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Fix enabling gradient as output for easy mode. (#3866)

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ashbhandare 2020-05-07 15:07:14 -07:00 committed by GitHub
parent 4ea10c9202
commit 424a00bf04
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2 changed files with 6 additions and 3 deletions
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2
orttraining/orttraining/python/ort_trainer.py
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@ -405,6 +405,7 @@ def create_ort_training_session_with_optimizer(model, device, training_optimizer
ort_parameters.allreduce_post_accumulation = allreduce_post_accumulation
ort_parameters.partition_optimizer = partition_optimizer
ort_parameters.enable_grad_norm_clip = enable_grad_norm_clip
ort_parameters.set_gradients_as_graph_outputs = False
output_types = {}
for output in model.graph.output:
@ -477,6 +478,7 @@ def create_ort_training_session_bind_parameters(model, device, world_rank=-1, wo
ort_parameters.world_rank = world_rank
ort_parameters.world_size = world_size
ort_parameters.gradient_accumulation_steps = gradient_accumulation_steps
ort_parameters.set_gradients_as_graph_outputs = True
torch_params = {}
output_types = {}

7
orttraining/orttraining/python/orttraining_pybind_state.cc
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@ -51,6 +51,7 @@ struct TrainingParameters {
int horizontal_parallel_size = 1;
bool partition_optimizer = false;
bool enable_grad_norm_clip = true;
bool set_gradients_as_graph_outputs = false;
};
struct TrainingConfigurationResult {
@ -94,7 +95,7 @@ TrainingConfigurationResult ConfigureSessionForTraining(
config.weight_names_to_not_train = parameters.weights_not_to_train;
config.immutable_weights = parameters.immutable_weights;
config.set_gradients_as_graph_outputs = false;
config.set_gradients_as_graph_outputs = parameters.set_gradients_as_graph_outputs;
config.gradient_accumulation_steps = parameters.gradient_accumulation_steps;
@ -115,7 +116,6 @@ TrainingConfigurationResult ConfigureSessionForTraining(
config.loss_name = parameters.loss_output_name;
if (!parameters.training_optimizer_name.empty()) {
config.set_gradients_as_graph_outputs = true;
training::TrainingSession::TrainingConfiguration::OptimizerConfiguration opt{};
opt.name = parameters.training_optimizer_name;
opt.learning_rate_input_name = parameters.lr_params_feed_name;
@ -181,7 +181,8 @@ void addObjectMethodsForTraining(py::module& m) {
.def_readwrite("world_size", &TrainingParameters::world_size)
.def_readwrite("gradient_accumulation_steps", &TrainingParameters::gradient_accumulation_steps)
.def_readwrite("partition_optimizer", &TrainingParameters::partition_optimizer)
.def_readwrite("enable_grad_norm_clip", &TrainingParameters::enable_grad_norm_clip);
.def_readwrite("enable_grad_norm_clip", &TrainingParameters::enable_grad_norm_clip)
.def_readwrite("set_gradients_as_graph_outputs", &TrainingParameters::set_gradients_as_graph_outputs);
py::class_<TrainingConfigurationResult> config_result(m, "TrainingConfigurationResult", "pbdoc(Configuration result for training.)pbdoc");
config_result.def(py::init())