Ensure that vmap is restored properly if an exception is thrown during frame eval #122074

aorenste · 2024-03-18T04:48:07Z

We save and restore the DynamicLayerStack during frame eval but since fx graph has no way to express a try/finally we just assume it will happen. If we throw an exception between the push and pop to the stack then we're left in a state that affects following operations poorly. Make sure that if it's in a bad state we restore it after frame eval.

Repro:
before:

$ rm test/dynamo_skips/TestSparseCPU.test_log1p_cpu_uint8
$ rm test/dynamo_expected_failures/FuncTorchHigherOrderOpTests.test_vmap_free_tensor
$ PYTORCH_TEST_WITH_DYNAMO=1 pytest test/jit/test_sparse.py test/dynamo/test_dynamic_shapes.py test/inductor/test_torchinductor_dynamic_shapes.py test/test_sparse.py -k 'test_log1p_cpu_uint8'
============= 1 passed, 8588 deselected in 9.75s =============
$ PYTORCH_TEST_WITH_DYNAMO=1 pytest test/jit/test_sparse.py test/dynamo/test_dynamic_shapes.py test/inductor/test_torchinductor_dynamic_shapes.py test/test_sparse.py -k
'test_vmap_free_tensor_dynamic_shapes or test_log1p_cpu_uint8'
================== short test summary info ===================
FAILED [0.0632s] test/test_sparse.py::TestSparseCPU::test_log1p_cpu_uint8 - AssertionError: "only Tensors of floating point dtype can require gradients"
does not match "You are attempting to call Tensor.requires_grad_() (or perhaps using torch.autograd.functional.* APIs) inside of a function ...
======= 1 failed, 1 skipped, 8587 deselected in 10.99s =======

(Note that adding test_vmap_free_tensor_dynamic_shapes causes test_vmap_free_tensor_dynamic_shapes to fail)
after:

$ rm test/dynamo_skips/TestSparseCPU.test_log1p_cpu_uint8
$ rm test/dynamo_expected_failures/FuncTorchHigherOrderOpTests.test_vmap_free_tensor
$ PYTORCH_TEST_WITH_DYNAMO=1 pytest test/jit/test_sparse.py test/dynamo/test_dynamic_shapes.py test/inductor/test_torchinductor_dynamic_shapes.py test/test_sparse.py -k 'test_log1p_cpu_uint8'
============= 1 passed, 8588 deselected in 9.89s =============
$ PYTORCH_TEST_WITH_DYNAMO=1 pytest test/jit/test_sparse.py test/dynamo/test_dynamic_shapes.py test/inductor/test_torchinductor_dynamic_shapes.py test/test_sparse.py -k
'test_vmap_free_tensor_dynamic_shapes or test_log1p_cpu_uint8'
======= 1 passed, 1 skipped, 8587 deselected in 11.34s =======

(test_vmap_free_tensor_dynamic_shapes passes either way)

Stack from ghstack (oldest at bottom):

-> Ensure that vmap is restored properly if an exception is thrown during frame eval #122074

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

…g frame eval [ghstack-poisoned]

pytorch-bot · 2024-03-18T04:48:11Z

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This comment was automatically generated by Dr. CI and updates every 15 minutes.

…hrown during frame eval" [ghstack-poisoned]

…hrown during frame eval" We save and restore the DynamicLayerStack during frame eval but since fx graph has no way to express a try/finally we just assume it will happen. If we throw an exception between the push and pop to the stack then we're left in a state that affects following operations poorly. Make sure that if it's in a bad state we restore it after frame eval. [ghstack-poisoned]

ezyang · 2024-03-19T04:16:26Z

giving this to @zou3519

zou3519 · 2024-03-19T12:44:15Z

torch/_dynamo/eval_frame.py

+                # Ensure that if an assertion occurs after graph pushes
+                # something onto the DynamicLayerStack then we pop it off (the
+                # constructed graph code isn't guarded with try/finally).
+                with torch._C._functorch._PreserveDynamicLayerStack():
+                    return fn(*args, **kwargs)


I'm kind of confused at this one. Dynamo has a mechanism to "undo the context manager" if tracing fails. Are you saying that Dynamo traced a graph, passed it to the backend ("eager" in this case), and that failed somewhere in the middle?

Correct. graph_module.py is producing python code which looks like this:

def forward(self, L_fn_closure_0_cell_contents : torch.Tensor, L_inputs_0_ : torch.Tensor): l_fn_closure_0_cell_contents = L_fn_closure_0_cell_contents arg = L_inputs_0_ lazy_load_decompositions = torch._functorch.vmap.lazy_load_decompositions() size = arg.size(0) ne = size != size _saved_tensors_hooks_disable = torch._C._autograd._saved_tensors_hooks_disable("torch.func transforms don\'t yet support saved tensor hooks. Please open an issue with your use case.") _vmap_increment_nesting = torch._C._functorch._vmap_increment_nesting(3, \'error\') _add_batch_dim = torch._C._functorch._add_batch_dim(arg, 0, 1); arg = None sum_1 = _add_batch_dim.sum(0) sum_2 = _add_batch_dim.sum(1); _add_batch_dim = None add = sum_1 + sum_2; sum_1 = sum_2 = None batched_output = add + l_fn_closure_0_cell_contents; add = l_fn_closure_0_cell_contents = None actual = torch._C._functorch._remove_batch_dim(batched_output, 1, size, 0); batched_output = size = None _vmap_decrement_nesting = torch._C._functorch._vmap_decrement_nesting() _saved_tensors_hooks_enable = torch._C._autograd._saved_tensors_hooks_enable() return (actual,)

the line

batched_output = add + l_fn_closure_0_cell_contents

is raising the error "TypeError: unsupported operand type(s) for +: 'Tensor' and 'function'"

Since the decrement isn't guarded with a try/finally it never happens and we end up out of sync.

…hrown during frame eval" We save and restore the DynamicLayerStack during frame eval but since fx graph has no way to express a try/finally we just assume it will happen. If we throw an exception between the push and pop to the stack then we're left in a state that affects following operations poorly. Make sure that if it's in a bad state we restore it after frame eval. Repro: before: ``` $ rm test/dynamo_skips/TestSparseCPU.test_log1p_cpu_uint8 $ rm test/dynamo_expected_failures/FuncTorchHigherOrderOpTests.test_vmap_free_tensor $ PYTORCH_TEST_WITH_DYNAMO=1 pytest test/jit/test_sparse.py test/dynamo/test_dynamic_shapes.py test/inductor/test_torchinductor_dynamic_shapes.py test/test_sparse.py -k 'test_log1p_cpu_uint8' ============= 1 passed, 8588 deselected in 9.75s ============= $ PYTORCH_TEST_WITH_DYNAMO=1 pytest test/jit/test_sparse.py test/dynamo/test_dynamic_shapes.py test/inductor/test_torchinductor_dynamic_shapes.py test/test_sparse.py -k 'test_vmap_free_tensor_dynamic_shapes or test_log1p_cpu_uint8' ================== short test summary info =================== FAILED [0.0632s] test/test_sparse.py::TestSparseCPU::test_log1p_cpu_uint8 - AssertionError: "only Tensors of floating point dtype can require gradients" does not match "You are attempting to call Tensor.requires_grad_() (or perhaps using torch.autograd.functional.* APIs) inside of a function ... ======= 1 failed, 1 skipped, 8587 deselected in 10.99s ======= ``` (Note that adding test_vmap_free_tensor_dynamic_shapes causes test_vmap_free_tensor_dynamic_shapes to fail) after: ``` $ rm test/dynamo_skips/TestSparseCPU.test_log1p_cpu_uint8 $ rm test/dynamo_expected_failures/FuncTorchHigherOrderOpTests.test_vmap_free_tensor $ PYTORCH_TEST_WITH_DYNAMO=1 pytest test/jit/test_sparse.py test/dynamo/test_dynamic_shapes.py test/inductor/test_torchinductor_dynamic_shapes.py test/test_sparse.py -k 'test_log1p_cpu_uint8' ============= 1 passed, 8588 deselected in 9.89s ============= $ PYTORCH_TEST_WITH_DYNAMO=1 pytest test/jit/test_sparse.py test/dynamo/test_dynamic_shapes.py test/inductor/test_torchinductor_dynamic_shapes.py test/test_sparse.py -k 'test_vmap_free_tensor_dynamic_shapes or test_log1p_cpu_uint8' ======= 1 passed, 1 skipped, 8587 deselected in 11.34s ======= ``` (test_vmap_free_tensor_dynamic_shapes passes either way) [ghstack-poisoned]

…g frame eval ghstack-source-id: ec14c1446f81d2b08b8005bfafe3b44587510749 Pull Request resolved: #122074

…g frame eval ghstack-source-id: 752f82729d6f16ff5eb621eed2d5065775a66295 Pull Request resolved: pytorch/pytorch#122074

oulgen

Looks reasonable to me, accepting to unblock. @zou3519 please comment if there's anything else you wanted to add

aorenste · 2024-05-06T18:13:03Z

@pytorchbot rebase

pytorchmergebot · 2024-05-06T18:14:36Z

@pytorchbot started a rebase job onto refs/remotes/origin/viable/strict. Check the current status here

[ghstack-poisoned]

pytorchmergebot · 2024-05-06T18:14:50Z

Successfully rebased gh/aorenste/23/orig onto refs/remotes/origin/viable/strict, please pull locally before adding more changes (for example, via ghstack checkout https://github.com/pytorch/pytorch/pull/122074)

aorenste · 2024-05-07T14:10:32Z

@pytorchbot merge

pytorchmergebot · 2024-05-07T14:13:02Z

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aorenste · 2024-05-07T15:57:27Z

@pytorchbot merge

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Merge failed

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aorenste · 2024-05-07T19:33:24Z

@pytorchbot merge -f existing failures fixed by #125706

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aorenste · 2024-05-07T19:34:06Z

@pytorchbot merge -f "existing failures fixed by #125706"

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The original change was about 9.5% slower than then backout. This improves it to be only about 1.41% slower than the backout. Fixes #126293 Ran torchbench 3 times on each change. Perf values before (stable), after (fix), and with #122074 backed out (backout): ``` ../inductor-tools/scripts/modelbench/inductor_single_run.sh single inference performance torchbench pyhpc_isoneutral_mixing amp first dynamic cpp stable: 43.948x 45.754x 44.906x fix: 47.505x 49.987x 47.493x backout: 48.243x 48.199x 48.192x ../inductor-tools/scripts/modelbench/inductor_single_run.sh single inference performance torchbench pyhpc_equation_of_state amp first static default stable: 15.224x 13.286x 15.354x fix: 16.402x 16.370x 16.183x backout: 16.554x 16.675x 16.787x ../inductor-tools/scripts/modelbench/inductor_single_run.sh single inference performance torchbench lennard_jones float32 first static default stable: 1.712x 1.651x 1.640x fix: 1.804x 1.798x 1.792x backout: 1.864x 1.824x 1.836x ``` [ghstack-poisoned]

The original change was about 9.5% slower than then backout. This improves it to be only about 1.41% slower than the backout. Fixes #126293 Ran torchbench 3 times on each change. Perf values before (stable), after (fix), and with #122074 backed out (backout): ``` ../inductor-tools/scripts/modelbench/inductor_single_run.sh single inference performance torchbench pyhpc_isoneutral_mixing amp first dynamic cpp stable: 43.948x 45.754x 44.906x fix: 47.505x 49.987x 47.493x backout: 48.243x 48.199x 48.192x ../inductor-tools/scripts/modelbench/inductor_single_run.sh single inference performance torchbench pyhpc_equation_of_state amp first static default stable: 15.224x 13.286x 15.354x fix: 16.402x 16.370x 16.183x backout: 16.554x 16.675x 16.787x ../inductor-tools/scripts/modelbench/inductor_single_run.sh single inference performance torchbench lennard_jones float32 first static default stable: 1.712x 1.651x 1.640x fix: 1.804x 1.798x 1.792x backout: 1.864x 1.824x 1.836x ``` ghstack-source-id: ecdcee8881a666a27530ce73f2c0d1b1276e7b20 Pull Request resolved: #126996

The original change was about 9.5% slower than then backout. This improves it to be only about 1.41% slower than the backout. Fixes #126293 Ran torchbench 3 times on each change. Perf values before (stable), after (fix), and with #122074 backed out (backout): ``` ../inductor-tools/scripts/modelbench/inductor_single_run.sh single inference performance torchbench pyhpc_isoneutral_mixing amp first dynamic cpp stable: 43.948x 45.754x 44.906x fix: 47.505x 49.987x 47.493x backout: 48.243x 48.199x 48.192x ../inductor-tools/scripts/modelbench/inductor_single_run.sh single inference performance torchbench pyhpc_equation_of_state amp first static default stable: 15.224x 13.286x 15.354x fix: 16.402x 16.370x 16.183x backout: 16.554x 16.675x 16.787x ../inductor-tools/scripts/modelbench/inductor_single_run.sh single inference performance torchbench lennard_jones float32 first static default stable: 1.712x 1.651x 1.640x fix: 1.804x 1.798x 1.792x backout: 1.864x 1.824x 1.836x ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx chenyang78 kadeng chauhang [ghstack-poisoned]

The original change was about 9.5% slower than then backout. This improves it to be only about 1.41% slower than the backout. Fixes #126293 Ran torchbench 3 times on each change. Perf values before (stable), after (fix), and with #122074 backed out (backout): ``` ../inductor-tools/scripts/modelbench/inductor_single_run.sh single inference performance torchbench pyhpc_isoneutral_mixing amp first dynamic cpp stable: 43.948x 45.754x 44.906x fix: 47.505x 49.987x 47.493x backout: 48.243x 48.199x 48.192x ../inductor-tools/scripts/modelbench/inductor_single_run.sh single inference performance torchbench pyhpc_equation_of_state amp first static default stable: 15.224x 13.286x 15.354x fix: 16.402x 16.370x 16.183x backout: 16.554x 16.675x 16.787x ../inductor-tools/scripts/modelbench/inductor_single_run.sh single inference performance torchbench lennard_jones float32 first static default stable: 1.712x 1.651x 1.640x fix: 1.804x 1.798x 1.792x backout: 1.864x 1.824x 1.836x ``` ghstack-source-id: 2342f889c59771845dd46ac5a6d1f3c1fe5d1d10 Pull Request resolved: #126996

The original change was about 9.5% slower than then before #122074 . This improves it to be only about 1.4% slower. Also touched up some unrelated nits that the linter complained about. Fixes #126293 Ran torchbench 3 times on each change. Perf values before (stable), after (fix), and with #122074 backed out (backout): ``` ../inductor-tools/scripts/modelbench/inductor_single_run.sh single inference performance torchbench pyhpc_isoneutral_mixing amp first dynamic cpp stable: 43.948x 45.754x 44.906x fix: 47.505x 49.987x 47.493x backout: 48.243x 48.199x 48.192x ../inductor-tools/scripts/modelbench/inductor_single_run.sh single inference performance torchbench pyhpc_equation_of_state amp first static default stable: 15.224x 13.286x 15.354x fix: 16.402x 16.370x 16.183x backout: 16.554x 16.675x 16.787x ../inductor-tools/scripts/modelbench/inductor_single_run.sh single inference performance torchbench lennard_jones float32 first static default stable: 1.712x 1.651x 1.640x fix: 1.804x 1.798x 1.792x backout: 1.864x 1.824x 1.836x ``` Pull Request resolved: #126996 Approved by: https://github.com/jansel

The original change was about 9.5% slower than then before pytorch#122074 . This improves it to be only about 1.4% slower. Also touched up some unrelated nits that the linter complained about. Fixes pytorch#126293 Ran torchbench 3 times on each change. Perf values before (stable), after (fix), and with pytorch#122074 backed out (backout): ``` ../inductor-tools/scripts/modelbench/inductor_single_run.sh single inference performance torchbench pyhpc_isoneutral_mixing amp first dynamic cpp stable: 43.948x 45.754x 44.906x fix: 47.505x 49.987x 47.493x backout: 48.243x 48.199x 48.192x ../inductor-tools/scripts/modelbench/inductor_single_run.sh single inference performance torchbench pyhpc_equation_of_state amp first static default stable: 15.224x 13.286x 15.354x fix: 16.402x 16.370x 16.183x backout: 16.554x 16.675x 16.787x ../inductor-tools/scripts/modelbench/inductor_single_run.sh single inference performance torchbench lennard_jones float32 first static default stable: 1.712x 1.651x 1.640x fix: 1.804x 1.798x 1.792x backout: 1.864x 1.824x 1.836x ``` Pull Request resolved: pytorch#126996 Approved by: https://github.com/jansel

Ensure that vmap is restored properly if an exception is thrown durin…

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Preserve dispatch state across function tracing #122073

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Ensure that vmap is restored properly if an exception is thrown durin…

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Ensure that vmap is restored properly if an exception is thrown durin…

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[inductor][cpu]lennard_jones, pyhpc_isoneutral_mixing and pyhpc_equation_of_state performance regression in 2024-05-12 nightly release #126293

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Fix perf regression caused by #122074 #126996

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