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Copy pathgenerate_samples.py
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executable file
·326 lines (284 loc) · 12.7 KB
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# coding=utf-8
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Sample Generate GPT2"""
import os
import stat
import random
import numpy as np
import torch
import torch.nn.functional as F
import argparse
import time
from datetime import datetime
from arguments import get_args
from utils import Timers
from pretrain_gpt2 import initialize_distributed
from pretrain_gpt2 import set_random_seed
from utils import load_checkpoint, get_checkpoint_iteration
from data_utils import get_tokenizer
import mpu
import deepspeed
from fp16 import FP16_Module
from model import GPT2Model
from model import DistributedDataParallel as DDP
from utils import print_rank_0
from pretrain_gpt2 import get_model
import math
from copy import deepcopy
from tqdm import tqdm
from generation import get_batch, filling_sequence, add_interlacing_beam_marks, magnify, inverse_prompt_score
from torchvision.utils import save_image
import torch.distributed as dist
def setup_model(args):
"""Setup model and optimizer."""
model = get_model(args)
if args.load is not None:
if args.deepspeed:
iteration, release, success = get_checkpoint_iteration(args)
path = os.path.join(args.load, str(iteration), "mp_rank_00_model_states.pt")
print('current device:', torch.cuda.current_device())
checkpoint = torch.load(path, map_location=torch.device('cpu'))
model.load_state_dict(checkpoint["module"])
print(f"Load model file {path}")
else:
_ = load_checkpoint(
model, None, None, args, load_optimizer_states=False)
return model
def _parse_and_to_tensor(text, img_size=256, query_template='{}'):
tokenizer = get_tokenizer()
text = query_template.format(*text.split('\t'))
seq = tokenizer.parse_query(text, img_size=img_size)
seq = torch.cuda.LongTensor(seq)
return seq
def get_context(args, query_template='{}'):
tokenizer = get_tokenizer()
terminate_runs = 0
img_size = 256 if args.generation_task != 'low-level super-resolution' else 128
ml = max(args.max_position_embeddings, args.max_position_embeddings_finetune)
output_path = args.output_path
if args.input_source == 'interactive':
assert not args.with_id, '--with-id is only used with file inputs.'
if args.generation_task == 'post-selection':
raise ValueError('post-selection only takes file inputs!')
while True:
raw_text = input("\nPlease Input Query (stop to exit) >>> ")
if not raw_text:
print('Query should not be empty!')
continue
if raw_text == "stop":
return
try:
seq = _parse_and_to_tensor(raw_text, img_size=img_size, query_template=query_template)
except (ValueError, FileNotFoundError) as e:
print(e)
continue
if len(seq) > ml:
print("\nSeq length", len(seq),
f"\nPlease give smaller context than {ml}!")
continue
yield (raw_text, seq, output_path)
else:
with open(args.input_source, 'r') as fin:
inputs = fin.readlines()
for line_no, raw_text in enumerate(inputs):
if line_no % dist.get_world_size() != dist.get_rank():
continue
rk = dist.get_rank()
print(f'Working on No. {line_no} on {rk}... ')
raw_text = raw_text.strip()
if len(raw_text) == 0:
continue
if args.with_id: # with id
parts = raw_text.split('\t')
output_path = os.path.join(args.output_path, parts[0])
raw_text = '\t'.join(parts[1:])
if args.generation_task == 'post-selection':
parts = raw_text.split('\t')
seqs = []
for part in parts[1:]:
try:
seq_single = _parse_and_to_tensor('\t'.join([part, parts[0]]), img_size=img_size, query_template=query_template)
seqs.append(seq_single)
except (ValueError, FileNotFoundError) as e:
print(e)
continue
seq = torch.stack(seqs)
else:
try:
seq = _parse_and_to_tensor(raw_text, img_size=img_size, query_template=query_template)
except (ValueError, FileNotFoundError) as e:
print(e)
continue
if len(seq) > ml:
print("\nSeq length", len(seq),
f"\nPlease give smaller context than {ml}!")
continue
yield (raw_text, seq, output_path)
def generate_images_once(model, args, raw_text, seq=None, num=8, query_template='{}', output_path='./samples'):
tokenizer = get_tokenizer()
if not os.path.exists(output_path):
os.makedirs(output_path)
if seq is None: # need parse
img_size = 256 if args.generation_task != 'low-level super-resolution' else 128
seq = _parse_and_to_tensor(raw_text, img_size=img_size, query_template=query_template)
model.eval()
with torch.no_grad():
print('show raw text:', raw_text)
start_time = time.time()
if args.generation_task in ['text2image', 'low-level super-resolution']:
invalid_slices = [slice(tokenizer.img_tokenizer.num_tokens, None)]
elif args.generation_task == 'image2text':
invalid_slices = [slice(0, tokenizer.img_tokenizer.num_tokens)]
else:
NotImplementedError
mbz = args.max_inference_batch_size
add_interlacing_beam_marks(seq, nb=min(num, mbz))
assert num < mbz or num % mbz == 0
output_tokens_list = []
for tim in range(max(num // mbz, 1)):
output_tokens_list.append(filling_sequence(model, seq.clone(), args))
torch.cuda.empty_cache()
output_tokens_list = torch.cat(output_tokens_list, dim=0)
print("\nTaken time {:.2f}\n".format(time.time() - start_time), flush=True)
print("\nContext:", raw_text, flush=True)
imgs, txts = [], []
for seq in output_tokens_list:
decoded_txts, decoded_imgs = tokenizer.DecodeIds(seq.tolist())
for i in range(len(decoded_imgs)):
if decoded_imgs[i].shape[-1] == 128:
decoded_imgs[i] = torch.nn.functional.interpolate(decoded_imgs[i], size=(256, 256))
if args.debug:
imgs.extend(decoded_imgs)
else:
imgs.append(decoded_imgs[-1]) # only the last image (target)
txts.append(decoded_txts)
if args.generation_task == 'image2text':
print(txts)
return
if args.debug:
output_file_prefix = raw_text.replace('/', '')[:20]
output_file = os.path.join(output_path, f"{output_file_prefix}-{datetime.now().strftime('%m-%d-%H-%M-%S')}.jpg")
imgs = torch.cat(imgs, dim=0)
print(txts)
print("\nSave to: ", output_file, flush=True)
save_image(imgs, output_file, normalize=True)
else:
print("\nSave to: ", output_path, flush=True)
for i in range(len(imgs)):
save_image(imgs[i], os.path.join(output_path,f'{i}.jpg'), normalize=True)
os.chmod(os.path.join(output_path,f'{i}.jpg'), stat.S_IRWXO+stat.S_IRWXG+stat.S_IRWXU)
save_image(torch.cat(imgs, dim=0), os.path.join(output_path,f'concat.jpg'), normalize=True)
os.chmod(os.path.join(output_path,f'concat.jpg'), stat.S_IRWXO+stat.S_IRWXG+stat.S_IRWXU)
def generate_images_continually(model, args):
if args.generation_task == 'text2image':
query_template = '[ROI1] {} [BASE] [BOI1] [MASK]*1024'
elif args.generation_task == 'image2text':
query_template = '[BASE] [BOI1] [Image]{} [EOI1] [ROI1] [MASK]*20'
elif args.generation_task == 'low-level super-resolution':
query_template = '[ROI1] {} [BASE] [BOI1] [Image]{} [EOI1] [ROI2] [POS0] [BASE] [BOI2] [MASK]*1024'
elif args.generation_task == 'super-resolution':
query_template = '[ROI1] {} [BASE] [BOI1] [Image]{}'
elif args.generation_task == 'post-selection':
query_template = '[BASE] [BOI1] [Image]{} [EOI1] [ROI1] {}'
else:
raise NotImplementedError
for raw_text, seq, output_path in get_context(args, query_template):
if args.generation_task == 'super-resolution':
super_resolution(model, args, raw_text, seq, output_path=output_path)
elif args.generation_task == 'post-selection':
post_selection(model, args, raw_text, seq, output_path=output_path)
else:
generate_images_once(model, args, raw_text, seq, num=args.batch_size, output_path=output_path)
def super_resolution(model, args, raw_text, seq, output_path="./samples"):
tokenizer = get_tokenizer()
model.eval()
if not os.path.exists(output_path):
os.makedirs(output_path)
with torch.no_grad():
start_time = time.time()
output_tokens_list = magnify(model, tokenizer, seq[-32**2:], seq[:-32**2], args)
print("\nTaken time {:.2f}\n".format(time.time() - start_time), flush=True)
print("\nContext:", raw_text, flush=True)
output_file_prefix = raw_text.replace('/', '')[:20]
output_file = os.path.join(output_path, f"{output_file_prefix}-{datetime.now().strftime('%m-%d-%H-%M-%S')}.jpg")
imgs = []
if args.debug:
imgs.append(torch.nn.functional.interpolate(tokenizer.img_tokenizer.DecodeIds(seq[-32**2:]), size=(512, 512)))
for seq in output_tokens_list:
decoded_txts, decoded_imgs = tokenizer.DecodeIds(seq.tolist())
imgs.extend(decoded_imgs)
imgs = torch.cat(imgs, dim=0)
print("\nSave to: ", output_file, flush=True)
save_image(imgs, output_file, normalize=True)
def post_selection(model, args, raw_text, seq, output_path):
tokenizer = get_tokenizer()
model.eval()
if not os.path.exists(output_path):
os.makedirs(output_path)
with torch.no_grad():
start_time = time.time()
num = seq.shape[0]
mbz = args.max_inference_batch_size
assert num < mbz or num % mbz == 0
scores = [inverse_prompt_score(model, seq[tim*mbz:(tim+1)*mbz], args)
for tim in range(max(num // mbz, 1))
]
scores = torch.cat(scores, dim=0)
# scores = inverse_prompt_score(model, seq, args) # once
print("\nTaken time {:.2f}\n".format(time.time() - start_time), flush=True)
print("\nContext:", raw_text, flush=True)
rank = dist.get_rank()
output_file = os.path.join(output_path, f"scores_rank_{rank}.txt")
with open(output_file, 'a') as fout:
fout.write(raw_text+'\n')
fout.write('\t'.join([str(x) for x in scores.tolist()])+'\n')
print("\nSave to: ", output_file, flush=True)
def prepare_tokenizer(args):
tokenizer = get_tokenizer(args)
num_tokens = tokenizer.num_tokens
before = num_tokens
after = before
multiple = args.make_vocab_size_divisible_by * \
mpu.get_model_parallel_world_size()
while (after % multiple) != 0:
after += 1
print_rank_0('> padded vocab (size: {}) with {} dummy '
'tokens (new size: {})'.format(
before, after - before, after))
args.vocab_size = after
print("prepare tokenizer done", flush=True)
return tokenizer
def main():
"""Main training program."""
print('Generate Samples')
# Disable CuDNN.
torch.backends.cudnn.enabled = False
# Arguments.
args = get_args()
# Pytorch distributed.
initialize_distributed(args)
# set device, this args.device is only used in inference
if args.device is not None:
device = int(args.device)
torch.cuda.set_device(device)
# Random seeds for reproducability.
set_random_seed(args.seed)
# get the tokenizer
tokenizer = prepare_tokenizer(args)
# Model, optimizer, and learning rate.
model = setup_model(args)
generate_images_continually(model, args)
if __name__ == "__main__":
main()
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