`runner.evaluate_loader` does not work with DataParallelEngine

[ShuhuaGao]

🐛 Bug Report

How To Reproduce

I have two GPUs and enable both of them. I copied the linear regression minimal example. After that, I checked

runner.engine
# <catalyst.engines.torch.DataParallelEngine at 0x7f67e25d72b0>

Then, the following line produced a long error message:

runner.evaluate_loader(loaders['valid'])

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
/home/shuhua/GitHub/Learn-DL/catalyst-tutorial/linear-regression.ipynb Cell 10' in <cell line: 1>()
----> [1](vscode-notebook-cell://ssh-remote%2B7b22686f73744e616d65223a22575333303930227d/home/shuhua/GitHub/Learn-DL/catalyst-tutorial/linear-regression.ipynb#ch0000009vscode-remote?line=0) runner.evaluate_loader(loaders['valid'])

File ~/miniconda3/lib/python3.9/site-packages/catalyst/runners/runner.py:490, in Runner.evaluate_loader(self, loader, callbacks, model, engine, seed, verbose)
    487     model = self.model
    488 assert model is not None
--> 490 self.train(
    491     model=model,
    492     engine=engine,
    493     loaders=OrderedDict([("valid", loader)]),
    494     num_epochs=1,
    495     verbose=verbose,
    496     callbacks=callbacks,
    497     valid_loader="valid",
    498     seed=seed,
    499 )
    501 return self.loader_metrics

File ~/miniconda3/lib/python3.9/site-packages/catalyst/runners/runner.py:377, in Runner.train(self, loaders, model, engine, criterion, optimizer, scheduler, callbacks, loggers, seed, hparams, num_epochs, logdir, resume, valid_loader, valid_metric, minimize_valid_metric, verbose, timeit, check, overfit, profile, load_best_on_end, cpu, fp16, ddp)
    375 self._load_best_on_end = load_best_on_end
    376 # run
--> 377 self.run()

File ~/miniconda3/lib/python3.9/site-packages/catalyst/core/runner.py:422, in IRunner.run(self)
    420 except (Exception, KeyboardInterrupt) as ex:
    421     self.exception = ex
--> 422     self._run_event("on_exception")
    423 return self

File ~/miniconda3/lib/python3.9/site-packages/catalyst/core/runner.py:365, in IRunner._run_event(self, event)
    363     getattr(callback, event)(self)
    364 if is_str_intersections(event, ("_end", "_exception")):
--> 365     getattr(self, event)(self)

File ~/miniconda3/lib/python3.9/site-packages/catalyst/core/runner.py:357, in IRunner.on_exception(self, runner)
    355 def on_exception(self, runner: "IRunner"):
    356     """Event handler."""
--> 357     raise self.exception

File ~/miniconda3/lib/python3.9/site-packages/catalyst/core/runner.py:419, in IRunner.run(self)
    413 """Runs the experiment.
    414 
    415 Returns:
    416     self, `IRunner` instance after the experiment
    417 """
    418 try:
--> 419     self._run()
    420 except (Exception, KeyboardInterrupt) as ex:
    421     self.exception = ex

File ~/miniconda3/lib/python3.9/site-packages/catalyst/core/runner.py:410, in IRunner._run(self)
    408 def _run(self) -> None:
    409     self.engine = self.get_engine()
--> 410     self.engine.spawn(self._run_local)

File ~/miniconda3/lib/python3.9/site-packages/catalyst/core/engine.py:59, in Engine.spawn(self, fn, *args, **kwargs)
     42 def spawn(self, fn: Callable, *args, **kwargs):
     43     """Spawns processes with specified ``fn`` and ``args``/``kwargs``.
     44 
     45     Args:
   (...)
     57         wrapped function (if needed).
     58     """
---> 59     return fn(*args, **kwargs)

File ~/miniconda3/lib/python3.9/site-packages/catalyst/core/runner.py:405, in IRunner._run_local(self, local_rank, world_size)
    403 self._local_rank, self._world_size = local_rank, world_size
    404 self._run_event("on_experiment_start")
--> 405 self._run_experiment()
    406 self._run_event("on_experiment_end")

File ~/miniconda3/lib/python3.9/site-packages/catalyst/core/runner.py:399, in IRunner._run_experiment(self)
    397     break
...
  File "/home/shuhua/miniconda3/lib/python3.9/site-packages/torch/nn/modules/linear.py", line 103, in forward
    return F.linear(input, self.weight, self.bias)
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:1 and cuda:0! (when checking argument for argument mat1 in method wrapper_addmm)

By contrast, if I use one GPU or CPU via setting os.environ["CUDA_VISIBLE_DEVICES"], then it works.

Pytorch DataParallel supports inference on multiple GPUs, right? I don't understand why evaluate_loader fails with DataParallelEngine.

### Environment # example checklist, fill with your info Catalyst version: 20.04. PyTorch version: 1.11.0 Python version: 3.9 CUDA runtime version: 11.4 Nvidia driver version: 472.39 ```

[Scitator]

hi, thanks for the issue! could you please try using evaluate_loader without any training?) as I see our implementation, we just run the experiment... so the problem could be with transferring model/data from train to validation experiment.

[ShuhuaGao]

I tried

1. Setting num_epochs=0 in runner.train, and the same error occurred.
2. Comment out totally the runner.train, and change evaluate_loader to

   runner.evaluate_loader(loaders['valid'], model=model)

Then there was no error, though the code is not useful.

[Scitator]

so, looks like we have some problems with handware-backend 😢 maybe, @ditwoo @bagxi could also review it our :)

[stale[bot]]

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