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未验证 提交 36739748 编写于 作者: Z zhangkaihuo 提交者： GitHub
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[Sparse]Rename sparse kernel (#45730)

上级 c084a7b1
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Showing with 271 addition and 310 deletion
paddle/phi/api/lib/sparse_api_custom_impl.cc
浏览文件 @ 36739748
......@@ -30,9 +30,9 @@ Tensor to_sparse_coo_impl(const Tensor& x, const int64_t sparse_dim) {
}
// 1. Get kernel signature and kernel
std::string kernel_name = "dense_to_sparse_coo";
std::string kernel_name = "dense_to_coo";
if (x.layout() == phi::DataLayout::SPARSE_CSR) {
kernel_name = "sparse_csr_to_coo";
kernel_name = "csr_to_coo";
}
auto kernel_key_set = ParseKernelKeyByInputArgs(x);
......@@ -88,9 +88,9 @@ Tensor to_sparse_csr_impl(const Tensor& x) {
return x;
}
// 1. Get kernel signature and kernel
std::string kernel_name = "dense_to_sparse_csr";
std::string kernel_name = "dense_to_csr";
if (x.layout() == phi::DataLayout::SPARSE_COO) {
kernel_name = "sparse_coo_to_csr";
kernel_name = "coo_to_csr";
}
auto kernel_key_set = ParseKernelKeyByInputArgs(x);
......@@ -151,9 +151,9 @@ Tensor to_dense_impl(const Tensor& x) {
}
// 1. Get kernel signature and kernel
std::string kernel_name = "sparse_coo_to_dense";
std::string kernel_name = "coo_to_dense";
if (x.layout() == phi::DataLayout::SPARSE_CSR) {
kernel_name = "sparse_csr_to_dense";
kernel_name = "csr_to_dense";
}
auto kernel_key_set = ParseKernelKeyByInputArgs(x);
......
paddle/phi/api/yaml/sparse_api.yaml
浏览文件 @ 36739748
......@@ -82,34 +82,13 @@
- api : conv3d
args : (Tensor x, Tensor kernel, int[] paddings, int[] dilations, int[] strides, int groups, bool subm, str key)
output : Tensor(out), Tensor(rulebook), Tensor(counter)
output : Tensor(out), Tensor(rulebook), Tensor(counter)
kernel :
func : conv3d_coo{sparse_coo, dense -> sparse_coo, dense, dense}
layout : x
intermediate: rulebook, counter
backward : conv3d_coo_grad
- api : coo_to_dense
args : (Tensor x)
output : Tensor(out)
invoke : to_dense_impl(x)
backward : coo_to_dense_grad
- api : create_sparse_coo_tensor
args : (Tensor values, Tensor indices, IntArray dense_shape)
output : Tensor(out)
kernel :
func : sparse_coo_tensor{dense, dense -> sparse_coo}
layout : values
data_type : values
backward : create_sparse_coo_tensor_grad
- api : dense_to_coo
args : (Tensor x, int64_t sparse_dim)
output : Tensor(out)
invoke : to_sparse_coo_impl(x, sparse_dim)
backward : dense_to_coo_grad
- api : divide
args : (Tensor x, Tensor y)
output : Tensor(out)
......@@ -224,6 +203,15 @@
layout : x
backward : softmax_grad
- api : sparse_coo_tensor
args : (Tensor values, Tensor indices, IntArray dense_shape)
output : Tensor(out)
kernel :
func : sparse_coo_tensor{dense, dense -> sparse_coo}
layout : values
data_type : values
backward : sparse_coo_tensor_grad
- api : sqrt
args : (Tensor x)
output : Tensor(out)
......@@ -272,24 +260,32 @@
- api : to_dense
args : (Tensor x)
output : Tensor(out)
invoke : to_dense_impl(x)
kernel :
func : coo_to_dense {sparse_coo -> dense},
csr_to_dense {sparse_csr -> dense}
backward : to_dense_grad
- api : to_sparse_coo
args : (Tensor x, int64_t sparse_dim)
output : Tensor(out)
invoke : to_sparse_coo_impl(x, sparse_dim)
kernel :
func : dense_to_coo { dense -> sparse_coo },
csr_to_coo { sparse_csr -> sparse_coo}
backward : to_sparse_coo_grad
- api : to_sparse_csr
args : (Tensor x)
output : Tensor(out)
invoke : to_sparse_csr_impl(x)
kernel :
func : dense_to_csr {dense -> sparse_csr},
coo_to_csr {sparse_coo -> sparse_csr}
- api : values
args : (Tensor x)
output : Tensor(out)
kernel :
func : coo_values{sparse_coo -> dense},
csr_values{sparse_csr -> dense}
func : values_coo{sparse_coo -> dense},
values_csr{sparse_csr -> dense}
layout : x
backward : values_grad
......
paddle/phi/api/yaml/sparse_bw_api.yaml
浏览文件 @ 36739748
......@@ -88,26 +88,6 @@
kernel :
func : conv3d_coo_grad{sparse_coo, dense, sparse_coo, dense, dense, sparse_coo -> sparse_coo, dense}
- backward_api : coo_to_dense_grad
forward : coo_to_dense(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : sparse_coo_to_dense_grad{sparse_coo, dense-> sparse_coo}
- backward_api : create_sparse_coo_tensor_grad
forward : create_sparse_coo_tensor(Tensor values, Tensor indices, IntArray dense_shape) -> Tensor(out)
args : (Tensor indices, Tensor out_grad)
output : Tensor(values_grad)
kernel :
func : sparse_coo_tensor_grad{dense, sparse_coo -> dense}
- backward_api : dense_to_coo_grad
forward : dense_to_coo(Tensor x, int64_t sparse_dim) -> Tensor(out)
args : (Tensor out_grad)
output : Tensor(x_grad)
invoke : coo_to_dense(out_grad)
- backward_api : divide_grad
forward : divide(Tensor x, Tensor y) -> Tensor(out)
args : (Tensor x, Tensor y, Tensor out, Tensor out_grad)
......@@ -239,6 +219,13 @@
kernel :
func : softmax_csr_grad{sparse_csr, sparse_csr -> sparse_csr}
- backward_api : sparse_coo_tensor_grad
forward : sparse_coo_tensor(Tensor values, Tensor indices, IntArray dense_shape) -> Tensor(out)
args : (Tensor indices, Tensor out_grad)
output : Tensor(values_grad)
kernel :
func : sparse_coo_tensor_grad{dense, sparse_coo -> dense}
- backward_api : sqrt_grad
forward : sqrt(Tensor x) -> Tensor(out)
args : (Tensor out, Tensor out_grad)
......@@ -279,12 +266,26 @@
func : tanh_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
tanh_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : to_dense_grad
forward : to_dense(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : coo_to_dense_grad{sparse_coo, dense -> sparse_coo}
- backward_api : to_sparse_coo_grad
forward : to_sparse_coo(Tensor x, int64_t sparse_dim) -> Tensor(out)
args : (Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : coo_to_dense { sparse_coo -> dense }
- backward_api : values_grad
forward : coo_values(Tensor x) -> Tensor(out)
forward : values_coo(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : coo_values_grad{sparse_coo, dense-> sparse_coo}
func : values_coo_grad{sparse_coo, dense-> sparse_coo}
- backward_api: fused_attention_grad
forward : fused_attention_csr(Tensor query, Tensor key, Tensor value, Tensor sparse_mask, Tensor key_padding_mask, Tensor attn_mask) -> Tensor(out), Tensor(softmax)
......
paddle/phi/kernels/sparse/cpu/elementwise_kernel.cc
浏览文件 @ 36739748
......@@ -270,15 +270,15 @@ void ElementWiseCooKernelImpl(const Context& dev_ctx,
const SparseCsrTensor& y, \
SparseCsrTensor* out) { \
funcs::name##Functor<T> functor; \
auto coo_x = SparseCsrToCoo<T>(dev_ctx, x); \
auto coo_y = SparseCsrToCoo<T>(dev_ctx, y); \
auto coo_x = CsrToCoo<T>(dev_ctx, x); \
auto coo_y = CsrToCoo<T>(dev_ctx, y); \
DenseTensor indeces; \
DenseTensor values; \
SparseCooTensor coo_out; \
coo_out.SetMember(indeces, values, x.dims()); \
ElementWiseCooKernelImpl<T, IntT, Context, funcs::name##Functor<T>>( \
dev_ctx, coo_x, coo_y, &coo_out, functor); \
*out = SparseCooToCsr<T>(dev_ctx, coo_out); \
*out = CooToCsr<T>(dev_ctx, coo_out); \
}
#define DEFINE_CSR_ELEMENTWISE_KERNEL(name) \
......
paddle/phi/kernels/sparse/cpu/sparse_utils_kernel.cc
浏览文件 @ 36739748
......@@ -63,10 +63,10 @@ inline int64_t GetNonZeroNum(const DenseTensor& dense,
}
template <typename T, typename Context>
void DenseToSparseCooKernel(const Context& dev_ctx,
const DenseTensor& x,
const int64_t sparse_dim,
SparseCooTensor* out) {
void DenseToCooKernel(const Context& dev_ctx,
const DenseTensor& x,
const int64_t sparse_dim,
SparseCooTensor* out) {
const T* x_data = x.data<T>();
const auto& x_dims = x.dims();
PADDLE_ENFORCE_LE(sparse_dim,
......@@ -107,9 +107,9 @@ void DenseToSparseCooKernel(const Context& dev_ctx,
}
template <typename T, typename IntT>
void SparseCsrToCooCPUKernel(const CPUContext& dev_ctx,
const SparseCsrTensor& x,
SparseCooTensor* out) {
void CsrToCooCPUKernel(const CPUContext& dev_ctx,
const SparseCsrTensor& x,
SparseCooTensor* out) {
const DDim& x_dims = x.dims();
const int64_t non_zero_num = x.cols().numel();
const auto& csr_crows = x.crows();
......@@ -157,19 +157,18 @@ void SparseCsrToCooCPUKernel(const CPUContext& dev_ctx,
}
template <typename T, typename Context>
void SparseCsrToCooKernel(const Context& dev_ctx,
const SparseCsrTensor& x,
SparseCooTensor* out) {
PD_VISIT_BASE_INTEGRAL_TYPES(
x.crows().dtype(), "SparseCsrToCooCPUKernel", ([&] {
SparseCsrToCooCPUKernel<T, data_t>(dev_ctx, x, out);
}));
void CsrToCooKernel(const Context& dev_ctx,
const SparseCsrTensor& x,
SparseCooTensor* out) {
PD_VISIT_BASE_INTEGRAL_TYPES(x.crows().dtype(), "CsrToCooCPUKernel", ([&] {
CsrToCooCPUKernel<T, data_t>(dev_ctx, x, out);
}));
}
template <typename T, typename IntT>
void SparseCooToCsrCPUKernel(const CPUContext& dev_ctx,
const SparseCooTensor& x,
SparseCsrTensor* out) {
void CooToCsrCPUKernel(const CPUContext& dev_ctx,
const SparseCooTensor& x,
SparseCsrTensor* out) {
const auto& x_dims = x.dims();
bool valid = x_dims.size() == 2 || x_dims.size() == 3;
PADDLE_ENFORCE_EQ(valid,
......@@ -247,19 +246,18 @@ void SparseCooToCsrCPUKernel(const CPUContext& dev_ctx,
}
template <typename T, typename Context>
void SparseCooToCsrKernel(const Context& dev_ctx,
const SparseCooTensor& x,
SparseCsrTensor* out) {
PD_VISIT_BASE_INTEGRAL_TYPES(
x.indices().dtype(), "SparseCooToCsrCPUKernel", ([&] {
SparseCooToCsrCPUKernel<T, data_t>(dev_ctx, x, out);
}));
void CooToCsrKernel(const Context& dev_ctx,
const SparseCooTensor& x,
SparseCsrTensor* out) {
PD_VISIT_BASE_INTEGRAL_TYPES(x.indices().dtype(), "CooToCsrCPUKernel", ([&] {
CooToCsrCPUKernel<T, data_t>(dev_ctx, x, out);
}));
}
template <typename T, typename IntT>
void SparseCooToDenseCPUKernel(const CPUContext& dev_ctx,
const SparseCooTensor& x,
DenseTensor* out) {
void CooToDenseCPUKernel(const CPUContext& dev_ctx,
const SparseCooTensor& x,
DenseTensor* out) {
const auto non_zero_num = x.nnz();
const auto dense_dims = x.dims();
const auto indices = x.indices();
......@@ -300,22 +298,22 @@ void SparseCooToDenseCPUKernel(const CPUContext& dev_ctx,
}
template <typename T, typename Context>
void SparseCooToDenseKernel(const Context& dev_ctx,
const SparseCooTensor& x,
DenseTensor* out) {
void CooToDenseKernel(const Context& dev_ctx,
const SparseCooTensor& x,
DenseTensor* out) {
PD_VISIT_BASE_INTEGRAL_TYPES(
x.indices().dtype(), "SparseCooToDenseCPUKernel", ([&] {
SparseCooToDenseCPUKernel<T, data_t>(dev_ctx, x, out);
x.indices().dtype(), "CooToDenseCPUKernel", ([&] {
CooToDenseCPUKernel<T, data_t>(dev_ctx, x, out);
}));
}
} // namespace sparse
} // namespace phi
PD_REGISTER_KERNEL(dense_to_sparse_coo,
PD_REGISTER_KERNEL(dense_to_coo,
CPU,
ALL_LAYOUT,
phi::sparse::DenseToSparseCooKernel,
phi::sparse::DenseToCooKernel,
float,
double,
paddle::float16,
......@@ -325,10 +323,10 @@ PD_REGISTER_KERNEL(dense_to_sparse_coo,
int,
int64_t) {}
PD_REGISTER_KERNEL(sparse_csr_to_coo,
PD_REGISTER_KERNEL(csr_to_coo,
CPU,
ALL_LAYOUT,
phi::sparse::SparseCsrToCooKernel,
phi::sparse::CsrToCooKernel,
float,
double,
paddle::float16,
......@@ -338,10 +336,10 @@ PD_REGISTER_KERNEL(sparse_csr_to_coo,
int,
int64_t) {}
PD_REGISTER_KERNEL(sparse_coo_to_csr,
PD_REGISTER_KERNEL(coo_to_csr,
CPU,
ALL_LAYOUT,
phi::sparse::SparseCooToCsrKernel,
phi::sparse::CooToCsrKernel,
float,
double,
phi::dtype::float16,
......@@ -351,10 +349,10 @@ PD_REGISTER_KERNEL(sparse_coo_to_csr,
int,
int64_t) {}
PD_REGISTER_KERNEL(dense_to_sparse_csr,
PD_REGISTER_KERNEL(dense_to_csr,
CPU,
ALL_LAYOUT,
phi::sparse::DenseToSparseCsrKernel,
phi::sparse::DenseToCsrKernel,
float,
double,
phi::dtype::float16,
......@@ -364,10 +362,10 @@ PD_REGISTER_KERNEL(dense_to_sparse_csr,
int,
int64_t) {}
PD_REGISTER_KERNEL(sparse_coo_to_dense,
PD_REGISTER_KERNEL(coo_to_dense,
CPU,
ALL_LAYOUT,
phi::sparse::SparseCooToDenseKernel,
phi::sparse::CooToDenseKernel,
float,
double,
phi::dtype::float16,
......@@ -377,10 +375,10 @@ PD_REGISTER_KERNEL(sparse_coo_to_dense,
int,
int64_t) {}
PD_REGISTER_KERNEL(sparse_csr_to_dense,
PD_REGISTER_KERNEL(csr_to_dense,
CPU,
ALL_LAYOUT,
phi::sparse::SparseCsrToDenseKernel,
phi::sparse::CsrToDenseKernel,
float,
double,
phi::dtype::float16,
......@@ -390,10 +388,10 @@ PD_REGISTER_KERNEL(sparse_csr_to_dense,
int,
int64_t) {}
PD_REGISTER_KERNEL(coo_values,
PD_REGISTER_KERNEL(values_coo,
CPU,
ALL_LAYOUT,
phi::sparse::CooValuesKernel,
phi::sparse::ValuesCooKernel,
float,
double,
phi::dtype::float16,
......@@ -405,10 +403,10 @@ PD_REGISTER_KERNEL(coo_values,
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
}
PD_REGISTER_KERNEL(csr_values,
PD_REGISTER_KERNEL(values_csr,
CPU,
ALL_LAYOUT,
phi::sparse::CsrValuesKernel,
phi::sparse::ValuesCsrKernel,
float,
double,
phi::dtype::float16,
......
paddle/phi/kernels/sparse/gpu/matmul_grad_kernel.cu
浏览文件 @ 36739748
......@@ -43,10 +43,10 @@ void MatmulCooDenseGradKernel(const Context& dev_ctx,
// 'cusparseSDDMM' only support CSR now, so use COO->CSR->COO,
// which will increase some expenses.
EmptyLikeCooKernel<T, Context>(dev_ctx, x, dx);
SparseCsrTensor dx_csr = SparseCooToCsr<T, Context>(dev_ctx, *dx);
SparseCsrTensor dx_csr = CooToCsr<T, Context>(dev_ctx, *dx);
sparse_blas.SDDMM(
false, true, static_cast<T>(1), dout, y, static_cast<T>(0), &dx_csr);
SparseCsrToCooKernel<T, Context>(dev_ctx, dx_csr, dx);
CsrToCooKernel<T, Context>(dev_ctx, dx_csr, dx);
}
// dy{Dense} = x'{SparseCoo} * dout{Dense}
......
paddle/phi/kernels/sparse/gpu/sparse_utils_kernel.cu
浏览文件 @ 36739748
......@@ -93,10 +93,10 @@ __global__ void GetNonZeroElementsAndIndices(const T* dense_data,
}
template <typename T, typename Context>
void DenseToSparseCooKernel(const Context& dev_ctx,
const DenseTensor& x,
const int64_t sparse_dim,
SparseCooTensor* out) {
void DenseToCooKernel(const Context& dev_ctx,
const DenseTensor& x,
const int64_t sparse_dim,
SparseCooTensor* out) {
const T* x_data = x.data<T>();
const auto& x_dims = x.dims();
PADDLE_ENFORCE_LE(sparse_dim,
......@@ -208,9 +208,9 @@ __global__ void ConvertCsrCrowsToCooRows(const IntT* crows_ptr,
}
template <typename T, typename IntT>
void SparseCsrToCooGPUKernel(const GPUContext& dev_ctx,
const SparseCsrTensor& x,
SparseCooTensor* out) {
void CsrToCooGPUKernel(const GPUContext& dev_ctx,
const SparseCsrTensor& x,
SparseCooTensor* out) {
const DDim& x_dims = x.dims();
const int64_t non_zero_num = x.cols().numel();
const auto& csr_crows = x.crows();
......@@ -274,13 +274,12 @@ void SparseCsrToCooGPUKernel(const GPUContext& dev_ctx,
}
template <typename T, typename Context>
void SparseCsrToCooKernel(const Context& dev_ctx,
const SparseCsrTensor& x,
SparseCooTensor* out) {
PD_VISIT_BASE_INTEGRAL_TYPES(
x.crows().dtype(), "SparseCsrToCooGPUKernel", ([&] {
SparseCsrToCooGPUKernel<T, data_t>(dev_ctx, x, out);
}));
void CsrToCooKernel(const Context& dev_ctx,
const SparseCsrTensor& x,
SparseCooTensor* out) {
PD_VISIT_BASE_INTEGRAL_TYPES(x.crows().dtype(), "CsrToCooGPUKernel", ([&] {
CsrToCooGPUKernel<T, data_t>(dev_ctx, x, out);
}));
}
template <typename IntT>
......@@ -343,9 +342,9 @@ __global__ void ConvertCooRowsToCsrCrows(
}
template <typename T, typename IntT>
void SparseCooToCsrGPUKernel(const GPUContext& dev_ctx,
const SparseCooTensor& x,
SparseCsrTensor* out) {
void CooToCsrGPUKernel(const GPUContext& dev_ctx,
const SparseCooTensor& x,
SparseCsrTensor* out) {
const auto& x_dims = x.dims();
bool valid = x_dims.size() == 2 || x_dims.size() == 3;
PADDLE_ENFORCE_EQ(valid,
......@@ -416,23 +415,22 @@ void SparseCooToCsrGPUKernel(const GPUContext& dev_ctx,
}
template <typename T, typename Context>
void SparseCooToCsrKernel(const Context& dev_ctx,
const SparseCooTensor& x,
SparseCsrTensor* out) {
PD_VISIT_BASE_INTEGRAL_TYPES(
x.indices().dtype(), "SparseCooToCsrGPUKernel", ([&] {
SparseCooToCsrGPUKernel<T, data_t>(dev_ctx, x, out);
}));
void CooToCsrKernel(const Context& dev_ctx,
const SparseCooTensor& x,
SparseCsrTensor* out) {
PD_VISIT_BASE_INTEGRAL_TYPES(x.indices().dtype(), "CooToCsrGPUKernel", ([&] {
CooToCsrGPUKernel<T, data_t>(dev_ctx, x, out);
}));
}
template <typename ValueT, typename IndicesT>
__global__ void KernelSparseCooToDense(const IndicesT* indices,
const int64_t* sparse_offsets,
const ValueT* data,
ValueT* dense_data,
const IndicesT non_zero_num,
const int64_t base_offset,
const int64_t sparse_dim) {
__global__ void KernelCooToDense(const IndicesT* indices,
const int64_t* sparse_offsets,
const ValueT* data,
ValueT* dense_data,
const IndicesT non_zero_num,
const int64_t base_offset,
const int64_t sparse_dim) {
int tid = threadIdx.x + blockIdx.x * blockDim.x;
for (int i = tid; i < non_zero_num; i += gridDim.x * blockDim.x) {
int64_t index = 0;
......@@ -447,9 +445,9 @@ __global__ void KernelSparseCooToDense(const IndicesT* indices,
}
template <typename T, typename IntT>
void SparseCooToDenseGPUKernel(const GPUContext& dev_ctx,
const SparseCooTensor& x,
DenseTensor* out) {
void CooToDenseGPUKernel(const GPUContext& dev_ctx,
const SparseCooTensor& x,
DenseTensor* out) {
const auto non_zero_num = x.nnz();
const auto dense_dims = x.dims();
const auto indices = x.indices();
......@@ -490,7 +488,7 @@ void SparseCooToDenseGPUKernel(const GPUContext& dev_ctx,
auto config =
phi::backends::gpu::GetGpuLaunchConfig1D(dev_ctx, non_zero_num, 1);
KernelSparseCooToDense<T, IntT>
KernelCooToDense<T, IntT>
<<<config.block_per_grid.x,
config.thread_per_block.x,
0,
......@@ -504,22 +502,22 @@ void SparseCooToDenseGPUKernel(const GPUContext& dev_ctx,
}
template <typename T, typename Context>
void SparseCooToDenseKernel(const Context& dev_ctx,
const SparseCooTensor& x,
DenseTensor* out) {
void CooToDenseKernel(const Context& dev_ctx,
const SparseCooTensor& x,
DenseTensor* out) {
PD_VISIT_BASE_INTEGRAL_TYPES(
x.indices().dtype(), "SparseCooToDenseGPUKernel", ([&] {
SparseCooToDenseGPUKernel<T, data_t>(dev_ctx, x, out);
x.indices().dtype(), "CooToDenseGPUKernel", ([&] {
CooToDenseGPUKernel<T, data_t>(dev_ctx, x, out);
}));
}
} // namespace sparse
} // namespace phi
PD_REGISTER_KERNEL(dense_to_sparse_coo,
PD_REGISTER_KERNEL(dense_to_coo,
GPU,
ALL_LAYOUT,
phi::sparse::DenseToSparseCooKernel,
phi::sparse::DenseToCooKernel,
float,
double,
phi::dtype::float16,
......@@ -529,10 +527,10 @@ PD_REGISTER_KERNEL(dense_to_sparse_coo,
int,
int64_t) {}
PD_REGISTER_KERNEL(sparse_csr_to_coo,
PD_REGISTER_KERNEL(csr_to_coo,
GPU,
ALL_LAYOUT,
phi::sparse::SparseCsrToCooKernel,
phi::sparse::CsrToCooKernel,
float,
double,
phi::dtype::float16,
......@@ -542,10 +540,10 @@ PD_REGISTER_KERNEL(sparse_csr_to_coo,
int,
int64_t) {}
PD_REGISTER_KERNEL(sparse_coo_to_csr,
PD_REGISTER_KERNEL(coo_to_csr,
GPU,
ALL_LAYOUT,
phi::sparse::SparseCooToCsrKernel,
phi::sparse::CooToCsrKernel,
float,
double,
phi::dtype::float16,
......@@ -555,10 +553,10 @@ PD_REGISTER_KERNEL(sparse_coo_to_csr,
int,
int64_t) {}
PD_REGISTER_KERNEL(dense_to_sparse_csr,
PD_REGISTER_KERNEL(dense_to_csr,
GPU,
ALL_LAYOUT,
phi::sparse::DenseToSparseCsrKernel,
phi::sparse::DenseToCsrKernel,
float,
double,
phi::dtype::float16,
......@@ -568,10 +566,10 @@ PD_REGISTER_KERNEL(dense_to_sparse_csr,
int,
int64_t) {}
PD_REGISTER_KERNEL(sparse_coo_to_dense,
PD_REGISTER_KERNEL(coo_to_dense,
GPU,
ALL_LAYOUT,
phi::sparse::SparseCooToDenseKernel,
phi::sparse::CooToDenseKernel,
float,
double,
phi::dtype::float16,
......@@ -581,10 +579,10 @@ PD_REGISTER_KERNEL(sparse_coo_to_dense,
int,
int64_t) {}
PD_REGISTER_KERNEL(sparse_csr_to_dense,
PD_REGISTER_KERNEL(csr_to_dense,
GPU,
ALL_LAYOUT,
phi::sparse::SparseCsrToDenseKernel,
phi::sparse::CsrToDenseKernel,
float,
double,
phi::dtype::float16,
......@@ -594,10 +592,10 @@ PD_REGISTER_KERNEL(sparse_csr_to_dense,
int,
int64_t) {}
PD_REGISTER_KERNEL(coo_values,
PD_REGISTER_KERNEL(values_coo,
GPU,
ALL_LAYOUT,
phi::sparse::CooValuesKernel,
phi::sparse::ValuesCooKernel,
float,
double,
phi::dtype::float16,
......@@ -609,10 +607,10 @@ PD_REGISTER_KERNEL(coo_values,
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
}
PD_REGISTER_KERNEL(csr_values,
PD_REGISTER_KERNEL(values_csr,
GPU,
ALL_LAYOUT,
phi::sparse::CsrValuesKernel,
phi::sparse::ValuesCsrKernel,
float,
double,
phi::dtype::float16,
......
paddle/phi/kernels/sparse/sparse_utils_grad_kernel.cc
浏览文件 @ 36739748
......@@ -20,7 +20,7 @@ namespace phi {
namespace sparse {
template <typename T, typename Context>
void CooValuesGradKernel(const Context& dev_ctx,
void ValuesCooGradKernel(const Context& dev_ctx,
const SparseCooTensor& x,
const DenseTensor& out_grad,
SparseCooTensor* x_grad) {
......@@ -28,20 +28,20 @@ void CooValuesGradKernel(const Context& dev_ctx,
}
template <typename T, typename Context>
void SparseCooToDenseGradKernel(const Context& dev_ctx,
const SparseCooTensor& x,
const DenseTensor& out_grad,
SparseCooTensor* x_grad) {
void CooToDenseGradKernel(const Context& dev_ctx,
const SparseCooTensor& x,
const DenseTensor& out_grad,
SparseCooTensor* x_grad) {
SparseMaskKernel<T, Context>(dev_ctx, out_grad, x, x_grad);
}
} // namespace sparse
} // namespace phi
PD_REGISTER_KERNEL(coo_values_grad,
PD_REGISTER_KERNEL(values_coo_grad,
CPU,
ALL_LAYOUT,
phi::sparse::CooValuesGradKernel,
phi::sparse::ValuesCooGradKernel,
float,
double,
uint8_t,
......@@ -52,10 +52,10 @@ PD_REGISTER_KERNEL(coo_values_grad,
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
}
PD_REGISTER_KERNEL(sparse_coo_to_dense_grad,
PD_REGISTER_KERNEL(coo_to_dense_grad,
CPU,
ALL_LAYOUT,
phi::sparse::SparseCooToDenseGradKernel,
phi::sparse::CooToDenseGradKernel,
float,
double,
uint8_t,
......@@ -80,10 +80,10 @@ PD_REGISTER_KERNEL(sparse_coo_tensor_grad,
}
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
PD_REGISTER_KERNEL(coo_values_grad,
PD_REGISTER_KERNEL(values_coo_grad,
GPU,
ALL_LAYOUT,
phi::sparse::CooValuesGradKernel,
phi::sparse::ValuesCooGradKernel,
float,
double,
phi::dtype::float16,
......@@ -94,10 +94,10 @@ PD_REGISTER_KERNEL(coo_values_grad,
int64_t) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
}
PD_REGISTER_KERNEL(sparse_coo_to_dense_grad,
PD_REGISTER_KERNEL(coo_to_dense_grad,
GPU,
ALL_LAYOUT,
phi::sparse::SparseCooToDenseGradKernel,
phi::sparse::CooToDenseGradKernel,
float,
double,
phi::dtype::float16,
......
paddle/phi/kernels/sparse/sparse_utils_grad_kernel.h
浏览文件 @ 36739748
......@@ -22,16 +22,16 @@ namespace phi {
namespace sparse {
template <typename T, typename Context>
void CooValuesGradKernel(const Context& dev_ctx,
void ValuesCooGradKernel(const Context& dev_ctx,
const SparseCooTensor& x,
const DenseTensor& out_grad,
SparseCooTensor* x_grad);
template <typename T, typename Context>
void SparseCooToDenseGradKernel(const Context& dev_ctx,
const SparseCooTensor& x,
const DenseTensor& out_grad,
SparseCooTensor* x_grad);
void CooToDenseGradKernel(const Context& dev_ctx,
const SparseCooTensor& x,
const DenseTensor& out_grad,
SparseCooTensor* x_grad);
template <typename T, typename Context>
void SparseCooTensorGradKernel(const Context& dev_ctx,
......
paddle/phi/kernels/sparse/sparse_utils_kernel.h
浏览文件 @ 36739748
......@@ -24,57 +24,55 @@ namespace phi {
namespace sparse {
template <typename T, typename Context>
void DenseToSparseCooKernel(const Context& dev_ctx,
const DenseTensor& x,
const int64_t sparse_dim,
SparseCooTensor* out);
void DenseToCooKernel(const Context& dev_ctx,
const DenseTensor& x,
const int64_t sparse_dim,
SparseCooTensor* out);
template <typename T, typename Context>
SparseCooTensor DenseToSparseCoo(const Context& dev_ctx,
const DenseTensor& x,
const int64_t sparse_dim) {
SparseCooTensor DenseToCoo(const Context& dev_ctx,
const DenseTensor& x,
const int64_t sparse_dim) {
DenseTensor indices;
DenseTensor values;
SparseCooTensor coo(indices, values, x.dims());
DenseToSparseCooKernel<T, Context>(dev_ctx, x, sparse_dim, &coo);
DenseToCooKernel<T, Context>(dev_ctx, x, sparse_dim, &coo);
return coo;
}
template <typename T, typename Context>
void SparseCsrToCooKernel(const Context& dev_ctx,
const SparseCsrTensor& x,
SparseCooTensor* out);
void CsrToCooKernel(const Context& dev_ctx,
const SparseCsrTensor& x,
SparseCooTensor* out);
template <typename T, typename Context>
SparseCooTensor SparseCsrToCoo(const Context& dev_ctx,
const SparseCsrTensor& x) {
SparseCooTensor CsrToCoo(const Context& dev_ctx, const SparseCsrTensor& x) {
DenseTensor indices;
DenseTensor values;
SparseCooTensor coo(indices, values, x.dims());
SparseCsrToCooKernel<T, Context>(dev_ctx, x, &coo);
CsrToCooKernel<T, Context>(dev_ctx, x, &coo);
return coo;
}
template <typename T, typename Context>
void SparseCooToCsrKernel(const Context& dev_ctx,
const SparseCooTensor& x,
SparseCsrTensor* out);
void CooToCsrKernel(const Context& dev_ctx,
const SparseCooTensor& x,
SparseCsrTensor* out);
template <typename T, typename Context>
SparseCsrTensor SparseCooToCsr(const Context& dev_ctx,
const SparseCooTensor& x) {
SparseCsrTensor CooToCsr(const Context& dev_ctx, const SparseCooTensor& x) {
DenseTensor crows;
DenseTensor cols;
DenseTensor non_zero_elements;
SparseCsrTensor csr(crows, cols, non_zero_elements, x.dims());
SparseCooToCsrKernel<T, Context>(dev_ctx, x, &csr);
CooToCsrKernel<T, Context>(dev_ctx, x, &csr);
return csr;
}
template <typename T, typename Context>
void DenseToSparseCsrKernel(const Context& dev_ctx,
const DenseTensor& x,
SparseCsrTensor* out) {
void DenseToCsrKernel(const Context& dev_ctx,
const DenseTensor& x,
SparseCsrTensor* out) {
const auto& x_dims = x.dims();
bool valid = x_dims.size() == 2 || x_dims.size() == 3;
PADDLE_ENFORCE_EQ(valid,
......@@ -85,61 +83,61 @@ void DenseToSparseCsrKernel(const Context& dev_ctx,
DenseTensor indices;
DenseTensor values;
SparseCooTensor coo(indices, values, x.dims());
DenseToSparseCooKernel<T, Context>(dev_ctx, x, sparse_dim, &coo);
SparseCooToCsrKernel<T, Context>(dev_ctx, coo, out);
DenseToCooKernel<T, Context>(dev_ctx, x, sparse_dim, &coo);
CooToCsrKernel<T, Context>(dev_ctx, coo, out);
}
template <typename T, typename Context>
SparseCsrTensor DenseToSparseCsr(const Context& dev_ctx, const DenseTensor& x) {
SparseCsrTensor DenseToCsr(const Context& dev_ctx, const DenseTensor& x) {
DenseTensor crows;
DenseTensor cols;
DenseTensor non_zero_elements;
SparseCsrTensor csr(crows, cols, non_zero_elements, x.dims());
DenseToSparseCsrKernel<T, Context>(dev_ctx, x, &csr);
DenseToCsrKernel<T, Context>(dev_ctx, x, &csr);
return csr;
}
template <typename T, typename Context>
void SparseCooToDenseKernel(const Context& dev_ctx,
const SparseCooTensor& x,
DenseTensor* out);
void CooToDenseKernel(const Context& dev_ctx,
const SparseCooTensor& x,
DenseTensor* out);
template <typename T, typename Context>
DenseTensor SparseCooToDense(const Context& dev_ctx, const SparseCooTensor& x) {
DenseTensor CooToDense(const Context& dev_ctx, const SparseCooTensor& x) {
DenseTensorMeta meta(x.dtype(), x.dims(), x.non_zero_elements().layout());
DenseTensor dense = phi::Empty(dev_ctx, std::move(meta));
SparseCooToDenseKernel<T, Context>(dev_ctx, x, &dense);
CooToDenseKernel<T, Context>(dev_ctx, x, &dense);
return dense;
}
template <typename T, typename Context>
void SparseCsrToDenseKernel(const Context& dev_ctx,
const SparseCsrTensor& x,
DenseTensor* out) {
void CsrToDenseKernel(const Context& dev_ctx,
const SparseCsrTensor& x,
DenseTensor* out) {
DenseTensor indices;
DenseTensor values;
SparseCooTensor coo(indices, values, x.dims());
SparseCsrToCooKernel<T, Context>(dev_ctx, x, &coo);
SparseCooToDenseKernel<T, Context>(dev_ctx, coo, out);
CsrToCooKernel<T, Context>(dev_ctx, x, &coo);
CooToDenseKernel<T, Context>(dev_ctx, coo, out);
}
template <typename T, typename Context>
DenseTensor SparseCsrToDense(const Context& dev_ctx, const SparseCsrTensor& x) {
DenseTensor CsrToDense(const Context& dev_ctx, const SparseCsrTensor& x) {
DenseTensorMeta meta(x.dtype(), x.dims(), x.non_zero_elements().layout());
DenseTensor dense = phi::Empty(dev_ctx, std::move(meta));
SparseCsrToDenseKernel<T, Context>(dev_ctx, x, &dense);
CsrToDenseKernel<T, Context>(dev_ctx, x, &dense);
return dense;
}
template <typename T, typename Context>
void CooValuesKernel(const Context& dev_ctx,
void ValuesCooKernel(const Context& dev_ctx,
const SparseCooTensor& x,
DenseTensor* out) {
*out = x.non_zero_elements();
}
template <typename T, typename Context>
void CsrValuesKernel(const Context& dev_ctx,
void ValuesCsrKernel(const Context& dev_ctx,
const SparseCsrTensor& x,
DenseTensor* out) {
*out = x.non_zero_elements();
......
paddle/phi/tests/api/test_sparse_utils_api.cc
浏览文件 @ 36739748
......@@ -23,7 +23,7 @@ limitations under the License. */
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/sparse_coo_tensor.h"
PD_DECLARE_KERNEL(dense_to_sparse_coo, CPU, ALL_LAYOUT);
PD_DECLARE_KERNEL(dense_to_coo, CPU, ALL_LAYOUT);
TEST(API, to_sparse_coo) {
const auto alloc = std::make_shared<paddle::experimental::DefaultAllocator>(
......
paddle/phi/tests/kernels/test_sparse_activation_dev_api.cc
浏览文件 @ 36739748
......@@ -47,7 +47,7 @@ TEST(DEV_API, sparse_relu) {
phi::Empty(dev_ctx_cpu,
DenseTensorMeta(DataType::FLOAT32, {3, 4}, DataLayout::NCHW));
memcpy(dense_x.data<float>(), data.data(), data.size() * sizeof(float));
auto sparse_coo = sparse::DenseToSparseCoo<float>(dev_ctx_cpu, dense_x, 2);
auto sparse_coo = sparse::DenseToCoo<float>(dev_ctx_cpu, dense_x, 2);
auto sparse_out = sparse::ReluCoo<float>(dev_ctx_cpu, sparse_coo);
DenseTensor dense_out =
......
paddle/phi/tests/kernels/test_sparse_elementwise_dev_api.cc
浏览文件 @ 36739748
......@@ -49,12 +49,9 @@ namespace tests {
const Sparse##type##Tensor& y, \
const DDim& dense_dims) { \
auto out = sparse::ElementWise##name##type<T>(dev_ctx_cpu, x, y); \
const DenseTensor denseX = \
sparse::Sparse##type##ToDense<T>(dev_ctx_cpu, x); \
const DenseTensor denseY = \
sparse::Sparse##type##ToDense<T>(dev_ctx_cpu, y); \
const DenseTensor denseOut = \
sparse::Sparse##type##ToDense<T>(dev_ctx_cpu, out); \
const DenseTensor denseX = sparse::type##ToDense<T>(dev_ctx_cpu, x); \
const DenseTensor denseY = sparse::type##ToDense<T>(dev_ctx_cpu, y); \
const DenseTensor denseOut = sparse::type##ToDense<T>(dev_ctx_cpu, out); \
auto expectResult = name<T>(dev_ctx_cpu, denseX, denseY); \
for (int j = 0; j < denseOut.numel(); ++j) { \
auto actualResultRow = denseOut.template data<T>()[j]; \
......@@ -114,8 +111,8 @@ TEST(DEV_API, sparse_elementwise_coo_kernel_double) {
.GetAllocator(paddle::platform::CPUPlace())
.get());
auto coo_x = sparse::DenseToSparseCoo<T>(dev_ctx_cpu, dense_x, sparse_dim);
auto coo_y = sparse::DenseToSparseCoo<T>(dev_ctx_cpu, dense_y, sparse_dim);
auto coo_x = sparse::DenseToCoo<T>(dev_ctx_cpu, dense_x, sparse_dim);
auto coo_y = sparse::DenseToCoo<T>(dev_ctx_cpu, dense_y, sparse_dim);
TestElementWiseAddCoo<T>(dev_ctx_cpu, coo_x, coo_y, dense_dims);
TestElementWiseSubtractCoo<T>(dev_ctx_cpu, coo_x, coo_y, dense_dims);
......@@ -159,8 +156,8 @@ TEST(DEV_API, sparse_elementwise_csr_kernel_float) {
.GetAllocator(paddle::platform::CPUPlace())
.get());
auto csr_x = sparse::DenseToSparseCsr<T>(dev_ctx_cpu, dense_x);
auto csr_y = sparse::DenseToSparseCsr<T>(dev_ctx_cpu, dense_y);
auto csr_x = sparse::DenseToCsr<T>(dev_ctx_cpu, dense_x);
auto csr_y = sparse::DenseToCsr<T>(dev_ctx_cpu, dense_y);
TestElementWiseAddCsr<T>(dev_ctx_cpu, csr_x, csr_y, dense_dims);
TestElementWiseSubtractCsr<T>(dev_ctx_cpu, csr_x, csr_y, dense_dims);
......@@ -190,20 +187,18 @@ TEST(DEV_API, sparse_elementwise_csr_kernel_float) {
dev_ctx_cpu, \
DenseTensorMeta(DataType::FLOAT32, dense_dims, DataLayout::NCHW)); \
\
phi::name##GradKernel<T>( \
dev_ctx_cpu, \
sparse::Sparse##type##ToDense<T>(dev_ctx_cpu, x), \
sparse::Sparse##type##ToDense<T>(dev_ctx_cpu, y), \
sparse::Sparse##type##ToDense<T>(dev_ctx_cpu, out), \
-1, \
&expectdx, \
&expectdy); \
phi::name##GradKernel<T>(dev_ctx_cpu, \
sparse::type##ToDense<T>(dev_ctx_cpu, x), \
sparse::type##ToDense<T>(dev_ctx_cpu, y), \
sparse::type##ToDense<T>(dev_ctx_cpu, out), \
-1, \
&expectdx, \
&expectdy); \
const DenseTensor densedX = \
sparse::Sparse##type##ToDense<T>(dev_ctx_cpu, dresult[0]); \
sparse::type##ToDense<T>(dev_ctx_cpu, dresult[0]); \
const DenseTensor densedY = \
sparse::Sparse##type##ToDense<T>(dev_ctx_cpu, dresult[1]); \
const DenseTensor denseOut = \
sparse::Sparse##type##ToDense<T>(dev_ctx_cpu, out); \
sparse::type##ToDense<T>(dev_ctx_cpu, dresult[1]); \
const DenseTensor denseOut = sparse::type##ToDense<T>(dev_ctx_cpu, out); \
\
for (int j = 0; j < densedX.numel(); ++j) { \
auto actualResultRow = densedX.template data<T>()[j]; \
......@@ -248,18 +243,16 @@ void TestElementWiseDivideCsrGrad(const Context& dev_ctx_cpu,
dev_ctx_cpu,
DenseTensorMeta(DataType::FLOAT32, dense_dims, DataLayout::NCHW));
phi::DivideGradKernel<T>(dev_ctx_cpu,
sparse::SparseCsrToDense<T>(dev_ctx_cpu, x),
sparse::SparseCsrToDense<T>(dev_ctx_cpu, y),
sparse::SparseCsrToDense<T>(dev_ctx_cpu, out),
sparse::SparseCsrToDense<T>(dev_ctx_cpu, out),
sparse::CsrToDense<T>(dev_ctx_cpu, x),
sparse::CsrToDense<T>(dev_ctx_cpu, y),
sparse::CsrToDense<T>(dev_ctx_cpu, out),
sparse::CsrToDense<T>(dev_ctx_cpu, out),
-1,
&expectdx,
&expectdy);
const DenseTensor densedX =
sparse::SparseCsrToDense<T>(dev_ctx_cpu, dresult[0]);
const DenseTensor densedY =
sparse::SparseCsrToDense<T>(dev_ctx_cpu, dresult[1]);
const DenseTensor denseOut = sparse::SparseCsrToDense<T>(dev_ctx_cpu, out);
const DenseTensor densedX = sparse::CsrToDense<T>(dev_ctx_cpu, dresult[0]);
const DenseTensor densedY = sparse::CsrToDense<T>(dev_ctx_cpu, dresult[1]);
const DenseTensor denseOut = sparse::CsrToDense<T>(dev_ctx_cpu, out);
for (int j = 0; j < densedX.numel(); ++j) {
auto actualResultRow = densedX.template data<T>()[j];
auto expectResultRow = expectdx.template data<T>()[j];
......@@ -291,18 +284,16 @@ void TestElementWiseDivideCooGrad(const Context& dev_ctx_cpu,
dev_ctx_cpu,
DenseTensorMeta(DataType::FLOAT32, dense_dims, DataLayout::NCHW));
phi::DivideGradKernel<T>(dev_ctx_cpu,
sparse::SparseCooToDense<T>(dev_ctx_cpu, x),
sparse::SparseCooToDense<T>(dev_ctx_cpu, y),
sparse::SparseCooToDense<T>(dev_ctx_cpu, out),
sparse::SparseCooToDense<T>(dev_ctx_cpu, out),
sparse::CooToDense<T>(dev_ctx_cpu, x),
sparse::CooToDense<T>(dev_ctx_cpu, y),
sparse::CooToDense<T>(dev_ctx_cpu, out),
sparse::CooToDense<T>(dev_ctx_cpu, out),
-1,
&expectdx,
&expectdy);
const DenseTensor densedX =
sparse::SparseCooToDense<T>(dev_ctx_cpu, dresult[0]);
const DenseTensor densedY =
sparse::SparseCooToDense<T>(dev_ctx_cpu, dresult[1]);
const DenseTensor denseOut = sparse::SparseCooToDense<T>(dev_ctx_cpu, out);
const DenseTensor densedX = sparse::CooToDense<T>(dev_ctx_cpu, dresult[0]);
const DenseTensor densedY = sparse::CooToDense<T>(dev_ctx_cpu, dresult[1]);
const DenseTensor denseOut = sparse::CooToDense<T>(dev_ctx_cpu, out);
for (int j = 0; j < densedX.numel(); ++j) {
auto actualResultRow = densedX.template data<T>()[j];
auto expectResultRow = expectdx.template data<T>()[j];
......@@ -356,11 +347,11 @@ TEST(DEV_API, sparse_elementwise_csr_grad_kernel_float) {
.GetAllocator(paddle::platform::CPUPlace())
.get());
auto csr_x = sparse::DenseToSparseCsr<T>(dev_ctx_cpu, dense_x);
auto csr_y = sparse::DenseToSparseCsr<T>(dev_ctx_cpu, dense_y);
auto csr_x = sparse::DenseToCsr<T>(dev_ctx_cpu, dense_x);
auto csr_y = sparse::DenseToCsr<T>(dev_ctx_cpu, dense_y);
auto dx = sparse::DenseToSparseCsr<T>(dev_ctx_cpu, dense_y);
auto dy = sparse::DenseToSparseCsr<T>(dev_ctx_cpu, dense_x);
auto dx = sparse::DenseToCsr<T>(dev_ctx_cpu, dense_y);
auto dy = sparse::DenseToCsr<T>(dev_ctx_cpu, dense_x);
TestElementWiseAddCsrGrad<T>(dev_ctx_cpu, csr_x, csr_y, dense_dims);
TestElementWiseSubtractCsrGrad<T>(dev_ctx_cpu, csr_x, csr_y, dense_dims);
......@@ -402,11 +393,11 @@ TEST(DEV_API, sparse_elementwise_coo_grad_kernel_double) {
.GetAllocator(paddle::platform::CPUPlace())
.get());
auto csr_x = sparse::DenseToSparseCoo<T>(dev_ctx_cpu, dense_x, sparse_dim);
auto csr_y = sparse::DenseToSparseCoo<T>(dev_ctx_cpu, dense_y, sparse_dim);
auto csr_x = sparse::DenseToCoo<T>(dev_ctx_cpu, dense_x, sparse_dim);
auto csr_y = sparse::DenseToCoo<T>(dev_ctx_cpu, dense_y, sparse_dim);
auto dx = sparse::DenseToSparseCoo<T>(dev_ctx_cpu, dense_y, sparse_dim);
auto dy = sparse::DenseToSparseCoo<T>(dev_ctx_cpu, dense_x, sparse_dim);
auto dx = sparse::DenseToCoo<T>(dev_ctx_cpu, dense_y, sparse_dim);
auto dy = sparse::DenseToCoo<T>(dev_ctx_cpu, dense_x, sparse_dim);
TestElementWiseAddCooGrad<T>(dev_ctx_cpu, csr_x, csr_y, dense_dims);
TestElementWiseSubtractCooGrad<T>(dev_ctx_cpu, csr_x, csr_y, dense_dims);
......
paddle/phi/tests/kernels/test_sparse_utils_dev_api.cc
浏览文件 @ 36739748
......@@ -94,8 +94,7 @@ void TestDenseToSparseCoo(const DenseTensor& dense_x,
.get());
// 1. test cpu
auto cpu_sparse_out =
sparse::DenseToSparseCoo<T>(dev_ctx_cpu, dense_x, sparse_dim);
auto cpu_sparse_out = sparse::DenseToCoo<T>(dev_ctx_cpu, dense_x, sparse_dim);
CheckResult<T, int64_t>(&dev_ctx_cpu,
cpu_sparse_out,
non_zero_data,
......@@ -129,8 +128,7 @@ void TestDenseToSparseCoo(const DenseTensor& dense_x,
DenseTensorMeta(dense_x.dtype(), dense_x.dims(), dense_x.layout()));
phi::Copy(dev_ctx_gpu, dense_x, phi::GPUPlace(), true, &d_dense_x);
auto sparse_out =
sparse::DenseToSparseCoo<T>(dev_ctx_gpu, d_dense_x, sparse_dim);
auto sparse_out = sparse::DenseToCoo<T>(dev_ctx_gpu, d_dense_x, sparse_dim);
CheckResult<T, int64_t>(&dev_ctx_gpu,
sparse_out,
non_zero_data,
......@@ -310,7 +308,7 @@ void TestSparseCsrToCoo(const DDim& dense_dims,
paddle::memory::allocation::AllocatorFacade::Instance()
.GetAllocator(phi::CPUPlace())
.get());
auto cpu_sparse_out = sparse::SparseCsrToCoo<T>(dev_ctx_cpu, csr);
auto cpu_sparse_out = sparse::CsrToCoo<T>(dev_ctx_cpu, csr);
CheckResult<T, int64_t>(&dev_ctx_cpu,
cpu_sparse_out,
non_zero_data,
......@@ -345,7 +343,7 @@ void TestSparseCsrToCoo(const DDim& dense_dims,
phi::Copy(dev_ctx_gpu, cols, d_cols.place(), true, &d_cols);
phi::Copy(dev_ctx_gpu, values, d_values.place(), true, &d_values);
phi::SparseCsrTensor d_csr(d_crows, d_cols, d_values, dense_dims);
auto cuda_sparse_out = sparse::SparseCsrToCoo<T>(dev_ctx_gpu, d_csr);
auto cuda_sparse_out = sparse::CsrToCoo<T>(dev_ctx_gpu, d_csr);
CheckResult<T, int64_t>(&dev_ctx_gpu,
cuda_sparse_out,
non_zero_data,
......@@ -491,7 +489,7 @@ void TestCooToCsr(const DDim& dense_dims,
paddle::memory::allocation::AllocatorFacade::Instance()
.GetAllocator(phi::CPUPlace())
.get());
auto cpu_sparse_out = sparse::SparseCooToCsr<T>(dev_ctx_cpu, coo);
auto cpu_sparse_out = sparse::CooToCsr<T>(dev_ctx_cpu, coo);
CheckCsrResult<T, int64_t>(&dev_ctx_cpu,
cpu_sparse_out,
non_zero_data,
......@@ -525,7 +523,7 @@ void TestCooToCsr(const DDim& dense_dims,
phi::Copy(dev_ctx_gpu, indices, phi::GPUPlace(), true, &d_indices);
phi::Copy(dev_ctx_gpu, values, phi::GPUPlace(), true, &d_values);
phi::SparseCooTensor d_coo(d_indices, d_values, dense_dims);
auto cuda_sparse_out = sparse::SparseCooToCsr<T>(dev_ctx_gpu, d_coo);
auto cuda_sparse_out = sparse::CooToCsr<T>(dev_ctx_gpu, d_coo);
CheckCsrResult<T, int64_t>(&dev_ctx_gpu,
cuda_sparse_out,
non_zero_data,
......@@ -591,7 +589,7 @@ void TestDenseToSparseCsr(const DenseTensor& dense_x,
.get());
// 1. test cpu
auto cpu_sparse_out = sparse::DenseToSparseCsr<T>(dev_ctx_cpu, dense_x);
auto cpu_sparse_out = sparse::DenseToCsr<T>(dev_ctx_cpu, dense_x);
CheckCsrResult<T, int64_t>(&dev_ctx_cpu,
cpu_sparse_out,
non_zero_data,
......@@ -624,7 +622,7 @@ void TestDenseToSparseCsr(const DenseTensor& dense_x,
.get());
dev_ctx_gpu.PartialInitWithAllocator();
phi::Copy(dev_ctx_gpu, dense_x, phi::GPUPlace(), true, &d_dense_x);
auto sparse_out = sparse::DenseToSparseCsr<T>(dev_ctx_gpu, d_dense_x);
auto sparse_out = sparse::DenseToCsr<T>(dev_ctx_gpu, d_dense_x);
CheckCsrResult<T, int64_t>(&dev_ctx_gpu,
sparse_out,
......@@ -731,7 +729,7 @@ void TestSparseCooToDense(const DDim& dense_dims,
SparseCooTensor coo(dense_indices, dense_elements, dense_dims);
DenseTensor dense_out = sparse::SparseCooToDense<T>(dev_ctx_cpu, coo);
DenseTensor dense_out = sparse::CooToDense<T>(dev_ctx_cpu, coo);
int cmp = memcmp(
&dense_data[0], dense_out.data<T>(), sizeof(T) * dense_data.size());
......@@ -763,7 +761,7 @@ void TestSparseCooToDense(const DDim& dense_dims,
phi::Copy(
dev_ctx_gpu, dense_elements, phi::GPUPlace(), true, &d_dense_elements);
SparseCooTensor coo_cuda(d_dense_indices, d_dense_elements, dense_dims);
auto dense_out_cuda = sparse::SparseCooToDense<T>(dev_ctx_gpu, coo_cuda);
auto dense_out_cuda = sparse::CooToDense<T>(dev_ctx_gpu, coo_cuda);
DenseTensor h_dense_out(alloc.get(),
DenseTensorMeta(dense_out_cuda.dtype(),
......@@ -878,7 +876,7 @@ void TestSparseCsrToDense(const DDim& dense_dims,
paddle::memory::allocation::AllocatorFacade::Instance()
.GetAllocator(phi::CPUPlace())
.get());
DenseTensor cpu_sparse_out = sparse::SparseCsrToDense<T>(dev_ctx_cpu, csr);
DenseTensor cpu_sparse_out = sparse::CsrToDense<T>(dev_ctx_cpu, csr);
int cmp_cpu = memcmp(cpu_sparse_out.data<T>(),
dense_data.data(),
sizeof(T) * dense_data.size());
......@@ -911,7 +909,7 @@ void TestSparseCsrToDense(const DDim& dense_dims,
phi::Copy(dev_ctx_gpu, cols, phi::GPUPlace(), true, &d_cols);
phi::Copy(dev_ctx_gpu, values, phi::GPUPlace(), true, &d_values);
phi::SparseCsrTensor d_csr(d_crows, d_cols, d_values, dense_dims);
auto cuda_sparse_out = sparse::SparseCsrToDense<T>(dev_ctx_gpu, d_csr);
auto cuda_sparse_out = sparse::CsrToDense<T>(dev_ctx_gpu, d_csr);
phi::DenseTensor h_out(alloc.get(), cpu_sparse_out.meta());
phi::Copy(dev_ctx_gpu, cuda_sparse_out, phi::CPUPlace(), true, &h_out);
int cmp_cuda =
......
python/paddle/fluid/dygraph/varbase_patch_methods.py
浏览文件 @ 36739748
......@@ -923,12 +923,7 @@ def monkey_patch_varbase():
print(sparse_x.values())
#[1, 2, 3, 4, 5]
"""
if self.is_sparse_coo() or self.is_sparse_csr():
return _C_ops.sparse_values(self)
else:
raise ValueError(
"only SparseCooTensor and SparseCsrTensor have method values")
return _C_ops.sparse_values(self)
@framework.dygraph_only
def to_dense(self):
......@@ -956,12 +951,7 @@ def monkey_patch_varbase():
# [4., 5., 0., 0.]]
"""
if self.is_sparse_coo():
return _C_ops.sparse_coo_to_dense(self)
elif self.is_sparse_csr():
return _C_ops.sparse_to_dense(self)
else:
return self
return _C_ops.sparse_to_dense(self)
@framework.dygraph_only
def to_sparse_coo(self, sparse_dim):
......@@ -987,16 +977,7 @@ def monkey_patch_varbase():
#values=[1., 2., 3., 4.]
"""
if self.is_sparse_csr():
return _C_ops.sparse_to_sparse_coo(self, sparse_dim)
elif self.is_sparse_coo():
return self
elif self.is_selected_rows():
raise ValueError(
"SelectedRows does not support to_sparse_coo method")
else:
#is dense tensor
return _C_ops.sparse_dense_to_coo(self, sparse_dim)
return _C_ops.sparse_to_sparse_coo(self, sparse_dim)
if framework._in_eager_mode_ and not hasattr(core, "eager"):
return
......
python/paddle/incubate/sparse/creation.py
浏览文件 @ 36739748
......@@ -166,7 +166,7 @@ def sparse_coo_tensor(indices,
"the number of dimensions(len(shape) must be sparse_dim({}) + dense_dim({}), but get {}"
.format(sparse_dim, dense_dim, len(shape)))
return _C_ops.sparse_create_sparse_coo_tensor(values, indices, shape)
return _C_ops.sparse_sparse_coo_tensor(values, indices, shape)
#TODO: need to support shape is None
......
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