mirror of
https://github.com/zebrajr/pytorch.git
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84abeaad5c
We want to log each symnode created so that we can do provenance tracking in the tlparse report generated for draft export. To do this, we want to assign a unique id to every symnode, which python's `id` function already does, and then for every expression created, we can find the provenance by tracing back through its arguments ids. This logging only happens when dtrace_structured is enabled, which is only when running draft export. An example output is as follows: <img width="799" alt="image" src="https://github.com/user-attachments/assets/88bb31b4-8c31-43fb-aa88-08b573b9f71d" /> For the increase in the compile_time_instruction_count benchmark, this seems unavoidable because I need to call `id` to get the unique identifier for each symnode. But I believe `id` is an inexpensive operation, so hopefully it should be ok? I tried doing the following: * Originally I was passing around `self`, which is a SymNode, which caused the compile time to be ~6.36M * I changed it to pass around `id(self)` instead, which reduced the compile time to ~6.33M * Then I changed it to be passed as a positional arg instead of a kwarg, which reduced the compile time to ~6.22M, but this doesn't seem to be a super worthwhile fix? #suppress-bc-linter Pull Request resolved: https://github.com/pytorch/pytorch/pull/146939 Approved by: https://github.com/oulgen
534 lines
18 KiB
Python
534 lines
18 KiB
Python
# Owner(s): ["oncall: export"]
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import copy
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import tempfile
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import unittest
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import torch
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from torch.export import Dim, export
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from torch.export._draft_export import draft_export, FailureType
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from torch.testing import FileCheck
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from torch.testing._internal.common_utils import IS_WINDOWS, run_tests, TestCase
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from torch.testing._internal.torchbind_impls import (
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_empty_tensor_queue,
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init_torchbind_implementations,
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)
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from torch.utils._pytree import tree_leaves
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class TestDraftExport(TestCase):
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def setUp(self):
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init_torchbind_implementations()
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@torch._library.register_fake_class("_TorchScriptTesting::_TensorQueue")
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class FakeTensorQueue:
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def __init__(self, queue):
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self.queue = queue
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@classmethod
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def __obj_unflatten__(cls, flattened_ctx):
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return cls(**dict(flattened_ctx))
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def push(self, x):
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self.queue.append(x)
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def pop(self):
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return self.queue.pop(0)
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def size(self):
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return len(self.queue)
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def is_empty(self):
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return len(self.queue) == 0
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def float_size(self):
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return float(len(self.queue))
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self.torch_bind_ops = [
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torch.ops._TorchScriptTesting.queue_pop,
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torch.ops._TorchScriptTesting.queue_push,
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torch.ops._TorchScriptTesting.queue_size,
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]
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def tearDown(self):
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torch._library.fake_class_registry.deregister_fake_class(
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"_TorchScriptTesting::_TensorQueue"
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)
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def test_missing_meta_kernel_custom_op(self):
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with torch.library._scoped_library("mylib", "FRAGMENT"):
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@torch.library.custom_op("mylib::foo2", mutates_args={})
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def foo2_impl(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
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return a + b
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class M(torch.nn.Module):
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def forward(self, a, b):
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res = torch.ops.mylib.foo2(a, b)
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return res
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inp = (torch.ones(3, 3), torch.ones(3, 3))
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ep, report = draft_export(M(), inp)
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self.assertEqual(len(report.failures), 1)
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self.assertEqual(
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report.failures[0].failure_type, FailureType.MISSING_FAKE_KERNEL
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)
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inp = (torch.randn(3, 3), torch.randn(3, 3))
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self.assertEqual(ep.module()(*inp), M()(*inp))
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def test_missing_meta_kernel_impl(self):
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with torch.library._scoped_library("mylib", "FRAGMENT") as lib:
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torch.library.define(
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"mylib::foo",
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"(Tensor a, Tensor b) -> Tensor",
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tags=torch.Tag.pt2_compliant_tag,
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lib=lib,
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)
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@torch.library.impl("mylib::foo", "cpu", lib=lib)
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def foo_impl(a, b):
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return a + b
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class M(torch.nn.Module):
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def forward(self, a, b):
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res = torch.ops.mylib.foo(a, b)
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res = torch.ops.mylib.foo(res, b)
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return res
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inp = (torch.ones(3, 3), torch.ones(3, 3))
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ep, report = draft_export(M(), inp)
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self.assertEqual(len(report.failures), 1)
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self.assertEqual(
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report.failures[0].failure_type, FailureType.MISSING_FAKE_KERNEL
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)
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inp = (torch.randn(3, 3), torch.randn(3, 3))
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self.assertEqual(ep.module()(*inp), M()(*inp))
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@unittest.skipIf(not torch.cuda.is_available(), "Requires cuda")
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def test_missing_meta_kernel_guard(self):
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with torch.library._scoped_library("mylib", "FRAGMENT"):
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@torch.library.custom_op("mylib::foo4", mutates_args={})
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def foo4_impl(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
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return a + b
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class M(torch.nn.Module):
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def forward(self, a, b):
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res1 = torch.ops.mylib.foo4(a, b)
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return res1
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inp = (
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torch.ones(3, 4),
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torch.ones(3, 4),
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)
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ep, report = draft_export(
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M(),
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inp,
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dynamic_shapes={
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"a": {0: Dim.DYNAMIC, 1: Dim.DYNAMIC},
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"b": {0: Dim.DYNAMIC, 1: Dim.DYNAMIC},
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},
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)
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inp = (torch.randn(2, 3), torch.randn(2, 3))
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self.assertEqual(ep.module()(*inp), M()(*inp))
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m = ep.module()
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with self.assertRaisesRegex(RuntimeError, "Tensor device mismatch!"):
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bad_device_inps = (
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torch.randn(2, 3, device=torch.device("cuda")),
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torch.randn(2, 3, device=torch.device("cuda")),
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)
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m(*bad_device_inps)
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with self.assertRaisesRegex(RuntimeError, "Tensor dtype mismatch!"):
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bad_dtype_inps = (
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torch.randn(2, 3, dtype=torch.float16),
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torch.randn(2, 3, dtype=torch.float16),
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)
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m(*bad_dtype_inps)
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def test_fake_infer_dense_in_memory_check(self):
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with torch.library._scoped_library("mylib", "FRAGMENT"):
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@torch.library.custom_op("mylib::foo5", mutates_args={})
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def foo5_impl(a: torch.Tensor) -> torch.Tensor:
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return a * 2
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@torch.library.custom_op("mylib::foo6", mutates_args={})
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def foo6_impl(a: torch.Tensor) -> torch.Tensor:
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return (a * 2)[:, :-1, :-1] # not dense in memory
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@torch.library.custom_op("mylib::foo7", mutates_args={})
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def foo7_impl(a: torch.Tensor) -> torch.Tensor:
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return (a * 2)[:, 1:-1, :] # non-zero storage offset
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class Foo(torch.nn.Module):
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def forward(self, x, opt):
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if opt == 0:
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return torch.ops.mylib.foo5(x)
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elif opt == 1:
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return torch.ops.mylib.foo6(x)
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else:
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return torch.ops.mylib.foo7(x)
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draft_export(Foo(), (torch.randn(80, 4, 4), 0))
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draft_export(Foo(), (torch.randn(80, 1, 4), 0))
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draft_export(Foo(), (torch.randn(1, 4, 1, 1, 4, 1, 4), 0))
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with self.assertRaisesRegex(
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RuntimeError,
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"a return was not dense in memory",
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):
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draft_export(Foo(), (torch.randn(4, 6, 8), 1))
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with self.assertRaisesRegex(
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RuntimeError,
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"a return has a non-zero storage offset",
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):
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draft_export(Foo(), (torch.randn(4, 6, 8), 2))
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def test_data_dependent_failure(self):
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with torch.library._scoped_library("mylib", "FRAGMENT") as lib:
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torch.library.define(
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"mylib::foo1",
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"(Tensor a, Tensor b) -> Tensor",
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tags=torch.Tag.pt2_compliant_tag,
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lib=lib,
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)
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@torch.library.impl("mylib::foo1", "cpu", lib=lib)
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def foo_impl(a, b):
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return a + b
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class M(torch.nn.Module):
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def forward(self, a, b, c):
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res = torch.ops.mylib.foo1(a, b)
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c_item = c.item()
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return res[:c_item]
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inp = (torch.ones(3, 3), torch.ones(3, 3), torch.tensor(3))
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ep, report = draft_export(M(), inp)
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self.assertTrue(len(report.failures) > 0)
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self.assertEqual(
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report.failures[0].failure_type, FailureType.MISSING_FAKE_KERNEL
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)
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self.assertEqual(
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report.failures[1].failure_type, FailureType.DATA_DEPENDENT_ERROR
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)
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inp = (torch.randn(3, 3), torch.randn(3, 3), torch.tensor(2))
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self.assertEqual(ep.module()(*inp), M()(*inp))
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def test_unbacked_div_mod_replacement(self):
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class M(torch.nn.Module):
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def forward(self, x):
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x = torch.zeros(x.item())
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x = x.unsqueeze(0).repeat(10, 2)
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return x.view(-1, 2, 2345)
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_, report = draft_export(M(), (torch.tensor([938]),))
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self.assertEqual(len(report.failures), 1)
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self.assertEqual(
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report.failures[0].failure_type, FailureType.DATA_DEPENDENT_ERROR
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)
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self.assertEqual(report.failures[0].data["expr"], "Eq(2*u1, 10)")
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def test_dedup_data_dependent_failure(self):
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class M(torch.nn.Module):
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def forward(self, x, y, z):
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res = 0
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for v in [x, y]:
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b = v.item()
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if b > 10:
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res += v * b
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else:
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res += v + b
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return z * res
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inp = (torch.tensor(5), torch.tensor(3), torch.tensor(2))
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ep, report = draft_export(M(), inp)
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self.assertEqual(len(report.failures), 1)
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self.assertEqual(
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report.failures[0].failure_type, FailureType.DATA_DEPENDENT_ERROR
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)
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inp = (torch.tensor(4), torch.tensor(2), torch.tensor(6))
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self.assertEqual(ep.module()(*inp), M()(*inp))
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def test_complex_data_dependent_expr(self):
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class M(torch.nn.Module):
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def forward(self, x, y):
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a = x.item()
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a = -a
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a = a // 3
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a = a + 5
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z = torch.cat([y, y])
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torch._check_is_size(a)
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torch._check(a < z.shape[0])
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return z[:a]
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_, report = draft_export(
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M(),
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(torch.tensor(6), torch.randn(5)),
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dynamic_shapes={"x": None, "y": {0: Dim.DYNAMIC}},
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)
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self.assertTrue(len(report.failures) > 0)
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self.assertEqual(
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report.failures[0].failure_type, FailureType.DATA_DEPENDENT_ERROR
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)
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self.assertTrue(len(report.expressions_created) > 4)
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def test_offsets(self):
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class M(torch.nn.Module):
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def forward(self, x):
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a = x.item()
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if a == 0:
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raise RuntimeError("bad")
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return x * a
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inp = (torch.tensor(3),)
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ep, report = draft_export(M(), inp)
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def test_shape_failure(self):
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class M(torch.nn.Module):
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def forward(self, a):
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assert a.shape[0] == 3
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return a * a
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inp = (torch.ones(3, 3),)
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ep, report = draft_export(M(), inp, dynamic_shapes={"a": {0: Dim("a0")}})
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self.assertEqual(len(report.failures), 1)
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self.assertEqual(
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report.failures[0].failure_type, FailureType.CONSTRAINT_VIOLATION_ERROR
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)
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inp = (torch.randn(3, 3),)
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self.assertEqual(ep.module()(*inp), M()(*inp))
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inp = (torch.randn(4, 3),)
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with self.assertRaises(RuntimeError):
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ep.module()(*inp)
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def test_side_effect1(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.register_buffer("a", torch.tensor(2))
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def forward(self, b):
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a_item = self.a.item()
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if a_item == 2:
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res = a_item * b
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else:
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res = (a_item + 1) * b
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self.a.add_(1)
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a_item = self.a.item()
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if a_item == 3:
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res = a_item * res
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else:
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res = (a_item + 1) * res
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return res
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inp = (torch.ones(3, 3),)
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mod = M()
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ep, report = draft_export(mod, inp)
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self.assertEqual(mod.a, torch.tensor(2))
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FileCheck().check_count("torch.ops.aten.add.default", 0, exactly=True).run(
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ep.graph_module.code
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)
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def test_side_effect_inps(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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def forward(self, x):
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x.sin_()
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return x
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inp = (torch.ones(3, 3),)
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ep, report = draft_export(M(), inp)
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self.assertTrue(report.successful())
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self.assertEqual(inp[0], torch.ones(3, 3))
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def test_torchbind(self):
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class Model(torch.nn.Module):
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def __init__(self) -> None:
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super().__init__()
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self.linear = torch.nn.Linear(2, 2)
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def forward(self, tq, x):
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x_cos = tq.pop() + tq.float_size() + self.linear(x)
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if tq.is_empty():
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x_sin = self.linear(tq.pop()) - tq.size() + x
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else:
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x_sin = tq.pop() + tq.size() + x
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return x_sin, x_cos, tq
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mod = Model()
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tq = _empty_tensor_queue()
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tq2 = copy.deepcopy(tq)
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a = torch.randn(2, 2)
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b = torch.randn(2, 2)
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tq.push(a)
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tq.push(b)
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tq3 = copy.deepcopy(tq)
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inp = (tq, torch.randn(2, 2))
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ep, report = draft_export(mod, inp)
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self.assertTrue(report.successful())
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self.assertEqual(tq2.size(), 0)
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self.assertEqual(tq3.size(), 2)
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self.assertEqual(tq.size(), 2)
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def test_override_size_and_dtype_mismatched_fake_kernels(self):
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class M(torch.nn.Module):
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def forward(self, a):
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return torch.ops.mylib.foo(a)
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@torch.library.custom_op("mylib::foo", mutates_args={})
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def foo(a: torch.Tensor) -> list[torch.Tensor]:
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x = a * 2
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y = a.repeat(2, 2)
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z = a.to(torch.bfloat16)
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return [x, y, z]
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@foo.register_fake
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def foo_fake_impl(a):
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x = torch.empty_like(a) # good
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y = torch.empty_like(a) # size mismatch
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z = torch.empty_like(a) # dtype mismatch
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return [x, y, z]
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mod = M()
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inputs = (torch.randn(3, 3),)
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with self.assertRaises(RuntimeError):
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with torch._functorch.config.patch(fake_tensor_propagate_real_tensors=True):
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export(mod, inputs, strict=True)
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ep, report = draft_export(mod, inputs)
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for ep_out, eager_out in zip(ep.module()(*inputs), mod(*inputs)):
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self.assertTrue(torch.allclose(ep_out, eager_out))
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self.assertEqual(ep_out.dtype, eager_out.dtype)
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self.assertEqual(len(report.failures), 2)
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self.assertEqual(
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report.failures[0].failure_type, FailureType.MISMATCHED_FAKE_KERNEL
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)
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self.assertEqual(
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report.failures[1].failure_type, FailureType.MISMATCHED_FAKE_KERNEL
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)
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self.assertEqual(
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sorted([f.data["reason"] for f in report.failures]),
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[
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"Dtypes torch.bfloat16 and torch.float32 are not equal!",
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"mismatch between fake value 3 and real value 6 ",
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],
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)
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def test_override_incorrectly_aliasing_kernel(self):
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class M(torch.nn.Module):
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def forward(self, a):
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return torch.ops.mylib.foo(a)
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@torch.library.custom_op("mylib::foo", mutates_args={})
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def foo(a: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
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return a * 2, a + 2
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@foo.register_fake
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def foo_fake_impl(a):
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return a, torch.empty_like(a) # incorrectly aliasing
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mod = M()
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inputs = (torch.randn(3, 3),)
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with self.assertRaisesRegex(
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RuntimeError,
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"Real tensor propagation found an aliasing mismatch",
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):
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with torch._functorch.config.patch(fake_tensor_propagate_real_tensors=True):
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export(mod, inputs, strict=True)
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ep, report = draft_export(mod, inputs)
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for ep_out, eager_out in zip(
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tree_leaves(ep.module()(*inputs)), tree_leaves(mod(*inputs))
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):
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self.assertTrue(torch.allclose(ep_out, eager_out))
|
|
self.assertEqual(ep_out.dtype, eager_out.dtype)
|
|
|
|
self.assertEqual(len(report.failures), 1)
|
|
self.assertEqual(
|
|
report.failures[0].failure_type, FailureType.MISMATCHED_FAKE_KERNEL
|
|
)
|
|
self.assertTrue(
|
|
"Mismatched aliasing spec between fake kernel and real kernel"
|
|
in report.failures[0].data["reason"]
|
|
)
|
|
|
|
def test_override_mismatched_fake_kernel_with_unbacked_symbols(self):
|
|
class M(torch.nn.Module):
|
|
def forward(self, a, b):
|
|
return torch.ops.mylib.foo(a, b)
|
|
|
|
@torch.library.custom_op("mylib::foo", mutates_args={})
|
|
def foo(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
|
|
return a[b.item()].to(torch.bfloat16)
|
|
|
|
@foo.register_fake
|
|
def foo_fake_impl(a, b):
|
|
ctx = torch.library.get_ctx()
|
|
u = ctx.new_dynamic_size()
|
|
return torch.empty(u, a.shape[1], dtype=a.dtype)
|
|
|
|
mod = M()
|
|
inputs = (torch.randn(100, 4), torch.tensor(10))
|
|
|
|
ep, report = draft_export(mod, inputs)
|
|
for ep_out, eager_out in zip(ep.module()(*inputs), mod(*inputs)):
|
|
self.assertTrue(torch.allclose(ep_out, eager_out))
|
|
self.assertEqual(ep_out.dtype, eager_out.dtype)
|
|
|
|
self.assertEqual(len(report.failures), 1)
|
|
self.assertEqual(
|
|
report.failures[0].failure_type, FailureType.MISMATCHED_FAKE_KERNEL
|
|
)
|
|
self.assertEqual(
|
|
report.failures[0].data["reason"],
|
|
"Dtypes torch.bfloat16 and torch.float32 are not equal!",
|
|
)
|
|
|
|
# https://github.com/pytorch/pytorch/issues/140625
|
|
@unittest.skipIf(IS_WINDOWS, "aoti_compile_and_package not supported on Windows")
|
|
def test_constantify_unbacked_symbol(self):
|
|
class M(torch.nn.Module):
|
|
def forward(self, x, y):
|
|
xt = torch.tensor(x.shape)
|
|
u0 = xt[0].item()
|
|
return y * torch.arange(u0)
|
|
|
|
mod = M()
|
|
example_inputs = (torch.randn(3, 5), torch.randn(3))
|
|
draft_ep, _ = draft_export(mod, example_inputs)
|
|
with tempfile.NamedTemporaryFile(suffix=".pt2") as f:
|
|
torch._inductor.aoti_compile_and_package(
|
|
draft_ep,
|
|
package_path=f.name,
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
run_tests()
|