Update benchmark scripts (#8)

benchmark/dspy/README.md

@@ -23,7 +23,7 @@ python3 bench_dspy_intro.py --backend sglang
 ```
 docker run --name tgi --rm -ti --gpus all --network host \
   -v /home/ubuntu/model_weights/Llama-2-7b-chat-hf:/Llama-2-7b-chat-hf \
-  ghcr.io/huggingface/text-generation-inference:1.1.0 \
+  ghcr.io/huggingface/text-generation-inference:1.3.0 \
   --model-id /Llama-2-7b-chat-hf --num-shard 1  --trust-remote-code \
   --max-input-length 2048 --max-total-tokens 4096 \
   --port 24000

benchmark/hellaswag/bench_other.py

@@ -57,6 +57,8 @@ def main(args):
             out = model + context + select(choices, name="answer")
             return choices.index(out["answer"])
 
+        call_select("Hello,", ["world", "earth"])
+
     elif args.backend == "lmql":
         import lmql
         model = lmql.model("meta-llama/Llama-2-7b-chat-hf",
@@ -135,6 +137,6 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--num-shot", type=int, default=20)
     parser.add_argument("--data-path", type=str, default="hellaswag_val.jsonl")
-    parser.add_argument("--num-questions", type=int, default=100)
+    parser.add_argument("--num-questions", type=int, default=200)
     args = add_common_other_args_and_parse(parser)
     main(args)

benchmark/hellaswag/bench_sglang.py

@@ -91,6 +91,6 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--num-shot", type=int, default=20)
     parser.add_argument("--data-path", type=str, default="hellaswag_val.jsonl")
-    parser.add_argument("--num-questions", type=int, default=100)
+    parser.add_argument("--num-questions", type=int, default=200)
     args = add_common_sglang_args_and_parse(parser)
     main(args)

benchmark/json_regex_decode/README.md → benchmark/json_decode_regex/README.md

@@ -17,14 +17,13 @@ outlines                  0.0.22
 
 ### Benchmark sglang
 
-Run llama-7b
+Run Llama-7B
 
 ```
 python3 -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000 
 ```
 
-Run mixtral-8x7b
-(When there is a CUDA out-of-memory error, try to reduce the `--mem-fraction-static`)
+Run Mixtral-8x7B
 
 ```
 python3 -m sglang.launch_server --model-path mistralai/Mixtral-8x7B-Instruct-v0.1 --port 30000 --tp-size 8
@@ -39,7 +38,7 @@ python3 bench_sglang.py --num-questions 10
 
 ### Benchmark vllm
 
-Run llama-7b
+Run Llama-7B
 
 ```
 python3 -m outlines.serve.serve --tokenizer-mode auto --model meta-llama/Llama-2-7b-chat-hf  --disable-log-requests --port 21000
@@ -54,7 +53,7 @@ python3 bench_other.py --backend vllm --num-questions 10
 
 ### Benchmark guidance
 
-Run llama-7b and benchmark
+Run Llama-7B and benchmark
 
 ```
 python3 bench_other.py --backend guidance --num-questions 10 --parallel 1

benchmark/json_regex_decode/bench_other.py → benchmark/json_decode_regex/bench_other.py

@@ -105,7 +105,7 @@ def main(args):
 
     with open(args.result_file, "a") as fout:
         value = {
-            "task": "json_regex_decode",
+            "task": "json_decode_regex",
             "backend": args.backend,
             "num_gpus": 1,
             "latency": round(latency, 3),

benchmark/json_regex_decode/bench_sglang.py → benchmark/json_decode_regex/bench_sglang.py

@@ -64,8 +64,6 @@ def main(args):
     # Run requests
     tic = time.time()
     states = json_decode.run_batch(arguments, temperature=0, num_threads=args.parallel)
-    for state in states:
-        state.sync()
     latency = time.time() - tic
 
     # Compute accuracy
@@ -80,7 +78,7 @@ def main(args):
 
     with open(args.result_file, "a") as fout:
         value = {
-            "task": "json_regex_decode",
+            "task": "json_decode_regex",
             "backend": args.backend,
             "num_gpus": 1,
             "latency": round(latency, 3),

benchmark/latency_throughput/README.md

@@ -3,19 +3,6 @@
 wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
 ```
 
-### Performance
-
-- Model: Llama-2-7b-chat-hf
-- `--num-prompts 2000 --request-rate 200`
-- On 4 A10 (24G) GPUs
-
-| Backend     | Throughput      | Latency  |
-| ----------- | --------------- | -------- |
-| srt         | 5.82 requests/s | 343.54 s |
-| vllm==0.2.6 | 3.93 requests/s | 509.08 s |
-| vllm==0.2.7 | 5.02 requests/s | 398.25 s |
-
- 
 ### SGLang
 ```
 python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000
@@ -28,7 +15,7 @@ python3 bench_throughput.py --backend srt --tokenizer meta-llama/Llama-2-7b-chat
 
 ### vLLM
 ```
-python3 -m vllm.entrypoints.api_server --model meta-llama/Llama-2-7b-chat-hf --disable-log-requests --swap-space 16
+python3 -m vllm.entrypoints.api_server --model meta-llama/Llama-2-7b-chat-hf --disable-log-requests --swap-space 16 --port 21000
 ```
 
 ```

benchmark/mmlu/bench_other.py

@@ -95,6 +95,9 @@ def evaluate(args, subject, dev_df, test_df):
                 max_tokens=max_tokens, temperature=0)
             return out["answer"]
 
+        # warmup
+        call_generate("Hello,", temperature=1.0, max_tokens=8)
+
     elif args.backend == "lmql":
         import lmql
         model = lmql.model("meta-llama/Llama-2-7b-chat-hf",

benchmark/multi_turns/README.md → benchmark/multi_turn_chat/README.md

 ### Benchmark sglang
 
-Run llama-7b
+Run Llama-7B
 
 ```
 python3 -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000
 ```
 
-Run mixtral-8x7b
+Run Mixtral-8x7B
 (When there is a CUDA out-of-memory error, try to reduce the `--mem-fraction-static`)
 
 ```
@@ -27,13 +27,13 @@ python3 bench_sglang.py --tokenizer meta-llama/Llama-2-7b-chat-hf --long
 
 ### Benchmark vLLM
 
-Run llama-7b
+Run Llama-7B
 
 ```
 python3 -m vllm.entrypoints.api_server --tokenizer-mode auto --model meta-llama/Llama-2-7b-chat-hf  --disable-log-requests --port 21000
 ```
 
-Run mixtral-8x7b
+Run Mixtral-8x7B
 
 ```
 python3 -m vllm.entrypoints.api_server --tokenizer-mode auto --model mistralai/Mixtral-8x7B-Instruct-v0.1 --disable-log-requests --port 21000 --tensor-parallel-size 8
@@ -53,13 +53,13 @@ python3 bench_other.py --tokenizer meta-llama/Llama-2-7b-chat-hf --backend vllm
 
 ### Benchmark guidance
 
-Benchmark llama-7b(short output)
+Benchmark Llama-7B (short output)
 
 ```
 python3 bench_other.py --tokenizer meta-llama/Llama-2-7b-chat-hf --backend guidance --parallel 1
 ```
 
-Benchmark llama-7b(long output)
+Benchmark Llama-7B (long output)
 
 ```
 python3 bench_other.py --tokenizer meta-llama/Llama-2-7b-chat-hf --backend guidance --parallel 1 --long

benchmark/multi_turns/bench_other.py → benchmark/multi_turn_chat/bench_other.py

@@ -99,7 +99,7 @@ def main(args):
 
     with open(args.result_file, "a") as fout:
         value = {
-            "task": "multi_turns",
+            "task": "multi_turn_chat",
             "backend": args.backend,
             "num_gpus": 1,
             "latency": round(latency, 3),

benchmark/multi_turns/bench_sglang.py → benchmark/multi_turn_chat/bench_sglang.py

@@ -21,8 +21,6 @@ def multi_turns(s, qas):
 
 
 def main(args):
-    print(args)
-
     tokenizer = get_tokenizer(args.tokenizer, trust_remote_code=args.trust_remote_code)
 
     multi_qas = gen_arguments(args, tokenizer)
@@ -33,8 +31,6 @@ def main(args):
     states = multi_turns.run_batch(
         multi_qas, temperature=0, backend=backend, num_threads=args.parallel
     )
-    for state in states:
-        state.sync()
     latency = time.time() - tic
 
     print(f"Latency: {latency:.3f}")
@@ -43,7 +39,7 @@ def main(args):
 
     with open(args.result_file, "a") as fout:
         value = {
-            "task": "multi_turns",
+            "task": "multi_turn_chat",
             "backend": args.backend,
             "num_gpus": 1,
             "latency": round(latency, 3),
@@ -74,4 +70,6 @@ if __name__ == "__main__":
         args.min_len_a = 256
         args.max_len_a = 512
         args.num_qa = 20
+
+    print(args)
     main(args)

benchmark/react/README.md

 ## Run benchmark
 
+NOTE: This is an implementation for replaying a given trace for throughput/latency benchmark purposes. It is not an actual ReAct agent implementation.
+
 ### Benchmark sglang
 ```
 python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000

benchmark/react/bench_other.py

@@ -124,6 +124,9 @@ def main(args):
             ))
             return out["result"]
 
+        # warmup
+        call_generate("Hello,", 1.0, 8, ".")
+
     else:
         raise ValueError(f"Invalid backend: {args.backend}")
 

benchmark/react/bench_sglang.py

@@ -82,9 +82,10 @@ Action 3: Finish[yes]
 """ + question)
     for i in range(1, len(triplets) + 2):
         s += "Thought " + str(i) + ":"
+        # NOTE: This is an implementation for replaying a given trace for benchmark purposes. It is not an actual ReAct agent implementation.
         ss = s.fork(1)
         ss[0] += sgl.gen(name="thought_action", max_tokens=200, stop="Observation")
-        # ss.join()
+        ss.join()
         # to verify the correctness of output, this should be collected
         # print(ss[0]["thought_action"])
         if i > len(triplets):

benchmark/tree_of_thought_deep/README.md

@@ -5,13 +5,15 @@ wget https://raw.githubusercontent.com/openai/grade-school-math/master/grade_sch
 
 ## Run benchmark
 
+NOTE: This is an implementation for throughput/latency benchmark purposes. The prompts are not tuned to achieve good accuracy on the GSM-8K tasks.
+
 ### Benchmark sglang
 ```
 python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000
 ```
 
 ```
-python3 bench_sglang.py --num-questions 32 --parallel 8
+python3 bench_sglang.py --num-questions 32
 python3 bench_sglang.py --num-questions 16 --parallel 1
 ```
 

benchmark/tree_of_thought_deep/bench_other.py

@@ -141,6 +141,9 @@ def main(args):
                     rets.append(out["answer"])
                 return rets
 
+        # warmup
+        call_generate("Hello,", 1.0, 8, ".", 1)
+
     # Run requests
     states = [None] * len(questions)
     def get_one_answer(i):