test_binary_ufuncs.py

import torch
import numpy as np

import itertools
from itertools import product
import math
import random
import unittest
import warnings
import operator
from functools import partial

from torch._six import inf, nan
from torch.testing._internal.common_utils import (
    TestCase, iter_indices, TEST_WITH_ASAN, run_tests,
    torch_to_numpy_dtype_dict, make_tensor, TEST_SCIPY, set_default_dtype)
from torch.testing._internal.common_device_type import (
    instantiate_device_type_tests, onlyCUDA, onlyCPU, dtypes, dtypesIfCUDA,
    dtypesIfCPU, deviceCountAtLeast, precisionOverride, onlyOnCPUAndCUDA,
    skipCUDAIfRocm, skipIf)
from torch.testing import all_types_and_complex_and

if TEST_SCIPY:
    import scipy.special

# TODO: remove this
def _generate_input(shape, dtype, device, with_extremal):
    if shape == ():
        x = torch.tensor((), dtype=dtype, device=device)
    else:
        if dtype.is_floating_point or dtype.is_complex:
            # work around torch.randn not being implemented for bfloat16
            if dtype == torch.bfloat16:
                x = torch.randn(*shape, device=device) * random.randint(30, 100)
                x = x.to(torch.bfloat16)
            else:
                x = torch.randn(*shape, dtype=dtype, device=device) * random.randint(30, 100)
            x[torch.randn(*shape) > 0.5] = 0
            if with_extremal and dtype.is_floating_point:
                # Use extremal values
                x[torch.randn(*shape) > 0.5] = float('nan')
                x[torch.randn(*shape) > 0.5] = float('inf')
                x[torch.randn(*shape) > 0.5] = float('-inf')
            elif with_extremal and dtype.is_complex:
                x[torch.randn(*shape) > 0.5] = complex('nan')
                x[torch.randn(*shape) > 0.5] = complex('inf')
                x[torch.randn(*shape) > 0.5] = complex('-inf')
        elif dtype == torch.bool:
            x = torch.zeros(shape, dtype=dtype, device=device)
            x[torch.randn(*shape) > 0.5] = True
        else:
            x = torch.randint(15, 100, shape, dtype=dtype, device=device)

    return x

# TODO: refactor this out
# Converts half/bfloat16 dtype to float when device is cpu
def _convert_t(dtype, device):
    if device == 'cpu' and dtype in {torch.half, torch.bfloat16}:
        return torch.float
    return dtype

# TODO: revise the tests to use make_tensor in common_utils.py instead
# Returns a tensor of the requested shape, dtype, and device
# Requesting a half CPU tensor returns a float CPU tensor with
# values representable by a half.
# Initialization uses randint for non-float types and randn for float types.
def _make_tensor(shape, dtype, device, fill_ones=False) -> torch.Tensor:
    # Returns a tensor filled with ones
    if fill_ones:
        return torch.ones(*shape, dtype=_convert_t(dtype, device), device=device)

    # Returns a tensor with random integer values
    if not (dtype.is_floating_point or dtype.is_complex):
        t = torch.randint(0, 10, shape, device=device)
        if dtype != torch.uint8:
            t = t - 5  # generate negative values also
        return t.to(_convert_t(dtype, device))

    # Populates the CPU tensor with floats representable as half/bfloat16
    if dtype == torch.half and device == 'cpu':
        return torch.randn(*shape, dtype=torch.float, device=device).half().float()
    if dtype == torch.bfloat16 and device == 'cpu':
        return torch.randn(*shape, dtype=torch.float, device=device).bfloat16().float()

    # Default: returns a tensor with random float values
    return torch.randn(shape, dtype=dtype, device=device).to(dtype=dtype)

# TODO: update to use opinfos consistently
class TestBinaryUfuncs(TestCase):

    def test_add_broadcast_empty(self, device):
        # empty + empty
        self.assertRaises(RuntimeError, lambda: torch.randn(5, 0, device=device) + torch.randn(0, 5, device=device))
        self.assertEqual(torch.randn(5, 0, device=device), torch.randn(0, device=device) + torch.randn(5, 0, device=device))
        self.assertEqual(torch.randn(5, 0, 0, device=device), torch.randn(0, device=device) + torch.randn(5, 0, 1, device=device))

        # scalar + empty
        self.assertEqual(torch.randn(5, 0, 6, device=device), torch.randn((), device=device) + torch.randn(5, 0, 6, device=device))

        # non-empty, empty
        self.assertEqual(torch.randn(0, device=device), torch.randn(0, device=device) + torch.randn(1, device=device))
        self.assertEqual(torch.randn(0, 7, 0, 6, 5, 0, 7, device=device),
                         torch.randn(0, 7, 0, 6, 5, 0, 1, device=device) + torch.randn(1, 1, 5, 1, 7, device=device))
        self.assertRaises(RuntimeError, lambda: torch.randn(7, 0, device=device) + torch.randn(2, 1, device=device))

    def test_addcmul_scalars_as_floats(self, device):
        # zero-dim variables that don't require grad should bind to scalar arguments
        x = torch.tensor(2.)
        y = torch.tensor(3., device=device)
        # 3 + (3 * 3) * 2
        self.assertEqual(y.addcmul(y, y, value=x), 21)

        x = torch.tensor(2., requires_grad=True)
        self.assertRaises(Exception, lambda: y.addcmul(y, y, value=x))

    # TODO: update to work on CUDA, too
    @onlyCPU
    def test_comparison_ops(self, device):
        x = torch.randn(5, 5)
        y = torch.randn(5, 5)

        eq = x == y
        for idx in iter_indices(x):
            self.assertEqual(x[idx] == y[idx], eq[idx] == 1)

        ne = x != y
        for idx in iter_indices(x):
            self.assertEqual(x[idx] != y[idx], ne[idx] == 1)

        lt = x < y
        for idx in iter_indices(x):
            self.assertEqual(x[idx] < y[idx], lt[idx] == 1)

        le = x <= y
        for idx in iter_indices(x):
            self.assertEqual(x[idx] <= y[idx], le[idx] == 1)

        gt = x > y
        for idx in iter_indices(x):
            self.assertEqual(x[idx] > y[idx], gt[idx] == 1)

        ge = x >= y
        for idx in iter_indices(x):
            self.assertEqual(x[idx] >= y[idx], ge[idx] == 1)

    # TODO: update to work on CUDA, too
    @onlyCPU
    def test_comparison_ops_must_take_bool_output(self, device):
        for op in [torch.lt, torch.le, torch.gt, torch.ge, torch.eq, torch.ne,
                   torch.logical_and, torch.logical_or, torch.logical_xor]:
            self.assertEqual(op(torch.tensor([True]), torch.tensor([False])).dtype, torch.bool)

    # TODO: update to work on CUDA, too
    @onlyCPU
    def test_inplace_comparison_ops_require_inputs_have_same_dtype(self, device):
        with self.assertRaisesRegex(RuntimeError, 'Expected object of scalar type'):
            for op in ['lt_', 'le_', 'gt_', 'ge_', 'eq_', 'ne_', 'logical_xor_', 'logical_and_', 'logical_or_']:
                x = torch.tensor([1], dtype=torch.int)
                y = torch.tensor([2], dtype=torch.long)
                in_place_method = getattr(x, op)
                in_place_method(y)

    # TODO: update to work on CUDA, too
    @onlyCPU
    def test_comparison_ops_check_for_scalar_overflow(self, device):
        s = 1 << 20
        t = torch.tensor([1 << 5], dtype=torch.uint8)
        with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'):
            self.assertTrue(t < s)
        with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'):
            self.assertTrue(s < t)
        with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'):
            self.assertTrue(t <= s)
        with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'):
            self.assertTrue(s <= t)
        with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'):
            self.assertTrue(t > s)
        with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'):
            self.assertTrue(s > t)
        with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'):
            self.assertTrue(t >= s)
        with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'):
            self.assertTrue(s >= t)
        with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'):
            self.assertTrue(t == s)
        with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'):
            self.assertTrue(s == t)
        with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'):
            self.assertTrue(t != s)
        with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'):
            self.assertTrue(s != t)

    # TODO: update to work on CUDA, too
    @onlyCPU
    def test_comparison_ops_check_for_zerodim_tensor_overflow(self, device):
        t1 = torch.tensor([1 << 5], dtype=torch.uint8)
        t2 = torch.tensor([1 << 30], dtype=torch.int32)
        ts1 = torch.tensor(1 << 20, dtype=torch.int32)
        ts2 = torch.tensor(1 << 40, dtype=torch.int64)
        with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'):
            self.assertTrue(t1 < ts1)
        with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'):
            self.assertTrue(ts2 < t2)
        with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'):
            self.assertTrue(t1 <= ts1)
        with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'):
            self.assertTrue(ts2 <= t2)
        with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'):
            self.assertTrue(t1 > ts1)
        with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'):
            self.assertTrue(ts2 > t2)
        with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'):
            self.assertTrue(t1 >= ts1)
        with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'):
            self.assertTrue(ts2 >= t2)
        with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'):
            self.assertTrue(t1 == ts1)
        with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'):
            self.assertTrue(ts2 == t2)
        with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'):
            self.assertTrue(t1 != ts1)
        with self.assertRaisesRegex(RuntimeError, 'value cannot be converted to type'):
            self.assertTrue(ts2 != t2)

    # TODO: update to work on CUDA, too
    @onlyCPU
    def test_bitwise_ops(self, device):
        x = torch.randn(5, 5).gt(0)
        y = torch.randn(5, 5).gt(0)

        and_result = x & y
        for idx in iter_indices(x):
            if and_result[idx]:
                self.assertTrue(x[idx] and y[idx])
            else:
                self.assertFalse(x[idx] and y[idx])

        or_result = x | y
        for idx in iter_indices(x):
            if or_result[idx]:
                self.assertTrue(x[idx] or y[idx])
            else:
                self.assertFalse(x[idx] or y[idx])

        xor_result = x ^ y
        for idx in iter_indices(x):
            if xor_result[idx]:
                self.assertTrue(x[idx] ^ y[idx])
            else:
                self.assertFalse(x[idx] ^ y[idx])

        x_clone = x.clone()
        x_clone &= y
        self.assertEqual(x_clone, and_result)

        x_clone = x.clone()
        x_clone |= y
        self.assertEqual(x_clone, or_result)

        x_clone = x.clone()
        x_clone ^= y
        self.assertEqual(x_clone, xor_result)

    def test_inplace_division(self, device):
        t = torch.rand(5, 5, device=device)
        id_before = id(t)
        t /= 2
        id_after = id(t)
        self.assertEqual(id_before, id_after)

    @dtypes(*torch.testing.get_all_dtypes(include_bool=False, include_complex=False))
    def test_div_rounding_modes(self, device, dtype):
        if dtype.is_floating_point:
            low, high = -10.0, 10.0
        else:
            info = torch.iinfo(dtype)
            low, high = info.min, info.max

        a = make_tensor((100,), device, dtype, low=low, high=high)
        b = make_tensor((100,), device, dtype, low=low, high=high)

        # Avoid division by zero so we can test (a / b) * b == a
        if dtype.is_floating_point:
            eps = 0.1
            b[(-eps < b) & (b < eps)] = eps
        else:
            b[b == 0] = 1

        if not dtype.is_floating_point:
            # floor(a / b) * b can be < a, so fixup slightly to avoid underflow
            a = torch.where(a < 0, a + b, a)

        d_true = torch.divide(a, b, rounding_mode=None)
        self.assertTrue(d_true.is_floating_point())
        self.assertEqual(d_true * b, a.to(d_true.dtype))

        d_floor = torch.divide(a, b, rounding_mode='floor')
        if dtype not in (torch.bfloat16, torch.half):
            self.assertEqual(d_floor * b + torch.remainder(a, b), a)
        else:
            self.assertEqual(d_floor * b + torch.remainder(a.float(), b.float()), a,
                             exact_dtype=False)

        d_trunc = torch.divide(a, b, rounding_mode='trunc')
        rounding_unsupported = (
            dtype == torch.half and device != 'cuda' or
            dtype == torch.bfloat16 and device != 'cpu')
        d_ref = d_true.float() if rounding_unsupported else d_true
        self.assertEqual(d_trunc, d_ref.trunc().to(dtype))

    @dtypes(torch.bfloat16, torch.half, torch.float32, torch.float64)
    def test_div_rounding_nonfinite(self, device, dtype):

        # Compare division of special floating point values against NumPy
        num = torch.tensor([1.0, -1.0, 0, 0.1, -0.1, np.pi, -np.pi, np.inf, -np.inf, np.nan],
                           dtype=dtype)
        # Divide by zero is tested seperately
        denom = num[num != 0]

        a, b = num[None, :].clone(), denom[:, None].clone()

        # Compare bfloat16 against NumPy float
        exact_dtype = dtype != torch.bfloat16
        if exact_dtype:
            an, bn = a.cpu().numpy(), b.cpu().numpy()
        else:
            an, bn = a.float().cpu().numpy(), b.float().cpu().numpy()

        for mode, np_ref in ((None, np.true_divide), ("floor", np.floor_divide)):
            with np.errstate(all='ignore'):
                expect = np_ref(an, bn)
            kwargs = dict(rounding_mode=mode) if mode is not None else {}
            with set_default_dtype(torch.double):
                actual = torch.divide(a, b, **kwargs)
            self.assertEqual(actual, torch.from_numpy(expect),
                             exact_device=False, exact_dtype=exact_dtype)

        # Compare contiguous (likely vectorized) against non-contiguous (not vectorized)
        a_noncontig = torch.empty([2 * i for i in a.shape], dtype=dtype, device=device)[::2, ::2]
        a_noncontig[:] = a
        b_noncontig = torch.empty([2 * i for i in b.shape], dtype=dtype, device=device)[::2, ::2]
        b_noncontig[:] = b

        for rounding_mode in (None, "trunc", "floor"):
            expect = torch.divide(a_noncontig, b_noncontig, rounding_mode=rounding_mode)
            actual = torch.divide(a, b, rounding_mode=rounding_mode)
            self.assertEqual(actual, expect)

    @dtypes(torch.bfloat16, torch.half, torch.float32, torch.float64)
    def test_divide_by_zero_rounding(self, device, dtype):
        a = torch.tensor([1.0, -1.0, 0, 0.1, -0.1, np.pi, -np.pi, np.inf, -np.inf, np.nan],
                         dtype=dtype)
        exact_dtype = (dtype != torch.bfloat16)
        if exact_dtype:
            an = a.cpu().numpy()
        else:
            an = a.float().cpu().numpy()

        zero = torch.zeros_like(a)

        # NOTE: NumPy's floor_divide rounding changed in 1.20.0 to be consistent with divide
        expect = np.divide(an, 0)
        for rounding_mode in (None, 'floor'):
            # CPU scalar
            actual = torch.divide(a, 0, rounding_mode=rounding_mode)
            self.assertEqual(actual, expect, exact_dtype=exact_dtype)
            # Device tensor
            actual = torch.divide(a, zero, rounding_mode=rounding_mode)
            self.assertEqual(actual, expect, exact_dtype=exact_dtype)

    @dtypes(*torch.testing.get_all_dtypes(
        include_bool=False, include_complex=False, include_bfloat16=False))
    def test_div_rounding_numpy(self, device, dtype):
        info = (torch.finfo(dtype) if dtype.is_floating_point
                else torch.iinfo(dtype))
        low, high = info.min, info.max

        # Compare division of random values against NumPy
        a = make_tensor((4096,), device, dtype, low=low, high=high)
        b = make_tensor((4096,), device, dtype, low=low, high=high)

        # Avoid division by zero which raises for integers and, for floats,
        # NumPy 1.20 changed floor_divide to follow IEEE rules for inf/nan
        # after dividing by zero.
        b[b == 0] = 1

        # Compare bfloat16 against NumPy float
        exact_dtype = dtype != torch.bfloat16

        if exact_dtype:
            an, bn = a.cpu().numpy(), b.cpu().numpy()
        else:
            an, bn = a.float().cpu().numpy(), b.float().cpu().numpy()

        for mode, np_ref in (
                (None, np.true_divide),
                ("floor", np.floor_divide),
                ("trunc", lambda a, b: np.trunc(np.true_divide(a, b)).astype(a.dtype))
        ):
            with np.errstate(all='ignore'):
                expect = torch.from_numpy(np_ref(an, bn))

            kwargs = dict(rounding_mode=mode) if mode is not None else {}
            # Contiguous (likely vectorized)
            with set_default_dtype(torch.double):
                actual = torch.divide(a, b, **kwargs)
            self.assertEqual(actual, expect, exact_device=False, exact_dtype=exact_dtype)

            # Non-contiguous (not vectorized)
            expect = expect[::2]
            with set_default_dtype(torch.double):
                actual = torch.divide(a[::2], b[::2], **kwargs)

            self.assertEqual(actual, expect, exact_device=False, exact_dtype=exact_dtype)

    # Tests that trying to add, inplace, a CUDA tensor to a CPU tensor
    #   throws the correct error message
    @onlyCUDA
    def test_cross_device_inplace_error_msg(self, device):
        a = torch.tensor(2.)
        b = torch.tensor(2., device=device)
        with self.assertRaisesRegex(RuntimeError,
                                    "Expected all tensors to be on the same device"):
            a += b

    # TODO: refactor this test into a more generic one, it's parked here currently
    @onlyOnCPUAndCUDA
    def test_out_resize_warning(self, device):
        a = torch.tensor((1, 2, 3), device=device, dtype=torch.float32)
        b = torch.tensor((4, 5, 6), device=device, dtype=torch.float32)

        unary_inputs = (a,)
        binary_inputs = (a, b)
        unary_ops = (torch.ceil, torch.exp)
        binary_ops = (torch.add, torch.sub)
        for op in (unary_ops + binary_ops):
            with warnings.catch_warnings(record=True) as w:
                warnings.simplefilter("always")
                inputs = unary_inputs if op in unary_ops else binary_inputs

                # No warnings
                op(*inputs, out=torch.empty(3, device=device))
                op(*inputs, out=torch.empty(0, device=device))
                self.assertEqual(len(w), 0)

                # Cases that throw warnings
                op(*inputs, out=torch.empty(2, device=device))
                self.assertEqual(len(w), 1)

    # Verifies that the inplace dunders (like idiv) actually are in place
    @onlyOnCPUAndCUDA
    def test_inplace_dunders(self, device):
        t = torch.randn((1,), device=device)
        expected = t.data_ptr()
        t += 1
        t -= 1
        t *= 1
        t /= 1
        with self.assertWarnsOnceRegex(UserWarning, 'floor_divide'):
            t //= 1
        t %= 1
        self.assertEqual(expected, t.data_ptr())

    def check_internal_mem_overlap(self, inplace_op, num_inputs,
                                   dtype, device,
                                   expected_failure=False):
        if isinstance(inplace_op, str):
            inplace_op = getattr(torch.Tensor, inplace_op)
        input = torch.randn(1, dtype=dtype, device=device).expand(3, 3)
        inputs = [input] + [torch.randn_like(input)
                            for i in range(num_inputs - 1)]
        if not expected_failure:
            with self.assertRaisesRegex(RuntimeError, 'single memory location'):
                inplace_op(*inputs)
        else:
            with self.assertRaises(AssertionError):
                with self.assertRaisesRegex(RuntimeError, 'single memory location'):
                    inplace_op(*inputs)

    def unary_check_input_output_mem_overlap(self, data, sz, op,
                                             expected_failure=False):

        def _test(op, output, input):
            output_exp = torch.empty_like(output)
            op(input, out=output_exp)
            self.assertEqual(op(input, out=output), output_exp, msg=op.__name__)

        # output is identical to input:
        _test(op, output=data[0:sz], input=data[0:sz])
        # output and input are independent:
        _test(op, output=data[0:sz], input=data[sz:2 * sz])
        # output partially overlaps with input:
        if not expected_failure:
            with self.assertRaisesRegex(RuntimeError, 'unsupported operation'):
                _test(op, data[0:sz], data[1:sz + 1])
        else:
            with self.assertRaises(AssertionError):
                with self.assertRaisesRegex(RuntimeError, 'unsupported operation'):
                    _test(op, data[0:sz], data[1:sz + 1])

    def binary_check_input_output_mem_overlap(self, op, device,
                                              expected_failure=False):
        sz = 3
        data = torch.randn(2 * sz, device=device)
        other = torch.randn(sz, device=device)

        self.unary_check_input_output_mem_overlap(
            data, sz, lambda input, out: op(other, input, out=out),
            expected_failure=expected_failure)

        self.unary_check_input_output_mem_overlap(
            data, sz, lambda input, out: op(input, other, out=out),
            expected_failure=expected_failure)

    @dtypes(torch.double)
    def test_binary_op_mem_overlap(self, device, dtype):
        ops = [
            ("add", True, True, 'cpu'),
            ("add", True, True, 'cuda'),
            ("mul", True, True, 'cpu'),
            ("mul", True, True, 'cuda'),
            ("sub", True, True, 'cpu'),
            ("sub", True, True, 'cuda'),
            ("div", True, True, 'cpu'),
            ("div", True, True, 'cuda'),
            ("pow", True, True, 'cpu'),
            ("pow", True, True, 'cuda'),
            ("fmod", True, True, 'cpu'),
            ("fmod", True, True, 'cuda'),
            ("atan2", True, True, 'cpu'),
            ("atan2", True, True, 'cuda'),
            ("hypot", True, True, 'cpu'),
            ("hypot", True, True, 'cuda'),
            ("igamma", True, True, 'cpu'),
            ("igamma", True, True, 'cuda'),
            ("igammac", True, True, 'cpu'),
            ("igammac", True, True, 'cuda'),
            ("nextafter", True, True, 'cpu'),
            ("nextafter", True, True, 'cuda'),
            ("le", True, True, 'cpu'),
            ("le", True, True, 'cuda'),
            ("lt", True, True, 'cpu'),
            ("lt", True, True, 'cuda'),
            ("ge", True, True, 'cpu'),
            ("ge", True, True, 'cuda'),
            ("gt", True, True, 'cpu'),
            ("gt", True, True, 'cuda'),
            ("eq", True, True, 'cpu'),
            ("eq", True, True, 'cuda'),
            ("ne", True, True, 'cpu'),
            ("ne", True, True, 'cuda'),
            ("logical_and", True, True, 'cpu'),
            ("logical_and", True, True, 'cuda'),
            ("logical_or", True, True, 'cpu'),
            ("logical_or", True, True, 'cuda'),
            ("logical_xor", True, True, 'cpu'),
            ("logical_xor", True, True, 'cuda'),
        ]

        for (fn, has_input_output_mem_overlap_check,
             has_internal_mem_overlap_check, dev) in ops:
            if dev != device:
                continue
            out_op = getattr(torch, fn)
            inplace_op = getattr(torch.Tensor, fn + '_')
            self.check_internal_mem_overlap(
                inplace_op, 2, dtype, device,
                expected_failure=not has_internal_mem_overlap_check)

            self.binary_check_input_output_mem_overlap(out_op, device,
                                                       expected_failure=not has_input_output_mem_overlap_check)

    def _do_pow_for_exponents(self, m1, exponents, pow_fn, atol):
        for num in exponents:
            if isinstance(num, int) and num < 0 and not m1.is_floating_point() and not m1.is_complex():
                with self.assertRaisesRegex(RuntimeError,
                                            r'Integers to negative integer powers are not allowed\.'):
                    torch.pow(m1[4], num)
            else:
                # base - tensor, exponent - number
                # contiguous
                res1 = torch.pow(m1[4], num)
                res2 = res1.clone().zero_()
                # `math.pow` has issues with complex exponentiation so we need to resort to normal `pow`.
                for i in range(res2.size(0)):
                    res2[i] = pow_fn(m1[4][i], num)
                rtol = 0 if atol is not None else None
                self.assertEqual(res1, res2, atol=atol, rtol=rtol)

                # non-contiguous
                res1 = torch.pow(m1[:, 4], num)
                res2 = res1.clone().zero_()
                for i in range(res2.size(0)):
                    res2[i] = pow_fn(m1[i, 4], num)
                self.assertEqual(res1, res2, atol=atol, rtol=rtol)

                # scalar ** tensor to enforce correct handling of dtypes for __rpow__().
                expected_dtype = torch.result_type(num, m1)
                res1 = num ** m1[4]
                res2 = torch.tensor(num, dtype=expected_dtype, device=m1.device) ** m1[4]
                self.assertEqual(res1, res2)
                self.assertEqual(res1.dtype, expected_dtype)

    @dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16))
    def test_pow(self, device, dtype):
        m1 = torch.empty(0, dtype=dtype, device=device)
        if m1.is_floating_point() or m1.is_complex():
            m1 = make_tensor((100, 100), low=0, high=1, dtype=dtype, device=device) + 0.5
        else:
            # math.pow will overflow and throw exceptions for large integers
            range_high = 4 if dtype in (torch.int8, torch.uint8) else 10
            m1 = make_tensor((100, 100), low=1, high=range_high, dtype=dtype, device=device)

        exponents = [-2.8, -2, -1, -0.5, 0, 0.5, 1, 2, 3, 4, 3.3]
        complex_exponents = [-2.5j, -1.0j, 0j, 1.0j, 2.5j, 1.0 + 1.0j, -1.0 - 1.5j, 3.3j]
        if m1.is_complex():
            self._do_pow_for_exponents(m1, exponents + complex_exponents, pow, 10e-4)
        else:
            self._do_pow_for_exponents(m1, exponents, math.pow, None)
            self._do_pow_for_exponents(m1, complex_exponents, pow, 10e-4)

        # base - number, exponent - tensor
        # contiguous
        res1 = torch.pow(3, m1[4])
        res2 = res1.clone().zero_()
        for i in range(res2.size(0)):
            res2[i] = pow(3, m1[4, i])
        self.assertEqual(res1, res2)

        # non-contiguous
        res1 = torch.pow(3, m1[:, 4])
        res2 = res1.clone().zero_()
        for i in range(res2.size(0)):
            res2[i] = pow(3, m1[i][4])
        self.assertEqual(res1, res2)

    # TODO: refactor all these tests using opinfos properly
    def _test_pow(self, base, exponent, np_exponent=None):
        if np_exponent is None:
            np_exponent = exponent

        def to_np(value):
            if isinstance(value, torch.Tensor):
                return value.cpu().numpy()
            return value

        try:
            np_res = np.power(to_np(base), to_np(np_exponent))
            expected = torch.from_numpy(np_res) if isinstance(np_res, np.ndarray) else torch.tensor(np_res, dtype=base.dtype)
        except ValueError as e:
            err_msg = "Integers to negative integer powers are not allowed."
            self.assertEqual(str(e), err_msg)
            out = torch.empty_like(base)
            test_cases = [
                lambda: base.pow(exponent),
                lambda: base.pow_(exponent),
                lambda: torch.pow(base, exponent),
                lambda: torch.pow(base, exponent, out=out)
            ]
            for test_case in test_cases:
                self.assertRaisesRegex(RuntimeError, err_msg, test_case)
        else:
            if isinstance(base, torch.Tensor):
                actual = base.pow(exponent)
                self.assertEqual(actual, expected.to(actual))
                actual = base.clone()
                # When base is a 0-dim cpu tensor and exp is a cuda tensor, we exp `pow` to work but `pow_` to fail, since
                # `pow` will try to create the output tensor on a cuda device, but `pow_` needs to use the cpu tensor as the output
                if (isinstance(exponent, torch.Tensor) and base.dim() == 0 and base.device.type == 'cpu' and
                        exponent.device.type == 'cuda'):
                    regex = 'Expected all tensors to be on the same device, but found at least two devices, cuda.* and cpu!'
                    self.assertRaisesRegex(RuntimeError, regex, base.pow_, exponent)
                elif torch.can_cast(torch.result_type(base, exponent), base.dtype):
                    actual2 = actual.pow_(exponent)
                    self.assertEqual(actual, expected)
                    self.assertEqual(actual2, expected)
                else:
                    self.assertRaisesRegex(RuntimeError, "Found dtype \\w+ but expected \\w+", lambda: actual.pow_(exponent))

            actual = torch.pow(base, exponent)
            self.assertEqual(actual, expected.to(actual))

            actual2 = torch.pow(base, exponent, out=actual)
            self.assertEqual(actual, expected.to(actual))
            self.assertEqual(actual2, expected.to(actual))

    # Tests pow() for integral, floating-type tensors, with integral, floating-type
    # exponents (tensor or scalar), respectively. noncontiguous tensors are also tested.
    def test_int_and_float_pow(self, device):

        def _test_int_and_float_pow(dt, low, high, dev):
            test_cases = (
                ((4, 4), 0, (4, 1)),
                ((3, 1), 4, (3, 1)),
                ((2,), 4, (1,)),
                ((1,), 2, ()),
                ((513, 513), 4, (513,)),
                ((5, 5, 5), 5, (5,)),
                ((), 2, ()),
            )
            for base_shape, exp_scalar, exp_shape in test_cases:
                base_tensor = make_tensor(base_shape, dtype=dt, device=dev, low=low, high=high)
                # int tensors don't take negative exponents
                if dt in [torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64]:
                    exp_tensor = make_tensor(exp_shape, dtype=dt, device=dev, low=0, high=high)
                else:
                    exp_tensor = make_tensor(exp_shape, dtype=dt, device=dev, low=low, high=high)
                self._test_pow(base_tensor, exp_scalar)
                self._test_pow(base_tensor, exp_tensor)
                # test non-contiguous tensors as well
                base_tensor = make_tensor(base_shape, dtype=dt, device=dev, low=low, high=high,
                                          noncontiguous=True)
                if dt in [torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64]:
                    exp_tensor = make_tensor(exp_shape, dtype=dt, device=dev, low=0, high=high,
                                             noncontiguous=True)
                else:
                    exp_tensor = make_tensor(exp_shape, dtype=dt, device=dev, low=low, high=high,
                                             noncontiguous=True)
                self._test_pow(base_tensor, exp_scalar)
                self._test_pow(base_tensor, exp_tensor)

        _test_int_and_float_pow(torch.int8, -2, 2, device)
        _test_int_and_float_pow(torch.uint8, 0, 3, device)
        _test_int_and_float_pow(torch.int16, -5, 5, device)
        _test_int_and_float_pow(torch.int64, -10, 10, device)
        _test_int_and_float_pow(torch.int32, -10, 10, device)
        _test_int_and_float_pow(torch.float16, 0., 5., device)
        _test_int_and_float_pow(torch.float32, 0., 10., device)
        _test_int_and_float_pow(torch.float64, 0., 10., device)
        # pow's output would have some NaNs as well
        _test_int_and_float_pow(torch.float32, -10., 10., device)
        _test_int_and_float_pow(torch.float64, -10., 10., device)

    # Tests that a Runtime error occurs when a base tensor cannot be resized
    # by pow's inplace variant due to PyTorch's broadcasting semantics.
    def test_pow_inplace_resizing_exception(self, device):
        test_cases = (
            ((), (3,)),
            ((2,), (2, 1)),
            ((2, 1), (2, 2)),
            ((2, 2), (2, 1, 1)),
        )
        test_inputs = list((make_tensor(base_size, dtype=torch.float64, device=device,
                                        high=10., low=0.),
                            make_tensor(exp_size, dtype=torch.float64, device=device,
                                        high=10., low=0.))
                           for base_size, exp_size in test_cases)
        for base, exponent in test_inputs:
            regex = "doesn't match the broadcast shape"
            self.assertRaisesRegex(RuntimeError, regex, base.pow_, exponent)

    def test_int_tensor_pow_neg_ints(self, device):
        ints = [torch.iinfo(torch.int32).min,
                -3, -2, -1, 0, 1, 2, 3,
                torch.iinfo(torch.int32).max]
        neg_ints = [torch.iinfo(torch.int32).min, -3, -2, -1]
        tensor = torch.tensor(ints, dtype=torch.int32, device=device)
        for pow in neg_ints:
            self._test_pow(tensor, pow)

    def test_long_tensor_pow_floats(self, device):
        ints = [0, 1, 23, 4567]
        floats = [0.0, 1 / 3, 1 / 2, 1.0, 3 / 2, 2.0]
        tensor = torch.tensor(ints, dtype=torch.int64, device=device)
        for pow in floats:
            self._test_pow(tensor, pow)

    @dtypes(*[torch.float32, torch.float64])
    def test_float_scalar_pow_float_tensor(self, device, dtype):
        floats = [2.0, -3 / 2, -1.0, -1 / 2, -1 / 3, 0.0,
                  1 / 3, 1 / 2, 1.0, 3 / 2, 2.0]
        exponent_shapes = (
            (1,),
            (2, 2),
            (2, 1),
            (2, 2, 2),
        )
        tensors = list(make_tensor(shape, dtype=dtype, device=device, low=0)
                       for shape in exponent_shapes)
        floats_tensor = torch.tensor(floats, dtype=dtype, device=device)
        for base in floats:
            self._test_pow(base, floats_tensor)
            for tensor in tensors:
                self._test_pow(base, tensor)

    @onlyCUDA
    def test_cuda_tensor_pow_scalar_tensor(self, device):
        cuda_tensors = [torch.randn((3, 3), device=device), torch.tensor(3.0, device=device)]
        scalar_tensors = [torch.tensor(5.0, device='cpu'), torch.tensor(-3), torch.tensor(1)]
        for base, exp in product(cuda_tensors, scalar_tensors):
            self._test_pow(base, exp)

    @onlyCUDA
    def test_cpu_tensor_pow_cuda_scalar_tensor(self, device):
        cuda_tensors = [torch.tensor(5.0, device='cuda'), torch.tensor(-3, device='cuda')]
        for exp in cuda_tensors:
            base = torch.randn((3, 3), device='cpu')
            regex = 'Expected all tensors to be on the same device, but found at least two devices, cuda.* and cpu!'
            self.assertRaisesRegex(RuntimeError, regex, torch.pow, base, exp)
        for exp in cuda_tensors:
            # Binary ops with a cpu + cuda tensor are allowed if the cpu tensor has 0 dimension
            base = torch.tensor(3.0, device='cpu')
            self._test_pow(base, exp)

    @onlyCUDA
    @dtypes(torch.complex64, torch.complex128)
    def test_pow_cuda_complex_extremal_failing(self, device, dtype):
        t = torch.tensor(complex(-1., float('inf')), dtype=dtype, device=device)
        with self.assertRaises(AssertionError):
            cuda_out = t.pow(2)
            cpu_out = t.cpu().pow(2)
            self.assertEqual(cpu_out, cuda_out)

    @onlyOnCPUAndCUDA
    @dtypes(*(torch.testing.get_all_dtypes(include_bool=False, include_bfloat16=False)))
    def test_complex_scalar_pow_tensor(self, device, dtype):
        complexes = [0.5j, 1. + 1.j, -1.5j, 2.2 - 1.6j, 1 + 0j]
        first_exp = make_tensor((100,), device, dtype, low=-2, high=2)
        second_exp = make_tensor((100,), device, dtype, low=-2, high=2, noncontiguous=True)
        first_exp[0] = first_exp[10] = first_exp[20] = 0
        second_exp[0] = second_exp[10] = second_exp[20] = 0
        for base in complexes:
            self._test_pow(base, first_exp)
            self._test_pow(base, second_exp)

    @onlyOnCPUAndCUDA
    def test_pow_scalar_type_promotion(self, device):
        # Test against a scalar and non-scalar input
        inputs = [17, [17]]
        for input in inputs:
            # We expect the computation to be performed in uint8 (overflowing to 0), and then cast to int64
            input_tensor_uint8 = torch.tensor(input, dtype=torch.uint8, device=device)
            out_uint8_computation = torch.pow(2, input_tensor_uint8, out=torch.tensor(0, dtype=torch.int64, device=device))

            # Computation should run in int64, and not overflow
            input_tensor_int64 = torch.tensor(input, dtype=torch.int64, device=device)
            out_int64_computation = torch.pow(2, input_tensor_int64, out=torch.tensor(0, dtype=torch.int64, device=device))

            self.assertNotEqual(out_uint8_computation, out_int64_computation)
            self.assertEqual(out_uint8_computation.to(dtype=torch.uint8), out_int64_computation.to(dtype=torch.uint8))

    def test_tensor_pow_tensor(self, dev):
        def rotate(l, n):
            return l[-n:] + l[:-n]

        def test_tensor_pow_tensor(values, torch_type, numpy_type):
            vals_tensor = torch.tensor(values, dtype=torch_type, device=dev)
            for i in range(len(values)):
                pows = rotate(values, i)
                pows_tensor = torch.tensor(pows, dtype=torch_type, device=dev)
                self._test_pow(vals_tensor, pows_tensor)

        ints = [0, 1, 2, 3]
        test_tensor_pow_tensor(ints, torch.uint8, np.uint8)
        test_tensor_pow_tensor(ints, torch.int8, np.int8)
        test_tensor_pow_tensor(ints, torch.int16, np.int16)
        test_tensor_pow_tensor(ints, torch.int32, np.int32)
        test_tensor_pow_tensor(ints, torch.int64, np.int64)

        floats = [-3.0, -2.0, -1.0, -1 / 2, -1 / 3,
                  0.0, 1 / 3, 1 / 2, 1.0, 2.0, 3.0]
        test_tensor_pow_tensor(floats, torch.float16, np.float16)
        test_tensor_pow_tensor(floats, torch.float32, np.float32)
        test_tensor_pow_tensor(floats, torch.float64, np.float64)


    def test_logical_xor_with_nontrivial_alignment(self, device):
        # test tensor that is not aligned to multiple of 16 bytes
        size = 128
        a = (torch.randn(size, device=device) > 0)
        b = (torch.randn(size, device=device) > 0)
        c = (torch.randn(size, device=device) > 0)
        non_trivial_alignment = [1, 2, 4, 8, 15]
        for i in non_trivial_alignment:
            for j in non_trivial_alignment:
                for k in non_trivial_alignment:
                    a_ = a[i: 100 + i]
                    b_ = b[j: 100 + j]
                    c_ = c[k: 100 + k]
                    torch.logical_xor(a_, b_, out=c_)
                    for x, y, z in zip(a_.tolist(), b_.tolist(), c_.tolist()):
                        self.assertEqual(x ^ y, z)

    @dtypes(torch.float)
    def test_add_with_tail(self, device, dtype):
        # test tensor where there is a tail which is not a multiple
        # of GPU warp size
        for tail_size in [1, 63, 67, 130]:
            size = 4096 + tail_size
            a = torch.randn(size, device=device, dtype=dtype)
            b = torch.randn(size, device=device, dtype=dtype)
            c = a + b
            for x, y, z in zip(a.tolist(), b.tolist(), c.tolist()):
                self.assertEqual(x + y, z)

    # Tests that CUDA tensors on different devices cannot be used in the same
    # binary operation, and that CUDA "scalars" cannot be used in the same
    # binary operation as non-scalar CPU tensors.
    @deviceCountAtLeast(2)
    @onlyCUDA
    def test_cross_device_binary_ops(self, devices):
        vals = (1., (2.,))
        cpu_tensor = torch.randn(2, 2)

        def do_test(op, a, b):
            with self.assertRaisesRegex(RuntimeError, "Expected all tensors.+"):
                op(a, b)
            with self.assertRaisesRegex(RuntimeError, "Expected all tensors.+"):
                op(b, a)
            with self.assertRaisesRegex(RuntimeError, "Expected all tensors.+"):
                op(a, cpu_tensor)
            with self.assertRaisesRegex(RuntimeError, "Expected all tensors.+"):
                op(cpu_tensor, a)

        for op in (operator.add, torch.add,
                   operator.sub, torch.sub,
                   operator.mul, torch.mul,
                   operator.truediv, torch.true_divide,
                   operator.floordiv, torch.floor_divide):
            for a, b in product(vals, vals):
                a = torch.tensor(a, device=devices[0])
                b = torch.tensor(b, device=devices[1])

            do_test(op, a, b)

    # This test ensures that a scalar Tensor can be safely used
    # in a binary operation in conjunction with a Tensor on all
    # available CUDA devices
    @deviceCountAtLeast(2)
    @onlyCUDA
    def test_binary_op_scalar_device_unspecified(self, devices):
        scalar_val = torch.tensor(1.)
        for default_device in devices:
            with torch.cuda.device(default_device):
                for device in devices:
                    device_obj = torch.device(device)
                    x = torch.rand(3, device=device)
                    y0 = x * scalar_val
                    self.assertEqual(y0.device, device_obj)
                    y1 = scalar_val * x
                    self.assertEqual(y1.device, device_obj)
                    self.assertEqual(y0, y1)

    def test_div_and_floordiv_vs_python(self, device):
        # Tests torch division ops which can handle both arguments being
        #   scalars.
        # NOTE: torch.floor_divide currently truncates instead of flooring.
        #   the quotient. See https://github.com/pytorch/pytorch/issues/43874.
        def _scalar_helper(python_op, torch_op):
            for a, b in product(range(-10, 10), range(-10, 10)):
                for op in (lambda x: x * .5, lambda x: math.floor(x)):
                    a = op(a)
                    b = op(b)

                    # Skips zero divisors
                    if b == 0:
                        continue

                    expected = python_op(a, b)

                    for op in (operator.truediv, torch.true_divide):
                        actual_scalar = torch_op(a, b)

                        a_t = torch.tensor(a, device=device)
                        b_t = torch.tensor(b, device=device)

                        actual_tensor = torch_op(a_t, b_t)
                        actual_first_tensor = torch_op(a_t, b)
                        actual_second_tensor = torch_op(a, b_t)

                        self.assertEqual(actual_scalar, expected_div)
                        self.assertEqual(actual_tensor.item(), expected_div)
                        self.assertEqual(actual_first_tensor, actual_tensor)
                        self.assertEqual(actual_second_tensor, actual_tensor)

            _scalar_helper(operator.truediv, operator.truediv)
            _scalar_helper(operator.truediv, torch.true_divide)
            with self.assertWarnsOnceRegex(UserWarning, 'floor_divide'):
                _scalar_helper(lambda a, b: math.trunc(a / b), operator.floordiv)
                _scalar_helper(lambda a, b: math.trunc(a / b), torch.floor_divide)

    # NOTE: torch.floor_divide currently truncates instead of flooring.
    # See https://github.com/pytorch/pytorch/issues/43874.
    @onlyOnCPUAndCUDA
    def test_div_and_floordiv_script_vs_python(self, device):
        # Creates jitted functions of two tensors
        def _wrapped_div(a, b):
            return a / b

        def _wrapped_floordiv(a, b):
            return a // b

        scripted_div = torch.jit.script(_wrapped_div)
        scripted_floordiv = torch.jit.script(_wrapped_floordiv)
        for a, b in product(range(-10, 10), range(-10, 10)):
            for op in (lambda x: x * .5, lambda x: math.floor(x)):
                a = op(a)
                b = op(b)

                # Skips zero divisors
                if b == 0:
                    continue

                expected_div = a / b
                expected_truncdiv = math.trunc(a / b)
                a_t = torch.tensor(a, device=device)
                b_t = torch.tensor(b, device=device)

                self.assertEqual(scripted_div(a_t, b_t), expected_div)
                with self.assertWarnsOnceRegex(UserWarning, 'floor_divide'):
                    self.assertEqual(scripted_floordiv(a_t, b_t), expected_truncdiv)

        # Creates jitted functions of one tensor
        def _wrapped_div_scalar(a):
            return a / 5

        # NOTE: the JIT implements division as torch.reciprocal(a) * 5
        def _wrapped_rdiv_scalar(a):
            return 5 / a

        def _wrapped_floordiv_scalar(a):
            return a // 5

        # NOTE: this fails if the input is not an integer tensor
        # See https://github.com/pytorch/pytorch/issues/45199
        def _wrapped_rfloordiv_scalar(a):
            return 5 // a

        scripted_div_scalar = torch.jit.script(_wrapped_div_scalar)
        scripted_rdiv_scalar = torch.jit.script(_wrapped_rdiv_scalar)
        scripted_floordiv_scalar = torch.jit.script(_wrapped_floordiv_scalar)
        scripted_rfloordiv_scalar = torch.jit.script(_wrapped_rfloordiv_scalar)

        for a in range(-10, 10):
            for op in (lambda x: x * .5, lambda x: math.floor(x)):
                a = op(a)

                a_t = torch.tensor(a, device=device)

                self.assertEqual(a / 5, scripted_div_scalar(a_t))
                with self.assertWarnsOnceRegex(UserWarning, 'floor_divide'):
                    self.assertEqual(math.trunc(a / 5), scripted_floordiv_scalar(a_t))

                # Skips zero divisors
                if a == 0:
                    continue

                self.assertEqual(5 / a, scripted_rdiv_scalar(a_t))

                # Handles Issue 45199 (see comment above)
                if a_t.is_floating_point():
                    with self.assertRaises(RuntimeError):
                        scripted_rfloordiv_scalar(a_t)
                else:
                    # This should emit a UserWarning, why doesn't it?
                    # See issue gh-52387
                    self.assertEqual(5 // a, scripted_rfloordiv_scalar(a_t))

    # NOTE: torch.floor_divide currently truncates instead of flooring
    #   the quotient. See https://github.com/pytorch/pytorch/issues/43874.
    @onlyOnCPUAndCUDA
    def test_idiv_and_ifloordiv_vs_python(self, device):
        def _wrapped_idiv_tensor(a, b):
            a /= b
            return a

        def _wrapped_idiv_scalar(a):
            a /= 5
            return a

        def _wrapped_true_divide__tensor(a, b):
            a.true_divide_(b)
            return a

        def _wrapped_true_divide__scalar(a):
            a.true_divide_(5)
            return a

        def _wrapped_floor_divide__tensor(a, b):
            a.floor_divide_(b)
            return a

        def _wrapped_floor_divide__scalar(a):
            a.floor_divide_(5)
            return a

        # The following functions are unsupported by the JIT
        def _wrapped_ifloordiv_tensor(a, b):
            a //= b
            return a

        def _wrapped_ifloordiv_scalar(a):
            a //= 5
            return a

        with self.assertRaises(torch.jit.frontend.NotSupportedError):
            scripted_ifloordiv_tensor = torch.jit.script(_wrapped_ifloordiv_tensor)

        with self.assertRaises(torch.jit.frontend.NotSupportedError):
            scripted_ifloordiv_scalar = torch.jit.script(_wrapped_ifloordiv_scalar)

        scripted_idiv_tensor = torch.jit.script(_wrapped_idiv_tensor)
        scripted_idiv_scalar = torch.jit.script(_wrapped_idiv_scalar)
        scripted_true_divide__tensor = torch.jit.script(_wrapped_true_divide__tensor)
        scripted_true_divide__scalar = torch.jit.script(_wrapped_true_divide__scalar)
        scripted_floor_divide__tensor = torch.jit.script(_wrapped_floor_divide__tensor)
        scripted_floor_divide__scalar = torch.jit.script(_wrapped_floor_divide__scalar)

        for a, b in product(range(-10, 10), range(-10, 10)):
            for op in (lambda x: x * .5, lambda x: math.floor(x)):
                a = op(a)
                b = op(b)

                # Skips zero divisors
                if b == 0:
                    continue

                expected_idiv = a / b
                expected_ifloordiv = a // b
                expected_itruncdiv = math.trunc(a / b)

                a_t = torch.tensor(a, device=device)
                b_t = torch.tensor(b, device=device)

                if a_t.is_floating_point():
                    tmp0 = a_t.clone()
                    tmp0 /= b

                    tmp1 = a_t.clone()
                    tmp1 /= b_t

                    self.assertEqual(tmp0.item(), expected_idiv)
                    self.assertEqual(tmp1.item(), expected_idiv)
                    self.assertEqual(scripted_true_divide__tensor(a_t.clone(), b_t).item(), expected_idiv)
                    self.assertEqual(scripted_true_divide__scalar(a_t.clone()).item(), a / 5)
                else:
                    tmp = a_t.clone()
                    with self.assertRaises(RuntimeError):
                        tmp /= b
                    with self.assertRaises(RuntimeError):
                        tmp /= b_t
                    with self.assertRaises(RuntimeError):
                        scripted_true_divide__tensor(tmp, b_t)
                    with self.assertRaises(RuntimeError):
                        scripted_true_divide__scalar(tmp)


                if not a_t.is_floating_point() and b_t.is_floating_point():
                    # Inplace modification fails because a float tensor is required
                    #   if the divisor is a float tensor
                    with self.assertRaises(RuntimeError), self.assertWarnsOnceRegex(UserWarning, "floor_divide"):
                        a_t.clone().floor_divide_(b_t)
                    with self.assertRaises(RuntimeError), self.assertWarnsOnceRegex(UserWarning, "floor_divide"):
                        scripted_floor_divide_tensor(a_t.clone(), b_t)
                    tmp = a_t.clone()
                    with self.assertRaises(RuntimeError), self.assertWarnsOnceRegex(UserWarning, "floor_divide"):
                        tmp //= b_t
                else:
                    # Inplace modification is OK when both or neither tensor is
                    #   a float tensor
                    with self.assertWarnsOnceRegex(UserWarning, "floor_divide"):
                        self.assertEqual(a_t.clone().floor_divide_(b_t).item(), expected_itruncdiv)
                        self.assertEqual(scripted_floor_divide__tensor(a_t.clone(), b_t).item(), expected_itruncdiv)
                    tmp = a_t.clone()
                    with self.assertWarnsOnceRegex(UserWarning, "floor_divide"):
                        tmp //= b_t
                    self.assertEqual(tmp.item(), expected_itruncdiv)

                with self.assertWarnsOnceRegex(UserWarning, "floor_divide"):
                    self.assertEqual(scripted_floor_divide__scalar(a_t), math.trunc(a / 5))

    # Tests binary op equivalence with Python builtin ops
    # Also tests that reverse operations are equivalent to forward ops
    # NOTE: division ops are tested separately above
    def test_binary_ops_with_scalars(self, device):
        for ops in ((operator.add, torch.add),
                    (operator.sub, torch.sub),
                    (operator.mul, torch.mul),
                    (operator.truediv, torch.div)):
            python_op, torch_op = ops

            for a, b in product(range(-10, 10), range(-10, 10)):
                for op in (lambda x: x * .5, lambda x: math.floor(x)):
                    a = op(a)
                    b = op(b)

                    # Skips zero divisors
                    if b == 0 or a == 0:
                        continue

                    a_tensor = torch.tensor(a, device=device)
                    b_tensor = torch.tensor(b, device=device)
                    a_tensor_cpu = a_tensor.cpu()
                    b_tensor_cpu = b_tensor.cpu()
                    vals = (a, b, a_tensor, b_tensor, a_tensor_cpu, b_tensor_cpu)

                    for args in product(vals, vals):
                        first, second = args

                        first_scalar = first if not isinstance(first, torch.Tensor) else first.item()
                        second_scalar = second if not isinstance(second, torch.Tensor) else second.item()
                        expected = python_op(first_scalar, second_scalar)

                        self.assertEqual(expected, python_op(first, second))
                        self.assertEqual(expected, torch_op(first, second))

    @dtypes(*product(torch.testing.get_all_dtypes(include_complex=False), torch.testing.get_all_dtypes(include_complex=False)))
    def test_maximum_minimum_type_promotion(self, device, dtypes):
        a = torch.tensor((0, 1), device=device, dtype=dtypes[0])
        b = torch.tensor((1, 0), device=device, dtype=dtypes[1])
        for op in (torch.maximum, torch.max, torch.fmax, torch.minimum, torch.min, torch.fmin):
            result = op(a, b)
            self.assertEqual(result.dtype, torch.result_type(a, b))

    @dtypes(*(torch.testing.get_all_int_dtypes() + [torch.bool]))
    def test_maximum_minimum_int_and_bool(self, device, dtype):
        ops = ((torch.maximum, torch.max, np.maximum), (torch.minimum, torch.min, np.minimum),
               (torch.fmax, None, np.fmax), (torch.fmin, None, np.fmin))
        rng = np.random.default_rng()
        a_np = np.array(rng.integers(-100, 100, size=10), dtype=torch_to_numpy_dtype_dict[dtype])
        b_np = np.array(rng.integers(-100, 100, size=10), dtype=torch_to_numpy_dtype_dict[dtype])

        for torch_op, alias, numpy_op in ops:
            a_tensor = torch.from_numpy(a_np).to(device=device, dtype=dtype)
            b_tensor = torch.from_numpy(b_np).to(device=device, dtype=dtype)
            tensor_result = torch_op(a_tensor, b_tensor)

            out = torch.empty_like(a_tensor)
            torch_op(a_tensor, b_tensor, out=out)

            numpy_result = numpy_op(a_np, b_np)

            if alias is not None:
                alias_result = alias(a_tensor, b_tensor)
                self.assertEqual(alias_result, tensor_result)

            self.assertEqual(tensor_result, numpy_result)
            self.assertEqual(out, numpy_result)

    @precisionOverride({torch.bfloat16: 1e-2})
    @dtypes(*(torch.testing.get_all_fp_dtypes()))
    def test_maximum_minimum_float(self, device, dtype):
        ops = ((torch.maximum, torch.max, np.maximum), (torch.minimum, torch.min, np.minimum),
               (torch.fmax, None, np.fmax), (torch.fmin, None, np.fmin))

        if dtype == torch.bfloat16:
            a_np = np.random.randn(10).astype(np.float64)
            b_np = np.random.randn(10).astype(np.float64)
        else:
            a_np = np.random.randn(10).astype(torch_to_numpy_dtype_dict[dtype])
            b_np = np.random.randn(10).astype(torch_to_numpy_dtype_dict[dtype])

        for torch_op, alias, numpy_op in ops:
            numpy_result = numpy_op(a_np, b_np)

            a_tensor = torch.from_numpy(a_np).to(device=device, dtype=dtype)
            b_tensor = torch.from_numpy(b_np).to(device=device, dtype=dtype)
            tensor_result = torch_op(a_tensor, b_tensor)
            out = torch.empty_like(a_tensor)
            torch_op(a_tensor, b_tensor, out=out)

            if alias is not None:
                alias_result = alias(a_tensor, b_tensor)
                self.assertEqual(alias_result, tensor_result)

            self.assertEqual(tensor_result, numpy_result)
            self.assertEqual(out, numpy_result)

    @dtypes(*(torch.testing.get_all_fp_dtypes()))
    def test_maximum_minimum_float_nan_and_inf(self, device, dtype):
        # np.maximum and np.minimum functions compare input arrays element-wisely.
        # if one of the elements being compared is a NaN, then that element is returned.
        ops = ((torch.maximum, torch.max, np.maximum), (torch.minimum, torch.min, np.minimum),
               (torch.fmax, None, np.fmax), (torch.fmin, None, np.fmin))
        a_vals = (float('inf'), -float('inf'), float('nan'), float('inf'), float('nan'), float('nan'), 1, float('nan'))
        b_vals = (-float('inf'), float('inf'), float('inf'), float('nan'), float('nan'), 0, float('nan'), -5)
        if dtype == torch.bfloat16:
            a_np = np.array(a_vals, dtype=np.float64)
            b_np = np.array(b_vals, dtype=np.float64)
        else:
            a_np = np.array(a_vals, dtype=torch_to_numpy_dtype_dict[dtype])
            b_np = np.array(b_vals, dtype=torch_to_numpy_dtype_dict[dtype])

        for torch_op, alias, numpy_op in ops:
            numpy_result = numpy_op(a_np, b_np)

            a_tensor = torch.from_numpy(a_np).to(device=device, dtype=dtype)
            b_tensor = torch.from_numpy(b_np).to(device=device, dtype=dtype)
            tensor_result = torch_op(a_tensor, b_tensor)

            out = torch.empty_like(a_tensor)
            torch_op(a_tensor, b_tensor, out=out)

            if alias is not None:
                alias_result = alias(a_tensor, b_tensor)
                self.assertEqual(alias_result, tensor_result)

            if dtype == torch.bfloat16:
                self.assertEqual(tensor_result, numpy_result, exact_dtype=False)
                self.assertEqual(out, numpy_result, exact_dtype=False)
            else:
                self.assertEqual(tensor_result, numpy_result)
                self.assertEqual(out, numpy_result)

    @dtypes(*product(torch.testing.get_all_complex_dtypes(), torch.testing.get_all_dtypes()))
    def test_maximum_minimum_complex(self, device, dtypes):
        for torch_op in (torch.maximum, torch.minimum, torch.max, torch.min, torch.fmax, torch.fmin):
            with self.assertRaisesRegex(RuntimeError, '.+not implemented for.+'):
                torch_op(torch.ones(1, device=device, dtype=dtypes[0]),
                         torch.ones(1, device=device, dtype=dtypes[1]))

            with self.assertRaisesRegex(RuntimeError, '.+not implemented for.+'):
                torch_op(torch.ones(1, device=device, dtype=dtypes[1]),
                         torch.ones(1, device=device, dtype=dtypes[0]))

    @onlyCUDA
    def test_maximum_minimum_cross_device(self, device):
        a = torch.tensor((1, 2, -1))
        b = torch.tensor((3, 0, 4), device=device)
        ops = (torch.maximum, torch.minimum)

        for torch_op in ops:
            with self.assertRaisesRegex(RuntimeError,
                                        "Expected all tensors to be on the same device"):
                torch_op(a, b)

            with self.assertRaisesRegex(RuntimeError,
                                        "Expected all tensors to be on the same device"):
                torch_op(b, a)

        # test cuda tensor and cpu scalar
        ops = ((torch.maximum, np.maximum), (torch.minimum, np.minimum))
        a_np = np.array(1)
        b_np = np.array([3, 0, 4])

        for torch_op, numpy_op in ops:
            a_tensor = torch.from_numpy(a_np)
            b_tensor = torch.from_numpy(b_np).to(device=device)
            tensor_result_1 = torch_op(a_tensor, b_tensor)
            numpy_result_1 = numpy_op(a_np, b_np)
            tensor_result_2 = torch_op(b_tensor, a_tensor)
            numpy_result_2 = numpy_op(b_np, a_np)

            self.assertEqual(tensor_result_1, numpy_result_1)
            self.assertEqual(tensor_result_2, numpy_result_2)

    # TODO: tests like this should be generic
    @dtypesIfCUDA(torch.half, torch.float, torch.double)
    @dtypes(torch.float, torch.double)
    def test_mul_intertype_scalar(self, device, dtype):
        x = torch.tensor(1.5, dtype=dtype, device=device)
        y = torch.tensor(3, dtype=torch.int32, device=device)

        self.assertEqual(x * y, 4.5)
        self.assertEqual(y * x, 4.5)

        with self.assertRaisesRegex(RuntimeError, "can't be cast to the desired output type"):
            y *= x
        x *= y
        self.assertEqual(x, 4.5)

    @onlyCPU
    @dtypes(*torch.testing.get_all_dtypes())
    def test_sub(self, device, dtype):
        m1 = torch.tensor([2.34, 4.44], dtype=dtype, device=device)
        m2 = torch.tensor([1.23, 2.33], dtype=dtype, device=device)

        if dtype == torch.bool:
            self.assertRaises(RuntimeError, lambda: m1 - m2)
        elif (dtype == torch.bfloat16 or dtype == torch.half):
            # bfloat16 has a lower precision so we have to have a separate check for it
            self.assertEqual(m1 - m2, torch.tensor([1.11, 2.11], dtype=dtype), atol=0.01, rtol=0)
        else:
            self.assertEqual(m1 - m2, torch.tensor([1.11, 2.11], dtype=dtype))

    # TODO: what is this test testing?
    @onlyCPU
    @dtypes(torch.float)
    def test_csub(self, device, dtype):
        # with a tensor
        a = torch.randn(100, 90, dtype=dtype, device=device)
        b = a.clone().normal_()

        res_add = torch.add(a, b, alpha=-1)
        res_csub = a.clone()
        res_csub.sub_(b)
        self.assertEqual(res_add, res_csub)

        # with a scalar
        a = torch.randn(100, 100, dtype=dtype, device=device)

        scalar = 123.5
        res_add = torch.add(a, -scalar)
        res_csub = a.clone()
        res_csub.sub_(scalar)
        self.assertEqual(res_add, res_csub)

    # TODO: reconcile with minimum/maximum tests
    @dtypesIfCUDA(torch.half, torch.float, torch.double)
    @dtypes(torch.float, torch.double)
    def test_min_max_binary_op_nan(self, device, dtype):
        a = torch.rand(1000, dtype=dtype, device=device)
        b = torch.rand(1000, dtype=dtype, device=device)

        # 0:250: a -- nan, b -- not nan
        a[:250] = float('nan')
        # 250:500: a -- not nan, b -- nan
        b[250:500] = float('nan')
        # 500:750: a and b both nan
        a[500:750] = float('nan')
        b[500:750] = float('nan')
        # 750:1000: neither nan

        ma = torch.max(a, b)
        mi = torch.min(a, b)

        for i in range(750):
            self.assertTrue(torch.isnan(ma[i]), "max(a, b): {}, a: {}, b: {}".format(ma[i], a[i], b[i]))
            self.assertTrue(torch.isnan(mi[i]), "min(a, b): {}, a: {}, b: {}".format(mi[i], a[i], b[i]))

        for i in range(750, 1000):
            self.assertFalse(torch.isnan(ma[i]), "max(a, b): {}, a: {}, b: {}".format(ma[i], a[i], b[i]))
            self.assertFalse(torch.isnan(mi[i]), "min(a, b): {}, a: {}, b: {}".format(mi[i], a[i], b[i]))

    @dtypes(*product(torch.testing.get_all_dtypes(include_complex=False),
                     torch.testing.get_all_dtypes(include_complex=False)))
    def test_copysign(self, device, dtypes):
        def _test_copysign_numpy(a, b):
            torch_result = torch.copysign(a, b)

            if a.dtype == torch.bfloat16:
                np_a = a.to(torch.float).cpu().numpy()
            else:
                np_a = a.cpu().numpy()

            if b.dtype == torch.bfloat16:
                np_b = b.to(torch.float).cpu().numpy()
            else:
                np_b = b.cpu().numpy()
            expected = torch.from_numpy(np.copysign(np_a, np_b))
            # To handle inconsistencies of type promotion between PyTorch and Numpy
            # Applied for both arguments having integral precision and bfloat16
            types = [torch.bool, torch.bfloat16] + torch.testing.get_all_int_dtypes()
            if a.dtype in types or b.dtype in types:
                promoted_type = torch.promote_types(torch_result.dtype, expected.dtype)
                torch_result = torch_result.to(promoted_type)
                expected = expected.to(promoted_type)

            # Verify Value
            self.assertEqual(torch_result, expected)
            # Verify Sign
            # Use double copysign to verify the correctnes of 0.0 and -0.0, since
            # it always True for self.assertEqual(0.0 == -0.0). So, we use 1 as the
            # magnitude to verify the sign between torch and numpy results, elementwise.
            # Special case: NaN conversions between FP32 and FP16 is not bitwise
            # equivalent to pass this assertion.
            if a.dtype != torch.float16 and b.dtype != torch.float16:
                self.assertEqual(torch.copysign(torch.tensor(1.0), torch_result),
                                 torch.copysign(torch.tensor(1.0), expected))

        # Compare Result with NumPy
        # Type promotion
        a = make_tensor((10, 10), device=device, dtype=dtypes[0], low=-9, high=9)
        b = make_tensor((10, 10), device=device, dtype=dtypes[1], low=-9, high=9)
        _test_copysign_numpy(a, b)

        # Broadcast
        a = make_tensor((10, 1, 10), device=device, dtype=dtypes[0], low=-9, high=9)
        b = make_tensor((10, 10), device=device, dtype=dtypes[1], low=-9, high=9)
        _test_copysign_numpy(a, b)

        a = make_tensor((10, 10), device=device, dtype=dtypes[0], low=-9, high=9)
        b = make_tensor((10, 1, 10), device=device, dtype=dtypes[1], low=-9, high=9)
        _test_copysign_numpy(a, b)

        # 0.0/-0.0/inf/-inf/nan
        cases = [0.0, -0.0, float('inf'), float('-inf'), float('nan')]
        # torch.bfloat16 can not hold '-nan'
        # torch.half can not hold '-nan' on CUDA
        types = [torch.float32, torch.float64]
        if device == 'cpu':
            types.append(torch.float16)
        if dtypes[0] in types:
            b = make_tensor((10, 10), device=device, dtype=dtypes[1], low=-9, high=9)
            for case in cases:
                _test_copysign_numpy(torch.tensor([case], device=device, dtype=dtypes[0]), b)

        if dtypes[1] in torch.testing.get_all_fp_dtypes():
            a = make_tensor((10, 10), device=device, dtype=dtypes[0], low=-9, high=9)
            for case in cases:
                _test_copysign_numpy(a, torch.tensor([case], device=device, dtype=dtypes[1]))

    @dtypes(torch.bfloat16, torch.float)
    def test_div(self, device, dtype):
        for op, method, inplace in ((torch.div, torch.Tensor.div, torch.Tensor.div_),
                                    (torch.true_divide, torch.Tensor.true_divide,
                                     torch.Tensor.true_divide_)):
            m1 = torch.randn(10, 10, dtype=torch.float, device=device).to(dtype=dtype)
            res1 = m1.clone()
            inplace(res1[:, 3], 2)
            res2 = m1.clone()
            for i in range(m1.size(0)):
                res2[i, 3] = res2[i, 3] / 2
            self.assertEqual(res1, res2)

            if dtype == torch.bfloat16:
                a1 = torch.tensor([4.2, 6.2], dtype=dtype, device=device)
                a2 = torch.tensor([2., 2.], dtype=dtype, device=device)
                self.assertEqual(op(a1, a2),
                                 torch.tensor([2.1, 3.1], dtype=dtype, device=device),
                                 atol=0.01, rtol=0)
                self.assertEqual(method(a1, a2), op(a1, a2))

    @dtypes(torch.bfloat16, torch.float)
    def test_true_divide_out(self, device, dtype):
        a1 = torch.tensor([4.2, 6.2], dtype=dtype, device=device)
        a2 = torch.tensor([2., 2.], dtype=dtype, device=device)
        res = torch.empty_like(a1)
        self.assertEqual(torch.true_divide(a1, a2, out=res),
                         torch.tensor([2.1, 3.1], dtype=dtype, device=device),
                         atol=0.01, rtol=0)

    @onlyCUDA
    @dtypes(torch.half)
    def test_divmul_scalar(self, device, dtype):
        x = torch.tensor(100., device=device, dtype=dtype)
        x_ref = x.float()
        scale = 1e5
        res = x.div(scale)
        expected = x_ref.div(scale)
        self.assertEqual(res, expected.to(dtype), atol=0., rtol=0.)
        x = torch.tensor(1e-5, device=device, dtype=dtype)
        x_ref = x.float()
        res = x.mul(scale)
        expected = x_ref.mul(scale)
        self.assertEqual(res, expected.to(dtype), atol=0., rtol=0.)
        res = scale * x
        self.assertEqual(res, expected.to(dtype), atol=0., rtol=0.)

    @dtypesIfCUDA(*set(torch.testing.get_all_math_dtypes('cuda')) - {torch.complex64, torch.complex128})
    @dtypes(*set(torch.testing.get_all_math_dtypes('cpu')) - {torch.complex64, torch.complex128})
    def test_floor_divide_tensor(self, device, dtype):
        x = torch.randn(10, device=device).mul(30).to(dtype)
        y = torch.arange(1, 11, dtype=dtype, device=device)

        with self.assertWarnsOnceRegex(UserWarning, "floor_divide"):
            z = x // y
        z_alt = torch.trunc(x.double() / y.double()).to(dtype)

        self.assertEqual(z.dtype, x.dtype)
        self.assertEqual(z, z_alt)

    @dtypesIfCUDA(*set(torch.testing.get_all_math_dtypes('cuda')) - {torch.complex64, torch.complex128})
    @dtypes(*set(torch.testing.get_all_math_dtypes('cpu')) - {torch.complex64, torch.complex128})
    def test_floor_divide_scalar(self, device, dtype):
        x = torch.randn(100, device=device).mul(10).to(dtype)

        with self.assertWarnsOnceRegex(UserWarning, "floor_divide"):
            z = x // 3
        z_alt = torch.tensor([math.trunc(v.item() / 3.) for v in x], dtype=x.dtype, device=device)

        self.assertEqual(z.dtype, x.dtype)
        self.assertEqual(z, z_alt)

    # Note: this tests fails on XLA
    @onlyOnCPUAndCUDA
    @dtypes(torch.float, torch.long)
    def test_floor_divide_out(self, device, dtype):
        x = torch.randn(10, device=device).mul(10).to(dtype)
        y = torch.arange(1, 11, dtype=dtype, device=device)
        o = torch.empty(10, dtype=dtype, device=device)

        with self.assertWarnsOnceRegex(UserWarning, "floor_divide"):
            torch.floor_divide(x, y, out=o)
            self.assertEqual(o, x // y)

            # Tests scalar with out
            torch.floor_divide(x, 2, out=o)
            self.assertEqual(o, x // 2)

            if dtype == torch.int:
                o = torch.empty(10, dtype=torch.float, device=device)
                torch.floor_divide(x, y, out=o)
                self.assertEqual(o, torch.floor_divide(x.float(), y.float()))

    @onlyCPU
    @dtypes(*torch.testing.get_all_math_dtypes('cpu'))
    def test_rdiv(self, device, dtype):
        if dtype is torch.float16:
            return
        elif dtype.is_complex:
            x = torch.rand(100, dtype=dtype, device=device).add(1).mul(4)
        else:
            x = torch.rand(100, device=device).add(1).mul(4).to(dtype)
        y = 30 / x
        z = torch.tensor([30 / v.item() for v in x], device=device)
        self.assertEqual(y, z, exact_dtype=False)

    @dtypes(*torch.testing.get_all_fp_dtypes(include_bfloat16=False))
    def test_fmod_remainder_by_zero_float(self, device, dtype):
        fn_list = (torch.fmod, torch.remainder)
        for fn in fn_list:
            # check floating-point tensor fmod/remainder to zero is nan on both CPU and GPU
            x = make_tensor((10, 10), device=device, dtype=dtype, low=-9, high=9)
            zero = torch.zeros_like(x)
            self.assertTrue(torch.all(fn(x, 0.0).isnan()))
            self.assertTrue(torch.all(fn(x, zero).isnan()))

    @onlyOnCPUAndCUDA  # Check Issue https://github.com/pytorch/pytorch/issues/48130
    @skipCUDAIfRocm  # Error happens on both ROCM and XLA
    @dtypes(*torch.testing.get_all_int_dtypes())
    def test_fmod_remainder_by_zero_integral(self, device, dtype):
        fn_list = (torch.fmod, torch.remainder)
        for fn in fn_list:
            # check integral tensor fmod/remainder to zero
            x = make_tensor((10, 10), device=device, dtype=dtype, low=-9, high=9)
            zero = torch.zeros_like(x)
            # RuntimeError on CPU
            if self.device_type == 'cpu':
                with self.assertRaisesRegex(RuntimeError, "ZeroDivisionError"):
                    fn(x, zero)
            # Different value for different dtype on CUDA:
            # Due to it's an undefined behavior, CUDA returns a pattern of all 1s
            # for integral dividend (other than int64) divided by zero. For int64,
            # CUDA returns all 1s for negative dividend, half 1s for positive dividend.
            # uint8: 0xff -> 255
            # int32: 0xffffffff -> -1
            else:
                if dtype == torch.int64:
                    self.assertEqual(fn(x, zero) == 4294967295, x >= 0)
                    self.assertEqual(fn(x, zero) == -1, x < 0)
                else:
                    value = 255 if dtype == torch.uint8 else -1
                    self.assertTrue(torch.all(fn(x, zero) == value))

    @dtypes(*torch.testing.get_all_dtypes(include_bfloat16=False, include_bool=False, include_complex=False))
    def test_fmod_remainder(self, device, dtype):
        # Use numpy as reference
        def _helper(x, mod, fns_list):
            for fn, inplace_fn, ref_fn in fns_list:
                np_x = x.cpu().numpy() if torch.is_tensor(x) else x
                np_mod = mod.cpu().numpy() if torch.is_tensor(mod) else mod
                exp = ref_fn(np_x, np_mod)
                exp = torch.from_numpy(exp)
                res = fn(x, mod)

                self.assertEqual(res, exp, exact_dtype=False)

                if torch.is_tensor(x):
                    # out
                    out = torch.empty(0, device=device, dtype=res.dtype)
                    fn(x, mod, out=out)
                    self.assertEqual(out, exp, exact_dtype=False)
                    self.assertEqual(out.size(), torch.Size([10, 10]))
                    # in-place (Type cast runtime error)
                    try:
                        inplace_fn(x, mod)
                        self.assertEqual(x, exp, exact_dtype=False)
                    except RuntimeError as e:
                        self.assertRegex(str(e), "result type (Half|Float|Double) "
                                                 "can't be cast to the desired output "
                                                 "type (Byte|Char|Short|Int|Long)")

        x = make_tensor((10, 10), device=device, dtype=dtype, low=-9, high=9)
        # mod with same dtype as x
        mod = make_tensor((10, 10), device=device, dtype=dtype, low=-9, high=9)
        # Exclude 0
        mod[mod == 0] = 1

        # Mods: Integer, Float, Tensor, Non-contiguous Tensor
        mods = [3, 2.3, mod, mod.t()]
        # mod with floating-point dtype
        if dtype in torch.testing.get_all_int_dtypes():
            mod_float = make_tensor((10, 10), device=device, dtype=torch.float, low=-9, high=9)
            mod[mod == 0] = 1
            mods.append(mod_float)

        for dividend, mod in product([x, x.t()], mods):
            _helper(dividend, mod,
                    ((torch.fmod, torch.Tensor.fmod_, np.fmod),
                     (torch.remainder, torch.Tensor.remainder_, np.remainder),))

        # Tests for torch.remainder(scalar, tensor)
        for dividend, mod in product([5, 3.14], mods):
            if torch.is_tensor(mod):
                _helper(dividend, mod,
                        ((torch.remainder, torch.Tensor.remainder_, np.remainder),))

    @dtypes(torch.float, torch.double)
    def test_remainder_fmod_large_dividend(self, device, dtype):
        alarge = 1e9
        pi = 3.14159265358979
        for avalue in [alarge, -alarge]:
            for bvalue in [pi, -pi]:
                a = torch.tensor([avalue], dtype=dtype, device=device)
                b = torch.tensor([bvalue], dtype=dtype, device=device)
                c = torch.remainder(a, b)
                d = torch.fmod(a, b)
                self.assertTrue((b[0] > 0) == (c[0] > 0))  # remainder has same sign as divisor
                self.assertTrue((a[0] > 0) == (d[0] > 0))  # fmod has same sign as dividend
                self.assertTrue(abs(c[0]) < abs(b[0]))     # remainder is within range of divisor
                self.assertTrue(abs(d[0]) < abs(b[0]))     # fmod is within range of divisor
                if ((a[0] > 0) == (b[0] > 0)):
                    self.assertTrue(c[0] == d[0])   # remainder is same as fmod
                else:
                    self.assertTrue(abs(c[0] - d[0]) == abs(b[0]))  # differ by one divisor

    @dtypesIfCPU(torch.bfloat16, torch.float32, torch.float64)
    @dtypes(torch.float32, torch.float64)
    def test_hypot(self, device, dtype):
        inputs = [
            (torch.randn(10, device=device).to(dtype), torch.randn(10, device=device).to(dtype)),
            (torch.randn((3, 3, 3), device=device).to(dtype), torch.randn((3, 3, 3), device=device).to(dtype)),
            (torch.randn((10, 1), device=device).to(dtype), torch.randn((10, 1), device=device).to(dtype).transpose(0, 1)),
            (torch.randint(100, (10, ), device=device, dtype=torch.long), torch.randn(10, device=device).to(dtype))
        ]
        for input in inputs:
            actual = torch.hypot(input[0], input[1])
            if dtype == torch.bfloat16:
                expected = torch.sqrt(input[0] * input[0] + input[1] * input[1])
            else:
                expected = np.hypot(input[0].cpu().numpy(), input[1].cpu().numpy())
            self.assertEqual(actual, expected)

    @onlyOnCPUAndCUDA
    @dtypes(torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64)
    def test_gcd(self, device, dtype):
        # Tests gcd(0, 0), gcd(0, a) cases
        t1 = torch.tensor([0, 10, 0], dtype=dtype, device=device)
        t2 = torch.tensor([0, 0, 10], dtype=dtype, device=device)
        actual = torch.gcd(t1, t2)
        expected = np.gcd([0, 10, 0], [0, 0, 10])
        self.assertEqual(actual, expected)

        if dtype == torch.uint8:
            # Test unsigned integers with potential sign issues (i.e., uint8 with value >= 128)
            a = torch.tensor([190, 210], device=device, dtype=dtype)
            b = torch.tensor([190, 220], device=device, dtype=dtype)
            actual = torch.gcd(a, b)
            expected = torch.tensor([190, 10], device=device, dtype=dtype)
        else:
            # Compares with NumPy
            a = torch.randint(-20, 20, (1024,), device=device, dtype=dtype)
            b = torch.randint(-20, 20, (1024,), device=device, dtype=dtype)
            actual = torch.gcd(a, b)
            expected = np.gcd(a.cpu().numpy(), b.cpu().numpy())
            self.assertEqual(actual, expected)

    @onlyOnCPUAndCUDA
    @dtypes(torch.int16, torch.int32, torch.int64)
    def test_lcm(self, device, dtype):
        # Tests lcm(0, 0), lcm(0, a) cases
        t1 = torch.tensor([0, 10, 0], dtype=dtype, device=device)
        t2 = torch.tensor([0, 0, 10], dtype=dtype, device=device)
        actual = torch.lcm(t1, t2)
        expected = np.lcm([0, 10, 0], [0, 0, 10])
        self.assertEqual(actual, expected)

        # Compares with NumPy
        a = torch.randint(-20, 20, (1024,), device=device, dtype=dtype)
        b = torch.randint(-20, 20, (1024,), device=device, dtype=dtype)
        actual = torch.lcm(a, b)
        expected = np.lcm(a.cpu().numpy(), b.cpu().numpy())
        self.assertEqual(actual, expected)

    @onlyOnCPUAndCUDA
    @dtypes(torch.float32, torch.float64)
    def test_nextafter(self, device, dtype):
        # Test special cases
        t1 = torch.tensor([0, 0, 10], device=device, dtype=dtype)
        t2 = torch.tensor([inf, -inf, 10], device=device, dtype=dtype)
        actual = torch.nextafter(t1, t2)
        expected = np.nextafter(t1.cpu().numpy(), t2.cpu().numpy())
        self.assertEqual(actual, expected, atol=0, rtol=0)

        actual = torch.nextafter(t2, t1)
        expected = np.nextafter(t2.cpu().numpy(), t1.cpu().numpy())
        self.assertEqual(actual, expected, atol=0, rtol=0)

        t1 = torch.tensor([0, nan], device=device, dtype=dtype)
        t2 = torch.tensor([nan, 0], device=device, dtype=dtype)
        self.assertTrue(torch.nextafter(t1, t2).isnan().all())

        a = torch.randn(100, device=device, dtype=dtype)
        b = torch.randn(100, device=device, dtype=dtype)
        actual = torch.nextafter(a, b)
        expected = np.nextafter(a.cpu().numpy(), b.cpu().numpy())
        self.assertEqual(actual, expected, atol=0, rtol=0)

    def _test_cop(self, torchfn, mathfn, dtype, device):
        def reference_implementation(res2):
            for i, j in iter_indices(sm1):
                idx1d = i * sm1.size(0) + j
                res2[i, j] = mathfn(sm1[i, j], sm2[idx1d])
            return res2

        # contiguous
        m1 = torch.randn(10, 10, 10, dtype=dtype, device=device)
        m2 = torch.randn(10, 10 * 10, dtype=dtype, device=device)
        sm1 = m1[4]
        sm2 = m2[4]

        res1 = torchfn(sm1, sm2.view(10, 10))
        res2 = reference_implementation(res1.clone())
        self.assertEqual(res1, res2)

        # non-contiguous
        m1 = torch.randn(10, 10, 10, dtype=dtype, device=device)
        m2 = torch.randn(10 * 10, 10 * 10, dtype=dtype, device=device)
        sm1 = m1[:, 4]
        sm2 = m2[:, 4]
        # view as sm1.size()
        sm2.set_(sm2.storage(), sm2.storage_offset(), sm1.size(), (sm2.stride()[0] * 10, sm2.stride()[0]))
        res1 = torchfn(sm1, sm2)
        # reference_implementation assumes 1-d sm2
        sm2.set_(sm2.storage(), sm2.storage_offset(), m2[:, 4].size(), m2[:, 4].stride())
        res2 = reference_implementation(res1.clone())
        self.assertEqual(res1, res2)

    @onlyCPU
    @dtypes(torch.float)
    def test_cdiv(self, device, dtype):
        self._test_cop(torch.div, lambda x, y: x / y, dtype, device)

    @onlyCPU
    @dtypes(torch.float)
    def test_cremainder(self, device, dtype):
        self._test_cop(torch.remainder, lambda x, y: x % y, dtype, device)

    @onlyCPU
    @dtypes(torch.float)
    def test_cmul(self, device, dtype):
        self._test_cop(torch.mul, lambda x, y: x * y, dtype, device)

    @onlyCPU
    @dtypes(torch.float)
    def test_cpow(self, device, dtype):
        self._test_cop(torch.pow, lambda x, y: nan if x < 0 else math.pow(x, y), dtype, device)

    @onlyCPU
    @dtypes(torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64)
    def test_floor_divide_zero(self, device, dtype):
        a = torch.tensor([0, 1], dtype=dtype, device=device)
        b = torch.tensor([0, 1], dtype=dtype, device=device)
        with self.assertRaisesRegex(RuntimeError, 'ZeroDivisionError'):
            with self.assertWarnsOnceRegex(UserWarning, "floor_divide"):
                a // b

    @unittest.skipIf(TEST_WITH_ASAN, "Integer overflows are not allowed under ASAN")
    @dtypes(*torch.testing.get_all_dtypes())
    def test_muldiv_scalar(self, device, dtype):
        x = make_tensor((10, 3), device, dtype, low=None, high=None)
        s = make_tensor((1,), 'cpu', dtype, low=None, high=None).item()
        y = torch.full_like(x, s)
        self.assertEqual(x * s, x * y)
        self.assertEqual(s * x, y * x)
        self.assertEqual(x / s, x / y)
        self.assertEqual(s / x, y / x)

    @dtypes(*tuple(itertools.combinations_with_replacement(torch.testing.get_all_dtypes(), 2)))
    def test_comparison_ops_type_promotion_and_broadcasting(self, device, dtypes):
        # issue #42660
        # testing all combinations of broadcasting and type promotion
        # with a range of dtypes and input shapes, and with extremal values
        def compare_with_numpy_bin_op(torch_fn, np_fn, x, y, out=None):
            # working around the fact that numpy doesn't support bfloat16
            # by letting numpy treat them as float32's
            x_np = x if x.dtype != torch.bfloat16 else x.to(torch.float32)
            y_np = y.cpu().numpy() if y.dtype != torch.bfloat16 else y.to(torch.float32).cpu().numpy()
            self.compare_with_numpy(lambda inp: torch_fn(inp, y, out=out) if out else torch_fn(inp, y),
                                    lambda inp: np_fn(inp, y_np, out=out) if out else np_fn(inp, y_np),
                                    x_np)

        complex_op_denylist = [torch.lt, torch.le, torch.gt, torch.ge]  # complex not supported
        input_sizes = [
            (1,),
            (10,),
            (10, 1),
            (1, 10),
            (4, 10),
            (64, 10),
            (12, 3)]
        op_pairs = [(torch.lt, np.less),
                    (torch.le, np.less_equal),
                    (torch.gt, np.greater),
                    (torch.ge, np.greater_equal),
                    (torch.eq, np.equal),
                    (torch.ne, np.not_equal),
                    (torch.logical_and, np.logical_and),
                    (torch.logical_or, np.logical_or),
                    (torch.logical_xor, np.logical_xor)]

        for size1 in input_sizes:
            size2 = (2,) + size1  # perform broadcasting
            for with_extremal in [False, True]:
                a = _generate_input(size1, dtypes[0], device, with_extremal)
                b = _generate_input(size2, dtypes[1], device, with_extremal)
                for torch_op, numpy_op in op_pairs:
                    if (dtypes[0].is_complex or dtypes[1].is_complex) and torch_op in complex_op_denylist:
                        continue
                    # functional version of op
                    compare_with_numpy_bin_op(torch_op, numpy_op, a, b)

                    # functional comparison ops always return bool tensors
                    self.assertEqual(torch_op(a, b).dtype, torch.bool)

                    # out version of op
                    out = torch.zeros(1, dtype=torch.complex128)  # all casts to complex128 are safe
                    compare_with_numpy_bin_op(torch_op, numpy_op, a, b, out=out)

    @onlyOnCPUAndCUDA
    @dtypes(torch.int8, torch.int16, torch.int32, torch.int64)
    def test_signed_shift(self, device, dtype):
        "Ensure that signed integer bit shifting works as expected."
        a = torch.tensor([-10, 10], device=device, dtype=dtype)  # [11...1110110, 1010]
        expected_l = torch.tensor([-40, 40], device=device, dtype=dtype)  # [11...11011000, 101000]
        self.assertEqual(a << 2, expected_l)
        self.compare_with_numpy(lambda x: x << 2, lambda x: np.left_shift(x, 2), a)
        expected_r = torch.tensor([-5, 5], device=device, dtype=dtype)  # [1111...111011, 101]
        self.assertEqual(a >> 1, expected_r)
        self.compare_with_numpy(lambda x: x >> 1, lambda x: np.right_shift(x, 1), a)

    def test_bitwise_and(self, device):
        for dtype in (torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64):
            a = torch.tensor([1, -2, 3], dtype=dtype, device=device)
            b = torch.tensor([2, 1, 3], dtype=dtype, device=device)
            expected_res = torch.tensor([0, 0, 3], dtype=dtype, device=device)
            b_scalar = 2
            expected_res_scalar = torch.tensor([0, 2, 2], dtype=dtype, device=device)

            # standard version
            self.assertEqual(torch.bitwise_and(a, b), expected_res)
            self.assertEqual(torch.bitwise_and(a, b_scalar), expected_res_scalar)

            # out
            c = torch.empty(0, dtype=dtype, device=device)
            torch.bitwise_and(a, b, out=c)
            self.assertEqual(c, expected_res)
            torch.bitwise_and(a, b_scalar, out=c)
            self.assertEqual(c, expected_res_scalar)

            # in-place
            a1 = a.clone()
            a1.bitwise_and_(b)
            self.assertEqual(a1, expected_res)
            a.bitwise_and_(b_scalar)
            self.assertEqual(a, expected_res_scalar)

        self.assertEqual(torch.tensor([False, True, False], device=device),
                         torch.bitwise_and(torch.tensor([True, True, False], device=device),
                                           torch.tensor([False, True, False], device=device)))

    def test_bitwise_or(self, device):
        for dtype in (torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64):
            a = torch.tensor([1, -2, 3], dtype=dtype, device=device)
            b = torch.tensor([2, 1, 3], dtype=dtype, device=device)
            expected_res = torch.tensor([3, -1, 3], dtype=dtype, device=device)
            b_scalar = 2
            expected_res_scalar = torch.tensor([3, -2, 3], dtype=dtype, device=device)

            # standard version
            self.assertEqual(torch.bitwise_or(a, b), expected_res)
            self.assertEqual(torch.bitwise_or(a, b_scalar), expected_res_scalar)

            # out
            c = torch.empty(0, dtype=dtype, device=device)
            torch.bitwise_or(a, b, out=c)
            self.assertEqual(c, expected_res)
            torch.bitwise_or(a, b_scalar, out=c)
            self.assertEqual(c, expected_res_scalar)

            # in-place
            a1 = a.clone()
            a1.bitwise_or_(b)
            self.assertEqual(a1, expected_res)
            a.bitwise_or_(b_scalar)
            self.assertEqual(a, expected_res_scalar)

        self.assertEqual(torch.tensor([True, True, False], device=device),
                         torch.bitwise_or(torch.tensor([True, True, False], device=device),
                                          torch.tensor([False, True, False], device=device)))

    def test_bitwise_xor(self, device):
        for dtype in (torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64):
            a = torch.tensor([1, -2, 3], dtype=dtype, device=device)
            b = torch.tensor([2, 1, 3], dtype=dtype, device=device)
            expected_res = torch.tensor([3, -1, 0], dtype=dtype, device=device)
            b_scalar = 2
            expected_res_scalar = torch.tensor([3, -4, 1], dtype=dtype, device=device)

            # standard version
            self.assertEqual(torch.bitwise_xor(a, b), expected_res)
            self.assertEqual(torch.bitwise_xor(a, b_scalar), expected_res_scalar)

            # out
            c = torch.empty(0, dtype=dtype, device=device)
            torch.bitwise_xor(a, b, out=c)
            self.assertEqual(c, expected_res)
            torch.bitwise_xor(a, b_scalar, out=c)
            self.assertEqual(c, expected_res_scalar)

            # in-place
            a1 = a.clone()
            a1.bitwise_xor_(b)
            self.assertEqual(a1, expected_res)
            a.bitwise_xor_(b_scalar)
            self.assertEqual(a, expected_res_scalar)

        self.assertEqual(torch.tensor([True, False, False], device=device),
                         torch.bitwise_xor(torch.tensor([True, True, False], device=device),
                                           torch.tensor([False, True, False], device=device)))

    @onlyOnCPUAndCUDA
    @dtypes(*list(product(torch.testing.get_all_dtypes(include_complex=False),
                          torch.testing.get_all_dtypes(include_complex=False))))
    def test_heaviside(self, device, dtypes):
        input_dtype = dtypes[0]
        values_dtype = dtypes[1]

        rng = np.random.default_rng()
        input = np.array(rng.integers(-10, 10, size=10),
                         dtype=torch_to_numpy_dtype_dict[input_dtype if (input_dtype != torch.bfloat16) else torch.float64])
        input[0] = input[3] = input[7] = 0
        values = np.array(rng.integers(-10, 10, size=10),
                          dtype=torch_to_numpy_dtype_dict[values_dtype if (values_dtype != torch.bfloat16) else torch.float64])
        np_result = torch.from_numpy(np.heaviside(input, values)).to(device=device, dtype=input_dtype)

        input = torch.from_numpy(input).to(device=device, dtype=input_dtype)
        values = torch.from_numpy(values).to(device=device, dtype=values_dtype)
        out = torch.empty_like(input)

        if input_dtype == values_dtype:
            torch_result = torch.heaviside(input, values)
            self.assertEqual(np_result, torch_result)

            torch_result = input.heaviside(values)
            self.assertEqual(np_result, torch_result)

            torch.heaviside(input, values, out=out)
            self.assertEqual(np_result, out)

            input.heaviside_(values)
            self.assertEqual(np_result, input)
        else:
            with self.assertRaisesRegex(RuntimeError, 'heaviside is not yet implemented for tensors with different dtypes.'):
                torch.heaviside(input, values)
            with self.assertRaisesRegex(RuntimeError, 'heaviside is not yet implemented for tensors with different dtypes.'):
                input.heaviside(values)
            with self.assertRaisesRegex(RuntimeError, 'heaviside is not yet implemented for tensors with different dtypes.'):
                torch.heaviside(input, values, out=out)
            with self.assertRaisesRegex(RuntimeError, 'heaviside is not yet implemented for tensors with different dtypes.'):
                input.heaviside_(values)

    @onlyCUDA
    def test_heaviside_cross_device(self, device):
        x = torch.tensor([-9, 5, 0, 6, -2, 2], device=device)
        y = torch.tensor(0)
        result = torch.heaviside(x, y)
        expect = torch.tensor([0, 1, 0, 1, 0, 1], device=device)
        self.assertEqual(result, expect)

        result = torch.heaviside(y, x)
        expect = torch.tensor([-9, 5, 0, 6, -2, 2], device=device)
        self.assertEqual(result, expect)

        x = torch.tensor([-9, 5, 0, 6, -2, 2])
        y = torch.tensor(0, device=device)
        with self.assertRaisesRegex(RuntimeError, 'Expected all tensors to be on the same device'):
            torch.heaviside(x, y)

        with self.assertRaisesRegex(RuntimeError, 'Expected all tensors to be on the same device'):
            torch.heaviside(y, x)

    @dtypes(*list(product(torch.testing.get_all_complex_dtypes(),
                          torch.testing.get_all_complex_dtypes())))
    def test_heaviside_complex(self, device, dtypes):
        input_dtype = dtypes[0]
        values_dtype = dtypes[1]

        data = (complex(0, -6), complex(-1, 3), complex(1, 1))
        input = torch.tensor(data, device=device, dtype=input_dtype)
        values = torch.tensor(data, device=device, dtype=values_dtype)
        out = torch.empty_like(input)
        real = input.real

        with self.assertRaisesRegex(RuntimeError, 'heaviside is not yet implemented for complex tensors.'):
            torch.heaviside(input, real)
        with self.assertRaisesRegex(RuntimeError, 'heaviside is not yet implemented for complex tensors.'):
            real.heaviside(values)
        with self.assertRaisesRegex(RuntimeError, 'heaviside is not yet implemented for complex tensors.'):
            input.heaviside_(values)
        with self.assertRaisesRegex(RuntimeError, 'heaviside is not yet implemented for complex tensors.'):
            torch.heaviside(real, real, out=out)

    def _test_logical(self, device, dtypes, op, a_, b_, expected_res_):
        expected_res = torch.tensor(expected_res_, dtype=dtypes[0], device=device)
        a = torch.tensor(a_, dtype=dtypes[0], device=device)
        b = torch.tensor(b_, dtype=dtypes[1], device=device)

        # new tensor
        self.assertEqual(expected_res.bool(), getattr(a, op)(b))
        # out
        c = torch.empty(0, dtype=torch.bool, device=device)
        getattr(torch, op)(a, b, out=c)
        self.assertEqual(expected_res.bool(), c)

        # in-place
        # TODO: remove when different dtypes as operands are supported
        if dtypes[0] != dtypes[1]:
            with self.assertRaises(RuntimeError):
                getattr(a, op + '_')(b)
            return

        getattr(a, op + '_')(b)
        self.assertEqual(expected_res, a)

    @dtypes(*product(torch.testing.get_all_dtypes(), torch.testing.get_all_dtypes()))
    def test_logical_xor(self, device, dtypes):
        self._test_logical(device, dtypes, 'logical_xor', [10, 0, 1, 0], [1, 0, 0, 10], [0, 0, 1, 1])

    @dtypes(*product(torch.testing.get_all_dtypes(), torch.testing.get_all_dtypes()))
    def test_logical_and(self, device, dtypes):
        self._test_logical(device, dtypes, 'logical_and', [10, 0, 1, 0], [1, 0, 0, 10], [1, 0, 0, 0])

    @dtypes(*product(torch.testing.get_all_dtypes(), torch.testing.get_all_dtypes()))
    def test_logical_or(self, device, dtypes):
        self._test_logical(device, dtypes, 'logical_or', [10, 0, 1, 0], [1, 0, 0, 10], [1, 0, 1, 1])

    def test_remainder_overflow(self, device):
        # Check Integer Overflows
        x = torch.tensor(23500, dtype=torch.int64, device=device)
        q = 392486996410368
        self.assertEqual(x % q, x)
        self.assertEqual(-x % q, q - x)
        self.assertEqual(x % -q, x - q)
        self.assertEqual(-x % -q, -x)

    def test_rpow(self, device):
        m = torch.randn(10, 10, device=device)
        self.assertEqual(torch.pow(2, m), 2**m)

        # test with scalar
        m = torch.randn(1, device=device).squeeze()
        assert m.dim() == 0, "m is intentionally a scalar"
        self.assertEqual(torch.pow(2, m), 2**m)

    @onlyCPU
    def test_ldexp(self, device):
        # random values
        mantissas = torch.randn(64, device=device)
        exponents = torch.randint(-31, 31, (64,), device=device, dtype=torch.int32)

        # basic test
        np_outcome = np.ldexp(mantissas.numpy(), exponents.numpy())
        pt_outcome_1 = torch.ldexp(mantissas, exponents)
        pt_outcome_2 = mantissas.ldexp(exponents)
        self.assertEqual(np_outcome, pt_outcome_1)
        self.assertEqual(np_outcome, pt_outcome_2)
        mantissas.ldexp_(exponents)
        self.assertEqual(np_outcome, mantissas)

        # test bounds
        mantissas = torch.tensor([float('inf'), float('-inf'), float('inf'), float('nan')], device=device)
        exponents = torch.randint(0, 31, (4,), device=device, dtype=torch.int32)
        np_outcome = np.ldexp(mantissas.numpy(), exponents.numpy())
        pt_outcome = torch.ldexp(mantissas, exponents)
        self.assertEqual(np_outcome, pt_outcome)

    @dtypes(torch.float, torch.double, torch.cfloat, torch.cdouble)
    def test_lerp(self, device, dtype):
        start_end_weight_shapes = [(), (5,), (5, 5)]
        for shapes in product(start_end_weight_shapes, start_end_weight_shapes, start_end_weight_shapes):
            start = torch.randn(shapes[0], device=device, dtype=dtype)
            end = torch.randn(shapes[1], device=device, dtype=dtype)

            # Tensor weights
            weights = [torch.randn(shapes[2], device=device, dtype=dtype), random.random()]
            if dtype.is_complex:
                weights += [complex(0, 1), complex(0.4, 1.2)]

            for weight in weights:
                actual = torch.lerp(start, end, weight)
                actual_method = start.lerp(end, weight)
                self.assertEqual(actual, actual_method)
                actual_out = torch.tensor(1., dtype=dtype, device=device)
                torch.lerp(start, end, weight, out=actual_out)
                self.assertEqual(actual, actual_out)
                expected = start + weight * (end - start)
                self.assertEqual(expected, actual)

    def _test_logaddexp(self, device, dtype, base2):
        if base2:
            ref_func = np.logaddexp2
            our_func = torch.logaddexp2
        else:
            ref_func = np.logaddexp
            our_func = torch.logaddexp

        def _test_helper(a, b):
            ref = ref_func(a.cpu().numpy(), b.cpu().numpy())
            v = our_func(a, b)
            self.assertEqual(ref, v)

        # simple test
        a = torch.randn(64, 2, dtype=dtype, device=device) - 0.5
        b = torch.randn(64, 2, dtype=dtype, device=device) - 0.5
        _test_helper(a, b)
        _test_helper(a[:3], b[:3])

        # large value test for numerical stability
        a *= 10000
        b *= 10000
        _test_helper(a, b)
        _test_helper(a[:3], b[:3])

        a = torch.tensor([float('inf'), float('-inf'), float('inf'), float("nan")], dtype=dtype, device=device)
        b = torch.tensor([float('inf'), float('-inf'), float('-inf'), float("nan")], dtype=dtype, device=device)
        _test_helper(a, b)

    @dtypes(torch.float32, torch.float64)
    def test_logaddexp(self, device, dtype):
        self._test_logaddexp(device, dtype, base2=False)

    @dtypes(torch.float32, torch.float64)
    def test_logaddexp2(self, device, dtype):
        self._test_logaddexp(device, dtype, base2=True)

    def test_add(self, device):
        dtypes = [torch.float, torch.double] + torch.testing.get_all_complex_dtypes()
        for dtype in dtypes:
            # [res] torch.add([res,] tensor1, tensor2)
            m1 = torch.randn(100, 100, dtype=dtype, device=device)
            v1 = torch.randn(100, dtype=dtype, device=device)

            # contiguous
            res1 = torch.add(m1[4], v1)
            res2 = res1.clone().zero_()
            for i in range(m1.size(1)):
                res2[i] = m1[4, i] + v1[i]
            self.assertEqual(res1, res2)

            m1 = torch.randn(100, 100, device=device)
            v1 = torch.randn(100, device=device)

            # non-contiguous
            res1 = torch.add(m1[:, 4], v1)
            res2 = res1.clone().zero_()
            for i in range(m1.size(0)):
                res2[i] = m1[i, 4] + v1[i]
            self.assertEqual(res1, res2)

            # [res] torch.add([res,] tensor, value)
            m1 = torch.randn(10, 10, device=device)

            # contiguous
            res1 = m1.clone()
            res1[3].add_(2)
            res2 = m1.clone()
            for i in range(m1.size(1)):
                res2[3, i] = res2[3, i] + 2
            self.assertEqual(res1, res2)

            # non-contiguous
            m1 = torch.randn(10, 10, device=device)
            res1 = m1.clone()
            res1[:, 3].add_(2)
            res2 = m1.clone()
            for i in range(m1.size(0)):
                res2[i, 3] = res2[i, 3] + 2
            self.assertEqual(res1, res2)

            # inter-type
            m1 = torch.randn(10, 10, dtype=dtype, device=device)
            self.assertEqual(m1 + 3, m1 + torch.tensor(3))
            self.assertEqual(3 + m1, torch.tensor(3) + m1)

            # contiguous + non-contiguous
            m1 = torch.randn(10, 10, dtype=dtype, device=device)
            m2 = torch.randn(10, 10, dtype=dtype, device=device).t()
            res = m1 + m2
            self.assertTrue(res.is_contiguous())
            self.assertEqual(res, m1 + m2.contiguous())

            # 1d + empty
            m1 = torch.tensor([1.0], dtype=dtype, device=device)
            m2 = torch.tensor([], dtype=dtype, device=device)
            self.assertEqual(m1 + m2, [])

        # inter-type unint8
        one = torch.tensor(1, dtype=torch.uint8, device=device)
        self.assertEqual(torch.add(one, 1), 2)
        self.assertEqual(torch.add(one, 1).dtype, torch.uint8)

        # bool
        m1 = torch.tensor([True, False, False, True, False, False], dtype=torch.bool, device=device)
        m2 = torch.tensor([True, True, False, False, False, True], dtype=torch.bool, device=device)
        expected = torch.tensor([True, True, False, True, False, True], dtype=torch.bool, device=device)
        self.assertEqual(m1 + m2, expected)

        # fused multiply add
        a = torch.zeros(2, 3, dtype=torch.bool, device=device)
        res = torch.add(a, a, alpha=0)
        expected = torch.zeros(2, 3, device=device).bool()
        self.assertEqual(res, expected)

        # bfloat16
        m1 = torch.tensor([1., 2.], dtype=torch.bfloat16)
        m2 = torch.tensor([3., 4.], dtype=torch.bfloat16)
        self.assertEqual(m1 + m2, torch.tensor([4., 6.], dtype=torch.bfloat16))

        # different alpha types
        m1 = torch.tensor([2 + 3j, 4 + 5j], dtype=torch.complex64, device=device)
        m2 = torch.tensor([4 + 5j, 2 + 3j], dtype=torch.complex64, device=device)
        # add complex numbers with float alpha
        res = torch.add(m1, m2, alpha=0.1)
        expected = torch.tensor([2.4000 + 3.5000j, 4.2000 + 5.3000j], dtype=torch.complex64, device=device)
        self.assertEqual(res, expected)

        # add complex numbers with complex alpha
        res = torch.add(m1, m2, alpha=complex(0.1, 0.2))
        expected = torch.tensor([1.4000 + 4.3000j, 3.6000 + 5.7000j], dtype=torch.complex64, device=device)
        self.assertEqual(res, expected)

        # add complex numbers with integer alpha
        res = torch.add(m1, m2, alpha=2)
        expected = torch.tensor([10. + 13.j, 8. + 11.j], dtype=torch.complex64, device=device)
        self.assertEqual(res, expected)

        # mismatched alpha
        m1 = torch.tensor([1], dtype=torch.int8, device=device)
        m2 = torch.tensor([2], dtype=torch.int8, device=device)
        self.assertRaisesRegex(RuntimeError,
                               r"Boolean alpha only supported for Boolean results\.",
                               lambda: torch.add(m1, m2, alpha=True))
        self.assertRaisesRegex(RuntimeError,
                               r"For integral input tensors, argument alpha must not be a floating point number\.",
                               lambda: torch.add(m1, m2, alpha=1.0))

        # mismatched alpha, float / double tensor and complex alpha
        msg = r"For non-complex input tensors, argument alpha must not be a complex number\."
        m1 = torch.tensor([3., 4.], device=device)
        m2 = torch.tensor([4., 3.], device=device)
        self.assertRaisesRegex(RuntimeError, msg,
                               lambda: torch.add(m1, m2, alpha=complex(0.1, 0.2)))

        m1 = torch.tensor([3., 4.], dtype=torch.double, device=device)
        m2 = torch.tensor([4., 3.], dtype=torch.double, device=device)
        self.assertRaisesRegex(RuntimeError, msg,
                               lambda: torch.add(m1, m2, alpha=complex(0.1, 0.2)))

        # complex
        m1 = torch.tensor((4.0000 + 4.0000j), dtype=torch.complex64)
        m2 = torch.tensor(4., dtype=torch.float64)
        self.assertRaisesRegex(RuntimeError, r"result type ComplexFloat can't be cast to the desired output type Double",
                               lambda: torch.add(m1, m1, out=m2))


    def test_sub_typing(self, device):
        m1 = torch.tensor([True, False, False, True, False, False], dtype=torch.bool, device=device)
        m2 = torch.tensor([True, True, False, False, False, True], dtype=torch.bool, device=device)
        self.assertRaisesRegex(RuntimeError,
                               r"Subtraction, the `\-` operator, with two bool tensors is not supported. "
                               r"Use the `\^` or `logical_xor\(\)` operator instead.",
                               lambda: m1 - m2)
        self.assertRaisesRegex(RuntimeError,
                               r"Subtraction, the `\-` operator, with a bool tensor is not supported. "
                               r"If you are trying to invert a mask, use the `\~` or `logical_not\(\)` operator instead.",
                               lambda: 1 - m1)
        self.assertRaisesRegex(RuntimeError,
                               r"Subtraction, the `\-` operator, with a bool tensor is not supported. "
                               r"If you are trying to invert a mask, use the `\~` or `logical_not\(\)` operator instead.",
                               lambda: m2 - 1)

        # mismatched alpha
        m1 = torch.tensor([1], dtype=torch.int8, device=device)
        m2 = torch.tensor([2], dtype=torch.int8, device=device)
        self.assertRaisesRegex(RuntimeError,
                               r"Boolean alpha only supported for Boolean results\.",
                               lambda: torch.sub(m1, m2, alpha=True))
        self.assertRaisesRegex(RuntimeError,
                               r"For integral input tensors, argument alpha must not be a floating point number\.",
                               lambda: torch.sub(m1, m2, alpha=1.0))

    def test_mul(self, device):
        m1 = torch.randn(10, 10, device=device)
        res1 = m1.clone()
        res1[:, 3].mul_(2)
        res2 = m1.clone()
        for i in range(res1.size(0)):
            res2[i, 3] = res2[i, 3] * 2
        self.assertEqual(res1, res2)

        a1 = torch.tensor([True, False, False, True], dtype=torch.bool, device=device)
        a2 = torch.tensor([True, False, True, False], dtype=torch.bool, device=device)
        self.assertEqual(a1 * a2, torch.tensor([True, False, False, False], dtype=torch.bool, device=device))

        if device == 'cpu':
            a1 = torch.tensor([0.1, 0.1], dtype=torch.bfloat16, device=device)
            a2 = torch.tensor([1.1, 0.1], dtype=torch.bfloat16, device=device)
            self.assertEqual(a1 * a2, torch.tensor([0.11, 0.01], dtype=torch.bfloat16, device=device), atol=0.01, rtol=0)
            self.assertEqual(a1.mul(a2), a1 * a2)

    def test_bool_tensor_comparison_ops(self, device):
        a = torch.tensor([True, False, True, False, True, False], dtype=torch.bool, device=device)
        b = torch.tensor([True, False, True, True, True, True], dtype=torch.bool, device=device)
        self.assertEqual(a == b, torch.tensor([1, 1, 1, 0, 1, 0], dtype=torch.bool, device=device))
        self.assertEqual(a != b, torch.tensor([0, 0, 0, 1, 0, 1], dtype=torch.bool, device=device))
        self.assertEqual(a < b, torch.tensor([0, 0, 0, 1, 0, 1], dtype=torch.bool, device=device))
        self.assertEqual(a > b, torch.tensor([0, 0, 0, 0, 0, 0], dtype=torch.bool, device=device))
        self.assertEqual(a >= b, torch.tensor([1, 1, 1, 0, 1, 0], dtype=torch.bool, device=device))
        self.assertEqual(a <= b, torch.tensor([1, 1, 1, 1, 1, 1], dtype=torch.bool, device=device))
        self.assertEqual(a > False, torch.tensor([1, 0, 1, 0, 1, 0], dtype=torch.bool, device=device))
        self.assertEqual(a == torch.tensor(True, dtype=torch.bool, device=device),
                         torch.tensor([1, 0, 1, 0, 1, 0], dtype=torch.bool, device=device))
        self.assertEqual(a == torch.tensor(0, dtype=torch.bool, device=device),
                         torch.tensor([0, 1, 0, 1, 0, 1], dtype=torch.bool, device=device))
        self.assertFalse(a.equal(b))

    @dtypes(*torch.testing.get_all_dtypes(include_complex=False))
    def test_logical(self, device, dtype):
        if dtype != torch.bool:
            x = torch.tensor([1, 2, 3, 4], device=device, dtype=dtype)
            b = torch.tensor([2], device=device, dtype=dtype)
            self.assertEqual(x.lt(2), torch.tensor([True, False, False, False]))
            self.assertEqual(x.le(2), torch.tensor([True, True, False, False]))
            self.assertEqual(x.ge(2), torch.tensor([False, True, True, True]))
            self.assertEqual(x.gt(2), torch.tensor([False, False, True, True]))
            self.assertEqual(x.eq(2), torch.tensor([False, True, False, False]))
            self.assertEqual(x.ne(2), torch.tensor([True, False, True, True]))

            self.assertEqual(x.lt(b), torch.tensor([True, False, False, False]))
            self.assertEqual(x.le(b), torch.tensor([True, True, False, False]))
            self.assertEqual(x.ge(b), torch.tensor([False, True, True, True]))
            self.assertEqual(x.gt(b), torch.tensor([False, False, True, True]))
            self.assertEqual(x.eq(b), torch.tensor([False, True, False, False]))
            self.assertEqual(x.ne(b), torch.tensor([True, False, True, True]))
        else:
            x = torch.tensor([True, False, True, False], device=device)
            self.assertEqual(x.lt(True), torch.tensor([False, True, False, True]))
            self.assertEqual(x.le(True), torch.tensor([True, True, True, True]))
            self.assertEqual(x.ge(True), torch.tensor([True, False, True, False]))
            self.assertEqual(x.gt(True), torch.tensor([False, False, False, False]))
            self.assertEqual(x.eq(True), torch.tensor([True, False, True, False]))
            self.assertEqual(x.ne(True), torch.tensor([False, True, False, True]))

    def test_atan2(self, device):
        def _test_atan2_with_size(size, device):
            a = torch.rand(size=size, device=device, dtype=torch.double)
            b = torch.rand(size=size, device=device, dtype=torch.double)
            actual = a.atan2(b)
            x = a.view(-1)
            y = b.view(-1)
            expected = torch.tensor([math.atan2(x[i].item(), y[i].item()) for i in range(x.numel())],
                                    device=device, dtype=torch.double)
            self.assertEqual(expected, actual.view(-1), rtol=0, atol=0.02)

        _test_atan2_with_size((2, 2), device)
        _test_atan2_with_size((3, 3), device)
        _test_atan2_with_size((5, 5), device)

    def test_atan2_edgecases(self, device):
        def _test_atan2(x, y, expected, device, dtype):
            expected_tensor = torch.tensor([expected], dtype=dtype, device=device)
            x_tensor = torch.tensor([x], dtype=dtype, device=device)
            y_tensor = torch.tensor([y], dtype=dtype, device=device)
            actual = torch.atan2(y_tensor, x_tensor)
            self.assertEqual(expected_tensor, actual, rtol=0, atol=0.02)

        for dtype in [torch.float, torch.double]:
            _test_atan2(0, 0, 0, device, dtype)
            _test_atan2(0, 1, math.pi / 2, device, dtype)
            _test_atan2(0, -1, math.pi / -2, device, dtype)
            _test_atan2(-1, 0, math.pi, device, dtype)
            _test_atan2(1, 0, 0, device, dtype)
            _test_atan2(-1, -1, math.pi * -3 / 4 , device, dtype)
            _test_atan2(1, 1, math.pi / 4 , device, dtype)
            _test_atan2(1, -1, math.pi / -4 , device, dtype)
            _test_atan2(-1, 1, math.pi * 3 / 4 , device, dtype)

    def test_trapz(self, device):
        def test_dx(sizes, dim, dx, device):
            t = torch.randn(sizes, device=device)
            actual = torch.trapz(t, dx=dx, dim=dim)
            expected = np.trapz(t.cpu().numpy(), dx=dx, axis=dim)
            self.assertEqual(expected.shape, actual.shape)
            self.assertEqual(expected, actual)

        def test_x(sizes, dim, x, device):
            t = torch.randn(sizes, device=device)
            actual = torch.trapz(t, x=torch.tensor(x, device=device), dim=dim)
            expected = np.trapz(t.cpu().numpy(), x=x, axis=dim)
            self.assertEqual(expected.shape, actual.shape)
            self.assertEqual(expected, actual.cpu())

        test_dx((2, 3, 4), 1, 1, device)
        test_dx((10, 2), 0, 0.1, device)
        test_dx((1, 10), 0, 2.3, device)
        test_dx((0, 2), 0, 1.0, device)
        test_dx((0, 2), 1, 1.0, device)
        test_x((2, 3, 4), 1, [1.0, 2.0, 3.0], device)
        test_x((10, 2), 0, [2.0, 3.0, 4.0, 7.0, 11.0, 14.0, 22.0, 26.0, 26.1, 30.3], device)
        test_x((1, 10), 0, [1.0], device)
        test_x((0, 2), 0, [], device)
        test_x((0, 2), 1, [1.0, 2.0], device)
        with self.assertRaisesRegex(
                IndexError,
                'Dimension out of range'):
            test_x((2, 3), 2, [], device)
            test_dx((2, 3), 2, 1.0, device)
        with self.assertRaisesRegex(
                RuntimeError,
                'There must be one `x` value for each sample point'):
            test_x((2, 3), 1, [1.0, 2.0], device)
            test_x((2, 3), 1, [1.0, 2.0, 3.0, 4.0], device)

    @dtypes(torch.double)
    def test_pow_scalar_overloads_mem_overlap(self, device, dtype):
        sz = 3
        doubles = torch.randn(2 * sz, dtype=dtype, device=device)
        self.check_internal_mem_overlap(
            lambda t: t.pow_(42), 1, dtype, device)
        self.unary_check_input_output_mem_overlap(
            doubles, sz, lambda input, out: torch.pow(input, 42, out=out))
        self.unary_check_input_output_mem_overlap(
            doubles, sz, lambda input, out: torch.pow(42, input, out=out))

    @dtypes(*list(product(torch.testing.get_all_dtypes(include_bool=False),
                          torch.testing.get_all_dtypes(include_bool=False))))
    def test_float_power(self, device, dtypes):
        def to_np(value):
            if isinstance(value, torch.Tensor) and value.dtype == torch.bfloat16:
                return value.to(torch.float).cpu().numpy()
            return value.cpu().numpy() if isinstance(value, torch.Tensor) else value

        base_dtype = dtypes[0]
        exp_dtype = dtypes[1]
        out_dtype = torch.complex128 if base_dtype.is_complex or exp_dtype.is_complex else torch.float64

        base = make_tensor((30,), device, base_dtype, low=1, high=100)
        # Complex and real results do not agree between PyTorch and NumPy when computing negative and zero power of 0
        # Related: https://github.com/pytorch/pytorch/issues/48000
        # base[0] = base[3] = base[7] = 0
        exp = make_tensor((30,), device, exp_dtype, low=-2, high=2)
        exp[0] = exp[4] = exp[6] = 0

        expected = torch.from_numpy(np.float_power(to_np(base), to_np(exp)))

        exponents = [-2.8, -2, -1, -0.5, 0.5, 1, 2]
        complex_exponents = exponents + [-2.5j, -1.0j, 1.0j, 2.5j, 1.0 + 1.0j, -1.0 - 1.5j, 3.3j]

        for op in (torch.float_power, torch.Tensor.float_power, torch.Tensor.float_power_):

            # Case of Tensor x Tensor
            if op is torch.Tensor.float_power_ and base_dtype != out_dtype:
                with self.assertRaisesRegex(RuntimeError, "operation's result requires dtype"):
                    op(base.clone(), exp)
            else:
                result = op(base.clone(), exp)
                self.assertEqual(expected, result)

            if op is torch.float_power:
                out = torch.empty_like(base).to(device=device, dtype=out_dtype)
                op(base, exp, out=out)
                self.assertEqual(expected, out)

            # Case of Tensor x Scalar
            for i in complex_exponents if exp_dtype.is_complex else exponents:
                out_dtype_scalar_exp = torch.complex128 if base_dtype.is_complex or type(i) == complex else torch.float64
                expected_scalar_exp = torch.from_numpy(np.float_power(to_np(base), i))

                if op is torch.Tensor.float_power_ and base_dtype != out_dtype_scalar_exp:
                    with self.assertRaisesRegex(RuntimeError, "operation's result requires dtype"):
                        op(base.clone(), i)
                else:
                    result = op(base.clone(), i)
                    self.assertEqual(expected_scalar_exp, result)

                if op is torch.float_power:
                    out = torch.empty_like(base).to(device=device, dtype=out_dtype_scalar_exp)
                    op(base, i, out=out)
                    self.assertEqual(expected_scalar_exp, out)

        # Case of Scalar x Tensor
        for i in complex_exponents if base_dtype.is_complex else exponents:
            out_dtype_scalar_base = torch.complex128 if exp_dtype.is_complex or type(i) == complex else torch.float64
            expected_scalar_base = torch.from_numpy(np.float_power(i, to_np(exp)))

            result = torch.float_power(i, exp)
            self.assertEqual(expected_scalar_base, result)

            out = torch.empty_like(exp).to(device=device, dtype=out_dtype_scalar_base)
            torch.float_power(i, exp, out=out)
            self.assertEqual(expected_scalar_base, out)

    def test_float_power_exceptions(self, device):
        def _promo_helper(x, y):
            for i in (x, y):
                if type(i) == complex:
                    return torch.complex128
                elif type(i) == torch.Tensor and i.is_complex():
                    return torch.complex128
            return torch.double

        test_cases = ((torch.tensor([-2, -1, 0, 1, 2], device=device), -.25),
                      (torch.tensor([-1.0j, 0j, 1.0j, 1.0 + 1.0j, -1.0 - 1.5j], device=device), 2.))
        for base, exp in test_cases:
            for out_dtype in (torch.long, torch.float, torch.double, torch.cdouble):
                out = torch.empty(1, device=device, dtype=out_dtype)
                required_dtype = _promo_helper(base, exp)

                if out.dtype == required_dtype:
                    torch.float_power(base, exp, out=out)
                else:
                    with self.assertRaisesRegex(RuntimeError, "operation's result requires dtype"):
                        torch.float_power(base, exp, out=out)

                if base.dtype == required_dtype:
                    torch.Tensor.float_power_(base.clone(), exp)
                else:
                    with self.assertRaisesRegex(RuntimeError, "operation's result requires dtype"):
                        torch.Tensor.float_power_(base.clone(), exp)

    @skipIf(not TEST_SCIPY, "Scipy required for the test.")
    @dtypes(*product(torch.testing.get_all_dtypes(include_complex=False, include_bfloat16=False),
                     torch.testing.get_all_dtypes(include_complex=False, include_bfloat16=False)))
    def test_xlogy_xlog1py(self, device, dtypes):
        x_dtype, y_dtype = dtypes

        def out_variant_helper(torch_fn, x, y):
            expected = torch_fn(x, y)
            out = torch.empty_like(expected)
            torch_fn(x, y, out=out)
            self.assertEqual(expected, out)

        def xlogy_inplace_variant_helper(x, y):
            if x.dtype in torch.testing.get_all_int_dtypes() + [torch.bool]:
                with self.assertRaisesRegex(RuntimeError,
                                            "can't be cast to the desired output type"):
                    x.clone().xlogy_(y)
            else:
                expected = torch.empty_like(x)
                torch.xlogy(x, y, out=expected)
                inplace_out = x.clone().xlogy_(y)
                self.assertEqual(expected, inplace_out)

        def test_helper(torch_fn, reference_fn, inputs, scalar=None):
            x, y, z = inputs
            torch_fn_partial = partial(torch_fn, x)
            reference_fn_partial = partial(reference_fn, x.cpu().numpy())
            self.compare_with_numpy(torch_fn_partial, reference_fn_partial, x, exact_dtype=False)
            self.compare_with_numpy(torch_fn_partial, reference_fn_partial, y, exact_dtype=False)
            self.compare_with_numpy(torch_fn_partial, reference_fn_partial, z, exact_dtype=False)

            val = scalar if scalar is not None else x
            out_variant_helper(torch_fn, val, x)
            out_variant_helper(torch_fn, val, y)
            out_variant_helper(torch_fn, val, z)

        # Tensor-Tensor Test (tensor of same and different shape)
        x = make_tensor((3, 2, 4, 5), device, x_dtype, low=0.5, high=1000)
        y = make_tensor((3, 2, 4, 5), device, y_dtype, low=0.5, high=1000)
        z = make_tensor((4, 5), device, y_dtype, low=0.5, high=1000)

        x_1p = make_tensor((3, 2, 4, 5), device, x_dtype, low=-0.5, high=1000)
        y_1p = make_tensor((3, 2, 4, 5), device, y_dtype, low=-0.5, high=1000)
        z_1p = make_tensor((4, 5), device, y_dtype, low=-0.5, high=1000)

        xlogy_fns = torch.xlogy, scipy.special.xlogy
        xlog1py_fns = torch.special.xlog1py, scipy.special.xlog1py

        test_helper(*xlogy_fns, (x, y, z))
        xlogy_inplace_variant_helper(x, x)
        xlogy_inplace_variant_helper(x, y)
        xlogy_inplace_variant_helper(x, z)
        test_helper(*xlog1py_fns, (x_1p, y_1p, z_1p))

        # Scalar-Tensor Test
        test_helper(*xlogy_fns, (x, y, z), 3.14)
        test_helper(*xlog1py_fns, (x_1p, y_1p, z_1p), 3.14)

        # Special Values Tensor-Tensor
        t = torch.tensor([-1., 0., 1., 2., float('inf'), -float('inf'), float('nan')], device=device)
        zeros = torch.zeros(7, dtype=y_dtype, device=device)

        def test_zeros_special_helper(torch_fn, reference_fn, scalar=False):
            zeros_t = 0 if scalar else zeros
            zeros_np = 0 if scalar else zeros.cpu().numpy()
            torch_fn_partial = partial(torch_fn, zeros_t)
            reference_fn_partial = partial(reference_fn, zeros_np)
            self.compare_with_numpy(torch_fn_partial, reference_fn_partial, t, exact_dtype=False)
            out_variant_helper(torch_fn, zeros_t, t)

        test_zeros_special_helper(*xlogy_fns)
        xlogy_inplace_variant_helper(zeros, t)
        test_zeros_special_helper(*xlog1py_fns)

        # Special Values Scalar-Tensor
        test_zeros_special_helper(*xlogy_fns, scalar=True)
        test_zeros_special_helper(*xlog1py_fns, scalar=True)

    def test_xlogy_xlog1py_scalar_type_promotion(self, device):
        # Test that python numbers don't participate in type promotion at the same
        # priority level as 0-dim tensors
        t = torch.randn((), dtype=torch.float32, device=device)

        self.assertEqual(t.dtype, torch.xlogy(t, 5).dtype)
        self.assertEqual(t.dtype, torch.xlogy(t, 5.).dtype)
        self.assertEqual(t.dtype, torch.special.xlog1py(t, 5).dtype)
        self.assertEqual(t.dtype, torch.special.xlog1py(t, 5.).dtype)

        self.assertEqual(t.dtype, torch.xlogy(5, t).dtype)
        self.assertEqual(t.dtype, torch.xlogy(5., t).dtype)
        self.assertEqual(t.dtype, torch.special.xlog1py(5, t).dtype)
        self.assertEqual(t.dtype, torch.special.xlog1py(5., t).dtype)

    @skipIf(not TEST_SCIPY, "Scipy required for the test.")
    def test_xlogy_xlog1py_bfloat16(self, device):
        def _compare_helper(x, y, torch_fn, reference_fn):
            x_np = x if isinstance(x, float) else x.cpu().to(torch.float).numpy()
            y_np = y if isinstance(y, float) else y.cpu().to(torch.float).numpy()
            expected = torch.from_numpy(reference_fn(x_np, y_np))
            actual = torch_fn(x, y)
            self.assertEqual(expected, actual, exact_dtype=False)

        x_dtype, y_dtype = torch.bfloat16, torch.bfloat16

        # Tensor-Tensor Test (tensor of same and different shape)
        x = make_tensor((3, 2, 4, 5), device, x_dtype, low=0.5, high=1000)
        y = make_tensor((3, 2, 4, 5), device, y_dtype, low=0.5, high=1000)
        z = make_tensor((4, 5), device, y_dtype, low=0.5, high=1000)

        x_1p = make_tensor((3, 2, 4, 5), device, x_dtype, low=-0.8, high=1000)
        y_1p = make_tensor((3, 2, 4, 5), device, y_dtype, low=-0.8, high=1000)
        z_1p = make_tensor((4, 5), device, y_dtype, low=-0.8, high=1000)

        xlogy_fns = torch.xlogy, scipy.special.xlogy
        xlog1py_fns = torch.special.xlog1py, scipy.special.xlog1py

        _compare_helper(x, x, *xlogy_fns)
        _compare_helper(x, y, *xlogy_fns)
        _compare_helper(x, z, *xlogy_fns)
        _compare_helper(x, 3.14, *xlogy_fns)
        _compare_helper(y, 3.14, *xlogy_fns)
        _compare_helper(z, 3.14, *xlogy_fns)

        _compare_helper(x_1p, x_1p, *xlog1py_fns)
        _compare_helper(x_1p, y_1p, *xlog1py_fns)
        _compare_helper(x_1p, z_1p, *xlog1py_fns)
        _compare_helper(x_1p, 3.14, *xlog1py_fns)
        _compare_helper(y_1p, 3.14, *xlog1py_fns)
        _compare_helper(z_1p, 3.14, *xlog1py_fns)

        # Special Values Tensor-Tensor
        t = torch.tensor([-1., 0., 1., 2., float('inf'), -float('inf'), float('nan')], device=device)
        zeros = torch.tensor(7, dtype=y_dtype, device=device)

        _compare_helper(t, zeros, *xlogy_fns)
        _compare_helper(t, 0., *xlogy_fns)

        _compare_helper(t, zeros, *xlog1py_fns)
        _compare_helper(t, 0., *xlog1py_fns)

tensor_binary_ops = [
    '__lt__', '__le__',
    '__gt__', '__ge__',
    '__eq__', '__ne__',

    '__add__', '__radd__', '__iadd__',
    '__sub__', '__rsub__', '__isub__',
    '__mul__', '__rmul__', '__imul__',
    '__matmul__', '__rmatmul__',
    '__truediv__', '__rtruediv__', '__itruediv__',
    '__floordiv__', '__rfloordiv__', '__ifloordiv__',
    '__mod__', '__rmod__', '__imod__',
    '__pow__', '__rpow__', '__ipow__',
    '__lshift__', '__ilshift__',
    '__rshift__', '__irshift__',
    '__and__', '__iand__',
    '__xor__', '__ixor__',
    '__or__', '__ior__',

    # Unsupported operators
    # '__imatmul__',
    # '__divmod__', '__rdivmod__', '__idivmod__',
    # '__rand__', '__ror__', '__rxor__', '__rlshift__', '__rrshift__',
]

# Test that binary math operations return NotImplemented for unknown types.
def generate_not_implemented_tests(cls):
    class UnknownType:
        pass

    # TODO: refactor to inline these
    _types = [
        torch.half, torch.float, torch.double,
        torch.int8, torch.short, torch.int, torch.long,
        torch.uint8
    ]

    # TODO: refactor to use make_tensor
    def _small_2d(dtype, device, has_zeros=True, fill_ones=False, oneish=False):
        t = _make_tensor((5, 5), dtype, device, fill_ones=fill_ones)
        if oneish:
            return t.clamp(min=_number(.99, 1, dtype), max=1.01)
        if not has_zeros:
            return t.clamp(min=(_number(_div_min, 1, dtype)))
        return t

    def create_test_func(op):
        @dtypes(*_types)
        def test(self, device, dtype):
            # Generate the inputs
            tensor = _small_2d(dtype, device)

            # Runs the tensor op on the device
            result = getattr(tensor, op)(UnknownType())
            self.assertEqual(result, NotImplemented)
        return test

    for op in tensor_binary_ops:
        test_name = "test_{}_not_implemented".format(op)
        assert not hasattr(cls, test_name), "{0} already in {1}".format(
            test_name, cls.__name__)

        setattr(cls, test_name, create_test_func(op))


generate_not_implemented_tests(TestBinaryUfuncs)
instantiate_device_type_tests(TestBinaryUfuncs, globals())

if __name__ == '__main__':
    run_tests()