Adding UniSRec model implemented on lightweight class hierarchy with pytorch preprocessing #306
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TOPAPEC wants to merge 7 commits intoMTSWebServices:mainfrom
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Adding UniSRec model implemented on lightweight class hierarchy with pytorch preprocessing #306TOPAPEC wants to merge 7 commits intoMTSWebServices:mainfrom
TOPAPEC wants to merge 7 commits intoMTSWebServices:mainfrom
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Standalone sequential recommender package, mimics ModelBase interface without touching existing rectools code. FlatSASRec - plain ID-embedding SASRec encoder. UniSRec - pretrained text embeddings + PCA/BN adaptor, 3-phase training (ID emb -> adaptor only -> full finetune). Uses lightweight rank_topk instead of TorchRanker, reuses SASRecDataPreparator for the data pipeline. 30 tests, smoke scripts for both models. Fix: NaN*0=NaN in IEEE 754 breaks attention padding masking via multiplication, switched to masked_fill.
New config options: - ffn_type: conv1d / linear_gelu / linear_relu + ffn_expansion - optimizer: adam / adamw - scheduler: cosine_warmup (with warmup_ratio, min_lr_ratio) - loss: softmax / BCE / gBCE / sampled_softmax (with gbce_t) - patience: early stopping via EarlyStopping callback + val split - data_preparator: accept custom preparator instance 31 tests passing.
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April 24, 2026 22:17
- Add hash-based ID mapping (splitmix64) as alternative to dense torch.unique mapping in build_sequences and align_embeddings. - Add UniSRecModel.export_to_onnx() for native ONNX export of encoder and item embeddings (project_all). - Add UniSRecModel.map_item_ids() for external→internal ID conversion at inference time (works for both dense and hash modes). - Remove FlatSASRecModel/FlatSASRecLightning (RecTools-coupled wrappers that duplicated UniSRecModel functionality). - Add tests: hash mapping (including string-derived IDs), ONNX export roundtrip, map_item_ids for both modes.
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New
rectools.fast_transformersmodule — standalone transformer sequential recommenders that work with raw torch tensors, without going throughDataset/pandas.GPU-native preprocessing.
build_sequences()builds left-padded interaction sequences entirely in torch (argsort + scatter). On ML-20M (20M interactions) this takes 0.5s vs 14.6s for the pandas-basedSASRecDataPreparator— roughly 30x faster.FlatSASRec. Pre-norm SASRec encoder with plain id-embeddings, no ItemNet hierarchy. Wraps into
FlatSASRecModel(inheritsModelBase) so it plugs into standard RecTools fit/recommend.UniSRec. Three-phase sequential recommender with pretrained text embeddings and a learnable PCA adaptor:
UniSRecModel.fit(user_ids, item_ids, timestamps)takes raw tensors end-to-end. Supports softmax/BCE/gBCE/sampled_softmax losses, Adam/AdamW, cosine warmup scheduler, gradient clipping, early stopping, checkpoint save/load. FFN blocks are configurable (conv1d, linear_gelu, linear_relu).rank_topk()— batched top-k with CSR viewed-item filtering and whitelist support.Benchmark (ML-20M, 10 epochs, softmax, Adam, n_factors=256)
UniSRec ID: +4.6% HR@10, +6.0% NDCG@10, 1.65x faster overall.
New files
Source (9 modules, 1683 lines):
rectools/fast_transformers/gpu_data.py—build_sequences,align_embeddings,GPUBatchDataset,make_dataloaderrectools/fast_transformers/net.py—FlatSASRec,SASRecBlockrectools/fast_transformers/lightning_wrap.py—FlatSASRecLightningrectools/fast_transformers/model.py—FlatSASRecModel,FlatSASRecConfigrectools/fast_transformers/ranking.py—rank_topkrectools/fast_transformers/unisrec_net.py—UniSRec,FeedForward,make_ffnrectools/fast_transformers/unisrec_lightning.py—UniSRecLightning, loss/optimizer/scheduler dispatchrectools/fast_transformers/unisrec_model.py—UniSRecModel(three-phase fit, checkpoint)Tests (143 tests, 1920 lines):
tests/fast_transformers/test_gpu_data.py— sequence building, alignment, dataset/dataloadertests/fast_transformers/test_net.py,test_lightning_wrap.py,test_model.py— FlatSASRec stacktests/fast_transformers/test_unisrec_net.py,test_unisrec_lightning.py,test_unisrec_model.py— UniSRec stacktests/fast_transformers/test_ranking.py— top-k, filtering, edge casesScripts:
scripts/compare_sasrec_unisrec.py— full benchmark with markdown report generationscripts/comparison_report.md— benchmark resultsTest plan
pytest tests/fast_transformers/ -q)FlatSASRecModelfit/recommend through the standard RecTools API on a small dataset