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BoyuanFeng · 2025-10-27T21:16:37Z

Graph partition relies on get_free_symbol_uses() to collect symbol inputs.

Lines 4869 to 4885 in ee7434b

    
                   def get_scheduler_node_symbol_uses( 
        
                       node: BaseSchedulerNode, 
        
                   ) -> OrderedSet[sympy.Symbol]: 
        
                       """ 
        
                       Gets symbols used in node. 
        
                       """ 
        
                       if isinstance(node, FusedSchedulerNode): 
        
                           return OrderedSet().union( 
        
                               *(get_scheduler_node_symbol_uses(snode) for snode in node.snodes) 
        
                           ) 
        
                       assert node.node is not None 
        
                       free_symbol_uses = node.node.get_free_symbol_uses() 
        
                       free_symbol_uses.update( 
        
                           *(get_layout_symints(ir_node) for ir_node in node.node.get_outputs()) 
        
                       ) 
        
                       return free_symbol_uses

I empirically observed that get_free_symbol_uses() becomes slower for larger graphs. Specifically, I tried to aten fallback for torchtitan which results in 10k+ aten nodes. When processing the 600-th node, it takes seconds to get_free_symbol_uses() for 1 node.

Why? Because get_free_symbol_uses() may recursively call another get_free_symbol_uses(), which could recursively run many times.

pytorch/torch/_inductor/ir.py

Lines 4541 to 4543 in ee7434b

    
           result = self.layout.get_free_symbol_uses( 
        
               unbacked_only 
        
           ) | self.data.get_free_symbol_uses(unbacked_only)

This PR fixes the issue by caching the results of get_free_symbol_uses(). I validated on torchtitan that the issue is fixed.

cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov @coconutruben

pytorch-bot · 2025-10-27T21:16:41Z

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laithsakka · 2025-10-28T06:20:50Z

seems reasonable are inductor nodes immutable? @eellison
if NOT wonder if we can do this optimization in a more safe way, within a context that i know that the nodes are not changing i can cache ? that would be dependent on the torchtitan case.

desertfire · 2025-10-28T14:19:53Z

+    def wrapper(self: Any, *args: P.args, **kwargs: P.kwargs) -> RV:
+        key = (id(self), args, tuple(sorted(kwargs.items())))
+        if key not in cache:
+            cache[key] = fn(self, *args, **kwargs)


Looking at how cache_on_self was implemented, I think we should do something similar here to further improve the performance.

eellison

Right, flexible layout might change stride, although it's unlikely it would induce a new symbol use. Would it be safer to only cache if the layout is fixed ?

BoyuanFeng · 2025-10-28T20:59:54Z

+
+    @offset.setter
+    def offset(self, value: Expr) -> None:
+        self.assert_free_symbol_uses_unchanged("offset", value)


error if free symbols are added or deleted after initialization.

eellison

sorry, one last question - at the point we call it all these nodes should have fixed layout. should we just only cache in the fixed layout case ? i think that will be a bit simpler.

desertfire · 2025-10-30T17:05:05Z

sorry, one last question - at the point we call it all these nodes should have fixed layout. should we just only cache in the fixed layout case ? i think that will be a bit simpler.

That is simpler but probably will have performance implication. @BoyuanFeng , I wonder how much performance difference it will be.

atalman · 2025-10-31T12:55:03Z

@pytorchmergebot revert -c nosignal -m "Failure: test/nn/test_convolution.py::TestConvolutionNN::test_conv3d_overflow_values GH job link HUD commit link"

pytorchmergebot · 2025-10-31T12:57:52Z

@pytorchbot successfully started a revert job. Check the current status here.
Questions? Feedback? Please reach out to the PyTorch DevX Team

pytorchmergebot · 2025-10-31T12:57:58Z

@BoyuanFeng your PR has been successfully reverted.

This reverts commit a6b1ef1. Reverted #166338 on behalf of https://github.com/atalman due to Failure: test/nn/test_convolution.py::TestConvolutionNN::test_conv3d_overflow_values [GH job link](https://github.com/pytorch/pytorch/actions/runs/18961173726/job/54149112920) [HUD commit link](https://hud.pytorch.org/pytorch/pytorch/commit/a6b1ef17173f56ba93ac97ff4384fa4060b5e41e) ([comment](#166338 (comment)))

BoyuanFeng · 2025-10-31T17:47:26Z

@atalman the failure is not related to this pr. I also cannot repro locally. Let me rebase and try ci again.

BoyuanFeng · 2025-10-31T21:16:31Z

@pytorchbot merge

pytorchmergebot · 2025-10-31T21:18:25Z

Merge started

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Learn more about merging in the wiki.

Questions? Feedback? Please reach out to the PyTorch DevX Team

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Graph partition relies on `get_free_symbol_uses()` to collect symbol inputs. https://github.com/pytorch/pytorch/blob/ee7434be822cf6e75b4566d8159f550ee233d8ae/torch/_inductor/scheduler.py#L4869-L4885 I empirically observed that `get_free_symbol_uses()` becomes slower for larger graphs. Specifically, I tried to aten fallback for torchtitan which results in 10k+ aten nodes. When processing the 600-th node, it takes seconds to `get_free_symbol_uses()` for 1 node. Why? Because `get_free_symbol_uses()` may recursively call another `get_free_symbol_uses()`, which could recursively run many times. https://github.com/pytorch/pytorch/blob/ee7434be822cf6e75b4566d8159f550ee233d8ae/torch/_inductor/ir.py#L4541-L4543 This PR fixes the issue by caching the results of `get_free_symbol_uses()`. I validated on torchtitan that the issue is fixed. Pull Request resolved: #166338 Approved by: https://github.com/eellison

This reverts commit a6b1ef1. Reverted #166338 on behalf of https://github.com/atalman due to Failure: test/nn/test_convolution.py::TestConvolutionNN::test_conv3d_overflow_values [GH job link](https://github.com/pytorch/pytorch/actions/runs/18961173726/job/54149112920) [HUD commit link](https://hud.pytorch.org/pytorch/pytorch/commit/a6b1ef17173f56ba93ac97ff4384fa4060b5e41e) ([comment](#166338 (comment)))

Graph partition relies on `get_free_symbol_uses()` to collect symbol inputs. https://github.com/pytorch/pytorch/blob/ee7434be822cf6e75b4566d8159f550ee233d8ae/torch/_inductor/scheduler.py#L4869-L4885 I empirically observed that `get_free_symbol_uses()` becomes slower for larger graphs. Specifically, I tried to aten fallback for torchtitan which results in 10k+ aten nodes. When processing the 600-th node, it takes seconds to `get_free_symbol_uses()` for 1 node. Why? Because `get_free_symbol_uses()` may recursively call another `get_free_symbol_uses()`, which could recursively run many times. https://github.com/pytorch/pytorch/blob/ee7434be822cf6e75b4566d8159f550ee233d8ae/torch/_inductor/ir.py#L4541-L4543 This PR fixes the issue by caching the results of `get_free_symbol_uses()`. I validated on torchtitan that the issue is fixed. Pull Request resolved: #166338 Approved by: https://github.com/eellison

Graph partition relies on `get_free_symbol_uses()` to collect symbol inputs. https://github.com/pytorch/pytorch/blob/ee7434be822cf6e75b4566d8159f550ee233d8ae/torch/_inductor/scheduler.py#L4869-L4885 I empirically observed that `get_free_symbol_uses()` becomes slower for larger graphs. Specifically, I tried to aten fallback for torchtitan which results in 10k+ aten nodes. When processing the 600-th node, it takes seconds to `get_free_symbol_uses()` for 1 node. Why? Because `get_free_symbol_uses()` may recursively call another `get_free_symbol_uses()`, which could recursively run many times. https://github.com/pytorch/pytorch/blob/ee7434be822cf6e75b4566d8159f550ee233d8ae/torch/_inductor/ir.py#L4541-L4543 This PR fixes the issue by caching the results of `get_free_symbol_uses()`. I validated on torchtitan that the issue is fixed. Pull Request resolved: pytorch#166338 Approved by: https://github.com/eellison

This reverts commit a6b1ef1. Reverted pytorch#166338 on behalf of https://github.com/atalman due to Failure: test/nn/test_convolution.py::TestConvolutionNN::test_conv3d_overflow_values [GH job link](https://github.com/pytorch/pytorch/actions/runs/18961173726/job/54149112920) [HUD commit link](https://hud.pytorch.org/pytorch/pytorch/commit/a6b1ef17173f56ba93ac97ff4384fa4060b5e41e) ([comment](pytorch#166338 (comment)))

Graph partition relies on `get_free_symbol_uses()` to collect symbol inputs. https://github.com/pytorch/pytorch/blob/ee7434be822cf6e75b4566d8159f550ee233d8ae/torch/_inductor/scheduler.py#L4869-L4885 I empirically observed that `get_free_symbol_uses()` becomes slower for larger graphs. Specifically, I tried to aten fallback for torchtitan which results in 10k+ aten nodes. When processing the 600-th node, it takes seconds to `get_free_symbol_uses()` for 1 node. Why? Because `get_free_symbol_uses()` may recursively call another `get_free_symbol_uses()`, which could recursively run many times. https://github.com/pytorch/pytorch/blob/ee7434be822cf6e75b4566d8159f550ee233d8ae/torch/_inductor/ir.py#L4541-L4543 This PR fixes the issue by caching the results of `get_free_symbol_uses()`. I validated on torchtitan that the issue is fixed. Pull Request resolved: pytorch#166338 Approved by: https://github.com/eellison

BoyuanFeng · 2025-11-04T18:43:07Z

@pytorchbot cherry-pick --onto release/2.9 --fixes "Inductor partition compilation infinite hang issue introduced in 2.9.0 breaking torchtitan" -c fixnewfeature

pytorchbot · 2025-11-04T18:48:31Z

Cherry picking #166338

Command git -C /home/runner/work/pytorch/pytorch cherry-pick -x dfebdcab86acbaa0eaa996b47595e5f27a66492e returned non-zero exit code 1

Auto-merging test/inductor/test_torchinductor.py
Auto-merging torch/_inductor/ir.py
CONFLICT (content): Merge conflict in torch/_inductor/ir.py
Auto-merging torch/_inductor/utils.py
CONFLICT (content): Merge conflict in torch/_inductor/utils.py
error: could not apply dfebdcab86a... [GraphPartition] cache get_free_symbol_uses (#166338)
hint: After resolving the conflicts, mark them with
hint: "git add/rm <pathspec>", then run
hint: "git cherry-pick --continue".
hint: You can instead skip this commit with "git cherry-pick --skip".
hint: To abort and get back to the state before "git cherry-pick",
hint: run "git cherry-pick --abort".
hint: Disable this message with "git config set advice.mergeConflict false"

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Raised by workflow job

Graph partition relies on `get_free_symbol_uses()` to collect symbol inputs. https://github.com/pytorch/pytorch/blob/ee7434be822cf6e75b4566d8159f550ee233d8ae/torch/_inductor/scheduler.py#L4869-L4885 I empirically observed that `get_free_symbol_uses()` becomes slower for larger graphs. Specifically, I tried to aten fallback for torchtitan which results in 10k+ aten nodes. When processing the 600-th node, it takes seconds to `get_free_symbol_uses()` for 1 node. Why? Because `get_free_symbol_uses()` may recursively call another `get_free_symbol_uses()`, which could recursively run many times. https://github.com/pytorch/pytorch/blob/ee7434be822cf6e75b4566d8159f550ee233d8ae/torch/_inductor/ir.py#L4541-L4543 This PR fixes the issue by caching the results of `get_free_symbol_uses()`. I validated on torchtitan that the issue is fixed. Pull Request resolved: #166338 Approved by: https://github.com/eellison (cherry picked from commit dfebdca)

Graph partition relies on `get_free_symbol_uses()` to collect symbol inputs. https://github.com/pytorch/pytorch/blob/ee7434be822cf6e75b4566d8159f550ee233d8ae/torch/_inductor/scheduler.py#L4869-L4885 I empirically observed that `get_free_symbol_uses()` becomes slower for larger graphs. Specifically, I tried to aten fallback for torchtitan which results in 10k+ aten nodes. When processing the 600-th node, it takes seconds to `get_free_symbol_uses()` for 1 node. Why? Because `get_free_symbol_uses()` may recursively call another `get_free_symbol_uses()`, which could recursively run many times. https://github.com/pytorch/pytorch/blob/ee7434be822cf6e75b4566d8159f550ee233d8ae/torch/_inductor/ir.py#L4541-L4543 This PR fixes the issue by caching the results of `get_free_symbol_uses()`. I validated on torchtitan that the issue is fixed. Pull Request resolved: #166338 Approved by: https://github.com/eellison (cherry picked from commit dfebdca) Co-authored-by: Boyuan Feng <[email protected]>

cache get_free_symbol_uses

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BoyuanFeng added ciflow/trunk Trigger trunk jobs on your pull request topic: not user facing topic category module: inductor labels Oct 27, 2025

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[DO NOT REVIEW] Inductor lite mode with CUDAGraph support #166320

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BoyuanFeng requested review from desertfire and eellison October 27, 2025 21:18

BoyuanFeng added 2 commits October 27, 2025 14:20

lint

46110df

Merge branch 'main' into bf/partition-cache-free-symbols

47d229f

eellison requested a review from laithsakka October 27, 2025 21:50

BoyuanFeng added 3 commits October 27, 2025 15:47

fix ConcatKernel

ab23224

add obj id to cache key

e4bc3e1

Merge branch 'main' into bf/partition-cache-free-symbols

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desertfire reviewed Oct 28, 2025

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check that FlexibleLayout does not change free symbol uses

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BoyuanFeng commented Oct 28, 2025

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BoyuanFeng added 6 commits October 28, 2025 14:03

use exec to make cache faster

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nit

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lint

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Merge branch 'main' into bf/partition-cache-free-symbols

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nit

48172eb

store on self.key

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BoyuanFeng requested review from desertfire and eellison October 30, 2025 00:13

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Merge branch 'main' into bf/partition-cache-free-symbols

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This was referenced Nov 4, 2025

[GraphPartition] cache get_free_symbol_uses (#166338) #166994

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[v2.9.1] Release Tracker #166758

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github-actions Bot deleted the bf/partition-cache-free-symbols branch December 5, 2025 02:18

	def get_scheduler_node_symbol_uses(
	node: BaseSchedulerNode,
	) -> OrderedSet[sympy.Symbol]:
	"""
	Gets symbols used in node.
	"""
	if isinstance(node, FusedSchedulerNode):
	return OrderedSet().union(
	*(get_scheduler_node_symbol_uses(snode) for snode in node.snodes)
	)
	assert node.node is not None
	free_symbol_uses = node.node.get_free_symbol_uses()
	free_symbol_uses.update(
	*(get_layout_symints(ir_node) for ir_node in node.node.get_outputs())
	)
	return free_symbol_uses

	result = self.layout.get_free_symbol_uses(
	unbacked_only
	) \| self.data.get_free_symbol_uses(unbacked_only)

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Cherry picking #166338

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