flex attention: fix dispatch order for tensor subclasses, avoid hardcoding call to faketensor impl in dynamo #151719

bdhirsh · 2025-04-18T23:14:13Z

This is enough to get @XilunWu 's stack in a state where his flex_attention DTensor implementations worked E2E for me. It also required these changes on the DTensor side, to properly add a DTensor rule for flex backward: P1789852198

There are two problems:

(1) in the normal dispatcher, we have a precedence ordering between modes and subclasses. Modes are dispatched to first, but modes are allowed to return NotImplemented, giving subclasses a chance to run.

This normally happens automatically in FakeTensorMode.__torch_dispatch__ and FunctionalTensorMode.__torch_dispatch__. However, since HOPs implement these two modes themselves, HOPs do not get this benefit. For now, I ended up hardcoding this NotImplemented logic directly into the functional/fake rules for flex attention.

Having to do this for every HOP seems a bit painful. If we could plumb every HOP through Fake[|Functional]TensorMode.__torch_dispatch__ then we would get this support. Another option could be to just assume that most HOP <> mode implementations want the same treatment by default, and hardcode this NotImplemented logic into torch/_ops.py. I'm not sure if we'd need a way for the HOP to opt out of this though.

(2) We were hardcoding a call to flex attention's fake implementation in dynamo to run fake prop. This is technically wrong for subclasses, because it doesn't give subclasses the chance to interpose on the op and desugar it before fake prop runs. I tweaked dynamo's logic to call the op, and let the dispatcher handle invoking the fake implementation.

Testing Xilun is adding some DTensor tests in his PR that will end up testing this logic. If folks would prefer, though, I can try to add a test that uses another subclass instead that is maybe more basic.

This is the tlparse that his DTensor test gnerated for me: https://manifold.edge.x2p.facebook.net/v0/read/tree/logs/hirsheybar/0196c1d3-a9a2-46ea-a46d-aa21618aa060/custom/rank_0/index.html?bucketName=tlparse_reports&apiKey=tlparse_reports-key&withPayload=1&timeoutMsec=10000

Stack from ghstack (oldest at bottom):

-> flex attention: fix dispatch order for tensor subclasses, avoid hardcoding call to faketensor impl in dynamo #151719

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

…oding call to faketensor impl in dynamo [ghstack-poisoned]

pytorch-bot · 2025-04-18T23:14:16Z

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…oding call to faketensor impl in dynamo ghstack-source-id: c4d5d49 Pull Request resolved: #151719

torch/_higher_order_ops/flex_attention.py

torch/_dynamo/variables/higher_order_ops.py

torch/_higher_order_ops/flex_attention.py

…avoid hardcoding call to faketensor impl in dynamo" This is enough to get XilunWu 's stack in a state where his flex_attention DTensor implementations worked E2E for me. It also required these changes on the DTensor side, to properly add a DTensor rule for flex backward: P1789852198 There are two problems: (1) in the normal dispatcher, we have a precedence ordering between modes and subclasses. Modes are dispatched to first, but modes are allowed to return NotImplemented, giving subclasses a chance to run. This normally happens automatically in `FakeTensorMode.__torch_dispatch__` and `FunctionalTensorMode.__torch_dispatch__`. However, since HOPs implement these two modes themselves, HOPs do not get this benefit. For now, I ended up hardcoding this `NotImplemented` logic directly into the functional/fake rules for flex attention. Having to do this for every HOP seems a bit painful. If we could plumb every HOP through `Fake[|Functional]TensorMode.__torch_dispatch__` then we would get this support. Another option could be to just assume that most HOP <> mode implementations want the same treatment by default, and hardcode this `NotImplemented` logic into `torch/_ops.py`. I'm not sure if we'd need a way for the HOP to opt out of this though. (2) We were hardcoding a call to flex attention's fake implementation in dynamo to run fake prop. This is technically wrong for subclasses, because it doesn't give subclasses the chance to interpose on the op and desugar it before fake prop runs. I tweaked dynamo's logic to call the op, and let the dispatcher handle invoking the fake implementation. **Testing** Xilun is adding some DTensor tests in his PR that will end up testing this logic. If folks would prefer, though, I can try to add a test that uses another subclass instead that is maybe more basic. This is the tlparse that his DTensor test gnerated for me: https://manifold.edge.x2p.facebook.net/v0/read/tree/logs/hirsheybar/0196c1d3-a9a2-46ea-a46d-aa21618aa060/custom/rank_0/index.html?bucketName=tlparse_reports&apiKey=tlparse_reports-key&withPayload=1&timeoutMsec=10000 cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx chenyang78 kadeng chauhang amjames [ghstack-poisoned]

…oding call to faketensor impl in dynamo ghstack-source-id: cc7c17d Pull Request resolved: #151719

…avoid hardcoding call to faketensor impl in dynamo" This is enough to get XilunWu 's stack in a state where his flex_attention DTensor implementations worked E2E for me. It also required these changes on the DTensor side, to properly add a DTensor rule for flex backward: P1789852198 There are two problems: (1) in the normal dispatcher, we have a precedence ordering between modes and subclasses. Modes are dispatched to first, but modes are allowed to return NotImplemented, giving subclasses a chance to run. This normally happens automatically in `FakeTensorMode.__torch_dispatch__` and `FunctionalTensorMode.__torch_dispatch__`. However, since HOPs implement these two modes themselves, HOPs do not get this benefit. For now, I ended up hardcoding this `NotImplemented` logic directly into the functional/fake rules for flex attention. Having to do this for every HOP seems a bit painful. If we could plumb every HOP through `Fake[|Functional]TensorMode.__torch_dispatch__` then we would get this support. Another option could be to just assume that most HOP <> mode implementations want the same treatment by default, and hardcode this `NotImplemented` logic into `torch/_ops.py`. I'm not sure if we'd need a way for the HOP to opt out of this though. (2) We were hardcoding a call to flex attention's fake implementation in dynamo to run fake prop. This is technically wrong for subclasses, because it doesn't give subclasses the chance to interpose on the op and desugar it before fake prop runs. I tweaked dynamo's logic to call the op, and let the dispatcher handle invoking the fake implementation. **Testing** Xilun is adding some DTensor tests in his PR that will end up testing this logic. If folks would prefer, though, I can try to add a test that uses another subclass instead that is maybe more basic. This is the tlparse that his DTensor test gnerated for me: https://manifold.edge.x2p.facebook.net/v0/read/tree/logs/hirsheybar/0196c1d3-a9a2-46ea-a46d-aa21618aa060/custom/rank_0/index.html?bucketName=tlparse_reports&apiKey=tlparse_reports-key&withPayload=1&timeoutMsec=10000 cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx chenyang78 kadeng chauhang amjames [ghstack-poisoned]

…oding call to faketensor impl in dynamo ghstack-source-id: e402015 Pull Request resolved: #151719

…avoid hardcoding call to faketensor impl in dynamo" This is enough to get XilunWu 's stack in a state where his flex_attention DTensor implementations worked E2E for me. It also required these changes on the DTensor side, to properly add a DTensor rule for flex backward: P1789852198 There are two problems: (1) in the normal dispatcher, we have a precedence ordering between modes and subclasses. Modes are dispatched to first, but modes are allowed to return NotImplemented, giving subclasses a chance to run. This normally happens automatically in `FakeTensorMode.__torch_dispatch__` and `FunctionalTensorMode.__torch_dispatch__`. However, since HOPs implement these two modes themselves, HOPs do not get this benefit. For now, I ended up hardcoding this `NotImplemented` logic directly into the functional/fake rules for flex attention. Having to do this for every HOP seems a bit painful. If we could plumb every HOP through `Fake[|Functional]TensorMode.__torch_dispatch__` then we would get this support. Another option could be to just assume that most HOP <> mode implementations want the same treatment by default, and hardcode this `NotImplemented` logic into `torch/_ops.py`. I'm not sure if we'd need a way for the HOP to opt out of this though. (2) We were hardcoding a call to flex attention's fake implementation in dynamo to run fake prop. This is technically wrong for subclasses, because it doesn't give subclasses the chance to interpose on the op and desugar it before fake prop runs. I tweaked dynamo's logic to call the op, and let the dispatcher handle invoking the fake implementation. **Testing** Xilun is adding some DTensor tests in his PR that will end up testing this logic. If folks would prefer, though, I can try to add a test that uses another subclass instead that is maybe more basic. This is the tlparse that his DTensor test gnerated for me: https://manifold.edge.x2p.facebook.net/v0/read/tree/logs/hirsheybar/0196c1d3-a9a2-46ea-a46d-aa21618aa060/custom/rank_0/index.html?bucketName=tlparse_reports&apiKey=tlparse_reports-key&withPayload=1&timeoutMsec=10000 cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx chenyang78 kadeng chauhang amjames [ghstack-poisoned]

…oding call to faketensor impl in dynamo ghstack-source-id: 0eeb838 Pull Request resolved: #151719

…avoid hardcoding call to faketensor impl in dynamo" This is enough to get XilunWu 's stack in a state where his flex_attention DTensor implementations worked E2E for me. It also required these changes on the DTensor side, to properly add a DTensor rule for flex backward: P1789852198 There are two problems: (1) in the normal dispatcher, we have a precedence ordering between modes and subclasses. Modes are dispatched to first, but modes are allowed to return NotImplemented, giving subclasses a chance to run. This normally happens automatically in `FakeTensorMode.__torch_dispatch__` and `FunctionalTensorMode.__torch_dispatch__`. However, since HOPs implement these two modes themselves, HOPs do not get this benefit. For now, I ended up hardcoding this `NotImplemented` logic directly into the functional/fake rules for flex attention. Having to do this for every HOP seems a bit painful. If we could plumb every HOP through `Fake[|Functional]TensorMode.__torch_dispatch__` then we would get this support. Another option could be to just assume that most HOP <> mode implementations want the same treatment by default, and hardcode this `NotImplemented` logic into `torch/_ops.py`. I'm not sure if we'd need a way for the HOP to opt out of this though. (2) We were hardcoding a call to flex attention's fake implementation in dynamo to run fake prop. This is technically wrong for subclasses, because it doesn't give subclasses the chance to interpose on the op and desugar it before fake prop runs. I tweaked dynamo's logic to call the op, and let the dispatcher handle invoking the fake implementation. **Testing** Xilun is adding some DTensor tests in his PR that will end up testing this logic. If folks would prefer, though, I can try to add a test that uses another subclass instead that is maybe more basic. This is the tlparse that his DTensor test gnerated for me: https://manifold.edge.x2p.facebook.net/v0/read/tree/logs/hirsheybar/0196c1d3-a9a2-46ea-a46d-aa21618aa060/custom/rank_0/index.html?bucketName=tlparse_reports&apiKey=tlparse_reports-key&withPayload=1&timeoutMsec=10000 cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx chenyang78 kadeng chauhang amjames [ghstack-poisoned]

…oding call to faketensor impl in dynamo ghstack-source-id: 17535e0 Pull Request resolved: #151719

[ghstack-poisoned]

ghstack-source-id: 02f4618 Pull Request resolved: #155851

drisspg · 2025-06-17T00:21:27Z

@zou3519 @bdhirsh @ydwu4
Update the code, added a test and addressed all comments

[ghstack-poisoned]

…oding call to faketensor impl in dynamo ghstack-source-id: 872151d Pull Request resolved: #151719

[ghstack-poisoned]

drisspg · 2025-06-17T23:57:55Z

torch/_dynamo/variables/higher_order_ops.py

@@ -2892,7 +2884,7 @@ def call_function(
                ),
                kwargs={},
            ),
-            example_value=example_value,
+            example_value=None,


By passing in None for example value wrap_fx_proxy will implicitly do the above

test/inductor/test_flex_attention.py

[ghstack-poisoned]

…oding call to faketensor impl in dynamo ghstack-source-id: 18b3717 Pull Request resolved: #151719

drisspg · 2025-06-18T02:25:50Z

@pytorchbot merge -i

pytorchmergebot · 2025-06-18T02:27:41Z

Merge started

Your change will be merged while ignoring the following 1 checks: inductor / linux-jammy-cpu-py3.9-gcc11-inductor / test (inductor_torchbench_cpu_smoketest_perf, 1, 1, linux.24xl.spr-metal)

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zou3519 · 2025-06-18T13:37:24Z

torch/_higher_order_ops/flex_attention.py

+    if has_user_subclass(
+        (
+            query,
+            key,
+            value,
+            score_mod,
+            block_mask,
+            scale,
+            kernel_options,
+            score_mod_other_buffers,
+            mask_mod_other_buffers,
+        ),
+        allowed_subclasses=(FakeTensor, FunctionalTensor),
+    ):
+        return NotImplemented


This should go into py_functionalize_impl so that other HOPs can benefit:

pytorch/torch/_ops.py

Lines 173 to 188 in 1bb9b18

def functionalize_dk_fn(*args: _P.args, **kwargs: _P.kwargs) -> _T:

return fn(CppFunctionalizeAPI(), *args, **kwargs)

def functionalize_dispatch_mode_fn(

mode: Optional[FunctionalTensorMode], *args: _P.args, **kwargs: _P.kwargs

) -> _T:

return fn(PythonFunctionalizeAPI(mode), *args, **kwargs)

def functionalize_functorch_fn(

interpreter, *args: _P.args, **kwargs: _P.kwargs

) -> _T:

return fn(FunctorchFunctionalizeAPI(interpreter), *args, **kwargs)

self.py_impl(DispatchKey.Functionalize)(functionalize_dk_fn)

self.py_impl(FunctionalTensorMode)(functionalize_dispatch_mode_fn)

self.py_impl(TransformType.Functionalize)(functionalize_functorch_fn)

.

All HOPs need this behavior, this PR only adds it for FlexAttention

I think Brian mentioned that he wasn't sure if we should make this default for everyone, do we need a way for hops to opt out?

Could just make it an option to py_functionalize_impl if we do need a way for HOPs to opt-out. But I don't think we should allow HOPs to opt out, there's not a way for operators to opt-out of this.

zou3519 · 2025-06-18T13:40:03Z

torch/_higher_order_ops/flex_attention.py

+    if has_user_subclass(
+        (
+            query,
+            key,
+            value,
+            score_mod,
+            block_mask,
+            scale,
+            kernel_options,
+            score_mod_other_buffers,
+            mask_mod_other_buffers,
+        ),
+        allowed_subclasses=(FakeTensor,),
+    ):
+        return NotImplemented


I don't see why this is necessary -- @register_fake will directly call FakeTensor's __torch_dispatch__, and FakeTensor already has this logic. So we shouldn't need to duplicate it.

zou3519 · 2025-06-18T13:40:35Z

torch/_higher_order_ops/flex_attention.py

+    if has_user_subclass(
+        (
+            query,
+            key,
+            value,
+            out,
+            logsumexp,
+            grad_out,
+            grad_logsumexp,
+            block_mask,
+            scale,
+            kernel_options,
+            score_mod_other_buffers,
+            mask_mod_other_buffers,
+        ),
+        allowed_subclasses=(FakeTensor, FunctionalTensor),
+    ):
+        return NotImplemented


Same as above -- if we put this into py_functionalize_impl, then we don't need to do this again for flex_attention_backward

zou3519

@drisspg , if you have bandwidth, could you try to address the comments?

drisspg · 2025-06-18T16:33:17Z

@zou3519 yeah ill open up a new one

flex attention: fix dispatch order for tensor subclasses, avoid hardc…

3b04657

…oding call to faketensor impl in dynamo [ghstack-poisoned]

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flex attention: fix dispatch order for tensor subclasses, avoid hardc…

7ef67ce

…oding call to faketensor impl in dynamo ghstack-source-id: c4d5d49 Pull Request resolved: #151719

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[cp] dispatch flex_attention to CP impl in TorchDispatchMode #151497

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SAC: fix recompute tag propagation for ops with list[tensor] inputs #152195

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…oding call to faketensor impl in dynamo ghstack-source-id: cc7c17d Pull Request resolved: #151719

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flex attention: fix dispatch order for tensor subclasses, avoid hardc…

f8fadbf

…oding call to faketensor impl in dynamo ghstack-source-id: e402015 Pull Request resolved: #151719

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flex attention: fix dispatch order for tensor subclasses, avoid hardc…

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…oding call to faketensor impl in dynamo ghstack-source-id: 0eeb838 Pull Request resolved: #151719

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Add a test for AsyncCollectiveTensor handling for maybe-view ops #152688

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flex attention: fix dispatch order for tensor subclasses, avoid hardc…

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…oding call to faketensor impl in dynamo ghstack-source-id: 17535e0 Pull Request resolved: #151719

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torch.compiled flex_attention + NJT raises RuntimeError: Attempting to use FunctionalTensor on its own. #154556

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patch PR #151719

b3269af

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patch PR #151719

7539e90

ghstack-source-id: 02f4618 Pull Request resolved: #155851

Update

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flex attention: fix dispatch order for tensor subclasses, avoid hardc…

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…oding call to faketensor impl in dynamo ghstack-source-id: 872151d Pull Request resolved: #151719

Update

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[WIP][RFC] Compilable flex_attention + Context Parallel #157015

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[fake tensor] fix issue of no attribute tags #156689

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	def functionalize_dk_fn(args: _P.args, *kwargs: _P.kwargs) -> _T:
	return fn(CppFunctionalizeAPI(), args, *kwargs)

	def functionalize_dispatch_mode_fn(
	mode: Optional[FunctionalTensorMode], args: _P.args, *kwargs: _P.kwargs
	) -> _T:
	return fn(PythonFunctionalizeAPI(mode), args, *kwargs)

	def functionalize_functorch_fn(
	interpreter, args: _P.args, *kwargs: _P.kwargs
	) -> _T:
	return fn(FunctorchFunctionalizeAPI(interpreter), args, *kwargs)

	self.py_impl(DispatchKey.Functionalize)(functionalize_dk_fn)
	self.py_impl(FunctionalTensorMode)(functionalize_dispatch_mode_fn)
	self.py_impl(TransformType.Functionalize)(functionalize_functorch_fn)

flex attention: fix dispatch order for tensor subclasses, avoid hardcoding call to faketensor impl in dynamo #151719

flex attention: fix dispatch order for tensor subclasses, avoid hardcoding call to faketensor impl in dynamo #151719

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