230 lines
8.9 KiB
Python
230 lines
8.9 KiB
Python
import os
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import platform
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import argparse
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import time
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import math
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import warnings
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import json
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import pandas as pd
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import torch
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import torch.nn.functional as F
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import torch.distributed as dist
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from contextlib import nullcontext
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from torch import optim
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from torch.nn.parallel import DistributedDataParallel
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from torch.utils.data import DataLoader, DistributedSampler
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from transformers import AutoTokenizer, AutoModel
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from model.model import Transformer
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from model.LMConfig import LMConfig
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from model.dataset import SFTDataset
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from model.vision_utils import get_vision_model, get_img_embedding
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warnings.filterwarnings('ignore')
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def count_parameters(model):
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return sum(p.numel() for p in model.parameters() if p.requires_grad)
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def Logger(content):
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if not ddp or dist.get_rank() == 0:
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print(content)
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def get_lr(it, all):
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warmup_iters = args.warmup_iters
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lr_decay_iters = all
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min_lr = args.learning_rate / 10
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if it < warmup_iters:
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return args.learning_rate * it / warmup_iters
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if it > lr_decay_iters:
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return min_lr
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decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters)
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assert 0 <= decay_ratio <= 1
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coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))
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return min_lr + coeff * (args.learning_rate - min_lr)
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def train_epoch(epoch, wandb):
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start_time = time.time()
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for step, (X, Y, loss_mask, image_process) in enumerate(train_loader):
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X = X.to(args.device)
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Y = Y.to(args.device)
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loss_mask = loss_mask.to(args.device)
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image_process = image_process.to(args.device)
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image_encoders = get_img_embedding(image_process, vision_model)
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lr = get_lr(epoch * iter_per_epoch + step, args.epochs * iter_per_epoch)
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for param_group in optimizer.param_groups:
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param_group['lr'] = lr
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with ctx:
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logits = model(X, Y, image_encoders=image_encoders).logits
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loss = F.cross_entropy(logits.view(-1, logits.size(-1)), Y.view(-1), ignore_index=0, reduction='none')
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loss_mask = loss_mask.view(-1)
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loss = torch.sum(loss * loss_mask) / loss_mask.sum()
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scaler.scale(loss).backward()
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if (step + 1) % args.accumulation_steps == 0:
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scaler.unscale_(optimizer)
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torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
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scaler.step(optimizer)
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scaler.update()
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optimizer.zero_grad(set_to_none=True)
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if step % args.log_interval == 0:
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spend_time = time.time() - start_time
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Logger(
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'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.7f} epoch_Time:{}min:'.format(
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epoch,
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args.epochs,
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step,
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iter_per_epoch,
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loss.item(),
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optimizer.param_groups[-1]['lr'],
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spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60))
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if (wandb is not None) and (not ddp or dist.get_rank() == 0):
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wandb.log({"loss": loss,
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"lr": optimizer.param_groups[-1]['lr'],
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"epoch_Time": spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60})
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if (step + 1) % args.save_interval == 0 and (not ddp or dist.get_rank() == 0):
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model.eval()
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moe_path = '_moe' if lm_config.use_moe else ''
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ckp = f'{args.save_dir}/{lm_config.dim}{moe_path}_vlm_sft.pth'
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if isinstance(model, torch.nn.parallel.DistributedDataParallel):
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state_dict = model.module.state_dict()
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else:
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state_dict = model.state_dict()
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torch.save(state_dict, ckp)
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model.train()
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def init_model(lm_config):
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tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer')
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moe_path = '_moe' if lm_config.use_moe else ''
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ckp = f'./out/{lm_config.dim}{moe_path}_vlm_pretrain.pth'
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model = Transformer(lm_config)
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state_dict = torch.load(ckp, map_location=args.device)
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# 处理不需要的前缀
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unwanted_prefix = '_orig_mod.'
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for k, v in list(state_dict.items()):
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if k.startswith(unwanted_prefix):
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state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
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# 加载到模型中
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model.load_state_dict(state_dict, strict=False)
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model = model.to(args.device)
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print(f'模型可学习参数: {count_parameters(model) / 1e6} 百万 = {count_parameters(model) / 1e9} B (Billion)')
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(vision_model, preprocess) = get_vision_model()
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vision_model = vision_model.to(args.device)
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return model, tokenizer, (vision_model, preprocess)
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def init_distributed_mode():
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if not ddp: return
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global ddp_local_rank, DEVICE
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dist.init_process_group(backend="nccl")
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ddp_rank = int(os.environ["RANK"])
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ddp_local_rank = int(os.environ["LOCAL_RANK"])
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ddp_world_size = int(os.environ["WORLD_SIZE"])
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DEVICE = f"cuda:{ddp_local_rank}"
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torch.cuda.set_device(DEVICE)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="MiniMind-V-SFT")
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parser.add_argument("--out_dir", type=str, default="out", help="Output directory")
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parser.add_argument("--epochs", type=int, default=19, help="Number of epochs")
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parser.add_argument("--batch_size", type=int, default=32, help="Batch size")
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parser.add_argument("--learning_rate", type=float, default=1e-6, help="Learning rate")
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parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu",
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help="Device to use")
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parser.add_argument("--dtype", type=str, default="bfloat16", help="Data type")
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parser.add_argument("--use_wandb", default=False, action="store_true", help="Use Weights & Biases")
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parser.add_argument("--wandb_project", type=str, default="MiniMind-V", help="Weights & Biases project name")
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parser.add_argument("--num_workers", type=int, default=8, help="Number of workers for data loading")
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parser.add_argument("--data_path", type=str, default="./dataset/LLaVA-Instruct/llava_instruct_230k.json",
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help="Path to training data")
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parser.add_argument("--ddp", action="store_true", help="Use DistributedDataParallel")
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parser.add_argument("--accumulation_steps", type=int, default=1, help="Gradient accumulation steps")
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parser.add_argument("--grad_clip", type=float, default=1.0, help="Gradient clipping threshold")
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parser.add_argument("--warmup_iters", type=int, default=0, help="Number of warmup iterations")
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parser.add_argument("--log_interval", type=int, default=100, help="Logging interval")
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parser.add_argument("--save_interval", type=int, default=100, help="Model saving interval")
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parser.add_argument('--local_rank', type=int, default=-1, help='local rank for distributed training')
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args = parser.parse_args()
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lm_config = LMConfig()
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max_seq_len = lm_config.max_seq_len
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args.save_dir = os.path.join(args.out_dir)
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os.makedirs(args.save_dir, exist_ok=True)
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os.makedirs(args.out_dir, exist_ok=True)
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tokens_per_iter = args.batch_size * max_seq_len
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torch.manual_seed(1337)
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device_type = "cuda" if "cuda" in args.device else "cpu"
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args.wandb_run_name = f"MiniMind-V-SFT-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LearningRate-{args.learning_rate}"
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ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast()
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ddp = int(os.environ.get("RANK", -1)) != -1 # is this a ddp run?
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ddp_local_rank, DEVICE = 0, "cuda:0"
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if ddp:
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init_distributed_mode()
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args.device = torch.device(DEVICE)
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if args.use_wandb and (not ddp or ddp_local_rank == 0):
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import wandb
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wandb.init(project=args.wandb_project, name=args.wandb_run_name)
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else:
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wandb = None
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model, tokenizer, (vision_model, preprocess) = init_model(lm_config)
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use_version = 0
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train_ds = SFTDataset(args.data_path, tokenizer, vision_model=(vision_model, preprocess),
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image_special_token=lm_config.image_special_token,
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max_length=max_seq_len)
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train_sampler = DistributedSampler(train_ds) if ddp else None
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train_loader = DataLoader(
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train_ds,
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batch_size=args.batch_size,
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pin_memory=True,
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drop_last=False,
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shuffle=False,
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num_workers=args.num_workers,
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sampler=train_sampler
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)
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scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype in ['float16', 'bfloat16']))
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optimizer = optim.Adam(model.parameters(), lr=args.learning_rate)
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if False and not lm_config.use_moe and platform.system() != 'Windows' and float(
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torch.__version__.split('.')[0]) >= 2:
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Logger("compiling the model... (takes a ~minute)")
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unoptimized_model = model
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model = torch.compile(model)
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if ddp:
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model._ddp_params_and_buffers_to_ignore = {"pos_cis"}
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model = DistributedDataParallel(model, device_ids=[ddp_local_rank])
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iter_per_epoch = len(train_loader)
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for epoch in range(args.epochs):
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train_epoch(epoch, wandb)
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