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evaluate_probes.py
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evaluate_probes.py
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import os
import pickle
import argparse
import einops
import torch
import numpy as np
from load import load_model
from sklearn.metrics import *
from utils import timestamp, MODEL_N_LAYERS, adjust_precision
from make_prompt_datasets import DATASET_MANAGERS, FEATURE_PROMPT_MAPPINGS
from train_probes import load_probe_result, load_all_probes, load_supervised_data
from sklearn.metrics.pairwise import cosine_similarity
from scipy.stats import spearmanr, kendalltau, pearsonr
from analysis.weight_composition import *
def save_evaluation(args, eval_result):
save_path = os.path.join(
os.getenv('RESULTS_DIR', 'results'),
args.experiment_name,
args.model,
args.entity_type,
args.feature_name,
'evaluations'
)
os.makedirs(save_path, exist_ok=True)
evaluation_metadata = [
args.evaluation_type,
args.experiment_type,
args.normalization_type,
args.label_processing,
args.activation_aggregation,
]
eval_name = '.'.join(evaluation_metadata) + '.p'
pickle.dump(
eval_result,
open(os.path.join(save_path, eval_name), 'wb')
)
def load_evaluation(
experiment_name, model, entity_type, feature_name,
evaluation_type, experiment_type, normalization_type='none',
label_processing='none', activation_aggregation='last'):
save_path = os.path.join(
os.getenv('RESULTS_DIR', 'results'),
experiment_name,
model,
entity_type,
feature_name,
'evaluations'
)
evaluation_metadata = [
evaluation_type,
experiment_type,
normalization_type,
label_processing,
activation_aggregation,
]
eval_name = '.'.join(evaluation_metadata) + '.p'
save_file = os.path.join(save_path, eval_name)
return pickle.load(open(save_file, 'rb'))
def load_all_probe_evals(experiment_name, model_name, entity_type, feature_name):
probe_path = os.path.join(
os.getenv('RESULTS_DIR', 'results'),
experiment_name,
model_name,
entity_type,
feature_name,
'evaluations'
)
experiment_metadata = (experiment_name, model_name, entity_type)
probe_files = os.listdir(probe_path)
probe_files = [f for f in probe_files if f.endswith('.p')]
probes = {}
for probe_file in probe_files:
probe_metadata = tuple(probe_file.split('.')[:-1])
probe_results = pickle.load(
open(os.path.join(probe_path, probe_file), 'rb'))
probes[experiment_metadata + probe_metadata] = probe_results
return probes
def evaluate_ranking(args, probe, prompt_name, layer, save_projections=False):
# load data
layer_activations, entity_values, test_ixs = load_supervised_data(
args, manager, args.feature_name, prompt_name, layer)
test_entity_values = entity_values[test_ixs]
feature_proj = (torch.tensor(layer_activations) @ probe).numpy()
all_spearman = spearmanr(feature_proj, entity_values)
test_spearman = spearmanr(feature_proj[test_ixs], test_entity_values)
train_spearman = spearmanr(
feature_proj[~test_ixs], entity_values[~test_ixs])
all_kendall = kendalltau(feature_proj, entity_values)
test_kendall = kendalltau(feature_proj[test_ixs], test_entity_values)
train_kendall = kendalltau(
feature_proj[~test_ixs], entity_values[~test_ixs])
all_pearson = pearsonr(feature_proj, entity_values)
test_pearson = pearsonr(feature_proj[test_ixs], test_entity_values)
train_pearson = pearsonr(
feature_proj[~test_ixs], entity_values[~test_ixs])
# TODO: add weights
result_dict = {
'all_spearman_coef': all_spearman.correlation,
'train_spearman_coef': train_spearman.correlation,
'test_spearman_coef': test_spearman.correlation,
'all_kendall_coef': all_kendall.correlation,
'train_kendall_coef': train_kendall.correlation,
'test_kendall_coef': test_kendall.correlation,
'all_pearson_coef': all_pearson.correlation,
'train_pearson_coef': train_pearson.correlation,
'test_pearson_coef': test_pearson.correlation,
'all_spearman_p': all_spearman.pvalue,
'train_spearman_p': train_spearman.pvalue,
'test_spearman_p': test_spearman.pvalue,
'all_kendall_p': all_kendall.pvalue,
'train_kendall_p': train_kendall.pvalue,
'test_kendall_p': test_kendall.pvalue,
'all_pearson_p': all_pearson.pvalue,
'train_pearson_p': train_pearson.pvalue,
'test_pearson_p': test_pearson.pvalue,
'norm': torch.norm(probe).item(),
}
if save_projections:
result_dict['feature_projection'] = feature_proj.astype(np.float16)
return result_dict
def evaluate_layer_and_prompt_generalization(args, probe):
transfer_results = {}
for layer in range(MODEL_N_LAYERS[args.model]):
prompt_key_list = FEATURE_PROMPT_MAPPINGS[args.entity_type][args.feature_name]
for transfer_prompt in prompt_key_list:
transfer_results[(layer, transfer_prompt)] = evaluate_ranking(
args, probe, transfer_prompt, layer)
return transfer_results
def evaluate_inter_feature_generalization():
raise NotImplementedError
def evaluate_probe(args, probe_results, layer):
raise NotImplementedError
if __name__ == "__main__":
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# experiment params
parser.add_argument(
'--experiment_name', type=str, help='Name of experiment for save dir')
parser.add_argument(
'--experiment_type', type=str, default='spearman_train',
help='Type of experiment to evaluate')
parser.add_argument(
'--evaluation_type', type=str, default='oos_generalization',
choices=['oos_generalization', 'composition',
'layer_and_prompt_generalization', 'cross_feature_generalization'])
parser.add_argument(
'--model', default='pythia-70m',
help='Name of model from TransformerLens')
parser.add_argument(
'--entity_type',
help='Name of feature collection (should be dir under processed_datasets/)')
parser.add_argument(
'--feature_name', type=str,
help='Name of feature to probe, must be in FEATURE_PROMPT_MAPPINGS')
# never changed from defaults
parser.add_argument(
'--normalization_type', type=str, default='none',
help='Type of normalization to apply to activations before training')
parser.add_argument(
'--label_processing', type=str, default='none',
help='Type of weighting to apply to labels before training')
parser.add_argument(
'--activation_aggregation', default='last',
help='Average activations across all tokens in a sequence')
args = parser.parse_args()
n_layers = MODEL_N_LAYERS[args.model]
if int(os.getenv('SLURM_CPUS_PER_TASK', -1)) > 0:
torch.set_num_threads(int(os.getenv('SLURM_CPUS_PER_TASK', 1)))
manager = DATASET_MANAGERS[args.entity_type]
if args.evaluation_type == 'composition':
torch.set_grad_enabled(False)
model = load_model(args.model)
probes = load_all_probes(
args.experiment_name, args.model, args.entity_type, args.feature_name)
eval_results = {}
for key in probes.keys():
experiment_type = key[3]
prompt_name = key[-1]
if experiment_type != args.experiment_type:
continue
for layer in probes[key].keys():
print(timestamp(),
f'running evaluation on {args.model} {args.evaluation_type}.{args.feature_name}.L{layer}')
probe_key = 'rank_probe' if experiment_type == 'lsq_train' else 'probe'
probe = torch.tensor(
probes[key][layer][probe_key]).to(torch.float32)
# run evaluations
if args.evaluation_type == 'oos_generalization':
probe_results = evaluate_ranking(
args, probe, prompt_name, layer, save_projections=True)
eval_results[(layer, prompt_name)] = probe_results
elif args.evaluation_type == 'layer_and_prompt_generalization':
probe_results = evaluate_layer_and_prompt_generalization(
args, probe)
eval_results[(layer, prompt_name)] = probe_results
elif args.evaluation_type == 'composition':
composition_results = evaluate_probe_composition(
model, probe)
eval_results[(layer, prompt_name)] = composition_results
elif args.evaluation_type == 'cross_feature_generalization':
generalization_results = evaluate_inter_feature_generalization(
args, probe)
eval_results[(layer, prompt_name)] = generalization_results
save_evaluation(args, eval_results)