Added input channel pre-processing to enable vision models functioning on MNIST #64
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
bakhtiarZ
pushed a commit
to bakhtiarZ/mase
that referenced
this pull request
Mar 18, 2024
* print args in cli
* print args, remove redundant args
* rename quantize *_bits to *_width, *_fraction_bits to *_frac
* add MaseTracer and mark_as_leaf
* replace "bits" with "width" when specifying bit width
* add integer matmul
* create MaseTracer
* only sync_dist on epoch end
* on_epoch=False for wrapper training step
* add new modifier and tracer. support custom func/module as leaf node
* remove old modifier
* supported save_name specified by users
* remove redundant comments
* get_dummy_inputs for bert-base-uncased and roberta-base
* save modified as pickle
* fixed bugs in MaseTracer
* OPTAttention mode 1 and 3 work
* traceable OPTDecoderLayer
* add func get_patched_nlp_model, which works in a way similar to get_nlp_model
* more facebook/opt models supported, not tested yet
* remove "mase_output" dir and use args.save instead
* fixed bugs in modifier, new get_dummy_inputs
* support training quantized facebook/opt
* use a unified cache dir
now we have
software/
|-- cache
|-- model_cache_dir
|-- dataset_cache_dir
|-- tokenizer_cache_dir
* update README.md and gitignore
* use user's output dir
* agreed --project_dir and --project for saving generated files
KelseyJing
pushed a commit
to KelseyJing/mase
that referenced
this pull request
Mar 18, 2024
* print args in cli
* print args, remove redundant args
* rename quantize *_bits to *_width, *_fraction_bits to *_frac
* add MaseTracer and mark_as_leaf
* replace "bits" with "width" when specifying bit width
* add integer matmul
* create MaseTracer
* only sync_dist on epoch end
* on_epoch=False for wrapper training step
* add new modifier and tracer. support custom func/module as leaf node
* remove old modifier
* supported save_name specified by users
* remove redundant comments
* get_dummy_inputs for bert-base-uncased and roberta-base
* save modified as pickle
* fixed bugs in MaseTracer
* OPTAttention mode 1 and 3 work
* traceable OPTDecoderLayer
* add func get_patched_nlp_model, which works in a way similar to get_nlp_model
* more facebook/opt models supported, not tested yet
* remove "mase_output" dir and use args.save instead
* fixed bugs in modifier, new get_dummy_inputs
* support training quantized facebook/opt
* use a unified cache dir
now we have
software/
|-- cache
|-- model_cache_dir
|-- dataset_cache_dir
|-- tokenizer_cache_dir
* update README.md and gitignore
* use user's output dir
* agreed --project_dir and --project for saving generated files
|
Hi, we found there is a bug the yml files that stop CI running from forked repos. It has now been fixed and merged into the upstream. Thanks, |
removed docker credentials (from upstream/main)
|
Hi, I just merged upstream changes into the PR branch. Does it work now? |
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.
I'm Mauro Marino from Group 0 (Mauro Marino, William Powell) working on Project 1 (TensorRT integration in MASE).
An issue was opened in which errors in processing MNIST dataset with vision models was brought to attention. This is traceable to MNIST dataset being grayscale, hence providing only one channel, whilst MASE convolutional models expect 3 channels.
The proposed fix applies a series of checks to ascertain that the model-dataset combination requires intervention (since some feedforward neural network models are able to run on MNIST without further action), then, if needed, overrides the model architecture when using MNIST by including a single Conv2d, mapping the single input channel to 3 output channels, before the first convolutional layer.