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"""
Approximate the bits/dimension for an image model.
"""
import argparse
import os, json
import torch as th
import numpy as np
import torch.distributed as dist
from improved_diffusion import dist_util, logger
from improved_diffusion.image_datasets import load_data
from improved_diffusion.text_datasets import load_data_text, load_synthetic_data
from improved_diffusion.script_util import (
model_and_diffusion_defaults,
create_model_and_diffusion,
add_dict_to_argparser,
args_to_dict,
)
from functools import partial
from transformers import set_seed
from improved_diffusion.test_util import get_weights, denoised_fn_round, compute_logp, load_results
def main():
set_seed(42)
args = create_argparser().parse_args()
# load configurations.
config_path = os.path.join(os.path.split(args.model_path)[0], "training_args.json")
print(config_path)
# sys.setdefaultencoding('utf-8')
with open(config_path, 'rb', ) as f:
training_args = json.load(f)
training_args['batch_size'] = args.batch_size
print(args.data_dir)
del training_args['data_dir']
# print(args.__dict__, training_args)
args.__dict__.update(training_args)
print(args.__dict__['batch_size'], training_args['batch_size'], args.clip_denoised, args.batch_size)
print(args.data_dir)
# if args.noise_level > 0.0: flag_noise=True #DEBUG
args.noise_level = 0.0
args.roc_train = 'diffusion_lm/ROCstory'
if args.modality == 'roc-aug':
args.modality = 'roc'
# DEBUG
dist_util.setup_dist()
logger.configure()
logger.log("creating model and diffusion...")
model, diffusion = create_model_and_diffusion(
**args_to_dict(args, model_and_diffusion_defaults().keys())
)
model.load_state_dict(th.load(args.model_path))
# model.load_state_dict(
# dist_util.load_state_dict(args.model_path, map_location="cpu")
# )
# diffusion.rescale_timesteps = False # IMPORTANT DEBUG --> REMOVE
model.to(dist_util.dev())
model.eval() # DEBUG
logger.log("creating data loader...")
if args.modality == 'image':
data = load_data(
data_dir=args.data_dir,
batch_size=args.batch_size,
image_size=args.image_size,
class_cond=args.class_cond,
deterministic=True,
)
elif args.modality == 'permuted_image':
# perm = np.arange(args.image_size * args.image_size)
# np.random.shuffle(perm)
model_path_base = os.path.split(args.model_path)[0]
print(f'load permutation to {model_path_base}/permutation.json')
with open(f'{model_path_base}/permutation.json', 'r') as f:
perm = json.load(f)
perm = np.array(perm)
data = load_data(
data_dir=args.data_dir,
batch_size=args.batch_size,
image_size=args.image_size,
class_cond=args.class_cond,
permutation=perm
)
elif args.modality == 'synth':
from improved_diffusion.rounding import load_models
model2, tokenizer = load_models(args.modality, args.experiment, args.model_name_or_path, args.in_channel,
os.path.split(args.model_path)[0])
data = load_synthetic_data(
data_dir=args.data_dir,
batch_size=args.batch_size,
image_size=args.image_size,
class_cond=args.class_cond,
data_args=args,
model=model2,
split='train',
# split='valid',
deterministic=True
)
elif args.modality == 'pos':
from improved_diffusion.rounding import load_models
model2, tokenizer = load_models(args.modality, args.experiment, args.model_name_or_path, args.in_channel,
os.path.split(args.model_path)[0])
data = load_synthetic_data(
data_dir=args.data_dir,
batch_size=args.batch_size,
image_size=args.image_size,
class_cond=args.class_cond,
data_args=args,
model=model2,
pos=True,
deterministic = True
)
else:
from improved_diffusion.rounding import load_models
model2, tokenizer = load_models(args.modality, args.experiment, args.model_name_or_path, args.in_channel,
os.path.split(args.model_path)[0])
# print(tokenizer)
# rev_tokenizer = {k:int(v) for k, v in tokenizer.items()}
rev_tokenizer = {v:k for k, v in tokenizer.items()}
if args.training_mode == 'e2e':
print('e2e, load the right model embeddings', '*'*80)
model2.weight = th.nn.Parameter(model.word_embedding.weight.clone().cpu())
# print(rev_tokenizer)
data = load_data_text(
data_dir=args.data_dir,
batch_size=args.batch_size,
image_size=args.image_size,
class_cond=args.class_cond,
data_args=args,
model=model2,
deterministic=True,
task_mode=args.modality,
padding_mode=args.padding_mode, # block, pad
split=args.split,
load_vocab=rev_tokenizer,
)
logger.log("evaluating...")
run_bpd_evaluation(model, diffusion, data, args.num_samples, args.clip_denoised, args, model2)
def run_bpd_evaluation(model, diffusion, data, num_samples, clip_denoised, args, model2):
all_bpd = []
all_metrics = {"vb": [], "mse": [], "xstart_mse": []}
num_complete = 0
model3 = get_weights(model2, args)
while num_complete < num_samples:
batch, model_kwargs = next(data)
batch = batch.to(dist_util.dev())
model_kwargs = {k: v.to(dist_util.dev()) for k, v in model_kwargs.items()}
model_kwargs['mapping_func'] = partial(compute_logp, args, model3.cuda())
minibatch_metrics = diffusion.calc_bpd_loop(
model, batch, clip_denoised=clip_denoised, model_kwargs=model_kwargs,
# denoised_fn=None,
denoised_fn=partial(denoised_fn_round, args, model3.cuda()) if args.clamp == 'clamp' else None,
)
for key, term_list in all_metrics.items():
terms = minibatch_metrics[key].mean(dim=0) / dist.get_world_size()
dist.all_reduce(terms)
term_list.append(terms.detach().cpu().numpy())
total_bpd = minibatch_metrics["total_bpd"]
total_bpd = total_bpd.mean() / dist.get_world_size()
dist.all_reduce(total_bpd)
all_bpd.append(total_bpd.item())
num_complete += dist.get_world_size() * batch.shape[0]
logger.log(f"done {num_complete} samples on {args.split}: bpd={np.mean(all_bpd)}, "
f"per token={np.mean(all_bpd) * args.in_channel} ", args.model_path)
temp_cat = np.mean(np.stack(all_metrics['vb']), axis=0)
if len(temp_cat) % 8 == 0:
print([y.sum() for y in np.split(np.mean(np.stack(all_metrics['vb']), axis=0), 8)])
else:
print(temp_cat[0].sum())
print([y.sum() for y in np.split(temp_cat[1:-1], 8)])
print(temp_cat[-1].sum())
vb_temp = np.mean(np.stack(all_metrics['vb']), axis=0)
print(vb_temp.shape, vb_temp.sum())
print(vb_temp[-10:])
if dist.get_rank() == 0:
for name, terms in all_metrics.items():
model_base_name = os.path.basename(
os.path.split(args.model_path)[0]) + f'.{os.path.split(args.model_path)[1]}'
# args.out_dir = os.path.join(args.out_dir, f"{model_base_name}.samples_{shape_str}.txt")
out_path = os.path.join(args.out_dir, f"{model_base_name}.{name}_{args.split}_{args.clamp}_terms.npz")
logger.log(f"saving {name} terms to {out_path}")
np.savez(out_path, np.mean(np.stack(terms), axis=0))
dist.barrier()
logger.log("evaluation complete")
if 'ema' in args.model_path:
json_path = os.path.join(os.path.split(args.model_path)[0], f'ema_score_{args.split}_nll.json')
elif args.clamp == 'noclamp':
json_path = os.path.join(os.path.split(args.model_path)[0], f'score_{args.split}_nll_noclamp.json')
else:
json_path = os.path.join(os.path.split(args.model_path)[0], f'score_{args.split}_nll.json')
print(f'written to {json_path}')
temp_cat = np.mean(np.stack(all_metrics['vb']), axis=0)
if len(temp_cat) % 8 == 0:
temp_cat = temp_cat
else:
temp_cat = temp_cat[1:-1]
json_dict = {
f'score_{args.split}_ppl_token': np.mean(all_bpd) * args.in_channel,
f'score_{args.split}_ppl_dim': np.mean(all_bpd),
f'break_down_{args.split}_dim' : [y.sum().item() for y in np.split(temp_cat, 8)],
f'last_10_{args.split}_dim': vb_temp[-10:].tolist(),
'source_file': out_path,
'num_samples':num_samples,
}
load_results(json_path, json_dict)
def create_argparser():
defaults = dict(
data_dir="", clip_denoised=False, num_samples=128, batch_size=64, model_path="",
out_dir="diffusion_lm/improved_diffusion/scores",
emb_scale_factor=1.0, split='train', debug_path='', clamp='clamp',
)
defaults.update(model_and_diffusion_defaults())
parser = argparse.ArgumentParser()
add_dict_to_argparser(parser, defaults)
return parser
if __name__ == "__main__":
main()
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