ColossalAI/extensions/csrc/cuda/multi_tensor_adam.cu

// modified from
// https://github.com/NVIDIA/apex/blob/master/csrc/multi_tensor_adam.cu
/* 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>
// Another possibility:
// #include <torch/all.h>

#include <assert.h>

#include "multi_tensor_apply.cuh"
#include "type_shim.h"

#define BLOCK_SIZE 512
#define ILP 4

typedef enum {
  ADAM_MODE_0 = 0,  // L2 regularization mode
  ADAM_MODE_1 = 1   // Decoupled weight decay mode(AdamW)
} adamMode_t;

using MATH_T = float;

template <typename T_g, typename T_p>
struct AdamFunctor {
  __device__ __forceinline__ void operator()(
      int chunk_size, volatile int *noop_gmem, TensorListMetadata<4> &tl,
      const float beta1, const float beta2, const float beta1_correction,
      const float beta2_correction, const float epsilon, const float lr,
      adamMode_t mode, const float decay, const float div_scale) {
    // I'd like this kernel to propagate infs/nans.
    // if(*noop_gmem == 1)
    //   return;

    int tensor_loc = tl.block_to_tensor[blockIdx.x];

    // potentially use to pass in list of scalar
    // int tensor_num = tl.start_tensor_this_launch + tensor_loc;

    int chunk_idx = tl.block_to_chunk[blockIdx.x];
    int n = tl.sizes[tensor_loc];

    T_g *g = (T_g *)tl.addresses[0][tensor_loc];
    g += chunk_idx * chunk_size;

    T_p *p = (T_p *)tl.addresses[1][tensor_loc];
    p += chunk_idx * chunk_size;

    T_p *m = (T_p *)tl.addresses[2][tensor_loc];
    m += chunk_idx * chunk_size;

    T_p *v = (T_p *)tl.addresses[3][tensor_loc];
    v += chunk_idx * chunk_size;

    n -= chunk_idx * chunk_size;

    // see note in multi_tensor_scale_kernel.cu
    for (int i_start = 0; i_start < n && i_start < chunk_size;
         i_start += blockDim.x * ILP) {
      MATH_T r_g[ILP];
      MATH_T r_p[ILP];
      MATH_T r_m[ILP];
      MATH_T r_v[ILP];
#pragma unroll
      for (int ii = 0; ii < ILP; ii++) {
        int i = i_start + threadIdx.x + ii * blockDim.x;
        if (i < n && i < chunk_size) {
          r_g[ii] = g[i];
          r_p[ii] = p[i];
          r_m[ii] = m[i];
          r_v[ii] = v[i];
        } else {
          r_g[ii] = MATH_T(0);
          r_p[ii] = MATH_T(0);
          r_m[ii] = MATH_T(0);
          r_v[ii] = MATH_T(0);
        }
      }
#pragma unroll
      for (int ii = 0; ii < ILP; ii++) {
        if (div_scale > 0) r_g[ii] /= div_scale;

        if (mode == ADAM_MODE_0) {  // L2
          r_g[ii] = r_g[ii] + (decay * r_p[ii]);
          r_m[ii] = beta1 * r_m[ii] + (1 - beta1) * r_g[ii];
          r_v[ii] = beta2 * r_v[ii] + (1 - beta2) * r_g[ii] * r_g[ii];
          MATH_T next_m_unbiased = r_m[ii] / beta1_correction;
          MATH_T next_v_unbiased = r_v[ii] / beta2_correction;
          MATH_T denom = sqrtf(next_v_unbiased) + epsilon;
          MATH_T update = next_m_unbiased / denom;
          r_p[ii] = r_p[ii] - (lr * update);
        } else {  // weight decay
          r_m[ii] = beta1 * r_m[ii] + (1 - beta1) * r_g[ii];
          r_v[ii] = beta2 * r_v[ii] + (1 - beta2) * r_g[ii] * r_g[ii];
          MATH_T next_m_unbiased = r_m[ii] / beta1_correction;
          MATH_T next_v_unbiased = r_v[ii] / beta2_correction;
          MATH_T denom = sqrtf(next_v_unbiased) + epsilon;
          MATH_T update = (next_m_unbiased / denom) + (decay * r_p[ii]);
          r_p[ii] = r_p[ii] - (lr * update);
        }
      }
#pragma unroll
      for (int ii = 0; ii < ILP; ii++) {
        int i = i_start + threadIdx.x + ii * blockDim.x;
        if (i < n && i < chunk_size) {
          p[i] = r_p[ii];
          m[i] = r_m[ii];
          v[i] = r_v[ii];
        }
      }
    }
  }
};

void multi_tensor_adam_cuda(int chunk_size, at::Tensor noop_flag,
                            std::vector<std::vector<at::Tensor>> tensor_lists,
                            const float lr, const float beta1,
                            const float beta2, const float epsilon,
                            const int step, const int mode,
                            const int bias_correction, const float weight_decay,
                            const float div_scale) {
  using namespace at;

  // Handle bias correction mode
  float bias_correction1 = 1.0f, bias_correction2 = 1.0f;
  if (bias_correction == 1) {
    bias_correction1 = 1 - std::pow(beta1, step);
    bias_correction2 = 1 - std::pow(beta2, step);
  }

  DISPATCH_FLOAT_AND_HALF_FOR_G_P(
      tensor_lists[0][0].scalar_type(), tensor_lists[1][0].scalar_type(), 0,
      "adam",
      multi_tensor_apply<4>(BLOCK_SIZE, chunk_size, noop_flag, tensor_lists,
                            AdamFunctor<g_scalar_t_0, p_scalar_t_0>(), beta1,
                            beta2, bias_correction1, bias_correction2, epsilon,
                            lr, (adamMode_t)mode, weight_decay, div_scale);)

  AT_CUDA_CHECK(cudaGetLastError());
}