474 lines
17 KiB
Python
474 lines
17 KiB
Python
"""
|
|
Hook-based correctness test for chunked prefill attention.
|
|
|
|
Uses PyTorch register_forward_hook() to capture real inference I/O,
|
|
then compares against reference computation to locate bugs.
|
|
|
|
This test targets the integration layer (context setup, cpu_block_table management)
|
|
which is where the needle test fails despite isolated attention tests passing.
|
|
"""
|
|
|
|
import os
|
|
os.environ["NANOVLLM_LOG_LEVEL"] = "DEBUG"
|
|
|
|
import torch
|
|
from random import randint, seed
|
|
from nanovllm import LLM, SamplingParams
|
|
from nanovllm.utils.context import get_context
|
|
from nanovllm.kvcache.chunked_attention import flash_attn_with_lse, merge_attention_outputs
|
|
from flash_attn.flash_attn_interface import flash_attn_varlen_func
|
|
|
|
|
|
# ============================================================
|
|
# Configuration
|
|
# ============================================================
|
|
|
|
MODEL_PATH = os.path.expanduser("~/models/Qwen3-4B-Instruct-2507/")
|
|
MAX_MODEL_LEN = 32 * 1024
|
|
NUM_GPU_BLOCKS = 2
|
|
INPUT_LEN = 16 * 1024 # 4K tokens = 4 chunks with 1K block size
|
|
BLOCK_SIZE = 1024
|
|
|
|
|
|
# ============================================================
|
|
# Global capture storage
|
|
# ============================================================
|
|
|
|
captures = []
|
|
|
|
|
|
# ============================================================
|
|
# Hook Functions
|
|
# ============================================================
|
|
|
|
def make_hook(layer_id):
|
|
"""Create a forward hook for a specific layer."""
|
|
def hook(module, inputs, output):
|
|
q, k, v = inputs
|
|
ctx = get_context()
|
|
|
|
# Only capture prefill phase
|
|
if not ctx.is_prefill:
|
|
return
|
|
|
|
chunk_idx = ctx.current_chunk_idx if hasattr(ctx, 'current_chunk_idx') else 0
|
|
|
|
capture_entry = {
|
|
'layer_id': layer_id,
|
|
'chunk_idx': chunk_idx,
|
|
'q': q.clone().cpu(),
|
|
'k': k.clone().cpu(),
|
|
'v': v.clone().cpu(),
|
|
'output': output.clone().cpu(),
|
|
'is_chunked_prefill': ctx.is_chunked_prefill,
|
|
}
|
|
|
|
# For debugging: also capture CPU cache state for layer 0
|
|
if layer_id == 0 and chunk_idx >= 2:
|
|
kvcache_manager = ctx.kvcache_manager if hasattr(ctx, 'kvcache_manager') else None
|
|
if kvcache_manager is not None and hasattr(kvcache_manager, 'offload_engine'):
|
|
oe = kvcache_manager.offload_engine
|
|
# Get what should have been loaded from CPU
|
|
cpu_k0 = oe.k_cache_cpu[0, 0].clone().cpu() # Layer 0, CPU block 0
|
|
cpu_k1 = oe.k_cache_cpu[0, 1].clone().cpu() # Layer 0, CPU block 1
|
|
capture_entry['cpu_k0'] = cpu_k0
|
|
capture_entry['cpu_k1'] = cpu_k1
|
|
|
|
captures.append(capture_entry)
|
|
return hook
|
|
|
|
|
|
def register_hooks(llm):
|
|
"""Register forward hooks on all Attention modules."""
|
|
hooks = []
|
|
model = llm.model_runner.model
|
|
|
|
for layer_idx, decoder_layer in enumerate(model.model.layers):
|
|
attn_module = decoder_layer.self_attn.attn
|
|
hook = attn_module.register_forward_hook(make_hook(layer_idx))
|
|
hooks.append(hook)
|
|
|
|
return hooks
|
|
|
|
|
|
# ============================================================
|
|
# Reference Computation
|
|
# ============================================================
|
|
|
|
def compute_reference(layer_id, chunk_idx, scale, debug=False):
|
|
"""
|
|
Compute reference attention output for a specific layer and chunk.
|
|
|
|
Uses the captured k, v from all chunks up to and including chunk_idx.
|
|
"""
|
|
# Filter captures for this layer
|
|
layer_captures = [c for c in captures
|
|
if c['layer_id'] == layer_id and c['chunk_idx'] <= chunk_idx]
|
|
|
|
if not layer_captures:
|
|
return None
|
|
|
|
# Get current chunk's q
|
|
current_capture = [c for c in layer_captures if c['chunk_idx'] == chunk_idx][0]
|
|
q = current_capture['q'].cuda().unsqueeze(0) # [1, seqlen, nheads, headdim]
|
|
|
|
# Collect all k, v up to current chunk
|
|
kv_list = []
|
|
for c in sorted(layer_captures, key=lambda x: x['chunk_idx']):
|
|
k = c['k'].cuda().unsqueeze(0) # [1, seqlen, nheads, headdim]
|
|
v = c['v'].cuda().unsqueeze(0)
|
|
kv_list.append((k, v, c['chunk_idx']))
|
|
|
|
if debug:
|
|
print(f" Reference for L{layer_id} C{chunk_idx}:")
|
|
print(f" q shape: {q.shape}, mean={q.mean().item():.4f}")
|
|
print(f" kv_list: {len(kv_list)} chunks")
|
|
for i, (k, v, cidx) in enumerate(kv_list):
|
|
print(f" chunk {cidx}: k.mean={k.mean().item():.4f}, v.mean={v.mean().item():.4f}")
|
|
|
|
o_acc, lse_acc = None, None
|
|
|
|
# Previous chunks: non-causal attention
|
|
for i in range(len(kv_list) - 1):
|
|
k, v, _ = kv_list[i]
|
|
o, lse = flash_attn_with_lse(q, k, v, softmax_scale=scale, causal=False)
|
|
if o_acc is None:
|
|
o_acc, lse_acc = o, lse
|
|
else:
|
|
o_acc, lse_acc = merge_attention_outputs(o_acc, lse_acc, o, lse)
|
|
|
|
# Current chunk: causal attention
|
|
k_cur, v_cur, _ = kv_list[-1]
|
|
o_cur, lse_cur = flash_attn_with_lse(q, k_cur, v_cur, softmax_scale=scale, causal=True)
|
|
|
|
if o_acc is None:
|
|
return o_cur.squeeze(0).cpu()
|
|
|
|
final_o, _ = merge_attention_outputs(o_acc, lse_acc, o_cur, lse_cur)
|
|
return final_o.squeeze(0).cpu()
|
|
|
|
|
|
def compute_standard_reference(layer_id, chunk_idx, scale, debug=False):
|
|
"""
|
|
Compute reference using standard flash attention (single pass with all K, V).
|
|
|
|
This simulates what standard (non-chunked) prefill would produce.
|
|
Concatenates all Q, K, V from chunks 0 to chunk_idx and runs a single
|
|
causal attention pass, then extracts the output for the current chunk.
|
|
"""
|
|
# Filter captures for this layer
|
|
layer_captures = [c for c in captures
|
|
if c['layer_id'] == layer_id and c['chunk_idx'] <= chunk_idx]
|
|
|
|
if not layer_captures:
|
|
return None
|
|
|
|
# Sort by chunk index
|
|
layer_captures = sorted(layer_captures, key=lambda x: x['chunk_idx'])
|
|
|
|
# Concatenate all Q, K, V
|
|
all_q = []
|
|
all_k = []
|
|
all_v = []
|
|
chunk_lengths = []
|
|
|
|
for c in layer_captures:
|
|
q = c['q'].cuda() # [seqlen, nheads, headdim]
|
|
k = c['k'].cuda()
|
|
v = c['v'].cuda()
|
|
all_q.append(q)
|
|
all_k.append(k)
|
|
all_v.append(v)
|
|
chunk_lengths.append(q.shape[0])
|
|
|
|
# Concatenate along sequence dimension
|
|
full_q = torch.cat(all_q, dim=0) # [total_seqlen, nheads, headdim]
|
|
full_k = torch.cat(all_k, dim=0)
|
|
full_v = torch.cat(all_v, dim=0)
|
|
|
|
total_len = full_q.shape[0]
|
|
|
|
if debug:
|
|
print(f" Standard Reference for L{layer_id} C{chunk_idx}:")
|
|
print(f" full_q shape: {full_q.shape}, mean={full_q.mean().item():.4f}")
|
|
print(f" full_k shape: {full_k.shape}, mean={full_k.mean().item():.4f}")
|
|
print(f" chunk_lengths: {chunk_lengths}")
|
|
|
|
# Run standard causal flash attention
|
|
# flash_attn_varlen_func expects: q, k, v with shape [total_seqlen, nheads, headdim]
|
|
cu_seqlens = torch.tensor([0, total_len], dtype=torch.int32, device='cuda')
|
|
|
|
full_o = flash_attn_varlen_func(
|
|
full_q, full_k, full_v,
|
|
cu_seqlens_q=cu_seqlens,
|
|
cu_seqlens_k=cu_seqlens,
|
|
max_seqlen_q=total_len,
|
|
max_seqlen_k=total_len,
|
|
softmax_scale=scale,
|
|
causal=True,
|
|
)
|
|
|
|
# Extract output for current chunk only
|
|
start_pos = sum(chunk_lengths[:-1])
|
|
end_pos = sum(chunk_lengths)
|
|
chunk_output = full_o[start_pos:end_pos]
|
|
|
|
if debug:
|
|
print(f" full_o shape: {full_o.shape}")
|
|
print(f" extracting positions [{start_pos}:{end_pos}]")
|
|
print(f" chunk_output shape: {chunk_output.shape}, mean={chunk_output.mean().item():.4f}")
|
|
|
|
return chunk_output.cpu()
|
|
|
|
|
|
# ============================================================
|
|
# Test Runner
|
|
# ============================================================
|
|
|
|
def run_test(verbose=True):
|
|
"""Run the hook-based chunked prefill correctness test."""
|
|
global captures
|
|
captures = []
|
|
|
|
if verbose:
|
|
print("=" * 70)
|
|
print("Test: Hook-Based Chunked Prefill Correctness")
|
|
print("=" * 70)
|
|
print(f"Model: {MODEL_PATH}")
|
|
print(f"Input length: {INPUT_LEN} tokens")
|
|
print(f"Block size: {BLOCK_SIZE}")
|
|
print(f"Expected chunks: {INPUT_LEN // BLOCK_SIZE}")
|
|
print()
|
|
|
|
# Initialize LLM with CPU offload
|
|
llm = LLM(
|
|
MODEL_PATH,
|
|
enforce_eager=True,
|
|
max_model_len=MAX_MODEL_LEN,
|
|
max_num_batched_tokens=MAX_MODEL_LEN,
|
|
enable_cpu_offload=True,
|
|
kvcache_block_size=BLOCK_SIZE,
|
|
num_gpu_blocks=NUM_GPU_BLOCKS,
|
|
)
|
|
|
|
# Get model info
|
|
num_layers = len(llm.model_runner.model.model.layers)
|
|
head_dim = llm.model_runner.model.model.layers[0].self_attn.attn.head_dim
|
|
scale = head_dim ** -0.5
|
|
|
|
if verbose:
|
|
print(f"Num layers: {num_layers}")
|
|
print(f"Head dim: {head_dim}")
|
|
print()
|
|
|
|
# Register hooks
|
|
hooks = register_hooks(llm)
|
|
if verbose:
|
|
print(f"Registered {len(hooks)} hooks")
|
|
|
|
# Generate random prompt
|
|
seed(42)
|
|
prompt_token_ids = [[randint(0, 10000) for _ in range(INPUT_LEN)]]
|
|
|
|
# Run prefill only (max_tokens=1)
|
|
if verbose:
|
|
print("Running inference...")
|
|
sampling_params = SamplingParams(temperature=0.6, max_tokens=1)
|
|
outputs = llm.generate(prompt_token_ids, sampling_params, use_tqdm=False)
|
|
|
|
# Remove hooks
|
|
for hook in hooks:
|
|
hook.remove()
|
|
|
|
# Analyze captures
|
|
if verbose:
|
|
print(f"\nCaptured {len(captures)} attention calls")
|
|
|
|
# Group by layer and chunk
|
|
chunks_per_layer = {}
|
|
for c in captures:
|
|
layer_id = c['layer_id']
|
|
chunk_idx = c['chunk_idx']
|
|
if layer_id not in chunks_per_layer:
|
|
chunks_per_layer[layer_id] = set()
|
|
chunks_per_layer[layer_id].add(chunk_idx)
|
|
|
|
if verbose:
|
|
print("Chunks per layer:", {k: sorted(v) for k, v in chunks_per_layer.items()})
|
|
print()
|
|
|
|
# First, verify CPU cache data integrity
|
|
if verbose:
|
|
print("\n--- CPU Cache Verification (Layer 0) ---")
|
|
# Get original k from chunk 0 and chunk 1 captures
|
|
chunk0_k = None
|
|
chunk1_k = None
|
|
chunk2_capture = None
|
|
for c in captures:
|
|
if c['layer_id'] == 0:
|
|
if c['chunk_idx'] == 0:
|
|
chunk0_k = c['k']
|
|
elif c['chunk_idx'] == 1:
|
|
chunk1_k = c['k']
|
|
elif c['chunk_idx'] == 2:
|
|
chunk2_capture = c
|
|
|
|
if chunk0_k is not None and chunk2_capture is not None and 'cpu_k0' in chunk2_capture:
|
|
cpu_k0 = chunk2_capture['cpu_k0']
|
|
diff_k0 = (chunk0_k - cpu_k0).abs().max().item()
|
|
print(f"Chunk 0 k vs CPU cache block 0: max_diff={diff_k0:.6f}")
|
|
if diff_k0 > 1e-3:
|
|
print(f" WARNING: CPU cache block 0 differs from original chunk 0 k!")
|
|
print(f" Original k[0,0,:5] = {chunk0_k[0,0,:5].tolist()}")
|
|
print(f" CPU k0[0,0,:5] = {cpu_k0[0,0,:5].tolist()}")
|
|
|
|
if chunk1_k is not None and chunk2_capture is not None and 'cpu_k1' in chunk2_capture:
|
|
cpu_k1 = chunk2_capture['cpu_k1']
|
|
diff_k1 = (chunk1_k - cpu_k1).abs().max().item()
|
|
print(f"Chunk 1 k vs CPU cache block 1: max_diff={diff_k1:.6f}")
|
|
if diff_k1 > 1e-3:
|
|
print(f" WARNING: CPU cache block 1 differs from original chunk 1 k!")
|
|
print(f" Original k[0,0,:5] = {chunk1_k[0,0,:5].tolist()}")
|
|
print(f" CPU k1[0,0,:5] = {cpu_k1[0,0,:5].tolist()}")
|
|
|
|
print()
|
|
|
|
# ================================================================
|
|
# Test 1: Verify against merge-based reference (same algorithm)
|
|
# ================================================================
|
|
if verbose:
|
|
print("--- Test 1: Merge-based Reference (verifies merge algorithm) ---")
|
|
|
|
all_passed_merge = True
|
|
results_merge = []
|
|
first_fail_debug = True
|
|
|
|
for c in captures:
|
|
layer_id = c['layer_id']
|
|
chunk_idx = c['chunk_idx']
|
|
|
|
if chunk_idx == 0:
|
|
continue
|
|
|
|
debug_this = (chunk_idx >= 2 and layer_id == 0 and first_fail_debug)
|
|
ref_output = compute_reference(layer_id, chunk_idx, scale, debug=debug_this)
|
|
if ref_output is None:
|
|
continue
|
|
|
|
actual_output = c['output']
|
|
diff = (actual_output - ref_output).abs()
|
|
max_diff = diff.max().item()
|
|
mean_diff = diff.mean().item()
|
|
|
|
tol = 1e-2
|
|
passed = max_diff < tol
|
|
all_passed_merge = all_passed_merge and passed
|
|
|
|
status = "PASS" if passed else "FAIL"
|
|
results_merge.append((layer_id, chunk_idx, passed, max_diff, mean_diff))
|
|
|
|
if verbose:
|
|
print(f"[{status}] Layer {layer_id:2d}, Chunk {chunk_idx}: "
|
|
f"max_diff={max_diff:.6f} mean_diff={mean_diff:.8f}")
|
|
|
|
if not passed and first_fail_debug:
|
|
first_fail_debug = False
|
|
print(f" Debug: actual_output shape={actual_output.shape}, mean={actual_output.mean().item():.4f}")
|
|
print(f" Debug: ref_output shape={ref_output.shape}, mean={ref_output.mean().item():.4f}")
|
|
max_idx = diff.argmax()
|
|
flat_actual = actual_output.flatten()
|
|
flat_ref = ref_output.flatten()
|
|
print(f" Debug: max_diff at idx={max_idx.item()}, actual={flat_actual[max_idx].item():.4f}, ref={flat_ref[max_idx].item():.4f}")
|
|
|
|
print()
|
|
|
|
# ================================================================
|
|
# Test 2: Verify against standard flash attention (single pass)
|
|
# ================================================================
|
|
if verbose:
|
|
print("--- Test 2: Standard FlashAttn Reference (verifies correctness vs non-chunked) ---")
|
|
|
|
all_passed_standard = True
|
|
results_standard = []
|
|
first_fail_debug = True
|
|
|
|
for c in captures:
|
|
layer_id = c['layer_id']
|
|
chunk_idx = c['chunk_idx']
|
|
|
|
if chunk_idx == 0:
|
|
continue
|
|
|
|
debug_this = (chunk_idx >= 2 and layer_id == 0 and first_fail_debug)
|
|
std_ref_output = compute_standard_reference(layer_id, chunk_idx, scale, debug=debug_this)
|
|
if std_ref_output is None:
|
|
continue
|
|
|
|
actual_output = c['output']
|
|
diff = (actual_output - std_ref_output).abs()
|
|
max_diff = diff.max().item()
|
|
mean_diff = diff.mean().item()
|
|
|
|
tol = 1e-2
|
|
passed = max_diff < tol
|
|
all_passed_standard = all_passed_standard and passed
|
|
|
|
status = "PASS" if passed else "FAIL"
|
|
results_standard.append((layer_id, chunk_idx, passed, max_diff, mean_diff))
|
|
|
|
if verbose:
|
|
print(f"[{status}] Layer {layer_id:2d}, Chunk {chunk_idx}: "
|
|
f"max_diff={max_diff:.6f} mean_diff={mean_diff:.8f}")
|
|
|
|
if not passed and first_fail_debug:
|
|
first_fail_debug = False
|
|
print(f" Debug: actual_output shape={actual_output.shape}, mean={actual_output.mean().item():.4f}")
|
|
print(f" Debug: std_ref_output shape={std_ref_output.shape}, mean={std_ref_output.mean().item():.4f}")
|
|
max_idx = diff.argmax()
|
|
flat_actual = actual_output.flatten()
|
|
flat_ref = std_ref_output.flatten()
|
|
print(f" Debug: max_diff at idx={max_idx.item()}, actual={flat_actual[max_idx].item():.4f}, ref={flat_ref[max_idx].item():.4f}")
|
|
|
|
print()
|
|
print("=" * 70)
|
|
|
|
# Summary
|
|
total_merge = len(results_merge)
|
|
passed_merge = sum(1 for r in results_merge if r[2])
|
|
total_standard = len(results_standard)
|
|
passed_standard = sum(1 for r in results_standard if r[2])
|
|
|
|
print(f"Merge-based reference: {passed_merge}/{total_merge} tests passed")
|
|
print(f"Standard FlashAttn ref: {passed_standard}/{total_standard} tests passed")
|
|
|
|
all_passed = all_passed_merge and all_passed_standard
|
|
|
|
if not all_passed_merge:
|
|
print("\nFailed merge-based tests:")
|
|
for layer_id, chunk_idx, passed, max_diff, mean_diff in results_merge:
|
|
if not passed:
|
|
print(f" - Layer {layer_id}, Chunk {chunk_idx}: max_diff={max_diff:.6f}")
|
|
|
|
if not all_passed_standard:
|
|
print("\nFailed standard FlashAttn tests:")
|
|
for layer_id, chunk_idx, passed, max_diff, mean_diff in results_standard:
|
|
if not passed:
|
|
print(f" - Layer {layer_id}, Chunk {chunk_idx}: max_diff={max_diff:.6f}")
|
|
|
|
print()
|
|
return all_passed
|
|
|
|
|
|
# ============================================================
|
|
# Main
|
|
# ============================================================
|
|
|
|
if __name__ == "__main__":
|
|
passed = run_test(verbose=True)
|
|
|
|
if passed:
|
|
print("test_chunked_prefill_hook: PASSED")
|
|
else:
|
|
print("test_chunked_prefill_hook: FAILED")
|
|
exit(1)
|