removed unused headers and lint

Author: Zonglin Peng

backends/cadence/reference/operators/im2row_out.cpp

@@ -1,6 +1,5 @@
 // (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
 
-#include <executorch/backends/cadence/reference/kernels/kernels.h>
 #include <executorch/backends/cadence/reference/operators/operators.h>
 
 #include <algorithm>

backends/cadence/reference/operators/operators.h

@@ -14,9 +14,8 @@ namespace native {
 namespace {
 using ::executorch::runtime::getLeadingDims;
 
-
 #define ET_FORALL_CADENCE_QUANTIZED_TYPES(_) \
-  _(uint8_t, Byte)                          \
+  _(uint8_t, Byte)                           \
   _(int8_t, Char)
 
 inline __attribute__((always_inline)) void linear_(
@@ -38,12 +37,7 @@ inline __attribute__((always_inline)) void linear_(
 
   // Given an N-dimensional input [d0, d1, d2, ..., d_{N-2}, d_{N-1}], the
   // leading dimensions is d0 * d1 * ... * d_{N-2}
-<<<<<<< HEAD
   int64_t leading_dims = getLeadingDims(input, input.dim() - 1);
-=======
-  int64_t leading_dims =
-      getLeadingDims(input, input.dim() - 1);
->>>>>>> 5ebcf7f03 (create quantized_linear_per_tensor_out in cpu)
 
   for (int i = 0; i < leading_dims; ++i) {
     for (int j = 0; j < M; ++j) {

backends/cadence/reference/operators/quantized_linear_out.cpp

@@ -6,7 +6,6 @@
  * LICENSE file in the root directory of this source tree.
  */
 
-#include <executorch/backends/cadence/reference/kernels/kernels.h>
 #include <executorch/backends/cadence/reference/operators/operators.h>
 #include <executorch/backends/cadence/reference/operators/quantized_ops.h>
 #include <executorch/runtime/kernel/kernel_includes.h>

backends/cadence/reference/operators/quantized_ops.h

@@ -5,8 +5,6 @@
 #include <executorch/backends/cadence/reference/kernels/kernels.h>
 #include <executorch/backends/cadence/reference/operators/operators.h>
 
-using executorch::runtime::getLeadingDims;
-
 // Generate kernels that perform elementwise arithmetic on two quantized
 // tensors. The tensors are either the same size, or the second tensor is a
 // scalar.
@@ -65,7 +63,8 @@ inline __attribute__((always_inline)) void quantized_linear_per_tensor_(
   // weight comes in shape [out_dim, in_dim]
   // output comes in empty with shape [leading_dims, out_dim]
   // Perform matrix multiply (M x N) x (N x P)' => M x P
-  const int64_t leading_dims = getLeadingDims(src, src.dim() - 1);
+  const int64_t leading_dims =
+      executorch::runtime::getLeadingDims(src, src.dim() - 1);
   const int64_t out_dim = weight.size(0); // = out_dim
   const int64_t in_dim = weight.size(1); // = in_dim
 
@@ -87,13 +86,8 @@ inline __attribute__((always_inline)) void quantized_linear_per_tensor_(
             (int32_t)weight_data[j * in_dim + k] - (int32_t)weight_zero_point;
         sum += x * w;
       }
-<<<<<<< HEAD
       out_data[i * out_dim + j] = ::impl::reference::kernels::quantize<T>(
           sum, requant_scale, out_zero_point);
-=======
-      out_data[i * out_dim + j] =
-          ::impl::reference::kernels::quantize<T>(sum, requant_scale, out_zero_point);
->>>>>>> 5ebcf7f03 (create quantized_linear_per_tensor_out in cpu)
     }
   }
 }
@@ -138,7 +132,8 @@ inline __attribute__((always_inline)) void quantized_linear_per_channel_(
   // weight comes in shape [out_dim, in_dim]
   // output comes in empty with shape [leading_dims, out_dim]
   // Perform matrix multiply (M x N) x (N x P)' => M x P
-  int64_t leading_dims = getLeadingDims(src, src.dim() - 1);
+  int64_t leading_dims =
+      executorch::runtime::getLeadingDims(src, src.dim() - 1);
   const int64_t out_dim = weight.size(0); // = out_dim
   const int64_t in_dim = weight.size(1); // = in_dim