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README.md
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README.md
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# AceMath-7B-Instruct
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---
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license: cc-by-nc-4.0
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language:
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- en
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pipeline_tag: text-generation
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tags:
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- nvidia
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- AceMath
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- math
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- CoT
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- pytorch
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---
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AceMath-7B-Instruct
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## Introduction
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We introduce AceMath, a family of frontier models designed for mathematical reasoning. The models in AceMath family, including AceMath-1.5B/7B/72B-Instruct and AceMath-7B/72B-RM, are <b>Improved using Qwen</b>.
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The AceMath-1.5B/7B/72B-Instruct models excel at solving English mathematical problems using Chain-of-Thought (CoT) reasoning, while the AceMath-7B/72B-RM models, as outcome reward models, specialize in evaluating and scoring mathematical solutions.
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The AceMath-1.5B/7B/72B-Instruct models are developed from the Qwen2.5-Math-1.5B/7B/72B-Base models, leveraging a multi-stage supervised fine-tuning (SFT) process: first with general-purpose SFT data, followed by math-specific SFT data. We are releasing all training data to support further research in this field.
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We only recommend using the AceMath models for solving math problems. To support other tasks, we also release AceInstruct-1.5B/7B/72B, a series of general-purpose SFT models designed to handle code, math, and general knowledge tasks. These models are built upon the Qwen2.5-1.5B/7B/72B-Base.
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For more information about AceMath, check our [website](https://research.nvidia.com/labs/adlr/acemath/) and [paper](https://arxiv.org/abs/2412.15084).
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## All Resources
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### AceMath Instruction Models
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- [AceMath-1.5B-Instruct](https://huggingface.co/nvidia/AceMath-1.5B-Instruct), [AceMath-7B-Instruct](https://huggingface.co/nvidia/AceMath-7B-Instruct), [AceMath-72B-Instruct](https://huggingface.co/nvidia/AceMath-72B-Instruct)
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### AceMath Reward Models
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- [AceMath-7B-RM](https://huggingface.co/nvidia/AceMath-7B-RM), [AceMath-72B-RM](https://huggingface.co/nvidia/AceMath-72B-RM)
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### Evaluation & Training Data
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- [AceMath-RewardBench](https://huggingface.co/datasets/nvidia/AceMath-RewardBench), [AceMath-Instruct Training Data](https://huggingface.co/datasets/nvidia/AceMath-Instruct-Training-Data), [AceMath-RM Training Data](https://huggingface.co/datasets/nvidia/AceMath-RM-Training-Data)
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### General Instruction Models
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- [AceInstruct-1.5B](https://huggingface.co/nvidia/AceInstruct-1.5B), [AceInstruct-7B](https://huggingface.co/nvidia/AceInstruct-7B), [AceInstruct-72B](https://huggingface.co/nvidia/AceInstruct-72B)
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## Benchmark Results (AceMath-Instruct + AceMath-72B-RM)
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<p align="center">
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<img src="./acemath-pic.png" alt="AceMath Benchmark Results" width="800">
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</p>
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We compare AceMath to leading proprietary and open-access math models in above Table. Our AceMath-7B-Instruct, largely outperforms the previous best-in-class Qwen2.5-Math-7B-Instruct (Average pass@1: 67.2 vs. 62.9) on a variety of math reasoning benchmarks, while coming close to the performance of 10× larger Qwen2.5-Math-72B-Instruct (67.2 vs. 68.2). Notably, our AceMath-72B-Instruct outperforms the state-of-the-art Qwen2.5-Math-72B-Instruct (71.8 vs. 68.2), GPT-4o (67.4) and Claude 3.5 Sonnet (65.6) by a margin. We also report the rm@8 accuracy (best of 8) achieved by our reward model, AceMath-72B-RM, which sets a new record on these reasoning benchmarks. This excludes OpenAI’s o1 model, which relies on scaled inference computation.
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## How to use
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "nvidia/AceMath-7B-Instruct"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
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prompt = "Jen enters a lottery by picking $4$ distinct numbers from $S=\\{1,2,3,\\cdots,9,10\\}.$ $4$ numbers are randomly chosen from $S.$ She wins a prize if at least two of her numbers were $2$ of the randomly chosen numbers, and wins the grand prize if all four of her numbers were the randomly chosen numbers. The probability of her winning the grand prize given that she won a prize is $\\tfrac{m}{n}$ where $m$ and $n$ are relatively prime positive integers. Find $m+n$."
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messages = [{"role": "user", "content": prompt}]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to("cuda")
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=2048
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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```
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## Correspondence to
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Zihan Liu (zihanl@nvidia.com), Yang Chen (yachen@nvidia.com), Wei Ping (wping@nvidia.com)
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## Citation
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If you find our work helpful, we’d appreciate it if you could cite us.
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<pre>
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@article{acemath2024,
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title={AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling},
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author={Liu, Zihan and Chen, Yang and Shoeybi, Mohammad and Catanzaro, Bryan and Ping, Wei},
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journal={arXiv preprint},
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year={2024}
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}
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</pre>
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## License
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All models in the AceMath family are for non-commercial use only, subject to [Terms of Use](https://openai.com/policies/row-terms-of-use/) of the data generated by OpenAI. We put the AceMath models under the license of [Creative Commons Attribution: Non-Commercial 4.0 International](https://spdx.org/licenses/CC-BY-NC-4.0).
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151643,
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"hidden_act": "silu",
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"hidden_size": 3584,
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"initializer_range": 0.02,
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"intermediate_size": 18944,
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"max_position_embeddings": 4096,
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"max_window_layers": 28,
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"model_type": "qwen2",
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"num_attention_heads": 28,
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"num_hidden_layers": 28,
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"num_key_value_heads": 4,
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"rms_norm_eps": 1e-06,
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"rope_theta": 10000,
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"sliding_window": 4096,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.44.0",
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"use_cache": true,
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"use_mrope": false,
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"use_sliding_window": false,
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"vocab_size": 152064
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}
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## Introduction
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This is the evaluation script used to reproduce math benchmarks scores for AceMath-1.5B/7B/72B-Instruct models based on their outputs. The benchmark can be downloaded from [Qwen2.5-Math](https://github.com/QwenLM/Qwen2.5-Math/tree/main/evaluation/data).
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## Calculate Scores
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```console
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python calculate_scores.py
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```
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from grader import is_equal
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import json
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import re
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def get_gold_list(datapath, dataset_name):
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assert dataset_name in ["gsm8k", "math", "minerva_math", "gaokao2023en", "olympiadbench", "collegemath"]
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gold_list = []
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with open(datapath, "r") as f:
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for line in f:
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item = json.loads(line)
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if dataset_name == "gsm8k":
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gold = item['answer'].split("#### ")[-1]
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elif dataset_name == "math":
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gold = item['answer']
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elif dataset_name == "minerva_math":
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pattern = r"\\boxed\{((?:[^{}]|\{(?:[^{}]|\{[^{}]*\})*\})*)\}"
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pattern_re = re.compile(pattern, re.DOTALL)
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solution = item['solution']
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matches = pattern_re.findall(solution)
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if len(matches) == 0:
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gold = None
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else:
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gold = matches[-1]
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elif dataset_name == "gaokao2023en":
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gold = re.sub(r'^\$(.*)\$$', r'\1', item['answer'])
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elif dataset_name == "olympiadbench":
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gold = re.sub(r'^\$(.*)\$$', r'\1', item['final_answer'][0])
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elif dataset_name == "collegemath":
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gold = re.sub(r'^\$(.*)\$$', r'\1', item['answer'])
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gold_list.append(gold)
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return gold_list
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def get_scores_on_math_benchmarks(model_output_path, test_gold_path, dataset_name):
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gold_list = get_gold_list(test_gold_path, dataset_name)
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"""TODO
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Get the output_list from model_output_path
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output_list is a list of string (List[str])
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Each string represents the model's response for a corresponding question in the benchmark
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Therefore, the length of output_list must match the length of gold_list.
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output_list = ...
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"""
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correct = 0
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for output, gold in zip(output_list, gold_list):
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if is_equal(output, gold, dataset_name):
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correct += 1
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print("accuracy on %s is %.4f" % (dataset_name, correct / len(gold_list)))
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if __name__ == "__main__":
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"""TODO
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Download test benchmarks from Qwen2.5-Math
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https://github.com/QwenLM/Qwen2.5-Math/tree/main/evaluation/data
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Prepare model_output_path and test_gold_path for each dataset
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"""
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test_gold_path = "PATH_OF_THE_BENCHMARK"
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model_output_path = "PATH_OF_YOUR_MODEL_OUTPUTS"
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dataset_name = "DATASET_NAME" # e.g., gsm8k, math, "minerva_math", "gaokao2023en", "olympiadbench", "collegemath"
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get_scores_on_math_benchmarks(model_output_path, test_gold_path, dataset_name)
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"""
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This script is adapted from Qwen2.5-Math
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https://github.com/QwenLM/Qwen2.5-Math/blob/main/evaluation/grader.py
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"""
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import re
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import regex
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import multiprocessing
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from math import isclose
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from typing import Union
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from collections import defaultdict
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from sympy import simplify, N
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from sympy.parsing.sympy_parser import parse_expr
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from sympy.parsing.latex import parse_latex
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def latex2sympy(sympy: str, variable_values={}):
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# record frac
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global frac_type
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if sympy.find(r'\frac') != -1:
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frac_type = r'\frac'
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if sympy.find(r'\dfrac') != -1:
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frac_type = r'\dfrac'
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if sympy.find(r'\tfrac') != -1:
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frac_type = r'\tfrac'
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sympy = sympy.replace(r'\dfrac', r'\frac')
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sympy = sympy.replace(r'\tfrac', r'\frac')
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# Translate Transpose
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sympy = sympy.replace(r'\mathrm{T}', 'T', -1)
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# Translate Derivative
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sympy = sympy.replace(r'\mathrm{d}', 'd', -1).replace(r'{\rm d}', 'd', -1)
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# Translate Matrix
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sympy = sympy.replace(r'\left[\begin{matrix}', r'\begin{bmatrix}', -1).replace(r'\end{matrix}\right]', r'\end{bmatrix}', -1)
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# Translate Permutation
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sympy = re.sub(r"\(([a-zA-Z0-9+\-*/\\ ]+?)\)_{([a-zA-Z0-9+\-*/\\ ]+?)}", r"\\frac{(\1)!}{((\1)-(\2))!}", sympy)
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# Remove \displaystyle
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sympy = sympy.replace(r'\displaystyle', ' ', -1)
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# Remove \quad
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sympy = sympy.replace(r'\quad', ' ', -1).replace(r'\qquad', ' ', -1).replace(r'~', ' ', -1).replace(r'\,', ' ', -1)
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# Remove $
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sympy = sympy.replace(r'$', ' ', -1)
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# variable values
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global VARIABLE_VALUES
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if len(variable_values) > 0:
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VARIABLE_VALUES = variable_values
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else:
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VARIABLE_VALUES = {}
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# setup listener
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matherror = MathErrorListener(sympy)
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# stream input
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stream = InputStream(sympy)
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lex = PSLexer(stream)
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lex.removeErrorListeners()
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lex.addErrorListener(matherror)
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tokens = CommonTokenStream(lex)
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parser = PSParser(tokens)
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# remove default console error listener
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parser.removeErrorListeners()
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parser.addErrorListener(matherror)
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# process the input
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return_data = None
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math = parser.math()
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# if a list
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if math.relation_list():
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return_data = []
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# go over list items
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relation_list = math.relation_list().relation_list_content()
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for list_item in relation_list.relation():
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expr = convert_relation(list_item)
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return_data.append(expr)
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# if not, do default
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else:
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relation = math.relation()
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return_data = convert_relation(relation)
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return return_data
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def math_answer_cleaning(answer, dataset_name):
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"""
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remove irrelevant strings and unify the answer format before checking whether the answers are equal
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"""
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def _is_completely_wrapped_by_text(input_string):
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pattern = r'^\\text{(.*)}$'
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match = re.match(pattern, input_string)
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if match:
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## input_string is completely wrapped by \text{}
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extracted_content = match.group(1)
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extracted_content = extracted_content.replace("(", "").replace(")", "").replace(",", "")
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return extracted_content
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else:
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return None
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## remove irrelevant \\text and space
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extracted_content = _is_completely_wrapped_by_text(answer)
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answer = extracted_content if extracted_content else answer
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## e.g., convert 5,\!460 into 5460; convert 14{,}916 into 14916 convert \$4 into 4
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answer = answer.replace(",\!", "").replace("{,}", "").replace("\$", "")
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## e.g., convert \dfrac{3}{2} into frac{3}{2}
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answer = answer.replace("dfrac{", "frac{").replace("tfrac{", "frac{")
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## e.g., convert 121^\circ into 121
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answer = answer.replace("^\circ", "")
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answer = answer.replace("^{\circ}", "")
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## remove \quad
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answer = answer.replace("\quad", "")
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## remove space
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answer = answer.replace(" ", "")
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## remove \n
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answer = answer.replace("\n", "").replace("\\n", "")
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## e.g., convert 3.54\times10^{10} into 3.54e10
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answer = re.sub(r'([+-]?\d*\.?\d+)[\\]times10\^{([+-]?\d+)}', r'\1e\2', answer)
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## e.g., convert 3.54\times10^10 into 3.54e10
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answer = re.sub(r'([+-]?\d*\.?\d+)[\\]times10\^([+-]?\d+)', r'\1e\2', answer)
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## e.g., convert 558\,\text{nm} into 558
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answer = re.sub(r'\\,\\text\{.*?\}', '', answer)
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## e.g., convert 558\text{nm} into 558
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answer = re.sub(r'\\text\{.*?\}', '', answer)
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## e.g., convert 2^{10} into 2^10
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answer = re.sub(r'(\d+)\^{(\d+)}', r'\1^\2', answer)
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## lowercase
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answer = answer.lower()
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if dataset_name == "collegemath":
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## convert 558\mathrm{ft} into 558
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answer = re.sub(r'\\mathrm\{.*?\}', '', answer)
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## clean noisy answer
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answer = re.sub(r'\$\([^)]*\)', '', answer)
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if answer.endswith("-"):
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answer = answer[:-1]
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if answer.endswith("."):
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answer = answer[:-1]
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if answer.endswith("hours"):
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answer = answer[:-len("hours")]
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## extract final answer after '=' or ':'
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if "=" in answer:
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answer = answer.split("=", 1)[1]
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if ":" in answer:
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answer = answer.split(":", 1)[1]
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## \emptyset and \oslash both reprsent empty set in latex
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answer = answer.replace("\\emptyset", "\\oslash")
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if dataset_name == "gsm8k":
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# Example: 5,600 -> 5600
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answer = answer.replace(',', '')
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if dataset_name == "gaokao2023en":
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unit_strings = ['students', 'dollars', 'boxes', 'feet', 'kilometers', 'meters', 'degreesontheBreadusscale', '$', 'a.m.', 'am', 'minutes']
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for unit in unit_strings:
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answer = answer.replace(unit, "")
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return answer
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def extract_final_answer(output):
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pattern_re = re.compile(r"\\boxed\{((?:[^{}]|\{(?:[^{}]|\{[^{}]*\})*\})*)\}", re.DOTALL)
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all_matches = pattern_re.findall(output)
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if len(all_matches) >= 1:
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extracted_answer = all_matches[-1]
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else:
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extracted_answer = None
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return extracted_answer, all_matches
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def round_number(answer):
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def _is_float(string):
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try:
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float(string)
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return True
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except:
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return False
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if _is_float(answer) and float(answer) < 1:
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## to consider the case like 5.56e-10 (convert 5.56e-10 into 5.6e-10)
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## still return a string type
|
||||
return f"{float(answer):.2g}"
|
||||
|
||||
return answer
|
||||
|
||||
|
||||
def choice_answer_clean(pred: str):
|
||||
pred = pred.strip("\n").rstrip(".").rstrip("/").strip(" ").lstrip(":")
|
||||
# Clean the answer based on the dataset
|
||||
tmp = re.findall(r"\b(A|B|C|D|E)\b", pred.upper())
|
||||
if tmp:
|
||||
pred = tmp
|
||||
else:
|
||||
pred = [pred.strip().strip(".")]
|
||||
pred = pred[-1]
|
||||
# Remove the period at the end, again!
|
||||
pred = pred.rstrip(".").rstrip("/")
|
||||
return pred
|
||||
|
||||
|
||||
def parse_digits(num):
|
||||
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 str_to_pmatrix(input_str):
|
||||
input_str = input_str.strip()
|
||||
matrix_str = re.findall(r"\{.*,.*\}", input_str)
|
||||
pmatrix_list = []
|
||||
|
||||
for m in matrix_str:
|
||||
m = m.strip("{}")
|
||||
pmatrix = r"\begin{pmatrix}" + m.replace(",", "\\") + r"\end{pmatrix}"
|
||||
pmatrix_list.append(pmatrix)
|
||||
|
||||
return ", ".join(pmatrix_list)
|
||||
|
||||
|
||||
def math_equal(
|
||||
prediction: Union[bool, float, str],
|
||||
reference: Union[float, str],
|
||||
include_percentage: bool = True,
|
||||
is_close: bool = True,
|
||||
timeout: bool = False,
|
||||
) -> bool:
|
||||
"""
|
||||
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 prediction is None or reference is None:
|
||||
return False
|
||||
if str(prediction.strip().lower()) == str(reference.strip().lower()):
|
||||
return True
|
||||
if (
|
||||
reference in ["A", "B", "C", "D", "E"]
|
||||
and choice_answer_clean(prediction) == reference
|
||||
):
|
||||
return True
|
||||
|
||||
# fraction equal
|
||||
if fraction_equal(prediction, reference):
|
||||
return True
|
||||
|
||||
try: # numerical equal
|
||||
if round_number(prediction) == round_number(reference):
|
||||
return True
|
||||
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 numeric_equal(prediction, item):
|
||||
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
|
||||
|
||||
# symbolic equal
|
||||
reference = str(reference).strip()
|
||||
prediction = str(prediction).strip()
|
||||
|
||||
## pmatrix (amps)
|
||||
if "pmatrix" in prediction and not "pmatrix" in reference:
|
||||
reference = str_to_pmatrix(reference)
|
||||
|
||||
## deal with [], (), {}
|
||||
pred_str, ref_str = prediction, reference
|
||||
if (
|
||||
prediction.startswith("[")
|
||||
and prediction.endswith("]")
|
||||
and not reference.startswith("(")
|
||||
) or (
|
||||
prediction.startswith("(")
|
||||
and prediction.endswith(")")
|
||||
and not reference.startswith("[")
|
||||
):
|
||||
pred_str = pred_str.strip("[]()")
|
||||
ref_str = ref_str.strip("[]()")
|
||||
for s in ["{", "}", "(", ")"]:
|
||||
ref_str = ref_str.replace(s, "")
|
||||
pred_str = pred_str.replace(s, "")
|
||||
if pred_str.lower() == ref_str.lower():
|
||||
return True
|
||||
|
||||
## [a, b] vs. [c, d], return a==c and b==d
|
||||
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
|
||||
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
|
||||
|
||||
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 timeout:
|
||||
if call_with_timeout(symbolic_equal_process, prediction, reference):
|
||||
return True
|
||||
else:
|
||||
if symbolic_equal(prediction, reference):
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def numeric_equal(prediction: float, reference: float):
|
||||
# Note that relative tolerance has significant impact
|
||||
# on the result of the synthesized GSM-Hard dataset
|
||||
# if reference.is_integer():
|
||||
# return isclose(reference, round(prediction), abs_tol=1e-4)
|
||||
# else:
|
||||
# prediction = round(prediction, len(str(reference).split(".")[-1]))
|
||||
return isclose(reference, prediction, rel_tol=1e-4)
|
||||
|
||||
|
||||
def fraction_equal(prediction, reference):
|
||||
def _calculate_numbers(input_string):
|
||||
try:
|
||||
result = eval(input_string)
|
||||
return result
|
||||
except:
|
||||
return None
|
||||
|
||||
reference = re.sub(r'\\frac{(.*?)}{(.*?)}', r'(\1/\2)', reference)
|
||||
prediction = re.sub(r'\\frac{(.*?)}{(.*?)}', r'(\1/\2)', prediction)
|
||||
|
||||
if reference == prediction:
|
||||
return True
|
||||
|
||||
reference = _calculate_numbers(reference)
|
||||
prediction = _calculate_numbers(prediction)
|
||||
|
||||
if reference and reference == prediction:
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
def symbolic_equal(a, b):
|
||||
def _parse(s):
|
||||
for f in [parse_latex, parse_expr, latex2sympy]:
|
||||
try:
|
||||
return f(s.replace("\\\\", "\\"))
|
||||
except:
|
||||
try:
|
||||
return f(s)
|
||||
except:
|
||||
pass
|
||||
return s
|
||||
|
||||
a = _parse(a)
|
||||
b = _parse(b)
|
||||
|
||||
# direct equal
|
||||
try:
|
||||
if str(a) == str(b) or a == b:
|
||||
return True
|
||||
except:
|
||||
pass
|
||||
|
||||
# simplify equal
|
||||
try:
|
||||
if a.equals(b) or simplify(a - b) == 0:
|
||||
return True
|
||||
except:
|
||||
pass
|
||||
|
||||
# equation equal
|
||||
try:
|
||||
if (abs(a.lhs - a.rhs)).equals(abs(b.lhs - b.rhs)):
|
||||
return True
|
||||
except:
|
||||
pass
|
||||
|
||||
try:
|
||||
if numeric_equal(float(N(a)), float(N(b))):
|
||||
return True
|
||||
except:
|
||||
pass
|
||||
|
||||
# matrix
|
||||
try:
|
||||
# if a and b are matrix
|
||||
if a.shape == b.shape:
|
||||
_a = a.applyfunc(lambda x: round(x, 3))
|
||||
_b = b.applyfunc(lambda x: round(x, 3))
|
||||
if _a.equals(_b):
|
||||
return True
|
||||
except:
|
||||
pass
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def symbolic_equal_process(a, b, output_queue):
|
||||
result = symbolic_equal(a, b)
|
||||
output_queue.put(result)
|
||||
|
||||
|
||||
def math_equal_process(prediction, reference, output_queue):
|
||||
result = math_equal(prediction, reference, timeout=True)
|
||||
output_queue.put(result)
|
||||
|
||||
|
||||
def call_with_timeout(func, *args, timeout=1, **kwargs):
|
||||
output_queue = multiprocessing.Queue()
|
||||
process_args = args + (output_queue,)
|
||||
process = multiprocessing.Process(target=func, args=process_args, kwargs=kwargs)
|
||||
process.start()
|
||||
process.join(timeout)
|
||||
|
||||
if process.is_alive():
|
||||
process.terminate()
|
||||
process.join()
|
||||
return False
|
||||
|
||||
return output_queue.get()
|
||||
|
||||
|
||||
def check_correctness_of_multiple_answer_cases(prediction, reference, all_matches):
|
||||
|
||||
if prediction.replace(",", "").replace("$", "") == reference.replace(",", "").replace("$", ""):
|
||||
return True
|
||||
|
||||
if not prediction.split("=")[-1] == reference.split("=")[-1].replace("$", ""):
|
||||
return False
|
||||
|
||||
if "," in reference or "or" in reference or "and" in reference:
|
||||
## there are multiple answers
|
||||
if len(all_matches) <= 1:
|
||||
return False
|
||||
|
||||
prediction1 = prediction.split("=")[-1]
|
||||
prediction2 = all_matches[-2].split("=")[-1]
|
||||
reference = reference.replace("$", "")
|
||||
if "or" in reference:
|
||||
gold_list = reference.split("or", 1)
|
||||
elif "and" in reference:
|
||||
gold_list = reference.split("and", 1)
|
||||
else:
|
||||
gold_list = reference.split(",", 1)
|
||||
|
||||
reference1 = gold_list[-1].split("=")[-1]
|
||||
reference2 = gold_list[-2].split("=")[-1]
|
||||
|
||||
if math_equal(prediction1, reference1) and math_equal(prediction2, reference2):
|
||||
return True
|
||||
elif math_equal(prediction2, reference1) and math_equal(prediction1, reference2):
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
else:
|
||||
return True
|
||||
|
||||
|
||||
def is_equal(model_output, reference, dataset_name):
|
||||
|
||||
extracted_model_answer, all_matches = extract_final_answer(model_output)
|
||||
if extracted_model_answer is None or reference is None:
|
||||
return False
|
||||
|
||||
extracted_model_answer = math_answer_cleaning(extracted_model_answer, dataset_name)
|
||||
reference = math_answer_cleaning(reference, dataset_name)
|
||||
|
||||
# if math_equal(prediction, reference, timeout=True):
|
||||
if call_with_timeout(math_equal_process, extracted_model_answer, reference):
|
||||
return True
|
||||
|
||||
if dataset_name == "collegemath":
|
||||
return check_correctness_of_multiple_answer_cases(extracted_model_answer, reference, all_matches)
|
||||
|
||||
return False
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
{
|
||||
"bos_token_id": 151643,
|
||||
"do_sample": false,
|
||||
"eos_token_id": 151643,
|
||||
"max_new_tokens": 2048,
|
||||
"transformers_version": "4.37.0"
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
Binary file not shown.
|
|
@ -0,0 +1,346 @@
|
|||
{
|
||||
"metadata": {
|
||||
"total_size": 15231233024
|
||||
},
|
||||
"weight_map": {
|
||||
"lm_head.weight": "model-00004-of-00004.safetensors",
|
||||
"model.embed_tokens.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.input_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.self_attn.k_proj.bias": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.self_attn.q_proj.bias": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.self_attn.v_proj.bias": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.input_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.self_attn.k_proj.bias": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.self_attn.q_proj.bias": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.self_attn.v_proj.bias": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.10.input_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.10.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.10.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.10.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.10.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.10.self_attn.k_proj.bias": "model-00002-of-00004.safetensors",
|
||||
"model.layers.10.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.10.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.10.self_attn.q_proj.bias": "model-00002-of-00004.safetensors",
|
||||
"model.layers.10.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.10.self_attn.v_proj.bias": "model-00002-of-00004.safetensors",
|
||||
"model.layers.10.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
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File diff suppressed because it is too large
Load Diff
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@ -0,0 +1,207 @@
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File diff suppressed because one or more lines are too long
Loading…
Reference in New Issue