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[Relax][TFLite] Add gather frontend expected IRModule tests #19516

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[Relax][TFLite] Add gather frontend expected IRModule tests #19516

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3 changes: 3 additions & 0 deletions python/tvm/relax/frontend/tflite/tflite_frontend.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1630,6 +1630,9 @@ def convert_gather_nd(self, op):

indices_dims = len(self._infer_shape(indices))
indices_t = relax.op.permute_dims(indices, axes=[-1] + list(range(indices_dims - 1)))
if indices_type == TensorType.INT32:
# Relax gather_nd requires int64 indices.
indices_t = relax.op.astype(indices_t, "int64")

out = relax.op.gather_nd(data, indices_t)
return out
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58 changes: 58 additions & 0 deletions tests/python/relax/test_frontend_tflite.py
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Expand Up @@ -1451,6 +1451,64 @@ def main(x: R.Tensor((2, 3), dtype="float32")) -> R.Tensor((2, 3), dtype="float3

verify(ReverseV2, Expected)


def test_gather():
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@gemini-code-assist gemini-code-assist Bot May 7, 2026

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medium

This test covers gather with int64 indices. For completeness, it would be beneficial to also add a test case for int32 indices, as TFLite's GATHER op supports both int32 and int64 for indices.

class Gather(tf.Module):
@tf.function(
input_signature=[
tf.TensorSpec(shape=(2, 3, 4), dtype=tf.float32),
tf.TensorSpec(shape=(2,), dtype=tf.int64),
]
)
def func(self, x, indices):
return tf.gather(x, indices, axis=1)

@I.ir_module
class Expected:
@R.function
def main(
x: R.Tensor((2, 3, 4), dtype="float32"),
indices: R.Tensor((2,), dtype="int64"),
) -> R.Tensor((2, 2, 4), dtype="float32"):
R.func_attr({"num_input": 2})
with R.dataflow():
lv: R.Tensor((2,), dtype="int32") = R.astype(indices, dtype="int32")
gv: R.Tensor((2, 2, 4), dtype="float32") = R.take(x, lv, axis=1, mode="fast")
R.output(gv)
return gv

verify(Gather, Expected)


def test_gather_nd():
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@gemini-code-assist gemini-code-assist Bot May 7, 2026

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medium

This test covers gather_nd with int32 indices, which is great for verifying the change in this PR. To make the test coverage for this operator more complete, could you also add a test case for int64 indices? This would ensure the int64 path is not broken by future changes and remains tested.

class GatherND(tf.Module):
@tf.function(
input_signature=[
tf.TensorSpec(shape=(2, 3, 4), dtype=tf.float32),
tf.TensorSpec(shape=(2, 2), dtype=tf.int32),
]
)
def func(self, x, indices):
return tf.gather_nd(x, indices)

@I.ir_module
class Expected:
@R.function
def main(
x: R.Tensor((2, 3, 4), dtype="float32"),
indices: R.Tensor((2, 2), dtype="int32"),
) -> R.Tensor((2, 4), dtype="float32"):
R.func_attr({"num_input": 2})
with R.dataflow():
lv: R.Tensor((2, 2), dtype="int32") = R.permute_dims(indices, axes=[-1, 0])
lv1: R.Tensor((2, 2), dtype="int64") = R.astype(lv, dtype="int64")
gv: R.Tensor((2, 4), dtype="float32") = R.gather_nd(x, lv1, batch_dims=0)
R.output(gv)
return gv

verify(GatherND, Expected)


def _make_conv2d_module(data_shape, kernel_shape, data_format, strides, padding):
class Conv2DModule(tf.Module):
@tf.function(
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