Backend-agnostic quantized_conv_nhwc (channels last)

backends/cadence/aot/TARGETS

@@ -129,7 +129,6 @@ python_library(
     ],
     typing = True,
     deps = [
-        "fbcode//executorch/backends/cadence/aot:utils",
         "fbcode//caffe2:torch",
         "fbcode//executorch/exir:scalar_type",
     ],

backends/cadence/aot/ref_implementations.py

@@ -23,8 +23,6 @@
     ScalarType.QINT32: torch.qint32,
 }
 
-_Number = bool | int | float
-
 
 @impl(m, "quantize_per_tensor")
 def quantize_per_tensor(
@@ -298,8 +296,7 @@ def quantized_layer_norm_per_tensor(
     )
 
 
-@impl(m, "quantized_conv_nchw")
-def quantized_conv_nchw(
+def quantized_conv(
     input_tensor: torch.Tensor,
     weight: torch.Tensor,
     bias: torch.Tensor,
@@ -374,6 +371,120 @@ def quantized_conv_nchw(
     )
 
 
+@impl(m, "quantized_conv_nchw")
+def quantized_conv_nchw(
+    input_tensor: torch.Tensor,
+    weight: torch.Tensor,
+    bias: torch.Tensor,
+    stride: tuple[int, int],
+    padding: tuple[int, int],
+    dilation: tuple[int, int],
+    groups: int,
+    in_zero_point: int,
+    weight_zero_point: torch.Tensor,
+    bias_scale: torch.Tensor,
+    output_scale: float,
+    output_zero_point: int,
+    out_multiplier: torch.Tensor,
+    out_shift: torch.Tensor,
+) -> torch.Tensor:
+    """
+    Quantized convolution operation.
+
+    Args:
+        - input_tensor (Tensor): The activations tensor
+        - weight (Tensor): The weight tensor
+        - bias (Tensor): The bias tensor
+        - stride (Tuple[int]): The stride of the convolution
+        - padding (Tuple[int]): The padding of the convolution
+        - dilation (Tuple[int]): The dilation of the convolution
+        - groups (int): The number of groups
+        - in_zero_point (int): The quantized mapping of zero for the input
+        - weight_zero_point (Tensor): The quantized mapping of zero for the weight
+        - bias_scale (Tensor): The quantized bias scale
+        - output_scale (float): The scale of the output
+        - output_zero_point (int): The zero point of the output
+        - out_multiplier (Tensor): Unused
+        - out_shift (Tensor): Unused
+    """
+    if not input_tensor.is_contiguous(memory_format=torch.contiguous_format):
+        raise ValueError("Input tensor must be in NCHW format")
+    return quantized_conv(
+        input_tensor,
+        weight,
+        bias,
+        stride,
+        padding,
+        dilation,
+        groups,
+        in_zero_point,
+        weight_zero_point,
+        bias_scale,
+        output_scale,
+        output_zero_point,
+        out_multiplier,
+        out_shift,
+    )
+
+
+@impl(m, "quantized_conv_nhwc")
+def quantized_conv_nhwc(
+    input_tensor: torch.Tensor,
+    weight: torch.Tensor,
+    bias: torch.Tensor,
+    stride: tuple[int, int],
+    padding: tuple[int, int],
+    dilation: tuple[int, int],
+    groups: int,
+    in_zero_point: int,
+    weight_zero_point: torch.Tensor,
+    bias_scale: torch.Tensor,
+    output_scale: float,
+    output_zero_point: int,
+    out_multiplier: torch.Tensor,
+    out_shift: torch.Tensor,
+) -> torch.Tensor:
+    """
+    Quantized convolution operation.
+
+    Args:
+        - input_tensor (Tensor): The activations tensor
+        - weight (Tensor): The weight tensor
+        - bias (Tensor): The bias tensor
+        - stride (Tuple[int]): The stride of the convolution
+        - padding (Tuple[int]): The padding of the convolution
+        - dilation (Tuple[int]): The dilation of the convolution
+        - groups (int): The number of groups
+        - in_zero_point (int): The quantized mapping of zero for the input
+        - weight_zero_point (Tensor): The quantized mapping of zero for the weight
+        - bias_scale (Tensor): The quantized bias scale
+        - output_scale (float): The scale of the output
+        - output_zero_point (int): The zero point of the output
+        - out_multiplier (Tensor): Unused
+        - out_shift (Tensor): Unused
+    """
+
+    if not input_tensor.is_contiguous(memory_format=torch.channels_last):
+        raise ValueError("Input tensor must be in NHWC format")
+
+    return quantized_conv(
+        input_tensor,
+        weight,
+        bias,
+        stride,
+        padding,
+        dilation,
+        groups,
+        in_zero_point,
+        weight_zero_point,
+        bias_scale,
+        output_scale,
+        output_zero_point,
+        out_multiplier,
+        out_shift,
+    )
+
+
 @impl(m, "requantize")
 def requantize(
     input: torch.Tensor,