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from pathlib import Path
from typing import Annotated, Any
import numpy as np
from pydantic import BaseModel, ConfigDict, Field, computed_field
from typing_extensions import Self
from src.display.formatting import make_clickable_model
from src.display.utils import AutoEvalColumn, ModelType, Precision, WeightType
from src.prepare import get_benchmarks, load_meta_toml
from src.submission.check_validity import is_model_on_hub
class EvalResultJson(BaseModel):
"""Model of the eval result json file."""
model_config: ConfigDict = ConfigDict(extra="allow", frozen=True)
config: "EvalResultJson_Config"
results: dict[str, dict[str, float | None]]
class EvalResultJson_Config(BaseModel):
"""`config` in the eval result json file."""
model_config: ConfigDict = ConfigDict(extra="allow", frozen=True)
model_name: Annotated[str, Field(..., description="The model name. e.g. Qwen/Qwen2.5-3B")]
model_key: Annotated[str, Field(..., description="The model key. e.g. 'qwen2.5_3b'")]
model_dtype: Annotated[str | None, Field(description="The model precision. e.g. torch.bfloat16")] = None
model_sha: Annotated[str, Field(description="The model sha. e.g. 3aab1f1954e9cc14eb9509a215f9e5ca08227a9b")] = ""
model_args: Annotated[str | None, Field(description="The model args.")] = None
class EvalResult(BaseModel):
"""Represents one full evaluation. Built from a combination of the result and request file for a given run."""
eval_name: Annotated[
str,
Field(
...,
description="The evaluation name. e.g. '{model_key}_{precision}', '{org}_{model_key}_{precision}' (unique identifier)",
),
]
full_model: Annotated[
str,
Field(
...,
description="The full model name. e.g. '{org}/{model_title}' (path on hub)",
),
]
org: str | None
model: str
link_url: str | None = None
revision: str # commit hash, "" if main
results: dict[str, float]
precision: Precision = Precision.Unknown
model_type: ModelType = ModelType.Unknown # Pretrained, fine tuned, ...
weight_type: WeightType = WeightType.Original # Original or Adapter
architecture: str = "Unknown"
license: str = "?"
likes: int = 0
num_params: int = 0
date: str = "" # submission date of request file
still_on_hub: bool = False
@computed_field
@property
def link(self) -> str | None:
"""Link to the model on the hub or other platform."""
if self.link_url:
# Use explicitly provided link
return self.link_url
if self.org and self.model:
# Use inferred link on HuggingFace
return f"https://huggingface.co/{self.org}/{self.model}"
return None
@classmethod
def init_from_json_file(cls, json_path: Path) -> Self:
"""Inits the result from the specific model result file"""
json_data = json_path.read_bytes()
return cls.init_from_json(json_data)
@classmethod
def init_from_json(cls, json_data: str | bytes | bytearray) -> Self:
"""Inits the result from the specific json data"""
data = EvalResultJson.model_validate_json(json_data)
return cls.init_from_model(data)
@classmethod
def init_from_dict(cls, raw_model: dict[str, Any]) -> Self:
"""Inits the result from the specific json content"""
data = EvalResultJson.model_validate(raw_model)
return cls.init_from_model(data)
@classmethod
def init_from_model(cls, data: EvalResultJson) -> Self:
BENCHMARKS = get_benchmarks()
config = data.config
# Precision
precision = Precision.from_str(config.model_dtype)
meta_toml = load_meta_toml()
# Get model and org
model_key: str = config.model_key or config.model_args or ""
org = None
link_url = None
m_repo = meta_toml.model_key_to_repo.get(model_key)
if m_repo is not None:
if m_repo.repo_id:
org, _, model_key = m_repo.repo_id.rpartition("/")
org = org or None
if m_repo.link:
link_url = m_repo.link
if not org:
result_key = f"{model_key}_{precision.value.name}"
else:
result_key = f"{org}_{model_key}_{precision.value.name}"
model_title = model_key
m_meta = meta_toml.model_key_to_model.get(model_key)
if m_meta is not None and m_meta.title:
model_title = m_meta.title
if org:
still_on_hub, _, model_config = is_model_on_hub(
f"{org}/{model_key}",
config.model_sha or "main",
trust_remote_code=True,
test_tokenizer=False,
)
else:
still_on_hub = False
model_config = None
architecture: str = "?"
if model_config is not None:
architectures: list[str] | None = getattr(model_config, "architectures", None)
if architectures:
architecture = ";".join(architectures)
# Extract results available in this file (some results are split in several files)
results: dict[str, float] = {}
for task in BENCHMARKS:
# We average all scores of a given metric (not all metrics are present in all files)
# TODO: support multiple metrics
metric_keys = ["caa", "acc"]
accs = np.array([
v.get(metric, np.nan)
for k, v in data.results.items()
if task.key == k
for metric in metric_keys
if metric in v
])
if accs.size == 0 or any(np.isnan(acc) for acc in accs):
continue
# mean_acc = np.mean(accs) * 100.0
mean_acc = np.mean(accs)
results[task.title] = float(mean_acc)
return cls.model_validate({
"eval_name": result_key,
"full_model": model_title,
"org": org or None,
"model": model_key,
"link_url": link_url or None,
"results": results,
"precision": precision,
"revision": config.model_sha or "",
"still_on_hub": still_on_hub,
"architecture": architecture,
})
def update_with_request_file(self, requests_path: Path | str) -> None:
"""Finds the relevant request file for the current model and updates info with it"""
# TODO: do nothing for now
return
request_file = get_request_file_for_model(requests_path, self.full_model, self.precision.value.name)
try:
with open(request_file) as f:
request: dict[str, Any] = json.load(f)
self.model_type = ModelType.from_str(request.get("model_type", ""))
self.weight_type = WeightType[request.get("weight_type", "Original")]
self.license = request.get("license", "?")
self.likes = request.get("likes", 0)
self.num_params = request.get("params", 0)
self.date = request.get("submitted_time", "")
except Exception as e:
print(
f"Could not find request file for {self.org}/{self.model} with precision {self.precision.value.name}. Error: {e}"
)
def to_dict(self) -> dict:
"""Converts the Eval Result to a dict compatible with our dataframe display"""
BENCHMARKS = get_benchmarks()
average = sum(v for v in self.results.values() if v is not None) / len(BENCHMARKS)
data_dict = {
"eval_name": self.eval_name, # not a column, just a save name,
AutoEvalColumn.precision.name: self.precision.value.name,
AutoEvalColumn.model_type.name: self.model_type.value.name,
AutoEvalColumn.model_type_symbol.name: self.model_type.value.symbol,
AutoEvalColumn.weight_type.name: self.weight_type.value.name,
AutoEvalColumn.architecture.name: self.architecture,
AutoEvalColumn.model.name: make_clickable_model(self.full_model, self.link),
AutoEvalColumn.revision.name: self.revision,
AutoEvalColumn.average.name: average,
AutoEvalColumn.license.name: self.license,
AutoEvalColumn.likes.name: self.likes,
AutoEvalColumn.params.name: self.num_params,
AutoEvalColumn.still_on_hub.name: self.still_on_hub,
}
for task in BENCHMARKS:
data_dict[task.title] = self.results.get(task.title, None)
return data_dict
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