[application] add lora sft example

.github/workflows/run_chatgpt_examples.yml

@@ -31,13 +31,12 @@ jobs:
 
       - name: Install Colossal-AI
         run: |
-          BUILD_EXT=1 pip install --no-cache-dir -v -e .
+          pip install --no-cache-dir -v -e .
 
       - name: Install ChatGPT
         run: |
           cd applications/ColossalChat
           pip install --no-cache-dir -v .
-          export BUILD_EXT=1
           pip install --no-cache-dir -r examples/requirements.txt
 
       - name: Install Transformers

applications/ColossalChat/README.md

@@ -29,6 +29,7 @@
 - [Alternative Option For RLHF: KTO](#alternative-option-for-rlhf-kahneman-tversky-optimization-kto)
 - [O1 Journey](#o1-journey)
   - [Inference with Self-refined MCTS](#inference-with-self-refined-mcts)
+- [SFT for DeepSeek V3/R1](#sft-for-deepseek-v3)
 - [FAQ](#faq)
   - [How to save/load checkpoint](#faq)
   - [How to train with limited resources](#faq)
@@ -389,6 +390,37 @@ You can find more examples in this [repo](https://github.com/XueFuzhao/Instructi
   - Cannot abide by OpenAI's policy: When generating prompts from OpenAI API, it always abides by its policy. So no violation case is in the datasets.
 </details>
 
+## SFT for DeepSeek V3
+
+We add a script to supervised-fintune the DeepSeek V3/R1 model with LoRA. The script is located in `examples/training_scripts/lora_fintune.py`. The script is similar to the SFT script for Coati7B, but with a few differences. This script is compatible with Peft.
+
+### Dataset preparation
+
+This script receives JSONL format file as input dataset. Each line of dataset should be a list of chat dialogues. E.g.
+```json
+[{"role": "user", "content": "Hello, how are you?"}, {"role": "assistant", "content": "I'm doing great. How can I help you today?"}]
+```
+```json
+[{"role": "user", "content": "火烧赤壁 曹操为何不拨打119求救？"}, {"role": "assistant", "content": "因为在三国时期，还没有电话和现代的消防系统，所以曹操无法拨打119求救。"}]
+```
+
+The dialogues can by multiple turns and it can contain system prompt. For more details, see the [chat_templating](https://huggingface.co/docs/transformers/main/chat_templating).
+
+### Model weights preparation
+
+We use bf16 weights for finetuning. If you downloaded fp8 DeepSeek V3/R1 weights, you can use the [script](https://github.com/deepseek-ai/DeepSeek-V3/blob/main/inference/fp8_cast_bf16.py) to convert the weights to bf16 via GPU. For Ascend NPU, you can use this [script](https://gitee.com/ascend/ModelZoo-PyTorch/blob/master/MindIE/LLM/DeepSeek/DeepSeek-V2/NPU_inference/fp8_cast_bf16.py).
+
+### Usage
+
+After preparing the dataset and model weights, you can run the script with the following command:
+```bash
+colossalai run --hostfile path-to-host-file --nproc_per_node 8 lora_finetune.py --pretrained path-to-DeepSeek-R1-bf16 --dataset path-to-dataset.jsonl --plugin moe --lr 2e-5 --max_length 256 -g --ep 8 --pp 3 --batch_size 24 --lora_rank 8 --lora_alpha 16 --num_epochs 2 --warmup_steps 8 --tensorboard_dir logs --save_dir DeepSeek-R1-bf16-lora
+```
+
+For more details of each argument, you can run `python lora_finetune.py --help`.
+
+The sample command does not use CPU offload to get better throughput. The minimum hardware requirement for sample command is 32 ascend 910B NPUs (with `ep=8,pp=4`) or 24 H100/H800 GPUs (with `ep=8,pp=3`). If you enable CPU offload by `--zero_cpu_offload`, the hardware requirement can be further reduced.
+
 ## FAQ
 
 <details><summary><b>How to save/load checkpoint</b></summary>
@@ -501,7 +533,7 @@ Thanks so much to all of our amazing contributors!
 - Keep in a sufficiently high running speed
 
 |  Model Pair   | Alpaca-7B ⚔ Coati-7B | Coati-7B ⚔ Alpaca-7B |
-| :-----------: | :------------------: | :------------------: |
+|:-------------:|:--------------------:|:--------------------:|
 | Better Cases  |     38 ⚔ **41**      |     **45** ⚔ 33      |
 |   Win Rate    |    48% ⚔ **52%**     |    **58%** ⚔ 42%     |
 | Average Score |   7.06 ⚔ **7.13**    |   **7.31** ⚔ 6.82    |

applications/ColossalChat/coati/dataset/loader.py

@@ -8,6 +8,7 @@
 from dataclasses import dataclass
 from typing import Dict, Iterator, List, Optional, Sequence, Union
 
+import jsonlines
 import torch
 import torch.nn.functional as F
 from coati.dataset.utils import chuncate_sequence, pad_to_max_len
@@ -344,3 +345,77 @@ def __len__(self) -> int:
 
     def set_start_index(self, start_index: int) -> None:
         self.start_index = start_index
+
+
+def apply_chat_template_and_mask(
+    tokenizer: PreTrainedTokenizer,
+    chat: List[Dict[str, str]],
+    max_length: Optional[int] = None,
+    padding: bool = True,
+    truncation: bool = True,
+    ignore_idx: int = -100,
+) -> Dict[str, torch.Tensor]:
+    tokens = []
+    assistant_mask = []
+    for i, msg in enumerate(chat):
+        msg_tokens = tokenizer.apply_chat_template([msg], tokenize=True)
+        # remove unexpected bos token
+        if i > 0 and msg_tokens[0] == tokenizer.bos_token_id:
+            msg_tokens = msg_tokens[1:]
+        tokens.extend(msg_tokens)
+        if msg["role"] == "assistant":
+            assistant_mask.extend([True] * len(msg_tokens))
+        else:
+            assistant_mask.extend([False] * len(msg_tokens))
+    attention_mask = [1] * len(tokens)
+    if max_length is not None:
+        if padding and len(tokens) < max_length:
+            to_pad = max_length - len(tokens)
+            if tokenizer.padding_side == "right":
+                tokens.extend([tokenizer.pad_token_id] * to_pad)
+                assistant_mask.extend([False] * to_pad)
+                attention_mask.extend([0] * to_pad)
+            else:
+                tokens = [tokenizer.pad_token_id] * to_pad + tokens
+                assistant_mask = [False] * to_pad + assistant_mask
+                attention_mask = [0] * to_pad + attention_mask
+        if truncation and len(tokens) > max_length:
+            tokens = tokens[:max_length]
+            assistant_mask = assistant_mask[:max_length]
+            attention_mask = attention_mask[:max_length]
+    input_ids = torch.tensor(tokens, dtype=torch.long)
+    attention_mask = torch.tensor(attention_mask, dtype=torch.long)
+    labels = input_ids.clone()
+    labels[~torch.tensor(assistant_mask, dtype=torch.bool)] = ignore_idx
+
+    return {
+        "input_ids": input_ids,
+        "attention_mask": attention_mask,
+        "labels": labels,
+    }
+
+
+class RawConversationDataset(Dataset):
+    """
+    Raw conversation dataset.
+    Each instance is a dictionary with fields `system`, `roles`, `messages`, `offset`, `sep_style`, `seps`.
+    """
+
+    def __init__(self, tokenizer: PreTrainedTokenizer, input_file: str, max_length: int) -> None:
+        self.tokenizer = tokenizer
+        self.raw_texts = []
+        with jsonlines.open(input_file) as f:
+            for line in f:
+                self.raw_texts.append(line)
+        self.tokenized_texts = [None] * len(self.raw_texts)
+        self.max_length = max_length
+
+    def __len__(self) -> int:
+        return len(self.raw_texts)
+
+    def __getitem__(self, index: int):
+        if self.tokenized_texts[index] is None:
+            message = self.raw_texts[index]
+            tokens = apply_chat_template_and_mask(self.tokenizer, message, self.max_length)
+            self.tokenized_texts[index] = dict(tokens)
+        return self.tokenized_texts[index]