[SWE-Paddle] Add task PaddlePaddle__Paddle-73855

[co63oc]

关联

• SWE-Paddle 共建 issue: SWE-Paddle 社区共建
• 来源 PR: [0-size Tensor Job2 No.56] Add 0-size Tensor support for paddle.nn.functional.dice_loss Paddle#73855

说明

新增 SWE-Paddle 任务 PaddlePaddle__Paddle-73855。

该任务覆盖 paddle.nn.functional.dice_loss 对 0-size Tensor 的处理。测试包含非 0-size Tensor regression case，以及 dice_loss 的目标场景，可在 CPU 环境运行。

验证结果

状态	P2P	dice_loss F2P
Base + tests/test.patch	PASS	FAIL
Base + test patch + solution/code.patch	PASS	PASS

Base P2P

========================================================= 1 passed, 1 warning in 1.28s =========================================================

Base F2P：dice_loss

    def test_api_with_dygraph(self):
        for place in get_places():
            paddle.disable_static(place)
            input = paddle.randn([0, 2]).astype(paddle.float64)
            input.stop_gradient = False
            label = paddle.randn([0, 1]).astype(paddle.int64)
            label.stop_gradient = False
>           out = paddle.nn.functional.dice_loss(input, label, 1e-5)
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

test_nn_dice_loss.py:49: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

input = Tensor(shape=[0, 2], dtype=float64, place=Place(cpu), stop_gradient=False, [])
label = Tensor(shape=[0, 1], dtype=int64, place=Place(cpu), stop_gradient=False, [])
epsilon = 1e-05, name = None

    def dice_loss(...):
        ...
        assert (
>           functools.reduce(operator.mul, input.shape) != 0
            and functools.reduce(operator.mul, label.shape) != 0
        ), "Any dimension of input and label cannot be equal to 0."
E       AssertionError: Any dimension of input and label cannot be equal to 0.

venv/lib/python3.12/site-packages/paddle/nn/functional/loss.py:114: AssertionError

Solution P2P

========================================================= 1 passed, 1 warning in 1.27s =========================================================

Solution F2P：dice_loss

========================================================= 1 passed, 1 warning in 1.20s =========================================================