Update vllm version to support llama3.1 (#705)

python/pyproject.toml

@@ -21,7 +21,7 @@ dependencies = [
 
 [project.optional-dependencies]
 srt = ["aiohttp", "fastapi", "hf_transfer", "huggingface_hub", "interegular", "packaging", "pillow",
-       "psutil", "pydantic", "torch", "uvicorn", "uvloop", "zmq", "vllm==0.5.1", "outlines>=0.0.44"]
+       "psutil", "pydantic", "torch", "uvicorn", "uvloop", "zmq", "vllm==0.5.3.post1", "outlines>=0.0.44"]
 openai = ["openai>=1.0", "tiktoken"]
 anthropic = ["anthropic>=0.20.0"]
 litellm = ["litellm>=1.0.0"]

python/sglang/srt/hf_transformers_utils.py

@@ -73,6 +73,8 @@ def get_context_length(config):
     rope_scaling = getattr(config, "rope_scaling", None)
     if rope_scaling:
         rope_scaling_factor = config.rope_scaling["factor"]
+        if config.rope_scaling["rope_type"] == "llama3":
+            rope_scaling_factor = 1
     else:
         rope_scaling_factor = 1
 

python/sglang/srt/models/llama2.py

@@ -5,14 +5,10 @@
 from typing import Any, Dict, Iterable, Optional, Tuple
 
 import torch
-import tqdm
 from torch import nn
 from transformers import LlamaConfig
 from vllm.config import CacheConfig
-from vllm.distributed import (
-    get_tensor_model_parallel_rank,
-    get_tensor_model_parallel_world_size,
-)
+from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
@@ -375,9 +371,6 @@ class LlamaForCausalLM(nn.Module):
                 weight_loader(param, loaded_weight)
 
         if name is None or loaded_weight is None:
-            if get_tensor_model_parallel_rank() == 0:
-                weights = tqdm.tqdm(weights, total=int(len(params_dict) * 1.5))
-
             for name, loaded_weight in weights:
                 load_weights_per_param(name, loaded_weight)
         else:

python/sglang/srt/server.py

@@ -222,6 +222,7 @@ def launch_server(
         detokenizer_port=ports[2],
         nccl_ports=ports[3:],
     )
+    logger.info(f"{server_args=}")
 
     # Handle multi-node tensor parallelism
     if server_args.nnodes > 1: