fix(nn): fix test for nn api: torch.nn.ParameterDict, torch.nn.ParameterList, torch.nn.Sequential-pytorch-AtomGit

已合并

fix(nn): fix test for nn api: torch.nn.ParameterDict, torch.nn.ParameterList, torch.nn.Sequential #32056

dinglaiping创建于 3月19日从已删除 :fixtest-nn-api-2.7.1 合入到 Ascend/pytorch： v2.7.1

fix(nn): fix test for nn api: torch.nn.ParameterDict, torch.nn.ParameterList, torch.nn.Sequential #32056

已合并

dinglaiping创建于 3月19日

从已删除 :fixtest-nn-api-2.7.1合入到Ascend/pytorch v2.7.1

共 2 个文件变更+17-9

Mtest/test_nn.py+12-8

Mtest/unsupported_test_cases/.pytorch-disabled-tests.json+5-1

	@@ -31650,5 +31650,9 @@
31650	"test_fake_autocast_fft_fftshift_npu_float32 (__main__.TestFakeTensorPRIVATEUSE1)": ["", [""]],	31650	"test_fake_autocast_fft_fftshift_npu_float32 (__main__.TestFakeTensorPRIVATEUSE1)": ["", [""]],
31651	"test_fake_autocast_fft_ifftshift_npu_float32 (__main__.TestFakeTensorPRIVATEUSE1)": ["", [""]],	31651	"test_fake_autocast_fft_ifftshift_npu_float32 (__main__.TestFakeTensorPRIVATEUSE1)": ["", [""]],
31652	"test_index_put_accumulate_large_tensor_npu (__main__.TestIndexingPRIVATEUSE1)": ["", ["910A"]],	31652	"test_index_put_accumulate_large_tensor_npu (__main__.TestIndexingPRIVATEUSE1)": ["", ["910A"]],
31653	- "test_serialization_array_with_empty (__main__.TestNpu)": ["", [""]]	31653	+ "test_serialization_array_with_empty (__main__.TestNpu)": ["", [""]],
		31654	+ "test_module_backcompat (__main__.TestNN)": ["", [""]],
		31655	+ "test_register_state_dict_pre_hook_backward_compat (__main__.TestNN)": ["", [""]],
		31656	+ "test_CTCLoss_empty_target_npu (__main__.TestNNDeviceTypePRIVATEUSE1)": ["", [""]],
		31657	+ "test_to_complex_npu (__main__.TestNNDeviceTypePRIVATEUSE1)": ["", [""]]
31654	}	31658	}

	@@ -32,17 +32,18 @@ from torch.types import _TensorOrTensors
32	from url import get_url	32	from url import get_url
33	import torch_npu	33	import torch_npu
34	import torch_npu.testing	34	import torch_npu.testing
		35	+from torch_npu.testing.common_utils import SupportedDevices
35	from torch.testing._internal.common_dtype import integral_types, get_all_math_dtypes, floating_types	36	from torch.testing._internal.common_dtype import integral_types, get_all_math_dtypes, floating_types
36	from torch.testing._internal.common_utils import freeze_rng_state, run_tests, TestCase, skipIfNoLapack, skipIfRocm, \	37	from torch.testing._internal.common_utils import freeze_rng_state, run_tests, TestCase, skipIfNoLapack, skipIfRocm, \
37	TEST_NUMPY, TEST_SCIPY, TEST_WITH_CROSSREF, TEST_WITH_ROCM, \	38	TEST_NUMPY, TEST_SCIPY, TEST_WITH_CROSSREF, TEST_WITH_ROCM, \
38	- download_file, get_function_arglist, load_tests, skipIfMps, \	39	+ download_file, get_function_arglist, load_tests, skipIfMPS, \
39	IS_PPC, TEST_PRIVATEUSE1, custom_device_mod, \	40	IS_PPC, TEST_PRIVATEUSE1, custom_device_mod, \
40	parametrize as parametrize_test, subtest, instantiate_parametrized_tests, \	41	parametrize as parametrize_test, subtest, instantiate_parametrized_tests, \
41	skipIfTorchDynamo, IS_WINDOWS, gcIfJetson, set_default_dtype	42	skipIfTorchDynamo, IS_WINDOWS, gcIfJetson, set_default_dtype
42	from torch.testing._internal.common_cuda import TEST_CUDNN, TEST_CUDNN_VERSION, PLATFORM_SUPPORTS_FLASH_ATTENTION	43	from torch.testing._internal.common_cuda import TEST_CUDNN, TEST_CUDNN_VERSION, PLATFORM_SUPPORTS_FLASH_ATTENTION
43	from torch.testing._internal.common_nn import NNTestCase, NewModuleTest, CriterionTest, \	44	from torch.testing._internal.common_nn import NNTestCase, NewModuleTest, CriterionTest, \
44	module_tests, criterion_tests, loss_reference_fns, _create_basic_net, \	45	module_tests, criterion_tests, loss_reference_fns, _create_basic_net, \
45	- ctcloss_reference, new_module_tests, single_batch_reference_fn, _test_bfloat16_ops, _test_module_empty_input	46	+ ctcloss_reference, get_new_module_tests, single_batch_reference_fn, _test_bfloat16_ops, _test_module_empty_input
46	from torch.testing._internal.common_device_type import instantiate_device_type_tests, dtypes, \	47	from torch.testing._internal.common_device_type import instantiate_device_type_tests, dtypes, \
47	precisionOverride, skipCUDAIfCudnnVersionLessThan, onlyCPU, \	48	precisionOverride, skipCUDAIfCudnnVersionLessThan, onlyCPU, \
48	skipCUDAIfRocm, skipCUDAIf, skipCUDAIfNotRocm, \	49	skipCUDAIfRocm, skipCUDAIf, skipCUDAIfNotRocm, \
	@@ -54,10 +55,10 @@ import torch.testing._internal.hypothesis_utils as hu
54	from torch.testing._internal.common_utils import _assertGradAndGradgradChecks, gradcheck, gradgradcheck, \	55	from torch.testing._internal.common_utils import _assertGradAndGradgradChecks, gradcheck, gradgradcheck, \
55	GRADCHECK_NONDET_TOL	56	GRADCHECK_NONDET_TOL
56	from torch.testing._internal.common_utils import dtype2prec_DONTUSE	57	from torch.testing._internal.common_utils import dtype2prec_DONTUSE
57	-from torch.testing._internal.common_cuda import tf32_on_and_off, tf32_is_not_fp32, tf32_off, tf32_on	58	+from torch.testing._internal.common_cuda import tf32_on_and_off, tf32_off, tf32_on
58		59
59		60
60	-AMPERE_OR_ROCM = TEST_WITH_ROCM or tf32_is_not_fp32()	61	+AMPERE_OR_ROCM = TEST_WITH_ROCM or torch.cuda.is_tf32_supported()
61		62
62	# load_tests from common_utils is used to automatically filter tests for	63	# load_tests from common_utils is used to automatically filter tests for
63	# sharding on sandcastle. This line silences flake warnings	64	# sharding on sandcastle. This line silences flake warnings
	@@ -7439,7 +7440,7 @@ def add_test(test_tmp, decorator=None):
7439	kwargs['extra_args'] = test_tmp.extra_args	7440	kwargs['extra_args'] = test_tmp.extra_args
7440		7441
7441	if 'dtype' in get_function_arglist(test_tmp.test_cuda):	7442	if 'dtype' in get_function_arglist(test_tmp.test_cuda):
7442	- if tf32_is_not_fp32() and test_tmp.with_tf32:	7443	+ if torch.cuda.is_tf32_supported() and test_tmp.with_tf32:
7443		7444
7444	def with_tf32_off(self, test_tmp=test_tmp, kwargs=kwargs):	7445	def with_tf32_off(self, test_tmp=test_tmp, kwargs=kwargs):
7445	with tf32_off():	7446	with tf32_off():
	@@ -7482,7 +7483,7 @@ def add_test(test_tmp, decorator=None):
7482	with tf32_off():	7483	with tf32_off():
7483	test_tmp.test_cuda(self, **kwargs)	7484	test_tmp.test_cuda(self, **kwargs)
7484		7485
7485	- if tf32_is_not_fp32() and test_tmp.with_tf32:	7486	+ if torch.cuda.is_tf32_supported() and test_tmp.with_tf32:
7486	add(cuda_test_name + '_fp32', with_tf32_off)	7487	add(cuda_test_name + '_fp32', with_tf32_off)
7487		7488
7488	def with_tf32_on(self, test_tmp=test_tmp, kwargs=kwargs):	7489	def with_tf32_on(self, test_tmp=test_tmp, kwargs=kwargs):
	@@ -7494,7 +7495,7 @@ def add_test(test_tmp, decorator=None):
7494	add(cuda_test_name, with_tf32_off)	7495	add(cuda_test_name, with_tf32_off)
7495		7496
7496		7497
7497	-for test_params in module_tests + new_module_tests:	7498	+for test_params in module_tests + get_new_module_tests():
7498	if 'constructor' not in test_params:	7499	if 'constructor' not in test_params:
7499	name = test_params.pop('module_name')	7500	name = test_params.pop('module_name')
7500	test_params['constructor'] = getattr(nn, name)	7501	test_params['constructor'] = getattr(nn, name)
	@@ -9598,6 +9599,7 @@ class TestNNDeviceType(NNTestCase):
9598		9599
9599	@parametrize_test("memory_format", [torch.contiguous_format, torch.channels_last])	9600	@parametrize_test("memory_format", [torch.contiguous_format, torch.channels_last])
9600	@parametrize_test("isize, osize", [(20, 11), (10, 15)])	9601	@parametrize_test("isize, osize", [(20, 11), (10, 15)])
		9602	+ @SupportedDevices(['Ascend910B', 'Ascend910_93'])
9601	def test_upsamplingNearestExact2d_correctness(self, device, memory_format, isize, osize):	9603	def test_upsamplingNearestExact2d_correctness(self, device, memory_format, isize, osize):
9602	in_t = torch.arange(isize * isize, dtype=torch.float, device=device).reshape(1, 1, isize, isize)	9604	in_t = torch.arange(isize * isize, dtype=torch.float, device=device).reshape(1, 1, isize, isize)
9603	in_t = in_t.contiguous(memory_format=memory_format)	9605	in_t = in_t.contiguous(memory_format=memory_format)
	@@ -9691,6 +9693,7 @@ class TestNNDeviceType(NNTestCase):
9691		9693
9692	@parametrize_test("memory_format", [torch.contiguous_format, torch.channels_last_3d])	9694	@parametrize_test("memory_format", [torch.contiguous_format, torch.channels_last_3d])
9693	@parametrize_test("isize, osize", [(20, 11), (10, 15)])	9695	@parametrize_test("isize, osize", [(20, 11), (10, 15)])
		9696	+ @SupportedDevices(['Ascend910B', 'Ascend910_93'])
9694	def test_upsamplingNearestExact3d_correctness(self, device, memory_format, isize, osize):	9697	def test_upsamplingNearestExact3d_correctness(self, device, memory_format, isize, osize):
9695	in_t = torch.arange(isize * isize * isize, dtype=torch.float, device=device)	9698	in_t = torch.arange(isize * isize * isize, dtype=torch.float, device=device)
9696	in_t = in_t.reshape(1, 1, isize, isize, isize)	9699	in_t = in_t.reshape(1, 1, isize, isize, isize)
	@@ -9788,6 +9791,7 @@ class TestNNDeviceType(NNTestCase):
9788	@parametrize_test("mode", ["nearest", "nearest-exact", "bilinear", "bicubic"])	9791	@parametrize_test("mode", ["nearest", "nearest-exact", "bilinear", "bicubic"])
9789	@parametrize_test("dtype", integral_types() + floating_types())	9792	@parametrize_test("dtype", integral_types() + floating_types())
9790	@onlyNativeDeviceTypes	9793	@onlyNativeDeviceTypes
		9794	+ @SupportedDevices(['Ascend910B', 'Ascend910_93'])
9791	def test_upsamplingBiMode2d_nonsupported_dtypes(self, device, antialias, num_channels, mode, dtype):	9795	def test_upsamplingBiMode2d_nonsupported_dtypes(self, device, antialias, num_channels, mode, dtype):
9792	x = torch.ones(1, num_channels, 32, 32, dtype=dtype, device=device)	9796	x = torch.ones(1, num_channels, 32, 32, dtype=dtype, device=device)
9793		9797
	@@ -10606,7 +10610,7 @@ class TestNNDeviceType(NNTestCase):
10606	self.assertEqual(logits_soft.grad, logits_hard.grad, atol=tol, rtol=0)	10610	self.assertEqual(logits_soft.grad, logits_hard.grad, atol=tol, rtol=0)
10607		10611
10608	@dtypesIfPRIVATEUSE1(torch.half, torch.float, torch.double)	10612	@dtypesIfPRIVATEUSE1(torch.half, torch.float, torch.double)
10609	- @skipIfMps	10613	+ @skipIfMPS
10610	@dtypes(torch.float, torch.double)	10614	@dtypes(torch.float, torch.double)
10611	def test_gumbel_softmax(self, device, dtype):	10615	def test_gumbel_softmax(self, device, dtype):
10612	self._test_gumbel_softmax_st_shapes(device, dtype, shape=[5], dim=0, count_expected=1)	10616	self._test_gumbel_softmax_st_shapes(device, dtype, shape=[5], dim=0, count_expected=1)