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BinaryOpsKernel.cpp
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BinaryOpsKernel.cpp
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#define TORCH_ASSERT_NO_OPERATORS
#include <ATen/native/BinaryOps.h>
#include <cmath>
#include <ATen/Dispatch.h>
#include <ATen/Parallel.h>
#include <ATen/OpMathType.h>
#include <ATen/cpu/vec/functional.h>
#include <ATen/cpu/vec/vec.h>
#include <ATen/native/TensorIterator.h>
#include <ATen/native/cpu/Loops.h>
#include <ATen/native/Math.h>
#include <ATen/native/cpu/LogAddExp.h>
#include <c10/macros/Macros.h>
#include <c10/util/TypeSafeSignMath.h>
namespace at::native {
namespace {
using namespace vec;
template <typename scalar_t, typename Op, typename opmath_t = at::opmath_type<scalar_t>,
typename std::enable_if_t<is_reduced_floating_point_v<scalar_t>, int> = 0>
inline Vectorized<scalar_t> binary_op_scalar(const Vectorized<scalar_t>& a, opmath_t b, const Op& op) {
Vectorized<opmath_t> a0, a1, vec_b(b);
std::tie(a0, a1) = convert_to_float<scalar_t>(a);
return convert_from_float<scalar_t>(op(a0, vec_b), op(a1, vec_b));
}
void add_clamp_kernel(TensorIterator& iter, const Scalar& alpha_scalar, const Scalar& min_val, const Scalar& max_val) {
AT_DISPATCH_ALL_TYPES(iter.dtype(), "add_clamp_cpu", [&]() {
auto alpha = alpha_scalar.to<scalar_t>();
auto alpha_vec = Vectorized<scalar_t>(alpha);
auto min_scalar = min_val.to<scalar_t>();
auto min_vec = Vectorized<scalar_t>(min_scalar);
auto max_scalar = max_val.to<scalar_t>();
auto max_vec = Vectorized<scalar_t>(max_scalar);
cpu_kernel_vec(iter,
[=](scalar_t a, scalar_t b) __ubsan_ignore_undefined__ -> scalar_t {
return std::min(max_scalar, std::max(min_scalar, static_cast<scalar_t>(a + alpha * b)));
},
[=](Vectorized<scalar_t> a, Vectorized<scalar_t> b) __ubsan_ignore_undefined__ {
auto add_clamp_res = vec::fmadd(b, alpha_vec, a);
add_clamp_res = vec::clamp_min(add_clamp_res, min_vec);
add_clamp_res = vec::clamp_max(add_clamp_res, max_vec);
return add_clamp_res;
});
});
}
void atan2_kernel(TensorIteratorBase& iter) {
AT_DISPATCH_FLOATING_TYPES_AND(kBFloat16, iter.dtype(), "atan2_cpu", [&]() {
cpu_kernel_vec(iter, [=](scalar_t a, scalar_t b) -> scalar_t {
return std::atan2(a, b);
},
[=](Vectorized<scalar_t> a, Vectorized<scalar_t> b) {
return a.atan2(b);
});
});
}
#if !defined(C10_MOBILE)
#define _AT_DISPATCH_ALL_TYPES_AND_BOOL(TYPE, NAME, ...) \
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND6( \
kComplexHalf, kHalf, kBool, kBFloat16, kFloat8_e5m2, kFloat8_e4m3fn, \
TYPE, NAME, __VA_ARGS__)
#define _AT_DISPATCH_ALL_TYPES_NO_BOOL(TYPE, NAME, ...) \
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND5( \
kComplexHalf, kHalf, kBFloat16, kFloat8_e5m2, kFloat8_e4m3fn, \
TYPE, NAME, __VA_ARGS__)
#define _AT_DISPATCH_MUL_TYPES(TYPE, NAME, ...) \
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND4( \
kHalf, kBFloat16, kFloat8_e5m2, kFloat8_e4m3fn, \
TYPE, NAME, __VA_ARGS__)
#else
#define _AT_DISPATCH_ALL_TYPES_AND_BOOL(TYPE, NAME, ...) \
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND4( \
kComplexHalf, kHalf, kBool, kBFloat16, \
TYPE, NAME, __VA_ARGS__)
#define _AT_DISPATCH_ALL_TYPES_NO_BOOL(TYPE, NAME, ...) \
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND3( \
kComplexHalf, kHalf, kBFloat16, \
TYPE, NAME, __VA_ARGS__)
#define _AT_DISPATCH_MUL_TYPES(TYPE, NAME, ...) \
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2( \
kHalf, kBFloat16, \
TYPE, NAME, __VA_ARGS__)
#endif
void mul_kernel(TensorIteratorBase& iter) {
auto dtype = iter.common_dtype();
if (dtype == ScalarType::Bool) {
cpu_kernel(iter, [=](bool a, bool b) -> bool { return a && b; });
} else if (dtype == kComplexHalf) {
cpu_kernel(
iter,
[=](c10::complex<at::Half> a,
c10::complex<at::Half> b) -> c10::complex<at::Half> {
using comp_t = c10::complex<float>;
return comp_t{a} * comp_t{b};
});
} else if (iter.is_scalar(2) && at::isReducedFloatingType(dtype)) {
AT_DISPATCH_REDUCED_FLOATING_TYPES(dtype, "mul_cpu_reduced_float", [&]() {
using opmath_t = at::opmath_type<scalar_t>;
opmath_t b = iter.original_scalar_value<opmath_t>(2);
iter.remove_operand(2);
cpu_kernel_vec(iter,
[=](scalar_t a) __ubsan_ignore_undefined__ -> scalar_t { return static_cast<opmath_t>(a) * b; },
[=](Vectorized<scalar_t> a) __ubsan_ignore_undefined__ {
return binary_op_scalar(a, b, [](const Vectorized<opmath_t>& x, const Vectorized<opmath_t>& y) { return x * y; });
});
});
} else {
_AT_DISPATCH_MUL_TYPES(dtype, "mul_cpu", [&]() {
cpu_kernel_vec(iter,
[=](scalar_t a, scalar_t b) __ubsan_ignore_undefined__ -> scalar_t { return a * b; },
[=](Vectorized<scalar_t> a, Vectorized<scalar_t> b) __ubsan_ignore_undefined__ {
return a * b;
});
});
}
}
void div_true_kernel(TensorIteratorBase& iter) {
const auto dtype = iter.common_dtype();
if (iter.is_scalar(2) && at::isReducedFloatingType(dtype)) {
AT_DISPATCH_REDUCED_FLOATING_TYPES(dtype, "div_cpu_reduced_float", [&]() {
using opmath_t = at::opmath_type<scalar_t>;
opmath_t b = iter.original_scalar_value<opmath_t>(2);
iter.remove_operand(2);
cpu_kernel_vec(iter,
[=](scalar_t a) __ubsan_ignore_float_divide_by_zero__ -> scalar_t {
return static_cast<opmath_t>(a) / b;
},
[=](Vectorized<scalar_t> a) {
return binary_op_scalar(a, b, [](const Vectorized<opmath_t>& x, const Vectorized<opmath_t>& y) { return x / y; });
});
});
} else {
AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND2(kBFloat16, kHalf, dtype, "div_cpu", [&]() {
cpu_kernel_vec(iter,
[](scalar_t a, scalar_t b) __ubsan_ignore_float_divide_by_zero__ -> scalar_t {
return a / b;
},
[](Vectorized<scalar_t> a, Vectorized<scalar_t> b) {
return a / b;
});
});
}
}
void div_trunc_kernel(TensorIteratorBase& iter) {
const auto dtype = iter.common_dtype();
if (isIntegralType(dtype, /*includeBool*/ false)) {
// There's no SIMD integer division, so don't try to vectorize it.
// TODO: if the divisor is a scalar, rewrite as multiplication by a constant.
AT_DISPATCH_INTEGRAL_TYPES(dtype, "div_trunc_cpu", [&]() {
cpu_kernel(iter, [](scalar_t a, scalar_t b) -> scalar_t {
TORCH_CHECK(b != 0, "ZeroDivisionError");
return a / b;
});
});
} else if (iter.is_scalar(2) && at::isReducedFloatingType(dtype)) {
AT_DISPATCH_REDUCED_FLOATING_TYPES(dtype, "div_trunc_cpu_reduced_float", [&]() {
using opmath_t = at::opmath_type<scalar_t>;
opmath_t b = iter.original_scalar_value<opmath_t>(2);
iter.remove_operand(2);
cpu_kernel_vec(iter,
[=](scalar_t a) __ubsan_ignore_float_divide_by_zero__ -> scalar_t {
return std::trunc(static_cast<opmath_t>(a) / b);
},
[=](Vectorized<scalar_t> a) {
return binary_op_scalar(a, b, [](const Vectorized<opmath_t>& x, const Vectorized<opmath_t>& y) { return (x / y).trunc(); });
});
});
} else {
AT_DISPATCH_FLOATING_TYPES_AND2(kBFloat16, kHalf, dtype, "div_trunc_cpu", [&]() {
cpu_kernel_vec(iter,
[](scalar_t a, scalar_t b) __ubsan_ignore_float_divide_by_zero__ -> scalar_t {
return std::trunc(a / b);
},
[](Vectorized<scalar_t> a, Vectorized<scalar_t> b) {
return (a / b).trunc();
});
});
}
}
template <typename scalar_t>
inline Vectorized<scalar_t> div_floor_floating_vec(const Vectorized<scalar_t>& a, const Vectorized<scalar_t>& b) {
using vec_t = Vectorized<scalar_t>;
auto mod = a.fmod(b);
auto div = (a - mod) / b;
const auto zero = vec_t(0);
auto mask = (mod != zero) & ((b < zero) ^ (mod < zero));
const auto one = vec_t(1);
div = vec_t::blendv(div, div - one, mask);
auto floordiv = div.floor();
mask = (div - floordiv) > vec_t(0.5);
floordiv = vec_t::blendv(floordiv, floordiv + one, mask);
const auto basic_div = a / b;
floordiv = vec_t::blendv(floordiv, zero.copysign(basic_div), div == zero);
floordiv = vec_t::blendv(floordiv, basic_div, b == zero);
return floordiv;
};
void div_floor_kernel(TensorIteratorBase& iter) {
const auto dtype = iter.common_dtype();
if (dtype == kByte) {
// In the special case of unsigned integer division, floor division is
// equivalent to truncation division (since the signs of the divisor and
// dividend are always the same)
return div_trunc_kernel(iter);
} else if (isIntegralType(dtype, /*includeBool*/ false)) {
// There's no SIMD integer division, so don't try to vectorize it.
AT_DISPATCH_INTEGRAL_TYPES(dtype, "div_floor_cpu", [&]() {
cpu_kernel(iter, [](scalar_t a, scalar_t b) -> scalar_t {
TORCH_CHECK(b != 0, "ZeroDivisionError");
return div_floor_integer(a, b);
});
});
} else {
// See NOTE: [Floor Division in Python]
if (iter.is_scalar(2) && at::isReducedFloatingType(dtype)) {
AT_DISPATCH_REDUCED_FLOATING_TYPES(dtype, "div_floor_cpu_reduced_float", [&]() {
using opmath_t = at::opmath_type<scalar_t>;
opmath_t b = iter.original_scalar_value<opmath_t>(2);
iter.remove_operand(2);
using vec_t = Vectorized<opmath_t>;
cpu_kernel_vec(iter,
[=](scalar_t a) -> scalar_t {
return div_floor_floating(static_cast<opmath_t>(a), b);
},
[=](Vectorized<scalar_t> a) {
return binary_op_scalar(a, b, [](const vec_t& x, const vec_t& y) { return div_floor_floating_vec(x, y); });
});
});
} else {
AT_DISPATCH_FLOATING_TYPES_AND2(kBFloat16, kHalf, dtype, "div_floor_cpu", [&]() {
using vec_t = Vectorized<scalar_t>;
cpu_kernel_vec(iter,
[](scalar_t a, scalar_t b) -> scalar_t {
return div_floor_floating(a, b);
},
[](vec_t a, vec_t b) -> vec_t {
return div_floor_floating_vec(a, b);
});
});
}
}
}
void remainder_kernel(TensorIteratorBase& iter) {
if (isIntegralType(iter.common_dtype(), /*includeBool*/ false)) {
AT_DISPATCH_INTEGRAL_TYPES(iter.common_dtype(), "remainder_cpu", [&]() {
cpu_kernel(iter, [](scalar_t a, scalar_t b) -> scalar_t {
TORCH_CHECK(b != 0, "ZeroDivisionError");
scalar_t r = a % b;
if ((r != 0) && (c10::is_negative(r) != c10::is_negative(b))) {
r += b;
}
return r;
});
});
} else if (iter.common_dtype() == kBFloat16) {
cpu_kernel_vec(iter,
[=](BFloat16 a, BFloat16 b) __ubsan_ignore_float_divide_by_zero__ -> BFloat16 {
float a0 = static_cast<float>(a);
float b0 = static_cast<float>(b);
float mod0 = std::fmod(a0, b0);
if ((mod0 != 0) && ((b0 < 0) != (mod0 < 0))) {
mod0 += b0;
}
return mod0;
},
[=](Vectorized<BFloat16> a, Vectorized<BFloat16> b) {
Vectorized<float> a0, a1, b0, b1;
std::tie(a0, a1) = convert_bfloat16_float(a);
std::tie(b0, b1) = convert_bfloat16_float(b);
auto mod0 = a0.fmod(b0);
auto mod1 = a1.fmod(b1);
const auto zero = Vectorized<float>(0);
auto mask0 = (mod0 != zero) & ((b0 < zero) ^ (mod0 < zero));
auto mask1 = (mod1 != zero) & ((b1 < zero) ^ (mod1 < zero));
a0 = Vectorized<float>::blendv(mod0, mod0 + b0, mask0);
a1 = Vectorized<float>::blendv(mod1, mod1 + b1, mask1);
return convert_float_bfloat16(a0, a1);
});
} else {
AT_DISPATCH_FLOATING_TYPES_AND_HALF(iter.common_dtype(), "remainder_cpu", [&]() {
cpu_kernel_vec(iter,
[=](scalar_t a, scalar_t b) __ubsan_ignore_float_divide_by_zero__ -> scalar_t {
scalar_t mod = std::fmod(a, b);
if ((mod != 0) && ((b < 0) != (mod < 0))) mod += b;
return mod;
},
[=](Vectorized<scalar_t> a, Vectorized<scalar_t> b) {
auto mod = a.fmod(b);
const auto zero = Vectorized<scalar_t>(0);
auto mask = (mod != zero) & ((b < zero) ^ (mod < zero));
return Vectorized<scalar_t>::blendv(mod, mod + b, mask);
});
});
}
}
void bitwise_and_kernel(TensorIteratorBase& iter) {
if (iter.dtype() == ScalarType::Bool) {
cpu_kernel(
iter,
[](bool a, bool b) {
return a && b;
});
} else {
AT_DISPATCH_INTEGRAL_TYPES(iter.dtype(), "bitwise_and_cpu", [&]() {
cpu_kernel_vec(
iter,
[](scalar_t a, scalar_t b) -> scalar_t {
return a & b;
},
[](Vectorized<scalar_t> a, Vectorized<scalar_t> b) {
return a & b;
});
});
}
}
void bitwise_or_kernel(TensorIteratorBase& iter) {
if (iter.dtype() == ScalarType::Bool) {
cpu_kernel(
iter,
[](bool a, bool b) {
return a || b;
});
} else {
AT_DISPATCH_INTEGRAL_TYPES(iter.dtype(), "bitwise_or_cpu", [&]() {
cpu_kernel_vec(
iter,
[](scalar_t a, scalar_t b) -> scalar_t {
return a | b;
},
[](Vectorized<scalar_t> a, Vectorized<scalar_t> b) {
return a | b;
});
});
}
}
void bitwise_xor_kernel(TensorIteratorBase& iter) {
if (iter.dtype() == ScalarType::Bool) {
// Boolean type does not work with ^ (bitwise XOR) in C++. bitwise_xor wraps this operation for both Boolean and
// integral types.
cpu_kernel(
iter,
[](bool a, bool b) {
return a != b;
});
} else {
AT_DISPATCH_INTEGRAL_TYPES(iter.dtype(), "bitwise_xor_cpu", [&]() {
cpu_kernel_vec(
iter,
[](scalar_t a, scalar_t b) -> scalar_t {
return a ^ b;
},
[](Vectorized<scalar_t> a, Vectorized<scalar_t> b) {
return a ^ b;
});
});
}
}
void lshift_kernel(TensorIteratorBase& iter) {
AT_DISPATCH_INTEGRAL_TYPES(iter.dtype(), "lshift_cpu", [&]() {
cpu_kernel_vec(iter,
[](scalar_t a, scalar_t b) -> scalar_t {
constexpr scalar_t max_shift = sizeof(scalar_t) * CHAR_BIT;
if ((static_cast<std::make_signed_t<scalar_t>>(b) < 0) || (b >= max_shift)) {
return 0;
}
return static_cast<std::make_unsigned_t<scalar_t>>(a) << b;
},
[](Vectorized<scalar_t> a, Vectorized<scalar_t> b) {
return a << b;
});
});
}
void logical_and_kernel(TensorIterator& iter) {
// See Note [special-case bool outputs]
if (iter.dtype() == ScalarType::Bool) {
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND3(kBool, kBFloat16, kHalf, iter.common_dtype(), "logical_and_cpu", [&]() {
cpu_kernel(iter,
[](scalar_t a, scalar_t b) -> bool {
return a && b;
});
});
} else {
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2(kBFloat16, kHalf, iter.common_dtype(), "logical_and_cpu", [&]() {
cpu_kernel(iter,
[](scalar_t a, scalar_t b) -> scalar_t {
return static_cast<scalar_t>(a && b);
});
});
}
}
void logical_or_kernel(TensorIterator& iter) {
// See Note [special-case bool outputs]
if (iter.dtype() == ScalarType::Bool) {
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND3(kBool, kBFloat16, kHalf, iter.common_dtype(), "logical_or_cpu", [&]() {
cpu_kernel(iter,
[](scalar_t a, scalar_t b) -> bool {
return a || b;
});
});
} else {
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND3(kBool, kBFloat16, kHalf, iter.common_dtype(), "logical_or_cpu", [&]() {
cpu_kernel(iter,
[](scalar_t a, scalar_t b) -> scalar_t {
return static_cast<scalar_t>(a || b);
});
});
}
}
void logical_xor_kernel(TensorIterator& iter) {
// See Note [special-case bool outputs]
if (iter.dtype() == ScalarType::Bool) {
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND3(kBool, kBFloat16, kHalf, iter.common_dtype(), "logical_xor_cpu", [&]() {
cpu_kernel(iter,
[](scalar_t a, scalar_t b) -> bool {
return bool(a) != bool(b);
});
});
} else {
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2(kBFloat16, kHalf, iter.common_dtype(), "logical_xor_cpu", [&]() {
cpu_kernel(iter,
[](scalar_t a, scalar_t b) -> scalar_t {
return static_cast<scalar_t>(bool(a) != bool(b));
});
});
}
}
void rshift_kernel(TensorIteratorBase& iter) {
AT_DISPATCH_INTEGRAL_TYPES(iter.dtype(), "rshift_cpu", [&]() {
cpu_kernel_vec(iter,
[](scalar_t a, scalar_t b) -> scalar_t {
// right shift value to retain sign bit for signed and no bits for unsigned
constexpr scalar_t max_shift = sizeof(scalar_t) * CHAR_BIT - std::is_signed_v<scalar_t>;
if ((static_cast<std::make_signed_t<scalar_t>>(b) < 0) || (b >= max_shift)) {
return a >> max_shift;
}
return a >> b;
},
[](Vectorized<scalar_t> a, Vectorized<scalar_t> b) {
return a >> b;
});
});
}
void lt_kernel(TensorIteratorBase& iter) {
// See Note [special-case bool outputs]
if (iter.dtype() == ScalarType::Bool) {
AT_DISPATCH_ALL_TYPES_AND3(kBool, kBFloat16, kHalf, iter.common_dtype(), "lt_cpu", [&]() {
cpu_kernel(iter,
[](scalar_t a, scalar_t b) -> bool {
return a < b;
});
});
} else {
AT_DISPATCH_ALL_TYPES_AND2(kBFloat16, kHalf, iter.common_dtype(), "lt_cpu", [&]() {
cpu_kernel_vec(
iter,
[](scalar_t a, scalar_t b) -> scalar_t {
return a < b;
},
[](Vectorized<scalar_t> a, Vectorized<scalar_t> b) -> Vectorized<scalar_t> {
return a.lt(b);
});
});
}
}
void le_kernel(TensorIteratorBase& iter) {
// See Note [special-case bool outputs]
if (iter.dtype() == ScalarType::Bool) {
AT_DISPATCH_ALL_TYPES_AND3(kBool, kBFloat16, kHalf, iter.common_dtype(), "le_cpu", [&]() {
cpu_kernel(iter,
[](scalar_t a, scalar_t b) -> bool {
return a <= b;
});
});
} else {
AT_DISPATCH_ALL_TYPES_AND2(kBFloat16, kHalf, iter.common_dtype(), "le_cpu", [&]() {
cpu_kernel_vec(
iter,
[](scalar_t a, scalar_t b) -> scalar_t {
return a <= b;
},
[](Vectorized<scalar_t> a, Vectorized<scalar_t> b) -> Vectorized<scalar_t> {
return a.le(b);
});
});
}
}
void gt_kernel(TensorIteratorBase& iter) {
// See Note [special-case bool outputs]
if (iter.dtype() == ScalarType::Bool) {
AT_DISPATCH_ALL_TYPES_AND3(kBool, kBFloat16, kHalf, iter.common_dtype(), "gt_cpu", [&]() {
cpu_kernel(iter,
[](scalar_t a, scalar_t b) -> bool {
return a > b;
});
});
} else {
AT_DISPATCH_ALL_TYPES_AND2(kBFloat16, kHalf, iter.common_dtype(), "gt_cpu", [&]() {
cpu_kernel_vec(
iter,
[](scalar_t a, scalar_t b) -> scalar_t {
return a > b;
},
[](Vectorized<scalar_t> a, Vectorized<scalar_t> b) -> Vectorized<scalar_t> {
return a.gt(b);
});
});
}
}
void ge_kernel(TensorIteratorBase& iter) {
// See Note [special-case bool outputs]
if (iter.dtype() == ScalarType::Bool) {
AT_DISPATCH_ALL_TYPES_AND3(kBool, kBFloat16, kHalf, iter.common_dtype(), "ge_cpu", [&]() {
cpu_kernel(iter,
[](scalar_t a, scalar_t b) -> bool {
return a >= b;
});
});
} else {
AT_DISPATCH_ALL_TYPES_AND2(kBFloat16, kHalf, iter.common_dtype(), "ge_cpu", [&]() {
cpu_kernel_vec(
iter,
[](scalar_t a, scalar_t b) -> scalar_t {
return a >= b;
},
[](Vectorized<scalar_t> a, Vectorized<scalar_t> b) -> Vectorized<scalar_t> {
return a.ge(b);
});
});
}
}
void eq_kernel(TensorIteratorBase& iter) {
// See Note [special-case bool outputs]
if (iter.dtype() == ScalarType::Bool) {
_AT_DISPATCH_ALL_TYPES_AND_BOOL(iter.common_dtype(), "eq_cpu", [&]() {
cpu_kernel(iter,
[](scalar_t a, scalar_t b) -> bool {
return a == b;
});
});
} else {
_AT_DISPATCH_ALL_TYPES_NO_BOOL(iter.common_dtype(), "eq_cpu", [&]() {
cpu_kernel_vec(
iter,
[](scalar_t a, scalar_t b) -> scalar_t {
return static_cast<scalar_t>(a == b);
},
[](Vectorized<scalar_t> a, Vectorized<scalar_t> b) -> Vectorized<scalar_t> {
return a.eq(b);
});
});
}
}
void ne_kernel(TensorIteratorBase& iter) {
// See Note [special-case bool outputs]
if (iter.dtype() == ScalarType::Bool) {
_AT_DISPATCH_ALL_TYPES_AND_BOOL(iter.common_dtype(), "ne_cpu", [&]() {
cpu_kernel(iter,
[](scalar_t a, scalar_t b) -> bool {
return a != b;
});
});
} else {
_AT_DISPATCH_ALL_TYPES_NO_BOOL(iter.common_dtype(), "ne_cpu", [&]() {
cpu_kernel_vec(
iter,
[](scalar_t a, scalar_t b) -> scalar_t {
return static_cast<scalar_t>(a != b);
},
[](Vectorized<scalar_t> a, Vectorized<scalar_t> b) -> Vectorized<scalar_t> {
return a.ne(b);
});
});
}
}
void maximum_kernel(TensorIteratorBase& iter) {
if (iter.dtype() == ScalarType::Bool) {
cpu_kernel(iter,
[](bool a, bool b) -> bool {
return a || b;
});
} else if (isIntegralType(iter.dtype(), /*includeBool=*/ false)) {
AT_DISPATCH_INTEGRAL_TYPES(iter.dtype(), "maximum_cpu", [&]() {
cpu_kernel_vec(iter,
[](scalar_t a, scalar_t b) -> scalar_t { return std::max(a, b); },
[](Vectorized<scalar_t> a, Vectorized<scalar_t> b) { return at::vec::maximum(a, b); });
});
} else {
AT_DISPATCH_FLOATING_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, iter.dtype(), "maximum_cpu", [&]() {
cpu_kernel_vec(iter,
[](scalar_t a, scalar_t b) -> scalar_t {
if (a != a || b != b) {
return std::numeric_limits<scalar_t>::quiet_NaN();
} else {
return std::max(a, b);
}
},
[](Vectorized<scalar_t> a, Vectorized<scalar_t> b) { return at::vec::maximum(a, b); });
});
}
}
void minimum_kernel(TensorIteratorBase& iter) {
if (iter.dtype() == ScalarType::Bool) {
cpu_kernel(iter,
[](bool a, bool b) -> bool {
return a && b;
});
} else if (isIntegralType(iter.dtype(), /*includeBool=*/ false)) {
AT_DISPATCH_INTEGRAL_TYPES(iter.dtype(), "minimum_cpu", [&]() {
cpu_kernel_vec(iter,
[](scalar_t a, scalar_t b) -> scalar_t { return std::min(a, b); },
[](Vectorized<scalar_t> a, Vectorized<scalar_t> b) { return at::vec::minimum(a, b); });
});
} else {
AT_DISPATCH_FLOATING_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, iter.dtype(), "minimum_cpu", [&]() {
cpu_kernel_vec(iter,
[](scalar_t a, scalar_t b) -> scalar_t {
if (a != a || b != b) {
return std::numeric_limits<scalar_t>::quiet_NaN();
} else {
return std::min(a, b);
}
},
[](Vectorized<scalar_t> a, Vectorized<scalar_t> b) { return at::vec::minimum(a, b); });
});
}
}
void fmax_kernel(TensorIteratorBase& iter) {
if (isFloatingType(iter.common_dtype())) {
AT_DISPATCH_FLOATING_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, iter.common_dtype(), "fmax_cpu", [&]() {
cpu_kernel(iter,
[](scalar_t a, scalar_t b) -> scalar_t {
return std::fmax(a, b);
});
});
} else {
maximum_kernel(iter);
}
}
void fmin_kernel(TensorIteratorBase& iter) {
if (isFloatingType(iter.common_dtype())) {
AT_DISPATCH_FLOATING_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, iter.common_dtype(), "fmin_cpu", [&]() {
cpu_kernel(iter,
[](scalar_t a, scalar_t b) -> scalar_t {
return std::fmin(a, b);
});
});
} else {
minimum_kernel(iter);
}
}
void smooth_l1_kernel(TensorIteratorBase& iter, double beta) {
if (iter.dtype() == kBFloat16) {
const float beta_val(beta);
const Vectorized<float> beta_val_vec(beta_val);
const Vectorized<float> point_five_vec(static_cast<float>(0.5));
cpu_kernel_vec(
iter,
[&beta_val](BFloat16 a, BFloat16 b) -> BFloat16 {
auto z = std::abs(float(a) - float(b));
return z < beta_val
? static_cast<float>(0.5) * z * z / beta_val
: z - static_cast<float>(0.5) * beta_val;
},
[&beta_val_vec, &point_five_vec](Vectorized<BFloat16> a, Vectorized<BFloat16> b) {
Vectorized<float> a0, a1, b0, b1;
std::tie(a0, a1) = convert_bfloat16_float(a);
std::tie(b0, b1) = convert_bfloat16_float(b);
auto z = (a0 - b0).abs();
a0 = Vectorized<float>::blendv(
point_five_vec * z * z / beta_val_vec, z - point_five_vec * beta_val_vec, z >= beta_val_vec);
z = (a1 - b1).abs();
a1 = Vectorized<float>::blendv(
point_five_vec * z * z / beta_val_vec, z - point_five_vec * beta_val_vec, z >= beta_val_vec);
return convert_float_bfloat16(a0, a1);
});
} else {
AT_DISPATCH_FLOATING_TYPES_AND(
kHalf, iter.dtype(), "smooth_l1_cpu", [&]() {
using Vec = Vectorized<scalar_t>;
const scalar_t beta_val(beta);
const Vec beta_val_vec(beta_val);
const Vec point_five_vec(static_cast<scalar_t>(0.5));
cpu_kernel_vec(
iter,
[&beta_val](scalar_t a, scalar_t b) -> scalar_t {
auto z = std::abs(a - b);
return z < beta_val
? static_cast<scalar_t>(0.5) * z * z / beta_val
: z - static_cast<scalar_t>(0.5) * beta_val;
},
[&beta_val_vec, &point_five_vec](Vec a, Vec b) {
auto z = (a - b).abs();
return Vec::blendv(
point_five_vec * z * z / beta_val_vec, z - point_five_vec * beta_val_vec, z >= beta_val_vec);
});
});
}
}
void huber_kernel(TensorIterator& iter, double delta) {
AT_DISPATCH_FLOATING_TYPES_AND2(kBFloat16, kHalf, iter.dtype(), "huber_cpu", [&]() {
using Vec = Vectorized<scalar_t>;
const scalar_t delta_val(delta);
const Vec delta_val_vec(delta_val);
const Vec point_five_vec(static_cast<scalar_t>(0.5));
cpu_kernel_vec(
iter,
[&delta_val](scalar_t a, scalar_t b) -> scalar_t {
auto z = std::abs(a - b);
return z < delta_val ? static_cast<scalar_t>(0.5) * z * z :
delta_val * (z - static_cast<scalar_t>(0.5) * delta_val);
},
[&delta_val_vec, &point_five_vec](Vec a, Vec b) {
auto z = (a - b).abs();
return Vec::blendv(point_five_vec * z * z,
delta_val_vec * (z - point_five_vec * delta_val_vec),
z >= delta_val_vec);
});
});
}
void sigmoid_backward_kernel(TensorIteratorBase& iter) {
if (isComplexType(iter.dtype())) {
AT_DISPATCH_COMPLEX_TYPES(iter.dtype(), "sigmoid_backward_cpu", [&]() {
auto one_vec = Vectorized<scalar_t>(scalar_t{1});
cpu_kernel_vec(
iter,
[=](scalar_t a, scalar_t b) -> scalar_t {
return a * std::conj((scalar_t(1) - b) * b);
},
[=](Vectorized<scalar_t> a, Vectorized<scalar_t> b) {
return a * ((one_vec - b) * b).conj();
});
});
} else if (iter.dtype() == kBFloat16) {
auto one_vec = Vectorized<float>((float)(1));
cpu_kernel_vec(
iter,
[=](BFloat16 a, BFloat16 b) -> BFloat16 {
float a0 = static_cast<float>(a);
float b0 = static_cast<float>(b);
return a0 * (float(1) - b0) * b0;
},
[=](Vectorized<BFloat16> a, Vectorized<BFloat16> b) {
Vectorized<float> a0, a1, b0, b1;
std::tie(a0, a1) = convert_bfloat16_float(a);
std::tie(b0, b1) = convert_bfloat16_float(b);
a0 = a0 * (one_vec - b0) * b0;
a1 = a1 * (one_vec - b1) * b1;
return convert_float_bfloat16(a0, a1);
});
} else {
AT_DISPATCH_FLOATING_TYPES_AND(kHalf, iter.dtype(), "sigmoid_backward_cpu", [&]() {
auto one_vec = Vectorized<scalar_t>((scalar_t)(1));
cpu_kernel_vec(
iter,
[=](scalar_t a, scalar_t b) -> scalar_t {
return a * (scalar_t(1) - b) * b;
},
[=](Vectorized<scalar_t> a, Vectorized<scalar_t> b) {
return a * (one_vec - b) * b;
});
});
}
}
void logit_backward_kernel(TensorIteratorBase& iter, const Scalar& eps_scalar) {
AT_DISPATCH_FLOATING_TYPES_AND(
kBFloat16, iter.dtype(), "logit_backward_cpu", [&]() {
const scalar_t eps = eps_scalar.to<scalar_t>();
const Vectorized<scalar_t> kZeroVec(scalar_t(0));
const Vectorized<scalar_t> kOneVec(scalar_t(1));
if (eps < scalar_t(0)) {
const Vectorized<scalar_t> kNanVec(
std::numeric_limits<scalar_t>::quiet_NaN());
cpu_kernel_vec(
iter,
[](scalar_t dy, scalar_t x) {
return (x < scalar_t(0) || x > scalar_t(1))
? std::numeric_limits<scalar_t>::quiet_NaN()
: ((x == scalar_t(0) || x == scalar_t(1))
? (dy * std::numeric_limits<scalar_t>::infinity())
: (dy / (x * (scalar_t(1) - x))));
},
[kZeroVec, kOneVec, kNanVec](
Vectorized<scalar_t> dy_vec, Vectorized<scalar_t> x_vec) {
return Vectorized<scalar_t>::blendv(
kNanVec,
dy_vec / (x_vec * (kOneVec - x_vec)),
(x_vec >= kZeroVec) & (x_vec <= kOneVec));
});
} else {
const scalar_t lo = eps;
const scalar_t hi = scalar_t(1) - eps;
const Vectorized<scalar_t> lo_vec(lo);
const Vectorized<scalar_t> hi_vec(hi);
cpu_kernel_vec(
iter,
[lo, hi](scalar_t dy, scalar_t x) {
return (x < lo || x > hi)
? scalar_t(0)
: ((x == scalar_t(0) || x == scalar_t(1))
? dy * std::numeric_limits<scalar_t>::infinity()
: dy / (x * (scalar_t(1) - x)));
},
[kZeroVec, kOneVec, lo_vec, hi_vec](
Vectorized<scalar_t> dy_vec, Vectorized<scalar_t> x_vec) {
return Vectorized<scalar_t>::blendv(
kZeroVec,
dy_vec / (x_vec * (kOneVec - x_vec)),
(x_vec >= lo_vec) & (x_vec <= hi_vec));
});
}
});
}
void tanh_backward_kernel(TensorIteratorBase& iter) {
if (isComplexType(iter.dtype())) {
AT_DISPATCH_COMPLEX_TYPES(iter.dtype(), "tanh_backward_cpu", [&]() {
auto one_vec = Vectorized<scalar_t>(scalar_t{1});
cpu_kernel_vec(
iter,
[=](scalar_t a, scalar_t b) -> scalar_t {
return a * std::conj(scalar_t{1} - b * b);
},
[=](Vectorized<scalar_t> a, Vectorized<scalar_t> b) {
return a * (one_vec - b * b).conj();
});
});
} else if (at::isReducedFloatingType(iter.dtype())) {
AT_DISPATCH_REDUCED_FLOATING_TYPES(iter.dtype(), "tanh_backward_cpu", [&]() {
auto one_vec = Vectorized<float>(float{1});
cpu_kernel_vec(
iter,
[=](scalar_t a, scalar_t b) -> scalar_t {
float a0 = float(a);
float b0 = float(b);
return a0 * (float{1} - b0 * b0);
},
[=](Vectorized<scalar_t> a, Vectorized<scalar_t> b) {
Vectorized<float> a0, a1, b0, b1;
std::tie(a0, a1) = convert_to_float<scalar_t>(a);
std::tie(b0, b1) = convert_to_float<scalar_t>(b);
a0 = a0 * (one_vec - b0 * b0);
a1 = a1 * (one_vec - b1 * b1);
return convert_from_float<scalar_t>(a0, a1);
});
});
} else {
AT_DISPATCH_FLOATING_TYPES(iter.dtype(), "tanh_backward_cpu", [&]() {
auto one_vec = Vectorized<scalar_t>(scalar_t{1});
cpu_kernel_vec(
iter,
[=](scalar_t a, scalar_t b) -> scalar_t {
return a * (scalar_t{1} - b * b);
},
[=](Vectorized<scalar_t> a, Vectorized<scalar_t> b) {
return a * (one_vec - b * b);
});
});
}
}
void mse_kernel(TensorIteratorBase& iter) {
if (iter.dtype() == ScalarType::Half) {
TORCH_WARN_ONCE("Applying the CPU mse kernel on half-type tensors. "
"This may be slower than using float or double-type tensors.");
}
AT_DISPATCH_FLOATING_TYPES_AND_HALF(iter.dtype(), "mse_cpu", [&]() {
cpu_kernel_vec(iter,
[=](scalar_t a, scalar_t b) -> scalar_t {
auto diff = a - b;
return diff * diff;
},
[=](Vectorized<scalar_t> a, Vectorized<scalar_t> b) {
auto diff = a - b;
return diff * diff;
});
});
}
void fmod_kernel(TensorIteratorBase& iter) {
if (isIntegralType(iter.common_dtype(), /*includeBool=*/ false)) {
AT_DISPATCH_INTEGRAL_TYPES(iter.common_dtype(), "fmod_cpu", [&]() {
cpu_kernel(iter, [=](scalar_t x, scalar_t d) -> scalar_t {
TORCH_CHECK(d != 0, "ZeroDivisionError");
return x % d;
});
});
} else {
AT_DISPATCH_FLOATING_TYPES_AND2(kBFloat16, kHalf, iter.common_dtype(), "fmod_cpu", [&]() {
cpu_kernel_vec(
iter,
[](scalar_t x, scalar_t d) -> scalar_t {
return std::fmod(x, d);
},
[](Vectorized<scalar_t> x, Vectorized<scalar_t> d) {
return x.fmod(d);
});
});
}
}
void logaddexp_kernel(TensorIteratorBase& iter) {
if (iter.dtype() == kBFloat16) {
cpu_kernel_vec(
iter,
[=](BFloat16 a, BFloat16 b) -> BFloat16 {
float a0 = static_cast<float>(a);
float b0 = static_cast<float>(b);
if (std::isinf(a0) && a0 == b0) {
return a0;
} else {
float m0 = std::max(a0, b0);
return m0 + std::log1p(std::exp(-std::abs(a0 - b0)));
}
},
[=](Vectorized<BFloat16> a, Vectorized<BFloat16> b) {
Vectorized<float> a0, a1, b0, b1;
std::tie(a0, a1) = convert_bfloat16_float(a);
std::tie(b0, b1) = convert_bfloat16_float(b);
Vectorized<float> inf(std::numeric_limits<float>::infinity());
Vectorized<float> m0 = maximum(a0, b0);
Vectorized<float> m1 = maximum(a1, b1);
a0 = Vectorized<float>::blendv(
m0 + (a0 - b0).abs().neg().exp().log1p(),
a0,
(a0 == b0) & (a0.abs() == inf));
a1 = Vectorized<float>::blendv(
m1 + (a1 - b1).abs().neg().exp().log1p(),
a1,
(a1 == b1) & (a1.abs() == inf));
return convert_float_bfloat16(a0, a1);
});
} else if (isComplexType(iter.dtype())) {
AT_DISPATCH_COMPLEX_TYPES(iter.dtype(), "logaddexp_cpu", [&]() {
cpu_kernel(
iter,
[=](scalar_t a, scalar_t b) -> scalar_t {
return _log_add_exp_helper(a, b);
});
});
} else {
AT_DISPATCH_FLOATING_TYPES(iter.dtype(), "logaddexp_cpu", [&]() {
cpu_kernel_vec(
iter,
[=](scalar_t a, scalar_t b) -> scalar_t {
if (std::isinf(a) && a == b) {
return a;
} else {
scalar_t m = std::max(a, b);
return m + std::log1p(std::exp(-std::abs(a - b)));
}
},
[=](Vectorized<scalar_t> a, Vectorized<scalar_t> b) {
Vectorized<scalar_t> inf(std::numeric_limits<scalar_t>::infinity());
Vectorized<scalar_t> m = maximum(a, b);
return Vectorized<scalar_t>::blendv(
m + (a - b).abs().neg().exp().log1p(),
a,
(a == b) & (a.abs() == inf));
});
});
}
}
void logaddexp2_kernel(TensorIteratorBase& iter) {
if (iter.dtype() == kBFloat16) {