Add more argument sanity checks for affine quantizer constructor

TrainingExtensions/torch/src/python/aimet_torch/v2/quantization/affine/encoding.py

@@ -69,19 +69,19 @@ def __init__(self, scale: torch.Tensor, offset: torch.Tensor, bitwidth: int, sig
                  block_size: Optional[Tuple[int, ...]] = None):
         ...
 
-    def __init__(self, scale: torch.Tensor, offset: torch.Tensor, *args, **kwargs):
+    def __init__(self, scale: torch.Tensor, offset: torch.Tensor, *args, **kwargs): # pylint: disable=too-many-locals
         self._scale = scale
         self._offset = offset
         full_args = (scale, offset, *args)
 
         # Pad positional args with None's such that len(args) == 4
         args = tuple(chain(args, repeat(None, 4 - len(args))))
-        arg0 = kwargs.get('qmin', kwargs.get('bitwidth', args[0]))
-        arg1 = kwargs.get('qmax', kwargs.get('signed', args[1]))
-        symmetry = kwargs.get('symmetry', args[2])
+        arg0 = kwargs.pop('qmin', kwargs.pop('bitwidth', args[0]))
+        arg1 = kwargs.pop('qmax', kwargs.pop('signed', args[1]))
+        symmetry = kwargs.pop('symmetry', args[2])
         if symmetry is None:
             symmetry = False
-        block_size = kwargs.get('block_size', args[3])
+        block_size = kwargs.pop('block_size', args[3])
 
         if arg1 is None or isinstance(arg1, bool):
             # (arg0, arg1) == (bitwidth, signed)
@@ -98,6 +98,14 @@ def __init__(self, scale: torch.Tensor, offset: torch.Tensor, *args, **kwargs):
         assert qmin is not None
         assert qmax is not None
 
+        if kwargs:
+            cls = type(self).__qualname__
+            unexpected_keys = ', '.join(kwargs.keys())
+            raise TypeError(f"{cls}.__init__ got unexpected keyword argument: {unexpected_keys}")
+
+        if qmin >= qmax:
+            raise ValueError(f"qmax should be strictly larger than qmin. Got qmax={qmax}, qmin={qmin}")
+
         self.qmin = qmin
         self.qmax = qmax
         self._symmetry = symmetry

TrainingExtensions/torch/src/python/aimet_torch/v2/quantization/affine/quantizer.py

@@ -97,35 +97,43 @@ def __init__(self, shape, *args, **kwargs):
 
         # Pad positional args with None's such that len(args) == 5
         args = tuple(chain(args, repeat(None, 5 - len(args))))
-        arg0 = kwargs.get('qmin', kwargs.get('bitwidth', args[0]))
-        arg1 = kwargs.get('qmax', args[1])
+        arg0 = kwargs.pop('qmin', kwargs.pop('bitwidth', args[0]))
+        arg1 = kwargs.pop('qmax', args[1])
 
         if arg1 is not None and not isinstance(arg1, bool):
             # (arg0, arg1, arg2) == (qmin, qmax, symmetric)
             qmin, qmax = arg0, arg1
-            symmetric = kwargs.get('symmetric', args[2])
+            symmetric = kwargs.pop('symmetric', args[2])
 
             if (qmin is None) or (qmax is None) or (symmetric is None):
                 raise self._arg_parsing_error(full_args, kwargs)
 
-            encoding_analyzer = kwargs.get('encoding_analyzer', args[3])
-            block_size = kwargs.get('block_size', args[4])
+            encoding_analyzer = kwargs.pop('encoding_analyzer', args[3])
+            block_size = kwargs.pop('block_size', args[4])
         else:
             # (arg0, arg1) == (bitwidth, symmetric)
             bitwidth = arg0
-            symmetric = kwargs.get('symmetric', args[1])
+            symmetric = kwargs.pop('symmetric', args[1])
 
             if (bitwidth is None) or (symmetric is None):
                 raise self._arg_parsing_error(full_args, kwargs)
 
             # We support two quantization modes: (unsigned) asymmetric and signed-symmetric
             qmin, qmax = _derive_qmin_qmax(bitwidth=bitwidth, signed=symmetric)
-            encoding_analyzer = kwargs.get('encoding_analyzer', args[2])
-            block_size = kwargs.get('block_size', args[3])
+            encoding_analyzer = kwargs.pop('encoding_analyzer', args[2])
+            block_size = kwargs.pop('block_size', args[3])
 
         assert qmin is not None
         assert qmax is not None
 
+        if kwargs:
+            cls = type(self).__qualname__
+            unexpected_keys = ', '.join(kwargs.keys())
+            raise TypeError(f"{cls}.__init__ got unexpected keyword argument: {unexpected_keys}")
+
+        if qmin >= qmax:
+            raise ValueError(f"qmax should be strictly larger than qmin. Got qmax={qmax}, qmin={qmin}")
+
         self.qmin = qmin
         self.qmax = qmax
         self._symmetric = symmetric

TrainingExtensions/torch/test/python/v2/quantization/affine/test_affine_quantizer.py

@@ -1506,6 +1506,22 @@ def test_parse_args_error():
     """
     with pytest.raises(TypeError):
         Quantize((1, 10), -128, 127)
+    """
+    When: Instantiate with (tuple, int, int, bool, 'foo'=any)
+    Then: Throw TypeError
+    """
+    with pytest.raises(TypeError):
+        Quantize((1, 10), -128, 127, True, foo=None)
+
+    """
+    When: Instantiate with qmin >= qmax
+    Then: Throw ValueError
+    """
+    with pytest.raises(ValueError):
+        Quantize((1, 10), 127, -128, True)
+
+    with pytest.raises(ValueError):
+        Quantize((1, 10), 127, 127, True)
 
     """
     When: Instantiate with (tuple, int, bool)