ColossalAI/csrc/multi_tensor_adam.cu

// modified from https://github.com/NVIDIA/apex/blob/master/csrc/multi_tensor_adam.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 "type_shim.h"
#include "multi_tensor_apply.cuh"

#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>
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)
    {
        // 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 = (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;

        // 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 (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)
{
    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);
    }

    // Assume single type across p,g,m1,m2 now
    DISPATCH_DOUBLE_FLOAT_AND_HALF(
        tensor_lists[0][0].scalar_type(), 0, "adam",
        multi_tensor_apply<4>(
            BLOCK_SIZE,
            chunk_size,
            noop_flag,
            tensor_lists,
            AdamFunctor<scalar_t_0>(),
            beta1,
            beta2,
            bias_correction1,
            bias_correction2,
            epsilon,
            lr,
            (adamMode_t)mode,
            weight_decay);)

    AT_CUDA_CHECK(cudaGetLastError());
}