// Copyright (c) 2024 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.

#include "paddle/phi/kernels/gpu/global_gather_kernel.h"
#include "paddle/phi/core/distributed/utils.h"
#include "paddle/phi/core/kernel_registry.h"
#if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL)
#include "paddle/phi/core/distributed/nccl_comm_context.h"
#include "paddle/phi/core/platform/device/gpu/nccl_helper.h"
#endif
#include "paddle/phi/core/utils/data_type.h"

namespace phi {

template <typename Context, typename T>
struct GlobalGatherFunctor {
  void operator()(const Context &dev_ctx,
                  const DenseTensor &x_in,
                  const DenseTensor &local_count_in,
                  const DenseTensor &global_count_in,
                  DenseTensor *out);
};
template <typename T>
struct GlobalGatherFunctor<GPUContext, T> {
  void operator()(const GPUContext &dev_ctx,
                  const DenseTensor &x_in,
                  const DenseTensor &local_count_in,
                  const DenseTensor &global_count_in,
                  DenseTensor *out) {
#if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL)
#if NCCL_VERSION_CODE >= 2703
    auto x = &x_in;
    auto local_count = &local_count_in;
    auto global_count = &global_count_in;
    auto local_count_type = local_count->dtype();
    auto global_count_type = global_count->dtype();
    if (local_count_type != DataType::INT64) {
      PADDLE_THROW(common::errors::InvalidArgument(
          "Please use int64 type in local_count."));
    }
    if (global_count_type != DataType::INT64) {
      PADDLE_THROW(common::errors::InvalidArgument(
          "Please use int64 type in global_count."));
    }

    const int64_t *cpu_local_count_data;
    const int64_t *cpu_global_count_data;
    auto local_count_len = 0;

    DenseTensor cpu_local_count;
    if (local_count->place().GetType() == AllocationType::CPU) {
      cpu_local_count_data = local_count->data<int64_t>();
      local_count_len = local_count->numel();
    } else {
      Copy(dev_ctx, *local_count, CPUPlace(), true, &cpu_local_count);
      cpu_local_count_data = cpu_local_count.data<int64_t>();
      local_count_len = cpu_local_count.numel();
    }

    DenseTensor cpu_global_count;
    if (global_count->place().GetType() == AllocationType::CPU) {
      cpu_global_count_data = global_count->data<int64_t>();
    } else {
      Copy(dev_ctx, *global_count, CPUPlace(), true, &cpu_global_count);
      cpu_global_count_data = cpu_global_count.data<int64_t>();
    }

    ncclDataType_t dtype = ToNCCLDataType(x->dtype());
    gpuStream_t stream = nullptr;
    stream = dev_ctx.stream();

    distributed::NCCLCommContext *comm_ctx = nullptr;
    int nranks = 0;

    comm_ctx =
        static_cast<distributed::NCCLCommContext *>(dev_ctx.GetCommContext());
    PADDLE_ENFORCE_NE(comm_ctx,
                      nullptr,
                      common::errors::Unavailable(
                          "NCCLCommContext is nullptr, collective op should "
                          "has ring_id attr."));
    nranks = comm_ctx->GetSize();

    auto in_feat = x->dims()[1];
    auto n_expert = local_count->dims()[0] / nranks;

    auto fwd_count = 0;
    for (auto i = 0; i < local_count_len; ++i) {
      fwd_count += cpu_local_count_data[i];
    }
    DDim out_dims = make_ddim({fwd_count, in_feat});
    int64_t *expert_ptr = new int64_t[n_expert * nranks];
    expert_ptr[0] = 0;
    auto tot_experts = n_expert * nranks;
    for (auto i = 1; i < tot_experts; ++i) {
      expert_ptr[i] = expert_ptr[i - 1] + cpu_local_count_data[i - 1];
    }
    auto send_ptr = 0;
    out->Resize(out_dims);
    dev_ctx.template Alloc<T>(out);

    for (auto i = 0; i < n_expert; ++i) {
      comm_ctx->GroupStart();
      for (auto j = 0; j < nranks; ++j) {
        int idx = i + j * n_expert;
        if (cpu_global_count_data[idx]) {
          auto send_buf = distributed::GetPartialTensor(
              *x, send_ptr * in_feat, cpu_global_count_data[idx] * in_feat);
          comm_ctx->Send(
              send_buf, cpu_global_count_data[idx] * in_feat, j, stream);
          send_ptr += cpu_global_count_data[idx];
        }
        if (cpu_local_count_data[idx]) {
          auto recv_buf = distributed::GetPartialTensor(
              *out,
              expert_ptr[idx] * in_feat,
              cpu_local_count_data[idx] * in_feat);
          comm_ctx->Recv(
              &recv_buf, cpu_local_count_data[idx] * in_feat, j, stream);
        }
      }
      comm_ctx->GroupEnd();
    }
#else
    PADDLE_THROW(
        common::errors::Unavailable("NCCL version >= 2.7.3 is needed."));
#endif
#else
    PADDLE_THROW(
        common::errors::Unavailable("PaddlePaddle should compile with GPU."));
#endif
  }
};

template <typename T, typename Context>
void GlobalGatherKernel(const Context &dev_ctx,
                        const DenseTensor &x,
                        const DenseTensor &local_count,
                        const DenseTensor &global_count,
                        DenseTensor *out) {
  GlobalGatherFunctor<GPUContext, T> functor_;
  functor_(dev_ctx, x, local_count, global_count, out);
}

}  // namespace phi

PD_REGISTER_KERNEL(global_gather,
                   GPU,
                   ALL_LAYOUT,
                   phi::GlobalGatherKernel,
                   float,
                   double,
                   int,
                   int64_t,
                   phi::float16) {
  kernel->InputAt(1).SetDataType(phi::DataType::INT64);
  kernel->InputAt(2).SetDataType(phi::DataType::INT64);
}