Add layout (1036 square) bundle + recognition pf64 for the 2-stage structured pipeline

.gitattributes

@@ -37,3 +37,7 @@ decoder/mineru_decode_int8lin_s1.aimodel/main.mlirb filter=lfs diff=lfs merge=lf
 decoder/tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text
 tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text
 vision/mineru_vision.aimodel/main.mlirb filter=lfs diff=lfs merge=lfs -text
+decoder/mineru_decode_int8lin_pf64.aimodel/main.mlirb filter=lfs diff=lfs merge=lfs -text
+layout/decoder/mineru_decode_int8lin_pf64.aimodel/main.mlirb filter=lfs diff=lfs merge=lfs -text
+layout/decoder/tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+layout/vision/mineru_vision.aimodel/main.mlirb filter=lfs diff=lfs merge=lfs -text

README.md

@@ -18,8 +18,9 @@ decodes on top. The host runs the model twice per page (layout prompt, then reco
 
 **⬇️ Converted `.aimodel` bundles:
 [mlboydaisuke/MinerU2.5-Pro-CoreAI](https://huggingface.co/mlboydaisuke/MinerU2.5-Pro-CoreAI)** —
-`vision/` (Qwen2-VL ViT, fp16) + `decoder/` (Qwen2 decoder, S=1 pipelined, int8lin) + `tokenizer/`.
-Apache-2.0.
+`vision/` + `decoder/` (768-token **recognition** grid, Qwen2-VL ViT fp16 + int8lin decoder, pf64
+chunked prefill) + `layout/` (a 1036² **square** recognition/detection grid for the 2-stage) +
+`tokenizer/`. Apache-2.0.
 
 ## Architecture
 
@@ -36,8 +37,13 @@ Apache-2.0.
   + `rope_shift_start` + `rope_shift_amount`. Zero embeds + `shift_start = 1<<30` → a plain Qwen2 text
   decoder.
 
-## Verified (M4 Max, GPU, Core AI pipelined engine)
+## Verified (M4 Max + iPhone 17 Pro, GPU, Core AI pipelined engine)
 
+- **iPhone 17 Pro, on-device, whole page read correctly** — the h18p AOT bundles run through
+  `KitMineruReader` in a real app (ReadDoc): a page photo → structured text (headings, paragraphs,
+  tables) with nothing leaving the device. **~4 s/page** (warm) via **chunked prefill**: the `pf64`
+  multifunction bundle feeds the 768 image tokens in **static S=64 chunks** (the engine auto-discovers
+  the `prefill` function) then decodes S=1 — the S=1 prefill (~10 s) drops to **~2.9 s**, no token cut.
 - **End-to-end real generation on the engine (shipped portrait 768 config): the sample page read
   verbatim** — letterbox → CLIP-norm → non-square patchify → GPU vision `.aimodel` → `image_embeds`
   → AOT-compiled (h16c) int8lin S=1 decoder, autoregressive greedy: title + paragraph + the **full
@@ -52,20 +58,37 @@ Apache-2.0.
 - **int8lin vs fp16: 13 / 706 argmax flips, all at visual-token positions (0 in the text region)** —
   the OCR text is preserved. Greedy generation byte-identical to fp16.
 
-## Run in app — `KitMineruReader` (iPhone + Mac)
+## Run in app — `KitMineruReader` (Mac; iPhone single-pass)
 
-The shipped bundle rides the kit's VL rope-shift runtime (`VLRuntime` + `VLArchitecture.mineru`).
-The page is letterboxed (aspect-fit + white pad) into a **fixed 672×896 portrait canvas**, CLIP-
-normalized, and non-square-patchified into a **32×24 merged grid (768 visual tokens)** — the
-`VLImagePreprocessor` non-square path added for Qwen2-VL. Two calls:
+Both grids ride the kit's VL rope-shift runtime (`VLRuntime`). Pages are CLIP-normalized and
+patchified (the `VLImagePreprocessor` non-square + `.stretch`/`.aspectFitPad` paths added for
+Qwen2-VL). Two entry points:
 
 ```swift
+// Single-pass (768 grid): whole page → plain text (reading order). Runs on iPhone (chunked prefill).
 let reader = try await KitMineruReader(catalog: "mineru2.5-pro")
-let markdown = try await reader.read(imageAt: documentURL)   // "Text Recognition:" whole-page
+let text = try await reader.read(imageAt: documentURL)
+
+// 2-stage (structured Markdown, tables as <table> HTML). Needs the layout bundle too.
+let reader = try await KitMineruReader(
+    visionDir: rec.vision, decoderDir: rec.decoder,
+    layoutVisionDir: layout.vision, layoutDecoderDir: layout.decoder)
+let markdown = try await reader.readStructured(imageAt: documentURL)
 ```
 
-v1 is a single whole-page `"Text Recognition:"` pass (correct for clean single-column pages). The
-2-stage layout→region pipeline is a follow-up.
+**2-stage** (`readStructured`, mirrors `mineru-vl-utils` `two_step_extract` + `json2md`):
+1. **Layout** on the page **stretched into 1036² square** (`VLArchitecture.mineruLayout`, 37×37 = 1369
+   tokens) → `Layout Detection:` emits `<|box_start|>…<|ref_start|>type…` blocks. A 768 portrait grid
+   mis-detects here — the square high-res grid is required. Boxes are 0–1 of the square, so they map
+   linearly onto the original page.
+2. **Recognition** per region on the **768 grid** (`.mineru`): each block is cropped from the original
+   and read by type — `Table Recognition:` emits **OTSL** (`<fcel>`/`<nl>`), kept (not skipped) and
+   converted to `<table>` HTML; `Formula Recognition:` → LaTeX; else plain text.
+3. **json2md** joins the region contents in reading order.
+
+Verified end-to-end in the ReadDoc Mac app: an 8-block page → title/paragraphs + a 5-row `<table>`,
+byte-identical to the reference. ~11 s load (both bundles) + ~11 s/page. The single-pass path also
+runs on iPhone; the 2-stage's 1036² layout grid is Mac-tier (two bundles, heavy).
 
 ## Pipeline (host side)
 

decoder/mineru_decode_int8lin_pf64.aimodel/main.hash

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decoder/mineru_decode_int8lin_pf64.aimodel/metadata.json

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layout/decoder/metadata.json

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+  "metadata_version": "0.2",
+  "kind": "llm",
+  "name": "mineru_decode_int8lin_pf64",
+  "assets": {
+    "main": "mineru_decode_int8lin_pf64.aimodel"
+  },
+  "language": {
+    "tokenizer": "/Users/majimadaisuke/code/coreai/_mineru_dl",
+    "vocab_size": 151936,
+    "max_context_length": 4096,
+    "embedded_tokenizer": true,
+    "function_map": {
+      "main": [
+        "main",
+        "prefill"
+      ]
+    }
+  },
+  "source": {
+    "model_definition": "torch",
+    "hf_model_id": "/Users/majimadaisuke/code/coreai/_mineru_dl"
+  },
+  "compression": null,
+  "compilation": {
+    "date": "2026-07-06T07:41:10.859485+00:00",
+    "targets": []
+  }
+}

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layout/decoder/tokenizer/chat_template.jinja

@@ -0,0 +1,7 @@
+{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system
+You are a helpful assistant.<|im_end|>
+{% endif %}<|im_start|>{{ message['role'] }}
+{% if message['content'] is string %}{{ message['content'] }}<|im_end|>
+{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>
+{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant
+{% endif %}

layout/decoder/tokenizer/special_tokens_map.json

@@ -0,0 +1,48 @@
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+  "additional_special_tokens": [
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+    "<|im_end|>",
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+    "<|rotate_down|>",
+    "<|rotate_left|>",
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+    "<|txt_contd|>",
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+  }
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