ditching lora nccl

example_trainer/config.py

@@ -105,13 +105,12 @@ class TrainingConfig(BaseModel):
     wandb_group: Optional[str] = Field(None, description="Wandb group name")
 
     # === Training Mode Configuration ===
-    weight_bridge_mode: Literal["shared_vllm", "lora_only", "lora_nccl", "none"] = Field(
+    weight_bridge_mode: Literal["shared_vllm", "lora_only", "none"] = Field(
         "none",
         description=(
             "How to synchronize weights with inference server. "
             "'shared_vllm': attach to vLLM's shared memory tensors and update in-place. "
             "'lora_only': keep base model frozen, train/swap LoRA adapters via HTTP. "
-            "'lora_nccl': LoRA training with NCCL direct weight transfer (torchtitan-style). "
             "'none': legacy mode, restart vLLM with new checkpoint files."
         ),
     )
@@ -149,30 +148,6 @@ class TrainingConfig(BaseModel):
         ),
     )
 
-    # === NCCL Weight Bridge Configuration (for lora_nccl mode) ===
-    nccl_init_method: str = Field(
-        "tcp://localhost:29500",
-        description=(
-            "NCCL process group init method for lora_nccl mode. "
-            "Format: tcp://host:port"
-        ),
-    )
-    nccl_world_size: int = Field(
-        2,
-        description=(
-            "Total number of processes in the NCCL weight bridge group. "
-            "Typically 2: trainer (rank 0) + vLLM server (rank 1). "
-            "For multi-GPU vLLM, this would be 1 + num_vllm_gpus."
-        ),
-    )
-    nccl_sync_every_step: bool = Field(
-        True,
-        description=(
-            "Whether to sync weights after every training step (true on-policy). "
-            "If False, syncs every vllm_restart_interval steps."
-        ),
-    )
-
     # === Single-Copy Mode Configuration ===
     single_copy: bool = Field(
         False,