forked from mindspore-Ecosystem/mindspore
!26664 Add support float64 as input type for ReduceProd GPU op.
Merge pull request !26664 from hezhenhao1/add_prod
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@ -52,5 +52,7 @@ MS_REG_GPU_KERNEL_ONE(ReduceProd, KernelAttr().AddInputAttr(kNumberTypeFloat16).
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ArrayReduceGpuKernel, half)
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MS_REG_GPU_KERNEL_ONE(ReduceProd, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
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ArrayReduceGpuKernel, float)
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MS_REG_GPU_KERNEL_ONE(ReduceProd, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
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ArrayReduceGpuKernel, double)
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} // namespace kernel
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} // namespace mindspore
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@ -972,7 +972,7 @@ class ReduceProd(_Reduce):
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ValueError: If `axis` is not one of the following: int, tuple or list.
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Supported Platforms:
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``Ascend`` ``GPU``
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``Ascend`` ``GPU`` ``CPU``
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Examples:
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>>> x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
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@ -19,152 +19,43 @@ import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.common.api import ms_function
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from mindspore.ops import operations as P
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x0 = np.random.rand(2, 3, 4, 4).astype(np.float32)
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axis0 = 3
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keep_dims0 = True
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x1 = np.random.rand(2, 3, 4, 4).astype(np.float16)
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axis1 = 3
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keep_dims1 = False
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x2 = np.random.rand(2, 3, 1, 4).astype(np.int8)
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axis2 = 2
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keep_dims2 = True
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x3 = np.random.rand(2, 3, 1, 4).astype(np.float32)
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axis3 = 2
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keep_dims3 = False
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x4 = np.random.rand(2, 3, 4, 4).astype(np.float16)
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axis4 = ()
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np_axis4 = None
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keep_dims4 = True
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x5 = np.random.rand(2, 3, 4, 4).astype(np.int8)
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axis5 = ()
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np_axis5 = None
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keep_dims5 = False
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x6 = np.random.rand(2, 3, 4, 4).astype(np.float32)
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axis6 = -2
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keep_dims6 = False
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x7 = np.random.rand(2, 3, 4, 4).astype(np.float16)
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axis7 = (-2, -1)
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keep_dims7 = True
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x8 = np.random.rand(1, 1, 1, 1).astype(np.float32)
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axis8 = ()
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np_axis8 = None
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keep_dims8 = True
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class ReduceProd(nn.Cell):
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def __init__(self):
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def __init__(self, keep_dims):
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super(ReduceProd, self).__init__()
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self.reduce_prod = P.ReduceProd(keep_dims=keep_dims)
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self.x0 = Tensor(x0)
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self.axis0 = axis0
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self.keep_dims0 = keep_dims0
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self.x1 = Tensor(x1)
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self.axis1 = axis1
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self.keep_dims1 = keep_dims1
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self.x2 = Tensor(x2)
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self.axis2 = axis2
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self.keep_dims2 = keep_dims2
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self.x3 = Tensor(x3)
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self.axis3 = axis3
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self.keep_dims3 = keep_dims3
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self.x4 = Tensor(x4)
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self.axis4 = axis4
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self.keep_dims4 = keep_dims4
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self.x5 = Tensor(x5)
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self.axis5 = axis5
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self.keep_dims5 = keep_dims5
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self.x6 = Tensor(x6)
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self.axis6 = axis6
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self.keep_dims6 = keep_dims6
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self.x7 = Tensor(x7)
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self.axis7 = axis7
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self.keep_dims7 = keep_dims7
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self.x8 = Tensor(x8)
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self.axis8 = axis8
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self.keep_dims8 = keep_dims8
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@ms_function
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def construct(self):
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return (P.ReduceProd(self.keep_dims0)(self.x0, self.axis0),
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P.ReduceProd(self.keep_dims1)(self.x1, self.axis1),
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P.ReduceProd(self.keep_dims2)(self.x2, self.axis2),
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P.ReduceProd(self.keep_dims3)(self.x3, self.axis3),
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P.ReduceProd(self.keep_dims4)(self.x4, self.axis4),
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P.ReduceProd(self.keep_dims5)(self.x5, self.axis5),
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P.ReduceProd(self.keep_dims6)(self.x6, self.axis6),
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P.ReduceProd(self.keep_dims7)(self.x7, self.axis7),
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P.ReduceProd(self.keep_dims8)(self.x8, self.axis8))
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def construct(self, x, axis):
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return self.reduce_prod(x, axis)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_reduce_prod():
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@pytest.mark.parametrize('decimal, dtype',
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[(1e-10, np.int8), (1e-3, np.float16), (1e-5, np.float32), (1e-8, np.float64)])
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@pytest.mark.parametrize('shape, axis, keep_dims',
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[((2, 3, 4, 4), 3, True), ((2, 3, 4, 4), 3, False), ((2, 3, 1, 4), 2, True),
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((2, 3, 1, 4), 2, False), ((2, 3, 4, 4), None, True), ((2, 3, 4, 4), None, False),
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((2, 3, 4, 4), -2, False), ((2, 3, 4, 4), (-2, -1), False), ((1, 1, 1, 1), None, True)])
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def test_reduce_prod(decimal, dtype, shape, axis, keep_dims):
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"""
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Feature: ALL To ALL
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Description: test cases for ReduceProd
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Expectation: the result match to numpy
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"""
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context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
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reduce_max = ReduceProd()
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output = reduce_max()
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x = np.random.rand(*shape).astype(dtype)
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tensor_x = Tensor(x)
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expect1 = np.prod(x1, axis=axis1, keepdims=keep_dims1)
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diff1 = abs(output[1].asnumpy() - expect1)
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error1 = np.ones(shape=expect1.shape) * 1.0e-5
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assert np.all(diff1 < error1)
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assert output[1].shape == expect1.shape
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reduce_prod = ReduceProd(keep_dims)
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ms_axis = axis if axis is not None else ()
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output = reduce_prod(tensor_x, ms_axis)
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expect2 = np.prod(x2, axis=axis2, keepdims=keep_dims2)
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diff2 = abs(output[2].asnumpy() - expect2)
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error2 = np.ones(shape=expect2.shape) * 1.0e-5
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assert np.all(diff2 < error2)
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assert output[2].shape == expect2.shape
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expect3 = np.prod(x3, axis=axis3, keepdims=keep_dims3)
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diff3 = abs(output[3].asnumpy() - expect3)
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error3 = np.ones(shape=expect3.shape) * 1.0e-5
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assert np.all(diff3 < error3)
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assert output[3].shape == expect3.shape
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expect4 = np.prod(x4, axis=np_axis4, keepdims=keep_dims4)
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diff4 = abs(output[4].asnumpy() - expect4)
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error4 = np.ones(shape=expect4.shape) * 1.0e-5
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assert np.all(diff4 < error4)
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assert output[4].shape == expect4.shape
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expect5 = np.prod(x5, axis=np_axis5, keepdims=keep_dims5)
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diff5 = abs(output[5].asnumpy() - expect5)
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error5 = np.ones(shape=expect5.shape) * 1.0e-5
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assert np.all(diff5 < error5)
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assert output[5].shape == expect5.shape
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expect6 = np.prod(x6, axis=axis6, keepdims=keep_dims6)
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diff6 = abs(output[6].asnumpy() - expect6)
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error6 = np.ones(shape=expect6.shape) * 1.0e-5
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assert np.all(diff6 < error6)
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assert output[6].shape == expect6.shape
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expect7 = np.prod(x7, axis=axis7, keepdims=keep_dims7)
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diff7 = abs(output[7].asnumpy() - expect7)
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error7 = np.ones(shape=expect7.shape) * 1.0e-5
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assert np.all(diff7 < error7)
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expect8 = np.prod(x8, axis=np_axis8, keepdims=keep_dims8)
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diff8 = abs(output[8].asnumpy() - expect8)
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error8 = np.ones(shape=expect8.shape) * 1.0e-5
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assert np.all(diff8 < error8)
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expect = np.prod(x, axis=axis, keepdims=keep_dims)
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diff = abs(output.asnumpy() - expect)
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error = np.ones(shape=expect.shape) * decimal
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assert np.all(diff < error)
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assert output.shape == expect.shape
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