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ForeachBinaryOpScalarList.cu
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ForeachBinaryOpScalarList.cu
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#include <ATen/Dispatch.h>
#include <ATen/native/ForeachUtils.h>
#include <ATen/native/cuda/ForeachFunctors.cuh>
#include <ATen/native/BinaryOps.h>
namespace at { namespace native {
template<template<class> class Op>
std::vector<Tensor> foreach_binary_op(TensorList tensors, at::ArrayRef<Scalar> scalars) {
std::vector<std::vector<at::Tensor>> tensor_lists;
std::vector<at::Tensor> vec_res;
vec_res.reserve(tensors.size());
for (const auto& t: tensors) {
vec_res.emplace_back(at::native::empty_like(t));
}
tensor_lists.emplace_back(tensors.vec());
tensor_lists.emplace_back(vec_res);
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND3(kBFloat16, kHalf, kBool, tensors[0].scalar_type(), "foreach_binary_op_scalarlist_cuda", [&]() {
using opmath_t = get_opmath_t<scalar_t>::opmath_t;
multi_tensor_apply<2, opmath_t>(tensor_lists,
scalars,
BinaryOpScalarListFunctor<scalar_t,
/* depth */ 2,
/* r_args_depth */ 1,
/* res_arg_index */ 1>(),
Op<opmath_t>());
});
return tensor_lists[1];
}
template<template<class> class Op>
void foreach_binary_op_(TensorList tensors, at::ArrayRef<Scalar> scalars) {
std::vector<std::vector<at::Tensor>> tensor_lists;
tensor_lists.emplace_back(tensors.vec());
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND3(kBFloat16, kHalf, kBool, tensors[0].scalar_type(), "foreach_binary_op_scalarlist_cuda_", [&]() {
using opmath_t = get_opmath_t<scalar_t>::opmath_t;
multi_tensor_apply<1, opmath_t>(tensor_lists,
scalars,
BinaryOpScalarListFunctor<scalar_t,
/* depth */ 1,
/* r_args_depth */ 1,
/* res_arg_index */ 0>(),
Op<opmath_t>());
});
}
#define FOREACH_BINARY_OP_SCALARLIST(NAME, OP, DIV_OP) \
void foreach_tensor_##NAME##_scalarlist_kernel_cuda_(TensorList tensors, at::ArrayRef<Scalar> scalars) { \
check_foreach_api_restrictions(tensors, scalars); \
if (!can_use_fast_route(tensors, scalars, DIV_OP)) { \
return at::native::foreach_tensor_##NAME##_scalarlist_kernel_slow_(tensors, scalars); \
} \
\
foreach_binary_op_<OP>(tensors, scalars); \
} \
\
std::vector<Tensor> foreach_tensor_##NAME##_scalarlist_kernel_cuda(TensorList tensors, at::ArrayRef<Scalar> scalars) { \
check_foreach_api_restrictions(tensors, scalars); \
if (!can_use_fast_route(tensors, scalars, DIV_OP)) { \
return at::native::foreach_tensor_##NAME##_scalarlist_kernel_slow(tensors, scalars); \
} \
\
return foreach_binary_op<OP>(tensors, scalars); \
}
FOREACH_BINARY_OP_SCALARLIST(add, std::plus, /*div_op*/ false);
FOREACH_BINARY_OP_SCALARLIST(mul, std::multiplies, /*div_op*/ false);
FOREACH_BINARY_OP_SCALARLIST(div, std::divides, /*div_op*/ true);
// This does not use FOREACH_BINARY_OP_SCALARLIST because
// In the case of subtraction, we dont allow scalar to be boolean following the torch.sub logic
void foreach_tensor_sub_scalarlist_kernel_cuda_(TensorList tensors, at::ArrayRef<Scalar> scalars) {
check_foreach_api_restrictions(tensors, scalars);
for (int i = 0; i < tensors.size(); i++) {
sub_check(tensors[i], scalars[i]);
}
if (!can_use_fast_route({tensors}, scalars)) {
return at::native::foreach_tensor_sub_scalarlist_kernel_slow_(tensors, scalars);
}
foreach_binary_op_<std::minus>(tensors, scalars);
}
std::vector<Tensor> foreach_tensor_sub_scalarlist_kernel_cuda(TensorList tensors, at::ArrayRef<Scalar> scalars) {
check_foreach_api_restrictions(tensors, scalars);
for (int i = 0; i < tensors.size(); i++) {
sub_check(tensors[i], scalars[i]);
}
if (!can_use_fast_route({tensors}, scalars)) {
return at::native::foreach_tensor_sub_scalarlist_kernel_slow(tensors, scalars);
}
return foreach_binary_op<std::minus>(tensors, scalars);
}
}} // namespace at::native