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# -*- coding: utf-8 -*-
# Owner(s): ["module: unknown"]
import logging
from torch import nn
from torch.ao.sparsity.sparsifier import utils
from torch.nn.utils import parametrize
import torch
from torch.testing._internal.common_utils import TestCase
logging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', level=logging.INFO)
class ModelUnderTest(nn.Module):
def __init__(self, bias=True):
super().__init__()
self.linear = nn.Linear(16, 16, bias=bias)
self.seq = nn.Sequential(
nn.Linear(16, 16, bias=bias),
nn.Linear(16, 16, bias=bias)
)
# Make sure the weights are not random
self.linear.weight = nn.Parameter(torch.zeros_like(self.linear.weight) + 1.0)
self.seq[0].weight = nn.Parameter(torch.zeros_like(self.seq[0].weight) + 2.0)
self.seq[1].weight = nn.Parameter(torch.zeros_like(self.seq[1].weight) + 3.0)
if bias:
self.linear = nn.Parameter(torch.zeros_like(self.linear.bias) + 10.0)
self.seq[0] = nn.Parameter(torch.zeros_like(self.seq[0].bias) + 20.0)
self.seq[0] = nn.Parameter(torch.zeros_like(self.seq[0].bias) + 30.0)
def forward(self, x):
x = self.linear(x)
x = self.seq(x)
return x
class TestFakeSparsity(TestCase):
def test_masking_logic(self):
model = nn.Linear(16, 16, bias=False)
model.weight = nn.Parameter(torch.eye(16))
x = torch.randn(3, 16)
self.assertEqual(torch.mm(x, torch.eye(16)), model(x))
mask = torch.zeros(16, 16)
sparsity = utils.FakeSparsity(mask)
parametrize.register_parametrization(model, 'weight', sparsity)
x = torch.randn(3, 16)
self.assertEqual(torch.zeros(3, 16), model(x))
def test_weights_parametrized(self):
model = ModelUnderTest(bias=False)
assert not hasattr(model.linear, 'parametrizations')
assert not hasattr(model.seq[0], 'parametrizations')
assert not hasattr(model.seq[1], 'parametrizations')
mask = torch.eye(16)
parametrize.register_parametrization(model.linear, 'weight',
utils.FakeSparsity(mask))
mask = torch.eye(16)
parametrize.register_parametrization(model.seq[0], 'weight',
utils.FakeSparsity(mask))
mask = torch.eye(16)
parametrize.register_parametrization(model.seq[1], 'weight',
utils.FakeSparsity(mask))
assert hasattr(model.linear, 'parametrizations')
assert parametrize.is_parametrized(model.linear, 'weight')
assert hasattr(model.seq[0], 'parametrizations')
assert parametrize.is_parametrized(model.linear, 'weight')
assert hasattr(model.seq[1], 'parametrizations')
assert parametrize.is_parametrized(model.linear, 'weight')
def test_state_dict_preserved(self):
model_save = ModelUnderTest(bias=False)
mask = torch.eye(16)
parametrize.register_parametrization(model_save.linear, 'weight',
utils.FakeSparsity(mask))
mask = torch.eye(16)
parametrize.register_parametrization(model_save.seq[0], 'weight',
utils.FakeSparsity(mask))
mask = torch.eye(16)
parametrize.register_parametrization(model_save.seq[1], 'weight',
utils.FakeSparsity(mask))
state_dict = model_save.state_dict()
model_load = ModelUnderTest(bias=False)
mask = torch.zeros(model_load.linear.weight.shape)
parametrize.register_parametrization(model_load.linear, 'weight',
utils.FakeSparsity(mask))
mask = torch.zeros(model_load.seq[0].weight.shape)
parametrize.register_parametrization(model_load.seq[0], 'weight',
utils.FakeSparsity(mask))
mask = torch.zeros(model_load.seq[1].weight.shape)
parametrize.register_parametrization(model_load.seq[1], 'weight',
utils.FakeSparsity(mask))
# Keep this strict, as we are not loading the 'mask'
model_load.load_state_dict(state_dict, strict=False)
# Check the parametrizations are preserved
assert hasattr(model_load.linear, 'parametrizations')
assert parametrize.is_parametrized(model_load.linear, 'weight')
assert hasattr(model_load.seq[0], 'parametrizations')
assert parametrize.is_parametrized(model_load.linear, 'weight')
assert hasattr(model_load.seq[1], 'parametrizations')
assert parametrize.is_parametrized(model_load.linear, 'weight')
# Check the weigths are preserved
self.assertEqual(model_save.linear.parametrizations['weight'].original,
model_load.linear.parametrizations['weight'].original)
self.assertEqual(model_save.seq[0].parametrizations['weight'].original,
model_load.seq[0].parametrizations['weight'].original)
self.assertEqual(model_save.seq[1].parametrizations['weight'].original,
model_load.seq[1].parametrizations['weight'].original)
# Check the masks are not preserved in the state_dict
# We store the state_dicts in the sparsifier, not in the model itself.
# TODO: Need to find a clean way of exporting the parametrized model
self.assertNotEqual(model_save.linear.parametrizations['weight'][0].mask,
model_load.linear.parametrizations['weight'][0].mask)
self.assertNotEqual(model_save.seq[0].parametrizations['weight'][0].mask,
model_load.seq[0].parametrizations['weight'][0].mask)
self.assertNotEqual(model_save.seq[1].parametrizations['weight'][0].mask,
model_load.seq[1].parametrizations['weight'][0].mask)
def test_jit_trace(self):
model = ModelUnderTest(bias=False)
mask = torch.eye(16)
parametrize.register_parametrization(model.linear, 'weight',
utils.FakeSparsity(mask))
mask = torch.eye(16)
parametrize.register_parametrization(model.seq[0], 'weight',
utils.FakeSparsity(mask))
mask = torch.eye(16)
parametrize.register_parametrization(model.seq[1], 'weight',
utils.FakeSparsity(mask))
# Tracing
example_x = torch.ones(3, 16)
model_trace = torch.jit.trace_module(model, {'forward': example_x})
x = torch.randn(3, 16)
y = model(x)
y_hat = model_trace(x)
self.assertEqual(y_hat, y)
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