[ET-VK] Add support for aten::upsample_bilinear2d ATen op (#10363)

Title says it all!

Differential Revision: [D73261394](https://our.internmc.facebook.com/intern/diff/D73261394/)

Author: pytorchbot

backends/vulkan/op_registry.py

@@ -540,6 +540,7 @@ def register_view_op(features: OpFeatures):
         exir_ops.edge.aten.ones.default,
         exir_ops.edge.aten.ones_like.default,
         exir_ops.edge.aten.upsample_nearest2d.vec,
+        exir_ops.edge.aten.upsample_bilinear2d.vec,
         exir_ops.edge.aten.zeros.default,
         exir_ops.edge.aten.zeros_like.default,
         exir_ops.edge.et_vk.grid_priors.default,

backends/vulkan/runtime/graph/ops/glsl/upsample_2d.glsl

@@ -0,0 +1,71 @@
+/*
+ * 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.
+ */
+
+#version 450 core
+
+#define PRECISION ${PRECISION}
+
+#define VEC4_T ${texel_load_type(DTYPE, STORAGE)}
+
+layout(std430) buffer;
+
+${layout_declare_tensor(B, "w", "t_out", DTYPE, STORAGE)}
+${layout_declare_tensor(B, "r", "t_in", DTYPE, STORAGE)}
+${layout_declare_ubo(B, "ivec3", "out_limits")}
+${layout_declare_ubo(B, "ivec3", "in_limits")}
+${layout_declare_ubo(B, "vec2", "recip_scales")}
+
+layout(local_size_x_id = 0, local_size_y_id = 1, local_size_z_id = 2) in;
+
+layout(constant_id = 3) const int align_corners = 0;
+
+void main() {
+  const ivec3 pos = ivec3(gl_GlobalInvocationID);
+
+  if (any(greaterThanEqual(pos, out_limits))) {
+    return;
+  }
+
+  ivec2 max_in_xy = in_limits.xy - 1;
+  vec2 scaled_xy;
+
+  if (align_corners == 1) {
+    scaled_xy = pos.xy * recip_scales;
+  } else {
+    scaled_xy = (pos.xy + 0.5) * recip_scales - 0.5;
+  }
+
+  $if MODE == "nearest":
+    const ivec2 ipos = clamp(ivec2(round(scaled_xy)), ivec2(0), max_in_xy);
+    VEC4_T out_tex = texelFetch(t_in, ivec3(ipos, pos.z), 0);
+  $elif MODE == "bilinear":
+    vec2 upper_xy = ceil(scaled_xy);
+    vec2 lower_xy = floor(scaled_xy);
+
+    // Clamp coordinates to valid input range
+    upper_xy = clamp(upper_xy, ivec2(0), max_in_xy);
+    lower_xy = clamp(lower_xy, ivec2(0), max_in_xy);
+
+    // Calculate interpolation weights
+    vec2 interp_weights = (scaled_xy - lower_xy);
+
+    // Sample the four nearest texels
+    VEC4_T sample00 = texelFetch(t_in, ivec3(lower_xy.x, lower_xy.y, pos.z), 0);
+    VEC4_T sample10 = texelFetch(t_in, ivec3(upper_xy.x, lower_xy.y, pos.z), 0);
+    VEC4_T sample01 = texelFetch(t_in, ivec3(lower_xy.x, upper_xy.y, pos.z), 0);
+    VEC4_T sample11 = texelFetch(t_in, ivec3(upper_xy.x, upper_xy.y, pos.z), 0);
+
+    // Perform bilinear interpolation
+    VEC4_T out_tex = mix(
+      mix(sample00, sample10, interp_weights.x),
+      mix(sample01, sample11, interp_weights.x),
+      interp_weights.y
+    );
+
+  imageStore(t_out, pos, out_tex);
+}

backends/vulkan/runtime/graph/ops/glsl/upsample_nearest2d.yaml renamed to backends/vulkan/runtime/graph/ops/glsl/upsample_2d.yaml

@@ -4,15 +4,16 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-upsample_nearest2d:
+upsample_2d:
   parameter_names_with_default_values:
-    NDIM: 3
     DTYPE: float
-    PACKING: C_packed
     STORAGE: texture3d
+    MODE: nearest
   generate_variant_forall:
     DTYPE:
       - VALUE: half
       - VALUE: float
   shader_variants:
     - NAME: upsample_nearest2d
+    - NAME: upsample_bilinear2d
+      MODE: bilinear

backends/vulkan/runtime/graph/ops/glsl/upsample_nearest2d.glsl

@@ -16,6 +16,8 @@
 
 namespace vkcompute {
 
+enum class UpsampleMode : int { NEAREST, BILINEAR };
+
 void resize_upsample_nearest2d_node(
     ComputeGraph* graph,
     const std::vector<ArgGroup>& args,
@@ -39,19 +41,12 @@ void resize_upsample_nearest2d_node(
   out->virtual_resize(out_sizes);
 }
 
-// ExecuTorch-Vulkan framework to add node
-// Args:
-//   in: will be converted from NCHW input tensor to 3D ARGB representation in
-//   openGL (via ExecuTorch) output_sizes: optional 2D array of targetting
-//   output size of H and W dimensions. >= input sizes;
-
-//      will be computed if only given the scale_factors.
-//   scale_factors: optional 2D array of scale factors for H and W dimensions.
-//      Will be computed if only given the output_sizes.
 void add_upsample_nearest2d_node(
     ComputeGraph& graph,
+    const UpsampleMode mode,
     const ValueRef in,
     const ValueRef output_sizes,
+    const ValueRef align_corners,
     const ValueRef scale_factors,
     const ValueRef out) {
   if (graph.val_is_none(output_sizes) && graph.val_is_none(scale_factors)) {
@@ -63,36 +58,61 @@ void add_upsample_nearest2d_node(
         "Invalid input, must provide ONLY one of output_sizes or scale_factors");
   }
 
-  vTensorPtr t_in = graph.get_tensor(in);
-  utils::uvec3 input_sizes = t_in->logical_limits();
+  int align_corners_val = 0;
+  if (is_valid(align_corners) && graph.get_bool(align_corners)) {
+    align_corners_val = 1;
+  }
+
+  utils::uvec3 in_limits = graph.logical_limits_of(in);
+  utils::uvec3 out_limits = graph.logical_limits_of(out);
+
+  uint32_t out_width = out_limits[0u];
+  uint32_t out_height = out_limits[1u];
 
-  utils::ivec2 input_size = {
-      utils::safe_downcast<int32_t>(input_sizes[0]),
-      utils::safe_downcast<int32_t>(input_sizes[1])};
-  utils::vec2 rev_scales = {
-      utils::safe_downcast<float>(1.0), utils::safe_downcast<float>(1.0)};
+  float scale_factor_x = float(in_limits[0u]) / float(out_width);
+  float scale_factor_y = float(in_limits[1u]) / float(out_height);
+
+  float recip_scale_factor_x = 1.0f / scale_factor_x;
+  float recip_scale_factor_y = 1.0f / scale_factor_y;
 
-  // Reverse scale factors that pre-computed before GLSL.
   if (!graph.val_is_none(output_sizes)) {
-    auto output_size_ref = graph.get_int_list(output_sizes);
-    rev_scales = {
-        utils::safe_downcast<float>(
-            (float)input_size[0] / output_size_ref->at(1)),
-        utils::safe_downcast<float>(
-            (float)input_size[1] / output_size_ref->at(0))};
+    IntListPtr output_size_ref = graph.get_int_list(output_sizes);
+    out_width = output_size_ref->at(1);
+    out_height = output_size_ref->at(0);
+
+    VK_CHECK_COND(out_width == out_limits[0u]);
+    VK_CHECK_COND(out_height == out_limits[1u]);
+
+  } else {
+    DoubleListPtr scales = graph.get_double_list(scale_factors);
+    scale_factor_x = scales->at(1);
+    scale_factor_y = scales->at(0);
 
+    VK_CHECK_COND(in_limits[0u] * scale_factor_x == out_width);
+    VK_CHECK_COND(in_limits[1u] * scale_factor_y == out_height);
+  }
+
+  if (align_corners_val == 1) {
+    recip_scale_factor_x = float(in_limits[0u] - 1) / float(out_width - 1);
+    recip_scale_factor_y = float(in_limits[1u] - 1) / float(out_height - 1);
   } else {
-    auto scales = graph.get_double_list(scale_factors);
-    rev_scales = {
-        utils::safe_downcast<float>(1.0 / scales->at(1)),
-        utils::safe_downcast<float>(1.0 / scales->at(0))};
+    recip_scale_factor_x = float(in_limits[0u]) / float(out_width);
+    recip_scale_factor_y = float(in_limits[1u]) / float(out_height);
   }
 
-  vTensorPtr t_out = graph.get_tensor(out);
+  utils::vec2 recip_scales = {recip_scale_factor_x, recip_scale_factor_y};
 
-  std::string kernel_name("upsample_nearest2d");
+  std::string kernel_name;
   kernel_name.reserve(kShaderNameReserve);
-  add_dtype_suffix(kernel_name, *t_out);
+  switch (mode) {
+    case UpsampleMode::NEAREST:
+      kernel_name = "upsample_nearest2d";
+      break;
+    case UpsampleMode::BILINEAR:
+      kernel_name = "upsample_bilinear2d";
+      break;
+  }
+  add_dtype_suffix(kernel_name, graph.dtype_of(out));
 
   graph.execute_nodes().emplace_back(new DispatchNode(
       graph,
@@ -103,21 +123,44 @@ void add_upsample_nearest2d_node(
       {{out, vkapi::MemoryAccessType::WRITE},
        {in, vkapi::MemoryAccessType::READ}},
       // Shader params buffers
-      {t_out->logical_limits_ubo(),
-       graph.create_params_buffer(input_size),
-       graph.create_params_buffer(rev_scales)},
+      {graph.logical_limits_ubo(out),
+       graph.logical_limits_ubo(in),
+       graph.create_params_buffer(recip_scales)},
       // Specialization Constants
-      {},
+      {align_corners_val},
       resize_upsample_nearest2d_node,
       {output_sizes, scale_factors}));
 }
 
-void upsample(ComputeGraph& graph, const std::vector<ValueRef>& args) {
-  return add_upsample_nearest2d_node(graph, args[0], args[1], args[2], args[3]);
+void upsample_nearest2d(
+    ComputeGraph& graph,
+    const std::vector<ValueRef>& args) {
+  return add_upsample_nearest2d_node(
+      graph,
+      UpsampleMode::NEAREST,
+      args[0],
+      args[1],
+      kDummyValueRef,
+      args[2],
+      args[3]);
+}
+
+void upsample_bilinear2d(
+    ComputeGraph& graph,
+    const std::vector<ValueRef>& args) {
+  return add_upsample_nearest2d_node(
+      graph,
+      UpsampleMode::BILINEAR,
+      args[0],
+      args[1],
+      args[2],
+      args[3],
+      args[4]);
 }
 
 REGISTER_OPERATORS {
-  VK_REGISTER_OP(aten.upsample_nearest2d.vec, upsample);
+  VK_REGISTER_OP(aten.upsample_nearest2d.vec, upsample_nearest2d);
+  VK_REGISTER_OP(aten.upsample_bilinear2d.vec, upsample_bilinear2d);
 }
 
 } // namespace vkcompute

backends/vulkan/runtime/graph/ops/impl/Upsample.cpp

@@ -430,21 +430,34 @@ def get_native_layer_norm_inputs():
     return test_suite
 
 
-@register_test_suite("aten.upsample_nearest2d.vec")
 def get_upsample_inputs():
-    test_suite = VkTestSuite(
-        [
-            # (input tensor shape, output 2D image size (H, W), output scaling factors)
-            ((2, 2, 2, 2), None, [1, 1]),
-            ((1, 1, 2, 2), None, [2, 2]),
-            ((1, 1, 2, 2), None, [2, 4]),
-            ((1, 1, 2, 2), None, [4, 2]),
-            ((1, 1, 2, 2), [2, 2], None),
-            ((1, 1, 2, 2), [2, 4], None),
-            ((1, 1, 2, 2), [3, 2], None),
-        ]
-    )
-    return test_suite
+    inputs_list = [
+        # (input tensor shape, output 2D image size (H, W), output scaling factors)
+        ((2, 2, 2, 2), None, [1, 1]),
+        ((1, 1, 2, 2), None, [2, 2]),
+        ((1, 1, 2, 2), None, [2, 4]),
+        ((1, 1, 2, 2), None, [4, 2]),
+        ((1, 1, 2, 2), [2, 2], None),
+        ((1, 1, 2, 2), [2, 4], None),
+        ((1, 1, 2, 2), [3, 2], None),
+    ]
+    return inputs_list
+
+
+@register_test_suite("aten.upsample_nearest2d.vec")
+def get_upsample_nearest2d_inputs():
+    inputs_list = get_upsample_inputs()
+    return VkTestSuite(inputs_list)
+
+
+@register_test_suite("aten.upsample_bilinear2d.vec")
+def get_upsample_bilinear2d_inputs():
+    base_inputs_list = get_upsample_inputs()
+    inputs_list = []
+    for input_case in base_inputs_list:
+        inputs_list.append((input_case[0], input_case[1], False, input_case[2]))
+        inputs_list.append((input_case[0], input_case[1], True, input_case[2]))
+    return VkTestSuite(inputs_list)
 
 
 @register_test_suite(["aten.full.default", "aten.full_like.default"])