mirror of
https://github.com/zebrajr/pytorch.git
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c19cda5782
Summary: Action following https://github.com/pytorch/pytorch/issues/66232 This change does require some context: there were several suggestions regarding what to do about this group of tests: tests that are core and crucial to all of PyTorch and are too broad to be owned by one team. 1. Let's add a "module: core" and put people behind it! This idea sounds appealing unless you are one of the people backing the label. From talking to albanD among others, this idea of putting all these core tests on the shoulder of a few people or one team isn't super fair and I have not yet found anyone willing to take on this job. 2. Taking advantage of the fact that we already have a triaging oncall that takes turns triaging issues, we can leave these tests essentially unlabeled and allow the oncall to triage these tests. Since these tests are crucial to PyTorch, we'll add the "high priority" label to mark them different from other unowned tests (see https://github.com/pytorch/pytorch/issues/67552). 3. I _could_ still create an unbacked label "module: core" and attribute these tests there, but I don't like the idea of creating a facade that the tests are "triaged" to a label when no one is actually taking a look. Now we could potentially break these tests down into smaller files so that each piece _could_ be owned by a team, but 1. I don't know if this is currently feasible and 2. This approach does not prevent that from happening in the future. Pull Request resolved: https://github.com/pytorch/pytorch/pull/67553 Reviewed By: albanD Differential Revision: D32025004 Pulled By: janeyx99 fbshipit-source-id: 1fb1aa4c27e305695ab6e80ae3d02f90519939c0
445 lines
20 KiB
Python
445 lines
20 KiB
Python
# Owner(s): ["module: nn"]
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import inspect
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import torch
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from unittest import mock
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from unittest.mock import MagicMock, patch
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from torch.testing import floating_types
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from torch.testing._internal.common_device_type import instantiate_device_type_tests, dtypes
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from torch.testing._internal.common_quantization import skipIfNoFBGEMM
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from torch.testing._internal.common_utils import TestCase, run_tests
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# Returns a database of args & kwargs that can be used to construct each module.
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# Each entry is in class -> (args, kwargs) format.
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# Example: torch.nn.Linear -> ([10, 5], {})
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# TODO: Merge this in with the initial ModuleInfo implementation.
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def build_constructor_arg_db():
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return {
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torch.nn.AdaptiveAvgPool1d: ((5,), {}),
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torch.nn.AdaptiveAvgPool2d: ((5,), {}),
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torch.nn.AdaptiveAvgPool3d: ((5,), {}),
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torch.nn.AdaptiveLogSoftmaxWithLoss: ((100, 20, [5, 10, 15]), {}),
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torch.nn.AdaptiveMaxPool1d: ((5,), {}),
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torch.nn.AdaptiveMaxPool2d: ((5,), {}),
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torch.nn.AdaptiveMaxPool3d: ((5,), {}),
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torch.nn.AlphaDropout: ((), {}),
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torch.nn.AvgPool1d: ((3,), {}),
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torch.nn.AvgPool2d: ((3,), {}),
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torch.nn.AvgPool3d: ((3,), {}),
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torch.nn.BCELoss: ((), {}),
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torch.nn.BCEWithLogitsLoss: ((), {}),
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torch.nn.BatchNorm1d: ((5,), {}),
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torch.nn.BatchNorm2d: ((5,), {}),
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torch.nn.BatchNorm3d: ((5,), {}),
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torch.nn.Bilinear: ((2, 3, 4), {}),
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torch.nn.CELU: ((), {}),
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torch.nn.CTCLoss: ((), {}),
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torch.nn.ChannelShuffle: ((4,), {}),
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torch.nn.ConstantPad1d: ((2, 3.5), {}),
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torch.nn.ConstantPad2d: ((2, 3.5), {}),
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torch.nn.ConstantPad3d: ((2, 3.5), {}),
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torch.nn.Conv1d: ((3, 3, 3), {}),
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torch.nn.Conv2d: ((3, 3, 3), {}),
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torch.nn.Conv3d: ((3, 3, 3), {}),
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torch.nn.ConvTranspose1d: ((3, 3, 3), {}),
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torch.nn.ConvTranspose2d: ((3, 3, 3), {}),
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torch.nn.ConvTranspose3d: ((3, 3, 3), {}),
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torch.nn.CosineEmbeddingLoss: ((), {}),
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torch.nn.CosineSimilarity: ((), {}),
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torch.nn.CrossEntropyLoss: ((), {}),
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torch.nn.CrossMapLRN2d: ((5,), {}),
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torch.nn.Dropout2d: ((), {}),
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torch.nn.Dropout3d: ((), {}),
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torch.nn.Dropout: ((), {}),
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torch.nn.ELU: ((), {}),
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torch.nn.Embedding: ((10, 5), {}),
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torch.nn.EmbeddingBag: ((10, 5), {}),
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torch.nn.FeatureAlphaDropout: ((), {}),
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torch.nn.Flatten: ((), {}),
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torch.nn.Fold: ((5, 2), {}),
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torch.nn.FractionalMaxPool2d: ((5, 2), {}),
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torch.nn.FractionalMaxPool3d: ((5, 2), {}),
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torch.nn.GELU: ((), {}),
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torch.nn.GLU: ((), {}),
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torch.nn.GRU: ((5, 10), {}),
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torch.nn.GRUCell: ((5, 10), {}),
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torch.nn.GaussianNLLLoss: ((), {}),
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torch.nn.GroupNorm: ((3, 6, 1e-5, True), {}),
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torch.nn.Hardshrink: ((), {}),
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torch.nn.Hardsigmoid: ((), {}),
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torch.nn.Hardswish: ((), {}),
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torch.nn.Hardtanh: ((), {}),
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torch.nn.HingeEmbeddingLoss: ((), {}),
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torch.nn.HuberLoss: ((), {}),
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torch.nn.Identity: ((), {}),
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torch.nn.InstanceNorm1d: ((5, 1e-5, 0.1, True), {}),
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torch.nn.InstanceNorm2d: ((5, 1e-5, 0.1, True), {}),
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torch.nn.InstanceNorm3d: ((5, 1e-5, 0.1, True), {}),
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torch.nn.KLDivLoss: ((), {}),
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torch.nn.L1Loss: ((), {}),
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torch.nn.LPPool1d: ((2, 3), {}),
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torch.nn.LPPool2d: ((2, 3), {}),
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torch.nn.LSTM: ((5, 10), {}),
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torch.nn.LSTMCell: ((5, 10), {}),
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torch.nn.LayerNorm: ((2,), {}),
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torch.nn.LazyBatchNorm1d: ((), {}),
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torch.nn.LazyBatchNorm2d: ((), {}),
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torch.nn.LazyBatchNorm3d: ((), {}),
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torch.nn.LazyConv1d: ((5, 2), {}),
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torch.nn.LazyConv2d: ((5, 2), {}),
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torch.nn.LazyConv3d: ((5, 2), {}),
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torch.nn.LazyConvTranspose1d: ((5, 2), {}),
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torch.nn.LazyConvTranspose2d: ((5, 2), {}),
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torch.nn.LazyConvTranspose3d: ((5, 2), {}),
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torch.nn.LazyInstanceNorm1d: ((), {}),
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torch.nn.LazyInstanceNorm2d: ((), {}),
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torch.nn.LazyInstanceNorm3d: ((), {}),
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torch.nn.LazyLinear: ((5,), {}),
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torch.nn.LeakyReLU: ((), {}),
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torch.nn.Linear: ((10, 5), {}),
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torch.nn.LocalResponseNorm: ((2,), {}),
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torch.nn.LogSigmoid: ((), {}),
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torch.nn.LogSoftmax: ((), {}),
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torch.nn.MSELoss: ((), {}),
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torch.nn.MarginRankingLoss: ((), {}),
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torch.nn.MaxPool1d: ((3,), {}),
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torch.nn.MaxPool2d: ((3,), {}),
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torch.nn.MaxPool3d: ((3,), {}),
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torch.nn.MaxUnpool1d: ((5,), {}),
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torch.nn.MaxUnpool2d: ((5,), {}),
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torch.nn.MaxUnpool3d: ((5,), {}),
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torch.nn.Mish: ((), {}),
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torch.nn.ModuleDict: ((), {}),
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torch.nn.ModuleList: ((), {}),
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torch.nn.MultiLabelMarginLoss: ((), {}),
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torch.nn.MultiLabelSoftMarginLoss: ((), {}),
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torch.nn.MultiMarginLoss: ((), {}),
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torch.nn.MultiheadAttention: ((100, 2), {}),
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torch.nn.NLLLoss2d: ((), {}),
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torch.nn.NLLLoss: ((), {}),
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torch.nn.PReLU: ((), {}),
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torch.nn.PairwiseDistance: ((), {}),
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torch.nn.ParameterDict: ((), {}),
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torch.nn.ParameterList: ((), {}),
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torch.nn.PixelShuffle: ((2,), {}),
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torch.nn.PixelUnshuffle: ((2,), {}),
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torch.nn.PoissonNLLLoss: ((), {}),
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torch.nn.RNN: ((5, 10), {}),
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torch.nn.RNNBase: (('LSTM', 5, 10), {}),
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torch.nn.RNNCell: ((5, 10), {}),
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torch.nn.RNNCellBase: ((5, 10, True, 2), {}),
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torch.nn.RReLU: ((), {}),
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torch.nn.ReLU6: ((), {}),
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torch.nn.ReLU: ((), {}),
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torch.nn.ReflectionPad1d: ((2,), {}),
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torch.nn.ReflectionPad2d: ((2,), {}),
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torch.nn.ReflectionPad3d: ((2,), {}),
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torch.nn.ReplicationPad1d: ((2,), {}),
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torch.nn.ReplicationPad2d: ((2,), {}),
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torch.nn.ReplicationPad3d: ((2,), {}),
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torch.nn.SELU: ((), {}),
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torch.nn.Sequential: ((), {}),
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torch.nn.SiLU: ((), {}),
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torch.nn.Sigmoid: ((), {}),
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torch.nn.SmoothL1Loss: ((), {}),
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torch.nn.SoftMarginLoss: ((), {}),
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torch.nn.Softmax2d: ((), {}),
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torch.nn.Softmax: ((), {}),
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torch.nn.Softmin: ((), {}),
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torch.nn.Softplus: ((), {}),
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torch.nn.Softshrink: ((), {}),
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torch.nn.Softsign: ((), {}),
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torch.nn.SyncBatchNorm: ((5,), {}),
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torch.nn.Tanh: ((), {}),
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torch.nn.Tanhshrink: ((), {}),
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torch.nn.Threshold: ((0.1, 20), {}),
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torch.nn.Transformer: ((), {}),
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torch.nn.TransformerDecoder: ((torch.nn.TransformerDecoderLayer, 3), {}),
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torch.nn.TransformerDecoderLayer: ((10, 2), {}),
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torch.nn.TransformerEncoder: ((torch.nn.TransformerEncoderLayer, 3), {}),
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torch.nn.TransformerEncoderLayer: ((10, 2), {}),
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torch.nn.TripletMarginLoss: ((), {}),
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torch.nn.TripletMarginWithDistanceLoss: ((), {}),
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torch.nn.Unflatten: ((1, (2, 5, 5)), {}),
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torch.nn.Unfold: ((3,), {}),
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torch.nn.Upsample: ((), {}),
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torch.nn.UpsamplingBilinear2d: ((), {}),
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torch.nn.UpsamplingNearest2d: ((), {}),
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torch.nn.ZeroPad2d: ((0,), {}),
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torch.nn.qat.Conv2d: ((3, 3, 3), {
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'qconfig': torch.ao.quantization.default_qconfig,
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}),
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torch.nn.qat.Conv3d: ((3, 3, 3), {
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'qconfig': torch.ao.quantization.default_qconfig,
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}),
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torch.nn.qat.Linear: ((5, 2), {
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'qconfig': torch.ao.quantization.default_qconfig,
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}),
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torch.nn.qat.EmbeddingBag: ((10, 12), {
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'qconfig': torch.ao.quantization.float_qparams_weight_only_qconfig,
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}),
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torch.nn.quantizable.LSTM: ((5, 6), {}),
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torch.nn.quantizable.LSTMCell: ((5, 6), {}),
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torch.nn.quantizable.MultiheadAttention: ((10, 2), {}),
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torch.nn.quantized.BatchNorm2d: ((2,), {}),
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torch.nn.quantized.BatchNorm3d: ((2,), {}),
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torch.nn.quantized.Conv1d: ((3, 3, 3), {}),
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torch.nn.quantized.Conv2d: ((3, 3, 3), {}),
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torch.nn.quantized.Conv3d: ((3, 3, 3), {}),
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torch.nn.quantized.ConvTranspose1d: ((3, 3, 3), {}),
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torch.nn.quantized.ConvTranspose2d: ((3, 3, 3), {}),
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torch.nn.quantized.ConvTranspose3d: ((16, 33, (3, 3, 5)), {
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'stride': (2, 1, 1),
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'padding': (4, 2, 2),
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'output_padding': (2, 2, 2),
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'dilation': (1, 1, 1),
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}),
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torch.nn.quantized.DeQuantize: ((), {}),
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torch.nn.quantized.ELU: ((0.01, 0), {}),
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torch.nn.quantized.Embedding: ((10, 3), {
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'factory_kwargs': {},
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}),
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torch.nn.quantized.EmbeddingBag: ((10, 3), {
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'factory_kwargs': {},
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}),
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torch.nn.quantized.GroupNorm: ((2, 3, torch.nn.Parameter(torch.tensor(2.)),
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torch.nn.Parameter(torch.tensor(2.)), 0.1, 0), {}),
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torch.nn.quantized.Hardswish: ((0.1, 0,), {}),
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torch.nn.quantized.InstanceNorm1d: ((2, torch.nn.Parameter(torch.tensor(2.)),
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torch.nn.Parameter(torch.tensor(2.)), 0.1, 0), {}),
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torch.nn.quantized.InstanceNorm2d: ((2, torch.nn.Parameter(torch.tensor(2.)),
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torch.nn.Parameter(torch.tensor(2.)), 0.1, 0), {}),
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torch.nn.quantized.InstanceNorm3d: ((2, torch.nn.Parameter(torch.tensor(2.)),
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torch.nn.Parameter(torch.tensor(2.)), 0.1, 0), {}),
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torch.nn.quantized.LayerNorm: ((2, torch.nn.Parameter(torch.tensor(2.)),
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torch.nn.Parameter(torch.tensor(2.)), 0.1, 0), {}),
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torch.nn.quantized.LeakyReLU: ((0.01, 0), {}),
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torch.nn.quantized.Linear: ((5, 2), {
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'factory_kwargs': {},
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}),
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torch.nn.quantized.MaxPool2d: ((3,), {}),
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torch.nn.quantized.Quantize: ((0.1, 0), {
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'dtype': torch.int16,
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'factory_kwargs': {},
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}),
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torch.nn.quantized.ReLU6: ((), {}),
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torch.nn.quantized.Sigmoid: ((0.1, 0), {}),
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torch.nn.quantized.FloatFunctional: ((), {}),
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torch.nn.quantized.FXFloatFunctional: ((), {}),
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torch.nn.quantized.QFunctional: ((), {}),
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}
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# Instantiates the given class with the given args, kwargs, optionally on a given device.
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def instantiate_class(cls, args, kwargs, extra_kwargs):
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return cls(*args, **kwargs) if extra_kwargs is None else cls(*args, **kwargs, **extra_kwargs)
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# Returns a function that calls the real implementation of a method
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# in addition to passing args to a mock object.
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def mock_wrapper(method):
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mock = MagicMock()
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def wrapper(self, *args, **kwargs):
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mock(*args, **kwargs)
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return method(self, *args, **kwargs)
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wrapper.mock = mock
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return wrapper
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# Returns a set of args / kwargs that can be used to construct the module.
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def get_example_args(module_cls, constructor_arg_db, extra_kwargs=None):
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assert module_cls in constructor_arg_db, \
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f"No entry for {module_cls} in the constructor arg DB. Please add it to pass these tests."
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args, kwargs = constructor_arg_db[module_cls]
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extra_kwargs = {} if extra_kwargs is None else extra_kwargs
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# Recursively instantiate args / kwargs that are class objects.
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args = [instantiate_class(arg, *get_example_args(arg, constructor_arg_db), extra_kwargs=extra_kwargs)
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if inspect.isclass(arg) else torch.nn.Parameter(arg.to(**extra_kwargs))
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if isinstance(arg, torch.nn.Parameter) else arg for arg in args]
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kwargs = {k: instantiate_class(v, *get_example_args(v, constructor_arg_db), extra_kwargs=extra_kwargs)
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if inspect.isclass(v) else torch.nn.Parameter(v.to(*extra_kwargs))
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if isinstance(v, torch.nn.Parameter) else v for k, v in kwargs.items()}
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kwargs.update(extra_kwargs)
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return args, kwargs
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def generate_test_func(test_cls, module_cls, constructor_arg_db,
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verify_kwargs=True, module_is_lazy=False, check_nonexistent_arg=True):
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# Generate a function for testing the given module.
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@dtypes(*floating_types())
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def run_test(test_cls, device, dtype, module_cls=module_cls):
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# Check if this module creates parameters or registers buffers.
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# The mock magic here passes through to the real Parameter / register_buffer
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# logic and is only used to check for calls.
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args, kwargs = get_example_args(module_cls, constructor_arg_db)
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# Some modules need to pass factory_kwargs so as not to conflict with existing args such as dtype.
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module_needs_factory_kwargs = 'factory_kwargs' in kwargs
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if module_needs_factory_kwargs:
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del kwargs['factory_kwargs']
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extra_kwargs = {
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'factory_kwargs': {
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'device': device,
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'dtype': dtype,
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}
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}
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else:
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extra_kwargs = {
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'device': device,
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'dtype': dtype,
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}
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parameter_new = mock_wrapper(torch.nn.Parameter.__new__)
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with patch.object(torch.nn.Parameter, '__new__', parameter_new):
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register_buffer = mock_wrapper(torch.nn.Module.register_buffer)
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with patch.object(torch.nn.Module, 'register_buffer', register_buffer):
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m = module_cls(*args, **kwargs)
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module_creates_params_or_buffers = parameter_new.mock.called or register_buffer.mock.called
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# == Verify factory kwargs are supported. ==
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if verify_kwargs and module_creates_params_or_buffers:
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args, kwargs = get_example_args(module_cls, constructor_arg_db,
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extra_kwargs=extra_kwargs)
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if module_is_lazy:
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# Ensure device and dtype are passed to all UninitializedParameters and UninitializedBuffers.
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uninit_param_new = mock_wrapper(torch.nn.UninitializedParameter.__new__)
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with patch.object(torch.nn.UninitializedParameter, '__new__', uninit_param_new):
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uninit_buffer_new = mock_wrapper(torch.nn.UninitializedBuffer.__new__)
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with patch.object(torch.nn.UninitializedBuffer, '__new__', uninit_buffer_new):
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m = module_cls(*args, **kwargs)
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uninit_param_new.mock.assert_has_calls(
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[mock.call(device=device, dtype=dtype) for _ in uninit_param_new.mock.mock_calls])
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uninit_buffer_new.mock.assert_has_calls(
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[mock.call(device=device, dtype=dtype) for _ in uninit_buffer_new.mock.mock_calls])
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else:
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# Check device placement and dtype for parameters and buffers.
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# Only verify floating point dtypes since that's what the kwarg applies to.
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# Note that dtype verification is also skipped if the module requires factory_kwargs.
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m = module_cls(*args, **kwargs)
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for name, param in m.named_parameters():
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test_cls.assertEqual(
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str(param.device), device,
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f'Parameter {name} is on {param.device.type} instead of the expected device {device}')
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if param.dtype.is_floating_point and not module_needs_factory_kwargs:
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test_cls.assertEqual(
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param.dtype, dtype,
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f'Parameter {name} is of dtype {param.dtype} instead of the expected dtype {dtype}')
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for name, buffer in m.named_buffers():
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test_cls.assertEqual(
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str(buffer.device), device,
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f'Buffer {name} is on {buffer.device.type} instead of the expected device {device}')
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if buffer.dtype.is_floating_point and not module_needs_factory_kwargs:
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test_cls.assertEqual(
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buffer.dtype, dtype,
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f'Buffer {name} is of dtype {buffer.dtype} instead of the expected dtype {dtype}')
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# == Verify passing a nonexistent arg errors out. ==
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if check_nonexistent_arg:
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with test_cls.assertRaises(TypeError):
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m = module_cls(*args, **kwargs, nonexistent_arg='foo')
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return run_test
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def generate_tests(test_cls, constructor_arg_db):
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# test all modules underneath these namespaces...
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NAMESPACES = [
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torch.nn,
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torch.nn.qat,
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torch.nn.quantizable,
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torch.nn.quantized,
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]
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# ...except these
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MODULES_TO_SKIP = {
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torch.nn.Module,
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torch.nn.Container, # deprecated
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torch.nn.NLLLoss2d, # deprecated
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torch.nn.quantized._ConvNd, # base class in __all__ for some reason
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# TODO: Remove these 2 from this list once the ASan issue is fixed.
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# See https://github.com/pytorch/pytorch/issues/55396
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torch.nn.quantized.Embedding,
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|
torch.nn.quantized.EmbeddingBag,
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}
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# no need to support kwargs for these modules even though
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|
# they have parameters / buffers because they are passed in
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# already instantiated
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MODULES_WITHOUT_KWARGS_SUPPORT = {
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|
torch.nn.BCELoss,
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|
torch.nn.BCEWithLogitsLoss,
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|
torch.nn.CrossEntropyLoss,
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|
torch.nn.FractionalMaxPool2d,
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|
torch.nn.FractionalMaxPool3d,
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|
torch.nn.MultiLabelSoftMarginLoss,
|
|
torch.nn.MultiMarginLoss,
|
|
torch.nn.NLLLoss,
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|
torch.nn.TransformerDecoder,
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|
torch.nn.TransformerEncoder,
|
|
}
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|
# modules that supported kwargs before
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|
MODULES_WITH_PREVIOUS_KWARGS = {
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|
torch.nn.Identity,
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|
}
|
|
# lazy modules don't instantiate parameters right away
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|
LAZY_MODULES = {
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|
torch.nn.LazyBatchNorm1d,
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|
torch.nn.LazyBatchNorm2d,
|
|
torch.nn.LazyBatchNorm3d,
|
|
torch.nn.LazyConv1d,
|
|
torch.nn.LazyConv2d,
|
|
torch.nn.LazyConv3d,
|
|
torch.nn.LazyConvTranspose1d,
|
|
torch.nn.LazyConvTranspose2d,
|
|
torch.nn.LazyConvTranspose3d,
|
|
torch.nn.LazyConvTranspose3d,
|
|
torch.nn.LazyInstanceNorm1d,
|
|
torch.nn.LazyInstanceNorm2d,
|
|
torch.nn.LazyInstanceNorm3d,
|
|
torch.nn.LazyLinear,
|
|
}
|
|
# these modules requires FBGEMM backend to instantiate
|
|
MODULES_THAT_REQUIRE_FBGEMM = {
|
|
torch.nn.quantized.Conv1d,
|
|
torch.nn.quantized.Conv2d,
|
|
torch.nn.quantized.Conv3d,
|
|
torch.nn.quantized.ConvTranspose1d,
|
|
torch.nn.quantized.ConvTranspose2d,
|
|
torch.nn.quantized.ConvTranspose3d,
|
|
torch.nn.quantized.Linear,
|
|
}
|
|
|
|
for namespace in NAMESPACES:
|
|
# the "nn" in "torch.nn"
|
|
namespace_basename = namespace.__name__.split('.')[-1]
|
|
for module_name in namespace.modules.__all__:
|
|
# class object for this module (e.g. torch.nn.Linear)
|
|
module_cls = getattr(namespace.modules, module_name)
|
|
if module_cls in MODULES_TO_SKIP:
|
|
continue
|
|
verify_kwargs = module_cls not in MODULES_WITHOUT_KWARGS_SUPPORT
|
|
module_is_lazy = module_cls in LAZY_MODULES
|
|
check_nonexistent_arg = module_cls not in MODULES_WITH_PREVIOUS_KWARGS
|
|
# Generate a function for testing this module and setattr it onto the test class.
|
|
run_test = generate_test_func(test_cls, module_cls, constructor_arg_db,
|
|
verify_kwargs=verify_kwargs,
|
|
module_is_lazy=module_is_lazy,
|
|
check_nonexistent_arg=check_nonexistent_arg)
|
|
test_name = f'test_{namespace_basename}_{module_name}'
|
|
if module_cls in MODULES_THAT_REQUIRE_FBGEMM:
|
|
run_test = skipIfNoFBGEMM(run_test)
|
|
setattr(TestModuleInit, test_name, run_test)
|
|
|
|
|
|
class TestModuleInit(TestCase):
|
|
_ignore_not_implemented_error = False
|
|
|
|
|
|
generate_tests(TestModuleInit, build_constructor_arg_db())
|
|
instantiate_device_type_tests(TestModuleInit, globals())
|
|
|
|
|
|
if __name__ == '__main__':
|
|
run_tests()
|