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未验证 提交 682acd22 编写于 作者: zhouweiwei2014's avatar zhouweiwei2014 提交者： GitHub
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[Sparse]add sparse unary api(sin/tan/pow/neg/log1p/square/cast...) (#44022)

上级 51e2933d
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Showing with 2036 addition and 846 deletion
paddle/fluid/pybind/eager_method.cc
浏览文件 @ 682acd22
......@@ -1473,6 +1473,27 @@ static PyObject* tensor_method_get_map_tensor(TensorObject* self,
EAGER_CATCH_AND_THROW_RETURN_NULL
}
static PyObject* tensor_method_get_non_zero_nums(TensorObject* self,
PyObject* args,
PyObject* kwargs) {
EAGER_TRY
PADDLE_ENFORCE(
self->tensor.is_sparse_coo_tensor() ||
self->tensor.is_sparse_csr_tensor(),
paddle::platform::errors::Fatal("this method is only effective for "
"SparseCooTensor or SparseCsrTensor"));
if (self->tensor.is_sparse_coo_tensor()) {
auto sparse_coo_tensor =
std::dynamic_pointer_cast<phi::SparseCooTensor>(self->tensor.impl());
return ToPyObject(sparse_coo_tensor->nnz());
} else {
auto sparse_csr_tensor =
std::dynamic_pointer_cast<phi::SparseCsrTensor>(self->tensor.impl());
return ToPyObject(sparse_csr_tensor->nnz());
}
EAGER_CATCH_AND_THROW_RETURN_NULL
}
static PyObject* tensor_method_get_non_zero_indices(TensorObject* self,
PyObject* args,
PyObject* kwargs) {
......@@ -1962,6 +1983,10 @@ PyMethodDef variable_methods[] = {
METH_VARARGS | METH_KEYWORDS,
NULL},
/***the method of sparse tensor****/
{"nnz",
(PyCFunction)(void (*)(void))tensor_method_get_non_zero_nums,
METH_VARARGS | METH_KEYWORDS,
NULL},
{"indices",
(PyCFunction)(void (*)(void))tensor_method_get_non_zero_indices,
METH_VARARGS | METH_KEYWORDS,
......
paddle/phi/api/yaml/generator/sparse_bw_api_gen.py
浏览文件 @ 682acd22
......@@ -109,6 +109,7 @@ def source_include(header_file_path):
#include "glog/logging.h"
#include "paddle/phi/api/include/sparse_api.h"
#include "paddle/phi/api/lib/api_gen_utils.h"
#include "paddle/phi/api/lib/kernel_dispatch.h"
#include "paddle/phi/api/lib/sparse_api_custom_impl.h"
......
paddle/phi/api/yaml/sparse_api.yaml
浏览文件 @ 682acd22
- api : abs
args : (Tensor x)
output : Tensor(out)
kernel :
func : abs_coo{sparse_coo -> sparse_coo},
abs_csr{sparse_csr -> sparse_csr}
layout : x
backward : abs_grad
- api : acos
args : (Tensor x)
output : Tensor(out)
kernel :
func : acos_coo{sparse_coo -> sparse_coo},
acos_csr{sparse_csr -> sparse_csr}
layout : x
backward : acos_grad
- api : acosh
args : (Tensor x)
output : Tensor(out)
kernel :
func : acosh_coo{sparse_coo -> sparse_coo},
acosh_csr{sparse_csr -> sparse_csr}
layout : x
backward : acosh_grad
- api : add
args : (Tensor x, Tensor y)
output : Tensor(out)
kernel :
func : add_coo_coo{sparse_coo -> sparse_coo},
add_csr_csr{sparse_csr -> sparse_csr}
func : add_coo_coo{sparse_coo, sparse_coo -> sparse_coo},
add_csr_csr{sparse_csr, sparse_csr -> sparse_csr}
layout : x
backward : add_grad
- api : asin
args : (Tensor x)
output : Tensor(out)
kernel :
func : asin_coo{sparse_coo -> sparse_coo},
asin_csr{sparse_csr -> sparse_csr}
layout : x
backward : asin_grad
- api : asinh
args : (Tensor x)
output : Tensor(out)
kernel :
func : asinh_coo{sparse_coo -> sparse_coo},
asinh_csr{sparse_csr -> sparse_csr}
layout : x
backward : asinh_grad
- api : atan
args : (Tensor x)
output : Tensor(out)
kernel :
func : atan_coo{sparse_coo -> sparse_coo},
atan_csr{sparse_csr -> sparse_csr}
layout : x
backward : atan_grad
- api : atanh
args : (Tensor x)
output : Tensor(out)
kernel :
func : atanh_coo{sparse_coo -> sparse_coo},
atanh_csr{sparse_csr -> sparse_csr}
layout : x
backward : atanh_grad
- api : cast
args : (Tensor x, DataType index_dtype=DataType::UNDEFINED, DataType value_dtype=DataType::UNDEFINED)
output : Tensor(out)
kernel :
func : cast_coo{sparse_coo -> sparse_coo},
cast_csr{sparse_csr -> sparse_csr}
layout : x
data_type : x
backward : cast_grad
- api : conv3d
args : (Tensor x, Tensor kernel, int[] paddings, int[] dilations, int[] strides, int groups, bool subm)
output : Tensor(out), Tensor(rulebook)
......@@ -41,38 +114,81 @@
args : (Tensor x, Tensor y)
output : Tensor(out)
kernel :
func : divide_coo_coo{sparse_coo -> sparse_coo},
divide_csr_csr{sparse_csr -> sparse_csr}
func : divide_coo_coo{sparse_coo, sparse_coo -> sparse_coo},
divide_csr_csr{sparse_csr, sparse_csr -> sparse_csr}
layout : x
backward : divide_grad
- api : divide_scalar
args : (Tensor x, float scalar)
output : Tensor(out)
kernel :
func : divide_coo_scalar{sparse_coo -> sparse_coo},
divide_csr_scalar{sparse_csr -> sparse_csr}
backward : divide_scalar_grad
- api : log1p
args : (Tensor x)
output : Tensor(out)
kernel :
func : log1p_coo{sparse_coo -> sparse_coo},
log1p_csr{sparse_csr -> sparse_csr}
layout : x
backward : log1p_grad
- api : multiply
args : (Tensor x, Tensor y)
output : Tensor(out)
kernel :
func : multiply_coo_coo{sparse_coo -> sparse_coo},
multiply_csr_csr{sparse_csr -> sparse_csr}
func : multiply_coo_coo{sparse_coo, sparse_coo -> sparse_coo},
multiply_csr_csr{sparse_csr, sparse_csr -> sparse_csr}
layout : x
backward : multiply_grad
- api : pow
args : (Tensor x, float factor)
output : Tensor(out)
kernel :
func : pow_coo{sparse_coo -> sparse_coo},
pow_csr{sparse_csr -> sparse_csr}
layout : x
backward : pow_grad
- api : relu
args : (Tensor x)
output : Tensor(out)
kernel :
func : sparse_coo_relu{sparse_coo -> sparse_coo},
sparse_csr_relu{sparse_csr -> sparse_csr}
func : relu_coo{sparse_coo -> sparse_coo},
relu_csr{sparse_csr -> sparse_csr}
layout : x
backward : relu_grad
- api : scale
args : (Tensor x, float scale, float bias, bool bias_after_scale)
output : Tensor(out)
kernel :
func : scale_coo{sparse_coo -> sparse_coo},
scale_csr{sparse_csr -> sparse_csr}
backward : scale_grad
- api : sin
args : (Tensor x)
output : Tensor(out@SparseCooTensor)
output : Tensor(out)
kernel :
func : sparse_coo_sin {sparse_coo -> sparse_coo},
sparse_csr_sin {sparse_csr -> sparse_csr}
func : sin_coo{sparse_coo -> sparse_coo},
sin_csr{sparse_csr -> sparse_csr}
layout : x
backward : sin_grad
- api : sinh
args : (Tensor x)
output : Tensor(out)
kernel :
func : sinh_coo{sparse_coo -> sparse_coo},
sinh_csr{sparse_csr -> sparse_csr}
layout : x
backward : sinh_grad
- api : softmax
args : (Tensor x, int axis=-1)
output : Tensor(out)
......@@ -85,26 +201,44 @@
args : (Tensor x)
output : Tensor(out)
kernel :
func : sparse_coo_sqrt{sparse_coo -> sparse_coo},
sparse_csr_sqrt{sparse_csr -> sparse_csr}
func : sqrt_coo{sparse_coo -> sparse_coo},
sqrt_csr{sparse_csr -> sparse_csr}
layout : x
backward : sqrt_grad
- api : square
args : (Tensor x)
output : Tensor(out)
kernel :
func : square_coo{sparse_coo -> sparse_coo},
square_csr{sparse_csr -> sparse_csr}
layout : x
backward : square_grad
- api : subtract
args : (Tensor x, Tensor y)
output : Tensor(out)
kernel :
func : subtract_coo_coo{sparse_coo -> sparse_coo},
subtract_csr_csr{sparse_csr -> sparse_csr}
func : subtract_coo_coo{sparse_coo, sparse_coo -> sparse_coo},
subtract_csr_csr{sparse_csr, sparse_csr -> sparse_csr}
layout : x
backward : subtract_grad
- api : tan
args : (Tensor x)
output : Tensor(out)
kernel :
func : tan_coo{sparse_coo -> sparse_coo},
tan_csr{sparse_csr -> sparse_csr}
layout : x
backward : tan_grad
- api : tanh
args : (Tensor x)
output : Tensor(out)
kernel :
func : sparse_coo_tanh{sparse_coo -> sparse_coo},
sparse_csr_tanh{sparse_csr -> sparse_csr}
func : tanh_coo{sparse_coo -> sparse_coo},
tanh_csr{sparse_csr -> sparse_csr}
layout : x
backward : tanh_grad
......
paddle/phi/api/yaml/sparse_bw_api.yaml
浏览文件 @ 682acd22
- backward_api : abs_grad
forward : tanh(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : abs_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
abs_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : acos_grad
forward : acos(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : acos_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
acos_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : acosh_grad
forward : acosh(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : acosh_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
acosh_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : add_grad
forward : add(Tensor x, Tensor y) -> Tensor(out)
args : (Tensor x, Tensor y, Tensor out_grad)
......@@ -6,6 +30,47 @@
func : add_coo_coo_grad{sparse_coo, sparse_coo, sparse_coo -> sparse_coo, sparse_coo},
add_csr_csr_grad{sparse_csr, sparse_csr, sparse_csr -> sparse_csr, sparse_csr}
- backward_api : asin_grad
forward : asin(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : asin_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
asin_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : asinh_grad
forward : asinh(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : asinh_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
asinh_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : atan_grad
forward : atan(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : atan_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
atan_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : atanh_grad
forward : atanh(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : atanh_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
atanh_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : cast_grad
forward : cast(Tensor x, DataType index_dtype, DataType value_dtype) -> Tensor(out)
args : (Tensor x, Tensor out_grad, DataType value_dtype)
output : Tensor(x_grad)
kernel :
func : cast_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
cast_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
data_type : out_grad
- backward_api : conv3d_grad
forward : conv3d (Tensor x, Tensor kernel, int[] paddings, int[] dilations, int[] strides, int groups, bool subm) -> Tensor(out@SparseCooTensor), Tensor(rulebook@DenseTensor)
args : (Tensor x, Tensor kernel, Tensor rulebook, Tensor out_grad, int[] paddings, int[] dilations, int[] strides, int groups, bool subm)
......@@ -41,6 +106,20 @@
func : divide_coo_coo_grad{sparse_coo, sparse_coo, sparse_coo, sparse_coo -> sparse_coo, sparse_coo},
divide_csr_csr_grad{sparse_csr, sparse_csr, sparse_csr, sparse_csr -> sparse_csr, sparse_csr}
- backward_api : divide_scalar_grad
forward : divide_scalar (Tensor x, float scalar) -> Tensor(out)
args : (Tensor out_grad, float scalar)
output : Tensor(x_grad)
invoke : divide_scalar(out_grad, scalar)
- backward_api : log1p_grad
forward : log1p(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : log1p_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
log1p_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : masked_matmul_grad
forward : masked_matmul(Tensor x, Tensor y, Tensor mask) -> Tensor(out)
args : (Tensor x, Tensor y, Tensor out_grad)
......@@ -71,19 +150,43 @@
func : mv_coo_grad{sparse_coo, dense, dense -> sparse_coo, dense},
mv_csr_grad{sparse_csr, dense, dense -> sparse_csr, dense}
- backward_api : pow_grad
forward : pow(Tensor x, float factor) -> Tensor(out)
args : (Tensor x, Tensor out_grad, float factor)
output : Tensor(x_grad)
kernel :
func : pow_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
pow_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : relu_grad
forward : relu(Tensor x) -> Tensor(out)
args : (Tensor out, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : sparse_coo_relu_grad {sparse_coo, sparse_coo -> sparse_coo}
func : relu_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
relu_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : scale_grad
forward : scale(Tensor x, float scale, float bias, bool bias_after_scale) -> Tensor(out)
args : (Tensor out_grad, float scale)
output : Tensor(x_grad)
invoke : scale(out_grad, scale, 0.0, true)
- backward_api : sin_grad
forward : sin(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : sparse_coo_sin_grad {sparse_coo, sparse_coo -> sparse_coo}
func : sin_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
sin_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : sinh_grad
forward : sinh(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : sinh_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
sinh_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : softmax_grad
forward : softmax(Tensor x, int axis=-1) -> Tensor(out)
......@@ -104,7 +207,16 @@
args : (Tensor out, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : sparse_coo_sqrt_grad {sparse_coo, sparse_coo -> sparse_coo}
func : sqrt_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
sqrt_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : square_grad
forward : square(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : square_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
square_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : subtract_grad
forward : subtract(Tensor x, Tensor y) -> Tensor(out)
......@@ -114,12 +226,21 @@
func : subtract_coo_coo_grad{sparse_coo, sparse_coo, sparse_coo -> sparse_coo, sparse_coo},
subtract_csr_csr_grad{sparse_csr, sparse_csr, sparse_csr -> sparse_csr, sparse_csr}
- backward_api : tan_grad
forward : tan(Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : tan_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
tan_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : tanh_grad
forward : tanh(Tensor x) -> Tensor(out)
args : (Tensor out, Tensor out_grad)
output : Tensor(x_grad)
kernel :
func : sparse_coo_tanh_grad {sparse_coo, sparse_coo -> sparse_coo}
func : tanh_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
tanh_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
- backward_api : values_grad
forward : coo_values(Tensor x) -> Tensor(out)
......
paddle/phi/kernels/activation_grad_kernel.h
浏览文件 @ 682acd22
......@@ -212,12 +212,17 @@ DECLARE_ACTIVATION_GRAD_KERNEL_DEPX(Acosh);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPX(Atanh);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPX(TanhShrink);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPX(Silu);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPX(Square);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPX(LogSigmoid);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPX(Log);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPX(Log2);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPX(Log10);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPX(Log1p);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPOUT(Exp);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPOUT(Expm1);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPOUT(Reciprocal);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPOUT(Rsqrt);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPOUT(Relu);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPOUT(Tanh);
DECLARE_ACTIVATION_GRAD_KERNEL_DEPOUT(Sigmoid);
......@@ -233,9 +238,12 @@ DECLARE_ACT_GRAD_KERNEL_WITH_ONE_ATTRS_DEPX(SoftShrink, lambda);
DECLARE_ACT_GRAD_KERNEL_WITH_ONE_ATTRS_DEPX(HardShrink, threshold);
DECLARE_ACT_GRAD_KERNEL_WITH_ONE_ATTRS_DEPX(Swish, beta);
DECLARE_ACT_GRAD_KERNEL_WITH_ONE_ATTRS_DEPX(Logit, eps);
DECLARE_ACT_GRAD_KERNEL_WITH_ONE_ATTRS_DEPX(Mish, threshold);
DECLARE_ACT_GRAD_KERNEL_WITH_ONE_ATTRS_DEPX(Celu, alpha);
DECLARE_ACT_GRAD_KERNEL_WITH_TWO_ATTRS_DEPX(BRelu, t_min, t_max);
DECLARE_ACT_GRAD_KERNEL_WITH_TWO_ATTRS_DEPX(STanh, scale_a, scale_b);
DECLARE_ACT_GRAD_KERNEL_WITH_TWO_ATTRS_DEPX(Softplus, beta, threshold);
DECLARE_ACT_GRAD_KERNEL_WITH_TWO_ATTRS_DEPOUT(HardSigmoid, slope, offset);
......
paddle/phi/kernels/activation_kernel.h
浏览文件 @ 682acd22
......@@ -40,12 +40,12 @@ namespace phi {
float attr2, \
DenseTensor* out);
DECLARE_ACTIVATION_KERNEL(Sin)
DECLARE_ACTIVATION_KERNEL(Cos)
DECLARE_ACTIVATION_KERNEL(Tan)
DECLARE_ACTIVATION_KERNEL(Acos)
DECLARE_ACTIVATION_KERNEL(Sin)
DECLARE_ACTIVATION_KERNEL(Asin)
DECLARE_ACTIVATION_KERNEL(Atan)
DECLARE_ACTIVATION_KERNEL(Acos)
DECLARE_ACTIVATION_KERNEL(Sinh)
DECLARE_ACTIVATION_KERNEL(Cosh)
DECLARE_ACTIVATION_KERNEL(Asinh)
......@@ -53,15 +53,14 @@ DECLARE_ACTIVATION_KERNEL(Acosh)
DECLARE_ACTIVATION_KERNEL(Atanh)
DECLARE_ACTIVATION_KERNEL(Relu)
DECLARE_ACTIVATION_KERNEL(Tanh)
DECLARE_ACTIVATION_KERNEL(TanhShrink)
DECLARE_ACTIVATION_KERNEL(Silu)
DECLARE_ACTIVATION_KERNEL(Exp)
DECLARE_ACTIVATION_KERNEL(Expm1)
DECLARE_ACTIVATION_KERNEL(Reciprocal)
DECLARE_ACTIVATION_KERNEL(Square)
DECLARE_ACTIVATION_KERNEL(Sqrt)
DECLARE_ACTIVATION_KERNEL(Rsqrt)
DECLARE_ACTIVATION_KERNEL(TanhShrink)
DECLARE_ACTIVATION_KERNEL(Silu)
DECLARE_ACTIVATION_KERNEL(Sigmoid)
DECLARE_ACTIVATION_KERNEL(LogSigmoid)
DECLARE_ACTIVATION_KERNEL(Log)
......@@ -77,28 +76,18 @@ DECLARE_ACTIVATION_KERNEL_WITH_ONE_ATTRS(LeakyRelu, alpha)
DECLARE_ACTIVATION_KERNEL_WITH_ONE_ATTRS(ThresholdedRelu, threshold)
DECLARE_ACTIVATION_KERNEL_WITH_ONE_ATTRS(Relu6, threshold)
DECLARE_ACTIVATION_KERNEL_WITH_ONE_ATTRS(SoftShrink, lambda)
DECLARE_ACTIVATION_KERNEL_WITH_ONE_ATTRS(Mish, threshold)
DECLARE_ACTIVATION_KERNEL_WITH_ONE_ATTRS(HardShrink, threshold)
DECLARE_ACTIVATION_KERNEL_WITH_ONE_ATTRS(SoftShrink, lambda)
DECLARE_ACTIVATION_KERNEL_WITH_ONE_ATTRS(Elu, alpha)
DECLARE_ACTIVATION_KERNEL_WITH_ONE_ATTRS(Swish, beta)
DECLARE_ACTIVATION_KERNEL_WITH_ONE_ATTRS(Celu, alpha)
DECLARE_ACTIVATION_KERNEL_WITH_ONE_ATTRS(Logit, eps)
DECLARE_ACTIVATION_KERNEL_WITH_TWO_ATTRS(BRelu, t_min, t_max)
DECLARE_ACTIVATION_KERNEL_WITH_TWO_ATTRS(STanh, scale_a, scale_b)
DECLARE_ACTIVATION_KERNEL_WITH_TWO_ATTRS(HardSigmoid, slope, offset)
DECLARE_ACTIVATION_KERNEL_WITH_TWO_ATTRS(Softplus, beta, threshold)
template <typename T, typename Context>
void LogitKernel(const Context& dev_ctx,
const DenseTensor& x,
float eps,
DenseTensor* out);
template <typename T, typename Context>
void MishKernel(const Context& dev_ctx,
const DenseTensor& x,
float threshold,
DenseTensor* out);
DECLARE_ACTIVATION_KERNEL_WITH_TWO_ATTRS(HardSigmoid, slope, offset)
template <typename T, typename Context>
void HardSwishKernel(const Context& dev_ctx,
......
paddle/phi/kernels/funcs/eigen/eigen_function.h
浏览文件 @ 682acd22
......@@ -118,6 +118,18 @@ struct EigenSub {
const InType& right);
};
template <typename EigenDevice, typename T>
struct EigenDiv {
using InType = Eigen::TensorMap<
Eigen::Tensor<const T, 1, Eigen::RowMajor, Eigen::DenseIndex>>;
using OutType =
Eigen::TensorMap<Eigen::Tensor<T, 1, Eigen::RowMajor, Eigen::DenseIndex>>;
static void Eval(const EigenDevice& dev,
OutType out,
const InType& in,
const T value);
};
template <typename EigenDevice, typename T, int Rank>
struct EigenSlice {
using Array = Eigen::DSizes<Eigen::DenseIndex, Rank>;
......
paddle/phi/kernels/funcs/eigen/elementwise.cc
浏览文件 @ 682acd22
......@@ -55,5 +55,22 @@ struct EigenSub<Eigen::DefaultDevice, T> {
template struct EigenSub<Eigen::DefaultDevice, float>;
template <typename T>
struct EigenDiv<Eigen::DefaultDevice, T> {
using InType = Eigen::TensorMap<
Eigen::Tensor<const T, 1, Eigen::RowMajor, Eigen::DenseIndex>>;
using OutType =
Eigen::TensorMap<Eigen::Tensor<T, 1, Eigen::RowMajor, Eigen::DenseIndex>>;
static void Eval(const Eigen::DefaultDevice& dev,
OutType out,
const InType& in,
const T value) {
out.device(dev) = in / value;
}
};
template struct EigenDiv<Eigen::DefaultDevice, float>;
template struct EigenDiv<Eigen::DefaultDevice, double>;
} // namespace funcs
} // namespace phi
paddle/phi/kernels/funcs/eigen/elementwise.cu
浏览文件 @ 682acd22
......@@ -55,5 +55,22 @@ struct EigenSub<Eigen::GpuDevice, T> {
template struct EigenSub<Eigen::GpuDevice, float>;
template <typename T>
struct EigenDiv<Eigen::GpuDevice, T> {
using InType = Eigen::TensorMap<
Eigen::Tensor<const T, 1, Eigen::RowMajor, Eigen::DenseIndex>>;
using OutType =
Eigen::TensorMap<Eigen::Tensor<T, 1, Eigen::RowMajor, Eigen::DenseIndex>>;
static void Eval(const Eigen::GpuDevice& dev,
OutType out,
const InType& in,
const T value) {
out.device(dev) = in / value;
}
};
template struct EigenDiv<Eigen::GpuDevice, float>;
template struct EigenDiv<Eigen::GpuDevice, double>;
} // namespace funcs
} // namespace phi
paddle/phi/kernels/sparse/cpu/unary_grad_kernel.cc 0 → 100644
浏览文件 @ 682acd22
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// 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/unary_grad_kernel.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/sparse/impl/unary_grad_kernel_impl.h"
#define PD_REGISTER_SPARSE_UNARY_CPU_GRAD_KERNEL(name, prefix) \
PD_REGISTER_KERNEL(name##_coo_grad, \
CPU, \
ALL_LAYOUT, \
phi::sparse::prefix##CooGradKernel, \
float, \
double) { \
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO); \
} \
\
PD_REGISTER_KERNEL(name##_csr_grad, \
CPU, \
ALL_LAYOUT, \
phi::sparse::prefix##CsrGradKernel, \
float, \
double) { \
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR); \
}
PD_REGISTER_SPARSE_UNARY_CPU_GRAD_KERNEL(sin, Sin)
PD_REGISTER_SPARSE_UNARY_CPU_GRAD_KERNEL(tan, Tan)
PD_REGISTER_SPARSE_UNARY_CPU_GRAD_KERNEL(asin, Asin)
PD_REGISTER_SPARSE_UNARY_CPU_GRAD_KERNEL(atan, Atan)
PD_REGISTER_SPARSE_UNARY_CPU_GRAD_KERNEL(sinh, Sinh)
PD_REGISTER_SPARSE_UNARY_CPU_GRAD_KERNEL(tanh, Tanh)
PD_REGISTER_SPARSE_UNARY_CPU_GRAD_KERNEL(asinh, Asinh)
PD_REGISTER_SPARSE_UNARY_CPU_GRAD_KERNEL(atanh, Atanh)
PD_REGISTER_SPARSE_UNARY_CPU_GRAD_KERNEL(sqrt, Sqrt)
PD_REGISTER_SPARSE_UNARY_CPU_GRAD_KERNEL(square, Square)
PD_REGISTER_SPARSE_UNARY_CPU_GRAD_KERNEL(log1p, Log1p)
PD_REGISTER_SPARSE_UNARY_CPU_GRAD_KERNEL(relu, Relu)
PD_REGISTER_SPARSE_UNARY_CPU_GRAD_KERNEL(abs, Abs)
PD_REGISTER_SPARSE_UNARY_CPU_GRAD_KERNEL(pow, Pow)
PD_REGISTER_KERNEL(cast_coo_grad,
CPU,
ALL_LAYOUT,
phi::sparse::CastCooGradKernel,
float,
double,
int8_t,
uint8_t,
int16_t,
int,
int64_t,
bool) {}
PD_REGISTER_KERNEL(cast_csr_grad,
CPU,
ALL_LAYOUT,
phi::sparse::CastCsrGradKernel,
float,
double,
int8_t,
uint8_t,
int16_t,
int,
int64_t,
bool) {}
paddle/phi/kernels/sparse/cpu/unary_kernel.cc 0 → 100644
浏览文件 @ 682acd22
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// 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/unary_kernel.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/eigen/common.h"
#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
#include "paddle/phi/kernels/sparse/impl/unary_grad_kernel_impl.h"
#include "paddle/phi/kernels/sparse/impl/unary_kernel_impl.h"
namespace phi {
namespace sparse {
template <typename T, typename Context>
void DivCooScalarKernel(const Context& dev_ctx,
const SparseCooTensor& x,
float scalar,
SparseCooTensor* out) {
EmptyLikeCooKernel<T, Context>(dev_ctx, x, out);
auto eigen_out =
phi::EigenVector<T>::Flatten(*(out->mutable_non_zero_elements()));
auto eigen_x = phi::EigenVector<T>::Flatten(x.non_zero_elements());
auto& dev = *dev_ctx.eigen_device();
phi::funcs::EigenDiv<std::decay_t<decltype(dev)>, T>::Eval(
dev, eigen_out, eigen_x, static_cast<T>(scalar));
}
template <typename T, typename Context>
void DivCsrScalarKernel(const Context& dev_ctx,
const SparseCsrTensor& x,
float scalar,
SparseCsrTensor* out) {
EmptyLikeCsrKernel<T, Context>(dev_ctx, x, out);
auto eigen_out =
phi::EigenVector<T>::Flatten(*(out->mutable_non_zero_elements()));
auto eigen_x = phi::EigenVector<T>::Flatten(x.non_zero_elements());
auto& dev = *dev_ctx.eigen_device();
phi::funcs::EigenDiv<std::decay_t<decltype(dev)>, T>::Eval(
dev, eigen_out, eigen_x, static_cast<T>(scalar));
}
} // namespace sparse
} // namespace phi
#define PD_REGISTER_SPARSE_UNARY_CPU_KERNEL(name, prefix) \
PD_REGISTER_KERNEL(name##_coo, \
CPU, \
ALL_LAYOUT, \
phi::sparse::prefix##CooKernel, \
float, \
double) { \
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO); \
} \
\
PD_REGISTER_KERNEL(name##_csr, \
CPU, \
ALL_LAYOUT, \
phi::sparse::prefix##CsrKernel, \
float, \
double) { \
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR); \
}
PD_REGISTER_SPARSE_UNARY_CPU_KERNEL(sin, Sin)
PD_REGISTER_SPARSE_UNARY_CPU_KERNEL(tan, Tan)
PD_REGISTER_SPARSE_UNARY_CPU_KERNEL(asin, Asin)
PD_REGISTER_SPARSE_UNARY_CPU_KERNEL(atan, Atan)
PD_REGISTER_SPARSE_UNARY_CPU_KERNEL(sinh, Sinh)
PD_REGISTER_SPARSE_UNARY_CPU_KERNEL(tanh, Tanh)
PD_REGISTER_SPARSE_UNARY_CPU_KERNEL(asinh, Asinh)
PD_REGISTER_SPARSE_UNARY_CPU_KERNEL(atanh, Atanh)
PD_REGISTER_SPARSE_UNARY_CPU_KERNEL(sqrt, Sqrt)
PD_REGISTER_SPARSE_UNARY_CPU_KERNEL(square, Square)
PD_REGISTER_SPARSE_UNARY_CPU_KERNEL(log1p, Log1p)
PD_REGISTER_SPARSE_UNARY_CPU_KERNEL(relu, Relu)
PD_REGISTER_SPARSE_UNARY_CPU_KERNEL(abs, Abs)
PD_REGISTER_SPARSE_UNARY_CPU_KERNEL(pow, Pow)
PD_REGISTER_SPARSE_UNARY_CPU_KERNEL(scale, Scale)
PD_REGISTER_KERNEL(divide_coo_scalar,
CPU,
ALL_LAYOUT,
phi::sparse::DivCooScalarKernel,
float,
double) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
}
PD_REGISTER_KERNEL(divide_csr_scalar,
CPU,
ALL_LAYOUT,
phi::sparse::DivCsrScalarKernel,
float,
double) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR);
}
PD_REGISTER_KERNEL(cast_coo,
CPU,
ALL_LAYOUT,
phi::sparse::CastCooKernel,
float,
double,
int8_t,
uint8_t,
int16_t,
int,
int64_t,
bool) {}
PD_REGISTER_KERNEL(cast_csr,
CPU,
ALL_LAYOUT,
phi::sparse::CastCsrKernel,
float,
double,
int8_t,
uint8_t,
int16_t,
int,
int64_t,
bool) {}
paddle/phi/kernels/sparse/gpu/unary_grad_kernel.cu 0 → 100644
浏览文件 @ 682acd22
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// 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/unary_grad_kernel.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/sparse/impl/unary_grad_kernel_impl.h"
#define PD_REGISTER_SPARSE_UNARY_GPU_GRAD_KERNEL(name, prefix) \
PD_REGISTER_KERNEL(name##_coo_grad, \
GPU, \
ALL_LAYOUT, \
phi::sparse::prefix##CooGradKernel, \
float, \
double) { \
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO); \
} \
\
PD_REGISTER_KERNEL(name##_csr_grad, \
GPU, \
ALL_LAYOUT, \
phi::sparse::prefix##CsrGradKernel, \
float, \
double) { \
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR); \
}
PD_REGISTER_SPARSE_UNARY_GPU_GRAD_KERNEL(sin, Sin)
PD_REGISTER_SPARSE_UNARY_GPU_GRAD_KERNEL(tan, Tan)
PD_REGISTER_SPARSE_UNARY_GPU_GRAD_KERNEL(asin, Asin)
PD_REGISTER_SPARSE_UNARY_GPU_GRAD_KERNEL(atan, Atan)
PD_REGISTER_SPARSE_UNARY_GPU_GRAD_KERNEL(sinh, Sinh)
PD_REGISTER_SPARSE_UNARY_GPU_GRAD_KERNEL(tanh, Tanh)
PD_REGISTER_SPARSE_UNARY_GPU_GRAD_KERNEL(asinh, Asinh)
PD_REGISTER_SPARSE_UNARY_GPU_GRAD_KERNEL(atanh, Atanh)
PD_REGISTER_SPARSE_UNARY_GPU_GRAD_KERNEL(sqrt, Sqrt)
PD_REGISTER_SPARSE_UNARY_GPU_GRAD_KERNEL(square, Square)
PD_REGISTER_SPARSE_UNARY_GPU_GRAD_KERNEL(log1p, Log1p)
PD_REGISTER_SPARSE_UNARY_GPU_GRAD_KERNEL(relu, Relu)
PD_REGISTER_SPARSE_UNARY_GPU_GRAD_KERNEL(abs, Abs)
PD_REGISTER_SPARSE_UNARY_GPU_GRAD_KERNEL(pow, Pow)
PD_REGISTER_KERNEL(cast_coo_grad,
GPU,
ALL_LAYOUT,
phi::sparse::CastCooGradKernel,
float,
double,
int8_t,
uint8_t,
int16_t,
int,
int64_t,
bool) {}
PD_REGISTER_KERNEL(cast_csr_grad,
GPU,
ALL_LAYOUT,
phi::sparse::CastCsrGradKernel,
float,
double,
int8_t,
uint8_t,
int16_t,
int,
int64_t,
bool) {}
paddle/phi/kernels/sparse/gpu/unary_kernel.cu 0 → 100644
浏览文件 @ 682acd22
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// 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/unary_kernel.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/elementwise_base.h"
#include "paddle/phi/kernels/sparse/impl/unary_kernel_impl.h"
namespace phi {
namespace sparse {
template <typename T>
struct DivScalarFunctor {
T value_;
explicit DivScalarFunctor(T value) : value_(value) {}
__device__ __forceinline__ T operator()(const T x) const {
return x / value_;
}
};
template <typename T, typename Context>
void DivCooScalarKernel(const Context& dev_ctx,
const SparseCooTensor& x,
float scalar,
SparseCooTensor* out) {
EmptyLikeCooKernel<T, Context>(dev_ctx, x, out);
std::vector<const DenseTensor*> ins = {&(x.non_zero_elements())};
std::vector<DenseTensor*> outs = {out->mutable_non_zero_elements()};
DivScalarFunctor<T> func(static_cast<T>(scalar));
funcs::ElementwiseKernel<T, DivScalarFunctor<T>>(dev_ctx, ins, &outs, func);
}
template <typename T, typename Context>
void DivCsrScalarKernel(const Context& dev_ctx,
const SparseCsrTensor& x,
float scalar,
SparseCsrTensor* out) {
EmptyLikeCsrKernel<T, Context>(dev_ctx, x, out);
std::vector<const DenseTensor*> ins = {&(x.non_zero_elements())};
std::vector<DenseTensor*> outs = {out->mutable_non_zero_elements()};
DivScalarFunctor<T> func(static_cast<T>(scalar));
funcs::ElementwiseKernel<T, DivScalarFunctor<T>>(dev_ctx, ins, &outs, func);
}
} // namespace sparse
} // namespace phi
#define PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(name, prefix) \
PD_REGISTER_KERNEL(name##_coo, \
GPU, \
ALL_LAYOUT, \
phi::sparse::prefix##CooKernel, \
float, \
double) { \
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO); \
} \
\
PD_REGISTER_KERNEL(name##_csr, \
GPU, \
ALL_LAYOUT, \
phi::sparse::prefix##CsrKernel, \
float, \
double) { \
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR); \
}
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(sin, Sin)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(tan, Tan)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(asin, Asin)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(atan, Atan)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(sinh, Sinh)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(tanh, Tanh)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(asinh, Asinh)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(atanh, Atanh)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(sqrt, Sqrt)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(square, Square)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(log1p, Log1p)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(relu, Relu)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(abs, Abs)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(pow, Pow)
PD_REGISTER_SPARSE_UNARY_GPU_KERNEL(scale, Scale)
PD_REGISTER_KERNEL(divide_coo_scalar,
GPU,
ALL_LAYOUT,
phi::sparse::DivCooScalarKernel,
float,
double) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
}
PD_REGISTER_KERNEL(divide_csr_scalar,
GPU,
ALL_LAYOUT,
phi::sparse::DivCsrScalarKernel,
float,
double) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR);
}
PD_REGISTER_KERNEL(cast_coo,
GPU,
ALL_LAYOUT,
phi::sparse::CastCooKernel,
float,
double,
int8_t,
uint8_t,
int16_t,
int,
int64_t,
bool) {}
PD_REGISTER_KERNEL(cast_csr,
GPU,
ALL_LAYOUT,
phi::sparse::CastCsrKernel,
float,
double,
int8_t,
uint8_t,
int16_t,
int,
int64_t,
bool) {}
paddle/phi/kernels/sparse/impl/unary_grad_kernel_impl.h 0 → 100644
浏览文件 @ 682acd22
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// 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.
#pragma once
#include "paddle/phi/core/sparse_coo_tensor.h"
#include "paddle/phi/core/sparse_csr_tensor.h"
#include "paddle/phi/core/tensor_utils.h"
#include "paddle/phi/kernels/abs_grad_kernel.h"
#include "paddle/phi/kernels/activation_grad_kernel.h"
#include "paddle/phi/kernels/cast_kernel.h"
#include "paddle/phi/kernels/sparse/empty_kernel.h"
#include "paddle/phi/kernels/sparse/impl/unary_kernel_impl.h"
namespace phi {
namespace sparse {
#define DEFINE_SPARSE_UNARY_GRAD_KERNEL(prefix) \
template <typename T, typename Context> \
void prefix##CooGradKernel(const Context& dev_ctx, \
const SparseCooTensor& x_or_out, \
const SparseCooTensor& dout, \
SparseCooTensor* dx) { \
EmptyLikeCooKernel<T, Context>(dev_ctx, x_or_out, dx); \
phi::prefix##GradKernel<T, Context>(dev_ctx, \
x_or_out.non_zero_elements(), \
dout.non_zero_elements(), \
dx->mutable_non_zero_elements()); \
} \
\
template <typename T, typename Context> \
void prefix##CsrGradKernel(const Context& dev_ctx, \
const SparseCsrTensor& x_or_out, \
const SparseCsrTensor& dout, \
SparseCsrTensor* dx) { \
EmptyLikeCsrKernel<T, Context>(dev_ctx, x_or_out, dx); \
phi::prefix##GradKernel<T, Context>(dev_ctx, \
x_or_out.non_zero_elements(), \
dout.non_zero_elements(), \
dx->mutable_non_zero_elements()); \
}
#define DEFINE_SPARSE_UNARY_GRAD_KERNEL_WITH_ONE_ATTR(prefix, attr) \
template <typename T, typename Context> \
void prefix##CooGradKernel(const Context& dev_ctx, \
const SparseCooTensor& x_or_out, \
const SparseCooTensor& dout, \
float attr, \
SparseCooTensor* dx) { \
EmptyLikeCooKernel<T, Context>(dev_ctx, x_or_out, dx); \
phi::prefix##GradKernel<T, Context>(dev_ctx, \
x_or_out.non_zero_elements(), \
dout.non_zero_elements(), \
attr, \
dx->mutable_non_zero_elements()); \
} \
\
template <typename T, typename Context> \
void prefix##CsrGradKernel(const Context& dev_ctx, \
const SparseCsrTensor& x_or_out, \
const SparseCsrTensor& dout, \
float attr, \
SparseCsrTensor* dx) { \
EmptyLikeCsrKernel<T, Context>(dev_ctx, x_or_out, dx); \
phi::prefix##GradKernel<T, Context>(dev_ctx, \
x_or_out.non_zero_elements(), \
dout.non_zero_elements(), \
attr, \
dx->mutable_non_zero_elements()); \
}
DEFINE_SPARSE_UNARY_GRAD_KERNEL(Sin)
DEFINE_SPARSE_UNARY_GRAD_KERNEL(Tan)
DEFINE_SPARSE_UNARY_GRAD_KERNEL(Asin)
DEFINE_SPARSE_UNARY_GRAD_KERNEL(Atan)
DEFINE_SPARSE_UNARY_GRAD_KERNEL(Sinh)
DEFINE_SPARSE_UNARY_GRAD_KERNEL(Tanh)
DEFINE_SPARSE_UNARY_GRAD_KERNEL(Asinh)
DEFINE_SPARSE_UNARY_GRAD_KERNEL(Atanh)
DEFINE_SPARSE_UNARY_GRAD_KERNEL(Sqrt)
DEFINE_SPARSE_UNARY_GRAD_KERNEL(Square)
DEFINE_SPARSE_UNARY_GRAD_KERNEL(Log1p)
DEFINE_SPARSE_UNARY_GRAD_KERNEL(Relu)
DEFINE_SPARSE_UNARY_GRAD_KERNEL(Abs)
DEFINE_SPARSE_UNARY_GRAD_KERNEL_WITH_ONE_ATTR(Pow, factor)
template <typename T, typename Context>
void CastCooGradKernel(const Context& dev_ctx,
const SparseCooTensor& x,
const SparseCooTensor& dout,
DataType value_dtype,
SparseCooTensor* dx) {
EmptyLikeCooKernel<T, Context>(dev_ctx, x, dx);
if (value_dtype == DataType::UNDEFINED) {
phi::Copy(dev_ctx,
dout.non_zero_elements(),
dev_ctx.GetPlace(),
false,
dx->mutable_non_zero_elements());
} else {
phi::CastKernel<T, Context>(dev_ctx,
dout.non_zero_elements(),
x.non_zero_elements().dtype(),
dx->mutable_non_zero_elements());
}
}
template <typename T, typename Context>
void CastCsrGradKernel(const Context& dev_ctx,
const SparseCsrTensor& x,
const SparseCsrTensor& dout,
DataType value_dtype,
SparseCsrTensor* dx) {
EmptyLikeCsrKernel<T, Context>(dev_ctx, x, dx);
if (value_dtype == DataType::UNDEFINED) {
phi::Copy(dev_ctx,
dout.non_zero_elements(),
dev_ctx.GetPlace(),
false,
dx->mutable_non_zero_elements());
} else {
phi::CastKernel<T, Context>(dev_ctx,
dout.non_zero_elements(),
x.non_zero_elements().dtype(),
dx->mutable_non_zero_elements());
}
}
} // namespace sparse
} // namespace phi
paddle/phi/kernels/sparse/impl/unary_kernel_impl.h 0 → 100644
浏览文件 @ 682acd22
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// 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.
#pragma once
#include "paddle/phi/core/meta_tensor.h"
#include "paddle/phi/core/sparse_coo_tensor.h"
#include "paddle/phi/core/sparse_csr_tensor.h"
#include "paddle/phi/core/tensor_utils.h"
#include "paddle/phi/core/visit_type.h"
#include "paddle/phi/kernels/abs_kernel.h"
#include "paddle/phi/kernels/activation_kernel.h"
#include "paddle/phi/kernels/cast_kernel.h"
#include "paddle/phi/kernels/scale_kernel.h"
#include "paddle/phi/kernels/sparse/empty_kernel.h"
#include "paddle/phi/kernels/trunc_kernel.h"
namespace phi {
namespace sparse {
#define DEFINE_SPARSE_UNARY_KERNEL(prefix) \
template <typename T, typename Context> \
void prefix##CooKernel(const Context& dev_ctx, \
const SparseCooTensor& x, \
SparseCooTensor* out) { \
EmptyLikeCooKernel<T, Context>(dev_ctx, x, out); \
phi::prefix##Kernel<T, Context>( \
dev_ctx, x.non_zero_elements(), out->mutable_non_zero_elements()); \
} \
\
template <typename T, typename Context> \
void prefix##CsrKernel(const Context& dev_ctx, \
const SparseCsrTensor& x, \
SparseCsrTensor* out) { \
EmptyLikeCsrKernel<T, Context>(dev_ctx, x, out); \
phi::prefix##Kernel<T, Context>( \
dev_ctx, x.non_zero_elements(), out->mutable_non_zero_elements()); \
}
#define DEFINE_SPARSE_UNARY_KERNEL_WITH_ONE_ATTR(prefix, attr) \
template <typename T, typename Context> \
void prefix##CooKernel(const Context& dev_ctx, \
const SparseCooTensor& x, \
float attr, \
SparseCooTensor* out) { \
EmptyLikeCooKernel<T, Context>(dev_ctx, x, out); \
phi::prefix##Kernel<T, Context>(dev_ctx, \
x.non_zero_elements(), \
attr, \
out->mutable_non_zero_elements()); \
} \
\
template <typename T, typename Context> \
void prefix##CsrKernel(const Context& dev_ctx, \
const SparseCsrTensor& x, \
float attr, \
SparseCsrTensor* out) { \
EmptyLikeCsrKernel<T, Context>(dev_ctx, x, out); \
phi::prefix##Kernel<T, Context>(dev_ctx, \
x.non_zero_elements(), \
attr, \
out->mutable_non_zero_elements()); \
}
DEFINE_SPARSE_UNARY_KERNEL(Sin)
DEFINE_SPARSE_UNARY_KERNEL(Tan)
DEFINE_SPARSE_UNARY_KERNEL(Asin)
DEFINE_SPARSE_UNARY_KERNEL(Atan)
DEFINE_SPARSE_UNARY_KERNEL(Sinh)
DEFINE_SPARSE_UNARY_KERNEL(Tanh)
DEFINE_SPARSE_UNARY_KERNEL(Asinh)
DEFINE_SPARSE_UNARY_KERNEL(Atanh)
DEFINE_SPARSE_UNARY_KERNEL(Sqrt)
DEFINE_SPARSE_UNARY_KERNEL(Square)
DEFINE_SPARSE_UNARY_KERNEL(Log1p)
DEFINE_SPARSE_UNARY_KERNEL(Relu)
DEFINE_SPARSE_UNARY_KERNEL(Abs)
DEFINE_SPARSE_UNARY_KERNEL_WITH_ONE_ATTR(Pow, factor)
template <typename T, typename Context>
void ScaleCooKernel(const Context& dev_ctx,
const SparseCooTensor& x,
float scale,
float bias,
bool bias_after_scale,
SparseCooTensor* out) {
EmptyLikeCooKernel<T, Context>(dev_ctx, x, out);
phi::ScaleKernel<T, Context>(dev_ctx,
x.non_zero_elements(),
scale,
bias,
bias_after_scale,
out->mutable_non_zero_elements());
}
template <typename T, typename Context>
void ScaleCsrKernel(const Context& dev_ctx,
const SparseCsrTensor& x,
float scale,
float bias,
bool bias_after_scale,
SparseCsrTensor* out) {
EmptyLikeCsrKernel<T, Context>(dev_ctx, x, out);
phi::ScaleKernel<T, Context>(dev_ctx,
x.non_zero_elements(),
scale,
bias,
bias_after_scale,
out->mutable_non_zero_elements());
}
template <typename T, typename Context>
void CastCooKernel(const Context& dev_ctx,
const SparseCooTensor& x,
DataType index_dtype,
DataType value_dtype,
SparseCooTensor* out) {
out->set_dims(x.dims());
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();
if (index_dtype == DataType::UNDEFINED) {
phi::Copy(dev_ctx, x_indices, dev_ctx.GetPlace(), false, out_indices);
} else {
phi::MetaTensor meta(out_indices);
meta.set_dims(x_indices.dims());
meta.set_dtype(index_dtype);
PD_VISIT_INTEGRAL_TYPES(x_indices.dtype(), "CastCooKernel", [&] {
phi::CastKernel<data_t, Context>(
dev_ctx, x_indices, index_dtype, out_indices);
});
}
if (value_dtype == DataType::UNDEFINED) {
phi::Copy(dev_ctx, x_values, dev_ctx.GetPlace(), false, out_values);
} else {
phi::MetaTensor meta(out_values);
meta.set_dims(x_values.dims());
meta.set_dtype(value_dtype);
phi::CastKernel<T, Context>(dev_ctx, x_values, value_dtype, out_values);
}
}
template <typename T, typename Context>
void CastCsrKernel(const Context& dev_ctx,
const SparseCsrTensor& x,
DataType index_dtype,
DataType value_dtype,
SparseCsrTensor* out) {
out->set_dims(x.dims());
const DenseTensor& x_crows = x.non_zero_crows();
const DenseTensor& x_cols = x.non_zero_cols();
const DenseTensor& x_values = x.non_zero_elements();
DenseTensor* out_crows = out->mutable_non_zero_crows();
DenseTensor* out_cols = out->mutable_non_zero_cols();
DenseTensor* out_values = out->mutable_non_zero_elements();
if (index_dtype == DataType::UNDEFINED) {
phi::Copy(dev_ctx, x_crows, dev_ctx.GetPlace(), false, out_crows);
phi::Copy(dev_ctx, x_cols, dev_ctx.GetPlace(), false, out_cols);
} else {
phi::MetaTensor crows_meta(out_crows);
crows_meta.set_dims(x_crows.dims());
crows_meta.set_dtype(index_dtype);
PD_VISIT_INTEGRAL_TYPES(x_crows.dtype(), "CastCsrKernel", [&] {
phi::CastKernel<data_t, Context>(
dev_ctx, x_crows, index_dtype, out_crows);
});
phi::MetaTensor cols_meta(out_cols);
cols_meta.set_dims(x_cols.dims());
cols_meta.set_dtype(index_dtype);
PD_VISIT_INTEGRAL_TYPES(x_cols.dtype(), "CastCsrKernel", [&] {
phi::CastKernel<data_t, Context>(dev_ctx, x_cols, index_dtype, out_cols);
});
}
if (value_dtype == DataType::UNDEFINED) {
phi::Copy(dev_ctx, x_values, dev_ctx.GetPlace(), false, out_values);
} else {
phi::MetaTensor meta(out_values);
meta.set_dims(x_values.dims());
meta.set_dtype(value_dtype);
phi::CastKernel<T, Context>(dev_ctx, x_values, value_dtype, out_values);
}
}
} // namespace sparse
} // namespace phi
paddle/phi/kernels/sparse/unary_grad_kernel.cc 已删除 100644 → 0
浏览文件 @ 51e2933d
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// 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/unary_grad_kernel.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/sparse_coo_tensor.h"
#include "paddle/phi/core/sparse_csr_tensor.h"
#include "paddle/phi/core/tensor_utils.h"
#include "paddle/phi/kernels/activation_grad_kernel.h"
#include "paddle/phi/kernels/empty_kernel.h"
#define DEFINE_SPARSE_UNARY_GRAD_KERNEL(DenseKernelFunc) \
namespace phi { \
namespace sparse { \
\
template <typename T, typename Context> \
void SparseCoo##DenseKernelFunc(const Context& dev_ctx, \
const SparseCooTensor& x_or_out, \
const SparseCooTensor& out_grad, \
SparseCooTensor* x_grad) { \
DenseTensor non_zero_indices = \
phi::EmptyLike<T, Context>(dev_ctx, x_or_out.non_zero_indices()); \
DenseTensor non_zero_elements = \
phi::EmptyLike<T, Context>(dev_ctx, x_or_out.non_zero_elements()); \
phi::Copy(dev_ctx, \
x_or_out.non_zero_indices(), \
dev_ctx.GetPlace(), \
false, \
&non_zero_indices); \
phi::DenseKernelFunc<T, Context>(dev_ctx, \
x_or_out.non_zero_elements(), \
out_grad.non_zero_elements(), \
&non_zero_elements); \
x_grad->SetMember( \
non_zero_indices, non_zero_elements, x_or_out.dims(), true); \
} \
\
template <typename T, typename Context> \
void SparseCsr##DenseKernelFunc(const Context& dev_ctx, \
const SparseCsrTensor& x_or_out, \
const SparseCsrTensor& out_grad, \
SparseCsrTensor* out) { \
DenseTensor non_zero_crows = \
phi::EmptyLike<T, Context>(dev_ctx, x_or_out.non_zero_crows()); \
DenseTensor non_zero_cols = \
phi::EmptyLike<T, Context>(dev_ctx, x_or_out.non_zero_cols()); \
DenseTensor non_zero_elements = \
phi::EmptyLike<T, Context>(dev_ctx, x_or_out.non_zero_elements()); \
phi::Copy(dev_ctx, \
x_or_out.non_zero_crows(), \
dev_ctx.GetPlace(), \
false, \
&non_zero_crows); \
phi::Copy(dev_ctx, \
x_or_out.non_zero_cols(), \
dev_ctx.GetPlace(), \
false, \
&non_zero_cols); \
phi::DenseKernelFunc<T, Context>(dev_ctx, \
x_or_out.non_zero_elements(), \
out_grad.non_zero_elements(), \
&non_zero_elements); \
out->SetMember( \
non_zero_crows, non_zero_cols, non_zero_elements, x_or_out.dims()); \
} \
} \
}
#define REGISTER_CPU_SPARSE_UNARY_KERNEL(kernel_name, DenseKernelFunc) \
PD_REGISTER_KERNEL(sparse_coo_##kernel_name, \
CPU, \
ALL_LAYOUT, \
phi::sparse::SparseCoo##DenseKernelFunc, \
float, \
double) { \
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO); \
} \
PD_REGISTER_KERNEL(sparse_csr_##kernel_name, \
CPU, \
ALL_LAYOUT, \
phi::sparse::SparseCsr##DenseKernelFunc, \
float, \
double) { \
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR); \
}
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
#define REGISTER_GPU_SPARSE_UNARY_KERNEL(kernel_name, DenseKernelFunc) \
PD_REGISTER_KERNEL(sparse_coo_##kernel_name, \
GPU, \
ALL_LAYOUT, \
phi::sparse::SparseCoo##DenseKernelFunc, \
float, \
double, \
phi::dtype::float16) { \
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO); \
} \
\
PD_REGISTER_KERNEL(sparse_csr_##kernel_name, \
GPU, \
ALL_LAYOUT, \
phi::sparse::SparseCsr##DenseKernelFunc, \
float, \
double, \
phi::dtype::float16) { \
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR); \
}
#else
// This macro definition is empty when GPU is disabled
#define REGISTER_GPU_SPARSE_UNARY_KERNEL(sparse_kernel_name, DenseKernelFunc)
#endif
#define REGISTER_SPARSE_UNARY_KERNEL(kernel_name, DenseKernelFunc) \
REGISTER_CPU_SPARSE_UNARY_KERNEL(kernel_name, DenseKernelFunc) \
REGISTER_GPU_SPARSE_UNARY_KERNEL(kernel_name, DenseKernelFunc)
#define DEFINE_AND_REGISTER_SPARSE_UNARY_GRAD_KERNEL(kernel_name, \
DenseKernelFunc) \
DEFINE_SPARSE_UNARY_GRAD_KERNEL(DenseKernelFunc) \
REGISTER_SPARSE_UNARY_KERNEL(kernel_name, DenseKernelFunc)
// NOTE: the following code is to bypass the restriction of Paddle
// kernel registration mechanism. Do NOT refactor them unless you
// know what you are doing.
// If you want to implement any new kernel, please follow `sin_grad`,
// `tanh_grad` etc, do NOT follow the following `relu_grad`.
DEFINE_SPARSE_UNARY_GRAD_KERNEL(ReluGradKernel)
PD_REGISTER_KERNEL(sparse_coo_relu_grad,
CPU,
ALL_LAYOUT,
phi::sparse::SparseCooReluGradKernel,
float,
double) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
}
PD_REGISTER_KERNEL(sparse_csr_relu_grad,
CPU,
ALL_LAYOUT,
phi::sparse::SparseCsrReluGradKernel,
float,
double) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR);
}
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
PD_REGISTER_KERNEL(sparse_coo_relu_grad,
GPU,
ALL_LAYOUT,
phi::sparse::SparseCooReluGradKernel,
float,
double,
phi::dtype::float16) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
}
PD_REGISTER_KERNEL(sparse_csr_relu_grad,
GPU,
ALL_LAYOUT,
phi::sparse::SparseCsrReluGradKernel,
float,
double,
phi::dtype::float16) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR);
}
#endif
DEFINE_AND_REGISTER_SPARSE_UNARY_GRAD_KERNEL(sin_grad, SinGradKernel)
DEFINE_AND_REGISTER_SPARSE_UNARY_GRAD_KERNEL(sqrt_grad, SqrtGradKernel)
DEFINE_AND_REGISTER_SPARSE_UNARY_GRAD_KERNEL(tanh_grad, TanhGradKernel)
paddle/phi/kernels/sparse/unary_grad_kernel.h
浏览文件 @ 682acd22
......@@ -17,25 +17,65 @@
#include "paddle/phi/core/sparse_coo_tensor.h"
#include "paddle/phi/core/sparse_csr_tensor.h"
#define DECLARE_SPARSE_UNARY_GRAD_KERNEL(name) \
template <typename T, typename Context> \
void SparseCoo##name##GradKernel(const Context& dev_ctx, \
const SparseCooTensor& x, \
const SparseCooTensor& out_grad, \
SparseCooTensor* x_grad); \
\
template <typename T, typename Context> \
void SparseCsr##name##GradKernel(const Context& dev_ctx, \
const SparseCsrTensor& x, \
const SparseCsrTensor& out_grad, \
SparseCsrTensor* x_grad);
namespace phi {
namespace sparse {
#define DECLARE_SPARSE_UNARY_GRAD_KERNEL(prefix) \
template <typename T, typename Context> \
void prefix##CooGradKernel(const Context& dev_ctx, \
const SparseCooTensor& x_or_out, \
const SparseCooTensor& dout, \
SparseCooTensor* dx); \
\
template <typename T, typename Context> \
void prefix##CsrGradKernel(const Context& dev_ctx, \
const SparseCsrTensor& x_or_out, \
const SparseCsrTensor& dout, \
SparseCsrTensor* dx);
#define DECLARE_SPARSE_UNARY_GRAD_KERNEL_WITH_ONE_ATTR(prefix, attr) \
template <typename T, typename Context> \
void prefix##CooGradKernel(const Context& dev_ctx, \
const SparseCooTensor& x_or_out, \
const SparseCooTensor& dout, \
float attr, \
SparseCooTensor* dx); \
\
template <typename T, typename Context> \
void prefix##CsrGradKernel(const Context& dev_ctx, \
const SparseCsrTensor& x_or_out, \
const SparseCsrTensor& dout, \
float attr, \
SparseCsrTensor* dx);
DECLARE_SPARSE_UNARY_GRAD_KERNEL(Sin)
DECLARE_SPARSE_UNARY_GRAD_KERNEL(Tan)
DECLARE_SPARSE_UNARY_GRAD_KERNEL(Asin)
DECLARE_SPARSE_UNARY_GRAD_KERNEL(Atan)
DECLARE_SPARSE_UNARY_GRAD_KERNEL(Sinh)
DECLARE_SPARSE_UNARY_GRAD_KERNEL(Asinh)
DECLARE_SPARSE_UNARY_GRAD_KERNEL(Atanh)
DECLARE_SPARSE_UNARY_GRAD_KERNEL(Relu)
DECLARE_SPARSE_UNARY_GRAD_KERNEL(Tanh)
DECLARE_SPARSE_UNARY_GRAD_KERNEL(Square)
DECLARE_SPARSE_UNARY_GRAD_KERNEL(Sqrt)
DECLARE_SPARSE_UNARY_GRAD_KERNEL(Sin)
DECLARE_SPARSE_UNARY_GRAD_KERNEL(Log1p)
DECLARE_SPARSE_UNARY_GRAD_KERNEL(Abs)
DECLARE_SPARSE_UNARY_GRAD_KERNEL_WITH_ONE_ATTR(Pow, factor)
template <typename T, typename Context>
void CastCooGradKernel(const Context& dev_ctx,
const SparseCooTensor& x,
const SparseCooTensor& dout,
DataType value_dtype,
SparseCooTensor* dx);
template <typename T, typename Context>
void CastCsrGradKernel(const Context& dev_ctx,
const SparseCsrTensor& x,
const SparseCsrTensor& dout,
DataType value_dtype,
SparseCsrTensor* dx);
} // namespace sparse
} // namespace phi
paddle/phi/kernels/sparse/unary_kernel.cc 已删除 100644 → 0
浏览文件 @ 51e2933d
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// 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/unary_kernel.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/sparse_coo_tensor.h"
#include "paddle/phi/core/sparse_csr_tensor.h"
#include "paddle/phi/core/tensor_utils.h"
#include "paddle/phi/kernels/activation_kernel.h"
#include "paddle/phi/kernels/empty_kernel.h"
#define DEFINE_SPARSE_UNARY_KERNEL(DenseKernelFunc) \
namespace phi { \
namespace sparse { \
\
template <typename T, typename Context> \
void SparseCoo##DenseKernelFunc(const Context& dev_ctx, \
const SparseCooTensor& x, \
SparseCooTensor* out) { \
DenseTensor non_zero_indices = \
phi::EmptyLike<T, Context>(dev_ctx, x.non_zero_indices()); \
DenseTensor non_zero_elements = \
phi::EmptyLike<T, Context>(dev_ctx, x.non_zero_elements()); \
phi::Copy(dev_ctx, \
x.non_zero_indices(), \
dev_ctx.GetPlace(), \
false, \
&non_zero_indices); \
phi::DenseKernelFunc<T, Context>( \
dev_ctx, x.non_zero_elements(), &non_zero_elements); \
out->SetMember(non_zero_indices, non_zero_elements, x.dims(), true); \
} \
\
template <typename T, typename Context> \
void SparseCsr##DenseKernelFunc(const Context& dev_ctx, \
const SparseCsrTensor& x, \
SparseCsrTensor* out) { \
DenseTensor non_zero_crows = \
phi::EmptyLike<T, Context>(dev_ctx, x.non_zero_crows()); \
DenseTensor non_zero_cols = \
phi::EmptyLike<T, Context>(dev_ctx, x.non_zero_cols()); \
DenseTensor non_zero_elements = \
phi::EmptyLike<T, Context>(dev_ctx, x.non_zero_elements()); \
phi::Copy(dev_ctx, \
x.non_zero_crows(), \
dev_ctx.GetPlace(), \
false, \
&non_zero_crows); \
phi::Copy(dev_ctx, \
x.non_zero_cols(), \
dev_ctx.GetPlace(), \
false, \
&non_zero_cols); \
phi::DenseKernelFunc<T, Context>( \
dev_ctx, x.non_zero_elements(), &non_zero_elements); \
out->SetMember( \
non_zero_crows, non_zero_cols, non_zero_elements, x.dims()); \
} \
} \
}
#define REGISTER_CPU_SPARSE_UNARY_KERNEL(kernel_name, DenseKernelFunc) \
PD_REGISTER_KERNEL(sparse_coo_##kernel_name, \
CPU, \
ALL_LAYOUT, \
phi::sparse::SparseCoo##DenseKernelFunc, \
float, \
double) { \
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO); \
} \
PD_REGISTER_KERNEL(sparse_csr_##kernel_name, \
CPU, \
ALL_LAYOUT, \
phi::sparse::SparseCsr##DenseKernelFunc, \
float, \
double) { \
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR); \
}
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
#define REGISTER_GPU_SPARSE_UNARY_KERNEL(kernel_name, DenseKernelFunc) \
PD_REGISTER_KERNEL(sparse_coo_##kernel_name, \
GPU, \
ALL_LAYOUT, \
phi::sparse::SparseCoo##DenseKernelFunc, \
float, \
double, \
phi::dtype::float16) { \
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO); \
} \
\
PD_REGISTER_KERNEL(sparse_csr_##kernel_name, \
GPU, \
ALL_LAYOUT, \
phi::sparse::SparseCsr##DenseKernelFunc, \
float, \
double, \
phi::dtype::float16) { \
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR); \
}
#else
// This macro definition is empty when GPU is disabled
#define REGISTER_GPU_SPARSE_UNARY_KERNEL(sparse_kernel_name, DenseKernelFunc)
#endif
#define REGISTER_SPARSE_UNARY_KERNEL(kernel_name, DenseKernelFunc) \
REGISTER_CPU_SPARSE_UNARY_KERNEL(kernel_name, DenseKernelFunc) \
REGISTER_GPU_SPARSE_UNARY_KERNEL(kernel_name, DenseKernelFunc)
#define DEFINE_AND_REGISTER_SPARSE_UNARY_KERNEL(kernel_name, DenseKernelFunc) \
DEFINE_SPARSE_UNARY_KERNEL(DenseKernelFunc) \
REGISTER_SPARSE_UNARY_KERNEL(kernel_name, DenseKernelFunc)
// NOTE: the following code is to bypass the restriction of Paddle
// kernel registration mechanism. Do NOT refactor them unless you
// know what you are doing.
// If you want to implement any new kernel, please follow `sin`,
// `tanh` etc, do NOT follow `sqrt`.
DEFINE_SPARSE_UNARY_KERNEL(SqrtKernel)
PD_REGISTER_KERNEL(sparse_coo_sqrt,
CPU,
ALL_LAYOUT,
phi::sparse::SparseCooSqrtKernel,
float,
double) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
}
PD_REGISTER_KERNEL(sparse_csr_sqrt,
CPU,
ALL_LAYOUT,
phi::sparse::SparseCsrSqrtKernel,
float,
double) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR);
}
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
PD_REGISTER_KERNEL(sparse_coo_sqrt,
GPU,
ALL_LAYOUT,
phi::sparse::SparseCooSqrtKernel,
float,
double,
phi::dtype::float16) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
}
PD_REGISTER_KERNEL(sparse_csr_sqrt,
GPU,
ALL_LAYOUT,
phi::sparse::SparseCsrSqrtKernel,
float,
double,
phi::dtype::float16) {
kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR);
}
#endif
DEFINE_AND_REGISTER_SPARSE_UNARY_KERNEL(sin, SinKernel)
DEFINE_AND_REGISTER_SPARSE_UNARY_KERNEL(tanh, TanhKernel)
DEFINE_AND_REGISTER_SPARSE_UNARY_KERNEL(relu, ReluKernel)
paddle/phi/kernels/sparse/unary_kernel.h
浏览文件 @ 682acd22
......@@ -14,35 +14,104 @@
#pragma once
#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/core/sparse_coo_tensor.h"
#include "paddle/phi/core/sparse_csr_tensor.h"
#include "paddle/phi/kernels/activation_kernel.h"
#include "paddle/phi/kernels/empty_kernel.h"
#define DECLARE_SPARSE_UNARY_KERNEL(name) \
namespace phi {
namespace sparse {
#define DECLARE_SPARSE_UNARY_KERNEL(prefix) \
template <typename T, typename Context> \
void SparseCoo##name##Kernel( \
void prefix##CooKernel( \
const Context& dev_ctx, const SparseCooTensor& x, SparseCooTensor* out); \
\
template <typename T, typename Context> \
void SparseCsr##name##Kernel( \
void prefix##CsrKernel( \
const Context& dev_ctx, const SparseCsrTensor& x, SparseCsrTensor* out);
namespace phi {
namespace sparse {
#define DECLARE_SPARSE_UNARY_KERNEL_WITH_ONE_ATTR(prefix, attr) \
template <typename T, typename Context> \
void prefix##CooKernel(const Context& dev_ctx, \
const SparseCooTensor& x, \
float attr, \
SparseCooTensor* out); \
\
template <typename T, typename Context> \
void prefix##CsrKernel(const Context& dev_ctx, \
const SparseCsrTensor& x, \
float attr, \
SparseCsrTensor* out);
DECLARE_SPARSE_UNARY_KERNEL(Sin)
DECLARE_SPARSE_UNARY_KERNEL(Tan)
DECLARE_SPARSE_UNARY_KERNEL(Asin)
DECLARE_SPARSE_UNARY_KERNEL(Atan)
DECLARE_SPARSE_UNARY_KERNEL(Sinh)
DECLARE_SPARSE_UNARY_KERNEL(Asinh)
DECLARE_SPARSE_UNARY_KERNEL(Atanh)
DECLARE_SPARSE_UNARY_KERNEL(Relu)
DECLARE_SPARSE_UNARY_KERNEL(Tanh)
DECLARE_SPARSE_UNARY_KERNEL(Square)
DECLARE_SPARSE_UNARY_KERNEL(Sqrt)
DECLARE_SPARSE_UNARY_KERNEL(Sin)
DECLARE_SPARSE_UNARY_KERNEL(Log1p)
DECLARE_SPARSE_UNARY_KERNEL(Abs)
DECLARE_SPARSE_UNARY_KERNEL_WITH_ONE_ATTR(Pow, factor)
template <typename T, typename Context>
void ScaleCooKernel(const Context& dev_ctx,
const SparseCooTensor& x,
float scale,
float bias,
bool bias_after_scale,
SparseCooTensor* out);
template <typename T, typename Context>
void ScaleCsrKernel(const Context& dev_ctx,
const SparseCsrTensor& x,
float scale,
float bias,
bool bias_after_scale,
SparseCsrTensor* out);
template <typename T, typename Context>
SparseCooTensor SparseRelu(const Context& dev_ctx, const SparseCooTensor& x) {
DenseTensor indices, values;
SparseCooTensor coo(indices, values, x.dims());
SparseCooReluKernel<T, Context>(dev_ctx, x, &coo);
void DivCooScalarKernel(const Context& dev_ctx,
const SparseCooTensor& x,
float scalar,
SparseCooTensor* out);
template <typename T, typename Context>
void DivCsrScalarKernel(const Context& dev_ctx,
const SparseCsrTensor& x,
float scalar,
SparseCsrTensor* out);
template <typename T, typename Context>
void CastCooKernel(const Context& dev_ctx,
const SparseCooTensor& x,
DataType index_dtype,
DataType value_dtype,
SparseCooTensor* out);
template <typename T, typename Context>
void CastCsrKernel(const Context& dev_ctx,
const SparseCsrTensor& x,
DataType index_dtype,
DataType value_dtype,
SparseCsrTensor* out);
template <typename T, typename Context>
SparseCooTensor ReluCoo(const Context& dev_ctx, const SparseCooTensor& x) {
SparseCooTensor coo;
ReluCooKernel<T, Context>(dev_ctx, x, &coo);
return coo;
}
template <typename T, typename Context>
SparseCooTensor ReluCsr(const Context& dev_ctx, const SparseCooTensor& x) {
SparseCooTensor csr;
ReluCsrKernel<T, Context>(dev_ctx, x, &csr);
return csr;
}
} // namespace sparse
} // namespace phi
paddle/phi/tests/kernels/test_sparse_activation_dev_api.cc
浏览文件 @ 682acd22
......@@ -49,7 +49,7 @@ TEST(DEV_API, sparse_relu) {
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_out = sparse::SparseRelu<float>(dev_ctx_cpu, sparse_coo);
auto sparse_out = sparse::ReluCoo<float>(dev_ctx_cpu, sparse_coo);
DenseTensor dense_out =
phi::EmptyLike<float>(dev_ctx_cpu, sparse_out.non_zero_elements());
ReluKernel<float>(dev_ctx_cpu, sparse_coo.non_zero_elements(), &dense_out);
......@@ -69,7 +69,7 @@ TEST(DEV_API, sparse_relu) {
SparseCooTensor sparse_out_grad(
sparse_coo.non_zero_indices(), dense_out, {3, 4});
sparse::SparseCooReluGradKernel<float>(
sparse::ReluCooGradKernel<float>(
dev_ctx_cpu, sparse_coo, sparse_out_grad, &sparse_grad_x);
cmp = memcmp(dense_grad_x.data<float>(),
......
python/paddle/fluid/tests/unittests/test_sparse_elementwise_op.py
浏览文件 @ 682acd22
......@@ -125,16 +125,14 @@ class TestSparseElementWiseAPI(unittest.TestCase):
def test_support_dtypes_csr(self):
paddle.device.set_device('cpu')
if paddle.device.get_device() == "cpu":
with _test_eager_guard():
for op in op_list:
self.func_test_csr(op)
for op in op_list:
self.func_test_csr(op)
def test_support_dtypes_coo(self):
paddle.device.set_device('cpu')
if paddle.device.get_device() == "cpu":
with _test_eager_guard():
for op in op_list:
self.func_test_coo(op)
for op in op_list:
self.func_test_coo(op)
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/test_sparse_model.py
浏览文件 @ 682acd22
......@@ -62,3 +62,7 @@ class TestGradientAdd(unittest.TestCase):
sparse_loss.backward()
assert np.allclose(x.grad.numpy(), sparse_x.grad.to_dense().numpy())
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_sparse_unary_op.py
浏览文件 @ 682acd22
......@@ -12,137 +12,142 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
from typing import Union, Callable
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid.framework import _test_eager_guard
from paddle import _C_ops
from paddle.fluid.framework import convert_np_dtype_to_dtype_
class TestSparseUnary(unittest.TestCase):
def assert_raises_on_dense_tensor(self, sparse_func):
with _test_eager_guard():
dense_x = paddle.ones((2, 3))
with self.assertRaises(NotImplementedError):
sparse_func(dense_x)
def compare_with_dense(
self,
x,
to_sparse: Callable[[paddle.Tensor], paddle.Tensor],
dense_func: Callable[[paddle.Tensor], paddle.Tensor],
sparse_func: Callable[[paddle.Tensor], paddle.Tensor],
test_gradient: bool,
):
def tensor_allclose(dense_tensor: paddle.Tensor,
sparse_tensor: paddle.Tensor):
dense_numpy = dense_tensor.numpy()
mask = ~np.isnan(dense_numpy)
return np.allclose(dense_numpy[mask],
sparse_tensor.to_dense().numpy()[mask])
fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
with _test_eager_guard():
dense_x = paddle.to_tensor(x,
dtype="float32",
stop_gradient=not test_gradient)
sparse_x = to_sparse(dense_x)
sparse_out = sparse_func(sparse_x)
dense_x = paddle.to_tensor(x,
dtype="float32",
stop_gradient=not test_gradient)
def to_sparse(self, x, format):
if format == 'coo':
return x.detach().to_sparse_coo(sparse_dim=x.ndim)
elif format == 'csr':
return x.detach().to_sparse_csr()
def check_result(self, dense_func, sparse_func, format, *args):
origin_x = paddle.rand([8, 16, 32], dtype='float32')
mask = paddle.randint(0, 2, [8, 16, 32]).astype('float32')
### check sparse coo with dense ###
dense_x = origin_x * mask
sp_x = self.to_sparse(dense_x, format)
sp_x.stop_gradient = False
if len(args) == 0:
sp_out = sparse_func(sp_x)
elif len(args) == 1:
sp_out = sparse_func(sp_x, args[0])
elif len(args) == 2:
sp_out = sparse_func(sp_x, args[0], args[1])
sp_out.backward()
dense_x.stop_gradient = False
if len(args) == 0:
dense_out = dense_func(dense_x)
elif len(args) == 1:
dense_out = dense_func(dense_x, args[0])
elif len(args) == 2:
if dense_func == paddle.cast:
dense_out = dense_func(dense_x, args[1])
int_dtype = convert_np_dtype_to_dtype_(args[0])
if sp_out.is_sparse_csr():
self.assertEqual(sp_out.crows().dtype, int_dtype)
self.assertEqual(sp_out.cols().dtype, int_dtype)
elif sp_out.is_sparse_coo():
self.assertEqual(sp_out.indices().dtype, int_dtype)
else:
dense_out = dense_func(dense_x, args[0], args[1])
dense_out.backward()
# compare forward
self.assertTrue(
np.allclose(sp_out.to_dense().numpy(), dense_out.numpy()))
# compare backward
if dense_func == paddle.sqrt:
expect_grad = np.nan_to_num(dense_x.grad.numpy(), 0., 0., 0.)
else:
expect_grad = (dense_x.grad * mask).numpy()
self.assertTrue(np.allclose(sp_x.grad.to_dense().numpy(), expect_grad))
def compare_with_dense(self, dense_func, sparse_func):
self.check_result(dense_func, sparse_func, 'coo')
self.check_result(dense_func, sparse_func, 'csr')
def compare_with_dense_one_attr(self, dense_func, sparse_func, attr1):
self.check_result(dense_func, sparse_func, 'coo', attr1)
self.check_result(dense_func, sparse_func, 'csr', attr1)
def compare_with_dense_two_attr(self, dense_func, sparse_func, attr1,
attr2):
self.check_result(dense_func, sparse_func, 'coo', attr1, attr2)
self.check_result(dense_func, sparse_func, 'csr', attr1, attr2)
assert tensor_allclose(dense_out, sparse_out)
def test_sparse_sin(self):
self.compare_with_dense(paddle.sin, paddle.incubate.sparse.sin)
if test_gradient:
dense_out.backward(dense_out)
sparse_out.backward(sparse_out)
assert tensor_allclose(dense_x.grad, sparse_x.grad)
fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
def test_sparse_tan(self):
self.compare_with_dense(paddle.tan, paddle.incubate.sparse.tan)
def test_sparse_relu(self):
x = [[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]]
sparse_dim = 2
self.compare_with_dense(
x,
lambda x: x.to_sparse_coo(sparse_dim),
paddle.nn.ReLU(),
paddle.incubate.sparse.nn.ReLU(),
True,
)
self.compare_with_dense(
x,
lambda x: x.to_sparse_csr(),
paddle.nn.ReLU(),
paddle.incubate.sparse.nn.ReLU(),
False,
)
self.assert_raises_on_dense_tensor(paddle.incubate.sparse.nn.ReLU())
def test_sparse_asin(self):
self.compare_with_dense(paddle.asin, paddle.incubate.sparse.asin)
def test_sparse_sqrt(self):
x = [[0, 16, 0, 0], [0, 0, 0, 0], [0, 4, 2, 0]]
sparse_dim = 2
self.compare_with_dense(
x,
lambda x: x.to_sparse_coo(sparse_dim),
paddle.sqrt,
paddle.incubate.sparse.sqrt,
True,
)
self.compare_with_dense(
x,
lambda x: x.to_sparse_csr(),
paddle.sqrt,
paddle.incubate.sparse.sqrt,
False,
)
self.assert_raises_on_dense_tensor(paddle.incubate.sparse.sqrt)
def test_sparse_atan(self):
self.compare_with_dense(paddle.atan, paddle.incubate.sparse.atan)
def test_sparse_sin(self):
x = [[0, 16, 0, 0], [0, 0, 0, 0], [0, 4, 2, 0]]
sparse_dim = 2
self.compare_with_dense(
x,
lambda x: x.to_sparse_coo(sparse_dim),
paddle.sin,
paddle.incubate.sparse.sin,
True,
)
self.compare_with_dense(
x,
lambda x: x.to_sparse_csr(),
paddle.sin,
paddle.incubate.sparse.sin,
False,
)
self.assert_raises_on_dense_tensor(paddle.incubate.sparse.sin)
def test_sparse_sinh(self):
self.compare_with_dense(paddle.sinh, paddle.incubate.sparse.sinh)
def test_sparse_tanh(self):
x = [[0, 16, 0, 0], [0, 0, 0, 0], [0, -4, 2, 0]]
sparse_dim = 2
self.compare_with_dense(
x,
lambda x: x.to_sparse_coo(sparse_dim),
paddle.tanh,
paddle.incubate.sparse.tanh,
True,
)
self.compare_with_dense(
x,
lambda x: x.to_sparse_csr(),
paddle.tanh,
paddle.incubate.sparse.tanh,
False,
)
self.assert_raises_on_dense_tensor(paddle.incubate.sparse.tanh)
self.compare_with_dense(paddle.tanh, paddle.incubate.sparse.tanh)
def test_sparse_asinh(self):
self.compare_with_dense(paddle.asinh, paddle.incubate.sparse.asinh)
def test_sparse_atanh(self):
self.compare_with_dense(paddle.atanh, paddle.incubate.sparse.atanh)
def test_sparse_sqrt(self):
self.compare_with_dense(paddle.sqrt, paddle.incubate.sparse.sqrt)
def test_sparse_square(self):
self.compare_with_dense(paddle.square, paddle.incubate.sparse.square)
def test_sparse_log1p(self):
self.compare_with_dense(paddle.log1p, paddle.incubate.sparse.log1p)
def test_sparse_relu(self):
self.compare_with_dense(paddle.nn.ReLU(),
paddle.incubate.sparse.nn.ReLU())
def test_sparse_abs(self):
self.compare_with_dense(paddle.abs, paddle.incubate.sparse.abs)
def test_sparse_neg(self):
self.compare_with_dense(paddle.neg, paddle.incubate.sparse.neg)
def test_sparse_pow(self):
self.compare_with_dense_one_attr(paddle.pow, paddle.incubate.sparse.pow,
3)
def test_sparse_mul_scalar(self):
self.compare_with_dense_one_attr(paddle.Tensor.__mul__,
paddle.incubate.sparse.multiply, 3)
def test_sparse_div_scalar(self):
self.compare_with_dense_one_attr(paddle.Tensor.__div__,
paddle.incubate.sparse.divide, 2)
def test_sparse_cast(self):
self.compare_with_dense_two_attr(paddle.cast,
paddle.incubate.sparse.cast, 'int16',
'float32')
self.compare_with_dense_two_attr(paddle.cast,
paddle.incubate.sparse.cast, 'int32',
'float64')
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/test_sparse_utils_op.py
浏览文件 @ 682acd22
......@@ -38,7 +38,6 @@ class TestSparseCreate(unittest.TestCase):
dense_shape,
stop_gradient=False)
# test the to_string.py
print(coo)
assert np.array_equal(indices, coo.indices().numpy())
assert np.array_equal(values, coo.values().numpy())
......@@ -49,6 +48,7 @@ class TestSparseCreate(unittest.TestCase):
dense_shape = [3, 3]
coo = paddle.incubate.sparse.sparse_coo_tensor(
indices, values, dense_shape)
assert np.array_equal(3, coo.nnz())
assert np.array_equal(indices, coo.indices().numpy())
assert np.array_equal(values, coo.values().numpy())
......@@ -78,7 +78,7 @@ class TestSparseCreate(unittest.TestCase):
csr = paddle.incubate.sparse.sparse_csr_tensor(
crows, cols, values, dense_shape)
# test the to_string.py
print(csr)
assert np.array_equal(5, csr.nnz())
assert np.array_equal(crows, csr.crows().numpy())
assert np.array_equal(cols, csr.cols().numpy())
assert np.array_equal(values, csr.values().numpy())
......
python/paddle/incubate/sparse/__init__.py
浏览文件 @ 682acd22
......@@ -15,27 +15,50 @@
from .creation import sparse_coo_tensor
from .creation import sparse_csr_tensor
from .unary import sqrt
from .unary import sin
from .unary import tan
from .unary import asin
from .unary import atan
from .unary import sinh
from .unary import tanh
from .unary import asinh
from .unary import atanh
from .unary import sqrt
from .unary import square
from .unary import log1p
from .unary import abs
from .unary import pow
from .unary import cast
from .unary import neg
from .binary import mv
from .binary import matmul
from .binary import masked_matmul
from .math import add
from .math import divide
from .math import multiply
from .math import subtract
from .binary import add
from .binary import divide
from .binary import multiply
from .binary import subtract
from . import nn
__all__ = [
'sparse_coo_tensor',
'sparse_csr_tensor',
'sqrt',
'sin',
'tan',
'asin',
'atan',
'sinh',
'tanh',
'asinh',
'atanh',
'sqrt',
'square',
'log1p',
'abs',
'pow',
'cast',
'neg',
'mv',
'matmul',
'masked_matmul',
......
python/paddle/incubate/sparse/binary.py
浏览文件 @ 682acd22
......@@ -13,10 +13,19 @@
# limitations under the License.
from paddle import _C_ops
from paddle.fluid.framework import dygraph_only
from paddle.fluid.framework import dygraph_only, core
__all__ = []
_int_dtype_ = [
core.VarDesc.VarType.UINT8,
core.VarDesc.VarType.INT8,
core.VarDesc.VarType.INT16,
core.VarDesc.VarType.INT32,
core.VarDesc.VarType.INT64,
core.VarDesc.VarType.BOOL,
]
@dygraph_only
def matmul(x, y, name=None):
......@@ -197,3 +206,191 @@ def mv(x, vec, name=None):
"""
return _C_ops.final_state_sparse_mv(x, vec)
def add(x, y, name=None):
"""
Add two sparse tensors element-wise. Input x and y's shape should be identical and have same sparse
type（SparseCooTensor or SparseCsrTensor）.If input is SparseCooTensor, x and y's sparse_dim should be identical.
The equation is:
.. math::
out = x + y
Args:
x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
y (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor: the result tensor.
Examples:
.. code-block:: python
import paddle
from paddle.fluid.framework import _test_eager_guard
paddle.device.set_device("cpu")
with _test_eager_guard():
x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32')
y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
sparse_x = x.to_sparse_csr()
sparse_y = y.to_sparse_csr()
sparse_z = paddle.incubate.sparse.add(sparse_x, sparse_y)
print(sparse_z.to_dense())
# [[ 0., -1., 0., 0.],
# [ 0., 2., -6., 0.],
# [ 6., 8., 4., 8.]]
"""
if y.dtype != x.dtype:
y = _C_ops.final_state_sparse_cast(y, None, x.dtype)
return _C_ops.final_state_sparse_add(x, y)
@dygraph_only
def subtract(x, y, name=None):
"""
Subtract two sparse tensors element-wise. Input x and y's shape should be identical and have same sparse
type（SparseCooTensor or SparseCsrTensor）.If input is SparseCooTensor, x and y's sparse_dim should be identical.
The equation is:
.. math::
out = x - y
Args:
x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
y (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor: the result tensor.
Examples:
.. code-block:: python
import paddle
from paddle.fluid.framework import _test_eager_guard
paddle.device.set_device("cpu")
with _test_eager_guard():
x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32')
y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
sparse_x = x.to_sparse_csr()
sparse_y = y.to_sparse_csr()
sparse_z = paddle.incubate.sparse.subtract(sparse_x, sparse_y)
print(sparse_z.to_dense())
# [[ 0., -1., 0., 4.],
# [ 0., -2., 0., 0.],
# [ 2., 2., -4., -8.]]
"""
if y.dtype != x.dtype:
y = _C_ops.final_state_sparse_cast(y, None, x.dtype)
return _C_ops.final_state_sparse_subtract(x, y)
@dygraph_only
def multiply(x, y, name=None):
"""
Multiply two sparse tensors element-wise. Input x and y's shape should be identical and have same sparse
type（SparseCooTensor or SparseCsrTensor）.If input is SparseCooTensor, x and y's sparse_dim should be identical.
The equation is:
.. math::
out = x * y
Args:
x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
y (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor: the result tensor.
Examples:
.. code-block:: python
import paddle
from paddle.fluid.framework import _test_eager_guard
paddle.device.set_device("cpu")
with _test_eager_guard():
x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32')
y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
sparse_x = x.to_sparse_csr()
sparse_y = y.to_sparse_csr()
sparse_z = paddle.incubate.sparse.multiply(sparse_x, sparse_y)
print(sparse_z.to_dense())
# [[ 0., 0., 0., -4.],
# [ 0., 0., 9., 0.],
# [ 8., 15., 0., 0.]]
"""
if isinstance(y, (int, float)):
return _C_ops.final_state_sparse_scale(x, float(y), 0.0, True)
else:
if y.dtype != x.dtype:
y = _C_ops.final_state_sparse_cast(y, None, x.dtype)
return _C_ops.final_state_sparse_multiply(x, y)
@dygraph_only
def divide(x, y, name=None):
"""
Divide two sparse tensors element-wise. Input x and y's shape should be identical and have same sparse
type（SparseCooTensor or SparseCsrTensor）.If input is SparseCooTensor, x and y's sparse_dim should be identical.
The equation is:
.. math::
out = x / y
Args:
x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
y (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor: the result tensor.
Examples:
.. code-block:: python
import paddle
from paddle.fluid.framework import _test_eager_guard
paddle.device.set_device("cpu")
with _test_eager_guard():
x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32')
y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
sparse_x = x.to_sparse_csr()
sparse_y = y.to_sparse_csr()
sparse_z = paddle.incubate.sparse.divide(sparse_x, sparse_y)
print(sparse_z.to_dense())
# [[ nan , -inf. , nan , -1. ],
# [ nan , 0. , 1. , nan ],
# [ 2. , 1.66666663, 0. , 0. ]]
"""
if x.dtype in _int_dtype_:
x = _C_ops.final_state_sparse_cast(x, None, core.VarDesc.VarType.FP32)
if isinstance(y, (int, float)):
return _C_ops.final_state_sparse_divide_scalar(x, float(y))
else:
if y.dtype != x.dtype:
y = _C_ops.final_state_sparse_cast(y, None, x.dtype)
return _C_ops.final_state_sparse_divide(x, y)
python/paddle/incubate/sparse/math.py 已删除 100644 → 0
浏览文件 @ 51e2933d
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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.
"""
sparse math functions
"""
from __future__ import print_function
from paddle import _C_ops, in_dynamic_mode, device, int32, int64
from paddle.tensor import cast
from paddle.incubate.sparse import sparse_csr_tensor
def _cast_coo(x, dtype, name=None):
indices = x.indices()
values = cast(x.values(), dtype)
return _C_ops.final_state_sparse_create_sparse_coo_tensor(
values, indices, x.shape)
def _cast_csr(x, dtype, name=None):
crows = x.crows()
cols = x.cols()
values = cast(x.values(), dtype)
return sparse_csr_tensor(crows, cols, values, x.shape)
def _cast(x, dtype, name=None):
if x.is_sparse_coo():
return _cast_coo(x, dtype, name)
return _cast_csr(x, dtype, name)
def add(x, y, name=None):
"""
Add two sparse tensors element-wise. Input x and y's shape should be identical and have same sparse
type（SparseCooTensor or SparseCsrTensor）.If input is SparseCooTensor, x and y's sparse_dim should be identical.
The equation is:
.. math::
out = x + y
Args:
x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
y (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor: the result tensor.
Examples:
.. code-block:: python
import paddle
from paddle.fluid.framework import _test_eager_guard
paddle.device.set_device("cpu")
with _test_eager_guard():
x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32')
y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
sparse_x = x.to_sparse_csr()
sparse_y = y.to_sparse_csr()
sparse_z = paddle.incubate.sparse.add(sparse_x, sparse_y)
print(sparse_z.to_dense())
# [[ 0., -1., 0., 0.],
# [ 0., 2., -6., 0.],
# [ 6., 8., 4., 8.]]
"""
assert device.get_device(
) == "cpu", "Currently, Sparse add only support CPU device."
assert in_dynamic_mode(), "Currently, Sparse API only support dynamic mode"
assert x.is_sparse_csr() == y.is_sparse_csr(
), f"Expect sparse tensor type to be same"
if x.is_sparse_coo() or x.is_sparse_csr():
return _C_ops.final_state_sparse_add(x, y)
else:
raise ValueError(
"Currently, sparse.add only support the input of SparseCooTensor or SparseCsrTensor"
)
def subtract(x, y, name=None):
"""
Subtract two sparse tensors element-wise. Input x and y's shape should be identical and have same sparse
type（SparseCooTensor or SparseCsrTensor）.If input is SparseCooTensor, x and y's sparse_dim should be identical.
The equation is:
.. math::
out = x - y
Args:
x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
y (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor: the result tensor.
Examples:
.. code-block:: python
import paddle
from paddle.fluid.framework import _test_eager_guard
paddle.device.set_device("cpu")
with _test_eager_guard():
x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32')
y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
sparse_x = x.to_sparse_csr()
sparse_y = y.to_sparse_csr()
sparse_z = paddle.incubate.sparse.subtract(sparse_x, sparse_y)
print(sparse_z.to_dense())
# [[ 0., -1., 0., 4.],
# [ 0., -2., 0., 0.],
# [ 2., 2., -4., -8.]]
"""
assert device.get_device(
) == "cpu", "Currently, Sparse subtract only support CPU device."
assert in_dynamic_mode(), "Currently, Sparse API only support dynamic mode"
assert x.is_sparse_csr() == y.is_sparse_csr(
), f"Expect sparse tensor type to be same"
if x.is_sparse_coo() or x.is_sparse_csr():
return _C_ops.final_state_sparse_subtract(x, y)
else:
raise ValueError(
"Currently, sparse.subtract only support the input of SparseCooTensor or SparseCsrTensor"
)
def multiply(x, y, name=None):
"""
Multiply two sparse tensors element-wise. Input x and y's shape should be identical and have same sparse
type（SparseCooTensor or SparseCsrTensor）.If input is SparseCooTensor, x and y's sparse_dim should be identical.
The equation is:
.. math::
out = x * y
Args:
x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
y (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor: the result tensor.
Examples:
.. code-block:: python
import paddle
from paddle.fluid.framework import _test_eager_guard
paddle.device.set_device("cpu")
with _test_eager_guard():
x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32')
y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
sparse_x = x.to_sparse_csr()
sparse_y = y.to_sparse_csr()
sparse_z = paddle.incubate.sparse.multiply(sparse_x, sparse_y)
print(sparse_z.to_dense())
# [[ 0., 0., 0., -4.],
# [ 0., 0., 9., 0.],
# [ 8., 15., 0., 0.]]
"""
assert device.get_device(
) == "cpu", "Currently, Sparse multiply only support CPU device."
assert in_dynamic_mode(), "Currently, Sparse API only support dynamic mode"
assert x.is_sparse_csr() == y.is_sparse_csr(
), f"Expect sparse tensor type to be same"
if x.is_sparse_coo() or x.is_sparse_csr():
return _C_ops.final_state_sparse_multiply(x, y)
else:
raise ValueError(
"Currently, sparse.multiply only support the input of SparseCooTensor or SparseCsrTensor"
)
def divide(x, y, name=None):
"""
Divide two sparse tensors element-wise. Input x and y's shape should be identical and have same sparse
type（SparseCooTensor or SparseCsrTensor）.If input is SparseCooTensor, x and y's sparse_dim should be identical.
The equation is:
.. math::
out = x / y
Args:
x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
y (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor: the result tensor.
Examples:
.. code-block:: python
import paddle
from paddle.fluid.framework import _test_eager_guard
paddle.device.set_device("cpu")
with _test_eager_guard():
x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32')
y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
sparse_x = x.to_sparse_csr()
sparse_y = y.to_sparse_csr()
sparse_z = paddle.incubate.sparse.divide(sparse_x, sparse_y)
print(sparse_z.to_dense())
# [[ nan , -inf. , nan , -1. ],
# [ nan , 0. , 1. , nan ],
# [ 2. , 1.66666663, 0. , 0. ]]
"""
assert device.get_device(
) == "cpu", "Currently, Sparse divide only support CPU device."
assert in_dynamic_mode(), "Currently, Sparse API only support dynamic mode"
assert x.is_sparse_csr() == y.is_sparse_csr(
), f"Expect sparse tensor type to be same"
if x.dtype in [int32, int64]:
if x.is_sparse_coo() or x.is_sparse_csr():
cx = _cast(x, 'float32')
cy = _cast(y, 'float32')
return _C_ops.final_state_sparse_divide(cx, cy)
else:
raise ValueError(
"Currently, sparse.divide only support the input of SparseCooTensor or SparseCsrTensor"
)
else:
if x.is_sparse_coo() or x.is_sparse_csr():
return _C_ops.final_state_sparse_divide(x, y)
else:
raise ValueError(
"Currently, sparse.divide only support the input of SparseCooTensor or SparseCsrTensor"
)
python/paddle/incubate/sparse/unary.py
浏览文件 @ 682acd22
......@@ -13,19 +13,79 @@
# limitations under the License.
from paddle import _C_ops
from paddle.fluid.framework import dygraph_only
from paddle.fluid.framework import dygraph_only, core, convert_np_dtype_to_dtype_
__all__ = []
@dygraph_only
def tanh(x, name=None):
def sin(x, name=None):
"""
sparse tanh activation, requiring x to be a sparse coo or sparse csr tensor.
Calculate elementwise sin of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = sin(x)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.sin(sparse_x)
"""
return _C_ops.final_state_sparse_sin(x)
@dygraph_only
def tan(x, name=None):
"""
Calculate elementwise tan of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = tanh(x)
out = tan(x)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.tan(sparse_x)
"""
return _C_ops.final_state_sparse_tan(x)
@dygraph_only
def asin(x, name=None):
"""
Calculate elementwise asin of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = asin(x)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
......@@ -39,21 +99,200 @@ def tanh(x, name=None):
.. code-block:: python
import paddle
from paddle.fluid.framework import _test_eager_guard
with _test_eager_guard():
dense_x = paddle.to_tensor([-2, 0, 1], dtype='float32')
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.tanh(sparse_x)
dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.asin(sparse_x)
"""
return _C_ops.final_state_sparse_asin(x)
@dygraph_only
def atan(x, name=None):
"""
Calculate elementwise atan of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = atan(x)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.atan(sparse_x)
"""
return _C_ops.final_state_sparse_atan(x)
@dygraph_only
def sinh(x, name=None):
"""
Calculate elementwise sinh of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = sinh(x)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.sinh(sparse_x)
"""
return _C_ops.final_state_sparse_sinh(x)
@dygraph_only
def asinh(x, name=None):
"""
Calculate elementwise asinh of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = asinh(x)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.asinh(sparse_x)
"""
return _C_ops.final_state_sparse_asinh(x)
@dygraph_only
def atanh(x, name=None):
"""
Calculate elementwise atanh of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = atanh(x)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.atanh(sparse_x)
"""
return _C_ops.final_state_sparse_atanh(x)
@dygraph_only
def tanh(x, name=None):
"""
Calculate elementwise tanh of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = tanh(x)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.tanh(sparse_x)
"""
return _C_ops.final_state_sparse_tanh(x)
@dygraph_only
def sqrt(x, name=None):
def square(x, name=None):
"""
Calculate square root of x, requiring x to be a sparse coo or sparse csr tensor.
Calculate elementwise square of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = square(x)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.square(sparse_x)
"""
return _C_ops.final_state_sparse_square(x)
@dygraph_only
def sqrt(x, name=None):
"""
Calculate elementwise sqrt of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = sqrt(x)
......@@ -70,24 +309,23 @@ def sqrt(x, name=None):
.. code-block:: python
import paddle
from paddle.fluid.framework import _test_eager_guard
with _test_eager_guard():
dense_x = paddle.to_tensor([4, 0, 1], dtype='float32')
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.sqrt(sparse_x)
dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.sqrt(sparse_x)
"""
return _C_ops.final_state_sparse_sqrt(x)
@dygraph_only
def sin(x, name=None):
def log1p(x, name=None):
"""
Calculate sin of x, requiring x to be a sparse coo or sparse csr tensor.
Calculate the natural log of (1+x), requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = sin(x)
out = ln(1+x)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
......@@ -101,11 +339,136 @@ def sin(x, name=None):
.. code-block:: python
import paddle
from paddle.fluid.framework import _test_eager_guard
with _test_eager_guard():
dense_x = paddle.to_tensor([-2, 0, 3], dtype='float32')
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.sin(sparse_x)
dense_x = paddle.to_tensor([-2, 0, 1], dtype='float32')
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.log1p(sparse_x)
"""
return _C_ops.final_state_sparse_sin(x)
return _C_ops.final_state_sparse_log1p(x)
@dygraph_only
def cast(x, index_dtype=None, value_dtype=None, name=None):
"""
cast non-zero-index of SparseTensor to `index_dtype`, non-zero-element of SparseTensor to
`value_dtype` , requiring x to be a SparseCooTensor or SparseCsrTensor.
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
index_dtype (np.dtype|str, optional): Data type of the index of SparseCooTensor,
or crows/cols of SparseCsrTensor. Can be uint8, int8, int16, int32, int64.
value_dtype (np.dtype|str, optional): Data type of the value of SparseCooTensor,
SparseCsrTensor. Can be bool, float16, float32, float64, int8, int32, int64, uint8.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2, 0, 1])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.cast(sparse_x, 'int32', 'float64')
"""
if index_dtype and not isinstance(index_dtype, core.VarDesc.VarType):
index_dtype = convert_np_dtype_to_dtype_(index_dtype)
if value_dtype and not isinstance(value_dtype, core.VarDesc.VarType):
value_dtype = convert_np_dtype_to_dtype_(value_dtype)
return _C_ops.final_state_sparse_cast(x, index_dtype, value_dtype)
@dygraph_only
def pow(x, factor, name=None):
"""
Calculate elementwise pow of x, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = x^{factor}
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
factor (float|int): factor of pow.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2, 0, 3], dtype='float32')
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.pow(sparse_x, 2)
"""
return _C_ops.final_state_sparse_pow(x, float(factor))
@dygraph_only
def neg(x, name=None):
"""
Calculate elementwise negative of x, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = -x
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2, 0, 3], dtype='float32')
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.neg(sparse_x)
"""
return _C_ops.final_state_sparse_scale(x, -1.0, 0.0, True)
@dygraph_only
def abs(x, name=None):
"""
Calculate elementwise absolute value of x, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = |x|
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2, 0, 3], dtype='float32')
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.abs(sparse_x)
"""
return _C_ops.final_state_sparse_abs(x)
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