Merge branch 'master' of github.com:clang-ykt/ONNF into shapeinference-pad

doc/gen_doc.py

@@ -54,6 +54,15 @@ ShapeInferenceList=['Exp', 'Tanh', 'Sinh', 'Cosh', 'Sigmoid', 'Relu',
 CanonicalList=['Add', 'Identity', 'ReduceL1', 'ReduceL2', 'ReduceLogSum',
                'ReduceLogSumExp', 'ReduceSumSquare']
 
+#add an Op in this list if the Op needs result type deduction which is required
+#when writing declarative rewriting rules. Deduced type is always
+#an UnrankedTensorType whose element type is the same as the first operand's
+#element type.
+#currenlty, there are only two build methods generated:
+# - one with operands and attributes having a separate parameter, and
+# - one with operands and attributes having aggregated parameters.
+custom_builder_ops_list = ['Abs', 'Mul', 'Exp', 'ReduceSum', 'ReduceSumSquare']
+
 manual_code_in_op_def = dict([
       ('DummyExample', '  let extraClassDeclaration = [{ \n'+
                     '    static StringRef getPermAttrName() { return "perm"; }\n'+
@@ -345,38 +354,23 @@ def gen_schema(schema) :
     #input
     s+= '\n'+line_indent+'let arguments = (ins '
     isfirst = True
-    if schema.inputs:
+    # add operands
-        isfirst = False
+    operand_ins = get_operand_ins(schema)
-        for input in schema.inputs:
+    for operand_type, operand_name in operand_ins:
-            if input != schema.inputs[0] :
+        if not isfirst:
             s+= ',\n           '
-            etypes=collect_types(schema, input)
+        else:
+            isfirst = False
+        s+=operand_type+':$'+operand_name
 
-            if OpSchema.FormalParameterOption.Optional == input.option:
+    # add attributes
-                #TODO: handle optional
+    attr_ins = get_attr_ins(schema)
-                print("warning: optional input for"+schema.name+' '+input.name)
+    for attr_type, attr_name in attr_ins:
-            elif OpSchema.FormalParameterOption.Variadic == input.option:
+        if not isfirst:
-                if input.isHomogeneous:
+            s += ',\n           '
-                    s+= 'Variadic<'
         else :
-                    #TODO handle  (variadic, heterogeneous)"
+            isfirst = False
-                    print("warning: (variadic, heterogeneous) for"+schema.name+' '+input.name)
+        s += attr_type+':$'+attr_name
-            if etypes == '':
-                s+= 'AnyTypeOf<[AnyMemRef, AnyTensor]>'
-            else:
-                s+= 'TensorOf<['+etypes+']>'
-
-            if OpSchema.FormalParameterOption.Optional == input.option:
-                #TODO: handle optional
-                t=''
-            elif OpSchema.FormalParameterOption.Variadic == input.option:
-                if input.isHomogeneous:
-                    s+= '>'
-                else:
-                    #TODO handle  (variadic, heterogeneous)"
-                    t=''
-            s+=':$'+input.name
-    s += gen_attr_ins(schema, isfirst)
     s+= ');'
 
     #output
@@ -395,6 +389,71 @@ def gen_schema(schema) :
     s+= ');\n'
 
     #s+= 'let hasCanonicalizer = 1;'
+
+    #TODO: any better way to do this.
+    def get_attr_type_for_builder(attr_type) :
+        if 'I64Attr' in attr_type :
+            mytype = 'IntegerAttr'
+        elif 'F32Attr' in attr_type :
+            mytype = 'FloatAttr'
+        elif 'I64ArrayAttr' in attr_type or 'F32ArrayAttr' in attr_type:
+            mytype = 'ArrayAttr'
+        elif 'StrAttr' in attr_type :
+            mytype = 'StringAttr'
+        elif 'strings' in attr_type :
+            mytype = 'ArrayAttr'
+        else :
+            mytype ='Attribute'
+        return mytype
+
+    def get_op_type_for_builder(op_type):
+        if op_type.startswith('Variadic'):
+            mytype = 'ValueRange'
+        else:
+            mytype = 'Value'
+        return mytype
+
+    # add custom builders
+    # use element type of the first operand to construct an UnrankedTensorType for the output.
+    if schema.name in custom_builder_ops_list:
+        if len(operand_ins) == 0:
+            print("warning: not generate custom build methods for " + schema.name + " since it does not have operands.")
+        else:
+            if get_op_type_for_builder(operand_ins[0][0]) == 'ValueRange':
+                first_operand = operand_ins[0][1]+'[0]'
+            else:
+                first_operand = operand_ins[0][1]
+
+            s += line_indent+'let builders = [\n'
+
+            # custom builders with operands and attributes having a seperate parameter.
+            # E.g. OpBuilder<"Builder *builder, OperationState &state, Value X, Value, Y, Attribute A", [{}]>
+            s += line_indent*2+'OpBuilder<"Builder *builder, OperationState &state'
+            for arg_type, arg_name in operand_ins:
+                s += ', '+get_op_type_for_builder(arg_type)+' '+arg_name
+            for attr_type, attr_name in attr_ins:
+                s += ', '+get_attr_type_for_builder(attr_type)+' '+attr_name
+            s += '", [{\n'
+            s += line_indent*3+'auto elementType = '+first_operand+'.getType().cast<TensorType>().getElementType();\n'
+            s += line_indent*3+'build(builder, state, UnrankedTensorType::get(elementType)'
+            for _, arg_name in operand_ins:
+                s += ', '+arg_name
+            for _, attr_name in attr_ins:
+                s += ', '+attr_name
+            s += ');\n'
+            s += line_indent*2+'}]>,\n'
+
+            # custom builders with all operands and attributes having aggregate parameters.
+            # E.g. OpBuilder<"Builder *builder, OperationState &state, ValueRange operands, ArrayRef<NamedAttribute> attributes", [{}]>'
+            s += line_indent*2+'OpBuilder<"Builder *builder, OperationState &state, ValueRange operands, ArrayRef<NamedAttribute> attributes", [{\n'
+            s += line_indent*3+'auto elementType = '+first_operand+'.getType().cast<TensorType>().getElementType();\n'
+            s += line_indent*3+'std::vector<mlir::Type> outputTypes;\n'
+            s += line_indent*3+'outputTypes.emplace_back(UnrankedTensorType::get(elementType));\n'
+            s += line_indent*3+'build(builder, state, outputTypes, operands, attributes);\n'
+            s += line_indent*2+'}]>'
+
+            s += '\n'+line_indent+'];\n'
+
     #add special code
     if schema.name in manual_code_in_op_def :
         s += manual_code_in_op_def[schema.name]
@@ -447,7 +506,41 @@ def gen_code(schema,fefile) :
     else:
         fefile.write(', '+variadicIn+', '+variadicOut+');\n')
 
-def gen_attr_ins(schema, isfirst) :
+def get_operand_ins(schema):
+    operand_type_and_name_list = []  # [(optype, opname)]
+    if schema.inputs:
+        for input in schema.inputs:
+            optype = ""
+
+            etypes=collect_types(schema, input)
+
+            if OpSchema.FormalParameterOption.Optional == input.option:
+                #TODO : handle optional
+                print("warning: optional input for"+schema.name+' '+input.name)
+            elif OpSchema.FormalParameterOption.Variadic == input.option:
+                if input.isHomogeneous:
+                    optype += 'Variadic<'
+                else:
+                    #TODO handle(variadic, heterogeneous) "
+                    print("warning: (variadic, heterogeneous) for"+schema.name+' '+input.name)
+            if etypes == '':
+                optype += 'AnyTypeOf<[AnyMemRef, AnyTensor]>'
+            else:
+                optype += 'TensorOf<['+etypes+']>'
+
+            if OpSchema.FormalParameterOption.Optional == input.option:
+                #TODO : handle optional
+                t=''
+            elif OpSchema.FormalParameterOption.Variadic == input.option:
+                if input.isHomogeneous:
+                    optype += '>'
+                else:
+                    #TODO handle(variadic, heterogeneous) "
+                    t=''
+            operand_type_and_name_list.append((optype, input.name))
+    return operand_type_and_name_list
+
+def get_attr_ins(schema) :
     
     def get_attr_type_basic(attr_type) :
         if attr_type == 'int' :
@@ -479,24 +572,22 @@ def gen_attr_ins(schema, isfirst) :
         mytype += ', "'+attr_default+'">'
         return mytype
 
+    attr_type_and_name_list = []  # :: [(attrtype, attrname)]
     attr_line = ''
     if schema.attributes:
         for _, attr in sorted(schema.attributes.items()):
             #attr_line = line_indent+line_indent+line_indent+line_indent
-            if not isfirst:
+            found = False
-                attr_line += ',\n           '
+            attr_type = ""
-            else :
-                isfirst = False
-            
             if schema.name+' '+attr.name in special_attr_defaults:
                 (attr_type_str, attr_default_str) = special_attr_defaults[schema.name+' '+attr.name]
-                attr_line += get_attr_type_with_default(attr_type_str, attr_default_str)
+                attr_type = get_attr_type_with_default(attr_type_str, attr_default_str)
-                attr_line += ':$'+attr.name
+                found = True
             elif attr.required:
                 s = Text(attr.type)
                 attr_type_str  = s[s.rfind('.') + 1:].lower()
-                attr_line += get_attr_type_basic(attr_type_str)
+                attr_type = get_attr_type_basic(attr_type_str)
-                attr_line += ':$'+attr.name
+                found = True
 
             # option holds either required or default value
             elif attr.default_value.name:
@@ -527,14 +618,15 @@ def gen_attr_ins(schema, isfirst) :
                 else:
                     default_value = format_value(default_value)
                     attr_option_str = default_value
-                attr_line += get_attr_type_with_default(attr_type_str, attr_option_str)
+                attr_type = get_attr_type_with_default(attr_type_str, attr_option_str)
-                attr_line += ':$'+attr.name
+                found = True
             else:
                 s = Text(attr.type)
                 attr_type_str  = s[s.rfind('.') + 1:].lower()
-                attr_line += get_attr_type_optional(attr_type_str)
+                attr_type = get_attr_type_optional(attr_type_str)
-                attr_line += ':$'+attr.name
+            if found:
-    return attr_line
+                attr_type_and_name_list.append((attr_type, attr.name))
+    return attr_type_and_name_list
 
 def main(args):  # type: (Type[Args]) -> None
     with io.open(args.changelog, 'w', newline='') as fout:

src/CMakeLists.txt

@@ -36,6 +36,11 @@ onnf_tablegen(onnx_combine.inc -gen-rewriters)
 add_public_tablegen_target(gen_onnx_combine)
 add_dependencies(compiler gen_onnx_combine)
 
+set(LLVM_TARGET_DEFINITIONS pass/onnx_rewrite.td)
+onnf_tablegen(onnx_rewrite.inc -gen-rewriters)
+add_public_tablegen_target(gen_onnx_rewrite)
+add_dependencies(compiler gen_onnx_rewrite)
+
 set(LLVM_TARGET_DEFINITIONS dialect/onnx/onnx.td)
 onnf_tablegen(onnx.hpp.inc -gen-op-decls "-I${CMAKE_SOURCE_DIR}/compiler/pass")
 onnf_tablegen(onnx.cpp.inc -gen-op-defs "-I${CMAKE_SOURCE_DIR}/compiler/pass")

src/dialect/onnx/onnxop.inc

@@ -14,6 +14,18 @@ def ONNXAbsOp:ONNX_Op<"Abs",
   }];
   let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
   let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$Y);
+  let builders = [
+    OpBuilder<"Builder *builder, OperationState &state, Value X", [{
+      auto elementType = X.getType().cast<TensorType>().getElementType();
+      build(builder, state, UnrankedTensorType::get(elementType), X);
+    }]>,
+    OpBuilder<"Builder *builder, OperationState &state, ValueRange operands, ArrayRef<NamedAttribute> attributes", [{
+      auto elementType = operands[0].getType().cast<TensorType>().getElementType();
+      std::vector<mlir::Type> outputTypes;
+      outputTypes.emplace_back(UnrankedTensorType::get(elementType));
+      build(builder, state, outputTypes, operands, attributes);
+    }]>
+  ];
 }
 
 def ONNXAcosOp:ONNX_Op<"Acos", 
@@ -649,6 +661,18 @@ def ONNXExpOp:ONNX_Op<"Exp",
   }];
   let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$input);
   let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$output);
+  let builders = [
+    OpBuilder<"Builder *builder, OperationState &state, Value input", [{
+      auto elementType = input.getType().cast<TensorType>().getElementType();
+      build(builder, state, UnrankedTensorType::get(elementType), input);
+    }]>,
+    OpBuilder<"Builder *builder, OperationState &state, ValueRange operands, ArrayRef<NamedAttribute> attributes", [{
+      auto elementType = operands[0].getType().cast<TensorType>().getElementType();
+      std::vector<mlir::Type> outputTypes;
+      outputTypes.emplace_back(UnrankedTensorType::get(elementType));
+      build(builder, state, outputTypes, operands, attributes);
+    }]>
+  ];
 }
 
 def ONNXExpandOp:ONNX_Op<"Expand", 
@@ -1763,6 +1787,18 @@ def ONNXMulOp:ONNX_Op<"Mul",
   let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$A,
            AnyTypeOf<[AnyMemRef, AnyTensor]>:$B);
   let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$C);
+  let builders = [
+    OpBuilder<"Builder *builder, OperationState &state, Value A, Value B", [{
+      auto elementType = A.getType().cast<TensorType>().getElementType();
+      build(builder, state, UnrankedTensorType::get(elementType), A, B);
+    }]>,
+    OpBuilder<"Builder *builder, OperationState &state, ValueRange operands, ArrayRef<NamedAttribute> attributes", [{
+      auto elementType = operands[0].getType().cast<TensorType>().getElementType();
+      std::vector<mlir::Type> outputTypes;
+      outputTypes.emplace_back(UnrankedTensorType::get(elementType));
+      build(builder, state, outputTypes, operands, attributes);
+    }]>
+  ];
 }
 
 def ONNXMultinomialOp:ONNX_Op<"Multinomial", 
@@ -2431,6 +2467,18 @@ def ONNXReduceSumOp:ONNX_Op<"ReduceSum",
            OptionalAttr<I64ArrayAttr>:$axes,
            DefaultValuedAttr<I64Attr, "1">:$keepdims);
   let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$reduced);
+  let builders = [
+    OpBuilder<"Builder *builder, OperationState &state, Value data, ArrayAttr axes, IntegerAttr keepdims", [{
+      auto elementType = data.getType().cast<TensorType>().getElementType();
+      build(builder, state, UnrankedTensorType::get(elementType), data, axes, keepdims);
+    }]>,
+    OpBuilder<"Builder *builder, OperationState &state, ValueRange operands, ArrayRef<NamedAttribute> attributes", [{
+      auto elementType = operands[0].getType().cast<TensorType>().getElementType();
+      std::vector<mlir::Type> outputTypes;
+      outputTypes.emplace_back(UnrankedTensorType::get(elementType));
+      build(builder, state, outputTypes, operands, attributes);
+    }]>
+  ];
 }
 
 def ONNXReduceSumSquareOp:ONNX_Op<"ReduceSumSquare", 
@@ -2449,6 +2497,18 @@ def ONNXReduceSumSquareOp:ONNX_Op<"ReduceSumSquare",
            OptionalAttr<I64ArrayAttr>:$axes,
            DefaultValuedAttr<I64Attr, "1">:$keepdims);
   let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$reduced);
+  let builders = [
+    OpBuilder<"Builder *builder, OperationState &state, Value data, ArrayAttr axes, IntegerAttr keepdims", [{
+      auto elementType = data.getType().cast<TensorType>().getElementType();
+      build(builder, state, UnrankedTensorType::get(elementType), data, axes, keepdims);
+    }]>,
+    OpBuilder<"Builder *builder, OperationState &state, ValueRange operands, ArrayRef<NamedAttribute> attributes", [{
+      auto elementType = operands[0].getType().cast<TensorType>().getElementType();
+      std::vector<mlir::Type> outputTypes;
+      outputTypes.emplace_back(UnrankedTensorType::get(elementType));
+      build(builder, state, outputTypes, operands, attributes);
+    }]>
+  ];
 }
 
 def ONNXReluOp:ONNX_Op<"Relu", 

src/pass/onnx_rewrite.cpp

@@ -17,252 +17,8 @@
 using namespace mlir;
 
 namespace {
-
+/// Include the patterns defined in the Declarative Rewrite framework.
-// There are two ways to write rewrite rules:
+#include "src/onnx_rewrite.inc"
-// - Declarative manner: specify rewrite rules in a TableGen record, and
-// - Manual Manner: subclass the mlir::RewritePattern.
-//
-// We prefer to use the former way as much as possible. However, there is a
-// limitation about operation definition specification (ODS) in TableGen that
-// requires us to write custom builders, that is
-// "all ODS-generated `build()` methods require specifying the result type(s),
-// unless the op has known traits like `SameOperandsAndResultType` that we can
-// use to auto-generate a `build()` method with result type deduction".
-//
-// More information about the limitation can be found here:
-// https://github.com/llvm/llvm-project/blob/master/mlir/docs/DeclarativeRewrites.md#building-operations
-//
-// Currently, we use the latter way of writing rewrite rules. There are two
-// reasons for this decision:
-// - To insert custom builders for operations, it is better to change the script
-// gen_doc.py to generate all possibles custom builders for a large class of
-// operations. At the time of this patch created, the gen_doc.py was changing,
-// so we decided to write manually to reduce conflicts.
-// - In declarative rewriting, we should deal with optional attributes. E.g. for
-// to handle optional attributes, but I haven't tried it yet.
-//
-// Once we have done the above issues, we will switch to use the declarative
-// manner.
-
-//===----------------------------------------------------------------------===//
-// ONNXReduceL1Op %X = ONNXReduceSumOp (ONNXAbsOp %X)
-//===----------------------------------------------------------------------===//
-struct ReduceL1OpPattern : public RewritePattern {
-  ReduceL1OpPattern(MLIRContext *context)
-      : RewritePattern(ONNXReduceL1Op::getOperationName(),
-                       {ONNXAbsOp::getOperationName(),
-                        ONNXReduceSumOp::getOperationName()},
-                       1, context) {}
-
-  PatternMatchResult matchAndRewrite(Operation *op,
-                                     PatternRewriter &rewriter) const override {
-    auto loc = op->getLoc();
-    auto opInput = op->getOperands()[0]; // %X
-    auto opResults = op->getResults();
-    auto opAttrs = op->getAttrs();
-
-    // Rewrite
-    ONNXAbsOp absOp;
-    {
-      auto elementType = opInput.getType().cast<TensorType>().getElementType();
-      absOp = rewriter.create<ONNXAbsOp>(
-          loc, UnrankedTensorType::get(elementType), opInput);
-    }
-
-    ONNXReduceSumOp sumOp;
-    {
-      SmallVector<Type, 4> types;
-      for (auto v : opResults) {
-        types.emplace_back(v.getType());
-      }
-
-      SmallVector<Value, 1> values;
-      values.emplace_back(absOp.getResult());
-
-      SmallVector<NamedAttribute, 4> attrs;
-      for (auto attr : opAttrs) {
-        attrs.emplace_back(attr);
-      }
-
-      sumOp = rewriter.create<ONNXReduceSumOp>(loc, types, values, attrs);
-    }
-
-    rewriter.replaceOp(op, sumOp.getResult());
-    return matchSuccess();
-  };
-};
-
-//===----------------------------------------------------------------------===//
-// ONNXReduceL2Op %X = ONNXSqrtOp (ONNXReduceSumSquareOp (%X))
-//===----------------------------------------------------------------------===//
-struct ReduceL2OpPattern : public RewritePattern {
-  ReduceL2OpPattern(MLIRContext *context)
-      : RewritePattern(ONNXReduceL2Op::getOperationName(),
-                       {ONNXSqrtOp::getOperationName(),
-                        ONNXReduceSumSquareOp::getOperationName()},
-                       1, context) {}
-
-  PatternMatchResult matchAndRewrite(Operation *op,
-                                     PatternRewriter &rewriter) const override {
-    auto loc = op->getLoc();
-    auto opInput = op->getOperands()[0]; // %X
-    auto opResults = op->getResults();
-    auto opAttrs = op->getAttrs();
-
-    // Rewrite
-    ONNXReduceSumSquareOp sumSquareOp;
-    {
-      auto elementType = opInput.getType().cast<TensorType>().getElementType();
-      sumSquareOp = rewriter.create<ONNXReduceSumSquareOp>(
-          loc, UnrankedTensorType::get(elementType), opInput, opAttrs);
-    }
-
-    ONNXSqrtOp sqrtOp;
-    {
-      SmallVector<Type, 4> types;
-      for (auto v : opResults) {
-        types.emplace_back(v.getType());
-      }
-      sqrtOp = rewriter.create<ONNXSqrtOp>(loc, types, sumSquareOp.getResult());
-    }
-
-    rewriter.replaceOp(op, sqrtOp.getResult());
-    return matchSuccess();
-  };
-};
-
-//===----------------------------------------------------------------------===//
-// ONNXReduceLogSumOp %X = ONNXLogOp (ONNXReduceSumOp (%X))
-//===----------------------------------------------------------------------===//
-struct ReduceLogSumOpPattern : public RewritePattern {
-  ReduceLogSumOpPattern(MLIRContext *context)
-      : RewritePattern(ONNXReduceLogSumOp::getOperationName(),
-                       {ONNXReduceSumOp::getOperationName(),
-                        ONNXLogOp::getOperationName()},
-                       1, context) {}
-
-  PatternMatchResult matchAndRewrite(Operation *op,
-                                     PatternRewriter &rewriter) const override {
-    auto loc = op->getLoc();
-    auto opInput = op->getOperands()[0]; // %X
-    auto opResults = op->getResults();
-    auto opAttrs = op->getAttrs();
-
-    // Rewrite
-    ONNXReduceSumOp sumOp;
-    {
-      auto elementType = opInput.getType().cast<TensorType>().getElementType();
-      sumOp = rewriter.create<ONNXReduceSumOp>(
-          loc, UnrankedTensorType::get(elementType), opInput, opAttrs);
-    }
-
-    ONNXLogOp logOp;
-    {
-      SmallVector<Type, 4> types;
-      for (auto v : opResults) {
-        types.emplace_back(v.getType());
-      }
-      logOp = rewriter.create<ONNXLogOp>(loc, types, sumOp.getResult());
-    }
-
-    rewriter.replaceOp(op, logOp.getResult());
-    return matchSuccess();
-  };
-};
-
-//===----------------------------------------------------------------------===//
-// ONNXReduceLogSumExpOp %X = ONNXReduceLogSumOp (ONNXExpOp %X)
-//===----------------------------------------------------------------------===//
-struct ReduceLogSumExpOpPattern : public RewritePattern {
-  ReduceLogSumExpOpPattern(MLIRContext *context)
-      : RewritePattern(ONNXReduceLogSumExpOp::getOperationName(),
-                       {ONNXExpOp::getOperationName(),
-                        ONNXReduceLogSumOp::getOperationName()},
-                       1, context) {}
-
-  PatternMatchResult matchAndRewrite(Operation *op,
-                                     PatternRewriter &rewriter) const override {
-    auto loc = op->getLoc();
-    auto opInput = op->getOperands()[0]; // %X
-    auto opResults = op->getResults();
-    auto opAttrs = op->getAttrs();
-
-    // Rewrite
-    ONNXExpOp expOp;
-    {
-      auto elementType = opInput.getType().cast<TensorType>().getElementType();
-      expOp = rewriter.create<ONNXExpOp>(
-          loc, UnrankedTensorType::get(elementType), opInput);
-    }
-
-    ONNXReduceLogSumOp logSumOp;
-    {
-      SmallVector<Type, 4> types;
-      for (auto v : opResults) {
-        types.emplace_back(v.getType());
-      }
-
-      SmallVector<Value, 1> values;
-      values.emplace_back(expOp.getResult());
-
-      SmallVector<NamedAttribute, 4> attrs;
-      for (auto attr : opAttrs) {
-        attrs.emplace_back(attr);
-      }
-      logSumOp = rewriter.create<ONNXReduceLogSumOp>(loc, types, values, attrs);
-    }
-
-    rewriter.replaceOp(op, logSumOp.getResult());
-    return matchSuccess();
-  };
-};
-
-//===----------------------------------------------------------------------===//
-// ONNXReduceSumSquareOp %X = ONNXReduceSumOp (ONNXMulOp %X, %X)
-//===----------------------------------------------------------------------===//
-struct ReduceSumSquareOpPattern : public RewritePattern {
-  ReduceSumSquareOpPattern(MLIRContext *context)
-      : RewritePattern(ONNXReduceSumSquareOp::getOperationName(),
-                       {ONNXMulOp::getOperationName(),
-                        ONNXReduceSumOp::getOperationName()},
-                       1, context) {}
-
-  PatternMatchResult matchAndRewrite(Operation *op,
-                                     PatternRewriter &rewriter) const override {
-    auto loc = op->getLoc();
-    auto opInput = op->getOperands()[0]; // %X
-    auto opResults = op->getResults();
-    auto opAttrs = op->getAttrs();
-
-    // Rewrite
-    ONNXMulOp mulOp;
-    {
-      auto elementType = opInput.getType().cast<TensorType>().getElementType();
-      mulOp = rewriter.create<ONNXMulOp>(
-          loc, UnrankedTensorType::get(elementType), opInput, opInput);
-    }
-
-    ONNXReduceSumOp sumOp;
-    {
-      SmallVector<Type, 4> types;
-      for (auto v : opResults) {
-        types.emplace_back(v.getType());
-      }
-
-      SmallVector<Value, 1> values;
-      values.emplace_back(mulOp.getResult());
-
-      SmallVector<NamedAttribute, 4> attrs;
-      for (auto attr : opAttrs) {
-        attrs.emplace_back(attr);
-      }
-      sumOp = rewriter.create<ONNXReduceSumOp>(loc, types, values, attrs);
-    }
-
-    rewriter.replaceOp(op, sumOp.getResult());
-    return matchSuccess();
-  };
-};
 
 //===----------------------------------------------------------------------===//
 // Rewrite:

src/pass/onnx_rewrite.td

@@ -0,0 +1,57 @@
+//===----------------------------------------------------------------------===//
+//=- onnx_rewrite.td - Pattern Match Rewriting for ONNX -*- tablegen -*----===//
+//
+// Copyright 2019 The IBM Research Authors.
+//
+// =============================================================================
+//
+// Defines language-specific pattern match optimizations for ONNX using
+// Declarative Rewrite Rules (DRR) specified using TableGen records.
+//
+
+#ifndef ONNX_REWRITE
+#define ONNX_REWRITE
+
+#ifndef OP_BASE
+include "dialect/onnx/onnx.td"
+#endif // OP_BASE
+
+/// Note: The DRR definition used for defining patterns is shown below:
+///
+/// class Pattern<
+///    dag sourcePattern, list<dag> resultPatterns,
+///    list<dag> additionalConstraints = [],
+///    dag benefitsAdded = (addBenefit 0)
+/// >;
+
+//===----------------------------------------------------------------------===//
+// ONNXReduceL1Op %X = ONNXReduceSumOp (ONNXAbsOp %X)
+//===----------------------------------------------------------------------===//
+def ReduceL1OpPattern: Pat<(ONNXReduceL1Op $oprd, $axes, $keepdims),
+                           (ONNXReduceSumOp (ONNXAbsOp $oprd), $axes, $keepdims)>;
+
+//===----------------------------------------------------------------------===//
+// ONNXReduceL2Op %X = ONNXSqrtOp (ONNXReduceSumSquareOp (%X))
+//===----------------------------------------------------------------------===//
+def ReduceL2OpPattern: Pat<(ONNXReduceL2Op $oprd, $axes, $keepdims),
+                           (ONNXSqrtOp (ONNXReduceSumSquareOp $oprd, $axes, $keepdims))>;
+
+//===----------------------------------------------------------------------===//
+// ONNXReduceLogSumOp %X = ONNXLogOp (ONNXReduceSumOp (%X))
+//===----------------------------------------------------------------------===//
+def ReduceLogSumOpPattern: Pat<(ONNXReduceLogSumOp $oprd, $axes, $keepdims),
+                           (ONNXLogOp (ONNXReduceSumOp $oprd, $axes, $keepdims))>;
+
+//===----------------------------------------------------------------------===//
+// ONNXReduceLogSumExpOp %X = ONNXReduceLogSumOp (ONNXExpOp %X)
+//===----------------------------------------------------------------------===//
+def ReduceLogSumExpOpPattern: Pat<(ONNXReduceLogSumExpOp $oprd, $axes, $keepdims),
+                                  (ONNXReduceLogSumOp (ONNXExpOp $oprd), $axes, $keepdims)>;
+
+//===----------------------------------------------------------------------===//
+// ONNXReduceSumSquareOp %X = ONNXReduceSumOp (ONNXMulOp %X, %X)
+//===----------------------------------------------------------------------===//
+def ReduceSumSquareOpPattern: Pat<(ONNXReduceSumSquareOp $oprd, $axes, $keepdims),
+                                  (ONNXReduceSumOp (ONNXMulOp $oprd, $oprd), $axes, $keepdims)>;
+
+#endif // ONNX_REWRITE