Add finetune

qwen/demo.py

@@ -1,4 +1,3 @@
-
 import torch
 from modelscope import snapshot_download
 from transformers import AutoModelForCausalLM, AutoTokenizer
@@ -11,7 +10,8 @@ seed = 4321
 torch.manual_seed(seed)
 torch.cuda.manual_seed_all(seed)
 
-model_dir = snapshot_download("qwen/Qwen-1_8B-Chat")
+# model_dir = snapshot_download("qwen/Qwen-1_8B-Chat")
+model_dir = "/home/colin/.cache/modelscope/hub/qwen/Qwen-1_8B-Chat"
 
 config, kwargs = AutoConfig.from_pretrained(
     model_dir,

qwen/finetune/.gitignore

@@ -0,0 +1 @@
+output_qwen

qwen/finetune/data.json

@@ -0,0 +1,15 @@
+[
+  {
+    "id": "identity_0",
+    "conversations": [
+      {
+        "from": "user",
+        "value": "你好"
+      },
+      {
+        "from": "assistant",
+        "value": "我是一个语言模型，我叫通义千问。"
+      }
+    ]
+  }
+]

qwen/finetune/ds_config_zero2.json

@@ -0,0 +1,52 @@
+{
+    "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
+}

qwen/finetune/ds_config_zero3.json

@@ -0,0 +1,59 @@
+{
+    "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": 3,
+        "offload_optimizer": {
+            "device": "none",
+            "pin_memory": true
+        },
+        "offload_param": {
+            "device": "none",
+            "pin_memory": true
+        },
+        "overlap_comm": true,
+        "contiguous_gradients": true,
+        "sub_group_size": 1e9,
+        "reduce_bucket_size": "auto",
+        "stage3_prefetch_bucket_size": "auto",
+        "stage3_param_persistence_threshold": "auto",
+        "stage3_max_live_parameters": 1e9,
+        "stage3_max_reuse_distance": 1e9,
+        "stage3_gather_16bit_weights_on_model_save": 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
+}

qwen/finetune/finetune.py

@@ -0,0 +1,364 @@
+# 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 modelscope import snapshot_download
+
+IGNORE_TOKEN_ID = LabelSmoother.ignore_index
+
+
+@dataclass
+class ModelArguments:
+    model_name_or_path: Optional[str] = field(default="qwen/Qwen-1_8B-Chat")
+    
+
+@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
+
+
+@dataclass
+class LoraArguments:
+    lora_r: int = 64
+    lora_alpha: int = 16
+    lora_dropout: float = 0.05
+    lora_target_modules: List[str] = field(
+        default_factory=lambda: ["c_attn", "c_proj", "w1", "w2"]
+    )
+    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:
+        if trainer.args.use_lora:
+            state_dict = get_peft_state_maybe_zero_3(
+                trainer.model.named_parameters(), bias
+            )
+        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 preprocess(
+    sources,
+    tokenizer: transformers.PreTrainedTokenizer,
+    max_len: int,
+    system_message: str = "You are a helpful assistant."
+) -> Dict:
+    roles = {"user": "<|im_start|>user", "assistant": "<|im_start|>assistant"}
+
+    im_start = tokenizer.im_start_id
+    im_end = tokenizer.im_end_id
+    nl_tokens = tokenizer('\n').input_ids
+    _system = tokenizer('system').input_ids + nl_tokens
+    _user = tokenizer('user').input_ids + nl_tokens
+    _assistant = tokenizer('assistant').input_ids + nl_tokens
+
+    # Apply prompt templates
+    input_ids, targets = [], []
+    for i, source in enumerate(sources):
+        if roles[source[0]["from"]] != roles["user"]:
+            source = source[1:]
+
+        input_id, target = [], []
+        system = [im_start] + _system + tokenizer(system_message).input_ids + [im_end] + nl_tokens
+        input_id += system
+        target += [im_start] + [IGNORE_TOKEN_ID] * (len(system)-3) + [im_end] + nl_tokens
+        assert len(input_id) == len(target)
+        for j, sentence in enumerate(source):
+            role = roles[sentence["from"]]
+            _input_id = tokenizer(role).input_ids + nl_tokens + \
+                tokenizer(sentence["value"]).input_ids + [im_end] + nl_tokens
+            input_id += _input_id
+            if role == '<|im_start|>user':
+                _target = [im_start] + [IGNORE_TOKEN_ID] * (len(_input_id)-3) + [im_end] + nl_tokens
+            elif role == '<|im_start|>assistant':
+                _target = [im_start] + [IGNORE_TOKEN_ID] * len(tokenizer(role).input_ids) + \
+                    _input_id[len(tokenizer(role).input_ids)+1:-2] + [im_end] + nl_tokens
+            else:
+                raise NotImplementedError
+            target += _target
+        assert len(input_id) == len(target)
+        input_id += [tokenizer.pad_token_id] * (max_len - len(input_id))
+        target += [IGNORE_TOKEN_ID] * (max_len - len(target))
+        input_ids.append(input_id[:max_len])
+        targets.append(target[:max_len])
+    input_ids = torch.tensor(input_ids, dtype=torch.int)
+    targets = torch.tensor(targets, dtype=torch.int)
+
+    return dict(
+        input_ids=input_ids,
+        labels=targets,
+        attention_mask=input_ids.ne(tokenizer.pad_token_id),
+    )
+
+
+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"][0],
+            labels=ret["labels"][0],
+            attention_mask=ret["attention_mask"][0],
+        )
+        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 train():
+    global local_rank
+
+    parser = transformers.HfArgumentParser(
+        (ModelArguments, DataArguments, TrainingArguments, LoraArguments)
+    )
+    (
+        model_args,
+        data_args,
+        training_args,
+        lora_args,
+    ) = parser.parse_args_into_dataclasses()
+
+    # This serves for single-gpu qlora.
+    if getattr(training_args, 'deepspeed', None) and int(os.environ.get("WORLD_SIZE", 1))==1:
+        training_args.distributed_state.distributed_type = DistributedType.DEEPSPEED
+
+    local_rank = training_args.local_rank
+
+    device_map = "auto"
+    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 "auto"
+        if len(training_args.fsdp) > 0 or deepspeed.is_deepspeed_zero3_enabled():
+            logging.warning(
+                "FSDP or ZeRO3 are incompatible with QLoRA."
+            )
+
+    model_dir = snapshot_download(model_args.model_name_or_path)
+
+    # Set RoPE scaling factor
+    config = transformers.AutoConfig.from_pretrained(
+        model_dir,
+        cache_dir=training_args.cache_dir,
+        trust_remote_code=True,
+    )
+    config.use_cache = False
+
+    # Load model and tokenizer
+
+
+    model = transformers.AutoModelForCausalLM.from_pretrained(
+        model_dir,
+        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 = transformers.AutoTokenizer.from_pretrained(
+        model_dir,
+        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 training_args.use_lora:
+        if lora_args.q_lora or 'chat' in model_dir.lower():
+            modules_to_save = None
+        else:
+            modules_to_save = ["wte", "lm_head"]
+        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.
+        )
+        if lora_args.q_lora:
+            model = prepare_model_for_kbit_training(
+                model, use_gradient_checkpointing=training_args.gradient_checkpointing
+            )
+
+        model = get_peft_model(model, lora_config)
+
+        # Print peft trainable params
+        model.print_trainable_parameters()
+
+        if training_args.gradient_checkpointing:
+            model.enable_input_require_grads()
+
+    # 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)
+
+
+if __name__ == "__main__":
+    train()

qwen/finetune/finetune_ds.sh

@@ -0,0 +1,90 @@
+#!/bin/bash
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+DIR=`pwd`
+
+# Guide:
+# This script supports distributed training on multi-gpu workers (as well as single-worker training).
+# Please set the options below according to the comments.
+# For multi-gpu workers training, these options should be manually set for each worker.
+# After setting the options, please run the script on each worker.
+
+# Number of GPUs per GPU worker
+GPUS_PER_NODE=$(python -c 'import torch; print(torch.cuda.device_count())')
+
+# Number of GPU workers, for single-worker training, please set to 1
+NNODES=${NNODES:-1}
+
+# The rank of this worker, should be in {0, ..., WORKER_CNT-1}, for single-worker training, please set to 0
+NODE_RANK=${NODE_RANK:-0}
+
+# The ip address of the rank-0 worker, for single-worker training, please set to localhost
+MASTER_ADDR=${MASTER_ADDR:localhost}
+
+# The port for communication
+MASTER_PORT=${MASTER_PORT:-6001}
+
+MODEL="Qwen/Qwen-7B" # Set the path if you do not want to load from huggingface directly
+# 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="path_to_data"
+
+function usage() {
+    echo '
+Usage: bash finetune/finetune_ds.sh [-m MODEL_PATH] [-d DATA_PATH]
+'
+}
+
+while [[ "$1" != "" ]]; do
+    case $1 in
+        -m | --model )
+            shift
+            MODEL=$1
+            ;;
+        -d | --data )
+            shift
+            DATA=$1
+            ;;
+        -h | --help )
+            usage
+            exit 0
+            ;;
+        * )
+            echo "Unknown argument ${1}"
+            exit 1
+            ;;
+    esac
+    shift
+done
+
+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.py \
+    --model_name_or_path $MODEL \
+    --data_path $DATA \
+    --bf16 True \
+    --output_dir output_qwen \
+    --num_train_epochs 5 \
+    --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 0.01 \
+    --lr_scheduler_type "cosine" \
+    --logging_steps 1 \
+    --report_to "none" \
+    --model_max_length 512 \
+    --gradient_checkpointing True \
+    --lazy_preprocess True \
+    --deepspeed finetune/ds_config_zero3.json

qwen/finetune/finetune_lora_ds.sh

@@ -0,0 +1,96 @@
+#!/bin/bash
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+DIR=`pwd`
+
+# Guide:
+# This script supports distributed training on multi-gpu workers (as well as single-worker training).
+# Please set the options below according to the comments.
+# For multi-gpu workers training, these options should be manually set for each worker.
+# After setting the options, please run the script on each worker.
+
+# Number of GPUs per GPU worker
+GPUS_PER_NODE=$(python -c 'import torch; print(torch.cuda.device_count())')
+
+# Number of GPU workers, for single-worker training, please set to 1
+NNODES=${NNODES:-1}
+
+# The rank of this worker, should be in {0, ..., WORKER_CNT-1}, for single-worker training, please set to 0
+NODE_RANK=${NODE_RANK:-0}
+
+# The ip address of the rank-0 worker, for single-worker training, please set to localhost
+MASTER_ADDR=${MASTER_ADDR:localhost}
+
+# The port for communication
+MASTER_PORT=${MASTER_PORT:-6001}
+
+MODEL="Qwen/Qwen-7B" # Set the path if you do not want to load from huggingface directly
+# 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="path_to_data"
+DS_CONFIG_PATH="finetune/ds_config_zero2.json"
+
+function usage() {
+    echo '
+Usage: bash finetune/finetune_lora_ds.sh [-m MODEL_PATH] [-d DATA_PATH] [--deepspeed DS_CONFIG_PATH]
+'
+}
+
+while [[ "$1" != "" ]]; do
+    case $1 in
+        -m | --model )
+            shift
+            MODEL=$1
+            ;;
+        -d | --data )
+            shift
+            DATA=$1
+            ;;
+        --deepspeed )
+            shift
+            DS_CONFIG_PATH=$1
+            ;;
+        -h | --help )
+            usage
+            exit 0
+            ;;
+        * )
+            echo "Unknown argument ${1}"
+            exit 1
+            ;;
+    esac
+    shift
+done
+
+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.py \
+    --model_name_or_path $MODEL \
+    --data_path $DATA \
+    --bf16 True \
+    --output_dir output_qwen \
+    --num_train_epochs 5 \
+    --per_device_train_batch_size 2 \
+    --per_device_eval_batch_size 1 \
+    --gradient_accumulation_steps 8 \
+    --evaluation_strategy "no" \
+    --save_strategy "steps" \
+    --save_steps 1000 \
+    --save_total_limit 10 \
+    --learning_rate 3e-4 \
+    --weight_decay 0.1 \
+    --adam_beta2 0.95 \
+    --warmup_ratio 0.01 \
+    --lr_scheduler_type "cosine" \
+    --logging_steps 1 \
+    --report_to "none" \
+    --model_max_length 512 \
+    --lazy_preprocess True \
+    --use_lora \
+    --gradient_checkpointing \
+    --deepspeed ${DS_CONFIG_PATH}

qwen/finetune/finetune_lora_single_gpu.sh

@@ -0,0 +1,65 @@
+#!/bin/bash
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+
+MODEL="qwen/Qwen-1_8B-Chat" # Set the path if you do not want to load from huggingface directly
+# 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="data.json"
+
+function usage() {
+    echo '
+Usage: bash finetune/finetune_lora_single_gpu.sh [-m MODEL_PATH] [-d DATA_PATH]
+'
+}
+
+while [[ "$1" != "" ]]; do
+    case $1 in
+        -m | --model )
+            shift
+            MODEL=$1
+            ;;
+        -d | --data )
+            shift
+            DATA=$1
+            ;;
+        -h | --help )
+            usage
+            exit 0
+            ;;
+        * )
+            echo "Unknown argument ${1}"
+            exit 1
+            ;;
+    esac
+    shift
+done
+
+export CUDA_VISIBLE_DEVICES=0
+
+python finetune.py \
+  --model_name_or_path $MODEL \
+  --data_path $DATA \
+  --bf16 False \
+  --output_dir output_qwen \
+  --num_train_epochs 5 \
+  --per_device_train_batch_size 2 \
+  --per_device_eval_batch_size 1 \
+  --gradient_accumulation_steps 8 \
+  --evaluation_strategy "no" \
+  --save_strategy "steps" \
+  --save_steps 1000 \
+  --save_total_limit 10 \
+  --learning_rate 3e-4 \
+  --weight_decay 0.1 \
+  --adam_beta2 0.95 \
+  --warmup_ratio 0.01 \
+  --lr_scheduler_type "cosine" \
+  --logging_steps 1 \
+  --report_to "none" \
+  --model_max_length 512 \
+  --lazy_preprocess True \
+  --gradient_checkpointing \
+  --use_lora
+
+# If you use fp16 instead of bf16, you should use deepspeed
+# --fp16 True --deepspeed finetune/ds_config_zero2.json

qwen/finetune/finetune_qlora_ds.sh

@@ -0,0 +1,93 @@
+#!/bin/bash
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+DIR=`pwd`
+
+# Guide:
+# This script supports distributed training on multi-gpu workers (as well as single-worker training).
+# Please set the options below according to the comments.
+# For multi-gpu workers training, these options should be manually set for each worker.
+# After setting the options, please run the script on each worker.
+
+# Number of GPUs per GPU worker
+GPUS_PER_NODE=$(python -c 'import torch; print(torch.cuda.device_count())')
+
+# Number of GPU workers, for single-worker training, please set to 1
+NNODES=${NNODES:-1}
+
+# The rank of this worker, should be in {0, ..., WORKER_CNT-1}, for single-worker training, please set to 0
+NODE_RANK=${NODE_RANK:-0}
+
+# The ip address of the rank-0 worker, for single-worker training, please set to localhost
+MASTER_ADDR=${MASTER_ADDR:localhost}
+
+# The port for communication
+MASTER_PORT=${MASTER_PORT:-6001}
+
+MODEL="Qwen/Qwen-7B-Chat-Int4" # Set the path if you do not want to load from huggingface directly
+# 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="path_to_data"
+
+function usage() {
+    echo '
+Usage: bash finetune/finetune_qlora_ds.sh [-m MODEL_PATH] [-d DATA_PATH]
+'
+}
+
+while [[ "$1" != "" ]]; do
+    case $1 in
+        -m | --model )
+            shift
+            MODEL=$1
+            ;;
+        -d | --data )
+            shift
+            DATA=$1
+            ;;
+        -h | --help )
+            usage
+            exit 0
+            ;;
+        * )
+            echo "Unknown argument ${1}"
+            exit 1
+            ;;
+    esac
+    shift
+done
+
+DISTRIBUTED_ARGS="
+    --nproc_per_node $GPUS_PER_NODE \
+    --nnodes $NNODES \
+    --node_rank $NODE_RANK \
+    --master_addr $MASTER_ADDR \
+    --master_port $MASTER_PORT
+"
+
+# Remember to use --fp16 instead of --bf16 due to autogptq
+torchrun $DISTRIBUTED_ARGS finetune.py \
+    --model_name_or_path $MODEL \
+    --data_path $DATA \
+    --fp16 True \
+    --output_dir output_qwen \
+    --num_train_epochs 5 \
+    --per_device_train_batch_size 2 \
+    --per_device_eval_batch_size 1 \
+    --gradient_accumulation_steps 8 \
+    --evaluation_strategy "no" \
+    --save_strategy "steps" \
+    --save_steps 1000 \
+    --save_total_limit 10 \
+    --learning_rate 3e-4 \
+    --weight_decay 0.1 \
+    --adam_beta2 0.95 \
+    --warmup_ratio 0.01 \
+    --lr_scheduler_type "cosine" \
+    --logging_steps 1 \
+    --report_to "none" \
+    --model_max_length 512 \
+    --lazy_preprocess True \
+    --use_lora \
+    --q_lora \
+    --gradient_checkpointing \
+    --deepspeed finetune/ds_config_zero2.json

qwen/finetune/finetune_qlora_single_gpu.sh

@@ -0,0 +1,66 @@
+#!/bin/bash
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+DIR=`pwd`
+
+MODEL="Qwen/Qwen-7B-Chat-Int4" # Set the path if you do not want to load from huggingface directly
+# 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="path_to_data"
+
+function usage() {
+    echo '
+Usage: bash finetune/finetune_qlora_single_gpu.sh [-m MODEL_PATH] [-d DATA_PATH]
+'
+}
+
+while [[ "$1" != "" ]]; do
+    case $1 in
+        -m | --model )
+            shift
+            MODEL=$1
+            ;;
+        -d | --data )
+            shift
+            DATA=$1
+            ;;
+        -h | --help )
+            usage
+            exit 0
+            ;;
+        * )
+            echo "Unknown argument ${1}"
+            exit 1
+            ;;
+    esac
+    shift
+done
+
+export CUDA_VISIBLE_DEVICES=0
+
+# Remember to use --fp16 instead of --bf16 due to autogptq
+python finetune.py \
+  --model_name_or_path $MODEL \
+  --data_path $DATA \
+  --fp16 True \
+  --output_dir output_qwen \
+  --num_train_epochs 5 \
+  --per_device_train_batch_size 2 \
+  --per_device_eval_batch_size 1 \
+  --gradient_accumulation_steps 8 \
+  --evaluation_strategy "no" \
+  --save_strategy "steps" \
+  --save_steps 1000 \
+  --save_total_limit 10 \
+  --learning_rate 3e-4 \
+  --weight_decay 0.1 \
+  --adam_beta2 0.95 \
+  --warmup_ratio 0.01 \
+  --lr_scheduler_type "cosine" \
+  --logging_steps 1 \
+  --report_to "none" \
+  --model_max_length 512 \
+  --lazy_preprocess True \
+  --gradient_checkpointing \
+  --use_lora \
+  --q_lora \
+  --deepspeed finetune/ds_config_zero2.json

test/dataset.py

@@ -0,0 +1,7 @@
+from datasets import load_dataset
+
+
+dataset = load_dataset("BAAI/COIG")
+
+d = dataset["Default"][0]
+dataset