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BinaryOps.cpp
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BinaryOps.cpp
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#include <ATen/native/BinaryOps.h>
#include <type_traits>
#include <ATen/ATen.h>
#include <ATen/Dispatch.h>
#include <ATen/MemoryOverlap.h>
#include <ATen/NativeFunctions.h>
#include <ATen/native/TensorIterator.h>
#include <ATen/ExpandUtils.h>
#include <ATen/RedispatchFunctions.h>
#include <torch/library.h>
namespace at {
namespace meta {
TORCH_META_FUNC2(add, Tensor) (
const Tensor& self, const Tensor& other, const Scalar& alpha
) {
build_borrowing_binary_op(maybe_get_output(), self, other);
native::alpha_check(dtype(), alpha);
}
TORCH_META_FUNC2(sub, Tensor) (
const Tensor& self, const Tensor& other, const Scalar& alpha
) {
native::sub_check(self, other);
build_borrowing_binary_op(maybe_get_output(), self, other);
native::alpha_check(dtype(), alpha);
}
TORCH_META_FUNC2(mul, Tensor) (
const Tensor& self, const Tensor& other
) {
build_borrowing_binary_op(maybe_get_output(), self, other);
}
TORCH_META_FUNC2(div, Tensor) (const Tensor& self, const Tensor& other) {
build_borrowing_binary_float_op(maybe_get_output(), self, other);
}
TORCH_META_FUNC2(div, Tensor_mode) (const Tensor& self, const Tensor& other, c10::optional<c10::string_view> rounding_mode) {
if (!rounding_mode.has_value()) {
build_borrowing_binary_float_op(maybe_get_output(), self, other);
// NOLINTNEXTLINE(bugprone-branch-clone)
} else if (*rounding_mode == "trunc") {
build_borrowing_binary_op(maybe_get_output(), self, other);
} else if (*rounding_mode == "floor") {
build_borrowing_binary_op(maybe_get_output(), self, other);
} else {
TORCH_CHECK(false,
"div expected rounding_mode to be one of None, 'trunc', or 'floor' "
"but found '", *rounding_mode, "'");
}
}
TORCH_META_FUNC(special_xlog1py) (const Tensor& self, const Tensor& other) {
build_borrowing_binary_float_op(maybe_get_output(), self, other);
}
TORCH_META_FUNC(special_zeta) (const Tensor& self, const Tensor& other) {
build_borrowing_binary_float_op(maybe_get_output(), self, other);
}
TORCH_META_FUNC(special_chebyshev_polynomial_t) (const Tensor& self, const Tensor& n) {
build_borrowing_binary_float_op(maybe_get_output(), self, n);
}
TORCH_META_FUNC(special_chebyshev_polynomial_u) (const Tensor& self, const Tensor& n) {
build_borrowing_binary_float_op(maybe_get_output(), self, n);
}
TORCH_META_FUNC(special_chebyshev_polynomial_v) (const Tensor& self, const Tensor& n) {
build_borrowing_binary_float_op(maybe_get_output(), self, n);
}
TORCH_META_FUNC(special_chebyshev_polynomial_w) (const Tensor& self, const Tensor& n) {
build_borrowing_binary_float_op(maybe_get_output(), self, n);
}
TORCH_META_FUNC(special_hermite_polynomial_h) (const Tensor& self, const Tensor& n) {
build_borrowing_binary_float_op(maybe_get_output(), self, n);
}
TORCH_META_FUNC(special_hermite_polynomial_he) (const Tensor& self, const Tensor& n) {
build_borrowing_binary_float_op(maybe_get_output(), self, n);
}
TORCH_META_FUNC(special_laguerre_polynomial_l) (const Tensor& self, const Tensor& n) {
build_borrowing_binary_float_op(maybe_get_output(), self, n);
}
TORCH_META_FUNC(special_legendre_polynomial_p) (const Tensor& self, const Tensor& n) {
build_borrowing_binary_float_op(maybe_get_output(), self, n);
}
TORCH_META_FUNC(special_shifted_chebyshev_polynomial_t) (const Tensor& self, const Tensor& n) {
build_borrowing_binary_float_op(maybe_get_output(), self, n);
}
TORCH_META_FUNC(special_shifted_chebyshev_polynomial_u) (const Tensor& self, const Tensor& n) {
build_borrowing_binary_float_op(maybe_get_output(), self, n);
}
TORCH_META_FUNC(special_shifted_chebyshev_polynomial_v) (const Tensor& self, const Tensor& n) {
build_borrowing_binary_float_op(maybe_get_output(), self, n);
}
TORCH_META_FUNC(special_shifted_chebyshev_polynomial_w) (const Tensor& self, const Tensor& n) {
build_borrowing_binary_float_op(maybe_get_output(), self, n);
}
TORCH_META_FUNC2(copysign, Tensor) (
const Tensor& self, const Tensor& other
) {
build_borrowing_binary_float_op(maybe_get_output(), self, other);
}
TORCH_META_FUNC(heaviside) (
const Tensor& self, const Tensor& other
) {
TORCH_CHECK(!self.is_complex() && !other.is_complex() &&
(maybe_get_output().defined() ? !maybe_get_output().is_complex() : true),
"heaviside is not yet implemented for complex tensors.");
TORCH_CHECK(self.dtype() == other.dtype() &&
(maybe_get_output().defined() ? maybe_get_output().dtype() == self.dtype() : true),
"heaviside is not yet implemented for tensors with different dtypes.");
build_binary_op(maybe_get_output(), self, other);
}
TORCH_META_FUNC(atan2) (const Tensor& self, const Tensor& other) {
build_borrowing_binary_float_op(maybe_get_output(), self, other);
}
TORCH_META_FUNC2(remainder, Tensor)(const Tensor& self, const Tensor& other) {
build_borrowing_binary_op(maybe_get_output(), self, other);
}
TORCH_META_FUNC2(bitwise_left_shift, Tensor) (
const Tensor& self, const Tensor& other
) {
build_borrowing_binary_op(maybe_get_output(), self, other);
}
TORCH_META_FUNC2(bitwise_right_shift, Tensor) (
const Tensor& self, const Tensor& other
) {
build_borrowing_binary_op(maybe_get_output(), self, other);
}
TORCH_META_FUNC2(bitwise_and, Tensor) (const Tensor& self, const Tensor& other) {
build_borrowing_binary_op(maybe_get_output(), self, other);
}
TORCH_META_FUNC2(bitwise_or, Tensor) (const Tensor& self, const Tensor& other) {
build_borrowing_binary_op(maybe_get_output(), self, other);
}
TORCH_META_FUNC2(bitwise_xor, Tensor) (const Tensor& self, const Tensor& other) {
build_borrowing_binary_op(maybe_get_output(), self, other);
}
TORCH_META_FUNC2(fmod, Tensor) (const Tensor& self, const Tensor& other) {
build_borrowing_binary_op(maybe_get_output(), self, other);
}
TORCH_META_FUNC2(xlogy, Tensor) (const Tensor& self, const Tensor& other) {
build_borrowing_binary_float_op(maybe_get_output(), self, other);
}
TORCH_META_FUNC(logit_backward) (const Tensor& grad_output, const Tensor& input, c10::optional<double> eps) {
build_borrowing_binary_op(maybe_get_output(), grad_output, input);
}
TORCH_META_FUNC(sigmoid_backward) (const Tensor& grad_output, const Tensor& output) {
build_borrowing_binary_op(maybe_get_output(), grad_output, output);
}
TORCH_META_FUNC(tanh_backward) (const Tensor& grad_output, const Tensor& output) {
build_borrowing_binary_op(maybe_get_output(), grad_output, output);
}
// These are normal binary ops that preserve dtype
#define CREATE_BINARY_META_FUNC(func) \
TORCH_META_FUNC(func) (const Tensor& self, const Tensor& other) { \
build_borrowing_binary_op(maybe_get_output(), self, other); \
}
CREATE_BINARY_META_FUNC(logaddexp);
CREATE_BINARY_META_FUNC(logaddexp2);
CREATE_BINARY_META_FUNC(gcd);
CREATE_BINARY_META_FUNC(lcm);
CREATE_BINARY_META_FUNC(hypot);
CREATE_BINARY_META_FUNC(igamma);
CREATE_BINARY_META_FUNC(igammac);
CREATE_BINARY_META_FUNC(nextafter);
TORCH_META_FUNC(maximum) (const Tensor& self, const Tensor& other) {
TORCH_CHECK(!self.is_complex() && !other.is_complex(), "maximum not implemented for complex tensors.");
build_borrowing_binary_op(maybe_get_output(), self, other);
}
TORCH_META_FUNC(minimum) (const Tensor& self, const Tensor& other) {
TORCH_CHECK(!self.is_complex() && !other.is_complex(), "minimum not implemented for complex tensors.");
build_borrowing_binary_op(maybe_get_output(), self, other);
}
TORCH_META_FUNC(fmax) (const Tensor& self, const Tensor& other) {
TORCH_CHECK(!self.is_complex() && !other.is_complex(), "fmax not implemented for complex tensors.");
build_binary_op(maybe_get_output(), self, other);
}
TORCH_META_FUNC(fmin) (const Tensor& self, const Tensor& other) {
TORCH_CHECK(!self.is_complex() && !other.is_complex(), "fmin not implemented for complex tensors.");
build_binary_op(maybe_get_output(), self, other);
}
#define CREATE_COMPARISON_SCALAR_TENSOR_META_FUNC(func) \
TORCH_META_FUNC2(func, Tensor)(const Tensor& self, const Tensor& other) { \
const Tensor& result = maybe_get_output(); \
build_borrowing_comparison_op(result, self, other); \
} \
\
TORCH_META_FUNC2(func, Scalar)(const Tensor& self, const Scalar& other) { \
auto other_tensor = \
native::wrapped_scalar_tensor(other); \
build_borrowing_except_last_argument_comparison_op(maybe_get_output(), self, other_tensor); \
}
CREATE_COMPARISON_SCALAR_TENSOR_META_FUNC(eq);
CREATE_COMPARISON_SCALAR_TENSOR_META_FUNC(ne);
CREATE_COMPARISON_SCALAR_TENSOR_META_FUNC(lt);
CREATE_COMPARISON_SCALAR_TENSOR_META_FUNC(le);
CREATE_COMPARISON_SCALAR_TENSOR_META_FUNC(gt);
CREATE_COMPARISON_SCALAR_TENSOR_META_FUNC(ge);
} // namespace meta
namespace native {
DEFINE_DISPATCH(add_clamp_stub);
DEFINE_DISPATCH(mul_stub);
DEFINE_DISPATCH(sub_stub);
DEFINE_DISPATCH(div_true_stub);
DEFINE_DISPATCH(div_floor_stub);
DEFINE_DISPATCH(div_trunc_stub);
DEFINE_DISPATCH(remainder_stub);
DEFINE_DISPATCH(atan2_stub);
DEFINE_DISPATCH(bitwise_and_stub);
DEFINE_DISPATCH(bitwise_or_stub);
DEFINE_DISPATCH(bitwise_xor_stub);
DEFINE_DISPATCH(lshift_stub);
DEFINE_DISPATCH(rshift_stub);
DEFINE_DISPATCH(logical_and_stub);
DEFINE_DISPATCH(logical_or_stub);
DEFINE_DISPATCH(logical_xor_stub);
DEFINE_DISPATCH(lt_stub);
DEFINE_DISPATCH(le_stub);
DEFINE_DISPATCH(gt_stub);
DEFINE_DISPATCH(ge_stub);
DEFINE_DISPATCH(eq_stub);
DEFINE_DISPATCH(ne_stub);
DEFINE_DISPATCH(sigmoid_backward_stub);
DEFINE_DISPATCH(logit_backward_stub);
DEFINE_DISPATCH(tanh_backward_stub);
DEFINE_DISPATCH(maximum_stub);
DEFINE_DISPATCH(minimum_stub);
DEFINE_DISPATCH(fmax_stub);
DEFINE_DISPATCH(fmin_stub);
DEFINE_DISPATCH(fmod_stub);
DEFINE_DISPATCH(logaddexp_stub);
DEFINE_DISPATCH(logaddexp2_stub);
DEFINE_DISPATCH(gcd_stub);
DEFINE_DISPATCH(lcm_stub);
DEFINE_DISPATCH(hypot_stub);
DEFINE_DISPATCH(igamma_stub);
DEFINE_DISPATCH(igammac_stub);
DEFINE_DISPATCH(nextafter_stub);
DEFINE_DISPATCH(heaviside_stub);
DEFINE_DISPATCH(copysign_stub);
DEFINE_DISPATCH(xlogy_stub);
DEFINE_DISPATCH(xlog1py_stub);
DEFINE_DISPATCH(zeta_stub);
DEFINE_DISPATCH(chebyshev_polynomial_t_stub);
DEFINE_DISPATCH(chebyshev_polynomial_u_stub);
DEFINE_DISPATCH(chebyshev_polynomial_v_stub);
DEFINE_DISPATCH(chebyshev_polynomial_w_stub);
DEFINE_DISPATCH(hermite_polynomial_h_stub);
DEFINE_DISPATCH(hermite_polynomial_he_stub);
DEFINE_DISPATCH(laguerre_polynomial_l_stub);
DEFINE_DISPATCH(legendre_polynomial_p_stub);
DEFINE_DISPATCH(shifted_chebyshev_polynomial_t_stub);
DEFINE_DISPATCH(shifted_chebyshev_polynomial_u_stub);
DEFINE_DISPATCH(shifted_chebyshev_polynomial_v_stub);
DEFINE_DISPATCH(shifted_chebyshev_polynomial_w_stub);
TORCH_IMPL_FUNC(sub_out) (
const Tensor& self, const Tensor& other, const Scalar& alpha, const Tensor& result
) {
add_stub(device_type(), *this, -alpha);
TORCH_INTERNAL_ASSERT(result.scalar_type() == output().dtype());
}
TORCH_IMPL_FUNC(mul_out) (
const Tensor& self, const Tensor& other, const Tensor& result
) {
mul_stub(device_type(), *this);
}
TORCH_IMPL_FUNC(div_out) (const Tensor& self, const Tensor& other, const Tensor& result) {
div_true_stub(device_type(), *this);
}
TORCH_IMPL_FUNC(div_out_mode) (
const Tensor& self, const Tensor& other, c10::optional<c10::string_view> rounding_mode, const Tensor& result
) {
if (!rounding_mode.has_value()) {
div_true_stub(device_type(), *this);
} else if (*rounding_mode == "trunc") {
div_trunc_stub(device_type(), *this);
} else if (*rounding_mode == "floor") {
div_floor_stub(device_type(), *this);
}
}
TORCH_IMPL_FUNC(logit_backward_out) (const Tensor& grad_output, const Tensor& input, c10::optional<double> eps, const Tensor& result) {
logit_backward_stub(device_type(), *this, Scalar(eps ? eps.value() : -1.0));
}
TORCH_IMPL_FUNC(sigmoid_backward_out) (const Tensor& grad_output, const Tensor& output, const Tensor& result) {
sigmoid_backward_stub(device_type(), *this);
}
TORCH_IMPL_FUNC(special_xlog1py_out) (const Tensor& self, const Tensor& other, const Tensor& result) {
xlog1py_stub(device_type(), *this);
}
TORCH_IMPL_FUNC(special_zeta_out) (const Tensor& self, const Tensor& other, const Tensor& result) {
zeta_stub(device_type(), *this);
}
TORCH_IMPL_FUNC(special_chebyshev_polynomial_t_out) (const Tensor& self, const Tensor& n, const Tensor& result) {
chebyshev_polynomial_t_stub(device_type(), *this);
}
TORCH_IMPL_FUNC(special_chebyshev_polynomial_u_out) (const Tensor& self, const Tensor& n, const Tensor& result) {
chebyshev_polynomial_u_stub(device_type(), *this);
}
TORCH_IMPL_FUNC(special_chebyshev_polynomial_v_out) (const Tensor& self, const Tensor& n, const Tensor& result) {
chebyshev_polynomial_v_stub(device_type(), *this);
}
TORCH_IMPL_FUNC(special_chebyshev_polynomial_w_out) (const Tensor& self, const Tensor& n, const Tensor& result) {
chebyshev_polynomial_w_stub(device_type(), *this);
}
TORCH_IMPL_FUNC(special_hermite_polynomial_h_out) (const Tensor& self, const Tensor& n, const Tensor& result) {
hermite_polynomial_h_stub(device_type(), *this);
}
TORCH_IMPL_FUNC(special_hermite_polynomial_he_out) (const Tensor& self, const Tensor& n, const Tensor& result) {
hermite_polynomial_he_stub(device_type(), *this);
}
TORCH_IMPL_FUNC(special_laguerre_polynomial_l_out) (const Tensor& self, const Tensor& n, const Tensor& result) {
laguerre_polynomial_l_stub(device_type(), *this);
}
TORCH_IMPL_FUNC(special_legendre_polynomial_p_out) (const Tensor& self, const Tensor& n, const Tensor& result) {
legendre_polynomial_p_stub(device_type(), *this);
}
TORCH_IMPL_FUNC(special_shifted_chebyshev_polynomial_t_out) (const Tensor& self, const Tensor& n, const Tensor& result) {
shifted_chebyshev_polynomial_t_stub(device_type(), *this);
}
TORCH_IMPL_FUNC(special_shifted_chebyshev_polynomial_u_out) (const Tensor& self, const Tensor& n, const Tensor& result) {
shifted_chebyshev_polynomial_u_stub(device_type(), *this);
}
TORCH_IMPL_FUNC(special_shifted_chebyshev_polynomial_v_out) (const Tensor& self, const Tensor& n, const Tensor& result) {
shifted_chebyshev_polynomial_v_stub(device_type(), *this);
}
TORCH_IMPL_FUNC(special_shifted_chebyshev_polynomial_w_out) (const Tensor& self, const Tensor& n, const Tensor& result) {
shifted_chebyshev_polynomial_w_stub(device_type(), *this);
}
TORCH_IMPL_FUNC(tanh_backward_out) (const Tensor& grad_output, const Tensor& output, const Tensor& result) {
tanh_backward_stub(device_type(), *this);
}
#define CREATE_BINARY_TORCH_IMPL_FUNC(func_out, func_stub) \
TORCH_IMPL_FUNC(func_out) (const Tensor& self, const Tensor& other, const Tensor& result) { \
func_stub(device_type(), *this); \
}
CREATE_BINARY_TORCH_IMPL_FUNC(bitwise_and_out, bitwise_and_stub);
CREATE_BINARY_TORCH_IMPL_FUNC(bitwise_or_out, bitwise_or_stub);
CREATE_BINARY_TORCH_IMPL_FUNC(bitwise_xor_out, bitwise_xor_stub);
CREATE_BINARY_TORCH_IMPL_FUNC(maximum_out, maximum_stub);
CREATE_BINARY_TORCH_IMPL_FUNC(minimum_out, minimum_stub);
CREATE_BINARY_TORCH_IMPL_FUNC(fmax_out, fmax_stub);
CREATE_BINARY_TORCH_IMPL_FUNC(fmin_out, fmin_stub);
CREATE_BINARY_TORCH_IMPL_FUNC(fmod_out, fmod_stub);
CREATE_BINARY_TORCH_IMPL_FUNC(logaddexp_out, logaddexp_stub);
CREATE_BINARY_TORCH_IMPL_FUNC(logaddexp2_out, logaddexp2_stub);
CREATE_BINARY_TORCH_IMPL_FUNC(gcd_out, gcd_stub);
CREATE_BINARY_TORCH_IMPL_FUNC(lcm_out, lcm_stub);
CREATE_BINARY_TORCH_IMPL_FUNC(hypot_out, hypot_stub);
CREATE_BINARY_TORCH_IMPL_FUNC(igamma_out, igamma_stub);
CREATE_BINARY_TORCH_IMPL_FUNC(igammac_out, igammac_stub);
CREATE_BINARY_TORCH_IMPL_FUNC(nextafter_out, nextafter_stub);
CREATE_BINARY_TORCH_IMPL_FUNC(remainder_out, remainder_stub);
CREATE_BINARY_TORCH_IMPL_FUNC(xlogy_out, xlogy_stub);
Tensor special_xlog1py(const Scalar& x, const Tensor& y) {
return at::special_xlog1py(wrapped_scalar_tensor(x), y);
}
Tensor special_xlog1py(const Tensor& x, const Scalar& y) {
return at::special_xlog1py(x, wrapped_scalar_tensor(y));
}
Tensor& special_xlog1py_out(const Scalar& self, const Tensor& other, Tensor& result) {
return at::special_xlog1py_out(result, wrapped_scalar_tensor(self), other);
}
Tensor& special_xlog1py_out(const Tensor& self, const Scalar& other, Tensor& result) {
return at::special_xlog1py_out(result, self, wrapped_scalar_tensor(other));
}
Tensor special_zeta(const Scalar& x, const Tensor& y) {
return at::special_zeta(wrapped_scalar_tensor(x), y);
}
Tensor special_zeta(const Tensor& x, const Scalar& y) {
return at::special_zeta(x, wrapped_scalar_tensor(y));
}
Tensor& special_zeta_out(const Scalar& self, const Tensor& other, Tensor& result) {
return at::special_zeta_out(result, wrapped_scalar_tensor(self), other);
}
Tensor& special_zeta_out(const Tensor& self, const Scalar& other, Tensor& result) {
return at::special_zeta_out(result, self, wrapped_scalar_tensor(other));
}
Tensor special_chebyshev_polynomial_t(const Scalar& x, const Tensor& n) {
return at::special_chebyshev_polynomial_t(wrapped_scalar_tensor(x), n);
}
Tensor special_chebyshev_polynomial_t(const Tensor& x, const Scalar& n) {
return at::special_chebyshev_polynomial_t(x, wrapped_scalar_tensor(n));
}
Tensor& special_chebyshev_polynomial_t_out(const Scalar& self, const Tensor& n, Tensor& result) {
return at::special_chebyshev_polynomial_t_out(result, wrapped_scalar_tensor(self), n);
}
Tensor& special_chebyshev_polynomial_t_out(const Tensor& self, const Scalar& n, Tensor& result) {
return at::special_chebyshev_polynomial_t_out(result, self, wrapped_scalar_tensor(n));
}
Tensor special_chebyshev_polynomial_u(const Scalar& x, const Tensor& n) {
return at::special_chebyshev_polynomial_u(wrapped_scalar_tensor(x), n);
}
Tensor special_chebyshev_polynomial_u(const Tensor& x, const Scalar& n) {
return at::special_chebyshev_polynomial_u(x, wrapped_scalar_tensor(n));
}
Tensor& special_chebyshev_polynomial_u_out(const Scalar& self, const Tensor& n, Tensor& result) {
return at::special_chebyshev_polynomial_u_out(result, wrapped_scalar_tensor(self), n);
}
Tensor& special_chebyshev_polynomial_u_out(const Tensor& self, const Scalar& n, Tensor& result) {
return at::special_chebyshev_polynomial_u_out(result, self, wrapped_scalar_tensor(n));
}
Tensor special_chebyshev_polynomial_v(const Scalar& x, const Tensor& n) {
return at::special_chebyshev_polynomial_v(wrapped_scalar_tensor(x), n);
}
Tensor special_chebyshev_polynomial_v(const Tensor& x, const Scalar& n) {
return at::special_chebyshev_polynomial_v(x, wrapped_scalar_tensor(n));
}
Tensor& special_chebyshev_polynomial_v_out(const Scalar& self, const Tensor& n, Tensor& result) {
return at::special_chebyshev_polynomial_v_out(result, wrapped_scalar_tensor(self), n);
}
Tensor& special_chebyshev_polynomial_v_out(const Tensor& self, const Scalar& n, Tensor& result) {
return at::special_chebyshev_polynomial_v_out(result, self, wrapped_scalar_tensor(n));
}
Tensor special_chebyshev_polynomial_w(const Scalar& x, const Tensor& n) {
return at::special_chebyshev_polynomial_w(wrapped_scalar_tensor(x), n);
}
Tensor special_chebyshev_polynomial_w(const Tensor& x, const Scalar& n) {
return at::special_chebyshev_polynomial_w(x, wrapped_scalar_tensor(n));
}
Tensor& special_chebyshev_polynomial_w_out(const Scalar& self, const Tensor& n, Tensor& result) {
return at::special_chebyshev_polynomial_w_out(result, wrapped_scalar_tensor(self), n);
}
Tensor& special_chebyshev_polynomial_w_out(const Tensor& self, const Scalar& n, Tensor& result) {
return at::special_chebyshev_polynomial_w_out(result, self, wrapped_scalar_tensor(n));
}
Tensor special_hermite_polynomial_h(const Scalar& x, const Tensor& n) {
return at::special_hermite_polynomial_h(wrapped_scalar_tensor(x), n);
}
Tensor special_hermite_polynomial_h(const Tensor& x, const Scalar& n) {
return at::special_hermite_polynomial_h(x, wrapped_scalar_tensor(n));
}
Tensor& special_hermite_polynomial_h_out(const Scalar& self, const Tensor& n, Tensor& result) {
return at::special_hermite_polynomial_h_out(result, wrapped_scalar_tensor(self), n);
}
Tensor& special_hermite_polynomial_h_out(const Tensor& self, const Scalar& n, Tensor& result) {
return at::special_hermite_polynomial_h_out(result, self, wrapped_scalar_tensor(n));
}
Tensor special_hermite_polynomial_he(const Scalar& x, const Tensor& n) {
return at::special_hermite_polynomial_he(wrapped_scalar_tensor(x), n);
}
Tensor special_hermite_polynomial_he(const Tensor& x, const Scalar& n) {
return at::special_hermite_polynomial_he(x, wrapped_scalar_tensor(n));
}
Tensor& special_hermite_polynomial_he_out(const Scalar& self, const Tensor& n, Tensor& result) {
return at::special_hermite_polynomial_he_out(result, wrapped_scalar_tensor(self), n);
}
Tensor& special_hermite_polynomial_he_out(const Tensor& self, const Scalar& n, Tensor& result) {
return at::special_hermite_polynomial_he_out(result, self, wrapped_scalar_tensor(n));
}
Tensor special_laguerre_polynomial_l(const Scalar& x, const Tensor& n) {
return at::special_laguerre_polynomial_l(wrapped_scalar_tensor(x), n);
}
Tensor special_laguerre_polynomial_l(const Tensor& x, const Scalar& n) {
return at::special_laguerre_polynomial_l(x, wrapped_scalar_tensor(n));
}
Tensor& special_laguerre_polynomial_l_out(const Scalar& self, const Tensor& n, Tensor& result) {
return at::special_laguerre_polynomial_l_out(result, wrapped_scalar_tensor(self), n);
}
Tensor& special_laguerre_polynomial_l_out(const Tensor& self, const Scalar& n, Tensor& result) {
return at::special_laguerre_polynomial_l_out(result, self, wrapped_scalar_tensor(n));
}
Tensor special_legendre_polynomial_p(const Scalar& x, const Tensor& n) {
return at::special_legendre_polynomial_p(wrapped_scalar_tensor(x), n);
}
Tensor special_legendre_polynomial_p(const Tensor& x, const Scalar& n) {
return at::special_legendre_polynomial_p(x, wrapped_scalar_tensor(n));
}
Tensor& special_legendre_polynomial_p_out(const Scalar& self, const Tensor& n, Tensor& result) {
return at::special_legendre_polynomial_p_out(result, wrapped_scalar_tensor(self), n);
}
Tensor& special_legendre_polynomial_p_out(const Tensor& self, const Scalar& n, Tensor& result) {
return at::special_legendre_polynomial_p_out(result, self, wrapped_scalar_tensor(n));
}
Tensor special_shifted_chebyshev_polynomial_t(const Scalar& x, const Tensor& n) {
return at::special_shifted_chebyshev_polynomial_t(wrapped_scalar_tensor(x), n);
}
Tensor special_shifted_chebyshev_polynomial_t(const Tensor& x, const Scalar& n) {
return at::special_shifted_chebyshev_polynomial_t(x, wrapped_scalar_tensor(n));
}
Tensor& special_shifted_chebyshev_polynomial_t_out(const Scalar& self, const Tensor& n, Tensor& result) {
return at::special_shifted_chebyshev_polynomial_t_out(result, wrapped_scalar_tensor(self), n);
}
Tensor& special_shifted_chebyshev_polynomial_t_out(const Tensor& self, const Scalar& n, Tensor& result) {
return at::special_shifted_chebyshev_polynomial_t_out(result, self, wrapped_scalar_tensor(n));
}
Tensor special_shifted_chebyshev_polynomial_u(const Scalar& x, const Tensor& n) {
return at::special_shifted_chebyshev_polynomial_u(wrapped_scalar_tensor(x), n);
}
Tensor special_shifted_chebyshev_polynomial_u(const Tensor& x, const Scalar& n) {
return at::special_shifted_chebyshev_polynomial_u(x, wrapped_scalar_tensor(n));
}
Tensor& special_shifted_chebyshev_polynomial_u_out(const Scalar& self, const Tensor& n, Tensor& result) {
return at::special_shifted_chebyshev_polynomial_u_out(result, wrapped_scalar_tensor(self), n);
}
Tensor& special_shifted_chebyshev_polynomial_u_out(const Tensor& self, const Scalar& n, Tensor& result) {
return at::special_shifted_chebyshev_polynomial_u_out(result, self, wrapped_scalar_tensor(n));
}
Tensor special_shifted_chebyshev_polynomial_v(const Scalar& x, const Tensor& n) {
return at::special_shifted_chebyshev_polynomial_v(wrapped_scalar_tensor(x), n);
}
Tensor special_shifted_chebyshev_polynomial_v(const Tensor& x, const Scalar& n) {
return at::special_shifted_chebyshev_polynomial_v(x, wrapped_scalar_tensor(n));
}
Tensor& special_shifted_chebyshev_polynomial_v_out(const Scalar& self, const Tensor& n, Tensor& result) {
return at::special_shifted_chebyshev_polynomial_v_out(result, wrapped_scalar_tensor(self), n);
}
Tensor& special_shifted_chebyshev_polynomial_v_out(const Tensor& self, const Scalar& n, Tensor& result) {
return at::special_shifted_chebyshev_polynomial_v_out(result, self, wrapped_scalar_tensor(n));
}
Tensor special_shifted_chebyshev_polynomial_w(const Scalar& x, const Tensor& n) {
return at::special_shifted_chebyshev_polynomial_w(wrapped_scalar_tensor(x), n);
}
Tensor special_shifted_chebyshev_polynomial_w(const Tensor& x, const Scalar& n) {
return at::special_shifted_chebyshev_polynomial_w(x, wrapped_scalar_tensor(n));
}
Tensor& special_shifted_chebyshev_polynomial_w_out(const Scalar& self, const Tensor& n, Tensor& result) {
return at::special_shifted_chebyshev_polynomial_w_out(result, wrapped_scalar_tensor(self), n);
}
Tensor& special_shifted_chebyshev_polynomial_w_out(const Tensor& self, const Scalar& n, Tensor& result) {
return at::special_shifted_chebyshev_polynomial_w_out(result, self, wrapped_scalar_tensor(n));
}
Tensor& special_gammainc_out(const Tensor& self, const Tensor& other, Tensor& result) {
return at::igamma_out(result, self, other);
}
Tensor special_gammainc(const Tensor& self, const Tensor& other) {
return at::igamma(self, other);
}
Tensor& special_gammaincc_out(const Tensor& self, const Tensor& other, Tensor& result) {
return at::igammac_out(result, self, other);
}
Tensor special_gammaincc(const Tensor& self, const Tensor& other) {
return at::igammac(self, other);
}
TORCH_IMPL_FUNC(atan2_out) (const Tensor& self, const Tensor& other, const Tensor& result) {
atan2_stub(device_type(), *this);
}
Tensor arctan2(const Tensor& self, const Tensor& other) {
return at::atan2(self, other);
}
Tensor& arctan2_(Tensor& self, const Tensor& other) {
return self.atan2_(other);
}
Tensor& arctan2_out(const Tensor& self, const Tensor& other, Tensor& result) {
return at::atan2_out(result, self, other);
}
Tensor& add_relu_impl(
Tensor& result, const Tensor& self, const Tensor& other, const Scalar& alpha) {
auto iter = TensorIterator::binary_op(result, self, other);
Scalar min_val;
Scalar max_val;
if (self.dtype() == at::kInt) {
min_val = 0;
max_val = std::numeric_limits<int32_t>::max();
} else if (self.dtype() == at::kLong) {
min_val = 0;
max_val = std::numeric_limits<int64_t>::max();
} else if (self.dtype() == at::kShort) {
min_val = 0;
max_val = std::numeric_limits<int16_t>::max();
} else if (self.dtype() == at::kChar) {
min_val = 0;
max_val = std::numeric_limits<int8_t>::max();
} else if (self.dtype() == at::kFloat) {
min_val = 0.0;
max_val = std::numeric_limits<float>::max();
} else if (self.dtype() == at::kDouble) {
min_val = 0.0;
max_val = std::numeric_limits<double>::max();
} else {
TORCH_INTERNAL_ASSERT(
false, "Unsupported datatype for add_relu:", self.dtype().name());
}
result = iter.output();
add_clamp_stub(iter.device_type(), iter, alpha, min_val, max_val);
return result;
}
Tensor& add_relu_out(const Tensor& self, const Tensor& other, const Scalar& alpha, Tensor& result) {
return add_relu_impl(result, self, other, alpha);
}
Tensor add_relu(const Tensor& self, const Tensor& other, const Scalar& alpha) {
Tensor result;
return add_relu_impl(result, self, other, alpha);
}
Tensor add_relu(const Tensor& self, const Scalar& other, const Scalar& alpha) {
return add_relu(self, wrapped_scalar_tensor(other), alpha);
}
Tensor& add_relu_(Tensor& self, const Tensor& other, const Scalar& alpha) {
return add_relu_impl(self, self, other, alpha);
}
Tensor& add_relu_(Tensor& self, const Scalar& other, const Scalar& alpha) {
return add_relu_(self, wrapped_scalar_tensor(other), alpha);
}
TORCH_IMPL_FUNC(copysign_out) (
const Tensor& self, const Tensor& other, const Tensor& result
) {
copysign_stub(device_type(), *this);
}
Tensor copysign(const Tensor& self, const Scalar& other) {
// redispatch!
return at::copysign(self, wrapped_scalar_tensor(other));
}
Tensor& copysign_(Tensor& self, const Scalar& other) {
// redispatch!
return self.copysign_(wrapped_scalar_tensor(other));
}
Tensor& copysign_out(const Tensor& self, const Scalar& other, Tensor& result) {
// redispatch!
return at::copysign_out(result, self, wrapped_scalar_tensor(other));
}
// WARNING: There doesn't appear to be any testing for this function
// with sparse self input.
Tensor div(const Tensor& self, const Scalar& other) {
return self.div(wrapped_scalar_tensor(other)); // redispatch!
}
// WARNING: This function, with a sparse self, is currently only
// exercised by DistributedDataParallelTest.test_sparse_gradients
// (you need to exercise it from C++, because this overload is never
// used for Python)
Tensor& div_(Tensor& self, const Scalar& other) {
return self.div_(wrapped_scalar_tensor(other)); // redispatch!
}
Tensor div(const Tensor& self, const Scalar& other, c10::optional<c10::string_view> rounding_mode) {
return self.div(wrapped_scalar_tensor(other), std::move(rounding_mode)); // redispatch!
}
Tensor& div_(Tensor& self, const Scalar& other, c10::optional<c10::string_view> rounding_mode) {
return self.div_(wrapped_scalar_tensor(other), std::move(rounding_mode)); // redispatch!
}
// divide, alias for div
Tensor& divide_out(const Tensor& self, const Tensor& other, Tensor& result) {
return at::div_out(result, self, other);
}
Tensor divide(const Tensor& self, const Tensor& other) {
return self.div(other);
}
Tensor& divide_(Tensor& self, const Tensor& other) {
return self.div_(other);
}
Tensor divide(const Tensor& self, const Scalar& other) {
return self.div(other);
}
Tensor& divide_(Tensor& self, const Scalar& other) {
return self.div_(other);
}
Tensor& divide_out(const Tensor& self, const Tensor& other, c10::optional<c10::string_view> rounding_mode, Tensor& result) {
return at::div_out(result, self, other, std::move(rounding_mode));
}
Tensor divide(const Tensor& self, const Tensor& other, c10::optional<c10::string_view> rounding_mode) {
return self.div(other, std::move(rounding_mode));
}
Tensor& divide_(Tensor& self, const Tensor& other, c10::optional<c10::string_view> rounding_mode) {
return self.div_(other, std::move(rounding_mode));
}
Tensor divide(const Tensor& self, const Scalar& other, c10::optional<c10::string_view> rounding_mode) {
return self.div(other, std::move(rounding_mode));
}
Tensor& divide_(Tensor& self, const Scalar& other, c10::optional<c10::string_view> rounding_mode) {
return self.div_(other, std::move(rounding_mode));
}
// true_divide, an alias for div
Tensor& true_divide_out(const Tensor& self, const Tensor& divisor, Tensor& result) {
return at::div_out(result, self, divisor);
}
Tensor true_divide(const Tensor& self, const Tensor& divisor) {
return self.div(divisor);
}
Tensor& true_divide_(Tensor& self, const Tensor& divisor) {
return self.div_(divisor);
}
Tensor true_divide(const Tensor& self, const Scalar& divisor) {
return self.div(divisor);
}
Tensor& true_divide_(Tensor& self, const Scalar& divisor) {
return self.div_(divisor);
}
Tensor& floor_divide_out(const Tensor& self, const Tensor& other, Tensor& result) {
auto iter = TensorIterator::binary_op(result, self, other);
div_floor_stub(iter.device_type(), iter);
if (!result.defined()) {
result = iter.output();
}
return result;
}
Tensor floor_divide(const Tensor& self, const Tensor& other) {
Tensor result;
auto iter = TensorIterator::binary_op(result, self, other);
div_floor_stub(iter.device_type(), iter);
return iter.output();
}
Tensor& floor_divide_(Tensor& self, const Tensor& other) {
return native::floor_divide_out(self, other, self);
}
// TODO: Make this structured to undo the perf regression from native:: removal
// in call here
Tensor mul(const Tensor& self, const Scalar& other) {
return at::mul(self, wrapped_scalar_tensor(other)); // redispatch!
}
Tensor& mul_(Tensor& self, const Scalar& other) {
return at::mul_out(self, wrapped_scalar_tensor(other), self); // redispatch!
}
Tensor& mul__scalar_sparse_csr(Tensor& self, const Scalar& other) {
self.values().mul_(other);
return self;
}
Device correct_out_device(const Tensor& self, const Tensor& other) {
if (self.device() == at::kCPU){
return other.device();
} else {
return self.device();
}
}
Tensor mul_zerotensor(const Tensor& self, const Tensor& other) {
auto out_device = correct_out_device(self, other);
// hack to use the TensorIterator to get the correct broadcasting and type promotion logic
auto device_ = Device(DeviceType::Meta);
auto meta_out = at::redispatch::mul(c10::DispatchKeySet(at::DispatchKey::Meta), self.to(device_), other.to(device_));
return at::_efficientzerotensor(meta_out.sizes(), meta_out.options().device(out_device));
}
Tensor div_zerotensor(const Tensor& self, const Tensor& other) {
auto out_device = correct_out_device(self, other);
// hack to use the TensorIterator to get the correct broadcasting and type promotion logic
auto device_ = Device(DeviceType::Meta);
auto meta_out = at::redispatch::div(c10::DispatchKeySet(at::DispatchKey::Meta), self.to(device_), other.to(device_));
if (self._is_zerotensor()) {
if (other._is_zerotensor()) {
// 0/0, return full NAN
return at::full(meta_out.sizes(), std::numeric_limits<float>::quiet_NaN(), meta_out.options().device(out_device));
}
else {
// 0/x, return zero tensor
return at::_efficientzerotensor(meta_out.sizes(), meta_out.options().device(out_device));
}
}
else {
if (other._is_zerotensor()) {
// x/0, return full INF
return at::full(meta_out.sizes(), std::numeric_limits<float>::infinity(), meta_out.options().device(out_device));
}
else {
// x/y -- unreachable, see TORCH_INTERNAL_ASSERT above
return at::_efficientzerotensor(meta_out.sizes(), meta_out.options().device(out_device));
}
}
}
Tensor maybe_add_maybe_sub(const Tensor& self, const Tensor& other, const Scalar& alpha) {
auto out_device = correct_out_device(self, other);
// hack to use the TensorIterator to get the correct broadcasting and type promotion logic
auto device_ = Device(DeviceType::Meta);
auto meta_out = at::redispatch::add(c10::DispatchKeySet(at::DispatchKey::Meta), self.to(device_), other.to(device_));
auto get_out_like = [&] (const Tensor& tensor)
{
auto sizes = meta_out.sizes();
return at::_to_copy(tensor.expand(sizes), meta_out.options().device(out_device));
};
if (self._is_zerotensor()) {
if (other._is_zerotensor()) {
return at::_efficientzerotensor(meta_out.sizes(), meta_out.options().device(out_device));
}
auto res = get_out_like(other);
return alpha.equal(1) ? res : res.mul(alpha);
} else {
return get_out_like(self);
}
}
Tensor add_zerotensor(const Tensor& self, const Tensor& other, const Scalar& alpha) {
return maybe_add_maybe_sub(self, other, alpha);
}
Tensor sub_zerotensor(const Tensor& self, const Tensor& other, const Scalar& alpha) {
return maybe_add_maybe_sub(self, other, -alpha);
}
Tensor linalg_cross_zerotensor(
const Tensor& input,
const Tensor& other,
const int64_t dim)
{
auto out_device = correct_out_device(input, other);
// hack to use the TensorIterator to get the correct broadcasting and type
// promotion logic (see add_zerotensor)
auto device = Device(DeviceType::Meta);
auto meta_out = at::redispatch::linalg_cross(
c10::DispatchKeySet(at::DispatchKey::Meta),
input.to(device),
other.to(device),
dim);
return at::_efficientzerotensor(
meta_out.sizes(),
meta_out.options().device(out_device));
}
// multiply, alias for mul
Tensor& multiply_out(const Tensor& self, const Tensor& other, Tensor& result) {
return at::mul_out(result, self, other);
}
Tensor multiply(const Tensor& self, const Tensor& other) {
return self.mul(other);
}
Tensor& multiply_(Tensor& self, const Tensor& other) {
return self.mul_(other);
}
Tensor multiply(const Tensor& self, const Scalar& other) {
return self.mul(other);
}
Tensor& multiply_(Tensor& self, const Scalar& other) {
return self.mul_(other);
}
Tensor sub(const Tensor& self, const Scalar& other, const Scalar& alpha) {
return at::sub(self, wrapped_scalar_tensor(other), alpha); // redispatch!
}
Tensor& sub_(Tensor& self, const Scalar& other, const Scalar& alpha) {
return self.sub_(wrapped_scalar_tensor(other), alpha); // redispatch!
}
// subtract, alias for sub
Tensor& subtract_out(const Tensor& self, const Tensor& other, const Scalar& alpha, Tensor& result) {
return at::sub_out(result, self, other, alpha);
}
Tensor subtract(const Tensor& self, const Tensor& other, const Scalar& alpha) {
return self.sub(other, alpha);