Fix get_default_qat_qconfig for PT 1.13 (pytorch#88876)

See https://github.com/pytorch/pytorch/pull/84329/files#r1019916766 for more context

Pull Request resolved: pytorch#88876
Approved by: https://github.com/jgong5, https://github.com/vkuzo

test/quantization/core/test_top_level_apis.py

@@ -59,3 +59,35 @@ def test_fake_quants(self) -> None:
         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])

torch/ao/quantization/qconfig.py

@@ -339,7 +339,7 @@ def get_default_qat_qconfig(backend='fbgemm', version=1):
                                                                 quant_min=0,
                                                                 quant_max=255),
                               weight=default_per_channel_weight_fake_quant)
-        if backend == 'x86':
+        elif backend == 'x86':
             qconfig = QConfig(activation=FakeQuantize.with_args(observer=MovingAverageMinMaxObserver,
                                                                 quant_min=0,
                                                                 quant_max=255,