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d8bfe4a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 | from core.tasks.base_task import BaseTask
from core.tasks.task_registry import register_task
from transformers import Trainer, TrainingArguments
from transformers.trainer import find_batch_size, EvalLoopOutput
from torch.utils.data import DataLoader
from core.datasets.cosyvoice_dataset import CosyVoiceDataset, CosyVoiceCollator
from core.datasets.samplers import DistributedDynamicBatchSampler
from models.cosyvoice.cosyvoice2 import CosyVoice2Model
from omegaconf import OmegaConf
from transformers import EvalPrediction
from models.cosyvoice.utils.common import np_accuracy, IGNORE_ID, th_accuracy
import torch.distributed as dist
import torch
import numpy as np
import os
def compute_metrics(eval_pred):
predictions, labels = eval_pred
acc = th_accuracy(
predictions,
labels,
ignore_label=IGNORE_ID,
)
return {"accuracy": acc}
class CustomTrainer(Trainer):
def __init__(self, sampler_cfg=None, *args, **kwargs):
super().__init__(*args, **kwargs)
self.sampler_cfg = sampler_cfg
def evaluation_loop(
self,
dataloader,
description,
prediction_loss_only=None,
ignore_keys=None,
metric_key_prefix="eval",
):
args = self.args
model = self._wrap_model(self.model, training=False, dataloader=dataloader)
if not self.is_in_train:
if args.fp16_full_eval:
model = model.to(dtype=torch.float16, device=args.device)
elif args.bf16_full_eval:
model = model.to(dtype=torch.bfloat16, device=args.device)
model.eval()
total_acc = 0.0
total_count = 0
total_loss = 0.0
observed_num_examples = 0
for step, inputs in enumerate(dataloader):
# forward
with torch.no_grad():
outputs = model(**inputs)
loss = outputs.get("loss", None)
acc = outputs.get("acc", None)
batch_size = find_batch_size(inputs) or 1
observed_num_examples += batch_size
if loss is not None:
total_loss += loss.detach().float().item() * batch_size
if acc is not None:
total_acc += float(acc.item()) * batch_size
total_count += batch_size
# 释放显存
del outputs
torch.cuda.empty_cache()
# ===== DDP：只在 rank 0 汇总 =====
if self.accelerator.num_processes > 1:
total_loss = self.accelerator.reduce(
torch.tensor(total_loss, device=args.device), reduction="sum"
).item()
total_acc = self.accelerator.reduce(
torch.tensor(total_acc, device=args.device), reduction="sum"
).item()
observed_num_examples = self.accelerator.reduce(
torch.tensor(observed_num_examples, device=args.device), reduction="sum"
).item()
metrics = {}
if observed_num_examples > 0:
metrics[f"{metric_key_prefix}_loss"] = total_loss / observed_num_examples
metrics[f"{metric_key_prefix}_accuracy"] = total_acc / total_count
metrics[f"{metric_key_prefix}_step"] = self.state.global_step
return EvalLoopOutput(
predictions=None,
label_ids=None,
metrics=metrics,
num_samples=observed_num_examples,
)
def get_train_dataloader(self):
if self.train_dataset is None:
raise ValueError("Trainer: training requires a train_dataset.")
# 使用自定义 DynamicBatchSampler
sampler = DistributedDynamicBatchSampler(
self.train_dataset.get_lengths(), **self.sampler_cfg
)
return DataLoader(
self.train_dataset,
batch_sampler=sampler,
collate_fn=self.data_collator,
num_workers=self.args.dataloader_num_workers,
pin_memory=self.args.dataloader_pin_memory,
)
def compute_loss(
self,
model,
inputs,
return_outputs=False,
num_items_in_batch=None,
):
outputs = model(**inputs)
loss = outputs["loss"]
acc = outputs.get("acc", None)
# ⭐ 关键：只在 logging_steps 那一步算 + log
if (
self.state.global_step > 0
and self.args.logging_steps > 0
and self.state.global_step % self.args.logging_steps == 0
):
# ⚠️ 用 self.log，而不是 print
self.log({"train_accuracy": acc.item(), "step": self.state.global_step})
if return_outputs:
return loss, outputs
return loss
@register_task("cosyvoice2")
class CosyVoice2Task(BaseTask):
def build_dataset(self):
dataset_cfg = self.config["datasets"]
dataset_cfg = OmegaConf.to_container(dataset_cfg, resolve=True)
train_split = dataset_cfg.pop("train_file")
valid_split = dataset_cfg.pop("valid_file")
train_dataset = CosyVoiceDataset(**dataset_cfg, split=train_split)
eval_dataset = CosyVoiceDataset(**dataset_cfg, split=valid_split)
print(f"train dataset size: {len(train_dataset)}")
print(f"eval dataset size: {len(eval_dataset)}")
return train_dataset, eval_dataset
def build_collator(self):
collator_cfg = self.config.get("collator", {})
return CosyVoiceCollator(**collator_cfg)
def build_model(self):
model_cfg = OmegaConf.to_container(self.config.get("model", {}), resolve=True)
pre_ckpt = model_cfg.pop("pretrained_path", "")
model = CosyVoice2Model(**model_cfg)
if os.path.exists(pre_ckpt):
state = torch.load(
pre_ckpt,
map_location="cpu",
)
model.model.load_state_dict(state)
print(f"load pretrained ckpt: {pre_ckpt}")
return model
def build_training_args(self):
args_cfg = self.config.get("trainer", {})
sampler_cfg = self.config.get("sampler", {}) # 透传到 Trainer
return TrainingArguments(**args_cfg), sampler_cfg
def build_trainer(self):
trainer = CustomTrainer(
model=self.model,
args=self.training_args,
sampler_cfg=self.sampler_cfg,
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
data_collator=self.collator,
compute_metrics=compute_metrics,
)
return trainer
def run(self):
# 构建组件
self.train_dataset, self.eval_dataset = self.build_dataset()
self.collator = self.build_collator()
self.model = self.build_model()
self.training_args, self.sampler_cfg = self.build_training_args()
self.trainer = self.build_trainer()
# 启动训练
print(f"train args: {self.trainer.args}", flush=True)
self.trainer.train()
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