# Tencent is pleased to support the open source community by making Angel available.
#
# Copyright (C) 2017-2018 THL A29 Limited, a Tencent company. All rights reserved.
#
# 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
#
# https://opensource.org/licenses/Apache-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 torch
from utils import scatter_add


@torch.jit.script
def spmm(index, value, m, matrix):
    # type: (Tensor, Tensor, int, Tensor) -> Tensor
    row, col = index[0], index[1]
    matrix = matrix if matrix.dim() > 1 else matrix.unsqueeze(-1)

    out = matrix[col]
    out = out * value.unsqueeze(-1)
    out = scatter_add(out, row, dim=0, dim_size=m)

    return out

bf4f0bcd3999acaf28cee60f01962e9c6b36f067
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@torch.jit.script
def spmm(index, value, m, matrix):
    row, col = index[0], index[1]
    matrix = matrix if matrix.dim() > 1 else matrix.unsqueeze(-1)
    out = matrix[col]
    out = out * value.unsqueeze(-1)
    out = scatter_add(out, row, dim=0, dim_size=m)
    return out

import torch
from utils import scatter_add


@torch.jit.script
def spmm(index, value, m, matrix):
    row, col = index[0], index[1]
    matrix = matrix if matrix.dim() > 1 else matrix.unsqueeze(-1)
    out = matrix[col]
    out = out * value.unsqueeze(-1)
    out = scatter_add(out, row, dim=0, dim_size=m)
    return out

# Tencent is pleased to support the open source community by making Angel available.
#
# Copyright (C) 2017-2018 THL A29 Limited, a Tencent company. All rights reserved.
#
# 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
#
# https://opensource.org/licenses/Apache-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 torch
from utils import scatter_add


@torch.jit.script
def spmm(index, value, m, matrix):
    # type: (Tensor, Tensor, int, Tensor) -> Tensor
    row, col = index[0], index[1]
    matrix = matrix if matrix.dim() > 1 else matrix.unsqueeze(-1)

    out = matrix[col]
    out = out * value.unsqueeze(-1)
    out = scatter_add(out, row, dim=0, dim_size=m)

    return out
