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[kernel] fix cpu adam kernel for pure fp16 and update tests #4919

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merged 2 commits into from
Oct 16, 2023
Merged

[kernel] fix cpu adam kernel for pure fp16 and update tests #4919

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bd4c0f3
[kernel] fix cpu adam
ver217 Oct 16, 2023
8e22669
[test] update gemini optim test
ver217 Oct 16, 2023
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238 changes: 62 additions & 176 deletions colossalai/kernel/cuda_native/csrc/cpu_adam.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -37,30 +37,17 @@ void Adam_Optimizer::Step_1(float *_params, float *grads, float *_exp_avg,
bool param_half_precision, bool grad_half_precision,
bool momentum_half_precision,
bool variance_half_precision, float loss_scale) {
size_t rounded_size = 0;
size_t rounded_size = ROUND_DOWN(_param_size, SIMD_WIDTH);

float betta1_minus1 = 1 - _betta1;
float betta2_minus1 = 1 - _betta2;
float step_size = -1 * _alpha / _bias_correction1;
float w_decay = -1 * _alpha * _weight_decay;

__half *params_cast_h = NULL;
__half *grads_cast_h = NULL;
__half *momentum_cast_h = NULL;
__half *variance_cast_h = NULL;

if (param_half_precision) {
params_cast_h = reinterpret_cast<__half *>(_params);
}
if (grad_half_precision) {
grads_cast_h = reinterpret_cast<__half *>(grads);
}
if (momentum_half_precision) {
momentum_cast_h = reinterpret_cast<__half *>(_exp_avg);
}
if (variance_half_precision) {
variance_cast_h = reinterpret_cast<__half *>(_exp_avg_sq);
}
__half *params_cast_h = reinterpret_cast<__half *>(_params);
__half *grads_cast_h = reinterpret_cast<__half *>(grads);
__half *momentum_cast_h = reinterpret_cast<__half *>(_exp_avg);
__half *variance_cast_h = reinterpret_cast<__half *>(_exp_avg_sq);

#if defined(__AVX512__) or defined(__AVX256__) or defined(__AVX2__)
AVX_Data betta1_4;
Expand All @@ -86,7 +73,6 @@ void Adam_Optimizer::Step_1(float *_params, float *grads, float *_exp_avg,
if (_weight_decay > 0)
weight_decay_4.data =
(_adamw_mode ? SIMD_SET(w_decay) : SIMD_SET(_weight_decay));
rounded_size = ROUND_DOWN(_param_size, SIMD_WIDTH);

for (size_t t = 0; t < rounded_size; t += TILE) {
size_t copy_size = TILE;
Expand All @@ -96,36 +82,23 @@ void Adam_Optimizer::Step_1(float *_params, float *grads, float *_exp_avg,
#pragma omp parallel for
for (size_t i = t; i < offset; i += SIMD_WIDTH) {
AVX_Data grad_4;
if (grad_half_precision) {
grad_4.data = SIMD_LOAD_HALF(grads_cast_h + i);
} else {
grad_4.data = SIMD_LOAD(grads + i);
}
this->simd_load(grad_half_precision, grads + i, grads_cast_h + i, grad_4);
if (loss_scale > 0) {
AVX_Data loss_scale_vec;
loss_scale_vec.data = SIMD_SET(loss_scale);
grad_4.data = SIMD_DIV(grad_4.data, loss_scale_vec.data);
}
AVX_Data momentum_4;
if (momentum_half_precision) {
momentum_4.data = SIMD_LOAD_HALF(momentum_cast_h + i);
} else {
momentum_4.data = SIMD_LOAD(_exp_avg + i);
}
this->simd_load(momentum_half_precision, _exp_avg + i,
momentum_cast_h + i, momentum_4);

AVX_Data variance_4;
if (variance_half_precision) {
variance_4.data = SIMD_LOAD_HALF(variance_cast_h + i);
} else {
variance_4.data = SIMD_LOAD(_exp_avg_sq + i);
}
this->simd_load(variance_half_precision, _exp_avg_sq + i,
variance_cast_h + i, variance_4);

AVX_Data param_4;
if (param_half_precision) {
param_4.data = SIMD_LOAD_HALF(params_cast_h + i);
} else {
param_4.data = SIMD_LOAD(_params + i);
}
this->simd_load(param_half_precision, _params + i, params_cast_h + i,
param_4);

if (_weight_decay > 0 && !_adamw_mode) {
grad_4.data = SIMD_FMA(param_4.data, weight_decay_4.data, grad_4.data);
Expand All @@ -147,21 +120,12 @@ void Adam_Optimizer::Step_1(float *_params, float *grads, float *_exp_avg,
}
param_4.data = SIMD_FMA(grad_4.data, step_size_4.data, param_4.data);

if (param_half_precision) {
SIMD_STORE_HALF((float *)(params_cast_h + i), param_4.data);
} else {
SIMD_STORE(_params + i, param_4.data);
}
if (momentum_half_precision) {
SIMD_STORE_HALF((float *)(momentum_cast_h + i), momentum_4.data);
} else {
SIMD_STORE(_exp_avg + i, momentum_4.data);
}
if (variance_half_precision) {
SIMD_STORE_HALF((float *)(variance_cast_h + i), variance_4.data);
} else {
SIMD_STORE(_exp_avg_sq + i, variance_4.data);
}
this->simd_store(param_half_precision, _params + i, params_cast_h + i,
param_4);
this->simd_store(momentum_half_precision, _exp_avg + i,
momentum_cast_h + i, momentum_4);
this->simd_store(variance_half_precision, _exp_avg_sq + i,
variance_cast_h + i, variance_4);
}
}
#endif
Expand Down Expand Up @@ -223,24 +187,12 @@ void Adam_Optimizer::Step_4(float *_params, float *grads, float *_exp_avg,
bool param_half_precision, bool grad_half_precision,
bool momentum_half_precision,
bool variance_half_precision, float loss_scale) {
size_t rounded_size = 0;

__half *params_cast_h = NULL;
__half *grads_cast_h = NULL;
__half *momentum_cast_h = NULL;
__half *variance_cast_h = NULL;
if (param_half_precision) {
params_cast_h = reinterpret_cast<__half *>(_params);
}
if (grad_half_precision) {
grads_cast_h = reinterpret_cast<__half *>(grads);
}
if (momentum_half_precision) {
momentum_cast_h = reinterpret_cast<__half *>(_exp_avg);
}
if (variance_half_precision) {
variance_cast_h = reinterpret_cast<__half *>(_exp_avg_sq);
}
size_t rounded_size = ROUND_DOWN(_param_size, SIMD_WIDTH * 4);

__half *params_cast_h = reinterpret_cast<__half *>(_params);
__half *grads_cast_h = reinterpret_cast<__half *>(grads);
__half *momentum_cast_h = reinterpret_cast<__half *>(_exp_avg);
__half *variance_cast_h = reinterpret_cast<__half *>(_exp_avg_sq);

#if defined(__AVX512__) or defined(__AVX256__) or defined(__AVX2__)
AVX_Data betta1_4;
Expand Down Expand Up @@ -270,7 +222,6 @@ void Adam_Optimizer::Step_4(float *_params, float *grads, float *_exp_avg,
if (_weight_decay > 0)
weight_decay_4.data =
(_adamw_mode ? SIMD_SET(w_decay) : SIMD_SET(_weight_decay));
rounded_size = ROUND_DOWN(_param_size, SIMD_WIDTH * 4);

for (size_t t = 0; t < rounded_size; t += TILE) {
size_t copy_size = TILE;
Expand All @@ -285,36 +236,21 @@ void Adam_Optimizer::Step_4(float *_params, float *grads, float *_exp_avg,
AVX_Data param_4[4];
#pragma unroll 4
for (int j = 0; j < 4; j++) {
if (grad_half_precision) {
grad_4[j].data = SIMD_LOAD_HALF(grads_cast_h + i + SIMD_WIDTH * j);
} else {
grad_4[j].data = SIMD_LOAD(grads + i + SIMD_WIDTH * j);
}
this->simd_load(grad_half_precision, grads + i + SIMD_WIDTH * j,
grads_cast_h + i + SIMD_WIDTH * j, grad_4[j]);

if (loss_scale > 0) {
AVX_Data loss_scale_vec;
loss_scale_vec.data = SIMD_SET(loss_scale);
grad_4[j].data = SIMD_DIV(grad_4[j].data, loss_scale_vec.data);
}

if (momentum_half_precision) {
momentum_4[j].data =
SIMD_LOAD_HALF(momentum_cast_h + i + SIMD_WIDTH * j);
} else {
momentum_4[j].data = SIMD_LOAD(_exp_avg + i + SIMD_WIDTH * j);
}
if (variance_half_precision) {
variance_4[j].data =
SIMD_LOAD_HALF(variance_cast_h + i + SIMD_WIDTH * j);
} else {
variance_4[j].data = SIMD_LOAD(_exp_avg_sq + i + SIMD_WIDTH * j);
}

if (param_half_precision) {
param_4[j].data = SIMD_LOAD_HALF(params_cast_h + i + SIMD_WIDTH * j);
} else {
param_4[j].data = SIMD_LOAD(_params + i + SIMD_WIDTH * j);
}
this->simd_load(momentum_half_precision, _exp_avg + i + SIMD_WIDTH * j,
momentum_cast_h + i + SIMD_WIDTH * j, momentum_4[j]);
this->simd_load(variance_half_precision,
_exp_avg_sq + i + SIMD_WIDTH * j,
variance_cast_h + i + SIMD_WIDTH * j, variance_4[j]);
this->simd_load(param_half_precision, _params + i + SIMD_WIDTH * j,
params_cast_h + i + SIMD_WIDTH * j, param_4[j]);

if (_weight_decay > 0 && !_adamw_mode) {
grad_4[j].data =
Expand All @@ -337,24 +273,13 @@ void Adam_Optimizer::Step_4(float *_params, float *grads, float *_exp_avg,
}
param_4[j].data =
SIMD_FMA(grad_4[j].data, step_size_4.data, param_4[j].data);
if (param_half_precision) {
SIMD_STORE_HALF((float *)(params_cast_h + i + SIMD_WIDTH * j),
param_4[j].data);
} else {
SIMD_STORE(_params + i + SIMD_WIDTH * j, param_4[j].data);
}
if (momentum_half_precision) {
SIMD_STORE_HALF((float *)(momentum_cast_h + i + SIMD_WIDTH * j),
momentum_4[j].data);
} else {
SIMD_STORE(_exp_avg + i + SIMD_WIDTH * j, momentum_4[j].data);
}
if (variance_half_precision) {
SIMD_STORE_HALF((float *)(variance_cast_h + i + SIMD_WIDTH * j),
variance_4[j].data);
} else {
SIMD_STORE(_exp_avg_sq + i + SIMD_WIDTH * j, variance_4[j].data);
}
this->simd_store(param_half_precision, _params + i + SIMD_WIDTH * j,
params_cast_h + i + SIMD_WIDTH * j, param_4[j]);
this->simd_store(momentum_half_precision, _exp_avg + i + SIMD_WIDTH * j,
momentum_cast_h + i + SIMD_WIDTH * j, momentum_4[j]);
this->simd_store(variance_half_precision,
_exp_avg_sq + i + SIMD_WIDTH * j,
variance_cast_h + i + SIMD_WIDTH * j, variance_4[j]);
}
}
}
Expand All @@ -378,23 +303,12 @@ void Adam_Optimizer::Step_8(float *_params, float *grads, float *_exp_avg,
bool param_half_precision, bool grad_half_precision,
bool momentum_half_precision,
bool variance_half_precision, float loss_scale) {
size_t rounded_size = 0;
__half *params_cast_h = NULL;
__half *grads_cast_h = NULL;
__half *momentum_cast_h = NULL;
__half *variance_cast_h = NULL;
if (param_half_precision) {
params_cast_h = reinterpret_cast<__half *>(_params);
}
if (grad_half_precision) {
grads_cast_h = reinterpret_cast<__half *>(grads);
}
if (momentum_half_precision) {
momentum_cast_h = reinterpret_cast<__half *>(_exp_avg);
}
if (variance_half_precision) {
variance_cast_h = reinterpret_cast<__half *>(_exp_avg_sq);
}
size_t rounded_size = ROUND_DOWN(_param_size, SIMD_WIDTH * 8);
__half *params_cast_h = reinterpret_cast<__half *>(_params);
__half *grads_cast_h = reinterpret_cast<__half *>(grads);
__half *momentum_cast_h = reinterpret_cast<__half *>(_exp_avg);
__half *variance_cast_h = reinterpret_cast<__half *>(_exp_avg_sq);

#if defined(__AVX512__) or defined(__AVX256__) or defined(__AVX2__)
AVX_Data betta1_4;
betta1_4.data = SIMD_SET(_betta1);
Expand Down Expand Up @@ -423,7 +337,6 @@ void Adam_Optimizer::Step_8(float *_params, float *grads, float *_exp_avg,
if (_weight_decay > 0)
weight_decay_4.data =
(_adamw_mode ? SIMD_SET(w_decay) : SIMD_SET(_weight_decay));
rounded_size = ROUND_DOWN(_param_size, SIMD_WIDTH * 8);

for (size_t t = 0; t < rounded_size; t += TILE) {
size_t copy_size = TILE;
Expand All @@ -438,36 +351,21 @@ void Adam_Optimizer::Step_8(float *_params, float *grads, float *_exp_avg,
AVX_Data param_4[8];
#pragma unroll 8
for (int j = 0; j < 8; j++) {
if (grad_half_precision) {
grad_4[j].data = SIMD_LOAD_HALF(grads_cast_h + i + SIMD_WIDTH * j);
} else {
grad_4[j].data = SIMD_LOAD(grads + i + SIMD_WIDTH * j);
}
this->simd_load(grad_half_precision, grads + i + SIMD_WIDTH * j,
grads_cast_h + i + SIMD_WIDTH * j, grad_4[j]);

if (loss_scale > 0) {
AVX_Data loss_scale_vec;
loss_scale_vec.data = SIMD_SET(loss_scale);
grad_4[j].data = SIMD_DIV(grad_4[j].data, loss_scale_vec.data);
}

if (momentum_half_precision) {
momentum_4[j].data =
SIMD_LOAD_HALF(momentum_cast_h + i + SIMD_WIDTH * j);
} else {
momentum_4[j].data = SIMD_LOAD(_exp_avg + i + SIMD_WIDTH * j);
}
if (variance_half_precision) {
variance_4[j].data =
SIMD_LOAD_HALF(variance_cast_h + i + SIMD_WIDTH * j);
} else {
variance_4[j].data = SIMD_LOAD(_exp_avg_sq + i + SIMD_WIDTH * j);
}

if (param_half_precision) {
param_4[j].data = SIMD_LOAD_HALF(params_cast_h + i + SIMD_WIDTH * j);
} else {
param_4[j].data = SIMD_LOAD(_params + i + SIMD_WIDTH * j);
}
this->simd_load(momentum_half_precision, _exp_avg + i + SIMD_WIDTH * j,
momentum_cast_h + i + SIMD_WIDTH * j, momentum_4[j]);
this->simd_load(variance_half_precision,
_exp_avg_sq + i + SIMD_WIDTH * j,
variance_cast_h + i + SIMD_WIDTH * j, variance_4[j]);
this->simd_load(param_half_precision, _params + i + SIMD_WIDTH * j,
params_cast_h + i + SIMD_WIDTH * j, param_4[j]);

if (_weight_decay > 0 && !_adamw_mode) {
grad_4[j].data =
Expand All @@ -490,25 +388,13 @@ void Adam_Optimizer::Step_8(float *_params, float *grads, float *_exp_avg,
param_4[j].data =
SIMD_FMA(grad_4[j].data, step_size_4.data, param_4[j].data);

if (param_half_precision) {
SIMD_STORE_HALF((float *)(params_cast_h + i + SIMD_WIDTH * j),
param_4[j].data);
} else {
SIMD_STORE(_params + i + SIMD_WIDTH * j, param_4[j].data);
}

if (momentum_half_precision) {
SIMD_STORE_HALF((float *)(momentum_cast_h + i + SIMD_WIDTH * j),
momentum_4[j].data);
} else {
SIMD_STORE(_exp_avg + i + SIMD_WIDTH * j, momentum_4[j].data);
}
if (variance_half_precision) {
SIMD_STORE_HALF((float *)(variance_cast_h + i + SIMD_WIDTH * j),
variance_4[j].data);
} else {
SIMD_STORE(_exp_avg_sq + i + SIMD_WIDTH * j, variance_4[j].data);
}
this->simd_store(param_half_precision, _params + i + SIMD_WIDTH * j,
params_cast_h + i + SIMD_WIDTH * j, param_4[j]);
this->simd_store(momentum_half_precision, _exp_avg + i + SIMD_WIDTH * j,
momentum_cast_h + i + SIMD_WIDTH * j, momentum_4[j]);
this->simd_store(variance_half_precision,
_exp_avg_sq + i + SIMD_WIDTH * j,
variance_cast_h + i + SIMD_WIDTH * j, variance_4[j]);
}
}
}
Expand Down
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