Add aten::_upsample_bilinear2d_aa.out (#13458)

Summary:
Trying to resolve #7031

Vibe-coded using the existing non-alias version in ET and Aten implementation in pytorch as reference, along with reference unittests in pytorch core

Test Plan:
1. Run https://gist.github.com/mergennachin/9b02aee4feb5acc83e71d8f902f5cca1
And then call `./cmake-out/executor_runner minicpmv_preprocessor.pte`

2. https://gist.github.com/mergennachin/a24e4509804de99caf906c9b79ea45fc

Reviewed By: manuelcandales

Differential Revision: D80343748

Pulled By: mergennachin

Author: mergennachin

kernels/portable/cpu/op_upsample_bilinear2d_aa.cpp

@@ -0,0 +1,305 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+#include <c10/util/irange.h>
+#include <algorithm>
+#include <cmath>
+
+#include <executorch/kernels/portable/cpu/util/upsample_util.h>
+#include <executorch/runtime/kernel/kernel_includes.h>
+
+namespace torch {
+namespace executor {
+namespace native {
+
+using executorch::aten::ArrayRef;
+using executorch::aten::SizesType;
+using std::optional;
+
+namespace {
+
+// Anti-aliasing filter matching PyTorch's implementation exactly
+template <typename T>
+inline T bilinear_aa_filter(T x) {
+  x = std::abs(x);
+  return (x < static_cast<T>(1.0)) ? (static_cast<T>(1.0) - x)
+                                   : static_cast<T>(0.0);
+}
+
+// Compute anti-aliasing weights exactly matching PyTorch's algorithm
+template <typename T>
+void compute_aa_weights_for_pixel(
+    int64_t output_idx,
+    int64_t input_size,
+    int64_t output_size,
+    bool align_corners,
+    int64_t* indices,
+    T* weights,
+    int64_t* num_contributors) {
+  const T scale = area_pixel_compute_scale<T>(
+      input_size, output_size, align_corners, optional<double>());
+
+  // PyTorch's center calculation for anti-aliasing
+  // Always uses scale * (i + 0.5) for anti-aliasing, regardless of
+  // align_corners
+  const T center = scale * (output_idx + static_cast<T>(0.5));
+
+  // PyTorch's support calculation for bilinear anti-aliasing
+  // interp_size = 2 for bilinear, so base support = 1.0
+  const T support = (scale >= static_cast<T>(1.0))
+      ? (static_cast<T>(1.0) * scale)
+      : static_cast<T>(1.0);
+
+  // PyTorch's exact range calculation
+  const int64_t xmin = std::max(
+      static_cast<int64_t>(center - support + static_cast<T>(0.5)),
+      static_cast<int64_t>(0));
+  const int64_t xmax = std::min(
+      static_cast<int64_t>(center + support + static_cast<T>(0.5)), input_size);
+
+  *num_contributors = std::min(xmax - xmin, static_cast<int64_t>(4));
+
+  // PyTorch's weight computation
+  T total_weight = static_cast<T>(0.0);
+  const T invscale = (scale >= static_cast<T>(1.0))
+      ? (static_cast<T>(1.0) / scale)
+      : static_cast<T>(1.0);
+
+  for (int64_t j = 0; j < *num_contributors; ++j) {
+    int64_t x = xmin + j;
+    // PyTorch's exact weight formula: (j + xmin - center + 0.5) * invscale
+    T arg = (static_cast<T>(j) + static_cast<T>(xmin) - center +
+             static_cast<T>(0.5)) *
+        invscale;
+    T weight = bilinear_aa_filter<T>(arg);
+    indices[j] = x;
+    weights[j] = weight;
+    total_weight += weight;
+  }
+
+  // Normalize weights to sum to 1 (PyTorch does this)
+  if (total_weight > static_cast<T>(0.0)) {
+    for (int64_t j = 0; j < *num_contributors; ++j) {
+      weights[j] /= total_weight;
+    }
+  }
+
+  // Clear unused weight slots
+  for (int64_t j = *num_contributors; j < 4; ++j) {
+    weights[j] = static_cast<T>(0.0);
+  }
+}
+
+template <typename CTYPE>
+void upsample_bilinear2d_aa_kernel_impl(
+    KernelRuntimeContext& ctx,
+    const Tensor& in,
+    bool align_corners,
+    const float scale_h,
+    const float scale_w,
+    Tensor& out) {
+  const auto in_data = in.const_data_ptr<CTYPE>();
+  auto out_data = out.mutable_data_ptr<CTYPE>();
+
+  const bool is_nchw =
+      is_contiguous_dim_order(in.dim_order().data(), in.dim_order().size());
+
+  if (is_nchw) {
+    // NCHW layout
+    for (int64_t n = 0; n < out.size(0); ++n) {
+      for (int64_t c = 0; c < out.size(1); ++c) {
+        const auto in_plane =
+            in_data + (n * in.size(1) + c) * in.size(2) * in.size(3);
+        auto out_plane =
+            out_data + (n * out.size(1) + c) * out.size(2) * out.size(3);
+
+        for (int64_t oh = 0; oh < out.size(2); ++oh) {
+          // Compute height weights for this output row
+          int64_t h_indices[4];
+          float h_weights[4];
+          int64_t h_num_contributors;
+          compute_aa_weights_for_pixel<float>(
+              oh,
+              in.size(2),
+              out.size(2),
+              align_corners,
+              h_indices,
+              h_weights,
+              &h_num_contributors);
+
+          for (int64_t ow = 0; ow < out.size(3); ++ow) {
+            // Compute width weights for this output column
+            int64_t w_indices[4];
+            float w_weights[4];
+            int64_t w_num_contributors;
+            compute_aa_weights_for_pixel<float>(
+                ow,
+                in.size(3),
+                out.size(3),
+                align_corners,
+                w_indices,
+                w_weights,
+                &w_num_contributors);
+
+            CTYPE value = 0;
+
+            // Apply anti-aliased interpolation
+            for (int64_t ih_idx = 0; ih_idx < h_num_contributors; ++ih_idx) {
+              int64_t ih = h_indices[ih_idx];
+              float h_weight = h_weights[ih_idx];
+
+              for (int64_t iw_idx = 0; iw_idx < w_num_contributors; ++iw_idx) {
+                int64_t iw = w_indices[iw_idx];
+                float w_weight = w_weights[iw_idx];
+
+                value += in_plane[ih * in.size(3) + iw] * h_weight * w_weight;
+              }
+            }
+
+            out_plane[oh * out.size(3) + ow] = value;
+          }
+        }
+      }
+    }
+  } else {
+    // NHWC layout
+    for (int64_t n = 0; n < out.size(0); ++n) {
+      const auto in_batch = in_data + n * in.size(1) * in.size(2) * in.size(3);
+      auto out_batch = out_data + n * out.size(1) * out.size(2) * out.size(3);
+
+      for (int64_t oh = 0; oh < out.size(2); ++oh) {
+        // Compute height weights for this output row
+        int64_t h_indices[4];
+        float h_weights[4];
+        int64_t h_num_contributors;
+        compute_aa_weights_for_pixel<float>(
+            oh,
+            in.size(2),
+            out.size(2),
+            align_corners,
+            h_indices,
+            h_weights,
+            &h_num_contributors);
+
+        for (int64_t ow = 0; ow < out.size(3); ++ow) {
+          // Compute width weights for this output column
+          int64_t w_indices[4];
+          float w_weights[4];
+          int64_t w_num_contributors;
+          compute_aa_weights_for_pixel<float>(
+              ow,
+              in.size(3),
+              out.size(3),
+              align_corners,
+              w_indices,
+              w_weights,
+              &w_num_contributors);
+
+          for (int64_t c = 0; c < out.size(1); ++c) {
+            CTYPE value = 0;
+
+            // Apply anti-aliased interpolation
+            for (int64_t ih_idx = 0; ih_idx < h_num_contributors; ++ih_idx) {
+              int64_t ih = h_indices[ih_idx];
+              float h_weight = h_weights[ih_idx];
+
+              for (int64_t iw_idx = 0; iw_idx < w_num_contributors; ++iw_idx) {
+                int64_t iw = w_indices[iw_idx];
+                float w_weight = w_weights[iw_idx];
+
+                value += in_batch[(ih * in.size(3) + iw) * in.size(1) + c] *
+                    h_weight * w_weight;
+              }
+            }
+
+            out_batch[(oh * out.size(3) + ow) * out.size(1) + c] = value;
+          }
+        }
+      }
+    }
+  }
+}
+
+} // namespace
+
+// Check function for anti-aliased bilinear upsampling
+bool check_upsample_bilinear2d_aa_args(
+    const Tensor& in,
+    const executorch::aten::OptionalArrayRef<int64_t>& output_size,
+    const bool align_corners,
+    const executorch::aten::OptionalArrayRef<double>& scale_factors,
+    Tensor& out) {
+  // Use the same checks as regular bilinear upsampling
+  return check_upsample_bilinear2d_args(
+      in, output_size, align_corners, scale_factors, out);
+}
+
+// Main entry point for anti-aliased bilinear upsampling
+Tensor& _upsample_bilinear2d_aa_out(
+    KernelRuntimeContext& ctx,
+    const Tensor& in,
+    const executorch::aten::ArrayRef<int64_t> output_size,
+    bool align_corners,
+    const std::optional<double> scale_h,
+    const std::optional<double> scale_w,
+    Tensor& out) {
+  // Preconditions (checked in check_..._args):
+  //  In and out tensors have same dtype.
+  //  In and out tensors are rank 4 and have same dim[0] and dim[1].
+  //  In and out tensors are NHWC or NCHW dim order.
+
+  // Custom validation for our specific interface (ArrayRef + optional
+  // individual scales)
+  ET_KERNEL_CHECK(ctx, in.dim() == 4, InvalidArgument, out);
+  ET_KERNEL_CHECK(ctx, out.dim() == 4, InvalidArgument, out);
+  ET_KERNEL_CHECK(
+      ctx, in.scalar_type() == out.scalar_type(), InvalidArgument, out);
+  ET_KERNEL_CHECK(ctx, output_size.size() == 2, InvalidArgument, out);
+  ET_KERNEL_CHECK(
+      ctx, output_size[0] > 0 && output_size[1] > 0, InvalidArgument, out);
+
+  // Ensure output tensor has correct dimensions
+  ET_KERNEL_CHECK(
+      ctx, out.size(0) == in.size(0), InvalidArgument, out); // batch
+  ET_KERNEL_CHECK(
+      ctx, out.size(1) == in.size(1), InvalidArgument, out); // channels
+  ET_KERNEL_CHECK(
+      ctx, out.size(2) == output_size[0], InvalidArgument, out); // height
+  ET_KERNEL_CHECK(
+      ctx, out.size(3) == output_size[1], InvalidArgument, out); // width
+
+  // Compute final scales - use provided scales if available, otherwise compute
+  // from sizes
+  double final_scale_h, final_scale_w;
+  if (scale_h.has_value() && scale_w.has_value()) {
+    final_scale_h = scale_h.value();
+    final_scale_w = scale_w.value();
+  } else {
+    // Compute scales from input/output sizes
+    final_scale_h =
+        static_cast<double>(output_size[0]) / static_cast<double>(in.size(2));
+    final_scale_w =
+        static_cast<double>(output_size[1]) / static_cast<double>(in.size(3));
+  }
+
+  const auto kernel_scale_h = area_pixel_compute_scale<double>(
+      in.sizes()[2], out.sizes()[2], align_corners, final_scale_h);
+  const auto kernel_scale_w = area_pixel_compute_scale<double>(
+      in.sizes()[3], out.sizes()[3], align_corners, final_scale_w);
+
+  ET_SWITCH_REALHBF16_TYPES(
+      in.scalar_type(), ctx, "_upsample_bilinear2d_aa.out", CTYPE, [&]() {
+        upsample_bilinear2d_aa_kernel_impl<CTYPE>(
+            ctx, in, align_corners, kernel_scale_h, kernel_scale_w, out);
+      });
+
+  return out;
+}
+
+} // namespace native
+} // namespace executor
+} // namespace torch

kernels/portable/functions.yaml

@@ -965,6 +965,11 @@
     - arg_meta: null
       kernel_name: torch::executor::upsample_bilinear2d_vec_out
 
+- op: _upsample_bilinear2d_aa.out
+  kernels:
+    - arg_meta: null
+      kernel_name: torch::executor::_upsample_bilinear2d_aa_out
+
 - op: upsample_nearest2d.vec_out
   kernels:
     - arg_meta: null

kernels/portable/test/TARGETS

@@ -20,6 +20,7 @@ runtime.cxx_library(
     deps = [
         "//executorch/extension/aten_util:aten_bridge",
         "//executorch/kernels/portable/cpu:op_upsample_bilinear2d",
+        "//executorch/kernels/portable/cpu:op_upsample_bilinear2d_aa",
         "//executorch/kernels/portable/cpu:op_upsample_nearest2d",
         "//executorch/runtime/core/exec_aten:lib",
     ],