wehub-resource-sync
78 lines
2.7 KiB
C++
78 lines
2.7 KiB
C++
// Copyright (c) 2025 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/api/backward/backward_api_base.h"
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#include "paddle/phi/api/include/api.h"
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#include "paddle/phi/backends/all_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/core/tensor_utils.h"
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#ifdef PADDLE_WITH_CUSTOM_DEVICE
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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_in,
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int rank,
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int nranks,
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bool use_model_parallel UNUSED,
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DenseTensor* out) {
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auto x = &x_in;
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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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dims[dims_size - 1] /= nranks;
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dev_ctx.template Alloc<T>(out);
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out->Resize(dims);
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std::vector<int64_t> split_list(nranks, dims[dims_size - 1]);
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int axis = dims_size - 1;
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auto x_tmp = std::make_shared<DenseTensor>();
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x_tmp->ShareDataWith(*x);
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paddle::Tensor x_tensor(x_tmp);
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auto outputs = paddle::experimental::split(x_tensor, split_list, axis);
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out->ShareDataWith(
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*reinterpret_cast<DenseTensor*>(outputs[rank].impl().get()));
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}
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} // namespace phi
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PD_REGISTER_KERNEL(c_split,
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Custom,
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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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#endif
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