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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 <torch/library.h>
namespace at {
namespace native {
DEFINE_DISPATCH(add_stub);
DEFINE_DISPATCH(add_clamp_stub);
DEFINE_DISPATCH(sub_stub);
DEFINE_DISPATCH(mul_stub);
DEFINE_DISPATCH(div_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(fmod_stub);
DEFINE_DISPATCH(fmod_scalar_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);
static Tensor wrapped_scalar_tensor(Scalar scalar) {
auto tensor = scalar_to_tensor(scalar);
tensor.unsafeGetTensorImpl()->set_wrapped_number(true);
return tensor;
}
Tensor& add_out(Tensor& result, const Tensor& self, const Tensor& other, Scalar alpha) {
auto iter = TensorIterator::binary_op(result, self, other);
alpha_check(iter.dtype(), alpha);
add_stub(iter.device_type(), iter, alpha);
TORCH_INTERNAL_ASSERT(result.scalar_type() == iter.output().dtype());
return result;
}
Tensor add(const Tensor& self, const Tensor& other, Scalar alpha) {
Tensor result;
auto iter = TensorIterator::binary_op(result, self, other);
alpha_check(iter.dtype(), alpha);
add_stub(iter.device_type(), iter, alpha);
return iter.output();
}
Tensor& add_(Tensor& self, const Tensor& other, Scalar alpha) {
return native::add_out(self, self, other, alpha);
}
Tensor& add_relu_impl(
Tensor& result, const Tensor& self, const Tensor& other, 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(
"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(Tensor& result, const Tensor& self, const Tensor& other, Scalar alpha) {
return add_relu_impl(result, self, other, alpha);
}
Tensor add_relu(const Tensor& self, const Tensor& other, Scalar alpha) {
Tensor result;
return add_relu_impl(result, self, other, alpha);
}
Tensor& add_relu_(Tensor& self, const Tensor& other, Scalar alpha) {
return add_relu_impl(self, self, other, alpha);
}
Tensor& copysign_out(Tensor& result, const Tensor& self, const Tensor& other) {
auto iter = TensorIterator::binary_float_op(result, self, other);
copysign_stub(iter.device_type(), iter);
return result;
}
Tensor copysign(const Tensor& self, const Tensor& other) {
Tensor result;
auto iter = TensorIterator::binary_float_op(result, self, other);
copysign_stub(iter.device_type(), iter);
return iter.output();
}
Tensor& copysign_(Tensor& self, const Tensor& other) {
return native::copysign_out(self, self, other);
}
Tensor copysign(const Tensor& self, Scalar other) {
return native::copysign(self, wrapped_scalar_tensor(other));
}
Tensor& copysign_(Tensor& self, Scalar other) {
return native::copysign_(self, wrapped_scalar_tensor(other));
}
Tensor& div_out(Tensor& result, const Tensor& self, const Tensor& other) {
auto iter = TensorIterator::binary_float_op(result, self, other);
div_stub(iter.device_type(), iter);
return result;
}
Tensor div(const Tensor& self, const Tensor& other) {
Tensor result;
auto iter = TensorIterator::binary_float_op(result, self, other);
div_stub(iter.device_type(), iter);
return iter.output();
}
Tensor& div_(Tensor& self, const Tensor& other) {
return native::div_out(self, self, other);
}
// WARNING: There doesn't appear to be any testing for this function
// with sparse self input.
Tensor div(const Tensor& self, 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, Scalar other) {
return self.div_(wrapped_scalar_tensor(other)); // redispatch!
}
// divide, alias for div
Tensor& divide_out(Tensor& result, const Tensor& self, const Tensor& other) {
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, Scalar other) {
return self.div(other);
}
Tensor& divide_(Tensor& self, Scalar other) {
return self.div_(other);
}
// true_divide, an alias for div
Tensor& true_divide_out(Tensor& result, const Tensor& self, const Tensor& divisor) {
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, Scalar divisor) {
return self.div(divisor);
}
Tensor& true_divide_(Tensor& self, Scalar divisor) {
return self.div_(divisor);
}
Tensor& remainder_out(Tensor& result, const Tensor& self, const Tensor& other) {
auto iter = TensorIterator::binary_op(result, self, other);
remainder_stub(iter.device_type(), iter);
return result;
}
Tensor remainder(const Tensor& self, const Tensor& other) {
Tensor result;
auto iter = TensorIterator::binary_op(result, self, other);
remainder_stub(iter.device_type(), iter);
return iter.output();
}
Tensor& remainder_(Tensor& self, const Tensor& other) {
return native::remainder_out(self, self, other);
}
Tensor& floor_divide_out(Tensor& result, const Tensor& self, const Tensor& other) {
auto iter = TensorIterator::binary_op(result, self, other);
div_stub(iter.device_type(), iter);
if (result.is_floating_point()) {
result.trunc_();
}
return result;
}
Tensor floor_divide(const Tensor& self, const Tensor& other) {
Tensor result;
auto iter = TensorIterator::binary_op(result, self, other);
div_stub(iter.device_type(), iter);
auto out = iter.output();
if (out.is_floating_point()) {
out.trunc_();
}
return out;
}
Tensor& floor_divide_(Tensor& self, const Tensor& other) {
return native::floor_divide_out(self, self, other);
}
Tensor& mul_out(Tensor& result, const Tensor& self, const Tensor& other) {
auto iter = TensorIterator::binary_op(result, self, other);
mul_stub(iter.device_type(), iter);
return result;
}
Tensor mul(const Tensor& self, const Tensor& other) {
Tensor result;
auto iter = TensorIterator::binary_op(result, self, other);
mul_stub(iter.device_type(), iter);
return iter.output();
}
Tensor& mul_(Tensor& self, const Tensor& other) {
return native::mul_out(self, self, other);
}
Tensor mul(const Tensor& self, Scalar other) {
return native::mul(self, wrapped_scalar_tensor(other));
}
Tensor& mul_(Tensor& self, Scalar other) {
return native::mul_(self, wrapped_scalar_tensor(other));
}
// multiply, alias for mul
Tensor& multiply_out(Tensor& result, const Tensor& self, const Tensor& other) {
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, Scalar other) {
return self.mul(other);
}
Tensor& multiply_(Tensor& self, Scalar other) {
return self.mul_(other);
}
Tensor& sub_out(Tensor& result, const Tensor& self, const Tensor& other, Scalar alpha) {
sub_check(self, other);
auto iter = TensorIterator::binary_op(result, self, other);
alpha_check(iter.dtype(), alpha);
sub_stub(iter.device_type(), iter, alpha);
TORCH_INTERNAL_ASSERT(result.scalar_type() == iter.output().dtype());
return result;
}
Tensor sub(const Tensor& self, const Tensor& other, Scalar alpha) {
sub_check(self, other);
Tensor result;
auto iter = TensorIterator::binary_op(result, self, other);
alpha_check(iter.dtype(), alpha);
sub_stub(iter.device_type(), iter, alpha);
return iter.output();
}
Tensor& sub_(Tensor& self, const Tensor& other, Scalar alpha) {
return native::sub_out(self, self, other, alpha);
}
Tensor sub(const Tensor& self, Scalar other, Scalar alpha) {
return native::sub(self, wrapped_scalar_tensor(other), alpha);
}
Tensor& sub_(Tensor& self, Scalar other, Scalar alpha) {
return native::sub_(self, wrapped_scalar_tensor(other), alpha);
}
// subtract, alias for sub
Tensor& subtract_out(Tensor& result, const Tensor& self, const Tensor& other, Scalar alpha) {
return at::sub_out(result, self, other, alpha);
}
Tensor subtract(const Tensor& self, const Tensor& other, Scalar alpha) {
return self.sub(other, alpha);
}
Tensor& subtract_(Tensor& self, const Tensor& other, Scalar alpha) {
return self.sub_(other, alpha);
}
Tensor subtract(const Tensor& self, Scalar other, Scalar alpha) {
return self.sub(other, alpha);
}
Tensor& subtract_(Tensor& self, Scalar other, Scalar alpha) {
return self.sub_(other, alpha);
}
Tensor& sigmoid_backward_out(Tensor& result, const Tensor& grad_output, const Tensor& output) {
auto iter = TensorIterator::binary_op(result, grad_output, output);
sigmoid_backward_stub(iter.device_type(), iter);
return result;
}
Tensor sigmoid_backward(const Tensor& grad_output, const Tensor& output) {
Tensor result;
auto iter = TensorIterator::binary_op(result, grad_output, output);
sigmoid_backward_stub(iter.device_type(), iter);
return iter.output();
}
Tensor& logit_backward_out(
Tensor& result,
const Tensor& grad_output,
const Tensor& input,
c10::optional<double> eps) {
auto iter = TensorIterator::binary_op(result, grad_output, input);
logit_backward_stub(
iter.device_type(), iter, Scalar(eps ? eps.value() : -1.0));
return result;
}
Tensor logit_backward(
const Tensor& grad_output,
const Tensor& input,
c10::optional<double> eps) {
Tensor result;
auto iter = TensorIterator::binary_op(result, grad_output, input);
logit_backward_stub(
iter.device_type(), iter, Scalar(eps ? eps.value() : -1.0));
return iter.output();
}
Tensor& tanh_backward_out(Tensor& result, const Tensor& grad_output, const Tensor& output) {
auto iter = TensorIterator::binary_op(result, grad_output, output);
tanh_backward_stub(iter.device_type(), iter);
return result;
}
Tensor tanh_backward(const Tensor& grad_output, const Tensor& output) {
Tensor result;
auto iter = TensorIterator::binary_op(result, grad_output, output);
tanh_backward_stub(iter.device_type(), iter);
return iter.output();
}
Tensor rsub(const Tensor& self, const Tensor& other, Scalar alpha) {
return native::sub(other, self, alpha);
}
Tensor& atan2_out(Tensor& result, const Tensor& self, const Tensor& other) {
auto iter = TensorIterator::binary_float_op(result, self, other);
atan2_stub(iter.device_type(), iter);
return result;
}
Tensor atan2(const Tensor& self, const Tensor& other) {
Tensor result;
auto iter = TensorIterator::binary_float_op(result, self, other);
atan2_stub(iter.device_type(), iter);
return iter.output();
}
Tensor& atan2_(Tensor& self, const Tensor& other) {
return native::atan2_out(self, self, other);
}
// These are still needed because we don't have C++ conversions from number
// types (int, float, etc.) to Tensor (only to Scalar). They're not exposed
// to Python.
static void check_convert(Scalar scalar, ScalarType scalarType) {
// Validate that is possible to convert scalar to tensor dtype without overflow
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND3(at::ScalarType::Bool, at::ScalarType::BFloat16, at::ScalarType::Half, scalarType, "check_convert", [&]{
scalar.to<scalar_t>();
});
}
static Tensor wrapped_scalar_tensor_and_check_convert(Scalar scalar, Tensor tensor) {
check_convert(scalar, tensor.scalar_type());
return wrapped_scalar_tensor(scalar);
}
Tensor add(const Tensor& self, Scalar other, Scalar alpha) {
return native::add(self, wrapped_scalar_tensor(other), alpha);
}
Tensor& add_(Tensor& self, Scalar other, Scalar alpha) {
return native::add_(self, wrapped_scalar_tensor(other), alpha);
}
Tensor remainder(const Tensor& self, Scalar other) {
Tensor other_tensor = wrapped_scalar_tensor(other);
// FIXME: 'other' is converted to match the dtype of 'self' to retain
// BC with TH, but in the future, we should use normal type promotion,
// like in numpy
return native::remainder(self, other_tensor.toType(self.scalar_type()));
}
Tensor& remainder_(Tensor& self, Scalar other) {
Tensor other_tensor = wrapped_scalar_tensor(other);
// FIXME: 'other' is converted to match the dtype of 'self' to retain
// BC with TH, but in the future, we should use normal type promotion,
// like in numpy
return native::remainder_(self, other_tensor.toType(self.scalar_type()));
}
Tensor& remainder_out(Tensor& result, const Tensor& self, Scalar other) {
Tensor other_tensor = wrapped_scalar_tensor(other);
// FIXME: 'other' is converted to match the dtype of 'self' to retain
// BC with TH, but in the future, we should use normal type promotion,
// like in numpy
return native::remainder_out(result, self, other_tensor.toType(self.scalar_type()));
}
Tensor rsub(const Tensor& self, Scalar other, Scalar alpha) {
return native::rsub(self, wrapped_scalar_tensor(other), alpha);
}
Tensor& bitwise_and_out(Tensor& result, const Tensor& self, const Tensor& other) {
auto iter = TensorIterator::binary_op(result, self, other);
bitwise_and_stub(iter.device_type(), iter);
return result;
}
Tensor bitwise_and(const Tensor& self, const Tensor& other) {
Tensor result = at::empty({0}, self.options());
at::bitwise_and_out(result, self, other);
return result;
}
Tensor& bitwise_and_(Tensor& self, const Tensor& other) {
return at::bitwise_and_out(self, self, other);
}
Tensor& bitwise_and_out(Tensor& result, const Tensor& self, Scalar other) {
return at::bitwise_and_out(result, self, wrapped_scalar_tensor(other));
}
Tensor bitwise_and(const Tensor& self, Scalar other) {
Tensor result = at::empty({0}, self.options());
return at::bitwise_and_out(result, self, other);
}
Tensor& bitwise_and_(Tensor& self, Scalar other) {
return at::bitwise_and_out(self, self, other);
}
// Legacy and interfaces. They are aliased to bitwise_and* functions
Tensor __and__(const Tensor& self, const Tensor& other) {
return at::bitwise_and(self, other);
}
Tensor __and__(const Tensor& self, Scalar other) {
return at::bitwise_and(self, other);
}
Tensor& __iand__(Tensor& self, const Tensor& other) {
return self.bitwise_and_(other);
}
Tensor& __iand__(Tensor& self, Scalar other) {
return self.bitwise_and_(other);
}
Tensor& bitwise_or_out(Tensor& result, const Tensor& self, const Tensor& other) {
auto iter = TensorIterator::binary_op(result, self, other);
bitwise_or_stub(iter.device_type(), iter);
return result;
}
Tensor bitwise_or(const Tensor& self, const Tensor& other) {
Tensor result = at::empty({0}, self.options());
at::bitwise_or_out(result, self, other);
return result;
}
Tensor& bitwise_or_(Tensor& self, const Tensor& other) {
return at::bitwise_or_out(self, self, other);
}
Tensor& bitwise_or_out(Tensor& result, const Tensor& self, Scalar other) {
return at::bitwise_or_out(result, self, wrapped_scalar_tensor(other));
}
Tensor bitwise_or(const Tensor& self, Scalar other) {
Tensor result = at::empty({0}, self.options());
return at::bitwise_or_out(result, self, other);
}
Tensor& bitwise_or_(Tensor& self, Scalar other) {
return at::bitwise_or_out(self, self, other);
}
// Legacy or interfaces. They are aliased to bitwise_or* functions
Tensor __or__(const Tensor& self, const Tensor& other) {
return at::bitwise_or(self, other);
}
Tensor __or__(const Tensor& self, Scalar other) {
return at::bitwise_or(self, other);
}
Tensor& __ior__(Tensor& self, const Tensor& other) {
return self.bitwise_or_(other);
}
Tensor& __ior__(Tensor& self, Scalar other) {
return self.bitwise_or_(other);
}
Tensor& bitwise_xor_out(Tensor& result, const Tensor& self, const Tensor& other) {
auto iter = TensorIterator::binary_op(result, self, other);
bitwise_xor_stub(iter.device_type(), iter);
return result;
}
Tensor bitwise_xor(const Tensor& self, const Tensor& other) {
Tensor result = at::empty({0}, self.options());
at::bitwise_xor_out(result, self, other);
return result;
}
Tensor& bitwise_xor_(Tensor& self, const Tensor& other) {
return at::bitwise_xor_out(self, self, other);
}
Tensor& bitwise_xor_out(Tensor& result, const Tensor& self, Scalar other) {
return at::bitwise_xor_out(result, self, wrapped_scalar_tensor(other));
}
Tensor bitwise_xor(const Tensor& self, Scalar other) {
Tensor result = at::empty({0}, self.options());
return at::bitwise_xor_out(result, self, other);
}
Tensor& bitwise_xor_(Tensor& self, Scalar other) {
return at::bitwise_xor_out(self, self, other);
}
// Legacy xor interfaces. They are aliased to bitwise_xor* functions
Tensor __xor__(const Tensor& self, const Tensor& other) {
return at::bitwise_xor(self, other);
}
Tensor __xor__(const Tensor& self, Scalar other) {
return at::bitwise_xor(self, other);
}
Tensor& __ixor__(Tensor& self, const Tensor& other) {
return self.bitwise_xor_(other);
}
Tensor& __ixor__(Tensor& self, Scalar other) {
return self.bitwise_xor_(other);
}
Tensor __lshift__(const Tensor& self, const Tensor& other) {
Tensor result;
auto iter = TensorIterator::binary_op(result, self, other);
lshift_stub(iter.device_type(), iter);
return iter.output();
}
Tensor __lshift__(const Tensor& self, Scalar other) {
Tensor result;
auto wrapper = wrapped_scalar_tensor(other).toType(self.scalar_type());
auto iter = TensorIterator::binary_op(result, self, wrapper);
lshift_stub(iter.device_type(), iter);
return iter.output();
}
Tensor& __ilshift__(Tensor& self, const Tensor& other) {
auto iter = TensorIterator::binary_op(self, self, other);
lshift_stub(iter.device_type(), iter);
return self;
}
Tensor& __ilshift__(Tensor& self, Scalar other) {
auto wrapper = wrapped_scalar_tensor(other).toType(self.scalar_type());
auto iter = TensorIterator::binary_op(self, self, wrapper);
lshift_stub(iter.device_type(), iter);
return self;
}
Tensor __rshift__(const Tensor& self, const Tensor& other) {
Tensor result;
auto iter = TensorIterator::binary_op(result, self, other);
rshift_stub(iter.device_type(), iter);
return iter.output();
}
Tensor __rshift__(const Tensor& self, Scalar other) {
Tensor result;
auto wrapper = wrapped_scalar_tensor(other).toType(self.scalar_type());
auto iter = TensorIterator::binary_op(result, self, wrapper);
rshift_stub(iter.device_type(), iter);
return iter.output();
}
Tensor& __irshift__(Tensor& self, const Tensor& other) {
auto iter = TensorIterator::binary_op(self, self, other);
rshift_stub(iter.device_type(), iter);
return self;
}
Tensor& __irshift__(Tensor& self, Scalar other) {
auto wrapper = wrapped_scalar_tensor(other).toType(self.scalar_type());
auto iter = TensorIterator::binary_op(self, self, wrapper);
rshift_stub(iter.device_type(), iter);
return self;
}
template <typename Stub>
Tensor& comparison_op_out(Tensor& result, const Tensor& self, const Tensor& other, Stub& stub) {
// Validate that is possible to convert zero-dim tensor's dtype to other dtype without overflow
if (self.scalar_type() != other.scalar_type()) {
if (self.dim() != 0 && other.dim() == 0) {
check_convert(other.item(), self.scalar_type());
} else if (self.dim() == 0 && other.dim() != 0) {
check_convert(self.item(), other.scalar_type());
}
}
auto iter = TensorIterator::comparison_op(result, self, other);
stub(iter.device_type(), iter);
return result;
}
template <typename OutImpl>
Tensor comparison_op(const Tensor& self, const Tensor& other, OutImpl& out_impl) {
Tensor result = at::empty({0}, self.options().dtype(kBool));
return out_impl(result, self, other);
}
// To avoid overflow during type promotion we will check that both dtypes of self and other are same
template <typename OutImpl>
Tensor& comparison_op_(Tensor& self, const Tensor& other, OutImpl& out_impl) {
TORCH_CHECK(self.dtype() == other.dtype(),
"Expected object of scalar type ", self.dtype(), " but got scalar type ",
other.dtype(), " for argument 'other'");
return out_impl(self, self, other);
}
// validates that is possible to convert Scalar other to self's dtype without overflow.
// This behavior is unique to comparison ops; arithmetic operations don't do this.
// In the future, we should reconsider this inconsistency and decide if we want to add the same check to arithmetic ops.
template <typename OutImpl>
Tensor& comparison_op_out(Tensor& result, const Tensor& self, Scalar other, OutImpl& out_impl) {
return out_impl(result, self, wrapped_scalar_tensor_and_check_convert(other, self));
}
template <typename OutImpl>
Tensor comparison_op(const Tensor& self, Scalar other, OutImpl& out_impl) {
return comparison_op(self, wrapped_scalar_tensor_and_check_convert(other, self), out_impl);
}
template <typename OutImpl>
Tensor& comparison_op_(Tensor& self, Scalar other, OutImpl& out_impl) {
return out_impl(self, self, wrapped_scalar_tensor_and_check_convert(other, self));
}
// We need explicit cast to OutFunc because each *_out func is overloaded twice. Without An explicit cast, merely
// referring to *_out function is ambiguious.
using OutFunc = std::add_const<Tensor&(&)(Tensor&, const Tensor&, const Tensor&)>::type;
Tensor& lt_out(Tensor& result, const Tensor& self, const Tensor& other) { return comparison_op_out(result, self, other, lt_stub); }
Tensor lt(const Tensor& self, const Tensor& other) { return comparison_op(self, other, static_cast<OutFunc>(at::lt_out)); }
Tensor& lt_(Tensor& self, const Tensor& other) { return comparison_op_(self, other, static_cast<OutFunc>(at::lt_out)); }
Tensor& lt_out(Tensor& result, const Tensor& self, Scalar other) { return comparison_op_out(result, self, other, static_cast<OutFunc>(at::lt_out)); }
Tensor lt(const Tensor& self, Scalar other) { return comparison_op(self, other, static_cast<OutFunc>(at::lt_out)); }
Tensor& lt_(Tensor& self, Scalar other) { return comparison_op_(self, other, static_cast<OutFunc>(at::lt_out)); }
// less, alias for torch.lt
Tensor& less_out(Tensor& result, const Tensor& self, const Tensor& other) { return at::lt_out(result, self, other); }
Tensor less(const Tensor& self, const Tensor& other) { return self.lt(other); }
Tensor& less_(Tensor& self, const Tensor& other) { return self.lt_(other); }
Tensor& less_out(Tensor& result, const Tensor& self, Scalar other) { return at::lt_out(result, self, other); }
Tensor less(const Tensor& self, Scalar other) { return self.lt(other); }
Tensor& less_(Tensor& self, Scalar other) { return self.lt_(other); }
Tensor& le_out(Tensor& result, const Tensor& self, const Tensor& other) { return comparison_op_out(result, self, other, le_stub); }
Tensor le(const Tensor& self, const Tensor& other) { return comparison_op(self, other, static_cast<OutFunc>(at::le_out)); }
Tensor& le_(Tensor& self, const Tensor& other) { return comparison_op_(self, other, static_cast<OutFunc>(at::le_out)); }
Tensor& le_out(Tensor& result, const Tensor& self, Scalar other) { return comparison_op_out(result, self, other, static_cast<OutFunc>(at::le_out)); }
Tensor le(const Tensor& self, Scalar other) { return comparison_op(self, other, static_cast<OutFunc>(at::le_out)); }
Tensor& le_(Tensor& self, Scalar other) { return comparison_op_(self, other, static_cast<OutFunc>(at::le_out)); }
// less_equal, alias for torch.le
Tensor& less_equal_out(Tensor& result, const Tensor& self, const Tensor& other) { return at::le_out(result, self, other); }
Tensor less_equal(const Tensor& self, const Tensor& other) { return self.le(other); }
Tensor& less_equal_(Tensor& self, const Tensor& other) { return self.le_(other); }
Tensor& less_equal_out(Tensor& result, const Tensor& self, Scalar other) { return at::le_out(result, self, other); }
Tensor less_equal(const Tensor& self, Scalar other) { return self.le(other); }
Tensor& less_equal_(Tensor& self, Scalar other) { return self.le_(other); }
Tensor& gt_out(Tensor& result, const Tensor& self, const Tensor& other) { return comparison_op_out(result, self, other, gt_stub); }
Tensor gt(const Tensor& self, const Tensor& other) { return comparison_op(self, other, static_cast<OutFunc>(at::gt_out)); }
Tensor& gt_(Tensor& self, const Tensor& other) { return comparison_op_(self, other, static_cast<OutFunc>(at::gt_out)); }
Tensor& gt_out(Tensor& result, const Tensor& self, Scalar other) { return comparison_op_out(result, self, other, static_cast<OutFunc>(at::gt_out)); }
Tensor gt(const Tensor& self, Scalar other) { return comparison_op(self, other, static_cast<OutFunc>(at::gt_out)); }
Tensor& gt_(Tensor& self, Scalar other) { return comparison_op_(self, other, static_cast<OutFunc>(at::gt_out)); }
// greater, alias for torch.gt
Tensor& greater_out(Tensor& result, const Tensor& self, const Tensor& other) { return at::gt_out(result, self, other); }
Tensor greater(const Tensor& self, const Tensor& other) { return self.gt(other); }
Tensor& greater_(Tensor& self, const Tensor& other) { return self.gt_(other); }
Tensor& greater_out(Tensor& result, const Tensor& self, Scalar other) { return at::gt_out(result, self, other); }
Tensor greater(const Tensor& self, Scalar other) { return self.gt(other); }
Tensor& greater_(Tensor& self, Scalar other) { return self.gt_(other); }
Tensor& ge_out(Tensor& result, const Tensor& self, const Tensor& other) { return comparison_op_out(result, self, other, ge_stub); }
Tensor ge(const Tensor& self, const Tensor& other) { return comparison_op(self, other, static_cast<OutFunc>(at::ge_out)); }
Tensor& ge_(Tensor& self, const Tensor& other) { return comparison_op_(self, other, static_cast<OutFunc>(at::ge_out)); }
Tensor& ge_out(Tensor& result, const Tensor& self, Scalar other) { return comparison_op_out(result, self, other, static_cast<OutFunc>(at::ge_out)); }
Tensor ge(const Tensor& self, Scalar other) { return comparison_op(self, other, static_cast<OutFunc>(at::ge_out)); }
Tensor& ge_(Tensor& self, Scalar other) { return comparison_op_(self, other, static_cast<OutFunc>(at::ge_out)); }
// greater_equal, alias for torch.ge
Tensor& greater_equal_out(Tensor& result, const Tensor& self, const Tensor& other) { return at::ge_out(result, self, other); }
Tensor greater_equal(const Tensor& self, const Tensor& other) { return self.ge(other); }
Tensor& greater_equal_(Tensor& self, const Tensor& other) { return self.ge_(other); }
Tensor& greater_equal_out(Tensor& result, const Tensor& self, Scalar other) { return at::ge_out(result, self, other); }
Tensor greater_equal(const Tensor& self, Scalar other) { return self.ge(other); }
Tensor& greater_equal_(Tensor& self, Scalar other) { return self.ge_(other); }
Tensor& eq_out(Tensor& result, const Tensor& self, const Tensor& other) { return comparison_op_out(result, self, other, eq_stub); }
Tensor eq(const Tensor& self, const Tensor& other) { return comparison_op(self, other, static_cast<OutFunc>(at::eq_out)); }
Tensor& eq_(Tensor& self, const Tensor& other) { return comparison_op_(self, other, static_cast<OutFunc>(at::eq_out)); }
Tensor& eq_out(Tensor& result, const Tensor& self, Scalar other) { return comparison_op_out(result, self, other, static_cast<OutFunc>(at::eq_out)); }
Tensor eq(const Tensor& self, Scalar other) { return comparison_op(self, other, static_cast<OutFunc>(at::eq_out)); }
Tensor& eq_(Tensor& self, Scalar other) { return comparison_op_(self, other, static_cast<OutFunc>(at::eq_out)); }
Tensor& ne_out(Tensor& result, const Tensor& self, const Tensor& other) { return comparison_op_out(result, self, other, ne_stub); }
Tensor ne(const Tensor& self, const Tensor& other) { return comparison_op(self, other, static_cast<OutFunc>(at::ne_out)); }
Tensor& ne_(Tensor& self, const Tensor& other) { return comparison_op_(self, other, static_cast<OutFunc>(at::ne_out)); }
Tensor& ne_out(Tensor& result, const Tensor& self, Scalar other) { return comparison_op_out(result, self, other, static_cast<OutFunc>(at::ne_out)); }
Tensor ne(const Tensor& self, Scalar other) { return comparison_op(self, other, static_cast<OutFunc>(at::ne_out)); }
Tensor& ne_(Tensor& self, Scalar other) { return comparison_op_(self, other, static_cast<OutFunc>(at::ne_out)); }
// not_equal, alias for torch.ne
Tensor& not_equal_out(Tensor& result, const Tensor& self, const Tensor& other) { return at::ne_out(result, self, other); }
Tensor not_equal(const Tensor& self, const Tensor& other) { return self.ne(other); }
Tensor& not_equal_(Tensor& self, const Tensor& other) { return self.ne_(other); }
Tensor& not_equal_out(Tensor& result, const Tensor& self, Scalar other) { return at::ne_out(result, self, other); }
Tensor not_equal(const Tensor& self, Scalar other) { return self.ne(other); }
Tensor& not_equal_(Tensor& self, Scalar other) { return self.ne_(other); }
Tensor& logical_and_out(Tensor& result, const Tensor& self, const Tensor& other) { return comparison_op_out(result, self, other, logical_and_stub); }
Tensor logical_and(const Tensor& self, const Tensor& other) { return comparison_op(self, other, static_cast<OutFunc>(at::logical_and_out)); }
Tensor& logical_and_(Tensor& self, const Tensor& other) { return comparison_op_(self, other, static_cast<OutFunc>(at::logical_and_out)); }
Tensor& logical_and_out(Tensor& result, const Tensor& self, Scalar other) { return comparison_op_out(result, self, other, static_cast<OutFunc>(at::logical_and_out)); }
Tensor logical_and(const Tensor& self, Scalar other) { return comparison_op(self, other, static_cast<OutFunc>(at::logical_and_out)); }
Tensor& logical_and_(Tensor& self, Scalar other) { return comparison_op_(self, other, static_cast<OutFunc>(at::logical_and_out)); }
Tensor& logical_or_out(Tensor& result, const Tensor& self, const Tensor& other) { return comparison_op_out(result, self, other, logical_or_stub); }
Tensor logical_or(const Tensor& self, const Tensor& other) { return comparison_op(self, other, static_cast<OutFunc>(at::logical_or_out)); }
Tensor& logical_or_(Tensor& self, const Tensor& other) { return comparison_op_(self, other, static_cast<OutFunc>(at::logical_or_out)); }
Tensor& logical_or_out(Tensor& result, const Tensor& self, Scalar other) { return comparison_op_out(result, self, other, static_cast<OutFunc>(at::logical_or_out)); }
Tensor logical_or(const Tensor& self, Scalar other) { return comparison_op(self, other, static_cast<OutFunc>(at::logical_or_out)); }
Tensor& logical_or_(Tensor& self, Scalar other) { return comparison_op_(self, other, static_cast<OutFunc>(at::logical_or_out)); }
Tensor& logical_xor_out(Tensor& result, const Tensor& self, const Tensor& other) { return comparison_op_out(result, self, other, logical_xor_stub); }
Tensor logical_xor(const Tensor& self, const Tensor& other) { return comparison_op(self, other, static_cast<OutFunc>(at::logical_xor_out)); }
Tensor& logical_xor_(Tensor& self, const Tensor& other) { return comparison_op_(self, other, static_cast<OutFunc>(at::logical_xor_out)); }
Tensor& logical_xor_out(Tensor& result, const Tensor& self, Scalar other) { return comparison_op_out(result, self, other, static_cast<OutFunc>(at::logical_xor_out)); }
Tensor logical_xor(const Tensor& self, Scalar other) { return comparison_op(self, other, static_cast<OutFunc>(at::logical_xor_out)); }
Tensor& logical_xor_(Tensor& self, Scalar other) { return comparison_op_(self, other, static_cast<OutFunc>(at::logical_xor_out)); }
Tensor& maximum_out(Tensor& result, const Tensor& self, const Tensor& other) {
TORCH_CHECK(!self.is_complex() && !other.is_complex(), "maximum does not support complex inputs.");
auto iter = TensorIterator::binary_op(result, self, other);
maximum_stub(iter.device_type(), iter);
return result;
}
Tensor maximum(const Tensor& self, const Tensor& other) {
TORCH_CHECK(!self.is_complex() && !other.is_complex(), "maximum does not support complex inputs.");
Tensor result;
auto iter = TensorIterator::binary_op(result, self, other);
maximum_stub(iter.device_type(), iter);
return iter.output();
}
// binary max, alias for maximum
Tensor& max_out(Tensor& result, const Tensor& self, const Tensor& other) {
return at::maximum_out(result, self, other);
}
Tensor max(const Tensor& self, const Tensor& other) {
return at::maximum(self, other);
}
Tensor& minimum_out(Tensor& result, const Tensor& self, const Tensor& other) {
TORCH_CHECK(!self.is_complex() && !other.is_complex(), "minimum does not support complex inputs.");
auto iter = TensorIterator::binary_op(result, self, other);
minimum_stub(iter.device_type(), iter);
return result;
}
Tensor minimum(const Tensor& self, const Tensor& other) {
TORCH_CHECK(!self.is_complex() && !other.is_complex(), "minimum does not support complex inputs.");
Tensor result;
auto iter = TensorIterator::binary_op(result, self, other);
minimum_stub(iter.device_type(), iter);
return iter.output();
}
// binary min, alias for minimum
Tensor& min_out(Tensor& result, const Tensor& self, const Tensor& other) {
return at::minimum_out(result, self, other);
}
Tensor min(const Tensor& self, const Tensor& other) {
return at::minimum(self, other);
}
Tensor floor_divide(const Tensor& self, Scalar other) {
return at::floor_divide(self, wrapped_scalar_tensor(other));
}
Tensor& floor_divide_(Tensor& self, Scalar other) {
return at::floor_divide_out(self, self, wrapped_scalar_tensor(other));
}
Tensor& fmod_out(Tensor & result, const Tensor& self, const Tensor& other) {
auto iter = TensorIterator::binary_op(result, self, other);
TORCH_CHECK(iter.device_type() == at::kCPU, "Native fmod only supports CPU");
fmod_stub(iter.device_type(), iter);
return result;
}
Tensor& fmod_out(Tensor & result, const Tensor& self, Scalar other) {
auto iter = TensorIterator::unary_op(result, self);
TORCH_CHECK(iter.device_type() == at::kCPU, "Native fmod only supports CPU");
fmod_scalar_stub(iter.device_type(), iter, other);
return result;
}
Tensor fmod(const Tensor& self, const Tensor & other) {
Tensor result = at::empty({0}, self.options());
return at::fmod_out(result, self, other);
}
Tensor fmod(const Tensor& self, Scalar other) {
Tensor result = at::empty({0}, self.options());
return at::fmod_out(result, self, other);
}
Tensor& fmod_(Tensor& self, const Tensor& other) {
return at::fmod_out(self, self, other);
}
Tensor& fmod_(Tensor& self, Scalar other) {
return at::fmod_out(self, self, other);
}
Tensor& logaddexp_out(Tensor& result, const Tensor& self, const Tensor& other) {
auto iter = TensorIterator::binary_op(result, self, other);
logaddexp_stub(iter.device_type(), iter);
return result;
}
Tensor logaddexp(const Tensor& self, const Tensor& other) {
Tensor result = at::empty({0}, self.options());
return at::logaddexp_out(result, self, other);
}
Tensor& logaddexp2_out(Tensor& result, const Tensor& self, const Tensor& other) {
auto iter = TensorIterator::binary_op(result, self, other);
logaddexp2_stub(iter.device_type(), iter);
return result;
}
Tensor logaddexp2(const Tensor& self, const Tensor& other) {
Tensor result = at::empty({0}, self.options());
return at::logaddexp2_out(result, self, other);
}
Tensor& gcd_out(Tensor& result, const Tensor& self, const Tensor& other) {
auto iter = TensorIterator::binary_op(result, self, other);
gcd_stub(iter.device_type(), iter);
return result;
}
Tensor gcd(const Tensor& self, const Tensor& other) {
Tensor result = at::empty({0}, self.options());
return at::gcd_out(result, self, other);
}
Tensor& gcd_(Tensor& self, const Tensor& other) {
return at::gcd_out(self, self, other);
}
Tensor& lcm_out(Tensor& result, const Tensor& self, const Tensor& other) {
auto iter = TensorIterator::binary_op(result, self, other);
lcm_stub(iter.device_type(), iter);
return result;
}
Tensor lcm(const Tensor& self, const Tensor& other) {
Tensor result = at::empty({0}, self.options());
return at::lcm_out(result, self, other);
}
Tensor& lcm_(Tensor& self, const Tensor& other) {
return at::lcm_out(self, self, other);
}
Tensor& hypot_out(Tensor& result, const Tensor& self, const Tensor& other) {
auto iter = TensorIterator::binary_op(result, self, other);
hypot_stub(iter.device_type(), iter);
return result;
}
Tensor hypot(const Tensor& self, const Tensor& other) {
Tensor result;
auto iter = TensorIterator::binary_op(result, self, other);
hypot_stub(iter.device_type(), iter);
return iter.output();
}
Tensor& hypot_(Tensor& self, const Tensor& other) {
return at::hypot_out(self, self, other);
}
Tensor& igamma_out(Tensor& result, const Tensor& self, const Tensor& other) {
auto iter = TensorIterator::binary_op(result, self, other);