Arm backend: Add extra_repr methods to MXFP modules

backends/arm/ao_ext/ops/mxfp_conv2d_op.py

@@ -10,8 +10,11 @@
 
 """
 
+from typing import cast
+
 import torch
 import torch.nn.functional as F
+
 from executorch.backends.arm.ao_ext.mxfp import (
     _cast_to_block_scaled_cpu_ref,
     mxfp_dtype_to_str,
@@ -257,6 +260,24 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
             output = output.to(self.output_dtype)
         return output
 
+    def extra_repr(self) -> str:
+        weight_qdata = cast(torch.Tensor, self.weight_qdata)
+        weight_shape = weight_qdata.shape
+        in_channels = _get_num_input_channels(weight_qdata, self.weight_dtype)
+        repr_parts = [
+            f"in_channels={in_channels}",
+            f"out_channels={weight_shape[0]}",
+            f"kernel_size={(weight_shape[1], weight_shape[2])}",
+            f"stride={self.stride}",
+            f"padding={self.padding}",
+            f"dilation={self.dilation}",
+            f"groups={self.groups}",
+            f"bias={self.bias is not None}",
+            f"weight_dtype={self.weight_dtype}",
+            f"block_size={self.block_size}",
+        ]
+        return ", ".join(repr_parts)
+
 
 def transform_conv2d_to_mxfp(
     module: torch.nn.Module,

backends/arm/ao_ext/ops/mxfp_linear_op.py

@@ -10,8 +10,11 @@
 
 """
 
+from typing import cast
+
 import torch
 import torch.nn.functional as F
+
 from executorch.backends.arm.ao_ext.mxfp import (
     _cast_to_block_scaled_cpu_ref,
     mxfp_dtype_to_str,
@@ -179,6 +182,19 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
             output = output.to(self.output_dtype)
         return output
 
+    def extra_repr(self) -> str:
+        weight_qdata = cast(torch.Tensor, self.weight_qdata)
+        weight_shape = weight_qdata.shape
+        in_features = _get_num_input_features(weight_qdata, self.weight_dtype)
+        repr_parts = [
+            f"in_features={in_features}",
+            f"out_features={weight_shape[1]}",
+            f"bias={self.bias is not None}",
+            f"weight_dtype={self.weight_dtype}",
+            f"block_size={self.block_size}",
+        ]
+        return ", ".join(repr_parts)
+
 
 def transform_linear_to_mxfp(
     module: torch.nn.Module,