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#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, typename FULL_T, typename index_t>
struct AdamFunctor
{
__device__ __forceinline__ void operator()(
index_t 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;
index_t 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;
index_t chunk_idx = tl.block_to_chunk[blockIdx.x];
index_t 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;
FULL_T* m = (FULL_T*)tl.addresses[2][tensor_loc];
m += chunk_idx*chunk_size;
FULL_T* v = (FULL_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(index_t 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];
}
}
}
}
};
template<typename T, typename FULL_T>
struct AdamCapturableFunctor
{
__device__ __forceinline__ void operator()(
int chunk_size,
volatile int* noop_gmem,
TensorListMetadata<4>& tl,
const float beta1,
const float beta2,
const int* step,
const int bias_correction,
const float epsilon,
const float* lr,
adamMode_t mode,
const float decay,
const float* inv_scale)
{
if(*noop_gmem == 1)
return;
float beta1_correction = 1.0f, beta2_correction = 1.0f;
if (bias_correction == 1) {
beta1_correction = 1 - pow(beta1, *step);
beta2_correction = 1 - pow(beta2, *step);
}
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;
FULL_T* m = (FULL_T*)tl.addresses[2][tensor_loc];
m += chunk_idx*chunk_size;
FULL_T* v = (FULL_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] = static_cast<MATH_T>(g[i]) * (*inv_scale);
g[i] = static_cast<T>(r_g[ii]);
r_p[ii] = static_cast<MATH_T>(p[i]);
r_m[ii] = static_cast<MATH_T>(m[i]);
r_v[ii] = static_cast<MATH_T>(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] = static_cast<T>(r_p[ii]);
m[i] = static_cast<T>(r_m[ii]);
v[i] = static_cast<T>(r_v[ii]);
}
}
}
}
};
template<typename T, typename FULL_T>
struct AdamCapturableMasterFunctor
{
__device__ __forceinline__ void operator()(
int chunk_size,
volatile int* noop_gmem,
TensorListMetadata<5>& tl,
const float beta1,
const float beta2,
const int* step,
const int bias_correction,
const float epsilon,
const float* lr,
adamMode_t mode,
const float decay,
const float* inv_scale)
{
if(*noop_gmem == 1)
return;
float beta1_correction = 1.0f, beta2_correction = 1.0f;
if (bias_correction == 1) {
beta1_correction = 1 - pow(beta1, *step);
beta2_correction = 1 - pow(beta2, *step);
}
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;
FULL_T* m = (FULL_T*)tl.addresses[2][tensor_loc];
m += chunk_idx*chunk_size;
FULL_T* v = (FULL_T*)tl.addresses[3][tensor_loc];
v += chunk_idx*chunk_size;
FULL_T* p_master = (FULL_T*)tl.addresses[4][tensor_loc];
p_master += 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] = static_cast<MATH_T>(g[i]) * (*inv_scale);
g[i] = static_cast<T>(r_g[ii]);
r_p[ii] = static_cast<MATH_T>(p_master[i]);
r_m[ii] = static_cast<MATH_T>(m[i]);
r_v[ii] = static_cast<MATH_T>(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] = static_cast<T>(r_p[ii]);
p_master[i] = static_cast<FULL_T>(r_p[ii]);
m[i] = static_cast<FULL_T>(r_m[ii]);
v[i] = static_cast<FULL_T>(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);
}
size_t max_size = 0;
bool requires_64bit_indexing = false;
for (auto it = tensor_lists.begin(); it != tensor_lists.end(); it++) {
for (auto it2 = it->begin(); it2 != it->end(); it2++) {
if (it2->numel() > max_size) {
max_size = it2->numel();
if (max_size >= INT_MAX) {
requires_64bit_indexing = true;
break;
}
}
}
if (requires_64bit_indexing) {
break;
}
}
if (requires_64bit_indexing) {
// Assume single type across p,g,m1,m2 now
DISPATCH_DOUBLE_FLOAT_HALF_AND_BFLOAT(
tensor_lists[0][0].scalar_type(), 0, "adam",
multi_tensor_apply<4>(
(int64_t) BLOCK_SIZE,
(int64_t) chunk_size,
noop_flag,
tensor_lists,
AdamFunctor<scalar_t_0, float, int64_t>(),
beta1,
beta2,
bias_correction1,
bias_correction2,
epsilon,
lr,
(adamMode_t) mode,
weight_decay); )
} else {
// Assume single type across p,g,m1,m2 now
DISPATCH_DOUBLE_FLOAT_HALF_AND_BFLOAT(
tensor_lists[0][0].scalar_type(), 0, "adam",
multi_tensor_apply<4>(
BLOCK_SIZE,
chunk_size,
noop_flag,
tensor_lists,
AdamFunctor<scalar_t_0, float, int32_t>(),
beta1,
beta2,
bias_correction1,
bias_correction2,
epsilon,
lr,
(adamMode_t) mode,
weight_decay); )
}
AT_CUDA_CHECK(cudaGetLastError());
}
void multi_tensor_adam_capturable_cuda(
int chunk_size,
at::Tensor noop_flag,
std::vector<std::vector<at::Tensor>> tensor_lists,
at::Tensor lr,
const float beta1,
const float beta2,
const float epsilon,
at::Tensor step,
const int mode,
const int bias_correction,
const float weight_decay,
at::Tensor inv_scale)
{
using namespace at;
DISPATCH_DOUBLE_FLOAT_HALF_AND_BFLOAT(
tensor_lists[0][0].scalar_type(), 0, "adam",
multi_tensor_apply<4>(
BLOCK_SIZE,
chunk_size,
noop_flag,
tensor_lists,
AdamCapturableFunctor<scalar_t_0, float>(),
beta1,
beta2,
step.data_ptr<int>(),
bias_correction,
epsilon,
lr.data_ptr<float>(),
(adamMode_t) mode,
weight_decay,
inv_scale.data_ptr<float>()); )
AT_CUDA_CHECK(cudaGetLastError());
}
void multi_tensor_adam_capturable_master_cuda(
int chunk_size,
at::Tensor noop_flag,
std::vector<std::vector<at::Tensor>> tensor_lists,
at::Tensor lr,
const float beta1,
const float beta2,
const float epsilon,
at::Tensor step,
const int mode,
const int bias_correction,
const float weight_decay,
at::Tensor inv_scale)
{
using namespace at;
DISPATCH_DOUBLE_FLOAT_HALF_AND_BFLOAT(
tensor_lists[0][0].scalar_type(), 0, "adam",
multi_tensor_apply<5>(
BLOCK_SIZE,
chunk_size,
noop_flag,
tensor_lists,
AdamCapturableMasterFunctor<scalar_t_0, float>(),
beta1,
beta2,
step.data_ptr<int>(),
bias_correction,
epsilon,
lr.data_ptr<float>(),
(adamMode_t) mode,
weight_decay,
inv_scale.data_ptr<float>()); )
AT_CUDA_CHECK(cudaGetLastError());
}
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