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未验证 提交 b6a4349d 编写于 作者: W wawltor 提交者： GitHub
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fix the error message for the math dir 

https://github.com/PaddlePaddle/Paddle/pull/27332
上级 ac82baa8
隐藏空白更改
内联 并排
Showing with 146 addition and 44 deletion
paddle/fluid/operators/math/beam_search.cc
浏览文件 @ b6a4349d
......@@ -87,7 +87,10 @@ class BeamSearchFunctor<platform::CPUDeviceContext, T> {
lod[0].assign(high_level.begin(), high_level.end());
lod[1].assign(low_level.begin(), low_level.end());
if (!framework::CheckLoD(lod)) {
PADDLE_THROW("lod %s is not right", framework::LoDToString(lod));
PADDLE_THROW(platform::errors::InvalidArgument(
"lod %s is not right in"
" beam_search, please check your code.",
framework::LoDToString(lod)));
}
selected_ids->set_lod(lod);
selected_scores->set_lod(lod);
......
paddle/fluid/operators/math/beam_search.cu
浏览文件 @ b6a4349d
......@@ -400,7 +400,10 @@ class BeamSearchFunctor<platform::CUDADeviceContext, T> {
context.Wait();
if (!framework::CheckLoD(selected_lod)) {
PADDLE_THROW("lod %s is not right", framework::LoDToString(selected_lod));
PADDLE_THROW(platform::errors::InvalidArgument(
"lod %s is not right in"
" beam_search, please check your code.",
framework::LoDToString(selected_lod)));
}
selected_ids->set_lod(selected_lod);
......
paddle/fluid/operators/math/blas.cc
浏览文件 @ b6a4349d
......@@ -20,7 +20,11 @@ namespace operators {
namespace math {
MatDescriptor CreateMatrixDescriptor(const framework::DDim &tensor_dim,
int num_flatten_cols, bool trans) {
PADDLE_ENFORCE_GT(tensor_dim.size(), 1);
PADDLE_ENFORCE_GT(
tensor_dim.size(), 1,
platform::errors::InvalidArgument("The tensor dim size should be greater "
"than 1, but reveived dim size is %d",
tensor_dim.size()));
MatDescriptor retv;
if (num_flatten_cols > 1) {
auto flatten_dim = framework::flatten_to_2d(tensor_dim, num_flatten_cols);
......
paddle/fluid/operators/math/blas_impl.cu.h
浏览文件 @ b6a4349d
......@@ -60,7 +60,8 @@ struct CUBlas<float> {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cublasSgemmStridedBatched(args...));
#else
PADDLE_THROW("SgemmStridedBatched is not supported on cuda <= 7.5");
PADDLE_THROW(platform::errors::Unimplemented(
"SgemmStridedBatched is not supported on cuda <= 7.5"));
#endif
}
......@@ -85,7 +86,8 @@ struct CUBlas<float> {
beta, C, Ctype, ldc));
});
#else
PADDLE_THROW("cublasSgemmEx is supported on cuda >= 8.0");
PADDLE_THROW(platform::errors::Unimplemented(
"cublasSgemmEx is not supported on cuda <= 7.5"));
#endif
}
......@@ -146,13 +148,15 @@ struct CUBlas<double> {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cublasDgemmStridedBatched(args...));
#else
PADDLE_THROW("DgemmStridedBatched is not supported on cuda <= 7.5");
PADDLE_THROW(platform::errors::Unimplemented(
"DgemmStridedBatched is not supported on cuda <= 7.5"));
#endif
}
template <typename... ARGS>
static void GEMM_EX(ARGS... args) {
PADDLE_THROW("Currently there are not cublasDgemmEx.");
PADDLE_THROW(platform::errors::Unimplemented(
"Currently there are not cublasDgemmEx."));
}
template <typename... ARGS>
......@@ -216,7 +220,8 @@ struct CUBlas<platform::float16> {
reinterpret_cast<const __half *>(beta), reinterpret_cast<__half *>(C),
ldc, strideC, batchCount));
#else
PADDLE_THROW("HgemmStridedBatched is not supported on cuda <= 7.5");
PADDLE_THROW(platform::errors::Unimplemented(
"HgemmStridedBatched is not supported on cuda <= 7.5"));
#endif
}
......@@ -247,7 +252,8 @@ struct CUBlas<platform::float16> {
beta, C, Ctype, ldc, computeType, algo));
});
#else
PADDLE_THROW("cublasGemmEx is supported on cuda >= 8.0");
PADDLE_THROW(platform::errors::Unimplemented(
"cublasGemmEx is not supported on cuda <= 7.5"));
#endif
}
};
......@@ -302,8 +308,12 @@ inline void Blas<platform::CUDADeviceContext>::GEMM(
(transB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
// TODO(kexinzhao): add processing code for compute capability < 53 case
PADDLE_ENFORCE_GE(context_.GetComputeCapability(), 53,
"cublas fp16 gemm requires GPU compute capability >= 53");
PADDLE_ENFORCE_GE(
context_.GetComputeCapability(), 53,
platform::errors::InvalidArgument(
"cublas fp16 gemm requires GPU compute capability >= 53,"
"but received %d",
context_.GetComputeCapability()));
float h_alpha = static_cast<float>(alpha);
float h_beta = static_cast<float>(beta);
......
paddle/fluid/operators/math/blas_impl.h
浏览文件 @ b6a4349d
......@@ -29,7 +29,8 @@ template <>
struct CBlas<int8_t> {
template <typename... ARGS>
static void VCOPY(ARGS... args) {
PADDLE_THROW("Blas VCOPY don't support int8_t");
PADDLE_THROW(platform::errors::Unimplemented(
"Blas VCOPY do not supported on CPU, please check your code"));
}
};
......@@ -347,22 +348,47 @@ struct CBlas<double> {
template <>
struct CBlas<platform::float16> {
static void GEMM(...) { PADDLE_THROW("float16 GEMM not supported on CPU"); }
static void GEMM(...) {
PADDLE_THROW(platform::errors::Unimplemented(
"float16 GEMM not supported on CPU, please check your code"));
}
static void SMM_GEMM(...) {
PADDLE_THROW("float16 SMM_GEMM not supported on CPU");
PADDLE_THROW(platform::errors::Unimplemented(
"float16 SMM_GEMM not supported on CPU, please check your code"));
}
static void VMUL(...) { PADDLE_THROW("float16 VMUL not supported on CPU"); }
static void VEXP(...) { PADDLE_THROW("float16 VEXP not supported on CPU"); }
static void VSQUARE(...) {
PADDLE_THROW("float16 VSQUARE not supported on CPU");
static void VMUL(...) {
PADDLE_THROW(platform::errors::Unimplemented(
"float16 VMUL not supported on CPU, please check your code"));
}
static void VPOW(...) { PADDLE_THROW("float16 VPOW not supported on CPU"); }
static void DOT(...) { PADDLE_THROW("float16 DOT not supported on CPU"); };
static void SCAL(...) { PADDLE_THROW("float16 SCAL not supported on CPU"); };
static void ASUM(...) { PADDLE_THROW("float16 ASUM not supported on CPU"); };
static void VEXP(...) {
PADDLE_THROW(platform::errors::Unimplemented(
"float16 VEXP not supported on CPU, please check your code"));
}
static void VSQUARE(...) {
PADDLE_THROW(platform::errors::Unimplemented(
"float16 VSQUARE not supported on CPU, please check your code"));
}
static void VPOW(...) {
PADDLE_THROW(platform::errors::Unimplemented(
"float16 VPOW not supported on CPU, please check your code"));
}
static void DOT(...) {
PADDLE_THROW(platform::errors::Unimplemented(
"float16 DOT not supported on CPU, please check your code"));
};
static void SCAL(...) {
PADDLE_THROW(platform::errors::Unimplemented(
"float16 SCAL not supported on CPU, please check your code"));
};
static void ASUM(...) {
PADDLE_THROW(platform::errors::Unimplemented(
"float16 ASUM not supported on CPU, please check your code"));
};
#ifdef PADDLE_WITH_MKLML
static void GEMM_BATCH(...) {
PADDLE_THROW("float16 GEMM_BATCH not supported on CPU");
PADDLE_THROW(platform::errors::Unimplemented(
"float16 GEMM_BATCH not supported on CPU, please check your code"));
}
#endif
};
......@@ -446,11 +472,18 @@ void Blas<DeviceContext>::MatMul(const framework::Tensor &mat_a, bool trans_a,
auto dim_a = mat_a.dims();
auto dim_b = mat_b.dims();
auto dim_out = mat_out->dims();
PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 && dim_out.size() == 2,
"The input and output of matmul be matrix");
PADDLE_ENFORCE(
mat_a.place() == mat_b.place() && mat_a.place() == mat_out->place(),
"The places of matrices must be same");
PADDLE_ENFORCE_EQ(
dim_a.size() == 2 && dim_b.size() == 2 && dim_out.size() == 2, true,
platform::errors::InvalidArgument(
"The input and output of matmul should be matrix, the dim size must "
"be 2,"
"but received dim size input_a:%d, input_b:%d, output:%d",
dim_a.size(), dim_b.size(), dim_out.size()));
PADDLE_ENFORCE_EQ(
mat_a.place() == mat_b.place() && mat_a.place() == mat_out->place(), true,
platform::errors::InvalidArgument("The places of matrices in the matmul "
"should be same, please check your "
"code."));
int M = dim_out[0];
int N = dim_out[1];
......@@ -715,7 +748,13 @@ void Blas<platform::CPUDeviceContext>::BatchedGEMMWithHead(
}
} else {
PADDLE_ENFORCE_EQ(W1, H2);
PADDLE_ENFORCE_EQ(
W1, H2,
platform::errors::InvalidArgument(
"The fisrt matrix width should be same as second matrix height,"
"but received fisrt matrix width %d"
", second matrix height %d",
W1, H2));
int ldc = W2 * head_number;
int sub_width = W1 / head_number;
......@@ -785,7 +824,14 @@ void Blas<DeviceContext>::MatMul(const framework::Tensor &mat_a,
const framework::Tensor &mat_b,
const MatDescriptor &dim_b, T alpha,
framework::Tensor *mat_out, T beta) const {
PADDLE_ENFORCE_EQ(dim_a.width_, dim_b.height_);
PADDLE_ENFORCE_EQ(
dim_a.width_, dim_b.height_,
platform::errors::InvalidArgument(
"The fisrt matrix width should be same as second matrix height,"
"but received fisrt matrix width %d"
", second matrix height %d",
dim_a.width_, dim_b.height_));
CBLAS_TRANSPOSE transA = !dim_a.trans_ ? CblasNoTrans : CblasTrans;
CBLAS_TRANSPOSE transB = !dim_b.trans_ ? CblasNoTrans : CblasTrans;
if (dim_a.batch_size_ == 0 && dim_b.batch_size_ == 0) {
......@@ -793,12 +839,14 @@ void Blas<DeviceContext>::MatMul(const framework::Tensor &mat_a,
dim_a.width_, alpha, mat_a.data<T>(),
mat_b.data<T>(), beta, mat_out->data<T>());
} else {
PADDLE_ENFORCE(dim_a.batch_size_ == dim_b.batch_size_ ||
dim_a.batch_size_ == 0 || dim_b.batch_size_ == 0,
"dim_a.batch_size should be equal to dim_b.batch_size, or "
"one of dim_a.batch_size and dim_b.batch_size should be 0. "
"But got dim_a.batch_size = %d, dim_b.batch_size = %d.",
dim_a.batch_size_, dim_b.batch_size_);
PADDLE_ENFORCE_EQ(
dim_a.batch_size_ == dim_b.batch_size_ || dim_a.batch_size_ == 0 ||
dim_b.batch_size_ == 0,
true, platform::errors::InvalidArgument(
"dim_a.batch_size should be equal to dim_b.batch_size, or "
"one of dim_a.batch_size and dim_b.batch_size should be 0. "
"But got dim_a.batch_size = %d, dim_b.batch_size = %d.",
dim_a.batch_size_, dim_b.batch_size_));
this->template BatchedGEMM<T>(
transA, transB, dim_a.height_, dim_b.width_, dim_a.width_, alpha,
mat_a.data<T>(), mat_b.data<T>(), beta, mat_out->data<T>(),
......@@ -834,15 +882,42 @@ void Blas<DeviceContext>::MatMulWithHead(const framework::Tensor &mat_a,
int head_number,
framework::Tensor *mat_out, T beta,
bool mat_b_split_vertical) const {
PADDLE_ENFORCE_EQ(dim_a.width_ % head_number, 0);
PADDLE_ENFORCE_GE(head_number, 1);
PADDLE_ENFORCE_LE(head_number, dim_a.width_);
PADDLE_ENFORCE_EQ(
dim_a.width_ % head_number, 0,
platform::errors::InvalidArgument(
"The first input width must be some times the head number"
"but received first input width %d"
", head_number %d",
dim_a.width_, head_number));
PADDLE_ENFORCE_GE(head_number, 1,
platform::errors::InvalidArgument(
"The head number should be greater equal 1,"
"but received head number %d",
head_number));
PADDLE_ENFORCE_LE(
head_number, dim_a.width_,
platform::errors::InvalidArgument(
"The head number should be less equal first input width,"
"but received first input width %d"
", head_number %d",
dim_a.width_, head_number));
CBLAS_TRANSPOSE transA = !dim_a.trans_ ? CblasNoTrans : CblasTrans;
CBLAS_TRANSPOSE transB = !dim_b.trans_ ? CblasNoTrans : CblasTrans;
if (mat_b_split_vertical) {
PADDLE_ENFORCE_EQ(dim_b.height_, dim_a.width_ / head_number);
PADDLE_ENFORCE_EQ(dim_b.width_ % head_number, 0);
PADDLE_ENFORCE_EQ(
dim_b.height_, dim_a.width_ / head_number,
platform::errors::InvalidArgument(
"The second input height should be equal than first input width,"
"but received second input height %d, first input width %d",
dim_b.height_, dim_a.width_ / head_number));
PADDLE_ENFORCE_EQ(
dim_a.width_ % head_number, 0,
platform::errors::InvalidArgument(
"The second input width should be some times the head number"
"but received second input width %d"
", head_number %d",
dim_b.width_, head_number));
}
if (dim_a.batch_size_ == 0 && dim_b.batch_size_ == 0) {
......@@ -888,9 +963,16 @@ void Blas<DeviceContext>::MatMulWithHead(const framework::Tensor &mat_a,
mat_out->data<T>() + sub_matC_offset, ldc);
}
} else {
PADDLE_ENFORCE_EQ((dim_a.batch_size_ == dim_b.batch_size_ ||
dim_a.batch_size_ == 0 || dim_b.batch_size_ == 0),
true);
PADDLE_ENFORCE_EQ(
(dim_a.batch_size_ == dim_b.batch_size_ || dim_a.batch_size_ == 0 ||
dim_b.batch_size_ == 0),
true,
platform::errors::InvalidArgument(
"The first input batch size should be equal than second input,"
"either two input batch size is 0, but received first input batch "
"size"
" %d, second input batch size %d",
dim_a.batch_size_, dim_b.batch_size_));
this->template BatchedGEMMWithHead<T>(
transA, transB, dim_a.width_, dim_a.height_, dim_b.width_,
......
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