File size: 5,522 Bytes
f0e7192 | 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 | """Explicit streaming evaluation; never invoked on test data during training."""
# ruff: noqa: PLC0415
import argparse
import json
import time
from pathlib import Path
from .data import classification_metrics, file_receipts, read_records
from .model import load_model
LONG_BATCH_THRESHOLD = 2048
def evaluate_records(
model,
tokenizer,
records,
label_to_id,
id_to_label,
max_length,
batch_size=16,
dtype="bfloat16",
):
import torch
device = next(model.parameters()).device
if model.config.problem_type != "single_label_classification":
raise ValueError("Softmax evaluation requires a single-label classifier")
was_training = model.training
model.eval()
encoded = [
tokenizer(row["text"], truncation=False, padding=False) for row in records
]
lengths = [len(row["input_ids"]) for row in encoded]
if max(lengths) > max_length:
raise ValueError(
"Evaluation input exceeds the explicit budget; truncation is forbidden"
)
order = sorted(range(len(records)), key=lengths.__getitem__)
results, timings, cursor = [None] * len(records), [], 0
while cursor < len(order):
size = 1 if lengths[order[cursor]] > LONG_BATCH_THRESHOLD else batch_size
indices = order[cursor : cursor + size]
if any(lengths[index] > LONG_BATCH_THRESHOLD for index in indices):
indices = indices[:1]
cursor += len(indices)
batch = tokenizer.pad(
[encoded[index] for index in indices], padding=True, return_tensors="pt"
)
batch = {key: value.to(device) for key, value in batch.items()}
if device.type == "cuda":
torch.cuda.synchronize()
start = time.perf_counter()
with torch.inference_mode(), torch.autocast(
device_type=device.type,
dtype=getattr(torch, dtype),
enabled=device.type == "cuda" and dtype != "float32",
):
logits = model(**batch).logits.float()
if not bool(torch.isfinite(logits).all()):
raise ValueError("Non-finite evaluation logits")
probabilities = logits.softmax(-1).cpu().tolist()
if device.type == "cuda":
torch.cuda.synchronize()
timings.append((time.perf_counter() - start) * 1000)
for position, index in enumerate(indices):
scores = probabilities[position]
results[index] = {
"id": records[index]["id"],
"prediction": max(range(len(scores)), key=scores.__getitem__),
"probabilities": scores,
"tokens": lengths[index],
}
gold = [label_to_id[row["label"]] for row in records]
predicted = [row["prediction"] for row in results]
metrics = classification_metrics(gold, predicted, id_to_label)
metrics["breakdowns"] = {}
for field in ["source", "language", "length_bucket", "position"]:
metrics["breakdowns"][field] = {}
for group in sorted({str(row.get(field, "unspecified")) for row in records}):
indices = [
i
for i, row in enumerate(records)
if str(row.get(field, "unspecified")) == group
]
metrics["breakdowns"][field][group] = classification_metrics(
[gold[i] for i in indices], [predicted[i] for i in indices], id_to_label
)
metrics["actual_tokens"] = {"min": min(lengths), "max": max(lengths)}
metrics["forward_batches_ms"] = timings
metrics["precision"] = {
"parameters": "float32",
"forward_autocast": (
dtype if device.type == "cuda" and dtype != "float32" else None
),
"device_type": device.type,
}
if was_training:
model.train()
return metrics, results
def main():
import torch
torch.set_num_threads(8)
parser = argparse.ArgumentParser()
parser.add_argument("--base", required=True)
parser.add_argument("--adapter")
parser.add_argument("--contract", required=True)
parser.add_argument("--data", nargs="+", required=True)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--max-length", type=int, default=32768)
parser.add_argument("--device", default="cuda")
parser.add_argument("--dtype", choices=["float32", "bfloat16"], default="bfloat16")
args = parser.parse_args()
if args.output.exists():
raise ValueError("Refusing to overwrite evaluation evidence")
model, tokenizer, labels, ids = load_model(args.base, args.contract, args.adapter)
if args.max_length > model.config.max_position_embeddings:
raise ValueError("Evaluation budget exceeds checkpoint position capacity")
model.to(args.device)
rows = read_records(args.data, labels)
metrics, predictions = evaluate_records(
model, tokenizer, rows, labels, ids, args.max_length, dtype=args.dtype
)
metrics["data_files"] = file_receipts(args.data)
args.output.parent.mkdir(parents=True, exist_ok=True)
args.output.write_text(json.dumps(metrics, indent=2) + "\n")
args.output.with_suffix(".predictions.jsonl").write_text(
"".join(json.dumps(row) + "\n" for row in predictions)
)
print(
json.dumps(
{
key: metrics[key]
for key in ["rows", "accuracy", "macro_f1", "actual_tokens"]
}
),
flush=True,
)
if __name__ == "__main__":
main()
|