extensions/csrc/kernel/cuda/multi_tensor_apply.cuh - main - githubmirror/ColossalAI

extensions/csrc/kernel/cuda/multi_tensor_apply.cuh
130 строк5 KB
傅剑寒
[Inference/Refactor] Refactor compilation mechanism and unified multi hw (#5613)
24 апр 2024, 09:17
24 апр 2024, 09:17279300d
// modified from
// https://github.com/NVIDIA/apex/blob/master/csrc/multi_tensor_apply.cuh
/* Copyright 2020 The Microsoft DeepSpeed Team
   Copyright NVIDIA/apex
   This file is adapted from fused adam in NVIDIA/apex, commit a109f85
   Licensed under the MIT License.
*/
#include <ATen/ATen.h>
#include <ATen/AccumulateType.h>
#include <ATen/cuda/CUDAContext.h>
#include <ATen/cuda/Exceptions.h>
#include <assert.h>
#include <c10/cuda/CUDAGuard.h>

#include "common/micros.h"

// #include <iostream>

// This header is the one-stop shop for all your multi-tensor apply needs.

// TODO:  Kernel arg size limit may be <4KB for some other cards (ie Jetson)
constexpr int depth_to_max_tensors[5] = {110, 64, 48, 36, 30};
constexpr int depth_to_max_blocks[5] = {320, 320, 320, 320, 320};

template <int n>
struct TensorListMetadata {
  void *addresses[n][depth_to_max_tensors[n - 1]];
  int sizes[depth_to_max_tensors[n - 1]];
  unsigned char block_to_tensor[depth_to_max_blocks[n - 1]];
  int block_to_chunk[depth_to_max_blocks[n - 1]];  // I fear this needs to be a
                                                   // full int.
  int start_tensor_this_launch;
};

template <typename T, typename U, typename... ArgTypes>
__global__ void multi_tensor_apply_kernel(int chunk_size,
                                          volatile int *noop_flag, T tl,
                                          U callable, ArgTypes... args) {
  // Hand the chunk information to the user-supplied functor to process however
  // it likes.
  callable(chunk_size, noop_flag, tl, args...);
}

template <int depth, typename T, typename... ArgTypes>
void multi_tensor_apply(
    int block_size, int chunk_size, const at::Tensor &noop_flag,
    const std::vector<std::vector<at::Tensor>> &tensor_lists, T callable,
    ArgTypes... args) {
  TORCH_CHECK(tensor_lists.size() == depth, "tensor_lists.size() != depth");
  int len0 = tensor_lists[0].size();
  TORCH_CHECK(len0 > 0, "tensor_lists[0].size() is not > 0");
  auto ref_device = tensor_lists[0][0].device();
  TORCH_CHECK(ref_device.type() == at::kCUDA, "expected input to be on cuda");
  for (int l = 0; l < tensor_lists.size();
       l++)  // No range-based for because I need indices
  {
    TORCH_CHECK(tensor_lists[l].size() == len0,
                "Size mismatch among tensor lists");
    for (int t = 0; t < tensor_lists[l].size(); t++) {
      // TODO:  Print which tensor fails.
      bool contiguous_memory = tensor_lists[l][t].is_contiguous();
#ifdef VERSION_GE_1_5
      contiguous_memory =
          (contiguous_memory ||
           tensor_lists[l][t].is_contiguous(at::MemoryFormat::ChannelsLast));
#endif
      TORCH_CHECK(contiguous_memory, "A tensor was not contiguous.");
      TORCH_CHECK(tensor_lists[l][t].device() == ref_device,
                  "A tensor was not on the same device as the first tensor");
      TORCH_CHECK(tensor_lists[l][t].numel() == tensor_lists[0][t].numel(),
                  "Size mismatch");
    }
  }

  int ntensors = tensor_lists[0].size();

  TensorListMetadata<depth> tl;

  const at::cuda::OptionalCUDAGuard device_guard(device_of(tensor_lists[0][0]));
  auto stream = at::cuda::getCurrentCUDAStream();

  tl.start_tensor_this_launch = 0;
  int loc_block_info = 0;
  int loc_tensor_info = 0;
  for (int t = 0; t < ntensors; t++) {
    tl.sizes[loc_tensor_info] = tensor_lists[0][t].numel();
    for (int d = 0; d < depth; d++)
      tl.addresses[d][loc_tensor_info] = tensor_lists[d][t].data_ptr();
    loc_tensor_info++;

    int chunks_this_tensor =
        (tensor_lists[0][t].numel() + chunk_size - 1) / chunk_size;

    for (int chunk = 0; chunk < chunks_this_tensor; chunk++) {
      // std::cout << chunks_this_tensor << std::endl;
      tl.block_to_tensor[loc_block_info] = loc_tensor_info - 1;
      tl.block_to_chunk[loc_block_info] = chunk;
      loc_block_info++;

      bool tensors_full = (loc_tensor_info == depth_to_max_tensors[depth - 1] &&
                           chunk == chunks_this_tensor - 1);
      bool blocks_full = (loc_block_info == depth_to_max_blocks[depth - 1]);
      bool last_chunk = (t == ntensors - 1 && chunk == chunks_this_tensor - 1);
      if (tensors_full || blocks_full || last_chunk) {
        // using accscalar_t = acc_type<scalar_t, true>;
        multi_tensor_apply_kernel<<<loc_block_info, block_size, 0, stream>>>(
            chunk_size, noop_flag.data_ptr<int>(), tl, callable, args...);

        AT_CUDA_CHECK(cudaGetLastError());

        // Reset.  The control flow possibilities here make my brain hurt.
        loc_block_info = 0;
        if (chunk == chunks_this_tensor - 1) {
          // std::cout << "Hit case 1 " << cond1 << " " << cond2 << " " << cond3
          // << std::endl;
          loc_tensor_info = 0;
          tl.start_tensor_this_launch = t + 1;
        } else {
          // std::cout << "Hit case 2 " << cond1 << " " << cond2 << " " << cond3
          // << std::endl;
          tl.sizes[0] = tl.sizes[loc_tensor_info - 1];
          for (int d = 0; d < depth; d++)
            tl.addresses[d][0] = tl.addresses[d][loc_tensor_info - 1];
          loc_tensor_info = 1;
          tl.start_tensor_this_launch = t;
        }
      }
    }
  }
}