# Owner(s): ["module: mta"] from contextlib import nullcontext from numbers import Number import random import re import torch import unittest import itertools import weakref 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, skipIfTorchDynamo, parametrize, gradcheck from torch.testing._internal.common_device_type import \ (instantiate_device_type_tests, dtypes, onlyCUDA, 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, ) _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, torch.Tensor)): # 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): self.func = func # 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 zero_size = kwargs.pop("zero_size") if ( is_cuda and torch.autograd.kineto_available() and torch.profiler.ProfilerActivity.CUDA in torch.profiler.supported_activities() ): with torch.profiler.profile() as p: actual = self.func(*inputs, **kwargs) keys = tuple([e.key for e in p.key_averages()]) mta_called = any("multi_tensor_apply_kernel" in k for k in keys) assert mta_called == (is_fastpath and (not zero_size)) else: actual = self.func(*inputs, **kwargs) # note(mkozuki): inplace foreach functions are void functions. return inputs[0] if self.is_inplace else actual class InplaceForeachVersionBumpCheck: def __init__(self, testcase: TestCase, tensorlist: "List[torch.Tensor]") -> None: self._testcase = testcase self._tensorlist = tensorlist self._orig_version_counts = [t._version for t in tensorlist] def __enter__(self): pass def __exit__(self, exc_type, exc_value, traceback): # note(crcrpar): some methods e.g. `_binary_test` could call the given inplace function multiple times self._testcase.assertGreaterEqual([t._version for t in self._tensorlist], self._orig_version_counts) def get_transform_func(num_tensors, dtype, device, is_fastpath): def transform(t): if not torch.is_tensor(t): return t if torch.is_tensor(t) and t.ndim == 0: return t return make_tensor( (num_tensors, num_tensors), dtype=dtype, device=device, requires_grad=True, noncontiguous=not is_fastpath, ) return transform def assert_multiple_grad_fns(tensors, test_case): test_case.assertEqual(len({t.grad_fn for t in tensors}), len(tensors), msg=f"{[t.grad_fn for t in tensors]}") def clone(arg): if isinstance(arg, (list, tuple)): return [clone(a) for a in arg] if torch.is_tensor(arg): return arg.clone().detach().requires_grad_() else: return arg # note(crcrpar): `zero_size` is `False` unless (dtype, device) == (torch.float32, "cuda") # as the pair would go through `multi_tensor_apply_kernel` if inputs are not zero size. class TestForeach(TestCase): @property def is_cuda(self): return self.device_type == 'cuda' def _get_funcs(self, op): return ( ForeachFuncWrapper(op.method_variant), RegularFuncWrapper(op.ref), ForeachFuncWrapper(op.inplace_variant), RegularFuncWrapper(op.ref_inplace), ) def _binary_test( self, dtype, op, ref, inputs, is_fastpath, is_inplace, *, alpha, scalar_self_arg: bool, zero_size: bool, ): if zero_size: with InplaceForeachVersionBumpCheck(self, inputs[0]) if op.is_inplace else nullcontext(): op(inputs, self.is_cuda, is_fastpath, zero_size=zero_size) return ref_inputs = [[t.clone().detach() for t in inputs[0]], inputs[1]] if is_inplace else inputs try: with InplaceForeachVersionBumpCheck(self, inputs[0]) if op.is_inplace else nullcontext(): actual = op(inputs, self.is_cuda, is_fastpath, zero_size=zero_size) except RuntimeError as e: with self.assertRaisesRegex(type(e), re.escape(str(e))): if not scalar_self_arg: ref(ref_inputs) else: [ref.func(ref_inputs[0], t) for t in ref_inputs[1]] else: expected = ref(ref_inputs) if not scalar_self_arg else [ref.func(ref_inputs[0], t) for t in ref_inputs[1]] self.assertEqual(actual, expected) if alpha is not None and not scalar_self_arg: kwargs = {'alpha': alpha} ref_inputs = inputs try: op_kwargs = {} op_kwargs.update(kwargs) op_kwargs['zero_size'] = zero_size with InplaceForeachVersionBumpCheck(self, inputs[0]) if op.is_inplace else nullcontext(): actual = op(inputs, self.is_cuda, is_fastpath, **op_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) @ops(foreach_binary_op_db) @parametrize("is_fastpath", (True, False)) def test_binary_op(self, device, dtype, op, is_fastpath): has_out_of_place = op.name not in {"_foreach_copy"} scalar_self_arg_test_complete = False for i, sample in enumerate(op.sample_inputs(device, dtype, noncontiguous=not is_fastpath)): (rhs_arg,) = sample.args zero_size = sample.kwargs.pop("zero_size") kwargs = {} or sample.kwargs alpha = kwargs.pop("alpha", None) disable_fastpath = kwargs.pop("disable_fastpath") if is_fastpath else False wrapped_op, ref, inplace_op, inplace_ref = self._get_funcs(op) if has_out_of_place: self._binary_test( dtype, wrapped_op, ref, [sample.input, rhs_arg], is_fastpath and not disable_fastpath, False, alpha=alpha, zero_size=zero_size, scalar_self_arg=False, ) self._binary_test( dtype, inplace_op, inplace_ref, [sample.input, rhs_arg], is_fastpath and not disable_fastpath, True, alpha=alpha, zero_size=zero_size, scalar_self_arg=False, ) if op.supports_autograd and dtype in floating_types() and not zero_size: transformed_sample = sample.transform(get_transform_func(len(sample.input), dtype, device, is_fastpath)) tensors = transformed_sample.input (rhs_arg,) = transformed_sample.args ref_tensors, ref_rhs_arg = clone(tensors), clone(rhs_arg) try: sum( wrapped_op([tensors, rhs_arg], is_cuda=False, is_fastpath=False, zero_size=zero_size) ).mean().backward() except RuntimeError: with self.assertRaises(RuntimeError): sum(ref([ref_tensors, ref_rhs_arg])).mean().backward() else: sum(ref([ref_tensors, ref_rhs_arg])).mean().backward() self.assertEqual([t.grad for t in tensors], [t.grad for t in ref_tensors]) if isinstance(rhs_arg, list) and isinstance(rhs_arg[0], torch.Tensor): self.assertEqual([t.grad for t in rhs_arg], [t.grad for t in ref_rhs_arg]) tensors = [t.clone().detach().requires_grad_().clone() for t in tensors] ref_tensors = [t.clone().detach().requires_grad_().clone() for t in tensors] inplace_op([tensors, rhs_arg], is_cuda=False, is_fastpath=False, zero_size=zero_size) assert_multiple_grad_fns(tensors, self) # note(crcrpar): the following ops' reference torch functions don't have the overload with Scalar/ScalarList. is_foreach_max_min_imum_with_scalar_or_scalarlist = ( inplace_op.func in (torch._foreach_minimum_, torch._foreach_maximum_) and ( isinstance(rhs_arg, Number) or (isinstance(rhs_arg, list) and isinstance(rhs_arg[0], Number)) ) ) if not is_foreach_max_min_imum_with_scalar_or_scalarlist: inplace_ref([ref_tensors, rhs_arg]) torch.autograd.backward(sum([t.clone() for t in tensors]).sum(), inputs=tensors) torch.autograd.backward(sum([t.clone() for t in ref_tensors]).sum(), inputs=ref_tensors) self.assertEqual([t.grad for t in tensors], [t.grad for t in ref_tensors]) if ( op.supports_scalar_self_arg and isinstance(rhs_arg, Number) and not scalar_self_arg_test_complete and not zero_size ): scalar_self_arg_test_complete = True self._binary_test( dtype, wrapped_op, ref, [rhs_arg, sample.input], is_fastpath, False, alpha=alpha, scalar_self_arg=True, zero_size=False, ) if op.supports_autograd and dtype == torch.float32 and not zero_size: transformed_sample = sample.transform( get_transform_func(len(sample.input), dtype, device, is_fastpath)) tensors = transformed_sample.input (rhs_arg,) = transformed_sample.args ref_tensors, ref_rhs_arg = clone(tensors), clone(rhs_arg) sum(wrapped_op( [rhs_arg, tensors], is_cuda=False, is_fastpath=False, zero_size=False )).mean().backward() sum([ref.func(ref_rhs_arg, t) for t in ref_tensors]).mean().backward() self.assertEqual([t.grad for t in tensors], [t.grad for t in ref_tensors]) @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, noncontiguous=not is_fastpath): assert isinstance(sample.args, tuple) assert len(sample.args) == 2 inputs = [sample.input, *sample.args] zero_size = sample.kwargs.pop("zero_size") kwargs = sample.kwargs disable_fastpath = kwargs.pop("disable_fastpath") if is_fastpath else False wrapped_op, ref, inplace_op, inplace_ref = self._get_funcs(op) values = kwargs.pop("values") self._pointwise_test( wrapped_op, ref, inputs, is_fastpath and not disable_fastpath, False, values=values, zero_size=zero_size ) self._pointwise_test( inplace_op, inplace_ref, inputs, is_fastpath and not disable_fastpath, True, values=values, zero_size=zero_size) if op.supports_autograd and dtype in floating_types() and not zero_size: transformed_sample = sample.transform(get_transform_func(len(sample.input), dtype, device, is_fastpath)) tensors = transformed_sample.input rhs_arg = transformed_sample.args ref_tensors, ref_rhs_arg = clone(tensors), clone(rhs_arg) try: sum( wrapped_op([tensors, *rhs_arg], is_cuda=False, is_fastpath=False, zero_size=zero_size) ).mean().backward() except RuntimeError: with self.assertRaises(RuntimeError): sum(ref([ref_tensors, *ref_rhs_arg])).mean().backward() else: sum(ref([ref_tensors, *ref_rhs_arg])).mean().backward() self.assertEqual([t.grad for t in tensors], [t.grad for t in ref_tensors]) for op_list, ref_list in zip(rhs_arg, ref_rhs_arg): if isinstance(op_list, list) and isinstance(op_list[0], torch.Tensor): self.assertEqual([t.grad for t in op_list], [t.grad for t in ref_list]) tensors = [t.clone().detach().requires_grad_().clone() for t in tensors] ref_tensors = [t.clone().detach().requires_grad_().clone() for t in tensors] inplace_op([tensors, *rhs_arg], is_cuda=False, is_fastpath=False, zero_size=zero_size) assert_multiple_grad_fns(tensors, self) inplace_ref([ref_tensors, *rhs_arg]) torch.autograd.backward(sum([t.clone() for t in tensors]).sum(), inputs=tensors) torch.autograd.backward(sum([t.clone() for t in ref_tensors]).sum(), inputs=ref_tensors) self.assertEqual([t.grad for t in tensors], [t.grad for t in ref_tensors]) if is_fastpath and isinstance(values, list) and not zero_size: 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 and not disable_fastpath, is_inplace, values=tensor_values, zero_size=False) self._pointwise_test( op_, ref_, inputs, is_fastpath and not disable_fastpath, is_inplace, values=tensor_values[0], custom_values_err="Expected packed scalar Tensor to be of dimension 1. Got 0 instead.", zero_size=False, ) if self.is_cuda: self._pointwise_test( op_, ref_, inputs, is_fastpath and not disable_fastpath, is_inplace, values=tensor_values.cuda(), custom_values_err="Expected scalars to be on CPU, got cuda:0 instead.", zero_size=False, ) self._pointwise_test( op_, ref_, inputs, is_fastpath and not disable_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.", zero_size=False, ) self._pointwise_test( op_, ref_, inputs, is_fastpath and not disable_fastpath, is_inplace, values=torch.tensor([[0, 1], [2, 3]])[:, 1], custom_values_err="Expected scalars to be contiguous.", zero_size=False, ) if not zero_size: # 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, zero_size=zero_size) self._pointwise_test( inplace_op, inplace_ref, inputs, is_fastpath and disable_fastpath, is_inplace=True, values=values, zero_size=zero_size) def _pointwise_test( self, op, ref, inputs, is_fastpath, is_inplace, *, values=None, custom_values_err=None, zero_size, ): kwargs = {'zero_size': zero_size} if zero_size: op(inputs, self.is_cuda, is_fastpath, **kwargs) return ref_inputs = [[t.clone().detach() for t in inputs[0]], inputs[1], inputs[2]] if is_inplace else inputs try: with (InplaceForeachVersionBumpCheck(self, inputs[0]) if is_inplace else nullcontext()): 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) else: expected = ref(ref_inputs) self.assertEqual(expected, actual) if values is not None: try: actual = op(inputs + [values], self.is_cuda, is_fastpath, **kwargs) 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, **kwargs): copied_inputs = [[t.clone().detach() for t in tensors] for tensors in inputs] try: with InplaceForeachVersionBumpCheck(self, inputs[0]): inplace(inputs, self.is_cuda, is_fastpath, **kwargs) 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) @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) samples = op.sample_inputs(device, dtype, noncontiguous=not is_fastpath) disable_fastpath = op.name == "_foreach_abs" and dtype in complex_types() for sample in samples: zero_size = sample.kwargs.pop('zero_size') inputs = [sample.input] if zero_size: if not op.has_no_out_of_place: wrapped_op(inputs, self.is_cuda, is_fastpath and not disable_fastpath, zero_size=zero_size) inplace_op(inputs, self.is_cuda, is_fastpath and not disable_fastpath, zero_size=zero_size) continue inputs = [sample.input] disable_fastpath = (op.name == "_foreach_abs" and dtype in complex_types()) or sample.kwargs.pop( "disable_fastpath" ) if not op.has_no_out_of_place: self.assertEqual( ref(inputs), wrapped_op(inputs, self.is_cuda, is_fastpath and not disable_fastpath, zero_size=zero_size), ) self._inplace_unary_test( inplace_op, inplace_ref, [sample.input], is_fastpath and not disable_fastpath, zero_size=zero_size ) if op.supports_autograd and dtype in floating_types() and not zero_size: tensors = [t.clone().detach().requires_grad_() for t in sample.input] ref_tensors = [t.clone().detach().requires_grad_() for t in tensors] if not op.has_no_out_of_place: out = wrapped_op.func(tensors) # tensors have different shapes torch.cat([t.view(-1) for t in out]).mean().backward() torch.cat([ref.func(t).view(-1) for t in ref_tensors]).mean().backward() self.assertEqual([t.grad for t in tensors], [t.grad for t in ref_tensors]) self.assertEqual(len({t.grad_fn for t in out}), 1) inplace_input_tensors = [t.clone().detach().requires_grad_() for t in tensors] inplace_inputs = [t.clone() for t in inplace_input_tensors] # set both to False to skip multi_tensor_apply_kernel check inplace_op([inplace_inputs], False, False, zero_size=zero_size) assert_multiple_grad_fns(inplace_inputs, self) # per-tensor `grad_fn` check. hook_buffer = [] def get_grad_fn_hook(i): def hook(grad_inputs, grad_outputs) -> None: hook_buffer.append(i) return hook for i, t in enumerate(inplace_inputs): t.grad_fn.register_hook(get_grad_fn_hook(i)) _ = torch.autograd.grad( inplace_inputs[0], inputs=(inplace_input_tensors[0],), grad_outputs=(torch.rand_like(inplace_inputs[0]),), retain_graph=True, ) self.assertEqual(hook_buffer, [0]) hook_buffer.clear() # tensors have different shapes. sum_of_cloned_tensors = torch.cat([t.view(-1) for t in inplace_inputs]).sum() grad_output = torch.rand_like(sum_of_cloned_tensors) grad_inputs = torch.autograd.grad( sum_of_cloned_tensors, inputs=tuple(inplace_input_tensors), grad_outputs=(grad_output,), retain_graph=False, ) self.assertEqual(hook_buffer, list(reversed(range(len(inplace_inputs))))) ref_inplace_input_tensors = [t.clone().detach().requires_grad_() for t in inplace_input_tensors] ref_inplace_inputs = [t.clone() for t in ref_inplace_input_tensors] ref_output = inplace_ref([ref_inplace_inputs]) ref_grad_inputs = torch.autograd.grad( torch.cat([t.view(-1) for t in ref_output]).sum(), inputs=tuple(ref_inplace_input_tensors), grad_outputs=(grad_output,), ) self.assertEqual(grad_inputs, ref_grad_inputs) @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, noncontiguous=not is_fastpath): ord = sample.kwargs.pop("ord") zero_size = sample.kwargs.pop("zero_size") disable_fastpath = sample.kwargs.pop("disable_fastpath", False) inputs = (sample.input,) wrapped_op, ref, _, _ = self._get_funcs(op) self.assertEqual( ref(inputs, ord=ord), wrapped_op( inputs, self.is_cuda, is_fastpath and not disable_fastpath, ord=ord, zero_size=zero_size, ), ) if op.supports_autograd and dtype in floating_types() and not zero_size: transformed_sample = sample.transform(get_transform_func(len(sample.input), dtype, device, is_fastpath)) tensors = transformed_sample.input ref_tensors = clone(tensors) sum(wrapped_op((tensors,), False, False, ord=ord, zero_size=zero_size)).backward() sum(ref((ref_tensors,), ord=ord)).backward() self.assertEqual([t.grad for t in tensors], [t.grad for t in ref_tensors]) @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( filter(lambda op: not op.has_no_out_of_place, foreach_binary_op_db), dtypes=OpDTypes.supported, ) 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) @ops( filter(lambda op: not op.has_no_out_of_place, 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( filter(lambda op: not op.has_no_out_of_place, 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]) @unittest.skipIf(not torch.cuda.is_available(), "CUDA not found") @ops( filter(lambda op: not op.has_no_out_of_place, foreach_binary_op_db), dtypes=OpDTypes.supported, ) def test_binary_op_list_slow_path(self, device, dtype, op): foreach_op, native_op, foreach_op_, native_op_ = self._get_funcs(op) # 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, zero_size=False, alpha=None, scalar_self_arg=False) self._binary_test( dtype, foreach_op_, native_op_, inputs, is_fastpath=False, is_inplace=True, zero_size=False, alpha=None, scalar_self_arg=False) # 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, zero_size=False, alpha=None, scalar_self_arg=False) self._binary_test( dtype, foreach_op_, native_op_, inputs, is_fastpath=False, is_inplace=True, zero_size=False, alpha=None, scalar_self_arg=False) # 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, zero_size=False, alpha=None, scalar_self_arg=False) self._binary_test( dtype, foreach_op_, native_op_, inputs, is_fastpath=False, is_inplace=True, zero_size=False, alpha=None, scalar_self_arg=False) # 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, zero_size=False, alpha=None, scalar_self_arg=False) self._binary_test( dtype, foreach_op_, native_op_, inputs, is_fastpath=False, is_inplace=True, zero_size=False, alpha=None, scalar_self_arg=False) @ops( filter(lambda op: not op.has_no_out_of_place, 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) self._binary_test(dtype, op, ref, inputs, True, False, zero_size=False, alpha=None, scalar_self_arg=False) self._binary_test( dtype, inplace_op, inplace_ref, inputs, True, True, zero_size=False, alpha=None, scalar_self_arg=False ) # 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): op.has_no_out_of_place = op.name != "_foreach_zero" method, ref, inplace_method, ref_inplace = self._get_funcs(op) # tensors: ['cuda', 'cpu] tensors = list(op.sample_inputs(device, dtype, num_input_tensors=[2]))[0].input tensors[1] = tensors[1].to("cpu") if op.has_no_out_of_place: try: actual = method((tensors,), False, False, zero_size=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, zero_size=False) except RuntimeError as e: with self.assertRaisesRegex(type(e), str(e)): ref_inplace((tensors,)) else: if op.has_no_out_of_place: self.assertEqual(expected, tensors) else: self.assertEqual([torch.zeros_like(t) for t in tensors], tensors) @onlyCUDA @ops(filter(lambda op: not op.has_no_out_of_place, 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(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] # first tensorlist is zero-size when float32 _cuda_tensors = list( op.sample_inputs(device, dtype, num_input_tensors=[3], same_size=True) )[int(dtype == torch.float32)].input _cpu_tensors = list(op.sample_inputs("cpu", dtype, num_input_tensors=[3], same_size=True))[0].input tensors1, tensors2, tensors3 = list(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, requries_grad=True, num_input_tensors=[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) actual = fn(inputs, is_cuda=True, is_fastpath=True, ord=ord, zero_size=False) 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, inplace_ref = self._get_funcs(op) args = [*sample.args] inputs = [sample.input, args[0]] zero_size = sample.kwargs.pop("zero_size") kwargs, ref_kwargs = {"zero_size": zero_size}, {} if isinstance(args[1], list): inputs.append(args[1]) else: kwargs["weight"] = args[1] ref_kwargs["weight"] = args[1] if dtype in integral_types() or dtype == torch.bool: 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:] with InplaceForeachVersionBumpCheck(self, inplace_inputs[0]): inplace_actual = inplace_op(inplace_inputs, self.is_cuda, is_fastpath, **kwargs) self.assertEqual(inplace_actual, expected) if op.supports_autograd and dtype in floating_types() and not zero_size: transformed_sample = sample.transform(get_transform_func(len(sample.input), dtype, device, is_fastpath)) args = [*transformed_sample.args] inputs = [transformed_sample.input, args[0]] kwargs, ref_kwargs = {}, {} if isinstance(args[1], list): inputs.append(args[1]) else: kwargs = ref_kwargs = {"weight": args[1]} ref_tensors = clone(transformed_sample.input) sum( wrapped_op((transformed_sample.input, *inputs[1:]), False, False, **kwargs, zero_size=zero_size) ).mean().backward() sum(ref((ref_tensors, *inputs[1:]), **ref_kwargs)).mean().backward() self.assertEqual( [t.grad for t in transformed_sample.input], [t.grad for t in ref_tensors], ) _tensors = [t.clone().detach().requires_grad_() for t in transformed_sample.input] _ref_tensors = [t.clone().detach().requires_grad_() for t in _tensors] tensors = [t.clone() for t in _tensors] inplace_op((tensors, *inputs[1:]), False, False, **kwargs, zero_size=False) ref_tensors = [t.clone() for t in _ref_tensors] inplace_ref((ref_tensors, *inputs[1:]), **ref_kwargs) assert_multiple_grad_fns(tensors, self) # tensors have different shapes. torch.autograd.backward(torch.cat([t.clone().view(-1) for t in tensors]).sum(), inputs=tensors) torch.autograd.backward(torch.cat([t.clone().view(-1) for t in ref_tensors]).sum(), inputs=ref_tensors) self.assertEqual([t.grad for t in tensors], [t.grad for t in ref_tensors]) @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 disable_fastpath = True if ord in (1, 2) and dtype in floating_types_and(torch.half, torch.bfloat16): disable_fastpath = False inputs = ([make_tensor((N,), dtype=dtype, device=device, noncontiguous=False)],) wrapped_op, ref, _, _ = self._get_funcs(op) self.assertEqual( ref(inputs, ord=ord), wrapped_op(inputs, self.is_cuda, not disable_fastpath, ord=ord, zero_size=False), ) @onlyCUDA @ops( foreach_unary_op_db + foreach_binary_op_db + foreach_pointwise_op_db + foreach_lerp_op_db, dtypes=(torch.float,), ) def test_inplace_foreach_leaf_check_and_grad_fn(self, device, dtype, op): inplace_op = op.inplace_variant if inplace_op is None: self.skipTest("no in-place op available") sample = list(op.sample_inputs(dtype=dtype, device=device, num_input_tensors=[2], same_size=True))[0] sample.input[0].requires_grad_(True) with self.assertRaisesRegex(RuntimeError, "a leaf Variable that requires grad"): inplace_op(sample.input, *sample.args) sample.input[1].requires_grad_(True) with self.assertRaisesRegex(RuntimeError, "a leaf Variable that requires grad"): inplace_op(sample.input, *sample.args) _tensors = [t.clone().detach().requires_grad_(i == 0) for i, t in enumerate(sample.input)] tensors = [t.clone() for t in _tensors] inplace_op(tensors, *sample.args) self.assertIsNotNone(tensors[0].grad_fn) self.assertIsNone(tensors[1].grad_fn) @onlyCUDA @ops( foreach_unary_op_db + foreach_binary_op_db + foreach_pointwise_op_db + foreach_lerp_op_db, dtypes=(torch.float,), ) def test_outplace_with_invalid_grads(self, device, dtype, op): if op.has_no_out_of_place: self.skipTest(f"{op.name} does not have out-of-place implementation") func, *_ = self._get_funcs(op) sample = list(op.sample_inputs(dtype=dtype, device=device, requires_grad=True, num_input_tensors=[2], same_size=True))[0] self.assertTrue(all(t.requires_grad for t in sample.input)) sample.kwargs.pop("disable_fastpath") if func.func in (torch._foreach_addcmul, torch._foreach_addcdiv): if sample.kwargs.get("values") is None: sample.kwargs.pop("values") (out1, out2) = func([sample.input, *sample.args], is_cuda=False, is_fastpath=False, **sample.kwargs) out1.backward(torch.ones_like(out1)) self.assertIsNotNone(sample.input[0].grad) self.assertIsNone(sample.input[1].grad) @ops( filter( lambda op: op.backward_requires_result, foreach_unary_op_db + foreach_binary_op_db + foreach_pointwise_op_db + foreach_lerp_op_db, ), dtypes=(torch.float32,), ) def test_lifetime_of_grad_fn_when_result_is_saved(self, device, dtype, op): def get_ref(func, sample): class Foo: pass out = func((sample.input, *sample.args), is_cuda=False, is_fastpath=False, **sample.kwargs) foo = Foo() meta_dict = out[0].grad_fn.metadata meta_dict[0] = foo ref = weakref.ref(foo) return out, ref def _test(func, sample): out, ref = get_ref(func, sample) self.assertIsNotNone(ref()) del out self.assertIsNone(ref()) func = self._get_funcs(op)[0] for sample in op.sample_inputs(device, dtype, requires_grad=True, num_input_tensors=[1]): for key in ("is_fastpath", "disable_fastpath"): if key in sample.kwargs: del sample.kwargs[key] # note: `_foreach_pow.Scalar` and `_foreach_pow.ScalarList` don't depend on `result` # see: https://github.com/pytorch/pytorch/blob/5403c7770cd9cdc05f6c216d593ea8e8ae328ff3/tools/autograd/derivatives.yaml#L3048-L3049 # noqa: B950 if op.name == "_foreach_pow": if ( (isinstance(sample.args[0], list) and isinstance(sample.args[0][0], Number)) or (isinstance(sample.args[0], Number) and not isinstance(sample.args[0], float)) ): continue if isinstance(sample.args[0], float): new_args = (sample.input,) sample.input = sample.args[0] sample.args = new_args _test(func, sample) @ops( foreach_unary_op_db + foreach_binary_op_db + foreach_pointwise_op_db + foreach_lerp_op_db, dtypes=OpDTypes.supported, allowed_dtypes=(torch.float64, torch.complex128), ) def test_outplace_forward_mode_AD(self, device, dtype, op): if not op.supports_forward_ad: self.skipTest("forward AD not supported") # note(crcrpar): without this, some unary functions fail, unlike inplace and/or complex. if dtype == torch.float64 and op.name in ( "_foreach_acos", "_foreach_asin", "_foreach_log10", "_foreach_log1p", "_foreach_log2", "_foreach_log", "_foreach_pow", "_foreach_sqrt", ): value_range = {"low": 0.5, "high": 1.0} else: value_range = {} for sample in op.sample_inputs( device, dtype, requires_grad=True, num_input_tensors=[5], **value_range, ): # Skip `_foreach_pow.ScalarAndTensor(Scalar, Tensor[])` if op.name == "_foreach_pow" and isinstance(sample.input, Number): continue def func(*tensorlist): kwargs = {"alpha": sample.kwargs["alpha"]} if "alpha" in sample.kwargs else {} return op.method_variant(tensorlist, *sample.args, **kwargs) working_sample, err_msg_pattern = check_forward_mode_AD_sample(op, sample, dtype, False) if not working_sample: if not err_msg_pattern: # lhs of float64 and rhs of complex. continue with self.assertRaisesRegex(RuntimeError, re.escape(err_msg_pattern)): gradcheck( func, sample.input, raise_exception=True, check_forward_ad=True, check_batched_forward_grad=False, check_backward_ad=False, check_batched_grad=False, ) else: gradcheck( func, sample.input, raise_exception=True, check_forward_ad=True, check_backward_ad=False, check_batched_grad=False, ) @ops( foreach_unary_op_db + foreach_binary_op_db + foreach_pointwise_op_db + foreach_lerp_op_db, dtypes=OpDTypes.supported, allowed_dtypes=(torch.float64, torch.complex128), ) def test_inplace_forward_mode_AD(self, device, dtype, op): if not op.supports_forward_ad: self.skipTest("forward AD not supported") for sample in op.sample_inputs( device, dtype, requires_grad=True, num_input_tensors=[5], same_size=True, ): # Call `clone` to avoid inplace modifications likewise # `torch.testing._internal.common_utils.TestGradients._get_safe_inplace` def inplace_func(*tensorlist): kwargs = {"alpha": sample.kwargs["alpha"]} if "alpha" in sample.kwargs else {} op.inplace_variant(tuple(t.clone() for t in tensorlist), *sample.args, **kwargs) return tensorlist working_sample, err_msg_pattern = check_forward_mode_AD_sample(op, sample, dtype, True) if not working_sample: with self.assertRaisesRegex(RuntimeError, re.escape(err_msg_pattern)): gradcheck( inplace_func, sample.input, raise_exception=True, check_forward_ad=True, check_backward_ad=False, check_batched_grad=False, ) else: gradcheck( inplace_func, sample.input, raise_exception=True, check_forward_ad=True, check_backward_ad=False, check_batched_grad=False, ) @unittest.skipIf(not (torch.cuda.is_available() and torch.cuda.device_count() > 1), "requires multiple GPUs") def test_tensors_grouping(self): num_tensors_per_list = 10 num_devices = torch.cuda.device_count() dtypes = (torch.float16, torch.float32, torch.float64) list1 = [ torch.tensor( i, device=torch.device("cuda", random.randint(0, num_devices - 1)), dtype=dtypes[random.randint(0, 2)], ) for i in range(num_tensors_per_list) ] list2 = [None for _ in list1] list3 = [torch.rand_like(t) for t in list1] nested_tensorlists = [list1, list2, list3] grouped_tensors = torch.utils._foreach_utils._group_tensors_by_device_and_dtype(nested_tensorlists, with_indices=True) num_tensors_seen = 0 for (device, dtype), ([l1, l2, l3], indices) in grouped_tensors.items(): for t in itertools.chain(l1, l3): self.assertEqual(t.device, device) self.assertEqual(t.dtype, dtype) num_tensors_seen += 1 self.assertEqual(len(l1), len(l2)) self.assertTrue(all(p is None for p in l2)) for i, index in enumerate(indices): self.assertEqual(l1[i], list1[index]) self.assertEqual(l2[i], list2[index]) self.assertEqual(l3[i], list3[index]) self.assertEqual(num_tensors_seen, 2 * num_tensors_per_list) @onlyCUDA def test_0dim_tensor_overload_exception(self): # check exceptions of fast path tensors = [make_tensor((2, 2), dtype=torch.float, device="cuda") for _ in range(2)] with self.assertRaisesRegex(RuntimeError, "scalar tensor expected to be 0 dim but"): torch._foreach_mul(tensors, torch.tensor([1.0, 1.0], device="cuda")) with self.assertRaisesRegex(RuntimeError, "scalar tensor expected to be on"): torch._foreach_mul(tensors, torch.tensor(1.0, device="cpu")) tensors = [make_tensor((2, 2), dtype=torch.float, device=d) for d in ("cpu", "cuda")] with self.assertRaisesRegex(RuntimeError, "scalar tensor expected to be 0 dim but"): torch._foreach_mul(tensors, torch.tensor([1.0, 1.0], device="cuda")) @onlyCUDA @ops(filter(lambda op: op.name == "_foreach_copy", foreach_binary_op_db)) def test_foreach_copy_with_multi_device_inputs(self, device, dtype, op): foreach_copy_ = op.inplace_variant copy_ = op.ref_inplace for non_blocking in (False, True): for sample in op.sample_inputs(device, dtype, noncontiguous=False): with torch.no_grad(): ref_input = [t.clone().detach() for t in sample.input] foreach_copy_(sample.input, sample.args[0], non_blocking) for t, s in zip(ref_input, sample.args[0]): copy_(t, s, non_blocking) self.assertEqual(sample.input, ref_input) if torch.cuda.device_count() > 1: device = torch.device("cuda", 1) rhs_tensors = [t.to(device) for t in sample.args[0]] foreach_copy_(sample.input, rhs_tensors, non_blocking) for t, s in zip(ref_input, rhs_tensors): copy_(t, s, non_blocking) self.assertEqual(ref_input, sample.input) # TODO(crcrpar): Hide this inside torch/testing/_internal. # would end up adding another layer to `foreach_inputs_sample_func.__call__` # so that we can use this function as something like the first argument of `filter` function. # Even after moving this function to testing, I personally think it'd be better to check the error message. def check_forward_mode_AD_sample(op, sample, dtype, is_inplace): if ( op.name == "_foreach_sub" and ( (isinstance(sample.args[0], list) and any(isinstance(a, bool) for a in sample.args[0])) or isinstance(sample.args[0], bool) ) ): return False, _BOOL_SUB_ERR_MSG rhs_arg_has_complex_number = sample.args and (( isinstance(sample.args[0], list) and any(isinstance(a, complex) for a in sample.args[0]) ) or ( isinstance(sample.args[0], complex) )) if rhs_arg_has_complex_number and dtype == torch.float64: if op.name in ("_foreach_clamp_max", "_foreach_clamp_min"): return False, "clamp is not supported for complex types" if not is_inplace: return False, "" else: if op.name == "_foreach_pow": return False, "Found dtype Double but expected ComplexDouble" if op.name in ("_foreach_add", "_foreach_sub", "_foreach_mul", "_foreach_div"): return False, "result type ComplexDouble can't be cast to the desired output type Double" return True, "" instantiate_device_type_tests(TestForeach, globals()) if __name__ == "__main__": run_tests()