add mask to mlx_lm model interface

llms/mlx_lm/models/cache.py

@@ -10,7 +10,6 @@ from mlx.utils import tree_flatten, tree_map, tree_unflatten
 def make_prompt_cache(
     model: nn.Module,
     max_kv_size: Optional[int] = None,
-    lengths: Optional[mx.array] = None,
 ) -> List[Any]:
     """
     Construct the model's cache for use when cgeneration.
@@ -23,22 +22,17 @@ def make_prompt_cache(
         max_kv_size (Optional[int]): If provided and the model does not have a
             ``make_cache`` method, a ``RotatingKVCache`` is used with a maximum
             size of ``max_kv_size``
-        lengths (Optional[array]): If provided these sequence lengths will be
-            used mask the KV cache. Useful for batch inputs.
     """
     if hasattr(model, "make_cache"):
         return model.make_cache()
 
     num_layers = len(model.layers)
     if max_kv_size is not None:
-        cache = [
+        return [
             RotatingKVCache(max_size=max_kv_size, keep=4) for _ in range(num_layers)
         ]
     else:
-        cache = [KVCache() for _ in range(num_layers)]
+        return [KVCache() for _ in range(num_layers)]
-
-    cache[0].lengths = lengths
-    return cache
 
 
 def save_prompt_cache(file_name: str, cache: List[Any], metadata: Dict[str, str] = {}):

llms/mlx_lm/models/cohere.py

@@ -155,11 +155,12 @@ class CohereModel(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
         h = self.embed_tokens(inputs)
 
-        mask = create_attention_mask(h, cache)
+        mask = mask or create_attention_mask(h, cache)
 
         if cache is None:
             cache = [None] * len(self.layers)
@@ -180,9 +181,10 @@ class Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
-        out = self.model(inputs, cache)
+        out = self.model(inputs, mask, cache)
         out = self.model.embed_tokens.as_linear(out)
         out = out * self.model.args.logit_scale
         return out

llms/mlx_lm/models/cohere2.py

@@ -6,7 +6,7 @@ from typing import Optional, Tuple
 import mlx.core as mx
 import mlx.nn as nn
 
-from .base import BaseModelArgs, create_causal_mask, scaled_dot_product_attention
+from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention
 from .cache import KVCache, RotatingKVCache
 
 
@@ -151,16 +151,12 @@ class CohereModel(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
         h = self.embed_tokens(inputs)
 
-        T = h.shape[1]
+        mask = mask or create_attention_mask(h, cache)
-        if T > 1:
-            offset = cache[0].offset if cache else 0
-            mask = create_causal_mask(T, offset).astype(h.dtype)
-        else:
-            mask = None
 
         if cache is None:
             cache = [None] * len(self.layers)
@@ -181,9 +177,10 @@ class Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
-        out = self.model(inputs, cache)
+        out = self.model(inputs, mask, cache)
         out = self.model.embed_tokens.as_linear(out)
         out = out * self.model.args.logit_scale
         return out

llms/mlx_lm/models/dbrx.py

@@ -197,11 +197,12 @@ class DBRX(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
         h = self.wte(inputs)
 
-        mask = create_attention_mask(h, cache)
+        mask = mask or create_attention_mask(h, cache)
 
         if cache is None:
             cache = [None] * len(self.blocks)
@@ -223,9 +224,10 @@ class Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
-        out = self.transformer(inputs, cache)
+        out = self.transformer(inputs, mask, cache)
         return self.lm_head(out)
 
     @property

llms/mlx_lm/models/deepseek.py

@@ -211,9 +211,10 @@ class DeepseekModel(nn.Module):
         self,
         x: mx.array,
         cache: Optional[Any] = None,
+        mask: Optional[mx.array] = None,
     ) -> mx.array:
         h = self.embed_tokens(x)
-        mask = create_attention_mask(h, cache)
+        mask = mask or create_attention_mask(h, cache)
 
         if cache is None:
             cache = [None] * len(self.layers)
@@ -236,8 +237,9 @@ class Model(nn.Module):
         self,
         inputs: mx.array,
         cache: Optional[Any] = None,
+        mask: Optional[mx.array] = None,
     ):
-        out = self.model(inputs, cache)
+        out = self.model(inputs, cache, mask)
         return self.lm_head(out)
 
     def sanitize(self, weights):

llms/mlx_lm/models/deepseek_v2.py

@@ -370,9 +370,10 @@ class DeepseekV2Model(nn.Module):
         self,
         x: mx.array,
         cache: Optional[Any] = None,
+        mask: Optional[mx.array] = None,
     ) -> mx.array:
         h = self.embed_tokens(x)
-        mask = create_attention_mask(h, cache)
+        mask = mask or create_attention_mask(h, cache)
 
         if cache is None:
             cache = [None] * len(self.layers)
@@ -395,8 +396,9 @@ class Model(nn.Module):
         self,
         inputs: mx.array,
         cache: Optional[Any] = None,
+        mask: Optional[mx.array] = None,
     ):
-        out = self.model(inputs, cache)
+        out = self.model(inputs, cache, mask)
         return self.lm_head(out)
 
     def sanitize(self, weights):

llms/mlx_lm/models/exaone.py

@@ -123,10 +123,11 @@ class ExaoneModel(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
         h = self.wte(inputs)
-        mask = create_attention_mask(h, cache)
+        mask = mask or create_attention_mask(h, cache)
 
         if cache is None:
             cache = [None] * len(self.h)
@@ -149,9 +150,10 @@ class Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
-        out = self.transformer(inputs, cache)
+        out = self.transformer(inputs, mask, cache)
         if self.args.tie_word_embeddings:
             out = self.transformer.wte.as_linear(out)
         else:

llms/mlx_lm/models/gemma.py

@@ -138,12 +138,13 @@ class GemmaModel(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
         h = self.embed_tokens(inputs)
         h = h * (self.args.hidden_size**0.5)
 
-        mask = create_attention_mask(h, cache)
+        mask = mask or create_attention_mask(h, cache)
 
         if cache is None:
             cache = [None] * len(self.layers)
@@ -164,9 +165,10 @@ class Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
-        out = self.model(inputs, cache)
+        out = self.model(inputs, mask, cache)
         out = self.model.embed_tokens.as_linear(out)
         return out
 

llms/mlx_lm/models/gemma2.py

@@ -160,12 +160,13 @@ class GemmaModel(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
         h = self.embed_tokens(inputs)
         h = h * (self.args.hidden_size**0.5)
 
-        mask = create_attention_mask(h, cache)
+        mask = mask or create_attention_mask(h, cache)
 
         if cache is None:
             cache = [None] * len(self.layers)
@@ -187,9 +188,10 @@ class Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
-        out = self.model(inputs, cache)
+        out = self.model(inputs, mask, cache)
         out = self.model.embed_tokens.as_linear(out)
         out = mx.tanh(out / self.final_logit_softcapping)
         out = out * self.final_logit_softcapping

llms/mlx_lm/models/gpt2.py

@@ -126,6 +126,7 @@ class GPT2Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
         _, L = inputs.shape
@@ -138,7 +139,7 @@ class GPT2Model(nn.Module):
             position_ids = mx.array(np.arange(L))
             hidden_states += self.wpe(position_ids)
 
-            mask = create_attention_mask(hidden_states, cache)
+            mask = mask or create_attention_mask(hidden_states, cache)
 
         if cache is None:
             cache = [None] * len(self.h)
@@ -159,9 +160,10 @@ class Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
-        out = self.model(inputs, cache)
+        out = self.model(inputs, mask, cache)
         out = self.model.wte.as_linear(out)
         return out
 

llms/mlx_lm/models/gpt_bigcode.py

@@ -137,6 +137,7 @@ class GPTBigCodeModel(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
         B, L = inputs.shape
@@ -149,7 +150,7 @@ class GPTBigCodeModel(nn.Module):
             position_ids = mx.array(np.arange(L))
             hidden_states += self.wpe(position_ids)
 
-            mask = create_attention_mask(hidden_states, cache)
+            mask = mask or create_attention_mask(hidden_states, cache)
 
         if cache is None:
             cache = [None] * len(self.h)
@@ -172,9 +173,10 @@ class Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
-        out = self.transformer(inputs, cache)
+        out = self.transformer(inputs, mask, cache)
         if self.args.tie_word_embeddings:
             out = self.transformer.wte.as_linear(out)
         else:

llms/mlx_lm/models/gpt_neox.py

@@ -146,13 +146,14 @@ class GPTNeoXModel(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
         _, L = inputs.shape
 
         hidden_states = self.embed_in(inputs)
 
-        mask = create_attention_mask(hidden_states, cache)
+        mask = mask or create_attention_mask(hidden_states, cache)
 
         if cache is None:
             cache = [None] * len(self.h)
@@ -176,9 +177,10 @@ class Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
-        out = self.model(inputs, cache)
+        out = self.model(inputs, mask, cache)
         return out
 
     def sanitize(self, weights):

llms/mlx_lm/models/hunyuan.py

@@ -239,11 +239,12 @@ class HunYuanModel(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
         h = self.embed_tokens(inputs)
 
-        mask = create_attention_mask(h, cache)
+        mask = mask or create_attention_mask(h, cache)
 
         if cache is None:
             cache = [None] * len(self.layers)
@@ -266,9 +267,10 @@ class Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
-        out = self.model(inputs, cache)
+        out = self.model(inputs, mask, cache)
         return self.model.embed_tokens.as_linear(out)
 
     def sanitize(self, weights):

llms/mlx_lm/models/internlm2.py

@@ -193,11 +193,12 @@ class InternLM2Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
         h = self.tok_embeddings(inputs)
 
-        mask = create_attention_mask(h, cache)
+        mask = mask or create_attention_mask(h, cache)
 
         if cache is None:
             cache = [None] * len(self.layers)
@@ -220,9 +221,10 @@ class Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
-        out = self.model(inputs, cache)
+        out = self.model(inputs, mask, cache)
         if self.args.tie_word_embeddings:
             out = self.model.tok_embeddings.as_linear(out)
         else:

llms/mlx_lm/models/llama.py

@@ -155,11 +155,12 @@ class LlamaModel(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
         h = self.embed_tokens(inputs)
 
-        mask = create_attention_mask(h, cache)
+        mask = mask or create_attention_mask(h, cache)
 
         if cache is None:
             cache = [None] * len(self.layers)
@@ -182,9 +183,10 @@ class Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
-        out = self.model(inputs, cache)
+        out = self.model(inputs, mask, cache)
         if self.args.tie_word_embeddings:
             out = self.model.embed_tokens.as_linear(out)
         else:

llms/mlx_lm/models/minicpm.py

@@ -158,11 +158,12 @@ class MiniCPMModel(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
         h = self.embed_tokens(inputs) * self.args.scale_emb
 
-        mask = create_attention_mask(h, cache)
+        mask = mask or create_attention_mask(h, cache)
 
         if cache is None:
             cache = [None] * len(self.layers)
@@ -186,9 +187,10 @@ class Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
-        out = self.model(inputs, cache)
+        out = self.model(inputs, mask, cache)
 
         if not self.args.tie_word_embeddings:
             out = self.lm_head(out / (self.args.hidden_size / self.args.dim_model_base))

llms/mlx_lm/models/mixtral.py

@@ -162,11 +162,12 @@ class MixtralModel(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
         h = self.embed_tokens(inputs)
 
-        mask = create_attention_mask(h, cache)
+        mask = mask or create_attention_mask(h, cache)
 
         if cache is None:
             cache = [None] * len(self.layers)
@@ -188,9 +189,10 @@ class Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
-        out = self.model(inputs, cache)
+        out = self.model(inputs, mask, cache)
         return self.lm_head(out)
 
     def sanitize(self, weights):

llms/mlx_lm/models/nemotron.py

@@ -176,11 +176,12 @@ class NemotronModel(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
         h = self.embed_tokens(inputs)
 
-        mask = create_attention_mask(h, cache)
+        mask = mask or create_attention_mask(h, cache)
 
         if cache is None:
             cache = [None] * len(self.layers)
@@ -203,9 +204,10 @@ class Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
-        out = self.model(inputs, cache)
+        out = self.model(inputs, mask, cache)
         if self.args.tie_word_embeddings:
             out = self.model.embed_tokens.as_linear(out)
         else:

llms/mlx_lm/models/olmo.py

@@ -124,11 +124,12 @@ class Transformer(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
         h = self.wte(inputs)
 
-        mask = create_attention_mask(h, cache)
+        mask = mask or create_attention_mask(h, cache)
 
         if cache is None:
             cache = [None] * len(self.blocks)
@@ -152,9 +153,10 @@ class OlmoModel(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
-        return self.transformer(inputs, cache)
+        return self.transformer(inputs, mask, cache)
 
 
 class Model(nn.Module):
@@ -167,9 +169,10 @@ class Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
-        return self.model(inputs, cache)
+        return self.model(inputs, mask, cache)
 
     @property
     def layers(self):

llms/mlx_lm/models/olmo2.py

@@ -163,10 +163,11 @@ class LlamaModel(nn.Module):
         self,
         inputs: mx.array,
         cache=None,
+        mask=None,
     ):
         h = self.embed_tokens(inputs)
 
-        mask = create_attention_mask(h, cache)
+        mask = mask or create_attention_mask(h, cache)
 
         if cache is None:
             cache = [None] * len(self.layers)
@@ -190,8 +191,9 @@ class Model(nn.Module):
         self,
         inputs: mx.array,
         cache=None,
+        mask=None,
     ):
-        out = self.model(inputs, cache)
+        out = self.model(inputs, cache, mask)
         if self.args.tie_word_embeddings:
             out = self.model.embed_tokens.as_linear(out)
         else:

llms/mlx_lm/models/openelm.py

@@ -178,11 +178,12 @@ class OpenELMModel(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
         h = self.token_embeddings(inputs)
 
-        mask = create_attention_mask(h, cache)
+        mask = mask or create_attention_mask(h, cache)
 
         if cache is None:
             cache = [None] * len(self.layers)
@@ -205,9 +206,10 @@ class Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
-        out = self.transformer(inputs, cache)
+        out = self.transformer(inputs, mask, cache)
         if self.args.share_input_output_layers:
             out = self.transformer.token_embeddings.as_linear(out)
         else:

llms/mlx_lm/models/phi.py

@@ -143,10 +143,10 @@ class PhiModel(nn.Module):
             config.hidden_size, eps=config.layer_norm_eps
         )
 
-    def __call__(self, x, cache):
+    def __call__(self, x, mask, cache):
         x = self.embed_tokens(x)
 
-        mask = create_attention_mask(x, cache)
+        mask = mask or create_attention_mask(x, cache)
 
         if cache is None:
             cache = [None] * len(self.layers)
@@ -167,9 +167,10 @@ class Model(nn.Module):
     def __call__(
         self,
         x: mx.array,
+        mask: mx.array = None,
         cache=None,
     ) -> mx.array:
-        y = self.model(x, cache)
+        y = self.model(x, mask, cache)
         return self.lm_head(y)
 
     @property

llms/mlx_lm/models/phi3.py

@@ -168,11 +168,12 @@ class Phi3Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
         h = self.embed_tokens(inputs)
 
-        mask = create_attention_mask(h, cache)
+        mask = mask or create_attention_mask(h, cache)
 
         if cache is None:
             cache = [None] * len(self.layers)
@@ -194,9 +195,10 @@ class Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
-        out = self.model(inputs, cache)
+        out = self.model(inputs, mask, cache)
         return self.lm_head(out)
 
     @property

llms/mlx_lm/models/phi3small.py

@@ -258,13 +258,14 @@ class Phi3Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
         h = self.embed_tokens(inputs)
         if self.mup_embedding_multiplier:
             h = self.mup_embedding_multiplier * h
 
-        mask = create_attention_mask(h, cache)
+        mask = mask or create_attention_mask(h, cache)
 
         if cache is None:
             cache = [None] * len(self.layers)
@@ -290,9 +291,10 @@ class Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
-        out = self.model(inputs, cache)
+        out = self.model(inputs, mask, cache)
         out = self.model.embed_tokens.as_linear(out)
         if self.mup_width_multiplier:
             out = out / self.mup_width_multiplier

llms/mlx_lm/models/phimoe.py

@@ -155,11 +155,12 @@ class PhiMoEModel(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ) -> mx.array:
         h = self.embed_tokens(inputs)
 
-        mask = create_attention_mask(h, cache)
+        mask = mask or create_attention_mask(h, cache)
 
         if cache is None:
             cache = [None] * len(self.layers)
@@ -181,9 +182,10 @@ class Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
-        out = self.model(inputs, cache)
+        out = self.model(inputs, mask, cache)
         return self.lm_head(out)
 
     def sanitize(self, weights):

llms/mlx_lm/models/phixtral.py

@@ -175,7 +175,7 @@ class Model(nn.Module):
         mask: mx.array = None,
         cache=None,
     ) -> mx.array:
-        mask = create_attention_mask(x, cache)
+        mask = mask or create_attention_mask(x, cache)
 
         y = self.transformer(x, mask, cache)
         return self.lm_head(y)

llms/mlx_lm/models/plamo.py

@@ -174,10 +174,11 @@ class PlamoModel(nn.Module):
         self,
         inputs: mx.array,
         cache: Optional[Any] = None,
+        mask: Optional[mx.array] = None,
     ) -> mx.array:
         h = self.embed_tokens(inputs)
 
-        mask = create_attention_mask(h, cache)
+        mask = mask or create_attention_mask(h, cache)
 
         if cache is None:
             cache = [None for _ in range(len(self.layers.layers))]
@@ -202,8 +203,9 @@ class Model(nn.Module):
         self,
         inputs: mx.array,
         cache: Optional[Any] = None,
+        mask: Optional[mx.array] = None,
     ) -> mx.array:
-        out = self.model(inputs, cache)
+        out = self.model(inputs, cache, mask)
         return self.lm_head(out)
 
     @property

llms/mlx_lm/models/qwen.py

@@ -123,7 +123,7 @@ class QwenModel(nn.Module):
     def __call__(self, inputs, mask=None, cache=None):
         x = self.wte(inputs)
 
-        mask = create_attention_mask(x, cache)
+        mask = mask or create_attention_mask(x, cache)
 
         if cache is None:
             cache = [None] * len(self.h)

llms/mlx_lm/models/qwen2.py

@@ -149,11 +149,12 @@ class Qwen2Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
         h = self.embed_tokens(inputs)
 
-        mask = create_attention_mask(h, cache)
+        mask = mask or create_attention_mask(h, cache)
 
         if cache is None:
             cache = [None] * len(self.layers)
@@ -176,9 +177,10 @@ class Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
-        out = self.model(inputs, cache)
+        out = self.model(inputs, mask, cache)
         if self.args.tie_word_embeddings:
             out = self.model.embed_tokens.as_linear(out)
         else:

llms/mlx_lm/models/qwen2_moe.py

@@ -187,11 +187,12 @@ class Qwen2MoeModel(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
         h = self.embed_tokens(inputs)
 
-        mask = create_attention_mask(h, cache)
+        mask = mask or create_attention_mask(h, cache)
 
         if cache is None:
             cache = [None] * len(self.layers)
@@ -213,9 +214,10 @@ class Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
-        out = self.model(inputs, cache)
+        out = self.model(inputs, mask, cache)
         return self.lm_head(out)
 
     def sanitize(self, weights):

llms/mlx_lm/models/recurrent_gemma.py

@@ -389,6 +389,7 @@ class Griffin(nn.Module):
     def __call__(
         self,
         tokens,
+        mask: mx.array = None,
         cache=None,
     ):
         x = self.embed_tokens(tokens)
@@ -402,7 +403,7 @@ class Griffin(nn.Module):
             if block.temporal_block_type != "recurrent":
                 mask_cache = [cache[i]]
 
-        mask = create_attention_mask(x, mask_cache)
+        mask = mask or create_attention_mask(x, mask_cache)
 
         for i, block in enumerate(self.layers):
             x = block(x, mask=mask, cache=cache[i])
@@ -418,12 +419,12 @@ class Model(nn.Module):
         self.model_type = config.model_type
         self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
 
-    def __call__(self, tokens: mx.array, cache=None) -> mx.array:
+    def __call__(self, tokens: mx.array, mask: mx.array = None, cache=None) -> mx.array:
         """
         Args:
           tokens: Sequence of input tokens.
         """
-        logits = self.model(tokens, cache=cache)
+        logits = self.model(tokens, mask=mask, cache=cache)
         if "lm_head" in self:
             logits = self.lm_head(logits)
         else:

llms/mlx_lm/models/stablelm.py

@@ -199,7 +199,7 @@ class Model(nn.Module):
         mask: mx.array = None,
         cache=None,
     ) -> mx.array:
-        mask = create_attention_mask(x, cache)
+        mask = mask or create_attention_mask(x, cache)
         y = self.model(x, mask, cache)
         return self.lm_head(y)
 

llms/mlx_lm/models/starcoder2.py

@@ -125,11 +125,12 @@ class Starcoder2Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
         h = self.embed_tokens(inputs)
 
-        mask = create_attention_mask(h, cache)
+        mask = mask or create_attention_mask(h, cache)
 
         if cache is None:
             cache = [None] * len(self.layers)
@@ -152,9 +153,10 @@ class Model(nn.Module):
     def __call__(
         self,
         inputs: mx.array,
+        mask: mx.array = None,
         cache=None,
     ):
-        out = self.model(inputs, cache)
+        out = self.model(inputs, mask, cache)
         if self.args.tie_word_embeddings:
             out = self.model.embed_tokens.as_linear(out)
         else:

llms/tests/test_models.py

@@ -182,7 +182,7 @@ class TestModels(unittest.TestCase):
             self.assertEqual(outputs.dtype, t)
 
             cache = make_prompt_cache(model)
-            outputs = model(inputs, cache)
+            outputs = model(inputs, cache=cache)
             self.assertEqual(outputs.shape, (1, 2, vocab_size))
             self.assertEqual(outputs.dtype, t)