根据我的训练日志，告诉我吞吐量以及TPS是多少。日志如下：{"current_steps": 1, "total_steps": 3, "loss": 4.7784, "lr": 0.0, "epoch": 1.0, "percentage": 33.33, "elapsed_time": "0:00:11", "remaining_time": "0:00:22", "throughput": 23213.23, "total_tokens": 262144} {"current_steps": 2, "total_steps": 3, "loss": 4.7778, "lr": 0.0001, "epoch": 2.0, "percentage": 66.67, "elapsed_time": "0:00:22", "remaining_time": "0:00:11", "throughput": 23737.19, "total_tokens": 524288} {"current_steps": 3, "total_steps": 3, "loss": 4.7336, "lr": 5e-05, "epoch": 3.0, "percentage": 100.0, "elapsed_time": "0:00:32", "remaining_time": "0:00:00", "throughput": 23875.55, "total_tokens": 786432} {"current_steps": 3, "total_steps": 3, "epoch": 3.0, "percentage": 100.0, "elapsed_time": "0:00:43", "remaining_time": "0:00:00", "throughput": 18153.96, "total_tokens": 786432}

### 计算吞吐量和TPS的方法 #### 一、定义关键术语 1. **吞吐量（Throughput）** 表示单位时间内处理的数据量，通常以样本数/秒或令牌数/秒表示。在日志中已提供字段 `throughput` 的具体值[^1]。 2. **TPS（Transactions Per Second）** TPS 是指每秒钟完成的任务数量，在深度学习领域可以理解为每秒完成的训练步数（steps）。其计算方式如下： $$ \text{TPS} = \frac{\text{已完成步骤数}}{\text{总耗时}} $$ #### 二、提取日志数据并计算 以下是基于提供的日志数据进行的具体计算： 1. **吞吐量验证** 日志中已经提供了 `throughput` 字段，可以直接读取该值作为参考。例如最后一个日志条目显示吞吐量为 `18153.96` 样本/秒[^2]。 2. **TPS 计算** 利用公式 $\text{TPS} = \frac{\text{current_steps}}{\text{elapsed_time_in_seconds}}$ 进行计算。需注意将时间转换为秒形式。 - 对于第一个日志条目： $$ \text{TPS}_1 = \frac{1}{11\,\text{seconds}} \approx 0.0909\,\text{steps/second} $$ - 对于第二个日志条目： $$ \text{TPS}_2 = \frac{2}{22\,\text{seconds}} \approx 0.0909\,\text{steps/second} $$ - 对于第三个日志条目： $$ \text{TPS}_3 = \frac{3}{32\,\text{seconds}} \approx 0.09375\,\text{steps/second} $$ - 对于第四个日志条目： $$ \text{TPS}_4 = \frac{3}{43\,\text{seconds}} \approx 0.06977\,\text{steps/second} $$ #### 三、实现代码示例 以下是一个 Python 脚本用于自动化计算吞吐量和 TPS： ```python import json def parse_log(logs): results = [] for log in logs: elapsed_time_parts = list(map(int, log["elapsed_time"].split(":"))) total_seconds = elapsed_time_parts[0]*60 + elapsed_time_parts[1] throughput_samples_per_second = log.get("throughput", None) tps = log["current_steps"] / total_seconds results.append({ "throughput": throughput_samples_per_second, "tps": round(tps, 5), "total_seconds": total_seconds }) return results logs_data = [ {"current_steps": 1, "total_steps": 3, "loss": 4.7784, "lr": 0.0, "epoch": 1.0, "percentage": 33.33, "elapsed_time": "0:00:11", "remaining_time": "0:00:22", "throughput": 23213.23, "total_tokens": 262144}, {"current_steps": 2, "total_steps": 3, "loss": 4.7778, "lr": 0.0001, "epoch": 2.0, "percentage": 66.67, "elapsed_time": "0:00:22", "remaining_time": "0:00:11", "throughput": 23737.19, "total_tokens": 524288}, {"current_steps": 3, "total_steps": 3, "loss": 4.7336, "lr": 5e-05, "epoch": 3.0, "percentage": 100.0, "elapsed_time": "0:00:32", "remaining_time": "0:00:00", "throughput": 23875.55, "total_tokens": 786432}, {"current_steps": 3, "total_steps": 3, "epoch": 3.0, "percentage": 100.0, "elapsed_time": "0:00:43", "remaining_time": "0:00:00", "throughput": 18153.96, "total_tokens": 786432} ] results = parse_log(logs_data) for idx, result in enumerate(results, start=1): print(f"Log {idx}: Throughput={result['throughput']} samples/s, TPS={result['tps']}") ``` 运行以上脚本会输出每个日志对应的吞吐量和 TPS 值。 --- ### 结论 通过上述方法可精确获取吞吐量与 TPS 数据，并利用编程工具进一步简化批量计算流程[^3]。