Use the existing benchmark infra to get some signals for AOT precompile pass rate on OSS models. Here we also measure and log the loading time.
```
python ./benchmarks/dynamo/huggingface.py --accuracy --inference --aot-precompile
python ./benchmarks/dynamo/timm_models.py --accuracy --inference --aot-precompile
python ./benchmarks/dynamo/torchbench.py --accuracy --inference --aot-precompile
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164906
Approved by: https://github.com/zhxchen17
Verify the deterministic mode with torch.compile benchmark scripts.
Here is what my testing script does (pasted in the end):
- run a model in default mode, save it's result
- run the model again in default mode, but distort the benchmarking results. Compare it with the saved result.
- Do the above again in deterministic mode.
I tried to test a few modes
- BertForMaskedLM and GoogleFnet: I can repro the numeric change by distorting the benchnmark result in the default mode. The non-determinism is gone in the deterministic mode
- DistillGPT2: I can not repro the numeric change by distorting the benchmarking result in the default mode. It does not surprise me much. Reduction order change does not always cause numeric change.
```
model=GoogleFnet
export TORCHINDUCTOR_WRITE_ARE_DETERMINISTIC_ALGORITHMS_ENABLED=0
export TORCHINDUCTOR_FORCE_DISABLE_CACHES=1 # disable autotune cache
export TORCHINDUCTOR_FX_GRAPH_REMOTE_CACHE=0
export TORCHINDUCTOR_FX_GRAPH_CACHE=0
export TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_shunting/
export TORCHINDUCTOR_BENCHMARK_KERNEL=1
export TORCHINDUCTOR_UNIQUE_KERNEL_NAMES=1
export INDUCTOR_TEST_DISABLE_FRESH_CACHE=1
# Non deterministic mode
# --float32 rather than --amp to make it easier to repro non-deterministic
echo "Save results for non-deterministic mode"
python benchmarks/dynamo/huggingface.py --backend inductor --float32 --accuracy --only $model --training --disable-cudagraphs --save-model-outputs-to=/tmp/saved-non-deterministic.pkl
echo "Compare results with distorted benchmarking in non-deterministic mode"
TORCHINDUCTOR_DISTORT_BENCHMARKING_RESULT=inverse python benchmarks/dynamo/huggingface.py --backend inductor --float32 --accuracy --only $model --training --disable-cudagraphs --compare-model-outputs-with=/tmp/saved-non-deterministic.pkl
echo "Save results for deterministic mode"
TORCHINDUCTOR_DETERMINISTIC=1 python benchmarks/dynamo/huggingface.py --backend inductor --float32 --accuracy --only $model --training --disable-cudagraphs --save-model-outputs-to=/tmp/saved-deterministic.pkl
echo "Compare results with distorted benchmarking in deterministic mode"
TORCHINDUCTOR_DETERMINISTIC=1 TORCHINDUCTOR_DISTORT_BENCHMARKING_RESULT=inverse python benchmarks/dynamo/huggingface.py --backend inductor --float32 --accuracy --only $model --training --disable-cudagraphs --compare-model-outputs-with=/tmp/saved-deterministic.pkl
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164904
Approved by: https://github.com/jansel, https://github.com/v0i0
Verify the deterministic mode with torch.compile benchmark scripts.
Here is what my testing script does (pasted in the end):
- run a model in default mode, save it's result
- run the model again in default mode, but distort the benchmarking results. Compare it with the saved result.
- Do the above again in deterministic mode.
I tried to test a few modes
- BertForMaskedLM and GoogleFnet: I can repro the numeric change by distorting the benchnmark result in the default mode. The non-determinism is gone in the deterministic mode
- DistillGPT2: I can not repro the numeric change by distorting the benchmarking result in the default mode. It does not surprise me much. Reduction order change does not always cause numeric change.
```
model=GoogleFnet
export TORCHINDUCTOR_WRITE_ARE_DETERMINISTIC_ALGORITHMS_ENABLED=0
export TORCHINDUCTOR_FORCE_DISABLE_CACHES=1 # disable autotune cache
export TORCHINDUCTOR_FX_GRAPH_REMOTE_CACHE=0
export TORCHINDUCTOR_FX_GRAPH_CACHE=0
export TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_shunting/
export TORCHINDUCTOR_BENCHMARK_KERNEL=1
export TORCHINDUCTOR_UNIQUE_KERNEL_NAMES=1
export INDUCTOR_TEST_DISABLE_FRESH_CACHE=1
# Non deterministic mode
# --float32 rather than --amp to make it easier to repro non-deterministic
echo "Save results for non-deterministic mode"
python benchmarks/dynamo/huggingface.py --backend inductor --float32 --accuracy --only $model --training --disable-cudagraphs --save-model-outputs-to=/tmp/saved-non-deterministic.pkl
echo "Compare results with distorted benchmarking in non-deterministic mode"
TORCHINDUCTOR_DISTORT_BENCHMARKING_RESULT=inverse python benchmarks/dynamo/huggingface.py --backend inductor --float32 --accuracy --only $model --training --disable-cudagraphs --compare-model-outputs-with=/tmp/saved-non-deterministic.pkl
echo "Save results for deterministic mode"
TORCHINDUCTOR_DETERMINISTIC=1 python benchmarks/dynamo/huggingface.py --backend inductor --float32 --accuracy --only $model --training --disable-cudagraphs --save-model-outputs-to=/tmp/saved-deterministic.pkl
echo "Compare results with distorted benchmarking in deterministic mode"
TORCHINDUCTOR_DETERMINISTIC=1 TORCHINDUCTOR_DISTORT_BENCHMARKING_RESULT=inverse python benchmarks/dynamo/huggingface.py --backend inductor --float32 --accuracy --only $model --training --disable-cudagraphs --compare-model-outputs-with=/tmp/saved-deterministic.pkl
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164904
Approved by: https://github.com/jansel, https://github.com/v0i0
ghstack dependencies: #164801, #164532
This adds a PR time benchmark that checks for runtime overhead on a very small graph. This will help track regressions in runtime overhead.
Example Results:
```
runtime_overhead_inductor,instruction_count,222645
runtime_overhead_inductor_inference_mode,instruction_count,234998
runtime_overhead_inductor_requires_grad,instruction_count,293556
runtime_overhead_inductor_requires_grad_backward,instruction_count,78181
runtime_overhead_inductor_dynamic,instruction_count,234870
runtime_overhead_inductor_inference_mode_dynamic,instruction_count,248711
runtime_overhead_inductor_requires_grad_dynamic,instruction_count,309979
runtime_overhead_inductor_requires_grad_backward_dynamic,instruction_count,77599
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163866
Approved by: https://github.com/jansel, https://github.com/mlazos, https://github.com/anijain2305
Potential issues
* gpt-oss-20b is probably too big (I can't run on my devserver)
* Mistral requires HF authentication
* Mistral also takes a while to run the performance checks (need to wait for CI)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163565
Approved by: https://github.com/huydhn
Summary:
This diff does a big refactor of PrecompileContext to make it considerably simpler: instead of being a CacheArtifactManager and managing a bunch of bytes, it simply stores two things: dynamo cache entries and backend cache entries. When asked, it stitches them together into PrecompileCacheEntries, which are stored by DynamoCache.
This structure then allows us to register DynamoCache to the regular Megacache API, instead of having two separate APIs that are confusing. It also lets us remove the autotune cache integration, since MegaCache API will automatically store autotune cache entries.
The intent here is that users who want to use caching precompile will simply be able to use torch.compiler.save_cache_artifacts as before, just with `torch.dynamo.config.caching_precompile` set to True. They can also directly interact with PrecompileContext if they wish to specifically only load Precompile entries, using PrecompileContext.create_cache_entries().
Saving single entries and such with DynamoCache still works normally.
Test Plan:
All existing unit tests pass.
Rollback Plan:
Differential Revision: D82380307
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162886
Approved by: https://github.com/zhxchen17
Update PyTorch to the latest Triton release candidate branch (release/3.5.x in triton-lang/triton)
Notably:
* this does *not* include the version number bump from 3.4 -> 3.5 (we'll do that in a follow-up PR)
* sam_fast is still failing, so we've disabled it temporarily https://github.com/pytorch/pytorch/issues/162282 and we are committed to fixing it, ideally before the branch cut but possibly as a cherry-pick into the release branch.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162278
Approved by: https://github.com/atalman
ghstack dependencies: #162244, #162309
Reland of https://github.com/pytorch/pytorch/pull/159923
Couple of fixes:
1. When we run into an operation we didn't proxy, we end up emitting fake constants. We detect this and warn using the FQN of the lifted constant. We warn because some internal users complained it was regressing their exportability.
2. Previous attribute mutation detection logic in non-strict didn't account for nested module structure. This fixes silent incorrectness issue of exporting esm and qwen in non-strict
3. We modify yolov3 to fix the previous silent incorrect behaviour
4. We use strict export for levit_128 because it errors in non-strict due to more strict side effect checking
When upgrading torchbench pin, opacus_cifar10 seems to not run on eager anymore. I verified this by pushing a temporary PR on master with new pin. So i added it to expect_fail list.
Differential Revision: [D81133908](https://our.internmc.facebook.com/intern/diff/D81133908)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/161589
Approved by: https://github.com/avikchaudhuri
