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
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
Fixes#128510.
https://github.com/pytorch/pytorch/pull/124451 makes LayoutLMForSequenceClassification hit the SDPA pattern 1 and then encounter the accuracy issue. The issue only happens with BF16 inference single thread. This PR tends to increase the model tolerance and make the check pass. Note that even the math-version SDPA could have the issue because of some small implementation diff.
The test log:
Single thread
```
correct_result: SequenceClassifierOutput(loss=tensor(0.5998), logits=tensor([[0.3301, 0.1338]], dtype=torch.bfloat16), hidden_states=None, attentions=None)
new_result: SequenceClassifierOutput(loss=tensor(0.6016), logits=tensor([[0.3281, 0.1357]], dtype=torch.bfloat16), hidden_states=None, attentions=None)
E0627 01:09:16.762789 140281313759104 torch/_dynamo/utils.py:1476] RMSE (res-fp64): 0.00151, (ref-fp64): 0.00046 and shape=torch.Size([1, 2]). res.dtype: torch.bfloat16, multiplier: 3.000000, tol: 0.001000
E0627 01:09:16.762972 140281313759104 torch/_dynamo/utils.py:1390] Accuracy failed for key name logits
fail_accuracy
```
Multiple threads
```
correct_result: SequenceClassifierOutput(loss=tensor(0.6007), logits=tensor([[0.3301, 0.1357]], dtype=torch.bfloat16), hidden_states=None, attentions=None)
new_result: SequenceClassifierOutput(loss=tensor(0.6016), logits=tensor([[0.3281, 0.1357]], dtype=torch.bfloat16), hidden_states=None, attentions=None)
pass
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129728
Approved by: https://github.com/jgong5, https://github.com/jansel
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126
Approved by: https://github.com/kit1980
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126
Approved by: https://github.com/kit1980
ghstack dependencies: #127122, #127123, #127124, #127125
Biggest movement is 4% HF inference, 9% TIMM inference. Note, this is max-autotune mode so we are more tolerant of compilation increases. We could improve compilation time by limiting:
```
# Take how many of the top triton kernels to benchmark epilogue
max_epilogue_benchmarked_choices = 3
```
There is a hf_Whisper failure which you can repro on main without this stack with `TORCHINDUCTOR_MAX_AUTOTUNE_GEMM_BACKENDS=TRITON TORCHINDUCTOR_MAX_AUTOTUNE=1 python benchmarks/dynamo/torchbench.py --backend inductor --amp --accuracy --training --only hf_Whisper`. When you turn off epilogue fusion, it fixes the accuracy. I bisected the failure to an epilogue, however when you compare the results of that epilogue with the corresponding separate kernels the results of the output are equivalent.
Inference:
<img width="1686" alt="image" src="https://github.com/pytorch/pytorch/assets/11477974/0b240080-cd33-4c08-89d3-583103b1fb0c">
Training:
<img width="1329" alt="Screenshot 2024-04-16 at 6 16 30 PM" src="https://github.com/pytorch/pytorch/assets/11477974/db0afcc9-7288-4c27-84ce-4fc1a5690788">
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124031
Approved by: https://github.com/Chillee, https://github.com/shunting314
ghstack dependencies: #124030, #122642, #123229, #122825
We need a higher tolerance for GPT2ForSequenceClassification since if I change --bfloat16 in
```
time python benchmarks/dynamo/huggingface.py --accuracy --inference --bfloat16 --backend inductor --disable-cudagraphs --only GPT2ForSequenceClassification
```
to --float16 or --float32 it will pass the accuracy check.
Adding --freezing can also make the test pass for this model. I think that's may be due to different fusion output being generated (depending on if constant propagation is happening controlled by freezing) and cause some small numerical difference.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120537
Approved by: https://github.com/jansel
The memory compression for these models is at parity, but because we interleave timings between torch.compile and eager run memory is duplicated between between eager and cudagraphs pool and causes OOM.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101837
Approved by: https://github.com/anijain2305
This pr accomplishes
1) Enables retries for downloading torchbenchmark and huggingface models in a similar method to how we do it for timm models right now.
2) creates a `_download_model` function for the hugging face and TIMM runners whose output I plan to use to preload the models somewhere if possible (please double check I'll be saving the right thing). Instead of retries, we plan to just add torchbench to a docker image as it is relatively small.
<!--
copilot:poem
-->
### <samp>🤖 Generated by Copilot at 3361a4c</samp>
> _We're the brave and bold coders of the `common.py` module_
> _We've made a handy function for downloading models_
> _We've shared it with our mates in the other runners_
> _So pull and push and try again, we'll get them all in time_
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101019
Approved by: https://github.com/huydhn, https://github.com/desertfire
Since the CI exclusions are hard-coded in our script, we might as well require them to match exactly. This solved some head scratching where I was like, "this model is not obviously excluded, why is it not showing up in CI."
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92761
Approved by: https://github.com/jansel
Reset the rng in hf before generating input and loading model, this makes the huggingface inputs+weights deterministic depending on the seed of the rng. This matches the behavior of the other test suites.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90936
Approved by: https://github.com/desertfire
