update codes

.gitignore

+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/en/_build/
+docs/zh_cn/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.vscode
+.idea
+.DS_Store
+
+# custom
+*.pkl
+*.pkl.json
+*.log.json
+# Pytorch
+*.pth
+*.py~
+*.sh~
\ No newline at end of file

LICENSE

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README.md

-# codellama_pytorch
+# CodeLlama
+Codellama模型是一系列7B、13B、34B和70B模型，使用500B-1T的tokens进行训练。
+## 论文
+`Code Llama: Open Foundation Models for Code`<br>
+[CodeLlama](https://scontent-lax3-1.xx.fbcdn.net/v/t39.2365-6/440937359_1249838219330505_1104237120116944930_n.pdf?_nc_cat=109&ccb=1-7&_nc_sid=3c67a6&_nc_ohc=nf68R9-64t8Q7kNvgHxIBjM&_nc_ht=scontent-lax3-1.xx&oh=00_AYBcUILDMCTVXhedseMkIiEnVwAkWPP9xejZllnlBiaK0Q&oe=66893420)
+## 模型结构
+codellama的模型结构主要基于llama2架构进行训练而来，使用了不同的训练方法得到了基于不同任务目的的代码生成模型。
+<div align=center>
+    <img src="./asserts/model_architecture.png"/>
+</div>
 
-code generation models by meta
+## 算法原理
\ No newline at end of file
+使用GQA模块能够带来更好的速度，使用GQA的head数量不同则会带来速度和性能平衡转换<br>
+使用了RoPE位置旋转编码来替代Embedding编码，使得模型获得更好的外推性。<br>
+<div align=center>
+    <img src="./asserts/model_blocks.png"/>
+</div>
+
+## 环境配置
+-v 路径、docker_name和imageID根据实际情况修改
+
+### Docker（方法一）
+
+```bash
+docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk24.04-py310
+docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro --shm-size=80G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
+
+cd /your_code_path/codellama_pytorch
+pip install -r requirements.txt  
+export HF_ENDPOINT=https://hf-mirror.com
+```
+
+### Dockerfile（方法二）
+
+```bash
+cd docker
+docker build --no-cache -t codellama:latest .
+docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro --shm-size=80G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
+
+cd /your_code_path/codellama_pytorch
+pip install -r requirements.txt  
+export HF_ENDPOINT=https://hf-mirror.com
+```
+
+### Anaconda（方法三）
+关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.hpccube.com/tool/)开发者社区下载安装。
+```
+DTK驱动: dtk24.04
+python: python3.10
+torch: 2.1.0
+```
+`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应`
+
+其它非深度学习库安装方式如下：
+```bash
+pip install -r requirements.txt  -i http://mirrors.huaweicloud.com/repository/pypi/simple
+export HF_ENDPOINT=https://hf-mirror.com
+```
+
+## 数据集
+finetune训练样例数据采用verilog-dataset-v3 [下载地址](https://hf-mirror.com/datasets/emilgoh/verilog-dataset-v3)<br>
+```angular2html
+.
+├── verilog-dataset-v3
+│   ├── README.md
+│   └── train.csv
+......
+```
+<div align=center>
+    <img src="./asserts/dataset.png"/>
+</div>
+
+## 训练
+### 单机两卡
+- lora微调
+具体参数更改请在train.sh文件中进行,以下为必要参数 <br>
+DATA_PATH="{数据集地址}" <br>
+MODEL_PATH="{预训练模型加载地址}" <br>
+- 全参微调 - 将lora_config地址空字符填充即可
+LORA_CONFIG=""
+```bash
+bash ./train.sh
+```
+
+## 推理
+基于Huggingface's Transformers进行推理.<br>
+模型下载后[模型下载地址](https://hf-mirror.com/meta-llama/CodeLlama-7b-Instruct-hf) 默认需存放至weights文件夹中<br>
+也可自行更改 inference.py文件中的 model_name 参数<br>
+```bash
+HIP_VISIBLE_DEVICES=0 python inference.py
+```
+## Result
+prompt：In Bash, how do I list all text files in the current directory (excluding subdirectories) that have been modified in the last month?",<br>
+result：
+<div align=center>
+    <img src="./asserts/result.png"/>
+</div>
+
+### 精度
+训练集verilog-dataset-v3
+
+|  device  | lora_train_loss | steps |
+|:--------:|:---------------:|:-----:|
+|  A800*2  |     0.4743      | 2580  |
+|  K100*2  |     0.4687      | 2580  |
+NV:绿色 DCU:红色
+<div align=center>
+    <img src="./asserts/loss.jpg"/>
+</div>
+
+## 应用场景
+### 算法类别
+代码生成
+
+### 热点应用行业
+制造,能源,教育
+
+## 预训练权重
+模型目录结构如下：
+```
+.
+└── CodeLlama-7b-Instruct-hf
+    ├── config.json
+    ├── generation_config.json
+    ├── LICENSE
+    ├── model-00001-of-00002.safetensors
+    ├── model-00002-of-00002.safetensors
+    ├── model.safetensors.index.json
+    ├── pytorch_model-00001-of-00003.bin
+    ├── pytorch_model-00002-of-00003.bin
+    ├── pytorch_model-00003-of-00003.bin
+    ├── pytorch_model.bin.index.json
+    ├── README.md
+    ├── special_tokens_map.json
+    ├── tokenizer_config.json
+    ├── tokenizer.json
+    ├── tokenizer.model
+    └── USE_POLICY.md
+```
+
+## 源码仓库及问题反馈
+- https://developer.hpccube.com/codes/modelzoo/starcoder2_pytorch
+
+## 参考资料
+- https://developer.hpccube.com/codes/modelzoo/codellama_pytorch
+- https://hf-mirror.com/meta-llama/CodeLlama-7b-Instruct-hf
+- https://github.com/meta-llama/codellama
+- https://hf-mirror.com/datasets/emilgoh/verilog-dataset-v3

docker/Dockerfile

+FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk24.04-py310
\ No newline at end of file

dp_configs/ds_config_stage3.json

+{
+    "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": "cpu",
+            "pin_memory": true
+        },
+        "offload_param": {
+            "device": "cpu",
+            "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": 20,
+    "train_batch_size": "auto",
+    "train_micro_batch_size_per_gpu": "auto",
+    "wall_clock_breakdown": false
+}
\ No newline at end of file

finetune.py

+import copy
+import random
+from dataclasses import dataclass, field
+from typing import Optional, Dict, Sequence
+
+import os
+import torch
+import torch.distributed
+import transformers
+from transformers import Trainer
+from datasets import load_dataset
+
+IGNORE_INDEX = -100
+EOT_TOKEN = "<|EOT|>"
+
+
+def build_instruction_prompt(instruction: str):
+    return '''
+You are an AI programming assistant, utilizing the DeepSeek Coder model, developed by DeepSeek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer.
+### Instruction:
+{}
+### Response:
+'''.format(instruction.strip()).lstrip()
+
+
+@dataclass
+class ModelArguments:
+    model_name_or_path: Optional[str] = field(default="./weights/CodeLlama-7b-Instruct-hf")
+
+
+@dataclass
+class DataArguments:
+    data_path: str = field(default=None, metadata={"help": "Path to the training data."})
+
+
+# training Default Arguments 继承于 Transform.TrainingArguments 的默认参数。
+@dataclass
+class TrainingArguments(transformers.TrainingArguments):
+    cache_dir: Optional[str] = field(default=None)
+    optim: str = field(default="adamw_torch")
+    model_max_length: int = field(
+        default=512,
+        metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."},
+    )
+    lora_config: str = field(default="",
+                             metadata={"help": "lora Finetuning configs path, for use ptft model to finetuning."},
+                             )
+
+
+def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output_dir: str):
+    """Collects the state dict and dump to disk."""
+    state_dict = trainer.model.state_dict()
+    if trainer.args.should_save:
+        cpu_state_dict = {key: value.cpu() for key, value in state_dict.items()}
+        del state_dict
+        trainer._save(output_dir, state_dict=cpu_state_dict)  # noqa
+
+
+def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer) -> Dict:
+    """Tokenize a list of strings."""
+    tokenized_list = [
+        tokenizer(
+            text,
+            return_tensors="pt",
+            padding="longest",
+            max_length=tokenizer.model_max_length,
+            truncation=True,
+        )
+        for text in strings
+    ]
+
+    input_ids = labels = [tokenized.input_ids[0] for tokenized in tokenized_list]
+    input_ids_lens = labels_lens = [
+        tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item() for tokenized in tokenized_list
+    ]
+
+    return dict(
+        input_ids=input_ids,
+        labels=labels,
+        input_ids_lens=input_ids_lens,
+        labels_lens=labels_lens,
+    )
+
+
+def preprocess(
+        sources: Sequence[str],
+        targets: Sequence[str],
+        tokenizer: transformers.PreTrainedTokenizer,
+) -> Dict:
+    """Preprocess the data by tokenizing."""
+    system_prompt = 'I want you to act as an IC designer, and implement the following in Verilog.'
+    instruction = 'Generate a Verilog module'
+
+    examples = [tokenizer.apply_chat_template([
+        {"role": "system", "content": f"{system_prompt}  {instruction}"},
+        {"role": "user", "content": f"{s}"},
+        {"role": "assistant", "content": f"{t}"},
+    ], tokenize=False) for s, t in zip(sources, targets)]
+
+    examples_tokenized, sources_tokenized = [_tokenize_fn(strings, tokenizer) for strings in (examples, sources)]
+    input_ids = examples_tokenized["input_ids"]
+
+    labels = copy.deepcopy(input_ids)
+    for label, source_len in zip(labels, sources_tokenized["input_ids_lens"]):
+        label[:source_len] = IGNORE_INDEX
+    return dict(input_ids=input_ids, labels=labels)
+
+
+@dataclass
+class DataCollatorForSupervisedDataset(object):
+    """Collate examples for supervised fine-tuning."""
+    tokenizer: transformers.PreTrainedTokenizer
+
+    def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
+        input_ids, labels = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels"))
+        input_ids = [torch.tensor(x) for x in input_ids]
+        input_ids = torch.nn.utils.rnn.pad_sequence(
+            input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id
+        )
+        labels = [torch.tensor(x) for x in labels]
+        labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX)
+
+        return dict(
+            input_ids=input_ids,
+            labels=labels,
+            attention_mask=input_ids.ne(self.tokenizer.pad_token_id),
+        )
+        # input_ids = torch.tensor(input_ids)
+        # labels = torch.tensor(labels)
+        # return dict(
+        #     input_ids=input_ids,
+        #     labels=labels,
+        #     attention_mask=input_ids,
+        # )
+
+
+def train_tokenize_function(examples, tokenizer):
+    sources = [
+        build_instruction_prompt(instruction)
+        for instruction in examples['description']
+    ]
+    targets = [output for output in examples['output']]
+    data_dict = preprocess(sources, targets, tokenizer)
+    return data_dict
+
+
+def train():
+    parser = transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
+    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    if training_args.local_rank == 0:
+        print('=' * 100)
+        print(training_args)
+
+    tokenizer = transformers.AutoTokenizer.from_pretrained(
+        model_args.model_name_or_path,
+        model_max_length=training_args.model_max_length,
+        padding_side="right",
+        use_fast=True,
+        trust_remote_code=True
+    )
+    tokenizer.pad_token_id = tokenizer.eos_token_id
+    tokenizer.pad_token = tokenizer.eos_token
+    tokenizer.padding_side = "left"
+    print("PAD Token:", tokenizer.pad_token, tokenizer.pad_token_id)
+    print("BOS Token", tokenizer.bos_token, tokenizer.bos_token_id)
+    print("EOS Token", tokenizer.eos_token, tokenizer.eos_token_id)
+
+    if training_args.local_rank == 0:
+        print("Load tokenizer from {} over.".format(model_args.model_name_or_path))
+
+    model = transformers.AutoModelForCausalLM.from_pretrained(
+        model_args.model_name_or_path,
+        torch_dtype=torch.bfloat16,
+        # low_cpu_mem_usage=True,
+        trust_remote_code=True,
+    )
+
+    if training_args.local_rank == 0:
+        print("Load model from {} over.".format(model_args.model_name_or_path))
+
+    raw_train_datasets = load_dataset(
+        'csv',
+        data_files=data_args.data_path,
+        split="train",
+        cache_dir=training_args.cache_dir
+    )
+    if training_args.local_rank > 0:
+        torch.distributed.barrier()
+
+    train_dataset = raw_train_datasets.map(
+        train_tokenize_function,
+        batched=True,
+        batch_size=3000,
+        num_proc=32,
+        remove_columns=raw_train_datasets.column_names,
+        load_from_cache_file=True,  # not args.overwrite_cache
+        desc="Running Encoding",
+        fn_kwargs={"tokenizer": tokenizer}
+    )
+
+    if training_args.local_rank == 0:
+        torch.distributed.barrier()
+
+    if training_args.local_rank == 0:
+        print("Training dataset samples:", len(train_dataset))
+        for index in random.sample(range(len(train_dataset)), 3):
+            print(
+                f"Sample {index} of the training set: {train_dataset[index]['input_ids']}, {train_dataset[index]['labels']}.")
+            print(f"Sample {index} of the training set: {tokenizer.decode(list(train_dataset[index]['input_ids']))}.")
+
+    if training_args.lora_config is not None and os.path.exists(training_args.lora_config):
+        from peft import (
+            get_peft_model,
+            LoraConfig,
+            TaskType,
+            prepare_model_for_kbit_training,
+            peft_model,
+            set_peft_model_state_dict,
+        )
+        import json
+        with open(training_args.lora_config) as f:
+            lora_config = json.load(f)
+
+        petf_lora_config = LoraConfig(
+            r=lora_config['r'],
+            lora_alpha=lora_config["lora_alpha"],
+            target_modules=lora_config["target_modules"],
+            fan_in_fan_out=False,
+            lora_dropout=lora_config["lora_dropout"],
+            bias=lora_config["bias"],
+            task_type="CAUSAL_LM",
+            inference_mode=False,
+        )
+        model.enable_input_require_grads()
+        model = get_peft_model(model, peft_config=petf_lora_config)
+        # if training_args.output_dir:
+        #     if os.path.exists(training_args.output_dir):
+        #         print(f"Restarting from {training_args.output_dir}")
+        #         adapters_weights = torch.load(training_args.output_dir)
+
+        #         set_peft_model_state_dict(model, adapters_weights)
+        #     else:
+        #         print(f"Checkpoint {training_args.output_dir} not found")
+        print(("#" * 10 + "\n") * 2 + "\n" + "\n")
+        print("using lora finetune!")
+        print("\n" + "\n" + ("#" * 10 + "\n") * 2)
+
+    data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer)
+    data_module = dict(train_dataset=train_dataset, eval_dataset=None, data_collator=data_collator)
+
+    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)
+
+
+if __name__ == "__main__":
+    train()

inference.py

+import torch
+from transformers import AutoModelForCausalLM, BitsAndBytesConfig, AutoTokenizer
+
+base_model = "./weights/CodeLlama-7b-Instruct-hf"
+max_gen_length = 4096
+description = "In Bash, how do I list all text files in the current directory (excluding subdirectories) that have been modified in the last month?"
+tokenizer = AutoTokenizer.from_pretrained(
+    base_model,
+    model_max_length=max_gen_length,
+)
+chat = [
+   {"role": "user", "content": f"{description}"},
+]
+print(tokenizer.apply_chat_template(chat, tokenize=False))
+model_input = tokenizer.apply_chat_template(chat,return_tensors="pt",truncation=True,).to("cuda")
+model = AutoModelForCausalLM.from_pretrained(
+    base_model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    low_cpu_mem_usage=True,
+    trust_remote_code=True,
+)
+model.eval()
+with torch.no_grad():
+    print(tokenizer.decode(model.generate(model_input, max_new_tokens=max_gen_length)[0], skip_special_tokens=True))

lora_config.json

+{
+"r":8,
+"lora_alpha":16,
+"target_modules":["q_proj","v_proj"],
+"fan_in_fan_out":"False",
+"lora_dropout":0.05,
+"bias":"none",
+"task_type":"CAUSAL_LM",
+"inference_mode":"False"
+}
\ No newline at end of file

model.properties

+# 模型唯一标识
+modelCode=650
+# 模型名称
+modelName=codellama_pytorch
+# 模型描述
+modelDescription=Codellama模型是一系列7B、13B、34B和70B模型，使用500B-1T的tokens进行训练。
+# 应用场景
+appScenario=推理,训练,代码生成,制造,能源,教育
+# 框架类型
+frameType=pytorch

requirements.txt

+#torch>=2.0
+#tokenizers>=0.14.0
+#transformers==4.35.0
+#accelerate
+#deepspeed==0.12.2
+sympy==1.12
+pebble 
+timeout-decorator 
+accelerate
+attrdict
+tqdm
+datasets
+tensorboardX
+peft

train.sh

+#export CUDA_VISIBLE_DEVICES=1,2
+DATA_PATH="./data/verilog-dataset-v3/train.csv"
+
+OUTPUT_PATH="./outputs"
+MODEL_PATH="./weights/CodeLlama-7b-Instruct-hf"
+DEEPSEPPD_CONFIG="./dp_configs/ds_config_stage3.json"
+LORA_CONFIG="./lora_config.json"
+
+deepspeed --include="localhost:2,3" --master_port 25923 fintune.py \
+    --model_name_or_path $MODEL_PATH \
+    --data_path $DATA_PATH \
+    --output_dir $OUTPUT_PATH \
+    --num_train_epochs 3 \
+    --model_max_length 2048 \
+    --per_device_train_batch_size 2 \
+    --per_device_eval_batch_size 1 \
+    --gradient_accumulation_steps 4 \
+    --evaluation_strategy "no" \
+    --save_strategy "steps" \
+    --save_steps 100 \
+    --save_total_limit 100 \
+    --learning_rate 2e-5 \
+    --warmup_steps 10 \
+    --logging_steps 1 \
+    --lr_scheduler_type "cosine" \
+    --gradient_checkpointing True \
+    --report_to "all" \
+    --deepspeed  $DEEPSEPPD_CONFIG \
+    --bf16 True \
+    --lora_config $LORA_CONFIG
+