def cli_main(
modify_parser: Optional[Callable[[argparse.ArgumentParser], None]] = None
) -> None:
parser = options.get_training_parser()
options.add_pruning_args(parser)
args = options.parse_args_and_arch(parser, modify_parser=modify_parser)
cfg = convert_namespace_to_omegaconf(args)
if args.profile:
with torch.cuda.profiler.profile():
with torch.autograd.profiler.emit_nvtx():
distributed_utils.call_main(cfg, main)
else:
distributed_utils.call_main(cfg, main)
def cli_main():
parser = options.get_validation_parser()
args = options.parse_args_and_arch(parser)
# only override args that are explicitly given on the command line
override_parser = options.get_validation_parser()
override_args = options.parse_args_and_arch(override_parser,
suppress_defaults=True)
# support multi-gpu validation, use all available gpus
default_world_size = max(1, torch.cuda.device_count())
if args.distributed_world_size < default_world_size:
args.distributed_world_size = default_world_size
override_args.distributed_world_size = default_world_size
distributed_utils.call_main(args, main, override_args=override_args)
def cli_main(
modify_parser: Optional[Callable[[argparse.ArgumentParser], None]] = None
) -> None:
# print('input:', modify_parser) # None
parser = options.get_training_parser()
print('parser:', parser)
print('\n modify_parser', modify_parser)
args = options.parse_args_and_arch(parser, modify_parser=modify_parser)
# print('args', args)
cfg = convert_namespace_to_omegaconf(args)
# print('cfg:', cfg)
if args.profile:
with torch.cuda.profiler.profile():
with torch.autograd.profiler.emit_nvtx():
distributed_utils.call_main(cfg, main)
else:
distributed_utils.call_main(cfg, main)
def _hydra_main(cfg: FairseqConfig, **kwargs) -> float:
add_defaults(cfg)
if cfg.common.reset_logging:
reset_logging() # Hydra hijacks logging, fix that
else:
# check if directly called or called through hydra_main
if HydraConfig.initialized():
with open_dict(cfg):
# make hydra logging work with ddp (see # see https://github.com/facebookresearch/hydra/issues/1126)
cfg.job_logging_cfg = OmegaConf.to_container(
HydraConfig.get().job_logging, resolve=True)
with omegaconf_no_object_check():
cfg = OmegaConf.create(
OmegaConf.to_container(cfg, resolve=True, enum_to_str=True))
OmegaConf.set_struct(cfg, True)
try:
if cfg.common.profile:
with torch.cuda.profiler.profile():
with torch.autograd.profiler.emit_nvtx():
distributed_utils.call_main(cfg, pre_main, **kwargs)
else:
distributed_utils.call_main(cfg, pre_main, **kwargs)
except BaseException as e:
if not cfg.common.suppress_crashes:
raise
else:
logger.error("Crashed! " + str(e))
# get best val and return - useful for sweepers
try:
best_val = metrics.get_smoothed_value(
"valid", cfg.checkpoint.best_checkpoint_metric)
except:
best_val = None
if best_val is None:
best_val = float("inf")
return best_val
def cli_main(
modify_parser: Optional[Callable[[argparse.ArgumentParser], None]] = None
) -> None:
parser = options.get_training_parser()
args = options.parse_args_and_arch(parser, modify_parser=modify_parser)
if args.debug:
args.device_id = 0
args.distributed_rank = 0
args.distributed_world_size = 1
#args.train_subset = args.valid_subset
args.num_workers = 0
args.dropout = 0
cfg = convert_namespace_to_omegaconf(args)
if args.profile:
with torch.cuda.profiler.profile():
with torch.autograd.profiler.emit_nvtx():
distributed_utils.call_main(cfg, main)
else:
distributed_utils.call_main(cfg, main)
def cli_main(path, model_overrides, name):
parser = options.get_eval_lm_parser()
args = options.parse_args_and_arch(parser)
args.max_sentences = 2
args.tokens_per_sample = 512
args.context_window = 400
# args.cpu = True
# args.num_shards = 300
gl._init()
gl.set_value('visualize', True)
gl.set_value('attn_weight_layers', [0 for _ in range(16)])
gl.set_value('attn_weight_heads', [0 for _ in range(8)])
gl.set_value('attn_weight_counts', [0 for _ in range(16)])
gl.set_value('current_layer', 0)
args.path = path
args.model_overrides = model_overrides
distributed_utils.call_main(args, main)
if name == 'layer':
attn_weight_layers = gl.get_value('attn_weight_layers')
attn_weight_counts = gl.get_value('attn_weight_counts')
attn_weight_layers = [
x / attn_weight_counts[idx]
for idx, x in enumerate(attn_weight_layers)
]
torch.save(attn_weight_layers, path + "." + name)
elif name == 'head':
attn_weight_heads = gl.get_value('attn_weight_heads')
attn_weight_counts = gl.get_value('attn_weight_counts')
attn_weight_heads = [
x / attn_weight_counts[idx]
for idx, x in enumerate(attn_weight_heads)
]
torch.save(attn_weight_heads, path + "." + name)
else:
exit(0)
def cli_main(
modify_parser: Optional[Callable[[argparse.ArgumentParser], None]] = None
) -> None:
parser = options.get_training_parser()
args = options.parse_args_and_arch(parser, modify_parser=modify_parser)
print_options_meaning_changes(args)
cfg = convert_namespace_to_omegaconf(args)
if cfg.common.use_plasma_view:
server = PlasmaStore(path=cfg.common.plasma_path)
logger.info(
f"Started plasma server pid {server.server.pid} {cfg.common.plasma_path}"
)
if args.profile:
with torch.cuda.profiler.profile():
with torch.autograd.profiler.emit_nvtx():
distributed_utils.call_main(cfg, main)
else:
distributed_utils.call_main(cfg, main)
def cli_main():
parser = options.get_interactive_generation_parser()
args = options.parse_args_and_arch(parser)
distributed_utils.call_main(convert_namespace_to_omegaconf(args), main)
def cli_main():
parser = options.get_interactive_generation_parser()
args = options.parse_args_and_arch(parser)
distributed_utils.call_main(args, main)
def cli_main():
parser = options.get_eval_lm_parser()
args = options.parse_args_and_arch(parser)
distributed_utils.call_main(args, main)
def cli_main():
parser = options.get_eval_lm_parser()
args = options.parse_args_and_arch(parser)
distributed_utils.call_main(convert_namespace_to_omegaconf(args), main)
def cli_main():
parser = options.get_rendering_parser()
add_distributed_training_args(parser)
args = options.parse_args_and_arch(parser)
distributed_utils.call_main(args, main)
def cli_main():
parser = options.get_generation_parser(interactive=False)
parser.add_argument('--no-print', action='store_true')
parser.add_argument('--truncate-size', default=512, type=int)
args = options.parse_args_and_arch(parser)
distributed_utils.call_main(args, main)
def cli_main():
parser = options.get_interactive_generation_parser()
parser.add_argument('--transformer-big-zhen', action='store_true')
args = options.parse_args_and_arch(parser)
distributed_utils.call_main(args, main)
