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enhance array_to_lod_tensor_op lod_tensor_to_array_op errors informaiton (#27386)

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* enhance array_to_lod_tensor_op lod_tensor_to_array_op errors information. test=develop
my_2.0rc
yongqiangma 5 years ago committed by GitHub
parent eb37ee2a26
commit aac57159c9
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 68 additions and 32 deletions
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66
paddle/fluid/operators/array_to_lod_tensor_op.cc
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@ -55,7 +55,8 @@ struct ArrayToLoDFunctor : public boost::static_visitor<void> {
#ifdef PADDLE_WITH_CUDA
Apply(static_cast<platform::CUDADeviceContext *>(pool.Get(place)));
#else
PADDLE_THROW("Fluid is not compiled with CUDA");
PADDLE_THROW(
platform::errors::Unavailable("Paddle is not compiled with CUDA."));
#endif
}
}
@ -95,7 +96,9 @@ class ArrayToLoDTensorOp : public framework::OperatorBase {
// Check dims, place and data type of input's elements and infer output's
// dim
PADDLE_ENFORCE(!x.empty(), "There's no element in the input array.");
PADDLE_ENFORCE_EQ(x.empty(), false,
platform::errors::PreconditionNotMet(
"There's no element in the input array."));
int rank = x[0].dims().size();
platform::Place place = x[0].place();
auto data_type = x[0].type();
@ -106,18 +109,27 @@ class ArrayToLoDTensorOp : public framework::OperatorBase {
for (size_t i = 1; i < x.size(); ++i) {
auto ins_i_dims = rank > 1 ? framework::slice_ddim(x[i].dims(), 1, rank)
: framework::make_ddim({0});
PADDLE_ENFORCE_EQ(ins_i_dims, ins_dims,
"The dimension of the %zu'th element in LoDTensorArray "
"differs from previous ones.",
i);
PADDLE_ENFORCE(x[i].place() == place,
"The place class of the %zu'th element in LoDTensorArray "
"differs from previous ones.",
i);
PADDLE_ENFORCE(x[i].type() == data_type,
"The date type of the %zu'th element in LoDTensorArray "
"differs from previous ones.",
i);
PADDLE_ENFORCE_EQ(
ins_i_dims, ins_dims,
platform::errors::InvalidArgument(
"The dimension of the %zu'th element in LoDTensorArray "
"differs from previous ones."
"The current dimension is %d, and the previous dimesion is %d.",
i, ins_i_dims, ins_dims));
PADDLE_ENFORCE_EQ(
x[i].place(), place,
platform::errors::InvalidArgument(
"The place class of the %zu'th element in LoDTensorArray "
"differs from previous ones."
"The current place is %d, and the previous place is %d.",
i, x[i].place(), place));
PADDLE_ENFORCE_EQ(
x[i].type(), data_type,
platform::errors::InvalidArgument(
"The date type of the %zu'th element in LoDTensorArray "
"differs from previous ones."
"The current data type is %d, and the previous data type is %d.",
i, x[i].type(), data_type));
batch_size += x[i].dims()[0];
}
auto ins_dim_vec = framework::vectorize(ins_dims);
@ -145,7 +157,13 @@ class ArrayToLoDTensorOp : public framework::OperatorBase {
ArrayToLoDFunctor functor;
for (size_t idx : table_item_idx) {
cur_level_lod.push_back(cur_level_lod.back() + table_items[idx].length);
PADDLE_ENFORCE_LE(table_items[idx].length, x.size());
PADDLE_ENFORCE_LE(table_items[idx].length, x.size(),
platform::errors::InvalidArgument(
"The RankTable items length should less than or "
"equal to Input(X) size,"
"but receive TankTable items length is %d , longer "
"than Input(X) size %d.",
table_items[idx].length, x.size()));
for (size_t x_idx = 0; x_idx < table_items[idx].length; ++x_idx) {
auto lod_and_offset = framework::GetSubLoDAndAbsoluteOffset(
x[x_idx].lod(), idx, idx + 1, 0);
@ -158,7 +176,13 @@ class ArrayToLoDTensorOp : public framework::OperatorBase {
VLOG(10) << "idx=" << idx << " x_idx=" << x_idx << " ["
<< ", " << end_offset << "]";
// Copy data
PADDLE_ENFORCE_GE(end_offset, start_offset);
PADDLE_ENFORCE_GE(
end_offset, start_offset,
platform::errors::InvalidArgument(
"The lod data start offset should smaller or equal to the end "
"offset,"
"but the start offset is %d, larger than end offset %d.",
start_offset, end_offset));
size_t len = end_offset - start_offset;
if (len == 0) {
continue;
@ -195,10 +219,12 @@ class ArrayToLoDTensorOpProtoMaker : public framework::OpProtoAndCheckerMaker {
class ArrayToLoDTensorInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE(context->HasInput("X"),
"ArrayToLoDTensorOp must have input X.");
PADDLE_ENFORCE(context->HasInput("RankTable"),
"ArrayToLoDTensorOp must have input RankTable.");
PADDLE_ENFORCE_EQ(
context->HasInput("X"), true,
platform::errors::NotFound("Input(X) of BmmOp should not be null."));
PADDLE_ENFORCE_EQ(context->HasInput("RankTable"), true,
platform::errors::NotFound(
"Input(RankTable) of BmmOp should not be null."));
// For compile-time, the first dim of input X and output Out should be -1.
// For runtime, the first dim of output Out should be the sum of all
// elements's first dim in input X. The output's dims will be re-computed in

4
paddle/fluid/operators/lod_rank_table_op.cc
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@ -71,7 +71,9 @@ output operators.
class LoDRankTableInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE(context->HasInput("X"), "LoDRankTable must have input X");
PADDLE_ENFORCE_EQ(
context->HasInput("X"), true,
platform::errors::NotFound("LoDRankTable must have input X."));
}
};

30
paddle/fluid/operators/lod_tensor_to_array_op.cc
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@ -66,7 +66,8 @@ struct LoDTensorToArrayFunctor : public boost::static_visitor<void> {
#ifdef PADDLE_WITH_CUDA
Apply(static_cast<platform::CUDADeviceContext *>(dev_ctx));
#else
PADDLE_THROW("Not compiled with cuda");
PADDLE_THROW(
platform::errors::Unavailable("Paddle is not compiled with CUDA."));
#endif
}
}
@ -114,8 +115,10 @@ class LoDTensorToArrayOp : public framework::OperatorBase {
PADDLE_ENFORCE_LT(
rank_level, x.lod().size(),
"Input should be a LoDTensor, and its lod_level should be at least %d",
rank_level + 1);
platform::errors::InvalidArgument(
"Input should be a LoDTensor, and its lod_level should be at "
"least %d, but given is %d.",
rank_level + 1, x.lod().size()));
out.resize(max_seq_len);
std::vector<std::vector<CopyRange>> copy_ranges(max_seq_len);
@ -195,14 +198,19 @@ NOTE: this operator is an internal component of DynamicRNN, and cannot be called
class LoDTensorToArrayInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE(context->HasInput("X"),
"Input(X) of LoDTensorToArrayOp should not be null.");
PADDLE_ENFORCE(
context->HasInput("RankTable"),
"Input(RankTable) of LoDTensorToArrayOp should not be null.");
PADDLE_ENFORCE(context->HasOutput("Out"),
"Output(Out) of LoDTensorToArrayOp should not be null.");
PADDLE_ENFORCE_EQ(
context->HasInput("X"), true,
platform::errors::NotFound(
"Input(X) of LoDTensorToArrayOp should not be null."));
PADDLE_ENFORCE_EQ(
context->HasInput("RankTable"), true,
platform::errors::NotFound(
"Input(RankTable) of LoDTensorToArrayOp should not be null."));
PADDLE_ENFORCE_EQ(
context->HasOutput("Out"), true,
platform::errors::NotFound(
"Output(Out) of LoDTensorToArrayOp should not be null."));
auto x_dim = context->GetInputDim("X");
// For compile-time, the first dim of input X and output Out should be -1.

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