[Infrt] add ir support for phi kernel batch_norm_infer.

paddle/infrt/dialect/phi/pass/phi_op_convert_pass.cc

@@ -97,12 +97,12 @@ void PhiOpConvertPass::convertStage() {
     }
     auto loc = getFunction().getLoc();
     builder.setInsertionPoint(op);
-    if (phi::KernelFactory::Instance().HasCompatiblePhiKernel(op_name)) {
-      std::string kernel_name = phi::TransToPhiKernelName(op_name);
+    op_name = phi::TransToPhiKernelName(op_name);
+    if (!::phi::OpUtilsMap::Instance().Contains(op_name)) {
       auto kernel_op = builder.create<infrt::KernelOp>(loc,
                                                        op->getResultTypes(),
                                                        op->getOperands(),
-                                                       kernel_name,
+                                                       op_name,
                                                        op->getAttrDictionary());
       op->replaceAllUsesWith(kernel_op.getResults());
     } else {

paddle/infrt/dialect/phi/pass/proto_arg_map_context.cc

@@ -32,17 +32,24 @@ bool ProtoArgumentMappingContext::HasOutput(const std::string& name) const {
 }
 
 bool ProtoArgumentMappingContext::HasAttr(const std::string& name) const {
+  if (name == "is_test") return true;
   return op_->hasAttr(name);
 }
 
 paddle::any ProtoArgumentMappingContext::Attr(const std::string& name) const {
-  mlir::Attribute attrs = op_->getAttr(name);
-  if (mlir::StringAttr str_attr = attrs.dyn_cast_or_null<mlir::StringAttr>()) {
+  if (name == "is_test") {
+    return paddle::any(true);
+  }
+  mlir::Attribute attr = op_->getAttr(name);
+  if (!attr) {
+    return paddle::any();
+  }
+  if (mlir::StringAttr str_attr = attr.dyn_cast<mlir::StringAttr>()) {
     return paddle::any(str_attr.str());
-  } else {
-    // ToDO: implementation in the ext PR.
-    return paddle::any(0);
   }
+
+  // ToDO: implementation in the ext PR.
+  return paddle::any(0);
 }
 
 size_t ProtoArgumentMappingContext::InputSize(const std::string& name) const {

paddle/infrt/host_context/value.h

@@ -147,6 +147,7 @@ class Value : public common::Object {
 #endif
   explicit Value(::phi::DenseTensor&& x) : data(std::move(x)) {}
   explicit Value(::phi::MetaTensor&& x) : data(std::move(x)) {}
+  explicit Value(::phi::MetaConfig&& x) : data(std::move(x)) {}
 #ifdef INFRT_WITH_TRT
   explicit Value(::infrt::backends::tensorrt::TrtEngine&& x)
       : data(std::move(x)) {}

paddle/infrt/kernel/phi/infershaped/infershaped_kernel_launcher.cc

@@ -14,6 +14,7 @@
 
 #include "paddle/infrt/kernel/phi/infershaped/infershaped_kernel_launcher.h"
 #include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/core/meta_tensor.h"
 
 namespace infrt {
 namespace kernel {
@@ -31,6 +32,10 @@ void InferShapedKernelLauncher::CreateKernelFrameForInferShape(
       infershape_kernel_frame_builder.AddArgument(value);
     }
   }
+  if (infershape_kernel_frame_builder.GetNumArgs() < arg_size_) {
+    infershape_kernel_frame_builder.AddArgument(
+        new host_context::Value(::phi::MetaConfig()));
+  }
 }
 
 void InferShapedKernelLauncher::BuildInferShapeCache(

paddle/infrt/kernel/phi/infershaped/infershaped_kernel_launcher.h

@@ -22,11 +22,8 @@ namespace infrt {
 namespace kernel {
 
 struct InferShapedKernelLauncher {
-  virtual void Invoke(host_context::KernelFrame* frame) = 0;
-
-  virtual ~InferShapedKernelLauncher() = default;
-
- protected:
+  explicit InferShapedKernelLauncher(int arg_size) : arg_size_(arg_size) {}
+  ~InferShapedKernelLauncher() = default;
   //! Initialize the kernel frame for InferShape kernel.
   // This method will create a new KernelFrame with all the Tensors(currently
   // only DenseHostTensor) converted into MetaTensors so that the infer-shape
@@ -46,6 +43,7 @@ struct InferShapedKernelLauncher {
   llvm::SmallVector<host_context::ValueRef, 3> values;
   llvm::SmallVector<::phi::DDim, 3> tensor_shape_cache;
   host_context::KernelFrameBuilder infershape_kernel_frame_builder;
+  const int arg_size_;
 };
 
 }  // namespace kernel

paddle/infrt/kernel/phi/infershaped/phi_kernel_launcher.h

@@ -24,46 +24,44 @@
 namespace infrt {
 namespace kernel {
 
+template <typename F>
+struct FuncArgStatics {};
+
+template <typename Return, typename... Args>
+struct FuncArgStatics<Return (*)(Args...)> {
+  constexpr static int arg_size = sizeof...(Args);
+};
+
 template <typename KernelFunc,
           KernelFunc kernel,
           typename InferShapedFunc,
           InferShapedFunc infershape>
-class KernelLauncher : public InferShapedKernelLauncher {
- public:
+void KernelLauncherFunc(host_context::KernelFrame* frame) {
+  static InferShapedKernelLauncher launcher(
+      FuncArgStatics<InferShapedFunc>::arg_size);
   static const uint16_t num_input_tensors{InferShapeHelper<KernelFunc>::count};
   static const bool turn_on_infer_shape_cache{true};
-  void Invoke(host_context::KernelFrame* frame) override {
+
 #ifndef NDEBUG
-    LOG(INFO) << "Kernel.frame: " << frame->DumpArgTypes();
+  LOG(INFO) << "Kernel.frame: " << frame->DumpArgTypes();
 #endif
-    // Build the infershape KernelFrame if needed.
-    // TODO(Superjomn) add unlikely here.
-    if (infershape_kernel_frame_builder.IsEmpty()) {
-      CreateKernelFrameForInferShape(frame);
+  // Build the infershape KernelFrame if needed.
+  // TODO(Superjomn) add unlikely here.
+  if (launcher.infershape_kernel_frame_builder.IsEmpty()) {
+    launcher.CreateKernelFrameForInferShape(frame);
 #ifndef NDEBUG
-      LOG(INFO) << "infershape.frame: "
-                << infershape_kernel_frame_builder.DumpArgTypes();
+    LOG(INFO) << "infershape.frame: "
+              << launcher.infershape_kernel_frame_builder.DumpArgTypes();
 #endif
+  }
+  if (turn_on_infer_shape_cache) {
+    if (launcher.IsShapeChanged(num_input_tensors)) {
+      ::infrt::host_context::KernelImpl<InferShapedFunc, infershape>::Invoke(
+          &launcher.infershape_kernel_frame_builder);
+      launcher.BuildInferShapeCache(num_input_tensors);
     }
-    if (turn_on_infer_shape_cache) {
-      if (!turn_on_infer_shape_cache || IsShapeChanged(num_input_tensors)) {
-        ::infrt::host_context::KernelImpl<InferShapedFunc, infershape>::Invoke(
-            &infershape_kernel_frame_builder);
-        BuildInferShapeCache(num_input_tensors);
-      }
-    }
-    ::infrt::host_context::KernelImpl<KernelFunc, kernel>::Invoke(frame);
   }
-};
-
-template <typename KernelFunc,
-          KernelFunc kernel,
-          typename InferShapedFunc,
-          InferShapedFunc infershape>
-void KernelLauncherFunc(
-    KernelLauncher<KernelFunc, kernel, InferShapedFunc, infershape> launcher,
-    host_context::KernelFrame* frame) {
-  launcher.Invoke(frame);
+  ::infrt::host_context::KernelImpl<KernelFunc, kernel>::Invoke(frame);
 }
 
 }  // namespace kernel

paddle/infrt/tests/dialect/phi/phi_test.mlir

@@ -1,14 +1,27 @@
 // RUN: infrtexec -i %s
 module  {
-  func @predict(%arg0: !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW> {
+  func @predict(%arg0: !infrt.dense_tensor<CPU, FP32, NCHW>, %arg1: !infrt.dense_tensor<CPU, FP32, NCHW>, %arg2: !infrt.dense_tensor<CPU, FP32, NCHW>, %arg3: !infrt.dense_tensor<CPU, FP32, NCHW>, %arg4: !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW> {
     %2 = "pd.abs"(%arg0) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
-    infrt.return %2 : !infrt.dense_tensor<CPU, FP32, NCHW>
+    %3 = "pd.matmul_v2"(%arg0, %2) {trans_x = false, trans_y = false} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
+    %Y, %MeanOut, %VarianceOut = "pd.batch_norm"(%3, %arg1, %arg2, %arg3, %arg4) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
+    infrt.return %Y : !infrt.dense_tensor<CPU, FP32, NCHW>
   }
   func @main() {
     %ctx = "phi_dt.create_context.cpu" (): () -> !phi.context<CPU>
-    %t = "phi_dt.create_dense_tensor.cpu" (%ctx) {precision=#infrt.precision<FP32>, layout=#infrt.layout<NCHW>, lod=[1:i64], dims=[1:i64]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
+    %t = "phi_dt.create_dense_tensor.cpu" (%ctx) {precision=#infrt.precision<FP32>, layout=#infrt.layout<NCHW>, lod=[1:i64], dims=[1:i64, 3:i64, 8:i64, 8:i64]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
     "phi_dt.fill_dense_tensor.f32"(%t) {value=[3.8:f32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> ()
-    %2 = infrt.call@predict(%t) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
+    %bias = "phi_dt.create_dense_tensor.cpu" (%ctx) {precision=#infrt.precision<FP32>, layout=#infrt.layout<NCHW>, lod=[1:i64], dims=[3:i64]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
+    "phi_dt.fill_dense_tensor.f32"(%bias) {value=[1.5:f32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> ()
+    %mean = "phi_dt.create_dense_tensor.cpu" (%ctx) {precision=#infrt.precision<FP32>, layout=#infrt.layout<NCHW>, lod=[1:i64], dims=[3:i64]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
+    "phi_dt.fill_dense_tensor.f32"(%mean) {value=[3.5:f32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> ()
+    %scale = "phi_dt.create_dense_tensor.cpu" (%ctx) {precision=#infrt.precision<FP32>, layout=#infrt.layout<NCHW>, lod=[1:i64], dims=[3:i64]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
+    "phi_dt.fill_dense_tensor.f32"(%scale) {value=[1.0:f32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> ()
+    %var = "phi_dt.create_dense_tensor.cpu" (%ctx) {precision=#infrt.precision<FP32>, layout=#infrt.layout<NCHW>, lod=[1:i64], dims=[3:i64]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
+    "phi_dt.fill_dense_tensor.f32"(%var) {value=[0.0:f32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> ()
+
+    %2 = infrt.call@predict(%t, %bias, %mean, %scale, %var) : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>,!infrt.dense_tensor<CPU, FP32, NCHW>,!infrt.dense_tensor<CPU, FP32, NCHW>,!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
+
+    //phi_dt.print_tensor(%t : !infrt.dense_tensor<CPU, FP32, NCHW>)
     phi_dt.print_tensor(%2 : !infrt.dense_tensor<CPU, FP32, NCHW>)
     infrt.return
   }

tools/infrt/generate_phi_kernel_dialect.py

@@ -22,7 +22,9 @@
     "i": 'SI32Attr',
     "b": 'BoolAttr',
     "l": 'SI64Attr',
-    "f": 'F32Attr'
+    "f": 'F32Attr',
+    "NSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE": 'StrAttr',
+    "St6vectorIiSaIiEE": 'I32ArrayAttr'
 }
 
 target_type_converter = {"CPU": "CPU", "GPU": "GPU"}

tools/infrt/get_phi_kernel_function.sh

@@ -38,35 +38,36 @@ python3 ${PADDLE_ROOT}/python/paddle/utils/code_gen/wrapped_infermeta_gen.py \
   --wrapped_infermeta_header_path ${temp_path}/generate.h \
   --wrapped_infermeta_source_path ${temp_path}/generate.cc
 
-grep PD_REGISTER_INFER_META_FN ${temp_path}/generate.cc  \
+find  ${PADDLE_ROOT}/paddle/phi/ -name "*.cc" | xargs grep PD_REGISTER_INFER_META_FN ${temp_path}/generate.cc \
   | awk -F "\(|,|::|\)" '{print $2, $4}' > ${temp_path}/wrap_info.txt
 
-
 #step 3:get ir's attr_name.
 ir_attr_name_info_file=`mktemp`
 # phi_cpu attr
-all_ir_name=`grep -Eo "PDTCPU_Kernel<.*\"" paddle/infrt/dialect/phi/ir/phi_cpu_kernels.td | awk -v FS="<" '{gsub(/\"/,"");print $2}'`
+all_ir_name=`grep -Eo "PDTCPU_Kernel<.*\"" ${PADDLE_ROOT}/paddle/infrt/dialect/phi/ir/phi_cpu_kernels.td | awk -v FS="<" '{gsub(/\"/,"");print $2}'`
 for ir in $all_ir_name
 do
-  attr_name=`grep "<\"$ir" -A 3 paddle/infrt/dialect/phi/ir/phi_cpu_kernels.td  | grep -Eo "Attr:.*)" \
+  attr_name=`grep "<\"$ir" -A 3 ${PADDLE_ROOT}/paddle/infrt/dialect/phi/ir/phi_cpu_kernels.td  | grep -Eo "Attr:.*)" \
   | awk '{gsub(/F32Attr/,"");gsub(/F64Attr/,"");gsub(/StrAttr/,"");gsub(/BoolAttr/,""); \
   gsub(/SI1Attr/,"");gsub(/SI8Attr/,"");gsub(/SI16Attr/,"");gsub(/SI32Attr/,"");gsub(/SI64Attr/,""); \
   gsub(/UI1Attr/,"");gsub(/UI8Attr/,"");gsub(/I16Attr/,"");gsub(/I32Attr/,"");gsub(/I64Attr/,""); \
   gsub(/I1Attr/,"");gsub(/I8Attr/,"");gsub(/UI16Attr/,"");gsub(/UI32Attr/,"");gsub(/UI64Attr/,""); \
+  gsub(/I32ArrayAttr/,"");gsub(/SI32ArrayAttr/,""); \
   gsub(/Attr/,"");gsub(/\)/,""); \
   gsub(/[,:]/,"");print $a}'`
   echo phi_cpu.$ir $attr_name >> $ir_attr_name_info_file
 done
 # phi_gpu attr
-all_ir_name=`grep -Eo "PDTGPU_Kernel<.*\"" paddle/infrt/dialect/phi/ir/phi_gpu_kernels.td | awk -v FS="<" '{gsub(/\"/,"");print $2}'`
+all_ir_name=`grep -Eo "PDTGPU_Kernel<.*\"" ${PADDLE_ROOT}/paddle/infrt/dialect/phi/ir/phi_gpu_kernels.td | awk -v FS="<" '{gsub(/\"/,"");print $2}'`
 for ir in $all_ir_name
 do
-  attr_name=`grep "<\"$ir" -A 3 paddle/infrt/dialect/phi/ir/phi_gpu_kernels.td  | grep -Eo "Attr:.*)" \
+  attr_name=`grep "<\"$ir" -A 3 ${PADDLE_ROOT}/paddle/infrt/dialect/phi/ir/phi_gpu_kernels.td  | grep -Eo "Attr:.*)" \
   | awk '{gsub(/F32Attr/,"");gsub(/F64Attr/,"");gsub(/StrAttr/,"");gsub(/BoolAttr/,""); \
   gsub(/SI1Attr/,"");gsub(/SI8Attr/,"");gsub(/SI16Attr/,"");gsub(/SI32Attr/,"");gsub(/SI64Attr/,""); \
   gsub(/UI1Attr/,"");gsub(/UI8Attr/,"");gsub(/I16Attr/,"");gsub(/I32Attr/,"");gsub(/I64Attr/,""); \
   gsub(/I1Attr/,"");gsub(/I8Attr/,"");gsub(/UI16Attr/,"");gsub(/UI32Attr/,"");gsub(/UI64Attr/,""); \
-  gsub(/Attr/,"");gsub(/\)/,""); \
+  gsub(/I32ArrayAttr/,"");gsub(/SI32ArrayAttr/,""); \
+  gsub(/Attr/,"");gsub(/\)/,"") \
   gsub(/[,:]/,"");print $a}'`
   echo phi_gpu.$ir $attr_name >> $ir_attr_name_info_file
 done

tools/infrt/get_phi_kernel_info.py

@@ -91,11 +91,10 @@ def merge(infer_meta_data, kernel_data, wrap_data):
     full_kernel_data = []
     for l in kernel_data:
         key = l.split()[0]
-        if key in meta_map:
-            if key in meta_map:
-                full_kernel_data.append((l + " " + wrap_map[key]).split())
-            else:
-                full_kernel_data.append((l + " " + meta_map[key]).split())
+        if key in wrap_map:
+            full_kernel_data.append((l + " " + wrap_map[key]).split())
+        elif key in meta_map:
+            full_kernel_data.append((l + " " + meta_map[key]).split())
         else:
             full_kernel_data.append((l + " unknown").split())
 
@@ -246,15 +245,10 @@ def gen_register_code_info(item: List[str], attr_data: Dict[str, List[str]]):
 registry->AddKernelWithAttrs("{ir_name}","""
 
             res += f"""
-    std::bind(&KernelLauncherFunc<decltype({kernel_func}),
+    &KernelLauncherFunc<decltype({kernel_func}),
                                   {kernel_func},
                                   decltype({infer_shape_func}),
                                   {infer_shape_func}>,
-              KernelLauncher<decltype({kernel_func}),
-                                  {kernel_func},
-                                  decltype({infer_shape_func}),
-                                  {infer_shape_func}>(),
-              std::placeholders::_1),
     {{{attr_names}}});
 """
 
@@ -263,15 +257,10 @@ def gen_register_code_info(item: List[str], attr_data: Dict[str, List[str]]):
 registry->AddKernel("{ir_name}","""
 
             res += f"""
-    std::bind(&KernelLauncherFunc<decltype({kernel_func}),
-                                  {kernel_func},
-                                  decltype({infer_shape_func}),
-                                  {infer_shape_func}>,
-              KernelLauncher<decltype({kernel_func}),
+    &KernelLauncherFunc<decltype({kernel_func}),
                                   {kernel_func},
                                   decltype({infer_shape_func}),
-                                  {infer_shape_func}>(),
-              std::placeholders::_1));
+                                  {infer_shape_func}>);
 """
 
     return res