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# Owner(s): ["oncall: quantization"]
import torch
import torch.ao.quantization
from torch.testing._internal.common_utils import TestCase
class TestDefaultObservers(TestCase):
observers = [
"default_affine_fixed_qparams_observer",
"default_debug_observer",
"default_dynamic_quant_observer",
"default_placeholder_observer",
"default_fixed_qparams_range_0to1_observer",
"default_fixed_qparams_range_neg1to1_observer",
"default_float_qparams_observer",
"default_float_qparams_observer_4bit",
"default_histogram_observer",
"default_observer",
"default_per_channel_weight_observer",
"default_reuse_input_observer",
"default_symmetric_fixed_qparams_observer",
"default_weight_observer",
"per_channel_weight_observer_range_neg_127_to_127",
"weight_observer_range_neg_127_to_127",
]
fake_quants = [
"default_affine_fixed_qparams_fake_quant",
"default_dynamic_fake_quant",
"default_embedding_fake_quant",
"default_embedding_fake_quant_4bit",
"default_fake_quant",
"default_fixed_qparams_range_0to1_fake_quant",
"default_fixed_qparams_range_neg1to1_fake_quant",
"default_fused_act_fake_quant",
"default_fused_per_channel_wt_fake_quant",
"default_fused_wt_fake_quant",
"default_histogram_fake_quant",
"default_per_channel_weight_fake_quant",
"default_symmetric_fixed_qparams_fake_quant",
"default_weight_fake_quant",
"fused_per_channel_wt_fake_quant_range_neg_127_to_127",
"fused_wt_fake_quant_range_neg_127_to_127",
]
def _get_observer_ins(self, observer):
obs_func = getattr(torch.ao.quantization, observer)
return obs_func()
def test_observers(self) -> None:
t = torch.rand(1, 2, 3, 4)
for observer in self.observers:
obs = self._get_observer_ins(observer)
obs.forward(t)
def test_fake_quants(self) -> None:
t = torch.rand(1, 2, 3, 4)
for observer in self.fake_quants:
obs = self._get_observer_ins(observer)
obs.forward(t)
class TestQConfig(TestCase):
REDUCE_RANGE_DICT = {
'fbgemm': (True, False),
'qnnpack': (False, False),
'onednn': (False, False),
'x86': (True, False),
}
def test_reduce_range_qat(self) -> None:
for backend, reduce_ranges in self.REDUCE_RANGE_DICT.items():
for version in range(2):
qconfig = torch.ao.quantization.get_default_qat_qconfig(backend, version)
fake_quantize_activ = qconfig.activation()
self.assertEqual(fake_quantize_activ.activation_post_process.reduce_range, reduce_ranges[0])
fake_quantize_weight = qconfig.weight()
self.assertEqual(fake_quantize_weight.activation_post_process.reduce_range, reduce_ranges[1])
def test_reduce_range(self) -> None:
for backend, reduce_ranges in self.REDUCE_RANGE_DICT.items():
for version in range(1):
qconfig = torch.ao.quantization.get_default_qconfig(backend, version)
fake_quantize_activ = qconfig.activation()
self.assertEqual(fake_quantize_activ.reduce_range, reduce_ranges[0])
fake_quantize_weight = qconfig.weight()
self.assertEqual(fake_quantize_weight.reduce_range, reduce_ranges[1])
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