[XPU][cherry-pick] add resnet50-D fusion, test=develop

* [XPU] add resnet50-D fusion test=develop test=xpu

lite/api/paddle_use_passes.h

@@ -62,6 +62,7 @@ USE_MIR_PASS(quantized_op_attributes_inference_pass);
 USE_MIR_PASS(control_flow_op_unused_inputs_and_outputs_eliminate_pass)
 USE_MIR_PASS(lite_scale_activation_fuse_pass);
 USE_MIR_PASS(__xpu__resnet_fuse_pass);
+USE_MIR_PASS(__xpu__resnet_d_fuse_pass);
 USE_MIR_PASS(__xpu__resnet_cbam_fuse_pass);
 USE_MIR_PASS(__xpu__multi_encoder_fuse_pass);
 USE_MIR_PASS(__xpu__embedding_with_eltwise_add_fuse_pass);

lite/core/optimizer.h

@@ -108,6 +108,7 @@ class Optimizer {
 #endif
           "identity_dropout_eliminate_pass",
           "__xpu__resnet_fuse_pass",
+          "__xpu__resnet_d_fuse_pass",
           "__xpu__resnet_cbam_fuse_pass",
           "__xpu__conv2d_fuse_pass",
           "__xpu__conv2d_link_previous_out_max_pass",

lite/kernels/xpu/__xpu__resnet50_compute.cc

@@ -34,6 +34,21 @@ void XPUResNet50Compute::PrepareForRun() {
   }
 }
 
+void XPUResNet50DtypeCompute::PrepareForRun() {
+  auto& param = this->Param<param_t>();
+
+  for (auto* filter : param.filter) {
+    arg_filter_.push_back(
+        reinterpret_cast<const int16_t*>(filter->data<float>()));
+  }
+  for (auto* bias : param.bias) {
+    arg_bias_.push_back(bias->data<float>());
+  }
+  for (auto* max_filter : param.max_filter) {
+    arg_max_filter_.push_back(max_filter->data<float>());
+  }
+}
+
 void XPUResNet50Compute::Run() {
   auto& param = this->Param<param_t>();
   auto& ctx = this->ctx_->As<XPUContext>();
@@ -50,6 +65,22 @@ void XPUResNet50Compute::Run() {
   CHECK_EQ(r, 0);
 }
 
+void XPUResNet50DtypeCompute::Run() {
+  auto& param = this->Param<param_t>();
+  auto& ctx = this->ctx_->As<XPUContext>();
+
+  int batch_size = param.input->dims()[0];
+  int r = xdnn::conv2d_int16_resnet_d<float, int16_t>(
+      ctx.GetRawContext(),                             /* context */
+      batch_size,                                      /* num */
+      param.input->data<float>(),                      /* bottom */
+      &arg_filter_[0],                                 /* weight_list */
+      param.output->mutable_data<float>(TARGET(kXPU)), /* top */
+      &arg_bias_[0],                                   /* bias_list */
+      &arg_max_filter_[0] /* max_filter_list */);
+  CHECK_EQ(r, 0);
+}
+
 }  // namespace xpu
 }  // namespace kernels
 }  // namespace lite
@@ -67,3 +98,16 @@ REGISTER_LITE_KERNEL(__xpu__resnet50,
     .BindInput("MaxFilter", {LiteType::GetTensorTy(TARGET(kXPU))})
     .BindOutput("Output", {LiteType::GetTensorTy(TARGET(kXPU))})
     .Finalize();
+
+REGISTER_LITE_KERNEL(__xpu__resnet50_d,
+                     kXPU,
+                     kFloat,
+                     kNCHW,
+                     paddle::lite::kernels::xpu::XPUResNet50DtypeCompute,
+                     def)
+    .BindInput("Input", {LiteType::GetTensorTy(TARGET(kXPU))})
+    .BindInput("Filter", {LiteType::GetTensorTy(TARGET(kXPU))})
+    .BindInput("Bias", {LiteType::GetTensorTy(TARGET(kXPU))})
+    .BindInput("MaxFilter", {LiteType::GetTensorTy(TARGET(kXPU))})
+    .BindOutput("Output", {LiteType::GetTensorTy(TARGET(kXPU))})
+    .Finalize();

lite/kernels/xpu/__xpu__resnet50_compute.h

@@ -38,6 +38,21 @@ class XPUResNet50Compute : public KernelLite<TARGET(kXPU), PRECISION(kFloat)> {
   std::vector<const float *> arg_bias_;
 };
 
+class XPUResNet50DtypeCompute
+    : public KernelLite<TARGET(kXPU), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::XPUResNet50Param;
+
+  virtual void PrepareForRun();
+
+  virtual void Run();
+
+ private:
+  std::vector<const int16_t *> arg_filter_;
+  std::vector<const float *> arg_max_filter_;
+  std::vector<const float *> arg_bias_;
+};
+
 }  // namespace xpu
 }  // namespace kernels
 }  // namespace lite

lite/operators/__xpu__resnet50_op.cc

@@ -62,3 +62,4 @@ bool XPUResNet50Op::AttachImpl(const cpp::OpDesc& op_desc, lite::Scope* scope) {
 }  // namespace paddle
 
 REGISTER_LITE_OP(__xpu__resnet50, paddle::lite::operators::XPUResNet50Op);
+REGISTER_LITE_OP(__xpu__resnet50_d, paddle::lite::operators::XPUResNet50Op);