ORT 1.20.0 Release: Cherry pick round 1 (#22526)

ORT 1.20.0 release preparation: Cherry pick round 1 Approved cherry pick comments --------- Co-authored-by: Edward Chen <[email protected]> Co-authored-by: Hector Li <[email protected]> Co-authored-by: Adrian Lizarraga <[email protected]> Co-authored-by: Patrice Vignola <[email protected]> Co-authored-by: Changming Sun <[email protected]> Co-authored-by: Yulong Wang <[email protected]>
microsoft · Oct 22, 2024 · 2d00351 · 2d00351
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diff --git a/cmake/onnxruntime_unittests.cmake b/cmake/onnxruntime_unittests.cmake
@@ -134,9 +134,14 @@ function(AddTest)
 
   if (IOS)
     # target_sources(${_UT_TARGET} PRIVATE ${TEST_SRC_DIR}/xctest/orttestmain.m)
+
+    set(_UT_IOS_BUNDLE_GUI_IDENTIFIER com.onnxruntime.utest.${_UT_TARGET})
+    # replace any characters that are not valid in a bundle identifier with '-'
+    string(REGEX REPLACE "[^a-zA-Z0-9\\.-]" "-" _UT_IOS_BUNDLE_GUI_IDENTIFIER ${_UT_IOS_BUNDLE_GUI_IDENTIFIER})
+
     set_target_properties(${_UT_TARGET} PROPERTIES FOLDER "ONNXRuntimeTest"
       MACOSX_BUNDLE_BUNDLE_NAME ${_UT_TARGET}
-      MACOSX_BUNDLE_GUI_IDENTIFIER com.onnxruntime.utest.${_UT_TARGET}
+      MACOSX_BUNDLE_GUI_IDENTIFIER ${_UT_IOS_BUNDLE_GUI_IDENTIFIER}
       MACOSX_BUNDLE_LONG_VERSION_STRING ${ORT_VERSION}
       MACOSX_BUNDLE_BUNDLE_VERSION ${ORT_VERSION}
       MACOSX_BUNDLE_SHORT_VERSION_STRING ${ORT_VERSION}
@@ -163,7 +168,7 @@ function(AddTest)
 
     set_target_properties(${_UT_TARGET}_xc PROPERTIES FOLDER "ONNXRuntimeXCTest"
       MACOSX_BUNDLE_BUNDLE_NAME ${_UT_TARGET}_xc
-      MACOSX_BUNDLE_GUI_IDENTIFIER com.onnxruntime.utest.${_UT_TARGET}
+      MACOSX_BUNDLE_GUI_IDENTIFIER ${_UT_IOS_BUNDLE_GUI_IDENTIFIER}
       MACOSX_BUNDLE_LONG_VERSION_STRING ${ORT_VERSION}
       MACOSX_BUNDLE_BUNDLE_VERSION ${ORT_VERSION}
       MACOSX_BUNDLE_SHORT_VERSION_STRING ${ORT_VERSION}

diff --git a/include/onnxruntime/core/session/onnxruntime_c_api.h b/include/onnxruntime/core/session/onnxruntime_c_api.h
@@ -3651,13 +3651,17 @@ struct OrtApi {
    *     - "73"
    *     - "75"
    *   "device_id": The ID of the device to use when setting 'htp_arch'. Defaults to "0" (for single device).
-       "enable_htp_fp16_precision": Used for float32 model for HTP backend.
-       Enable the float32 model to be inferenced with fp16 precision. Otherwise, it will be fp32 precision.
-         - "0": With fp32 precision.
-         - "1": Default. With fp16 precision.
-       "enable_htp_weight_sharing": Enable QNN weight sharing feature while compiling multiple graphs into one QNN context.
-         - "0": Default. Disabled.
-         - "1": Enabled.
+   *   "enable_htp_fp16_precision": Used for float32 model for HTP backend.
+   *   Enable the float32 model to be inferenced with fp16 precision. Otherwise, it will be fp32 precision.
+   *     - "0": With fp32 precision.
+   *     - "1": Default. With fp16 precision.
+   *   "enable_htp_weight_sharing": Enable QNN weight sharing feature while compiling multiple graphs into one QNN context.
+   *     - "0": Default. Disabled.
+   *     - "1": Enabled.
+   *   "offload_graph_io_quantization": Offload graph input quantization and graph output dequantization to another
+   *   execution provider (typically CPU EP).
+   *     - "0": Default. Disabled. QNN EP will handle quantization and dequantization of graph I/O.
+   *     - "1": Enabled.
    *
    * SNPE supported keys:
    *   "runtime": SNPE runtime engine, options: "CPU", "CPU_FLOAT32", "GPU", "GPU_FLOAT32_16_HYBRID", "GPU_FLOAT16",

diff --git a/onnxruntime/core/providers/coreml/builders/impl/depthtospace_op_builder.cc b/onnxruntime/core/providers/coreml/builders/impl/depthtospace_op_builder.cc
@@ -145,6 +145,20 @@ bool DepthToSpaceOpBuilder::IsOpSupportedImpl(const Node& node, const OpBuilderI
       LOGS(logger, VERBOSE) << "DepthToSpace: CRD mode requires static shape";
       return false;
     }
+
+    if (mode == "DCR" && input_params.coreml_version < 7) {
+      int32_t input_type = ONNX_NAMESPACE::TensorProto_DataType_UNDEFINED;
+      GetType(*input_defs[0], input_type, logger);
+
+      if (input_type == ONNX_NAMESPACE::TensorProto_DataType_FLOAT16) {
+        // In CoreML version 6 (e.g., on an iOS 16 simulator) with DCR mode and float16 input, the output is all zeros
+        // in this unit test: TensorOpTest/1.DepthToSpaceTest_4.
+        // However, CoreML version 7 is fine.
+        // Don't support CoreML version < 7, DCR mode, and float16 input.
+        LOGS(logger, VERBOSE) << "DepthToSpace: DCR mode with float16 input requires at least CoreML version 7.";
+        return false;
+      }
+    }
   } else {
     if (mode != "DCR") {
       LOGS(logger, VERBOSE) << "DepthToSpace: " << mode << " mode is not supported";

diff --git a/onnxruntime/core/providers/cpu/nn/layer_norm_impl.cc b/onnxruntime/core/providers/cpu/nn/layer_norm_impl.cc
@@ -24,16 +24,16 @@ void ComputeJob(
     const T* bias_data,
     const ptrdiff_t task_idx,
     const int64_t norm_size,
-    IAllocatorUniquePtr<float>& scale_float_uptr,
-    IAllocatorUniquePtr<float>& bias_float_uptr,
+    const float* scale_float_ptr,
+    const float* bias_float_ptr,
     float epsilon,
     bool simplified,
     T* Y_data,
     U* mean_data,
     U* inv_std_dev_data,
     AllocatorPtr alloc) {
-  ORT_UNUSED_PARAMETER(scale_float_uptr);  // only used in MLFloat16 overload
-  ORT_UNUSED_PARAMETER(bias_float_uptr);   // only used in MLFloat16 overload
+  ORT_UNUSED_PARAMETER(scale_float_ptr);  // only used in MLFloat16 overload
+  ORT_UNUSED_PARAMETER(bias_float_ptr);   // only used in MLFloat16 overload
   ORT_UNUSED_PARAMETER(alloc);
 
   const T* p_input = X_data + task_idx * norm_size;
@@ -82,14 +82,17 @@ void ComputeJob(
     const MLFloat16* bias_data,
     const ptrdiff_t task_idx,
     const int64_t norm_size,
-    IAllocatorUniquePtr<float>& scale_float_uptr,
-    IAllocatorUniquePtr<float>& bias_float_uptr,
+    const float* scale_float_ptr,
+    const float* bias_float_ptr,
     float epsilon,
     bool simplified,
     MLFloat16* Y_data,
     U* mean_data,
     U* inv_std_dev_data,
     AllocatorPtr alloc) {
+  ORT_UNUSED_PARAMETER(scale_data);  // only used in float/double overload
+  ORT_UNUSED_PARAMETER(bias_data);   // only used in float/double overload
+
   const MLFloat16* p_input = X_data + task_idx * norm_size;
   MLFloat16* p_output = Y_data + task_idx * norm_size;
 
@@ -117,22 +120,10 @@ void ComputeJob(
     mean_square = sqrt(mean_square / norm_size - mean * mean + epsilon);
   }
 
-  if (!scale_float_uptr) {
-    scale_float_uptr = std::move(input_float_uptr);  // overwrite input with scale values, since they have the same size
-    MlasConvertHalfToFloatBuffer(scale_data, scale_float_uptr.get(), num_elems);
-  }
-
-  if (bias_data && !bias_float_uptr) {
-    bias_float_uptr = IAllocator::MakeUniquePtr<float>(alloc, num_elems);
-    MlasConvertHalfToFloatBuffer(bias_data, bias_float_uptr.get(), num_elems);
-  }
-
-  const float* scale_float_ptr = scale_float_uptr.get();
-  const float* bias_float_ptr = bias_float_uptr.get();
   for (size_t h = 0; h < num_elems; h++) {
     if (simplified) {
       output_float_ptr[h] = output_float_ptr[h] / mean_square * scale_float_ptr[h];
-    } else if (nullptr == bias_data) {
+    } else if (nullptr == bias_float_ptr) {
       output_float_ptr[h] = (output_float_ptr[h] - mean) / mean_square * scale_float_ptr[h];
     } else {
       output_float_ptr[h] = (output_float_ptr[h] - mean) / mean_square * scale_float_ptr[h] + bias_float_ptr[h];
@@ -166,7 +157,13 @@ void ConvertMLFloat16ToFloatIfNeeded(const Tensor& tensor, AllocatorPtr alloc, I
 }  // namespace
 
 LayerNormImpl::LayerNormImpl(const OpKernelInfo& op_kernel_info, bool simplified, bool contrib_op)
-    : OpKernel(op_kernel_info), simplified_{simplified}, contrib_op_{contrib_op}, scale_fp32_(nullptr), bias_fp32_(nullptr) {
+    : OpKernel(op_kernel_info),
+      simplified_{simplified},
+      contrib_op_{contrib_op},
+      prepacked_scale_fp32_data_(nullptr),
+      prepacked_scale_fp32_size_(0),
+      prepacked_bias_fp32_data_(nullptr),
+      prepacked_bias_fp32_size_(0) {
   ORT_ENFORCE(op_kernel_info.GetAttr("axis", &axis_).IsOK());
   ORT_ENFORCE(op_kernel_info.GetAttr<float>("epsilon", &epsilon_).IsOK());
 }
@@ -175,15 +172,15 @@ template <typename T, typename U>
 Status LayerNormImpl::ComputeImpl(OpKernelContext* p_ctx, int64_t orig_axis, float epsilon, bool simplified) const {
   // Inputs
   const Tensor* X = p_ctx->Input<Tensor>(0);
-  const Tensor* scale = p_ctx->Input<Tensor>(1);
-  const Tensor* bias = p_ctx->Input<Tensor>(2);
+  const Tensor* scale = prepacked_scale_fp32_data_ ? nullptr : p_ctx->Input<Tensor>(1);
+  const Tensor* bias = prepacked_bias_fp32_data_ ? nullptr : p_ctx->Input<Tensor>(2);
   const T* X_data = X->Data<T>();
-  const T* scale_data = scale->Data<T>();
+  const T* scale_data = scale ? scale->Data<T>() : nullptr;
   const T* bias_data = (simplified || nullptr == bias) ? nullptr : bias->Data<T>();
 
   const TensorShape& x_shape = X->Shape();
-  const TensorShape& scale_shape = scale->Shape();
-  const TensorShape& bias_shape = bias->Shape();
+  size_t scale_size = scale ? static_cast<size_t>(scale->Shape().Size()) : prepacked_scale_fp32_size_;
+  size_t bias_size = bias ? static_cast<size_t>(bias->Shape().Size()) : prepacked_bias_fp32_size_;
   Tensor* Y = p_ctx->Output(0, x_shape);
   T* Y_data = Y->MutableData<T>();
 
@@ -218,7 +215,7 @@ Status LayerNormImpl::ComputeImpl(OpKernelContext* p_ctx, int64_t orig_axis, flo
 
   AllocatorPtr alloc;
   ORT_RETURN_IF_ERROR(p_ctx->GetTempSpaceAllocator(&alloc));
-  return ComputeWithoutContext<T, U>(X_data, x_shape, scale_data, scale_shape, bias_data, bias_shape, Y_data, mean_data,
+  return ComputeWithoutContext<T, U>(X_data, x_shape, scale_data, scale_size, bias_data, bias_size, Y_data, mean_data,
                                      inv_std_dev_data, thread_pool, axis, epsilon, simplified, alloc);
 }
 
@@ -237,9 +234,11 @@ Status LayerNormImpl::PrePack(const Tensor& tensor, int input_idx, AllocatorPtr
 
   is_packed = false;
   if (input_idx == 1) {  // scale
-    ConvertMLFloat16ToFloatIfNeeded(tensor, alloc, scale_fp32_, is_packed);
+    prepacked_scale_fp32_size_ = static_cast<size_t>(tensor.Shape().Size());
+    ConvertMLFloat16ToFloatIfNeeded(tensor, alloc, prepacked_scale_fp32_data_, is_packed);
   } else if (input_idx == 2) {  // bias
-    ConvertMLFloat16ToFloatIfNeeded(tensor, alloc, bias_fp32_, is_packed);
+    prepacked_bias_fp32_size_ = static_cast<size_t>(tensor.Shape().Size());
+    ConvertMLFloat16ToFloatIfNeeded(tensor, alloc, prepacked_bias_fp32_data_, is_packed);
   }
 
   return Status::OK();
@@ -250,9 +249,9 @@ Status LayerNormImpl::ComputeWithoutContext(
     const T* X_data,
     const TensorShape& x_shape,
     const T* scale_data,
-    const TensorShape& scale_shape,
+    size_t scale_size,
     const T* bias_data,
-    const TensorShape& bias_shape,
+    size_t bias_size,
     T* Y_data,
     U* mean_data,
     U* inv_std_dev_data,
@@ -264,19 +263,34 @@ Status LayerNormImpl::ComputeWithoutContext(
   int64_t norm_count = x_shape.SizeToDimension(onnxruntime::narrow<size_t>(axis));
   int64_t norm_size = x_shape.SizeFromDimension(onnxruntime::narrow<size_t>(axis));
 
-  const auto scale_size = scale_shape.Size();
-  const auto bias_size = (bias_data) ? bias_shape.Size() : 0;
-  if (scale_size != norm_size || (bias_data && bias_size != norm_size)) {
+  if (static_cast<int64_t>(scale_size) != norm_size || (bias_data && static_cast<int64_t>(bias_size) != norm_size)) {
     return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
                            "Size of X.shape()[axis:] == ", norm_size,
                            ". Size of scale and bias (if provided) must match this. Got scale size of ",
                            scale_size, " and bias size of ", bias_size);
   }
 
+  IAllocatorUniquePtr<float> scale_fp32;
+  IAllocatorUniquePtr<float> bias_fp32;
+  if constexpr (std::is_same_v<T, MLFloat16>) {
+    if (prepacked_scale_fp32_data_ == nullptr) {
+      const size_t num_elems = static_cast<size_t>(norm_size);
+      scale_fp32 = IAllocator::MakeUniquePtr<float>(alloc, num_elems);
+      MlasConvertHalfToFloatBuffer(scale_data, scale_fp32.get(), num_elems);
+    }
+    if (prepacked_bias_fp32_data_ == nullptr && bias_data) {
+      const size_t num_elems = static_cast<size_t>(norm_size);
+      bias_fp32 = IAllocator::MakeUniquePtr<float>(alloc, num_elems);
+      MlasConvertHalfToFloatBuffer(bias_data, bias_fp32.get(), num_elems);
+    }
+  }
+
   concurrency::ThreadPool::TryBatchParallelFor(
       thread_pool, static_cast<int32_t>(norm_count),
       [&](ptrdiff_t task_idx) {
-        ComputeJob(X_data, scale_data, bias_data, task_idx, norm_size, scale_fp32_, bias_fp32_,
+        ComputeJob(X_data, scale_data, bias_data, task_idx, norm_size,
+                   prepacked_scale_fp32_data_ ? prepacked_scale_fp32_data_.get() : scale_fp32.get(),
+                   prepacked_bias_fp32_data_ ? prepacked_bias_fp32_data_.get() : bias_fp32.get(),
                    epsilon, simplified, Y_data, mean_data, inv_std_dev_data, alloc);
       },
       0);

diff --git a/onnxruntime/core/providers/cpu/nn/layer_norm_impl.h b/onnxruntime/core/providers/cpu/nn/layer_norm_impl.h
@@ -24,9 +24,9 @@ class LayerNormImpl : public OpKernel {
       const T* X_data,
       const TensorShape& x_shape,
       const T* scale_data,
-      const TensorShape& scale_shape,
+      size_t scale_size,
       const T* bias_data,
-      const TensorShape& bias_shape,
+      size_t bias_size,
       T* Y_data,
       U* mean_data,
       U* inv_std_dev,
@@ -63,8 +63,10 @@ class LayerNormImpl : public OpKernel {
   float epsilon_;
   const bool simplified_;
   const bool contrib_op_;
-  mutable IAllocatorUniquePtr<float> scale_fp32_;
-  mutable IAllocatorUniquePtr<float> bias_fp32_;
+  IAllocatorUniquePtr<float> prepacked_scale_fp32_data_;
+  size_t prepacked_scale_fp32_size_;
+  IAllocatorUniquePtr<float> prepacked_bias_fp32_data_;
+  size_t prepacked_bias_fp32_size_;
 };
 
 }  // namespace onnxruntime