rename quant con

Author: Zonglin Peng

backends/cadence/reference/operators/CMakeLists.txt

@@ -82,13 +82,13 @@ target_include_directories(
 add_library(
   custom_ops
   "quantized_linear_out.cpp"
+  "quantized_conv_out.cpp"
   "quantized_relu_out.cpp"
   "quantized_layer_norm.cpp"
   "quantize_per_tensor.cpp"
   "dequantize_per_tensor.cpp"
   "quantized_matmul_out.cpp"
   "im2row_out.cpp"
-  "convolution_out.cpp"
 )
 target_include_directories(
   custom_ops PUBLIC ${ROOT_DIR}/.. ${CMAKE_BINARY_DIR}

backends/cadence/reference/operators/convolution_out.cpp renamed to backends/cadence/reference/operators/quantized_conv_out.cpp

@@ -273,134 +273,6 @@ __attribute__((noinline)) void conv2d_nhwc_core_generic(
   }
 }
 
-void convolution_nchw(
-    const Tensor& input,
-    const Tensor& weight,
-    const Tensor& bias,
-    IntArrayRef stride,
-    IntArrayRef padding,
-    IntArrayRef dilation,
-    int16_t groups,
-    Tensor& output) {
-  bool conv1d = input.dim() == 3;
-  // input = [n, c, h, w]
-  const int n = input.size(0);
-  const int c = input.size(1);
-  const int h = conv1d ? 1 : input.size(2);
-  const int w = conv1d ? input.size(2) : input.size(3);
-  // weight = [oc, wc, wh, ww]
-  const int oc = weight.size(0);
-  const int wc = weight.size(1);
-  const int wh = conv1d ? 1 : weight.size(2);
-  const int ww = conv1d ? weight.size(2) : weight.size(3);
-  // output = [n, oc, oh, ow]
-  const int oh = conv1d ? 1 : output.size(2);
-  const int ow = conv1d ? output.size(2) : output.size(3);
-
-  float* __restrict__ p_out = output.mutable_data_ptr<float>();
-  const float* __restrict__ p_in = input.const_data_ptr<float>();
-  const float* __restrict__ p_weight = weight.const_data_ptr<float>();
-  const float* __restrict__ p_bias = bias.const_data_ptr<float>();
-
-  conv2d_nchw_core_generic<>(
-      p_in,
-      p_weight,
-      p_bias,
-      p_out,
-      n,
-      c,
-      h,
-      w,
-      oc,
-      wc,
-      wh,
-      ww,
-      oh,
-      ow,
-      conv1d ? 1 : stride[0],
-      conv1d ? stride[0] : stride[1],
-      conv1d ? 0 : padding[0],
-      conv1d ? padding[0] : padding[1],
-      conv1d ? 1 : dilation[0],
-      conv1d ? dilation[0] : dilation[1],
-      groups);
-}
-
-void convolution_nhwc(
-    const Tensor& input,
-    const Tensor& weight,
-    const Tensor& bias,
-    IntArrayRef stride,
-    IntArrayRef padding,
-    IntArrayRef dilation,
-    int16_t groups,
-    Tensor& output) {
-  bool conv1d = input.dim() == 3;
-  // input = [n, h, w, c]
-  const int n = input.size(0);
-  const int h = conv1d ? 1 : input.size(1);
-  const int w = conv1d ? input.size(1) : input.size(2);
-  const int c = conv1d ? input.size(2) : input.size(3);
-
-  // weight = [oc, wh, ww, wc]
-  const int oc = weight.size(0);
-  const int wh = conv1d ? 1 : weight.size(1);
-  const int ww = conv1d ? weight.size(1) : weight.size(2);
-  const int wc = conv1d ? weight.size(2) : weight.size(3);
-
-  // output = [n, oh, ow, oc]
-  const int oh = conv1d ? 1 : output.size(1);
-  const int ow = conv1d ? output.size(1) : output.size(2);
-
-  float* __restrict__ p_out = output.mutable_data_ptr<float>();
-  const float* __restrict__ p_in = input.const_data_ptr<float>();
-  const float* __restrict__ p_weight = weight.const_data_ptr<float>();
-  const float* __restrict__ p_bias = bias.const_data_ptr<float>();
-
-  conv2d_nhwc_core_generic<>(
-      p_in,
-      p_weight,
-      p_bias,
-      p_out,
-      n,
-      h,
-      w,
-      c,
-      oc,
-      wh,
-      ww,
-      wc,
-      oh,
-      ow,
-      conv1d ? 1 : stride[0],
-      conv1d ? stride[0] : stride[1],
-      conv1d ? 0 : padding[0],
-      conv1d ? padding[0] : padding[1],
-      conv1d ? 1 : dilation[0],
-      conv1d ? dilation[0] : dilation[1],
-      groups);
-}
-
-void convolution_out(
-    __ET_UNUSED KernelRuntimeContext& ctx,
-    const Tensor& input,
-    const Tensor& weight,
-    const Tensor& bias,
-    IntArrayRef stride,
-    IntArrayRef padding,
-    IntArrayRef dilation,
-    int64_t groups,
-    bool channel_last,
-    Tensor& output) {
-  if (channel_last) {
-    convolution_nhwc(
-        input, weight, bias, stride, padding, dilation, groups, output);
-  } else {
-    convolution_nchw(
-        input, weight, bias, stride, padding, dilation, groups, output);
-  }
-}
-
 // The quantized convolution kernel. in_scale and weight_scale are implicit in
 // bias_scale, since it is a product of the two. The kernel will branch to
 // quantized::conv1d or quantized::conv2d based on the dimensionality of