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ForeachUnaryOp.cu
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#include <ATen/Dispatch.h>
#include <ATen/native/ForeachUtils.h>
#include <ATen/native/cuda/ForeachFunctors.cuh>
namespace at { namespace native {
template <template<class> class Op>
std::vector<Tensor> foreach_unary_op_complex(TensorList tensors) {
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(std::move(vec_res));
AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND1(ScalarType::Half, tensors[0].scalar_type(), "foreach_unary_op_cuda", [&]() {
using opmath_t = get_opmath_t<scalar_t>::opmath_t;
multi_tensor_apply<2>(tensor_lists,
UnaryOpFunctor<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_unary_op_complex_(TensorList tensors) {
std::vector<std::vector<at::Tensor>> tensor_lists;
tensor_lists.emplace_back(tensors.vec());
AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND1(ScalarType::Half, tensors[0].scalar_type(), "foreach_unary_op_cuda_", [&]() {
using opmath_t = get_opmath_t<scalar_t>::opmath_t;
multi_tensor_apply<1>(tensor_lists,
UnaryOpFunctor<scalar_t,
/* depth */ 1,
/* r_args_depth */ 1,
/* res_arg_index */ 0>(),
Op<opmath_t>());
});
}
template <template<class> class Op>
std::vector<Tensor> foreach_unary_op_complex_bfloat16(TensorList tensors) {
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(std::move(vec_res));
AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND2(ScalarType::Half, ScalarType::BFloat16, tensors[0].scalar_type(), "foreach_unary_op_cuda", [&]() {
using opmath_t = get_opmath_t<scalar_t>::opmath_t;
multi_tensor_apply<2>(tensor_lists,
UnaryOpFunctor<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_unary_op_complex_bfloat16_(TensorList tensors) {
std::vector<std::vector<at::Tensor>> tensor_lists;
tensor_lists.emplace_back(tensors.vec());
AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND2(ScalarType::Half, ScalarType::BFloat16, tensors[0].scalar_type(), "foreach_unary_op_cuda_", [&]() {
using opmath_t = get_opmath_t<scalar_t>::opmath_t;
multi_tensor_apply<1>(tensor_lists,
UnaryOpFunctor<scalar_t,
/* depth */ 1,
/* r_args_depth */ 1,
/* res_arg_index */ 0>(),
Op<opmath_t>());
});
}
template <template<class> class Op>
std::vector<Tensor> foreach_unary_op(TensorList tensors) {
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(std::move(vec_res));
AT_DISPATCH_FLOATING_TYPES_AND(ScalarType::Half, tensors[0].scalar_type(), "foreach_unary_op_cuda", [&]() {
using opmath_t = get_opmath_t<scalar_t>::opmath_t;
multi_tensor_apply<2>(tensor_lists,
UnaryOpFunctor<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_unary_op_(TensorList tensors) {
std::vector<std::vector<at::Tensor>> tensor_lists;
tensor_lists.emplace_back(tensors.vec());
AT_DISPATCH_FLOATING_TYPES_AND_HALF(tensors[0].scalar_type(), "foreach_unary_op_cuda_", [&]() {
using opmath_t = get_opmath_t<scalar_t>::opmath_t;
multi_tensor_apply<1>(tensor_lists,
UnaryOpFunctor<scalar_t,
/* depth */ 1,
/* r_args_depth */ 1,
/* res_arg_index */ 0>(),
Op<opmath_t>());
});
}
template <template<class> class Op>
void foreach_op_unary_(TensorList tensors) {
std::vector<std::vector<at::Tensor>> tensor_lists;
tensor_lists.emplace_back(tensors.vec());
AT_DISPATCH_FLOATING_TYPES_AND_HALF(tensors[0].scalar_type(), "foreach_unary_op_cuda_", [&]() {
using opmath_t = get_opmath_t<scalar_t>::opmath_t;
multi_tensor_apply<1>(tensor_lists,
UnaryOpFunctor<scalar_t,
/* depth */ 1,
/* r_args_depth */ 1,
/* res_arg_index */ 0>(),
Op<opmath_t>());
});
}
template <template<class> class Op>
std::vector<Tensor> foreach_unary_op_bfloat16(TensorList tensors) {
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(std::move(vec_res));
AT_DISPATCH_FLOATING_TYPES_AND2(ScalarType::Half, ScalarType::BFloat16, tensors[0].scalar_type(), "foreach_unary_op_cuda", [&]() {
using opmath_t = get_opmath_t<scalar_t>::opmath_t;
multi_tensor_apply<2>(tensor_lists,
UnaryOpFunctor<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_unary_op_bfloat16_(TensorList tensors) {
std::vector<std::vector<at::Tensor>> tensor_lists;
tensor_lists.emplace_back(tensors.vec());
AT_DISPATCH_FLOATING_TYPES_AND2(ScalarType::Half, ScalarType::BFloat16, tensors[0].scalar_type(), "foreach_unary_op_cuda_", [&]() {
using opmath_t = get_opmath_t<scalar_t>::opmath_t;
multi_tensor_apply<1>(tensor_lists,
UnaryOpFunctor<scalar_t,
/* depth */ 1,
/* r_args_depth */ 1,
/* res_arg_index */ 0>(),
Op<opmath_t>());
});
}
#define FOREACH_UNARY_OP_COMPLEX(NAME, NAME1) \
template<typename T> \
struct NAME1 { \
__device__ T operator()(T t) const { return std::NAME(t); } \
}; \
\
std::vector<Tensor> foreach_tensor_##NAME##_cuda(TensorList tensors) { \
check_foreach_api_restrictions(tensors); \
if (!can_use_fast_route(tensors)) { \
return at::native::foreach_tensor_##NAME##_slow(tensors); \
} \
\
return foreach_unary_op_complex<NAME1>(tensors); \
} \
\
void foreach_tensor_##NAME##_cuda_(TensorList tensors) { \
check_foreach_api_restrictions(tensors); \
if (!can_use_fast_route(tensors)) { \
return at::native::foreach_tensor_##NAME##_slow_(tensors); \
} \
\
foreach_unary_op_complex_<NAME1>(tensors); \
}
#define FOREACH_UNARY_OP_COMPLEX_BFLOAT16(NAME, NAME1) \
template<typename T> \
struct NAME1 { \
__device__ T operator()(T t) const { return std::NAME(t); } \
}; \
\
std::vector<Tensor> foreach_tensor_##NAME##_cuda(TensorList tensors) { \
check_foreach_api_restrictions(tensors); \
if (!can_use_fast_route(tensors)) { \
return at::native::foreach_tensor_##NAME##_slow(tensors); \
} \
\
return foreach_unary_op_complex_bfloat16<NAME1>(tensors); \
} \
\
void foreach_tensor_##NAME##_cuda_(TensorList tensors) { \
check_foreach_api_restrictions(tensors); \
if (!can_use_fast_route(tensors)) { \
return at::native::foreach_tensor_##NAME##_slow_(tensors); \
} \
\
foreach_unary_op_complex_bfloat16_<NAME1>(tensors); \
}
#define FOREACH_UNARY_OP(NAME, NAME1) \
template<typename T> \
struct NAME1 { \
__device__ T operator()(T t) const { return std::NAME(t); } \
}; \
\
std::vector<Tensor> foreach_tensor_##NAME##_cuda(TensorList tensors) { \
check_foreach_api_restrictions(tensors); \
if (!can_use_fast_route(tensors)) { \
return at::native::foreach_tensor_##NAME##_slow(tensors); \
} \
\
return foreach_unary_op<NAME1>(tensors); \
} \
\
void foreach_tensor_##NAME##_cuda_(TensorList tensors) { \
check_foreach_api_restrictions(tensors); \
if (!can_use_fast_route(tensors)) { \
return at::native::foreach_tensor_##NAME##_slow_(tensors); \
} \
\
foreach_unary_op_<NAME1>(tensors); \
}
#define FOREACH_UNARY_OP_BFLOAT16(NAME, NAME1) \
template<typename T> \
struct NAME1 { \
__device__ T operator()(T t) const { return std::NAME(t); } \
}; \
\
std::vector<Tensor> foreach_tensor_##NAME##_cuda(TensorList tensors) { \
check_foreach_api_restrictions(tensors); \
\
if (!can_use_fast_route(tensors)) { \
return at::native::foreach_tensor_##NAME##_slow(tensors); \
} \
\
return foreach_unary_op_bfloat16<NAME1>(tensors); \
} \
\
void foreach_tensor_##NAME##_cuda_(TensorList tensors) { \
check_foreach_api_restrictions(tensors); \
\
if (!can_use_fast_route(tensors)) { \
return at::native::foreach_tensor_##NAME##_slow_(tensors); \
} \
\
foreach_unary_op_bfloat16_<NAME1>(tensors); \
}
FOREACH_UNARY_OP(ceil, Ceil);
FOREACH_UNARY_OP(erfc, Erfc);
FOREACH_UNARY_OP(expm1, Expm1);
FOREACH_UNARY_OP(floor, Floor);
FOREACH_UNARY_OP(lgamma, Lgamma);
FOREACH_UNARY_OP_BFLOAT16(log1p, Log1p);
FOREACH_UNARY_OP_BFLOAT16(erf, Erf);
FOREACH_UNARY_OP_COMPLEX(acos, Acos);
FOREACH_UNARY_OP_COMPLEX(asin, Asin);
FOREACH_UNARY_OP_COMPLEX(atan, Atan);
FOREACH_UNARY_OP_COMPLEX(cosh, Cosh);
FOREACH_UNARY_OP_COMPLEX(tan, Tan);
FOREACH_UNARY_OP_COMPLEX(sin, Sin);
FOREACH_UNARY_OP_COMPLEX(sinh, Sinh);
FOREACH_UNARY_OP_COMPLEX_BFLOAT16(exp, Exp);
FOREACH_UNARY_OP_COMPLEX_BFLOAT16(sqrt, Sqrt);
FOREACH_UNARY_OP_COMPLEX_BFLOAT16(cos, Cos);
FOREACH_UNARY_OP_COMPLEX_BFLOAT16(tanh, Tanh);
FOREACH_UNARY_OP_COMPLEX_BFLOAT16(log, Log);
FOREACH_UNARY_OP_COMPLEX_BFLOAT16(log10, Log10);
FOREACH_UNARY_OP_COMPLEX_BFLOAT16(log2, Log2);
//
// Special cases
//
std::vector<Tensor> foreach_tensor_neg_cuda(TensorList tensors) {
check_foreach_api_restrictions(tensors);
if (!can_use_fast_route(tensors)) {
return at::native::foreach_tensor_neg_slow(tensors);
}
return foreach_unary_op_complex_bfloat16<std::negate>(tensors);
}
void foreach_tensor_neg_cuda_(TensorList tensors) {
check_foreach_api_restrictions(tensors);
if (!can_use_fast_route(tensors)) {
return at::native::foreach_tensor_neg_slow_(tensors);
}
foreach_unary_op_complex_bfloat16_<std::negate>(tensors);
}
template<typename T> \
struct Round { \
__device__ T operator()(T t) const { return std::nearbyint(t); } \
};
std::vector<Tensor> foreach_tensor_round_cuda(TensorList tensors) {
check_foreach_api_restrictions(tensors);
if (!can_use_fast_route(tensors)) {
return at::native::foreach_tensor_round_slow(tensors);
}
return foreach_unary_op<Round>(tensors);
}
void foreach_tensor_round_cuda_(TensorList tensors) {
check_foreach_api_restrictions(tensors);
if (!can_use_fast_route(tensors)) {
return at::native::foreach_tensor_round_slow_(tensors);
}
foreach_unary_op_<Round>(tensors);
}
// Abs have to go via slow path in case of a complex type.
// This is because foreach kernels can't return a different dtype than passed, while
// abs with complex input will produce float output.
template<typename T>
struct Abs {
__device__ T operator()(T t) const { return std::abs(t); }
};
std::vector<Tensor> foreach_tensor_abs_cuda(TensorList tensors) {
check_foreach_api_restrictions(tensors);
bool has_complex = false;
for (auto t : tensors) {
if (at::isComplexType(t.scalar_type())) {
has_complex = true;
}
}
if (!can_use_fast_route(tensors) || has_complex) {
return at::native::foreach_tensor_abs_slow(tensors);
}
return foreach_unary_op_complex_bfloat16<Abs>(tensors);
}
void foreach_tensor_abs_cuda_(TensorList tensors) {
check_foreach_api_restrictions(tensors);
bool has_complex = false;
for (auto t : tensors) {
if (at::isComplexType(t.scalar_type())) {
has_complex = true;
}
}
if (!can_use_fast_route(tensors) || has_complex) {
return at::native::foreach_tensor_abs_slow_(tensors);
}
foreach_unary_op_complex_bfloat16_<Abs>(tensors);
}
template<typename T>
struct Trunc {
__device__ T operator()(T t) const { return t - std::trunc(t); }
};
std::vector<Tensor> foreach_tensor_frac_cuda(TensorList tensors) {
check_foreach_api_restrictions(tensors);
if (!can_use_fast_route(tensors)) {
return at::native::foreach_tensor_frac_slow(tensors);
}
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(std::move(vec_res));
AT_DISPATCH_FLOATING_TYPES_AND_HALF(tensors[0].scalar_type(), "foreach_unary_op_cuda", [&]() {
using opmath_t = get_opmath_t<scalar_t>::opmath_t;
multi_tensor_apply<2>(tensor_lists,
UnaryOpFunctor<scalar_t,
/* depth */ 2,
/* r_args_depth */ 1,
/* res_arg_index */ 1>(),
Trunc<opmath_t>());
});
return tensor_lists[1];
}
void foreach_tensor_frac_cuda_(TensorList tensors) {
check_foreach_api_restrictions(tensors);
if (!can_use_fast_route(tensors)) {
return at::native::foreach_tensor_frac_slow_(tensors);
}
std::vector<std::vector<at::Tensor>> tensor_lists;
tensor_lists.emplace_back(tensors.vec());
AT_DISPATCH_FLOATING_TYPES_AND_HALF(tensors[0].scalar_type(), "foreach_unary_op_cuda_", [&]() {
using opmath_t = get_opmath_t<scalar_t>::opmath_t;
multi_tensor_apply<1>(tensor_lists,
UnaryOpFunctor<scalar_t,
/* depth */ 1,
/* r_args_depth */ 1,
/* res_arg_index */ 0>(),
Trunc<opmath_t>());
});
}
template<typename T>
struct Sigmoid {
T one = T(1);
__device__ T operator()(T t) const { return (one / (one + std::exp(-t))); }
};
std::vector<Tensor> foreach_tensor_sigmoid_cuda(TensorList tensors) {
check_foreach_api_restrictions(tensors);
if (!can_use_fast_route(tensors)) {
return at::native::foreach_tensor_sigmoid_slow(tensors);
}
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(std::move(vec_res));
AT_DISPATCH_FLOATING_TYPES_AND2(ScalarType::Half, ScalarType::BFloat16, tensors[0].scalar_type(), "foreach_unary_op_cuda", [&]() {
using opmath_t = get_opmath_t<scalar_t>::opmath_t;
multi_tensor_apply<2>(tensor_lists,
UnaryOpFunctor<scalar_t,
/* depth */ 2,
/* r_args_depth */ 1,
/* res_arg_index */ 1>(),
Sigmoid<opmath_t>());
});
return tensor_lists[1];
}
void foreach_tensor_sigmoid_cuda_(TensorList tensors) {
check_foreach_api_restrictions(tensors);
if (!can_use_fast_route(tensors)) {
return at::native::foreach_tensor_sigmoid_slow_(tensors);
}
std::vector<std::vector<at::Tensor>> tensor_lists;
tensor_lists.emplace_back(tensors.vec());
AT_DISPATCH_FLOATING_TYPES_AND2(ScalarType::Half, ScalarType::BFloat16, tensors[0].scalar_type(), "foreach_unary_op_cuda_", [&]() {
using opmath_t = get_opmath_t<scalar_t>::opmath_t;
multi_tensor_apply<1>(tensor_lists,
UnaryOpFunctor<scalar_t,
/* depth */ 1,
/* r_args_depth */ 1,
/* res_arg_index */ 0>(),
Sigmoid<opmath_t>());
});
}
template<typename T>
struct Reciprocal {
T one = T(1);
__device__ T operator()(T t) const { return (one / t); }
};
std::vector<Tensor> foreach_tensor_reciprocal_cuda(TensorList tensors) {
check_foreach_api_restrictions(tensors);
if (!can_use_fast_route(tensors)) {
return at::native::foreach_tensor_reciprocal_slow(tensors);
}
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(std::move(vec_res));
AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND2(ScalarType::Half, ScalarType::BFloat16, tensors[0].scalar_type(), "foreach_unary_op_cuda", [&]() {
using opmath_t = get_opmath_t<scalar_t>::opmath_t;
multi_tensor_apply<2>(tensor_lists,
UnaryOpFunctor<scalar_t,
/* depth */ 2,
/* r_args_depth */ 1,
/* res_arg_index */ 1>(),
Reciprocal<opmath_t>());
});
return tensor_lists[1];
}
void foreach_tensor_reciprocal_cuda_(TensorList tensors) {
check_foreach_api_restrictions(tensors);
if (!can_use_fast_route(tensors)) {
return at::native::foreach_tensor_reciprocal_slow_(tensors);
}
std::vector<std::vector<at::Tensor>> tensor_lists;
tensor_lists.emplace_back(tensors.vec());
AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND2(ScalarType::Half, ScalarType::BFloat16, tensors[0].scalar_type(), "foreach_unary_op_cuda_", [&]() {
using opmath_t = get_opmath_t<scalar_t>::opmath_t;
multi_tensor_apply<1>(tensor_lists,
UnaryOpFunctor<scalar_t,
/* depth */ 1,
/* r_args_depth */ 1,
/* res_arg_index */ 0>(),
Reciprocal<opmath_t>());
});
}
template<typename T>
struct Truncf {
__device__ T operator()(T t) const { return std::trunc(t); }
};
std::vector<Tensor> foreach_tensor_trunc_cuda(TensorList tensors) {
check_foreach_api_restrictions(tensors);
if (!can_use_fast_route(tensors)) {
return at::native::foreach_tensor_trunc_slow(tensors);
}
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(std::move(vec_res));
AT_DISPATCH_FLOATING_TYPES_AND_HALF(tensors[0].scalar_type(), "foreach_unary_op_cuda", [&]() {
using opmath_t = get_opmath_t<scalar_t>::opmath_t;
multi_tensor_apply<2>(tensor_lists,
UnaryOpFunctor<scalar_t,
/* depth */ 2,
/* r_args_depth */ 1,
/* res_arg_index */ 1>(),
Truncf<opmath_t>());
});
return tensor_lists[1];
}
void foreach_tensor_trunc_cuda_(TensorList tensors) {
check_foreach_api_restrictions(tensors);
if (!can_use_fast_route(tensors)) {
return at::native::foreach_tensor_trunc_slow_(tensors);
}
std::vector<std::vector<at::Tensor>> tensor_lists;
tensor_lists.emplace_back(tensors.vec());
AT_DISPATCH_FLOATING_TYPES_AND_HALF(tensors[0].scalar_type(), "foreach_unary_op_cuda_", [&]() {
using opmath_t = get_opmath_t<scalar_t>::opmath_t;
multi_tensor_apply<1>(tensor_lists,
UnaryOpFunctor<scalar_t,
/* depth */ 1,
/* r_args_depth */ 1,
/* res_arg_index */ 0>(),
Truncf<opmath_t>());
});
}
}} // namespace at::native