Add support for numpy

Author: abetlen

llama_cpp/llama.py

@@ -20,6 +20,9 @@
 from . import llama_cpp
 from .llama_types import *
 
+import numpy as np
+import numpy.typing as npt
+
 
 class LlamaCache:
     """Cache for a llama.cpp model."""
@@ -73,11 +76,15 @@ def __init__(
         self,
         eval_tokens: Deque[int],
         eval_logits: Deque[List[float]],
+        input_ids: npt.NDArray[np.intc],
+        scores: npt.NDArray[np.single],
         llama_state,  # type: llama_cpp.Array[llama_cpp.c_uint8]
         llama_state_size: int,
     ):
         self.eval_tokens = eval_tokens
         self.eval_logits = eval_logits
+        self.input_ids = input_ids
+        self.scores = scores
         self.llama_state = llama_state
         self.llama_state_size = llama_state_size
 
@@ -207,27 +214,24 @@ def __init__(
 
         self._n_vocab = self.n_vocab()
         self._n_ctx = self.n_ctx()
-        data = (llama_cpp.llama_token_data * self._n_vocab)(
-            *[
-                llama_cpp.llama_token_data(
-                    id=llama_cpp.llama_token(i),
-                    logit=llama_cpp.c_float(0.0),
-                    p=llama_cpp.c_float(0.0),
-                )
-                for i in range(self._n_vocab)
-            ]
-        )
         size = llama_cpp.c_size_t(self._n_vocab)
-        sorted = False
+        sorted = llama_cpp.c_bool(False)
+        self._candidates_data = np.array(
+            [], dtype=[("id", np.intc), ("logit", np.single), ("p", np.single)]
+        )
+        self._candidates_data.resize(3, self._n_vocab)
         candidates = llama_cpp.llama_token_data_array(
-            data=data,
+            data=self._candidates_data.ctypes.data_as(llama_cpp.llama_token_data_p),
             size=size,
             sorted=sorted,
         )
         self._candidates = candidates
         self._token_nl = Llama.token_nl()
         self._token_eos = Llama.token_eos()
 
+        self._input_ids = np.array([], dtype=np.intc)
+        self._scores = np.ndarray((0, self._n_vocab), dtype=np.single)
+
     def tokenize(self, text: bytes, add_bos: bool = True) -> List[int]:
         """Tokenize a string.
 
@@ -319,13 +323,19 @@ def eval(self, tokens: Sequence[int]):
                 raise RuntimeError(f"llama_eval returned {return_code}")
             # Save tokens
             self.eval_tokens.extend(batch)
+            self._input_ids: npt.NDArray[np.intc] = np.concatenate(
+                (self._input_ids, np.array(batch, dtype=np.intc)), axis=0
+            )
             # Save logits
             rows = n_tokens if self.params.logits_all else 1
             n_vocab = self._n_vocab
             cols = n_vocab
             logits_view = llama_cpp.llama_get_logits(self.ctx)
             logits = [logits_view[i * cols : (i + 1) * cols] for i in range(rows)]
             self.eval_logits.extend(logits)
+            self._scores: npt.NDArray[np.single] = np.concatenate(
+                (self._scores, np.array(logits, dtype=np.single)), axis=0
+            )
 
     def _sample(
         self,
@@ -354,18 +364,23 @@ def _sample(
             if last_n_tokens_size.value < 0
             else last_n_tokens_size
         )
-        logits = self.eval_logits[-1]
+        logits: npt.NDArray[np.single] = self._scores[-1, :]
 
         if logits_processor is not None:
-            logits = logits_processor(list(self.eval_tokens), logits)
-            self.eval_logits[-1] = logits
+            logits = np.array(
+                logits_processor(list(self.eval_tokens), logits.tolist()),
+                dtype=np.single,
+            )
+            self._scores[-1, :] = logits
+            self.eval_logits[-1] = logits.tolist()
 
         nl_logit = logits[self._token_nl]
         candidates = self._candidates
-        for i, logit in enumerate(logits):
-            candidates.data[i].id = llama_cpp.llama_token(i)
-            candidates.data[i].logit = llama_cpp.c_float(logit)
-            candidates.data[i].p = llama_cpp.c_float(0.0)
+        candidates_data = self._candidates_data
+        candidates_data["id"] = np.arange(n_vocab, dtype=np.intc)  # type: ignore
+        candidates_data["logit"] = logits
+        candidates_data["p"] = np.zeros(n_vocab, dtype=np.single)
+        candidates.data = candidates_data.ctypes.data_as(llama_cpp.llama_token_data_p)
         candidates.sorted = llama_cpp.c_bool(False)
         candidates.size = llama_cpp.c_size_t(n_vocab)
         llama_cpp.llama_sample_repetition_penalty(
@@ -1371,6 +1386,8 @@ def save_state(self) -> LlamaState:
         return LlamaState(
             eval_tokens=self.eval_tokens.copy(),
             eval_logits=self.eval_logits.copy(),
+            scores=self._scores.copy(),
+            input_ids=self._input_ids.copy(),
             llama_state=llama_state_compact,
             llama_state_size=n_bytes,
         )
@@ -1379,6 +1396,8 @@ 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()
+        self._scores = state.scores.copy()
+        self._input_ids = state.input_ids.copy()
         state_size = state.llama_state_size
         if llama_cpp.llama_set_state_data(self.ctx, state.llama_state) != state_size:
             raise RuntimeError("Failed to set llama state data")

setup.py

@@ -16,9 +16,7 @@
     license="MIT",
     package_dir={"llama_cpp": "llama_cpp", "llama_cpp.server": "llama_cpp/server"},
     packages=["llama_cpp", "llama_cpp.server"],
-    install_requires=[
-        "typing-extensions>=4.5.0",
-    ],
+    install_requires=["typing-extensions>=4.5.0", "numpy>=1.24.2"],
     extras_require={
         "server": ["uvicorn>=0.21.1", "fastapi>=0.95.0", "sse-starlette>=1.3.3"],
     },