Rope theta to support Coda Llama (#121)

* rope theta for llama model

* llama chat/code

* nit

llama/README.md

@@ -3,7 +3,8 @@
 An example of generating text with Llama (1 or 2) using MLX.
 
 Llama is a set of open source language models from Meta AI Research[^1][^2]
-ranging from 7B to 70B parameters.
+ranging from 7B to 70B parameters. This example also supports Llama Chat and
+Code Llama.
 
 ### Setup
 

llama/llama.py

@@ -23,6 +23,7 @@ class ModelArgs:
     n_kv_heads: int
     norm_eps: float
     vocab_size: int
+    rope_theta: float
 
 
 class RMSNorm(nn.Module):
@@ -39,6 +40,27 @@ class RMSNorm(nn.Module):
         return self.weight * output
 
 
+class RoPE(nn.RoPE):
+    def __init__(self, dims: int, traditional: bool = False, base: float = 10000):
+        super().__init__(dims, traditional)
+        self.base = base
+
+    def __call__(self, x, offset: int = 0):
+        shape = x.shape
+        x = mx.reshape(x, (-1, shape[-2], shape[-1]))
+        N = x.shape[1] + offset
+        costheta, sintheta = RoPE.create_cos_sin_theta(
+            N, self.dims, offset=offset, base=self.base, dtype=x.dtype
+        )
+
+        rope = (
+            self._compute_traditional_rope if self.traditional else self._compute_rope
+        )
+        rx = rope(costheta, sintheta, x)
+
+        return mx.reshape(rx, shape)
+
+
 class Attention(nn.Module):
     def __init__(self, args: ModelArgs):
         super().__init__()
@@ -55,7 +77,7 @@ class Attention(nn.Module):
         self.wk = nn.Linear(args.dim, args.n_kv_heads * args.head_dim, bias=False)
         self.wv = nn.Linear(args.dim, args.n_kv_heads * args.head_dim, bias=False)
         self.wo = nn.Linear(args.n_heads * args.head_dim, args.dim, bias=False)
-        self.rope = nn.RoPE(args.head_dim, traditional=True)
+        self.rope = RoPE(args.head_dim, traditional=True, base=args.rope_theta)
 
     def __call__(
         self,
@@ -315,7 +337,9 @@ def load_model(model_path):
             config["hidden_dim"] = weights["layers.0.feed_forward.w1.weight"].shape[0]
         if config.get("vocab_size", -1) < 0:
             config["vocab_size"] = weights["output.weight"].shape[-1]
-        unused = ["multiple_of", "ffn_dim_multiplier", "rope_theta"]
+        if "rope_theta" not in config:
+            config["rope_theta"] = 10000
+        unused = ["multiple_of", "ffn_dim_multiplier"]
         for k in unused:
             if k in config:
                 config.pop(k)

phi2/phi2.py

@@ -206,7 +206,10 @@ if __name__ == "__main__":
 
         if (len(tokens) % 10) == 0:
             mx.eval(tokens)
-            eos_index = next((i for i, t in enumerate(tokens) if t.item() == tokenizer.eos_token_id), None)
+            eos_index = next(
+                (i for i, t in enumerate(tokens) if t.item() == tokenizer.eos_token_id),
+                None,
+            )
 
             if eos_index is not None:
                 tokens = tokens[:eos_index]