Fix (runtime_act): fix negative group_dim handling

src/brevitas/export/inference/handler.py

@@ -131,7 +131,7 @@ def forward(self, x: Tensor, unused_scale: Tensor = None) -> Tuple[Tensor]:
 
         # If we skip quant tensor, we return the flattened version of the groupwise tensor
         if self.skip_create_quant_tensor:
-            start_dim = self.group_dim if self.group_dim != -1 else -2
+            start_dim = self.group_dim if self.group_dim > 0 else self.group_dim - 1
             x = x.flatten(start_dim, start_dim + 1)
         output_args = tuple([x] + list(other))
         return output_args
@@ -278,7 +278,7 @@ def forward(self, x: Tensor) -> Tuple[Tensor]:
 
         # If we skip quant tensor, we return the flattened version of the groupwise tensor
         if self.skip_create_quant_tensor:
-            start_dim = self.group_dim if self.group_dim != -1 else -2
+            start_dim = self.group_dim if self.group_dim > 0 else self.group_dim - 1
             x = x.flatten(start_dim, start_dim + 1)
         output_args = tuple([x] + list(other))
         return output_args

src/brevitas/proxy/groupwise_float_parameter_quant.py

@@ -30,7 +30,7 @@ def group_size(self):
 
     def apply_input_view(self, x):
         x = super().apply_input_view(x)
-        start_dim = self.group_dim if self.group_dim != -1 else -2
+        start_dim = start_dim = self.group_dim if self.group_dim > 0 else self.group_dim - 1
         return x.flatten(start_dim, start_dim + 1)
 
     def create_quant_tensor(self, qt_args: Tuple[Any]) -> GroupwiseFloatQuantTensor:

src/brevitas/proxy/groupwise_float_runtime_quant.py

@@ -23,7 +23,7 @@ def group_size(self):
 
     def apply_input_view(self, x):
         x = super().apply_input_view(x)
-        start_dim = self.group_dim if self.group_dim != -1 else -2
+        start_dim = start_dim = self.group_dim if self.group_dim > 0 else self.group_dim - 1
         return x.flatten(start_dim, start_dim + 1)
 
     def create_quant_tensor(

src/brevitas/proxy/groupwise_int_parameter_quant.py

@@ -30,7 +30,7 @@ def group_size(self):
 
     def apply_input_view(self, x):
         x = super().apply_input_view(x)
-        start_dim = self.group_dim if self.group_dim != -1 else -2
+        start_dim = start_dim = self.group_dim if self.group_dim > 0 else self.group_dim - 1
         return x.flatten(start_dim, start_dim + 1)
 
     def create_quant_tensor(self, qt_args: Tuple[Any]) -> GroupwiseIntQuantTensor:

src/brevitas/proxy/groupwise_int_runtime_quant.py

@@ -23,7 +23,7 @@ def group_size(self):
 
     def apply_input_view(self, x):
         x = super().apply_input_view(x)
-        start_dim = self.group_dim if self.group_dim != -1 else -2
+        start_dim = start_dim = self.group_dim if self.group_dim > 0 else self.group_dim - 1
         return x.flatten(start_dim, start_dim + 1)
 
     def create_quant_tensor(

src/brevitas/quant/solver/common.py

@@ -181,7 +181,7 @@ def stats_reduce_dim(scaling_stats_op, scaling_per_output, group_dim=None):
         elif scaling_per_output == ScalingPerOutputType.TENSOR:
             return None
         elif scaling_per_output == ScalingPerOutputType.GROUP:
-            reduce_dim = group_dim + 1 if group_dim != -1 else -1
+            reduce_dim = group_dim + 1 if group_dim > 0 else group_dim
             return reduce_dim
 
     @value

src/brevitas/quant_tensor/groupwise_float_quant_tensor.py

@@ -97,7 +97,7 @@ def expand(self):
 
     @staticmethod
     def from_expanded(value, group_size, group_dim, compress=False):
-        group_dim = group_dim if group_dim != -1 else -2
+        group_dim = group_dim if group_dim > 0 else group_dim - 1
         size = list(value.shape)
         assert size[group_dim] % group_size == 0, 'Input channel is not divisible by group size'
         if compress:

src/brevitas/quant_tensor/groupwise_int_quant_tensor.py

@@ -83,7 +83,7 @@ def expand(self):
 
     @staticmethod
     def from_expanded(value, group_size, group_dim, compress=False):
-        group_dim = group_dim if group_dim != -1 else -2
+        group_dim = group_dim if group_dim > 0 else group_dim - 1
         size = list(value.shape)
         assert size[group_dim] % group_size == 0, 'Input channel is not divisible by group size'
         if compress:

src/brevitas/utils/quant_utils.py

@@ -220,9 +220,8 @@ def float_to_int_impl_to_enum(module):
 
 
 def groupwise_dequant_expand(value_, scale_, zero_point_, group_dim, dequant_shape):
-    final_shape = dequant_shape
     curr_shape = value_.shape
-    start_dim = group_dim if group_dim != -1 else -2
+    start_dim = group_dim if group_dim > 0 else group_dim - 1
     new_value = value_.flatten(start_dim, start_dim + 1)
     if scale_.shape != ():
         new_scale = scale_.expand(curr_shape).flatten(start_dim, start_dim + 1)
@@ -237,7 +236,7 @@ def groupwise_dequant_expand(value_, scale_, zero_point_, group_dim, dequant_sha
     # First, we compute how much we padded along the group_dim shape
     # Then, we unbind the tensor along the group_dim shape, and drop the padded columns
     # Finally, we stack the remaining tensors
-    unpadding_shape = final_shape[group_dim]
+    unpadding_shape = dequant_shape[group_dim]
     residual = new_value.shape[group_dim] - unpadding_shape
 
     if residual > 0:

src/brevitas_examples/common/generative/quantize.py

@@ -388,10 +388,10 @@ def generate_quantizers(
             elif input_quant_granularity == 'per_group':
                 q_scaled_quant = sym_input_quant.let(
                     **{
-                        'group_dim': 2, 'group_size': input_group_size})
+                        'group_dim': -1, 'group_size': input_group_size})
                 k_transposed_quant = sym_input_quant.let(
                     **{
-                        'group_dim': 1, 'group_size': input_group_size})
+                        'group_dim': -2, 'group_size': input_group_size})
             v_quant = q_scaled_quant
             attn_output_weights_quant = q_scaled_quant
         else: