Skip to content

[Bug] Relax ONNX Gather mishandles negative indices #19531

@ALinrunrun

Description

@ALinrunrun

Expected behavior

TVM Relax should preserve ONNX Gather semantics for negative indices.

For ONNX Gather, negative indices are valid and should count from the end of the selected axis. For example:

X = [1, 2, 3, 4, 5]
I = [-1, -3, 0]

The expected output is:

[5, 3, 1]

This matches ONNX Runtime.

Actual behavior

TVM Relax produces a different output after importing the ONNX model and applying LegalizeOps:

ORT: [5. 3. 1.]
TVM: [0. 0. 1.]

It looks like the negative indices are not handled according to ONNX semantics. In this case, -1 and -3 are effectively producing 0 instead of selecting elements from the end of the input tensor.

Environment

TVM: 0.14 environment / Relax ONNX frontend
ONNX Runtime: 1.23
Python: 3.11
Target: llvm
OS: Linux

Steps to reproduce

import numpy as np
import onnx
from onnx import helper, TensorProto
import onnxruntime as ort
import tvm
from tvm import relax
from tvm.relax.frontend.onnx import from_onnx


x_info = helper.make_tensor_value_info("X", TensorProto.FLOAT, [5])
i_info = helper.make_tensor_value_info("I", TensorProto.INT64, [3])
y_info = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [3])

node = helper.make_node("Gather", ["X", "I"], ["Y"], axis=0)
graph = helper.make_graph([node], "g", [x_info, i_info], [y_info])
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 17)])
model.ir_version = 9
onnx.checker.check_model(model)

x = np.array([1.0, 2.0, 3.0, 4.0, 5.0], dtype=np.float32)
idx = np.array([-1, -3, 0], dtype=np.int64)

ort_out = ort.InferenceSession(
    model.SerializeToString(),
    providers=["CPUExecutionProvider"],
).run(None, {"X": x, "I": idx})[0]

mod = from_onnx(model)
mod = relax.transform.LegalizeOps()(mod)

ex = relax.build(mod, tvm.target.Target("llvm"))
dev = tvm.cpu(0)
vm = relax.VirtualMachine(ex, dev)

tvm_out = vm["main"](
    tvm.runtime.tensor(x, device=dev),
    tvm.runtime.tensor(idx, device=dev),
).numpy()

print("ORT:", ort_out)
print("TVM:", tvm_out)

Triage

  • needs-triage

Metadata

Metadata

Assignees

No one assigned

    Labels

    needs-triagePRs or issues that need to be investigated by maintainers to find the right assignees to address ittype: bug

    Type

    No type
    No fields configured for issues without a type.

    Projects

    No projects

    Milestone

    No milestone

    Relationships

    None yet

    Development

    No branches or pull requests

    Issue actions