Update llama_cpp and add kv_cache api support

llama_cpp/llama_cpp.py

@@ -1,6 +1,6 @@
 import ctypes
 
-from ctypes import c_int, c_float, c_char_p, c_void_p, c_bool, POINTER, Structure, Array
+from ctypes import c_int, c_float, c_char_p, c_void_p, c_bool, POINTER, Structure, Array, c_uint8, c_size_t
 
 import pathlib
 from itertools import chain
@@ -101,6 +101,36 @@ def llama_model_quantize(
 _lib.llama_model_quantize.argtypes = [c_char_p, c_char_p, c_int, c_int]
 _lib.llama_model_quantize.restype = c_int
 
+# Returns the KV cache that will contain the context for the
+# ongoing prediction with the model.
+def llama_get_kv_cache(ctx: llama_context_p):
+    return _lib.llama_get_kv_cache(ctx)
+
+_lib.llama_get_kv_cache.argtypes = [llama_context_p]
+_lib.llama_get_kv_cache.restype = POINTER(c_uint8)
+
+# Returns the size of the KV cache
+def llama_get_kv_cache_size(ctx: llama_context_p) -> c_size_t:
+    return _lib.llama_get_kv_cache_size(ctx)
+
+_lib.llama_get_kv_cache_size.argtypes = [llama_context_p]
+_lib.llama_get_kv_cache_size.restype = c_size_t
+
+# Returns the number of tokens in the KV cache
+def llama_get_kv_cache_token_count(ctx: llama_context_p) -> c_int:
+    return _lib.llama_get_kv_cache_token_count(ctx)
+
+_lib.llama_get_kv_cache_token_count.argtypes = [llama_context_p]
+_lib.llama_get_kv_cache_token_count.restype = c_int
+
+
+# Sets the KV cache containing the current context for the model
+def llama_set_kv_cache(ctx: llama_context_p, kv_cache, n_size: c_size_t, n_token_count: c_int):
+    return _lib.llama_set_kv_cache(ctx, kv_cache, n_size, n_token_count)
+
+_lib.llama_set_kv_cache.argtypes = [llama_context_p, POINTER(c_uint8), c_size_t, c_int]
+_lib.llama_set_kv_cache.restype = None
+
 
 # Run the llama inference to obtain the logits and probabilities for the next token.
 # tokens + n_tokens is the provided batch of new tokens to process