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Mutiply allows non-tensor data input (#27690)

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Mutiply allows non-tensor data input
my_2.0rc
joejiong 4 years ago committed by GitHub
parent 55e63763ec
commit 2bcb7c0a2f
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2 changed files with 87 additions and 0 deletions
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75
python/paddle/fluid/tests/unittests/test_multiply.py
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@ -26,6 +26,7 @@ class TestMultiplyAPI(unittest.TestCase):
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(
@ -42,6 +43,21 @@ class TestMultiplyAPI(unittest.TestCase):
res = outs[0]
return res
def __run_static_graph_case_with_numpy_input(self, x_data, y_data, axis=-1):
with program_guard(Program(), Program()):
paddle.enable_static()
res = tensor.multiply(x_data, y_data, 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)
@ -49,27 +65,52 @@ class TestMultiplyAPI(unittest.TestCase):
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: 1-d array
x_data = np.random.rand(200)
y_data = np.random.rand(200)
res = self.__run_static_graph_case_with_numpy_input(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 with_primitives: 2-d array
x_data = np.random.rand(2, 500)
y_data = np.random.rand(2, 500)
res = self.__run_static_graph_case_with_numpy_input(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 with_primitives: broadcast
x_data = np.random.rand(2, 500)
y_data = np.random.rand(500)
res = self.__run_static_graph_case_with_numpy_input(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)
@ -77,24 +118,50 @@ class TestMultiplyAPI(unittest.TestCase):
expected = np.multiply(x_data, y_data[..., np.newaxis])
self.assertTrue(np.allclose(res, expected))
# test static computation graph with_primitives: broadcast with axis
x_data = np.random.rand(2, 300, 40)
y_data = np.random.rand(300)
res = self.__run_static_graph_case_with_numpy_input(
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 with numpy tensor: 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)
@ -102,6 +169,14 @@ class TestMultiplyAPI(unittest.TestCase):
expected = np.multiply(x_data, y_data[..., np.newaxis])
self.assertTrue(np.allclose(res, expected))
# test dynamic computation graph with numpy tensor: broadcast with axis
x_data = np.random.rand(2, 300, 40)
y_data = np.random.rand(300)
res = self.__run_dynamic_graph_case_with_numpy_input(
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."""

12
python/paddle/tensor/math.py
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@ -472,15 +472,27 @@ def multiply(x, y, axis=-1, name=None):
"""
op_type = 'elementwise_mul'
act = None
if x.dtype != y.dtype:
raise TypeError(
'Input tensors must be same type, but received type of x: %s, type of y: %s '
% (x.dtype, y.dtype))
if in_dygraph_mode():
if not isinstance(x, (paddle.Tensor)):
x = paddle.to_tensor(x)
if not isinstance(y, (paddle.Tensor)):
y = paddle.to_tensor(y)
return _elementwise_op_in_dygraph(
x, y, axis=axis, act=act, op_name=op_type)
if not isinstance(x, (paddle.Tensor, Variable)):
x = paddle.static.data(
name='x', shape=x.shape, dtype=x.dtype)
if not isinstance(y, (paddle.Tensor, Variable)):
y = paddle.static.data(
name='y', shape=y.shape, dtype=y.dtype)
return _elementwise_op(LayerHelper(op_type, **locals()))
def maximum(x, y, axis=-1, name=None):

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