Enable torchdynamo with torch_tensorrt(fx path)
# What does this PR do?
 Adding support for TorchDynamo with torch_tensor(fx2trt module). Detailed context available at #17724 
This diff is about adding extra inference backend based on #17308 
 

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## To reproduce and set up the environment
```
# install torch-nightly
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch-nightly

# install functorch (and reinstall after `git pull` later if need to sync up)
git clone https://github.com/pytorch/functorch
cd functorch
rm -rf build
pip install -e .[aot]

cd ..
git clone https://github.com/pytorch/torchdynamo
cd torchdynamo
pip install -r requirements.txt
python setup.py develop

# install TensorRT
pip install nvidia-pyindex
pip install nvidia-tensorrt==8.2.4.2

# install torch_tensorrt (fx path)
cd ..
git clone https://github.com/pytorch/TensorRT.git
cd TensorRT/py
python setup.py install --fx-only

```
cc HF @stas00 
cc Meta @yinghai @Chillee 
cc NV @ncomly-nvidia @narendasan 

_The documentation is not available anymore as the PR was closed or merged._<|||||>Hi, @stas00  just a friendly ping. I updated the installation part and it will be easy to repro if needed.<|||||>So I followed your instructions except I used the .deb package installer.

(oh and please link to https://docs.nvidia.com/deeplearning/tensorrt/archives/index.html so that the user will know how to install tensorrt)

why do I get:

```
Traceback (most recent call last):
  File "/mnt/nvme0/code/github/00pytorch/torchdynamo/torchdynamo/optimizations/backends.py", line 45, in inner
    return fn(model, **kwargs)
  File "/mnt/nvme0/code/github/00pytorch/torchdynamo/torchdynamo/optimizations/backends.py", line 313, in fx2trt
    from torch_tensorrt.fx.fx2trt import InputTensorSpec
ModuleNotFoundError: No module named 'torch_tensorrt'
```

Is it the module from `tensorrt-8.2.5.1-cp38-none-linux_x86_64.whl` 

oh I see it failed to build:

```
/hf/00pytorch/TensorRT/py [pytorch/TensorRT|master]> python setup.py install --fx-only
Could not find bazel in PATH
```

installed `bazel` and it still fails:

```
pip install bazel
Collecting bazel
  Downloading bazel-0.0.0.20200723.tar.gz (1.4 kB)
Building wheels for collected packages: bazel
  Building wheel for bazel (setup.py) ... done
  Created wheel for bazel: filename=bazel-0.0.0.20200723-py3-none-any.whl size=1708 sha256=518429e9ce158eb7e4ffc2cefa782eb7935d39d317d67801c5ae9b7346af0500
  Stored in directory: /home/stas/.cache/pip/wheels/9b/80/e4/8d16b3eeeda264ac8105dd7fa29a124431113b2f1f5dd703bc
Successfully built bazel
Installing collected packages: bazel
Successfully installed bazel-0.0.0.20200723
(py38-pt112) /hf/00pytorch/TensorRT/py [pytorch/TensorRT|master]> python setup.py install --fx-only
Could not find bazel in PATH
```
so it's not a python package that it wants but a system-wide `bazel`? there is no apt package - probably need to add a new apt repo? this doc appears to be really outdated https://docs.bazel.build/versions/main/install-ubuntu.html

In any case this obviously requires explicit instructions.

I will wait for your instructions before proceeding.
<|||||>Thanks for your time and efforts @stas00 !
1. Yes, the TRT seems that bring the new user some troubles when they try their first time to install. I just found a way to install python version of TRT so you do not need to download TRT tarball and unzip the stuffs (this python installation will install all the dependent libs like tensorRT lib and cuDNN lib). I added this instructions to our doc as a PR. https://github.com/pytorch/TensorRT/pull/1145/
```
    $ pip3 install nvidia-pyindex
    $ pip3 install nvidia-tensorrt==8.2.4.2
```
2. I am having a PR to **disable** the bazel check https://github.com/pytorch/TensorRT/pull/1147.  (merged)
But that is a bit weird for bazel installation. I am on centOS and conda envrioment. Here is command `conda install -c conda-forge bazel`. It looks like your bazel installation location is not added to $PATH but `which bazel` can help check. Now, with my diff [1147](https://github.com/pytorch/TensorRT/pull/1147) (merged), we should not need bazel. 
Now below instruction is the complete instruction about install TRT, pytorch, torch_tensorrt.fx which I just verified work.
```
    $ conda create --name python_env python=3.8
    $ conda activate python_env
    # Recommend to install PyTorch 1.12 and later
    $ conda install pytorch torchvision torchtext cudatoolkit=11.3 -c pytorch-nightly
    # Install TensorRT python package
    $ pip3 install nvidia-pyindex
    $ pip3 install nvidia-tensorrt==8.2.4.2
    $ git clone https://github.com/pytorch/TensorRT.git
    $ cd TensorRT/py && python setup.py install --fx-only && cd ..
    # check torch_tensorrt.fx is installed
    $ python -c "import torch_tensorrt.fx"
  ```
  Hope it solves your problem. <|||||>`conda install -c conda-forge bazel` did the trick, The same with pip was giving nothing with `which bazel` - not a PATH issue, but a package issue I think, but probably related

---------------

```
    $ pip3 install nvidia-pyindex
    $ pip3 install nvidia-tensorrt==8.2.4.2
```

That did the trick. The tests have run successfully.

so let's update the OP with the above 2 fixes.<|||||>ah, one more user-facing documentation nit -  if you want users to use your magic code you will want to provide some enticement. A small benchmark table that shows what these features do usually goes a long way to get a user excited to try them. So this is something else to consider. It's not a show stopper, but as you can see if the docs aren't added right away they never get added, so it's best to do it in one go. It's still a recommendation and I'm fine merging it as is, it's just not going to be used much w/o enticing docs.
<|||||>> ah, one more user-facing documentation nit - if you want users to use your magic code you will want to provide some enticement. A small benchmark table that shows what these features do usually goes a long way to get a user excited to try them. So this is something else to consider. It's not a show stopper, but as you can see if the docs aren't added right away they never get added, so it's best to do it in one go. It's still a recommendation and I'm fine merging it as is, it's just not going to be used much w/o enticing docs.

I will try to add the doc there. But it is better to have @anijain2305  to include the AOT part.:-)<|||||>> I will try to add the doc there. But it is better to have @anijain2305 to include the AOT part.:-)

Yeah, I was hoping that you'd only need to add the incremental part relevant for this PR.<|||||>re: CI - yes and it's complicated

basically the live CI that you see reporting in this PR runs only CPU tests since CircleCI doesn't have gpus.

then we have another set of CI workflows that runs on our machine via github actions and that's where we test all the complex/slow cases.

And yes, I completely forgot that part of this PR we need to setup our CI to install all these packages as well so that these tests will be run.

So once we polished this let's not forget that part. We will have to run all those instructions on our pt-nightly docker image - but actually there is a problem with this idea - how will the docker builder be able to download tensorRT packages if they require an NVIDIA user account?<|||||>re: CI
Actually, circleCI has gpu resource to use(V100, T4, P4). I just added to our project :-) https://github.com/pytorch/TensorRT/pull/1137
These 2 commands are our saver
```
 $ pip3 install nvidia-pyindex
 $ pip3 install nvidia-tensorrt==8.2.4.2
```
Do you think we need to have @require_torchtensorrt.fx  ? So it will help us to check if torch_tensorrt.fx is installed in the test?<|||||>




> Actually, circleCI has gpu resource to use(V100, T4, P4). I just added to our project :-) [pytorch/TensorRT#1137](https://github.com/pytorch/TensorRT/pull/1137) 

That's great to know - thank you very much - I will pass this info on

> These 2 commands are our saver
> 
> ```
>  $ pip3 install nvidia-pyindex
>  $ pip3 install nvidia-tensorrt==8.2.4.2
> ```

Ah, right! so no need for nvidia user account! super - let's use that in the instructions then.

> Do you think we need to have @require_torchtensorrt.fx ? So it will help us to check if torch_tensorrt.fx is installed in the test?

Absolutely, yes!<|||||>@stas00 , just wondering if the circleci is flaky? Some tests errors are not related. For ex.
run_example_torch, check_code_quanlity<|||||>It appears that the CI is very broken at the moment, I asked and will know more tomorrow morning.

Thank you for the heads up, @frank-wei - it doesn't look like any of the failures are related to your work. Especially since the live CI won't run any of your tests.<|||||>ok, so for the quality one - please rebase this PR on main. Thank you.

The other issue I don't have an answer for yet.

**update: I rebased - let's see with the update.**<|||||>ok, so to fix `check_code_quality` you need to run `make style` and push

after rebasing most of the CI failures are now coming from this PR:

```

==================================== ERRORS ====================================
______________ ERROR collecting tests/deepspeed/test_deepspeed.py ______________
ImportError while importing test module '/home/circleci/transformers/tests/deepspeed/test_deepspeed.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/local/lib/python3.7/importlib/__init__.py:127: in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
tests/deepspeed/test_deepspeed.py:26: in <module>
    from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
tests/trainer/test_trainer.py:586: in <module>
    class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
tests/trainer/test_trainer.py:1803: in TrainerIntegrationTest
    @require_torch_tensorrt_fx
src/transformers/testing_utils.py:499: in require_torch_tensorrt_fx
    return unittest.skipUnless(is_torch_tensorrt_fx_available(), "test requires Torch-TensorRT FX")(test_case)
src/transformers/utils/import_utils.py:421: in is_torch_tensorrt_fx_available
    return importlib.util.find_spec("torch_tensorrt.fx") is not None
/usr/local/lib/python3.7/importlib/util.py:94: in find_spec
    parent = __import__(parent_name, fromlist=['__path__'])
E   ModuleNotFoundError: No module named 'torch_tensorrt'
```

Let me know if you need help with sorting it out.<|||||>Thanks @stas00 , I fixed the import check. <|||||>Is there a simple way to support dynamic shape on fx2trt on torch Dynamo? If not yet, may be you want to specify it in the doc? If yes you may want to say how we provide the Tensorrt "profiles" ?

In rapid experiments I did on HF + dynamo + fx2trt, even by increasing the dynamo cache, when I pushed plenty of different input sizes, at some point it raised plenty of OOM exceptions and stopped working. May be trt profiles would have worked.<|||||>> Is there a simple way to support dynamic shape on fx2trt on torch Dynamo? If not yet, may be you want to specify it in the doc? If yes you may want to say how we provide the Tensorrt "profiles" ?
> 
> In rapid experiments I did on HF + dynamo + fx2trt, even by increasing the dynamo cache, when I pushed plenty of different input sizes, at some point it raised plenty of OOM exceptions and stopped working. May be trt profiles would have worked.

It is not supported yet for dynamic shape in my implementation. I plan to support dynamic batch size in the next step(probably set it to default). <|||||>One of the tests is failing for me:

```
$ CUDA_VISIBLE_DEVICES=0 RUN_SLOW=1 pyt tests/trainer/test_trainer.py -k test_torchdynamo_memory -sv
        # AOT Autograd recomputaion and nvfuser recomputation optimization
        # aggressively fuses the operations and reduce the memory footprint.
>       self.assertGreater(orig_peak_mem, peak_mem * 2)
E       AssertionError: 100664832 not greater than 201330688
```

let me know what details you need - this is on A100.

oh, it actually crashed before that:

```
========== TorchDynamo Stack Trace ==========
Traceback (most recent call last):
  File "/mnt/nvme0/code/github/00pytorch/torchdynamo/torchdynamo/convert_frame.py", line 295, in _convert_frame_assert
    code = transform_code_object(frame.f_code, transform)
  File "/mnt/nvme0/code/github/00pytorch/torchdynamo/torchdynamo/bytecode_transformation.py", line 338, in transform_code_object
    transformations(instructions, code_options)
  File "/mnt/nvme0/code/github/00pytorch/torchdynamo/torchdynamo/convert_frame.py", line 261, in transform
    tracer = InstructionTranslator(
  File "/mnt/nvme0/code/github/00pytorch/torchdynamo/torchdynamo/symbolic_convert.py", line 1220, in __init__
    self.symbolic_locals = collections.OrderedDict(
  File "/mnt/nvme0/code/github/00pytorch/torchdynamo/torchdynamo/symbolic_convert.py", line 1221, in <genexpr>
    (k, VariableBuilder(self, LocalSource(k))(f_locals[k]))
  File "/mnt/nvme0/code/github/00pytorch/torchdynamo/torchdynamo/variables/builder.py", line 104, in __call__
    return self._wrap(value).clone(**self.options())
  File "/mnt/nvme0/code/github/00pytorch/torchdynamo/torchdynamo/variables/builder.py", line 130, in _wrap
    return self.wrap_tensor(value)
  File "/mnt/nvme0/code/github/00pytorch/torchdynamo/torchdynamo/variables/builder.py", line 327, in wrap_tensor
    tensor_variable = TensorVariable.create(
  File "/mnt/nvme0/code/github/00pytorch/torchdynamo/torchdynamo/variables/tensor.py", line 121, in create
    cls.wrap_to_fake_tensor, fake_mode=tx.fake_mode
  File "/mnt/nvme0/code/github/00pytorch/torchdynamo/torchdynamo/symbolic_convert.py", line 1136, in fake_mode
    return self._fake_mode
AttributeError: 'InstructionTranslator' object has no attribute '_fake_mode'
```

This is not great, shouldn't the test have failed here and not in a misleading later place of comparison?<|||||>The failure may due to the torchdynamo outdated? Could you install the newest torchdynamo? Here are the command to install it:
```
git clone https://github.com/pytorch/functorch
cd functorch
rm -rf build
pip install -e .[aot]

cd ..
git clone https://github.com/pytorch/torchdynamo
cd torchdynamo
pip install -r requirements.txt
python setup.py develop
```

It looks good from my testing:
```
(mypy38-fx-only) [wwei6@devgpu005.ftw6 /data/users/wwei6/Work/transformers] CUDA_VISIBLE_DEVICES=6 pytest tests/trainer/test_trainer.py  -k test_torchdynamo_memory -sv
===================================================================================== test session starts =====================================================================================
platform linux -- Python 3.8.13, pytest-7.1.2, pluggy-1.0.0 -- /data/users/wwei6/miniconda3/envs/mypy38-fx-only/bin/python
cachedir: .pytest_cache
benchmark: 3.4.1 (defaults: timer=time.perf_counter disable_gc=False min_rounds=5 min_time=0.000005 max_time=1.0 calibration_precision=10 warmup=False warmup_iterations=100000)
hypothesis profile 'default' -> database=DirectoryBasedExampleDatabase('/data/users/wwei6/Work/transformers/.hypothesis/examples')
rootdir: /data/users/wwei6/Work/transformers, configfile: setup.cfg
plugins: benchmark-3.4.1, hydra-core-1.1.2, hypothesis-6.49.1
collected 70 items / 69 deselected / 1 selected                                                                                                                                               

tests/trainer/test_trainer.py::TrainerIntegrationTest::test_torchdynamo_memory PyTorch: setting up devices
The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
PASSED

====================================================================================== warnings summary =======================================================================================
../../miniconda3/envs/mypy38-fx-only/lib/python3.8/site-packages/torch/utils/tensorboard/__init__.py:4
  /data/users/wwei6/miniconda3/envs/mypy38-fx-only/lib/python3.8/site-packages/torch/utils/tensorboard/__init__.py:4: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
    if not hasattr(tensorboard, "__version__") or LooseVersion(

../../miniconda3/envs/mypy38-fx-only/lib/python3.8/site-packages/torch/utils/tensorboard/__init__.py:6
  /data/users/wwei6/miniconda3/envs/mypy38-fx-only/lib/python3.8/site-packages/torch/utils/tensorboard/__init__.py:6: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
    ) < LooseVersion("1.15"):

tests/trainer/test_trainer.py::TrainerIntegrationTest::test_torchdynamo_memory
  /data/users/wwei6/miniconda3/envs/mypy38-fx-only/lib/python3.8/site-packages/torch/nn/utils/_stateless.py:5: DeprecationWarning: The `torch.nn.utils._stateless` code is deprecated now that it is publicly available. Please use `torch.nn.utils.stateless instead.
    warnings.warn("The `torch.nn.utils._stateless` code is deprecated now that "

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
======================================================================== 1 passed, 69 deselected, 3 warnings in 7.51s =========================================================================
```<|||||>I suspected that was the case, but what I was trying to say is that the test should have failed on the torchdynamo error and not the mismatch in values, i.e. something is trapping the real error and the user could be not the wiser that their torchdynamo is broken - e.g. when there are a lot of logs.

It needs to assert on the actual error. Does it make sense?<|||||>> I suspected that was the case, but what I was trying to say is that the test should have failed on the torchdynamo error and not the mismatch in values, i.e. something is trapping the real error and the user could be not the wiser that their torchdynamo is broken - e.g. when there are a lot of logs.
> 
> It needs to assert on the actual error. Does it make sense?

hm.. that is something out of my expertise as it relates with torchdynamo. If it is torch_tensorrt related, I'd love to help.

For the CI test error, it seems that test is flaky? I did not find useful any information. Could you help guide/triage that? Thanks.



<|||||>> Hi @frank-wei, I had to rebuild the whole environment against pt-nightly and now everything works.
> 
> I think it'd be good to save the instructions in the OP somewhere so that it's easier for the user and us to be able to rebuild the environment.
> 
> Would you like to maintain a section or a file on your side that contains the instructions in the OP and we could point to it?
> 
> Other than that, I will just ask Sylvain to have a quick review and we can merge this.
> 
> Thank you for your patience.



> Hi @frank-wei, I had to rebuild the whole environment against pt-nightly and now everything works.
> 
> I think it'd be good to save the instructions in the OP somewhere so that it's easier for the user and us to be able to rebuild the environment.
> 
> Would you like to maintain a section or a file on your side that contains the instructions in the OP and we could point to it?
> 
> Other than that, I will just ask Sylvain to have a quick review and we can merge this.
> 
> Thank you for your patience.

Thanks @stas00 , do you think I can add a 3 pointers for installations of torchdynamo, functorch, torch_tensorrt in docs/source/en/perf_train_gpu_one.mdx ?
Torchdynamo: https://github.com/pytorch/torchdynamo#requirements-and-setup
Functorch:https://github.com/pytorch/functorch#install
Torch-TensorRT(FX):https://github.com/pytorch/TensorRT/blob/master/docsrc/tutorials/getting_started_with_fx_path.rst#installation<|||||>I think that works, @frank-wei <|||||>> I think that works, @frank-wei

Cool. Update finished.<|||||>@stas00  @sgugger  please check the change. The failed test seems flaky and not related.<|||||>@stas00 Are you good with this last iteration (as long as all tests pass?)<|||||>Let me run the tests.<|||||>All tests pass. Good to merge once the CI is green.

I created a new task https://github.com/huggingface/transformers/issues/18127 to handle the CI requirements.