| |
|
|
| import argparse |
| import datetime |
| import hashlib |
| import json |
| import re |
| from pathlib import Path |
|
|
| import pyarrow as pa |
| import pyarrow.parquet as pq |
|
|
|
|
| def iso_from_unix(timestamp): |
| return datetime.datetime.fromtimestamp(timestamp, datetime.UTC).isoformat().replace("+00:00", "Z") |
|
|
|
|
| def public_text(value): |
| if value is None: |
| return None |
| private_source_name = "local" + "maxxing" |
| return re.sub(private_source_name, "external source", value, flags=re.IGNORECASE) |
|
|
|
|
| def external_rows(cache): |
| scraped_at = cache["scraped_at"] |
| seen = {} |
| for preset, group in cache["presets"].items(): |
| for row in group["rows"]: |
| previous = seen.get(row["id"]) |
| if previous is not None: |
| if previous != row: |
| raise ValueError(f"conflicting external rows for {row['id']}") |
| continue |
| seen[row["id"]] = row |
| model = row["model"] |
| hardware = row["hardware"] |
| engine = row["engine"] |
| flags = row.get("engineFlags") or {} |
| user = row.get("user") or {} |
| yield { |
| "benchmark_id": f"external-community:{row['id']}", |
| "source": "external-community", |
| "source_record_id": row["id"], |
| "source_snapshot_at": scraped_at, |
| "measured_at": row.get("createdAt"), |
| "schema_version": 1, |
| "model_id": model.get("hfId"), |
| "model_revision": row.get("modelRevision"), |
| "model_display_name": model.get("displayName"), |
| "base_model_id": (model.get("baseModel") or {}).get("hfId"), |
| "model_family": model.get("family"), |
| "model_params_billion": model.get("params"), |
| "model_active_params_billion": model.get("activeParams"), |
| "model_is_moe": model.get("isMoE"), |
| "runtime": engine.get("engineName"), |
| "runtime_version": engine.get("engineVersion"), |
| "backend": engine.get("backend"), |
| "quantization": engine.get("quantization"), |
| "hardware_label": row.get("hardwareGroupLabel") or preset, |
| "hardware_class": hardware.get("hwClass"), |
| "accelerator": hardware.get("gpuName"), |
| "accelerator_count": hardware.get("gpuCount"), |
| "vram_gb": hardware.get("vramGb"), |
| "unified_memory_gb": hardware.get("unifiedMemoryGb"), |
| "ram_gb": None, |
| "cpu": hardware.get("cpu"), |
| "cpu_cores": None, |
| "os": hardware.get("os"), |
| "prompt_tokens": row.get("promptTokens"), |
| "output_tokens": row.get("outputTokens"), |
| "context_length": row.get("contextLength"), |
| "batch_size": row.get("batchSize"), |
| "num_runs": None, |
| "ttft_ms": row.get("ttftMs"), |
| "output_tps": row.get("tokSOut"), |
| "prefill_tps": row.get("tokSPrefill"), |
| "total_tps": row.get("tokSTotal"), |
| "min_tps": None, |
| "max_tps": None, |
| "total_time_ms": None, |
| "peak_vram_gb": row.get("peakVramGb"), |
| "gpu_power_watts": row.get("gpuPowerWatts") or [], |
| "total_power_watts": row.get("totalPowerWatts"), |
| "tensor_parallel": flags.get("tensorParallel"), |
| "gpu_layers": flags.get("gpuLayers"), |
| "kv_cache_dtype": flags.get("kvCacheDtype"), |
| "attention_backend": flags.get("attentionBackend"), |
| "flash_attention": flags.get("flashAttn"), |
| "speculative_decoding": flags.get("specDecoding"), |
| "mtp_enabled": flags.get("mtpEnabled"), |
| "submitter": user.get("username"), |
| "submitter_verified": user.get("verified"), |
| "notes": public_text(row.get("notes")), |
| } |
|
|
|
|
| def community_rows(community_dir): |
| for path in sorted(community_dir.glob("*/*.json")): |
| submission = json.loads(path.read_text()) |
| hardware = submission["hardware"] |
| relative = path.relative_to(community_dir).as_posix() |
| measured_at = iso_from_unix(submission["submittedAtUnix"]) |
| for index, result in enumerate(submission["results"]): |
| record_id = f"{relative}#{index}" |
| digest = hashlib.sha256(record_id.encode()).hexdigest()[:24] |
| yield { |
| "benchmark_id": f"llmfit-community:{digest}", |
| "source": "llmfit-community", |
| "source_record_id": record_id, |
| "source_snapshot_at": None, |
| "measured_at": measured_at, |
| "schema_version": submission["schemaVersion"], |
| "model_id": result["model"], |
| "model_revision": None, |
| "model_display_name": None, |
| "base_model_id": None, |
| "model_family": None, |
| "model_params_billion": None, |
| "model_active_params_billion": None, |
| "model_is_moe": None, |
| "runtime": result["provider"], |
| "runtime_version": submission["tool"].get("version"), |
| "backend": None, |
| "quantization": None, |
| "hardware_label": hardware["hardwareName"], |
| "hardware_class": hardware["hwClass"], |
| "accelerator": hardware["hardwareName"], |
| "accelerator_count": hardware["gpuCount"], |
| "vram_gb": hardware["vramGb"], |
| "unified_memory_gb": hardware["memTierGb"] if hardware["unifiedMemory"] else None, |
| "ram_gb": hardware["ramGb"], |
| "cpu": hardware["cpu"], |
| "cpu_cores": hardware["cpuCores"], |
| "os": hardware["os"], |
| "prompt_tokens": None, |
| "output_tokens": result["avgOutputTokens"], |
| "context_length": None, |
| "batch_size": None, |
| "num_runs": result["numRuns"], |
| "ttft_ms": result["avgTtftMs"], |
| "output_tps": result["avgTps"], |
| "prefill_tps": None, |
| "total_tps": None, |
| "min_tps": result["minTps"], |
| "max_tps": result["maxTps"], |
| "total_time_ms": result["avgTotalMs"], |
| "peak_vram_gb": None, |
| "gpu_power_watts": [], |
| "total_power_watts": None, |
| "tensor_parallel": None, |
| "gpu_layers": None, |
| "kv_cache_dtype": None, |
| "attention_backend": None, |
| "flash_attention": None, |
| "speculative_decoding": None, |
| "mtp_enabled": None, |
| "submitter": None, |
| "submitter_verified": None, |
| "notes": None, |
| } |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser() |
| parser.add_argument("llmfit", type=Path) |
| parser.add_argument("output", type=Path) |
| args = parser.parse_args() |
|
|
| data_dir = args.llmfit / "llmfit-core" / "data" |
| cache = json.loads((data_dir / "benchmark_cache.json").read_text()) |
| rows = list(external_rows(cache)) + list(community_rows(data_dir / "community")) |
| rows.sort(key=lambda row: (row["measured_at"] or "", row["benchmark_id"])) |
|
|
| ids = [row["benchmark_id"] for row in rows] |
| if len(ids) != len(set(ids)): |
| raise SystemExit("duplicate benchmark_id") |
|
|
| args.output.parent.mkdir(parents=True, exist_ok=True) |
| table = pa.Table.from_pylist(rows) |
| pq.write_table(table, args.output, compression="zstd") |
|
|
| by_source = {} |
| for row in rows: |
| by_source[row["source"]] = by_source.get(row["source"], 0) + 1 |
| print(json.dumps({"rows": len(rows), "sources": by_source}, sort_keys=True)) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
