Fix the default arguments of bench_offline_throughput.py & simplify detokenizer manager (#2042)

python/sglang/bench_offline_throughput.py

 """
 Benchmark the throughput of using the offline LLM engine.
 This script does not launch a server.
-It accepts the same arguments as launch_server.py and additional benchmark arguments
+It accepts server arguments (the same as launch_server.py) and benchmark arguments (the same as bench_serving.py).
 
 # Usage
 ## Sharegpt dataset with default args
-python -m sglang.bench_offline_throughput --model-path meta-llama/Meta-Llama-3-8B-Instruct
+python -m sglang.bench_offline_throughput --model-path meta-llama/Meta-Llama-3.1-8B-Instruct
 
 ## Random dataset with default args
-python -m sglang.bench_offline_throughput --model-path meta-llama/Meta-Llama-3-8B-Instruct --dataset-name random
+python -m sglang.bench_offline_throughput --model-path meta-llama/Meta-Llama-3.1-8B-Instruct --dataset-name random
 
 ## Shared prefix dataset with default args
-python -m sglang.bench_offline_throughput --model-path meta-llama/Meta-Llama-3-8B-Instruct --dataset-name generated-shared-prefix
+python -m sglang.bench_offline_throughput --model-path meta-llama/Meta-Llama-3.1-8B-Instruct --dataset-name generated-shared-prefix
 
 ## Sharegpt dataset on runtime backend
-python -m sglang.bench_offline_throughput --model-path meta-llama/Meta-Llama-3-8B-Instruct --backend runtime
+python -m sglang.bench_offline_throughput --model-path meta-llama/Meta-Llama-3.1-8B-Instruct --backend runtime
 """
 
 import argparse
@@ -23,7 +23,7 @@ import json
 import logging
 import random
 import time
-from typing import List, Tuple
+from typing import List, Optional, Tuple
 
 import numpy as np
 
@@ -45,14 +45,15 @@ class BenchArgs:
     dataset_name: str = "sharegpt"
     dataset_path: str = ""
     num_prompts: int = 1000
-    sharegpt_output_len: int = 256
-    random_input_len: int = 256
-    random_output_len: int = 256
+    sharegpt_output_len: Optional[int] = None
+    random_input_len: int = 1024
+    random_output_len: int = 1024
     random_range_ratio: float = 0.0
-    gen_num_groups: int = 8
+    gen_num_groups: int = 64
     gen_prompts_per_group: int = 16
-    gen_system_prompt_len: int = 128
-    gen_question_len: int = 256
+    gen_system_prompt_len: int = 2048
+    gen_question_len: int = 128
+    gen_output_len: int = 256
     disable_ignore_eos: bool = False
     seed: int = 1
 
@@ -129,6 +130,12 @@ class BenchArgs:
             default=BenchArgs.gen_question_len,
             help="Question length, used" "only for generate-shared-prefix",
         )
+        parser.add_argument(
+            "--gen-output-len",
+            type=int,
+            default=BenchArgs.gen_output_len,
+            help="Target length in tokens for outputs in generated-shared-prefix dataset",
+        )
         parser.add_argument(
             "--disable-ignore-eos",
             type=bool,
@@ -139,12 +146,8 @@ class BenchArgs:
 
     @classmethod
     def from_cli_args(cls, args: argparse.Namespace):
-        # use the default value's type to case the args into correct types.
-        attrs = [(attr.name, type(attr.default)) for attr in dataclasses.fields(cls)]
-        print(attrs)
-        return cls(
-            **{attr: attr_type(getattr(args, attr)) for attr, attr_type in attrs}
-        )
+        attrs = [attr.name for attr in dataclasses.fields(cls)]
+        return cls(**{attr: getattr(args, attr) for attr in attrs})
 
 
 def throughput_test_once(
@@ -224,6 +227,7 @@ def throughput_test(
     random.seed(bench_args.seed)
     np.random.seed(bench_args.seed)
 
+    # Read dataset
     input_requests = get_dataset(bench_args, tokenizer)
 
     warmup_requests = sample_random_requests(

python/sglang/bench_serving.py

@@ -1241,10 +1241,12 @@ if __name__ == "__main__":
     parser.add_argument(
         "--random-input-len",
         type=int,
+        default=1024,
         help="Number of input tokens per request, used only for random dataset.",
     )
     parser.add_argument(
         "--random-output-len",
+        default=1024,
         type=int,
         help="Number of output tokens per request, used only for random dataset.",
     )

python/sglang/srt/managers/detokenizer_manager.py

@@ -100,20 +100,6 @@ class DetokenizerManager:
 
             if isinstance(recv_obj, BatchEmbeddingOut):
                 # If it is embedding model, no detokenization is needed.
-                self.send_to_tokenizer.send_pyobj(
-                    BatchEmbeddingOut(
-                        rids=recv_obj.rids,
-                        embeddings=recv_obj.embeddings,
-                        meta_info=recv_obj.meta_info,
-                        finished_reason=recv_obj.finished_reason,
-                    )
-                )
-                continue
-            elif isinstance(recv_obj, UpdateWeightReqOutput):
-                # If it is a weight update request, no detokenization is needed.
-                self.send_to_tokenizer.send_pyobj(recv_obj)
-                continue
-            elif isinstance(recv_obj, GetMemPoolSizeReqOutput):
                 self.send_to_tokenizer.send_pyobj(recv_obj)
                 continue
             else:

python/sglang/srt/managers/scheduler.py

@@ -114,6 +114,9 @@ class Scheduler:
             self.recv_from_tokenizer = get_zmq_socket(
                 context, zmq.PULL, port_args.scheduler_input_ipc_name
             )
+            self.send_to_tokenizer = get_zmq_socket(
+                context, zmq.PUSH, port_args.tokenizer_ipc_name
+            )
 
             if server_args.skip_tokenizer_init:
                 # Directly send to the tokenizer/api
@@ -127,6 +130,7 @@ class Scheduler:
                 )
         else:
             self.recv_from_tokenizer = None
+            self.send_to_tokenizer = SimpleNamespace(send_pyobj=lambda x: None)
             self.send_to_detokenizer = SimpleNamespace(send_pyobj=lambda x: None)
 
         # Init tokenizer
@@ -421,7 +425,7 @@ class Scheduler:
                 self.abort_request(recv_req)
             elif isinstance(recv_req, UpdateWeightReqInput):
                 success, message = self.update_weights(recv_req)
-                self.send_to_detokenizer.send_pyobj(
+                self.send_to_tokenizer.send_pyobj(
                     UpdateWeightReqOutput(success, message)
                 )
             elif isinstance(recv_req, ProfileReq):
@@ -430,7 +434,7 @@ class Scheduler:
                 else:
                     self.stop_profile()
             elif isinstance(recv_req, GetMemPoolSizeReq):
-                self.send_to_detokenizer.send_pyobj(
+                self.send_to_tokenizer.send_pyobj(
                     GetMemPoolSizeReqOutput(self.max_total_num_tokens)
                 )
             else:

test/srt/test_overlap_schedule.py

@@ -11,16 +11,24 @@ from sglang.test.test_utils import run_mmlu_test
 
 class TestOverlapSchedule(unittest.TestCase):
     def test_no_radix_attention_chunked_prefill(self):
-        run_mmlu_test(disable_radix_cache=True, chunked_prefill_size=32)
+        run_mmlu_test(
+            disable_radix_cache=True, chunked_prefill_size=32, enable_overlap=True
+        )
 
     def test_no_radix_attention_no_chunked_prefill(self):
-        run_mmlu_test(disable_radix_cache=True, chunked_prefill_size=-1)
+        run_mmlu_test(
+            disable_radix_cache=True, chunked_prefill_size=-1, enable_overlap=True
+        )
 
     def test_radix_attention_chunked_prefill(self):
-        run_mmlu_test(disable_radix_cache=False, chunked_prefill_size=32)
+        run_mmlu_test(
+            disable_radix_cache=False, chunked_prefill_size=32, enable_overlap=True
+        )
 
     def test_radix_attention_no_chunked_prefill(self):
-        run_mmlu_test(disable_radix_cache=False, chunked_prefill_size=-1)
+        run_mmlu_test(
+            disable_radix_cache=False, chunked_prefill_size=-1, enable_overlap=True
+        )
 
 
 if __name__ == "__main__":