Fix+tests for get_lr from lr_scheduler before training starts #310
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ShadenSmith
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Aug 8, 2020
tjruwase
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Aug 10, 2020
samyam
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Aug 10, 2020
jeffra
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Jul 18, 2022
* Staging compression library v1 (#314) * prototype * add sparse/row/head pruning * add bert test examples, not testing yet * rm moq * add deepspeed based glue example to test compression * add get/set attr * tested replacement module * Custimized Linear Layer Accuracy Checked without any compression technique * sparse pruning tested * head pruning tested * row pruning tested * enable act dy quantization * change l1 mask to buffer for better resume training * add final model saving helper function, only for sparse prunin now * tested sparse pruning resume training and final model saving * row pruning resume training and final saving checked * head pruning resuming training / final model saving * rm bert from deepspeed * restruct the code * add mixed-precision quantization support * add binary/ternary support * add weight quantization FP16 assert * add conv2d * add compression function * move config generation to deepspeed side, need elton to take a look * add activation quantization support * add sparse pruning support * add row pruning * add head pruning * add channel pruning * support matching patterns for module names * update * fix typo in fix_compression * add compression scheduler, rm the offset scheduler from MoQ * fix some errors in head pruning, support redudent clearning (naive version) * add dim-reduction redudent clearning * update linear layer * make cnn example work * add bn2d * fix bias issue * add static act quantization * support mpu row/colomn parallel linear layer * add skip_bias_add for mpu linear layers * make mpu compress work, remove_redundent is not tested yet * fix several small errors * add conv1d to linear converter function * add conv1d to linear converter function * add conv1d to linear converter function * make dy-act-quantization per-token or per-image * cleaning part of the code; more is coming * enable forward weight quantization which supports both FP32 and some tricky settings * update readme * Update README.md * naming cleaning * fix static activation loading issue * update parameter * Update utils.py fix a typo * fix typo * fix typo * replace expand_as with view * Zheweiyao/compression library (#304) * add forward weight quantization constraint * add quantize_weight_in_forward warning: a lot of features are not supported * offset 0 fixing * add forward weight quantization constraint * add quantize_weight_in_forward warning: a lot of features are not supported * offset 0 fixing * fix a small issue * omit bias if the model does not have bias * add contiguous to aviod memory issue * add scale associated to weight, so people can quantize the weight after training * add fix weight quantization, change name based on constant.py file * disable eigen-based MoQ * When a method is disable (enable: false), we do not need to initialize its related parameters * weight quantization cleaning * fix get_quantize_enabled missing problem * fix redundent cleaning issue, make sure we either get mask from related-module or we enable the method in config * sort the redundent cleaning step, so we always do quantization, then sparse pruning, then others * a lot of comment cleaning and args explanation * add args in config-json.md * fix format issue * fix quantization offset step=1 with FP16 optimizer * Zheweiyao/compression library from s1 (#305) * add binary/ternary support for FP32 training; this is used to resolve FP16 unstable extreme compression training * add embedding quantization support * Xiaoxia/compression library v1 (#307) * add layer reduction (Xiaoxia/Zhewei) * fixing bug for sym activation and clean layer reduction (Xiaoxia) * fixing compression initialization (Xiaoxia/Zhewei) * fix format issue (#310) * Xiaoxia/compression library v1 (#311) * add layer reduction * fixing bug for sym activation and clean layer reduction * fixingn compression initialization * pre-commit... * Zheweiyao/compression library from s1 (#312) * fix format issue * fix the accuracy mismatch after quantization cleaning * fix clean_model bug and add layer_reduction configuration Co-authored-by: yaozhewei <[email protected]> Co-authored-by: Elton Zheng <[email protected]> Co-authored-by: Jeff Rasley <[email protected]> * switch to deepspeed comm * dummy tutorial * improve config json * Zheweiyao/compression library based on s2 (#315) * change the name and merge layer reduction to init_compression * add conv1d to linear test unit, fix errors introduced by merging studient initialtization to init_compression * Update config-json.md * fix for cifar10 channel pruning * fix the block_eigenvalue is None bug * fix the block_eigenvalue is None bug * move compression-related constants and configs to compression * tutorial and json config Co-authored-by: Xiaoxia (Shirley) Wu <[email protected]> Co-authored-by: yaozhewei <[email protected]> Co-authored-by: Elton Zheng <[email protected]> Co-authored-by: Jeff Rasley <[email protected]> Co-authored-by: xiaoxiawu <[email protected]> Co-authored-by: xiaoxiawu <[email protected]>
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User was running into log(0) error with WarmupLR scheduler since they were trying to call get_lr() before the first training step.