[Inductor] Restrict block analysis to only match integer dims and strides #149615
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/149615
Note: Links to docs will display an error until the docs builds have been completed. ❌ 4 New Failures, 7 Unrelated FailuresAs of commit fb3bce7 with merge base 98bd2bd ( NEW FAILURES - The following jobs have failed:
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@pytorchbot label "topic: not user facing" |
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Similar here - @blaine-rister would you midn reviewing this one ? |
| # dim_mod1_: 4, stride_mod1_: 1, stride_mod4_: 0, stride_mod2_: 0, stride_mod0_: 0 | ||
| # } | ||
| # This is now fixed by ensuring that that wild symbols only match nonnegative integers | ||
| def test_ensure_integral_dims_and_strides(self): |
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Thanks for the test case! This is a good one.
| @@ -167,7 +171,11 @@ def match_affine_block_expr( | |||
| stride. | |||
| """ | |||
| index = cls._preprocess(index) | |||
| stride = sympy.Wild("stride", exclude=[index_var]) | |||
| stride = sympy.Wild( | |||
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nit: it might make sense to use a helper function here, since these properties are non-trivial. Something like this might work:
class BlockPatternMatcher:
_indexing_wild = functools.partial(sympy.Wild, properties=[x.is_integer])
| wild = functools.partial( | ||
| sympy.Wild, | ||
| exclude=[index_var], | ||
| properties=[lambda x: x.is_integer and x.is_nonnegative], |
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Good catch--strides should be integers.
Do we also need them to be positive, or is that too restrictive? I'm not sure how Inductor will handle this under the hood, but at least in normal Python we can have negative strides. This would be a good test case:
import torch
def foo(x, y):
return x[:-1:] + y # Slice in reverse order via a negative stride
x, y = (torch.randn(8) for _ in range(2))
foo(x, y)
# TODO test with torch.compile + block pointers...
For dims, I agree that they need to be positive integers.
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Yes you're right, although support for generating negative strides seems limited to functions like torch.flip (see #59786). I've changed the code to include negative values for strides.
import torch
from torch._inductor import config
# Matches the following:
# index_relative_to_xyr_index = -256*((xindex//64)) - (ModularIndexing(xindex, 1, 8)) - 16*(ModularIndexing(xindex, 8, 8)) + 1911
# subexpr = -256*((xindex//64)) - (ModularIndexing(xindex, 1, 8)) - 16*(ModularIndexing(xindex, 8, 8))
# BlockParameters(shape=[8, 8, 8], block_shape=[
# ((XBLOCK + 63)//64),
# Min(8, ((XBLOCK + 7)//8)),
# Min(8, XBLOCK)
# ], strides=[-256, -16, -1],
# offsets=[(xoffset//64), ModularIndexing(xoffset, 8, 8),
# ModularIndexing(xoffset, 1, 8)])
# constant_offset = 1911
def fn(x, y):
return torch.flip(x, [0, 1, 2]) + y # Slice in reverse order via a negative stride
def discontiguous_tensor(view_size, device="cpu"):
full_size = tuple(2 * dim for dim in view_size)
full = torch.randn(full_size).to(device)
view = torch.as_strided(full, view_size, full.stride())
return view
x, y = (discontiguous_tensor((8, 8, 8)) for _ in range(2))
result_eager = fn(x, y)
config.triton.use_block_ptr= True
foo_compile = torch.compile(fn)
result_compile = foo_compile(x, y)
torch.testing.assert_close(result_eager, result_compile)There was a problem hiding this comment.
Nice find! It wouldn't hurt to add this test case to the CI as well.
blaine-rister
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blaine-rister
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May 10, 2025
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Restrict block analysis to only match dimension sizes and strides that are integers. E.g.
sympycan match index expressions likeModularIndexing(xindex, 4, 4)) + 4*(ModularIndexing(xindex, 32, 2))with the candidate below that is invalid.Fixes #ISSUE_NUMBER
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov