Unifying TBE API using List (Backend) #3563
Open
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
|
This pull request was exported from Phabricator. Differential Revision: D68054868 |
✅ Deploy Preview for pytorch-fbgemm-docs ready!
To edit notification comments on pull requests, go to your Netlify site configuration. |
spcyppt
force-pushed
the
export-D68054868
branch
from
5f02b4b to
f906f10
Compare
spcyppt
added a commit
to spcyppt/FBGEMM
that referenced
this pull request
Jan 16, 2025
Summary: X-link: facebookresearch/FBGEMM#649 As the number of arguments in TBE keeps growing, some of the optimizers run into number of arguments limitation (i.e., 64) during pytorch operation registration. **For long-term growth and maintenance, we hence redesign TBE API by packing some of the arguments into list. Note that not all arguments are packed.** We pack the arguments as a list for each type. For **common** arguments, we pack - weights and arguments of type `Momentum` into TensorList - other tensors and optional tensors to list of optional tensors `aux_tensor` - `int` arguments into `aux_int` - `float` arguments into `aux_float` - `bool` arguments into `aux_bool`. Similarly for **optimizer-specific** arguments, we pack - arguments of type `Momentum` that are *__not__ optional* into TensorList - *optional* tensors to list of optional tensors `optim_tensor` - `int` arguments into `optim_int` - `float` arguments into `optim_float` - `bool` arguments into `optim_bool`. We see issues with pytorch registration across packing SymInt in python-C++, so we unroll and pass SymInt arguments individually. **This significantly reduces number of arguments.** For example, `split_embedding_codegen_lookup_rowwise_adagrad_with_counter_function`, which currently has 61 arguments only have 26 arguments with this API design. Please refer to the design doc on which arguments are packed and signature. Design doc: https://docs.google.com/document/d/1dCBg7dcf7Yq9FHVrvXsAmFtBxkDi9o6u0r-Ptd4UDPE/edit?tab=t.0#heading=h.6bip5pwqq8xb Full signature for each optimizer lookup function will be provided shortly. Differential Revision: D68054868
|
This pull request was exported from Phabricator. Differential Revision: D68054868 |
spcyppt
added a commit
to spcyppt/FBGEMM
that referenced
this pull request
Jan 16, 2025
Summary: X-link: facebookresearch/FBGEMM#649 As the number of arguments in TBE keeps growing, some of the optimizers run into number of arguments limitation (i.e., 64) during pytorch operation registration. **For long-term growth and maintenance, we hence redesign TBE API by packing some of the arguments into list. Note that not all arguments are packed.** We pack the arguments as a list for each type. For **common** arguments, we pack - weights and arguments of type `Momentum` into TensorList - other tensors and optional tensors to list of optional tensors `aux_tensor` - `int` arguments into `aux_int` - `float` arguments into `aux_float` - `bool` arguments into `aux_bool`. Similarly for **optimizer-specific** arguments, we pack - arguments of type `Momentum` that are *__not__ optional* into TensorList - *optional* tensors to list of optional tensors `optim_tensor` - `int` arguments into `optim_int` - `float` arguments into `optim_float` - `bool` arguments into `optim_bool`. We see issues with pytorch registration across packing SymInt in python-C++, so we unroll and pass SymInt arguments individually. **This significantly reduces number of arguments.** For example, `split_embedding_codegen_lookup_rowwise_adagrad_with_counter_function`, which currently has 61 arguments only have 26 arguments with this API design. Please refer to the design doc on which arguments are packed and signature. Design doc: https://docs.google.com/document/d/1dCBg7dcf7Yq9FHVrvXsAmFtBxkDi9o6u0r-Ptd4UDPE/edit?tab=t.0#heading=h.6bip5pwqq8xb Full signature for each optimizer lookup function will be provided shortly. Reviewed By: sryap Differential Revision: D68054868
spcyppt
force-pushed
the
export-D68054868
branch
from
f906f10 to
e7408fa
Compare
|
This pull request was exported from Phabricator. Differential Revision: D68054868 |
Summary: X-link: facebookresearch/FBGEMM#649 As the number of arguments in TBE keeps growing, some of the optimizers run into number of arguments limitation (i.e., 64) during pytorch operation registration. **For long-term growth and maintenance, we hence redesign TBE API by packing some of the arguments into list. Note that not all arguments are packed.** We pack the arguments as a list for each type. For **common** arguments, we pack - weights and arguments of type `Momentum` into TensorList - other tensors and optional tensors to list of optional tensors `aux_tensor` - `int` arguments into `aux_int` - `float` arguments into `aux_float` - `bool` arguments into `aux_bool`. Similarly for **optimizer-specific** arguments, we pack - arguments of type `Momentum` that are *__not__ optional* into TensorList - *optional* tensors to list of optional tensors `optim_tensor` - `int` arguments into `optim_int` - `float` arguments into `optim_float` - `bool` arguments into `optim_bool`. We see issues with pytorch registration across packing SymInt in python-C++, so we unroll and pass SymInt arguments individually. **This significantly reduces number of arguments.** For example, `split_embedding_codegen_lookup_rowwise_adagrad_with_counter_function`, which currently has 61 arguments only have 26 arguments with this API design. Please refer to the design doc on which arguments are packed and signature. Design doc: https://docs.google.com/document/d/1dCBg7dcf7Yq9FHVrvXsAmFtBxkDi9o6u0r-Ptd4UDPE/edit?tab=t.0#heading=h.6bip5pwqq8xb Full signature for each optimizer lookup function will be provided shortly. Reviewed By: sryap Differential Revision: D68054868
spcyppt
force-pushed
the
export-D68054868
branch
from
e7408fa to
8d6fa66
Compare
|
This pull request was exported from Phabricator. Differential Revision: D68054868 |
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.
Summary:
As the number of arguments in TBE keeps growing, some of the optimizers run into number of arguments limitation (i.e., 64) during pytorch operation registration.
For long-term growth and maintenance, we hence redesign TBE API by packing some of the arguments into list. Note that not all arguments are packed.
We pack the arguments as a list for each type.
For common arguments, we pack
Momentuminto TensorListaux_tensorintarguments intoaux_intfloatarguments intoaux_floatboolarguments intoaux_bool.Similarly for optimizer-specific arguments, we pack
Momentumthat are not optional into TensorListoptim_tensorintarguments intooptim_intfloatarguments intooptim_floatboolarguments intooptim_bool.We see issues with pytorch registration across packing SymInt in python-C++, so we unroll and pass SymInt arguments individually.
This significantly reduces number of arguments. For example,
split_embedding_codegen_lookup_rowwise_adagrad_with_counter_function, which currently has 61 arguments only have 26 arguments with this API design.Please refer to the design doc on which arguments are packed and signature.
Design doc:
https://docs.google.com/document/d/1dCBg7dcf7Yq9FHVrvXsAmFtBxkDi9o6u0r-Ptd4UDPE/edit?tab=t.0#heading=h.6bip5pwqq8xb
Full signature for each optimizer lookup function will be provided shortly.
Differential Revision: D68054868