Improve universal checkpoint #5289
Merged
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
tjruwase
reviewed
Mar 18, 2024
tjruwase
approved these changes
Mar 18, 2024
tjruwase
reviewed
Mar 20, 2024
github-merge-queue
bot
removed this pull request from the merge queue due to failed status checks
rraminen
pushed a commit
to ROCm/DeepSpeed
that referenced
this pull request
May 9, 2024
This PR includes the following improvement regarding universal checkpoint. - Restoring step A universal checkpoint saves the training step count taken from the engine. In microsoft#5263, we fixed to always set this count to restore training step count to optimizer's states per-param (`optimizer_state['state`][param]['step']`) and a param_group. However, this approach does not restore the optimizer's state and param groups precisely due to different behaviors of optimizers. Torch's Adam doesn't make `step` in a param groups and only uses `optimizer_state['state'][param]['step']`. Apex's fused adam only uses `step` in a param groups. DeepSpeed's fused adam creates `step` in a param groups and never updates. It only uses `optimizer_state['state'][param]['step']`. Consequently, this leads to discrepancies between the restored and original states of the optimizer and param groups. This PR modifies the restoration process to ensure that the step number in the optimizer's state and param groups matches those in the original setup, effectively aligning the restored and original optimizer states and param groups. - Unit tests of DP size scaling This PR also adds unit tests to verify universal checkpointing. They run training with DP, save a checkpoint, and converts in to a universal checkpoint. Then they load the checkpoint with a different DP size and validate that parameters and the all-gathered (ZeRO 1/2) optimizer states match. - Fix bug of loading with `load_optimizer_states=False` The loader doesn't load parameters from a universal checkpoint when `load_optimizer_states=False`. microsoft@c8c0498 fixes this issue.
dbyoung18
pushed a commit
to dbyoung18/DeepSpeed
that referenced
this pull request
Jun 11, 2024
This PR includes the following improvement regarding universal checkpoint. - Restoring step A universal checkpoint saves the training step count taken from the engine. In microsoft#5263, we fixed to always set this count to restore training step count to optimizer's states per-param (`optimizer_state['state`][param]['step']`) and a param_group. However, this approach does not restore the optimizer's state and param groups precisely due to different behaviors of optimizers. Torch's Adam doesn't make `step` in a param groups and only uses `optimizer_state['state'][param]['step']`. Apex's fused adam only uses `step` in a param groups. DeepSpeed's fused adam creates `step` in a param groups and never updates. It only uses `optimizer_state['state'][param]['step']`. Consequently, this leads to discrepancies between the restored and original states of the optimizer and param groups. This PR modifies the restoration process to ensure that the step number in the optimizer's state and param groups matches those in the original setup, effectively aligning the restored and original optimizer states and param groups. - Unit tests of DP size scaling This PR also adds unit tests to verify universal checkpointing. They run training with DP, save a checkpoint, and converts in to a universal checkpoint. Then they load the checkpoint with a different DP size and validate that parameters and the all-gathered (ZeRO 1/2) optimizer states match. - Fix bug of loading with `load_optimizer_states=False` The loader doesn't load parameters from a universal checkpoint when `load_optimizer_states=False`. microsoft@c8c0498 fixes this issue.
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This PR includes the following improvement regarding universal checkpoint.
A universal checkpoint saves the training step count taken from the engine. In
#5263, we fixed to always set this count to restore training step count to optimizer's states per-param (
optimizer_state['state][param]['step']`) and a param_group. However, this approach does not restore the optimizer's state and param groups precisely due to different behaviors of optimizers.Torch's Adam doesn't make
stepin a param groups and only usesoptimizer_state['state'][param]['step']. Apex's fused adam only usesstepin a param groups. DeepSpeed's fused adam createsstepin a param groups and never updates. It only usesoptimizer_state['state'][param]['step'].Consequently, this leads to discrepancies between the restored and original states of the optimizer and param groups.
This PR modifies the restoration process to ensure that the step number in the optimizer's state and param groups matches those in the original setup, effectively aligning the restored and original optimizer states and param groups.
This PR also adds unit tests to verify universal checkpointing. They run training with DP, save a checkpoint, and converts in to a universal checkpoint. Then they load the checkpoint with a different DP size and validate that parameters and the all-gathered (ZeRO 1/2) optimizer states match.
load_optimizer_states=FalseThe loader doesn't load parameters from a universal checkpoint when
load_optimizer_states=False. c8c0498 fixes this issue.