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Delete extra file

This commit is contained in:
ZPaC 2020-08-26 19:36:06 +08:00
parent 8d41931456
commit 442b38dc20
4 changed files with 88 additions and 87 deletions
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4
mindspore/ccsrc/backend/kernel_compiler/cpu/ps/sparse_apply_ftrl_ps_kernel.cc
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@ -88,10 +88,6 @@ void SparseApplyFtrlPSKernel::ReInit(const std::vector<AddressPtr> &inputs) {
bool SparseApplyFtrlPSKernel::Execute(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
const std::vector<AddressPtr> &outputs) {
ReInit(inputs);
int *indices = reinterpret_cast<int *>(inputs[4]->addr);
for (size_t i = 0; i < inputs[4]->size / sizeof(int); i++) {
indices[i] -= row_offset_;
}
return Launch(inputs, workspace, outputs);
}

4
mindspore/ccsrc/backend/kernel_compiler/cpu/ps/sparse_apply_lazy_adam_ps_kernel.cc
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@ -86,10 +86,6 @@ bool SparseApplyLazyAdamPSKernel::Execute(const std::vector<AddressPtr> &inputs,
const std::vector<AddressPtr> &workspace,
const std::vector<AddressPtr> &outputs) {
ReInit(inputs);
int *indices = reinterpret_cast<int *>(inputs[10]->addr);
for (size_t i = 0; i < inputs[10]->size / sizeof(int); i++) {
indices[i] -= row_offset_;
}
return Launch(inputs, workspace, outputs);
}

68
mindspore/ccsrc/frontend/parallel/ps/parameter_server.h
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@ -511,28 +511,27 @@ void ParameterServer<T>::UpdateWeights() {
MS_EXCEPTION_IF_NULL(optimizer);
std::shared_ptr<OptimizerInfo> optim_info = optim_infos_[key];
if (optim_info == nullptr) {
continue;
}
const std::vector<kernel::AddressPtr> &inputs = optim_info->inputs();
const std::vector<kernel::AddressPtr> &workspaces = optim_info->workspaces();
const std::vector<kernel::AddressPtr> &outputs = optim_info->outputs();
if (optim_info != nullptr) {
const std::vector<kernel::AddressPtr> &inputs = optim_info->inputs();
const std::vector<kernel::AddressPtr> &workspaces = optim_info->workspaces();
const std::vector<kernel::AddressPtr> &outputs = optim_info->outputs();
std::shared_ptr<std::vector<std::shared_ptr<std::vector<size_t>>>> shapes =
std::make_shared<std::vector<std::shared_ptr<std::vector<size_t>>>>();
std::shared_ptr<std::vector<size_t>> indices_shape = std::make_shared<std::vector<size_t>>();
indices_shape->emplace_back(optim_info->indice_size());
shapes->push_back(indices_shape);
std::shared_ptr<std::vector<std::shared_ptr<std::vector<size_t>>>> shapes =
std::make_shared<std::vector<std::shared_ptr<std::vector<size_t>>>>();
std::shared_ptr<std::vector<size_t>> indices_shape = std::make_shared<std::vector<size_t>>();
indices_shape->emplace_back(optim_info->indice_size());
shapes->push_back(indices_shape);
if (original_optim_inputs_shape_.count(key) != 0) {
for (auto &input_shapes : *(original_optim_inputs_shape_[key])) {
shapes->push_back(input_shapes);
if (original_optim_inputs_shape_.count(key) != 0) {
for (auto &input_shapes : *(original_optim_inputs_shape_[key])) {
shapes->push_back(input_shapes);
}
}
optimizer->ReInit(shapes);
optim_info->ComputeMean(shapes, worker_num_, pserver_num_, rank_id_);
optimizer->Execute(inputs, workspaces, outputs);
optim_info->Reset();
}
optimizer->ReInit(shapes);
optim_info->ComputeMean(shapes, worker_num_, pserver_num_, rank_id_);
optimizer->Execute(inputs, workspaces, outputs);
optim_info->Reset();
if (!is_embedding_[key]) {
tokens_[key] = worker_num_;
}
@ -545,23 +544,26 @@ template <typename T>
void ParameterServer<T>::AccumGrad(const Keys &keys, const Values &values, const Lengths &lengths) {
std::unique_lock<std::mutex> lock(mutex_);
const Key &key = keys[0];
std::shared_ptr<OptimizerInfo> optim_info = optim_infos_[key];
bool no_sparse_grad = values.size() == 1 && values[0] == -100;
if (!no_sparse_grad) {
std::shared_ptr<OptimizerInfo> optim_info = optim_infos_[key];
// Create or update the optimizer info
if (optim_info == nullptr) {
const std::shared_ptr<OptimizerInfoBuilder> &builder = optim_info_builders_[weight_key_to_optims_[key]];
std::shared_ptr<kernel::ps::PServerKernel> pserver_kernel = optimizers_[key];
if (pserver_kernel == nullptr) {
MS_LOG(EXCEPTION) << "no optimizer found for key " << key << " optim name " << weight_key_to_optims_[key];
// Create or update the optimizer info
if (optim_info == nullptr) {
const std::shared_ptr<OptimizerInfoBuilder> &builder = optim_info_builders_[weight_key_to_optims_[key]];
std::shared_ptr<kernel::ps::PServerKernel> pserver_kernel = optimizers_[key];
if (pserver_kernel == nullptr) {
MS_LOG(EXCEPTION) << "no optimizer found for key " << key << " optim name " << weight_key_to_optims_[key];
}
MS_EXCEPTION_IF_NULL(pserver_kernel);
OptimizerInfo *optim =
builder->Build(pserver_kernel, weights_[key], keys, values, lengths, optim_inputs_shape_[key], worker_num_);
optim_info.reset(optim);
optim_infos_[key] = optim_info;
} else {
optim_info->Update(values, lengths);
optim_info->Accumulate(values, lengths);
}
MS_EXCEPTION_IF_NULL(pserver_kernel);
OptimizerInfo *optim =
builder->Build(pserver_kernel, weights_[key], keys, values, lengths, optim_inputs_shape_[key], worker_num_);
optim_info.reset(optim);
optim_infos_[key] = optim_info;
} else {
optim_info->Update(values, lengths);
optim_info->Accumulate(values, lengths);
}
grads_accum_counter_[key] += 1;

99
mindspore/ccsrc/frontend/parallel/ps/worker_proxy.h
View File

@ -112,7 +112,7 @@ class WorkerProxy : public ::ps::KVWorker<T> {
std::unique_ptr<::ps::Customer> general_customer_;
std::unordered_map<::ps::Key, std::shared_ptr<std::vector<::ps::Range>>> embedding_table_ranges_;
std::unordered_map<int, std::vector<::ps::KVPairs<T>>> lookup_results_;
std::unordered_map<int, ::ps::KVPairs<T>> gathered_response_;
std::unordered_map<int, std::map<int, ::ps::KVPairs<T>>> gathered_response_;
std::mutex mutex_;
Slicer lookup_slicer_;
Slicer sparse_slicer_;
@ -337,12 +337,19 @@ int WorkerProxy<T>::AddGeneralRspCB(const ::ps::SArray<::ps::Key> &keys, ::ps::S
int ts = general_customer_->NewRequest(::ps::kServerGroup);
const auto &callback = [this, ts, keys, vals, lens, cb]() mutable {
mutex_.lock();
auto &kvs = gathered_response_[ts];
std::map<int, ::ps::KVPairs<T>> server_kvs = gathered_response_[ts];
mutex_.unlock();
*vals = kvs.vals;
if (lens) {
*lens = kvs.lens;
vals->clear();
for (auto kvs : server_kvs) {
for (auto val : kvs.second.vals) {
vals->push_back(val);
}
if (lens) {
for (auto len : kvs.second.lens) {
lens->push_back(len);
}
}
}
mutex_.lock();
@ -464,43 +471,50 @@ void WorkerProxy<T>::SparseSlicer(int timestamp, const ::ps::KVPairs<T> &send, c
}
}
size_t indices_size = indice_ids.size();
int slice_segment_size = indices_size * segment_size;
T *src_grad_data = new T[slice_segment_size];
int *src_indice_data = new int[indices_size];
PrepareSparseGradient(begin, end, distinct_ids, indice_to_grads, indice_data, segment_size, src_grad_data,
src_indice_data);
if (indices_size > 0) {
int slice_segment_size = indices_size * segment_size;
T *src_grad_data = new T[slice_segment_size];
int *src_indice_data = new int[indices_size];
PrepareSparseGradient(begin, end, distinct_ids, indice_to_grads, indice_data, segment_size, src_grad_data,
src_indice_data);
// Reduce the sparse gradient and indice
T *new_grad = new T[slice_segment_size];
int *new_indices = new int[indices_size];
mindspore::kernel::SparseGradient<int> unique_sparse_grad({new_grad, new_indices, indices_size});
Util::ReduceSparseGradient(src_grad_data, src_indice_data, indices_size, segment_size, first_dim_size,
outer_dim_size, &unique_sparse_grad);
// Reduce the sparse gradient and indice
T *new_grad = new T[slice_segment_size];
int *new_indices = new int[indices_size];
mindspore::kernel::SparseGradient<int> unique_sparse_grad({new_grad, new_indices, indices_size});
Util::ReduceSparseGradient(src_grad_data, src_indice_data, indices_size, segment_size, first_dim_size,
outer_dim_size, &unique_sparse_grad);
// Update the length of reduce sparse gradient and indice
::ps::SArray<int> reduced_lens;
reduced_lens.CopyFrom(kvs.lens);
reduced_lens[grad_index] = unique_sparse_grad.indices_size_ * segment_size;
reduced_lens[indice_index] = unique_sparse_grad.indices_size_;
// Update the length of reduce sparse gradient and indice
::ps::SArray<int> reduced_lens;
reduced_lens.CopyFrom(kvs.lens);
reduced_lens[grad_index] = unique_sparse_grad.indices_size_ * segment_size;
reduced_lens[indice_index] = unique_sparse_grad.indices_size_;
// Build the sparse value to be sent
size_t total_size = 0;
for (auto size : reduced_lens) {
total_size += size;
// Build the sparse value to be sent
size_t total_size = 0;
for (auto size : reduced_lens) {
total_size += size;
}
::ps::SArray<T> reduced_data(total_size, 0);
BuildSparseValue(reduced_lens, grad_index, indice_index, data, unique_sparse_grad.value_,
unique_sparse_grad.indices_, &reduced_data);
kvs.lens = reduced_lens;
kvs.vals = reduced_data;
}
::ps::SArray<T> reduced_data(total_size, 0);
BuildSparseValue(reduced_lens, grad_index, indice_index, data, unique_sparse_grad.value_,
unique_sparse_grad.indices_, &reduced_data);
kvs.lens = reduced_lens;
kvs.vals = reduced_data;
if (indices_size <= 0) {
sliced->at(i).first = false;
} else {
sliced->at(i).first = true;
expected_result_count_[timestamp] += 1;
::ps::SArray<T> no_keys;
::ps::SArray<T> no_vals;
::ps::SArray<T> no_lens;
no_keys.push_back(key);
no_vals.push_back(-100);
kvs.vals = no_vals;
kvs.lens = no_lens;
}
sliced->at(i).first = true;
expected_result_count_[timestamp] += 1;
}
}
@ -554,8 +568,8 @@ void WorkerProxy<T>::BuildSparseValue(const ::ps::SArray<int> &lengths, const si
}
// Fill the reduced indice
int indice_offset = grad_offset + lengths[grad_index];
data_size = lengths[indice_index] * sizeof(T);
int indice_offset = grad_offset + data_size;
T *indice_data = reduced_data->data() + indice_offset;
T *convert = new T[lengths[indice_index]];
for (int i = 0; i < lengths[indice_index]; i++) {
@ -656,7 +670,7 @@ void WorkerProxy<T>::ProcessLookupResult(const ::ps::Message &msg) {
lookup_results_[ts].push_back(kvs);
mutex_.unlock();
}
if (lookup_customer_->NumResponse(ts) == expected_result_count_[ts] - 1) {
if (lookup_customer_->NumResponse(ts) + 1 == server_num_) {
const auto &cb = lookup_callbacks_[ts];
cb();
lookup_callbacks_.erase(ts);
@ -676,15 +690,8 @@ void WorkerProxy<T>::ProcessResponse(const ::ps::Message &msg) {
kvs.lens = msg.data[2];
}
mutex_.lock();
for (auto key : kvs.keys) {
gathered_response_[ts].keys.push_back(key);
}
for (auto val : kvs.vals) {
gathered_response_[ts].vals.push_back(val);
}
for (auto len : kvs.lens) {
gathered_response_[ts].lens.push_back(len);
}
int rsp_server_rank = ::ps::Postoffice::Get()->IDtoRank(msg.meta.sender);
gathered_response_[ts][rsp_server_rank] = kvs;
mutex_.unlock();
if (general_customer_->NumResponse(ts) + 1 == server_num_) {
const auto &cb = general_callbacks_[ts];