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[Fix][TOPI] Allow empty GatherElements indices #20990

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[Fix][TOPI] Allow empty GatherElements indices #20990

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5 changes: 2 additions & 3 deletions include/tvm/topi/transform.h
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Original file line number Diff line number Diff line change
Expand Up @@ -1526,9 +1526,8 @@ inline Tensor gather(const Tensor& data, int axis, const Tensor& indices,
}
TVM_FFI_ICHECK_GE(axis, 0);
TVM_FFI_ICHECK_LT(axis, ndim_d);
if (indices->shape[axis].as<IntImmObj>()) {
size_t indices_dim_i = static_cast<size_t>(GetConstInt(indices->shape[axis]));
TVM_FFI_ICHECK_GE(indices_dim_i, 1);
if (const auto* indices_dim = indices->shape[axis].as<IntImmObj>()) {
TVM_FFI_ICHECK_GE(indices_dim->value, 0);
}
// Index tensors are validated by integer element kind; vector lane encoding is irrelevant here.
PrimType indices_ty = indices->dtype;
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34 changes: 34 additions & 0 deletions tests/python/relax/test_frontend_onnx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2249,6 +2249,40 @@ def test_gather_elements(data_shape, indices_shape, axis):
check_correctness(model, inputs=input_values)


@pytest.mark.skipif(not tvm_tirx.testing.device_enabled("llvm"), reason="LLVM is required")
@pytest.mark.parametrize(
"data_shape,indices_shape,axis",
[
((2, 3), (0, 3), 0),
((2, 3), (2, 0), -1),
((0, 3), (0, 3), 0),
((0, 3), (0, 2), 1),
((2, 3), (1, 3), 0),
],
)
def test_gather_elements_with_empty_indices(data_shape, indices_shape, axis):
graph = helper.make_graph(
[helper.make_node("GatherElements", ["data", "indices"], ["y"], axis=axis)],
"gather_elements_with_empty_indices",
[
helper.make_tensor_value_info("data", TensorProto.FLOAT, data_shape),
helper.make_tensor_value_info("indices", TensorProto.INT64, indices_shape),
],
[helper.make_tensor_value_info("y", TensorProto.FLOAT, indices_shape)],
)
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
onnx.checker.check_model(model)
# Give the importer concrete input shapes to isolate TOPI lowering.
mod = from_onnx(model, shape_dict={"data": data_shape, "indices": indices_shape})
vm = relax.VirtualMachine(tvm.compile(mod, target="llvm"), tvm_tirx.cpu())

data = np.arange(np.prod(data_shape), dtype=np.float32).reshape(data_shape)
indices = np.zeros(indices_shape, dtype=np.int64)
actual = vm["main"](tvm_tirx.runtime.tensor(data), tvm_tirx.runtime.tensor(indices)).numpy()
expected = np.take_along_axis(data, indices, axis)
np.testing.assert_array_equal(actual, expected, strict=True)


@pytest.mark.parametrize(
"data_shape, indices_shape, axis",
[
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