CoreML: Add ML Program ConvTranspose (#21416)

### Description
<!-- Describe your changes. -->
Add ML Program ConvTranspose
- some limitations to simplify the implementation for now
- some limitations due to flaky CoreML output

Added support for non-contiguous MLMultiArray output as we see that with
some unit tests when the CPU-only flag is not set (e.g. innermost dim
has min size of 16 but test output only has 8 values).
- support only one non-contiguous dim to keep it simple
- manually tested as we don't have a setup that can test objective-c
code
- test code is in model.mm and can be enabled via ifdef if we need to
validate any future changes



### 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. -->
Address operator gaps in high priority model.

---------

Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>

cmake/onnxruntime_unittests.cmake

@@ -679,7 +679,10 @@ if(onnxruntime_USE_RKNPU)
 endif()
 
 if(onnxruntime_USE_COREML)
-  list(APPEND onnxruntime_test_framework_src_patterns  ${TEST_SRC_DIR}/providers/coreml/*)
+  list(APPEND onnxruntime_test_framework_src_patterns ${TEST_SRC_DIR}/providers/coreml/*.cc)
+  if(APPLE)
+    list(APPEND onnxruntime_test_framework_src_patterns ${TEST_SRC_DIR}/providers/coreml/*.mm)
+  endif()
   list(APPEND onnxruntime_test_framework_libs onnxruntime_providers_coreml coreml_proto)
   list(APPEND onnxruntime_test_providers_dependencies onnxruntime_providers_coreml coreml_proto)
   list(APPEND onnxruntime_test_providers_libs onnxruntime_providers_coreml coreml_proto)

onnxruntime/core/providers/coreml/builders/impl/convtranspose_op_builder.cc

@@ -0,0 +1,218 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+#include "core/providers/common.h"
+#include "core/providers/coreml/builders/helper.h"
+#include "core/providers/coreml/builders/impl/base_op_builder.h"
+#include "core/providers/coreml/builders/impl/builder_utils.h"
+#include "core/providers/coreml/builders/model_builder.h"
+#include "core/providers/coreml/builders/op_builder_factory.h"
+#include "core/providers/coreml/shape_utils.h"
+#include "core/providers/shared/utils/utils.h"
+
+using namespace CoreML::Specification;
+
+namespace onnxruntime {
+namespace coreml {
+
+class ConvTransposeOpBuilder : public BaseOpBuilder {
+  Status AddToModelBuilderImpl(ModelBuilder& model_builder, const Node& node,
+                               const logging::Logger& logger) const override;
+
+  bool IsOpSupportedImpl(const Node& /* node */, const OpBuilderInputParams& /* input_params */,
+                         const logging::Logger& /* logger */) const override;
+
+  bool SupportsMLProgram() const override { return true; }
+};
+
+Status ConvTransposeOpBuilder::AddToModelBuilderImpl([[maybe_unused]] ModelBuilder& model_builder,
+                                                     [[maybe_unused]] const Node& node,
+                                                     const logging::Logger& /*logger*/) const {
+#if defined(COREML_ENABLE_MLPROGRAM)
+  using namespace CoreML::Specification::MILSpec;  // NOLINT
+  const auto input_defs = node.InputDefs();
+  const auto output_defs = node.OutputDefs();
+  const auto& input_name = input_defs[0]->Name();
+
+  NodeAttrHelper helper(node);
+
+  // https://apple.github.io/coremltools/source/coremltools.converters.mil.mil.ops.defs.html#coremltools.converters.mil.mil.ops.defs.iOS15.conv.conv_transpose
+  std::unique_ptr<Operation> op = model_builder.CreateOperation(node, "conv_transpose");
+  const auto& op_type = op->type();
+
+  AddOperationInput(*op, "x", input_name);
+  AddOperationInput(*op, "weight", input_defs[1]->Name());
+
+  if (input_defs.size() > 2) {
+    AddOperationInput(*op, "bias", input_defs[2]->Name());
+  }
+
+  // we know this input has a valid shape due to the check in IsOpSupportedImpl. ignore N and C dims.
+  const auto num_spatial_dims = input_defs[1]->Shape()->dim_size() - 2;
+
+  // Spec says strides/dilations/pads are optional but reality is they're required for at least the iOS15 target
+  // which is CoreML5. Due to that we just add everything for simplicity.
+  const auto strides = helper.Get("strides", std::vector<int64_t>(num_spatial_dims, 1));
+  const auto dilations = helper.Get("dilations", std::vector<int64_t>(num_spatial_dims, 1));
+
+  AddOperationInput(*op, "strides", model_builder.AddConstant(op_type, "strides", strides));
+  AddOperationInput(*op, "dilations", model_builder.AddConstant(op_type, "dilations", dilations));
+
+  const std::optional<int64_t> groups = helper.GetInt64("group");
+  if (groups) {
+    AddOperationInput(*op, "groups", model_builder.AddScalarConstant(op_type, "groups", *groups));
+  }
+
+  // if we can enable output_shape, this code works. see IsOpSupportedImpl for the reason it's disabled.
+  // const auto output_shape = helper.GetInt64s("output_shape");
+  // if (output_shape) {
+  //  AddOperationInput(*op, "output_shape", model_builder.AddConstant(op_type, "output_shape", *output_shape));
+  //  // these are required despite the spec saying otherwise
+  //  AddOperationInput(*op, "pad_type", model_builder.AddScalarConstant(op_type, "pad_type", std::string("valid")));
+  //  std::vector<int64_t> pads(num_spatial_dims * 2, 0);
+  //  AddOperationInput(*op, "pad", model_builder.AddConstant(op_type, "pad", pads));
+  //} else {
+  //  AddPadTypeAndPads(*op, model_builder, op_type, helper, num_spatial_dims);
+  //}
+
+  AddPadTypeAndPads(*op, model_builder, op_type, helper, num_spatial_dims);
+
+  AddOperationOutput(*op, *output_defs[0]);
+
+  model_builder.AddOperation(std::move(op));
+#endif  // defined(COREML_ENABLE_MLPROGRAM)
+
+  return Status::OK();
+}
+
+bool ConvTransposeOpBuilder::IsOpSupportedImpl(const Node& node, const OpBuilderInputParams& input_params,
+                                               const logging::Logger& logger) const {
+  if (!input_params.create_mlprogram) {
+    LOGS(logger, VERBOSE) << "ConvTranspose: ML Program required";
+    return false;
+  }
+
+  // ML Program
+  // - const weight until CoreML7 (iOS17)
+  //   - require constant for now as non-const would be unusual and we rely on the shape of W to be known to validate
+  //     the kernel_shape can be used
+  // - const bias
+  // - const pad
+  //   - if auto_pad is same_upper or same_lower the output[i] - (input[i] * strides[i]) must be divisible by 2
+  //     as the pads must be equally split as there's no upper/lower option in CoreML
+  //     - punting on supporting this for now
+  //   - must be symmetric for CoreML to do the right thing
+  // - const strides/dilations/groups
+  // - output_shape CoreML output is inconsistent so disabled for now
+  //
+  // NOTE: need to test with/without the COREML_FLAG_USE_CPU_ONLY flag being set to get an idea of how flaky the CoreML
+  // behavior is.
+  // Update /onnxruntime/test/util/default_providers.cc:DefaultCoreMLExecutionProvider to do so
+
+  const auto& input_defs = node.InputDefs();
+
+  std::vector<int64_t> input_shape;
+  if (!GetShape(*input_defs[0], input_shape, logger)) {
+    // requires the rank at least to be known
+    LOGS(logger, VERBOSE) << "ConvTranspose: failed to get input shape";
+    return false;
+  }
+
+  // for simplicity require weight to be constant
+  const auto& weight_arg = *input_defs[1];
+  const auto& weight_name = input_defs[1]->Name();
+  const auto* weight = input_params.graph_viewer.GetConstantInitializer(weight_name);
+  if (!weight) {
+    LOGS(logger, VERBOSE) << "ConvTranspose: weight must be constant";
+    return false;
+  }
+
+  if (input_defs.size() > 2 && !input_params.graph_viewer.GetConstantInitializer(input_defs[2]->Name())) {
+    LOGS(logger, VERBOSE) << "ConvTranspose: bias must be constant";
+    return false;
+  }
+
+  std::vector<int64_t> weight_shape;
+  if (!GetShape(weight_arg, weight_shape, logger)) {
+    // impossible as it's a constant initializer
+    LOGS(logger, VERBOSE) << "ConvTranspose: failed to get weight shape";
+    return false;
+  }
+
+  int64_t num_spatial_dims = narrow<int64_t>(weight_shape.size()) - 2;
+
+  NodeAttrHelper helper(node);
+
+  // Punt on SAME_UPPER/SAME_LOWER for now.
+  // We could infer that 'same' -> 'same_upper' based on the CoreML conv spec having 'same' and 'same_lower' but
+  // need to validate that assertion.
+  // Additionally, if the pads size is equal, there's no difference between same_upper and same_lower.
+  // To do that we'd need the 'output_shape' attribute to check against.
+  // Can add this handling if/when needed.
+  auto autopad = StringToAutoPadType(helper.Get("auto_pad", "NOTSET"));
+  if (autopad == AutoPadType::SAME_LOWER || autopad == AutoPadType::SAME_UPPER) {
+    LOGS(logger, VERBOSE) << "ConvTranspose: support for SAME_LOWER/SAME_UPPER is not implemented yet";
+    return false;
+  } else if (autopad == AutoPadType::NOTSET) {
+    // CoreML output is inconsistent between CPU_ONLY and ALL if the pads aren't all the same value.
+    // CPU matches the expected output, but other devices don't seem to (at least on macOS).
+    auto onnx_pads = *helper.GetInt64s("pads");  // 'pads' are required if auto_pad is NOTSET
+    const auto pad_value = onnx_pads[0];
+    if (!std::all_of(onnx_pads.begin() + 1, onnx_pads.end(),
+                     [pad_value](auto value) { return value == pad_value; })) {
+      LOGS(logger, VERBOSE) << "ConvTranspose: all pad values must be the same for CoreML to return "
+                               "consistent results";
+      return false;
+    }
+  }
+
+  // there's no input to specify a kernel shape in CoreML.
+  // it's OK if a specified kernel_shape matches kH and kW dims of the weight input.
+  auto kernel_shape = helper.GetInt64s("kernel_shape");
+  if (kernel_shape) {
+    bool valid = true;
+
+    if (static_cast<int64_t>(kernel_shape->size()) == num_spatial_dims) {
+      for (int i = 0; i < num_spatial_dims; ++i) {
+        // check the specified kernel shape matches the weight shape. skip the initial N and C dims in the latter.
+        if ((*kernel_shape)[i] != weight_shape[i + 2]) {
+          valid = false;
+          break;
+        }
+      }
+    } else {
+      valid = false;
+    }
+
+    if (!valid) {
+      LOGS(logger, VERBOSE) << "ConvTranspose: kernel_shape attribute does not match the weight shape";
+      return false;
+    }
+  }
+
+  // In theory this can be supported, but running with COREML_FLAG_USE_CPU_ONLY produces output that doesn't match
+  // ONNX. Running without that flag produces the expected output. Madness...
+  auto output_shape = helper.GetInt64s("output_shape");
+  if (output_shape) {
+    LOGS(logger, VERBOSE) << "ConvTranspose: output_shape is not supported as the CoreML output is inconsistent";
+    return false;
+  }
+
+  // output_padding, if specified, must be the default value of all zeros as there's no equivalent in CoreML.
+  auto output_padding = helper.GetInt64s("output_padding");
+  if (output_padding &&
+      std::any_of(output_padding->begin(), output_padding->end(), [](auto value) { return value != 0; })) {
+    LOGS(logger, VERBOSE) << "ConvTranspose: output_padding is not supported";
+    return false;
+  }
+
+  return true;
+}
+
+void CreateConvTransposeOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations) {
+  op_registrations.builders.push_back(std::make_unique<ConvTransposeOpBuilder>());
+  op_registrations.op_builder_map.emplace(op_type, op_registrations.builders.back().get());
+}
+
+}  // namespace coreml
+}  // namespace onnxruntime

onnxruntime/core/providers/coreml/builders/impl/resize_op_builder.cc

@@ -427,13 +427,13 @@ bool ResizeOpBuilder::IsOpSupportedImpl(const Node& node, const OpBuilderInputPa
         auto h_in = input_shape[input_rank - 2];
         auto w_in = input_shape[input_rank - 1];
 
-        if (!utils::IsScalingByAFactorOfN(h_in, scale_h)) {
+        if (!utils::ReciprocalIsAFactorOfN(h_in, scale_h)) {
           LOGS(logger, VERBOSE) << "Resize: downsampling scale " << scale_h
                                 << " is not a factor of input height: " << h_in;
           return false;
         }
 
-        if (!utils::IsScalingByAFactorOfN(w_in, scale_w)) {
+        if (!utils::ReciprocalIsAFactorOfN(w_in, scale_w)) {
           LOGS(logger, VERBOSE) << "Resize: downsampling scale " << scale_w
                                 << " is not a factor of input width: " << w_in;
           return false;