From c0008fd46134d60a3a41b022bf9156cea5b145e5 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 11 十二月 2023 10:10:40 +0800
Subject: [PATCH] Merge branch 'dev_gzf_funasr2' into main
---
funasr/cli/train_cli.py | 163 ++++++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 163 insertions(+), 0 deletions(-)
diff --git a/funasr/cli/train_cli.py b/funasr/cli/train_cli.py
new file mode 100644
index 0000000..54cd2e8
--- /dev/null
+++ b/funasr/cli/train_cli.py
@@ -0,0 +1,163 @@
+import argparse
+import logging
+import os
+import sys
+from io import BytesIO
+from collections.abc import Sequence
+import torch
+import hydra
+from omegaconf import DictConfig, OmegaConf
+from funasr.torch_utils.set_all_random_seed import set_all_random_seed
+# from funasr.model_class_factory1 import model_choices
+from funasr.modules.lora.utils import mark_only_lora_as_trainable
+from funasr.optimizers import optim_choices
+from funasr.schedulers import scheduler_choices
+from funasr.torch_utils.load_pretrained_model import load_pretrained_model
+from funasr.torch_utils.initialize import initialize
+from funasr.datasets.data_sampler import BatchSampler
+# from funasr.tokenizer.build_tokenizer import build_tokenizer
+# from funasr.tokenizer.token_id_converter import TokenIDConverter
+from funasr.tokenizer.funtoken import build_tokenizer
+from funasr.datasets.dataset_jsonl import AudioDataset
+from funasr.cli.trainer import Trainer
+# from funasr.utils.load_fr_py import load_class_from_path
+from funasr.utils.dynamic_import import dynamic_import
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+
+
+def preprocess_config(cfg: DictConfig):
+ for key, value in cfg.items():
+ if value == 'None':
+ cfg[key] = None
+
+
+
+@hydra.main()
+def main(kwargs: DictConfig):
+ # preprocess_config(kwargs)
+ # import pdb; pdb.set_trace()
+ # set random seed
+ set_all_random_seed(kwargs.get("seed", 0))
+ torch.backends.cudnn.enabled = kwargs.get("cudnn_enabled", torch.backends.cudnn.enabled)
+ torch.backends.cudnn.benchmark = kwargs.get("cudnn_benchmark", torch.backends.cudnn.benchmark)
+ torch.backends.cudnn.deterministic = kwargs.get("cudnn_deterministic", True)
+
+ local_rank = int(os.environ.get('LOCAL_RANK', 0))
+ # Check if we are using DDP or FSDP
+ use_ddp = 'WORLD_SIZE' in os.environ and int(os.environ["WORLD_SIZE"]) > 1
+ use_fsdp = kwargs.get("use_fsdp", None)
+ if use_ddp or use_fsdp:
+ dist.init_process_group(backend=kwargs.get("backend", "nccl"), init_method='env://')
+ torch.cuda.set_device(local_rank)
+
+
+ # build_tokenizer
+ tokenizer = build_tokenizer(
+ token_type=kwargs.get("token_type", "char"),
+ bpemodel=kwargs.get("bpemodel", None),
+ delimiter=kwargs.get("delimiter", None),
+ space_symbol=kwargs.get("space_symbol", "<space>"),
+ non_linguistic_symbols=kwargs.get("non_linguistic_symbols", None),
+ g2p_type=kwargs.get("g2p_type", None),
+ token_list=kwargs.get("token_list", None),
+ unk_symbol=kwargs.get("unk_symbol", "<unk>"),
+ )
+
+ # import pdb;
+ # pdb.set_trace()
+ # build model
+ # model_class = model_choices.get_class(kwargs.get("model", "asr"))
+ # model_class = load_class_from_path(kwargs.get("model").split(":"))
+ model_class = dynamic_import(kwargs.get("model"))
+ model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=len(tokenizer.token_list))
+ frontend = model.frontend
+ # init_param
+ init_param = kwargs.get("init_param", None)
+ if init_param is not None:
+ init_param = eval(init_param)
+ if isinstance(init_param, Sequence):
+ init_param = (init_param,)
+ logging.info("init_param is not None: ", init_param)
+ for p in init_param:
+ logging.info(f"Loading pretrained params from {p}")
+ load_pretrained_model(
+ model=model,
+ init_param=p,
+ ignore_init_mismatch=kwargs.get("ignore_init_mismatch", True),
+ oss_bucket=kwargs.get("oss_bucket", None),
+ )
+ else:
+ initialize(model, kwargs.get("init", "kaiming_normal"))
+
+ # import pdb;
+ # pdb.set_trace()
+ # freeze_param
+ freeze_param = kwargs.get("freeze_param", None)
+ if freeze_param is not None:
+ freeze_param = eval(freeze_param)
+ if isinstance(freeze_param, Sequence):
+ freeze_param = (freeze_param,)
+ logging.info("freeze_param is not None: ", freeze_param)
+ for t in freeze_param:
+ for k, p in model.named_parameters():
+ if k.startswith(t + ".") or k == t:
+ logging.info(f"Setting {k}.requires_grad = False")
+ p.requires_grad = False
+
+
+ if use_ddp:
+ model = model.cuda(local_rank)
+ model = DDP(model, device_ids=[local_rank],
+ find_unused_parameters=kwargs.get("train_conf", {}).get("find_unused_parameters", False))
+ elif use_fsdp:
+ model = FSDP(model).cuda(local_rank)
+ else:
+ model = model.to(device=kwargs.get("device", "cuda"))
+
+
+ # optim
+ optim = kwargs.get("optim", "adam")
+ assert optim in optim_choices
+ optim_class = optim_choices.get(optim)
+ optim = optim_class(model.parameters(), **kwargs.get("optim_conf"))
+
+ # scheduler
+ scheduler = kwargs.get("scheduler", "warmuplr")
+ assert scheduler in scheduler_choices
+ scheduler_class = scheduler_choices.get(scheduler)
+ scheduler = scheduler_class(optim, **kwargs.get("scheduler_conf"))
+
+
+ # dataset
+ dataset_tr = AudioDataset(kwargs.get("train_data_set_list"), frontend=frontend, tokenizer=tokenizer, **kwargs.get("dataset_conf"))
+
+ # dataloader
+ batch_sampler = BatchSampler(dataset_tr, **kwargs.get("dataset_conf"), **kwargs.get("dataset_conf").get("batch_conf"))
+ dataloader_tr = torch.utils.data.DataLoader(dataset_tr,
+ collate_fn=dataset_tr.collator,
+ batch_sampler=batch_sampler,
+ num_workers=kwargs.get("num_workers", 0),
+ pin_memory=True)
+
+ trainer = Trainer(
+ model=model,
+ optim=optim,
+ scheduler=scheduler,
+ dataloader_train=dataloader_tr,
+ dataloader_val=None,
+ local_rank=local_rank,
+ use_ddp=use_ddp,
+ use_fsdp=use_fsdp,
+ **kwargs.get("train_conf"),
+ )
+ trainer.run()
+
+ if use_ddp or use_fsdp:
+ torch.distributed.destroy_process_group()
+
+
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
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