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from dataclasses import dataclass, make_dataclass
from enum import Enum
import pandas as pd
def fields(raw_class):
return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
@dataclass
class Task:
benchmark: str
metric: str
col_name: str
class Tasks(Enum):
# XXX include me back at some point
# nqopen = Task("nq8", "em", "NQ Open/EM")
# triviaqa = Task("tqa8", "em", "TriviaQA/EM")
# truthfulqa_mc1 = Task("truthfulqa_mc1", "acc", "TruthQA MC1/Acc")
# truthfulqa_mc2 = Task("truthfulqa_mc2", "acc", "TruthQA MC2/Acc")
# truthfulqa_gen = Task("truthfulqa_gen", "rougeL_acc", "TruthQA Gen/ROUGE")
# xsum_r = Task("xsum_v2", "rougeL", "XSum/ROUGE")
# xsum_f = Task("xsum_v2", "factKB", "XSum/factKB")
# xsum_b = Task("xsum_v2", "bertscore_precision", "XSum/BERT-P")
# cnndm_r = Task("cnndm_v2", "rougeL", "CNN-DM/ROUGE")
# cnndm_f = Task("cnndm_v2", "factKB", "CNN-DM/factKB")
# cnndm_b = Task("cnndm_v2", "bertscore_precision", "CNN-DM/BERT-P")
# race = Task("race", "acc", "RACE/Acc")
# squadv2 = Task("squadv2", "exact", "SQUaDv2/EM")
# memotrap = Task("memo-trap_v2", "acc", "MemoTrap/Acc")
# ifeval = Task("ifeval", "prompt_level_strict_acc", "IFEval/Acc")
# faithdial = Task("faithdial_hallu_v2", "acc", "FaithDial/Acc")
# halueval_qa = Task("halueval_qa", "acc", "HaluQA/Acc")
# halueval_summ = Task("halueval_summarization", "acc", "HaluSumm/Acc")
# halueval_dial = Task("halueval_dialogue", "acc", "HaluDial/Acc")
# # XXX include me back at some point
selfcheck = Task("selfcheckgpt", "max-selfcheckgpt", "SelfCheckGPT")
mmlu = Task("mmlu", "acc", "MMLU/Acc (5-shot)")
# These classes are for user facing column names,
# to avoid having to change them all around the code
# when a modif is needed
@dataclass
class ColumnContent:
name: str
type: str
displayed_by_default: bool
hidden: bool = False
never_hidden: bool = False
dummy: bool = False
auto_eval_column_dict = []
# Init
auto_eval_column_dict.append(["model_type_symbol", ColumnContent, ColumnContent("T", "str", True, never_hidden=True)])
auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
# #Scores
# # auto_eval_column_dict.append(["average", ColumnContent, ColumnContent("Avg", "number", True)])
# Inference framework
auto_eval_column_dict.append(["inference_framework", ColumnContent, ColumnContent("Inference framework", "str", True)])
for task in Tasks:
auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
# System performance metrics
auto_eval_column_dict.append([f"{task.name}_end_to_end_time", ColumnContent, ColumnContent(f"{task.value.col_name} End-to-end time (s)", "number", True)])
auto_eval_column_dict.append([f"{task.name}_prefilling_time", ColumnContent, ColumnContent(f"{task.value.col_name} Prefilling time (s)", "number", True)])
auto_eval_column_dict.append([f"{task.name}_decoding_throughput", ColumnContent, ColumnContent(f"{task.value.col_name} Decoding throughput (tok/s)", "number", True)])
# Model information
auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", False)])
auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False)])
auto_eval_column_dict.append(["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True)])
auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", False)])
auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False)])
auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False)])
auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False)])
auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])
# Dummy column for the search bar (hidden by the custom CSS)
auto_eval_column_dict.append(["dummy", ColumnContent, ColumnContent("model_name_for_query", "str", False, dummy=True)])
# We use make dataclass to dynamically fill the scores from Tasks
AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)
@dataclass(frozen=True)
class EvalQueueColumn: # Queue column
model = ColumnContent("model", "markdown", True)
revision = ColumnContent("revision", "str", True)
private = ColumnContent("private", "bool", True)
precision = ColumnContent("precision", "str", True)
weight_type = ColumnContent("weight_type", "str", "Original")
status = ColumnContent("status", "str", True)
@dataclass
class ModelDetails:
name: str
symbol: str = "" # emoji, only for the model type
class ModelType(Enum):
PT = ModelDetails(name="pretrained", symbol="🟢")
FT = ModelDetails(name="fine-tuned on domain-specific datasets", symbol="🔶")
chat = ModelDetails(name="chat models (RLHF, DPO, IFT, ...)", symbol="💬")
merges = ModelDetails(name="base merges and moerges", symbol="🤝")
Unknown = ModelDetails(name="", symbol="?")
def to_str(self, separator=" "):
return f"{self.value.symbol}{separator}{self.value.name}"
@staticmethod
def from_str(type):
if "fine-tuned" in type or "🔶" in type:
return ModelType.FT
if "pretrained" in type or "🟢" in type:
return ModelType.PT
if any([k in type for k in ["instruction-tuned", "RL-tuned", "chat", "🟦", "⭕", "💬"]]):
return ModelType.chat
if "merge" in type or "🤝" in type:
return ModelType.merges
return ModelType.Unknown
class InferenceFramework(Enum):
# "moe-infinity", hf-chat
MoE_Infinity = ModelDetails("moe-infinity")
HF_Chat = ModelDetails("hf-chat")
Unknown = ModelDetails("?")
def to_str(self):
return self.value.name
@staticmethod
def from_str(inference_framework: str):
if inference_framework in ["moe-infinity"]:
return InferenceFramework.MoE_Infinity
if inference_framework in ["hf-chat"]:
return InferenceFramework.HF_Chat
return InferenceFramework.Unknown
class WeightType(Enum):
Adapter = ModelDetails("Adapter")
Original = ModelDetails("Original")
Delta = ModelDetails("Delta")
class Precision(Enum):
float32 = ModelDetails("float32")
float16 = ModelDetails("float16")
bfloat16 = ModelDetails("bfloat16")
qt_8bit = ModelDetails("8bit")
qt_4bit = ModelDetails("4bit")
qt_GPTQ = ModelDetails("GPTQ")
Unknown = ModelDetails("?")
@staticmethod
def from_str(precision: str):
if precision in ["torch.float32", "float32"]:
return Precision.float32
if precision in ["torch.float16", "float16"]:
return Precision.float16
if precision in ["torch.bfloat16", "bfloat16"]:
return Precision.bfloat16
if precision in ["8bit"]:
return Precision.qt_8bit
if precision in ["4bit"]:
return Precision.qt_4bit
if precision in ["GPTQ", "None"]:
return Precision.qt_GPTQ
return Precision.Unknown
# Column selection
COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
TYPES = [c.type for c in fields(AutoEvalColumn) if not c.hidden]
COLS_LITE = [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]
TYPES_LITE = [c.type for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]
EVAL_COLS = [c.name for c in fields(EvalQueueColumn)]
EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]
BENCHMARK_COLS = [t.value.col_name for t in Tasks]
# NUMERIC_INTERVALS = {
# "?": pd.Interval(-1, 0, closed="right"),
# "~1.5": pd.Interval(0, 2, closed="right"),
# "~3": pd.Interval(2, 4, closed="right"),
# "~7": pd.Interval(4, 9, closed="right"),
# "~13": pd.Interval(9, 20, closed="right"),
# "~35": pd.Interval(20, 45, closed="right"),
# "~60": pd.Interval(45, 70, closed="right"),
# "70+": pd.Interval(70, 10000, closed="right"),
# }
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