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[API Compatibility No.354] add torch.addcmul and torch.Tensor.addcmul -part #814

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[API Compatibility No.354] add torch.addcmul and torch.Tensor.addcmul -part #814

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20 changes: 2 additions & 18 deletions paconvert/api_mapping.json
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -431,14 +431,7 @@
]
},
"torch.Tensor.addcmul": {
"Matcher": "AddCMulMatcher",
"min_input_args": 2,
"args_list": [
"tensor1",
"tensor2",
"*",
"value"
]
"Matcher": "ChangePrefixMatcher"
},
"torch.Tensor.addcmul_": {
"Matcher": "AddCMul_Matcher",
Expand Down Expand Up @@ -3681,16 +3674,7 @@
]
},
"torch.addcmul": {
"Matcher": "AddCMulMatcher",
"min_input_args": 3,
"args_list": [
"input",
"tensor1",
"tensor2",
"*",
"value",
"out"
]
"Matcher": "ChangePrefixMatcher"
},
"torch.addmm": {
"Matcher": "GenericMatcher",
Expand Down
72 changes: 72 additions & 0 deletions tests/test_Tensor_addcmul.py
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Original file line number Diff line number Diff line change
Expand Up @@ -100,3 +100,75 @@ def test_case_6():
"""
)
obj.run(pytorch_code, ["result"])


def test_case_7():
"""2D tensor test"""
pytorch_code = textwrap.dedent(
"""
import torch
tensor1 = torch.tensor([[1., 2.], [3., 4.]])
tensor2 = torch.tensor([[5., 6.], [7., 8.]])
input = torch.tensor([[1., 1.], [1., 1.]])
result = input.addcmul(tensor1, tensor2, value=2)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_8():
"""3D tensor test"""
pytorch_code = textwrap.dedent(
"""
import torch
tensor1 = torch.tensor([[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]]])
tensor2 = torch.tensor([[[1., 1.], [1., 1.]], [[1., 1.], [1., 1.]]])
input = torch.tensor([[[0., 0.], [0., 0.]], [[0., 0.], [0., 0.]]])
result = input.addcmul(tensor1, tensor2)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_9():
"""float64 dtype test"""
pytorch_code = textwrap.dedent(
"""
import torch
tensor1 = torch.tensor([1., 2., 3.], dtype=torch.float64)
tensor2 = torch.tensor([4., 5., 6.], dtype=torch.float64)
input = torch.tensor([7., 8., 9.], dtype=torch.float64)
result = input.addcmul(tensor1, tensor2, value=2.0)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_10():
"""gradient computation test"""
pytorch_code = textwrap.dedent(
"""
import torch
tensor1 = torch.tensor([1., 2., 3.], requires_grad=True)
tensor2 = torch.tensor([4., 5., 6.], requires_grad=True)
input = torch.tensor([7., 8., 9.], requires_grad=True)
result = input.addcmul(tensor1, tensor2, value=2.0)
result.sum().backward()
input_grad = input.grad
"""
)
obj.run(pytorch_code, ["result", "input_grad"], check_stop_gradient=False)


def test_case_11():
"""expression argument test"""
pytorch_code = textwrap.dedent(
"""
import torch
tensor1 = torch.tensor([1., 2., 3.])
tensor2 = torch.tensor([4., 5., 6.])
input = torch.tensor([7., 8., 9.])
result = input.addcmul(tensor1, tensor2, value=1 + 1)
"""
)
obj.run(pytorch_code, ["result"])
72 changes: 72 additions & 0 deletions tests/test_addcmul.py
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Original file line number Diff line number Diff line change
Expand Up @@ -114,3 +114,75 @@ def test_case_7():
"""
)
obj.run(pytorch_code, ["result"])


def test_case_8():
"""2D tensor test"""
pytorch_code = textwrap.dedent(
"""
import torch
tensor1 = torch.tensor([[1., 2.], [3., 4.]])
tensor2 = torch.tensor([[5., 6.], [7., 8.]])
input = torch.tensor([[1., 1.], [1., 1.]])
result = torch.addcmul(input, tensor1, tensor2, value=2)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_9():
"""3D tensor test"""
pytorch_code = textwrap.dedent(
"""
import torch
tensor1 = torch.tensor([[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]]])
tensor2 = torch.tensor([[[1., 1.], [1., 1.]], [[1., 1.], [1., 1.]]])
input = torch.tensor([[[0., 0.], [0., 0.]], [[0., 0.], [0., 0.]]])
result = torch.addcmul(input, tensor1, tensor2)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_10():
"""float64 dtype test"""
pytorch_code = textwrap.dedent(
"""
import torch
tensor1 = torch.tensor([1., 2., 3.], dtype=torch.float64)
tensor2 = torch.tensor([4., 5., 6.], dtype=torch.float64)
input = torch.tensor([7., 8., 9.], dtype=torch.float64)
result = torch.addcmul(input, tensor1, tensor2, value=2.0)
"""
)
obj.run(pytorch_code, ["result"])


def test_case_11():
"""gradient computation test"""
pytorch_code = textwrap.dedent(
"""
import torch
tensor1 = torch.tensor([1., 2., 3.], requires_grad=True)
tensor2 = torch.tensor([4., 5., 6.], requires_grad=True)
input = torch.tensor([7., 8., 9.], requires_grad=True)
result = torch.addcmul(input, tensor1, tensor2, value=2.0)
result.sum().backward()
input_grad = input.grad
"""
)
obj.run(pytorch_code, ["result", "input_grad"], check_stop_gradient=False)


def test_case_12():
"""expression argument test"""
pytorch_code = textwrap.dedent(
"""
import torch
tensor1 = torch.tensor([1., 2., 3.])
tensor2 = torch.tensor([4., 5., 6.])
input = torch.tensor([7., 8., 9.])
result = torch.addcmul(input, tensor1, tensor2, value=1 + 1)
"""
)
obj.run(pytorch_code, ["result"])