wehub-resource-sync
99 lines
3.2 KiB
C++
99 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/p_send_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/core/utils/data_type.h"
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#include "paddle/phi/kernels/funcs/send_recv_functor.h"
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#if defined(PADDLE_WITH_XPU_BKCL)
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#include "paddle/phi/core/distributed/bkcl_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 PSendKernel(const Context& dev_ctx,
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const DenseTensor& x,
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int peer,
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bool dynamic_shape) {
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#if defined(PADDLE_WITH_XPU_BKCL)
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auto comm_ctx =
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GetCommContext<Context, distributed::BKCLCommContext>(dev_ctx, peer);
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XPUStream stream = dev_ctx.stream();
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if (dynamic_shape) {
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send_shape_info<Context, distributed::BKCLCommContext, XPUStream>(
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dev_ctx, x, comm_ctx, peer, stream);
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}
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comm_ctx->Send(x, x.numel(), peer, stream);
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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 p_send kernel."));
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#endif
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}
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template <typename T, typename Context>
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void PSendArrayKernel(const Context& dev_ctx,
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const TensorArray& x_array,
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int peer) {
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#if defined(PADDLE_WITH_XPU_BKCL)
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auto comm_ctx =
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GetCommContext<Context, distributed::BKCLCommContext>(dev_ctx, peer);
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XPUStream stream = dev_ctx.stream();
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for (size_t idx = 0; idx < x_array.size(); idx++) {
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VLOG(3) << "DenseTensorArray: idx(" << idx << ")";
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auto x = x_array.at(idx);
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comm_ctx->Send(x, x.numel(), peer, stream);
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VLOG(3) << "rank " << comm_ctx->GetRank() << " send "
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<< common::product(x.dims()) << " to " << peer;
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}
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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 p_send_array kernel."));
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#endif
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}
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} // namespace phi
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PD_REGISTER_KERNEL(p_send,
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XPU,
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ALL_LAYOUT,
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phi::PSendKernel,
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float,
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double,
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uint8_t,
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int,
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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_send_array,
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XPU,
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ALL_LAYOUT,
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phi::PSendArrayKernel,
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float,
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double,
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uint8_t,
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int,
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int64_t,
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phi::bfloat16,
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phi::float16) {}
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