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[mlir][tosa] Fix lowering of tosa.conv2d #73240

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Merged
merged 1 commit into from
Dec 1, 2023
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[mlir][tosa] Fix lowering of tosa.conv2d #73240

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4 changes: 2 additions & 2 deletions mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -136,7 +136,7 @@ static SmallVector<Value> inferDynamicDimsForConv(
for (uint32_t i = 0, s = inputSizeDims.size(); i < s; ++i) {
int64_t inputDim = inputSizeDims[i];
int64_t kernelDim = kernelSizeDims[i];
if (inputTy.isDynamicDim(inputDim)) {
if (resultTy.isDynamicDim(inputDim)) {
auto padTop = padAttr[i * 2];
auto padBottom = padAttr[i * 2 + 1];
auto stride = strideAttr[i];
Expand All @@ -153,7 +153,7 @@ static SmallVector<Value> inferDynamicDimsForConv(

// Get the batch/channels dimensions.
for (int i = 0; i < inputRank; i++) {
if (inputTy.isDynamicDim(i) && !dynDims[i])
if (resultTy.isDynamicDim(i) && !dynDims[i])
dynDims[i] = rewriter.create<tensor::DimOp>(loc, input, i);
}

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23 changes: 23 additions & 0 deletions mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
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Expand Up @@ -497,6 +497,29 @@ func.func @conv2d_dyn_w_h(%input: tensor<1x?x?x27xf32>, %weights: tensor<28x3x3x

// -----

// CHECK: [[$MAP1:.+]] = affine_map<(d0, d1, d2, d3) -> (d3)>
// CHECK: [[$MAP2:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>

func.func @conv2d_dyn_output(%input: tensor<2x6x5x4xf32>, %weights: tensor<4x3x3x4xf32>, %bias: tensor<4xf32>) {
// %[[C0:.+]] = arith.constant 0 : index
// %[[DIM0:.+]] = tensor.dim %input, %[[C0]] : tensor<2x6x5x4xf32>
// %[[INIT_CONV:.+]] = tensor.empty(%[[DIM0]]) : tensor<?x4x3x4xf32>
// %[[ZERO:.+]] = arith.constant 0.000000e+00 : f32
// %[[FILL:.+]] = linalg.fill
// %[[INIT_GENERIC:.+]] = tensor.empty([[DIM0]]) : tensor<?x4x3x4xf32>

// %[[CONV:.+]] = linalg.conv_2d_nhwc_fhwc {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<2x6x5x4xf32>, tensor<4x3x3x4xf32>) outs(%[[INIT_CONV]] : tensor<?x4x3x4xf32>) -> tensor<?x4x3x4xf32>
// linalg.generic {indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2, %[[CONV]] : tensor<4xf32>, tensor<?x4x3x4xf32>) outs(%[[INIT_GENERIC]] : tensor<?x4x3x4xf32>) {
// %[[ADD:.+]] = arith.addf
// linalg.yield %[[ADD]] : f32
// } -> tensor<?x4x3x4xf32>

%0 = tosa.conv2d %input, %weights, %bias {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x6x5x4xf32 >, tensor<4x3x3x4xf32>, tensor<4xf32>) -> tensor<?x4x3x4xf32>
return
}

// -----

// CHECK-LABEL: @conv2d_padded_f32
func.func @conv2d_padded_f32(%input: tensor<1x47x40x28xf32>, %weights: tensor<28x3x3x28xf32>, %bias: tensor<28xf32>) -> () {
// CHECK: %[[C0:.+]] = arith.constant 0
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