def test_insert_quant_dequant_conv_dynamic(self):
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.conv = torch.nn.Conv2d(3, 5, 3).float()
def forward(self, x):
return self.conv(x)
m = torch.jit.script(M())
m = prepare_dynamic_script(m, {'': default_dynamic_qconfig})
data = torch.randn(1, 3, 10, 10, dtype=torch.float)
m(data)
m = wrap_cpp_module(
torch._C._jit_pass_insert_quant_dequant(m._c, "forward", False,
True))
assert len(m._modules._c.items()) == 1, \
'Expected to have single submodule of conv'
m(data)
quant_func = "aten::quantize_per_tensor"
# quantizing activations
FileCheck().check("aten::_choose_qparams_per_tensor") \
.check(quant_func) \
.check("prim::CallMethod[name=\"forward\"]") \
.check_not(quant_func) \
.check("return") \
.run(str(get_forward_graph(m._c)))
# quantizing weight in forward function of conv module, no choose_qparams
FileCheck().check_not("aten::_choose_qparams_per_tensor") \
.check(quant_func) \
.check("prim::CallMethod[name=\"_conv_forward\"]") \
.check_not(quant_func) \
.check("return") \
.run(str(get_forward_graph(m.conv._c)))
# shouldn't have quant/dequant in _conv_foward function
FileCheck().check_not(quant_func) \
.check("aten::conv2d") \
.check_not(quant_func) \
.check("return") \
.run(str(get_module_method(m, 'conv', '_conv_forward').graph))
def test_insert_quant_dequant_linear_dynamic(self):
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.fc1 = torch.nn.Linear(5, 5).float()
self.fc2 = torch.nn.Linear(5, 5).float()
def forward(self, x):
x = self.fc1(x)
return self.fc2(x)
m = torch.jit.script(M())
m = prepare_dynamic_script(m, {'': default_dynamic_qconfig})
data = torch.randn(5, 5, dtype=torch.float)
m(data)
m = wrap_cpp_module(
torch._C._jit_pass_insert_quant_dequant(m._c, "forward", False,
True))
assert len(m._modules._c.items()) == 2, \
'Expected to have two submodule of linear'
m(data)
quant_func = "aten::quantize_per_tensor"
# quantizing activations
FileCheck().check("aten::_choose_qparams_per_tensor") \
.check(quant_func) \
.check("prim::CallMethod[name=\"forward\"]") \
.check("aten::_choose_qparams_per_tensor") \
.check(quant_func) \
.check("prim::CallMethod[name=\"forward\"]") \
.check_not(quant_func) \
.check("return") \
.run(str(get_forward_graph(m._c)))
# quantizing weight in forward function of fc module, no choose_qparams
FileCheck().check_not("aten::_choose_qparams_per_tensor") \
.check(quant_func) \
.check("prim::CallFunction") \
.check_not(quant_func) \
.check("return") \
.run(str(get_forward_graph(m.fc1._c)))
def test_prepare_dynamic_lstm(self):
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.lstm = torch.nn.LSTM(2, 2).to(dtype=torch.float)
def forward(self, x):
return self.lstm(x)
from torch.quantization.observer import default_dynamic_quant_observer, _MinMaxTensorListObserver
qconfig = QConfigDynamic(activation=default_dynamic_quant_observer,
weight=_MinMaxTensorListObserver)
m = torch.jit.script(M())
m = prepare_dynamic_script(m, {'': qconfig})
assert len(attrs_with_prefix(m.lstm, '_observer_')) == 1
FileCheck().check('_MinMaxTensorListObserver = prim::GetAttr[name="_observer_0') \
.check("aten::lstm") \
.check("return") \
.run(str(get_module_method(m, 'lstm', 'forward__0').graph))
def test_prepare_dynamic(self):
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.fc = torch.nn.Linear(5, 5)
def forward(self, x):
return self.fc(x)
m = torch.jit.script(M())
m = prepare_dynamic_script(m, {'': default_dynamic_qconfig})
# for input of FC for dynamic quant
assert len(attrs_with_prefix(m, '_observer_')) == 1
# for weight
assert len(attrs_with_prefix(m.fc, '_observer_')) == 1
FileCheck().check('DynamicQuantObserver = prim::GetAttr[name="_observer_') \
.check('prim::GetAttr[name="fc"]') \
.check('prim::CallMethod') \
.check_not('Observer = prim::GetAttr[name="_observer_') \
.run(m.graph)
def test_prepare_dynamic_child_qconfig(self):
class Sub(torch.nn.Module):
def __init__(self):
super(Sub, self).__init__()
self.fc = torch.nn.Linear(5, 5)
def forward(self, x):
return self.fc(x)
class M(torch.nn.Module):
def __init__(self):
super(M, self).__init__()
self.conv = torch.nn.Conv2d(3, 5, 3)
self.sub = Sub()
def forward(self, x):
return self.sub(self.conv(x))
m = torch.jit.script(M())
# only quantize child module.
m = prepare_dynamic_script(m, {'sub.fc': default_dynamic_qconfig})
# input of sub for dynamic quant
assert len(attrs_with_prefix(m, '_observer_')) == 1
# not quantized
assert len(attrs_with_prefix(m.conv, '_observer_')) == 0
# no observers since we observe in the outer most call site
assert len(attrs_with_prefix(m.sub, '_observer_')) == 0
# weight of linear
assert len(attrs_with_prefix(m.sub.fc, '_observer_')) == 1
FileCheck().check('prim::GetAttr[name="sub') \
.check('prim::CallMethod') \
.check('DynamicQuantObserver = prim::GetAttr[name="_observer_') \
.check('prim::CallMethod') \
.check_not('Observer = prim::GetAttr[name="_observer_') \
.run(m.graph)
