Remove FullyConnectedOp from TOSA Dialect

* This patch removes FullyConncected Operator from the TOSA Dialect
and all associated tests and transforms.

* TosaDecomposeConv2D pass is also removed. Please expect this as a
  backend optimization

Signed-off-by: Tai Ly <[email protected]>
Change-Id: Ib8c928cb21daf325f00cdad302680af2d7c13da5

Author: Tai78641

mlir/include/mlir/Dialect/Tosa/IR/TosaOpBase.td

@@ -150,15 +150,6 @@ def Tosa_TransConvOpQuantInfoBuilder : OpBuilder<
                                   outputShape, acc_type);
   }]>;
 
-// The tosa.fully_connected op has its own builder as it does not have
-// strides/dilation/padding.
-def Tosa_FCOpQuantInfoBuilder : OpBuilder<
-  (ins "Type":$outputType, "Value":$input, "Value":$weight, "Value":$bias),
-  [{
-    buildFCOpWithQuantInfo($_builder, $_state, outputType,
-                           input, weight, bias);
-  }]>;
-
 // The tosa.matmul op is also intended to be generated where a fully_connected
 // op must be constructed where the weight is not a constant. In this case,
 // the fully_connected op must be expressed using matmul.

mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td

@@ -224,32 +224,6 @@ def Tosa_FFT2dOp : Tosa_InferShapedTypeOp<"fft2d"> {
   }];
 }
 
-//===----------------------------------------------------------------------===//
-// Operator: fully_connected
-//===----------------------------------------------------------------------===//
-def Tosa_FullyConnectedOp : Tosa_InferShapedTypeOp<"fully_connected"> {
-  let summary = "Fully Connected operator";
-
-  let description = [{
-    Performs a fully connected network.
-  }];
-
-  let arguments = (ins
-    Tosa_Tensor2D:$input,
-    TosaTensorRankOf<[Tosa_Weight], [2]>:$weight,
-    Tosa_Tensor1D:$bias,
-    OptionalAttr<I32Attr>:$input_zp,
-    OptionalAttr<I32Attr>:$weight_zp
-  );
-
-  let results = (outs
-    Tosa_Tensor2D:$output
-  );
-
-  let builders = [Tosa_FCOpQuantInfoBuilder];
-  let hasVerifier = 1;
-}
-
 //===----------------------------------------------------------------------===//
 // Operator: matmul
 //===----------------------------------------------------------------------===//

mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td

@@ -81,7 +81,7 @@ def Tosa_AnyNumber : AnyTypeOf<[Tosa_Int, Tosa_QuantizedInt, AnyFloat],
                                 "number">;
 
 // For weight tensors from tosa::Conv2DOp, tosa::Conv3DOp,
-// tosa::DepthwiseConv2DOp, tosa::TransposeConv2DOp, tosa::FullyConnectedOp
+// tosa::DepthwiseConv2DOp, tosa::TransposeConv2DOp
 def Tosa_Weight : AnyTypeOf<[Tosa_Int4, Tosa_Int8,
                              Tosa_QuantizedInt, AnyFloat]>;
 

mlir/include/mlir/Dialect/Tosa/Transforms/Passes.h

@@ -26,7 +26,6 @@ namespace tosa {
 
 // Expose Rewrite Functions that decompose TOSA Ops into further TOSA Ops.
 // The rewrites can be selectively added to a conversion pass.
-void populateTosaDecomposeConv2D(MLIRContext *ctx, RewritePatternSet &patterns);
 void populateTosaDecomposeTransposeConv(MLIRContext *ctx,
                                         RewritePatternSet &patterns);
 void populateTosaDecomposeDepthwise(MLIRContext *ctx,

mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp

@@ -607,84 +607,6 @@ class MatMulConverter : public OpConversionPattern<tosa::MatMulOp> {
   }
 };
 
-class FullyConnectedConverter
-    : public OpConversionPattern<tosa::FullyConnectedOp> {
-public:
-  using OpConversionPattern<tosa::FullyConnectedOp>::OpConversionPattern;
-  LogicalResult
-  matchAndRewrite(tosa::FullyConnectedOp op, OpAdaptor adaptor,
-                  ConversionPatternRewriter &rewriter) const final {
-    Location loc = op.getLoc();
-    auto outputTy = cast<ShapedType>(op.getType());
-    auto input = op.getInput();
-    auto inputTy = cast<ShapedType>(input.getType());
-
-    auto bias = op.getBias();
-
-    auto weight = op.getWeight();
-    auto weightTy = cast<ShapedType>(weight.getType());
-    auto weightShape = weightTy.getShape();
-
-    auto outputETy = outputTy.getElementType();
-
-    SmallVector<Value> dynDims;
-    dynDims.resize(cast<ShapedType>(op->getResult(0).getType()).getRank());
-
-    if (!inputTy.hasRank() || inputTy.isDynamicDim(0)) {
-      dynDims[0] = rewriter.create<tensor::DimOp>(loc, input, 0);
-    }
-
-    if (!weightTy.hasRank() || weightTy.isDynamicDim(0)) {
-      dynDims[1] = rewriter.create<tensor::DimOp>(loc, weight, 0);
-    }
-
-    SmallVector<Value> filteredDims = condenseValues(dynDims);
-
-    SmallVector<int64_t> permutation = {1, 0};
-    auto permutationAttr = rewriter.getI64TensorAttr(permutation);
-    Value permutationValue =
-        rewriter.create<arith::ConstantOp>(loc, permutationAttr);
-
-    SmallVector<int64_t> newWeightShape = {weightShape[1], weightShape[0]};
-    Type newWeightTy =
-        RankedTensorType::get(newWeightShape, weightTy.getElementType());
-
-    Value transposedWeight = rewriter.create<tosa::TransposeOp>(
-        loc, newWeightTy, weight, permutationValue);
-
-    Value biasEmptyTensor = rewriter.create<tensor::EmptyOp>(
-        loc, outputTy.getShape(), outputETy, filteredDims);
-
-    Value broadcastBias =
-        linalgBroadcastAndMaybeExtSI(rewriter, loc, bias, biasEmptyTensor);
-
-    if (!op.getInputZp() && !op.getWeightZp()) {
-      Value matmul = rewriter
-                         .create<linalg::MatmulOp>(
-                             loc, TypeRange{op.getType()},
-                             ValueRange{input, transposedWeight}, broadcastBias)
-                         ->getResult(0);
-
-      rewriter.replaceOp(op, matmul);
-      return success();
-    }
-
-    auto inputZp = rewriter.create<arith::ConstantOp>(loc, op.getInputZpAttr());
-    auto outputZp =
-        rewriter.create<arith::ConstantOp>(loc, op.getWeightZpAttr());
-    Value matmul =
-        rewriter
-            .create<linalg::QuantizedMatmulOp>(
-                loc, TypeRange{op.getType()},
-                ValueRange{input, transposedWeight, inputZp, outputZp},
-                broadcastBias)
-            ->getResult(0);
-
-    rewriter.replaceOp(op, matmul);
-    return success();
-  }
-};
-
 class MaxPool2dConverter : public OpConversionPattern<tosa::MaxPool2dOp> {
 public:
   using OpConversionPattern::OpConversionPattern;
@@ -1090,7 +1012,6 @@ void mlir::tosa::populateTosaToLinalgNamedConversionPatterns(
       DepthwiseConvConverter,
       MatMulConverter,
       AvgPool2dConverter,
-      FullyConnectedConverter,
       TransposeConverter
   >(patterns->getContext());
 

mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamedPass.cpp

@@ -62,7 +62,6 @@ struct TosaToLinalgNamed
     target.addIllegalOp<tosa::MaxPool2dOp>();
     target.addIllegalOp<tosa::AvgPool2dOp>();
     target.addIllegalOp<tosa::MatMulOp>();
-    target.addIllegalOp<tosa::FullyConnectedOp>();
     target.addIllegalOp<tosa::TransposeOp>();
 
     target.markUnknownOpDynamicallyLegal([](Operation *) { return true; });

mlir/lib/Dialect/Tosa/IR/TosaOps.cpp

@@ -566,26 +566,9 @@ static void buildTransConvOpWithQuantInfo(
   result.addTypes(finalOutputType);
 }
 
-/// The tosa.fully_connected op has its own builder as it does not have
-/// strides/dilation/padding.
-static void buildFCOpWithQuantInfo(OpBuilder &builder, OperationState &result,
-                                   Type outputType, Value input, Value weight,
-                                   Value bias) {
-
-  result.addOperands({input, weight, bias});
-  auto quantAttr = ::buildConvOpQuantizationAttr(builder, input, weight);
-  if (quantAttr) {
-    result.addAttribute("quantization_info", quantAttr);
-    result.addTypes(
-        buildConvOpResultTypeInfo(builder, outputType, input, weight));
-  } else {
-    result.addTypes(outputType);
-  }
-}
-
-/// The tosa.matmul op is also intended to be generated where a
-/// fully_connected op must be constructed where the weight is not a constant.
-/// In this case, the fully_connected op must be expressed using matmul.
+/// The tosa.matmul op is also intended to be generated where a fully_connected
+/// op must be constructed where the weight is not a constant. In this case,
+/// the fully_connected op must be expressed using matmul.
 /// TODO: Add link to the leglization document explaining this.
 static void buildMatMulOpWithQuantInfo(OpBuilder &builder,
                                        OperationState &result, Type outputType,
@@ -889,76 +872,6 @@ bool tosa::EqualOp::isCompatibleReturnTypes(TypeRange l, TypeRange r) {
   return succeeded(verifyCompatibleShape(l[0], r[0]));
 }
 
-LogicalResult tosa::FullyConnectedOp::inferReturnTypeComponents(
-    MLIRContext *context, ::std::optional<Location> location,
-    FullyConnectedOp::Adaptor adaptor,
-    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
-  ShapeAdaptor inputShape(adaptor.getInput().getType());
-  ShapeAdaptor weightShape(adaptor.getWeight().getType());
-  ShapeAdaptor biasShape(adaptor.getBias().getType());
-
-  // All shapes are dynamic.
-  SmallVector<int64_t> outShape;
-  outShape.resize(2, ShapedType::kDynamic);
-
-  if (inputShape.hasRank()) {
-    outShape[0] = inputShape.getDimSize(0);
-  }
-
-  if (weightShape.hasRank()) {
-    outShape[1] = weightShape.getDimSize(0);
-  }
-
-  if (biasShape.hasRank()) {
-    outShape[1] = outShape[1] == ShapedType::kDynamic ? biasShape.getDimSize(0)
-                                                      : outShape[1];
-  }
-
-  inferredReturnShapes.push_back(ShapedTypeComponents(outShape));
-  return success();
-}
-
-LogicalResult FullyConnectedOp::verify() {
-  // All TOSA conv ops have an input() and weight().
-  auto inputType = llvm::dyn_cast<RankedTensorType>(getInput().getType());
-
-  RankedTensorType weightType =
-      llvm::dyn_cast<RankedTensorType>(getWeight().getType());
-
-  // Must be ranked tensor types
-  if (!inputType) {
-    emitOpError("expect a ranked tensor for input, got ") << getInput();
-    return failure();
-  }
-  if (!weightType) {
-    emitOpError("expect a ranked tensor for weight, got ") << getWeight();
-    return failure();
-  }
-
-  auto inputEType = inputType.getElementType();
-  auto weightEType = weightType.getElementType();
-
-  bool inputIsQuant = !llvm::isa<FloatType>(inputEType);
-  bool weightIsQuant = !llvm::isa<FloatType>(weightEType);
-
-  // Either both must be quantized or both unquantized.
-  if (inputIsQuant != weightIsQuant) {
-    emitOpError(
-        "expect both input and weight to be float or not together, got ")
-        << inputEType << " and " << weightEType;
-    return failure();
-  }
-
-  // Quantized type must have constructed the quantizationattr, and unquantized
-  // types should not have a quantizationattr.
-  if ((inputIsQuant && !getInputZp()) || (!inputIsQuant && getInputZp())) {
-    emitOpError("input zero point is required for quantized type, and not "
-                "allowed for float type");
-    return failure();
-  }
-  return success();
-}
-
 LogicalResult tosa::MatMulOp::inferReturnTypeComponents(
     MLIRContext *context, ::std::optional<Location> location,
     MatMulOp::Adaptor adaptor,

mlir/lib/Dialect/Tosa/Transforms/CMakeLists.txt

@@ -1,6 +1,5 @@
 add_mlir_dialect_library(MLIRTosaTransforms
   TosaDecomposeTransposeConv.cpp
-  TosaDecomposeConv2D.cpp
   TosaDecomposeDepthwise.cpp
   TosaFolders.cpp
   TosaInferShapes.cpp