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run transfermer decoder success

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pull/5864/head
cjh9368 5 years ago
parent e0c7ad784b
commit 9d8fd9252d
7 changed files with 18 additions and 45 deletions
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2
mindspore/lite/src/ops/gather.cc
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@ -91,7 +91,7 @@ int Gather::InferShape(std::vector<tensor::Tensor *> inputs_, std::vector<tensor
std::vector<int> out_shape{in_shape};
out_shape.erase(out_shape.begin() + axis);
for (int i = 0; i < indices_rank; i++) {
out_shape.insert(out_shape.begin() + axis, indices_shape[i]);
out_shape.insert(out_shape.begin() + axis + i, indices_shape[i]);
}
output->set_shape(out_shape);
return RET_OK;

8
mindspore/lite/src/runtime/kernel/arm/base/fullconnection_base.cc
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@ -55,7 +55,7 @@ kernel::LiteKernel *CpuFullConnectionInt8KernelCreator(const std::vector<lite::t
}
int RestoreFullconnectWeight(lite::tensor::Tensor *input_tensor) {
MS_ASSERT(input_tensor != nullptr);
if (input_tensor->data_type() != kNumberTypeUInt8) {
if (input_tensor->data_type() != kNumberTypeInt8) {
MS_LOG(ERROR) << "full connect input type error" << input_tensor->data_type();
return RET_ERROR;
}
@ -63,7 +63,7 @@ int RestoreFullconnectWeight(lite::tensor::Tensor *input_tensor) {
MS_LOG(ERROR) << "no quant param";
return RET_ERROR;
}
const auto* quant_data = static_cast<const uint8_t*>(input_tensor->Data());
const auto* quant_data = static_cast<const int8_t*>(input_tensor->Data());
auto* dequant_data = static_cast<float *>(malloc(input_tensor->DataSize() * sizeof(float)));
if (dequant_data == nullptr) {
MS_LOG(ERROR) << "malloc faile";
@ -108,7 +108,7 @@ kernel::LiteKernel *CpuFullConnectionFp32KernelCreator(const std::vector<lite::t
MS_ASSERT(desc.type == schema::PrimitiveType_Concat);
auto *weight_tensor = inputs.at(kWeightIndex);
auto *restore_data = weight_tensor->Data();
if (primitive->GetQuantType() == schema::QuantType_WeightQuant) {
if (!weight_tensor->GetQuantParams().empty()) {
RestoreFullconnectWeight(inputs.at(kWeightIndex));
}
auto kernel = new (std::nothrow) FullconnectionCPUKernel(opParameter, inputs, outputs, ctx, primitive);
@ -123,7 +123,7 @@ kernel::LiteKernel *CpuFullConnectionFp32KernelCreator(const std::vector<lite::t
<< schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(opParameter->type_));
return nullptr;
}
if (primitive->GetQuantType() == schema::QuantType_WeightQuant) {
if (!weight_tensor->GetQuantParams().empty()) {
weight_tensor->FreeData();
weight_tensor->SetData(restore_data);
}

9
mindspore/lite/src/runtime/kernel/arm/fp32/arithmetic_self.cc
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@ -116,11 +116,6 @@ int RestoreMulWeight(lite::tensor::Tensor *input_tensor) {
return RET_OK;
}
int ArithmeticSelfCPUKernel::Run() {
void *restore_data = nullptr;
if (primitive_->GetQuantType() == schema::QuantType_WeightQuant) {
restore_data = in_tensors_[1]->Data();
RestoreMulWeight(in_tensors_[1]);
}
auto ret = Prepare();
if (ret != RET_OK) {
MS_LOG(ERROR) << "Prepare fail!ret: " << ret;
@ -135,10 +130,6 @@ int ArithmeticSelfCPUKernel::Run() {
MS_LOG(ERROR) << "ArithmeticSelfRun error error_code[" << ret << "]";
return ret;
}
if (primitive_->GetQuantType() == schema::QuantType_WeightQuant) {
in_tensors_[1]->FreeData();
in_tensors_[1]->SetData(restore_data);
}
return RET_OK;
}

26
mindspore/lite/src/runtime/kernel/arm/int8/gather_int8.cc
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@ -35,29 +35,11 @@ int GatherInt8CPUKernel::Init() {
axis_ = (reinterpret_cast<GatherParameter *>(op_parameter_))->axis_;
batchDims_ = (reinterpret_cast<GatherParameter *>(op_parameter_))->batchDims_;
auto in_quant_args = in_tensors_.at(0)->GetQuantParams();
auto ind_quant_args = in_tensors_.at(1)->GetQuantParams();
auto out_quant_args = out_tensors_.at(0)->GetQuantParams();
param_.alpha_ = in_quant_args.front().scale / out_quant_args.front().scale;
param_.zp_in_ = in_quant_args.front().zeroPoint;
param_.zp_out_ = out_quant_args.front().zeroPoint;
auto indices_ptr = reinterpret_cast<int8_t *>(in_tensors_.at(1)->Data());
if (indices_ != nullptr) {
free(indices_);
indices_ = nullptr;
}
int count = in_tensors_.at(1)->ElementsNum();
indices_ = reinterpret_cast<int *>(malloc(count * sizeof(int)));
if (indices_ == nullptr) {
MS_LOG(ERROR) << "Gather Malloc indices_ error!";
return RET_ERROR;
}
(void)memset(indices_, 0, count * sizeof(int));
for (int i = 0; i < count; ++i) {
indices_[i] =
static_cast<int>(round((indices_ptr[i] - ind_quant_args.front().zeroPoint) * ind_quant_args.front().scale));
}
if (!InferShapeDone()) {
return RET_OK;
}
@ -73,6 +55,7 @@ int GatherInt8CPUKernel::DoGather(int task_id) {
auto input_ptr = reinterpret_cast<int8_t *>(input_tensor->Data());
auto output_ptr = reinterpret_cast<int8_t *>(out_tensor->Data());
auto indices_ptr = reinterpret_cast<int32_t *>(out_tensor->Data());
auto in_shape = input_tensor->shape();
int in_rank = in_shape.size();
@ -80,8 +63,8 @@ int GatherInt8CPUKernel::DoGather(int task_id) {
const int limit = in_shape[axis_];
for (int i = 0; i < indices_element_size; ++i) {
if (indices_[i] >= limit) {
MS_LOG(ERROR) << " indice data: " << indices_[i] << " is not in [ 0, " << limit - 1 << " ]";
if (indices_ptr[i] >= limit) {
MS_LOG(ERROR) << " indice data: " << indices_ptr[i] << " is not in [ 0, " << limit - 1 << " ]";
return RET_ERROR;
}
}
@ -103,7 +86,7 @@ int GatherInt8CPUKernel::DoGather(int task_id) {
int error_code;
input_ptr += thread_stride * limit;
output_ptr += thread_stride * indices_element_size;
error_code = GatherInt8(input_ptr, output_ptr, count, inner_size, limit, indices_, indices_element_size, param_);
error_code = GatherInt8(input_ptr, output_ptr, count, inner_size, limit, indices_ptr, indices_element_size, param_);
if (error_code != RET_OK) {
return RET_ERROR;
@ -127,6 +110,7 @@ int GatherInt8CPUKernel::Run() {
MS_LOG(ERROR) << "Prepare fail!ret: " << prepare_ret;
return prepare_ret;
}
int error_code = ParallelLaunch(THREAD_POOL_DEFAULT, GatherInt8Run, this, thread_count_);
if (error_code != RET_OK) {
MS_LOG(ERROR) << "Gather function error error_code[" << error_code << "]";

3
mindspore/lite/src/runtime/kernel/arm/int8/gather_int8.h
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@ -30,8 +30,6 @@ class GatherInt8CPUKernel : public LiteKernel {
const mindspore::lite::PrimitiveC *primitive)
: LiteKernel(parameter, inputs, outputs, ctx, primitive), thread_count_(ctx->thread_num_) {}
~GatherInt8CPUKernel() {
free(indices_);
indices_ = nullptr;
}
int Init() override;
@ -40,7 +38,6 @@ class GatherInt8CPUKernel : public LiteKernel {
int DoGather(int task_id);
private:
int *indices_ = nullptr;
int thread_count_;
int batchDims_;
int axis_;

13
mindspore/lite/tools/anf_exporter/anf_exporter.cc
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@ -129,7 +129,7 @@ void AnfExporter::SetGraphInputIndex(const std::unique_ptr<schema::MetaGraphT> &
for (auto node : graph_input_nodes_) {
for (auto input : node->inputIndex) {
auto tensor = meta_graphT->allTensors[input].get();
if (tensor->data.empty()) {
if (tensor->nodeType != schema::NodeType_CNode && tensor->data.empty()) {
tensor->nodeType = schema::NodeType_ValueNode;
tensor->format = schema::Format_NHWC;
if (!IsContain(meta_graphT->inputIndex, input)) {
@ -261,7 +261,6 @@ int AnfExporter::ConvertInputParameter(const std::shared_ptr<AnfNode> input_anod
return RET_OK;
}
auto paramTensor = std::make_unique<schema::TensorT>();
paramTensor->nodeType = schema::NodeType_ValueNode;
paramTensor->format = schema::Format_NHWC;
auto abstractBase = paramNode->abstract();
if (abstractBase == nullptr) {
@ -341,11 +340,10 @@ int AnfExporter::SetOpInputNode(const CNodePtr &cnode, const std::unique_ptr<sch
if (cnode->inputs().size() <= 1) {
return RET_OK;
}
bool is_graph_input = true;
bool is_graph_input = false;
for (size_t i = 1; i < cnode->inputs().size(); i++) {
auto input_node = cnode->input(i);
if (input_node->isa<CNode>()) {
is_graph_input = false;
auto ret = ConvertInputCNode(input_node, fb_node);
if (ret != RET_OK) {
MS_LOG(ERROR) << "ConvertInputCNode failed";
@ -357,6 +355,9 @@ int AnfExporter::SetOpInputNode(const CNodePtr &cnode, const std::unique_ptr<sch
MS_LOG(ERROR) << "ConvertInputParameter failed";
return RET_ERROR;
}
if (!input_node->cast<ParameterPtr>()->has_default()) {
is_graph_input = true;
}
} else if (input_node->isa<ValueNode>()) {
auto ret = ConvertInputValueNode(input_node, meta_graphT, fb_node);
if (ret != RET_OK) {
@ -382,7 +383,7 @@ void AnfExporter::SetOpOutputNode(const CNodePtr &cnode, const std::unique_ptr<s
auto tuple = std::reinterpret_pointer_cast<abstract::AbstractTuple>(cnode->abstract());
for (size_t i = 0; i < tuple->size(); i++) {
auto msTensor = new schema::TensorT();
msTensor->nodeType = schema::NodeType_Parameter;
msTensor->nodeType = schema::NodeType_CNode;
fb_node->outputIndex.emplace_back(meta_graphT->allTensors.size());
if (tuple->size() == 1) {
node_id_map_[cnode_name] = meta_graphT->allTensors.size();
@ -399,7 +400,7 @@ void AnfExporter::SetOpOutputNode(const CNodePtr &cnode, const std::unique_ptr<s
}
} else {
auto ms_tensor = new schema::TensorT();
ms_tensor->nodeType = schema::NodeType_Parameter;
ms_tensor->nodeType = schema::NodeType_CNode;
fb_node->outputIndex.emplace_back(meta_graphT->allTensors.size());
node_id_map_[cnode_name] = meta_graphT->allTensors.size();
meta_graphT->allTensors.emplace_back(ms_tensor);

2
mindspore/lite/tools/anf_importer/import_from_meta_graphT.cc
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@ -59,8 +59,8 @@ int AnfImporterFromMetaGraphT::ConverterConstTensor() {
std::memcpy(tensor_data, tensor->data.data(), size);
param_value->set_tensor_addr(tensor_data);
param_value->set_tensor_size(size);
parameter->set_default_param(param_value);
}
parameter->set_default_param(param_value);
AddNode(i, parameter);
}
return RET_OK;

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