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ForeachPointwiseOp.cu
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#include <ATen/Dispatch.h>
#include <ATen/native/ForeachUtils.h>
#include <ATen/native/cuda/ForeachFunctors.cuh>
#include <ATen/NumericUtils.h>
namespace at { namespace native {
template<template<class> class Op>
std::vector<Tensor> foreach_pointwise_op(TensorList input, TensorList tensors1, TensorList tensors2, const Scalar& scalar) {
std::vector<std::vector<at::Tensor>> tensor_lists;
std::vector<at::Tensor> vec_res;
vec_res.reserve(input.size());
for (const auto& t: input) {
vec_res.emplace_back(at::native::empty_like(t));
}
tensor_lists.emplace_back(input.vec());
tensor_lists.emplace_back(tensors1.vec());
tensor_lists.emplace_back(tensors2.vec());
tensor_lists.emplace_back(std::move(vec_res));
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2(kHalf, kBFloat16, input[0].scalar_type(), "foreach_pointwise_op_cuda", [&]() {
using opmath_t = at::opmath_type<scalar_t>;
multi_tensor_apply<4>(tensor_lists,
PointwiseOpScalarFunctor<scalar_t,
/* depth */ 4,
/* r_args_depth */ 3,
/* res_arg_index */ 3>(),
Op<opmath_t>(),
scalar.to<opmath_t>());
});
return tensor_lists[3];
}
template<template<class> class Op>
void foreach_pointwise_op_(TensorList input, TensorList tensors1, TensorList tensors2, const Scalar& scalar) {
std::vector<std::vector<at::Tensor>> tensor_lists;
tensor_lists.emplace_back(input.vec());
tensor_lists.emplace_back(tensors1.vec());
tensor_lists.emplace_back(tensors2.vec());
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2(kHalf, kBFloat16, input[0].scalar_type(), "foreach_pointwise_op__cuda", [&]() {
using opmath_t = at::opmath_type<scalar_t>;
multi_tensor_apply<3>(tensor_lists,
PointwiseOpScalarFunctor<scalar_t,
/* depth */ 3,
/* r_args_depth */ 3,
/* res_arg_index */ 0>(),
Op<opmath_t>(),
scalar.to<opmath_t>());
});
}
template<template<class> class Op>
void foreach_pointwise_op_(TensorList input, TensorList tensors1, TensorList tensors2, at::ArrayRef<Scalar> scalars) {
std::vector<std::vector<at::Tensor>> tensor_lists;
tensor_lists.reserve(3);
tensor_lists.emplace_back(input.vec());
tensor_lists.emplace_back(tensors1.vec());
tensor_lists.emplace_back(tensors2.vec());
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2(kHalf, kBFloat16, input[0].scalar_type(), "foreach_pointwise_op__cuda", [&]() {
using opmath_t = at::opmath_type<scalar_t>;
multi_tensor_apply<3, opmath_t>(tensor_lists,
scalars,
PointwiseOpScalarListFunctor<scalar_t,
/* depth */ 3,
/* r_args_depth */ 3,
/* res_arg_index */ 0>(),
Op<opmath_t>());
});
}
template<template<class> class Op>
std::vector<Tensor> foreach_pointwise_op(TensorList input, TensorList tensors1, TensorList tensors2, at::ArrayRef<Scalar> scalars) {
std::vector<std::vector<at::Tensor>> tensor_lists;
tensor_lists.reserve(4);
std::vector<at::Tensor> vec_res;
vec_res.reserve(input.size());
for (const auto& t: input) {
vec_res.emplace_back(at::native::empty_like(t));
}
tensor_lists.emplace_back(input.vec());
tensor_lists.emplace_back(tensors1.vec());
tensor_lists.emplace_back(tensors2.vec());
tensor_lists.emplace_back(std::move(vec_res));
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2(kHalf, kBFloat16, input[0].scalar_type(), "foreach_pointwise_op_cuda", [&]() {
using opmath_t = at::opmath_type<scalar_t>;
multi_tensor_apply<4, opmath_t>(tensor_lists,
scalars,
PointwiseOpScalarListFunctor<scalar_t,
/* depth */ 4,
/* r_args_depth */ 3,
/* res_arg_index */ 3>(),
Op<opmath_t>());
});
return tensor_lists[3];
}
#define FOREACH_POINTWISE_OP_SCALAR(NAME, OP) \
std::vector<Tensor> foreach_tensor_##NAME##_scalar_cuda(TensorList input, TensorList tensors1, TensorList tensors2, const Scalar& scalar) { \
check_foreach_api_restrictions(input, tensors1, tensors2); \
\
if (!can_use_fast_route({input, tensors1, tensors2}, scalar) || has_integral_tensor(input, /* includeBool */ true)) { \
return at::native::foreach_tensor_##NAME##_scalar_slow(input, tensors1, tensors2, scalar); \
} \
\
return foreach_pointwise_op<OP>(input, tensors1, tensors2, scalar); \
} \
\
void foreach_tensor_##NAME##_scalar_cuda_(TensorList input, TensorList tensors1, TensorList tensors2, const Scalar& scalar) { \
check_foreach_api_restrictions(input, tensors1, tensors2); \
\
if (!can_use_fast_route({input, tensors1, tensors2}, scalar) || has_integral_tensor(input, /* includeBool */ true)) { \
return at::native::foreach_tensor_##NAME##_scalar_slow_(input, tensors1, tensors2, scalar); \
} \
\
foreach_pointwise_op_<OP>(input, tensors1, tensors2, scalar); \
}
#define FOREACH_POINTWISE_OP_SCALARLIST(NAME, OP) \
std::vector<Tensor> foreach_tensor_##NAME##_scalarlist_cuda(TensorList input, TensorList tensors1, TensorList tensors2, at::ArrayRef<Scalar> scalars) { \
check_foreach_api_restrictions(input, tensors1, tensors2, scalars); \
\
if (!can_use_fast_route({input, tensors1, tensors2}, scalars) || has_integral_tensor(input, /* includeBool */ true)) { \
return at::native::foreach_tensor_##NAME##_scalarlist_slow(input, tensors1, tensors2, scalars); \
} \
\
return foreach_pointwise_op<OP>(input, tensors1, tensors2, scalars); \
} \
\
void foreach_tensor_##NAME##_scalarlist_cuda_(TensorList input, TensorList tensors1, TensorList tensors2, at::ArrayRef<Scalar> scalars) { \
check_foreach_api_restrictions(input, tensors1, tensors2, scalars); \
\
if (!can_use_fast_route({input, tensors1, tensors2}, scalars) || has_integral_tensor(input, /* includeBool */ true)) { \
return at::native::foreach_tensor_##NAME##_scalarlist_slow_(input, tensors1, tensors2, scalars); \
} \
\
foreach_pointwise_op_<OP>(input, tensors1, tensors2, scalars); \
}
FOREACH_POINTWISE_OP_SCALAR(addcmul, std::multiplies);
FOREACH_POINTWISE_OP_SCALAR(addcdiv, std::divides);
FOREACH_POINTWISE_OP_SCALARLIST(addcmul, std::multiplies);
FOREACH_POINTWISE_OP_SCALARLIST(addcdiv, std::divides);
// Why bool tensors are pushed to slowpath?
// Because `AT_DISPATCH_ALL_TYPES_AND` is used below.
// TODO(mkozuki): Check whether it's possible to handle bool tensors in fastpath.
#define FOREACH_MAXIMUM_MINIMUM_OP(NAME, OP) \
std::vector<Tensor> foreach_tensor_##NAME##_cuda(TensorList tensors1, TensorList tensors2) { \
check_foreach_api_restrictions(tensors1, tensors2); \
if (!can_use_fast_route({tensors1, tensors2}) || has_bool_tensor(tensors1)) { \
return at::native::foreach_tensor_##NAME##_slow(tensors1, tensors2); \
} \
\
std::vector<std::vector<at::Tensor>> tensor_lists; \
std::vector<at::Tensor> vec_res; \
vec_res.reserve(tensors1.size()); \
for (const auto& t: tensors1) { \
vec_res.emplace_back(at::native::empty_like(t)); \
} \
\
tensor_lists.emplace_back(tensors1.vec()); \
tensor_lists.emplace_back(tensors2.vec()); \
tensor_lists.emplace_back(std::move(vec_res)); \
\
AT_DISPATCH_ALL_TYPES_AND2(kHalf, kBFloat16, tensors1[0].scalar_type(), "foreach_maximum_minimum_op_cuda", [&]() { \
using opmath_t = at::opmath_type<scalar_t>; \
auto op = [] GPU_LAMBDA (opmath_t a, opmath_t b) -> opmath_t { \
opmath_t c = a OP b ? a : b; \
if (_isnan(a)) { \
c = a; \
} \
return c;}; \
multi_tensor_apply<3>(tensor_lists, \
PointwiseOpListFunctor<scalar_t, 3>(), \
op); \
}); \
\
return tensor_lists[2]; \
} \
FOREACH_MAXIMUM_MINIMUM_OP(maximum, >)
FOREACH_MAXIMUM_MINIMUM_OP(minimum, <)
}} // namespace at::native