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@ -24,120 +24,49 @@
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namespace mindspore {
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namespace lite {
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STATUS HuffmanEncode::GetParamValueLitePtr(const std::shared_ptr<AnfNode> &input_node, ParamValueLitePtr *param_value) {
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if (!input_node->isa<Parameter>()) {
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return RET_CONTINUE;
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}
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auto abstract_base = input_node->abstract();
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if (abstract_base == nullptr) {
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MS_LOG(ERROR) << "Abstract of parameter is nullptr, " << input_node->fullname_with_scope();
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return RET_ERROR;
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}
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if (!utils::isa<abstract::AbstractTensorPtr>(abstract_base)) {
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MS_LOG(ERROR) << "Abstract of parameter should be abstract tensor, " << input_node->fullname_with_scope();
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return RET_ERROR;
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}
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auto abstract_tensor = utils::cast<abstract::AbstractTensorPtr>(abstract_base);
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if (abstract_tensor->element() == nullptr) {
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MS_LOG(ERROR) << "abstract tensor element is nullptr, " << input_node->fullname_with_scope();
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STATUS HuffmanEncode::DoHuffmanEncode(const ParamValueLitePtr &weight, const std::shared_ptr<PrimitiveC> &primitive_c,
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void *quant_datas, const size_t &bit_num) {
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if (quant_datas == nullptr) {
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MS_LOG(ERROR) << "quant data is nullptr";
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return RET_ERROR;
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}
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auto tensor_type = abstract_tensor->element()->GetTypeTrack();
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MS_ASSERT(tensor_type != nullptr);
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auto tensor_type_id = tensor_type->type_id();
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if (tensor_type_id != kNumberTypeInt8) {
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return RET_CONTINUE;
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}
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auto param_node = input_node->cast<ParameterPtr>();
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if (param_node == nullptr) {
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MS_LOG(ERROR) << "parameter node is nullptr, " << input_node->fullname_with_scope();
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return RET_ERROR;
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}
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if (!param_node->has_default()) {
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MS_LOG(WARNING) << "param_node don't have default: " << input_node->fullname_with_scope();
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return RET_CONTINUE;
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}
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*param_value = std::static_pointer_cast<ParamValueLite>(param_node->default_param());
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return RET_OK;
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}
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STATUS HuffmanEncode::DoHuffmanEncode(const FuncGraphPtr &func_graph, const int &bit_num) {
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auto cnodes = func_graph->GetOrderedCnodes();
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for (auto &cnode : cnodes) {
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auto primitive_c = GetValueNode<std::shared_ptr<PrimitiveC>>(cnode->input(0));
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if (primitive_c == nullptr) {
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MS_LOG(ERROR) << "primitive_c is nullptr: " << cnode->fullname_with_scope();
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return RET_ERROR;
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}
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if (primitive_c->quant_type() != schema::QuantType_WeightQuant) {
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continue;
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auto *raw_datas = static_cast<int8_t *>(quant_datas);
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size_t elem_count = weight->tensor_shape_size();
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size_t packed_size = elem_count * bit_num;
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HuffmanPriorityQueue pq;
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auto status = GetHuffmanPriorityQueue(raw_datas, elem_count, &pq);
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if (status != RET_OK) {
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MS_LOG(ERROR) << "GetHuffmanPriorityQueue failed";
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return status;
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}
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status = BuildHuffmanTree(&pq);
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if (status != RET_OK) {
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MS_LOG(ERROR) << "BuildHuffmanTree failed";
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return status;
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}
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status = DoHuffmanCompress(raw_datas, elem_count);
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if (status != RET_OK) {
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MS_LOG(ERROR) << "DoHuffmanCompress failed";
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return status;
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}
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size_t ch_size = huffman_encoded_str_.length();
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if (ch_size < packed_size) {
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auto encode_data = new (std::nothrow) char[ch_size];
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if (encode_data == nullptr) {
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MS_LOG(ERROR) << "new char[] failed.";
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return RET_MEMORY_FAILED;
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}
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for (size_t i = 1; i < cnode->inputs().size(); i++) {
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auto input_node = cnode->input(i);
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ParamValueLitePtr param_value;
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auto status = GetParamValueLitePtr(input_node, ¶m_value);
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if (status == RET_CONTINUE) {
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continue;
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} else if (status == RET_ERROR) {
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MS_LOG(ERROR) << "Get param value lite ptr failed. " << cnode->fullname_with_scope();
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return RET_ERROR;
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}
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size_t elem_count = param_value->tensor_shape_size();
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size_t packed_size = param_value->tensor_size();
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auto *raw_datas = static_cast<int8_t *>(param_value->tensor_addr());
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if (raw_datas == nullptr) {
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MS_LOG(ERROR) << "rawDatas is nullptr";
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return RET_ERROR;
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}
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if (bit_num < 8 && bit_num > 0) {
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auto dst_data = new (std::nothrow) int8_t[elem_count];
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if (dst_data == nullptr) {
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MS_LOG(ERROR) << "new int8_t[] failed";
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return RET_ERROR;
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}
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DequantUtil::UnpackUtil<int8_t, uint8_t>(raw_datas, packed_size, bit_num, dst_data);
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if (memcpy_s(raw_datas, elem_count, dst_data, elem_count) != EOK) {
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MS_LOG(ERROR) << "memcpy_s failed.";
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return RET_MEMORY_FAILED;
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}
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}
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HuffmanPriorityQueue pq;
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status = GetHuffmanPriorityQueue(raw_datas, elem_count, &pq);
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if (status != RET_OK) {
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MS_LOG(ERROR) << "GetHuffmanPriorityQueue failed";
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return status;
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}
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status = BuildHuffmanTree(&pq);
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if (status != RET_OK) {
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MS_LOG(ERROR) << "BuildHuffmanTree failed";
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return status;
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}
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status = DoHuffmanCompress(raw_datas, elem_count);
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if (status != RET_OK) {
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MS_LOG(ERROR) << "DoHuffmanCompress failed";
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return status;
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}
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size_t ch_size = huffman_encoded_str_.length();
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if (ch_size < packed_size) {
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auto encode_data = new (std::nothrow) char[ch_size];
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if (encode_data == nullptr) {
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MS_LOG(ERROR) << "new char[] failed.";
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delete[] raw_datas;
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return RET_MEMORY_FAILED;
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}
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delete[] raw_datas;
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if (memcpy_s(encode_data, ch_size, huffman_encoded_str_.c_str(), ch_size) != EOK) {
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MS_LOG(ERROR) << "memcpy_s failed.";
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delete[] encode_data;
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return RET_MEMORY_FAILED;
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}
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param_value->SetTensorData(encode_data, ch_size);
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primitive_c->SetEnableHuffmanCode(true);
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}
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huffman_encoded_str_.clear();
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huffman_table_.clear();
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if (memcpy_s(encode_data, ch_size, huffman_encoded_str_.c_str(), ch_size) != EOK) {
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MS_LOG(ERROR) << "memcpy_s failed.";
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delete[] encode_data;
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return RET_MEMORY_FAILED;
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}
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weight->SetTensorData(encode_data, ch_size);
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primitive_c->set_enable_huffman_code(true);
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}
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huffman_encoded_str_.clear();
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huffman_table_.clear();
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return RET_SUCCESS;
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}
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mindspore-ci-bot
4 years ago
committed by
Gitee