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[API Compatibility] enhance the tests for loss apis -part #874

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01b8ec0
enhance the tests for loss apis
Manfredss Apr 27, 2026
e553572
update
Manfredss Apr 27, 2026
80cf546
change prefix
Manfredss May 3, 2026
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510 changes: 37 additions & 473 deletions paconvert/api_mapping.json
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33 changes: 33 additions & 0 deletions tests/test_nn_BCELoss.py
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Expand Up @@ -194,3 +194,36 @@ def test_case_11():
"""
)
obj.run(pytorch_code, ["result"])


def test_case_12():
"""*args unpacking"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn as nn
input = torch.tensor([[0.2837, 0.0297, 0.0355],
[ 0.9112, 0.7526, 0.4061]])
target = torch.tensor([[1.,0.,1.],[0.,1.,0.]])
weight = torch.tensor([0.5,0.2,0.3])
args = (weight,)
loss = torch.nn.BCELoss(*args, reduction='sum')
result = loss(input, target)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_13():
"""Different dim (1D) and float64 dtype"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn as nn
input = torch.tensor([0.5, 0.6, 0.7, 0.4], dtype=torch.float64)
target = torch.tensor([1., 0., 1., 0.], dtype=torch.float64)
loss = torch.nn.BCELoss(reduction='none')
result = loss(input, target)
"""
)
obj.run(pytorch_code, ["result"])
31 changes: 31 additions & 0 deletions tests/test_nn_CosineEmbeddingLoss.py
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Expand Up @@ -548,3 +548,34 @@ def test_case_36():
"""
)
obj.run(pytorch_code, ["result"])


def test_case_37():
"""*args unpacking with reduction kwarg"""
pytorch_code = textwrap.dedent(
"""
import torch
input1 = torch.Tensor([[1.6, 1.2, -0.5], [3.2, 2.6, -5.8]]).type(torch.float32)
input2 = torch.Tensor([[0.5, 0.5, -1.8], [2.3, -1.4, 1.1]]).type(torch.float32)
label = torch.Tensor([1, -1]).type(torch.int64)
args = (0.5,)
loss = torch.nn.CosineEmbeddingLoss(*args, reduction='sum')
result = loss(input1, input2, label)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_38():
"""Out-of-order keyword arguments"""
pytorch_code = textwrap.dedent(
"""
import torch
input1 = torch.Tensor([[1.6, 1.2, -0.5], [3.2, 2.6, -5.8]]).type(torch.float32)
input2 = torch.Tensor([[0.5, 0.5, -1.8], [2.3, -1.4, 1.1]]).type(torch.float32)
label = torch.Tensor([1, -1]).type(torch.int64)
loss = torch.nn.CosineEmbeddingLoss(reduction='none', margin=0.3)
result = loss(input1, input2, label)
"""
)
obj.run(pytorch_code, ["result"])
75 changes: 75 additions & 0 deletions tests/test_nn_GaussianNLLLoss.py
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Expand Up @@ -92,3 +92,78 @@ def test_case_5():
"""
)
obj.run(pytorch_code, ["result"])


def test_case_6():
"""All keyword arguments, reduction='none'"""
pytorch_code = textwrap.dedent(
"""
import torch
loss = torch.nn.GaussianNLLLoss(full=True, eps=1e-06, reduction='none')
input = torch.tensor([[0.5, -0.3], [1.2, 0.7]], dtype=torch.float32)
label = torch.tensor([[0.4, -0.2], [1.0, 0.5]], dtype=torch.float32)
variance = torch.tensor([[0.1, 0.2], [0.3, 0.4]], dtype=torch.float32)
result = loss(input, label, variance)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_7():
"""Out-of-order keyword arguments"""
pytorch_code = textwrap.dedent(
"""
import torch
loss = torch.nn.GaussianNLLLoss(reduction='sum', eps=1e-08, full=False)
input = torch.tensor([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=torch.float32)
label = torch.tensor([[0.0, 0.1, 0.2], [0.3, 0.4, 0.5]], dtype=torch.float32)
variance = torch.tensor([[0.5, 0.5, 0.5], [0.5, 0.5, 0.5]], dtype=torch.float32)
result = loss(input, label, variance)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_8():
"""Default args (no kwargs)"""
pytorch_code = textwrap.dedent(
"""
import torch
loss = torch.nn.GaussianNLLLoss()
input = torch.tensor([[1.5, 2.3], [-0.5, 1.7]], dtype=torch.float32)
label = torch.tensor([[1.0, 2.0], [-0.3, 1.5]], dtype=torch.float32)
variance = torch.tensor([[0.2, 0.3], [0.4, 0.5]], dtype=torch.float32)
result = loss(input, label, variance)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_9():
"""3D inputs"""
pytorch_code = textwrap.dedent(
"""
import torch
loss = torch.nn.GaussianNLLLoss(full=False, eps=1e-06, reduction='mean')
input = torch.ones([2, 3, 4], dtype=torch.float32) * 0.5
label = torch.ones([2, 3, 4], dtype=torch.float32)
variance = torch.ones([2, 3, 4], dtype=torch.float32) * 2.0
result = loss(input, label, variance)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_10():
"""float64 dtype"""
pytorch_code = textwrap.dedent(
"""
import torch
loss = torch.nn.GaussianNLLLoss(eps=1e-10)
input = torch.tensor([[0.5, -0.3], [1.2, 0.7]], dtype=torch.float64)
label = torch.tensor([[0.4, -0.2], [1.0, 0.5]], dtype=torch.float64)
variance = torch.tensor([[0.1, 0.2], [0.3, 0.4]], dtype=torch.float64)
result = loss(input, label, variance)
"""
)
obj.run(pytorch_code, ["result"])
29 changes: 29 additions & 0 deletions tests/test_nn_HingeEmbeddingLoss.py
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Expand Up @@ -358,3 +358,32 @@ def test_case_25():
"""
)
obj.run(pytorch_code, ["result"])


def test_case_26():
"""*args unpacking"""
pytorch_code = textwrap.dedent(
"""
import torch
input = torch.Tensor([[1, -2, 3], [0, -1, 2]]).type(torch.float32)
label = torch.Tensor([[-1, 1, -1], [1, 1, 1]]).type(torch.float32)
args = (1.5,)
cri = torch.nn.HingeEmbeddingLoss(*args, reduction='sum')
result = cri(input, label)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_27():
"""Out-of-order keyword arguments"""
pytorch_code = textwrap.dedent(
"""
import torch
input = torch.Tensor([[1, -2, 3], [0, -1, 2]]).type(torch.float32)
label = torch.Tensor([[-1, 1, -1], [1, 1, 1]]).type(torch.float32)
cri = torch.nn.HingeEmbeddingLoss(reduction='none', margin=2.0)
result = cri(input, label)
"""
)
obj.run(pytorch_code, ["result"])
31 changes: 31 additions & 0 deletions tests/test_nn_L1Loss.py
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Expand Up @@ -184,3 +184,34 @@ def test_case_11():
"""
)
obj.run(pytorch_code, ["result"])


def test_case_12():
"""*args unpacking"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn as nn
input = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
target = torch.tensor([[1.5, 1.5], [3.5, 3.5]])
args = ()
loss = torch.nn.L1Loss(*args, reduction='sum')
result = loss(input, target)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_13():
"""3D inputs with float64 dtype"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn as nn
input = torch.ones([2, 3, 4], dtype=torch.float64) * 0.5
target = torch.ones([2, 3, 4], dtype=torch.float64)
loss = torch.nn.L1Loss(reduction='none')
result = loss(input, target)
"""
)
obj.run(pytorch_code, ["result"])
31 changes: 31 additions & 0 deletions tests/test_nn_MSELoss.py
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Original file line number Diff line number Diff line change
Expand Up @@ -184,3 +184,34 @@ def test_case_11():
"""
)
obj.run(pytorch_code, ["result"])


def test_case_12():
"""*args unpacking"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn as nn
input = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
target = torch.tensor([[1.5, 1.5], [3.5, 3.5]])
args = ()
loss = torch.nn.MSELoss(*args, reduction='sum')
result = loss(input, target)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_13():
"""3D inputs with float64 dtype"""
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn as nn
input = torch.ones([2, 3, 4], dtype=torch.float64) * 0.5
target = torch.ones([2, 3, 4], dtype=torch.float64)
loss = torch.nn.MSELoss(reduction='none')
result = loss(input, target)
"""
)
obj.run(pytorch_code, ["result"])
31 changes: 31 additions & 0 deletions tests/test_nn_MarginRankingLoss.py
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Expand Up @@ -383,3 +383,34 @@ def test_case_25():
"""
)
obj.run(pytorch_code, ["result"])


def test_case_26():
"""*args unpacking"""
pytorch_code = textwrap.dedent(
"""
import torch
input = torch.Tensor([[1, 2], [3, 4]]).type(torch.float32)
other = torch.Tensor([[2, 1], [2, 4]]).type(torch.float32)
label = torch.Tensor([[1, -1], [-1, -1]]).type(torch.float32)
args = (0.5,)
loss = torch.nn.MarginRankingLoss(*args, reduction='sum')
result = loss(input, other, label)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_27():
"""Out-of-order keyword arguments"""
pytorch_code = textwrap.dedent(
"""
import torch
input = torch.Tensor([[1, 2], [3, 4]]).type(torch.float32)
other = torch.Tensor([[2, 1], [2, 4]]).type(torch.float32)
label = torch.Tensor([[1, -1], [-1, -1]]).type(torch.float32)
loss = torch.nn.MarginRankingLoss(reduction='none', margin=1.0)
result = loss(input, other, label)
"""
)
obj.run(pytorch_code, ["result"])
29 changes: 29 additions & 0 deletions tests/test_nn_MultiLabelMarginLoss.py
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Expand Up @@ -121,3 +121,32 @@ def test_case_8():
"""
)
obj.run(pytorch_code, ["result"])


def test_case_9():
"""*args unpacking"""
pytorch_code = textwrap.dedent(
"""
import torch
args = ()
loss = torch.nn.MultiLabelMarginLoss(*args, reduction='none')
input = torch.tensor([[0.1, 0.2, 0.4, 0.8], [0.2, 0.5, 0.3, 0.1]]).to(dtype=torch.float32)
label = torch.LongTensor([[3, 0, -1, -1], [0, 2, -1, -1]])
result = loss(input, label)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_10():
"""Out-of-order keyword arguments"""
pytorch_code = textwrap.dedent(
"""
import torch
loss = torch.nn.MultiLabelMarginLoss(reduction='sum', reduce=None, size_average=None)
input = torch.tensor([[0.1, 0.2, 0.4, 0.8], [0.2, 0.5, 0.3, 0.1]]).to(dtype=torch.float32)
label = torch.LongTensor([[3, 0, -1, -1], [0, 2, -1, -1]])
result = loss(input, label)
"""
)
obj.run(pytorch_code, ["result"])
30 changes: 30 additions & 0 deletions tests/test_nn_MultiLabelSoftMarginLoss.py
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Expand Up @@ -85,3 +85,33 @@ def test_case_5():
"""
)
obj.run(pytorch_code, ["result"])


def test_case_6():
"""*args unpacking"""
pytorch_code = textwrap.dedent(
"""
import torch
weight = torch.tensor([1.0, 0.5, 1.0])
args = (weight,)
loss = torch.nn.MultiLabelSoftMarginLoss(*args, reduction='sum')
input = torch.tensor([[1, -2, 3], [0, -1, 2]]).to(dtype=torch.float32)
label = torch.tensor([[-1, 1, -1], [1, 1, 1]]).to(dtype=torch.float32)
result = loss(input, label)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_7():
"""Out-of-order keyword arguments"""
pytorch_code = textwrap.dedent(
"""
import torch
loss = torch.nn.MultiLabelSoftMarginLoss(reduction='none', weight=None)
input = torch.tensor([[1, -2, 3], [0, -1, 2]]).to(dtype=torch.float32)
label = torch.tensor([[-1, 1, -1], [1, 1, 1]]).to(dtype=torch.float32)
result = loss(input, label)
"""
)
obj.run(pytorch_code, ["result"])
29 changes: 29 additions & 0 deletions tests/test_nn_MultiMarginLoss.py
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Expand Up @@ -87,3 +87,32 @@ def test_case_5():
"""
)
obj.run(pytorch_code, ["result"])


def test_case_6():
"""*args unpacking"""
pytorch_code = textwrap.dedent(
"""
import torch
args = (1, 1.0)
loss = torch.nn.MultiMarginLoss(*args, reduction='sum')
input = torch.tensor([[1, -2, 3], [0, -1, 2], [1, 0, 1]]).to(dtype=torch.float32)
label = torch.tensor([0, 1, 2])
result = loss(input, label)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_7():
"""Out-of-order keyword arguments"""
pytorch_code = textwrap.dedent(
"""
import torch
loss = torch.nn.MultiMarginLoss(reduction='none', margin=2.0, p=2)
input = torch.tensor([[1, -2, 3], [0, -1, 2], [1, 0, 1]]).to(dtype=torch.float32)
label = torch.tensor([0, 1, 2])
result = loss(input, label)
"""
)
obj.run(pytorch_code, ["result"])
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