Fix ddp tests + .test() #2512
Fix ddp tests + .test() #2512
Conversation
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Hello @williamFalcon! Thanks for updating this PR. There are currently no PEP 8 issues detected in this Pull Request. Cheers! 🍻 Comment last updated at 2020-07-07 16:09:33 UTC |
Codecov Report
@@ Coverage Diff @@
## master #2512 +/- ##
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+ Coverage 88% 90% +1%
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Files 69 69
Lines 5629 5669 +40
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+ Hits 4964 5077 +113
+ Misses 665 592 -73 |
| @@ -163,6 +165,10 @@ def train_fx(trial_hparams, cluster_manager, _): | |||
| else: | |||
| XLA_AVAILABLE = True | |||
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| pid = os.getpid() | |||
| rng1 = np.random.RandomState(pid) | |||
| RANDOM_PORTS = rng1.randint(10000, 19999, 100) | |||
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does this cause a failure for a distributed cluster > 100 nodes?
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the random_port thing is only used in non-multi node ddp.
| torch.cuda.empty_cache() | ||
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| if self.global_rank == 0 and q is not None: | ||
| q.put(self.checkpoint_callback.best_model_path) |
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this feels hacky, what are we trying to do here? return the state of a callback to the main node? why put this specific attribute in the queue?
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I think he did this so that one can call .test in the main process, which will access the best model to test on. But I agree with you, this seems fragile and dangerous to modify state across processes, there's gotta be a better way. Could one put the whole trainer in the queue in theory?
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i tried that but it didn’t work. i think in a different PR we can do something like state_dict for the trainer.
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i agree this isn't optimal but let's get a release that fixes all the test issues (which this PR does) and then we can figure out a longer term strategy
| @@ -22,7 +22,7 @@ def wrapped_fn(*args, **kwargs): | |||
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| def _warn(*args, **kwargs): | |||
| warnings.warn(*args, **kwargs) | |||
| warnings.warn(UserWarning(*args, **kwargs)) | |||
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this causes the failing test on deprecation warnings, @williamFalcon
| @@ -377,17 +384,18 @@ def set_nvidia_flags(self, is_slurm_managing_tasks, data_parallel_device_ids): | |||
| # don't make this debug... this is good UX | |||
| rank_zero_info(f'CUDA_VISIBLE_DEVICES: [{os.environ["CUDA_VISIBLE_DEVICES"]}]') | |||
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| def set_random_port(self): | |||
| def set_random_port(self, force=False): | |||
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@jeremyjordan this function is only ever called from ddp on a single node... not distributed
| rng1 = np.random.RandomState(pid) | ||
| default_port = rng1.randint(10000, 19999, 1)[0] | ||
| # pick a random port first | ||
| assert self.num_nodes == 1, 'random port can only be called from single node training' |
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@jeremyjordan added this to make sure it's used as expected
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I'm understanding this, it looks like this will disable multi-node support (at least, I'm not able to run across multiple nodes anymore due to this assertion - see issue here: flatironinstitute/deepblast#46)
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@mortonjt can you open a github issue about this and explain how you launched your script?
| @@ -446,15 +455,24 @@ def spawn_ddp_children(self, model): | |||
| sleep(delay) | |||
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| local_rank = 0 | |||
| self.ddp_train(local_rank, model, is_master=True) | |||
| results = self.ddp_train(local_rank, q=None, model=model, is_master=True) | |||
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@tgaddair hey! any chance you can look at this? |
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Hey @williamFalcon, what was the issue? I see this PR has been merged. Is there still something that needs to be fixed on the Horovod side for this? |
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I don't think this is Horovod's fault. Something witht the checkpoint paths changed. The tests are now simply skipped, which is not good :( |
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perfect! @awaelchli . |
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| assert event_acc.summary_metadata['_hparams_/experiment'].plugin_data.plugin_name == 'hparams' | ||
| assert event_acc.summary_metadata['_hparams_/experiment'].plugin_data.content == hparams_data | ||
| # |
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I added a reminder in the related issue #2371 that we should fix this
| @@ -88,6 +88,7 @@ def test_horovod_cpu_implicit(tmpdir): | |||
| _run_horovod(trainer_options) | |||
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| @pytest.mark.skipif(True, reason="fix hv") | |||
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just @pytest.mark.skip(reason="fix hv")
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