Feat (tests): add new tests for proxy

tests/brevitas/proxy/test_proxy.py

@@ -0,0 +1,80 @@
+import pytest
+
+from brevitas.nn import QuantLinear
+from brevitas.nn.quant_activation import QuantReLU
+from brevitas.quant.scaled_int import Int8AccumulatorAwareWeightQuant
+from brevitas.quant.scaled_int import Int8BiasPerTensorFloatInternalScaling
+from brevitas.quant.scaled_int import Int8WeightPerChannelFloatDecoupled
+from brevitas.quant.scaled_int import Int8WeightPerTensorFloat
+from brevitas_examples.common.generative.quantizers import Int8DynamicActPerTensorFloat
+
+
+class TestProxy:
+
+    def test_bias_proxy(self):
+        model = QuantLinear(10, 5, bias_quant=Int8BiasPerTensorFloatInternalScaling)
+        assert model.bias_quant.scale() is not None
+        assert model.bias_quant.zero_point() is not None
+        assert model.bias_quant.bit_width() is not None
+
+        model.bias_quant.disable_quant = True
+        assert model.bias_quant.scale() is None
+        assert model.bias_quant.zero_point() is None
+        assert model.bias_quant.bit_width() is None
+
+    def test_weight_proxy(self):
+        model = QuantLinear(10, 5, weight_quant=Int8WeightPerTensorFloat)
+        assert model.weight_quant.scale() is not None
+        assert model.weight_quant.zero_point() is not None
+        assert model.weight_quant.bit_width() is not None
+
+        model.weight_quant.disable_quant = True
+        assert model.weight_quant.scale() is None
+        assert model.weight_quant.zero_point() is None
+        assert model.weight_quant.bit_width() is None
+
+    def test_weight_decoupled_proxy(self):
+        model = QuantLinear(10, 5, weight_quant=Int8WeightPerChannelFloatDecoupled)
+        assert model.weight_quant.pre_scale() is not None
+        assert model.weight_quant.pre_zero_point() is not None
+
+        model.weight_quant.disable_quant = True
+        assert model.weight_quant.pre_scale() is None
+        assert model.weight_quant.pre_zero_point() is None
+
+    def test_weight_decoupled_with_input_proxy(self):
+        model = QuantLinear(10, 5, weight_quant=Int8AccumulatorAwareWeightQuant)
+        with pytest.raises(NotImplementedError):
+            model.weight_quant.scale()
+        with pytest.raises(NotImplementedError):
+            model.weight_quant.zero_point()
+
+        with pytest.raises(NotImplementedError):
+            model.weight_quant.pre_scale()
+        with pytest.raises(NotImplementedError):
+            model.weight_quant.pre_zero_point()
+
+    def test_act_proxy(self):
+        model = QuantReLU()
+        assert model.act_quant.scale() is not None
+        assert model.act_quant.zero_point() is not None
+        assert model.act_quant.bit_width() is not None
+
+        model.act_quant.disable_quant = True
+        assert model.act_quant.scale() is None
+        assert model.act_quant.zero_point() is None
+        assert model.act_quant.bit_width() is None
+
+    def test_act_proxy(self):
+        model = QuantReLU(Int8DynamicActPerTensorFloat)
+
+        with pytest.raises(RuntimeError, match="Scale for Dynamic Act Quant is input-dependant"):
+            model.act_quant.scale()
+        with pytest.raises(RuntimeError,
+                           match="Zero point for Dynamic Act Quant is input-dependant"):
+            model.act_quant.zero_point()
+
+        assert model.act_quant.bit_width() is not None
+
+        model.act_quant.disable_quant = True
+        assert model.act_quant.bit_width() is None

tests/brevitas/proxy/test_weight_scaling.py

@@ -1,7 +1,6 @@
 # Copyright (C) 2023, Advanced Micro Devices, Inc. All rights reserved.
 # SPDX-License-Identifier: BSD-3-Clause
 
-import pytest
 from torch import nn
 
 from brevitas import config