xutianchun
5 years ago
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1ca715c7e7
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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 "tools/converter/quantizer/weight_quantizer.h"
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#include <list>
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#include <string>
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#include "src/common/common.h"
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#include "ir/dtype/type_id.h"
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using std::string;
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using std::vector;
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namespace mindspore {
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namespace lite {
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namespace quant {
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WeightQuantizer::WeightQuantizer(FuncGraphPtr graph, const string &weightSize,
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const std::string &convWeightChannelThreshold, const std::string &bitNum)
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: Quantizer(graph) {
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auto quantSize = static_cast<size_t>(std::stoull(weightSize));
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this->bitNum = static_cast<size_t>(std::stoull(bitNum));
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auto convQuantWeightChannelThreshold = static_cast<size_t>(std::stoull(convWeightChannelThreshold));
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// TODO(...): update stractory
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mStrategy.reset(new QuantStrategy(quantSize, convQuantWeightChannelThreshold));
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}
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// uint32_t GetConvChannel(TensorDefT *weight) {
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// uint32_t channel = 0;
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// const vector<int> dims = weight->dims;
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// switch (weight->format) {
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// case Format_NCHW:
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// case Format_KCHW:
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// case Format_NC4HW4:
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// channel = static_cast<uint32_t>(dims[NCHW_N]);
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// break;
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// case Format_NHWC:
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// case Format_HWKC:
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// channel = static_cast<uint32_t>(dims[NHWC_N]);
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// break;
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// case Format_HWCK:
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// channel = static_cast<uint32_t>(dims[HWCK_K]);
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// break;
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// case Format_CKHW:
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// channel = static_cast<uint32_t>(dims[CKHW_K]);
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// break;
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// default:
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// MS_LOGE("Unsupported format: %d", weight->format);
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// return 0;
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// }
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// return channel;
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// }
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STATUS WeightQuantizer::DoConvQuantize(const std::list<CNodePtr> &nodes) {
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for (auto &cnode : nodes) {
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if (!mStrategy->CanConvOpQuantized(cnode)) {
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continue;
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}
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auto inputNode = cnode->input(2);
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if (!inputNode->isa<Parameter>()) {
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return RET_ERROR;
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}
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auto paramNode = inputNode->cast<ParameterPtr>();
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if (!paramNode->has_default()) {
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return RET_ERROR;
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}
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ParamValueLitePtr paramValue = std::static_pointer_cast<ParamValueLite>(paramNode->default_param());
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auto status = QuantFilter(paramValue, QuantType_WeightQuant, 127, -128, bitNum);
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if (status != RET_OK) {
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MS_LOG(ERROR) << "QuantFilter failed : " << status;
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return status;
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}
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}
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return RET_OK;
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}
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STATUS WeightQuantizer::DoMulQuantize(const std::list<CNodePtr> &nodes) {
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for (auto &node : nodes) {
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if (!mStrategy->CanMulOpQuantized(node)) {
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continue;
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}
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ParamValueLitePtr paramValue = nullptr;
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for (size_t i = 1; i < node->size(); i++) {
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auto inputNode = node->input(i);
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if (inputNode->isa<Parameter>() == true) {
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auto paramNode = inputNode->cast<ParameterPtr>();
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if ((paramNode != nullptr) && (paramNode->has_default() == true)) {
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paramValue = std::static_pointer_cast<ParamValueLite>(paramNode->default_param());
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if ((paramValue == nullptr) || (paramValue->tensor_size() == 0)
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|| (paramValue->tensor_shape().size() != 4)
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|| (paramValue->tensor_addr() == nullptr)
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|| (paramValue->tensor_type() != mindspore::kNumberTypeFloat32)) {
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paramValue = nullptr;
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continue;
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} else {
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break;
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}
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}
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}
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}
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if (paramValue == nullptr) {
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MS_LOG(ERROR) << "No valid input param node !";
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continue;
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}
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auto status = QuantFilter(paramValue, QuantType_WeightQuant, 127, -128, bitNum);
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if (status != RET_OK) {
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MS_LOG(ERROR) << "QunatFilter failed" << status;
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return RET_ERROR;
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}
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}
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return RET_OK;
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}
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STATUS WeightQuantizer::DoQuantize(FuncGraphPtr funcGraph) {
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auto ret = RET_OK;
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auto cnodes = funcGraph->GetOrderedCnodes();
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ret = DoConvQuantize(cnodes);
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if (ret != RET_OK) {
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MS_LOG(ERROR) << "DoConvQuantize failed :" << ret;
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return ret;
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}
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ret = DoMulQuantize(cnodes);
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if (ret != RET_OK) {
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MS_LOG(ERROR) << "DoMulQuantize failed :" << ret;
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return ret;
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}
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return ret;
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}
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} // namespace quant
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} // namespace lite
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} // namespace mindspore
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@ -1,53 +0,0 @@
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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 WEIGHT_QUANTIZER_H
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#define WEIGHT_QUANTIZER_H
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#include <memory>
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#include <list>
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#include <string>
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#include "tools/converter/quantizer/quantizer.h"
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#include "tools/converter/quantizer/quantize_util.h"
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#include "ir/func_graph.h"
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#include "ir/anf.h"
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#include "include/model.h"
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#include "base/base.h"
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#include "abstract/dshape.h"
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namespace mindspore {
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namespace lite {
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namespace quant {
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class WeightQuantizer : public Quantizer {
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public:
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WeightQuantizer(FuncGraphPtr graph, const std::string& weightSize,
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const std::string& covWeightChannelThreshold, const std::string& bitNum);
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~WeightQuantizer() = default;
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STATUS DoQuantize(FuncGraphPtr funcGraph) override;
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STATUS DoConvQuantize(const std::list<CNodePtr> &nodes);
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STATUS DoMulQuantize(const std::list<CNodePtr> &nodes);
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private:
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std::unique_ptr<QuantStrategy> mStrategy;
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size_t bitNum;
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};
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} // namespace quant
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} // namespace lite
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} // namespace mindspore
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#endif
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