Supported Pruning Algorithms in NNI¶
NNI provides several pruning algorithms that reproducing from the papers. In pruning v2, NNI split the pruning algorithm into more detailed components. This means users can freely combine components from different algorithms, or easily use a component of their own implementation to replace a step in the original algorithm to implement their own pruning algorithm.
Right now, pruning algorithms with how to generate masks in one step are implemented as pruners, and how to schedule sparsity in each iteration are implemented as iterative pruners.
Pruner
Iterative Pruner
Level Pruner¶
This is a basic pruner, and in some papers called it magnitude pruning or fine-grained pruning.
It will mask the weight in each specified layer with smaller absolute value by a ratio configured in the config list.
Usage¶
from nni.algorithms.compression.v2.pytorch.pruning import LevelPruner
config_list = [{ 'sparsity': 0.8, 'op_types': ['default'] }]
pruner = LevelPruner(model, config_list)
masked_model, masks = pruner.compress()
For detailed example please refer to examples/model_compress/pruning/v2/level_pruning_torch.py
User configuration for Level Pruner¶
PyTorch
- class nni.algorithms.compression.v2.pytorch.pruning.LevelPruner(model: torch.nn.modules.module.Module, config_list: List[Dict])[source]¶
- Parameters
model (torch.nn.Module) – Model to be pruned
config_list (List[Dict]) –
- Supported keys:
sparsity : This is to specify the sparsity for each layer in this config to be compressed.
sparsity_per_layer : Equals to sparsity.
op_types : Operation types to be pruned.
op_names : Operation names to be pruned.
op_partial_names: Operation partial names to be pruned, will be autocompleted by NNI.
exclude : Set True then the layers setting by op_types and op_names will be excluded from pruning.
L1 Norm Pruner¶
L1 norm pruner computes the l1 norm of the layer weight on the first dimension, then prune the weight blocks on this dimension with smaller l1 norm values. i.e., compute the l1 norm of the filters in convolution layer as metric values, compute the l1 norm of the weight by rows in linear layer as metric values.
For more details, please refer to PRUNING FILTERS FOR EFFICIENT CONVNETS.
In addition, L1 norm pruner also supports dependency-aware mode.
Usage¶
from nni.algorithms.compression.v2.pytorch.pruning import L1NormPruner
config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
pruner = L1NormPruner(model, config_list)
masked_model, masks = pruner.compress()
For detailed example please refer to examples/model_compress/pruning/v2/norm_pruning_torch.py
User configuration for L1 Norm Pruner¶
PyTorch
- class nni.algorithms.compression.v2.pytorch.pruning.L1NormPruner(model: torch.nn.modules.module.Module, config_list: List[Dict], mode: str = 'normal', dummy_input: Optional[torch.Tensor] = None)[source]¶
- Parameters
model (torch.nn.Module) – Model to be pruned
config_list (List[Dict]) –
- Supported keys:
sparsity : This is to specify the sparsity for each layer in this config to be compressed.
sparsity_per_layer : Equals to sparsity.
op_types : Conv2d and Linear are supported in L1NormPruner.
op_names : Operation names to be pruned.
op_partial_names: Operation partial names to be pruned, will be autocompleted by NNI.
exclude : Set True then the layers setting by op_types and op_names will be excluded from pruning.
mode (str) – ‘normal’ or ‘dependency_aware’. If prune the model in a dependency-aware way, this pruner will prune the model according to the l1-norm of weights and the channel-dependency or group-dependency of the model. In this way, the pruner will force the conv layers that have dependencies to prune the same channels, so the speedup module can better harvest the speed benefit from the pruned model. Note that, if set ‘dependency_aware’ , the dummy_input cannot be None, because the pruner needs a dummy input to trace the dependency between the conv layers.
dummy_input (Optional[torch.Tensor]) – The dummy input to analyze the topology constraints. Note that, the dummy_input should on the same device with the model.
L2 Norm Pruner¶
L2 norm pruner is a variant of L1 norm pruner. It uses l2 norm as metric to determine which weight elements should be pruned.
L2 norm pruner also supports dependency-aware mode.
Usage¶
from nni.algorithms.compression.v2.pytorch.pruning import L2NormPruner
config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
pruner = L2NormPruner(model, config_list)
masked_model, masks = pruner.compress()
For detailed example please refer to examples/model_compress/pruning/v2/norm_pruning_torch.py
User configuration for L2 Norm Pruner¶
PyTorch
- class nni.algorithms.compression.v2.pytorch.pruning.L2NormPruner(model: torch.nn.modules.module.Module, config_list: List[Dict], mode: str = 'normal', dummy_input: Optional[torch.Tensor] = None)[source]¶
- Parameters
model (torch.nn.Module) – Model to be pruned
config_list (List[Dict]) –
- Supported keys:
sparsity : This is to specify the sparsity for each layer in this config to be compressed.
sparsity_per_layer : Equals to sparsity.
op_types : Conv2d and Linear are supported in L1NormPruner.
op_names : Operation names to be pruned.
op_partial_names: Operation partial names to be pruned, will be autocompleted by NNI.
exclude : Set True then the layers setting by op_types and op_names will be excluded from pruning.
mode (str) – ‘normal’ or ‘dependency_aware’. If prune the model in a dependency-aware way, this pruner will prune the model according to the l2-norm of weights and the channel-dependency or group-dependency of the model. In this way, the pruner will force the conv layers that have dependencies to prune the same channels, so the speedup module can better harvest the speed benefit from the pruned model. Note that, if set ‘dependency_aware’ , the dummy_input cannot be None, because the pruner needs a dummy input to trace the dependency between the conv layers.
dummy_input (Optional[torch.Tensor]) – The dummy input to analyze the topology constraints. Note that, the dummy_input should on the same device with the model.
FPGM Pruner¶
FPGM pruner prunes the blocks of the weight on the first dimension with the smallest geometric median. FPGM chooses the weight blocks with the most replaceable contribution.
For more details, please refer to Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration.
FPGM pruner also supports dependency-aware mode.
Usage¶
from nni.algorithms.compression.v2.pytorch.pruning import FPGMPruner
config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
pruner = FPGMPruner(model, config_list)
masked_model, masks = pruner.compress()
For detailed example please refer to examples/model_compress/pruning/v2/fpgm_pruning_torch.py
User configuration for FPGM Pruner¶
PyTorch
- class nni.algorithms.compression.v2.pytorch.pruning.FPGMPruner(model: torch.nn.modules.module.Module, config_list: List[Dict], mode: str = 'normal', dummy_input: Optional[torch.Tensor] = None)[source]¶
- Parameters
model (torch.nn.Module) – Model to be pruned
config_list (List[Dict]) –
- Supported keys:
sparsity : This is to specify the sparsity for each layer in this config to be compressed.
sparsity_per_layer : Equals to sparsity.
op_types : Conv2d and Linear are supported in FPGMPruner.
op_names : Operation names to be pruned.
op_partial_names: Operation partial names to be pruned, will be autocompleted by NNI.
exclude : Set True then the layers setting by op_types and op_names will be excluded from pruning.
mode (str) – ‘normal’ or ‘dependency_aware’. If prune the model in a dependency-aware way, this pruner will prune the model according to the FPGM of weights and the channel-dependency or group-dependency of the model. In this way, the pruner will force the conv layers that have dependencies to prune the same channels, so the speedup module can better harvest the speed benefit from the pruned model. Note that, if set ‘dependency_aware’ , the dummy_input cannot be None, because the pruner needs a dummy input to trace the dependency between the conv layers.
dummy_input (Optional[torch.Tensor]) – The dummy input to analyze the topology constraints. Note that, the dummy_input should on the same device with the model.
Slim Pruner¶
Slim pruner adds sparsity regularization on the scaling factors of batch normalization (BN) layers during training to identify unimportant channels. The channels with small scaling factor values will be pruned.
For more details, please refer to Learning Efficient Convolutional Networks through Network Slimming.
Usage¶
import nni
from nni.algorithms.compression.v2.pytorch.pruning import SlimPruner
# make sure you have used nni.trace to wrap the optimizer class before initialize
traced_optimizer = nni.trace(torch.optim.Adam)(model.parameters())
config_list = [{ 'sparsity': 0.8, 'op_types': ['BatchNorm2d'] }]
pruner = SlimPruner(model, config_list, trainer, traced_optimizer, criterion, training_epochs=1)
masked_model, masks = pruner.compress()
For detailed example please refer to examples/model_compress/pruning/v2/slim_pruning_torch.py
User configuration for Slim Pruner¶
PyTorch
- class nni.algorithms.compression.v2.pytorch.pruning.SlimPruner(model: torch.nn.modules.module.Module, config_list: List[Dict], trainer: Callable[[torch.nn.modules.module.Module, torch.optim.optimizer.Optimizer, Callable], None], traced_optimizer: nni.common.serializer.Traceable, criterion: Callable[[torch.Tensor, torch.Tensor], torch.Tensor], training_epochs: int, scale: float = 0.0001, mode='global')[source]¶
- Parameters
model (torch.nn.Module) – Model to be pruned
config_list (List[Dict]) –
- Supported keys:
sparsity : This is to specify the sparsity for each layer in this config to be compressed.
sparsity_per_layer : Equals to sparsity.
total_sparsity : This is to specify the total sparsity for all layers in this config, each layer may have different sparsity.
max_sparsity_per_layer : Always used with total_sparsity. Limit the max sparsity of each layer.
op_types : Only BatchNorm2d is supported in SlimPruner.
op_names : Operation names to be pruned.
op_partial_names: Operation partial names to be pruned, will be autocompleted by NNI.
exclude : Set True then the layers setting by op_types and op_names will be excluded from pruning.
trainer (Callable[[Module, Optimizer, Callable], None]) –
A callable function used to train model or just inference. Take model, optimizer, criterion as input. The model will be trained or inferenced training_epochs epochs.
Example:
def trainer(model: Module, optimizer: Optimizer, criterion: Callable[[Tensor, Tensor], Tensor]): training = model.training model.train(mode=True) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = criterion(output, target) loss.backward() # If you don't want to update the model, you can skip `optimizer.step()`, and set train mode False. optimizer.step() model.train(mode=training)
traced_optimizer (nni.common.serializer.Traceable(torch.optim.Optimizer)) – The traced optimizer instance which the optimizer class is wrapped by nni.trace. E.g. traced_optimizer = nni.trace(torch.nn.Adam)(model.parameters()).
criterion (Callable[[Tensor, Tensor], Tensor]) – The criterion function used in trainer. Take model output and target value as input, and return the loss.
training_epochs (int) – The epoch number for training model to sparsify the BN weight.
scale (float) – Penalty parameter for sparsification, which could reduce overfitting.
mode (str) – ‘normal’ or ‘global’. If prune the model in a global way, all layer weights with same config will be considered uniformly. That means a single layer may not reach or exceed the sparsity setting in config, but the total pruned weights meet the sparsity setting.
Activation APoZ Rank Pruner¶
Activation APoZ rank pruner is a pruner which prunes on the first weight dimension,
with the smallest importance criterion APoZ
calculated from the output activations of convolution layers to achieve a preset level of network sparsity.
The pruning criterion APoZ
is explained in the paper Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures.
The APoZ is defined as:
\(APoZ_{c}^{(i)} = APoZ\left(O_{c}^{(i)}\right)=\frac{\sum_{k}^{N} \sum_{j}^{M} f\left(O_{c, j}^{(i)}(k)=0\right)}{N \times M}\)
Activation APoZ rank pruner also supports dependency-aware mode.
Usage¶
import nni
from nni.algorithms.compression.v2.pytorch.pruning import ActivationAPoZRankPruner
# make sure you have used nni.trace to wrap the optimizer class before initialize
traced_optimizer = nni.trace(torch.optim.Adam)(model.parameters())
config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
pruner = ActivationAPoZRankPruner(model, config_list, trainer, traced_optimizer, criterion, training_batches=20)
masked_model, masks = pruner.compress()
For detailed example please refer to examples/model_compress/pruning/v2/activation_pruning_torch.py
User configuration for Activation APoZ Rank Pruner¶
PyTorch
- class nni.algorithms.compression.v2.pytorch.pruning.ActivationAPoZRankPruner(model: torch.nn.modules.module.Module, config_list: List[Dict], trainer: Callable[[torch.nn.modules.module.Module, torch.optim.optimizer.Optimizer, Callable], None], traced_optimizer: nni.common.serializer.Traceable, criterion: Callable[[torch.Tensor, torch.Tensor], torch.Tensor], training_batches: int, activation: str = 'relu', mode: str = 'normal', dummy_input: Optional[torch.Tensor] = None)[source]¶
Activation Mean Rank Pruner¶
Activation mean rank pruner is a pruner which prunes on the first weight dimension,
with the smallest importance criterion mean activation
calculated from the output activations of convolution layers to achieve a preset level of network sparsity.
The pruning criterion mean activation
is explained in section 2.2 of the paper Pruning Convolutional Neural Networks for Resource Efficient Inference.
Activation mean rank pruner also supports dependency-aware mode.
Usage¶
import nni
from nni.algorithms.compression.v2.pytorch.pruning import ActivationMeanRankPruner
# make sure you have used nni.trace to wrap the optimizer class before initialize
traced_optimizer = nni.traces(torch.optim.Adam)(model.parameters())
config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
pruner = ActivationMeanRankPruner(model, config_list, trainer, traced_optimizer, criterion, training_batches=20)
masked_model, masks = pruner.compress()
For detailed example please refer to examples/model_compress/pruning/v2/activation_pruning_torch.py
User configuration for Activation Mean Rank Pruner¶
PyTorch
- class nni.algorithms.compression.v2.pytorch.pruning.ActivationMeanRankPruner(model: torch.nn.modules.module.Module, config_list: List[Dict], trainer: Callable[[torch.nn.modules.module.Module, torch.optim.optimizer.Optimizer, Callable], None], traced_optimizer: nni.common.serializer.Traceable, criterion: Callable[[torch.Tensor, torch.Tensor], torch.Tensor], training_batches: int, activation: str = 'relu', mode: str = 'normal', dummy_input: Optional[torch.Tensor] = None)[source]¶
Taylor FO Weight Pruner¶
Taylor FO weight pruner is a pruner which prunes on the first weight dimension, based on estimated importance calculated from the first order taylor expansion on weights to achieve a preset level of network sparsity. The estimated importance is defined as the paper Importance Estimation for Neural Network Pruning.
\(\widehat{\mathcal{I}}_{\mathcal{S}}^{(1)}(\mathbf{W}) \triangleq \sum_{s \in \mathcal{S}} \mathcal{I}_{s}^{(1)}(\mathbf{W})=\sum_{s \in \mathcal{S}}\left(g_{s} w_{s}\right)^{2}\)
Taylor FO weight pruner also supports dependency-aware mode.
What’s more, we provide a global-sort mode for this pruner which is aligned with paper implementation.
Usage¶
import nni
from nni.algorithms.compression.v2.pytorch.pruning import TaylorFOWeightPruner
# make sure you have used nni.trace to wrap the optimizer class before initialize
traced_optimizer = nni.trace(torch.optim.Adam)(model.parameters())
config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
pruner = TaylorFOWeightPruner(model, config_list, trainer, traced_optimizer, criterion, training_batches=20)
masked_model, masks = pruner.compress()
For detailed example please refer to examples/model_compress/pruning/v2/taylorfo_pruning_torch.py
User configuration for Activation Mean Rank Pruner¶
PyTorch
- class nni.algorithms.compression.v2.pytorch.pruning.TaylorFOWeightPruner(model: torch.nn.modules.module.Module, config_list: List[Dict], trainer: Callable[[torch.nn.modules.module.Module, torch.optim.optimizer.Optimizer, Callable], None], traced_optimizer: nni.common.serializer.Traceable, criterion: Callable[[torch.Tensor, torch.Tensor], torch.Tensor], training_batches: int, mode: str = 'normal', dummy_input: Optional[torch.Tensor] = None)[source]¶
- Parameters
model (torch.nn.Module) – Model to be pruned
config_list (List[Dict]) –
- Supported keys:
sparsity : This is to specify the sparsity for each layer in this config to be compressed.
sparsity_per_layer : Equals to sparsity.
total_sparsity : This is to specify the total sparsity for all layers in this config, each layer may have different sparsity.
max_sparsity_per_layer : Always used with total_sparsity. Limit the max sparsity of each layer.
op_types : Conv2d and Linear are supported in TaylorFOWeightPruner.
op_names : Operation names to be pruned.
op_partial_names: Operation partial names to be pruned, will be autocompleted by NNI.
exclude : Set True then the layers setting by op_types and op_names will be excluded from pruning.
trainer (Callable[[Module, Optimizer, Callable]) –
A callable function used to train model or just inference. Take model, optimizer, criterion as input. The model will be trained or inferenced training_epochs epochs.
Example:
def trainer(model: Module, optimizer: Optimizer, criterion: Callable[[Tensor, Tensor], Tensor]): training = model.training model.train(mode=True) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = criterion(output, target) loss.backward() # If you don't want to update the model, you can skip `optimizer.step()`, and set train mode False. optimizer.step() model.train(mode=training)
traced_optimizer (nni.common.serializer.Traceable(torch.optim.Optimizer)) – The traced optimizer instance which the optimizer class is wrapped by nni.trace. E.g. traced_optimizer = nni.trace(torch.nn.Adam)(model.parameters()).
criterion (Callable[[Tensor, Tensor], Tensor]) – The criterion function used in trainer. Take model output and target value as input, and return the loss.
training_batches (int) – The batch number used to collect activations.
mode (str) –
‘normal’, ‘dependency_aware’ or ‘global’.
If prune the model in a dependency-aware way, this pruner will prune the model according to the taylorFO and the channel-dependency or group-dependency of the model. In this way, the pruner will force the conv layers that have dependencies to prune the same channels, so the speedup module can better harvest the speed benefit from the pruned model. Note that, if set ‘dependency_aware’ , the dummy_input cannot be None, because the pruner needs a dummy input to trace the dependency between the conv layers.
If prune the model in a global way, all layer weights with same config will be considered uniformly. That means a single layer may not reach or exceed the sparsity setting in config, but the total pruned weights meet the sparsity setting.
dummy_input (Optional[torch.Tensor]) – The dummy input to analyze the topology constraints. Note that, the dummy_input should on the same device with the model.
ADMM Pruner¶
Alternating Direction Method of Multipliers (ADMM) is a mathematical optimization technique, by decomposing the original nonconvex problem into two subproblems that can be solved iteratively. In weight pruning problem, these two subproblems are solved via 1) gradient descent algorithm and 2) Euclidean projection respectively.
During the process of solving these two subproblems, the weights of the original model will be changed. Then a fine-grained pruning will be applied to prune the model according to the config list given.
This solution framework applies both to non-structured and different variations of structured pruning schemes.
For more details, please refer to A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers.
Usage¶
import nni
from nni.algorithms.compression.v2.pytorch.pruning import ADMMPruner
# make sure you have used nni.trace to wrap the optimizer class before initialize
traced_optimizer = nni.trace(torch.optim.Adam)(model.parameters())
config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
pruner = ADMMPruner(model, config_list, trainer, traced_optimizer, criterion, iterations=10, training_epochs=1)
masked_model, masks = pruner.compress()
For detailed example please refer to examples/model_compress/pruning/v2/admm_pruning_torch.py
User configuration for ADMM Pruner¶
PyTorch
- class nni.algorithms.compression.v2.pytorch.pruning.ADMMPruner(model: torch.nn.modules.module.Module, config_list: List[Dict], trainer: Callable[[torch.nn.modules.module.Module, torch.optim.optimizer.Optimizer, Callable], None], traced_optimizer: nni.common.serializer.Traceable, criterion: Callable[[torch.Tensor, torch.Tensor], torch.Tensor], iterations: int, training_epochs: int)[source]¶
ADMM (Alternating Direction Method of Multipliers) Pruner is a kind of mathematical optimization technique. The metric used in this pruner is the absolute value of the weight. In each iteration, the weight with small magnitudes will be set to zero. Only in the final iteration, the mask will be generated and apply to model wrapper.
The original paper refer to: https://arxiv.org/abs/1804.03294.
- Parameters
model (torch.nn.Module) – Model to be pruned.
config_list (List[Dict]) –
- Supported keys:
sparsity : This is to specify the sparsity for each layer in this config to be compressed.
sparsity_per_layer : Equals to sparsity.
rho : Penalty parameters in ADMM algorithm.
op_types : Operation types to be pruned.
op_names : Operation names to be pruned.
op_partial_names: Operation partial names to be pruned, will be autocompleted by NNI.
exclude : Set True then the layers setting by op_types and op_names will be excluded from pruning.
trainer (Callable[[Module, Optimizer, Callable]) –
A callable function used to train model or just inference. Take model, optimizer, criterion as input. The model will be trained or inferenced training_epochs epochs.
Example:
def trainer(model: Module, optimizer: Optimizer, criterion: Callable[[Tensor, Tensor], Tensor]): training = model.training model.train(mode=True) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = criterion(output, target) loss.backward() # If you don't want to update the model, you can skip `optimizer.step()`, and set train mode False. optimizer.step() model.train(mode=training)
traced_optimizer (nni.common.serializer.Traceable(torch.optim.Optimizer)) – The traced optimizer instance which the optimizer class is wrapped by nni.trace. E.g. traced_optimizer = nni.trace(torch.nn.Adam)(model.parameters()).
criterion (Callable[[Tensor, Tensor], Tensor]) – The criterion function used in trainer. Take model output and target value as input, and return the loss.
iterations (int) – The total iteration number in admm pruning algorithm.
training_epochs (int) – The epoch number for training model in each iteration.
Movement Pruner¶
Movement pruner is an implementation of movement pruning. This is a “fine-pruning” algorithm, which means the masks may change during each fine-tuning step. Each weight element will be scored by the opposite of the sum of the product of weight and its gradient during each step. This means the weight elements moving towards zero will accumulate negative scores, the weight elements moving away from zero will accumulate positive scores. The weight elements with low scores will be masked during inference.
The following figure from the paper shows the weight pruning by movement pruning.
For more details, please refer to Movement Pruning: Adaptive Sparsity by Fine-Tuning.
Usage¶
import nni
from nni.algorithms.compression.v2.pytorch.pruning import MovementPruner
# make sure you have used nni.trace to wrap the optimizer class before initialize
traced_optimizer = nni.trace(torch.optim.Adam)(model.parameters())
config_list = [{'op_types': ['Linear'], 'op_partial_names': ['bert.encoder'], 'sparsity': 0.9}]
pruner = MovementPruner(model, config_list, trainer, traced_optimizer, criterion, 10, 3000, 27000)
masked_model, masks = pruner.compress()
User configuration for Movement Pruner¶
PyTorch
- class nni.algorithms.compression.v2.pytorch.pruning.MovementPruner(model: torch.nn.modules.module.Module, config_list: List[Dict], trainer: Callable[[torch.nn.modules.module.Module, torch.optim.optimizer.Optimizer, Callable], None], traced_optimizer: nni.common.serializer.Traceable, criterion: Callable[[torch.Tensor, torch.Tensor], torch.Tensor], training_epochs: int, warm_up_step: int, cool_down_beginning_step: int)[source]¶
- Parameters
model (torch.nn.Module) – Model to be pruned.
config_list (List[Dict]) –
- Supported keys:
sparsity : This is to specify the sparsity for each layer in this config to be compressed.
sparsity_per_layer : Equals to sparsity.
op_types : Operation types to be pruned.
op_names : Operation names to be pruned.
op_partial_names: Operation partial names to be pruned, will be autocompleted by NNI.
exclude : Set True then the layers setting by op_types and op_names will be excluded from pruning.
trainer (Callable[[Module, Optimizer, Callable]) –
A callable function used to train model or just inference. Take model, optimizer, criterion as input. The model will be trained or inferenced training_epochs epochs.
Example:
def trainer(model: Module, optimizer: Optimizer, criterion: Callable[[Tensor, Tensor], Tensor]): training = model.training model.train(mode=True) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = criterion(output, target) loss.backward() # If you don't want to update the model, you can skip `optimizer.step()`, and set train mode False. optimizer.step() model.train(mode=training)
traced_optimizer (nni.common.serializer.Traceable(torch.optim.Optimizer)) – The traced optimizer instance which the optimizer class is wrapped by nni.trace. E.g. traced_optimizer = nni.trace(torch.nn.Adam)(model.parameters()).
criterion (Callable[[Tensor, Tensor], Tensor]) – The criterion function used in trainer. Take model output and target value as input, and return the loss.
training_epochs (int) – The total epoch number for training the model. Make sure the total optimizer.step() in training_epochs is bigger than cool_down_beginning_step.
warm_up_step (int) – The total optimizer.step() number before start pruning for warm up. Make sure warm_up_step is smaller than cool_down_beginning_step.
cool_down_beginning_step (int) – The number of steps at which sparsity stops growing, note that the sparsity stop growing doesn’t mean masks not changed. The sparsity after each optimizer.step() is: total_sparsity * (1 - (1 - (current_step - warm_up_step) / (cool_down_beginning_step - warm_up_step)) ** 3).
Reproduced Experiment¶
Model |
Dataset |
Remaining Weights |
MaP acc.(paper/ours) |
MvP acc.(paper/ours) |
---|---|---|---|---|
Bert base |
MNLI - Dev |
10% |
77.8% / 73.6% |
79.3% / 78.8% |
Linear Pruner¶
Linear pruner is an iterative pruner, it will increase sparsity evenly from scratch during each iteration.
For example, the final sparsity is set as 0.5, and the iteration number is 5, then the sparsity used in each iteration are [0, 0.1, 0.2, 0.3, 0.4, 0.5]
.
Usage¶
from nni.algorithms.compression.v2.pytorch.pruning import LinearPruner
config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
pruner = LinearPruner(model, config_list, pruning_algorithm='l1', total_iteration=10, finetuner=finetuner)
pruner.compress()
_, model, masks, _, _ = pruner.get_best_result()
For detailed example please refer to examples/model_compress/pruning/v2/iterative_pruning_torch.py
User configuration for Linear Pruner¶
PyTorch
- class nni.algorithms.compression.v2.pytorch.pruning.LinearPruner(model: torch.nn.modules.module.Module, config_list: List[Dict], pruning_algorithm: str, total_iteration: int, log_dir: str = '.', keep_intermediate_result: bool = False, finetuner: Optional[Callable[[torch.nn.modules.module.Module], None]] = None, speed_up: bool = False, dummy_input: Optional[torch.Tensor] = None, evaluator: Optional[Callable[[torch.nn.modules.module.Module], float]] = None, pruning_params: Dict = {})[source]¶
- Parameters
model (Module) – The origin unwrapped pytorch model to be pruned.
config_list (List[Dict]) – The origin config list provided by the user.
pruning_algorithm (str) – Supported pruning algorithm [‘level’, ‘l1’, ‘l2’, ‘fpgm’, ‘slim’, ‘apoz’, ‘mean_activation’, ‘taylorfo’, ‘admm’]. This iterative pruner will use the chosen corresponding pruner to prune the model in each iteration.
total_iteration (int) – The total iteration number.
log_dir (str) – The log directory use to saving the result, you can find the best result under this folder.
keep_intermediate_result (bool) – If keeping the intermediate result, including intermediate model and masks during each iteration.
finetuner (Optional[Callable[[Module], None]]) – The finetuner handled all finetune logic, use a pytorch module as input. It will be called at the end of each iteration, usually for neutralizing the accuracy loss brought by the pruning in this iteration.
speed_up (bool) – If set True, speed up the model at the end of each iteration to make the pruned model compact.
dummy_input (Optional[torch.Tensor]) – If speed_up is True, dummy_input is required for tracing the model in speed up.
evaluator (Optional[Callable[[Module], float]]) – Evaluate the pruned model and give a score. If evaluator is None, the best result refers to the latest result.
pruning_params (Dict) – If the chosen pruning_algorithm has extra parameters, put them as a dict to pass in.
AGP Pruner¶
This is an iterative pruner, which the sparsity is increased from an initial sparsity value \(s_{i}\) (usually 0) to a final sparsity value \(s_{f}\) over a span of \(n\) pruning iterations, starting at training step \(t_{0}\) and with pruning frequency \(\Delta t\):
\(s_{t}=s_{f}+\left(s_{i}-s_{f}\right)\left(1-\frac{t-t_{0}}{n \Delta t}\right)^{3} \text { for } t \in\left\{t_{0}, t_{0}+\Delta t, \ldots, t_{0} + n \Delta t\right\}\)
For more details please refer to To prune, or not to prune: exploring the efficacy of pruning for model compression.
Usage¶
from nni.algorithms.compression.v2.pytorch.pruning import AGPPruner
config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
pruner = AGPPruner(model, config_list, pruning_algorithm='l1', total_iteration=10, finetuner=finetuner)
pruner.compress()
_, model, masks, _, _ = pruner.get_best_result()
For detailed example please refer to examples/model_compress/pruning/v2/iterative_pruning_torch.py
User configuration for AGP Pruner¶
PyTorch
- class nni.algorithms.compression.v2.pytorch.pruning.AGPPruner(model: torch.nn.modules.module.Module, config_list: List[Dict], pruning_algorithm: str, total_iteration: int, log_dir: str = '.', keep_intermediate_result: bool = False, finetuner: Optional[Callable[[torch.nn.modules.module.Module], None]] = None, speed_up: bool = False, dummy_input: Optional[torch.Tensor] = None, evaluator: Optional[Callable[[torch.nn.modules.module.Module], float]] = None, pruning_params: Dict = {})[source]¶
- Parameters
model (Module) – The origin unwrapped pytorch model to be pruned.
config_list (List[Dict]) – The origin config list provided by the user.
pruning_algorithm (str) – Supported pruning algorithm [‘level’, ‘l1’, ‘l2’, ‘fpgm’, ‘slim’, ‘apoz’, ‘mean_activation’, ‘taylorfo’, ‘admm’]. This iterative pruner will use the chosen corresponding pruner to prune the model in each iteration.
total_iteration (int) – The total iteration number.
log_dir (str) – The log directory use to saving the result, you can find the best result under this folder.
keep_intermediate_result (bool) – If keeping the intermediate result, including intermediate model and masks during each iteration.
finetuner (Optional[Callable[[Module], None]]) – The finetuner handled all finetune logic, use a pytorch module as input. It will be called at the end of each iteration, usually for neutralizing the accuracy loss brought by the pruning in this iteration.
speed_up (bool) – If set True, speed up the model at the end of each iteration to make the pruned model compact.
dummy_input (Optional[torch.Tensor]) – If speed_up is True, dummy_input is required for tracing the model in speed up.
evaluator (Optional[Callable[[Module], float]]) – Evaluate the pruned model and give a score. If evaluator is None, the best result refers to the latest result.
pruning_params (Dict) – If the chosen pruning_algorithm has extra parameters, put them as a dict to pass in.
Lottery Ticket Pruner¶
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks, authors Jonathan Frankle and Michael Carbin,provides comprehensive measurement and analysis, and articulate the lottery ticket hypothesis: dense, randomly-initialized, feed-forward networks contain subnetworks (winning tickets) that – when trained in isolation – reach test accuracy comparable to the original network in a similar number of iterations.
In this paper, the authors use the following process to prune a model, called iterative prunning:
Randomly initialize a neural network f(x;theta_0) (where theta0 follows D{theta}).
Train the network for j iterations, arriving at parameters theta_j.
Prune p% of the parameters in theta_j, creating a mask m.
Reset the remaining parameters to their values in theta_0, creating the winning ticket f(x;m*theta_0).
Repeat step 2, 3, and 4.
If the configured final sparsity is P (e.g., 0.8) and there are n times iterative pruning, each iterative pruning prunes 1-(1-P)^(1/n) of the weights that survive the previous round.
Usage¶
from nni.algorithms.compression.v2.pytorch.pruning import LotteryTicketPruner
config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
pruner = LotteryTicketPruner(model, config_list, pruning_algorithm='l1', total_iteration=10, finetuner=finetuner, reset_weight=True)
pruner.compress()
_, model, masks, _, _ = pruner.get_best_result()
For detailed example please refer to examples/model_compress/pruning/v2/iterative_pruning_torch.py
User configuration for Lottery Ticket Pruner¶
PyTorch
- class nni.algorithms.compression.v2.pytorch.pruning.LotteryTicketPruner(model: torch.nn.modules.module.Module, config_list: List[Dict], pruning_algorithm: str, total_iteration: int, log_dir: str = '.', keep_intermediate_result: bool = False, finetuner: Optional[Callable[[torch.nn.modules.module.Module], None]] = None, speed_up: bool = False, dummy_input: Optional[torch.Tensor] = None, evaluator: Optional[Callable[[torch.nn.modules.module.Module], float]] = None, reset_weight: bool = True, pruning_params: Dict = {})[source]¶
- Parameters
model (Module) – The origin unwrapped pytorch model to be pruned.
config_list (List[Dict]) – The origin config list provided by the user.
pruning_algorithm (str) – Supported pruning algorithm [‘level’, ‘l1’, ‘l2’, ‘fpgm’, ‘slim’, ‘apoz’, ‘mean_activation’, ‘taylorfo’, ‘admm’]. This iterative pruner will use the chosen corresponding pruner to prune the model in each iteration.
total_iteration (int) – The total iteration number.
log_dir (str) – The log directory use to saving the result, you can find the best result under this folder.
keep_intermediate_result (bool) – If keeping the intermediate result, including intermediate model and masks during each iteration.
finetuner (Optional[Callable[[Module], None]]) – The finetuner handled all finetune logic, use a pytorch module as input. It will be called at the end of each iteration if reset_weight is False, will be called at the beginning of each iteration otherwise.
speed_up (bool) – If set True, speed up the model at the end of each iteration to make the pruned model compact.
dummy_input (Optional[torch.Tensor]) – If speed_up is True, dummy_input is required for tracing the model in speed up.
evaluator (Optional[Callable[[Module], float]]) – Evaluate the pruned model and give a score. If evaluator is None, the best result refers to the latest result.
reset_weight (bool) – If set True, the model weight will reset to the original model weight at the end of each iteration step.
pruning_params (Dict) – If the chosen pruning_algorithm has extra parameters, put them as a dict to pass in.
Simulated Annealing Pruner¶
We implement a guided heuristic search method, Simulated Annealing (SA) algorithm. As mentioned in the paper, this method is enhanced on guided search based on prior experience. The enhanced SA technique is based on the observation that a DNN layer with more number of weights often has a higher degree of model compression with less impact on overall accuracy.
Randomly initialize a pruning rate distribution (sparsities).
While current_temperature < stop_temperature:
generate a perturbation to current distribution
Perform fast evaluation on the perturbated distribution
accept the perturbation according to the performance and probability, if not accepted, return to step 1
cool down, current_temperature <- current_temperature * cool_down_rate
For more details, please refer to AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates.
Usage¶
from nni.algorithms.compression.v2.pytorch.pruning import SimulatedAnnealingPruner
config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
pruner = SimulatedAnnealingPruner(model, config_list, pruning_algorithm='l1', evaluator=evaluator, cool_down_rate=0.9, finetuner=finetuner)
pruner.compress()
_, model, masks, _, _ = pruner.get_best_result()
For detailed example please refer to examples/model_compress/pruning/v2/simulated_anealing_pruning_torch.py
User configuration for Simulated Annealing Pruner¶
PyTorch
- class nni.algorithms.compression.v2.pytorch.pruning.SimulatedAnnealingPruner(model: torch.nn.modules.module.Module, config_list: List[Dict], evaluator: Callable[[torch.nn.modules.module.Module], float], start_temperature: float = 100, stop_temperature: float = 20, cool_down_rate: float = 0.9, perturbation_magnitude: float = 0.35, pruning_algorithm: str = 'level', pruning_params: Dict = {}, log_dir: str = '.', keep_intermediate_result: bool = False, finetuner: Optional[Callable[[torch.nn.modules.module.Module], None]] = None, speed_up: bool = False, dummy_input: Optional[torch.Tensor] = None)[source]¶
- Parameters
model (Module) – The origin unwrapped pytorch model to be pruned.
config_list (List[Dict]) – The origin config list provided by the user.
evaluator (Callable[[Module], float]) – Evaluate the pruned model and give a score.
start_temperature (float) – Start temperature of the simulated annealing process.
stop_temperature (float) – Stop temperature of the simulated annealing process.
cool_down_rate (float) – Cool down rate of the temperature.
perturbation_magnitude (float) – Initial perturbation magnitude to the sparsities. The magnitude decreases with current temperature.
pruning_algorithm (str) – Supported pruning algorithm [‘level’, ‘l1’, ‘l2’, ‘fpgm’, ‘slim’, ‘apoz’, ‘mean_activation’, ‘taylorfo’, ‘admm’]. This iterative pruner will use the chosen corresponding pruner to prune the model in each iteration.
pruning_params (Dict) – If the chosen pruning_algorithm has extra parameters, put them as a dict to pass in.
log_dir (str) – The log directory use to saving the result, you can find the best result under this folder.
keep_intermediate_result (bool) – If keeping the intermediate result, including intermediate model and masks during each iteration.
finetuner (Optional[Callable[[Module], None]]) – The finetuner handled all finetune logic, use a pytorch module as input, will be called in each iteration.
speed_up (bool) – If set True, speed up the model at the end of each iteration to make the pruned model compact.
dummy_input (Optional[torch.Tensor]) – If speed_up is True, dummy_input is required for tracing the model in speed up.
Auto Compress Pruner¶
For total iteration number \(N\), AutoCompressPruner prune the model that survive the previous iteration for a fixed sparsity ratio (e.g., \(1-{(1-0.8)}^{(1/N)}\)) to achieve the overall sparsity (e.g., \(0.8\)):
1. Generate sparsities distribution using SimulatedAnnealingPruner
2. Perform ADMM-based pruning to generate pruning result for the next iteration.
For more details, please refer to AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates.
Usage¶
import nni
from nni.algorithms.compression.v2.pytorch.pruning import AutoCompressPruner
# make sure you have used nni.trace to wrap the optimizer class before initialize
traced_optimizer = nni.trace(torch.optim.Adam)(model.parameters())
config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
admm_params = {
'trainer': trainer,
'traced_optimizer': traced_optimizer,
'criterion': criterion,
'iterations': 10,
'training_epochs': 1
}
sa_params = {
'evaluator': evaluator
}
pruner = AutoCompressPruner(model, config_list, 10, admm_params, sa_params, finetuner=finetuner)
pruner.compress()
_, model, masks, _, _ = pruner.get_best_result()
The full script can be found here.
User configuration for Auto Compress Pruner¶
PyTorch
- class nni.algorithms.compression.v2.pytorch.pruning.AutoCompressPruner(model: torch.nn.modules.module.Module, config_list: List[Dict], total_iteration: int, admm_params: Dict, sa_params: Dict, log_dir: str = '.', keep_intermediate_result: bool = False, finetuner: Optional[Callable[[torch.nn.modules.module.Module], None]] = None, speed_up: bool = False, dummy_input: Optional[torch.Tensor] = None, evaluator: Optional[Callable[[torch.nn.modules.module.Module], float]] = None)[source]¶
- Parameters
model (Module) – The origin unwrapped pytorch model to be pruned.
config_list (List[Dict]) – The origin config list provided by the user.
total_iteration (int) – The total iteration number.
evaluator (Callable[[Module], float]) – Evaluate the pruned model and give a score.
admm_params (Dict) –
The parameters passed to the ADMMPruner.
- trainerCallable[[Module, Optimizer, Callable].
A callable function used to train model or just inference. Take model, optimizer, criterion as input. The model will be trained or inferenced training_epochs epochs.
- traced_optimizernni.common.serializer.Traceable(torch.optim.Optimizer)
The traced optimizer instance which the optimizer class is wrapped by nni.trace. E.g. traced_optimizer = nni.trace(torch.nn.Adam)(model.parameters()).
- criterionCallable[[Tensor, Tensor], Tensor].
The criterion function used in trainer. Take model output and target value as input, and return the loss.
- iterationsint.
The total iteration number in admm pruning algorithm.
- training_epochsint.
The epoch number for training model in each iteration.
sa_params (Dict) –
The parameters passed to the SimulatedAnnealingPruner.
- evaluatorCallable[[Module], float]. Required.
Evaluate the pruned model and give a score.
- start_temperaturefloat. Default: 100.
Start temperature of the simulated annealing process.
- stop_temperaturefloat. Default: 20.
Stop temperature of the simulated annealing process.
- cool_down_ratefloat. Default: 0.9.
Cooldown rate of the temperature.
- perturbation_magnitudefloat. Default: 0.35.
Initial perturbation magnitude to the sparsities. The magnitude decreases with current temperature.
- pruning_algorithmstr. Default: ‘level’.
Supported pruning algorithm [‘level’, ‘l1’, ‘l2’, ‘fpgm’, ‘slim’, ‘apoz’, ‘mean_activation’, ‘taylorfo’, ‘admm’].
- pruning_paramsDict. Default: {}.
If the chosen pruning_algorithm has extra parameters, put them as a dict to pass in.
log_dir (str) – The log directory used to save the result, you can find the best result under this folder.
keep_intermediate_result (bool) – If keeping the intermediate result, including intermediate model and masks during each iteration.
finetuner (Optional[Callable[[Module], None]]) – The finetuner handles all finetune logic, takes a pytorch module as input. It will be called at the end of each iteration, usually for neutralizing the accuracy loss brought by the pruning in this iteration.
speed_up (bool) – If set True, speed up the model at the end of each iteration to make the pruned model compact.
dummy_input (Optional[torch.Tensor]) – If speed_up is True, dummy_input is required for tracing the model in speed up.
AMC Pruner¶
AMC pruner leverages reinforcement learning to provide the model compression policy. According to the author, this learning-based compression policy outperforms conventional rule-based compression policy by having a higher compression ratio, better preserving the accuracy and freeing human labor.
For more details, please refer to AMC: AutoML for Model Compression and Acceleration on Mobile Devices.
Usage¶
PyTorch code
from nni.algorithms.compression.v2.pytorch.pruning import AMCPruner
config_list = [{'op_types': ['Conv2d'], 'total_sparsity': 0.5, 'max_sparsity_per_layer': 0.8}]
pruner = AMCPruner(400, model, config_list, dummy_input, evaluator, finetuner=finetuner)
pruner.compress()
The full script can be found here.
User configuration for AMC Pruner¶
PyTorch
- class nni.algorithms.compression.v2.pytorch.pruning.AMCPruner(total_episode: int, model: torch.nn.modules.module.Module, config_list: List[Dict], dummy_input: torch.Tensor, evaluator: Callable[[torch.nn.modules.module.Module], float], pruning_algorithm: str = 'l1', log_dir: str = '.', keep_intermediate_result: bool = False, finetuner: Optional[Callable[[torch.nn.modules.module.Module], None]] = None, ddpg_params: dict = {}, pruning_params: dict = {}, target: str = 'flops')[source]¶
A pytorch implementation of AMC: AutoML for Model Compression and Acceleration on Mobile Devices. (https://arxiv.org/pdf/1802.03494.pdf) Suggust config all total_sparsity in config_list a same value. AMC pruner will treat the first sparsity in config_list as the global sparsity.
- Parameters
total_episode (int) – The total episode number.
model (Module) – The model to be pruned.
config_list (List[Dict]) –
- Supported keys :
total_sparsity : This is to specify the total sparsity for all layers in this config, each layer may have different sparsity.
max_sparsity_per_layer : Always used with total_sparsity. Limit the max sparsity of each layer.
op_types : Operation type to be pruned.
op_names : Operation name to be pruned.
op_partial_names: Operation partial names to be pruned, will be autocompleted by NNI.
exclude : Set True then the layers setting by op_types and op_names will be excluded from pruning.
dummy_input (torch.Tensor) – dummy_input is required for speed-up and tracing the model in RL environment.
evaluator (Callable[[Module], float]) – Evaluate the pruned model and give a score.
pruning_algorithm (str) – Supported pruning algorithm [‘l1’, ‘l2’, ‘fpgm’, ‘apoz’, ‘mean_activation’, ‘taylorfo’]. This iterative pruner will use the chosen corresponding pruner to prune the model in each iteration.
log_dir (str) – The log directory use to saving the result, you can find the best result under this folder.
keep_intermediate_result (bool) – If keeping the intermediate result, including intermediate model and masks during each iteration.
finetuner (Optional[Callable[[Module], None]]) – The finetuner handled all finetune logic, use a pytorch module as input, will be called in each iteration.
ddpg_params (Dict) – Configuration dict to configure the DDPG agent, any key unset will be set to default implicitly. - hidden1: hidden num of first fully connect layer. Default: 300 - hidden2: hidden num of second fully connect layer. Default: 300 - lr_c: learning rate for critic. Default: 1e-3 - lr_a: learning rate for actor. Default: 1e-4 - warmup: number of episodes without training but only filling the replay memory. During warmup episodes, random actions ares used for pruning. Default: 100 - discount: next Q value discount for deep Q value target. Default: 0.99 - bsize: minibatch size for training DDPG agent. Default: 64 - rmsize: memory size for each layer. Default: 100 - window_length: replay buffer window length. Default: 1 - tau: moving average for target network being used by soft_update. Default: 0.99 - init_delta: initial variance of truncated normal distribution. Default: 0.5 - delta_decay: delta decay during exploration. Default: 0.99 # parameters for training ddpg agent - max_episode_length: maximum episode length. Default: 1e9 - epsilon: linear decay of exploration policy. Default: 50000
pruning_params (Dict) – If the pruner corresponding to the chosen pruning_algorithm has extra parameters, put them as a dict to pass in.
target (str) – ‘flops’ or ‘params’. Note that the sparsity in other pruners always means the parameters sparse, but in AMC, you can choose flops sparse. This parameter is used to explain what the sparsity setting in config_list refers to.