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[API Compatibility] update test for Optimizer.step() #894

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[API Compatibility] update test for Optimizer.step() #894

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8 changes: 0 additions & 8 deletions paconvert/api_mapping.json
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Original file line number Diff line number Diff line change
Expand Up @@ -9774,14 +9774,6 @@
"paddle_api": "paddle.optimizer.Optimizer.state_dict",
"min_input_args": 0
},

@zhwesky2010 zhwesky2010 Jun 29, 2026

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step应该是一个call调用

@algorithm1832 algorithm1832 Jun 30, 2026

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如果在api_mapping中使用ChangePrefixMatcher会导致opt.step()变成paddle.optim.Optimizer.step()，所以我放到attribute_mapping里面了

"torch.optim.Optimizer.step": {
"Matcher": "GenericMatcher",
"paddle_api": "paddle.optimizer.Optimizer.step",
"min_input_args": 0,
"unsupport_args": [
"closure"
]
},
"torch.optim.Optimizer.zero_grad": {
"Matcher": "ZeroGradMatcher",
"paddle_api": "paddle.optimizer.Optimizer.clear_gradients",
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3 changes: 3 additions & 0 deletions paconvert/attribute_mapping.json
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Expand Up @@ -181,6 +181,9 @@
"torch.nn": {
"Matcher": "ChangePrefixMatcher"
},
"torch.optim.Optimizer.step": {
"Matcher": "ChangePrefixMatcher"
},
"torch.pi": {
"Matcher": "ChangePrefixMatcher"
},
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37 changes: 19 additions & 18 deletions tests/test_optim_Optimizer_step.py
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Expand Up @@ -14,6 +14,7 @@

import textwrap

import numpy as np
from apibase import APIBase


Expand All @@ -30,7 +31,10 @@ def compare(
rtol=1.0e-6,
atol=0.0,
):
assert paddle_result == pytorch_result
if isinstance(paddle_result, np.ndarray):
np.testing.assert_array_equal(paddle_result, pytorch_result)
else:
assert paddle_result == pytorch_result


obj = optimOptimizerAPIBase("torch.optim.Optimizer.step")
Expand All @@ -47,27 +51,27 @@ def test_case_1():
result = type(optim.step)
"""
)
obj.run(
pytorch_code,
["result"],
)
obj.run(pytorch_code, ["result"])


def _test_case_2():
def test_case_2():
pytorch_code = textwrap.dedent(
"""
import torch
import torch.nn as nn
theta = torch.tensor([1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0], requires_grad=True)
l = torch.nn.Linear(10, 1)
optim = torch.optim.SGD(l.parameters(), lr = 1.0)
torch.nn.init.constant_(l.weight, 3.0)
torch.nn.init.constant_(l.bias, 4.0)
optim = torch.optim.SGD(l.parameters(), lr=1.0)
z = l(theta)
z.backward()
optim.step()
result = optim.state_dict()
weight = l.weight.data.numpy(force=True)
bias = l.bias.data.numpy(force=True)
"""
)
obj.run(pytorch_code, ["result"])
obj.run(pytorch_code, ["weight", "bias"])


def test_case_3():
Expand All @@ -78,7 +82,9 @@ def test_case_3():

theta = torch.tensor([1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0], requires_grad=True)
l = torch.nn.Linear(10, 1)
optim = torch.optim.LBFGS(l.parameters(), lr=0.01)
torch.nn.init.constant_(l.weight, 3.0)
torch.nn.init.constant_(l.bias, 4.0)
optim = torch.optim.SGD(l.parameters(), lr=0.01)

def closure():
optim.zero_grad()
Expand All @@ -87,13 +93,8 @@ def closure():
loss.backward()
return loss

optim.step(closure=closure)
result = optim.state_dict()
loss_value = optim.step(closure=closure)
result = loss_value.numpy(force=True)
"""
)
obj.run(
pytorch_code,
["result"],
unsupport=True,
reason="paddle does not support 'closure' now!",
)
obj.run(pytorch_code, ["result"])
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