[ci] disable pip cache for ci (#9654)

.github/workflows/tests.yml

@@ -67,8 +67,6 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python }}
-          cache: "pip"
-          cache-dependency-path: "**/requirements*.txt"
 
       - name: Install dependencies
         run: |

src/llamafactory/extras/constants.py

@@ -114,6 +114,7 @@ class AttentionFunction(str, Enum):
     DISABLED = "disabled"
     SDPA = "sdpa"
     FA2 = "fa2"
+    FA3 = "fa3"
 
 
 class EngineName(str, Enum):

src/llamafactory/model/model_utils/attention.py

@@ -29,16 +29,19 @@ logger = logging.get_logger(__name__)
 
 
 def configure_attn_implementation(config: "PretrainedConfig", model_args: "ModelArguments") -> None:
+    from transformers.utils import is_flash_attn_2_available
+
     if getattr(config, "model_type", None) == "gpt_oss":
         from transformers.integrations.hub_kernels import load_and_register_kernel
+
         flash_attn3_kernel = "kernels-community/vllm-flash-attn3"
         load_and_register_kernel(flash_attn3_kernel)
         setattr(config, "_attn_implementation", flash_attn3_kernel)
         setattr(config, "_attn_implementation_internal", flash_attn3_kernel)
-        model_args.flash_attn = flash_attn3_kernel
-        return
+        model_args.flash_attn = AttentionFunction.FA3
 
-    from transformers.utils import is_flash_attn_2_available
+        logger.info_rank0("Using FlashAttention-3 with attention sink for the gpt-oss model.")
+        return
 
     if getattr(config, "model_type", None) == "gemma2":
         if model_args.flash_attn == AttentionFunction.AUTO or model_args.flash_attn == AttentionFunction.FA2:

src/llamafactory/model/model_utils/liger_kernel.py

@@ -78,8 +78,11 @@ def apply_liger_kernel(
     elif model_type == "qwen3_moe":
         from liger_kernel.transformers import apply_liger_kernel_to_qwen3_moe as apply_liger_kernel
     elif model_type == "gpt_oss":
-        # Install manually from https://github.com/Comet0322/Liger-Kernel
+        try:
             from liger_kernel.transformers import apply_liger_kernel_to_gpt_oss as apply_liger_kernel
+        except ImportError:
+            logger.warning_rank0("Please install liger-kernel from https://github.com/Comet0322/Liger-Kernel.")
+            return
     else:
         logger.warning_rank0("Current model does not support liger kernel.")
         return

src/llamafactory/model/model_utils/moe.py

@@ -82,6 +82,11 @@ def add_z3_leaf_module(model: "PreTrainedModel") -> None:
 
         _set_z3_leaf_modules(model, [Glm4vMoeTextMoE])
 
+    if model_type == "gpt_oss":
+        from transformers.models.gpt_oss.modeling_gpt_oss import GptOssMLP
+
+        _set_z3_leaf_modules(model, [GptOssMLP])
+
     if model_type == "jamba":
         from transformers.models.jamba.modeling_jamba import JambaSparseMoeBlock
 
@@ -132,10 +137,6 @@ def add_z3_leaf_module(model: "PreTrainedModel") -> None:
 
         _set_z3_leaf_modules(model, [Qwen3OmniMoeThinkerTextSparseMoeBlock])
 
-    if model_type == "gpt_oss":
-        from transformers.models.gpt_oss.modeling_gpt_oss import GptOssMLP
-        
-        _set_z3_leaf_modules(model, [GptOssMLP])
 
 def configure_moe(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
     if not is_trainable or not model_args.moe_aux_loss_coef: