[Kunlun] 2.0 cherry-pick:Support for Baidu Kunlun XPU multi card training (#29713)

* [Kunlun] PR1:Support one Kunlun card training in parallel executor (#29337)

* [Kunlun] PR2: Support MultiDevicePass and BKCL in parallel executor (#29574)

* [Kunlun] bug fix of PR2: Support MultiDevicePass and BKCL in parallel executor  (#29926)

* add bkcl.so in whl for kunlun (#29947)

* [Kunlun] bug fix of PR2: Support MultiDevicePass and BKCL in parallel executor  (#29961)
Co-authored-by: NQingshuChen <qingshu.chen714@gmail.com>

CMakeLists.txt

@@ -29,7 +29,8 @@ include(generic)            # simplify cmake module
 find_package(CUDA QUIET)
 option(WITH_GPU         "Compile PaddlePaddle with NVIDIA GPU"          ${CUDA_FOUND})
 option(WITH_TENSORRT    "Compile PaddlePaddle with NVIDIA TensorRT"     OFF)
-option(WITH_XPU         "Compile PaddlePaddle with BAIDU KUNLUN"        OFF)
+option(WITH_XPU         "Compile PaddlePaddle with BAIDU KUNLUN XPU"    OFF)
+option(WITH_WIN_DUMP_DBG "Compile with windows core dump debug mode"    OFF)
 if (WITH_GPU  AND WITH_XPU)
     message(FATAL_ERROR "Error when compile GPU and XPU at the same time")
 endif()
@@ -141,6 +142,7 @@ option(WITH_DGC   "Use DGC(Deep Gradient Compression) or not" ${WITH_DISTRIBUTE}
 option(SANITIZER_TYPE "Choose the type of sanitizer, options are: Address, Leak, Memory, Thread, Undefined" OFF)
 option(WITH_LITE   "Compile Paddle Fluid with Lite Engine" OFF)
 option(WITH_NCCL   "Compile PaddlePaddle with NCCL support"             ON)
+option(WITH_XPU_BKCL    "Compile PaddlePaddle with BAIDU KUNLUN XPU BKCL"   OFF)
 option(WITH_CRYPTO   "Compile PaddlePaddle with crypto support"         ON)
 option(WITH_ARM   "Compile PaddlePaddle with arm support"         OFF)
 option(WITH_SW   "Compile PaddlePaddle with sw support"         OFF)
@@ -188,6 +190,13 @@ if (NOT WITH_GPU AND WITH_NCCL)
         "Disable NCCL when compiling without GPU" FORCE)
 endif()
 
+if (NOT WITH_XPU AND WITH_XPU_BKCL)
+    MESSAGE(WARNING
+        "Disable BKCL when compiling without XPU. Force WITH_XPU_BKCL=OFF.")
+    set(WITH_XPU_BKCL OFF CACHE STRING
+        "Disable BKCL when compiling without XPU" FORCE)
+endif()
+
 if(WITH_NCCL)
      add_definitions("-DPADDLE_WITH_NCCL")
      include(nccl)

cmake/external/xpu.cmake

@@ -47,4 +47,18 @@ set_property(TARGET shared_xpuapi PROPERTY IMPORTED_LOCATION "${XPU_API_LIB}")
 generate_dummy_static_lib(LIB_NAME "xpulib" GENERATOR "xpu.cmake")
 
 TARGET_LINK_LIBRARIES(xpulib ${XPU_API_LIB} ${XPU_RT_LIB})
+
+if (WITH_XPU_BKCL)
+  MESSAGE(STATUS "Compile with XPU BKCL!")
+  ADD_DEFINITIONS(-DPADDLE_WITH_XPU_BKCL)
+
+  SET(XPU_BKCL_LIB_NAME         "libbkcl.so")
+  SET(XPU_BKCL_LIB              "${XPU_LIB_DIR}/${XPU_BKCL_LIB_NAME}")
+  SET(XPU_BKCL_INC_DIR          "${THIRD_PARTY_PATH}/install/xpu/include")
+  INCLUDE_DIRECTORIES(${XPU_BKCL_INC_DIR})
+  TARGET_LINK_LIBRARIES(xpulib ${XPU_API_LIB} ${XPU_RT_LIB} ${XPU_BKCL_LIB})
+else(WITH_XPU_BKCL)
+  TARGET_LINK_LIBRARIES(xpulib ${XPU_API_LIB} ${XPU_RT_LIB} )
+endif(WITH_XPU_BKCL)
+
 ADD_DEPENDENCIES(xpulib ${XPU_PROJECT})

paddle/fluid/framework/details/all_reduce_op_handle.cc

@@ -43,6 +43,19 @@ AllReduceOpHandle::AllReduceOpHandle(ir::Node *node,
                         "number of local scopes is %d.",
                         places_.size(), local_scopes_.size()));
 }
+#elif defined(PADDLE_WITH_XPU_BKCL)
+AllReduceOpHandle::AllReduceOpHandle(ir::Node *node,
+                                     const std::vector<Scope *> &local_scopes,
+                                     const std::vector<platform::Place> &places,
+                                     const platform::BKCLCommunicator *ctxs)
+    : BKCLOpHandleBase(node, places, ctxs), local_scopes_(local_scopes) {
+  PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size(),
+                    platform::errors::InvalidArgument(
+                        "The number of places and the number of local scopes "
+                        "should be equal, but got number of places is %d and "
+                        "number of local scopes is %d.",
+                        places_.size(), local_scopes_.size()));
+}
 #else
 AllReduceOpHandle::AllReduceOpHandle(ir::Node *node,
                                      const std::vector<Scope *> &local_scopes,
@@ -98,6 +111,9 @@ void AllReduceOpHandle::AllReduceImpl(
   places.reserve(num_places);
   int64_t numel = -1;
   bool is_gpu_place = false;
+#if defined(PADDLE_WITH_XPU_BKCL)
+  bool is_xpu_place = false;
+#endif
   auto dtype = static_cast<framework::proto::VarType::Type>(0);
   for (size_t i = 0; i < local_exec_scopes_.size(); ++i) {
     auto &local_scope = local_exec_scopes_[i];
@@ -117,6 +133,9 @@ void AllReduceOpHandle::AllReduceImpl(
               in_var_handles[i]->name(), numel));
       dtype = lod_tensor.type();
       is_gpu_place = platform::is_gpu_place(lod_tensor.place());
+#if defined(PADDLE_WITH_XPU_BKCL)
+      is_xpu_place = platform::is_xpu_place(lod_tensor.place());
+#endif
     }
     PADDLE_ENFORCE_EQ(
         numel, static_cast<int64_t>(lod_tensor.numel()),
@@ -128,6 +147,12 @@ void AllReduceOpHandle::AllReduceImpl(
         platform::errors::PreconditionNotMet(
             "The dtype of tensors of the same variable in different local "
             "scopes should be equal."));
+#if defined(PADDLE_WITH_XPU_BKCL)
+    PADDLE_ENFORCE_EQ(is_xpu_place, platform::is_xpu_place(lod_tensor.place()),
+                      platform::errors::PreconditionNotMet(
+                          "The place type of tensors of the same variable "
+                          "in different local scopes should be equal."));
+#endif
     PADDLE_ENFORCE_EQ(is_gpu_place, platform::is_gpu_place(lod_tensor.place()),
                       platform::errors::PreconditionNotMet(
                           "The place type of tensors of the same variable "
@@ -179,6 +204,25 @@ void AllReduceOpHandle::AllReduceFunc(
 #else
     PADDLE_THROW(
         platform::errors::PreconditionNotMet("Not compiled with CUDA."));
+#endif
+  } else if (is_xpu_place(places[0])) {
+#if defined(PADDLE_WITH_XPU_BKCL)
+    PADDLE_ENFORCE_NOT_NULL(bkcl_ctxs_,
+                            platform::errors::InvalidArgument(
+                                "The bkcl context should not be NULL."));
+    BKCLDataType bkcl_dtype = platform::ToBKCLDataType(dtype);
+    std::vector<std::function<void()>> all_reduce_calls;
+    for (size_t i = 0; i < local_exec_scopes_.size(); ++i) {
+      auto &p = places[i];
+      void *buffer = const_cast<void *>(lod_tensor_data.at(i));
+      all_reduce_calls.emplace_back([=] {
+        BKCLAllReduce(p, buffer, buffer, numel, bkcl_dtype, BKCL_ADD);
+      });
+    }
+    BKCLAllReduceFunc(all_reduce_calls);
+#else
+    PADDLE_THROW(
+        platform::errors::PreconditionNotMet("Not compiled with BKCL."));
 #endif
   } else {  // Special handle CPU only Operator's gradient. Like CRF
     auto &trg = *local_exec_scopes_[0]
@@ -205,6 +249,27 @@ void AllReduceOpHandle::AllReduceFunc(
   VLOG(10) << Name() << " size:" << numel * SizeOfType(dtype);
 }
 
+#if defined(PADDLE_WITH_XPU_BKCL)
+void AllReduceOpHandle::BKCLAllReduceFunc(
+    const std::vector<std::function<void()>> &all_reduce_calls) {
+  this->RunAndRecordEvent([&] {
+    if (all_reduce_calls.size() == 1UL) {
+      all_reduce_calls[0]();
+    } else {
+      PADDLE_ENFORCE_EQ(
+          bkcl_group_start(), BKCL_SUCCESS,
+          platform::errors::PreconditionNotMet("bkcl_group_start failed"));
+      for (auto &call : all_reduce_calls) {
+        call();
+      }
+      PADDLE_ENFORCE_EQ(
+          bkcl_group_end(), BKCL_SUCCESS,
+          platform::errors::PreconditionNotMet("bkcl_group_end failed"));
+    }
+  });
+}
+#endif
+
 #if defined(PADDLE_WITH_NCCL)
 void AllReduceOpHandle::NCCLAllReduceFunc(
     const std::vector<std::function<void()>> &all_reduce_calls) {

paddle/fluid/framework/details/all_reduce_op_handle.h

@@ -34,6 +34,9 @@ class NCCLCommunicator;
 #if defined(PADDLE_WITH_NCCL)
 #include "paddle/fluid/framework/details/nccl_op_handle.h"
 #include "paddle/fluid/platform/nccl_helper.h"
+#elif defined(PADDLE_WITH_XPU_BKCL)
+#include "paddle/fluid/framework/details/bkcl_op_handle.h"
+#include "paddle/fluid/platform/bkcl_helper.h"
 #endif
 
 namespace paddle {
@@ -46,6 +49,12 @@ class AllReduceOpHandle : public NCCLOpHandleBase {
   AllReduceOpHandle(ir::Node *node, const std::vector<Scope *> &local_scopes,
                     const std::vector<platform::Place> &places,
                     const platform::NCCLCommunicator *ctxs);
+#elif defined(PADDLE_WITH_XPU_BKCL)
+class AllReduceOpHandle : public BKCLOpHandleBase {
+ public:
+  AllReduceOpHandle(ir::Node *node, const std::vector<Scope *> &local_scopes,
+                    const std::vector<platform::Place> &places,
+                    const platform::BKCLCommunicator *ctxs);
 #else
 class AllReduceOpHandle : public OpHandleBase {
  public:
@@ -65,8 +74,8 @@ class AllReduceOpHandle : public OpHandleBase {
 
   std::vector<Scope *> local_scopes_;
 
-#ifndef PADDLE_WITH_NCCL
-  // NCCLOpHandleBase already have these attributes.
+#if !(PADDLE_WITH_NCCL || PADDLE_WITH_XPU_BKCL)
+  // NCCLOpHandleBase and BKCLOpHandleBase already have these attributes.
   // Will polish it by class inheritance framework.
   std::vector<platform::Place> places_;
 #endif
@@ -78,6 +87,11 @@ class AllReduceOpHandle : public OpHandleBase {
   void SyncNCCLAllReduce();
 #endif
 
+#if defined(PADDLE_WITH_XPU_BKCL)
+  void BKCLAllReduceFunc(
+      const std::vector<std::function<void()>> &all_reduce_calls);
+#endif
+
   void AllReduceImpl(const std::vector<VarHandle *> &in_var_handles,
                      const std::vector<VarHandle *> &out_var_handles);
 

paddle/fluid/framework/details/bkcl_op_handle.h

+//   Copyright (c) 2020 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.
+
+#pragma once
+
+#include "xpu/bkcl.h"
+
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "paddle/fluid/framework/details/op_handle_base.h"
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/platform/bkcl_helper.h"
+
+DECLARE_bool(sync_bkcl_allreduce);
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+class BKCLOpHandleBase : public OpHandleBase {
+ public:
+  BKCLOpHandleBase(ir::Node* node, const std::vector<platform::Place>& places,
+                   const platform::BKCLCommunicator* bkcl_ctxs)
+      : OpHandleBase(node), places_(places), bkcl_ctxs_(bkcl_ctxs) {
+    if (bkcl_ctxs == nullptr) {
+      return;
+    }
+    // init device context
+    auto default_bkcl_ctxs = bkcl_ctxs_->DefaultFlatCtx();
+    for (auto& p : places_) {
+      this->SetDeviceContext(p, default_bkcl_ctxs->DevCtx(p));
+    }
+  }
+
+  virtual ~BKCLOpHandleBase() {}
+
+  void SetRunEnv(int run_order, bool use_hierarchical_allreduce) {
+    PADDLE_ENFORCE_GE(
+        run_order, 0,
+        platform::errors::InvalidArgument(
+            "The argument run_order must be >= 0, but got %d.", run_order));
+    PADDLE_ENFORCE_NE(use_hierarchical_allreduce, true,
+                      platform::errors::Unimplemented(
+                          "xpu doesn't support hierarchical_allreduce"));
+
+    run_order_ = run_order;
+    use_hierarchical_allreduce_ = use_hierarchical_allreduce;
+
+    VLOG(10) << "SetRunEnv "
+             << " run_order:" << run_order
+             << ", use_hierarchical_allreduce:" << use_hierarchical_allreduce;
+
+    if (bkcl_ctxs_ == nullptr) {
+      return;
+    }
+
+    if (!use_hierarchical_allreduce_) {
+      auto ctxs = bkcl_ctxs_->GetFlatCtx(run_order);
+      for (auto& p : places_) {
+        this->SetDeviceContext(p, ctxs->DevCtx(p));
+      }
+      return;
+    }
+  }
+
+  void FlatBKCLAllReduce(platform::Place place, const void* sendbuff,
+                         void* recvbuff, size_t count, BKCLDataType datatype,
+                         BKCLOp op) {
+    PADDLE_ENFORCE_GE(
+        run_order_, 0,
+        platform::errors::InvalidArgument(
+            "The argument run_order_ must be >= 0, but got %d.", run_order_));
+    auto flat_bkcl_ctxs = bkcl_ctxs_->GetFlatCtx(run_order_);
+    int dev_id = BOOST_GET_CONST(platform::XPUPlace, place).device;
+    auto& bkcl_ctx = flat_bkcl_ctxs->at(dev_id);
+    auto comm = bkcl_ctx.comm_;
+
+    VLOG(10) << "before all reduce buffer:" << sendbuff << ", numel:" << count
+             << ", dev_id:" << dev_id << ", dtype:" << datatype
+             << ", place:" << place;
+
+    PADDLE_ENFORCE_EQ(
+        bkcl_all_reduce(comm, sendbuff, recvbuff, count, datatype, op, NULL),
+        BKCL_SUCCESS,
+        platform::errors::PreconditionNotMet("bckl all reduce failed"));
+  }
+
+  void BKCLAllReduce(platform::Place place, const void* sendbuff,
+                     void* recvbuff, size_t count, BKCLDataType datatype,
+                     BKCLOp op) {
+    PADDLE_ENFORCE_GE(
+        run_order_, 0,
+        platform::errors::InvalidArgument(
+            "The argument run_order_ must be >= 0, but got %d.", run_order_));
+    PADDLE_ENFORCE_EQ(use_hierarchical_allreduce_, false,
+                      platform::errors::Unimplemented(
+                          "xpu doesn't support hierarchical all reduce"));
+    if (!use_hierarchical_allreduce_) {
+      FlatBKCLAllReduce(place, sendbuff, recvbuff, count, datatype, op);
+      return;
+    }
+  }
+
+ protected:
+  std::vector<platform::Place> places_;
+  const platform::BKCLCommunicator* bkcl_ctxs_{nullptr};
+  // When multi trainer call collective function, they need run the same order.
+  // Or the program will hang.So we use allreduce_deps_pass to set this
+  // run_order_.
+  int run_order_{0};
+  // Use 2d allreduce or not.
+  bool use_hierarchical_allreduce_{false};
+};
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/broadcast_op_handle.cc

@@ -80,7 +80,7 @@ void BroadcastOpHandle::BroadcastOneVar(
             &VariableVisitor::GetMutableTensor(out_var));
       });
     }
-  } else {
+  } else if (platform::is_gpu_place(in_tensor.place())) {
 #if defined(PADDLE_WITH_NCCL)
     VarHandle *out_handle = nullptr;
     int root_id =
@@ -141,6 +141,72 @@ void BroadcastOpHandle::BroadcastOneVar(
 #else
     PADDLE_THROW(
         platform::errors::PreconditionNotMet("Not compiled with NCLL."));
+#endif
+  } else {
+#if defined(PADDLE_WITH_XPU_BKCL)
+    VarHandle *out_handle = nullptr;
+    int root_id = BOOST_GET_CONST(platform::XPUPlace, in_tensor.place()).device;
+    std::vector<std::function<void()>> broadcast_calls;
+
+    int type = platform::ToBKCLDataType(in_tensor.type());
+    size_t numel = static_cast<size_t>(in_tensor.numel());
+
+    for (auto out_var_handle : out_var_handles) {
+      Variable *out_var = var_scopes.at(out_var_handle->scope_idx())
+                              ->FindVar(out_var_handle->name());
+
+      int dst_id =
+          BOOST_GET_CONST(platform::XPUPlace, out_var_handle->place()).device;
+
+      auto &bkcl_ctx = bkcl_ctxs_->at(dst_id);
+
+      void *send_recv_buffer = nullptr;
+      if (root_id == dst_id) {
+        send_recv_buffer = const_cast<void *>(in_tensor.data<void>());
+        out_handle = out_var_handle;
+      } else {
+        send_recv_buffer = VariableVisitor::GetMutableTensor(out_var)
+                               .Resize(in_tensor.dims())
+                               .mutable_data(out_var_handle->place());
+      }
+
+      broadcast_calls.emplace_back([send_recv_buffer, numel, type, root_id,
+                                    &bkcl_ctx] {
+        PADDLE_ENFORCE_EQ(
+            bkcl_broadcast(bkcl_ctx.comm(), send_recv_buffer, send_recv_buffer,
+                           numel, static_cast<BKCLDataType>(type), root_id,
+                           nullptr),
+            BKCL_SUCCESS,
+            platform::errors::Unavailable("bkcl_broadcast failed"));
+      });
+    }
+
+    WaitInputVarGenerated();
+    this->RunAndRecordEvent([&] {
+      {
+        PADDLE_ENFORCE_EQ(
+            bkcl_group_start(), BKCL_SUCCESS,
+            platform::errors::Unavailable("bkcl_group_start failed"));
+        for (auto &call : broadcast_calls) {
+          call();
+        }
+        PADDLE_ENFORCE_EQ(
+            bkcl_group_end(), BKCL_SUCCESS,
+            platform::errors::Unavailable("bkcl_group_end failed"));
+      }
+
+      if (!out_handle->IsTheSameVar(in_var_handle)) {
+        auto out_var = var_scopes.at(in_var_handle.scope_idx())
+                           ->FindVar(out_var_handles[0]->name());
+        paddle::framework::TensorCopy(
+            in_tensor, in_var_handle.place(),
+            *(dev_ctxes_.at(in_var_handle.place())),
+            &VariableVisitor::GetMutableTensor(out_var));
+      }
+    });
+#else
+    PADDLE_THROW(
+        platform::errors::PreconditionNotMet("Not compiled with BKCL."));
 #endif
   }
 }

paddle/fluid/framework/details/broadcast_op_handle.h

@@ -34,12 +34,19 @@ class Node;
 }  // namespace ir
 }  // namespace framework
 namespace platform {
+#if defined(PADDLE_WITH_NCCL)
 struct NCCLContextMap;
+#endif
+#if defined(PADDLE_WITH_XPU_BKCL)
+struct BKCLContextMap;
+#endif
 }  // namespace platform
 }  // namespace paddle
 
 #if defined(PADDLE_WITH_NCCL)
 #include "paddle/fluid/platform/nccl_helper.h"
+#elif defined(PADDLE_WITH_XPU_BKCL)
+#include "paddle/fluid/platform/bkcl_helper.h"
 #endif
 
 namespace paddle {
@@ -63,11 +70,26 @@ struct BroadcastOpHandle : public OpHandleBase {
       }
     }
   }
-#else
+#endif
+#if defined(PADDLE_WITH_XPU_BKCL)
+  BroadcastOpHandle(ir::Node *node, const std::vector<Scope *> &local_scopes,
+                    const std::vector<platform::Place> &places,
+                    const platform::BKCLContextMap *bkcl_ctxs)
+      : OpHandleBase(node),
+        local_scopes_(local_scopes),
+        places_(places),
+        bkcl_ctxs_(bkcl_ctxs) {
+    if (bkcl_ctxs_) {
+      for (auto &p_ctx : bkcl_ctxs_->contexts_) {
+        this->SetDeviceContext(platform::XPUPlace(p_ctx.first),
+                               p_ctx.second.ctx_.get());
+      }
+    }
+  }
+#endif
   BroadcastOpHandle(ir::Node *node, const std::vector<Scope *> &local_scopes,
                     const std::vector<platform::Place> &places)
       : OpHandleBase(node), local_scopes_(local_scopes), places_(places) {}
-#endif
 
   std::string Name() const override;
 
@@ -86,6 +108,8 @@ struct BroadcastOpHandle : public OpHandleBase {
   std::vector<platform::Place> places_;
 #if defined(PADDLE_WITH_NCCL)
   const platform::NCCLContextMap *nccl_ctxs_;
+#elif defined(PADDLE_WITH_XPU_BKCL)
+  const platform::BKCLContextMap *bkcl_ctxs_;
 #endif
 
   void InitOutputValue(const VarHandle &in_var_handle,

paddle/fluid/framework/details/broadcast_op_handle_test.cc

@@ -18,10 +18,12 @@ namespace paddle {
 namespace framework {
 namespace details {
 
+using DeviceType = paddle::platform::DeviceType;
+
 TEST(BroadcastTester, TestCPUBroadcastTestLodTensor) {
   TestBroadcastOpHandle test_op;
   size_t input_scope_idx = 0;
-  test_op.InitCtxOnGpu(false);
+  test_op.InitCtxOnDevice(p::kCPU);
   test_op.InitBroadcastOp(input_scope_idx);
   test_op.TestBroadcastLodTensor(input_scope_idx);
 }
@@ -29,7 +31,7 @@ TEST(BroadcastTester, TestCPUBroadcastTestLodTensor) {
 TEST(BroadcastTester, TestCPUBroadcastTestSelectedRows) {
   TestBroadcastOpHandle test_op;
   size_t input_scope_idx = 0;
-  test_op.InitCtxOnGpu(false);
+  test_op.InitCtxOnDevice(p::kCPU);
   test_op.InitBroadcastOp(input_scope_idx);
   test_op.TestBroadcastSelectedRows(input_scope_idx);
 }
@@ -38,7 +40,7 @@ TEST(BroadcastTester, TestCPUBroadcastTestSelectedRows) {
 TEST(BroadcastTester, TestGPUBroadcastTestLodTensor) {
   TestBroadcastOpHandle test_op;
   size_t input_scope_idx = 0;
-  test_op.InitCtxOnGpu(true);
+  test_op.InitCtxOnDevice(p::kCUDA);
   test_op.InitBroadcastOp(input_scope_idx);
   test_op.TestBroadcastLodTensor(input_scope_idx);
 }
@@ -46,12 +48,22 @@ TEST(BroadcastTester, TestGPUBroadcastTestLodTensor) {
 TEST(BroadcastTester, TestGPUBroadcastTestSelectedRows) {
   TestBroadcastOpHandle test_op;
   size_t input_scope_idx = 0;
-  test_op.InitCtxOnGpu(true);
+  test_op.InitCtxOnDevice(p::kCUDA);
   test_op.InitBroadcastOp(input_scope_idx);
   test_op.TestBroadcastSelectedRows(input_scope_idx);
 }
 #endif
 
+#if defined(PADDLE_WITH_XPU_BKCL)
+TEST(BroadcastTester, TestXPUBroadcastTestLodTensor) {
+  TestBroadcastOpHandle test_op;
+  size_t input_scope_idx = 0;
+  test_op.InitCtxOnDevice(p::kXPU);
+  test_op.InitBroadcastOp(input_scope_idx);
+  test_op.TestBroadcastLodTensor(input_scope_idx);
+}
+#endif
+
 }  // namespace details
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/details/broadcast_op_handle_test.h

@@ -33,6 +33,8 @@ struct VarHandle;
 namespace f = paddle::framework;
 namespace p = paddle::platform;
 
+using DeviceType = paddle::platform::DeviceType;
+
 // test data amount
 const f::DDim kDims = {20, 20};
 
@@ -45,11 +47,15 @@ struct TestBroadcastOpHandle {
   std::vector<VarHandleBase*> vars_;
   std::vector<std::unique_ptr<ir::Node>> nodes_;
   std::vector<p::Place> place_list_;
-  bool use_gpu_;
+  DeviceType use_device_;
 #if defined(PADDLE_WITH_NCCL)
   std::unique_ptr<platform::NCCLContextMap> nccl_ctxs_;
 #endif
 
+#if defined(PADDLE_WITH_XPU_BKCL)
+  std::unique_ptr<platform::BKCLContextMap> bkcl_ctxs_;
+#endif
+
   void WaitAll() {
     for (size_t j = 0; j < ctxs_.size(); ++j) {
       ctxs_[j]->Wait();
@@ -58,12 +64,36 @@ struct TestBroadcastOpHandle {
     if (nccl_ctxs_) {
       nccl_ctxs_->WaitAll();
     }
+#endif
+#if defined(PADDLE_WITH_XPU_BKCL)
+    if (bkcl_ctxs_) {
+      bkcl_ctxs_->WaitAll();
+    }
 #endif
   }
 
-  void InitCtxOnGpu(bool use_gpu) {
-    use_gpu_ = use_gpu;
-    if (use_gpu_) {
+  void InitCtxOnDevice(DeviceType use_device) {
+    use_device_ = use_device;
+    if (use_device_ == p::kXPU) {
+#if defined(PADDLE_WITH_XPU_BKCL)
+      int count = p::GetXPUDeviceCount();
+      if (count <= 1) {
+        LOG(WARNING) << "Cannot test multi-xpu Broadcast, because the XPU "
+                        "device count is "
+                     << count;
+        exit(0);
+      }
+      for (int i = 0; i < count; ++i) {
+        auto p = p::XPUPlace(i);
+        place_list_.push_back(p);
+        ctxs_.emplace_back(new p::XPUDeviceContext(p));
+      }
+      bkcl_ctxs_.reset(new platform::BKCLContextMap(place_list_));
+#else
+      PADDLE_THROW(
+          platform::errors::PreconditionNotMet("Not compiled with BKCL."));
+#endif
+    } else if (use_device_ == p::kCUDA) {
 #if defined(PADDLE_WITH_NCCL)
       int count = p::GetCUDADeviceCount();
       if (count <= 1) {
@@ -89,6 +119,9 @@ struct TestBroadcastOpHandle {
         place_list_.push_back(p);
         ctxs_.emplace_back(new p::CPUDeviceContext(p));
       }
+#if defined(PADDLE_WITH_XPU_BKCL)
+      bkcl_ctxs_.reset(nullptr);
+#endif
 #if defined(PADDLE_WITH_NCCL)
       nccl_ctxs_.reset(nullptr);
 #endif
@@ -109,22 +142,25 @@ struct TestBroadcastOpHandle {
 
     nodes_.emplace_back(
         ir::CreateNodeForTest("node0", ir::Node::Type::kOperation));
-    if (use_gpu_) {
+    if (use_device_ == p::kCUDA) {
 #if defined(PADDLE_WITH_NCCL)
       op_handle_ = new BroadcastOpHandle(nodes_.back().get(), local_scopes_,
                                          place_list_, nccl_ctxs_.get());
 #else
       PADDLE_THROW(
-          platform::errors::PreconditionNotMet("Not compiled with NCLL."));
+          platform::errors::PreconditionNotMet("Not compiled with NCCL."));
 #endif
-    } else {
-#if defined(PADDLE_WITH_NCCL)
+    } else if (use_device_ == p::kXPU) {
+#if defined(PADDLE_WITH_XPU_BKCL)
       op_handle_ = new BroadcastOpHandle(nodes_.back().get(), local_scopes_,
-                                         place_list_, nccl_ctxs_.get());
+                                         place_list_, bkcl_ctxs_.get());
 #else
+      PADDLE_THROW(
+          platform::errors::PreconditionNotMet("Not compiled with BKCL."));
+#endif
+    } else {
       op_handle_ = new BroadcastOpHandle(nodes_.back().get(), local_scopes_,
                                          place_list_);
-#endif
     }
 
     op_handle_->SetLocalExecScopes(scope_map);
@@ -147,7 +183,7 @@ struct TestBroadcastOpHandle {
     op_handle_->AddInput(dummy_var_handle);
 
     for (size_t j = 0; j < place_list_.size(); ++j) {
-      if (!use_gpu_) {
+      if (use_device_ != p::kCUDA) {
         op_handle_->SetDeviceContext(place_list_[j], ctxs_[j].get());
       }
       nodes_.emplace_back(
@@ -273,7 +309,8 @@ struct TestBroadcastOpHandle {
     f::LoD lod{{0, 10, 20}};
     auto send_vector = InitLoDTensor("input", input_scope_idx, lod);
 
-    op_handle_->Run(false);
+    DeviceType use_device = p::kCPU;
+    op_handle_->Run(use_device);
 
     WaitAll();
     for (size_t j = 0; j < place_list_.size(); ++j) {
@@ -287,7 +324,8 @@ struct TestBroadcastOpHandle {
     int height = static_cast<int>(kDims[0] * 2);
     auto send_vector = InitSelectedRows("input", input_scope_idx, rows, height);
 
-    op_handle_->Run(false);
+    DeviceType use_device = p::kCPU;
+    op_handle_->Run(use_device);
 
     WaitAll();
     for (size_t j = 0; j < place_list_.size(); ++j) {

paddle/fluid/framework/details/build_strategy.cc

@@ -313,10 +313,13 @@ ir::Graph *BuildStrategy::Apply(ir::Graph *graph,
                                 const std::vector<Scope *> &local_scopes,
                                 const size_t &nranks,
 #if defined(PADDLE_WITH_NCCL)
-                                const bool use_cuda,
+                                DeviceType use_device,
                                 platform::NCCLCommunicator *nccl_ctxs) const {
+#elif defined(PADDLE_WITH_XPU) && defined(PADDLE_WITH_XPU_BKCL)
+                                DeviceType use_device,
+                                platform::BKCLCommunicator *bkcl_ctxs) const {
 #else
-                                const bool use_cuda) const {
+                                DeviceType use_device) const {
 #endif
   VLOG(1) << "apply all passes";
   // Create a default one if not finalized by user.
@@ -336,9 +339,16 @@ ir::Graph *BuildStrategy::Apply(ir::Graph *graph,
       pass->Set<size_t>(kNRanks, new size_t(nranks));
 
 #if defined(PADDLE_WITH_NCCL)
-      platform::NCCLCommunicator *nctx = use_cuda ? nccl_ctxs : nullptr;
+      platform::NCCLCommunicator *nctx =
+          (use_device == p::kCUDA) ? nccl_ctxs : nullptr;
       pass->Erase(kNCCLCtxs);
       pass->SetNotOwned<platform::NCCLCommunicator>(kNCCLCtxs, nctx);
+#elif defined(PADDLE_WITH_XPU) && defined(PADDLE_WITH_XPU_BKCL)
+      // ToDo: more check
+      platform::BKCLCommunicator *bkcl_ctx =
+          (use_device == p::kXPU) ? bkcl_ctxs : nullptr;
+      pass->Erase(kBKCLCtxs);
+      pass->SetNotOwned<platform::BKCLCommunicator>(kBKCLCtxs, bkcl_ctx);
 #endif
     } else if (pass->Type() == "fuse_all_reduce_op_pass") {
       pass->Erase(kNRanks);
@@ -349,12 +359,24 @@ ir::Graph *BuildStrategy::Apply(ir::Graph *graph,
       pass->SetNotOwned<const std::vector<Scope *>>(kLocalScopes,
                                                     &local_scopes);
 #if defined(PADDLE_WITH_NCCL)
-      platform::NCCLCommunicator *nctx = use_cuda ? nccl_ctxs : nullptr;
+      platform::NCCLCommunicator *nctx =
+          (use_device == p::kCUDA) ? nccl_ctxs : nullptr;
       pass->Erase(kNCCLCtxs);
       pass->SetNotOwned<platform::NCCLCommunicator>(kNCCLCtxs, nctx);
       pass->Erase(kUseHierarchicalAllReduce);
       pass->Set<bool>(kUseHierarchicalAllReduce,
                       new bool(use_hierarchical_allreduce_));
+#elif defined(PADDLE_WITH_XPU) && defined(PADDLE_WITH_XPU_BKCL)
+      platform::BKCLCommunicator *nctx =
+          (use_device == p::kXPU) ? bkcl_ctxs : nullptr;
+      pass->Erase(kBKCLCtxs);
+      pass->SetNotOwned<platform::BKCLCommunicator>(kBKCLCtxs, nctx);
+      pass->Erase(kUseHierarchicalAllReduce);
+      PADDLE_ENFORCE_EQ(use_hierarchical_allreduce_, false,
+                        platform::errors::Unimplemented(
+                            "xpu doesn't support hierarchical_allreduce"));
+      pass->Set<bool>(kUseHierarchicalAllReduce,
+                      new bool(use_hierarchical_allreduce_));
 #endif
     } else if (pass->Type() == "coalesce_grad_tensor_pass") {
       pass->Erase(kNRanks);
@@ -364,35 +386,47 @@ ir::Graph *BuildStrategy::Apply(ir::Graph *graph,
                 << enable_sequential_execution_;
     } else if (pass->Type() == "all_reduce_deps_pass") {
 #if defined(PADDLE_WITH_NCCL)
-      platform::NCCLCommunicator *nctx = use_cuda ? nccl_ctxs : nullptr;
+      platform::NCCLCommunicator *nctx =
+          (use_device == p::kCUDA) ? nccl_ctxs : nullptr;
       pass->Erase(kNCCLCtxs);
       pass->SetNotOwned<platform::NCCLCommunicator>(kNCCLCtxs, nctx);
       pass->Erase(kUseHierarchicalAllReduce);
       pass->Set<bool>(kUseHierarchicalAllReduce,
                       new bool(use_hierarchical_allreduce_));
+#elif defined(PADDLE_WITH_XPU) && defined(PADDLE_WITH_XPU_BKCL)
+      platform::BKCLCommunicator *nctx =
+          (use_device == p::kXPU) ? bkcl_ctxs : nullptr;
+      pass->Erase(kBKCLCtxs);
+      pass->SetNotOwned<platform::BKCLCommunicator>(kBKCLCtxs, nctx);
+      pass->Erase(kUseHierarchicalAllReduce);
+      PADDLE_ENFORCE_EQ(use_hierarchical_allreduce_, false,
+                        platform::errors::Unimplemented(
+                            "xpu doesn't support hierarchical_allreduce"));
+      pass->Set<bool>(kUseHierarchicalAllReduce,
+                      new bool(use_hierarchical_allreduce_));
 #endif
       VLOG(1) << "SeqOnlyAllReduceOps:" << SeqOnlyAllReduceOps(*this)
               << ", num_trainers:" << num_trainers_;
     } else if (pass->Type() == "fuse_relu_depthwise_conv_pass") {
-      if (!use_cuda) {
+      if (use_device != p::kCUDA) {
         LOG(WARNING) << "fuse_relu_depthwise_conv_pass is only supported on "
                         "GPU, skipped.";
         continue;
       }
     } else if (pass->Type() == "fusion_group_pass") {
-      pass->Set<bool>("use_gpu", new bool(use_cuda));
-      if (!use_cuda) {
+      pass->Set<bool>("use_gpu", new bool((use_device == p::kCUDA)));
+      if (use_device != p::kCUDA) {
         LOG(WARNING) << "fusion_group_pass is only supported on GPU, skipped.";
         continue;
       }
     } else if (pass->Type() == "fuse_bn_act_pass") {
-      if (!use_cuda) {
+      if (use_device != p::kCUDA) {
         LOG(WARNING) << "fuse_bn_act_pass is only supported on "
                         "GPU, skipped.";
         continue;
       }
     } else if (pass->Type() == "fuse_bn_add_act_pass") {
-      if (!use_cuda) {
+      if (use_device != p::kCUDA) {
         LOG(WARNING) << "fuse_bn_add_act_pass is only supported on "
                         "GPU, skipped.";
         continue;
@@ -401,7 +435,7 @@ ir::Graph *BuildStrategy::Apply(ir::Graph *graph,
       pass->Set("mkldnn_enabled_op_types",
                 new std::unordered_set<std::string>(mkldnn_enabled_op_types_));
     } else if (pass->Type() == "backward_optimizer_op_deps_pass") {
-      if (!use_cuda) {
+      if (use_device != p::kCUDA) {
         VLOG(1) << "backward_optimizer_op_deps_pass is only supported on "
                    "GPU, skipped.";
         continue;

paddle/fluid/framework/details/build_strategy.h

@@ -41,11 +41,15 @@ class NCCLCommunicator;
 
 #if defined(PADDLE_WITH_NCCL)
 #include "paddle/fluid/platform/nccl_helper.h"
+#elif defined(PADDLE_WITH_XPU) && defined(PADDLE_WITH_XPU_BKCL)
+#include "paddle/fluid/platform/bkcl_helper.h"
 #endif
 
 namespace paddle {
 namespace framework {
 namespace details {
+using DeviceType = paddle::platform::DeviceType;
+namespace p = paddle::platform;
 
 struct BuildStrategy {
   // ParallelExecutor supports two modes of ReduceStrategy, kAllReduce and
@@ -147,6 +151,7 @@ struct BuildStrategy {
 
   // NCCL config
   size_t nccl_comm_num_{1};
+  size_t bkcl_comm_num_{1};
   // The picture is here:
   // https://github.com/PaddlePaddle/Paddle/pull/17263#discussion_r285411396
   bool use_hierarchical_allreduce_{false};
@@ -181,10 +186,13 @@ struct BuildStrategy {
                    const std::vector<Scope *> &local_scopes,
                    const size_t &nranks,
 #if defined(PADDLE_WITH_NCCL)
-                   const bool use_cuda,
+                   DeviceType use_device,
                    platform::NCCLCommunicator *nccl_ctxs) const;
+#elif defined(PADDLE_WITH_XPU) && defined(PADDLE_WITH_XPU_BKCL)
+                   DeviceType use_device,
+                   platform::BKCLCommunicator *bkcl_ctxs) const;
 #else
-                   const bool use_cuda) const;
+                   DeviceType use_device) const;
 #endif
 
   // If set true, ParallelExecutor would build the main_program into multiple

paddle/fluid/framework/details/execution_strategy.h

@@ -14,17 +14,19 @@
 
 #pragma once
 #include <cstddef>  // for size_t
+#include "paddle/fluid/platform/device_context.h"
 
 namespace paddle {
 namespace framework {
 namespace details {
-
+using DeviceType = paddle::platform::DeviceType;
+namespace p = paddle::platform;
 struct ExecutionStrategy {
   enum ExecutorType { kDefault = 0, kExperimental = 1 };
 
   // num_threads indicates the size of thread pool.
   size_t num_threads_{0};
-  bool use_cuda_{true};
+  DeviceType use_device_ = p::kCUDA;
   // Note that allow_op_delay is invalid now.
   bool allow_op_delay_{false};
   // num_iteration_per_drop_scope indicates how many

paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc

@@ -330,7 +330,7 @@ bool FastThreadedSSAGraphExecutor::RunOpSync(OpHandleBase *op) {
   try {
     VLOG(10) << op << " " << op->Name() << " : " << op->DebugString();
     if (LIKELY(!strategy_.dry_run_)) {
-      op->Run(strategy_.use_cuda_);
+      op->Run(strategy_.use_device_);
     }
     VLOG(10) << op << " " << op->Name() << " Done ";
     return true;

paddle/fluid/framework/details/fused_all_reduce_op_handle.cc

@@ -37,6 +37,13 @@ FusedAllReduceOpHandle::FusedAllReduceOpHandle(
     const platform::NCCLCommunicator *ctxs)
     : AllReduceOpHandle(node, local_scopes, places, ctxs),
       num_of_all_reduce_(num_of_all_reduce) {}
+#elif defined(PADDLE_WITH_XPU_BKCL)
+FusedAllReduceOpHandle::FusedAllReduceOpHandle(
+    ir::Node *node, const std::vector<Scope *> &local_scopes,
+    const std::vector<platform::Place> &places, const size_t num_of_all_reduce,
+    const platform::BKCLCommunicator *ctxs)
+    : AllReduceOpHandle(node, local_scopes, places, ctxs),
+      num_of_all_reduce_(num_of_all_reduce) {}
 #else
 FusedAllReduceOpHandle::FusedAllReduceOpHandle(
     ir::Node *node, const std::vector<Scope *> &local_scopes,
@@ -73,9 +80,14 @@ void FusedAllReduceOpHandle::RunImpl() {
           "handles is %d, and the number of  output variable handles is %d.",
           in_var_handles.size(), out_var_handles.size()));
 
-  // Note: some gradient op doesn't have CUDAKernel, so the gradients of
-  // those op are in CPUPlace, in this case, the all reduce should not be fused.
+// Note: some gradient op doesn't have CUDAKernel, so the gradients of
+// those op are in CPUPlace, in this case, the all reduce should not be fused.
+#if defined(PADDLE_WITH_XPU_BKCL)
+  // TODO(liuyuhui): XPU don't support fuse all reduce for now
+  if (InputIsInDifferentPlace(in_var_handles) || true) {
+#else
   if (InputIsInDifferentPlace(in_var_handles)) {
+#endif
     for (size_t j = 0; j < num_of_all_reduce_; ++j) {
       std::vector<VarHandle *> dev_inputs;
       std::vector<VarHandle *> dev_outputs;

paddle/fluid/framework/details/fused_all_reduce_op_handle.h

@@ -36,6 +36,8 @@ class NCCLCommunicator;
 #if defined(PADDLE_WITH_NCCL)
 #include "paddle/fluid/framework/details/nccl_op_handle.h"
 #include "paddle/fluid/platform/nccl_helper.h"
+#elif defined(PADDLE_WITH_XPU_BKCL)
+#include "paddle/fluid/platform/bkcl_helper.h"
 #endif
 
 namespace paddle {
@@ -49,6 +51,13 @@ struct FusedAllReduceOpHandle : public AllReduceOpHandle {
                          const std::vector<platform::Place> &places,
                          const size_t num_of_all_reduce,
                          const platform::NCCLCommunicator *ctxs);
+#elif defined(PADDLE_WITH_XPU_BKCL)
+struct FusedAllReduceOpHandle : public AllReduceOpHandle {
+  FusedAllReduceOpHandle(ir::Node *node,
+                         const std::vector<Scope *> &local_scopes,
+                         const std::vector<platform::Place> &places,
+                         const size_t num_of_all_reduce,
+                         const platform::BKCLCommunicator *ctxs);
 #else
 struct FusedAllReduceOpHandle : public AllReduceOpHandle {
   FusedAllReduceOpHandle(ir::Node *node,

paddle/fluid/framework/details/fused_broadcast_op_handle.h

@@ -52,11 +52,18 @@ struct FusedBroadcastOpHandle : public BroadcastOpHandle {
                          const std::vector<platform::Place> &places,
                          const platform::NCCLContextMap *nccl_ctx)
       : BroadcastOpHandle(node, local_scopes, places, nccl_ctx) {}
-#else
-  FusedBroadcastOpHandle(ir::Node* node, const std::vector<Scope*> local_scopes,
-                         const std::vector<platform::Place>& places)
-      : BroadcastOpHandle(node, local_scopes, places) {}
 #endif
+#if defined(PADDLE_WITH_XPU_BKCL)
+  FusedBroadcastOpHandle(ir::Node *node,
+                         const std::vector<Scope *> local_scopes,
+                         const std::vector<platform::Place> &places,
+                         const platform::BKCLContextMap *bkcl_ctx)
+      : BroadcastOpHandle(node, local_scopes, places, bkcl_ctx) {}
+#endif
+  FusedBroadcastOpHandle(ir::Node *node,
+                         const std::vector<Scope *> local_scopes,
+                         const std::vector<platform::Place> &places)
+      : BroadcastOpHandle(node, local_scopes, places) {}
   std::string Name() const override;
 
  protected:

paddle/fluid/framework/details/fused_broadcast_op_handle_test.cc

@@ -32,6 +32,7 @@ namespace framework {
 namespace details {
 
 struct VarHandle;
+using DeviceType = paddle::platform::DeviceType;
 
 struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
   std::vector<std::string> out_varnames_;
@@ -55,7 +56,7 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
     // create op handle node
     nodes_.emplace_back(
         ir::CreateNodeForTest("fused_broadcast", ir::Node::Type::kOperation));
-    if (use_gpu_) {
+    if (use_device_ == p::kCUDA) {
 #if defined(PADDLE_WITH_NCCL)
       op_handle_ = new FusedBroadcastOpHandle(
           nodes_.back().get(), local_scopes_, place_list_, nccl_ctxs_.get());
@@ -63,14 +64,17 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
       PADDLE_THROW(
           platform::errors::PreconditionNotMet("Not compiled with CUDA."));
 #endif
-    } else {
-#if defined(PADDLE_WITH_NCCL)
+    } else if (use_device_ == p::kXPU) {
+#if defined(PADDLE_WITH_XPU_BKCL)
       op_handle_ = new FusedBroadcastOpHandle(
-          nodes_.back().get(), local_scopes_, place_list_, nccl_ctxs_.get());
+          nodes_.back().get(), local_scopes_, place_list_, bkcl_ctxs_.get());
 #else
+      PADDLE_THROW(
+          platform::errors::PreconditionNotMet("Not compiled with XPU."));
+#endif
+    } else {
       op_handle_ = new FusedBroadcastOpHandle(nodes_.back().get(),
                                               local_scopes_, place_list_);
-#endif
     }
 
     op_handle_->SetLocalExecScopes(scope_map);
@@ -108,7 +112,8 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
           InitLoDTensor(varname, input_scope_idxes[i], lod, val_scalar));
     }
 
-    op_handle_->Run(false);
+    DeviceType use_device = p::kCPU;
+    op_handle_->Run(use_device);
 
     WaitAll();
     for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
@@ -131,7 +136,8 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
                                              rows, height, val_scalar));
     }
 
-    op_handle_->Run(false);
+    DeviceType use_device = p::kCPU;
+    op_handle_->Run(use_device);
 
     WaitAll();
     for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
@@ -147,7 +153,7 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
 TEST(FusedBroadcastTester, CPULodTensor) {
   TestFusedBroadcastOpHandle test_op;
   std::vector<size_t> input_scope_idxes = {0, 1};
-  test_op.InitCtxOnGpu(false);
+  test_op.InitCtxOnDevice(p::kCPU);
   test_op.InitFusedBroadcastOp(input_scope_idxes);
   test_op.TestFusedBroadcastLoDTensor(input_scope_idxes);
 }
@@ -155,7 +161,7 @@ TEST(FusedBroadcastTester, CPULodTensor) {
 TEST(FusedBroadcastTester, CPUSelectedRows) {
   TestFusedBroadcastOpHandle test_op;
   std::vector<size_t> input_scope_idxes = {0, 1};
-  test_op.InitCtxOnGpu(false);
+  test_op.InitCtxOnDevice(p::kCPU);
   test_op.InitFusedBroadcastOp(input_scope_idxes);
   test_op.TestFusedBroadcastSelectedRows(input_scope_idxes);
 }
@@ -164,7 +170,7 @@ TEST(FusedBroadcastTester, CPUSelectedRows) {
 TEST(FusedBroadcastTester, GPULodTensor) {
   TestFusedBroadcastOpHandle test_op;
   std::vector<size_t> input_scope_idxes = {0, 1};
-  test_op.InitCtxOnGpu(true);
+  test_op.InitCtxOnDevice(p::kCUDA);
   test_op.InitFusedBroadcastOp(input_scope_idxes);
   test_op.TestFusedBroadcastLoDTensor(input_scope_idxes);
 }
@@ -172,12 +178,22 @@ TEST(FusedBroadcastTester, GPULodTensor) {
 TEST(FusedBroadcastTester, GPUSelectedRows) {
   TestFusedBroadcastOpHandle test_op;
   std::vector<size_t> input_scope_idxes = {0, 1};
-  test_op.InitCtxOnGpu(true);
+  test_op.InitCtxOnDevice(p::kCUDA);
   test_op.InitFusedBroadcastOp(input_scope_idxes);
   test_op.TestFusedBroadcastSelectedRows(input_scope_idxes);
 }
 #endif
 
+#if defined(PADDLE_WITH_XPU_BKCL)
+TEST(FusedBroadcastTester, XPULodTensor) {
+  TestFusedBroadcastOpHandle test_op;
+  std::vector<size_t> input_scope_idxes = {0, 1};
+  test_op.InitCtxOnDevice(p::kXPU);
+  test_op.InitFusedBroadcastOp(input_scope_idxes);
+  test_op.TestFusedBroadcastLoDTensor(input_scope_idxes);
+}
+#endif
+
 }  // namespace details
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/details/gather_op_handle_test.cc

@@ -27,6 +27,8 @@ struct DummyVarHandle;
 namespace f = paddle::framework;
 namespace p = paddle::platform;
 
+using DeviceType = paddle::platform::DeviceType;
+
 // test data amount
 const f::DDim kDims = {20, 20};
 
@@ -171,7 +173,8 @@ struct TestGatherOpHandle {
     out_selected_rows->mutable_value()->ShareDataWith(
         in_selected_rows->value());
 
-    op_handle_->Run(false);
+    DeviceType use_device = p::kCPU;
+    op_handle_->Run(use_device);
 
     WaitAll();
 

paddle/fluid/framework/details/multi_devices_helper.h

@@ -55,6 +55,7 @@ constexpr char kPlaces[] = "places";
 constexpr char kGlobalScope[] = "global_scope";
 constexpr char kLocalScopes[] = "local_scopes";
 constexpr char kNCCLCtxs[] = "nccl_ctxs";
+constexpr char kBKCLCtxs[] = "bkcl_ctxs";
 constexpr char kUseHierarchicalAllReduce[] = "use_hierarchical_allreduce";
 
 // aux variables to represent dependency. Useful to resolve data hazard.

paddle/fluid/framework/details/op_handle_base.cc

@@ -82,20 +82,74 @@ void OpHandleBase::InitCUDA() {
       }
     }
   }
+#else
+  PADDLE_THROW(platform::errors::PermissionDenied(
+      "Paddle can't use CUDA device since it's not compiled with CUDA,"
+      "Please recompile or reinstall Paddle with GPU support."));
+#endif
+}
+
+void OpHandleBase::InitXPU() {
+#ifdef PADDLE_WITH_XPU
+  if (IsMultiDeviceTransfer() && dev_ctxes_.size() > 0) {
+    for (auto &out_var : outputs_) {
+      auto *out_var_handle = dynamic_cast<VarHandle *>(out_var);
+      if (out_var_handle) {
+        // TODO(liuyuhui): XPU now don't support sync events, add later.
+      }
+    }
+  } else {
+    PADDLE_ENFORCE_EQ(dev_ctxes_.size(), 1UL,
+                      platform::errors::InvalidArgument(
+                          "%s should have only one dev_ctx.", Name()));
+    auto &place = dev_ctxes_.begin()->first;
+    int dev_id = BOOST_GET_CONST(platform::XPUPlace, place).device;
+    PADDLE_ENFORCE_EQ(
+        xpu_set_device(dev_id), XPU_SUCCESS,
+        platform::errors::PreconditionNotMet("xpu_set_device failed"));
+    for (auto &out_var : outputs_) {
+      auto *out_var_handle = dynamic_cast<VarHandle *>(out_var);
+      if (out_var_handle) {
+        PADDLE_ENFORCE_EQ(
+            platform::is_same_place(place, out_var_handle->place()), true,
+            platform::errors::InvalidArgument(
+                "The place of output(%s) is not consistent with the "
+                "place of current op(%s).",
+                out_var_handle->Name(), Name()));
+      }
+    }
+  }
+#else
+  PADDLE_THROW(platform::errors::PermissionDenied(
+      "Paddle can't use XPU device since it's not compiled with XPU,"
+      "Please recompile or reinstall Paddle with XPU support."));
 #endif
 }
 
-void OpHandleBase::Run(bool use_cuda) {
+void OpHandleBase::Run(DeviceType use_device) {
 #ifdef PADDLE_WITH_CUDA
-  if (events_.empty() && use_cuda && dev_ctxes_.size() > 0) {
+  if (events_.empty() && use_device == p::kCUDA && dev_ctxes_.size() > 0) {
     InitCUDA();
   }
 #else
-  PADDLE_ENFORCE_EQ(use_cuda, false,
-                    platform::errors::InvalidArgument(
-                        "Argument use_cuda should be false when Paddle is not "
-                        "compiled with CUDA."));
+  PADDLE_ENFORCE_NE(
+      use_device, p::kCUDA,
+      platform::errors::InvalidArgument(
+          "Argument use_device should not be kCUDA when Paddle is not "
+          "compiled with CUDA."));
+#endif
+
+  if (use_device == p::kXPU && dev_ctxes_.size() > 0) {
+#ifdef PADDLE_WITH_XPU
+    InitXPU();
+#else
+    PADDLE_ENFORCE_NE(
+        use_device, p::kXPU,
+        platform::errors::InvalidArgument(
+            "Argument use_device should not be kXPU when Paddle is not "
+            "compiled with XPU."));
 #endif
+  }
 
   // skip running current op, used with inplace_addto_op_pass
   if (skip_running_) {

paddle/fluid/framework/details/op_handle_base.h

@@ -19,6 +19,7 @@
 #include <unordered_set>
 #include <vector>
 
+#include "paddle/fluid/framework/details/execution_strategy.h"
 #include "paddle/fluid/framework/details/var_handle.h"
 #include "paddle/fluid/framework/ir/node.h"
 #include "paddle/fluid/platform/device_context.h"
@@ -42,7 +43,8 @@ class Node;
 }  // namespace ir
 
 namespace details {
-
+using DeviceType = paddle::platform::DeviceType;
+namespace p = paddle::platform;
 // Wraps ir::Node and provide helper utilities.
 // It's responsible for populating necessary fields of ir::Node.
 class OpHandleBase {
@@ -71,7 +73,7 @@ class OpHandleBase {
 
   virtual std::string Name() const = 0;
 
-  void Run(bool use_cuda);
+  void Run(DeviceType use_device);
 
   virtual void RecordWaitEventOnCtx(platform::DeviceContext *waited_ctx);
 
@@ -144,6 +146,7 @@ class OpHandleBase {
   virtual void RunImpl() = 0;
 
   virtual void InitCUDA();
+  virtual void InitXPU();
 
   ir::Node *node_;
   std::vector<VarHandleBase *> inputs_;

paddle/fluid/framework/details/reduce_op_handle.cc

@@ -212,10 +212,64 @@ void ReduceOpHandle::RunImpl() {
 #else
       PADDLE_THROW(
           platform::errors::PreconditionNotMet("Not compiled with CUDA."));
+#endif
+    } else if (paddle::platform::is_xpu_place(lod_tensors[0]->place())) {
+#if defined(PADDLE_WITH_XPU_BKCL)
+      auto pre_in = pre_in_var->Get<framework::LoDTensor>();
+      VariableVisitor::ShareDimsAndLoD(*pre_in_var, out_var);
+      VariableVisitor::GetMutableTensor(out_var).mutable_data(
+          out_var_handle->place(), pre_in.type());
+
+      auto out_p = out_var_handle->place();
+      int root_id = BOOST_GET_CONST(platform::XPUPlace, out_p).device;
+      std::vector<std::function<void()>> all_reduce_calls;
+      for (size_t i = 0; i < var_scopes.size(); ++i) {
+        auto &p = in_places[i];
+        auto &lod_tensor = *lod_tensors[i];
+
+        int dev_id = BOOST_GET_CONST(platform::XPUPlace, p).device;
+        auto &bkcl_ctx = bkcl_ctxs_->at(dev_id);
+
+        void *buffer = const_cast<void *>(lod_tensor.data<void>());
+        void *recvbuffer = nullptr;
+        if (root_id == dev_id) {
+          recvbuffer =
+              out_var->GetMutable<framework::LoDTensor>()->mutable_data(
+                  out_var_handle->place());
+        }
+
+        int type = platform::ToBKCLDataType(lod_tensor.type());
+        size_t numel = static_cast<size_t>(lod_tensor.numel());
+        all_reduce_calls.emplace_back([buffer, recvbuffer, type, numel, root_id,
+                                       &bkcl_ctx] {
+          PADDLE_ENFORCE_EQ(bkcl_reduce(bkcl_ctx.comm(), buffer, recvbuffer,
+                                        numel, static_cast<BKCLDataType>(type),
+                                        BKCL_ADD, root_id, nullptr),
+                            BKCL_SUCCESS, platform::errors::Unavailable(
+                                              "bkcl_all_reduce failed"));
+        });
+      }
+
+      WaitInputVarGenerated();
+      this->RunAndRecordEvent([&] {
+        PADDLE_ENFORCE_EQ(
+            bkcl_group_start(), BKCL_SUCCESS,
+            platform::errors::Unavailable("bkcl_group_start failed"));
+        for (auto &call : all_reduce_calls) {
+          call();
+        }
+        PADDLE_ENFORCE_EQ(
+            bkcl_group_end(), BKCL_SUCCESS,
+            platform::errors::Unavailable("bkcl_group_end failed"));
+      });
+#else
+      PADDLE_THROW(
+          platform::errors::PreconditionNotMet("Not compiled with XPU."));
 #endif
     } else {
       PADDLE_THROW(platform::errors::InvalidArgument(
-          "The place of tensor should be CPUPlace or CUDAPlace, but got %s.",
+          "The place of tensor should be CPUPlace, CUDAPlace or XPUPlace, but "
+          "got %s.",
           lod_tensors[0]->place()));
     }
   }

paddle/fluid/framework/details/reduce_op_handle.h

@@ -41,6 +41,8 @@ struct NCCLContextMap;
 }  // namespace paddle
 #if defined(PADDLE_WITH_NCCL)
 #include "paddle/fluid/platform/nccl_helper.h"
+#elif defined(PADDLE_WITH_XPU_BKCL)
+#include "paddle/fluid/platform/bkcl_helper.h"
 #endif
 
 namespace paddle {
@@ -93,6 +95,22 @@ struct ReduceOpHandle : public OpHandleBase {
       }
     }
   }
+#elif defined(PADDLE_WITH_XPU_BKCL)
+  const platform::BKCLContextMap *bkcl_ctxs_;
+  ReduceOpHandle(ir::Node *node, const std::vector<Scope *> &local_scopes,
+                 const std::vector<platform::Place> &places,
+                 const platform::BKCLContextMap *bkcl_ctxs)
+      : OpHandleBase(node),
+        local_scopes_(local_scopes),
+        places_(places),
+        bkcl_ctxs_(bkcl_ctxs) {
+    if (bkcl_ctxs_) {
+      for (auto &p_ctx : bkcl_ctxs_->contexts_) {
+        this->SetDeviceContext(platform::XPUPlace(p_ctx.first),
+                               p_ctx.second.ctx_.get());
+      }
+    }
+  }
 #else
   ReduceOpHandle(ir::Node *node, const std::vector<Scope *> &local_scopes,
                  const std::vector<platform::Place> &places)

paddle/fluid/framework/details/reduce_op_handle_test.cc

@@ -25,6 +25,8 @@ namespace details {
 namespace f = paddle::framework;
 namespace p = paddle::platform;
 
+using DeviceType = paddle::platform::DeviceType;
+
 // test data amount
 const f::DDim kDims = {20, 20};
 
@@ -196,7 +198,8 @@ struct TestReduceOpHandle {
     out_selected_rows->mutable_value()->ShareDataWith(
         in_selected_rows->value());
 
-    op_handle_->Run(false);
+    DeviceType use_device = p::kCPU;
+    op_handle_->Run(use_device);
 
     WaitAll();
 
@@ -260,7 +263,8 @@ struct TestReduceOpHandle {
 
     out_lodtensor->ShareDataWith(in_lodtensor);
 
-    op_handle_->Run(false);
+    DeviceType use_device = p::kCPU;
+    op_handle_->Run(use_device);
 
     WaitAll();
 

paddle/fluid/framework/details/scale_loss_grad_op_handle.cc

@@ -58,6 +58,17 @@ struct ScaleLossGradFunctor {
     auto *out_data = out_->mutable_data<OutT>(place_);
     if (platform::is_cpu_place(place_)) {
       *out_data = static_cast<OutT>(coeff_);
+    } else if (platform::is_xpu_place(place_)) {
+#if defined(PADDLE_WITH_XPU)
+      OutT cast_coeff = static_cast<OutT>(coeff_);
+      memory::Copy(BOOST_GET_CONST(platform::XPUPlace, place_), out_data,
+                   platform::CPUPlace(), &cast_coeff, SizeOfType(out_dtype_));
+      VLOG(10) << place_ << "RUN Scale loss grad op";
+#else
+      PADDLE_THROW(platform::errors::PermissionDenied(
+          "Paddle can't use XPU device since it's not compiled with XPU,"
+          "Please recompile or reinstall Paddle with XPU support."));
+#endif
     } else {
 #ifdef PADDLE_WITH_CUDA
       OutT cast_coeff = static_cast<OutT>(coeff_);
@@ -66,7 +77,10 @@ struct ScaleLossGradFunctor {
                    platform::CPUPlace(), &cast_coeff, SizeOfType(out_dtype_),
                    stream);
       VLOG(10) << place_ << "RUN Scale loss grad op";
-
+#else
+      PADDLE_THROW(platform::errors::PermissionDenied(
+          "Paddle can't use CUDA device since it's not compiled with CUDA,"
+          "Please recompile or reinstall Paddle with GPU support."));
 #endif
     }
   }

paddle/fluid/framework/details/threaded_ssa_graph_executor.cc

@@ -348,7 +348,7 @@ bool ThreadedSSAGraphExecutor::RunOpSync(OpHandleBase *op) {
   try {
     VLOG(10) << op << " " << op->Name() << " : " << op->DebugString();
     if (LIKELY(!strategy_.dry_run_)) {
-      op->Run(strategy_.use_cuda_);
+      op->Run(strategy_.use_device_);
     }
     VLOG(10) << op << " " << op->Name() << " Done ";
     return true;

paddle/fluid/framework/ir/memory_optimize_pass/test_reference_count_pass_last_lived_ops.cc

@@ -30,6 +30,7 @@ DECLARE_double(eager_delete_tensor_gb);
 
 namespace paddle {
 namespace framework {
+namespace p = paddle::platform;
 
 static std::vector<platform::Place> CreatePlaces(size_t num, bool use_cuda) {
   std::vector<platform::Place> result;
@@ -88,7 +89,7 @@ class ReferenceCountPassTestHelper {
     FLAGS_eager_delete_tensor_gb = -1;
 
     details::ExecutionStrategy exec_strategy;
-    exec_strategy.use_cuda_ = use_cuda;
+    exec_strategy.use_device_ = use_cuda ? p::kCUDA : p::kCPU;
 
     executor_.reset(new ParallelExecutor(CreatePlaces(1, use_cuda), {}, "",
                                          &scope_, {}, exec_strategy,

paddle/fluid/framework/ir/multi_devices_graph_pass/fuse_all_reduce_op_pass.cc

@@ -41,6 +41,9 @@ class FuseAllReduceOpPass : public ir::Pass {
 #if defined(PADDLE_WITH_NCCL)
     auto *multi_nccl_ctxs =
         &Get<platform::NCCLCommunicator>(details::kNCCLCtxs);
+#elif defined(PADDLE_WITH_XPU_BKCL)
+    auto *multi_bkcl_ctxs =
+        &Get<platform::BKCLCommunicator>(details::kBKCLCtxs);
 #endif
 
     ir::Graph &result = *graph;
@@ -92,6 +95,9 @@ class FuseAllReduceOpPass : public ir::Pass {
 #if defined(PADDLE_WITH_NCCL)
       InsertFusedAllReduce(places, local_scopes, group_size,
                            group_all_reduce_ops, multi_nccl_ctxs, &result);
+#elif defined(PADDLE_WITH_XPU_BKCL)
+      InsertFusedAllReduce(places, local_scopes, group_size,
+                           group_all_reduce_ops, multi_bkcl_ctxs, &result);
 #else
       InsertFusedAllReduce(places, local_scopes, group_size,
                            group_all_reduce_ops, &result);
@@ -154,6 +160,8 @@ class FuseAllReduceOpPass : public ir::Pass {
                             const std::vector<ir::Node *> &all_reduce_ops,
 #if defined(PADDLE_WITH_NCCL)
                             const platform::NCCLCommunicator *multi_nccl_ctxs,
+#elif defined(PADDLE_WITH_XPU_BKCL)
+                            const platform::BKCLCommunicator *multi_bkcl_ctxs,
 #endif
                             ir::Graph *result) const {
     std::vector<details::VarHandleBase *> inputs;
@@ -182,6 +190,9 @@ class FuseAllReduceOpPass : public ir::Pass {
 #if defined(PADDLE_WITH_NCCL)
     CreateFusedAllReduceOp(inputs, outputs, num_of_all_reduce, places,
                            local_scopes, multi_nccl_ctxs, result);
+#elif defined(PADDLE_WITH_XPU_BKCL)
+    CreateFusedAllReduceOp(inputs, outputs, num_of_all_reduce, places,
+                           local_scopes, multi_bkcl_ctxs, result);
 #else
     CreateFusedAllReduceOp(inputs, outputs, num_of_all_reduce, places,
                            local_scopes, result);
@@ -197,12 +208,18 @@ class FuseAllReduceOpPass : public ir::Pass {
       const std::vector<Scope *> &local_scopes,
 #if defined(PADDLE_WITH_NCCL)
       const platform::NCCLCommunicator *multi_nccl_ctxs,
+#elif defined(PADDLE_WITH_XPU_BKCL)
+      const platform::BKCLCommunicator *multi_bkcl_ctxs,
 #endif
       ir::Graph *result) const {
 #if defined(PADDLE_WITH_NCCL)
     auto *op_handle = new details::FusedAllReduceOpHandle(
         result->CreateEmptyNode("fused_all_reduce", ir::Node::Type::kOperation),
         local_scopes, places, num_of_all_reduce, multi_nccl_ctxs);
+#elif defined(PADDLE_WITH_XPU_BKCL)
+    auto *op_handle = new details::FusedAllReduceOpHandle(
+        result->CreateEmptyNode("fused_all_reduce", ir::Node::Type::kOperation),
+        local_scopes, places, num_of_all_reduce, multi_bkcl_ctxs);
 #else
     auto *op_handle = new details::FusedAllReduceOpHandle(
         result->CreateEmptyNode("fused_all_reduce", ir::Node::Type::kOperation),
@@ -221,6 +238,10 @@ class FuseAllReduceOpPass : public ir::Pass {
     if (!multi_nccl_ctxs) {
       SetCommunicationContext(places, op_handle);
     }
+#elif defined(PADDLE_WITH_XPU_BKCL)
+    if (!multi_bkcl_ctxs) {
+      SetCommunicationContext(places, op_handle);
+    }
 #else
     SetCommunicationContext(places, op_handle);
 #endif

paddle/fluid/framework/ir/multi_devices_graph_pass/multi_devices_graph_pass.cc

@@ -162,6 +162,12 @@ void MultiDevSSAGraphBuilderBase::Init() const {
   if (multi_nccl_ctxs_) {
     nccl_ctxs_ = multi_nccl_ctxs_->DefaultFlatCtx();
   }
+#elif defined(PADDLE_WITH_XPU_BKCL)
+  multi_bkcl_ctxs_ = &Get<platform::BKCLCommunicator>(details::kBKCLCtxs);
+  bkcl_ctxs_ = nullptr;
+  if (multi_bkcl_ctxs_) {
+    bkcl_ctxs_ = multi_bkcl_ctxs_->DefaultFlatCtx();
+  }
 #endif
   PADDLE_ENFORCE_EQ(
       places_.size(), local_scopes_.size(),
@@ -371,6 +377,11 @@ void MultiDevSSAGraphBuilderBase::SetCommunicationContext(
     op_handle->SetDeviceContext(p,
                                 platform::DeviceContextPool::Instance().Get(p));
   }
+#elif defined(PADDLE_WITH_XPU_BKCL)
+  if (bkcl_ctxs_ == nullptr) {
+    op_handle->SetDeviceContext(p,
+                                platform::DeviceContextPool::Instance().Get(p));
+  }
 #else
   op_handle->SetDeviceContext(p,
                               platform::DeviceContextPool::Instance().Get(p));
@@ -384,6 +395,10 @@ void MultiDevSSAGraphBuilderBase::CreateBroadcastOp(ir::Graph *result,
   auto *op_handle = new details::BroadcastOpHandle(
       result->CreateEmptyNode("broadcast", ir::Node::Type::kOperation),
       local_scopes_, places_, nccl_ctxs_);
+#elif defined(PADDLE_WITH_XPU_BKCL)
+  auto *op_handle = new details::BroadcastOpHandle(
+      result->CreateEmptyNode("broadcast", ir::Node::Type::kOperation),
+      local_scopes_, places_, bkcl_ctxs_);
 #else
   auto *op_handle = new details::BroadcastOpHandle(
       result->CreateEmptyNode("broadcast", ir::Node::Type::kOperation),
@@ -417,6 +432,10 @@ void MultiDevSSAGraphBuilderBase::CreateFusedBroadcastOp(
   auto *op_handle = new details::FusedBroadcastOpHandle(
       result->CreateEmptyNode("fused_broadcast", ir::Node::Type::kOperation),
       local_scopes_, places_, nccl_ctxs_);
+#elif defined(PADDLE_WITH_XPU_BKCL)
+  auto *op_handle = new details::FusedBroadcastOpHandle(
+      result->CreateEmptyNode("fused_broadcast", ir::Node::Type::kOperation),
+      local_scopes_, places_, bkcl_ctxs_);
 #else
   auto *op_handle = new details::FusedBroadcastOpHandle(
       result->CreateEmptyNode("fused_broadcast", ir::Node::Type::kOperation),
@@ -487,6 +506,11 @@ void MultiDevSSAGraphBuilderBase::CreateAllReduceOp(ir::Graph *result,
         new details::AllReduceOpHandle(
             result->CreateEmptyNode("allreduce", ir::Node::Type::kOperation),
             scopes, places, multi_nccl_ctxs_));
+#elif defined(PADDLE_WITH_XPU_BKCL)
+    result->Get<GraphOps>(kGraphOps).emplace_back(
+        new details::AllReduceOpHandle(
+            result->CreateEmptyNode("allreduce", ir::Node::Type::kOperation),
+            scopes, places, multi_bkcl_ctxs_));
 #else
     result->Get<GraphOps>(kGraphOps).emplace_back(
         new details::AllReduceOpHandle(
@@ -565,6 +589,10 @@ details::VarHandle *MultiDevSSAGraphBuilderBase::CreateReduceOp(
   result->Get<GraphOps>(kGraphOps).emplace_back(new details::ReduceOpHandle(
       result->CreateEmptyNode("reduce", ir::Node::Type::kOperation),
       local_scopes_, places_, nccl_ctxs_));
+#elif defined(PADDLE_WITH_XPU_BKCL)
+  result->Get<GraphOps>(kGraphOps).emplace_back(new details::ReduceOpHandle(
+      result->CreateEmptyNode("reduce", ir::Node::Type::kOperation),
+      local_scopes_, places_, bkcl_ctxs_));
 #else
   result->Get<GraphOps>(kGraphOps).emplace_back(new details::ReduceOpHandle(
       result->CreateEmptyNode("reduce", ir::Node::Type::kOperation),

paddle/fluid/framework/ir/multi_devices_graph_pass/multi_devices_graph_pass.h

@@ -39,8 +39,13 @@ class Graph;
 
 namespace paddle {
 namespace platform {
+#if defined(PADDLE_WITH_NCCL)
 class NCCLContextMap;
 class NCCLCommunicator;
+#elif defined(PADDLE_WITH_XPU_BKCL)
+class BKCLContextMap;
+class BKCLCommunicator;
+#endif
 }
 
 namespace framework {
@@ -114,6 +119,9 @@ class MultiDevSSAGraphBuilderBase : public ir::Pass {
 #if defined(PADDLE_WITH_NCCL)
   mutable platform::NCCLContextMap *nccl_ctxs_{nullptr};
   mutable platform::NCCLCommunicator *multi_nccl_ctxs_{nullptr};
+#elif defined(PADDLE_WITH_XPU_BKCL)
+  mutable platform::BKCLContextMap *bkcl_ctxs_{nullptr};
+  mutable platform::BKCLCommunicator *multi_bkcl_ctxs_{nullptr};
 #endif
 
   mutable std::string loss_var_name_;

paddle/fluid/framework/parallel_executor.cc

@@ -93,6 +93,8 @@ class ParallelExecutorPrivate {
     }
   }
 
+  bool IsUseCUDA(DeviceType use_device);
+
   void SetHasFeed(size_t dev_idx, bool has_feed = true);
 
   bool AllowPartialFeed() const;
@@ -268,6 +270,90 @@ class ParallelExecutorPrivate {
   }
 #endif
 
+#if defined(PADDLE_WITH_XPU_BKCL)
+  void InitBKCLCtxs(framework::Scope *scope, const BuildStrategy &bst) {
+    VLOG(1) << "bkcl comm num:" << bst.bkcl_comm_num_ << ", nranks:" << nranks_
+            << ", num_trainers:" << bst.num_trainers_
+            << ", trainer_id:" << bst.trainer_id_;
+
+    PADDLE_ENFORCE_EQ(bst.use_hierarchical_allreduce_, false,
+                      platform::errors::Unimplemented(
+                          "xpu doesn't support use_hierarchical_allreduce"));
+
+    std::vector<BKCLUniqueId *> flat_bkcl_ids;
+    if (nranks_ == 1) {
+      // FIXME(gongwb): need not to create bkclid when nranks==1
+      bkcl_ctxs_->InitFlatCtxs(places_, flat_bkcl_ids, bst.num_trainers_,
+                               bst.trainer_id_);
+      return;
+    }
+
+    if (bst.enable_parallel_graph_) {
+      VLOG(1) << "use only one bkclid in pg model";
+
+      BKCLUniqueId *bkcl_id = nullptr;
+
+      std::string var_name = platform::GetFlatBKCLVarName(0);
+      auto bkcl_id_var = scope->FindVar(var_name);
+      std::unique_ptr<BKCLUniqueId> id(new BKCLUniqueId());
+      if (bkcl_id_var) {
+        bkcl_id = bkcl_id_var->GetMutable<BKCLUniqueId>();
+      } else {
+        PADDLE_ENFORCE_EQ(
+            bkcl_get_unique_id(id.get()), BKCL_SUCCESS,
+            platform::errors::Unavailable("bkcl get unique id failed"));
+        bkcl_id = id.get();
+      }
+
+      flat_bkcl_ids.push_back(bkcl_id);
+
+      bkcl_ctxs_->InitFlatCtxs(places_, flat_bkcl_ids, bst.num_trainers_,
+                               bst.trainer_id_);
+      VLOG(1) << "init bst bkcl context complete!";
+      return;
+    }
+
+    // num_trainers ==1 && places > 1
+    if (bst.num_trainers_ == 1) {
+      bkcl_ctxs_->InitFlatCtxs(places_, flat_bkcl_ids, bst.num_trainers_,
+                               bst.trainer_id_);
+      return;
+    }
+
+    for (int i = 0; i < static_cast<int>(bst.bkcl_comm_num_); i++) {
+      std::string var_name = platform::GetFlatBKCLVarName(i);
+      auto bkcl_id_var = scope->FindVar(var_name);
+      PADDLE_ENFORCE_NOT_NULL(
+          bkcl_id_var,
+          platform::errors::NotFound("can't find %s bkcl_id_var", var_name));
+      auto bkcl_id = bkcl_id_var->GetMutable<BKCLUniqueId>();
+      flat_bkcl_ids.push_back(bkcl_id);
+    }
+
+    bkcl_ctxs_->InitFlatCtxs(places_, flat_bkcl_ids, bst.num_trainers_,
+                             bst.trainer_id_);
+  }
+
+  void InitOrGetBKCLCommunicator(framework::Scope *scope,
+                                 const BuildStrategy &bst) {
+    const std::string var_name = "BKCLCommunicator";
+    auto var = scope->FindVar(var_name);
+    if (var != nullptr) {
+      PADDLE_ENFORCE_EQ(var->IsInitialized(), true,
+                        platform::errors::PreconditionNotMet(
+                            "if %s exists, it must be initialized", var_name));
+      VLOG(1) << "find " << var_name
+              << " in scope, so use it and does not recreate!";
+      bkcl_ctxs_ = var->GetMutable<platform::BKCLCommunicator>();
+      return;
+    }
+
+    VLOG(1) << "not find " << var_name << " in scope, so recreate it!";
+    bkcl_ctxs_ = scope->Var(var_name)->GetMutable<platform::BKCLCommunicator>();
+    InitBKCLCtxs(scope, bst);
+  }
+#endif
+
   inline bool IsPersistable(const std::string &name) const {
     auto iter = is_persistable_.find(name);
     return iter != is_persistable_.end() && iter->second;
@@ -284,9 +370,11 @@ class ParallelExecutorPrivate {
 
 #if defined(PADDLE_WITH_NCCL)
   platform::NCCLCommunicator *nccl_ctxs_{nullptr};
+#elif defined(PADDLE_WITH_XPU_BKCL)
+  platform::BKCLCommunicator *bkcl_ctxs_{nullptr};
 #endif
   bool own_local_scope_;
-  bool use_cuda_;
+  DeviceType use_device_;
   bool use_all_reduce_;
   size_t nranks_;
 
@@ -296,6 +384,10 @@ class ParallelExecutorPrivate {
   details::ParallelSSAGraphExecutor *inference_executor_{nullptr};
 };
 
+bool ParallelExecutorPrivate::IsUseCUDA(DeviceType use_device) {
+  return use_device == p::kCUDA;
+}
+
 void ParallelExecutorPrivate::SetHasFeed(size_t dev_idx, bool has_feed) {
   if (inference_executor_) {
     inference_executor_->SetHasFeed(dev_idx, has_feed);
@@ -340,7 +432,7 @@ ir::Graph *ParallelExecutorPrivate::ApplyMemoryOptimizePass(ir::Graph *graph) {
     auto addto_pass = ir::PassRegistry::Instance().Get("inplace_addto_op_pass");
     addto_pass->SetNotOwned(ir::kMemOptVarInfoMapList, &mem_opt_var_infos_);
     addto_pass->SetNotOwned(ir::kLastLiveOpsOfVars, &last_live_ops_of_vars);
-    addto_pass->SetNotOwned(ir::kUseCuda, &use_cuda_);
+    addto_pass->Set(ir::kUseCuda, new bool(use_device_ == p::kCUDA));
     VLOG(10) << "Start to apply inplace_addto_op_pass";
     graph = addto_pass->Apply(graph);
     VLOG(10) << "inplace_addto_op_pass Applied";
@@ -351,7 +443,7 @@ ir::Graph *ParallelExecutorPrivate::ApplyMemoryOptimizePass(ir::Graph *graph) {
         ir::PassRegistry::Instance().Get("buffer_shared_inplace_pass");
     inplace_pass->SetNotOwned(ir::kMemOptVarInfoMapList, &mem_opt_var_infos_);
     inplace_pass->SetNotOwned(ir::kLastLiveOpsOfVars, &last_live_ops_of_vars);
-    inplace_pass->SetNotOwned(ir::kUseCuda, &use_cuda_);
+    inplace_pass->Set(ir::kUseCuda, new bool(use_device_ == p::kCUDA));
     VLOG(10) << "Start to apply buffer_shared_inplace_pass";
     graph = inplace_pass->Apply(graph);
     VLOG(10) << "buffer_shared_inplace_pass Applied";
@@ -366,7 +458,8 @@ ir::Graph *ParallelExecutorPrivate::ApplyMemoryOptimizePass(ir::Graph *graph) {
                                             &mem_opt_var_infos_);
     cross_op_memory_reuse_pass->SetNotOwned(ir::kLastLiveOpsOfVars,
                                             &last_live_ops_of_vars);
-    cross_op_memory_reuse_pass->SetNotOwned(ir::kUseCuda, &use_cuda_);
+    cross_op_memory_reuse_pass->Set(ir::kUseCuda,
+                                    new bool(use_device_ == p::kCUDA));
     VLOG(10) << "Start to apply buffer_shared_cross_op_memory_reuse_pass";
     graph = cross_op_memory_reuse_pass->Apply(graph);
     VLOG(10) << "buffer_shared_cross_op_memory_reuse_pass Applied";
@@ -386,8 +479,8 @@ ir::Graph *ParallelExecutorPrivate::ApplyMemoryOptimizePass(ir::Graph *graph) {
       continue;
     }
     std::unique_ptr<GarbageCollector> gc;
-#ifdef PADDLE_WITH_CUDA
     if (platform::is_gpu_place(place)) {
+#ifdef PADDLE_WITH_CUDA
       if (IsFastEagerDeletionModeEnabled()) {
         gc.reset(new UnsafeFastGPUGarbageCollector(
             BOOST_GET_CONST(platform::CUDAPlace, place), max_memory_size));
@@ -396,20 +489,29 @@ ir::Graph *ParallelExecutorPrivate::ApplyMemoryOptimizePass(ir::Graph *graph) {
             BOOST_GET_CONST(platform::CUDAPlace, place), max_memory_size));
       }
       VLOG(10) << "Created " << i << "-th GarbageCollector at " << place;
-    } else {
+#else
+      PADDLE_THROW(platform::errors::PermissionDenied(
+          "Paddle can't use CUDA device since it's not compiled with CUDA,"
+          "Please recompile or reinstall Paddle with GPU support."));
 #endif
-      if (platform::is_cpu_place(place)) {
-        gc.reset(new CPUGarbageCollector(
-            BOOST_GET_CONST(platform::CPUPlace, place), max_memory_size));
-        VLOG(10) << "Created GarbageCollector at " << place;
-      } else {
-        PADDLE_THROW(platform::errors::PreconditionNotMet(
-            "Unsupported place for garbage collection"));
-      }
-#ifdef PADDLE_WITH_CUDA
-    }
+    } else if (platform::is_xpu_place(place)) {
+#if defined(PADDLE_WITH_XPU)
+      gc.reset(new XPUGarbageCollector(
+          BOOST_GET_CONST(platform::XPUPlace, place), max_memory_size));
+      VLOG(10) << "Created " << i << "-th GarbageCollector at " << place;
+#else
+      PADDLE_THROW(platform::errors::PermissionDenied(
+          "Paddle can't use XPU device since it's not compiled with XPU,"
+          "Please recompile or reinstall Paddle with XPU support."));
 #endif
-
+    } else if (platform::is_cpu_place(place)) {
+      gc.reset(new CPUGarbageCollector(
+          BOOST_GET_CONST(platform::CPUPlace, place), max_memory_size));
+      VLOG(10) << "Created GarbageCollector at " << place;
+    } else {
+      PADDLE_THROW(platform::errors::PreconditionNotMet(
+          "Unsupported place for garbage collection"));
+    }
     gcs_.emplace(place, std::move(gc));
   }
 
@@ -510,13 +612,10 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
                                    const BuildStrategy &build_strategy,
                                    ir::Graph *graph)
     : member_(new ParallelExecutorPrivate(places, scope)) {
-  PADDLE_ENFORCE(places.size() > 0 && !is_xpu_place(places[0]),
-                 platform::errors::Unavailable(
-                     "XPU is not supported in ParallelExecutor"));
   InitP2P(places);
   ir::InitReaderQueueDeviceCount(graph, *(member_->global_scope_),
                                  member_->places_.size());
-  member_->use_cuda_ = exec_strategy.use_cuda_;
+  member_->use_device_ = exec_strategy.use_device_;
   member_->build_strategy_ = build_strategy;
   member_->use_all_reduce_ = member_->build_strategy_.reduce_ ==
                              BuildStrategy::ReduceStrategy::kAllReduce;
@@ -529,7 +628,7 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
     member_->use_all_reduce_ = true;
   }
 #if defined(PADDLE_WITH_CUDA) && defined(_WIN32)
-  if (member_->use_cuda_) {
+  if (member_->IsUseCUDA(member_->use_device_)) {
     PADDLE_ENFORCE_EQ(
         places.size(), 1,
         platform::errors::Unavailable("Windows can support Single GPU only."));
@@ -537,19 +636,30 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
 #endif
 
 #if defined(PADDLE_WITH_CUDA) && !defined(PADDLE_WITH_NCCL)
-  PADDLE_ENFORCE_EQ(
-      places.size(), 1,
-      platform::errors::PermissionDenied(
-          "Your machine has multiple cards, "
-          "but the WITH_NCCL option is not turned on during compilation, "
-          "and you cannot use multi-card training or prediction. "
-          "Please recompile and turn on the WITH_NCCL option."));
+  if (member_->IsUseCUDA(member_->use_device_)) {
+    PADDLE_ENFORCE_EQ(
+        places.size(), 1,
+        platform::errors::PermissionDenied(
+            "Your machine has multiple cards, "
+            "but the WITH_NCCL option is not turned on during compilation, "
+            "and you cannot use multi-card training or prediction. "
+            "Please recompile and turn on the WITH_NCCL option."));
+  }
 #endif
 
+  std::string device_name;
+  if (member_->use_device_ == p::kCPU) {
+    device_name = "CPU";
+  } else if (member_->use_device_ == p::kCUDA) {
+    device_name = "CUDA";
+  } else {
+    device_name = "XPU";
+  }
+
   VLOG(1) << string::Sprintf(
       "The Program will be executed on %s using ParallelExecutor, %lu "
       "cards are used, so %lu programs are executed in parallel.",
-      (member_->use_cuda_ ? "CUDA" : "CPU"), places.size(), places.size());
+      device_name, places.size(), places.size());
 
   // Step 1. Bcast the bcast_vars to devs.
   // Create local scopes
@@ -573,7 +683,7 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
 
   std::vector<ir::Graph *> graphs;
   if (member_->build_strategy_.async_mode_) {
-    PADDLE_ENFORCE_EQ(member_->use_cuda_, false,
+    PADDLE_ENFORCE_EQ(member_->IsUseCUDA(member_->use_device_), false,
                       platform::errors::Unavailable(
                           "gpu mode does not support async_mode_ now!"));
     graphs.push_back(graph);
@@ -596,7 +706,7 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
               << "you can force it off by env FLAGS_enable_parallel_graph=0";
   }
 
-  if (member_->use_cuda_ && member_->nranks_ > 1) {
+  if (member_->IsUseCUDA(member_->use_device_) && member_->nranks_ > 1) {
 #if defined(PADDLE_WITH_NCCL)
     member_->InitOrGetNCCLCommunicator(scope, &member_->build_strategy_);
 
@@ -616,6 +726,27 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
       auto &nccl_ctx = nccl_ctxs->at(member_->places_[dev_id]);
       dev_ctx->set_nccl_comm(nccl_ctx.comm());
     }
+#else
+    PADDLE_THROW(
+        platform::errors::PreconditionNotMet("Not compiled with CUDA."));
+#endif
+  }
+  if (member_->use_device_ == p::kXPU && member_->nranks_ > 1) {
+#if defined(PADDLE_WITH_XPU_BKCL)
+    member_->InitOrGetBKCLCommunicator(scope, member_->build_strategy_);
+
+    auto *bkcl_ctxs =
+        member_->bkcl_ctxs_->GetSyncBatchNormCtx(scope, member_->places_);
+    auto &pool = platform::DeviceContextPool::Instance();
+    for (size_t dev_id = 0; dev_id < member_->places_.size(); ++dev_id) {
+      auto *dev_ctx = static_cast<platform::XPUDeviceContext *>(
+          pool.Get(member_->places_[dev_id]));
+      auto &bkcl_ctx = bkcl_ctxs->at(member_->places_[dev_id]);
+      dev_ctx->set_bkcl_context(bkcl_ctx.comm());
+    }
+#else
+    PADDLE_THROW(
+        platform::errors::PreconditionNotMet("Not compiled with XPU."));
 #endif
   }
   // broadcast parameters from the 0th device to others:
@@ -645,36 +776,55 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
     VLOG(3) << "use local async mode";
     graph = member_->build_strategy_.Apply(
         graph, {member_->places_[0]}, loss_var_name,
-        {member_->local_scopes_[0]}, 1, member_->use_cuda_,
+        {member_->local_scopes_[0]}, 1, member_->use_device_,
         member_->nccl_ctxs_);
     for (size_t i = 1; i < member_->places_.size(); ++i) {
       graphs[i] = member_->build_strategy_.Apply(
           graphs[i], {member_->places_[i]}, loss_var_name,
-          {member_->local_scopes_[i]}, 1, member_->use_cuda_,
+          {member_->local_scopes_[i]}, 1, member_->use_device_,
           member_->nccl_ctxs_);
       async_graphs[i] = graphs[i];
     }
   } else {
     graph = member_->build_strategy_.Apply(
         graph, member_->places_, loss_var_name, member_->local_scopes_,
-        member_->nranks_, member_->use_cuda_, member_->nccl_ctxs_);
+        member_->nranks_, member_->use_device_, member_->nccl_ctxs_);
+  }
+#elif defined(PADDLE_WITH_XPU_BKCL)
+  if (member_->build_strategy_.async_mode_) {
+    VLOG(3) << "use local async mode";
+    graph = member_->build_strategy_.Apply(
+        graph, {member_->places_[0]}, loss_var_name,
+        {member_->local_scopes_[0]}, 1, member_->use_device_,
+        member_->bkcl_ctxs_);
+    for (size_t i = 1; i < member_->places_.size(); ++i) {
+      graphs[i] = member_->build_strategy_.Apply(
+          graphs[i], {member_->places_[i]}, loss_var_name,
+          {member_->local_scopes_[i]}, 1, member_->use_device_,
+          member_->bkcl_ctxs_);
+      async_graphs[i] = graphs[i];
+    }
+  } else {
+    graph = member_->build_strategy_.Apply(
+        graph, member_->places_, loss_var_name, member_->local_scopes_,
+        member_->nranks_, member_->use_device_, member_->bkcl_ctxs_);
   }
 #else
   if (member_->build_strategy_.async_mode_) {
     VLOG(3) << "use local async mode";
     graph = member_->build_strategy_.Apply(
         graph, {member_->places_[0]}, loss_var_name,
-        {member_->local_scopes_[0]}, 1, member_->use_cuda_);
+        {member_->local_scopes_[0]}, 1, member_->use_device_);
     for (size_t i = 1; i < member_->places_.size(); ++i) {
       graphs[i] = member_->build_strategy_.Apply(
           graphs[i], {member_->places_[i]}, loss_var_name,
-          {member_->local_scopes_[i]}, 1, member_->use_cuda_);
+          {member_->local_scopes_[i]}, 1, member_->use_device_);
       async_graphs[i] = graphs[i];
     }
   } else {
     graph = member_->build_strategy_.Apply(
         graph, member_->places_, loss_var_name, member_->local_scopes_,
-        member_->nranks_, member_->use_cuda_);
+        member_->nranks_, member_->use_device_);
   }
 #endif
 
@@ -854,6 +1004,63 @@ void ParallelExecutor::BCastParamsToDevices(
         }
         nccl_ctxs->WaitAll();
       }
+#endif
+    } else if (paddle::platform::is_xpu_place(main_tensor.place())) {
+#if defined(PADDLE_WITH_XPU_BKCL)
+      std::vector<void *> buffers;
+      buffers.reserve(member_->places_.size());
+      size_t numel = main_tensor.numel();
+      // TODO(liuyuhui): BKCL only support parameters using float type,
+      // other parameters need to be strongly converted to float before
+      // broadcasting,
+      // but broadcast is equivalent to no type of operation, does not affect
+      // correctness.
+      BKCLDataType data_type = BKCL_FLOAT;
+      // BKCLDataType data_type = platform::ToBKCLDataType(main_tensor.type());
+      for (size_t i = 0; i < member_->places_.size(); ++i) {
+        auto place = member_->places_[i];
+        void *buffer;
+
+        if (i == 0 && trainer_id == 0) {
+          buffer = const_cast<void *>(main_tensor.data<void>());
+        } else {
+          auto local_scope = member_->local_scopes_[i];
+          auto *t = local_scope->Var(var)->GetMutable<LoDTensor>();
+          t->Resize(dims);
+          buffer = t->mutable_data(place, main_tensor.type());
+        }
+        buffers.push_back(buffer);
+      }
+
+      PADDLE_ENFORCE_EQ(member_->places_.size(), buffers.size(),
+                        platform::errors::PreconditionNotMet(
+                            "variables' buffer size to bcast is %d, which is "
+                            "NOT equal to places size %d",
+                            buffers.size(), member_->places_.size()));
+      {
+        auto *bkcl_ctxs = member_->bkcl_ctxs_->DefaultFlatCtx();
+
+        PADDLE_ENFORCE_EQ(
+            bkcl_group_start(), BKCL_SUCCESS,
+            platform::errors::Unavailable("bkcl_group_start failed"));
+        for (size_t i = 0; i < member_->places_.size(); ++i) {
+          auto &bkcl_ctx = bkcl_ctxs->at(member_->places_[i]);
+          if (main_tensor.type() == framework::proto::VarType::INT64) {
+            numel *= 2;
+          }
+          PADDLE_ENFORCE_EQ(
+              bkcl_broadcast(bkcl_ctx.comm(), buffers[i], buffers[i], numel,
+                             data_type, 0, NULL),
+              BKCL_SUCCESS,
+              platform::errors::Unavailable("bkcl_broadcast failed"));
+        }
+        PADDLE_ENFORCE_EQ(
+            bkcl_group_end(), BKCL_SUCCESS,
+            platform::errors::Unavailable("bkcl_group_end failed"));
+      }
+#else
+      PADDLE_THROW(
+          platform::errors::PreconditionNotMet("Not compiled with BKCL."));
 #endif
     } else {
       platform::CPUPlace cpu;
@@ -872,7 +1079,8 @@ void ParallelExecutor::BCastParamsToDevices(
         // FIXME(zcd): LR_DECAY_COUNTER should not be shared. This is a hot fix.
         if (member_->build_strategy_.async_mode_) {
           share_memory();
-        } else if (member_->use_all_reduce_ || member_->use_cuda_ ||
+        } else if (member_->use_all_reduce_ ||
+                   member_->IsUseCUDA(member_->use_device_) ||
                    var == "@LR_DECAY_COUNTER@") {
           copy_memory();
         } else {
@@ -1103,7 +1311,7 @@ bool ParallelExecutor::EnableParallelGraphExecution(
     }
   }
 
-  if (!member_->use_all_reduce_ || !member_->use_cuda_) {
+  if (!member_->use_all_reduce_ || !member_->IsUseCUDA(member_->use_device_)) {
     if (build_strategy.enable_sequential_execution_ ||
         exec_strategy.type_ == ExecutionStrategy::ExecutorType::kExperimental) {
       enable_parallel_graph = false;

paddle/fluid/framework/parallel_executor.h

@@ -43,6 +43,8 @@ class ParallelExecutorPrivate;
 
 using details::BuildStrategy;
 using details::ExecutionStrategy;
+namespace p = paddle::platform;
+using DeviceType = paddle::platform::DeviceType;
 
 class ParallelExecutor {
   DISABLE_COPY_AND_ASSIGN(ParallelExecutor);

paddle/fluid/framework/var_type_traits.cc

@@ -30,6 +30,10 @@
 #include "paddle/fluid/operators/cudnn_rnn_cache.h"
 #endif
 
+#if defined(PADDLE_WITH_XPU_BKCL)
+#include "paddle/fluid/platform/bkcl_helper.h"
+#endif
+
 namespace paddle {
 namespace framework {
 

paddle/fluid/framework/var_type_traits.h

@@ -31,6 +31,10 @@
 #endif
 #endif
 
+#if defined(PADDLE_WITH_XPU_BKCL)
+#include "xpu/bkcl.h"
+#endif
+
 // Users should add forward declarations here
 namespace paddle {
 
@@ -41,6 +45,10 @@ class Communicator;
 class NCCLCommunicator;
 #endif
 #endif
+
+#if defined(PADDLE_WITH_XPU_BKCL)
+class BKCLCommunicator;
+#endif
 }  // namespace platform
 
 namespace framework {
@@ -148,6 +156,9 @@ using VarTypeRegistry = detail::VarTypeRegistryImpl<
     ncclUniqueId, platform::Communicator, platform::NCCLCommunicator,
 #endif
     operators::CudnnRNNCache,
+#endif
+#if defined(PADDLE_WITH_XPU_BKCL)
+    BKCLUniqueId, platform::BKCLCommunicator,
 #endif
     int, float>;
 

paddle/fluid/framework/var_type_traits_test.cc

@@ -31,6 +31,9 @@
 #include "paddle/fluid/operators/conv_cudnn_op_cache.h"
 #include "paddle/fluid/operators/cudnn_rnn_cache.h"
 #endif
+#if defined(PADDLE_WITH_XPU_BKCL)
+#include "paddle/fluid/platform/bkcl_helper.h"
+#endif
 
 namespace paddle {
 namespace framework {

paddle/fluid/platform/bkcl_helper.h

+//   Copyright (c) 2020 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.
+
+#ifndef _WIN32
+#if defined(PADDLE_WITH_XPU_BKCL)
+#pragma once
+
+#include <stdio.h>
+#include <memory>
+#include <string>
+#include <thread>  // NOLINT
+#include <typeindex>
+#include <unordered_map>
+#include <vector>
+
+#include "paddle/fluid/framework/data_type.h"
+#include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/platform/device_context.h"
+#include "paddle/fluid/platform/enforce.h"
+#include "paddle/fluid/platform/float16.h"
+#include "paddle/fluid/platform/place.h"
+#include "xpu/bkcl.h"
+#include "xpu/runtime.h"
+
+#define BKCL_ID_VARNAME "BKCLID"
+
+namespace paddle {
+namespace platform {
+
+inline BKCLDataType ToBKCLDataType(framework::proto::VarType::Type type) {
+  if (type == framework::proto::VarType::FP32) {
+    return BKCL_FLOAT;
+  } else {
+    PADDLE_THROW(
+        platform::errors::Unimplemented("BKCL currently only support FP32, "
+                                        "other data types are not supported."));
+  }
+}
+
+struct BKCLContext {
+  std::unique_ptr<platform::XPUDeviceContext> ctx_;
+  BKCLContext_t comm_;
+
+  explicit BKCLContext(int dev_id)
+      : ctx_(new platform::XPUDeviceContext(XPUPlace(dev_id))),
+        comm_{nullptr} {}
+
+  BKCLContext_t comm() const { return comm_; }
+
+  int device_id() const {
+    return BOOST_GET_CONST(platform::XPUPlace, ctx_->GetPlace()).device;
+  }
+};
+
+struct InitBKCLPara {
+  BKCLUniqueId *bkcl_id;
+  int rank;
+  int nranks;
+  int dev_id;
+  BKCLContext_t *ctx;
+};
+
+static void *init_bkcl_context_func(void *args) {
+  struct InitBKCLPara *para = (struct InitBKCLPara *)args;
+  PADDLE_ENFORCE_EQ(xpu_set_device(para->dev_id), XPU_SUCCESS,
+                    platform::errors::PreconditionNotMet(
+                        "xpu_set_device failed[%d]", para->dev_id));
+  PADDLE_ENFORCE_EQ(
+      bkcl_init_rank(para->ctx, para->rank, para->nranks, para->bkcl_id),
+      BKCL_SUCCESS,
+      platform::errors::PreconditionNotMet("bkcl_init_rank failed"));
+  return nullptr;
+}
+
+struct BKCLContextMap {
+  std::unordered_map<int, BKCLContext> contexts_;
+  std::vector<int> order_;
+  std::vector<platform::Place> places_;
+  size_t num_trainers_;
+  size_t trainer_id_;
+  BKCLUniqueId *bkcl_id_;
+
+  explicit BKCLContextMap(const std::vector<platform::Place> &places,
+                          BKCLUniqueId *bkcl_id = nullptr,
+                          size_t num_trainers = 1, size_t trainer_id = 0) {
+    places_ = places;
+    bkcl_id_ = bkcl_id;
+    num_trainers_ = num_trainers;
+    trainer_id_ = trainer_id;
+  }
+
+  // Synchronization is required and can only be initialized with
+  // multithreading.
+  int init() {
+    PADDLE_ENFORCE_EQ(!places_.empty(), true,
+                      platform::errors::InvalidArgument(
+                          "The BKCL place should not be empty."));
+    order_.reserve(places_.size());
+    for (auto &p : places_) {
+      int dev_id = BOOST_GET_CONST(platform::XPUPlace, p).device;
+      order_.emplace_back(dev_id);
+      contexts_.emplace(dev_id, BKCLContext(dev_id));
+    }
+    PADDLE_ENFORCE_EQ(
+        order_.size(), contexts_.size(),
+        platform::errors::Unavailable("BKCL Context Map does not support "
+                                      "contain two or more same device"));
+
+    std::unique_ptr<BKCLContext_t[]> comms(new BKCLContext_t[order_.size()]);
+    std::unique_ptr<InitBKCLPara[]> paras(new InitBKCLPara[order_.size()]);
+    std::unique_ptr<pthread_t[]> pids(new pthread_t[order_.size()]);
+    BKCLResult_t ret;
+    BKCLUniqueId id;
+    // if num_trainers == 1, should create a new bkcl id for local comms.
+    if (num_trainers_ == 1 && bkcl_id_ == nullptr) {
+      ret = bkcl_get_unique_id(&id);
+      PADDLE_ENFORCE_EQ(BKCL_SUCCESS, ret,
+                        platform::errors::PreconditionNotMet(
+                            "bkcl get unique id failed [%d]", ret));
+      bkcl_id_ = &id;
+    }
+    PADDLE_ENFORCE_NOT_NULL(bkcl_id_, platform::errors::InvalidArgument(
+                                          "The BKCL id should not be null."));
+    {
+      int nranks = num_trainers_ * order_.size();
+      for (size_t i = 0; i < order_.size(); ++i) {
+        int rank;
+        if (order_.size() > 1) {
+          rank = trainer_id_ * order_.size() + i;
+        } else {
+          rank = trainer_id_;
+        }
+        VLOG(1) << "init bkcl rank:" << rank << ", nranks:" << nranks
+                << ", xpu_id:" << order_[i];
+        paras[i].rank = rank;
+        paras[i].nranks = nranks;
+        paras[i].dev_id = order_[i];
+        paras[i].bkcl_id = bkcl_id_;
+        paras[i].ctx = &comms[i];
+        PADDLE_ENFORCE_EQ(
+            pthread_create(&pids[i], nullptr, init_bkcl_context_func,
+                           reinterpret_cast<void *>(&paras[i])),
+            0, platform::errors::External("pthread_create failed"));
+      }
+      for (size_t i = 0; i < order_.size(); i++) {
+        pthread_join(pids[i], nullptr);
+      }
+    }
+    int i = 0;
+    for (auto &dev_id : order_) {
+      contexts_.at(dev_id).comm_ = comms[i++];
+    }
+    return 0;
+  }
+
+  BKCLContextMap(const BKCLContextMap &other) = delete;
+  BKCLContextMap &operator=(const BKCLContextMap &other) = delete;
+
+  XPUDeviceContext *DevCtx(int dev_id) const { return at(dev_id).ctx_.get(); }
+
+  XPUDeviceContext *DevCtx(platform::Place p) const {
+    return DevCtx(BOOST_GET_CONST(platform::XPUPlace, p).device);
+  }
+
+  const BKCLContext &at(platform::Place p) const {
+    return this->at(BOOST_GET_CONST(platform::XPUPlace, p).device);
+  }
+
+  const BKCLContext &at(int dev_id) const { return contexts_.at(dev_id); }
+
+  void WaitAll() {
+    for (auto &p : contexts_) {
+      p.second.ctx_->Wait();
+    }
+  }
+};
+
+inline std::string GetFlatBKCLVarName(size_t pos) {
+  if (pos == 0) {
+    return BKCL_ID_VARNAME;
+  }
+  return string::Sprintf("%s_%d", BKCL_ID_VARNAME, static_cast<int>(pos));
+}
+
+class BKCLCommunicator {
+ public:
+  BKCLCommunicator() {}
+  virtual ~BKCLCommunicator() {}
+
+  BKCLContextMap *DefaultFlatCtx() const {
+    if (flat_ctxs_.size() == 0) {
+      return nullptr;
+    }
+
+    return flat_ctxs_[0].get();
+  }
+
+  std::vector<std::unique_ptr<BKCLContextMap>> *GetFlatCtxs() {
+    return &flat_ctxs_;
+  }
+
+  BKCLContextMap *GetFlatCtx(size_t run_order) const {
+    return flat_ctxs_[run_order % flat_ctxs_.size()].get();
+  }
+
+  BKCLContextMap *GetRunEnvBKCLCtx(size_t run_order,
+                                   bool use_hierarchical_allreduce) const {
+    PADDLE_ENFORCE_EQ(use_hierarchical_allreduce, false,
+                      platform::errors::Unimplemented(
+                          "Hierarchical all reduce is not support for XPU"));
+    return GetFlatCtx(run_order);
+  }
+
+  /*
+   *It meets error when allreduce ophandle and sync_batch_norm_op use
+   *bkcl_all_reduce
+   *parallelly. So create a new bkcl comm for sync_batch_norm_op. And these
+   *codes should be polished with a unified bkcl management.
+  */
+  BKCLContextMap *GetSyncBatchNormCtx(
+      framework::Scope *scope, const std::vector<platform::Place> &places) {
+    auto *bkcl_id_var = scope->FindVar(BKCL_ID_VARNAME);
+    if (bkcl_id_var != nullptr) {
+      return DefaultFlatCtx();
+    }
+
+    if (sync_batch_norm_ctx_.get() == nullptr) {
+      sync_batch_norm_ctx_.reset(new BKCLContextMap(places));
+      sync_batch_norm_ctx_->init();
+    }
+    return sync_batch_norm_ctx_.get();
+  }
+
+  void InitFlatCtxs(const std::vector<platform::Place> &places,
+                    const std::vector<BKCLUniqueId *> &bkcl_ids,
+                    size_t trainers_num, size_t trainer_id) {
+    if (bkcl_ids.size() == 0) {
+      auto ptr = new platform::BKCLContextMap(places);
+      ptr->init();
+      VLOG(1) << "init local trainer";
+      flat_ctxs_.emplace_back(ptr);
+      return;
+    }
+
+    PADDLE_ENFORCE_EQ(bkcl_ids.size(), 1,
+                      platform::errors::Unimplemented(
+                          "Multi-all-reduce-ring is not support for XPU"));
+    for (size_t i = 0; i < bkcl_ids.size(); i++) {
+      auto ptr = new platform::BKCLContextMap(places, bkcl_ids[i], trainers_num,
+                                              trainer_id);
+      ptr->init();
+      VLOG(1) << "init trainer_id:" << trainer_id << ", comm no:" << i;
+      flat_ctxs_.emplace_back(ptr);
+    }
+  }
+
+ protected:
+  // Support multi bkcl comm on default bkcl ring while BKCLContextMap can't.
+  std::vector<std::unique_ptr<BKCLContextMap>> flat_ctxs_;
+
+  // just used for sync_batch_norm op.
+  std::unique_ptr<BKCLContextMap> sync_batch_norm_ctx_;
+};
+
+}  // namespace platform
+}  // namespace paddle
+
+#endif  // PADDLE_WITH_XPU_BKCL
+#endif

paddle/fluid/platform/device_context.cc

@@ -29,23 +29,39 @@ namespace memory {
 
 AllocationPtr Alloc(const platform::DeviceContext& dev_ctx, size_t size) {
   auto place = dev_ctx.GetPlace();
-#ifdef PADDLE_WITH_CUDA
-  if (size == 0 || !platform::is_gpu_place(place)) {
+  if (size == 0) {
     return Alloc(place, size);
   }
-  auto* default_dev_ctx = static_cast<platform::CUDADeviceContext*>(
-      platform::DeviceContextPool::Instance().Get(place));
-  auto& desired_dev_ctx =
-      static_cast<const platform::CUDADeviceContext&>(dev_ctx);
-  if (default_dev_ctx->stream() == desired_dev_ctx.stream()) {
+
+  if (platform::is_gpu_place(place)) {
+#ifdef PADDLE_WITH_CUDA
+    auto* default_dev_ctx = static_cast<platform::CUDADeviceContext*>(
+        platform::DeviceContextPool::Instance().Get(place));
+    auto& desired_dev_ctx =
+        static_cast<const platform::CUDADeviceContext&>(dev_ctx);
+    if (default_dev_ctx->stream() == desired_dev_ctx.stream()) {
+      return Alloc(place, size);
+    } else {
+      return allocation::CUDADeviceContextAllocatorPool::Instance().Alloc(
+          desired_dev_ctx, size);
+    }
+#else
+    PADDLE_THROW(platform::errors::PermissionDenied(
+        "Paddle can't use CUDA device since it's not compiled with CUDA,"
+        "Please recompile or reinstall Paddle with GPU support."));
+#endif
+  } else if (platform::is_xpu_place(place)) {
+#ifdef PADDLE_WITH_XPU
+    // TODO(liuyuhui): Consider xpu stream later
     return Alloc(place, size);
-  } else {
-    return allocation::CUDADeviceContextAllocatorPool::Instance().Alloc(
-        desired_dev_ctx, size);
-  }
 #else
-  return Alloc(place, size);
+    PADDLE_THROW(platform::errors::PermissionDenied(
+        "Paddle can't use XPU device since it's not compiled with XPU,"
+        "Please recompile or reinstall Paddle with XPU support."));
 #endif
+  } else {
+    return Alloc(place, size);
+  }
 }
 
 }  // namespace memory

paddle/fluid/platform/device_context.h

@@ -30,6 +30,10 @@ limitations under the License. */
 #include "paddle/fluid/platform/gpu_info.h"
 #endif
 
+#if defined(PADDLE_WITH_XPU_BKCL)
+#include "xpu/bkcl.h"
+#endif
+
 #ifdef PADDLE_WITH_MKLDNN
 #include "mkldnn.hpp"
 #include "paddle/fluid/framework/data_layout.h"
@@ -52,11 +56,29 @@ struct GpuDevice;
 
 #ifdef PADDLE_WITH_XPU
 #include "paddle/fluid/platform/xpu_header.h"
+#include "paddle/fluid/platform/xpu_info.h"
 #endif
 
 namespace paddle {
 namespace platform {
 
+#ifdef PADDLE_WITH_CUDA
+/*Set the value of the global variable allow_tf32_cublas*/
+void SetAllowTF32Cublas(bool active);
+/*Get the global variable allow_tf32_cublas value*/
+bool AllowTF32Cublas();
+#endif  // PADDLE_WITH_CUDA
+
+enum DeviceType {
+  CPU = 0,
+  CUDA = 1,
+  XPU = 2,
+};
+
+constexpr DeviceType kCPU = DeviceType::CPU;
+constexpr DeviceType kCUDA = DeviceType::CUDA;
+constexpr DeviceType kXPU = DeviceType::XPU;
+
 class DeviceContext {
  public:
   virtual ~DeviceContext() PADDLE_MAY_THROW {}
@@ -100,9 +122,20 @@ class XPUDeviceContext : public DeviceContext {
   /*! \brief  Wait for all operations completion in the stream. */
   void Wait() const override;
 
+#ifdef PADDLE_WITH_XPU_BKCL
+  /*! \brief  Return bkcl context. */
+  BKCLContext_t bkcl_context() const { return bkcl_context_; }
+
+  /*! \brief  Set bkcl context. */
+  void set_bkcl_context(BKCLContext_t context) { bkcl_context_ = context; }
+#endif
+
  private:
   XPUPlace place_;
   xpu::Context* context_;
+#ifdef PADDLE_WITH_XPU_BKCL
+  BKCLContext_t bkcl_context_;
+#endif
 
   // Need to be the same with other DeviceContext,
   // Eventhough eigen_device_ is not used in XPU
@@ -535,8 +568,8 @@ class MKLDNNDeviceContext : public CPUDeviceContext {
   const std::string& GetKeySuffix(void) const { return key_suffix_; }
 
   // Disable adding  thread ID to the key
-  void DisableThreadInfoInKey(void) { key_attach_thread_id_ = false; };
-  bool IsThreadIdUsedInKey(void) const { return key_attach_thread_id_; };
+  void DisableThreadInfoInKey(void) { key_attach_thread_id_ = false; }
+  bool IsThreadIdUsedInKey(void) const { return key_attach_thread_id_; }
 
   // Prevent next ResetBlobMap()
   void BlockNextCacheClearing();

paddle/fluid/pybind/pybind.cc

@@ -1307,6 +1307,7 @@ All parameter, weight, gradient are variables in Paddle.
        "The module will return special predefined variable name in Paddle")
       .def("empty", []() { return kEmptyVarName; })
       .def("temp", []() { return kTempVarName; });
+
   // clang-format off
   py::class_<paddle::platform::DeviceContext>(m, "DeviceContext")
       .def_static("create",
@@ -1492,7 +1493,9 @@ All parameter, weight, gradient are variables in Paddle.
 #endif
       .def("__repr__", string::to_string<const platform::XPUPlace &>)
       .def("__str__", string::to_string<const platform::XPUPlace &>);
-
+#ifdef PADDLE_WITH_XPU
+  m.def("get_xpu_device_count", platform::GetXPUDeviceCount);
+#endif
   py::class_<paddle::platform::CPUPlace>(m, "CPUPlace", R"DOC(
     CPUPlace is a descriptor of a device.
     It represents a CPU device on which a tensor will be allocated and a model will run.
@@ -2072,6 +2075,11 @@ All parameter, weight, gradient are variables in Paddle.
                                               exec_strategy=exec_strategy)
         )DOC");
 
+  py::enum_<paddle::platform::DeviceType>(m, "DeviceType", py::arithmetic())
+      .value("CPU", paddle::platform::DeviceType::CPU)
+      .value("CUDA", paddle::platform::DeviceType::CUDA)
+      .value("XPU", paddle::platform::DeviceType::XPU);
+
   exec_strategy.def(py::init())
       .def_property(
           "num_threads",
@@ -2102,14 +2110,12 @@ All parameter, weight, gradient are variables in Paddle.
                     exec_strategy.num_threads = 4
             )DOC")
       .def_property(
-          "use_cuda",
-          [](const ExecutionStrategy &self) { return self.use_cuda_; },
-          [](ExecutionStrategy &self, bool use_cuda) {
-            self.use_cuda_ = use_cuda;
-          })  // FIXME(chengduo): Doesn't add doc for 'use_cuda', use_cuda may
-      // make user confuse, because ParallelExecutor has a parameter named
-      // 'use_cuda' too, in current implementation, ParallelExecutor's
-      // 'use_cuda' will rewrite ExecutionStrategy's 'use_cuda'.
+          "_use_device",
+          [](const ExecutionStrategy &self) { return self.use_device_; },
+          [](ExecutionStrategy &self, paddle::platform::DeviceType use_device) {
+            self.use_device_ = use_device;
+          })  // NOTE(liuyuhui): Doesn't add doc for 'use_device', because
+              // use_device isn‘t exposed to users.
       .def_property(
           "allow_op_delay",
           [](const ExecutionStrategy &self) { return self.allow_op_delay_; },

python/paddle/fluid/compiler.py

@@ -18,7 +18,7 @@ import six
 import sys
 from .. import compat as cpt
 from . import framework
-from .framework import cuda_places, cpu_places
+from .framework import cuda_places, cpu_places, xpu_places
 
 from . import core
 
@@ -28,6 +28,7 @@ ExecutionStrategy = core.ParallelExecutor.ExecutionStrategy
 BuildStrategy = core.ParallelExecutor.BuildStrategy
 InferNativeConfig = core.NativeConfig
 InferAnalysisConfig = core.AnalysisConfig
+DeviceType = core.DeviceType
 
 
 def _place_obj(place):
@@ -316,7 +317,7 @@ class CompiledProgram(object):
             "Subclass of CompiledProgram should implement _with_distributed method."
         )
 
-    def _compile_data_parallel(self, places, use_cuda=False, scope=None):
+    def _compile_data_parallel(self, places, use_device, scope=None):
         if self._share_vars_from:
             if scope:
                 sys.stderr.write("share_vars_from is set, scope is ignored.\n")
@@ -342,16 +343,23 @@ class CompiledProgram(object):
 
         if self._exec_strategy is None:
             self._exec_strategy = ExecutionStrategy()
-        self._exec_strategy.use_cuda = use_cuda
+        self._exec_strategy._use_device = use_device
 
         if self._exec_strategy.num_threads == 0:
-            if self._exec_strategy.use_cuda:
+            if self._exec_strategy._use_device == DeviceType.CUDA:
                 # Experiments on se-resnext shows that too many threads hurt
                 # performance. Worth tunning for other models in the future.
                 self._exec_strategy.num_threads = len(places) * 4
+            elif self._exec_strategy._use_device == DeviceType.XPU:
+                # Currently only single thread is supported in Kunlun XPU.
+                self._exec_strategy.num_threads = 1
             else:
                 self._exec_strategy.num_threads = len(places) * 2
 
+        if self._exec_strategy._use_device == DeviceType.XPU:
+            assert self._exec_strategy.num_threads == 1, \
+                "Currently only single thread is supported in Kunlun XPU."
+
         if self._build_strategy.num_trainers > 1:
             assert self._is_data_parallel, \
                 "If you use multi-trainer to train the model, you should use "\
@@ -377,7 +385,7 @@ class CompiledProgram(object):
             self._build_strategy.enable_sequential_execution = True
 
         if self._program is not None and self._program._enable_dgc:
-            assert use_cuda, "DGC only used under CUDA environment."
+            assert self._exec_strategy._use_device == DeviceType.CUDA, "DGC only used under CUDA environment."
             assert self._build_strategy.num_trainers * len(
                 places) > 1, "DGC is not avaliable for single card training."
             assert self._build_strategy.reduce_strategy == BuildStrategy.ReduceStrategy.AllReduce, "DGC \
@@ -447,11 +455,14 @@ class CompiledProgram(object):
                 raise NotImplementedError(
                     "If optimizer is used in control flow, "
                     "training on multi-places is not supported now.")
-
+            if isinstance(self._place, core.CUDAPlace):
+                use_device = DeviceType.CUDA
+            elif isinstance(self._place, core.XPUPlace):
+                use_device = DeviceType.XPU
+            else:
+                use_device = DeviceType.CPU
             self._executor = self._compile_data_parallel(
-                use_cuda=isinstance(self._place, core.CUDAPlace),
-                scope=self._scope,
-                places=self._places)
+                use_device=use_device, scope=self._scope, places=self._places)
         return self
 
     def _get_places(self, place, place_list):
@@ -461,7 +472,11 @@ class CompiledProgram(object):
                 assert p._type() == place._type(), \
                     "Place type not match. You may set wrong type of places."
         else:
-            place_list = cuda_places() if isinstance(
-                place, core.CUDAPlace) else cpu_places()
+            if isinstance(place, core.CUDAPlace):
+                place_list = cuda_places()
+            elif isinstance(place, core.XPUPlace):
+                place_list = xpu_places()
+            else:
+                place_list = cpu_places()
         assert place_list, "No places for execution."
         return place_list

python/paddle/fluid/framework.py

@@ -46,6 +46,7 @@ __all__ = [
     'name_scope',
     'cuda_places',
     'cpu_places',
+    'xpu_places',
     'cuda_pinned_places',
     'in_dygraph_mode',
     'is_compiled_with_cuda',
@@ -353,6 +354,15 @@ def _cuda_ids():
     return device_ids
 
 
+def _xpu_ids():
+    xpus_env = os.getenv("FLAGS_selected_xpus")
+    if xpus_env:
+        device_ids = [int(s) for s in xpus_env.split(",")]
+    else:
+        device_ids = six.moves.range(core.get_xpu_device_count())
+    return device_ids
+
+
 def is_compiled_with_xpu():
     """
     Whether this whl package can be used to run the model on XPU.
@@ -429,6 +439,44 @@ def cuda_places(device_ids=None):
     return [core.CUDAPlace(dev_id) for dev_id in device_ids]
 
 
+def xpu_places(device_ids=None):
+    """
+    **Note**:
+        For multi-card tasks, please use `FLAGS_selected_xpus` environment variable to set the visible XPU device.
+    This function creates a list of :code:`paddle.XPUPlace` objects.
+    If :code:`device_ids` is None, environment variable of
+    :code:`FLAGS_selected_xpus` would be checked first. For example, if
+    :code:`FLAGS_selected_xpus=0,1,2`, the returned list would
+    be [paddle.XPUPlace(0), paddle.XPUPlace(1), paddle.XPUPlace(2)].
+    If :code:`FLAGS_selected_xpus` is not set, all visible
+    xpu places would be returned.
+    If :code:`device_ids` is not None, it should be the device
+    ids of XPUs. For example, if :code:`device_ids=[0,1,2]`,
+    the returned list would be 
+    [paddle.XPUPlace(0), paddle.XPUPlace(1), paddle.XPUPlace(2)].
+    
+    Parameters:
+        device_ids (list or tuple of int, optional): list of XPU device ids.
+    Returns:
+        list of paddle.XPUPlace: Created XPU place list.
+    Examples:
+        .. code-block:: python
+        
+            import paddle
+            import paddle.static as static
+            
+            paddle.enable_static()
+            xpu_places = static.xpu_places()
+    """
+    assert core.is_compiled_with_xpu(), \
+        "Not compiled with XPU"
+    if device_ids is None:
+        device_ids = _xpu_ids()
+    elif not isinstance(device_ids, (list, tuple)):
+        device_ids = [device_ids]
+    return [core.XPUPlace(dev_id) for dev_id in device_ids]
+
+
 def cpu_places(device_count=None):
     """
     This function creates a list of :code:`paddle.CPUPlace` objects, and returns the created list.

python/paddle/fluid/tests/unittests/parallel_executor_test_base.py

@@ -28,13 +28,14 @@ import sys
 from feed_data_reader import FeedDataReader
 
 __all__ = ['TestParallelExecutorBase']
+DeviceType = core.DeviceType
 
 
 class TestParallelExecutorBase(unittest.TestCase):
     @classmethod
     def check_network_convergence(cls,
                                   method,
-                                  use_cuda=True,
+                                  use_device=DeviceType.CUDA,
                                   iter=5,
                                   batch_size=None,
                                   feed_dict=None,
@@ -74,7 +75,9 @@ class TestParallelExecutorBase(unittest.TestCase):
             feed_dict, loss = cls.build_model(feed_dict, get_data_from_feeder,
                                               main, method, optimizer)
 
-        place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+        place = fluid.CUDAPlace(
+            0) if use_device == DeviceType.CUDA else fluid.XPUPlace(
+                0) if use_device == DeviceType.XPU else fluid.CPUPlace()
         exe = fluid.Executor(place)
         exe.run(startup)
 
@@ -82,7 +85,7 @@ class TestParallelExecutorBase(unittest.TestCase):
             enable_inplace, enable_sequential_execution, fuse_all_optimizer_ops,
             fuse_all_reduce_ops, fuse_elewise_add_act_ops,
             fuse_relu_depthwise_conv, use_fast_executor, use_ir_memory_optimize,
-            use_reduce, use_cuda)
+            use_reduce, use_device)
 
         if use_parallel_executor:
             binary = compiler.CompiledProgram(main).with_data_parallel(
@@ -94,7 +97,8 @@ class TestParallelExecutorBase(unittest.TestCase):
 
         if batch_size is not None:
             batch_size *= fluid.core.get_cuda_device_count(
-            ) if use_cuda else int(
+            ) if use_device == DeviceType.CUDA else fluid.core.get_xpu_device_count(
+            ) if use_device == DeviceType.XPU else int(
                 os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
 
         begin = time.time()
@@ -123,7 +127,7 @@ class TestParallelExecutorBase(unittest.TestCase):
     @classmethod
     def check_pass_conflict(cls,
                             method,
-                            use_cuda=True,
+                            use_device=DeviceType.CUDA,
                             feed_dict=None,
                             get_data_from_feeder=None,
                             use_reduce=False,
@@ -143,7 +147,9 @@ class TestParallelExecutorBase(unittest.TestCase):
             feed_dict, loss = cls.build_model(feed_dict, get_data_from_feeder,
                                               main, method, optimizer)
 
-        place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+        place = fluid.CUDAPlace(
+            0) if use_device == DeviceType.CUDA else fluid.XPUPlace(
+                0) if use_device == DeviceType.XPU else fluid.CPUPlace()
         exe = fluid.Executor(place)
         exe.run(startup)
 
@@ -151,7 +157,7 @@ class TestParallelExecutorBase(unittest.TestCase):
             enable_inplace, enable_sequential_execution, fuse_all_optimizer_ops,
             fuse_all_reduce_ops, fuse_elewise_add_act_ops,
             fuse_relu_depthwise_conv, use_fast_executor, use_ir_memory_optimize,
-            use_reduce, use_cuda)
+            use_reduce, use_device)
 
         binary = compiler.CompiledProgram(main).with_data_parallel(
             loss_name=loss.name,
@@ -165,7 +171,7 @@ class TestParallelExecutorBase(unittest.TestCase):
                      fuse_all_optimizer_ops, fuse_all_reduce_ops,
                      fuse_elewise_add_act_ops, fuse_relu_depthwise_conv,
                      use_fast_executor, use_ir_memory_optimize, use_reduce,
-                     use_cuda):
+                     use_device):
         exec_strategy = fluid.ExecutionStrategy()
         if use_fast_executor:
             exec_strategy.use_experimental_executor = True
@@ -180,8 +186,17 @@ class TestParallelExecutorBase(unittest.TestCase):
         build_strategy.enable_inplace = enable_inplace
         build_strategy.enable_sequential_execution = enable_sequential_execution
 
-        if use_cuda and core.is_compiled_with_cuda():
+        if use_device == DeviceType.CUDA and core.is_compiled_with_cuda():
             build_strategy.remove_unnecessary_lock = True
+        if use_device == DeviceType.XPU and core.is_compiled_with_xpu():
+            build_strategy.fuse_elewise_add_act_ops = False
+            build_strategy.fuse_relu_depthwise_conv = False
+            build_strategy.fuse_all_optimizer_ops = False
+            build_strategy.fuse_all_reduce_ops = False
+            build_strategy.memory_optimize = False
+            build_strategy.enable_inplace = False
+            build_strategy.enable_sequential_execution = False
+
         return build_strategy, exec_strategy
 
     @classmethod

python/paddle/fluid/tests/unittests/seresnext_net.py

@@ -19,6 +19,7 @@ fluid.core._set_eager_deletion_mode(-1, -1, False)
 import paddle.fluid.layers.ops as ops
 from paddle.fluid.layers.learning_rate_scheduler import cosine_decay
 from simple_nets import init_data
+from seresnext_test_base import DeviceType
 import math
 import os
 os.environ['CPU_NUM'] = str(4)
@@ -169,28 +170,32 @@ def optimizer(learning_rate=0.01):
 model = SE_ResNeXt50Small
 
 
-def batch_size(use_cuda):
-    if use_cuda:
+def batch_size(use_device):
+    if use_device == DeviceType.CUDA:
         # Paddle uses 8GB P4 GPU for unittest so we decreased the batch size.
         return 8
     return 12
 
 
-def iter(use_cuda):
-    if use_cuda:
+def iter(use_device):
+    if use_device == DeviceType.CUDA:
         return 10
     return 1
 
 
 gpu_img, gpu_label = init_data(
-    batch_size=batch_size(use_cuda=True), img_shape=img_shape, label_range=999)
+    batch_size=batch_size(use_device=DeviceType.CUDA),
+    img_shape=img_shape,
+    label_range=999)
 cpu_img, cpu_label = init_data(
-    batch_size=batch_size(use_cuda=False), img_shape=img_shape, label_range=999)
+    batch_size=batch_size(use_device=DeviceType.CPU),
+    img_shape=img_shape,
+    label_range=999)
 feed_dict_gpu = {"image": gpu_img, "label": gpu_label}
 feed_dict_cpu = {"image": cpu_img, "label": cpu_label}
 
 
-def feed_dict(use_cuda):
-    if use_cuda:
+def feed_dict(use_device):
+    if use_device == DeviceType.CUDA:
         return feed_dict_gpu
     return feed_dict_cpu

python/paddle/fluid/tests/unittests/seresnext_test_base.py

@@ -15,34 +15,35 @@
 from __future__ import print_function
 import seresnext_net
 import paddle.fluid.core as core
-from parallel_executor_test_base import TestParallelExecutorBase
+from parallel_executor_test_base import TestParallelExecutorBase, DeviceType
+from parallel_executor_test_base import DeviceType
 import numpy as np
 
 
 class TestResnetBase(TestParallelExecutorBase):
     def _compare_result_with_origin_model(self,
                                           check_func,
-                                          use_cuda,
+                                          use_device,
                                           delta2=1e-5,
                                           compare_seperately=True):
-        if use_cuda and not core.is_compiled_with_cuda():
+        if use_device == DeviceType.CUDA and not core.is_compiled_with_cuda():
             return
 
         func_1_first_loss, func_1_last_loss = self.check_network_convergence(
             seresnext_net.model,
-            feed_dict=seresnext_net.feed_dict(use_cuda),
-            iter=seresnext_net.iter(use_cuda),
-            batch_size=seresnext_net.batch_size(use_cuda),
-            use_cuda=use_cuda,
+            feed_dict=seresnext_net.feed_dict(use_device),
+            iter=seresnext_net.iter(use_device),
+            batch_size=seresnext_net.batch_size(use_device),
+            use_device=use_device,
             use_reduce=False,
             optimizer=seresnext_net.optimizer)
 
         func_2_first_loss, func_2_last_loss = check_func(
             seresnext_net.model,
-            feed_dict=seresnext_net.feed_dict(use_cuda),
-            iter=seresnext_net.iter(use_cuda),
-            batch_size=seresnext_net.batch_size(use_cuda),
-            use_cuda=use_cuda)
+            feed_dict=seresnext_net.feed_dict(use_device),
+            iter=seresnext_net.iter(use_device),
+            batch_size=seresnext_net.batch_size(use_device),
+            use_device=use_device)
 
         if compare_seperately:
             for loss in zip(func_1_first_loss, func_2_first_loss):

python/paddle/fluid/tests/unittests/test_fuse_all_reduce_pass.py

@@ -14,7 +14,7 @@
 
 from simple_nets import simple_fc_net, fc_with_batchnorm, init_data, bow_net
 from fake_reader import fake_imdb_reader
-from parallel_executor_test_base import TestParallelExecutorBase
+from parallel_executor_test_base import TestParallelExecutorBase, DeviceType
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 from functools import partial
@@ -30,12 +30,12 @@ class TestFuseAllReduceOpsBase(TestParallelExecutorBase):
 
     def compare_fuse_all_reduce_ops(self,
                                     model,
-                                    use_cuda,
+                                    use_device,
                                     init_feed_dict=None,
                                     get_data_from_feeder=None,
                                     optimizer=None,
                                     fuse_all_optimizer_ops=False):
-        if use_cuda and not core.is_compiled_with_cuda():
+        if use_device == DeviceType.CUDA and not core.is_compiled_with_cuda():
             return
 
         feed_dict_data = None
@@ -47,7 +47,7 @@ class TestFuseAllReduceOpsBase(TestParallelExecutorBase):
             model,
             feed_dict=feed_dict_data,
             get_data_from_feeder=get_data_from_feeder,
-            use_cuda=use_cuda,
+            use_device=use_device,
             fuse_all_reduce_ops=False,
             fuse_all_optimizer_ops=fuse_all_optimizer_ops,
             optimizer=optimizer)
@@ -55,7 +55,7 @@ class TestFuseAllReduceOpsBase(TestParallelExecutorBase):
             model,
             feed_dict=feed_dict_data,
             get_data_from_feeder=get_data_from_feeder,
-            use_cuda=use_cuda,
+            use_device=use_device,
             fuse_all_reduce_ops=True,
             fuse_all_optimizer_ops=fuse_all_optimizer_ops,
             optimizer=optimizer)
@@ -73,28 +73,30 @@ class TestFuseAllReduceOpsBase(TestParallelExecutorBase):
 
 
 class TestFuseAllReduceOps(TestFuseAllReduceOpsBase):
-    def _decorate_compare_fused_all_reduce(self, model, use_cuda):
+    def _decorate_compare_fused_all_reduce(self, model, use_device):
         self.compare_fuse_all_reduce_ops(
             model,
-            use_cuda,
+            use_device,
             init_feed_dict=init_data,
             optimizer=self.optimizer,
             fuse_all_optimizer_ops=True)
 
     def test_simple_fc_with_fuse_all_reduce(self):
-        self._decorate_compare_fused_all_reduce(simple_fc_net, True)
-        self._decorate_compare_fused_all_reduce(simple_fc_net, False)
+        self._decorate_compare_fused_all_reduce(simple_fc_net, DeviceType.CUDA)
+        self._decorate_compare_fused_all_reduce(simple_fc_net, DeviceType.CPU)
 
     def test_batchnorm_fc_with_fuse_all_reduce(self):
-        self._decorate_compare_fused_all_reduce(fc_with_batchnorm, True)
-        self._decorate_compare_fused_all_reduce(fc_with_batchnorm, False)
+        self._decorate_compare_fused_all_reduce(fc_with_batchnorm,
+                                                DeviceType.CUDA)
+        self._decorate_compare_fused_all_reduce(fc_with_batchnorm,
+                                                DeviceType.CPU)
 
 
 class TestFuseAllReduceOpsAndOptiOps(TestFuseAllReduceOps):
-    def _decorate_compare_fused_all_reduce(self, model, use_cuda):
+    def _decorate_compare_fused_all_reduce(self, model, use_device):
         self.compare_fuse_all_reduce_ops(
             model,
-            use_cuda,
+            use_device,
             init_feed_dict=init_data,
             optimizer=self.optimizer,
             fuse_all_optimizer_ops=True)
@@ -115,17 +117,17 @@ class TestFuseAllReduceOpsWithSparseGrad(TestFuseAllReduceOpsBase):
         feeder = fluid.DataFeeder(feed_list=["words", "label"], place=place)
         return feeder.feed(self.train_data)
 
-    def _decorate_compare_fused_all_reduce(self, model, use_cuda):
+    def _decorate_compare_fused_all_reduce(self, model, use_device):
         self.compare_fuse_all_reduce_ops(
             model,
-            use_cuda,
+            use_device,
             get_data_from_feeder=self.get_data_from_feeder,
             optimizer=self.optimizer)
 
     def test_simple_bow_net_with_fuse_all_reduce(self):
         model = partial(bow_net, dict_dim=self.word_dict_len, is_sparse=True)
-        self._decorate_compare_fused_all_reduce(model, True)
-        self._decorate_compare_fused_all_reduce(model, False)
+        self._decorate_compare_fused_all_reduce(model, DeviceType.CUDA)
+        self._decorate_compare_fused_all_reduce(model, DeviceType.CPU)
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/test_fuse_elewise_add_act_pass.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from simple_nets import simple_fc_net, fc_with_batchnorm, init_data
-from parallel_executor_test_base import TestParallelExecutorBase
+from parallel_executor_test_base import TestParallelExecutorBase, DeviceType
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 import unittest
@@ -25,8 +25,8 @@ class TestMNIST(TestParallelExecutorBase):
     def setUpClass(cls):
         os.environ['CPU_NUM'] = str(4)
 
-    def _compare_fuse_elewise_add_act_ops(self, model, use_cuda):
-        if use_cuda and not core.is_compiled_with_cuda():
+    def _compare_fuse_elewise_add_act_ops(self, model, use_device):
+        if use_device == DeviceType.CUDA and not core.is_compiled_with_cuda():
             return
         img, label = init_data()
 
@@ -45,7 +45,7 @@ class TestMNIST(TestParallelExecutorBase):
             model,
             feed_dict={"image": img,
                        "label": label},
-            use_cuda=use_cuda,
+            use_device=use_device,
             fuse_elewise_add_act_ops=False,
             use_ir_memory_optimize=False,
             enable_inplace=False,
@@ -54,7 +54,7 @@ class TestMNIST(TestParallelExecutorBase):
             model,
             feed_dict={"image": img,
                        "label": label},
-            use_cuda=use_cuda,
+            use_device=use_device,
             fuse_elewise_add_act_ops=True,
             use_ir_memory_optimize=False,
             enable_inplace=False,
@@ -66,12 +66,14 @@ class TestMNIST(TestParallelExecutorBase):
             self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
 
     def test_simple_fc_with_fuse_op(self):
-        self._compare_fuse_elewise_add_act_ops(simple_fc_net, True)
-        self._compare_fuse_elewise_add_act_ops(simple_fc_net, False)
+        self._compare_fuse_elewise_add_act_ops(simple_fc_net, DeviceType.CUDA)
+        self._compare_fuse_elewise_add_act_ops(simple_fc_net, DeviceType.CPU)
 
     def test_batchnorm_fc_with_fuse_op(self):
-        self._compare_fuse_elewise_add_act_ops(fc_with_batchnorm, True)
-        self._compare_fuse_elewise_add_act_ops(fc_with_batchnorm, False)
+        self._compare_fuse_elewise_add_act_ops(fc_with_batchnorm,
+                                               DeviceType.CUDA)
+        self._compare_fuse_elewise_add_act_ops(fc_with_batchnorm,
+                                               DeviceType.CPU)
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/test_fuse_optimizer_pass.py

@@ -14,7 +14,7 @@
 
 from simple_nets import simple_fc_net, fc_with_batchnorm, init_data, bow_net
 from fake_reader import fake_imdb_reader
-from parallel_executor_test_base import TestParallelExecutorBase
+from parallel_executor_test_base import TestParallelExecutorBase, DeviceType
 from functools import partial
 import paddle
 import paddle.fluid as fluid
@@ -34,25 +34,25 @@ class TestFuseOptimizationOps(TestParallelExecutorBase):
 
     def _compare_fused_optimizer_ops(self,
                                      model,
-                                     use_cuda,
+                                     use_device,
                                      feed_dict=None,
                                      get_data_from_feeder=None,
                                      optimizer=fluid.optimizer.Adam):
-        if use_cuda and not core.is_compiled_with_cuda():
+        if use_device == DeviceType.CUDA and not core.is_compiled_with_cuda():
             return
 
         not_fuse_op_first_loss, not_fuse_op_last_loss = self.check_network_convergence(
             model,
             feed_dict=feed_dict,
             get_data_from_feeder=get_data_from_feeder,
-            use_cuda=use_cuda,
+            use_device=use_device,
             fuse_all_optimizer_ops=False,
             optimizer=optimizer)
         fuse_op_first_loss, fuse_op_last_loss = self.check_network_convergence(
             model,
             feed_dict=feed_dict,
             get_data_from_feeder=get_data_from_feeder,
-            use_cuda=use_cuda,
+            use_device=use_device,
             fuse_all_optimizer_ops=True,
             optimizer=optimizer)
 
@@ -61,10 +61,11 @@ class TestFuseOptimizationOps(TestParallelExecutorBase):
         for loss in zip(not_fuse_op_last_loss, fuse_op_last_loss):
             self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
 
-    def _decorate_compare_fused_optimizer_ops(self, model, use_cuda, optimizer):
+    def _decorate_compare_fused_optimizer_ops(self, model, use_device,
+                                              optimizer):
         self._compare_fused_optimizer_ops(
             model,
-            use_cuda,
+            use_device,
             feed_dict=self._get_feed_dict(),
             optimizer=optimizer)
 
@@ -75,9 +76,9 @@ class TestFuseAdamOps(TestFuseOptimizationOps):
 
     def test_batchnorm_fc_with_fuse_op(self):
         self._decorate_compare_fused_optimizer_ops(
-            fc_with_batchnorm, True, optimizer=self.optimizer)
+            fc_with_batchnorm, DeviceType.CUDA, optimizer=self.optimizer)
         self._decorate_compare_fused_optimizer_ops(
-            fc_with_batchnorm, False, optimizer=self.optimizer)
+            fc_with_batchnorm, DeviceType.CPU, optimizer=self.optimizer)
 
 
 class TestFuseSGDOps(TestFuseAdamOps):
@@ -106,10 +107,11 @@ class TestSpareFuseAdamOps(TestFuseOptimizationOps):
         feeder = fluid.DataFeeder(feed_list=["words", "label"], place=place)
         return feeder.feed(self.train_data)
 
-    def _decorate_compare_fused_optimizer_ops(self, model, use_cuda, optimizer):
+    def _decorate_compare_fused_optimizer_ops(self, model, use_device,
+                                              optimizer):
         self._compare_fused_optimizer_ops(
             model,
-            use_cuda,
+            use_device,
             get_data_from_feeder=self._get_data_from_feeder,
             optimizer=optimizer)
 
@@ -119,9 +121,9 @@ class TestSpareFuseAdamOps(TestFuseOptimizationOps):
     def test_simple_bow_net_with_fuse_op(self):
         model = partial(bow_net, dict_dim=self.word_dict_len, is_sparse=True)
         self._decorate_compare_fused_optimizer_ops(
-            model, True, optimizer=self.optimizer)
+            model, DeviceType.CUDA, optimizer=self.optimizer)
         self._decorate_compare_fused_optimizer_ops(
-            model, False, optimizer=self.optimizer)
+            model, DeviceType.CPU, optimizer=self.optimizer)
 
 
 class TestSpareFuseSGDOps(TestSpareFuseAdamOps):
@@ -138,18 +140,18 @@ class TestSpareFuseMomentumOps(TestSpareFuseAdamOps):
 class TestPassConflictBase(TestFuseAdamOps):
     def _compare_fused_optimizer_ops(self,
                                      model,
-                                     use_cuda,
+                                     use_device,
                                      feed_dict=None,
                                      get_data_from_feeder=None,
                                      optimizer=fluid.optimizer.Adam):
-        if use_cuda and not core.is_compiled_with_cuda():
+        if use_device == DeviceType.CUDA and not core.is_compiled_with_cuda():
             return
 
         self.check_pass_conflict(
             model,
             feed_dict=feed_dict,
             get_data_from_feeder=get_data_from_feeder,
-            use_cuda=use_cuda,
+            use_device=use_device,
             fuse_all_optimizer_ops=True,
             optimizer=optimizer,
             enable_sequential_execution=True)
@@ -161,9 +163,9 @@ class TestFuseAdamOpsPassConflict(TestPassConflictBase):
 
     def test_batchnorm_fc_with_fuse_op(self):
         self._decorate_compare_fused_optimizer_ops(
-            fc_with_batchnorm, True, optimizer=self.optimizer)
+            fc_with_batchnorm, DeviceType.CPU, optimizer=self.optimizer)
         self._decorate_compare_fused_optimizer_ops(
-            fc_with_batchnorm, False, optimizer=self.optimizer)
+            fc_with_batchnorm, DeviceType.CUDA, optimizer=self.optimizer)
 
 
 class TestFuseSGDOpsPassConflict(TestFuseAdamOpsPassConflict):

python/paddle/fluid/tests/unittests/test_fuse_relu_depthwise_conv_pass.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from parallel_executor_test_base import TestParallelExecutorBase
+from parallel_executor_test_base import TestParallelExecutorBase, DeviceType
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 import numpy as np
@@ -72,8 +72,8 @@ class TestMNIST(TestParallelExecutorBase):
         label = np.ones(shape=[32, 1], dtype='int64')
         return img, label
 
-    def _compare(self, model, use_cuda, random_data=True, only_forward=False):
-        if use_cuda and not core.is_compiled_with_cuda():
+    def _compare(self, model, use_device, random_data=True, only_forward=False):
+        if use_device == DeviceType.CUDA and not core.is_compiled_with_cuda():
             return
         img, label = self._init_data(random_data)
 
@@ -90,7 +90,7 @@ class TestMNIST(TestParallelExecutorBase):
             model,
             feed_dict={"image": img,
                        "label": label},
-            use_cuda=use_cuda,
+            use_device=use_device,
             fuse_relu_depthwise_conv=True,
             use_ir_memory_optimize=True,
             optimizer=_optimizer)
@@ -98,7 +98,7 @@ class TestMNIST(TestParallelExecutorBase):
             model,
             feed_dict={"image": img,
                        "label": label},
-            use_cuda=use_cuda,
+            use_device=use_device,
             fuse_relu_depthwise_conv=False,
             optimizer=_optimizer)
 
@@ -108,12 +108,12 @@ class TestMNIST(TestParallelExecutorBase):
             self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
 
     def test_simple_depthwise_with_fuse_op(self):
-        self._compare(simple_depthwise_net, True)
-        self._compare(simple_depthwise_net, False)
+        self._compare(simple_depthwise_net, DeviceType.CUDA)
+        self._compare(simple_depthwise_net, DeviceType.CPU)
 
     def test_simple_depthwise_with_fuse_op_only_forward(self):
-        self._compare(simple_depthwise_net, True, only_forward=True)
-        self._compare(simple_depthwise_net, False, only_forward=True)
+        self._compare(simple_depthwise_net, DeviceType.CUDA, only_forward=True)
+        self._compare(simple_depthwise_net, DeviceType.CPU, only_forward=True)
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/test_ir_inplace_pass.py

@@ -19,7 +19,7 @@ import unittest
 import numpy as np
 import paddle.fluid.core as core
 import paddle.fluid as fluid
-from parallel_executor_test_base import TestParallelExecutorBase
+from parallel_executor_test_base import TestParallelExecutorBase, DeviceType
 
 
 def fc_with_batchnorm(use_feed):
@@ -58,7 +58,7 @@ class TestIrInplace(TestParallelExecutorBase):
             fc_with_batchnorm,
             feed_dict={"image": img,
                        "label": label},
-            use_cuda=True,
+            use_device=DeviceType.CUDA,
             use_ir_memory_optimize=ir_memory_optimize,
             enable_inplace=enable_inplace)
 

python/paddle/fluid/tests/unittests/test_ir_memory_optimize_ifelse_op.py

@@ -75,7 +75,7 @@ class TestIrMemoryOptimizeIfElseOp(unittest.TestCase):
             exe = Executor(place)
 
             exec_strategy = fluid.ExecutionStrategy()
-            exec_strategy.use_cuda = use_cuda
+            exec_strategy._use_device = core.DeviceType.CUDA if use_cuda else core.DeviceType.CPU
 
             build_strategy = fluid.BuildStrategy()
             build_strategy.memory_optimize = use_mem_opt

python/paddle/fluid/tests/unittests/test_ir_memory_optimize_pass.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from parallel_executor_test_base import TestParallelExecutorBase
+from parallel_executor_test_base import TestParallelExecutorBase, DeviceType
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 import numpy as np
@@ -60,8 +60,8 @@ class TestMNIST(TestParallelExecutorBase):
         label = np.ones(shape=[32, 1], dtype='int64')
         return img, label
 
-    def _compare_ir_memory_optimize(self, model, use_cuda):
-        if use_cuda and not core.is_compiled_with_cuda():
+    def _compare_ir_memory_optimize(self, model, use_device):
+        if use_device == DeviceType.CUDA and not core.is_compiled_with_cuda():
             return
 
         img, label = self._dummy_data()
@@ -69,13 +69,13 @@ class TestMNIST(TestParallelExecutorBase):
             model,
             feed_dict={"image": img,
                        "label": label},
-            use_cuda=use_cuda,
+            use_device=use_device,
             use_ir_memory_optimize=False)
         first_loss1, last_loss1 = self.check_network_convergence(
             model,
             feed_dict={"image": img,
                        "label": label},
-            use_cuda=use_cuda,
+            use_device=use_device,
             use_ir_memory_optimize=True)
         for loss in zip(first_loss0, first_loss1):
             self.assertAlmostEqual(loss[0], loss[1], delta=1e-6)
@@ -83,12 +83,12 @@ class TestMNIST(TestParallelExecutorBase):
             self.assertAlmostEqual(loss[0], loss[1], delta=1e-6)
 
     def test_simple_fc_net(self):
-        self._compare_ir_memory_optimize(simple_fc_net, False)
-        self._compare_ir_memory_optimize(simple_fc_net, True)
+        self._compare_ir_memory_optimize(simple_fc_net, DeviceType.CPU)
+        self._compare_ir_memory_optimize(simple_fc_net, DeviceType.CUDA)
 
     def test_fc_with_reshape_net(self):
-        self._compare_ir_memory_optimize(fc_with_inplace_net, False)
-        self._compare_ir_memory_optimize(fc_with_inplace_net, True)
+        self._compare_ir_memory_optimize(fc_with_inplace_net, DeviceType.CPU)
+        self._compare_ir_memory_optimize(fc_with_inplace_net, DeviceType.CUDA)
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/test_ir_memory_optimize_transformer.py

@@ -23,7 +23,7 @@ import paddle.dataset.wmt16 as wmt16
 
 os.environ['FLAGS_eager_delete_tensor_gb'] = "0.0"
 
-from parallel_executor_test_base import TestParallelExecutorBase
+from parallel_executor_test_base import TestParallelExecutorBase, DeviceType
 from test_parallel_executor_transformer import get_feed_data_reader, transformer
 
 
@@ -35,14 +35,14 @@ class TestTransformerWithIR(TestParallelExecutorBase):
             # check python transpiler
             self.check_network_convergence(
                 transformer,
-                use_cuda=True,
+                use_device=DeviceType.CUDA,
                 feed_data_reader=get_feed_data_reader(),
                 use_ir_memory_optimize=False,
                 iter=2)
             # check IR memory optimize
             self.check_network_convergence(
                 transformer,
-                use_cuda=True,
+                use_device=DeviceType.CUDA,
                 feed_data_reader=get_feed_data_reader(),
                 use_ir_memory_optimize=True,
                 iter=2)

python/paddle/fluid/tests/unittests/test_mix_precision_all_reduce_fuse.py

@@ -24,7 +24,7 @@ import numpy as np
 import paddle
 import paddle.fluid as fluid
 from simple_nets import init_data
-from parallel_executor_test_base import TestParallelExecutorBase
+from parallel_executor_test_base import TestParallelExecutorBase, DeviceType
 
 batch_size = 12
 img_shape = [1, 28, 28]
@@ -68,7 +68,7 @@ def _optimizer(learning_rate=1e-6):
 
 
 class TestResnet(TestParallelExecutorBase):
-    def check_model(self, use_cuda):
+    def check_model(self, use_device):
         img, label = init_data(
             batch_size=batch_size, img_shape=img_shape, label_range=9)
         img = np.float16(img)
@@ -78,13 +78,13 @@ class TestResnet(TestParallelExecutorBase):
             conv_net,
             feed_dict=feed_dict,
             iter=10,
-            use_cuda=use_cuda,
+            use_device=use_device,
             fuse_all_reduce_ops=True,
             optimizer=_optimizer)
 
     def test_model(self):
         if core.is_compiled_with_cuda():
-            self.check_model(True)
+            self.check_model(DeviceType.CUDA)
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/test_parallel_executor_mnist.py

@@ -18,9 +18,11 @@ import unittest
 
 import numpy as np
 import paddle.fluid.core as core
+import paddle
 import os
 import paddle.fluid as fluid
-from parallel_executor_test_base import TestParallelExecutorBase
+from parallel_executor_test_base import TestParallelExecutorBase, DeviceType
+from parallel_executor_test_base import DeviceType
 
 
 def simple_fc_net(use_feed):
@@ -76,10 +78,13 @@ class TestMNIST(TestParallelExecutorBase):
 
     def _compare_reduce_and_allreduce(self,
                                       model,
-                                      use_cuda,
+                                      use_device,
                                       delta1=1e-6,
                                       delta2=1e-4):
-        if use_cuda and not core.is_compiled_with_cuda():
+        if use_device == DeviceType.CUDA and not core.is_compiled_with_cuda():
+            return
+
+        if use_device == DeviceType.XPU and not core.is_compiled_with_xpu():
             return
 
         img, label = self._init_data()
@@ -88,14 +93,14 @@ class TestMNIST(TestParallelExecutorBase):
             model,
             feed_dict={"image": img,
                        "label": label},
-            use_cuda=use_cuda,
+            use_device=use_device,
             use_reduce=False)
 
         reduce_first_loss, reduce_last_loss = self.check_network_convergence(
             model,
             feed_dict={"image": img,
                        "label": label},
-            use_cuda=use_cuda,
+            use_device=use_device,
             use_reduce=True)
 
         for loss in zip(all_reduce_first_loss, reduce_first_loss):
@@ -104,8 +109,11 @@ class TestMNIST(TestParallelExecutorBase):
             self.assertAlmostEqual(loss[0], loss[1], delta=delta2)
 
     # simple_fc
-    def check_simple_fc_convergence(self, use_cuda, use_reduce=False):
-        if use_cuda and not core.is_compiled_with_cuda():
+    def check_simple_fc_convergence(self, use_device, use_reduce=False):
+        if use_device == DeviceType.CUDA and not core.is_compiled_with_cuda():
+            return
+
+        if use_device == DeviceType.XPU and not core.is_compiled_with_xpu():
             return
 
         img, label = self._init_data()
@@ -114,23 +122,26 @@ class TestMNIST(TestParallelExecutorBase):
             simple_fc_net,
             feed_dict={"image": img,
                        "label": label},
-            use_cuda=use_cuda,
+            use_device=use_device,
             use_reduce=use_reduce)
 
     def test_simple_fc(self):
-        # use_cuda
-        self.check_simple_fc_convergence(True)
-        self.check_simple_fc_convergence(False)
+        # use_device
+        self.check_simple_fc_convergence(DeviceType.CUDA)
+        self.check_simple_fc_convergence(DeviceType.CPU)
+        self.check_simple_fc_convergence(DeviceType.XPU)
 
     def test_simple_fc_with_new_strategy(self):
-        # use_cuda, use_reduce
+        # use_device, use_reduce
         # NOTE: the computation result of nccl_reduce is non-deterministic,
         # related issue: https://github.com/NVIDIA/nccl/issues/157
-        self._compare_reduce_and_allreduce(simple_fc_net, True, 1e-5, 1e-2)
-        self._compare_reduce_and_allreduce(simple_fc_net, False, 1e-5, 1e-2)
+        self._compare_reduce_and_allreduce(simple_fc_net, DeviceType.CUDA, 1e-5,
+                                           1e-2)
+        self._compare_reduce_and_allreduce(simple_fc_net, DeviceType.CPU, 1e-5,
+                                           1e-2)
 
-    def check_simple_fc_parallel_accuracy(self, use_cuda):
-        if use_cuda and not core.is_compiled_with_cuda():
+    def check_simple_fc_parallel_accuracy(self, use_device):
+        if use_device == DeviceType.CUDA and not core.is_compiled_with_cuda():
             return
 
         img, label = self._init_data()
@@ -139,13 +150,13 @@ class TestMNIST(TestParallelExecutorBase):
             method=simple_fc_net,
             feed_dict={"image": img,
                        "label": label},
-            use_cuda=use_cuda,
+            use_device=use_device,
             use_parallel_executor=False)
         parallel_first_loss, parallel_last_loss = self.check_network_convergence(
             method=simple_fc_net,
             feed_dict={"image": img,
                        "label": label},
-            use_cuda=use_cuda,
+            use_device=use_device,
             use_parallel_executor=True)
 
         self.assertAlmostEquals(
@@ -156,33 +167,38 @@ class TestMNIST(TestParallelExecutorBase):
             np.mean(parallel_last_loss), single_last_loss, delta=1e-6)
 
     def test_simple_fc_parallel_accuracy(self):
-        self.check_simple_fc_parallel_accuracy(True)
-        self.check_simple_fc_parallel_accuracy(False)
+        self.check_simple_fc_parallel_accuracy(DeviceType.CUDA)
+        self.check_simple_fc_parallel_accuracy(DeviceType.CPU)
 
-    def check_batchnorm_fc_convergence(self, use_cuda, use_fast_executor):
-        if use_cuda and not core.is_compiled_with_cuda():
+    def check_batchnorm_fc_convergence(self, use_device, use_fast_executor):
+        if use_device == DeviceType.CUDA and not core.is_compiled_with_cuda():
+            return
+        if use_device == DeviceType.XPU and not core.is_compiled_with_xpu():
             return
-
         img, label = self._init_data()
 
         self.check_network_convergence(
             fc_with_batchnorm,
             feed_dict={"image": img,
                        "label": label},
-            use_cuda=use_cuda,
+            use_device=use_device,
             use_fast_executor=use_fast_executor)
 
     def test_batchnorm_fc(self):
-        for use_cuda in (False, True):
+        for use_device in (DeviceType.CPU, DeviceType.CUDA):
             for use_fast_executor in (False, True):
-                self.check_batchnorm_fc_convergence(use_cuda, use_fast_executor)
+                self.check_batchnorm_fc_convergence(use_device,
+                                                    use_fast_executor)
 
     def test_batchnorm_fc_with_new_strategy(self):
         # NOTE: the computation result of nccl_reduce is non-deterministic,
         # related issue: https://github.com/NVIDIA/nccl/issues/157
-        self._compare_reduce_and_allreduce(fc_with_batchnorm, True, 1e-5, 1e-2)
-        self._compare_reduce_and_allreduce(fc_with_batchnorm, False, 1e-5, 1e-2)
+        self._compare_reduce_and_allreduce(fc_with_batchnorm, DeviceType.CUDA,
+                                           1e-5, 1e-2)
+        self._compare_reduce_and_allreduce(fc_with_batchnorm, DeviceType.CPU,
+                                           1e-5, 1e-2)
 
 
 if __name__ == '__main__':
+    paddle.enable_static()
     unittest.main()

python/paddle/fluid/tests/unittests/test_parallel_executor_pg.py

@@ -21,7 +21,7 @@ import os
 os.environ['FLAGS_enable_parallel_graph'] = str(1)
 import paddle.fluid.core as core
 import os
-from parallel_executor_test_base import TestParallelExecutorBase
+from parallel_executor_test_base import TestParallelExecutorBase, DeviceType
 from simple_nets import simple_fc_net, init_data
 
 
@@ -31,8 +31,8 @@ class TestMNIST(TestParallelExecutorBase):
         os.environ['CPU_NUM'] = str(4)
 
     # simple_fc
-    def check_simple_fc_convergence(self, use_cuda, use_reduce=False):
-        if use_cuda and not core.is_compiled_with_cuda():
+    def check_simple_fc_convergence(self, use_device, use_reduce=False):
+        if use_device == DeviceType.CUDA and not core.is_compiled_with_cuda():
             return
 
         img, label = init_data()
@@ -40,15 +40,15 @@ class TestMNIST(TestParallelExecutorBase):
             simple_fc_net,
             feed_dict={"image": img,
                        "label": label},
-            use_cuda=use_cuda,
+            use_device=use_device,
             use_reduce=use_reduce)
 
     def test_simple_fc(self):
-        # use_cuda
+        # use_device
         self.check_simple_fc_convergence(True)
 
-    def check_simple_fc_parallel_accuracy(self, use_cuda):
-        if use_cuda and not core.is_compiled_with_cuda():
+    def check_simple_fc_parallel_accuracy(self, use_device):
+        if use_device and not core.is_compiled_with_cuda():
             return
 
         img, label = init_data()
@@ -56,13 +56,13 @@ class TestMNIST(TestParallelExecutorBase):
             method=simple_fc_net,
             feed_dict={"image": img,
                        "label": label},
-            use_cuda=use_cuda,
+            use_device=use_device,
             use_parallel_executor=False)
         parallel_first_loss, parallel_last_loss = self.check_network_convergence(
             method=simple_fc_net,
             feed_dict={"image": img,
                        "label": label},
-            use_cuda=use_cuda,
+            use_device=use_device,
             use_parallel_executor=True)
 
         self.assertAlmostEquals(
@@ -73,7 +73,7 @@ class TestMNIST(TestParallelExecutorBase):
             np.mean(parallel_last_loss), single_last_loss, delta=1e-6)
 
     def test_simple_fc_parallel_accuracy(self):
-        self.check_simple_fc_parallel_accuracy(True)
+        self.check_simple_fc_parallel_accuracy(DeviceType.CUDA)
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/test_parallel_executor_seresnext_base_cpu.py

@@ -15,7 +15,7 @@
 from __future__ import print_function
 import unittest
 import seresnext_net
-from seresnext_test_base import TestResnetBase
+from seresnext_test_base import TestResnetBase, DeviceType
 from functools import partial
 
 
@@ -30,7 +30,10 @@ class TestResnetCPU(TestResnetBase):
             optimizer=seresnext_net.optimizer,
             use_parallel_executor=False)
         self._compare_result_with_origin_model(
-            check_func, use_cuda=False, compare_seperately=False, delta2=1e-3)
+            check_func,
+            use_device=DeviceType.CPU,
+            compare_seperately=False,
+            delta2=1e-3)
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/test_parallel_executor_seresnext_base_gpu.py

@@ -15,7 +15,7 @@
 from __future__ import print_function
 import unittest
 import seresnext_net
-from seresnext_test_base import TestResnetBase
+from seresnext_test_base import TestResnetBase, DeviceType
 from functools import partial
 
 
@@ -30,7 +30,7 @@ class TestResnetGPU(TestResnetBase):
             optimizer=seresnext_net.optimizer,
             use_parallel_executor=False)
         self._compare_result_with_origin_model(
-            check_func, use_cuda=True, compare_seperately=False)
+            check_func, use_device=DeviceType.CUDA, compare_seperately=False)
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/test_parallel_executor_seresnext_with_fuse_all_reduce_cpu.py

@@ -19,7 +19,7 @@ fluid.core._set_fuse_parameter_memory_size(131072)
 
 import unittest
 import seresnext_net
-from seresnext_test_base import TestResnetBase
+from seresnext_test_base import TestResnetBase, DeviceType
 from functools import partial
 
 
@@ -31,7 +31,8 @@ class TestResnetWithFuseAllReduceCPU(TestResnetBase):
             self.check_network_convergence,
             optimizer=seresnext_net.optimizer,
             fuse_all_reduce_ops=True)
-        self._compare_result_with_origin_model(check_func, use_cuda=False)
+        self._compare_result_with_origin_model(
+            check_func, use_device=DeviceType.CPU)
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/test_parallel_executor_seresnext_with_fuse_all_reduce_gpu.py

@@ -19,7 +19,7 @@ fluid.core._set_fuse_parameter_memory_size(131072)
 
 import unittest
 import seresnext_net
-from seresnext_test_base import TestResnetBase
+from seresnext_test_base import TestResnetBase, DeviceType
 from functools import partial
 
 
@@ -32,7 +32,7 @@ class TestResnetWithFuseAllReduceGPU(TestResnetBase):
             optimizer=seresnext_net.optimizer,
             fuse_all_reduce_ops=True)
         self._compare_result_with_origin_model(
-            check_func, use_cuda=True, delta2=1e-2)
+            check_func, use_device=DeviceType.CUDA, delta2=1e-2)
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/test_parallel_executor_seresnext_with_reduce_cpu.py

@@ -14,30 +14,30 @@
 
 from __future__ import print_function
 import unittest
-from parallel_executor_test_base import TestParallelExecutorBase
+from parallel_executor_test_base import TestParallelExecutorBase, DeviceType
 import seresnext_net
 import paddle.fluid.core as core
 
 
 class TestResnetWithReduceBase(TestParallelExecutorBase):
-    def _compare_reduce_and_allreduce(self, use_cuda, delta2=1e-5):
-        if use_cuda and not core.is_compiled_with_cuda():
+    def _compare_reduce_and_allreduce(self, use_device, delta2=1e-5):
+        if use_device == DeviceType.CUDA and not core.is_compiled_with_cuda():
             return
 
         all_reduce_first_loss, all_reduce_last_loss = self.check_network_convergence(
             seresnext_net.model,
-            feed_dict=seresnext_net.feed_dict(use_cuda),
-            iter=seresnext_net.iter(use_cuda),
-            batch_size=seresnext_net.batch_size(use_cuda),
-            use_cuda=use_cuda,
+            feed_dict=seresnext_net.feed_dict(use_device),
+            iter=seresnext_net.iter(use_device),
+            batch_size=seresnext_net.batch_size(use_device),
+            use_device=use_device,
             use_reduce=False,
             optimizer=seresnext_net.optimizer)
         reduce_first_loss, reduce_last_loss = self.check_network_convergence(
             seresnext_net.model,
-            feed_dict=seresnext_net.feed_dict(use_cuda),
-            iter=seresnext_net.iter(use_cuda),
-            batch_size=seresnext_net.batch_size(use_cuda),
-            use_cuda=use_cuda,
+            feed_dict=seresnext_net.feed_dict(use_device),
+            iter=seresnext_net.iter(use_device),
+            batch_size=seresnext_net.batch_size(use_device),
+            use_device=use_device,
             use_reduce=True,
             optimizer=seresnext_net.optimizer)
 
@@ -46,25 +46,25 @@ class TestResnetWithReduceBase(TestParallelExecutorBase):
         for loss in zip(all_reduce_last_loss, reduce_last_loss):
             self.assertAlmostEquals(loss[0], loss[1], delta=loss[0] * delta2)
 
-        if not use_cuda:
+        if not use_device:
             return
 
         all_reduce_first_loss_seq, all_reduce_last_loss_seq = self.check_network_convergence(
             seresnext_net.model,
-            feed_dict=seresnext_net.feed_dict(use_cuda),
-            iter=seresnext_net.iter(use_cuda),
-            batch_size=seresnext_net.batch_size(use_cuda),
-            use_cuda=use_cuda,
+            feed_dict=seresnext_net.feed_dict(use_device),
+            iter=seresnext_net.iter(use_device),
+            batch_size=seresnext_net.batch_size(use_device),
+            use_device=use_device,
             use_reduce=False,
             optimizer=seresnext_net.optimizer,
             enable_sequential_execution=True)
 
         reduce_first_loss_seq, reduce_last_loss_seq = self.check_network_convergence(
             seresnext_net.model,
-            feed_dict=seresnext_net.feed_dict(use_cuda),
-            iter=seresnext_net.iter(use_cuda),
-            batch_size=seresnext_net.batch_size(use_cuda),
-            use_cuda=use_cuda,
+            feed_dict=seresnext_net.feed_dict(use_device),
+            iter=seresnext_net.iter(use_device),
+            batch_size=seresnext_net.batch_size(use_device),
+            use_device=use_device,
             use_reduce=True,
             optimizer=seresnext_net.optimizer,
             enable_sequential_execution=True)
@@ -87,7 +87,8 @@ class TestResnetWithReduceBase(TestParallelExecutorBase):
 
 class TestResnetWithReduceCPU(TestResnetWithReduceBase):
     def test_seresnext_with_reduce(self):
-        self._compare_reduce_and_allreduce(use_cuda=False, delta2=1e-3)
+        self._compare_reduce_and_allreduce(
+            use_device=DeviceType.CPU, delta2=1e-3)
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/test_parallel_executor_seresnext_with_reduce_gpu.py

@@ -14,12 +14,13 @@
 
 from __future__ import print_function
 import unittest
-from test_parallel_executor_seresnext_with_reduce_cpu import TestResnetWithReduceBase
+from test_parallel_executor_seresnext_with_reduce_cpu import TestResnetWithReduceBase, DeviceType
 
 
 class TestResnetWithReduceGPU(TestResnetWithReduceBase):
     def test_seresnext_with_reduce(self):
-        self._compare_reduce_and_allreduce(use_cuda=True, delta2=1e-2)
+        self._compare_reduce_and_allreduce(
+            use_device=DeviceType.CUDA, delta2=1e-2)
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/test_parallel_executor_transformer.py

@@ -17,7 +17,7 @@ from __future__ import print_function
 import paddle.fluid as fluid
 import transformer_model
 import numpy as np
-from parallel_executor_test_base import TestParallelExecutorBase
+from parallel_executor_test_base import TestParallelExecutorBase, DeviceType
 import unittest
 import paddle
 import paddle.fluid.core as core
@@ -191,16 +191,16 @@ class TestTransformer(TestParallelExecutorBase):
         if core.is_compiled_with_cuda():
             self.check_network_convergence(
                 transformer,
-                use_cuda=True,
+                use_device=DeviceType.CUDA,
                 feed_data_reader=get_feed_data_reader())
             self.check_network_convergence(
                 transformer,
-                use_cuda=True,
+                use_device=DeviceType.CUDA,
                 enable_sequential_execution=True,
                 feed_data_reader=get_feed_data_reader())
         self.check_network_convergence(
             transformer,
-            use_cuda=False,
+            use_device=DeviceType.CPU,
             iter=2,
             feed_data_reader=get_feed_data_reader())
 

python/paddle/fluid/tests/unittests/test_program_prune_backward.py

@@ -22,7 +22,7 @@ import paddle.fluid as fluid
 import paddle.fluid.core as core
 from simple_nets import init_data, simple_fc_net, fc_with_batchnorm
 import seresnext_net
-from test_parallel_executor_transformer import transformer, get_feed_data_reader
+from test_parallel_executor_transformer import transformer, get_feed_data_reader, DeviceType
 from fake_reader import fake_imdb_reader
 
 
@@ -219,7 +219,7 @@ class TestProgramPruneBackward(unittest.TestCase):
         with self.program_scope_guard():
             self.check_prune_correctness(
                 method=seresnext_net.model,
-                feed_dict=seresnext_net.feed_dict(use_cuda=False),
+                feed_dict=seresnext_net.feed_dict(use_device=DeviceType.CPU),
                 optimizer=seresnext_net.optimizer)
 
     def test_transformer(self):

python/paddle/fluid/tests/unittests/xpu/test_xpu_place.py

+#   copyright (c) 2020 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.
+
+from __future__ import print_function
+
+import unittest
+import os
+import paddle
+import numpy as np
+import paddle.fluid as fluid
+from paddle.fluid import core
+import paddle.static as static
+
+
+class Test_XPU_Places(unittest.TestCase):
+    def assert_places_equal(self, places0, places1):
+        self.assertEqual(len(places0), len(places1))
+        for place0, place1 in zip(places0, places1):
+            self.assertEqual(type(place0), type(place1))
+            self.assertEqual(place0.get_device_id(), place1.get_device_id())
+
+    def test_check_preset_envs(self):
+        if core.is_compiled_with_xpu():
+            os.environ["FLAGS_selected_xpus"] = "0"
+            place_list = static.xpu_places()
+            self.assert_places_equal([fluid.XPUPlace(0)], place_list)
+
+    def test_check_no_preset_envs(self):
+        if core.is_compiled_with_xpu():
+            place_list = static.xpu_places(0)
+            self.assert_places_equal([fluid.XPUPlace(0)], place_list)
+
+
+if __name__ == '__main__':
+    paddle.enable_static()
+    unittest.main()

python/paddle/static/__init__.py

@@ -20,7 +20,7 @@ __all__ = [
     'default_main_program', 'default_startup_program', 'Program', 'data',
     'InputSpec', 'save', 'load', 'save_inference_model', 'load_inference_model',
     'load_program_state', 'set_program_state', 'cpu_places', 'cuda_places',
-    'Variable'
+    'xpu_places', 'Variable'
 ]
 
 from . import nn
@@ -44,6 +44,7 @@ from ..fluid.framework import name_scope  #DEFINE_ALIAS
 from ..fluid.framework import program_guard  #DEFINE_ALIAS
 from ..fluid.framework import cpu_places  #DEFINE_ALIAS
 from ..fluid.framework import cuda_places  #DEFINE_ALIAS
+from ..fluid.framework import xpu_places  #DEFINE_ALIAS
 from ..fluid.framework import Variable  #DEFINE_ALIAS
 from ..fluid.layers.control_flow import Print  #DEFINE_ALIAS
 from ..fluid.layers.nn import py_func  #DEFINE_ALIAS

python/setup.py.in

@@ -321,6 +321,10 @@ if '${WITH_XPU}' == 'ON':
     package_data['paddle.libs']+=['${XPU_API_LIB_NAME}',
                                   '${XPU_RT_LIB_NAME}']
 
+if '${WITH_XPU_BKCL}' == 'ON':
+    shutil.copy('${XPU_BKCL_LIB}', libs_path)
+    package_data['paddle.libs']+=['${XPU_BKCL_LIB_NAME}']
+
 # copy libfuild_framework.so to libs
 if os.name != 'nt' and sys.platform != 'darwin':
     paddle_framework_lib='${FLUID_FRAMEWORK_SHARED_LIB}'

tools/wlist.json

@@ -413,7 +413,8 @@
         "CRFDecoding.forward",
         "SequenceTagging.forward",
         "XPUPlace",
-        "is_compiled_with_xpu"
+        "is_compiled_with_xpu",
+        "xpu_places"
     ],
     "gpu_not_white":[
         "deformable_conv",