forked from mindspore-Ecosystem/mindspore
add scatter_nd_add and scatter_nd_sub op for gpu
This commit is contained in:
parent
00dee4e3bd
commit
d8d906df3a
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@ -0,0 +1,363 @@
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/**
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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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#include "backend/kernel_compiler/gpu/arrays/scatter_nd_functor_gpu_kernel.h"
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namespace mindspore {
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namespace kernel {
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// ScatterNdUpdate
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MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat64)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeFloat64)
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.AddOutputAttr(kNumberTypeFloat64),
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ScatterNdFunctorKernel, double, int)
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MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat64)
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.AddInputAttr(kNumberTypeInt64)
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.AddInputAttr(kNumberTypeFloat64)
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.AddOutputAttr(kNumberTypeFloat64),
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ScatterNdFunctorKernel, double, int64_t)
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MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat32)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeFloat32)
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.AddOutputAttr(kNumberTypeFloat32),
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ScatterNdFunctorKernel, float, int)
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MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat32)
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.AddInputAttr(kNumberTypeInt64)
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.AddInputAttr(kNumberTypeFloat32)
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.AddOutputAttr(kNumberTypeFloat32),
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ScatterNdFunctorKernel, float, int64_t)
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MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat16)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeFloat16)
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.AddOutputAttr(kNumberTypeFloat16),
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ScatterNdFunctorKernel, half, int)
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MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat16)
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.AddInputAttr(kNumberTypeInt64)
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.AddInputAttr(kNumberTypeFloat16)
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.AddOutputAttr(kNumberTypeFloat16),
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ScatterNdFunctorKernel, half, int64_t)
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MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
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KernelAttr()
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeInt32)
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.AddOutputAttr(kNumberTypeInt32),
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ScatterNdFunctorKernel, int, int)
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MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
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KernelAttr()
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeInt64)
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.AddInputAttr(kNumberTypeInt32)
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.AddOutputAttr(kNumberTypeInt32),
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ScatterNdFunctorKernel, int, int64_t)
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MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
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KernelAttr()
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.AddInputAttr(kNumberTypeInt16)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeInt16)
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.AddOutputAttr(kNumberTypeInt16),
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ScatterNdFunctorKernel, int16_t, int)
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MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
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KernelAttr()
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.AddInputAttr(kNumberTypeInt16)
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.AddInputAttr(kNumberTypeInt64)
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.AddInputAttr(kNumberTypeInt16)
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.AddOutputAttr(kNumberTypeInt16),
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ScatterNdFunctorKernel, int16_t, int64_t)
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MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
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KernelAttr()
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.AddInputAttr(kNumberTypeUInt8)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeUInt8)
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.AddOutputAttr(kNumberTypeUInt8),
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ScatterNdFunctorKernel, uint8_t, int)
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MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
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KernelAttr()
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.AddInputAttr(kNumberTypeUInt8)
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.AddInputAttr(kNumberTypeInt64)
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.AddInputAttr(kNumberTypeUInt8)
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.AddOutputAttr(kNumberTypeUInt8),
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ScatterNdFunctorKernel, uint8_t, int64_t)
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MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
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KernelAttr()
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.AddInputAttr(kNumberTypeInt8)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeInt8)
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.AddOutputAttr(kNumberTypeInt8),
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ScatterNdFunctorKernel, int8_t, int)
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MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
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KernelAttr()
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.AddInputAttr(kNumberTypeInt8)
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.AddInputAttr(kNumberTypeInt64)
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.AddInputAttr(kNumberTypeInt8)
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.AddOutputAttr(kNumberTypeInt8),
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ScatterNdFunctorKernel, int8_t, int64_t)
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MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
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KernelAttr()
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.AddInputAttr(kNumberTypeBool)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeBool)
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.AddOutputAttr(kNumberTypeBool),
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ScatterNdFunctorKernel, bool, int)
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MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
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KernelAttr()
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.AddInputAttr(kNumberTypeBool)
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.AddInputAttr(kNumberTypeInt64)
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.AddInputAttr(kNumberTypeBool)
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.AddOutputAttr(kNumberTypeBool),
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ScatterNdFunctorKernel, bool, int64_t)
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// ScatterNdAdd
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MS_REG_GPU_KERNEL_TWO(ScatterNdAdd,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat64)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeFloat64)
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.AddOutputAttr(kNumberTypeFloat64),
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ScatterNdFunctorKernel, double, int)
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MS_REG_GPU_KERNEL_TWO(ScatterNdAdd,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat64)
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.AddInputAttr(kNumberTypeInt64)
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.AddInputAttr(kNumberTypeFloat64)
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.AddOutputAttr(kNumberTypeFloat64),
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ScatterNdFunctorKernel, double, int64_t)
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MS_REG_GPU_KERNEL_TWO(ScatterNdAdd,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat32)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeFloat32)
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.AddOutputAttr(kNumberTypeFloat32),
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ScatterNdFunctorKernel, float, int)
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MS_REG_GPU_KERNEL_TWO(ScatterNdAdd,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat32)
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.AddInputAttr(kNumberTypeInt64)
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.AddInputAttr(kNumberTypeFloat32)
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.AddOutputAttr(kNumberTypeFloat32),
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ScatterNdFunctorKernel, float, int64_t)
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MS_REG_GPU_KERNEL_TWO(ScatterNdAdd,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat16)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeFloat16)
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.AddOutputAttr(kNumberTypeFloat16),
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ScatterNdFunctorKernel, half, int)
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MS_REG_GPU_KERNEL_TWO(ScatterNdAdd,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat16)
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.AddInputAttr(kNumberTypeInt64)
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.AddInputAttr(kNumberTypeFloat16)
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.AddOutputAttr(kNumberTypeFloat16),
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ScatterNdFunctorKernel, half, int64_t)
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MS_REG_GPU_KERNEL_TWO(ScatterNdAdd,
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KernelAttr()
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeInt32)
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.AddOutputAttr(kNumberTypeInt32),
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ScatterNdFunctorKernel, int, int)
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MS_REG_GPU_KERNEL_TWO(ScatterNdAdd,
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KernelAttr()
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeInt64)
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.AddInputAttr(kNumberTypeInt32)
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.AddOutputAttr(kNumberTypeInt32),
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ScatterNdFunctorKernel, int, int64_t)
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MS_REG_GPU_KERNEL_TWO(ScatterNdAdd,
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KernelAttr()
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.AddInputAttr(kNumberTypeInt16)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeInt16)
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.AddOutputAttr(kNumberTypeInt16),
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ScatterNdFunctorKernel, int16_t, int)
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MS_REG_GPU_KERNEL_TWO(ScatterNdAdd,
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KernelAttr()
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.AddInputAttr(kNumberTypeInt16)
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.AddInputAttr(kNumberTypeInt64)
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.AddInputAttr(kNumberTypeInt16)
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.AddOutputAttr(kNumberTypeInt16),
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ScatterNdFunctorKernel, int16_t, int64_t)
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MS_REG_GPU_KERNEL_TWO(ScatterNdAdd,
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KernelAttr()
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.AddInputAttr(kNumberTypeUInt8)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeUInt8)
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.AddOutputAttr(kNumberTypeUInt8),
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ScatterNdFunctorKernel, uint8_t, int)
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MS_REG_GPU_KERNEL_TWO(ScatterNdAdd,
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KernelAttr()
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.AddInputAttr(kNumberTypeUInt8)
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.AddInputAttr(kNumberTypeInt64)
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.AddInputAttr(kNumberTypeUInt8)
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.AddOutputAttr(kNumberTypeUInt8),
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ScatterNdFunctorKernel, uint8_t, int64_t)
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MS_REG_GPU_KERNEL_TWO(ScatterNdAdd,
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KernelAttr()
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.AddInputAttr(kNumberTypeInt8)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeInt8)
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.AddOutputAttr(kNumberTypeInt8),
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ScatterNdFunctorKernel, int8_t, int)
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MS_REG_GPU_KERNEL_TWO(ScatterNdAdd,
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KernelAttr()
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.AddInputAttr(kNumberTypeInt8)
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.AddInputAttr(kNumberTypeInt64)
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.AddInputAttr(kNumberTypeInt8)
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.AddOutputAttr(kNumberTypeInt8),
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ScatterNdFunctorKernel, int8_t, int64_t)
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MS_REG_GPU_KERNEL_TWO(ScatterNdAdd,
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KernelAttr()
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.AddInputAttr(kNumberTypeBool)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeBool)
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.AddOutputAttr(kNumberTypeBool),
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ScatterNdFunctorKernel, bool, int)
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MS_REG_GPU_KERNEL_TWO(ScatterNdAdd,
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KernelAttr()
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.AddInputAttr(kNumberTypeBool)
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.AddInputAttr(kNumberTypeInt64)
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.AddInputAttr(kNumberTypeBool)
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.AddOutputAttr(kNumberTypeBool),
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ScatterNdFunctorKernel, bool, int64_t)
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// ScatterNdSub
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MS_REG_GPU_KERNEL_TWO(ScatterNdSub,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat64)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeFloat64)
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.AddOutputAttr(kNumberTypeFloat64),
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ScatterNdFunctorKernel, double, int)
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MS_REG_GPU_KERNEL_TWO(ScatterNdSub,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat64)
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.AddInputAttr(kNumberTypeInt64)
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.AddInputAttr(kNumberTypeFloat64)
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.AddOutputAttr(kNumberTypeFloat64),
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ScatterNdFunctorKernel, double, int64_t)
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MS_REG_GPU_KERNEL_TWO(ScatterNdSub,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat32)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeFloat32)
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.AddOutputAttr(kNumberTypeFloat32),
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ScatterNdFunctorKernel, float, int)
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MS_REG_GPU_KERNEL_TWO(ScatterNdSub,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat32)
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.AddInputAttr(kNumberTypeInt64)
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.AddInputAttr(kNumberTypeFloat32)
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.AddOutputAttr(kNumberTypeFloat32),
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ScatterNdFunctorKernel, float, int64_t)
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MS_REG_GPU_KERNEL_TWO(ScatterNdSub,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat16)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeFloat16)
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.AddOutputAttr(kNumberTypeFloat16),
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ScatterNdFunctorKernel, half, int)
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MS_REG_GPU_KERNEL_TWO(ScatterNdSub,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat16)
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.AddInputAttr(kNumberTypeInt64)
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.AddInputAttr(kNumberTypeFloat16)
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.AddOutputAttr(kNumberTypeFloat16),
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ScatterNdFunctorKernel, half, int64_t)
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MS_REG_GPU_KERNEL_TWO(ScatterNdSub,
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KernelAttr()
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeInt32)
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.AddOutputAttr(kNumberTypeInt32),
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ScatterNdFunctorKernel, int, int)
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MS_REG_GPU_KERNEL_TWO(ScatterNdSub,
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KernelAttr()
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeInt64)
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.AddInputAttr(kNumberTypeInt32)
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.AddOutputAttr(kNumberTypeInt32),
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ScatterNdFunctorKernel, int, int64_t)
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MS_REG_GPU_KERNEL_TWO(ScatterNdSub,
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KernelAttr()
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.AddInputAttr(kNumberTypeInt16)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeInt16)
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.AddOutputAttr(kNumberTypeInt16),
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ScatterNdFunctorKernel, int16_t, int)
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MS_REG_GPU_KERNEL_TWO(ScatterNdSub,
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KernelAttr()
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.AddInputAttr(kNumberTypeInt16)
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.AddInputAttr(kNumberTypeInt64)
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.AddInputAttr(kNumberTypeInt16)
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.AddOutputAttr(kNumberTypeInt16),
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ScatterNdFunctorKernel, int16_t, int64_t)
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MS_REG_GPU_KERNEL_TWO(ScatterNdSub,
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KernelAttr()
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.AddInputAttr(kNumberTypeUInt8)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeUInt8)
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.AddOutputAttr(kNumberTypeUInt8),
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ScatterNdFunctorKernel, uint8_t, int)
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MS_REG_GPU_KERNEL_TWO(ScatterNdSub,
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KernelAttr()
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.AddInputAttr(kNumberTypeUInt8)
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.AddInputAttr(kNumberTypeInt64)
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.AddInputAttr(kNumberTypeUInt8)
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.AddOutputAttr(kNumberTypeUInt8),
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ScatterNdFunctorKernel, uint8_t, int64_t)
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MS_REG_GPU_KERNEL_TWO(ScatterNdSub,
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KernelAttr()
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.AddInputAttr(kNumberTypeInt8)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeInt8)
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.AddOutputAttr(kNumberTypeInt8),
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ScatterNdFunctorKernel, int8_t, int)
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MS_REG_GPU_KERNEL_TWO(ScatterNdSub,
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KernelAttr()
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.AddInputAttr(kNumberTypeInt8)
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.AddInputAttr(kNumberTypeInt64)
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.AddInputAttr(kNumberTypeInt8)
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.AddOutputAttr(kNumberTypeInt8),
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ScatterNdFunctorKernel, int8_t, int64_t)
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MS_REG_GPU_KERNEL_TWO(ScatterNdSub,
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KernelAttr()
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.AddInputAttr(kNumberTypeBool)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeBool)
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.AddOutputAttr(kNumberTypeBool),
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ScatterNdFunctorKernel, bool, int)
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MS_REG_GPU_KERNEL_TWO(ScatterNdSub,
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KernelAttr()
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.AddInputAttr(kNumberTypeBool)
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.AddInputAttr(kNumberTypeInt64)
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.AddInputAttr(kNumberTypeBool)
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.AddOutputAttr(kNumberTypeBool),
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ScatterNdFunctorKernel, bool, int64_t)
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} // namespace kernel
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} // namespace mindspore
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@ -14,21 +14,30 @@
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* limitations under the License.
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*/
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#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_ARRAYS_SCATTER_ND_UPDATE_GPU_KERNEL_H_
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#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_ARRAYS_SCATTER_ND_UPDATE_GPU_KERNEL_H_
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#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_ARRAYS_SCATTER_ND_FUNCTOR_GPU_KERNEL_H_
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#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_ARRAYS_SCATTER_ND_FUNCTOR_GPU_KERNEL_H_
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#include <vector>
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#include <string>
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#include <map>
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#include "backend/kernel_compiler/gpu/gpu_kernel.h"
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#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
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#include "backend/kernel_compiler/gpu/cuda_impl/scatter_nd_update_impl.cuh"
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#include "backend/kernel_compiler/gpu/cuda_impl/scatter_nd_functor_impl.cuh"
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namespace mindspore {
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namespace kernel {
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static const std::map<std::string, ScatterNdFunctorType> kScatterNdFunctorTypeMap = {
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{"ScatterNdUpdate", SCATTER_ND_FUNC_UPDATE},
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{"ScatterNdAdd", SCATTER_ND_FUNC_ADD},
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{"ScatterNdSub", SCATTER_ND_FUNC_SUB},
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};
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template <typename T, typename S>
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class ScatterNdUpdateKernel : public GpuKernel {
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class ScatterNdFunctorKernel : public GpuKernel {
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public:
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ScatterNdUpdateKernel() { ResetResource(); }
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~ScatterNdUpdateKernel() {
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ScatterNdFunctorKernel() { ResetResource(); }
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~ScatterNdFunctorKernel() {
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if (indices_stride_ != nullptr) {
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device::gpu::GPUMemoryAllocator::GetInstance().FreeTensorMem(static_cast<void *>(indices_stride_));
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}
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@ -54,9 +63,9 @@ class ScatterNdUpdateKernel : public GpuKernel {
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CHECK_CUDA_RET_WITH_EXCEPT(kernel_node_,
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cudaMemcpyAsync(indices_stride_, &out_strides_[0], indices_len, cudaMemcpyHostToDevice,
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reinterpret_cast<cudaStream_t>(stream_ptr)),
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"cudaMemcpyAsync failed in ScatterNdUpdateGpuFwdKernel::Launch.");
|
||||
CalScatterNdUpdate(unit_size_, num_units_, index_depth_, indices_stride_, indices, updates, input,
|
||||
reinterpret_cast<cudaStream_t>(stream_ptr));
|
||||
"cudaMemcpyAsync failed in ScatterNdFunctorGpuFwdKernel::Launch.");
|
||||
CalScatterNdFunctor(scatter_nd_functor_type_, unit_size_, num_units_, index_depth_, indices_stride_, indices,
|
||||
updates, input, reinterpret_cast<cudaStream_t>(stream_ptr));
|
||||
CHECK_CUDA_RET_WITH_EXCEPT(kernel_node_,
|
||||
cudaMemcpyAsync(&output[0], &input[0], input_size_ * sizeof(T), cudaMemcpyDeviceToDevice,
|
||||
reinterpret_cast<cudaStream_t>(stream_ptr)),
|
||||
|
|
@ -65,15 +74,22 @@ class ScatterNdUpdateKernel : public GpuKernel {
|
|||
}
|
||||
|
||||
bool Init(const CNodePtr &kernel_node) override {
|
||||
std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node);
|
||||
auto iter = kScatterNdFunctorTypeMap.find(kernel_name);
|
||||
if (iter == kScatterNdFunctorTypeMap.end()) {
|
||||
MS_LOG(EXCEPTION) << "ScatterNd functor " << kernel_name << " is not supported.";
|
||||
} else {
|
||||
scatter_nd_functor_type_ = iter->second;
|
||||
}
|
||||
kernel_node_ = kernel_node;
|
||||
size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
|
||||
if (input_num != 3) {
|
||||
MS_LOG(ERROR) << "Input number is " << input_num << ", but ScatterNdUpdate needs 3 inputs.";
|
||||
MS_LOG(ERROR) << "Input number is " << input_num << ", but " << kernel_name << " needs 3 inputs.";
|
||||
return false;
|
||||
}
|
||||
size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
|
||||
if (output_num != 1) {
|
||||
MS_LOG(ERROR) << "Output number is " << output_num << ", but ScatterNdUpdate has 1 output.";
|
||||
MS_LOG(ERROR) << "Output number is " << output_num << ", but " << kernel_name << " has 1 output.";
|
||||
return false;
|
||||
}
|
||||
|
||||
|
|
@ -151,6 +167,7 @@ class ScatterNdUpdateKernel : public GpuKernel {
|
|||
}
|
||||
|
||||
private:
|
||||
ScatterNdFunctorType scatter_nd_functor_type_;
|
||||
size_t input_size_;
|
||||
size_t indices_size_;
|
||||
size_t updates_size_;
|
||||
|
|
@ -167,4 +184,4 @@ class ScatterNdUpdateKernel : public GpuKernel {
|
|||
};
|
||||
} // namespace kernel
|
||||
} // namespace mindspore
|
||||
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_ARRAYS_SCATTER_ND_UPDATE_GPU_KERNEL_H_
|
||||
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_ARRAYS_SCATTER_ND_FUNCTOR_GPU_KERNEL_H_
|
||||
|
|
@ -1,134 +0,0 @@
|
|||
/**
|
||||
* Copyright 2021 Huawei Technologies Co., Ltd
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
#include "backend/kernel_compiler/gpu/arrays/scatter_nd_update_gpu_kernel.h"
|
||||
|
||||
namespace mindspore {
|
||||
namespace kernel {
|
||||
MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
|
||||
KernelAttr()
|
||||
.AddInputAttr(kNumberTypeFloat64)
|
||||
.AddInputAttr(kNumberTypeInt32)
|
||||
.AddInputAttr(kNumberTypeFloat64)
|
||||
.AddOutputAttr(kNumberTypeFloat64),
|
||||
ScatterNdUpdateKernel, double, int)
|
||||
MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
|
||||
KernelAttr()
|
||||
.AddInputAttr(kNumberTypeFloat64)
|
||||
.AddInputAttr(kNumberTypeInt64)
|
||||
.AddInputAttr(kNumberTypeFloat64)
|
||||
.AddOutputAttr(kNumberTypeFloat64),
|
||||
ScatterNdUpdateKernel, double, int64_t)
|
||||
MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
|
||||
KernelAttr()
|
||||
.AddInputAttr(kNumberTypeFloat32)
|
||||
.AddInputAttr(kNumberTypeInt32)
|
||||
.AddInputAttr(kNumberTypeFloat32)
|
||||
.AddOutputAttr(kNumberTypeFloat32),
|
||||
ScatterNdUpdateKernel, float, int)
|
||||
MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
|
||||
KernelAttr()
|
||||
.AddInputAttr(kNumberTypeFloat32)
|
||||
.AddInputAttr(kNumberTypeInt64)
|
||||
.AddInputAttr(kNumberTypeFloat32)
|
||||
.AddOutputAttr(kNumberTypeFloat32),
|
||||
ScatterNdUpdateKernel, float, int64_t)
|
||||
MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
|
||||
KernelAttr()
|
||||
.AddInputAttr(kNumberTypeFloat16)
|
||||
.AddInputAttr(kNumberTypeInt32)
|
||||
.AddInputAttr(kNumberTypeFloat16)
|
||||
.AddOutputAttr(kNumberTypeFloat16),
|
||||
ScatterNdUpdateKernel, half, int)
|
||||
MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
|
||||
KernelAttr()
|
||||
.AddInputAttr(kNumberTypeFloat16)
|
||||
.AddInputAttr(kNumberTypeInt64)
|
||||
.AddInputAttr(kNumberTypeFloat16)
|
||||
.AddOutputAttr(kNumberTypeFloat16),
|
||||
ScatterNdUpdateKernel, half, int64_t)
|
||||
MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
|
||||
KernelAttr()
|
||||
.AddInputAttr(kNumberTypeInt32)
|
||||
.AddInputAttr(kNumberTypeInt32)
|
||||
.AddInputAttr(kNumberTypeInt32)
|
||||
.AddOutputAttr(kNumberTypeInt32),
|
||||
ScatterNdUpdateKernel, int, int)
|
||||
MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
|
||||
KernelAttr()
|
||||
.AddInputAttr(kNumberTypeInt32)
|
||||
.AddInputAttr(kNumberTypeInt64)
|
||||
.AddInputAttr(kNumberTypeInt32)
|
||||
.AddOutputAttr(kNumberTypeInt32),
|
||||
ScatterNdUpdateKernel, int, int64_t)
|
||||
MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
|
||||
KernelAttr()
|
||||
.AddInputAttr(kNumberTypeInt16)
|
||||
.AddInputAttr(kNumberTypeInt32)
|
||||
.AddInputAttr(kNumberTypeInt16)
|
||||
.AddOutputAttr(kNumberTypeInt16),
|
||||
ScatterNdUpdateKernel, int16_t, int)
|
||||
MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
|
||||
KernelAttr()
|
||||
.AddInputAttr(kNumberTypeInt16)
|
||||
.AddInputAttr(kNumberTypeInt64)
|
||||
.AddInputAttr(kNumberTypeInt16)
|
||||
.AddOutputAttr(kNumberTypeInt16),
|
||||
ScatterNdUpdateKernel, int16_t, int64_t)
|
||||
MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
|
||||
KernelAttr()
|
||||
.AddInputAttr(kNumberTypeUInt8)
|
||||
.AddInputAttr(kNumberTypeInt32)
|
||||
.AddInputAttr(kNumberTypeUInt8)
|
||||
.AddOutputAttr(kNumberTypeUInt8),
|
||||
ScatterNdUpdateKernel, uint8_t, int)
|
||||
MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
|
||||
KernelAttr()
|
||||
.AddInputAttr(kNumberTypeUInt8)
|
||||
.AddInputAttr(kNumberTypeInt64)
|
||||
.AddInputAttr(kNumberTypeUInt8)
|
||||
.AddOutputAttr(kNumberTypeUInt8),
|
||||
ScatterNdUpdateKernel, uint8_t, int64_t)
|
||||
MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
|
||||
KernelAttr()
|
||||
.AddInputAttr(kNumberTypeInt8)
|
||||
.AddInputAttr(kNumberTypeInt32)
|
||||
.AddInputAttr(kNumberTypeInt8)
|
||||
.AddOutputAttr(kNumberTypeInt8),
|
||||
ScatterNdUpdateKernel, int8_t, int)
|
||||
MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
|
||||
KernelAttr()
|
||||
.AddInputAttr(kNumberTypeInt8)
|
||||
.AddInputAttr(kNumberTypeInt64)
|
||||
.AddInputAttr(kNumberTypeInt8)
|
||||
.AddOutputAttr(kNumberTypeInt8),
|
||||
ScatterNdUpdateKernel, int8_t, int64_t)
|
||||
MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
|
||||
KernelAttr()
|
||||
.AddInputAttr(kNumberTypeBool)
|
||||
.AddInputAttr(kNumberTypeInt32)
|
||||
.AddInputAttr(kNumberTypeBool)
|
||||
.AddOutputAttr(kNumberTypeBool),
|
||||
ScatterNdUpdateKernel, bool, int)
|
||||
MS_REG_GPU_KERNEL_TWO(ScatterNdUpdate,
|
||||
KernelAttr()
|
||||
.AddInputAttr(kNumberTypeBool)
|
||||
.AddInputAttr(kNumberTypeInt64)
|
||||
.AddInputAttr(kNumberTypeBool)
|
||||
.AddOutputAttr(kNumberTypeBool),
|
||||
ScatterNdUpdateKernel, bool, int64_t)
|
||||
} // namespace kernel
|
||||
} // namespace mindspore
|
||||
|
|
@ -0,0 +1,181 @@
|
|||
/**
|
||||
* Copyright 2021 Huawei Technologies Co., Ltd
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
#include "backend/kernel_compiler/gpu/cuda_impl/util.cuh"
|
||||
#include "backend/kernel_compiler/gpu/cuda_impl/scatter_nd_functor_impl.cuh"
|
||||
|
||||
template <typename T, typename S>
|
||||
__global__ void ScatterNdUpdate(const size_t unit_size, const size_t index_depth, const size_t updates_size,
|
||||
const S *out_strides, const S *indices, const T *updates, T *input) {
|
||||
int i, j;
|
||||
for (size_t read_index = blockIdx.x * blockDim.x + threadIdx.x; read_index < (updates_size);
|
||||
read_index += blockDim.x * gridDim.x) {
|
||||
size_t write_index = 0;
|
||||
bool out_bound = false;
|
||||
|
||||
i = read_index / unit_size;
|
||||
j = read_index % unit_size;
|
||||
|
||||
for (size_t k = 0; k < index_depth; k++) {
|
||||
S indices_i = indices[i * index_depth + k];
|
||||
out_bound |= indices_i < 0;
|
||||
write_index += indices_i * out_strides[k] * unit_size;
|
||||
}
|
||||
|
||||
write_index += j;
|
||||
|
||||
if (!out_bound) {
|
||||
input[write_index] = updates[read_index];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <typename T, typename S>
|
||||
__global__ void ScatterNdAdd(const size_t unit_size, const size_t index_depth, const size_t updates_size,
|
||||
const S *out_strides, const S *indices, const T *updates, T *input) {
|
||||
int i, j;
|
||||
for (size_t read_index = blockIdx.x * blockDim.x + threadIdx.x; read_index < (updates_size);
|
||||
read_index += blockDim.x * gridDim.x) {
|
||||
size_t write_index = 0;
|
||||
bool out_bound = false;
|
||||
|
||||
i = read_index / unit_size;
|
||||
j = read_index % unit_size;
|
||||
|
||||
for (size_t k = 0; k < index_depth; k++) {
|
||||
S indices_i = indices[i * index_depth + k];
|
||||
out_bound |= indices_i < 0;
|
||||
write_index += indices_i * out_strides[k] * unit_size;
|
||||
}
|
||||
|
||||
write_index += j;
|
||||
|
||||
if (!out_bound) {
|
||||
MsAtomicAdd(&input[write_index], updates[read_index]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <typename T, typename S>
|
||||
__global__ void ScatterNdSub(const size_t unit_size, const size_t index_depth, const size_t updates_size,
|
||||
const S *out_strides, const S *indices, const T *updates, T *input) {
|
||||
int i, j;
|
||||
for (size_t read_index = blockIdx.x * blockDim.x + threadIdx.x; read_index < (updates_size);
|
||||
read_index += blockDim.x * gridDim.x) {
|
||||
size_t write_index = 0;
|
||||
bool out_bound = false;
|
||||
|
||||
i = read_index / unit_size;
|
||||
j = read_index % unit_size;
|
||||
|
||||
for (size_t k = 0; k < index_depth; k++) {
|
||||
S indices_i = indices[i * index_depth + k];
|
||||
out_bound |= indices_i < 0;
|
||||
write_index += indices_i * out_strides[k] * unit_size;
|
||||
}
|
||||
|
||||
write_index += j;
|
||||
|
||||
if (!out_bound) {
|
||||
MsAtomicAdd(&input[write_index], -updates[read_index]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <typename T, typename S>
|
||||
void CalScatterNdFunctor(enum ScatterNdFunctorType func_type, const size_t &unit_size, const size_t &num_units,
|
||||
const size_t &index_depth, const S *out_strides, const S *indices, const T *updates, T *input,
|
||||
cudaStream_t cuda_stream) {
|
||||
const size_t updates_size = unit_size * num_units;
|
||||
switch (func_type) {
|
||||
case SCATTER_ND_FUNC_UPDATE:
|
||||
return ScatterNdUpdate<<<GET_BLOCKS(updates_size), GET_THREADS, 0, cuda_stream>>>(
|
||||
unit_size, index_depth, updates_size, out_strides, indices, updates, input);
|
||||
case SCATTER_ND_FUNC_ADD:
|
||||
return ScatterNdAdd<<<GET_BLOCKS(updates_size), GET_THREADS, 0, cuda_stream>>>(
|
||||
unit_size, index_depth, updates_size, out_strides, indices, updates, input);
|
||||
case SCATTER_ND_FUNC_SUB:
|
||||
return ScatterNdSub<<<GET_BLOCKS(updates_size), GET_THREADS, 0, cuda_stream>>>(
|
||||
unit_size, index_depth, updates_size, out_strides, indices, updates, input);
|
||||
default:
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
template void CalScatterNdFunctor<double, int64_t>(enum ScatterNdFunctorType func_type, const size_t &unit_size,
|
||||
const size_t &num_units, const size_t &index_depth,
|
||||
const int64_t *out_strides, const int64_t *indices,
|
||||
const double *updates, double *input, cudaStream_t cuda_stream);
|
||||
template void CalScatterNdFunctor<double, int32_t>(enum ScatterNdFunctorType func_type, const size_t &unit_size,
|
||||
const size_t &num_units, const size_t &index_depth,
|
||||
const int32_t *out_strides, const int32_t *indices,
|
||||
const double *updates, double *input, cudaStream_t cuda_stream);
|
||||
template void CalScatterNdFunctor<float, int64_t>(enum ScatterNdFunctorType func_type, const size_t &unit_size,
|
||||
const size_t &num_units, const size_t &index_depth,
|
||||
const int64_t *out_strides, const int64_t *indices,
|
||||
const float *updates, float *input, cudaStream_t cuda_stream);
|
||||
template void CalScatterNdFunctor<float, int32_t>(enum ScatterNdFunctorType func_type, const size_t &unit_size,
|
||||
const size_t &num_units, const size_t &index_depth,
|
||||
const int32_t *out_strides, const int32_t *indices,
|
||||
const float *updates, float *input, cudaStream_t cuda_stream);
|
||||
template void CalScatterNdFunctor<half, int64_t>(enum ScatterNdFunctorType func_type, const size_t &unit_size,
|
||||
const size_t &num_units, const size_t &index_depth,
|
||||
const int64_t *out_strides, const int64_t *indices,
|
||||
const half *updates, half *input, cudaStream_t cuda_stream);
|
||||
template void CalScatterNdFunctor<half, int32_t>(enum ScatterNdFunctorType func_type, const size_t &unit_size,
|
||||
const size_t &num_units, const size_t &index_depth,
|
||||
const int32_t *out_strides, const int32_t *indices,
|
||||
const half *updates, half *input, cudaStream_t cuda_stream);
|
||||
template void CalScatterNdFunctor<int32_t, int64_t>(enum ScatterNdFunctorType func_type, const size_t &unit_size,
|
||||
const size_t &num_units, const size_t &index_depth,
|
||||
const int64_t *out_strides, const int64_t *indices,
|
||||
const int32_t *updates, int32_t *input, cudaStream_t cuda_stream);
|
||||
template void CalScatterNdFunctor<int32_t, int32_t>(enum ScatterNdFunctorType func_type, const size_t &unit_size,
|
||||
const size_t &num_units, const size_t &index_depth,
|
||||
const int32_t *out_strides, const int32_t *indices,
|
||||
const int32_t *updates, int32_t *input, cudaStream_t cuda_stream);
|
||||
template void CalScatterNdFunctor<int16_t, int64_t>(enum ScatterNdFunctorType func_type, const size_t &unit_size,
|
||||
const size_t &num_units, const size_t &index_depth,
|
||||
const int64_t *out_strides, const int64_t *indices,
|
||||
const int16_t *updates, int16_t *input, cudaStream_t cuda_stream);
|
||||
template void CalScatterNdFunctor<int16_t, int32_t>(enum ScatterNdFunctorType func_type, const size_t &unit_size,
|
||||
const size_t &num_units, const size_t &index_depth,
|
||||
const int32_t *out_strides, const int32_t *indices,
|
||||
const int16_t *updates, int16_t *input, cudaStream_t cuda_stream);
|
||||
template void CalScatterNdFunctor<uint8_t, int64_t>(enum ScatterNdFunctorType func_type, const size_t &unit_size,
|
||||
const size_t &num_units, const size_t &index_depth,
|
||||
const int64_t *out_strides, const int64_t *indices,
|
||||
const uint8_t *updates, uint8_t *input, cudaStream_t cuda_stream);
|
||||
template void CalScatterNdFunctor<uint8_t, int32_t>(enum ScatterNdFunctorType func_type, const size_t &unit_size,
|
||||
const size_t &num_units, const size_t &index_depth,
|
||||
const int32_t *out_strides, const int32_t *indices,
|
||||
const uint8_t *updates, uint8_t *input, cudaStream_t cuda_stream);
|
||||
template void CalScatterNdFunctor<int8_t, int64_t>(enum ScatterNdFunctorType func_type, const size_t &unit_size,
|
||||
const size_t &num_units, const size_t &index_depth,
|
||||
const int64_t *out_strides, const int64_t *indices,
|
||||
const int8_t *updates, int8_t *input, cudaStream_t cuda_stream);
|
||||
template void CalScatterNdFunctor<int8_t, int32_t>(enum ScatterNdFunctorType func_type, const size_t &unit_size,
|
||||
const size_t &num_units, const size_t &index_depth,
|
||||
const int32_t *out_strides, const int32_t *indices,
|
||||
const int8_t *updates, int8_t *input, cudaStream_t cuda_stream);
|
||||
template void CalScatterNdFunctor<bool, int64_t>(enum ScatterNdFunctorType func_type, const size_t &unit_size,
|
||||
const size_t &num_units, const size_t &index_depth,
|
||||
const int64_t *out_strides, const int64_t *indices,
|
||||
const bool *updates, bool *input, cudaStream_t cuda_stream);
|
||||
template void CalScatterNdFunctor<bool, int32_t>(enum ScatterNdFunctorType func_type, const size_t &unit_size,
|
||||
const size_t &num_units, const size_t &index_depth,
|
||||
const int32_t *out_strides, const int32_t *indices,
|
||||
const bool *updates, bool *input, cudaStream_t cuda_stream);
|
||||
|
|
@ -14,13 +14,21 @@
|
|||
* limitations under the License.
|
||||
*/
|
||||
|
||||
#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_CUDA_IMPL_SCATTER_ND_UPDATE_IMPL_CUH_
|
||||
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_CUDA_IMPL_SCATTER_ND_UPDATE_IMPL_CUH_
|
||||
#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_CUDA_IMPL_SCATTER_ND_FUNCTOR_IMPL_CUH_
|
||||
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_CUDA_IMPL_SCATTER_ND_FUNCTOR_IMPL_CUH_
|
||||
|
||||
#include "runtime/device/gpu/cuda_common.h"
|
||||
|
||||
template <typename T, typename S>
|
||||
void CalScatterNdUpdate(const size_t &unit_size, const size_t &num_units, const size_t &index_depth,
|
||||
const S *out_strides, const S *indices, const T *updates, T *input, cudaStream_t cuda_stream);
|
||||
enum ScatterNdFunctorType {
|
||||
SCATTER_ND_FUNC_UPDATE = 0,
|
||||
SCATTER_ND_FUNC_ADD,
|
||||
SCATTER_ND_FUNC_SUB,
|
||||
SCATTER_ND_FUNC_INVALID_TYPE = 255
|
||||
};
|
||||
|
||||
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_CUDA_IMPL_SCATTER_ND_UPDATE_IMPL_CUH_
|
||||
template <typename T, typename S>
|
||||
void CalScatterNdFunctor(enum ScatterNdFunctorType func_type, const size_t &unit_size, const size_t &num_units,
|
||||
const size_t &index_depth, const S *out_strides, const S *indices, const T *updates, T *input,
|
||||
cudaStream_t cuda_stream);
|
||||
|
||||
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_CUDA_IMPL_SCATTER_ND_FUNCTOR_IMPL_CUH_
|
||||
|
|
@ -1,116 +0,0 @@
|
|||
/**
|
||||
* Copyright 2021 Huawei Technologies Co., Ltd
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
#include "backend/kernel_compiler/gpu/cuda_impl/scatter_nd_update_impl.cuh"
|
||||
|
||||
template <typename T, typename S>
|
||||
__global__ void ScatterNdUpdate(const size_t unit_size, const size_t index_depth, const size_t updates_size,
|
||||
const S *out_strides, const S *indices, const T *updates, T *input) {
|
||||
int i, j;
|
||||
for (size_t read_index = blockIdx.x * blockDim.x + threadIdx.x; read_index < (updates_size);
|
||||
read_index += blockDim.x * gridDim.x) {
|
||||
size_t write_index = 0;
|
||||
bool out_bound = false;
|
||||
|
||||
i = read_index / unit_size;
|
||||
j = read_index % unit_size;
|
||||
|
||||
for (size_t k = 0; k < index_depth; k++) {
|
||||
S indices_i = indices[i * index_depth + k];
|
||||
out_bound |= indices_i < 0;
|
||||
write_index += indices_i * out_strides[k] * unit_size;
|
||||
}
|
||||
|
||||
write_index += j;
|
||||
|
||||
if (!out_bound) {
|
||||
input[write_index] = updates[read_index];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <typename T, typename S>
|
||||
void CalScatterNdUpdate(const size_t &unit_size, const size_t &num_units, const size_t &index_depth,
|
||||
const S *out_strides, const S *indices, const T *updates, T *input, cudaStream_t cuda_stream) {
|
||||
const size_t updates_size = unit_size * num_units;
|
||||
ScatterNdUpdate<<<GET_BLOCKS(updates_size), GET_THREADS, 0, cuda_stream>>>(unit_size, index_depth, updates_size,
|
||||
out_strides, indices, updates, input);
|
||||
}
|
||||
|
||||
template void CalScatterNdUpdate<double, int64_t>(const size_t &unit_size, const size_t &num_units,
|
||||
const size_t &index_depth, const int64_t *out_strides,
|
||||
const int64_t *indices, const double *updates, double *input,
|
||||
cudaStream_t cuda_stream);
|
||||
template void CalScatterNdUpdate<double, int32_t>(const size_t &unit_size, const size_t &num_units,
|
||||
const size_t &index_depth, const int32_t *out_strides,
|
||||
const int32_t *indices, const double *updates, double *input,
|
||||
cudaStream_t cuda_stream);
|
||||
template void CalScatterNdUpdate<float, int64_t>(const size_t &unit_size, const size_t &num_units,
|
||||
const size_t &index_depth, const int64_t *out_strides,
|
||||
const int64_t *indices, const float *updates, float *input,
|
||||
cudaStream_t cuda_stream);
|
||||
template void CalScatterNdUpdate<float, int32_t>(const size_t &unit_size, const size_t &num_units,
|
||||
const size_t &index_depth, const int32_t *out_strides,
|
||||
const int32_t *indices, const float *updates, float *input,
|
||||
cudaStream_t cuda_stream);
|
||||
template void CalScatterNdUpdate<half, int64_t>(const size_t &unit_size, const size_t &num_units,
|
||||
const size_t &index_depth, const int64_t *out_strides,
|
||||
const int64_t *indices, const half *updates, half *input,
|
||||
cudaStream_t cuda_stream);
|
||||
template void CalScatterNdUpdate<half, int32_t>(const size_t &unit_size, const size_t &num_units,
|
||||
const size_t &index_depth, const int32_t *out_strides,
|
||||
const int32_t *indices, const half *updates, half *input,
|
||||
cudaStream_t cuda_stream);
|
||||
template void CalScatterNdUpdate<int32_t, int64_t>(const size_t &unit_size, const size_t &num_units,
|
||||
const size_t &index_depth, const int64_t *out_strides,
|
||||
const int64_t *indices, const int32_t *updates, int32_t *input,
|
||||
cudaStream_t cuda_stream);
|
||||
template void CalScatterNdUpdate<int32_t, int32_t>(const size_t &unit_size, const size_t &num_units,
|
||||
const size_t &index_depth, const int32_t *out_strides,
|
||||
const int32_t *indices, const int32_t *updates, int32_t *input,
|
||||
cudaStream_t cuda_stream);
|
||||
template void CalScatterNdUpdate<int16_t, int64_t>(const size_t &unit_size, const size_t &num_units,
|
||||
const size_t &index_depth, const int64_t *out_strides,
|
||||
const int64_t *indices, const int16_t *updates, int16_t *input,
|
||||
cudaStream_t cuda_stream);
|
||||
template void CalScatterNdUpdate<int16_t, int32_t>(const size_t &unit_size, const size_t &num_units,
|
||||
const size_t &index_depth, const int32_t *out_strides,
|
||||
const int32_t *indices, const int16_t *updates, int16_t *input,
|
||||
cudaStream_t cuda_stream);
|
||||
template void CalScatterNdUpdate<uint8_t, int64_t>(const size_t &unit_size, const size_t &num_units,
|
||||
const size_t &index_depth, const int64_t *out_strides,
|
||||
const int64_t *indices, const uint8_t *updates, uint8_t *input,
|
||||
cudaStream_t cuda_stream);
|
||||
template void CalScatterNdUpdate<uint8_t, int32_t>(const size_t &unit_size, const size_t &num_units,
|
||||
const size_t &index_depth, const int32_t *out_strides,
|
||||
const int32_t *indices, const uint8_t *updates, uint8_t *input,
|
||||
cudaStream_t cuda_stream);
|
||||
template void CalScatterNdUpdate<int8_t, int64_t>(const size_t &unit_size, const size_t &num_units,
|
||||
const size_t &index_depth, const int64_t *out_strides,
|
||||
const int64_t *indices, const int8_t *updates, int8_t *input,
|
||||
cudaStream_t cuda_stream);
|
||||
template void CalScatterNdUpdate<int8_t, int32_t>(const size_t &unit_size, const size_t &num_units,
|
||||
const size_t &index_depth, const int32_t *out_strides,
|
||||
const int32_t *indices, const int8_t *updates, int8_t *input,
|
||||
cudaStream_t cuda_stream);
|
||||
template void CalScatterNdUpdate<bool, int64_t>(const size_t &unit_size, const size_t &num_units,
|
||||
const size_t &index_depth, const int64_t *out_strides,
|
||||
const int64_t *indices, const bool *updates, bool *input,
|
||||
cudaStream_t cuda_stream);
|
||||
template void CalScatterNdUpdate<bool, int32_t>(const size_t &unit_size, const size_t &num_units,
|
||||
const size_t &index_depth, const int32_t *out_strides,
|
||||
const int32_t *indices, const bool *updates, bool *input,
|
||||
cudaStream_t cuda_stream);
|
||||
|
|
@ -4479,7 +4479,7 @@ class ScatterNdAdd(_ScatterNdOp):
|
|||
ValueError: If the shape of `updates` is not equal to `indices_shape[:-1] + x_shape[indices_shape[-1]:]`.
|
||||
|
||||
Supported Platforms:
|
||||
``Ascend``
|
||||
``Ascend`` ``GPU``
|
||||
|
||||
Examples:
|
||||
>>> input_x = Parameter(Tensor(np.array([1, 2, 3, 4, 5, 6, 7, 8]), mindspore.float32), name="x")
|
||||
|
|
@ -4556,7 +4556,7 @@ class ScatterNdSub(_ScatterNdOp):
|
|||
ValueError: If the shape of `updates` is not equal to `indices_shape[:-1] + x_shape[indices_shape[-1]:]`.
|
||||
|
||||
Supported Platforms:
|
||||
``Ascend``
|
||||
``Ascend`` ``GPU``
|
||||
|
||||
Examples:
|
||||
>>> input_x = Parameter(Tensor(np.array([1, 2, 3, 4, 5, 6, 7, 8]), mindspore.float32), name="x")
|
||||
|
|
|
|||
|
|
@ -0,0 +1,234 @@
|
|||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
import mindspore.context as context
|
||||
import mindspore.nn as nn
|
||||
from mindspore import Tensor, Parameter
|
||||
import mindspore.common.dtype as mstype
|
||||
import mindspore.ops as ops
|
||||
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
||||
|
||||
func_map = {
|
||||
"update": ops.ScatterNdUpdate,
|
||||
"add": ops.ScatterNdAdd,
|
||||
"sub": ops.ScatterNdSub,
|
||||
}
|
||||
|
||||
|
||||
class TestScatterNdFuncNet(nn.Cell):
|
||||
def __init__(self, func, lock, inputx, indices, updates):
|
||||
super(TestScatterNdFuncNet, self).__init__()
|
||||
|
||||
self.scatter_func = func_map[func](use_locking=lock)
|
||||
self.inputx = Parameter(inputx, name="inputx")
|
||||
self.indices = Parameter(indices, name="indices")
|
||||
self.updates = Parameter(updates, name="updates")
|
||||
|
||||
def construct(self):
|
||||
out = self.scatter_func(self.inputx, self.indices, self.updates)
|
||||
return out
|
||||
|
||||
|
||||
def scatter_nd_func_net(func, inputx, indices, updates):
|
||||
lock = True
|
||||
net = TestScatterNdFuncNet(func, lock, inputx, indices, updates)
|
||||
return net()
|
||||
|
||||
|
||||
def scatter_nd_func_use_locking_false_net(func, inputx, indices, updates):
|
||||
lock = False
|
||||
net = TestScatterNdFuncNet(func, lock, inputx, indices, updates)
|
||||
return net()
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_traning
|
||||
@pytest.mark.env_onecard
|
||||
def test_scatter_nd_func_small_float32():
|
||||
inputx = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mstype.float32)
|
||||
indices = Tensor(np.array([[0, 0], [1, 1]]), mstype.int32)
|
||||
updates = Tensor(np.array([1.0, 2.2]), mstype.float32)
|
||||
|
||||
# update
|
||||
output = scatter_nd_func_net("update", inputx, indices, updates)
|
||||
expected = np.array([[1.0, 0.3, 3.6], [0.4, 2.2, -3.2]])
|
||||
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
||||
|
||||
# add
|
||||
output = scatter_nd_func_net("add", inputx, indices, updates)
|
||||
expected = np.array([[0.9, 0.3, 3.6], [0.4, 2.7, -3.2]])
|
||||
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
||||
|
||||
# sub
|
||||
output = scatter_nd_func_net("sub", inputx, indices, updates)
|
||||
expected = np.array([[-1.1, 0.3, 3.6], [0.4, -1.7, -3.2]])
|
||||
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_scatter_nd_func_input_updated():
|
||||
inputx = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mstype.float32)
|
||||
indices = Tensor(np.array([[0, 0], [1, 1]]), mstype.int32)
|
||||
updates = Tensor(np.array([1.0, 2.2]), mstype.float32)
|
||||
lock = True
|
||||
|
||||
# update
|
||||
net = TestScatterNdFuncNet("update", lock, inputx, indices, updates)
|
||||
net()
|
||||
expected = np.array([[1.0, 0.3, 3.6], [0.4, 2.2, -3.2]])
|
||||
np.testing.assert_array_almost_equal(net.inputx.asnumpy(), expected)
|
||||
|
||||
# add
|
||||
net = TestScatterNdFuncNet("add", lock, inputx, indices, updates)
|
||||
net()
|
||||
expected = np.array([[0.9, 0.3, 3.6], [0.4, 2.7, -3.2]])
|
||||
np.testing.assert_array_almost_equal(net.inputx.asnumpy(), expected)
|
||||
|
||||
# sub
|
||||
net = TestScatterNdFuncNet("sub", lock, inputx, indices, updates)
|
||||
net()
|
||||
expected = np.array([[-1.1, 0.3, 3.6], [0.4, -1.7, -3.2]])
|
||||
np.testing.assert_array_almost_equal(net.inputx.asnumpy(), expected)
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_traning
|
||||
@pytest.mark.env_onecard
|
||||
def test_scatter_nd_func_small_float32_using_locking_false():
|
||||
inputx = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mstype.float32)
|
||||
indices = Tensor(np.array([[0, 0], [1, 1]]), mstype.int32)
|
||||
updates = Tensor(np.array([1.0, 2.2]), mstype.float32)
|
||||
|
||||
# update
|
||||
output = scatter_nd_func_use_locking_false_net("update", inputx, indices, updates)
|
||||
expected = np.array([[1.0, 0.3, 3.6], [0.4, 2.2, -3.2]])
|
||||
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
||||
|
||||
# add
|
||||
output = scatter_nd_func_use_locking_false_net("add", inputx, indices, updates)
|
||||
expected = np.array([[0.9, 0.3, 3.6], [0.4, 2.7, -3.2]])
|
||||
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
||||
|
||||
# sub
|
||||
output = scatter_nd_func_use_locking_false_net("sub", inputx, indices, updates)
|
||||
expected = np.array([[-1.1, 0.3, 3.6], [0.4, -1.7, -3.2]])
|
||||
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_traning
|
||||
@pytest.mark.env_onecard
|
||||
def test_scatter_nd_func_small_int32():
|
||||
inputx = Tensor(np.array([1, 2, 3, 4, 5, 6, 7, 8]), mstype.float32)
|
||||
indices = Tensor(np.array([[4], [3], [1], [7]]), mstype.int32)
|
||||
updates = Tensor(np.array([9, 10, 11, 12]), mstype.float32)
|
||||
|
||||
# update
|
||||
output = scatter_nd_func_net("update", inputx, indices, updates)
|
||||
expected = np.array([1, 11, 3, 10, 9, 6, 7, 12])
|
||||
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
||||
|
||||
# add
|
||||
output = scatter_nd_func_net("add", inputx, indices, updates)
|
||||
expected = np.array([1, 13, 3, 14, 14, 6, 7, 20])
|
||||
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
||||
|
||||
# sub
|
||||
output = scatter_nd_func_net("sub", inputx, indices, updates)
|
||||
expected = np.array([1, -9, 3, -6, -4, 6, 7, -4])
|
||||
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_traning
|
||||
@pytest.mark.env_onecard
|
||||
def test_scatter_nd_func_multi_dims():
|
||||
inputx = Tensor(np.zeros((4, 4, 4)), mstype.float32)
|
||||
indices = Tensor(np.array([[0], [2]]), mstype.int32)
|
||||
updates = Tensor(
|
||||
np.array(
|
||||
[
|
||||
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
|
||||
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
|
||||
]
|
||||
),
|
||||
mstype.float32,
|
||||
)
|
||||
|
||||
# update
|
||||
output = scatter_nd_func_net("update", inputx, indices, updates)
|
||||
expected = np.array(
|
||||
[
|
||||
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
|
||||
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
|
||||
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
|
||||
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
|
||||
]
|
||||
)
|
||||
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
||||
|
||||
# add
|
||||
output = scatter_nd_func_net("add", inputx, indices, updates)
|
||||
expected = np.array(
|
||||
[
|
||||
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
|
||||
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
|
||||
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
|
||||
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
|
||||
]
|
||||
)
|
||||
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
||||
|
||||
# sub
|
||||
output = scatter_nd_func_net("sub", inputx, indices, updates)
|
||||
expected = np.array(
|
||||
[
|
||||
[[-5, -5, -5, -5], [-6, -6, -6, -6], [-7, -7, -7, -7], [-8, -8, -8, -8]],
|
||||
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
|
||||
[[-5, -5, -5, -5], [-6, -6, -6, -6], [-7, -7, -7, -7], [-8, -8, -8, -8]],
|
||||
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
|
||||
]
|
||||
)
|
||||
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_traning
|
||||
@pytest.mark.env_onecard
|
||||
def test_scatter_nd_func_one_value():
|
||||
inputx = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mstype.float32)
|
||||
indices = Tensor(np.array([[0, 1]]), mstype.int32)
|
||||
updates = Tensor(np.array([1.0]), mstype.float32)
|
||||
|
||||
# update
|
||||
output = scatter_nd_func_net("update", inputx, indices, updates)
|
||||
expected = np.array([[-0.1, 1.0, 3.6], [0.4, 0.5, -3.2]])
|
||||
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
||||
|
||||
# add
|
||||
output = scatter_nd_func_net("add", inputx, indices, updates)
|
||||
expected = np.array([[-0.1, 1.3, 3.6], [0.4, 0.5, -3.2]])
|
||||
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
||||
|
||||
# sub
|
||||
output = scatter_nd_func_net("sub", inputx, indices, updates)
|
||||
expected = np.array([[-0.1, -0.7, 3.6], [0.4, 0.5, -3.2]])
|
||||
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
|
||||
|
|
@ -1,131 +0,0 @@
|
|||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
import mindspore.context as context
|
||||
import mindspore.nn as nn
|
||||
from mindspore import Tensor, Parameter
|
||||
import mindspore.common.dtype as mstype
|
||||
import mindspore.ops as ops
|
||||
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_traning
|
||||
@pytest.mark.env_onecard
|
||||
def test_op1():
|
||||
class ScatterNdUpdate(nn.Cell):
|
||||
def __init__(self):
|
||||
super(ScatterNdUpdate, self).__init__()
|
||||
self.scatter_nd_update = ops.ScatterNdUpdate()
|
||||
self.x = Parameter(
|
||||
Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mstype.float32), name="x"
|
||||
)
|
||||
|
||||
def construct(self, indices, update):
|
||||
return self.scatter_nd_update(self.x, indices, update)
|
||||
|
||||
indices = Tensor(np.array([[0, 0], [1, 1]]), mstype.int32)
|
||||
update = Tensor(np.array([1.0, 2.2]), mstype.float32)
|
||||
|
||||
scatter_nd_update = ScatterNdUpdate()
|
||||
scatter_nd_update(indices, update)
|
||||
expect = [[1.0, 0.3, 3.6], [0.4, 2.2, -3.2]]
|
||||
assert np.allclose(scatter_nd_update.x.data.asnumpy(), np.array(expect, np.float))
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_traning
|
||||
@pytest.mark.env_onecard
|
||||
def test_op2():
|
||||
class ScatterNdUpdate(nn.Cell):
|
||||
def __init__(self):
|
||||
super(ScatterNdUpdate, self).__init__()
|
||||
self.scatter_nd_update = ops.ScatterNdUpdate()
|
||||
self.x = Parameter(Tensor(np.array([1, 2, 3, 4, 5, 6, 7, 8]), mstype.float32), name="x")
|
||||
|
||||
def construct(self, indices, update):
|
||||
return self.scatter_nd_update(self.x, indices, update)
|
||||
|
||||
indices = Tensor(np.array([[4], [3], [1], [7]]), mstype.int32)
|
||||
update = Tensor(np.array([9, 10, 11, 12]), mstype.float32)
|
||||
|
||||
scatter_nd_update = ScatterNdUpdate()
|
||||
scatter_nd_update(indices, update)
|
||||
expect = [1, 11, 3, 10, 9, 6, 7, 12]
|
||||
assert np.allclose(scatter_nd_update.x.data.asnumpy(), np.array(expect, dtype=float))
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_traning
|
||||
@pytest.mark.env_onecard
|
||||
def test_op3():
|
||||
class ScatterNdUpdate(nn.Cell):
|
||||
def __init__(self):
|
||||
super(ScatterNdUpdate, self).__init__()
|
||||
self.scatter_nd_update = ops.ScatterNdUpdate()
|
||||
self.x = Parameter(Tensor(np.zeros((4, 4, 4)), mstype.float32), name="x")
|
||||
|
||||
def construct(self, indices, update):
|
||||
return self.scatter_nd_update(self.x, indices, update)
|
||||
|
||||
indices = Tensor(np.array([[0], [2]]), mstype.int32)
|
||||
update = Tensor(
|
||||
np.array(
|
||||
[
|
||||
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
|
||||
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
|
||||
]
|
||||
),
|
||||
mstype.float32,
|
||||
)
|
||||
|
||||
scatter_nd_update = ScatterNdUpdate()
|
||||
scatter_nd_update(indices, update)
|
||||
expect = [
|
||||
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
|
||||
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
|
||||
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
|
||||
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
|
||||
]
|
||||
assert np.allclose(scatter_nd_update.x.data.asnumpy(), np.array(expect, dtype=float))
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_traning
|
||||
@pytest.mark.env_onecard
|
||||
def test_op4():
|
||||
class ScatterNdUpdate(nn.Cell):
|
||||
def __init__(self):
|
||||
super(ScatterNdUpdate, self).__init__()
|
||||
self.scatter_nd_update = ops.ScatterNdUpdate()
|
||||
self.x = Parameter(
|
||||
Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mstype.float32), name="x"
|
||||
)
|
||||
|
||||
def construct(self, indices, update):
|
||||
return self.scatter_nd_update(self.x, indices, update)
|
||||
|
||||
indices = Tensor(np.array([[0, 1]]), mstype.int32)
|
||||
update = Tensor(np.array([1.0]), mstype.float32)
|
||||
|
||||
scatter_nd_update = ScatterNdUpdate()
|
||||
out = scatter_nd_update(indices, update)
|
||||
assert np.allclose(out.asnumpy(), scatter_nd_update.x.data.asnumpy())
|
||||
expect = [[-0.1, 1.0, 3.6], [0.4, 0.5, -3.2]]
|
||||
assert np.allclose(out.asnumpy(), np.array(expect, np.float))
|
||||
Loading…
Reference in New Issue