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11
README.md
View File

@ -72,8 +72,6 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
[24/04/21] We supported **[Mixture-of-Depths](https://arxiv.org/abs/2404.02258)** according to [AstraMindAI's implementation](https://github.com/astramind-ai/Mixture-of-depths). See `examples/extras/mod` for usage.
[24/04/19] We supported **Meta Llama 3** model series.
[24/04/16] We supported **[BAdam](https://arxiv.org/abs/2404.02827)**. See `examples/extras/badam` for usage.
[24/04/16] We supported **[unsloth](https://github.com/unslothai/unsloth)**'s long-sequence training (Llama-2-7B-56k within 24GB). It achieves **117%** speed and **50%** memory compared with FlashAttention-2, more benchmarks can be found in [this page](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison).
@ -112,7 +110,7 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
[23/09/23] We integrated MMLU, C-Eval and CMMLU benchmarks in this repo. See [this example](#evaluation) to evaluate your models.
[23/09/10] We supported **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)**. Try `--flash_attn` argument to enable FlashAttention-2 if you are using RTX4090, A100 or H100 GPUs.
[23/09/10] We supported **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)**. Try `--flash_attn fa2` argument to enable FlashAttention-2 if you are using RTX4090, A100 or H100 GPUs.
[23/08/12] We supported **RoPE scaling** to extend the context length of the LLaMA models. Try `--rope_scaling linear` argument in training and `--rope_scaling dynamic` argument at inference to extrapolate the position embeddings.
@ -151,8 +149,9 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
| [LLaMA-2](https://huggingface.co/meta-llama) | 7B/13B/70B | q_proj,v_proj | llama2 |
| [LLaMA-3](https://huggingface.co/meta-llama) | 8B/70B | q_proj,v_proj | llama3 |
| [Mistral/Mixtral](https://huggingface.co/mistralai) | 7B/8x7B/8x22B | q_proj,v_proj | mistral |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | att_proj | olmo |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | q_proj,v_proj | - |
| [Phi-1.5/2](https://huggingface.co/microsoft) | 1.3B/2.7B | q_proj,v_proj | - |
| [Phi-3](https://huggingface.co/microsoft) | 3.8B | qkv_proj | phi |
| [Qwen](https://huggingface.co/Qwen) | 1.8B/7B/14B/72B | c_attn | qwen |
| [Qwen1.5 (Code/MoE)](https://huggingface.co/Qwen) | 0.5B/1.8B/4B/7B/14B/32B/72B | q_proj,v_proj | qwen |
| [StarCoder2](https://huggingface.co/bigcode) | 3B/7B/15B | q_proj,v_proj | - |
@ -333,7 +332,7 @@ To enable FlashAttention-2 on the Windows platform, you need to install the prec
</details>
### Train with LLaMA Board GUI
### Train with LLaMA Board GUI (powered by [Gradio](https://github.com/gradio-app/gradio))
> [!IMPORTANT]
> LLaMA Board GUI only supports training on a single GPU, please use [CLI](#command-line-interface) for distributed training.
@ -458,7 +457,7 @@ If you have a project that should be incorporated, please contact via email or c
This repository is licensed under the [Apache-2.0 License](LICENSE).
Please follow the model licenses to use the corresponding model weights: [Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command-R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [InternLM2](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2](https://ai.meta.com/llama/license/) / [LLaMA-3](https://llama.meta.com/llama3/license/) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yuan](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
Please follow the model licenses to use the corresponding model weights: [Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command-R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [InternLM2](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2](https://ai.meta.com/llama/license/) / [LLaMA-3](https://llama.meta.com/llama3/license/) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yuan](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
## Citation

11
README_zh.md
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@ -72,8 +72,6 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
[24/04/21] 我们基于 [AstraMindAI 的仓库](https://github.com/astramind-ai/Mixture-of-depths)支持了 **[混合深度训练](https://arxiv.org/abs/2404.02258)**。详细用法请参照 `examples/extras/mod`。
[24/04/19] 我们支持了 **Meta Llama 3** 系列模型。
[24/04/16] 我们支持了 **[BAdam](https://arxiv.org/abs/2404.02827)**。详细用法请参照 `examples/extras/badam`。
[24/04/16] 我们支持了 **[unsloth](https://github.com/unslothai/unsloth)** 的长序列训练（24GB 可训练 Llama-2-7B-56k）。该方法相比 FlashAttention-2 提供了 **117%** 的训练速度和 **50%** 的显存节约。更多数据请见[此页面](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison)。
@ -112,7 +110,7 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
[23/09/23] 我们在项目中集成了 MMLU、C-Eval 和 CMMLU 评估集。使用方法请参阅[此示例](#模型评估)。
[23/09/10] 我们支持了 **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)**。如果您使用的是 RTX4090、A100 或 H100 GPU，请使用 `--flash_attn` 参数以启用 FlashAttention-2。
[23/09/10] 我们支持了 **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)**。如果您使用的是 RTX4090、A100 或 H100 GPU，请使用 `--flash_attn fa2` 参数以启用 FlashAttention-2。
[23/08/12] 我们支持了 **RoPE 插值**来扩展 LLaMA 模型的上下文长度。请使用 `--rope_scaling linear` 参数训练模型或使用 `--rope_scaling dynamic` 参数评估模型。
@ -151,8 +149,9 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
| [LLaMA-2](https://huggingface.co/meta-llama) | 7B/13B/70B | q_proj,v_proj | llama2 |
| [LLaMA-3](https://huggingface.co/meta-llama) | 8B/70B | q_proj,v_proj | llama3 |
| [Mistral/Mixtral](https://huggingface.co/mistralai) | 7B/8x7B/8x22B | q_proj,v_proj | mistral |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | att_proj | olmo |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | q_proj,v_proj | - |
| [Phi-1.5/2](https://huggingface.co/microsoft) | 1.3B/2.7B | q_proj,v_proj | - |
| [Phi-3](https://huggingface.co/microsoft) | 3.8B | qkv_proj | phi |
| [Qwen](https://huggingface.co/Qwen) | 1.8B/7B/14B/72B | c_attn | qwen |
| [Qwen1.5 (Code/MoE)](https://huggingface.co/Qwen) | 0.5B/1.8B/4B/7B/14B/32B/72B | q_proj,v_proj | qwen |
| [StarCoder2](https://huggingface.co/bigcode) | 3B/7B/15B | q_proj,v_proj | - |
@ -333,7 +332,7 @@ pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/downl
</details>
### 利用 LLaMA Board 可视化界面训练
### 利用 LLaMA Board 可视化界面训练（由 [Gradio](https://github.com/gradio-app/gradio) 驱动）
> [!IMPORTANT]
> LLaMA Board 可视化界面目前仅支持单 GPU 训练，请使用[命令行接口](#命令行接口)来进行多 GPU 分布式训练。
@ -458,7 +457,7 @@ export USE_MODELSCOPE_HUB=1 # Windows 使用 `set USE_MODELSCOPE_HUB=1`
本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源。
使用模型权重时，请遵循对应的模型协议：[Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command-R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [InternLM2](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2](https://ai.meta.com/llama/license/) / [LLaMA-3](https://llama.meta.com/llama3/license/) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yuan](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
使用模型权重时，请遵循对应的模型协议：[Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command-R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [InternLM2](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2](https://ai.meta.com/llama/license/) / [LLaMA-3](https://llama.meta.com/llama3/license/) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yuan](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
## 引用

1
requirements.txt
View File

@ -15,3 +15,4 @@ fastapi
sse-starlette
matplotlib
fire
packaging

3
scripts/test_mllm.py
View File

@ -47,7 +47,8 @@ def apply_lora(base_model_path, model_path, lora_path):
model.save_pretrained(model_path)
tokenizer.save_pretrained(model_path)
processor.image_processor.save_pretrained(model_path)
if 'instructblip' in model_path:
processor.qformer_tokenizer.save_pretrained(model_path)
def main(
model_path: str,

18
src/llmtuner/chat/vllm_engine.py
View File

@ -2,9 +2,9 @@ import uuid
from typing import TYPE_CHECKING, AsyncGenerator, AsyncIterator, Dict, List, Optional, Sequence
from ..data import get_template_and_fix_tokenizer
from ..extras.misc import get_device_count
from ..extras.misc import get_device_count, infer_optim_dtype
from ..extras.packages import is_vllm_available
from ..model import load_tokenizer
from ..model import load_config, load_tokenizer
from .base_engine import BaseEngine, Response
@ -23,10 +23,20 @@ class VllmEngine(BaseEngine):
finetuning_args: "FinetuningArguments",
generating_args: "GeneratingArguments",
) -> None:
config = load_config(model_args) # may download model from ms hub
load_dtype = infer_optim_dtype(model_dtype=getattr(config, "torch_dtype", None))
self.can_generate = finetuning_args.stage == "sft"
self.tokenizer = load_tokenizer(model_args)
self.tokenizer.padding_side = "left"
self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template)
self.generating_args = generating_args.to_dict()
engine_args = AsyncEngineArgs(
model=model_args.model_name_or_path,
trust_remote_code=True,
download_dir=model_args.cache_dir,
dtype=str(load_dtype).split(".")[-1],
max_model_len=model_args.vllm_maxlen,
tensor_parallel_size=get_device_count() or 1,
gpu_memory_utilization=model_args.vllm_gpu_util,
@ -35,10 +45,6 @@ class VllmEngine(BaseEngine):
enforce_eager=model_args.vllm_enforce_eager,
)
self.model = AsyncLLMEngine.from_engine_args(engine_args)
self.tokenizer = load_tokenizer(model_args)
self.tokenizer.padding_side = "left"
self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template)
self.generating_args = generating_args.to_dict()
async def _generate(
self,

52
src/llmtuner/data/template.py
View File

@ -503,6 +503,7 @@ _register_template(
name="chatml",
format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
format_separator=EmptyFormatter(slots=["\n"]),
stop_words=["<|im_end|>", "<|im_start|>"],
replace_eos=True,
@ -513,6 +514,7 @@ _register_template(
name="chatml_de",
format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
format_separator=EmptyFormatter(slots=["\n"]),
default_system="Du bist ein freundlicher und hilfsbereiter KI-Assistent.",
stop_words=["<|im_end|>", "<|im_start|>"],
@ -550,6 +552,32 @@ _register_template(
)
_register_template(
name="dbrx",
format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
format_separator=EmptyFormatter(slots=["\n"]),
default_system=(
"You are DBRX, created by Databricks. You were last updated in December 2023. "
"You answer questions based on information available up to that point.\n"
"YOU PROVIDE SHORT RESPONSES TO SHORT QUESTIONS OR STATEMENTS, but provide thorough "
"responses to more complex and open-ended questions.\nYou assist with various tasks, "
"from writing to coding (using markdown for code blocks — remember to use ``` with "
"code, JSON, and tables).\n(You do not have real-time data access or code execution "
"capabilities. You avoid stereotyping and provide balanced perspectives on "
"controversial topics. You do not provide song lyrics, poems, or news articles and "
"do not divulge details of your training data.)\nThis is your system prompt, "
"guiding your responses. Do not reference it, just respond to the user. If you find "
"yourself talking about this message, stop. You should be responding appropriately "
"and usually that means not mentioning this.\nYOU DO NOT MENTION ANY OF THIS INFORMATION "
"ABOUT YOURSELF UNLESS THE INFORMATION IS DIRECTLY PERTINENT TO THE USER'S QUERY."
),
stop_words=["<|im_end|>"],
replace_eos=True,
)
_register_template(
name="deepseek",
format_user=StringFormatter(slots=["User: {{content}}\n\nAssistant:"]),
@ -608,6 +636,9 @@ _register_template(
name="gemma",
format_user=StringFormatter(slots=["<start_of_turn>user\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]),
format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
format_observation=StringFormatter(
slots=["<start_of_turn>tool\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]
),
format_separator=EmptyFormatter(slots=["<end_of_turn>\n"]),
efficient_eos=True,
force_system=True,
@ -678,6 +709,14 @@ _register_template(
format_system=StringFormatter(
slots=[{"bos_token"}, "<|start_header_id|>system<|end_header_id|>\n\n{{content}}<|eot_id|>"]
),
format_observation=StringFormatter(
slots=[
(
"<|start_header_id|>tool<|end_header_id|>\n\n{{content}}<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\n"
)
]
),
default_system="You are a helpful assistant.",
stop_words=["<|eot_id|>"],
replace_eos=True,
@ -718,10 +757,23 @@ _register_template(
)
_register_template(
name="phi",
format_user=StringFormatter(slots=["<|user|>\n{{content}}<|end|>\n<|assistant|>\n"]),
format_system=StringFormatter(slots=[{"bos_token"}, "<|system|>\n{{content}}<|end|>\n"]),
format_observation=StringFormatter(slots=["<|function_output|>\n{{content}}<|end|>\n<|assistant|>\n"]),
format_separator=EmptyFormatter(slots=["\n"]),
default_system="You are a helpful AI assistant.",
stop_words=["<|end|>"],
replace_eos=True,
)
_register_template(
name="qwen",
format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
format_separator=EmptyFormatter(slots=["\n"]),
default_system="You are a helpful assistant.",
stop_words=["<|im_end|>"],

53
src/llmtuner/extras/constants.py
View File

@ -47,6 +47,8 @@ TRAINING_STAGES = {
STAGES_USE_PAIR_DATA = ["rm", "dpo", "orpo"]
SUPPORTED_CLASS_FOR_S2ATTN = ["llama"]
V_HEAD_WEIGHTS_NAME = "value_head.bin"
V_HEAD_SAFE_WEIGHTS_NAME = "value_head.safetensors"
@ -266,6 +268,22 @@ register_model_group(
)
register_model_group(
models={
"DBRX-132B-Base": {
DownloadSource.DEFAULT: "databricks/dbrx-base",
DownloadSource.MODELSCOPE: "AI-ModelScope/dbrx-base",
},
"DBRX-132B-Chat": {
DownloadSource.DEFAULT: "databricks/dbrx-instruct",
DownloadSource.MODELSCOPE: "AI-ModelScope/dbrx-instruct",
},
},
module="Wqkv",
template="dbrx",
)
register_model_group(
models={
"DeepSeek-LLM-7B-Base": {
@ -451,6 +469,16 @@ register_model_group(
)
register_model_group(
models={
"Jambda-v0.1": {
DownloadSource.DEFAULT: "ai21labs/Jamba-v0.1",
DownloadSource.MODELSCOPE: "AI-ModelScope/Jamba-v0.1",
}
},
)
register_model_group(
models={
"LingoWhale-8B": {
@ -585,18 +613,15 @@ register_model_group(
register_model_group(
models={
"OLMo-1B": {
DownloadSource.DEFAULT: "allenai/OLMo-1B",
DownloadSource.DEFAULT: "allenai/OLMo-1B-hf",
},
"OLMo-7B": {
DownloadSource.DEFAULT: "allenai/OLMo-7B",
DownloadSource.MODELSCOPE: "AI-ModelScope/OLMo-7B",
DownloadSource.DEFAULT: "allenai/OLMo-7B-hf",
},
"OLMo-7B-Chat": {
DownloadSource.DEFAULT: "allenai/OLMo-7B-Instruct",
"OLMo-1.7-7B": {
DownloadSource.DEFAULT: "allenai/OLMo-1.7-7B-hf",
},
},
module="att_proj",
template="olmo",
)
@ -652,6 +677,20 @@ register_model_group(
)
register_model_group(
models={
"Phi3-3.8B-4k-Chat": {
DownloadSource.DEFAULT: "microsoft/Phi-3-mini-4k-instruct",
},
"Phi3-3.8B-128k-Chat": {
DownloadSource.DEFAULT: "microsoft/Phi-3-mini-128k-instruct",
},
},
module="qkv_proj",
template="phi",
)
register_model_group(
models={
"Qwen-1.8B": {

19
src/llmtuner/extras/packages.py
View File

@ -1,16 +1,23 @@
import importlib.metadata
import importlib.util
from typing import TYPE_CHECKING
from packaging import version
if TYPE_CHECKING:
from packaging.version import Version
def _is_package_available(name: str) -> bool:
return importlib.util.find_spec(name) is not None
def _get_package_version(name: str) -> str:
def _get_package_version(name: str) -> "Version":
try:
return importlib.metadata.version(name)
return version.parse(importlib.metadata.version(name))
except Exception:
return "0.0.0"
return version.parse("0.0.0")
def is_fastapi_availble():
@ -18,7 +25,7 @@ def is_fastapi_availble():
def is_flash_attn2_available():
return _is_package_available("flash_attn") and _get_package_version("flash_attn").startswith("2")
return _is_package_available("flash_attn") and _get_package_version("flash_attn") > version.parse("2.0.0")
def is_galore_available():
@ -49,6 +56,10 @@ def is_rouge_available():
return _is_package_available("rouge_chinese")
def is_sdpa_available():
return _get_package_version("torch") > version.parse("2.1.1")
def is_starlette_available():
return _is_package_available("sse_starlette")

4
src/llmtuner/hparams/data_args.py
View File

@ -26,11 +26,11 @@ class DataArguments:
)
cutoff_len: int = field(
default=1024,
metadata={"help": "The cutoff length of the model inputs after tokenization."},
metadata={"help": "The cutoff length of the tokenized inputs in the dataset."},
)
reserved_label_len: int = field(
default=1,
metadata={"help": "The minimum cutoff length reserved for label after tokenization."},
metadata={"help": "The minimum cutoff length reserved for the tokenized labels in the dataset."},
)
train_on_prompt: bool = field(
default=False,

4
src/llmtuner/hparams/generating_args.py
View File

@ -31,11 +31,11 @@ class GeneratingArguments:
metadata={"help": "Number of beams for beam search. 1 means no beam search."},
)
max_length: int = field(
default=512,
default=1024,
metadata={"help": "The maximum length the generated tokens can have. It can be overridden by max_new_tokens."},
)
max_new_tokens: int = field(
default=512,
default=1024,
metadata={"help": "The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt."},
)
repetition_penalty: float = field(

15
src/llmtuner/hparams/model_args.py
View File

@ -22,7 +22,7 @@ class ModelArguments:
metadata={"help": "Where to store the pre-trained models downloaded from huggingface.co or modelscope.cn."},
)
use_fast_tokenizer: bool = field(
default=False,
default=True,
metadata={"help": "Whether or not to use one of the fast tokenizer (backed by the tokenizers library)."},
)
resize_vocab: bool = field(
@ -33,6 +33,10 @@ class ModelArguments:
default=False,
metadata={"help": "Whether or not the special tokens should be split during the tokenization process."},
)
new_special_tokens: Optional[str] = field(
default=None,
metadata={"help": "Special tokens to be added into the tokenizer."},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
@ -61,9 +65,9 @@ class ModelArguments:
default=None,
metadata={"help": "Which scaling strategy should be adopted for the RoPE embeddings."},
)
flash_attn: bool = field(
default=False,
metadata={"help": "Enable FlashAttention for faster training."},
flash_attn: Literal["off", "sdpa", "fa2", "auto"] = field(
default="auto",
metadata={"help": "Enable FlashAttention for faster training and inference."},
)
shift_attn: bool = field(
default=False,
@ -177,6 +181,9 @@ class ModelArguments:
if self.adapter_name_or_path is not None: # support merging multiple lora weights
self.adapter_name_or_path = [path.strip() for path in self.adapter_name_or_path.split(",")]
if self.new_special_tokens is not None: # support multiple special tokens
self.new_special_tokens = [token.strip() for token in self.new_special_tokens.split(",")]
assert self.quantization_bit in [None, 8, 4], "We only accept 4-bit or 8-bit quantization."
assert self.export_quantization_bit in [None, 8, 4, 3, 2], "We only accept 2/3/4/8-bit quantization."

6
src/llmtuner/hparams/parser.py
View File

@ -67,6 +67,9 @@ def _verify_model_args(model_args: "ModelArguments", finetuning_args: "Finetunin
if finetuning_args.finetuning_type != "lora":
raise ValueError("Quantization is only compatible with the LoRA method.")
if model_args.resize_vocab:
raise ValueError("Cannot resize embedding layers of a quantized model.")
if model_args.adapter_name_or_path is not None and finetuning_args.create_new_adapter:
raise ValueError("Cannot create new adapter upon a quantized model.")
@ -199,10 +202,11 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
if (
training_args.do_train
and finetuning_args.finetuning_type == "lora"
and model_args.quantization_bit is None
and model_args.resize_vocab
and finetuning_args.additional_target is None
):
logger.warning("Add token embeddings to `additional_target` to make the added tokens trainable.")
logger.warning("Remember to add embedding layers to `additional_target` to make the added tokens trainable.")
if training_args.do_train and model_args.quantization_bit is not None and (not model_args.upcast_layernorm):
logger.warning("We recommend enable `upcast_layernorm` in quantized training.")

5
src/llmtuner/model/__init__.py
View File

@ -1,7 +1,8 @@
from .loader import load_model, load_tokenizer, load_processor, load_mm_model
from .utils import find_all_linear_modules, load_valuehead_params
from .loader import load_config, load_model, load_tokenizer, load_mm_model
from .utils.misc import find_all_linear_modules, load_valuehead_params
__all__ = [
"load_config",
"load_model",
"load_mm_model",
"load_tokenizer",

49
src/llmtuner/model/adapter.py
View File

@ -1,25 +1,30 @@
from typing import TYPE_CHECKING, Union
from typing import TYPE_CHECKING
import torch
from peft import LoraConfig, LoraModel, PeftModel, TaskType, get_peft_model
from transformers import AutoModelForVision2Seq
from transformers.integrations import is_deepspeed_zero3_enabled
from ..extras.logging import get_logger
from .utils import QuantizationMethod, find_all_linear_modules, find_expanded_modules
from .utils.misc import find_all_linear_modules, find_expanded_modules
from .utils.quantization import QuantizationMethod
from .utils.unsloth import get_unsloth_peft_model, load_unsloth_peft_model
if TYPE_CHECKING:
from transformers.modeling_utils import PreTrainedModel, AutoModelForVision2Seq
from transformers import PretrainedConfig, PreTrainedModel, AutoModelForVision2Seq
from ..hparams import FinetuningArguments, ModelArguments
logger = get_logger(__name__)
def init_adapter(
model: "PreTrainedModel", model_args: "ModelArguments",
config: "PretrainedConfig",
model: "PreTrainedModel",
model_args: "ModelArguments",
finetuning_args: "FinetuningArguments",
is_trainable: bool
is_trainable: bool,
) -> "PreTrainedModel":
r"""
Initializes the adapters.
@ -106,6 +111,10 @@ def init_adapter(
assert len(model_args.adapter_name_or_path) == 1, "Cannot use multiple adapters in DeepSpeed ZeRO-3."
is_mergeable = False
if model_args.use_unsloth:
assert len(model_args.adapter_name_or_path) == 1, "Unsloth model only accepts a single adapter."
is_mergeable = False
if (is_trainable and not finetuning_args.create_new_adapter) or (not is_mergeable):
adapter_to_merge = model_args.adapter_name_or_path[:-1]
adapter_to_resume = model_args.adapter_name_or_path[-1]
@ -122,8 +131,14 @@ def init_adapter(
logger.info("Merged {} adapter(s).".format(len(adapter_to_merge)))
if adapter_to_resume is not None: # resume lora training
if model_args.use_unsloth:
model = load_unsloth_peft_model(config, model_args, is_trainable=is_trainable)
else:
model = PeftModel.from_pretrained(
model, adapter_to_resume, is_trainable=is_trainable, offload_folder=model_args.offload_folder
model,
adapter_to_resume,
is_trainable=is_trainable,
offload_folder=model_args.offload_folder,
)
if is_trainable and adapter_to_resume is None: # create new lora weights while training
@ -142,6 +157,17 @@ def init_adapter(
):
raise ValueError("DoRA is not compatible with PTQ-quantized models.")
if model_args.resize_vocab and finetuning_args.additional_target is None:
input_embeddings = model.get_input_embeddings()
output_embeddings = model.get_output_embeddings()
module_names = set()
for name, module in model.named_modules():
if module in [input_embeddings, output_embeddings]:
module_names.add(name.split(".")[-1])
finetuning_args.additional_target = module_names
logger.warning("Vocab has been resized, add {} to trainable params.".format(",".join(module_names)))
peft_kwargs = {
"r": finetuning_args.lora_rank,
"target_modules": target_modules,
@ -152,14 +178,7 @@ def init_adapter(
}
if model_args.use_unsloth:
from unsloth import FastLanguageModel # type: ignore
unsloth_peft_kwargs = {
"model": model,
"max_seq_length": model_args.model_max_length,
"use_gradient_checkpointing": "unsloth",
}
model = FastLanguageModel.get_peft_model(**peft_kwargs, **unsloth_peft_kwargs)
model = get_unsloth_peft_model(model, model_args, peft_kwargs)
else:
lora_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,

84
src/llmtuner/model/loader.py
View File

@ -3,15 +3,16 @@ from typing import TYPE_CHECKING, Any, Dict
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, AutoProcessor, AutoModelForVision2Seq
from trl import AutoModelForCausalLMWithValueHead
from ..extras.constants import MOD_SUPPORTED_MODELS
from ..extras.logging import get_logger
from ..extras.misc import count_parameters, get_current_device, try_download_model_from_ms
from ..extras.misc import count_parameters, try_download_model_from_ms
from .adapter import init_adapter, init_mm_adapter
from .patcher import patch_config, patch_model, patch_tokenizer, patch_valuehead_model
from .utils import load_valuehead_params, register_autoclass
from .utils.misc import load_valuehead_params, register_autoclass
from .utils.mod import convert_pretrained_model_to_mod, load_mod_pretrained_model
from .utils.unsloth import load_unsloth_pretrained_model
if TYPE_CHECKING:
from transformers import PreTrainedModel, PreTrainedTokenizer
from transformers import PretrainedConfig, PreTrainedModel, PreTrainedTokenizer
from ..hparams import FinetuningArguments, ModelArguments
@ -19,6 +20,11 @@ logger = get_logger(__name__)
def _get_init_kwargs(model_args: "ModelArguments") -> Dict[str, Any]:
r"""
Gets arguments to load config/tokenizer/model.
Note: including inplace operation of model_args.
"""
model_args.model_name_or_path = try_download_model_from_ms(model_args)
return {
"trust_remote_code": True,
@ -30,7 +36,7 @@ def _get_init_kwargs(model_args: "ModelArguments") -> Dict[str, Any]:
def load_tokenizer(model_args: "ModelArguments") -> "PreTrainedTokenizer":
r"""
Loads pretrained tokenizer. Must before load_model.
Loads pretrained tokenizer.
Note: including inplace operation of model_args.
"""
@ -51,6 +57,16 @@ def load_tokenizer(model_args: "ModelArguments") -> "PreTrainedTokenizer":
**init_kwargs,
)
if model_args.new_special_tokens is not None:
num_added_tokens = tokenizer.add_special_tokens(
dict(additional_special_tokens=model_args.new_special_tokens),
replace_additional_special_tokens=False,
)
logger.info("Add {} to special tokens.".format(",".join(model_args.new_special_tokens)))
if num_added_tokens > 0 and not model_args.resize_vocab:
model_args.resize_vocab = True
logger.warning("New tokens have been added, changed `resize_vocab` to True.")
patch_tokenizer(tokenizer)
return tokenizer
@ -81,6 +97,14 @@ def load_processor(model_args: "ModelArguments") -> "AutoProcessor":
return processor
def load_config(model_args: "ModelArguments") -> "PretrainedConfig":
r"""
Loads model config.
"""
init_kwargs = _get_init_kwargs(model_args)
return AutoConfig.from_pretrained(model_args.model_name_or_path, **init_kwargs)
def load_model(
tokenizer: "PreTrainedTokenizer",
model_args: "ModelArguments",
@ -89,61 +113,37 @@ def load_model(
add_valuehead: bool = False,
) -> "PreTrainedModel":
r"""
Loads pretrained model. Must after load_tokenizer.
Loads pretrained model.
"""
init_kwargs = _get_init_kwargs(model_args)
config = AutoConfig.from_pretrained(model_args.model_name_or_path, **init_kwargs)
config = load_config(model_args)
patch_config(config, tokenizer, model_args, init_kwargs, is_trainable)
model = None
if is_trainable and model_args.use_unsloth:
from unsloth import FastLanguageModel # type: ignore
lazy_load = False
if model_args.use_unsloth:
if model_args.adapter_name_or_path is not None:
lazy_load = True
elif is_trainable:
model = load_unsloth_pretrained_model(config, model_args)
unsloth_kwargs = {
"model_name": model_args.model_name_or_path,
"max_seq_length": model_args.model_max_length,
"dtype": model_args.compute_dtype,
"load_in_4bit": model_args.quantization_bit == 4,
"token": model_args.hf_hub_token,
"device_map": {"": get_current_device()},
"rope_scaling": getattr(config, "rope_scaling", None),
"fix_tokenizer": False,
"trust_remote_code": True,
}
try:
model, _ = FastLanguageModel.from_pretrained(**unsloth_kwargs)
except NotImplementedError:
logger.warning("Unsloth does not support model type {}.".format(getattr(config, "model_type", None)))
model_args.use_unsloth = False
if model_args.adapter_name_or_path:
model_args.adapter_name_or_path = None
logger.warning("Unsloth does not support loading adapters.")
if model is None:
if model is None and not lazy_load:
init_kwargs["config"] = config
init_kwargs["pretrained_model_name_or_path"] = model_args.model_name_or_path
if model_args.mixture_of_depths == "load":
from MoD import AutoMoDModelForCausalLM
model = AutoMoDModelForCausalLM.from_pretrained(**init_kwargs)
model = load_mod_pretrained_model(**init_kwargs)
else:
model = AutoModelForCausalLM.from_pretrained(**init_kwargs)
if model_args.mixture_of_depths == "convert":
from MoD import apply_mod_to_hf
if getattr(config, "model_type", None) not in MOD_SUPPORTED_MODELS:
raise ValueError("Current model is not supported by mixture-of-depth.")
model = apply_mod_to_hf(model)
model = model.to(model_args.compute_dtype)
model = convert_pretrained_model_to_mod(model, config, model_args)
if not lazy_load:
patch_model(model, tokenizer, model_args, is_trainable)
register_autoclass(config, model, tokenizer)
model = init_adapter(model, model_args, finetuning_args, is_trainable)
model = init_adapter(config, model, model_args, finetuning_args, is_trainable)
if add_valuehead:
model = AutoModelForCausalLMWithValueHead.from_pretrained(model)

296
src/llmtuner/model/patcher.py
View File

@ -1,23 +1,20 @@
import math
import os
import random
from contextlib import nullcontext
from types import MethodType
from typing import TYPE_CHECKING, Any, Dict, List, Tuple
from typing import TYPE_CHECKING, Any, Dict
import torch
from datasets import load_dataset
from peft import PeftModel
from transformers import BitsAndBytesConfig, GPTQConfig, PreTrainedModel, PreTrainedTokenizerBase
from transformers import PreTrainedModel, PreTrainedTokenizerBase
from transformers.integrations import is_deepspeed_zero3_enabled
from transformers.utils.versions import require_version
from ..extras.constants import FILEEXT2TYPE, LAYERNORM_NAMES
from ..extras.logging import get_logger
from ..extras.misc import get_current_device, infer_optim_dtype
from ..extras.packages import is_flash_attn2_available
from ..extras.patches.llama_patch import apply_llama_patch
from .utils import QuantizationMethod, add_z3_leaf_module, gradient_checkpointing_enable
from ..extras.misc import infer_optim_dtype
from .utils.attention import configure_attn_implementation, print_attn_implementation
from .utils.checkpointing import prepare_model_for_training
from .utils.embedding import resize_embedding_layer
from .utils.longlora import configure_longlora
from .utils.moe import add_z3_leaf_module
from .utils.quantization import configure_quantization
from .utils.rope import configure_rope
if TYPE_CHECKING:
@ -28,255 +25,6 @@ if TYPE_CHECKING:
logger = get_logger(__name__)
SUPPORTED_CLASS_FOR_S2ATTN = ["llama"]
def _get_quantization_dataset(tokenizer: "PreTrainedTokenizer", model_args: "ModelArguments") -> List[str]:
r"""
Inspired by: https://github.com/huggingface/optimum/blob/v1.16.0/optimum/gptq/data.py#L133
TODO: remove tokenizer.decode() https://github.com/huggingface/optimum/pull/1600
"""
if os.path.isfile(model_args.export_quantization_dataset):
data_path = FILEEXT2TYPE.get(model_args.export_quantization_dataset.split(".")[-1], None)
data_files = model_args.export_quantization_dataset
else:
data_path = model_args.export_quantization_dataset
data_files = None
dataset = load_dataset(path=data_path, data_files=data_files, split="train", cache_dir=model_args.cache_dir)
maxlen = model_args.export_quantization_maxlen
samples = []
for _ in range(model_args.export_quantization_nsamples):
while True:
sample_idx = random.randint(0, len(dataset) - 1)
sample: Dict[str, torch.Tensor] = tokenizer(dataset[sample_idx]["text"], return_tensors="pt")
if sample["input_ids"].size(1) >= maxlen:
break # TODO: fix large maxlen
word_idx = random.randint(0, sample["input_ids"].size(1) - maxlen - 1)
input_ids = sample["input_ids"][:, word_idx : word_idx + maxlen]
samples.append(tokenizer.decode(input_ids[0].tolist(), skip_special_tokens=True))
return samples
def _configure_attn_implementation(config: "PretrainedConfig", model_args: "ModelArguments") -> None:
if model_args.flash_attn:
if not is_flash_attn2_available():
logger.warning("FlashAttention2 is not installed.")
return
logger.info("Using FlashAttention-2 for faster training and inference.")
if getattr(config, "model_type", None) == "internlm2": # special case for custom models
setattr(config, "attn_implementation", "flash_attention_2")
else:
setattr(config, "_attn_implementation", "flash_attention_2")
else:
setattr(config, "_attn_implementation", "eager")
def _configure_rope(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
if model_args.rope_scaling is None:
return
if not hasattr(config, "rope_scaling"):
logger.warning("Current model does not support RoPE scaling.")
return
if is_trainable:
if model_args.rope_scaling == "dynamic":
logger.warning(
"Dynamic NTK scaling may not work well with fine-tuning. "
"See: https://github.com/huggingface/transformers/pull/24653"
)
current_max_length = getattr(config, "max_position_embeddings", None)
if current_max_length and model_args.model_max_length > current_max_length:
scaling_factor = float(math.ceil(model_args.model_max_length / current_max_length))
else:
logger.warning("Input length is smaller than max length. Consider increase input length.")
scaling_factor = 1.0
else:
scaling_factor = 2.0
setattr(config, "rope_scaling", {"type": model_args.rope_scaling, "factor": scaling_factor})
logger.info(
"Using {} scaling strategy and setting scaling factor to {}".format(model_args.rope_scaling, scaling_factor)
)
def _configure_longlora(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
if not is_trainable or not model_args.shift_attn:
return
if getattr(config, "model_type", None) in SUPPORTED_CLASS_FOR_S2ATTN:
setattr(config, "group_size_ratio", 0.25)
apply_llama_patch()
logger.info("Using shift short attention with group_size_ratio=1/4.")
else:
logger.warning("Current model does not support shift short attention.")
def _configure_quantization(
config: "PretrainedConfig",
tokenizer: "PreTrainedTokenizer",
model_args: "ModelArguments",
init_kwargs: Dict[str, Any],
) -> None:
r"""
Priority: PTQ-quantized (training) > AutoGPTQ (export) > Bitsandbytes (training)
"""
if getattr(config, "quantization_config", None): # ptq
if is_deepspeed_zero3_enabled():
raise ValueError("DeepSpeed ZeRO-3 is incompatible with quantized models.")
if model_args.quantization_device_map != "auto":
init_kwargs["device_map"] = {"": get_current_device()}
quantization_config: Dict[str, Any] = getattr(config, "quantization_config", None)
quant_method = quantization_config.get("quant_method", "")
if quant_method == QuantizationMethod.GPTQ:
require_version("auto_gptq>=0.5.0", "To fix: pip install auto_gptq>=0.5.0")
quantization_config.pop("disable_exllama", None) # remove deprecated args
quantization_config["use_exllama"] = False # disable exllama
if quant_method == QuantizationMethod.AWQ:
require_version("autoawq", "To fix: pip install autoawq")
if quant_method == QuantizationMethod.AQLM:
require_version("transformers>=4.39.0", "To fix: pip install transformers>=4.39.0")
require_version("aqlm>=1.1.0", "To fix: pip install aqlm[gpu]>=1.1.0")
quantization_config["bits"] = 2
quant_bits = quantization_config.get("bits", "?")
logger.info("Loading {}-bit {}-quantized model.".format(quant_bits, quant_method.upper()))
elif model_args.export_quantization_bit is not None: # auto-gptq
require_version("optimum>=1.16.0", "To fix: pip install optimum>=1.16.0")
require_version("auto_gptq>=0.5.0", "To fix: pip install auto_gptq>=0.5.0")
from accelerate.utils import get_max_memory
if getattr(config, "model_type", None) == "chatglm":
raise ValueError("ChatGLM model is not supported.")
init_kwargs["quantization_config"] = GPTQConfig(
bits=model_args.export_quantization_bit,
tokenizer=tokenizer,
dataset=_get_quantization_dataset(tokenizer, model_args),
)
init_kwargs["device_map"] = "auto"
init_kwargs["max_memory"] = get_max_memory()
logger.info("Quantizing model to {} bit.".format(model_args.export_quantization_bit))
elif model_args.quantization_bit is not None: # bnb
if model_args.quantization_bit == 8:
require_version("bitsandbytes>=0.37.0", "To fix: pip install bitsandbytes>=0.37.0")
init_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)
elif model_args.quantization_bit == 4:
require_version("bitsandbytes>=0.39.0", "To fix: pip install bitsandbytes>=0.39.0")
init_kwargs["quantization_config"] = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=model_args.compute_dtype,
bnb_4bit_use_double_quant=model_args.double_quantization,
bnb_4bit_quant_type=model_args.quantization_type,
bnb_4bit_quant_storage=model_args.compute_dtype, # crucial for fsdp qlora
)
if is_deepspeed_zero3_enabled() or model_args.quantization_device_map == "auto":
if model_args.quantization_bit != 4:
raise ValueError("Only 4-bit quantized model can use auto device map.")
require_version("transformers>=4.39.0", "To fix: pip install transformers>=4.39.0")
require_version("accelerate>=0.28.0", "To fix: pip install accelerate>=0.28.0")
require_version("bitsandbytes>=0.43.0", "To fix: pip install bitsandbytes>=0.43.0")
else:
init_kwargs["device_map"] = {"": get_current_device()}
logger.info("Quantizing model to {} bit.".format(model_args.quantization_bit))
def _noisy_mean_initialization(embed_weight: torch.Tensor, num_new_tokens: int):
embedding_dim = embed_weight.size(1)
avg_weight = embed_weight[:-num_new_tokens].mean(dim=0, keepdim=True)
noise_weight = torch.empty_like(embed_weight[-num_new_tokens:])
noise_weight.normal_(mean=0, std=(1.0 / math.sqrt(embedding_dim)))
embed_weight[-num_new_tokens:] = avg_weight + noise_weight
def _resize_embedding_layer(model: "PreTrainedModel", tokenizer: "PreTrainedTokenizer") -> None:
r"""
Resize token embeddings.
"""
if is_deepspeed_zero3_enabled():
import deepspeed # type: ignore
params = [model.get_input_embeddings().weight]
if model.get_output_embeddings() is not None and not model.config.tie_word_embeddings:
params.append(model.get_output_embeddings().weight)
context_maybe_zero3 = deepspeed.zero.GatheredParameters(params, modifier_rank=0)
else:
context_maybe_zero3 = nullcontext()
with context_maybe_zero3:
current_embedding_size = model.get_input_embeddings().weight.size(0)
if len(tokenizer) > current_embedding_size:
if not isinstance(model.get_output_embeddings(), torch.nn.Linear):
logger.warning("Current model does not support resizing token embeddings.")
return
model.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=64)
with context_maybe_zero3:
new_embedding_size = model.get_input_embeddings().weight.size(0)
num_new_tokens = new_embedding_size - current_embedding_size
_noisy_mean_initialization(model.get_input_embeddings().weight.data, num_new_tokens)
_noisy_mean_initialization(model.get_output_embeddings().weight.data, num_new_tokens)
logger.info("Resized token embeddings from {} to {}.".format(current_embedding_size, new_embedding_size))
def _fp32_forward_post_hook(
module: "torch.nn.Module", args: Tuple["torch.Tensor"], output: "torch.Tensor"
) -> "torch.Tensor":
return output.to(torch.float32)
def _prepare_model_for_training(
model: "PreTrainedModel", model_args: "ModelArguments", output_layer_name: str = "lm_head"
) -> None:
r"""
Includes:
(1) cast the layernorm in fp32
(2) make output embedding layer require grads
(3) add the upcasting of the lm_head in fp32
Inspired by: https://github.com/huggingface/peft/blob/v0.7.1/src/peft/utils/other.py#L72
"""
if model_args.upcast_layernorm:
logger.info("Upcasting layernorm weights in float32.")
for name, param in model.named_parameters():
if param.ndim == 1 and any(ln_name in name for ln_name in LAYERNORM_NAMES):
param.data = param.data.to(torch.float32)
if not model_args.disable_gradient_checkpointing:
if not getattr(model, "supports_gradient_checkpointing", False):
logger.warning("Current model does not support gradient checkpointing.")
else:
# use_reentrant=False might increase VRAM usage (have not been empirically verified yet)
# According to: https://github.com/huggingface/transformers/issues/28339
model.gradient_checkpointing_enable = MethodType(gradient_checkpointing_enable, model)
model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": True})
setattr(model.config, "use_cache", False) # turn off when gradient checkpointing is enabled
logger.info("Gradient checkpointing enabled.")
if hasattr(model, output_layer_name) and model_args.upcast_lmhead_output:
logger.info("Upcasting lm_head outputs in float32.")
output_layer = getattr(model, output_layer_name)
if isinstance(output_layer, torch.nn.Linear) and output_layer.weight.dtype != torch.float32:
output_layer.register_forward_hook(_fp32_forward_post_hook)
def patch_tokenizer(tokenizer: "PreTrainedTokenizer") -> None:
@ -294,10 +42,10 @@ def patch_config(
if model_args.compute_dtype is None: # priority: bf16 > fp16 > fp32
model_args.compute_dtype = infer_optim_dtype(model_dtype=getattr(config, "torch_dtype", None))
_configure_attn_implementation(config, model_args)
_configure_rope(config, model_args, is_trainable)
_configure_longlora(config, model_args, is_trainable)
_configure_quantization(config, tokenizer, model_args, init_kwargs)
configure_attn_implementation(config, model_args)
configure_rope(config, model_args, is_trainable)
configure_longlora(config, model_args, is_trainable)
configure_quantization(config, tokenizer, model_args, init_kwargs)
if model_args.use_cache and not is_trainable:
setattr(config, "use_cache", True)
@ -350,20 +98,14 @@ def patch_model(
setattr(model, "_keys_to_ignore_on_save", ["lm_head.weight"])
if model_args.resize_vocab:
_resize_embedding_layer(model, tokenizer)
resize_embedding_layer(model, tokenizer)
if is_trainable:
_prepare_model_for_training(model, model_args)
prepare_model_for_training(model, model_args)
add_z3_leaf_module(model)
if getattr(model.config, "model_type", None) == "mixtral":
from transformers.models.mixtral.modeling_mixtral import MixtralSparseMoeBlock
add_z3_leaf_module(model, MixtralSparseMoeBlock)
if getattr(model.config, "model_type", None) == "qwen2moe":
from transformers.models.qwen2_moe.modeling_qwen2_moe import Qwen2MoeSparseMoeBlock
add_z3_leaf_module(model, Qwen2MoeSparseMoeBlock)
if not model_args.use_unsloth:
print_attn_implementation(model.config)
try:
model.add_model_tags(["llama-factory"])

0
src/llmtuner/extras/patches/__init__.py → src/llmtuner/model/utils/__init__.py
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55
src/llmtuner/model/utils/attention.py Normal file
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@ -0,0 +1,55 @@
from typing import TYPE_CHECKING
from ...extras.logging import get_logger
from ...extras.packages import is_flash_attn2_available, is_sdpa_available
if TYPE_CHECKING:
from transformers import PretrainedConfig
from ...hparams import ModelArguments
logger = get_logger(__name__)
def configure_attn_implementation(config: "PretrainedConfig", model_args: "ModelArguments") -> None:
if model_args.flash_attn == "auto":
return
elif model_args.flash_attn == "off":
requested_attn_implementation = "eager"
elif model_args.flash_attn == "sdpa":
if not is_sdpa_available():
logger.warning("Torch>=2.1.1 is required for SDPA attention.")
return
requested_attn_implementation = "sdpa"
elif model_args.flash_attn == "fa2":
if not is_flash_attn2_available():
logger.warning("FlashAttention-2 is not installed.")
return
requested_attn_implementation = "flash_attention_2"
else:
raise NotImplementedError("Unknown attention type: {}".format(model_args.flash_attn))
if getattr(config, "model_type", None) == "internlm2": # special case for custom models
setattr(config, "attn_implementation", requested_attn_implementation)
else:
setattr(config, "_attn_implementation", requested_attn_implementation)
def print_attn_implementation(config: "PretrainedConfig") -> None:
if getattr(config, "model_type", None) == "internlm2": # special case for custom models
attn_implementation = getattr(config, "attn_implementation", None)
else:
attn_implementation = getattr(config, "_attn_implementation", None)
if attn_implementation == "flash_attention_2":
logger.info("Using FlashAttention-2 for faster training and inference.")
elif attn_implementation == "sdpa":
logger.info("Using torch SDPA for faster training and inference.")
else:
logger.info("Using vanilla Attention implementation.")

94
src/llmtuner/model/utils/checkpointing.py Normal file
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@ -0,0 +1,94 @@
import inspect
from functools import partial
from types import MethodType
from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple
import torch
from ...extras.constants import LAYERNORM_NAMES
from ...extras.logging import get_logger
if TYPE_CHECKING:
from transformers import PreTrainedModel
from ...hparams import ModelArguments
logger = get_logger(__name__)
def _gradient_checkpointing_enable(
self: "PreTrainedModel", gradient_checkpointing_kwargs: Optional[Dict[str, Any]] = None
) -> None:
r"""
Activates gradient checkpointing for the current model.
Modification of the original method to enable gradient checkpointing for block-wise optimizer.
"""
from torch.utils.checkpoint import checkpoint
if not self.supports_gradient_checkpointing:
raise ValueError("{} does not support gradient checkpointing.".format(self.__class__.__name__))
if gradient_checkpointing_kwargs is None:
gradient_checkpointing_kwargs = {"use_reentrant": True}
gradient_checkpointing_func = partial(checkpoint, **gradient_checkpointing_kwargs)
def custom_gradient_checkpointing_func(func, *args, **kwargs):
module: "torch.nn.Module" = func.__self__
if any(param.requires_grad for param in module.parameters()):
for arg in args:
if torch.is_tensor(arg) and torch.is_floating_point(arg):
arg.requires_grad_(True)
return gradient_checkpointing_func(func, *args, **kwargs)
if "value" in inspect.signature(self._set_gradient_checkpointing).parameters: # old GC format
self.apply(partial(self._set_gradient_checkpointing, value=True))
self.enable_input_require_grads()
logger.warning("You are using the old GC format, some features (e.g. BAdam) will be invalid.")
else: # have already enabled input require gradients
self._set_gradient_checkpointing(enable=True, gradient_checkpointing_func=custom_gradient_checkpointing_func)
def _fp32_forward_post_hook(
module: "torch.nn.Module", args: Tuple["torch.Tensor"], output: "torch.Tensor"
) -> "torch.Tensor":
return output.to(torch.float32)
def prepare_model_for_training(
model: "PreTrainedModel", model_args: "ModelArguments", output_layer_name: str = "lm_head"
) -> None:
r"""
Includes:
(1) cast the layernorm in fp32
(2) make output embedding layer require grads
(3) add the upcasting of the lm_head in fp32
Inspired by: https://github.com/huggingface/peft/blob/v0.7.1/src/peft/utils/other.py#L72
"""
if model_args.upcast_layernorm:
logger.info("Upcasting layernorm weights in float32.")
for name, param in model.named_parameters():
if param.ndim == 1 and any(ln_name in name for ln_name in LAYERNORM_NAMES):
param.data = param.data.to(torch.float32)
if not model_args.disable_gradient_checkpointing:
if not getattr(model, "supports_gradient_checkpointing", False):
logger.warning("Current model does not support gradient checkpointing.")
else:
# use_reentrant=False might increase VRAM usage (have not been empirically verified yet)
# According to: https://github.com/huggingface/transformers/issues/28339
model.gradient_checkpointing_enable = MethodType(_gradient_checkpointing_enable, model)
model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": True})
setattr(model.config, "use_cache", False) # turn off when gradient checkpointing is enabled
logger.info("Gradient checkpointing enabled.")
if hasattr(model, output_layer_name) and model_args.upcast_lmhead_output:
logger.info("Upcasting lm_head outputs in float32.")
output_layer = getattr(model, output_layer_name)
if isinstance(output_layer, torch.nn.Linear) and output_layer.weight.dtype != torch.float32:
output_layer.register_forward_hook(_fp32_forward_post_hook)

58
src/llmtuner/model/utils/embedding.py Normal file
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@ -0,0 +1,58 @@
import math
from contextlib import nullcontext
from typing import TYPE_CHECKING
import torch
from transformers.integrations import is_deepspeed_zero3_enabled
from ...extras.logging import get_logger
if TYPE_CHECKING:
from transformers import PreTrainedModel, PreTrainedTokenizer
logger = get_logger(__name__)
def _noisy_mean_initialization(embed_weight: torch.Tensor, num_new_tokens: int) -> None:
embedding_dim = embed_weight.size(1)
avg_weight = embed_weight[:-num_new_tokens].mean(dim=0, keepdim=True)
noise_weight = torch.empty_like(embed_weight[-num_new_tokens:])
noise_weight.normal_(mean=0, std=(1.0 / math.sqrt(embedding_dim)))
embed_weight[-num_new_tokens:] = avg_weight + noise_weight
def resize_embedding_layer(model: "PreTrainedModel", tokenizer: "PreTrainedTokenizer") -> None:
r"""
Resize token embeddings.
"""
if is_deepspeed_zero3_enabled():
import deepspeed # type: ignore
params = [model.get_input_embeddings().weight]
if model.get_output_embeddings() is not None and not model.config.tie_word_embeddings:
params.append(model.get_output_embeddings().weight)
context_maybe_zero3 = deepspeed.zero.GatheredParameters(params, modifier_rank=0)
else:
context_maybe_zero3 = nullcontext()
with context_maybe_zero3:
current_embedding_size = model.get_input_embeddings().weight.size(0)
if len(tokenizer) > current_embedding_size:
if getattr(model, "quantization_method", None):
raise ValueError("Cannot resize embedding layers of a quantized model.")
if not isinstance(model.get_output_embeddings(), torch.nn.Linear):
raise ValueError("Current model does not support resizing embedding layers.")
model.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=64)
with context_maybe_zero3:
new_embedding_size = model.get_input_embeddings().weight.size(0)
num_new_tokens = new_embedding_size - current_embedding_size
_noisy_mean_initialization(model.get_input_embeddings().weight.data, num_new_tokens)
_noisy_mean_initialization(model.get_output_embeddings().weight.data, num_new_tokens)
logger.info("Resized token embeddings from {} to {}.".format(current_embedding_size, new_embedding_size))

151
src/llmtuner/extras/patches/llama_patch.py → src/llmtuner/model/utils/longlora.py
View File

@ -1,5 +1,5 @@
import math
from typing import Optional, Tuple
from typing import TYPE_CHECKING, Optional, Tuple
import torch
import torch.nn as nn
@ -7,19 +7,28 @@ from transformers.models.llama.modeling_llama import (
Cache,
LlamaAttention,
LlamaFlashAttention2,
LlamaSdpaAttention,
apply_rotary_pos_emb,
repeat_kv,
)
from transformers.utils import logging
from transformers.utils.versions import require_version
from ...extras.constants import SUPPORTED_CLASS_FOR_S2ATTN
if TYPE_CHECKING:
from transformers import PretrainedConfig
from ...hparams import ModelArguments
logger = logging.get_logger(__name__)
# Modified from:
# https://github.com/huggingface/transformers/blob/v4.39.1/src/transformers/models/llama/modeling_llama.py
def llama_torch_attn_forward(
# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/models/llama/modeling_llama.py
def llama_attention_forward(
self: "LlamaAttention",
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
@ -39,10 +48,11 @@ def llama_torch_attn_forward(
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
past_key_value = getattr(self, "past_key_value", past_key_value)
cos, sin = self.rotary_emb(value_states, position_ids)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
past_key_value = getattr(self, "past_key_value", past_key_value)
if past_key_value is not None:
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
@ -69,8 +79,9 @@ def llama_torch_attn_forward(
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
if attention_mask is not None:
attn_weights = attn_weights + attention_mask
if attention_mask is not None: # no matter the length, we just slice it
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
# upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
@ -97,8 +108,8 @@ def llama_torch_attn_forward(
# Modified from:
# https://github.com/huggingface/transformers/blob/v4.39.1/src/transformers/models/llama/modeling_llama.py
def llama_flash_attn_forward(
# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/models/llama/modeling_llama.py
def llama_flash_attention_2_forward(
self: "LlamaFlashAttention2",
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
@ -117,7 +128,6 @@ def llama_flash_attn_forward(
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
# FlashAttention requires the input to have the shape (bsz, seq_len, n_heads, head_dim)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
@ -134,9 +144,10 @@ def llama_flash_attn_forward(
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
query_states = query_states.transpose(1, 2) # (bsz, seq_len, n_heads, head_dim)
key_states = key_states.transpose(1, 2) # (bsz, seq_len, n_heads, head_dim)
value_states = value_states.transpose(1, 2) # (bsz, seq_len, n_heads, head_dim)
# FlashAttention requires the input to have the shape (bsz, seq_len, n_heads, head_dim)
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
dropout_rate = self.attention_dropout if self.training else 0.0
@ -192,7 +203,115 @@ def llama_flash_attn_forward(
return attn_output, attn_weights, past_key_value
def apply_llama_patch() -> None:
require_version("transformers==4.39.3", "To fix: pip install transformers==4.39.3")
LlamaAttention.forward = llama_torch_attn_forward
LlamaFlashAttention2.forward = llama_flash_attn_forward
# Modified from:
# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/models/llama/modeling_llama.py
def llama_sdpa_attention_forward(
self: "LlamaSdpaAttention",
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional["Cache"] = None,
output_attentions: bool = False,
cache_position: Optional[torch.LongTensor] = None,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
if output_attentions:
logger.warning_once("SDPA does not support `output_attentions=True`. Falling back to the vanilla attention")
return llama_attention_forward(
self,
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
cache_position=cache_position,
**kwargs,
)
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
cos, sin = self.rotary_emb(value_states, position_ids)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
if getattr(self.config, "group_size_ratio", None) and self.training: # shift
groupsz = int(q_len * getattr(self.config, "group_size_ratio"))
assert q_len % groupsz == 0, "q_len {} should be divisible by group size {}.".format(q_len, groupsz)
num_groups = q_len // groupsz
def shift(state: torch.Tensor) -> torch.Tensor:
state = state.transpose(1, 2) # output: (bsz, seq_len, n_heads, head_dim)
state = torch.cat(
(state[:, :, : self.num_heads // 2], state[:, :, self.num_heads // 2 :].roll(-groupsz // 2, dims=1)),
dim=2,
)
return state.reshape(bsz * num_groups, groupsz, self.num_heads, self.head_dim).transpose(1, 2)
query_states, key_states, value_states = shift(query_states), shift(key_states), shift(value_states)
if attention_mask is not None:
attention_mask = attention_mask[:, :, :groupsz, :groupsz].repeat(num_groups, 1, 1, 1)
causal_mask = attention_mask
if attention_mask is not None:
causal_mask = causal_mask[:, :, :, :groupsz]
query_states = query_states.contiguous()
key_states = key_states.contiguous()
value_states = value_states.contiguous()
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=causal_mask,
dropout_p=self.attention_dropout if self.training else 0.0,
is_causal=causal_mask is None and q_len > 1,
)
attn_output = attn_output.transpose(1, 2).contiguous()
if getattr(self.config, "group_size_ratio", None) and self.training: # shift back
attn_output.reshape(bsz, q_len, self.num_heads, self.head_dim)
attn_output = torch.cat(
(
attn_output[:, :, : self.num_heads // 2],
attn_output[:, :, self.num_heads // 2 :].roll(groupsz // 2, dims=1),
)
)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
return attn_output, None, past_key_value
def _apply_llama_patch() -> None:
require_version("transformers==4.40.0", "To fix: pip install transformers==4.40.0")
LlamaAttention.forward = llama_attention_forward
LlamaFlashAttention2.forward = llama_flash_attention_2_forward
LlamaSdpaAttention.forward = llama_sdpa_attention_forward
def configure_longlora(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
if not is_trainable or not model_args.shift_attn:
return
if getattr(config, "model_type", None) in SUPPORTED_CLASS_FOR_S2ATTN:
setattr(config, "group_size_ratio", 0.25)
_apply_llama_patch()
logger.info("Using shift short attention with group_size_ratio=1/4.")
else:
logger.warning("Current model does not support shift short attention.")

74
src/llmtuner/model/utils.py → src/llmtuner/model/utils/misc.py
View File

@ -1,51 +1,23 @@
import inspect
from enum import Enum, unique
from functools import partial
from typing import TYPE_CHECKING, Any, Dict, List, Optional
from typing import TYPE_CHECKING, Dict, List
import torch
from transformers import PreTrainedModel
from transformers.integrations import is_deepspeed_zero3_enabled
from transformers.utils import cached_file
from transformers.utils.versions import require_version
from ..extras.constants import V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
from ..extras.logging import get_logger
from ...extras.constants import V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
from ...extras.logging import get_logger
from .quantization import QuantizationMethod
if TYPE_CHECKING:
from transformers import PretrainedConfig, PreTrainedTokenizer
from ..hparams import ModelArguments
from ...hparams import ModelArguments
logger = get_logger(__name__)
@unique
class QuantizationMethod(str, Enum):
r"""
Borrowed from `transformers.utils.quantization_config.QuantizationMethod`.
"""
BITS_AND_BYTES = "bitsandbytes"
GPTQ = "gptq"
AWQ = "awq"
AQLM = "aqlm"
QUANTO = "quanto"
def add_z3_leaf_module(model: "PreTrainedModel", module: "torch.nn.Module") -> None:
r"""
Sets module as a leaf module to skip partitioning in deepspeed zero3.
"""
if is_deepspeed_zero3_enabled():
require_version("deepspeed>=0.13.0", "To fix: pip install deepspeed>=0.13.0")
from deepspeed.utils import set_z3_leaf_modules # type: ignore
set_z3_leaf_modules(model, [module])
def find_all_linear_modules(model: "PreTrainedModel") -> List[str]:
r"""
Finds all available modules to apply lora or galore.
@ -102,42 +74,6 @@ def find_expanded_modules(model: "PreTrainedModel", target_modules: List[str], n
return module_names
def gradient_checkpointing_enable(
self: "PreTrainedModel", gradient_checkpointing_kwargs: Optional[Dict[str, Any]] = None
) -> None:
r"""
Activates gradient checkpointing for the current model.
Modification of the original method to enable gradient checkpointing for block-wise optimizer.
"""
from torch.utils.checkpoint import checkpoint
if not self.supports_gradient_checkpointing:
raise ValueError("{} does not support gradient checkpointing.".format(self.__class__.__name__))
if gradient_checkpointing_kwargs is None:
gradient_checkpointing_kwargs = {"use_reentrant": True}
gradient_checkpointing_func = partial(checkpoint, **gradient_checkpointing_kwargs)
def custom_gradient_checkpointing_func(func, *args, **kwargs):
module: "torch.nn.Module" = func.__self__
if any(param.requires_grad for param in module.parameters()):
for arg in args:
if torch.is_tensor(arg) and torch.is_floating_point(arg):
arg.requires_grad_(True)
return gradient_checkpointing_func(func, *args, **kwargs)
if "value" in inspect.signature(self._set_gradient_checkpointing).parameters: # old GC format
self.apply(partial(self._set_gradient_checkpointing, value=True))
self.enable_input_require_grads()
logger.warning("You are using the old GC format, some features (e.g. BAdam) will be invalid.")
else: # have already enabled input require gradients
self._set_gradient_checkpointing(enable=True, gradient_checkpointing_func=custom_gradient_checkpointing_func)
def load_valuehead_params(path_or_repo_id: str, model_args: "ModelArguments") -> Dict[str, torch.Tensor]:
r"""
Loads value head parameters from Hugging Face Hub or local disk.

28
src/llmtuner/model/utils/mod.py Normal file
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@ -0,0 +1,28 @@
from typing import TYPE_CHECKING
from ...extras.constants import MOD_SUPPORTED_MODELS
if TYPE_CHECKING:
from transformers import PretrainedConfig, PreTrainedModel
from ...hparams import ModelArguments
def load_mod_pretrained_model(**init_kwargs) -> "PreTrainedModel":
from MoD import AutoMoDModelForCausalLM
return AutoMoDModelForCausalLM.from_pretrained(**init_kwargs)
def convert_pretrained_model_to_mod(
model: "PreTrainedModel", config: "PretrainedConfig", model_args: "ModelArguments"
) -> "PreTrainedModel":
from MoD import apply_mod_to_hf
if getattr(config, "model_type", None) not in MOD_SUPPORTED_MODELS:
raise ValueError("Current model is not supported by mixture-of-depth.")
model = apply_mod_to_hf(model)
model = model.to(model_args.compute_dtype)
return model

39
src/llmtuner/model/utils/moe.py Normal file
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@ -0,0 +1,39 @@
from typing import TYPE_CHECKING
from transformers.integrations import is_deepspeed_zero3_enabled
from transformers.utils.versions import require_version
if TYPE_CHECKING:
from transformers import PreTrainedModel
def add_z3_leaf_module(model: "PreTrainedModel") -> None:
r"""
Sets module as a leaf module to skip partitioning in deepspeed zero3.
"""
if not is_deepspeed_zero3_enabled():
return
require_version("deepspeed>=0.13.0", "To fix: pip install deepspeed>=0.13.0")
from deepspeed.utils import set_z3_leaf_modules # type: ignore
if getattr(model.config, "model_type", None) == "mixtral":
from transformers.models.mixtral.modeling_mixtral import MixtralSparseMoeBlock
set_z3_leaf_modules(model, [MixtralSparseMoeBlock])
if getattr(model.config, "model_type", None) == "qwen2moe":
from transformers.models.qwen2_moe.modeling_qwen2_moe import Qwen2MoeSparseMoeBlock
set_z3_leaf_modules(model, [Qwen2MoeSparseMoeBlock])
if getattr(model.config, "model_type", None) == "jamba":
from transformers.models.jamba.modeling_jamba import JambaSparseMoeBlock
set_z3_leaf_modules(model, [JambaSparseMoeBlock])
if getattr(model.config, "model_type", None) == "dbrx":
from transformers.models.dbrx.modeling_dbrx import DbrxFFN
set_z3_leaf_modules(model, [DbrxFFN])

146
src/llmtuner/model/utils/quantization.py Normal file
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@ -0,0 +1,146 @@
import os
import random
from enum import Enum, unique
from typing import TYPE_CHECKING, Any, Dict, List
import torch
from datasets import load_dataset
from transformers import BitsAndBytesConfig, GPTQConfig
from transformers.integrations import is_deepspeed_zero3_enabled
from transformers.utils.versions import require_version
from ...extras.constants import FILEEXT2TYPE
from ...extras.logging import get_logger
from ...extras.misc import get_current_device
if TYPE_CHECKING:
from transformers import PretrainedConfig, PreTrainedTokenizer
from ...hparams import ModelArguments
logger = get_logger(__name__)
@unique
class QuantizationMethod(str, Enum):
r"""
Borrowed from `transformers.utils.quantization_config.QuantizationMethod`.
"""
BITS_AND_BYTES = "bitsandbytes"
GPTQ = "gptq"
AWQ = "awq"
AQLM = "aqlm"
QUANTO = "quanto"
def _get_quantization_dataset(tokenizer: "PreTrainedTokenizer", model_args: "ModelArguments") -> List[str]:
r"""
Inspired by: https://github.com/huggingface/optimum/blob/v1.16.0/optimum/gptq/data.py#L133
TODO: remove tokenizer.decode() https://github.com/huggingface/optimum/pull/1600
"""
if os.path.isfile(model_args.export_quantization_dataset):
data_path = FILEEXT2TYPE.get(model_args.export_quantization_dataset.split(".")[-1], None)
data_files = model_args.export_quantization_dataset
else:
data_path = model_args.export_quantization_dataset
data_files = None
dataset = load_dataset(path=data_path, data_files=data_files, split="train", cache_dir=model_args.cache_dir)
maxlen = model_args.export_quantization_maxlen
samples = []
for _ in range(model_args.export_quantization_nsamples):
while True:
sample_idx = random.randint(0, len(dataset) - 1)
sample: Dict[str, torch.Tensor] = tokenizer(dataset[sample_idx]["text"], return_tensors="pt")
if sample["input_ids"].size(1) >= maxlen:
break # TODO: fix large maxlen
word_idx = random.randint(0, sample["input_ids"].size(1) - maxlen - 1)
input_ids = sample["input_ids"][:, word_idx : word_idx + maxlen]
samples.append(tokenizer.decode(input_ids[0].tolist(), skip_special_tokens=True))
return samples
def configure_quantization(
config: "PretrainedConfig",
tokenizer: "PreTrainedTokenizer",
model_args: "ModelArguments",
init_kwargs: Dict[str, Any],
) -> None:
r"""
Priority: PTQ-quantized (training) > AutoGPTQ (export) > Bitsandbytes (training)
"""
if getattr(config, "quantization_config", None): # ptq
if is_deepspeed_zero3_enabled():
raise ValueError("DeepSpeed ZeRO-3 is incompatible with quantized models.")
if model_args.quantization_device_map != "auto":
init_kwargs["device_map"] = {"": get_current_device()}
quantization_config: Dict[str, Any] = getattr(config, "quantization_config", None)
quant_method = quantization_config.get("quant_method", "")
if quant_method == QuantizationMethod.GPTQ:
require_version("auto_gptq>=0.5.0", "To fix: pip install auto_gptq>=0.5.0")
quantization_config.pop("disable_exllama", None) # remove deprecated args
quantization_config["use_exllama"] = False # disable exllama
if quant_method == QuantizationMethod.AWQ:
require_version("autoawq", "To fix: pip install autoawq")
if quant_method == QuantizationMethod.AQLM:
require_version("transformers>=4.39.0", "To fix: pip install transformers>=4.39.0")
require_version("aqlm>=1.1.0", "To fix: pip install aqlm[gpu]>=1.1.0")
quantization_config["bits"] = 2
quant_bits = quantization_config.get("bits", "?")
logger.info("Loading {}-bit {}-quantized model.".format(quant_bits, quant_method.upper()))
elif model_args.export_quantization_bit is not None: # auto-gptq
require_version("optimum>=1.16.0", "To fix: pip install optimum>=1.16.0")
require_version("auto_gptq>=0.5.0", "To fix: pip install auto_gptq>=0.5.0")
from accelerate.utils import get_max_memory
if getattr(config, "model_type", None) == "chatglm":
raise ValueError("ChatGLM model is not supported.")
init_kwargs["quantization_config"] = GPTQConfig(
bits=model_args.export_quantization_bit,
tokenizer=tokenizer,
dataset=_get_quantization_dataset(tokenizer, model_args),
)
init_kwargs["device_map"] = "auto"
init_kwargs["max_memory"] = get_max_memory()
logger.info("Quantizing model to {} bit.".format(model_args.export_quantization_bit))
elif model_args.quantization_bit is not None: # bnb
if model_args.quantization_bit == 8:
require_version("bitsandbytes>=0.37.0", "To fix: pip install bitsandbytes>=0.37.0")
init_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)
elif model_args.quantization_bit == 4:
require_version("bitsandbytes>=0.39.0", "To fix: pip install bitsandbytes>=0.39.0")
init_kwargs["quantization_config"] = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=model_args.compute_dtype,
bnb_4bit_use_double_quant=model_args.double_quantization,
bnb_4bit_quant_type=model_args.quantization_type,
bnb_4bit_quant_storage=model_args.compute_dtype, # crucial for fsdp qlora
)
if is_deepspeed_zero3_enabled() or model_args.quantization_device_map == "auto":
if model_args.quantization_bit != 4:
raise ValueError("Only 4-bit quantized model can use auto device map.")
require_version("transformers>=4.39.0", "To fix: pip install transformers>=4.39.0")
require_version("accelerate>=0.28.0", "To fix: pip install accelerate>=0.28.0")
require_version("bitsandbytes>=0.43.0", "To fix: pip install bitsandbytes>=0.43.0")
else:
init_kwargs["device_map"] = {"": get_current_device()}
logger.info("Quantizing model to {} bit.".format(model_args.quantization_bit))

47
src/llmtuner/model/utils/rope.py Normal file
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@ -0,0 +1,47 @@
import math
from typing import TYPE_CHECKING
from ...extras.logging import get_logger
if TYPE_CHECKING:
from transformers import PretrainedConfig
from ...hparams import ModelArguments
logger = get_logger(__name__)
def configure_rope(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
if model_args.rope_scaling is None:
return
if not hasattr(config, "rope_scaling"):
logger.warning("Current model does not support RoPE scaling.")
return
if is_trainable:
if model_args.rope_scaling == "dynamic":
logger.warning(
"Dynamic NTK scaling may not work well with fine-tuning. "
"See: https://github.com/huggingface/transformers/pull/24653"
)
current_max_length = getattr(config, "max_position_embeddings", None)
if current_max_length and model_args.model_max_length > current_max_length:
logger.info(
"Enlarge max model length from {} to {}.".format(current_max_length, model_args.model_max_length)
)
setattr(config, "max_position_embeddings", model_args.model_max_length)
scaling_factor = float(math.ceil(model_args.model_max_length / current_max_length))
else:
logger.warning("Input length is smaller than max length. Consider increase input length.")
scaling_factor = 1.0
else:
scaling_factor = 2.0
setattr(config, "rope_scaling", {"type": model_args.rope_scaling, "factor": scaling_factor})
logger.info(
"Using {} scaling strategy and setting scaling factor to {}".format(model_args.rope_scaling, scaling_factor)
)

88
src/llmtuner/model/utils/unsloth.py Normal file
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@ -0,0 +1,88 @@
from typing import TYPE_CHECKING, Any, Dict, Optional
from ...extras.logging import get_logger
from ...extras.misc import get_current_device
if TYPE_CHECKING:
from transformers import PretrainedConfig, PreTrainedModel
from ...hparams import ModelArguments
logger = get_logger(__name__)
def _get_unsloth_kwargs(
config: "PretrainedConfig", model_name_or_path: str, model_args: "ModelArguments"
) -> Dict[str, Any]:
return {
"model_name": model_name_or_path,
"max_seq_length": model_args.model_max_length or 4096,
"dtype": model_args.compute_dtype,
"load_in_4bit": model_args.quantization_bit == 4,
"token": model_args.hf_hub_token,
"device_map": {"": get_current_device()},
"rope_scaling": getattr(config, "rope_scaling", None),
"fix_tokenizer": False,
"trust_remote_code": True,
"use_gradient_checkpointing": "unsloth",
}
def load_unsloth_pretrained_model(
config: "PretrainedConfig", model_args: "ModelArguments"
) -> Optional["PreTrainedModel"]:
r"""
Optionally loads pretrained model with unsloth. Used in training.
"""
from unsloth import FastLanguageModel
unsloth_kwargs = _get_unsloth_kwargs(config, model_args.model_name_or_path, model_args)
try:
model, _ = FastLanguageModel.from_pretrained(**unsloth_kwargs)
except NotImplementedError:
logger.warning("Unsloth does not support model type {}.".format(getattr(config, "model_type", None)))
model = None
model_args.use_unsloth = False
return model
def get_unsloth_peft_model(
model: "PreTrainedModel", model_args: "ModelArguments", peft_kwargs: Dict[str, Any]
) -> "PreTrainedModel":
r"""
Gets the peft model for the pretrained model with unsloth. Used in training.
"""
from unsloth import FastLanguageModel
unsloth_peft_kwargs = {
"model": model,
"max_seq_length": model_args.model_max_length,
"use_gradient_checkpointing": "unsloth",
}
return FastLanguageModel.get_peft_model(**peft_kwargs, **unsloth_peft_kwargs)
def load_unsloth_peft_model(
config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool
) -> "PreTrainedModel":
r"""
Loads peft model with unsloth. Used in both training and inference.
"""
from unsloth import FastLanguageModel
unsloth_kwargs = _get_unsloth_kwargs(config, model_args.adapter_name_or_path[0], model_args)
try:
if not is_trainable:
unsloth_kwargs["use_gradient_checkpointing"] = False
model, _ = FastLanguageModel.from_pretrained(**unsloth_kwargs)
except NotImplementedError:
raise ValueError("Unsloth does not support model type {}.".format(getattr(config, "model_type", None)))
if not is_trainable:
FastLanguageModel.for_inference(model)
return model

3
src/llmtuner/train/utils.py
View File

@ -61,6 +61,9 @@ def create_modelcard_and_push(
if data_args.dataset is not None:
kwargs["dataset"] = [dataset.strip() for dataset in data_args.dataset.split(",")]
if model_args.use_unsloth:
kwargs["tags"] = kwargs["tags"] + ["unsloth"]
if not training_args.do_train:
pass
elif training_args.push_to_hub:

2
src/llmtuner/webui/chatter.py
View File

@ -72,7 +72,7 @@ class WebChatModel(ChatModel):
finetuning_type=get("top.finetuning_type"),
quantization_bit=int(get("top.quantization_bit")) if get("top.quantization_bit") in ["8", "4"] else None,
template=get("top.template"),
flash_attn=(get("top.booster") == "flash_attn"),
flash_attn="fa2" if get("top.booster") == "flashattn2" else "auto",
use_unsloth=(get("top.booster") == "unsloth"),
rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") in ["linear", "dynamic"] else None,
infer_backend=get("infer.infer_backend"),

2
src/llmtuner/webui/components/top.py
View File

@ -33,7 +33,7 @@ def create_top() -> Dict[str, "Component"]:
quantization_bit = gr.Dropdown(choices=["none", "8", "4"], value="none")
template = gr.Dropdown(choices=list(templates.keys()), value="default")
rope_scaling = gr.Radio(choices=["none", "linear", "dynamic"], value="none")
booster = gr.Radio(choices=["none", "flashattn", "unsloth"], value="none")
booster = gr.Radio(choices=["none", "flashattn2", "unsloth"], value="none")
model_name.change(list_adapters, [model_name, finetuning_type], [adapter_path], queue=False).then(
get_model_path, [model_name], [model_path], queue=False

2
src/llmtuner/webui/components/train.py
View File

@ -138,7 +138,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
with gr.Row():
lora_rank = gr.Slider(value=8, minimum=1, maximum=1024, step=1)
lora_alpha = gr.Slider(value=16, minimum=1, maximum=2048, step=1)
lora_dropout = gr.Slider(value=0.1, minimum=0, maximum=1, step=0.01)
lora_dropout = gr.Slider(value=0, minimum=0, maximum=1, step=0.01)
loraplus_lr_ratio = gr.Slider(value=0, minimum=0, maximum=64, step=0.01)
create_new_adapter = gr.Checkbox()

6
src/llmtuner/webui/runner.py
View File

@ -67,7 +67,7 @@ class Runner:
if not model_path:
return ALERTS["err_no_path"][lang]
if len(dataset) == 0:
if not dataset:
return ALERTS["err_no_dataset"][lang]
if not from_preview and self.demo_mode:
@ -122,7 +122,7 @@ class Runner:
quantization_bit=int(get("top.quantization_bit")) if get("top.quantization_bit") in ["8", "4"] else None,
template=get("top.template"),
rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") in ["linear", "dynamic"] else None,
flash_attn=(get("top.booster") == "flashattn"),
flash_attn="fa2" if get("top.booster") == "flashattn2" else "auto",
use_unsloth=(get("top.booster") == "unsloth"),
dataset_dir=get("train.dataset_dir"),
dataset=",".join(get("train.dataset")),
@ -222,7 +222,7 @@ class Runner:
quantization_bit=int(get("top.quantization_bit")) if get("top.quantization_bit") in ["8", "4"] else None,
template=get("top.template"),
rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") in ["linear", "dynamic"] else None,
flash_attn=(get("top.booster") == "flashattn"),
flash_attn="fa2" if get("top.booster") == "flashattn2" else "auto",
use_unsloth=(get("top.booster") == "unsloth"),
dataset_dir=get("eval.dataset_dir"),
dataset=",".join(get("eval.dataset")),