Merge branch 'main' of https://github.com/BUAADreamer/LLaMA-Factory

README.md

@@ -70,14 +70,16 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
 
 ## Changelog
 
+[24/05/14] We supported training and inference on the Ascend NPU devices. Check [installation](#installation) section for details.
+
 [24/05/13] We supported fine-tuning the **Yi-1.5** series models.
 
 [24/04/26] We supported fine-tuning the **LLaVA-1.5** multimodal LLMs. See [examples](examples/README.md) for usage.
 
-[24/04/22] We provided a **[Colab notebook](https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing)** for fine-tuning the Llama-3 model on a free T4 GPU. Two Llama-3-derived models fine-tuned using LLaMA Factory are available at Hugging Face, check [Llama3-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat) and [Llama3-Chinese](https://huggingface.co/zhichen/Llama3-Chinese) for details.
-
 <details><summary>Full Changelog</summary>
 
+[24/04/22] We provided a **[Colab notebook](https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing)** for fine-tuning the Llama-3 model on a free T4 GPU. Two Llama-3-derived models fine-tuned using LLaMA Factory are available at Hugging Face, check [Llama3-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat) and [Llama3-Chinese](https://huggingface.co/zhichen/Llama3-Chinese) for details.
+
 [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](examples/README.md) for usage.
 
 [24/04/16] We supported **[BAdam](https://arxiv.org/abs/2404.02827)**. See [examples](examples/README.md) for usage.
@@ -328,7 +330,7 @@ Extra dependencies available: torch, metrics, deepspeed, bitsandbytes, vllm, gal
 
 <details><summary>For Windows users</summary>
 
-If you want to enable the quantized LoRA (QLoRA) on the Windows platform, you will be required to install a pre-built version of `bitsandbytes` library, which supports CUDA 11.1 to 12.2, please select the appropriate [release version](https://github.com/jllllll/bitsandbytes-windows-webui/releases/tag/wheels) based on your CUDA version.
+If you want to enable the quantized LoRA (QLoRA) on the Windows platform, you need to install a pre-built version of `bitsandbytes` library, which supports CUDA 11.1 to 12.2, please select the appropriate [release version](https://github.com/jllllll/bitsandbytes-windows-webui/releases/tag/wheels) based on your CUDA version.
 
 ```bash
 pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.2.post2-py3-none-win_amd64.whl
@@ -338,6 +340,23 @@ To enable FlashAttention-2 on the Windows platform, you need to install the prec
 
 </details>
 
+<details><summary>For Ascend NPU users</summary>
+
+To utilize Ascend NPU devices for (distributed) training and inference, you need to install the **[torch-npu](https://gitee.com/ascend/pytorch)** package and the **[Ascend CANN Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**.
+
+| Requirement  | Minimum | Recommend |
+| ------------ | ------- | --------- |
+| CANN         | 8.0.RC1 | 8.0.RC1   |
+| torch        | 2.2.0   | 2.2.0     |
+| torch-npu    | 2.2.0   | 2.2.0     |
+| deepspeed    | 0.13.2  | 0.13.2    |
+
+Remember to use `ASCEND_RT_VISIBLE_DEVICES` instead of `CUDA_VISIBLE_DEVICES` to specify the device to use.
+
+If you cannot infer model on NPU devices, try setting `do_sample: false` in the configurations.
+
+</details>
+
 ### Data Preparation
 
 Please refer to [data/README.md](data/README.md) for checking the details about the format of dataset files. You can either use datasets on HuggingFace / ModelScope hub or load the dataset in local disk.

README_zh.md

@@ -70,14 +70,16 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
 
 ## 更新日志
 
+[24/05/14] 我们支持了昇腾 NPU 设备的训练和推理。详情请查阅[安装](#安装-llama-factory)部分。
+
 [24/05/13] 我们支持了 Yi-1.5 系列模型的微调。
 
 [24/04/26] 我们支持了多模态模型 **LLaVA-1.5** 的微调。详细用法请参照 [examples](examples/README_zh.md)。
 
-[24/04/22] 我们提供了在免费 T4 GPU 上微调 Llama-3 模型的 **[Colab 笔记本](https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing)**。Hugging Face 社区公开了两个利用 LLaMA Factory 微调的 Llama-3 模型，详情请见 [Llama3-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat) 和 [Llama3-Chinese](https://huggingface.co/zhichen/Llama3-Chinese)。
-
 <details><summary>展开日志</summary>
 
+[24/04/22] 我们提供了在免费 T4 GPU 上微调 Llama-3 模型的 **[Colab 笔记本](https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing)**。Hugging Face 社区公开了两个利用 LLaMA Factory 微调的 Llama-3 模型，详情请见 [Llama3-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat) 和 [Llama3-Chinese](https://huggingface.co/zhichen/Llama3-Chinese)。
+
 [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](examples/README_zh.md)。
 
 [24/04/16] 我们支持了 **[BAdam](https://arxiv.org/abs/2404.02827)**。详细用法请参照 [examples](examples/README_zh.md)。
@@ -338,6 +340,23 @@ pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/downl
 
 </details>
 
+<details><summary>昇腾 NPU 用户指南</summary>
+
+如果使用昇腾 NPU 设备进行（分布式）训练或推理，需要安装 **[torch-npu](https://gitee.com/ascend/pytorch)** 库和 **[Ascend CANN Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**。
+
+| 依赖项       | 至少     | 推荐      |
+| ------------ | ------- | --------- |
+| CANN         | 8.0.RC1 | 8.0.RC1   |
+| torch        | 2.2.0   | 2.2.0     |
+| torch-npu    | 2.2.0   | 2.2.0     |
+| deepspeed    | 0.13.2  | 0.13.2    |
+
+请记得使用 `ASCEND_RT_VISIBLE_DEVICES` 而非 `CUDA_VISIBLE_DEVICES` 来指定您使用的设备。
+
+如果遇到无法正常推理的情况，请尝试设置 `do_sample: false`。
+
+</details>
+
 ### 数据准备
 
 关于数据集文件的格式，请参考 [data/README_zh.md](data/README_zh.md) 的内容。你可以使用 HuggingFace / ModelScope 上的数据集或加载本地数据集。

examples/README.md

@@ -7,6 +7,7 @@ Make sure to execute these commands in the `LLaMA-Factory` directory.
 - [LoRA Fine-Tuning on A Single GPU](#lora-fine-tuning-on-a-single-gpu)
 - [QLoRA Fine-Tuning on a Single GPU](#qlora-fine-tuning-on-a-single-gpu)
 - [LoRA Fine-Tuning on Multiple GPUs](#lora-fine-tuning-on-multiple-gpus)
+- [LoRA Fine-Tuning on Multiple NPUs](#lora-fine-tuning-on-multiple-npus)
 - [Full-Parameter Fine-Tuning on Multiple GPUs](#full-parameter-fine-tuning-on-multiple-gpus)
 - [Merging LoRA Adapters and Quantization](#merging-lora-adapters-and-quantization)
 - [Inferring LoRA Fine-Tuned Models](#inferring-lora-fine-tuned-models)
@@ -124,6 +125,14 @@ bash examples/lora_multi_gpu/multi_node.sh
 bash examples/lora_multi_gpu/ds_zero3.sh
 ```
 
+### LoRA Fine-Tuning on Multiple NPUs
+
+#### Supervised Fine-Tuning with DeepSpeed ZeRO-0
+
+```bash
+bash examples/lora_multi_npu/ds_zero0.sh
+```
+
 ### Full-Parameter Fine-Tuning on Multiple GPUs
 
 #### Supervised Fine-Tuning with Accelerate on Single Node

examples/README_zh.md

@@ -7,6 +7,7 @@
 - [单 GPU LoRA 微调](#单-gpu-lora-微调)
 - [单 GPU QLoRA 微调](#单-gpu-qlora-微调)
 - [多 GPU LoRA 微调](#多-gpu-lora-微调)
+- [多 NPU LoRA 微调](#多-npu-lora-微调)
 - [多 GPU 全参数微调](#多-gpu-全参数微调)
 - [合并 LoRA 适配器与模型量化](#合并-lora-适配器与模型量化)
 - [推理 LoRA 模型](#推理-lora-模型)
@@ -124,6 +125,14 @@ bash examples/lora_multi_gpu/multi_node.sh
 bash examples/lora_multi_gpu/ds_zero3.sh
 ```
 
+### 多 NPU LoRA 微调
+
+#### 使用 DeepSpeed ZeRO-0 训练
+
+```bash
+bash examples/lora_multi_npu/ds_zero0.sh
+```
+
 ### 多 GPU 全参数微调
 
 #### 使用 DeepSpeed 进行单节点训练

examples/deepspeed/ds_z0_config.json

@@ -0,0 +1,28 @@
+{
+  "train_batch_size": "auto",
+  "train_micro_batch_size_per_gpu": "auto",
+  "gradient_accumulation_steps": "auto",
+  "gradient_clipping": "auto",
+  "zero_allow_untested_optimizer": true,
+  "fp16": {
+    "enabled": "auto",
+    "loss_scale": 0,
+    "loss_scale_window": 1000,
+    "initial_scale_power": 16,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
+  "bf16": {
+    "enabled": "auto"
+  },
+  "zero_optimization": {
+    "stage": 0,
+    "allgather_partitions": true,
+    "allgather_bucket_size": 5e8,
+    "overlap_comm": true,
+    "reduce_scatter": true,
+    "reduce_bucket_size": 5e8,
+    "contiguous_gradients": true,
+    "round_robin_gradients": true
+  }
+}

examples/full_multi_gpu/multi_node.sh

@@ -6,7 +6,7 @@ RANK=0
 MASTER_ADDR=192.168.0.1
 MASTER_PORT=29500
 
-CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.run \
+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun \
     --nproc_per_node $NPROC_PER_NODE \
     --nnodes $NNODES \
     --node_rank $RANK \

examples/full_multi_gpu/single_node.sh

@@ -1,9 +1,15 @@
 #!/bin/bash
 
 NPROC_PER_NODE=4
+NNODES=1
+RANK=0
+MASTER_ADDR=127.0.0.1
+MASTER_PORT=29500
 
-CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.run \
+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun \
     --nproc_per_node $NPROC_PER_NODE \
-    --nnodes 1 \
-    --standalone \
+    --nnodes $NNODES \
+    --node_rank $RANK \
+    --master_addr $MASTER_ADDR \
+    --master_port $MASTER_PORT \
     src/train.py examples/full_multi_gpu/llama3_full_sft.yaml

examples/lora_multi_gpu/ds_zero3.sh

@@ -1,9 +1,15 @@
 #!/bin/bash
 
 NPROC_PER_NODE=4
+NNODES=1
+RANK=0
+MASTER_ADDR=127.0.0.1
+MASTER_PORT=29500
 
-CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.run \
+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun \
     --nproc_per_node $NPROC_PER_NODE \
-    --nnodes 1 \
-    --standalone \
+    --nnodes $NNODES \
+    --node_rank $RANK \
+    --master_addr $MASTER_ADDR \
+    --master_port $MASTER_PORT \
     src/train.py examples/lora_multi_gpu/llama3_lora_sft_ds.yaml

examples/lora_multi_npu/ds_zero0.sh

@@ -0,0 +1,15 @@
+#!/bin/bash
+
+NPROC_PER_NODE=4
+NNODES=1
+RANK=0
+MASTER_ADDR=127.0.0.1
+MASTER_PORT=29500
+
+ASCEND_RT_VISIBLE_DEVICES=0,1,2,3 torchrun \
+    --nproc_per_node $NPROC_PER_NODE \
+    --nnodes $NNODES \
+    --node_rank $RANK \
+    --master_addr $MASTER_ADDR \
+    --master_port $MASTER_PORT \
+    src/train.py examples/lora_multi_npu/llama3_lora_sft_ds.yaml

examples/lora_multi_npu/llama3_lora_sft_ds.yaml

@@ -0,0 +1,42 @@
+# model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+
+# method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_target: q_proj,v_proj
+
+# ddp
+ddp_timeout: 180000000
+deepspeed: examples/deepspeed/ds_z0_config.json
+
+# dataset
+dataset: identity,alpaca_gpt4_en
+template: llama3
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+# output
+output_dir: saves/llama3-8b/lora/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+# train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 2
+learning_rate: 0.0001
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_steps: 0.1
+fp16: true
+
+# eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+evaluation_strategy: steps
+eval_steps: 500

src/llmtuner/api/app.py

@@ -51,7 +51,7 @@ def create_app(chat_model: "ChatModel") -> "FastAPI":
         allow_methods=["*"],
         allow_headers=["*"],
     )
-    api_key = os.environ.get("API_KEY", None)
+    api_key = os.environ.get("API_KEY")
     security = HTTPBearer(auto_error=False)
 
     async def verify_api_key(auth: Annotated[Optional[HTTPAuthorizationCredentials], Depends(security)]):

src/llmtuner/chat/hf_engine.py

@@ -65,12 +65,13 @@ class HuggingfaceEngine(BaseEngine):
         prompt_length = len(prompt_ids)
         inputs = torch.tensor([prompt_ids], device=model.device)
 
-        do_sample = input_kwargs.pop("do_sample", None)
-        temperature = input_kwargs.pop("temperature", None)
-        top_p = input_kwargs.pop("top_p", None)
-        top_k = input_kwargs.pop("top_k", None)
-        num_return_sequences = input_kwargs.pop("num_return_sequences", None)
-        repetition_penalty = input_kwargs.pop("repetition_penalty", None)
+        do_sample = input_kwargs.pop("do_sample", generating_args["do_sample"])
+        temperature = input_kwargs.pop("temperature", generating_args["temperature"])
+        top_p = input_kwargs.pop("top_p", generating_args["top_p"])
+        top_k = input_kwargs.pop("top_k", generating_args["top_k"])
+        num_return_sequences = input_kwargs.pop("num_return_sequences", 1)
+        repetition_penalty = input_kwargs.pop("repetition_penalty", generating_args["repetition_penalty"])
+        length_penalty = input_kwargs.pop("length_penalty", generating_args["length_penalty"])
         max_length = input_kwargs.pop("max_length", None)
         max_new_tokens = input_kwargs.pop("max_new_tokens", None)
         stop = input_kwargs.pop("stop", None)
@@ -78,14 +79,16 @@ class HuggingfaceEngine(BaseEngine):
         if stop is not None:
             raise ValueError("Stop parameter is not supported in Huggingface engine yet.")
 
+        generating_args = generating_args.copy()
         generating_args.update(
             dict(
-                do_sample=do_sample if do_sample is not None else generating_args["do_sample"],
-                temperature=temperature or generating_args["temperature"],
-                top_p=top_p or generating_args["top_p"],
-                top_k=top_k or generating_args["top_k"],
-                num_return_sequences=num_return_sequences or 1,
-                repetition_penalty=repetition_penalty or generating_args["repetition_penalty"],
+                do_sample=do_sample,
+                temperature=temperature,
+                top_p=top_p,
+                top_k=top_k,
+                num_return_sequences=num_return_sequences,
+                repetition_penalty=repetition_penalty,
+                length_penalty=length_penalty,
                 eos_token_id=[tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids,
                 pad_token_id=tokenizer.pad_token_id,
             )
@@ -94,6 +97,10 @@ class HuggingfaceEngine(BaseEngine):
         if isinstance(num_return_sequences, int) and num_return_sequences > 1:
             generating_args["do_sample"] = True
 
+        if not generating_args["do_sample"]:
+            generating_args.pop("temperature", None)
+            generating_args.pop("top_p", None)
+
         if max_length:
             generating_args.pop("max_new_tokens", None)
             generating_args["max_length"] = max_length

src/llmtuner/chat/vllm_engine.py

@@ -89,43 +89,35 @@ class VllmEngine(BaseEngine):
         )
         prompt_length = len(prompt_ids)
 
-        temperature = input_kwargs.pop("temperature", None)
-        top_p = input_kwargs.pop("top_p", None)
-        top_k = input_kwargs.pop("top_k", None)
-        num_return_sequences = input_kwargs.pop("num_return_sequences", None)
-        repetition_penalty = input_kwargs.pop("repetition_penalty", None)
+        use_beam_search = self.generating_args["num_beams"] > 1
+        temperature = input_kwargs.pop("temperature", self.generating_args["temperature"])
+        top_p = input_kwargs.pop("top_p", self.generating_args["top_p"])
+        top_k = input_kwargs.pop("top_k", self.generating_args["top_k"])
+        num_return_sequences = input_kwargs.pop("num_return_sequences", 1)
+        repetition_penalty = input_kwargs.pop("repetition_penalty", self.generating_args["repetition_penalty"])
+        length_penalty = input_kwargs.pop("length_penalty", self.generating_args["length_penalty"])
         max_length = input_kwargs.pop("max_length", None)
         max_new_tokens = input_kwargs.pop("max_new_tokens", None)
         stop = input_kwargs.pop("stop", None)
 
-        generating_args = self.generating_args.copy()
-        generating_args.update(
-            dict(
-                temperature=temperature or generating_args["temperature"],
-                top_p=top_p or generating_args["top_p"],
-                top_k=top_k or generating_args["top_k"],
-                num_return_sequences=num_return_sequences or 1,
-                repetition_penalty=repetition_penalty or generating_args["repetition_penalty"],
-            )
-        )
-
+        max_tokens = self.generating_args["max_new_tokens"] or self.generating_args["max_length"]
         if max_length:
-            generating_args["max_new_tokens"] = max_length - prompt_length
+            max_tokens = max_length - prompt_length if max_length > prompt_length else 1
 
         if max_new_tokens:
-            generating_args["max_new_tokens"] = max_new_tokens
+            max_tokens = max_new_tokens
 
         sampling_params = SamplingParams(
-            n=generating_args["num_return_sequences"],
-            repetition_penalty=generating_args["repetition_penalty"],
-            temperature=generating_args["temperature"],
-            top_p=generating_args["top_p"],
-            top_k=generating_args["top_k"],
-            use_beam_search=generating_args["num_beams"] > 1,
-            length_penalty=generating_args["length_penalty"],
+            n=num_return_sequences,
+            repetition_penalty=repetition_penalty,
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k,
+            use_beam_search=use_beam_search,
+            length_penalty=length_penalty,
             stop=stop,
             stop_token_ids=[self.tokenizer.eos_token_id] + self.tokenizer.additional_special_tokens_ids,
-            max_tokens=generating_args["max_new_tokens"],
+            max_tokens=max_tokens,
             skip_special_tokens=True,
         )
 

src/llmtuner/extras/callbacks.py

@@ -53,7 +53,7 @@ class LogCallback(TrainerCallback):
         self.aborted = False
         self.do_train = False
         """ Web UI """
-        self.webui_mode = bool(int(os.environ.get("LLAMABOARD_ENABLED", "0")))
+        self.webui_mode = os.environ.get("LLAMABOARD_ENABLED", "0").lower() in ["true", "1"]
         if self.webui_mode:
             signal.signal(signal.SIGABRT, self._set_abort)
             self.logger_handler = LoggerHandler(output_dir)

src/llmtuner/extras/misc.py

@@ -58,7 +58,7 @@ class AverageMeter:
 
 
 def check_dependencies() -> None:
-    if int(os.environ.get("DISABLE_VERSION_CHECK", "0")):
+    if os.environ.get("DISABLE_VERSION_CHECK", "0").lower() in ["true", "1"]:
         logger.warning("Version checking has been disabled, may lead to unexpected behaviors.")
     else:
         require_version("transformers>=4.37.2", "To fix: pip install transformers>=4.37.2")

src/llmtuner/extras/ploting.py

@@ -21,6 +21,9 @@ def smooth(scalars: List[float]) -> List[float]:
     r"""
     EMA implementation according to TensorBoard.
     """
+    if len(scalars) == 0:
+        return []
+
     last = scalars[0]
     smoothed = []
     weight = 1.8 * (1 / (1 + math.exp(-0.05 * len(scalars))) - 0.5)  # a sigmoid function
@@ -32,6 +35,9 @@ def smooth(scalars: List[float]) -> List[float]:
 
 
 def gen_loss_plot(trainer_log: List[Dict[str, Any]]) -> "matplotlib.figure.Figure":
+    r"""
+    Plots loss curves in LlamaBoard.
+    """
     plt.close("all")
     plt.switch_backend("agg")
     fig = plt.figure()
@@ -51,6 +57,9 @@ def gen_loss_plot(trainer_log: List[Dict[str, Any]]) -> "matplotlib.figure.Figur
 
 
 def plot_loss(save_dictionary: os.PathLike, keys: List[str] = ["loss"]) -> None:
+    r"""
+    Plots loss curves and saves the image.
+    """
     plt.switch_backend("agg")
     with open(os.path.join(save_dictionary, TRAINER_STATE_NAME), "r", encoding="utf-8") as f:
         data = json.load(f)

src/llmtuner/model/patcher.py

@@ -1,9 +1,10 @@
+import os
 from types import MethodType
 from typing import TYPE_CHECKING, Any, Dict
 
 import torch
 from peft import PeftModel
-from transformers import PreTrainedModel, PreTrainedTokenizerBase
+from transformers import PreTrainedModel, PreTrainedTokenizerBase, is_torch_npu_available
 from transformers.integrations import is_deepspeed_zero3_enabled
 
 from ..extras.logging import get_logger
@@ -44,6 +45,10 @@ 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))
 
+    if is_torch_npu_available():
+        use_jit_compile = os.environ.get("JIT_COMPILE", "0").lower() in ["true", "1"]
+        torch.npu.set_compile_mode(jit_compile=use_jit_compile)
+
     configure_attn_implementation(config, model_args)
     configure_rope(config, model_args, is_trainable)
     configure_longlora(config, model_args, is_trainable)
@@ -57,7 +62,7 @@ def patch_config(
         logger.info("Using KV cache for faster generation.")
 
     if getattr(config, "model_type", None) == "qwen":
-        setattr(config, "use_flash_attn", model_args.flash_attn)
+        setattr(config, "use_flash_attn", model_args.flash_attn == "fa2")
         for dtype_name, dtype in [("fp16", torch.float16), ("bf16", torch.bfloat16), ("fp32", torch.float32)]:
             setattr(config, dtype_name, model_args.compute_dtype == dtype)
 

src/llmtuner/model/utils/attention.py

@@ -22,7 +22,7 @@ def configure_attn_implementation(config: "PretrainedConfig", model_args: "Model
 
     elif model_args.flash_attn == "sdpa":
         if not is_sdpa_available():
-            logger.warning("Torch>=2.1.1 is required for SDPA attention.")
+            logger.warning("torch>=2.1.1 is required for SDPA attention.")
             return
 
         requested_attn_implementation = "sdpa"
@@ -52,4 +52,4 @@ def print_attn_implementation(config: "PretrainedConfig") -> None:
     elif attn_implementation == "sdpa":
         logger.info("Using torch SDPA for faster training and inference.")
     else:
-        logger.info("Using vanilla Attention implementation.")
+        logger.info("Using vanilla attention implementation.")

src/llmtuner/webui/interface.py

@@ -71,12 +71,12 @@ def create_web_demo() -> gr.Blocks:
 
 
 def run_web_ui() -> None:
-    gradio_share = bool(int(os.environ.get("GRADIO_SHARE", "0")))
+    gradio_share = os.environ.get("GRADIO_SHARE", "0").lower() in ["true", "1"]
     server_name = os.environ.get("GRADIO_SERVER_NAME", "0.0.0.0")
     create_ui().queue().launch(share=gradio_share, server_name=server_name)
 
 
 def run_web_demo() -> None:
-    gradio_share = bool(int(os.environ.get("GRADIO_SHARE", "0")))
+    gradio_share = os.environ.get("GRADIO_SHARE", "0").lower() in ["true", "1"]
     server_name = os.environ.get("GRADIO_SERVER_NAME", "0.0.0.0")
     create_web_demo().queue().launch(share=gradio_share, server_name=server_name)

src/webui.py

@@ -4,7 +4,7 @@ from llmtuner.webui.interface import create_ui
 
 
 def main():
-    gradio_share = bool(int(os.environ.get("GRADIO_SHARE", "0")))
+    gradio_share = os.environ.get("GRADIO_SHARE", "0").lower() in ["true", "1"]
     server_name = os.environ.get("GRADIO_SERVER_NAME", "0.0.0.0")
     create_ui().queue().launch(share=gradio_share, server_name=server_name)