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#include <torch/csrc/jit/mobile/model_tracer/MobileModelRunner.h>
#include <torch/csrc/jit/mobile/model_tracer/TensorUtils.h>
namespace torch {
namespace jit {
namespace mobile {
std::vector<std::vector<at::IValue>> MobileModelRunner::
ivalue_to_bundled_inputs(const c10::IValue& bundled_inputs) {
CAFFE_ENFORCE(
bundled_inputs.isList(),
"Expected get_all_bundled_inputs to ",
"return a list but got a ",
bundled_inputs.tagKind(),
" instead");
c10::List<at::IValue> all_inputs = bundled_inputs.toList();
CAFFE_ENFORCE(
!all_inputs.empty(),
"Expected at least 1 bundled input, ",
"but found none. Please use ",
"torch.utils.bundled_inputs.augment_model_with_bundled_inputs to add.");
std::vector<std::vector<at::IValue>> ret;
for (at::IValue input : all_inputs) {
CAFFE_ENFORCE(
input.isTuple(),
"Expected list element to be a tuple ",
"but got a ",
input.tagKind(),
" instead");
ret.push_back(input.toTupleRef().elements());
}
return ret;
}
std::unordered_map<std::string, std::string> MobileModelRunner::
ivalue_to_bundled_inputs_map(const c10::IValue& bundled_inputs) {
CAFFE_ENFORCE(
bundled_inputs.isGenericDict(),
"Expected get_bundled_inputs_functions_and_info to ",
"return a dict but got a ",
bundled_inputs.tagKind(),
" instead");
c10::Dict<at::IValue, at::IValue> all_inputs = bundled_inputs.toGenericDict();
CAFFE_ENFORCE(
!all_inputs.empty(),
"Expected at least 1 function with bundled inputs, ",
"but found none. Please use ",
"torch.utils.bundled_inputs.augment_model_with_bundled_inputs to add.");
std::unordered_map<std::string, std::string> ret;
for (auto& input : all_inputs) {
at::IValue function_name = input.key();
at::IValue nested_dict = input.value();
CAFFE_ENFORCE(
function_name.isString(),
"Expected function with inputs to be a string ",
"but got a ",
function_name.tagKind(),
" instead");
CAFFE_ENFORCE(
nested_dict.isGenericDict(),
"Expected function name to map to dictionary ",
"but got a ",
nested_dict.tagKind(),
" instead");
// Got the nested dict now need to convert that into std types
c10::Dict<at::IValue, at::IValue> function_and_info_ival_dict =
nested_dict.toGenericDict();
std::unordered_map<std::string, std::vector<std::string>>
function_and_info_dict;
for (auto& entry : function_and_info_ival_dict) {
at::IValue key = entry.key();
at::IValue value = entry.value();
CAFFE_ENFORCE(
key.isString(),
"Expected extra information key to be a string ",
"but got a ",
value.tagKind(),
" instead");
CAFFE_ENFORCE(
value.isList(),
"Expected extra information values to be a list ",
"but got a ",
value.tagKind(),
" instead");
// Got the value of the nested dict entry now need to convert it to std
// types
std::vector<std::string> data_list;
c10::List<at::IValue> ival_data = value.toList();
for (at::IValue data : ival_data) {
CAFFE_ENFORCE(
data.isString(),
"Expected list element of nested dict entries to be a string ",
"but got a ",
data.tagKind(),
" instead");
data_list.push_back(data.toStringRef());
}
// Add entry into std type mapping
function_and_info_dict[key.toStringRef()] = data_list;
}
// Could store the full mapping of std types, but the 'info' section isnt
// needed here
std::string input_function =
function_and_info_dict["get_inputs_function_name"][0];
ret[function_name.toStringRef()] = input_function;
}
return ret;
}
std::vector<std::vector<at::IValue>> MobileModelRunner::
get_all_bundled_inputs() {
auto has_bundled_input = module_->find_method("get_all_bundled_inputs");
CAFFE_ENFORCE(
has_bundled_input,
"Model does not have bundled inputs. ",
"Use torch.utils.bundled_inputs.augment_model_with_bundled_inputs to add.");
c10::IValue bundled_inputs = module_->run_method("get_all_bundled_inputs");
return ivalue_to_bundled_inputs(bundled_inputs);
}
std::unordered_map<std::string, std::vector<std::vector<at::IValue>>>
MobileModelRunner::get_many_functions_bundled_inputs() {
auto has_bundled_input =
module_->find_method("get_bundled_inputs_functions_and_info");
CAFFE_ENFORCE(
has_bundled_input,
"Model does not have bundled inputs. ",
"Use torch.utils.bundled_inputs.augment_many_model_functions_with_bundled_inputs to add.");
auto ival_bundled_inputs_mapping =
module_->run_method("get_bundled_inputs_functions_and_info");
auto bundled_inputs_mapping =
ivalue_to_bundled_inputs_map(ival_bundled_inputs_mapping);
std::unordered_map<std::string, std::vector<std::vector<at::IValue>>> ret;
for (auto& entry : bundled_inputs_mapping) {
std::string function_name = entry.first;
std::string function_to_call = entry.second;
auto has_func_to_call = module_->find_method(function_to_call);
CAFFE_ENFORCE(
has_func_to_call,
"Model does not have ",
function_to_call,
"Use torch.utils.bundled_inputs.augment_many_model_functions_with_bundled_inputs to add.");
c10::IValue bundled_inputs = module_->run_method(function_to_call);
ret[function_name] = ivalue_to_bundled_inputs(bundled_inputs);
}
return ret;
}
std::vector<at::IValue> MobileModelRunner::run_with_inputs(
std::vector<std::vector<at::IValue>> const& bundled_inputs) {
std::vector<at::IValue> ret;
ret.reserve(bundled_inputs.size());
for (std::vector<at::IValue> const& input : bundled_inputs) {
ret.emplace_back(module_->forward(input));
}
return ret;
}
std::vector<at::IValue> MobileModelRunner::run_with_inputs(
const std::string& function_name,
std::vector<std::vector<at::IValue>> const& bundled_inputs) const {
std::vector<at::IValue> ret;
ret.reserve(bundled_inputs.size());
auto has_bundled_input = module_->find_method(function_name);
CAFFE_ENFORCE(
has_bundled_input,
"Model does not have the method named ",
function_name,
"Please ensure that it was exported correctly");
for (std::vector<at::IValue> const& input : bundled_inputs) {
auto func = module_->get_method(function_name);
ret.emplace_back(func(input));
}
return ret;
}
void MobileModelRunner::run_argless_functions(
const std::vector<std::string>& functions) {
for (auto& function_name : functions) {
if (module_->find_method(function_name)) {
module_->run_method(function_name);
}
}
}
bool MobileModelRunner::set_has_metal_gpu_operators(
std::set<std::string> const& op_list) {
for (std::string const& op : op_list) {
if (op.find("metal::") == 0 || op.find("metal_prepack::") == 0 ||
op.find("metal_prepack_unet::") == 0) {
return true;
}
}
return false;
}
void MobileModelRunner::for_each_tensor_in_bundled_inputs(
std::function<void(const ::at::Tensor&)> const& func) {
if (has_new_style_bundled_inputs()) {
// Get the bundled inputs and access the arg level ivalues stored within
auto bundled_inputs_mapping = this->get_many_functions_bundled_inputs();
// Loop over functions
for (auto& entry : bundled_inputs_mapping) {
std::vector<std::vector<at::IValue>> bundled_inputs = entry.second;
// Loop through inputs
for (const std::vector<at::IValue>& input : bundled_inputs) {
// Loop through values in an input
for (const at::IValue& iv : input) {
for_each_tensor_in_ivalue(iv, func);
}
}
}
} else {
c10::IValue iv = module_->run_method("get_all_bundled_inputs");
for_each_tensor_in_ivalue(iv, func);
}
}
} // namespace mobile
} // namespace jit
} // namespace torch
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