Develop/experiments (#59)

* Add gradient accumulation, fix lr scheduler

* fix FP16 optimizer and adapted torch amp with tensor parallel (#18)

* fixed bugs in compatibility between torch amp and tensor parallel and performed some minor fixes

* fixed trainer

* Revert "fixed trainer"

This reverts commit 2e0b0b7699.

* improved consistency between trainer, engine and schedule (#23)

Co-authored-by: 1SAA <c2h214748@gmail.com>

* Split conv2d, class token, positional embedding in 2d, Fix random number in ddp
Fix convergence in cifar10, Imagenet1000

* Integrate 1d tensor parallel in Colossal-AI (#39)

* fixed 1D and 2D convergence (#38)

* optimized 2D operations

* fixed 1D ViT convergence problem

* Feature/ddp (#49)

* remove redundancy func in setup (#19) (#20)

* use env to control the language of doc (#24) (#25)

* Support TP-compatible Torch AMP and Update trainer API (#27)

* Add gradient accumulation, fix lr scheduler

* fix FP16 optimizer and adapted torch amp with tensor parallel (#18)

* fixed bugs in compatibility between torch amp and tensor parallel and performed some minor fixes

* fixed trainer

* Revert "fixed trainer"

This reverts commit 2e0b0b7699.

* improved consistency between trainer, engine and schedule (#23)

Co-authored-by: 1SAA <c2h214748@gmail.com>

Co-authored-by: 1SAA <c2h214748@gmail.com>
Co-authored-by: ver217 <lhx0217@gmail.com>

* add an example of ViT-B/16 and remove w_norm clipping in LAMB (#29)

* add explanation for ViT example (#35) (#36)

* support torch ddp

* fix loss accumulation

* add log for ddp

* change seed

* modify timing hook

Co-authored-by: Frank Lee <somerlee.9@gmail.com>
Co-authored-by: 1SAA <c2h214748@gmail.com>
Co-authored-by: binmakeswell <binmakeswell@gmail.com>

* Feature/pipeline (#40)

* remove redundancy func in setup (#19) (#20)

* use env to control the language of doc (#24) (#25)

* Support TP-compatible Torch AMP and Update trainer API (#27)

* Add gradient accumulation, fix lr scheduler

* fix FP16 optimizer and adapted torch amp with tensor parallel (#18)

* fixed bugs in compatibility between torch amp and tensor parallel and performed some minor fixes

* fixed trainer

* Revert "fixed trainer"

This reverts commit 2e0b0b7699.

* improved consistency between trainer, engine and schedule (#23)

Co-authored-by: 1SAA <c2h214748@gmail.com>

Co-authored-by: 1SAA <c2h214748@gmail.com>
Co-authored-by: ver217 <lhx0217@gmail.com>

* add an example of ViT-B/16 and remove w_norm clipping in LAMB (#29)

* add explanation for ViT example (#35) (#36)

* optimize communication of pipeline parallel

* fix grad clip for pipeline

Co-authored-by: Frank Lee <somerlee.9@gmail.com>
Co-authored-by: 1SAA <c2h214748@gmail.com>
Co-authored-by: binmakeswell <binmakeswell@gmail.com>

* optimized 3d layer to fix slow computation ; tested imagenet performance with 3d; reworked lr_scheduler config definition; fixed launch args; fixed some printing issues; simplified apis of 3d layers (#51)

* Update 2.5d layer code to get a similar accuracy on imagenet-1k dataset

* update api for better usability (#58)

update api for better usability

Co-authored-by: 1SAA <c2h214748@gmail.com>
Co-authored-by: ver217 <lhx0217@gmail.com>
Co-authored-by: puck_WCR <46049915+WANG-CR@users.noreply.github.com>
Co-authored-by: binmakeswell <binmakeswell@gmail.com>
Co-authored-by: アマデウス <kurisusnowdeng@users.noreply.github.com>
Co-authored-by: BoxiangW <45734921+BoxiangW@users.noreply.github.com>

colossalai/nn/lr_scheduler/delayed.py

@@ -48,8 +48,10 @@ class DelayerScheduler(_LRScheduler):
         if self.finished:
             if epoch is None:
                 self.after_scheduler.step(None)
+                self._last_lr = self.after_scheduler.get_last_lr()
             else:
                 self.after_scheduler.step(epoch - self.delay_epochs)
+                self._last_lr = self.after_scheduler.get_last_lr()
         else:
             return super(DelayerScheduler, self).step(epoch)
 
@@ -66,6 +68,7 @@ class WarmupScheduler(_LRScheduler):
     :param last_epoch: The index of last epoch, defaults to -1
     :type last_epoch: int, optional
     """
+
     def __init__(self, optimizer, warmup_epochs, after_scheduler, last_epoch=-1):
         self.warmup_epochs = int(warmup_epochs)
         self.after_scheduler = after_scheduler
@@ -85,8 +88,10 @@ class WarmupScheduler(_LRScheduler):
         if self.finished:
             if epoch is None:
                 self.after_scheduler.step(None)
+                self._last_lr = self.after_scheduler.get_last_lr()
             else:
                 self.after_scheduler.step(epoch - self.warmup_epochs)
+                self._last_lr = self.after_scheduler.get_last_lr()
         else:
             return super().step(epoch)
 
@@ -136,7 +141,9 @@ class WarmupDelayerScheduler(_LRScheduler):
         if self.finished:
             if epoch is None:
                 self.after_scheduler.step(None)
+                self._last_lr = self.after_scheduler.get_last_lr()
             else:
                 self.after_scheduler.step(epoch - self.warmup_epochs)
+                self._last_lr = self.after_scheduler.get_last_lr()
         else:
             return super().step(epoch)