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new 9ba27ff271 [relax][frontend][tflite] Add tests for
l2_normalization/slice/reverse_v2 (#19371)
9ba27ff271 is described below
commit 9ba27ff271d3b62f665d4bdac7bbb04f54607118
Author: Ahmad Jahaf <[email protected]>
AuthorDate: Sat Apr 11 20:33:58 2026 +0300
[relax][frontend][tflite] Add tests for l2_normalization/slice/reverse_v2
(#19371)
This PR adds Relax TFLite frontend test coverage for:
- L2_NORMALIZATION
- SLICE
- REVERSE_V2
Part of fixing #18971.
---
.../tvm/relax/frontend/tflite/tflite_frontend.py | 20 ++++++---
tests/python/relax/test_frontend_tflite.py | 49 ++++++++++++++++++++++
2 files changed, 63 insertions(+), 6 deletions(-)
diff --git a/python/tvm/relax/frontend/tflite/tflite_frontend.py
b/python/tvm/relax/frontend/tflite/tflite_frontend.py
index 16d5cb636b..d7b56e597b 100644
--- a/python/tvm/relax/frontend/tflite/tflite_frontend.py
+++ b/python/tvm/relax/frontend/tflite/tflite_frontend.py
@@ -800,7 +800,12 @@ class OperatorConverter:
)
# TFL uses only the default epsilon value
- out = relax.op.nn.l2_normalize(in_expr, eps=1e-12,
axis=[input_tensor_rank - 1])
+ # Implement L2 normalization: output = input / sqrt(sum(input^2) + eps)
+ # L2 normalization is applied along the last axis
+ squared = relax.op.square(in_expr)
+ sum_squared = relax.op.sum(squared, axis=input_tensor_rank - 1,
keepdims=True)
+ denom = relax.op.sqrt(relax.op.add(sum_squared, relax.const(1e-12,
"float32")))
+ out = relax.op.divide(in_expr, denom)
# if we have fused activation fn
if output_tensor.qnn_params:
@@ -2251,8 +2256,11 @@ class OperatorConverter:
else:
end[i] += begin[i]
- out = relax.op.strided_slice(in_expr, begin, end)
-
+ # Create axes list for all dimensions being sliced
+ axes = list(range(input_tensor_rank))
+ begin = [int(v) for v in begin]
+ end = [int(v) for v in end]
+ out = relax.op.strided_slice(in_expr, axes=axes, begin=begin, end=end)
return out
def convert_select(self, op):
@@ -3555,7 +3563,7 @@ class OperatorConverter:
axis = self.get_tensor_value(input_tensors[1])
if isinstance(axis, np.ndarray):
assert axis.size == 1, "only one value is expected."
- axis = int(axis)
+ axis = int(axis.flat[0])
ndims = len(input_tensors[0].tensor.ShapeAsNumpy())
assert -1 - ndims <= axis <= ndims, "axis out of range"
@@ -3628,9 +3636,9 @@ class OperatorConverter:
axis = self.get_tensor_value(input_tensors[1])
if isinstance(axis, np.ndarray):
assert len(axis) == 1, "TFLite does not support multi-axis yet"
- axis = int(axis)
+ axis = int(axis.flat[0])
- out = relax.op.reverse(input_expr, axis)
+ out = relax.op.flip(input_expr, axis)
return out
def convert_matrix_set_diag(self, op):
diff --git a/tests/python/relax/test_frontend_tflite.py
b/tests/python/relax/test_frontend_tflite.py
index c237d4db8f..c9a8470f42 100644
--- a/tests/python/relax/test_frontend_tflite.py
+++ b/tests/python/relax/test_frontend_tflite.py
@@ -709,6 +709,55 @@ def test_reduce(tf_op, relax_op, axis, out_shape):
verify(TfInput, Expected)
+def test_l2_normalization():
+ class L2Normalization(tf.Module):
+ @tf.function(input_signature=[tf.TensorSpec(shape=(2, 4),
dtype=tf.float32)])
+ def func(self, x):
+ return tf.nn.l2_normalize(x, axis=-1)
+
+ verify(L2Normalization)
+
+
+def test_slice():
+ class Slice(tf.Module):
+ @tf.function(input_signature=[tf.TensorSpec(shape=(3, 4),
dtype=tf.float32)])
+ def func(self, x):
+ return tf.slice(x, begin=[1, 1], size=[2, 2])
+
+ @I.ir_module
+ class Expected:
+ @R.function
+ def main(x: R.Tensor((3, 4), dtype="float32")) -> R.Tensor((2, 2),
dtype="float32"):
+ R.func_attr({"num_input": 1})
+ with R.dataflow():
+ gv: R.Tensor((2, 2), dtype="float32") = R.strided_slice(
+ x, axes=[0, 1], begin=[1, 1], end=[3, 3]
+ )
+ R.output(gv)
+ return gv
+
+ verify(Slice, Expected)
+
+
+def test_reverse_v2():
+ class ReverseV2(tf.Module):
+ @tf.function(input_signature=[tf.TensorSpec(shape=(2, 3),
dtype=tf.float32)])
+ def func(self, x):
+ return tf.reverse(x, axis=[1])
+
+ @I.ir_module
+ class Expected:
+ @R.function
+ def main(x: R.Tensor((2, 3), dtype="float32")) -> R.Tensor((2, 3),
dtype="float32"):
+ R.func_attr({"num_input": 1})
+ with R.dataflow():
+ gv: R.Tensor((2, 3), dtype="float32") = R.flip(x, axis=1)
+ R.output(gv)
+ return gv
+
+ verify(ReverseV2, Expected)
+
+
def _make_conv2d_module(data_shape, kernel_shape, data_format, strides,
padding):
class Conv2DModule(tf.Module):
@tf.function(
