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Convert local-only self.X attributes to locals (#25430)

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fzyzcjy
2026-05-16 09:05:43 +08:00
committed by GitHub
parent 18c16f8660
commit 824ad24149
2 changed files with 5 additions and 5 deletions
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python/sglang/srt/managers/tokenizer_manager.py
+1 -1
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@@ -445,7 +445,7 @@ class TokenizerManager(TokenizerControlMixin, TokenizerManagerScoreMixin):
self.disaggregation_mode = DisaggregationMode( self.disaggregation_mode = DisaggregationMode(
self.server_args.disaggregation_mode self.server_args.disaggregation_mode
) )
self.bootstrap_server = start_disagg_service(self.server_args) start_disagg_service(self.server_args)
# Single-source counter for auto-assigning fake bootstrap_room. # Single-source counter for auto-assigning fake bootstrap_room.
self.fake_bootstrap_room_counter = 0 self.fake_bootstrap_room_counter = 0
python/sglang/srt/model_executor/model_runner.py
+4 -4
View File
@@ -1500,8 +1500,8 @@ class ModelRunner(ModelRunnerKVCacheMixin):
# Register model for layerwise NVTX profiling if enabled # Register model for layerwise NVTX profiling if enabled
if self.server_args.enable_layerwise_nvtx_marker: if self.server_args.enable_layerwise_nvtx_marker:
self.pyt_hooks = PytHooks() pyt_hooks = PytHooks()
self.pyt_hooks.register_hooks(self.model, module_prefix="model") pyt_hooks.register_hooks(self.model, module_prefix="model")
if self.server_args.kv_cache_dtype == "fp8_e4m3": if self.server_args.kv_cache_dtype == "fp8_e4m3":
if self.server_args.quantization_param_path is not None: if self.server_args.quantization_param_path is not None:
@@ -2080,8 +2080,8 @@ class ModelRunner(ModelRunnerKVCacheMixin):
) )
# We need to get device after patch otherwise the device would be wrong # We need to get device after patch otherwise the device would be wrong
self.device_module = torch.get_device_module(self.device) device_module = torch.get_device_module(self.device)
infered_device = self.device_module.current_device() infered_device = device_module.current_device()
named_tensors = [ named_tensors = [
(name, _unwrap_tensor(tensor, tp_rank=self.tp_rank, device=infered_device)) (name, _unwrap_tensor(tensor, tp_rank=self.tp_rank, device=infered_device))