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Update deepspeed_lr_schedules.py #314

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merged 1 commit into from
Aug 12, 2020
Merged

Update deepspeed_lr_schedules.py #314

merged 1 commit into from
Aug 12, 2020

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jeffra
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@jeffra jeffra commented Aug 12, 2020

get_lr() should return a list not a single float, fixes this issue

@jeffra jeffra merged commit 3437342 into master Aug 12, 2020
@jeffra jeffra deleted the jeffra-patch-1 branch September 15, 2020 20:30
jeffra added a commit that referenced this pull request 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 <zheweiy@berkeley.edu>
Co-authored-by: Elton Zheng <eltonz@microsoft.com>
Co-authored-by: Jeff Rasley <jerasley@microsoft.com>

* 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 <94406484+xiaoxiawu-microsoft@users.noreply.github.com>
Co-authored-by: yaozhewei <zheweiy@berkeley.edu>
Co-authored-by: Elton Zheng <eltonz@microsoft.com>
Co-authored-by: Jeff Rasley <jerasley@microsoft.com>
Co-authored-by: xiaoxiawu <yxiaoxiawu@microsoft.com>
Co-authored-by: xiaoxiawu <xiaoxiawu@microsoft.com>
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2 participants