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pytorch-labs / LeanRL

LeanRL is a fork of CleanRL, where selected PyTorch scripts optimized for performance using compile and cudagraphs.
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Some installation issues #2

Open vwxyzjn opened 2 months ago

vwxyzjn commented 2 months ago

There seem to be some issues with getting the environment set up. I tried two installation methods.

Installation 1

One is to do 

pip install -r requirements/requirements-envpool.txt

which seems to get stuck finding a torchrl_nightly version

Ignoring importlib-metadata: markers 'python_version >= "3.8" and python_version < "3.10"' don't match your environment
Collecting absl-py==1.4.0 (from -r requirements/requirements-envpool.txt (line 1))
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Collecting appdirs==1.4.4 (from -r requirements/requirements-envpool.txt (line 2))
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Collecting bitmath==1.3.3.1 (from -r requirements/requirements-envpool.txt (line 3))
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  Preparing metadata (setup.py) ... done
Collecting cachetools==5.3.0 (from -r requirements/requirements-envpool.txt (line 4))
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Collecting charset-normalizer==3.1.0 (from -r requirements/requirements-envpool.txt (line 6))
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Collecting click==8.1.3 (from -r requirements/requirements-envpool.txt (line 7))
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Collecting docker-pycreds==0.4.0 (from -r requirements/requirements-envpool.txt (line 16))
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Collecting docstring-parser==0.15 (from -r requirements/requirements-envpool.txt (line 17))
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Collecting envpool==0.6.6 (from -r requirements/requirements-envpool.txt (line 19))
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Collecting farama-notifications==0.0.4 (from -r requirements/requirements-envpool.txt (line 20))
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Collecting gitdb==4.0.10 (from -r requirements/requirements-envpool.txt (line 22))
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Collecting google-auth-oauthlib==0.4.6 (from -r requirements/requirements-envpool.txt (line 23))
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Collecting google-auth==2.18.0 (from -r requirements/requirements-envpool.txt (line 24))
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Collecting graphviz==0.20.1 (from -r requirements/requirements-envpool.txt (line 25))
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Collecting gym-notices==0.0.8 (from -r requirements/requirements-envpool.txt (line 26))
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Collecting gym==0.23.1 (from -r requirements/requirements-envpool.txt (line 27))
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  Installing build dependencies ... done
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  Preparing metadata (pyproject.toml) ... done
Collecting gymnasium==0.28.1 (from -r requirements/requirements-envpool.txt (line 28))
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Collecting hbutils==0.8.6 (from -r requirements/requirements-envpool.txt (line 29))
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Collecting huggingface-hub==0.11.1 (from -r requirements/requirements-envpool.txt (line 30))
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Collecting imageio-ffmpeg==0.3.0 (from -r requirements/requirements-envpool.txt (line 31))
  Using cached imageio_ffmpeg-0.3.0-py3-none-manylinux2010_x86_64.whl.metadata (1.4 kB)
Collecting imageio==2.28.1 (from -r requirements/requirements-envpool.txt (line 32))
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Collecting jax-jumpy==1.0.0 (from -r requirements/requirements-envpool.txt (line 34))
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Collecting kiwisolver==1.4.4 (from -r requirements/requirements-envpool.txt (line 35))
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Collecting matplotlib==3.5.3 (from -r requirements/requirements-envpool.txt (line 38))
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Collecting moviepy==1.0.3 (from -r requirements/requirements-envpool.txt (line 39))
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Collecting numpy==1.24.4 (from -r requirements/requirements-envpool.txt (line 40))
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Collecting oauthlib==3.2.2 (from -r requirements/requirements-envpool.txt (line 41))
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Collecting pandas==1.3.5 (from -r requirements/requirements-envpool.txt (line 43))
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Collecting pathtools==0.1.2 (from -r requirements/requirements-envpool.txt (line 44))
  Using cached pathtools-0.1.2.tar.gz (11 kB)
  Preparing metadata (setup.py) ... done
Collecting proglog==0.1.10 (from -r requirements/requirements-envpool.txt (line 45))
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Collecting protobuf==3.20.3 (from -r requirements/requirements-envpool.txt (line 46))
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Collecting psutil==5.9.5 (from -r requirements/requirements-envpool.txt (line 47))
  Using cached psutil-5.9.5-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (21 kB)
Collecting pyasn1-modules==0.3.0 (from -r requirements/requirements-envpool.txt (line 48))
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Collecting pyasn1==0.5.0 (from -r requirements/requirements-envpool.txt (line 49))
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Collecting pygame==2.1.0 (from -r requirements/requirements-envpool.txt (line 50))
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Collecting pygments==2.15.1 (from -r requirements/requirements-envpool.txt (line 51))
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Collecting pyparsing==3.0.9 (from -r requirements/requirements-envpool.txt (line 52))
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Collecting python-dateutil==2.8.2 (from -r requirements/requirements-envpool.txt (line 53))
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Collecting pytimeparse==1.1.8 (from -r requirements/requirements-envpool.txt (line 54))
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Collecting pytz==2023.3 (from -r requirements/requirements-envpool.txt (line 55))
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Collecting pyyaml==6.0.1 (from -r requirements/requirements-envpool.txt (line 56))
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Collecting rich==11.2.0 (from -r requirements/requirements-envpool.txt (line 57))
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Collecting rsa==4.7.2 (from -r requirements/requirements-envpool.txt (line 58))
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Collecting setproctitle==1.3.2 (from -r requirements/requirements-envpool.txt (line 59))
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Collecting shtab==1.6.4 (from -r requirements/requirements-envpool.txt (line 60))
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Collecting six==1.16.0 (from -r requirements/requirements-envpool.txt (line 61))
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Collecting smmap==5.0.0 (from -r requirements/requirements-envpool.txt (line 62))
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Collecting stable-baselines3==2.0.0 (from -r requirements/requirements-envpool.txt (line 63))
  Using cached stable_baselines3-2.0.0-py3-none-any.whl.metadata (5.4 kB)
Collecting tenacity==8.2.3 (from -r requirements/requirements-envpool.txt (line 64))
  Using cached tenacity-8.2.3-py3-none-any.whl.metadata (1.0 kB)
Collecting tensorboard-data-server==0.6.1 (from -r requirements/requirements-envpool.txt (line 65))
  Using cached tensorboard_data_server-0.6.1-py3-none-manylinux2010_x86_64.whl.metadata (1.1 kB)
Collecting tensorboard-plugin-wit==1.8.1 (from -r requirements/requirements-envpool.txt (line 66))
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Collecting tensorboard==2.11.2 (from -r requirements/requirements-envpool.txt (line 67))
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Collecting tqdm (from -r requirements/requirements-envpool.txt (line 68))
  Using cached tqdm-4.66.5-py3-none-any.whl.metadata (57 kB)
Collecting treevalue==1.4.10 (from -r requirements/requirements-envpool.txt (line 69))
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Collecting types-protobuf==4.23.0.1 (from -r requirements/requirements-envpool.txt (line 70))
  Using cached types_protobuf-4.23.0.1-py3-none-any.whl.metadata (1.3 kB)
Collecting typing-extensions==4.5.0 (from -r requirements/requirements-envpool.txt (line 71))
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Collecting tyro==0.5.10 (from -r requirements/requirements-envpool.txt (line 72))
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Collecting wandb==0.13.11 (from -r requirements/requirements-envpool.txt (line 73))
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Collecting wheel==0.40.0 (from -r requirements/requirements-envpool.txt (line 74))
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Collecting torchrl-nightly (from -r requirements/requirements-envpool.txt (line 75))
  Using cached torchrl_nightly-2024.9.19-cp310-cp310-manylinux1_x86_64.whl.metadata (39 kB)
Collecting tensordict-nightly (from -r requirements/requirements-envpool.txt (line 76))
  Using cached tensordict_nightly-2024.9.19-cp310-cp310-manylinux1_x86_64.whl.metadata (9.0 kB)
Collecting requests-oauthlib>=0.7.0 (from google-auth-oauthlib==0.4.6->-r requirements/requirements-envpool.txt (line 23))
  Using cached requests_oauthlib-2.0.0-py2.py3-none-any.whl.metadata (11 kB)
Collecting urllib3<2.0 (from google-auth==2.18.0->-r requirements/requirements-envpool.txt (line 24))
  Using cached urllib3-1.26.20-py2.py3-none-any.whl.metadata (50 kB)
Requirement already satisfied: setuptools>=50.0 in /home/costa/.pyenv/versions/miniforge3-22.11.1-4/envs/leanrl3/lib/python3.10/site-packages (from hbutils==0.8.6->-r requirements/requirements-envpool.txt (line 29)) (74.1.2)
Collecting requests (from huggingface-hub==0.11.1->-r requirements/requirements-envpool.txt (line 30))
  Using cached requests-2.32.3-py3-none-any.whl.metadata (4.6 kB)
Collecting pillow>=8.3.2 (from imageio==2.28.1->-r requirements/requirements-envpool.txt (line 32))
  Using cached pillow-10.4.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (9.2 kB)
Collecting fonttools>=4.22.0 (from matplotlib==3.5.3->-r requirements/requirements-envpool.txt (line 38))
  Using cached fonttools-4.53.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (162 kB)
Collecting torch>=1.11 (from stable-baselines3==2.0.0->-r requirements/requirements-envpool.txt (line 63))
  Using cached torch-2.4.1-cp310-cp310-manylinux1_x86_64.whl.metadata (26 kB)
Collecting grpcio>=1.24.3 (from tensorboard==2.11.2->-r requirements/requirements-envpool.txt (line 67))
  Using cached grpcio-1.66.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.9 kB)
Collecting werkzeug>=1.0.1 (from tensorboard==2.11.2->-r requirements/requirements-envpool.txt (line 67))
  Using cached werkzeug-3.0.4-py3-none-any.whl.metadata (3.7 kB)
Collecting GitPython!=3.1.29,>=1.0.0 (from wandb==0.13.11->-r requirements/requirements-envpool.txt (line 73))
  Using cached GitPython-3.1.43-py3-none-any.whl.metadata (13 kB)
Collecting sentry-sdk>=1.0.0 (from wandb==0.13.11->-r requirements/requirements-envpool.txt (line 73))
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INFO: pip is looking at multiple versions of torchrl-nightly to determine which version is compatible with other requirements. This could take a while.
Collecting torchrl-nightly (from -r requirements/requirements-envpool.txt (line 75))
  Using cached torchrl_nightly-2024.9.16-cp310-cp310-manylinux1_x86_64.whl.metadata (34 kB)
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  Using cached torchrl_nightly-2024.9.10-cp310-cp310-manylinux1_x86_64.whl.metadata (34 kB)
INFO: pip is still looking at multiple versions of torchrl-nightly to determine which version is compatible with other requirements. This could take a while.
  Using cached torchrl_nightly-2024.9.9-cp310-cp310-manylinux1_x86_64.whl.metadata (34 kB)
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INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. See https://pip.pypa.io/warnings/backtracking for guidance. If you want to abort this run, press Ctrl + C.
  Using cached torchrl_nightly-2024.9.4-cp310-cp310-manylinux1_x86_64.whl.metadata (34 kB)
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Installation 2

I tried building an environment from scratch, with two separate machines with different GPUs, all getting the same error as follows:

absl-py==2.1.0
certifi==2024.8.30
charset-normalizer==3.3.2
click==8.1.7
cloudpickle==3.0.0
dm-env==1.6
dm-tree==0.1.8
docker-pycreds==0.4.0
docstring_parser==0.16
envpool==0.8.4
Farama-Notifications==0.0.4
filelock==3.13.1
fsspec==2024.6.1
gitdb==4.0.11
GitPython==3.1.43
gym==0.23.1
gym-notices==0.0.8
gymnasium==0.28.1
idna==3.10
jax-jumpy==1.0.0
Jinja2==3.1.4
markdown-it-py==3.0.0
MarkupSafe==2.1.5
mdurl==0.1.2
mpmath==1.3.0
networkx==3.3
numpy==2.0.2
nvidia-cublas-cu12==12.1.3.1
nvidia-cuda-cupti-cu12==12.1.105
nvidia-cuda-nvrtc-cu12==12.1.105
nvidia-cuda-runtime-cu12==12.1.105
nvidia-cudnn-cu12==9.1.0.70
nvidia-cufft-cu12==11.0.2.54
nvidia-curand-cu12==10.3.2.106
nvidia-cusolver-cu12==11.4.5.107
nvidia-cusparse-cu12==12.1.0.106
nvidia-nccl-cu12==2.21.5
nvidia-nvjitlink-cu12==12.1.105
nvidia-nvtx-cu12==12.1.105
optree==0.12.1
orjson==3.10.7
packaging==24.1
platformdirs==4.3.6
protobuf==5.28.2
psutil==6.0.0
Pygments==2.18.0
pytorch-triton==3.1.0+5fe38ffd73
PyYAML==6.0.2
requests==2.32.3
rich==13.8.1
sentry-sdk==2.14.0
setproctitle==1.3.3
shtab==1.7.1
six==1.16.0
smmap==5.0.1
sympy==1.13.1
tensordict-nightly==2024.9.19
torch==2.5.0.dev20240911+cu121
torchrl-nightly==2024.9.19
tqdm==4.66.5
types-protobuf==5.27.0.20240907
typing_extensions==4.12.2
tyro==0.8.11
urllib3==2.2.3
wandb==0.18.1
Traceback (most recent call last):
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/leanrl/ppo_atari_envpool_torchcompile.py", line 378, in <module>
    next_obs, next_done, container = rollout(next_obs, next_done, avg_returns=avg_returns)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/leanrl/ppo_atari_envpool_torchcompile.py", line 207, in rollout
    next_obs, reward, next_done, action, logprob, value = act_and_step_func(obs)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 465, in _fn
    return fn(*args, **kwargs)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/leanrl/ppo_atari_envpool_torchcompile.py", line 193, in act_and_step_func
    action, logprob, _, value = policy(obs=obs)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 1292, in __call__
    return self._torchdynamo_orig_callable(
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 1087, in __call__
    result = self._inner_convert(
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 530, in __call__
    return _compile(
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 933, in _compile
    guarded_code = compile_inner(code, one_graph, hooks, transform)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 675, in compile_inner
    return _compile_inner(code, one_graph, hooks, transform)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_utils_internal.py", line 87, in wrapper_function
    return function(*args, **kwargs)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 708, in _compile_inner
    out_code = transform_code_object(code, transform)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/bytecode_transformation.py", line 1322, in transform_code_object
    transformations(instructions, code_options)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 220, in _fn
    return fn(*args, **kwargs)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 643, in transform
    tracer.run()
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2776, in run
    super().run()
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 979, in run
    while self.step():
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 891, in step
    self.dispatch_table[inst.opcode](self, inst)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 569, in wrapper
    return inner_fn(self, inst)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1598, in CALL_FUNCTION
    self.call_function(fn, args, {})
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 826, in call_function
    self.push(fn.call_function(self, args, kwargs))  # type: ignore[arg-type]
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/variables/user_defined.py", line 938, in call_function
    return self.call_method(tx, "__call__", args, kwargs)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/variables/user_defined.py", line 798, in call_method
    return UserMethodVariable(method, self, source=source).call_function(
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 400, in call_function
    return super().call_function(tx, args, kwargs)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 339, in call_function
    return super().call_function(tx, args, kwargs)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 111, in call_function
    return tx.inline_user_function_return(self, [*self.self_args(), *args], kwargs)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 832, in inline_user_function_return
    return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2991, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 3119, in inline_call_
    tracer.run()
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 979, in run
    while self.step():
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 891, in step
    self.dispatch_table[inst.opcode](self, inst)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 569, in wrapper
    return inner_fn(self, inst)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1676, in CALL_FUNCTION_EX
    self.call_function(fn, argsvars.items, kwargsvars)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 826, in call_function
    self.push(fn.call_function(self, args, kwargs))  # type: ignore[arg-type]
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/variables/lazy.py", line 156, in realize_and_forward
    return getattr(self.realize(), name)(*args, **kwargs)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/variables/torch.py", line 953, in call_function
    tensor_variable = wrap_fx_proxy(
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/variables/builder.py", line 2045, in wrap_fx_proxy
    return wrap_fx_proxy_cls(target_cls=TensorVariable, **kwargs)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/variables/builder.py", line 2132, in wrap_fx_proxy_cls
    example_value = get_fake_value(proxy.node, tx, allow_non_graph_fake=True)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 2103, in get_fake_value
    raise TorchRuntimeError(str(e)).with_traceback(e.__traceback__) from None
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 2038, in get_fake_value
    ret_val = wrap_fake_exception(
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 1595, in wrap_fake_exception
    return fn()
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 2039, in <lambda>
    lambda: run_node(tx.output, node, args, kwargs, nnmodule)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 2171, in run_node
    raise RuntimeError(make_error_message(e)).with_traceback(
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 2153, in run_node
    return node.target(*args, **kwargs)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_ops.py", line 716, in __call__
    return self._op(*args, **kwargs)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_library/autograd.py", line 113, in autograd_impl
    result = forward_no_grad(*args, Metadata(keyset, keyword_only_args))
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_library/autograd.py", line 40, in forward_no_grad
    result = op.redispatch(keyset & _C._after_autograd_keyset, *args, **kwargs)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_ops.py", line 721, in redispatch
    return self._handle.redispatch_boxed(keyset, *args, **kwargs)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/utils/_stats.py", line 21, in wrapper
    return fn(*args, **kwargs)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_subclasses/fake_tensor.py", line 1238, in __torch_dispatch__
    return self.dispatch(func, types, args, kwargs)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_subclasses/fake_tensor.py", line 1692, in dispatch
    return self._cached_dispatch_impl(func, types, args, kwargs)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_subclasses/fake_tensor.py", line 1348, in _cached_dispatch_impl
    output = self._dispatch_impl(func, types, args, kwargs)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_subclasses/fake_tensor.py", line 2001, in _dispatch_impl
    result = maybe_fake_impl(*args, **kwargs)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_library/utils.py", line 20, in __call__
    return self.func(*args, **kwargs)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/library.py", line 1156, in inner
    return func(*args, **kwargs)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_library/custom_ops.py", line 608, in fake_impl
    raise RuntimeError(
torch._dynamo.exc.TorchRuntimeError: Failed running call_function mylib.step.default(*(FakeTensor(..., device='cuda:0', size=(8,), dtype=torch.int64),), **{}):
There was no fake impl registered for <CustomOpDef(mylib::step)>. This is necessary for torch.compile/export/fx tracing to work. Please use `step_func.register_fake` to add an fake impl.

from user code:
   File "/net/nfs.cirrascale/allennlp/costa/LeanRL/leanrl/ppo_atari_envpool_torchcompile.py", line 196, in torch_dynamo_resume_in_act_and_step_func_at_193
    next_obs, reward, next_done, info = step_func(action)
  File "/net/nfs.cirrascale/allennlp/costa/LeanRL/.venv/lib/python3.10/site-packages/torch/_library/custom_ops.py", line 669, in __call__
    return self._opoverload(*args, **kwargs)

Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information

You can suppress this exception and fall back to eager by setting:
    import torch._dynamo
    torch._dynamo.config.suppress_errors = True
vwxyzjn commented 2 months ago

To fix the default installation, I tried removing # torchrl-nightly # tensordict-nightly from requirements/requirements-envpool.txt and then install from it.

Then I was able to run 

pip install --upgrade --pre torch --index-url https://download.pytorch.org/whl/nightly/cu124
pip install tensordict-nightly
python leanrl/ppo_atari_envpool_torchcompile.py \
    --seed 1 \
    --total-timesteps 50000 \
    --compile \
    --cudagraphs

However still ran into that torch._dynamo.exc.TorchRuntimeError: Failed running call_function mylib.step.default issue.

roger-creus commented 2 months ago

same issues here! were you able to solve them @vwxyzjn ?

I am able to run the code only if I comment this CustomOp Line. However, then the code runs at 1.8k fps with compile and cudagraphs instead of the reported 6.8k

vmoens commented 2 months ago

These should be fixed by #4 (hopefully!) LMK if it isn't!

roger-creus commented 2 months ago

@vmoens we can install and run the code now without any problems. However, I am currently unable to get the fps reported in the README. I am getting 400fps for ppo (cleanRL) and 1900 for ppo (leanRL with compile and cudagraphs).

vmoens commented 2 months ago

@vmoens we can install and run the code now without any problems. However, I am currently unable to get the fps reported in the README. I am getting 400fps for ppo (cleanRL) and 1900 for ppo (leanRL with compile and cudagraphs).

That's even a better speed up than the one reported no?

roger-creus commented 2 months ago

Not really, but something was wrong on my end. After rebooting my computers, I am able to reproduce the results in the readme (at least for the compiled+cudagraphs version). Concretely, running on a machine with 32 cores I am getting:

ppo_atari_envpool.py -- 3.4k sps ppo_atari_envpool_torchcompile.py -- 6.1k sps

As you see I am able to reproduce your results (which are awesome!) but the baseline speed is also faster on my end, probably because the number of cores I am using, which envpool can actually make good use of them. Great work! :)