Fix server for openai package (#877)

* fix

* fixes for 9b

llms/mlx_lm/models/recurrent_gemma.py

@@ -14,7 +14,6 @@ class ModelArgs(BaseModelArgs):
     hidden_size: int
     attention_bias: bool
     conv1d_width: int
-    embeddings_scale_by_sqrt_dim: bool
     hidden_size: int
     intermediate_size: int
     logits_soft_cap: float
@@ -25,7 +24,14 @@ class ModelArgs(BaseModelArgs):
     rope_theta: float
     attention_window_size: int
     vocab_size: int
-    _block_types: List[str]
+    embeddings_scale_by_sqrt_dim: bool = True
+    block_types: Optional[List[str]] = None
+    _block_types: Optional[List[str]] = None
+
+    def __post_init__(self):
+        # For some reason these have different names in 2B and 9B
+        if self.block_types is None:
+            self.block_types = self._block_types
 
 
 def create_window_causal_mask(N: int, window_size: int):
@@ -202,6 +208,8 @@ class RGLRU(nn.Module):
 
         # Apply gamma normalization to the input.
         multiplier = mx.sqrt(1 - a_square)
+        if cache is None:
+            multiplier[:, 0, :] = 1.0
         normalized_x = gated_x * multiplier.astype(x.dtype)
 
         y, last_h = rnn_scan(
@@ -404,8 +412,8 @@ class ResidualBlock(nn.Module):
         raw_x = x
 
         inputs_normalized = self.temporal_pre_norm(raw_x)
-        x = self.temporal_block(inputs_normalized, cache=cache, mask=mask)
 
+        x = self.temporal_block(inputs_normalized, cache=cache, mask=mask)
         residual = x + raw_x
 
         x = self.channel_pre_norm(residual)
@@ -427,7 +435,7 @@ class Griffin(nn.Module):
         )
 
         self.scale_by_sqrt_dim = config.embeddings_scale_by_sqrt_dim
-        block_types = config._block_types
+        block_types = config.block_types
 
         self.layers = [
             ResidualBlock(
@@ -461,14 +469,7 @@ class Griffin(nn.Module):
         for i, block in enumerate(self.layers):
             x = block(x, mask=mask, cache=cache[i])
 
-        x = self.final_norm(x)
-        logits = self.embed_tokens.as_linear(x)
-
-        c = self.config.logits_soft_cap
-        if c:
-            logits = mx.tanh(logits / c) * c
-
-        return logits
+        return self.final_norm(x)
 
 
 class Model(nn.Module):
@@ -476,13 +477,23 @@ class Model(nn.Module):
     def __init__(self, config):
         self.args = config
         self.model = Griffin(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
 
     def __call__(self, tokens: mx.array, cache=None) -> mx.array:
         """
         Args:
           tokens: Sequence of input tokens.
         """
-        return self.model(tokens, cache=cache)
+        logits = self.model(tokens, cache=cache)
+        if "lm_head" in self:
+            logits = self.lm_head(logits)
+        else:
+            logits = self.model.embed_tokens.as_linear(logits)
+
+        c = self.args.logits_soft_cap
+        if c:
+            logits = mx.tanh(logits / c) * c
+        return logits
 
     @property
     def layers(self):
@@ -493,6 +504,8 @@ class Model(nn.Module):
         for k, v in weights.items():
             if "conv_1d.weight" in k and v.ndim == 3:
                 weights[k] = v.squeeze(1).T
+        if "lm_head.weight" not in weights:
+            self.pop("lm_head")
         return weights
 
     def make_cache(self):

llms/mlx_lm/server.py

@@ -173,6 +173,7 @@ class APIHandler(BaseHTTPRequestHandler):
         endpoints = {
             "/v1/completions": self.handle_text_completions,
             "/v1/chat/completions": self.handle_chat_completions,
+            "/chat/completions": self.handle_chat_completions,
         }
 
         if self.path not in endpoints: