[feat] Added debug hook to offload_engine.py.
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Zijie Tian
267
tests/test_debug_verification.py
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267
tests/test_debug_verification.py
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"""
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Test script for verifying KV cache offload correctness using debug hooks.
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Strategy:
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1. Inject distinctive K/V values (K=chunk_idx+1, V=-(chunk_idx+1))
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2. Register debug hook to receive loaded tensor
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3. Hook reads tensor values to verify correct block was loaded
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4. No verification logic in framework - all external
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This tests the framework's normal async execution path.
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"""
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import os
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os.environ["NANOVLLM_LOG_LEVEL"] = "INFO"
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from random import randint, seed
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from typing import Dict, List, Tuple
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import torch
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from torch import Tensor
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from nanovllm import LLM, SamplingParams
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from nanovllm.utils.context import get_context
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# ============================================================
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# Configuration
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# ============================================================
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MODEL_PATH = os.path.expanduser("~/models/Qwen3-0.6B/")
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MAX_MODEL_LEN = 32 * 1024
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NUM_GPU_BLOCKS = 4
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INPUT_LEN = 32 * 1024
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BLOCK_SIZE = 1024
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# ============================================================
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# External state (managed by test, not framework)
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# ============================================================
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# Record all load operations: list of {cpu_block_id, k_value, v_value, ...}
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load_log: List[Dict] = []
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# Track current chunk for grouping loads
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current_chunk: List[int] = [0] # mutable container
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# ============================================================
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# Debug hook - receives loaded tensor directly
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# ============================================================
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def debug_load_hook(slot_idx: int, layer_id: int, cpu_block_id: int, k: Tensor, v: Tensor) -> None:
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"""
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Debug hook called after each H2D load.
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Reads tensor values to verify which block was actually loaded.
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"""
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# Only record layer 0 for efficiency
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if layer_id != 0:
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return
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# Read tensor values (the distinctive pattern we injected)
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k_val = k.float().mean().item()
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v_val = v.float().mean().item()
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load_log.append({
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"chunk_idx": current_chunk[0],
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"slot_idx": slot_idx,
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"cpu_block_id": cpu_block_id,
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"k_value": k_val,
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"v_value": v_val,
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})
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# ============================================================
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# Pattern injection hook - injects distinctive values into K/V
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# ============================================================
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def make_pattern_injection_hook(layer_id):
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"""Inject distinctive patterns: K = chunk_idx + 1, V = -(chunk_idx + 1)"""
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def hook(module, inputs):
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ctx = get_context()
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if not ctx.is_prefill:
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return inputs
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if layer_id != 0:
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return inputs
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chunk_idx = ctx.current_chunk_idx if hasattr(ctx, 'current_chunk_idx') else 0
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current_chunk[0] = chunk_idx # Update for debug_load_hook
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if len(inputs) >= 3:
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q, k, v = inputs[0], inputs[1], inputs[2]
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k_pattern = float(chunk_idx + 1)
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v_pattern = float(-(chunk_idx + 1))
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k_new = torch.full_like(k, k_pattern)
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v_new = torch.full_like(v, v_pattern)
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return (q, k_new, v_new) + inputs[3:]
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return inputs
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return hook
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# ============================================================
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# Verification functions (all external, not in framework)
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# ============================================================
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def verify_load_order() -> Tuple[int, int, List[Dict]]:
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"""Verify blocks were loaded in correct order by checking K values."""
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# Group loads by chunk
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chunk_loads: Dict[int, List[Tuple[int, float]]] = {}
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for log in load_log:
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chunk = log["chunk_idx"]
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if chunk not in chunk_loads:
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chunk_loads[chunk] = []
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chunk_loads[chunk].append((log["cpu_block_id"], log["k_value"]))
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correct = 0
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incorrect = 0
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errors = []
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for chunk in sorted(chunk_loads.keys()):
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loads = chunk_loads[chunk]
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# Expected: blocks [0, 1, ..., chunk-1] with K values [1, 2, ..., chunk]
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expected_blocks = list(range(chunk))
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actual_blocks = [block_id for block_id, _ in loads]
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# Also verify K values match expected pattern
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k_values = [k_val for _, k_val in loads]
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expected_k_values = [float(b + 1) for b in expected_blocks]
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blocks_ok = actual_blocks == expected_blocks
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# Check K values with tolerance
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k_ok = all(abs(a - e) < 1e-2 for a, e in zip(k_values, expected_k_values)) if len(k_values) == len(expected_k_values) else False
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if blocks_ok and k_ok:
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correct += 1
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else:
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incorrect += 1
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errors.append({
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"chunk_idx": chunk,
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"expected_blocks": expected_blocks,
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"actual_blocks": actual_blocks,
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"expected_k": expected_k_values,
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"actual_k": k_values,
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})
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return correct, incorrect, errors
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def print_verification_summary():
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"""Print verification results."""
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correct, incorrect, errors = verify_load_order()
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# Group for display
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chunk_loads: Dict[int, List[int]] = {}
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for log in load_log:
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chunk = log["chunk_idx"]
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if chunk not in chunk_loads:
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chunk_loads[chunk] = []
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chunk_loads[chunk].append(log["cpu_block_id"])
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print(f"\n{'='*60}")
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print("Debug Verification Summary")
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print(f"{'='*60}")
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print(f"\n1. Load Operations:")
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print(f" Total H2D loads recorded: {len(load_log)}")
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print(f" Chunks with correct order: {correct}")
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print(f" Chunks with incorrect order: {incorrect}")
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if incorrect > 0:
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print(f"\n Errors:")
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for err in errors[:5]:
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print(f" Chunk {err['chunk_idx']}:")
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print(f" Expected blocks: {err['expected_blocks']}")
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print(f" Actual blocks: {err['actual_blocks']}")
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print(f" K values: {[f'{v:.1f}' for v in err['actual_k']]}")
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print(f"\n2. Load Order Sample (first 5 and last 2 chunks):")
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sorted_chunks = sorted(chunk_loads.keys())
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display_chunks = sorted_chunks[:5] + sorted_chunks[-2:] if len(sorted_chunks) > 7 else sorted_chunks
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for chunk in display_chunks:
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blocks = chunk_loads[chunk]
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expected = list(range(chunk))
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status = "OK" if blocks == expected else "WRONG"
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print(f" Chunk {chunk}: {blocks} [{status}]")
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print(f"\n{'='*60}")
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# ============================================================
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# Main Test Script
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# ============================================================
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print("Initializing LLM with CPU offload...")
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llm = LLM(
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MODEL_PATH,
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enforce_eager=True,
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max_model_len=MAX_MODEL_LEN,
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max_num_batched_tokens=MAX_MODEL_LEN,
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enable_cpu_offload=True,
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kvcache_block_size=BLOCK_SIZE,
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num_gpu_blocks=NUM_GPU_BLOCKS,
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dtype="float16",
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)
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# Get offload engine and enable debug mode
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kvcache_manager = llm.model_runner.kvcache_manager
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offload_engine = kvcache_manager.offload_engine
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offload_engine.enable_debug_mode()
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# Register our debug hook
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offload_engine.register_debug_hook(debug_load_hook)
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print("Debug mode enabled with custom hook")
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# Register pattern injection hooks
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hooks = []
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model = llm.model_runner.model
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for layer_idx, decoder_layer in enumerate(model.model.layers):
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attn_module = decoder_layer.self_attn.attn
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pre_hook = attn_module.register_forward_pre_hook(make_pattern_injection_hook(layer_idx))
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hooks.append(pre_hook)
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print(f"Registered {len(hooks)} pattern injection hooks")
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# Generate input
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seed(42)
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prompt_token_ids = [[randint(0, 10000) for _ in range(INPUT_LEN)]]
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num_chunks = INPUT_LEN // BLOCK_SIZE
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print(f"\nInput: {INPUT_LEN} tokens, {num_chunks} chunks expected")
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print(f"GPU blocks: {NUM_GPU_BLOCKS}, Block size: {BLOCK_SIZE}")
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# Run prefill
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print("\n" + "=" * 60)
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print("Starting Prefill...")
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print("=" * 60)
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sampling_params = SamplingParams(temperature=0.6, ignore_eos=True, max_tokens=1)
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outputs = llm.generate(prompt_token_ids, sampling_params, use_tqdm=False)
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# Remove hooks
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for hook in hooks:
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hook.remove()
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offload_engine.remove_debug_hook(debug_load_hook)
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# Verify and print
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print("\n" + "=" * 60)
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print("Post-Execution Verification")
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print("=" * 60)
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print_verification_summary()
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# Final verdict
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correct, incorrect, _ = verify_load_order()
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expected_loads = num_chunks * (num_chunks - 1) // 2
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actual_loads = len(load_log)
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print(f"\nResults:")
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print(f" Total loads: {actual_loads} (expected: {expected_loads})")
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print(f" Order verification: {correct} correct, {incorrect} incorrect")
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print("\n" + "=" * 60)
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all_passed = incorrect == 0 and actual_loads == expected_loads
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if all_passed:
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print("test_debug_verification: PASSED")
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else:
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print("test_debug_verification: FAILED")
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print("=" * 60)
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offload_engine.disable_debug_mode()
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