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import glob
import json
import math
import os
from dataclasses import dataclass
from src.display.formatting import make_clickable_model
from src.display.utils import AutoEvalColumn, ModelType, Tasks, Precision, WeightType
from src.submission.check_validity import is_model_on_hub
from src.evaluation.model_trace_eval import compute_model_trace_p_value
from src.evaluation.initialize_models import is_model_allowed
@dataclass
class EvalResult:
"""Represents one perplexity evaluation result."""
eval_name: str # org_model_precision (uid)
full_model: str # org/model (path on hub)
org: str
model: str
revision: str # commit hash, "" if main
results: dict
precision: Precision = Precision.Unknown
model_type: ModelType = ModelType.PT # Default to pretrained
weight_type: WeightType = WeightType.Original
architecture: str = "Unknown"
still_on_hub: bool = False
@classmethod
def init_from_json_file(self, json_filepath):
"""Inits the result from the specific model result file"""
with open(json_filepath) as fp:
data = json.load(fp)
config = data.get("config")
# Precision
precision = Precision.from_str(config.get("model_dtype"))
# Get model and org
org_and_model = config.get("model_name", config.get("model_args", None))
org_and_model = org_and_model.split("/", 1)
if len(org_and_model) == 1:
org = None
model = org_and_model[0]
result_key = f"{model}_{precision.value.name}"
else:
org = org_and_model[0]
model = org_and_model[1]
result_key = f"{org}_{model}_{precision.value.name}"
full_model = "/".join(org_and_model)
still_on_hub, _, model_config = is_model_on_hub(
full_model, config.get("model_sha", "main"), trust_remote_code=True, test_tokenizer=False
)
architecture = "?"
if model_config is not None:
architectures = getattr(model_config, "architectures", None)
if architectures:
architecture = ";".join(architectures)
# No perplexity extraction - we only care about p-values
results = {}
return self(
eval_name=result_key,
full_model=full_model,
org=org,
model=model,
results=results,
precision=precision,
revision=config.get("model_sha", ""),
still_on_hub=still_on_hub,
architecture=architecture
)
def to_dict(self):
"""Converts the Eval Result to a dict compatible with our dataframe display - P-VALUES ONLY"""
import sys
sys.stderr.write(f"\n=== PROCESSING RESULT TO_DICT (P-VALUES ONLY) ===\n")
sys.stderr.write(f"Processing result for model: {self.full_model}\n")
sys.stderr.flush()
# Create data dictionary - NO TASK PROCESSING AT ALL
data_dict = {}
# Add core columns
data_dict["eval_name"] = self.eval_name
data_dict[AutoEvalColumn.precision.name] = self.precision.value.name
data_dict[AutoEvalColumn.model_type.name] = self.model_type.value.name
data_dict[AutoEvalColumn.model_type_symbol.name] = self.model_type.value.symbol
data_dict[AutoEvalColumn.weight_type.name] = self.weight_type.value.name
data_dict[AutoEvalColumn.architecture.name] = self.architecture
data_dict[AutoEvalColumn.model.name] = make_clickable_model(self.full_model)
data_dict[AutoEvalColumn.revision.name] = self.revision
data_dict[AutoEvalColumn.still_on_hub.name] = self.still_on_hub
# Add default values for missing model info
data_dict[AutoEvalColumn.license.name] = "Unknown"
data_dict[AutoEvalColumn.params.name] = 0
data_dict[AutoEvalColumn.likes.name] = 0
# Compute model trace p-value
sys.stderr.write(f"🧬 COMPUTING MODEL TRACE P-VALUE FOR: {self.full_model}\n")
sys.stderr.flush()
try:
model_trace_p_value = compute_model_trace_p_value(
self.full_model,
self.revision if self.revision else "main",
self.precision.value.name.lower()
)
if model_trace_p_value is not None:
sys.stderr.write(f"✅ P-value: {model_trace_p_value}\n")
else:
sys.stderr.write(f"⚠️ P-value computation failed\n")
except Exception as e:
sys.stderr.write(f"💥 Exception during p-value computation: {e}\n")
model_trace_p_value = None
data_dict[AutoEvalColumn.model_trace_p_value.name] = model_trace_p_value
sys.stderr.write(f"=== END PROCESSING - ONLY P-VALUES ===\n")
sys.stderr.flush()
return data_dict
def get_raw_eval_results(results_path: str) -> list[EvalResult]:
"""From the path of the results folder root, extract all perplexity results"""
import sys
sys.stderr.write(f"\nSearching for result files in: {results_path}\n")
sys.stderr.flush()
model_result_filepaths = []
for root, _, files in os.walk(results_path):
# Process all JSON files, regardless of other files in the directory
for file in files:
if file.endswith(".json"):
model_result_filepaths.append(os.path.join(root, file))
sys.stderr.write(f"Found {len(model_result_filepaths)} result files\n")
sys.stderr.flush()
eval_results = {}
for model_result_filepath in model_result_filepaths:
try:
sys.stderr.write(f"\nProcessing file: {model_result_filepath}\n")
sys.stderr.flush()
# Quick pre-check: Try to extract model name from file before full processing
try:
with open(model_result_filepath, 'r') as f:
data = json.load(f)
config = data.get("config", {})
model_name = config.get("model_name", "")
if model_name and not is_model_allowed(model_name):
sys.stderr.write(f"⏭️ Skipping non-allowed model file: {model_result_filepath} (model: {model_name})\n")
sys.stderr.flush()
continue
except Exception as e:
sys.stderr.write(f"⚠️ Error pre-checking file {model_result_filepath}: {e}\n")
sys.stderr.flush()
continue
# Creation of result
eval_result = EvalResult.init_from_json_file(model_result_filepath)
sys.stderr.write(f"Created result object for: {eval_result.full_model}\n")
sys.stderr.flush()
# Store results of same eval together
eval_name = eval_result.eval_name
if eval_name in eval_results.keys():
eval_results[eval_name].results.update({k: v for k, v in eval_result.results.items() if v is not None})
sys.stderr.write(f"Updated existing result for {eval_name}\n")
sys.stderr.flush()
else:
eval_results[eval_name] = eval_result
sys.stderr.write(f"Added new result for {eval_name}\n")
sys.stderr.flush()
except Exception as e:
sys.stderr.write(f"Error processing result file {model_result_filepath}: {e}\n")
import traceback
sys.stderr.write(f"Traceback: {traceback.format_exc()}\n")
sys.stderr.flush()
continue
results = []
sys.stderr.write(f"\nProcessing {len(eval_results)} evaluation results\n")
sys.stderr.flush()
for v in eval_results.values():
try:
sys.stderr.write(f"\nConverting result to dict for: {v.full_model}\n")
sys.stderr.flush()
# Filter to only allowed models
if not is_model_allowed(v.full_model):
sys.stderr.write(f"⏭️ Skipping non-allowed model: {v.full_model}\n")
sys.stderr.flush()
continue
v.to_dict() # we test if the dict version is complete
results.append(v)
sys.stderr.write("Successfully converted and added result\n")
sys.stderr.flush()
except KeyError as e:
sys.stderr.write(f"Error converting result to dict: {e}\n")
import traceback
sys.stderr.write(f"Traceback: {traceback.format_exc()}\n")
sys.stderr.flush()
continue
sys.stderr.write(f"\nReturning {len(results)} processed results\n")
sys.stderr.flush()
return results
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