mock and reuse

tests/test_events.py

@@ -63,62 +63,6 @@ def event_counter_callback():
     return EventCounterCallback()
 
 
-@pytest.fixture
-def trainer(
-    model,
-    optimizer,
-    train_dataset,
-    evaluator1,
-    evaluator2,
-    event_counter_callback,
-    request,
-):
-    # extract parameters from the test function
-    params = request.param
-    precision = params.get('precision', 'fp32')
-    max_duration = params.get('max_duration', '1ep')
-    save_interval = params.get('save_interval', '1ep')
-    device = params.get('device', 'cpu')
-    deepspeed_zero_stage = params.get('deepspeed_zero_stage', None)
-    use_fsdp = params.get('use_fsdp', False)
-
-    deepspeed_config = None
-    if deepspeed_zero_stage:
-        deepspeed_config = {'zero_optimization': {'stage': deepspeed_zero_stage}}
-
-    parallelism_config = None
-    if use_fsdp:
-        parallelism_config = {
-            'fsdp': {
-                'sharding_strategy': 'FULL_SHARD',
-                'mixed_precision': 'PURE',
-                'backward_prefetch': 'BACKWARD_PRE',
-            },
-        }
-
-    return Trainer(
-        model=model,
-        train_dataloader=DataLoader(
-            dataset=train_dataset,
-            batch_size=4,
-            sampler=dist.get_sampler(train_dataset),
-            num_workers=0,
-        ),
-        eval_dataloader=(evaluator1, evaluator2),
-        device_train_microbatch_size=2,
-        precision=precision,
-        train_subset_num_batches=1,
-        eval_subset_num_batches=1,
-        max_duration=max_duration,
-        save_interval=save_interval,
-        optimizers=optimizer,
-        callbacks=[event_counter_callback],
-        device=device,
-        deepspeed_config=deepspeed_config,
-        parallelism_config=parallelism_config,
-    )
-
-
 @pytest.mark.parametrize('event', list(Event))
 def test_event_values(event: Event):
     assert event.name.lower() == event.value
@@ -177,9 +121,22 @@ def test_event_calls(
     event_counter_callback,
 ):
     with patch.object(Trainer, 'save_checkpoint', return_value=None):
-        # mock forward and backward to speed up
-        with patch.object(model, 'forward', return_value=torch.tensor(0.0)) as mock_forward, \
-             patch.object(model, 'backward', return_value=None) as mock_backward:
+        # mock forward method
+        with patch.object(model, 'forward', return_value=torch.tensor(0.0)):
+            # initialize the Trainer with the current parameters
+            deepspeed_config = None
+            if deepspeed_zero_stage:
+                deepspeed_config = {'zero_optimization': {'stage': deepspeed_zero_stage}}
+
+            parallelism_config = None
+            if use_fsdp:
+                parallelism_config = {
+                    'fsdp': {
+                        'sharding_strategy': 'FULL_SHARD',
+                        'mixed_precision': 'PURE',
+                        'backward_prefetch': 'BACKWARD_PRE',
+                    },
+                }
 
             trainer_instance = Trainer(
                 model=model,
@@ -199,16 +156,8 @@ def test_event_calls(
                 optimizers=optimizer,
                 callbacks=[event_counter_callback],
                 device=device,
-                deepspeed_config={'zero_optimization': {
-                    'stage': deepspeed_zero_stage,
-                }} if deepspeed_zero_stage else None,
-                parallelism_config={
-                    'fsdp': {
-                        'sharding_strategy': 'FULL_SHARD',
-                        'mixed_precision': 'PURE',
-                        'backward_prefetch': 'BACKWARD_PRE',
-                    },
-                } if use_fsdp else None,
+                deepspeed_config=deepspeed_config,
+                parallelism_config=parallelism_config,
             )
 
             trainer_instance.fit()