def test_qconfig_dict(self):
data = [(torch.randn(10, 5, dtype=torch.float) * 20, 1)]
# Eager mode
qconfig = QConfig(activation=Observer, weight=WeightObserver)
eager_module = AnnotatedNestedModel()
eager_module.fc3.qconfig = qconfig
eager_module.sub2.fc1.qconfig = qconfig
# Assign weights
eager_module.sub1.fc.weight.data.fill_(1.0)
eager_module.sub2.fc1.module.weight.data.fill_(1.0)
eager_module.sub2.fc2.weight.data.fill_(1.0)
eager_module.fc3.module.weight.data.fill_(1.0)
script_module = torch.jit.script(NestedModel())
# Copy weights for eager_module
script_module.sub1.fc.weight = eager_module.sub1.fc.weight
script_module.sub2.fc1.weight = eager_module.sub2.fc1.module.weight
script_module.sub2.fc2.weight = eager_module.sub2.fc2.weight
script_module.fc3.weight = eager_module.fc3.module.weight
# Quantize eager module
quantized_eager_module = quantize(eager_module, default_eval_fn, data)
def get_forward(m):
return m._c._get_method('forward')
# Quantize script_module
torch._C._jit_pass_constant_propagation(
get_forward(script_module).graph)
ScriptedObserver = torch.jit.script(Observer())
ScriptedWeightObserver = torch.jit.script(WeightObserver())
scripted_qconfig = QConfig(activation=ScriptedObserver._c,
weight=ScriptedWeightObserver._c)
qconfig_dict = {'sub2.fc1': scripted_qconfig, 'fc3': scripted_qconfig}
torch._C._jit_pass_insert_observers(script_module._c, "forward",
qconfig_dict)
# Run script_module and Collect statistics
get_forward(script_module)(data[0][0])
# Insert quantize and dequantize calls
script_module._c = torch._C._jit_pass_insert_quant_dequant(
script_module._c, "forward")
# Note that observer modules are not removed right now
torch._C._jit_pass_quant_fusion(
script_module._c._get_method('forward').graph)
get_forward(script_module)(data[0][0])
eager_result = quantized_eager_module(data[0][0])
script_result = get_forward(script_module)(data[0][0])
self.assertEqual(eager_result, script_result)
def test_nested1(self):
r"""Test quantization for nested model, top level 'fc3' and
'fc1' of submodule 'sub2', 'sub2.fc2' is not quantized
"""
model = AnnotatedNestedModel()
def checkPrepModules(model, before_calib=False):
if before_calib:
self.checkObservers(model)
self.checkNoPrepModules(model)
self.checkNoPrepModules(model.sub1)
self.checkNoPrepModules(model.sub1.fc)
self.checkNoPrepModules(model.sub1.relu)
self.checkNoPrepModules(model.sub2)
self.checkHasPrepModules(model.sub2.fc1)
self.checkNoPrepModules(model.sub2.fc2)
self.checkHasPrepModules(model.fc3)
model = prepare(model)
checkPrepModules(model, True)
test_only_eval_fn(model, self.calib_data)
model = convert(model)
def checkQuantized(model):
checkPrepModules(model)
self.checkLinear(model.sub1.fc)
self.checkWrappedQuantizedLinear(model.fc3)
self.checkWrappedQuantizedLinear(model.sub2.fc1)
self.checkLinear(model.sub2.fc2)
test_only_eval_fn(model, self.calib_data)
self.checkScriptable(model, self.calib_data)
checkQuantized(model)
# test one line API
model = quantize(AnnotatedNestedModel(), test_only_eval_fn,
self.calib_data)
checkQuantized(model)
