Fix conversion + inference errors. - Mistral (#176)

* Fix conversion + inference errors.

* wire rope_theta throuugh to nn.RoPE

---------

Co-authored-by: Awni Hannun <awni@apple.com>

llms/llama/llama.py

@@ -41,27 +41,6 @@ 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__()
@@ -78,7 +57,7 @@ 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 = RoPE(
+        self.rope = nn.RoPE(
             args.head_dim, traditional=args.rope_traditional, base=args.rope_theta
         )
 

llms/llama/requirements.txt

@@ -1,3 +1,3 @@
-mlx
+mlx>=0.0.6
 sentencepiece
 torch

llms/mistral/mistral.py

@@ -23,6 +23,7 @@ class ModelArgs:
     n_kv_heads: int
     norm_eps: float
     vocab_size: int
+    rope_theta: float = 10000
 
 
 class RMSNorm(nn.Module):
@@ -55,7 +56,7 @@ 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 = nn.RoPE(args.head_dim, traditional=True, base=args.rope_theta)
 
     def __call__(
         self,
@@ -274,8 +275,8 @@ if __name__ == "__main__":
     for token, ntoks in zip(generate(prompt, model, args.temp), range(args.max_tokens)):
         tokens.append(token)
         if ntoks == 0:
-            toc = time.time()
             mx.eval(tokens)
+            toc = time.time()
             prompt_tps = prompt.size / (toc - tic)
             tic = time.time()
 

llms/mistral/requirements.txt

@@ -1,4 +1,4 @@
-mlx
+mlx>=0.0.6
 sentencepiece
 torch
 numpy