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[TorchOnnxToTorch] Fix DepthToSpace conversion in CRD mode #4351

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[TorchOnnxToTorch] Fix DepthToSpace conversion in CRD mode #4351

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40 changes: 27 additions & 13 deletions lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -2102,26 +2102,27 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
binder.getLoc(), c, cstBlockSizeSquare);
cDivBlockSizeSquare = rewriter.create<Torch::AtenIntFloatOp>(
binder.getLoc(), cDivBlockSizeSquare);
Value reshapeSizesList = rewriter.create<Torch::PrimListConstructOp>(
binder.getLoc(),
Torch::ListType::get(Torch::IntType::get(input.getContext())),
llvm::SmallVector<Value>{b, cstBlockSize, cstBlockSize,
cDivBlockSizeSquare, h, w});
int64_t cDivBlockSizeSquareInt =
inputSizes[1] == Torch::kUnknownSize
? Torch::kUnknownSize
: inputSizes[1] / (blockSize * blockSize);
SmallVector<int64_t, 6> reshapeSizesInt{
inputSizes[0], blockSize, blockSize,
cDivBlockSizeSquareInt, inputSizes[2], inputSizes[3]};
Value reshapedInput = rewriter.create<Torch::AtenReshapeOp>(
binder.getLoc(),
inputTy.getWithSizesAndDtype(reshapeSizesInt,
inputTy.getOptionalDtype()),
input, reshapeSizesList);

Value transposedInput;
Value reshapeSizesList;
if (mode == "DCR") {
reshapeSizesList = rewriter.create<Torch::PrimListConstructOp>(
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@sahas3 sahas3 Oct 29, 2025 •
edited
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I am not familiar with the ONNX part of the codebase. Are there e2e tests that will lock down the numeric for DCR/CRD mode? If not, can you add some please?

binder.getLoc(),
Torch::ListType::get(Torch::IntType::get(input.getContext())),
llvm::SmallVector<Value>{b, cstBlockSize, cstBlockSize,
cDivBlockSizeSquare, h, w});
SmallVector<int64_t, 6> reshapeSizesInt{
inputSizes[0], blockSize, blockSize,
cDivBlockSizeSquareInt, inputSizes[2], inputSizes[3]};
Value reshapedInput = rewriter.create<Torch::AtenReshapeOp>(
binder.getLoc(),
inputTy.getWithSizesAndDtype(reshapeSizesInt,
inputTy.getOptionalDtype()),
input, reshapeSizesList);
if (failed(createTorchTransposeOp(
rewriter, binder.getLoc(), reshapedInput,
/*dimA=*/1, /*dimB=*/3, transposedInput)))
Expand All @@ -2134,6 +2135,19 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
binder.op, "Failed to create TorchTranspose op");
} else {
// mode == "CRD"
reshapeSizesList = rewriter.create<Torch::PrimListConstructOp>(
binder.getLoc(),
Torch::ListType::get(Torch::IntType::get(input.getContext())),
llvm::SmallVector<Value>{b, cDivBlockSizeSquare, cstBlockSize,
cstBlockSize, h, w});
SmallVector<int64_t, 6> reshapeSizesInt{
inputSizes[0], cDivBlockSizeSquareInt, blockSize,
blockSize, inputSizes[2], inputSizes[3]};
Value reshapedInput = rewriter.create<Torch::AtenReshapeOp>(
binder.getLoc(),
inputTy.getWithSizesAndDtype(reshapeSizesInt,
inputTy.getOptionalDtype()),
input, reshapeSizesList);
if (failed(createTorchTransposeOp(
rewriter, binder.getLoc(), reshapedInput,
/*dimA=*/2, /*dimB=*/4, transposedInput)))
Expand Down
6 changes: 3 additions & 3 deletions test/Conversion/TorchOnnxToTorch/simple_ops_a_to_f.mlir
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Original file line number Diff line number Diff line change
Expand Up @@ -1836,7 +1836,7 @@ func.func @test_depthtospace_example(%arg0: !torch.vtensor<[1,8,2,3],f32>) -> !t
// CHECK: %[[TRANSPOSE_2:.*]] = torch.aten.transpose.int %[[TRANSPOSE_1]], %[[C4_1]], %[[C5]] : !torch.vtensor<[1,2,2,2,2,3],f32>, !torch.int, !torch.int -> !torch.vtensor<[1,2,2,2,3,2],f32>
// CHECK: %[[MUL:.*]] = torch.aten.mul.int %[[SIZE_1]], %[[C2_0]] : !torch.int, !torch.int -> !torch.int
// CHECK: %[[MUL_0:.*]] = torch.aten.mul.int %[[SIZE_2]], %[[C2_0]] : !torch.int, !torch.int -> !torch.int
// CHECK: %[[RESHAPE_LIST_0:.*]] = torch.prim.ListConstruct %[[SIZE]], %5, %[[MUL]], %[[MUL_0]] : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[RESHAPE_LIST_0:.*]] = torch.prim.ListConstruct %[[SIZE]], %[[INT]], %[[MUL]], %[[MUL_0]] : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[RESULT:.*]] = torch.aten.reshape %[[TRANSPOSE_2]], %[[RESHAPE_LIST_0]] : !torch.vtensor<[1,2,2,2,3,2],f32>, !torch.list<int> -> !torch.vtensor<[1,2,4,6],f32>
// CHECK: return %[[RESULT]] : !torch.vtensor<[1,2,4,6],f32
%0 = torch.operator "onnx.DepthToSpace"(%arg0) {torch.onnx.blocksize = 2 : si64, torch.onnx.mode = "DCR"} : (!torch.vtensor<[1,8,2,3],f32>) -> !torch.vtensor<[1,2,4,6],f32>
Expand All @@ -1859,7 +1859,7 @@ func.func @test_depthtospace_crd_mode_example(%arg0: !torch.vtensor<[1,8,2,3],f3
// CHECK: %[[C4:.*]] = torch.constant.int 4
// CHECK: %[[DIV:.*]] = torch.aten.div.int %[[SIZE_0]], %[[C4]] : !torch.int, !torch.int -> !torch.float
// CHECK: %[[INT:.*]] = torch.aten.Int.float %[[DIV]] : !torch.float -> !torch.int
// CHECK: %[[RESHAPE_LIST:.*]] = torch.prim.ListConstruct %[[SIZE]], %[[C2_0]], %[[C2_0]], %[[INT]], %[[SIZE_1]], %[[SIZE_2]] : (!torch.int, !torch.int, !torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[RESHAPE_LIST:.*]] = torch.prim.ListConstruct %[[SIZE]], %[[INT]], %[[C2_0]], %[[C2_0]], %[[SIZE_1]], %[[SIZE_2]] : (!torch.int, !torch.int, !torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[RESHAPE:.*]] = torch.aten.reshape %arg0, %[[RESHAPE_LIST]] : !torch.vtensor<[1,8,2,3],f32>, !torch.list<int> -> !torch.vtensor<[1,2,2,2,2,3],f32>
// CHECK: %[[C2_1:.*]] = torch.constant.int 2
// CHECK: %[[C4_0:.*]] = torch.constant.int 4
Expand All @@ -1872,7 +1872,7 @@ func.func @test_depthtospace_crd_mode_example(%arg0: !torch.vtensor<[1,8,2,3],f3
// CHECK: %[[TRANSPOSE_2:.*]] = torch.aten.transpose.int %[[TRANSPOSE_1]], %[[C4_1]], %[[C5]] : !torch.vtensor<[1,2,2,2,2,3],f32>, !torch.int, !torch.int -> !torch.vtensor<[1,2,2,2,3,2],f32>
// CHECK: %[[MUL:.*]] = torch.aten.mul.int %[[SIZE_1]], %[[C2_0]] : !torch.int, !torch.int -> !torch.int
// CHECK: %[[MUL_0:.*]] = torch.aten.mul.int %[[SIZE_2]], %[[C2_0]] : !torch.int, !torch.int -> !torch.int
// CHECK: %[[RESHAPE_LIST_0:.*]] = torch.prim.ListConstruct %[[SIZE]], %5, %[[MUL]], %[[MUL_0]] : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[RESHAPE_LIST_0:.*]] = torch.prim.ListConstruct %[[SIZE]], %[[INT]], %[[MUL]], %[[MUL_0]] : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[RESULT:.*]] = torch.aten.reshape %[[TRANSPOSE_2]], %[[RESHAPE_LIST_0]] : !torch.vtensor<[1,2,2,2,3,2],f32>, !torch.list<int> -> !torch.vtensor<[1,2,4,6],f32>
// CHECK: return %[[RESULT]] : !torch.vtensor<[1,2,4,6],f32
%0 = torch.operator "onnx.DepthToSpace"(%arg0) {torch.onnx.blocksize = 2 : si64, torch.onnx.mode = "CRD"} : (!torch.vtensor<[1,8,2,3],f32>) -> !torch.vtensor<[1,2,4,6],f32>
Expand Down
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