v0.4.9: RCA evidence archived + HANDOVER §0.4

scripts/proc_probe.py

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+# Processor probe: does the eval-path processor call actually embed the
+# image? (NO model weights needed - processor only. Runs in seconds.)
+import os, sys
+os.environ["HF_HUB_DISABLE_XET"] = "1"
+sys.path.insert(0, "/content/qwenjev")
+sys.path.insert(0, "/content/qwenjev/train")
+from pathlib import Path
+
+from transformers import AutoProcessor
+
+proc = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-2B-Instruct")
+
+# two DIFFERENT 32x32 frames (drastic difference, like the 109-cell change)
+gA = [[(r * 32 + c) % 16 for c in range(32)] for r in range(32)]
+gB = [[((r * 32 + c) * 7 + 3) % 16 for c in range(32)] for r in range(32)]
+
+from qwenjev.vision import render_grid_pil
+imgA, imgB = render_grid_pil(gA), render_grid_pil(gB)
+print("pil images:", imgA.size, imgB.size, imgA.mode)
+
+prompt = "<|im_start|>user\nSTATE:\ntest state\n\nQUESTION:\nWhich candidate?<|im_end|>\n<|im_start|>assistant\n"
+
+texts = [
+    proc.apply_chat_template(
+        [{"role": "user", "content": [{"type": "image"},
+                                      {"type": "text", "text": l}]}],
+        add_generation_prompt=True, tokenize=False)
+    for l in ("CANDIDATE: ACTION1", "CANDIDATE: ACTION6")
+]
+print("--- chat template text[0] head ---")
+print(texts[0][:220].replace("\n", "\\n"))
+
+# exactly the eval-path call: images kwarg, padding, pt
+batch = proc(text=texts, images=[imgA, imgB], padding=True, return_tensors="pt")
+print("--- batch keys ---")
+print(sorted(batch.keys()))
+if "pixel_values" in batch:
+    pv = batch["pixel_values"]
+    print("pixel_values:", tuple(pv.shape), pv.dtype,
+          "mean=", float(pv.float().mean()), "std=", float(pv.float().std()))
+    print("image_grid_thw:", batch.get("image_grid_thw"))
+    # vision token count inside input_ids (Qwen image pad id 151655)
+    import torch
+    n_vis = int((batch["input_ids"] == 151655).sum())
+    print("vision pad tokens in input_ids:", n_vis)
+else:
+    print("!!! NO pixel_values: the eval path is TEXT-ONLY — BUG CONFIRMED")
+# text-only comparison: same call WITHOUT images
+batch2 = proc(text=[t.replace("[ vision content omitted ]", "") for t in texts],
+              padding=True, return_tensors="pt")
+print("--- no-image batch keys ---", sorted(batch2.keys()))
+print("PROBE_DONE")