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[API Compatibility] add torch.relu #802

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[API Compatibility] add torch.relu #802

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705f213
add torch.nn.functional.relu, relu_, leaky_relu and leaky_relu_
Manfredss Jan 11, 2026
8fe8bda
Merge branch 'master' of https://github.com/paddlepaddle/paconvert in…
Manfredss Jan 26, 2026
2c18427
[API Compatibility] Mark relu and leaky_relu APIs as aligned
Manfredss Jan 26, 2026
f3dca27
enhance tests for api relu, relu_, leaky_relu and leaky_relu_
Manfredss Feb 4, 2026
ea420a2
fix
Manfredss Feb 4, 2026
25e4727
add torch.relu with ChangePrefixMatcher
Manfredss Feb 13, 2026
f0eeb64
Merge branch 'master' of https://github.com/paddlepaddle/paconvert in…
Manfredss Feb 28, 2026
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10 changes: 1 addition & 9 deletions paconvert/api_mapping.json
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Original file line number Diff line number Diff line change
Expand Up @@ -11006,15 +11006,7 @@
"Matcher": "ChangePrefixMatcher"
},
"torch.relu": {
"Matcher": "GenericMatcher",
"paddle_api": "paddle.nn.functional.relu",
"min_input_args": 1,
"args_list": [
"input"
],
"kwargs_change": {
"input": "x"
}
"Matcher": "ChangePrefixMatcher"
},
"torch.remainder": {
"Matcher": "ChangePrefixMatcher"
Expand Down
130 changes: 130 additions & 0 deletions tests/test_nn_functional_leaky_relu.py
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Original file line number Diff line number Diff line change
Expand Up @@ -124,3 +124,133 @@ def test_case_8():
"""
)
obj.run(pytorch_code, ["result"])


def test_case_9():
"""1D tensor input"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([-1.5, -0.5, 0.0, 0.5, 1.5])
result = F.leaky_relu(x)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_10():
"""2D tensor input"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([[-1.0, 2.0], [3.0, -4.0]])
result = F.leaky_relu(x, 0.1)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_11():
"""float64 dtype"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([-1.5, -0.5, 0.0, 0.5, 1.5], dtype=torch.float64)
result = F.leaky_relu(x, negative_slope=0.2)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_12():
"""4D tensor (batch, channel, height, width)"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([[[[-1.0, 2.0], [3.0, -4.0]], [[0.5, -0.5], [-1.5, 1.5]]]])
result = F.leaky_relu(x, 0.1)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_13():
"""All negative values"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([-1.0, -2.0, -3.0, -4.0])
result = F.leaky_relu(x, 0.2)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_14():
"""Default negative_slope (0.01)"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([-10.0, -5.0, 0.0, 5.0, 10.0])
result = F.leaky_relu(x)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_15():
"""Small negative_slope"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([-1.0, 0.0, 1.0, 2.0])
result = F.leaky_relu(x, 0.05)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_16():
"""Large negative_slope"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([-2.0, -1.0, 0.0, 1.0, 2.0])
result = F.leaky_relu(x, negative_slope=0.5)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_17():
"""Expression as input"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([1.0, 2.0, 3.0])
result = F.leaky_relu(x - 2.0, 0.1)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_18():
"""3D tensor"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([[[-1.0, 0.5, 1.0], [2.0, -0.5, -1.5]]])
result = F.leaky_relu(x, negative_slope=0.15)
"""
)
obj.run(pytorch_code, ["result"])
91 changes: 91 additions & 0 deletions tests/test_nn_functional_leaky_relu_.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -95,3 +95,94 @@ def test_case_6():
"""
)
obj.run(pytorch_code, ["x"])


def test_case_7():
"""1D tensor input"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([-1.5, -0.5, 0.0, 0.5, 1.5])
F.leaky_relu_(x)
"""
)
obj.run(pytorch_code, ["x"])


def test_case_8():
"""2D tensor input"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([[-1.0, 2.0], [3.0, -4.0]])
F.leaky_relu_(x, 0.1)
"""
)
obj.run(pytorch_code, ["x"])


def test_case_9():
"""float64 dtype"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([-1.5, -0.5, 0.0, 0.5, 1.5], dtype=torch.float64)
F.leaky_relu_(x, negative_slope=0.2)
"""
)
obj.run(pytorch_code, ["x"])


def test_case_10():
"""4D tensor (batch, channel, height, width)"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([[[[-1.0, 2.0], [3.0, -4.0]], [[0.5, -0.5], [-1.5, 1.5]]]])
F.leaky_relu_(x, 0.1)
"""
)
obj.run(pytorch_code, ["x"])


def test_case_11():
"""All negative values"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([-1.0, -2.0, -3.0, -4.0])
F.leaky_relu_(x, 0.2)
"""
)
obj.run(pytorch_code, ["x"])


def test_case_12():
"""3D tensor"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([[[-1.0, 0.5, 1.0], [2.0, -0.5, -1.5]]])
F.leaky_relu_(x, negative_slope=0.15)
"""
)
obj.run(pytorch_code, ["x"])


def test_case_13():
"""Large negative_slope"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([-2.0, -1.0, 0.0, 1.0, 2.0])
F.leaky_relu_(x, negative_slope=0.5)
"""
)
obj.run(pytorch_code, ["x"])
130 changes: 130 additions & 0 deletions tests/test_nn_functional_relu.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -153,3 +153,133 @@ def test_case_10():
"""
)
obj.run(pytorch_code, ["result"])


def test_case_11():
"""1D tensor input"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([-1.5, -0.5, 0.0, 0.5, 1.5])
result = F.relu(x)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_12():
"""2D tensor input"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([[-1.0, 2.0], [3.0, -4.0]])
result = F.relu(x)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_13():
"""float64 dtype"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([-1.5, -0.5, 0.0, 0.5, 1.5], dtype=torch.float64)
result = F.relu(x)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_14():
"""4D tensor (batch, channel, height, width)"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([[[[-1.0, 2.0], [3.0, -4.0]], [[0.5, -0.5], [-1.5, 1.5]]]])
result = F.relu(x)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_15():
"""All negative values"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([-1.0, -2.0, -3.0, -4.0])
result = F.relu(x)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_16():
"""All positive values"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([1.0, 2.0, 3.0, 4.0])
result = F.relu(x)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_17():
"""All zeros"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([0.0, 0.0, 0.0])
result = F.relu(x)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_18():
"""Mixed positive and negative values"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([-1.0, 0.0, 1.0, 2.0])
result = F.relu(x)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_19():
"""Variable as input"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
data = torch.tensor([-2.0, -1.0, 0.0, 1.0, 2.0])
result = F.relu(data)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_20():
"""Expression as input"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn.functional as F
x = torch.tensor([1.0, 2.0, 3.0])
result = F.relu(x - 2.0)
"""
)
obj.run(pytorch_code, ["result"])
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