Add StoppingCriteria and LogitsProcessor to generate to match huggingface API

llama_cpp/llama.py

@@ -4,7 +4,17 @@ import uuid
 import time
 import math
 import multiprocessing
-from typing import List, Optional, Union, Generator, Sequence, Iterator, Deque, Tuple, Callable
+from typing import (
+    List,
+    Optional,
+    Union,
+    Generator,
+    Sequence,
+    Iterator,
+    Deque,
+    Tuple,
+    Callable,
+)
 from collections import deque, OrderedDict
 
 from . import llama_cpp
@@ -72,6 +82,24 @@ class LlamaState:
         self.llama_state_size = llama_state_size
 
 
+LogitsProcessor = Callable[[List[int], List[float]], List[float]]
+
+
+class LogitsProcessorList(List[LogitsProcessor]):
+    def __call__(self, input_ids: List[int], scores: List[float]) -> List[float]:
+        for processor in self:
+            scores = processor(input_ids, scores)
+        return scores
+
+
+StoppingCriteria = Callable[[List[int], List[float]], bool]
+
+
+class StoppingCriteriaList(List[StoppingCriteria]):
+    def __call__(self, input_ids: List[int], logits: List[float]) -> bool:
+        return any([stopping_criteria(input_ids, logits) for stopping_criteria in self])
+
+
 class Llama:
     """High-level Python wrapper for a llama.cpp model."""
 
@@ -316,12 +344,10 @@ class Llama:
         mirostat_tau: llama_cpp.c_float,
         mirostat_eta: llama_cpp.c_float,
         penalize_nl: bool = True,
-        logits_processors: List[Callable[[List[int], List[float]], List[float]]] = None
+        logits_processor: Optional[LogitsProcessorList] = None,
     ):
         assert self.ctx is not None
         assert len(self.eval_logits) > 0
-        if logits_processors is None:
-            logits_processors = []
 
         n_vocab = self.n_vocab()
         n_ctx = self.n_ctx()
@@ -332,10 +358,10 @@ class Llama:
             else last_n_tokens_size
         )
         logits = self.eval_logits[-1]
-        for processor in logits_processors:
-            logits = processor(list(self.eval_tokens), logits)
 
-        self.eval_logits[-1] = logits
+        if logits_processor is not None:
+            logits = logits_processor(list(self.eval_tokens), logits)
+
         nl_logit = logits[self._token_nl]
         candidates = self._candidates
         for i, logit in enumerate(logits):
@@ -444,8 +470,7 @@ class Llama:
         mirostat_eta: float = 0.1,
         mirostat_tau: float = 5.0,
         penalize_nl: bool = True,
-        logits_processors:  List[Callable[[List[int], List[float]], List[float]]] = None
+        logits_processor: Optional[LogitsProcessorList] = None,
-
     ):
         """Sample a token from the model.
 
@@ -478,8 +503,7 @@ class Llama:
             mirostat_tau=llama_cpp.c_float(mirostat_tau),
             mirostat_eta=llama_cpp.c_float(mirostat_eta),
             penalize_nl=penalize_nl,
-            logits_processors=logits_processors
+            logits_processor=logits_processor,
-
         )
 
     def generate(
@@ -496,7 +520,8 @@ class Llama:
         mirostat_mode: int = 0,
         mirostat_tau: float = 5.0,
         mirostat_eta: float = 0.1,
-        logits_processors: List[Callable[[List[int], List[float]], List[float]]] = None
+        logits_processor: Optional[LogitsProcessorList] = None,
+        stopping_criteria: Optional[StoppingCriteriaList] = None,
     ) -> Generator[int, Optional[Sequence[int]], None]:
         """Create a generator of tokens from a prompt.
 
@@ -554,8 +579,12 @@ class Llama:
                 mirostat_mode=mirostat_mode,
                 mirostat_tau=mirostat_tau,
                 mirostat_eta=mirostat_eta,
-                logits_processors=logits_processors
+                logits_processor=logits_processor,
             )
+            if stopping_criteria is not None and stopping_criteria(
+                list(self.eval_tokens), self.eval_logits[-1]
+            ):
+                return
             tokens_or_none = yield token
             tokens = [token]
             if tokens_or_none is not None:
@@ -651,14 +680,9 @@ class Llama:
         mirostat_tau: float = 5.0,
         mirostat_eta: float = 0.1,
         model: Optional[str] = None,
-        logits_processors: List[Callable[[List[int], List[float]], List[float]]] = None,
-        stopping_criterias: List[Callable[[List[int], List[float]], bool]] = None,
     ) -> Union[Iterator[Completion], Iterator[CompletionChunk]]:
         assert self.ctx is not None
 
-        if stopping_criterias is None:
-            stopping_criterias = []
-
         completion_id: str = f"cmpl-{str(uuid.uuid4())}"
         created: int = int(time.time())
         completion_tokens: List[int] = []
@@ -720,7 +744,6 @@ class Llama:
             frequency_penalty=frequency_penalty,
             presence_penalty=presence_penalty,
             repeat_penalty=repeat_penalty,
-            logits_processors=logits_processors
         ):
             if token == self._token_eos:
                 text = self.detokenize(completion_tokens)
@@ -728,14 +751,6 @@ class Llama:
                 break
 
             completion_tokens.append(token)
-            for stopping_crit in stopping_criterias:
-                if stopping_crit(completion_tokens, None):
-                    text = self.detokenize(completion_tokens)
-                    finish_reason = "stop"
-                    break
-
-            if finish_reason == "stop":
-                break
 
             all_text = self.detokenize(completion_tokens)
 
@@ -1035,8 +1050,6 @@ class Llama:
         mirostat_tau: float = 5.0,
         mirostat_eta: float = 0.1,
         model: Optional[str] = None,
-        logits_processors: List[Callable[[List[int], List[float]], List[float]]] = None,
-        stopping_criterias: List[Callable[[List[int], List[float]], bool]] = None
     ) -> Union[Completion, Iterator[CompletionChunk]]:
         """Generate text from a prompt.
 
@@ -1079,9 +1092,6 @@ class Llama:
             mirostat_tau=mirostat_tau,
             mirostat_eta=mirostat_eta,
             model=model,
-            logits_processors=logits_processors,
-            stopping_criterias=stopping_criterias
-
         )
         if stream:
             chunks: Iterator[CompletionChunk] = completion_or_chunks