mirror of
https://github.com/zebrajr/pytorch.git
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c106ee8515
## Fixes https://github.com/pytorch/pytorch/issues/159462 also fixes #163569 , #163604 ## summary the issue is caused by the wrong stride of conv1d's result generated by meta_conv:4d5b3f2d5a/torch/_meta_registrations.py (L2453-L2471)and the wrong stride will be used to codegen size assert in inductor:4d5b3f2d5a/torch/_inductor/ir.py (L6152-L6163)## reason So why the computed stride is wrong in the meta_conv function? because the corresponding backend will convert conv1d to conv2d and change the input tensor' size and memory_format(channel last). but the meta_conv do not do this transformation, so a mismatch happend.4d5b3f2d5a/aten/src/ATen/native/Convolution.cpp (L1502-L1510)just add corresponding logic in meta_conv. Pull Request resolved: https://github.com/pytorch/pytorch/pull/160408 Approved by: https://github.com/eellison, https://github.com/jansel, https://github.com/mlazos
208 lines
7.3 KiB
Python
208 lines
7.3 KiB
Python
# Owner(s): ["module: inductor"]
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import unittest
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from sympy import Symbol, sympify
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import torch
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from torch._inductor.fx_utils import count_flops_fx, countable_fx
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from torch._inductor.utils import get_device_tflops, sympy_str, sympy_subs
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from torch._inductor.virtualized import V
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from torch.testing._internal.common_device_type import (
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dtypes,
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instantiate_device_type_tests,
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)
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from torch.testing._internal.common_utils import run_tests, TestCase
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class TestUtils(TestCase):
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def test_zip_schema(self):
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def foo(x: torch.Tensor) -> None:
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pass
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result = torch.library.custom_op("mylib::foo", foo, mutates_args={"x"})
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schema = result._opoverload._schema
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g = torch.tensor([11, 2])
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found = False
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for arg, val in torch._library.utils.zip_schema(schema, [], {"x": g}):
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if arg.name == "x":
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found = True
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self.assertTrue(found)
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found = False
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for arg, val in torch._library.utils.zip_schema(schema, [g], {}):
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if arg.name == "x":
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found = True
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self.assertTrue(found)
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def testSympySubs(self):
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# integer and nonnegetaive attributes are preserved.
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expr = Symbol("x")
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result = sympy_subs(expr, {expr: "y"})
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self.assertEqual(result.name, "y")
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self.assertEqual(result.is_integer, None)
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self.assertEqual(result.is_nonnegative, None)
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expr = Symbol("x", integer=True, nonnegative=False)
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result = sympy_subs(expr, {expr: "y"})
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self.assertEqual(result.name, "y")
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self.assertEqual(result.is_integer, True)
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self.assertEqual(result.is_nonnegative, False)
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# invalid replacement.
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expr = Symbol("x", integer=True)
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result = sympy_subs(expr, {Symbol("x"): Symbol("y")})
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self.assertEqual(result.name, "x")
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# valid replacement since properties match.
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expr = Symbol("x", integer=True)
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result = sympy_subs(expr, {Symbol("x", integer=True): Symbol("y")})
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self.assertEqual(result.name, "y")
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# invalid replacement.
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expr = Symbol("x", integer=None)
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result = sympy_subs(expr, {Symbol("x", integer=False): Symbol("y")})
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self.assertEqual(result.name, "x")
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# replaced can't be string
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self.assertRaises(AssertionError, sympy_subs, expr, {"x": "y"})
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# replaced can be an expression
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expr = Symbol("x")
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expr = abs(expr)
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self.assertEqual(expr.is_integer, None)
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self.assertEqual(expr.is_nonnegative, None)
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# replace abs(x) with y
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# propagte abs(x) sympy properties.
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result = sympy_subs(expr, {expr: Symbol("y")})
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self.assertEqual(result.name, "y")
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self.assertEqual(result.is_integer, None)
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self.assertEqual(result.is_nonnegative, None)
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def test_sympy_str(self):
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self.assertEqual(sympy_str(sympify("a+b+c")), "a + b + c")
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self.assertEqual(sympy_str(sympify("a*b+c")), "c + a * b")
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self.assertEqual(sympy_str(sympify("a+b*(c+d)")), "a + b * (c + d)")
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self.assertEqual(sympy_str(sympify("(a+b)*(c+d)")), "(a + b) * (c + d)")
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self.assertEqual(sympy_str(sympify("-a")), "-a")
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self.assertEqual(sympy_str(sympify("a-b")), "a - b")
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self.assertEqual(sympy_str(sympify("a+-b")), "a - b")
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def test_flops_fx(self):
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def create_fx_node(
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aten: torch._ops.OpOverloadPacket, args, kwargs
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) -> tuple[torch.fx.Node, torch.fx.Node]:
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node1 = torch.fx.Node(
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graph=torch.fx.Graph(),
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name="",
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op="call_function",
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target=aten,
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args=args,
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kwargs=kwargs,
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)
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name: str = aten.overloads()[0]
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op_overload: torch._ops.OpOverload = getattr(aten, name)
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node2 = torch.fx.Node(
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graph=torch.fx.Graph(),
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name="",
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op="call_function",
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target=op_overload,
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args=args,
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kwargs=kwargs,
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)
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return node1, node2
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with V.set_fake_mode(
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torch._subclasses.FakeTensorMode(allow_non_fake_inputs=True)
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):
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trues = [
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(
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torch.ops.aten.addmm,
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(torch.Tensor(4, 4), torch.Tensor(4, 5), torch.Tensor(5, 4)),
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{},
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),
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(
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torch.ops.aten.bmm,
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(torch.Tensor(10, 4, 5), torch.Tensor(10, 5, 4)),
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{},
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),
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(torch.ops.aten.mm, (torch.Tensor(2, 3), torch.Tensor(3, 2)), {}),
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(
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torch.ops.aten.convolution,
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(
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torch.Tensor(2, 2, 3),
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torch.Tensor(2, 2, 2),
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torch.Tensor(2),
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(1,),
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(0,),
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(1,),
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True,
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(0,),
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1,
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),
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{},
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),
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(
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torch.ops.aten._convolution,
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(
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torch.Tensor(2, 2, 2),
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torch.Tensor(2, 2, 2),
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torch.Tensor(2),
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(1,),
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(0,),
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(1,),
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True,
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(0,),
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1,
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False,
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True,
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False,
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),
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{},
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),
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]
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# we don't support pointwise ops
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falses = [
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(
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torch.ops.aten.add,
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(torch.Tensor(1, 2, 3), torch.Tensor(1, 2, 3)),
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{},
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),
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(
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torch.ops.aten.mul,
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(torch.Tensor(1, 2, 3), torch.Tensor(1, 2, 3)),
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{},
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),
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]
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for t, args, kwargs in trues:
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fx_node_1, fx_node_2 = create_fx_node(t, args, kwargs)
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self.assertTrue(
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countable_fx(fx_node_1), f"Expected true {t}: {fx_node_1}"
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)
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self.assertTrue(
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countable_fx(fx_node_2), f"Expected true {t}: {fx_node_2}"
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)
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self.assertNotEqual(count_flops_fx(fx_node_1), None)
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self.assertNotEqual(count_flops_fx(fx_node_2), None)
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for f, args, kwargs in falses:
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fx_node_1, fx_node_2 = create_fx_node(f, args, kwargs)
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self.assertFalse(
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countable_fx(fx_node_1), f"Expected false {f}: {fx_node_1}"
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)
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self.assertFalse(
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countable_fx(fx_node_2), f"Expected false {f}: {fx_node_2}"
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)
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@unittest.skipIf(not torch.cuda.is_available(), "skip if no device")
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@dtypes(torch.float16, torch.bfloat16, torch.float32)
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def test_get_device_tflops(self, dtype):
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ret = get_device_tflops(dtype)
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self.assertTrue(type(ret) == float)
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instantiate_device_type_tests(TestUtils, globals())
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if __name__ == "__main__":
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run_tests()
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