This PR adds support for tracing `torch._C._pop_torch_function_stack()` without graph breaking and in order to verify the state change also adds replay of mutations to the torch function mode stack via side_effects appending supplemental bytecode as we do for other python mutable objects.
Details:
To represent the torch function mode stack symbolically a deque field is added to the instruction translator. When the InstructionTranslator is initialized, all modes are read from the current torch function mode stack, and stashed in a global weak ref for later access (using existing sources) without needing to push/pop the python/cpp torch function mode stack.
During tracing, when `_pop_torch_function_stack` is encountered a value is popped from this deque and the variable tracker representing the mode is returned. To ensure the true torch function mode stack matches this state, `TorchFunctionModeStackVariable`, a singleton, is marked as mutated, this adds it to side effects, where during final codegen, side effects will codegen a call to a python helper which will update the python torch function mode stack.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133131
Approved by: https://github.com/jansel
ghstack dependencies: #133130, #133729
This PR adds a guard on the torch function mode stack state at the beginning of tracing. The way this is implemented is via a new leaf guard which is passed the initial stack state at construction and compares it to the stack state at the time the guard is run.
Details:
The stack state is extracted via popping all modes, appending them to a list, and pushing all modes back. This list is stored on the output graph and read during guard construction to pass to the stack mode guard. There the length and types of the modes are recorded. Next time the guard is run it compares this recorded state to the current mode stack state.
To implement this in python a helper function was added to utils.py and this is used if cpp guards are not enabled.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133130
Approved by: https://github.com/anijain2305
`torch.cuda.Event` objects are different from `torch.cuda.Stream` in that events are not pooled, meaning we can't look up a previously created CUDA event object by ID. This prevents CUDA event object created outside of the Dynamo graph from being used within the graph (since Dynamo needs a way to emit a `call_function` line in the graph that does the retrieval of the event object for downstream op use). This PR adds a simple object pool within Dynamo utility, to support looking up CUDA event object by ID from within the Dynamo graph.
After this PR, if a user creates a CUDA event object outside of the graph and use that event within the graph, the behavior will exactly match eager.
Test commands:
- `pytest -rA test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_event_created_outside_of_graph`
- `pytest -rA test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_event_across_graph_break`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133635
Approved by: https://github.com/yifuwang
ghstack dependencies: #133532, #133531, #133636
Fixes#132290
This PR attempts a more invasive / complete solution than the one from #132338, which removes immediate tensor fields from the `tensor_dict` copy stored in node meta. The approach taken here is to store only those fields of the `tensor_dict` which are absolutely utilized somewhere else.
So far, this appears to be limited to:
* `_dynamo_static_input_type`
* `tag` (at least in the tests). Discussion at #94080 appears to indicate this is depended on for export
(CI may point out more)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132805
Approved by: https://github.com/mlazos
Summary:
- We add Inductor logs for what tensors we tried to reinplace, what
tensors we were unable to reinplace, and of those tensors, which of
those might be bugs (the "missed reinplacing opportunities"). You can
tell this by reading the Inductor output graph but the logs make it
easier to figure out.
- Add a dynamo_compile counter for missed reinplacing opportunities. The
goal is to see how widespread existing problems (if any) are. We've had
trouble getting all of the edge cases for the reinplacing pass; the
counter will help us hunt down issues.
Test Plan:
- tested locally
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132758
Approved by: https://github.com/eellison
Need to revert due to internal hangs: S437700
This reverts commit b6c1490cc02316ffe85e5ae74651d80f0158ba64.
Revert "[dynamo] implement IteratorVariable and polyfill fallbacks for enumerate (#131725)"
This reverts commit 2576dbbc35d66e8e9ed6cb12216ccc424cb87ec3.
Revert "[dynamo] add itertools repeat/count bytecode reconstruction (#131716)"
This reverts commit 35b4de32fafc5ad024c20ef1275711bffc557ae9.
Revert "[dynamo] add lazy IteratorVariable implementations for map and zip (#131413)"
This reverts commit 7d282d87550787d8269593093519c2ad7c5032cd.
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132528
Approved by: https://github.com/ZainRizvi
Need to revert due to internal hangs: S437700
This reverts commit b6c1490cc02316ffe85e5ae74651d80f0158ba64.
Revert "[dynamo] implement IteratorVariable and polyfill fallbacks for enumerate (#131725)"
This reverts commit 2576dbbc35d66e8e9ed6cb12216ccc424cb87ec3.
Revert "[dynamo] add itertools repeat/count bytecode reconstruction (#131716)"
This reverts commit 35b4de32fafc5ad024c20ef1275711bffc557ae9.
Revert "[dynamo] add lazy IteratorVariable implementations for map and zip (#131413)"
This reverts commit 7d282d87550787d8269593093519c2ad7c5032cd.
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132528
Approved by: https://github.com/ZainRizvi
Fixes https://github.com/pytorch/pytorch/issues/130750.
Repro of lazy/eager `map` discrepancy without `islice`:
```python
def fn(a, b):
y = 1
def f(x):
nonlocal y
y += 1
return x
l = list(zip([a, b], map(f, [1, 2, 3, 4])))
return a + y
```
The major change is that we implement `MapVariable` and `ZipVariable` based on `IteratorVariable`. Before, `map` and `zip` were being traced by immediately unpacking the result as a `TupleVariable`, which is wrong in cases such as the example above.
`MapVariable`s are not allowed to be unpacked while `ZipVariable`s can only be unpacked if all of its iterables can also be unpacked.
We also add new `[has_]force_unpack_var_sequence` methods to `VariableTracker` for the case where it is safe to unpack the entire sequence lazily, e.g., when building a list from a map (i.e. `list(map(f, ...))`).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131413
Approved by: https://github.com/anijain2305
This PR marks all buffers and parameters of an NNModule as static using the `mark_static_address` API. As a result, when tensors are passed to AOT, the `tensor_dict` metadata of placeholder nodes will contain the `static_address_type` key, indicating which graph argument positions are static for cudagraphs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130391
Approved by: https://github.com/anijain2305
This PR batch the fix for a few accuracy failures issues during training by raising tolerance. I do that only for models that I think it fails not due to real issue.
## sebotnet33ts_256
The accuracy test for this model start to fail around June 05 [link](https://hud.pytorch.org/benchmark/timm_models/inductor_with_cudagraphs?dashboard=torchinductor&startTime=Sun%2C%2002%20Jun%202024%2007%3A19%3A38%20GMT&stopTime=Tue%2C%2002%20Jul%202024%2007%3A19%3A38%20GMT&granularity=day&mode=training&dtype=amp&lBranch=main&lCommit=04a0d856207d83c2031e4b9cb6825ba3e0092850&rBranch=main&rCommit=e62925930f6a62f6aeeb1fe1a661a9bd3352b53d&model=sebotnet33ts_256).
I can not repro locally, but from the log from the dashboard:
```
RMSE (res-fp64): 0.09441, (ref-fp64): 0.02971 and shape=torch.Size([1536]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.040000
```
raising the tolerance should fix it.
## DebertaForQuestionAnswering
This model fails accuracy test on the dashboard only in max-autotune mode. I can not repro locally by command:
```
TORCHINDUCTOR_MAX_AUTOTUNE=1 time python benchmarks/dynamo/huggingface.py --accuracy --no-translation-validation --training --amp --backend inductor --device cuda --only DebertaForQuestionAnswering
```
From error message on the dashboard:
```
RMSE (res-fp64): 0.01803, (ref-fp64): 0.00537 and shape=torch.Size([2]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.010000
```
0.02 tolerance should suppress this error.
## gluon_inception_v3
This model fail on the dashboard in max-autotune mode. I can not repro locally by command
```
TORCHINDUCTOR_MAX_AUTOTUNE=1 time python benchmarks/dynamo/timm_models.py --accuracy --training --amp --backend inductor --disable-cudagraphs --device cuda --only gluon_inception_v3
```
From error message on the dashboard
```
RMSE (res-fp64): 0.02798, (ref-fp64): 0.00730 and shape=torch.Size([384]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.010000
Accuracy failed for key name Mixed_7c.branch3x3dbl_3a.bn.running_var
```
raising tolerance should suppress this error.
# mobilenetv3_large_100
Fail in MA model. I can not repro locally by command
```
TORCHINDUCTOR_MAX_AUTOTUNE=1 time python benchmarks/dynamo/timm_models.py --accuracy --training --amp --backend inductor --disable-cudagraphs --device cuda --only
```
The error message on the dashboard is
```
RMSE (res-fp64): 0.29754, (ref-fp64): 0.05205 and shape=torch.Size([]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.040000
```
The tensor is so small that the noise can be high. I use larger multiplier for smaller tensor in torch._dynamo.utils.same.
# yolov3
Fail on dashboard with error
```
Error on the dashboard: RMSE (res-fp64): 0.01278, (ref-fp64): 0.00246 and shape=torch.Size([256]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.001000
```
Fix it by using a larger multiplier for smaller tensors and raising the tolereance.
# timm_efficientdet
Fail on the dashboard with error
```
E0623 18:37:43.638000 139924418725056 torch/_dynamo/utils.py:1468] RMSE (res-fp64): 0.00096, (ref-fp64): 0.00009 and shape=torch.Size([2]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.001000
```
But I can not repro locally with command
```
time python benchmarks/dynamo/torchbench.py --backend inductor --amp --performance --only timm_efficientdet --training
```
Raise the tolerance should fix.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129941
Approved by: https://github.com/jansel
ghstack dependencies: #129996
I'm debugging the accuracy failure for training vision_maskrcnn.
Unfortunately I could not succeed to run it locally (I've check pined commits for torchbenchmars/torchvision are correct, and reinstalled torchbenchmark for mask_rcnn). I get this error:
```
eager run fail: AssertionError: targets should not be none when in training mode
```
(Command: time python benchmarks/dynamo/torchbench.py --backend inductor --amp --performance --training --only vision_maskrcnn )
But look at the log from the dashboard
```
E0623 19:17:59.085000 140114670171328 torch/_dynamo/utils.py:1468] RMSE (res-fp64): nan, (ref-fp64): nan and shape=torch.Size([1024, 256, 1, 1]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.001000
```
We can see both the reference number and the pt2 number are NaN. I change torch._dynamo.utils.same to return true if both RMSE values are NaN.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129996
Approved by: https://github.com/jansel
This PR batch the fix for a few accuracy failures issues during training by raising tolerance. I do that only for models that I think it fails not due to real issue.
## sebotnet33ts_256
The accuracy test for this model start to fail around June 05 [link](https://hud.pytorch.org/benchmark/timm_models/inductor_with_cudagraphs?dashboard=torchinductor&startTime=Sun%2C%2002%20Jun%202024%2007%3A19%3A38%20GMT&stopTime=Tue%2C%2002%20Jul%202024%2007%3A19%3A38%20GMT&granularity=day&mode=training&dtype=amp&lBranch=main&lCommit=04a0d856207d83c2031e4b9cb6825ba3e0092850&rBranch=main&rCommit=e62925930f6a62f6aeeb1fe1a661a9bd3352b53d&model=sebotnet33ts_256).
I can not repro locally, but from the log from the dashboard:
```
RMSE (res-fp64): 0.09441, (ref-fp64): 0.02971 and shape=torch.Size([1536]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.040000
```
raising the tolerance should fix it.
## DebertaForQuestionAnswering
This model fails accuracy test on the dashboard only in max-autotune mode. I can not repro locally by command:
```
TORCHINDUCTOR_MAX_AUTOTUNE=1 time python benchmarks/dynamo/huggingface.py --accuracy --no-translation-validation --training --amp --backend inductor --device cuda --only DebertaForQuestionAnswering
```
From error message on the dashboard:
```
RMSE (res-fp64): 0.01803, (ref-fp64): 0.00537 and shape=torch.Size([2]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.010000
```
0.02 tolerance should suppress this error.
## gluon_inception_v3
This model fail on the dashboard in max-autotune mode. I can not repro locally by command
```
TORCHINDUCTOR_MAX_AUTOTUNE=1 time python benchmarks/dynamo/timm_models.py --accuracy --training --amp --backend inductor --disable-cudagraphs --device cuda --only gluon_inception_v3
```
From error message on the dashboard
```
RMSE (res-fp64): 0.02798, (ref-fp64): 0.00730 and shape=torch.Size([384]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.010000
Accuracy failed for key name Mixed_7c.branch3x3dbl_3a.bn.running_var
```
raising tolerance should suppress this error.
# mobilenetv3_large_100
Fail in MA model. I can not repro locally by command
```
TORCHINDUCTOR_MAX_AUTOTUNE=1 time python benchmarks/dynamo/timm_models.py --accuracy --training --amp --backend inductor --disable-cudagraphs --device cuda --only
```
The error message on the dashboard is
```
RMSE (res-fp64): 0.29754, (ref-fp64): 0.05205 and shape=torch.Size([]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.040000
```
The tensor is so small that the noise can be high. I use larger multiplier for smaller tensor in torch._dynamo.utils.same.
# yolov3
Fail on dashboard with error
```
Error on the dashboard: RMSE (res-fp64): 0.01278, (ref-fp64): 0.00246 and shape=torch.Size([256]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.001000
```
Fix it by using a larger multiplier for smaller tensors and raising the tolereance.
# timm_efficientdet
Fail on the dashboard with error
```
E0623 18:37:43.638000 139924418725056 torch/_dynamo/utils.py:1468] RMSE (res-fp64): 0.00096, (ref-fp64): 0.00009 and shape=torch.Size([2]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.001000
```
But I can not repro locally with command
```
time python benchmarks/dynamo/torchbench.py --backend inductor --amp --performance --only timm_efficientdet --training
```
Raise the tolerance should fix.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129941
Approved by: https://github.com/jansel
ghstack dependencies: #129996
I'm debugging the accuracy failure for training vision_maskrcnn.
Unfortunately I could not succeed to run it locally (I've check pined commits for torchbenchmars/torchvision are correct, and reinstalled torchbenchmark for mask_rcnn). I get this error:
```
eager run fail: AssertionError: targets should not be none when in training mode
```
(Command: time python benchmarks/dynamo/torchbench.py --backend inductor --amp --performance --training --only vision_maskrcnn )
But look at the log from the dashboard
```
E0623 19:17:59.085000 140114670171328 torch/_dynamo/utils.py:1468] RMSE (res-fp64): nan, (ref-fp64): nan and shape=torch.Size([1024, 256, 1, 1]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.001000
```
We can see both the reference number and the pt2 number are NaN. I change torch._dynamo.utils.same to return true if both RMSE values are NaN.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129996
Approved by: https://github.com/jansel
