Replace the channel dimension index op trait by op interface

PiperOrigin-RevId: 290144065
Change-Id: If77b13f2685b7e2c5ba3f3aa5a44358d13ea1dae

tensorflow/compiler/mlir/lite/BUILD

@@ -191,7 +191,6 @@ cc_library(
     ],
     hdrs = [
         "ir/tfl_ops.h",
-        "ir/tfl_traits.h",
         "transforms/passes.h",
         "utils/attribute_utils.h",
         "//tensorflow/compiler/mlir/lite/quantization:quantization_traits.h",

tensorflow/compiler/mlir/lite/ir/tfl_ops.h

@@ -27,7 +27,6 @@ limitations under the License.
 #include "mlir/IR/StandardTypes.h"  // TF:llvm-project
 #include "mlir/Support/Functional.h"  // TF:llvm-project
 #include "mlir/Support/LLVM.h"  // TF:llvm-project
-#include "tensorflow/compiler/mlir/lite/ir/tfl_traits.h"
 #include "tensorflow/compiler/mlir/lite/quantization/quantization_traits.h"
 #include "tensorflow/lite/schema/schema_generated.h"
 

tensorflow/compiler/mlir/lite/ir/tfl_ops.td

@@ -268,10 +268,20 @@ def TFL_StatefulOp : OpInterface<"StatefulOpInterface"> {
 }
 
 //===----------------------------------------------------------------------===//
-// TFL native op trait for channel indices.
+// TFL op interface for output channel index.
 
-class ChannelDimIndex<int index>
-    : ParamNativeOpTrait<"TFL::ChannelDimIndex", !cast<string>(index)>;
+def TFL_ChannelDimIndexInterface : OpInterface<"ChannelDimIndexInterface"> {
+  let description = [{
+    Interface for defining the index of out channel index.
+  }];
+
+  let methods = [
+    InterfaceMethod<
+      [{Returns the dimension index of the output channels.}],
+      "int", "GetChannelDimIndex", (ins)
+    >,
+  ];
+}
 
 //===----------------------------------------------------------------------===//
 // TFL op base class.
@@ -300,7 +310,7 @@ class TFL_Op<string mnemonic, list<OpTrait> traits = []> :
 
 class TFL_ConvOp<string mnemonic, string opSummary, int index> :
     TFL_Op<mnemonic, [NoSideEffect, AccumulatorUniformScale<2, 0, 1>,
-    ChannelDimIndex<index>, AffineOpCoefficient<index, 1>]> {
+    TFL_ChannelDimIndexInterface, AffineOpCoefficient<index, 1>]> {
   let summary = opSummary # " operator";
 
   let description = [{
@@ -630,7 +640,12 @@ def TFL_ExternalConstOp : Op<TFL_Dialect, "external_const", [NoSideEffect]> {
   let results = (outs AnyTensor:$output);
 }
 
-def TFL_Conv2DOp : TFL_ConvOp<"conv_2d", "Convolution", 0>;
+def TFL_Conv2DOp : TFL_ConvOp<"conv_2d", "Convolution", 0> {
+  let extraClassDeclaration = [{
+    // StatefulOpInterface:
+    int GetChannelDimIndex() { return 0; }
+  }];
+}
 
 def TFL_CosOp: TFL_Op<"cos", [
     NoSideEffect, SameOperandsAndResultType, NoQuantizableResult]> {
@@ -650,6 +665,11 @@ def TFL_CosOp: TFL_Op<"cos", [
 def TFL_DepthwiseConv2DOp :
     TFL_ConvOp<"depthwise_conv_2d", "Depthwise-separable convolution", 3> {
   let arguments = !con(TFL_Conv2DOp.arguments, (ins I32Attr:$depth_multiplier));
+
+  let extraClassDeclaration = [{
+    // StatefulOpInterface:
+    int GetChannelDimIndex() { return 3; }
+  }];
 }
 
 def TFL_FCWO_Default  : StrEnumAttrCase<"DEFAULT">;
@@ -663,7 +683,8 @@ def TFL_FullyConnectedOptionsWeightFormatAttr :
 
 // TODO(jpienaar): Update post discussion on semantics of FC OP.
 def TFL_FullyConnectedOp : TFL_Op<"fully_connected", [
-    NoSideEffect, AccumulatorUniformScale<2, 0, 1>, ChannelDimIndex<0>,
+    NoSideEffect, AccumulatorUniformScale<2, 0, 1>,
+    TFL_ChannelDimIndexInterface,
     AffineOpCoefficient<-1, 1>]> {
   let summary = "Fully connected op";
 
@@ -685,6 +706,11 @@ def TFL_FullyConnectedOp : TFL_Op<"fully_connected", [
   let verifier = [{ return Verify(*this); }];
 
   let hasOptions = 1;
+
+  let extraClassDeclaration = [{
+    // ChannelDimIndexInterface:
+    int GetChannelDimIndex() { return 0; }
+  }];
 }
 
 def TFL_GatherOp : TFL_Op<"gather", [

tensorflow/compiler/mlir/lite/ir/tfl_traits.h

@@ -1,47 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// This file defines the op traits used in the MLIR TensorFlow Lite dialect.
-
-#ifndef TENSORFLOW_COMPILER_MLIR_LITE_IR_TFL_TRAITS_H_
-#define TENSORFLOW_COMPILER_MLIR_LITE_IR_TFL_TRAITS_H_
-
-#include "mlir/IR/OpDefinition.h"
-#include "mlir/Support/LLVM.h"  // TF:llvm-project
-
-namespace mlir {
-namespace OpTrait {
-namespace TFL {
-// The trait to specify the channel dimension index of the input (first operand)
-// of an affine TFL op (Conv2D, DepthwiseConv2D, FullyConnected).
-//
-//   class Conv2DOp
-//       : public Op<Conv2DOp, OpTrait::TFL::ChannelDimIndex<0>::Impl> {
-//
-template <int Index>
-class ChannelDimIndex {
- public:
-  template <typename ConcreteType>
-  class Impl : public TraitBase<ConcreteType, ChannelDimIndex<Index>::Impl> {
-   public:
-    static int GetChannelDimIndex() { return Index; }
-  };
-};
-
-}  // namespace TFL
-}  // namespace OpTrait
-}  // namespace mlir
-
-#endif  // TENSORFLOW_COMPILER_MLIR_LITE_IR_TFL_TRAITS_H_

tensorflow/compiler/mlir/lite/quantization/quantization_driver.cc

@@ -35,7 +35,6 @@ limitations under the License.
 #include "mlir/IR/Value.h"  // TF:llvm-project
 #include "mlir/Support/LLVM.h"  // TF:llvm-project
 #include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
-#include "tensorflow/compiler/mlir/lite/ir/tfl_traits.h"
 #include "tensorflow/compiler/mlir/lite/quantization/quantization_traits.h"
 #include "tensorflow/compiler/mlir/lite/quantization/quantization_utils.h"
 #include "tensorflow/core/platform/logging.h"

tensorflow/compiler/mlir/lite/transforms/optimize.cc

@@ -366,7 +366,8 @@ struct FuseBinaryOpToFollowingAffineOp : public OpRewritePattern<AffineOpType> {
       // so we have to update the bias.
       if (llvm::isa<SubOp>(binary_op)) cst_value.changeSign();
 
-      auto bias_and_slice = GetBiasDimAndSliceSize(filter_type.getShape());
+      auto bias_and_slice =
+          GetBiasDimAndSliceSize(filter_type.getShape(), fc_op);
       int64_t bias_size = bias_and_slice.first;
       int64_t slice_size = bias_and_slice.second;
       ShapedType new_bias_type =
@@ -438,10 +439,10 @@ struct FuseBinaryOpToFollowingAffineOp : public OpRewritePattern<AffineOpType> {
   // has tailing channel dimension. This function is to provide a utility to
   // create the above information from the op property.
   static std::pair<int64_t, int64_t> GetBiasDimAndSliceSize(
-      ArrayRef<int64_t> filter_shape) {
+      ArrayRef<int64_t> filter_shape, AffineOpType op) {
     // Channel dimension index is specified as op property
     auto channel_index_iter = filter_shape.begin();
-    std::advance(channel_index_iter, AffineOpType::GetChannelDimIndex());
+    std::advance(channel_index_iter, op.GetChannelDimIndex());
     // The slide size is the size of the data in higher dimensions.
     int64_t slice_size =
         std::accumulate(std::next(channel_index_iter), filter_shape.end(), 1,