forked from mindspore-Ecosystem/mindspore
ReduceProd gpu kernel initial commit
update testcase fix ci fix ci
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@ -42,5 +42,11 @@ MS_REG_GPU_KERNEL_ONE(ReduceAny, KernelAttr().AddInputAttr(kNumberTypeBool).AddO
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ArrayReduceGpuKernel, bool)
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MS_REG_GPU_KERNEL_ONE(ReduceAll, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
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ArrayReduceGpuKernel, bool)
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MS_REG_GPU_KERNEL_ONE(ReduceProd, KernelAttr().AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8),
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ArrayReduceGpuKernel, int8_t)
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MS_REG_GPU_KERNEL_ONE(ReduceProd, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(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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} // namespace kernel
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} // namespace mindspore
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@ -27,9 +27,10 @@
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namespace mindspore {
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namespace kernel {
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const std::map<std::string, cudnnReduceTensorOp_t> kReduceTypeMap = {
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{"ReduceMax", CUDNN_REDUCE_TENSOR_MAX}, {"ReduceMean", CUDNN_REDUCE_TENSOR_AVG},
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{"ReduceSum", CUDNN_REDUCE_TENSOR_ADD}, {"ReduceMin", CUDNN_REDUCE_TENSOR_MIN},
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{"ReduceAny", CUDNN_REDUCE_TENSOR_MAX}, {"ReduceAll", CUDNN_REDUCE_TENSOR_MUL},
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{"ReduceMax", CUDNN_REDUCE_TENSOR_MAX}, {"ReduceMean", CUDNN_REDUCE_TENSOR_AVG},
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{"ReduceSum", CUDNN_REDUCE_TENSOR_ADD}, {"ReduceMin", CUDNN_REDUCE_TENSOR_MIN},
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{"ReduceAny", CUDNN_REDUCE_TENSOR_MAX}, {"ReduceAll", CUDNN_REDUCE_TENSOR_MUL},
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{"ReduceProd", CUDNN_REDUCE_TENSOR_MUL},
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};
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template <typename T>
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class ArrayReduceGpuKernel : public GpuKernel {
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@ -0,0 +1,170 @@
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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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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super(ReduceProd, self).__init__()
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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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@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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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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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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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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