Update of generate_step to support repetition_penalty and repetition_…

…context_size params

outlines/models/mlxlm.py

@@ -1,5 +1,16 @@
 import dataclasses
-from typing import TYPE_CHECKING, Generator, Iterator, List, Optional, Tuple, Union
+from typing import (
+    TYPE_CHECKING,
+    Iterator,
+    List,
+    Optional,
+    Tuple,
+    TypedDict,
+    Union,
+    Generator,
+)
+
+from typing_extensions import Unpack
 
 from .transformers import TransformerTokenizer
 
@@ -12,6 +23,14 @@
     from outlines.processors import OutlinesLogitsProcessor
 
 
+
+class MLXLMParams(TypedDict, total=False):
+    top_p: float						# so top_p can be passed as a parameter to generate() without defining a sampler
+    repetition_penalty: float
+    repetition_context_size: int
+
+
+
 class MLXLM:
     """
     Represents an `mlx_lm` model
@@ -28,24 +47,28 @@ def __init__(
             tokenizer._tokenizer
         )  # _tokenizer is HF Tokenizer
 
+
     def generate(
         self,
         prompts: Union[str, List[str]],
         generation_parameters: "GenerationParameters",
         logits_processor,
         sampling_parameters: "SamplingParameters",
+        **mlx_lm_params: Unpack[MLXLMParams],
     ) -> str:
         streamer = self.stream(
-            prompts, generation_parameters, logits_processor, sampling_parameters
+            prompts, generation_parameters, logits_processor, sampling_parameters, **mlx_lm_params
         )
         return "".join(list(streamer))
 
+
     def stream(
         self,
         prompts: Union[str, List[str]],
         generation_parameters: "GenerationParameters",
         logits_processor,
         sampling_parameters: "SamplingParameters",
+        **mlx_lm_params: Unpack[MLXLMParams],
     ) -> Iterator[str]:
         """Generate text using `mlx_lm`.
 
@@ -63,6 +86,9 @@ def stream(
             An instance of `SamplingParameters`, a dataclass that contains
             the name of the sampler to use and related parameters as available
             in Outlines.
+        mlx_lm_params
+            Of type `MLXLMParams`.
+
         Returns
         -------
         The generated text.
@@ -100,6 +126,7 @@ def stream(
             "top_p": top_p,
             "sampler": sampler,
             "logits_processor": logits_processor,
+            **mlx_lm_params
         }
 
         # Adapted from
@@ -121,40 +148,61 @@ def stream(
         detokenizer.finalize()
         yield detokenizer.last_segment
 
+
     def generate_step(
         self,
         prompt: "mx.array",
         temp: Optional[float],
         top_p: Optional[float],
         sampler: str,
         logits_processor: "OutlinesLogitsProcessor",
+        repetition_penalty: Optional[float] = None,
+        repetition_context_size: Optional[int] = 20,
     ) -> Generator[Tuple[int, float], None, None]:
         """
         Adapted from
-        https://github.com/ml-explore/mlx-examples/blob/4872727/llms/mlx_lm/utils.py#L129
+            https://github.com/ml-explore/mlx-examples/blob/4872727/llms/mlx_lm/utils.py#L129
+        and updated (on Sept 2024 to add repetition_* args) from
+            https://github.com/ml-explore/mlx-examples/blob/bd29aec299c8fa59c161a9c1207bfc59db31d845/llms/mlx_lm/utils.py#L149
 
         A generator producing token ids based on the given prompt from the model.
 
             Args:
                 prompt (mx.array): The input prompt.
                 temp (float): The temperature for sampling, if 0 the argmax is used.
-                  Default: ``0``.
+                    Default: ``0``.
                 top_p (float, optional): Nulceus sampling, higher means model considers
-                  more less likely words.
+                    more less likely words.
                 sampler (str): The sampler string defined by SequenceGeneratorAdapter
                 logits_processor (OutlinesLogitsProcessor): Augment logits before sampling.
+                repetition_penalty (float, optional): The penalty factor for repeating tokens. 
+                    1.0 for no penalty. >1.0 for penalty. Default: ``None``.
+                repetition_context_size (int, optional): The number of tokens to
+                    consider for repetition penalty. Default: ``20``.
+
+            Yields:
+                Generator[Tuple[mx.array, mx.array], None, None]: A generator producing
+                    one token and a vector of log probabilities.
         """
         import mlx.core as mx
         import mlx_lm
 
-        temperature: float = temp or 1.0
+        if repetition_penalty:
+            if not isinstance(repetition_penalty, float) or repetition_penalty <= 0:
+                raise ValueError(
+                    f"repetition_penalty must be a non-negative float, got {repetition_penalty}" )
+            if not isinstance(repetition_context_size, int) or repetition_context_size <= 2:
+                raise ValueError(
+					f"repetition_context_size must be a positive integer > 2, got {repetition_context_size}" )
+
 
         def sample(logits: "mx.array") -> Tuple["mx.array", float]:
             softmax_logits = mx.softmax(logits)
 
-            if temperature == 0.0 or sampler == "greedy":
+            if temp == 0.0 or sampler == "greedy":		# temp == 0, not temperature, which can never be 0
                 token = mx.argmax(logits, axis=-1)
             elif sampler == "multinomial":
+                temperature: float = temp or 1.0
                 if top_p is not None and top_p > 0 and top_p < 1.0:
                     token = mlx_lm.sample_utils.top_p_sampling(
                         logits, top_p, temperature
@@ -167,13 +215,22 @@ def sample(logits: "mx.array") -> Tuple["mx.array", float]:
             prob = softmax_logits[0, token]
             return token, prob
 
+
+        # Create the KV cache for generation
         kv_heads = (
             [self.model.n_kv_heads] * len(self.model.layers)
             if isinstance(self.model.n_kv_heads, int)
             else self.model.n_kv_heads
         )
         cache = [mlx_lm.models.base.KVCache(self.model.head_dim, n) for n in kv_heads]
 
+
+        # Init the repetition context
+        repetition_context = prompt.tolist()
+        if repetition_context_size:
+            repetition_context = repetition_context[-repetition_context_size:]
+
+
         # kv cache contains processed input IDs, we pass the unprocessed inputs and cache to model()
         unprocessed_input_ids = prompt
         generated_ids: List[int] = []
@@ -182,6 +239,10 @@ def sample(logits: "mx.array") -> Tuple["mx.array", float]:
             logits = self.model(unprocessed_input_ids[None], cache=cache)
             logits = logits[:, -1, :]
 
+            if repetition_penalty:
+                logits = mlx_lm.utils.apply_repetition_penalty(
+                    logits, repetition_context, repetition_penalty )
+
             if logits_processor is not None:
                 # convert to logits_processor 1d expectation, apply, then convert back
                 logits_1d = logits.reshape(-1)
@@ -191,11 +252,17 @@ def sample(logits: "mx.array") -> Tuple["mx.array", float]:
             new_token_single, prob = sample(logits)
             new_token = new_token_single.item()
             yield new_token, prob
+
+            if repetition_penalty:
+                repetition_context.append(new_token)
+                if repetition_context_size and len(repetition_context) > repetition_context_size:
+                    repetition_context = repetition_context[-repetition_context_size:]
 
             generated_ids.append(new_token)
             unprocessed_input_ids = new_token_single
 
 
+
 def mlxlm(
     model_name: str,
     tokenizer_config: dict = {},