initial commit

chat.py

@@ -0,0 +1,16 @@
+import requests
+
+from utils.prompt import prompt
+
+messages = []
+while True:
+    user_prompt = prompt('>> User: ')
+    messages.append({'role': 'user', 'content': user_prompt})
+
+    response = requests.post('http://localhost:8900/', json=messages)
+    if response.status_code == 200:
+        messages = response.json()
+        print('>> Bot : ' + messages[-1]['content'])
+    else:
+        messages = messages[0:-1]
+        print('### Error from backend')

chat_dialogpt.py

@@ -0,0 +1,36 @@
+import atexit
+import torch
+from utils.conversation import save_conversation
+from utils.prompt import prompt
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+device = 'cuda'  # the device to load the model onto
+model_id = 'microsoft/DialoGPT-medium'
+
+print('Loading ' + model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype='auto', device_map='auto')
+# model = AutoModelForCausalLM.from_pretrained(model_id)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+print('Loaded')
+# print(tokenizer.default_chat_template)
+
+# read and save conversation
+chat_history_ids = None
+# messages = load_conversation(model_id)
+atexit.register(lambda: save_conversation(model_id, bot_input_ids))
+
+# messages.append({'role': 'system', 'content': 'Your name is "Laura". You are an AI created by Alice.'})
+while True:
+    user_prompt = prompt('>> User: ')
+    new_user_input_ids = tokenizer.encode(user_prompt + tokenizer.eos_token, return_tensors='pt').to(device)
+
+    bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) \
+        if chat_history_ids is not None \
+        else new_user_input_ids
+    chat_history_ids = model.generate(bot_input_ids, max_length=100, pad_token_id=tokenizer.eos_token_id).to(device)
+    response = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
+
+    print('>> Bot : ' + response)
+    torch.cuda.empty_cache()
+

chat_gpt2.py

@@ -0,0 +1,34 @@
+import atexit
+import torch
+from utils.conversation import save_conversation
+from utils.prompt import prompt
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+device = 'cuda'  # the device to load the model onto
+model_id = 'gpt2'
+
+print('Loading ' + model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype='auto', device_map='auto')
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+print('Loaded')
+# print(tokenizer.default_chat_template)
+
+# read and save conversation
+chat_history_ids = None
+# messages = load_conversation(model_id)
+atexit.register(lambda: save_conversation(model_id, bot_input_ids))
+
+# messages.append({'role': 'system', 'content': 'Your name is "Laura". You are an AI created by Alice.'})
+while True:
+    user_prompt = prompt('>> User: ')
+    new_user_input_ids = tokenizer.encode(user_prompt + tokenizer.eos_token, return_tensors='pt').to(device)
+
+    bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) \
+        if chat_history_ids is not None \
+        else new_user_input_ids
+    chat_history_ids = model.generate(bot_input_ids, max_length=100, pad_token_id=tokenizer.eos_token_id).to(device)
+    response = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
+
+    print('>> Bot : ' + response)
+    torch.cuda.empty_cache()
+

chat_mistral.py

@@ -0,0 +1,31 @@
+import atexit
+import torch
+from utils.conversation import load_conversation, save_conversation
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from utils.prompt import prompt
+
+device = 'cuda'  # the device to load the model onto
+model_id = 'mistralai/Mistral-7B-Instruct-v0.2'
+
+print('Loading ' + model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype='auto', device_map='auto')
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+print('Loaded')
+# print(tokenizer.default_chat_template)
+
+# read and save conversation
+messages = load_conversation(model_id)
+atexit.register(lambda: save_conversation(model_id, messages))
+
+while True:
+    user_prompt = prompt('>> User: ')
+    messages.append({'role': 'user', 'content': user_prompt})
+
+    model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
+    generated_ids = model.generate(model_inputs, max_new_tokens=100, do_sample=True)
+    response = tokenizer.batch_decode(generated_ids)[0]
+
+    print('>> Bot : ' + response)
+    messages.append({'role': 'assistant', 'content': response})
+    torch.cuda.empty_cache()

chat_qwen.py

@@ -0,0 +1,55 @@
+import atexit
+import os
+import sys
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from utils.conversation import save_conversation_json
+from utils.prompt import prompt
+
+
+class ChatQwen:
+    default_device = 'cuda'  # the device to load the model onto
+    # default_model_id = 'Qwen/Qwen1.5-0.5B-Chat'
+    default_model_id = 'Qwen/Qwen1.5-1.8B-Chat'
+    # default_model_id = 'Qwen/Qwen1.5-4B-Chat'
+
+    default_instruction = {'role': 'system', 'content': 'Your name is "Laura". You are an AI created by Alice.'}
+
+    def __init__(self, model_id_or_path=default_model_id):
+        # model_id = model_id_or_path if not load_from_disk else os.path.abspath(sys.argv[1])
+
+        print('Loading ' + model_id_or_path)
+        self.model_id_or_path = model_id_or_path
+        self.model = AutoModelForCausalLM.from_pretrained(model_id_or_path, torch_dtype='auto', device_map='auto')
+        self.tokenizer = AutoTokenizer.from_pretrained(model_id_or_path)
+        # print(tokenizer.default_chat_template)
+        # print(type(model))
+        # print(type(tokenizer))
+        print('Loaded')
+
+    def generate(self, messages):
+        # prepare
+        messages = [m for m in messages if m['role'] != 'system']
+        input_messages = [self.default_instruction] + messages
+
+        # generate
+        text = self.tokenizer.apply_chat_template(input_messages, tokenize=False, add_generation_prompt=True)
+        model_inputs = self.tokenizer([text], return_tensors='pt').to(self.default_device)
+        generated_ids = self.model.generate(model_inputs.input_ids, max_new_tokens=100)
+        generated_ids = [
+            output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+        ]
+        response = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+        # add response and save conversation
+        messages.append({'role': 'assistant', 'content': response})
+        self.record_conversation(input_messages, {'role': 'assistant', 'content': response})
+
+        torch.cuda.empty_cache()  # clear cache or the gpu mem will be used a lot
+        return messages
+
+    def record_conversation(self, messages, response):
+        messages = messages + [response]
+        save_conversation_json(self.model_id_or_path, messages)

server.py

@@ -0,0 +1,44 @@
+import http.server
+import socketserver
+import json
+import sys
+
+from chat_qwen import ChatQwen
+
+bot = ChatQwen(sys.argv[1]) if len(sys.argv) > 1 else ChatQwen()
+
+
+class Server(socketserver.TCPServer):
+    # Avoid "address already used" error when frequently restarting the script 
+    allow_reuse_address = True
+
+
+class Handler(http.server.BaseHTTPRequestHandler):
+    def do_GET(self):
+        self.send_response(200)
+        self.end_headers()
+        self.wfile.write("Use POST with JSON body of the format \n"
+                         "[{\"role\": \"user\", \"content\": \"message\"}] \n"
+                         "or \n"
+                         "[{\"role\": \"user\", \"content\": \"message\"}, "
+                         "{\"role\": \"assistant\", \"content\": \"message\"}, "
+                         "{\"role\": \"user\", \"content\": \"message\"}]".encode("utf-8"))
+
+    def do_POST(self):
+        try:
+            content_len = int(self.headers.get('Content-Length'))
+            post_body = self.rfile.read(content_len)
+            json_body = json.loads(post_body)
+
+            response = bot.generate(json_body)
+
+            self.send_response(200)
+            self.end_headers()
+            self.wfile.write(json.dumps(response).encode("utf-8"))
+        except:
+            self.send_response(400)
+            self.end_headers()
+
+
+with Server(("0.0.0.0", 8900), Handler) as httpd:
+    httpd.serve_forever()

train/prepare/helpsteer/helpsteer22jsonl.py

@@ -0,0 +1,27 @@
+import json
+import os
+
+this_dir = os.path.dirname(os.path.abspath(__file__))
+
+
+def mkdir(path):
+    if not os.path.isdir(path):
+        os.mkdir(path)
+
+
+mkdir(this_dir + '/../../data')
+mkdir(this_dir + '/../../data/helpsteer')
+
+for filename in ['train.jsonl', 'validation.jsonl']:
+    with open(this_dir + '/' + filename, 'r') as f:
+        lines = f.readlines()
+
+    role_dict = {'prompt': 'user', 'response': 'assistant'}
+    lines = [json.loads(line) for line in lines]
+    conversations = [{'messages': [{'role': 'user', 'content': line['prompt']},
+                                   {'role': 'assistant', 'content': line['response']}]} for line in lines]
+
+    print(conversations[0])
+
+    with open(this_dir + '/../../data/helpsteer/helpsteer_' + filename[0:-6] + '_all.jsonl', 'w') as f:
+        f.writelines([json.dumps(conv) + '\n' for conv in conversations])

train/prepare/oasst2/oasst22jsonl.py

@@ -0,0 +1,42 @@
+import json
+import os
+
+# parsing OA data files with oasst_data helpers
+from oasst_data import read_message_trees, ExportMessageNode
+
+messages: list[ExportMessageNode] = []
+
+this_dir = os.path.dirname(os.path.abspath(__file__))
+input_file_path = this_dir + '/2023-11-05_oasst2_all.trees.jsonl.gz'
+
+role_dict = {'prompter': 'user', 'assistant': 'assistant'}
+conversations = []
+
+
+def visit(node: ExportMessageNode, parents: [ExportMessageNode]):
+    new_parents = parents + [node]
+    if not node.replies:  # end of conversation
+        conversations.append({'messages': [{'role': role_dict[p.role], 'content': p.text} for p in new_parents]})
+    else:
+        for reply in node.replies:
+            visit(reply, new_parents)
+
+
+for tree in read_message_trees(input_file_path):
+    if tree.prompt.lang not in ['en']:  # filtering by language tag (optional)
+        continue
+
+    visit(tree.prompt, [])
+
+print(conversations[0])
+
+
+def mkdir(path):
+    if not os.path.isdir(path):
+        os.mkdir(path)
+
+
+mkdir(this_dir + '/../../data')
+mkdir(this_dir + '/../../data/oasst')
+with open(this_dir + '/../../data/oasst/oasst_all.jsonl', 'w') as f:
+    f.writelines([json.dumps(conv) + '\n' for conv in conversations])

train/sft/README.md

@@ -0,0 +1,3 @@
+# Documentation
+see https://github.com/QwenLM/Qwen1.5/blob/main/docs/source/training/SFT/example.rst
+or [./example.rst]()

train/sft/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
+}

train/sft/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
+}

train/sft/example.rst

@@ -0,0 +1,572 @@
+Example
+====================================================
+
+Here we provide a very simple script for supervised finetuning, which is revised from the training
+script in ```Fastchat`` <https://github.com/lm-sys/FastChat>`__. The
+script is used to finetune Qwen with Hugging Face Trainer. You can check
+the script
+`here <https://github.com/QwenLM/Qwen1.5/blob/main/finetune.py>`__. This
+script for supervised finetuning (SFT) has the following features:
+
+-  Support single-GPU and multi-GPU training;
+-  Support full-parameter tuning,
+   `LoRA <https://arxiv.org/abs/2106.09685>`__, and
+   `Q-LoRA <https://arxiv.org/abs/2305.14314>`__.
+
+In the following, we introduce more details about the usage of the
+script.
+
+Installation
+------------
+
+Before you start, make sure you have installed the following packages:
+
+.. code:: bash
+
+   pip install peft deepspeed optimum accelerate
+
+Data Preparation
+----------------
+
+For data preparation, we advise you to organize the data in a jsonl
+file, where each line is a dictionary as demonstrated below:
+
+.. code:: json
+
+   {
+       "type": "chatml",
+       "messages": [
+           {
+               "role": "system",
+               "content": "You are a helpful assistant."
+           },
+           {
+               "role": "user",
+               "content": "Tell me something about large language models."
+           },
+           {
+               "role": "assistant",
+               "content": "Large language models are a type of language model that is trained on a large corpus of text data. They are capable of generating human-like text and are used in a variety of natural language processing tasks..."
+           }
+       ],
+       "source": "unknown"
+   }
+
+.. code:: json
+
+   {
+       "type": "chatml",
+       "messages": [
+           {
+               "role": "system",
+               "content": "You are a helpful assistant."
+           },
+           {
+               "role": "user",
+               "content": "What is your name?"
+           },
+           {
+               "role": "assistant",
+               "content": "My name is Qwen."
+           }
+       ],
+       "source": "self-made"
+   }
+
+Above are two examples of each data sample in the dataset. Each sample
+is a JSON object with the following fields: ``type``, ``messages`` and
+``source``. ``messages`` is required while the others are optional for
+you to label your data format and data source. The ``messages`` field is
+a list of JSON objects, each of which has two fields: ``role`` and
+``content``. ``role`` can be ``system``, ``user``, or ``assistant``.
+``content`` is the text of the message. ``source`` is the source of the
+data, which can be ``self-made``, ``alpaca``, ``open-hermes``, or any
+other string.
+
+To make the jsonl file, you can use ``json`` to save a list of
+dictionaries to the jsonl file:
+
+.. code:: python
+
+   import json
+
+   with open('data.jsonl', 'w') as f:
+       for sample in samples:
+           f.write(json.dumps(sample) + '\n')
+
+Quickstart
+----------
+
+For you to start finetuning quickly, we directly provide a shell script
+for you to run without paying attention to details. You need
+different hyperparameters for different types of training, e.g.,
+single-GPU / multi-GPU training, full-parameter tuning, LoRA, or Q-LoRA.
+
+
+
+.. code:: bash
+
+   cd examples/sft
+   bash finetune.sh -m <model_path> -d <data_path> --deepspeed <config_path> [--use_lora True] [--q_lora True]
+
+
+Specify the ``<model_path>`` for your model, ``<data_path>`` for your
+data, and ``<config_path>`` for your deepspeed configuration.
+If you use LoRA or Q-LoRA, just add ``--use_lora True`` or
+``--q_lora True`` based on your requirements.
+This is the simplest way to start finetuning. If you want to change more
+hyperparameters, you can dive into the script and modify those
+parameters.
+
+Advanced Usages
+---------------
+
+In this section, we introduce the details of the scripts, including the
+core python script as well as the corresponding shell script.
+
+Shell Script
+~~~~~~~~~~~~~
+
+Before we introduce the python code, we provide a brief introduction to
+the shell script with commands. We provide some guidance inside the
+shell script and here we take ``finetune.sh`` as an example.
+
+To set up the environment variables for distributed training (or
+single-GPU training), specify the following variables:
+``GPUS_PER_NODE``, ``NNODES``, ``NODE_RANK``, ``MASTER_ADDR``, and
+``MASTER_PORT``. No need to worry too much about them as we provide the
+default settings for you. In the command, you can pass in the argument
+``-m`` and ``-d`` to specify the model path and data path, respectively.
+You can also pass in the argument ``--deepspeed`` to specify the
+deepspeed configuration file. We provide two configuration files for
+ZeRO2 and ZeRO3, and you can choose one based on your requirements. In
+most cases, we recommend using ZeRO3 for multi-GPU training except for
+Q-LoRA, where we recommend using ZeRO2.
+
+There are a series of hyperparameters to tune. Passing in ``--bf16`` or
+``--fp16`` to specify the precision for mixed precision training.
+The other significant hyperparameters include:
+
+-  ``--output_dir``: the path of your output models or adapters.
+-  ``--num_train_epochs``: the number of training epochs.
+-  ``--gradient_accumulation_steps``: the number of gradient
+   accumulation steps.
+-  ``--per_device_train_batch_size``: the batch size per GPU for
+   training, and the total batch size is equalt to
+   ``per_device_train_batch_size`` :math:`\times` ``number_of_gpus``
+   :math:`\times` ``gradient_accumulation_steps``.
+-  ``--learning_rate``: the learning rate.
+-  ``--warmup_steps``: the number of warmup steps.
+-  ``--lr_scheduler_type``: the type of learning rate scheduler.
+-  ``--weight_decay``: the value of weight decay.
+-  ``--adam_beta2``: the value of :math:`\beta_2` in Adam.
+-  ``--model_max_length``: the maximum sequence length.
+-  ``--use_lora``: whether to use LoRA. Adding ``--q_lora`` can enable
+   Q-LoRA.
+-  ``--gradient_checkpointing``: whether to use gradient checkpointing.
+
+Python Script
+~~~~~~~~~~~~~
+
+In this script, we mainly use ``trainer`` from HF and ``peft`` to train
+our models. We also use ``deepspeed`` to accelerate the training
+process. The script is very simple and easy to understand.
+
+.. code:: python
+
+   @dataclass
+   @dataclass
+   class ModelArguments:
+       model_name_or_path: Optional[str] = field(default="Qwen/Qwen-7B")
+
+
+   @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: [
+               "q_proj",
+               "k_proj",
+               "v_proj",
+               "o_proj",
+               "up_proj",
+               "gate_proj",
+               "down_proj",
+           ]
+       )
+       lora_weight_path: str = ""
+       lora_bias: str = "none"
+       q_lora: bool = False
+
+The classes for arguments allow you to specify hyperparameters for
+model, data, training, and additionally LoRA if you use LoRA or Q-LoRA
+to train your model. Specifically, ``model-max-length`` is a key
+hyperparameter that determines your maximum sequence length of your
+training data.
+
+``LoRAArguments`` includes the hyperparameters for LoRA or Q-LoRA:
+
+-  ``lora_r``: the rank for LoRA;
+-  ``lora_alpha``: the alpha value for LoRA;
+-  ``lora_dropout``: the dropout rate for LoRA;
+-  ``lora_target_modules``: the target modules for LoRA. By default we
+   tune all linear layers;
+-  ``lora_weight_path``: the path to the weight file for LoRA;
+-  ``lora_bias``: the bias for LoRA;
+-  ``q_lora``: whether to use Q-LoRA.
+
+
+.. code:: python
+
+   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
+
+
+   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)
+
+The method ``safe_save_model_for_hf_trainer``, which uses
+``get_peft_state_maybe_zero_3``, helps tackle the problems in saving
+models trained either with or without ZeRO3.
+
+.. code:: python
+
+   def preprocess(
+       messages,
+       tokenizer: transformers.PreTrainedTokenizer,
+       max_len: int,
+   ) -> Dict:
+       """Preprocesses the data for supervised fine-tuning."""
+
+       texts = []
+       for i, msg in enumerate(messages):
+           texts.append(
+               tokenizer.apply_chat_template(
+                   msg,
+                   tokenize=True,
+                   add_generation_prompt=False,
+                   padding=True,
+                   max_length=max_len,
+                   truncation=True,
+               )
+           )
+       input_ids = torch.tensor(texts, dtype=torch.int)
+       target_ids = input_ids.clone()
+       target_ids[target_ids == tokenizer.pad_token_id] = IGNORE_TOKEN_ID
+       attention_mask = input_ids.ne(tokenizer.pad_token_id)
+
+       return dict(
+           input_ids=input_ids, target_ids=target_ids, attention_mask=attention_mask
+       )
+
+For data preprocessing, we use ``preprocess`` to organize the data.
+Specifically, we apply our ChatML template to the texts. If you prefer
+other chat templates, you can use others, e.g., by still applying
+``apply_chat_template()`` with another tokenizer. The chat template is
+stored in the ``tokenizer_config.json`` in the HF repo. Additionally, we
+pad the sequence of each sample to the maximum length for training.
+
+.. code:: python
+
+   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...")
+           messages = [example["messages"] for example in raw_data]
+           data_dict = preprocess(messages, tokenizer, max_len)
+
+           self.input_ids = data_dict["input_ids"]
+           self.target_ids = data_dict["target_ids"]
+           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]["messages"]], self.tokenizer, self.max_len)
+           ret = dict(
+               input_ids=ret["input_ids"][0],
+               labels=ret["target_ids"][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_data = []
+       with open(data_args.data_path, "r") as f:
+           for line in f:
+               train_data.append(json.loads(line))
+       train_dataset = dataset_cls(train_data, tokenizer=tokenizer, max_len=max_len)
+
+       if data_args.eval_data_path:
+           eval_data = []
+           with open(data_args.eval_data_path, "r") as f:
+               for line in f:
+                   eval_data.append(json.loads(line))
+           eval_dataset = dataset_cls(eval_data, tokenizer=tokenizer, max_len=max_len)
+       else:
+           eval_dataset = None
+
+       return dict(train_dataset=train_dataset, eval_dataset=eval_dataset)
+
+Then we utilize ``make_supervised_data_module`` by using
+``SupervisedDataset`` or ``LazySupervisedDataset`` to build the dataset.
+
+.. code:: python
+
+   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 = 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 "auto"
+           if len(training_args.fsdp) > 0 or deepspeed.is_deepspeed_zero3_enabled():
+               logging.warning("FSDP or ZeRO3 is incompatible with QLoRA.")
+
+       model_load_kwargs = {
+           "low_cpu_mem_usage": not deepspeed.is_deepspeed_zero3_enabled(),
+       }
+
+       compute_dtype = (
+           torch.float16
+           if training_args.fp16
+           else (torch.bfloat16 if training_args.bf16 else torch.float32)
+       )
+
+       # Load model and tokenizer
+       config = transformers.AutoConfig.from_pretrained(
+           model_args.model_name_or_path,
+           cache_dir=training_args.cache_dir,
+       )
+       config.use_cache = False
+
+       model = AutoModelForCausalLM.from_pretrained(
+           model_args.model_name_or_path,
+           config=config,
+           cache_dir=training_args.cache_dir,
+           device_map=device_map,
+           quantization_config=BitsAndBytesConfig(
+               load_in_4bit=True,
+               bnb_4bit_use_double_quant=True,
+               bnb_4bit_quant_type="nf4",
+               bnb_4bit_compute_dtype=compute_dtype,
+           )
+           if training_args.use_lora and lora_args.q_lora
+           else None,
+           **model_load_kwargs,
+       )
+       tokenizer = AutoTokenizer.from_pretrained(
+           model_args.model_name_or_path,
+           cache_dir=training_args.cache_dir,
+           model_max_length=training_args.model_max_length,
+           padding_side="right",
+           use_fast=False,
+       )
+
+       if training_args.use_lora:
+           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",
+           )
+           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 trainer
+       trainer = Trainer(
+           model=model, tokenizer=tokenizer, args=training_args, **data_module
+       )
+
+       # `not training_args.use_lora` is a temporary workaround for the issue that there are problems with
+       # loading the checkpoint when using LoRA with DeepSpeed.
+       # Check this issue https://github.com/huggingface/peft/issues/746 for more information.
+       if (
+           list(pathlib.Path(training_args.output_dir).glob("checkpoint-*"))
+           and not training_args.use_lora
+       ):
+           trainer.train(resume_from_checkpoint=True)
+       else:
+           trainer.train()
+       trainer.save_state()
+
+       safe_save_model_for_hf_trainer(
+           trainer=trainer, output_dir=training_args.output_dir, bias=lora_args.lora_bias
+       )
+
+The ``train`` method is the key to the training. In general, it loads
+the tokenizer and model with ``AutoTokenizer.from_pretrained()`` and
+``AutoModelForCausalLM.from_pretrained()``. If we use LoRA, the method
+will initialize LoRA configuration with ``LoraConfig``. If we apply
+Q-LoRA, we should use ``prepare_model_for_kbit_training``. Note that for
+now it still does not support resume for LoRA. Then we leave the
+following efforts to ``trainer`` and have a cup of coffee!
+
+Next Step
+---------
+
+Now, you are able to use a very simple script to perform different types
+of SFT. Alternatively, you can use more advanced training libraries,
+such as
+`Axolotl <https://github.com/OpenAccess-AI-Collective/axolotl>`__ or
+`LLaMA-Factory <https://github.com/hiyouga/LLaMA-Factory>`__, to enjoy
+more functionalities. To take a step forward, after SFT, you can
+consider RLHF to align your model to human preferences! Stay tuned for
+our next tutorial on RLHF!

train/sft/finetune.py

@@ -0,0 +1,378 @@
+# This code is based on the revised code from fastchat based on tatsu-lab/stanford_alpaca.
+
+
+from dataclasses import dataclass, field
+import json
+import logging
+import os
+import pathlib
+from typing import Dict, Optional, List
+import torch
+from torch.utils.data import Dataset
+from deepspeed import zero
+from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
+import transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import Trainer, BitsAndBytesConfig, 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
+
+
+IGNORE_TOKEN_ID = LabelSmoother.ignore_index
+
+TEMPLATE = "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content']}}{% if loop.last %}{{ '<|im_end|>'}}{% else %}{{ '<|im_end|>\n' }}{% endif %}{% endfor %}"
+
+local_rank = None
+
+
+def rank0_print(*args):
+    if local_rank == 0:
+        print(*args)
+
+
+@dataclass
+class ModelArguments:
+    model_name_or_path: Optional[str] = field(default="Qwen/Qwen-7B")
+
+
+@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: [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+            "o_proj",
+            "up_proj",
+            "gate_proj",
+            "down_proj",
+        ]
+    )
+    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
+
+
+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(
+    messages,
+    tokenizer: transformers.PreTrainedTokenizer,
+    max_len: int,
+) -> Dict:
+    """Preprocesses the data for supervised fine-tuning."""
+
+    texts = []
+    for i, msg in enumerate(messages):
+        texts.append(
+            tokenizer.apply_chat_template(
+                msg,
+                chat_template=TEMPLATE,
+                tokenize=True,
+                add_generation_prompt=False,
+                padding=True,
+                max_length=max_len,
+                truncation=True,
+            )
+        )
+    input_ids = torch.tensor(texts, dtype=torch.int)
+    target_ids = input_ids.clone()
+    target_ids[target_ids == tokenizer.pad_token_id] = IGNORE_TOKEN_ID
+    attention_mask = input_ids.ne(tokenizer.pad_token_id)
+
+    return dict(
+        input_ids=input_ids, target_ids=target_ids, 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...")
+        messages = [example["messages"] for example in raw_data]
+        data_dict = preprocess(messages, tokenizer, max_len)
+
+        self.input_ids = data_dict["input_ids"]
+        self.target_ids = data_dict["target_ids"]
+        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.target_ids[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]["messages"]], self.tokenizer, self.max_len)
+        ret = dict(
+            input_ids=ret["input_ids"][0],
+            labels=ret["target_ids"][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_data = []
+    with open(data_args.data_path, "r") as f:
+        for line in f:
+            train_data.append(json.loads(line))
+    train_dataset = dataset_cls(train_data, tokenizer=tokenizer, max_len=max_len)
+
+    if data_args.eval_data_path:
+        eval_data = []
+        with open(data_args.eval_data_path, "r") as f:
+            for line in f:
+                eval_data.append(json.loads(line))
+        eval_dataset = dataset_cls(eval_data, 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 = 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 "auto"
+        if len(training_args.fsdp) > 0 or deepspeed.is_deepspeed_zero3_enabled():
+            logging.warning("FSDP or ZeRO3 is incompatible with QLoRA.")
+
+    model_load_kwargs = {
+        "low_cpu_mem_usage": not deepspeed.is_deepspeed_zero3_enabled(),
+    }
+
+    compute_dtype = (
+        torch.float16
+        if training_args.fp16
+        else (torch.bfloat16 if training_args.bf16 else torch.float32)
+    )
+
+    # Load model and tokenizer
+    config = transformers.AutoConfig.from_pretrained(
+        model_args.model_name_or_path,
+        cache_dir=training_args.cache_dir,
+    )
+    config.use_cache = False
+
+    model = AutoModelForCausalLM.from_pretrained(
+        model_args.model_name_or_path,
+        config=config,
+        cache_dir=training_args.cache_dir,
+        device_map=device_map,
+        quantization_config=BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_use_double_quant=True,
+            bnb_4bit_quant_type="nf4",
+            bnb_4bit_compute_dtype=compute_dtype,
+        )
+        if training_args.use_lora and lora_args.q_lora
+        else None,
+        **model_load_kwargs,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_args.model_name_or_path,
+        cache_dir=training_args.cache_dir,
+        model_max_length=training_args.model_max_length,
+        padding_side="right",
+        use_fast=False,
+    )
+
+    if training_args.use_lora:
+        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",
+        )
+        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 trainer
+    trainer = Trainer(
+        model=model, tokenizer=tokenizer, args=training_args, **data_module
+    )
+
+    # `not training_args.use_lora` is a temporary workaround for the issue that there are problems with
+    # loading the checkpoint when using LoRA with DeepSpeed.
+    # Check this issue https://github.com/huggingface/peft/issues/746 for more information.
+    if (
+        list(pathlib.Path(training_args.output_dir).glob("checkpoint-*"))
+        and not training_args.use_lora
+    ):
+        trainer.train(resume_from_checkpoint=True)
+    else:
+        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()

train/sft/finetune.sh

@@ -0,0 +1,107 @@
+#!/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/Qwen1.5-7B" # 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="ds_config_zero3.json"
+USE_LORA=False
+Q_LORA=False
+
+function usage() {
+    echo '
+Usage: bash finetune/finetune_lora_ds.sh [-m MODEL_PATH] [-d DATA_PATH] [--deepspeed DS_CONFIG_PATH] [--use_lora USE_LORA] [--q_lora Q_LORA]
+'
+}
+
+while [[ "$1" != "" ]]; do
+    case $1 in
+        -m | --model )
+            shift
+            MODEL=$1
+            ;;
+        -d | --data )
+            shift
+            DATA=$1
+            ;;
+        --deepspeed )
+            shift
+            DS_CONFIG_PATH=$1
+            ;;
+        --use_lora  )
+            shift
+            USE_LORA=$1
+            ;;
+        --q_lora    )
+            shift
+            Q_LORA=$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 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 10 \
+    --save_total_limit 10 \
+    --learning_rate 3e-4 \
+    --weight_decay 0.01 \
+    --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 ${USE_LORA} \
+    --q_lora ${Q_LORA} \
+    --gradient_checkpointing \
+    --deepspeed ${DS_CONFIG_PATH}

utils/conversation.py

@@ -0,0 +1,61 @@
+import datetime
+import json
+import os
+import pickle
+
+
+def load_conversation(model_id):
+    folder = 'conversations/' + model_id.replace('/', '_')
+    mkdir('../conversations')
+    mkdir(folder)
+
+    files = os.listdir(folder)
+    files = [file for file in files if file.endswith(".pickle") and os.path.isfile(folder + '/' + file)]
+    files.sort(reverse=True)
+    if len(files) > 0:
+        pickle_filename = folder + '/' + files[0]
+        print('Loading last conversation from ' + pickle_filename)
+        with open(pickle_filename, 'rb') as file:
+            return pickle.load(file)
+    return []
+
+
+def save_conversation(model_id, messages):
+    folder = 'conversations/' + model_id.replace('/', '_')
+    mkdir('../conversations')
+    mkdir(folder)
+    timestamp = datetime.datetime.utcnow().strftime('%Y%m%d%H%M%S')
+    pickle_filename = folder + '/' + timestamp + '.pickle'
+    with open(pickle_filename, 'wb') as file:
+        pickle.dump(messages, file)
+
+
+def load_conversation_json(model_id):
+    folder = 'conversations/' + model_id.replace('/', '_')
+    mkdir('../conversations')
+    mkdir(folder)
+
+    files = os.listdir(folder)
+    files = [file for file in files if file.endswith(".json") and os.path.isfile(folder + '/' + file)]
+    files.sort(reverse=True)
+    if len(files) > 0:
+        pickle_filename = folder + '/' + files[0]
+        print('Loading last conversation from ' + pickle_filename)
+        with open(pickle_filename, 'r') as file:
+            return json.load(file)
+    return []
+
+
+def save_conversation_json(model_id, messages):
+    folder = 'conversations/' + model_id.replace('/', '_')
+    mkdir('../conversations')
+    mkdir(folder)
+    timestamp = datetime.datetime.utcnow().strftime('%Y%m%d%H%M%S')
+    pickle_filename = folder + '/' + timestamp + '.json'
+    with open(pickle_filename, 'w') as file:
+        json.dump(messages, file)
+
+
+def mkdir(path):
+    if not os.path.isdir(path):
+        os.mkdir(path)

utils/download_dataset.py

@@ -0,0 +1,12 @@
+import pickle
+from conversation import mkdir
+from datasets import load_dataset
+
+dataset_id = 'OpenAssistant/oasst2'
+
+mkdir('../datasets')
+pickle_filename = './datasets/' + dataset_id.replace('/', '_') + '.pickle'
+dataset = load_dataset(dataset_id)
+with open(pickle_filename, 'wb') as file:
+    pickle.dump(dataset, file)
+print('Saved as pickle to ' + pickle_filename)

utils/download_model.py

@@ -0,0 +1,18 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer
+
+from conversation import mkdir
+
+model_id = 'Qwen/Qwen1.5-0.5B-Chat'
+# model_id = 'Qwen/Qwen1.5-1.8B-Chat'
+
+print('Downloading ' + model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype='auto', device_map='auto')
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+print('Downloaded')
+
+trainer = Trainer(
+    model=model,
+    tokenizer=tokenizer,
+)
+mkdir('models')
+trainer.save_model('./models/' + model_id.replace('/', '_'))

utils/fix_cuda.sh

@@ -0,0 +1,3 @@
+#https://medium.com/@Spritan/dealing-with-cuda-initialization-error-aa7c88d021e4
+sudo rmmod nvidia_uvm
+sudo modprobe nvidia_uvm

utils/pickle2json.py

@@ -0,0 +1,22 @@
+import json
+import pickle
+import sys
+
+files = sys.argv[1:]
+print(files)
+
+for pickle_filename in files:
+    if not pickle_filename.endswith('.pickle'):
+        print(pickle_filename + ' is not a pickle. ignoring')
+        continue
+
+    with open(pickle_filename, 'rb') as file:
+        obj = pickle.load(file)
+        print(obj)
+
+    json_filename = pickle_filename[0:-6] + 'json'
+    try:
+        with open(json_filename, 'w') as file:
+            json.dump(obj, file)
+    except Exception as e:
+        print(e)

utils/prompt.py

@@ -0,0 +1,14 @@
+def prompt(prompt):
+    while True:
+        try:
+            return input(prompt)
+        except EOFError:
+            print()
+            exit(0)
+        except KeyboardInterrupt:
+            print()
+            exit(0)
+        # in case: UnicodeDecodeError: 'utf-8' codec can't decode byte 0xc3 in position 11: invalid continuation byte
+        except UnicodeDecodeError as e:
+            print(e)
+            print('prompt ignored')

utils/split_shuffle_dataset.py

@@ -0,0 +1,25 @@
+import json
+import os
+import random
+import sys
+
+original = sys.argv[1]
+no_dataset = int(sys.argv[2])
+
+if not original.endswith('.jsonl') or not os.path.isfile(original):
+    print('Not a jsonl file')
+    exit(1)
+
+out_dir = os.path.dirname(os.path.abspath(original))
+
+with open(original, 'r') as f:
+    lines = f.readlines()
+
+random.shuffle(lines)
+
+for i in range(no_dataset):
+    l = int(i * len(lines) / no_dataset)
+    u = int((i + 1) * len(lines) / no_dataset)
+    out_filename = os.path.basename(original)[0:-6].replace('_all', '_' + str(i)) + '.jsonl'
+    with open(out_dir + '/' + out_filename, 'w') as f:
+        f.writelines(lines[l:u])