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Optimize the error message for OP (#27617)

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* Optimize the error message for OPs.

* Optimize the error message for OPs in details.
my_2.0rc
xiemoyuan 4 years ago committed by GitHub
parent baddedfdf1
commit 6c1acf34ed
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GPG Key ID: 4AEE18F83AFDEB23
8 changed files with 61 additions and 38 deletions
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3
paddle/fluid/operators/beam_search_decode_op.cc
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@ -117,7 +117,8 @@ void BeamSearchDecodeFunctor::apply() const {
template <>
void BeamSearchDecodeFunctor::apply<bool>() const {
PADDLE_THROW("beam search decode op does not support bool!");
PADDLE_THROW(platform::errors::InvalidArgument(
"beam search decode op does not support bool!"));
}
class BeamSearchDecodeOp : public framework::OperatorBase {

2
paddle/fluid/operators/chunk_eval_op.h
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@ -146,7 +146,7 @@ class ChunkEvalKernel : public framework::OpKernel<T> {
tag_end = -1;
tag_single = -1;
} else {
PADDLE_THROW("Unknown chunk scheme.");
PADDLE_THROW(platform::errors::InvalidArgument("Unknown chunk scheme."));
}
other_chunk_type = num_chunk_types = context.Attr<int>("num_chunk_types");
excluded_chunk_types.insert(

4
paddle/fluid/operators/cudnn_lstm_op.cc
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@ -274,8 +274,8 @@ template <typename T>
class NotImpleKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_THROW(
"CPU is not support for this kernel now. Will be add in the future");
PADDLE_THROW(platform::errors::Unimplemented(
"CPU is not support for this kernel now. Will be add in the future"));
}
};

5
paddle/fluid/operators/edit_distance_op.cu
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@ -111,8 +111,9 @@ class EditDistanceGPUKernel : public framework::OpKernel<T> {
if (normalized) {
for (size_t i = 1; i < ref_lod.size(); ++i) {
PADDLE_ENFORCE(ref_lod[i] > ref_lod[i - 1],
"Reference string %d is empty.", i);
PADDLE_ENFORCE_GT(ref_lod[i], ref_lod[i - 1],
platform::errors::InvalidArgument(
"Reference string %d is empty.", i));
}
}

14
paddle/fluid/operators/edit_distance_op.h
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@ -58,8 +58,9 @@ class EditDistanceKernel : public framework::OpKernel<T> {
if (normalized) {
for (size_t i = 1; i < ref_lod.size(); ++i) {
PADDLE_ENFORCE(ref_lod[i] > ref_lod[i - 1],
"Reference string %d is empty.", i);
PADDLE_ENFORCE_GT(ref_lod[i], ref_lod[i - 1],
platform::errors::InvalidArgument(
"Reference string %d is empty.", i));
}
}
auto num_strs = hyp_lod.size() - 1;
@ -106,10 +107,11 @@ class EditDistanceKernel : public framework::OpKernel<T> {
}
if (normalized) {
PADDLE_ENFORCE(n > 0,
"The reference string (#%d) cannot be empty "
"when Attr(normalized) is enabled.",
n);
PADDLE_ENFORCE_GT(n, 0UL,
platform::errors::InvalidArgument(
"The reference string (#%d) cannot be empty "
"when Attr(normalized) is enabled.",
n));
distance = distance / n;
}
out[num] = distance;

5
paddle/fluid/operators/expand_as_op.cc
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@ -89,8 +89,9 @@ class ExpandAsGradOp : public framework::OperatorWithKernel {
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true);
PADDLE_ENFORCE_EQ(ctx->HasInput(framework::GradVarName("Out")), true);
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "ExpandAs");
OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
framework::GradVarName("Out"), "ExpandAs");
auto x_dims = ctx->GetInputDim("X");
auto x_grad_name = framework::GradVarName("X");

30
paddle/fluid/operators/expand_as_op.h
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@ -61,7 +61,10 @@ class ExpandAsKernel : public framework::OpKernel<T> {
switch (rank) {
REP_EXPAND_AS_TEMPLATE(MAX_RANK_SUPPORTED)
default:
PADDLE_THROW("Only support tensor with rank being between 1 and 6.");
PADDLE_THROW(platform::errors::InvalidArgument(
"Only support tensor with rank being between 1 and 6. But received "
"tensor X's rank = %d.",
rank));
}
}
@ -77,13 +80,19 @@ class ExpandAsKernel : public framework::OpKernel<T> {
auto x_dims = in0->dims();
auto y_dims = target_tensor->dims();
for (int i = 0; i < y_dims.size(); ++i) {
PADDLE_ENFORCE_NE(x_dims[i], 0, "X(input) should not have 0 dim");
PADDLE_ENFORCE_NE(
x_dims[i], 0UL,
platform::errors::InvalidArgument(
"X(input) should not have 0 dim. But received x_dims[%d] = 0.",
i));
bcast_dims[i] = y_dims[i] / x_dims[i];
bcast_dims_remainder += y_dims[i] % x_dims[i];
}
PADDLE_ENFORCE_EQ(bcast_dims_remainder, 0,
"X(input) could not be broadcast together with remapped "
"shape(expand tensor's shape)");
PADDLE_ENFORCE_EQ(
bcast_dims_remainder, 0UL,
platform::errors::InvalidArgument(
"X(input) could not be broadcast together with remapped "
"shape(expand tensor's shape)"));
framework::DDim out_dims(in_dims);
for (size_t i = 0; i < bcast_dims.size(); ++i) {
out_dims[i] *= bcast_dims[i];
@ -137,7 +146,10 @@ class ExpandAsGradKernel : public framework::OpKernel<T> {
switch (dims) {
REP_EXPAND_AS_GRAD_TEMPLATE(MAX_RANK_SUPPORTED)
default:
PADDLE_THROW("Only support tensor with rank being between 1 and 6.");
PADDLE_THROW(platform::errors::InvalidArgument(
"Only support tensor with rank being between 1 and 6. But "
"received tensor's rank = %d.",
dims));
}
}
}
@ -149,12 +161,6 @@ class ExpandAsGradKernel : public framework::OpKernel<T> {
const std::vector<int>& reduce_dims_vec) const {
size_t reshape_size = reshape_dims_vec.size();
size_t reduce_size = reduce_dims_vec.size();
PADDLE_ENFORCE_EQ(reshape_size, reshape_dims_vec.size(),
"Inconsistent size between template Dims and "
"reshape dimensions.");
PADDLE_ENFORCE_EQ(reduce_size, reduce_dims_vec.size(),
"Inconsistent size between template Dims and "
"reduce dimensions.");
auto* in0 = context.Input<Tensor>(framework::GradVarName("Out"));
auto* out0 = context.Output<Tensor>(framework::GradVarName("X"));
out0->mutable_data<T>(context.GetPlace());

36
paddle/fluid/operators/linear_chain_crf_op.h
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@ -27,9 +27,10 @@ static inline T NormalizeL1(T* x, size_t len) {
// (This comment is from the old LinearChainCRFLayer.)
// Right now, we just bet that sum won't be zero. If this really happens, we
// will figure out what should be done then.
PADDLE_ENFORCE(sum,
"The unnormalized probabilities of all possible unfinished "
"sequences must be greater than 0.");
PADDLE_ENFORCE_GT(
sum, 0., platform::errors::InvalidArgument(
"The unnormalized probabilities of all possible unfinished "
"sequences must be greater than 0."));
T s = 1. / sum;
for (size_t i = 0; i < len; ++i) x[i] *= s;
return sum;
@ -84,13 +85,19 @@ class LinearChainCRFOpKernel : public framework::OpKernel<T> {
const Tensor* label_length = ctx.Input<framework::Tensor>("Length");
length_data = label_length->data<int64_t>();
seq_num = label_length->numel();
PADDLE_ENFORCE_EQ(seq_num, emission_dims[0],
"the size of Input(length) must be equal to "
"emission_dims[0].");
PADDLE_ENFORCE_EQ(
seq_num, emission_dims[0],
platform::errors::InvalidArgument(
"the size of Input(length) must be equal to "
"emission_dims[0]. But input_size = %d, emission_dims[0] = %d.",
seq_num, emission_dims[0]));
auto label_dims = label->dims();
PADDLE_ENFORCE_EQ(seq_num, label_dims[0],
"the size of Input(length) must be equal to "
"label_dims[0].");
PADDLE_ENFORCE_EQ(
seq_num, label_dims[0],
platform::errors::InvalidArgument(
"the size of Input(length) must be equal to "
"label_dims[0]. But input_size = %d, label_dims[0] = %d.",
seq_num, label_dims[0]));
batch_size = emission_dims[0] * emission_dims[1];
tag_num = emission_dims[2];
@ -102,7 +109,9 @@ class LinearChainCRFOpKernel : public framework::OpKernel<T> {
math::set_constant(ctx.device_context(), alpha, 0.0);
} else {
in_lod = ctx.Input<LoDTensor>("Label")->lod();
PADDLE_ENFORCE_NE(in_lod.size(), 0, "Input(Label) must be a sequence.");
PADDLE_ENFORCE_NE(in_lod.size(), 0,
platform::errors::InvalidArgument(
"Input(Label) must be a sequence."));
seq_num = in_lod[0].size() - 1;
batch_size = emission_dims[0];
tag_num = emission_dims[1];
@ -204,7 +213,8 @@ class LinearChainCRFOpKernel : public framework::OpKernel<T> {
const int64_t* lbl = label.data<int64_t>();
PADDLE_ENFORCE_LT(
static_cast<size_t>(*std::max_element(lbl, lbl + seq_length)), tag_num,
"An invalid tag label that execesses the largest tag number.");
platform::errors::InvalidArgument(
"An invalid tag label that execesses the largest tag number."));
// Calculate the nominator part, which depends on the label sequence.
ll += w[lbl[0]] /*start transition*/ + x[lbl[0]] +
@ -254,7 +264,9 @@ class LinearChainCRFGradOpKernel : public framework::OpKernel<T> {
{emission_dims[0] * emission_dims[1], emission_dims[2]});
} else {
in_lod = ctx.Input<LoDTensor>("Label")->lod();
PADDLE_ENFORCE_NE(in_lod.size(), 0, "Input(Label) must be a sequence.");
PADDLE_ENFORCE_NE(in_lod.size(), 0,
platform::errors::InvalidArgument(
"Input(Label) must be a sequence."));
seq_num = static_cast<int64_t>(in_lod[0].size() - 1);
}

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