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c179c12 | 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 | """MLX-VLM runtime for Qwen3.6 ModelOpt hybrid FP8/NVFP4 checkpoints.
The language model keeps the lossless ModelOpt-to-MLX representation used by
``modeling_mlx_qwen36_modelopt_hybrid.py``. The vision tower remains in its
original BF16 representation and is delegated to MLX-VLM's native Qwen3.5 MoE
vision implementation.
This module intentionally exports the same public symbols as an MLX-VLM model
package so a model-local loader can select it without changing ``model_type``.
"""
from dataclasses import dataclass, field
from typing import Dict
import mlx.core as mx
import mlx.nn as nn
from mlx.utils import tree_flatten, tree_unflatten
from mlx_vlm.models.qwen3_5_moe import LanguageModel, TextConfig, VisionConfig
from mlx_vlm.models.qwen3_5_moe import Model as BaseModel
from mlx_vlm.models.qwen3_5_moe import ModelConfig as BaseModelConfig
from mlx_vlm.models.qwen3_5_moe import VisionModel
from mlx_vlm.models.switch_layers import SwitchLinear
@dataclass
class ModelConfig(BaseModelConfig):
mlx_modelopt_quantization: Dict[str, str] = field(default_factory=dict)
class ScaledQuantizedLinear(nn.Module):
"""Weight-quantized dense linear with a ModelOpt tensor scale."""
def __init__(
self,
input_dims: int,
output_dims: int,
*,
group_size: int,
bits: int,
mode: str,
bias: bool = False,
):
super().__init__()
if input_dims % group_size:
raise ValueError(
f"input_dims={input_dims} is not divisible by group_size={group_size}"
)
if (input_dims * bits) % 32:
raise ValueError(
f"input_dims={input_dims}, bits={bits} cannot be packed into uint32"
)
self.group_size = group_size
self.bits = bits
self.mode = mode
self.weight = mx.zeros(
(output_dims, input_dims * bits // 32), dtype=mx.uint32
)
self.scales = mx.zeros(
(output_dims, input_dims // group_size), dtype=mx.uint8
)
self.global_scale = mx.ones((), dtype=mx.float32)
if bias:
self.bias = mx.zeros((output_dims,))
self.freeze()
@classmethod
def from_linear(cls, linear: nn.Module, kind: str):
output_dims, input_dims = linear.weight.shape
has_bias = linear.get("bias") is not None
if kind == "scaled_mxfp8":
params = dict(group_size=32, bits=8, mode="mxfp8")
elif kind == "scaled_nvfp4":
params = dict(group_size=16, bits=4, mode="nvfp4")
else:
raise ValueError(f"Unsupported dense quantization kind: {kind}")
return cls(input_dims, output_dims, bias=has_bias, **params)
def __call__(self, x):
y = mx.quantized_matmul(
x,
self["weight"],
self["scales"],
transpose=True,
group_size=self.group_size,
bits=self.bits,
mode=self.mode,
)
y = y * self["global_scale"].astype(y.dtype)
if "bias" in self:
y = y + self["bias"]
return y
class ScaledNVFP4SwitchLinear(nn.Module):
"""Expert linear using MLX gather_qmm and per-expert tensor scales."""
group_size = 16
bits = 4
mode = "nvfp4"
def __init__(
self,
input_dims: int,
output_dims: int,
num_experts: int,
*,
bias: bool = False,
):
super().__init__()
if input_dims % self.group_size:
raise ValueError(
f"input_dims={input_dims} is not divisible by {self.group_size}"
)
self.weight = mx.zeros(
(num_experts, output_dims, input_dims * self.bits // 32),
dtype=mx.uint32,
)
self.scales = mx.zeros(
(num_experts, output_dims, input_dims // self.group_size),
dtype=mx.uint8,
)
self.global_scales = mx.ones((num_experts,), dtype=mx.float32)
if bias:
self.bias = mx.zeros((num_experts, output_dims))
self.freeze()
@classmethod
def from_switch_linear(cls, linear: SwitchLinear):
num_experts, output_dims, input_dims = linear.weight.shape
has_bias = linear.get("bias") is not None
return cls(input_dims, output_dims, num_experts, bias=has_bias)
@property
def input_dims(self):
return self.scales.shape[2] * self.group_size
@property
def output_dims(self):
return self.weight.shape[1]
@property
def num_experts(self):
return self.weight.shape[0]
def __call__(self, x, indices, sorted_indices=False):
y = mx.gather_qmm(
x,
self["weight"],
self["scales"],
rhs_indices=indices,
transpose=True,
group_size=self.group_size,
bits=self.bits,
mode=self.mode,
sorted_indices=sorted_indices,
)
scale = self["global_scales"][indices].astype(y.dtype)[..., None, None]
y = y * scale
if "bias" in self:
y = y + mx.expand_dims(self["bias"][indices], -2)
return y
def _replace_quantized_modules(model: nn.Module, quantization: Dict[str, str]):
leaves = dict(
tree_flatten(model.leaf_modules(), is_leaf=lambda m: isinstance(m, nn.Module))
)
missing = sorted(set(quantization) - set(leaves))
if missing:
preview = "\n ".join(missing[:20])
raise ValueError(f"Quantized module paths are absent from the model:\n {preview}")
for path, kind in quantization.items():
module = leaves[path]
if kind in ("scaled_mxfp8", "scaled_nvfp4"):
if not isinstance(module, nn.Linear):
raise TypeError(f"{path} is {type(module).__name__}, expected Linear")
leaves[path] = ScaledQuantizedLinear.from_linear(module, kind)
elif kind == "scaled_nvfp4_switch":
if not isinstance(module, SwitchLinear):
raise TypeError(
f"{path} is {type(module).__name__}, expected SwitchLinear"
)
leaves[path] = ScaledNVFP4SwitchLinear.from_switch_linear(module)
else:
raise ValueError(f"Unknown quantization kind {kind!r} for {path}")
model.update_modules(tree_unflatten(list(leaves.items())))
class Model(BaseModel):
def __init__(self, config: ModelConfig):
super().__init__(config)
_replace_quantized_modules(self, config.mlx_modelopt_quantization)
def sanitize(self, weights):
"""Map only raw source keys; converted language keys are already sanitized."""
sanitized = {}
for key, value in weights.items():
if "mtp." in key:
continue
if key.startswith("model.language_model.visual"):
key = key.replace(
"model.language_model.visual", "vision_tower", 1
)
elif key.startswith("model.language_model"):
key = key.replace(
"model.language_model", "language_model.model", 1
)
elif key.startswith("model.visual"):
key = key.replace("model.visual", "vision_tower", 1)
elif key.startswith("lm_head"):
key = key.replace("lm_head", "language_model.lm_head", 1)
sanitized[key] = value
return sanitized
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