Merge branch 'dev' of github.com:Xilinx/brevitas into feat/remove_qop…

…_export

requirements/requirements-export.txt

@@ -1,2 +1,2 @@
-onnx
+onnx==1.15
 onnxoptimizer

requirements/requirements-finn-integration.txt

@@ -1,5 +1,5 @@
 bitstring
-onnx
+onnx==1.15
 onnxoptimizer
 onnxruntime>=1.15.0
 qonnx

requirements/requirements-ort-integration.txt

@@ -1,4 +1,4 @@
-onnx
+onnx==1.15
 onnxoptimizer
 onnxruntime>=1.15.0
 qonnx

src/brevitas/nn/quant_avg_pool.py

@@ -93,14 +93,10 @@ def __init__(
             output_size: Union[int, Tuple[int, int]],
             trunc_quant: Optional[AccQuantType] = RoundTo8bit,
             return_quant_tensor: bool = True,
-            cache_kernel_size_stride: bool = True,
             **kwargs):
         AdaptiveAvgPool2d.__init__(self, output_size=output_size)
         QuantLayerMixin.__init__(self, return_quant_tensor)
         TruncMixin.__init__(self, trunc_quant=trunc_quant, **kwargs)
-        self.cache_kernel_size_stride = cache_kernel_size_stride
-        self._cached_kernel_size = None
-        self._cached_kernel_stride = None
 
     @property
     def channelwise_separable(self) -> bool:
@@ -114,15 +110,8 @@ def requires_export_handler(self):
     def padding(self):
         return 0
 
-    @property
-    def kernel_size(self):
-        return self._cached_kernel_size
-
-    @property
-    def stride(self):
-        return self._cached_kernel_stride
-
-    def compute_kernel_size_stride(self, input_shape, output_shape):
+    @staticmethod
+    def compute_kernel_size_stride(input_shape, output_shape):
         kernel_size_list = []
         stride_list = []
         for inp, out in zip(input_shape, output_shape):
@@ -141,10 +130,6 @@ def forward(self, input: Union[Tensor, QuantTensor]):
             self._set_global_is_quant_layer(False)
             return out
 
-        if self.cache_kernel_size_stride:
-            self._cached_kernel_size = k_size
-            self._cached_kernel_stride = stride
-
         if isinstance(x, QuantTensor):
             y = x.set(value=super(TruncAdaptiveAvgPool2d, self).forward(x.value))
             k_size, stride = self.compute_kernel_size_stride(x.value.shape[2:], y.value.shape[2:])

src/brevitas/quant_tensor/__init__.py

@@ -22,11 +22,11 @@
 
 class QuantTensorBase(NamedTuple):
     value: Tensor
-    scale: Optional[Tensor]
-    zero_point: Optional[Tensor]
-    bit_width: Optional[Tensor]
-    signed_t: Optional[Tensor]
-    training_t: Optional[Tensor]
+    scale: Tensor
+    zero_point: Tensor
+    bit_width: Tensor
+    signed_t: Tensor
+    training_t: Tensor
 
 
 def _unpack_quant_tensor(input_data):
@@ -61,17 +61,11 @@ def __new__(cls, value, scale, zero_point, bit_width, signed, training):
 
     @property
     def signed(self):
-        if self.signed_t is not None:
-            return self.signed_t.item()
-        else:
-            return None
+        return self.signed_t.item()
 
     @property
     def training(self):
-        if self.training_t is not None:
-            return self.training_t.item()
-        else:
-            return None
+        return self.training_t.item()
 
     def __torch_function__(self, func, types, args=(), kwargs=None):
         if kwargs is None:
@@ -129,8 +123,7 @@ def device(self):
         value_device = self.value.device
         is_same_device = True
         for t in [self.scale, self.zero_point, self.bit_width]:
-            if t is not None:
-                is_same_device &= value_device == t.device
+            is_same_device &= value_device == t.device
         if not is_same_device:
             raise RuntimeError("Value and metadata are on different devices")
         return value_device
@@ -193,13 +186,13 @@ def is_zero_zero_point(tensor):
         return (tensor.zero_point == 0.).all()
 
     def check_scaling_factors_same(self, other):
-        if self.training is not None and self.training:
+        if self.training:
             return True
         if not torch.allclose(self.scale, other.scale):
             raise RuntimeError("Scaling factors are different")
 
     def check_zero_points_same(self, other):
-        if self.training is not None and self.training:
+        if self.training:
             return True
         if not torch.allclose(self.zero_point, other.zero_point):
             raise RuntimeError("Zero points are different")
@@ -226,7 +219,7 @@ def transpose(self, *args, **kwargs):
         tensor_meta = {
             'scale': self.scale, 'zero_point': self.zero_point, 'bit_width': self.bit_width}
         for k, tm in tensor_meta.items():
-            if tm is not None and len(value.shape) == len(tm.shape):
+            if len(value.shape) == len(tm.shape):
                 tensor_meta[k] = tm.transpose(*args, **kwargs)
         return self.set(value=value, **tensor_meta)
 
@@ -235,7 +228,7 @@ def permute(self, *args, **kwargs):
         tensor_meta = {
             'scale': self.scale, 'zero_point': self.zero_point, 'bit_width': self.bit_width}
         for k, tm in tensor_meta.items():
-            if tm is not None and len(value.shape) == len(tm.shape):
+            if len(value.shape) == len(tm.shape):
                 tensor_meta[k] = tm.permute(*args, **kwargs)
         return self.set(value=value, **tensor_meta)
 
@@ -359,8 +352,6 @@ def __add__(self, other):
                 bit_width=output_bit_width,
                 signed=output_signed,
                 training=output_training)
-        elif isinstance(other, QuantTensor):
-            output = self.value + other.value
         else:
             output = self.value + other
         return output
@@ -389,8 +380,6 @@ def __mul__(self, other):
                 bit_width=output_bit_width,
                 signed=output_signed,
                 training=output_training)
-        elif isinstance(other, QuantTensor):
-            output = self.value * other.value
         else:
             output = self.value * other
         return output
@@ -420,8 +409,6 @@ def __truediv__(self, other):
                 bit_width=output_bit_width,
                 signed=output_signed,
                 training=output_training)
-        elif isinstance(other, QuantTensor):
-            output = self.value / other.value
         else:
             output = self.value / other
         return output