add precompute kv_cache in Llama class

nexa/gguf/llama/llama.py

@@ -31,6 +31,7 @@
 
 from nexa.gguf.llama.llama_types import *
 from nexa.gguf.llama.llama_grammar import LlamaGrammar
+from nexa.gguf.llama.llama_cache import BaseLlamaCache
 from nexa.gguf.llama.llama_tokenizer import BaseLlamaTokenizer, LlamaTokenizer
 import nexa.gguf.llama.llama_cpp as llama_cpp
 import nexa.gguf.llama.llama_chat_format as llama_chat_format
@@ -350,6 +351,8 @@ def __init__(
         # Sampling Params
         self.last_n_tokens_size = last_n_tokens_size
 
+        self.cache: Optional[BaseLlamaCache] = None
+
         self.lora_base = lora_base
         self.lora_scale = lora_scale
         self.lora_path = lora_path
@@ -596,6 +599,14 @@ def detokenize(
             The detokenized string.
         """
         return self.tokenizer_.detokenize(tokens, prev_tokens=prev_tokens, special=special)
+
+    def set_cache(self, cache: Optional[BaseLlamaCache]):
+        """Set the cache.
+
+        Args:
+            cache: The cache to set.
+        """
+        self.cache = cache
 
     def set_seed(self, seed: int):
         """Set the random seed.
@@ -1211,6 +1222,23 @@ def logit_bias_processor(
             raise ValueError(
                 "logprobs is not supported for models created with logits_all=False"
             )
+
+        if self.cache:
+            try:
+                cache_item = self.cache[prompt_tokens]
+                cache_prefix_len = Llama.longest_token_prefix(
+                    cache_item.input_ids.tolist(), prompt_tokens
+                )
+                eval_prefix_len = Llama.longest_token_prefix(
+                    self._input_ids.tolist(), prompt_tokens
+                )
+                if cache_prefix_len > eval_prefix_len:
+                    self.load_state(cache_item)
+                    if self.verbose:
+                        print("Llama._create_completion: cache hit", file=sys.stderr)
+            except KeyError:
+                if self.verbose:
+                    print("Llama._create_completion: cache miss", file=sys.stderr)
 
         if seed is not None:
             self._ctx.set_rng_seed(seed)
@@ -1552,8 +1580,19 @@ def logit_bias_processor(
                     }
                 ],
             }
+            if self.cache:
+                if self.verbose:
+                    print("Llama._create_completion: cache save", file=sys.stderr)
+                self.cache[prompt_tokens + completion_tokens] = self.save_state()
+                if self.verbose:
+                    print("Llama._create_completion: cache saved", file=sys.stderr)
             return
 
+        if self.cache:
+            if self.verbose:
+                print("Llama._create_completion: cache save", file=sys.stderr)
+            self.cache[prompt_tokens + completion_tokens] = self.save_state()
+
         text_str = text.decode("utf-8", errors="ignore")
 
         if echo: