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Poptorch 3.1.0 supports dictionary inputs #16683

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Poptorch 3.1.0 supports dictionary inputs #16683

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41 changes: 15 additions & 26 deletions tests/tests_pytorch/accelerators/test_ipu.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,17 +37,6 @@
import poptorch


class IPUModel(BoringModel):
def training_step(self, batch, batch_idx):
return self.step(batch)

def validation_step(self, batch, batch_idx):
return self.step(batch)

def test_step(self, batch, batch_idx):
return self.step(batch)


class IPUClassificationModel(ClassificationModel):
def training_step(self, batch, batch_idx):
x, y = batch
Expand Down Expand Up @@ -115,7 +104,7 @@ def test_no_warning_strategy(tmpdir):
@RunIf(ipu=True)
@pytest.mark.parametrize("devices", [1, 4])
def test_all_stages(tmpdir, devices):
model = IPUModel()
model = BoringModel()
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, accelerator="ipu", devices=devices)
trainer.fit(model)
trainer.validate(model)
Expand All @@ -126,7 +115,7 @@ def test_all_stages(tmpdir, devices):
@RunIf(ipu=True)
@pytest.mark.parametrize("devices", [1, 4])
def test_inference_only(tmpdir, devices):
model = IPUModel()
model = BoringModel()

trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, accelerator="ipu", devices=devices)
trainer.validate(model)
Expand Down Expand Up @@ -181,7 +170,7 @@ def setup(self, trainer: Trainer, pl_module: LightningModule, stage: str) -> Non
assert trainer.precision == "16"
raise SystemExit

model = IPUModel()
model = BoringModel()
trainer = Trainer(
default_root_dir=tmpdir, fast_dev_run=True, accelerator="ipu", devices=1, precision=16, callbacks=TestCallback()
)
Expand All @@ -200,7 +189,7 @@ def on_train_start(self, trainer: Trainer, pl_module: LightningModule) -> None:
assert param.dtype == torch.float16
raise SystemExit

model = IPUModel()
model = BoringModel()
model = model.half()
trainer = Trainer(
default_root_dir=tmpdir, fast_dev_run=True, accelerator="ipu", devices=1, precision=16, callbacks=TestCallback()
Expand Down Expand Up @@ -234,7 +223,7 @@ def on_train_start(self, trainer: Trainer, pl_module: LightningModule) -> None:
assert poptorch_model._options.toDict()["device_iterations"] == 2
raise SystemExit

model = IPUModel()
model = BoringModel()
trainer = Trainer(
default_root_dir=tmpdir,
fast_dev_run=True,
Expand All @@ -260,7 +249,7 @@ def on_train_start(self, trainer: Trainer, pl_module: LightningModule) -> None:
assert poptorch_model._options.Training.toDict()["gradient_accumulation"] == 2
raise SystemExit

model = IPUModel()
model = BoringModel()
trainer = Trainer(
default_root_dir=tmpdir,
fast_dev_run=True,
Expand All @@ -277,7 +266,7 @@ def on_train_start(self, trainer: Trainer, pl_module: LightningModule) -> None:
def test_stages_correct(tmpdir):
"""Ensure all stages correctly are traced correctly by asserting the output for each stage."""

class StageModel(IPUModel):
class StageModel(BoringModel):
def training_step(self, batch, batch_idx):
loss = super().training_step(batch, batch_idx)
# tracing requires a loss value that depends on the model.
Expand Down Expand Up @@ -321,7 +310,7 @@ def on_predict_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, da

@RunIf(ipu=True)
def test_different_accumulate_grad_batches_fails(tmpdir):
model = IPUModel()
model = BoringModel()
trainer = Trainer(default_root_dir=tmpdir, accelerator="ipu", devices=1, accumulate_grad_batches={1: 2})
with pytest.raises(
MisconfigurationException, match="IPUs currently does not support different `accumulate_grad_batches`"
Expand All @@ -331,7 +320,7 @@ def test_different_accumulate_grad_batches_fails(tmpdir):

@RunIf(ipu=True)
def test_clip_gradients_fails(tmpdir):
model = IPUModel()
model = BoringModel()
trainer = Trainer(default_root_dir=tmpdir, accelerator="ipu", devices=1, gradient_clip_val=10)
with pytest.raises(MisconfigurationException, match="IPUs currently do not support clipping gradients."):
trainer.fit(model)
Expand All @@ -340,7 +329,7 @@ def test_clip_gradients_fails(tmpdir):
@RunIf(ipu=True)
def test_autoreport(tmpdir):
"""Ensure autoreport dumps to a file."""
model = IPUModel()
model = BoringModel()
autoreport_path = os.path.join(tmpdir, "report/")
trainer = Trainer(
default_root_dir=tmpdir,
Expand All @@ -358,7 +347,7 @@ def test_autoreport(tmpdir):
def test_manual_poptorch_dataloader(tmpdir):
model_options = poptorch.Options()

class IPUTestModel(IPUModel):
class IPUTestModel(BoringModel):
def train_dataloader(self):
dataloader = super().train_dataloader()
# save to instance to compare the reference later
Expand Down Expand Up @@ -389,7 +378,7 @@ def train_dataloader(self):
@RunIf(ipu=True)
def test_manual_poptorch_opts(tmpdir):
"""Ensure if the user passes manual poptorch Options, we run with the correct object."""
model = IPUModel()
model = BoringModel()
inference_opts = poptorch.Options()
training_opts = poptorch.Options()

Expand Down Expand Up @@ -418,7 +407,7 @@ def test_manual_poptorch_opts_custom(tmpdir):
"""Ensure if the user passes manual poptorch Options with custom parameters set, we respect them in our
poptorch options and the dataloaders."""

model = IPUModel()
model = BoringModel()
training_opts = poptorch.Options()
training_opts.deviceIterations(8)
training_opts.replicationFactor(2)
Expand Down Expand Up @@ -511,7 +500,7 @@ def test_replication_factor(tmpdir):
def test_default_opts(tmpdir):
"""Ensure default opts are set correctly in the IPUStrategy."""

model = IPUModel()
model = BoringModel()

trainer = Trainer(default_root_dir=tmpdir, accelerator="ipu", devices=1, fast_dev_run=True)
trainer.fit(model)
Expand All @@ -529,7 +518,7 @@ def test_default_opts(tmpdir):
def test_multi_optimizers_fails(tmpdir):
"""Ensure if there are multiple optimizers, we throw an exception."""

class TestModel(IPUModel):
class TestModel(BoringModel):
def configure_optimizers(self):
return [torch.optim.Adam(self.parameters()), torch.optim.Adam(self.parameters())]

Expand Down