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fix gather weight quant bug

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pull/11454/head
xutianchun 4 years ago
parent f654167045
commit 9428ffe860
8 changed files with 76 additions and 53 deletions
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11
mindspore/lite/src/dequant.cc
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@ -51,7 +51,7 @@ void DequantUtil::UnPackToInt(const schema::Tensor *input_tensor, void *unpack_i
}
std::map<Tensor *, std::pair<TypeId, void *>> DequantUtil::DequantTensor(const std::vector<Tensor *> &in_tensors,
TypeId data_type) {
TypeId data_type, bool need_restore) {
std::map<Tensor *, std::pair<TypeId, void *>> tensor_origin_data;
if (data_type == TypeId::kNumberTypeFloat32 || data_type == TypeId::kNumberTypeFloat16) {
for (auto weight_tensor : in_tensors) {
@ -59,16 +59,21 @@ std::map<Tensor *, std::pair<TypeId, void *>> DequantUtil::DequantTensor(const s
auto *restore_data = weight_tensor->data_c();
auto restore_type = weight_tensor->data_type();
bool dequant_flag = !weight_tensor->quant_params().empty() && weight_tensor->quant_params().front().inited &&
restore_data != nullptr;
restore_data != nullptr &&
(restore_type == kNumberTypeInt8 || restore_type == kNumberTypeInt16);
if (dequant_flag) {
auto *dequant_weight = DequantUtil::DequantWeight(weight_tensor);
if (dequant_weight == nullptr) {
MS_LOG(ERROR) << "dequant data is nullptr.";
return tensor_origin_data;
}
if (need_restore) {
tensor_origin_data[weight_tensor] = {restore_type, restore_data};
} else {
weight_tensor->FreeData();
}
weight_tensor->set_data(dequant_weight);
weight_tensor->set_data_type(kNumberTypeFloat32);
tensor_origin_data[weight_tensor] = {restore_type, restore_data};
}
}
}

4
mindspore/lite/src/dequant.h
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@ -34,7 +34,7 @@ class DequantUtil {
static void UnPackToInt(const schema::Tensor *input_tensor, void *weight_unpack_data);
static std::map<Tensor *, std::pair<TypeId, void *>> DequantTensor(const std::vector<Tensor *> &in_tensors,
TypeId data_type);
TypeId data_type, bool need_restore = true);
static void RestoreTensorData(const std::map<Tensor *, std::pair<TypeId, void *>> &tensor_origin_data_map);
@ -79,7 +79,7 @@ class DequantUtil {
auto var_corr = param.var_corr;
auto mean_corr = param.mean_corr;
if (var_corr < 0 || var_corr > 10) {
MS_LOG(WARNING) << "unexpeted var_corr: " << var_corr;
MS_LOG(WARNING) << "unexpected var_corr: " << var_corr;
var_corr = 1;
}
for (size_t j = 0; j < per_channel_size; j++) {

37
mindspore/lite/src/lite_session.cc
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@ -38,10 +38,6 @@
namespace mindspore {
namespace lite {
static std::vector<schema::PrimitiveType> packed_op = {
schema::PrimitiveType_Conv2D, schema::PrimitiveType_DeConv2D, schema::PrimitiveType_DepthwiseConv2D,
schema::PrimitiveType_DeDepthwiseConv2D, schema::PrimitiveType_MatMul};
// this method will not check whether tensor_idx is a weight tensor index, caller should ensure this.
static bool WeightTensorNeedCopy(const lite::Model *model, const uint32_t tensor_idx) {
#ifdef SUPPORT_TRAIN
@ -92,8 +88,13 @@ int LiteSession::ConvertTensorsData(const lite::Model *model, size_t tensor_inde
lite::Tensor *dst_tensor) {
MS_ASSERT(src_tensor != nullptr);
MS_ASSERT(dst_tensor != nullptr);
auto NeedUnPack = [&src_tensor, &dst_tensor]() -> bool {
auto data_type = src_tensor->dataType();
int pack_size = src_tensor->data()->size();
int org_size = dst_tensor->Size();
return (pack_size != org_size) && (data_type == kNumberTypeInt8 || data_type == kNumberTypeInt16);
};
auto src_category = TensorCategory(src_tensor);
auto data_type = src_tensor->dataType();
if ((src_category == Tensor::Category::CONST_TENSOR || src_category == Tensor::Category::CONST_SCALAR) &&
src_tensor->data() != nullptr && src_tensor->data()->size() > 0) {
if (src_tensor->dataType() == kObjectTypeTensorType) {
@ -112,18 +113,20 @@ int LiteSession::ConvertTensorsData(const lite::Model *model, size_t tensor_inde
MS_LOG(ERROR) << "Data from tensor is nullptr";
return RET_NULL_PTR;
}
memcpy(dst_data, src_tensor->data()->data(), dst_tensor->Size());
if (NeedUnPack()) {
DequantUtil::UnPackToInt(src_tensor, dst_data);
} else {
memcpy(dst_data, src_tensor->data()->data(), dst_tensor->Size());
}
copyed_tensor_idxes_.emplace_back(tensor_index);
} else {
int pack_size = src_tensor->data()->size();
int org_size = dst_tensor->Size();
if (pack_size != org_size && (data_type == kNumberTypeInt8 || data_type == kNumberTypeInt16)) {
auto ret = dst_tensor->MallocData();
if (ret != RET_OK) {
MS_LOG(ERROR) << "Malloc data for tensor failed ";
return RET_ERROR;
if (NeedUnPack()) {
auto dst_data = dst_tensor->MutableData();
if (dst_data == nullptr) {
MS_LOG(ERROR) << "Data from tensor is nullptr";
return RET_NULL_PTR;
}
DequantUtil::UnPackToInt(src_tensor, dst_tensor->MutableData());
DequantUtil::UnPackToInt(src_tensor, dst_data);
copyed_tensor_idxes_.emplace_back(tensor_index);
} else {
dst_tensor->set_data(const_cast<unsigned char *>(src_tensor->data()->data()));
@ -713,12 +716,12 @@ int LiteSession::InitGPURuntime() {
session::LiteSession *session::LiteSession::CreateSession(const lite::Context *context) {
auto session = new (std::nothrow) lite::LiteSession();
if (session == nullptr) {
MS_LOG(ERROR) << "create sesssion failed";
MS_LOG(ERROR) << "create session failed";
return nullptr;
}
auto ret = session->Init(context);
if (ret != mindspore::lite::RET_OK) {
MS_LOG(ERROR) << "init sesssion failed";
MS_LOG(ERROR) << "init session failed";
delete session;
return nullptr;
}
@ -729,7 +732,7 @@ session::LiteSession *session::LiteSession::CreateSession(const char *model_buf,
const lite::Context *context) {
auto *session = LiteSession::CreateSession(context);
if (session == nullptr) {
MS_LOG(ERROR) << "Create sesssion failed";
MS_LOG(ERROR) << "Create session failed";
return nullptr;
}
auto *model = lite::ImportFromBuffer(model_buf, size, true);

26
mindspore/lite/src/runtime/kernel/arm/fp32/lstm_fp32.cc
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@ -107,8 +107,10 @@ int LstmCPUKernel::InitWeightBias() {
}
memcpy(weight_h_ptr_, weight_h->MutableData(), weight_h->ElementsNum() * sizeof(float));
std::vector<int> w_shape = weight_i->shape();
auto hidden_size = w_shape.at(1) / 4;
// init bias
int bias_num = lstm_parm_->bidirectional_ ? 2 * 4 * lstm_parm_->hidden_size_ : 4 * lstm_parm_->hidden_size_;
int bias_num = lstm_parm_->bidirectional_ ? 2 * 4 * hidden_size : 4 * hidden_size;
bias_ptr_ = reinterpret_cast<float *>(malloc(bias_num * sizeof(float)));
if (bias_ptr_ == nullptr) {
MS_LOG(ERROR) << "LstmCPUKernel malloc bias_ptr_ error.";
@ -116,13 +118,13 @@ int LstmCPUKernel::InitWeightBias() {
}
auto bias_data = reinterpret_cast<float *>(in_tensors_.at(3)->MutableData());
const int state_bias_offset = 4 * lstm_parm_->hidden_size_;
const int state_bias_offset = 4 * hidden_size;
for (int i = 0; i < state_bias_offset; i++) {
bias_ptr_[i] = bias_data[i] + bias_data[i + state_bias_offset];
}
if (lstm_parm_->bidirectional_) {
bias_data += 4 * lstm_parm_->hidden_size_ * 2;
auto backward_bias = bias_ptr_ + 4 * lstm_parm_->hidden_size_;
bias_data += 4 * hidden_size * 2;
auto backward_bias = bias_ptr_ + 4 * hidden_size;
for (int i = 0; i < state_bias_offset; i++) {
backward_bias[i] = bias_data[i] + bias_data[i + state_bias_offset];
}
@ -131,6 +133,14 @@ int LstmCPUKernel::InitWeightBias() {
}
int LstmCPUKernel::Init() {
FreeTmpBuffer();
auto ret = InitWeightBias();
if (ret != RET_OK) {
MS_LOG(ERROR) << "LstmCPUKernel InitWeightBias error.";
FreeTmpBuffer();
return RET_ERROR;
}
if (!InferShapeDone()) {
return RET_OK;
}
@ -138,20 +148,12 @@ int LstmCPUKernel::Init() {
}
int LstmCPUKernel::ReSize() {
FreeTmpBuffer();
auto ret = InitParam();
if (ret != RET_OK) {
MS_LOG(ERROR) << "LstmCPUKernel InitParam error.";
return RET_ERROR;
}
ret = InitWeightBias();
if (ret != RET_OK) {
MS_LOG(ERROR) << "LstmCPUKernel InitWeightBias error.";
FreeTmpBuffer();
return RET_ERROR;
}
ret = InitBuffer();
if (ret != RET_OK) {
MS_LOG(ERROR) << "LstmCPUKernel InitBuffer error.";

11
mindspore/lite/src/scheduler.cc
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@ -184,6 +184,13 @@ kernel::LiteKernel *Scheduler::FindBackendKernel(const std::vector<Tensor *> &in
const Model::Node *node) {
MS_ASSERT(primitive != nullptr);
TypeId data_type = GetFirstFp32Fp16OrInt8Type(in_tensors);
bool need_restore = true;
if (primitive->quant_type() == schema::QuantType_WeightQuant) {
data_type = kNumberTypeFloat32;
}
if (!IsContain(packed_op, (schema::PrimitiveType)primitive->Type())) {
need_restore = false;
}
kernel::KernelKey desc{kCPU, data_type, static_cast<schema::PrimitiveType>(primitive->Type())};
#if SUPPORT_GPU
if (context_->IsGpuEnabled()) {
@ -216,7 +223,7 @@ kernel::LiteKernel *Scheduler::FindBackendKernel(const std::vector<Tensor *> &in
if (mindspore::lite::IsSupportFloat16() &&
((context_->IsCpuFloat16Enabled() && data_type == kNumberTypeFloat32) || data_type == kNumberTypeFloat16)) {
kernel::KernelKey fp16_cpu_desc{desc.arch, kNumberTypeFloat16, desc.type};
auto tensor_origin_data_map = DequantUtil::DequantTensor(in_tensors, fp16_cpu_desc.data_type);
auto tensor_origin_data_map = DequantUtil::DequantTensor(in_tensors, fp16_cpu_desc.data_type, need_restore);
auto *kernel =
KernelRegistry::GetInstance()->GetKernel(in_tensors, out_tensors, primitive, context_, fp16_cpu_desc);
DequantUtil::RestoreTensorData(tensor_origin_data_map);
@ -230,7 +237,7 @@ kernel::LiteKernel *Scheduler::FindBackendKernel(const std::vector<Tensor *> &in
MS_LOG(DEBUG) << "Get fp16 op failed, back to fp32 op.";
desc.data_type = kNumberTypeFloat32;
}
auto tensor_origin_data_map = DequantUtil::DequantTensor(in_tensors, desc.data_type);
auto tensor_origin_data_map = DequantUtil::DequantTensor(in_tensors, desc.data_type, need_restore);
auto *kernel = KernelRegistry::GetInstance()->GetKernel(in_tensors, out_tensors, primitive, context_, desc);
DequantUtil::RestoreTensorData(tensor_origin_data_map);
if (kernel != nullptr) {

6
mindspore/lite/src/scheduler.h
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@ -26,6 +26,12 @@
#include "src/ops/primitive_c.h"
namespace mindspore::lite {
static std::vector<schema::PrimitiveType> packed_op = {
schema::PrimitiveType_Conv2D, schema::PrimitiveType_DeConv2D,
schema::PrimitiveType_DepthwiseConv2D, schema::PrimitiveType_DeDepthwiseConv2D,
schema::PrimitiveType_MatMul, schema::PrimitiveType_Lstm};
class Scheduler {
public:
Scheduler(const InnerContext *ctx, Model *src_model, std::vector<Tensor *> *src_tensors)

32
mindspore/lite/tools/converter/quantizer/weight_quantizer.cc
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@ -253,11 +253,11 @@ STATUS WeightQuantizer::DoLstmQuntize(CNodePtr cnode) {
}
auto status = RET_ERROR;
if (type_id_ == kNumberTypeInt8) {
status =
QuantFilter<int8_t>(param_value, primitive_c, QuantType_WeightQuant, quant_max_, quant_min_, bit_num_, false);
status = QuantFilter<int8_t>(param_value, primitive_c, QuantType_WeightQuant, quant_max_, quant_min_, bit_num_,
false, 1);
} else if (type_id_ == kNumberTypeInt16) {
status =
QuantFilter<int16_t>(param_value, primitive_c, QuantType_WeightQuant, quant_max_, quant_min_, bit_num_, false);
status = QuantFilter<int16_t>(param_value, primitive_c, QuantType_WeightQuant, quant_max_, quant_min_, bit_num_,
false, 1);
}
if (status != RET_OK) {
MS_LOG(ERROR) << "QuantFilter failed : " << status;
@ -316,11 +316,11 @@ STATUS WeightQuantizer::DoLstmQuntize(CNodePtr cnode) {
}
auto status = RET_ERROR;
if (type_id_ == kNumberTypeInt8) {
status =
QuantFilter<int8_t>(param_value, primitive_c, QuantType_WeightQuant, quant_max_, quant_min_, bit_num_, false);
status = QuantFilter<int8_t>(param_value, primitive_c, QuantType_WeightQuant, quant_max_, quant_min_, bit_num_,
false, 3);
} else if (type_id_ == kNumberTypeInt16) {
status = QuantFilter<int16_t>(param_value, primitive_c, QuantType_WeightQuant, quant_max_, quant_min_, bit_num_,
false);
false, 3);
}
if (status != RET_OK) {
MS_LOG(ERROR) << "QuantFilter failed : " << status;
@ -340,10 +340,10 @@ STATUS WeightQuantizer::DoGatherQuntize(CNodePtr cnode) {
auto primitive_c = GetValueNode<std::shared_ptr<PrimitiveC>>(cnode->input(0));
MS_ASSERT(primitive_c != nullptr);
auto weight_h = cnode->input(1);
auto first_input = cnode->input(1);
ParameterPtr param_node;
ParamValueLitePtr param_value;
GetLiteParameter(weight_h, &param_node, &param_value);
GetLiteParameter(first_input, &param_node, &param_value);
if (param_node == nullptr || param_value == nullptr || param_value->tensor_type() != TypeId::kNumberTypeFloat32) {
MS_LOG(INFO) << "This Gather op " << cnode->fullname_with_scope() << " can not quant weight";
return RET_OK;
@ -358,10 +358,10 @@ STATUS WeightQuantizer::DoGatherQuntize(CNodePtr cnode) {
auto status = RET_ERROR;
if (type_id_ == kNumberTypeInt8) {
status =
QuantFilter<int8_t>(param_value, primitive_c, QuantType_WeightQuant, quant_max_, quant_min_, bit_num_, false);
QuantFilter<int8_t>(param_value, primitive_c, QuantType_WeightQuant, quant_max_, quant_min_, bit_num_, false, 0);
} else if (type_id_ == kNumberTypeInt16) {
status =
QuantFilter<int16_t>(param_value, primitive_c, QuantType_WeightQuant, quant_max_, quant_min_, bit_num_, false);
QuantFilter<int16_t>(param_value, primitive_c, QuantType_WeightQuant, quant_max_, quant_min_, bit_num_, false, 0);
}
if (status != RET_OK) {
MS_LOG(ERROR) << "QuantFilter failed : " << status;
@ -510,7 +510,7 @@ STATUS WeightQuantizer::RunFp32Graph(FuncGraphPtr func_graph) {
return RET_OK;
}
STATUS WeightQuantizer::DoMiexedQuant(FuncGraphPtr func_graph) {
STATUS WeightQuantizer::DoMixedQuant(FuncGraphPtr func_graph) {
// 0.2 Parse input calib files
auto status = CollectCalibInputs(config_param_.image_paths, config_param_.batch_count, &images_);
if (status != RET_OK) {
@ -652,7 +652,7 @@ STATUS WeightQuantizer::DoMiexedQuant(FuncGraphPtr func_graph) {
delete quant_sm.model;
return RET_ERROR;
}
// 3. compare betwen quant and fp32
// 3. compare between quant and fp32
auto quant_outputs = quant_session->GetOutputs();
mean_error += CompareOutputData<float>(fp32_output_tensors_[i], quant_outputs);
} // end_for: calib data loop
@ -690,8 +690,8 @@ STATUS WeightQuantizer::DoFixedQuant(FuncGraphPtr func_graph) {
for (auto &cnode : func_graph->GetOrderedCnodes()) {
auto primitive_c = GetValueNode<std::shared_ptr<PrimitiveC>>(cnode->input(0));
if (primitive_c == nullptr) {
MS_LOG(ERROR) << "primitive_c is nullptr";
return RET_ERROR;
MS_LOG(DEBUG) << cnode->fullname_with_scope() << " : primitive_c is nullptr";
continue;
}
auto op_name = cnode->fullname_with_scope();
auto op_type = (schema::PrimitiveType)primitive_c->Type();
@ -744,7 +744,7 @@ STATUS WeightQuantizer::DoQuantize(FuncGraphPtr func_graph) {
quant_min_ = -(1 << (unsigned int)(this->bit_num_ - 1));
type_id_ = kNumberTypeInt8;
MS_LOG(INFO) << "Do mixed bit quantization";
return DoMiexedQuant(func_graph);
return DoMixedQuant(func_graph);
}
return DoFixedQuant(func_graph);

2
mindspore/lite/tools/converter/quantizer/weight_quantizer.h
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@ -62,7 +62,7 @@ class WeightQuantizer : public Quantizer {
std::vector<std::vector<std::string>> images_; // multi_input, [[mode_input_0], [model_input_1]...]
std::vector<std::unordered_map<std::string, mindspore::tensor::MSTensor *>> fp32_output_tensors_;
STATUS DoMiexedQuant(FuncGraphPtr);
STATUS DoMixedQuant(FuncGraphPtr);
STATUS SetAbstract(ParamValueLitePtr param_value, ParameterPtr param_node, std::shared_ptr<PrimitiveC> primitive_c);
STATUS DoFixedQuant(FuncGraphPtr);
STATUS RunFp32Graph(FuncGraphPtr);

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