pillar1(R2): HF per-layer hidden state dump tool

tools/pillar1/hf_dump.py runs a prompt through Qwen3-0.6B FP32 (HF
reference) and saves every post-layer hidden state + logits to npz.

Structure captured for prompt "Hello" (1 token, dim=1024):
  emb                 — embedding output
  h0..h27             — output of each of 28 transformer layers
  logits              — post-final-norm + lm_head [1, vocab=151936]
  tokens              — input token IDs

Top-5 logits on "Hello": "Answer" 8.139, "Question" 8.077 (matches
R1 smoke test generation " Answer! I'm a bit confused...").

This is the ground-truth reference. R3 will add a matching dump to
our engine and compute layer-by-layer diff to find the first
divergence.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Author: unamedkr

tools/pillar1/hf_dump.py

@@ -0,0 +1,53 @@
+#!/usr/bin/env python3
+"""HF Qwen3-0.6B per-layer hidden state dump.
+
+Runs a fixed prompt through the reference model, captures the hidden
+state after the embedding and after each of the 28 transformer layers,
+and saves to an npz file for diffing against our engine's dump.
+
+Usage:
+    python hf_dump.py "Hello"
+    -> writes hf_dump.npz with arrays emb, h0, h1, ..., h27, final_norm, logits
+"""
+import sys
+import numpy as np
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+MODEL = "Qwen/Qwen3-0.6B"
+PROMPT = sys.argv[1] if len(sys.argv) > 1 else "Hello"
+OUT = sys.argv[2] if len(sys.argv) > 2 else "tools/pillar1/hf_dump.npz"
+
+print(f"prompt: {PROMPT!r}")
+
+tok = AutoTokenizer.from_pretrained(MODEL)
+model = AutoModelForCausalLM.from_pretrained(MODEL, dtype=torch.float32, device_map="cpu")
+model.eval()
+
+ids = tok.encode(PROMPT, return_tensors="pt")
+print(f"tokens: {ids.tolist()[0]}")
+
+with torch.no_grad():
+    out = model(ids, output_hidden_states=True, use_cache=False)
+
+# out.hidden_states is a tuple of (n_layers+1) tensors, each [1, seq_len, dim]
+# [0] = after embedding (before any layer)
+# [i] = after layer (i-1)
+# final logits after final norm + lm_head: out.logits [1, seq_len, vocab]
+arrays = {}
+for i, h in enumerate(out.hidden_states):
+    key = "emb" if i == 0 else f"h{i-1}"
+    arrays[key] = h[0].cpu().float().numpy()  # [seq_len, dim]
+    print(f"  {key}: shape={arrays[key].shape}, first8={arrays[key][-1, :8]}")
+
+arrays["logits"] = out.logits[0].cpu().float().numpy()  # [seq_len, vocab]
+print(f"  logits: shape={arrays['logits'].shape}, top5_last=", end="")
+last = arrays["logits"][-1]
+top5 = np.argsort(-last)[:5]
+print({int(t): f'{last[t]:.3f} ({tok.decode([int(t)])!r})' for t in top5})
+
+# Also capture tokens for comparison
+arrays["tokens"] = np.array(ids.tolist()[0], dtype=np.int64)
+
+np.savez(OUT, **arrays)
+print(f"saved {OUT}")