part 5 of Lightning-AI#3733 (Lightning-AI#3774)

* ref: part 4 of Lightning-AI#3733

* ref: part 4 of Lightning-AI#3733

* ref: part 4 of Lightning-AI#3733

tests/backends/test_ddp.py

@@ -0,0 +1,57 @@
+import pytest
+import torch
+import os
+from tests.backends import ddp_model
+from tests.utilities.dist import call_training_script
+
+
+@pytest.mark.parametrize('cli_args', [
+    pytest.param('--max_epochs 1 --gpus 2 --distributed_backend ddp'),
+])
+@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
+def test_multi_gpu_model_ddp_fit_only(tmpdir, cli_args):
+    # call the script
+    std, err = call_training_script(ddp_model, cli_args, 'fit', tmpdir, timeout=120)
+
+    # load the results of the script
+    result_path = os.path.join(tmpdir, 'ddp.result')
+    result = torch.load(result_path)
+
+    # verify the file wrote the expected outputs
+    assert result['status'] == 'complete'
+
+
+@pytest.mark.parametrize('cli_args', [
+    pytest.param('--max_epochs 1 --gpus 2 --distributed_backend ddp'),
+])
+@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
+def test_multi_gpu_model_ddp_test_only(tmpdir, cli_args):
+    # call the script
+    call_training_script(ddp_model, cli_args, 'test', tmpdir)
+
+    # load the results of the script
+    result_path = os.path.join(tmpdir, 'ddp.result')
+    result = torch.load(result_path)
+
+    # verify the file wrote the expected outputs
+    assert result['status'] == 'complete'
+
+
+# @pytest.mark.parametrize('cli_args', [
+#     pytest.param('--max_epochs 1 --gpus 2 --distributed_backend ddp'),
+# ])
+# @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
+# def test_multi_gpu_model_ddp_fit_test(tmpdir, cli_args):
+#     # call the script
+#     call_training_script(ddp_model, cli_args, 'fit_test', tmpdir, timeout=20)
+#
+#     # load the results of the script
+#     result_path = os.path.join(tmpdir, 'ddp.result')
+#     result = torch.load(result_path)
+#
+#     # verify the file wrote the expected outputs
+#     assert result['status'] == 'complete'
+#
+#     model_outs = result['result']
+#     for out in model_outs:
+#         assert out['test_acc'] > 0.90

tests/models/test_gpu.py

@@ -1,25 +1,18 @@
-import os
-import subprocess
-import sys
 from collections import namedtuple
-from pathlib import Path
 from unittest.mock import patch
 
 import pytest
 import torch
 from torchtext.data import Batch, Dataset, Example, Field, LabelField
 
-import pytorch_lightning
 import tests.base.develop_pipelines as tpipes
 import tests.base.develop_utils as tutils
 from pytorch_lightning import Trainer
 from pytorch_lightning.core import memory
 from pytorch_lightning.utilities import device_parser
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.base import EvalModelTemplate
-from tests.models.data.ddp import train_test_variations
 from pytorch_lightning.accelerators.gpu_backend import GPUBackend
-from pytorch_lightning.accelerators.cpu_backend import CPUBackend
 
 
 PRETEND_N_OF_GPUS = 16
@@ -84,34 +77,6 @@ def test_multi_gpu_model_dp(tmpdir):
     memory.get_memory_profile('min_max')
 
 
-@pytest.mark.parametrize('cli_args', [
-    pytest.param('--max_epochs 1 --gpus 2 --distributed_backend ddp'),
-])
-@pytest.mark.parametrize('variation', train_test_variations.get_variations())
-@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
-def test_multi_gpu_model_ddp(tmpdir, cli_args, variation):
-    """ Runs a basic training and test run with distributed_backend=ddp. """
-    file = Path(train_test_variations.__file__).absolute()
-    cli_args = cli_args.split(' ') if cli_args else []
-    cli_args += ['--default_root_dir', str(tmpdir)]
-    cli_args += ['--variation', variation]
-    command = [sys.executable, str(file)] + cli_args
-
-    # need to set the PYTHONPATH in case pytorch_lightning was not installed into the environment
-    env = os.environ.copy()
-    env['PYTHONPATH'] = f'{pytorch_lightning.__file__}:' + env.get('PYTHONPATH', '')
-
-    # for running in ddp mode, we need to lauch it's own process or pytest will get stuck
-    p = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env)
-
-    std, err = p.communicate(timeout=60)
-    std = std.decode('utf-8').strip()
-    err = err.decode('utf-8').strip()
-    assert std, f"{variation} produced no output"
-    if p.returncode > 0:
-        pytest.fail(err)
-
-
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
 @pytest.mark.parametrize('gpus', [1, [0], [1]])
 def test_single_gpu_model(tmpdir, gpus):
@@ -129,28 +94,6 @@ def test_single_gpu_model(tmpdir, gpus):
     tpipes.run_model_test(trainer_options, model)
 
 
-@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
-def test_ddp_all_dataloaders_passed_to_fit(tmpdir):
-    """Make sure DDP works with dataloaders passed to fit()"""
-    tutils.set_random_master_port()
-
-    model = EvalModelTemplate()
-    fit_options = dict(train_dataloader=model.train_dataloader(),
-                       val_dataloaders=model.val_dataloader())
-
-    trainer = Trainer(
-        default_root_dir=tmpdir,
-        progress_bar_refresh_rate=0,
-        max_epochs=1,
-        limit_train_batches=0.2,
-        limit_val_batches=0.2,
-        gpus=[0, 1],
-        distributed_backend='ddp_spawn'
-    )
-    result = trainer.fit(model, **fit_options)
-    assert result == 1, "DDP doesn't work with dataloaders passed to fit()."
-
-
 @pytest.fixture
 def mocked_device_count(monkeypatch):
     def device_count():