def test_quantization(tmpdir, observe: str, fuse: bool, convert: bool):
"""Parity test for quant model."""
seed_everything(42)
dm = RegressDataModule()
accelerator = "gpu" if torch.cuda.is_available() else "cpu"
trainer_args = dict(default_root_dir=tmpdir, max_epochs=7, accelerator=accelerator, devices=1)
model = RegressionModel()
qmodel = copy.deepcopy(model)
trainer = Trainer(**trainer_args)
trainer.fit(model, datamodule=dm)
org_size = get_model_size_mb(model)
org_score = torch.mean(torch.tensor([mape(model(x), y) for x, y in dm.test_dataloader()]))
fusing_layers = [(f"layer_{i}", f"layer_{i}a") for i in range(3)] if fuse else None
qcb = QuantizationAwareTraining(
observer_type=observe,
modules_to_fuse=fusing_layers,
quantize_on_fit_end=convert,
observer_enabled_stages=("train", "validate"),
)
trainer = Trainer(callbacks=[qcb], **trainer_args)
trainer.fit(qmodel, datamodule=dm)
quant_calls = qcb._forward_calls
assert quant_calls == qcb._forward_calls
quant_score = torch.mean(torch.tensor([mape(qmodel(x), y) for x, y in dm.test_dataloader()]))
# test that the test score is almost the same as with pure training
assert torch.allclose(org_score, quant_score, atol=0.45)
model_path = trainer.checkpoint_callback.best_model_path
curr_epoch = trainer.current_epoch
trainer_args.update(dict(max_epochs=1, enable_checkpointing=False))
if not convert:
trainer = Trainer(callbacks=[QuantizationAwareTraining()], **trainer_args)
trainer.fit(qmodel, datamodule=dm)
qmodel.eval()
torch.quantization.convert(qmodel, inplace=True)
quant_size = get_model_size_mb(qmodel)
# test that the trained model is smaller then initial
size_ratio = quant_size / org_size
assert size_ratio < 0.65
# todo: make it work also with strict loading
qmodel2 = RegressionModel.load_from_checkpoint(model_path, strict=False)
quant2_score = torch.mean(torch.tensor([mape(qmodel2(x), y) for x, y in dm.test_dataloader()]))
assert torch.allclose(org_score, quant2_score, atol=0.45)
# test without and with QAT callback
trainer_args.update(max_epochs=curr_epoch + 1)
qmodel2 = RegressionModel()
trainer = Trainer(callbacks=[QuantizationAwareTraining()], **trainer_args)
trainer.fit(qmodel2, datamodule=dm, ckpt_path=model_path)
quant2_score = torch.mean(torch.tensor([mape(qmodel2(x), y) for x, y in dm.test_dataloader()]))
# test that the test score is almost the same as with pure training
assert torch.allclose(org_score, quant2_score, atol=0.45)
def test_quantization(tmpdir, observe: str, fuse: bool, convert: bool):
"""Parity test for quant model"""
seed_everything(42)
dm = RegressDataModule()
trainer_args = dict(default_root_dir=tmpdir,
max_epochs=7,
gpus=int(torch.cuda.is_available()))
model = RegressionModel()
qmodel = copy.deepcopy(model)
trainer = Trainer(**trainer_args)
trainer.fit(model, datamodule=dm)
org_size = model.model_size
org_score = torch.mean(
torch.tensor([
mean_relative_error(model(x), y) for x, y in dm.test_dataloader()
]))
fusing_layers = [(f"layer_{i}", f"layer_{i}a")
for i in range(3)] if fuse else None
qcb = QuantizationAwareTraining(observer_type=observe,
modules_to_fuse=fusing_layers,
quantize_on_fit_end=convert)
trainer = Trainer(callbacks=[qcb], **trainer_args)
trainer.fit(qmodel, datamodule=dm)
quant_calls = qcb._forward_calls
assert quant_calls == qcb._forward_calls
quant_score = torch.mean(
torch.tensor([
mean_relative_error(qmodel(x), y) for x, y in dm.test_dataloader()
]))
# test that the test score is almost the same as with pure training
assert torch.allclose(org_score, quant_score, atol=0.45)
model_path = trainer.checkpoint_callback.best_model_path
trainer_args.update(dict(max_epochs=1, checkpoint_callback=False))
if not convert:
trainer = Trainer(callbacks=[QuantizationAwareTraining()],
**trainer_args)
trainer.fit(qmodel, datamodule=dm)
qmodel.eval()
torch.quantization.convert(qmodel, inplace=True)
quant_size = qmodel.model_size
# test that the trained model is smaller then initial
size_ratio = quant_size / org_size
assert size_ratio < 0.65
# todo: make it work also with strict loading
qmodel2 = RegressionModel.load_from_checkpoint(model_path, strict=False)
quant2_score = torch.mean(
torch.tensor([
mean_relative_error(qmodel2(x), y)
for x, y in dm.test_dataloader()
]))
assert torch.allclose(org_score, quant2_score, atol=0.45)