release initial code

Co-authored-by: Ying Sheng <sqy1415@gmail.com>
Co-authored-by: Liangsheng Yin <hnyls2002@gmail.com>
Co-authored-by: Zhiqiang Xie <xiezhq@stanford.edu>
Co-authored-by: parasol-aser <3848358+parasol-aser@users.noreply.github.com>
Co-authored-by: LiviaSun <33578456+ChuyueSun@users.noreply.github.com>
Co-authored-by: Cody Yu <hao.yu.cody@gmail.com>

benchmark/generative_agents/bench_other.py

@@ -0,0 +1,104 @@
+import argparse
+from functools import partial
+import json
+import time
+from pathlib import Path
+
+from tqdm import tqdm
+from sglang.test.test_utils import (
+    add_common_other_args_and_parse,
+    call_generate_lightllm,
+    call_generate_vllm,
+    call_generate_srt_raw,
+)
+from sglang.utils import read_jsonl, dump_state_text
+
+from agent_functions import (
+    poignancy_event_prompt,
+    generate_event_triple_prompt,
+    generate_pronunciatio_prompt,
+    action_location_sector_prompt,
+    action_location_object_prompt,
+)
+
+
+def main(args):
+    lines = read_jsonl(args.data_path)[:args.num_events]
+    mapping = {
+        "poignancy_event": poignancy_event_prompt,
+        "generate_event_triple": generate_event_triple_prompt,
+        "generate_pronunciatio": generate_pronunciatio_prompt,
+        "action_location_sector": action_location_sector_prompt,
+        "action_location_object": action_location_object_prompt,
+    }
+
+    arguments = [mapping[k](**v) for l in lines for k, v in l.items()]
+    states = []
+
+    # Select backend
+    if args.backend == "lightllm":
+        url = f"{args.host}:{args.port}/generate"
+        call_generate = partial(call_generate_lightllm, url=url)
+    elif args.backend == "vllm":
+        url = f"{args.host}:{args.port}/generate"
+        call_generate = partial(call_generate_vllm, url=url)
+    elif args.backend == "srt-raw":
+        url = f"{args.host}:{args.port}/generate"
+        call_generate = partial(call_generate_srt_raw, url=url)
+    elif args.backend == "guidance":
+        from guidance import models, gen
+
+        model = models.LlamaCpp(
+            str(Path.home()) + "/model_weights/Llama-2-7b-chat.gguf",
+            n_gpu_layers=-1,
+            n_ctx=4096,
+        )
+
+        def call_generate(prompt, temperature, max_tokens, stop):
+            out = model + prompt + gen(
+                name="result",
+                max_tokens=max_tokens,
+                temperature=temperature,
+                stop=stop,
+            )
+            return out["result"]
+
+    else:
+        raise ValueError(f"Invalid backend: {args.backend}")
+
+    def get_one_answer(arg):
+        answer = call_generate(**arg, temperature=0)
+        states.append(answer)
+
+    tic = time.time()
+    # we always sequentially execute agent calls to maintain its dependency
+    for arg in tqdm(arguments):
+        get_one_answer(arg)
+    latency = time.time() - tic
+
+    print(f"Latency: {latency:.3f}")
+
+    # Write results
+    dump_state_text(f"tmp_output_{args.backend}.txt", states)
+
+    with open(args.result_file, "a") as fout:
+        value = {
+            "task": "Generative Agents",
+            "backend": args.backend,
+            "num_gpus": 1,
+            "latency": round(latency, 3),
+            # to pack weighted functions as a single agent            
+            "num_requests": len(arguments) / len(mapping),
+            "other": {
+                "parallel": args.parallel,
+            },
+        }
+        fout.write(json.dumps(value) + "\n")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--data-path", type=str, default="agent_calls.jsonl")
+    parser.add_argument("--num-events", type=int, default=10)
+    args = add_common_other_args_and_parse(parser)
+    main(args)