`python benchmarks/dynamo/torchbench.py --multiprocess` currently fails due to initializing distributed multiple times:
```
torch.distributed.DistNetworkError: The server socket has failed to listen on any local network address. The server socket has failed to bind to [::]:6789 (errno: 98 - Address already in use). The server socket has failed to bind to 0.0.0.0:6789
(errno: 98 - Address already in use).
```
Because torchbench calls itself via mp.spawn, there is the parent run (with `--multiprocess`) and child runs (with `--multiprocess --only <model>`).
This PR addresses this by fixing two issues:
1) distributed is initialized once in parent run and once in child runs, it should be initialized only in child runs where we have accurate rank and world size info
2) torchbench overrides CUDA_VISIBLE_DEVICES/world_size sometimes, but it shouldn't for distributed use cases where we want to use all available gpus
I am also adding a CI test to cover this type of issue in #109311
### Test plan
parent run test: `python benchmarks/dynamo/torchbench.py --ci --accuracy --timing --explain --inductor --device cuda --inference --bfloat16 --output /home/xmfan/local/pytorch/test/test-reports/inference_torchbench.csv --multiprocess`
child run test: `python benchmarks/dynamo/torchbench.py --ci --accuracy --timing --explain --inductor --device cuda --inference --bfloat16 --output /home/xmfan/local/pytorch/test/test-reports/inference_torchbench.csv --multiprocess --only simple_gpt`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109657
Approved by: https://github.com/H-Huang
Previously, the code for passing inputs to exported program was:
```
if kwargs:
return (args, kwargs)
else:
return args
```
However, this causes some inconsistency where if the original input contains args and kwargs, the treespec would be a tuple containing a tuple of arguments, and a dictionary of keyword arguments. But if the original input only contained args, the treespec would just be a tuple of arguments. This inconsistency causes some inconveniences in the runtime.
So I updated the code to just always keep the kwargs around.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109160
Approved by: https://github.com/zhxchen17, https://github.com/avikchaudhuri
Summary: Switch AOTInductor unit tests and integration tests to invoke the same runtime interface. This is only an effort to unify the usage of the runtime. The interface scrutiny will come in later PRs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108663
Approved by: https://github.com/ezyang
ghstack dependencies: #108653
**This PR is a 99% copy paste of Sam Gross** (@colesbury) work at https://github.com/pytorch/pytorch/pull/100642. Copied from there
--------
The NN_MODULE guard now subsumes guards on Module attributes. The check_fn will fail if the module attributes are changed (such as Module.training), parameters, submodules, and buffers are added or removed, and if fields are changed on the type itself.
This gives up specificity in the guard check -- if any field is changed the check_fn fails -- for faster overall checks.
-----
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108528
Approved by: https://github.com/ezyang
Summary: Move AOTInductor runtime header files into its own subdirectory, to separate them from to-be-added libtorch C interface.
Reviewed By: frank-wei
Differential Revision: D48905038
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108564
Approved by: https://github.com/frank-wei
Summary:
Include the constants into AOTInductor .so file.
We do not modify existing API signatures but create necessary format with weight lifted out instead.
Test Plan:
test/inductor/test_aot_inductor.py
Reviewers:
Subscribers:
Tasks:
Tags:
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107718
Approved by: https://github.com/angelayi, https://github.com/eellison
Generate diagnostic reports to monitor the internal stages of the export process. This tool aids in unblocking model exports and debugging the exporter.
#### Settings
~~1. Choose if you want to produce a .sarif file and specify its location.~~
1. Updated: saving .sarif file should be done by `export_output.save_sarif_log(dst)`, similar to saving exported onnx model `export_output.save(model_dst)`.
2. Customize diagnostic options:
- Set the desired verbosity for diagnostics.
- Treat warnings as errors.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106741
Approved by: https://github.com/titaiwangms, https://github.com/justinchuby, https://github.com/malfet
RFC: https://github.com/pytorch/rfcs/pull/54
First commit is the contents of https://github.com/Quansight-Labs/numpy_pytorch_interop/
We have already been using this in core for the last few months as a external dependency. This PR pulls all these into core.
In the next commits, I do a number of things in this order
- Fix a few small issues
- Make the tests that this PR adds pass
- Bend backwards until lintrunner passes
- Remove the optional dependency on `torch_np` and simply rely on the upstreamed code
- Fix a number dynamo tests that were passing before (they were not tasting anything I think) and are not passing now.
Missing from this PR (but not blocking):
- Have a flag that deactivates tracing NumPy functions and simply breaks. There used to be one but after the merge stopped working and I removed it. @lezcano to investigate.
- https://github.com/pytorch/pytorch/pull/106431#issuecomment-1667079543. @voznesenskym to submit a fix after we merge.
All the tests in `tests/torch_np` take about 75s to run.
This was a work by @ev-br, @rgommers @honno and I. I did not create this PR via ghstack (which would have been convenient) as this is a collaboration, and ghstack doesn't allow for shared contributions.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106211
Approved by: https://github.com/ezyang
https://github.com/pytorch/pytorch/issues/105555
Existing flow first exports and then calls torch._inductor.aot_compile. However, export calls aot_autograd with the core aten decomposition table, and then torch._inductor.aot_compile calls aot_autograd again with the inductor decomposition table. The 2nd calling of aot_autograd is supposedly causing some problems, and seems excessive, so instead we will create a new function, torch._export.aot_compiler which will export using the inductor decomposition table, pass it to inductor's compile_fx_aot, and because it has already been exported, avoid recalling aot_autograd.
```
def aot_compile(
f: Callable,
args: Tuple[Any],
kwargs: Optional[Dict[str, Any]] = None,
constraints: Optional[List[Constraint]] = None,
) -> Tuple[str, ExportedProgram]:
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105977
Approved by: https://github.com/desertfire, https://github.com/zhxchen17, https://github.com/eellison
Includes stable diffusion, whisper, llama7b and clip
To get this to work I had to Pass in hf auth token to all ci jobs, github does not pass in secrets from parent to child automatically. There's a likelihood HF will rate limit us in case please revert this PR and I'll work on adding a cache next - cc @voznesenskym @penguinwu @anijain2305 @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @aakhundov @malfet
Something upstream changed in torchbench too where now `hf_Bert` and `hf_Bert_large` are both failing on some dynamic shape looking error which I'm not sure how to debug yet so for now felt a bit gross but added a skip since others are building on top this work @ezyang
`llamav2_7b_16h` cannot pass through accuracy checks cause it OOMs on deepcloning extra inputs this seems to make it not need to show up in expected numbers csv, will figure this when we update the pin with https://github.com/pytorch/benchmark/pull/1803 cc @H-Huang @xuzhao9 @cpuhrsch
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106009
Approved by: https://github.com/malfet
### Description
As an alternative to PR #105774, which provides a standalone, end-to-end minification script that covers all types of failures and has more functionality, this PR adds the ability to minify models when they fail the eval loop (accuracy checks). Both this PR and the other one can be merged without issue.
### Purpose
The goal is to leverage the minifier to minify models that fail accuracy checks, allowing failed models to be debugged more easily. The ideal use-case is trying to run a model suite on a backend where operator coverage is not known or is limited. If models can compile but fails the eval loop, having the repro script for each model is valuable for any developer that's trying to fix the issue.
### Functionality
- Create minify flag that minifies models when they fail accuracy check
- Produce minified graph for each model, and save it into repro script
- Move repro script to output directory/base Dynamo directory
- Enable functionality for running an entire model suite (Hugging Face, timm, and TorchBench) by prepending model name to repro script
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106201
Approved by: https://github.com/ezyang
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
Previously, we had a problem when partitioning forward-backward dynamic graphs, which is that we could end up with a backward graph that mentions a symbol in an input tensor (e.g., `f32[s0 + s1]`), but without this symbol being otherwise bound elsewhere. When this happens, we have no way of actually deriving the values of `s0` and `s1`. Our fix for this in https://github.com/pytorch/pytorch/pull/93059 was to just retrace the graph, so that s0 + s1 got allocated a new symbol s2 and everything was happy. However, this strategy had other problems, namely (1) we lost all information from the previous ShapeEnv, including guards and (2) we end up allocating a LOT of fresh new symbols in backwards.
With this change, we preserve the same ShapeEnv between forward and backwards. How do we do this? We simply require that every symbol which may be present inside tensors, ALSO be a plain SymInt input to the graph. This invariant is enforced by Dynamo. Once we have done this, we can straightforwardly modify the partitioner to preserve these SymInt as saved for backwards, if they are needed in the backwards graph to preserve the invariant as well.
This apparently breaks yolov3, but since everything else is OK I'm merging this as obviously good and investigating later.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/99089
Approved by: https://github.com/voznesenskym
Dynamo benchmark --verbose is broken:
```
Traceback (most recent call last):
File "/scratch/ybliang/work/repos/pytorch/benchmarks/dynamo/torchbench.py", line 400, in <module>
torchbench_main()
File "/scratch/ybliang/work/repos/pytorch/benchmarks/dynamo/torchbench.py", line 396, in torchbench_main
main(TorchBenchmarkRunner(), original_dir)
File "/scratch/ybliang/work/repos/pytorch/benchmarks/dynamo/common.py", line 1967, in main
return maybe_fresh_cache(
File "/scratch/ybliang/work/repos/pytorch/benchmarks/dynamo/common.py", line 993, in inner
return fn(*args, **kwargs)
File "/scratch/ybliang/work/repos/pytorch/benchmarks/dynamo/common.py", line 2135, in run
torch._dynamo.config.log_level = logging.DEBUG
File "/scratch/ybliang/work/repos/pytorch/torch/_dynamo/config_utils.py", line 67, in __setattr__
raise AttributeError(f"{self.__name__}.{name} does not exist")
AttributeError: torch._dynamo.config.log_level does not exist
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/99224
Approved by: https://github.com/voznesenskym
Symbolic shapes compile time on full CI with inductor is horribly long (even though our aot_eager local runs seemed to suggest that the added latency was only 10s per model.) To patch over the problem for now, run the benchmark suite with dynamic batch only. This should absolve a lot of sins.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97912
Approved by: https://github.com/janeyx99, https://github.com/desertfire
