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未验证 提交 eec4e034 编写于 作者: zhouweiwei2014's avatar zhouweiwei2014 提交者： GitHub
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[Sparse] support batch compute of SparseTensor matmul/masked_matmul/softmax (#43703)

上级 fa9586a7
隐藏空白更改
内联 并排
Showing with 457 addition and 232 deletion
paddle/fluid/platform/dynload/cusparse.h
浏览文件 @ eec4e034
......@@ -31,24 +31,22 @@ namespace dynload {
#if defined(PADDLE_WITH_CUDA)
// APIs available after CUDA 11.0
#if CUDA_VERSION >= 11000
#define CUSPARSE_ROUTINE_EACH(__macro) \
__macro(cusparseCreate); \
__macro(cusparseSetStream); \
__macro(cusparseCreateMatDescr); \
__macro(cusparseDestroy); \
__macro(cusparseSnnz); \
__macro(cusparseDnnz); \
__macro(cusparseSetMatType); \
__macro(cusparseSetMatIndexBase); \
__macro(cusparseCreateCsr); \
__macro(cusparseCreateCoo); \
__macro(cusparseCreateDnMat); \
__macro(cusparseSpMM_bufferSize); \
__macro(cusparseSpMM); \
__macro(cusparseDestroySpMat); \
__macro(cusparseDestroyDnMat); \
__macro(cusparseDnMatSetStridedBatch); \
__macro(cusparseCsrSetStridedBatch);
#define CUSPARSE_ROUTINE_EACH(__macro) \
__macro(cusparseCreate); \
__macro(cusparseSetStream); \
__macro(cusparseCreateMatDescr); \
__macro(cusparseDestroy); \
__macro(cusparseSnnz); \
__macro(cusparseDnnz); \
__macro(cusparseSetMatType); \
__macro(cusparseSetMatIndexBase); \
__macro(cusparseCreateCsr); \
__macro(cusparseCreateCoo); \
__macro(cusparseCreateDnMat); \
__macro(cusparseSpMM_bufferSize); \
__macro(cusparseSpMM); \
__macro(cusparseDestroySpMat); \
__macro(cusparseDestroyDnMat);
CUSPARSE_ROUTINE_EACH(PLATFORM_DECLARE_DYNAMIC_LOAD_CUSPARSE_WRAP)
#endif
......@@ -62,8 +60,17 @@ CUSPARSE_ROUTINE_EACH(PLATFORM_DECLARE_DYNAMIC_LOAD_CUSPARSE_WRAP)
CUSPARSE_ROUTINE_EACH_R2(PLATFORM_DECLARE_DYNAMIC_LOAD_CUSPARSE_WRAP)
#endif
#if CUDA_VERSION >= 11070
#define CUSPARSE_ROUTINE_EACH_R3(__macro) \
__macro(cusparseDnMatSetStridedBatch); \
__macro(cusparseCooSetStridedBatch); \
__macro(cusparseCsrSetStridedBatch);
CUSPARSE_ROUTINE_EACH_R3(PLATFORM_DECLARE_DYNAMIC_LOAD_CUSPARSE_WRAP)
#endif
#endif // PADDLE_WITH_CUDA
#undef PLATFORM_DECLARE_DYNAMIC_LOAD_CUSPARSE_WRAP
} // namespace dynload
} // namespace platform
......
paddle/phi/backends/dynload/cusparse.h
浏览文件 @ eec4e034
......@@ -43,24 +43,22 @@ extern void *cusparse_dso_handle;
#if defined(PADDLE_WITH_CUDA)
// APIs available after CUDA 11.0
#if CUDA_VERSION >= 11000
#define CUSPARSE_ROUTINE_EACH(__macro) \
__macro(cusparseCreate); \
__macro(cusparseSetStream); \
__macro(cusparseCreateMatDescr); \
__macro(cusparseDestroy); \
__macro(cusparseSnnz); \
__macro(cusparseDnnz); \
__macro(cusparseSetMatType); \
__macro(cusparseSetMatIndexBase); \
__macro(cusparseCreateCsr); \
__macro(cusparseCreateCoo); \
__macro(cusparseCreateDnMat); \
__macro(cusparseSpMM_bufferSize); \
__macro(cusparseSpMM); \
__macro(cusparseDestroySpMat); \
__macro(cusparseDestroyDnMat); \
__macro(cusparseDnMatSetStridedBatch); \
__macro(cusparseCsrSetStridedBatch);
#define CUSPARSE_ROUTINE_EACH(__macro) \
__macro(cusparseCreate); \
__macro(cusparseSetStream); \
__macro(cusparseCreateMatDescr); \
__macro(cusparseDestroy); \
__macro(cusparseSnnz); \
__macro(cusparseDnnz); \
__macro(cusparseSetMatType); \
__macro(cusparseSetMatIndexBase); \
__macro(cusparseCreateCsr); \
__macro(cusparseCreateCoo); \
__macro(cusparseCreateDnMat); \
__macro(cusparseSpMM_bufferSize); \
__macro(cusparseSpMM); \
__macro(cusparseDestroySpMat); \
__macro(cusparseDestroyDnMat);
CUSPARSE_ROUTINE_EACH(DECLARE_DYNAMIC_LOAD_CUSPARSE_WRAP)
#endif
......@@ -74,8 +72,17 @@ CUSPARSE_ROUTINE_EACH(DECLARE_DYNAMIC_LOAD_CUSPARSE_WRAP)
CUSPARSE_ROUTINE_EACH_R2(DECLARE_DYNAMIC_LOAD_CUSPARSE_WRAP)
#endif
#if CUDA_VERSION >= 11070
#define CUSPARSE_ROUTINE_EACH_R3(__macro) \
__macro(cusparseDnMatSetStridedBatch); \
__macro(cusparseCooSetStridedBatch); \
__macro(cusparseCsrSetStridedBatch);
CUSPARSE_ROUTINE_EACH_R3(DECLARE_DYNAMIC_LOAD_CUSPARSE_WRAP)
#endif
#endif // PADDLE_WITH_CUDA
#undef DECLARE_DYNAMIC_LOAD_CUSPARSE_WRAP
} // namespace dynload
} // namespace phi
paddle/phi/kernels/funcs/sparse/sparse_blas.h
浏览文件 @ eec4e034
......@@ -28,33 +28,23 @@ class SparseBlas {
public:
explicit SparseBlas(const DeviceContext& dev_ctx) : dev_ctx_(dev_ctx) {}
// TODO(zhouwei25): implement "COO @ DENSE -> DENSE" of DSDMM
template <typename T>
void DSDMM(bool transa,
bool transb,
T alpha,
const phi::SparseCooTensor& mat_a,
const phi::DenseTensor& mat_b,
T beta,
phi::DenseTensor* mat_c) const;
template <typename T>
void DSDMM(bool transa,
bool transb,
T alpha,
const phi::SparseCsrTensor& mat_a,
const phi::DenseTensor& mat_b,
T beta,
phi::DenseTensor* mat_c) const;
template <typename T, typename TensorType>
void SPMM(bool transa,
bool transb,
T alpha,
const TensorType& mat_a,
const phi::DenseTensor& mat_b,
T beta,
phi::DenseTensor* mat_out) const;
template <typename T>
template <typename T, typename TensorType>
void SDDMM(bool transa,
bool transb,
T alpha,
const phi::DenseTensor& mat_a,
const phi::DenseTensor& mat_b,
T beta,
phi::SparseCsrTensor* mat_c) const;
TensorType* mat_out) const;
private:
const DeviceContext& dev_ctx_;
......@@ -66,8 +56,8 @@ class SparseBlasT : private SparseBlas<DeviceContext> {
using SparseBlas<DeviceContext>::SparseBlas;
template <typename... ARGS>
void DSDMM(ARGS... args) const {
Base()->template DSDMM<T>(args...);
void SPMM(ARGS... args) const {
Base()->template SPMM<T>(args...);
}
template <typename... ARGS>
......
paddle/phi/kernels/funcs/sparse/sparse_blas_impl.cu.h
浏览文件 @ eec4e034
......@@ -47,6 +47,61 @@ inline cusparseOperation_t GetTransposeOperation(const bool trans) {
}
}
template <typename T, typename IntT>
inline void CreateCsrDescriptor(const phi::SparseCsrTensor& x,
const phi::GPUContext& dev_ctx,
cusparseSpMatDescr_t* descriptor) {
std::vector<int64_t> xdim_vec = phi::vectorize(x.dims());
auto x_ndims = xdim_vec.size();
PADDLE_ENFORCE_GE(
x_ndims,
2,
phi::errors::InvalidArgument("the dim size of SparseCsrTensor must be "
"greater than or eaqual to 2."));
int64_t M = xdim_vec[x_ndims - 2];
int64_t N = xdim_vec[x_ndims - 1];
int batch_size = 1;
for (int i = 0; i < x_ndims - 2; i++) {
batch_size *= xdim_vec[i];
}
PADDLE_ENFORCE_EQ(x.non_zero_crows().numel(),
batch_size * (M + 1),
phi::errors::PreconditionNotMet(
"the length of SparseCsrTensor crows is not right."));
const IntT* crows_data = x.non_zero_crows().data<IntT>();
const IntT* cols_data = x.non_zero_cols().data<IntT>();
const T* values_data = x.non_zero_elements().data<T>();
int64_t batch_nnz = x.nnz() / batch_size;
cudaDataType_t gpu_type = GetGpuDataType<T>();
dev_ctx.CusparseCall([&](cusparseHandle_t handle) {
phi::dynload::cusparseCreateCsr(descriptor,
M,
N,
batch_nnz,
const_cast<IntT*>(crows_data),
const_cast<IntT*>(cols_data),
const_cast<T*>(values_data),
CUSPARSE_INDEX_64I,
CUSPARSE_INDEX_64I,
CUSPARSE_INDEX_BASE_ZERO,
gpu_type);
});
if (batch_size > 1) {
#if CUDA_VERSION >= 11070
dev_ctx.CusparseCall([&](cusparseHandle_t handle) {
phi::dynload::cusparseCsrSetStridedBatch(
*descriptor, batch_size, M + 1, batch_nnz);
});
#else
PADDLE_THROW(phi::errors::Unimplemented(
"Batch Sparse matmul use 'cusparseCsrSetStridedBatch', which is "
"supported from CUDA 11.7"));
#endif
}
}
template <typename T>
class CuSparseSpMatDescriptor {
public:
......@@ -55,45 +110,9 @@ class CuSparseSpMatDescriptor {
: dev_ctx_(dev_ctx) {
PD_VISIT_INTEGRAL_TYPES(
x.non_zero_crows().dtype(), "CuSparseSpMatDescriptor", ([&] {
const data_t* crows_data = x.non_zero_crows().data<data_t>();
const data_t* cols_data = x.non_zero_cols().data<data_t>();
const T* values_data = x.non_zero_elements().data<T>();
int64_t nnz = x.nnz();
std::vector<int64_t> xdim_vec = phi::vectorize(x.dims());
auto x_ndims = xdim_vec.size();
int64_t M = xdim_vec[x_ndims - 2];
int64_t N = xdim_vec[x_ndims - 1];
int batch_size = 1;
for (int i = 0; i < x_ndims - 2; i++) {
batch_size *= xdim_vec[i];
}
cudaDataType_t gpu_type = GetGpuDataType<T>();
dev_ctx_.CusparseCall([&](cusparseHandle_t handle) {
phi::dynload::cusparseCreateCsr(&descriptor_,
M,
N,
nnz,
const_cast<data_t*>(crows_data),
const_cast<data_t*>(cols_data),
const_cast<T*>(values_data),
CUSPARSE_INDEX_64I,
CUSPARSE_INDEX_64I,
CUSPARSE_INDEX_BASE_ZERO,
gpu_type);
});
PADDLE_ENFORCE_EQ(x.non_zero_crows().numel(), batch_size * (M + 1));
PADDLE_ENFORCE_EQ(x.non_zero_cols().numel(), x.nnz());
if (batch_size > 1) {
dev_ctx_.CusparseCall([&](cusparseHandle_t handle) {
phi::dynload::cusparseCsrSetStridedBatch(
descriptor_, batch_size, M + 1, nnz);
});
}
CreateCsrDescriptor<T, data_t>(x, dev_ctx_, &descriptor_);
}));
VLOG(6) << "Create cusparseSpMatDescr_t " << &descriptor_;
VLOG(6) << "Create csr cusparseSpMatDescr_t " << &descriptor_;
}
~CuSparseSpMatDescriptor() {
......@@ -116,9 +135,14 @@ class CuSparseDnMatDescriptor {
explicit CuSparseDnMatDescriptor(const phi::DenseTensor& x,
const phi::GPUContext& dev_ctx)
: dev_ctx_(dev_ctx) {
const T* x_data = x.data<T>();
std::vector<int64_t> xdim_vec = phi::vectorize(x.dims());
auto x_ndims = xdim_vec.size();
PADDLE_ENFORCE_GE(
x_ndims,
2,
phi::errors::InvalidArgument("the dim size of DenseTensor must be "
"greater than or eaqual to 2."));
int64_t M = xdim_vec[x_ndims - 2];
int64_t N = xdim_vec[x_ndims - 1];
int batch_size = 1;
......@@ -126,6 +150,7 @@ class CuSparseDnMatDescriptor {
batch_size *= xdim_vec[i];
}
const T* x_data = x.data<T>();
cudaDataType_t gpu_type = GetGpuDataType<T>();
dev_ctx_.CusparseCall([&](cusparseHandle_t handle) {
phi::dynload::cusparseCreateDnMat(&descriptor_,
......@@ -139,10 +164,16 @@ class CuSparseDnMatDescriptor {
PADDLE_ENFORCE_EQ(x.numel(), batch_size * M * N);
if (batch_size > 1) {
#if CUDA_VERSION >= 11070
dev_ctx_.CusparseCall([&](cusparseHandle_t handle) {
phi::dynload::cusparseDnMatSetStridedBatch(
descriptor_, batch_size, M * N);
});
#else
PADDLE_THROW(phi::errors::Unimplemented(
"Batch Sparse matmul use 'cusparseDnMatSetStridedBatch', which is "
"supported from CUDA 11.7"));
#endif
}
VLOG(6) << "Create cusparseDnMatDescr_t " << &descriptor_;
}
......@@ -162,20 +193,19 @@ class CuSparseDnMatDescriptor {
};
template <>
template <typename T>
void SparseBlas<phi::GPUContext>::DSDMM(bool transa,
bool transb,
T alpha,
const phi::SparseCsrTensor& mat_a,
const phi::DenseTensor& mat_b,
T beta,
phi::DenseTensor* mat_c) const {
cudaDataType_t gpu_type = GetGpuDataType<T>();
template <typename T, typename TensorType>
void SparseBlas<phi::GPUContext>::SPMM(bool transa,
bool transb,
T alpha,
const TensorType& mat_a,
const phi::DenseTensor& mat_b,
T beta,
phi::DenseTensor* mat_out) const {
auto a_descriptor = CuSparseSpMatDescriptor<T>(mat_a, dev_ctx_);
auto b_descriptor = CuSparseDnMatDescriptor<T>(mat_b, dev_ctx_);
auto c_descriptor = CuSparseDnMatDescriptor<T>(*mat_c, dev_ctx_);
auto out_descriptor = CuSparseDnMatDescriptor<T>(*mat_out, dev_ctx_);
cudaDataType_t gpu_type = GetGpuDataType<T>();
size_t buffer_size = 0;
dev_ctx_.CusparseCall([&](cusparseHandle_t handle) {
phi::dynload::cusparseSpMM_bufferSize(handle,
......@@ -185,7 +215,7 @@ void SparseBlas<phi::GPUContext>::DSDMM(bool transa,
a_descriptor.descriptor(),
b_descriptor.descriptor(),
&beta,
c_descriptor.descriptor(),
out_descriptor.descriptor(),
gpu_type,
CUSPARSE_SPMM_ALG_DEFAULT,
&buffer_size);
......@@ -202,7 +232,7 @@ void SparseBlas<phi::GPUContext>::DSDMM(bool transa,
a_descriptor.descriptor(),
b_descriptor.descriptor(),
&beta,
c_descriptor.descriptor(),
out_descriptor.descriptor(),
gpu_type,
CUSPARSE_SPMM_ALG_DEFAULT,
tmp_buffer_ptr);
......@@ -211,19 +241,19 @@ void SparseBlas<phi::GPUContext>::DSDMM(bool transa,
#if CUDA_VERSION >= 11030
template <>
template <typename T>
template <typename T, typename TensorType>
void SparseBlas<phi::GPUContext>::SDDMM(bool transa,
bool transb,
T alpha,
const phi::DenseTensor& mat_a,
const phi::DenseTensor& mat_b,
T beta,
phi::SparseCsrTensor* mat_c) const {
TensorType* mat_out) const {
cudaDataType_t gpu_type = GetGpuDataType<T>();
auto a_descriptor = CuSparseDnMatDescriptor<T>(mat_a, dev_ctx_);
auto b_descriptor = CuSparseDnMatDescriptor<T>(mat_b, dev_ctx_);
auto c_descriptor = CuSparseSpMatDescriptor<T>(*mat_c, dev_ctx_);
auto out_descriptor = CuSparseSpMatDescriptor<T>(*mat_out, dev_ctx_);
size_t buffer_size = 0;
dev_ctx_.CusparseCall([&](cusparseHandle_t handle) {
......@@ -234,7 +264,7 @@ void SparseBlas<phi::GPUContext>::SDDMM(bool transa,
a_descriptor.descriptor(),
b_descriptor.descriptor(),
&beta,
c_descriptor.descriptor(),
out_descriptor.descriptor(),
gpu_type,
CUSPARSE_SDDMM_ALG_DEFAULT,
&buffer_size);
......@@ -252,7 +282,7 @@ void SparseBlas<phi::GPUContext>::SDDMM(bool transa,
a_descriptor.descriptor(),
b_descriptor.descriptor(),
&beta,
c_descriptor.descriptor(),
out_descriptor.descriptor(),
gpu_type,
CUSPARSE_SDDMM_ALG_DEFAULT,
tmp_buffer_ptr);
......@@ -266,7 +296,7 @@ void SparseBlas<phi::GPUContext>::SDDMM(bool transa,
a_descriptor.descriptor(),
b_descriptor.descriptor(),
&beta,
c_descriptor.descriptor(),
out_descriptor.descriptor(),
gpu_type,
CUSPARSE_SDDMM_ALG_DEFAULT,
tmp_buffer_ptr);
......
paddle/phi/kernels/sparse/cpu/softmax_grad_kernel.cc
浏览文件 @ eec4e034
......@@ -38,11 +38,17 @@ void SoftmaxCsrGradKernel(const Context& dev_ctx,
"SparseCsrTensor only support axis=-1 for softmax, "
"which is faster when reading data by row (axis=-1)"));
EmptyLikeCsrKernel<T, Context>(dev_ctx, dout, dx);
auto out_dim = out.dims();
int rows = 1;
for (int i = 0; i < out_dim.size() - 1; ++i) {
rows *= out_dim[i];
auto out_rank = out_dim.size();
int batch_size = 1;
int row_number = 1;
for (int i = 0; i < out_rank - 1; ++i) {
if (i < out_rank - 2) {
batch_size *= out_dim[i];
} else if (i == out_rank - 2) {
row_number = out_dim[i];
}
}
const DenseTensor& out_crows = out.non_zero_crows();
......@@ -50,7 +56,6 @@ void SoftmaxCsrGradKernel(const Context& dev_ctx,
const DenseTensor& dout_values = dout.non_zero_elements();
DenseTensor* dx_values = dx->mutable_non_zero_elements();
int row_first = 0;
int row_nnz = 0;
const T* out_data = out_values.data<T>();
const T* dout_data = dout_values.data<T>();
......@@ -60,20 +65,24 @@ void SoftmaxCsrGradKernel(const Context& dev_ctx,
PD_VISIT_INTEGRAL_TYPES(
out.non_zero_crows().dtype(), "SoftmaxCsrGradKernel", ([&] {
const data_t* out_crows_data = out_crows.data<data_t>();
for (int i = 0; i < rows; ++i) {
row_first = static_cast<int>(out_crows_data[i]);
row_nnz = static_cast<int>(out_crows_data[i + 1] - out_crows_data[i]);
out_data = out_data + row_first;
dout_data = dout_data + row_first;
dx_data = dx_data + row_first;
T sum = 0;
phi::funcs::vec_mul_reduce<T, plt::avx>(
row_nnz, dout_data, out_data, &sum);
phi::funcs::vec_add_bias<T, plt::avx>(
row_nnz, static_cast<T>(-1) * sum, dout_data, dx_data);
phi::funcs::vec_mul<T, plt::avx>(row_nnz, dx_data, out_data, dx_data);
for (int i = 0; i < batch_size; ++i) {
for (int j = 0; j < row_number; ++j) {
int crow_idx = i * (row_number + 1) + j;
row_nnz = static_cast<int>(out_crows_data[crow_idx + 1] -
out_crows_data[crow_idx]);
T sum = 0;
phi::funcs::vec_mul_reduce<T, plt::avx>(
row_nnz, dout_data, out_data, &sum);
phi::funcs::vec_add_bias<T, plt::avx>(
row_nnz, static_cast<T>(-1) * sum, dout_data, dx_data);
phi::funcs::vec_mul<T, plt::avx>(
row_nnz, dx_data, out_data, dx_data);
out_data = out_data + row_nnz;
dout_data = dout_data + row_nnz;
dx_data = dx_data + row_nnz;
}
}
}));
}
......
paddle/phi/kernels/sparse/cpu/softmax_kernel.cc
浏览文件 @ eec4e034
......@@ -37,18 +37,23 @@ void SoftmaxCsrKernel(const Context& dev_ctx,
"SparseCsrTensor only support axis=-1 for softmax, "
"which is faster when reading data by row (axis=-1)"));
EmptyLikeCsrKernel<T, Context>(dev_ctx, x, out);
auto x_dim = x.dims();
auto x_rank = x_dim.size();
int batch_size = 1;
int row_number = 1;
for (int i = 0; i < x_dim.size() - 1; ++i) {
row_number *= x_dim[i];
for (int i = 0; i < x_rank - 1; ++i) {
if (i < x_rank - 2) {
batch_size *= x_dim[i];
} else if (i == x_rank - 2) {
row_number = x_dim[i];
}
}
const DenseTensor& x_crows = x.non_zero_crows();
const DenseTensor& x_values = x.non_zero_elements();
DenseTensor* out_values = out->mutable_non_zero_elements();
int row_first = 0;
int row_nnz = 0;
T row_max_val = 0;
const T* x_data = x_values.data<T>();
......@@ -58,23 +63,26 @@ void SoftmaxCsrKernel(const Context& dev_ctx,
PD_VISIT_INTEGRAL_TYPES(
x.non_zero_crows().dtype(), "CsrSoftmaxKernel", ([&] {
const data_t* x_crows_data = x_crows.data<data_t>();
for (int i = 0; i < row_number; ++i) {
row_first = static_cast<int>(x_crows_data[i]);
row_nnz = static_cast<int>(x_crows_data[i + 1] - x_crows_data[i]);
x_data = x_data + row_first;
out_data = out_data + row_first;
row_max_val = *std::max_element(x_data, x_data + row_nnz);
phi::funcs::vec_add_bias<T, plt::avx>(
row_nnz, static_cast<T>(-1) * row_max_val, x_data, out_data);
phi::funcs::vec_exp<T>(row_nnz, out_data, out_data);
T sum = 0;
phi::funcs::vec_sum<T, plt::avx>(row_nnz, out_data, &sum);
phi::funcs::vec_scal<T, plt::avx>(
row_nnz, static_cast<T>(1) / sum, out_data, out_data);
for (int i = 0; i < batch_size; ++i) {
for (int j = 0; j < row_number; ++j) {
int crow_idx = i * (row_number + 1) + j;
row_nnz = static_cast<int>(x_crows_data[crow_idx + 1] -
x_crows_data[crow_idx]);
row_max_val = *std::max_element(x_data, x_data + row_nnz);
phi::funcs::vec_add_bias<T, plt::avx>(
row_nnz, static_cast<T>(-1) * row_max_val, x_data, out_data);
phi::funcs::vec_exp<T>(row_nnz, out_data, out_data);
T sum = 0;
phi::funcs::vec_sum<T, plt::avx>(row_nnz, out_data, &sum);
phi::funcs::vec_scal<T, plt::avx>(
row_nnz, static_cast<T>(1) / sum, out_data, out_data);
x_data = x_data + row_nnz;
out_data = out_data + row_nnz;
}
}
}));
}
......
paddle/phi/kernels/sparse/empty_kernel.cc
浏览文件 @ eec4e034
......@@ -22,6 +22,25 @@ limitations under the License. */
namespace phi {
namespace sparse {
template <typename T, typename Context>
void EmptyLikeCooKernel(const Context& dev_ctx,
const SparseCooTensor& x,
SparseCooTensor* out) {
const DenseTensor& x_indices = x.non_zero_indices();
const DenseTensor& x_values = x.non_zero_elements();
DenseTensor* out_indices = out->mutable_non_zero_indices();
DenseTensor* out_values = out->mutable_non_zero_elements();
phi::Copy(dev_ctx, x_indices, dev_ctx.GetPlace(), false, out_indices);
phi::Copy(dev_ctx, x_values, dev_ctx.GetPlace(), false, out_values);
out_values->Resize(x_values.dims());
dev_ctx.template Alloc<T>(out_values);
out->set_dims(x.dims());
}
template <typename T, typename Context>
void EmptyLikeCsrKernel(const Context& dev_ctx,
const SparseCsrTensor& x,
......@@ -34,17 +53,33 @@ void EmptyLikeCsrKernel(const Context& dev_ctx,
DenseTensor* out_cols = out->mutable_non_zero_cols();
DenseTensor* out_values = out->mutable_non_zero_elements();
out->set_dims(x.dims());
phi::Copy(dev_ctx, x_crows, dev_ctx.GetPlace(), false, out_crows);
phi::Copy(dev_ctx, x_cols, dev_ctx.GetPlace(), false, out_cols);
out_values->Resize(x_values.dims());
dev_ctx.template Alloc<T>(out_values);
out->set_dims(x.dims());
}
} // namespace sparse
} // namespace phi
PD_REGISTER_KERNEL(empty_like_coo,
CPU,
ALL_LAYOUT,
phi::sparse::EmptyLikeCooKernel,
float,
double,
int8_t,
uint8_t,
int16_t,
int,
int64_t,
bool) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
}
PD_REGISTER_KERNEL(empty_like_csr,
CPU,
ALL_LAYOUT,
......@@ -61,6 +96,21 @@ PD_REGISTER_KERNEL(empty_like_csr,
}
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
PD_REGISTER_KERNEL(empty_like_coo,
GPU,
ALL_LAYOUT,
phi::sparse::EmptyLikeCooKernel,
float,
double,
int8_t,
uint8_t,
int16_t,
int,
int64_t,
bool) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
}
PD_REGISTER_KERNEL(empty_like_csr,
GPU,
ALL_LAYOUT,
......
paddle/phi/kernels/sparse/empty_kernel.h
浏览文件 @ eec4e034
......@@ -14,11 +14,17 @@ limitations under the License. */
#pragma once
#include "paddle/phi/core/sparse_coo_tensor.h"
#include "paddle/phi/core/sparse_csr_tensor.h"
namespace phi {
namespace sparse {
template <typename T, typename Context>
void EmptyLikeCooKernel(const Context& dev_ctx,
const SparseCooTensor& x,
SparseCooTensor* out);
template <typename T, typename Context>
void EmptyLikeCsrKernel(const Context& dev_ctx,
const SparseCsrTensor& x,
......
paddle/phi/kernels/sparse/gpu/matmul_grad_kernel.cu
浏览文件 @ eec4e034
......@@ -21,6 +21,7 @@ limitations under the License. */
#include "paddle/phi/kernels/copy_kernel.h"
#include "paddle/phi/kernels/empty_kernel.h"
#include "paddle/phi/kernels/funcs/sparse/sparse_blas.h"
#include "paddle/phi/kernels/sparse/empty_kernel.h"
#include "paddle/phi/kernels/transpose_kernel.h"
namespace phi {
......@@ -38,23 +39,8 @@ void CsrDenseMatmulGradKernel(const Context& dev_ctx,
// dx{SparseCsr} = dout{Dense} * y'{Dense}
if (dx) {
// InferMeta of SparseCsrTensor 'dx'
dx->set_dims(x.dims());
phi::Copy(dev_ctx,
x.non_zero_crows(),
dev_ctx.GetPlace(),
false,
dx->mutable_non_zero_crows());
phi::Copy(dev_ctx,
x.non_zero_cols(),
dev_ctx.GetPlace(),
false,
dx->mutable_non_zero_cols());
DenseTensor* values = dx->mutable_non_zero_elements();
values->Resize(x.non_zero_elements().dims());
dev_ctx.template Alloc<T>(values);
// InferMeta of SparseCsrTensor 'dx', CreateLikeInferMeta
EmptyLikeCsrKernel<T, Context>(dev_ctx, x, dx);
sparse_blas.SDDMM(
false, true, static_cast<T>(1), dout, y, static_cast<T>(0), dx);
......@@ -69,13 +55,13 @@ void CsrDenseMatmulGradKernel(const Context& dev_ctx,
dev_ctx.template Alloc<T>(dy);
sparse_blas.DSDMM(
sparse_blas.SPMM(
true, false, static_cast<T>(1), x, dout, static_cast<T>(0), dy);
}
#else
PADDLE_THROW(phi::errors::Unimplemented(
" backward of 'sparse.mm' use cusparseSDDMM, Only "
"support it from CUDA 11.3"));
"backward of 'sparse.matmul' use cusparseSDDMM, which is supported from "
"CUDA 11.3"));
#endif
}
......@@ -97,7 +83,7 @@ void CsrMaskedMatmulGradKernel(const Context& dev_ctx,
meta_dx.set_dtype(x.dtype());
dev_ctx.template Alloc<T>(dx);
sparse_blas.DSDMM(
sparse_blas.SPMM(
false, true, static_cast<T>(1), dout, y, static_cast<T>(0), dx);
}
......@@ -109,7 +95,7 @@ void CsrMaskedMatmulGradKernel(const Context& dev_ctx,
std::swap(trans_dim_vec[rank - 1], trans_dim_vec[rank - 2]);
DenseTensor trans_dy = phi::Empty<T, Context>(dev_ctx, trans_dim_vec);
sparse_blas.DSDMM(
sparse_blas.SPMM(
true, false, static_cast<T>(1), dout, x, static_cast<T>(0), &trans_dy);
// InferMeta of DenseTensor 'dy'
......
paddle/phi/kernels/sparse/gpu/matmul_kernel.cu
浏览文件 @ eec4e034
......@@ -26,6 +26,7 @@ limitations under the License. */
#include "paddle/phi/kernels/copy_kernel.h"
#include "paddle/phi/kernels/empty_kernel.h"
#include "paddle/phi/kernels/funcs/sparse/sparse_blas.h"
#include "paddle/phi/kernels/sparse/empty_kernel.h"
namespace phi {
namespace sparse {
......@@ -59,7 +60,7 @@ void CsrDenseMatmulKernel(const Context& dev_ctx,
PADDLE_ENFORCE_EQ(xdim_vec[i],
ydim_vec[i],
phi::errors::InvalidArgument(
"x.dim[%d] and x.dim[%d] must match.", i, i));
"x.dim[%d] and x.dim[%d] must be eaqul.", i, i));
}
PADDLE_ENFORCE_GE(
......@@ -80,11 +81,11 @@ void CsrDenseMatmulKernel(const Context& dev_ctx,
dev_ctx.template Alloc<T>(out);
auto sparse_blas = phi::funcs::sparse::GetSparseBlas<Context, T>(dev_ctx);
sparse_blas.DSDMM(
sparse_blas.SPMM(
false, false, static_cast<T>(1), x, y, static_cast<T>(0), out);
#else
PADDLE_THROW(
phi::errors::Unimplemented(" forward of 'sparse.mm' use cusparseSpMM, "
phi::errors::Unimplemented("forward of 'sparse.matmul' use cusparseSpMM, "
"which is supported from CUDA 11.0"));
#endif
}
......@@ -159,32 +160,16 @@ void CsrMaskedMatmulKernel(const Context& dev_ctx,
"The shape of Input(x) and Input(y) is not suitable for matmul "
"opetation, mask_dim[-1] must be eaqual to y_dim[-1]."));
// InferMeta of SparseCsrTensor 'out'
out->set_dims(mask.dims());
phi::Copy(dev_ctx,
mask.non_zero_crows(),
dev_ctx.GetPlace(),
false,
out->mutable_non_zero_crows());
phi::Copy(dev_ctx,
mask.non_zero_cols(),
dev_ctx.GetPlace(),
false,
out->mutable_non_zero_cols());
DenseTensor* values = out->mutable_non_zero_elements();
values->Resize(mask.non_zero_elements().dims());
dev_ctx.template Alloc<T>(values);
// InferMeta of SparseCsrTensor 'out', CreateLikeInferMeta
EmptyLikeCsrKernel<T, Context>(dev_ctx, mask, out);
auto sparse_blas = phi::funcs::sparse::GetSparseBlas<Context, T>(dev_ctx);
sparse_blas.SDDMM(
false, false, static_cast<T>(1), x, y, static_cast<T>(0), out);
#else
PADDLE_THROW(
phi::errors::Unimplemented(" forward of 'sparse.masked_mm' use "
"cusparseSDDMM, which is supported from "
"CUDA 11.3"));
PADDLE_THROW(phi::errors::Unimplemented(
"forward of 'sparse.masked_matmul' use cusparseSDDMM, which is supported "
"from CUDA 11.3"));
#endif
}
......
paddle/phi/kernels/sparse/gpu/softmax_grad_kernel.cu
浏览文件 @ eec4e034
......@@ -12,12 +12,13 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/phi/kernels/sparse/softmax_grad_kernel.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/visit_type.h"
#include "paddle/phi/kernels/funcs/math_cuda_utils.h"
#include "paddle/phi/kernels/sparse/empty_kernel.h"
#include "paddle/phi/kernels/sparse/softmax_grad_kernel.h"
namespace phi {
namespace sparse {
......@@ -27,13 +28,20 @@ __global__ void SoftmaxGradGpuKernel(const IntT* out_crows,
const T* out_values,
const T* dout_values,
T* dx_values,
int row_number) {
int row_number,
int total_row_number) {
// dx = (dout - sum(dout * out)) * out
int row = blockIdx.x * blockDim.y + threadIdx.y;
int non_zero_idx = threadIdx.x;
if (row >= row_number) return;
int row_first = static_cast<int>(out_crows[row]);
int row_nnz = static_cast<int>(out_crows[row + 1] - out_crows[row]);
if (row >= total_row_number) return;
int cur_batch = row / row_number;
int crow_idx = cur_batch * (row_number + 1) + (row % row_number);
int cur_batch_offset = 0;
for (int i = 1; i < cur_batch + 1; ++i) {
cur_batch_offset += out_crows[i * (row_number + 1) - 1];
}
int row_first = cur_batch_offset + static_cast<int>(out_crows[crow_idx]);
int row_nnz = static_cast<int>(out_crows[crow_idx + 1] - out_crows[crow_idx]);
if (row_nnz == 0) return;
int kIteration = (row_nnz + warpSize - 1) / warpSize;
......@@ -70,12 +78,18 @@ void SoftmaxCsrGradKernel(const Context& dev_ctx,
EmptyLikeCsrKernel<T, Context>(dev_ctx, dout, dx);
auto out_dim = out.dims();
auto out_rank = out_dim.size();
int total_row_number = 1;
int row_number = 1;
for (int i = 0; i < out_dim.size() - 1; ++i) {
row_number *= out_dim[i];
for (int i = 0; i < out_rank - 1; ++i) {
total_row_number *= out_dim[i];
if (i == out_rank - 2) {
row_number = out_dim[i];
}
}
dim3 grid((row_number + 3) / 4);
dim3 grid((total_row_number + 3) / 4);
dim3 block(32, 4);
PD_VISIT_INTEGRAL_TYPES(
......@@ -85,7 +99,8 @@ void SoftmaxCsrGradKernel(const Context& dev_ctx,
out.non_zero_elements().data<T>(),
dout.non_zero_elements().data<T>(),
dx->mutable_non_zero_elements()->data<T>(),
row_number);
row_number,
total_row_number);
}));
}
......
paddle/phi/kernels/sparse/gpu/softmax_kernel.cu
浏览文件 @ eec4e034
......@@ -12,6 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/phi/kernels/sparse/softmax_kernel.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/visit_type.h"
......@@ -19,7 +21,6 @@ limitations under the License. */
#include "paddle/phi/kernels/funcs/activation_functor.h"
#include "paddle/phi/kernels/funcs/math_cuda_utils.h"
#include "paddle/phi/kernels/sparse/empty_kernel.h"
#include "paddle/phi/kernels/sparse/softmax_kernel.h"
namespace phi {
namespace sparse {
......@@ -28,13 +29,20 @@ template <typename T, typename IntT = int>
__global__ void SoftmaxGpuKernel(const IntT* x_crows,
const T* x_values,
T* out_values,
int row_number) {
int row_number,
int total_row_number) {
// out = exp(x-x_max) / sum(exp(x-x_max))
int row = blockIdx.x * blockDim.y + threadIdx.y;
int non_zero_idx = threadIdx.x;
if (row >= row_number) return;
int row_first = static_cast<int>(x_crows[row]);
int row_nnz = static_cast<int>(x_crows[row + 1] - x_crows[row]);
if (row >= total_row_number) return;
int cur_batch = row / row_number;
int crow_idx = cur_batch * (row_number + 1) + (row % row_number);
int cur_batch_offset = 0;
for (int i = 1; i < cur_batch + 1; ++i) {
cur_batch_offset += x_crows[i * (row_number + 1) - 1];
}
int row_first = cur_batch_offset + static_cast<int>(x_crows[crow_idx]);
int row_nnz = static_cast<int>(x_crows[crow_idx + 1] - x_crows[crow_idx]);
if (row_nnz == 0) return;
int kIteration = (row_nnz + warpSize - 1) / warpSize;
......@@ -81,17 +89,20 @@ void SoftmaxCsrKernel(const Context& dev_ctx,
"SparseCsrTensor only support axis=-1 for softmax, "
"which is faster when reading data by row (axis=-1)"));
EmptyLikeCsrKernel<T, Context>(dev_ctx, x, out);
auto x_dim = x.dims();
auto x_rank = x_dim.size();
int total_row_number = 1;
int row_number = 1;
for (int i = 0; i < x_dim.size() - 1; ++i) {
row_number *= x_dim[i];
for (int i = 0; i < x_rank - 1; ++i) {
total_row_number *= x_dim[i];
if (i == x_rank - 2) {
row_number = x_dim[i];
}
}
dim3 grid((row_number + 3) / 4);
dim3 block(32, 4);
DenseTensor tmp_tensor =
phi::EmptyLike<T, Context>(dev_ctx, x.non_zero_elements());
dim3 grid((total_row_number + 3) / 4);
dim3 block(32, 4);
PD_VISIT_INTEGRAL_TYPES(x.non_zero_crows().dtype(), "CsrSoftmaxKernel", ([&] {
SoftmaxGpuKernel<T, data_t>
......@@ -99,7 +110,8 @@ void SoftmaxCsrKernel(const Context& dev_ctx,
x.non_zero_crows().data<data_t>(),
x.non_zero_elements().data<T>(),
out->mutable_non_zero_elements()->data<T>(),
row_number);
row_number,
total_row_number);
}));
}
......
python/paddle/fluid/tests/unittests/test_sparse_matmul_op.py
浏览文件 @ eec4e034
......@@ -114,7 +114,77 @@ class TestCsrMaskedMatmul2D(unittest.TestCase):
self.assertTrue(np.allclose(np_y_grad, y.grad.numpy()))
#TODO(zhouwei25): support unit test of batch 'paddle.sparse.mm/masked_mm'
@unittest.skipIf(
not paddle.is_compiled_with_cuda() or get_cuda_version() < 11070,
"paddle is not compiled with CUDA and cuda version need to >= 11.7")
class TestCsrDenseMatmul3D(unittest.TestCase):
# x: csr, y: dense, out: dense
def test_matmul(self):
with _test_eager_guard():
paddle.set_default_dtype('float32')
origin_x = paddle.rand([16, 16, 12])
mask = paddle.randint(0, 2, [16, 12])
origin_x = origin_x * mask
origin_y = paddle.rand([16, 12, 10])
dense_x = origin_x.detach()
dense_x.stop_gradient = False
dense_y = origin_y.detach()
dense_y.stop_gradient = False
dense_out = paddle.matmul(dense_x, dense_y)
dense_out.backward()
sp_x = origin_x.detach().to_sparse_csr()
sp_x.stop_gradient = False
sp_y = origin_y.detach()
sp_y.stop_gradient = False
sp_out = paddle.incubate.sparse.matmul(sp_x, sp_y)
sp_out.backward()
self.assertTrue(np.allclose(sp_out.numpy(), dense_out.numpy()))
self.assertTrue(
np.allclose(sp_x.grad.to_dense().numpy(),
(dense_x.grad * mask).numpy()))
self.assertTrue(np.allclose(sp_y.grad.numpy(),
dense_y.grad.numpy()))
@unittest.skipIf(
not paddle.is_compiled_with_cuda() or get_cuda_version() < 11070,
"paddle is not compiled with CUDA and cuda version need to >= 11.7")
class TestCsrMaskedMatmul3D(unittest.TestCase):
# x: dense, y: dense, out: csr
def test_matmul(self):
with _test_eager_guard():
paddle.set_default_dtype('float64')
origin_x = paddle.rand([16, 16, 12])
origin_y = paddle.rand([16, 12, 10])
mask = paddle.randint(0, 2, [16, 10])
dense_x = origin_x.detach()
dense_x.stop_gradient = False
dense_y = origin_y.detach()
dense_y.stop_gradient = False
dense_out = paddle.matmul(dense_x, dense_y)
dense_out = dense_out * mask
dense_out.backward()
sp_x = origin_x.detach()
sp_x.stop_gradient = False
sp_y = origin_y.detach()
sp_y.stop_gradient = False
sp_out = paddle.incubate.sparse.masked_matmul(
sp_x, sp_y, dense_out.to_sparse_csr())
sp_out.backward()
self.assertTrue(
np.allclose(sp_out.to_dense().numpy(), dense_out.numpy()))
self.assertTrue(np.allclose(sp_x.grad.numpy(),
dense_x.grad.numpy()))
self.assertTrue(np.allclose(sp_y.grad.numpy(),
dense_y.grad.numpy()))
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_sparse_softmax.py → python/paddle/fluid/tests/unittests/test_sparse_softmax_op.py
浏览文件 @ eec4e034
......@@ -28,10 +28,10 @@ np.random.seed(2022)
class TestCsrSoftmax(unittest.TestCase):
def test_softmax(self):
def test_softmax2d(self):
with _test_eager_guard():
mask = np.random.rand(1, 5) < 0.5
np_x = np.random.rand(1, 5) * mask
mask = np.random.rand(16, 128) < 0.5
np_x = np.random.rand(16, 128) * mask
np_csr = sp.csr_matrix(np_x)
row_number = np_csr.shape[0]
......@@ -56,6 +56,7 @@ class TestCsrSoftmax(unittest.TestCase):
# dx = (dout - sum(dout * out)) * out, dout=rand_x
out.backward(csr.detach())
dx = np.array([])
for i in range(row_number):
start = np_csr.indptr[i]
end = np_csr.indptr[i + 1]
......@@ -64,7 +65,7 @@ class TestCsrSoftmax(unittest.TestCase):
out = np_out[start:end]
dout = np_csr.data[start:end]
sum = np.sum(dout * out, keepdims=True)
dx = (dout - sum) * out
dx = np.concatenate([dx, (dout - sum) * out])
self.assertTrue(np.allclose(csr.grad.crows().numpy(),
np_csr.indptr))
......@@ -72,6 +73,55 @@ class TestCsrSoftmax(unittest.TestCase):
np_csr.indices))
self.assertTrue(np.allclose(csr.grad.values().numpy(), dx))
def test_softmax3d(self):
with _test_eager_guard():
batchNum = 16
mask = np.random.rand(batchNum, 16, 128) < 0.5
np_x = np.random.rand(batchNum, 16, 128) * mask
np_out_list = []
np_out = np.array([])
for i in range(batchNum):
np_csr = sp.csr_matrix(np_x[i, :, :])
row_number = np_csr.shape[0]
for j in range(row_number, ):
start = np_csr.indptr[j]
end = np_csr.indptr[j + 1]
if start == end:
continue
x = np_csr.data[start:end]
x_max = np.max(x, keepdims=True)
x_exp = np.exp(x - x_max)
x_exp_sum = np.sum(x_exp, keepdims=True)
np_out_list.append(x_exp / x_exp_sum)
np_out = np.concatenate([np_out, x_exp / x_exp_sum])
csr = paddle.to_tensor(np_x, stop_gradient=False).to_sparse_csr()
m = paddle.incubate.sparse.nn.Softmax()
out = m(csr)
self.assertTrue(np.allclose(out.values().numpy(), np_out))
# dx = (dout - sum(dout * out)) * out, dout=rand_x
out.backward(csr.detach())
dx = np.array([])
batch_offset = 0
for i in range(batchNum):
np_csr = sp.csr_matrix(np_x[i, :, :])
row_number = np_csr.shape[0]
for j in range(row_number):
start = np_csr.indptr[j]
end = np_csr.indptr[j + 1]
if start == end:
continue
dout = np_csr.data[start:end]
out = np_out[batch_offset + start:batch_offset + end]
sum = np.sum(dout * out, keepdims=True)
dx = np.concatenate([dx, (dout - sum) * out])
batch_offset += np_csr.nnz
self.assertTrue(np.allclose(csr.grad.values().numpy(), dx))
if __name__ == "__main__":
unittest.main()
python/paddle/incubate/sparse/nn/functional/activation.py
浏览文件 @ eec4e034
......@@ -55,7 +55,7 @@ def softmax(x, axis=-1, name=None):
sparse softmax activation, x must be SparseCsrTensor or SparseCooTensor.
Note:
Only supported axis=-1 for SparseCsrTensor, which is faster when read data
Only support axis=-1 for SparseCsrTensor, which is faster when read data
by row (axis=-1).
From the point of view of dense matrix, for each row :math:`i` and each column :math:`j`
......
python/paddle/incubate/sparse/nn/layer/activation.py
浏览文件 @ eec4e034
......@@ -66,7 +66,7 @@ class Softmax(Layer):
sparse softmax activation, x must be SparseCsrTensor or SparseCooTensor.
Note:
Only supported axis=-1 for SparseCsrTensor, which is faster when read data
Only support axis=-1 for SparseCsrTensor, which is faster when read data
by row (axis=-1).
From the point of view of dense matrix, for each row :math:`i` and each column :math:`j`
......
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