Format (#593)

benchmark/latency_throughput/bench_one.py

@@ -92,4 +92,4 @@ if __name__ == "__main__":
     print(ret)
 
     speed = args.batch_size * max_new_tokens / latency
-    print(f"latency: {latency:.2f} s, speed: {speed:.2f} token/s")
+    print(f"latency: {latency:.2f} s, speed: {speed:.2f} token/s")

benchmark/latency_throughput/bench_serving.py

@@ -307,8 +307,9 @@ def main(args: argparse.Namespace):
     avg_per_output_token_latency = np.mean(
         [latency / output_len for _, output_len, latency in REQUEST_LATENCY]
     )
-    decoding_throughput = np.sum([
-        output_len for _, output_len, _ in REQUEST_LATENCY]) / benchmark_time
+    decoding_throughput = (
+        np.sum([output_len for _, output_len, _ in REQUEST_LATENCY]) / benchmark_time
+    )
 
     print(f"Total time: {benchmark_time:.2f} s")
     print(f"Request throughput: {args.num_prompts / benchmark_time:.2f} requests/s")

benchmark/line_retrieval/gen_data.py

@@ -48,9 +48,9 @@ def generate_lines(random_words, num_lines, redirect_ratio):
         )
         for i in redirect_indices:
             target_idx = np.random.choice(min(i * 2 + 100, num_lines))
-            lines[
-                i
-            ] = f"Line {indices[i]}: The REGISTER_CONTENT is the same as Line {indices[target_idx]}."
+            lines[i] = (
+                f"Line {indices[i]}: The REGISTER_CONTENT is the same as Line {indices[target_idx]}."
+            )
             redirects[i] = target_idx
 
     # Build links and find sources

benchmark/mmlu/bench_sglang.py

@@ -80,10 +80,12 @@ def main(args):
         for i in range(test_df.shape[0]):
             prompt_end = format_example(test_df, i, include_answer=False)
 
-            arguments.append({
-                "examples": few_shot_examples,
-                "question": prompt_end,
-            })
+            arguments.append(
+                {
+                    "examples": few_shot_examples,
+                    "question": prompt_end,
+                }
+            )
 
             label = test_df.iloc[i, test_df.shape[1] - 1]
             labels.append(label)
@@ -134,7 +136,9 @@ def main(args):
 
     pt = 0
     for subject, num_qs in zip(subjects[: args.nsub], num_questions):
-        print(f"subject: {subject}, #q:{num_qs}, acc: {np.mean(cors[pt: pt + num_qs]):.3f}")
+        print(
+            f"subject: {subject}, #q:{num_qs}, acc: {np.mean(cors[pt: pt + num_qs]):.3f}"
+        )
         pt += num_qs
     assert pt == len(cors)
     weighted_acc = np.mean(cors)