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Error description optimize for math dir

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Error description optimize for math dir
disable_ut_1
Jack Zhou 4 years ago committed by GitHub
parent 5c1bafbbc6
commit 9437ce36c4
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
9 changed files with 271 additions and 123 deletions
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18
paddle/fluid/operators/math/math_function.cc
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@ -128,9 +128,23 @@ struct RowwiseAdd<platform::CPUDeviceContext, T> {
const framework::Tensor& input,
const framework::Tensor& vector, framework::Tensor* output) {
auto in_dims = input.dims();
auto out_dims = output->dims();
auto size = input.numel() / in_dims[0];
PADDLE_ENFORCE_EQ(vector.numel(), size);
PADDLE_ENFORCE_EQ(output->dims(), in_dims);
PADDLE_ENFORCE_EQ(
vector.numel(), size,
platform::errors::InvalidArgument(
"The input vector size"
" should be equal to the size of each row of input tensor."
" Expected vector size=%d, but received %d",
size, vector.numel()));
const char* in_dims_cstr = in_dims.to_str().c_str();
const char* out_dims_cstr = out_dims.to_str().c_str();
PADDLE_ENFORCE_EQ(out_dims, in_dims,
platform::errors::InvalidArgument(
"The output tensor shape should be same as the input"
" tensor shape. Expected output tensor shape: %s,"
" but received %s",
in_dims_cstr, out_dims_cstr));
auto in = framework::EigenMatrix<T>::From(input);
auto vec = framework::EigenVector<T>::Flatten(vector);

33
paddle/fluid/operators/math/math_function.cu
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@ -88,9 +88,24 @@ struct RowwiseAdd<platform::CUDADeviceContext, T> {
const framework::Tensor& input,
const framework::Tensor& vector, framework::Tensor* output) {
auto in_dims = input.dims();
auto out_dims = output->dims();
auto size = input.numel() / in_dims[0];
PADDLE_ENFORCE_EQ(vector.numel(), size);
PADDLE_ENFORCE_EQ(output->dims(), in_dims);
PADDLE_ENFORCE_EQ(
vector.numel(), size,
platform::errors::InvalidArgument(
"The input vector size"
" should be equal to the size of each row of input tensor."
" Expected vector size=%d, but received %d",
size, vector.numel()));
const char* in_dims_cstr = in_dims.to_str().c_str();
const char* out_dims_cstr = out_dims.to_str().c_str();
PADDLE_ENFORCE_EQ(
out_dims, in_dims,
platform::errors::InvalidArgument(
"The output tensor shape should be same as the input tensor"
" shape. Expected output tensor shape: %s,"
" but received %s",
in_dims_cstr, out_dims_cstr));
int blocks = 512;
int grids = (input.numel() + blocks - 1) / blocks;
RowwiseAddKernel<T><<<grids, blocks, 0, context.stream()>>>(
@ -113,7 +128,12 @@ void ColwiseSum<platform::CUDADeviceContext, double>::operator()(
framework::Tensor* vector) {
auto in_dims = input.dims();
auto size = input.numel() / in_dims[0];
PADDLE_ENFORCE_EQ(vector->numel(), size);
PADDLE_ENFORCE_EQ(vector->numel(), size,
platform::errors::InvalidArgument(
"The size of input vector"
" should be equal to the size of input tensor column"
" dimension. Expected vector size=%d, but received %d",
size, vector->numel()));
framework::Tensor one;
one.mutable_data<double>({in_dims[0]}, context.GetPlace());
SetConstant<platform::CUDADeviceContext, double> set;
@ -134,7 +154,12 @@ void RowwiseSum<platform::CUDADeviceContext, double>::operator()(
framework::Tensor* vector) {
auto in_dims = input.dims();
auto size = input.numel() / in_dims[0];
PADDLE_ENFORCE_EQ(vector->numel(), in_dims[0]);
PADDLE_ENFORCE_EQ(vector->numel(), in_dims[0],
platform::errors::InvalidArgument(
"The size of input vector"
" should be equal to the size of input tensor row"
" dimension. Expected vector size=%d, but received %d",
in_dims[0], vector->numel()));
framework::Tensor one;
one.mutable_data<double>({size}, context.GetPlace());
SetConstant<platform::CUDADeviceContext, double> set;

65
paddle/fluid/operators/math/math_function_impl.h
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@ -59,7 +59,12 @@ void ColwiseSum<DeviceContext, T>::operator()(const DeviceContext& context,
framework::Tensor* out) {
auto in_dims = input.dims();
auto size = input.numel() / in_dims[0];
PADDLE_ENFORCE_EQ(out->numel(), size);
PADDLE_ENFORCE_EQ(out->numel(), size,
platform::errors::InvalidArgument(
"The size of output tensor "
"should be equal to the size of input tensor column"
" dimension. Expected output size=%d, but received %d",
size, out->numel()));
auto in = framework::EigenMatrix<T>::From(input);
auto vec = framework::EigenVector<T>::Flatten(*out);
@ -78,7 +83,13 @@ class ColwiseSum<platform::CPUDeviceContext, T> {
auto& in_dims = input.dims();
auto height = in_dims[0];
auto size = in_dims[1];
PADDLE_ENFORCE_EQ(out->numel(), size);
PADDLE_ENFORCE_EQ(
out->numel(), size,
platform::errors::InvalidArgument(
"The size of output tensor "
"should be equal to the size of input tensor column"
" dimension. Expected output size=%d, but received %d",
size, out->numel()));
T* out_buf = out->mutable_data<T>(out->place());
const T* in_buf = input.data<T>();
@ -100,8 +111,16 @@ void RowwiseMean<DeviceContext, T>::operator()(const DeviceContext& context,
const framework::Tensor& input,
framework::Tensor* out) {
auto in_dims = input.dims();
PADDLE_ENFORCE_EQ(in_dims.size(), 2U);
PADDLE_ENFORCE_EQ(out->numel(), in_dims[0]);
PADDLE_ENFORCE_EQ(in_dims.size(), 2U, platform::errors::InvalidArgument(
"The rank of input tensor "
"should be 2, but received %d",
in_dims.size()));
PADDLE_ENFORCE_EQ(out->numel(), in_dims[0],
platform::errors::InvalidArgument(
"The size of output tensor "
"should be equal to the size of input tensor row"
" dimension. Expected output size=%d, but received %d",
in_dims[0], out->numel()));
auto in = framework::EigenMatrix<T>::From(input);
auto vec = framework::EigenVector<T>::Flatten(*out);
@ -118,10 +137,19 @@ class RowwiseMean<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& context,
const framework::Tensor& input, framework::Tensor* out) {
auto& in_dims = input.dims();
PADDLE_ENFORCE_EQ(in_dims.size(), 2U);
PADDLE_ENFORCE_EQ(in_dims.size(), 2U, platform::errors::InvalidArgument(
"The rank of input tensor "
"should be 2, but received %d",
in_dims.size()));
auto height = in_dims[0];
auto size = in_dims[1];
PADDLE_ENFORCE_EQ(out->numel(), height);
PADDLE_ENFORCE_EQ(
out->numel(), height,
platform::errors::InvalidArgument(
"The size of output tensor "
"should be equal to the size of input tensor row"
" dimension. Expected output size=%d, but received %d",
height, out->numel()));
auto inv_size = 1.0 / size;
T* out_buf = out->mutable_data<T>(out->place());
const T* in_buf = input.data<T>();
@ -141,8 +169,16 @@ void RowwiseSum<DeviceContext, T>::operator()(const DeviceContext& context,
const framework::Tensor& input,
framework::Tensor* out) {
auto in_dims = input.dims();
PADDLE_ENFORCE_EQ(in_dims.size(), 2U);
PADDLE_ENFORCE_EQ(out->numel(), in_dims[0]);
PADDLE_ENFORCE_EQ(in_dims.size(), 2U, platform::errors::InvalidArgument(
"The rank of input tensor "
"should be 2, but received %d",
in_dims.size()));
PADDLE_ENFORCE_EQ(out->numel(), in_dims[0],
platform::errors::InvalidArgument(
"The size of output tensor "
"should be equal to the size of input tensor row"
" dimension. Expected output size=%d, but received %d",
in_dims[0], out->numel()));
auto in = framework::EigenMatrix<T>::From(input);
auto vec = framework::EigenVector<T>::Flatten(*out);
@ -159,10 +195,19 @@ class RowwiseSum<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& context,
const framework::Tensor& input, framework::Tensor* out) {
auto& in_dims = input.dims();
PADDLE_ENFORCE_EQ(in_dims.size(), 2U);
PADDLE_ENFORCE_EQ(in_dims.size(), 2U, platform::errors::InvalidArgument(
"The rank of input tensor "
"should be 2, but received %d",
in_dims.size()));
auto height = in_dims[0];
auto size = in_dims[1];
PADDLE_ENFORCE_EQ(out->numel(), height);
PADDLE_ENFORCE_EQ(
out->numel(), height,
platform::errors::InvalidArgument(
"The size of output tensor "
"should be equal to the size of input tensor row"
" dimension. Expected output size=%d, but received %d",
height, out->numel()));
T* out_buf = out->mutable_data<T>(out->place());
const T* in_buf = input.data<T>();

6
paddle/fluid/operators/math/math_function_test.cc
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@ -224,7 +224,11 @@ TEST(math_funciton, set_constant) {
auto* ctx = new paddle::platform::CPUDeviceContext();
paddle::operators::math::set_constant(*ctx, &t, 10);
for (int64_t i = 0; i < t.numel(); ++i) {
PADDLE_ENFORCE_EQ(10, t.data<int>()[i]);
PADDLE_ENFORCE_EQ(10, t.data<int>()[i],
paddle::platform::errors::InvalidArgument(
"Each value of input"
"tensor should be 10, but received %d.",
t.data<int>()[i]));
}
delete ctx;
}

7
paddle/fluid/operators/math/math_function_test.cu
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@ -18,7 +18,12 @@
void fill_fp16_data(paddle::platform::float16* in_ptr, size_t size,
const std::vector<float>& data) {
PADDLE_ENFORCE_EQ(size, data.size());
PADDLE_ENFORCE_EQ(
size, data.size(),
paddle::platform::errors::InvalidArgument(
"The size of argument data should"
" be equal to the argument size. Expected %d, but received %d.",
size, data.size()));
for (size_t i = 0; i < data.size(); ++i) {
in_ptr[i] = paddle::platform::float16(data[i]);
}

10
paddle/fluid/operators/math/padding.h
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@ -85,8 +85,9 @@ void PaddingFunctor(int rank, const framework::ExecutionContext& context,
PadFunction<DeviceContext, T, 6>(context, pads, src, pad_value, out);
break;
default:
PADDLE_THROW(
"PadOp only support tensors with no more than 6 dimensions.");
PADDLE_THROW(platform::errors::Unimplemented(
"PadOp only support tensors with no more"
" than 6 dimensions currently."));
}
}
@ -114,8 +115,9 @@ void PaddingGradFunctor(int rank, const framework::ExecutionContext& context,
PadGradFunction<DeviceContext, T, 6>(context, pads, src, out);
break;
default:
PADDLE_THROW(
"PadOp only support tensors with no more than 6 dimensions.");
PADDLE_THROW(platform::errors::Unimplemented(
"PadOp only support tensors with no more"
" than 6 dimensions currently."));
}
}

7
paddle/fluid/operators/math/sampler.h
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@ -19,6 +19,8 @@ limitations under the License. */
#include <random>
#include <vector>
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace operators {
namespace math {
@ -31,7 +33,10 @@ namespace math {
class Sampler {
public:
explicit Sampler(int64_t range, unsigned int seed = 0UL) : range_(range) {
// PADDLE_ENFORCE_GT(range, 0, "Range should be greater than 0.");
PADDLE_ENFORCE_GT(range, 0, platform::errors::InvalidArgument(
"Range should be"
" greater than 0, but recevied %d.",
range));
if (seed == 0) {
std::random_device r;
seed_ = r();

124
paddle/fluid/operators/math/vol2col.cc
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@ -34,10 +34,16 @@ class Vol2ColFunctor<platform::CPUDeviceContext, T> {
const std::vector<int>& strides,
const std::vector<int>& paddings, framework::Tensor* col,
const DataLayout data_layout) const {
PADDLE_ENFORCE_EQ(vol.dims().size(), 4,
"The dimension of vol should be 4.");
PADDLE_ENFORCE_EQ(col->dims().size(), 7,
"The dimension of col should be 7.");
PADDLE_ENFORCE_EQ(
vol.dims().size(), 4,
platform::errors::InvalidArgument("The dimension of"
" vol should be 4, but received %d.",
vol.dims().size()));
PADDLE_ENFORCE_EQ(
col->dims().size(), 7,
platform::errors::InvalidArgument("The dimension of"
"col should be 7, but received %d.",
col->dims().size()));
int input_channels =
(data_layout != DataLayout::kNHWC ? vol.dims()[0] : vol.dims()[3]);
@ -65,27 +71,33 @@ class Vol2ColFunctor<platform::CPUDeviceContext, T> {
int pad_w_left = paddings_size_is_6 ? paddings[4] : paddings[2];
int pad_w_right = paddings_size_is_6 ? paddings[5] : paddings[2];
PADDLE_ENFORCE_EQ((input_depth + pad_d_forth + pad_d_back -
((dilations[0] * (filter_depth - 1) + 1))) /
strides[0] +
1,
output_depth,
"input_depth and output_depth are "
"mismatching.");
PADDLE_ENFORCE_EQ((input_height + pad_h_up + pad_h_down -
((dilations[1] * (filter_height - 1) + 1))) /
strides[1] +
1,
output_height,
"input_height and output_height are "
"mismatching.");
PADDLE_ENFORCE_EQ((input_width + pad_w_left + pad_w_right -
((dilations[2] * (filter_width - 1) + 1))) /
strides[2] +
1,
output_width,
"input_width and output_width are "
"mismatching.");
auto input_depth_tmp = (input_depth + pad_d_forth + pad_d_back -
((dilations[0] * (filter_depth - 1) + 1))) /
strides[0] +
1;
PADDLE_ENFORCE_EQ(
input_depth_tmp, output_depth,
platform::errors::InvalidArgument(
"input_depth(%d) and output_depth(%d) are mismatching.",
input_depth_tmp, output_depth));
auto input_height_tmp = (input_height + pad_h_up + pad_h_down -
((dilations[1] * (filter_height - 1) + 1))) /
strides[1] +
1;
PADDLE_ENFORCE_EQ(
input_height_tmp, output_height,
platform::errors::InvalidArgument(
"input_height(%d) and output_height(%d) are mismatching.",
input_height_tmp, output_height));
auto input_width_tmp = (input_width + pad_w_left + pad_w_right -
((dilations[2] * (filter_width - 1) + 1))) /
strides[2] +
1;
PADDLE_ENFORCE_EQ(
input_width_tmp, output_width,
platform::errors::InvalidArgument(
"input_width(%d) and output_width(%d) are mismatching.",
input_width_tmp, output_width));
const T* vol_data = vol.data<T>();
T* col_data = col->data<T>();
@ -140,10 +152,16 @@ class Col2VolFunctor<platform::CPUDeviceContext, T> {
const std::vector<int>& strides,
const std::vector<int>& paddings, framework::Tensor* vol,
const DataLayout data_layout) const {
PADDLE_ENFORCE_EQ(vol->dims().size(), 4,
"The dimension of vol should be 4.");
PADDLE_ENFORCE_EQ(col.dims().size(), 7,
"The dimension of col should be 7.");
PADDLE_ENFORCE_EQ(
vol->dims().size(), 4,
platform::errors::InvalidArgument("The dimension of vol"
" should be 4, but received %d.",
vol->dims().size()));
PADDLE_ENFORCE_EQ(
col.dims().size(), 7,
platform::errors::InvalidArgument("The dimension of col"
" should be 7, but received %d.",
col.dims().size()));
int input_channels =
(data_layout != DataLayout::kNHWC ? vol->dims()[0] : vol->dims()[3]);
@ -170,27 +188,33 @@ class Col2VolFunctor<platform::CPUDeviceContext, T> {
int pad_w_left = paddings_size_is_6 ? paddings[4] : paddings[2];
int pad_w_right = paddings_size_is_6 ? paddings[5] : paddings[2];
PADDLE_ENFORCE_EQ((input_depth + pad_d_forth + pad_d_back -
((dilations[0] * (filter_depth - 1) + 1))) /
strides[0] +
1,
output_depth,
"input_depth and output_depth are "
"mismatching.");
PADDLE_ENFORCE_EQ((input_height + pad_h_up + pad_h_down -
((dilations[1] * (filter_height - 1) + 1))) /
strides[1] +
1,
output_height,
"input_height and output_height are "
"mismatching.");
PADDLE_ENFORCE_EQ((input_width + pad_w_left + pad_w_right -
((dilations[2] * (filter_width - 1) + 1))) /
strides[2] +
1,
output_width,
"input_width and output_width are "
"mismatching.");
auto input_depth_tmp = (input_depth + pad_d_forth + pad_d_back -
((dilations[0] * (filter_depth - 1) + 1))) /
strides[0] +
1;
PADDLE_ENFORCE_EQ(input_depth_tmp, output_depth,
platform::errors::InvalidArgument(
"input_depth(%d)"
" and output_depth(%d) are mismatching.",
input_depth_tmp, output_depth));
auto input_height_tmp = (input_height + pad_h_up + pad_h_down -
((dilations[1] * (filter_height - 1) + 1))) /
strides[1] +
1;
PADDLE_ENFORCE_EQ(input_height_tmp, output_height,
platform::errors::InvalidArgument(
"input_height(%d)"
" and output_height(%d) are mismatching.",
input_height_tmp, output_height));
auto input_width_tmp = (input_width + pad_w_left + pad_w_right -
((dilations[2] * (filter_width - 1) + 1))) /
strides[2] +
1;
PADDLE_ENFORCE_EQ(input_width_tmp, output_width,
platform::errors::InvalidArgument(
"input_width(%d)"
" and output_width(%d) are mismatching.",
input_width_tmp, output_width));
T* vol_data = vol->data<T>();
const T* col_data = col.data<T>();

124
paddle/fluid/operators/math/vol2col.cu
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@ -90,10 +90,16 @@ class Vol2ColFunctor<platform::CUDADeviceContext, T> {
const std::vector<int>& strides,
const std::vector<int>& paddings, framework::Tensor* col,
const DataLayout data_layout) const {
PADDLE_ENFORCE_EQ(vol.dims().size(), 4,
"The dimension of vol should be 4.");
PADDLE_ENFORCE_EQ(col->dims().size(), 7,
"The dimension of col should be 7.");
PADDLE_ENFORCE_EQ(
vol.dims().size(), 4,
platform::errors::InvalidArgument("The dimension of"
" vol should be 4, but received %d.",
vol.dims().size()));
PADDLE_ENFORCE_EQ(
col->dims().size(), 7,
platform::errors::InvalidArgument("The dimension of"
"col should be 7, but received %d.",
col->dims().size()));
int input_channels =
(data_layout != DataLayout::kNHWC ? vol.dims()[0] : vol.dims()[3]);
@ -117,27 +123,33 @@ class Vol2ColFunctor<platform::CUDADeviceContext, T> {
int pad_h_down = paddings_size_is_6 ? paddings[3] : paddings[1];
int pad_w_left = paddings_size_is_6 ? paddings[4] : paddings[2];
int pad_w_right = paddings_size_is_6 ? paddings[5] : paddings[2];
PADDLE_ENFORCE_EQ((input_depth + pad_d_forth + pad_d_back -
((dilations[0] * (filter_depth - 1) + 1))) /
strides[0] +
1,
output_depth,
"input_depth and output_depth are "
"mismatching.");
PADDLE_ENFORCE_EQ((input_height + pad_h_up + pad_h_down -
((dilations[1] * (filter_height - 1) + 1))) /
strides[1] +
1,
output_height,
"input_height and output_height are "
"mismatching.");
PADDLE_ENFORCE_EQ((input_width + pad_w_left + pad_w_right -
((dilations[2] * (filter_width - 1) + 1))) /
strides[2] +
1,
output_width,
"input_width and output_width are "
"mismatching.");
auto input_depth_tmp = (input_depth + pad_d_forth + pad_d_back -
((dilations[0] * (filter_depth - 1) + 1))) /
strides[0] +
1;
PADDLE_ENFORCE_EQ(
input_depth_tmp, output_depth,
platform::errors::InvalidArgument(
"input_depth(%d) and output_depth(%d) are mismatching.",
input_depth_tmp, output_depth));
auto input_height_tmp = (input_height + pad_h_up + pad_h_down -
((dilations[1] * (filter_height - 1) + 1))) /
strides[1] +
1;
PADDLE_ENFORCE_EQ(
input_height_tmp, output_height,
platform::errors::InvalidArgument(
"input_height(%d) and output_height(%d) are mismatching.",
input_height_tmp, output_height));
auto input_width_tmp = (input_width + pad_w_left + pad_w_right -
((dilations[2] * (filter_width - 1) + 1))) /
strides[2] +
1;
PADDLE_ENFORCE_EQ(
input_width_tmp, output_width,
platform::errors::InvalidArgument(
"input_width(%d) and output_width(%d) are mismatching.",
input_width_tmp, output_width));
int num_outputs =
input_channels * output_depth * output_height * output_width;
@ -241,10 +253,16 @@ class Col2VolFunctor<platform::CUDADeviceContext, T> {
const std::vector<int>& strides,
const std::vector<int>& paddings, framework::Tensor* vol,
const DataLayout data_layout) const {
PADDLE_ENFORCE_EQ(vol->dims().size(), 4,
"The dimension of vol should be 4.");
PADDLE_ENFORCE_EQ(col.dims().size(), 7,
"The dimension of col should be 7.");
PADDLE_ENFORCE_EQ(
vol->dims().size(), 4,
platform::errors::InvalidArgument("The dimension of vol"
" should be 4, but received %d.",
vol->dims().size()));
PADDLE_ENFORCE_EQ(
col.dims().size(), 7,
platform::errors::InvalidArgument("The dimension of col"
" should be 7, but received %d.",
col.dims().size()));
int input_channels =
(data_layout != DataLayout::kNHWC ? vol->dims()[0] : vol->dims()[3]);
@ -269,27 +287,33 @@ class Col2VolFunctor<platform::CUDADeviceContext, T> {
int pad_w_left = paddings_size_is_6 ? paddings[4] : paddings[2];
int pad_w_right = paddings_size_is_6 ? paddings[5] : paddings[2];
PADDLE_ENFORCE_EQ((input_depth + pad_d_forth + pad_d_back -
((dilations[0] * (filter_depth - 1) + 1))) /
strides[0] +
1,
output_depth,
"input_depth and output_depth are "
"mismatching.");
PADDLE_ENFORCE_EQ((input_height + pad_h_up + pad_h_down -
((dilations[1] * (filter_height - 1) + 1))) /
strides[1] +
1,
output_height,
"input_height and output_height are "
"mismatching.");
PADDLE_ENFORCE_EQ((input_width + pad_w_left + pad_w_right -
((dilations[2] * (filter_width - 1) + 1))) /
strides[2] +
1,
output_width,
"input_width and output_width are "
"mismatching.");
auto input_depth_tmp = (input_depth + pad_d_forth + pad_d_back -
((dilations[0] * (filter_depth - 1) + 1))) /
strides[0] +
1;
PADDLE_ENFORCE_EQ(input_depth_tmp, output_depth,
platform::errors::InvalidArgument(
"input_depth(%d)"
" and output_depth(%d) are mismatching.",
input_depth_tmp, output_depth));
auto input_height_tmp = (input_height + pad_h_up + pad_h_down -
((dilations[1] * (filter_height - 1) + 1))) /
strides[1] +
1;
PADDLE_ENFORCE_EQ(input_height_tmp, output_height,
platform::errors::InvalidArgument(
"input_height(%d)"
" and output_height(%d) are mismatching.",
input_height_tmp, output_height));
auto input_width_tmp = (input_width + pad_w_left + pad_w_right -
((dilations[2] * (filter_width - 1) + 1))) /
strides[2] +
1;
PADDLE_ENFORCE_EQ(input_width_tmp, output_width,
platform::errors::InvalidArgument(
"input_width(%d)"
" and output_width(%d) are mismatching.",
input_width_tmp, output_width));
int num_kernels = input_channels * input_depth * input_height * input_width;

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