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f618189 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 | # Copyright 2024-2025 ModelCloud.ai
# Copyright 2024-2025 qubitium@modelcloud.ai
# Contact: qubitium@modelcloud.ai, x.com/qubitium
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
from enum import Enum
try:
from enum import EnumType
except ImportError:
EnumType = type(Enum)
from typing import Dict, List, Optional, Type, Union
from .evalplus import patch_evalplus
class EVAL:
class LM_EVAL(str, Enum):
ARC_CHALLENGE = "arc_challenge"
GSM8K_COT = "gsm8k_cot"
GSM8K_PLATINUM_COT = "gsm8k_platinum_cot"
HELLASWAG = "hellaswag"
MMLU = "mmlu"
GPQA = "gpqa"
ARC_EASY = "arc_easy"
BOOLQ = "boolq"
OPENBOOKQA = "openbookqa"
class EVALPLUS(str, Enum):
HUMAN = "humaneval"
MBPP = "mbpp"
class MMLU_PRO(str, Enum):
BIOLOGY = "biology"
BUSINESS = "business"
CHEMISTRY = "chemistry"
COMPUTER_SCIENCE = "computer science"
ECONOMICS = "economics"
ENGINEERING = "engineering"
HEALTH = "health"
HISTORY = "history"
LAW = "law"
MATH = "math"
OTHER = "other"
PHILOSOPHY = "philosophy"
PHYSICS = "physics"
PSYCHOLOGY = "psychology"
@classmethod
def get_tasks_for_framework(cls, framework: Union[str, Type[Enum]]) -> list:
if isinstance(framework, EnumType):
framework = framework.__name__
if not hasattr(cls, framework):
raise ValueError(f"No such EVAL framework: `{framework}`")
enum_class = getattr(cls, framework)
return list(enum_class)
@classmethod
def get_task_enums(cls):
task_lists = []
for name in dir(cls):
attr = getattr(cls, name)
if isinstance(attr, type) and issubclass(attr, Enum):
task_lists.extend(list(attr))
return task_lists
@classmethod
def get_full_name(cls, member):
return f"{cls.__name__}.{member.__class__.__name__}.{member.name}"
@classmethod
def get_all_tasks_string(cls):
full_names = []
for name in dir(cls):
attr = getattr(cls, name)
if isinstance(attr, type) and issubclass(attr, Enum):
full_names.extend(cls.get_full_name(member) for member in attr)
return ', '.join(full_names)
@classmethod
def get_task_groups_from_tasks(cls, tasks: Union[str, List[str]]) -> Dict[Type[Enum], List[str]]:
"""Group tasks by their evaluation framework.
Args:
tasks: Either a single task name or list of task names
Returns:
Dictionary mapping framework enum classes to lists of tasks
Example: {EVAL.LM_EVAL: ["arc_challenge", "hellaswag"], EVAL.EVALPLUS: ["humaneval"]}
Raises:
ValueError: If any task doesn't match a known framework
"""
if isinstance(tasks, str):
tasks = [tasks]
# Create a mapping of task values to their enum classes
task_to_framework = {}
# Populate the mapping for all frameworks
for framework in [cls.LM_EVAL, cls.EVALPLUS, cls.MMLU_PRO]:
for task in framework:
task_to_framework[task.value] = framework
# Group tasks by their framework
task_groups = {}
unknown_tasks = []
for task in tasks:
if task in task_to_framework:
framework = task_to_framework[task]
if framework not in task_groups:
task_groups[framework] = []
task_groups[framework].append(task)
else:
unknown_tasks.append(task)
if unknown_tasks:
raise ValueError(f"Unknown tasks: {unknown_tasks}")
return task_groups
def evalplus(
model,
dataset: str,
batch: int = 1,
trust_remote_code: bool = False,
output_file: Optional[str] = None,
backend: str = 'gptqmodel'
):
patch_evalplus(model)
try:
from evalplus.evaluate import evaluate
except BaseException:
raise ValueError("evalplus is not installed. Please install via `pip install gptqmodel[evalplus]`.")
assert dataset in ["humaneval", "mbpp"], f"Invalid dataset {dataset}"
evaluate(dataset=dataset, model=model, backend=backend, bs=batch, trust_remote_code=trust_remote_code, output_file=output_file,
greedy=True)
if output_file is None:
output_file = model.strip("./").replace("/", "--") + "_gptqmodel_temp_0.0_eval_results.json"
output_file = os.path.join("evalplus_results", dataset, output_file)
if not os.path.exists(output_file):
raise FileNotFoundError(f"No such file: {output_file}")
try:
with open(output_file, 'r') as file:
data = json.load(file)
except json.JSONDecodeError:
raise ValueError(f"Failed to decode JSON: {output_file}")
try:
pass_at_k = data["pass_at_k"]
base = float(pass_at_k["base"]["pass@1"])
plus = float(pass_at_k["plus"]["pass@1"])
base_formatted = format(base, ".3f")
plus_formatted = format(plus, ".3f")
except KeyError as e:
raise ValueError(f"Required key not found in JSON: {str(e)}")
except ValueError as e:
raise ValueError(f"Data format error: {str(e)}")
return base_formatted, plus_formatted, output_file
def evalplus_make_table(results):
print("| Tasks | base tests | base + extra tests |")
print("|-------------|------------|--------------------|")
for task, metrics in results.items():
print(f"| {task} | {metrics['base tests']} | {metrics['base + extra tests']} |")
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