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cpu_adagrad.cpp
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243 lines (218 loc) · 8.24 KB
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// Copyright (c) Microsoft Corporation.
// SPDX-License-Identifier: Apache-2.0
// DeepSpeed Team
#include "cpu_adagrad.h"
#include <torch/extension.h>
#include <iostream>
#include <memory>
#include <type_traits>
#include <unordered_map>
#if defined(__ENABLE_CUDA__)
#include <cuda_runtime_api.h>
#include "cublas_v2.h"
#include "cuda.h"
#include "curand.h"
#include "custom_cuda_layers.h"
#endif
static std::unordered_map<int, std::shared_ptr<void>> s_optimizers;
// C++ interface
void Adagrad_Optimizer::Step_1(float* _params,
float* grads,
float* _exp_avg_sq,
size_t _param_size,
ds_half_precision_t* dev_params,
bool half_precision)
{
size_t rounded_size = 0;
#if defined(__AVX512__) or defined(__AVX256__)
Step_AVX<1>(
&rounded_size, _params, grads, _exp_avg_sq, _param_size, dev_params, half_precision);
#endif
if (_param_size > rounded_size) {
float step_size = -1 * _alpha;
ds_half_precision_t* grads_cast_h;
ds_half_precision_t* params_cast_h;
if (half_precision) {
grads_cast_h = reinterpret_cast<ds_half_precision_t*>(grads);
params_cast_h = reinterpret_cast<ds_half_precision_t*>(_params);
}
for (size_t t = rounded_size; t < _param_size; t += TILE) {
size_t copy_size = TILE;
if ((t + TILE) > _param_size) copy_size = _param_size - t;
size_t offset = copy_size + t;
#if defined(__ENABLE_CUDA__)
if ((t / TILE) >= 2) { cudaStreamSynchronize(_streams[_buf_index]); }
#endif
#pragma omp parallel for
for (size_t k = t; k < offset; k++) {
float grad = half_precision ? (float)grads_cast_h[k] : grads[k];
float param = half_precision ? (float)params_cast_h[k] : _params[k];
float momentum = grads[k];
float variance = _exp_avg_sq[k];
if (_weight_decay > 0) { grad = param * _weight_decay + grad; }
variance += grad * grad;
grad = sqrt(variance);
grad += _eps;
grad = momentum / grad;
param = grad * step_size + param;
#if defined(__ENABLE_CUDA__)
if (dev_params) _doubled_buffer[_buf_index][k - t] = param;
#endif
if (half_precision)
params_cast_h[k] = (ds_half_precision_t)param;
else
_params[k] = param;
// STORE UPDATE TERM TO GRAD'S MEMORY
grads[k] = grad * step_size;
_exp_avg_sq[k] = variance;
}
#if defined(__ENABLE_CUDA__)
if (dev_params) {
launch_param_update(
_doubled_buffer[_buf_index], dev_params + t, (copy_size), _streams[_buf_index]);
_buf_index = !_buf_index;
}
#endif
}
}
}
void Adagrad_Optimizer::Step_4(float* _params,
float* grads,
float* _exp_avg_sq,
size_t _param_size,
ds_half_precision_t* dev_params,
bool half_precision)
{
size_t rounded_size = 0;
#if defined(__AVX512__) or defined(__AVX256__)
Step_AVX<4>(
&rounded_size, _params, grads, _exp_avg_sq, _param_size, dev_params, half_precision);
#endif
if (_param_size > rounded_size)
Step_1((_params + rounded_size),
(grads + rounded_size),
(_exp_avg_sq + rounded_size),
(_param_size - rounded_size),
(dev_params != nullptr ? (dev_params + rounded_size) : dev_params),
half_precision);
}
int create_adagrad_optimizer(int optimizer_id,
float alpha = 1e-2,
float eps = 1e-8,
float weight_decay = 0,
bool should_log = false)
{
auto opt = std::make_shared<Adagrad_Optimizer>(alpha, eps, weight_decay);
s_optimizers[optimizer_id] = opt;
if (should_log) {
std::string avx_type = "";
#if defined(__AVX512__)
avx_type = "AVX512";
#else
#if defined(__AVX256__)
avx_type = "AVX2";
#else
avx_type = "scalar";
#endif
#endif
printf("Adagrad Optimizer #%d is created with %s arithmetic capability.\n",
optimizer_id,
avx_type.c_str());
printf("Config: alpha=%f, weight_decay=%f\n", alpha, weight_decay);
}
return 0;
}
void Adagrad_Optimizer::Step_8(float* _params,
float* grads,
float* _exp_avg_sq,
size_t _param_size,
ds_half_precision_t* dev_params,
bool half_precision)
{
size_t rounded_size = 0;
#if defined(__AVX512__) or defined(__AVX256__)
Step_AVX<8>(
&rounded_size, _params, grads, _exp_avg_sq, _param_size, dev_params, half_precision);
#endif
if (_param_size > rounded_size)
Step_4((_params + rounded_size),
(grads + rounded_size),
(_exp_avg_sq + rounded_size),
(_param_size - rounded_size),
(dev_params != nullptr ? (dev_params + rounded_size) : dev_params),
half_precision);
}
int ds_adagrad_step(int optimizer_id,
size_t step,
float lr,
float epsilon,
float weight_decay,
torch::Tensor& params,
torch::Tensor& grads,
torch::Tensor& exp_avg_sq)
{
auto params_c = params.contiguous();
auto grads_c = grads.contiguous();
auto exp_avg_sq_c = exp_avg_sq.contiguous();
float* params_ptr = (float*)params_c.data_ptr();
float* grads_ptr = (float*)grads_c.data_ptr();
float* exp_avg_sq_ptr = (float*)exp_avg_sq_c.data_ptr();
std::shared_ptr<Adagrad_Optimizer> opt =
std::static_pointer_cast<Adagrad_Optimizer>(s_optimizers[optimizer_id]);
opt->IncrementStep(step);
opt->update_state(lr, epsilon, weight_decay);
opt->Step_8(params_ptr, grads_ptr, exp_avg_sq_ptr, params_c.numel());
#if defined(__ENABLE_CUDA__)
opt->SynchronizeStreams();
#endif
return 0;
}
int ds_adagrad_step_plus_copy(int optimizer_id,
size_t step,
float lr,
float epsilon,
float weight_decay,
torch::Tensor& params,
torch::Tensor& grads,
torch::Tensor& exp_avg_sq,
torch::Tensor& gpu_params)
{
#if defined(__ENABLE_CUDA__)
auto params_c = params.contiguous();
auto gpu_params_c = gpu_params.contiguous();
auto exp_avg_sq_c = exp_avg_sq.contiguous();
auto grads_c = grads.contiguous();
float* params_ptr = (float*)params_c.data_ptr();
float* grads_ptr = (float*)grads_c.data_ptr();
ds_half_precision_t* gpu_params_ptr = (ds_half_precision_t*)gpu_params_c.data_ptr();
float* exp_avg_sq_ptr = (float*)exp_avg_sq_c.data_ptr();
std::shared_ptr<Adagrad_Optimizer> opt =
std::static_pointer_cast<Adagrad_Optimizer>(s_optimizers[optimizer_id]);
opt->IncrementStep(step);
opt->update_state(lr, epsilon, weight_decay);
opt->Step_8(params_ptr,
grads_ptr,
exp_avg_sq_ptr,
params_c.numel(),
gpu_params_ptr,
(params.options().dtype() == at::kHalf));
opt->SynchronizeStreams();
#else
assert(false);
#endif
return 0;
}
int destroy_adagrad_optimizer(int optimizer_id)
{
s_optimizers.erase(optimizer_id);
return 0;
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
{
m.def("adagrad_update", &ds_adagrad_step, "DeepSpeed CPU Adagrad update (C++)");
m.def("adagrad_update_copy",
&ds_adagrad_step_plus_copy,
"DeepSpeed CPU Adagrad update and param copy (C++)");
m.def("create_adagrad", &create_adagrad_optimizer, "DeepSpeed CPU Adagrad (C++)");
m.def("destroy_adagrad", &destroy_adagrad_optimizer, "DeepSpeed CPU Adagrad destroy (C++)");
}