Merge branch 'main' into feat/batch_generate

llms/mlx_lm/utils.py

@@ -18,7 +18,7 @@ from mlx.utils import tree_flatten
 from transformers import PreTrainedTokenizer
 
 # Local imports
-from .models.base import KVCache, RotatingKVCache
+from .models import base, cache
 from .sample_utils import categorical_sampling, min_p_sampling, top_p_sampling
 from .tokenizer_utils import TokenizerWrapper, load_tokenizer
 from .tuner.utils import dequantize as dequantize_model
@@ -124,26 +124,6 @@ def apply_repetition_penalty(logits: mx.array, tokens: mx.array, penalty: float)
     return logits
 
 
-def make_kv_caches(
-    model: nn.Module, max_kv_size: Optional[int] = None
-) -> List[Union[KVCache, RotatingKVCache]]:
-    if hasattr(model, "make_cache"):
-        return model.make_cache()
-
-    kv_heads = (
-        [model.n_kv_heads] * len(model.layers)
-        if isinstance(model.n_kv_heads, int)
-        else model.n_kv_heads
-    )
-    if max_kv_size is not None:
-        return [
-            RotatingKVCache(model.head_dim, n, max_size=max_kv_size, keep=4)
-            for n in kv_heads
-        ]
-    else:
-        return [KVCache(model.head_dim, n) for n in kv_heads]
-
-
 def generate_step(
     prompts: mx.array,
     model: nn.Module,
@@ -155,7 +135,7 @@ def generate_step(
     min_tokens_to_keep: int = 1,
     prefill_step_size: int = 512,
     max_kv_size: Optional[int] = None,
-    cache_history: Optional[List[Tuple[mx.array, mx.array]]] = None,
+    prompt_cache: Optional[Any] = None,
     logit_bias: Optional[Dict[int, float]] = None,
     logits_processor: Optional[List[Callable[[mx.array, mx.array], mx.array]]] = None,
 ) -> Generator[Tuple[mx.array, mx.array], None, None]:
@@ -180,6 +160,8 @@ def generate_step(
         prefill_step_size (int): Step size for processing the prompt.
         max_kv_size (int, optional): Maximum size of the key-value cache. Old
           entries (except the first 4 tokens) will be overwritten.
+        prompt_cache (List[Any], optional): A pre-computed prompt cache. Note, if
+          provided, the cache will be updated in place.
         logit_bias (dictionary, optional): Additive logit bias.
         logits_processor (List[Callable[[mx.array, mx.array], mx.array]], optional):
             A list of functions that take tokens and logits and return the processed
@@ -243,20 +225,13 @@ def generate_step(
     tokens = None
 
     # Create the KV cache for generation
-    cache = make_kv_caches(model, max_kv_size)
-
-    if cache_history is not None:
-        if len(cache_history) != len(cache):
-            raise ValueError("Wrong number of layers in the cache history")
-
-        # Set the history in the cache objects and evaluate them to prepare for
-        # generation.
-        for c, h in zip(cache, cache_history):
-            c.update_and_fetch(h[0], h[1])
-        mx.eval([c.state for c in cache])
+    if prompt_cache is None:
+        prompt_cache = cache.make_prompt_cache(model, max_kv_size)
+    elif len(prompt_cache) != len(model.layers):
+        raise ValueError("Wrong number of layers in the prompt cache.")
 
     def _step(y):
-        logits = model(y, cache=cache)
+        logits = model(y, cache=prompt_cache)
         logits = logits[:, -1, :]
 
         if logits_processor:
@@ -270,7 +245,7 @@ def generate_step(
         return y, logprobs
 
     while y.shape[1] > prefill_step_size:
-        model(y[:, :prefill_step_size], cache=cache)
+        model(y[:, :prefill_step_size], cache=prompt_cache)
         mx.eval([c.state for c in cache])
         y = y[:, prefill_step_size:]
 
@@ -312,9 +287,9 @@ def stream_generate(
     detokenizer = tokenizer.detokenizer
 
     detokenizer.reset()
-    for (token, _), n in zip(
-        generate_step(prompt_tokens[None], model, **kwargs),
+    for _, (token, _) in zip(
         range(max_tokens),
+        generate_step(prompt_tokens, model, **kwargs),
     ):
         token = token.item()
         if token == tokenizer.eos_token_id:
@@ -365,9 +340,9 @@ def generate(
     tic = time.perf_counter()
     detokenizer.reset()
 
-    for (token, logprobs), n in zip(
-        generate_step(prompt_tokens[None], model, **kwargs),
+    for n, (token, logprobs) in zip(
         range(max_tokens),
+        generate_step(prompt_tokens[None], model, **kwargs),
     ):
         token = token.item()
         if n == 0: