Remove monkey_patch_vllm_dummy_weight_loader (#2064)

python/sglang/srt/managers/scheduler.py

@@ -895,7 +895,7 @@ class Scheduler:
             logits_output, next_token_ids, bid = result
 
             if self.enable_overlap:
-                logits_output, next_token_ids = self.tp_worker.resulve_batch_result(bid)
+                logits_output, next_token_ids = self.tp_worker.resolve_batch_result(bid)
             else:
                 # Move next_token_ids and logprobs to cpu
                 if batch.return_logprob:
@@ -970,7 +970,7 @@ class Scheduler:
         self.num_generated_tokens += len(batch.reqs)
 
         if self.enable_overlap:
-            logits_output, next_token_ids = self.tp_worker.resulve_batch_result(bid)
+            logits_output, next_token_ids = self.tp_worker.resolve_batch_result(bid)
             next_token_logprobs = logits_output.next_token_logprobs
         else:
             # Move next_token_ids and logprobs to cpu

python/sglang/srt/managers/tp_worker_overlap_thread.py

@@ -141,7 +141,7 @@ class TpModelWorkerClient:
             self.launch_event.set()
             self.output_queue.put((copy_event, logits_output, next_token_ids))
 
-    def resulve_batch_result(self, bid: int):
+    def resolve_batch_result(self, bid: int):
         copy_event, logits_output, next_token_ids = self.output_queue.get()
         while not copy_event.query():
             time.sleep(1e-5)

python/sglang/srt/model_executor/model_runner.py

@@ -58,7 +58,6 @@ from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import (
     enable_show_time_cost,
     get_available_gpu_memory,
-    monkey_patch_vllm_dummy_weight_loader,
     monkey_patch_vllm_p2p_access_check,
 )
 
@@ -242,7 +241,6 @@ class ModelRunner:
                     raise RuntimeError("SGLang only supports sm75 and above.")
 
         # Prepare the vllm model config
-        monkey_patch_vllm_dummy_weight_loader()
         self.load_config = LoadConfig(
             load_format=self.server_args.load_format,
             download_dir=self.server_args.download_dir,
@@ -261,7 +259,6 @@ class ModelRunner:
             self.vllm_model_config.hf_config.update(
                 self.model_config.model_override_args
             )
-        self.dtype = self.vllm_model_config.dtype
 
         # Load the model
         self.model = get_model(
@@ -278,6 +275,7 @@ class ModelRunner:
             if hasattr(self.model, "get_attention_sliding_window_size")
             else None
         )
+        self.dtype = self.vllm_model_config.dtype
 
         logger.info(
             f"Load weight end. "

python/sglang/srt/utils.py

@@ -405,57 +405,6 @@ def monkey_patch_vllm_p2p_access_check(gpu_id: int):
     setattr(tgt, "gpu_p2p_access_check", lambda *arg, **kwargs: True)
 
 
-def monkey_patch_vllm_dummy_weight_loader():
-    """
-    Monkey patch the dummy weight loader in vllm to call process_weights_after_loading.
-    """
-
-    from vllm.model_executor.model_loader.loader import (
-        CacheConfig,
-        DeviceConfig,
-        DummyModelLoader,
-        LoRAConfig,
-        ModelConfig,
-        ParallelConfig,
-        SchedulerConfig,
-        _initialize_model,
-        initialize_dummy_weights,
-        nn,
-        set_default_torch_dtype,
-    )
-
-    def load_model(
-        self,
-        *,
-        model_config: ModelConfig,
-        device_config: DeviceConfig,
-        lora_config: Optional[LoRAConfig],
-        parallel_config: ParallelConfig,
-        scheduler_config: SchedulerConfig,
-        cache_config: CacheConfig,
-    ) -> nn.Module:
-        with set_default_torch_dtype(model_config.dtype):
-            with torch.device(device_config.device):
-                model = _initialize_model(
-                    model_config,
-                    self.load_config,
-                    lora_config,
-                    cache_config,
-                )
-
-            for _, module in model.named_modules():
-                quant_method = getattr(module, "quant_method", None)
-                if quant_method is not None:
-                    quant_method.process_weights_after_loading(module)
-
-            # NOTE(woosuk): For accurate performance evaluation, we assign
-            # random values to the weights.
-            initialize_dummy_weights(model)
-        return model.eval()
-
-    setattr(DummyModelLoader, "load_model", load_model)
-
-
 vllm_all_gather_backup = None
 
 

test/srt/test_bench_latency.py

@@ -13,7 +13,7 @@ class TestBenchLatency(unittest.TestCase):
         output_throughput = run_bench_latency(DEFAULT_MODEL_NAME_FOR_TEST, [])
 
         if is_in_ci():
-            assert output_throughput > 130, f"{output_throughput=}"
+            self.assertGreater(output_throughput, 135)
 
     def test_moe_default(self):
         output_throughput = run_bench_latency(
@@ -21,7 +21,7 @@ class TestBenchLatency(unittest.TestCase):
         )
 
         if is_in_ci():
-            assert output_throughput > 125, f"{output_throughput=}"
+            self.assertGreater(output_throughput, 125)
 
 
 if __name__ == "__main__":

test/srt/test_bench_serving.py

@@ -20,7 +20,7 @@ class TestBenchServing(unittest.TestCase):
         )
 
         if is_in_ci():
-            assert res["output_throughput"] > 2830
+            self.assertGreater(res["output_throughput"], 2850)
 
     def test_offline_throughput_non_stream_small_batch_size(self):
         res = run_bench_serving(
@@ -35,7 +35,7 @@ class TestBenchServing(unittest.TestCase):
         )
 
         if is_in_ci():
-            assert res["output_throughput"] > 1000
+            self.assertGreater(res["output_throughput"], 950)
 
     def test_offline_throughput_without_radix_cache(self):
         res = run_bench_serving(
@@ -46,7 +46,7 @@ class TestBenchServing(unittest.TestCase):
         )
 
         if is_in_ci():
-            assert res["output_throughput"] > 2880
+            self.assertGreater(res["output_throughput"], 2900)
 
     def test_offline_throughput_without_chunked_prefill(self):
         res = run_bench_serving(
@@ -57,7 +57,7 @@ class TestBenchServing(unittest.TestCase):
         )
 
         if is_in_ci():
-            assert res["output_throughput"] > 2600
+            self.assertGreater(res["output_throughput"], 2600)
 
     def test_offline_throughput_with_triton_attention_backend(self):
         res = run_bench_serving(
@@ -73,7 +73,7 @@ class TestBenchServing(unittest.TestCase):
         )
 
         if is_in_ci():
-            assert res["output_throughput"] > 2930
+            self.assertGreater(res["output_throughput"], 2950)
 
     def test_offline_throughput_default_fp8(self):
         res = run_bench_serving(
@@ -84,7 +84,7 @@ class TestBenchServing(unittest.TestCase):
         )
 
         if is_in_ci():
-            assert res["output_throughput"] > 3100
+            self.assertGreater(res["output_throughput"], 3200)
 
     def test_online_latency_default(self):
         res = run_bench_serving(
@@ -95,9 +95,9 @@ class TestBenchServing(unittest.TestCase):
         )
 
         if is_in_ci():
-            assert res["median_e2e_latency_ms"] < 12000
+            self.assertLess(res["median_e2e_latency_ms"], 12000)
-            assert res["median_ttft_ms"] < 80
+            self.assertLess(res["median_ttft_ms"], 80)
-            assert res["median_itl_ms"] < 12
+            self.assertLess(res["median_itl_ms"], 11)
 
     def test_moe_offline_throughput_default(self):
         res = run_bench_serving(
@@ -108,7 +108,7 @@ class TestBenchServing(unittest.TestCase):
         )
 
         if is_in_ci():
-            assert res["output_throughput"] > 1850
+            self.assertGreater(res["output_throughput"], 1900)
 
     def test_moe_offline_throughput_without_radix_cache(self):
         res = run_bench_serving(
@@ -119,7 +119,7 @@ class TestBenchServing(unittest.TestCase):
         )
 
         if is_in_ci():
-            assert res["output_throughput"] > 1950
+            self.assertGreater(res["output_throughput"], 1950)
 
 
 if __name__ == "__main__":