Switch to fast RMS/LN Norm (#603)

* use nn.RMSNorm, use sdpa, cleanup

* bump mlx versions

* minor update

* use fast layer norm

* version bump

* update requirement for whisper

* update requirement for gguf

llms/mlx_lm/models/plamo.py

@@ -6,7 +6,6 @@ import mlx.nn as nn
 import numpy as np
 
 from .base import BaseModelArgs
-from .layers import RMSNorm
 
 
 @dataclass
@@ -82,9 +81,6 @@ class Attention(nn.Module):
 
         # expand shared kv
         assert self.k_num_heads == self.v_num_heads
-        repeats = self.config.n_shared_head
-        key_states = mx.repeat(key_states, repeats, axis=1)
-        value_states = mx.repeat(value_states, repeats, axis=1)
 
         kv_seq_len = 0
         if cache is not None:
@@ -97,12 +93,14 @@ class Attention(nn.Module):
             key_states = mx.concatenate([cache[0], key_states], axis=2)
             value_states = mx.concatenate([cache[1], value_states], axis=2)
 
-        scores = (query_states * self.scale) @ key_states.transpose(0, 1, 3, 2)
-        if attention_mask is not None:
-            scores += attention_mask
-        scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(scores.dtype)
-        output = (scores @ value_states).transpose(0, 2, 1, 3).reshape(bsz, q_len, -1)
-
+        output = mx.fast.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            scale=self.scale,
+            mask=attention_mask,
+        )
+        output = output.transpose(0, 2, 1, 3).reshape(bsz, q_len, -1)
         return self.o_proj(output), (key_states, value_states)
 
 
@@ -127,7 +125,7 @@ class PlamoDecoderLayer(nn.Module):
         self.hidden_size = config.hidden_size
         self.self_attn = Attention(config)
         self.mlp = MLP(config)
-        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.norm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 
     def __call__(
         self,
@@ -170,7 +168,7 @@ class PlamoModel(nn.Module):
 
         self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
         self.layers = PlamoDecoder(config)  # type: ignore
-        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.norm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 
     def __call__(
         self,