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Improve GPU delegate graph partitioning logic

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1. Allow offloading computation starting from intermediate nodes of a model in GPU delegate. And when there are multiple partitions, only offload the first largest partition.

2. Unify GetOpsToReplace and GetOpsToReplaceFromGraphWithDequantize into the same function.

RELNOTES: [TFLite] Allow GPU acceleration starting with internal nodes
PiperOrigin-RevId: 300889638
Change-Id: I09da4043532a95a9efb4f167c8af4243c3889a45
This commit is contained in:
Chao Mei 2020-03-13 23:25:31 -07:00 committed by TensorFlower Gardener
parent 01641aee30
commit 3492a0f355
6 changed files with 634 additions and 312 deletions
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2
tensorflow/lite/BUILD
View File

@ -416,6 +416,7 @@ cc_library(
hdrs = ["util.h"],
copts = TFLITE_DEFAULT_COPTS + tflite_copts(),
deps = [
":kernel_api",
"//tensorflow/lite/c:common",
"//tensorflow/lite/schema:schema_fbs",
],
@ -432,6 +433,7 @@ cc_test(
deps = [
":util",
"//tensorflow/lite/c:common",
"//tensorflow/lite/schema:schema_fbs",
"@com_google_googletest//:gtest",
],
)

542
tensorflow/lite/delegates/gpu/common/model_builder.cc
View File

@ -20,9 +20,12 @@ limitations under the License.
#include <algorithm>
#include <cstdint>
#include <cstring>
#include <list>
#include <memory>
#include <set>
#include <string>
#include <unordered_map>
#include <utility>
#include <vector>
#include <fp16.h>
@ -36,6 +39,7 @@ limitations under the License.
#include "tensorflow/lite/c/builtin_op_data.h"
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/context.h"
#include "tensorflow/lite/context_util.h"
#include "tensorflow/lite/delegates/gpu/common/custom_parsers.h"
#include "tensorflow/lite/delegates/gpu/common/data_type.h"
#include "tensorflow/lite/delegates/gpu/common/model.h"
@ -2746,96 +2750,309 @@ std::unique_ptr<TFLiteOperationParser> NewOperationParser(
return absl::make_unique<UnsupportedOperationParser>();
}
// A utility class to remove Dequantize op in the graph.
class DequantizeOpRemover {
Status GetNodeAndRegistration(TfLiteContext* context, int node_id,
TfLiteNode** tflite_node,
TfLiteRegistration** registration) {
if (context->GetNodeAndRegistration(context, node_id, tflite_node,
registration) != kTfLiteOk) {
return InvalidArgumentError(absl::StrCat(
"Couldn't get node and registration info for op: ", node_id));
}
return OkStatus();
}
using IsNodeSupportedFn =
std::function<Status(TfLiteContext*, TfLiteNode*, TfLiteRegistration*)>;
// A utility class to help model graph parition and decide the partition to be
// offloaded to GPU.
// TODO(b/151152967): move the following to lite/delegates/utils
class GraphPartitionHelper {
public:
bool MayAddNode(const TfLiteNode& node, int32_t op_code, int node_id,
const TfLiteTensor* tensors) {
if (op_code == kTfLiteBuiltinDequantize &&
tensors[node.inputs->data[0]].type == TfLiteType::kTfLiteFloat16) {
dequant_nodes_[node.outputs->data[0]] = {node_id, node.inputs->data[0]};
input_tensors_.insert(node.inputs->data[0]);
return true;
GraphPartitionHelper(TfLiteContext* context,
IsNodeSupportedFn is_node_supported_fn)
: is_node_supported_fn_(is_node_supported_fn), context_(context) {}
virtual ~GraphPartitionHelper() { TfLiteIntArrayFree(supported_nodes_); }
// Partitions the graph into multiple subgraphs, each of which is in
// dependency order with others
virtual Status Partition(std::set<std::string>* unsupported_nodes_info) {
RETURN_IF_ERROR(PrepareSupportedNodes(unsupported_nodes_info));
TfLiteDelegateParams* partition_params_array_ = nullptr;
int num_partitions_ = 0;
if (context_->PreviewDelegatePartitioning(context_, supported_nodes_,
&partition_params_array_,
&num_partitions_) != kTfLiteOk) {
return InvalidArgumentError("Unable to preview delegate partition.");
}
return false;
for (int i = 0; i < num_partitions_; ++i) {
partitions_.push_back(partition_params_array_ + i);
}
return OkStatus();
}
// Remap inputs of 'node' to the inputs of the preceding dequant if there's a
// one.
// inputs_from_dequant: records the node id of a dequantize node whose
// output tensor is one of the input tensor of this 'node'.
// orig_inputs: records original input tensor ids of this node if any input is
// remapped.
void MayRemapInputTensors(TfLiteNode* node,
std::vector<int>* inputs_from_dequant,
std::vector<int>* orig_inputs) const {
inputs_from_dequant->clear();
orig_inputs->clear();
if (dequant_nodes_.empty()) return;
// Returns the first n largest partitions or all if #partitions is less than
// 'n'. Note that partitions are ranked according to the number of nodes that
// a partition has, and the returned TfLiteDelegateParams objects are *owned*
// by the TfLite runtime.
std::vector<TfLiteDelegateParams*> GetFirstNLargestPartitions(int n) {
const int total = num_partitions();
// We only sort partitions according to their sizes if necessary.
if (n < total) {
partitions_.sort(CompareTwoPartitions);
}
std::vector<TfLiteDelegateParams*> results;
auto p_it = partitions_.begin();
for (int i = 0; i < std::min(total, n); ++i, ++p_it) {
results.push_back(*p_it);
}
return results;
}
TfLiteIntArray* inputs = node->inputs;
orig_inputs->reserve(inputs->size);
// Fix the node's inputs (i.e. prune out the preceding dequantize node)
// in order to test if it is supported on the GPU.
for (int j = 0; j < inputs->size; ++j) {
const int input_tid = inputs->data[j];
orig_inputs->push_back(input_tid);
const auto it = dequant_nodes_.find(input_tid);
if (it != dequant_nodes_.end()) {
inputs_from_dequant->push_back(it->second.node_id);
// Remap inputs of this node to the inputs of the preceding dequant.
inputs->data[j] = it->second.input_tensor_id;
int num_total_nodes() const { return num_total_nodes_; }
int num_partitions() const { return partitions_.size(); }
private:
static bool CompareTwoPartitions(TfLiteDelegateParams* left,
TfLiteDelegateParams* right) {
// Reverse sort
return left->nodes_to_replace->size > right->nodes_to_replace->size;
}
Status PrepareSupportedNodes(
std::set<std::string>* unsupported_nodes_info = nullptr) {
TfLiteIntArray* execution_plan = nullptr;
if (context_->GetExecutionPlan(context_, &execution_plan) != kTfLiteOk) {
return InvalidArgumentError("Unable to get graph execution plan.");
}
num_total_nodes_ = execution_plan->size;
supported_nodes_ = TfLiteIntArrayCreate(num_total_nodes_);
supported_nodes_->size = 0;
for (int node_id : TfLiteIntArrayView(execution_plan)) {
TfLiteNode* node;
TfLiteRegistration* registration;
auto status =
GetNodeAndRegistration(context_, node_id, &node, &registration);
if (!status.ok()) {
supported_nodes_->size = 0;
return status;
}
status = IsNodeSupported(context_, node, registration, node_id);
if (status.ok()) {
supported_nodes_->data[supported_nodes_->size++] = node_id;
} else if (unsupported_nodes_info) {
unsupported_nodes_info->insert(
absl::StrCat(GetOpNameByRegistration(*registration), ": ",
status.error_message()));
}
}
return OkStatus();
}
// May restore inputs of 'node' to 'orig_inputs' if there're inputs from
// dequant nodes (i.e. denoted by 'inputs_from_dequant'). We will also mark
// such dequantize nodes to be preserved.
void MayRestoreInputTensors(TfLiteNode* node,
const std::vector<int>& inputs_from_dequant,
const std::vector<int>& orig_inputs) {
if (inputs_from_dequant.empty()) return;
// The number of total nodes passed in for partition (i.e. the
// execution_plan size)
int num_total_nodes_ = 0;
for (int j = 0; j < node->inputs->size; ++j) {
node->inputs->data[j] = orig_inputs[j];
}
// Mark those dequantize nodes to be presevered in the graph.
dequant_nodes_to_save_.insert(dequant_nodes_to_save_.end(),
inputs_from_dequant.begin(),
inputs_from_dequant.end());
// Tells whether a node is replaceable.
const IsNodeSupportedFn is_node_supported_fn_;
TfLiteIntArray* supported_nodes_; // owns the memory
protected:
virtual Status IsNodeSupported(TfLiteContext* context, TfLiteNode* node,
TfLiteRegistration* registration,
int node_id) {
return is_node_supported_fn_(context, node, registration);
}
void RemovePreservedNodesFrom(std::vector<int>* nodes) const {
for (const int nid : dequant_nodes_to_save_) {
auto it = std::find(nodes->begin(), nodes->end(), nid);
if (it != nodes->end()) nodes->erase(it);
TfLiteContext* const context_ = nullptr;
// Doesn't own the memory of each TfLiteDelegateParams object as it's
// managed by the TfLite runtime itself. See
// TfLiteContext::PreviewDelegatePartitioning for details.
std::list<TfLiteDelegateParams*> partitions_;
};
class GraphWithDequantPartitionHelper : public GraphPartitionHelper {
public:
GraphWithDequantPartitionHelper(TfLiteContext* context,
IsNodeSupportedFn is_node_supported_fn)
: GraphPartitionHelper(context, std::move(is_node_supported_fn)) {}
Status Partition(std::set<std::string>* unsupported_nodes_info) override {
auto status = GraphPartitionHelper::Partition(unsupported_nodes_info);
// Clean up those partitions that have a single dequant op. NoteThose
// removed dequant ops have to be reserved in the graph and should not be
// delegated.
RemoveSingleDequantNodePartitions();
return status;
}
// Returns a list of node indices of all nodes from the first n largest
// partitions. If there are fewer paritions than n, all nodes will be
// returned. The partition is ranked according to the number of nodes.
std::vector<int> GetNodesOfFirstNLargestPartitions(int n) {
// We first get partitions to reduce the number of nodes to be checked in
// deciding which dequant ops could actually be replaced. And then we
// remap input-tensor to dequant nodes' inputs and remove those
// to-be-reserved dequant nodes.
auto first_nps = GetFirstNLargestPartitions(n);
std::vector<int> ops_to_replace;
for (const auto p : first_nps) {
auto nodes = p->nodes_to_replace;
ops_to_replace.insert(ops_to_replace.end(), nodes->data,
nodes->data + nodes->size);
}
RemapInputTensors(ops_to_replace);
RemoveReservedDequantsFromNodes(&ops_to_replace);
return ops_to_replace;
}
protected:
Status IsNodeSupported(TfLiteContext* context, TfLiteNode* node,
TfLiteRegistration* registration,
int node_id) override {
// If we need to handle dequant nodes, we have to remap input tensors of
// this node if some of them come from a dequant node before testing if
// the node is supported.
std::vector<int> orig_inputs;
if (RecordAndRemapInputTensors(registration->builtin_code, node_id, node,
&orig_inputs)) {
// We have a dequant op here. Note that we retrun an Ok status because a
// dequant node is first added as supported. Later, this dequant node
// will be removed if it has to be preserved in the graph which happens
// when its immediate downstream nodes cannot be supported.
return OkStatus();
}
const auto status = GraphPartitionHelper::IsNodeSupported(
context, node, registration, node_id);
RestoreToOrigInputTensors(node, orig_inputs);
return status;
}
private:
struct NodeInfo {
int node_id;
int input_tensor_id;
};
// Record 'node' if it is a dequant op (i.e. a fp16 one here) and return true.
// When it's not a dequant op, remap its inputs to the inputs of the preceding
// dequant if there's a one and returns false. 'orig_inputs' records original
// input tensor ids of this node if any input is remapped.
bool RecordAndRemapInputTensors(int32_t op_code, int node_id,
TfLiteNode* node,
std::vector<int>* orig_inputs) {
orig_inputs->clear();
// Record the dequant node.
if (op_code == kTfLiteBuiltinDequantize &&
context_->tensors[node->inputs->data[0]].type ==
TfLiteType::kTfLiteFloat16) {
dequant_nodes_[node->outputs->data[0]] = node->inputs->data[0];
return true;
}
// For a dequantize op, there's no need to remap its input tensors.
if (dequant_nodes_.empty()) return false;
RemapInputTensors(node, orig_inputs);
return false;
}
// A map recording dequantize nodes of this graph. A dequantize node is
// identified by the output tensor id. The value is a NodeInfo.
std::unordered_map<int, NodeInfo> dequant_nodes_;
// Restore inputs of 'node' to 'orig_inputs' only if two sizes match.
void RestoreToOrigInputTensors(TfLiteNode* node,
const std::vector<int>& orig_inputs) {
if (node->inputs->size != orig_inputs.size()) return;
for (int j = 0; j < node->inputs->size; ++j) {
node->inputs->data[j] = orig_inputs[j];
}
}
// A set of input tensor ids of dequantize nodes.
std::set<int> input_tensors_;
// Remap input tensors of every node in 'nodes' (i.e. node indices) if some of
// them are from dequant ops.
void RemapInputTensors(const std::vector<int>& nodes) const {
for (int node_id : nodes) {
TfLiteNode* node;
TfLiteRegistration* registration;
GetNodeAndRegistration(context_, node_id, &node, &registration)
.IgnoreError();
RemapInputTensors(node, nullptr /* orig_inputs*/);
}
}
// The node ids of dequantize nodes that has to be preserved in the graph.
std::vector<int> dequant_nodes_to_save_;
void RemoveSingleDequantNodePartitions() {
auto it = partitions_.begin();
while (it != partitions_.end()) {
auto p = *it;
if (p->nodes_to_replace->size != 1) {
++it;
continue;
}
int node_id = p->nodes_to_replace->data[0];
TfLiteNode* node = nullptr;
TfLiteRegistration* registration = nullptr;
GetNodeAndRegistration(context_, node_id, &node, &registration)
.IgnoreError();
if (registration->builtin_code != kTfLiteBuiltinDequantize) {
++it;
continue;
}
// Note such dequant nodes have to be preserved in the graph as dequant
// ops are not actually supported in the GPU delegate.
dequant_nodes_to_save_.insert(node_id);
it = partitions_.erase(it);
}
}
void RemoveReservedDequantsFromNodes(std::vector<int>* nodes) {
if (dequant_nodes_to_save_.empty()) return;
auto it = nodes->begin();
while (it != nodes->end()) {
if (dequant_nodes_to_save_.find(*it) == dequant_nodes_to_save_.end()) {
++it;
continue;
}
it = nodes->erase(it);
}
}
// Remap input tensors of a single 'node' if some of come from a dequant op.
// If 'orig_inputs' isn't nullptr, it records original input tensor ids of
// this node if any input is remapped.
void RemapInputTensors(TfLiteNode* node,
std::vector<int>* orig_inputs) const {
TfLiteIntArray* inputs = node->inputs;
auto inputs_view = TfLiteIntArrayView(inputs);
// Prepopulate 'orig_inputs' first and clear it if there's no input from a
// dequant op.
if (orig_inputs) {
orig_inputs->clear();
orig_inputs->reserve(inputs->size);
for (auto tid : inputs_view) {
orig_inputs->push_back(tid);
}
}
// Fix this node's inputs (i.e. prune out the preceding dequantize node) in
// order to test if it is supported.
bool is_remapped = false;
for (int j = 0; j < inputs->size; ++j) {
const int input_tid = inputs->data[j];
const auto it = dequant_nodes_.find(input_tid);
if (it != dequant_nodes_.end()) {
inputs->data[j] = it->second;
is_remapped = true;
}
}
if (!is_remapped && orig_inputs) orig_inputs->clear();
}
// A map recording dequantize nodes's input/output tensors of this selected
// graph. The key is the output tensor id, and the value is the input tensor
// id.
std::unordered_map<int, int> dequant_nodes_;
// A set of dequant nodes as in node indices that have to be preserved in the
// graph.
std::set<int> dequant_nodes_to_save_;
};
} // namespace
Status ConvertTfLiteTensorToTensorRef(const TfLiteTensor& tflite_tensor,
TensorRef<BHWC>* tensor_ref) {
tensor_ref->type = ToDataType(tflite_tensor.type);
return ExtractTensorShape(tflite_tensor, &tensor_ref->shape);
}
Status IsSupported(const TfLiteContext* context, TfLiteNode* node,
const TfLiteRegistration* registration) {
@ -2856,158 +3073,63 @@ bool IsAllFloatTensors(const TfLiteContext* context,
return true;
}
std::string GetOpNameByRegistration(const TfLiteRegistration* registration) {
auto op = registration->builtin_code;
std::string result =
EnumNameBuiltinOperator(static_cast<BuiltinOperator>(op));
if (op == kTfLiteBuiltinCustom) {
result += " " + std::string(registration->custom_name);
}
return result;
}
} // namespace
Status GetNodeAndRegistration(TfLiteContext* context, int node_id,
TfLiteNode** tflite_node,
TfLiteRegistration** registration) {
if (context->GetNodeAndRegistration(context, node_id, tflite_node,
registration) != kTfLiteOk) {
return InvalidArgumentError(absl::StrCat(
"Couldn't get node and registration info for op: ", node_id));
}
return OkStatus();
}
TfLiteIntArray* GetOpsToReplaceFromGraphWithDequantize(TfLiteContext* context) {
TfLiteIntArray* execution_plan = nullptr;
if (context->GetExecutionPlan(context, &execution_plan) != kTfLiteOk) {
context->ReportError(context, "Unable to get graph execution plan.");
return nullptr;
}
std::set<std::string> errors;
std::vector<int> ops_to_replace;
DequantizeOpRemover dequant_remover;
for (int i = 0; i < execution_plan->size; ++i) {
const int node_id = execution_plan->data[i];
TfLiteNode* node = nullptr;
TfLiteRegistration* registration = nullptr;
auto status =
GetNodeAndRegistration(context, node_id, &node, &registration);
if (!status.ok()) {
context->ReportError(context, status.error_message().c_str());
return nullptr;
}
if (dequant_remover.MayAddNode(*node, registration->builtin_code, node_id,
context->tensors)) {
// For now, add the node to the list of ops to replace.
ops_to_replace.push_back(node_id);
continue;
}
// Record the node id of a dequantize node whose output tensor is one of the
// input tensor of this node.
std::vector<int> inputs_from_dequant;
// Original input tensor ids of this node.
std::vector<int> orig_inputs;
dequant_remover.MayRemapInputTensors(node, &inputs_from_dequant,
&orig_inputs);
status = IsSupported(context, node, registration);
if (status.ok() &&
// TODO(eignasheva): resolve sub operation support for metal delegate
// registration->builtin_code != kTfLiteBuiltinSub &&
IsAllFloatTensors(context, node->inputs) &&
IsAllFloatTensors(context, node->outputs) && errors.empty()) {
// Node is supported and there were no previous errors.
ops_to_replace.push_back(node_id);
} else {
// The node is not replaceable, record an error message.
errors.insert(absl::StrCat(GetOpNameByRegistration(registration), ": ",
status.error_message()));
dequant_remover.MayRestoreInputTensors(node, inputs_from_dequant,
orig_inputs);
}
}
if (!errors.empty()) {
std::string unsupported = absl::StrJoin(errors, "\n");
std::string error_message =
"Next operations are not supported by GPU delegate:\n" + unsupported +
"\nFirst " + std::to_string(ops_to_replace.size()) +
" operations will run on the GPU, and the remaining " +
std::to_string(execution_plan->size - ops_to_replace.size()) +
" on the CPU.";
context->ReportError(context, error_message.c_str());
}
dequant_remover.RemovePreservedNodesFrom(&ops_to_replace);
return ConvertVectorToTfLiteIntArray(ops_to_replace);
Status ConvertTfLiteTensorToTensorRef(const TfLiteTensor& tflite_tensor,
TensorRef<BHWC>* tensor_ref) {
tensor_ref->type = ToDataType(tflite_tensor.type);
return ExtractTensorShape(tflite_tensor, &tensor_ref->shape);
}
// TODO(impjdi): Check number of input/output tensors and their dimensions.
// TODO(impjdi): Check ops' parameters.
TfLiteIntArray* GetOpsToReplace(TfLiteContext* context) {
TfLiteIntArray* execution_plan = nullptr;
if (context->GetExecutionPlan(context, &execution_plan) != kTfLiteOk) {
context->ReportError(context, "Unable to get graph execution plan.");
IsNodeSupportedFn node_supported_fn =
[=](TfLiteContext* context, TfLiteNode* node,
TfLiteRegistration* registration) -> Status {
RETURN_IF_ERROR(IsSupported(context, node, registration));
return (IsAllFloatTensors(context, node->inputs) &&
IsAllFloatTensors(context, node->outputs))
? OkStatus()
: FailedPreconditionError(
"OP is supported, but tensor type isn't matched!");
};
GraphWithDequantPartitionHelper partition_helper(context, node_supported_fn);
std::set<std::string> unsupported_nodes_info;
auto status = partition_helper.Partition(&unsupported_nodes_info);
if (!status.ok()) {
TF_LITE_KERNEL_LOG(context, status.error_message().c_str());
return nullptr;
}
// Dispatch to another function if graph has Dequantize nodes.
for (int i = 0; i < execution_plan->size; ++i) {
const int node_id = execution_plan->data[i];
TfLiteNode* node = nullptr;
TfLiteRegistration* registration = nullptr;
auto status =
GetNodeAndRegistration(context, node_id, &node, &registration);
if (!status.ok()) {
context->ReportError(context, status.error_message().c_str());
return nullptr;
}
if (registration->builtin_code == kTfLiteBuiltinDequantize &&
context->tensors[node->inputs->data[0]].type ==
TfLiteType::kTfLiteFloat16) {
return GetOpsToReplaceFromGraphWithDequantize(context);
}
}
// We simply get 1st largest partition, but we could later explore whether
// getting more partitions could lead to better performance, i.e. by
// parameterizing '1' here.
std::vector<int> ops_to_replace =
partition_helper.GetNodesOfFirstNLargestPartitions(1);
// No Dequantize nodes. Iterate through graph and find ops to replace.
TfLiteIntArray* subgraph = TfLiteIntArrayCreate(execution_plan->size);
subgraph->size = 0;
std::set<std::string> errors;
for (int i = 0; i < execution_plan->size; ++i) {
const int node_id = execution_plan->data[i];
TfLiteNode* node;
TfLiteRegistration* registration;
auto status =
GetNodeAndRegistration(context, node_id, &node, &registration);
if (!status.ok()) {
context->ReportError(context, status.error_message().c_str());
return nullptr;
}
status = IsSupported(context, node, registration);
if (status.ok() &&
// TODO(eignasheva): resolve sub operation support for metal delegate
// registration->builtin_code != kTfLiteBuiltinSub &&
IsAllFloatTensors(context, node->inputs) &&
IsAllFloatTensors(context, node->outputs)) {
if (errors.empty()) subgraph->data[subgraph->size++] = node_id;
if (!unsupported_nodes_info.empty()) {
std::string unsupported = absl::StrJoin(unsupported_nodes_info, "\n");
std::string error_message = absl::StrCat(
"Following operations are not supported by GPU delegate:\n",
unsupported, "\n");
if (!ops_to_replace.empty()) {
absl::StrAppendFormat(
&error_message,
"%d operations will run on the GPU (first node: "
"%d, last node: %d), and the remaining %d",
ops_to_replace.size(), ops_to_replace.front(), ops_to_replace.back(),
partition_helper.num_total_nodes() - ops_to_replace.size());
} else {
errors.insert(absl::StrCat(GetOpNameByRegistration(registration), ": ",
status.error_message()));
absl::StrAppend(&error_message,
"No operations will run on the GPU, and all ",
partition_helper.num_total_nodes());
}
absl::StrAppend(&error_message, " operations will run on the CPU.");
TF_LITE_KERNEL_LOG(context, error_message.c_str());
}
if (!errors.empty()) {
std::string unsupported = absl::StrJoin(errors, "\n");
std::string error_message =
"Next operations are not supported by GPU delegate:\n" + unsupported +
"\nFirst " + std::to_string(subgraph->size) +
" operations will run on the GPU, and the remaining " +
std::to_string(execution_plan->size - subgraph->size) + " on the CPU.";
context->ReportError(context, error_message.c_str());
}
return subgraph;
return ConvertVectorToTfLiteIntArray(ops_to_replace);
}
Status BuildModel(TfLiteContext* context,
@ -3047,7 +3169,7 @@ Status BuildModel(TfLiteContext* context,
const auto status =
operations[i]->Parse(tflite_node, registration, graph, &reader);
if (!status.ok()) {
return InternalError(absl::StrCat(GetOpNameByRegistration(registration),
return InternalError(absl::StrCat(GetOpNameByRegistration(*registration),
": ", status.error_message()));
}
}

360
tensorflow/lite/delegates/gpu/common/model_builder_test.cc
View File

@ -120,9 +120,52 @@ TEST(ModelBuilderTest, ConvertTfLiteTensorToTensorRefFailsForRankGT3) {
EXPECT_FALSE(status.ok());
}
class InterpreterFp16 {
class DelegatedInterpreter {
public:
explicit InterpreterFp16(TfLiteBuiltinOperator op) {
explicit DelegatedInterpreter(int num_nodes) {
exec_plan_ = TfLiteIntArrayCreate(num_nodes);
}
virtual ~DelegatedInterpreter() {
TfLiteIntArrayFree(exec_plan_);
for (auto params : delegate_params_) {
TfLiteIntArrayFree(params.nodes_to_replace);
TfLiteIntArrayFree(params.input_tensors);
TfLiteIntArrayFree(params.output_tensors);
}
}
// Get the TfLiteContext to be mocked for swapping out functions that have to
// be called inside delegate (i.e. in delegat kernel mode).
TfLiteContext* context() { return interpreter_.primary_subgraph().context(); }
std::vector<std::pair<TfLiteNode, TfLiteRegistration>>&
nodes_and_registration() {
return interpreter_.primary_subgraph().nodes_and_registration();
}
TfLiteIntArray* exec_plan() const { return exec_plan_; }
TfLiteDelegateParams* add_delegate_params() {
delegate_params_.push_back(TfLiteDelegateParams());
return &delegate_params_.back();
}
TfLiteDelegateParams* delegate_params() { return &delegate_params_.front(); }
int num_delegate_params() { return delegate_params_.size(); }
protected:
Interpreter interpreter_;
private:
// The manually-set execution plan for this delegated interpreter.
TfLiteIntArray* exec_plan_;
// The TfLiteDelegateParams object that's manually populated inside the mocked
// TfLiteContext::PreviewDelegatePartitioning.
std::vector<TfLiteDelegateParams> delegate_params_;
};
class InterpreterFp16 : public DelegatedInterpreter {
public:
explicit InterpreterFp16(TfLiteBuiltinOperator op) : DelegatedInterpreter(3) {
void* builtin_data = malloc(sizeof(int));
EXPECT_EQ(interpreter_.AddTensors(5), kTfLiteOk);
EXPECT_EQ(interpreter_.SetInputs({0, 1}), kTfLiteOk);
@ -187,22 +230,23 @@ class InterpreterFp16 {
3, TfLiteType::kTfLiteFloat32, "t3", dims, quantization, false),
kTfLiteOk);
exec_plan_ = TfLiteIntArrayCreate(3);
exec_plan_->data[0] = 0;
exec_plan_->data[1] = 1;
exec_plan_->data[2] = 2;
exec_plan()->data[0] = 0;
exec_plan()->data[1] = 1;
exec_plan()->data[2] = 2;
}
~InterpreterFp16() { TfLiteIntArrayFree(exec_plan_); }
Subgraph* GetSubgraph() { return interpreter_.subgraph(0); }
TfLiteIntArray* exec_plan() const { return exec_plan_; }
private:
Interpreter interpreter_;
TfLiteIntArray* exec_plan_;
};
// **NOTE**: we have several interpreter instances created at global scope to
// test *exactly* the GetOpsToReplace function alone, and not the sequence of
// function calls that includes GetOpsToReplace when calling
// ModifyGraphWithDelegate. A TfLiteContext is needed to test GetOpsToReplace,
// but TfLiteContexts intentionally make it difficult to call certain functions
// in a non-delegate context (see tensorflow/lite/subgraph/subgraph.cc for
// details) We create our own GetExecutionPlan, GetNodeAndRegistration and
// PreviewDelegatePartitioning lambdas inside each test, but we can't use local
// captures without changing the function signature. Therefore, this test data
// lives at global scope in order to be accessible inside the lambda.
InterpreterFp16* interpreter_fp16_add_op =
new InterpreterFp16(kTfLiteBuiltinAdd);
@ -218,7 +262,8 @@ TEST(ModelBuilderTest, GetOpsToReplacePrunesFp16DequantizeNodes) {
// t0 (FP16) --> Add -> t4
// t2 (FP16) --/
//
TfLiteContext* context = interpreter_fp16_add_op->GetSubgraph()->context();
TfLiteContext* context = interpreter_fp16_add_op->context();
// These functions are meant to be called inside delegates. Swap out
// for similar functions to permit direct calling of GetOpsToReplace.
context->GetExecutionPlan = [](struct TfLiteContext* context,
@ -229,12 +274,30 @@ TEST(ModelBuilderTest, GetOpsToReplacePrunesFp16DequantizeNodes) {
context->GetNodeAndRegistration = [](struct TfLiteContext*, int node_index,
TfLiteNode** node,
TfLiteRegistration** registration) {
auto& node_and_reg = interpreter_fp16_add_op->GetSubgraph()
->nodes_and_registration()[node_index];
auto& node_and_reg =
interpreter_fp16_add_op->nodes_and_registration()[node_index];
*node = &node_and_reg.first;
*registration = &node_and_reg.second;
return kTfLiteOk;
};
context->PreviewDelegatePartitioning =
[](struct TfLiteContext* context, const TfLiteIntArray* nodes_to_replace,
TfLiteDelegateParams** partition_params_array, int* num_partitions) {
auto params = interpreter_fp16_add_op->add_delegate_params();
params->nodes_to_replace = TfLiteIntArrayCreate(3);
params->nodes_to_replace->data[0] = 0;
params->nodes_to_replace->data[1] = 1;
params->nodes_to_replace->data[2] = 2;
params->input_tensors = TfLiteIntArrayCreate(2);
params->input_tensors->data[0] = 0;
params->input_tensors->data[1] = 2;
params->output_tensors = TfLiteIntArrayCreate(1);
params->output_tensors->data[0] = 4;
*partition_params_array = interpreter_fp16_add_op->delegate_params();
*num_partitions = interpreter_fp16_add_op->num_delegate_params();
return kTfLiteOk;
};
TfLiteIntArray* ops_to_replace = GetOpsToReplace(context);
@ -251,17 +314,6 @@ TEST(ModelBuilderTest, GetOpsToReplacePrunesFp16DequantizeNodes) {
TfLiteIntArrayFree(ops_to_replace);
}
// This interpreter instance is created at global scope to test *exactly*
// the GetOpsToReplace function alone, and not the sequence of function calls
// that includes GetOpsToReplace when calling ModifyGraphWithDelegate.
// A TfLiteContext is needed to test GetOpsToReplace, but TfLiteContexts
// intentionally make it difficult to call certain functions in a
// non-delegate context (see tensorflow/lite/subgraph/subgraph.cc for details)
// We create our own GetExecutionPlan and GetNodeAndRegistration lambdas
// inside each test, but we can't use local captures without changing the
// function signature. Therefore, this test data lives at global scope
// in order to be accessible inside the lambda.
InterpreterFp16* interpreter_fp16_gt_op =
new InterpreterFp16(kTfLiteBuiltinGreater);
@ -274,7 +326,7 @@ TEST(ModelBuilderTest, GetOpsToReplaceKeepsFp16DequantizeNodes) {
// Because there is no GPU equivalent for the Greater op, we don't prune
// the Dequantize nodes.
TfLiteContext* context = interpreter_fp16_gt_op->GetSubgraph()->context();
TfLiteContext* context = interpreter_fp16_gt_op->context();
// These functions are meant to be called inside delegates. Swap out
// for similar functions to permit direct calling of GetOpsToReplace.
context->GetExecutionPlan = [](struct TfLiteContext* context,
@ -285,12 +337,37 @@ TEST(ModelBuilderTest, GetOpsToReplaceKeepsFp16DequantizeNodes) {
context->GetNodeAndRegistration = [](struct TfLiteContext*, int node_index,
TfLiteNode** node,
TfLiteRegistration** registration) {
auto& node_and_reg = interpreter_fp16_gt_op->GetSubgraph()
->nodes_and_registration()[node_index];
auto& node_and_reg =
interpreter_fp16_gt_op->nodes_and_registration()[node_index];
*node = &node_and_reg.first;
*registration = &node_and_reg.second;
return kTfLiteOk;
};
context->PreviewDelegatePartitioning =
[](struct TfLiteContext* context, const TfLiteIntArray* nodes_to_replace,
TfLiteDelegateParams** partition_params_array, int* num_partitions) {
auto params = interpreter_fp16_gt_op->add_delegate_params();
// First partition for DequantNode (t0->t1)
params->nodes_to_replace = TfLiteIntArrayCreate(1);
params->nodes_to_replace->data[0] = 0;
params->input_tensors = TfLiteIntArrayCreate(1);
params->input_tensors->data[0] = 0;
params->output_tensors = TfLiteIntArrayCreate(1);
params->output_tensors->data[0] = 1;
// Second partition for DequantNode (t2->t3)
params = interpreter_fp16_add_op->add_delegate_params();
params->nodes_to_replace = TfLiteIntArrayCreate(1);
params->nodes_to_replace->data[0] = 0;
params->input_tensors = TfLiteIntArrayCreate(1);
params->input_tensors->data[0] = 0;
params->output_tensors = TfLiteIntArrayCreate(1);
params->output_tensors->data[0] = 1;
*partition_params_array = interpreter_fp16_gt_op->delegate_params();
*num_partitions = interpreter_fp16_gt_op->num_delegate_params();
return kTfLiteOk;
};
TfLiteIntArray* ops_to_replace = GetOpsToReplace(context);
@ -309,9 +386,9 @@ TEST(ModelBuilderTest, GetOpsToReplaceKeepsFp16DequantizeNodes) {
TfLiteIntArrayFree(ops_to_replace);
}
class InterpreterFp32 {
class InterpreterFp32 : public DelegatedInterpreter {
public:
InterpreterFp32() {
InterpreterFp32() : DelegatedInterpreter(2) {
void* builtin_data = malloc(sizeof(int));
EXPECT_EQ(interpreter_.AddTensors(4), kTfLiteOk);
EXPECT_EQ(interpreter_.SetInputs({0, 2}), kTfLiteOk);
@ -363,34 +440,24 @@ class InterpreterFp32 {
interpreter_.SetTensorParametersReadWrite(
2, TfLiteType::kTfLiteFloat32, "t2", dims, quantization, false),
kTfLiteOk);
exec_plan_ = TfLiteIntArrayCreate(2);
exec_plan_->data[0] = 0;
exec_plan_->data[1] = 1;
exec_plan()->data[0] = 0;
exec_plan()->data[1] = 1;
}
~InterpreterFp32() { TfLiteIntArrayFree(exec_plan_); }
Subgraph* GetSubgraph() { return interpreter_.subgraph(0); }
TfLiteIntArray* exec_plan() const { return exec_plan_; }
private:
Interpreter interpreter_;
TfLiteIntArray* exec_plan_;
};
InterpreterFp32* interpreter_fp32 = new InterpreterFp32();
TEST(ModelBuilderTest, GetOpsToReplaceDoesNotPruneUint8) {
// A graph with a Dequant node with uint8 input
// is not pruned. The delegate will attempt to replace it
// with a GPU op, but this op is currently not supported on
// the GPU. Therefore, the Dequant op and all downstream ops
// will be scheduled to run on the CPU.
// A graph with a Dequant node with uint8 input is not pruned. As this op is
// currently not supported on the GPU. Therefore, the Dequant op will be
// scheduled to run on the CPU while the remaining supported op Add on the
// GPU.
//
// t0 (uint8) --> Dequant --> t1 (FP32) --> Add -> t3
// t2 (FP32) --/
//
TfLiteContext* context = interpreter_fp32->GetSubgraph()->context();
TfLiteContext* context = interpreter_fp32->context();
// These functions are meant to be called inside delegates. Swap out
// for similar functions to permit direct calling of GetOpsToReplace.
@ -402,24 +469,43 @@ TEST(ModelBuilderTest, GetOpsToReplaceDoesNotPruneUint8) {
context->GetNodeAndRegistration = [](struct TfLiteContext*, int node_index,
TfLiteNode** node,
TfLiteRegistration** registration) {
auto& node_and_reg =
interpreter_fp32->GetSubgraph()->nodes_and_registration()[node_index];
auto& node_and_reg = interpreter_fp32->nodes_and_registration()[node_index];
*node = &node_and_reg.first;
*registration = &node_and_reg.second;
return kTfLiteOk;
};
context->PreviewDelegatePartitioning =
[](struct TfLiteContext* context, const TfLiteIntArray* nodes_to_replace,
TfLiteDelegateParams** partition_params_array, int* num_partitions) {
auto params = interpreter_fp32->add_delegate_params();
params->nodes_to_replace = TfLiteIntArrayCreate(1);
params->nodes_to_replace->data[0] = 1;
params->input_tensors = TfLiteIntArrayCreate(2);
params->input_tensors->data[0] = 1;
params->input_tensors->data[1] = 2;
params->output_tensors = TfLiteIntArrayCreate(1);
params->output_tensors->data[0] = 3;
*partition_params_array = interpreter_fp32->delegate_params();
*num_partitions = interpreter_fp32->num_delegate_params();
return kTfLiteOk;
};
TfLiteIntArray* ops_to_replace = GetOpsToReplace(context);
// No ops are run on the GPU, since the Dequant op is not pruned and must run
// on the CPU.
EXPECT_EQ(ops_to_replace->size, 0);
// As the Dequant op is not pruned and the ADD op could run on GPU, we have
// 1 partition.
EXPECT_EQ(ops_to_replace->size, 1);
// ADD at index 1.
EXPECT_EQ(1, ops_to_replace->data[0]);
TfLiteIntArrayFree(ops_to_replace);
}
class InterpreterMultiNode {
class InterpreterMultiNode : public DelegatedInterpreter {
public:
explicit InterpreterMultiNode(bool add_op_first = true) {
explicit InterpreterMultiNode(bool add_op_first = true)
: DelegatedInterpreter(5) {
void* builtin_data = malloc(sizeof(int));
EXPECT_EQ(interpreter_.AddTensors(8), kTfLiteOk);
EXPECT_EQ(interpreter_.SetInputs({0, 1, 2}), kTfLiteOk);
@ -552,38 +638,29 @@ class InterpreterMultiNode {
interpreter_.SetTensorParametersReadWrite(
7, TfLiteType::kTfLiteFloat32, "t5", dims, quantization, false),
kTfLiteOk);
exec_plan_ = TfLiteIntArrayCreate(5);
exec_plan_->data[0] = 0;
exec_plan_->data[1] = 1;
exec_plan_->data[2] = 2;
exec_plan_->data[3] = 3;
exec_plan_->data[4] = 4;
exec_plan()->data[0] = 0;
exec_plan()->data[1] = 1;
exec_plan()->data[2] = 2;
exec_plan()->data[3] = 3;
exec_plan()->data[4] = 4;
}
~InterpreterMultiNode() { TfLiteIntArrayFree(exec_plan_); }
Subgraph* GetSubgraph() { return interpreter_.subgraph(0); }
TfLiteIntArray* exec_plan() const { return exec_plan_; }
private:
Interpreter interpreter_;
TfLiteIntArray* exec_plan_;
};
InterpreterMultiNode* interpreter_mn = new InterpreterMultiNode();
TEST(ModelBuilderTest, GetOpsToReplaceSelectsCorrectDequants) {
TEST(ModelBuilderTest, GetOpsToReplaceSelectsCorrectDequantsAddFirst) {
// A graph with three Dequant nodes feeding two ops, 'Add' and 'Greater'.
// 'Add' can be replaced by the GPU delegate, but 'Greater' can not.
// t0 (FP16) --> Dequant --> t3 (FP32) --> Greater -> t6
// t1 (FP16) --> Dequant --> t4 (FP32) --/
// --\
// t3 (FP16) --> Dequant --> t5 (FP32) --> Add -> t7
// t0 (FP16) --> Dequant(0) --> t3 (FP32) --> Greater(4) -> t6
// t1 (FP16) --> Dequant(1) --> t4 (FP32) --/
// --\
// t3 (FP16) --> Dequant(2) --> t5 (FP32) --> Add(3) -> t7
//
// OpsToReplace should replace the 'Add' op and the Dequant outputing
// t5, but leave the other Dequant nodes because 'Greater' must run
// on the CPU.
TfLiteContext* context = interpreter_mn->GetSubgraph()->context();
TfLiteContext* context = interpreter_mn->context();
// These functions are meant to be called inside delegates. Swap out
// for similar functions to permit direct calling of GetOpsToReplace.
@ -595,12 +672,48 @@ TEST(ModelBuilderTest, GetOpsToReplaceSelectsCorrectDequants) {
context->GetNodeAndRegistration = [](struct TfLiteContext*, int node_index,
TfLiteNode** node,
TfLiteRegistration** registration) {
auto& node_and_reg =
interpreter_mn->GetSubgraph()->nodes_and_registration()[node_index];
auto& node_and_reg = interpreter_mn->nodes_and_registration()[node_index];
*node = &node_and_reg.first;
*registration = &node_and_reg.second;
return kTfLiteOk;
};
context->PreviewDelegatePartitioning =
[](struct TfLiteContext* context, const TfLiteIntArray* nodes_to_replace,
TfLiteDelegateParams** partition_params_array, int* num_partitions) {
auto params = interpreter_mn->add_delegate_params();
// First partition for DequantNode(0)
params->nodes_to_replace = TfLiteIntArrayCreate(1);
params->nodes_to_replace->data[0] = 0;
params->input_tensors = TfLiteIntArrayCreate(1);
params->input_tensors->data[0] = 0;
params->output_tensors = TfLiteIntArrayCreate(1);
params->output_tensors->data[0] = 3;
// Second partition for DequantNode(1)
params = interpreter_mn->add_delegate_params();
params->nodes_to_replace = TfLiteIntArrayCreate(1);
params->nodes_to_replace->data[0] = 1;
params->input_tensors = TfLiteIntArrayCreate(1);
params->input_tensors->data[0] = 1;
params->output_tensors = TfLiteIntArrayCreate(1);
params->output_tensors->data[0] = 4;
// Third partition for DequantNode(1), DequantNode(2), ADD(3)
params = interpreter_mn->add_delegate_params();
params->nodes_to_replace = TfLiteIntArrayCreate(3);
params->nodes_to_replace->data[0] = 1;
params->nodes_to_replace->data[1] = 2;
params->nodes_to_replace->data[2] = 3;
params->input_tensors = TfLiteIntArrayCreate(2);
params->input_tensors->data[0] = 1;
params->input_tensors->data[0] = 3;
params->output_tensors = TfLiteIntArrayCreate(1);
params->output_tensors->data[0] = 7;
*partition_params_array = interpreter_mn->delegate_params();
*num_partitions = interpreter_mn->num_delegate_params();
return kTfLiteOk;
};
TfLiteIntArray* ops_to_replace = GetOpsToReplace(context);
@ -624,19 +737,22 @@ TEST(ModelBuilderTest, GetOpsToReplaceSelectsCorrectDequants) {
InterpreterMultiNode* interpreter_mn2 =
new InterpreterMultiNode(/*add_op_first=*/false);
TEST(ModelBuilderTest, GetOpsToReplaceRestoresInputsOnErrors) {
// A graph with three Dequant nodes feeding two ops, 'Greater' and 'Add'.
TEST(ModelBuilderTest, GetOpsToReplaceSelectsCorrectDequantsGreaterFirst) {
// A graph with three Dequant nodes feeding two ops, 'Add' and 'Greater'.
// 'Add' can be replaced by the GPU delegate, but 'Greater' can not.
// t0 (FP16) --> Dequant --> t3 (FP32) --> Greater -> t6
// t1 (FP16) --> Dequant --> t4 (FP32) --/
// --\
// t3 (FP16) --> Dequant --> t5 (FP32) --> Add -> t7
// t0 (FP16) --> Dequant(0) --> t3 (FP32) --> Greater(3) -> t6
// t1 (FP16) --> Dequant(1) --> t4 (FP32) --/
// --\
// t3 (FP16) --> Dequant(2) --> t5 (FP32) --> Add(4) -> t7
//
// 'Greater' comes first in the execution plan though, so Add should not
// be scheduled to run on the Gpu. Further, it's inputs should remain t4
// and t5.
TfLiteContext* context = interpreter_mn->GetSubgraph()->context();
// Note: the graph dependency is exactly same w/ that in
// GetOpsToReplaceSelectsCorrectDequantsAddFirst, but the unsupported
// 'Greater' op appears first in the execution plan. Despite this,
// OpsToReplace should still replace the 'Add' op and the Dequant outputing
// t5, but leave the other Dequant nodes because 'Greater' must run
// on the CPU.
TfLiteContext* context = interpreter_mn2->context();
// These functions are meant to be called inside delegates. Swap out
// for similar functions to permit direct calling of GetOpsToReplace.
@ -648,27 +764,67 @@ TEST(ModelBuilderTest, GetOpsToReplaceRestoresInputsOnErrors) {
context->GetNodeAndRegistration = [](struct TfLiteContext*, int node_index,
TfLiteNode** node,
TfLiteRegistration** registration) {
auto& node_and_reg =
interpreter_mn2->GetSubgraph()->nodes_and_registration()[node_index];
auto& node_and_reg = interpreter_mn2->nodes_and_registration()[node_index];
*node = &node_and_reg.first;
*registration = &node_and_reg.second;
return kTfLiteOk;
};
context->PreviewDelegatePartitioning =
[](struct TfLiteContext* context, const TfLiteIntArray* nodes_to_replace,
TfLiteDelegateParams** partition_params_array, int* num_partitions) {
auto params = interpreter_mn2->add_delegate_params();
// First partition for DequantNode(0)
params->nodes_to_replace = TfLiteIntArrayCreate(1);
params->nodes_to_replace->data[0] = 0;
params->input_tensors = TfLiteIntArrayCreate(1);
params->input_tensors->data[0] = 0;
params->output_tensors = TfLiteIntArrayCreate(1);
params->output_tensors->data[0] = 3;
// Second partition for DequantNode(1)
params = interpreter_mn2->add_delegate_params();
params->nodes_to_replace = TfLiteIntArrayCreate(1);
params->nodes_to_replace->data[0] = 1;
params->input_tensors = TfLiteIntArrayCreate(1);
params->input_tensors->data[0] = 1;
params->output_tensors = TfLiteIntArrayCreate(1);
params->output_tensors->data[0] = 4;
// Third partition for DequantNode(1), DequantNode(2), ADD(4)
params = interpreter_mn2->add_delegate_params();
params->nodes_to_replace = TfLiteIntArrayCreate(3);
params->nodes_to_replace->data[0] = 1;
params->nodes_to_replace->data[1] = 2;
params->nodes_to_replace->data[2] = 4;
params->input_tensors = TfLiteIntArrayCreate(2);
params->input_tensors->data[0] = 1;
params->input_tensors->data[0] = 3;
params->output_tensors = TfLiteIntArrayCreate(1);
params->output_tensors->data[0] = 7;
*partition_params_array = interpreter_mn2->delegate_params();
*num_partitions = interpreter_mn2->num_delegate_params();
return kTfLiteOk;
};
TfLiteIntArray* ops_to_replace = GetOpsToReplace(context);
// Verify that no ops will be replaced.
EXPECT_EQ(ops_to_replace->size, 0);
EXPECT_EQ(ops_to_replace->size, 2);
// Op at index 2 is the Dequant op (t3 -> t5).
EXPECT_EQ(ops_to_replace->data[0], 2);
// Op at index 4 is the Add op.
EXPECT_EQ(ops_to_replace->data[1], 4);
TfLiteNode* node = nullptr;
TfLiteRegistration* registration = nullptr;
// Verify that Add op has fp32 inputs.
context->GetNodeAndRegistration(context, 4, &node, &registration);
EXPECT_EQ(registration->builtin_code, kTfLiteBuiltinAdd);
// Verify that Add op has fp16 inputs.
context->GetNodeAndRegistration(context, ops_to_replace->data[1], &node,
&registration);
EXPECT_EQ(context->tensors[node->inputs->data[0]].type,
TfLiteType::kTfLiteFloat32);
TfLiteType::kTfLiteFloat16);
EXPECT_EQ(context->tensors[node->inputs->data[1]].type,
TfLiteType::kTfLiteFloat32);
TfLiteType::kTfLiteFloat16);
TfLiteIntArrayFree(ops_to_replace);
}

12
tensorflow/lite/util.cc
View File

@ -17,6 +17,7 @@ limitations under the License.
#include <complex>
#include <cstring>
#include "tensorflow/lite/builtin_ops.h"
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/schema/schema_generated.h"
@ -131,4 +132,15 @@ bool IsUnresolvedCustomOp(const TfLiteRegistration& registration) {
registration.invoke == &UnresolvedOpInvoke;
}
std::string GetOpNameByRegistration(const TfLiteRegistration& registration) {
auto op = registration.builtin_code;
std::string result =
EnumNameBuiltinOperator(static_cast<BuiltinOperator>(op));
if ((op == kTfLiteBuiltinCustom || op == kTfLiteBuiltinDelegate) &&
registration.custom_name) {
result += " " + std::string(registration.custom_name);
}
return result;
}
} // namespace tflite

3
tensorflow/lite/util.h
View File

@ -21,6 +21,7 @@ limitations under the License.
#ifndef TENSORFLOW_LITE_UTIL_H_
#define TENSORFLOW_LITE_UTIL_H_
#include <string>
#include <vector>
#include "tensorflow/lite/c/common.h"
@ -73,6 +74,8 @@ TfLiteRegistration CreateUnresolvedCustomOp(const char* custom_op_name);
// Checks whether the provided op is an unresolved custom op.
bool IsUnresolvedCustomOp(const TfLiteRegistration& registration);
// Returns a descriptive name with the given op TfLiteRegistration.
std::string GetOpNameByRegistration(const TfLiteRegistration& registration);
} // namespace tflite
#endif // TENSORFLOW_LITE_UTIL_H_

27
tensorflow/lite/util_test.cc
View File

@ -20,6 +20,7 @@ limitations under the License.
#include <gmock/gmock.h>
#include <gtest/gtest.h>
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/schema/schema_generated.h"
namespace tflite {
namespace {
@ -90,6 +91,32 @@ TEST(CombineHashes, TestHashOutputsDifferent) {
EXPECT_NE(output1, output2);
}
TEST(GetOpNameByRegistration, ValidBuiltinCode) {
TfLiteRegistration registration;
registration.builtin_code = tflite::BuiltinOperator_ADD;
const auto op_name = GetOpNameByRegistration(registration);
EXPECT_EQ("ADD", op_name);
}
TEST(GetOpNameByRegistration, InvalidBuiltinCode) {
TfLiteRegistration registration;
registration.builtin_code = -1;
const auto op_name = GetOpNameByRegistration(registration);
EXPECT_EQ("", op_name);
}
TEST(GetOpNameByRegistration, CustomName) {
TfLiteRegistration registration;
registration.builtin_code = tflite::BuiltinOperator_CUSTOM;
registration.custom_name = "TestOp";
auto op_name = GetOpNameByRegistration(registration);
EXPECT_EQ("CUSTOM TestOp", op_name);
registration.builtin_code = tflite::BuiltinOperator_DELEGATE;
registration.custom_name = "TestDelegate";
op_name = GetOpNameByRegistration(registration);
EXPECT_EQ("DELEGATE TestDelegate", op_name);
}
} // namespace
} // namespace tflite