mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
fa44e165ff
Retry of https://github.com/pytorch/pytorch/pull/76875. It was reverted due to torchvision failures, but it turned out that the failures were caused by a different PR. irparser previously didn't support these, which would cause failures in log_extract.py Pull Request resolved: https://github.com/pytorch/pytorch/pull/77377 Approved by: https://github.com/datumbox
383 lines
12 KiB
Python
383 lines
12 KiB
Python
# Owner(s): ["oncall: jit"]
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from typing import Any, Dict, List, Optional, Tuple
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from torch.testing._internal.jit_utils import JitTestCase, make_global
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from torch.testing import FileCheck
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from torch import jit
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from jit.test_module_interface import TestModuleInterface # noqa: F401
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import unittest
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import os
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import sys
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import torch
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import torch.testing._internal.jit_utils
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import torch.nn as nn
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from torch.testing._internal.common_utils import freeze_rng_state
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# Make the helper files in test/ importable
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pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
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sys.path.append(pytorch_test_dir)
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if __name__ == '__main__':
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raise RuntimeError("This test file is not meant to be run directly, use:\n\n"
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"\tpython test/test_jit.py TESTNAME\n\n"
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"instead.")
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class TestMisc(JitTestCase):
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def test_joined_str(self):
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def func(x):
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hello, test = "Hello", "test"
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print(f"{hello + ' ' + test}, I'm a {test}")
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print("format blank")
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hi = 'hi'
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print(f"stuff before {hi}")
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print(f"{hi} stuff after")
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return x + 1
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x = torch.arange(4., requires_grad=True)
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# TODO: Add support for f-strings in string parser frontend
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# self.checkScript(func, [x], optimize=True, capture_output=True)
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with self.capture_stdout() as captured:
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out = func(x)
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scripted = torch.jit.script(func)
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with self.capture_stdout() as captured_script:
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out_script = func(x)
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self.assertEqual(out, out_script)
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self.assertEqual(captured, captured_script)
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@unittest.skipIf(sys.version_info[:2] < (3, 7), "`dataclasses` module not present on < 3.7")
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def test_dataclass_error(self):
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from dataclasses import dataclass
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@dataclass
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class NormalizationInfo(object):
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mean: float = 0.0
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def compute(self, total_rows):
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return self.mean
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def fn():
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return NormalizationInfo(1, 2, 3, 4, 5)
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with self.assertRaisesRegex(OSError, "could not get source code"):
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torch.jit.script(fn)
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def test_kwarg_support(self):
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with self.assertRaisesRegex(torch.jit.frontend.NotSupportedError, "variable number of arguments"):
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class M(torch.nn.Module):
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def forward(self, *, n_tokens: int, device_name: str = 2):
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pass
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torch.jit.script(M())
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class M(torch.nn.Module):
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def forward(self, *, n_tokens: int, device_name: str):
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return n_tokens, device_name
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sm = torch.jit.script(M())
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with self.assertRaisesRegex(RuntimeError, "missing value for argument 'n_tokens'"):
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sm()
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with self.assertRaisesRegex(RuntimeError, "positional arg"):
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sm(3, 'hello')
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self.assertEqual(sm(n_tokens=3, device_name='hello'), (3, 'hello'))
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def test_tuple_subscripted_assign(self):
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with self.assertRaisesRegex(RuntimeError, "subscripted assignment"):
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@torch.jit.script
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def foo(a: Tuple[int, int]) -> None:
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a[0] = a[1]
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with self.assertRaisesRegex(RuntimeError, "augmented assignment"):
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@torch.jit.script
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def bar(a: Tuple[int, int]) -> None:
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a[0] += a[1]
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def test_subexpression_List_Future(self):
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@torch.jit.script
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def fn(x: List[torch.jit.Future[int]]) -> torch.jit.Future[int]:
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return x[0]
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FileCheck().check('Future[int]').check('Future[int]').run(fn.graph)
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def test_subexpression_Future_annotate(self):
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@torch.jit.script
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def fn() -> torch.jit.Future[int]:
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x: List[torch.jit.Future[int]] = []
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return x[0]
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FileCheck().check("Future[int][]").run(fn.graph)
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def test_future_isinstance(self):
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@torch.jit.script
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def fn(x: Any) -> torch.jit.Future[int]:
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assert isinstance(x, jit.Future[int])
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return x
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FileCheck().check("Future[int]").run(fn.graph)
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def test_str_refine_any(self):
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def forward(x: Any) -> str:
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if isinstance(x, str):
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return x
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return "foo"
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forward = torch.jit.script(forward)
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self.assertEqual(forward(1), "foo")
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self.assertEqual(forward("bar"), "bar")
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def test_subexpression_Tuple_int_int_Future(self):
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@torch.jit.script
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def fn(x: Tuple[int, int, torch.jit.Future[int]]) -> Tuple[int, torch.jit.Future[int]]:
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return x[0], x[2]
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FileCheck().check('(int, int, Future[int])').check('(int, Future[int])').run(fn.graph)
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def test_subexpression_Dict_int_Future(self):
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@torch.jit.script
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def fn(x: Dict[int, torch.jit.Future[int]], y: int) -> torch.jit.Future[int]:
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return x[y]
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FileCheck().check('Dict(int, Future(int))').check('Future[int]').run(fn.graph)
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def test_subexpression_Optional(self):
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@torch.jit.script
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def fn(x: Optional[Dict[int, torch.jit.Future[int]]]) -> Optional[torch.jit.Future[int]]:
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if x is not None:
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return x[0]
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else:
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return None
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FileCheck().check('Dict(int, Future(int))?').run(fn.graph)
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def test_if_returning_any(self):
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"""
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Check that an if statement can return different
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types early from each branch when the return
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type of the function is Any.
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"""
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def if_function(inp: torch.Tensor) -> Any:
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if inp.shape[0] == 1:
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return inp * inp
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else:
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return "str"
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self.checkScript(if_function, (torch.randn(5),))
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def test_hacked_twin(self):
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def gen_data():
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with freeze_rng_state():
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return torch.randn(10), torch.randint(10, (20,)), torch.randn(20)
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input, index, value, = gen_data()
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input1, index1, value1, = gen_data()
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out1 = torch.ops.aten.index_put.hacked_twin(input, [index], value, accumulate=False)
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out2 = torch.index_put(input1, [index1], value1, accumulate=False)
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self.assertEqual(out1, out2)
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torch.ops.aten.index_put_.hacked_twin(input, [index], value, accumulate=False)
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torch.index_put_(input1, [index1], value1, accumulate=False)
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self.assertEqual(input, input1)
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def test_export_opnames_interface(self):
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@torch.jit.interface
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class OneTwoModule(nn.Module):
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def one(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
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pass
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def two(self, x: torch.Tensor) -> torch.Tensor:
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pass
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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pass
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class FooMod(nn.Module):
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def one(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
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return x + y
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def two(self, x: torch.Tensor) -> torch.Tensor:
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return 2 * x
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.one(self.two(x), x)
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class BarMod(nn.Module):
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def one(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
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return x * y
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def two(self, x: torch.Tensor) -> torch.Tensor:
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return 2 / x
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.two(self.one(x, x))
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make_global(OneTwoModule)
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class M(nn.Module):
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sub : OneTwoModule
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def __init__(self):
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super(M, self).__init__()
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self.sub = BarMod()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.sub.forward(x)
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def use_module_interface(mod_list: List[OneTwoModule], x: torch.Tensor):
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return mod_list[0].forward(x) + mod_list[1].forward(x)
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torch._C._enable_mobile_interface_call_export()
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scripted_M_mod = torch.jit.script(M())
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self.assertTrue(set(['aten::mul.Scalar', 'aten::mul.Tensor', 'aten::reciprocal']).issubset(
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set(torch.jit.export_opnames(scripted_M_mod))))
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scripted_M_mod.sub = torch.jit.script(FooMod())
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self.assertTrue(set(['aten::add.Tensor', 'aten::mul.Scalar']).issubset(
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set(torch.jit.export_opnames(scripted_M_mod))))
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def test_math_inf(self):
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from math import inf
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def foo():
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return inf
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self.checkScript(foo, ())
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def test_list_literal_infer(self):
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def expects_intlist(x: List[int]):
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x.append(3)
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return x
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def foo():
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return expects_intlist([])
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self.checkScript(foo, ())
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def annotated_list_fail():
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return expects_intlist(torch.jit.annotate([], List[Tensor]))
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with self.assertRaises(RuntimeError):
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torch.jit.script(annotated_list_fail)
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def non_temporary_fail():
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a = []
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return expects_intlist(a)
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with self.assertRaises(RuntimeError):
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torch.jit.script(non_temporary_fail)
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@torch.jit.script
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def test_return():
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return []
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FileCheck().check("Tensor[] = prim::ListConstruct").run(test_return.graph)
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def test_legacy_tensor_constructor(self):
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# testing PyObject overload
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def test_all_dtypes():
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return (
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torch.BoolTensor([2]),
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torch.LongTensor([3]),
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torch.ByteTensor([4]),
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torch.CharTensor([5]),
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torch.DoubleTensor([6]),
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torch.FloatTensor([7]),
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torch.IntTensor([8]),
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torch.ShortTensor([1]),
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torch.HalfTensor([1]),
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)
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self.checkScript(test_all_dtypes, ())
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# now test empty overload
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def empty_overload():
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return torch.LongTensor(2, 3, 4)
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eager = empty_overload()
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jit = torch.jit.script(empty_overload)()
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eager[:] = 1
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jit[:] = 1
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self.assertEqual(eager, jit)
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def no_inputs():
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return torch.DoubleTensor()
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self.checkScript(no_inputs, ())
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# bad schema
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def multiple_args():
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return torch.LongTensor(1, [2])
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with self.assertRaisesRegex(RuntimeError, "multiple positional arguments that were not all integers"):
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torch.jit.script(multiple_args)
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# kwarg bad schema
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def bad_kwarg():
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return torch.LongTensor(hello="1")
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with self.assertRaisesRegex(RuntimeError, "hello"):
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torch.jit.script(bad_kwarg)
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def test_broadcasting_list(self):
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"""
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Test BroadcastingList and torch.nn._size_N_t alias
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"""
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from torch._jit_internal import BroadcastingList2
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from torch.nn.common_types import _size_2_t
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def sum_i(x: _size_2_t) -> int:
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return x[0] + x[1]
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def sum_f(x: BroadcastingList2[float]) -> float:
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return x[0] + x[1]
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self.assertTrue(torch.jit.script(sum_i)(4) == 8)
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self.assertTrue(torch.jit.script(sum_f)(4.5) == 9.)
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def test_parse_ir_annotate(self):
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ir = """
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graph():
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%3 : int[] = prim::Constant[value=annotate(List[int], [])]()
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return (%3)
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"""
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graph = torch._C.parse_ir(ir, True)
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func = torch._C._create_function_from_graph("forward", graph)
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ret = func()
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self.assertTrue(ret == [])
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def test_parse_ir_single_element_tensor_positive(self):
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ir = """
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graph():
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%7 : Long(1, strides=[1], requires_grad=0, device=cpu) = prim::Constant[value={0}]()
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return (%7)
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"""
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graph = torch._C.parse_ir(ir, True)
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func = torch._C._create_function_from_graph("forward", graph)
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ret = func()
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self.assertTrue(ret.numel() == 1)
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self.assertTrue(len(ret.size()) == 1)
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def test_parse_ir_single_element_tensor_negative(self):
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ir = """
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graph():
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%7 : Long(1, strides=[1], requires_grad=0, device=cpu) = prim::Constant[value={-17}]()
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return (%7)
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"""
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graph = torch._C.parse_ir(ir, True)
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func = torch._C._create_function_from_graph("forward", graph)
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ret = func()
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self.assertTrue(ret.numel() == 1)
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self.assertTrue(len(ret.size()) == 1)
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