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op_map.cc
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/****************************************************************************
*
* Copyright (c) 2021 Vivante Corporation
*
* Permission is hereby granted, free of charge, to any person obtaining a
* copy of this software and associated documentation files (the "Software"),
* to deal in the Software without restriction, including without limitation
* the rights to use, copy, modify, merge, publish, distribute, sublicense,
* and/or sell copies of the Software, and to permit persons to whom the
* Software is furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
*THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
* DEALINGS IN THE SOFTWARE.
*
*****************************************************************************/
#include <algorithm>
#include <array>
#include <cstring>
#include <iostream>
#include <limits>
#include <memory>
#include <numeric>
#include <tuple>
#include <vector>
#include "op_map.h"
#include "tensorflow/lite/context_util.h"
#include "tensorflow/lite/kernels/internal/reference/reference_ops.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/internal/types.h"
#include "tensorflow/lite/minimal_logging.h"
#include "tim/vx/ops.h"
#include "utils.h"
#include "vsi_npu_custom_op.h"
using namespace tflite;
namespace {
inline tim::vx::PadType TflitePadTypeToVsiPadType(TfLitePadding pad) {
switch (pad) {
case kTfLitePaddingUnknown:
return tim::vx::PadType::AUTO;
case kTfLitePaddingValid:
return tim::vx::PadType::VALID;
case kTfLitePaddingSame:
return tim::vx::PadType::SAME;
default:
TFLITE_LOG_PROD(TFLITE_LOG_ERROR, "Unsuppoted pad type: %d", pad);
break;
}
return tim::vx::PadType::AUTO;
}
/// Insert activation layer before the `original_tensor`
/// Return the input tensor of new activation layer
std::shared_ptr<tim::vx::Tensor> ProcessFusedActivation(
vx::delegate::Delegate* delegate,
TfLiteFusedActivation fused_activation,
const std::shared_ptr<tim::vx::Tensor>& original_tensor) {
std::shared_ptr<tim::vx::Operation> op = nullptr;
switch (fused_activation) {
case kTfLiteActNone:
return original_tensor;
case kTfLiteActRelu:
op = delegate->GetGraph()->CreateOperation<tim::vx::ops::Relu>();
break;
case kTfLiteBuiltinReluN1To1:
op = delegate->GetGraph()->CreateOperation<tim::vx::ops::Relu1>();
break;
case kTfLiteActRelu6:
op = delegate->GetGraph()->CreateOperation<tim::vx::ops::Relu6>();
break;
case kTfLiteActTanh:
op = delegate->GetGraph()->CreateOperation<tim::vx::ops::Tanh>();
break;
case kTfLiteActSigmoid:
op = delegate->GetGraph()->CreateOperation<tim::vx::ops::Sigmoid>();
break;
default:
TFLITE_LOG_PROD(TFLITE_LOG_WARNING,
"Unsupported fused activation: %d",
fused_activation);
}
auto processed_tensor = delegate->GetGraph()->CreateTensor(
original_tensor->GetSpec().AsTransientSpec());
(*op).BindInput(processed_tensor);
(*op).BindOutput(original_tensor);
delegate->GetOps().push_back(op);
delegate->GetTensors().push_back(processed_tensor);
return processed_tensor;
}
std::shared_ptr<tim::vx::Tensor> ReverseInputTensor(
vx::delegate::Delegate* delegate,
const std::shared_ptr<tim::vx::Tensor>& original_tensor,
std::vector<int32_t> axis) {
auto reversed_tensor =
delegate->GetGraph()->CreateTensor(original_tensor->GetSpec());
std::shared_ptr<tim::vx::Operation> op =
delegate->GetGraph()->CreateOperation<tim::vx::ops::Reverse>(axis);
(*op).BindInput(original_tensor);
(*op).BindOutput(reversed_tensor);
delegate->GetOps().push_back(op);
delegate->GetTensors().push_back(reversed_tensor);
return reversed_tensor;
}
bool ResizeToTransposeConv(
vx::delegate::Delegate* delegate,
std::vector<std::shared_ptr<tim::vx::Tensor>>& inputs,
std::vector<std::shared_ptr<tim::vx::Tensor>>& outputs,
tim::vx::ResizeType resizeType,
uint32_t channel,
uint32_t scale_w,
uint32_t scale_h) {
uint32_t kernel_w = 0;
uint32_t kernel_h = 0;
uint32_t pad_w = 0;
uint32_t pad_h = 0;
std::vector<float> weight_data;
if (resizeType == tim::vx::ResizeType::BILINEAR) {
kernel_w = vx::delegate::utils::CalcWeightSizeForBilinear(scale_w);
kernel_h = vx::delegate::utils::CalcWeightSizeForBilinear(scale_h);
pad_w = vx::delegate::utils::CalcPadSizeForBilinear(scale_w);
pad_h = vx::delegate::utils::CalcPadSizeForBilinear(scale_h);
weight_data.resize(kernel_h * kernel_w * channel * channel);
vx::delegate::utils::GenerateWeightsDataForBilinear(
weight_data.data(),
{kernel_w, kernel_h, channel, channel},
scale_w,
scale_h);
} else if (resizeType == tim::vx::ResizeType::NEAREST_NEIGHBOR) {
kernel_w = scale_w;
kernel_h = scale_h;
pad_w = 0;
pad_h = 0;
weight_data.resize(kernel_h * kernel_w * channel * channel);
vx::delegate::utils::GenerateWeightDataForNearest(
weight_data.data(), {kernel_w, kernel_h, channel, channel});
}
auto weight_spec = tim::vx::TensorSpec(tim::vx::DataType::FLOAT32,
{kernel_w, kernel_h, channel, channel},
tim::vx::TensorAttribute::CONSTANT);
std::shared_ptr<tim::vx::Tensor> weight_tensor;
auto input_type = inputs[0]->GetDataType();
auto input_quant = inputs[0]->GetQuantization();
uint32_t kernel_size = kernel_h * kernel_w * channel * channel;
std::vector<uint8_t> weight_quant_data(kernel_size);
if (input_quant.Type() == tim::vx::QuantType::ASYMMETRIC) {
float scale = input_quant.Scales()[0];
int32_t zp = input_quant.ZeroPoints()[0];
if (input_type == tim::vx::DataType::INT8) {
std::vector<int8_t> quant_i8;
vx::delegate::utils::Quantize<int8_t>(weight_data, scale, zp, quant_i8);
weight_spec.SetDataType(tim::vx::DataType::INT8);
memcpy(weight_quant_data.data(), quant_i8.data(), kernel_size);
} else if (input_type == tim::vx::DataType::UINT8) {
std::vector<uint8_t> quant_u8;
vx::delegate::utils::Quantize<uint8_t>(weight_data, scale, zp, quant_u8);
weight_spec.SetDataType(tim::vx::DataType::UINT8);
memcpy(weight_quant_data.data(), quant_u8.data(), kernel_size);
}
weight_spec.SetQuantization(input_quant);
weight_tensor = delegate->GetGraph()->CreateTensor(
weight_spec, weight_quant_data.data());
} else {
weight_tensor =
delegate->GetGraph()->CreateTensor(weight_spec, weight_data.data());
}
std::array<uint32_t, 2> ksize{kernel_w, kernel_h};
std::array<uint32_t, 2> stride{scale_w, scale_h};
std::array<uint32_t, 2> output_padding{0, 0};
std::array<uint32_t, 4> pad{pad_w, pad_w, pad_h, pad_h};
auto op = delegate->GetGraph()->CreateOperation<tim::vx::ops::DeConv2d>(
channel,
tim::vx::PadType::SAME,
ksize,
stride,
output_padding,
pad,
1,
tim::vx::DataLayout::CWHN);
std::vector<std::shared_ptr<tim::vx::Tensor>> final_inputs;
final_inputs.push_back(inputs[0]);
final_inputs.push_back(weight_tensor);
(*op).BindInputs(final_inputs);
(*op).BindOutput(outputs[0]);
delegate->GetOps().push_back(std::move(op));
return true;
}
enum class ActionTargetType { INPUT, OUTPUT, STATE };
struct IAction {
virtual ActionTargetType GetActionTargetType() const = 0;
virtual bool process(vx::delegate::Delegate* delegate,
std::vector<std::shared_ptr<tim::vx::Tensor>>& inputs,
std::vector<std::shared_ptr<tim::vx::Tensor>>& outputs,
std::vector<std::shared_ptr<tim::vx::Tensor>>& states,
const void* params) const = 0;
virtual ~IAction(){};
};
template <ActionTargetType type, int Port>
struct ActionBase : public IAction {
ActionTargetType type_{type};
int port_{Port};
bool process(vx::delegate::Delegate* delegate,
std::vector<std::shared_ptr<tim::vx::Tensor>>& inputs,
std::vector<std::shared_ptr<tim::vx::Tensor>>& outputs,
std::vector<std::shared_ptr<tim::vx::Tensor>>& states,
const void* params) const override {
return true;
}
ActionTargetType GetActionTargetType() const final { return type_; }
};
template <int Port, typename T_Param>
struct FusedActivationAction
: public ActionBase<ActionTargetType::OUTPUT, Port> {
bool process(vx::delegate::Delegate* delegate,
std::vector<std::shared_ptr<tim::vx::Tensor>>& inputs,
std::vector<std::shared_ptr<tim::vx::Tensor>>& outputs,
std::vector<std::shared_ptr<tim::vx::Tensor>>& states,
const void* params) const final {
const auto builtin = reinterpret_cast<const T_Param*>(params);
outputs[this->port_] = ProcessFusedActivation(
delegate, builtin->activation, outputs[this->port_]);
return true;
}
};
template <typename T_Param, typename... Actions>
struct OpMapperBase : public vx::op_map::IOpMapper {
std::vector<std::unique_ptr<IAction>> actions_;
OpMapperBase() {
(void)std::initializer_list<int>{
0, (actions_.emplace_back(std::make_unique<Actions>()), 0)...};
}
size_t GetParamSize() const override { return sizeof(T_Param); }
bool IsSupported(TfLiteContext* context,
TfLiteNode* node,
const TfLiteRegistration* registration) const override {
for (int i = 0; i < node->inputs->size; i++) {
int input_index = node->inputs->data[i];
if (input_index < 0) {
continue;
}
if (context->tensors[input_index].type == kTfLiteInt16) {
TFLITE_LOG_PROD(TFLITE_LOG_ERROR, "Int16 input is not supported");
return false;
}
if (context->tensors[input_index].type == kTfLiteInt64) {
TFLITE_LOG_PROD(TFLITE_LOG_ERROR, "Int64 input is not supported");
return false;
}
if (context->tensors[input_index].dims->size > 6) {
TFLITE_LOG_PROD(
TFLITE_LOG_ERROR,
"vx-delegate doesn't support the tensor whose dimension "
"is greater than 6.");
return false;
}
for (int j = 0; j < context->tensors[input_index].dims->size; j++) {
if (context->tensors[input_index].dims->data[j] == 0) {
TFLITE_LOG_PROD(TFLITE_LOG_ERROR,
"vx-delegate doesn't support the tensor of which one "
"of dims is 0");
return false;
}
}
}
for (int i = 0; i < node->outputs->size; i++) {
int output_index = node->outputs->data[i];
if (context->tensors[output_index].type == kTfLiteInt16) {
TFLITE_LOG_PROD(TFLITE_LOG_ERROR, "Int16 output is not supported");
return false;
}
if (context->tensors[output_index].type == kTfLiteInt64) {
TFLITE_LOG_PROD(TFLITE_LOG_ERROR, "Int64 output is not supported");
return false;
}
for (int j = 0; j < context->tensors[output_index].dims->size; j++) {
if (context->tensors[output_index].dims->data[j] == 0) {
TFLITE_LOG_PROD(TFLITE_LOG_ERROR,
"vx-delegate doesn't support the tensor of which one "
"of dims is 0");
return false;
}
}
}
return IsOpSupported(context, node, registration);
}
virtual bool IsOpSupported(TfLiteContext* context,
TfLiteNode* node,
const TfLiteRegistration* registration) const {
return true;
}
bool MapOp(vx::delegate::Delegate* delegate,
std::vector<std::shared_ptr<tim::vx::Tensor>> inputs,
std::vector<std::shared_ptr<tim::vx::Tensor>> outputs,
std::vector<std::shared_ptr<tim::vx::Tensor>> states,
const void* params) {
bool status = true;
for (auto& a : actions_) {
if (a->GetActionTargetType() == ActionTargetType::INPUT) {
a->process(delegate, inputs, outputs, states, params);
}
}
for (auto it = actions_.rbegin(); it != actions_.rend(); it++) {
if ((*it)->GetActionTargetType() == ActionTargetType::OUTPUT) {
(*it)->process(delegate, inputs, outputs, states, params);
}
}
status = HandleMapOp(delegate, inputs, outputs, states, params);
return status;
}
virtual bool HandleMapOp(
vx::delegate::Delegate* delegate,
std::vector<std::shared_ptr<tim::vx::Tensor>>& inputs,
std::vector<std::shared_ptr<tim::vx::Tensor>>& outputs,
std::vector<std::shared_ptr<tim::vx::Tensor>>& states,
const void* params) {
return HandleMapOp(delegate, inputs, outputs, params);
}
virtual bool HandleMapOp(
vx::delegate::Delegate* delegate,
std::vector<std::shared_ptr<tim::vx::Tensor>>& inputs,
std::vector<std::shared_ptr<tim::vx::Tensor>>& outputs,
const void* params) {
return false;
}
};
} // namespace
namespace vx {
namespace op_map {
template <typename T_OperationType>
struct SimpleOpMapper : public OpMapperBase<EmptyStructPlaceholder> {
std::string name_;
SimpleOpMapper(std::string name) : name_(name) {}
bool HandleMapOp(vx::delegate::Delegate* delegate,
std::vector<std::shared_ptr<tim::vx::Tensor>>& inputs,
std::vector<std::shared_ptr<tim::vx::Tensor>>& outputs,
const void* params) override {
TFLITE_LOG(TFLITE_LOG_INFO, "Creating %s op", name_.c_str());
auto op = delegate->GetGraph()->CreateOperation<T_OperationType>();
(*op).BindInputs(inputs).BindOutputs(outputs);
delegate->GetOps().push_back(std::move(op));
return true;
}
};
template <typename T_OperationType, typename T_Param>
struct SimpleOpWithFusedActiovationMapper
: public OpMapperBase<T_Param, FusedActivationAction<0, T_Param>> {
std::string name_;
SimpleOpWithFusedActiovationMapper(std::string name) : name_(name) {}
bool HandleMapOp(vx::delegate::Delegate* delegate,
std::vector<std::shared_ptr<tim::vx::Tensor>>& inputs,
std::vector<std::shared_ptr<tim::vx::Tensor>>& outputs,
const void* params) override {
TFLITE_LOG(TFLITE_LOG_INFO, "Creating %s op", name_.c_str());
auto op = delegate->GetGraph()->CreateOperation<T_OperationType>();
(*op).BindInputs(inputs).BindOutputs(outputs);
delegate->GetOps().push_back(std::move(op));
return true;
}
};
template <typename T_Param>
struct Conv2dKind
: public OpMapperBase<T_Param, FusedActivationAction<0, T_Param>> {};
struct FullyConnectedMapper
: public OpMapperBase<
TfLiteFullyConnectedParams,
FusedActivationAction<0, TfLiteFullyConnectedParams>> {
bool IsOpSupported(TfLiteContext* context,
TfLiteNode* node,
const TfLiteRegistration* registration) const override {
const auto builtin =
reinterpret_cast<const TfLiteFullyConnectedParams*>(node->builtin_data);
auto input_tensor = context->tensors[node->inputs->data[0]];
auto weight_tensor = context->tensors[node->inputs->data[1]];
if (input_tensor.type != weight_tensor.type) {
TFLITE_LOG_PROD(TFLITE_LOG_ERROR,
"hybrid data type is not supported in fullyconnected.");
return false;
}
if (builtin->weights_format ==
kTfLiteFullyConnectedWeightsFormatShuffled4x16Int8) {
TFLITE_LOG_PROD(TFLITE_LOG_ERROR, "Shuffled weight is not supported");
return false;
}
for (int i = 0; i < node->inputs->size; i++) {
int input_index = node->inputs->data[i];
if (input_index >= 0 && input_index < context->tensors_size &&
context->tensors[input_index].type == kTfLiteInt16) {
TFLITE_LOG_PROD(TFLITE_LOG_ERROR, "Int16 input is not supported");
return false;
}
}
return true;
}
bool HandleMapOp(vx::delegate::Delegate* delegate,
std::vector<std::shared_ptr<tim::vx::Tensor>>& inputs,
std::vector<std::shared_ptr<tim::vx::Tensor>>& outputs,
const void* params) override {
TFLITE_LOG(TFLITE_LOG_INFO, "Creating fully connected op");
const auto builtin =
reinterpret_cast<const TfLiteFullyConnectedParams*>(params);
auto input_tensor = inputs[0];
auto weight_tensor = inputs[1];
if (input_tensor->GetShape().size() > 2 ||
(input_tensor->GetShape().size() == 2 &&
input_tensor->GetShape()[0] != weight_tensor->GetShape()[0])) {
uint32_t input_size = weight_tensor->GetShape()[0];
uint32_t total_input_size = 1;
for (int i = 0; i < input_tensor->GetShape().size(); i++) {
total_input_size *= input_tensor->GetShape()[i];
}
uint32_t input_batch = total_input_size / input_size;
auto reshape_output = delegate->GetGraph()->CreateTensor(
input_tensor->GetSpec().AsTransientSpec());
std::vector<uint32_t> new_shape{input_size, input_batch};
auto reshape_op =
delegate->GetGraph()->CreateOperation<tim::vx::ops::Reshape>(
new_shape);
(*reshape_op).BindInput(inputs[0]);
(*reshape_op).BindOutput(reshape_output);
delegate->GetOps().push_back(reshape_op);
inputs[0] = reshape_output;
}
auto op =
delegate->GetGraph()->CreateOperation<tim::vx::ops::FullyConnected>(
0, weight_tensor->GetShape()[1]);
(*op).BindInputs(inputs);
(*op).BindOutputs(outputs);
delegate->GetOps().push_back(std::move(op));
return true;
}
};
struct SoftmaxMapper : public OpMapperBase<TfLiteSoftmaxParams> {
bool IsOpSupported(TfLiteContext* context,
TfLiteNode* node,
const TfLiteRegistration* registration) const override {
int input_index = node->inputs->data[0];
auto input_dims = context->tensors[input_index].dims;
if (input_dims->data[1] > 65535 || input_dims->data[2] > 65535) {
TFLITE_LOG_PROD(
TFLITE_LOG_ERROR,
"vx-delegate doesn't support tensor height/width > 65535");
return false;
}
return true;
}
bool HandleMapOp(vx::delegate::Delegate* delegate,
std::vector<std::shared_ptr<tim::vx::Tensor>>& inputs,
std::vector<std::shared_ptr<tim::vx::Tensor>>& outputs,
const void* params) override {
TFLITE_LOG(TFLITE_LOG_INFO, "Creating softmax op");
auto builtin = reinterpret_cast<const TfLiteSoftmaxParams*>(params);
auto op = delegate->GetGraph()->CreateOperation<tim::vx::ops::Softmax>(
builtin->beta, 0);
(*op).BindInputs(inputs).BindOutputs(outputs);
delegate->GetOps().push_back(std::move(op));
return true;
}
};
struct Conv2dMapper : public Conv2dKind<TfLiteConvParams> {
virtual bool IsOpSupported(TfLiteContext* context,
TfLiteNode* node,
const TfLiteRegistration* registration) const {
auto input_tensor = context->tensors[node->inputs->data[0]];
auto weight_tensor = context->tensors[node->inputs->data[1]];
if (input_tensor.type != weight_tensor.type) {
TFLITE_LOG_PROD(TFLITE_LOG_ERROR,
"hybrid data type is not supported in conv2d.");
return false;
}
return true;
}
bool HandleMapOp(vx::delegate::Delegate* delegate,
std::vector<std::shared_ptr<tim::vx::Tensor>>& inputs,
std::vector<std::shared_ptr<tim::vx::Tensor>>& outputs,
const void* params) override {
TFLITE_LOG(TFLITE_LOG_INFO, "Creating Conv2d op");
const auto builtin = reinterpret_cast<const TfLiteConvParams*>(params);
uint32_t weights = inputs[1]->GetShape()[3];
uint32_t kernel_h = inputs[1]->GetShape()[2];
uint32_t kernel_w = inputs[1]->GetShape()[1];
auto op = delegate->GetGraph()->CreateOperation<tim::vx::ops::Conv2d>(
static_cast<int32_t>(weights),
TflitePadTypeToVsiPadType(builtin->padding),
std::array<uint32_t, 2>({kernel_w, kernel_h}),
std::array<uint32_t, 2>(
{builtin->stride_width, builtin->stride_height}),
std::array<uint32_t, 2>(
{builtin->dilation_width_factor, builtin->dilation_height_factor}),
0,
tim::vx::DataLayout::CWHN);
(*op).BindInputs(inputs);
(*op).BindOutputs(outputs);
delegate->GetOps().push_back(std::move(op));
return true;
}
};
struct TransposeConvMapper : public OpMapperBase<TfLiteTransposeConvParams> {
bool HandleMapOp(vx::delegate::Delegate* delegate,
std::vector<std::shared_ptr<tim::vx::Tensor>>& inputs,
std::vector<std::shared_ptr<tim::vx::Tensor>>& outputs,
const void* params) override {
TFLITE_LOG(TFLITE_LOG_INFO, "Create TransposeConv op");
const auto builtin =
reinterpret_cast<const TfLiteTransposeConvParams*>(params);
auto padding = TflitePadTypeToVsiPadType(builtin->padding);
auto stride_width = builtin->stride_width;
auto stride_height = builtin->stride_height;
std::vector<int32_t> output_shape(inputs[0]->GetShape()[0]);
inputs[0]->CopyDataFromTensor(output_shape.data());
uint32_t input_width = inputs[2]->GetShape()[1];
uint32_t input_height = inputs[2]->GetShape()[2];
uint32_t ksize_width = inputs[1]->GetShape()[1];
;
uint32_t ksize_height = inputs[1]->GetShape()[2];
;
uint32_t weights = inputs[1]->GetShape()[3];
int32_t pad_left_inter = static_cast<int32_t>(
ksize_width + stride_width * (input_width - 1) - output_shape[2]);
uint32_t pad_left = pad_left_inter;
uint32_t pad_right = pad_left_inter - pad_left;
int32_t pad_top_inter = static_cast<int32_t>(
ksize_height + stride_height * (input_height - 1) - output_shape[1]);
uint32_t pad_top = pad_top_inter / 2;
uint32_t pad_bottom = pad_top_inter - pad_top;
std::array<uint32_t, 2> ksize{ksize_width, ksize_height};
std::array<uint32_t, 2> stride{stride_width, stride_height};
std::array<uint32_t, 2> output_padding{0, 0};
std::array<uint32_t, 4> pad{pad_left, pad_right, pad_top, pad_bottom};
auto op = delegate->GetGraph()->CreateOperation<tim::vx::ops::DeConv2d>(
weights,
padding,
ksize,
stride,
output_padding,
pad,
1,
tim::vx::DataLayout::CWHN,
tim::vx::DataLayout::IcWHOc);
std::vector<std::shared_ptr<tim::vx::Tensor>> input_tensor;
input_tensor.push_back(inputs[2]);
input_tensor.push_back(inputs[1]);
if (inputs.size() == 4) {
input_tensor.push_back(inputs[3]);
}
(*op).BindInputs(input_tensor);
(*op).BindOutputs(outputs);
delegate->GetOps().push_back(std::move(op));
return true;
}
};
template <tim::vx::PoolType poolType>
struct Pool2dMapper : public Conv2dKind<TfLitePoolParams> {
virtual bool IsOpSupported(TfLiteContext* context,
TfLiteNode* node,
const TfLiteRegistration* registration) const {
return true;
}
bool HandleMapOp(vx::delegate::Delegate* delegate,
std::vector<std::shared_ptr<tim::vx::Tensor>>& inputs,
std::vector<std::shared_ptr<tim::vx::Tensor>>& outputs,
const void* params) override {
TFLITE_LOG(
TFLITE_LOG_INFO, "Creating Pool2d(%d) op", static_cast<int>(poolType));
const auto builtin = reinterpret_cast<const TfLitePoolParams*>(params);
auto op = delegate->GetGraph()->CreateOperation<tim::vx::ops::Pool2d>(
poolType,
TflitePadTypeToVsiPadType(builtin->padding),
std::array<uint32_t, 2>(
{builtin->filter_width, builtin->filter_height}),
std::array<uint32_t, 2>(
{builtin->stride_width, builtin->stride_height}),
tim::vx::RoundType::FLOOR,
tim::vx::DataLayout::CWHN);
(*op).BindInputs(inputs);
(*op).BindOutputs(outputs);
delegate->GetOps().push_back(std::move(op));
return true;
}
};
struct DepthwiseConv2dMapper : public Conv2dKind<TfLiteDepthwiseConvParams> {
virtual bool IsOpSupported(TfLiteContext* context,
TfLiteNode* node,
const TfLiteRegistration* registration) const {
auto input_tensor = context->tensors[node->inputs->data[0]];
auto weight_tensor = context->tensors[node->inputs->data[1]];
if (input_tensor.type != weight_tensor.type) {
TFLITE_LOG_PROD(TFLITE_LOG_ERROR,
"hybrid data type is not supported in DepthwiseConv2d.");
return false;
}
return true;
}
bool HandleMapOp(vx::delegate::Delegate* delegate,
std::vector<std::shared_ptr<tim::vx::Tensor>>& inputs,
std::vector<std::shared_ptr<tim::vx::Tensor>>& outputs,
const void* params) override {
TFLITE_LOG(TFLITE_LOG_INFO, "Creating DepthwiseConv2d op");
const auto builtin =
reinterpret_cast<const TfLiteDepthwiseConvParams*>(params);
uint32_t weights = inputs[1]->GetShape()[0];
uint32_t kernel_h = inputs[1]->GetShape()[2];
uint32_t kernel_w = inputs[1]->GetShape()[1];
auto op = delegate->GetGraph()->CreateOperation<tim::vx::ops::Conv2d>(
static_cast<int32_t>(weights),
TflitePadTypeToVsiPadType(builtin->padding),
std::array<uint32_t, 2>({kernel_w, kernel_h}),
std::array<uint32_t, 2>(
{builtin->stride_width, builtin->stride_height}),
std::array<uint32_t, 2>(
{builtin->dilation_width_factor, builtin->dilation_height_factor}),
builtin->depth_multiplier,
tim::vx::DataLayout::CWHN);
(*op).BindInputs(inputs);
(*op).BindOutputs(outputs);
delegate->GetOps().push_back(std::move(op));
return true;
}
};
struct ConcatenationMapper
: public OpMapperBase<TfLiteConcatenationParams,
FusedActivationAction<0, TfLiteConcatenationParams>> {
bool HandleMapOp(vx::delegate::Delegate* delegate,
std::vector<std::shared_ptr<tim::vx::Tensor>>& inputs,
std::vector<std::shared_ptr<tim::vx::Tensor>>& outputs,
const void* params) override {
TFLITE_LOG(TFLITE_LOG_INFO, "Creating Concatenation op");
const auto builtin =
reinterpret_cast<const TfLiteConcatenationParams*>(params);
auto output_tensor = outputs[0];
auto axis = vx::delegate::utils::ConvertAxis(builtin->axis,
inputs[0]->GetShape().size());
auto op = delegate->GetGraph()->CreateOperation<tim::vx::ops::Concat>(
axis, inputs.size());
(*op).BindInputs(inputs);
(*op).BindOutputs(outputs);
delegate->GetOps().push_back(std::move(op));
return true;
}
};
struct LocalResponseNormalizationMapper
: public OpMapperBase<TfLiteLocalResponseNormParams> {
bool HandleMapOp(vx::delegate::Delegate* delegate,
std::vector<std::shared_ptr<tim::vx::Tensor>>& inputs,
std::vector<std::shared_ptr<tim::vx::Tensor>>& outputs,
const void* params) override {
TFLITE_LOG(TFLITE_LOG_INFO, "Creating LRN op");
const auto builtin =
reinterpret_cast<const TfLiteLocalResponseNormParams*>(params);
auto op = delegate->GetGraph()
->CreateOperation<tim::vx::ops::LocalResponseNormalization>(
builtin->radius,
builtin->alpha,
builtin->beta,
builtin->bias,
0);
(*op).BindInputs(inputs).BindOutputs(outputs);
delegate->GetOps().push_back(std::move(op));
return true;
}
};
struct L2NormalizationMapper
: public OpMapperBase<TfLiteL2NormParams,
FusedActivationAction<0, TfLiteL2NormParams>> {
bool HandleMapOp(vx::delegate::Delegate* delegate,
std::vector<std::shared_ptr<tim::vx::Tensor>>& inputs,
std::vector<std::shared_ptr<tim::vx::Tensor>>& outputs,
const void* params) override {
TFLITE_LOG(TFLITE_LOG_INFO, "Creating L2Normaliztion op");
auto op =
delegate->GetGraph()->CreateOperation<tim::vx::ops::L2Normalization>(0);
(*op).BindInputs(inputs).BindOutputs(outputs);
delegate->GetOps().push_back(std::move(op));
return true;
}
};
struct ReshapeMapper : public OpMapperBase<TfLiteReshapeParams> {
bool HandleMapOp(vx::delegate::Delegate* delegate,
std::vector<std::shared_ptr<tim::vx::Tensor>>& inputs,
std::vector<std::shared_ptr<tim::vx::Tensor>>& outputs,
const void* params) override {
TFLITE_LOG(TFLITE_LOG_INFO, "Creating Reshape op");
const auto builtin = reinterpret_cast<const TfLiteReshapeParams*>(params);
std::vector<int32_t> new_shape;
uint32_t total_size = 1, negative_index = 0;
std::vector<uint32_t> no_nagetive_shape;
bool do_shape_inference = false;
// The new shape may be passed to reshape op by
// builtin prarameters or inputs[1], the two formats should be handled.
if (inputs.size() == 2 &&
inputs[1]->GetDataType() == tim::vx::DataType::INT32 &&
inputs[1]->GetShape().size() == 1) {
std::vector<int32_t> shape_from_input1(inputs[1]->GetShape()[0]);
inputs[1]->CopyDataFromTensor(shape_from_input1.data());
new_shape.assign(shape_from_input1.rbegin(), shape_from_input1.rend());
} else {
for (int i = builtin->num_dimensions - 1; i >= 0; i--) {
new_shape.push_back(static_cast<int32_t>(builtin->shape[i]));
}
}
for (uint32_t i = 0; i < inputs[0]->GetShape().size(); ++i) {
total_size *= inputs[0]->GetShape().at(i);
}
for (uint32_t i = 0; i < new_shape.size(); ++i) {
if (new_shape.at(i) != -1) {
total_size /= new_shape.at(i);
no_nagetive_shape.push_back(new_shape.at(i));
} else {
do_shape_inference = true;
negative_index = i;
no_nagetive_shape.push_back(0); // hold a place for changes to the
// value
}
}
if (do_shape_inference) {
no_nagetive_shape.at(negative_index) = total_size;
}
auto op = delegate->GetGraph()->CreateOperation<tim::vx::ops::Reshape>(
no_nagetive_shape);
(*op).BindInput(inputs[0]);
(*op).BindOutput(outputs[0]);
delegate->GetOps().push_back(std::move(op));
return true;
}
};
struct StridedSliceMapper : public OpMapperBase<TfLiteStridedSliceParams> {
virtual bool IsOpSupported(TfLiteContext* context,
TfLiteNode* node,
const TfLiteRegistration* registration) const {
TFLITE_LOG(TFLITE_LOG_INFO, "Check StridedSlice");
const auto builtin =
reinterpret_cast<const TfLiteStridedSliceParams*>(node->builtin_data);
if (builtin->new_axis_mask) {
TFLITE_LOG_PROD(TFLITE_LOG_ERROR, "new_axis_mask > 0 is not supported");
return false;
}
if (builtin->ellipsis_mask) {
TFLITE_LOG_PROD(TFLITE_LOG_ERROR, "ellipsis_mask > 0 is not supported");
return false;
}
return true;
}
bool HandleMapOp(vx::delegate::Delegate* delegate,
std::vector<std::shared_ptr<tim::vx::Tensor>>& inputs,
std::vector<std::shared_ptr<tim::vx::Tensor>>& outputs,
const void* params) override {
TFLITE_LOG(TFLITE_LOG_INFO, "Creating StridedSlice op");
const auto builtin =
reinterpret_cast<const TfLiteStridedSliceParams*>(params);
auto input_tensor = inputs[0];
auto begin_tensor = inputs[1];
auto end_tensor = inputs[2];
auto strides_tensor = inputs[3];
auto output_tensor = outputs[0];
auto const& input_shape = input_tensor->GetShape();
int begin_mask = builtin->begin_mask;
int end_mask = builtin->end_mask;
int ellipsis_mask = builtin->ellipsis_mask;
int shrink_axis_mask = builtin->shrink_axis_mask;
std::vector<int32_t> begin_dims(begin_tensor->GetShape()[0]);
begin_tensor->CopyDataFromTensor(begin_dims.data());
for (size_t i = 0; i < begin_dims.size(); i++) {
if (begin_mask & (1 << i)) {
begin_dims[i] = -1;
}
}
std::reverse(begin_dims.begin(), begin_dims.end());
std::vector<int32_t> end_dims(end_tensor->GetShape()[0]);
int32_t end_pos = 1 + std::accumulate(input_shape.begin(),
input_shape.end(),
0,
[](int32_t lhs, int32_t rhs) {
return std::max(lhs, rhs);
});
end_tensor->CopyDataFromTensor(end_dims.data());
for (size_t i = 0; i < end_dims.size(); i++) {
if (end_mask & (1 << i)) {
end_dims[i] = end_pos;
}
}
std::reverse(end_dims.begin(), end_dims.end());
std::vector<int32_t> strides_dims(strides_tensor->GetShape()[0]);
strides_tensor->CopyDataFromTensor(strides_dims.data());
std::reverse(strides_dims.begin(), strides_dims.end());
if (ellipsis_mask) {
TFLITE_LOG_PROD(TFLITE_LOG_WARNING, "ellipsis_mask > 0 is not supported");
} else {
size_t i = begin_dims.size();
for (; i < input_shape.size(); i++) {
begin_dims.insert(begin_dims.begin(), -1);
}
i = end_dims.size();
for (; i < input_shape.size(); i++) {
end_dims.insert(end_dims.begin(), end_pos);
}
i = strides_dims.size();
for (; i < input_shape.size(); i++) {
strides_dims.insert(strides_dims.begin(), -1);
}
}
for (size_t i = 0; i < begin_dims.size(); i++) {
begin_dims[i] = begin_dims[i] == -1 ? 0 : begin_dims[i];
}
for (size_t i = 0; i < end_dims.size(); i++) {
end_dims[i] = end_dims[i] == end_pos ? input_shape[i] : end_dims[i];
end_dims[i] = end_dims[i] > static_cast<int32_t>(input_shape[i])
? input_shape[i]
: end_dims[i];
}
for (size_t i = 0; i < strides_dims.size(); i++) {
strides_dims[i] = strides_dims[i] == -1 ? 1 : strides_dims[i];
}
if (begin_mask) {
int32_t t = 0;
int32_t input_dim = input_shape.size();
for (size_t i = 0; i < input_dim; i++) {
if (begin_mask & (1 << i)) {
t = t | (1 << (input_dim - i - 1));
}
}
begin_mask = t;
}
if (shrink_axis_mask) {
int32_t t = 0;
int32_t input_dim = input_shape.size();
for (size_t i = 0; i < input_dim; i++) {
if (shrink_axis_mask & (1 << i)) {
t = t | (1 << (input_dim - i - 1));
}
}
shrink_axis_mask = t;
}
if (end_mask) {
int32_t t = 0;
int32_t input_dim = input_shape.size();
for (size_t i = 0; i < input_dim; i++) {
if (end_mask & (1 << i)) {
t = t | (1 << (input_dim - i - 1));
}
}
end_mask = t;
}
auto op = delegate->GetGraph()->CreateOperation<tim::vx::ops::StridedSlice>(
begin_dims,
end_dims,
strides_dims,
begin_mask,
end_mask,
shrink_axis_mask);
(*op).BindInput(input_tensor);
(*op).BindOutput(output_tensor);
delegate->GetOps().push_back(std::move(op));
return true;
}
};
struct PadMapper : public OpMapperBase<EmptyStructPlaceholder> {
bool HandleMapOp(vx::delegate::Delegate* delegate,
std::vector<std::shared_ptr<tim::vx::Tensor>>& inputs,
std::vector<std::shared_ptr<tim::vx::Tensor>>& outputs,
const void* params) override {
TFLITE_LOG(TFLITE_LOG_INFO, "Creating Pad op");
auto padding = inputs[1];
std::vector<uint32_t> padding_shape = padding->GetShape();