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train4.py

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+import json
+import pdb
+from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
+from transformers.models.auto.modeling_auto import MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES
+import copy
+from transformers.modeling_outputs import (
+    MoeCausalLMOutputWithPast,
+    MoeModelOutputWithPast,
+)
+from collections import defaultdict
+import numpy as np
+import math
+from torch import nn
+# import pandas as pd
+from transformers.cache_utils import Cache, DynamicCache, StaticCache
+from dataclasses import dataclass
+# from transformers.models.olmoe.configuration_olmoe import OlmoeConfig
+# from transformers.models.olmoe.modeling_olmoe import OlmoeMLP, OlmoeAttention, OlmoeFlashAttention2, OlmoeSdpaAttention, OlmoeRMSNorm, OlmoeSparseMoeBlock, apply_rotary_pos_emb, repeat_kv, OlmoeRotaryEmbedding
+import os
+import sys
+import torch.distributed as dist
+from tqdm import tqdm
+from torch.utils.data import DataLoader
+from torch.utils.data.distributed import DistributedSampler
+import transformers
+import pickle
+
+# from transformers.models.olmoe.configuration_olmoe import OlmoeConfig
+from dataset import *
+# from utils import flash_attn_forward, flash_attn_prepare_decoder_attention_mask, get_multiround_data
+# from peft import (get_peft_model, PeftModel)
+import random
+# from config import *
+from datasets import Dataset, DatasetDict, load_dataset
+import wandb
+import gc
+import os
+import argparse
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import functools
+from torch.optim.lr_scheduler import StepLR
+import torch.nn.functional as F
+import torch.distributed as dist
+import torch.multiprocessing as mp
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data.distributed import DistributedSampler
+
+from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
+    checkpoint_wrapper, CheckpointImpl)
+
+from torch.distributed.fsdp import (
+    FullyShardedDataParallel as FSDP,
+    MixedPrecision,
+    BackwardPrefetch,
+    ShardingStrategy,
+    FullStateDictConfig,
+    StateDictType,
+)
+from torch.distributed.fsdp.wrap import (
+    transformer_auto_wrap_policy,
+    enable_wrap,
+    wrap,
+)
+from functools import partial
+from torch.utils.data import DataLoader
+from pathlib import Path
+from typing import Type, List, Optional, Tuple, Union
+from modelforseminat_v5 import *
+from transformers import get_cosine_schedule_with_warmup
+# from torch.optim.lr_scheduler import _LRScheduler
+
+# class WarmupCosineScheduler(_LRScheduler):
+
+#     def __init__(self,
+#                  optimizer,
+#                  warmup_steps,
+#                  total_steps,
+#                  min_lr=0.0,
+#                  last_epoch=-1):
+#         # self.warmup_steps = warmup_steps
+#         self.total_steps = total_steps
+#         self.min_lr = min_lr
+#         if isinstance(warmup_steps, float) and 0 < warmup_steps < 1:
+#             self.warmup_steps = int(warmup_steps * total_steps)
+#         else:
+#             self.warmup_steps = int(warmup_steps)
+#         super().__init__(optimizer, last_epoch)
+
+#     def get_lr(self):
+#         step = self.last_epoch + 1
+#         lrs = []
+
+#         for base_lr in self.base_lrs:
+#             if step < self.warmup_steps:
+#                 # Linear warmup
+#                 lr = base_lr * step / self.warmup_steps
+#             else:
+#                 # Cosine decay
+#                 progress = (step - self.warmup_steps) / max(
+#                     1, self.total_steps - self.warmup_steps)
+#                 cosine_decay = 0.5 * (1 + math.cos(math.pi * progress))
+#                 lr = self.min_lr + (base_lr - self.min_lr) * cosine_decay
+
+#             lrs.append(lr)
+
+#         return lrs
+
+
+
+
+
+
+################################# FSDP Config #####################################
+def setup():
+    # initialize the process group
+    local_rank = int(os.environ['LOCAL_RANK'])
+    torch.cuda.set_device(local_rank)
+    dist.init_process_group(
+          backend='nccl',
+          init_method='env://',
+           )
+
+
+def cleanup():
+    gc.collect()
+    torch.cuda.empty_cache()
+    dist.destroy_process_group()
+
+
+def get_fsdp_device():
+    # 每个进程初始化分布式环境后调用
+    local_rank = int(os.environ.get("LOCAL_RANK", 0))  # torchrun 自动设置
+    device = torch.device(f"cuda:{local_rank}")
+    torch.cuda.set_device(device)
+    return device
+
+
+# def load_trained_model(model_name):
+#     DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+#     olmo_path = "/AIRvePFS/ai4science/users/ai4science/users/zyk/seminat_backup/model/OLMo-2-0425-1B"
+#     pt_path = "/AIRvePFS/ai4science/users/ai4science/users/zyk/seminat/ckp/sft-v4-0616-1w-1e3-chunklimit5-jueduipos/sft-v4-1e3-len4-fc-chunklimit4-jueduipos-epoch_136.pt"
+#     config_path = "/AIRvePFS/ai4science/users/ai4science/users/zyk/seminat_backup/model/OLMo-2-0425-1B/config.json"
+
+
+#     config = AutoConfig.from_pretrained(olmo_path)
+#     model = Olmo2ForCausalLMForSemiNAT.from_pretrained(olmo_path,
+#                                                     config=config,
+#                                                     torch_dtype=torch.bfloat16)
+#     state_dict = torch.load(pt_path, map_location=DEVICE, weights_only=True)
+#     missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
+#     print(
+#         f"Loaded with {len(missing_keys)} missing keys and {len(unexpected_keys)} unexpected keys."
+#     )
+#     if missing_keys:
+#         print("Missing keys:", missing_keys)
+#     if unexpected_keys:
+#         print("Unexpected keys:", unexpected_keys)
+
+#     model = model.to(DEVICE)
+
+#     tokenizer = AutoTokenizer.from_pretrained(olmo_path)
+
+#     return model, tokenizer
+
+
+
+# def setup_model(model_name,device):
+#     model = Olmo2ForCausalLMForSemiNAT.from_pretrained(model_name,torch_dtype=torch.bfloat16,device_map=device)
+#     tokenizer = AutoTokenizer.from_pretrained(model_name)
+#     # config = AutoConfig.from_pretrained(model_name)
+#     # model = Olmo2ForCausalLMForSemiNAT(config)  # 注意这里不用 from_pretrained
+#     # tokenizer = AutoTokenizer.from_pretrained(model_name)
+#     return model, tokenizer
+
+def setup_model(model_name, type):
+    # pdb.set_trace()
+    if type == "bf16":
+        model = Olmo2ForCausalLMForSemiNAT.from_pretrained(
+            model_name, 
+            torch_dtype=torch.bfloat16
+        )
+    elif type == "fp16":
+        model = Olmo2ForCausalLMForSemiNAT.from_pretrained(
+            model_name, 
+            torch_dtype=torch.float16
+        )
+    else:
+        model = Olmo2ForCausalLMForSemiNAT.from_pretrained(
+            model_name
+        )
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    # config = AutoConfig.from_pretrained(model_name)
+    # model = Olmo2ForCausalLMForSemiNAT(config)  # 注意这里不用 from_pretrained
+    # tokenizer = AutoTokenizer.from_pretrained(model_name)
+    return model, tokenizer
+
+def collate_fn(batch):
+    # 过滤 None
+    batch = [x for x in batch if x is not None]
+    if len(batch) == 0:
+        return None  # 如果整 batch 都无效
+
+    input_ids, labels, attention_mask, slice_arr, slice_label = zip(*batch)
+
+    return (
+        torch.stack(input_ids),
+        torch.stack(labels),
+        torch.stack(attention_mask),
+        torch.stack(slice_arr),
+        torch.stack(slice_label)
+    )
+
+def fsdp_main(args):
+    local_rank = int(os.environ['LOCAL_RANK'])
+    rank = int(os.environ['RANK'])
+    world_size = int(os.environ['WORLD_SIZE'])
+    if args.use_wandb and rank == 0:
+        wandb.init(entity="SemiNAT", project="SemiNAT-Debug", name=args.run_name)
+
+    local_rank = int(os.environ['LOCAL_RANK'])
+    device = f"cuda:{local_rank}"
+    
+    # model, tokenizer = setup_model(args.model_path, args.dtype, device)
+    model, tokenizer = setup_model(args.model_path,device)
+
+    # model, tokenizer = load_trained_model(args.model_path)
+
+    model.config.chunk_size_limit = args.chunk_size_limit
+
+
+
+    # if ".pkl" in args.data_path:
+    #     train_dataset = pickle.load(open(args.data_path, "rb"))
+    # else:
+    #     datasets = pd.read_parquet(args.data_path)
+    #     train_dataset = eval(f"{args.data_type}")(
+    #         tokenizer,
+    #         datasets,  
+    #         args.max_length,  
+    #         args.data_processess_num)
+
+    # train_sampler = DistributedSampler(train_dataset,
+    #                                    rank=rank,
+    #                                    num_replicas=world_size,
+    #                                    shuffle=True)
+    # train_dataloader = DataLoader(dataset=train_dataset,
+    #                               sampler=train_sampler,
+    #                               batch_size=args.batch_size)
+
+
+    train_dataset = eval(f"{args.data_type}")(
+        tokenizer,
+        args.data_path,
+        args.max_length
+    )
+    train_sampler = DistributedSampler(train_dataset,
+                                   rank=rank,
+                                   num_replicas=world_size,
+                                   shuffle=True)
+
+    train_dataloader = DataLoader(dataset=train_dataset,
+                                sampler=train_sampler,
+                                batch_size=args.batch_size,
+                                num_workers=args.data_processess_num,
+                                collate_fn=collate_fn)
+
+    # pdb.set_trace()
+
+    print(f"Size of train dataset: {len(train_dataset)}")
+
+    setup()
+
+    # Olmo2DecoderLayerForSemiNAT_auto_wrap_policy = functools.partial(
+    #     transformer_auto_wrap_policy,
+    #     transformer_layer_cls={
+    #         Olmo2DecoderLayerForSemiNAT,    
+    #         NATEncoderForSemiNAT,
+    #         NATDecoderForSemiNAT,
+    #     })
+
+    Olmo2DecoderLayerForSemiNAT_auto_wrap_policy = functools.partial(
+        transformer_auto_wrap_policy,
+        transformer_layer_cls={
+            Olmo2DecoderLayer,          
+            Olmo2DecoderLayerForSemiNAT
+            }
+        )
+
+    
+
+    sharding_strategy: ShardingStrategy = ShardingStrategy.FULL_SHARD  #for Zero2 and FULL_SHARD for Zero3
+    torch.cuda.set_device(local_rank)
+    # local_rank = int(os.environ['LOCAL_RANK'])
+    # device = torch.device(f"cuda:{local_rank}")
+    # model = model.to(device)
+
+    # if bf16_ready:
+    mp_policy = MixedPrecision(
+        param_dtype=torch.bfloat16,
+        reduce_dtype=torch.bfloat16,
+        buffer_dtype=torch.bfloat16,
+    )
+    # else:
+    # mp_policy = None  # defaults to fp32
+
+    # if args.use_lora:
+    #     model = get_peft_model(model, lora_config)
+
+    # pdb.set_trace()
+    # model is on CPU before input to FSDP
+    model = FSDP(model,
+                 auto_wrap_policy=Olmo2DecoderLayerForSemiNAT_auto_wrap_policy,
+                 mixed_precision=mp_policy,
+                 sharding_strategy=sharding_strategy,
+                 device_id=torch.cuda.current_device(),
+                 use_orig_params=True)
+
+    optimizer = optim.AdamW(
+        model.parameters(),
+        lr=args.lr,
+        betas=args.betas,
+        weight_decay=args.weight_decay,
+        eps=args.eps,
+    )
+
+    # pdb.set_trace()
+
+    # scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
+    # scheduler = WarmupCosineScheduler(
+    #     optimizer=optimizer,  # 优化器对象
+    #     warmup_steps=args.warmup_steps,  # warmup 步数（或比例）
+    #     total_steps=args.total_steps,  # 总训练步数
+    #     min_lr=args.min_lr  # 最小学习率
+    # )
+
+
+     
+    num_training_steps = args.epochs * len(train_dataloader) # 总训练步数
+    num_warmup_steps = num_training_steps * args.warmup_ratio
+
+    scheduler = get_cosine_schedule_with_warmup(
+        optimizer,
+        num_warmup_steps=num_warmup_steps,
+        num_training_steps=num_training_steps
+    )
+
+    torch.autograd.set_detect_anomaly(True)
+
+    loss1_list = []
+    loss2_list = []
+    loss_list = []
+
+    global_step = 0
+
+
+
+    start_time = time.time()
+
+    for epoch in range(1, args.epochs + 1):
+        # t0 = time.time()
+        model.train()
+        local_rank = int(os.environ['LOCAL_RANK'])  
+        # fsdp_loss = torch.zeros(2).to(local_rank)
+
+        if train_sampler:
+            train_sampler.set_epoch(epoch)
+        if rank == 0:
+            inner_pbar = tqdm(range(len(train_dataloader)),
+                              colour="blue",
+                              desc="r0 Training Epoch")
+        
+        memories = []
+        
+        for batch in train_dataloader:
+            if batch is None:
+                continue
+            optimizer.zero_grad()
+            loss1, loss2 = model(input_ids=batch[0],
+                                 labels=batch[1],
+                                 attention_mask=batch[2],
+                                 slice_pos=batch[3],
+                                 slice_label=batch[4],
+                                 use_cache=False).loss
+            loss = loss1 + loss2
+            # loss = loss2
+            loss1_list.append(loss1.item())
+            loss2_list.append(loss2.item())
+            loss_list.append(loss.item())
+            # pdb.set_trace()
+
+            # if torch.isnan(loss):
+            #     print(f"Step {global_step}: loss is NaN, entering pdb …")
+            #     pdb.set_trace()
+
+            # print(f"loss1:{loss1},loss2:{loss2}")
+            loss.backward()
+
+            # 按参数计算
+            # for name, module in model.named_modules():
+            #     total_norm = 0.0
+            #     param_count = 0
+            #     for param in module.parameters(recurse=False):
+            #         if param.grad is not None:
+            #             total_norm += param.grad.data.norm(2).item()**2
+            #             param_count += 1
+            #     if param_count > 0:
+            #         if args.use_wandb and rank == 0:
+            #             total_norm = total_norm**0.5
+            #             wandb.log({f"grad_norm/{name}": total_norm},
+            #                     step=global_step)
+
+
+            optimizer.step()
+
+            
+            mem = torch.cuda.memory_allocated() / (1024 ** 2)
+            memories.append(mem)
+
+            global_step += 1
+
+            if global_step % args.save_steps == 0:
+                save_policy = FullStateDictConfig(offload_to_cpu=True,
+                                                  rank0_only=True)
+                with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT,
+                                          save_policy):
+                    cpu_state = model.state_dict()
+
+                if rank == 0:
+                    print(f"--> steps: {str(global_step)} saving model ...")
+                    if not os.path.exists(args.save_path):
+                        os.makedirs(args.save_path)
+                    save_name = f"{args.save_name}-steps_{str(global_step)}.pt"
+                    print(f"--> saving as model name {save_name}")
+                    save_path = os.path.join(args.save_path, save_name)
+                    torch.save(cpu_state, save_path)
+
+            if rank == 0:
+                inner_pbar.update(1)
+                if args.use_wandb and rank == 0:
+                    wandb.log({
+                        "length prediction loss":
+                        sum(loss1_list[-20:]) / len(loss1_list[-20:]),
+                        "nat loss":
+                        sum(loss2_list[-20:]) / len(loss2_list[-20:]),
+                        "loss":
+                        sum(loss_list[-20:]) / len(loss_list[-20:]),
+                        "lr": scheduler.get_last_lr()[0]
+                    })
+
+
+        avg_mem = sum(memories) / len(memories)
+        print(f"Average memory usage over {len(memories)} steps: {avg_mem:.2f} MB")
+
+
+        dist.all_reduce(loss, op=dist.ReduceOp.SUM)
+
+        if rank == 0:
+            inner_pbar.close()
+
+        scheduler.step()
+
+        # if rank == 0:
+        #     print(f"--> entering save model state")
+
+        # save_policy = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
+        # with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT,
+        #                           save_policy):
+        #     cpu_state = model.state_dict()
+
+        # if rank == 0:
+        #     print(f"--> epoch: {str(epoch)} saving model ...")
+        #     if not os.path.exists(args.save_path):
+        #         os.makedirs(args.save_path)
+        #     save_name = f"{args.save_name}-epoch_{str(epoch)}.pt"
+        #     print(f"--> saving as model name {save_name}")
+        #     save_path = os.path.join(args.save_path, save_name)
+        #     torch.save(cpu_state, save_path)
+
+
+    end_time = time.time()
+    print(f"Training time: {end_time - start_time} seconds")
+
+    dist.barrier()
+    cleanup()
+
+
+################################# FSDP Config #####################################
+
+if __name__ == "__main__":
+    # Training settings
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--batch-size',
+                        type=int,
+                        default=4,
+                        metavar='N',
+                        help='input batch size for training (default: 64)')
+    parser.add_argument('--model_path', type=str)
+    parser.add_argument('--save_path', type=str)
+    parser.add_argument('--save_name', type=str)
+    parser.add_argument('--data_path', type=str)
+    parser.add_argument('--data_type', type=str)
+    parser.add_argument('--run_name', type=str)
+    parser.add_argument('--max_length', type=int)
+    parser.add_argument('--chunk_size_limit', type=int)
+    parser.add_argument('--save_steps', type=int, default=5000)
+    parser.add_argument('--data_processess_num', type=int, default=8)
+    parser.add_argument('--epochs',
+                        type=int,
+                        default=2,
+                        metavar='N',
+                        help='number of epochs to train (default: 3)')
+    parser.add_argument('--lr',
+                        type=float,
+                        default=.002,
+                        metavar='LR',
+                        help='learning rate (default: .002)')
+    parser.add_argument('--weight_decay', type=float)
+    parser.add_argument('--betas', type=float, nargs=2)
+    parser.add_argument('--eps', type=float)
+    parser.add_argument('--warmup_ratio', type=float)
+    parser.add_argument('--seed',
+                        type=int,
+                        default=1,
+                        metavar='S',
+                        help='random seed (default: 1)')
+    parser.add_argument('--use_lora', action='store_true', default=False)
+    parser.add_argument("--use_wandb",
+                        action="store_true",
+                        help="whether to use wandb")
+    parser.add_argument('--dtype', type=str)
+    args = parser.parse_args()
+
+    torch.manual_seed(args.seed)
+
+    fsdp_main(args)