Arm backend: Add support for QAT+per-channel combo (#13511)

Fixes a bug where an unsupported observer was used for QAT combined with
per-channel quantization.

Signed-off-by: Oscar Andersson <[email protected]>

Author: oscarandersson8218

backends/arm/quantizer/arm_quantizer.py

@@ -101,18 +101,20 @@ def get_symmetric_quantization_config(
     weight_observer_or_fake_quant_ctr: ObserverOrFakeQuantizeConstructor = (
         MinMaxObserver
     )
+
     # Determine the right observer/fake-quant constructor
     if is_qat:
-        # Set plain fake-quant with true min/max
-        weight_observer_or_fake_quant_ctr = FakeQuantize
+        if is_per_channel:
+            weight_observer_or_fake_quant_ctr = PerChannelMinMaxObserver
+        else:
+            # Set plain fake-quant with true min/max
+            weight_observer_or_fake_quant_ctr = FakeQuantize
     else:
         # PTQ: set min/max observer
         weight_observer_or_fake_quant_ctr = (
             PerChannelMinMaxObserver if is_per_channel else MinMaxObserver
         )
 
-    extra_args = {"eps": 2**-12}
-
     weight_quantization_spec = QuantizationSpec(
         dtype=torch.int8,
         quant_min=weight_qmin,

backends/arm/test/misc/test_bn_relu_folding_qat.py

@@ -40,13 +40,17 @@ def forward(self, x: torch.Tensor):
 
 
 models = {
-    "conv_bn_relu": ConvModule(batch_norm=True),
-    "conv_relu": ConvModule(batch_norm=False),
+    # name : (model, is_per_channel)
+    "conv_bn_relu_per_channel": (ConvModule(batch_norm=True), True),
+    "conv_relu_per_channel": (ConvModule(batch_norm=False), True),
+    "conv_bn_relu_per_tensor": (ConvModule(batch_norm=True), False),
+    "conv_relu_per_tensor": (ConvModule(batch_norm=False), False),
 }
 
 
-@common.parametrize("model", models)
-def test_qat_tosa_INT(model: torch.nn.Module):
+@common.parametrize("test_data", models)
+def test_qat_tosa_INT(test_data):
+    model, per_channel = test_data
     pipeline = TosaPipelineINT[input_t1](model, model.test_data, [], [], qtol=1)
     tosa_version = conftest.get_option("tosa_version")
     tosa_profiles = {
@@ -59,7 +63,7 @@ def test_qat_tosa_INT(model: torch.nn.Module):
         Quantize(
             quantizer=quantizer,
             quantization_config=get_symmetric_quantization_config(
-                is_qat=True, is_per_channel=False
+                is_qat=True, is_per_channel=per_channel
             ),
             is_qat=True,
         ),