| # Owner(s): ["module: mta"] |
| |
| from numbers import Number |
| import random |
| import re |
| import torch |
| import unittest |
| |
| from torch.testing import make_tensor |
| from torch.testing._comparison import default_tolerances |
| from torch.testing._internal.common_utils import TestCase, run_tests, TEST_WITH_ROCM, TEST_WITH_SLOW, skipIfTorchDynamo, parametrize |
| from torch.testing._internal.common_device_type import \ |
| (instantiate_device_type_tests, dtypes, onlyCUDA, skipMeta, ops, OpDTypes) |
| from torch.testing._internal.common_methods_invocations import ( |
| foreach_unary_op_db, foreach_binary_op_db, foreach_pointwise_op_db, |
| foreach_reduce_op_db, foreach_lerp_op_db) |
| from torch.testing._internal.common_dtype import ( |
| all_types_and_complex_and, integral_types, complex_types, |
| floating_types_and, floating_types, integral_types_and, |
| ) |
| |
| # Includes some values such that N * N won't be a multiple of 4, |
| # which should ensure we test the vectorized and non-vectorized |
| # kernel code paths. |
| N_values = [20, 23] if not TEST_WITH_SLOW else [23, 30, 300] |
| Scalars = ( |
| random.randint(1, 10), |
| 1.0 - random.random(), |
| True, |
| complex(1.0 - random.random(), 1.0 - random.random()), |
| ) |
| |
| |
| def getScalarLists(N): |
| return ( |
| ("int", [random.randint(0, 9) + 1 for _ in range(N)]), |
| ("float", [1.0 - random.random() for _ in range(N)]), |
| ("complex", [complex(1.0 - random.random(), 1.0 - random.random()) for _ in range(N)]), |
| ("bool", [True for _ in range(N)]), |
| ("mixed", [1, 2.0, 3.0 + 4.5j] + [3.0 for _ in range(N - 3)]), |
| ("mixed", [True, 1, 2.0, 3.0 + 4.5j] + [3.0 for _ in range(N - 4)]), |
| ) |
| |
| |
| _BOOL_SUB_ERR_MSG = "Subtraction, the `-` operator" |
| |
| |
| class RegularFuncWrapper: |
| |
| def __init__(self, func): |
| self.func = func |
| |
| def __call__(self, inputs, values=None, **kwargs): |
| if values is not None: |
| assert len(inputs) == 3 |
| if isinstance(values, Number): |
| values = [values for _ in range(len(inputs[0]))] |
| return [self.func(*i, value=values[idx], **kwargs) for idx, i in enumerate(zip(*inputs))] |
| if len(inputs) == 2 and isinstance(inputs[1], Number): |
| # binary op with tensorlist and scalar. |
| inputs[1] = [inputs[1] for _ in range(len(inputs[0]))] |
| return [self.func(*i, **kwargs) for i in zip(*inputs)] |
| |
| |
| class ForeachFuncWrapper: |
| |
| def __init__(self, func, n_expected_cudaLaunchKernels): |
| self.func = func |
| self.n_expected_cudaLaunchKernels = n_expected_cudaLaunchKernels |
| # Some foreach functions don't have in-place implementations. |
| self._is_inplace = False if func is None else func.__name__.endswith('_') |
| |
| def __call__(self, inputs, is_cuda, is_fastpath, **kwargs): |
| actual = None |
| if ( |
| is_cuda and |
| torch.autograd.kineto_available() and |
| torch.profiler.ProfilerActivity.CUDA in torch.profiler.supported_activities() |
| ): |
| with torch.profiler.profile(activities=(torch.profiler.ProfilerActivity.CPU,)) as p: |
| actual = self.func(*inputs, **kwargs) |
| for e in p.key_averages(): |
| if e.key == 'cudaLaunchKernel': |
| if is_fastpath: |
| assert e.count == self.n_expected_cudaLaunchKernels, \ |
| f'{e.count} != {self.n_expected_cudaLaunchKernels}' |
| else: |
| assert e.count > self.n_expected_cudaLaunchKernels, \ |
| f'{e.count} <= {self.n_expected_cudaLaunchKernels}' |
| else: |
| actual = self.func(*inputs, **kwargs) |
| # note(mkozuki): inplace foreach functions are void functions. |
| return inputs[0] if self._is_inplace else actual |
| |
| |
| class TestForeach(TestCase): |
| |
| @property |
| def is_cuda(self): |
| return self.device_type == 'cuda' |
| |
| # note(mkozuki): It might be the case that the expected number of `cudaLaunchKernel`s |
| # is greater than 1 once foreach functions internally separate their input `TensorList`s by |
| # devices & dtypes into vectors of tensors. |
| def _get_funcs(self, op, n_expected_cudaLaunchKernels: int): |
| return ( |
| ForeachFuncWrapper(op.method_variant, n_expected_cudaLaunchKernels), |
| RegularFuncWrapper(op.ref), |
| ForeachFuncWrapper(op.inplace_variant, n_expected_cudaLaunchKernels), |
| RegularFuncWrapper(op.ref_inplace), |
| ) |
| |
| def _binary_test(self, dtype, op, ref, inputs, is_fastpath, is_inplace, *, alpha=None): |
| ref_inputs = [[t.clone().detach() for t in inputs[0]], inputs[1]] if is_inplace else inputs |
| |
| try: |
| actual = op(inputs, self.is_cuda, is_fastpath) |
| except RuntimeError as e: |
| with self.assertRaisesRegex(type(e), re.escape(str(e))): |
| ref(ref_inputs) |
| else: |
| expected = ref(ref_inputs) |
| self.assertEqual(actual, expected) |
| if alpha is not None: |
| kwargs = {'alpha': alpha} |
| ref_inputs = inputs |
| try: |
| actual = op(inputs, self.is_cuda, is_fastpath, **kwargs) |
| except RuntimeError as e: |
| with self.assertRaisesRegex(type(e), re.escape(str(e))): |
| ref(ref_inputs, **kwargs) |
| else: |
| expected = ref(ref_inputs, **kwargs) |
| if dtype in (torch.float16, torch.bfloat16) and TEST_WITH_ROCM: |
| self.assertEqual(expected, actual, atol=1.e-3, rtol=default_tolerances(dtype)[0]) |
| else: |
| self.assertEqual(expected, actual) |
| |
| @skipMeta |
| @ops(foreach_binary_op_db) |
| @parametrize("is_fastpath", (True, False)) |
| def test_binary_op(self, device, dtype, op, is_fastpath): |
| for sample in op.sample_inputs(device, dtype, noncontiguous=not is_fastpath): |
| rhs_arg, = sample.args |
| kwargs = {} or sample.kwargs |
| alpha = kwargs.pop("alpha", None) |
| disable_fastpath = kwargs.pop("disable_fastpath") if is_fastpath else False |
| |
| n_expected_cudaLaunchKernels = len(sample.input) if disable_fastpath else 1 |
| # TODO(crcrpar): Look into why the following is needed |
| if ( |
| is_fastpath and disable_fastpath and dtype == torch.float16 and self.is_cuda and |
| op.name in ("_foreach_mul", "_foreach_div") and |
| (isinstance(rhs_arg, list) and not torch.is_tensor(rhs_arg[0])) |
| ): |
| n_expected_cudaLaunchKernels -= 1 |
| if ( |
| disable_fastpath and op.ref in (torch.clamp_min, torch.clamp_max) and |
| ( |
| torch.result_type(sample.input[0], rhs_arg[0]) != dtype |
| if isinstance(rhs_arg, list) else torch.result_type(sample.input[0], rhs_arg) != dtype |
| ) |
| ): |
| n_expected_cudaLaunchKernels = len(sample.input) * 2 # fallback op launches 2 kernels on these cases |
| wrapped_op, ref, inplace_op, inplace_ref = self._get_funcs(op, n_expected_cudaLaunchKernels) |
| self._binary_test( |
| dtype, wrapped_op, ref, [sample.input, rhs_arg], is_fastpath, False, alpha=alpha) |
| self._binary_test( |
| dtype, inplace_op, inplace_ref, [sample.input, rhs_arg], is_fastpath, True, alpha=alpha) |
| |
| @ops(foreach_pointwise_op_db) |
| @parametrize("is_fastpath", (True, False)) |
| def test_pointwise_op(self, device, dtype, op, is_fastpath): |
| for sample in op.sample_inputs(device, dtype): |
| if not is_fastpath: |
| sample = sample.noncontiguous() |
| assert isinstance(sample.args, tuple) |
| assert len(sample.args) == 2 |
| inputs = [sample.input, *sample.args] |
| kwargs = sample.kwargs |
| disable_fastpath = kwargs.pop("disable_fastpath") if is_fastpath else False |
| n_expected_cudaLaunchKernels = len(sample.input) if disable_fastpath else 1 |
| wrapped_op, ref, inplace_op, inplace_ref = self._get_funcs(op, n_expected_cudaLaunchKernels) |
| values = kwargs.pop("values") |
| self._pointwise_test(wrapped_op, ref, inputs, is_fastpath, False, values=values) |
| self._pointwise_test(inplace_op, inplace_ref, inputs, is_fastpath, True, values=values) |
| |
| if is_fastpath and isinstance(values, list): |
| sample = sample.transform(lambda t: t.clone().detach() if torch.is_tensor(t) else t) |
| inputs = [sample.input, *sample.args] |
| tensor_values = torch.tensor(values) |
| # 1D Tensor of scalars |
| for is_inplace, op_, ref_ in ((False, wrapped_op, ref), (True, inplace_op, inplace_ref)): |
| self._pointwise_test(op_, ref_, inputs, is_fastpath, is_inplace, values=tensor_values) |
| self._pointwise_test( |
| op_, ref_, inputs, is_fastpath, is_inplace, values=tensor_values[0], |
| custom_values_err="Expected packed scalar Tensor to be of dimension 1. Got 0 instead.") |
| if self.is_cuda: |
| self._pointwise_test( |
| op_, ref_, inputs, is_fastpath, is_inplace, values=tensor_values.cuda(), |
| custom_values_err="Expected scalars to be on CPU, got cuda:0 instead.") |
| self._pointwise_test( |
| op_, ref_, inputs, is_fastpath, is_inplace, values=tensor_values[:2], |
| custom_values_err=f"Expected length of scalars to match input of length {len(values)} but got 2 instead.") |
| self._pointwise_test( |
| op_, ref_, inputs, is_fastpath, is_inplace, values=torch.tensor([[0, 1], [2, 3]])[:, 1], |
| custom_values_err="Expected scalars to be contiguous.") |
| |
| # Tests of implicit broadcasting |
| N = len(sample.input) |
| inputs = [ |
| [ |
| make_tensor((N, N), device=device, dtype=dtype, noncontiguous=not is_fastpath) for _ in range(N) |
| ], |
| [ |
| make_tensor((N - i, 1), device=device, dtype=dtype, noncontiguous=not is_fastpath) for i in range(N) |
| ], |
| [ |
| make_tensor((1, N - i), device=device, dtype=dtype, noncontiguous=not is_fastpath) for i in range(N) |
| ], |
| ] |
| self._pointwise_test( |
| wrapped_op, ref, inputs, is_fastpath and disable_fastpath, is_inplace=False, values=values) |
| self._pointwise_test( |
| inplace_op, inplace_ref, inputs, is_fastpath and disable_fastpath, is_inplace=True, values=values) |
| |
| def _pointwise_test(self, op, ref, inputs, is_fastpath, is_inplace, *, values=None, custom_values_err=None): |
| ref_inputs = [[t.clone().detach() for t in inputs[0]], inputs[1], inputs[2]] if is_inplace else inputs |
| try: |
| actual = op(inputs, self.is_cuda, is_fastpath) |
| except RuntimeError as e: |
| with self.assertRaisesRegex(type(e), re.escape(str(e))): |
| ref(ref_inputs) |
| else: |
| expected = ref(ref_inputs) |
| self.assertEqual(expected, actual) |
| if values is not None: |
| try: |
| actual = op(inputs + [values], self.is_cuda, is_fastpath) |
| except RuntimeError as e: |
| # Match with error messages from regular non-foreach reference if no |
| # custom error message was provided. |
| if custom_values_err is None: |
| with self.assertRaisesRegex(type(e), re.escape(str(e))): |
| ref(ref_inputs, values=values) |
| else: |
| self.assertEqual(re.escape(str(e)), re.escape(custom_values_err)) |
| else: |
| expected = ref(ref_inputs, values=values) |
| self.assertEqual(expected, actual) |
| |
| # note(mkozuki): why `try-except` for both fastpath? |
| # - inputs for fastpath can be integer tensors. |
| # - this is because opinfo dtypes are configured for out-place implementation |
| # - for integer inputs, trigonometric functions and exponential function returns float outputs, |
| # which causes "result type Float can't be case to the desired type" error. |
| # Thus, `try-except` is used even if `is_fastpath` is `True`. |
| def _inplace_unary_test(self, inplace, inplace_ref, inputs, is_fastpath): |
| copied_inputs = [[t.clone().detach() for t in tensors] for tensors in inputs] |
| try: |
| inplace(inputs, self.is_cuda, is_fastpath) |
| except RuntimeError as e: |
| with self.assertRaisesRegex(type(e), re.escape(str(e))): |
| inplace_ref(copied_inputs) |
| else: |
| inplace_ref(copied_inputs), |
| self.assertEqual(copied_inputs, inputs) |
| |
| @skipMeta |
| @ops(foreach_unary_op_db) |
| @parametrize("is_fastpath", (True, False)) |
| def test_unary_op(self, device, dtype, op, is_fastpath): |
| wrapped_op, ref, inplace_op, inplace_ref = self._get_funcs(op, 1) |
| samples = op.sample_inputs(device, dtype) |
| disable_fastpath = op.name == "_foreach_abs" and dtype in complex_types() |
| for sample in samples: |
| if not is_fastpath: |
| sample = sample.noncontiguous() |
| inputs = [sample.input] |
| self.assertEqual(ref(inputs), wrapped_op(inputs, self.is_cuda, is_fastpath and not disable_fastpath)) |
| self._inplace_unary_test(inplace_op, inplace_ref, [sample.input], is_fastpath and not disable_fastpath) |
| |
| @ops(foreach_reduce_op_db) |
| @parametrize("is_fastpath", (True, False)) |
| def test_reduce_op(self, device, dtype, op, is_fastpath): |
| for sample in op.sample_inputs(device, dtype): |
| if not is_fastpath: |
| sample = sample.noncontiguous() |
| ord = sample.kwargs.pop("ord") |
| disable_fastpath = sample.kwargs.pop("disable_fastpath", False) |
| if is_fastpath: |
| n_expected_cudaLaunchKernels = len(sample.input) if disable_fastpath else 3 |
| else: |
| n_expected_cudaLaunchKernels = len(sample.input) - 1 |
| |
| inputs = (sample.input,) |
| wrapped_op, ref, _, _ = self._get_funcs(op, n_expected_cudaLaunchKernels) |
| self.assertEqual(ref(inputs, ord=ord), wrapped_op(inputs, self.is_cuda, is_fastpath, ord=ord)) |
| |
| @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=OpDTypes.supported) |
| def test_binary_op_scalar_with_overlapping_tensors(self, device, dtype, op): |
| print(op, device, dtype) |
| 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) |
| |
| @skipIfTorchDynamo("Different error msgs, TODO") |
| @ops(foreach_binary_op_db, dtypes=OpDTypes.supported) |
| 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=OpDTypes.supported) |
| 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) |
| |
| @ops(foreach_binary_op_db, dtypes=floating_types_and(torch.half, torch.bfloat16)) |
| def test_binary_op_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) |
| ], |
| ) |
| op, ref, inplace_op, inplace_ref = self._get_funcs(op, 1) |
| self._binary_test(dtype, op, ref, inputs, True, False) |
| self._binary_test(dtype, inplace_op, inplace_ref, inputs, True, 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 = list(op.sample_inputs(device, dtype, num_input_tensors=[2]))[0].input |
| 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 = list(op.sample_inputs(device, dtype, num_input_tensors=[2], same_size=True))[0].input |
| _cpu_tensors = list(op.sample_inputs("cpu", dtype, num_input_tensors=[2], same_size=True))[0].input |
| 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 = list(op.sample_inputs(device, dtype, num_input_tensors=[3], same_size=True))[0].input |
| _cpu_tensors = list(op.sample_inputs("cpu", dtype, num_input_tensors=[3], same_size=True))[0].input |
| 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 list(op.sample_inputs(device, dtype, [N], low=1))[0].input], |
| # 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) |
| |
| @parametrize("is_fastpath", (True, False)) |
| @ops(foreach_lerp_op_db) |
| def test_lerp(self, device, dtype, op, is_fastpath): |
| for sample in op.sample_inputs(device, dtype, noncontiguous=not is_fastpath): |
| wrapped_op, ref, inplace_op, _ = self._get_funcs(op, 1) |
| |
| args = [*sample.args] |
| inputs = [sample.input, args[0]] |
| |
| kwargs, ref_kwargs = {}, {} |
| if isinstance(args[1], list): |
| inputs.append(args[1]) |
| else: |
| kwargs = ref_kwargs = {"weight": args[1]} |
| |
| if ( |
| dtype in integral_types() or |
| dtype == torch.bool or |
| (not self.is_cuda and dtype == torch.half) |
| ): |
| with self.assertRaises(RuntimeError): |
| wrapped_op(inputs, self.is_cuda, is_fastpath, **kwargs) |
| return |
| actual = wrapped_op(inputs, self.is_cuda, is_fastpath, **kwargs) |
| expected = ref(inputs, **ref_kwargs) |
| self.assertEqual(actual, expected) |
| |
| inplace_inputs = [[t.clone() for t in inputs[0]]] + inputs[1:] |
| inplace_actual = inplace_op(inplace_inputs, self.is_cuda, is_fastpath, **kwargs) |
| self.assertEqual(inplace_actual, expected) |
| |
| @onlyCUDA |
| @ops(foreach_reduce_op_db) |
| def test_foreach_reduce_large_input(self, device, dtype, op): |
| # test inputs larger than kChunkSize = 65536 |
| ord, N = 2, 65536 * 2 |
| n_expected_kernels = 3 if dtype in floating_types_and(torch.half, torch.bfloat16) else 1 |
| inputs = [make_tensor((N,), dtype=dtype, device=device, noncontiguous=False)], |
| wrapped_op, ref, _, _ = self._get_funcs(op, n_expected_kernels) |
| self.assertEqual(ref(inputs, ord=ord), wrapped_op(inputs, self.is_cuda, True, ord=ord)) |
| |
| |
| instantiate_device_type_tests(TestForeach, globals()) |
| |
| if __name__ == '__main__': |
| run_tests() |
