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update torch.quantile, torch.nanquantile and corresponding tensor methods #383

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merged 2 commits into from
Apr 3, 2024
Merged

update torch.quantile, torch.nanquantile and corresponding tensor methods #383

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b3eb56b
update quantile
RedContritio Apr 2, 2024
441f868
update tests about interpolation arg
RedContritio Apr 3, 2024
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19 changes: 6 additions & 13 deletions paconvert/api_mapping.json
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Original file line number Diff line number Diff line change
Expand Up @@ -2466,10 +2466,7 @@
],
"kwargs_change": {
"dim": "axis"
},
"unsupport_args": [
"interpolation"
]
}
},
"torch.Tensor.nansum": {
"Matcher": "GenericMatcher",
Expand Down Expand Up @@ -2785,7 +2782,9 @@
"args_list": [
"q",
"dim",
"keepdim"
"keepdim",
"*",
"interpolation"
],
"kwargs_change": {
"dim": "axis"
Expand Down Expand Up @@ -8747,10 +8746,7 @@
"kwargs_change": {
"input": "x",
"dim": "axis"
},
"unsupport_args": [
"interpolation "
]
}
},
"torch.nansum": {
"Matcher": "GenericMatcher",
Expand Down Expand Up @@ -13500,10 +13496,7 @@
"kwargs_change": {
"input": "x",
"dim": "axis"
},
"unsupport_args": [
"interpolation "
]
}
},
"torch.rad2deg": {
"Matcher": "GenericMatcher",
Expand Down
17 changes: 15 additions & 2 deletions tests/test_Tensor_nanquantile.py
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Original file line number Diff line number Diff line change
Expand Up @@ -97,6 +97,19 @@ def test_case_7():
obj.run(
pytorch_code,
["result"],
unsupport=True,
reason="Paddle not support this parameter",
)


# generated by validate_unittest autofix, based on test_case_7
def test_case_8():
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.tensor([[0]], dtype=torch.float64)
result = x.nanquantile(interpolation='higher', keepdim=True, dim=1, q=0.3)
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可以针对 interpolation 多测几个不同的值，验收下interpolation的不同取值是不是都完全写对了

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updated

"""
)
obj.run(
pytorch_code,
["result"],
)
7 changes: 3 additions & 4 deletions tests/test_Tensor_quantile.py
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Original file line number Diff line number Diff line change
Expand Up @@ -132,13 +132,12 @@ def test_case_10():
obj.run(pytorch_code, ["result"])


# torch.Tensor.quantile not support interpolation
def _test_case_11():
def test_case_11():
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.tensor([[ 0.0795, -1.2117, 0.9765], [ 1.1707, 0.6706, 0.4884]],dtype=torch.float64)
result=x.quantile(q=0.6, dim=1, keepdim=False, interpolation='linear')
x = torch.tensor([[ 0.0795, -1.2117, 0.9765], [ 1.1707, 0.6706, 0.4884]], dtype=torch.float64)
result = x.quantile(q=0.6, dim=1, keepdim=False, interpolation='linear')
"""
)
obj.run(pytorch_code, ["result"])
21 changes: 15 additions & 6 deletions tests/test_nanquantile.py
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Original file line number Diff line number Diff line change
Expand Up @@ -114,12 +114,21 @@ def test_case_9():
"""
import torch
x = torch.tensor([[0]], dtype=torch.float64)
result = torch.nanquantile(x=x, q=0.3, dim=1, keepdim=True, interpolation='higher')
out = torch.tensor([[1]], dtype=torch.float64)
result = torch.nanquantile(input=x, q=0.3, dim=1, keepdim=True, interpolation='higher', out=out)
"""
)
obj.run(
pytorch_code,
["result"],
unsupport=True,
reason="Paddle not support this parameter",
obj.run(pytorch_code, ["result", "out"])


# generated by validate_unittest autofix, based on test_case_9
def test_case_10():
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.tensor([[0]], dtype=torch.float64)
out = torch.tensor([[1]], dtype=torch.float64)
result = torch.nanquantile(out=out, interpolation='higher', keepdim=True, dim=1, q=0.3, input=x)
"""
)
obj.run(pytorch_code, ["result", "out"])
39 changes: 35 additions & 4 deletions tests/test_quantile.py
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Original file line number Diff line number Diff line change
Expand Up @@ -86,12 +86,43 @@ def test_case_7():
"""
import torch
x = torch.tensor([[ 0.0795, -1.2117, 0.9765], [ 1.1707, 0.6706, 0.4884]], dtype=torch.float64)
result = torch.quantile(x=x, q=0.3, dim=1, keepdim=True, interpolation='higher')
out = torch.tensor([], dtype=torch.float64)
result = torch.quantile(input=x, q=0.3, dim=1, keepdim=True, interpolation='higher', out=out)
"""
)
obj.run(
pytorch_code,
["result"],
unsupport=True,
reason="Paddle not support this parameter",
["result", "out"],
)


# generated by validate_unittest autofix, based on test_case_7
def test_case_8():
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.tensor([[ 0.0795, -1.2117, 0.9765], [ 1.1707, 0.6706, 0.4884]], dtype=torch.float64)
out = torch.tensor([], dtype=torch.float64)
result = torch.quantile(x, 0.3, 1, True)
"""
)
obj.run(
pytorch_code,
["result", "out"],
)


# generated by validate_unittest autofix, based on test_case_7
def test_case_9():
pytorch_code = textwrap.dedent(
"""
import torch
x = torch.tensor([[ 0.0795, -1.2117, 0.9765], [ 1.1707, 0.6706, 0.4884]], dtype=torch.float64)
out = torch.tensor([], dtype=torch.float64)
result = torch.quantile(out=out, interpolation='higher', keepdim=True, dim=1, q=0.3, input=x)
"""
)
obj.run(
pytorch_code,
["result", "out"],
)