train_code

eval/eval.sh

@@ -5,7 +5,4 @@ SAVE_NAME=$2
 
 
 
-python -m torch.distributed.launch --use-env     --nproc_per_node ${NPROC_PER_NODE:-1}     --nnodes ${WORLD_SIZE:-1}     --node_rank ${RANK:-0} --master_addr ${MASTER_ADDR:-127.0.0.1}     --master_port ${MASTER_PORT:-12345}     eval/evaluate_vqa.py     --checkpoint $EVAL_PTH         --batch-size 4        --num-workers 2 --save_name $SAVE_NAME
-
+python -m torch.distributed.launch --use-env     --nproc_per_node ${NPROC_PER_NODE:-1}     --nnodes ${WORLD_SIZE:-1}     --node_rank ${RANK:-0} --master_addr ${MASTER_ADDR:-127.0.0.1}     --master_port ${MASTER_PORT:-12345}     eval/evaluate_vqa.py     --checkpoint $EVAL_PTH         --batch-size 4        --num-workers 2 --save_name $SAVE_NAME
\ No newline at end of file
-
-

finetune/ds_config_zero2.json

+{
+    "fp16": {
+        "enabled": "auto",
+        "loss_scale": 0,
+        "loss_scale_window": 1000,
+        "initial_scale_power": 16,
+        "hysteresis": 2,
+        "min_loss_scale": 1
+    },
+    "bf16": {
+        "enabled": "auto"
+    },
+    "optimizer": {
+        "type": "AdamW",
+        "params": {
+            "lr": "auto",
+            "betas": "auto",
+            "eps": "auto",
+            "weight_decay": "auto"
+        }
+    },
+    "scheduler": {
+        "type": "WarmupLR",
+        "params": {
+            "warmup_min_lr": "auto",
+            "warmup_max_lr": "auto",
+            "warmup_num_steps": "auto"
+        }
+    },
+
+    "zero_optimization": {
+        "stage": 2,
+        "offload_optimizer": {
+            "device": "none",
+            "pin_memory": true
+        },
+        "allgather_partitions": true,
+        "allgather_bucket_size": 2e8,
+        "overlap_comm": true,
+        "reduce_scatter": true,
+        "reduce_bucket_size": 2e8,
+        "contiguous_gradients": true
+    },
+
+    "gradient_accumulation_steps": "auto",
+    "gradient_clipping": "auto",
+    "steps_per_print": 100,
+    "train_batch_size": "auto",
+    "train_micro_batch_size_per_gpu": "auto",
+    "wall_clock_breakdown": false
+}
\ No newline at end of file

finetune/finetune_ds_debug.sh

+#!/bin/bash
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+DIR=`pwd`
+
+GPUS_PER_NODE=8
+NNODES=1
+NODE_RANK=0
+MASTER_ADDR=localhost
+MASTER_PORT=6001
+
+MODEL="Qwen/Qwen-VL" # We use the first version of Qwen-VL
+# ATTENTION: specify the path to your training data, which should be a json file consisting of a list of conversations.
+# See the section for finetuning in README for more information.
+DATA="pathto/data"
+
+DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE \
+    --nnodes $NNODES \
+    --node_rank $NODE_RANK \
+    --master_addr $MASTER_ADDR \
+    --master_port $MASTER_PORT"
+
+
+torchrun $DISTRIBUTED_ARGS finetune_multitask.py \
+    --model_name_or_path $MODEL \
+    --data_path $DATA \
+    --bf16 True \
+    --fix_vit True \
+    --output_dir output_model \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 \
+    --gradient_accumulation_steps 16 \
+    --evaluation_strategy "no" \
+    --save_strategy "steps" \
+    --save_steps 1000 \
+    --save_total_limit 10 \
+    --learning_rate 1e-5 \
+    --weight_decay 0.1 \
+    --adam_beta2 0.95 \
+    --warmup_ratio 0.02 \
+    --lr_scheduler_type "cosine" \
+    --logging_steps 1 \
+    --report_to "none" \
+    --model_max_length 2048 \
+    --gradient_checkpointing \
+    --lazy_preprocess True \
+    --deepspeed finetune/ds_config_zero2.json
+
+
+

finetune_multitask.py

+# This code is based on the revised code from fastchat based on tatsu-lab/stanford_alpaca.
+
+
+from dataclasses import dataclass, field
+import json
+import math
+import logging
+import os
+from typing import Dict, Optional, List
+import torch
+from torch.utils.data import Dataset
+from deepspeed import zero
+from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
+import transformers
+from transformers import Trainer, GPTQConfig, deepspeed
+from transformers.trainer_pt_utils import LabelSmoother
+from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
+from accelerate.utils import DistributedType
+from monkey_model.modeling_monkey import MonkeyLMHeadModel
+from monkey_model.tokenization_qwen import QWenTokenizer
+from monkey_model.configuration_monkey import MonkeyConfig
+IGNORE_TOKEN_ID = LabelSmoother.ignore_index
+
+
+@dataclass
+class ModelArguments:
+    model_name_or_path: Optional[str] = field(default="")
+
+
+@dataclass
+class DataArguments:
+    data_path: str = field(
+        default=None, metadata={"help": "Path to the training data."}
+    )
+    eval_data_path: str = field(
+        default=None, metadata={"help": "Path to the evaluation data."}
+    )
+    lazy_preprocess: bool = False
+
+
+@dataclass
+class TrainingArguments(transformers.TrainingArguments):
+    cache_dir: Optional[str] = field(default=None)
+    optim: str = field(default="adamw_torch")
+    model_max_length: int = field(
+        default=8192,
+        metadata={
+            "help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."
+        },
+    )
+    use_lora: bool = False
+    fix_vit: bool = True
+
+
+@dataclass
+class LoraArguments:
+    lora_r: int = 16
+    lora_alpha: int = 32
+    lora_dropout: float = 0.05
+    lora_target_modules: List[str] = field(
+        default_factory=lambda: ["in_proj","out_proj","c_fc"] ##["in_proj","out_proj","c_fc"]
+    )
+    lora_weight_path: str = ""
+    lora_bias: str = "none"
+    q_lora: bool = False
+
+
+def maybe_zero_3(param):
+    if hasattr(param, "ds_id"):
+        assert param.ds_status == ZeroParamStatus.NOT_AVAILABLE
+        with zero.GatheredParameters([param]):
+            param = param.data.detach().cpu().clone()
+    else:
+        param = param.detach().cpu().clone()
+    return param
+
+
+# Borrowed from peft.utils.get_peft_model_state_dict
+def get_peft_state_maybe_zero_3(named_params, bias):
+    if bias == "none":
+        to_return = {k: t for k, t in named_params if "lora_" in k}
+    elif bias == "all":
+        to_return = {k: t for k, t in named_params if "lora_" in k or "bias" in k}
+    elif bias == "lora_only":
+        to_return = {}
+        maybe_lora_bias = {}
+        lora_bias_names = set()
+        for k, t in named_params:
+            if "lora_" in k:
+                to_return[k] = t
+                bias_name = k.split("lora_")[0] + "bias"
+                lora_bias_names.add(bias_name)
+            elif "bias" in k:
+                maybe_lora_bias[k] = t
+        for k, t in maybe_lora_bias:
+            if bias_name in lora_bias_names:
+                to_return[bias_name] = t
+    else:
+        raise NotImplementedError
+    to_return = {k: maybe_zero_3(v) for k, v in to_return.items()}
+    return to_return
+
+local_rank = None
+
+def rank0_print(*args):
+    if local_rank == 0:
+        print(*args)
+
+
+def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output_dir: str, bias="none"):
+    """Collects the state dict and dump to disk."""
+    # check if zero3 mode enabled
+    if deepspeed.is_deepspeed_zero3_enabled():
+        state_dict = trainer.model_wrapped._zero3_consolidated_16bit_state_dict()
+    else:
+        state_dict = trainer.model.state_dict()
+    if trainer.args.should_save and trainer.args.local_rank == 0:
+        trainer._save(output_dir, state_dict=state_dict)
+
+
+
+def format_tokenizer(tokenizer, message, return_target=False, label=False):
+    _input_ids = tokenizer(message).input_ids
+    input_ids =  _input_ids 
+    if return_target:
+        if label:
+            target = input_ids
+        else:
+            target =  [IGNORE_TOKEN_ID] * (len(_input_ids)) 
+        return input_ids, target
+    else:
+        return input_ids
+
+def preprocess(
+               source,
+               tokenizer,
+               max_len,
+               system_message: str = "You are a helpful assistant.",
+               padding=True
+               ):
+
+
+    # Apply prompt templates
+    input_ids, targets = [], []
+    user, assistant = source[0], source[1]
+
+    user_input =  user['value']
+    assistant_input =  assistant['value']
+    message_l = [user_input, assistant_input]
+    for i, message in enumerate(message_l):
+        try:
+            _input_ids, _target = format_tokenizer(tokenizer, message, return_target=True, label=True if i == len(message_l) - 1 else False)  # <img> 有些text会有img标签，所以使用<img>作为特殊id有问题，标签数量不对等会报错
+        except Exception as e:
+            print(e)
+            continue
+        input_ids += _input_ids
+        targets += _target
+
+        assert len(_input_ids) == len(_input_ids)
+    if padding:
+
+        input_ids += [-1]+[tokenizer.pad_token_id] * (max_len - len(input_ids)-1)
+
+        targets += [tokenizer.pad_token_id] +[IGNORE_TOKEN_ID] * (max_len - len(targets)-1)
+        targets = targets[:max_len]
+        input_ids = input_ids[:max_len]
+        
+
+    input_ids = torch.tensor(input_ids, dtype=torch.int)
+    targets = torch.tensor(targets, dtype=torch.int)
+    attention_mask=input_ids.ne(tokenizer.pad_token_id)
+    input_ids[input_ids == -1 ] = tokenizer.pad_token_id
+    return dict(
+        input_ids=input_ids,
+        labels=targets,
+        attention_mask=attention_mask,
+    )
+
+class SupervisedDataset(Dataset):
+    """Dataset for supervised fine-tuning."""
+
+    def __init__(self, raw_data, tokenizer: transformers.PreTrainedTokenizer, max_len: int):
+        super(SupervisedDataset, self).__init__()
+
+        rank0_print("Formatting inputs...")
+        sources = [example["conversations"] for example in raw_data]
+        data_dict = preprocess(sources, tokenizer, max_len)
+
+        self.input_ids = data_dict["input_ids"]
+        self.labels = data_dict["labels"]
+        self.attention_mask = data_dict["attention_mask"]
+
+    def __len__(self):
+        return len(self.input_ids)
+
+    def __getitem__(self, i) -> Dict[str, torch.Tensor]:
+        return dict(
+            input_ids=self.input_ids[i],
+            labels=self.labels[i],
+            attention_mask=self.attention_mask[i],
+        )
+
+
+class LazySupervisedDataset(Dataset):
+    """Dataset for supervised fine-tuning."""
+
+    def __init__(self, raw_data, tokenizer: transformers.PreTrainedTokenizer, max_len: int):
+        super(LazySupervisedDataset, self).__init__()
+        self.tokenizer = tokenizer
+        self.max_len = max_len
+
+        rank0_print("Formatting inputs...Skip in lazy mode")
+        self.tokenizer = tokenizer
+        self.raw_data = raw_data
+        self.cached_data_dict = {}
+
+    def __len__(self):
+        return len(self.raw_data)
+
+    def __getitem__(self, i) -> Dict[str, torch.Tensor]:
+        if i in self.cached_data_dict:
+            return self.cached_data_dict[i]
+
+        ret = preprocess(self.raw_data[i]["conversations"], self.tokenizer, self.max_len)
+        ret = dict(
+            input_ids=ret["input_ids"],
+            labels=ret["labels"],
+            attention_mask=ret["attention_mask"],
+        )
+        self.cached_data_dict[i] = ret
+
+        return ret
+
+
+def make_supervised_data_module(
+    tokenizer: transformers.PreTrainedTokenizer, data_args, max_len,
+) -> Dict:
+    """Make dataset and collator for supervised fine-tuning."""
+    dataset_cls = (
+        LazySupervisedDataset if data_args.lazy_preprocess else SupervisedDataset
+    )
+    rank0_print("Loading data...")
+
+    train_json = json.load(open(data_args.data_path, "r"))
+    train_dataset = dataset_cls(train_json, tokenizer=tokenizer, max_len=max_len)
+    
+    if data_args.eval_data_path:
+        eval_json = json.load(open(data_args.eval_data_path, "r"))
+        eval_dataset = dataset_cls(eval_json, tokenizer=tokenizer, max_len=max_len)
+    else:
+        eval_dataset = None
+    return dict(train_dataset=train_dataset, eval_dataset=eval_dataset)
+
+
+def print_trainable_params(model: torch.nn.Module):
+    trainable_params, all_param = 0, 0
+    for param in model.parameters():
+        num_params = param.numel()
+        all_param += num_params
+        if param.requires_grad:
+            trainable_params += num_params
+    rank0_print("trainable params: {:d} || all params: {:d} || trainable%: {:.4f}".format(
+        trainable_params, all_param, 100 * trainable_params / all_param))
+    # for name,p in model.named_parameters():
+    #     if p.requires_grad and "transformer.h" not in name:
+    #         print(name)
+
+def train():
+    global local_rank
+    
+    parser = transformers.HfArgumentParser(
+        (ModelArguments, DataArguments, TrainingArguments, LoraArguments)
+    )
+    (
+        model_args,
+        data_args,
+        training_args,
+        lora_args,
+    ) = parser.parse_args_into_dataclasses()
+
+    if getattr(training_args, 'deepspeed', None) and getattr(lora_args, 'q_lora', False):
+        training_args.distributed_state.distributed_type = DistributedType.DEEPSPEED
+
+    compute_dtype = (
+        torch.float16
+        if training_args.fp16
+        else (torch.bfloat16 if training_args.bf16 else torch.float32)
+    )
+
+    local_rank = training_args.local_rank
+
+    device_map = None
+    world_size = int(os.environ.get("WORLD_SIZE", 1))
+    ddp = world_size != 1
+    if lora_args.q_lora:
+        device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)} if ddp else None
+        if len(training_args.fsdp) > 0 or deepspeed.is_deepspeed_zero3_enabled():
+            logging.warning(
+                "FSDP or ZeRO3 are not incompatible with QLoRA."
+            )
+
+    # Set RoPE scaling factor
+    config = MonkeyConfig.from_pretrained(
+        "monkey_model",
+        cache_dir=training_args.cache_dir,
+        trust_remote_code=True,
+    )
+    rank0_print(config)
+    config.use_cache = False
+
+    # Load model and tokenizer
+    rank0_print("loading base model")
+    model = MonkeyLMHeadModel.from_pretrained(
+        model_args.model_name_or_path,
+        config=config,
+        cache_dir=training_args.cache_dir,
+        device_map=device_map,
+        trust_remote_code=True,
+        quantization_config=GPTQConfig(
+            bits=4, disable_exllama=True
+        )
+        if training_args.use_lora and lora_args.q_lora
+        else None,
+    )
+
+
+    tokenizer = QWenTokenizer.from_pretrained(
+        "monkey_model",
+        cache_dir=training_args.cache_dir,
+        model_max_length=training_args.model_max_length,
+        padding_side="right",
+        use_fast=False,
+        trust_remote_code=True,
+    )
+    tokenizer.pad_token_id = tokenizer.eod_id
+
+
+
+    if not training_args.use_lora:
+        if training_args.fix_vit and hasattr(model,'transformer') and hasattr(model.transformer,'visual'):
+            model.transformer.visual.requires_grad_(False)
+            if hasattr(model.transformer.visual,'attn_pool'):
+                model.transformer.visual.attn_pool.requires_grad_(True)
+
+            for k,v in model.named_parameters():
+                if "lora" in k :
+                    v.requires_grad_(True)
+
+    if training_args.use_lora:
+        if lora_args.q_lora or "chat" in model_args.model_name_or_path.lower():
+            modules_to_save = None
+        else:
+            modules_to_save = []
+        lora_config = LoraConfig(
+            r=lora_args.lora_r,
+            lora_alpha=lora_args.lora_alpha,
+            target_modules=lora_args.lora_target_modules,
+            lora_dropout=lora_args.lora_dropout,
+            bias=lora_args.lora_bias,
+            task_type="CAUSAL_LM",
+            modules_to_save=modules_to_save  # This argument serves for adding new tokens.
+        )
+
+        model = get_peft_model(model, lora_config)
+
+        if training_args.gradient_checkpointing:
+            model.enable_input_require_grads()
+    
+
+    print_trainable_params(model)
+    # Load data
+    data_module = make_supervised_data_module(
+        tokenizer=tokenizer, data_args=data_args, max_len=training_args.model_max_length
+    )
+    # Start trainner
+    trainer = Trainer(
+        model=model, tokenizer=tokenizer, args=training_args, **data_module
+    )
+
+    trainer.train()
+    trainer.save_state()
+
+    safe_save_model_for_hf_trainer(trainer=trainer, output_dir=training_args.output_dir, bias=lora_args.lora_bias)
+
+import numpy as np
+import random
+def setup_seed(seed):
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+    # torch.backends.cudnn.deterministic = True
+    # torch.backends.cudnn.benchmark = False
+    os.environ["PYTHONHASHSEED"] = str(seed)
+if __name__ == "__main__":
+    setup_seed(46)
+    train()