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[XPU][Fleet] Support multi-card infer for xpu (#50490)
* support xpu multi-card infer * add ut * clean code * clean code * fix * fix * fix * fix
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73 changes: 73 additions & 0 deletions
73
paddle/fluid/inference/tests/api/analyzer_dist_model_xpu_tester.cc
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| // Copyright (c) 2023 PaddlePaddle Authors. 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 | ||
| // | ||
| // http://www.apache.org/licenses/LICENSE-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. | ||
|
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| #include "gtest/gtest.h" | ||
| #include "paddle/fluid/framework/block_desc.h" | ||
| #include "paddle/fluid/framework/op_desc.h" | ||
| #include "paddle/fluid/framework/program_desc.h" | ||
| #include "paddle/fluid/framework/scope.h" | ||
| #include "paddle/fluid/inference/tests/api/tester_helper.h" | ||
| #include "paddle/fluid/inference/utils/singleton.h" | ||
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| namespace paddle { | ||
| namespace inference { | ||
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| TEST(test_dist_model_xpu, dist_model_xpu) { | ||
| std::cout << "Analysis Predictor DistModel XPU test." << std::endl; | ||
| AnalysisConfig config; | ||
| config.SetModel(FLAGS_infer_model + "/__model__", | ||
| FLAGS_infer_model + "/__params__"); | ||
| config.SwitchUseFeedFetchOps(false); | ||
| config.EnableXpu(); | ||
| config.SetXpuDeviceId(0); | ||
| DistConfig dist_config; | ||
| dist_config.SetRanks(1, 0); | ||
| dist_config.EnableDistModel(true); | ||
| dist_config.SetEndpoints({""}, ""); | ||
| config.SetDistConfig(dist_config); | ||
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| auto predictor = paddle_infer::CreatePredictor(config); | ||
| int batch_size = 1; | ||
| int channels = 1; | ||
| int height = 48; | ||
| int width = 512; | ||
| int nums = batch_size * channels * height * width; | ||
| std::cout << "Created predictor." << std::endl; | ||
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| float* input = new float[nums]; | ||
| for (int i = 0; i < nums; ++i) input[i] = 0; | ||
| auto input_names = predictor->GetInputNames(); | ||
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| auto input_t = predictor->GetInputHandle(input_names[0]); | ||
| input_t->Reshape({batch_size, channels, height, width}); | ||
| input_t->CopyFromCpu(input); | ||
| std::cout << "Input data." << std::endl; | ||
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| predictor->Run(); | ||
| std::cout << "Zero Copy Run." << std::endl; | ||
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| std::vector<float> out_data; | ||
| auto output_names = predictor->GetOutputNames(); | ||
| auto output_t = predictor->GetOutputHandle(output_names[0]); | ||
| std::vector<int> output_shape = output_t->shape(); | ||
| int out_num = std::accumulate( | ||
| output_shape.begin(), output_shape.end(), 1, std::multiplies<int>()); | ||
| out_data.resize(out_num); | ||
| output_t->CopyToCpu(out_data.data()); | ||
| std::cout << "Output data." << std::endl; | ||
| delete[] input; | ||
| } | ||
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| } // namespace inference | ||
| } // namespace paddle |
118 changes: 118 additions & 0 deletions
118
paddle/fluid/operators/collective/c_broadcast_op_xpu.cc
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| /* Copyright (c) 2023 PaddlePaddle Authors. 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 | ||
| http://www.apache.org/licenses/LICENSE-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. */ | ||
|
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| #include "paddle/fluid/operators/collective/c_broadcast_op.h" | ||
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| #ifdef PADDLE_WITH_XPU_BKCL | ||
| #include "paddle/fluid/platform/collective_helper.h" | ||
| #include "paddle/fluid/platform/device/xpu/bkcl_helper.h" | ||
| #endif | ||
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| namespace paddle { | ||
| namespace operators { | ||
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| template <typename T> | ||
| class CBroadcastOpXPUKernel : public framework::OpKernel<T> { | ||
| public: | ||
| void Compute(const framework::ExecutionContext& ctx) const override { | ||
| #if defined(PADDLE_WITH_XPU_BKCL) | ||
| auto x = ctx.Input<phi::DenseTensor>("X"); | ||
| auto out = ctx.Output<phi::DenseTensor>("Out"); | ||
| size_t numel = x->numel(); | ||
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| BKCLDataType dtype = | ||
| platform::ToBKCLDataType(framework::TransToProtoVarType(x->dtype())); | ||
| int ring_id = ctx.Attr<int>("ring_id"); | ||
| auto place = ctx.GetPlace(); | ||
| auto comm = | ||
| paddle::platform::BKCLCommContext::Instance().Get(ring_id, place); | ||
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| XPUStream stream = nullptr; | ||
| auto dev_ctx = platform::DeviceContextPool::Instance().Get(place); | ||
| if (ctx.Attr<bool>("use_calc_stream")) { | ||
| stream = static_cast<platform::XPUDeviceContext*>(dev_ctx) | ||
| ->x_context() | ||
| ->xpu_stream; | ||
| } else { | ||
| stream = comm->stream(); | ||
| } | ||
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| int root = ctx.Attr<int>("root"); | ||
| VLOG(3) << "begin bkcl broadcast, parameter is: " | ||
| << "root " << root << ", comm: " << comm->comm() | ||
| << ", stream: " << stream; | ||
| void* send_recv_buffer = nullptr; | ||
| if (root == comm->rank()) { | ||
| // API: BKCLResult_t bkcl_broadcast(const BKCLContext_t ctx, | ||
| // const void* sendbuf, | ||
| // void* recvbuf, | ||
| // size_t count, BKCLDataType datatype, | ||
| // int root, | ||
| // XPUStream stream); | ||
| send_recv_buffer = reinterpret_cast<void*>(const_cast<T*>(x->data<T>())); | ||
| auto ret = bkcl_broadcast(comm->comm(), | ||
| send_recv_buffer, | ||
| send_recv_buffer, | ||
| numel, | ||
| dtype, | ||
| root, | ||
| stream); | ||
| PADDLE_ENFORCE_EQ(ret, | ||
| BKCL_SUCCESS, | ||
| platform::errors::PreconditionNotMet( | ||
| "XPU BKCL c_broadcast execute failed")); | ||
| if (out != x) { | ||
| framework::TensorCopy( | ||
| *static_cast<const phi::DenseTensor*>(x), | ||
| place, | ||
| *platform::DeviceContextPool::Instance().Get(place), | ||
| static_cast<phi::DenseTensor*>(out)); | ||
| } | ||
| } else { | ||
| auto& dev_ctx = ctx.template device_context<platform::XPUDeviceContext>(); | ||
| dev_ctx.template Alloc<T>(out); | ||
| send_recv_buffer = out->data<T>(); | ||
| auto ret = bkcl_broadcast(comm->comm(), | ||
| send_recv_buffer, | ||
| send_recv_buffer, | ||
| numel, | ||
| dtype, | ||
| root, | ||
| stream); | ||
| PADDLE_ENFORCE_EQ(ret, | ||
| BKCL_SUCCESS, | ||
| platform::errors::PreconditionNotMet( | ||
| "XPU BKCL c_broadcast execute failed")); | ||
| } | ||
|
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| VLOG(3) << "rank " << comm->rank() << " invoke Bcast. received " | ||
| << phi::product(out->dims()); | ||
| out->Resize(x->dims()); | ||
| out->set_lod(x->lod()); | ||
| #else | ||
| PADDLE_THROW(platform::errors::PreconditionNotMet( | ||
| "PaddlePaddle should be compiled with XPU and BKCL.")); | ||
| #endif | ||
| } | ||
| }; | ||
|
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| } // namespace operators | ||
| } // namespace paddle | ||
|
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| namespace ops = paddle::operators; | ||
| namespace plat = paddle::platform; | ||
|
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| REGISTER_OP_XPU_KERNEL(c_broadcast, | ||
| ops::CBroadcastOpXPUKernel<float>, | ||
| ops::CBroadcastOpXPUKernel<plat::float16>); |
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