Originally, my goal for this PR was to remove the `dynamic_shapes` tests in torch/_dynamo/variables/builder.py. However, one thing lead to another, and it turns out that it was easiest to do all of the following in one go:
* Unconditionally allocate a ShapeEnv, no matter if dynamic_shapes is enabled or not (torch/_dynamo/output_graph.py). There is a small adjustment to export torch/_dynamo/eval_frame.py to account for the fact that a ShapeEnv always exists, even if you're not doing symbolic export.
* Remove dynamic_shapes test from unspec logic (torch/_dynamo/variables/builder.py), the original goal
* Specialize strides and storage offset if all sizes are dynamic (torch/fx/experimental/symbolic_shapes.py). This is required to deal with unconditional ShapeEnv: if a ShapeEnv exist, fake tensor-ification may choose to allocate symbols. The idea is that with `automatic_dynamic_shapes == False`, Dynamo should never request dynamic sizes, but this invariant was not upheld for nontrivial strides/offset.
The rest are just auxiliary fixups from the above:
* Workaround bug in FakeTensorProp where sometimes it doesn't return a FakeTensor (torch/fx/passes/fake_tensor_prop.py), see https://github.com/pytorch/pytorch/pull/103395 for follow up
* Make ShapeProp correctly handle int inputs (torch/fx/passes/shape_prop.py)
* Disable indexing strength reduction if `assume_static_by_default` is False (torch/_inductor/codegen/triton.py)
* Fix hf_T5_generate to NOT toggle `assume_static_by_default` if dynamic shapes is not enabled (benchmarks/dynamo/common.py); technically this is not necessary anymore but it's in for safety.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103302
Approved by: https://github.com/voznesenskym
Previously, cudagraphs and dynamic_shapes were incompatible and enabling
dynamic shapes would forcibly disable cudagraphs. This new strategy
I think is better. The idea is essentially that cudagraphs is an
"optimization" that happens to guard on every input. When cudagraphs
is on, we force everything static, and this automatically does the right
thing because we will force a recompile if sizes change.
This obsoletes https://github.com/pytorch/pytorch/pull/101813
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103290
Approved by: https://github.com/voznesenskym
Even if you passed in --amp we would run inference in float32.
`AlbertForMaskedLM` goes from 1.305 float32 to 1.724x amp, and then again to 1.910x with freezing. Benchmark numbers for amp are about to go way up lol.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103220
Approved by: https://github.com/desertfire
The "tolerance" option evaluates the model on the baseline device in eager mode (default: CPU) compared to the test device (e.g., CUDA, XLA, etc.) and compares the output tensors to determine the absolute tolerance value based on the [formula](https://pytorch.org/docs/stable/generated/torch.allclose.html). It then saves the results in a CSV file. This comparison highlights the tolerance/accuracy difference between XLA and GPU/CPU devices and can also be used to evaluate newer accelerators. This feature aims to identify accuracy failures on the test device (e.g., XLA) and facilitate quick bug triaging.
This feature enables the following capabilities:
1. Ability to monitor accuracy issues of backends
2. Provide more informative picture on accuracy beyond pass/ fail status
3. Having a dump of accuracy information will help triage models accordingly
The data generated using this feature is in the [spreadsheet](https://docs.google.com/spreadsheets/d/1A8BAzSqfAw0Q5rgzK5Gk__Uy7qhuynh8tedxKnH-t94/edit#gid=0).
The spreadsheet data can be used to compile the below summary table:
| Suite | Max Tolerance | | No. of models with high inaccuracy(>=0.005) | | Mean Tolerance | |
|------------------ |:-------------:|:--------:|:-------------------------------------------:|:--------:|:--------------:|:--------:|
| | xla | inductor | xla | inductor | xla | inductor |
| huggingface | 0.1169 | 0.0032 | 1 | 0 | 0.0022 | 0.0005 |
| timm_models | 0.0373 | 2.8892 | 10 | 8 | 0.0028 | 0.7044 |
| torchbench | 3.013 | 3.0381 | 6 | 2 | 0.0016 | 0.0016 |
| All models | 3.013 | 3.0381 | 17 | 10 | 0.0028 | 0.7044 |
I used PyTorch release/2.0 branch and corresponding [commit_pin](https://github.com/pytorch/pytorch/blob/release/2.0/.github/ci_commit_pins/xla.txt) for XLA to generate the above data.
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102218
Approved by: https://github.com/jansel
With the TQDM changes in #100969 -- the models names ended up getting hidden from the benchmark printouts. We would print the model name with no newline, then tqdm would print a `\r` and overwrite the name of the running model.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101627
Approved by: https://github.com/ezyang
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
