lidanger
diff --git a/‎llama_cpp/llama.py
Lines changed: 39 additions & 4 deletions b/‎llama_cpp/llama.py
Lines changed: 39 additions & 4 deletions
@@ -4,7 +4,7 @@
 import time
 import math
 import multiprocessing
-from typing import List, Optional, Union, Generator, Sequence, Iterator
+from typing import List, Optional, Union, Generator, Sequence, Iterator, Deque
 from collections import deque
 
 from . import llama_cpp
@@ -20,6 +20,18 @@ class LlamaCache:
     pass
 
 
+class LlamaState:
+    def __init__(
+        self,
+        eval_tokens: Deque[llama_cpp.llama_token],
+        eval_logits: Deque[List[float]],
+        llama_state,
+    ):
+        self.eval_tokens = eval_tokens
+        self.eval_logits = eval_logits
+        self.llama_state = llama_state
+
+
 class Llama:
     """High-level Python wrapper for a llama.cpp model."""
 
@@ -85,8 +97,8 @@ def __init__(
 
         self.last_n_tokens_size = last_n_tokens_size
         self.n_batch = min(n_ctx, n_batch)
-        self.eval_tokens: deque[llama_cpp.llama_token] = deque(maxlen=n_ctx)
-        self.eval_logits: deque[List[float]] = deque(maxlen=n_ctx)
+        self.eval_tokens: Deque[llama_cpp.llama_token] = deque(maxlen=n_ctx)
+        self.eval_logits: Deque[List[float]] = deque(maxlen=n_ctx)
 
         ### HACK: This is a hack to work around the fact that the llama.cpp API does not yet support
         ###       saving and restoring state, this allows us to continue a completion if the last
@@ -204,7 +216,10 @@ def eval(self, tokens: Sequence[llama_cpp.llama_token]):
                 cols = int(n_vocab)
                 rows = n_tokens
                 logits_view = llama_cpp.llama_get_logits(self.ctx)
-                logits = [[logits_view[i * cols + j] for j in range(cols)] for i in range(rows)]
+                logits = [
+                    [logits_view[i * cols + j] for j in range(cols)]
+                    for i in range(rows)
+                ]
                 self.eval_logits.extend(logits)
 
     def sample(
@@ -828,6 +843,26 @@ def __setstate__(self, state):
             verbose=state["verbose"],
         )
 
+    def save_state(self) -> LlamaState:
+        assert self.ctx is not None
+        state_size = llama_cpp.llama_get_state_size(self.ctx)
+        llama_state = (llama_cpp.c_uint8 * int(state_size))()
+        if llama_cpp.llama_copy_state_data(self.ctx, llama_state) != state_size:
+            raise RuntimeError("Failed to copy llama state data")
+        return LlamaState(
+            eval_tokens=self.eval_tokens.copy(),
+            eval_logits=self.eval_logits.copy(),
+            llama_state=llama_state,
+        )
+
+    def load_state(self, state: LlamaState) -> None:
+        assert self.ctx is not None
+        self.eval_tokens = state.eval_tokens.copy()
+        self.eval_logits = state.eval_logits.copy()
+        state_size = llama_cpp.llama_get_state_size(self.ctx)
+        if llama_cpp.llama_set_state_data(self.ctx, state.llama_state) != state_size:
+            raise RuntimeError("Failed to set llama state data")
+
     @staticmethod
     def token_eos() -> llama_cpp.llama_token:
         """Return the end-of-sequence token."""