File size: 6,441 Bytes
18eb93c 41e0423 18eb93c d195287 18eb93c d195287 18eb93c d195287 18eb93c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 | #!/usr/bin/env python3
"""
Dump a cached .pt sample to JSON for manual debugging.
Usage:
python scripts/dump_cache_sample.py # Dump first sample
python scripts/dump_cache_sample.py --index 5 # Dump sample at index 5
python scripts/dump_cache_sample.py --file data/cache/sample_ABC123.pt # Dump specific file
python scripts/dump_cache_sample.py --output debug.json # Custom output path
"""
import argparse
import json
import sys
import os
# Add project root to path so torch.load can find project modules when unpickling
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import torch
import numpy as np
from pathlib import Path
from datetime import datetime
def convert_to_serializable(obj):
"""Recursively convert non-JSON-serializable objects."""
if obj is None:
return None
if isinstance(obj, (str, int, float, bool)):
return obj
if isinstance(obj, (np.integer,)):
return int(obj)
if isinstance(obj, (np.floating,)):
return float(obj)
if isinstance(obj, np.ndarray):
return {"__type__": "ndarray", "shape": list(obj.shape), "dtype": str(obj.dtype), "data": obj.tolist()}
if isinstance(obj, torch.Tensor):
data = obj.tolist()
# Truncate large tensors for readability
if obj.numel() > 50:
flat = obj.flatten().tolist()
data = flat[:20] + [f"... ({obj.numel()} elements total)"]
return {"__type__": "tensor", "shape": list(obj.shape), "dtype": str(obj.dtype), "data": data}
# Handle EmbeddingPooler specifically
if type(obj).__name__ == 'EmbeddingPooler':
try:
items = obj.get_all_items()
return {
"__type__": "EmbeddingPooler",
"count": len(items),
"items": [convert_to_serializable(item) for item in items]
}
except:
return {"__type__": "EmbeddingPooler", "repr": str(obj)}
if isinstance(obj, datetime):
return {"__type__": "datetime", "value": obj.isoformat()}
if isinstance(obj, bytes):
return {"__type__": "bytes", "length": len(obj), "preview": obj[:100].hex() if len(obj) > 0 else ""}
if isinstance(obj, dict):
return {str(k): convert_to_serializable(v) for k, v in obj.items()}
if isinstance(obj, (list, tuple)):
return [convert_to_serializable(item) for item in obj]
if isinstance(obj, set):
return {"__type__": "set", "data": list(obj)}
# Fallback: try str representation
try:
return {"__type__": type(obj).__name__, "repr": str(obj)[:500]}
except:
return {"__type__": "unknown", "repr": "<not serializable>"}
def main():
parser = argparse.ArgumentParser(description="Dump cached .pt sample to JSON")
parser.add_argument("--index", "-i", type=int, default=0, help="Index of sample to dump (default: 0)")
parser.add_argument("--file", "-f", type=str, default=None, help="Direct path to .pt file (overrides --index)")
parser.add_argument("--cache_dir", "-c", type=str, default="data/cache", help="Cache directory (default: data/cache)")
parser.add_argument("--output", "-o", type=str, default=None, help="Output JSON path (default: auto-generated)")
parser.add_argument("--compact", action="store_true", help="Compact JSON output (no indentation)")
args = parser.parse_args()
# Determine which file to load
if args.file:
filepath = Path(args.file)
if not filepath.exists():
print(f"ERROR: File not found: {filepath}")
return 1
else:
cache_dir = Path(args.cache_dir)
if not cache_dir.is_dir():
print(f"ERROR: Cache directory not found: {cache_dir}")
return 1
cached_files = sorted(cache_dir.glob("sample_*.pt"))
if not cached_files:
print(f"ERROR: No sample_*.pt files found in {cache_dir}")
return 1
if args.index >= len(cached_files):
print(f"ERROR: Index {args.index} out of range. Found {len(cached_files)} files.")
return 1
filepath = cached_files[args.index]
print(f"Loading: {filepath}")
# Load the .pt file
try:
data = torch.load(filepath, map_location="cpu", weights_only=False)
except Exception as e:
print(f"ERROR: Failed to load file: {e}")
return 1
# Convert to JSON-serializable format
print("Converting to JSON-serializable format...")
serializable_data = convert_to_serializable(data)
# Add metadata
output_data = {
"__metadata__": {
"source_file": str(filepath.absolute()),
"dumped_at": datetime.now().isoformat(),
"cache_format": "context" if isinstance(data, dict) and "event_sequence" in data else "legacy"
},
"data": serializable_data
}
# Determine output path
if args.output:
output_path = Path(args.output)
else:
# Default: Save to current directory (root) instead of inside cache dir
output_path = Path.cwd() / filepath.with_suffix(".json").name
# Write JSON
print(f"Writing to: {output_path}")
indent = None if args.compact else 2
with open(output_path, "w") as f:
json.dump(output_data, f, indent=indent, ensure_ascii=False)
# Print summary
if isinstance(data, dict):
print(f"\n=== Summary ===")
print(f"Top-level keys: {list(data.keys())}")
print(f"Cache format: {'context' if 'event_sequence' in data else 'legacy'}")
if 'event_sequence' in data:
print(f"Event count: {len(data['event_sequence'])}")
if 'trades' in data:
print(f"Trade count: {len(data['trades'])}")
if 'source_token' in data:
print(f"Source token: {data['source_token']}")
if 'class_id' in data:
print(f"Class ID: {data['class_id']}")
if 'context_bucket' in data:
print(f"Context bucket: {data['context_bucket']}")
if 'context_score' in data:
print(f"Context score: {data['context_score']}")
if 'quality_score' in data:
print(f"Quality score: {data['quality_score']}")
print(f"\nDone! JSON saved to: {output_path}")
return 0
if __name__ == "__main__":
exit(main())
|