mindspore-ci-bot
4 years ago
committed by
Gitee
commit
97159386ae
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef MINDSPORE_LITE_NNACL_LSH_PROJECTION_PARAMETER_H_
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#define MINDSPORE_LITE_NNACL_LSH_PROJECTION_PARAMETER_H_
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#include "nnacl/op_base.h"
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typedef struct LshProjectionParameter {
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OpParameter op_parameter_;
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int lsh_type_;
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int hash_shape_[2];
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int in_item_num_;
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size_t in_item_size_;
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size_t seed_size_;
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size_t key_size_;
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int64_t real_dst_count;
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int task_id_;
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int64_t count_unit_;
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} LshProjectionParameter;
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#endif // MINDSPORE_LITE_NNACL_LSH_PROJECTION_PARAMETER_H_
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "src/runtime/kernel/arm/fp32/lsh_projection.h"
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#include "include/errorcode.h"
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#include "src/kernel_registry.h"
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#include "src/runtime/runtime_api.h"
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#include "src/common/string_util.h"
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using mindspore::kernel::KERNEL_ARCH::kCPU;
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using mindspore::lite::KernelRegistrar;
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using mindspore::lite::RET_ERROR;
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using mindspore::lite::RET_MEMORY_FAILED;
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using mindspore::lite::RET_OK;
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using mindspore::schema::PrimitiveType_LshProjection;
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namespace mindspore::kernel {
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namespace {
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constexpr int kSparseType = 1;
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constexpr int kDenseType = 2;
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} // namespace
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int LshProjectionCPUKernel::Init() {
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if (!InferShapeDone()) {
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return RET_OK;
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}
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return ReSize();
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}
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int LshProjectionCPUKernel::ReSize() { return RET_OK; }
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int LshProjectionCPUKernel::Run() {
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auto ret = Prepare();
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if (ret != RET_OK) {
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MS_LOG(ERROR) << "Prepare fail!ret: " << ret;
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return ret;
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}
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auto input_tensor0 = in_tensors_.at(0);
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auto input_tensor1 = in_tensors_.at(1);
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auto out_tensor0 = out_tensors_.at(0);
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hash = reinterpret_cast<float *>(input_tensor0->MutableData());
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in_data = reinterpret_cast<char *>(input_tensor1->MutableData());
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weight = in_tensors_.size() == 2 ? nullptr : reinterpret_cast<float *>(in_tensors_.at(2)->MutableData());
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output = reinterpret_cast<int32_t *>(out_tensor0->MutableData());
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const size_t seed_size = sizeof(float);
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const size_t input_item_size =
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input_tensor1->ElementsNum() * sizeof(input_tensor1->data_type()) / input_tensor1->DimensionSize(0);
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const size_t key_size = seed_size + input_item_size;
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lsh_param_->seed_size_ = seed_size;
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lsh_param_->in_item_size_ = input_item_size;
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lsh_param_->key_size_ = key_size;
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lsh_param_->in_item_num_ = input_tensor1->DimensionSize(0);
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memcpy(lsh_param_->hash_shape_, input_tensor0->shape().data(), sizeof(int) * input_tensor0->shape().size());
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elements_num_ = input_tensor0->DimensionSize(0);
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count_unit_ = thread_num_ > 1 ? UP_DIV(elements_num_, thread_num_) : elements_num_;
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ret = ParallelLaunch(this->context_->thread_pool_, LshProjectionRun, this, thread_num_);
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return ret;
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}
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int LshProjectionRun(void *cdata, int task_id) {
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auto lsh_projection = reinterpret_cast<LshProjectionCPUKernel *>(cdata);
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lsh_projection->DoExecute(task_id);
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return RET_OK;
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}
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int LshProjectionCPUKernel::DoExecute(int task_id) {
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int64_t real_dst_count = MSMIN(elements_num_ - task_id * count_unit_, count_unit_);
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lsh_param_->real_dst_count = real_dst_count;
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lsh_param_->task_id_ = task_id;
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lsh_param_->count_unit_ = count_unit_;
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if (real_dst_count <= 0) {
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return lite::RET_OK;
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}
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switch (lsh_param_->lsh_type_) {
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case kSparseType:
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LshProjectionSparse(hash, in_data, weight, output, lsh_param_);
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break;
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case kDenseType:
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LshProjectionDense(hash, in_data, weight, output, lsh_param_);
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break;
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default:
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return RET_ERROR;
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}
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return RET_OK;
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}
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int LshProjectionCPUKernel::GetSignBit(char *in_data, float *weight, float seed, LshProjectionParameter *para) {
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double score = 0.0;
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for (int i = 0; i < para->in_item_num_; i++) {
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char *key = static_cast<char *>(ctx_->allocator->Malloc(lsh_param_->key_size_));
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if (key == nullptr) {
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MS_LOG(ERROR) << "malloc key failed.";
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return RET_ERROR;
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}
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memcpy(key, &seed, para->seed_size_);
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memcpy(key + para->seed_size_, in_data, para->in_item_size_);
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in_data += para->in_item_size_;
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double hash_sign = static_cast<double>(mindspore::lite::StringHash64(key, para->key_size_));
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if (weight == nullptr) {
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score += hash_sign;
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} else {
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score += weight[i] * hash_sign;
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}
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ctx_->allocator->Free(key);
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}
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return (score > 0) ? 1 : 0;
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}
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void LshProjectionCPUKernel::LshProjectionSparse(float *hash, char *in_data, float *weight, int32_t *output,
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LshProjectionParameter *para) {
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int start = para->task_id_ * para->count_unit_;
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int end = start + para->real_dst_count;
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for (int i = start; i < end; i++) {
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int32_t hash_sign = 0;
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for (int j = 0; j < para->hash_shape_[1]; j++) {
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int bit = GetSignBit(in_data, weight, hash[i * para->hash_shape_[1] + j], para);
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hash_sign = (hash_sign << 1) | bit;
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}
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output[i] = hash_sign + i * (1 << para->hash_shape_[1]);
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}
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}
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void LshProjectionCPUKernel::LshProjectionDense(float *hash, char *in_data, float *weight, int32_t *output,
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LshProjectionParameter *para) {
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int start = para->task_id_ * para->count_unit_;
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int end = start + para->real_dst_count;
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for (int i = start; i < end; i++) {
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for (int j = 0; j < para->hash_shape_[1]; j++) {
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output[i * para->hash_shape_[1] + j] = GetSignBit(in_data, weight, hash[i * para->hash_shape_[1] + j], para);
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}
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}
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}
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kernel::LiteKernel *CpuLshProjectionFp32KernelCreator(const std::vector<lite::Tensor *> &inputs,
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const std::vector<lite::Tensor *> &outputs,
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OpParameter *op_parameter, const lite::InnerContext *ctx,
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const kernel::KernelKey &desc,
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const mindspore::lite::PrimitiveC *primitive) {
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if (op_parameter == nullptr) {
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MS_LOG(ERROR) << "Input op_parameter is nullptr!";
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return nullptr;
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}
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if (ctx == nullptr) {
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MS_LOG(ERROR) << "Input context is nullptr!";
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return nullptr;
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}
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MS_ASSERT(desc.type == schema::PrimitiveType_LshProjection);
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auto *kernel = new (std::nothrow) LshProjectionCPUKernel(op_parameter, inputs, outputs, ctx, primitive);
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if (kernel == nullptr) {
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MS_LOG(ERROR) << "new LshProjectionCPUKernel fail!";
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return nullptr;
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}
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auto ret = kernel->Init();
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if (ret != RET_OK) {
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MS_LOG(ERROR) << "Init kernel failed! name: " << op_parameter->name_ << ", type: "
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<< schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(op_parameter->type_));
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delete kernel;
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return nullptr;
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}
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return kernel;
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}
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REG_KERNEL(kCPU, kNumberTypeFloat32, PrimitiveType_LshProjection, CpuLshProjectionFp32KernelCreator)
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} // namespace mindspore::kernel
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_LSH_PROJECTION_H_
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#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_LSH_PROJECTION_H_
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#include <vector>
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#include "nnacl/lsh_projection_parameter.h"
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#include "src/lite_kernel.h"
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#include "schema/model_generated.h"
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namespace mindspore::kernel {
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class LshProjectionCPUKernel : public LiteKernel {
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public:
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LshProjectionCPUKernel(OpParameter *parameter, const std::vector<lite::Tensor *> &inputs,
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const std::vector<lite::Tensor *> &outputs, const lite::InnerContext *ctx,
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const mindspore::lite::PrimitiveC *primitive)
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: LiteKernel(parameter, inputs, outputs, ctx, primitive), thread_num_(ctx->thread_num_) {
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lsh_param_ = reinterpret_cast<LshProjectionParameter *>(op_parameter_);
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}
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~LshProjectionCPUKernel() = default;
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int Init() override;
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int ReSize() override;
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int Run() override;
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int DoExecute(int task_id);
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int GetSignBit(char *in_data, float *weight, float seed, LshProjectionParameter *para);
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void LshProjectionSparse(float *hash, char *in_data, float *weight, int32_t *output, LshProjectionParameter *param);
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void LshProjectionDense(float *hash, char *in_data, float *weight, int32_t *output, LshProjectionParameter *param);
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private:
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LshProjectionParameter *lsh_param_ = nullptr;
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const lite::InnerContext *ctx_;
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int thread_num_;
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int64_t elements_num_;
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int64_t count_unit_;
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float *hash = nullptr;
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char *in_data = nullptr;
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float *weight = nullptr;
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int32_t *output = nullptr;
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};
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int LshProjectionRun(void *cdata, int task_id);
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} // namespace mindspore::kernel
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#endif // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_LSH_PROJECTION_H_
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@ -0,0 +1,164 @@
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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||||||
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* you may not use this file except in compliance with the License.
|
||||||
|
* You may obtain a copy of the License at
|
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|
*
|
||||||
|
* http://www.apache.org/licenses/LICENSE-2.0
|
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|
*
|
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|
* Unless required by applicable law or agreed to in writing, software
|
||||||
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
* See the License for the specific language governing permissions and
|
||||||
|
* limitations under the License.
|
||||||
|
*/
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|
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#include <iostream>
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#include "schema/inner/model_generated.h"
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#include "src/common/log_adapter.h"
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#include "common/common_test.h"
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#include "mindspore/lite/nnacl/lsh_projection_parameter.h"
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#include "mindspore/lite/src/kernel_registry.h"
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#include "mindspore/lite/src/lite_kernel.h"
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#include "mindspore/lite/src/tensor.h"
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namespace mindspore {
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namespace {
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constexpr int kSparseType = 1;
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constexpr int kDenseType = 2;
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} // namespace
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class TestLshProjectionFp32 : public mindspore::CommonTest {
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public:
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TestLshProjectionFp32() {}
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};
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TEST_F(TestLshProjectionFp32, Dense1DInputs) {
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lite::Tensor in_tensor0(kNumberTypeFloat, {3, 2});
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lite::Tensor in_tensor1(kNumberTypeInt32, {5});
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lite::Tensor in_tensor2(kNumberTypeFloat, {5});
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lite::Tensor out_tensor(kNumberTypeInt32, {6});
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float input_data0[] = {0.123, 0.456, -0.321, 1.234, 5.678, -4.321};
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int32_t input_data1[] = {12345, 54321, 67890, 9876, -12345678};
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float input_data2[] = {1.0, 1.0, 1.0, 1.0, 1.0};
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int32_t output_data[6] = {0};
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in_tensor0.SetData(input_data0);
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in_tensor1.SetData(input_data1);
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in_tensor2.SetData(input_data2);
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out_tensor.SetData(output_data);
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std::vector<lite::Tensor *> inputs = {&in_tensor0, &in_tensor1, &in_tensor2};
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std::vector<lite::Tensor *> outputs = {&out_tensor};
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LshProjectionParameter parameter = {};
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parameter.lsh_type_ = kDenseType;
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kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_LshProjection};
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auto creator = lite::KernelRegistry::GetInstance()->GetCreator(desc);
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ASSERT_NE(creator, nullptr);
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||||||
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||||||
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auto ctx = std::make_shared<lite::InnerContext>();
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||||||
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ctx->thread_num_ = 3;
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ASSERT_EQ(lite::RET_OK, ctx->Init());
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||||||
|
auto kernel = creator(inputs, outputs, reinterpret_cast<OpParameter *>(¶meter), ctx.get(), desc, nullptr);
|
||||||
|
ASSERT_NE(kernel, nullptr);
|
||||||
|
|
||||||
|
auto ret = kernel->Run();
|
||||||
|
EXPECT_EQ(0, ret);
|
||||||
|
|
||||||
|
std::vector<int32_t> except_result = {0, 0, 0, 1, 0, 0};
|
||||||
|
PrintData("output data", output_data, 6);
|
||||||
|
CompareOutputData(output_data, except_result.data(), 6, 0.000001);
|
||||||
|
|
||||||
|
in_tensor0.SetData(nullptr);
|
||||||
|
in_tensor1.SetData(nullptr);
|
||||||
|
out_tensor.SetData(nullptr);
|
||||||
|
}
|
||||||
|
|
||||||
|
TEST_F(TestLshProjectionFp32, Sparse1DInputs) {
|
||||||
|
lite::Tensor in_tensor0(kNumberTypeFloat, {3, 2});
|
||||||
|
lite::Tensor in_tensor1(kNumberTypeInt32, {5});
|
||||||
|
lite::Tensor out_tensor(kNumberTypeInt32, {3});
|
||||||
|
|
||||||
|
float input_data0[] = {0.123, 0.456, -0.321, 1.234, 5.678, -4.321};
|
||||||
|
int32_t input_data1[] = {12345, 54321, 67890, 9876, -12345678};
|
||||||
|
int32_t output_data[3] = {0};
|
||||||
|
in_tensor0.SetData(input_data0);
|
||||||
|
in_tensor1.SetData(input_data1);
|
||||||
|
out_tensor.SetData(output_data);
|
||||||
|
|
||||||
|
std::vector<lite::Tensor *> inputs = {&in_tensor0, &in_tensor1};
|
||||||
|
std::vector<lite::Tensor *> outputs = {&out_tensor};
|
||||||
|
|
||||||
|
LshProjectionParameter parameter = {};
|
||||||
|
parameter.lsh_type_ = kSparseType;
|
||||||
|
kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_LshProjection};
|
||||||
|
|
||||||
|
auto creator = lite::KernelRegistry::GetInstance()->GetCreator(desc);
|
||||||
|
ASSERT_NE(creator, nullptr);
|
||||||
|
|
||||||
|
auto ctx = std::make_shared<lite::InnerContext>();
|
||||||
|
ctx->thread_num_ = 1;
|
||||||
|
ASSERT_EQ(lite::RET_OK, ctx->Init());
|
||||||
|
auto kernel = creator(inputs, outputs, reinterpret_cast<OpParameter *>(¶meter), ctx.get(), desc, nullptr);
|
||||||
|
ASSERT_NE(kernel, nullptr);
|
||||||
|
|
||||||
|
auto ret = kernel->Run();
|
||||||
|
EXPECT_EQ(0, ret);
|
||||||
|
|
||||||
|
std::vector<int32_t> except_result = {0, 5, 8};
|
||||||
|
PrintData("output data", output_data, 3);
|
||||||
|
CompareOutputData(output_data, except_result.data(), 3, 0.000001);
|
||||||
|
|
||||||
|
in_tensor0.SetData(nullptr);
|
||||||
|
in_tensor1.SetData(nullptr);
|
||||||
|
out_tensor.SetData(nullptr);
|
||||||
|
}
|
||||||
|
|
||||||
|
TEST_F(TestLshProjectionFp32, Sparse3DInputs) {
|
||||||
|
lite::Tensor in_tensor0(kNumberTypeFloat, {3, 2});
|
||||||
|
lite::Tensor in_tensor1(kNumberTypeInt32, {5, 2, 2});
|
||||||
|
lite::Tensor in_tensor2(kNumberTypeFloat, {5});
|
||||||
|
lite::Tensor out_tensor(kNumberTypeInt32, {3});
|
||||||
|
|
||||||
|
float input_data0[] = {0.123, 0.456, -0.321, 1.234, 5.678, -4.321};
|
||||||
|
int32_t input_data1[] = {1234, 2345, 3456, 1234, 4567, 5678, 6789, 4567, 7891, 8912,
|
||||||
|
9123, 7890, -987, -876, -765, -987, -543, -432, -321, -543};
|
||||||
|
float input_data2[] = {0.12, 0.34, 0.56, 0.67, 0.78};
|
||||||
|
int32_t output_data[3] = {0};
|
||||||
|
in_tensor0.SetData(input_data0);
|
||||||
|
in_tensor1.SetData(input_data1);
|
||||||
|
in_tensor2.SetData(input_data2);
|
||||||
|
out_tensor.SetData(output_data);
|
||||||
|
|
||||||
|
std::vector<lite::Tensor *> inputs = {&in_tensor0, &in_tensor1, &in_tensor2};
|
||||||
|
std::vector<lite::Tensor *> outputs = {&out_tensor};
|
||||||
|
|
||||||
|
LshProjectionParameter parameter = {};
|
||||||
|
parameter.lsh_type_ = kSparseType;
|
||||||
|
kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_LshProjection};
|
||||||
|
|
||||||
|
auto creator = lite::KernelRegistry::GetInstance()->GetCreator(desc);
|
||||||
|
ASSERT_NE(creator, nullptr);
|
||||||
|
|
||||||
|
auto ctx = std::make_shared<lite::InnerContext>();
|
||||||
|
ctx->thread_num_ = 3;
|
||||||
|
ASSERT_EQ(lite::RET_OK, ctx->Init());
|
||||||
|
auto kernel = creator(inputs, outputs, reinterpret_cast<OpParameter *>(¶meter), ctx.get(), desc, nullptr);
|
||||||
|
ASSERT_NE(kernel, nullptr);
|
||||||
|
|
||||||
|
auto ret = kernel->Run();
|
||||||
|
EXPECT_EQ(0, ret);
|
||||||
|
|
||||||
|
std::vector<int32_t> except_result = {2, 5, 9};
|
||||||
|
PrintData("output data", output_data, 3);
|
||||||
|
CompareOutputData(output_data, except_result.data(), 3, 0.000001);
|
||||||
|
|
||||||
|
in_tensor0.SetData(nullptr);
|
||||||
|
in_tensor1.SetData(nullptr);
|
||||||
|
out_tensor.SetData(nullptr);
|
||||||
|
}
|
||||||
|
} // namespace mindspore
|
||||||
Loading…
Reference in new issue