wehub-resource-sync
93 lines
3.2 KiB
C++
93 lines
3.2 KiB
C++
// Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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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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#include "paddle/phi/kernels/c_split_kernel.h"
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#include "paddle/phi/backends/xpu/enforce_xpu.h"
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#include "paddle/phi/core/kernel_registry.h"
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namespace phi {
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template <typename T, typename Context>
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void CSplitKernel(const Context& dev_ctx,
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const DenseTensor& x,
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int rank,
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int nranks,
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bool use_model_parallel,
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DenseTensor* out) {
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#if defined(PADDLE_WITH_XPU_BKCL)
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using XPUType = typename XPUTypeTrait<T>::Type;
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PADDLE_ENFORCE_GE(rank,
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0,
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common::errors::PreconditionNotMet(
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"The value of rank (%d) for c_split must be "
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"greater than or equal to 0.",
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rank));
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PADDLE_ENFORCE_GE(nranks,
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2,
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common::errors::PreconditionNotMet(
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"The value of nranks (%d) for c_split must be "
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"greater than or equal to 2.",
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nranks));
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PADDLE_ENFORCE_LT(rank,
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nranks,
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common::errors::PreconditionNotMet(
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"The value of rank (%d) for c_split must be "
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"less than that of nranks (%d).",
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rank,
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nranks));
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auto dims = x.dims();
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auto dims_size = dims.size();
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// final dim
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int64_t end_size = dims[dims_size - 1];
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// remain dim
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auto remain_ddim = slice_ddim(dims, 0, dims_size - 1);
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int64_t remain_numel = common::product(remain_ddim);
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dims[dims_size - 1] /= nranks;
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out->Resize(dims);
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dev_ctx.Alloc(out, x.dtype());
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std::vector<XPUType*> output_list(nranks, nullptr);
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output_list.at(rank) = reinterpret_cast<XPUType*>(out->data<T>());
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std::vector<int64_t> split_list(nranks, dims[dims_size - 1]);
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int axis = 1;
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auto ret = xpu::split(dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(x.data<T>()),
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output_list,
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{remain_numel, end_size},
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split_list,
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axis);
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PADDLE_ENFORCE_XDNN_SUCCESS(ret, "split");
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#else
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PADDLE_THROW(common::errors::PreconditionNotMet(
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"PaddlePaddle is not compiled with DWITH_XPU_BKCL, please recompile with "
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"DWITH_XPU_BKCL for using c_split_kernel."));
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#endif
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}
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} // namespace phi
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PD_REGISTER_KERNEL(c_split,
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XPU,
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ALL_LAYOUT,
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phi::CSplitKernel,
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float,
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int,
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phi::float16,
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phi::bfloat16) {}
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