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a0b72ca 0bcd821 a0b72ca 0bcd821 a0b72ca 0bcd821 a0b72ca 0bcd821 a0b72ca | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 | """Train BERT tiny, evaluating three NanoBEIR datasets every 20%.
Adapted from the multi-vector training skill template and training_contrastive.py.
Run from the repository root with Python and the training dependencies installed.
Use --smoke-test for one step, or --push-to-hub to upload the best checkpoint.
Use --long-run to continue the initial model for 10,000 steps on the full dataset.
Run without --long-run first to create the local initial model.
"""
import argparse
import json
import logging
import shutil
from contextlib import nullcontext
from pathlib import Path
import torch
from datasets import load_dataset
from transformers import BertConfig, BertModel, BertTokenizer, TrainerCallback, set_seed
from sentence_transformers import (
MultiVectorEncoder,
MultiVectorEncoderModelCardData,
MultiVectorEncoderTrainer,
MultiVectorEncoderTrainingArguments,
)
from sentence_transformers.base.modules import Dense, Normalize, Transformer
from sentence_transformers.base.sampler import BatchSamplers
from sentence_transformers.multi_vector_encoder.evaluation import MultiVectorNanoBEIREvaluator
from sentence_transformers.multi_vector_encoder.losses import MultiVectorMultipleNegativesRankingLoss
from sentence_transformers.multi_vector_encoder.modules import MultiVectorMask
RUN_NAME = "bert-tiny-msmarco"
REPO_ID = "multi-vector-encoder-testing/bert-tiny-multi-vector"
class LogProgress(TrainerCallback):
def on_log(self, args, state, control, logs=None, **kwargs):
values = {
key: value
for key, value in (logs or {}).items()
if key in ("loss", "learning_rate", "eval_loss", "eval_NanoBEIR_mean_maxsim_ndcg@10")
}
if values:
logging.info("Step %s/%s: %s", state.global_step, state.max_steps, values)
def autocast_ctx():
if not torch.cuda.is_available():
return nullcontext()
dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
return torch.autocast("cuda", dtype=dtype)
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--smoke-test", action="store_true")
parser.add_argument("--push-to-hub", action="store_true")
parser.add_argument("--long-run", action="store_true")
cli = parser.parse_args()
repo_id = REPO_ID
run_name = RUN_NAME + ("-long" if cli.long_run else "") + ("-smoke" if cli.smoke_test else "")
output_dir = Path("models") / run_name
output_dir.mkdir(parents=True, exist_ok=True)
Path("logs").mkdir(exist_ok=True)
logging.basicConfig(
format="%(asctime)s - %(message)s",
level=logging.INFO,
handlers=[logging.StreamHandler(), logging.FileHandler(f"logs/{run_name}.log", mode="w")],
force=True,
)
for noisy in ("httpx", "httpcore", "huggingface_hub", "urllib3", "filelock", "fsspec"):
logging.getLogger(noisy).setLevel(logging.WARNING)
set_seed(12)
if torch.cuda.is_available():
torch.set_float32_matmul_precision("high")
card = MultiVectorEncoderModelCardData(
language="en",
license="mit",
model_name="BERT tiny multi-vector encoder trained on MS MARCO",
model_id=repo_id,
)
if cli.long_run:
initial_model = Path("models") / RUN_NAME / "final"
if not initial_model.is_dir():
parser.error("Run without --long-run first to create the local initial model.")
model = MultiVectorEncoder(str(initial_model), model_card_data=card)
model.model_card_data.set_base_model("prajjwal1/bert-tiny")
else:
# The original checkpoint lacks model_type, which recent AutoConfig versions require.
base_dir = output_dir / "base"
base_model = BertModel.from_pretrained(
"prajjwal1/bert-tiny", config=BertConfig.from_pretrained("prajjwal1/bert-tiny")
)
base_model.save_pretrained(base_dir)
BertTokenizer.from_pretrained("prajjwal1/bert-tiny").save_pretrained(base_dir)
del base_model
transformer = Transformer(
str(base_dir),
query_length=32,
document_length=256,
query_expansion={"strategy": "min", "length": 32},
)
model = MultiVectorEncoder(
modules=[
transformer,
Dense(128, 128, bias=False, activation_function=None, module_input_name="token_embeddings"),
MultiVectorMask(),
Normalize(module_input_name="token_embeddings"),
],
model_card_data=card,
)
model.model_card_data.set_base_model("prajjwal1/bert-tiny")
batch_size = 128 if cli.long_run else 32
train_size, eval_size = (batch_size * 2, 32) if cli.smoke_test else (16_000, 128)
split = "train" if cli.long_run and not cli.smoke_test else f"train[:{train_size + eval_size}]"
dataset = load_dataset("sentence-transformers/msmarco-bm25", "triplet", split=split).select_columns(
["query", "positive", "negative"]
)
if cli.long_run and not cli.smoke_test:
eval_size = 1024
dataset = dataset.train_test_split(test_size=eval_size, seed=12)
evaluator = MultiVectorNanoBEIREvaluator(dataset_names=["msmarco", "nq", "fiqa2018"], batch_size=64)
logging.info("Baseline evaluation on three NanoBEIR datasets")
with autocast_ctx():
baseline_metrics = evaluator(model, output_path=str(output_dir), steps=0)
baseline_eval = baseline_metrics[evaluator.primary_metric]
full_evaluator = None
full_baseline = None
if cli.long_run and not cli.smoke_test:
full_evaluator = MultiVectorNanoBEIREvaluator(batch_size=64)
full_output = output_dir / "full_eval"
full_output.mkdir(exist_ok=True)
logging.info("Baseline evaluation on all 13 NanoBEIR datasets")
with autocast_ctx():
full_baseline = full_evaluator(model, output_path=str(full_output), steps=0)
(output_dir / "baseline.json").write_text(
json.dumps({"selection": baseline_metrics, "full": full_baseline}, indent=2), encoding="utf-8"
)
args = MultiVectorEncoderTrainingArguments(
output_dir=str(output_dir),
max_steps=1 if cli.smoke_test else 10_000 if cli.long_run else 500,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=32,
learning_rate=1e-5 if cli.long_run else 3e-5,
weight_decay=0.01,
warmup_steps=0.05,
bf16=torch.cuda.is_available() and torch.cuda.is_bf16_supported(),
fp16=torch.cuda.is_available() and not torch.cuda.is_bf16_supported(),
batch_sampler=BatchSamplers.NO_DUPLICATES,
eval_strategy="steps",
eval_steps=0.2,
save_strategy="steps",
save_steps=0.2,
save_total_limit=2,
logging_steps=0.005 if cli.long_run else 0.02,
logging_first_step=True,
disable_tqdm=True,
load_best_model_at_end=True,
metric_for_best_model=f"eval_{evaluator.primary_metric}",
greater_is_better=True,
report_to="none",
run_name=run_name,
seed=12,
)
trainer = MultiVectorEncoderTrainer(
model=model,
args=args,
train_dataset=dataset["train"],
eval_dataset=dataset["test"],
loss=MultiVectorMultipleNegativesRankingLoss(model, scale=1.0),
evaluator=evaluator,
callbacks=[LogProgress()],
)
logging.info("Training configuration: %s", args.to_dict())
trainer.train()
logging.info("Evaluating the best checkpoint on the same three datasets")
with autocast_ctx():
final_metrics = evaluator(model, output_path=str(output_dir / "eval"))
score = final_metrics[evaluator.primary_metric]
full_final = None
if full_evaluator is not None:
logging.info("Evaluating the best checkpoint on all 13 NanoBEIR datasets")
with autocast_ctx():
full_final = full_evaluator(model, output_path=str(full_output))
baseline_eval = full_baseline[full_evaluator.primary_metric]
score = full_final[full_evaluator.primary_metric]
delta = score - baseline_eval
verdict = "WIN" if delta >= 0.005 else "MARGINAL" if delta >= 0 else "REGRESSION"
logging.info("VERDICT: %s | score=%.4f | baseline=%.4f | delta=%+.4f", verdict, score, baseline_eval, delta)
final_dir = output_dir / "final"
model.save_pretrained(str(final_dir))
shutil.copy2(__file__, final_dir / "train.py")
results = {
"baseline": baseline_metrics,
"final": final_metrics,
"best_checkpoint": trainer.state.best_model_checkpoint,
"history": trainer.state.log_history,
"verdict": verdict,
"full_baseline": full_baseline,
"full_final": full_final,
"configuration": vars(cli),
"training_args": args.to_dict(),
}
(final_dir / "results.json").write_text(json.dumps(results, indent=2), encoding="utf-8")
logging.info("Saved model, training script, and metrics to %s", final_dir)
if cli.push_to_hub and not cli.smoke_test:
try:
url = model.push_to_hub(repo_id, local_model_path=str(final_dir))
logging.info("Uploaded to %s", url)
except Exception:
logging.exception("Hub upload failed. The model is saved at %s", final_dir)
if __name__ == "__main__":
main()
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