Add offload_kqv option to llama and server

llama_cpp/llama.py

@@ -752,6 +752,7 @@ class Llama:
         mul_mat_q: bool = True,
         logits_all: bool = False,
         embedding: bool = False,
+        offload_kqv: bool = False,
         # Sampling Params
         last_n_tokens_size: int = 64,
         # LoRA Params
@@ -817,6 +818,7 @@ class Llama:
             yarn_orig_ctx: YaRN original context size
             logits_all: Return logits for all tokens, not just the last token. Must be True for completion to return logprobs.
             embedding: Embedding mode only.
+            offload_kqv: Offload K, Q, V to GPU.
             last_n_tokens_size: Maximum number of tokens to keep in the last_n_tokens deque.
             lora_base: Optional path to base model, useful if using a quantized base model and you want to apply LoRA to an f16 model.
             lora_path: Path to a LoRA file to apply to the model.
@@ -903,6 +905,7 @@ class Llama:
         self.context_params.mul_mat_q = mul_mat_q
         self.context_params.logits_all = logits_all
         self.context_params.embedding = embedding
+        self.context_params.offload_kqv = offload_kqv
 
         # Sampling Params
         self.last_n_tokens_size = last_n_tokens_size

llama_cpp/server/app.py

@@ -100,6 +100,9 @@ class Settings(BaseSettings):
     )
     logits_all: bool = Field(default=True, description="Whether to return logits.")
     embedding: bool = Field(default=True, description="Whether to use embeddings.")
+    offload_kqv: bool = Field(
+        default=False, description="Whether to offload kqv to the GPU."
+    )
     # Sampling Params
     last_n_tokens_size: int = Field(
         default=64,
@@ -409,6 +412,7 @@ def create_app(settings: Optional[Settings] = None):
         mul_mat_q=settings.mul_mat_q,
         logits_all=settings.logits_all,
         embedding=settings.embedding,
+        offload_kqv=settings.offload_kqv,
         # Sampling Params
         last_n_tokens_size=settings.last_n_tokens_size,
         # LoRA Params