This serves as a quick-start for ASP (Automatic SParsity), a tool that enables sparse training and inference for PyTorch models by adding 2 lines of Python.
For details on "Channel Permutations for N:M Sparsity," please see the permutation_tests directory.
from apex.contrib.sparsity import ASP
Apart from the import statement, it is sufficient to add just the following line of code before the training phase to augment the model and the optimizer for sparse training/inference:
ASP.prune_trained_model(model, optimizer)
In the context of a typical PyTorch training loop, it might look like this:
ASP.prune_trained_model(model, optimizer)
x, y = DataLoader(args)
for epoch in range(epochs):
y_pred = model(x)
loss = loss_function(y_pred, y)
loss.backward()
optimizer.step()
torch.save(...)
The prune_trained_model step calculates the sparse mask and applies it to the weights. This is done once, i.e., sparse locations in the weights matrix remain fixed after this step.
The following approach serves as a guiding example on how to generate a pruned model that can use Sparse Tensor Cores in the NVIDIA Ampere Architecture. This approach generates a model for deployment, i.e. inference mode.
(1) Given a fully trained (dense) network, prune parameter values in a 2:4 sparse pattern.
(2) Fine-tune the pruned model with optimization method and hyper-parameters (learning-rate, schedule, number of epochs, etc.) exactly as those used to obtain the trained model.
(3) (If required) Quantize the model.
In code, below is a sketch on how to use ASP for this approach (steps 1 and 2 above).
model = define_model(..., pretrained=True) # define model architecture and load parameter tensors with trained values (by reading a trained checkpoint)
criterion = ... # compare ground truth with model predition; use the same criterion as used to generate the dense trained model
optimizer = ... # optimize model parameters; use the same optimizer as used to generate the dense trained model
lr_scheduler = ... # learning rate scheduler; use the same schedule as used to generate the dense trained model
from apex.contrib.sparsity import ASP
ASP.prune_trained_model(model, optimizer) #pruned a trained model
x, y = DataLoader(args)
for epoch in range(epochs): # train the pruned model for the same number of epochs as used to generate the dense trained model
y_pred = model(x)
loss = criterion(y_pred, y)
lr_scheduler.step()
loss.backward()
optimizer.step()
torch.save(...) # saves the pruned checkpoint with sparsity masks
If your goal is to easily perpare a network for accelerated inference, please follow the recipe above. However, ASP can also be used to perform experiments in advanced techniques like training with sparsity from initialization. For example, in order to recompute the sparse mask in between training steps, use the following method:
ASP.compute_sparse_masks()
A more thorough example can be found in ./test/toy_problem.py.
We introduce channel permutations as an advanced method to maximize the accuracy of structured sparse networks. By permuting weight matrices along their channel dimension and adjusting the surrounding layers appropriately, we demonstrate accuracy recovery for even small, parameter-efficient networks, without affecting inference run-time.
The final accuracy has a strong relationship with the quality of permutations. We provide the default algorithms to search for high-quality permutations. The permutation search process can be accelerated by the Apex CUDA extension: apex.contrib.sparsity.permutation_search_kernels
If you want to use the GPU to accelerate the permutation search process, we recommend installing Apex with permutation search CUDA extension via
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--permutation_search" ./
If you want to disable the permutation search process, please pass the allow_permutation=False to init_model_for_pruning function. For example:
ASP.init_model_for_pruning(model, mask_calculator="m4n2_1d", verbosity=2, whitelist=[torch.nn.Linear, torch.nn.Conv2d], allow_recompute_mask=False, allow_permutation=False)
Please notice, when using multi-GPUs we should set the identical random seed for all GPUs to make sure the same results generated in permutation search. The library has implemented the set_identical_seed function in permutation_lib.py, and be called in ASP library. We still suggest the users to set the identical random seed when using multi-GPUs in their code, the example code is as follows:
import torch
import numpy
import random
torch.manual_seed(identical_seed)
torch.cuda.manual_seed_all(identical_seed)
numpy.random.seed(identical_seed)
random.seed(identical_seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
More details about sparsity support on the NVIDIA Ampere GPU with Sparse Tensor Cores can refer to our white paper.
@article{mishra2021accelerating,
title={Accelerating sparse deep neural networks},
author={Mishra, Asit and Latorre, Jorge Albericio and Pool, Jeff and Stosic, Darko and Stosic, Dusan and Venkatesh, Ganesh and Yu, Chong and Micikevicius, Paulius},
journal={arXiv preprint arXiv:2104.08378},
year={2021}
}
The details about sparsity with permutation can refer to our paper published in Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2021):
@inproceedings{pool2021channel,
author = {Pool, Jeff and Yu, Chong},
booktitle = {Advances in Neural Information Processing Systems ({NeurIPS})},
title = {Channel Permutations for {N:M} Sparsity},
url = {https://proceedings.neurips.cc/paper/2021/file/6e8404c3b93a9527c8db241a1846599a-Paper.pdf},
volume = {34},
year = {2021}
}