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pytorch-bot bot commented Oct 13, 2024

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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/137861

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❌ 121 New Failures

As of commit 03de794 with merge base 8fbf866 (image):

NEW FAILURES - The following jobs have failed:

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This PR needs a release notes: label

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cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx ipiszy yf225 chenyang78 kadeng muchulee8 ColinPeppler amjames desertfire chauhang

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@Chillee Chillee changed the title Turned most IRNodes into dataclasses Turned Views, Layouts, and Loops into proper dataclasses Oct 13, 2024
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FYI: In case this bites you: python/cpython#109409

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Chillee added a commit that referenced this pull request Oct 24, 2024
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Chillee added a commit that referenced this pull request Oct 25, 2024
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super().__init__(

also should have kwargs for appropriate dataclass init

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Hey, @Chillee! I'm from the issue #136196

I'm catching this runtime error that should be relevant to your changes.

After debug prints, I see that the buffers in the assertion have the type of SymInt

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Chillee commented Oct 28, 2024

@alexdremov ah I don't think that's related, i think it's another limitation related to symbolic shapes today.

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@alexdremov ah I don't think that's related, i think it's another limitation related to symbolic shapes today.

Are there quick fixes?

If no, the initial issue should be left open as combination of dynamic shapes + flex attention is not working as of this PR.

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Chillee commented Oct 28, 2024

@alexdremov The example in that particular issue works, right?

The error you're running into is most likely because you're doing something like

Q_LEN = q.shape[0]
def score_mod(score, b, h, q, kv):
    return score + (Q_LEN - kv)

This can be worked around by converting Q_LEN into a tensor.

There's some discussion here #137779

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alexdremov commented Oct 28, 2024

This can be worked around by converting Q_LEN into a tensor.

I see, I'll try doing this thing.

My example is nothing fancy. It's just sequence length padding + chunking

# pseudocode-ish

lens = torch.ones((B, ), device='cuda').long() # sequence lengths

def generator(b, h, q_idx, kv_idx):
    chunking = f(q_idx, kv_idx)  # only simple torch operations inside f
    padding = (q_idx < lens[b]) & (kv_idx < lens[b])
    return chunking & padding

# for self attention
block_mask = create_block_mask(generator, B=B, H=None, Q_LEN=Q_LEN, KV_LEN=KV_LEN)

But I don't see where SymInt comes from. Q_LEN and KV_LEN are indeed derived from input shapes

UPD: well, I guess capturing lens is the problem. What are possible hacks?
UPD2: seems like it's not the problem. Symbolic Q_LEN and KV_LEN are

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Looks like this PR hasn't been updated in a while so we're going to go ahead and mark this as Stale.
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@github-actions github-actions bot added the Stale label Dec 27, 2024
@github-actions github-actions bot closed this Jan 26, 2025
@github-actions github-actions bot deleted the gh/chillee/367/head branch February 26, 2025 02:05
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