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https://github.com/pytorch/pytorch.git
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8dc6001057
* Modified the SymInt schema to also store the hint of the SymInt if it is represented as a symbol so that when we reconstruct the SymInt, the hint will also exist on the node. * GraphModuleDeserializer.deserialize now also optionally map of symbol names to range. ReplaceSymSizeOpPass should not be needed after https://github.com/pytorch/pytorch/pull/103107 lands Pull Request resolved: https://github.com/pytorch/pytorch/pull/103273 Approved by: https://github.com/avikchaudhuri, https://github.com/zhxchen17
335 lines
11 KiB
Python
335 lines
11 KiB
Python
# Owner(s): ["module: dynamo"]
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import unittest
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import torch
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import torch._dynamo as torchdynamo
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from torch._export import dynamic_dim, export
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from torch._export.db.case import ExportCase, normalize_inputs, SupportLevel
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from torch._export.db.examples import all_examples
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from torch._export.serde.serialize import (
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ExportedProgramSerializer,
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deserialize,
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serialize,
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)
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from torch._subclasses.fake_tensor import FakeTensor
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from torch.fx.experimental.symbolic_shapes import is_concrete_int
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import torch.utils._pytree as pytree
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from torch.testing._internal.common_utils import (
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instantiate_parametrized_tests,
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parametrize,
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run_tests,
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TestCase,
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)
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def get_filtered_export_db_tests():
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unsupported_tags = {"torch.cond", "torch.map"}
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unsupported_test_names = {
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"dynamic_shape_constructor", # 'NoneType' object has no attribute 'from_tensor'
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"dictionary", # Graph output must be a tuple()
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"fn_with_kwargs", # export doesn't support kwargs yet
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"scalar_output", # Tracing through 'f' must produce a single graph
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}
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return [
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(name, case)
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for name, case in all_examples().items()
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if (
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case.support_level == SupportLevel.SUPPORTED and
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not (unsupported_tags & case.tags) and
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name not in unsupported_test_names
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)
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]
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@unittest.skipIf(not torchdynamo.is_dynamo_supported(), "dynamo doesn't support")
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class TestSerialize(TestCase):
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def test_serialize_multiple_returns_from_node(self) -> None:
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class MyModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x, w, b):
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return torch.nn.functional.layer_norm(
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x,
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x.size()[1:],
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weight=w,
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bias=b,
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eps=1e-5,
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)
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exported_module = export(
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MyModule(),
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(
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torch.ones([512, 512], requires_grad=True),
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torch.ones([512]),
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torch.ones([512]),
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),
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)
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serialized, _ = ExportedProgramSerializer().serialize(exported_module)
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node = serialized.graph_module.graph.nodes[-7]
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self.assertEqual(node.target, "torch._ops.aten.var_mean.correction")
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# aten::native_layer_norm returns 3 tensnors
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self.assertEqual(len(node.outputs), 2)
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# check the names are unique
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seen = set()
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for output in node.outputs:
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name = output.as_tensor.name
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self.assertNotIn(name, seen)
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seen.add(name)
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def test_serialize_list_returns(self) -> None:
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class MyModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x):
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return torch.split(x, 2)
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input = torch.arange(10.0).reshape(5, 2)
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input.requires_grad = True
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exported_module = export(MyModule(), (input,))
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serialized, _ = ExportedProgramSerializer().serialize(exported_module)
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node = serialized.graph_module.graph.nodes[-1]
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self.assertEqual(node.target, "torch._ops.aten.split.Tensor")
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self.assertEqual(len(node.outputs), 1)
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# Input looks like:
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# tensor([[0, 1],
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# [2, 3],
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# [4, 5],
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# [6, 7],
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# [8, 9]])
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# Output looks like:
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# (tensor([[0, 1],
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# [2, 3]]),
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# tensor([[4, 5],
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# [6, 7]]),
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# tensor([[8, 9]]))
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self.assertEqual(len(node.outputs[0].as_tensors), 3)
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# check the names are unique
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seen = set()
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for output in node.outputs[0].as_tensors:
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name = output.name
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self.assertNotIn(name, seen)
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seen.add(name)
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def test_multi_return_some_unused(self) -> None:
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"""
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Make sure the serialized output matches the op schema, even if some of
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the arguments are never used in the graph.
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"""
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class MyModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x):
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return torch.ops.aten.var_mean.correction(x, [1])[0]
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exported_module = export(
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MyModule(),
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(torch.ones([512, 512], requires_grad=True),),
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)
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serialized, _ = ExportedProgramSerializer().serialize(exported_module)
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node = serialized.graph_module.graph.nodes[-1]
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self.assertEqual(node.target, "torch._ops.aten.var_mean.correction")
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self.assertEqual(len(node.outputs), 2)
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# check the names are unique
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seen = set()
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for output in node.outputs:
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name = output.as_tensor.name
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self.assertNotIn(name, seen)
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seen.add(name)
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def test_kwargs_default(self) -> None:
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"""
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Tests that the kwargs default values are serialized even if they are not
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specified
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"""
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def f(x: torch.Tensor) -> torch.Tensor:
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values = torch.randn(3, 2)
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return torch.searchsorted(x, values, side="right", right=True)
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x, _ = torch.sort(torch.randn(3, 4))
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exported_module = export(f, (x,))
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serialized, _ = ExportedProgramSerializer().serialize(exported_module)
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node = serialized.graph_module.graph.nodes[-1]
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self.assertEqual(node.target, "torch._ops.aten.searchsorted.Tensor")
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self.assertEqual(len(node.inputs), 6)
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self.assertEqual(node.inputs[2].arg.as_bool, False)
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self.assertEqual(node.inputs[3].arg.as_bool, True)
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self.assertEqual(node.inputs[4].arg.as_string, "right")
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self.assertEqual(node.inputs[5].arg.as_none, ())
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@unittest.skipIf(not torchdynamo.is_dynamo_supported(), "dynamo doesn't support")
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class TestDeserialize(TestCase):
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def check_graph(self, fn, inputs, constraints=None) -> None:
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"""Export a graph, serialize it, deserialize it, and compare the results."""
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# TODO(angelayi): test better with some sort of wrapper
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constraints = [] if constraints is None else constraints
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ep = export(fn, inputs, constraints)
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serialized_struct, state_dict = serialize(ep)
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deserialized_ep = deserialize(serialized_struct, state_dict)
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orig_outputs = ep(*inputs)
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loaded_outputs = deserialized_ep(*inputs)
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flat_orig_outputs, _ = pytree.tree_flatten(orig_outputs)
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flat_loaded_outputs, _ = pytree.tree_flatten(loaded_outputs)
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for orig, loaded in zip(flat_orig_outputs, flat_loaded_outputs):
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self.assertEqual(type(orig), type(loaded))
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if isinstance(orig, torch.Tensor):
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self.assertTrue(torch.allclose(orig, loaded))
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else:
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self.assertEqual(orig, loaded)
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for node1, node2 in zip(ep.graph.nodes, deserialized_ep.graph.nodes):
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# Check "val" metadata
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val1 = node1.meta.get("val", None)
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val2 = node2.meta.get("val", None)
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if val1 is None or val2 is None:
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# Either both are None
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self.assertEqual(val1, val2)
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elif isinstance(val1, FakeTensor) and isinstance(val2, FakeTensor):
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# Or both are fake tensors with the same shape/dtype
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self.assertEqual(len(val1.shape), len(val2.shape))
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for s1, s2 in zip(val1.shape, val2.shape):
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if is_concrete_int(s1) and is_concrete_int(s2):
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self.assertEqual(s1, s2)
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else:
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self.assertEqual(str(s1), str(s2))
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self.assertEqual(val1.dtype, val2.dtype)
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elif isinstance(val1, list) and isinstance(val2, list):
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# Or both are fake tensors lists with one element and with the
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# same shape/dtype
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self.assertTrue(len(val1) == 1 and len(val2) == 1)
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self.assertEqual(val1[0].shape, val2[0].shape)
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self.assertEqual(val1[0].dtype, val2[0].dtype)
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else:
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# For expressions like 's0 < 10' can only compare through string
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self.assertEqual(str(val1), str(val2))
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def test_multi_return(self) -> None:
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"""
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Test multiple return from a single node (ex. layer_norm has 2 outputs)
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"""
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class MyModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x, w, b):
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return torch.nn.functional.layer_norm(
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x,
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x.size()[1:],
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weight=w,
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bias=b,
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eps=1e-5,
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)
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inputs = (
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torch.ones([512, 512], requires_grad=True),
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torch.ones([512]),
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torch.ones([512]),
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)
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self.check_graph(MyModule(), inputs)
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def test_basic(self) -> None:
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class MyModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x):
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x = x + x
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x = x * x
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x = x / x
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return x, x.clone()
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inputs = (torch.ones([512], requires_grad=True),)
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self.check_graph(MyModule(), inputs)
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def test_dynamic(self) -> None:
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class DynamicShapeSimpleModel(torch.nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, a, b, c) -> torch.Tensor:
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d = (torch.matmul(a, b) + c) / 2
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d_s0 = d.shape[0]
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d_s1 = d.shape[1]
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d_s3 = d_s0 * d_s1
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e = d.view(d_s3)
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return torch.cat([e, e])
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inputs = (torch.randn(2, 4), torch.randn(4, 7), torch.randn(2, 7))
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constraints = [
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dynamic_dim(inputs[0], 0),
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dynamic_dim(inputs[2], 0),
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dynamic_dim(inputs[2], 0) == dynamic_dim(inputs[0], 0),
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]
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self.check_graph(DynamicShapeSimpleModel(), inputs, constraints)
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def test_sym_bool(self):
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def f(x, y):
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return x.size(0) in y
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self.check_graph(f, (torch.ones(2), torch.ones(3)))
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def test_shape(self):
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def f(x):
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z, y = x.size()
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return z + y + x[0], z
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inputs = (torch.ones(2, 3),)
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constraints = [
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dynamic_dim(inputs[0], 0),
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dynamic_dim(inputs[0], 1),
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]
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self.check_graph(f, inputs, constraints)
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def test_module(self):
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class M(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.linear1 = torch.nn.Linear(3, 3)
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self.relu = torch.nn.ReLU()
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self.linear2 = torch.nn.Linear(3, 5)
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def forward(self, x):
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x = self.linear1(x)
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x = self.linear1(x)
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x = torch.nn.functional.relu(x)
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x = self.linear2(x)
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return x
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inputs = (torch.randn(3, 3),)
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self.check_graph(M(), inputs)
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@parametrize(
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"name,case",
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get_filtered_export_db_tests(),
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name_fn=lambda name, case: "case_{}".format(name),
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)
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def test_exportdb_supported(self, name: str, case: ExportCase) -> None:
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model = case.model
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inputs = normalize_inputs(case.example_inputs)
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self.check_graph(model, inputs.args)
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instantiate_parametrized_tests(TestDeserialize)
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if __name__ == '__main__':
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run_tests()
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