github
/
ColossalAI
mirror of https://github.com/hpcaitech/ColossalAI
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

[NFC] polish colossalai/kernel/cuda_native/csrc/multi_tensor_adam.cu code style (#667)

Browse Source
pull/673/head
encmps 3 years ago committed by binmakeswell
parent e4bcff9b0f
commit 79ccfa4310
1 changed files with 112 additions and 150 deletions
Show Stats Download Patch File Download Diff File

262
colossalai/kernel/cuda_native/csrc/multi_tensor_adam.cu
Unescape View File

@ -1,4 +1,5 @@
// modified from https://github.com/NVIDIA/apex/blob/master/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>
@ -8,170 +9,131 @@
#include <assert.h>
#include "type_shim.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)
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)
{
// 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];
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 *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);
}
}
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);
}
}
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];
}
}
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);
}
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);)
AT_CUDA_CHECK(cudaGetLastError());
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);
}
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);)
AT_CUDA_CHECK(cudaGetLastError());
}
Write Preview
Loading…
Cancel
Save