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!3529 Pass optimzier attributes to push kernel and parameter server. 

Merge pull request !3529 from ZPaC/pass-optimizer-attrs-to-push
This commit is contained in:
mindspore-ci-bot 2020-07-27 21:07:34 +08:00 committed by Gitee
parent 6e7c673c90 af62d4020b
commit 79225e044a
14 changed files with 117 additions and 201 deletions
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3
mindspore/ccsrc/backend/kernel_compiler/cpu/ps/pserver_kernel.h
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@ -31,8 +31,9 @@ class PServerKernel {
~PServerKernel() = default;
PServerKernel(const PServerKernel &) = delete;
PServerKernel &operator=(const PServerKernel &) = delete;
virtual void InitKernel(const std::shared_ptr<std::vector<std::shared_ptr<std::vector<size_t>>>> &) {}
virtual void InitKernel(const CNodePtr &cnode,
const std::shared_ptr<std::vector<std::shared_ptr<std::vector<size_t>>>> &) {}
virtual void ReInit(const std::shared_ptr<std::vector<std::shared_ptr<std::vector<size_t>>>> &) {}
virtual bool Execute(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
const std::vector<AddressPtr> &outputs) = 0;

8
mindspore/ccsrc/backend/kernel_compiler/cpu/ps/sparse_apply_adam_ps_kernel.cc
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@ -23,7 +23,7 @@ namespace mindspore {
namespace kernel {
namespace ps {
void SparseApplyAdamPSKernel::InitKernel(
const std::shared_ptr<std::vector<std::shared_ptr<std::vector<size_t>>>> &shapes) {
const CNodePtr &cnode, const std::shared_ptr<std::vector<std::shared_ptr<std::vector<size_t>>>> &shapes) {
const std::vector<std::shared_ptr<std::vector<size_t>>> &shape_vec = *shapes;
std::vector<size_t> &var_shape = *(shape_vec[0]);
std::vector<size_t> &m_shape = *(shape_vec[1]);
@ -55,11 +55,9 @@ void SparseApplyAdamPSKernel::InitKernel(
if (grad_shape[0] != indices_size_) {
MS_LOG(ERROR) << "The first dimension of grad shape must be equal to indices";
}
/*
if (AnfAlgo::HasNodeAttr(USE_NESTEROV, kernel_node)) {
use_nesterov_ = AnfAlgo::GetNodeAttr<bool>(kernel_node, "use_nesterov");
if (AnfAlgo::HasNodeAttr(USE_NESTEROV, cnode)) {
use_nesterov_ = AnfAlgo::GetNodeAttr<bool>(cnode, "use_nesterov");
}
*/
workspace_size_list_.emplace_back(indices_size_ * var_outer_dim_size_ * sizeof(float));
workspace_size_list_.emplace_back(indices_size_ * sizeof(int));
workspace_size_list_.emplace_back(indices_size_ * var_outer_dim_size_ * sizeof(float));

3
mindspore/ccsrc/backend/kernel_compiler/cpu/ps/sparse_apply_adam_ps_kernel.h
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@ -30,7 +30,8 @@ class SparseApplyAdamPSKernel : public SparseApplyAdamCPUKernel, public PServerK
SparseApplyAdamPSKernel(size_t rank_id, size_t pserver_num) : PServerKernel(rank_id, pserver_num) {}
~SparseApplyAdamPSKernel() override = default;
void InitKernel(const std::shared_ptr<std::vector<std::shared_ptr<std::vector<size_t>>>> &) override;
void InitKernel(const CNodePtr &cnode,
const std::shared_ptr<std::vector<std::shared_ptr<std::vector<size_t>>>> &) override;
void ReInit(const std::shared_ptr<std::vector<std::shared_ptr<std::vector<size_t>>>> &) override;
bool Execute(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
const std::vector<AddressPtr> &outputs) override;

22
mindspore/ccsrc/backend/kernel_compiler/cpu/ps/sparse_apply_ftrl_ps_kernel.cc
View File

@ -20,7 +20,7 @@ namespace mindspore {
namespace kernel {
namespace ps {
void SparseApplyFtrlPSKernel::InitKernel(
const std::shared_ptr<std::vector<std::shared_ptr<std::vector<size_t>>>> &shapes) {
const CNodePtr &cnode, const std::shared_ptr<std::vector<std::shared_ptr<std::vector<size_t>>>> &shapes) {
const std::vector<std::shared_ptr<std::vector<size_t>>> &shape_vec = *shapes;
std::vector<size_t> var_shape = *(shape_vec[0]);
std::vector<size_t> accum_shape = *(shape_vec[1]);
@ -46,10 +46,22 @@ void SparseApplyFtrlPSKernel::InitKernel(
if (grad_shape[0] != indices_size_) {
MS_LOG(EXCEPTION) << "The first dimension of grad shape must be equal to indices";
}
lr_ = 0.01;
l1_ = 1e-8;
l2_ = 1e-8;
lr_power_ = -0.5;
lr_ = AnfAlgo::GetNodeAttr<float>(cnode, "lr");
if (lr_ <= 0) {
MS_LOG(EXCEPTION) << "lr should be a positive scalar";
}
l1_ = AnfAlgo::GetNodeAttr<float>(cnode, "l1");
if (l1_ < 0) {
MS_LOG(EXCEPTION) << "l1 should be a non-negative scalar";
}
l2_ = AnfAlgo::GetNodeAttr<float>(cnode, "l2");
if (l2_ < 0) {
MS_LOG(EXCEPTION) << "l2 should be a non-negative scalar";
}
lr_power_ = AnfAlgo::GetNodeAttr<float>(cnode, "lr_power");
if (lr_power_ > 0) {
MS_LOG(EXCEPTION) << "lr_power should be a non-positive scalar";
}
workspace_size_list_.emplace_back(indices_size_ * var_outer_dim_size_ * sizeof(float));
workspace_size_list_.emplace_back(indices_size_ * sizeof(int));
workspace_size_list_.emplace_back(indices_size_ * var_outer_dim_size_ * sizeof(float));

3
mindspore/ccsrc/backend/kernel_compiler/cpu/ps/sparse_apply_ftrl_ps_kernel.h
View File

@ -30,7 +30,8 @@ class SparseApplyFtrlPSKernel : public SparseApplyFtrlCPUKernel, public PServerK
SparseApplyFtrlPSKernel(size_t rank_id, size_t pserver_num) : PServerKernel(rank_id, pserver_num) {}
~SparseApplyFtrlPSKernel() override = default;
void InitKernel(const std::shared_ptr<std::vector<std::shared_ptr<std::vector<size_t>>>> &) override;
void InitKernel(const CNodePtr &cnode,
const std::shared_ptr<std::vector<std::shared_ptr<std::vector<size_t>>>> &) override;
void ReInit(const std::shared_ptr<std::vector<std::shared_ptr<std::vector<size_t>>>> &) override;
bool Execute(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,

8
mindspore/ccsrc/backend/kernel_compiler/cpu/ps/sparse_apply_lazy_adam_ps_kernel.cc
View File

@ -23,7 +23,7 @@ namespace mindspore {
namespace kernel {
namespace ps {
void SparseApplyLazyAdamPSKernel::InitKernel(
const std::shared_ptr<std::vector<std::shared_ptr<std::vector<size_t>>>> &shapes) {
const CNodePtr &cnode, const std::shared_ptr<std::vector<std::shared_ptr<std::vector<size_t>>>> &shapes) {
const std::vector<std::shared_ptr<std::vector<size_t>>> &shape_vec = *shapes;
std::vector<size_t> &var_shape = *(shape_vec[0]);
std::vector<size_t> &m_shape = *(shape_vec[1]);
@ -55,11 +55,9 @@ void SparseApplyLazyAdamPSKernel::InitKernel(
if (grad_shape[0] != indices_size_) {
MS_LOG(ERROR) << "The first dimension of grad shape must be equal to indices";
}
/*
if (AnfAlgo::HasNodeAttr(USE_NESTEROV, kernel_node)) {
use_nesterov_ = AnfAlgo::GetNodeAttr<bool>(kernel_node, "use_nesterov");
if (AnfAlgo::HasNodeAttr(USE_NESTEROV, cnode)) {
use_nesterov_ = AnfAlgo::GetNodeAttr<bool>(cnode, "use_nesterov");
}
*/
workspace_size_list_.emplace_back(indices_size_ * var_outer_dim_size_ * sizeof(float));
workspace_size_list_.emplace_back(indices_size_ * sizeof(int));
workspace_size_list_.emplace_back(indices_size_ * var_outer_dim_size_ * sizeof(float));

3
mindspore/ccsrc/backend/kernel_compiler/cpu/ps/sparse_apply_lazy_adam_ps_kernel.h
View File

@ -30,7 +30,8 @@ class SparseApplyLazyAdamPSKernel : public SparseApplyLazyAdamCPUKernel, public
SparseApplyLazyAdamPSKernel(size_t rank_id, size_t pserver_num) : PServerKernel(rank_id, pserver_num) {}
~SparseApplyLazyAdamPSKernel() override = default;
void InitKernel(const std::shared_ptr<std::vector<std::shared_ptr<std::vector<size_t>>>> &) override;
void InitKernel(const CNodePtr &cnode,
const std::shared_ptr<std::vector<std::shared_ptr<std::vector<size_t>>>> &) override;
void ReInit(const std::shared_ptr<std::vector<std::shared_ptr<std::vector<size_t>>>> &) override;
bool Execute(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
const std::vector<AddressPtr> &outputs) override;

5
mindspore/ccsrc/frontend/parallel/ps/common.h
View File

@ -57,15 +57,20 @@ constexpr char kMomentum[] = "momentum";
constexpr char kApplyMomentum[] = "ApplyMomentum";
constexpr char kSparseAdam[] = "Adam";
constexpr char kSparseFtrl[] = "Ftrl";
constexpr char kApplyMomentumOp[] = "Momentum";
constexpr char kSparseAdamOp[] = "Adam";
constexpr char kSparseFtrlOp[] = "FTRL";
constexpr int kInitWeightsCmd = 10;
constexpr int kInitWeightToOptimIdCmd = 11;
constexpr int kInitOptimInputsShapeCmd = 12;
constexpr int kInitKeyToPushNodeIdCmd = 13;
constexpr int kInitEmbeddingsCmd = 20;
constexpr int kEmbeddingLookupCmd = 30;
constexpr int kFinalizeCmd = 40;
constexpr size_t kInvalidKey = UINT64_MAX;
constexpr int kInvalidID = -1;
using Key = ::ps::Key;
using Keys = ::ps::SArray<Key>;

26
mindspore/ccsrc/frontend/parallel/ps/parameter_server.h Executable file → Normal file
View File

@ -28,6 +28,7 @@
#include <thread>
#include <cmath>
#include <random>
#include <list>
#include "ir/func_graph.h"
#include "backend/session/session_basic.h"
#include "backend/session/anf_runtime_algorithm.h"
@ -116,6 +117,7 @@ class ParameterServer {
bool ReadyForUpdateWeights();
bool ReadyForAccumGrads();
void ResetGradAccumCount();
const CNodePtr GetCNode(const std::string &name) const;
size_t pserver_num_;
size_t worker_num_;
@ -132,6 +134,7 @@ class ParameterServer {
std::unordered_map<Key, std::shared_ptr<OptimizerInfo>> optim_infos_;
std::unordered_map<std::string, std::shared_ptr<OptimizerInfoBuilder>> optim_info_builders_;
std::unordered_map<Key, std::string> weight_key_to_optims_;
std::unordered_map<Key, std::string> weight_key_to_optim_op_;
std::unordered_map<Key, WeightPtr> weights_;
std::unordered_map<Key, WeightPtr> grads_;
std::unordered_map<Key, size_t> grads_accum_counter_;
@ -277,7 +280,6 @@ bool ParameterServer<T>::Init(const FuncGraphPtr &func_graph) {
handler_->Init();
InitOptimInfoBuilders();
ps_->set_request_handle(*handler_);
thread_.reset(new std::thread(&ParameterServer::UpdateWeights, this));
return true;
@ -299,6 +301,7 @@ void ParameterServer<T>::InitWeightKeyToOptims(const Key &key, const int &optim_
return;
}
weight_key_to_optims_[key] = Util::optimizer_name(optim_id);
weight_key_to_optim_op_[key] = Util::optimizer_node_name(optim_id);
}
template <typename T>
@ -321,27 +324,42 @@ void ParameterServer<T>::InitOptimInputsShape(const Keys &keys, const Values &va
}
if (weight_key_to_optims_.count(key) > 0) {
const std::string &optim_name = weight_key_to_optims_[key];
const std::string &optim_op_name = weight_key_to_optim_op_[key];
if (optimizers_.count(key) == 0 && optim_inputs_shape_.count(key) > 0) {
const CNodePtr cnode = GetCNode(optim_op_name);
MS_EXCEPTION_IF_NULL(cnode);
if (optim_name == kSparseAdam) {
std::shared_ptr<PServerKernel> optimizer =
std::make_shared<kernel::ps::SparseApplyLazyAdamPSKernel>(rank_id_, pserver_num_);
optimizer->InitKernel(optim_inputs_shape_[key]);
optimizer->InitKernel(cnode, optim_inputs_shape_[key]);
optimizers_[key] = optimizer;
} else if (optim_name == kApplyMomentum) {
std::shared_ptr<PServerKernel> optimizer =
std::make_shared<kernel::ps::ApplyMomentumPSKernel>(rank_id_, pserver_num_);
optimizer->InitKernel(optim_inputs_shape_[key]);
optimizer->InitKernel(cnode, optim_inputs_shape_[key]);
optimizers_[key] = optimizer;
} else if (optim_name == kSparseFtrl) {
std::shared_ptr<PServerKernel> optimizer =
std::make_shared<kernel::ps::SparseApplyFtrlPSKernel>(rank_id_, pserver_num_);
optimizer->InitKernel(optim_inputs_shape_[key]);
optimizer->InitKernel(cnode, optim_inputs_shape_[key]);
optimizers_[key] = optimizer;
}
}
}
}
template <typename T>
const CNodePtr ParameterServer<T>::GetCNode(const std::string &name) const {
std::list<CNodePtr> cnodes = func_graph_->GetOrderedCnodes();
for (CNodePtr cnode : cnodes) {
std::string fullname = cnode->fullname_with_scope();
if (fullname.find(name) != std::string::npos && fullname.find("Push") != std::string::npos) {
return cnode;
}
}
return nullptr;
}
template <typename T>
void ParameterServer<T>::InitWeight(const Key &key, const WeightPtr &weight) {
MS_LOG(INFO) << "Initializing weight for key " << key;

14
mindspore/ccsrc/frontend/parallel/ps/util.cc
View File

@ -33,6 +33,13 @@ std::unordered_map<int, std::string> Util::id_to_optimizers{
{1, kSparseAdam},
{2, kSparseFtrl},
};
std::unordered_map<int, std::string> Util::id_to_optimizer_nodes{
{0, kApplyMomentumOp},
{1, kSparseAdamOp},
{2, kSparseFtrlOp},
};
bool Util::IsParamServerMode() { return IsRoleOfWorker() || IsRoleOfPServer() || IsRoleOfScheduler(); }
bool Util::IsRoleOfWorker() {
@ -112,6 +119,13 @@ std::string Util::optimizer_name(int id) {
return "";
}
std::string Util::optimizer_node_name(int id) {
if (id_to_optimizer_nodes.count(id) > 0) {
return id_to_optimizer_nodes[id];
}
return "";
}
bool Util::is_optimizer(std::string name) { return optimizer_to_ids.count(name) > 0; }
int Util::LocalShard(int first_dim, int rank_id, int server_num) {

2
mindspore/ccsrc/frontend/parallel/ps/util.h
View File

@ -34,12 +34,14 @@ class Util {
static void SetInternalEnvVar();
static int optimizer_id(std::string name);
static std::string optimizer_name(int id);
static std::string optimizer_node_name(int id);
static bool is_optimizer(std::string name);
static int LocalShard(int first_dim, int rank_id, int server_num);
private:
static std::unordered_map<std::string, int> optimizer_to_ids;
static std::unordered_map<int, std::string> id_to_optimizers;
static std::unordered_map<int, std::string> id_to_optimizer_nodes;
};
} // namespace ps
} // namespace parallel

8
mindspore/nn/optim/__init__.py
View File

@ -20,14 +20,14 @@ The optimizer is used to calculate and update the gradients.
"""
from .optimizer import Optimizer
from .momentum import Momentum
from .adam import Adam, PSAdam, AdamWeightDecay
from .adam import Adam, AdamWeightDecay
from .lamb import Lamb
from .sgd import SGD
from .lars import LARS
from .ftrl import FTRL, PSFTRL
from .ftrl import FTRL
from .rmsprop import RMSProp
from .proximal_ada_grad import ProximalAdagrad
from .lazyadam import LazyAdam
__all__ = ['Optimizer', 'Momentum', 'LARS', 'Adam', 'PSAdam', 'AdamWeightDecay', 'LazyAdam',
'Lamb', 'SGD', 'FTRL', 'PSFTRL', 'RMSProp', 'ProximalAdagrad']
__all__ = ['Optimizer', 'Momentum', 'LARS', 'Adam', 'AdamWeightDecay', 'LazyAdam',
'Lamb', 'SGD', 'FTRL', 'RMSProp', 'ProximalAdagrad']

119
mindspore/nn/optim/adam.py
View File

@ -27,7 +27,6 @@ from mindspore._checkparam import Rel
from .optimizer import Optimizer
_adam_opt = C.MultitypeFuncGraph("adam_opt")
_adam_push_pull_opt = C.MultitypeFuncGraph("_adam_push_pull_opt")
@_adam_opt.register("Tensor", "Tensor", "Tensor", "Tensor", "Number", "Tensor", "Tensor", "Tensor",
@ -85,77 +84,42 @@ def _update_run_op(beta1, beta2, eps, lr, weight_decay, param, m, v, gradient, d
return gradient
@_adam_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "IndexedSlices",
"Tensor", "Tensor", "Tensor", "Bool")
def _run_opt_with_sparse(opt, sparse_opt, beta1_power, beta2_power, beta1, beta2, eps, lr, gradient, params,
moment1, moment2, ps_parameter):
@_adam_opt.register("Function", "Function", "Function", "Function", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
"Tensor", "IndexedSlices", "Tensor", "Tensor", "Tensor", "Bool")
def _run_opt_with_sparse(opt, sparse_opt, push, pull, beta1_power, beta2_power, beta1, beta2, eps, lr,
gradient, params, moment1, moment2, ps_parameter):
"""Apply sparse adam optimizer to the weight parameter when the gradient is sparse."""
success = True
indices = gradient.indices()
values = gradient.values()
if ps_parameter:
op_shape = P.Shape()
_ps_pull = P.Pull()
_ps_push = P.Push("Adam", [0, 1, 2])
shapes = (op_shape(params), op_shape(moment1), op_shape(moment2),
op_shape(beta1_power), op_shape(beta2_power), op_shape(lr), op_shape(beta1),
op_shape(beta2), op_shape(eps), op_shape(values), op_shape(indices))
success = F.depend(success, _ps_pull(_ps_push((beta1_power, beta2_power, lr, beta1, beta2,
eps, values, indices), shapes), params))
success = F.depend(success, pull(push((beta1_power, beta2_power, lr, beta1, beta2,
eps, values, indices), shapes), params))
else:
success = F.depend(success, sparse_opt(params, moment1, moment2, beta1_power, beta2_power, lr, beta1, beta2,
eps, values, indices))
return success
@_adam_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
"Tensor", "Tensor", "Tensor", "Bool")
def _run_opt_with_one_number(opt, sparse_opt, beta1_power, beta2_power, beta1, beta2, eps, lr, gradient, params,
moment1, moment2, ps_parameter):
@_adam_opt.register("Function", "Function", "Function", "Function", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
"Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Bool")
def _run_opt_with_one_number(opt, sparse_opt, push, pull, beta1_power, beta2_power, beta1, beta2, eps, lr, gradient,
params, moment1, moment2, ps_parameter):
"""Apply adam optimizer to the weight parameter using Tensor."""
success = True
if ps_parameter:
op_shape = P.Shape()
_ps_pull = P.Pull()
_ps_push = P.Push("Adam", [0, 1, 2])
success = F.depend(success, _ps_pull(_ps_push((beta1_power, beta2_power, lr, beta1, beta2, eps, gradient),
(op_shape(params), op_shape(moment1), op_shape(moment2))),
params))
success = F.depend(success, pull(push((beta1_power, beta2_power, lr, beta1, beta2, eps, gradient),
(op_shape(params), op_shape(moment1), op_shape(moment2))), params))
else:
success = F.depend(success, opt(params, moment1, moment2, beta1_power, beta2_power, lr, beta1, beta2,
eps, gradient))
return success
@_adam_push_pull_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
"Tensor", "IndexedSlices", "Tensor", "Tensor", "Tensor")
def _run_push_pull_opt_with_sparse(push, pull, beta1_power, beta2_power, beta1, beta2, eps, lr, gradient, params,
moment1, moment2):
"""Apply sparse adam optimizer by push and pull to the weight parameter when the gradient is sparse."""
success = True
op_shape = P.Shape()
values = gradient.values()
indices = gradient.indices()
shapes = (op_shape(params), op_shape(moment1), op_shape(moment2),
op_shape(beta1_power), op_shape(beta2_power), op_shape(lr), op_shape(beta1),
op_shape(beta2), op_shape(eps), op_shape(values), op_shape(indices))
success = F.depend(success, pull(push((beta1_power, beta2_power, lr, beta1, beta2,
eps, values, indices), shapes), params))
return success
@_adam_push_pull_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
"Tensor", "Tensor", "Tensor", "Tensor", "Tensor")
def _run_push_pull_opt_with_one_number(push, pull, beta1_power, beta2_power, beta1, beta2, eps, lr, gradient, params,
moment1, moment2):
"""Apply adam optimizer by push and pull to the weight parameter using Tensor."""
success = True
op_shape = P.Shape()
success = F.depend(success, pull(push((beta1_power, beta2_power, lr, beta1, beta2, eps, gradient),
(op_shape(params), op_shape(moment1), op_shape(moment2))), params))
return success
def _check_param_value(beta1, beta2, eps, prim_name):
"""Check the type of inputs."""
validator.check_value_type("beta1", beta1, [float], prim_name)
@ -285,6 +249,10 @@ class Adam(Optimizer):
self.opt = P.Adam(use_locking, use_nesterov)
self.sparse_opt = P.FusedSparseAdam(use_locking, use_nesterov)
self._ps_pull = P.Pull()
self._ps_push = P.Push("Adam", [0, 1, 2])
self._ps_push.add_prim_attr("use_nesterov", use_nesterov)
def construct(self, gradients):
params = self.parameters
moment1 = self.moment1
@ -298,63 +266,16 @@ class Adam(Optimizer):
beta2_power = self.beta2_power * self.beta2
self.beta2_power = beta2_power
if self.is_group_lr:
success = self.map_(F.partial(_adam_opt, self.opt, self.sparse_opt, beta1_power, beta2_power,
self.beta1, self.beta2, self.eps),
success = self.map_(F.partial(_adam_opt, self.opt, self.sparse_opt, self._ps_push, self._ps_pull,
beta1_power, beta2_power, self.beta1, self.beta2, self.eps),
lr, gradients, params, moment1, moment2, self.ps_parameters)
else:
success = self.map_(F.partial(_adam_opt, self.opt, self.sparse_opt, beta1_power, beta2_power,
self.beta1, self.beta2, self.eps, lr),
success = self.map_(F.partial(_adam_opt, self.opt, self.sparse_opt, self._ps_push, self._ps_pull,
beta1_power, beta2_power, self.beta1, self.beta2, self.eps, lr),
gradients, params, moment1, moment2, self.ps_parameters)
return success
class PSAdam(Optimizer):
'''The same usage as Adam optimizer except the parameters are set PS mode.'''
def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-8, use_locking=False,
use_nesterov=False, weight_decay=0.0, loss_scale=1.0):
super(PSAdam, self).__init__(learning_rate, params, weight_decay, loss_scale)
_check_param_value(beta1, beta2, eps, self.cls_name)
validator.check_value_type("use_locking", use_locking, [bool], self.cls_name)
validator.check_value_type("use_nesterov", use_nesterov, [bool], self.cls_name)
self.beta1 = Tensor(beta1, mstype.float32)
self.beta2 = Tensor(beta2, mstype.float32)
self.beta1_power = Parameter(initializer(1, [1], mstype.float32), name="beta1_power")
self.beta2_power = Parameter(initializer(1, [1], mstype.float32), name="beta2_power")
self.eps = Tensor(eps, mstype.float32)
self.moment1 = self.parameters.clone(prefix="moment1", init='zeros')
self.moment2 = self.parameters.clone(prefix="moment2", init='zeros')
self.hyper_map = C.HyperMap()
self.push = P.Push("Adam", [0, 1, 2])
self.push.add_prim_attr("primitive_target", "CPU")
self.pull = P.Pull()
self.pull.add_prim_attr("primitive_target", "CPU")
def construct(self, gradients):
params = self.parameters
moment1 = self.moment1
moment2 = self.moment2
gradients = self.decay_weight(gradients)
gradients = self.scale_grad(gradients)
lr = self.get_lr()
beta1_power = self.beta1_power * self.beta1
self.beta1_power = beta1_power
beta2_power = self.beta2_power * self.beta2
self.beta2_power = beta2_power
if self.is_group_lr:
success = self.map_(F.partial(_adam_push_pull_opt, self.push, self.pull, beta1_power, beta2_power,
self.beta1, self.beta2, self.eps),
lr, gradients, params, moment1, moment2)
else:
success = self.map_(F.partial(_adam_push_pull_opt, self.push, self.pull, beta1_power, beta2_power,
self.beta1, self.beta2, self.eps, lr),
gradients, params, moment1, moment2)
return success
class AdamWeightDecay(Optimizer):
"""
Implements Adam algorithm weight decay fix.

94
mindspore/nn/optim/ftrl.py
View File

@ -21,68 +21,40 @@ from mindspore._checkparam import Rel
from .optimizer import Optimizer, _apply_decay, _grad_scale
_ftrl_opt = C.MultitypeFuncGraph("ftrl_opt")
_ftrl_push_pull_opt = C.MultitypeFuncGraph("ftrl_opt")
@_ftrl_opt.register("Function", "Function", "Number", "Number", "Number", "Tensor", "Tensor", "IndexedSlices", "Tensor",
"Tensor", "Bool")
def _tensor_run_opt_with_sparse(opt, spars_opt, l1, l2, lr_power, learning_rate, linear, gradient, weight, moment,
ps_parameter):
@_ftrl_opt.register("Function", "Function", "Function", "Function", "Number", "Number", "Number", "Tensor", "Tensor",
"IndexedSlices", "Tensor", "Tensor", "Bool")
def _tensor_run_opt_with_sparse(opt, spars_opt, push, pull, l1, l2, lr_power, learning_rate, linear,
gradient, weight, moment, ps_parameter):
"""Apply sparse ftrl optimizer to the weight parameter when the gradient is sparse."""
success = True
indices = gradient.indices()
values = gradient.values()
if ps_parameter:
op_shape = P.Shape()
_ps_pull = P.Pull()
_ps_push = P.Push("Ftrl", [0, 1, 2])
shapes = (op_shape(weight), op_shape(moment), op_shape(linear), op_shape(values), op_shape(indices))
success = F.depend(success, _ps_pull(_ps_push((values, indices), shapes), weight))
success = F.depend(success, pull(push((values, indices), shapes), weight))
else:
success = F.depend(success, spars_opt(weight, moment, linear, values, indices))
return success
@_ftrl_opt.register("Function", "Function", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor", "Tensor",
"Tensor", "Bool")
def _tensor_run_opt(opt, spars_opt, l1, l2, lr_power, learning_rate, linear, gradient, weight, moment, ps_parameter):
@_ftrl_opt.register("Function", "Function", "Function", "Function", "Number", "Number", "Number", "Tensor", "Tensor",
"Tensor", "Tensor", "Tensor", "Bool")
def _tensor_run_opt(opt, spars_opt, push, pull, l1, l2, lr_power, learning_rate, linear,
gradient, weight, moment, ps_parameter):
"""Apply ftrl optimizer to the weight parameter."""
success = True
if ps_parameter:
op_shape = P.Shape()
_ps_pull = P.Pull()
_ps_push = P.Push("Ftrl", [0, 1, 2])
success = F.depend(success, _ps_pull(_ps_push((gradient, learning_rate, l1, l2, lr_power),
(op_shape(weight), op_shape(moment), op_shape(linear))), weight))
success = F.depend(success, pull(push((gradient, learning_rate, l1, l2, lr_power),
(op_shape(weight), op_shape(moment), op_shape(linear))), weight))
else:
success = F.depend(success, opt(weight, moment, linear, gradient, learning_rate, l1, l2, lr_power))
return success
@_ftrl_push_pull_opt.register("Function", "Function", "Tensor", "Number", "Number", "Number", "Tensor", "IndexedSlices",
"Tensor", "Tensor")
def _tensor_run_push_pull_opt_with_sparse(push, pull, learning_rate, l1, l2, lr_power, linear, gradient,
weight, moment):
success = True
op_shape = P.Shape()
values = gradient.values()
indices = gradient.indices()
shapes = (op_shape(weight), op_shape(moment), op_shape(linear), op_shape(values), op_shape(indices))
success = F.depend(success, pull(push((values, indices), shapes), weight))
return success
@_ftrl_push_pull_opt.register("Function", "Function", "Tensor", "Number", "Number", "Number", "Tensor", "Tensor",
"Tensor", "Tensor")
def _tensor_run_push_pull_opt_with_one_number(push, pull, learning_rate, l1, l2, lr_power, linear, gradient,
weight, moment):
success = True
op_shape = P.Shape()
success = F.depend(success, pull(push((gradient, learning_rate, l1, l2, lr_power),
(op_shape(weight), op_shape(moment), op_shape(linear))), weight))
return success
def _check_param(initial_accum, lr_power, l1, l2, use_locking, prim_name=None):
"""Check param."""
validator.check_value_type("initial_accum", initial_accum, [float], prim_name)
@ -188,6 +160,12 @@ class FTRL(Optimizer):
self.hyper_map = C.HyperMap()
self.opt = P.ApplyFtrl(use_locking=use_locking)
self.sparse_opt = P.FusedSparseFtrl(learning_rate, l1, l2, lr_power, use_locking=use_locking)
self._ps_pull = P.Pull()
self._ps_push = P.Push("Ftrl", [0, 1, 2])
self._ps_push.add_prim_attr("lr", learning_rate)
self._ps_push.add_prim_attr("l1", l1)
self._ps_push.add_prim_attr("l2", l2)
self._ps_push.add_prim_attr("lr_power", lr_power)
def construct(self, grads):
params = self.parameters
@ -197,41 +175,7 @@ class FTRL(Optimizer):
grads = self.scale_grad(grads)
lr = self.get_lr()
success = self.map_(F.partial(_ftrl_opt, self.opt, self.sparse_opt, self.l1, self.l2, self.lr_power, lr),
success = self.map_(F.partial(_ftrl_opt, self.opt, self.sparse_opt, self._ps_push, self._ps_pull,
self.l1, self.l2, self.lr_power, lr),
linear, grads, params, moments, self.ps_parameters)
return success
class PSFTRL(Optimizer):
def __init__(self, params, initial_accum=0.1, learning_rate=0.001, lr_power=-0.5, l1=0.0, l2=0.0,
use_locking=False, loss_scale=1.0, weight_decay=0.0):
super(PSFTRL, self).__init__(learning_rate, params, loss_scale=loss_scale)
if self.is_group:
raise RuntimeError(f"The {self.cls_name} optimizer cannot support group setting.")
_check_param(initial_accum, lr_power, l1, l2, use_locking, self.cls_name)
self.moments = self.parameters.clone(prefix="moments", init=initial_accum)
self.linear = self.parameters.clone(prefix="linear", init='zeros')
self.l1 = l1
self.l2 = l2
self.lr_power = lr_power
self.weight_decay = weight_decay
self.decay_tf = tuple((lambda: True)() for x in self.parameters)
self.hyper_map = C.HyperMap()
self.push = P.Push("Ftrl", [0, 1, 2])
self.push.add_prim_attr("primitive_target", "CPU")
self.pull = P.Pull()
self.pull.add_prim_attr("primitive_target", "CPU")
def construct(self, grads):
params = self.parameters
moments = self.moments
linear = self.linear
lr = self.learning_rate
if self.weight_decay > 0.0:
grads = self.hyper_map(F.partial(_apply_decay, self.weight_decay), self.decay_tf, params, grads)
grads = self.scale_grad(grads)
success = self.map_(F.partial(_ftrl_push_pull_opt, self.push, self.pull, lr, self.l1, self.l2, self.lr_power),
linear, grads, params, moments)
return success