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import torch
from dnntools.sparsification import GRUSparsifier, LinearSparsifier, Conv1dSparsifier, ConvTranspose1dSparsifier
def mark_for_sparsification(module, params):
setattr(module, 'sparsify', True)
setattr(module, 'sparsification_params', params)
return module
def create_sparsifier(module, start, stop, interval):
sparsifier_list = []
for m in module.modules():
if hasattr(m, 'sparsify'):
if isinstance(m, torch.nn.GRU):
sparsifier_list.append(
GRUSparsifier([(m, m.sparsification_params)], start, stop, interval)
)
elif isinstance(m, torch.nn.Linear):
sparsifier_list.append(
LinearSparsifier([(m, m.sparsification_params)], start, stop, interval)
)
elif isinstance(m, torch.nn.Conv1d):
sparsifier_list.append(
Conv1dSparsifier([(m, m.sparsification_params)], start, stop, interval)
)
elif isinstance(m, torch.nn.ConvTranspose1d):
sparsifier_list.append(
ConvTranspose1dSparsifier([(m, m.sparsification_params)], start, stop, interval)
)
else:
print(f"[create_sparsifier] warning: module {m} marked for sparsification but no suitable sparsifier exists.")
def sparsify(verbose=False):
for sparsifier in sparsifier_list:
sparsifier.step(verbose)
return sparsify
def count_parameters(model, verbose=False):
total = 0
for name, p in model.named_parameters():
count = torch.ones_like(p).sum().item()
if verbose:
print(f"{name}: {count} parameters")
total += count
return total
def estimate_nonzero_parameters(module):
num_zero_parameters = 0
if hasattr(module, 'sparsify'):
params = module.sparsification_params
if isinstance(module, torch.nn.Conv1d) or isinstance(module, torch.nn.ConvTranspose1d):
num_zero_parameters = torch.ones_like(module.weight).sum().item() * (1 - params[0])
elif isinstance(module, torch.nn.GRU):
num_zero_parameters = module.input_size * module.hidden_size * (3 - params['W_ir'][0] - params['W_iz'][0] - params['W_in'][0])
num_zero_parameters += module.hidden_size * module.hidden_size * (3 - params['W_hr'][0] - params['W_hz'][0] - params['W_hn'][0])
elif isinstance(module, torch.nn.Linear):
num_zero_parameters = module.in_features * module.out_features * params[0]
else:
raise ValueError(f'unknown sparsification method for module of type {type(module)}')
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