File size: 6,954 Bytes
597f7c0 | 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 | """Test-only loaders for alternate Qwen3-VL text-encoder artifacts.
The released mixed NVFP4/FP8 loader remains immutable. This module reuses its
validated tensor mapping and module implementation while accepting the
ComfyUI scaled-FP8 policy (252 language projections, all FP8 E4M3).
"""
from __future__ import annotations
from collections import Counter
from pathlib import Path
from typing import Any
import torch
from accelerate import init_empty_weights
from safetensors import safe_open
from transformers import AutoConfig, AutoProcessor, AutoTokenizer
EXPECTED_FP8_SHA256 = (
"54bd5144df0bbc25dd6ccadfcb826b521445a1b06ae5a42570bdd2974ca87094"
)
EXPECTED_FP8_PROJECTION_COUNT = 252
def _base_loader() -> Any:
import quant_text_encoder
return quant_text_encoder
def _install_scaled_fp8_linears(
hf_module: torch.nn.Module,
artifact_path: Path,
) -> dict[str, Any]:
base = _base_loader()
format_counts: Counter[str] = Counter()
installed: list[str] = []
storage_bytes = 0
original_bf16_bytes = 0
full_precision_matrix_mult_counts: Counter[bool] = Counter()
with safe_open(str(artifact_path), framework="pt", device="cpu") as handle:
config_keys = sorted(
key for key in handle.keys() if key.endswith(".comfy_quant")
)
for config_key in config_keys:
layer_key = config_key.removesuffix(".comfy_quant")
quant_config = base._decode_quant_config(
handle.get_tensor(config_key)
)
quant_format = quant_config["format"]
if quant_format != "float8_e4m3fn":
raise RuntimeError(
f"{layer_key}: expected float8_e4m3fn, got {quant_format}"
)
full_precision_matrix_mult_counts[
bool(quant_config.get("full_precision_matrix_mult", False))
] += 1
module_name = base._artifact_layer_to_hf_module(layer_key)
parent, leaf = base._resolve_parent(hf_module, module_name)
original = getattr(parent, leaf)
if not isinstance(original, torch.nn.Linear):
raise TypeError(
f"{module_name}: expected torch.nn.Linear, got "
f"{type(original).__name__}"
)
if original.bias is not None:
raise ValueError(f"{module_name}: quantized projection has a bias")
replacement = base.PublishedQuantLinear(
in_features=original.in_features,
out_features=original.out_features,
quant_format=quant_format,
qdata=handle.get_tensor(f"{layer_key}.weight"),
weight_scale=handle.get_tensor(f"{layer_key}.weight_scale"),
weight_scale_2=None,
)
setattr(parent, leaf, replacement)
format_counts[quant_format] += 1
installed.append(module_name)
original_bf16_bytes += (
original.in_features * original.out_features * 2
)
storage_bytes += replacement.qdata.nbytes
storage_bytes += replacement.weight_scale.nbytes
summary = {
"installed_module_count": len(installed),
"format_counts": dict(format_counts),
"full_precision_matrix_mult_counts": {
str(key).lower(): value
for key, value in sorted(
full_precision_matrix_mult_counts.items()
)
},
"original_projection_bf16_bytes": original_bf16_bytes,
"packed_projection_bytes": storage_bytes,
"projection_saving_bytes": original_bf16_bytes - storage_bytes,
"projection_saving_gib": (
(original_bf16_bytes - storage_bytes) / float(1 << 30)
),
}
if (
summary["installed_module_count"] != EXPECTED_FP8_PROJECTION_COUNT
or summary["format_counts"]
!= {"float8_e4m3fn": EXPECTED_FP8_PROJECTION_COUNT}
or summary["full_precision_matrix_mult_counts"] != {"false": 252}
):
raise RuntimeError(f"unexpected scaled-FP8 policy: {summary}")
return summary
def load_scaled_fp8_text_encoder(
*,
text_encoder_dir: str | Path,
artifact_path: str | Path,
tokenizer_max_length: int,
dit_structure: dict[str, Any],
use_packed_text_infer: bool,
attn_type: str = "flash2",
) -> tuple[torch.nn.Module, dict[str, Any]]:
"""Build Mage's Qwen wrapper from the 252-projection scaled-FP8 file."""
base = _base_loader()
from mage_flow.models.modules.text_encoder import (
CustomQwen3VLForConditionalGeneration,
TextEncoder,
_resolve_hf_attn_impl,
)
text_encoder_dir = Path(text_encoder_dir).resolve()
artifact_path = Path(artifact_path).resolve()
actual_sha256 = base.sha256(artifact_path)
if actual_sha256 != EXPECTED_FP8_SHA256:
raise RuntimeError(
"scaled-FP8 text artifact SHA-256 mismatch: "
f"{actual_sha256}"
)
config = AutoConfig.from_pretrained(
str(text_encoder_dir),
local_files_only=True,
)
hf_attn_implementation = _resolve_hf_attn_impl(attn_type)
with init_empty_weights():
hf_module = CustomQwen3VLForConditionalGeneration._from_config(
config,
attn_implementation=hf_attn_implementation,
dtype=torch.bfloat16,
)
quant_summary = _install_scaled_fp8_linears(
hf_module,
artifact_path,
)
load_summary = base._load_nonquantized_weights(
hf_module,
artifact_path,
)
text_encoder = TextEncoder.__new__(TextEncoder)
torch.nn.Module.__init__(text_encoder)
text_encoder.model_name = str(text_encoder_dir)
text_encoder.tokenizer_max_length = int(tokenizer_max_length)
text_encoder.tokenizer = AutoTokenizer.from_pretrained(
str(text_encoder_dir),
local_files_only=True,
)
text_encoder.tokenizer.padding_side = "right"
text_encoder.processor = AutoProcessor.from_pretrained(
str(text_encoder_dir),
local_files_only=True,
)
text_encoder.hf_module = hf_module.eval().requires_grad_(False)
text_encoder.prompt_template_encode = ""
text_encoder.prompt_template_encode_start_idx = 0
text_encoder.dit_structure = dict(dit_structure)
text_encoder.use_packed_text_infer = bool(use_packed_text_infer)
text_encoder.eval().requires_grad_(False)
return (
text_encoder,
{
"artifact": str(artifact_path),
"artifact_sha256": actual_sha256,
"attention_backend": attn_type,
"hf_attention_implementation": hf_attn_implementation,
"quantized": quant_summary,
"nonquantized": load_summary,
},
)
__all__ = [
"EXPECTED_FP8_PROJECTION_COUNT",
"EXPECTED_FP8_SHA256",
"load_scaled_fp8_text_encoder",
]
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