Create debug_ee.py

debug_ee.py

@@ -0,0 +1,117 @@
+"""
+EE Sanity Check Script
+Run this locally (not on HF Spaces) to verify the transform is correct.
+
+Usage:
+  python debug_ee.py --original Qwen/Qwen3-0.6B --ee your/model-dp-ee --seed 424242
+"""
+import torch
+import numpy as np
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import argparse
+
+def get_sigma(hidden_size, seed):
+    rng = np.random.default_rng(seed)
+    sigma = rng.permutation(hidden_size)
+    sigma_inv = np.argsort(sigma)
+    return sigma, sigma_inv
+
+def run_check(original_name, ee_name, seed, prompt="Hello, how are you?"):
+    print(f"\n{'='*60}")
+    print(f"Original : {original_name}")
+    print(f"EE model : {ee_name}")
+    print(f"Seed     : {seed}")
+    print(f"Prompt   : {prompt}")
+    print('='*60)
+
+    tokenizer = AutoTokenizer.from_pretrained(original_name, trust_remote_code=True)
+    inputs = tokenizer(prompt, return_tensors="pt")
+    input_ids = inputs.input_ids
+
+    print("\n[1] Loading original model...")
+    orig = AutoModelForCausalLM.from_pretrained(
+        original_name, torch_dtype=torch.float32, device_map="cpu", trust_remote_code=True
+    )
+    orig.eval()
+
+    print("[2] Loading EE model...")
+    ee = AutoModelForCausalLM.from_pretrained(
+        ee_name, torch_dtype=torch.float32, device_map="cpu", trust_remote_code=True
+    )
+    ee.eval()
+
+    hidden_size = orig.config.hidden_size
+    sigma, sigma_inv = get_sigma(hidden_size, seed)
+
+    # --- Check 1: Does the EE embed layer match original? ---
+    orig_embed = orig.model.embed_tokens.weight.data
+    ee_embed   = ee.model.embed_tokens.weight.data
+    embed_match = torch.allclose(orig_embed, ee_embed, atol=1e-3)
+    print(f"\n[CHECK 1] Embed layers identical: {embed_match}")
+    if not embed_match:
+        diff = (orig_embed - ee_embed).abs().max().item()
+        print(f"  ⚠️  Max diff: {diff:.6f} — EE embed was permuted, this BREAKS client-side encryption")
+        print(f"  → Re-run transform with the embed layer skipped (see transform_fix.py)")
+
+    # --- Check 2: Run plain forward on original ---
+    print("\n[CHECK 2] Running plain forward on original...")
+    with torch.no_grad():
+        plain_embeds = orig.model.embed_tokens(input_ids)
+        orig_out = orig(inputs_embeds=plain_embeds, output_hidden_states=False)
+    orig_logits = orig_out.logits  # (1, seq, vocab)
+
+    # --- Check 3: Run encrypted forward on EE model ---
+    print("[CHECK 3] Running encrypted forward on EE model...")
+    with torch.no_grad():
+        encrypted_embeds = plain_embeds[..., sigma]
+        ee_out = ee(inputs_embeds=encrypted_embeds, output_hidden_states=False)
+    ee_logits = ee_out.logits
+
+    # --- Check 4: Do logits match? ---
+    logit_match = torch.allclose(orig_logits, ee_logits, atol=1e-1)
+    max_diff = (orig_logits - ee_logits).abs().max().item()
+    print(f"\n[CHECK 4] Logits match (atol=0.1): {logit_match}")
+    print(f"  Max logit diff: {max_diff:.4f}")
+    if not logit_match:
+        print("  ⚠️  Logits differ — equivariance is BROKEN")
+        # Find where it breaks — check RoPE suspicion
+        print("\n  Diagnosing: checking if RoPE is the culprit...")
+        print("  RoPE applies rotation in head_dim space (64), not hidden space (1024)")
+        print("  If q_proj/k_proj output is permuted (because output==hidden_size),")
+        print("  the head_dim slices fed to RoPE will be scrambled → broken attention")
+
+    # --- Check 5: Greedy decode comparison ---
+    print("\n[CHECK 5] Greedy decode comparison (10 tokens)...")
+    with torch.no_grad():
+        orig_ids = orig.generate(input_ids, max_new_tokens=10, do_sample=False)
+        ee_ids   = ee.generate(inputs_embeds=encrypted_embeds,
+                               attention_mask=inputs.attention_mask,
+                               max_new_tokens=10, do_sample=False,
+                               pad_token_id=tokenizer.eos_token_id)
+
+    orig_text = tokenizer.decode(orig_ids[0], skip_special_tokens=True)
+    ee_text   = tokenizer.decode(ee_ids[0],   skip_special_tokens=True)
+    print(f"  Original output : {repr(orig_text)}")
+    print(f"  EE model output : {repr(ee_text)}")
+    print(f"  Match: {orig_text == ee_text}")
+
+    if orig_text != ee_text:
+        print("\n  ⚠️  OUTPUTS DIFFER. Most likely causes in order:")
+        print("  1. Embed layer was permuted in EE model (Check 1 above)")
+        print("  2. RoPE disruption — q_proj/k_proj output rows were permuted")
+        print("     FIX: do NOT permute output rows of q_proj and k_proj")
+        print("     because their outputs are split into heads for RoPE rotation")
+        print("  3. Model on Hub is stale — re-run transform and re-push")
+
+    print(f"\n{'='*60}\n")
+    return embed_match and logit_match
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--original", required=True)
+    parser.add_argument("--ee",       required=True)
+    parser.add_argument("--seed",     type=int, required=True)
+    parser.add_argument("--prompt",   default="Hello, how are you?")
+    args = parser.parse_args()
+    run_check(args.original, args.ee, args.seed, args.prompt)