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API/OP (Some SL API) error message enhancement (#24441)

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* polish some sl api error message, test=develop

* polish python input check of stride slice, test=develop

* fix unittest bugs, test=develop
release/2.0-alpha
Chen Weihang 5 years ago committed by GitHub
parent a97d5a6153
commit 05d20e5793
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GPG Key ID: 4AEE18F83AFDEB23
11 changed files with 259 additions and 127 deletions
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45
paddle/fluid/operators/sequence_ops/sequence_scatter_op.cc
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@ -74,23 +74,25 @@ class SequenceScatterOp : public framework::OperatorWithKernel {
void InferShape(framework::InferShapeContext* ctx) const override {
// Enforce has inputs and outputs
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SequenceScatterOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Ids"),
"Input(Ids) of SequenceScatterOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Updates"),
"Input(Updates) of SequenceScatterOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of SequenceScatterOp should not be null.");
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "SequenceScatter");
OP_INOUT_CHECK(ctx->HasInput("Ids"), "Input", "Ids", "SequenceScatter");
OP_INOUT_CHECK(ctx->HasInput("Updates"), "Input", "Updates",
"SequenceScatter");
OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "SequenceScatter");
// Set output dim the same as input
auto ref_dims = ctx->GetInputDim("X");
ctx->SetOutputDim("Out", ref_dims);
// Enforce the Updates and Ids are the same shape
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Updates")[0],
ctx->GetInputDim("Ids")[0],
"Updates and Ids should have same shape.");
auto updates_dim = ctx->GetInputDim("Updates");
auto ids_dim = ctx->GetInputDim("Ids");
PADDLE_ENFORCE_EQ(
updates_dim[0], ids_dim[0],
platform::errors::InvalidArgument(
"The shape of SequenceScatter operator's input Updates and Ids do "
"not match, receive Updates's shape is [%s], Ids's shape is [%s].",
updates_dim, ids_dim));
// Enforce LoD of ids and updates be the same
if (ctx->IsRuntime()) {
@ -101,12 +103,21 @@ class SequenceScatterOp : public framework::OperatorWithKernel {
auto& ids_lod = ids_var->Get<LoDTensor>().lod();
auto& updates_lod = updates_var->Get<LoDTensor>().lod();
PADDLE_ENFORCE_EQ(ids_lod.size(), 1,
"Currently only level 1 LoD could be"
" processed by sequence scatter op.");
PADDLE_ENFORCE_EQ(updates_lod.size(), 1,
"Currently only level 1 LoD "
"could be processed by sequence scatter op.");
PADDLE_ENFORCE_EQ(
ids_lod.size(), 1,
platform::errors::InvalidArgument(
"The SequenceScatter operator’s Input Ids holds wrong LoD "
"information. Currently SequenceScatter operator can only deal "
"with one level LoD for input Ids, but received LoD level is %d.",
ids_lod.size()));
PADDLE_ENFORCE_EQ(
updates_lod.size(), 1,
platform::errors::InvalidArgument(
"The SequenceScatter operator’s Input Updates holds wrong LoD "
"information. Currently SequenceScatter operator can only deal "
"with one level LoD for input Updates, but received LoD level is "
"%d.",
ids_lod.size()));
}
}

49
paddle/fluid/operators/sequence_ops/sequence_scatter_op.h
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@ -35,8 +35,9 @@ class SequenceScatterOpKernel : public framework::OpKernel<T> {
auto& ids_lod = ids->lod();
PADDLE_ENFORCE_EQ(ids_lod.empty(), false,
"Input(Ids) Tensor of SequenceScatterOp does not contain "
"LoD information.");
platform::errors::InvalidArgument(
"Input(Ids) Tensor of SequenceScatter operator does "
"not contain LoD information."));
// Initialize out as same as x
out->mutable_data<T>(ctx.GetPlace());
@ -46,9 +47,12 @@ class SequenceScatterOpKernel : public framework::OpKernel<T> {
auto out_dims = out->dims();
for (int i = 0; i < x_dims.size(); ++i)
PADDLE_ENFORCE(x_dims[i] == out_dims[i],
"Input and output shape of "
"sequence scatter op must exactly be the same.");
PADDLE_ENFORCE_EQ(x_dims[i], out_dims[i],
platform::errors::InvalidArgument(
"Input(X) and output(Out) shape of SequenceScatter "
"operator do not match. Received input(X)'s shape "
"is [%s], output(Out)'s shape is [%s].",
x_dims, out_dims));
size_t slice_size = 1;
for (int i = 1; i < x_dims.size(); ++i) slice_size *= x_dims[i];
@ -56,8 +60,13 @@ class SequenceScatterOpKernel : public framework::OpKernel<T> {
auto lod_vec = ids_lod[0];
unsigned int seg = 0;
for (int i = 0; i < ids->dims()[0]; ++i) {
PADDLE_ENFORCE_LT(seg, lod_vec.size() - 1,
"Segment num must not exceed batch size.\n");
PADDLE_ENFORCE_LT(
seg, lod_vec.size() - 1,
platform::errors::OutOfRange("The segment index is out of bound in "
"SequenceScatter operator, it must be "
"less than batch size. The segment "
"index is %d, the batch size is %d.",
seg, lod_vec.size()));
int lower_bound = lod_vec[seg];
int upper_bound = lod_vec[seg + 1];
if (i >= lower_bound && i < upper_bound) {
@ -77,8 +86,11 @@ template <typename T>
class SequenceScatterGradientOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()),
"This kernel only runs on CPU.");
PADDLE_ENFORCE_EQ(
platform::is_cpu_place(ctx.GetPlace()), true,
platform::errors::Unimplemented("Device dose not match. The "
"SequenceScatterGradientOpKernel can "
"only run on CPU device."));
auto* dX = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* dUpdates = ctx.Output<LoDTensor>(framework::GradVarName("Updates"));
auto* ids = ctx.Input<LoDTensor>("Ids");
@ -94,9 +106,13 @@ class SequenceScatterGradientOpKernel : public framework::OpKernel<T> {
auto dout_dims = dOut->dims();
for (int i = 0; i < dx_dims.size(); ++i)
PADDLE_ENFORCE(dx_dims[i] == dout_dims[i],
"Input and output shape of "
"sequence scatter grad op must exactly be the same.");
PADDLE_ENFORCE_EQ(dx_dims[i], dout_dims[i],
platform::errors::InvalidArgument(
"Input(Out@GRAD) and output(X@GRAD) shape of "
"SequenceScatterGradient operator do not match. "
"Received input(Out@GRAD)'s shape is [%s], "
"output(X@GRAD)'s shape is [%s].",
dout_dims, dx_dims));
size_t slice_size = 1;
for (int i = 1; i < dx_dims.size(); ++i) slice_size *= dx_dims[i];
@ -105,8 +121,13 @@ class SequenceScatterGradientOpKernel : public framework::OpKernel<T> {
unsigned int seg = 0;
for (int i = 0; i < ids->dims()[0]; ++i) {
PADDLE_ENFORCE_LT(seg, lod_vec.size() - 1,
"Segment num must not exceed batch size.\n");
PADDLE_ENFORCE_LT(
seg, lod_vec.size() - 1,
platform::errors::OutOfRange(
"The segment index is out of bound in SequenceScatterGradient "
"operator, it must be less than batch size. The segment index is "
"%d, the batch size is %d.",
seg, lod_vec.size()));
int lower_bound = lod_vec[seg];
int upper_bound = lod_vec[seg + 1];
if (i >= lower_bound && i < upper_bound) {

32
paddle/fluid/operators/sequence_ops/sequence_slice_op.cc
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@ -23,14 +23,10 @@ class SequenceSliceOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SequenceSliceOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Offset"),
"Input(Offset) of SequenceSliceOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Length"),
"Input(Length) of SequenceSliceOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of SequenceSliceOp should not be null.");
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "SequenceSlice");
OP_INOUT_CHECK(ctx->HasInput("Offset"), "Input", "Offset", "SequenceSlice");
OP_INOUT_CHECK(ctx->HasInput("Length"), "Input", "Length", "SequenceSlice");
OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "SequenceSlice");
auto input_dims = ctx->GetInputDim("X");
auto offset_dim = ctx->GetInputDim("Offset");
@ -38,10 +34,18 @@ class SequenceSliceOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(
offset_dim.size(), 2UL,
"Only support one level sequence now, The rank of offset must be 2.");
platform::errors::InvalidArgument(
"Input Offset dimension error. SequenceSlice operator only support "
"one level sequence now, the dimension of input Offset must be 2, "
"but received dimension is %d.",
offset_dim.size()));
PADDLE_ENFORCE_EQ(
length_dim.size(), 2UL,
"Only support one level sequence now, The rank of Length must be 2.");
platform::errors::InvalidArgument(
"Input Length dimension error. SequenceSlice operator only support "
"one level sequence now, the dimension of input Length must be 2, "
"but received dimension is %d.",
offset_dim.size()));
// Initialize the output's dims to maximum,
// and re-set to real dims by the value of Offset and Length at kernel
@ -62,10 +66,10 @@ class SequenceSliceGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"The gradient of Out should not be null.");
PADDLE_ENFORCE(ctx->HasOutputs(framework::GradVarName("X")),
"The gradient of X should not be null.");
OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
framework::GradVarName("Out"), "SequenceSliceGrad");
OP_INOUT_CHECK(ctx->HasOutputs(framework::GradVarName("X")), "Output",
framework::GradVarName("X"), "SequenceSliceGrad");
ctx->SetOutputsDim(framework::GradVarName("X"), ctx->GetInputsDim("X"));
}

44
paddle/fluid/operators/sequence_ops/sequence_slice_op.h
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@ -49,18 +49,32 @@ class SequenceSliceOpKernel : public framework::OpKernel<T> {
auto* out = ctx.Output<LoDTensor>("Out");
auto lod = in->lod();
PADDLE_ENFORCE_EQ(
lod.empty(), false,
"Input(X) Tensor of SequenceSliceOp does not contain LoD information.");
PADDLE_ENFORCE_EQ(lod.empty(), false,
platform::errors::InvalidArgument(
"Input(X) Tensor of SequenceSlice operator does not "
"contain LoD information."));
PADDLE_ENFORCE_EQ(
lod.size(), 1UL,
platform::errors::InvalidArgument(
"LoD information error. SequenceSlice operator only support one "
"level sequence now, but received LoD level is %d.",
lod.size()));
auto n = lod[0].size() - 1;
PADDLE_ENFORCE_EQ(lod.size(), 1UL, "Only support one level sequence now.");
PADDLE_ENFORCE_EQ(
n, static_cast<size_t>(length->dims()[0]),
"The size of input-sequence and length-array should be the same");
platform::errors::InvalidArgument(
"Input length shape error. The length of input LoD sequence and "
"input length-array‘s first dimension should be equal, but the LoD "
"sequence length is %d, the length-array‘s first dimension is %d.",
n, static_cast<size_t>(length->dims()[0])));
PADDLE_ENFORCE_EQ(
n, static_cast<size_t>(offset->dims()[0]),
"The size of input-sequence and offset-array should be the same");
platform::errors::InvalidArgument(
"Input offset shape error. The length of input LoD sequence and "
"input offset-array‘s first dimension should be equal, but the LoD "
"sequence length is %d, the offset-array‘s first dimension is %d.",
n, static_cast<size_t>(offset->dims()[0])));
const int64_t* offset_data = offset->data<int64_t>();
const int64_t* length_data = length->data<int64_t>();
@ -79,11 +93,21 @@ class SequenceSliceOpKernel : public framework::OpKernel<T> {
for (size_t i = 0; i < n; ++i) {
PADDLE_ENFORCE_LE(0, offset_data[i],
"The offset[%d] must be nonnegative.", i);
platform::errors::InvalidArgument(
"The input offset[%d]'s value is negative, its "
"value is %d, expect it to be non-negative.",
i, offset_data[i]));
PADDLE_ENFORCE_LE(0, length_data[i],
"The length[%d] must be nonnegative.", i);
PADDLE_ENFORCE_LE(lod[0][i] + offset_data[i] + length_data[i],
lod[0][i + 1], "The target tensor's length overflow.");
platform::errors::InvalidArgument(
"The input length[%d]'s value is negative, its "
"value is %d, expect it to be non-negative.",
i, offset_data[i]));
PADDLE_ENFORCE_LE(
lod[0][i] + offset_data[i] + length_data[i], lod[0][i + 1],
platform::errors::OutOfRange(
"The slice end index of target tensor is out of range. expect it "
"less than or equal to %d, but the actual slice end index is %d.",
lod[0][i + 1], lod[0][i] + offset_data[i] + length_data[i]));
}
out->mutable_data<T>(ctx.GetPlace());

35
paddle/fluid/operators/sequence_ops/sequence_softmax_op.cc
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@ -23,10 +23,8 @@ class SequenceSoftmaxOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SequenceSoftmaxOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of SequenceSoftmaxOp should not be null.");
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "SequenceSoftmax");
OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "SequenceSoftmax");
ctx->ShareDim("X", /*->*/ "Out");
ctx->ShareLoD("X", /*->*/ "Out");
@ -108,21 +106,22 @@ class SequenceSoftmaxGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Out"),
"Input(Out) of SequenceSoftmaxGradOp should not be null.");
PADDLE_ENFORCE(
ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) of SequenceSoftmaxGradOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SequenceSoftmaxOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
"Output(X@GRAD) of SequenceSoftmaxOp should not be null.");
OP_INOUT_CHECK(ctx->HasInput("Out"), "Input", "Out", "SequenceSoftmaxGrad");
OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
"Out@GRAD", "SequenceSoftmaxGrad");
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "SequenceSoftmaxGrad");
OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Output",
"X@GRAD", "SequenceSoftmaxGrad");
auto out_dim = ctx->GetInputDim("Out");
auto out_grad_dim = ctx->GetInputDim(framework::GradVarName("Out"));
PADDLE_ENFORCE_EQ(
ctx->GetInputDim("Out"),
ctx->GetInputDim(framework::GradVarName("Out")),
"Input(Out) and Input(Out@GRAD) of SequenceSoftmaxGradOp should be of "
"the same shape.");
out_dim, out_grad_dim,
platform::errors::InvalidArgument(
"The shape of Input(Out) and Input(Out@GRAD) of "
"SequenceSoftmaxGrad operator do not match. The Input(Out)'s shape "
"is [%s], the Input(Out@GRAD)'s shape is [%s].",
out_dim, out_grad_dim));
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}

31
paddle/fluid/operators/sequence_ops/sequence_softmax_op.h
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@ -95,20 +95,27 @@ class SequenceSoftmaxKernel : public framework::OpKernel<T> {
auto lod = x->lod();
auto dims = x->dims();
PADDLE_ENFORCE_EQ(lod.empty(), false,
"Input(X) Tensor of SequenceSoftmaxOp does not contain "
"LoD information.");
PADDLE_ENFORCE_EQ(
lod.empty(), false,
platform::errors::InvalidArgument(
"Input(X) Tensor of SequenceSoftmax operator does not contain "
"LoD information."));
const size_t level = lod.size() - 1;
PADDLE_ENFORCE_GT(
lod.size(), 0U,
"The LoD level of Input X should be larger than 0 (lod.size() > 0).");
PADDLE_ENFORCE_EQ(dims[0], static_cast<int64_t>(lod[level].back()),
"The first dimension of Input(X) should be equal to the "
"sum of all sequences' lengths.");
PADDLE_ENFORCE_EQ(dims[0], x->numel(),
"The width of each timestep in Input(X) of "
"SequenceSoftmaxOp should be 1.");
PADDLE_ENFORCE_EQ(
dims[0], static_cast<int64_t>(lod[level].back()),
platform::errors::InvalidArgument(
"The first dimension of Input(X) should be equal to the sum of all "
"sequences' lengths. But the first dimension of Input(X) is %d, "
"the sum of all sequences' lengths is %d.",
dims[0], static_cast<int64_t>(lod[level].back())));
PADDLE_ENFORCE_EQ(
dims[0], x->numel(),
platform::errors::InvalidArgument(
"The width of each timestep in Input(X) of SequenceSoftmax "
"operator should be 1. But the first dimension of Input(X) is %d, "
"the number of elements is %d.",
dims[0], x->numel()));
out->mutable_data<T>(ctx.GetPlace());

66
paddle/fluid/operators/strided_slice_op.cc
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@ -29,14 +29,16 @@ class StridedSliceOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE_EQ(ctx->HasInput("Input"), true,
"Input (Input) of slice op should not be null.");
PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
"Output (Out) of slice op should not be null.");
OP_INOUT_CHECK(ctx->HasInput("Input"), "Input", "Input", "StridedSlice");
OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "StridedSlice");
auto in_dims = ctx->GetInputDim("Input");
PADDLE_ENFORCE_LT(in_dims.size(), 7,
"The rank of input should be less than 7.");
PADDLE_ENFORCE_LT(
in_dims.size(), 7,
platform::errors::InvalidArgument(
"The dimension of StridedSlice operator's input should be less "
"than 7, but received dimension is %d.",
in_dims.size()));
auto starts = ctx->Attrs().Get<std::vector<int>>("starts");
auto ends = ctx->Attrs().Get<std::vector<int>>("ends");
auto strides = ctx->Attrs().Get<std::vector<int>>("strides");
@ -50,20 +52,26 @@ class StridedSliceOp : public framework::OperatorWithKernel {
if (ctx->HasInputs("StartsTensorList")) {
auto StartsTensorList = ctx->Inputs("StartsTensorList");
PADDLE_ENFORCE_GT(StartsTensorList.size(), 0,
"StartsTensorList size can't be zero");
PADDLE_ENFORCE_GT(
StartsTensorList.size(), 0,
platform::errors::InvalidArgument(
"StridedSlice operator's StartsTensorList is empty."));
starts_size = StartsTensorList.size();
}
if (ctx->HasInputs("EndsTensorList")) {
auto EndsTensorList = ctx->Inputs("EndsTensorList");
PADDLE_ENFORCE_GT(EndsTensorList.size(), 0,
"EndsTensorList size can't be zero");
PADDLE_ENFORCE_GT(
EndsTensorList.size(), 0,
platform::errors::InvalidArgument(
"StridedSlice operator's EndsTensorList is empty."));
ends_size = EndsTensorList.size();
}
if (ctx->HasInputs("StridesTensorList")) {
auto StridesTensorList = ctx->Inputs("StridesTensorList");
PADDLE_ENFORCE_GT(StridesTensorList.size(), 0,
"StridesTensorList size can't be zero");
PADDLE_ENFORCE_GT(
StridesTensorList.size(), 0,
platform::errors::InvalidArgument(
"StridedSlice operator's StridesTensorList is empty."));
strides_size = StridesTensorList.size();
}
@ -73,18 +81,31 @@ class StridedSliceOp : public framework::OperatorWithKernel {
tensor_input = true;
}
if (!ctx->HasInput("EndsTensor")) {
PADDLE_ENFORCE_EQ(ends_size, axes.size(),
"The size of ends must be equal to the size of axes.");
PADDLE_ENFORCE_EQ(
ends_size, axes.size(),
platform::errors::InvalidArgument(
"The size of ends attribute in StridedSlice operator is not "
"equal to the size of axes attribute. The ends attribute's size "
"is %d, axes attribute's size is %d.",
ends_size, axes.size()));
}
if (!ctx->HasInput("StartsTensor")) {
PADDLE_ENFORCE_EQ(
starts_size, axes.size(),
"The size of starts must be equal to the size of axes.");
platform::errors::InvalidArgument(
"The size of starts attribute in StridedSlice operator is not "
"equal to the size of axes attribute. The starts attribute's "
"size is %d, axes attribute's size is %d.",
starts_size, axes.size()));
}
if (!ctx->HasInput("StridesTensor")) {
PADDLE_ENFORCE_EQ(
strides_size, axes.size(),
"The size of strides must be equal to the size of axes.");
platform::errors::InvalidArgument(
"The size of strides attribute in StridedSlice operator is not "
"equal to the size of axes attribute. The strides attribute's "
"size is %d, axes attribute's size is %d.",
strides_size, axes.size()));
}
// we need to analysis strided slice op is valid for
// the parameter that we get from python front
@ -101,7 +122,10 @@ class StridedSliceOp : public framework::OperatorWithKernel {
for (size_t i = 0; i < decrease_axis.size(); ++i) {
if (ctx->IsRuntime() && infer_flags[i] != -1) {
PADDLE_ENFORCE_EQ(out_dims[decrease_axis[i]], 1,
"decrease dim should be 1");
platform::errors::InvalidArgument(
"the size of decrease dimension should be 1, "
"but received %d.",
out_dims[decrease_axis[i]]));
}
out_dims[decrease_axis[i]] = 0;
}
@ -219,9 +243,11 @@ class StridedSliceOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE_EQ(ctx->HasInput("Input"), true, "Input should not be null");
PADDLE_ENFORCE_EQ(ctx->HasInput(framework::GradVarName("Out")), true,
"Input(Out@GRAD) should not be null");
OP_INOUT_CHECK(ctx->HasInput("Input"), "Input", "Input",
"StridedSliceGrad");
OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
"Out@GRAD", "StridedSliceGrad");
auto x_dims = ctx->GetInputDim("Input");
auto x_grad_name = framework::GradVarName("Input");
if (ctx->HasOutput(x_grad_name)) {

16
paddle/fluid/operators/strided_slice_op.h
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@ -54,7 +54,9 @@ static void StridedSliceOutDims(
continue;
}
PADDLE_ENFORCE_NE(stride_index, 0, "stride must not to be zero");
PADDLE_ENFORCE_NE(stride_index, 0,
platform::errors::InvalidArgument(
"stride index in StridedSlice operator is 0."));
int axis_size = in_dims[axes_index];
if (axis_size < 0) {
continue;
@ -78,8 +80,9 @@ static void StridedSliceOutDims(
((stride_index < 0 && (start_index <= end_index)) ||
(stride_index > 0 && (start_index >= end_index)));
PADDLE_ENFORCE_EQ(zero_dim_condition, false,
"starts and end must meet requirement in different "
"stride conditiont");
platform::errors::InvalidArgument(
"The start index and end index are invalid for their "
"corresponding stride."));
int left = std::max(0, std::min(start_index, end_index));
int right = std::min(axis_size, std::max(start_index, end_index));
int step = std::abs(stride_index);
@ -249,8 +252,11 @@ class StridedSliceKernel : public framework::OpKernel<T> {
if (decrease_axis.size() > 0) {
std::vector<int> new_out_shape;
for (size_t i = 0; i < decrease_axis.size(); ++i) {
PADDLE_ENFORCE_EQ(out_dims[decrease_axis[i]], 1,
"decrease dim should be 1");
PADDLE_ENFORCE_EQ(
out_dims[decrease_axis[i]], 1,
platform::errors::InvalidArgument(
"the size of decrease dimension should be 1, but received %d.",
out_dims[decrease_axis[i]]));
out_dims_origin[decrease_axis[i]] = 0;
}

33
python/paddle/fluid/layers/nn.py
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@ -10671,18 +10671,31 @@ def strided_slice(input, axes, starts, ends, strides):
sliced_2 = fluid.layers.strided_slice(input, axes=axes, starts=[minus_3, 0, 2], ends=ends, strides=strides_2)
# sliced_2 is input[:, 0:3:1, 0:2:1, 2:4:2].
"""
if not isinstance(starts, (list, tuple, Variable)):
raise ValueError(
"Input starts must be an Variable, python list or tuple.")
if not isinstance(ends, (list, tuple, Variable)):
raise ValueError(
"Input ends must be an Variable, python list or tuple.")
if not isinstance(strides, (list, tuple, Variable)):
raise ValueError(
"Input strides must be an Variable, python list or tuple.")
helper = LayerHelper('strided_slice', **locals())
check_variable_and_dtype(input, 'input',
['float32', 'float64', 'int32', 'int64'],
'strided_slice')
check_type(axes, 'axes', (list, tuple), 'strided_slice')
check_type(starts, 'starts', (list, tuple, Variable), 'strided_slice')
check_type(ends, 'ends', (list, tuple, Variable), 'strided_slice')
check_type(strides, 'strides', (list, tuple, Variable), 'strided_slice')
def check_list_elements_dtype(list_input, input_name):
if isinstance(list_input, Variable):
check_dtype(list_input.dtype, input_name, ['int32'],
'strided_slice')
else:
for i, var in enumerate(list_input):
var_name = input_name + '[' + str(i) + ']'
if isinstance(var, Variable):
check_dtype(var.dtype, var_name, ['int32'], 'strided_slice')
check_list_elements_dtype(axes, 'axes')
check_list_elements_dtype(starts, 'starts')
check_list_elements_dtype(ends, 'ends')
check_list_elements_dtype(strides, 'strides')
def get_new_list_tensor(old_list):
new_list_tensor = []
for dim in old_list:

29
python/paddle/fluid/layers/sequence_lod.py
Unescape View File

@ -240,6 +240,8 @@ def sequence_softmax(input, use_cudnn=False, name=None):
assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_softmax', **locals())
check_variable_and_dtype(input, 'input', ['float32', 'float64'],
'sequence_softmax')
dtype = helper.input_dtype()
softmax_out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
@ -558,10 +560,10 @@ def sequence_slice(input, offset, length, name=None):
Args:
input(Variable): LoDTensor, The input Variable which consists of the complete
sequences.The data type is float32 or float64.
offset(Variable): LoDTensor, The offset to slice each sequence.The data
sequences.The data type can be float32, float64, int32 or int64
offset(Variable): LoDTensor, The offset to slice each sequence. The data
type is int32 or int64.
length(Variable): LoDTensor, The length of each subsequence.The data
length(Variable): LoDTensor, The length of each subsequence. The data
type is int32 or int64.
name(str|None): The default value is None. Normally there is no need
for user to set this property. For more information,
@ -586,6 +588,15 @@ def sequence_slice(input, offset, length, name=None):
assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper("sequence_slice", **locals())
check_variable_and_dtype(input, 'input',
['float32', 'float64', 'int32', 'int64'],
'sequence_slice')
check_variable_and_dtype(offset, 'offset', ['int32', 'int64'],
'sequence_slice')
check_variable_and_dtype(length, 'length', ['int32', 'int64'],
'sequence_slice')
dtype = helper.input_dtype()
out = helper.create_variable_for_type_inference(dtype)
@ -1135,7 +1146,7 @@ def sequence_scatter(input, index, updates, name=None):
Args:
input (Variable): A Tensor with shape of :math:`[N, k_1... k_n]`. Supported data types: float32, float64, int32, int64.
index (Variable): A LoDTensor contains index information. Its LoD level must be 1 and its data type must be int64.
index (Variable): A LoDTensor contains index information. Its LoD level must be 1 and its data type can be int32 or int64.
updates (Variable): A LodTensor contains updates information. It has the same LoD level with the index and has the
same data type with the input. Supported data types: float32, float64, int32, int64.
name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information,
@ -1159,6 +1170,16 @@ def sequence_scatter(input, index, updates, name=None):
assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_scatter', **locals())
check_variable_and_dtype(input, 'input',
['float32', 'float64', 'int32', 'int64'],
'sequence_scatter')
check_variable_and_dtype(index, 'index', ['int32', 'int64'],
'sequence_scatter')
check_variable_and_dtype(updates, 'updates',
['float32', 'float64', 'int32', 'int64'],
'sequence_scatter')
dtype = helper.input_dtype()
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(

6
python/paddle/fluid/tests/unittests/test_strided_slice_op.py
Unescape View File

@ -444,11 +444,11 @@ class TestStridedSliceAPI(unittest.TestCase):
minus_1 = fluid.layers.fill_constant([1], "int32", -1)
minus_3 = fluid.layers.fill_constant([1], "int32", -3)
starts = fluid.layers.data(
name='starts', shape=[3], append_batch_size=False)
name='starts', shape=[3], dtype='int32', append_batch_size=False)
ends = fluid.layers.data(
name='ends', shape=[3], append_batch_size=False)
name='ends', shape=[3], dtype='int32', append_batch_size=False)
strides = fluid.layers.data(
name='strides', shape=[3], append_batch_size=False)
name='strides', shape=[3], dtype='int32', append_batch_size=False)
x = fluid.layers.data(
name="x",

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