ColossalAI/colossalai/kernel/cuda_native/csrc/multi_tensor_lamb.cu

// modified from
// https://github.com/NVIDIA/apex/blob/master/csrc/multi_tensor_lamb.cu
#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

template <typename T>
__device__ __forceinline__ bool is_aligned(T *p) {
  return ((uint64_t)p) % (ILP * sizeof(T)) == 0;
}

template <typename T>
__device__ __forceinline__ void load_store(T *dst, T *src, int dst_offset,
                                           int src_offset) {
  typedef
      typename std::aligned_storage<ILP * sizeof(T), ILP * alignof(T)>::type LT;
  ((LT *)dst)[dst_offset] = ((LT *)src)[src_offset];
}

typedef enum {
  MOMENT_MODE_0 = 0,  // L2 regularization mode
  MOMENT_MODE_1 = 1   // Decoupled weight decay mode
} adamMode_t;

std::tuple<at::Tensor, at::Tensor> multi_tensor_l2norm_cuda(
    int chunk_size, at::Tensor noop_flag,
    std::vector<std::vector<at::Tensor>> tensor_lists,
    at::optional<bool> per_tensor_python);

using MATH_T = float;

template <typename T>
struct LAMBStage1Functor {
  __device__ __forceinline__ void operator()(
      int chunk_size, volatile int *noop_gmem, TensorListMetadata<4> &tl,
      const float beta1, const float beta2, const float beta3,
      const float beta1_correction, const float beta2_correction,
      const float epsilon, adamMode_t mode, const float decay,
      const float *global_grad_norm, const float max_global_grad_norm) {
    // I'd like this kernel to propagate infs/nans.
    // if(*noop_gmem == 1)
    //   return;

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

    float clipped_global_grad_norm =
        (*global_grad_norm) > max_global_grad_norm
            ? (*global_grad_norm) / max_global_grad_norm
            : 1.0f;

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

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

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

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

    n -= chunk_idx * chunk_size;

    MATH_T r_g[ILP];
    MATH_T r_p[ILP];
    MATH_T r_m[ILP];
    MATH_T r_v[ILP];
    // to make things simple, we put aligned case in a different code path
    if (n % ILP == 0 && chunk_size % ILP == 0 && is_aligned(g) &&
        is_aligned(p) && is_aligned(m) && is_aligned(v)) {
      T l_g[ILP];
      T l_p[ILP];
      T l_m[ILP];
      T l_v[ILP];
      for (int i_start = threadIdx.x;
           i_start * ILP < n && i_start * ILP < chunk_size;
           i_start += blockDim.x) {
        // load
        load_store(l_g, g, 0, i_start);
        if (decay != 0) load_store(l_p, p, 0, i_start);
        load_store(l_m, m, 0, i_start);
        load_store(l_v, v, 0, i_start);
        // unpack
#pragma unroll
        for (int ii = 0; ii < ILP; ii++) {
          r_g[ii] = l_g[ii];
          if (decay == 0) {
            r_p[ii] = MATH_T(0);
          } else {
            r_p[ii] = l_p[ii];
          }
          r_m[ii] = l_m[ii];
          r_v[ii] = l_v[ii];
        }
#pragma unroll
        for (int ii = 0; ii < ILP; ii++) {
          if (mode == MOMENT_MODE_0) {
            MATH_T scaled_grad = r_g[ii] / clipped_global_grad_norm;
            // L2 on scaled grad
            scaled_grad = scaled_grad + decay * r_p[ii];
            r_m[ii] = r_m[ii] * beta1 + beta3 * scaled_grad;
            r_v[ii] = r_v[ii] * beta2 + (1 - beta2) * scaled_grad * scaled_grad;
            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;
            r_p[ii] = next_m_unbiased / denom;
          } else {
            MATH_T scaled_grad = r_g[ii] / clipped_global_grad_norm;
            r_m[ii] = r_m[ii] * beta1 + beta3 * scaled_grad;
            r_v[ii] = r_v[ii] * beta2 + (1 - beta2) * scaled_grad * scaled_grad;
            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;
            r_p[ii] = (next_m_unbiased / denom) + (decay * r_p[ii]);
          }
        }
#pragma unroll
        for (int ii = 0; ii < ILP; ii++) {
          l_p[ii] = r_p[ii];
          l_m[ii] = r_m[ii];
          l_v[ii] = r_v[ii];
        }
        // store
        load_store(g, l_p, i_start, 0);
        load_store(m, l_m, i_start, 0);
        load_store(v, l_v, i_start, 0);
      }
    } else {
      // 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];
            // special ?optimization? for lamb stage 1
            if (decay == 0) {
              r_p[ii] = MATH_T(0);
            } else {
              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 (mode == MOMENT_MODE_0) {
            MATH_T scaled_grad = r_g[ii] / clipped_global_grad_norm;
            // L2 on scaled grad
            scaled_grad = scaled_grad + decay * r_p[ii];
            r_m[ii] = r_m[ii] * beta1 + beta3 * scaled_grad;
            r_v[ii] = r_v[ii] * beta2 + (1 - beta2) * scaled_grad * scaled_grad;
            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;
            r_p[ii] = next_m_unbiased / denom;
          } else {
            MATH_T scaled_grad = r_g[ii] / clipped_global_grad_norm;
            r_m[ii] = r_m[ii] * beta1 + beta3 * scaled_grad;
            r_v[ii] = r_v[ii] * beta2 + (1 - beta2) * scaled_grad * scaled_grad;
            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;
            r_p[ii] = (next_m_unbiased / denom) + (decay * r_p[ii]);
          }
        }
#pragma unroll
        for (int ii = 0; ii < ILP; ii++) {
          int i = i_start + threadIdx.x + ii * blockDim.x;
          if (i < n && i < chunk_size) {
            g[i] = r_p[ii];
            m[i] = r_m[ii];
            v[i] = r_v[ii];
          }
        }
      }
    }
  }
};

// Step 2 reads in 'update' value and per-tensor param_norm and update_norm.
// It computes new parameter value.
template <typename T>
struct LAMBStage2Functor {
  __device__ __forceinline__ void operator()(
      int chunk_size, volatile int *noop_gmem, TensorListMetadata<2> &tl,
      const float *per_tensor_param_norm, const float *per_tensor_update_norm,
      const float learning_rate, const float decay, bool use_nvlamb) {
    // I'd like this kernel to propagate infs/nans.
    // if(*noop_gmem == 1)
    //   return;

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

    MATH_T ratio = learning_rate;
    // nvlamb: apply adaptive learning rate to all parameters
    // otherwise, only apply to those with non-zero weight decay
    if (use_nvlamb || (decay != 0.0)) {
      float param_norm = per_tensor_param_norm[tensor_num];
      float update_norm = per_tensor_update_norm[tensor_num];
      ratio = (update_norm != 0.0f && param_norm != 0.0f)
                  ? learning_rate * (param_norm / update_norm)
                  : learning_rate;
    }

    T *update = (T *)tl.addresses[0][tensor_loc];
    update += chunk_idx * chunk_size;

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

    n -= chunk_idx * chunk_size;

    // to make things simple, we put aligned case in a different code path
    if (n % ILP == 0 && chunk_size % ILP == 0 && is_aligned(p) &&
        is_aligned(update)) {
      T r_p[ILP];
      T r_update[ILP];
      for (int i_start = threadIdx.x;
           i_start * ILP < n && i_start * ILP < chunk_size;
           i_start += blockDim.x) {
        // load
        load_store(r_p, p, 0, i_start);
        load_store(r_update, update, 0, i_start);
#pragma unroll
        for (int ii = 0; ii < ILP; ii++) {
          r_p[ii] = static_cast<MATH_T>(r_p[ii]) -
                    (ratio * static_cast<MATH_T>(r_update[ii]));
        }
        load_store(p, r_p, i_start, 0);
      }
    } else {
      for (int i_start = 0; i_start < n && i_start < chunk_size;
           i_start += blockDim.x * ILP) {
        MATH_T r_p[ILP];
        MATH_T r_update[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_p[ii] = p[i];
            r_update[ii] = update[i];
          }
        }
#pragma unroll
        for (int ii = 0; ii < ILP; ii++) {
          r_p[ii] = r_p[ii] - (ratio * r_update[ii]);
        }
#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];
          }
        }
      }
    }
  }
};

void multi_tensor_lamb_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 bias_correction,
                            const float weight_decay, const int grad_averaging,
                            const int mode, at::Tensor global_grad_norm,
                            const float max_grad_norm,
                            at::optional<bool> use_nvlamb_python) {
  using namespace at;
  // Master weight and 32bit momentum(potentially changing) is not handled by
  // this So we assume every tensor are all in the same type

  bool use_nvlamb =
      use_nvlamb_python.has_value() ? use_nvlamb_python.value() : false;

  // 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);
  }

  // Handle grad averaging mode
  float beta3 = 1.0f;
  if (grad_averaging == 1) beta3 = 1 - beta1;

  std::vector<std::vector<at::Tensor>> grad_list(tensor_lists.begin(),
                                                 tensor_lists.begin() + 1);
  std::vector<std::vector<at::Tensor>> param_list(tensor_lists.begin() + 1,
                                                  tensor_lists.begin() + 2);

  // Compute per tensor param norm
  auto param_norm_tuple =
      multi_tensor_l2norm_cuda(chunk_size, noop_flag, param_list, true);

  // We now in-place modify grad to store update before compute its norm
  // Generally this is not a issue since people modify grad in step() method all
  // the time We can also grab list of empty tensor to avoid this, but I'd like
  // to save space/cpu code
  DISPATCH_FLOAT_AND_HALF(
      tensor_lists[0][0].scalar_type(), 0, "lamb_stage_1",
      multi_tensor_apply<4>(BLOCK_SIZE, chunk_size, noop_flag, tensor_lists,
                            LAMBStage1Functor<scalar_t_0>(), beta1, beta2,
                            beta3,  // 1-beta1 or 1 depends on averaging mode
                            bias_correction1, bias_correction2, epsilon,
                            (adamMode_t)mode, weight_decay,
                            global_grad_norm.DATA_PTR<float>(), max_grad_norm);)

  // Compute update norms
  auto update_norm_tuple =
      multi_tensor_l2norm_cuda(chunk_size, noop_flag, grad_list, true);

  std::vector<std::vector<at::Tensor>> grad_param_list(
      tensor_lists.begin(), tensor_lists.begin() + 2);

  DISPATCH_FLOAT_AND_HALF(
      tensor_lists[0][0].scalar_type(), 0, "lamb_stage_2",
      multi_tensor_apply<2>(BLOCK_SIZE, chunk_size, noop_flag, grad_param_list,
                            LAMBStage2Functor<scalar_t_0>(),
                            std::get<1>(param_norm_tuple).DATA_PTR<float>(),
                            std::get<1>(update_norm_tuple).DATA_PTR<float>(),
                            lr, weight_decay, use_nvlamb);)

  AT_CUDA_CHECK(cudaGetLastError());
}