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
Debugging is happening in https://github.com/pytorch/pytorch/issues/123126 .
Upgrading triton cause accuracy failure for mixer_b16_224 and levit_128 .
mixer_b16_224 is debugged specifically. It due to extra FMA instructions being used in a single kernel. That kernel itself only introduce small numerical difference. We conclude that this is not some 'real' accuracy issue and we should raise the tolerance to unblock the triton pin update.
The tolerance is picked such that the CI accuracy test can pass.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123484
Approved by: https://github.com/jansel
Fix https://github.com/pytorch/pytorch/issues/120545 . The reason why these models fail accuracy test with freezing is due to the conv-batchnorm fusion. Conv-batchnorm fusion causes relative big numerical churn.
For the failed TIMM models, raising the tolerance to `8 * 1e-2` can make the test pass.
For the failed TB models, the numerical difference is too large. Having a discussion with @eellison , we decided to skip them with freezing for now.
One the other hand, we probably should dig more why the conv-bn fusion cause such large numerical difference.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121054
Approved by: https://github.com/eellison
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
Fixes#97382#95416 fixed a critical bug in dynamo benchmark, where AMP tests fall back to eager mode before that PR. However, after that PR, we found [a list of TIMM models amp + eager + training testing failed](https://docs.google.com/spreadsheets/d/1DEhirVOkj15Lu4UNawIUon9MqkVLaWqyT-DQPif5NHk/edit#gid=0).
Now we identified the root cause is: high loss values make gradient checking harder, as small changes in accumulation order upset accuracy checks. We should switch to the helper function ```reduce_to_scalar_loss``` which has been used by Torchbench tests.
After switching to ```reduce_to_scalar_loss```, TIMM models accuracy pass rate grows from 67.74% to 91.94% in my local test. The rest 5 failed models(ese_vovnet19b_dw, fbnetc_100, mnasnet_100, mobilevit_s, sebotnet33ts_256) need further investigation and handling, but I think it should be similar reason.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97423
Approved by: https://github.com/Chillee
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
Fixes https://github.com/pytorch/torchdynamo/issues/1995
Running `python benchmarks/dynamo/timm_models.py --performance --float32 -dcuda --output=out.csv --training --inductor --only bad_model_name` gives
```
Traceback (most recent call last):
File "benchmarks/dynamo/timm_models.py", line 338, in <module>
main(TimmRunnner())
File "/scratch/williamwen/work/pytorch/benchmarks/dynamo/common.py", line 1660, in main
return maybe_fresh_cache(run, args.cold_start_latency and args.only)(
File "/scratch/williamwen/work/pytorch/benchmarks/dynamo/common.py", line 833, in inner
return fn(*args, **kwargs)
File "/scratch/williamwen/work/pytorch/benchmarks/dynamo/common.py", line 2000, in run
) = runner.load_model(device, model_name, batch_size=batch_size)
File "benchmarks/dynamo/timm_models.py", line 215, in load_model
raise RuntimeError(f"Failed to load model '{model_name}'")
RuntimeError: Failed to load model 'bad_model_name'
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91049
Approved by: https://github.com/ezyang
Moving to train mode for TIMM models and also raising batch size for accuracy testing.
Raising batch size seems to remove a lot of noise/instability coming from batch_norm decomposition.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89780
Approved by: https://github.com/ngimel
