def __init__(self, args, task, model, criterion, quantizer=None):
self.args = args
self.task = task
# catalog shared parameters
shared_params = _catalog_shared_params(model)
self.tpu = getattr(args, 'tpu', False)
self.cuda = torch.cuda.is_available() and not args.cpu and not self.tpu
if self.cuda:
self.device = torch.device('cuda')
elif self.tpu:
self.device = utils.get_tpu_device(args)
else:
self.device = torch.device('cpu')
# copy model and criterion to current device/dtype
self._criterion = criterion
self._model = model
if self.tpu:
import torch_xla.core.xla_model as xm
self._model = xm.send_cpu_data_to_device(self._model, self.device)
if args.fp16:
self._criterion = self._criterion.half()
self._model = self._model.half()
elif args.bf16:
self._criterion = self._criterion.to(dtype=torch.bfloat16)
self._model = self._model.to(dtype=torch.bfloat16)
self._criterion = self._criterion.to(device=self.device)
self._model = self._model.to(device=self.device)
# check that shared parameters are preserved after device transfer
for shared_param in shared_params:
ref = _get_module_by_path(self._model, shared_param[0])
for path in shared_param[1:]:
logger.info(
'detected shared parameter: {} <- {}'.format(shared_param[0], path)
)
_set_module_by_path(self._model, path, ref)
self._dummy_batch = "DUMMY" # indicates we don't have a dummy batch at first
self._lr_scheduler = None
self._num_updates = 0
self._num_xla_compiles = 0 # for TPUs
self._optim_history = None
self._optimizer = None
self._warn_once = set()
self._wrapped_criterion = None
self._wrapped_model = None
# TODO(myleott): support tpu
if self.cuda and self.data_parallel_world_size > 1:
self._grad_norm_buf = torch.cuda.DoubleTensor(self.data_parallel_world_size)
else:
self._grad_norm_buf = None
self.quantizer = quantizer
if self.quantizer is not None:
self.quantizer.set_trainer(self)
# get detailed cuda environment
if self.cuda:
self.cuda_env = utils.CudaEnvironment()
if self.data_parallel_world_size > 1:
self.cuda_env_arr = distributed_utils.all_gather_list(self.cuda_env)
else:
self.cuda_env_arr = [self.cuda_env]
if self.data_parallel_rank == 0:
utils.CudaEnvironment.pretty_print_cuda_env_list(self.cuda_env_arr)
else:
self.cuda_env = None
self.cuda_env_arr = None
metrics.log_start_time("wall", priority=790, round=0)
self._start_time = time.time()
self._previous_training_time = 0
self._cumulative_training_time = None
def __init__(self,
cfg: FairseqConfig,
task,
model,
criterion,
quantizer=None):
if isinstance(cfg, Namespace):
logger.warning(
"argparse.Namespace configuration is deprecated! Automatically converting to OmegaConf"
)
cfg = convert_namespace_to_omegaconf(cfg)
self.cfg = cfg
self.task = task
# catalog shared parameters
shared_params = _catalog_shared_params(model)
self.tpu = cfg.common.tpu
self.cuda = torch.cuda.is_available(
) and not cfg.common.cpu and not self.tpu
if self.cuda:
self.device = torch.device("cuda")
elif self.tpu:
self.device = utils.get_tpu_device()
else:
self.device = torch.device("cpu")
# copy model and criterion to current device/dtype
self._criterion = criterion
self._model = model
if cfg.common.fp16:
self._criterion = self._criterion.half()
self._model = self._model.half()
elif cfg.common.bf16:
self._criterion = self._criterion.to(dtype=torch.bfloat16)
self._model = self._model.to(dtype=torch.bfloat16)
if not cfg.distributed_training.pipeline_model_parallel:
self._criterion = self._criterion.to(device=self.device)
self._model = self._model.to(device=self.device)
self.pipeline_model_parallel = cfg.distributed_training.pipeline_model_parallel
self.last_device = None
if self.cuda and self.pipeline_model_parallel:
self.last_device = torch.device(
cfg.distributed_training.pipeline_devices[-1])
# check that shared parameters are preserved after device transfer
for shared_param in shared_params:
ref = _get_module_by_path(self._model, shared_param[0])
for path in shared_param[1:]:
logger.info("detected shared parameter: {} <- {}".format(
shared_param[0], path))
_set_module_by_path(self._model, path, ref)
self._dummy_batch = None # indicates we don't have a dummy batch at first
self._lr_scheduler = None
self._num_updates = 0
self._num_xla_compiles = 0 # for TPUs
self._optim_history = None
self._optimizer = None
self._warn_once = set()
self._wrapped_criterion = None
self._wrapped_model = None
# TODO(myleott): support tpu
if self.cuda and self.data_parallel_world_size > 1:
self._grad_norm_buf = torch.cuda.DoubleTensor(
self.data_parallel_world_size)
else:
self._grad_norm_buf = None
self.quantizer = quantizer
if self.quantizer is not None:
self.quantizer.set_trainer(self)
# get detailed cuda environment
if self.cuda:
self.cuda_env = utils.CudaEnvironment()
if self.data_parallel_world_size > 1:
self.cuda_env_arr = distributed_utils.all_gather_list(
self.cuda_env, group=distributed_utils.get_global_group())
else:
self.cuda_env_arr = [self.cuda_env]
if self.data_parallel_rank == 0:
utils.CudaEnvironment.pretty_print_cuda_env_list(
self.cuda_env_arr)
else:
self.cuda_env = None
self.cuda_env_arr = None
metrics.log_start_time("wall", priority=790, round=0)
self._start_time = time.time()
self._previous_training_time = 0
self._cumulative_training_time = None
#!/usr/bin/env python # -*- coding: utf-8 -*- # !@Time : 2021/4/20 下午9:49 # !@Author : miracleyin @email: [email protected] # !@File : cuda.py import torch from fairseq import utils cuda_env = utils.CudaEnvironment() utils.CudaEnvironment.pretty_print_cuda_env_list([cuda_env]) device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')