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[mlx-lm] Use sdpa in llama / mistral model (#515)
* use sdpa * update a few more models * version * fix stablelm type
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@@ -44,8 +44,6 @@ class Attention(nn.Module):
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self.n_heads = n_heads = args.num_attention_heads
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self.n_kv_heads = n_kv_heads = args.num_key_value_heads
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self.repeats = n_heads // n_kv_heads
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head_dim = args.hidden_size // n_heads
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self.scale = head_dim**-0.5
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@@ -81,10 +79,6 @@ class Attention(nn.Module):
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keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
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values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
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if self.repeats > 1:
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keys = mx.repeat(keys, self.repeats, axis=1)
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values = mx.repeat(values, self.repeats, axis=1)
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if cache is not None:
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key_cache, value_cache = cache
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queries = self.rope(queries, offset=key_cache.shape[2])
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@@ -95,11 +89,10 @@ class Attention(nn.Module):
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queries = self.rope(queries)
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keys = self.rope(keys)
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scores = (queries * self.scale) @ keys.transpose(0, 1, 3, 2)
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if mask is not None:
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scores += mask
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scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(scores.dtype)
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output = (scores @ values).transpose(0, 2, 1, 3).reshape(B, L, -1)
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output = mx.fast.scaled_dot_product_attention(
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queries, keys, values, scale=self.scale, mask=mask
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)
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output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
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return self.o_proj(output), (keys, values)
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