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# Owner(s): ["oncall: quantization"]
import torch
import torch.nn as nn
from torch.testing._internal.common_quantization import QuantizationTestCase
from torch.ao.quantization.fuse_modules import fuse_modules
import torch.ao.quantization._equalize as _equalize
import copy
class TestEqualizeEager(QuantizationTestCase):
def checkChannelsEqualized(self, tensor1, tensor2, output_axis, input_axis):
''' Checks the channel ranges of tensor1, tensor2 are the same,
which is an indication that equalization has been applied correctly
'''
output_channel_tensor1 = _equalize.channel_range(tensor1, output_axis)
input_channel_tensor2 = _equalize.channel_range(tensor2, input_axis)
# ensuring the channels ranges of tensor1's input is the same as
# tensor2's output
self.assertEqual(output_channel_tensor1, input_channel_tensor2)
def getModule(self, model, name):
''' Given the name is a submodule to a model, return the submodule
'''
curr = model
name = name.split('.')
for subname in name:
curr = curr._modules[subname]
return curr
def test_cross_layer_equalization(self):
''' applies _equalize.cross_layer_equalization on two modules and checks
to make sure channels ranges are equivalent
'''
module1 = nn.Conv2d(3, 4, 2)
module2 = nn.Linear(4, 4)
module1_output_channel_axis = 0
module2_input_channel_axis = 1
_equalize.cross_layer_equalization(module1, module2)
mod_tensor1, mod_tensor2 = module1.weight, module2.weight
self.checkChannelsEqualized(mod_tensor1, mod_tensor2, module1_output_channel_axis, module2_input_channel_axis)
def test_converged(self):
''' Sanity checks on _equalize.converged working
identical modules should return true
modules with high difference in weights should return false
'''
module1 = nn.Linear(3, 3)
module2 = nn.Linear(3, 3)
module1.weight = nn.parameter.Parameter(torch.ones(module1.weight.size()))
module2.weight = nn.parameter.Parameter(torch.zeros(module1.weight.size()))
# input is a dictionary
dictionary_1 = {'linear1': module1}
dictionary_2 = {'linear1': module2}
self.assertTrue(_equalize.converged(dictionary_1, dictionary_1, 1e-6))
self.assertFalse(_equalize.converged(dictionary_1, dictionary_2, 1e-6))
def test_equalize(self):
''' First checks to see if _equalize.equalize can handle multiple
pair modules as input
then checks correctness of the function by ensuring the equalized
and unequalized versions of the model yield the same output
given the same input
'''
class ChainModule(nn.Module):
def __init__(self):
super(ChainModule, self).__init__()
self.linear1 = nn.Linear(3, 4)
self.linear2 = nn.Linear(4, 5)
self.linear3 = nn.Linear(5, 6)
def forward(self, x):
x = self.linear1(x)
x = self.linear2(x)
x = self.linear3(x)
return x
chain1 = ChainModule()
chain2 = copy.deepcopy(chain1)
_equalize.equalize(chain1, [['linear1', 'linear2'], ['linear2', 'linear3']], 1e-6)
linear1 = self.getModule(chain1, 'linear1')
linear2 = self.getModule(chain1, 'linear2')
linear3 = self.getModule(chain1, 'linear3')
self.checkChannelsEqualized(linear1.weight, linear2.weight, 0, 1)
self.checkChannelsEqualized(linear2.weight, linear3.weight, 0, 1)
input = torch.randn(20, 3)
self.assertEqual(chain1(input), chain2(input))
def test_equalize_fused_convrelu(self):
''' Checks to see if eager mode equalization supports fused
ConvReLU2d models
A model with 3 ConvReLU2d is constructed. Next, the conv2d and relu
layers are fused together and adjacent conv2d layers have cross-layer
equalization applied. Finally, we ensure that the channels have been
equalized and that the equalized and unequalized versions of the model
yield the same output given the same input
'''
class M(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 3, 1).to(dtype=torch.float)
self.relu1 = nn.ReLU(inplace=False).to(dtype=torch.float)
self.conv2 = nn.Conv2d(3, 3, 1).to(dtype=torch.float)
self.relu2 = nn.ReLU(inplace=False).to(dtype=torch.float)
self.conv3 = nn.Conv2d(3, 3, 1).to(dtype=torch.float)
self.relu3 = nn.ReLU(inplace=False).to(dtype=torch.float)
def forward(self, x):
x = self.conv1(x)
x = self.relu1(x)
x = self.conv2(x)
x = self.relu2(x)
x = self.conv3(x)
x = self.relu3(x)
return x
model = M()
fused_model1 = fuse_modules(model, [['conv1', 'relu1'], ['conv2', 'relu2'], ['conv3', 'relu3']])
fused_model2 = copy.deepcopy(fused_model1)
_equalize.equalize(fused_model1, [['conv1', 'conv2'], ['conv2', 'conv3']], 1e-6)
conv1 = self.getModule(fused_model1, 'conv1')[0]
conv2 = self.getModule(fused_model1, 'conv2')[0]
conv3 = self.getModule(fused_model1, 'conv3')[0]
self.checkChannelsEqualized(conv1.weight, conv2.weight, 0, 1)
self.checkChannelsEqualized(conv2.weight, conv3.weight, 0, 1)
input = torch.randn(3, 3, 1, 1)
self.assertEqual(fused_model1(input), fused_model2(input))
self.assertEqual(fused_model1(input), model(input))
def test_equalize_fused_linearrelu(self):
''' Checks to see if eager mode equalization supports fused
LinearReLU models
A model with 3 LinearReLU is constructed. Next, the linear and relu
layers are fused together and adjacent linear layers have cross-layer
equalization applied. Finally, we ensure that the channels have been
equalized and that the equalized and unequalized versions of the model
yield the same output given the same input
'''
class M(nn.Module):
def __init__(self):
super().__init__()
self.linear1 = nn.Linear(3, 4)
self.relu1 = nn.ReLU(inplace=False).to(dtype=torch.float)
self.linear2 = nn.Linear(4, 5)
self.relu2 = nn.ReLU(inplace=False).to(dtype=torch.float)
self.linear3 = nn.Linear(5, 6)
self.relu3 = nn.ReLU(inplace=False).to(dtype=torch.float)
def forward(self, x):
x = self.linear1(x)
x = self.relu1(x)
x = self.linear2(x)
x = self.relu2(x)
x = self.linear3(x)
x = self.relu3(x)
return x
model = M()
fused_model1 = fuse_modules(model, [['linear1', 'relu1'], ['linear2', 'relu2'], ['linear3', 'relu3']])
fused_model2 = copy.deepcopy(fused_model1)
_equalize.equalize(fused_model1, [['linear1', 'linear2'], ['linear2', 'linear3']], 1e-6)
linear1 = self.getModule(fused_model1, 'linear1')[0]
linear2 = self.getModule(fused_model1, 'linear2')[0]
linear3 = self.getModule(fused_model1, 'linear3')[0]
self.checkChannelsEqualized(linear1.weight, linear2.weight, 0, 1)
self.checkChannelsEqualized(linear2.weight, linear3.weight, 0, 1)
input = torch.randn(20, 3)
self.assertEqual(fused_model1(input), fused_model2(input))
self.assertEqual(fused_model1(input), model(input))
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