Misc fix for min_p_sampling, --cuda-graph-bs (#2761)

.github/workflows/pr-test.yml

@@ -66,12 +66,14 @@ jobs:
       - name: Run test
         timeout-minutes: 25
         run: |
-          cd test/srt
           RANGE=${{ matrix.range }}
           range_begin=${RANGE%-*}
           range_end=${RANGE#*-}
+
+          cd test/srt
           python3 run_suite.py --suite per-commit --range-begin ${range_begin} --range-end ${range_end}
 
+
   unit-test-backend-2-gpu:
     if: github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request'
     runs-on: 2-gpu-runner

benchmark/kernels/fused_moe_triton/tuning_fused_moe_triton.py

@@ -228,6 +228,7 @@ class BenchmarkWorker:
                 hidden_size,
                 topk,
                 dtype_str,
+                False,
             )
         else:
             config = op_config[min(op_config.keys(), key=lambda x: abs(x - num_tokens))]

python/pyproject.toml

@@ -16,14 +16,20 @@ classifiers = [
 dependencies = ["requests", "tqdm", "numpy", "IPython", "setproctitle"]
 
 [project.optional-dependencies]
-runtime_common = ["aiohttp", "decord", "fastapi",
+runtime_common = [
+    "aiohttp", "decord", "fastapi",
     "hf_transfer", "huggingface_hub", "interegular", "modelscope",
     "orjson", "outlines>=0.0.44,<0.1.0",
     "packaging", "pillow", "prometheus-client>=0.20.0",
     "psutil", "pydantic", "python-multipart",
     "pyzmq>=25.1.2", "torchao>=0.7.0", "uvicorn", "uvloop",
-    "xgrammar>=0.1.6"]
-srt = ["sglang[runtime_common]", "torch", "vllm>=0.6.3.post1,<=0.6.4.post1", "cuda-python", "flashinfer==0.1.6", "sgl-kernel>=0.0.2.post11"]
+    "xgrammar>=0.1.6"
+]
+srt = [
+    "sglang[runtime_common]", "cuda-python",
+    "sgl-kernel>=0.0.2.post11", "torch", "vllm>=0.6.3.post1,<=0.6.4.post1",
+    "flashinfer==0.1.6"
+]
 
 # HIP (Heterogeneous-computing Interface for Portability) for AMD
 # => base docker rocm/vllm-dev:20241022, not from public vllm whl

python/sglang/bench_serving.py

@@ -563,7 +563,7 @@ def sample_sharegpt_requests(
         raise ValueError("output_len too small")
 
     # Download sharegpt if necessary
-    if not os.path.isfile(dataset_path):
+    if not os.path.isfile(dataset_path) and dataset_path == "":
         dataset_path = download_and_cache_file(SHAREGPT_URL)
 
     # Load the dataset.
@@ -1064,8 +1064,11 @@ async def benchmark(
             "total_output_tokens_retokenized": metrics.total_output_retokenized,
             "mean_e2e_latency_ms": metrics.mean_e2e_latency_ms,
             "median_e2e_latency_ms": metrics.median_e2e_latency_ms,
+            "mean_ttft_ms": metrics.mean_ttft_ms,
             "median_ttft_ms": metrics.median_ttft_ms,
+            "mean_itl_ms": metrics.mean_itl_ms,
             "median_itl_ms": metrics.median_itl_ms,
+            "input_throughput": metrics.input_throughput,
             "output_throughput": metrics.output_throughput,
             "sharegpt_output_len": args.sharegpt_output_len,
             "random_input_len": args.random_input_len,

python/sglang/srt/layers/logits_processor.py

@@ -117,6 +117,11 @@ class LogitsProcessor(nn.Module):
         self.final_logit_softcapping = getattr(
             self.config, "final_logit_softcapping", None
         )
+        if (
+            self.final_logit_softcapping is not None
+            and self.final_logit_softcapping < 0
+        ):
+            self.final_logit_softcapping = None
 
     def forward(
         self,

python/sglang/srt/layers/moe/fused_moe_triton/fused_moe.py

@@ -1011,6 +1011,17 @@ def fused_experts_impl(
                 out_hidden_states[begin_chunk_idx:end_chunk_idx],
             )
         else:
+            if topk_ids.shape[1] == 1:
+                out_hidden_states[begin_chunk_idx:end_chunk_idx].copy_(
+                    intermediate_cache3[:, 0]
+                )
+            elif topk_ids.shape[1] == 2:
+                torch.add(
+                    intermediate_cache3[:, 0],
+                    intermediate_cache3[:, 1],
+                    out=out_hidden_states[begin_chunk_idx:end_chunk_idx],
+                ).squeeze(dim=1)
+            elif topk_ids.shape[1] > 2:
                 torch.sum(
                     intermediate_cache3.view(*intermediate_cache3.shape),
                     dim=1,

python/sglang/srt/layers/quantization/__init__.py

 # Adapted from https://raw.githubusercontent.com/vllm-project/vllm/v0.5.5/vllm/model_executor/layers/quantization/__init__.py
 
-from typing import Callable, Dict, Optional, Type
+from typing import Dict, Type
 
-import torch
 from vllm.model_executor.layers.quantization.aqlm import AQLMConfig
 from vllm.model_executor.layers.quantization.awq import AWQConfig
 from vllm.model_executor.layers.quantization.awq_marlin import AWQMarlinConfig

python/sglang/srt/managers/data_parallel_controller.py

@@ -20,6 +20,7 @@ import threading
 from enum import Enum, auto
 
 import psutil
+import setproctitle
 import zmq
 
 from sglang.srt.managers.io_struct import (
@@ -230,6 +231,7 @@ def run_data_parallel_controller_process(
     port_args: PortArgs,
     pipe_writer,
 ):
+    setproctitle.setproctitle("sglang::data_parallel_controller")
     configure_logger(server_args)
     parent_process = psutil.Process().parent()
 

python/sglang/srt/managers/scheduler.py

@@ -1516,8 +1516,9 @@ class Scheduler:
         return success, message
 
     def update_weights_from_distributed(
-        self, recv_req: UpdateWeightsFromDistributedReqInput
-    ):
+        self,
+        recv_req: UpdateWeightsFromDistributedReqInput,
+    ) -> Tuple[bool, str]:
         """Update the online model parameter."""
         success, message = self.tp_worker.update_weights_from_distributed(recv_req)
         if success:

python/sglang/srt/metrics/collector.py

@@ -114,26 +114,20 @@ class TokenizerMetricsCollector:
             documentation="Histogram of time to first token in seconds.",
             labelnames=labels.keys(),
             buckets=[
-                0.001,
-                0.005,
-                0.01,
-                0.02,
-                0.04,
-                0.06,
-                0.08,
                 0.1,
                 0.25,
                 0.5,
                 0.75,
-                1.0,
-                2.5,
-                5.0,
-                7.5,
-                10.0,
-                15.0,
-                20.0,
-                25.0,
-                30.0,
+                1,
+                2,
+                5,
+                10,
+                20,
+                40,
+                60,
+                80,
+                120,
+                160,
             ],
         )
 
@@ -168,21 +162,19 @@ class TokenizerMetricsCollector:
             documentation="Histogram of End-to-end request latency in seconds",
             labelnames=labels.keys(),
             buckets=[
-                0.3,
+                0.1,
+                0.25,
                 0.5,
-                0.8,
-                1.0,
-                1.5,
-                2.0,
-                2.5,
-                5.0,
-                10.0,
-                15.0,
-                20.0,
-                30.0,
-                40.0,
-                50.0,
-                60.0,
+                1,
+                2,
+                5,
+                10,
+                20,
+                40,
+                60,
+                80,
+                120,
+                160,
             ],
         )
 

python/sglang/srt/model_executor/cuda_graph_runner.py

@@ -124,6 +124,13 @@ class CudaGraphRunner:
         self.tp_size = self.model_runner.tp_size
 
         # Batch sizes to capture
+        self.capture_bs = self.model_runner.server_args.cuda_graph_bs
+        if self.capture_bs is None:
+            if model_runner.server_args.disable_cuda_graph_padding:
+                self.capture_bs = list(range(1, 33)) + [64, 128]
+            else:
+                self.capture_bs = [1, 2, 4] + [i * 8 for i in range(1, 21)]
+
         if model_runner.server_args.disable_cuda_graph_padding:
             self.capture_bs = list(range(1, 33)) + [64, 128]
         else:
@@ -340,8 +347,8 @@ class CudaGraphRunner:
             top_logprobs_nums=[0] * bs,
             positions=positions,
             global_num_tokens=global_num_tokens,
-            mrope_positions=mrope_positions,
             gathered_buffer=gathered_buffer,
+            mrope_positions=mrope_positions,
             spec_algorithm=self.model_runner.spec_algorithm,
             spec_info=spec_info,
             capture_hidden_mode=(

python/sglang/srt/model_executor/model_runner.py

@@ -89,6 +89,7 @@ class ModelRunner:
         self.is_draft_worker = is_draft_worker
         self.is_generation = model_config.is_generation
         self.is_multimodal = model_config.is_multimodal
+        self.should_log = tp_rank == 0
         self.spec_algorithm = SpeculativeAlgorithm.from_string(
             server_args.speculative_algorithm
         )
@@ -117,15 +118,21 @@ class ModelRunner:
 
         if self.is_multimodal:
             self.mem_fraction_static *= 0.95
+            logger.info(
+                f"Automatically reduce --mem-fraction-static to {self.mem_fraction_static:.3f} "
+                f"because this is a multimodal model."
+            )
+
             if self.model_config.hf_config.architectures == [
                 "MllamaForConditionalGeneration"
             ]:
                 logger.info("Automatically turn off --chunked-prefill-size for mllama.")
                 server_args.chunked_prefill_size = -1
-            # TODO: qwen2-vl does not support radix cache now, set disable_radix_cache=True automatically
+
             if self.model_config.hf_config.architectures == [
                 "Qwen2VLForConditionalGeneration"
             ]:
+                # TODO: qwen2-vl does not support radix cache now, set disable_radix_cache=True automatically
                 logger.info(
                     "Automatically turn off --chunked-prefill-size and disable radix cache for qwen2-vl."
                 )

python/sglang/srt/sampling/sampling_batch_info.py

@@ -232,6 +232,7 @@ class SamplingBatchInfo:
         self.logit_bias = SamplingBatchInfo.merge_bias_tensor(
             self.logit_bias, other.logit_bias, len(self), len(other), self.device
         )
+        self.need_min_p_sampling = self.need_min_p_sampling or other.need_min_p_sampling
 
     def apply_logits_bias(self, logits: torch.Tensor):
         # Apply logit_bias

python/sglang/srt/server.py

@@ -127,14 +127,12 @@ async def health() -> Response:
 async def health_generate(request: Request) -> Response:
     """Check the health of the inference server by generating one token."""
 
+    sampling_params = {"max_new_tokens": 1, "temperature": 0.7}
+
     if tokenizer_manager.is_generation:
-        gri = GenerateReqInput(
-            input_ids=[0], sampling_params={"max_new_tokens": 1, "temperature": 0.7}
-        )
+        gri = GenerateReqInput(input_ids=[0], sampling_params=sampling_params)
     else:
-        gri = EmbeddingReqInput(
-            input_ids=[0], sampling_params={"max_new_tokens": 1, "temperature": 0.7}
-        )
+        gri = EmbeddingReqInput(input_ids=[0], sampling_params=sampling_params)
 
     try:
         async for _ in tokenizer_manager.generate_request(gri, request):

python/sglang/srt/server_args.py

@@ -148,6 +148,7 @@ class ServerArgs:
     enable_torch_compile: bool = False
     torch_compile_max_bs: int = 32
     cuda_graph_max_bs: Optional[int] = None
+    cuda_graph_bs: Optional[List[int]] = None
     torchao_config: str = ""
     enable_nan_detection: bool = False
     enable_p2p_check: bool = False
@@ -803,6 +804,12 @@ class ServerArgs:
             default=ServerArgs.cuda_graph_max_bs,
             help="Set the maximum batch size for cuda graph.",
         )
+        parser.add_argument(
+            "--cuda-graph-bs",
+            type=int,
+            nargs="+",
+            help="Set the list of batch sizes for cuda graph.",
+        )
         parser.add_argument(
             "--torchao-config",
             type=str,

python/sglang/srt/utils.py

@@ -709,13 +709,14 @@ def broadcast_pyobj(
     data: List[Any],
     rank: int,
     dist_group: Optional[torch.distributed.ProcessGroup] = None,
+    src: int = 0,
 ):
     """Broadcast inputs from rank=0 to all other ranks with torch.dist backend."""
 
     if rank == 0:
         if len(data) == 0:
             tensor_size = torch.tensor([0], dtype=torch.long)
-            dist.broadcast(tensor_size, src=0, group=dist_group)
+            dist.broadcast(tensor_size, src=src, group=dist_group)
         else:
             serialized_data = pickle.dumps(data)
             size = len(serialized_data)
@@ -724,19 +725,19 @@ def broadcast_pyobj(
             )
             tensor_size = torch.tensor([size], dtype=torch.long)
 
-            dist.broadcast(tensor_size, src=0, group=dist_group)
-            dist.broadcast(tensor_data, src=0, group=dist_group)
+            dist.broadcast(tensor_size, src=src, group=dist_group)
+            dist.broadcast(tensor_data, src=src, group=dist_group)
         return data
     else:
         tensor_size = torch.tensor([0], dtype=torch.long)
-        dist.broadcast(tensor_size, src=0, group=dist_group)
+        dist.broadcast(tensor_size, src=src, group=dist_group)
         size = tensor_size.item()
 
         if size == 0:
             return []
 
         tensor_data = torch.empty(size, dtype=torch.uint8)
-        dist.broadcast(tensor_data, src=0, group=dist_group)
+        dist.broadcast(tensor_data, src=src, group=dist_group)
 
         serialized_data = bytes(tensor_data.cpu().numpy())
         data = pickle.loads(serialized_data)

python/sglang/test/test_utils.py

@@ -532,6 +532,8 @@ def run_bench_serving(
     request_rate,
     other_server_args,
     dataset_name="random",
+    dataset_path="",
+    tokenizer=None,
     random_input_len=4096,
     random_output_len=2048,
     disable_stream=False,
@@ -553,9 +555,9 @@ def run_bench_serving(
         host=None,
         port=None,
         dataset_name=dataset_name,
-        dataset_path="",
+        dataset_path=dataset_path,
         model=None,
-        tokenizer=None,
+        tokenizer=tokenizer,
         num_prompts=num_prompts,
         sharegpt_output_len=None,
         random_input_len=random_input_len,
@@ -657,16 +659,16 @@ STDERR_FILENAME = "stderr.txt"
 STDOUT_FILENAME = "stdout.txt"
 
 
-def read_output(output_lines):
+def read_output(output_lines: List[str], filename: str = STDERR_FILENAME):
     """Print the output in real time with another thread."""
-    while not os.path.exists(STDERR_FILENAME):
+    while not os.path.exists(filename):
         time.sleep(1)
 
     pt = 0
     while pt >= 0:
-        if pt > 0 and not os.path.exists(STDERR_FILENAME):
+        if pt > 0 and not os.path.exists(filename):
             break
-        lines = open(STDERR_FILENAME).readlines()
+        lines = open(filename).readlines()
         for line in lines[pt:]:
             print(line, end="", flush=True)
             output_lines.append(line)
@@ -747,6 +749,33 @@ def run_and_check_memory_leak(
         assert has_abort
 
 
+def run_command_and_capture_output(command, env: Optional[dict] = None):
+    stdout = open(STDOUT_FILENAME, "w")
+    stderr = open(STDERR_FILENAME, "w")
+    process = subprocess.Popen(
+        command, stdout=stdout, stderr=stderr, env=env, text=True
+    )
+
+    # Launch a thread to stream the output
+    output_lines = []
+    t = threading.Thread(target=read_output, args=(output_lines, STDOUT_FILENAME))
+    t.start()
+
+    # Join the process
+    process.wait()
+
+    stdout.close()
+    stderr.close()
+    if os.path.exists(STDOUT_FILENAME):
+        os.remove(STDOUT_FILENAME)
+    if os.path.exists(STDERR_FILENAME):
+        os.remove(STDERR_FILENAME)
+    kill_process_tree(process.pid)
+    t.join()
+
+    return output_lines
+
+
 def run_mmlu_test(
     disable_radix_cache=False,
     enable_mixed_chunk=False,