Improve code styles (#4021)

benchmark/kernels/fused_moe_triton/benchmark_torch_compile_fused_moe.py

@@ -30,6 +30,11 @@ def get_model_config(model_name: str, tp_size: int):
         topk = config.num_experts_per_tok
         intermediate_size = config.moe_intermediate_size
         shard_intermediate_size = 2 * intermediate_size // tp_size
+    elif config.architectures[0] in ["DeepseekV2ForCausalLM", "DeepseekV3ForCausalLM"]:
+        E = config.n_routed_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
     elif config.architectures[0] in [
         "Grok1ForCausalLM",
         "Grok1ImgGen",
@@ -39,11 +44,6 @@ def get_model_config(model_name: str, tp_size: int):
         topk = config.num_experts_per_tok
         intermediate_size = config.moe_intermediate_size
         shard_intermediate_size = 2 * intermediate_size // tp_size
-    elif config.architectures[0] in ["DeepseekV2ForCausalLM", "DeepseekV3ForCausalLM"]:
-        E = config.n_routed_experts
-        topk = config.num_experts_per_tok
-        intermediate_size = config.intermediate_size
-        shard_intermediate_size = 2 * intermediate_size // tp_size
     else:
         # Default: Mixtral
         E = config.num_local_experts

python/sglang/bench_serving.py

@@ -393,7 +393,7 @@ async def async_request_sglang_generate(
                                     output.itl.extend([adjust_itl] * num_new_tokens)
 
                                 most_recent_timestamp = timestamp
-                                generated_text = data["text"]
+                                last_output_len = output_len
 
                     output.generated_text = generated_text
                     output.success = True

python/sglang/lang/backend/runtime_endpoint.py

@@ -329,12 +329,7 @@ class RuntimeEndpoint(BaseBackend):
 
 def compute_normalized_prompt_logprobs(input_logprobs):
     values = [x[0] for x in input_logprobs if x[0]]
-    try:
     return sum(values) / len(values)
-    except TypeError:
-        print(f"{input_logprobs=}", flush=True)
-        print(f"{input_logprobs[0]=}", flush=True)
-        exit(-1)
 
 
 class Runtime:

python/sglang/srt/layers/logits_processor.py

@@ -64,7 +64,7 @@ class LogitsProcessorOutput:
 
     ## Part 3: Prefill-only. This part will be assigned in python/sglang/srt/layers/logits_processor.py::LogitsProcessor
     # The logprobs of input tokens.        shape: [#token]
-    input_token_logprobs: torch.Tensor = None
+    input_token_logprobs: Optional[torch.Tensor] = None
     # The logprobs and ids of the top-k tokens in input positions.  shape: [#seq, #token, k]
     input_top_logprobs_val: List = None
     input_top_logprobs_idx: List = None

python/sglang/srt/layers/moe/ep_moe/layer.py

@@ -181,7 +181,6 @@ class EPMoE(torch.nn.Module):
 
     def forward(self, hidden_states: torch.Tensor, router_logits: torch.Tensor):
         assert self.quant_method is not None
-        assert self.activation == "silu"
 
         if self.grouped_gemm_runner is None:
             self.grouped_gemm_runner = GroupedGemmRunner(

python/sglang/srt/managers/data_parallel_controller.py

@@ -198,8 +198,6 @@ class DataParallelController:
         self.max_total_num_tokens = scheduler_info[0]["max_total_num_tokens"]
         self.max_req_input_len = scheduler_info[0]["max_req_input_len"]
 
-        print(f"{scheduler_info=}")
-
     def round_robin_scheduler(self, req):
         self.workers[self.round_robin_counter].send_pyobj(req)
         self.round_robin_counter = (self.round_robin_counter + 1) % len(self.workers)
@@ -222,7 +220,6 @@ class DataParallelController:
                         TokenizedEmbeddingReqInput,
                     ),
                 ):
-                    logger.info("dispatching")
                     self.dispatching(recv_req)
                 else:
                     # Send other control messages to first worker of tp group

python/sglang/srt/managers/io_struct.py

@@ -158,7 +158,7 @@ class GenerateReqInput:
                 # Expand parallel_sample_num
                 num = self.batch_size * self.parallel_sample_num
 
-            if self.image_data is None:
+            if not self.image_data:
                 self.image_data = [None] * num
             elif not isinstance(self.image_data, list):
                 self.image_data = [self.image_data] * num

python/sglang/srt/managers/schedule_batch.py

@@ -282,6 +282,8 @@ class Req:
         # If we want to abort the request in the middle of the event loop, set this to true
         # Note: We should never set finished_reason in the middle, the req will get filtered and never respond
         self.to_abort = False
+        # This carries the error message for `.to_abort` and will be attached to the finished_reason at the end of the event loop
+        self.to_abort_message: str = "Unknown error"
         self.stream = stream
         self.eos_token_ids = eos_token_ids
 
@@ -359,8 +361,6 @@ class Req:
         # The tokens is prefilled but need to be considered as decode tokens
         # and should be updated for the decode logprobs
         self.last_update_decode_tokens = 0
-        # The relative logprob_start_len in an extend batch
-        self.extend_logprob_start_len = 0
 
         # Embedding (return values)
         self.embedding = None
@@ -377,9 +377,6 @@ class Req:
         self.spec_verify_ct = 0
         self.lora_path = lora_path
 
-        # This carries the error message for `.to_abort` and will be attached to the finished_reason at the end of the event loop
-        self.to_abort_message: str = "Unknown error"
-
     @property
     def seqlen(self):
         return len(self.origin_input_ids) + len(self.output_ids)

python/sglang/srt/managers/scheduler.py

@@ -358,7 +358,6 @@ class Scheduler:
         self.cum_spec_accept_count = 0
         self.last_decode_stats_tic = time.time()
         self.return_health_check_ct = 0
-        self.stream_interval = server_args.stream_interval
         self.current_stream = torch.get_device_module(self.device).current_stream()
         if self.device == "cpu":
             self.current_stream.synchronize = lambda: None  # No-op for CPU
@@ -444,11 +443,6 @@ class Scheduler:
                 },
             )
 
-        # The largest prefill length of a single request
-        self._largest_prefill_len: int = 0
-        # The largest context length (prefill + generation) of a single request
-        self._largest_prefill_decode_len: int = 0
-
         # Init request dispatcher
         self._request_dispatcher = TypeBasedDispatcher(
             [
@@ -2309,8 +2303,6 @@ def run_scheduler_process(
     if get_bool_env_var("SGLANG_SET_CPU_AFFINITY"):
         set_gpu_proc_affinity(server_args.tp_size, server_args.nnodes, gpu_id)
 
-    parent_process = psutil.Process().parent()
-
     # Create a scheduler and run the event loop
     try:
         scheduler = Scheduler(server_args, port_args, gpu_id, tp_rank, dp_rank)

python/sglang/srt/metrics/collector.py

@@ -238,120 +238,6 @@ class TokenizerMetricsCollector:
             ],
         )
 
-        self.histogram_prefill_prealloc_duration = Histogram(
-            name="sglang:prefill_prealloc_duration_seconds",
-            documentation="Histogram of prefill prealloc duration in seconds.",
-            labelnames=labels.keys(),
-            buckets=[
-                0.1,
-                0.3,
-                0.5,
-                0.7,
-                0.9,
-                1,
-                2,
-                4,
-                6,
-                8,
-                10,
-                20,
-                40,
-                60,
-                80,
-                120,
-                160,
-            ],
-        )
-
-        self.histogram_prefill_queue_duration = Histogram(
-            name="sglang:prefill_queue_duration_seconds",
-            documentation="Histogram of prefill queue duration in seconds.",
-            labelnames=labels.keys(),
-            buckets=[
-                0.1,
-                0.3,
-                0.5,
-                0.7,
-                0.9,
-                2,
-                4,
-                8,
-                16,
-                64,
-            ],
-        )
-
-        self.histogram_prefill_forward_duration = Histogram(
-            name="sglang:prefill_forward_duration_seconds",
-            documentation="Histogram of prefill forward duration in seconds.",
-            labelnames=labels.keys(),
-            buckets=[
-                0.1,
-                0.3,
-                0.5,
-                0.7,
-                0.9,
-                2,
-                4,
-                8,
-                16,
-                64,
-            ],
-        )
-
-        self.histogram_prefill_transfer_duration = Histogram(
-            name="sglang:prefill_transfer_duration_seconds",
-            documentation="Histogram of prefill transfer duration in seconds.",
-            labelnames=labels.keys(),
-            buckets=[
-                0.050,
-                0.100,
-                0.150,
-                0.200,
-                0.300,
-                0.400,
-                0.500,
-                1.000,
-                2.000,
-            ],
-        )
-
-        self.histogram_decode_prealloc_duration = Histogram(
-            name="sglang:decode_prealloc_duration_seconds",
-            documentation="Histogram of decode prealloc duration in seconds.",
-            labelnames=labels.keys(),
-            buckets=[
-                0.1,
-                0.3,
-                0.5,
-                0.7,
-                0.9,
-                2,
-                4,
-                8,
-                16,
-                64,
-            ],
-        )
-
-        self.histogram_decode_queue_duration = Histogram(
-            name="sglang:decode_queue_duration_seconds",
-            documentation="Histogram of decode queue duration in seconds.",
-            labelnames=labels.keys(),
-            buckets=[
-                0.1,
-                0.3,
-                0.5,
-                0.7,
-                0.9,
-                2,
-                4,
-                8,
-                16,
-                64,
-            ],
-        )
-
     def _log_histogram(self, histogram, data: Union[int, float]) -> None:
         histogram.labels(**self.labels).observe(data)
 

python/sglang/srt/model_executor/forward_batch_info.py

@@ -284,7 +284,9 @@ class ForwardBatch:
             ):
                 ret.extend_num_tokens = batch.extend_num_tokens
                 positions, ret.extend_start_loc = compute_position_triton(
-                    ret.extend_prefix_lens, ret.extend_seq_lens, ret.extend_num_tokens
+                    ret.extend_prefix_lens,
+                    ret.extend_seq_lens,
+                    ret.extend_num_tokens,
                 )
             else:
                 positions, ret.extend_start_loc = compute_position_torch(

python/sglang/srt/server_args.py

@@ -62,7 +62,6 @@ class ServerArgs:
     chat_template: Optional[str] = None
     is_embedding: bool = False
     revision: Optional[str] = None
-    skip_tokenizer_init: bool = False
 
     # Port for the HTTP server
     host: str = "127.0.0.1"
@@ -563,7 +562,7 @@ class ServerArgs:
             "--download-dir",
             type=str,
             default=ServerArgs.download_dir,
-            help="Model download directory.",
+            help="Model download directory for huggingface.",
         )
         parser.add_argument(
             "--base-gpu-id",

python/sglang/test/few_shot_gsm8k.py

@@ -93,9 +93,11 @@ def run_eval(args):
     tic = time.time()
     states = few_shot_gsm8k.run_batch(
         arguments,
-        temperature=0,
+        temperature=args.temperature if hasattr(args, "temperature") else 0,
         num_threads=args.parallel,
         progress_bar=True,
+        return_logprob=getattr(args, "return_logprob", None),
+        logprob_start_len=getattr(args, "logprob_start_len", None),
     )
     latency = time.time() - tic
 
@@ -141,5 +143,6 @@ if __name__ == "__main__":
     parser.add_argument("--parallel", type=int, default=128)
     parser.add_argument("--host", type=str, default="http://127.0.0.1")
     parser.add_argument("--port", type=int, default=30000)
+    parser.add_argument("--temperature", type=float, default=0.0)
     args = parser.parse_args()
     run_eval(args)

sgl-kernel/src/sgl-kernel/__init__.py

@@ -8,16 +8,19 @@ if os.path.exists("/usr/local/cuda/targets/x86_64-linux/lib/libcudart.so.12"):
         "/usr/local/cuda/targets/x86_64-linux/lib/libcudart.so.12",
         mode=ctypes.RTLD_GLOBAL,
     )
-from .version import __version__
 
-if torch.version.hip is not None:
+from sgl_kernel.version import __version__
+
+if torch.version.cuda:
     from sgl_kernel.ops import (
-        all_reduce_reg,
-        all_reduce_unreg,
-        allocate_meta_buffer,
         apply_rope_with_cos_sin_cache_inplace,
         bmm_fp8,
-        dispose,
+        build_tree_kernel,
+        build_tree_kernel_efficient,
+        cublas_grouped_gemm,
+        custom_dispose,
+        custom_reduce,
+        fp8_blockwise_scaled_mm,
         fp8_scaled_mm,
         fused_add_rmsnorm,
         gelu_and_mul,
@@ -25,63 +28,32 @@ if torch.version.hip is not None:
         gemma_fused_add_rmsnorm,
         gemma_rmsnorm,
         get_graph_buffer_ipc_meta,
-        get_meta_buffer_ipc_handle,
-        init_custom_ar,
+        init_custom_reduce,
         int8_scaled_mm,
         lightning_attention_decode,
-        meta_size,
         min_p_sampling_from_probs,
         moe_align_block_size,
-        register_buffer,
         register_graph_buffers,
         rmsnorm,
         sampling_scaling_penalties,
+        sgl_per_token_group_quant_fp8,
         silu_and_mul,
         top_k_renorm_prob,
         top_k_top_p_sampling_from_probs,
         top_p_renorm_prob,
+        tree_speculative_sampling_target_only,
     )
 
-    __all__ = [
-        "all_reduce_reg",
-        "all_reduce_unreg",
-        "allocate_meta_buffer",
-        "apply_rope_with_cos_sin_cache_inplace",
-        "bmm_fp8",
-        "dispose",
-        "fp8_scaled_mm",
-        "fused_add_rmsnorm",
-        "gelu_and_mul",
-        "gelu_tanh_and_mul",
-        "gemma_fused_add_rmsnorm",
-        "gemma_rmsnorm",
-        "get_graph_buffer_ipc_meta",
-        "get_meta_buffer_ipc_handle",
-        "init_custom_ar",
-        "int8_scaled_mm",
-        "lightning_attention_decode",
-        "meta_size",
-        "min_p_sampling_from_probs",
-        "moe_align_block_size",
-        "register_buffer",
-        "register_graph_buffers",
-        "rmsnorm",
-        "sampling_scaling_penalties",
-        "silu_and_mul",
-        "top_k_renorm_prob",
-        "top_k_top_p_sampling_from_probs",
-        "top_p_renorm_prob",
-    ]
 else:
+    assert torch.version.hip
+
     from sgl_kernel.ops import (
+        all_reduce_reg,
+        all_reduce_unreg,
+        allocate_meta_buffer,
         apply_rope_with_cos_sin_cache_inplace,
         bmm_fp8,
-        build_tree_kernel,
-        build_tree_kernel_efficient,
-        cublas_grouped_gemm,
-        custom_dispose,
-        custom_reduce,
-        fp8_blockwise_scaled_mm,
+        dispose,
         fp8_scaled_mm,
         fused_add_rmsnorm,
         gelu_and_mul,
@@ -89,23 +61,26 @@ else:
         gemma_fused_add_rmsnorm,
         gemma_rmsnorm,
         get_graph_buffer_ipc_meta,
-        init_custom_reduce,
+        get_meta_buffer_ipc_handle,
+        init_custom_ar,
         int8_scaled_mm,
         lightning_attention_decode,
+        meta_size,
         min_p_sampling_from_probs,
         moe_align_block_size,
+        register_buffer,
         register_graph_buffers,
         rmsnorm,
         sampling_scaling_penalties,
-        sgl_per_token_group_quant_fp8,
         silu_and_mul,
         top_k_renorm_prob,
         top_k_top_p_sampling_from_probs,
         top_p_renorm_prob,
-        tree_speculative_sampling_target_only,
     )
 
-    __all__ = [
+
+__all__ = [
+    "__version__",
     "apply_rope_with_cos_sin_cache_inplace",
     "bmm_fp8",
     "cublas_grouped_gemm",
@@ -135,4 +110,4 @@ else:
     "top_k_top_p_sampling_from_probs",
     "top_p_renorm_prob",
     "tree_speculative_sampling_target_only",
-    ]
+]