mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
53df1c11cd
For custom ops that do not have a meta kernel, draft export automatically creates a meta kernel based on the tracing example inputs. To ensure that these assumptions made during tracing is clear to the user, we add assertions into the traced exported program:
An example graph:
```
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, a: "f32[s0, s1]", b: "f32[s2, s3]"):
# File: /data/users/angelayi/pytorch/test/export/test_draft_export.py:172 in forward, code: res1 = torch.ops.mylib.foo4(a, b)
_assert_tensor_metadata = torch.ops.aten._assert_tensor_metadata(a, dtype = torch.float32, device = device(type='cpu')); _assert_tensor_metadata = None
_assert_tensor_metadata_1 = torch.ops.aten._assert_tensor_metadata(b, dtype = torch.float32, device = device(type='cpu')); _assert_tensor_metadata_1 = None
foo4: "f32[u2, u3]" = torch.ops.mylib.foo4.default(a, b); a = b = None
return (foo4,)
```
Differential Revision: [D66321129](https://our.internmc.facebook.com/intern/diff/D66321129)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141072
Approved by: https://github.com/pianpwk
ghstack dependencies: #141071
407 lines
13 KiB
Python
407 lines
13 KiB
Python
# Owner(s): ["oncall: export"]
|
|
import copy
|
|
import unittest
|
|
from typing import List, Tuple
|
|
|
|
import torch
|
|
from torch.export import Dim, export
|
|
from torch.export._draft_export import draft_export, FailureType
|
|
from torch.testing import FileCheck
|
|
from torch.testing._internal.common_utils import run_tests, TestCase
|
|
from torch.testing._internal.torchbind_impls import (
|
|
_empty_tensor_queue,
|
|
init_torchbind_implementations,
|
|
)
|
|
from torch.utils._pytree import tree_leaves
|
|
|
|
|
|
class TestDraftExport(TestCase):
|
|
def setUp(self):
|
|
init_torchbind_implementations()
|
|
|
|
@torch._library.register_fake_class("_TorchScriptTesting::_TensorQueue")
|
|
class FakeTensorQueue:
|
|
def __init__(self, queue):
|
|
self.queue = queue
|
|
|
|
@classmethod
|
|
def __obj_unflatten__(cls, flattened_ctx):
|
|
return cls(**dict(flattened_ctx))
|
|
|
|
def push(self, x):
|
|
self.queue.append(x)
|
|
|
|
def pop(self):
|
|
return self.queue.pop(0)
|
|
|
|
def size(self):
|
|
return len(self.queue)
|
|
|
|
def is_empty(self):
|
|
return len(self.queue) == 0
|
|
|
|
def float_size(self):
|
|
return float(len(self.queue))
|
|
|
|
self.torch_bind_ops = [
|
|
torch.ops._TorchScriptTesting.queue_pop,
|
|
torch.ops._TorchScriptTesting.queue_push,
|
|
torch.ops._TorchScriptTesting.queue_size,
|
|
]
|
|
|
|
def tearDown(self):
|
|
torch._library.fake_class_registry.deregister_fake_class(
|
|
"_TorchScriptTesting::_TensorQueue"
|
|
)
|
|
|
|
def test_missing_meta_kernel_custom_op(self):
|
|
with torch.library._scoped_library("mylib", "FRAGMENT") as lib:
|
|
|
|
@torch.library.custom_op("mylib::foo2", mutates_args={})
|
|
def foo2_impl(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
|
|
return a + b
|
|
|
|
class M(torch.nn.Module):
|
|
def forward(self, a, b):
|
|
res = torch.ops.mylib.foo2(a, b)
|
|
return res
|
|
|
|
inp = (torch.ones(3, 3), torch.ones(3, 3))
|
|
|
|
ep, report = draft_export(M(), inp)
|
|
|
|
self.assertEqual(len(report.failures), 1)
|
|
self.assertEqual(
|
|
report.failures[0].failure_type, FailureType.MISSING_FAKE_KERNEL
|
|
)
|
|
|
|
inp = (torch.randn(3, 3), torch.randn(3, 3))
|
|
self.assertEqual(ep.module()(*inp), M()(*inp))
|
|
|
|
def test_missing_meta_kernel_impl(self):
|
|
with torch.library._scoped_library("mylib", "FRAGMENT") as lib:
|
|
torch.library.define(
|
|
"mylib::foo",
|
|
"(Tensor a, Tensor b) -> Tensor",
|
|
tags=torch.Tag.pt2_compliant_tag,
|
|
lib=lib,
|
|
)
|
|
|
|
@torch.library.impl("mylib::foo", "cpu", lib=lib)
|
|
def foo_impl(a, b):
|
|
return a + b
|
|
|
|
class M(torch.nn.Module):
|
|
def forward(self, a, b):
|
|
res = torch.ops.mylib.foo(a, b)
|
|
return res
|
|
|
|
inp = (torch.ones(3, 3), torch.ones(3, 3))
|
|
|
|
ep, report = draft_export(M(), inp)
|
|
|
|
self.assertEqual(len(report.failures), 1)
|
|
self.assertEqual(
|
|
report.failures[0].failure_type, FailureType.MISSING_FAKE_KERNEL
|
|
)
|
|
|
|
inp = (torch.randn(3, 3), torch.randn(3, 3))
|
|
self.assertEqual(ep.module()(*inp), M()(*inp))
|
|
|
|
@unittest.skipIf(not torch.cuda.is_available(), "Requires cuda")
|
|
def test_missing_meta_kernel_guard(self):
|
|
with torch.library._scoped_library("mylib", "FRAGMENT") as lib:
|
|
|
|
@torch.library.custom_op("mylib::foo4", mutates_args={})
|
|
def foo4_impl(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
|
|
return a + b
|
|
|
|
class M(torch.nn.Module):
|
|
def forward(self, a, b):
|
|
res1 = torch.ops.mylib.foo4(a, b)
|
|
return res1
|
|
|
|
inp = (
|
|
torch.ones(3, 4),
|
|
torch.ones(3, 4),
|
|
)
|
|
|
|
ep, report = draft_export(
|
|
M(),
|
|
inp,
|
|
dynamic_shapes={
|
|
"a": {0: Dim.DYNAMIC, 1: Dim.DYNAMIC},
|
|
"b": {0: Dim.DYNAMIC, 1: Dim.DYNAMIC},
|
|
},
|
|
)
|
|
|
|
inp = (torch.randn(2, 3), torch.randn(2, 3))
|
|
self.assertEqual(ep.module()(*inp), M()(*inp))
|
|
m = ep.module()
|
|
with self.assertRaisesRegex(RuntimeError, "Tensor device mismatch!"):
|
|
bad_device_inps = (
|
|
torch.randn(2, 3, device=torch.device("cuda")),
|
|
torch.randn(2, 3, device=torch.device("cuda")),
|
|
)
|
|
m(*bad_device_inps)
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "Tensor dtype mismatch!"):
|
|
bad_dtype_inps = (
|
|
torch.randn(2, 3, dtype=torch.float16),
|
|
torch.randn(2, 3, dtype=torch.float16),
|
|
)
|
|
m(*bad_dtype_inps)
|
|
|
|
def test_data_dependent_failure(self):
|
|
with torch.library._scoped_library("mylib", "FRAGMENT") as lib:
|
|
torch.library.define(
|
|
"mylib::foo1",
|
|
"(Tensor a, Tensor b) -> Tensor",
|
|
tags=torch.Tag.pt2_compliant_tag,
|
|
lib=lib,
|
|
)
|
|
|
|
@torch.library.impl("mylib::foo1", "cpu", lib=lib)
|
|
def foo_impl(a, b):
|
|
return a + b
|
|
|
|
@torch.library.register_fake("mylib::foo1", lib=lib)
|
|
def mylib_foo_default_fake(*args, **kwargs):
|
|
ctx = torch.library.get_ctx()
|
|
fake_shape = [ctx.new_dynamic_size() for _ in range(2)]
|
|
return torch.empty(fake_shape, dtype=torch.float32, device="cpu")
|
|
|
|
class M(torch.nn.Module):
|
|
def forward(self, a, b, c):
|
|
res = torch.ops.mylib.foo1(a, b)
|
|
|
|
c_item = c.item()
|
|
return res[:c_item]
|
|
|
|
inp = (torch.ones(3, 3), torch.ones(3, 3), torch.tensor(3))
|
|
|
|
ep, report = draft_export(M(), inp)
|
|
self.assertTrue(len(report.failures) > 0)
|
|
self.assertEqual(
|
|
report.failures[0].failure_type, FailureType.DATA_DEPENDENT_ERROR
|
|
)
|
|
|
|
inp = (torch.randn(3, 3), torch.randn(3, 3), torch.tensor(2))
|
|
self.assertEqual(ep.module()(*inp), M()(*inp))
|
|
|
|
def test_dedup_data_dependent_failure(self):
|
|
class M(torch.nn.Module):
|
|
def forward(self, x, y, z):
|
|
res = 0
|
|
for v in [x, y]:
|
|
if v.item() > 10:
|
|
res += v * v
|
|
else:
|
|
res += v + v
|
|
|
|
return z * res
|
|
|
|
inp = (torch.tensor(5), torch.tensor(3), torch.tensor(2))
|
|
|
|
ep, report = draft_export(M(), inp)
|
|
self.assertTrue(len(report.failures) > 0)
|
|
self.assertEqual(
|
|
report.failures[0].failure_type, FailureType.DATA_DEPENDENT_ERROR
|
|
)
|
|
|
|
inp = (torch.tensor(4), torch.tensor(2), torch.tensor(6))
|
|
self.assertEqual(ep.module()(*inp), M()(*inp))
|
|
|
|
def test_offsets(self):
|
|
class M(torch.nn.Module):
|
|
def forward(self, x):
|
|
a = x.item()
|
|
if a == 0:
|
|
raise RuntimeError("bad")
|
|
return x * a
|
|
|
|
inp = (torch.tensor(3),)
|
|
ep, report = draft_export(M(), inp)
|
|
|
|
def test_shape_failure(self):
|
|
class M(torch.nn.Module):
|
|
def forward(self, a):
|
|
assert a.shape[0] == 3
|
|
return a * a
|
|
|
|
inp = (torch.ones(3, 3),)
|
|
|
|
ep, report = draft_export(M(), inp, dynamic_shapes={"a": {0: Dim("a0")}})
|
|
|
|
self.assertEqual(len(report.failures), 1)
|
|
self.assertEqual(
|
|
report.failures[0].failure_type, FailureType.CONSTRAINT_VIOLATION_ERROR
|
|
)
|
|
|
|
inp = (torch.randn(3, 3),)
|
|
self.assertEqual(ep.module()(*inp), M()(*inp))
|
|
|
|
inp = (torch.randn(4, 3),)
|
|
with self.assertRaises(RuntimeError):
|
|
ep.module()(*inp)
|
|
|
|
def test_side_effect1(self):
|
|
class M(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.register_buffer("a", torch.tensor(2))
|
|
|
|
def forward(self, b):
|
|
a_item = self.a.item()
|
|
if a_item == 2:
|
|
res = a_item * b
|
|
else:
|
|
res = (a_item + 1) * b
|
|
|
|
self.a.add_(1)
|
|
a_item = self.a.item()
|
|
|
|
if a_item == 3:
|
|
res = a_item * res
|
|
else:
|
|
res = (a_item + 1) * res
|
|
return res
|
|
|
|
inp = (torch.ones(3, 3),)
|
|
mod = M()
|
|
ep, report = draft_export(mod, inp)
|
|
self.assertEqual(mod.a, torch.tensor(2))
|
|
FileCheck().check_count("torch.ops.aten.add.default", 0, exactly=True).run(
|
|
ep.graph_module.code
|
|
)
|
|
|
|
def test_side_effect_inps(self):
|
|
class M(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
def forward(self, x):
|
|
x.sin_()
|
|
return x
|
|
|
|
inp = (torch.ones(3, 3),)
|
|
ep, report = draft_export(M(), inp)
|
|
self.assertTrue(report.successful())
|
|
self.assertEqual(inp[0], torch.ones(3, 3))
|
|
|
|
def test_torchbind(self):
|
|
class Model(torch.nn.Module):
|
|
def __init__(self) -> None:
|
|
super().__init__()
|
|
self.linear = torch.nn.Linear(2, 2)
|
|
|
|
def forward(self, tq, x):
|
|
x_cos = tq.pop() + tq.float_size() + self.linear(x)
|
|
if tq.is_empty():
|
|
x_sin = self.linear(tq.pop()) - tq.size() + x
|
|
else:
|
|
x_sin = tq.pop() + tq.size() + x
|
|
return x_sin, x_cos, tq
|
|
|
|
mod = Model()
|
|
tq = _empty_tensor_queue()
|
|
tq2 = copy.deepcopy(tq)
|
|
a = torch.randn(2, 2)
|
|
b = torch.randn(2, 2)
|
|
tq.push(a)
|
|
tq.push(b)
|
|
tq3 = copy.deepcopy(tq)
|
|
inp = (tq, torch.randn(2, 2))
|
|
ep, report = draft_export(mod, inp)
|
|
self.assertTrue(report.successful())
|
|
self.assertEqual(tq2.size(), 0)
|
|
self.assertEqual(tq3.size(), 2)
|
|
self.assertEqual(tq.size(), 2)
|
|
|
|
def test_override_size_and_dtype_mismatched_fake_kernels(self):
|
|
class M(torch.nn.Module):
|
|
def forward(self, a):
|
|
return torch.ops.mylib.foo(a)
|
|
|
|
@torch.library.custom_op("mylib::foo", mutates_args={})
|
|
def foo(a: torch.Tensor) -> List[torch.Tensor]:
|
|
x = a * 2
|
|
y = a.repeat(2, 2)
|
|
z = a.to(torch.bfloat16)
|
|
return [x, y, z]
|
|
|
|
@foo.register_fake
|
|
def foo_fake_impl(a):
|
|
x = torch.empty_like(a) # good
|
|
y = torch.empty_like(a) # size mismatch
|
|
z = torch.empty_like(a) # dtype mismatch
|
|
return [x, y, z]
|
|
|
|
mod = M()
|
|
inputs = (torch.randn(3, 3),)
|
|
with self.assertRaises(RuntimeError):
|
|
with torch._functorch.config.patch(fake_tensor_propagate_real_tensors=True):
|
|
export(mod, inputs)
|
|
|
|
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), 2)
|
|
self.assertEqual(
|
|
report.failures[0].failure_type, FailureType.MISMATCHED_FAKE_KERNEL
|
|
)
|
|
self.assertEqual(
|
|
report.failures[1].failure_type, FailureType.MISMATCHED_FAKE_KERNEL
|
|
)
|
|
self.assertEqual(
|
|
sorted([f.data["reason"] for f in report.failures]),
|
|
[
|
|
"Dtypes torch.bfloat16 and torch.float32 are not equal!",
|
|
"mismatch between fake value 3 and real value 6 ",
|
|
],
|
|
)
|
|
|
|
def test_override_incorrectly_aliasing_kernel(self):
|
|
class M(torch.nn.Module):
|
|
def forward(self, a):
|
|
return torch.ops.mylib.foo(a)
|
|
|
|
@torch.library.custom_op("mylib::foo", mutates_args={})
|
|
def foo(a: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
return a * 2, a + 2
|
|
|
|
@foo.register_fake
|
|
def foo_fake_impl(a):
|
|
return a, torch.empty_like(a) # incorrectly aliasing
|
|
|
|
mod = M()
|
|
inputs = (torch.randn(3, 3),)
|
|
with self.assertRaisesRegex(
|
|
RuntimeError,
|
|
"Real tensor propagation found an aliasing mismatch",
|
|
):
|
|
with torch._functorch.config.patch(fake_tensor_propagate_real_tensors=True):
|
|
export(mod, inputs)
|
|
|
|
ep, report = draft_export(mod, inputs)
|
|
for ep_out, eager_out in zip(
|
|
tree_leaves(ep.module()(*inputs)), tree_leaves(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.assertTrue(
|
|
"Mismatched aliasing spec between fake kernel and real kernel"
|
|
in report.failures[0].data["reason"]
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
run_tests()
|