Remove cached triton launcher (#656)

python/sglang/srt/utils.py

@@ -5,6 +5,7 @@ import fcntl
 import logging
 import os
 import random
+import resource
 import socket
 import struct
 import time
@@ -16,6 +17,7 @@ import numpy as np
 import psutil
 import requests
 import torch
+import torch.distributed as dist
 import triton
 from fastapi.responses import JSONResponse
 from packaging import version as pkg_version
@@ -184,71 +186,6 @@ def get_int_token_logit_bias(tokenizer, vocab_size):
     return logit_bias
 
 
-def wrap_kernel_launcher(kernel):
-    """A faster launcher for triton kernels."""
-    if int(triton.__version__.split(".")[0]) >= 3:
-        return None
-
-    gpu_id = torch.cuda.current_device()
-    kernels = kernel.cache[gpu_id].values()
-    kernel = next(iter(kernels))
-
-    # Different trition versions use different low-level names
-    if hasattr(kernel, "cu_function"):
-        kfunction = kernel.cu_function
-    else:
-        kfunction = kernel.function
-
-    if hasattr(kernel, "c_wrapper"):
-        run = kernel.c_wrapper
-    else:
-        run = kernel.run
-
-    add_cluster_dim = True
-
-    def ret_func(grid, num_warps, *args):
-        nonlocal add_cluster_dim
-
-        try:
-            if add_cluster_dim:
-                run(
-                    grid[0],
-                    grid[1],
-                    grid[2],
-                    num_warps,
-                    1,
-                    1,
-                    1,
-                    1,
-                    kernel.shared,
-                    0,
-                    kfunction,
-                    None,
-                    None,
-                    kernel,
-                    *args,
-                )
-            else:
-                run(
-                    grid[0],
-                    grid[1],
-                    grid[2],
-                    num_warps,
-                    kernel.shared,
-                    0,
-                    kfunction,
-                    None,
-                    None,
-                    kernel,
-                    *args,
-                )
-        except TypeError:
-            add_cluster_dim = not add_cluster_dim
-            ret_func(grid, num_warps, *args)
-
-    return ret_func
-
-
 def is_multimodal_model(model):
     from sglang.srt.model_config import ModelConfig
 
@@ -512,7 +449,6 @@ def get_ip_address(ifname):
 
 def send_addrs_to_rank_0(model_port_args, server_args):
     assert server_args.node_rank != 0 and server_args.dp_size == 1
-    import torch.distributed as dist
 
     ifname = os.environ.get(
         "SGLANG_SOCKET_IFNAME", os.environ.get("NCCL_SOCKET_IFNAME", "eth0")
@@ -544,7 +480,6 @@ def send_addrs_to_rank_0(model_port_args, server_args):
 
 def receive_addrs(model_port_args, server_args):
     assert server_args.node_rank == 0 and server_args.dp_size == 1
-    import torch.distributed as dist
 
     ifname = os.environ.get(
         "SGLANG_SOCKET_IFNAME", os.environ.get("NCCL_SOCKET_IFNAME", "eth0")
@@ -577,3 +512,14 @@ def receive_addrs(model_port_args, server_args):
 
     dist.barrier()
     dist.destroy_process_group()
+
+
+def set_ulimit(target_soft_limit=65535):
+    resource_type = resource.RLIMIT_NOFILE
+    current_soft, current_hard = resource.getrlimit(resource_type)
+
+    if current_soft < target_soft_limit:
+        try:
+            resource.setrlimit(resource_type, (target_soft_limit, current_hard))
+        except ValueError as e:
+            logger.warn(f"Fail to set RLIMIT_NOFILE: {e}")