fix some fp16 subgraph bugs

mindspore/lite/src/ops/arithmetic_compare.cc

@@ -21,13 +21,9 @@ namespace lite {
 
 int ArithmeticCompare::InferShape(std::vector<Tensor *> inputs_, std::vector<Tensor *> outputs_) {
   auto res = Arithmetic::InferShape(inputs_, outputs_);
-  if (res == RET_OK) {
+  auto output = outputs_.front();
-    auto output = outputs_.front();
+  output->set_data_type(TypeId::kNumberTypeBool);
-    output->set_data_type(TypeId::kNumberTypeBool);
+  return res;
-    return RET_OK;
-  } else {
-    return res;
-  }
 }
 }  // namespace lite
 }  // namespace mindspore

mindspore/lite/src/runtime/kernel/arm/fp32/pad_fp32.cc

@@ -304,10 +304,12 @@ int PadCPUKernel::CheckPaddings(int *paddings, int length, int *input_shape, int
   for (auto i = 0; i < length; ++i) {
     int max_valid = input_shape[i] - offset;
     if (paddings[i * 2] > max_valid) {
-      MS_LOG(WARNING) << prefix << "paddings " << paddings[i * 2] << "should be less than " << max_valid + 1;
+      MS_LOG(ERROR) << prefix << "paddings " << paddings[i * 2] << "should be less than " << max_valid + 1;
+      return RET_ERROR;
     }
     if (paddings[i * 2 + 1] > max_valid) {
-      MS_LOG(WARNING) << prefix << "paddings " << paddings[i * 2 + 1] << "should be less than " << max_valid + 1;
+      MS_LOG(ERROR) << prefix << "paddings " << paddings[i * 2 + 1] << "should be less than " << max_valid + 1;
+      return RET_ERROR;
     }
   }
   return RET_OK;

mindspore/lite/src/runtime/kernel/arm/fp32/shape_fp32.cc

@@ -77,5 +77,6 @@ kernel::LiteKernel *CpuShapeFp32KernelCreator(const std::vector<lite::Tensor *>
 }
 
 REG_KERNEL(kCPU, kNumberTypeFloat32, PrimitiveType_Shape, CpuShapeFp32KernelCreator)
+REG_KERNEL(kCPU, kNumberTypeFloat16, PrimitiveType_Shape, CpuShapeFp32KernelCreator)
 REG_KERNEL(kCPU, kNumberTypeInt8, PrimitiveType_Shape, CpuShapeFp32KernelCreator)
 }  // namespace mindspore::kernel

mindspore/lite/src/sub_graph_kernel.cc

@@ -238,6 +238,9 @@ int CpuFp16SubGraph::PreProcess() {
   }
   for (auto kernel : this->nodes_) {
     for (auto tensor : kernel->out_tensors()) {
+      if (kernel->Type() == schema::PrimitiveType_Cast) {
+        continue;
+      }
       if (tensor->data_type() == kNumberTypeFloat32) {
         tensor->set_data_type(kNumberTypeFloat16);
       }

mindspore/lite/test/models_onnx.cfg

@@ -36,3 +36,4 @@ udnie-9.onnx
 adversarial_pruning.onnx
 residual_distill_res34_cifar10_bs_1_update.onnx
 residual_distill_res50_cifar10_bs_1_update.onnx
+encoder.onnx;1,32,83

mindspore/lite/test/models_tflite_fp16.cfg

@@ -40,7 +40,11 @@ deeplabv3_257_mv_gpu.tflite 3
 multi_person_mobilenet_v1_075_float.tflite 9
 #hiai_vad.tflite 20
 ide_label_base.tflite 22
-#ide_label_retrained.tflite 80
+# ide_label_retrained.tflite involves a softmax-like structure whose output channel is 12.
+# The values in the first few channels are extremely small and casted into 0 in the fp16 subgraph.
+# The next slice operator takes out data from the first 6 channels, which are all zero, causing the error of
+# dividing 0 in the following operator.
+#ide_label_retrained.tflite
 ml_ei_headpose.tflite 3
 ml_ei_landmark.tflite 3
 mnist.tflite 4