Kezhan/execute graph refactoring

include/onnxruntime/core/framework/allocator.h

@@ -72,6 +72,10 @@ struct OrtDevice {
   DeviceId device_id;
 };
 
+inline bool operator==(const OrtDevice& left, const OrtDevice& other) {
+  return left.Id() == other.Id() && left.MemType() == other.MemType() && left.Type() == other.Type();
+}
+
 struct OrtAllocatorInfo {
   // use string for name, so we could have customized allocator in execution provider.
   const char* name;

onnxruntime/core/framework/allocation_planner.cc

@@ -347,6 +347,10 @@ class PlannerImpl {
 
   Status ComputeUseCounts() {
     // Note: for every ml-value, its definition must appear before all its uses in a topological sort of a valid model
+    std::unordered_set<std::string> graph_inputs;
+    for (auto& graph_input : graph_viewer_.GetInputsIncludingInitializers()) {
+      graph_inputs.insert(graph_input->Name());
+    }
 
     for (auto graph_input : graph_viewer_.GetInputs()) {
       OrtValueIndex index = Index(graph_input->Name());
@@ -371,15 +375,7 @@ class PlannerImpl {
     for (SequentialExecutionPlan::NodeExecutionPlan& step : plan_.execution_plan) {
       auto pnode = graph_viewer_.GetNode(step.node_index);
       if (pnode == nullptr) return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "Can not find the node ", step.node_index);
-      for (auto node_input : pnode->InputDefs()) {
-        if (node_input->Exists())
-          UseCount(node_input->Name())++;
-      }
 
-      for (auto node_input : pnode->ImplicitInputDefs()) {
-        if (node_input->Exists())
-          UseCount(node_input->Name())++;
-      }
       // Identify where each output of this node should be allocated.
       // This is determined by the opkernel bound to the node.
       const KernelCreateInfo* kernel_create_info = nullptr;
@@ -394,31 +390,45 @@ class PlannerImpl {
         if (!pnode->Name().empty()) errormsg << " (node " << pnode->Name() << ")";
         return Status(ONNXRUNTIME, FAIL, errormsg.str());
       }
-
       auto exec_provider = execution_providers_.Get(*pnode);
       if (exec_provider == nullptr) {
         return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "Can not find the execution provider ",
                                pnode->GetExecutionProviderType());
       }
 
-      auto& default_allocator_info = exec_provider->GetAllocator(0, OrtMemTypeDefault)->Info();
+      // increment UseCount and add location information if applicable for the provided input def
+      auto process_input = [&graph_inputs, &exec_provider, &p_kernelDef, this](const NodeArg& input, size_t arg_idx) {
+        const auto& name = input.Name();
+        UseCount(name)++;
+
+        // If it's a graph input or outer scope node arg, set its plan.
+        // NOTE: Copy nodes should have already been added if a graph input is fed as input
+        // to nodes assigned to different providers.
+        if (graph_inputs.find(name) != graph_inputs.cend() ||
+            std::find_if(outer_scope_node_args_.cbegin(), outer_scope_node_args_.cend(),
+                         [&name](const NodeArg* value) {
+                           return value && value->Name() == name;
+                         }) != outer_scope_node_args_.cend()) {
+          OrtValueIndex index = Index(name);
+          plan_.SetLocation(static_cast<size_t>(index),
+                            exec_provider->GetAllocator(0, p_kernelDef->InputMemoryType(arg_idx))->Info());
+        }
+
+        return Status::OK();
+      };
+
+      ORT_RETURN_IF_ERROR(Node::ForEachWithIndex(pnode->InputDefs(), process_input));
+      ORT_RETURN_IF_ERROR(Node::ForEachWithIndex(pnode->ImplicitInputDefs(), process_input));
+
       auto outputs = pnode->OutputDefs();
       auto num_outputs = outputs.size();
-
       for (size_t i = 0; i < num_outputs; ++i) {
         auto* node_output = outputs[i];
         if (!node_output->Exists()) continue;
         OrtValueIndex index = Index(node_output->Name());
         ProcessDef(index, node_output);
         ++UseCount(index);
-        if (strcmp(default_allocator_info.name, CPU) != 0) {
-          // By default, outputs of this node are allocated on the default device allocator,
-          // except for outputs marked for allocation in MemoryType:
-          auto memory_type = p_kernelDef->OutputMemoryType(i);
-          plan_.SetLocation(static_cast<size_t>(index), memory_type == OrtMemTypeDefault
-                                                            ? default_allocator_info
-                                                            : exec_provider->GetAllocator(0, memory_type)->Info());
-        }
+        plan_.SetLocation(static_cast<size_t>(index), exec_provider->GetAllocator(0, p_kernelDef->OutputMemoryType(i))->Info());
       }
       // if sync is needed, mark allocation plan as create_fence_if_async=true
       // note that the input arg may come from an execution provider (i.e. CPU) that does not support async,

onnxruntime/core/framework/feeds_fetches_manager.h

@@ -48,9 +48,8 @@ struct FeedsFetchesInfo {
 class FeedsFetchesManager {
  public:
   struct MLValueCopyInfo {
-    int allocation_device_id = 0;
+    OrtDevice target_device;
     const IExecutionProvider* allocation_provider = nullptr;
-    const IExecutionProvider* copy_provider = nullptr;
   };
 
   static Status Create(const std::vector<std::string>& feed_names, const std::vector<std::string>& output_names,

onnxruntime/core/framework/graph_partitioner.cc

@@ -175,10 +175,6 @@ Status GraphPartitioner::Partition(Graph& graph, bool export_dll, FuncManager& f
         //prepare the func kernel
         KernelDefBuilder builder;
         BuildFusedKernelDef(builder, *node);
-        if (node->GetExecutionProviderType() == onnxruntime::kNGraphExecutionProvider || node->GetExecutionProviderType() == onnxruntime::kNnapiExecutionProvider) {
-          builder.SetDefaultInputsMemoryType(OrtMemTypeCPUInput);
-          builder.SetDefaultOutputMemoryType(OrtMemTypeCPUOutput);
-        }
         ORT_RETURN_IF_ERROR(fused_kernel_registry->Register(
             builder, static_cast<KernelCreatePtrFn>([](const OpKernelInfo& info) -> OpKernel* { return new FunctionKernel(info); })));
       }

onnxruntime/core/framework/session_state.h

@@ -141,17 +141,18 @@ class SessionState {
      * \param p_node0 Nullable
      * \param kci0 Nullable
      */
-    NodeInfo(size_t index0, const onnxruntime::Node* p_node0, const KernelCreateInfo* kci0)
+    NodeInfo(size_t index0, const onnxruntime::Node* p_node0, const KernelCreateInfo* kci0, const OrtDevice& device0)
         : index(index0),
           p_node(p_node0),
-          kci(kci0) {
-    }
+          kci(kci0),
+          device(&device0) {}
 
     size_t index;
     // Nullable
     const onnxruntime::Node* p_node = nullptr;
     // Nullable
     const KernelCreateInfo* kci = nullptr;
+    const OrtDevice* device = nullptr;
   };
 
   using NameNodeInfoMapType = std::unordered_map<std::string, std::vector<NodeInfo>>;

onnxruntime/core/framework/session_state_initializer.cc

@@ -351,6 +351,8 @@ common::Status SaveInputOutputNamesToNodeMapping(const onnxruntime::Graph& graph
   if (implicit_inputs && implicit_inputs->empty()) {
     implicit_inputs = nullptr;
   }
+  const auto* exec_plan = session_state.GetExecutionPlan();
+  const auto& name_to_id = session_state.GetOrtValueNameIdxMap();
 
   for (auto& node : graph.Nodes()) {
     // note that KernelCreateInfo may not exist for custom kernel
@@ -365,7 +367,11 @@ common::Status SaveInputOutputNamesToNodeMapping(const onnxruntime::Graph& graph
                 return Status::OK();
               }
 
-              SessionState::NodeInfo node_info(index, &node, kci);
+              int arg_index;
+              ORT_RETURN_IF_ERROR(name_to_id.GetIdx(arg.Name(), arg_index));
+              const auto& device = exec_plan->GetLocation(arg_index).device;
+
+              SessionState::NodeInfo node_info(index, &node, kci, device);
 
               if (IsArgNameInInputsOutputs(arg.Name(), graph_inputs)) {
                 ORT_RETURN_IF_ERROR(session_state.AddInputNameToNodeInfoMapping(arg.Name(), node_info));
@@ -397,8 +403,13 @@ common::Status SaveInputOutputNamesToNodeMapping(const onnxruntime::Graph& graph
       // copy to/from CPU to go through the control flow nodes where possible/applicable.
       // the processing for the subgraph where the implicit input is consumed will do the real check on whether any
       // copy to a different device is required
-      SessionState::NodeInfo node_info(std::numeric_limits<size_t>::max(), &node, kci);
       for (const auto& input_def : node_implicit_inputs) {
+        int arg_index;
+        //Question: the implicit input may not be found in this session state name to id map, but in parent session state name to id map.
+        //@Scott
+        ORT_RETURN_IF_ERROR(name_to_id.GetIdx(input_def->Name(), arg_index));
+        auto& device = exec_plan->GetLocation(arg_index).device;
+        SessionState::NodeInfo node_info(std::numeric_limits<size_t>::max(), &node, kci, device);
         ORT_RETURN_IF_ERROR(session_state.AddInputNameToNodeInfoMapping(input_def->Name(), node_info));
       }
     }
@@ -413,7 +424,6 @@ common::Status SaveInputOutputNamesToNodeMapping(const onnxruntime::Graph& graph
 
   auto& input_map = session_state.GetInputNodeInfoMap();
   auto end_map = input_map.cend();
-  SessionState::NodeInfo empty_node_info(std::numeric_limits<size_t>::max(), nullptr, nullptr);
 
   for (const auto& graph_input : graph_inputs) {
     const auto& name = graph_input->Name();
@@ -422,6 +432,10 @@ common::Status SaveInputOutputNamesToNodeMapping(const onnxruntime::Graph& graph
       // utils::CopyOneInputAcrossDevices will use the input OrtValue as is given we don't believe it's used anywhere.
       LOGS(session_state.Logger(), INFO) << (graph.IsSubgraph() ? "Subgraph" : "Graph") << " input with name "
                                          << name << " is not used by any node.";
+      int arg_index;
+      ORT_RETURN_IF_ERROR(name_to_id.GetIdx(name, arg_index));
+      auto& device = exec_plan->GetLocation(arg_index).device;
+      SessionState::NodeInfo empty_node_info(std::numeric_limits<size_t>::max(), nullptr, nullptr, device);
       ORT_RETURN_IF_ERROR(session_state.AddInputNameToNodeInfoMapping(name, empty_node_info));
     }
   }

onnxruntime/core/framework/utils.cc

@@ -23,9 +23,18 @@ AllocatorPtr GetAllocator(const SessionState& session_state, const OrtAllocatorI
   return session_state.GetExecutionProviders().GetAllocator(allocator_info);
 }
 
-common::Status AllocateHelper(const IExecutionProvider& execution_provider, int device_id, const Tensor& fetched_tensor,
+bool ProviderIsCpuBased(const std::string& provider_type) {
+  return provider_type == onnxruntime::kCpuExecutionProvider ||
+         provider_type == onnxruntime::kMklDnnExecutionProvider ||
+         provider_type == onnxruntime::kNGraphExecutionProvider ||
+         provider_type == onnxruntime::kNupharExecutionProvider ||
+         provider_type == onnxruntime::kOpenVINOExecutionProvider ||
+         provider_type == onnxruntime::kNnapiExecutionProvider;
+}
+
+common::Status AllocateHelper(const IExecutionProvider& execution_provider, const OrtDevice& device, const Tensor& fetched_tensor,
                               OrtValue& output_mlvalue) {
-  auto allocator = execution_provider.GetAllocator(device_id, OrtMemTypeDefault);
+  auto allocator = execution_provider.GetAllocator(device.Id(), OrtMemTypeDefault);
   if (!allocator) {
     return Status(common::ONNXRUNTIME, common::FAIL, "invalid allocator");
   }
@@ -62,20 +71,20 @@ static Status CopyMLValue(const DataTransferManager& data_transfer_mgr,
                           const FeedsFetchesManager::MLValueCopyInfo& copy_info,
                           const OrtValue& source_mlvalue,
                           OrtValue& target_mlvalue) {
-  if (copy_info.copy_provider == nullptr) {
+  if (copy_info.allocation_provider == nullptr){
     target_mlvalue = source_mlvalue;
-  } else {
-    auto& source_tensor = source_mlvalue.Get<Tensor>();
+    return Status::OK();
+  }
 
-    if (!target_mlvalue.IsAllocated()) {
-      ORT_RETURN_IF_ERROR(utils::AllocateHelper(*copy_info.allocation_provider, copy_info.allocation_device_id,
-                                                source_tensor, target_mlvalue));
-    }
+  auto& source_tensor = source_mlvalue.Get<Tensor>();
+  if (!target_mlvalue.IsAllocated()) {
+    ORT_RETURN_IF_ERROR(utils::AllocateHelper(*copy_info.allocation_provider, copy_info.target_device,
+                                              source_tensor, target_mlvalue));
+  }
 
-    Tensor* p_output_tensor = target_mlvalue.GetMutable<Tensor>();
+  Tensor* p_output_tensor = target_mlvalue.GetMutable<Tensor>();
 
-    ORT_RETURN_IF_ERROR(data_transfer_mgr.CopyTensor(source_tensor, *p_output_tensor));
-  }
+  ORT_RETURN_IF_ERROR(data_transfer_mgr.CopyTensor(source_tensor, *p_output_tensor));
 
   return Status::OK();
 }
@@ -86,8 +95,6 @@ common::Status CopyOneInputAcrossDevices(const SessionState& session_state, cons
                                          FeedsFetchesManager::MLValueCopyInfo& copy_info) {
   needed_copy = false;
 
-  //TODO: make it configurable
-  const int target_device_id = 0;
   std::vector<SessionState::NodeInfo> node_info_vec;
   ORT_RETURN_IF_ERROR(session_state.GetInputNodeInfo(input_name, node_info_vec));
 
@@ -111,51 +118,23 @@ common::Status CopyOneInputAcrossDevices(const SessionState& session_state, cons
       break;
     }
 
-    auto& required_provider_type = GetNodeInputProviderType(node_info);
-    auto& input_tensor = orig_mlvalue.Get<Tensor>();
-    auto& input_tensor_loc = input_tensor.Location();
-
-    auto* p_input_provider = exec_providers.Get(input_tensor_loc);
-    if (!p_input_provider) {
-      p_input_provider = exec_providers.Get(onnxruntime::kCpuExecutionProvider);
-      ORT_ENFORCE(p_input_provider);
-    }
-
-    //no copy for nGraph
-    if (required_provider_type == onnxruntime::kNGraphExecutionProvider) {
-      new_mlvalue = orig_mlvalue;
-      break;
-    }
-
-    auto input_provider_type = p_input_provider->Type();
-    if (input_provider_type == required_provider_type && input_tensor_loc.mem_type == OrtMemTypeDefault) {
-      new_mlvalue = orig_mlvalue;
-      break;
-    }
-
-    // If a node requires input on cpu and input tensor is allocated with pinned memory allocator, don't do copy
-    if (required_provider_type == onnxruntime::kCpuExecutionProvider &&
-        input_tensor_loc.mem_type == OrtMemTypeCPU) {
+    auto& required_device = *node_info.device;
+    auto& input_tensor_device = orig_mlvalue.Get<Tensor>().Location().device;
+    if (required_device == input_tensor_device) {
+      // No copy needed for same device.
       new_mlvalue = orig_mlvalue;
       break;
     }
 
+    auto& required_provider_type = GetNodeInputProviderType(node_info);
     auto* required_provider = exec_providers.Get(required_provider_type);
-    ORT_ENFORCE(required_provider);
-
-    auto* p_copy_provider = (required_provider_type != onnxruntime::kCpuExecutionProvider)
-                                ? required_provider
-                                : p_input_provider;
-
-    copy_info.allocation_device_id = target_device_id;
+    copy_info.target_device = required_device;
     copy_info.allocation_provider = required_provider;
-    copy_info.copy_provider = p_copy_provider;
 
     ORT_RETURN_IF_ERROR(CopyMLValue(session_state.GetDataTransferMgr(), copy_info, orig_mlvalue, new_mlvalue));
 
     needed_copy = true;
 
-    // } loop of node_info_vec
   } while (false);
 
   return Status::OK();
@@ -344,43 +323,26 @@ static common::Status CopyOutputsAcrossDevices(const SessionState& session_state
       continue;
     }
 
-    auto& fetched_tensor = fetched_mlvalue.Get<Tensor>();
-    auto& fetched_tensor_location = fetched_tensor.Location();
-    auto* p_fetched_provider = execution_providers.Get(fetched_tensor_location);
-    if (!p_fetched_provider) {
-      p_fetched_provider = cpu_execution_provider;
-    }
-
-    auto fetched_provider_type = p_fetched_provider->Type();
-    auto& output_mlvalue = user_fetches[idx];
-
     const IExecutionProvider* p_output_provider = nullptr;
-
+    auto target_device = OrtDevice();
+    auto& output_mlvalue = user_fetches[idx];
     if (output_mlvalue.IsAllocated()) {
       Tensor* p_output_tensor = output_mlvalue.GetMutable<Tensor>();
+      target_device = p_output_tensor->Location().device;
       p_output_provider = execution_providers.Get(p_output_tensor->Location());
     }
+    auto fetch_result_device = fetched_mlvalue.Get<Tensor>().Location().device;
+    if (target_device == fetch_result_device) {
+      user_fetches[idx] = fetched_mlvalue;
+      continue;
+    }
 
     if (!p_output_provider) {
       p_output_provider = cpu_execution_provider;
     }
 
-    auto output_provider_type = p_output_provider->Type();
-
-    if (fetched_provider_type == output_provider_type ||
-        (p_output_provider == cpu_execution_provider && fetched_tensor_location.mem_type == OrtMemTypeCPUOutput)) {
-      user_fetches[idx] = fetched_mlvalue;
-      continue;
-    }
-
     needed_copy = true;
-
-    auto* p_copy_provider = (fetched_provider_type != onnxruntime::kCpuExecutionProvider)
-                                ? p_fetched_provider
-                                : p_output_provider;
-
-    const int device_id = 0;  // TODO: As per comment in the copy input code, make this configurable.
-    FeedsFetchesManager::MLValueCopyInfo copy_info{device_id, p_output_provider, p_copy_provider};
+    FeedsFetchesManager::MLValueCopyInfo copy_info{target_device, p_output_provider};
     ORT_RETURN_IF_ERROR(CopyMLValue(session_state.GetDataTransferMgr(), copy_info, fetched_mlvalue, output_mlvalue));
 
     if (copiers) {
@@ -410,11 +372,7 @@ static common::Status CachedCopyOutputsAcrossDevices(
 
 static DeviceCopyCheck CheckExecutionProviders(const ExecutionProviders& execution_providers) {
   for (const auto& execution_provider : execution_providers) {
-    if (execution_provider->Type() != onnxruntime::kCpuExecutionProvider &&
-        execution_provider->Type() != onnxruntime::kMklDnnExecutionProvider &&
-        execution_provider->Type() != onnxruntime::kNGraphExecutionProvider &&
-        execution_provider->Type() != onnxruntime::kNupharExecutionProvider &&
-        execution_provider->Type() != onnxruntime::kOpenVINOExecutionProvider) {
+    if (!ProviderIsCpuBased(execution_provider->Type())) {
       return DeviceCopyCheck::Unknown;
     }
   }

onnxruntime/core/providers/cuda/cuda_execution_provider.cc

@@ -72,6 +72,11 @@ CUDAExecutionProvider::CUDAExecutionProvider(const CUDAExecutionProviderInfo& in
   DeviceAllocatorRegistrationInfo pinned_allocator_info(
       {OrtMemTypeCPUOutput, [](int) { return std::make_unique<CUDAPinnedAllocator>(0, CUDA_PINNED); }, std::numeric_limits<size_t>::max()});
   InsertAllocator(CreateAllocator(pinned_allocator_info, device_id_));
+
+  // TODO: this is actually used for the cuda kernels which explicitly ask for inputs from CPU.
+  // This will be refactored/removed when allocator and execution provider are decoupled.
+  DeviceAllocatorRegistrationInfo cpu_allocator_info({OrtMemTypeCPUInput, [](int) { return std::make_unique<CPUAllocator>(std::make_unique<OrtAllocatorInfo>("CUDA_CPU", OrtAllocatorType::OrtDeviceAllocator, OrtDevice(), 0, OrtMemTypeCPUInput)); }, std::numeric_limits<size_t>::max()});
+  InsertAllocator(CreateAllocator(cpu_allocator_info));
 }
 
 CUDAExecutionProvider::~CUDAExecutionProvider() {

onnxruntime/test/providers/provider_test_utils.cc

@@ -440,7 +440,6 @@ void OpTester::Run(ExpectResult expect_result,
     std::unordered_map<std::string, OrtValue> feeds;
     std::vector<std::string> output_names;
     FillFeedsAndOutputNames(feeds, output_names);
-
     // Run the model
     SessionOptions so;
     so.session_logid = op_;