Mutiply bug allow non-tensor data input

[Joejiong]

PR types

Bug fixes

PR changes

APIs

Describe

from __future__ import print_function
import paddle
import paddle.tensor as tensor
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
import numpy as np
import unittest


class TestMultiplyAPI(unittest.TestCase):
    """TestMultiplyAPI."""

    def __run_static_graph_case(self, x_data, y_data, axis=-1):
        with program_guard(Program(), Program()):
            paddle.enable_static()
            x = paddle.static.data(
                name='x', shape=x_data.shape, dtype=x_data.dtype)
            y = paddle.static.data(
                name='y', shape=y_data.shape, dtype=y_data.dtype)
            res = tensor.multiply(x, y, axis=axis)

            place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
            ) else fluid.CPUPlace()
            exe = fluid.Executor(place)
            outs = exe.run(fluid.default_main_program(),
                           feed={'x': x_data,
                                 'y': y_data},
                           fetch_list=[res])
            res = outs[0]
            return res

    def __run_dynamic_graph_case(self, x_data, y_data, axis=-1):
        paddle.disable_static()
        x = paddle.to_tensor(x_data)
        y = paddle.to_tensor(y_data)
        res = paddle.multiply(x, y, axis=axis)
        return res.numpy()

    def __run_dynamic_graph_case_with_numpy_input(self, x_data, y_data,
                                                  axis=-1):
        paddle.disable_static()
        res = paddle.multiply(x_data, y_data, axis=axis)
        return res.numpy()

    def test_multiply(self):
        """test_multiply."""
        np.random.seed(7)
        # test static computation graph: 1-d array
        x_data = np.random.rand(200)
        y_data = np.random.rand(200)
        res = self.__run_static_graph_case(x_data, y_data)
        self.assertTrue(np.allclose(res, np.multiply(x_data, y_data)))

        # test static computation graph: 2-d array
        x_data = np.random.rand(2, 500)
        y_data = np.random.rand(2, 500)
        res = self.__run_static_graph_case(x_data, y_data)
        self.assertTrue(np.allclose(res, np.multiply(x_data, y_data)))

        # test static computation graph: broadcast
        x_data = np.random.rand(2, 500)
        y_data = np.random.rand(500)
        res = self.__run_static_graph_case(x_data, y_data)
        self.assertTrue(np.allclose(res, np.multiply(x_data, y_data)))

        # test static computation graph: broadcast with axis
        x_data = np.random.rand(2, 300, 40)
        y_data = np.random.rand(300)
        res = self.__run_static_graph_case(x_data, y_data, axis=1)
        expected = np.multiply(x_data, y_data[..., np.newaxis])
        self.assertTrue(np.allclose(res, expected))

        # test dynamic computation graph: 1-d array
        x_data = np.random.rand(200)
        y_data = np.random.rand(200)
        res = self.__run_dynamic_graph_case(x_data, y_data)
        self.assertTrue(np.allclose(res, np.multiply(x_data, y_data)))

        # test dynamic numpy input computation graph: 1-d array
        x_data = np.random.rand(200)
        y_data = np.random.rand(200)
        res = self.__run_dynamic_graph_case_with_numpy_input(x_data, y_data)
        self.assertTrue(np.allclose(res, np.multiply(x_data, y_data)))

        # test dynamic computation graph: 2-d array
        x_data = np.random.rand(20, 50)
        y_data = np.random.rand(20, 50)
        res = self.__run_dynamic_graph_case(x_data, y_data)
        self.assertTrue(np.allclose(res, np.multiply(x_data, y_data)))

        # test dynamic numpy input computation graph: 1-d array
        x_data = np.random.rand(20, 50)
        y_data = np.random.rand(20, 50)
        res = self.__run_dynamic_graph_case_with_numpy_input(x_data, y_data)
        self.assertTrue(np.allclose(res, np.multiply(x_data, y_data)))

        # test dynamic computation graph: broadcast
        x_data = np.random.rand(2, 500)
        y_data = np.random.rand(500)
        res = self.__run_dynamic_graph_case(x_data, y_data)
        self.assertTrue(np.allclose(res, np.multiply(x_data, y_data)))

        # test dynamic computation graph: broadcast
        x_data = np.random.rand(2, 500)
        y_data = np.random.rand(500)
        res = self.__run_dynamic_graph_case_with_numpy_input(x_data, y_data)
        self.assertTrue(np.allclose(res, np.multiply(x_data, y_data)))

        # test dynamic computation graph: broadcast with axis
        x_data = np.random.rand(2, 300, 40)
        y_data = np.random.rand(300)
        res = self.__run_dynamic_graph_case(x_data, y_data, axis=1)
        expected = np.multiply(x_data, y_data[..., np.newaxis])
        self.assertTrue(np.allclose(res, expected))


class TestMultiplyError(unittest.TestCase):
    """TestMultiplyError."""

    def test_errors(self):
        """test_errors."""
        # test static computation graph: dtype can not be int8
        paddle.enable_static()
        with program_guard(Program(), Program()):
            x = paddle.static.data(name='x', shape=[100], dtype=np.int8)
            y = paddle.static.data(name='y', shape=[100], dtype=np.int8)
            self.assertRaises(TypeError, tensor.multiply, x, y)

        # test static computation graph: inputs must be broadcastable 
        with program_guard(Program(), Program()):
            x = paddle.static.data(name='x', shape=[20, 50], dtype=np.float64)
            y = paddle.static.data(name='y', shape=[20], dtype=np.float64)
            self.assertRaises(fluid.core.EnforceNotMet, tensor.multiply, x, y)

        np.random.seed(7)
        # test dynamic computation graph: dtype can not be int8
        paddle.disable_static()
        x_data = np.random.randn(200).astype(np.int8)
        y_data = np.random.randn(200).astype(np.int8)
        x = paddle.to_tensor(x_data)
        y = paddle.to_tensor(y_data)
        self.assertRaises(fluid.core.EnforceNotMet, paddle.multiply, x, y)

        # test dynamic computation graph: inputs must be broadcastable
        x_data = np.random.rand(200, 5)
        y_data = np.random.rand(200)
        x = paddle.to_tensor(x_data)
        y = paddle.to_tensor(y_data)
        self.assertRaises(fluid.core.EnforceNotMet, paddle.multiply, x, y)

        # test dynamic computation graph: inputs must be broadcastable(python)
        x_data = np.random.rand(200, 5)
        y_data = np.random.rand(200)
        x = paddle.to_tensor(x_data)
        y = paddle.to_tensor(y_data)
        self.assertRaises(fluid.core.EnforceNotMet, paddle.multiply, x, y)

        # test dynamic computation graph: dtype must be same
        x_data = np.random.randn(200).astype(np.int64)
        y_data = np.random.randn(200).astype(np.float64)
        x = paddle.to_tensor(x_data)
        y = paddle.to_tensor(y_data)
        self.assertRaises(TypeError, paddle.multiply, x, y)


if __name__ == '__main__':
    unittest.main()

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[zhhsplendid]

代码我觉得没有问题，测试有一些代码我觉得不是很好，已评论，因为ci已经过了，我这边先approve，要这个PR改还是下一个PR改请自己决定。

[zhhsplendid]

program是静态图才有的，所以最好paddle.enable_static写在with program_guard前面

[zhhsplendid]

program是静态图才有的，所以最好paddle.enable_static写在with program_guard前面

[zhhsplendid]

将来可以尽量用2.0 迁移的写法，比如paddle.CPUPlace, paddle.CUDAPlace，paddle.static.Executor

[zhhsplendid]

像我以前说过，python里面用注释分隔测试并不是最好的方法，你可以直接创建一个测试method:

def test_static_multiply_1d(self) 这类的