[infrt] unify the paddle dialect operation name. test=develop (#38354)

paddle/infrt/dialect/mlir_loader.cc

@@ -34,7 +34,7 @@ namespace infrt::dialect {
 
 mlir::OwningModuleRef LoadMlirSource(mlir::MLIRContext* context,
                                      const std::string& mlir_source) {
-  context->allowUnregisteredDialects();
+  // context->allowUnregisteredDialects();
   RegisterCinnDialects(context->getDialectRegistry());
   context->getDialectRegistry().insert<mlir::StandardOpsDialect>();
 
@@ -54,7 +54,7 @@ mlir::OwningModuleRef LoadMlirSource(mlir::MLIRContext* context,
 
 mlir::OwningModuleRef LoadMlirFile(const std::string& file_name,
                                    mlir::MLIRContext* context) {
-  context->allowUnregisteredDialects();
+  // context->allowUnregisteredDialects();
   RegisterCinnDialects(context->getDialectRegistry());
   context->getDialectRegistry().insert<mlir::StandardOpsDialect>();
 

paddle/infrt/dialect/mlir_tests/paddle_ops.mlir

 func @ops() {
-  %a = pd.Feed() : tensor<?xf32>
-  %b = pd.Feed() : tensor<?xf32>
+  %a = pd.feed() : tensor<?xf32>
+  %b = pd.feed() : tensor<?xf32>
 
-  %c = "pd.Matmul"(%a, %b) {transpose_x=true, transpose_y=false} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
+  %c = "pd.matmul"(%a, %b) {transpose_x=true, transpose_y=false} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
 
   infrt.return
 }

paddle/infrt/dialect/mlir_tests/rewrite.mlir

 // CHECK-LABEL: @main
 func @main() -> tensor<?xf32> {
-  %a = "pd.Feed"() : () -> tensor<?xf32>
-  %b = "pd.Feed"() : () -> tensor<?xf32>
-  %bias = "pd.Feed"() : () -> tensor<?xf32>
+  %a = "pd.feed"() : () -> tensor<?xf32>
+  %b = "pd.feed"() : () -> tensor<?xf32>
+  %bias = "pd.feed"() : () -> tensor<?xf32>
 
-  %b1 = "pd.Feed"() : () -> tensor<?xf32>
-  %b2 = "pd.Feed"() : () -> tensor<?xf32>
-  %bias1 = "pd.Feed"() : () -> tensor<?xf32>
-  %bias2 = "pd.Feed"() : () -> tensor<?xf32>
+  %b1 = "pd.feed"() : () -> tensor<?xf32>
+  %b2 = "pd.feed"() : () -> tensor<?xf32>
+  %bias1 = "pd.feed"() : () -> tensor<?xf32>
+  %bias2 = "pd.feed"() : () -> tensor<?xf32>
 
-  %c = "pd.Matmul"(%a, %b) {transpose_y=false} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
-  %d = "pd.ElementwiseAdd"(%c, %bias) {axis=1:i32} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
-  %e = "pd.Relu6"(%d) {} : (tensor<?xf32>) -> tensor<?xf32>
+  %c = "pd.matmul"(%a, %b) {transpose_y=false} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
+  %d = "pd.elementwise_add"(%c, %bias) {axis=1:i32} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
+  %e = "pd.relu6"(%d) {} : (tensor<?xf32>) -> tensor<?xf32>
 
-  %c1 = "pd.Matmul"(%e, %b1) {transpose_x=false, transpose_y=false} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
-  %d1 = "pd.ElementwiseAdd"(%c1, %bias1) {axis=1:i32} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
-  %e1 = "pd.Relu"(%d1) {} : (tensor<?xf32>) -> tensor<?xf32>
+  %c1 = "pd.matmul"(%e, %b1) {transpose_x=false, transpose_y=false} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
+  %d1 = "pd.elementwise_add"(%c1, %bias1) {axis=1:i32} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
+  %e1 = "pd.relu"(%d1) {} : (tensor<?xf32>) -> tensor<?xf32>
 
-  %c2 = "pd.Matmul"(%e1, %b2) {transpose_x=true, transpose_y=false} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
-  %d2 = "pd.ElementwiseAdd"(%c2, %bias2) {axis=1:i32} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
-  %e2 = "pd.Relu"(%d2) {} : (tensor<?xf32>) -> tensor<?xf32>
+  %c2 = "pd.matmul"(%e1, %b2) {transpose_x=true, transpose_y=false} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
+  %d2 = "pd.elementwise_add"(%c2, %bias2) {axis=1:i32} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
+  %e2 = "pd.relu"(%d2) {} : (tensor<?xf32>) -> tensor<?xf32>
   infrt.return %e2 : tensor<?xf32>
 }
\ No newline at end of file

paddle/infrt/dialect/mlir_tests/rewrite_conv_bn.mlir

 // CHECK-LABEL: @main
 func @main() -> tensor<?xf32> {
-  %a = "pd.Feed"() : () -> tensor<?x3x256x256xf32>
-  %filter = "pd.Constant"(){value = dense<1.000000e+00> : tensor<3x64x3x3xf32>} : () -> tensor<3x64x3x3xf32> 
-  %bias = "pd.Constant"(){value = dense<1.000000e+00> : tensor<64xf32>} : () -> tensor<64xf32>
+  %a = "pd.feed"() : () -> tensor<?x3x256x256xf32>
+  %filter = "pd.constant"(){value = dense<1.000000e+00> : tensor<3x64x3x3xf32>} : () -> tensor<3x64x3x3xf32> 
+  %bias = "pd.constant"(){value = dense<1.000000e+00> : tensor<64xf32>} : () -> tensor<64xf32>
 
-  %scale = "pd.Constant"(){value = dense<1.000000e+00> : tensor<64xf32>} : () -> tensor<64xf32>
-  %bias2 = "pd.Constant"(){value = dense<1.000000e+00> : tensor<64xf32>} : () -> tensor<64xf32>
-  %mean = "pd.Constant"(){value = dense<1.000000e+00> : tensor<64xf32>} : () -> tensor<64xf32>
-  %var = "pd.Constant"(){value = dense<1.000000e+00> : tensor<64xf32>} : () -> tensor<64xf32>
+  %scale = "pd.constant"(){value = dense<1.000000e+00> : tensor<64xf32>} : () -> tensor<64xf32>
+  %bias2 = "pd.constant"(){value = dense<1.000000e+00> : tensor<64xf32>} : () -> tensor<64xf32>
+  %mean = "pd.constant"(){value = dense<1.000000e+00> : tensor<64xf32>} : () -> tensor<64xf32>
+  %var = "pd.constant"(){value = dense<1.000000e+00> : tensor<64xf32>} : () -> tensor<64xf32>
 
   %c = "pd.conv2d"(%a, %filter, %bias) {} : (tensor<?x3x256x256xf32>, tensor<3x64x3x3xf32>, tensor<64xf32>) -> tensor<?x3x256x256xf32>
   %d = "pd.batch_norm"(%c, %scale, %bias2, %mean, %var) {} : (tensor<?x3x256x256xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>) -> tensor<?x3x256x256xf32>

paddle/infrt/dialect/mlir_tests/trt_ops.mlir

+// CHECK-LABEL: @main
+func @main() -> tensor<?xf32> {
+  %a = "pd.feed"() : () -> tensor<?xf32>
+  %b = "pd.feed"() : () -> tensor<?xf32>
+  %bias = "pd.feed"() : () -> tensor<?xf32>
+  %c = "pd.feed"() : () -> tensor<?xf32>
+  %b1 = "pd.feed"() : () -> tensor<?xf32>
+  %b2 = "pd.feed"() : () -> tensor<?xf32>
+  %bias1 = "pd.feed"() : () -> tensor<?xf32>
+  %bias2 = "pd.feed"() : () -> tensor<?xf32>
+
+  %d = "pd.elementwise_add"(%c, %bias) {axis=1:i32} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
+  %e = "pd.relu6"(%d) {} : (tensor<?xf32>) -> tensor<?xf32>
+
+  %c1 = "pd.matmul"(%e, %b1) {transpose_x=false, transpose_y=false} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
+  %d1 = "pd.elementwise_add"(%c1, %bias1) {axis=1:i32} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
+  %e1 = "pd.relu"(%d1) {} : (tensor<?xf32>) -> tensor<?xf32>
+
+  %c2 = "pd.matmul"(%e1, %b2) {transpose_x=true, transpose_y=false} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
+  %d2 = "pd.elementwise_add"(%c2, %bias2) {axis=1:i32} : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
+  %e2 = "pd.relu"(%d2) {} : (tensor<?xf32>) -> tensor<?xf32>
+  
+  "pd.fetch"(%e2) :(tensor<?xf32>)->()
+}

paddle/infrt/dialect/pd_ops.td

@@ -41,7 +41,7 @@ def PD_GraphOp : PD_Op<"graph", [SingleBlockImplicitTerminator<"FetchOp">]> {
   let results = (outs Variadic<PD_Tensor>:$outputs);
 }
 
-def PD_ConstantOp : PD_Op<"Constant", [NoSideEffect, ConstantLike, DeclareOpInterfaceMethods<InferTypeOpInterface>, AllTypesMatch<["value", "output"]>]> {
+def PD_ConstantOp : PD_Op<"constant", [NoSideEffect, ConstantLike, DeclareOpInterfaceMethods<InferTypeOpInterface>, AllTypesMatch<["value", "output"]>]> {
   let summary = "constant Op";
   let description = [{}];
 
@@ -54,7 +54,7 @@ def PD_ConstantOp : PD_Op<"Constant", [NoSideEffect, ConstantLike, DeclareOpInte
   ];
 }
 
-def PD_AbsOp : PD_Op<"Abs", [NoSideEffect, SameOperandsAndResultType]> {
+def PD_AbsOp : PD_Op<"abs", [NoSideEffect, SameOperandsAndResultType]> {
   let summary = "Computes the absolute value of a tensor";
 
   let description = [{
@@ -74,7 +74,7 @@ def PD_SqrtOp : PD_Op<"sqrt", [NoSideEffect, SameOperandsAndResultType]> {
   let results = (outs PD_Tensor:$y);
 }
 
-def PD_ReluOp : PD_Op<"Relu", [NoSideEffect, SameOperandsAndResultType]> {
+def PD_ReluOp : PD_Op<"relu", [NoSideEffect, SameOperandsAndResultType]> {
   let summary = "Computes the Relu of a tensor";
 
   let description = [{
@@ -85,7 +85,7 @@ def PD_ReluOp : PD_Op<"Relu", [NoSideEffect, SameOperandsAndResultType]> {
   let hasCanonicalizer = 1;
 }
 
-def PD_Relu6Op : PD_Op<"Relu6", [NoSideEffect, SameOperandsAndResultType]> {
+def PD_Relu6Op : PD_Op<"relu6", [NoSideEffect, SameOperandsAndResultType]> {
   let summary = "Computes the Relu6 of a tensor";
 
   let description = [{
@@ -95,7 +95,7 @@ def PD_Relu6Op : PD_Op<"Relu6", [NoSideEffect, SameOperandsAndResultType]> {
   let results = (outs PD_Tensor:$y);
 }
 
-def PD_ElementwiseAdd : PD_Op<"ElementwiseAdd", [NoSideEffect, Commutative, DeclareOpInterfaceMethods<InferTypeOpInterface>]> {
+def PD_ElementwiseAdd : PD_Op<"elementwise_add", [NoSideEffect, Commutative, DeclareOpInterfaceMethods<InferTypeOpInterface>]> {
   let summary = "ElementwiseAdd Op";
   let description = [{
   }];
@@ -106,7 +106,7 @@ def PD_ElementwiseAdd : PD_Op<"ElementwiseAdd", [NoSideEffect, Commutative, Decl
   let hasFolder = 1;
 }
 
-def PD_ElementwiseSub : PD_Op<"ElementwiseSub", [NoSideEffect, DeclareOpInterfaceMethods<InferTypeOpInterface>]> {
+def PD_ElementwiseSub : PD_Op<"elementwise_sub", [NoSideEffect, DeclareOpInterfaceMethods<InferTypeOpInterface>]> {
   let summary = "ElementwiseSub Op";
   let description = [{
   }];
@@ -115,7 +115,7 @@ def PD_ElementwiseSub : PD_Op<"ElementwiseSub", [NoSideEffect, DeclareOpInterfac
   let results = (outs PD_Tensor:$out);
 }
 
-def PD_ElementwiseMul : PD_Op<"ElementwiseMul", [NoSideEffect, Commutative, DeclareOpInterfaceMethods<InferTypeOpInterface>]> {
+def PD_ElementwiseMul : PD_Op<"elementwise_mul", [NoSideEffect, Commutative, DeclareOpInterfaceMethods<InferTypeOpInterface>]> {
   let summary = "ElementwiseMul Op";
   let description = [{
   }];
@@ -124,7 +124,7 @@ def PD_ElementwiseMul : PD_Op<"ElementwiseMul", [NoSideEffect, Commutative, Decl
   let results = (outs PD_Tensor:$out);
 }
 
-def PD_ElementwiseDiv : PD_Op<"ElementwiseDiv", [NoSideEffect, DeclareOpInterfaceMethods<InferTypeOpInterface>]> {
+def PD_ElementwiseDiv : PD_Op<"elementwise_div", [NoSideEffect, DeclareOpInterfaceMethods<InferTypeOpInterface>]> {
   let summary = "ElementwiseDiv Op";
   let description = [{
   }];
@@ -133,7 +133,7 @@ def PD_ElementwiseDiv : PD_Op<"ElementwiseDiv", [NoSideEffect, DeclareOpInterfac
   let results = (outs PD_Tensor:$out);
 }
 
-def PD_MatmulOp : PD_Op<"Matmul", [NoSideEffect]> {
+def PD_MatmulOp : PD_Op<"matmul", [NoSideEffect]> {
   let summary = "Computes the matrix mulplication result of two tensors";
   let description = [{
   }];
@@ -181,7 +181,7 @@ def PD_BatchNormOp : PD_Op<"batch_norm", [NoSideEffect]> {
   let hasCanonicalizer = 1;
 }
 
-def PD_FusedFC : PD_Op<"FC", [NoSideEffect]> {
+def PD_FusedFC : PD_Op<"fc", [NoSideEffect]> {
     let summary = "Computes the Fully Connected result of two tensors";
     let description = [{
     }];
@@ -190,7 +190,7 @@ def PD_FusedFC : PD_Op<"FC", [NoSideEffect]> {
     let results = (outs PD_Tensor:$out);
 }
 
-def PD_FusedRepeatedFCRelu : PD_Op<"RepeatedFCRelu", [SameVariadicOperandSize, NoSideEffect]> {
+def PD_FusedRepeatedFCRelu : PD_Op<"fusion_repeated_fc_relu", [SameVariadicOperandSize, NoSideEffect]> {
     let summary = "";
     let description = [{ }];
 

paddle/infrt/dialect/print_ir.cc

@@ -115,7 +115,7 @@ int main(int argc, char **argv) {
   cl::ParseCommandLineOptions(argc, argv, "mlir demo");
 
   mlir::MLIRContext *context = infrt::Global::getMLIRContext();
-  context->allowUnregisteredDialects();
+  // context->allowUnregisteredDialects();
   auto &registry = context->getDialectRegistry();
   infrt::RegisterCinnDialects(registry);