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

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merged 1 commit into from
Mar 7, 2025
Merged

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

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35 changes: 17 additions & 18 deletions mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -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);
Expand Down Expand Up @@ -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},
Expand All @@ -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);
Expand Down
27 changes: 26 additions & 1 deletion mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
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Original file line number Diff line number Diff line change
Expand Up @@ -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]]
Expand All @@ -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"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
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I suspect value needs updating to values after #129943

%weight_zp = "tosa.const"() <{value = 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)>

Expand Down
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