Another

Author: carmocca

tests/tests_pytorch/strategies/test_sharded_strategy.py

@@ -324,28 +324,6 @@ def configure_optimizers(self):
         return OSS(params=base_optimizer.param_groups, optim=type(base_optimizer), **base_optimizer.defaults)
 
 
-@RunIf(min_cuda_gpus=2, fairscale=True)
-@pytest.mark.parametrize("strategy", (pytest.param("ddp_sharded", marks=RunIf(standalone=True)), "ddp_sharded_spawn"))
-def test_ddp_sharded_strategy_checkpoint_multi_gpu_fairscale_optimizer(tmpdir, strategy):
-    """Test to ensure that checkpoint is saved correctly when using fairscale optimizers."""
-    model = BoringFairScaleOptimizerModel()
-    with pytest.deprecated_call(match="FairScale has been deprecated in v1.9.0"):
-        trainer = Trainer(accelerator="gpu", devices=2, strategy=strategy, max_steps=1)
-
-    trainer.fit(model)
-
-    checkpoint_path = os.path.join(tmpdir, "model.pt")
-    # need to broadcast because tmpdir is different on each process
-    checkpoint_path = trainer.strategy.broadcast(checkpoint_path)
-    trainer.save_checkpoint(checkpoint_path)
-    trainer.strategy.barrier()  # ensure the checkpoint is saved before load
-    saved_model = BoringModel.load_from_checkpoint(checkpoint_path)
-
-    # Assert model parameters are identical after loading
-    for trained_param, loaded_param in zip(model.parameters(), saved_model.parameters()):
-        assert torch.equal(trained_param.to("cpu"), loaded_param)
-
-
 @RunIf(min_cuda_gpus=2, fairscale=True)
 def test_ddp_sharded_strategy_fit_ckpt_path_downsize_gpus(tmpdir):
     model = ModelWithAdamOptimizer()