Make Llama instance pickleable. Closes #27

llama_cpp/llama.py

@@ -651,6 +651,45 @@ class Llama:
             llama_cpp.llama_free(self.ctx)
             self.ctx = None
 
+    def __getstate__(self):
+        return dict(
+            verbose=self.verbose,
+            model_path=self.model_path,
+            n_ctx=self.params.n_ctx,
+            n_parts=self.params.n_parts,
+            seed=self.params.seed,
+            f16_kv=self.params.f16_kv,
+            logits_all=self.params.logits_all,
+            vocab_only=self.params.vocab_only,
+            use_mlock=self.params.use_mlock,
+            embedding=self.params.embedding,
+            last_n_tokens_size=self.last_n_tokens_size,
+            last_n_tokens_data=self.last_n_tokens_data,
+            tokens_consumed=self.tokens_consumed,
+            n_batch=self.n_batch,
+            n_threads=self.n_threads,
+        )
+
+    def __setstate__(self, state):
+        self.__init__(
+            model_path=state["model_path"],
+            n_ctx=state["n_ctx"],
+            n_parts=state["n_parts"],
+            seed=state["seed"],
+            f16_kv=state["f16_kv"],
+            logits_all=state["logits_all"],
+            vocab_only=state["vocab_only"],
+            use_mlock=state["use_mlock"],
+            embedding=state["embedding"],
+            n_threads=state["n_threads"],
+            n_batch=state["n_batch"],
+            last_n_tokens_size=state["last_n_tokens_size"],
+            verbose=state["verbose"],
+        )
+        self.last_n_tokens_data=state["last_n_tokens_data"]
+        self.tokens_consumed=state["tokens_consumed"]
+
+
     @staticmethod
     def token_eos() -> llama_cpp.llama_token:
         """Return the end-of-sequence token."""

tests/test_llama.py

@@ -77,3 +77,20 @@ def test_llama_patch(monkeypatch):
     chunks = llama.create_completion(text, max_tokens=2, stream=True)
     assert "".join(chunk["choices"][0]["text"] for chunk in chunks) == " j"
     assert completion["choices"][0]["finish_reason"] == "length"
+
+
+def test_llama_pickle():
+    import pickle
+    import tempfile
+    fp = tempfile.TemporaryFile()
+    llama = llama_cpp.Llama(model_path=MODEL, vocab_only=True)
+    pickle.dump(llama, fp)
+    fp.seek(0)
+    llama = pickle.load(fp)
+
+    assert llama
+    assert llama.ctx is not None
+
+    text = b"Hello World"
+
+    assert llama.detokenize(llama.tokenize(text)) == text