debate

.github/workflows/run_chatgpt_examples.yml

@@ -28,9 +28,8 @@ jobs:
       - name: Checkout ColossalAI
         uses: actions/checkout@v2
 
-      - name: Install ColossalAI and ChatGPT
+      - name: Install ChatGPT
         run: |
-          pip install -e .
           cd applications/Chat
           pip install -v .
           pip install -r examples/requirements.txt

.github/workflows/run_chatgpt_unit_tests.yml

@@ -30,9 +30,8 @@ jobs:
       - name: Checkout ColossalAI
         uses: actions/checkout@v2
 
-      - name: Install ColossalAI and ChatGPT
+      - name: Install ChatGPT
         run: |
-          pip install -e .
           cd applications/Chat
           pip install -v .
           pip install -r requirements-test.txt

README.md

@@ -25,6 +25,7 @@
 </div>
 
 ## Latest News
+* [2023/09] [70 Billion Parameter LLaMA2 Model Training Accelerated by 195%](https://www.hpc-ai.tech/blog/70b-llama2-training)
 * [2023/07] [HPC-AI Tech Raises 22 Million USD in Series A Funding](https://www.hpc-ai.tech/blog/hpc-ai-tech-raises-22-million-usd-in-series-a-funding-to-fuel-team-expansion-and-business-growth)
 * [2023/07] [65B Model Pretraining Accelerated by 38%, Best Practices for Building LLaMA-Like Base Models Open-Source](https://www.hpc-ai.tech/blog/large-model-pretraining)
 * [2023/03] [ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b)
@@ -50,7 +51,7 @@
  <li>
    <a href="#Parallel-Training-Demo">Parallel Training Demo</a>
    <ul>
-     <li><a href="#LLaMA">LLaMA</a></li>
+     <li><a href="#LLaMA2">LLaMA 1/2</a></li>
      <li><a href="#GPT-3">GPT-3</a></li>
      <li><a href="#GPT-2">GPT-2</a></li>
      <li><a href="#BERT">BERT</a></li>
@@ -217,8 +218,16 @@ Acceleration of [AlphaFold Protein Structure](https://alphafold.ebi.ac.uk/)
 <p align="right">(<a href="#top">back to top</a>)</p>
 
 ## Parallel Training Demo
+### LLaMA2
+<p align="center">
+<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/llama2_pretraining.png" width=600/>
+</p>
+
+- 70 billion parameter LLaMA2 model training accelerated by 195%
+[[code]](https://github.com/hpcaitech/ColossalAI/tree/example/llama/examples/language/llama)
+[[blog]](https://www.hpc-ai.tech/blog/70b-llama2-training)
 
-### LLaMA
+### LLaMA1
 <p align="center">
 <img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/examples/images/LLaMA_pretraining.png" width=600/>
 </p>

applications/Chat/coati/dataset/sft_dataset.py

@@ -19,7 +19,7 @@
 from torch.utils.data import Dataset
 from tqdm import tqdm
 from transformers import PreTrainedTokenizer
-
+from coati.models.chatglm.chatglm_tokenizer import ChatGLMTokenizer 
 from colossalai.logging import get_dist_logger
 
 from .utils import is_rank_0, jload
@@ -71,6 +71,42 @@ def _preprocess(sources: Sequence[str],
     return sequences_token["input_ids"], labels, sequences_token["attention_mask"]
 
 
+def _preprocess_chatglm(sources: Sequence[str],
+                targets: Sequence[str],
+                tokenizer: PreTrainedTokenizer,
+                max_length: int,
+                ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """
+    Preprocess the data by tokenizing.
+    None for attention mask, ChatGLM will calculate attention mask according to input ids
+    """
+
+    labels = []
+    input_ids = []
+    for source, target in zip(sources, targets):
+        source_id = tokenizer.encode(text=source, add_special_tokens=False)
+        target_id = tokenizer.encode(text=target, add_special_tokens=False)
+        input_id = tokenizer.build_inputs_with_special_tokens(source_id, target_id)
+        # truncate
+        sp_token_list = [tokenizer.gmask_token_id, tokenizer.bos_token_id]
+        truncate_length = max(0, len(input_id) - max_length)
+        input_id = input_id[truncate_length: ]
+        if truncate_length == len(source_id) + 1:
+            input_id = sp_token_list + input_id[1: ]
+        elif truncate_length > len(source_id) + 1:
+            input_id = sp_token_list + input_id[2: ]
+
+        context_length = input_id.index(tokenizer.bos_token_id)
+        mask_position = context_length - 1
+        label = [IGNORE_INDEX] * context_length + input_id[mask_position+1:]
+
+        pad_len = max_length - len(input_id)
+        input_id = input_id + [tokenizer.pad_token_id] * pad_len
+        input_ids.append(input_id)
+        labels.append(label + [IGNORE_INDEX] * pad_len)
+    return torch.tensor(input_ids), torch.tensor(labels), None
+
+
 class SFTDataset(Dataset):
     """
     Dataset for sft model
@@ -94,18 +130,25 @@ def __init__(self,
             data["completion"] + tokenizer.eos_token
             for data in tqdm(dataset, disable=not is_rank_0())
         ]
-
-        self.input_ids, self.labels, self.attention_mask = \
-            _preprocess(sources, targets, tokenizer, max_length)
+        if isinstance(tokenizer, ChatGLMTokenizer):
+            self.input_ids, self.labels, self.attention_mask = \
+                _preprocess_chatglm(sources, targets, tokenizer, max_length)
+        else:
+            self.input_ids, self.labels, self.attention_mask = \
+                _preprocess(sources, targets, tokenizer, max_length)
 
     def __len__(self):
         length = self.input_ids.shape[0]
         return length
 
     def __getitem__(self, idx):
-        return dict(input_ids=self.input_ids[idx],
-                    labels=self.labels[idx],
-                    attention_mask=self.attention_mask[idx])
+        if self.attention_mask is not None:
+            return dict(input_ids=self.input_ids[idx],
+                        labels=self.labels[idx],
+                        attention_mask=self.attention_mask[idx])
+        else:
+            return dict(input_ids=self.input_ids[idx],
+                        labels=self.labels[idx])
 
 
 class SupervisedDataset(Dataset):
@@ -137,14 +180,22 @@ def __init__(self,
         ]
 
         logger.info("Tokenizing inputs... This may take some time...")
-        self.input_ids, self.labels, self.attention_mask = \
-            _preprocess(sources, targets, tokenizer, max_length)
+        if isinstance(tokenizer, ChatGLMTokenizer):
+            self.input_ids, self.labels, self.attention_mask = \
+                _preprocess_chatglm(sources, targets, tokenizer, max_length)
+        else:
+            self.input_ids, self.labels, self.attention_mask = \
+                _preprocess(sources, targets, tokenizer, max_length)
 
     def __len__(self):
         length = self.input_ids.shape[0]
         return length
 
     def __getitem__(self, idx):
-        return dict(input_ids=self.input_ids[idx],
-                    labels=self.labels[idx],
-                    attention_mask=self.attention_mask[idx])
+        if self.attention_mask is not None:
+            return dict(input_ids=self.input_ids[idx],
+                        labels=self.labels[idx],
+                        attention_mask=self.attention_mask[idx])
+        else:
+            return dict(input_ids=self.input_ids[idx],
+                        labels=self.labels[idx])

applications/Chat/coati/models/chatglm/__init__.py

@@ -0,0 +1,3 @@
+from .chatglm_actor import ChatGLMActor
+
+__all__ = ['ChatGLMActor']

applications/Chat/coati/models/chatglm/chatglm_actor.py

@@ -0,0 +1,34 @@
+from typing import Optional
+
+import torch
+from .configuration_chatglm import ChatGLMConfig
+from .modeling_chatglm import ChatGLMForConditionalGeneration
+
+from ..base import Actor
+
+
+class ChatGLMActor(Actor):
+    """
+    ChatGLM Actor model.
+
+    Args:
+        pretrained (str): Pretrained model name or path.
+        config (ChatGLMConfig): Model config.
+        checkpoint (bool): Enable gradient checkpointing.
+
+    do not support lora for now.
+    """
+
+    def __init__(self,
+                 pretrained: str = None,
+                 config: Optional[ChatGLMConfig] = None,
+                 checkpoint: bool = False) -> None:
+        if pretrained is not None:
+            model = ChatGLMForConditionalGeneration.from_pretrained(pretrained)
+        elif config is not None:
+            model = ChatGLMForConditionalGeneration(config)
+        else:
+            model = ChatGLMForConditionalGeneration(ChatGLMConfig())
+        if checkpoint:
+            model.gradient_checkpointing_enable()
+        super().__init__(model, lora_rank=0, lora_train_bias='none')