector<rnn::MemoryAttr> memories;
};

Expand All @@ -61,8 +54,6 @@ struct ArgumentName {
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
Expand All @@ -72,15 +63,15 @@ struct ArgumentName {
* Prepare inputs for each step net.
*/
void SegmentInputs(const std::vector<Scope*>& step_scopes,
const std::vector<Link>& inlinks, const size_t seq_len,
bool infer_shape_mode);
const std::vector<std::string>& inlinks,
const size_t seq_len, bool infer_shape_mode);

/**
* Process outputs of step nets and merge to variables.
*/
void ConcatOutputs(const std::vector<Scope*>& step_scopes,
const std::vector<Link>& outlinks, const size_t seq_len,
bool infer_shape_mode);
const std::vector<std::string>& outlinks,
const size_t seq_len, bool infer_shape_mode);

void LinkMemories(const std::vector<Scope*>& step_scopes,
const std::vector<MemoryAttr>& memories, const size_t step_id,
Expand Down
16 changes: 6 additions & 10 deletions python/paddle/v2/framework/tests/test_recurrent_op.py
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Expand Up @@ -59,7 +59,6 @@ def setUp(self):

def test_forward(self):
output = self.rnn.forward()
print 'output', output


def create_tensor(scope, name, shape, np_data):
Expand Down Expand Up @@ -103,7 +102,7 @@ def forward(self):
ctx = core.DeviceContext.create(core.CPUPlace())
self.rnnop.infer_shape(self.scope)
self.rnnop.run(self.scope, ctx)
return np.array(self.scope.find_var("h").get_tensor())
return np.array(self.scope.find_var("h@mem").get_tensor())

def create_global_variables(self):
# create inlink
Expand All @@ -123,8 +122,7 @@ def create_global_variables(self):
create_tensor(self.scope, "h_boot", [self.batch_size, self.input_dim],
h_boot_np_data)
self.scope.new_var("step_scopes")
self.scope.new_var("h@alias")
self.scope.new_var("h")
self.scope.new_var("h@mem")

def create_rnn_op(self):
# create RNNOp
Expand All @@ -134,20 +132,18 @@ def create_rnn_op(self):
boot_memories=["h_boot"],
step_net="stepnet",
# outputs
outlinks=["h"],
outlinks=["h@mem"],
step_scopes="step_scopes",
# attributes
inlink_alias=["x@alias"],
outlink_alias=["h@alias"],
pre_memories=["h@pre"],
memories=["h@alias"])
memories=["h@mem"])

def create_step_net(self):
stepnet = core.Net.create()
x_fc_op = Operator("mul", X="x@alias", Y="W", Out="Wx")
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")
sig_op = Operator("sigmoid", X="sum", Y="h@alias")
sig_op = Operator("sigmoid", X="sum", Y="h@mem")

for op in [x_fc_op, h_fc_op, sum_op, sig_op]:
stepnet.append_op(op)
Expand Down