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optimize lstmp and sample_logits op, test=develop (#16845)

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* optimize lstmp and sample_logits op, test=develop

* update op_use_default_grad_op_maker.spec, test=develop

* delete useless file,test=develop

* append 0 to dim variable to avoid memory reusage, test=develop
revert-16839-cmakelist_change
liuwei1031 6 years ago committed by GitHub
parent 84b7a7291e
commit 072db0938b
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GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 81 additions and 25 deletions
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1
paddle/fluid/op_use_default_grad_op_maker.spec
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@ -18,7 +18,6 @@ gru
hierarchical_sigmoid
lrn
lstm_unit
lstmp
max_pool2d_with_index
max_pool3d_with_index
maxout

49
paddle/fluid/operators/lstmp_op.cc
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@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/lstmp_op.h"
#include <memory>
#include <string>
namespace paddle {
@ -45,6 +46,7 @@ class LSTMPOp : public framework::OperatorWithKernel {
"Output(BatchHidden) of LSTMP operator should not be null.");
auto in_dims = ctx->GetInputDim("Input");
PADDLE_ENFORCE_EQ(in_dims.size(), 2,
"Input(X)'s rank of LSTMP operator must be 2.");
@ -269,13 +271,47 @@ Users can choose to use fully-connected operator before LSTMP operator.
}
};
class LSTMPGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
auto* grad_op = new framework::OpDesc();
grad_op->SetType("lstmp_grad");
grad_op->SetInput("Weight", Input("Weight"));
grad_op->SetInput("ProjWeight", Input("ProjWeight"));
grad_op->SetInput("Bias", Input("Bias"));
grad_op->SetInput("Projection", Output("Projection"));
grad_op->SetInput("Cell", Output("Cell"));
grad_op->SetInput("BatchGate", Output("BatchGate"));
grad_op->SetInput("BatchCellPreAct", Output("BatchCellPreAct"));
grad_op->SetInput("BatchHidden", Output("BatchHidden"));
grad_op->SetInput("H0", Input("H0"));
grad_op->SetInput("C0", Input("C0"));
grad_op->SetInput(framework::GradVarName("Projection"),
OutputGrad("Projection"));
grad_op->SetOutput(framework::GradVarName("Input"), InputGrad("Input"));
grad_op->SetOutput(framework::GradVarName("Weight"), InputGrad("Weight"));
grad_op->SetOutput(framework::GradVarName("ProjWeight"),
InputGrad("ProjWeight"));
grad_op->SetOutput(framework::GradVarName("Bias"), InputGrad("Bias"));
grad_op->SetOutput(framework::GradVarName("H0"), InputGrad("H0"));
grad_op->SetOutput(framework::GradVarName("C0"), InputGrad("C0"));
grad_op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
class LSTMPGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Input"),
"Input(Input) of LSTMP operator should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Projection"),
"Input(Projection) of LSTMP operator should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Cell"),
@ -298,7 +334,8 @@ class LSTMPGradOp : public framework::OperatorWithKernel {
ctx->SetOutputDim(g_name, ctx->GetInputDim(name));
};
SetOutGradDim("Input");
ctx->SetOutputDim(framework::GradVarName("Input"),
ctx->GetInputDim("BatchGate"));
SetOutGradDim("Weight");
SetOutGradDim("ProjWeight");
SetOutGradDim("Bias");
@ -310,7 +347,8 @@ class LSTMPGradOp : public framework::OperatorWithKernel {
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
ctx.Input<framework::LoDTensor>("Input")->type(), ctx.device_context());
ctx.Input<framework::LoDTensor>("BatchGate")->type(),
ctx.device_context());
}
};
@ -318,8 +356,7 @@ class LSTMPGradOp : public framework::OperatorWithKernel {
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(lstmp, ops::LSTMPOp, ops::LSTMPOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(lstmp, ops::LSTMPOp, ops::LSTMPOpMaker, ops::LSTMPGradMaker);
REGISTER_OPERATOR(lstmp_grad, ops::LSTMPGradOp);
REGISTER_OP_CPU_KERNEL(
lstmp, ops::LSTMPKernel<paddle::platform::CPUDeviceContext, float>,

4
paddle/fluid/operators/lstmp_op.h
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@ -267,7 +267,6 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
}
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<LoDTensor>("Input");
auto* weight = ctx.Input<Tensor>("Weight");
auto* proj_weight = ctx.Input<Tensor>("ProjWeight");
auto* bias = ctx.Input<Tensor>("Bias");
@ -323,7 +322,8 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
ordered_c0_g.mutable_data<T>(c0_g->dims(), ctx.GetPlace());
}
auto in_dims = input->dims();
// batch_gate dims equal to input dims
auto in_dims = batch_gate->dims();
auto out_dims = cell_out->dims();
framework::DDim proj_dims({in_dims[0], proj_weight->dims()[1]});
int frame_size = static_cast<int>(in_dims[1] / 4);

46
paddle/fluid/operators/sample_logits_op.cc
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@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/sample_logits_op.h"
#include <memory>
#include "paddle/fluid/operators/math/sample_prob.h"
namespace paddle {
@ -60,6 +61,10 @@ class SampleLogitsOpMaker : public framework::OpProtoAndCheckerMaker {
"(Tensor, default: Tensor<float>), A 2-D tensor with shape [N, NT + S]."
"The probabilites of sampled positive and negtive labels.")
.AsIntermediate();
AddOutput("LogitsDim", "Store dim information of Logits for gradient op")
.AsIntermediate();
AddOutput("LabelsDim", "Store dim information of Logits for gradient op")
.AsIntermediate();
AddOutput("SampledLogits",
"(Tensor, default: Tensor<float>), A 2-D tensor with shape"
"[N, NT + S]. The outputs value of sampled logits, which will be"
@ -121,6 +126,10 @@ class SampleLogitsOp : public framework::OperatorWithKernel {
"Output(SampledLogits) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput("SampledLabels"),
"Output(SampledLabels) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput("LogitsDim"),
"Output(LogitsDim) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput("LabelsDim"),
"Output(LabelsDim) should be not null.");
auto logits_dims = ctx->GetInputDim("Logits");
auto labels_dims = ctx->GetInputDim("Labels");
@ -137,6 +146,15 @@ class SampleLogitsOp : public framework::OperatorWithKernel {
ctx->SetOutputDim("Probabilities", {logits_dims[0], num_sampled_classes});
ctx->SetOutputDim("SampledLogits", {logits_dims[0], num_sampled_classes});
ctx->SetOutputDim("SampledLabels", {logits_dims[0], labels_dims[1]});
// append 0 to shape variable to avoid optimized by memory optimize pass
auto logits_dim_vec = framework::vectorize(logits_dims);
logits_dim_vec.push_back(0);
ctx->SetOutputDim("LogitsDim", framework::make_ddim(logits_dim_vec));
auto labels_dim_vec = framework::vectorize(labels_dims);
labels_dim_vec.push_back(0);
ctx->SetOutputDim("LabelsDim", framework::make_ddim(labels_dim_vec));
}
protected:
@ -155,28 +173,27 @@ class SampleLogitsOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Logits"),
"Input(Logits) should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Labels"),
"Input(Labels) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("LogitsDim"),
"Input(LogitsDim) should not be null.");
PADDLE_ENFORCE(ctx->HasInput("LabelsDim"),
"Input(LabelsDim) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("Samples"),
"Input(Samples) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("SampledLogits"),
"Input(SampledLogits) should be not null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("SampledLogits")),
"Input(SampledLogits@Grad) should not be null.");
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("Logits")),
"Output(Logits@Grad) should be not null.");
auto logit_dims = ctx->GetInputDim("Logits");
auto label_dims = ctx->GetInputDim("Labels");
PADDLE_ENFORCE_EQ(label_dims.size(), 2UL,
auto logits_dims = ctx->GetInputDim("LogitsDim");
logits_dims = framework::DDim(logits_dims.Get(), logits_dims.size() - 1);
auto labels_dims = ctx->GetInputDim("LabelsDim");
labels_dims = framework::DDim(labels_dims.Get(), labels_dims.size() - 1);
PADDLE_ENFORCE_EQ(labels_dims.size(), 2UL,
"The label should be a 2-D tensor.");
PADDLE_ENFORCE_EQ(logit_dims.size(), 2UL,
PADDLE_ENFORCE_EQ(logits_dims.size(), 2UL,
"The logits should be a 2-D tensor.");
ctx->SetOutputDim(framework::GradVarName("Logits"),
ctx->GetInputDim("Logits"));
ctx->SetOutputDim(framework::GradVarName("Logits"), logits_dims);
}
protected:
@ -199,10 +216,9 @@ class SampleLogitsGradMaker : public framework::SingleGradOpDescMaker {
std::unique_ptr<framework::OpDesc> Apply() const override {
auto* grad_op = new framework::OpDesc();
grad_op->SetType("sample_logits_grad");
grad_op->SetInput("Logits", Input("Logits"));
grad_op->SetInput("Labels", Input("Labels"));
grad_op->SetInput("LogitsDim", Output("LogitsDim"));
grad_op->SetInput("LabelsDim", Output("LabelsDim"));
grad_op->SetInput("Samples", Output("Samples"));
grad_op->SetInput("SampledLogits", Output("SampledLogits"));
grad_op->SetInput(framework::GradVarName("SampledLogits"),
OutputGrad("SampledLogits"));
grad_op->SetOutput(framework::GradVarName("Logits"), InputGrad("Logits"));

6
python/paddle/fluid/layers/nn.py
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@ -6278,6 +6278,8 @@ def sampled_softmax_with_cross_entropy(logits,
sampled_label = helper.create_variable_for_type_inference(dtype='int64')
sampled_softlabel = helper.create_variable_for_type_inference(
dtype=logits.dtype)
logits_dim = helper.create_variable_for_type_inference(dtype=logits.dtype)
labels_dim = helper.create_variable_for_type_inference(dtype=label.type)
helper.append_op(
type='sample_logits',
@ -6291,7 +6293,9 @@ def sampled_softmax_with_cross_entropy(logits,
'Samples': samples,
'Probabilities': probabilities,
'SampledLabels': sampled_label,
'SampledLogits': sampled_logits
'SampledLogits': sampled_logits,
'LogitsDim': logits_dim,
'LabelsDim': labels_dim
},
attrs={
'use_customized_samples': use_customized_samples,

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