W&B properties removed

docs/tutorials/quant_tensor_quant_conv2d_overview.ipynb

@@ -129,8 +129,8 @@
     }
    ],
    "source": [
-    "print(f'Is weight quant enabled: {default_quant_conv.is_weight_quant_enabled}')\n",
-    "print(f'Is bias quant enabled: {default_quant_conv.is_bias_quant_enabled}')\n",
+    "print(f'Is weight quant enabled: {default_quant_conv.weight_quant.is_quant_enabled}')\n",
+    "print(f'Is bias quant enabled: {default_quant_conv.bias_quant.is_quant_enabled}')\n",
     "print(f'Is input quant enabled: {default_quant_conv.is_input_quant_enabled}')\n",
     "print(f'Is output quant enabled: {default_quant_conv.output_quant.is_quant_enabled}')"
    ]

src/brevitas/export/onnx/standard/qoperator/handler/parameter.py

@@ -41,7 +41,7 @@ def int_bias(module: QuantWBIOL):
 
     @classmethod
     def validate(cls, module: QuantWBIOL, requires_quant_bias=True):
-        assert module.is_weight_quant_enabled, 'Weight quant required'
+        assert module.weight_quant.is_quant_enabled, 'Weight quant required'
         assert module.output_quant.is_quant_enabled, 'Output quant required'
         # Handling narrow_range is across the network is difficult do to the fact that
         # it's not part of QuantTensor, and so it can't be cached
@@ -52,8 +52,8 @@ def validate(cls, module: QuantWBIOL, requires_quant_bias=True):
         cls.validate_8b_bit_width(module.input_quant.bit_width(), le_then=True)
         cls.validate_8b_bit_width(module.output_quant.bit_width(), le_then=True)
         if module.bias is not None and requires_quant_bias:
-            assert module.is_bias_quant_enabled
-            assert module.is_quant_bias_signed
+            assert module.bias_quant.is_quant_enabled
+            assert module.bias_quant.is_signed
             cls.validate_32b_bit_width(module.bias_quant.bit_width(), le_then=True)
 
     def prepare_for_export(self, module: Union[QuantConv1d, QuantConv2d]):

src/brevitas/graph/gpxq.py

@@ -212,7 +212,7 @@ def __init__(
     @property
     def layer_requires_input_quant(self):
         # some weight quantizers require a quant input (e.g., A2Q)
-        check_1 = self.layer.weight_quant_requires_quant_input
+        check_1 = self.layer.weight_quant.requires_quant_input
         # if input_quant is enabled, then we will store its information
         check_2 = self.layer.input_quant.is_quant_enabled
         # GPFA2Q requires the quantized input to be stored

src/brevitas/nn/mixin/act.py

@@ -32,19 +32,6 @@ def __init__(self, input_quant: Optional[ActQuantType], **kwargs):
             input_passthrough_act=True,
             **kwargs)
 
-    # @property
-    # def is_input_quant_enabled(self):
-    #     return self.input_quant.is_quant_enabled
-
-    # @property
-    # def is_quant_input_narrow_range(self):  # TODO make abstract once narrow range can be cached
-    #     return self.input_quant.is_narrow_range
-
-    # @property
-    # @abstractmethod
-    # def is_quant_input_signed(self):
-    #     pass
-
 
 class QuantOutputMixin(QuantProxyMixin):
     __metaclass__ = ABCMeta
@@ -61,19 +48,6 @@ def __init__(self, output_quant: Optional[ActQuantType], **kwargs):
             output_passthrough_act=True,
             **kwargs)
 
-    # @property
-    # def is_output_quant_enabled(self):
-    #     return self.output_quant.is_quant_enabled
-
-    # @property
-    # def is_quant_output_narrow_range(self):  # TODO make abstract once narrow range can be cached
-    #     return self.output_quant.is_narrow_range
-
-    # @property
-    # @abstractmethod
-    # def is_quant_output_signed(self):
-    #     pass
-
 
 class QuantNonLinearActMixin(QuantProxyMixin):
     __metaclass__ = ABCMeta
@@ -98,28 +72,3 @@ def __init__(
             none_quant_injector=NoneActQuant,
             **prefixed_kwargs,
             **kwargs)
-
-    # @property
-    # def is_act_quant_enabled(self):
-    #     return self.act_quant.is_quant_enabled
-
-    # @property
-    # def is_quant_act_narrow_range(self):  # TODO make abstract once narrow range can be cached
-    #     return self.act_quant.is_narrow_range
-
-    # @property
-    # @abstractmethod
-    # def is_quant_act_signed(self):
-    #     pass
-
-    # @abstractmethod
-    # def quant_act_scale(self):
-    #     pass
-
-    # @abstractmethod
-    # def quant_act_zero_point(self):
-    #     pass
-
-    # @abstractmethod
-    # def quant_act_bit_width(self):
-    #     pass

src/brevitas/nn/mixin/base.py

@@ -116,7 +116,7 @@ def unpack_input(self, inp: Union[Tensor, QuantTensor]) -> Union[Tensor, QuantTe
                 cached_inp = _CachedIO(inp.detach(), self.cache_quant_io_metadata_only)
                 if hasattr(self, 'input_quant'):
                     self.input_quant._cached_act = cached_inp
-                if hasattr(self, 'weight_quant') and self.weight_quant_requires_quant_input:
+                if hasattr(self, 'weight_quant') and self.weight_quant.requires_quant_input:
                     self.weight_quant._cached_act = cached_inp
         if not torch._C._get_tracing_state():
             if isinstance(inp, QuantTensor):

src/brevitas/nn/mixin/parameter.py

@@ -43,22 +43,6 @@ def __init__(self, weight_quant: Optional[WeightQuantType], **kwargs):
     def output_channel_dim(self) -> int:
         pass
 
-    @property
-    def is_weight_quant_enabled(self):
-        return self.weight_quant.is_quant_enabled
-
-    @property
-    def is_quant_weight_narrow_range(self):
-        return self.weight_quant.is_narrow_range
-
-    # @property
-    # def is_quant_weight_signed(self):
-    #     return self.weight_quant.is_signed
-
-    @property
-    def weight_quant_requires_quant_input(self):
-        return self.weight_quant.requires_quant_input
-
     def quant_weight(
             self,
             quant_input: Optional[QuantTensor] = None,
@@ -86,10 +70,10 @@ def quant_weight(
                 slice(*s) if s is not None else slice(s) for s in subtensor_slice_list)
         else:
             weight_slice_tuple = slice(None)
-        if self.weight_quant_requires_quant_input:
+        if self.weight_quant.requires_quant_input:
             input_bit_width = None
             input_is_signed = None
-            if self.is_weight_quant_enabled:
+            if self.weight_quant.is_quant_enabled:
                 if quant_input is None:
                     input_bit_width = self.input_quant.bit_width()
                     input_is_signed = self.input_quant.signed()
@@ -138,24 +122,8 @@ def __init__(
             **kwargs)
         self.cache_inference_quant_bias = cache_inference_bias
 
-    @property
-    def is_bias_quant_enabled(self):
-        return self.bias_quant.is_quant_enabled
-
-    @property
-    def is_quant_bias_narrow_range(self):
-        if self.bias is None:
-            return None
-        return self.bias_quant.is_narrow_range
-
-    @property
-    def is_quant_bias_signed(self):
-        if self.bias is None or not self.is_bias_quant_enabled:
-            return None
-        return self.bias_quant.is_signed
-
     def int_bias(self, float_datatype=False):
-        if self.bias is None or not self.is_bias_quant_enabled:
+        if self.bias is None or not self.bias_quant.is_quant_enabled:
             return None
         quant_bias = self.quant_bias()
         return quant_bias.int(float_datatype=float_datatype)