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Phase 4: Post-Processing
Reuses logic from filter_ifeval_data.py to:
1. Restructure kwargs (flat dict -> per-instruction list)
2. Filter conflicting constraints
3. Validate responses with lm-eval IFEval checker
4. Keep only samples where prompt_level_strict_acc == True
Key Functions from filter_ifeval_data.py:
- build_instruction_kwargs() (lines 257-285)
- filter_not_valid_rows() (lines 288-305)
- get_ifeval_results() (lines 308-321)
Key Data Structures from filter_ifeval_data.py:
- INSTRUCTION_ARGS (lines 11-41)
- LANGUAGE_TO_CODE (lines 223-254)
- IFEVAL_INSTRUCTION_CONFLICTS (lines 70-221)
"""
import argparse
import json
from tqdm import tqdm
from main_ifeval_code.filter_ifeval_data_pt import (
INSTRUCTION_ARGS,
LANGUAGE_TO_CODE,
IFEVAL_INSTRUCTION_CONFLICTS,
build_instruction_kwargs,
filter_not_valid_rows,
get_ifeval_results,
)
from main_ifeval_code.config import (
PHASE3_OUTPUT,
PHASE4_OUTPUT,
)
from main_ifeval_code.utils import (
iter_jsonl_batches,
write_jsonl_line,
count_jsonl_lines,
)
# -----------------------------------------------------------------------
# STEP 4.1: RESTRUCTURE KWARGS
# Reuses build_instruction_kwargs() from filter_ifeval_data.py (lines 257-285)
# -----------------------------------------------------------------------
def restructure_kwargs(item: dict) -> dict:
"""
Transform flat kwargs dict into per-instruction kwargs list.
Wraps build_instruction_kwargs() from filter_ifeval_data.py.
"""
result = build_instruction_kwargs(item)
item["kwargs"] = result.get("kwargs", item.get("kwargs"))
item["valid_kwargs_json"] = result.get("valid_kwargs_json", False)
return item
# -----------------------------------------------------------------------
# STEP 4.2: FILTER CONFLICTING CONSTRAINTS
# Reuses filter_not_valid_rows() from filter_ifeval_data.py (lines 288-305)
# -----------------------------------------------------------------------
def is_valid_row(item: dict) -> bool:
"""
Check if a row has valid kwargs and no conflicting constraints.
Wraps filter_not_valid_rows() from filter_ifeval_data.py.
"""
return filter_not_valid_rows(item)
# -----------------------------------------------------------------------
# STEP 4.3: VALIDATE WITH LM-EVAL
# Reuses get_ifeval_results() from filter_ifeval_data.py (lines 308-321)
# -----------------------------------------------------------------------
def validate_response(item: dict) -> dict:
"""
Validate response against constraints using lm-eval IFEval checker.
Wraps get_ifeval_results() from filter_ifeval_data.py.
"""
# Rename instruction to prompt (as done in filter_ifeval_data.py line 329)
item["prompt"] = item.pop("instruction", item.get("prompt"))
# Add 'key' field required by lm-eval process_results
if "key" not in item:
item["key"] = item.get("id", 0)
results = get_ifeval_results(item)
item.update(results)
return item
# -----------------------------------------------------------------------
# MAIN PROCESSING
# -----------------------------------------------------------------------
def process_item(item: dict) -> dict | None:
"""
Process a single item through all post-processing steps.
Returns None if the item should be filtered out.
"""
# Step 4.1: Restructure kwargs
item = restructure_kwargs(item)
# Step 4.2: Filter invalid/conflicting
if not is_valid_row(item):
return None
# Step 4.3: Validate with lm-eval
item = validate_response(item)
# Step 4.4: Filter by strict accuracy
if not item.get("prompt_level_strict_acc", False):
return None
# Clean up intermediate fields
if "valid_kwargs_json" in item:
del item["valid_kwargs_json"]
return item
def main(
input_file: str = PHASE3_OUTPUT,
output_file: str = PHASE4_OUTPUT,
batch_size: int = 100,
):
"""
Run post-processing on Phase 3 output.
Note: This is CPU-bound (no LLM calls), so we process sequentially.
The main bottleneck is the lm-eval validation.
"""
# Count total items
total_items = count_jsonl_lines(input_file)
print(f"Processing {total_items} items from {input_file}...")
# Track stats
stats = {
"total": 0,
"invalid_kwargs": 0,
"conflicts": 0,
"failed_validation": 0,
"passed": 0,
}
# Debug: track first few failures of each type
DEBUG_LIMIT = 10
debug_samples = {
"invalid_kwargs": [],
"conflicts": [],
"failed_validation": [],
}
# Process items
pbar = tqdm(total=total_items, desc="Post-processing")
for batch in iter_jsonl_batches(
input_file,
batch_size,
start_from_id=0,
required_fields=["instruction", "response", "instruction_id_list", "kwargs"],
):
for item in batch:
stats["total"] += 1
item_id = item.get("id", stats["total"])
# Step 4.1: Restructure kwargs
item = restructure_kwargs(item)
if not item.get("valid_kwargs_json", False):
stats["invalid_kwargs"] += 1
if len(debug_samples["invalid_kwargs"]) < DEBUG_LIMIT:
debug_samples["invalid_kwargs"].append({
"id": item_id,
"raw_kwargs": item.get("kwargs"),
"instruction_id_list": item.get("instruction_id_list"),
})
pbar.update(1)
continue
# Step 4.2: Filter invalid/conflicting
if not is_valid_row(item):
stats["conflicts"] += 1
if len(debug_samples["conflicts"]) < DEBUG_LIMIT:
debug_samples["conflicts"].append({
"id": item_id,
"instruction_id_list": item.get("instruction_id_list"),
})
pbar.update(1)
continue
# Step 4.3: Validate with lm-eval
item = validate_response(item)
# Step 4.4: Filter by strict accuracy
if not item.get("prompt_level_strict_acc", False):
stats["failed_validation"] += 1
if len(debug_samples["failed_validation"]) < DEBUG_LIMIT:
debug_samples["failed_validation"].append({
"id": item_id,
"instruction_id_list": item.get("instruction_id_list"),
"kwargs": item.get("kwargs"),
"inst_level_strict_acc": item.get("inst_level_strict_acc"),
"validation_error": item.get("validation_error"), # Capture exception
"response_preview": item.get("response", "")[:500],
})
pbar.update(1)
continue
# Clean up intermediate fields
if "valid_kwargs_json" in item:
del item["valid_kwargs_json"]
# Write passing item
write_jsonl_line(output_file, item)
stats["passed"] += 1
pbar.update(1)
pbar.close()
# Print summary
print("\n" + "=" * 50)
print("Post-processing Summary")
print("=" * 50)
print(f"Total processed: {stats['total']:,}")
print(f"Invalid kwargs: {stats['invalid_kwargs']:,}")
print(f"Conflicting: {stats['conflicts']:,}")
print(f"Failed validation: {stats['failed_validation']:,}")
print(f"Passed (final): {stats['passed']:,}")
print(f"Pass rate: {stats['passed']/max(stats['total'],1)*100:.1f}%")
print("=" * 50)
print(f"Output: {output_file}")
# Print debug samples
if debug_samples["invalid_kwargs"]:
print("\n" + "=" * 50)
print(f"DEBUG: Sample INVALID KWARGS failures (first {DEBUG_LIMIT}):")
print("=" * 50)
for sample in debug_samples["invalid_kwargs"]:
print(f"\n--- ID: {sample['id']} ---")
print(f"instruction_id_list: {sample['instruction_id_list']}")
print(f"raw_kwargs: {sample['raw_kwargs'][:500] if sample['raw_kwargs'] else 'None'}...")
if debug_samples["conflicts"]:
print("\n" + "=" * 50)
print(f"DEBUG: Sample CONFLICT failures (first {DEBUG_LIMIT}):")
print("=" * 50)
for sample in debug_samples["conflicts"]:
print(f"\n--- ID: {sample['id']} ---")
print(f"instruction_id_list: {sample['instruction_id_list']}")
if debug_samples["failed_validation"]:
print("\n" + "=" * 50)
print(f"DEBUG: Sample VALIDATION failures (first {DEBUG_LIMIT}):")
print("=" * 50)
for sample in debug_samples["failed_validation"]:
print(f"\n--- ID: {sample['id']} ---")
print(f"instruction_id_list: {sample['instruction_id_list']}")
print(f"kwargs: {sample['kwargs']}")
print(f"inst_level_strict_acc: {sample['inst_level_strict_acc']}")
if sample.get('validation_error'):
print(f"EXCEPTION: {sample['validation_error']}")
print(f"response_preview: {sample['response_preview']}...")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Phase 4: Post-Processing")
parser.add_argument("--input", default=PHASE3_OUTPUT, help="Input JSONL file from Phase 3")
parser.add_argument("--output", default=PHASE4_OUTPUT, help="Output JSONL file")
parser.add_argument("--batch-size", type=int, default=100, help="Batch size for reading")
args = parser.parse_args()
main(
input_file=args.input,
output_file=args.output,
batch_size=args.batch_size,
)
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