cherry pick resize grad pr (#18255)

### Description
Cherry picking Resize Grad PR #17772 



### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->

Co-authored-by: Baiju Meswani <bmeswani@microsoft.com>

onnxruntime/python/tools/symbolic_shape_infer.py

@@ -234,7 +234,6 @@ def __init__(self, int_max, auto_merge, guess_output_rank, verbose, prefix=""):
             "upsample_nearest1d": self._infer_aten_upsample,
             "upsample_nearest2d": self._infer_aten_upsample,
             "upsample_nearest3d": self._infer_aten_upsample,
-            "upsample_bilinear2d": self._infer_aten_upsample,
         }
         self.run_ = True
         self.suggested_merge_ = {}

orttraining/orttraining/core/graph/gradient_builder.cc

@@ -2087,5 +2087,13 @@ IMPLEMENT_GRADIENT_BUILDER(GetConvTransposeGradient) {
               SrcNodeAttributes())};
 }
 
+IMPLEMENT_GRADIENT_BUILDER(GetResizeGradient) {
+  return std::vector<NodeDef>{
+      NodeDef(OpDef{"ResizeGrad", kMSDomain, 1},
+              {GO(0), I(0), I(1), I(2)},
+              {GI(0)},
+              SrcNodeAttributes())};
+}
+
 }  // namespace training
 }  // namespace onnxruntime

orttraining/orttraining/core/graph/gradient_builder.h

@@ -89,6 +89,7 @@ DECLARE_GRADIENT_BUILDER(GetGRUGradient)
 DECLARE_GRADIENT_BUILDER(GetReciprocalGradient)
 DECLARE_GRADIENT_BUILDER(GetLeakyReluGradient)
 DECLARE_GRADIENT_BUILDER(GetConvTransposeGradient)
+DECLARE_GRADIENT_BUILDER(GetResizeGradient)
 
 DECLARE_GRADIENT_BUILDER(GetExternalGradient)
 

orttraining/orttraining/core/graph/gradient_builder_registry.cc

@@ -121,6 +121,7 @@ void GradientBuilderRegistry::RegisterGradientBuilders() {
   REGISTER_GRADIENT_BUILDER("Reciprocal", GetReciprocalGradient);
   REGISTER_GRADIENT_BUILDER("LeakyRelu", GetLeakyReluGradient);
   REGISTER_GRADIENT_BUILDER("ConvTranspose", GetConvTransposeGradient);
+  REGISTER_GRADIENT_BUILDER("Resize", GetResizeGradient);
 
   REGISTER_GRADIENT_BUILDER("ExternalGradient", GetExternalGradient);
 };

orttraining/orttraining/core/graph/training_op_defs.cc

@@ -4923,6 +4923,26 @@ Return true if all elements are true and false otherwise.
           "T",
           {"tensor(float16)", "tensor(float)", "tensor(double)"},
           "Constrain input and output types to float tensors.");
+
+  ONNX_CONTRIB_OPERATOR_SCHEMA(ResizeGrad)
+      .SetDomain(kMSDomain)
+      .SinceVersion(1)
+      .Input(0, "dY", "Gradient of output Y.", "T")
+      .Input(1, "X", "Input tensor to the Resize operator.", "T")
+      .Input(2, "roi", "The roi input to the Resize operator.", "T", OpSchema::Optional)
+      .Input(3, "scales", "The scales input to the Resize operator.", "tensor(float)", OpSchema::Optional)
+      .Output(0, "dX", "Gradient of the input X.", "T")
+      .AllowUncheckedAttributes()
+      .TypeConstraint(
+          "T",
+          {"tensor(float16)", "tensor(float)", "tensor(double)"},
+          "Constrain input and output types to float tensors.")
+      .TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext& ctx) {
+        propagateElemTypeFromInputToOutput(ctx, 1, 0);
+        if (hasInputShape(ctx, 1)) {
+          propagateShapeFromInputToOutput(ctx, 1, 0);
+        }
+      });
 }
 
 }  // namespace training

orttraining/orttraining/python/training/ortmodule/_custom_gradient_registry.py

@@ -271,8 +271,3 @@ def upsample_nearest2d_gradient():
 @register_gradient("org.pytorch.aten", "ATen", "upsample_nearest3d", "vec")
 def upsample_nearest3d_gradient():
     return _upsample_gradient("upsample_nearest3d_backward", 3)
-
-
-@register_gradient("org.pytorch.aten", "ATen", "upsample_bilinear2d", "vec")
-def upsample_bilinear2d_gradient():
-    return _upsample_gradient("upsample_bilinear2d_backward", 2)

orttraining/orttraining/python/training/ortmodule/_custom_op_symbolic_registry.py

@@ -829,16 +829,3 @@ def upsample_nearest2d(g, input, output_size, scale_factors):
 @register_symbolic("upsample_nearest3d")
 def upsample_nearest3d(g, input, output_size, scale_factors):
     return _upsample_nearest(g, input, output_size, scale_factors, "upsample_nearest3d")
-
-
-@register_symbolic("upsample_bilinear2d")
-def upsample_bilinear2d(g, input, output_size, align_corners, scale_factors):
-    return g.op(
-        "org.pytorch.aten::ATen",
-        input,
-        output_size,
-        align_corners,
-        scale_factors,
-        operator_s="upsample_bilinear2d",
-        overload_name_s="vec",
-    )

orttraining/orttraining/test/gradient/gradient_ops_test.cc

@@ -3235,6 +3235,41 @@ TEST(GradientCheckerTest, ConvTransposeGrad) {
   execution_providers.push_back(DefaultCudaExecutionProvider());
   ConvTransposeGradientCheckerTest(&execution_providers);
 }
+
+// TODO: Enable test for ROCM
+TEST(GradientCheckerTest, ResizeGrad) {
+  std::vector<std::unique_ptr<IExecutionProvider>> execution_providers;
+  execution_providers.push_back(DefaultCudaExecutionProvider());
+  const std::vector<ONNX_NAMESPACE::AttributeProto> attributes = {
+      MakeAttribute("coordinate_transformation_mode", "half_pixel"),
+      MakeAttribute("cubic_coeff_a", -0.75f),
+      MakeAttribute("exclude_outside", static_cast<int64_t>(0)),
+      MakeAttribute("extrapolation_value", 0.0f),
+      MakeAttribute("mode", "linear"),
+      MakeAttribute("nearest_mode", "floor")};
+
+  float max_error;
+  GradientChecker<float, float, float> gradient_checker;
+  OpDef op_def{"Resize", kOnnxDomain, 18};
+
+  TensorInfo x_info({1, 2, 4, 4}, true);
+  TensorInfo roi_info({4}, false, nullptr, DataTypeImpl::GetTensorType<float>());
+  TensorInfo scales_info({4}, false, nullptr, DataTypeImpl::GetTensorType<float>());
+
+  TensorInfo y_info({1, 2, 8, 8}, true);
+
+  std::vector<std::vector<float>> x_datas = {{0.2f, 0.4f, 0.6f, 0.8f, 0.2f, 0.4f, 0.6f, 0.8f,
+                                              0.2f, 0.4f, 0.6f, 0.8f, 0.2f, 0.4f, 0.6f, 0.8f,
+                                              0.2f, 0.4f, 0.6f, 0.8f, 0.2f, 0.4f, 0.6f, 0.8f,
+                                              0.2f, 0.4f, 0.6f, 0.8f, 0.2f, 0.4f, 0.6f, 0.8f},
+                                             {1.0f, 1.0f, 1.0f, 1.0f},
+                                             {1.0f, 1.0f, 2.0f, 2.0f}};
+
+  ASSERT_STATUS_OK(gradient_checker.ComputeGradientError(op_def, {x_info, roi_info, scales_info},
+                                                         {y_info}, &max_error, x_datas, attributes, true, false, &execution_providers));
+  EXPECT_IS_TINY(max_error);
+}
+
 #endif  // USE_CUDA
 
 }  // namespace test

orttraining/orttraining/test/python/orttraining_test_ortmodule_api.py

@@ -1773,13 +1773,17 @@ def run_step(model, input):
     _test_helpers.assert_values_are_close(ort_input.grad, pt_input.grad)
 
 
-def test_aten_upsample_bilinear():
+@pytest.mark.parametrize("interpolate_size_scale", ({"size": (8, 12)}, {"scale_factor": 4.7}))
+@pytest.mark.parametrize("align_corners", (True, False))
+def test_resize_grad_correctness_bilinear_2d(interpolate_size_scale, align_corners):
     class _NeuralNetUpsampleBilinear(torch.nn.Module):
         def __init__(self):
             super().__init__()
 
         def forward(self, input):
-            return torch.nn.functional.interpolate(input, size=(8, 12), mode="bilinear")
+            return torch.nn.functional.interpolate(
+                input, align_corners=align_corners, mode="bilinear", **interpolate_size_scale
+            )
 
     device = "cuda"
     pt_model = _NeuralNetUpsampleBilinear().to(device)

orttraining/orttraining/test/training_ops/cuda/resize_grad_test.cc

@@ -0,0 +1,227 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+#include "test/providers/compare_provider_test_utils.h"
+#include "test/providers/provider_test_utils.h"
+#include "test/util/include/default_providers.h"
+
+namespace onnxruntime::test {
+
+#if defined(USE_CUDA) || defined(USE_ROCM)
+
+namespace {
+
+void AddResizeGradAttributes(OpTester& test, const std::string& coordinate_transformation_mode) {
+  test.AddAttribute<std::string>("mode", "linear");
+  test.AddAttribute<std::string>("coordinate_transformation_mode", coordinate_transformation_mode);
+}
+
+}  // namespace
+
+TEST(ResizeGradTest, ResizeGradWithSizes) {
+  std::vector<std::unique_ptr<IExecutionProvider>> providers;
+#ifdef USE_CUDA
+  providers.emplace_back(DefaultCudaExecutionProvider());
+#elif USE_ROCM
+  providers.emplace_back(DefaultRocmExecutionProvider());
+#endif
+
+  OpTester test("ResizeGrad", 1, onnxruntime::kMSDomain);
+
+  AddResizeGradAttributes(test, "half_pixel");
+
+  std::vector<float> dY(128, 1.0f);
+  std::vector<int64_t> dY_shape = {1, 2, 8, 8};
+
+  std::vector<float> X(32, 1.0f);
+  std::vector<int64_t> X_shape = {1, 2, 4, 4};
+
+  std::vector<float> dX(32, 4.0f);
+  std::vector<int64_t> dX_shape = X_shape;
+
+  test.AddInput<float>("dY", dY_shape, dY);
+  test.AddInput<float>("X", X_shape, X);
+
+  test.AddOutput<float>("dX", dX_shape, dX);
+
+  test.Run(OpTester::ExpectResult::kExpectSuccess, "", {}, nullptr, &providers);
+}
+
+TEST(ResizeGradTest, ResizeGradWithSizesHalf) {
+  std::vector<std::unique_ptr<IExecutionProvider>> providers;
+#ifdef USE_CUDA
+  providers.emplace_back(DefaultCudaExecutionProvider());
+#elif USE_ROCM
+  providers.emplace_back(DefaultRocmExecutionProvider());
+#endif
+
+  OpTester test("ResizeGrad", 1, onnxruntime::kMSDomain);
+
+  AddResizeGradAttributes(test, "half_pixel");
+
+  std::vector<float> dY(128, 1.0f);
+  std::vector<MLFloat16> dY_half(dY.size());
+  ConvertFloatToMLFloat16(dY.data(), dY_half.data(), static_cast<int>(dY.size()));
+  std::vector<int64_t> dY_shape = {1, 2, 8, 8};
+
+  std::vector<float> X(32, 1.0f);
+  std::vector<MLFloat16> X_half(X.size());
+  ConvertFloatToMLFloat16(X.data(), X_half.data(), static_cast<int>(X.size()));
+  std::vector<int64_t> X_shape = {1, 2, 4, 4};
+
+  std::vector<float> dX(32, 4.0f);
+  std::vector<MLFloat16> dX_half(dX.size());
+  ConvertFloatToMLFloat16(dX.data(), dX_half.data(), static_cast<int>(dX.size()));
+  std::vector<int64_t> dX_shape = X_shape;
+
+  test.AddInput<MLFloat16>("dY", dY_shape, dY_half);
+  test.AddInput<MLFloat16>("X", X_shape, X_half);
+
+  test.AddOutput<MLFloat16>("dX", dX_shape, dX_half);
+
+  test.Run(OpTester::ExpectResult::kExpectSuccess, "", {}, nullptr, &providers);
+}
+
+TEST(ResizeGradTest, ResizeGradWithSizesAndAlignCorners) {
+  std::vector<std::unique_ptr<IExecutionProvider>> providers;
+#ifdef USE_CUDA
+  providers.emplace_back(DefaultCudaExecutionProvider());
+#elif USE_ROCM
+  providers.emplace_back(DefaultRocmExecutionProvider());
+#endif
+
+  OpTester test("ResizeGrad", 1, onnxruntime::kMSDomain);
+
+  AddResizeGradAttributes(test, "align_corners");
+
+  std::vector<float> dY(128, 1.0f);
+  std::vector<int64_t> dY_shape = {1, 2, 8, 8};
+
+  std::vector<float> X(32, 1.0f);
+  std::vector<int64_t> X_shape = {1, 2, 4, 4};
+
+  std::vector<float> dX({2.9388f, 3.9184f, 3.9184f, 2.9388f, 3.9184f, 5.2245f, 5.2245f, 3.9184f,
+                         3.9184f, 5.2245f, 5.2245f, 3.9184f, 2.9388f, 3.9184f, 3.9184f, 2.9388f,
+                         2.9388f, 3.9184f, 3.9184f, 2.9388f, 3.9184f, 5.2245f, 5.2245f, 3.9184f,
+                         3.9184f, 5.2245f, 5.2245f, 3.9184f, 2.9388f, 3.9184f, 3.9184f, 2.9388f});
+  std::vector<int64_t> dX_shape = X_shape;
+
+  test.AddInput<float>("dY", dY_shape, dY);
+  test.AddInput<float>("X", X_shape, X);
+
+  test.AddOutput<float>("dX", dX_shape, dX);
+
+  test.Run(OpTester::ExpectResult::kExpectSuccess, "", {}, nullptr, &providers);
+}
+
+TEST(ResizeGradTest, ResizeGradWithScales) {
+  std::vector<std::unique_ptr<IExecutionProvider>> providers;
+#ifdef USE_CUDA
+  providers.emplace_back(DefaultCudaExecutionProvider());
+#elif USE_ROCM
+  providers.emplace_back(DefaultRocmExecutionProvider());
+#endif
+
+  OpTester test("ResizeGrad", 1, onnxruntime::kMSDomain);
+
+  AddResizeGradAttributes(test, "half_pixel");
+
+  std::vector<float> dY(72, 1.0f);
+  std::vector<int64_t> dY_shape = {1, 2, 6, 6};
+
+  std::vector<float> X(32, 1.0f);
+  std::vector<int64_t> X_shape = {1, 2, 4, 4};
+
+  std::vector<float> dX({2.7128f, 2.9550f, 2.7612f, 1.4533f, 2.9550f, 3.2189f, 3.0078f, 1.5830f,
+                         2.7612f, 3.0078f, 2.8106f, 1.4792f, 1.4533f, 1.5830f, 1.4792f, 0.7785f,
+                         2.7128f, 2.9550f, 2.7612f, 1.4533f, 2.9550f, 3.2189f, 3.0078f, 1.5830f,
+                         2.7612f, 3.0078f, 2.8106f, 1.4792f, 1.4533f, 1.5830f, 1.4792f, 0.7785f});
+  std::vector<int64_t> dX_shape = X_shape;
+
+  test.AddInput<float>("dY", dY_shape, dY);
+  test.AddInput<float>("X", X_shape, X);
+  test.AddInput<float>("", {0}, {});
+  test.AddInput<float>("scales", {4}, {1.0f, 1.0f, 1.7f, 1.7f});
+
+  test.AddOutput<float>("dX", dX_shape, dX);
+
+  test.Run(OpTester::ExpectResult::kExpectSuccess, "", {}, nullptr, &providers);
+}
+
+TEST(ResizeGradTest, ResizeGradWithScalesHalf) {
+  std::vector<std::unique_ptr<IExecutionProvider>> providers;
+#ifdef USE_CUDA
+  providers.emplace_back(DefaultCudaExecutionProvider());
+#elif USE_ROCM
+  providers.emplace_back(DefaultRocmExecutionProvider());
+#endif
+
+  OpTester test("ResizeGrad", 1, onnxruntime::kMSDomain);
+
+  AddResizeGradAttributes(test, "half_pixel");
+
+  std::vector<float> dY(72, 1.0f);
+  std::vector<MLFloat16> dY_half(dY.size());
+  ConvertFloatToMLFloat16(dY.data(), dY_half.data(), static_cast<int>(dY.size()));
+  std::vector<int64_t> dY_shape = {1, 2, 6, 6};
+
+  std::vector<float> X(32, 1.0f);
+  std::vector<MLFloat16> X_half(X.size());
+  ConvertFloatToMLFloat16(X.data(), X_half.data(), static_cast<int>(X.size()));
+  std::vector<int64_t> X_shape = {1, 2, 4, 4};
+
+  std::vector<float> dX({2.7128f, 2.9550f, 2.7612f, 1.4533f, 2.9550f, 3.2189f, 3.0078f, 1.5830f,
+                         2.7612f, 3.0078f, 2.8106f, 1.4792f, 1.4533f, 1.5830f, 1.4792f, 0.7785f,
+                         2.7128f, 2.9550f, 2.7612f, 1.4533f, 2.9550f, 3.2189f, 3.0078f, 1.5830f,
+                         2.7612f, 3.0078f, 2.8106f, 1.4792f, 1.4533f, 1.5830f, 1.4792f, 0.7785f});
+  std::vector<MLFloat16> dX_half(dX.size());
+  ConvertFloatToMLFloat16(dX.data(), dX_half.data(), static_cast<int>(dX.size()));
+  std::vector<int64_t> dX_shape = X_shape;
+
+  test.AddInput<MLFloat16>("dY", dY_shape, dY_half);
+  test.AddInput<MLFloat16>("X", X_shape, X_half);
+  test.AddInput<float>("", {0}, {});
+  test.AddInput<float>("scales", {4}, {1.0f, 1.0f, 1.7f, 1.7f});
+
+  test.AddOutput<MLFloat16>("dX", dX_shape, dX_half);
+
+  test.Run(OpTester::ExpectResult::kExpectSuccess, "", {}, nullptr, &providers);
+}
+
+TEST(ResizeGradTest, ResizeGradWithScalesAndAlignCorners) {
+  std::vector<std::unique_ptr<IExecutionProvider>> providers;
+#ifdef USE_CUDA
+  providers.emplace_back(DefaultCudaExecutionProvider());
+#elif USE_ROCM
+  providers.emplace_back(DefaultRocmExecutionProvider());
+#endif
+
+  OpTester test("ResizeGrad", 1, onnxruntime::kMSDomain);
+
+  AddResizeGradAttributes(test, "align_corners");
+
+  std::vector<float> dY(72, 1.0f);
+  std::vector<int64_t> dY_shape = {1, 2, 6, 6};
+
+  std::vector<float> X(32, 1.0f);
+  std::vector<int64_t> X_shape = {1, 2, 4, 4};
+
+  std::vector<float> dX({1.9600f, 2.2400f, 2.2400f, 1.9600f, 2.2400f, 2.5600f, 2.5600f, 2.2400f,
+                         2.2400f, 2.5600f, 2.5600f, 2.2400f, 1.9600f, 2.2400f, 2.2400f, 1.9600f,
+                         1.9600f, 2.2400f, 2.2400f, 1.9600f, 2.2400f, 2.5600f, 2.5600f, 2.2400f,
+                         2.2400f, 2.5600f, 2.5600f, 2.2400f, 1.9600f, 2.2400f, 2.2400f, 1.9600f});
+  std::vector<int64_t> dX_shape = X_shape;
+
+  test.AddInput<float>("dY", dY_shape, dY);
+  test.AddInput<float>("X", X_shape, X);
+  test.AddInput<float>("", {0}, {});
+  test.AddInput<float>("scales", {4}, {1.0f, 1.0f, 1.7f, 1.7f});
+
+  test.AddOutput<float>("dX", dX_shape, dX);
+
+  test.Run(OpTester::ExpectResult::kExpectSuccess, "", {}, nullptr, &providers);
+}
+
+#endif  // defined(USE_CUDA) || defined(USE_ROCM)
+
+}  // namespace onnxruntime::test