[mlir][TOSA] Fix linalg lowering of depthwise conv2d (#130282)

Current lowering for tosa.depthwise_conv2d assumes if both zero points
are zero then it's a floating-point operation by hardcoding the use of a
arith.addf in the lowered code. Fix code to check for the element type
to decide what add operation to use.

Author: RoboTux

mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp

@@ -477,27 +477,21 @@ class DepthwiseConvConverter
       return rewriter.notifyMatchFailure(
           op, "weight zero point must be zero for non-int8 integer types");
 
-    bool hasZp = (inputZpVal != 0) || (weightZpVal != 0);
     auto weightShape = weightTy.getShape();
     auto resultShape = resultTy.getShape();
 
     // Apply padding as necessary.
-    TypedAttr zeroAttr = rewriter.getZeroAttr(inputETy);
-    if (hasZp) {
-      int64_t intMin =
-          APInt::getSignedMinValue(inputETy.getIntOrFloatBitWidth())
-              .getSExtValue();
-      int64_t intMax =
-          APInt::getSignedMaxValue(inputETy.getIntOrFloatBitWidth())
-              .getSExtValue();
+    int64_t intMin = APInt::getSignedMinValue(inputETy.getIntOrFloatBitWidth())
+                         .getSExtValue();
+    int64_t intMax = APInt::getSignedMaxValue(inputETy.getIntOrFloatBitWidth())
+                         .getSExtValue();
 
-      if (inputZpVal < intMin || inputZpVal > intMax)
-        return rewriter.notifyMatchFailure(
-            op, "tosa.depthwise_conv op quantization has zp outside of input "
-                "range");
+    if (inputZpVal < intMin || inputZpVal > intMax)
+      return rewriter.notifyMatchFailure(
+          op, "tosa.depthwise_conv op quantization has zp outside of input "
+              "range");
 
-      zeroAttr = rewriter.getIntegerAttr(inputETy, inputZpVal);
-    }
+    TypedAttr zeroAttr = rewriter.getIntegerAttr(inputETy, inputZpVal);
 
     llvm::SmallVector<int64_t> pad;
     pad.resize(2, 0);
@@ -536,7 +530,7 @@ class DepthwiseConvConverter
     indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultRank));
     indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultRank));
 
-    if (!hasZp) {
+    if (inputZpVal == 0 && weightZpVal == 0) {
       Value conv = rewriter
                        .create<linalg::DepthwiseConv2DNhwcHwcmOp>(
                            loc, linalgConvTy, ValueRange{input, weight},
@@ -556,8 +550,13 @@ class DepthwiseConvConverter
                   getNParallelLoopsAttrs(resultRank),
                   [&](OpBuilder &nestedBuilder, Location nestedLoc,
                       ValueRange args) {
-                    Value added = nestedBuilder.create<arith::AddFOp>(
-                        loc, args[0], args[1]);
+                    Value added;
+                    if (llvm::isa<FloatType>(inputETy))
+                      added = nestedBuilder.create<arith::AddFOp>(loc, args[0],
+                                                                  args[1]);
+                    else
+                      added = nestedBuilder.create<arith::AddIOp>(loc, args[0],
+                                                                  args[1]);
                     nestedBuilder.create<linalg::YieldOp>(nestedLoc, added);
                   })
               .getResult(0);

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

@@ -798,9 +798,10 @@ func.func @depthwise_conv2d_dyn_w_h(%arg0: tensor<2x?x?x3xf32>, %arg1: tensor<3x
   // CHECK: arith.subi
   // CHECK: arith.muli
   // CHECK: arith.divui
+  // CHECK: [[CST0:%.+]] = arith.constant 0
   // CHECK: %[[PADDED:.+]] = tensor.pad %arg0 low[0, 1, 3, 0] high[0, 2, 4, 0] {
   // CHECK: ^bb0(%[[ARG3:[0-9a-zA-Z_]+]]: index, %[[ARG4:[0-9a-zA-Z_]+]]: index, %[[ARG5:[0-9a-zA-Z_]+]]: index, %[[ARG6:[0-9a-zA-Z_]+]]: index):
-  // CHECK: tensor.yield %cst : f32
+  // CHECK: tensor.yield [[CST0]] : f32
   // CHECK:  } : tensor<2x?x?x3xf32> to tensor<2x?x?x3xf32>
   // CHECK: %[[CONV:.+]] = linalg.depthwise_conv_2d_nhwc_hwcm {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<[1, 2]> : tensor<2xi64>} ins(%[[PADDED]], %arg1 : tensor<2x?x?x3xf32>, tensor<3x6x3x5xf32>) outs(%{{.*}} : tensor<2x?x?x3x5xf32>) -> tensor<2x?x?x3x5xf32>
   // CHECK: %[[COLLAPSED:.+]] = tensor.collapse_shape %[[CONV]] {{\[}}[0], [1], [2], [3, 4]]
@@ -812,6 +813,30 @@ func.func @depthwise_conv2d_dyn_w_h(%arg0: tensor<2x?x?x3xf32>, %arg1: tensor<3x
 
 // -----
 
+// CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d3)>
+// CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
+
+// CHECK-LABEL: @depthwise_int_conv_zero_zp
+func.func @depthwise_int_conv_zero_zp(%arg0 : tensor<1x7x5x3xi8>, %arg1 : tensor<3x1x3x11xi8>, %arg2 : tensor<33xi32>) -> () {
+  // CHECK: [[INIT:%.+]] = tensor.empty()
+  // CHECK: [[CST0:%.+]] = arith.constant 0
+  // CHECK: [[FILL:%.+]] = linalg.fill ins([[CST0]]{{.*}}outs([[INIT]]
+  // CHECK: [[OUT:%.+]] = tensor.empty()
+  // CHECK: [[DEPTH:%.+]] = linalg.depthwise_conv_2d_nhwc_hwcm {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x7x5x3xi8>, tensor<3x1x3x11xi8>) outs([[FILL]] : tensor<1x5x5x3x11xi32>)
+  // CHECK: [[COLLAPSED:%.+]] = tensor.collapse_shape [[DEPTH]] {{\[}}[0], [1], [2], [3, 4]]
+  // CHECK: [[BIAS:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2, [[COLLAPSED]] : tensor<33xi32>, tensor<1x5x5x33xi32>) outs([[OUT]] : tensor<1x5x5x33xi32>) {
+  // CHECK: ^bb0(%[[ARG3:[0-9a-zA-Z_]+]]: i32, %[[ARG4:[0-9a-zA-Z_]+]]: i32, %[[ARG5:[0-9a-zA-Z_]+]]: i32):
+  // CHECK:   [[ADD:%.+]] = arith.addi %[[ARG3]], %[[ARG4]] : i32
+  // CHECK:   linalg.yield [[ADD]] : i32
+  // CHECK: } -> tensor<1x5x5x33xi32>
+  %input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+  %weight_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
+  %2 = tosa.depthwise_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = i32, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>, dilation = array<i64: 1, 1> } : (tensor<1x7x5x3xi8>, tensor<3x1x3x11xi8>, tensor<33xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x5x5x33xi32>
+  return
+}
+
+// -----
+
 // CHECK: #[[$MAP1:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d4)>
 // CHECK: #[[$MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2, d3, d4)>