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!2386 Add multiple process for computation of optimizer in cpu

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Merge pull request !2386 from YuJianfeng/master
pull/2386/MERGE
mindspore-ci-bot 5 years ago committed by Gitee
parent f96bf04b5f 6c1bc1c6a9
commit 12e7ddae0a
7 changed files with 260 additions and 103 deletions
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17
mindspore/ccsrc/kernel/common_utils.cc
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@ -20,6 +20,7 @@
#include <iostream>
#include <utility>
#include <fstream>
#include <thread>
#include "nlohmann/json.hpp"
#include "session/anf_runtime_algorithm.h"
#include "common/utils.h"
@ -876,5 +877,21 @@ bool IsWeightBoundary(const AnfNodePtr &node) {
}
return false;
}
void MultiThreadCompute(const MultiThreadComputeFunc &func, MultiThreadComputeParams *params, size_t thread_num,
size_t total_compute_size) {
std::vector<std::thread> threads;
threads.reserve(thread_num);
size_t start = 0;
size_t once_compute_size = (total_compute_size + thread_num - 1) / thread_num;
while (start < total_compute_size) {
size_t end = (start + once_compute_size) > total_compute_size ? total_compute_size : (start + once_compute_size);
threads.emplace_back(std::thread(func, params, start, end));
start += once_compute_size;
}
for (size_t i = 0; i < threads.size(); ++i) {
threads[i].join();
}
}
} // namespace kernel
} // namespace mindspore

23
mindspore/ccsrc/kernel/common_utils.h
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@ -78,6 +78,27 @@ struct SparseGradient {
size_t indices_size_;
};
struct MultiThreadComputeParams {
float *var_;
float *accum_;
float *linear_;
float *m_;
float *m_t_;
float *v_;
float lr_;
float l1_;
float l2_;
float lr_power_;
float beta1_;
float beta2_;
float epsilon_;
SparseGradient sparse_grad_;
size_t var_first_dim_size_;
size_t var_outer_dim_size_;
bool use_nesterov_;
};
using MultiThreadComputeFunc = std::function<void(MultiThreadComputeParams *param, size_t start, size_t end)>;
bool CheckCache(const std::string &kernel_name);
KernelPackPtr SearchCache(const std::string &kernel_name, const std::string &processor);
KernelPackPtr InsertCache(const std::string &kernel_name, const std::string &processor);
@ -107,6 +128,8 @@ void GetValidKernelNodes(const FuncGraphPtr &func_graph, std::vector<AnfNodePtr>
bool GetInputTensorValue(const AnfNodePtr &anf_node, size_t input_idx, nlohmann::json *const node_json);
void GetGraphRealOutput(const FuncGraphPtr &func_graph, std::vector<std::pair<AnfNodePtr, size_t>> *node_list);
bool IsWeightBoundary(const AnfNodePtr &node);
void MultiThreadCompute(const MultiThreadComputeFunc &func, MultiThreadComputeParams *params, size_t thread_num,
size_t total_compute_size);
} // namespace kernel
} // namespace mindspore

109
mindspore/ccsrc/kernel/cpu/sparse_apply_adam_cpu_kernel.cc
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@ -14,12 +14,66 @@
* limitations under the License.
*/
#include "kernel/cpu/sparse_apply_adam_cpu_kernel.h"
#include "kernel/common_utils.h"
#include "device/cpu/cpu_device_address.h"
namespace mindspore {
namespace kernel {
namespace {
constexpr size_t kSparseApplyAdamInputSize = 11;
void ComputeAdam(MultiThreadComputeParams *input_params, size_t start, size_t end) {
MS_EXCEPTION_IF_NULL(input_params);
auto m = input_params->m_;
auto m_t = input_params->m_t_;
auto v = input_params->v_;
auto beta1 = input_params->beta1_;
auto beta2 = input_params->beta2_;
auto use_nesterov = input_params->use_nesterov_;
auto unique_sparse_grad = input_params->sparse_grad_;
auto var_first_dim_size = input_params->var_first_dim_size_;
auto var_outer_dim_size = input_params->var_outer_dim_size_;
for (size_t i = start; i < end; ++i) {
int index = unique_sparse_grad.indices_[i];
if (index < 0 || IntToSize(index) >= var_first_dim_size) {
MS_LOG(EXCEPTION) << "Index " << index << " in indices is out of range after unique process";
}
size_t start_index = var_outer_dim_size * index;
size_t end_index = start_index + var_outer_dim_size;
for (size_t j = start_index, k = var_outer_dim_size * i; j < end_index; ++j, ++k) {
auto summed_grad = unique_sparse_grad.value_[k];
m[j] += (1 - beta1) * summed_grad;
v[j] += (1 - beta2) * summed_grad * summed_grad;
if (use_nesterov) {
m_t[j] = m[j] * beta1 + (1 - beta1) * summed_grad;
}
}
}
}
void ComputeMomentum(MultiThreadComputeParams *input_params, size_t start, size_t end) {
MS_EXCEPTION_IF_NULL(input_params);
auto m = input_params->m_;
auto v = input_params->v_;
auto beta1 = input_params->beta1_;
auto beta2 = input_params->beta2_;
for (size_t i = start; i < end; ++i) {
m[i] *= beta1;
v[i] *= beta2;
}
}
void ComputeWeight(MultiThreadComputeParams *input_params, size_t start, size_t end) {
MS_EXCEPTION_IF_NULL(input_params);
auto var = input_params->var_;
auto m = input_params->m_;
auto v = input_params->v_;
auto lr = input_params->lr_;
auto epsilon = input_params->epsilon_;
for (size_t i = start; i < end; ++i) {
var[i] -= lr * m[i] / (std::sqrt(v[i]) + epsilon);
}
}
} // namespace
void SparseApplyAdamCPUKernel::InitInputOutputSize(const CNodePtr &kernel_node) {
@ -64,29 +118,6 @@ void SparseApplyAdamCPUKernel::InitKernel(const CNodePtr &kernel_node) {
}
}
void SparseApplyAdamCPUKernel::UpdateSparseMomentum(const SparseGradient &unique_sparse_grad, float *m, float *m_t,
float *v, float beta1, float beta2) const {
MS_EXCEPTION_IF_NULL(m);
MS_EXCEPTION_IF_NULL(m_t);
MS_EXCEPTION_IF_NULL(v);
for (size_t i = 0; i < unique_sparse_grad.indices_size_; ++i) {
int index = unique_sparse_grad.indices_[i];
if (index < 0 || IntToSize(index) >= var_first_dim_size_) {
MS_LOG(EXCEPTION) << "Index " << index << " in indices is out of range after unique process";
}
size_t start_index = var_outer_dim_size_ * index;
size_t end_index = start_index + var_outer_dim_size_;
for (size_t j = start_index, k = var_outer_dim_size_ * i; j < end_index; ++j, ++k) {
auto summed_grad = unique_sparse_grad.value_[k];
m[j] += (1 - beta1) * summed_grad;
v[j] += (1 - beta2) * summed_grad * summed_grad;
if (use_nesterov_) {
m_t[j] = m[j] * beta1 + (1 - beta1) * summed_grad;
}
}
}
}
bool SparseApplyAdamCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
const std::vector<kernel::AddressPtr> &workspace,
const std::vector<kernel::AddressPtr> & /*outputs*/) {
@ -115,21 +146,31 @@ bool SparseApplyAdamCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inp
ReduceSparseGradient(SparseGradient({grad, indices, indices_size_}), &unique_sparse_grad, var_first_dim_size_,
var_outer_dim_size_);
size_t total_dim_size = var_first_dim_size_ * var_outer_dim_size_;
// Update momentum
lr = lr * std::sqrt(1 - beta2_power) / (1 - beta1_power);
for (size_t i = 0; i < total_dim_size; ++i) {
m[i] *= beta1;
v[i] *= beta2;
}
MultiThreadComputeParams input_params;
input_params.m_ = m;
input_params.v_ = v;
input_params.beta1_ = beta1;
input_params.beta2_ = beta2;
const size_t kThreadNum = 16;
MultiThreadCompute(ComputeMomentum, &input_params, kThreadNum, total_dim_size);
std::vector<float> m_t(m, m + total_dim_size);
UpdateSparseMomentum(unique_sparse_grad, m, m_t.data(), v, beta1, beta2);
// Update weight
input_params.m_t_ = m_t.data();
input_params.use_nesterov_ = use_nesterov_;
input_params.sparse_grad_ = unique_sparse_grad;
input_params.var_first_dim_size_ = var_first_dim_size_;
input_params.var_outer_dim_size_ = var_outer_dim_size_;
MultiThreadCompute(ComputeAdam, &input_params, kThreadNum, unique_sparse_grad.indices_size_);
if (use_nesterov_) {
m = m_t.data();
}
for (size_t i = 0; i < total_dim_size; ++i) {
var[i] -= lr * m[i] / (std::sqrt(v[i]) + epsilon);
input_params.m_ = input_params.m_t_;
}
input_params.var_ = var;
input_params.lr_ = lr;
input_params.epsilon_ = epsilon;
MultiThreadCompute(ComputeWeight, &input_params, kThreadNum, total_dim_size);
return true;
}
} // namespace kernel

3
mindspore/ccsrc/kernel/cpu/sparse_apply_adam_cpu_kernel.h
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@ -20,7 +20,6 @@
#include <memory>
#include "kernel/cpu/cpu_kernel.h"
#include "kernel/cpu/cpu_kernel_factory.h"
#include "kernel/common_utils.h"
namespace mindspore {
namespace kernel {
@ -35,8 +34,6 @@ class SparseApplyAdamCPUKernel : public CPUKernel {
const std::vector<AddressPtr> &outputs) override;
private:
void UpdateSparseMomentum(const SparseGradient &unique_sparse_grad, float *m, float *m_t, float *v, float beta1,
float beta2) const;
size_t indices_size_{0};
size_t var_first_dim_size_{0};
size_t var_outer_dim_size_{1};

81
mindspore/ccsrc/kernel/cpu/sparse_apply_ftrl_cpu_kernel.cc
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@ -21,6 +21,47 @@ namespace mindspore {
namespace kernel {
namespace {
constexpr size_t kSparseApplyFtrlInputSize = 5;
void ComputeFtrl(MultiThreadComputeParams *input_params, size_t start, size_t end) {
MS_EXCEPTION_IF_NULL(input_params);
auto var = input_params->var_;
auto accum = input_params->accum_;
auto linear = input_params->linear_;
auto lr = input_params->lr_;
auto l1 = input_params->l1_;
auto l2 = input_params->l2_;
auto lr_power = input_params->lr_power_;
auto unique_sparse_grad = input_params->sparse_grad_;
auto var_first_dim_size = input_params->var_first_dim_size_;
auto var_outer_dim_size = input_params->var_outer_dim_size_;
for (size_t i = start; i < end; ++i) {
int index = unique_sparse_grad.indices_[i];
if (index < 0 || IntToSize(index) >= var_first_dim_size) {
MS_LOG(EXCEPTION) << "Index " << index << " in indices is out of range after unique process";
}
size_t start_index = var_outer_dim_size * index;
size_t end_index = start_index + var_outer_dim_size;
for (size_t j = start_index, k = var_outer_dim_size * i; j < end_index; ++j, ++k) {
auto summed_grad = unique_sparse_grad.value_[k];
auto accum_new = accum[j] + summed_grad * summed_grad;
if (lr_power == -0.5) {
linear[j] += summed_grad - (std::sqrt(accum_new) - std::sqrt(accum[j])) / lr * var[j];
} else {
linear[j] += summed_grad - (std::pow(accum_new, -lr_power) - std::pow(accum[j], -lr_power)) / lr * var[j];
}
auto x = Sign(linear[j]) * l1 - linear[j];
float y;
if (lr_power == -0.5) {
y = std::sqrt(accum_new) / lr + 2 * l2;
} else {
y = std::pow(accum_new, -lr_power) / lr + 2 * l2;
}
auto pre_shrink = x / y;
var[j] = std::fabs(linear[j]) > l1 ? pre_shrink : 0;
accum[j] = accum_new;
}
}
}
} // namespace
void SparseApplyFtrlCPUKernel::InitInputOutputSize(const CNodePtr &kernel_node) {
@ -96,33 +137,19 @@ bool SparseApplyFtrlCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inp
ReduceSparseGradient(SparseGradient({grad, indices, indices_size_}), &unique_sparse_grad, var_first_dim_size_,
var_outer_dim_size_);
for (size_t i = 0; i < unique_sparse_grad.indices_size_; ++i) {
int index = unique_sparse_grad.indices_[i];
if (index < 0 || IntToSize(index) >= var_first_dim_size_) {
MS_LOG(EXCEPTION) << "Index " << index << " in indices is out of range after unique process";
}
size_t start_index = var_outer_dim_size_ * index;
size_t end_index = start_index + var_outer_dim_size_;
for (size_t j = start_index, k = var_outer_dim_size_ * i; j < end_index; ++j, ++k) {
auto summed_grad = unique_sparse_grad.value_[k];
auto accum_new = accum[j] + summed_grad * summed_grad;
if (lr_power_ == -0.5) {
linear[j] += summed_grad - (std::sqrt(accum_new) - std::sqrt(accum[j])) / lr_ * var[j];
} else {
linear[j] += summed_grad - (std::pow(accum_new, -lr_power_) - std::pow(accum[j], -lr_power_)) / lr_ * var[j];
}
auto x = Sign(linear[j]) * l1_ - linear[j];
float y;
if (lr_power_ == -0.5) {
y = std::sqrt(accum_new) / lr_ + 2 * l2_;
} else {
y = std::pow(accum_new, -lr_power_) / lr_ + 2 * l2_;
}
auto pre_shrink = x / y;
var[j] = std::fabs(linear[j]) > l1_ ? pre_shrink : 0;
accum[j] = accum_new;
}
}
MultiThreadComputeParams input_params;
input_params.var_ = var;
input_params.accum_ = accum;
input_params.linear_ = linear;
input_params.lr_ = lr_;
input_params.l1_ = l1_;
input_params.l2_ = l2_;
input_params.lr_power_ = lr_power_;
input_params.sparse_grad_ = unique_sparse_grad;
input_params.var_first_dim_size_ = var_first_dim_size_;
input_params.var_outer_dim_size_ = var_outer_dim_size_;
const size_t kThreadNum = 16;
MultiThreadCompute(ComputeFtrl, &input_params, kThreadNum, unique_sparse_grad.indices_size_);
return true;
}
} // namespace kernel

65
mindspore/ccsrc/kernel/cpu/sparse_apply_lazy_adam_cpu_kernel.cc
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@ -21,6 +21,39 @@ namespace mindspore {
namespace kernel {
namespace {
constexpr size_t kSparseApplyLazyAdamInputSize = 11;
void ComputeLazyAdam(MultiThreadComputeParams *input_params, size_t start, size_t end) {
MS_EXCEPTION_IF_NULL(input_params);
auto var = input_params->var_;
auto m = input_params->m_;
auto v = input_params->v_;
auto lr = input_params->lr_;
auto beta1 = input_params->beta1_;
auto beta2 = input_params->beta2_;
auto epsilon = input_params->epsilon_;
auto use_nesterov = input_params->use_nesterov_;
auto unique_sparse_grad = input_params->sparse_grad_;
auto var_first_dim_size = input_params->var_first_dim_size_;
auto var_outer_dim_size = input_params->var_outer_dim_size_;
for (size_t i = start; i < end; ++i) {
int index = unique_sparse_grad.indices_[i];
if (index < 0 || IntToSize(index) >= var_first_dim_size) {
MS_LOG(EXCEPTION) << "Index " << index << " in indices is out of range";
}
size_t start_index = var_outer_dim_size * index;
size_t end_index = start_index + var_outer_dim_size;
for (size_t j = start_index, k = var_outer_dim_size * i; j < end_index; ++j, ++k) {
auto summed_grad = unique_sparse_grad.value_[k];
m[j] = beta1 * m[j] + (1 - beta1) * summed_grad;
v[j] = beta2 * v[j] + (1 - beta2) * summed_grad * summed_grad;
if (use_nesterov) {
var[j] -= lr * (m[j] * beta1 + (1 - beta1) * summed_grad) / (std::sqrt(v[j]) + epsilon);
} else {
var[j] -= lr * m[j] / (std::sqrt(v[j]) + epsilon);
}
}
}
}
} // namespace
void SparseApplyLazyAdamCPUKernel::InitInputOutputSize(const CNodePtr &kernel_node) {
@ -94,24 +127,20 @@ bool SparseApplyLazyAdamCPUKernel::Launch(const std::vector<kernel::AddressPtr>
var_outer_dim_size_);
lr = lr * std::sqrt(1 - beta2_power) / (1 - beta1_power);
for (size_t i = 0; i < unique_sparse_grad.indices_size_; ++i) {
int index = unique_sparse_grad.indices_[i];
if (index < 0 || IntToSize(index) >= var_first_dim_size_) {
MS_LOG(EXCEPTION) << "Index " << index << " in indices is out of range";
}
size_t start_index = var_outer_dim_size_ * index;
size_t end_index = start_index + var_outer_dim_size_;
for (size_t j = start_index, k = var_outer_dim_size_ * i; j < end_index; ++j, ++k) {
auto summed_grad = unique_sparse_grad.value_[k];
m[j] = beta1 * m[j] + (1 - beta1) * summed_grad;
v[j] = beta2 * v[j] + (1 - beta2) * summed_grad * summed_grad;
if (use_nesterov_) {
var[j] -= lr * (m[j] * beta1 + (1 - beta1) * summed_grad) / (std::sqrt(v[j]) + epsilon);
} else {
var[j] -= lr * m[j] / (std::sqrt(v[j]) + epsilon);
}
}
}
MultiThreadComputeParams input_params;
input_params.var_ = var;
input_params.m_ = m;
input_params.v_ = v;
input_params.lr_ = lr;
input_params.beta1_ = beta1;
input_params.beta2_ = beta2;
input_params.epsilon_ = epsilon;
input_params.use_nesterov_ = use_nesterov_;
input_params.sparse_grad_ = unique_sparse_grad;
input_params.var_first_dim_size_ = var_first_dim_size_;
input_params.var_outer_dim_size_ = var_outer_dim_size_;
const size_t kThreadNum = 16;
MultiThreadCompute(ComputeLazyAdam, &input_params, kThreadNum, unique_sparse_grad.indices_size_);
return true;
}
} // namespace kernel

65
mindspore/ccsrc/kernel/cpu/sparse_apply_proximal_adagrad_cpu_kernel.cc
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@ -21,6 +21,39 @@ namespace mindspore {
namespace kernel {
namespace {
constexpr size_t kSparseApplyProximalAdagradInputSize = 7;
void ComputeProximalAdagrad(MultiThreadComputeParams *input_params, size_t start, size_t end) {
MS_EXCEPTION_IF_NULL(input_params);
auto var = input_params->var_;
auto accum = input_params->accum_;
auto lr = input_params->lr_;
auto l1 = input_params->l1_;
auto l2 = input_params->l2_;
auto unique_sparse_grad = input_params->sparse_grad_;
auto var_first_dim_size = input_params->var_first_dim_size_;
auto var_outer_dim_size = input_params->var_outer_dim_size_;
for (size_t i = start; i < end; ++i) {
int index = unique_sparse_grad.indices_[i];
if (index < 0 || IntToSize(index) >= var_first_dim_size) {
MS_LOG(EXCEPTION) << "Index " << index << " in indices is out of range after unique process";
}
size_t start_index = var_outer_dim_size * index;
size_t end_index = start_index + var_outer_dim_size;
for (size_t j = start_index, k = var_outer_dim_size * i; j < end_index; ++j, ++k) {
auto summed_grad = unique_sparse_grad.value_[k];
accum[j] += summed_grad * summed_grad;
auto learning_rate = lr * (1 / std::sqrt(accum[j]));
auto prox_v = var[j];
prox_v -= summed_grad * learning_rate;
if (l1 > 0) {
var[j] = Sign(prox_v) * std::fmax(std::fabs(prox_v) - learning_rate * l1, static_cast<float>(0.0)) /
(1 + l2 * learning_rate);
} else {
var[j] = prox_v / (1 + l2 * learning_rate);
}
}
}
}
} // namespace
void SparseApplyProximalAdagradCPUKernel::InitInputOutputSize(const CNodePtr &kernel_node) {
@ -90,27 +123,17 @@ bool SparseApplyProximalAdagradCPUKernel::Launch(const std::vector<kernel::Addre
ReduceSparseGradient(SparseGradient({grad, indices, indices_size_}), &unique_sparse_grad, var_first_dim_size_,
var_outer_dim_size_);
for (size_t i = 0; i < unique_sparse_grad.indices_size_; ++i) {
int index = unique_sparse_grad.indices_[i];
if (index < 0 || IntToSize(index) >= var_first_dim_size_) {
MS_LOG(EXCEPTION) << "Index " << index << " in indices is out of range after unique process";
}
size_t start_index = var_outer_dim_size_ * index;
size_t end_index = start_index + var_outer_dim_size_;
for (size_t j = start_index, k = var_outer_dim_size_ * i; j < end_index; ++j, ++k) {
auto summed_grad = unique_sparse_grad.value_[k];
accum[j] += summed_grad * summed_grad;
auto learning_rate = lr * (1 / std::sqrt(accum[j]));
auto prox_v = var[j];
prox_v -= summed_grad * learning_rate;
if (l1 > 0) {
var[j] = Sign(prox_v) * std::fmax(std::fabs(prox_v) - learning_rate * l1, static_cast<float>(0.0)) /
(1 + l2 * learning_rate);
} else {
var[j] = prox_v / (1 + l2 * learning_rate);
}
}
}
MultiThreadComputeParams input_params;
input_params.var_ = var;
input_params.accum_ = accum;
input_params.lr_ = lr;
input_params.l1_ = l1;
input_params.l2_ = l2;
input_params.sparse_grad_ = unique_sparse_grad;
input_params.var_first_dim_size_ = var_first_dim_size_;
input_params.var_outer_dim_size_ = var_outer_dim_size_;
const size_t kThreadNum = 16;
MultiThreadCompute(ComputeProximalAdagrad, &input_params, kThreadNum, unique_sparse_grad.indices_size_);
return true;
}
} // namespace kernel

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