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from dataclasses import dataclass, make_dataclass
from enum import Enum
import pandas as pd
from src.display.about import Tasks
def fields(raw_class):
return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
# 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
## Leaderboard columns
auto_eval_column_dict = [["model_type_symbol", ColumnContent, ColumnContent("T", "str", True, never_hidden=True)],
["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)],
["model_report", ColumnContent, ColumnContent("Report", "markdown", True, never_hidden=True)]
]
# Init
# Scores
for task in Tasks:
auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "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, dummy=True)])
# auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("Params (B)", "number", False)])
# auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False, dummy=True)])
auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False, dummy=True)])
# auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False, dummy=True)])
# 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)
# For the queue columns in the submission tab
@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)
# All the model information that we might need
@dataclass
class ModelDetails:
name: str
display_name: str = ""
symbol: str = "" # emoji
class ModelType(Enum):
OPEN = ModelDetails(name="Publicly Available", symbol="🟢")
Unknown = ModelDetails(name="Private", symbol="🔒")
def to_str(self, separator=" "):
return f"{self.value.symbol}{separator}{self.value.name}"
@staticmethod
def from_str(type):
if "open" in type or "🟢" in type:
return ModelType.OPEN
return ModelType.Unknown
class WeightType(Enum):
Adapter = ModelDetails("Adapter")
Original = ModelDetails("Original")
Delta = ModelDetails("Delta")
class Precision(Enum):
float16 = ModelDetails("float16")
bfloat16 = ModelDetails("bfloat16")
qt_gptq_3bit = ModelDetails("GPTQ-3bit")
qt_gptq_4bit = ModelDetails("GPTQ-4bit")
qt_gptq_8bit = ModelDetails("GPTQ-8bit")
qt_awq_3bit = ModelDetails("AWQ-3bit")
qt_awq_4bit = ModelDetails("AWQ-4bit")
qt_awq_8bit = ModelDetails("AWQ-8bit")
Unknown = ModelDetails("🔒")
def from_str(precision):
if precision in ["torch.float16", "float16"]:
return Precision.float16
if precision in ["torch.bfloat16", "bfloat16"]:
return Precision.bfloat16
if precision in ["GPTQ-3bit"]:
return Precision.qt_gptq_3bit
if precision in ["GPTQ-4bit"]:
return Precision.qt_gptq_4bit
if precision in ["GPTQ-8bit"]:
return Precision.qt_gptq_8bit
if precision in ["AWQ-3bit"]:
return Precision.qt_awq_3bit
if precision in ["AWQ-4bit"]:
return Precision.qt_awq_4bit
if precision in ["AWQ-8bit"]:
return Precision.qt_awq_8bit
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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