Merge branch 'main' of https://github.com/microsoft/onnxruntime into …

…pythonop_inplace

cmake/onnxruntime.cmake

@@ -240,7 +240,7 @@ install(TARGETS onnxruntime
         PUBLIC_HEADER DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/onnxruntime
         ARCHIVE   DESTINATION ${CMAKE_INSTALL_LIBDIR}
         LIBRARY   DESTINATION ${CMAKE_INSTALL_LIBDIR}
-        RUNTIME   DESTINATION ${CMAKE_INSTALL_LIBDIR}
+        RUNTIME   DESTINATION ${CMAKE_INSTALL_BINDIR}
         FRAMEWORK DESTINATION ${CMAKE_INSTALL_BINDIR})
 
 

cmake/onnxruntime_providers.cmake

@@ -366,7 +366,7 @@ if (NOT onnxruntime_MINIMAL_BUILD AND NOT onnxruntime_EXTENDED_MINIMAL_BUILD
   install(TARGETS onnxruntime_providers_shared
           ARCHIVE  DESTINATION ${CMAKE_INSTALL_LIBDIR}
           LIBRARY  DESTINATION ${CMAKE_INSTALL_LIBDIR}
-          RUNTIME  DESTINATION ${CMAKE_INSTALL_LIBDIR}
+          RUNTIME  DESTINATION ${CMAKE_INSTALL_BINDIR}
   )
 endif()
 
@@ -819,7 +819,7 @@ if (onnxruntime_USE_TENSORRT)
           PUBLIC_HEADER DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/onnxruntime
           ARCHIVE  DESTINATION ${CMAKE_INSTALL_LIBDIR}
           LIBRARY  DESTINATION ${CMAKE_INSTALL_LIBDIR}
-          RUNTIME  DESTINATION ${CMAKE_INSTALL_LIBDIR})
+          RUNTIME  DESTINATION ${CMAKE_INSTALL_BINDIR})
 endif()
 
 if (onnxruntime_USE_VITISAI)
@@ -1434,6 +1434,14 @@ if (onnxruntime_USE_MIGRAPHX)
       message(STATUS "MIGRAPHX GPU STREAM SYNC is DISABLED")
   endif()
 
+  if (onnxruntime_ENABLE_TRAINING_OPS)
+    onnxruntime_add_include_to_target(onnxruntime_providers_migraphx onnxruntime_training)
+    target_link_libraries(onnxruntime_providers_migraphx PRIVATE onnxruntime_training)
+    if (onnxruntime_ENABLE_TRAINING_TORCH_INTEROP)
+      onnxruntime_add_include_to_target(onnxruntime_providers_migraphx Python::Module)
+    endif()
+  endif()
+
   install(TARGETS onnxruntime_providers_migraphx
           ARCHIVE  DESTINATION ${CMAKE_INSTALL_LIBDIR}
           LIBRARY  DESTINATION ${CMAKE_INSTALL_LIBDIR}

onnxruntime/core/providers/cuda/cuda_execution_provider.cc

@@ -373,7 +373,7 @@ Status CUDAExecutionProvider::OnRunStart() {
   // always set CUDA device when session::Run() in case it runs in a worker thread
   CUDA_RETURN_IF_ERROR(cudaSetDevice(GetDeviceId()));
   if (IsGraphCaptureEnabled() && GetPerThreadContext().IsGraphCaptureAllowed() && !GetPerThreadContext().IsGraphCaptured()) {
-    LOGS_DEFAULT(INFO) << "Capturing the cuda graph for this model";
+    LOGS(*GetLogger(), INFO) << "Capturing the cuda graph for this model";
     GetPerThreadContext().CaptureBegin();
   }
   return Status::OK();
@@ -2410,7 +2410,7 @@ static bool RNNNeedFallbackToCPU(const onnxruntime::Node& node,
   return false;
 }
 
-static bool ConvTransposeNeedFallbackToCPU(const onnxruntime::Node& node) {
+static bool ConvTransposeNeedFallbackToCPU(const onnxruntime::Node& node, const logging::Logger& logger) {
   const auto& node_attributes = node.GetAttributes();
   // Check attributes
   for (auto& attr : node_attributes) {
@@ -2428,7 +2428,7 @@ static bool ConvTransposeNeedFallbackToCPU(const onnxruntime::Node& node) {
       int rank = pads_size / 2;
       for (int i = 0; i < rank; i++) {
         if (pads.Get(i) != pads.Get(i + rank)) {
-          LOGS_DEFAULT(WARNING) << "Dropping the ConvTranspose node: " << node.Name()
+          LOGS(logger, WARNING) << "Dropping the ConvTranspose node: " << node.Name()
                                 << " to CPU because it requires asymmetric padding which the CUDA EP"
                                 << " currently does not support";
           return true;
@@ -2450,7 +2450,7 @@ static bool ConvTransposeNeedFallbackToCPU(const onnxruntime::Node& node) {
       // symmetric padding.
       // TODO: Remove this after we have supported asymmetric padding in the CUDA ConvTranspose kernel
       if (auto_pad_attr == "SAME_UPPER" || auto_pad_attr == "SAME_LOWER") {
-        LOGS_DEFAULT(WARNING) << "Dropping the ConvTranspose node: " << node.Name()
+        LOGS(logger, WARNING) << "Dropping the ConvTranspose node: " << node.Name()
                               << " to CPU because it uses the auto_pad attribute which may lead to asymmetric padding which"
                               << " the CUDA EP currently does not support";
         return true;
@@ -2487,20 +2487,26 @@ std::vector<std::unique_ptr<ComputeCapability>>
 CUDAExecutionProvider::GetCapability(const onnxruntime::GraphViewer& graph,
                                      const IKernelLookup& kernel_lookup) const {
   InlinedVector<NodeIndex> candidates;
+  // A subset of the above vector. A subset of the tentative_nodes might be moved to CPU.
+  InlinedVector<NodeIndex> tentative_nodes;
+  const logging::Logger& logger = *GetLogger();
   for (auto& node_index : graph.GetNodesInTopologicalOrder()) {
     const auto* p_node = graph.GetNode(node_index);
     if (p_node == nullptr)
       continue;
 
     const auto& node = *p_node;
     if (!node.GetExecutionProviderType().empty()) {
+      if (node.GetExecutionProviderType() == kCudaExecutionProvider) {
+        candidates.push_back(node.Index());
+      }
       continue;
     }
 
     const KernelCreateInfo* cuda_kernel_def = kernel_lookup.LookUpKernel(node);
     // none of the provided registries has a CUDA kernel for this node
     if (cuda_kernel_def == nullptr) {
-      LOGS_DEFAULT(INFO) << "CUDA kernel not found in registries for Op type: " << node.OpType() << " node name: " << node.Name();
+      LOGS(logger, INFO) << "CUDA kernel not found in registries for Op type: " << node.OpType() << " node name: " << node.Name();
       continue;
     }
 
@@ -2520,7 +2526,7 @@ CUDAExecutionProvider::GetCapability(const onnxruntime::GraphViewer& graph,
       not_supported = RNNNeedFallbackToCPU(node, activations_supported, node.OpType());
       force_inside = !not_supported;
     } else if ("ConvTranspose" == node.OpType()) {
-      not_supported = ConvTransposeNeedFallbackToCPU(node);
+      not_supported = ConvTransposeNeedFallbackToCPU(node, logger);
       force_inside = !not_supported;
     } else if ("Cast" == node.OpType()) {
       not_supported = CastNeedFallbackToCPU(node);
@@ -2529,17 +2535,18 @@ CUDAExecutionProvider::GetCapability(const onnxruntime::GraphViewer& graph,
 
     if (!force_inside && not_supported) {
       if (not_supported) {
-        LOGS_DEFAULT(WARNING) << "CUDA kernel not supported. Fallback to CPU execution provider for Op type: " << node.OpType() << " node name: " << node.Name();
+        LOGS(logger, WARNING) << "CUDA kernel not supported. Fallback to CPU execution provider for Op type: " << node.OpType() << " node name: " << node.Name();
       }
     } else {
+      tentative_nodes.push_back(node.Index());
       candidates.push_back(node.Index());
     }
   }
 
   // For CUDA EP, exclude the subgraph that is preferred to be placed in CPU
   // These are usually shape related computation subgraphs
   // Following logic can be extended for other EPs
-  auto cpu_nodes = GetCpuPreferredNodes(graph, kernel_lookup, candidates);
+  auto cpu_nodes = GetCpuPreferredNodes(graph, kernel_lookup, tentative_nodes);
   std::vector<std::unique_ptr<ComputeCapability>> result;
   for (auto& node_index : candidates) {
     if (cpu_nodes.count(node_index) > 0)

onnxruntime/core/providers/shared_library/provider_api.h

@@ -350,6 +350,9 @@ void InitProviderOrtApi();
   if ((logger).OutputIsEnabled(::onnxruntime::logging::Severity::k##severity, ::onnxruntime::logging::DataType::SYSTEM)) \
   CREATE_MESSAGE(logger, severity, category, ::onnxruntime::logging::DataType::SYSTEM)->Stream()
 
+#define LOGS(logger, severity) \
+  LOGS_CATEGORY(logger, severity, ::onnxruntime::logging::Category::onnxruntime)
+
 #define LOGS_DEFAULT_CATEGORY(severity, category) \
   LOGS_CATEGORY(::onnxruntime::logging::LoggingManager::DefaultLogger(), severity, category)
 

tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage2/requirements.txt

@@ -2,7 +2,7 @@ pandas
 scikit-learn
 numpy==1.21.6 ; python_version < '3.11'
 numpy==1.24.2 ; python_version >= '3.11'
-transformers==v4.4.2
+transformers==v4.16.1
 rsa==4.9
 tensorboard>=2.2.0,<2.5.0
 h5py