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201 | class QATTrainer(Trainer):
@classmethod
def train_from_config(cls, cfg: Union[DictConfig, dict]) -> Tuple[nn.Module, Tuple]:
"""
Perform quantization aware training (QAT) according to a recipe configuration.
This method will instantiate all the objects specified in the recipe, build and quantize the model,
and calibrate the quantized model. The resulting quantized model and the output of the trainer.train()
method will be returned.
The quantized model will be exported to ONNX along with other checkpoints.
:param cfg: The parsed DictConfig object from yaml recipe files or a dictionary.
:return: A tuple containing the quantized model and the output of trainer.train() method.
:rtype: Tuple[nn.Module, Tuple]
:raises ValueError: If the recipe does not have the required key `quantization_params` or
`checkpoint_params.checkpoint_path` in it.
:raises NotImplementedError: If the recipe requests multiple GPUs or num_gpus is not equal to 1.
:raises ImportError: If pytorch-quantization import was unsuccessful
"""
if _imported_pytorch_quantization_failure is not None:
raise _imported_pytorch_quantization_failure
# INSTANTIATE ALL OBJECTS IN CFG
cfg = hydra.utils.instantiate(cfg)
# TRIGGER CFG MODIFYING CALLBACKS
cfg = cls._trigger_cfg_modifying_callbacks(cfg)
if "quantization_params" not in cfg:
raise ValueError("Your recipe does not have quantization_params. Add them to use QAT.")
if "checkpoint_path" not in cfg.checkpoint_params:
raise ValueError("Starting checkpoint is a must for QAT finetuning.")
num_gpus = core_utils.get_param(cfg, "num_gpus")
multi_gpu = core_utils.get_param(cfg, "multi_gpu")
device = core_utils.get_param(cfg, "device")
if num_gpus != 1:
raise NotImplementedError(
f"Recipe requests multi_gpu={cfg.multi_gpu} and num_gpus={cfg.num_gpus}. QAT is proven to work correctly only with multi_gpu=OFF and num_gpus=1"
)
setup_device(device=device, multi_gpu=multi_gpu, num_gpus=num_gpus)
# INSTANTIATE DATA LOADERS
train_dataloader = dataloaders.get(
name=get_param(cfg, "train_dataloader"),
dataset_params=copy.deepcopy(cfg.dataset_params.train_dataset_params),
dataloader_params=copy.deepcopy(cfg.dataset_params.train_dataloader_params),
)
val_dataloader = dataloaders.get(
name=get_param(cfg, "val_dataloader"),
dataset_params=copy.deepcopy(cfg.dataset_params.val_dataset_params),
dataloader_params=copy.deepcopy(cfg.dataset_params.val_dataloader_params),
)
if "calib_dataloader" in cfg:
calib_dataloader_name = get_param(cfg, "calib_dataloader")
calib_dataloader_params = copy.deepcopy(cfg.dataset_params.calib_dataloader_params)
calib_dataset_params = copy.deepcopy(cfg.dataset_params.calib_dataset_params)
else:
calib_dataloader_name = get_param(cfg, "train_dataloader")
calib_dataloader_params = copy.deepcopy(cfg.dataset_params.train_dataloader_params)
calib_dataset_params = copy.deepcopy(cfg.dataset_params.train_dataset_params)
# if we use whole dataloader for calibration, don't shuffle it
# HistogramCalibrator collection routine is sensitive to order of batches and produces slightly different results
# if we use several batches, we don't want it to be from one class if it's sequential in dataloader
# model is in eval mode, so BNs will not be affected
calib_dataloader_params.shuffle = cfg.quantization_params.calib_params.num_calib_batches is not None
# we don't need training transforms during calibration, distribution of activations will be skewed
calib_dataset_params.transforms = cfg.dataset_params.val_dataset_params.transforms
calib_dataloader = dataloaders.get(
name=calib_dataloader_name,
dataset_params=calib_dataset_params,
dataloader_params=calib_dataloader_params,
)
# BUILD MODEL
model = models.get(
model_name=cfg.arch_params.get("model_name", None) or cfg.architecture,
num_classes=cfg.get("num_classes", None) or cfg.arch_params.num_classes,
arch_params=cfg.arch_params,
strict_load=cfg.checkpoint_params.strict_load,
pretrained_weights=cfg.checkpoint_params.pretrained_weights,
checkpoint_path=cfg.checkpoint_params.checkpoint_path,
load_backbone=False,
)
model.to(device_config.device)
# QUANTIZE MODEL
model.eval()
fuse_repvgg_blocks_residual_branches(model)
q_util = SelectiveQuantizer(
default_quant_modules_calibrator_weights=cfg.quantization_params.selective_quantizer_params.calibrator_w,
default_quant_modules_calibrator_inputs=cfg.quantization_params.selective_quantizer_params.calibrator_i,
default_per_channel_quant_weights=cfg.quantization_params.selective_quantizer_params.per_channel,
default_learn_amax=cfg.quantization_params.selective_quantizer_params.learn_amax,
verbose=cfg.quantization_params.calib_params.verbose,
)
q_util.register_skip_quantization(layer_names=cfg.quantization_params.selective_quantizer_params.skip_modules)
q_util.quantize_module(model)
# CALIBRATE MODEL
logger.info("Calibrating model...")
calibrator = QuantizationCalibrator(
verbose=cfg.quantization_params.calib_params.verbose,
torch_hist=True,
)
calibrator.calibrate_model(
model,
method=cfg.quantization_params.calib_params.histogram_calib_method,
calib_data_loader=calib_dataloader,
num_calib_batches=cfg.quantization_params.calib_params.num_calib_batches or len(train_dataloader),
percentile=get_param(cfg.quantization_params.calib_params, "percentile", 99.99),
)
calibrator.reset_calibrators(model) # release memory taken by calibrators
# VALIDATE PTQ MODEL AND PRINT SUMMARY
logger.info("Validating PTQ model...")
trainer = Trainer(experiment_name=cfg.experiment_name, ckpt_root_dir=get_param(cfg, "ckpt_root_dir", default_val=None))
valid_metrics_dict = trainer.test(model=model, test_loader=val_dataloader, test_metrics_list=cfg.training_hyperparams.valid_metrics_list)
results = ["PTQ Model Validation Results"]
results += [f" - {metric:10}: {value}" for metric, value in valid_metrics_dict.items()]
logger.info("\n".join(results))
# TRAIN
if cfg.quantization_params.ptq_only:
logger.info("cfg.quantization_params.ptq_only=True. Performing PTQ only!")
suffix = "ptq"
res = None
else:
model.train()
recipe_logged_cfg = {"recipe_config": OmegaConf.to_container(cfg, resolve=True)}
trainer = Trainer(experiment_name=cfg.experiment_name, ckpt_root_dir=get_param(cfg, "ckpt_root_dir", default_val=None))
torch.cuda.empty_cache()
res = trainer.train(
model=model,
train_loader=train_dataloader,
valid_loader=val_dataloader,
training_params=cfg.training_hyperparams,
additional_configs_to_log=recipe_logged_cfg,
)
suffix = "qat"
# EXPORT QUANTIZED MODEL TO ONNX
input_shape = next(iter(val_dataloader))[0].shape
os.makedirs(trainer.checkpoints_dir_path, exist_ok=True)
qdq_onnx_path = os.path.join(trainer.checkpoints_dir_path, f"{cfg.experiment_name}_{'x'.join((str(x) for x in input_shape))}_{suffix}.onnx")
# TODO: modify SG's convert_to_onnx for quantized models and use it instead
export_quantized_module_to_onnx(
model=model.cpu(),
onnx_filename=qdq_onnx_path,
input_shape=input_shape,
input_size=input_shape,
train=False,
)
logger.info(f"Exported {suffix.upper()} ONNX to {qdq_onnx_path}")
return model, res
|