!6011 fix opencl context set for fp16

Merge pull request !6011 from wandongdong/master

mindspore/lite/src/lite_session.cc

@@ -307,6 +307,7 @@ int LiteSession::Init(Context *context) {
 #if SUPPORT_GPU
   if (context_->device_type_ == DT_GPU) {
     auto opencl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
+    opencl_runtime->SetFp16Enable(context_->float16_priority);
     opencl_runtime->Init();
     MS_LOG(INFO) << "Init OpenCL runtime.";
   }

mindspore/lite/src/runtime/kernel/opencl/subgraph_opencl_kernel.cc

@@ -95,8 +95,9 @@ int SubGraphOpenCLKernel::GenToFormatOp(const std::vector<lite::Tensor *> &in_te
 
     out_tensors->emplace_back(new_tensor);
     KernelKey desc{kGPU, kNumberTypeFloat32, schema::PrimitiveType_ToFormat};
-    if (lite::opencl::OpenCLRuntime::GetInstance()->GetFp16Enable()) {
+    if (mem_type == OpenCLMemType::IMG && lite::opencl::OpenCLRuntime::GetInstance()->GetFp16Enable()) {
       desc.data_type = kNumberTypeFloat16;
+      new_tensor->set_data_type(kNumberTypeFloat16);
     }
     OpenCLToFormatParameter *parameter = new (std::nothrow) OpenCLToFormatParameter;
     MS_ASSERT(parameter);
@@ -112,11 +113,11 @@ int SubGraphOpenCLKernel::GenToFormatOp(const std::vector<lite::Tensor *> &in_te
     out_parameters->emplace_back(parameter);
     LiteKernel *in_convert_op = nullptr;
     if (mem_type == OpenCLMemType::IMG) {
-      in_convert_op =
+      in_convert_op = lite::GetOpenCLKernel({in_tensors[i]}, {new_tensor}, reinterpret_cast<OpParameter *>(parameter),
-        lite::GetOpenCLKernel({in_tensors[i]}, {new_tensor}, reinterpret_cast<OpParameter *>(parameter), nullptr, desc);
+                                            context_, desc);
     } else {
-      in_convert_op =
+      in_convert_op = lite::GetOpenCLKernel({new_tensor}, {in_tensors[i]}, reinterpret_cast<OpParameter *>(parameter),
-        lite::GetOpenCLKernel({new_tensor}, {in_tensors[i]}, reinterpret_cast<OpParameter *>(parameter), nullptr, desc);
+                                            context_, desc);
     }
     MS_ASSERT(in_convert_op);
     if (in_convert_op == nullptr) {

mindspore/lite/src/runtime/kernel/opencl/subgraph_opencl_kernel.h

@@ -34,8 +34,10 @@ class SubGraphOpenCLKernel : public SubGraphKernel {
   explicit SubGraphOpenCLKernel(const std::vector<lite::Tensor *> inputs, const std::vector<lite::Tensor *> outputs,
                                 const std::vector<kernel::LiteKernel *> inKernels,
                                 const std::vector<kernel::LiteKernel *> outKernels,
-                                const std::vector<kernel::LiteKernel *> nodes)
+                                const std::vector<kernel::LiteKernel *> nodes,
-      : SubGraphKernel(inputs, outputs, inKernels, outKernels, nodes, nullptr, nullptr) {}
+                                const lite::Context *ctx = nullptr,
+                                const mindspore::lite::PrimitiveC *primitive = nullptr)
+      : SubGraphKernel(inputs, outputs, inKernels, outKernels, nodes, ctx, primitive) {}
   ~SubGraphOpenCLKernel() override;
 
   int Init() override;

mindspore/lite/src/scheduler.cc

@@ -178,6 +178,12 @@ void Scheduler::ConstructSubgraphs(std::vector<kernel::LiteKernel *> *kernels) {
   std::vector<kernel::LiteKernel *> subgraph_kernels;
   size_t sub_cnt{0};
   for (auto temp_kernels : sub_kernels_list) {
+    std::vector<Tensor *> output_tensor = kernel::LiteKernelUtil::SubgraphOutputTensors(temp_kernels);
+    for (auto tensor : output_tensor) {
+      if (context_->float16_priority && tensor->data_type() == kNumberTypeFloat16) {
+        tensor->set_data_type(kNumberTypeFloat32);
+      }
+    }
     kernel::KERNEL_ARCH arch = temp_kernels.front()->desc().arch;
     if (arch == kernel::KERNEL_ARCH::kCPU) {
       for (auto kernel : temp_kernels) {
@@ -185,12 +191,6 @@ void Scheduler::ConstructSubgraphs(std::vector<kernel::LiteKernel *> *kernels) {
           tensor->set_allocator(context_->allocator.get());
         }
       }
-      std::vector<Tensor *> output_tensor = kernel::LiteKernelUtil::SubgraphOutputTensors(temp_kernels);
-      for (auto tensor : output_tensor) {
-        if (context_->float16_priority && tensor->data_type() == kNumberTypeFloat16) {
-          tensor->set_data_type(kNumberTypeFloat32);
-        }
-      }
       std::copy(temp_kernels.begin(), temp_kernels.end(), std::back_inserter(subgraph_kernels));
     } else {
       auto subgraph_kernel = CreateSubKernel(temp_kernels, arch);
@@ -213,8 +213,8 @@ kernel::LiteKernel *Scheduler::CreateSubKernel(const std::vector<kernel::LiteKer
     std::vector<Tensor *> output_tensors = kernel::LiteKernelUtil::SubgraphOutputTensors(kernels);
     std::vector<kernel::LiteKernel *> input_kernels = kernel::LiteKernelUtil::SubgraphInputKernels(kernels);
     std::vector<kernel::LiteKernel *> output_kernels = kernel::LiteKernelUtil::SubgraphOutputKernels(kernels);
-    sub_kernel =
+    sub_kernel = new kernel::SubGraphOpenCLKernel(input_tensors, output_tensors, input_kernels, output_kernels, kernels,
-      new kernel::SubGraphOpenCLKernel(input_tensors, output_tensors, input_kernels, output_kernels, kernels);
+                                                  context_, nullptr);
     sub_kernel->Init();
   } else if (arch == kernel::KERNEL_ARCH::kNPU) {
     MS_LOG(ERROR) << "NPU kernel is not supported";