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Configuration error
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5000a45 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 | #!/usr/bin/env python3
from __future__ import annotations
import argparse
import json
import sys
import time
from pathlib import Path
from typing import Any, Dict, Tuple
import numpy as np
import torch
ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT))
from scripts.collect_qwen_05b_measurements import EPS, benchmark_qwen_task
from scripts.qwen_05b_spec import QwenKernelTask, qwen_05b_tasks
TASK_BY_ID = {task.task_id: task for task in qwen_05b_tasks()}
def _bench_callable(fn, args: Tuple[Any, ...], repeats: int, warmup: int) -> float:
for _ in range(max(1, warmup)):
fn(*args)
torch.cuda.synchronize()
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
durations = []
for _ in range(max(1, repeats)):
torch.cuda.synchronize()
start.record()
fn(*args)
end.record()
end.synchronize()
durations.append(start.elapsed_time(end))
return float(np.median(np.asarray(durations, dtype=np.float32)))
def _build_qwen_callable(task: QwenKernelTask, seed: int):
torch.manual_seed(seed)
if task.family == "softmax":
x = torch.randn((task.m, task.n), device="cuda", dtype=torch.float16)
def fn(inp: torch.Tensor):
return torch.softmax(inp, dim=-1)
return fn, (x,)
if task.family == "rmsnorm":
x = torch.randn((task.m, task.n), device="cuda", dtype=torch.float16)
def fn(inp: torch.Tensor):
return inp.float() * torch.rsqrt(inp.float().pow(2).mean(dim=-1, keepdim=True) + EPS)
return fn, (x,)
if task.family == "gemm":
a = torch.randn((task.m, task.k), device="cuda", dtype=torch.float16)
b = torch.randn((task.k, task.n), device="cuda", dtype=torch.float16)
def fn(lhs: torch.Tensor, rhs: torch.Tensor):
return torch.matmul(lhs, rhs)
return fn, (a, b)
raise ValueError(f"Unsupported family: {task.family}")
def _benchmark_torch(task: QwenKernelTask, seed: int, repeats: int, warmup: int) -> Dict[str, float]:
eager_fn, args = _build_qwen_callable(task, seed)
eager_latency_ms = _bench_callable(eager_fn, args, repeats=repeats, warmup=warmup)
compiled_fn = torch.compile(eager_fn)
torch.cuda.synchronize()
start = time.perf_counter()
compiled_fn(*args)
torch.cuda.synchronize()
compile_plus_first_call_ms = float((time.perf_counter() - start) * 1000.0)
compiled_latency_ms = _bench_callable(compiled_fn, args, repeats=repeats, warmup=warmup)
return {
"eager_latency_ms": eager_latency_ms,
"compile_plus_first_call_ms": compile_plus_first_call_ms,
"compiled_latency_ms": compiled_latency_ms,
}
def _task_best_configs(eval_results: Dict[str, Any]) -> Dict[str, Dict[str, Dict[str, Any]]]:
task_map: Dict[str, Dict[str, Dict[str, Any]]] = {}
for section in eval_results["results"].values():
for method in ("random", "surrogate"):
for run in section["task_runs"][method]:
task_map.setdefault(run["task"], {})[method] = run["best_overall"]["config"]
return task_map
def main() -> None:
parser = argparse.ArgumentParser(description="Benchmark eager/torch.compile and best Triton configs for Qwen2.5-0.5B exact kernels.")
parser.add_argument("--generalization-results", type=Path, default=Path("outputs/qwen_05b_generalization_eval.json"))
parser.add_argument("--repeats", type=int, default=100)
parser.add_argument("--warmup", type=int, default=10)
parser.add_argument("--seed", type=int, default=123)
parser.add_argument("--output", type=Path, default=Path("outputs/qwen_05b_runtime_references.json"))
args = parser.parse_args()
generalization_results = json.loads(args.generalization_results.read_text(encoding="utf-8"))
task_configs = _task_best_configs(generalization_results)
results = {}
for idx, task_id in enumerate(sorted(task_configs.keys())):
task = TASK_BY_ID[task_id]
seed = args.seed + idx
torch_metrics = _benchmark_torch(task, seed=seed, repeats=args.repeats, warmup=args.warmup)
triton_results = {
method: benchmark_qwen_task(
task=task,
block_size=int(config["block_size"]),
num_warps=int(config["num_warps"]),
num_stages=int(config["num_stages"]),
repeats=args.repeats,
warmup=args.warmup,
seed=seed,
).__dict__
for method, config in task_configs[task_id].items()
}
results[task_id] = {
"family": task.family,
"role": task.role,
"mode": task.mode,
"torch": torch_metrics,
"triton": triton_results,
"speedups": {
method: {
"vs_eager": float(torch_metrics["eager_latency_ms"] / row["median_ms"]),
"vs_compiled": float(torch_metrics["compiled_latency_ms"] / row["median_ms"]),
}
for method, row in triton_results.items()
},
}
summary = {
"generalization_results": str(args.generalization_results),
"repeats": args.repeats,
"warmup": args.warmup,
"seed": args.seed,
"task_count": len(results),
"results": results,
}
args.output.parent.mkdir(parents=True, exist_ok=True)
with args.output.open("w", encoding="utf-8") as handle:
json.dump(summary, handle, indent=2)
print(json.dumps(summary, indent=2))
if __name__ == "__main__":
main()
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