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test_mobile_optimizer.py
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test_mobile_optimizer.py
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import unittest
import torch
import torch.backends.xnnpack
import torch.utils.bundled_inputs
from torch.testing._internal.jit_utils import get_forward, get_forward_graph
from torch.utils.mobile_optimizer import *
from torch.nn import functional as F
from torch._C import MobileOptimizerType
FileCheck = torch._C.FileCheck
class TestOptimizer(unittest.TestCase):
@unittest.skipUnless(torch.backends.xnnpack.enabled,
" XNNPACK must be enabled for these tests."
" Please build with USE_XNNPACK=1.")
def test_optimize_for_mobile(self):
batch_size = 2
input_channels_per_group = 6
height = 16
width = 16
output_channels_per_group = 6
groups = 4
kernel_h = kernel_w = 3
stride_h = stride_w = 1
pad_h = pad_w = 1
dilation = 1
input_channels = input_channels_per_group * groups
output_channels = output_channels_per_group * groups
kernels = (kernel_h, kernel_w)
strides = (stride_h, stride_w)
paddings = (pad_h, pad_w)
dilations = (dilation, dilation)
conv_weight_shape = (output_channels, input_channels_per_group, kernel_h, kernel_w)
conv_bias_shape = (output_channels)
input_data = torch.rand((batch_size, input_channels, height, width))
conv_weight = torch.rand((output_channels, input_channels_per_group, kernel_h, kernel_w))
conv_bias = torch.rand((output_channels))
result = F.conv2d(input_data, conv_weight, conv_bias, strides, paddings, dilations, groups)
weight_output_dim = 24
linear_input_shape = result.shape[1]
linear_weight_shape = (weight_output_dim, linear_input_shape)
class MyTestModule(torch.nn.Module):
def __init__(self):
super(MyTestModule, self).__init__()
self.conv_weight = torch.nn.Parameter(torch.Tensor(torch.rand(conv_weight_shape)))
self.conv_bias = torch.nn.Parameter(torch.Tensor(torch.rand((conv_bias_shape))))
self.linear_weight = torch.nn.Parameter(torch.Tensor(torch.rand(linear_weight_shape)))
self.linear_bias = torch.nn.Parameter(torch.Tensor(torch.rand((weight_output_dim))))
self.strides = strides
self.paddings = paddings
self.dilations = dilations
self.groups = groups
def forward(self, x):
o = F.conv2d(x, self.conv_weight, self.conv_bias,
self.strides, self.paddings, self.dilations, self.groups)
o = F.relu(o)
o = o.permute([0, 2, 3, 1])
o = F.linear(o, self.linear_weight, self.linear_bias)
return F.relu(o)
class BNTestModule(torch.nn.Module):
def __init__(self):
super(BNTestModule, self).__init__()
self.conv = torch.nn.Conv2d(1, 20, 5, 1)
self.bn = torch.nn.BatchNorm2d(num_features=20)
self.bn.eps = 0.0023
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return x
data_shape = (batch_size, input_channels, height, width)
input_data = torch.normal(1, 20, size=data_shape)
scripted_model = torch.jit.script(MyTestModule())
scripted_model.eval()
initial_result = scripted_model(input_data)
optimized_scripted_model = optimize_for_mobile(scripted_model)
optimized_result = optimized_scripted_model(input_data)
FileCheck().check_not("Tensor = aten::conv2d") \
.check_not("Tensor = prim::CallFunction") \
.check_not("prepacked::conv2d_clamp_prepack") \
.check_count("prepacked::conv2d_clamp_run", 1, exactly=True) \
.check_not("prepacked::linear_clamp_prepack") \
.check_count("prepacked::linear_clamp_run", 1, exactly=True) \
.run(optimized_scripted_model.graph)
torch.testing.assert_allclose(initial_result, optimized_result, rtol=1e-2, atol=1e-3)
optimization_blacklist_no_prepack = {MobileOptimizerType.INSERT_FOLD_PREPACK_OPS}
optimized_scripted_model_no_prepack = optimize_for_mobile(scripted_model, optimization_blacklist_no_prepack)
optimized_result_no_prepack = optimized_scripted_model_no_prepack(input_data)
FileCheck().check_count("Tensor = aten::conv2d", 1, exactly=True) \
.check_not("prepacked::linear_clamp_run") \
.check_not("prepacked::conv2d_clamp_run") \
.run(optimized_scripted_model_no_prepack.graph)
torch.testing.assert_allclose(initial_result, optimized_result_no_prepack, rtol=1e-2, atol=1e-3)
bn_test_module = BNTestModule()
bn_scripted_module = torch.jit.script(bn_test_module)
bn_scripted_module.eval()
self.assertEqual(len(torch.jit.export_opnames(bn_scripted_module)), 13)
FileCheck().check_count("prim::CallMethod[name=\"forward\"]", 2, exactly=True) \
.run(str(get_forward(bn_scripted_module._c).graph))
optimization_blacklist_no_prepack = {MobileOptimizerType.INSERT_FOLD_PREPACK_OPS}
bn_fold_scripted_module = optimize_for_mobile(bn_scripted_module, optimization_blacklist_no_prepack)
self.assertEqual(len(torch.jit.export_opnames(bn_fold_scripted_module)), 1)
FileCheck().check_count("prim::CallMethod[name=\"forward\"]", 1, exactly=True) \
.run(str(get_forward_graph(bn_fold_scripted_module._c)))
bn_input = torch.rand(1, 1, 6, 6)
torch.testing.assert_allclose(bn_scripted_module(bn_input), bn_fold_scripted_module(bn_input), rtol=1e-2, atol=1e-3)
optimization_blacklist_no_fold_bn = {MobileOptimizerType.CONV_BN_FUSION}
no_bn_fold_scripted_module = optimize_for_mobile(bn_scripted_module, optimization_blacklist_no_fold_bn)
FileCheck().check_count("aten::batch_norm", 1, exactly=True) \
.run(str(get_forward_graph(no_bn_fold_scripted_module._c)))
bn_input = torch.rand(1, 1, 6, 6)
torch.testing.assert_allclose(bn_scripted_module(bn_input), no_bn_fold_scripted_module(bn_input), rtol=1e-2, atol=1e-3)
def test_generate_mobile_module_lints(self):
class MyTestModule(torch.nn.Module):
def __init__(self):
super(MyTestModule, self).__init__()
self.fc = torch.nn.Linear(4, 4)
self.dropout = torch.nn.Dropout(p=0.5)
def forward(self, inputs):
out = self.fc(inputs)
out = self.dropout(out)
return out
class MyBNModule(torch.nn.Module):
def __init__(self):
super(MyBNModule, self).__init__()
self.bn = torch.nn.BatchNorm2d(4, affine=True)
def forward(self, inputs):
bn = self.bn(inputs)
return bn
class MyBundledInputModule(torch.nn.Module):
def __init__(self):
super(MyBundledInputModule, self).__init__()
def forward(self, inputs):
return inputs
def get_lint_count_by_type(lint_type, module_lint_List):
return len([lint_dict for lint_dict in module_lint_List if lint_dict['name'] == lint_type.name])
test_module = torch.jit.script(MyTestModule())
test_module_lint_list = generate_mobile_module_lints(test_module)
self.assertEqual(len(test_module_lint_list), 4)
self.assertEqual(get_lint_count_by_type(LintCode.BUNDLED_INPUT, test_module_lint_list), 1)
self.assertEqual(get_lint_count_by_type(LintCode.DROPOUT, test_module_lint_list), 1)
self.assertEqual(get_lint_count_by_type(LintCode.REQUIRES_GRAD, test_module_lint_list), 2)
bn_module = torch.jit.script(MyBNModule())
bn_module_lint_list = generate_mobile_module_lints(bn_module)
self.assertEqual(len(bn_module_lint_list), 4)
self.assertEqual(get_lint_count_by_type(LintCode.BUNDLED_INPUT, bn_module_lint_list), 1)
self.assertEqual(get_lint_count_by_type(LintCode.BATCHNORM, bn_module_lint_list), 1)
self.assertEqual(get_lint_count_by_type(LintCode.REQUIRES_GRAD, bn_module_lint_list), 2)
bi_module = torch.jit.script(MyBundledInputModule())
torch.utils.bundled_inputs.augment_model_with_bundled_inputs(
bi_module, [(torch.tensor([1]),)], [])
bi_module_lint_list = generate_mobile_module_lints(bi_module)
self.assertEqual(len(bi_module_lint_list), 0)
if __name__ == '__main__':
unittest.main()