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ForeachOpsKernels.cpp
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#include <vector>
#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
#include <ATen/core/Tensor.h>
#include <ATen/native/ForeachUtils.h>
#include <c10/util/irange.h>
#ifndef AT_PER_OPERATOR_HEADERS
#include <ATen/Functions.h>
#include <ATen/NativeFunctions.h>
#include <ATen/Operators.h>
#else
#include <ATen/ops/_foreach_abs_native.h>
#include <ATen/ops/_foreach_acos_native.h>
#include <ATen/ops/_foreach_add_native.h>
#include <ATen/ops/_foreach_addcdiv_native.h>
#include <ATen/ops/_foreach_addcmul_native.h>
#include <ATen/ops/_foreach_asin_native.h>
#include <ATen/ops/_foreach_atan_native.h>
#include <ATen/ops/_foreach_ceil_native.h>
#include <ATen/ops/_foreach_clamp_max_native.h>
#include <ATen/ops/_foreach_clamp_min_native.h>
#include <ATen/ops/_foreach_copy_native.h>
#include <ATen/ops/_foreach_cos_native.h>
#include <ATen/ops/_foreach_cosh_native.h>
#include <ATen/ops/_foreach_div_native.h>
#include <ATen/ops/_foreach_erf_native.h>
#include <ATen/ops/_foreach_erfc_native.h>
#include <ATen/ops/_foreach_exp_native.h>
#include <ATen/ops/_foreach_expm1_native.h>
#include <ATen/ops/_foreach_floor_native.h>
#include <ATen/ops/_foreach_frac_native.h>
#include <ATen/ops/_foreach_lerp_native.h>
#include <ATen/ops/_foreach_lgamma_native.h>
#include <ATen/ops/_foreach_log10_native.h>
#include <ATen/ops/_foreach_log1p_native.h>
#include <ATen/ops/_foreach_log2_native.h>
#include <ATen/ops/_foreach_log_native.h>
#include <ATen/ops/_foreach_max_native.h>
#include <ATen/ops/_foreach_maximum_native.h>
#include <ATen/ops/_foreach_minimum_native.h>
#include <ATen/ops/_foreach_mul_native.h>
#include <ATen/ops/_foreach_neg_native.h>
#include <ATen/ops/_foreach_norm_native.h>
#include <ATen/ops/_foreach_pow_native.h>
#include <ATen/ops/_foreach_reciprocal_native.h>
#include <ATen/ops/_foreach_round_native.h>
#include <ATen/ops/_foreach_sigmoid_native.h>
#include <ATen/ops/_foreach_sign_native.h>
#include <ATen/ops/_foreach_sin_native.h>
#include <ATen/ops/_foreach_sinh_native.h>
#include <ATen/ops/_foreach_sqrt_native.h>
#include <ATen/ops/_foreach_sub_native.h>
#include <ATen/ops/_foreach_tan_native.h>
#include <ATen/ops/_foreach_tanh_native.h>
#include <ATen/ops/_foreach_trunc_native.h>
#include <ATen/ops/_foreach_zero_native.h>
#include <ATen/ops/copy.h>
#include <ATen/ops/linalg_vector_norm.h>
#include <ATen/ops/max.h>
#include <ATen/ops/maximum.h>
#include <ATen/ops/minimum.h>
#include <ATen/ops/pow.h>
#endif
namespace at::native {
#define FOREACH_BINARY_OP_TENSOR(OP) \
void foreach_tensor_##OP##_tensor_kernel_slow_( \
TensorList tensors, const Tensor& scalar) { \
TORCH_CHECK( \
scalar.dim() == 0 && scalar.numel() == 1, \
"scalar tensor expected to be 0 dim but it has ", \
scalar.dim(), \
" dimensions and ", \
scalar.numel(), \
" elements."); \
check_foreach_api_restrictions(tensors); \
\
for (auto& t : tensors) { \
t.OP##_(scalar); \
} \
} \
\
std::vector<Tensor> foreach_tensor_##OP##_tensor_kernel_slow( \
TensorList tensors, const Tensor& scalar) { \
TORCH_CHECK( \
scalar.dim() == 0 && scalar.numel() == 1, \
"scalar tensor expected to be 0 dim but it has ", \
scalar.dim(), \
" dimensions and ", \
scalar.numel(), \
" elements."); \
check_foreach_api_restrictions(tensors); \
\
std::vector<Tensor> result; \
result.reserve(tensors.size()); \
for (const auto& t : tensors) { \
result.emplace_back(t.OP(scalar)); \
} \
\
return result; \
}
#define FOREACH_BINARY_OP_TENSOR_ALPHA(OP) \
void foreach_tensor_##OP##_tensor_kernel_slow_( \
TensorList tensors, const Tensor& scalar, const Scalar& alpha) { \
TORCH_CHECK( \
scalar.dim() == 0 && scalar.numel() == 1, \
"scalar tensor expected to be 0 dim but it has ", \
scalar.dim(), \
" dimensions and ", \
scalar.numel(), \
" elements."); \
check_foreach_api_restrictions(tensors); \
\
for (auto& t : tensors) { \
t.OP##_(scalar, alpha); \
} \
} \
\
std::vector<Tensor> foreach_tensor_##OP##_tensor_kernel_slow( \
TensorList tensors, const Tensor& scalar, const Scalar& alpha) { \
TORCH_CHECK( \
scalar.dim() == 0 && scalar.numel() == 1, \
"scalar tensor expected to be 0 dim but it has ", \
scalar.dim(), \
" dimensions and ", \
scalar.numel(), \
" elements."); \
check_foreach_api_restrictions(tensors); \
\
std::vector<Tensor> result; \
result.reserve(tensors.size()); \
for (const auto& t : tensors) { \
result.emplace_back(t.OP(scalar, alpha)); \
} \
\
return result; \
}
#define FOREACH_BINARY_OP_SCALAR(OP) \
void foreach_tensor_##OP##_scalar_kernel_slow_( \
TensorList tensors, const Scalar& scalar) { \
check_foreach_api_restrictions(tensors); \
\
for (auto& t : tensors) { \
t.OP##_(scalar); \
} \
} \
\
std::vector<Tensor> foreach_tensor_##OP##_scalar_kernel_slow( \
TensorList tensors, const Scalar& scalar) { \
check_foreach_api_restrictions(tensors); \
\
std::vector<Tensor> result; \
result.reserve(tensors.size()); \
for (const auto& t : tensors) { \
result.emplace_back(t.OP(scalar)); \
} \
\
return result; \
}
#define FOREACH_BINARY_OP_SCALARLIST(OP) \
void foreach_tensor_##OP##_scalarlist_kernel_slow_( \
TensorList tensors, at::ArrayRef<Scalar> scalars) { \
check_foreach_api_restrictions(tensors, scalars); \
\
for (const auto i : c10::irange(tensors.size())) { \
tensors[i].OP##_(scalars[i]); \
} \
} \
\
std::vector<Tensor> foreach_tensor_##OP##_scalarlist_kernel_slow( \
TensorList tensors, at::ArrayRef<Scalar> scalars) { \
check_foreach_api_restrictions(tensors, scalars); \
std::vector<Tensor> result; \
result.reserve(tensors.size()); \
for (const auto i : c10::irange(tensors.size())) { \
result.emplace_back(tensors[i].OP(scalars[i])); \
} \
\
return result; \
}
#define FOREACH_BINARY_OP_LIST(OP) \
std::vector<Tensor> foreach_tensor_##OP##_list_kernel_slow( \
TensorList tensors1, TensorList tensors2) { \
check_foreach_api_restrictions(tensors1, tensors2); \
\
std::vector<Tensor> result; \
result.reserve(tensors1.size()); \
for (const auto i : c10::irange(tensors1.size())) { \
result.emplace_back(tensors1[i].OP(tensors2[i])); \
} \
\
return result; \
} \
\
void foreach_tensor_##OP##_list_kernel_slow_( \
TensorList tensors1, TensorList tensors2) { \
check_foreach_api_restrictions(tensors1, tensors2); \
\
for (const auto i : c10::irange(tensors1.size())) { \
tensors1[i].OP##_(tensors2[i]); \
} \
}
#define FOREACH_BINARY_OP_LIST_ALPHA(OP) \
std::vector<Tensor> foreach_tensor_##OP##_list_kernel_slow( \
TensorList tensors1, TensorList tensors2, const Scalar& alpha) { \
check_foreach_api_restrictions(tensors1, tensors2); \
\
std::vector<Tensor> result; \
result.reserve(tensors1.size()); \
for (const auto i : c10::irange(tensors1.size())) { \
result.emplace_back(tensors1[i].OP(tensors2[i], alpha)); \
} \
\
return result; \
} \
\
void foreach_tensor_##OP##_list_kernel_slow_( \
TensorList tensors1, TensorList tensors2, const Scalar& alpha) { \
check_foreach_api_restrictions(tensors1, tensors2); \
\
for (const auto i : c10::irange(tensors1.size())) { \
tensors1[i].OP##_(tensors2[i], alpha); \
} \
}
#define FOREACH_UNARY_OP(OP) \
std::vector<Tensor> foreach_tensor_##OP##_slow(TensorList tensors) { \
check_foreach_api_restrictions(tensors); \
\
std::vector<Tensor> result; \
result.reserve(tensors.size()); \
for (const auto& t : tensors) { \
result.emplace_back(t.OP()); \
} \
\
return result; \
} \
\
void foreach_tensor_##OP##_slow_(TensorList tensors) { \
check_foreach_api_restrictions(tensors); \
\
for (auto& t : tensors) { \
t.OP##_(); \
} \
}
#define FOREACH_POINTWISE_OP_SCALAR(OP) \
std::vector<Tensor> foreach_tensor_##OP##_scalar_slow( \
TensorList input, \
TensorList tensors1, \
TensorList tensors2, \
const Scalar& scalar) { \
check_foreach_api_restrictions(input, tensors1, tensors2); \
\
std::vector<Tensor> result; \
for (const auto i : c10::irange(input.size())) { \
result.emplace_back(input[i].OP(tensors1[i], tensors2[i], scalar)); \
} \
\
return result; \
} \
\
void foreach_tensor_##OP##_scalar_slow_( \
TensorList input, \
TensorList tensors1, \
TensorList tensors2, \
const Scalar& scalar) { \
check_foreach_api_restrictions(input, tensors1, tensors2); \
\
for (const auto i : c10::irange(input.size())) { \
input[i].OP##_(tensors1[i], tensors2[i], scalar); \
} \
}
#define FOREACH_POINTWISE_OP_SCALARLIST(OP) \
std::vector<Tensor> foreach_tensor_##OP##_scalarlist_slow( \
TensorList input, \
TensorList tensors1, \
TensorList tensors2, \
at::ArrayRef<Scalar> scalars) { \
check_foreach_api_restrictions(input, tensors1, tensors2, scalars); \
\
std::vector<Tensor> result; \
for (const auto i : c10::irange(input.size())) { \
result.emplace_back(input[i].OP(tensors1[i], tensors2[i], scalars[i])); \
} \
\
return result; \
} \
\
void foreach_tensor_##OP##_scalarlist_slow_( \
TensorList input, \
TensorList tensors1, \
TensorList tensors2, \
at::ArrayRef<Scalar> scalars) { \
check_foreach_api_restrictions(input, tensors1, tensors2, scalars); \
\
for (const auto i : c10::irange(input.size())) { \
input[i].OP##_(tensors1[i], tensors2[i], scalars[i]); \
} \
}
#define FOREACH_POINTWISE_OP_TENSOR(OP) \
std::vector<Tensor> foreach_tensor_##OP##_tensor_slow( \
TensorList input, \
TensorList tensors1, \
TensorList tensors2, \
const Tensor& scalars_) { \
auto scalars = convert_tensor_to_scalar_list(scalars_, input.size()); \
check_foreach_api_restrictions(input, tensors1, tensors2, scalars); \
return foreach_tensor_##OP##_scalarlist_slow( \
input, tensors1, tensors2, scalars); \
} \
\
void foreach_tensor_##OP##_tensor_slow_( \
TensorList input, \
TensorList tensors1, \
TensorList tensors2, \
const Tensor& scalars_) { \
auto scalars = convert_tensor_to_scalar_list(scalars_, input.size()); \
check_foreach_api_restrictions(input, tensors1, tensors2, scalars); \
foreach_tensor_##OP##_scalarlist_slow_( \
input, tensors1, tensors2, scalars); \
}
FOREACH_BINARY_OP_LIST_ALPHA(add);
FOREACH_BINARY_OP_LIST_ALPHA(sub);
FOREACH_BINARY_OP_LIST_ALPHA(lerp);
FOREACH_BINARY_OP_TENSOR_ALPHA(add);
FOREACH_BINARY_OP_TENSOR(mul);
FOREACH_BINARY_OP_TENSOR(div);
FOREACH_BINARY_OP_SCALAR(add);
FOREACH_BINARY_OP_SCALAR(sub);
FOREACH_BINARY_OP_SCALAR(mul);
FOREACH_BINARY_OP_SCALAR(div);
FOREACH_BINARY_OP_SCALAR(clamp_min);
FOREACH_BINARY_OP_SCALAR(clamp_max);
FOREACH_BINARY_OP_SCALAR(pow);
FOREACH_BINARY_OP_SCALARLIST(add);
FOREACH_BINARY_OP_SCALARLIST(sub);
FOREACH_BINARY_OP_SCALARLIST(mul);
FOREACH_BINARY_OP_SCALARLIST(div);
FOREACH_BINARY_OP_SCALARLIST(clamp_min);
FOREACH_BINARY_OP_SCALARLIST(clamp_max);
FOREACH_BINARY_OP_SCALARLIST(pow);
FOREACH_BINARY_OP_LIST(mul);
FOREACH_BINARY_OP_LIST(div);
FOREACH_BINARY_OP_LIST(clamp_min);
FOREACH_BINARY_OP_LIST(clamp_max);
FOREACH_BINARY_OP_LIST(pow);
// _foreach_copy_
void foreach_tensor_copy_list_kernel_slow_(
TensorList self,
TensorList src,
const bool non_blocking) {
check_foreach_api_restrictions(self, src);
for (const auto i : c10::irange(self.size())) {
self[i].copy_(src[i], non_blocking);
}
}
FOREACH_UNARY_OP(sqrt);
FOREACH_UNARY_OP(exp);
FOREACH_UNARY_OP(abs);
FOREACH_UNARY_OP(acos);
FOREACH_UNARY_OP(asin);
FOREACH_UNARY_OP(atan);
FOREACH_UNARY_OP(ceil);
FOREACH_UNARY_OP(cos);
FOREACH_UNARY_OP(cosh);
FOREACH_UNARY_OP(erf);
FOREACH_UNARY_OP(erfc);
FOREACH_UNARY_OP(expm1);
FOREACH_UNARY_OP(floor);
FOREACH_UNARY_OP(log);
FOREACH_UNARY_OP(log10);
FOREACH_UNARY_OP(log1p);
FOREACH_UNARY_OP(log2);
FOREACH_UNARY_OP(neg);
FOREACH_UNARY_OP(tan);
FOREACH_UNARY_OP(tanh);
FOREACH_UNARY_OP(sin);
FOREACH_UNARY_OP(sinh);
FOREACH_UNARY_OP(round);
FOREACH_UNARY_OP(lgamma);
FOREACH_UNARY_OP(frac);
FOREACH_UNARY_OP(trunc);
FOREACH_UNARY_OP(reciprocal);
FOREACH_UNARY_OP(sigmoid);
FOREACH_UNARY_OP(sign);
FOREACH_POINTWISE_OP_SCALAR(addcdiv);
FOREACH_POINTWISE_OP_SCALAR(addcmul);
FOREACH_POINTWISE_OP_SCALARLIST(addcdiv);
FOREACH_POINTWISE_OP_SCALARLIST(addcmul);
FOREACH_POINTWISE_OP_TENSOR(addcdiv);
FOREACH_POINTWISE_OP_TENSOR(addcmul);
#define FOREACH_TERNARY_OP(OP) \
std::vector<Tensor> foreach_tensor_ternary_##OP##_slow( \
TensorList tensors1, TensorList tensors2, TensorList tensors3) { \
check_foreach_api_restrictions(tensors1, tensors2, tensors3); \
std::vector<Tensor> result; \
for (const auto i : c10::irange(tensors1.size())) { \
result.emplace_back(tensors1[i].OP(tensors2[i], tensors3[i])); \
} \
return result; \
} \
\
void foreach_tensor_ternary_##OP##_slow_( \
TensorList tensors1, TensorList tensors2, TensorList tensors3) { \
check_foreach_api_restrictions(tensors1, tensors2, tensors3); \
for (const auto i : c10::irange(tensors1.size())) { \
tensors1[i].OP##_(tensors2[i], tensors3[i]); \
} \
}
FOREACH_TERNARY_OP(lerp);
void foreach_tensor_zero_slow_(TensorList tensors) {
check_foreach_api_restrictions(tensors);
for (auto& t : tensors) {
t.zero_();
}
}
std::vector<Tensor> foreach_tensor_norm_slow(
TensorList tensors,
const Scalar& ord,
std::optional<ScalarType> dtype) {
check_foreach_api_restrictions(tensors);
std::vector<Tensor> result;
for (const auto& t : tensors) {
result.emplace_back(at::linalg_vector_norm(t, ord, {}, false, dtype));
}
return result;
}
std::vector<Tensor> foreach_tensor_max_slow(TensorList tensors) {
check_foreach_api_restrictions(tensors);
std::vector<Tensor> result;
for (const auto& t : tensors) {
result.emplace_back(at::max(t));
}
return result;
}
std::vector<Tensor> foreach_scalar_pow_list_kernel_slow(
const Scalar& self,
TensorList exponent) {
check_foreach_api_restrictions(exponent);
std::vector<Tensor> result;
result.reserve(exponent.size());
for (const auto& t : exponent) {
result.emplace_back(at::pow(self, t));
}
return result;
}
} // namespace at::native