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feat: support aten.log1p converter (#2823)
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import torch | ||
import torch.nn as nn | ||
from parameterized import parameterized | ||
from torch.testing._internal.common_utils import run_tests | ||
from torch_tensorrt import Input | ||
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from .harness import DispatchTestCase | ||
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class TestLog1pConverter(DispatchTestCase): | ||
@parameterized.expand( | ||
[ | ||
((10,), torch.float), | ||
((1, 20), torch.float), | ||
((2, 3, 4), torch.float), | ||
((2, 3, 4, 5), torch.float), | ||
] | ||
) | ||
def test_log1p_float(self, input_shape, dtype): | ||
class Log1p(nn.Module): | ||
def forward(self, input): | ||
return torch.ops.aten.log1p.default(input) | ||
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inputs = [ | ||
torch.randn(input_shape, dtype=dtype).abs() + 0.001 | ||
] # ensure positive input | ||
self.run_test( | ||
Log1p(), | ||
inputs, | ||
) | ||
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@parameterized.expand( | ||
[ | ||
((10,), torch.int, 0, 5), | ||
((1, 20), torch.int, 0, 10), | ||
((2, 3, 4), torch.int, 0, 5), | ||
((2, 3, 4, 5), torch.int, 0, 5), | ||
] | ||
) | ||
def test_log1p_int(self, input_shape, dtype, low, high): | ||
class Log1p(nn.Module): | ||
def forward(self, input): | ||
return torch.ops.aten.log1p.default(input) | ||
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||
inputs = [ | ||
torch.randint(low, high, input_shape, dtype=dtype).abs() + 0.001 | ||
] # ensure positive input | ||
self.run_test( | ||
Log1p(), | ||
inputs, | ||
) | ||
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||
@parameterized.expand( | ||
[ | ||
(torch.full((1, 20), 2, dtype=torch.float),), | ||
(torch.full((2, 3, 4), 3, dtype=torch.float),), | ||
(torch.full((2, 3, 4, 5), 4, dtype=torch.float),), | ||
] | ||
) | ||
def test_log1p_const_float(self, data): | ||
class Log1p(nn.Module): | ||
def forward(self, input): | ||
return torch.ops.aten.log1p.default(input) | ||
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inputs = [data] | ||
self.run_test( | ||
Log1p(), | ||
inputs, | ||
) | ||
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if __name__ == "__main__": | ||
run_tests() |