mirror of
https://github.com/pytorch/pytorch.git
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673 lines
34 KiB
Python
673 lines
34 KiB
Python
# Owner(s): ["module: mta"]
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import itertools
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from numbers import Number
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import random
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import re
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import torch
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import unittest
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from torch.testing import make_tensor
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from torch.testing._comparison import default_tolerances
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from torch.testing._internal.common_utils import TestCase, run_tests, TEST_WITH_ROCM, TEST_WITH_SLOW
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from torch.testing._internal.common_device_type import \
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(instantiate_device_type_tests, dtypes, onlyCUDA, skipMeta, ops)
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from torch.testing._internal.common_methods_invocations import (
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foreach_unary_op_db, foreach_binary_op_db, foreach_pointwise_op_db, foreach_minmax_op_db,
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foreach_reduce_op_db)
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from torch.testing._internal.common_dtype import (
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all_types_and_complex_and, all_types_and, integral_types, complex_types,
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floating_types_and, floating_types, integral_types_and,
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)
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# Includes some values such that N * N won't be a multiple of 4,
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# which should ensure we test the vectorized and non-vectorized
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# kernel code paths.
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N_values = [20, 23] if not TEST_WITH_SLOW else [23, 30, 300]
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Scalars = (
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random.randint(1, 10),
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1.0 - random.random(),
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True,
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complex(1.0 - random.random(), 1.0 - random.random()),
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)
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def getScalarLists(N):
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return (
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("int", [random.randint(0, 9) + 1 for _ in range(N)]),
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("float", [1.0 - random.random() for _ in range(N)]),
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("complex", [complex(1.0 - random.random(), 1.0 - random.random()) for _ in range(N)]),
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("bool", [True for _ in range(N)]),
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("mixed", [1, 2.0, 3.0 + 4.5j] + [3.0 for _ in range(N - 3)]),
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("mixed", [True, 1, 2.0, 3.0 + 4.5j] + [3.0 for _ in range(N - 4)]),
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)
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_BOOL_SUB_ERR_MSG = "Subtraction, the `-` operator"
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class RegularFuncWrapper:
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def __init__(self, func):
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self.func = func
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def __call__(self, inputs, values=None, **kwargs):
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if values is not None:
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assert len(inputs) == 3
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if isinstance(values, Number):
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values = [values for _ in range(len(inputs[0]))]
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return [self.func(*i, value=values[idx], **kwargs) for idx, i in enumerate(zip(*inputs))]
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if len(inputs) == 2 and isinstance(inputs[1], Number):
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# binary op with tensorlist and scalar.
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inputs[1] = [inputs[1] for _ in range(len(inputs[0]))]
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return [self.func(*i, **kwargs) for i in zip(*inputs)]
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class ForeachFuncWrapper:
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def __init__(self, func, n_expected_cudaLaunchKernels):
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self.func = func
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self.n_expected_cudaLaunchKernels = n_expected_cudaLaunchKernels
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# Some foreach functions don't have in-place implementations.
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self._is_inplace = False if func is None else func.__name__.endswith('_')
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def __call__(self, inputs, is_cuda, is_fastpath, **kwargs):
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actual = None
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if (
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is_cuda and
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torch.autograd.kineto_available() and
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torch.profiler.ProfilerActivity.CUDA in torch.profiler.supported_activities()
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):
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with torch.profiler.profile(activities=(torch.profiler.ProfilerActivity.CPU,)) as p:
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actual = self.func(*inputs, **kwargs)
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for e in p.key_averages():
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if e.key == 'cudaLaunchKernel':
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if is_fastpath:
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assert e.count == self.n_expected_cudaLaunchKernels
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else:
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assert e.count > self.n_expected_cudaLaunchKernels
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else:
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actual = self.func(*inputs, **kwargs)
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# note(mkozuki): inplace foreach functions are void functions.
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return inputs[0] if self._is_inplace else actual
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class TestForeach(TestCase):
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@property
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def is_cuda(self):
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return self.device_type == 'cuda'
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# note(mkozuki): It might be the case that the expected number of `cudaLaunchKernel`s
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# is greater than 1 once foreach functions internally separate their input `TensorList`s by
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# devices & dtypes into vectors of tensors.
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def _get_funcs(self, op, n_expected_cudaLaunchKernels):
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return (
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ForeachFuncWrapper(op.method_variant, n_expected_cudaLaunchKernels),
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RegularFuncWrapper(op.ref),
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ForeachFuncWrapper(op.inplace_variant, n_expected_cudaLaunchKernels),
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RegularFuncWrapper(op.ref_inplace),
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)
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def _binary_test(self, dtype, op, ref, inputs, is_fastpath, is_inplace, *, alpha=None):
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ref_inputs = [[t.clone().detach() for t in inputs[0]], inputs[1]] if is_inplace else inputs
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try:
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actual = op(inputs, self.is_cuda, is_fastpath)
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except RuntimeError as e:
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with self.assertRaisesRegex(type(e), re.escape(str(e))):
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ref(ref_inputs)
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else:
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expected = ref(ref_inputs)
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self.assertEqual(actual, expected)
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if alpha is not None:
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kwargs = {'alpha': alpha}
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ref_inputs = inputs
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try:
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actual = op(inputs, self.is_cuda, is_fastpath, **kwargs)
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except RuntimeError as e:
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with self.assertRaisesRegex(type(e), re.escape(str(e))):
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ref(ref_inputs, **kwargs)
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else:
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expected = ref(ref_inputs, **kwargs)
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if dtype in (torch.float16, torch.bfloat16) and TEST_WITH_ROCM:
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self.assertEqual(expected, actual, atol=1.e-3, rtol=default_tolerances(dtype)[0])
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else:
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self.assertEqual(expected, actual)
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def _test_binary_op_tensorlists(self, device, dtype, opinfo, N, is_fastpath, disable_fastpath):
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n_expected_cudaLaunchKernels = N if disable_fastpath else 1
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op, ref, inplace_op, inplace_ref = self._get_funcs(opinfo, n_expected_cudaLaunchKernels)
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inputs = [
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opinfo.sample_inputs(device, dtype, N, noncontiguous=not is_fastpath),
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opinfo.sample_inputs(device, dtype, N, noncontiguous=not is_fastpath),
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]
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self._binary_test(dtype, op, ref, inputs, is_fastpath, is_inplace=False)
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self._binary_test(dtype, inplace_op, inplace_ref, inputs, is_fastpath, is_inplace=True)
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if opinfo.supports_alpha_param:
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alpha = None
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if dtype in integral_types():
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alpha = 3
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elif dtype.is_complex:
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alpha = complex(3, 3)
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else:
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alpha = 3.14
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self._binary_test(dtype, op, ref, inputs, is_fastpath, is_inplace=False, alpha=alpha)
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self._binary_test(dtype, inplace_op, inplace_ref, inputs, is_fastpath, is_inplace=True, alpha=alpha)
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# Tests of implicit broadcasting
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# When sizes of tensors don't match, foreach functions are supposed to choose slow path
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# even if this methods's argument `is_fastpath` is True.
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# `cudaLaunchKernel` will be equal to `N`. For assert in `ForeachFuncWrapper` to pass,
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# we pass `is_fastpath and disable_fastpath` to `_binary_test`'s argument of is_fastpath.
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# as n_expected_cudaLaunchKernels is N if disable_fastpath.
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inputs = [
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opinfo.sample_inputs(device, dtype, N, noncontiguous=not is_fastpath),
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[
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make_tensor((N - i , 1), device=device, dtype=dtype, noncontiguous=not is_fastpath) for i in range(N)
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],
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]
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self._binary_test(dtype, op, ref, inputs, is_fastpath and disable_fastpath, is_inplace=False)
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self._binary_test(
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dtype, inplace_op, inplace_ref, inputs, is_fastpath and disable_fastpath, is_inplace=True)
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@skipMeta
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@ops(foreach_binary_op_db)
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def test_binary_op_tensorlists_fastpath(self, device, dtype, op):
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for N in N_values:
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disable_fastpath = op.ref == torch.div and dtype in integral_types_and(torch.bool)
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if op.ref == torch.add and dtype == torch.bool:
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disable_fastpath = True
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self._test_binary_op_tensorlists(device, dtype, op, N, True, disable_fastpath)
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@ops(foreach_binary_op_db)
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def test_binary_op_tensorlists_slowpath(self, device, dtype, op):
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for N in N_values:
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self._test_binary_op_tensorlists(device, dtype, op, N, False, False)
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def _test_binary_op_scalar(self, device, dtype, opinfo, N, scalar, is_fastpath, disable_fastpath):
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n_expected_cudaLaunchKernels = N if disable_fastpath else 1
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op, ref, inplace_op, inplace_ref = self._get_funcs(opinfo, n_expected_cudaLaunchKernels)
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inputs = [opinfo.sample_inputs(device, dtype, N, noncontiguous=not is_fastpath), scalar]
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self._binary_test(dtype, op, ref, inputs, is_fastpath, is_inplace=False)
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self._binary_test(dtype, inplace_op, inplace_ref, inputs, is_fastpath, is_inplace=True)
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@skipMeta
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@ops(foreach_binary_op_db)
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def test_binary_op_scalar_fastpath(self, device, dtype, op):
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for N, scalar in itertools.product(N_values, Scalars):
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disable_fastpath = op.ref == torch.div and dtype in integral_types_and(torch.bool)
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if isinstance(scalar, int):
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disable_fastpath |= dtype == torch.bool
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if isinstance(scalar, float):
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disable_fastpath |= dtype in integral_types_and(torch.bool)
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if isinstance(scalar, bool):
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disable_fastpath |= dtype == torch.bool
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if op.ref in (torch.add, torch.mul):
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disable_fastpath = False
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if isinstance(scalar, complex):
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disable_fastpath |= dtype not in complex_types()
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self._test_binary_op_scalar(device, dtype, op, N, scalar, True, disable_fastpath)
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@ops(foreach_binary_op_db)
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def test_binary_op_scalar_slowpath(self, device, dtype, op):
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for N, scalar in itertools.product(N_values, Scalars):
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self._test_binary_op_scalar(device, dtype, op, N, scalar, False, False)
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def _test_binary_op_scalarlist(self, device, dtype, opinfo, N, scalarlist, is_fastpath, disable_fastpath):
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n_expected_cudaLaunchKernels = N if disable_fastpath else 1
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op, ref, inplace_op, inplace_ref = self._get_funcs(opinfo, n_expected_cudaLaunchKernels)
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inputs = [opinfo.sample_inputs(device, dtype, N, noncontiguous=not is_fastpath), scalarlist]
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self._binary_test(dtype, op, ref, inputs, is_fastpath, is_inplace=False)
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self._binary_test(dtype, inplace_op, inplace_ref, inputs, is_fastpath, is_inplace=True)
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# note(mkozuki): Why two functions depending on with/without bool?
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# `foreach_sub` & `foreach_sub_` do `sub_check(tensors[i], scalars[i])` from i=1...N.
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# So, if scalarlist has one or more bool values, `foreach_sub` and `foreach_sub_`
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# raise bool subtraction error before doing any math.
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# While regular `sub` and `sub_` do some math until they encounter bool.
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# So, foreach sub's throw bool sub error first. However, regular sub's throw different
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# errors depending on the order of scalarlist. To keep actual unit test impl simple,
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# separating mixed scalarlist tests. By setting the first element of scalarlist to bool,
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# they are expected to throw bool sub error even in inplace test.
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@skipMeta
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@ops(foreach_binary_op_db)
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def test_binary_op_scalarlist_fastpath(self, device, dtype, op):
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for N in N_values:
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for type_str, scalarlist in getScalarLists(N):
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bool_int_div = op.ref == torch.div and dtype in integral_types_and(torch.bool)
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disable_fastpath = bool_int_div
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if type_str == "int":
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disable_fastpath |= dtype == torch.bool
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if type_str == "float":
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disable_fastpath |= dtype in integral_types_and(torch.bool)
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if type_str == "complex":
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disable_fastpath |= dtype not in complex_types()
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if type_str == "mixed":
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disable_fastpath |= True and dtype not in complex_types()
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self._test_binary_op_scalarlist(device, dtype, op, N, scalarlist, True, disable_fastpath)
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@ops(foreach_binary_op_db)
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def test_binary_op_scalarlist_slowpath(self, device, dtype, op):
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for N in N_values:
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for _, scalarlist in getScalarLists(N):
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self._test_binary_op_scalarlist(device, dtype, op, N, scalarlist, False, False)
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def _pointwise_test(self, dtype, op, ref, inputs, is_fastpath, is_inplace, *, values=None):
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ref_inputs = [[t.clone().detach() for t in inputs[0]], inputs[1], inputs[2]] if is_inplace else inputs
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try:
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actual = op(inputs, self.is_cuda, is_fastpath)
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except RuntimeError as e:
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with self.assertRaisesRegex(type(e), re.escape(str(e))):
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ref(ref_inputs)
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else:
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expected = ref(ref_inputs)
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self.assertEqual(expected, actual)
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if values is not None:
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try:
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actual = op(inputs + [values], self.is_cuda, is_fastpath)
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except RuntimeError as e:
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with self.assertRaisesRegex(type(e), re.escape(str(e))):
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ref(ref_inputs, values=values)
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else:
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expected = ref(ref_inputs, values=values)
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self.assertEqual(expected, actual)
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def _test_pointwise_op(self, device, dtype, opinfo, N, is_fastpath, disable_fastpath, *, values=None):
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n_expected_cudaLaunchKernels = N if disable_fastpath else 1
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op, ref, inplace_op, inplace_ref = self._get_funcs(opinfo, n_expected_cudaLaunchKernels)
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inputs = [
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opinfo.sample_inputs(device, dtype, N, noncontiguous=not is_fastpath),
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opinfo.sample_inputs(device, dtype, N, noncontiguous=not is_fastpath),
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opinfo.sample_inputs(device, dtype, N, noncontiguous=not is_fastpath),
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]
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self._pointwise_test(dtype, op, ref, inputs, is_fastpath, is_inplace=False, values=values)
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self._pointwise_test(dtype, inplace_op, inplace_ref, inputs, is_fastpath, is_inplace=True, values=values)
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# Tests of implicit broadcasting
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inputs = [
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opinfo.sample_inputs(device, dtype, N, noncontiguous=not is_fastpath, same_size=True),
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[
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make_tensor((N - i, 1), device=device, dtype=dtype, noncontiguous=not is_fastpath) for i in range(N)
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],
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[
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make_tensor((1, N - i), device=device, dtype=dtype, noncontiguous=not is_fastpath) for i in range(N)
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],
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]
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self._pointwise_test(dtype, op, ref, inputs, is_fastpath and disable_fastpath, is_inplace=False, values=values)
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self._pointwise_test(
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dtype, inplace_op, inplace_ref, inputs, is_fastpath and disable_fastpath, is_inplace=True, values=values)
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@skipMeta
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@ops(foreach_pointwise_op_db)
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def test_pointwise_op_fastpath(self, device, dtype, op):
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disable_fastpath = dtype in integral_types_and(torch.bool)
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# for N, scalar in itertools.product(N_values, Scalars):
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for N in N_values:
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self._test_pointwise_op(device, dtype, op, N, True, disable_fastpath)
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for scalar in Scalars:
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self._test_pointwise_op(device, dtype, op, N, True, disable_fastpath, values=scalar)
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for _, scalarlist in getScalarLists(N):
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self._test_pointwise_op(
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device, dtype, op, N, True, disable_fastpath, values=scalarlist)
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@ops(foreach_pointwise_op_db)
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def test_pointwise_op_slowpath(self, device, dtype, op):
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# for N, scalar in itertools.product(N_values, Scalars):
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for N in N_values:
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self._test_pointwise_op(device, dtype, op, N, False, False)
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for scalar in Scalars:
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self._test_pointwise_op(device, dtype, op, N, False, False, values=scalar)
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for _, scalarlist in getScalarLists(N):
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self._test_pointwise_op(
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device, dtype, op, N, False, False, values=scalarlist)
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# note(mkozuki): fastpath test uses dtypes which fastpath implementation supports.
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# To confirm the dtypes of `OpInfo` cover the dtypes that the function support,
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# this test does not use `try-except` for fastpath.
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def _regular_unary_test(self, dtype, op, ref, inputs, is_fastpath):
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if is_fastpath:
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self.assertEqual(ref(inputs), op(inputs, self.is_cuda, is_fastpath))
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return
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try:
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actual = op(inputs, self.is_cuda, is_fastpath)
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except RuntimeError as e:
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with self.assertRaisesRegex(type(e), re.escape(str(e))):
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ref(inputs)
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else:
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expected = ref(inputs)
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self.assertEqual(actual, expected)
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# note(mkozuki): why `try-except` for both fastpath?
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# - inputs for fastpath can be integer tensors.
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# - this is becase opinfo dtypes are configured for outpulace implementation
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# - for integer inputs, trigonometric functions and exponential function returns float outputs,
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# which causes "result type Float can't be case to the desired type" error.
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# Thus, `try-except` is used even if `is_fastpath` is `True`.
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def _inplace_unary_test(self, dtype, inplace, inplace_ref, inputs, is_fastpath):
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copied_inputs = [[t.clone().detach() for t in tensors] for tensors in inputs]
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try:
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inplace(inputs, self.is_cuda, is_fastpath)
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except RuntimeError as e:
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with self.assertRaisesRegex(type(e), re.escape(str(e))):
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inplace_ref(copied_inputs)
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else:
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inplace_ref(copied_inputs),
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self.assertEqual(copied_inputs, inputs)
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def _test_unary(self, device, dtype, opinfo, N, is_fastpath):
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op, ref, inplace_op, inplace_ref = self._get_funcs(opinfo, 1)
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inputs = opinfo.sample_inputs(device, dtype, N, noncontiguous=not is_fastpath),
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# note(mkozuki): Complex inputs for `_foreach_abs` go through slowpath.
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if opinfo.name == "_foreach_abs" and dtype in complex_types():
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is_fastpath = False
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self._regular_unary_test(dtype, op, ref, inputs, is_fastpath)
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self._inplace_unary_test(dtype, inplace_op, inplace_ref, inputs, is_fastpath)
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@skipMeta
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@ops(foreach_unary_op_db)
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def test_unary_fastpath(self, device, dtype, op):
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for N in N_values:
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self._test_unary(device, dtype, op, N, is_fastpath=True)
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@ops(foreach_unary_op_db, dtypes=all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool))
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def test_unary_slowpath(self, device, dtype, op):
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for N in N_values:
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self._test_unary(device, dtype, op, N, is_fastpath=False)
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def _minmax_test(self, opinfo, inputs, is_fastpath, n_expected_cudaLaunchKernels):
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op, ref, _, _ = self._get_funcs(opinfo, n_expected_cudaLaunchKernels)
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self.assertEqual(ref(inputs), op(inputs, self.is_cuda, is_fastpath))
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# note(mkozuki): in-place of foreach_minimum and foreach_maximum aren't implemented.
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@ops(foreach_minmax_op_db)
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def test_minmax_fastpath(self, device, dtype, op):
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for N in N_values:
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inputs = tuple(op.sample_inputs(device, dtype, N) for _ in range(2))
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self._minmax_test(op, inputs, True, N if dtype == torch.bool else 1)
|
|
|
|
@ops(foreach_minmax_op_db,
|
|
dtypes=all_types_and(torch.half, torch.bfloat16, torch.bool))
|
|
def test_minmax_slowpath(self, device, dtype, op):
|
|
for N in N_values:
|
|
inputs = tuple(op.sample_inputs(device, dtype, N, noncontiguous=True) for _ in range(2))
|
|
self._minmax_test(op, inputs, False, 1)
|
|
|
|
# note(mkozuki): ForeachFuncInfo's of both `_foreach_maximum` and `_foreach_minimum` include integer types.
|
|
# so, manually limit dtypes to fp types for inf&nan tests.
|
|
@ops(foreach_minmax_op_db, dtypes=floating_types_and(torch.half, torch.bfloat16))
|
|
def test_minmax_float_inf_nan(self, device, dtype, op):
|
|
inputs = (
|
|
[
|
|
torch.tensor([float('inf')], device=device, dtype=dtype),
|
|
torch.tensor([-float('inf')], device=device, dtype=dtype),
|
|
torch.tensor([float('nan')], device=device, dtype=dtype),
|
|
torch.tensor([float('nan')], device=device, dtype=dtype)
|
|
],
|
|
[
|
|
torch.tensor([-float('inf')], device=device, dtype=dtype),
|
|
torch.tensor([float('inf')], device=device, dtype=dtype),
|
|
torch.tensor([float('inf')], device=device, dtype=dtype),
|
|
torch.tensor([float('nan')], device=device, dtype=dtype)
|
|
],
|
|
)
|
|
self._minmax_test(op, inputs, True, 1)
|
|
|
|
def _reduce_test(self, opinfo, inputs, ord, is_fastpath, n_expected_cudaLaunchKernels):
|
|
op, ref, _, _ = self._get_funcs(opinfo, n_expected_cudaLaunchKernels)
|
|
self.assertEqual(ref(inputs, ord=ord), op(inputs, self.is_cuda, is_fastpath, ord=ord))
|
|
|
|
@ops(foreach_reduce_op_db)
|
|
def test_reduce_fastpath(self, device, dtype, op):
|
|
for N, ord in itertools.product(N_values, (0, 1, 2, -1, -2)):
|
|
if ord in (1, 2) and dtype in floating_types_and(torch.half, torch.bfloat16):
|
|
n_expected_cudaLaunchKernels = 3
|
|
else:
|
|
n_expected_cudaLaunchKernels = N
|
|
inputs = op.sample_inputs(device, dtype, N, noncontiguous=False),
|
|
self._reduce_test(op, inputs, ord, True, n_expected_cudaLaunchKernels)
|
|
|
|
@ops(foreach_reduce_op_db)
|
|
def test_reduce_slowpath(self, device, dtype, op):
|
|
for N, ord in itertools.product(N_values, (0, 1, 2, -1, -2)):
|
|
inputs = op.sample_inputs(device, dtype, N, noncontiguous=True),
|
|
self._reduce_test(op, inputs, ord, False, 1)
|
|
|
|
@dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool))
|
|
def test_add_scalar_with_empty_list_and_empty_tensor(self, device, dtype):
|
|
# TODO: enable empty list case
|
|
for tensors in [[torch.randn([0])]]:
|
|
res = torch._foreach_add(tensors, 1)
|
|
self.assertEqual(res, tensors)
|
|
|
|
torch._foreach_add_(tensors, 1)
|
|
self.assertEqual(res, tensors)
|
|
|
|
@ops(foreach_binary_op_db, dtypes=all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool))
|
|
def test_binary_op_scalar_with_overlapping_tensors(self, device, dtype, op):
|
|
foreach_op, ref = op.method_variant, op.ref
|
|
tensors = [torch.ones(1, 1, device=device, dtype=dtype).expand(2, 1, 3)]
|
|
|
|
if ref == torch.sub and dtype == torch.bool:
|
|
with self.assertRaisesRegex(RuntimeError, re.escape(_BOOL_SUB_ERR_MSG)):
|
|
[ref(t, 1) for t in tensors]
|
|
with self.assertRaisesRegex(RuntimeError, re.escape(_BOOL_SUB_ERR_MSG)):
|
|
foreach_op(tensors, 1)
|
|
return
|
|
|
|
expected = [ref(t, 1) for t in tensors]
|
|
res = foreach_op(tensors, 1)
|
|
self.assertEqual(res, expected)
|
|
|
|
# note(mkozuki): this test case fails with Meta at least in my local environment.
|
|
# The message was
|
|
# `AssertionError: NotImplementedError("Could not run 'aten::_foreach_add.Scalar' with arguments from the 'Meta' backend.`
|
|
@skipMeta
|
|
@ops(foreach_binary_op_db, allowed_dtypes=[torch.float])
|
|
def test_binary_op_scalar_with_different_tensor_dtypes(self, device, dtype, op):
|
|
foreach_op = op.method_variant
|
|
tensors = [torch.tensor([1.1], dtype=torch.float, device=device),
|
|
torch.tensor([1], dtype=torch.long, device=device)]
|
|
runtime_error = None
|
|
try:
|
|
foreach_op(tensors, 1)
|
|
except RuntimeError as e:
|
|
runtime_error = e
|
|
self.assertIsNone(runtime_error)
|
|
|
|
@ops(foreach_binary_op_db, dtypes=all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool))
|
|
def test_binary_op_list_error_cases(self, device, dtype, op):
|
|
foreach_op, foreach_op_, ref, ref_ = op.method_variant, op.inplace_variant, op.ref, op.ref_inplace
|
|
tensors1 = []
|
|
tensors2 = []
|
|
|
|
# Empty lists
|
|
with self.assertRaisesRegex(RuntimeError, "There were no tensor arguments to this function"):
|
|
foreach_op(tensors1, tensors2)
|
|
with self.assertRaisesRegex(RuntimeError, "There were no tensor arguments to this function"):
|
|
foreach_op_(tensors1, tensors2)
|
|
|
|
# One empty list
|
|
tensors1.append(torch.tensor([1], device=device, dtype=dtype))
|
|
with self.assertRaisesRegex(RuntimeError, "Tensor list must have same number of elements as scalar list."):
|
|
foreach_op(tensors1, tensors2)
|
|
with self.assertRaisesRegex(RuntimeError, "Tensor list must have same number of elements as scalar list."):
|
|
foreach_op_(tensors1, tensors2)
|
|
|
|
# Lists have different amount of tensors
|
|
tensors2.append(torch.tensor([1], device=device))
|
|
tensors2.append(torch.tensor([1], device=device))
|
|
with self.assertRaisesRegex(RuntimeError, "Tensor lists must have the same number of tensors, got 1 and 2"):
|
|
foreach_op(tensors1, tensors2)
|
|
with self.assertRaisesRegex(RuntimeError, "Tensor lists must have the same number of tensors, got 1 and 2"):
|
|
foreach_op_(tensors1, tensors2)
|
|
|
|
# Corresponding tensors with different sizes that aren't compatible with broadcast
|
|
# If sizes are different then foreach chooses slow path, thus error messages are expected
|
|
# to be the same as torch regular function.
|
|
tensors1 = [torch.zeros(10, 10, device=device, dtype=dtype) for _ in range(10)]
|
|
tensors2 = [torch.ones(11, 11, device=device, dtype=dtype) for _ in range(10)]
|
|
try:
|
|
foreach_op(tensors1, tensors2)
|
|
except RuntimeError as e:
|
|
with self.assertRaisesRegex(type(e), re.escape(str(e))):
|
|
[ref(t1, t2) for t1, t2 in zip(tensors1, tensors2)]
|
|
try:
|
|
foreach_op_(tensors1, tensors2)
|
|
except RuntimeError as e:
|
|
with self.assertRaisesRegex(type(e), re.escape(str(e))):
|
|
[ref_(t1, t2) for t1, t2 in zip(tensors1, tensors2)]
|
|
|
|
# different devices
|
|
if self.device_type == "cuda" and torch.cuda.device_count() > 1:
|
|
tensor1 = torch.zeros(10, 10, device="cuda:0", dtype=dtype)
|
|
tensor2 = torch.ones(10, 10, device="cuda:1", dtype=dtype)
|
|
if dtype == torch.bool and foreach_op == torch._foreach_sub:
|
|
with self.assertRaisesRegex(RuntimeError, re.escape(_BOOL_SUB_ERR_MSG)):
|
|
foreach_op([tensor1], [tensor2])
|
|
with self.assertRaisesRegex(RuntimeError, re.escape(_BOOL_SUB_ERR_MSG)):
|
|
foreach_op_([tensor1], [tensor2])
|
|
return
|
|
with self.assertRaisesRegex(RuntimeError, "Expected all tensors to be on the same device"):
|
|
foreach_op([tensor1], [tensor2])
|
|
if dtype in integral_types_and(torch.bool) and foreach_op == torch._foreach_div:
|
|
with self.assertRaisesRegex(RuntimeError, "result type"):
|
|
foreach_op_([tensor1], [tensor2])
|
|
else:
|
|
with self.assertRaisesRegex(RuntimeError, "Expected all tensors to be on the same device"):
|
|
foreach_op_([tensor1], [tensor2])
|
|
|
|
@skipMeta
|
|
@unittest.skipIf(not torch.cuda.is_available(), "CUDA not found")
|
|
@ops(foreach_binary_op_db, dtypes=all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool))
|
|
def test_binary_op_list_slow_path(self, device, dtype, op):
|
|
# note(mkozuki): why `n_expected_cudaLaunchKernels=0`?
|
|
# In this test, foreach functions don't go through fast path,
|
|
# but as there is only one tensor in each list of tensors,
|
|
# `cudaLaunchKernel` is 1 so ForeachFuncWrapper internal assert fails.
|
|
foreach_op, native_op, foreach_op_, native_op_ = self._get_funcs(op, n_expected_cudaLaunchKernels=0)
|
|
# 0-strides
|
|
tensor1 = make_tensor((10, 10), dtype=dtype, device=device)
|
|
tensor2 = make_tensor((1,), device=device, dtype=dtype).expand_as(tensor1)
|
|
inputs = ([tensor1], [tensor2])
|
|
self._binary_test(dtype, foreach_op, native_op, inputs, is_fastpath=False, is_inplace=False)
|
|
self._binary_test(dtype, foreach_op_, native_op_, inputs, is_fastpath=False, is_inplace=True)
|
|
|
|
# different strides
|
|
tensor1 = torch.zeros(10, 10, device=device, dtype=dtype)
|
|
tensor2 = torch.ones(10, 10, device=device, dtype=dtype)
|
|
inputs = ([tensor1], [tensor2.t()])
|
|
self._binary_test(dtype, foreach_op, native_op, inputs, is_fastpath=False, is_inplace=False)
|
|
self._binary_test(dtype, foreach_op_, native_op_, inputs, is_fastpath=False, is_inplace=True)
|
|
|
|
# non contiguous
|
|
tensor1 = make_tensor((5, 2, 1, 3), device=device, dtype=dtype, noncontiguous=True)
|
|
tensor2 = make_tensor((5, 2, 1, 3), device=device, dtype=dtype, noncontiguous=True)
|
|
self.assertFalse(tensor1.is_contiguous())
|
|
self.assertFalse(tensor2.is_contiguous())
|
|
inputs = ([tensor1], [tensor2])
|
|
self._binary_test(dtype, foreach_op, native_op, inputs, is_fastpath=False, is_inplace=False)
|
|
self._binary_test(dtype, foreach_op_, native_op_, inputs, is_fastpath=False, is_inplace=True)
|
|
|
|
# sliced tensor
|
|
tensor1 = make_tensor((5, 2, 1, 3), device=device, dtype=dtype)
|
|
tensor2 = make_tensor((5, 2, 1, 3 * 7), device=device, dtype=dtype)[:, :, :, ::7]
|
|
inputs = ([tensor1], [tensor2])
|
|
self._binary_test(dtype, foreach_op, native_op, inputs, is_fastpath=False, is_inplace=False)
|
|
self._binary_test(dtype, foreach_op_, native_op_, inputs, is_fastpath=False, is_inplace=True)
|
|
|
|
# note: Below three tests (postfixed with `_tensors_on_different_devices`)
|
|
# checks whether foreach works with lists of tensors on different devices
|
|
# but tensors of the same index are on the same device, e.g., ['cuda', 'cpu].
|
|
@onlyCUDA
|
|
@ops(foreach_unary_op_db)
|
|
def test_unary_op_tensors_on_different_devices(self, device, dtype, op):
|
|
method, ref, inplace_method, ref_inplace = self._get_funcs(op, 1)
|
|
# tensors: ['cuda', 'cpu]
|
|
tensors = op.sample_inputs(device, dtype, 2)
|
|
tensors[1] = tensors[1].to('cpu')
|
|
try:
|
|
actual = method((tensors,), False, False)
|
|
except RuntimeError as e:
|
|
with self.assertRaisesRegex(type(e), str(e)):
|
|
ref((tensors,))
|
|
else:
|
|
expected = ref((tensors,))
|
|
self.assertEqual(expected, actual)
|
|
|
|
try:
|
|
inplace_method((tensors,), False, False)
|
|
except RuntimeError as e:
|
|
with self.assertRaisesRegex(type(e), str(e)):
|
|
ref_inplace((tensors,))
|
|
else:
|
|
self.assertEqual(expected, tensors)
|
|
|
|
@onlyCUDA
|
|
@ops(foreach_binary_op_db)
|
|
def test_binary_op_tensors_on_different_devices(self, device, dtype, op):
|
|
# `tensors1`: ['cuda', 'cpu']
|
|
# `tensors2`: ['cuda', 'cpu']
|
|
_cuda_tensors = op.sample_inputs(device, dtype, 2, same_size=True)
|
|
_cpu_tensors = op.sample_inputs('cpu', dtype, 2, same_size=True)
|
|
tensors1, tensors2 = list(tensors for tensors in zip(_cuda_tensors, _cpu_tensors))
|
|
|
|
foreach_op, foreach_op_ = op.method_variant, op.inplace_variant
|
|
native_op, native_op_ = op.ref, op.ref_inplace
|
|
try:
|
|
actual = foreach_op(tensors1, tensors2)
|
|
except RuntimeError as e:
|
|
with self.assertRaisesRegex(type(e), re.escape(str(e))):
|
|
[native_op(t1, t2) for t1, t2 in zip(tensors1, tensors2)]
|
|
else:
|
|
expected = [native_op(t1, t2) for t1, t2 in zip(tensors1, tensors2)]
|
|
self.assertEqual(expected, actual)
|
|
try:
|
|
foreach_op_(tensors1, tensors2)
|
|
except RuntimeError as e:
|
|
with self.assertRaisesRegex(type(e), re.escape(str(e))):
|
|
[native_op_(t1, t2) for t1, t2 in zip(tensors1, tensors2)]
|
|
else:
|
|
self.assertEqual(actual, tensors1)
|
|
|
|
@onlyCUDA
|
|
@ops(foreach_pointwise_op_db, allowed_dtypes=floating_types())
|
|
def test_pointwise_op_tensors_on_different_devices(self, device, dtype, op):
|
|
# tensors1: ['cuda', 'cpu]
|
|
# tensors2: ['cuda', 'cpu]
|
|
# tensors3: ['cuda', 'cpu]
|
|
_cuda_tensors = op.sample_inputs(device, dtype, 3, same_size=True)
|
|
_cpu_tensors = op.sample_inputs('cpu', dtype, 3, same_size=True)
|
|
tensors1, tensors2, tensors3 = list(tensors for tensors in zip(_cuda_tensors, _cpu_tensors))
|
|
|
|
foreach_op, foreach_op_, native_op = op.method_variant, op.inplace_variant, op.ref
|
|
actual = foreach_op(tensors1, tensors2, tensors3)
|
|
expected = [native_op(*_cuda_tensors), native_op(*_cpu_tensors)]
|
|
self.assertEqual(expected, actual)
|
|
|
|
# note(mkozuki): Limiting dtypes to FP32&FP64, we can safely run inplace ops.
|
|
foreach_op_(tensors1, tensors2, tensors3)
|
|
self.assertEqual(expected, tensors1)
|
|
|
|
# note: BFloat16 has the same number of exponent bits as FP32
|
|
# so if squared L2 norm overflows in BF16, then it also overflows in FP32.
|
|
@onlyCUDA
|
|
@ops(foreach_reduce_op_db, allowed_dtypes=(torch.half, torch.bfloat16))
|
|
def test_foreach_l2_large_value_input(self, device, dtype, op):
|
|
ord, N = 2, 10
|
|
max_value = torch.finfo(dtype).max
|
|
scaler = torch.tensor([max_value]).sqrt().to(device=device, dtype=dtype)
|
|
inputs = [t * scaler for t in op.sample_inputs(device, dtype, N, noncontiguous=False, low=1)],
|
|
# make sure that the min. of squared L2 norm value per tensor is greater than the max value of `dtype`.
|
|
self.assertTrue(scaler * scaler * N > max_value)
|
|
fn, ref_fn, *_ = self._get_funcs(op, 3)
|
|
actual = fn(inputs, is_cuda=True, is_fastpath=True, ord=ord)
|
|
expect = ref_fn(inputs, ord=ord)
|
|
if dtype == torch.float16:
|
|
# making sure the reference L2 norm values are in the range of FP16.
|
|
self.assertFalse(any(torch.isinf(e) for e in expect))
|
|
else:
|
|
self.assertTrue(all(torch.isinf(e) for e in expect))
|
|
self.assertEqual(expect, actual, equal_nan=False)
|
|
|
|
|
|
instantiate_device_type_tests(TestForeach, globals())
|
|
|
|
if __name__ == '__main__':
|
|
run_tests()
|