[misc] fix ci with uv (#9676)

src/llamafactory/data/mm_plugin.py

@@ -26,6 +26,7 @@ from typing import TYPE_CHECKING, BinaryIO, Literal, Optional, TypedDict, Union
 
 import numpy as np
 import torch
+import torchaudio
 from transformers.image_utils import get_image_size, is_valid_image, to_numpy_array
 from transformers.models.mllama.processing_mllama import (
     convert_sparse_cross_attention_mask_to_dense,
@@ -34,16 +35,7 @@ from transformers.models.mllama.processing_mllama import (
 from typing_extensions import NotRequired, override
 
 from ..extras.constants import AUDIO_PLACEHOLDER, IGNORE_INDEX, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
-from ..extras.packages import (
-    is_librosa_available,
-    is_pillow_available,
-    is_pyav_available,
-    is_transformers_version_greater_than,
-)
-
-
-if is_librosa_available():
-    import librosa
+from ..extras.packages import is_pillow_available, is_pyav_available, is_transformers_version_greater_than
 
 
 if is_pillow_available():
@@ -316,7 +308,14 @@ class MMPluginMixin:
         results, sampling_rates = [], []
         for audio in audios:
             if not isinstance(audio, np.ndarray):
-                audio, sampling_rate = librosa.load(audio, sr=sampling_rate)
+                audio, sr = torchaudio.load(audio)
+                if audio.shape[0] > 1:
+                    audio = audio.mean(dim=0, keepdim=True)
+
+                if sr != sampling_rate:
+                    audio = torchaudio.functional.resample(audio, sr, sampling_rate)
+
+                audio = audio.squeeze(0).numpy()
 
             results.append(audio)
             sampling_rates.append(sampling_rate)
@@ -500,13 +499,17 @@ class ErnieVLPlugin(BasePlugin):
             while IMAGE_PLACEHOLDER in content:
                 image_seqlen = image_grid_thw[image_idx].prod() // merge_length if self.expand_mm_tokens else 1
                 content = content.replace(
-                    IMAGE_PLACEHOLDER, f"Picture {image_idx + 1}:<|IMAGE_START|>{image_token * image_seqlen}<|IMAGE_END|>", 1
+                    IMAGE_PLACEHOLDER,
+                    f"Picture {image_idx + 1}:<|IMAGE_START|>{image_token * image_seqlen}<|IMAGE_END|>",
+                    1,
                 )
                 image_idx += 1
             while VIDEO_PLACEHOLDER in content:
                 video_seqlen = video_grid_thw[video_idx].prod() // merge_length if self.expand_mm_tokens else 1
                 content = content.replace(
-                    VIDEO_PLACEHOLDER, f"Video {video_idx + 1}:<|VIDEO_START|>{video_token * video_seqlen}<|VIDEO_END|>", 1
+                    VIDEO_PLACEHOLDER,
+                    f"Video {video_idx + 1}:<|VIDEO_START|>{video_token * video_seqlen}<|VIDEO_END|>",
+                    1,
                 )
                 video_idx += 1
             message["content"] = content

src/llamafactory/extras/misc.py

@@ -332,3 +332,7 @@ def fix_proxy(ipv6_enabled: bool = False) -> None:
     if ipv6_enabled:
         os.environ.pop("http_proxy", None)
         os.environ.pop("HTTP_PROXY", None)
+        os.environ.pop("https_proxy", None)
+        os.environ.pop("HTTPS_PROXY", None)
+        os.environ.pop("all_proxy", None)
+        os.environ.pop("ALL_PROXY", None)

src/llamafactory/model/loader.py

@@ -15,7 +15,6 @@
 import os
 from typing import TYPE_CHECKING, Any, Optional, TypedDict
 
-import torch
 from transformers import (
     AutoConfig,
     AutoModelForCausalLM,
@@ -158,6 +157,7 @@ def load_model(
     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
+        init_kwargs["torch_dtype"] = "auto"
 
         if model_args.mixture_of_depths == "load":
             model = load_mod_pretrained_model(**init_kwargs)

src/llamafactory/model/patcher.py

@@ -156,16 +156,13 @@ def patch_config(
     # deepspeed zero3 is not compatible with low_cpu_mem_usage
     init_kwargs["low_cpu_mem_usage"] = model_args.low_cpu_mem_usage and (not is_deepspeed_zero3_enabled())
 
-    # do not cast data type of the model deepspeed zero3 without qlora
-    if not (is_deepspeed_zero3_enabled() and model_args.quantization_bit is None):
-        init_kwargs["torch_dtype"] = "auto"
+    # fsdp/deepspeed zero3 does not need device map
+    if not (is_deepspeed_zero3_enabled() or is_fsdp_enabled()) and init_kwargs["low_cpu_mem_usage"]:
+        if "device_map" not in init_kwargs and model_args.device_map:
+            init_kwargs["device_map"] = model_args.device_map  # device map requires low_cpu_mem_usage=True
 
-        if init_kwargs["low_cpu_mem_usage"] and not is_fsdp_enabled():  # fsdp does not need device map
-            if "device_map" not in init_kwargs and model_args.device_map:
-                init_kwargs["device_map"] = model_args.device_map  # device map requires low_cpu_mem_usage=True
-
-            if init_kwargs.get("device_map", None) == "auto":
-                init_kwargs["offload_folder"] = model_args.offload_folder
+        if init_kwargs.get("device_map", None) == "auto":
+            init_kwargs["offload_folder"] = model_args.offload_folder
 
 
 def patch_model(

src/llamafactory/train/test_utils.py

@@ -84,7 +84,7 @@ def load_reference_model(
         model: AutoModelForCausalLMWithValueHead = AutoModelForCausalLMWithValueHead.from_pretrained(
             model_path, torch_dtype=torch.float16, device_map="auto"
         )
-        
+
         return model
 
     model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, device_map="auto")

src/llamafactory/webui/locales.py

@@ -35,35 +35,40 @@ LOCALES = {
             "value": (
                 "<h3><center>Visit <a href='https://github.com/hiyouga/LLaMA-Factory' target='_blank'>"
                 "GitHub Page</a> <a href='https://llamafactory.readthedocs.io/en/latest/' target='_blank'>"
-                "Documentation</a></center></h3>"
+                "Documentation</a> <a href='https://blog.llamafactory.net/en/' target='_blank'>"
+                "Blog</a></center></h3>"
             ),
         },
         "ru": {
             "value": (
                 "<h3><center>Посетить <a href='https://github.com/hiyouga/LLaMA-Factory' target='_blank'>"
                 "страницу GitHub</a> <a href='https://llamafactory.readthedocs.io/en/latest/' target='_blank'>"
-                "Документацию</a></center></h3>"
+                "Документацию</a> <a href='https://blog.llamafactory.net/en/' target='_blank'>"
+                "Блог</a></center></h3>"
             ),
         },
         "zh": {
             "value": (
                 "<h3><center>访问 <a href='https://github.com/hiyouga/LLaMA-Factory' target='_blank'>"
                 "GitHub 主页</a> <a href='https://llamafactory.readthedocs.io/zh-cn/latest/' target='_blank'>"
-                "官方文档</a></center></h3>"
+                "官方文档</a> <a href='https://blog.llamafactory.net/' target='_blank'>"
+                "博客</a></center></h3>"
             ),
         },
         "ko": {
             "value": (
                 "<h3><center><a href='https://github.com/hiyouga/LLaMA-Factory' target='_blank'>"
                 "GitHub 페이지</a> <a href='https://llamafactory.readthedocs.io/en/latest/' target='_blank'>"
-                "공식 문서</a>를 방문하세요.</center></h3>"
+                "공식 문서</a> <a href='https://blog.llamafactory.net/en/' target='_blank'>"
+                "블로그</a>를 방문하세요.</center></h3>"
             ),
         },
         "ja": {
             "value": (
                 "<h3><center><a href='https://github.com/hiyouga/LLaMA-Factory' target='_blank'>"
                 "GitHub ページ</a> <a href='https://llamafactory.readthedocs.io/en/latest/' target='_blank'>"
-                "ドキュメント</a>にアクセスする</center></h3>"
+                "ドキュメント</a> <a href='https://blog.llamafactory.net/en/' target='_blank'>"
+                "ブログ</a>にアクセスする</center></h3>"
             ),
         },
     },