#!/usr/bin/env python3 import subprocess, time, json, sys, os, requests, argparse from pathlib import Path # ========================= # ⚙️ GLOBAL SETTINGS # ========================= # HARDWARE: 1x Strix Halo (128GB, RDNA 3.5) GPU_UTIL = "0.90" # 1. THROUGHPUT CONFIG OFF_NUM_PROMPTS = 200 OFF_FORCED_OUTPUT = "512" # Default fallback if not specified in MODEL_TABLE DEFAULT_BATCH_TOKENS = "8192" # Fallbacks FALLBACK_INPUT_LEN = 1024 FALLBACK_OUTPUT_LEN = 512 RESULTS_DIR = Path("benchmark_results") RESULTS_DIR.mkdir(exist_ok=True) # ========================= # 🛠️ MODEL CONFIGURATION 🛠️ # ========================= MODEL_TABLE = { # 1. Llama 3.1 8B Instruct # MAD uses 131k tokens. We scale to 32k for 32GB VRAM safety. "meta-llama/Meta-Llama-3.1-8B-Instruct": { "trust_remote": False, "valid_tp": [1, 2], "max_num_seqs": "64", "max_tokens": "32768" }, "google/gemma-3-12b-it": { "trust_remote": False, "valid_tp": [1, 2], "max_num_seqs": "64", "max_tokens": "32768" }, # 2. GPT-OSS 20B (MXFP4) # MAD Row 0 uses 8192. We match this exactly. "openai/gpt-oss-20b": { "trust_remote": True, "valid_tp": [1, 2], "max_num_seqs": "64", "max_tokens": "8192" }, "openai/gpt-oss-120b": { "trust_remote": True, "valid_tp": [1], "max_num_seqs": "64", "max_tokens": "8192" }, "Qwen/Qwen3-14B-AWQ": { "trust_remote": True, "valid_tp": [1], # Too big for single GPU "max_num_seqs": "32", # Lower concurrency for safety "max_tokens": "16384", # Lower batch size because Eager mode is CPU intensive "enforce_eager": False, "env": {"VLLM_USE_TRITON_AWQ": "1"} # Fixes "Unsupported Hardware" error }, # 4. Qwen 30B 4-bit "cpatonn/Qwen3-Coder-30B-A3B-Instruct-GPTQ-4bit": { "trust_remote": True, "enforce_eager": False, "valid_tp": [1, 2], "max_num_seqs": "64", "max_tokens": "32768" }, # 5. Qwen 80B AWQ # Size: ~48GB. Fits on 2x32GB (64GB). Leftover for Cache: ~16GB. # Config: 20k ctx fits in that cache. Eager mode required for stability. "dazipe/Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16": { "trust_remote": True, "valid_tp": [1], # Too big for single GPU "max_num_seqs": "32", # Lower concurrency for safety "max_tokens": "16384", # Lower batch size because Eager mode is CPU intensive "enforce_eager": True, "env": {"VLLM_USE_TRITON_AWQ": "1"} # Fixes "Unsupported Hardware" error }, } MODELS_TO_RUN = [ "meta-llama/Meta-Llama-3.1-8B-Instruct", "google/gemma-3-12b-it", "Qwen/Qwen3-14B-AWQ", "openai/gpt-oss-20b", "openai/gpt-oss-120b", "cpatonn/Qwen3-Coder-30B-A3B-Instruct-GPTQ-4bit", "dazipe/Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16", ] # ========================= # UTILS # ========================= def log(msg): print(f"\n[BENCH] {msg}") def get_gpu_count(): try: # Using rocm-smi --showid to list GPUs. # Output format: "GPU[0] : Device Name: ..." res = subprocess.run(["rocm-smi", "--showid"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True) if res.returncode == 0: return 1 # Force return 1 for Strix Halo APU else: log("rocm-smi failed, defaulting to 1 GPU (Hardcoded Fallback)") return 1 except Exception as e: log(f"Error detecting GPUs: {e}, defaulting to 1 GPU") return 1 def kill_vllm(): subprocess.run("pgrep -f 'vllm serve' | xargs -r kill -9", shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) time.sleep(5) def nuke_vllm_cache(): cache = Path.home() / ".cache" / "vllm" if cache.exists(): try: subprocess.run(["rm", "-rf", str(cache)], check=True) cache.mkdir(parents=True, exist_ok=True) time.sleep(2) except: pass def get_dataset(): data_path = Path("ShareGPT_V3_unfiltered_cleaned_split.json") if data_path.exists(): return str(data_path) log("Downloading ShareGPT dataset...") url = "https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json" try: r = requests.get(url, stream=True, timeout=15) r.raise_for_status() with open(data_path, 'wb') as f: for chunk in r.iter_content(chunk_size=8192): f.write(chunk) return str(data_path) except Exception as e: log(f"WARNING: ShareGPT download failed ({e}). using RANDOM.") return None def get_model_args(model, tp_size): config = MODEL_TABLE.get(model, {"max_num_seqs": "32"}) # Allow per-model GPU utilization override util = config.get("gpu_util", GPU_UTIL) cmd = [ "--model", model, "--gpu-memory-utilization", util, "--dtype", "auto", "--tensor-parallel-size", str(tp_size), "--max-num-seqs", config["max_num_seqs"] ] # Optional: if a model really needs a hard limit, we can still support "ctx" in config, # but by default we rely on auto. if "ctx" in config: cmd.extend(["--max-model-len", config["ctx"]]) if config.get("trust_remote"): cmd.append("--trust-remote-code") if config.get("enforce_eager"): cmd.append("--enforce-eager") return cmd def run_throughput(model, tp_size, backend_name="Default", output_dir=RESULTS_DIR, extra_env=None): if tp_size not in MODEL_TABLE[model]["valid_tp"]: return model_safe = model.replace("/", "_") output_dir_path = Path(output_dir) output_dir_path.mkdir(parents=True, exist_ok=True) output_file = output_dir_path / f"{model_safe}_tp{tp_size}_throughput.json" if output_file.exists(): log(f"SKIP {model} (TP={tp_size} | {backend_name})") return dataset_path = get_dataset() dataset_args = ["--dataset-name", "sharegpt", "--dataset-path", dataset_path] if dataset_path else ["--input-len", "1024"] # Retrieve Model-Specific Batch Tokens batch_tokens = MODEL_TABLE[model].get("max_tokens", DEFAULT_BATCH_TOKENS) log(f"START {model} (TP={tp_size} | {backend_name}) [Batch: {batch_tokens}]...") kill_vllm() nuke_vllm_cache() cmd = ["vllm", "bench", "throughput"] + get_model_args(model, tp_size) cmd.extend([ "--num-prompts", str(OFF_NUM_PROMPTS), "--max-num-batched-tokens", batch_tokens, "--output-len", OFF_FORCED_OUTPUT, "--output-json", str(output_file), "--disable-log-stats" ]) cmd.extend(dataset_args) # ENV Setup: Global + Model Specific env = os.environ.copy() # Inject model specific env vars (e.g. for AWQ) model_env = MODEL_TABLE[model].get("env", {}) env.update(model_env) # Extra Env if extra_env: env.update(extra_env) try: subprocess.run(cmd, check=True, env=env) except: log(f"ERROR: Failed {model} [{backend_name}]") def print_summary(tps): print(f"\n{'MODEL':<40} | {'TP':<2} | {'Triton':<8} | {'ROCm':<8}") print("-" * 75) for m in MODELS_TO_RUN: msafe = m.replace("/", "_") for tp in tps: if tp not in MODEL_TABLE[m]["valid_tp"]: continue # Default try: p1 = RESULTS_DIR / f"{msafe}_tp{tp}_throughput.json" d1 = json.loads(p1.read_text()) val1 = f"{d1.get('tokens_per_second', 0):.1f}" except: val1 = "N/A" # ROCm try: p2 = Path("benchmark_results_rocm_attn/benchmark_results") / f"{msafe}_tp{tp}_throughput.json" d2 = json.loads(p2.read_text()) val2 = f"{d2.get('tokens_per_second', 0):.1f}" except: val2 = "N/A" name_cell = m.split('/')[-1] print(f"{name_cell:<40} | {tp:<2} | {val1:<8} | {val2:<8}") print("-" * 75) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--tp", type=int, nargs="+", default=[1]) args = parser.parse_args() gpu_count = get_gpu_count() log(f"Detected {gpu_count} AMD GPU(s)") valid_tp_args = [t for t in args.tp if t <= gpu_count] if not valid_tp_args: log(f"Requested TP={args.tp} but only {gpu_count} GPU(s) detected. Nothing to run.") sys.exit(0) kill_vllm() for tp in valid_tp_args: for m in MODELS_TO_RUN: # 1. Default (Triton) run_throughput(m, tp, "Default", RESULTS_DIR) # 2. ROCm Attention run_throughput(m, tp, "ROCm-Attn", "benchmark_results_rocm_attn/benchmark_results", { "VLLM_V1_USE_PREFILL_DECODE_ATTENTION": "1", "VLLM_USE_TRITON_FLASH_ATTN": "0" }) print_summary(valid_tp_args)