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models.py
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models.py
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# Copyright © 2023 Apple Inc.
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple
import mlx.core as mx
import mlx.nn as nn
from mlx.utils import tree_map, tree_unflatten
@dataclass
class ModelArgs:
dim: int
n_layers: int
head_dim: int
hidden_dim: int
n_heads: int
n_kv_heads: int
norm_eps: float
vocab_size: int
class LoRALinear(nn.Module):
def __init__(self, input_dims: int, output_dims: int, lora_rank: int = 8, bias: bool = False):
super().__init__()
# Regular linear layer weights
self.linear = nn.Linear(input_dims, output_dims, bias=bias)
# Low rank lora weights
scale = 1 / math.sqrt(input_dims)
self.lora_a = mx.random.uniform(
low=-scale,
high=scale,
shape=(input_dims, lora_rank),
)
self.lora_b = mx.zeros(shape=(lora_rank, output_dims))
@staticmethod
def from_linear(linear: nn.Linear, rank: int = 8):
output_dims, input_dims = linear.weight.shape
lora_lin = LoRALinear(input_dims, output_dims, rank)
lora_lin.linear = linear
return lora_lin
def __call__(self, x):
y = self.linear(x.astype(self.linear.weight.dtype))
z = (x @ self.lora_a) @ self.lora_b
return y + 2.0 * z
class RMSNorm(nn.Module):
def __init__(self, dims: int, eps: float = 1e-5):
super().__init__()
self.weight = mx.ones((dims,))
self.eps = eps
def _norm(self, x):
return x * mx.rsqrt(x.square().mean(-1, keepdims=True) + self.eps)
def __call__(self, x):
output = self._norm(x.astype(mx.float32)).astype(x.dtype)
return self.weight * output
class Attention(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
self.args = args
self.n_heads: int = args.n_heads
self.n_kv_heads: int = args.n_kv_heads
self.repeats = self.n_heads // self.n_kv_heads
self.scale = self.args.head_dim**-0.5
self.wq = nn.Linear(args.dim, args.n_heads * args.head_dim, bias=False)
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)
def __call__(
self,
x: mx.array,
mask: Optional[mx.array] = None,
cache: Optional[Tuple[mx.array, mx.array]] = None,
) -> mx.array:
B, L, D = x.shape
queries, keys, values = self.wq(x), self.wk(x), self.wv(x)
# Prepare the queries, keys and values for the attention computation
queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
def repeat(a):
a = mx.concatenate([mx.expand_dims(a, 2)] * self.repeats, axis=2)
return a.reshape([B, self.n_heads, L, -1])
if self.repeats > 1:
keys, values = map(repeat, (keys, values))
if cache is not None:
key_cache, value_cache = cache
queries = self.rope(queries, offset=key_cache.shape[2])
keys = self.rope(keys, offset=key_cache.shape[2])
keys = mx.concatenate([key_cache, keys], axis=2)
values = mx.concatenate([value_cache, values], axis=2)
else:
queries = self.rope(queries)
keys = self.rope(keys)
scores = (queries * self.scale) @ keys.transpose(0, 1, 3, 2)
if mask is not None:
scores += mask
scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(scores.dtype)
output = (scores @ values).transpose(0, 2, 1, 3).reshape(B, L, -1)
return self.wo(output), (keys, values)
class FeedForward(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
self.w1 = nn.Linear(args.dim, args.hidden_dim, bias=False)
self.w2 = nn.Linear(args.hidden_dim, args.dim, bias=False)
self.w3 = nn.Linear(args.dim, args.hidden_dim, bias=False)
def __call__(self, x) -> mx.array:
return self.w2(nn.silu(self.w1(x)) * self.w3(x))
class TransformerBlock(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
self.n_heads = args.n_heads
self.dim = args.dim
self.attention = Attention(args)
self.feed_forward = FeedForward(args=args)
self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)
self.args = args
def __call__(
self,
x: mx.array,
mask: Optional[mx.array] = None,
cache: Optional[Tuple[mx.array, mx.array]] = None,
) -> mx.array:
r, cache = self.attention(self.attention_norm(x), mask, cache)
h = x + r
r = self.feed_forward(self.ffn_norm(h))
out = h + r
return out, cache
class Model(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
self.args = args
self.vocab_size = args.vocab_size
self.n_layers = args.n_layers
assert self.vocab_size > 0
self.tok_embeddings = nn.Embedding(args.vocab_size, args.dim)
self.layers = [TransformerBlock(args=args) for _ in range(args.n_layers)]
self.norm = RMSNorm(args.dim, eps=args.norm_eps)
self.output = nn.Linear(args.dim, args.vocab_size, bias=False)
def __call__(
self,
inputs: mx.array,
cache=None,
):
h = self.tok_embeddings(inputs)
mask = None
if h.shape[1] > 1:
mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
mask = mask.astype(h.dtype)
if cache is None:
cache = [None] * len(self.layers)
for e, layer in enumerate(self.layers):
h, cache[e] = layer(h, mask, cache[e])
return self.output(self.norm(h)), cache