Donato Capitella
182 baris
6.1 KiB
Python
182 baris
6.1 KiB
Python
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import os
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import json
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import re
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from pathlib import Path
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# Config
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BENCHMARK_DIR = Path("../benchmarks/benchmark_results")
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OUTPUT_FILE = Path("results.json")
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# Regex to parse model name for quantization and parameters
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# Examples:
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# "meta-llama/Meta-Llama-3.1-8B-In
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# struct"
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# "cpatonn/Qwen3-Coder-30B-A3B-Instruct-GPTQ-4bit"
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# "RedHatAI/Llama-3.1-8B-Instruct-FP8-block"
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PARAMS_REGEX = r"(\d+(?:\.\d+)?)B"
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QUANT_REGEX = r"(FP8|AWQ|GPTQ|BF16|4bit|Int4)"
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def extract_meta(model_name):
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# Params
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params_match = re.search(PARAMS_REGEX, model_name, re.IGNORECASE)
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params_b = float(params_match.group(1)) if params_match else None
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# Quant
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quant_match = re.search(QUANT_REGEX, model_name, re.IGNORECASE)
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quant = quant_match.group(1).upper() if quant_match else "BF16" # Default assumption if no tag? Or unknown.
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# Refine quant if 4bit
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if quant == "4BIT" or quant == "INT4":
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if "GPTQ" in model_name: quant = "GPTQ-4bit"
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elif "AWQ" in model_name: quant = "AWQ-4bit"
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else: quant = "4-bit"
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return params_b, quant
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def parse_logs():
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runs = []
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if not BENCHMARK_DIR.exists():
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print(f"Error: {BENCHMARK_DIR} does not exist!")
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return []
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print(f"Scanning {BENCHMARK_DIR}...")
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# Files are flat in the dir: {model_safe}_tp{tp}_{type}.json
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# or latency: {model_safe}_tp{tp}_qps{q}_latency.json
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# We need to group by (model, tp) to form cohesive records if we want,
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# BUT the webapp expects a list of "runs".
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# Looking at the example JSON, each "run" is a single test point (e.g. "pp2048 @ d16384" OR "tg32 @ d16384")
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# Actually, looking at the provided valid example:
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# "test": "pp512", "tps_mean": 2708.86 ...
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# Our data:
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# throughput.json -> tokens_per_second. This is usually "decoding" or a mix?
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# vLLM bench throughput usually streams tokens.
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# Let's look at what run_vllm_bench.py produces.
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# Throughput: --input-len 1024 --output-len 512.
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# This is effectively a mixed batch.
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# We'll label it "Throughput (1024/512)" or just "Throughput"
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# Latency: qps-based.
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files = list(BENCHMARK_DIR.glob("*.json"))
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for f in files:
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fname = f.name
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try:
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data = json.loads(f.read_text())
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except:
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print(f"Skipping bad JSON: {fname}")
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continue
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# Infer metadata from filename
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# Format: {model_safe}_tp{tp}_{suffix}
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# Suffix can be: "throughput.json" or "qps{q}_latency.json"
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# We need model name. The script replaces / with _ in filenames.
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# But we verify against the known models list? Or just parse string.
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# We can reconstruct roughly.
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# Split by "_tp" which is a strong delimiter
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parts = fname.split("_tp")
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if len(parts) < 2: continue
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model_part = parts[0]
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rest = parts[1] # "1_throughput.json" or "2_qps1.0_latency.json"
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# TP
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tp_match = re.match(r"^(\d+)", rest)
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if not tp_match: continue
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tp = int(tp_match.group(1))
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# Env mapping
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env = f"TP{tp}"
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# Model Name Restoration (best effort or matching)
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# In the script: model.replace("/", "_")
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# We can reverse this if we have the list, but for now let's just use the clean string?
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# The webapp uses "model_clean" and "model".
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# Let's assume standard "org_model" format -> "org/model"
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if "_" in model_part:
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# Heuristic: First _ is likely the slash
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model_display = model_part.replace("_", "/", 1)
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else:
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model_display = model_part
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params_b, quant = extract_meta(model_display)
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base_run = {
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"model": model_display,
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"model_clean": model_display,
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"env": env,
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"gpu_config": "dual" if tp > 1 else "single",
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"quant": quant,
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"params_b": params_b,
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"name_params_b": params_b,
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# Defaults
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"backend": "vLLM",
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"error": False
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}
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if "throughput" in fname:
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# Throughput run
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# data has "tokens_per_second"
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tps = data.get("tokens_per_second", 0)
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run = base_run.copy()
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run["test"] = "Throughput"
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run["tps_mean"] = tps
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# If tps is 0 or missing, it might be an error?
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if tps == 0 and "error" in str(data).lower():
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run["error"] = True
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runs.append(run)
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elif "latency" in fname:
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# Latency run
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# raw_output has strings like "Mean TTFT: 12.3 ms", "Mean TPOT: 45.6 ms"
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raw = data.get("raw_output", "")
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qps_match = re.search(r"_qps([\d\.]+)_", fname)
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qps = qps_match.group(1) if qps_match else "?"
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# Extract metrics
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ttft = 0.0
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tpot = 0.0
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ttft_m = re.search(r"(?:Mean TTFT|TTFT).*?([\d\.]+)", raw)
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if ttft_m: ttft = float(ttft_m.group(1))
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tpot_m = re.search(r"(?:Mean TPOT|TPOT).*?([\d\.]+)", raw)
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if tpot_m: tpot = float(tpot_m.group(1))
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# We create TWO entries? Or how does the webapp handle multiple metrics?
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# Example webapp table columns are "Backends" showing ONE value.
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# But grouping is by "Test".
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# So we can have a test called "TTFT (QPS 1.0)" and "TPOT (QPS 1.0)"
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# Entry 1: TTFT
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r1 = base_run.copy()
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r1["test"] = f"TTFT @ QPS {qps}"
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r1["tps_mean"] = ttft # Using tps_mean field for the numeric value
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runs.append(r1)
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# Entry 2: TPOT
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r2 = base_run.copy()
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r2["test"] = f"TPOT @ QPS {qps}"
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r2["tps_mean"] = tpot
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runs.append(r2)
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return runs
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if __name__ == "__main__":
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data = {"runs": parse_logs()}
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runs_count = len(data["runs"])
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print(f"Parsed {runs_count} runs.")
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with open(OUTPUT_FILE, "w") as f:
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json.dump(data, f, indent=2)
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print(f"Written to {OUTPUT_FILE}")
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