Summary:
Original PR at https://github.com/pytorch/pytorch/pull/104977. Landing from fbcode instead.
Add an aot_inductor backend (Export+AOTInductor) in the benchmarking harness. Note it is not a dynamo backend.
Moved files from torch/_inductor/aot_inductor_include to torch/csrc/inductor as a more standard way for exposing headers
Created a caching function in benchmarks/dynamo/common.py for compiling, loading and caching the .so file, as a proxy for a pure C++ deployment, but easier for benchmarking.
Differential Revision: D47452591
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105221
Approved by: https://github.com/jansel
The general idea is to do a separate CUDA graph for each size. Because of cuda graph trees, these graphs will all share the same memory pool, so your memory usage will only be the worst case memory usage of the biggest dynamic size you want. This requires an extra dispatch in the cudagraphified callable. You must pay for a CUDA graph recording for every dynamic size you encounter, but this is MUCH cheaper than running the entire PT2 compile stack, so I expect you to still see benefits.
This was surprisingly easy to do.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105064
Approved by: https://github.com/voznesenskym
This PR disables translation validation (TV) when running the benchmark suits on
performance workflows: inductor with A100s.
In summary, the changes are:
- Add flag for turning TV on and off on _benchmarks/dynamo/common.py_
- Turn TV on only on CI accuracy builds
- Add `--no-translation-validation` target flag to _.ci/pytorch/test.sh_
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104887
Approved by: https://github.com/ezyang
As of now, translation validation runs to its completion. However, Z3 is time
consuming. PR #104464, for example, disables translation validation for a few benchmarks.
Instead, this PR introduces a timeout for translation validation. In that case, Z3 will
return `unknown`, since it wasn't able to prove or disprove the assertions. Then, we log
it as a warning, but don't stop execution.
Here's a summary of the changes:
- Added an environment variable for turning translation validation on and off
- Added an environment variable for setting the translation validation timeout
- Possibly reverts the changes in #104464
- ~~Move from "QF_NRA" to "QF_NIRA" logic~~
- ~~It makes more sense, given the nature of the problems~~
- "QF_NRA" seems to solve more instances of _dynamo/test_dynamic_shapes.py_
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104654
Approved by: https://github.com/ezyang
issues resolved: https://github.com/pytorch/pytorch/issues/104294
local test on TB and TIMM
* python benchmarks/dynamo/torchbench.py -d cuda --inference --accuracy --progress --export --print-dataframe-summary
* python benchmarks/dynamo/timm_models.py -d cuda --inference --accuracy --progress --export --print-dataframe-summary
why not HF
* huggingface use kwargs (dict) to torch.nn.module
* we will need to support kwargs in torch._export.export, which is in progress
local test result
timm 95% pass rate (58 ouf of 61 passed) P781702926
* 1 x [export specific]1 x ERROR:common:Mutating module attribute rel_indices during export
* 1 x[not relevant to export] Unknown model (SelecSls42b)
* 1 x [not relevant to export] Failed to load model: HTTP Error 409: Public access is not permitted on this storage account
torchbench 54% pass rate (41 out of 75 passed) P781690552
* 7 x ERROR:common:Dynamo input and output is a strict subset of traced input/output
* 3 x ERROR:common:call_method NNModuleVariable() / UserDefinedObjectVariable
* 3 x ERROR:common:Mutating module attribute {xx} during export.
* 2 x ERROR:common:inline in skipfiles
* 2 x ERROR:common:Consider annotating your code using constrain_as_*(). It appears that you're trying
* 1 x ERROR:common:guard on data-dependent symbolic int/float
* 1 x ERROR:common:Tensor.tolist
* 1 x ERROR:common:Tensor.numpy. Turn on config.numpy_ndarray_as_tensor and install torch_np to support tensor.numpy(). [may be dev * env?]
* 1 x ERROR:common:missing: BUILD_SET
* 1 x ERROR:common:whole graph export entails exactly one guard export
* 1 x ERROR:common:call_function BuiltinVariable(str) [GetAttrVariable(UserMethodVariable(<function
* 1 x ERROR:common:Dynamic slicing on data-dependent value is not supported
* 1 x ERROR:common:Failed running call_function <function interpolate at 0x7f60a8361ea0>(*(FakeTensor(..., device='cuda:0', size=(1, 3, * 427,
* 1 x ERROR:common:Dynamo attempts to add additional input during export: value=0.6177528500556946, source=RandomValueSource(random_call_index=0)
* 1 x Found following user inputs located at [16, 17, 18, 19, 20, 21, 22] are mutated. This is currently banned in the aot_export workflow.
* 1 x RuntimeError: cumsum_cuda_kernel does not have a deterministic implementation
* 4 x pass_due_to_skip
* 1 x eager_2nd_run_OOM
* 1 x fail_accuracy
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104382
Approved by: https://github.com/zhxchen17
Some notes:
* I now manually turn off `_generate` jobs from running with cudagraphs, as it is unrealistic to expect to cudagraph autoregressive generation up to max sequence length, this would imply compiling the entire unrolled sequence generation. Concretely, cm3leon_generate was timing out post this change, likely due to the compile time slowdown of dynamic shapes ON TOP OF accidentally unrolling all the loops
* A few torch._dynamo.reset tactically inserted to force recompiles on tests that expected it
* expectedFailureAutomaticDynamic flip into patching automatic_dynamic_shapes=False
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103623
Approved by: https://github.com/voznesenskym
This PR turns translation validation on by default for tests and accuracy benchmark
runs. It also installs Z3 on CI.
The main changes are:
- Add `--no-translation-validation` as an option in _test/run_tests.py_
- Set `PYTORCH_TEST_WITH_TV` environment variable
- Add `TEST_WITH_TV` variable in _torch/testing/_internal/common_utils.py_
- Turn translation validation on for accuracy benchmarks in _benchmarks/dynamo/common.py_
- Add Z3 installation on CI scripts
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103611
Approved by: https://github.com/ezyang
- Extend dynamo bench interface with '--compilers onnx' and '--compilers dynamo-onnx'
- ONNX bench exports model to onnx and runs in ONNX Runtime.
- Introduce error aggregation and report.
- Scripts to build ONNX deps and running ONNX bench.
- Huggingface accuracy check workaround for ONNX.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103135
Approved by: https://github.com/thiagocrepaldi, https://github.com/jansel
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, https://github.com/eellison
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
