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# Owner(s): ["oncall: quantization"]
import torch
import torch.nn.intrinsic as nni
import torch.nn.qat as nnqat
import torch.nn.quantized._reference as nnqr
from torch.testing._internal.common_quantization import QuantizationTestCase
from torch.ao.quantization.backend_config import (
BackendConfig,
BackendPatternConfig,
DTypeConfig,
DTypeWithConstraints,
ObservationType,
)
from torch.ao.quantization.fake_quantize import FixedQParamsFakeQuantize
from torch.ao.quantization.fuser_method_mappings import reverse_sequential_wrapper2
from torch.ao.quantization.fx.quantization_patterns import _default_root_node_getter
from torch.ao.quantization.observer import default_fixed_qparams_range_0to1_observer
class TestBackendConfig(QuantizationTestCase):
# =============
# DTypeConfig
# =============
dtype_config1 = DTypeConfig(
input_dtype=torch.quint8,
output_dtype=torch.quint8,
weight_dtype=torch.qint8,
bias_dtype=torch.float
)
dtype_config2 = DTypeConfig(
input_dtype=torch.float16,
output_dtype=torch.float,
is_dynamic=True
)
activation_dtype_with_constraints = DTypeWithConstraints(
dtype=torch.quint8,
quant_min_lower_bound=0,
quant_max_upper_bound=127,
scale_min_lower_bound=2 ** -12,
)
weight_dtype_with_constraints = DTypeWithConstraints(
dtype=torch.qint8,
quant_min_lower_bound=-128,
quant_max_upper_bound=127,
scale_min_lower_bound=2 ** -12,
)
dtype_config3 = DTypeConfig(
input_dtype=activation_dtype_with_constraints,
output_dtype=activation_dtype_with_constraints,
weight_dtype=weight_dtype_with_constraints,
)
dtype_config_dict1_legacy = {
"input_dtype": torch.quint8,
"output_dtype": torch.quint8,
"weight_dtype": torch.qint8,
"bias_dtype": torch.float,
}
dtype_config_dict2_legacy = {
"input_dtype": torch.float16,
"output_dtype": torch.float,
"is_dynamic": True,
}
dtype_config_dict1 = {
"input_dtype": DTypeWithConstraints(dtype=torch.quint8),
"output_dtype": DTypeWithConstraints(torch.quint8),
"weight_dtype": DTypeWithConstraints(torch.qint8),
"bias_dtype": torch.float,
}
dtype_config_dict2 = {
"input_dtype": DTypeWithConstraints(dtype=torch.float16),
"output_dtype": DTypeWithConstraints(dtype=torch.float),
"is_dynamic": True,
}
dtype_config_dict3 = {
"input_dtype": activation_dtype_with_constraints,
"output_dtype": activation_dtype_with_constraints,
"weight_dtype": weight_dtype_with_constraints,
}
def test_dtype_config_from_dict(self):
self.assertEqual(DTypeConfig.from_dict(self.dtype_config_dict1_legacy), self.dtype_config1)
self.assertEqual(DTypeConfig.from_dict(self.dtype_config_dict2_legacy), self.dtype_config2)
self.assertEqual(DTypeConfig.from_dict(self.dtype_config_dict1), self.dtype_config1)
self.assertEqual(DTypeConfig.from_dict(self.dtype_config_dict2), self.dtype_config2)
self.assertEqual(DTypeConfig.from_dict(self.dtype_config_dict3), self.dtype_config3)
def test_dtype_config_to_dict(self):
self.assertEqual(self.dtype_config1.to_dict(), self.dtype_config_dict1)
self.assertEqual(self.dtype_config2.to_dict(), self.dtype_config_dict2)
self.assertEqual(self.dtype_config3.to_dict(), self.dtype_config_dict3)
# ======================
# BackendPatternConfig
# ======================
_fuser_method = reverse_sequential_wrapper2(nni.LinearReLU)
_num_tensor_args_to_observation_type = {
0: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
1: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
2: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
}
_input_type_to_index = {
"bias": 0,
"input": 1,
"weight": 2,
}
_fake_quantize = FixedQParamsFakeQuantize.with_args(observer=default_fixed_qparams_range_0to1_observer)
def _extra_inputs_getter(self, p):
return (torch.rand(3, 3),)
def _get_backend_op_config1(self):
return BackendPatternConfig((torch.nn.ReLU, torch.nn.Linear)) \
.set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT) \
.add_dtype_config(self.dtype_config1) \
.add_dtype_config(self.dtype_config2) \
.set_root_module(torch.nn.Linear) \
.set_qat_module(nnqat.Linear) \
.set_reference_quantized_module(nnqr.Linear) \
.set_fused_module(nni.LinearReLU) \
.set_fuser_method(self._fuser_method)
def _get_backend_op_config2(self):
return BackendPatternConfig(torch.add) \
.add_dtype_config(self.dtype_config2) \
._set_root_node_getter(_default_root_node_getter) \
._set_extra_inputs_getter(self._extra_inputs_getter) \
._set_num_tensor_args_to_observation_type(self._num_tensor_args_to_observation_type) \
._set_input_type_to_index(self._input_type_to_index) \
._set_input_output_observed(False) \
._set_overwrite_output_fake_quantize(self._fake_quantize) \
._set_overwrite_output_observer(default_fixed_qparams_range_0to1_observer)
def _get_backend_pattern_config_dict1(self):
return {
"pattern": (torch.nn.ReLU, torch.nn.Linear),
"observation_type": ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
"dtype_configs": [self.dtype_config_dict1, self.dtype_config_dict2],
"root_module": torch.nn.Linear,
"qat_module": nnqat.Linear,
"reference_quantized_module_for_root": nnqr.Linear,
"fused_module": nni.LinearReLU,
"fuser_method": self._fuser_method,
}
def _get_backend_pattern_config_dict2(self):
return {
"pattern": torch.add,
"observation_type": ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
"dtype_configs": [self.dtype_config_dict2],
"root_node_getter": _default_root_node_getter,
"extra_inputs_getter": self._extra_inputs_getter,
"num_tensor_args_to_observation_type": self._num_tensor_args_to_observation_type,
"input_type_to_index": self._input_type_to_index,
"input_output_observed": False,
"overwrite_output_fake_quantize": self._fake_quantize,
"overwrite_output_observer": default_fixed_qparams_range_0to1_observer
}
def test_backend_op_config_set_observation_type(self):
conf = BackendPatternConfig(torch.nn.Linear)
self.assertEqual(conf.observation_type, ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT)
conf.set_observation_type(ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT)
self.assertEqual(conf.observation_type, ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT)
def test_backend_op_config_add_dtype_config(self):
conf = BackendPatternConfig(torch.nn.Linear)
self.assertEqual(len(conf.dtype_configs), 0)
conf.add_dtype_config(self.dtype_config1)
conf.add_dtype_config(self.dtype_config2)
self.assertEqual(len(conf.dtype_configs), 2)
self.assertEqual(conf.dtype_configs[0], self.dtype_config1)
self.assertEqual(conf.dtype_configs[1], self.dtype_config2)
def test_backend_op_config_set_root_module(self):
conf = BackendPatternConfig(nni.LinearReLU)
self.assertTrue(conf.root_module is None)
conf.set_root_module(torch.nn.Linear)
self.assertEqual(conf.root_module, torch.nn.Linear)
def test_backend_op_config_set_qat_module(self):
conf = BackendPatternConfig(torch.nn.Linear)
self.assertTrue(conf.qat_module is None)
conf.set_qat_module(nnqat.Linear)
self.assertEqual(conf.qat_module, nnqat.Linear)
def test_backend_op_config_set_reference_quantized_module(self):
conf = BackendPatternConfig(torch.nn.Linear)
self.assertTrue(conf.reference_quantized_module is None)
conf.set_reference_quantized_module(nnqr.Linear)
self.assertEqual(conf.reference_quantized_module, nnqr.Linear)
def test_backend_op_config_set_fused_module(self):
conf = BackendPatternConfig((torch.nn.ReLU, torch.nn.Linear))
self.assertTrue(conf.fused_module is None)
conf.set_fused_module(nni.LinearReLU)
self.assertEqual(conf.fused_module, nni.LinearReLU)
def test_backend_op_config_set_fuser_method(self):
conf = BackendPatternConfig((torch.nn.ReLU, torch.nn.Linear))
self.assertTrue(conf.fuser_method is None)
conf.set_fuser_method(self._fuser_method)
self.assertEqual(conf.fuser_method, self._fuser_method)
def test_backend_op_config_set_root_node_getter(self):
conf = BackendPatternConfig((torch.nn.ReLU, torch.nn.Linear))
self.assertTrue(conf._root_node_getter is None)
conf._set_root_node_getter(_default_root_node_getter)
self.assertEqual(conf._root_node_getter, _default_root_node_getter)
def test_backend_op_config_set_extra_inputs_getter(self):
conf = BackendPatternConfig(torch.nn.Linear)
self.assertTrue(conf._extra_inputs_getter is None)
conf._set_extra_inputs_getter(self._extra_inputs_getter)
self.assertEqual(conf._extra_inputs_getter, self._extra_inputs_getter)
def test_backend_op_config_set_num_tensor_args_to_observation_type(self):
conf = BackendPatternConfig(torch.add)
self.assertEqual(len(conf._num_tensor_args_to_observation_type), 0)
conf._set_num_tensor_args_to_observation_type(self._num_tensor_args_to_observation_type)
self.assertEqual(conf._num_tensor_args_to_observation_type, self._num_tensor_args_to_observation_type)
def test_backend_op_config_set_input_type_to_index(self):
conf = BackendPatternConfig(torch.addmm)
self.assertEqual(len(conf._input_type_to_index), 0)
conf._set_input_type_to_index(self._input_type_to_index)
self.assertEqual(conf._input_type_to_index, self._input_type_to_index)
def test_backend_op_config_set_input_output_observed(self):
conf = BackendPatternConfig(torch.nn.Embedding)
self.assertTrue(conf._input_output_observed is None)
conf._set_input_output_observed(False)
self.assertEqual(conf._input_output_observed, False)
def test_backend_op_config_set_overwrite_output_fake_quantize(self):
conf = BackendPatternConfig(torch.sigmoid)
self.assertTrue(conf._overwrite_output_fake_quantize is None)
conf._set_overwrite_output_fake_quantize(self._fake_quantize)
self.assertEqual(conf._overwrite_output_fake_quantize, self._fake_quantize)
def test_backend_op_config_set_overwrite_output_observer(self):
conf = BackendPatternConfig(torch.sigmoid)
self.assertTrue(conf._overwrite_output_observer is None)
conf._set_overwrite_output_observer(default_fixed_qparams_range_0to1_observer)
self.assertEqual(conf._overwrite_output_observer, default_fixed_qparams_range_0to1_observer)
def test_backend_op_config_from_dict(self):
conf_dict1 = self._get_backend_pattern_config_dict1()
conf1 = BackendPatternConfig.from_dict(conf_dict1)
self.assertEqual(conf1.pattern, (torch.nn.ReLU, torch.nn.Linear))
self.assertEqual(conf1.observation_type, ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT)
self.assertEqual(conf1.root_module, torch.nn.Linear)
self.assertEqual(conf1.qat_module, nnqat.Linear)
self.assertEqual(conf1.reference_quantized_module, nnqr.Linear)
self.assertEqual(conf1.fused_module, nni.LinearReLU)
self.assertEqual(conf1.fuser_method, self._fuser_method)
self.assertTrue(conf1._root_node_getter is None)
self.assertTrue(conf1._extra_inputs_getter is None)
self.assertEqual(len(conf1._num_tensor_args_to_observation_type), 0)
self.assertEqual(len(conf1._input_type_to_index), 0)
self.assertTrue(conf1._input_output_observed is None)
self.assertTrue(conf1._overwrite_output_fake_quantize is None)
self.assertTrue(conf1._overwrite_output_observer is None)
# Test temporary/internal keys
conf_dict2 = self._get_backend_pattern_config_dict2()
conf2 = BackendPatternConfig.from_dict(conf_dict2)
self.assertEqual(conf2.pattern, torch.add)
self.assertEqual(conf2.observation_type, ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT)
self.assertTrue(conf2.root_module is None)
self.assertTrue(conf2.qat_module is None)
self.assertTrue(conf2.reference_quantized_module is None)
self.assertTrue(conf2.fused_module is None)
self.assertTrue(conf2.fuser_method is None)
self.assertEqual(conf2._root_node_getter, _default_root_node_getter)
self.assertEqual(conf2._extra_inputs_getter, self._extra_inputs_getter)
self.assertEqual(conf2._num_tensor_args_to_observation_type, self._num_tensor_args_to_observation_type)
self.assertEqual(conf2._input_type_to_index, self._input_type_to_index)
self.assertEqual(conf2._input_output_observed, False)
self.assertEqual(conf2._overwrite_output_fake_quantize, self._fake_quantize)
self.assertEqual(conf2._overwrite_output_observer, default_fixed_qparams_range_0to1_observer)
def test_backend_op_config_to_dict(self):
conf1 = self._get_backend_op_config1()
conf2 = self._get_backend_op_config2()
conf_dict1 = self._get_backend_pattern_config_dict1()
conf_dict2 = self._get_backend_pattern_config_dict2()
self.assertEqual(conf1.to_dict(), conf_dict1)
self.assertEqual(conf2.to_dict(), conf_dict2)
# ===============
# BackendConfig
# ===============
def test_backend_config_set_name(self):
conf = BackendConfig("name1")
self.assertEqual(conf.name, "name1")
conf.set_name("name2")
self.assertEqual(conf.name, "name2")
def test_backend_config_set_backend_pattern_config(self):
conf = BackendConfig("name1")
self.assertEqual(len(conf.configs), 0)
backend_op_config1 = self._get_backend_op_config1()
backend_op_config2 = self._get_backend_op_config2()
conf.set_backend_pattern_config(backend_op_config1)
self.assertEqual(conf.configs, {
(torch.nn.ReLU, torch.nn.Linear): backend_op_config1,
})
conf.set_backend_pattern_config(backend_op_config2)
self.assertEqual(conf.configs, {
(torch.nn.ReLU, torch.nn.Linear): backend_op_config1,
torch.add: backend_op_config2
})
def test_backend_config_from_dict(self):
op1 = self._get_backend_op_config1()
op2 = self._get_backend_op_config2()
op_dict1 = self._get_backend_pattern_config_dict1()
op_dict2 = self._get_backend_pattern_config_dict2()
conf_dict = {
"name": "name1",
"configs": [op_dict1, op_dict2],
}
conf = BackendConfig.from_dict(conf_dict)
self.assertEqual(conf.name, "name1")
self.assertEqual(len(conf.configs), 2)
key1 = (torch.nn.ReLU, torch.nn.Linear)
key2 = torch.add
self.assertTrue(key1 in conf.configs)
self.assertTrue(key2 in conf.configs)
self.assertEqual(conf.configs[key1].to_dict(), op_dict1)
self.assertEqual(conf.configs[key2].to_dict(), op_dict2)
def test_backend_config_to_dict(self):
op1 = self._get_backend_op_config1()
op2 = self._get_backend_op_config2()
op_dict1 = self._get_backend_pattern_config_dict1()
op_dict2 = self._get_backend_pattern_config_dict2()
conf = BackendConfig("name1").set_backend_pattern_config(op1).set_backend_pattern_config(op2)
conf_dict = {
"name": "name1",
"configs": [op_dict1, op_dict2],
}
self.assertEqual(conf.to_dict(), conf_dict)
if __name__ == '__main__':
raise RuntimeError("This _test file is not meant to be run directly, use:\n\n"
"\tpython _test/_test_quantization.py TESTNAME\n\n"
"instead.")
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