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new 7196ac3995 [Test][TFLite] Add unit tests for `LEAKY_RELU`,
`HARD_SWISH` `ReLU_N1_to_1` and `LOG_SOFTMAX` (#19388)
7196ac3995 is described below
commit 7196ac399524aa10a9e6d39a208f92456c3cfb42
Author: Felix Hirwa Nshuti <[email protected]>
AuthorDate: Sat Apr 11 19:46:58 2026 +0200
[Test][TFLite] Add unit tests for `LEAKY_RELU`, `HARD_SWISH` `ReLU_N1_to_1`
and `LOG_SOFTMAX` (#19388)
This PR adds unit test coverage for activation function operators
in the Relax TFLite frontend, as part of
https://github.com/apache/tvm/issues/18971
- Added the `(tf.nn.log_softmax, R.nn.log_softmax)` entry to elementwise
unit test
- Added unit test for `LEAK_RELU` with default alpha
- Added unit test for `HARD_SWISH`
- Added unit test for `ReLU_N1_to_1`
**Bugs fixed:**
- `relax.op.tensor.clip` -> `relax.op.clip` in `_relu6` helper of
`convert_hard_swish`
- `relax.op.nn.leaky_relu` -> `relax.op.nn.leakyrelu` in
`convert_leaky_relu`
---
.../tvm/relax/frontend/tflite/tflite_frontend.py | 4 +-
tests/python/relax/test_frontend_tflite.py | 67 ++++++++++++++++++++++
2 files changed, 69 insertions(+), 2 deletions(-)
diff --git a/python/tvm/relax/frontend/tflite/tflite_frontend.py
b/python/tvm/relax/frontend/tflite/tflite_frontend.py
index d7b56e597b..8b2f70a0f5 100644
--- a/python/tvm/relax/frontend/tflite/tflite_frontend.py
+++ b/python/tvm/relax/frontend/tflite/tflite_frontend.py
@@ -1010,7 +1010,7 @@ class OperatorConverter:
output_tensor = output_tensors[0]
def _relu6(data):
- return relax.op.tensor.clip(data, 0.0, 6.0)
+ return relax.op.clip(data, min=0.0, max=6.0)
def _hard_swish(data):
return data * _relu6(data + relax.const(3.0)) / relax.const(6.0)
@@ -1094,7 +1094,7 @@ class OperatorConverter:
if input_tensor.qnn_params:
in_expr = self.dequantize(in_expr, input_tensor)
- out = relax.op.nn.leaky_relu(in_expr, alpha_tensor)
+ out = relax.op.nn.leakyrelu(in_expr, alpha_tensor)
if output_tensor.qnn_params:
out = self.quantize(out, output_tensor)
diff --git a/tests/python/relax/test_frontend_tflite.py
b/tests/python/relax/test_frontend_tflite.py
index c9a8470f42..195d2f5542 100644
--- a/tests/python/relax/test_frontend_tflite.py
+++ b/tests/python/relax/test_frontend_tflite.py
@@ -523,6 +523,7 @@ def test_square():
(tf.math.rsqrt, R.rsqrt),
(tf.nn.softmax, R.nn.softmax),
(tf.math.sqrt, R.sqrt),
+ (tf.nn.log_softmax, R.nn.log_softmax),
],
)
def test_element_wise(tf_op, relax_op):
@@ -1918,5 +1919,71 @@ def test_space_to_depth():
verify(SpaceToDepth, Expected)
+def test_leaky_relu():
+ class LeakyReLU(tf.Module):
+ @tf.function(input_signature=[tf.TensorSpec(shape=(1, 30),
dtype=tf.float32)])
+ def func(self, x):
+ return tf.nn.leaky_relu(x, alpha=0.2)
+
+ @I.ir_module
+ class Expected:
+ @R.function
+ def main(x: R.Tensor((1, 30), dtype="float32")) -> R.Tensor((1, 30),
dtype="float32"):
+ R.func_attr({"num_input": 1})
+ with R.dataflow():
+ gv: R.Tensor((1, 30), dtype="float32") = R.nn.leakyrelu(
+ x, alpha=0.20000000298023224
+ )
+ R.output(gv)
+ return gv
+
+ verify(LeakyReLU, Expected)
+
+
+def test_hard_swish():
+ class HardSwish(tf.Module):
+ @tf.function(input_signature=[tf.TensorSpec(shape=(1, 30),
dtype=tf.float32)])
+ def func(self, x):
+ return x * tf.nn.relu6(x + 3) / 6
+
+ @I.ir_module
+ class Expected:
+ @R.function
+ def main(x: R.Tensor((1, 30), dtype="float32")) -> R.Tensor((1, 30),
dtype="float32"):
+ R.func_attr({"num_input": 1})
+ with R.dataflow():
+ lv: R.Tensor((1, 30), dtype="float32") = R.add(x, R.const(3.0,
dtype="float32"))
+ lv1: R.Tensor((1, 30), dtype="float32") = R.clip(
+ lv, R.prim_value(T.float64(0.0)),
R.prim_value(T.float64(6.0))
+ )
+ lv2: R.Tensor((1, 30), dtype="float32") = R.multiply(x, lv1)
+ gv: R.Tensor((1, 30), dtype="float32") = R.divide(
+ lv2, R.const(6.0, dtype="float32")
+ )
+ R.output(gv)
+ return gv
+
+ verify(HardSwish, Expected)
+
+
+def test_relu_n1_to_1():
+ class ReLU_N1_to_1(tf.Module):
+ @tf.function(input_signature=[tf.TensorSpec(shape=(1, 30),
dtype=tf.float32)])
+ def func(self, x):
+ return tf.clip_by_value(x, -1.0, 1.0)
+
+ @I.ir_module
+ class Expected:
+ @R.function
+ def main(x: R.Tensor((1, 30), dtype="float32")) -> R.Tensor((1, 30),
dtype="float32"):
+ R.func_attr({"num_input": 1})
+ with R.dataflow():
+ gv: R.Tensor((1, 30), dtype="float32") = R.clip(x, min=-1,
max=1)
+ R.output(gv)
+ return gv
+
+ verify(ReLU_N1_to_1, Expected)
+
+
if __name__ == "__main__":
pytest.main(["-s", __file__])
