Json Decode && Mutl-Turns (#4)

flashinfer @ 88b9496e

-Subproject commit 00cf5f46fdbb4f1dbd9277fe3b842621c1d9e7dc
+Subproject commit 88b9496e1a726ddb353eb42887cfc0ab32c99460

benchmark/json_regex_decode/README.md

+## Run benchmark
+
+### Build dataset
+```
+pip install wikipedia
+python3 build_dataset.py
+```
+
+### Dependencies
+
+```
+llama_cpp_python          0.2.19
+guidance                  0.1.10
+vllm                      0.2.5
+outlines                  0.0.22
+```
+
+### Benchmark sglang
+
+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`)
+
+```
+python3 -m sglang.launch_server --model-path mistralai/Mixtral-8x7B-Instruct-v0.1 --port 30000 --tp-size 8
+```
+
+Benchmark
+
+```
+python3 bench_sglang.py --num-questions 10
+```
+
+
+### Benchmark vllm
+
+Run llama-7b
+
+```
+python3 -m outlines.serve.serve --tokenizer-mode auto --model meta-llama/Llama-2-7b-chat-hf  --disable-log-requests --port 21000
+```
+
+Benchmark
+
+```
+python3 bench_other.py --backend vllm --num-questions 10
+```
+
+
+### Benchmark guidance
+
+Run llama-7b and benchmark
+
+```
+python3 bench_other.py --backend guidance --num-questions 10 --parallel 1
+```
\ No newline at end of file

benchmark/json_regex_decode/bench_other.py

+import argparse
+import json
+import time
+from concurrent.futures import ThreadPoolExecutor
+from functools import partial
+
+from sglang.test.test_utils import (
+    add_common_other_args_and_parse,
+    call_generate_outlines,
+)
+from sglang.utils import dump_state_text, read_jsonl
+from sglang.lang.ir import REGEX_INT, REGEX_STRING, REGEX_FLOAT
+from tqdm import tqdm
+
+REGEX_LIST = r"\[(" + REGEX_STRING + ", )*" + REGEX_STRING + r"\]"
+
+
+# fmt: off
+def json_decode(document, generate):
+    s = "Please extract the information of a city from the following wikipedia page.\n"
+    s += "Page begin.\n" + document + "Page end.\n"
+    s += "Here is the name, country, and symbol of the city in JSON format.\n"
+    s += "{\n"
+    s += '  "name": '
+    s += generate(s, max_tokens=8, regex=REGEX_STRING + ",") + "\n"
+    s += '  "country": '
+    s += generate(s, max_tokens=8, regex=REGEX_STRING + ",") + "\n"
+    s += '  "latitude": '
+    s += generate(s, max_tokens=8, regex=REGEX_FLOAT + ",") + "\n"
+    s += '  "population": '
+    s += generate(s, max_tokens=8, regex=REGEX_INT + ",") + "\n"
+    s += '  "top 3 landmarks": '
+    s += generate(s, max_tokens=24, regex=REGEX_LIST) + "\n"
+    s += "}\n"
+
+    return s
+# fmt: on
+
+
+def main(args):
+    lines = read_jsonl(args.data_path)
+    arguments = []
+    for i in range(len(lines[: args.num_questions])):
+        arguments.append(
+            {
+                "document": lines[i]["document"],
+            }
+        )
+    states = [None] * len(arguments)
+
+    # Select backend
+    if args.backend == "vllm":
+        url = f"{args.host}:{args.port}/generate"
+        generate = partial(call_generate_outlines, url=url, temperature=0)
+    elif args.backend == "guidance":
+        from guidance import gen, models
+
+        model = models.LlamaCpp(
+            "/home/ubuntu/model_weights/Llama-2-7b-chat-hf/ggml-model-f16.gguf",
+            n_gpu_layers=-1,
+            n_ctx=4096,
+        )
+
+        def generate(prompt, max_tokens, stop=None, regex=None):
+            out = (
+                model
+                + prompt
+                + gen(
+                    name="answer",
+                    max_tokens=max_tokens,
+                    temperature=0,
+                    stop=stop,
+                    regex=regex,
+                )
+            )
+            return out["answer"]
+
+        # warmup
+        for _ in range(3):
+            generate("Hello!" * 10, max_tokens=64, stop=None)
+    else:
+        raise ValueError(f"Invalid backend: {args.backend}")
+
+    # Run requests
+    def get_one_answer(i):
+        states[i] = json_decode(generate=generate, **arguments[i])
+
+    tic = time.time()
+    if args.parallel == 1:
+        for i in tqdm(range(len(arguments))):
+            get_one_answer(i)
+    else:
+        with ThreadPoolExecutor(args.parallel) as executor:
+            rets = executor.map(get_one_answer, list(range(len(arguments))))
+            for _ in rets:
+                pass
+
+    latency = time.time() - tic
+
+    # Compute accuracy
+    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": "json_regex_decode",
+            "backend": args.backend,
+            "num_gpus": 1,
+            "latency": round(latency, 3),
+            "num_requests": args.num_questions,
+            "other": {
+                "parallel": args.parallel,
+            },
+        }
+        fout.write(json.dumps(value) + "\n")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--data-path", type=str, default="questions.jsonl")
+    parser.add_argument("--num-questions", type=int, default=20)
+    args = add_common_other_args_and_parse(parser)
+    main(args)

benchmark/json_regex_decode/bench_sglang.py

+import argparse
+import json
+import time
+
+import sglang as sgl
+from sglang.lang.ir import REGEX_INT, REGEX_STRING, REGEX_FLOAT
+from sglang.test.test_utils import (
+    add_common_sglang_args_and_parse,
+    select_sglang_backend,
+)
+from sglang.utils import dump_state_text, read_jsonl
+
+REGEX_LIST = r"\[(" + REGEX_STRING + ", )*" + REGEX_STRING + r"\]"
+
+# fmt: off
+@sgl.function
+def json_warm_up(s):
+    s += "The information about Hogwarts is in the following JSON format.\n"
+    with s.var_scope("json_output"):
+        s += "{\n"
+        s += '  "name": ' + sgl.gen("name", max_tokens=8, regex=REGEX_STRING + ",") + "\n"
+        s += '  "country": ' + sgl.gen("country", max_tokens=8, regex=REGEX_STRING + ",") + "\n"
+        s += '  "latitude": ' + sgl.gen("latitude", max_tokens=8, regex=REGEX_FLOAT + ",") + "\n"
+        s += '  "population": ' + sgl.gen("population", max_tokens=8, regex=REGEX_INT + ",") + "\n"
+        s += '  "top 3 landmarks": ' + sgl.gen( "landmarks", max_tokens=24, regex=REGEX_LIST) + "\n"
+        s += "}\n"
+    print(f'The warmp up json result is:\n{s["json_output"]}')
+# fmt: on
+
+# fmt: off
+@sgl.function
+def json_decode(s, document):
+    s += "Please extract the information of a city from the following wikipedia page.\n"
+    s += "Page begin.\n" + document + "Page end.\n"
+    s += "Here is the name, country, and symbol of the city in JSON format.\n"
+    with s.var_scope("json_output"):
+        s += "{\n"
+        s += '  "name": ' + sgl.gen("name", max_tokens=8, regex=REGEX_STRING + ",") + "\n"
+        s += '  "country": ' + sgl.gen("country", max_tokens=8, regex=REGEX_STRING + ",") + "\n"
+        s += '  "latitude": ' + sgl.gen("latitude", max_tokens=8, regex=REGEX_FLOAT + ",") + "\n"
+        s += '  "population": ' + sgl.gen("population", max_tokens=8, regex=REGEX_INT + ",") + "\n"
+        s += '  "top 3 landmarks": ' + sgl.gen( "landmarks", max_tokens=24, regex=REGEX_LIST) + "\n"
+        s += "}\n"
+# fmt: on
+
+
+def main(args):
+    lines = read_jsonl(args.data_path)
+    arguments = []
+    for i in range(len(lines[: args.num_questions])):
+        arguments.append(
+            {
+                "document": lines[i]["document"],
+            }
+        )
+
+    # Select backend
+    backend = select_sglang_backend(args)
+    sgl.set_default_backend(backend)
+
+    # Warm up
+    json_warm_up.run().sync()
+
+    # 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
+    print(f"Latency: {latency:.3f}")
+
+    # Write results
+    dump_state_text(f"tmp_output_{args.backend}.txt", states)
+
+    with open(f"tmp_{args.backend}_json_results.txt", "w") as fout:
+        for state in states:
+            fout.write(state["json_output"] + "\n")
+
+    with open(args.result_file, "a") as fout:
+        value = {
+            "task": "json_regex_decode",
+            "backend": args.backend,
+            "num_gpus": 1,
+            "latency": round(latency, 3),
+            "num_requests": args.num_questions,
+            "other": {
+                "parallel": args.parallel,
+            },
+        }
+        fout.write(json.dumps(value) + "\n")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--data-path", type=str, default="questions.jsonl")
+    parser.add_argument("--num-questions", type=int, default=20)
+    args = add_common_sglang_args_and_parse(parser)
+    main(args)

benchmark/json_regex_decode/build_dataset.py

+import json
+
+import transformers
+import wikipedia
+
+model_path = "meta-llama/Llama-2-7b-chat-hf"
+t = transformers.AutoTokenizer.from_pretrained(model_path)
+city_names = [
+    "los angles",
+    "london",
+    "tokyo",
+    "beijing",
+    "singapore",
+    "paris",
+    "dubai",
+    "sydney",
+    "moscow",
+    "rome",
+    "toronto",
+    "rio de janeiro",
+    "istanbul",
+    "berlin",
+    "auckland",
+    "buenos aires",
+    "mexico city",
+    "mumbai",
+    "seoul",
+    "bangkok",
+    "cairo",
+    "athens",
+    "jerusalem",
+]
+
+
+def get_content(city_name):
+    content = str(wikipedia.page(city_name).content)
+    content = content.replace("\n\n", "\n")
+
+    tokens = t.encode(content)
+
+    expected_tokens = 3000
+    truncate_len = int((expected_tokens / len(tokens)) * len(content))
+    truncate_content = content[:truncate_len]
+    truncate_tokens = t.encode(truncate_content)
+
+    # Count token
+    print(
+        f"city_name: {city_name}, #tokens: {len(tokens)}, #truncate tokens: {len(truncate_tokens)}"
+    )
+
+    return truncate_content
+
+
+if __name__ == "__main__":
+    with open("questions.jsonl", "w") as fout:
+        for city_name in city_names:
+            truncate_content = get_content(city_name)
+            fout.write(json.dumps({"document": truncate_content}) + "\n")

benchmark/multi_turns/README.md

+### Benchmark sglang
+
+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`)
+
+```
+python3 -m sglang.launch_server --model-path mistralai/Mixtral-8x7B-Instruct-v0.1 --port 30000 --tp-size 8
+```
+
+Benchmark(short output)
+
+```
+python3 bench_sglang.py --tokenizer meta-llama/Llama-2-7b-chat-hf
+```
+
+Benchmark(long output)
+
+```
+python3 bench_sglang.py --tokenizer meta-llama/Llama-2-7b-chat-hf --long
+```
+
+### Benchmark vLLM
+
+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
+
+```
+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
+```
+
+Benchmark(short output)
+
+```
+python3 bench_other.py --tokenizer meta-llama/Llama-2-7b-chat-hf --backend vllm
+```
+
+Benchmark(long output)
+
+```
+python3 bench_other.py --tokenizer meta-llama/Llama-2-7b-chat-hf --backend vllm --long
+```
+
+### Benchmark guidance
+
+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)
+
+```
+python3 bench_other.py --tokenizer meta-llama/Llama-2-7b-chat-hf --backend guidance --parallel 1 --long
+```
\ No newline at end of file

benchmark/multi_turns/bench_other.py

+import json
+import time
+from argparse import ArgumentParser
+from concurrent.futures import ThreadPoolExecutor
+
+import requests
+from sglang.test.test_utils import add_common_other_args_and_parse
+from sglang.utils import dump_state_text
+from tqdm import tqdm
+from vllm.transformers_utils.tokenizer import get_tokenizer
+
+from data_gen import gen_arguments
+
+
+def get_generate(args):
+    # Select backend
+    if args.backend == "vllm":
+        url = f"{args.host}:{args.port}/generate"
+
+        def generate(prompt, max_tokens, stop=None, temperature=0, url=url, n=1):
+            data = {
+                "prompt": prompt,
+                "temperature": temperature,
+                "max_tokens": max_tokens,
+                "ignore_eos": True,
+                "stop": stop,
+                "stream": False,
+                "n": n,
+            }
+            res = requests.post(url, json=data)
+            assert res.status_code == 200
+            return res.json()["text"][0][len(prompt) :]
+
+    elif args.backend == "guidance":
+        from guidance import gen, models
+
+        model = models.LlamaCpp(
+            "/home/ubuntu/model_weights/Llama-2-7b-chat-hf/ggml-model-f16.gguf",
+            n_gpu_layers=-1,
+            n_ctx=4096,
+        )
+
+        def generate(prompt, max_tokens, stop=None):
+            out = (
+                model
+                + prompt
+                + gen(name="answer", max_tokens=max_tokens, temperature=0, stop=stop)
+            )
+            return out["answer"]
+
+        # warmup
+        for _ in range(3):
+            generate("Hello!" * 10, max_tokens=64, stop=None)
+    else:
+        raise ValueError(f"Invalid backend: {args.backend}")
+
+    return generate
+
+
+def multi_turns(generate, qas):
+    s = ""
+    for qa in qas:
+        s += qa["prompt"]
+        s += generate(s, max_tokens=qa["new_tokens"]) 
+
+    return s
+
+
+def main(args):
+    print(args)
+
+    tokenizer = get_tokenizer(args.tokenizer, trust_remote_code=args.trust_remote_code)
+
+    multi_qas = gen_arguments(args, tokenizer)
+
+    states = [None] * args.num_qa
+
+    generate = get_generate(args)
+
+    def get_one_answer(i):
+        states[i] = multi_turns(generate=generate, **multi_qas[i])
+
+    tic = time.time()
+    if args.parallel == 1:
+        for i in tqdm(range(len(multi_qas))):
+            get_one_answer(i)
+    else:
+        with ThreadPoolExecutor(args.parallel) as executor:
+            rets = executor.map(get_one_answer, list(range(len(multi_qas))))
+            for _ in rets:
+                pass
+
+    latency = time.time() - tic
+
+    # Compute accuracy
+    print(f"Latency: {latency:.3f}")
+
+    dump_state_text(f"tmp_output_{args.backend}.txt", states)
+
+    with open(args.result_file, "a") as fout:
+        value = {
+            "task": "multi_turns",
+            "backend": args.backend,
+            "num_gpus": 1,
+            "latency": round(latency, 3),
+            "num_requests": args.num_qa,
+            "num_turns": args.turns,
+            "other": {
+                "parallel": args.parallel,
+                "output_mode": "long" if args.long else "short",
+            },
+        }
+        fout.write(json.dumps(value) + "\n")
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser()
+    parser.add_argument("--turns", type=int, default=4)
+    parser.add_argument("--num-qa", type=int, default=20)
+    parser.add_argument("--min-len-q", type=int, default=256)
+    parser.add_argument("--max-len-q", type=int, default=512)
+    parser.add_argument("--min-len-a", type=int, default=4)
+    parser.add_argument("--max-len-a", type=int, default=8)
+    parser.add_argument("--tokenizer", type=str, required=True)
+    parser.add_argument("--trust-remote-code", action="store_true")
+    parser.add_argument("--long", action="store_true")
+    args = add_common_other_args_and_parse(parser)
+
+    if args.long:
+        args.min_len_a = 256
+        args.max_len_a = 512
+        args.num_qa = 20
+    main(args)

benchmark/multi_turns/bench_sglang.py

+import json
+import time
+from argparse import ArgumentParser
+
+import sglang as sgl
+from sglang.test.test_utils import (
+    add_common_sglang_args_and_parse,
+    select_sglang_backend,
+)
+from sglang.utils import dump_state_text
+from vllm.transformers_utils.tokenizer import get_tokenizer
+
+from data_gen import gen_arguments
+
+
+@sgl.function
+def multi_turns(s, qas):
+    for qa in qas:
+        s += qa["prompt"]
+        s += sgl.gen(max_tokens=qa["new_tokens"], ignore_eos=True) 
+
+
+def main(args):
+    print(args)
+
+    tokenizer = get_tokenizer(args.tokenizer, trust_remote_code=args.trust_remote_code)
+
+    multi_qas = gen_arguments(args, tokenizer)
+
+    backend = select_sglang_backend(args)
+
+    tic = time.time()
+    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}")
+
+    dump_state_text(f"tmp_output_{args.backend}.txt", states)
+
+    with open(args.result_file, "a") as fout:
+        value = {
+            "task": "multi_turns",
+            "backend": args.backend,
+            "num_gpus": 1,
+            "latency": round(latency, 3),
+            "num_requests": args.num_qa,
+            "num_turns": args.turns,
+            "other": {
+                "parallel": args.parallel,
+                "output_mode": "long" if args.long else "short",
+            },
+        }
+        fout.write(json.dumps(value) + "\n")
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser()
+    parser.add_argument("--turns", type=int, default=4)
+    parser.add_argument("--num-qa", type=int, default=20)
+    parser.add_argument("--min-len-q", type=int, default=256)
+    parser.add_argument("--max-len-q", type=int, default=512)
+    parser.add_argument("--min-len-a", type=int, default=4)
+    parser.add_argument("--max-len-a", type=int, default=8)
+    parser.add_argument("--tokenizer", type=str, required=True)
+    parser.add_argument("--trust-remote-code", action="store_true")
+    parser.add_argument("--long", action="store_true")
+    args = add_common_sglang_args_and_parse(parser)
+
+    if args.long:
+        args.min_len_a = 256
+        args.max_len_a = 512
+        args.num_qa = 20
+    main(args)

benchmark/multi_turns/data_gen.py

+import random
+import string
+
+random.seed(42)
+
+
+def gen_prompt(tokenizer, token_num):
+    cha_set = string.ascii_letters + string.digits
+    ret = "".join(random.choices(cha_set, k=token_num))
+    while len(tokenizer(ret).input_ids) < token_num:
+        ret += random.choice(cha_set)
+    return ret
+
+
+def gen_arguments(args, tokenizer):
+    multi_qas = [{"qas": []} for _ in range(args.num_qa)]
+    for i in range(args.num_qa):
+        qas = multi_qas[i]["qas"]
+        for _ in range(args.turns):
+            prompt_len = random.randint(args.min_len_q, args.max_len_q)
+            new_tokens = random.randint(args.min_len_a, args.max_len_a)
+            qas.append(
+                {
+                    "prompt": gen_prompt(tokenizer, prompt_len),
+                    "new_tokens": new_tokens,
+                }
+            )
+
+    return multi_qas

python/sglang/api.py

@@ -37,6 +37,7 @@ def gen(
     top_k: Optional[int] = None,
     frequency_penalty: Optional[float] = None,
     presence_penalty: Optional[float] = None,
+    ignore_eos: Optional[bool] = None,
     dtype: Optional[type] = None,
     choices: Optional[List[str]] = None,
     regex: Optional[str] = None,
@@ -60,6 +61,7 @@ def gen(
         top_k,
         frequency_penalty,
         presence_penalty,
+        ignore_eos,
         dtype,
         regex,
     )
@@ -74,6 +76,7 @@ def gen_int(
     top_k: Optional[int] = None,
     frequency_penalty: Optional[float] = None,
     presence_penalty: Optional[float] = None,
+    ignore_eos: Optional[bool] = None,
 ):
     return SglGen(
         name,
@@ -84,6 +87,7 @@ def gen_int(
         top_k,
         frequency_penalty,
         presence_penalty,
+        ignore_eos,
         int,
         None,
     )
@@ -98,6 +102,7 @@ def gen_string(
     top_k: Optional[int] = None,
     frequency_penalty: Optional[float] = None,
     presence_penalty: Optional[float] = None,
+    ignore_eos: Optional[bool] = None,
 ):
     return SglGen(
         name,
@@ -108,6 +113,7 @@ def gen_string(
         top_k,
         frequency_penalty,
         presence_penalty,
+        ignore_eos,
         str,
         None,
     )

python/sglang/backend/anthropic.py

@@ -4,7 +4,7 @@ import numpy as np
 from sglang.backend.base_backend import BaseBackend
 from sglang.lang.chat_template import get_chat_template
 from sglang.lang.interpreter import StreamExecutor
-from sglang.lang.ir import SamplingParams
+from sglang.lang.ir import SglSamplingParams
 
 try:
     import anthropic
@@ -28,7 +28,7 @@ class Anthropic(BaseBackend):
     def generate(
         self,
         s: StreamExecutor,
-        sampling_params: SamplingParams,
+        sampling_params: SglSamplingParams,
     ):
         prompt = s.text_
         ret = anthropic.Anthropic().completions.create(
@@ -43,7 +43,7 @@ class Anthropic(BaseBackend):
     def generate_stream(
         self,
         s: StreamExecutor,
-        sampling_params: SamplingParams,
+        sampling_params: SglSamplingParams,
     ):
         prompt = s.text_
         generator = anthropic.Anthropic().completions.create(

python/sglang/backend/base_backend.py

@@ -2,7 +2,7 @@ from typing import Callable, List, Optional, Union
 
 from sglang.lang.chat_template import get_chat_template
 from sglang.lang.interpreter import StreamExecutor
-from sglang.lang.ir import SamplingParams
+from sglang.lang.ir import SglSamplingParams
 
 
 class BaseBackend:
@@ -48,14 +48,14 @@ class BaseBackend:
     def generate(
         self,
         s: StreamExecutor,
-        sampling_params: SamplingParams,
+        sampling_params: SglSamplingParams,
     ):
         raise NotImplementedError()
 
     def generate_stream(
         self,
         s: StreamExecutor,
-        sampling_params: SamplingParams,
+        sampling_params: SglSamplingParams,
     ):
         raise NotImplementedError()
 

python/sglang/backend/openai.py

@@ -4,7 +4,7 @@ import numpy as np
 from sglang.backend.base_backend import BaseBackend
 from sglang.lang.chat_template import get_chat_template
 from sglang.lang.interpreter import StreamExecutor
-from sglang.lang.ir import SamplingParams
+from sglang.lang.ir import SglSamplingParams
 
 try:
     import openai
@@ -73,7 +73,7 @@ class OpenAI(BaseBackend):
     def generate(
         self,
         s: StreamExecutor,
-        sampling_params: SamplingParams,
+        sampling_params: SglSamplingParams,
     ):
         if sampling_params.dtype is None:
             if self.is_chat_model:
@@ -122,7 +122,7 @@ class OpenAI(BaseBackend):
     def generate_stream(
         self,
         s: StreamExecutor,
-        sampling_params: SamplingParams,
+        sampling_params: SglSamplingParams,
     ):
         if sampling_params.dtype is None:
             if self.is_chat_model:

python/sglang/backend/runtime_endpoint.py

@@ -7,7 +7,7 @@ from sglang.backend.base_backend import BaseBackend
 from sglang.global_config import global_config
 from sglang.lang.chat_template import get_chat_template_by_model_path
 from sglang.lang.interpreter import StreamExecutor
-from sglang.lang.ir import SamplingParams, SglArgument
+from sglang.lang.ir import SglSamplingParams, SglArgument
 from sglang.utils import encode_image_base64, find_printable_text, http_request
 
 
@@ -55,7 +55,7 @@ class RuntimeEndpoint(BaseBackend):
     def generate(
         self,
         s: StreamExecutor,
-        sampling_params: SamplingParams,
+        sampling_params: SglSamplingParams,
     ):
         if sampling_params.dtype is None:
             data = {
@@ -87,7 +87,7 @@ class RuntimeEndpoint(BaseBackend):
     def generate_stream(
         self,
         s: StreamExecutor,
-        sampling_params: SamplingParams,
+        sampling_params: SglSamplingParams,
     ):
         if sampling_params.dtype is None:
             data = {

python/sglang/backend/tgi.py

@@ -7,7 +7,7 @@ from typing import List, Optional, Union
 from sglang.backend.base_backend import BaseBackend
 from sglang.lang.chat_template import get_chat_template_by_model_path
 from sglang.lang.interpreter import StreamExecutor
-from sglang.lang.ir import SamplingParams
+from sglang.lang.ir import SglSamplingParams
 from sglang.utils import http_request
 
 
@@ -138,7 +138,7 @@ class TGI(BaseBackend):
         self,
         s: StreamExecutor,
         choices: List[str],
-        sampling_params: SamplingParams,
+        sampling_params: SglSamplingParams,
     ):
         decision = self.retry_for_expected(
             s.text_,
@@ -152,7 +152,7 @@ class TGI(BaseBackend):
         s: StreamExecutor,
         max_tokens: int,
         stop: Union[str, List[str]],
-        sampling_params: SamplingParams,
+        sampling_params: SglSamplingParams,
         dtype: Optional[str] = None,
     ):
         if dtype is None:

python/sglang/lang/compiler.py

@@ -6,7 +6,7 @@ from typing import List, Union
 from sglang.global_config import global_config
 from sglang.lang.interpreter import ProgramState, StreamExecutor, pin_program
 from sglang.lang.ir import (
-    SamplingParams,
+    SglSamplingParams,
     SglArgument,
     SglConstantText,
     SglExpr,
@@ -140,7 +140,7 @@ class CompiledFunction:
         kwargs = {k: SglArgument(k, v) for k, v in kwargs.items()}
         kwargs.update(self.function.bind_arguments)
 
-        default_sampling_para = SamplingParams(
+        default_sampling_para = SglSamplingParams(
             max_new_tokens=max_new_tokens,
             stop=stop,
             temperature=temperature,
@@ -173,7 +173,7 @@ class CompiledFunction:
 
         backend = backend or global_config.default_backend
 
-        default_sampling_para = SamplingParams(
+        default_sampling_para = SglSamplingParams(
             max_new_tokens=max_new_tokens,
             stop=stop,
             temperature=temperature,

python/sglang/lang/interpreter.py

@@ -292,7 +292,7 @@ class StreamExecutor:
 
         assert isinstance(other, SglExpr), f"{other}"
 
-        if isinstance(other, (SglConstantText, SglArgument)):
+        if isinstance(other, SglConstantText):
             self._execute_fill(other.value)
         elif isinstance(other, SglGen):
             self._execute_gen(other)
@@ -332,8 +332,6 @@ class StreamExecutor:
 
     def _execute_image(self, expr: SglImage):
         path = expr.path
-        if isinstance(path, SglArgument):
-            path = path.value
 
         base64_data = encode_image_base64(path)
 
@@ -419,7 +417,7 @@ class StreamExecutor:
                 "role": expr.role,
                 "content": [{"type": "text", "text": new_text}],
             }
-            for (image_path, image_base64_data) in self.cur_images:
+            for image_path, image_base64_data in self.cur_images:
                 last_msg["content"].append(
                     {
                         "type": "image_url",
@@ -480,6 +478,7 @@ class StreamExecutor:
             "top_k",
             "frequency_penalty",
             "presence_penalty",
+            "ignore_eos", 
             "dtype",
             "regex",
         ]:

python/sglang/lang/ir.py

@@ -13,7 +13,7 @@ REGEX_STRING = r"\"[\w\d\s]*\""  # bugs with regex r"\".*\"" in interegular pkg
 
 
 @dataclasses.dataclass
-class SamplingParams:
+class SglSamplingParams:
     max_new_tokens: int = 16
     stop: Union[str, List[str]] = ()
     temperature: float = 1.0
@@ -21,13 +21,14 @@ class SamplingParams:
     top_k: int = -1  # -1 means disable
     frequency_penalty: float = 0.0
     presence_penalty: float = 0.0
+    ignore_eos: bool = False
 
     # for constrained generation, not included in to_xxx_kwargs
     dtype: Optional[str] = None
     regex: Optional[str] = None
 
     def clone(self):
-        return SamplingParams(
+        return SglSamplingParams(
             self.max_new_tokens,
             self.stop,
             self.temperature,
@@ -67,6 +68,7 @@ class SamplingParams:
             "top_k": self.top_k,
             "frequency_penalty": self.frequency_penalty,
             "presence_penalty": self.presence_penalty,
+            "ignore_eos": self.ignore_eos,
             "regex": self.regex,
         }
 
@@ -98,13 +100,14 @@ class SglFunction:
         top_k: int = -1,
         frequency_penalty: float = 0.0,
         presence_penalty: float = 0.0,
+        ignore_eos: bool = False,
         stream: bool = False,
         backend=None,
         **kwargs,
     ):
         from sglang.lang.interpreter import run_program
 
-        default_sampling_para = SamplingParams(
+        default_sampling_para = SglSamplingParams(
             max_new_tokens=max_new_tokens,
             stop=stop,
             temperature=temperature,
@@ -112,9 +115,9 @@ class SglFunction:
             top_k=top_k,
             frequency_penalty=frequency_penalty,
             presence_penalty=presence_penalty,
+            ignore_eos=ignore_eos,
         )
         backend = backend or global_config.default_backend
-        kwargs = {k: SglArgument(k, v) for k, v in kwargs.items()}
         return run_program(self, backend, args, kwargs, default_sampling_para, stream)
 
     def run_batch(
@@ -128,6 +131,7 @@ class SglFunction:
         top_k: int = -1,
         frequency_penalty: float = 0.0,
         presence_penalty: float = 0.0,
+        ignore_eos: bool = False,
         backend=None,
         num_threads: Union[str, int] = "auto",
         progress_bar: bool = False,
@@ -139,7 +143,7 @@ class SglFunction:
             return []
         assert isinstance(batch_kwargs[0], dict)
 
-        default_sampling_para = SamplingParams(
+        default_sampling_para = SglSamplingParams(
             max_new_tokens=max_new_tokens,
             stop=stop,
             temperature=temperature,
@@ -147,11 +151,9 @@ class SglFunction:
             top_k=top_k,
             frequency_penalty=frequency_penalty,
             presence_penalty=presence_penalty,
+            ignore_eos=ignore_eos,
         )
         backend = backend or global_config.default_backend
-        batch_kwargs = [
-            {k: SglArgument(k, v) for k, v in kwargs.items()} for kwargs in batch_kwargs
-        ]
         return run_program_batch(
             self,
             backend,
@@ -321,12 +323,13 @@ class SglGen(SglExpr):
         top_k,
         frequency_penalty,
         presence_penalty,
+        ignore_eos,
         dtype,
         regex,
     ):
         super().__init__()
         self.name = name
-        self.sampling_params = SamplingParams(
+        self.sampling_params = SglSamplingParams(
             max_new_tokens=max_new_tokens,
             stop=stop,
             temperature=temperature,
@@ -334,6 +337,7 @@ class SglGen(SglExpr):
             top_k=top_k,
             frequency_penalty=frequency_penalty,
             presence_penalty=presence_penalty,
+            ignore_eos=ignore_eos,
             dtype=dtype,
             regex=regex,
         )

python/sglang/lang/tracer.py

@@ -40,7 +40,8 @@ def extract_prefix_by_tracing(program, backend):
     try:
         with TracingScope(tracer):
             tracer.ret_value = program.func(tracer, **arguments)
-    except StopTracing:
+    except (StopTracing, TypeError):
+        # Some exceptions may not be catched
         pass
 
     # Run and cache prefix

python/sglang/srt/backend_config.py

+"""
+Backend configurations, may vary with different serving platforms.
+"""
+from dataclasses import dataclass
+
+
+@dataclass
+class BackendConfig:
+    extend_dependency_time: float = 0.03
+
+
+GLOBAL_BACKEND_CONFIG = BackendConfig()