diff --git a/torchvision/csrc/ops/quantized/cpu/qroi_align_kernel.cpp b/torchvision/csrc/ops/quantized/cpu/qroi_align_kernel.cpp
index cfd5ec4ee97..20d4f503501 100644
--- a/torchvision/csrc/ops/quantized/cpu/qroi_align_kernel.cpp
+++ b/torchvision/csrc/ops/quantized/cpu/qroi_align_kernel.cpp
@@ -1,5 +1,4 @@
 #include <ATen/ATen.h>
-#include <ATen/native/quantized/affine_quantizer.h>
 #include <torch/library.h>
 
 #include "../../cpu/roi_align_common.h"
@@ -9,6 +8,90 @@ namespace ops {
 
 namespace {
 
+// BEGIN copy-pasted code from pytorch core
+// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/quantized/affine_quantizer_base.cpp
+// We're vendoring the quantize_val() and dequantize_val() functions here. The
+// reason is that these functions belong in at::native, which is incompatible
+// with android xplat support.
+
+// FIXME: Remove this section once we can use at::native for android xplat
+// builds, or when quantize_val() and dequantize_val() aren't in at::native
+
+#ifdef USE_FBGEMM
+template <typename T>
+T quantize_val(double scale, int64_t zero_point, float value) {
+  // Internally, fbgemm::Quantize uses std::nearbyint.
+  // std::nearbyint results in nearest integer value according to the current
+  // rounding mode and the default rounding mode is rounds to even in half-way
+  // cases in most popular processor architectures like x86 and ARM. This is
+  // typically faster than an alternatives like std::round that rounds half-way
+  // cases away from zero, and can be consistent with SIMD implementations for
+  // example in x86 using _mm512_cvtps_epi32 or mm512_round_ps with
+  // _MM_FROUND_CUR_DIRECTION option that also follow the current rounding mode.
+  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+  int32_t qvalue;
+  // NOLINTNEXTLINE(bugprone-signed-char-misuse)
+  qvalue = fbgemm::Quantize<typename T::underlying, false /*LEGACY*/>(
+      value,
+      static_cast<int32_t>(zero_point),
+      static_cast<float>(scale),
+      /*result_precision=*/CHAR_BIT * sizeof(typename T::underlying));
+  return static_cast<T>(qvalue);
+}
+
+template <typename T>
+inline float dequantize_val(double scale, int64_t zero_point, T value) {
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  fbgemm::TensorQuantizationParams qparams;
+  qparams.scale = static_cast<float>(scale);
+  qparams.zero_point = static_cast<int32_t>(zero_point);
+  return fbgemm::Dequantize<typename T::underlying>(value.val_, qparams);
+}
+#else // USE_FBGEMM
+
+#if defined(__ANDROID__) && !defined(__NDK_MAJOR__)
+template <class T>
+inline float Round(const float x) {
+  return ::nearbyintf(x);
+}
+inline double Round(const double x) {
+  return ::nearbyint(x);
+}
+#else
+template <class T>
+inline T Round(const T x) {
+  return std::nearbyint(x);
+}
+#endif
+
+template <typename T>
+T quantize_val(double scale, int64_t zero_point, float value) {
+  // std::nearbyint results in nearest integer value according to the current
+  // rounding mode and the default rounding mode is rounds to even in half-way
+  // cases in most popular processor architectures like x86 and ARM. This is
+  // typically faster than an alternatives like std::round that rounds half-way
+  // cases away from zero, and can be consistent with SIMD implementations for
+  // example in x86 using _mm512_cvtps_epi32 or mm512_round_ps with
+  // _MM_FROUND_CUR_DIRECTION option that also follow the current rounding mode.
+  int64_t qvalue;
+  constexpr int64_t qmin = std::numeric_limits<typename T::underlying>::min();
+  constexpr int64_t qmax = std::numeric_limits<typename T::underlying>::max();
+  float inv_scale = 1.0f / static_cast<float>(scale);
+  qvalue = static_cast<int64_t>(zero_point + Round(value * inv_scale));
+  qvalue = std::max<int64_t>(qvalue, qmin);
+  qvalue = std::min<int64_t>(qvalue, qmax);
+  return static_cast<T>(qvalue);
+}
+
+template <typename T>
+float dequantize_val(double scale, int64_t zero_point, T value) {
+  // We need to convert the qint8 value to float to ensure the subtraction
+  // subexpression returns a float
+  return (static_cast<float>(value.val_) - zero_point) * scale;
+}
+#endif // USE_FBGEMM
+// END copy-pasted code from pytorch core
+
 template <typename T>
 void qroi_align_forward_kernel_impl(
     int n_rois,
@@ -46,19 +129,19 @@ void qroi_align_forward_kernel_impl(
     // Do not using rounding; this implementation detail is critical
     float offset = aligned ? 0.5 : 0.;
     float roi_start_w =
-        at::native::dequantize_val(rois_scale, rois_zp, offset_rois[1]) *
+        dequantize_val(rois_scale, rois_zp, offset_rois[1]) *
             spatial_scale -
         offset;
     float roi_start_h =
-        at::native::dequantize_val(rois_scale, rois_zp, offset_rois[2]) *
+        dequantize_val(rois_scale, rois_zp, offset_rois[2]) *
             spatial_scale -
         offset;
     float roi_end_w =
-        at::native::dequantize_val(rois_scale, rois_zp, offset_rois[3]) *
+        dequantize_val(rois_scale, rois_zp, offset_rois[3]) *
             spatial_scale -
         offset;
     float roi_end_h =
-        at::native::dequantize_val(rois_scale, rois_zp, offset_rois[4]) *
+        dequantize_val(rois_scale, rois_zp, offset_rois[4]) *
             spatial_scale -
         offset;
 
@@ -134,8 +217,7 @@ void qroi_align_forward_kernel_impl(
 
           output_val /= count; // Average pooling
 
-          output[index] =
-              at::native::quantize_val<T>(input_scale, input_zp, output_val);
+          output[index] = quantize_val<T>(input_scale, input_zp, output_val);
         } // for pw
       } // for ph
     } // for c