apache--tvm/3rdparty/tensorrt_llm/custom_allreduce_kernels.cu

/*
 * Copyright (c) 2022-2024, NVIDIA CORPORATION.  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 <cuda_bf16.h>
#include <cuda_fp16.h>
#include <dlpack/dlpack.h>
#include <stdint.h>
#include <tvm/runtime/logging.h>

#include "custom_allreduce_kernels.h"

namespace tensorrt_llm {

static inline __device__ void st_flag_release(uint32_t& flag, uint32_t* flag_addr) {
#if __CUDA_ARCH__ >= 700
  asm volatile("st.global.release.sys.b32 [%1], %0;" ::"r"(flag), "l"(flag_addr));
#else
  __threadfence_system();
  asm volatile("st.global.volatile.b32 [%1], %0;" ::"r"(flag), "l"(flag_addr));
#endif
}

////////////////////////////////////////////////////////////////////////////////////////////////////

static inline __device__ void ld_flag_acquire(uint32_t& flag, uint32_t* flag_addr) {
#if __CUDA_ARCH__ >= 700
  asm volatile("ld.global.acquire.sys.b32 %0, [%1];" : "=r"(flag) : "l"(flag_addr));
#else
  asm volatile("ld.global.volatile.b32 %0, [%1];" : "=r"(flag) : "l"(flag_addr));
#endif
}

////////////////////////////////////////////////////////////////////////////////////////////////////

// Type Converter that packs data format to 128 bits data type
//
using PackedFloat = union {
  int4 packed;
  float unpacked[4];
};

using PackedHalf = union {
  int4 packed;
  half2 unpacked[4];
};

template <typename T>
struct PackedOn16Bytes {};

template <>
struct PackedOn16Bytes<float> {
  using Type = PackedFloat;
};

template <>
struct PackedOn16Bytes<half> {
  using Type = PackedHalf;
};

using PackedBFloat16 = union {
  int4 packed;
  __nv_bfloat162 unpacked[4];
};

template <>
struct PackedOn16Bytes<__nv_bfloat16> {
  using Type = PackedBFloat16;
};

// add two 128b data
template <typename T>
inline __device__ int4 add128b(T& a, T& b) {
  T c;
  c.unpacked[0] = a.unpacked[0] + b.unpacked[0];
  c.unpacked[1] = a.unpacked[1] + b.unpacked[1];
  c.unpacked[2] = a.unpacked[2] + b.unpacked[2];
  c.unpacked[3] = a.unpacked[3] + b.unpacked[3];
  return c.packed;
}

__inline__ __device__ void multi_gpu_barrier(uint32_t** signals, const uint32_t flag,
                                             const size_t rank, const size_t world_size,
                                             int const tidx, int const bidx) {
  // At the end of the function, we now that has least block 0 from all others GPUs have reached
  // that point.
  uint32_t volatile* my_signals = signals[rank];
  if (tidx < world_size) {
    // The 1st block notifies the other ranks.
    if (bidx == 0) {
      signals[tidx][rank] = flag;
    }

    // Busy-wait until all ranks are ready.
    while (my_signals[tidx] != flag) {
    }
  }

  // Make sure we can move on...
  __syncthreads();
}

__global__ void multiGpuBarrierKernel(AllReduceParams params) {
  multi_gpu_barrier(params.peer_barrier_ptrs_out, params.barrier_flag, params.local_rank,
                    params.ranks_per_node, threadIdx.x, blockIdx.x);
}

template <typename T, int RANKS_PER_NODE>
static __global__ void oneShotAllReduceKernel(AllReduceParams params) {
  int const bidx = blockIdx.x;
  int const tidx = threadIdx.x;

  // The number of elements packed into one for comms
  static constexpr int NUM_ELTS = 16 / sizeof(T);

  // Packed data type for comms
  using PackedStruct = typename PackedOn16Bytes<T>::Type;

  multi_gpu_barrier(params.peer_barrier_ptrs_in, params.barrier_flag, params.local_rank,
                    RANKS_PER_NODE, tidx, bidx);

  // The source pointers. Distributed round-robin for the different warps.
  T const* src_d[RANKS_PER_NODE];
#pragma unroll
  for (int ii = 0; ii < RANKS_PER_NODE; ++ii) {
    int rank = (params.local_rank + ii) % RANKS_PER_NODE;
    src_d[ii] = reinterpret_cast<T*>(params.peer_comm_buffer_ptrs[rank]);
  }

  // The location in the destination array (load 8 fp16 or load 4 fp32 using LDG.128).
  size_t offset = bidx * params.elts_per_block + tidx * NUM_ELTS;
  // The end of the segment computed by that block.
  size_t max_offset = min((bidx + 1) * params.elts_per_block, params.elts_per_rank);

  // Each block accumulates the values from the different GPUs on the same node.
  for (size_t iter_offset = offset; iter_offset < max_offset;
       iter_offset += blockDim.x * NUM_ELTS) {
    // Iterate over the different ranks/devices on the node to load the values.
    PackedStruct vals[RANKS_PER_NODE];
#pragma unroll
    for (int ii = 0; ii < RANKS_PER_NODE; ++ii) {
      vals[ii].packed = *reinterpret_cast<int4 const*>(&src_d[ii][iter_offset]);
    }

    // Sum the values from the different ranks.
    PackedStruct sums;
    sums.packed = {0, 0, 0, 0};
#pragma unroll
    for (int ii = 0; ii < RANKS_PER_NODE; ++ii) {
      sums.packed = add128b(sums, vals[ii]);
    }

    // Store to the destination buffer.
    *reinterpret_cast<int4*>(&reinterpret_cast<T*>(params.local_output_buffer_ptr)[iter_offset]) =
        sums.packed;
  }
}

template <typename T, int RANKS_PER_NODE>
static __global__ void twoShotAllReduceKernel(AllReduceParams params) {
  // The block index.
  int const bidx = blockIdx.x;
  // The thread index with the block.
  int const tidx = threadIdx.x;

  // The number of elements packed into one for comms
  static constexpr int NUM_ELTS = 16 / sizeof(T);

  // Packed data type for comms
  using PackedType = typename PackedOn16Bytes<T>::Type;

  // The location in the destination array (load 8 fp16 or load 4 fp32 using LDG.128).
  const size_t block_offset = bidx * params.elts_per_block + tidx * NUM_ELTS;
  const size_t block_start = params.rank_offset + block_offset;
  // The end of the segment computed by that block.
  size_t max_offset =
      min(block_start + params.elts_per_block, params.rank_offset + params.elts_per_rank);

  multi_gpu_barrier(params.peer_barrier_ptrs_in, params.barrier_flag, params.local_rank,
                    RANKS_PER_NODE, tidx, bidx);

  // The source pointers. Distributed round-robin for the different warps.
  T* src_d[RANKS_PER_NODE];
  // The destination ranks for round-robin gathering
  size_t dst_rank[RANKS_PER_NODE];
#pragma unroll
  for (int ii = 0; ii < RANKS_PER_NODE; ++ii) {
    int rank = (params.local_rank + ii) % RANKS_PER_NODE;
    src_d[ii] = reinterpret_cast<T*>(params.peer_comm_buffer_ptrs[rank]);
    dst_rank[ii] = rank;
  }

  // Each block accumulates the values from the different GPUs on the same node.
  for (size_t local_offset = block_start; local_offset < max_offset;
       local_offset += blockDim.x * NUM_ELTS) {
    // Iterate over the different ranks/devices on the node to load the values.
    PackedType vals[RANKS_PER_NODE];
#pragma unroll
    for (int ii = 0; ii < RANKS_PER_NODE; ++ii) {
      vals[ii].packed = *reinterpret_cast<int4 const*>(&src_d[ii][local_offset]);
    }

    // Sum the values from the different ranks.
    PackedType sums;
    sums.packed = {0, 0, 0, 0};
#pragma unroll
    for (int ii = 0; ii < RANKS_PER_NODE; ++ii) {
      sums.packed = add128b(sums, vals[ii]);
    }

    // Store to the local buffer.
    *reinterpret_cast<int4*>(&src_d[0][local_offset]) = sums.packed;
  }

  // sync threads to make sure all block threads have the sums
  __syncthreads();

  // barriers among the blocks with the same idx (release-acquire semantics)
  if (tidx < RANKS_PER_NODE) {
    // The all blocks notifies the other ranks.
    uint32_t flag_block_offset = RANKS_PER_NODE + bidx * RANKS_PER_NODE;
    st_flag_release(params.barrier_flag,
                    params.peer_barrier_ptrs_in[tidx] + flag_block_offset + params.local_rank);

    // Busy-wait until all ranks are ready.
    uint32_t rank_barrier = 0;
    uint32_t* peer_barrier_d =
        params.peer_barrier_ptrs_in[params.local_rank] + flag_block_offset + tidx;
    do {
      ld_flag_acquire(rank_barrier, peer_barrier_d);
    } while (rank_barrier != params.barrier_flag);
  }

  // sync threads to make sure all other ranks has the final partial results
  __syncthreads();

  size_t max_block_offset = min(block_offset + params.elts_per_block, params.elts_per_rank);
  // Gather all needed elts from other intra-node ranks
  for (size_t local_offset = block_offset; local_offset < max_block_offset;
       local_offset += blockDim.x * NUM_ELTS) {
#pragma unroll
    for (int ii = 0; ii < RANKS_PER_NODE; ++ii) {
      // use round-robin gathering from other ranks
      size_t offset_rank = dst_rank[ii] * params.elts_per_rank + local_offset;
      if (offset_rank >= params.elts_total) {
        continue;
      }
      *reinterpret_cast<int4*>(&reinterpret_cast<T*>(params.local_output_buffer_ptr)[offset_rank]) =
          *reinterpret_cast<int4*>(&src_d[ii][offset_rank]);
    }
  }
}

////////////////////////////////////////////////////////////////////////////////////////////////////

inline int divUp(int a, int b) { return (a + b - 1) / b; }

std::tuple<int, int> kernelLaunchConfig(AllReduceStrategyType algo, AllReduceParams& param,
                                        size_t elts_per_thread) {
  TVM_FFI_ICHECK(param.elts_total % elts_per_thread == 0);

  int blocks_per_grid = 1, threads_per_block = DEFAULT_BLOCK_SIZE;

  const size_t total_threads = param.elts_total / elts_per_thread;
  switch (algo) {
    case AllReduceStrategyType::ONESHOT: {        // one stage all reduce algo
      if (total_threads <= DEFAULT_BLOCK_SIZE) {  // local reduce
        threads_per_block = WARP_SIZE * divUp(total_threads, WARP_SIZE);
        blocks_per_grid = 1;
      } else {  // local reduce
        threads_per_block = DEFAULT_BLOCK_SIZE;
        blocks_per_grid = divUp(total_threads, DEFAULT_BLOCK_SIZE);
        blocks_per_grid = std::min(static_cast<int>(MAX_ALL_REDUCE_BLOCKS), blocks_per_grid);
      }
      param.elts_per_rank = param.elts_total;
      param.elts_per_block =
          elts_per_thread * divUp(param.elts_per_rank, elts_per_thread * blocks_per_grid);
      break;
    }
    case AllReduceStrategyType::TWOSHOT: {  // two stage all reduce algo
      const size_t elts_per_rank = param.elts_total / param.ranks_per_node;
      TVM_FFI_ICHECK(elts_per_rank % elts_per_thread == 0);

      size_t total_threads = elts_per_rank / elts_per_thread;
      total_threads = WARP_SIZE * ((total_threads + WARP_SIZE - 1) / WARP_SIZE);
      TVM_FFI_ICHECK(total_threads % WARP_SIZE == 0);

      while (total_threads % blocks_per_grid != 0 ||
             total_threads / blocks_per_grid > DEFAULT_BLOCK_SIZE) {
        blocks_per_grid += 1;
      }

      threads_per_block = total_threads / blocks_per_grid;

      // NOTE: need to adjust here
      if (static_cast<size_t>(blocks_per_grid) > MAX_ALL_REDUCE_BLOCKS) {
        size_t iter_factor = 1;
        while (blocks_per_grid / iter_factor > MAX_ALL_REDUCE_BLOCKS ||
               blocks_per_grid % iter_factor) {
          iter_factor += 1;
        }
        blocks_per_grid /= iter_factor;
      }
      param.elts_per_rank = param.elts_total / param.ranks_per_node;
      param.elts_per_block = param.elts_per_rank / blocks_per_grid;
      param.elts_per_block = elts_per_thread * divUp(param.elts_per_block, elts_per_thread);
      param.rank_offset = param.rank * param.elts_per_rank;
      break;
    }
    default:
      LOG(FATAL) << ("Algorithm not supported here.");
  }

  return std::make_tuple(blocks_per_grid, threads_per_block);
}

////////////////////////////////////////////////////////////////////////////////////////////////////

template <typename T, int RANKS_PER_NODE>
void dispatchARKernels(AllReduceStrategyType algo, AllReduceParams& param, int blocks_per_grid,
                       int threads_per_block, cudaStream_t stream) {
  if (algo == AllReduceStrategyType::ONESHOT) {
    oneShotAllReduceKernel<T, RANKS_PER_NODE>
        <<<blocks_per_grid, threads_per_block, 0, stream>>>(param);
  } else {
    twoShotAllReduceKernel<T, RANKS_PER_NODE>
        <<<blocks_per_grid, threads_per_block, 0, stream>>>(param);
  }
}

template <typename T>
void invokeOneOrTwoShotAllReduceKernel(AllReduceParams& param, AllReduceStrategyType strat,
                                       cudaStream_t stream) {
  TVM_FFI_ICHECK(strat == AllReduceStrategyType::ONESHOT || strat == AllReduceStrategyType::TWOSHOT);
  auto last_error = cudaGetLastError();
  if (last_error != cudaSuccess) {
    LOG(INFO) << "cuda error:" << cudaGetErrorString(last_error);
  }

  size_t elts_per_thread = 16 / sizeof(T);
  auto [blocks_per_grid, threads_per_block] = kernelLaunchConfig(strat, param, elts_per_thread);
  switch (param.ranks_per_node) {
    case 2:
      dispatchARKernels<T, 2>(strat, param, blocks_per_grid, threads_per_block, stream);
      break;
    case 4:
      dispatchARKernels<T, 4>(strat, param, blocks_per_grid, threads_per_block, stream);
      break;
    case 6:
      dispatchARKernels<T, 6>(strat, param, blocks_per_grid, threads_per_block, stream);
      break;
    case 8:
      dispatchARKernels<T, 8>(strat, param, blocks_per_grid, threads_per_block, stream);
      break;
    default:
      break;
  }
  last_error = cudaGetLastError();
  if (last_error != cudaSuccess) {
    LOG(INFO) << "cuda error:" << cudaGetErrorString(last_error);
  }
}

void invokeMultiGpuBarrier(AllReduceParams& param, cudaStream_t stream) {
  multiGpuBarrierKernel<<<1, param.ranks_per_node, 0, stream>>>(param);
}

void customAllReduce(AllReduceParams& params, void* data, size_t elts, DLDataType dataType,
                     AllReduceStrategyType strat, cudaStream_t stream) {
  params.local_output_buffer_ptr = data;
  params.elts_total = elts;

  if (dataType.code == kDLFloat && dataType.bits == 32) {
    invokeOneOrTwoShotAllReduceKernel<float>(params, strat, stream);
  } else if (dataType.code == kDLFloat && dataType.bits == 16) {
    invokeOneOrTwoShotAllReduceKernel<half>(params, strat, stream);
  } else if (dataType.code == kDLBfloat && dataType.bits == 16) {
    invokeOneOrTwoShotAllReduceKernel<__nv_bfloat16>(params, strat, stream);
  } else {
    LOG(FATAL) << ("Unsupported dataType for customAllReduce");
  }
}

}  // namespace tensorrt_llm