fix kv cache

src/llmtuner/hparams/model_args.py

@@ -101,6 +101,10 @@ class ModelArguments:
         default="offload",
         metadata={"help": "Path to offload model weights."},
     )
+    use_cache: bool = field(
+        default=True,
+        metadata={"help": "Whether or not to use KV cache in generation."},
+    )
     hf_hub_token: Optional[str] = field(
         default=None,
         metadata={"help": "Auth token to log in with Hugging Face Hub."},

src/llmtuner/model/patcher.py

@@ -115,6 +115,9 @@ def _configure_attn_implementation(model_args: "ModelArguments", init_kwargs: Di
 
 
 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
@@ -141,7 +144,10 @@ def _configure_rope(config: "PretrainedConfig", model_args: "ModelArguments", is
     )
 
 
-def _configure_longlora(config: "PretrainedConfig") -> None:
+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()
@@ -242,7 +248,7 @@ def _prepare_model_for_training(
             # According to: https://github.com/huggingface/transformers/issues/28339
             model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": True})
             model.enable_input_require_grads()
-            model.config.use_cache = False  # turn off when gradient checkpointing is enabled
+            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:
@@ -276,15 +282,14 @@ def patch_config(
             setattr(config, dtype_name, model_args.compute_dtype == dtype)
 
     _configure_attn_implementation(model_args, init_kwargs)
-
-    if model_args.rope_scaling is not None:
-        _configure_rope(config, model_args, is_trainable)
-
-    if is_trainable and model_args.shift_attn:
-        _configure_longlora(config)
-
+    _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)
+        logger.info("Using KV cache for faster generation.")
+
     init_kwargs["torch_dtype"] = model_args.compute_dtype
     if not is_deepspeed_zero3_enabled():
         init_kwargs["low_cpu_mem_usage"] = model_args.low_cpu_mem_usage