mirror of
https://gitcode.com/JianFeeeee/HomeAgent.git
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a37bc7333e
问题：cmd/homed 里 `case "onnx": qwen.New(modelDir)` 把模型适配写进了核心， `type=onnx` 名义上是格式、实际写死了一个模型家族；2117 行 Qwen 专属代码 （BPE、chat template、M-RoPE、Vision_gN 命名）住在内核树里，还带着一对 `//go:build onnxruntime` 的 stub。加任何新模型都要改内核。 现在核心只认一个模型无关的公共契约（pkg/embedding）： - 输入是不透明的 Data+MIME，解码/预处理/时序分组全归 provider - 能力是数据（Info.Modalities），不是接口方法——新增模态无需改核心接口 - 不支持的模态返回 embedding.ErrUnsupportedModality（可 errors.Is 识别） - 按名字注册，重复注册 panic；Options 是 provider 私有命名空间，核心不解释 改动： - 新增 pkg/embedding：Modality/Purpose/Input/Info/Provider/Config + 注册表 （Open 校验 Info，ValidateVector 在入库前拦下维度错与非有限值） - providers/qwen3vl：Qwen 实现整体移出内核（git mv），实现公共 SPI 并自注册 - internal/memory/vector：新增 ProviderAdapter（公共 SPI → 内部小接口）； ErrModalityUnsupported 改为公共哨兵别名；删除 VideoEmbedder 可选接口 （那正是「核心为每个新模态长方法」的坏味道） - http embedder 也变成普通 provider（注册名 http） - cmd/homed：删除 qwen import 与 onnx/http 分支，改为按 provider 名打开 + 透传 options.*；provider 打开失败只警告并禁用多模态检索，不影响启动 - config：multimodal_space.type/onnx./http.* → provider + options.* - 删除 internal/memory/qwen（整体搬迁） 测试： - pkg/embedding：注册表隔离/未知名字/非法 Info 自动关闭/ValidateVector - vector：适配器原样透传字节与 MIME、维度错被拦、Close 幂等且停止使用、 两个哨兵 errors.Is 互通 - providers/qwen3vl：新增公共 SPI 全链路集成测试（Open→Info→Embed→ 未知模态哨兵），并明确断言 Info 不声明 video 已知未完成（不得当作已验证）： - 视频冻结回归 TestEmbedderVideoMatchesONNXReference **显式跳过**：Go 侧 video 模板缺少 processor 按时间组插入的字面时间戳文本 （<0.0 seconds>/<1.0 seconds>），同一输入 Python seq=1190(1152+38)、 Go 只有 22 个文本 token。时间戳也占 M-RoPE 位置，故现有 M-RoPE 自洽断言 通过不能证明与官方实现一致。修复属 provider 内部工作。 - 视觉侧三档已导出并逐档校验通过（cos 1.000000119/1.000000119/1.000000000） 验证：go build ./... ；go vet -tags onnxruntime ./... ； go test -short ./internal/memory/... ./internal/agent/core/... ./internal/sdk/... ./pkg/... ；onnxruntime 下 providers/qwen3vl 全绿（视频为显式 skip）
758 lines
36 KiB
Python
758 lines
36 KiB
Python
#!/usr/bin/env python3
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"""自动拉取 Qwen3-VL-Embedding-2B 并导出 HomeAgent 用的三段式 ONNX 统一向量空间。
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产物（写入 --out 目录，约 8GB）::
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TokenEmbedding.onnx input_ids -> hidden
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Transformer.onnx hidden + deepstack + RoPE + causal mask -> embedding
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Vision.onnx(+.data) pixel_values -> 3 层 DeepStack + 主视觉特征
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tokenizer.json / tokenizer_config.json / chat_template.jinja
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embed_config.json Go 侧读取的布局与契约常量
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为什么是「三段」而不是一张图
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--------------------------
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文本与图像共用同一 token embedding、同一 28 层 Transformer、同一 last-token
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池化与同一 fingerprint；分段只是部署形式。把 RoPE 与视觉特征散射留在 Go 计算，
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是为了避开旧式 tracer 把 seq=598 / visual=576 烘焙进图里——那样签名上写着
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dynamic_axes，实际却只能用导出的那个长度运行。
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Vision 为什么固定 768×768（不支持原生多帧视频）
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--------------------------------------------
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Qwen3-VL 的视觉塔把 ``grid_thw`` 当 Python 值消费（``grid_thw.tolist()``），
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legacy tracer 会把它的内容固化成常量：实测导出后 ONNX 图里根本没有
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``grid_thw`` 输入，用别的帧数调用会直接报 Invalid input name。因此这里把
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grid 固定为 (1, 48, 48)，并在导出处做 PyTorch↔ONNX 一致性校验。
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视频由上层抽帧后逐帧按图像编码——同一模型、同一维度、同一 fingerprint，
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只是不做跨帧时序注意力；音频不在本空间覆盖范围内（见 vector.ErrModalityUnsupported）。
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自检是不可省的
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------------
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导出脚本必须自己证明产物正确，而不是只比较有没有报错：
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1. 分段 PyTorch（TokenEmbedding+Transformer+Vision 的组合）对比完整模型前向；
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2. 再用 onnxruntime 跑导出后的三段图，对比完整模型前向。
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两步都要求 cos ≥ 0.999999，否则以非零码退出——绝不产出一个「能加载但算错」的模型。
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"""
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from __future__ import annotations
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import argparse
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import gc
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import json
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import os
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import shutil
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import subprocess
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import sys
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import time
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import numpy as np
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import torch
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INSTRUCTION = "Represent the user's input."
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IMAGE_SIZE = 768
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PATCH_SIZE = 16
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TEMPORAL_PATCH = 2
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SPATIAL_MERGE = 2
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# 每个时间组合并后的视觉 token 数：(768/16/2)^2 = 576。
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VISUAL_TOKENS_PER_GROUP = (IMAGE_SIZE // PATCH_SIZE // SPATIAL_MERGE) ** 2
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MAX_LENGTH = 1024 # 768x768 有 576 个视觉 token；512 会截断视觉占位符
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DEFAULT_MODEL_ID = "Qwen/Qwen3-VL-Embedding-2B"
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REFERENCE_TEXT = "今天天气怎么样"
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REFERENCE_IMAGE_RGB = (200, 30, 30)
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# 视频参考：4 帧、4 种颜色 → 2 个时间组。用可区分的颜色，
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# 这样帧顺序（组 g 的 tp0←帧2g、tp1←帧2g+1）写错时参考向量立刻不匹配。
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REFERENCE_VIDEO_RGB = [(10, 10, 10), (200, 20, 20), (20, 200, 20), (20, 20, 200)]
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DEFAULT_VIDEO_GROUPS = (2, 3, 4)
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# MAX_LENGTH 由 main() 按 --video-groups 调大；做成模块级是因为文本/图像/视频
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# 三个输入构造函数共用它。
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MAX_LENGTH = 1024
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def max_length_for(video_groups) -> int:
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"""足够容纳最大视频档的序列长度。
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图像路径只需 598 token（1 组），但视频是 G×576：G=2 就要 1190，
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G=4 要 2342。实测过：若沿用图像的 1024，处理器会因截断而报
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「Mismatch in video token count between text and input_ids」。
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模板文本实测约 38 token，这里留 256 余量（允许将来插入更长的指令）。
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"""
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return max(1024, max(video_groups) * VISUAL_TOKENS_PER_GROUP + 256)
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def log(msg: str) -> None:
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print(f"[{time.strftime('%H:%M:%S')}] {msg}", flush=True)
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# ─────────────────────────── 模型获取 ───────────────────────────
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def pull_model(model_id: str, store: str) -> str:
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"""把模型拉到本地并返回快照目录。优先 HuggingFace，失败回落 ModelScope。
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两个源都必须显式给出目标目录：默认的 HF blobs+snapshots 结构会把权重存成
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符号链接树，导出脚本按固定文件名读取时很容易踩到不存在的路径。
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HF_ENDPOINT 会被 huggingface_hub 自动识别，因此国内镜像（如
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https://hf-mirror.com ）无需额外参数。
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"""
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os.makedirs(store, exist_ok=True)
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local = os.path.join(store, model_id.replace("/", "--"))
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marker = os.path.join(local, "config.json")
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if os.path.exists(marker):
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log(f"复用已下载模型: {local}")
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return local
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errors = []
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try:
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from huggingface_hub import snapshot_download
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log(f"从 HuggingFace 拉取 {model_id} -> {local}（HF_ENDPOINT={os.environ.get('HF_ENDPOINT', '默认')}）")
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snapshot_download(repo_id=model_id, local_dir=local, max_workers=4)
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if os.path.exists(marker):
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return local
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errors.append("huggingface: 下载完成但缺少 config.json")
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except Exception as exc: # noqa: BLE001 - 需要回落到 ModelScope
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errors.append(f"huggingface: {exc}")
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log(f"HuggingFace 拉取失败：{exc}")
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try:
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from modelscope import snapshot_download as ms_snapshot
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log(f"从 ModelScope 拉取 {model_id} -> {local}")
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ms_snapshot(model_id, local_dir=local)
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if os.path.exists(marker):
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return local
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errors.append("modelscope: 下载完成但缺少 config.json")
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except Exception as exc: # noqa: BLE001
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errors.append(f"modelscope: {exc}")
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log(f"ModelScope 拉取失败：{exc}")
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raise SystemExit("模型拉取失败：\n - " + "\n - ".join(errors))
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# ─────────────────────────── 模型分段 ───────────────────────────
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class TokenEmbedding(torch.nn.Module):
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def __init__(self, embed_tokens):
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super().__init__()
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self.embed_tokens = embed_tokens
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def forward(self, input_ids):
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return self.embed_tokens(input_ids)
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class Transformer(torch.nn.Module):
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"""28 层语言模型 + 前置 3 层 DeepStack 相加 + final norm + last-token 池化。
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池化放在图里（而不是 Go）是有意的：last-token 的位置由 attention_mask 决定，
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一旦 Go 侧算错位置就会静默取到 padding 的 hidden，而向量照样归一化、照样
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能比余弦——那种错误只能靠与参考向量对比才能发现。
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"""
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def __init__(self, lm):
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super().__init__()
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self.layers = lm.layers
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self.norm = lm.norm
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def forward(self, hidden, deepstack_0, deepstack_1, deepstack_2,
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rotary_cos, rotary_sin, causal_mask):
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deep = (deepstack_0, deepstack_1, deepstack_2)
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for i, layer in enumerate(self.layers):
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hidden = layer(
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hidden_states=hidden,
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attention_mask=causal_mask,
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position_embeddings=(rotary_cos, rotary_sin),
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use_cache=False,
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)
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if i < 3:
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hidden = hidden + deep[i]
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return self.norm(hidden)[:, -1]
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class VisionTower(torch.nn.Module):
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"""视觉塔：固定 grid 的 patch 张量 -> 主视觉特征 + 3 层 DeepStack。
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grid_thw 作为 buffer 固化。原因见模块 docstring：legacy tracer 无法把
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grid_thw 保留为运行时输入，写成输入只会得到一个实际不含该输入的图。
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"""
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def __init__(self, visual, grid_thw):
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super().__init__()
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self.visual = visual
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self.register_buffer("grid_thw", grid_thw)
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def forward(self, pixel_values):
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out = self.visual(pixel_values, grid_thw=self.grid_thw, return_dict=True)
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d = out.deepstack_features
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return d[0], d[1], d[2], out.pooler_output
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# ─────────────────────────── 输入构造 ───────────────────────────
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def render_text(instruction: str, text: str) -> str:
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"""与 Go 侧 tokenizer.renderInstructionInput 逐字符一致。
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指令放 system、正文放 user、以 assistant 起始符结尾。差一个特殊 token，
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last-token 池化取到的位置就变了，嵌入也就不同——而且不会报错。
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"""
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return (f"<|im_start|>system\n{instruction}<|im_end|>\n"
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f"<|im_start|>user\n{text}<|im_end|>\n<|im_start|>assistant\n")
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def text_position_ids(attention_mask):
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"""无 padding 的单批语义下，三个 RoPE 轴都等于累计可见位置。"""
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pos = attention_mask.long().cumsum(-1) - 1
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return pos.clamp(min=0).unsqueeze(0).expand(3, -1, -1)
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def text_inputs(processor, lm, text):
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rendered = render_text(INSTRUCTION, text)
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x = processor.tokenizer([rendered], return_tensors="pt", truncation=True,
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max_length=MAX_LENGTH, padding=True)
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pos = text_position_ids(x["attention_mask"])
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hidden = lm.embed_tokens(x["input_ids"])
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cos, sin = lm.rotary_emb(hidden, pos)
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zero = torch.zeros_like(hidden)
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return x, hidden, (zero, zero, zero), cos, sin, causal_mask(hidden.shape[1]), pos
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def image_inputs(processor, model, image, groups=1):
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"""构造视觉输入。
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groups=1 走图像路径（<|image_pad|>）；groups>1 走视频路径
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（<|video_pad|>，2×groups 帧，相邻两帧一个时间组）。
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两者模板结构一致，只差占位符与组数。
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"""
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lm = model.model.language_model
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if groups == 1:
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conv = [{"role": "system", "content": [{"type": "text", "text": INSTRUCTION}]},
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{"role": "user", "content": [{"type": "image", "image": image}]}]
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rendered = processor.apply_chat_template([conv], add_generation_prompt=True, tokenize=False)
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x = processor(text=rendered, images=[image], do_resize=False,
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return_tensors="pt", truncation=True, max_length=MAX_LENGTH)
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with torch.no_grad():
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vo = model.model.visual(x["pixel_values"], grid_thw=x["image_grid_thw"], return_dict=True)
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pos, _ = model.model.get_rope_index(
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x["input_ids"], x["mm_token_type_ids"],
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image_grid_thw=x["image_grid_thw"], attention_mask=x["attention_mask"])
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visual_mask = x["mm_token_type_ids"] == 1
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else:
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frames = video_frames(groups)
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conv = [{"role": "system", "content": [{"type": "text", "text": INSTRUCTION}]},
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{"role": "user", "content": [{"type": "video", "video": frames}]}]
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rendered = processor.apply_chat_template([conv], add_generation_prompt=True, tokenize=False)
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# do_sample_frames=False 至关重要：处理器默认按 fps 重采样视频，
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# 未提供 video_metadata 时回落到 fps=24，会把任何帧数都改成 grid_t=2
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# （实测 4/6/8 帧都变成 1152 个视觉 token）。那会把「G 帧」变成
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# 「2 帧」，且在导出阶段看起来一切正常。
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x = processor(text=rendered, videos=[frames], do_resize=False, do_sample_frames=False,
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return_tensors="pt", truncation=True, max_length=MAX_LENGTH)
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got_groups = int(x["video_grid_thw"][0][0])
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if got_groups != groups:
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raise SystemExit(
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f"视频时间组数 {got_groups}，期望 {groups}（处理器重采样了帧？"
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"确认 do_sample_frames=False 未被覆盖）")
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with torch.no_grad():
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vo = model.model.visual(x["pixel_values_videos"], grid_thw=x["video_grid_thw"], return_dict=True)
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pos, _ = model.model.get_rope_index(
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x["input_ids"], x["mm_token_type_ids"],
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video_grid_thw=x["video_grid_thw"], attention_mask=x["attention_mask"])
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visual_mask = x["mm_token_type_ids"] == 2
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hidden = lm.embed_tokens(x["input_ids"]).clone()
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if int(visual_mask.sum()) != groups * VISUAL_TOKENS_PER_GROUP:
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# 截断、模板改动、占位符扩展异常都会落到这里。它能区分
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# 「真的错了」与「只是看起来像」，比后续 scatter 报形状不符清楚得多。
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raise SystemExit(
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f"groups={groups} 视觉 token 数 {int(visual_mask.sum())}，期望 "
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f"{groups * VISUAL_TOKENS_PER_GROUP}（max_length={MAX_LENGTH}；"
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"截断会导致此错，请提高 --video-groups 推导出的 max_length）")
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hidden[visual_mask] = vo.pooler_output
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deep = []
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for d in vo.deepstack_features:
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full = torch.zeros_like(hidden)
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full[visual_mask] = d
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deep.append(full)
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cos, sin = lm.rotary_emb(hidden, pos)
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return x, hidden, tuple(deep), cos, sin, causal_mask(hidden.shape[1]), pos
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def video_frames(groups: int):
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from PIL import Image
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fps = list(REFERENCE_VIDEO_RGB)
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while len(fps) < 2 * groups:
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fps.append(fps[len(fps) % len(REFERENCE_VIDEO_RGB)])
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return [Image.new("RGB", (IMAGE_SIZE, IMAGE_SIZE), c) for c in fps[: 2 * groups]]
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def causal_mask(seq: int) -> torch.Tensor:
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m = torch.full((1, 1, seq, seq), torch.finfo(torch.float32).min)
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return torch.triu(m, diagonal=1)
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def pool_last(hidden, mask):
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last = mask.shape[1] - mask.flip(1).argmax(1) - 1
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return hidden[torch.arange(hidden.shape[0], device=hidden.device), last]
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# ─────────────────────────── 校验 ───────────────────────────
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def compare(name: str, a, b, floor: float = 0.999999) -> float:
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a = torch.nn.functional.normalize(torch.as_tensor(a).float(), dim=-1)
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b = torch.nn.functional.normalize(torch.as_tensor(b).float(), dim=-1)
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cos = float((a * b).sum())
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md = float((a - b).abs().max())
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log(f" {name}: cos={cos:.9f} maxdiff={md:.3e}")
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if cos < floor:
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raise SystemExit(f"导出校验失败：{name} 与完整模型不等价 (cos={cos:.9f})")
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return cos
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def verify_split_torch(model, processor, transformer) -> None:
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log("校验①：分段 PyTorch vs 完整模型")
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x, h, d, cos, sin, cm, pos = text_inputs(processor, model.model.language_model, REFERENCE_TEXT)
|
||
with torch.no_grad():
|
||
got = transformer(h, *d, cos, sin, cm)
|
||
ref = model.model(input_ids=x["input_ids"], attention_mask=x["attention_mask"],
|
||
position_ids=pos, use_cache=False).last_hidden_state[:, -1]
|
||
compare("text/split-torch", got, ref)
|
||
|
||
img = reference_image()
|
||
x, h, d, cos, sin, cm, _ = image_inputs(processor, model, img)
|
||
with torch.no_grad():
|
||
got = transformer(h, *d, cos, sin, cm)
|
||
ref = model.model(input_ids=x["input_ids"], attention_mask=x["attention_mask"],
|
||
pixel_values=x["pixel_values"], image_grid_thw=x["image_grid_thw"],
|
||
mm_token_type_ids=x["mm_token_type_ids"],
|
||
use_cache=False).last_hidden_state[:, -1]
|
||
compare("image/split-torch", got, ref)
|
||
log(f" 形状: seq={h.shape[1]} visual={(x['mm_token_type_ids'] == 1).sum().item()}")
|
||
|
||
|
||
def unit(vec):
|
||
"""L2 归一化。
|
||
|
||
必须对**写出的参考向量**归一化：ONNX 图返回的是 final norm 之后的原始 last hidden，
|
||
而 Go 侧的 public 接口返回的是归一化后的向量。若参考用原始值，Go 测试会全线不匹配——
|
||
且这个差异看起来像“模型不对”，实际上只是两边对“向量”的定义不同。
|
||
"""
|
||
v = np.asarray(vec, dtype=np.float64)
|
||
n = float(np.linalg.norm(v))
|
||
return v / n if n > 0 else v
|
||
|
||
|
||
RESULT_PREFIX = "@@VERIFY_RESULT@@"
|
||
|
||
|
||
def onnx_session(path: str):
|
||
import onnxruntime as ort
|
||
|
||
return ort.InferenceSession(path, providers=["CPUExecutionProvider"])
|
||
|
||
|
||
def load_model(model_dir: str):
|
||
"""加载 FP32 CPU 全模型与处理器（导出与校验共用同一套加载参数）。"""
|
||
from transformers import AutoProcessor
|
||
from transformers.models.qwen3_vl.modeling_qwen3_vl import Qwen3VLForConditionalGeneration
|
||
|
||
processor = AutoProcessor.from_pretrained(model_dir, trust_remote_code=True, padding_side="right")
|
||
model = Qwen3VLForConditionalGeneration.from_pretrained(
|
||
model_dir, dtype=torch.float32, low_cpu_mem_usage=True).eval()
|
||
return model, processor
|
||
|
||
|
||
def verify_case(case: str, out_dir: str, model, processor) -> dict:
|
||
"""在**单个进程内**只校验一个用例，返回该用例的参考片段。
|
||
|
||
一个用例一个进程是有意的：这里必须同时驻留 PyTorch 全模型（~8GB）与
|
||
Transformer.onnx（~7.5GB）。若在同一进程里连着校验图像与各档视频，
|
||
每档新建的视觉图（~1.6GB/张）不会及时释放，峰值是它们之和——
|
||
在 17GB 内存的机器上会被 OOM 杀掉（实测：校验到视频档时 python3 被 kill，
|
||
total-vm 26GB）。拆成子进程后峰值等于单个用例，且某一档崩了不影响其余档。
|
||
"""
|
||
ts = onnx_session(os.path.join(out_dir, "TokenEmbedding.onnx"))
|
||
xs = onnx_session(os.path.join(out_dir, "Transformer.onnx"))
|
||
lm = model.model.language_model
|
||
|
||
def run_transform(hidden, deep, cos, sin):
|
||
seq = hidden.shape[1]
|
||
return xs.run(None, {
|
||
"hidden": hidden.astype(np.float32),
|
||
"deepstack_0": deep[0].astype(np.float32),
|
||
"deepstack_1": deep[1].astype(np.float32),
|
||
"deepstack_2": deep[2].astype(np.float32),
|
||
"rotary_cos": cos.astype(np.float32),
|
||
"rotary_sin": sin.astype(np.float32),
|
||
"causal_mask": causal_mask(seq).numpy(),
|
||
})[0]
|
||
|
||
if case == "text":
|
||
x, _h, _d, cos, sin, _cm, pos = text_inputs(processor, lm, REFERENCE_TEXT)
|
||
with torch.no_grad():
|
||
ref = model.model(input_ids=x["input_ids"], attention_mask=x["attention_mask"],
|
||
position_ids=pos, use_cache=False).last_hidden_state[:, -1].numpy()
|
||
hidden = ts.run(None, {"input_ids": x["input_ids"].numpy().astype(np.int64)})[0]
|
||
zero = np.zeros_like(hidden)
|
||
got = run_transform(hidden, (zero, zero, zero), cos.numpy(), sin.numpy())
|
||
cos_v = compare("text/onnx-vs-full", got, ref)
|
||
return {"case": case, "cos": cos_v, "reference": {
|
||
"text": REFERENCE_TEXT,
|
||
"text_vector_prefix": [float(v) for v in unit(got[0])[:12]],
|
||
"text_norm_raw": float(np.linalg.norm(got[0])),
|
||
"dim": int(got.shape[1]),
|
||
}}
|
||
|
||
# 图像与视频共用同一条后半段（视觉塔 → 按掩码散射 → 语言 Transformer）：
|
||
# 两者的差异只在「视觉图 + 输入张量名 + token 类型 + 视觉 token 数」。
|
||
if case == "image":
|
||
x, _h, _d, cos, sin, _cm, _ = image_inputs(processor, model, reference_image())
|
||
pixels = x["pixel_values"]
|
||
visual_path = os.path.join(out_dir, "Vision.onnx")
|
||
token_type, want_tokens, name = 1, VISUAL_TOKENS_PER_GROUP, "image"
|
||
full_kwargs = {"pixel_values": x["pixel_values"], "image_grid_thw": x["image_grid_thw"]}
|
||
prefix_key, norm_key = "image_vector_prefix", "image_norm_raw"
|
||
extra: dict[str, object] = {
|
||
"image_rgb": list(REFERENCE_IMAGE_RGB),
|
||
"image_size": IMAGE_SIZE,
|
||
}
|
||
elif case.startswith("video_g"):
|
||
groups = int(case.split("_g", 1)[1])
|
||
x, _h, _d, cos, sin, _cm, _ = image_inputs(processor, model, None, groups=groups)
|
||
pixels = x["pixel_values_videos"]
|
||
visual_path = os.path.join(out_dir, f"Vision_g{groups}.onnx")
|
||
token_type, want_tokens, name = 2, groups * VISUAL_TOKENS_PER_GROUP, case
|
||
full_kwargs = {"pixel_values_videos": x["pixel_values_videos"],
|
||
"video_grid_thw": x["video_grid_thw"]}
|
||
prefix_key, norm_key = "video_vector_prefix", "video_norm_raw"
|
||
extra = {
|
||
"video_groups": groups,
|
||
"video_frame_rgb": [list(c) for c in REFERENCE_VIDEO_RGB[: 2 * groups]],
|
||
}
|
||
else:
|
||
raise SystemExit(f"未知校验用例: {case}")
|
||
|
||
vs = onnx_session(visual_path)
|
||
with torch.no_grad():
|
||
ref = model.model(input_ids=x["input_ids"], attention_mask=x["attention_mask"],
|
||
mm_token_type_ids=x["mm_token_type_ids"],
|
||
use_cache=False, **full_kwargs).last_hidden_state[:, -1].numpy()
|
||
hidden = ts.run(None, {"input_ids": x["input_ids"].numpy().astype(np.int64)})[0]
|
||
vis = vs.run(None, {"pixel_values": pixels.numpy().astype(np.float32)})
|
||
mask = x["mm_token_type_ids"].numpy() == token_type
|
||
# 视觉区间长度不对，说明模板/占位符/档位三者有一处错了。单独报错比
|
||
# 后面 scatter 抛「形状不符」清楚得多。
|
||
if int(mask.sum()) != want_tokens:
|
||
raise SystemExit(f"{name}: 视觉 token 数 {int(mask.sum())}，期望 {want_tokens}")
|
||
hidden = hidden.copy()
|
||
hidden[mask] = vis[3]
|
||
deep = []
|
||
for d in vis[:3]:
|
||
full = np.zeros_like(hidden)
|
||
full[mask] = d
|
||
deep.append(full)
|
||
got = run_transform(hidden, tuple(deep), cos.numpy(), sin.numpy())
|
||
cos_v = compare(f"{name}/onnx-vs-full", got, ref)
|
||
extra[prefix_key] = [float(v) for v in unit(got[0])[:12]]
|
||
extra[norm_key] = float(np.linalg.norm(got[0]))
|
||
extra["dim"] = int(got.shape[1])
|
||
return {"case": case, "cos": cos_v, "reference": extra}
|
||
|
||
def verify_onnx(out_dir: str, video_groups, require_video: bool = True, model_dir: str = "") -> dict:
|
||
"""逐用例在子进程里校验导出后的 ONNX 图，汇总冻结参考向量。
|
||
|
||
返回参考向量（供 Go 侧测试冻结使用）：Go 必须复现同一套预处理与模板，
|
||
因此这里把同一输入下的期望向量前若干维导出。
|
||
"""
|
||
log("校验②：导出后的 ONNX 三段图 vs 完整模型（每个用例一个进程）")
|
||
cases = case_list(out_dir, video_groups, require_video)
|
||
reference: dict[str, object] = {}
|
||
verified: list = []
|
||
for case in cases:
|
||
# 先把子进程跑完，再决定要不要采它的参考值。
|
||
# 历史教训：曾经把「参考只取第一档」写成在调用前 continue，
|
||
# 结果 video_g3/g4 根本没被校验，而脚本仍然 exit 0 ——
|
||
# 一个「通过」的假象比报错危险得多。
|
||
res = run_verify_child(case, out_dir, model_dir)
|
||
verified.append(case)
|
||
log(f" {case}: cos={res['cos']:.9f}")
|
||
if case.startswith("video_g") and "video_groups" in reference:
|
||
# 参考只取第一档（Go 侧回归用一档就够），但这一档本身已经真的校验过。
|
||
continue
|
||
reference.update(res["reference"])
|
||
# 覆盖度必须与计划一致：少跑一个用例就不算校验完成。
|
||
if verified != cases:
|
||
raise SystemExit(f"校验覆盖不完整：计划 {cases}，实际 {verified}")
|
||
log(f"校验覆盖 {len(verified)} 个用例: {', '.join(verified)}")
|
||
if "dim" not in reference:
|
||
raise SystemExit("校验没有产出 dim")
|
||
return reference
|
||
|
||
|
||
def case_list(out_dir: str, video_groups, require_video: bool) -> list:
|
||
"""要校验的用例列表。视频档缺图时：刚导出完必须报错，校验旧目录则跳过。"""
|
||
cases = ["text", "image"]
|
||
for groups in video_groups:
|
||
path = os.path.join(out_dir, f"Vision_g{groups}.onnx")
|
||
if os.path.exists(path):
|
||
cases.append(f"video_g{groups}")
|
||
elif require_video:
|
||
raise SystemExit(f"缺少 {path}（--video-groups 包含 {groups} 但未导出）")
|
||
else:
|
||
log(f"跳过视频档 G={groups}：目录里没有 {os.path.basename(path)}")
|
||
return cases
|
||
|
||
|
||
def run_verify_child(case: str, out_dir: str, model_dir: str) -> dict:
|
||
"""在子进程里校验一个用例并取回它的参考片段。"""
|
||
cmd = [sys.executable, os.path.abspath(__file__), "--out", out_dir, "--verify-case", case]
|
||
if model_dir:
|
||
cmd += ["--model-dir", model_dir]
|
||
log(f" 校验 {case}（独立进程）")
|
||
proc = subprocess.run(cmd, capture_output=True, text=True)
|
||
if proc.returncode != 0:
|
||
out = ((proc.stderr or "") + (proc.stdout or "")).strip().splitlines()
|
||
raise SystemExit(f"校验 {case} 失败（exit={proc.returncode}）:\n" + "\n".join(out[-20:]))
|
||
for line in reversed((proc.stdout or "").splitlines()):
|
||
if line.startswith(RESULT_PREFIX):
|
||
return json.loads(line[len(RESULT_PREFIX):])
|
||
raise SystemExit(
|
||
f"校验 {case} 的子进程没有输出结果行；stdout 末尾: {(proc.stdout or '')[-300:]!r}")
|
||
|
||
|
||
def video_groups_from_config(out_dir: str):
|
||
"""读取产物自带的 video_groups，读不到则返回 None。
|
||
|
||
max_length 由 video_groups 推导，而推导结果必须与导出时一致，否则校验
|
||
会因截断而报「视觉 token 数不符」。产物自己的 config 是权威来源，
|
||
比让调用方记得重传 --video-groups 可靠。
|
||
"""
|
||
try:
|
||
with open(os.path.join(out_dir, "embed_config.json")) as f:
|
||
cfg = json.load(f)
|
||
except (OSError, ValueError):
|
||
return None
|
||
groups = cfg.get("video_groups")
|
||
if isinstance(groups, list) and groups and all(isinstance(g, int) and g >= 2 for g in groups):
|
||
return sorted(groups)
|
||
return None
|
||
|
||
|
||
def reference_image():
|
||
from PIL import Image
|
||
|
||
return Image.new("RGB", (IMAGE_SIZE, IMAGE_SIZE), REFERENCE_IMAGE_RGB)
|
||
|
||
|
||
# ─────────────────────────── 导出 ───────────────────────────
|
||
|
||
def export_graphs(out_dir: str, model, processor, model_dir: str, transformer, video_groups) -> None:
|
||
os.makedirs(out_dir, exist_ok=True)
|
||
# 清掉旧产物，避免 fingerprint 把死文件算进去（旧图/旧外部权重会让
|
||
# 空间指纹变化，触发一次毫无意义的全量重算）。
|
||
for name in os.listdir(out_dir):
|
||
p = os.path.join(out_dir, name)
|
||
if os.path.isfile(p):
|
||
os.remove(p)
|
||
|
||
lm = model.model.language_model
|
||
|
||
log("导出 TokenEmbedding.onnx")
|
||
ids = torch.tensor([[151643, 151643]], dtype=torch.long)
|
||
with torch.no_grad():
|
||
torch.onnx.export(
|
||
TokenEmbedding(lm.embed_tokens).eval(), (ids,), os.path.join(out_dir, "TokenEmbedding.onnx"),
|
||
input_names=["input_ids"], output_names=["hidden"],
|
||
dynamic_axes={"input_ids": {1: "seq"}, "hidden": {1: "seq"}},
|
||
opset_version=17, do_constant_folding=True, dynamo=False,
|
||
)
|
||
|
||
log("导出 Transformer.onnx")
|
||
x, h, d, cos, sin, cm, _ = image_inputs(processor, model, reference_image())
|
||
with torch.no_grad():
|
||
torch.onnx.export(
|
||
transformer, (h, *d, cos, sin, cm), os.path.join(out_dir, "Transformer.onnx"),
|
||
input_names=["hidden", "deepstack_0", "deepstack_1", "deepstack_2",
|
||
"rotary_cos", "rotary_sin", "causal_mask"],
|
||
output_names=["embedding"],
|
||
dynamic_axes={
|
||
"hidden": {1: "seq"}, "deepstack_0": {1: "seq"}, "deepstack_1": {1: "seq"},
|
||
"deepstack_2": {1: "seq"}, "rotary_cos": {1: "seq"}, "rotary_sin": {1: "seq"},
|
||
"causal_mask": {2: "seq", 3: "seq"},
|
||
},
|
||
opset_version=17, do_constant_folding=True, dynamo=False,
|
||
)
|
||
|
||
log("导出 Vision.onnx（图像，固定 grid 1×48×48）")
|
||
grid = torch.tensor([[1, IMAGE_SIZE // PATCH_SIZE, IMAGE_SIZE // PATCH_SIZE]], dtype=torch.long)
|
||
xi, _h, _d, _c, _s, _cm, _p = image_inputs(processor, model, reference_image())
|
||
pv = xi["pixel_values"]
|
||
with torch.no_grad():
|
||
torch.onnx.export(
|
||
VisionTower(model.model.visual, grid).eval(), (pv,), os.path.join(out_dir, "Vision.onnx"),
|
||
input_names=["pixel_values"],
|
||
output_names=["deepstack_feature_0", "deepstack_feature_1",
|
||
"deepstack_feature_2", "vision_hidden_states"],
|
||
opset_version=17, do_constant_folding=True, dynamo=False,
|
||
)
|
||
|
||
# 视频：每个时间组数一张图。grid_thw 被 legacy tracer 固化为常量，
|
||
# 所以“动态时间轴”不可行（实测导出的图里根本没有 grid_thw 输入）；
|
||
# 反过来，每档导一张则完全可验证。
|
||
for groups in video_groups:
|
||
name = f"Vision_g{groups}.onnx"
|
||
log(f"导出 {name}（视频，固定 grid {groups}×48×48 ⇒ {2 * groups} 帧）")
|
||
vgrid = torch.tensor([[groups, IMAGE_SIZE // PATCH_SIZE, IMAGE_SIZE // PATCH_SIZE]], dtype=torch.long)
|
||
vx, _h, _d, _c, _s, _cm, _p = image_inputs(processor, model, None, groups=groups)
|
||
vpv = vx["pixel_values_videos"]
|
||
with torch.no_grad():
|
||
torch.onnx.export(
|
||
VisionTower(model.model.visual, vgrid).eval(), (vpv,), os.path.join(out_dir, name),
|
||
input_names=["pixel_values"],
|
||
output_names=["deepstack_feature_0", "deepstack_feature_1",
|
||
"deepstack_feature_2", "vision_hidden_states"],
|
||
opset_version=17, do_constant_folding=True, dynamo=False,
|
||
)
|
||
|
||
for name in ("tokenizer.json", "tokenizer_config.json", "chat_template.jinja", "added_tokens.json"):
|
||
src = os.path.join(model_dir, name)
|
||
if os.path.exists(src):
|
||
shutil.copy2(src, os.path.join(out_dir, name))
|
||
|
||
|
||
def write_config(out_dir: str, model, processor, video_groups) -> None:
|
||
cfg = model.config
|
||
text_cfg = getattr(cfg, "text_config", cfg)
|
||
rope_scaling = getattr(text_cfg, "rope_scaling", None) or {}
|
||
mrope_section = rope_scaling.get("mrope_section") or [24, 20, 20]
|
||
vision = cfg.vision_config
|
||
meta = {
|
||
"arch": "qwen3-vl-embedding-2b-multimodal",
|
||
"runtime": "homeagent-onnx-three-part",
|
||
"dim": int(getattr(text_cfg, "hidden_size", 2048)),
|
||
"max_length": MAX_LENGTH,
|
||
"instruction": INSTRUCTION,
|
||
"pooling": "last_token",
|
||
"normalize": True,
|
||
"image_size": IMAGE_SIZE,
|
||
"patch_size": int(vision.patch_size),
|
||
"temporal_patch_size": int(vision.temporal_patch_size),
|
||
"spatial_merge_size": int(vision.spatial_merge_size),
|
||
"image_mean": [0.5, 0.5, 0.5],
|
||
"image_std": [0.5, 0.5, 0.5],
|
||
"rope_theta": float(getattr(text_cfg, "rope_theta", 5000000)),
|
||
"mrope_section": [int(v) for v in mrope_section],
|
||
"num_layers": int(getattr(text_cfg, "num_hidden_layers", 28)),
|
||
"supports_native_video": True,
|
||
"video_groups": list(video_groups),
|
||
"unsupported_modalities": ["audio"],
|
||
"notes": ("视频每个时间组数（G）各一张 Vision 图：grid_thw 被 legacy tracer "
|
||
"固化为常量，无法做成运行时输入；用错档会因维度不符报错。"
|
||
"帧：相邻两帧构成一个时间组，temporal 槽 tp0←帧2g、tp1←帧2g+1。"
|
||
"音频不在 Qwen3-VL 原生模态内（无 audio_token_id），需另一模型。"),
|
||
}
|
||
with open(os.path.join(out_dir, "embed_config.json"), "w") as f:
|
||
json.dump(meta, f, ensure_ascii=False, indent=2)
|
||
log(f"写出 embed_config.json dim={meta['dim']} rope_theta={meta['rope_theta']} "
|
||
f"mrope={meta['mrope_section']} video_groups={meta['video_groups']}")
|
||
|
||
|
||
def main() -> int:
|
||
ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
|
||
ap.add_argument("--out", required=True, help="ONNX 产物目录（写入约 8GB）")
|
||
ap.add_argument("--model-dir", default="", help="已下载的模型目录；给出则跳过自动拉取")
|
||
ap.add_argument("--model-id", default=DEFAULT_MODEL_ID, help=f"模型仓库 id（默认 {DEFAULT_MODEL_ID}）")
|
||
ap.add_argument("--model-store", default=os.path.expanduser("~/.cache/homeagent-qwen-models"),
|
||
help="自动拉取时的模型存放目录")
|
||
ap.add_argument("--no-reference", action="store_true",
|
||
help="不写 <out>/qwen_reference.json（默认会写；Go 测试靠它做冻结回归）")
|
||
ap.add_argument("--skip-verify", action="store_true", help="跳过导出后校验（仅调试用，不推荐）")
|
||
ap.add_argument("--video-groups", default=",".join(str(g) for g in DEFAULT_VIDEO_GROUPS),
|
||
help="逗号分隔的视频时间组数，每档导一张 Vision_g{N}.onnx"
|
||
f"（默认 {','.join(str(g) for g in DEFAULT_VIDEO_GROUPS)}；"
|
||
"G 组合 2G 帧，即默认 4/6/8 帧）")
|
||
ap.add_argument("--verify-only", action="store_true",
|
||
help="不重新导出，只校验已存在的 <out> 并（重新）写出参考向量")
|
||
ap.add_argument("--verify-case", default="",
|
||
help=argparse.SUPPRESS) # 内部用：单用例校验子进程
|
||
args = ap.parse_args()
|
||
try:
|
||
video_groups = [int(g) for g in str(args.video_groups).split(",") if str(g).strip()]
|
||
except ValueError:
|
||
raise SystemExit(f"--video-groups 必须是逗号分隔的整数，得到 {args.video_groups!r}")
|
||
if not video_groups or any(g < 2 for g in video_groups):
|
||
raise SystemExit("--video-groups 需至少一个 >=2 的整数（单图档是 Vision.onnx，不用列）")
|
||
|
||
if args.verify_case or args.verify_only:
|
||
cfg_groups = video_groups_from_config(args.out)
|
||
if cfg_groups and cfg_groups != video_groups:
|
||
log(f"按产物 embed_config.json 使用 video_groups={cfg_groups}（命令行是 {video_groups}）")
|
||
video_groups = cfg_groups
|
||
|
||
global MAX_LENGTH
|
||
MAX_LENGTH = max_length_for(video_groups)
|
||
log(f"max_length={MAX_LENGTH}（按最大档 {max(video_groups)} 组×{VISUAL_TOKENS_PER_GROUP} 推导）")
|
||
|
||
if args.verify_case:
|
||
model_dir = args.model_dir or pull_model(args.model_id, args.model_store)
|
||
model, processor = load_model(model_dir)
|
||
res = verify_case(args.verify_case, args.out, model, processor)
|
||
print(RESULT_PREFIX + json.dumps(res, ensure_ascii=False), flush=True)
|
||
return 0
|
||
|
||
if args.verify_only:
|
||
# 校验既有产物目录：既能确认线上在用的图没坏，也能给旧目录补上参考向量。
|
||
for name in ("TokenEmbedding.onnx", "Transformer.onnx", "Vision.onnx"):
|
||
if not os.path.exists(os.path.join(args.out, name)):
|
||
raise SystemExit(f"{args.out} 下缺少 {name}，无法 --verify-only")
|
||
model_dir = args.model_dir or pull_model(args.model_id, args.model_store)
|
||
reference = verify_onnx(args.out, video_groups, require_video=False, model_dir=model_dir)
|
||
if not args.no_reference:
|
||
path = os.path.join(args.out, "qwen_reference.json")
|
||
with open(path, "w") as f:
|
||
json.dump(reference, f, ensure_ascii=False, indent=2)
|
||
log(f"写出冻结参考向量: {path}")
|
||
log(f"校验完成: {args.out}")
|
||
return 0
|
||
|
||
if args.model_dir:
|
||
model_dir = args.model_dir
|
||
if not os.path.exists(os.path.join(model_dir, "config.json")):
|
||
raise SystemExit(f"--model-dir {model_dir} 下没有 config.json")
|
||
log(f"使用本地模型: {model_dir}")
|
||
else:
|
||
model_dir = pull_model(args.model_id, args.model_store)
|
||
|
||
# transformers / PIL 只在真正导出时才需要（拉取模型本身只用 huggingface_hub）；
|
||
# 这里提前导入一次，缺依赖时给出清晰报错而不是走到深处才炸。
|
||
from PIL import Image # noqa: F401
|
||
|
||
log("加载 processor / model（FP32，CPU）")
|
||
model, processor = load_model(model_dir)
|
||
transformer = Transformer(model.model.language_model).eval()
|
||
|
||
verify_split_torch(model, processor, transformer)
|
||
export_graphs(args.out, model, processor, model_dir, transformer, video_groups)
|
||
write_config(args.out, model, processor, video_groups)
|
||
|
||
# 校验在子进程里跑，父进程先把模型释放掉，把内存完全让给子进程。
|
||
del transformer, model
|
||
gc.collect()
|
||
|
||
reference = None if args.skip_verify else verify_onnx(args.out, video_groups, model_dir=model_dir)
|
||
# 参考写进产物目录本身：这样任何一个 ONNX 目录都自带「它应当给出什么输出」，
|
||
# Go 测试无需额外配置就能找到，也不会出现「模型换了、参考还是旧的」的错配。
|
||
if reference is not None and not args.no_reference:
|
||
path = os.path.join(args.out, "qwen_reference.json")
|
||
with open(path, "w") as f:
|
||
json.dump(reference, f, ensure_ascii=False, indent=2)
|
||
log(f"写出冻结参考向量: {path}")
|
||
|
||
total = sum(os.path.getsize(os.path.join(args.out, n))
|
||
for n in os.listdir(args.out) if os.path.isfile(os.path.join(args.out, n)))
|
||
log(f"完成: {args.out}（{total / 2**30:.2f} GiB）")
|
||
log("Go 侧用法: core.memory.multimodal_space.type=onnx + "
|
||
f"core.memory.multimodal_space.onnx.model_dir={args.out}")
|
||
return 0
|
||
|
||
|
||
if __name__ == "__main__":
|
||
sys.exit(main())
|