[tosa][mlir] Fix FullyConnected to correctly order dimensions

[rsuderman]

Summary

MatMul and FullyConnected have transposed dimensions for the weights.
Also, removed uneeded tensor reshape for bias.

[mravishankar]

mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp

751

I understand that you want to do this, but just to throw this out there, might want to actually have the transpose variants of matmul. You could write compiler passes to combine transpose with matmuls and present different variants to code-generation.

[rsuderman]

mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp

751

In progress of discussing named op variants of matmul. To avoid having this be a block issue I'll submit and update to whatever solution we find.

mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp

 
 class FullyConnectedConverter
     : public OpConversionPattern<tosa::FullyConnectedOp> {
 public:
   using OpConversionPattern<tosa::FullyConnectedOp>::OpConversionPattern;
   LogicalResult
   matchAndRewrite(tosa::FullyConnectedOp op, ArrayRef<Value> args,
                   ConversionPatternRewriter &rewriter) const final {
-    tosa::FullyConnectedOp::Adaptor adaptor(args);
-
     Location loc = op.getLoc();
     auto outputTy = op.getType().cast<ShapedType>();
-    auto biasTy = op->getOperand(2).getType().cast<ShapedType>();
+    auto input = op.input();
+    auto weight = op.weight();
+    auto bias = op.bias();
 
-    // Reshaping the bias from n to [1, n] for broadcasting
-    SmallVector<int64_t> biasShapeReshaped;
-    biasShapeReshaped.push_back(1);
-    biasShapeReshaped.push_back(biasTy.getShape()[0]);
-
-    RankedTensorType reshapedBias =
-        RankedTensorType::get(biasShapeReshaped, outputTy.getElementType());
-    auto reshapeResult =
-        rewriter.create<tosa::ReshapeOp>(loc, reshapedBias, args[2])
-            ->getResult(0);
+    auto weightTy = weight.getType().cast<ShapedType>();
+    auto biasTy = bias.getType().cast<ShapedType>();
+
+    auto weightShape = weightTy.getShape();
+
+    if (op.quantization_info())
+      return failure();
 
     // Creating maps for the output of MatMul and the bias
     SmallVector<AffineMap, 4> indexingMaps;
-    indexingMaps.push_back(createAffineMapForType(reshapedBias, rewriter));
+
+    // Broadcast the bias.
+    indexingMaps.push_back(AffineMap::get(/*dimCount=*/2, /*symbolCount=*/0,
+                                          {rewriter.getAffineDimExpr(1)},
+                                          rewriter.getContext()));
+
     indexingMaps.push_back(rewriter.getMultiDimIdentityMap(outputTy.getRank()));
 
     auto initTensor =
         rewriter
             .create<linalg::InitTensorOp>(loc, outputTy.getShape(),
                                           outputTy.getElementType())
             ->getResults();
 
     auto linalgOp =
         rewriter
             .create<linalg::GenericOp>(
-                loc, outputTy, reshapeResult, initTensor, indexingMaps,
+                loc, outputTy, bias, initTensor, indexingMaps,
                 getNParallelLoopsAttrs(outputTy.getRank()),
                 [&](OpBuilder &nested_builder, Location nested_loc,
                     ValueRange args) {
                   nested_builder.create<linalg::YieldOp>(loc, *args.begin());
                 })
             ->getResults();
 
+    SmallVector<int64_t> permutation{1, 0};
+    auto permutationAttr = DenseIntElementsAttr::get(
+        RankedTensorType::get({2}, rewriter.getI64Type()), permutation);
+    Value permutationValue = rewriter.create<ConstantOp>(loc, permutationAttr);
+
+    SmallVector<int64_t> newWeightShape{weightShape[1], weightShape[0]};
+    Type newWeightTy =
+        RankedTensorType::get(newWeightShape, biasTy.getElementType());
+
+    Value transposedWeight = rewriter.create<tosa::TransposeOp>(
+        loc, newWeightTy, weight, permutationValue);
+
     rewriter.replaceOpWithNewOp<linalg::MatmulOp>(
-        op, TypeRange{op.getType()},
-        ValueRange{adaptor.input(), adaptor.weight()}, linalgOp);
+        op, TypeRange{op.getType()}, ValueRange{input, transposedWeight},
+        linalgOp);
     return success();
   }
 };
 
 class Conv2DConverter : public OpConversionPattern<tosa::Conv2DOp> {
 public:
   using OpConversionPattern<tosa::Conv2DOp>::OpConversionPattern;
   LogicalResult

mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir

func @matmul(%arg0: tensor<5x3xf32>, %arg1: tensor<3x6xf32>, %arg2: tensor<6xf32>) -> (tensor<5x6xf32>) {
   // CHECK: [[FILLED:%.+]] = linalg.fill([[INIT]], [[C0]]) : tensor<5x6xf32>, f32 -> tensor<5x6xf32>
   // CHECK: linalg.matmul ins(%arg0, %arg1 : tensor<5x3xf32>, tensor<3x6xf32>) outs([[FILLED]] : tensor<5x6xf32>) -> tensor<5x6xf32>
   %0 = "tosa.matmul"(%arg0, %arg1) : (tensor<5x3xf32>, tensor<3x6xf32>)  -> (tensor<5x6xf32>)
   return %0 : tensor<5x6xf32>
 }
 
 // -----
 
-// CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)>
-// CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1) -> (0, d1)>
+// CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d1)>
+// CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1) -> (d0, d1)>
+// CHECK: #[[$MAP2:.*]] = affine_map<(d0, d1) -> (d1, d0)>
 
 // CHECK-LABEL: @fully_connected
-func @fully_connected(%arg0: tensor<5x3xf32>, %arg1: tensor<3x6xf32>, %arg2: tensor<6xf32>) -> (tensor<5x6xf32>) {
-  // CHECK: [[RS:%.+]] = linalg.tensor_reshape %arg2 [#[[$MAP0]]]
-  // CHECK: [[INIT:%.+]] = linalg.init_tensor [5, 6]
-  // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP0]]], iterator_types = ["parallel", "parallel"]} ins([[RS]] : tensor<1x6xf32>) outs([[INIT]] : tensor<5x6xf32>) {
-  // CHECK: ^bb0([[IN:%.+]]: f32, [[MULTIPLIER:%.+]]: f32):
-  // CHECK: linalg.matmul ins(%arg0, %arg1 : tensor<5x3xf32>, tensor<3x6xf32>) outs([[GENERIC]] : tensor<5x6xf32>) -> tensor<5x6xf32>
-  %0 = "tosa.fully_connected"(%arg0, %arg1, %arg2) : (tensor<5x3xf32>, tensor<3x6xf32>, tensor<6xf32>)  -> (tensor<5x6xf32>)
+func @fully_connected(%arg0: tensor<5x3xf32>, %arg1: tensor<6x3xf32>, %arg2: tensor<6xf32>) -> (tensor<5x6xf32>) {
+  // CHECK: [[INITB:%.+]] = linalg.init_tensor [5, 6]
+  // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel"]} ins(%arg2 : tensor<6xf32>) outs([[INITB]] : tensor<5x6xf32>) {
+  // CHECK: ^bb0([[IN:%.+]]: f32, [[UNUSED:%.+]]: f32):
+  // CHECK:   linalg.yield [[IN]] : f32
+  // CHECK: [[INITT:%.+]] = linalg.init_tensor [3, 6]
+  // CHECK: [[TRANSPOSE:%.+]] = linalg.generic {indexing_maps = [#[[$MAP2]], #[[$MAP1]]], iterator_types = ["parallel", "parallel"]} ins(%arg1 : tensor<6x3xf32>) outs([[INITT]]
+  // CHECK: ^bb0([[IN:%.+]]: f32, [[UNUSED:%.+]]: f32):
+  // CHECK:   linalg.yield [[IN]] : f32
+  // CHECK: linalg.matmul ins(%arg0, [[TRANSPOSE]] : tensor<5x3xf32>, tensor<3x6xf32>) outs([[GENERIC]] : tensor<5x6xf32>) -> tensor<5x6xf32>
+  %0 = "tosa.fully_connected"(%arg0, %arg1, %arg2) : (tensor<5x3xf32>, tensor<6x3xf32>, tensor<6xf32>)  -> (tensor<5x6xf32>)
   return %0 : tensor<5x6xf32>
 }
 
 // -----
 
 func @pad_float(%arg0 : tensor<1x2xf32>) -> (tensor<4x9xf32>) {
   %0 = constant dense<[[1, 2], [3, 4]]> : tensor<2x2xi32>
   // CHECK: [[INDEX0:%.+]] = constant 0 : index