[router] cache-aware load-balancing router v1 (#2114)

benchmark/multi_turn_chat/long_prompt_multi_turn.py

@@ -1,21 +1,24 @@
 import itertools
 import json
+import os
 import random
 import string
 import threading
 import time
 from argparse import ArgumentParser
+from pathlib import Path
+from typing import Union
+
+from tqdm import tqdm
 
 import sglang as sgl
-from sglang.srt.hf_transformers_utils import get_tokenize
+from sglang.srt.hf_transformers_utils import get_tokenizer
 from sglang.test.test_utils import (
     add_common_sglang_args_and_parse,
     select_sglang_backend,
 )
 from sglang.utils import dump_state_text
 
-random.seed(42)
-
 
 def gen_prompt(tokenizer, token_num):
     all_available_tokens = list(tokenizer.get_vocab().values())
@@ -24,12 +27,34 @@ def gen_prompt(tokenizer, token_num):
     return ret
 
 
+def get_cache_path(args):
+    # Create cache directory under ~/.cache/sglang
+    cache_dir = Path.home() / ".cache" / "sglang"
+
+    # Create a unique cache filename based on the arguments that affect generation
+    cache_key = f"qa_{args.num_qa}_{args.turns}_{args.system_prompt_len}_{args.len_q}_{args.len_a}_{args.tokenizer.replace('/', '_')}.json"
+    return cache_dir / cache_key
+
+
 def gen_arguments(args, tokenizer):
-    multi_qas = [
-        {"system_prompt": gen_prompt(tokenizer, args.system_prompt_len), "qas": []}
-        for _ in range(args.num_qa)
-    ]
-    for i in range(args.num_qa):
+    cache_path = get_cache_path(args)
+
+    # Try to load from cache first
+    if cache_path.exists():
+        print(f"Loading cached arguments from {cache_path}")
+        with open(cache_path, "r") as f:
+            return json.load(f)
+
+    print("Generating new arguments...")
+    # First progress bar for system prompts
+    multi_qas = []
+    for _ in tqdm(range(args.num_qa), desc="Generating system prompts"):
+        multi_qas.append(
+            {"system_prompt": gen_prompt(tokenizer, args.system_prompt_len), "qas": []}
+        )
+
+    # Nested progress bars for QA pairs
+    for i in tqdm(range(args.num_qa), desc="Generating QA pairs"):
         qas = multi_qas[i]["qas"]
         for j in range(args.turns):
             qas.append(
@@ -38,14 +63,21 @@ def gen_arguments(args, tokenizer):
                     "new_tokens": args.len_a,
                 }
             )
+
+    # Save to cache
+    cache_path.parent.mkdir(parents=True, exist_ok=True)
+    with open(cache_path, "w") as f:
+        json.dump(multi_qas, f)
+    print(f"Cached arguments saved to {cache_path}")
+
     return multi_qas
 
 
 @sgl.function
 def multi_turns(s, system_prompt, qas):
     s += system_prompt
 
-    for qa in qas:
+    for i, qa in enumerate(qas):
         s += qa["prompt"]
         s += sgl.gen(max_tokens=qa["new_tokens"], ignore_eos=True)
 
@@ -62,7 +94,7 @@ def main(args):
         multi_qas,
         temperature=0,
         backend=backend,
-        num_threads=args.parallel,
+        num_threads="auto",
         progress_bar=True,
     )
     latency = time.time() - tic
@@ -75,7 +107,6 @@ def main(args):
         value = {
             "task": "multi_turn_system_prompt_chat",
             "backend": args.backend,
-            "num_gpus": 1,
             "latency": round(latency, 3),
             "num_requests": args.num_qa,
             "num_turns": args.turns,

python/sglang/bench_serving.py

@@ -727,9 +727,9 @@ def sample_generated_shared_prefix_requests(
     total_input_tokens = 0
     total_output_tokens = 0
 
-    for group_idx in range(num_groups):
+    for group_idx in tqdm(range(num_groups), desc="Generating system prompt"):
         system_prompt = system_prompts[group_idx]
-        for prompt_idx in range(prompts_per_group):
+        for prompt_idx in tqdm(range(prompts_per_group), desc="Generating questions"):
             question = questions[group_idx * prompts_per_group + prompt_idx]
             full_prompt = f"{system_prompt}\n\n{question}"
             prompt_len = len(tokenizer.encode(full_prompt))

python/sglang/test/few_shot_gsm8k.py

@@ -48,9 +48,13 @@ def run_eval(args):
     # Select backend
     set_default_backend(RuntimeEndpoint(f"{args.host}:{args.port}"))
 
-    # Read data
-    url = "https://raw.githubusercontent.com/openai/grade-school-math/master/grade_school_math/data/test.jsonl"
-    filename = download_and_cache_file(url)
+    if args.data_path is None:
+        # Read data
+        url = "https://raw.githubusercontent.com/openai/grade-school-math/master/grade_school_math/data/test.jsonl"
+        filename = download_and_cache_file(url)
+    else:
+        filename = args.data_path
+
     lines = list(read_jsonl(filename))
 
     # Construct prompts

rust/Cargo.toml

@@ -24,6 +24,8 @@ futures-util = "0.3"
 serde_json = "1.0"
 pyo3 = { version = "0.22.5", features = ["extension-module"] }
 tokenizers = { version = "0.20.3", features = ["http"] }
+dashmap = "6.1.0"
+http = "1.1.0"
 
 [profile.release]
 lto = "thin"

rust/README.md

@@ -46,6 +46,9 @@ pip install <path-to-wheel>
 #### Option B: Development Mode
 
 For development purposes, you can install the package in editable mode:
+
+Warning: Using editable python binding can suffer from performance degradation!! Please build a fresh wheel for every update if you want to test performance.
+
 ```bash
 pip install -e .
 ```