[Relax][ONNX] Normalize negative indices before the take call for Gather operator (#19525)

Hi Committers,

This PR is trying to fix issues
#19436. Any suggestions would be
appreciated if you are available.

### Root Cause
1. ONNX `Gather` allows negative indices (counting from the end of the
target axis).
2. In the Relax ONNX importer, `Gather` was lowered directly to
`relax.op.take` without normalizing negative indices first.
3. This created semantic mismatch / incorrect behavior in downstream
lowering paths that assume non-negative indices.
4. Test failures were also caused by pytest parametrization issues:
  - using ONNX `TensorProto` enum values directly as NumPy dtypes,
- and tuple-style parametrization triggering fixture interpretation
errors.
  
### Solutions
1. Added conditional negative-index normalization in `Gather._impl_v13`:
  - apply only for signed index dtypes,
  - use: `idx < 0 ? idx + axis_extent : idx`,
- derive `axis_extent` from shape/runtime expression to support dynamic
shapes.
2. Skipped normalization for unsigned index dtypes to avoid redundant
graph ops/checks.

---------

Co-authored-by: cchung100m <cchung100m@users.noreply.github.com>

Author: cchung100m

python/tvm/relax/frontend/onnx/onnx_frontend.py

@@ -1106,6 +1106,25 @@ def _impl_v13(cls, bb, inputs, attr, params):
             shape_val = data[np_index]
             return relax.PrimValue(shape_val)
 
+        indices_dtype = indices.struct_info.dtype
+        if not indices_dtype.startswith("uint"):
+            data_shape = bb.normalize(relax.op.shape_of(data))
+            data_shape_tensor = bb.normalize(relax.op.shape_to_tensor(data_shape))
+            axis_extent = bb.normalize(
+                relax.op.take(data_shape_tensor, relax.const(axis, "int64"), axis=0, mode="wrap")
+            )
+
+            if indices_dtype !="int64":
+                axis_extent = bb.normalize(relax.op.astype(axis_extent, indices_dtype))
+
+            indices = bb.normalize(
+                relax.op.where(
+                    relax.op.less(indices, relax.const(0, indices_dtype)),
+                    relax.op.add(indices, axis_extent),
+                    indices,
+                )
+            )
+
         return relax.op.take(data, indices, axis)
 
 

tests/python/relax/test_frontend_onnx.py

@@ -874,6 +874,68 @@ def _verify_gather(data_shape, indices, out_shape, axis=0):
     _verify_gather([3, 3], [[0, 2]], [3, 1, 2], 1)
 
 
+@pytest.mark.parametrize(
+    "axis, indices, out_shape",
+    [
+        (0, [-1, 0], [2, 4]),
+        (1, [-1, 0], [3, 2]),
+        (
+            1, 
+            [[-1, 0], [1, -2]], 
+            [3, 2, 2],
+        ),
+    ],
+)
+@pytest.mark.parametrize("indices_type", [TensorProto.INT64, TensorProto.INT32])
+def test_gather_negative_indices(axis, indices, out_shape, indices_type):
+    gather_node = helper.make_node("Gather", ["data", "indices"], ["y"], axis=axis)
+    indices_shape = np.asarray(indices).shape
+
+    graph = helper.make_graph(
+        [gather_node],
+        "gather_negative_indices_test",
+        inputs=[
+            helper.make_tensor_value_info("data", TensorProto.FLOAT, [3, 4]),
+            helper.make_tensor_value_info("indices", indices_type, indices_shape),
+        ],
+        outputs=[helper.make_tensor_value_info("y", TensorProto.FLOAT, out_shape)],
+    )
+
+    model = helper.make_model(graph, producer_name="gather_negative_indices_test")
+    indices_np_dtype = {
+        TensorProto.INT64: np.int64,
+        TensorProto.INT32: np.int32,
+    }[indices_type]
+    input_values = {
+        "data": np.random.randn(3, 4).astype("float32"),
+        "indices": np.array(indices).astype(indices_np_dtype),
+    }
+    check_correctness(model, inputs=input_values)
+
+
+@pytest.mark.parametrize("indices_type", [TensorProto.INT64, TensorProto.INT32])
+def test_gather_negative_indices_ir_normalization(indices_type):
+    gather_node = helper.make_node("Gather", ["data", "indices"], ["y"], axis=1)
+    graph = helper.make_graph(
+        [gather_node],
+        "gather_negative_indices_ir_test",
+        inputs=[
+            helper.make_tensor_value_info("data", TensorProto.FLOAT, [3, 4]),
+            helper.make_tensor_value_info("indices", indices_type, [2]),
+        ],
+        outputs=[helper.make_tensor_value_info("y", TensorProto.FLOAT, [3, 2])],
+    )
+
+    model = helper.make_model(graph, producer_name="gather_negative_indices_ir_test")
+    tvm_model = from_onnx(model, opset=13, keep_params_in_input=True)
+    call_ops = collect_relax_call_ops(tvm_model["main"])
+
+    assert "relax.where" in call_ops
+    assert "relax.less" in call_ops
+    assert "relax.add" in call_ops
+    assert "relax.take" in call_ops
+
+
 @pytest.mark.parametrize(
     "data_shape, indices_shape, axis",
     [