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@ -15,6 +15,7 @@
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*/
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#include "frontend/parallel/ps/optimizer_info.h"
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#include <map>
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#include <memory>
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#include "frontend/parallel/ps/util.h"
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@ -37,13 +38,6 @@ size_t OptimizerInfo::grad_index() { return 0; }
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size_t OptimizerInfo::indices_index() { return 0; }
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void OptimizerInfo::UpdateWeight(const WeightPtr &weight) {
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AddressPtr weight_addr = std::make_shared<kernel::Address>();
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weight_addr->addr = weight->data();
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weight_addr->size = weight->size();
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inputs_[0] = weight_addr;
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}
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void DenseOptimInfo::Accumulate(const Values &values, const Lengths &lengths) {
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float *accum_grad_data = reinterpret_cast<float *>(gradient()->addr);
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size_t size = gradient()->size / sizeof(float);
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@ -60,8 +54,7 @@ void DenseOptimInfo::Accumulate(const Values &values, const Lengths &lengths) {
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}
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}
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void DenseOptimInfo::ComputeMean(const std::shared_ptr<std::vector<std::shared_ptr<std::vector<size_t>>>> &, size_t n,
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size_t server_num, size_t rank_id) {
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void DenseOptimInfo::ComputeMean(const std::vector<std::vector<size_t>> &, size_t n, size_t, size_t) {
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if (n > 1) {
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float *accum_grad_data = reinterpret_cast<float *>(gradient()->addr);
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size_t size = gradient()->size / sizeof(float);
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@ -88,6 +81,7 @@ void SparseOptimInfo::Accumulate(const Values &values, const Lengths &lengths) {
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auto ret = memcpy_s(accum_grad_data + grads_offset_, incr_grad_size, incr_grad_data, incr_grad_size);
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if (ret != 0) {
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MS_LOG(EXCEPTION) << "memcpy_s error, errorno(" << ret << ")";
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return;
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}
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grads_offset_ += lengths[grad_index];
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gradient()->size += incr_grad_size;
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@ -107,13 +101,14 @@ void SparseOptimInfo::Accumulate(const Values &values, const Lengths &lengths) {
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memcpy_s(accum_indices_data + indices_offset_, incr_indice_data_size, incr_indice_data, incr_indice_data_size);
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if (ret2 != 0) {
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MS_LOG(EXCEPTION) << "memcpy_s error, errorno(" << ret2 << ")";
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return;
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}
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indices_offset_ += lengths[indices_index];
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indices()->size += incr_indice_data_size;
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}
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void SparseOptimInfo::ComputeMean(const std::shared_ptr<std::vector<std::shared_ptr<std::vector<size_t>>>> &shapes,
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size_t n, size_t server_num, size_t rank_id) {
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void SparseOptimInfo::ComputeMean(const std::vector<std::vector<size_t>> &shapes, size_t n, size_t server_num,
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size_t rank_id) {
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size_t indices_size = static_cast<size_t>(indices()->size / sizeof(int));
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int segment_size = gradient()->size / indices()->size;
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@ -121,16 +116,15 @@ void SparseOptimInfo::ComputeMean(const std::shared_ptr<std::vector<std::shared_
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std::vector<int> new_indices(indices_size);
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mindspore::kernel::SparseGradient<int> unique_sparse_grad({new_grad.data(), new_indices.data(), indices_size});
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const std::vector<std::shared_ptr<std::vector<size_t>>> &shape_vec = *shapes;
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if (shape_vec.size() < 2 || shape_vec[1] == nullptr) {
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// const std::vector<std::shared_ptr<std::vector<size_t>>> &shape_vec = *shapes;
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if (shapes.size() < 2 || shapes[1].empty()) {
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MS_LOG(EXCEPTION) << "No input shape found";
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}
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auto input_shapes = shape_vec.size() > 0 ? shape_vec[1] : nullptr;
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MS_EXCEPTION_IF_NULL(input_shapes);
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if (input_shapes->size() == 0) {
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auto input_shapes = shapes[1];
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if (input_shapes.size() == 0) {
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MS_LOG(EXCEPTION) << "Invalid input shapes";
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}
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int first_dim_size = input_shapes->front();
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int first_dim_size = input_shapes.front();
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int outer_dim_size = segment_size;
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if (first_dim_size == 0 || outer_dim_size == 0) {
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@ -140,7 +134,7 @@ void SparseOptimInfo::ComputeMean(const std::shared_ptr<std::vector<std::shared_
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float *grad_data = reinterpret_cast<float *>(gradient()->addr);
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int *indices_data = reinterpret_cast<int *>(indices()->addr);
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size_t original_row_count = input_shapes->front();
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size_t original_row_count = input_shapes.front();
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if (original_row_count > 0) {
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size_t offset = 0;
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std::map<int, int> rank_dims = Util::AllRankLocalShard(original_row_count, rank_id, server_num);
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@ -162,11 +156,13 @@ void SparseOptimInfo::ComputeMean(const std::shared_ptr<std::vector<std::shared_
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auto ret = memcpy_s(gradient()->addr, reduced_grad_size, unique_sparse_grad.value_, reduced_grad_size);
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if (ret != 0) {
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MS_LOG(EXCEPTION) << "memcpy_s error, errorno(" << ret << ")";
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return;
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}
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int reduced_indice_size = unique_sparse_grad.indices_size_ * sizeof(int);
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ret = memcpy_s(indices()->addr, reduced_indice_size, unique_sparse_grad.indices_, reduced_indice_size);
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if (ret != 0) {
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MS_LOG(EXCEPTION) << "memcpy_s error, errorno(" << ret << ")";
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return;
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}
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gradient()->size = reduced_grad_size;
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@ -197,11 +193,12 @@ MomentumOptimInfo::MomentumOptimInfo(const AddressPtr &weight, const AddressPtr
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}
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void MomentumOptimInfo::Update(const Values &values, const Lengths &lens) {
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size_t lr_offset = 0;
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const size_t lr_offset = 0;
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float *lr = values.data() + lr_offset;
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auto ret = memcpy_s(inputs_[2]->addr, sizeof(float), lr, sizeof(float));
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if (ret != 0) {
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MS_LOG(EXCEPTION) << "memcpy_s error, errorno(" << ret << ")";
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return;
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}
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}
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@ -243,6 +240,7 @@ void SparseAdamOptimInfo::Update(const Values &values, const Lengths &lens) {
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auto ret = memcpy_s(beta1_power->addr, size * bytes, data_ptr + offset, size * bytes);
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if (ret != 0) {
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MS_LOG(EXCEPTION) << "memcpy_s error, errorno(" << ret << ")";
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return;
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}
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offset += size;
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@ -251,6 +249,7 @@ void SparseAdamOptimInfo::Update(const Values &values, const Lengths &lens) {
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ret = memcpy_s(beta2_power->addr, size * bytes, data_ptr + offset, size * bytes);
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if (ret != 0) {
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MS_LOG(EXCEPTION) << "memcpy_s error, errorno(" << ret << ")";
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return;
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}
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offset += size;
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@ -259,6 +258,7 @@ void SparseAdamOptimInfo::Update(const Values &values, const Lengths &lens) {
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ret = memcpy_s(lr->addr, size * bytes, data_ptr + offset, size * bytes);
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if (ret != 0) {
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MS_LOG(EXCEPTION) << "memcpy_s error, errorno(" << ret << ")";
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return;
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}
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offset += size;
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@ -267,6 +267,7 @@ void SparseAdamOptimInfo::Update(const Values &values, const Lengths &lens) {
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ret = memcpy_s(beta1->addr, size * bytes, data_ptr + offset, size * bytes);
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if (ret != 0) {
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MS_LOG(EXCEPTION) << "memcpy_s error, errorno(" << ret << ")";
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return;
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}
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offset += size;
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@ -275,6 +276,7 @@ void SparseAdamOptimInfo::Update(const Values &values, const Lengths &lens) {
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ret = memcpy_s(beta2->addr, size * bytes, data_ptr + offset, size * bytes);
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if (ret != 0) {
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MS_LOG(EXCEPTION) << "memcpy_s error, errorno(" << ret << ")";
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return;
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}
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offset += size;
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@ -283,6 +285,7 @@ void SparseAdamOptimInfo::Update(const Values &values, const Lengths &lens) {
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ret = memcpy_s(epsilon->addr, size * bytes, data_ptr + offset, size * bytes);
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if (ret != 0) {
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MS_LOG(EXCEPTION) << "memcpy_s error, errorno(" << ret << ")";
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return;
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}
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}
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ZPaC
4 years ago