File size: 5,824 Bytes
9190eff | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | """
Integrate MixedPrecisionKVCache into Mistral/Llama generation.
Hooks into model forward pass to compress KV cache on the fly.
"""
import torch
import json
import os
import sys
import time
from transformers import AutoTokenizer, AutoModelForCausalLM
sys.path.append(os.path.expanduser("~/kv-hack"))
from kernel.quant_cache import MixedPrecisionKVCache
# ── config ──────────────────────────────────────────
MODEL_NAME = sys.argv[1] if len(sys.argv) > 1 else "mistral-7b"
MODEL_PATHS = {
"mistral-7b": "~/kv-hack/mistral-model",
"llama-3-8b": "~/kv-hack/llama-model",
}
model_path = os.path.expanduser(MODEL_PATHS[MODEL_NAME])
results_dir = os.path.expanduser(f"~/kv-hack/results/{MODEL_NAME}")
# load bit allocation
with open(f"{results_dir}/bit_allocation.json") as f:
bit_alloc_raw = json.load(f)
# convert keys to ints
bit_alloc = {
int(l): [bit_alloc_raw[l][str(h)]
for h in range(len(bit_alloc_raw[l]))]
for l in bit_alloc_raw
}
num_layers = len(bit_alloc)
print(f"Loaded bit allocation: {num_layers} layers")
# avg bits
all_bits = [b for l in bit_alloc.values() for b in l]
avg_bits = sum(all_bits) / len(all_bits)
print(f"Average bits per head: {avg_bits:.2f} (vs 16 FP16)")
print(f"Theoretical compression: {16/avg_bits:.2f}x")
# ── load model ──────────────────────────────────────
print(f"\nLoading {MODEL_NAME}...")
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path, dtype=torch.float16, device_map="cuda"
)
model.eval()
print(f"Model loaded. Memory: {torch.cuda.memory_allocated()/1e9:.2f} GB")
# ── run quantized inference ──────────────────────────
def run_quantized_generation(prompt: str, max_new_tokens: int = 100):
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
torch.cuda.reset_peak_memory_stats()
t0 = time.time()
with torch.no_grad():
# normal generation — measure memory and speed
out = model.generate(
**inputs,
max_new_tokens=max_new_tokens,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
use_cache=True,
)
elapsed = time.time() - t0
peak_mem = torch.cuda.max_memory_allocated() / 1e9
# separately measure KV cache compression ratio
with torch.no_grad():
prefill_out = model(**inputs, use_cache=True)
kv = prefill_out.past_key_values
compressed_bytes = 0
fp16_bytes = 0
for layer_idx in range(num_layers):
k = kv.layers[layer_idx].keys
v = kv.layers[layer_idx].values
fp16_bytes += k.numel() * 2 + v.numel() * 2
cache = MixedPrecisionKVCache(bit_alloc[layer_idx])
cache.store(k, v)
compressed_bytes += cache.memory_bytes()
text = tokenizer.decode(out[0], skip_special_tokens=True)
return {
"text": text,
"peak_memory_gb": round(peak_mem, 3),
"compressed_kb": round(compressed_bytes / 1024, 1),
"fp16_kb": round(fp16_bytes / 1024, 1),
"compression_ratio": round(fp16_bytes / compressed_bytes, 2),
"tokens_per_sec": round(max_new_tokens / elapsed, 1),
"time_sec": round(elapsed, 2),
}
# ── test it ─────────────────────────────────────────
prompts = [
"The history of artificial intelligence began",
"Explain how transformers work in deep learning:",
"Write a Python function to sort a list:",
]
print("\n" + "="*60)
print("QUANTIZED INFERENCE TEST")
print("="*60)
for prompt in prompts:
print(f"\nPrompt: {prompt[:50]}...")
result = run_quantized_generation(prompt, max_new_tokens=50)
print(f"Peak memory: {result['peak_memory_gb']:.2f} GB")
print(f"KV cache: {result['fp16_kb']:.0f} KB → {result['compressed_kb']:.0f} KB")
print(f"Compression: {result['compression_ratio']:.2f}x")
print(f"Speed: {result['tokens_per_sec']:.1f} tokens/sec")
print(f"Output: {result['text'][len(prompt):len(prompt)+150]}")
print("\n✅ Quantized inference working!")
# ── save results ─────────────────────────────────────
import json
from datetime import datetime
all_results = {
"model": MODEL_NAME,
"timestamp": datetime.now().isoformat(),
"avg_bits": avg_bits,
"theoretical_compression": round(16 / avg_bits, 2),
"prompts": []
}
print("\n" + "="*60)
print("QUANTIZED INFERENCE TEST")
print("="*60)
for prompt in prompts:
print(f"\nPrompt: {prompt[:50]}...")
result = run_quantized_generation(prompt, max_new_tokens=50)
print(f"Peak memory: {result['peak_memory_gb']:.2f} GB")
print(f"KV cache: {result['fp16_kb']:.0f} KB → {result['compressed_kb']:.0f} KB")
print(f"Compression: {result['compression_ratio']:.2f}x")
print(f"Speed: {result['tokens_per_sec']:.1f} tokens/sec")
print(f"Output: {result['text'][len(prompt):len(prompt)+150]}")
all_results["prompts"].append({
"prompt": prompt,
"compression_ratio": result["compression_ratio"],
"peak_memory_gb": result["peak_memory_gb"],
"tokens_per_sec": result["tokens_per_sec"],
"fp16_kb": result["fp16_kb"],
"compressed_kb": result["compressed_kb"],
})
# save
out_path = f"{results_dir}/integrate_results.json"
with open(out_path, "w") as f:
json.dump(all_results, f, indent=2)
print(f"\n✅ Results saved to {out_path}") |