Benchmark for reasoning models (#3532)

Co-authored-by: Chayenne <zhaochen20@outlook.com>

benchmark/reasoning_benchmark/README.md

+# Run benchmark
+
+This benchmark is primarily intended to be used with reasoning models like `DeepSeek-R1` and its distilled models like `DeepSeek-R1-Distill-Qwen-1.5B`. Please use
+
+```bash
+pip install antlr4-python3-runtime
+```
+
+for `parse_latex` which we use for symbolic equality check.
+
+## Benchmark sglang
+
+1. Launch the Server
+
+```bash
+python3 -m sglang.launch_server --model-path deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B --port 30000
+```
+
+Note that depending on the GPU this benchmark will take quiet some time. To employ data parallelism please use:
+
+```bash
+python3 -m sglang_router.launch_server --model-path deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B --port 30000 --dp-size 4
+```
+
+2. Benchmarking
+
+We use [suggested](https://github.com/deepseek-ai/DeepSeek-R1) parameters of `temperature=0.6`, `top_p=.95`, `max_new_tokens=32768`. The command line argument `num-tries` can be used to evaluate the model multiple times on the same question. We use the suggested `64` from the repo for AIME 2024. For LIMO, we use `8` as the number of tries due to the size of the dataset.
+
+By default evaluate on LIMO dataset.
+
+```bash
+python3 bench_sglang.py --parallel 256 --num-tries 64 --port 30000
+```
+
+Evaluate on AIME 2024 dataset.
+
+```bash
+python3 bench_sglang.py --parallel 256 --port 30000 --data-path Maxwell-Jia/AIME_2024 --question-key Problem --answer-key Answer --num-tries 64
+```
+
+Evaluate on [AIME 2025 I dataset](https://huggingface.co/datasets/opencompass/AIME2025). For benchmark result see [here](https://matharena.ai/).
+
+```bash
+python3 bench_sglang.py --parallel 256 --port 30000 --data-path opencompass/AIME2025 --question-key question --answer-key answer --num-tries 64
+```
+
+
+## Results
+
+| Dataset    | Num Tries | Accuracy | Reference |
+|------------|-----------|----------|-----------|
+| LIMO       | 8         | 47.7%    | ?         |
+| AIME 2024  | 64        | 33.2%    | 28.9%     |
+| AIME 2025 I| 64        | 29.9%    | 25.0%     |

benchmark/reasoning_benchmark/answer_extraction.py

+# Adapted from https://github.com/deepseek-ai/DeepSeek-Math/blob/main/evaluation/data_processing/answer_extraction.py
+
+import re
+
+import regex
+
+
+def _fix_fracs(string):
+    substrs = string.split("\\frac")
+    new_str = substrs[0]
+    if len(substrs) > 1:
+        substrs = substrs[1:]
+        for substr in substrs:
+            new_str += "\\frac"
+            if len(substr) > 0 and substr[0] == "{":
+                new_str += substr
+            else:
+                try:
+                    assert len(substr) >= 2
+                except:
+                    return string
+                a = substr[0]
+                b = substr[1]
+                if b != "{":
+                    if len(substr) > 2:
+                        post_substr = substr[2:]
+                        new_str += "{" + a + "}{" + b + "}" + post_substr
+                    else:
+                        new_str += "{" + a + "}{" + b + "}"
+                else:
+                    if len(substr) > 2:
+                        post_substr = substr[2:]
+                        new_str += "{" + a + "}" + b + post_substr
+                    else:
+                        new_str += "{" + a + "}" + b
+    string = new_str
+    return string
+
+
+def _fix_a_slash_b(string):
+    if len(string.split("/")) != 2:
+        return string
+    a = string.split("/")[0]
+    b = string.split("/")[1]
+    try:
+        if "sqrt" not in a:
+            a = int(a)
+        if "sqrt" not in b:
+            b = int(b)
+        assert string == "{}/{}".format(a, b)
+        new_string = "\\frac{" + str(a) + "}{" + str(b) + "}"
+        return new_string
+    except:
+        return string
+
+
+def _fix_sqrt(string):
+    _string = re.sub(r"\\sqrt(-?[0-9.a-zA-Z]+)", r"\\sqrt{\1}", string)
+    _string = re.sub(r"\\sqrt\s+(\w+)$", r"\\sqrt{\1}", _string)
+    return _string
+
+
+def _fix_tan(string):
+    _string = re.sub(r"\\tan(-?[0-9.a-zA-Z]+)", r"\\tan{\1}", string)
+    _string = re.sub(r"\\tan\s+(\w+)$", r"\\tan{\1}", _string)
+    return _string
+
+
+def strip_string(string):
+    string = str(string).strip()
+    # linebreaks
+    string = string.replace("\n", "")
+
+    # right "."
+    string = string.rstrip(".")
+
+    # remove inverse spaces
+    string = string.replace("\\!", "")
+    # string = string.replace("\\ ", "")
+
+    # replace \\ with \
+    # string = string.replace("\\\\", "\\")
+    # string = string.replace("\\\\", "\\")
+
+    if string.startswith("\\text{") and string.endswith("}"):
+        string = string.split("{", 1)[1][:-1]
+
+    # replace tfrac and dfrac with frac
+    string = string.replace("tfrac", "frac")
+    string = string.replace("dfrac", "frac")
+    string = string.replace("cfrac", "frac")
+
+    # remove \left and \right
+    string = string.replace("\\left", "")
+    string = string.replace("\\right", "")
+
+    # Remove unit: miles, dollars if after is not none
+    _string = re.sub(r"\\text{.*?}$", "", string).strip()
+    if _string != "" and _string != string:
+        # print("Warning: unit not removed: '{}' -> '{}'".format(string, _string))
+        string = _string
+
+    # Remove circ (degrees)
+    string = string.replace("^{\\circ}", "").strip()
+    string = string.replace("^\\circ", "").strip()
+
+    string = regex.sub(r"\{(c|m)?m\}(\^(2|3))?", "", string).strip()
+    string = regex.sub(r"p\.m\.$", "", string).strip()
+    string = regex.sub(r"(\d)\s*t$", r"\1", string).strip()
+
+    # remove dollar signs
+    string = string.replace("\\$", "")
+    string = string.replace("$", "")
+
+    # string = string.replace("\\text", "")
+    string = string.replace("x\\in", "")
+
+    # remove percentage
+    string = string.replace("\\%", "%")
+    string = string.replace("\%", "%")
+    # string = string.replace("%", "")
+
+    # " 0." equivalent to " ." and "{0." equivalent to "{." Alternatively, add "0" if "." is the start of the string
+    string = string.replace(" .", " 0.")
+    string = string.replace("{.", "{0.")
+
+    # cdot
+    string = string.replace("\\cdot", "")
+
+    # inf
+    string = string.replace("infinity", "\\infty")
+    if "\\infty" not in string:
+        string = string.replace("inf", "\\infty")
+    string = string.replace("+\\inity", "\\infty")
+
+    # and
+    # string = string.replace("and", "")
+    string = string.replace("\\mathbf", "")
+    string = string.replace("\\mathrm", "")
+
+    # use regex to remove \mbox{...}
+    string = re.sub(r"\\mbox{.*?}", "", string)
+
+    # quote
+    string.replace("'", "")
+    string.replace('"', "")
+
+    # i, j
+    if "j" in string and "i" not in string:
+        string = string.replace("j", "i")
+
+    # replace a.000b where b is not number or b is end, with ab, use regex
+    string = re.sub(r"(\d+)\.0+([^\d])", r"\1\2", string)
+    string = re.sub(r"(\d+)\.0+$", r"\1", string)
+
+    # if empty, return empty string
+    if len(string) == 0:
+        return string
+    if string[0] == ".":
+        string = "0" + string
+
+    # to consider: get rid of e.g. "k = " or "q = " at beginning
+    # if len(string.split("=")) == 2:
+    #     if len(string.split("=")[0]) <= 2:
+    #         string = string.split("=")[1]
+
+    string = _fix_sqrt(string)
+    string = _fix_tan(string)
+    string = string.replace(" ", "")
+
+    # \frac1b or \frac12 --> \frac{1}{b} and \frac{1}{2}, etc. Even works with \frac1{72} (but not \frac{72}1). Also does a/b --> \\frac{a}{b}
+    string = _fix_fracs(string)
+
+    # NOTE: X/Y changed to \frac{X}{Y} in dataset, but in simple cases fix in case the model output is X/Y
+    string = _fix_a_slash_b(string)
+
+    string = regex.sub(r"(\\|,|\.)+$", "", string)
+
+    return string
+
+
+def extract_boxed_answers(text):
+    answers = []
+    for piece in text.split("boxed{")[1:]:
+        n = 0
+        for i in range(len(piece)):
+            if piece[i] == "{":
+                n += 1
+            elif piece[i] == "}":
+                n -= 1
+                if n < 0:
+                    if i + 1 < len(piece) and piece[i + 1] == "%":
+                        answers.append(piece[: i + 1])
+                    else:
+                        answers.append(piece[:i])
+                    break
+    return answers
+
+
+def extract_program_output(pred_str):
+    """
+    extract output between the last ```output\n...\n```
+    """
+    if "```output" not in pred_str:
+        return ""
+    if "```output" in pred_str:
+        pred_str = pred_str.split("```output")[-1]
+    if "```" in pred_str:
+        pred_str = pred_str.split("```")[0]
+    output = pred_str.strip()
+    return output
+
+
+def extract_answer(pred_str, exhaust=False):
+    pred = []
+    if "final answer is $" in pred_str and "$. I hope" in pred_str:
+        tmp = pred_str.split("final answer is $", 1)[1]
+        pred = [tmp.split("$. I hope", 1)[0].strip()]
+    elif "boxed" in pred_str:
+        pred = extract_boxed_answers(pred_str)
+    elif "he answer is" in pred_str:
+        pred = [pred_str.split("he answer is")[-1].strip()]
+    else:
+        program_output = extract_program_output(pred_str)
+        if program_output != "":
+            # fall back to program
+            pred.append(program_output)
+        else:  # use the last number
+            pattern = "-?\d*\.?\d+"
+            ans = re.findall(pattern, pred_str.replace(",", ""))
+            if len(ans) >= 1:
+                ans = ans[-1]
+            else:
+                ans = ""
+            if ans:
+                pred.append(ans)
+
+    # multiple line
+    _pred = []
+    for ans in pred:
+        ans = ans.strip().split("\n")[0]
+        ans = ans.lstrip(":")
+        ans = ans.rstrip(".")
+        ans = ans.rstrip("/")
+        ans = strip_string(ans)
+        _pred.append(ans)
+    if exhaust:
+        return _pred
+    else:
+        return _pred[-1] if _pred else ""
+
+
+def extract_math_answer(question, reasoning, task):
+    answer = []
+    for ans in extract_answer(reasoning, exhaust=True):
+        if "separated by commas" in question and all(ch not in ans for ch in "()[]"):
+            answer.extend([a.strip() for a in ans.split(",")])
+        elif regex.search(r"\\text\{\s*and\s*\}", ans):
+            answer.extend(
+                [
+                    a.strip()
+                    for a in regex.sub(r"\\text\{\s*and\s*\}", "[SEP]", ans).split(
+                        "[SEP]"
+                    )
+                ]
+            )
+        else:
+            answer.append(ans.strip())
+    return answer

benchmark/reasoning_benchmark/bench_sglang.py

+import argparse
+import json
+import time
+
+import answer_extraction
+import eval_utils
+from datasets import load_dataset
+
+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
+
+
+@sgl.function
+def reasoning_gen(s, question: str):
+    s += sgl.user(
+        question
+        + "\nPlease reason step by step, and put your final answer within \boxed{}."
+    )
+    s += sgl.assistant(
+        sgl.gen(
+            "answer",
+        )
+    )
+
+
+def convert_dataset(path: str, question_key: str, answer_key: str, num_tries: int):
+    raw_dataset = load_dataset(path)
+    questions = []
+    answers = []
+    for data in raw_dataset["train"]:
+        question = data[question_key]
+        answer = data[answer_key]
+        for _ in range(num_tries):
+            questions.append({"question": question})
+            answers.append({"answer": answer})
+    return questions, answers
+
+
+def main(args):
+    # Select backend
+    sgl.set_default_backend(select_sglang_backend(args))
+
+    # Get dataset
+    questions, answers = convert_dataset(
+        args.data_path, args.question_key, args.answer_key, args.num_tries
+    )
+
+    # Run requests
+    tic = time.time()
+    states = reasoning_gen.run_batch(
+        questions,
+        num_threads=args.parallel,
+        progress_bar=True,
+        temperature=0.6,
+        max_new_tokens=32768,
+        top_p=0.95,
+    )
+    latency = time.time() - tic
+
+    # Extract answers
+    correct = 0
+    for i, state in enumerate(states):
+        try:
+            pred_answer = answer_extraction.extract_math_answer(
+                questions[i]["question"], state["answer"], "limo"
+            )
+            gt_answer = str(answers[i]["answer"])
+            # Use last answer if multiple were extracted
+            pred_answer = (
+                pred_answer[-1] if isinstance(pred_answer, list) else pred_answer
+            )
+            correct += 1 if eval_utils.math_equal(pred_answer, gt_answer) else 0
+        except Exception as e:
+            print(f"Error extracting answer: {e}")
+            pass
+
+    # Calculate accuracy
+    accuracy = correct / len(questions)
+    print(f"Accuracy: {accuracy}")
+
+    # Calculate output throughput
+    num_output_tokens = sum(
+        s.get_meta_info("answer")["completion_tokens"] for s in states
+    )
+    output_throughput = num_output_tokens / latency
+    print(f"Output throughput: {output_throughput} token/s")
+
+    # Dump results
+    dump_state_text(f"tmp_output_{args.backend}.txt", states)
+
+    # Write results
+    with open(args.result_file, "a") as fout:
+        value = {
+            "task": "limo",
+            "backend": args.backend,
+            "latency": round(latency, 3),
+            "accuracy": round(accuracy, 3),
+            "num_requests": len(questions),
+            "other": {
+                "num_questions": len(questions),
+                "parallel": args.parallel,
+            },
+        }
+        fout.write(json.dumps(value) + "\n")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--data-path", type=str, default="GAIR/LIMO")
+    parser.add_argument("--question-key", type=str, default="question")
+    parser.add_argument("--answer-key", type=str, default="answer")
+    parser.add_argument("--num-tries", type=int, default=1)
+    add_common_sglang_args_and_parse(parser)
+    args = parser.parse_args()
+    main(args)

benchmark/reasoning_benchmark/eval_utils.py

+# Adapted from https://github.com/deepseek-ai/DeepSeek-Math/blob/main/evaluation/eval/eval_utils.py
+
+from math import isclose
+
+import regex
+from sympy import N, simplify
+from sympy.parsing.latex import parse_latex
+from sympy.parsing.sympy_parser import parse_expr
+
+
+def parse_digits(num):
+    # format: 234.23 || 23%
+    num = regex.sub(",", "", str(num))
+    try:
+        return float(num)
+    except:
+        if num.endswith("%"):
+            num = num[:-1]
+            if num.endswith("\\"):
+                num = num[:-1]
+            try:
+                return float(num) / 100
+            except:
+                pass
+    return None
+
+
+def is_digit(num):
+    # paired with parse_digits
+    return parse_digits(num) is not None
+
+
+def symbolic_equal(a, b):
+    def _parse(s):
+        for f in [parse_latex, parse_expr]:
+            try:
+                return f(s)
+            except:
+                pass
+        return s
+
+    a = _parse(a)
+    b = _parse(b)
+
+    try:
+        if simplify(a - b) == 0:
+            return True
+    except:
+        pass
+
+    try:
+        if isclose(N(a), N(b), abs_tol=1e-3):
+            return True
+    except:
+        pass
+    return False
+
+
+def math_equal(prediction, reference, include_percentage=True, is_close=True):
+    """
+    Exact match of math if and only if:
+    1. numerical equal: both can convert to float and are equal
+    2. symbolic equal: both can convert to sympy expression and are equal
+    """
+    if str(prediction) == str(reference):
+        return True
+
+    try:  # 1. numerical equal
+        if is_digit(prediction) and is_digit(reference):
+            prediction = parse_digits(prediction)
+            reference = parse_digits(reference)
+            # number questions
+            if include_percentage:
+                gt_result = [reference / 100, reference, reference * 100]
+            else:
+                gt_result = [reference]
+            for item in gt_result:
+                try:
+                    if is_close:
+                        if isclose(item, prediction, abs_tol=1e-3):
+                            return True
+                    else:
+                        if item == prediction:
+                            return True
+                except Exception:
+                    continue
+            return False
+    except:
+        pass
+
+    if not prediction and prediction not in [0, False]:
+        return False
+
+    # 2. symbolic equal
+    reference = str(reference).strip()
+    prediction = str(prediction).strip()
+
+    if (
+        regex.match(r"(\(|\[).+(\)|\])", prediction) is not None
+        and regex.match(r"(\(|\[).+(\)|\])", reference) is not None
+    ):
+        pred_parts = prediction[1:-1].split(",")
+        ref_parts = reference[1:-1].split(",")
+        if len(pred_parts) == len(ref_parts):
+            if all(
+                [
+                    math_equal(
+                        pred_parts[i], ref_parts[i], include_percentage, is_close
+                    )
+                    for i in range(len(pred_parts))
+                ]
+            ):
+                return True
+
+    # Add back matrix comparison
+    if (
+        (
+            prediction.startswith("\\begin{pmatrix}")
+            or prediction.startswith("\\begin{bmatrix}")
+        )
+        and (
+            prediction.endswith("\\end{pmatrix}")
+            or prediction.endswith("\\end{bmatrix}")
+        )
+        and (
+            reference.startswith("\\begin{pmatrix}")
+            or reference.startswith("\\begin{bmatrix}")
+        )
+        and (
+            reference.endswith("\\end{pmatrix}") or reference.endswith("\\end{bmatrix}")
+        )
+    ):
+        pred_lines = [
+            line.strip()
+            for line in prediction[
+                len("\\begin{pmatrix}") : -len("\\end{pmatrix}")
+            ].split("\\\\")
+            if line.strip()
+        ]
+        ref_lines = [
+            line.strip()
+            for line in reference[
+                len("\\begin{pmatrix}") : -len("\\end{pmatrix}")
+            ].split("\\\\")
+            if line.strip()
+        ]
+        matched = True
+        if len(pred_lines) == len(ref_lines):
+            for pred_line, ref_line in zip(pred_lines, ref_lines):
+                pred_parts = pred_line.split("&")
+                ref_parts = ref_line.split("&")
+                if len(pred_parts) == len(ref_parts):
+                    if not all(
+                        [
+                            math_equal(
+                                pred_parts[i],
+                                ref_parts[i],
+                                include_percentage,
+                                is_close,
+                            )
+                            for i in range(len(pred_parts))
+                        ]
+                    ):
+                        matched = False
+                        break
+                else:
+                    matched = False
+                if not matched:
+                    break
+        else:
+            matched = False
+        if matched:
+            return True
+
+    # Add back equation comparison
+    if prediction.count("=") == 1 and reference.count("=") == 1:
+        pred = prediction.split("=")
+        pred = f"{pred[0].strip()} - ({pred[1].strip()})"
+        ref = reference.split("=")
+        ref = f"{ref[0].strip()} - ({ref[1].strip()})"
+        if symbolic_equal(pred, ref) or symbolic_equal(f"-({pred})", ref):
+            return True
+    elif (
+        prediction.count("=") == 1
+        and len(prediction.split("=")[0].strip()) <= 2
+        and "=" not in reference
+    ):
+        if math_equal(
+            prediction.split("=")[1], reference, include_percentage, is_close
+        ):
+            return True
+    elif (
+        reference.count("=") == 1
+        and len(reference.split("=")[0].strip()) <= 2
+        and "=" not in prediction
+    ):
+        if math_equal(
+            prediction, reference.split("=")[1], include_percentage, is_close
+        ):
+            return True
+
+    # symbolic equal with sympy
+    if symbolic_equal(prediction, reference):
+        return True
+
+    return False