[infrt] fix bug in emit si32 attribute. (#40860)

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

@@ -97,8 +97,9 @@ void PhiOpConvertPass::convertStage() {
     }
     auto loc = getFunction().getLoc();
     builder.setInsertionPoint(op);
+
+    if (!::phi::OpUtilsMap::Instance().HasArgumentMappingFn(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(),

paddle/infrt/host_context/mlir_to_runtime_translate.cc

@@ -130,7 +130,7 @@ boost::optional<int32_t> MlirToRuntimeTranslator::EmitAttribute(
   if (attr.isa<mlir::IntegerAttr>()) {
     auto val = attr.cast<mlir::IntegerAttr>();
     if (val.getType().isInteger(32)) {
-      return val.getInt();
+      return val.getValue().getSExtValue();
     }
   }
   return boost::none;
@@ -142,7 +142,7 @@ boost::optional<int64_t> MlirToRuntimeTranslator::EmitAttribute(
   if (attr.isa<mlir::IntegerAttr>()) {
     auto val = attr.cast<mlir::IntegerAttr>();
     if (val.getType().isInteger(64)) {
-      return val.getInt();
+      return val.getValue().getSExtValue();
     }
   }
   return boost::none;
@@ -233,7 +233,7 @@ boost::optional<std::string> MlirToRuntimeTranslator::EmitAttribute(
                                                                                \
     std::vector<type__> res;                                                   \
     for (auto& v : array) {                                                    \
-      res.push_back(v.cast<mlir::IntegerAttr>().getInt());                     \
+      res.push_back(v.cast<mlir::IntegerAttr>().getValue().getSExtValue());    \
     }                                                                          \
     return res;                                                                \
   }

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

 // RUN: infrtexec -i %s
 module  {
-  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> {
+  func @predict(%arg0: !infrt.dense_tensor<CPU, FP32, NCHW>,%filter: !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>
     %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>
+    %4 = "pd.conv2d"(%3, %filter) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [2 : i32, 2 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
+    %Y, %MeanOut, %VarianceOut = "pd.batch_norm"(%4, %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>)
+    %out = "pd.relu"(%Y) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
+    %5 = "pd.elementwise_add"(%out, %out) {axis = -1:si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
+    infrt.return %5 : !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, 3:i64, 8:i64, 8: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], dims=[1, 3, 8, 8]}: (!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>) -> ()
-    %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>)
+    %filter = "phi_dt.create_dense_tensor.cpu" (%ctx) {precision=#infrt.precision<FP32>, layout=#infrt.layout<NCHW>, lod=[1], dims=[3, 3, 8, 8]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
+    "phi_dt.fill_dense_tensor.f32"(%filter) {value=[3.8:f32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> ()
+    %bias = "phi_dt.create_dense_tensor.cpu" (%ctx) {precision=#infrt.precision<FP32>, layout=#infrt.layout<NCHW>, lod=[1], dims=[3]}: (!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>)
+    %mean = "phi_dt.create_dense_tensor.cpu" (%ctx) {precision=#infrt.precision<FP32>, layout=#infrt.layout<NCHW>, lod=[1], dims=[3]}: (!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>)
+    %scale = "phi_dt.create_dense_tensor.cpu" (%ctx) {precision=#infrt.precision<FP32>, layout=#infrt.layout<NCHW>, lod=[1], dims=[3]}: (!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>)
+    %var = "phi_dt.create_dense_tensor.cpu" (%ctx) {precision=#infrt.precision<FP32>, layout=#infrt.layout<NCHW>, lod=[1], dims=[3]}: (!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>
+    %2 = infrt.call@predict(%t, %filter, %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>) -> !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>)

paddle/phi/core/compat/op_utils.h

@@ -124,6 +124,10 @@ class OpUtilsMap {
         {std::move(op_type), std::move(base_kernel_name)});
   }
 
+  bool HasArgumentMappingFn(const std::string& op_type) const {
+    return arg_mapping_fn_map_.count(op_type);
+  }
+
   void InsertArgumentMappingFn(std::string op_type, ArgumentMappingFn fn) {
     PADDLE_ENFORCE_EQ(
         arg_mapping_fn_map_.count(op_type),