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Enhance sequence_expand operator #9100

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merged 6 commits into from
Mar 20, 2018
Merged

Enhance sequence_expand operator #9100

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352fa41
Finish adapting forward.
pkuyym Mar 15, 2018
bf3f56e
Finish adaption for backward.
pkuyym Mar 15, 2018
58730ba
Enhance unit test.
pkuyym Mar 16, 2018
3b03e37
Refine some ENFORCE.
pkuyym Mar 16, 2018
2f2c5f5
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
pkuyym Mar 20, 2018
2c22552
Fix some comments and adapt test_machine_translation.py.
pkuyym Mar 20, 2018
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2 changes: 2 additions & 0 deletions paddle/fluid/operators/math/math_function.cc
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Expand Up @@ -371,6 +371,8 @@ template struct RowwiseAdd<platform::CPUDeviceContext, double>;

template struct ColwiseSum<platform::CPUDeviceContext, float>;
template struct ColwiseSum<platform::CPUDeviceContext, double>;
template struct ColwiseSum<platform::CPUDeviceContext, int>;
template struct ColwiseSum<platform::CPUDeviceContext, int64_t>;

template struct RowwiseSum<platform::CPUDeviceContext, float>;
template struct RowwiseSum<platform::CPUDeviceContext, double>;
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2 changes: 2 additions & 0 deletions paddle/fluid/operators/math/math_function.cu
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Expand Up @@ -422,6 +422,8 @@ struct RowwiseAdd<platform::CUDADeviceContext, T> {
template struct RowwiseAdd<platform::CUDADeviceContext, float>;
template struct RowwiseAdd<platform::CUDADeviceContext, double>;
template struct ColwiseSum<platform::CUDADeviceContext, float>;
template struct ColwiseSum<platform::CUDADeviceContext, int>;
template struct ColwiseSum<platform::CUDADeviceContext, int64_t>;
// template struct ColwiseSum<platform::CUDADeviceContext, double>;
// The ColwiseSum<platform::CUDADeviceContext, double> failed in debug mode,
// and only failed for this case. So reimplemented it.
Expand Down
186 changes: 124 additions & 62 deletions paddle/fluid/operators/sequence_expand_op.cc
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Expand Up @@ -17,23 +17,79 @@ limitations under the License. */
namespace paddle {
namespace operators {

using framework::Tensor;
using framework::LoDTensor;

class SequenceExpandOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;

protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"));
PADDLE_ENFORCE(ctx->HasOutput("Out"));
PADDLE_ENFORCE(ctx->HasInput("Y"));
framework::DDim out_dim;
auto y_dim = ctx->GetInputDim("Y");
out_dim = ctx->GetInputDim("X");
out_dim[0] = y_dim[0];
ctx->ShareLoD("Y", "Out");
ctx->SetOutputDim("Out", out_dim);
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SequenceExpandOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Y"),
"Input(Y) of SequenceExpandOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of SequenceExpandOp should not be null.");

auto x_dims = ctx->GetInputDim("X");
auto out_dims = x_dims;
int ref_level = ctx->Attrs().Get<int>("ref_level");

PADDLE_ENFORCE_GE(x_dims.size(), 2,
"Dimension number of Input(X) should be at least 2.");

if (ctx->IsRuntime()) {
framework::Variable* x_var =
boost::get<framework::Variable*>(ctx->GetInputVarPtrs("X")[0]);
framework::Variable* y_var =
boost::get<framework::Variable*>(ctx->GetInputVarPtrs("Y")[0]);

auto& x_lod = x_var->Get<LoDTensor>().lod();
auto& y_lod = y_var->Get<LoDTensor>().lod();

PADDLE_ENFORCE_LE(x_lod.size(), 1,
"Level number of Input(X)'s lod should not be "
"greater than 1.");
PADDLE_ENFORCE_GT(y_lod.size(), 0,
"Level number of Input(Y)'s lod should be "
"greater than 0.");
PADDLE_ENFORCE(
ref_level == -1 ||
(ref_level >= 0 && ref_level < static_cast<int>(y_lod.size())),
"Invlid `ref_level`, which should be either equal to -1 "
"or in [0, %d)",
y_lod.size());

if (ref_level == -1) ref_level = y_lod.size() - 1;

if (x_lod.size() > 0) {
PADDLE_ENFORCE(x_lod[0].size() == y_lod[ref_level].size(),
"Level number of Input(X)'s lod could be 0. Otherwise "
"size of Input(X)'s first level lod should be equal to "
"size of Input(Y)'s referred level lod.");
}

int64_t out_first_dim = 0;
if (y_lod[ref_level].size() <= 1) {
out_first_dim = x_dims[0];
} else {
for (size_t i = 1; i < y_lod[ref_level].size(); ++i) {
int x_seq_len = 1;
if (x_lod.size() == 1) {
x_seq_len = x_lod[0][i] - x_lod[0][i - 1];
}
out_first_dim +=
(y_lod[ref_level][i] - y_lod[ref_level][i - 1]) * x_seq_len;
}
}
out_dims[0] = out_first_dim;
ctx->SetOutputDim("Out", out_dims);
} else {
out_dims[0] = -1;
ctx->SetOutputDim("Out", out_dims);
ctx->ShareLoD("X", /*->*/ "Out");
}
}
};

Expand All @@ -42,83 +98,81 @@ class SequenceExpandOpMaker : public framework::OpProtoAndCheckerMaker {
SequenceExpandOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(Tensor or LoDTensor) The input(X) of this operator can be a "
"LoDTensor or a base Tensor.");
"(LoDTensor, default LoDTensor<float>) A 2-D LoDTensor whose lod "
"level is at most 1.");
AddInput("Y",
"(LoDTensor)The reference input(Y) of sequence_expand op."
"It must be a LoDTensor with k-level(k>0)."
"The input(X) will be expanded according to LOD of input(Y)."
"The element numbers of last level in input(Y) "
"must be equal to dims[0] of input(X).");
"(LoDTensor, default LoDTensor<float>) Referred LoDTensor whose "
"lod (specified level) is referred by Input(X).");
AddOutput("Out",
"(LodTensor)The output of sequence_expand op."
"The lod of output will be as same as input(Y)'s lod.");
"(LodTensor, default LoDTensor<float>) Output LoDTensor which is "
"generated from Input(X) by referring lod of Input(Y).");
AddAttr<int>("ref_level", "Specify lod level of Input(Y).").SetDefault(-1);
AddComment(R"DOC(
Sequence Expand Operator.

This operator expands input(X) according to LOD of input(Y).
This operator expands `X` according to specified level lod of `Y`. Current
implementation constaints that lod level of `X` should be at most 1. Attribute
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constaints -> requires

I feel the semantics of this op very difficult to understand. Is there an op in other framework that has the same effect?

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I don't think so, since LoDTensor is a peculiar concept in PaddlePaddle and this operator is dependent on the LoD of input variable.

`ref_level` is used to specify which level lod of `Y` is referred to expand `X`.
If set `ref_level` to -1, then last level lod of `Y` would be referred.
Please note, rank of `X` should be at least 2, when the rank exceeds 2, `X`
would be viewed as a 2-D tensor.

Following are cases to better explain how this works:

Case 1:

Given a 2-level LoDTensor input(X)
X.lod = [[0, 2, 3],
[0, 1, 3, 4]]
X.data = [a, b, c, d]
Given a 1-level LoDTensor input(X)
X.lod = [[0, 2, 4]]
X.data = [[a], [b], [c], [d]]
X.dims = [4, 1]
and input(Y)
Y.lod = [[0, 2, 4],
[0, 3, 6, 7, 8]]
with condition len(Y.lod[-1]) -1 == X.dims[0]
then we get 2-level LoDTensor
Out.lod = [[0, 2, 4],
[0, 3, 6, 7, 8]]
Out.data = [a, a, a, b, b, b, c, d]
ref_level: 0
then we get 1-level LoDTensor
Out.lod = [[0, 2, 4, 6, 8]]
Out.data = [[a], [b], [a], [b], [c], [d], [c], [d]]
Out.dims = [8, 1]

Case 2:

Given 1-level LoDTensor input(X)
X.lod = [[0, 1, 4]]
X.data = [[a], [b], [c], [d]]
X.dims = [4, 1]
and input(Y)
Y.lod = [[0, 2, 4],
[0, 3, 6, 6, 8]]
ref_level: 0
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what would happen if this if -1?

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When set ref_level to -1, the LoD size would be inconsistent between [0, 1, 4] and [0, 3, 6, 6, 8].

then we get 1-level LoDTensor
Out.lod = [[0, 1, 2, 5, 8]]
Out.data = [[a], [a], [b], [c], [d], [b], [c], [d]]
Out.dims = [8, 1]

Case 3:

Given a common Tensor input(X)
X.data = [a, b, c]
X.data = [[a], [b], [c]]
X.dims = [3, 1]
and input(Y)
Y.lod = [[0, 2, 3, 6]]
with condition len(Y.lod[-1]) -1 == X.dims[0]
then we get 1-level LoDTensor
Out.lod = [[0, 2, 3, 6]]
Out.data = [a, a, b, c, c, c]
ref_level: -1
then we get a common Tensor
Out.data = [[a], [a], [b], [c], [c], [c]]
Out.dims = [6, 1]

Case 3:
Case 4:

Given a common Tensor input(X)
X.data = [[a, b], [c, d], [e, f]]
X.dims = [3, 2]
and input(Y)
Y.lod = [[0, 2, 3, 6]]
with condition len(Y.lod[-1]) -1 == X.dims[0]
then we get 1-level LoDTensor
Out.lod = [[0, 2, 3, 6]]
Out.data = [[a,b], [a,b] [c,d], [e, f], [e, f], [e, f]]
ref_level: 0
then we get a common LoDTensor
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The LoD of output tensor should be given here?

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Out is a Tensor.

Out.data = [[a, b], [a, b] [c, d], [e, f], [e, f], [e, f]]
Out.dims = [6, 2]

Case 4:

Given 2-level a LoDTensor input(X)
X.lod = [[0, 2, 3],
[0, 1, 3, 4]]
X.data = [a, b, c, d]
X.dims = [4, 1]
and input(Y)
Y.lod = [[0, 2, 4],
[0, 3, 6, 6, 8]]
with condition len(Y.lod[-1]) -1 == X.dims[0]
then we get 2-level LoDTensor
Out.lod = [[0, 2, 4],
[0, 3, 6, 6, 8]]
Out.data = [a, a, a, b, b, b, d, d]
Out.dims = [8, 1]


)DOC");
}
};
Expand All @@ -129,12 +183,14 @@ class SequenceExpandOpGrad : public framework::OperatorWithKernel {

protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"));
PADDLE_ENFORCE(ctx->HasInput("Out"));
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Out"), "Input(Out) should not be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"The input(Out@GRAD) should not be null");
"Input(Out@GRAD) should not be null.");

auto x_dims = ctx->GetInputDim("X");
auto x_grad_name = framework::GradVarName("X");

if (ctx->HasOutput(x_grad_name)) {
ctx->SetOutputDim(x_grad_name, x_dims);
}
Expand All @@ -149,7 +205,13 @@ REGISTER_OP(sequence_expand, ops::SequenceExpandOp, ops::SequenceExpandOpMaker,
sequence_expand_grad, ops::SequenceExpandOpGrad);
REGISTER_OP_CPU_KERNEL(
sequence_expand,
ops::SequenceExpandKernel<paddle::platform::CPUDeviceContext, float>);
ops::SequenceExpandKernel<paddle::platform::CPUDeviceContext, float>,
ops::SequenceExpandKernel<paddle::platform::CPUDeviceContext, double>,
ops::SequenceExpandKernel<paddle::platform::CPUDeviceContext, int>,
ops::SequenceExpandKernel<paddle::platform::CPUDeviceContext, int64_t>);
REGISTER_OP_CPU_KERNEL(
sequence_expand_grad,
ops::SequenceExpandGradKernel<paddle::platform::CPUDeviceContext, float>);
ops::SequenceExpandGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::SequenceExpandGradKernel<paddle::platform::CPUDeviceContext, double>,
ops::SequenceExpandGradKernel<paddle::platform::CPUDeviceContext, int>,
ops::SequenceExpandGradKernel<paddle::platform::CPUDeviceContext, int64_t>);
11 changes: 9 additions & 2 deletions paddle/fluid/operators/sequence_expand_op.cu
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Expand Up @@ -18,7 +18,14 @@ limitations under the License. */
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
sequence_expand,
ops::SequenceExpandKernel<paddle::platform::CUDADeviceContext, float>);
ops::SequenceExpandKernel<paddle::platform::CUDADeviceContext, float>,
ops::SequenceExpandKernel<paddle::platform::CUDADeviceContext, double>,
ops::SequenceExpandKernel<paddle::platform::CUDADeviceContext, int>,
ops::SequenceExpandKernel<paddle::platform::CUDADeviceContext, int64_t>);
REGISTER_OP_CUDA_KERNEL(
sequence_expand_grad,
ops::SequenceExpandGradKernel<paddle::platform::CUDADeviceContext, float>);
ops::SequenceExpandGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::SequenceExpandGradKernel<paddle::platform::CUDADeviceContext, double>,
ops::SequenceExpandGradKernel<paddle::platform::CUDADeviceContext, int>,
ops::SequenceExpandGradKernel<paddle::platform::CUDADeviceContext,
int64_t>);
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