Add test for DynamicActQuant

src/brevitas/export/common/handler/qcdq.py

@@ -276,10 +276,6 @@ def symbolic_execution(self, x: Tensor):
 class DynamicQDQCastActQuantProxyHandlerMixin(DynamicQMixin, DQCastMixin, ABC):
     handled_layer = DynamicActQuantProxyFromInjector
 
-    def validate(self, module):
-        super().validate(module)
-        assert module.is_signed == False, "Only unsigned quantization is supported"
-
     def prepare_for_export(self, module):
         if module.is_quant_enabled:
             self.validate(module)

src/brevitas/export/onnx/standard/qcdq/handler.py

@@ -94,7 +94,11 @@ def int32_dtype(cls):
 
     def validate(self, module):
         super().validate(module)
-        # ONNX DynamicQuantizeLinear supports only 8b output with round to nearest even.
+
+        assert module.is_signed == False, "Only unsigned quantization supported"
+        assert module.quant_injector.scaling_stats_op == 'min_max', "Only min_max scaling op supported"
+        # ONNX QuantizeLinear supports only 8b output with round to nearest even.
+        # Below 8b quantization is supported through clipping.
         assert module.rounding_mode.upper() == 'ROUND', 'Only round to nearest even supported'
         # Below 8b quantization is not supported.
         self.validate_8b_bit_width(module.bit_width(), le_then=False)

src/brevitas_examples/common/generative/quantizers.py

@@ -127,11 +127,10 @@ class Int8ActDynamicPerTensorFloat(ActDynamicProxyMixin, Int8ActPerTensorFloat):
     """
     Symmetric quantizer with per tensor dynamic scale.
     """
-    proxy_class = DynamicActQuantProxyFromInjector
     scaling_impl = RuntimeDynamicStatsScaling
     scaling_stats_input_view_shape_impl = OverTensorView
-    scaling_stats_op = 'max'
-    dynamic_scaling_broadcastable_shape = (-1,)
+    scaling_stats_op = 'min_max'
+    dynamic_scaling_broadcastable_shape = this.scaling_shape
     stats_reduce_dim = 0
 
 
@@ -141,7 +140,7 @@ class Int8ActDynamicPerRowFloat(ActDynamicProxyMixin, Int8ActPerRowFloat):
     """
     scaling_impl = RuntimeDynamicStatsScaling
     scaling_stats_input_view_shape_impl = OverBatchOverOutputChannelView
-    scaling_stats_op = 'max'
+    scaling_stats_op = 'min_max'
 
 
 class Int8ActDynamicPerGroupFloat(ActDynamicProxyMixin, Int8ActPerRowFloat):
@@ -150,7 +149,7 @@ class Int8ActDynamicPerGroupFloat(ActDynamicProxyMixin, Int8ActPerRowFloat):
     """
     scaling_impl = RuntimeDynamicGroupStatsScaling
     keepdim = True
-    scaling_stats_op = 'max'
+    scaling_stats_op = 'min_max'
 
     @value
     def stats_reduce_dim(group_dim):
@@ -159,11 +158,11 @@ def stats_reduce_dim(group_dim):
 
 class ShiftedUint8ActDynamicPerTensorFloat(ActDynamicProxyMixin, ShiftedUint8ActPerTensorFloat):
     """
-    Asymmetric quantizer with per tensor dynamic scale.
+    Symmetric quantizer with per tensor dynamic scale.
     """
     scaling_impl = RuntimeDynamicStatsScaling
     scaling_stats_input_view_shape_impl = OverTensorView
-    scaling_stats_op = 'max'
+    scaling_stats_op = 'min_max'
     zero_point_stats_impl = NegativeMinOrZero
-    dynamic_scaling_broadcastable_shape = (-1,)
+    dynamic_scaling_broadcastable_shape = this.scaling_shape
     stats_reduce_dim = 0

tests/brevitas_ort/common.py

@@ -30,6 +30,7 @@
 from brevitas.quant.shifted_scaled_int import ShiftedUint8ActPerTensorFloat
 from brevitas.quant.shifted_scaled_int import ShiftedUint8WeightPerChannelFloat
 from brevitas.quant.shifted_scaled_int import ShiftedUint8WeightPerTensorFloat
+from brevitas_examples.common.generative.quantizers import ShiftedUint8ActDynamicPerTensorFloat
 
 SEED = 123456
 OUT_CH = 16
@@ -114,7 +115,8 @@ def recursive_allclose(ort_output, brevitas_output, tolerance):
 def is_brevitas_ort_close(
         model, np_input, export_name, export_type, tolerance=None, first_output_only=False):
     input_t = torch.from_numpy(np_input)
-    brevitas_output = model(input_t)
+    with torch.no_grad():
+        brevitas_output = model(input_t)
 
     if tolerance is not None and export_type == 'qcdq':
         tolerance = tolerance * brevitas_output.scale  # Float Output, tolerance is +/- output scale

tests/brevitas_ort/test_quant_module.py

@@ -27,13 +27,18 @@ def test_ort_wbiol(model, export_type, current_cases):
     impl = case_id.split('-')[
         -2]  # Inverse list of definition, 'export_type' is -1, 'impl' is -2, etc.
     quantizer = case_id.split('-')[-6]
+    o_bit_width = case_id.split('-')[-5]
+    i_bit_width = case_id.split('-')[-3]
 
     if impl in ('QuantConvTranspose1d', 'QuantConvTranspose2d') and export_type == 'qop':
         pytest.skip('Export of ConvTranspose is not supported for QOperation')
     if 'per_channel' in quantizer and 'asymmetric' in quantizer:
         pytest.skip('Per-channel zero-point is not well supported in ORT.')
     if 'QuantLinear' in impl and 'asymmetric' in quantizer:
         pytest.skip('ORT execution is unreliable and fails randomly on a subset of cases.')
+    if 'dynamic' in quantizer and ((o_bit_width != "o8" and i_bit_width != "i8") or
+                                   export_type != "qcdq"):
+        pytest.skip('Dynamic Act Quant supported only for 8bit and QCDQ export')
 
     if impl in ('QuantLinear'):
         in_size = (1, IN_CH)