Summary:
Add the following inductor fx graph cache stats to dynamo compile
- inductor_fx_cache_hit_count
- inductor_fx_cache_miss_count
- inductor_fx_cache_backend_type
- inductor_fx_cache_hit_keys
- inductor_fx_cache_miss_keys
- remote_cache_version
Test Plan: Run local tests and staging logger: P1683061460
Differential Revision: D66232206
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141190
Approved by: https://github.com/masnesral
Adding some dynamo timed for the purpose of better understanding AOTI compilation time.
Probably would require a few more passes. A lot of time is spent in Scheduler.__init__, and not enough annotations are there.
run_command_and_check takes a lot time as well. But there is probably not much we can do. Maybe we can add a config to tune C++ optimization level?
traces:
<img width="1205" alt="Screenshot 2024-11-08 at 4 41 10 PM" src="https://github.com/user-attachments/assets/61645264-b3af-4d4a-804d-700b0f831c7c">
Differential Revision: D65554141
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140198
Approved by: https://github.com/desertfire
Here's the overview:
There's a new contextmanager singleton called MetricsContext. Entering the MetricsContext is how we demarcate the boundary on which we'll create a single CompilationMetrics object, and therefore, a single dynamo_compile log entry. While we're inside the MetricsContext, we can update/set many different metrics. Most importantly: `dynamo_timed` can also update the in-progress MetricsContext. In the proposal here, we tell `dynamo_timed` that we want it to do so by providing the name of the MetricsContext field to increment. There can be many `dynamo_timed` calls in different parts of the code updating different fields. Then when the MetricsContext exits, that's when the logging of everything gathered finally happens. One potential footgun is trying to use `dynamo_timed` when we haven't entered the MetricsContext, but we assert on that problem. Another problem is that we re-enter the context recursively, but we watch for that and do the logging only when the outermost exits.
Some specifics:
* Introduce MetricsContext - a context manager that on exit, records the CompilationMetrics (which also logs to dynamo_compile).
* Completely remove the concept of frame_phase_timing. Instead, update the MetricsContext during compilation, either directly or via dynamo_timed.
* Remove some globals we previously used to accumulate counters to later populate a CompilationMetrics. We use CompilationMetrics set/update/increment APIs instead.
* `record_compilation_metrics` is now called on exit from MetricsContext.
* Populate legacy CompilationMetrics fields right before logging, inside `record_compilation_metrics`.
* Remove the one-off `add_remote_cache_time_saved` helper; capture that timing directly into the MetricsContext.
And specifically, several changes to dynamo_timed:
* "Modernize" the parameters and update all callsites accordingly.
* Move the backwards logging of the CompilationMetrics to the backwards compile location.
* Add a parameter for which CompilationMetrics field to update
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139849
Approved by: https://github.com/ezyang
Here's the overview:
There's a new contextmanager singleton called MetricsContext. Entering the MetricsContext is how we demarcate the boundary on which we'll create a single CompilationMetrics object, and therefore, a single dynamo_compile log entry. While we're inside the MetricsContext, we can update/set many different metrics. Most importantly: `dynamo_timed` can also update the in-progress MetricsContext. In the proposal here, we tell `dynamo_timed` that we want it to do so by providing the name of the MetricsContext field to increment. There can be many `dynamo_timed` calls in different parts of the code updating different fields. Then when the MetricsContext exits, that's when the logging of everything gathered finally happens. One potential footgun is trying to use `dynamo_timed` when we haven't entered the MetricsContext, but we assert on that problem. Another problem is that we re-enter the context recursively, but we watch for that and do the logging only when the outermost exits.
Some specifics:
* Introduce MetricsContext - a context manager that on exit, records the CompilationMetrics (which also logs to dynamo_compile).
* Completely remove the concept of frame_phase_timing. Instead, update the MetricsContext during compilation, either directly or via dynamo_timed.
* Remove some globals we previously used to accumulate counters to later populate a CompilationMetrics. We use CompilationMetrics set/update/increment APIs instead.
* `record_compilation_metrics` is now called on exit from MetricsContext.
* Populate legacy CompilationMetrics fields right before logging, inside `record_compilation_metrics`.
* Remove the one-off `add_remote_cache_time_saved` helper; capture that timing directly into the MetricsContext.
And specifically, several changes to dynamo_timed:
* "Modernize" the parameters and update all callsites accordingly.
* Move the backwards logging of the CompilationMetrics to the backwards compile location.
* Add a parameter for which CompilationMetrics field to update
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139849
Approved by: https://github.com/ezyang
ghstack dependencies: #140094
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
