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[API Compatibility] add torch.special.logit -part #855

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[API Compatibility] add torch.special.logit -part #855

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13 changes: 1 addition & 12 deletions paconvert/api_mapping.json
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Original file line number Diff line number Diff line change
Expand Up @@ -11791,18 +11791,7 @@
}
},
"torch.special.logit": {
"Matcher": "GenericMatcher",
"paddle_api": "paddle.logit",
"min_input_args": 1,
"args_list": [
"input",
"eps",
"*",
"out"
],
"kwargs_change": {
"input": "x"
}
"Matcher": "ChangePrefixMatcher"
},
"torch.special.logsumexp": {
"Matcher": "ChangePrefixMatcher"
Expand Down
61 changes: 61 additions & 0 deletions tests/test_Tensor_logit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,3 +61,64 @@ def test_case_4():
"""
)
obj.run(pytorch_code, ["result"])


def test_case_5():
"""2D input with eps"""
pytorch_code = textwrap.dedent(
"""
import torch
input = torch.tensor([[0.2, 0.5, 0.8], [0.1, 0.9, 0.3]])
result = input.logit(eps=1e-6)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_6():
"""3D input, no eps"""
pytorch_code = textwrap.dedent(
"""
import torch
input = torch.tensor([[[0.2, 0.8], [0.4, 0.6]], [[0.1, 0.9], [0.3, 0.7]]])
result = input.logit()
"""
)
obj.run(pytorch_code, ["result"])


def test_case_7():
"""float64 dtype"""
pytorch_code = textwrap.dedent(
"""
import torch
input = torch.tensor([0.2796, 0.9331, 0.6486], dtype=torch.float64)
result = input.logit(eps=1e-6)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_8():
"""Gradient computation"""
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.tensor([0.2796, 0.9331, 0.6486], requires_grad=True)
y = x.logit(eps=1e-6)
y.sum().backward()
x_grad = x.grad
"""
)
obj.run(pytorch_code, ["y", "x_grad"], check_stop_gradient=False)


def test_case_9():
"""Chained call"""
pytorch_code = textwrap.dedent(
"""
import torch
result = torch.tensor([0.2, 0.5, 0.8]).logit(1e-6)
"""
)
obj.run(pytorch_code, ["result"])
124 changes: 124 additions & 0 deletions tests/test_logit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -102,3 +102,127 @@ def test_case_7():
"""
)
obj.run(pytorch_code, ["result", "out"])


def test_case_8():
"""No eps, keyword input="""
pytorch_code = textwrap.dedent(
"""
import torch
input = torch.tensor([0.2796, 0.9331, 0.6486, 0.1523, 0.6516])
result = torch.logit(input=input)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_9():
"""No eps, positional only"""
pytorch_code = textwrap.dedent(
"""
import torch
input = torch.tensor([0.2796, 0.9331, 0.6486, 0.1523, 0.6516])
result = torch.logit(input)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_10():
"""2D input with eps"""
pytorch_code = textwrap.dedent(
"""
import torch
input = torch.tensor([[0.2, 0.5, 0.8], [0.1, 0.9, 0.3]])
result = torch.logit(input, eps=1e-6)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_11():
"""3D input, no eps"""
pytorch_code = textwrap.dedent(
"""
import torch
input = torch.tensor([[[0.2, 0.8], [0.4, 0.6]], [[0.1, 0.9], [0.3, 0.7]]])
result = torch.logit(input)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_12():
"""float64 dtype"""
pytorch_code = textwrap.dedent(
"""
import torch
input = torch.tensor([0.2796, 0.9331, 0.6486], dtype=torch.float64)
result = torch.logit(input, eps=1e-6)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_13():
"""out parameter without eps"""
pytorch_code = textwrap.dedent(
"""
import torch
input = torch.tensor([0.2796, 0.9331, 0.6486, 0.1523, 0.6516])
out = torch.zeros(5)
result = torch.logit(input, out=out)
"""
)
obj.run(pytorch_code, ["result", "out"])


def test_case_14():
"""Reordered kwargs: out, input, eps"""
pytorch_code = textwrap.dedent(
"""
import torch
input = torch.tensor([0.2796, 0.9331, 0.6486, 0.1523, 0.6516])
out = torch.zeros(5)
result = torch.logit(out=out, input=input, eps=1e-6)
"""
)
obj.run(pytorch_code, ["result", "out"])


def test_case_15():
"""Variable unpacking"""
pytorch_code = textwrap.dedent(
"""
import torch
args = (torch.tensor([0.2796, 0.9331, 0.6486, 0.1523, 0.6516]), 1e-6)
result = torch.logit(*args)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_16():
"""Expression as eps argument"""
pytorch_code = textwrap.dedent(
"""
import torch
input = torch.tensor([0.2796, 0.9331, 0.6486, 0.1523, 0.6516])
result = torch.logit(input, 1e-3 * 1e-3)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_17():
"""Gradient computation"""
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.tensor([0.2796, 0.9331, 0.6486], requires_grad=True)
y = torch.logit(x, eps=1e-6)
y.sum().backward()
x_grad = x.grad
"""
)
obj.run(pytorch_code, ["y", "x_grad"], check_stop_gradient=False)
112 changes: 112 additions & 0 deletions tests/test_special_logit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -87,3 +87,115 @@ def test_case_6():
"""
)
obj.run(pytorch_code, ["result"])


def test_case_7():
"""No eps, keyword input="""
pytorch_code = textwrap.dedent(
"""
import torch
input = torch.tensor([0.2796, 0.9331, 0.6486, 0.1523, 0.6516])
result = torch.special.logit(input=input)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_8():
"""2D input with eps"""
pytorch_code = textwrap.dedent(
"""
import torch
input = torch.tensor([[0.2, 0.5, 0.8], [0.1, 0.9, 0.3]])
result = torch.special.logit(input, eps=1e-6)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_9():
"""3D input, no eps"""
pytorch_code = textwrap.dedent(
"""
import torch
input = torch.tensor([[[0.2, 0.8], [0.4, 0.6]], [[0.1, 0.9], [0.3, 0.7]]])
result = torch.special.logit(input)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_10():
"""float64 dtype"""
pytorch_code = textwrap.dedent(
"""
import torch
input = torch.tensor([0.2796, 0.9331, 0.6486], dtype=torch.float64)
result = torch.special.logit(input, eps=1e-6)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_11():
"""out parameter without eps"""
pytorch_code = textwrap.dedent(
"""
import torch
input = torch.tensor([0.2796, 0.9331, 0.6486, 0.1523, 0.6516])
out = torch.zeros(5)
result = torch.special.logit(input, out=out)
"""
)
obj.run(pytorch_code, ["result", "out"])


def test_case_12():
"""Reordered kwargs: out, input, eps"""
pytorch_code = textwrap.dedent(
"""
import torch
input = torch.tensor([0.2796, 0.9331, 0.6486, 0.1523, 0.6516])
out = torch.zeros(5)
result = torch.special.logit(out=out, input=input, eps=1e-6)
"""
)
obj.run(pytorch_code, ["result", "out"])


def test_case_13():
"""Variable unpacking"""
pytorch_code = textwrap.dedent(
"""
import torch
args = (torch.tensor([0.2796, 0.9331, 0.6486, 0.1523, 0.6516]), 1e-6)
result = torch.special.logit(*args)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_14():
"""Expression as eps argument"""
pytorch_code = textwrap.dedent(
"""
import torch
input = torch.tensor([0.2796, 0.9331, 0.6486, 0.1523, 0.6516])
result = torch.special.logit(input, 1e-3 * 1e-3)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_15():
"""Gradient computation"""
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.tensor([0.2796, 0.9331, 0.6486], requires_grad=True)
y = torch.special.logit(x, eps=1e-6)
y.sum().backward()
x_grad = x.grad
"""
)
obj.run(pytorch_code, ["y", "x_grad"], check_stop_gradient=False)