From 4a7a984a5f3e3f894f86ce82e76ddd13d8a42a20 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 11 三月 2024 17:56:30 +0800
Subject: [PATCH] Dev gzf (#1465)
---
examples/industrial_data_pretraining/llm_asr/conf/whisper_vicuna_linear.yaml | 5
funasr/schedulers/lambdalr_cus.py | 17 ++++
funasr/schedulers/__init__.py | 2
funasr/datasets/llm_datasets_vicuna/samplers.py | 9 ++
funasr/bin/train_llm.py | 200 ++++++++++++++++++++++++++++++++++++++++++++++++++
5 files changed, 231 insertions(+), 2 deletions(-)
diff --git a/examples/industrial_data_pretraining/llm_asr/conf/whisper_vicuna_linear.yaml b/examples/industrial_data_pretraining/llm_asr/conf/whisper_vicuna_linear.yaml
index 331ac0d..8a01d14 100644
--- a/examples/industrial_data_pretraining/llm_asr/conf/whisper_vicuna_linear.yaml
+++ b/examples/industrial_data_pretraining/llm_asr/conf/whisper_vicuna_linear.yaml
@@ -65,8 +65,9 @@
optim: adamw
optim_conf:
lr: 0.0001
- weight_decay: 0.000001
-scheduler: warmuplr
+ weight_decay: 0
+
+scheduler: custom_lambdalr
scheduler_conf:
warmup_steps: 1000
diff --git a/funasr/bin/train_llm.py b/funasr/bin/train_llm.py
new file mode 100644
index 0000000..3c93371
--- /dev/null
+++ b/funasr/bin/train_llm.py
@@ -0,0 +1,200 @@
+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+
+import os
+import sys
+import torch
+import hydra
+import logging
+import argparse
+from io import BytesIO
+import torch.distributed as dist
+from collections.abc import Sequence
+from omegaconf import DictConfig, OmegaConf
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+
+from funasr.register import tables
+from funasr.optimizers import optim_classes
+from funasr.train_utils.trainer import Trainer
+from funasr.schedulers import scheduler_classes
+from funasr.train_utils.initialize import initialize
+from funasr.download.download_from_hub import download_model
+from funasr.models.lora.utils import mark_only_lora_as_trainable
+from funasr.train_utils.set_all_random_seed import set_all_random_seed
+from funasr.train_utils.load_pretrained_model import load_pretrained_model
+# from funasr.tokenizer.build_tokenizer import build_tokenizer
+# from funasr.tokenizer.token_id_converter import TokenIDConverter
+# from funasr.tokenizer.funtoken import build_tokenizer
+
+
+@hydra.main(config_name=None, version_base=None)
+def main_hydra(kwargs: DictConfig):
+ if kwargs.get("debug", False):
+ import pdb; pdb.set_trace()
+
+ assert "model" in kwargs
+ if "model_conf" not in kwargs:
+ logging.info("download models from model hub: {}".format(kwargs.get("hub", "ms")))
+ kwargs = download_model(is_training=kwargs.get("is_training", True), **kwargs)
+
+
+ main(**kwargs)
+
+
+def main(**kwargs):
+ print(kwargs)
+
+ # 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))
+ if local_rank == 0:
+ tables.print()
+ # 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)
+
+ # save config.yaml
+ if (use_ddp or use_fsdp) and dist.get_rank() == 0 or not (use_ddp or use_fsdp) and local_rank == 0:
+ os.makedirs(kwargs.get("output_dir", "./"), exist_ok=True)
+ yaml_file = os.path.join(kwargs.get("output_dir", "./"), "config.yaml")
+ OmegaConf.save(config=kwargs, f=yaml_file)
+ logging.info("config.yaml is saved to: %s", yaml_file)
+
+ tokenizer = kwargs.get("tokenizer", None)
+ if tokenizer is not None:
+ tokenizer_class = tables.tokenizer_classes.get(tokenizer)
+ tokenizer = tokenizer_class(**kwargs["tokenizer_conf"])
+ kwargs["tokenizer"] = tokenizer
+
+ # build frontend if frontend is none None
+ frontend = kwargs.get("frontend", None)
+ if frontend is not None:
+ frontend_class = tables.frontend_classes.get(frontend)
+ frontend = frontend_class(**kwargs["frontend_conf"])
+ kwargs["frontend"] = frontend
+ kwargs["input_size"] = frontend.output_size()
+
+
+ # build model
+ model_class = tables.model_classes.get(kwargs["model"])
+ vocab_size = len(tokenizer.token_list) if hasattr(tokenizer, "token_list") else None
+ vocab_size = len(tokenizer.get_vocab()) if hasattr(tokenizer, "get_vocab") else vocab_size
+ model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=vocab_size)
+
+
+
+ # init_param
+ init_param = kwargs.get("init_param", None)
+ if init_param is not None:
+ if not isinstance(init_param, (list, tuple)):
+ init_param = (init_param,)
+ logging.info("init_param is not None: %s", init_param)
+ for p in init_param:
+ if os.path.exists(p):
+ logging.info(f"Loading pretrained params from {p}")
+ load_pretrained_model(
+ model=model,
+ path=p,
+ ignore_init_mismatch=kwargs.get("ignore_init_mismatch", True),
+ oss_bucket=kwargs.get("oss_bucket", None),
+ scope_map=kwargs.get("scope_map", []),
+ excludes=kwargs.get("excludes", None),
+ )
+ else:
+ logging.info(f"Checkpoint does not exist, init randomly: {p}")
+ elif kwargs.get("init", None):
+ initialize(model, kwargs.get("init", "kaiming_normal"))
+ else:
+ print("No initialize method")
+
+
+ # 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: %s", 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_classes
+ optim_class = optim_classes.get(optim)
+ optim = optim_class(model.parameters(), **kwargs.get("optim_conf"))
+
+ # scheduler
+ scheduler = kwargs.get("scheduler", "warmuplr")
+ assert scheduler in scheduler_classes
+ scheduler_class = scheduler_classes.get(scheduler)
+ scheduler = scheduler_class(optim, **kwargs.get("scheduler_conf"))
+
+
+ # dataset
+ dataset_class = tables.dataset_classes.get(kwargs.get("dataset", "AudioDataset"))
+ dataset_tr = dataset_class(kwargs.get("train_data_set_list"), frontend=frontend, tokenizer=tokenizer, is_training=True, **kwargs.get("dataset_conf"))
+ dataset_val = dataset_class(kwargs.get("valid_data_set_list"), frontend=frontend, tokenizer=tokenizer, is_training=False, **kwargs.get("dataset_conf"))
+
+ # dataloader
+ batch_sampler = kwargs["dataset_conf"].get("batch_sampler", "DynamicBatchLocalShuffleSampler")
+ batch_sampler_val = None
+ if batch_sampler is not None:
+ batch_sampler_class = tables.batch_sampler_classes.get(batch_sampler)
+ batch_sampler = batch_sampler_class(dataset_tr, **kwargs.get("dataset_conf"))
+ batch_sampler_val = batch_sampler_class(dataset_val, is_training=False, **kwargs.get("dataset_conf"))
+ dataloader_tr = torch.utils.data.DataLoader(dataset_tr,
+ collate_fn=dataset_tr.collator,
+ batch_sampler=batch_sampler,
+ num_workers=kwargs.get("dataset_conf").get("num_workers", 4),
+ pin_memory=True)
+
+ dataloader_val = torch.utils.data.DataLoader(dataset_val,
+ collate_fn=dataset_val.collator,
+ batch_sampler=batch_sampler_val,
+ num_workers=kwargs.get("dataset_conf").get("num_workers", 4),
+ pin_memory=True)
+ trainer = Trainer(
+ model=model,
+ optim=optim,
+ scheduler=scheduler,
+ dataloader_train=dataloader_tr,
+ dataloader_val=dataloader_val,
+ local_rank=local_rank,
+ use_ddp=use_ddp,
+ use_fsdp=use_fsdp,
+ output_dir=kwargs.get("output_dir", "./exp"),
+ resume=kwargs.get("resume", True),
+ **kwargs.get("train_conf"),
+ )
+ trainer.run()
+
+ if use_ddp or use_fsdp:
+ torch.distributed.destroy_process_group()
+
+
+
+if __name__ == "__main__":
+ main_hydra()
\ No newline at end of file
diff --git a/funasr/datasets/llm_datasets_vicuna/samplers.py b/funasr/datasets/llm_datasets_vicuna/samplers.py
index 512250d..fe840e2 100644
--- a/funasr/datasets/llm_datasets_vicuna/samplers.py
+++ b/funasr/datasets/llm_datasets_vicuna/samplers.py
@@ -142,6 +142,15 @@
def set_epoch(self, epoch):
self.epoch = epoch
+
+def CustomDistributedBatchSampler_fn(dataset, **kwargs):
+ dataloader_args = {"dataset": dataset}
+ dataloader_args["batch_sampler"] = CustomDistributedBatchSampler(dataset, **kwargs)
+ dataloader_args["num_workers"] = kwargs.get("num_workers", 4)
+ dataloader_args["pin_memory"] = kwargs.get("pin_memory", True)
+
+ return dataloader_args
+
@tables.register("batch_sampler_classes", "CustomDistributedBatchSampler")
class CustomDistributedBatchSampler(Sampler):
def __init__(self, dataset,
diff --git a/funasr/schedulers/__init__.py b/funasr/schedulers/__init__.py
index 0d1a578..39f8c0e 100644
--- a/funasr/schedulers/__init__.py
+++ b/funasr/schedulers/__init__.py
@@ -6,6 +6,7 @@
from funasr.schedulers.noam_lr import NoamLR
from funasr.schedulers.tri_stage_scheduler import TriStageLR
from funasr.schedulers.warmup_lr import WarmupLR
+from funasr.schedulers.lambdalr_cus import CustomLambdaLR
scheduler_classes = dict(
ReduceLROnPlateau=torch.optim.lr_scheduler.ReduceLROnPlateau,
@@ -20,4 +21,5 @@
cycliclr=torch.optim.lr_scheduler.CyclicLR,
onecyclelr=torch.optim.lr_scheduler.OneCycleLR,
CosineAnnealingWarmRestarts=torch.optim.lr_scheduler.CosineAnnealingWarmRestarts,
+ custom_lambdalr=CustomLambdaLR,
)
diff --git a/funasr/schedulers/lambdalr_cus.py b/funasr/schedulers/lambdalr_cus.py
new file mode 100644
index 0000000..0123cc2
--- /dev/null
+++ b/funasr/schedulers/lambdalr_cus.py
@@ -0,0 +1,17 @@
+
+import torch
+from torch.optim.lr_scheduler import _LRScheduler
+
+class CustomLambdaLR(_LRScheduler):
+ def __init__(self, optimizer, warmup_steps, last_epoch=-1):
+ self.warmup_steps = warmup_steps
+ super().__init__(optimizer, last_epoch)
+
+ def get_lr(self):
+ if self.last_epoch < self.warmup_steps:
+ return [
+ base_lr * min(self.last_epoch / self.warmup_steps, 1)
+ for base_lr in self.base_lrs
+ ]
+ else:
+ return [base_lr for base_lr in self.base_lrs]
\ No newline at end of file
--
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