Clean up ET_Check in depthwise conv, replace with assert

Differential Revision: D80952627

Pull Request resolved: pytorch#13655

Author: ethansfng

backends/cadence/hifi/operators/op_quantized_conv_nchw_asym8sxsym8s_asym8s_per_tensor_out.cpp

@@ -89,67 +89,101 @@ void xa_opt_quantized_conv_nchw_asym8sxsym8s_asym8s(
 
   WORD32 scratch_size = 0;
 
-  if (groups == 1) {
-    WORD32 out_data_format = 1;
-
-    WORD8* ptr1 = (WORD8*)kernels::allocate_temp_memory(
-        ctx,
-        ((batches * input_channels * input_height * input_width) + 8) *
-            sizeof(WORD8));
-
-    WORD8* ptr2 = (WORD8*)kernels::allocate_temp_memory(
-        ctx,
-        ((out_channels * kernel_channels * kernel_height * kernel_width) + 8) *
-            sizeof(WORD8));
-
-    WORD8* pin = (WORD8*)ALIGN_PTR(ptr1, 8);
-    WORD8* pkernel = (WORD8*)ALIGN_PTR(ptr2, 8);
-
-    WORD32 p_inp_shape[kNnlibMaxDim];
-    p_inp_shape[0] = input.size(0);
-    p_inp_shape[1] = input_channels;
-    p_inp_shape[2] = input_height;
-    p_inp_shape[3] = input_width;
-
-    WORD32 p_out_shape[kNnlibMaxDim];
-    p_out_shape[0] = input.size(0);
-    p_out_shape[1] = input_height;
-    p_out_shape[2] = input_width;
-    p_out_shape[3] = input_channels;
-
-    WORD32 p_permute_vec[kNnlibMaxDim] = {0, 2, 3, 1};
-
-    xa_nn_transpose_8_8(
-        pin,
-        p_out_shape,
-        p_inp,
-        p_inp_shape,
-        p_permute_vec,
-        kNnlibMaxDim,
-        kNnlibMaxDim);
-
-    WORD32 p_inp_shape1[kNnlibMaxDim];
-    p_inp_shape1[0] = out_channels;
-    p_inp_shape1[1] = kernel_channels;
-    p_inp_shape1[2] = kernel_height;
-    p_inp_shape1[3] = kernel_width;
-
-    WORD32 p_out_shape1[kNnlibMaxDim];
-    p_out_shape1[0] = out_channels;
-    p_out_shape1[1] = kernel_height;
-    p_out_shape1[2] = kernel_width;
-    p_out_shape1[3] = kernel_channels;
-
-    xa_nn_transpose_8_8(
+  ET_CHECK_MSG(groups == 1, "Only groups=1 supported for regular convolution");
+  WORD32 out_data_format = 1;
+
+  WORD8* ptr1 = (WORD8*)kernels::allocate_temp_memory(
+      ctx,
+      ((batches * input_channels * input_height * input_width) + 8) *
+          sizeof(WORD8));
+
+  WORD8* ptr2 = (WORD8*)kernels::allocate_temp_memory(
+      ctx,
+      ((out_channels * kernel_channels * kernel_height * kernel_width) + 8) *
+          sizeof(WORD8));
+
+  WORD8* pin = (WORD8*)ALIGN_PTR(ptr1, 8);
+  WORD8* pkernel = (WORD8*)ALIGN_PTR(ptr2, 8);
+
+  WORD32 p_inp_shape[kNnlibMaxDim];
+  p_inp_shape[0] = input.size(0);
+  p_inp_shape[1] = input_channels;
+  p_inp_shape[2] = input_height;
+  p_inp_shape[3] = input_width;
+
+  WORD32 p_out_shape[kNnlibMaxDim];
+  p_out_shape[0] = input.size(0);
+  p_out_shape[1] = input_height;
+  p_out_shape[2] = input_width;
+  p_out_shape[3] = input_channels;
+
+  WORD32 p_permute_vec[kNnlibMaxDim] = {0, 2, 3, 1};
+
+  xa_nn_transpose_8_8(
+      pin,
+      p_out_shape,
+      p_inp,
+      p_inp_shape,
+      p_permute_vec,
+      kNnlibMaxDim,
+      kNnlibMaxDim);
+
+  WORD32 p_inp_shape1[kNnlibMaxDim];
+  p_inp_shape1[0] = out_channels;
+  p_inp_shape1[1] = kernel_channels;
+  p_inp_shape1[2] = kernel_height;
+  p_inp_shape1[3] = kernel_width;
+
+  WORD32 p_out_shape1[kNnlibMaxDim];
+  p_out_shape1[0] = out_channels;
+  p_out_shape1[1] = kernel_height;
+  p_out_shape1[2] = kernel_width;
+  p_out_shape1[3] = kernel_channels;
+
+  xa_nn_transpose_8_8(
+      pkernel,
+      p_out_shape1,
+      p_kernel,
+      p_inp_shape1,
+      p_permute_vec,
+      kNnlibMaxDim,
+      kNnlibMaxDim);
+
+  scratch_size = xa_nn_conv2d_getsize(
+      input_height,
+      input_width,
+      input_channels,
+      kernel_height,
+      kernel_width,
+      kernel_channels,
+      dilation_height,
+      dilation_width,
+      y_stride,
+      y_padding,
+      x_stride,
+      x_padding,
+      out_height,
+      out_width,
+      out_channels,
+      inp_precision,
+      kernel_precision,
+      out_data_format);
+
+  scratch_size = scratch_size < 0 ? 0 : scratch_size;
+
+  ptr_scratch = (WORD32*)kernels::allocate_temp_memory(ctx, scratch_size);
+
+  p_scratch = (pVOID)ALIGN_PTR(ptr_scratch, 8);
+
+  for (int _n = 0; _n < batches; _n++) {
+    WORD8* in_batch = pin + _n * input_channels * input_height * input_width;
+    WORD8* out_batch = p_out + _n * out_channels * out_height * out_width;
+
+    xa_nn_conv2d_per_chan_sym8sxasym8s(
+        out_batch,
+        in_batch,
         pkernel,
-        p_out_shape1,
-        p_kernel,
-        p_inp_shape1,
-        p_permute_vec,
-        kNnlibMaxDim,
-        kNnlibMaxDim);
-
-    scratch_size = xa_nn_conv2d_getsize(
+        p_bias,
         input_height,
         input_width,
         input_channels,
@@ -158,59 +192,20 @@ void xa_opt_quantized_conv_nchw_asym8sxsym8s_asym8s(
         kernel_channels,
         dilation_height,
         dilation_width,
-        y_stride,
-        y_padding,
+        out_channels,
         x_stride,
+        y_stride,
         x_padding,
+        y_padding,
         out_height,
         out_width,
-        out_channels,
-        inp_precision,
-        kernel_precision,
-        out_data_format);
-
-    scratch_size = scratch_size < 0 ? 0 : scratch_size;
-
-    ptr_scratch = (WORD32*)kernels::allocate_temp_memory(ctx, scratch_size);
-
-    p_scratch = (pVOID)ALIGN_PTR(ptr_scratch, 8);
-
-    for (int _n = 0; _n < batches; _n++) {
-      WORD8* in_batch = pin + _n * input_channels * input_height * input_width;
-      WORD8* out_batch = p_out + _n * out_channels * out_height * out_width;
-
-      xa_nn_conv2d_per_chan_sym8sxasym8s(
-          out_batch,
-          in_batch,
-          pkernel,
-          p_bias,
-          input_height,
-          input_width,
-          input_channels,
-          kernel_height,
-          kernel_width,
-          kernel_channels,
-          dilation_height,
-          dilation_width,
-          out_channels,
-          x_stride,
-          y_stride,
-          x_padding,
-          y_padding,
-          out_height,
-          out_width,
-          input_zero_bias,
-          out_multiplier32,
-          out_shift32,
-          out_zero_bias,
-          out_data_format,
-          p_scratch);
-    }
-    return;
+        input_zero_bias,
+        out_multiplier32,
+        out_shift32,
+        out_zero_bias,
+        out_data_format,
+        p_scratch);
   }
-
-  // Depthwise convolutions are now handled by specialized operators
-  ET_CHECK_MSG(groups == 1, "Only groups=1 supported for regular convolution");
 }
 
 void quantized_conv_nchw_asym8sxsym8s_asym8s_per_tensor_out(