v0.7.0

Major Features and Improvements

Train/Eval/Export

• Support train/eval/export on cpu #27
• Support TRT export (Beta) #30 #32 #41 #43 #58 #59 #89
• Support AOT export (WIP) #79

Model

• Optimize TDM gen tree speed #33
• TDM Support string id #72
• Rank and Match models support sample weight #50 #57 #63 #65
• Add zero collision hash embedding #60
• Add intervention methods for multi-target learning #49
• Add Autodis and MLP embedding for raw features #73 #75
• Add task space for multi-target learning loss #82
• Add dual augmented two-tower match model #83
• Add HSTU (WIP) #55

Feature

• pyfg support CPU without avx512 #20
• ExprFeature support l2_norm|dot|euclid_dist #35
• Add fg bucketize only mode & refactor fg_encoded to fg_mode #62
• Make default bucketize value configurable #94
• Support multi-value sequence #96
• Support vocab file #97

Dataset

• Enhance stability for credential of OdpsDataset #45
• Add complex type and credential support for sampler when use odps dataset #52
• Support CsvDataset with null columns #56
• Negative sampler support string id #70

Config

• Support easyrec config convert to tzrec config #37 #39 #51 #90

Upgrade

• Release official dlc image #26
• Upgrade pytorch to v2.6 torchrec to v1.1.0 #99

Note

For TorchEasyRec 0.7.x, you should use Docker image version 0.7.

• For the GPU version (CUDA 12.4):
  • mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easyrec/tzrec-devel:0.7-cu124
  • PyTorch: v2.6 CUDA: v12.4 FBGEMM: v1.1.0 TorchRec: v1.1.0 Python: v3.11
• For the CPU version:
  • mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easyrec/tzrec-devel:0.7-cpu
  • PyTorch: v2.6 FBGEMM: v1.1.0 TorchRec: v1.1.0 Python: v3.11

Bug Fixes and Other Changes

• [bugfix] remove redundant sequence key in feature input names when fg_mode is DAG by @tiankongdeguiji in #21
• fix quota_name for add feature info by @chengaofei in #22
• update config delete drop feature config by @chengaofei in #23
• [feat] make docker compat with gpu driver 470 by @tiankongdeguiji in #24
• [bugfix] fix dlc tutorial doc by @tiankongdeguiji in #25
• [bugfix] fix dbmtl model doc by @tiankongdeguiji in #28
• [feat] add pai dlc and dsw dependency in docker by @tiankongdeguiji in #29
• [feat] update easyrec dinggroup qrcode by @tiankongdeguiji in #31
• [feat] update pyfg doc to 0.3.5 by @tiankongdeguiji in #34
• [bugfix] fix fg arrow handler with sample mask by @tiankongdeguiji in #38
• [feat] add unique test work dir by @tiankongdeguiji in #40
• [bugfix] add id field of negative sampler to selected columns by @tiankongdeguiji in #42
• [bugfix] prevent predict hang when subthread or subproc exception by @tiankongdeguiji in #44
• [bugfix] input_tile=3: make dataparser to get user feats before creat… by @yjjinjie in #46
• [bugfix] fix sequence feature doc by @tiankongdeguiji in #48
• [feat] optimize is_user_feat of Feature when use dag by @tiankongdeguiji in #53
• [bugfix] refine sample weight compatibility & refine label dtype check & relax predict pipeline check & fix num_rows < num_workers when use OdpsDataset by @tiankongdeguiji in #54
• [feat] add doc for training with maxcompute tables on DLC by @yanzhen1233 in #47
• create fg will use resource name by @chengaofei in #64
• [bugfix] fix is_sparse of LookupFeature and MatchFeature when use vocab_dict by @tiankongdeguiji in #66
• [bugfix] fix odps quota in hitrate.py & refine error info of CsvReader and ParquetReader by @tiankongdeguiji in #67
• [bugfix] fix mtl model label in ut by @tiankongdeguiji in #68
• [bugfix] fix calculate_shard_storages to handle optimizer correctly by @tiankongdeguiji in #69
• [feat] add LOG_LEVEL environ variable by @tiankongdeguiji in #71
• [bugfix] fix predict when num_workers = 0 by @tiankongdeguiji in #74
• [bugfix] fix duplicate server launch error in odps sampler test by @tiankongdeguiji in #76
• [feat] refactor batch_size to tile_size in Batch dataclass by @tiankongdeguiji in #77
• [feat]add total_loss to the plogger and summary_writer by @eric-gecheng in #78
• [bugfix] fix weighted feature when INPUT_TILE=2 by @tiankongdeguiji in #80
• [bugfix] fix negative sample table with multiple partitions by @tiankongdeguiji in #81
• [bugfix] readme typo by @eric-gecheng in #85
• [doc] fix task space doc error by @chengaofei in #86
• [bugfix] add div_no_nan and prevent divide by zero loss weight by @tiankongdeguiji in #88
• [feat] remove sample weight and labels when export by @tiankongdeguiji in #91
• [feat] configure the shell to be bash by default in docker environments by @tiankongdeguiji in #92
• [doc] creat fg json doc add upload fg json to mc method by @chengaofei in #95

New Contributors

• @yjjinjie made their first contribution in #30
• @eric-gecheng made their first contribution in #50
• @yanzhen1233 made their first contribution in #47
• @Dave-AdamsWANG made their first contribution in #49
• @chengmengli06 made their first contribution in #79
• @iWelkin-coder made their first contribution in #55

Full Changelog: v0.6.0...v0.7.0

v0.6.0

We are excited to announce the release of TorchEasyRec 0.6.0, the first public release for TorchEasyRec.

Major Features and Improvements

• High-performance training, evaluation, and prediction with GPUs.
• Supported a variety of input data types, including MaxCompute Table, OSS files, CSV files, Parquet files doc here.
• Supported a variety of feature types, including IdFeature, RawFeature, ComboFeature, LookupFeature, MatchFeature, ExprFeature, OverlapFeature, TokenizeFeature, SequenceIdFeature, SequenceRawFeature, and SequenceFeature. The feature generation operations is also efficient and robust doc here.
• Supported a variety of models, including DSSM, TDM, DeepFM, MultiTower, DIN, MMoE, DBMTL, PLE. It is also easy to implement customized models.
• Supported a variety of loss, including binary_cross_entropy, softmax_cross_entropy, l2_loss, jrc_loss doc here.
• Supported VariationalDropout feature selection.
• Easy to deploy a TorchEasyRec model as a high-performance inference service using the TorchEasyRec Processor.

Bug Fixes and Other Changes

• [bugfix] fix train_eval may hang when use OdpsDataset and set is_orderby_partition=true by @tiankongdeguiji in
• [bugfix] fix offline predict input tile model with sequence by @tiankongdeguiji in #14

Note

For TorchEasyRec 0.6.x, you should use Docker image version 0.6.

• For the GPU version (CUDA 12.1):
  • mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easyrec/tzrec-devel:0.6-cu121
• For the CPU version:
  • mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easyrec/tzrec-devel:0.6-cpu

New Contributors

• @tiankongdeguiji made their first contribution in #1
• @jjbbong made their first contribution in #3
• @chengaofei made their first contribution in #4

Full Changelog: https://github.com/alibaba/TorchEasyRec/commits/v0.6.0