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models.py
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models.py
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'''
builds models with functions to enable pruning / temporary noising of params
'''
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from types import MethodType
from math import ceil, floor
from architectures_and_layers.custom_batch_norm import _BatchNorm
from architectures_and_layers import resnet2056_arch, resnet18_arch, vgg_arch
from scipy.stats import truncnorm
from numpy.random import permutation
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
_prunable_batch_forward = _BatchNorm._prunable_batch_forward # batch norm with a mask for pruning
###\############################
# code to make models prunable #
################################
def _prune(self, iterations, N_iters):
'''
prune each prunable layer in the network
'''
avg_pruned_magnitude = 0
i = 0
for layer in [x for x in self.modules() if hasattr(x, 'prune_frac')]:
iteration = iterations.pop(0)
N_iter = N_iters.pop(0)
if iteration:
temp = layer.prune_layer(iteration, N_iter)
if np.isnan(temp):
print('nan pruned mag!!')
continue
else:
assert temp > -1e-5, 'negative pruned magnitude!!'
avg_pruned_magnitude += max(temp,0)
i+=1
if i==0:
return -1 # magnitude calculated is always positive, so negative means no pruning
return avg_pruned_magnitude / i
def _prunable_conv_forward(self, input):
'''
modify the forward pass to mask pruned filters
'''
return F.conv2d(input, self.weight.transpose(0,3).mul(self.mask).transpose(0,3),
self.bias, self.stride, self.padding, self.dilation, self.groups)
def _noisy_conv_forward(self, input):
'''
modify the forward pass to add noise to targeted filters
'''
# add noise to weights for self.batches_of_noise batches.
# (this will add noise during test batches too, which creates a relatively large
# change in the weights when gaussian noise is added because no training is
# offsetting the effect)
if self.batches_since_targeting <= self.batches_of_noise:
self.batches_since_targeting += 1
w = self.weight.data
unpruned_filter_std = w[self.mask.bool()][0].std().item()
if 'restore' in self.noise:
bn_w = self.next_bn_layer.weight.data
bn_b = self.next_bn_layer.bias.data
bn_mu = self.next_bn_layer.running_mean.data
bn_sig2 = self.next_bn_layer.running_var.data
if self.noise[:4] == 'iter':
if self.batches_since_targeting%900 == 1:
print('targeting newest weights')
pruned = self.newly_pruned
else:
if self.batches_since_targeting%900 == 1:
print('targeting all weights (cumulative)')
pruned = ~self.mask.bool()
if self.batches_since_targeting == 1 and 'restore' in self.noise:
self.orig = w[pruned]
self.orig_bn_weight = bn_w[pruned]
self.orig_bn_bias = bn_b[pruned]
self.orig_bn_mu = bn_mu[pruned]
self.orig_bn_sig2 = bn_sig2[pruned]
if 'gaussian' in self.noise:
if self.batches_since_targeting%900 == 1:
print('adding gaussian noise')
w[pruned] += torch.distributions.Normal(0, unpruned_filter_std
).sample(w[pruned].shape).cuda()
else:
if self.batches_since_targeting%900 == 1:
print('multiplying zeroing noise')
w[pruned] *= 0
if 'restore' in self.noise:
bn_w[pruned] *= 0
bn_b[pruned] *= 0
if (self.batches_since_targeting > self.batches_of_noise) and ('restore'
in self.noise):
w[pruned] = self.orig
bn_w[pruned] = self.orig_bn_weight
bn_b[pruned] = self.orig_bn_bias
bn_mu[pruned] = self.orig_bn_mu
bn_sig2[pruned] = self.orig_bn_sig2
return F.conv2d(input, self.weight,
self.bias, self.stride, self.padding, self.dilation, self.groups)
def _make_conv_prunable(layer, prune_frac, target_large, prune_random=False,
next_bn_layer = None, noise = None, scoring = 'L2'):
'''
modify a conv layer to make it prunable with an l1 or l2 norm score,
note the following BN layer will be masked wherever a filter in this
conv layer is masked
'''
assert type(layer) is nn.modules.conv.Conv2d, print(type(layer))
layer.prune_layer = MethodType(_prune_layer, layer)
# the pruning mask
layer.mask = torch.ones_like(layer.weight.transpose(0,3)[0,0,0], device=device)
# a big number that won't be reached, used in noise experiments
layer.batches_since_targeting = 1e7
# if applying noise, the type (Gaussian or zeroing)
layer.noise = noise
layer.forward = MethodType(_prunable_conv_forward, layer)
if layer.noise:
if not ('Prune' in layer.noise):
# test after initial prune, then train, so 79+391 batches per epoch.
# (assuming a batch size of 128 and 50k train + 10k test).
# we apply noise for batches_of_noise batches
layer.batches_of_noise = int(layer.noise.split("_")[1])
layer.forward = MethodType(_noisy_conv_forward, layer)
# fraction of weights to remove
layer.prune_frac = prune_frac
# True: prune largest weights, False: smallest
layer.target_large = target_large
# True: prune random weights and override target_large
layer.prune_random = prune_random
layer.scoring = scoring
if layer.noise == None:
# only mask the next BN layer if a noise experiment is not being run
# because we test the effect of applying noise to the conv weights
next_bn_layer.mask = layer.mask
next_bn_layer.forward = MethodType(_prunable_batch_forward, next_bn_layer)
else:
layer.next_bn_layer = next_bn_layer
if 'activations' in layer.scoring:
layer.post_shortcut_running_mean = torch.tensor([0.],device=device)
layer.counter = 0 # only update this running mean every 10 batches
def _make_bn_prunable(layer, prune_frac, target_large, prune_random=False,
scoring = 'EBN'):
'''
modify a batch norm layer to make it prunable with the E[BN] approach from our appendix
'''
assert type(layer) in (nn.modules.batchnorm.BatchNorm2d,nn.modules.batchnorm.BatchNorm1d
), print(type(layer))
layer.prune_layer = MethodType(_prune_layer, layer)
# the pruning mask
layer.mask = torch.ones_like(layer.weight, device=device)
layer.forward = MethodType(_prunable_batch_forward, layer)
# fraction of weights to remove
layer.prune_frac = prune_frac
# True: prune largest weights, False: smallest
layer.target_large = target_large
# True: prune random weights and override target_large
layer.prune_random = prune_random
layer.post_relu_running_mean = torch.tensor([0.], device = device)
layer.scoring = scoring
def _bn_expectation(self):
'''
computes expected value of post-ReLU, batch-normalized feature-map-activations
if 'alt' is in the scoring method, then will use a variant of the E[BN] method
that provided more stability in our ResNet18 experiments
'''
def _normalize(x, mu, std):
return (x-mu)/std
# alpha is the point in the std normal dist corresponding to 0 (the
# ReLU-truncation point) in the BN dist
if 'alt' in layer.scoring:
w = self.weight.detach().to(device)
else:
w = self.weight.detach().abs().to(device)
alpha = _normalize(0, self.bias.detach().to(device), w)
n = torch.distributions.normal.Normal(torch.tensor([0.], device=device),
torch.tensor([1.], device=device))
# Z is the area under the curve retained in the normalized analog of the
# ReLU-truncated dist
Z = 1 - n.cdf(alpha)
# the expected value of the truncated distribution (what makes it past the ReLU)
trunc_expected = self.bias.detach().to(device) + n.log_prob(alpha).exp() / Z * w
# if Z=0, alpha=inf, weight=0, bias < 0
trunc_expected[Z==0] = 0
# the expected value of batch-normalized, post-ReLU feature maps is
# n.cdf(alpha)*0 + (1-n.cdf(alpha))*trunc_expected
self.expectation = Z * trunc_expected
# Z=nan implies alpha = 0/0, so bias and weight are 0
self.expectation[torch.isnan(Z)] = 0
layer.calc_expectation = MethodType(_bn_expectation, layer)
def _prune_layer(self, iteration, N_iter):
noise = None
if hasattr(self, 'noise'):
noise = self.noise
if iteration == 1:
self.N_filters = self.weight.size(0)
self.final_kept_filters = ceil((1-self.prune_frac) * self.N_filters)
# calculate the number of filters (k) to prune this iteration
self.current_kept_filters = ceil(self.final_kept_filters +
(self.N_filters - self.final_kept_filters) * (N_iter-iteration)/(N_iter))
pruned_count = (~self.mask.bool()).sum()
k = int(self.N_filters - self.current_kept_filters - pruned_count)
# score pruning targets
if 'L1' in self.scoring:
print('pruning with filter L1 norm...')
target = self.weight.data[self.mask.bool()].view(self.mask.int().sum(), -1).norm(p=1,dim=1)
elif 'EBN' in self.scoring:
print('pruning with E[BN]')
self.calc_expectation()
target = self.expectation.data[self.mask.bool()]
elif 'activations' in self.scoring:
print("pruning with post-shortcut activations' L1 norm")
target = self.post_shortcut_running_mean.data[self.mask.bool()]
else:
print('pruning with filter L2 norm')
target = self.weight.data[self.mask.bool()].view(self.mask.int().sum(), -1).norm(dim=1)
# prune if k>0
if k > 0:
k_weights, _ = torch.topk(target, k, largest=self.target_large)
cutoff_weight = float(k_weights[-1])
if self.target_large:
meets_cutoff = target < cutoff_weight
else:
meets_cutoff = target > cutoff_weight
if (~meets_cutoff).sum() == len(meets_cutoff):
print("you're trying to prune the whole layer, no variance in "+
"weights is likely. proceeding by pruning randomly")
meets_cutoff[k:] = True
if (~meets_cutoff).sum() > k:
print("you're trying to prune too much, sparsity from weight decay "+
"is likely. proceeding by pruning only the scheduled # of weights")
temp = meets_cutoff[~meets_cutoff]
temp[k:] = True
meets_cutoff[~meets_cutoff] = temp
assert (~meets_cutoff).sum() == k, 'pruned fewer weights than should have!!!'
if self.prune_random:
meets_cutoff = meets_cutoff[permutation(range(len(meets_cutoff)))]
#magnitude of cut weights
avg_pruned_magnitude = float(target[~meets_cutoff].mean())
print('avg score of pruned weights was {}'.format(avg_pruned_magnitude))
if noise != None:
if not ('Prune' in self.noise):
self.batches_since_targeting = 0
temp = self.mask.data.clone()
temp[self.mask.bool()] += (~meets_cutoff).float()
self.newly_pruned = temp==2
self.mask.data[self.mask.bool()] = meets_cutoff.float()
if noise == None:
self.weight.data[~self.mask.bool()] *= 0
return avg_pruned_magnitude
return float('nan')
#############################
######## models #############
#############################
def resnetxx(dataset = "cifar10",
prune_frac = None,
target_large = True,
prune_random=False,
scoring = 'L2',
layers = None):
'''
set up resnet20 or resnet56 for pruning.
we only prune layers that are in the blocks.
shortcut connections to a pruned filter get pruned via logic in
_mask_shortcuts_forward_C() function
'''
if layers == 56:
model = resnet2056_arch.resnet56() #assume cifar10
else:
assert layers == 20
model = resnet2056_arch.resnet20() #assume cifar10
def _mask_shortcuts_forward_C(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
#out += self.shortcut(x)
# instead of the prior line, use this to prevent shortcuts to pruned filters
out[:,self.conv2.mask.bool()] += self.shortcut(x)[:,self.conv2.mask.bool()]
out = F.relu(out)
return out
if prune_frac is not None:
mods = [*model.layer1.modules()] + [*model.layer2.modules()] + [*model.layer3.modules()]
bn_indices = list(np.nonzero([type(x)==torch.nn.modules.batchnorm.BatchNorm2d
for x in mods])[0][::-1])
conv_indices = list(np.nonzero([type(x)==torch.nn.modules.conv.Conv2d
for x in mods])[0][::-1])
assert len(bn_indices) == len(prune_frac)
assert (np.array(bn_indices) - np.array(conv_indices) == 1).all()
prune_frac_len = len(prune_frac)
prunable_layers = len(bn_indices)
model.targeted_bn_layer_indices = bn_indices[:prune_frac_len]
for layer in model.targeted_bn_layer_indices:
_make_conv_prunable(mods[layer-1], prune_frac.pop(), target_large,
prune_random, next_bn_layer = mods[layer], scoring=scoring)
blocks = list(model.layer1) + list(model.layer2) + list(model.layer3)
for block in blocks:
block.forward = MethodType(_mask_shortcuts_forward_C, block)
assert len(prune_frac)==0
model.prune = MethodType(_prune, model)
return model
def resnet20(dataset = "cifar10",
prune_frac = None,
num_classes = 10,
target_large = True,
scoring = 'L2',
prune_random=False,
noise = None):
# only running cifar10 experiments with this model
assert num_classes == 10
# didn't test adding noise
assert noise == None
return resnetxx(dataset = dataset,
prune_frac = prune_frac,
target_large = target_large,
prune_random=prune_random,
scoring = scoring,
layers=20)
def resnet56(dataset = "cifar10",
prune_frac = None,
num_classes = 10,
target_large = True,
scoring = 'L2',
prune_random=False,
noise = None):
# only running cifar10 experiments with this model
assert num_classes == 10
# didn't test adding noise
assert noise == None
return resnetxx(dataset = dataset,
prune_frac = prune_frac,
target_large = target_large,
prune_random=prune_random,
scoring = scoring,
layers=56)
def resnet18(dataset = "cifar10",
prune_frac = None,
num_classes = 10,
target_large = True,
scoring = 'L2',
prune_random=False,
noise = None):
'''
set up resnet18 for pruning.
we only prune layers that are in the blocks.
shortcut connections to a pruned filter get pruned via logic in
_mask_shortcuts_forward_C or _mask_shortcuts_forward_B (the latter
is for our custom E[BN] pruning approach)
'''
model = resnet18_arch.ResNet18(num_classes=num_classes)
def _mask_shortcuts_forward_B(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
#out += self.shortcut(x)
# instead of the prior line, use this to prevent shortcuts to pruned filters
out[:,self.bn2.mask.bool()] += self.shortcut(x)[:,self.bn2.mask.bool()]
out = F.relu(out)
return out
def _mask_shortcuts_forward_C(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
#out += self.shortcut(x)
# instead of the prior line, use this to prevent shortcuts to pruned filters
out[:,self.conv2.mask.bool()] += self.shortcut(x)[:,self.conv2.mask.bool()]
out = F.relu(out)
return out
if 'activations' in scoring:
def _mask_shortcuts_forward_C(self, x):
out = self.bn1(self.conv1(x))
# only compute the mean if using training data (every 10 batches)
if self.training:
self.conv1.counter+=1
if self.conv1.counter%10==0:
self.conv1.post_shortcut_running_mean = .1*out.abs().sum(
dim=[0,2,3]).detach() + .9*self.conv1.post_shortcut_running_mean.detach()
out = F.relu(out)
out = self.bn2(self.conv2(out))
#out += self.shortcut(x)
# instead of the prior line, use this to prevent shortcuts to pruned filters
out[:,self.conv2.mask.bool()] += self.shortcut(x)[:,self.conv2.mask.bool()]
if self.training and self.conv1.counter%10==0:
self.conv2.post_shortcut_running_mean = .1*out.abs().sum(
dim=[0,2,3]).detach() + .9*self.conv2.post_shortcut_running_mean.detach()
out = F.relu(out)
return out
if prune_frac is not None:
mods = [*model.layer1.modules()] + [*model.layer2.modules()] + [
*model.layer3.modules()] + [*model.layer4.modules()]
bn_indices = list(np.nonzero([type(x)==torch.nn.modules.batchnorm.BatchNorm2d
for x in mods])[0][::-1])
conv_indices = list(np.nonzero([type(x)==torch.nn.modules.conv.Conv2d
for x in mods])[0][::-1])
# shortcut conv layers don't need their own mask,
# we prune them by using the masks of the filters they add to
for i in range(len(conv_indices)):
if mods[conv_indices[i]].weight.shape[-2:] == torch.Size([1,1]):
bn_indices[i] = conv_indices[i] = 999
for i in range(3):
bn_indices.remove(999)
conv_indices.remove(999)
assert (np.array(bn_indices) - np.array(conv_indices) == 1).all()
prune_frac_len = len(prune_frac)
prunable_layers = len(bn_indices)
model.targeted_bn_layer_indices = bn_indices[:prune_frac_len]
for layer in model.targeted_bn_layer_indices:
if 'EBN' in scoring:
_make_bn_prunable(mods[layer], prune_frac.pop(), target_large, prune_random,
scoring=scoring)
else:
_make_conv_prunable(mods[layer-1], prune_frac.pop(), target_large,
prune_random, next_bn_layer = mods[layer], scoring=scoring)
pruned_blocks = list(model.layer4)
if prune_frac_len > 4:
# we've given a fraction for multiple blocks
pruned_blocks += list(model.layer3) + list(model.layer2) + list(model.layer1)
for block in pruned_blocks:
if 'EBN' in scoring:
block.forward = MethodType(_mask_shortcuts_forward_B, block)
else:
block.forward = MethodType(_mask_shortcuts_forward_C, block)
assert len(prune_frac)==0
model.prune = MethodType(_prune, model)
return model
def vgg11_bn(dataset = "cifar10",
prune_frac = None,
num_classes = 10,
target_large = True,
scoring = 'L2',
prune_random=False,
noise = None):
model = vgg_arch.vgg11_bn(num_classes = num_classes)
if dataset in ["cifar10", "mnist", "cifar100"]:
model.classifier = nn.Sequential(nn.Linear(512, num_classes))
if prune_frac is not None:
model.targeted_bn_layer_indices = [-3,-6,-10,-13]
for layer in model.targeted_bn_layer_indices:
if 'EBN' in scoring:
_make_bn_prunable(model.features[layer], prune_frac.pop(), target_large,
prune_random, scoring=scoring)
else:
_make_conv_prunable(model.features[layer-1], prune_frac.pop(), target_large,
prune_random, next_bn_layer = model.features[layer], noise=noise, scoring=scoring)
assert len(prune_frac)==0
model.prune = MethodType(_prune, model)
return model