scripts/baseline.py

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import time, json, os, sys

# ── 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 = f"~/kv-hack/results/{MODEL_NAME}"
os.makedirs(os.path.expanduser(results_dir), exist_ok=True)
# ────────────────────────────────────────────────────

print(f"Running baseline for: {MODEL_NAME}")
print("Loading model...")
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    dtype=torch.float16,
    device_map="cuda"
)
model.eval()

results = {}

for ctx_len in [1024, 4096, 8192]:
    print(f"\nTesting context length: {ctx_len}")
    input_ids = torch.randint(1, 1000, (1, ctx_len)).cuda()

    # warmup
    with torch.no_grad():
        for _ in range(2):
            out = model(input_ids, use_cache=True)

    torch.cuda.synchronize()
    torch.cuda.reset_peak_memory_stats()

    # measure
    times = []
    with torch.no_grad():
        for _ in range(5):
            t0 = time.time()
            out = model(input_ids, use_cache=True)
            torch.cuda.synchronize()
            times.append(time.time() - t0)

    peak_mem = torch.cuda.max_memory_allocated() / 1e9
    avg_time = sum(times) / len(times)

    results[ctx_len] = {
        "peak_memory_gb": round(peak_mem, 2),
        "avg_prefill_ms": round(avg_time * 1000, 1),
    }
    print(f"  Peak memory: {peak_mem:.2f} GB")
    print(f"  Avg prefill: {avg_time*1000:.1f} ms")

# decode speed
print("\nTesting decode speed...")
input_ids = torch.randint(1, 1000, (1, 512)).cuda()
with torch.no_grad():
    t0 = time.time()
    out = model.generate(
        input_ids,
        max_new_tokens=100,
        do_sample=False,
        pad_token_id=tokenizer.eos_token_id
    )
    torch.cuda.synchronize()
    elapsed = time.time() - t0

tokens_per_sec = 100 / elapsed
results["decode_tokens_per_sec"] = round(tokens_per_sec, 1)
print(f"  Decode speed: {tokens_per_sec:.1f} tokens/sec")

# save
out_path = os.path.expanduser(f"{results_dir}/baseline.json")
with open(out_path, "w") as f:
    json.dump(results, f, indent=2)

print(f"\n✅ Saved to {out_path}")
print(json.dumps(results, indent=2))