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deconv weight quant

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pull/6298/head
kai00 4 years ago
parent 95710ae970
commit 2628263077
10 changed files with 84 additions and 35 deletions
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24
mindspore/lite/src/runtime/kernel/arm/fp32/deconvolution.cc
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@ -231,9 +231,23 @@ kernel::LiteKernel *CpuDeConvFp32KernelCreator(const std::vector<lite::Tensor *>
const mindspore::lite::PrimitiveC *primitive) {
MS_ASSERT(opParameter != nullptr);
MS_ASSERT(desc.type == schema::PrimitiveType_DeConv2D);
auto *weight_tensor = inputs.at(kWeightIndex);
auto *restore_data = weight_tensor->MutableData();
if (weight_tensor->data_type() == kNumberTypeInt8 || primitive->GetQuantType() == schema::QuantType_WeightQuant) {
auto *dequant_weight = kernel::LiteKernelUtil::DequantWeight(weight_tensor);
if (dequant_weight == nullptr) {
MS_LOG(ERROR) << "dequant data is nullptr.";
return nullptr;
}
weight_tensor->SetData(dequant_weight);
}
auto kernel = new (std::nothrow) kernel::DeConvolutionCPUKernel(opParameter, inputs, outputs, ctx, primitive);
if (kernel == nullptr) {
MS_LOG(ERROR) << "kernel is nullptr.";
if (weight_tensor->data_type() == kNumberTypeInt8 || primitive->GetQuantType() == schema::QuantType_WeightQuant) {
weight_tensor->FreeData();
weight_tensor->SetData(restore_data);
}
return nullptr;
}
auto ret = kernel->Init();
@ -241,8 +255,18 @@ kernel::LiteKernel *CpuDeConvFp32KernelCreator(const std::vector<lite::Tensor *>
delete kernel;
MS_LOG(ERROR) << "Init kernel failed, name: " << opParameter->name_ << ", type: "
<< schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(opParameter->type_));
if (weight_tensor->data_type() == kNumberTypeInt8 || primitive->GetQuantType() == schema::QuantType_WeightQuant) {
weight_tensor->FreeData();
weight_tensor->SetData(restore_data);
}
return nullptr;
}
if (weight_tensor->data_type() == kNumberTypeInt8 || primitive->GetQuantType() == schema::QuantType_WeightQuant) {
weight_tensor->FreeData();
weight_tensor->SetData(restore_data);
}
return kernel;
}

22
mindspore/lite/src/runtime/kernel/arm/fp32/deconvolution_depthwise.cc
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@ -199,10 +199,24 @@ kernel::LiteKernel *CpuDeconvDwFp32KernelCreator(const std::vector<lite::Tensor
const mindspore::lite::PrimitiveC *primitive) {
MS_ASSERT(opParameter != nullptr);
MS_ASSERT(desc.type == schema::PrimitiveType_DeDepthwiseConv2D);
auto *weight_tensor = inputs.at(kWeightIndex);
auto *restore_data = weight_tensor->MutableData();
if (weight_tensor->data_type() == kNumberTypeInt8 || primitive->GetQuantType() == schema::QuantType_WeightQuant) {
auto *dequant_weight = kernel::LiteKernelUtil::DequantWeight(weight_tensor);
if (dequant_weight == nullptr) {
MS_LOG(ERROR) << "dequant data is nullptr.";
return nullptr;
}
weight_tensor->SetData(dequant_weight);
}
auto kernel =
new (std::nothrow) kernel::DeconvolutionDepthwiseCPUKernel(opParameter, inputs, outputs, ctx, primitive);
if (kernel == nullptr) {
MS_LOG(ERROR) << "kernel is nullptr.";
if (weight_tensor->data_type() == kNumberTypeInt8 || primitive->GetQuantType() == schema::QuantType_WeightQuant) {
weight_tensor->FreeData();
weight_tensor->SetData(restore_data);
}
return nullptr;
}
auto ret = kernel->Init();
@ -210,8 +224,16 @@ kernel::LiteKernel *CpuDeconvDwFp32KernelCreator(const std::vector<lite::Tensor
delete kernel;
MS_LOG(ERROR) << "Init kernel failed, name: " << opParameter->name_ << ", type: "
<< schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(opParameter->type_));
if (weight_tensor->data_type() == kNumberTypeInt8 || primitive->GetQuantType() == schema::QuantType_WeightQuant) {
weight_tensor->FreeData();
weight_tensor->SetData(restore_data);
}
return nullptr;
}
if (weight_tensor->data_type() == kNumberTypeInt8 || primitive->GetQuantType() == schema::QuantType_WeightQuant) {
weight_tensor->FreeData();
weight_tensor->SetData(restore_data);
}
return kernel;
}

9
mindspore/lite/tools/converter/legacy_optimizer/graph/weight_format_transform_pass.cc
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@ -53,16 +53,19 @@ STATUS WeightFormatTransformPass::QuantDataFormatTrans(MetaGraphT *graph) {
MS_ASSERT(node != nullptr);
MS_ASSERT(node->primitive != nullptr);
auto opType = node->primitive->value.type;
if (opType != PrimitiveType_Conv2D && opType != PrimitiveType_DepthwiseConv2D) {
if (opType != PrimitiveType_Conv2D && opType != PrimitiveType_DepthwiseConv2D &&
opType != PrimitiveType_DeConv2D && opType != PrimitiveType_DeDepthwiseConv2D) {
continue;
}
MS_ASSERT(node->inputIndex.size() >= 2);
auto weightIndex = node->inputIndex.at(1);
MS_ASSERT(subGraph->allTensors.size() > weightIndex);
auto &weightTensor = graph->allTensors[weightIndex];
MS_ASSERT(weightTensor->dataType == DataType_DT_UINT8 || weightTensor->dataType == DataType_DT_FLOAT);
MS_ASSERT(weightTensor->dataType == DataType_DT_UINT8 || weightTensor->dataType == DataType_DT_FLOAT ||
weightTensor->dataType == DataType_DT_INT8);
STATUS status;
if (opType == PrimitiveType_Conv2D || opType == PrimitiveType_DepthwiseConv2D) { // weight should be HWCK
if (opType == PrimitiveType_Conv2D || opType == PrimitiveType_DepthwiseConv2D ||
opType == PrimitiveType_DeConv2D || opType == PrimitiveType_DeDepthwiseConv2D) { // weight should be HWCK
Format curDstFormat;
if (this->dstFormat == Format_NUM_OF_FORMAT) {
curDstFormat = Format_KHWC;

7
mindspore/lite/tools/converter/parser/caffe/caffe_model_parser.cc
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@ -80,7 +80,7 @@ schema::MetaGraphT *CaffeModelParser::ParseToFb(const std::string &modelFile, co
return nullptr;
}
status = ParseLayer(proto, weight, &tensorCache, metaGraph.get());
status = ParseLayer(proto, weight, &tensorCache, metaGraph.get(), quantType);
if (status != RET_OK) {
MS_LOG(ERROR) << "ParseLayer failed " << status;
ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
@ -177,7 +177,8 @@ STATUS CaffeModelParser::SetGraphTensorIndex(const caffe::NetParameter &proto, T
}
STATUS CaffeModelParser::ParseLayer(const caffe::NetParameter &proto, const caffe::NetParameter &weight,
TensorCache *tensorCache, schema::MetaGraphT *subGraphDef) {
TensorCache *tensorCache, schema::MetaGraphT *subGraphDef,
const QuantType &quantType) {
for (int i = 0; i < proto.layer_size(); i++) {
auto layer = proto.layer(i);
@ -214,7 +215,7 @@ STATUS CaffeModelParser::ParseLayer(const caffe::NetParameter &proto, const caff
std::unique_ptr<schema::CNodeT> op = std::make_unique<schema::CNodeT>();
op->name = layer.name();
op->quantType = quantType;
if (layer.type() == "Split") {
for (int j = 0; j < layer.top_size(); ++j) {
splitLayer.emplace(layer.top(j), layer.bottom(0));

2
mindspore/lite/tools/converter/parser/caffe/caffe_model_parser.h
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@ -50,7 +50,7 @@ class CaffeModelParser : public ModelParser {
schema::MetaGraphT *subGraphDef);
STATUS ParseLayer(const caffe::NetParameter &proto, const caffe::NetParameter &weight, TensorCache *tensorCache,
schema::MetaGraphT *subGraphDef);
schema::MetaGraphT *subGraphDef, const QuantType &quantType);
STATUS GetModelInput(const caffe::NetParameter &proto, TensorCache *tensorCache);

5
mindspore/lite/tools/converter/parser/onnx/onnx_model_parser.cc
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@ -247,9 +247,10 @@ STATUS OnnxModelParser::ParseOnnxGivenFillNode(const onnx::NodeProto &onnx_node,
STATUS OnnxModelParser::ParseOnnxNodeToDstOp(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node,
schema::CNodeT *dst_op, schema::TensorT *dst_tensor,
TensorCache *tensor_cache) {
TensorCache *tensor_cache, const QuantType &quantType) {
// change op_type() to name(), that is unique
dst_op->name = onnx_node.op_type() + "_" + onnx_node.output(0);
dst_op->quantType = quantType;
// dst_op->fmkType = FmkType_ONNX;
MS_LOG(DEBUG) << "onnx op name " << onnx_node.op_type() << ", dst op name: " << dst_op->name << ", input size "
<< onnx_node.input_size();
@ -520,7 +521,7 @@ schema::MetaGraphT *OnnxModelParser::ParseToFb(const std::string &modelFile, con
std::unique_ptr<schema::CNodeT> dst_op = std::make_unique<schema::CNodeT>();
std::unique_ptr<schema::TensorT> dst_tensor = std::make_unique<schema::TensorT>();
status = ParseOnnxNodeToDstOp(onnx_graph, onnx_node, dst_op.get(), dst_tensor.get(), &tensor_cache);
status = ParseOnnxNodeToDstOp(onnx_graph, onnx_node, dst_op.get(), dst_tensor.get(), &tensor_cache, quantType);
if (status != RET_OK) {
MS_LOG(ERROR) << "parse node " << onnx_node.op_type() << " failed";
ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);

3
mindspore/lite/tools/converter/parser/onnx/onnx_model_parser.h
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@ -61,7 +61,8 @@ class OnnxModelParser : public ModelParser {
TensorCache *tensor_cache, int *index);
STATUS ParseOnnxNodeToDstOp(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node,
schema::CNodeT *dst_op, schema::TensorT *dst_tensor, TensorCache *tensor_cache);
schema::CNodeT *dst_op, schema::TensorT *dst_tensor, TensorCache *tensor_cache,
const QuantType &quantType);
void ParseOnnxGemmNode(const onnx::GraphProto &onnx_graph, const onnx::NodeProto &onnx_node,
schema::MetaGraphT *graph, TensorCache *tensor_cache);

36
mindspore/lite/tools/converter/quantizer/quantize_util.cc
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@ -32,22 +32,24 @@ using std::vector;
namespace mindspore {
namespace lite {
namespace quant {
const std::array<std::string, 4> QuantStrategy::mConvTypes = {
{"Conv2D", "DeConv2D", "DepthwiseConv2D", "DeDepthwiseConv2D"}};
const std::array<std::string, 4> QuantStrategy::mMulTypes = {{"Mul", "MatMul", "BatchMatMul", "FullConnection"}};
const std::vector<schema::PrimitiveType> QuantStrategy::conv_types = {
schema::PrimitiveType_DeConv2D, schema::PrimitiveType_DeDepthwiseConv2D,
schema::PrimitiveType_Conv2D, schema::PrimitiveType_DepthwiseConv2D};
const std::vector<schema::PrimitiveType> QuantStrategy::mul_types = {
schema::PrimitiveType_Mul, schema::PrimitiveType_MatMul, schema::PrimitiveType_FullConnection};
QuantStrategy::QuantStrategy(size_t weightSize, size_t convWeightQuantChannelThreshold)
: mWeightSize(weightSize), mConvWeightQuantChannelThreshold(convWeightQuantChannelThreshold) {}
bool QuantStrategy::CanConvOpQuantized(const CNodePtr &node) const {
size_t i = 0;
for (i = 0; i < mConvTypes.size(); i++) {
if (node->fullname_with_scope().find(mConvTypes[i]) == 0) {
break;
}
auto primitive_c = GetValueNode<std::shared_ptr<PrimitiveC>>(node->input(0));
if (primitive_c == nullptr) {
MS_LOG(ERROR) << "primitive_c is nullptr";
return false;
}
if ((i == mConvTypes.size()) || (node->size() < 3)) {
if (!IsContain(conv_types, (schema::PrimitiveType)primitive_c->Type())) {
return false;
}
if (node->size() < 3) {
return false;
}
@ -107,13 +109,13 @@ bool QuantStrategy::CanOpPostQuantized(AnfNodePtr &node) const {
}
bool QuantStrategy::CanMulOpQuantized(const CNodePtr &node) const {
size_t i = 0;
for (i = 0; i < mMulTypes.size(); i++) {
if (node->fullname_with_scope().find(mMulTypes[i]) == 0) {
break;
}
auto primitive_c = GetValueNode<std::shared_ptr<PrimitiveC>>(node->input(0));
if (primitive_c == nullptr) {
MS_LOG(ERROR) << "primitive_c is nullptr";
return false;
}
if (i == mMulTypes.size()) {
if (!IsContain(mul_types, (schema::PrimitiveType)primitive_c->Type())) {
return false;
}

5
mindspore/lite/tools/converter/quantizer/quantize_util.h
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@ -57,9 +57,8 @@ class QuantStrategy {
private:
size_t mWeightSize;
size_t mConvWeightQuantChannelThreshold;
static const std::array<std::string, 4> mConvTypes;
static const std::array<std::string, 4> mMulTypes;
static const std::vector<schema::PrimitiveType> conv_types;
static const std::vector<schema::PrimitiveType> mul_types;
};
STATUS CalQuantizationParams(schema::QuantParamT *quantParam, double mMin, double mMax, bool narrowRange, int quant_max,

6
mindspore/lite/tools/converter/quantizer/weight_quantizer.cc
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@ -69,13 +69,9 @@ STATUS WeightQuantizer::DoConvQuantize(const std::list<CNodePtr> &nodes) {
std::vector<schema::QuantParamT> quant_params;
primitive_c->AddInputQuantParam(quant_params);
auto op_type = (schema::PrimitiveType)primitive_c->Type();
bool depthwise = op_type == schema::PrimitiveType_DepthwiseConv2D ? true : false;
auto status =
QuantFilter<int8_t>(param_value, primitive_c, QuantType_WeightQuant,
quant_max, quant_min, bitNum, true, depthwise);
quant_max, quant_min, bitNum, true, false);
if (status != RET_OK) {
MS_LOG(ERROR) << "QuantFilter failed : " << status;
return status;

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