Donato Capitella
219 linhas
7.1 KiB
Python
219 linhas
7.1 KiB
Python
#!/usr/bin/env python3
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import subprocess, time, json, sys, os, requests, argparse
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from pathlib import Path
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# =========================
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# ⚙️ GLOBAL SETTINGS
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# =========================
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try:
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import models
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except ImportError:
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# If running locally and models.py is in ../scripts?
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# Or if running in /opt where models.py is alongside.
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# We will try adding current dir to path just in case
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sys.path.append(os.getcwd())
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try:
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import models
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except ImportError:
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# Fallback for local structure: assuming this is in benchmarks/ and models is in scripts/
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sys.path.append(str(Path(__file__).parent.parent / "scripts"))
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import models
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# Import from shared config
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MODEL_TABLE = models.MODEL_TABLE
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MODELS_TO_RUN = models.MODELS_TO_RUN
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GPU_UTIL = models.GPU_UTIL
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OFF_NUM_PROMPTS = models.OFF_NUM_PROMPTS
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OFF_FORCED_OUTPUT = models.OFF_FORCED_OUTPUT
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DEFAULT_BATCH_TOKENS = models.DEFAULT_BATCH_TOKENS
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# Fallbacks
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FALLBACK_INPUT_LEN = 1024
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FALLBACK_OUTPUT_LEN = 512
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RESULTS_DIR = Path("benchmark_results")
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RESULTS_DIR.mkdir(exist_ok=True)
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# =========================
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# UTILS
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# =========================
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def log(msg): print(f"\n[BENCH] {msg}")
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def get_gpu_count():
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try:
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# Using rocm-smi --showid to list GPUs.
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# Output format: "GPU[0] : Device Name: ..."
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res = subprocess.run(["rocm-smi", "--showid"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
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if res.returncode == 0:
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return 1 # Force return 1 for Strix Halo APU
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else:
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log("rocm-smi failed, defaulting to 1 GPU (Hardcoded Fallback)")
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return 1
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except Exception as e:
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log(f"Error detecting GPUs: {e}, defaulting to 1 GPU")
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return 1
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def kill_vllm():
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subprocess.run("pgrep -f 'vllm serve' | xargs -r kill -9",
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shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
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time.sleep(5)
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def nuke_vllm_cache():
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cache = Path.home() / ".cache" / "vllm"
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if cache.exists():
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try:
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subprocess.run(["rm", "-rf", str(cache)], check=True)
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cache.mkdir(parents=True, exist_ok=True)
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time.sleep(2)
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except: pass
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def get_dataset():
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data_path = Path("ShareGPT_V3_unfiltered_cleaned_split.json")
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if data_path.exists(): return str(data_path)
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log("Downloading ShareGPT dataset...")
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url = "https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json"
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try:
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r = requests.get(url, stream=True, timeout=15)
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r.raise_for_status()
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with open(data_path, 'wb') as f:
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for chunk in r.iter_content(chunk_size=8192): f.write(chunk)
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return str(data_path)
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except Exception as e:
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log(f"WARNING: ShareGPT download failed ({e}). using RANDOM.")
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return None
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def get_model_args(model, tp_size):
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config = MODEL_TABLE.get(model, {"max_num_seqs": "32"})
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# Allow per-model GPU utilization override
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util = config.get("gpu_util", GPU_UTIL)
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cmd = [
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"--model", model,
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"--gpu-memory-utilization", util,
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"--dtype", "auto",
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"--tensor-parallel-size", str(tp_size),
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"--max-num-seqs", config["max_num_seqs"]
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]
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# Optional: if a model really needs a hard limit, we can still support "ctx" in config,
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# but by default we rely on auto.
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if "ctx" in config:
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cmd.extend(["--max-model-len", config["ctx"]])
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if config.get("trust_remote"): cmd.append("--trust-remote-code")
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if config.get("enforce_eager"): cmd.append("--enforce-eager")
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return cmd
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def run_throughput(model, tp_size, backend_name="Default", output_dir=RESULTS_DIR, extra_env=None):
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if tp_size not in MODEL_TABLE[model]["valid_tp"]: return
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model_safe = model.replace("/", "_")
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output_dir_path = Path(output_dir)
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output_dir_path.mkdir(parents=True, exist_ok=True)
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output_file = output_dir_path / f"{model_safe}_tp{tp_size}_throughput.json"
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if output_file.exists():
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log(f"SKIP {model} (TP={tp_size} | {backend_name})")
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return
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dataset_path = get_dataset()
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dataset_args = ["--dataset-name", "sharegpt", "--dataset-path", dataset_path] if dataset_path else ["--input-len", "1024"]
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# Retrieve Model-Specific Batch Tokens
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batch_tokens = MODEL_TABLE[model].get("max_tokens", DEFAULT_BATCH_TOKENS)
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log(f"START {model} (TP={tp_size} | {backend_name}) [Batch: {batch_tokens}]...")
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kill_vllm()
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nuke_vllm_cache()
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cmd = ["vllm", "bench", "throughput"] + get_model_args(model, tp_size)
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cmd.extend([
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"--num-prompts", str(OFF_NUM_PROMPTS),
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"--max-num-batched-tokens", batch_tokens,
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"--output-len", OFF_FORCED_OUTPUT,
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"--output-json", str(output_file),
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"--disable-log-stats"
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])
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cmd.extend(dataset_args)
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# ENV Setup: Global + Model Specific
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env = os.environ.copy()
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# Inject model specific env vars (e.g. for AWQ)
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model_env = MODEL_TABLE[model].get("env", {})
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env.update(model_env)
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# Extra Env
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if extra_env:
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env.update(extra_env)
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try:
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subprocess.run(cmd, check=True, env=env)
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except:
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log(f"ERROR: Failed {model} [{backend_name}]")
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def print_summary(tps):
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print(f"\n{'MODEL':<40} | {'TP':<2} | {'Triton':<8} | {'ROCm':<8}")
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print("-" * 75)
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for m in MODELS_TO_RUN:
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msafe = m.replace("/", "_")
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for tp in tps:
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if tp not in MODEL_TABLE[m]["valid_tp"]: continue
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# Default
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try:
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p1 = RESULTS_DIR / f"{msafe}_tp{tp}_throughput.json"
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d1 = json.loads(p1.read_text())
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val1 = f"{d1.get('tokens_per_second', 0):.1f}"
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except: val1 = "N/A"
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# ROCm
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try:
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p2 = Path("benchmark_results_rocm_attn/benchmark_results") / f"{msafe}_tp{tp}_throughput.json"
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d2 = json.loads(p2.read_text())
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val2 = f"{d2.get('tokens_per_second', 0):.1f}"
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except: val2 = "N/A"
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name_cell = m.split('/')[-1]
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print(f"{name_cell:<40} | {tp:<2} | {val1:<8} | {val2:<8}")
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print("-" * 75)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--tp", type=int, nargs="+", default=[1])
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args = parser.parse_args()
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gpu_count = get_gpu_count()
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log(f"Detected {gpu_count} AMD GPU(s)")
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valid_tp_args = [t for t in args.tp if t <= gpu_count]
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if not valid_tp_args:
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log(f"Requested TP={args.tp} but only {gpu_count} GPU(s) detected. Nothing to run.")
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sys.exit(0)
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kill_vllm()
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for tp in valid_tp_args:
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for m in MODELS_TO_RUN:
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# 1. Default (Triton)
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run_throughput(m, tp, "Default", RESULTS_DIR)
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# 2. ROCm Attention
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run_throughput(m, tp, "ROCm-Attn", "benchmark_results_rocm_attn/benchmark_results", {
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"VLLM_V1_USE_PREFILL_DECODE_ATTENTION": "1",
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"VLLM_USE_TRITON_FLASH_ATTN": "0"
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})
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print_summary(valid_tp_args)
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