Update tests/models/*.py to use devices instead of gpus or ipus

tests/models/test_amp.py

@@ -77,13 +77,18 @@ def _assert_autocast_enabled(self):
     ],
 )
 @pytest.mark.parametrize("precision", [16, "bf16"])
-@pytest.mark.parametrize("num_processes", [1, 2])
-def test_amp_cpus(tmpdir, strategy, precision, num_processes):
+@pytest.mark.parametrize("devices", [1, 2])
+def test_amp_cpus(tmpdir, strategy, precision, devices):
     """Make sure combinations of AMP and training types work if supported."""
     tutils.reset_seed()
 
     trainer = Trainer(
-        default_root_dir=tmpdir, num_processes=num_processes, max_epochs=1, strategy=strategy, precision=precision
+        default_root_dir=tmpdir,
+        accelerator="cpu",
+        devices=devices,
+        max_epochs=1,
+        strategy=strategy,
+        precision=precision,
     )
 
     model = AMPTestModel()
@@ -97,12 +102,19 @@ def test_amp_cpus(tmpdir, strategy, precision, num_processes):
 @RunIf(min_gpus=2, min_torch="1.10")
 @pytest.mark.parametrize("strategy", [None, "dp", "ddp_spawn"])
 @pytest.mark.parametrize("precision", [16, "bf16"])
-@pytest.mark.parametrize("gpus", [1, 2])
-def test_amp_gpus(tmpdir, strategy, precision, gpus):
+@pytest.mark.parametrize("devices", [1, 2])
+def test_amp_gpus(tmpdir, strategy, precision, devices):
     """Make sure combinations of AMP and training types work if supported."""
     tutils.reset_seed()
 
-    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, gpus=gpus, strategy=strategy, precision=precision)
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_epochs=1,
+        accelerator="gpu",
+        devices=devices,
+        strategy=strategy,
+        precision=precision,
+    )
 
     model = AMPTestModel()
     trainer.fit(model)
@@ -141,7 +153,8 @@ def test_amp_gpu_ddp_slurm_managed(tmpdir):
     trainer = Trainer(
         default_root_dir=tmpdir,
         max_epochs=1,
-        gpus=[0],
+        accelerator="gpu",
+        devices=[0],
         strategy="ddp_spawn",
         precision=16,
         callbacks=[checkpoint],
@@ -195,7 +208,9 @@ def configure_optimizers(self):
     model = CustomModel()
     model.training_epoch_end = None
 
-    trainer = Trainer(default_root_dir=tmpdir, max_steps=5, precision=16, amp_backend="apex", gpus=1)
+    trainer = Trainer(
+        default_root_dir=tmpdir, max_steps=5, precision=16, amp_backend="apex", accelerator="gpu", devices=1
+    )
     assert str(trainer.amp_backend) == "AMPType.APEX"
     trainer.fit(model)
     assert trainer.state.finished, f"Training failed with {trainer.state}"

tests/models/test_cpu.py

@@ -136,8 +136,8 @@ def test_multi_cpu_model_ddp(tmpdir):
         max_epochs=1,
         limit_train_batches=0.4,
         limit_val_batches=0.2,
-        gpus=None,
-        num_processes=2,
+        accelerator="cpu",
+        devices=2,
         strategy="ddp_spawn",
     )
 

tests/models/test_gpu.py

@@ -47,7 +47,8 @@ def test_multi_gpu_none_backend(tmpdir):
         max_epochs=1,
         limit_train_batches=0.2,
         limit_val_batches=0.2,
-        gpus=2,
+        accelerator="gpu",
+        devices=2,
     )
 
     dm = ClassifDataModule()
@@ -56,16 +57,17 @@ def test_multi_gpu_none_backend(tmpdir):
 
 
 @RunIf(min_gpus=2)
-@pytest.mark.parametrize("gpus", [1, [0], [1]])
-def test_single_gpu_model(tmpdir, gpus):
+@pytest.mark.parametrize("devices", [1, [0], [1]])
+def test_single_gpu_model(tmpdir, devices):
     """Make sure single GPU works (DP mode)."""
     trainer_options = dict(
         default_root_dir=tmpdir,
         enable_progress_bar=False,
         max_epochs=1,
         limit_train_batches=0.1,
         limit_val_batches=0.1,
-        gpus=gpus,
+        accelerator="gpu",
+        devices=devices,
     )
 
     model = BoringModel()
@@ -94,7 +96,7 @@ def device_count():
 
 # Asking for a gpu when non are available will result in a MisconfigurationException
 @pytest.mark.parametrize(
-    ["gpus", "expected_root_gpu", "strategy"],
+    ["devices", "expected_root_gpu", "strategy"],
     [
         (1, None, "ddp"),
         (3, None, "ddp"),
@@ -105,13 +107,13 @@ def device_count():
         ("-1", None, "ddp"),
     ],
 )
-def test_root_gpu_property_0_raising(mocked_device_count_0, gpus, expected_root_gpu, strategy):
+def test_root_gpu_property_0_raising(mocked_device_count_0, devices, expected_root_gpu, strategy):
     with pytest.raises(MisconfigurationException):
-        Trainer(gpus=gpus, strategy=strategy)
+        Trainer(accelerator="gpu", devices=devices, strategy=strategy)
 
 
 @pytest.mark.parametrize(
-    ["gpus", "expected_root_gpu"],
+    ["devices", "expected_root_gpu"],
     [
         pytest.param(None, None, id="No gpus, expect gpu root device to be None"),
         pytest.param([0], 0, id="Oth gpu, expect gpu root device to be 0."),
@@ -120,12 +122,12 @@ def test_root_gpu_property_0_raising(mocked_device_count_0, gpus, expected_root_
         pytest.param([1, 2], 1, id="[1, 2] gpus, expect gpu root device to be 1."),
     ],
 )
-def test_determine_root_gpu_device(gpus, expected_root_gpu):
-    assert device_parser.determine_root_gpu_device(gpus) == expected_root_gpu
+def test_determine_root_gpu_device(devices, expected_root_gpu):
+    assert device_parser.determine_root_gpu_device(devices) == expected_root_gpu
 
 
 @pytest.mark.parametrize(
-    ["gpus", "expected_gpu_ids"],
+    ["devices", "expected_gpu_ids"],
     [
         (None, None),
         (0, None),
@@ -143,31 +145,31 @@ def test_determine_root_gpu_device(gpus, expected_root_gpu):
         pytest.param("-1", list(range(PRETEND_N_OF_GPUS)), id="'-1' - use all gpus"),
     ],
 )
-def test_parse_gpu_ids(mocked_device_count, gpus, expected_gpu_ids):
-    assert device_parser.parse_gpu_ids(gpus) == expected_gpu_ids
+def test_parse_gpu_ids(mocked_device_count, devices, expected_gpu_ids):
+    assert device_parser.parse_gpu_ids(devices) == expected_gpu_ids
 
 
-@pytest.mark.parametrize("gpus", [0.1, -2, False, [-1], [None], ["0"], [0, 0]])
-def test_parse_gpu_fail_on_unsupported_inputs(mocked_device_count, gpus):
+@pytest.mark.parametrize("devices", [0.1, -2, False, [-1], [None], ["0"], [0, 0]])
+def test_parse_gpu_fail_on_unsupported_inputs(mocked_device_count, devices):
     with pytest.raises(MisconfigurationException):
-        device_parser.parse_gpu_ids(gpus)
+        device_parser.parse_gpu_ids(devices)
 
 
-@pytest.mark.parametrize("gpus", [[1, 2, 19], -1, "-1"])
-def test_parse_gpu_fail_on_non_existent_id(mocked_device_count_0, gpus):
+@pytest.mark.parametrize("devices", [[1, 2, 19], -1, "-1"])
+def test_parse_gpu_fail_on_non_existent_id(mocked_device_count_0, devices):
     with pytest.raises(MisconfigurationException):
-        device_parser.parse_gpu_ids(gpus)
+        device_parser.parse_gpu_ids(devices)
 
 
 def test_parse_gpu_fail_on_non_existent_id_2(mocked_device_count):
     with pytest.raises(MisconfigurationException):
         device_parser.parse_gpu_ids([1, 2, 19])
 
 
-@pytest.mark.parametrize("gpus", [-1, "-1"])
-def test_parse_gpu_returns_none_when_no_devices_are_available(mocked_device_count_0, gpus):
+@pytest.mark.parametrize("devices", [-1, "-1"])
+def test_parse_gpu_returns_none_when_no_devices_are_available(mocked_device_count_0, devices):
     with pytest.raises(MisconfigurationException):
-        device_parser.parse_gpu_ids(gpus)
+        device_parser.parse_gpu_ids(devices)
 
 
 @mock.patch.dict(
@@ -198,7 +200,7 @@ def test_torchelastic_gpu_parsing(mocked_device_count, mocked_is_available, gpus
 
 @RunIf(min_gpus=1)
 def test_single_gpu_batch_parse():
-    trainer = Trainer(gpus=1)
+    trainer = Trainer(accelerator="gpu", devices=1)
 
     # non-transferrable types
     primitive_objects = [None, {}, [], 1.0, "x", [None, 2], {"x": (1, 2), "y": None}]

tests/models/test_hooks.py

@@ -203,7 +203,8 @@ def train_dataloader(self):
         max_epochs=1,
         enable_model_summary=False,
         strategy="ddp",
-        gpus=2,
+        accelerator="gpu",
+        devices=2,
     )
     trainer.fit(model)
 
@@ -553,7 +554,6 @@ def training_step(self, batch, batch_idx):
         dict(name="training_epoch_end", args=([dict(loss=ANY)] * train_batches,)),
         dict(name="Callback.on_train_epoch_end", args=(trainer, model)),
         # `ModelCheckpoint.save_checkpoint` is called here from `Callback.on_train_epoch_end`
-        dict(name="Callback.state_dict"),
         dict(name="Callback.on_save_checkpoint", args=(trainer, model, saved_ckpt)),
         dict(name="on_save_checkpoint", args=(saved_ckpt,)),
         dict(name="on_train_epoch_end"),
@@ -627,7 +627,6 @@ def test_trainer_model_hook_system_fit_no_val_and_resume(tmpdir):
         dict(name="setup", kwargs=dict(stage="fit")),
         dict(name="on_load_checkpoint", args=(loaded_ckpt,)),
         dict(name="Callback.on_load_checkpoint", args=(trainer, model, {"foo": True})),
-        dict(name="Callback.load_state_dict", args=({"foo": True},)),
         dict(name="configure_sharded_model"),
         dict(name="Callback.on_configure_sharded_model", args=(trainer, model)),
         dict(name="configure_optimizers"),
@@ -649,7 +648,6 @@ def test_trainer_model_hook_system_fit_no_val_and_resume(tmpdir):
         *model._train_batch(trainer, model, steps_after_reload, current_batch=1, current_epoch=1),
         dict(name="training_epoch_end", args=([dict(loss=ANY)] * train_batches,)),
         dict(name="Callback.on_train_epoch_end", args=(trainer, model)),
-        dict(name="Callback.state_dict"),
         dict(name="Callback.on_save_checkpoint", args=(trainer, model, saved_ckpt)),
         dict(name="on_save_checkpoint", args=(saved_ckpt,)),
         dict(name="on_train_epoch_end"),

tests/models/test_onnx.py

@@ -96,7 +96,8 @@ def test_model_saves_on_multi_gpu(tmpdir):
         max_epochs=1,
         limit_train_batches=10,
         limit_val_batches=10,
-        gpus=[0, 1],
+        accelerator="gpu",
+        devices=[0, 1],
         strategy="ddp_spawn",
         enable_progress_bar=False,
     )

tests/models/test_restore.py

@@ -399,7 +399,8 @@ def test_running_test_pretrained_model_distrib_dp(tmpdir):
         limit_val_batches=5,
         callbacks=[checkpoint],
         logger=logger,
-        gpus=[0, 1],
+        accelerator="gpu",
+        devices=[0, 1],
         strategy="dp",
         default_root_dir=tmpdir,
     )
@@ -445,7 +446,8 @@ def test_running_test_pretrained_model_distrib_ddp_spawn(tmpdir):
         limit_val_batches=2,
         callbacks=[checkpoint],
         logger=logger,
-        gpus=[0, 1],
+        accelerator="gpu",
+        devices=[0, 1],
         strategy="ddp_spawn",
         default_root_dir=tmpdir,
     )
@@ -564,7 +566,7 @@ def test_dp_resume(tmpdir):
     model = CustomClassificationModelDP(lr=0.1)
     dm = ClassifDataModule()
 
-    trainer_options = dict(max_epochs=1, gpus=2, strategy="dp", default_root_dir=tmpdir)
+    trainer_options = dict(max_epochs=1, accelerator="gpu", devices=2, strategy="dp", default_root_dir=tmpdir)
 
     # get logger
     logger = tutils.get_default_logger(tmpdir)

tests/models/test_tpu.py

@@ -50,7 +50,7 @@ def val_dataloader(self):
 
 @RunIf(tpu=True)
 @pl_multi_process_test
-def test_model_tpu_cores_1(tmpdir):
+def test_model_devices_1(tmpdir):
     """Make sure model trains on TPU."""
     tutils.reset_seed()
     trainer_options = dict(
@@ -90,7 +90,7 @@ def test_model_tpu_index(tmpdir, tpu_core):
 
 @RunIf(tpu=True)
 @pl_multi_process_test
-def test_model_tpu_cores_8(tmpdir):
+def test_model_devices_8(tmpdir):
     """Make sure model trains on TPU."""
     tutils.reset_seed()
     trainer_options = dict(
@@ -110,7 +110,7 @@ def test_model_tpu_cores_8(tmpdir):
 
 @RunIf(tpu=True)
 @pl_multi_process_test
-def test_model_16bit_tpu_cores_1(tmpdir):
+def test_model_16bit_devices_1(tmpdir):
     """Make sure model trains on TPU."""
     tutils.reset_seed()
     trainer_options = dict(
@@ -152,7 +152,7 @@ def test_model_16bit_tpu_index(tmpdir, tpu_core):
 
 @RunIf(tpu=True)
 @pl_multi_process_test
-def test_model_16bit_tpu_cores_8(tmpdir):
+def test_model_16bit_devices_8(tmpdir):
     """Make sure model trains on TPU."""
     tutils.reset_seed()
     trainer_options = dict(