Upload 08_reshard.py

08_reshard.py

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+#!/usr/bin/env python3
+"""
+08_reshard.py — Teilt die externe Datendatei des Decoders in <=1.9 GB grosse
+Chunks auf (Chromium 2-GB-ArrayBuffer-Limit / Transformers.js Multi-Chunk).
+
+Chunk-Namensschema (belegt via onnx-community/Phi-4-mini-instruct-web-q4f16
+und ORT-Web-Fehlermeldung in transformers.js Issue #1460):
+    Chunk 0 -> <base>.onnx_data        (ohne Suffix)
+    Chunk 1 -> <base>.onnx_data_1
+    Chunk k -> <base>.onnx_data_k
+config.json:
+    "use_external_data_format": {"decoder_model_merged_q4f16.onnx": <n_chunks>}
+
+Verifiziert am Ende den Ladevorgang mit onnxruntime (nicht onnx.checker,
+der bei >2 GB an protobuf scheitert).
+
+Start:  python3.13 08_reshard.py
+"""
+import os, onnx, onnxruntime as ort
+from onnx import TensorProto
+
+SRC_DIR = "/root/train/gemma4-bund-final-v3/onnx"
+IN      = os.path.join(SRC_DIR, "decoder_model_merged_q4f16.onnx")
+OUT_DIR = "/root/train/gemma4-bund-reshard"
+BASE    = "decoder_model_merged_q4f16.onnx_data"
+CAP     = 1_900_000_000  # 1.9 GB Sicherheitsgrenze unter 2^31
+
+os.makedirs(OUT_DIR, exist_ok=True)
+
+def cname(i):
+    return BASE if i == 0 else f"{BASE}_{i}"
+
+print("=== A) Modell laden (inkl. externer Daten)", flush=True)
+m = onnx.load(IN, load_external_data=True)
+
+print("=== B) Tensoren auf Chunks verteilen", flush=True)
+idx, off, n = 0, 0, 0
+f = open(os.path.join(OUT_DIR, cname(0)), "wb")
+for t in m.graph.initializer:
+    if not t.raw_data:          # kleine inline-Tensoren bleiben im Graph
+        continue
+    b  = t.raw_data
+    sz = len(b)
+    if off + sz > CAP and off > 0:
+        f.close(); idx += 1; off = 0
+        f = open(os.path.join(OUT_DIR, cname(idx)), "wb")
+    f.write(b)
+    t.ClearField("raw_data")
+    t.data_location = TensorProto.EXTERNAL
+    del t.external_data[:]
+    for k, v in (("location", cname(idx)), ("offset", str(off)), ("length", str(sz))):
+        e = t.external_data.add(); e.key = k; e.value = v
+    off += sz; n += 1
+f.close()
+n_chunks = idx + 1
+print(f"    externalisierte Tensoren: {n}, Chunks: {n_chunks}", flush=True)
+
+print("=== C) Graph speichern (Pointer, ohne Daten neu zu schreiben)", flush=True)
+out_onnx = os.path.join(OUT_DIR, "decoder_model_merged_q4f16.onnx")
+onnx.save(m, out_onnx, save_as_external_data=False)
+for i in range(n_chunks):
+    p = os.path.join(OUT_DIR, cname(i))
+    print(f"    {cname(i)}: {os.path.getsize(p)/1e9:.2f} GB", flush=True)
+
+print("=== D) Verifikation: Laden mit onnxruntime (CPU)", flush=True)
+sess = ort.InferenceSession(out_onnx, ort.SessionOptions(),
+                            providers=["CPUExecutionProvider"])
+print(f"    OK — Session erstellt, Inputs total: {len(sess.get_inputs())}", flush=True)
+
+print("\n=== FERTIG. config.json-Eintrag:", flush=True)
+print(f'    "use_external_data_format": {{"decoder_model_merged_q4f16.onnx": {n_chunks}}}', flush=True)
+print("Naechste Schritte: embed_tokens_q4f16.* unveraendert dazukopieren, "
+      "alles nach HF, dann Bundesrechner-WebGPU-Test.", flush=True)