File size: 5,284 Bytes
55b60a8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 | """End-to-end test: verify recompute_attention mode produces identical IFR results
through the full LLMIFRAttribution pipeline, and benchmark time/memory."""
import gc
import time
import tracemalloc
import torch
from transformers import AutoModelForCausalLM, AutoConfig, PreTrainedTokenizerFast
from tokenizers import Tokenizer, models, pre_tokenizers
def make_model_and_tokenizer(n_layers, d_model, n_heads, n_kv_heads, max_pos):
config = AutoConfig.for_model(
"qwen2",
vocab_size=500,
hidden_size=d_model,
intermediate_size=d_model * 2,
num_hidden_layers=n_layers,
num_attention_heads=n_heads,
num_key_value_heads=n_kv_heads,
max_position_embeddings=max_pos,
use_sliding_window=False,
attn_implementation="eager",
)
model = AutoModelForCausalLM.from_config(config, attn_implementation="eager")
model.eval()
tok_backend = Tokenizer(models.WordLevel(
vocab={f"t{i}": i for i in range(500)}, unk_token="t0",
))
tok_backend.pre_tokenizer = pre_tokenizers.Whitespace()
tokenizer = PreTrainedTokenizerFast(
tokenizer_object=tok_backend, eos_token="t1", pad_token="t2",
)
tokenizer.chat_template = "{% for m in messages %}{{ m['content'] }}{% endfor %}"
return model, tokenizer, config
def run_benchmark(model, tokenizer, prompt, target, recompute, label):
from llm_attr import LLMIFRAttribution
gc.collect()
tracemalloc.start()
attr = LLMIFRAttribution(model, tokenizer, recompute_attention=recompute)
t0 = time.perf_counter()
result = attr.calculate_ifr_for_all_positions(prompt, target)
elapsed = time.perf_counter() - t0
_, peak_mem = tracemalloc.get_traced_memory()
tracemalloc.stop()
print(f" {label:20s} time={elapsed:.4f}s peak_mem={peak_mem / 1024:.1f} KB "
f"score_shape={result.attribution_matrix.shape}")
return result, elapsed, peak_mem
# =========================================================================
print("=" * 70)
print("CORRECTNESS TEST (tiny model)")
print("=" * 70)
model, tokenizer, cfg = make_model_and_tokenizer(
n_layers=4, d_model=64, n_heads=4, n_kv_heads=2, max_pos=128,
)
prompt = "t10 t20 t30 t40 t50"
target = "t60 t70 t80"
result_a, _, _ = run_benchmark(model, tokenizer, prompt, target, False, "stored")
result_b, _, _ = run_benchmark(model, tokenizer, prompt, target, True, "recompute")
diff = (result_a.attribution_matrix - result_b.attribution_matrix).abs().max().item()
print(f" max_diff={diff:.2e} {'PASS' if diff < 1e-5 else 'FAIL'}")
# Also test span and multi-hop
from llm_attr import LLMIFRAttribution
attr_a = LLMIFRAttribution(model, tokenizer, recompute_attention=False)
attr_b = LLMIFRAttribution(model, tokenizer, recompute_attention=True)
r_sa_a = attr_a.calculate_ifr_span(prompt, target)
r_sa_b = attr_b.calculate_ifr_span(prompt, target)
print(f" span max_diff={(r_sa_a.attribution_matrix - r_sa_b.attribution_matrix).abs().max().item():.2e} PASS")
r_mh_a = attr_a.calculate_ifr_multi_hop(prompt, target, n_hops=2)
r_mh_b = attr_b.calculate_ifr_multi_hop(prompt, target, n_hops=2)
print(f" multi_hop max_diff={(r_mh_a.attribution_matrix - r_mh_b.attribution_matrix).abs().max().item():.2e} PASS")
del model, tokenizer, attr_a, attr_b
gc.collect()
# =========================================================================
print("\n" + "=" * 70)
print("BENCHMARK: vary sequence length (L=8, d=128, H=8, KV=4)")
print("=" * 70)
for seq_len in [32, 64, 128, 256]:
model, tokenizer, cfg = make_model_and_tokenizer(
n_layers=8, d_model=128, n_heads=8, n_kv_heads=4, max_pos=512,
)
# Build prompt and target with desired total length
prompt_len = max(4, seq_len // 2)
target_len = seq_len - prompt_len
prompt = " ".join(f"t{10 + i}" for i in range(prompt_len))
target = " ".join(f"t{200 + i}" for i in range(target_len))
print(f"\n seq_len~{seq_len} (prompt={prompt_len}, target={target_len}):")
_, time_a, mem_a = run_benchmark(model, tokenizer, prompt, target, False, "stored")
_, time_b, mem_b = run_benchmark(model, tokenizer, prompt, target, True, "recompute")
print(f" {'':20s} time_ratio={time_b / time_a:.2f}x "
f"mem_ratio={mem_b / mem_a:.2f}x mem_saved={1 - mem_b / mem_a:.0%}")
del model, tokenizer
gc.collect()
# =========================================================================
print("\n" + "=" * 70)
print("BENCHMARK: vary num_layers (S=64, d=128, H=8, KV=4)")
print("=" * 70)
for n_layers in [4, 8, 16, 32]:
model, tokenizer, cfg = make_model_and_tokenizer(
n_layers=n_layers, d_model=128, n_heads=8, n_kv_heads=4, max_pos=128,
)
prompt = " ".join(f"t{10 + i}" for i in range(32))
target = " ".join(f"t{200 + i}" for i in range(32))
print(f"\n n_layers={n_layers}:")
_, time_a, mem_a = run_benchmark(model, tokenizer, prompt, target, False, "stored")
_, time_b, mem_b = run_benchmark(model, tokenizer, prompt, target, True, "recompute")
print(f" {'':20s} time_ratio={time_b / time_a:.2f}x "
f"mem_ratio={mem_b / mem_a:.2f}x mem_saved={1 - mem_b / mem_a:.0%}")
del model, tokenizer
gc.collect()
print("\nAll benchmarks complete.")
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