wehub-resource-sync
148 lines
4.6 KiB
Plaintext
148 lines
4.6 KiB
Plaintext
// 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/p_recv_kernel.h"
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#include "glog/logging.h"
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#include "paddle/phi/backends/all_context.h"
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#include "paddle/phi/common/memory_utils.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/send_recv_functor.h"
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#if defined(PADDLE_WITH_NCCL) || \
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defined(PADDLE_WITH_RCCL) && NCCL_VERSION_CODE >= 2703
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#include "paddle/phi/core/distributed/nccl_comm_context.h"
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#endif
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namespace phi {
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template <typename T, typename Context>
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void PRecvKernel(const Context& dev_ctx,
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int peer,
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DataType dtype,
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const std::vector<int>& out_shape,
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bool dynamic_shape,
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DenseTensor* out) {
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#if defined(PADDLE_WITH_NCCL) || \
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defined(PADDLE_WITH_RCCL) && NCCL_VERSION_CODE >= 2703
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auto comm_ctx =
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GetCommContext<Context, distributed::NCCLCommContext>(dev_ctx, peer);
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gpuStream_t stream = dev_ctx.stream();
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// auto data_type = TransToPhiDataType(dtype);
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if (dynamic_shape) {
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DDim new_dim =
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recv_shape_info<Context, distributed::NCCLCommContext, gpuStream_t>(
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dev_ctx, out, comm_ctx, peer);
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out->Resize(new_dim);
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}
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dev_ctx.Alloc(out, dtype);
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comm_ctx->Recv(out, out->numel(), peer, stream);
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#else
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PADDLE_THROW(
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errors::PreconditionNotMet("PaddlePaddle should compile with GPU."
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"and NCCL version >= 2.7.3 is needed."));
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#endif
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}
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template <typename T, typename Context>
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void PRecvArrayKernel(const Context& dev_ctx,
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int peer,
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DataType dtype,
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const std::vector<int>& out_shape,
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TensorArray* out_array) {
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#if defined(PADDLE_WITH_NCCL) || \
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defined(PADDLE_WITH_RCCL) && NCCL_VERSION_CODE >= 2703
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auto comm_ctx =
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GetCommContext<Context, distributed::NCCLCommContext>(dev_ctx, peer);
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gpuStream_t stream = dev_ctx.stream();
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for (size_t idx = 0; idx < out_shape.size(); ++idx) {
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VLOG(3) << "DenseTensorArray: idx(" << idx << ")";
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auto out = out_array->at(idx);
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auto out_dims = out.dims();
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dev_ctx.Alloc(&out, dtype);
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comm_ctx->Recv(&out, out.numel(), peer, stream);
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VLOG(3) << "rank " << comm_ctx->GetRank() << " recv "
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<< common::product(out_dims) << " from " << peer;
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}
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#else
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PADDLE_THROW(
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errors::PreconditionNotMet("PaddlePaddle should compile with GPU."
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"and NCCL version >= 2.7.3 is needed."));
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#endif
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}
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} // namespace phi
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#if NCCL_VERSION_CODE >= 21000
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PD_REGISTER_KERNEL(p_recv,
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GPU,
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ALL_LAYOUT,
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phi::PRecvKernel,
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float,
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double,
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int,
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bool,
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int8_t,
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uint8_t,
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int16_t,
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int64_t,
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phi::bfloat16,
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phi::float16) {}
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PD_REGISTER_KERNEL(p_recv_array,
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GPU,
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ALL_LAYOUT,
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phi::PRecvArrayKernel,
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float,
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double,
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int,
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bool,
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int8_t,
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uint8_t,
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int64_t,
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phi::bfloat16,
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phi::float16) {}
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#else
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PD_REGISTER_KERNEL(p_recv,
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GPU,
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ALL_LAYOUT,
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phi::PRecvKernel,
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float,
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double,
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int,
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bool,
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int8_t,
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uint8_t,
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int16_t,
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int64_t,
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phi::float16) {}
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PD_REGISTER_KERNEL(p_recv_array,
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GPU,
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ALL_LAYOUT,
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phi::PRecvArrayKernel,
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float,
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double,
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int,
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bool,
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int8_t,
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uint8_t,
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int64_t,
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phi::float16) {}
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#endif
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