short-transformers 0.4.0

Cutting transformers layers

:scissors: Short Transformers

• [Unofficial] Pytorch implementation of layer pruning proposed in The Unreasonable Ineffectiveness of the Deeper Layers.
• The repository reproduces and extends original methods by offering different layer pruning criteria.

Installation:

pip install short-transformers

Required additional dependencies: transformers, datasets.

Quickstart:

from short_transformers import ShortTransformer
from datasets import load_dataset

# load from path/hf_hub
model = ShortTransformer.from_pretrained(model_name)

# or use hf model
# model = ShortTransformer.from_model(hf_model)

# load hf dataset
dataset = load_dataset("allenai/c4", "en", split="validation", streaming=True)

# remove 5 layers, use the dataset to find the least important layers to remove
short_model = model.remove_layers(block_size=5, dataset=dataset, limit=1000)

# continue training to heal after the cut
# ...

# save as hf model
short_mdoel.save_pretrained(output_path)

Both short_model and the saved model are fully compatible with transformers. See examples/basic.py for a complete working example.

Pruning in steps:

Pruning can composed step-by-step and customized:

1. Analyze model layers:

from datasets import load_dataset
from short_transformers import ShortTransformer
from short_transformers.utils import (
    draw_diagram,
    get_scored_blocks,
    get_best_pruning_start,
)
# load from path/hf_hub
model_name = "meta-llama/Meta-Llama-3-8B"

model = ShortTransformer.from_pretrained(model_name, device_map="auto")

dataset = load_dataset("allenai/c4", "en", split="validation", streaming=True)

# calculate distances between inputs/outputs from/to model layers
# results in a triangular numpy array of shape (layer_count, layer_count)
# results[x, y] - averaged distances for block of size x starting at layer y
results = model.analyse_layers(
    dataset=dataset,
    key="text",
    limit=100,
    max_length=1000,
)

# draw results
# diagrams style matches the style of original article
# "The Unreasonable Ineffectiveness of the Deeper Layers"
draw_diagram(results, "results.png", title="Meta-Llama-3-8B")

Example output:

2. Find optimal block_size and start_layer:

# find optimial block of size 'block_size' to prune
start_layer = get_best_pruning_start(results, block_size=5)

# evaluate all possibe block sizes to prune,
# for each block returns score 0-1
# which is averaged over samples distance between input and output to/from a block
block_score = get_scored_blocks(results, return_md=True, threshold=0.3)

Example output:

Block_size	Removed_layers	Score (avg dist)
1	25-25	0.123
2	24-25	0.155
3	25-27	0.181
4	24-27	0.204
5	23-27	0.226
6	22-27	0.248
7	22-28	0.268
8	20-27	0.291

3. Pruning layers:

# prune 5-layers block
model.prune(start_layer=start_layer, block_size=5)

# save the pruned model
model.save_pretrained("model_output_dir")

See example/prune_in_steps.py for a complete working example.

4. Changing the pruning method:

Default pruning method is based on angular distance of the last token. It is possible to overwrite the distance by using model.set_metric(some_callable) before model.analyse_layers().

# ...
from short_transformers.dist import get_angular_distance_ith_token

model_name = "meta-llama/Meta-Llama-3-8B"
model = ShortTransformer.from_pretrained(model_name, device_map="auto")

# choose metric
# calculate distances based on the angular distance of the i=0 token
model.set_metric(get_angular_distance_ith_token(i=0))

# load dataset ...

results = model.analyse_layers(
    dataset=dataset,
    tokenizer=tokenizer,
    key="text",
    limit=1,
    max_length=1000,
)

Supported pruning methods:

• based on layer input/output distances:

  • angular distance of the last token (original)
  • averaged angular distances of all tokens

• todo: based on layer linear replacement trining loss

Citing:

If you use Short Transformers in your research, please cite with the following BibText

@misc{russak2024shorttransformers,
    title  = {ShortTransformers, optimal layer pruning tools},
    author = {Melisa Russak},
    url    = {https://github.com/melisa/short-transformers},
    year   = {2024}
}

@misc{gromov2024unreasonable,
      title={The Unreasonable Ineffectiveness of the Deeper Layers}, 
      author={Andrey Gromov and Kushal Tirumala and Hassan Shapourian and Paolo Glorioso and Daniel A. Roberts},
      year={2024},
      eprint={2403.17887},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}