Update sampling api

llama_cpp/llama.py

@@ -127,7 +127,9 @@ class Llama:
         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_logits: Deque[List[llama_cpp.c_float]] = deque(
+            maxlen=n_ctx if logits_all else 1
+        )
 
         self.cache: Optional[LlamaCache] = None
 
@@ -236,17 +238,90 @@ class Llama:
             )
             if int(return_code) != 0:
                 raise RuntimeError(f"llama_eval returned {return_code}")
+            # Save tokens
             self.eval_tokens.extend(batch)
-            if self.params.logits_all:
+            # Save logits
-                n_vocab = llama_cpp.llama_n_vocab(self.ctx)
+            rows = n_tokens if self.params.logits_all else 1
-                cols = int(n_vocab)
+            n_vocab = llama_cpp.llama_n_vocab(self.ctx)
-                rows = n_tokens
+            cols = int(n_vocab)
-                logits_view = llama_cpp.llama_get_logits(self.ctx)
+            logits_view = llama_cpp.llama_get_logits(self.ctx)
-                logits = [
+            logits: List[List[llama_cpp.c_float]] = [
-                    [logits_view[i * cols + j] for j in range(cols)]
+                [logits_view[i * cols + j] for j in range(cols)] for i in range(rows)
-                    for i in range(rows)
+            ]
-                ]
+            self.eval_logits.extend(logits)
-                self.eval_logits.extend(logits)
+
+    def _sample_top_p_top_k(
+        self,
+        last_n_tokens_data,  # type: llama_cpp.Array[llama_cpp.llama_token]
+        last_n_tokens_size: llama_cpp.c_int,
+        top_k: llama_cpp.c_int,
+        top_p: llama_cpp.c_float,
+        temp: llama_cpp.c_float,
+        repeat_penalty: llama_cpp.c_float,
+    ):
+        assert self.ctx is not None
+        assert len(self.eval_logits) > 0
+        n_vocab = int(llama_cpp.llama_n_vocab(self.ctx))
+        logits = self.eval_logits[-1]
+        data = (llama_cpp.llama_token_data * n_vocab)(
+            *[
+                llama_cpp.llama_token_data(
+                    id=llama_cpp.llama_token(i),
+                    logit=logits[i],
+                    p=llama_cpp.c_float(0.0),
+                )
+                for i in range(n_vocab)
+            ]
+        )
+        size = llama_cpp.c_size_t(n_vocab)
+        sorted = False
+        candidates = llama_cpp.llama_token_data_array(
+            data=data,
+            size=size,
+            sorted=sorted,
+        )
+        llama_cpp.llama_sample_repetition_penalty(
+            ctx=self.ctx,
+            last_tokens_data=last_n_tokens_data,
+            last_tokens_size=last_n_tokens_size,
+            candidates=llama_cpp.ctypes.pointer(candidates),
+            penalty=repeat_penalty,
+        )
+        if temp == 0.0:
+            return llama_cpp.llama_sample_token_greedy(
+                ctx=self.ctx,
+                candidates=llama_cpp.ctypes.pointer(candidates),
+            )
+        else:
+            llama_cpp.llama_sample_top_k(
+                ctx=self.ctx,
+                candidates=llama_cpp.ctypes.pointer(candidates),
+                k=top_k,
+            )
+            llama_cpp.llama_sample_tail_free(
+                ctx=self.ctx,
+                candidates=llama_cpp.ctypes.pointer(candidates),
+                z=llama_cpp.c_float(1.0),
+            )
+            llama_cpp.llama_sample_typical(
+                ctx=self.ctx,
+                candidates=llama_cpp.ctypes.pointer(candidates),
+                p=llama_cpp.c_float(1.0)
+            )
+            llama_cpp.llama_sample_top_p(
+                ctx=self.ctx,
+                candidates=llama_cpp.ctypes.pointer(candidates),
+                p=top_p,
+            )
+            llama_cpp.llama_sample_temperature(
+                ctx=self.ctx,
+                candidates=llama_cpp.ctypes.pointer(candidates),
+                temp=temp,
+            )
+            return llama_cpp.llama_sample_token(
+                ctx=self.ctx,
+                candidates=llama_cpp.ctypes.pointer(candidates),
+            )
 
     def sample(
         self,
@@ -270,8 +345,7 @@ class Llama:
         last_n_tokens_data = [llama_cpp.llama_token(0)] * max(
             0, self.last_n_tokens_size - len(self.eval_tokens)
         ) + list(self.eval_tokens)[-self.last_n_tokens_size :]
-        return llama_cpp.llama_sample_top_p_top_k(
+        return self._sample_top_p_top_k(
-            ctx=self.ctx,
             last_n_tokens_data=(llama_cpp.llama_token * self.last_n_tokens_size)(
                 *last_n_tokens_data
             ),
@@ -470,15 +544,15 @@ class Llama:
             all_text = self.detokenize(completion_tokens)
 
             # Contains multi-byte UTF8
-            for k,char in enumerate(all_text[-3:]):
+            for k, char in enumerate(all_text[-3:]):
                 k = 3 - k
-                for num,pattern in [(2, 192), (3, 224), (4, 240)]:
+                for num, pattern in [(2, 192), (3, 224), (4, 240)]:
                     # Bitwise AND check
-                    if (num > k and pattern & char == pattern):
+                    if num > k and pattern & char == pattern:
                         multibyte_fix = num - k
 
             # Stop incomplete bytes from passing
-            if (multibyte_fix > 0):
+            if multibyte_fix > 0:
                 multibyte_fix -= 1
                 continue
 
@@ -531,7 +605,9 @@ class Llama:
                 "model": self.model_path,
                 "choices": [
                     {
-                        "text": text[returned_characters:].decode("utf-8", errors="ignore"),
+                        "text": text[returned_characters:].decode(
+                            "utf-8", errors="ignore"
+                        ),
                         "index": 0,
                         "logprobs": None,
                         "finish_reason": finish_reason,
@@ -558,7 +634,8 @@ class Llama:
 
             all_tokens = prompt_tokens + completion_tokens
             all_token_strs = [
-                self.detokenize([token]).decode("utf-8", errors="ignore") for token in all_tokens
+                self.detokenize([token]).decode("utf-8", errors="ignore")
+                for token in all_tokens
             ]
             all_logprobs = [
                 [Llama.logit_to_logprob(logit) for logit in row]
@@ -577,7 +654,9 @@ class Llama:
                 )
                 token_logprobs.append(sorted_logprobs[int(token)][0])
                 top_logprob = {
-                    self.detokenize([llama_cpp.llama_token(i)]).decode("utf-8", errors="ignore"): logprob
+                    self.detokenize([llama_cpp.llama_token(i)]).decode(
+                        "utf-8", errors="ignore"
+                    ): logprob
                     for logprob, i in sorted_logprobs[:logprobs]
                 }
                 top_logprob.update({token_str: sorted_logprobs[int(token)][0]})

llama_cpp/llama_cpp.py

@@ -495,7 +495,9 @@ _lib.llama_sample_softmax.restype = None
 
 
 # @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
-def llama_sample_top_k(ctx: llama_context_p, candidates, k: c_int, min_keep: c_int):
+def llama_sample_top_k(
+    ctx: llama_context_p, candidates, k: c_int, min_keep: c_size_t = c_size_t(1)
+):
     return _lib.llama_sample_top_k(ctx, candidates, k, min_keep)
 
 
@@ -503,13 +505,15 @@ _lib.llama_sample_top_k.argtypes = [
     llama_context_p,
     llama_token_data_array_p,
     c_int,
-    c_int,
+    c_size_t,
 ]
 _lib.llama_sample_top_k.restype = None
 
 
 # @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
-def llama_sample_top_p(ctx: llama_context_p, candidates, p: c_float, min_keep: c_int):
+def llama_sample_top_p(
+    ctx: llama_context_p, candidates, p: c_float, min_keep: c_size_t = c_size_t(1)
+):
     return _lib.llama_sample_top_p(ctx, candidates, p, min_keep)
 
 
@@ -517,14 +521,14 @@ _lib.llama_sample_top_p.argtypes = [
     llama_context_p,
     llama_token_data_array_p,
     c_float,
-    c_int,
+    c_size_t,
 ]
 _lib.llama_sample_top_p.restype = None
 
 
 # @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
 def llama_sample_tail_free(
-    ctx: llama_context_p, candidates, z: c_float, min_keep: c_int
+    ctx: llama_context_p, candidates, z: c_float, min_keep: c_size_t = c_size_t(1)
 ):
     return _lib.llama_sample_tail_free(ctx, candidates, z, min_keep)
 
@@ -533,13 +537,15 @@ _lib.llama_sample_tail_free.argtypes = [
     llama_context_p,
     llama_token_data_array_p,
     c_float,
-    c_int,
+    c_size_t,
 ]
 _lib.llama_sample_tail_free.restype = None
 
 
 # @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
-def llama_sample_typical(ctx: llama_context_p, candidates, p: c_float, min_keep: c_int):
+def llama_sample_typical(
+    ctx: llama_context_p, candidates, p: c_float, min_keep: c_size_t = c_size_t(1)
+):
     return _lib.llama_sample_typical(ctx, candidates, p, min_keep)
 
 
@@ -547,7 +553,7 @@ _lib.llama_sample_typical.argtypes = [
     llama_context_p,
     llama_token_data_array_p,
     c_float,
-    c_int,
+    c_size_t,
 ]
 _lib.llama_sample_typical.restype = None