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591cda4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 | """Native MLX implementation of LiquidAI/LFM2.5-Encoder-350M.
This is an encoder/masked-language model: attention and short convolutions are
bidirectional. It intentionally does not use mlx-lm's causal generation API.
"""
from __future__ import annotations
import json
from dataclasses import dataclass
from pathlib import Path
from typing import Optional
import mlx.core as mx
import mlx.nn as nn
@dataclass
class ModelArgs:
vocab_size: int
hidden_size: int
num_hidden_layers: int
num_attention_heads: int
num_key_value_heads: int
norm_eps: float
conv_bias: bool
conv_L_cache: int
block_dim: int
intermediate_size: int
block_multiple_of: int
block_ffn_dim_multiplier: float
block_auto_adjust_ff_dim: bool
layer_types: list[str]
rope_theta: float = 1_000_000.0
@classmethod
def from_dict(cls, config: dict) -> "ModelArgs":
rope = config.get("rope_parameters") or {}
fields = {
"vocab_size",
"hidden_size",
"num_hidden_layers",
"num_attention_heads",
"num_key_value_heads",
"norm_eps",
"conv_bias",
"conv_L_cache",
"block_dim",
"intermediate_size",
"block_multiple_of",
"block_ffn_dim_multiplier",
"block_auto_adjust_ff_dim",
"layer_types",
}
values = {key: config[key] for key in fields}
values["rope_theta"] = rope.get("rope_theta", config.get("rope_theta", 1_000_000.0))
return cls(**values)
class Attention(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
dim = args.hidden_size
self.n_heads = args.num_attention_heads
self.n_kv_heads = args.num_key_value_heads
self.head_dim = dim // self.n_heads
self.scale = self.head_dim**-0.5
self.q_layernorm = nn.RMSNorm(self.head_dim, eps=args.norm_eps)
self.k_layernorm = nn.RMSNorm(self.head_dim, eps=args.norm_eps)
self.q_proj = nn.Linear(dim, self.n_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(dim, self.n_kv_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(dim, self.n_kv_heads * self.head_dim, bias=False)
self.out_proj = nn.Linear(self.n_heads * self.head_dim, dim, bias=False)
self.rope = nn.RoPE(self.head_dim, base=args.rope_theta, traditional=False)
def __call__(self, x: mx.array, mask: Optional[mx.array] = None) -> mx.array:
batch, length, _ = x.shape
queries = self.q_proj(x).reshape(batch, length, self.n_heads, -1)
keys = self.k_proj(x).reshape(batch, length, self.n_kv_heads, -1)
values = self.v_proj(x).reshape(batch, length, self.n_kv_heads, -1)
queries = self.q_layernorm(queries).transpose(0, 2, 1, 3)
keys = self.k_layernorm(keys).transpose(0, 2, 1, 3)
values = values.transpose(0, 2, 1, 3)
queries = self.rope(queries)
keys = self.rope(keys)
output = mx.fast.scaled_dot_product_attention(
queries, keys, values, scale=self.scale, mask=mask
)
output = output.transpose(0, 2, 1, 3).reshape(batch, length, -1)
return self.out_proj(output)
class ShortConv(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
self.kernel_size = args.conv_L_cache
self.conv = nn.Conv1d(
in_channels=args.hidden_size,
out_channels=args.hidden_size,
kernel_size=self.kernel_size,
groups=args.hidden_size,
bias=args.conv_bias,
)
self.in_proj = nn.Linear(args.hidden_size, 3 * args.hidden_size, bias=args.conv_bias)
self.out_proj = nn.Linear(args.hidden_size, args.hidden_size, bias=args.conv_bias)
def __call__(self, x: mx.array, padding_mask: Optional[mx.array] = None) -> mx.array:
if padding_mask is not None:
x = mx.where(padding_mask[..., None], x, 0)
gate_b, gate_c, value = mx.split(self.in_proj(x), 3, axis=-1)
gated = gate_b * value
pad = self.kernel_size // 2
gated = mx.pad(gated, [(0, 0), (pad, pad), (0, 0)])
conv_out = self.conv(gated)
if conv_out.shape[1] > x.shape[1]:
conv_out = conv_out[:, : x.shape[1], :]
elif conv_out.shape[1] < x.shape[1]:
conv_out = mx.pad(conv_out, [(0, 0), (0, x.shape[1] - conv_out.shape[1]), (0, 0)])
return self.out_proj(gate_c * conv_out)
class MLP(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
ff_dim = args.intermediate_size
if args.block_auto_adjust_ff_dim:
ff_dim = int(2 * ff_dim / 3)
ff_dim = int(args.block_ffn_dim_multiplier * ff_dim)
multiple = args.block_multiple_of
ff_dim = multiple * ((ff_dim + multiple - 1) // multiple)
self.w1 = nn.Linear(args.block_dim, ff_dim, bias=False)
self.w3 = nn.Linear(args.block_dim, ff_dim, bias=False)
self.w2 = nn.Linear(ff_dim, args.block_dim, bias=False)
def __call__(self, x: mx.array) -> mx.array:
return self.w2(nn.silu(self.w1(x)) * self.w3(x))
class EncoderLayer(nn.Module):
def __init__(self, args: ModelArgs, layer_type: str):
super().__init__()
self.is_attention_layer = layer_type == "full_attention"
if self.is_attention_layer:
self.self_attn = Attention(args)
else:
self.conv = ShortConv(args)
self.feed_forward = MLP(args)
self.operator_norm = nn.RMSNorm(args.hidden_size, eps=args.norm_eps)
self.ffn_norm = nn.RMSNorm(args.hidden_size, eps=args.norm_eps)
def __call__(
self,
x: mx.array,
attention_mask: Optional[mx.array],
padding_mask: Optional[mx.array],
) -> mx.array:
normalized = self.operator_norm(x)
if self.is_attention_layer:
residual = self.self_attn(normalized, mask=attention_mask)
else:
residual = self.conv(normalized, padding_mask=padding_mask)
hidden = x + residual
return hidden + self.feed_forward(self.ffn_norm(hidden))
class Lfm2BidirectionalModel(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
self.layers = [EncoderLayer(args, layer_type) for layer_type in args.layer_types]
self.embedding_norm = nn.RMSNorm(args.hidden_size, eps=args.norm_eps)
def __call__(
self, input_ids: mx.array, attention_mask: Optional[mx.array] = None
) -> mx.array:
hidden = self.embed_tokens(input_ids)
padding_mask = None
additive_mask = None
if attention_mask is not None:
padding_mask = attention_mask.astype(mx.bool_)
additive_mask = mx.where(
padding_mask[:, None, None, :],
mx.array(0.0, dtype=hidden.dtype),
mx.array(-1e9, dtype=hidden.dtype),
)
for layer in self.layers:
hidden = layer(hidden, additive_mask, padding_mask)
return self.embedding_norm(hidden)
class Model(nn.Module):
"""Bidirectional encoder with a tied masked-language-model head."""
def __init__(self, args: ModelArgs):
super().__init__()
self.args = args
self.lfm2 = Lfm2BidirectionalModel(args)
def __call__(
self,
input_ids: mx.array,
attention_mask: Optional[mx.array] = None,
*,
return_hidden: bool = False,
) -> mx.array | tuple[mx.array, mx.array]:
hidden = self.lfm2(input_ids, attention_mask=attention_mask)
logits = self.lfm2.embed_tokens.as_linear(hidden)
return (logits, hidden) if return_hidden else logits
def load_model(path: str | Path) -> Model:
path = Path(path)
with (path / "config.json").open() as handle:
args = ModelArgs.from_dict(json.load(handle))
model = Model(args)
weights = mx.load(str(path / "model.safetensors"))
model.load_weights(list(weights.items()), strict=True)
mx.eval(model.parameters())
return model
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