Tiny support range ratio in GSP in bench serving

python/sglang/bench_serving.py

@@ -817,6 +817,7 @@ def get_dataset(args, tokenizer, model_id=None):
             system_prompt_len=args.gsp_system_prompt_len,
             question_len=args.gsp_question_len,
             output_len=args.gsp_output_len,
+            range_ratio=getattr(args, "gsp_range_ratio", 1.0),
             tokenizer=tokenizer,
             args=args,
         )
@@ -1235,6 +1236,14 @@ def sample_sharegpt_requests(
     return filtered_dataset
 
 
+def compute_random_lens(full_len: int, range_ratio: float, num: int):
+    return np.random.randint(
+        max(int(full_len * range_ratio), 1),
+        full_len + 1,
+        size=num,
+    )
+
+
 def sample_random_requests(
     input_len: int,
     output_len: int,
@@ -1245,15 +1254,15 @@ def sample_random_requests(
     random_sample: bool = True,
     return_text: bool = True,
 ) -> List[DatasetRow]:
-    input_lens = np.random.randint(
-        max(int(input_len * range_ratio), 1),
-        input_len + 1,
-        size=num_prompts,
+    input_lens = compute_random_lens(
+        full_len=input_len,
+        range_ratio=range_ratio,
+        num=num_prompts,
     )
-    output_lens = np.random.randint(
-        int(output_len * range_ratio),
-        output_len + 1,
-        size=num_prompts,
+    output_lens = compute_random_lens(
+        full_len=output_len,
+        range_ratio=range_ratio,
+        num=num_prompts,
     )
 
     if random_sample:
@@ -1476,11 +1485,15 @@ def sample_image_requests(
         )
 
     # Sample text lengths
-    input_lens = np.random.randint(
-        max(int(input_len * range_ratio), 1), input_len + 1, size=num_requests
+    input_lens = compute_random_lens(
+        full_len=input_len,
+        range_ratio=range_ratio,
+        num=num_requests,
     )
-    output_lens = np.random.randint(
-        int(output_len * range_ratio), output_len + 1, size=num_requests
+    output_lens = compute_random_lens(
+        full_len=output_len,
+        range_ratio=range_ratio,
+        num=num_requests,
     )
 
     def _gen_random_image_data_uri(
@@ -1576,30 +1589,51 @@ def sample_generated_shared_prefix_requests(
     system_prompt_len: int,
     question_len: int,
     output_len: int,
+    range_ratio: float,
     tokenizer: PreTrainedTokenizerBase,
     args: argparse.Namespace,
 ) -> List[DatasetRow]:
     """Generate benchmark requests with shared system prompts using random tokens and caching."""
     cache_path = get_gen_prefix_cache_path(args, tokenizer)
 
     # Try to load from cache first
-    if cache_path.exists():
+    if cache_path.exists() and range_ratio == 1:
         print(f"\nLoading cached generated input data from {cache_path}")
         with open(cache_path, "rb") as f:
             return pickle.load(f)
 
-    print("\nGenerating new input data...")
+    print(
+        f"\nGenerating new input data... "
+        f"({num_groups=}, {prompts_per_group}, {system_prompt_len=}, {question_len=}, {output_len=}, {range_ratio=})"
+    )
+
+    system_prompt_lens = compute_random_lens(
+        full_len=system_prompt_len,
+        range_ratio=range_ratio,
+        num=num_groups,
+    )
+    question_lens = compute_random_lens(
+        full_len=question_len,
+        range_ratio=range_ratio,
+        num=num_groups * prompts_per_group,
+    )
+    output_lens = compute_random_lens(
+        full_len=output_len,
+        range_ratio=range_ratio,
+        num=num_groups * prompts_per_group,
+    )
+    del system_prompt_len, question_len, output_len
 
     # Generate system prompts for each group
     system_prompts = []
-    for _ in range(num_groups):
-        system_prompt = gen_prompt(tokenizer, system_prompt_len)
+    for i in range(num_groups):
+        system_prompt = gen_prompt(tokenizer, system_prompt_lens[i].item())
         system_prompts.append(system_prompt)
 
     # Generate questions
     questions = []
-    for _ in range(num_groups * prompts_per_group):
-        question = gen_prompt(tokenizer, question_len)
+    for i in range(num_groups * prompts_per_group):
+        question = gen_prompt(tokenizer, question_lens[i].item())
         questions.append(question)
 
     # Combine system prompts with questions
@@ -1612,19 +1646,20 @@ def sample_generated_shared_prefix_requests(
         for prompt_idx in tqdm(
             range(prompts_per_group), desc="Generating questions", leave=False
         ):
-            question = questions[group_idx * prompts_per_group + prompt_idx]
+            flat_index = group_idx * prompts_per_group + prompt_idx
+            question = questions[flat_index]
             full_prompt = f"{system_prompt}\n\n{question}"
             prompt_len = len(tokenizer.encode(full_prompt))
 
             input_requests.append(
                 DatasetRow(
                     prompt=full_prompt,
                     prompt_len=prompt_len,
-                    output_len=output_len,
+                    output_len=output_lens[flat_index].item(),
                 )
             )
             total_input_tokens += prompt_len
-            total_output_tokens += output_len
+            total_output_tokens += output_lens[flat_index].item()
 
     # Shuffle questions
     random.shuffle(input_requests)
@@ -2861,6 +2896,13 @@ def __call__(self, parser, namespace, values, option_string=None):
         default=256,
         help="Target length in tokens for outputs in generated-shared-prefix dataset",
     )
+    parser.add_argument(
+        "--gsp-range-ratio",
+        type=float,
+        # WARN: The default 1.0 is for backward compatibility, and is different from the default 0.0 for random dataset
+        default=1.0,
+        help="Range of sampled ratio of input/output length, used only for gsp dataset.",
+    )
     mooncake_group = parser.add_argument_group("mooncake dataset arguments")
     mooncake_group.add_argument(
         "--mooncake-slowdown-factor",