Clean up model loader (#1440)

python/sglang/srt/managers/tp_worker.py

@@ -415,7 +415,7 @@ class ModelTpServer:
 
         # Truncate prompts that are too long
         if len(req.origin_input_ids) >= self.max_req_input_len:
-            logger.warn(
+            logger.warning(
                 "Request length is longer than the KV cache pool size or "
                 "the max context length. Truncated!!!"
             )
@@ -936,6 +936,8 @@ class ModelTpServer:
         if success:
             flash_cache_success = self.flush_cache()
             assert flash_cache_success, "Cache flush failed after updating weights"
+        else:
+            logger.error(message)
         return success, message
 
 

python/sglang/srt/model_executor/model_runner.py

@@ -54,11 +54,9 @@ from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import (
     get_available_gpu_memory,
     is_generation_model,
-    is_llama3_405b_fp8_head_16,
     is_multimodal_model,
     monkey_patch_vllm_dummy_weight_loader,
     monkey_patch_vllm_p2p_access_check,
-    monkey_patch_vllm_qvk_linear_loader,
 )
 
 logger = logging.getLogger(__name__)
@@ -166,10 +164,13 @@ class ModelRunner:
         return min_per_gpu_memory
 
     def load_model(self):
-        torch.set_num_threads(1)
         logger.info(
             f"Load weight begin. avail mem={get_available_gpu_memory(self.gpu_id):.2f} GB"
         )
+
+        # This can reduce thread conflicts and speed up weight loading.
+        torch.set_num_threads(1)
+
         if torch.cuda.get_device_capability()[0] < 8:
             logger.info(
                 "Compute capability below sm80. Use float16 due to lack of bfloat16 support."
@@ -178,6 +179,7 @@ class ModelRunner:
             if torch.cuda.get_device_capability()[1] < 5:
                 raise RuntimeError("SGLang only supports sm75 and above.")
 
+        # Prepare the vllm model config
         monkey_patch_vllm_dummy_weight_loader()
         self.device_config = DeviceConfig()
         self.load_config = LoadConfig(load_format=self.server_args.load_format)
@@ -188,23 +190,16 @@ class ModelRunner:
             tokenizer_mode=None,
             trust_remote_code=self.server_args.trust_remote_code,
             dtype=self.server_args.dtype,
-            seed=42,
+            seed=self.server_args.random_seed,
             skip_tokenizer_init=True,
         )
-
-        # A temporary hack to fix the num_heads for meta-llama/Meta-Llama-3.1-405B-FP8 checkpoints
-        # Drop this after Sept, 2024.
-        if is_llama3_405b_fp8_head_16(self.model_config) and self.tp_size <= 8:
-            self.model_config.hf_config.num_key_value_heads = 8
-            self.vllm_model_config.hf_config.num_key_value_heads = 8
-            monkey_patch_vllm_qvk_linear_loader()
-
-        self.dtype = self.vllm_model_config.dtype
         if self.model_config.model_override_args is not None:
             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(
             model_config=self.vllm_model_config,
             load_config=self.load_config,
@@ -255,20 +250,20 @@ class ModelRunner:
                 tokenizer_mode=None,
                 trust_remote_code=self.server_args.trust_remote_code,
                 dtype=self.server_args.dtype,
-                seed=42,
+                seed=self.server_args.random_seed,
                 skip_tokenizer_init=True,
             )
         except Exception as e:
-            logger.error(f"Failed to load model config: {e}")
-            return False, "Failed to update model weights"
+            message = f"Failed to load model config: {e}."
+            return False, message
 
         load_config = LoadConfig(load_format=load_format)
 
         # Only support vllm DefaultModelLoader for now
         loader = get_model_loader(load_config)
         if not isinstance(loader, DefaultModelLoader):
-            logger.error("Failed to get weights iterator: Unsupported loader")
-            return False, "Failed to update model weights"
+            message = f"Failed to get model loader: {loader}."
+            return False, message
 
         def get_weight_iter(config):
             iter = loader._get_weights_iterator(
@@ -293,14 +288,14 @@ class ModelRunner:
             try:
                 iter = get_weight_iter(vllm_model_config)
             except Exception as e:
-                message = f"Failed to get weights iterator: {e}"
-                logger.error(message)
+                message = f"Failed to get weights iterator: {e}."
                 return False, message
             try:
                 model = model_load_weights(self.model, iter)
             except Exception as e:
-                message = f"Failed to update weights: {e}. \n Rolling back to original weights"
-                logger.error(message)
+                message = (
+                    f"Failed to update weights: {e}.\nRolling back to original weights."
+                )
                 del iter
                 gc.collect()
                 iter = get_weight_iter(self.vllm_model_config)
@@ -315,7 +310,7 @@ class ModelRunner:
         self.model_config.path = model_path
 
         logger.info("Update weights end.")
-        return True, "Succeeded to update model weights"
+        return True, "Succeeded to update model weights."
 
     def init_lora_manager(self):
         self.lora_manager = LoRAManager(

python/sglang/srt/server.py

@@ -152,7 +152,7 @@ async def flush_cache():
 async def update_weights(obj: UpdateWeightReqInput, request: Request):
 
     success, message = await tokenizer_manager.update_weights(obj, request)
-    content = {"message": message, "success": str(success)}
+    content = {"success": success, "message": message}
     if success:
         return JSONResponse(
             content,

python/sglang/srt/utils.py

@@ -187,7 +187,7 @@ def allocate_init_ports(
         cur_port += 1
 
     if port is not None and ret_ports[0] != port:
-        logger.warn(
+        logger.warning(
             f"WARNING: Port {port} is not available. Use port {ret_ports[0]} instead."
         )
 
@@ -623,56 +623,7 @@ def set_ulimit(target_soft_limit=65535):
         try:
             resource.setrlimit(resource_type, (target_soft_limit, current_hard))
         except ValueError as e:
-            logger.warn(f"Fail to set RLIMIT_NOFILE: {e}")
-
-
-def is_llama3_405b_fp8_head_16(model_config):
-    """Return whether the model is meta-llama/Meta-Llama-3.1-405B-FP8 with 16 kv heads."""
-    if (
-        model_config.hf_config.architectures[0] == "LlamaForCausalLM"
-        and model_config.hf_config.hidden_size == 16384
-        and model_config.hf_config.intermediate_size == 53248
-        and model_config.hf_config.num_hidden_layers == 126
-        and model_config.hf_config.num_key_value_heads == 16
-        and hasattr(model_config.hf_config, "quantization_config")
-        and model_config.hf_config.quantization_config["quant_method"] == "fbgemm_fp8"
-    ):
-        return True
-    return False
-
-
-def monkey_patch_vllm_qvk_linear_loader():
-    """A temporary hack to fix the num_heads for meta-llama/Meta-Llama-3.1-405B-FP8 checkpoints."""
-    from vllm.model_executor.layers.linear import QKVParallelLinear
-
-    origin_weight_loader = QKVParallelLinear.weight_loader
-
-    def get_original_weight(loaded_weight, head_dim):
-        n_kv_head = loaded_weight.shape[0] // (2 * head_dim)
-        dim = loaded_weight.shape[1]
-        for i in range(n_kv_head):
-            loaded_weight[i * head_dim : (i + 1) * head_dim, :] = loaded_weight[
-                2 * i * head_dim : (2 * i + 1) * head_dim, :
-            ]
-        original_kv_weight = loaded_weight[: n_kv_head * head_dim, :]
-        assert original_kv_weight.shape == (n_kv_head * head_dim, dim)
-        return original_kv_weight
-
-    def weight_loader_srt(
-        self,
-        param: Parameter,
-        loaded_weight: torch.Tensor,
-        loaded_shard_id: Optional[str] = None,
-    ):
-        if (
-            loaded_shard_id in ["k", "v"]
-            and loaded_weight.shape[0] == self.head_size * self.total_num_kv_heads * 2
-        ):
-            loaded_weight = get_original_weight(loaded_weight, self.head_size)
-
-        origin_weight_loader(self, param, loaded_weight, loaded_shard_id)
-
-    setattr(QKVParallelLinear, "weight_loader", weight_loader_srt)
+            logger.warning(f"Fail to set RLIMIT_NOFILE: {e}")
 
 
 def add_api_key_middleware(app, api_key: str):

test/srt/test_update_weights.py

@@ -44,7 +44,6 @@ class TestReplaceWeights(unittest.TestCase):
         )
         print(json.dumps(response.json()))
         print("=" * 100)
-        # return the "text" in response
         text = response.json()["text"]
         return text
 
@@ -61,7 +60,9 @@ class TestReplaceWeights(unittest.TestCase):
                 "model_path": model_path,
             },
         )
+        ret = response.json()
         print(json.dumps(response.json()))
+        return ret
 
     def test_replace_weights(self):
         origin_model_path = self.get_model_info()
@@ -70,7 +71,8 @@ class TestReplaceWeights(unittest.TestCase):
 
         # update weights
         new_model_path = "meta-llama/Meta-Llama-3.1-8B"
-        self.run_update_weights(new_model_path)
+        ret = self.run_update_weights(new_model_path)
+        assert ret["success"]
 
         updated_model_path = self.get_model_info()
         print(f"updated_model_path: {updated_model_path}")
@@ -81,7 +83,9 @@ class TestReplaceWeights(unittest.TestCase):
         assert origin_response[:32] != updated_response[:32]
 
         # update weights back
-        self.run_update_weights(origin_model_path)
+        ret = self.run_update_weights(origin_model_path)
+        assert ret["success"]
+
         updated_model_path = self.get_model_info()
         assert updated_model_path == origin_model_path
 
@@ -95,7 +99,8 @@ class TestReplaceWeights(unittest.TestCase):
 
         # update weights
         new_model_path = "meta-llama/Meta-Llama-3.1-8B-1"
-        self.run_update_weights(new_model_path)
+        ret = self.run_update_weights(new_model_path)
+        assert not ret["success"]
 
         updated_model_path = self.get_model_info()
         print(f"updated_model_path: {updated_model_path}")