Merge Ov2

Purpose / 用途

Use this skill when merging a standalone ViT encoder and LLM into a unified LlavaOnevision2 HuggingFace checkpoint, and when validating that the merged weights and inference outputs are consistent with the originals.

当需要将独立的 ViT encoder 和 LLM 合并成统一的 LlavaOnevision2 HuggingFace checkpoint，并验证合并后权重和推理输出与原始模型一致时，使用这个 skill。

Prerequisites / 前置条件

• Container llava_megatron_container_ax running with GPU access
• All paths below assume execution inside the container at /workspace/LLaVA-OneVision-2
• PYTHONPATH=transformers_impl:. must be set for all Python commands
• For large models, use tmpfs (/train_tmp) for I/O performance

容器 llava_megatron_container_ax 需启动并有 GPU 访问权限。以下所有路径假设在容器内 /workspace/LLaVA-OneVision-2 执行。所有 Python 命令需设置 PYTHONPATH=transformers_impl:.。大模型建议用内存盘 /train_tmp。

Architecture / 架构

What merge_ov2 does / merge_ov2 做了什么

ViT encoder (e.g. onevision_encoder_patch16_0424)
  + LLM (e.g. Qwen3-4B-Instruct-2507)
  + Processor (e.g. lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct)
  → Unified LlavaOnevision2ForConditionalGeneration checkpoint

Key transformations during merge:

合并时的关键转换：

1. ViT weights are prefixed with visual. (e.g. encoder.layers.0.self_attn.q_proj.weight → visual.encoder.layers.0.self_attn.qkv.weight)
2. QKV fusion: separate q_proj / k_proj / v_proj are concatenated into fused self_attn.qkv (introduces ~1e-7 bf16 divergence)
3. LLM weights are prefixed with language_model. (e.g. model.layers.0.self_attn.q_proj.weight → language_model.model.layers.0.self_attn.q_proj.weight)
4. Adapter (multi_modal_projector) is randomly initialized if no adapter checkpoint is provided
5. layernorm_post from ViT is dropped (not used in LlavaOnevision2)
6. class_embedding may not exist in some ViT encoders (e.g. patch16 variant)

Source code layout / 源码结构

transformers_impl/merge_ov2/
├── __main__.py          # CLI entry point
├── cli.py               # Argument parsing for merge / validate / dry-run
├── remap.py             # Weight key remapping logic
├── loader.py            # Weight loading from source checkpoints
├── save.py              # Save merged checkpoint
├── io.py                # I/O utilities
├── utils.py             # Shared utilities
├── variants/
│   ├── dense.py         # Dense model variant
│   └── moe.py           # MoE model variant
└── validators/
    ├── vit_layerwise.py   # ViT layer-wise weight validator
    ├── vit_blockorder.py  # ViT block-order validator (patch14+sms=2 only)
    ├── llm_parallel.py    # LLM parallel validator
    ├── llm_sequential.py  # LLM sequential validator
    └── e2e.py             # End-to-end validator

CLI Reference / CLI 参考

Subcommand: merge

Remap + load + (optional validate) + save.

PYTHONPATH=transformers_impl:. python -m merge_ov2 merge \
  --variant dense \
  --vit /path/to/vit_encoder \
  --llm /path/to/llm \
  --processor /path/to/processor \
  --out /path/to/output \
  --spatial-merge-size 2 \
  --target-dtype bf16 \
  --vit-validator-strategy layerwise

Subcommand: validate

Validate an already-merged checkpoint against original sources.

验证已合并的 checkpoint 与原始模型的一致性。

PYTHONPATH=transformers_impl:. python -m merge_ov2 validate \
  --variant dense \
  --ckpt /path/to/merged_checkpoint \
  --vit /path/to/vit_encoder \
  --llm /path/to/llm \
  --processor /path/to/processor \
  --vit-validator-strategy layerwise

Subcommand: dry-run

Remap only; report load coverage; no save.

仅做 remap，报告加载覆盖率，不保存。

PYTHONPATH=transformers_impl:. python -m merge_ov2 dry-run \
  --variant dense \
  --vit /path/to/vit_encoder \
  --llm /path/to/llm \
  --processor /path/to/processor
# --spatial-merge-size defaults to 2 (the current production p14m2 variant);
# pass --spatial-merge-size 3 explicitly for the legacy p14m33 / p16m3 layouts.

Concrete Example / 具体示例

Merging Qwen3-4B + onevision-encoder-large-lang-tf57 (patch14, sms=2, current production)

docker exec llava_megatron_container_ax bash -c '
cd /workspace/LLaVA-OneVision-2 && \
PYTHONPATH=transformers_impl:. python -u -m merge_ov2 merge \
  --variant dense \
  --vit /train_tmp/onevision-encoder-large-lang-tf57 \
  --llm /train_tmp/Qwen3-4B-Instruct-2507 \
  --processor lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct \
  --out /train_tmp/llava_onevision2_4b_p14m2 \
  --target-dtype bf16 \
  --vit-validator-strategy layerwise \
  --img /train_tmp/sample.jpg \
  --sample-text "Hello, world!"
'

--spatial-merge-size is omitted because the CLI defaults to 2. Output checkpoint config: patch_size=14, spatial_merge_size=2, image_size=28*N, hidden_size=1024, 24 ViT layers, 36 LLM layers. The sample image must be a multiple of patch_size * spatial_merge_size = 28 in both dimensions (e.g. 504x504 — still works since 504 = 28 × 18).

--spatial-merge-size 省略，CLI 缺省为 2。输出 checkpoint 的配置: patch_size=14, spatial_merge_size=2, image_size=28*N, hidden_size=1024, 24 ViT 层, 36 LLM 层。样图尺寸必须是 patch_size * spatial_merge_size = 28 的整数倍（如 504x504，因 504 = 28 × 18 仍合法）。

Legacy 4b_p14m33 (sms=3) merge: same command, append --spatial-merge-size 3 and change --out to /train_tmp/llava_onevision2_4b_p14m33. Image size must then be a multiple of 42.

旧的 4b_p14m33（sms=3）合并：同样命令，加 --spatial-merge-size 3，--out 改为 /train_tmp/llava_onevision2_4b_p14m33。样图尺寸需为 42 的整数倍。

Variant Cheat Sheet / Variant 参数对照表

OV2 4B has two real-world variants. The --patch-size (set by the ViT checkpoint), --spatial-merge-size, and --vit-validator-strategy must all match — using the wrong validator silently crashes inside reshape ops.

OV2 4B 有两套真实使用的 variant。--patch-size（由 ViT checkpoint 决定）、 --spatial-merge-size 和 --vit-validator-strategy 必须配齐 —— 用错 validator 会在 reshape 里直接崩。

Why layerwise is required for non-(patch14+sms=2) variants / 为什么非 patch14+sms=2 的 variant 必须用 layerwise: vit_blockorder.py's reshape hard-codes patch_size=14, spatial_merge_size=2. Any other combo (sms=3, or different patch_size with sms≠2) breaks the reshape: RuntimeError: shape '[...]' is invalid for input of size N. 4b and 4b_p14m2 are the only variants that satisfy the hardcoded assumption, so they may use either blockorder or layerwise; everything else must use layerwise.

vit_blockorder.py 的 reshape 写死了 patch_size=14, spatial_merge_size=2 的尺寸假设。任何其他组合（sms=3，或非 2 的 sms）都会让维度对不上， 抛 RuntimeError: shape '[...]' is invalid for input of size N。 只有 4b 和 4b_p14m2 满足硬编码假设，可以用 blockorder 或 layerwise； 其余 variant 必须用 layerwise。

Post-Merge Validation / 合并后验证

Always prefer the validate subcommand over hand-written scripts. It runs the same three validators (vit, llm, e2e) used during merge, against an already-saved checkpoint. Use this when:

• you skipped validation during merge (--validate-skip)
• you manually patched a saved checkpoint and want to re-verify
• you downloaded a checkpoint and want to confirm parity

优先使用 validate 子命令而不是手写脚本。它会对一份已保存的 checkpoint 跑和 merge 时同样的三个 validator（vit、llm、e2e）。适用场景：

• merge 时跳过了验证（--validate-skip）
• 手动改过 checkpoint 后重验证
• 下载了 checkpoint 想确认一致性

docker exec llava_megatron_container_ax bash -c '
cd /workspace/LLaVA-OneVision-2 && \
PYTHONPATH=transformers_impl:. python -m merge_ov2 validate \
  --variant dense \
  --ckpt /train_tmp/llava_onevision2_4b_p14m2 \
  --vit /train_tmp/onevision-encoder-large-lang-tf57 \
  --llm /train_tmp/Qwen3-4B-Instruct-2507 \
  --processor lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct \
  --img /train_tmp/sample.jpg \
  --sample-text "Hello, world!" \
  --vit-validator-strategy layerwise
'

Skipping validators / 跳过 validator

--validate-skip is append-style; pass it once per validator to skip. Three validators exist: vit, llm, e2e. Run only the ones you need to save GPU time.

--validate-skip 是 append 模式，每跳一个就传一次。一共三个 validator： vit、llm、e2e。只跑你需要的，省 GPU 时间。

# Skip e2e only (run vit + llm) / 只跳 e2e
... --validate-skip e2e

# Validate only LLM (skip vit + e2e) / 只验证 LLM
... --validate-skip vit --validate-skip e2e

# Skip everything (no parity check) / 全跳过
... --validate-skip vit --validate-skip llm --validate-skip e2e

When you skip vit, --qwen-processor and --img may be omitted; when you skip llm, --sample-text may be omitted. The CLI enforces only the flags required by the validators you actually run.

跳了 vit 时可以省略 --qwen-processor 和 --img；跳了 llm 时可以 省略 --sample-text。CLI 只对实际要跑的 validator 强制要求对应的参数。

Next step: Megatron conversion + consistency / 下一步：转 Megatron + 一致性测试

After merge + validate succeeds, the standard next step is HF → mcore conversion plus the 6-check end-to-end HF↔mcore consistency suite. That workflow lives in the llava-onevision2-consistency skill — load it with skill(name="llava-onevision2-consistency").

merge + validate 通过后，标准下一步是 HF → mcore 转换 + 6 项 HF↔mcore 端到端 一致性测试。流程在 llava-onevision2-consistency skill 里 —— skill(name="llava-onevision2-consistency") 加载。

Reverse direction: mcore → HF (deploy / round-trip) / 反向：mcore → HF（部署 / 回环验证）

For the p14m2 variant, the reverse conversion ships as two scripts:

# mcore → HF (auto-detects /release subdir; pass either form)
bash examples/llava_onevision2/convert/convert_4b_p14m2_mcore_to_hf.sh \
    /train_tmp/llava_onevision2_4b_p14m2_mcore_tp1pp1 \
    /train_tmp/llava_onevision2_4b_p14m2_hf_out \
    1 1

Round-trip mcore → HF → mcore (TP=1 PP=1) is bitwise identical to the original mcore checkpoint (588 non-empty tensors, max abs diff = 0.000e+00). This is the strongest correctness guarantee for the reverse path. Use round-trip when changing TP/PP layout without retraining.

对于 p14m2 variant，反向转换提供两个脚本（mcore→HF 用于部署/debug/推理， mcore→release 用于通过 HF 中转改 TP/PP 切分）。回环 mcore→HF→mcore 在 TP=1 PP=1 下与原始 mcore checkpoint 逐位一致（588 个非空 tensor，max abs diff = 0.000e+00）， 这是反向路径正确性的最强保证。在不重训的前提下改 TP/PP layout 时使用回环。

Note: convert_4b_p14m2_mcore_to_hf.sh auto-detects <load>/release — pass either the parent dir (/path/to/mcore_ckpt) or the explicit release path (/path/to/mcore_ckpt/release). Sibling scripts (4b, p14m3, p16m3, 8b, 30b) still require the explicit /release path.

convert_4b_p14m2_mcore_to_hf.sh 会自动检测 <load>/release — 父目录 (/path/to/mcore_ckpt) 或显式 release 路径 (/path/to/mcore_ckpt/release) 都可以传。Sibling 脚本（4b、p14m3、p16m3、8b、30b） 仍然要求显式 /release 路径。

Manual validation scripts (OOM fallback) / 手写脚本（OOM 后备方案）

Only use these when the built-in validators OOM (e.g. 30B+ MoE on a single 80 GB GPU). They load only the visual or language sub-component to halve memory pressure.

只在内置 validator OOM 时用（如 30B+ MoE 单卡 80 GB）。手写脚本只加载 visual 或 language 子组件来缓解显存压力。

Test 1: ViT Weight Consistency / ViT 权重一致性

Compare every ViT weight tensor between the original encoder and the merged checkpoint.

逐 tensor 比较原始 encoder 和合并 checkpoint 中的 ViT 权重。

# Inside container, PYTHONPATH=transformers_impl:.
import torch
from safetensors.torch import load_file
import torch.nn.functional as F

merged_dir = "/train_tmp/llava_onevision2_4b_p14m2"
vit_dir = "/train_tmp/onevision-encoder-large-lang-tf57"

# Load weights
merged_w = {}
for f in ["model-00001-of-00002.safetensors", "model-00002-of-00002.safetensors"]:
    merged_w.update(load_file(f"{merged_dir}/{f}"))
vit_w = load_file(f"{vit_dir}/model.safetensors")

# Compare non-QKV weights (direct mapping with "visual." prefix)
for vit_key, vit_val in vit_w.items():
    if "q_proj" in vit_key or "k_proj" in vit_key or "v_proj" in vit_key:
        continue
    if "layernorm_post" in vit_key:  # dropped in merge
        continue
    merged_key = f"visual.{vit_key}"
    merged_val = merged_w[merged_key]
    cos = F.cosine_similarity(vit_val.flatten().float(), merged_val.flatten().float(), dim=0)
    assert cos > 0.9999, f"FAIL {vit_key}: cos={cos}"

# Compare QKV weights (fused: q+k+v → qkv)
for i in range(24):  # 24 encoder layers
    for suffix in ["weight", "bias"]:
        q = vit_w[f"encoder.layers.{i}.self_attn.q_proj.{suffix}"]
        k = vit_w[f"encoder.layers.{i}.self_attn.k_proj.{suffix}"]
        v = vit_w[f"encoder.layers.{i}.self_attn.v_proj.{suffix}"]
        fused = torch.cat([q, k, v], dim=0)
        merged = merged_w[f"visual.encoder.layers.{i}.self_attn.qkv.{suffix}"]
        cos = F.cosine_similarity(fused.flatten().float(), merged.flatten().float(), dim=0)
        assert cos > 0.9999, f"FAIL QKV layer {i} {suffix}: cos={cos}"

print("ViT weight consistency OK")

Expected: All cosine similarities > 0.9999. QKV may have ~1e-7 divergence due to bf16 cat.

预期: 所有余弦相似度 > 0.9999。QKV 因 bf16 拼接可能有 ~1e-7 的微小差异。

Test 2: ViT Inference Consistency / ViT 推理一致性

Full forward pass through independent patch_embed → layernorm_pre → 24 encoder layers.

独立的 patch_embed → layernorm_pre → 24 encoder 层的完整前向传播。

import torch, sys
import torch.nn.functional as F
from PIL import Image
from transformers import AutoModel, AutoModelForCausalLM, CLIPImageProcessor

DEVICE = torch.device("cuda:0")
DTYPE = torch.bfloat16
merged_dir = "/train_tmp/llava_onevision2_4b_p14m2"
vit_dir = "/train_tmp/onevision-encoder-large-lang-tf57"
sms, patch_size = 2, 14
pixel_unit = patch_size * sms  # 28

# Use a small synthetic image (must be multiple of pixel_unit)
image = Image.new("RGB", (504, 504), color="red")
h, w = 504, 504

# Load merged model's visual component
model = AutoModelForCausalLM.from_pretrained(
    merged_dir, torch_dtype=DTYPE,
    low_cpu_mem_usage=True, trust_remote_code=True,
    attn_implementation="flash_attention_2",
)
merged_visual = model.model.visual.to(DEVICE).eval()
del model.model.language_model
import gc; gc.collect()

# Load original ViT — IMPORTANT: use AutoModel + trust_remote_code, do NOT
# import from `transformers_impl/onevision_encoder` (local copy can drift
# from the modeling_*.py shipped inside the checkpoint, producing sim≈-0.02)
orig_vit = AutoModel.from_pretrained(
    vit_dir, torch_dtype=DTYPE, trust_remote_code=True,
    attn_implementation="flash_attention_2",
).to(DEVICE).eval()

# Prepare pixel values
clip_proc = CLIPImageProcessor.from_pretrained(vit_dir)
clip_px = clip_proc(images=image, return_tensors="pt",
                    do_resize=False, do_center_crop=False)["pixel_values"]
clip_px = clip_px.to(dtype=DTYPE, device=DEVICE)
grid_h, grid_w = h // patch_size, w // patch_size

# Use orig_vit's full forward (row-major output)
with torch.no_grad(), torch.amp.autocast("cuda", dtype=DTYPE):
    orig_out = orig_vit(clip_px).last_hidden_state  # (1, N, D)

    # Merged visual: block layout forward
    # NOTE: canonical RoPE helpers live in merge_ov2/utils.py — do NOT copy
    # them inline. The Megatron-side canonical implementation is in
    # aiak_training_llm/models/llava_onevision2/onevision_encoder_model.py
    # but cannot be imported from transformers_impl (would create a reverse
    # dep on the training framework).
    from merge_ov2.utils import (
        convert_rope_to_block_layout_by_positions,
        rowmajor_to_block,
    )

    def extract_block_patches(img_tensor, ps, s):
        b, c, ph, pw = img_tensor.shape
        h2, w2 = ph // ps, pw // ps
        patches = img_tensor.reshape(b, c, h2, ps, w2, ps).permute(0, 2, 4, 1, 3, 5).reshape(h2, w2, c, ps, ps)
        h_m, w_m = h2 // s, w2 // s
        patches = patches.reshape(h_m, s, w_m, s, c, ps, ps).permute(0, 2, 1, 3, 4, 5, 6).contiguous()
        return patches.reshape(-1, c, ps, ps)

    block_patches = extract_block_patches(clip_px, ps=patch_size, s=sms)
    merged_pre = merged_visual.layernorm_pre(merged_visual.embeddings(block_patches).unsqueeze(0))

    # Build RoPE
    grid_thw = torch.tensor([[1, grid_h, grid_w]], device=DEVICE)
    t_idx = torch.arange(1, device=DEVICE, dtype=torch.float32)
    h_idx = torch.arange(grid_h, device=DEVICE, dtype=torch.float32)
    w_idx = torch.arange(grid_w, device=DEVICE, dtype=torch.float32)
    mt, mh, mw = torch.meshgrid(t_idx, h_idx, w_idx, indexing="ij")
    patch_positions = torch.stack([mt, mh, mw], dim=-1).reshape(-1, 3)
    merged_freqs = merged_visual.video_rope.forward_from_positions(patch_positions)
    merged_freqs = convert_rope_to_block_layout_by_positions(
        merged_freqs, patch_positions, spatial_merge_size=sms, grid_thw=grid_thw)
    block_rope = torch.cat([merged_freqs, merged_freqs], dim=-1).unsqueeze(0)

    # Run encoder layers
    merged_h = merged_pre
    for i in range(len(merged_visual.encoder.layers)):
        merged_h = merged_visual.encoder.layers[i](
            merged_h, attention_mask=None, rotary_pos_emb=block_rope,
            output_attentions=False, cu_seqlens=None, max_seqlen=None)[0]

    # Convert orig row-major output to block layout for comparison
    # (rowmajor_to_block already imported from merge_ov2.utils above)
    orig_block = rowmajor_to_block(orig_out[0], 1, grid_h, grid_w, sms)
    cos = F.cosine_similarity(merged_h[0].flatten().float(), orig_block.flatten().float(), dim=0)
    diff = (merged_h[0] - orig_block).abs().mean().item()
    print(f"ViT inference: cos={cos:.8f}, diff={diff:.8e}")
    # bf16 24-layer accumulation: realistic min cos ≈ 0.98, not 0.999.
    # See "bf16 numerical thresholds" in Known Issues below.
    assert cos > 0.98, f"ViT inference mismatch: cos={cos}"

Expected: cos ≥ 0.98 (bf16 24-layer accumulation). Use fp32 for cos ≥ 0.999.

预期: cos ≥ 0.98（bf16 24 层累积）。要 cos ≥ 0.999 请用 fp32。

Note on image size: Use small images (e.g. 480x480) to avoid GPU OOM. The image dimensions must be multiples of patch_size * spatial_merge_size.

关于图像大小: 用小图（如 480x480）避免 GPU OOM。图像尺寸必须是 patch_size * spatial_merge_size 的整数倍。

Test 3: LLM Inference Consistency / LLM 推理一致性

Pure text forward pass comparing logits from the original LLM vs the merged model's language_model.

纯文本前向传播，比较原始 LLM 和合并模型的 language_model 的 logits。

import torch
import torch.nn.functional as F
from transformers import AutoModelForCausalLM, AutoTokenizer

DEVICE = torch.device("cuda:0")
DTYPE = torch.bfloat16
merged_dir = "/train_tmp/llava_onevision2_4b_p14m2"
llm_dir = "/train_tmp/Qwen3-4B-Instruct-2507"

tokenizer = AutoTokenizer.from_pretrained(llm_dir, trust_remote_code=True)
input_ids = tokenizer("Hello, world!", return_tensors="pt")["input_ids"].to(DEVICE)

# Load original LLM
orig_llm = AutoModelForCausalLM.from_pretrained(llm_dir, torch_dtype=DTYPE,
                                                 trust_remote_code=True).to(DEVICE).eval()
with torch.no_grad():
    orig_logits = orig_llm(input_ids).logits
del orig_llm
import gc; gc.collect(); torch.cuda.empty_cache()

# Load merged model's language_model
merged = AutoModelForCausalLM.from_pretrained(merged_dir, torch_dtype=DTYPE,
                                               low_cpu_mem_usage=True, trust_remote_code=True)
merged_lm = merged.model.language_model.to(DEVICE).eval()
del merged.model.visual
gc.collect()

with torch.no_grad():
    merged_logits = merged_lm(input_ids).logits

cos = F.cosine_similarity(orig_logits.flatten().float(), merged_logits.flatten().float(), dim=0)
diff = (orig_logits - merged_logits).abs().max().item()
print(f"LLM logits: cos={cos:.8f}, max_diff={diff:.8e}")
# bf16 logits: cos ≈ 0.9999, max_diff < 5e-2 is healthy. fp32 gives diff = 0.
assert cos > 0.999, f"LLM logits mismatch: cos={cos}"
assert diff < 5e-2, f"LLM logits diff too large: {diff}"

Expected: bf16 → cos ≈ 0.9999, max_diff < 5e-2 (LLM weights copied verbatim, only RMSNorm/MLP bf16 noise). fp32 → cos = 1.0, diff = 0.0.

预期: bf16 → cos ≈ 0.9999, max_diff < 5e-2（LLM 权重直接复制，只有 RMSNorm/MLP 的 bf16 噪声）。fp32 → cos = 1.0, diff = 0.0。

What is NOT tested / 未覆盖的部分

Known Issues & Workarounds / 已知问题和解决方案

1. blockorder ViT validator only works for patch14+sms=2

vit_blockorder.py does a reshape that hard-codes patch14+sms=2 dimensions. Any other combination (patch16+sms=3, patch14+sms=3, etc.) crashes with RuntimeError: shape '[...]' is invalid for input of size N. Use --vit-validator-strategy layerwise for everything except the legacy 4b (patch14+sms=2) variant.

vit_blockorder.py 的 reshape 写死了 patch14+sms=2 的尺寸。其他任何组合 （patch16+sms=3、patch14+sms=3 等）都会崩。除了旧的 4b（patch14+sms=2） 之外，全部用 --vit-validator-strategy layerwise。

2. GPU OOM during validation

The built-in validator loads the full merged model + original ViT simultaneously. For 4B+ models on a single 80GB GPU, this may OOM. Workaround: use the standalone scripts above (they load only the visual component, deleting language_model first).

内置 validator 同时加载完整合并模型和原始 ViT。4B+ 模型在单张 80GB GPU 上可能 OOM。解决方案：用上面的独立脚本（只加载 visual 部分，先删 language_model）。

3. Original ViT embeddings output shape mismatch

Some ViT encoders output (N, 1, D) from embeddings() while merged visual outputs (N, D). The full model() forward handles this internally, so use orig_vit(pixel_values).last_hidden_state instead of calling embeddings() + layers manually for the original ViT.

有些 ViT encoder 的 embeddings() 输出 (N, 1, D) 而合并后的 visual 输出 (N, D)。用 orig_vit(pixel_values).last_hidden_state 调用完整 forward 而非手动逐层调用。

4. Block layout conversion for comparison

Original ViT outputs features in row-major order; merged visual uses block layout (grouped by spatial_merge_size). Use rowmajor_to_block() to align before comparison.

原始 ViT 输出 row-major 顺序的特征；合并后的 visual 使用 block layout（按 spatial_merge_size 分组）。比较前用 rowmajor_to_block() 对齐。

5. CRITICAL — Load original ViT via AutoModel + trust_remote_code, NOT a local import

When validating against the original ViT, use:

from transformers import AutoModel
orig_vit = AutoModel.from_pretrained(
    vit_dir, torch_dtype=DTYPE, trust_remote_code=True,
    attn_implementation="flash_attention_2",
)

Do NOT do from onevision_encoder import OneVisionEncoderModel from transformers_impl/onevision_encoder/. The local copy of modeling_onevision_encoder.py can drift from the modeling_*.py shipped inside the checkpoint directory (e.g. RoPE construction, attention impl, embedding signature). When they disagree, layerwise sim collapses to ~−0.024 even though the weights are byte-identical, and the failure mode looks like "wrong weights" but isn't. This bit us during the 4b_p14m33 merge against onevision-encoder-large-lang-tf57.

AutoModel + trust_remote_code always loads the modeling code that ships with the checkpoint, guaranteeing parity with whoever produced the weights.

验证 orig ViT 时必须用 AutoModel.from_pretrained(..., trust_remote_code=True)， 不要 from onevision_encoder import OneVisionEncoderModel。本地的 transformers_impl/onevision_encoder/ 与 checkpoint 自带的 modeling 文件 可能漂移（RoPE、attention 实现、embedding 接口），导致权重一致但 sim ≈ −0.024， 错觉是"权重错了"，实际是 modeling 代码不匹配。AutoModel + trust_remote_code 保证加载 checkpoint 自带的 modeling，与产 checkpoint 的环境完全一致。

6. bf16 numerical thresholds (validators tuned for bf16, not fp32)

The built-in validators are tuned for --target-dtype bf16. Realistic thresholds:

Why so loose for ViT? 24 transformer layers in bf16 accumulate ~2% relative error end-to-end. A characteristic healthy bf16 signature is "mid-layer cos dips to 0.98 then climbs back to 0.99 by the last layer" — this is bf16 RoPE accumulation noise, not a weight bug. If you're seeing cos < 0.95 at every layer (not just middle), suspect modeling-code drift (Issue #5), not weight error.

内置 validator 的阈值是按 bf16 调的。ViT layerwise 中间层 cos 跌到 0.98、 末层回升到 0.99 是 bf16 RoPE 累积噪声的健康特征，不是权重错位。 如果是"每一层"都 < 0.95（不只是中间层），怀疑 modeling 代码漂移 （见 Issue #5），不是权重问题。

7. Canonical helpers live in merge_ov2/utils.py — do NOT copy

convert_rope_to_block_layout, convert_rope_to_block_layout_by_positions, _infer_hw_from_positions, rowmajor_to_block are all canonical in merge_ov2/utils.py. The Megatron-side definition in aiak_training_llm/models/llava_onevision2/onevision_encoder_model.py:604 is the upstream reference, but transformers_impl/ cannot import from aiak_training_llm/ (would create a reverse dep on the training framework), hence the controlled re-implementation in merge_ov2/utils.py.

When writing manual debug scripts, always import from merge_ov2.utils:

from merge_ov2.utils import (
    convert_rope_to_block_layout_by_positions,
    rowmajor_to_block,
    cosine_similarity,
    load_image,
)

Do not copy these functions inline (we accumulated a 100-line drift in vit_layerwise.py this way before consolidating). Old broken imports like from llavaonevision2.modeling_llava_onevision2_moe import convert_rope_to_block_layout_by_positions never worked — that function never existed in that module.

convert_rope_to_block_layout*、rowmajor_to_block 等 helper 在 merge_ov2/utils.py 是 canonical 定义。不要 inline 复制（会和 utils 版本漂移）。Megatron 侧 aiak_training_llm/.../onevision_encoder_model.py:604 是上游参考，但 transformers_impl/ 不能反向 import 训练框架。

8. cli.py does not call logging.set_verbosity_info() — validators use print(flush=True)

The CLI does not raise transformers.logging verbosity, so any logger.info(...) inside validators (which run as part of merge/validate) is swallowed at the default WARNING level. To work around this, vit_layerwise.py and peers emit progress via print(..., flush=True) instead of logger.info.

If you want logger-style output instead, the proper fix is to add logging.set_verbosity_info() in cli.py near argument parsing — but that changes behavior for all subcommands, so it's been left as tech debt for now.

cli.py 没调 logging.set_verbosity_info()，validator 里的 logger.info 会被默认 WARNING 等级吞掉。所以 vit_layerwise.py 等用 print(..., flush=True) 输出进度，是绕过这个症状的权宜之计。要根治 就在 cli.py 加 logging.set_verbosity_info()，但会影响所有子命令的行为， 暂作 tech debt。

Quick Reference: Key Weight Mappings / 快速参考：关键权重映射