Change tie_weights to no-op and add docstring for weight tying clarification#503
Merged
2015aroras merged 8 commits intoallenai:mainfrom Apr 2, 2024
Merged
Change tie_weights to no-op and add docstring for weight tying clarification#5032015aroras merged 8 commits intoallenai:mainfrom
2015aroras merged 8 commits intoallenai:mainfrom
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The `tie_weights` function in the `OLMoForCausalLM` class is now a no-op
because weight tying is already handled in other parts of the codebase:
1. During model initialization in `olmo/model.py`:
- The `ff_out` layer is conditionally defined based on the `weight_tying`
configuration using `if not config.weight_tying: self.transformer.update(...)`.
2. When computing logits in the `forward` method:
- The `wte` weights are used directly if `weight_tying` is enabled using
`if self.config.weight_tying: logits = F.linear(x, self.transformer.wte.weight, None)`.
Since weight tying is handled explicitly in these places, there is no need
for the `tie_weights` function to perform any additional weight tying.
The function is updated with a docstring that explains the reasoning behind
the no-op and provides references to the relevant code snippets where weight
tying is handled.
This change improves code clarity and avoids redundant weight tying logic.
AkshitaB
approved these changes
Mar 15, 2024
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This pull request addresses #485 and updates the
tie_weightsfunction in theOLMoForCausalLMclass to be a no-op and adds a docstring explaining the reasoning behind this change.The
tie_weightsfunction is being made a no-op because weight tying is already handled explicitly in other parts of the codebase. This change avoids redundant weight tying logic and improves code clarity.The added docstring explains why the function is a no-op and directs developers to the specific locations where weight tying is handled. This improves code readability and maintainability.
This doesn't fix the warning message from the
acceleratelibrary, which expects a fundamentally different mechanism for weight tying.@2015aroras ready for your initial review—thanks!