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3da88e3 | 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 | from __future__ import annotations
import gc
import hashlib
import importlib.metadata
import json
import logging
import os
from pathlib import Path
import mlx.core as mx
from transformers import AutoTokenizer
from .ops import attention, rms_norm, rotate, silu
from .weights import Linear, WeightStore
log = logging.getLogger(__name__)
class TextEncoder:
"""Qwen3-VL's first fifty language layers, with no final RMS normalization.
Layers stream one at a time. Conditioning is cached by weights, exact token
presentation and precision policy; it is independent of diffusion seed.
"""
def __init__(
self, path, *, cache_dir="cache", bits=None, group_size=64, query_chunk=0
):
self.path = Path(path)
self.config = json.loads((self.path / "config.json").read_text())
self.text_config = self.config["text_config"]
self.store = WeightStore(self.path)
self.tokenizer = AutoTokenizer.from_pretrained(
str(self.path), local_files_only=True
)
self.cache_dir = Path(cache_dir)
self.bits, self.group_size, self.query_chunk = bits, group_size, query_chunk
def encode(self, prompt, *, images=(), on_layer=None):
self.cache_hit = False
if not prompt.strip():
raise ValueError("Prompt must be nonempty")
if images:
from .vision import image_presentation
ids, tags, positions, vision = image_presentation(self, prompt, images)
else:
raw = self.tokenizer.encode(prompt, add_special_tokens=False)[:4096]
ids = raw + [0] * (-len(raw) % 8)
tags = mx.ones(len(raw), mx.int32)
positions = None
vision = None
identity = {
"version": 5,
"weights": self.store.fingerprint(),
"config": self.config,
"versions": {
p: importlib.metadata.version(p)
for p in ("mlx", "numpy", "Pillow", "transformers")
},
"ids": ids,
"bits": self.bits,
"group_size": self.group_size,
"query_chunk": self.query_chunk,
"tap": 50,
"tf32": os.environ.get("MLX_ENABLE_TF32"),
"images": [
hashlib.sha256(im.tobytes()).hexdigest() + str(im.size) for im in images
],
}
if images:
identity["vision_layer_norm_revision"] = 2
key = hashlib.sha256(json.dumps(identity, sort_keys=True).encode()).hexdigest()[
:24
]
target = self.cache_dir / f"conditioning-{key}.safetensors"
if target.exists():
data = mx.load(str(target))
mx.eval(data)
self.cache_hit = True
log.info(
"Conditioning cache hit: %d effective tokens", data["hidden"].shape[0]
)
return data["hidden"], data["tags"]
prefix = "model.language_model"
x = self.store.get(prefix + ".embed_tokens.weight", rows=ids)[None]
mx.eval(x)
if vision is not None:
from .vision import encode_vision
features, deep = encode_vision(
self.store, self.config["vision_config"], vision
)
image_indices = mx.array(
[i for i, t in enumerate(ids) if t == self.config["image_token_id"]]
)
x[0, image_indices] = features
cfg = self.text_config
dim, nq, nk = (
cfg["head_dim"],
cfg["num_attention_heads"],
cfg["num_key_value_heads"],
)
if positions is None:
positions = mx.broadcast_to(
mx.arange(len(ids), dtype=mx.float32)[None], (3, len(ids))
)
else:
positions = mx.array(positions, mx.float32)
inv = 1 / (cfg["rope_theta"] ** (mx.arange(0, dim, 2, dtype=mx.float32) / dim))
angles = positions[:, :, None] * inv[None, None]
# Qwen3-VL interleaves spatial axes into the temporal frequency stream.
sections = cfg["rope_scaling"]["mrope_section"]
freq = angles[0]
for axis, offset in ((1, 1), (2, 2)):
stop = sections[axis] * 3
freq[:, offset:stop:3] = angles[axis, :, offset:stop:3]
freq = mx.concatenate((freq, freq), axis=-1)
rope = (
mx.cos(freq).astype(mx.bfloat16)[None, :, None],
mx.sin(freq).astype(mx.bfloat16)[None, :, None],
)
mx.eval(rope)
for i in range(50):
layer = f"{prefix}.layers.{i}"
def linear(suffix, layer=layer):
return Linear.load(
self.store,
layer + "." + suffix,
bits=self.bits,
group_size=self.group_size,
)
qproj, kproj, vproj = (
linear("self_attn." + p + "_proj") for p in ("q", "k", "v")
)
oproj = linear("self_attn.o_proj")
h = rms_norm(
x,
self.store.get(layer + ".input_layernorm.weight"),
cfg["rms_norm_eps"],
rounded=True,
)
q = qproj(h).reshape(1, len(ids), nq, dim)
k = kproj(h).reshape(1, len(ids), nk, dim)
v = vproj(h).reshape(1, len(ids), nk, dim)
q = rms_norm(
q,
self.store.get(layer + ".self_attn.q_norm.weight"),
cfg["rms_norm_eps"],
rounded=True,
)
k = rms_norm(
k,
self.store.get(layer + ".self_attn.k_norm.weight"),
cfg["rms_norm_eps"],
rounded=True,
)
q, k = rotate(q, *rope), rotate(k, *rope)
h = attention(q, k, v, causal=True, query_chunk=self.query_chunk).reshape(
1, len(ids), -1
)
x = x + oproj(h)
mx.eval(x)
del qproj, kproj, vproj, oproj, q, k, v, h
gate, up, down = (
linear("mlp." + p + "_proj") for p in ("gate", "up", "down")
)
h = rms_norm(
x,
self.store.get(layer + ".post_attention_layernorm.weight"),
cfg["rms_norm_eps"],
rounded=True,
)
x = x + down(silu(gate(h)) * up(h))
if vision is not None and i < len(deep):
x = x.at[0, image_indices].add(deep[i])
mx.eval(x)
del gate, up, down, h
if on_layer:
on_layer(i, x)
if (i + 1) % 10 == 0:
log.info("Text encoder %d/50", i + 1)
mx.clear_cache()
hidden = x[0, : len(tags)].astype(mx.bfloat16)
mx.eval(hidden)
self.cache_dir.mkdir(parents=True, exist_ok=True)
tmp = target.with_suffix(".tmp.safetensors")
mx.save_safetensors(
str(tmp),
{"hidden": hidden, "tags": tags},
metadata={"identity": json.dumps(identity)},
)
tmp.replace(target)
gc.collect()
mx.clear_cache()
return hidden, tags
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