def run_all(
model_type="gpt2",
device="cuda",
out_dir=".",
random_weights=False,
template_indices=None,
):
print("Model:", model_type, flush=True)
# Set up all the potential combinations.
professions = get_profession_list()
templates = get_template_list(template_indices)
intervention_types = get_intervention_types()
# Initialize Model and Tokenizer.
model = Model(device=device,
gpt2_version=model_type,
random_weights=random_weights)
tokenizer = (
GPT2Tokenizer if model.is_gpt2 else TransfoXLTokenizer
if model.is_txl else XLNetTokenizer if model.is_xlnet else
BertTokenizer if model.is_bert else DistilBertTokenizer if model.
is_distilbert else RobertaTokenizer).from_pretrained(model_type)
# Set up folder if it does not exist.
dt_string = datetime.now().strftime("%Y%m%d")
folder_name = dt_string + "_neuron_intervention"
base_path = os.path.join(out_dir, "results", folder_name)
if random_weights:
base_path = os.path.join(base_path, "random")
if not os.path.exists(base_path):
os.makedirs(base_path)
# Iterate over all possible templates.
for temp in templates:
print("Running template '{}' now...".format(temp), flush=True)
# Fill in all professions into current template
interventions = construct_interventions(temp, professions, tokenizer,
device)
# Consider all the intervention types
for itype in intervention_types:
print("\t Running with intervention: {}".format(itype), flush=True)
# Run actual exp.
intervention_results = model.neuron_intervention_experiment(
interventions, itype, alpha=1.0)
df = convert_results_to_pd(interventions, intervention_results)
# Generate file name.
temp_string = "_".join(temp.replace("{}", "X").split())
model_type_string = model_type
fname = "_".join([temp_string, itype, model_type_string])
# Finally, save each exp separately.
df.to_csv(os.path.join(base_path, fname + ".csv"))
def run_all(
model_type="gpt2",
device="cuda",
out_dir=".",
grammar_file=None,
structure=None,
random_weights=False,
template_indices=None,
):
print("Model:", model_type, flush=True)
# Set up all the potential combinations.
if grammar_file is not None:
if structure is None:
raise Exception(
"Error: grammar file given but no structure specified")
grammar = read_grammar(grammar_file)
professions = get_profession_list(grammar=grammar, structure=structure)
templates = get_template_list(structure=structure, grammar=grammar)
intervention_types = get_intervention_types(bias_type="structural")
else:
professions = get_profession_list()
templates = get_template_list(template_indices)
intervention_types = get_intervention_types()
# Initialize Model and Tokenizer.
tokenizer = GPT2Tokenizer.from_pretrained(model_type)
model = Model(device=device,
gpt2_version=model_type,
random_weights=random_weights)
# Set up folder if it does not exist.
dt_string = datetime.now().strftime("%Y%m%d")
folder_name = dt_string + "_neuron_intervention"
base_path = os.path.join(out_dir, "results", folder_name)
if random_weights:
base_path = os.path.join(base_path, "random")
if not os.path.exists(base_path):
os.makedirs(base_path)
# Iterate over all possible templates.
for number in ('sing', 'pl'):
for temp in templates[number]:
print("Running template '{}' now...".format(temp), flush=True)
# Fill in all professions into current template
if structure == "simple_agreement":
other_number = 'sing' if number == 'pl' else 'pl'
interventions = construct_interventions(
temp,
professions[number],
tokenizer,
device,
structure=structure,
number=number,
subs=professions[other_number])
else:
interventions = construct_interventions(temp,
professions[number],
tokenizer,
device,
structure=structure,
number=number)
# Consider all the intervention types
for itype in intervention_types:
print("\t Running with intervention: {}".format(itype),
flush=True)
# Run actual exp.
intervention_results = model.neuron_intervention_experiment(
interventions, itype, alpha=1.0)
df = convert_results_to_pd(interventions, intervention_results)
# Generate file name.
temp_string = "_".join(temp.replace("{}", "X").split())
model_type_string = model_type
fname = "_".join([temp_string, itype, model_type_string])
# Finally, save each exp separately.
df.to_csv(os.path.join(base_path, fname + ".csv"))