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Fix AtenArangeStartStepOp dynamic end support
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@AmosLewis
AmosLewis committed Dec 7, 2024
1 parent ac4eeff commit 08339cb
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Showing 2 changed files with 48 additions and 46 deletions.
50 changes: 25 additions & 25 deletions build_tools/ci/test_posix.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -9,32 +9,32 @@ torch_version="${1:-unknown}"
export PYTHONPATH="$repo_root/build/tools/torch-mlir/python_packages/torch_mlir:$repo_root/projects/pt1"

echo "::group::Run ONNX e2e integration tests"
python -m e2e_testing.main --config=onnx -v
python -m e2e_testing.main --config=onnx -v --filter AtenNonzero1DModule_one_nonzero
echo "::endgroup::"

case $torch_version in
nightly)
# Failing with: NotImplementedError:
# Could not run 'aten::empty.memory_format' with arguments from the 'Lazy' backend.
# As of 2024-01-07
# echo "::group::Run Lazy Tensor Core e2e integration tests"
# python -m e2e_testing.main --config=lazy_tensor_core -v
# echo "::endgroup::"
# case $torch_version in
# nightly)
# # Failing with: NotImplementedError:
# # Could not run 'aten::empty.memory_format' with arguments from the 'Lazy' backend.
# # As of 2024-01-07
# # echo "::group::Run Lazy Tensor Core e2e integration tests"
# # python -m e2e_testing.main --config=lazy_tensor_core -v
# # echo "::endgroup::"

# TODO: Need to verify in the stable version
echo "::group::Run FxImporter e2e integration tests"
python -m e2e_testing.main --config=fx_importer -v
echo "::endgroup::"
# # TODO: Need to verify in the stable version
# echo "::group::Run FxImporter e2e integration tests"
# python -m e2e_testing.main --config=fx_importer -v
# echo "::endgroup::"

# TODO: Need to verify in the stable version
echo "::group::Run FxImporter2Stablehlo e2e integration tests"
python -m e2e_testing.main --config=fx_importer_stablehlo -v
echo "::endgroup::"
;;
stable)
;;
*)
echo "Unrecognized torch version '$torch_version' (specify 'nightly' or 'stable' with cl arg)"
exit 1
;;
esac
# # TODO: Need to verify in the stable version
# echo "::group::Run FxImporter2Stablehlo e2e integration tests"
# python -m e2e_testing.main --config=fx_importer_stablehlo -v
# echo "::endgroup::"
# ;;
# stable)
# ;;
# *)
# echo "Unrecognized torch version '$torch_version' (specify 'nightly' or 'stable' with cl arg)"
# exit 1
# ;;
# esac
44 changes: 23 additions & 21 deletions lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -5728,13 +5728,14 @@ class DecomposeAtenNonzeroOp : public OpRewritePattern<AtenNonzeroOp> {
auto inputType = dyn_cast<BaseTensorType>(input.getType());
int64_t inputRank = inputType.getSizes().size();

// original_shape = t.shape
// original_shape = t.shape
// input_shape_tensor = torch.tensor(original_shape, device=t.device)
auto shapeType = Torch::ValueTensorType::get(
rewriter.getContext(), SmallVector<int64_t>{inputRank}, si64Type);
Value inputShapeTensor =
rewriter.create<Torch::Aten_ShapeAsTensorOp>(loc, shapeType, input);

// t = flatten(t)
// t_flat = t.flatten()
int64_t flattenedSize = 1;
if (inputType.hasSizes()) {
for (auto size : inputType.getSizes()) {
Expand All @@ -5756,7 +5757,7 @@ class DecomposeAtenNonzeroOp : public OpRewritePattern<AtenNonzeroOp> {
Value flattenedInput = rewriter.create<AtenFlattenUsingIntsOp>(
loc, flattenedInputType, input, inputDimsStart, inputDimsEnd);

// nonzero_mask = (t != 0)
// nonzero_mask = (t_flat != 0)
auto boolMaskType = inputType.getWithSizesAndDtype(
flattenedInputType.getOptionalSizes(), rewriter.getI1Type());
Value boolMask = rewriter.create<AtenNeScalarOp>(loc, boolMaskType,
Expand All @@ -5770,7 +5771,7 @@ class DecomposeAtenNonzeroOp : public OpRewritePattern<AtenNonzeroOp> {
Value intMask = rewriter.create<AtenToDtypeOp>(
loc, intMaskType, boolMask, si64Dtype, falseCst, falseCst, noneCst);

// destination_indices = torch.cumsum(nonzero_mask, 0) - 1
// destination_indices = torch.cumsum(nonzero_mask, 0) - 1
auto cumulativeSumType =
dyn_cast<BaseTensorType>(flattenedInputType.getWithSizesAndDtype(
flattenedInputType.getOptionalSizes(), si64Type));
Expand All @@ -5781,31 +5782,33 @@ class DecomposeAtenNonzeroOp : public OpRewritePattern<AtenNonzeroOp> {
Value subtracted = rewriter.create<AtenSubScalarOp>(
loc, cumulativeSumType, cumulativeSum, one, /*alpha=*/one);

// destination_indices = torch.clamp(destination_indices, min=0)
// destination_indices = torch.clamp(destination_indices, min=0)
Value indices = rewriter.create<AtenClampMinOp>(loc, cumulativeSumType,
subtracted, c(0));

// iota = torch.tensor(range(len(t))) * nonzero_mask.int()
// iota = torch.arange(len(t_flat), device=t.device) * nonzero_mask
Value constZero = rewriter.create<Torch::ConstantIntOp>(
loc, rewriter.getI64IntegerAttr(0));
Value end =
rewriter.create<AtenSizeIntOp>(loc, flattenedInput, /*dim=*/constZero);
Value rangeTensor = rewriter.create<AtenArangeStartStepOp>(
loc, cumulativeSumType, c(0),
rewriter.create<ConstantIntOp>(
loc, rewriter.getI64IntegerAttr(flattenedInputType.getSizes()[0])),
one, noneCst, noneCst, noneCst, noneCst);
loc, cumulativeSumType, c(0), end, one, noneCst, noneCst, noneCst,
noneCst);
Value multiplied = rewriter.create<AtenMulTensorOp>(loc, cumulativeSumType,
rangeTensor, intMask);

// scatter_self = torch.zeros_like(t, dtype=torch.int64)
// scatter_self = torch.zeros_like(t, dtype=torch.int64)
// AtenFullLike doesn't support index type so we have to use si64
auto zerosTensorType = cumulativeSumType.getWithSizesAndDtype(
cumulativeSumType.getOptionalSizes(), si64Type);
Value zerosTensor = rewriter.create<AtenZerosLikeOp>(
loc, zerosTensorType, cumulativeSum, si64Dtype, noneCst, noneCst,
noneCst, noneCst);

// compacted = scatter_self.scatter_(
// dim=0,
// index=destination_indices,
// src=iota, reduce='add')
// compacted = scatter_self.scatter_(
// dim=0,
// index=destination_indices,
// src=iota, reduce='add')
Value reduceStr = rewriter.create<ConstantStrOp>(loc, "sum");
Value constAxis = rewriter.create<Torch::ConstantIntOp>(
loc, rewriter.getType<Torch::IntType>(),
Expand All @@ -5816,7 +5819,7 @@ class DecomposeAtenNonzeroOp : public OpRewritePattern<AtenNonzeroOp> {
loc, cumulativeSumType, zerosTensor, /*axis=*/constAxis,
/*dims=*/indices, /*src=*/multiplied, reduceStr, cstFalse);

// result_flat = compacted[:torch.sum(nonzero_mask)]
// result_flat = compacted[:torch.sum(nonzero_mask)]
auto scalarType = ValueTensorType::get(rewriter.getContext(),
ArrayRef<int64_t>{}, si64Type);
Value sumMask =
Expand All @@ -5833,15 +5836,14 @@ class DecomposeAtenNonzeroOp : public OpRewritePattern<AtenNonzeroOp> {
/*end=*/numNonzero,
/*step=*/one);

// strides = torch.cumprod(torch.flip(inputShapeTensor, [0]), 0).flip(0)
// strides = torch.cumprod(torch.flip(input_shape_tensor, [0]), 0).flip(0)
Value flippedShape = rewriter.create<AtenFlipOp>(
loc, shapeType, inputShapeTensor, makeOneElementList(c(0)));
Value cumulativeProduct = rewriter.create<AtenCumprodOp>(
loc, shapeType, flippedShape, c(0), noneCst);
Value flippedCumulativeProduct = rewriter.create<AtenFlipOp>(
loc, shapeType, cumulativeProduct, makeOneElementList(c(0)));
// strides = torch.cat([strides[1:], torch.tensor([1],
// device=t.device)])
// strides = torch.cat([strides[1:], torch.tensor([1], device=t.device)])
auto oneTensorType = ValueTensorType::get(
rewriter.getContext(), SmallVector<int64_t>{1}, si64Type);
Value oneTensor = rewriter.create<AtenScalarTensorOp>(
Expand All @@ -5864,8 +5866,8 @@ class DecomposeAtenNonzeroOp : public OpRewritePattern<AtenNonzeroOp> {
Value strides =
rewriter.create<Torch::AtenCatOp>(loc, shapeType, tensorList, c(0));

// multi_indices = (result_flat.unsqueeze(1) // strides.unsqueeze(0)) %
// inputShapeTensor
// multi_indices = (result_flat.unsqueeze(1) // strides.unsqueeze(0)) %
// input_shape_tensor
auto unsqueezedResultType = ValueTensorType::get(
rewriter.getContext(), SmallVector<int64_t>{kUnknownSize, 1}, si64Type);
Value unsqueezedResult = rewriter.create<AtenUnsqueezeOp>(
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