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[tflchef] Tidy disabled codes (#13640)
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This will tidy disabled codes.

ONE-DCO-1.0-Signed-off-by: SaeHie Park <[email protected]>
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@seanshpark
seanshpark authored Aug 12, 2024
1 parent 13e8954 commit a1c5053
Showing 1 changed file with 0 additions and 219 deletions.
219 changes: 0 additions & 219 deletions compiler/tflchef/core/src/ModelChef.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -177,20 +177,6 @@ std::set<std::string> gather_customcode_set(const ::tflchef::ModelRecipe &model_
namespace
{

// TODO remove
#if 0
struct CookParams
{
std::vector<flatbuffers::Offset<::tflite::Buffer>> &buffer_vec;
std::vector<flatbuffers::Offset<::tflite::OperatorCode>> &code_vec;
std::vector<flatbuffers::Offset<::tflite::SubGraph>> &subgraph_vec;
std::unique_ptr<flatbuffers::FlatBufferBuilder> &flatbuffer_builder;
std::map<tflite::BuiltinOperator, int32_t> &builtin_code_map;
std::vector<std::string> &custom_code_vec;
std::string noname;
};
#endif

struct ModelChef
{
void cook(const ::tflchef::ModelRecipe &model_recipe);
Expand Down Expand Up @@ -952,211 +938,6 @@ GeneratedModel cook(const ::tflchef::ModelRecipe &model_recipe)

mc.cook(model_recipe);

#if 0
// TODO remove references

//
// Create FlatBufferBuilder
//
std::unique_ptr<flatbuffers::FlatBufferBuilder> &flatbuffer_builder = mc.flatbuffer_builder;

// Operand-related
std::vector<flatbuffers::Offset<::tflite::Buffer>> &buffer_vec = mc.buffer_vec;

// Operation-related
std::vector<flatbuffers::Offset<::tflite::OperatorCode>> &code_vec = mc.code_vec;

// SignatureDef-related
std::vector<flatbuffers::Offset<::tflite::SignatureDef>> &signdef_vec = mc.signdef_vec;

// Graphs-related
std::vector<flatbuffers::Offset<::tflite::SubGraph>> &subgraph_vec = mc.subgraph_vec;

// Create OperatorCode with Builtin Operator
mc.builtin_code_map = gather_builtincode_map(model_recipe);
std::map<tflite::BuiltinOperator, int32_t> &builtin_code_map = mc.builtin_code_map;
for (auto const &opcode : builtin_code_map)
{
tflite::OperatorCodeBuilder code_builder{*flatbuffer_builder};
// 127 is BuiltinOperator_PLACEHOLDER_FOR_GREATER_OP_CODES
// This is the way to handle deprecated builtin code
// See
// https://github.com/tensorflow/tensorflow/blob/a0afe8f9218be5eb9ed5dffc2dff652996da8c28/tensorflow/lite/schema/schema.fbs#L1061-L1077
if (opcode.first < 127)
{
code_builder.add_deprecated_builtin_code(opcode.first);
}
else
{
code_builder.add_deprecated_builtin_code(
::tflite::BuiltinOperator_PLACEHOLDER_FOR_GREATER_OP_CODES);
}
code_builder.add_version(opcode.second);
code_builder.add_builtin_code(opcode.first);
auto code = code_builder.Finish();
// Update OperatorCode vector
code_vec.emplace_back(code);
}

// Create OperatorCode with Custom Operator
std::set<std::string> custom_code_set = gather_customcode_set(model_recipe);
mc.custom_code_vec = {custom_code_set.begin(), custom_code_set.end()};
std::vector<std::string> &custom_code_vec = mc.custom_code_vec;

for (auto opcode : custom_code_vec)
{
auto custom_code = flatbuffer_builder->CreateString(opcode);
tflite::OperatorCodeBuilder code_builder{*flatbuffer_builder};
code_builder.add_deprecated_builtin_code(tflite::BuiltinOperator_CUSTOM);
code_builder.add_custom_code(custom_code);
code_builder.add_builtin_code(tflite::BuiltinOperator_CUSTOM);
auto code = code_builder.Finish();
// Update OperatorCode vector
code_vec.emplace_back(code);
}

// Create an Empty Buffer
//
// Buffer 0 SHOULD be an empty buffer in TensorFlow Lite model file
// (Please refer to the comment for Tensor.buffer field in schema)
{
tflite::BufferBuilder buffer_builder{*flatbuffer_builder};
buffer_vec.emplace_back(buffer_builder.Finish());
}

// symbol_tables stores symbol_table of each sub graph
// this is used to find tensor ID(index) with tensor name
std::vector<std::map<std::string, int32_t>> symbol_tables;

//
// Create Main graph
//

mc.graph_name = "main";
auto table = cook_graph<::tflchef::ModelRecipe>(model_recipe, mc);
symbol_tables.push_back(table);

//
// Create subgraphs if exist
//
for (int g = 0; g < model_recipe.graph_size(); ++g)
{
const auto &graph = model_recipe.graph(g);

std::ostringstream stringStream;
stringStream << "sub_" << (g + 1);

mc.graph_name = stringStream.str();

auto table = cook_graph<::tflchef::Graph>(graph, mc);
symbol_tables.push_back(table);
}

// Create Signature-Def
//
for (int s = 0; s < model_recipe.signature_def_size(); ++s)
{
// load from recipe
const auto &rec_signature_def = model_recipe.signature_def(s);

std::vector<flatbuffers::Offset<::tflite::TensorMap>> tensormap_inputs;
std::vector<flatbuffers::Offset<::tflite::TensorMap>> tensormap_outputs;

// which subgraph index to cook
auto subgraph_index = 0;
if (rec_signature_def.has_subgraph_index())
{
subgraph_index = rec_signature_def.subgraph_index();
}
assert(subgraph_index < symbol_tables.size());
auto &symbol_table = symbol_tables[subgraph_index];

// cook for inputs
for (int si = 0; si < rec_signature_def.inputs_size(); ++si)
{
// recipe for input TensorMap
auto rec_tm_input = rec_signature_def.inputs(si);
auto name = flatbuffer_builder->CreateString(rec_tm_input.name());
uint32_t tensor_index = 0;
// either tensor or tensor_index should exist
assert(rec_tm_input.has_tensor() || rec_tm_input.has_tensor_index());
if (rec_tm_input.has_tensor())
{
// we can get tensor_index from symbol_table
auto tensor = rec_tm_input.tensor();
tensor_index = symbol_table[tensor];
}
else
{
// or we can use tensor_index itself
tensor_index = rec_tm_input.tensor_index();
}

::tflite::TensorMapBuilder tensormap_builder{*flatbuffer_builder};
tensormap_builder.add_name(name);
tensormap_builder.add_tensor_index(tensor_index);
tensormap_inputs.push_back(tensormap_builder.Finish());
}
// cook for outputs, same as inputs
for (int so = 0; so < rec_signature_def.outputs_size(); ++so)
{
auto rec_tm_output = rec_signature_def.outputs(so);
auto name = flatbuffer_builder->CreateString(rec_tm_output.name());
uint32_t tensor_index = 0;
assert(rec_tm_output.has_tensor() || rec_tm_output.has_tensor_index());
if (rec_tm_output.has_tensor())
{
auto tensor = rec_tm_output.tensor();
tensor_index = symbol_table[tensor];
}
else
{
tensor_index = rec_tm_output.tensor_index();
}

::tflite::TensorMapBuilder tensormap_builder{*flatbuffer_builder};
tensormap_builder.add_name(name);
tensormap_builder.add_tensor_index(tensor_index);
tensormap_outputs.push_back(tensormap_builder.Finish());
}

auto inputs = flatbuffer_builder->CreateVector(tensormap_inputs);
auto outputs = flatbuffer_builder->CreateVector(tensormap_outputs);
auto signature_key = flatbuffer_builder->CreateString(rec_signature_def.signature_key());
// TODO add validation for signature_key

::tflite::SignatureDefBuilder signature_def_builder{*flatbuffer_builder};
signature_def_builder.add_inputs(inputs);
signature_def_builder.add_outputs(outputs);
signature_def_builder.add_signature_key(signature_key);
signature_def_builder.add_subgraph_index(rec_signature_def.subgraph_index());

signdef_vec.emplace_back(signature_def_builder.Finish());
}

// Create "Model" arguments
auto buffers = flatbuffer_builder->CreateVector(buffer_vec);
auto signdefs = flatbuffer_builder->CreateVector(signdef_vec);
auto operator_codes = flatbuffer_builder->CreateVector(code_vec);
auto subgraphs = flatbuffer_builder->CreateVector(subgraph_vec);
auto description = flatbuffer_builder->CreateString("Generated by tflchef");

// Create "Model"
tflite::ModelBuilder model_builder{*flatbuffer_builder};

model_builder.add_version(3);
model_builder.add_operator_codes(operator_codes);
model_builder.add_signature_defs(signdefs);
model_builder.add_subgraphs(subgraphs);
model_builder.add_description(description);
model_builder.add_buffers(buffers);

auto model = model_builder.Finish();

// Finalize
::tflite::FinishModelBuffer(*flatbuffer_builder, model);
#endif

// Return "GenerateModel"
return GeneratedModel{std::move(gen_model)};
}
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