is_expendable argument reduces memory usage for command line script

This commit is contained in:
Brett Kuprel 2022-06-30 06:43:10 -04:00
parent 38377107da
commit 1e18ba0ffa
4 changed files with 81 additions and 31 deletions

View File

@ -15,7 +15,7 @@ parser.set_defaults(torch=False)
parser.add_argument('--text', type=str, default='alien life') parser.add_argument('--text', type=str, default='alien life')
parser.add_argument('--seed', type=int, default=7) parser.add_argument('--seed', type=int, default=7)
parser.add_argument('--image_path', type=str, default='generated') parser.add_argument('--image_path', type=str, default='generated')
parser.add_argument('--sample_token_count', type=int, default=256) # for debugging parser.add_argument('--token_count', type=int, default=256) # for debugging
def ascii_from_image(image: Image.Image, size: int) -> str: def ascii_from_image(image: Image.Image, size: int) -> str:
@ -42,20 +42,21 @@ def generate_image(
text: str, text: str,
seed: int, seed: int,
image_path: str, image_path: str,
sample_token_count: int token_count: int
): ):
is_expendable = True
if is_torch: if is_torch:
image_generator = MinDalleTorch(is_mega, sample_token_count) image_generator = MinDalleTorch(is_mega, is_expendable, token_count)
image_tokens = image_generator.generate_image_tokens(text, seed)
if sample_token_count < image_generator.config['image_length']: if token_count < image_generator.config['image_length']:
image_tokens = image_generator.generate_image_tokens(text, seed)
print('image tokens', list(image_tokens.to('cpu').detach().numpy())) print('image tokens', list(image_tokens.to('cpu').detach().numpy()))
return return
else: else:
image = image_generator.generate_image(text, seed) image = image_generator.generate_image(text, seed)
else: else:
image_generator = MinDalleFlax(is_mega) image_generator = MinDalleFlax(is_mega, is_expendable=True)
image = image_generator.generate_image(text, seed) image = image_generator.generate_image(text, seed)
save_image(image, image_path) save_image(image, image_path)
@ -71,5 +72,5 @@ if __name__ == '__main__':
text=args.text, text=args.text,
seed=args.seed, seed=args.seed,
image_path=args.image_path, image_path=args.image_path,
sample_token_count=args.sample_token_count token_count=args.token_count
) )

View File

@ -6,7 +6,7 @@ from .text_tokenizer import TextTokenizer
from .load_params import load_vqgan_torch_params, load_dalle_bart_flax_params from .load_params import load_vqgan_torch_params, load_dalle_bart_flax_params
from .models.vqgan_detokenizer import VQGanDetokenizer from .models.vqgan_detokenizer import VQGanDetokenizer
class MinDalle: class MinDalleBase:
def __init__(self, is_mega: bool): def __init__(self, is_mega: bool):
self.is_mega = is_mega self.is_mega = is_mega
model_name = 'dalle_bart_{}'.format('mega' if is_mega else 'mini') model_name = 'dalle_bart_{}'.format('mega' if is_mega else 'mini')
@ -25,11 +25,15 @@ class MinDalle:
merges = f.read().split("\n")[1:-1] merges = f.read().split("\n")[1:-1]
self.model_params = load_dalle_bart_flax_params(model_path) self.model_params = load_dalle_bart_flax_params(model_path)
self.tokenizer = TextTokenizer(vocab, merges) self.tokenizer = TextTokenizer(vocab, merges)
def init_detokenizer(self):
print("initializing VQGanDetokenizer")
params = load_vqgan_torch_params('./pretrained/vqgan')
self.detokenizer = VQGanDetokenizer() self.detokenizer = VQGanDetokenizer()
vqgan_params = load_vqgan_torch_params('./pretrained/vqgan') self.detokenizer.load_state_dict(params)
self.detokenizer.load_state_dict(vqgan_params) del params
def tokenize_text(self, text: str) -> numpy.ndarray: def tokenize_text(self, text: str) -> numpy.ndarray:

View File

@ -3,18 +3,25 @@ import numpy
from PIL import Image from PIL import Image
import torch import torch
from .min_dalle import MinDalle from .min_dalle_base import MinDalleBase
from .models.dalle_bart_encoder_flax import DalleBartEncoderFlax from .models.dalle_bart_encoder_flax import DalleBartEncoderFlax
from .models.dalle_bart_decoder_flax import DalleBartDecoderFlax from .models.dalle_bart_decoder_flax import DalleBartDecoderFlax
class MinDalleFlax(MinDalle): class MinDalleFlax(MinDalleBase):
def __init__(self, is_mega: bool): def __init__(self, is_mega: bool, is_expendable: bool = False):
super().__init__(is_mega) super().__init__(is_mega)
self.is_expendable = is_expendable
print("initializing MinDalleFlax") print("initializing MinDalleFlax")
if not is_expendable:
self.init_encoder()
self.init_decoder()
self.init_detokenizer()
print("loading encoder")
self.encoder = DalleBartEncoderFlax( def init_encoder(self):
print("initializing DalleBartEncoderFlax")
self.encoder: DalleBartEncoderFlax = DalleBartEncoderFlax(
attention_head_count = self.config['encoder_attention_heads'], attention_head_count = self.config['encoder_attention_heads'],
embed_count = self.config['d_model'], embed_count = self.config['d_model'],
glu_embed_count = self.config['encoder_ffn_dim'], glu_embed_count = self.config['encoder_ffn_dim'],
@ -23,7 +30,9 @@ class MinDalleFlax(MinDalle):
layer_count = self.config['encoder_layers'] layer_count = self.config['encoder_layers']
).bind({'params': self.model_params.pop('encoder')}) ).bind({'params': self.model_params.pop('encoder')})
print("loading decoder")
def init_decoder(self):
print("initializing DalleBartDecoderFlax")
self.decoder = DalleBartDecoderFlax( self.decoder = DalleBartDecoderFlax(
image_token_count = self.config['image_length'], image_token_count = self.config['image_length'],
text_token_count = self.config['max_text_length'], text_token_count = self.config['max_text_length'],
@ -39,20 +48,30 @@ class MinDalleFlax(MinDalle):
def generate_image(self, text: str, seed: int) -> Image.Image: def generate_image(self, text: str, seed: int) -> Image.Image:
text_tokens = self.tokenize_text(text) text_tokens = self.tokenize_text(text)
if self.is_expendable: self.init_encoder()
print("encoding text tokens") print("encoding text tokens")
encoder_state = self.encoder(text_tokens) encoder_state = self.encoder(text_tokens)
if self.is_expendable: del self.encoder
if self.is_expendable:
self.init_decoder()
params = self.model_params.pop('decoder')
else:
params = self.model_params['decoder']
print("sampling image tokens") print("sampling image tokens")
image_tokens = self.decoder.sample_image_tokens( image_tokens = self.decoder.sample_image_tokens(
text_tokens, text_tokens,
encoder_state, encoder_state,
jax.random.PRNGKey(seed), jax.random.PRNGKey(seed),
self.model_params['decoder'] params
) )
if self.is_expendable: del self.decoder
image_tokens = torch.tensor(numpy.array(image_tokens)) image_tokens = torch.tensor(numpy.array(image_tokens))
if self.is_expendable: self.init_detokenizer()
print("detokenizing image") print("detokenizing image")
image = self.detokenizer.forward(image_tokens).to(torch.uint8) image = self.detokenizer.forward(image_tokens).to(torch.uint8)
if self.is_expendable: del self.detokenizer
image = Image.fromarray(image.to('cpu').detach().numpy()) image = Image.fromarray(image.to('cpu').detach().numpy())
return image return image

View File

@ -9,17 +9,30 @@ torch.set_grad_enabled(False)
torch.set_num_threads(os.cpu_count()) torch.set_num_threads(os.cpu_count())
from .load_params import convert_dalle_bart_torch_from_flax_params from .load_params import convert_dalle_bart_torch_from_flax_params
from .min_dalle import MinDalle from .min_dalle_base import MinDalleBase
from .models.dalle_bart_encoder_torch import DalleBartEncoderTorch from .models.dalle_bart_encoder_torch import DalleBartEncoderTorch
from .models.dalle_bart_decoder_torch import DalleBartDecoderTorch from .models.dalle_bart_decoder_torch import DalleBartDecoderTorch
class MinDalleTorch(MinDalle): class MinDalleTorch(MinDalleBase):
def __init__(self, is_mega: bool, sample_token_count: int = 256): def __init__(
self,
is_mega: bool,
is_expendable: bool = False,
token_count: int = 256
):
super().__init__(is_mega) super().__init__(is_mega)
self.is_expendable = is_expendable
self.token_count = token_count
print("initializing MinDalleTorch") print("initializing MinDalleTorch")
if not is_expendable:
self.init_encoder()
self.init_decoder()
self.init_detokenizer()
print("loading encoder")
def init_encoder(self):
print("initializing DalleBartEncoderTorch")
self.encoder = DalleBartEncoderTorch( self.encoder = DalleBartEncoderTorch(
layer_count = self.config['encoder_layers'], layer_count = self.config['encoder_layers'],
embed_count = self.config['d_model'], embed_count = self.config['d_model'],
@ -28,18 +41,22 @@ class MinDalleTorch(MinDalle):
text_token_count = self.config['max_text_length'], text_token_count = self.config['max_text_length'],
glu_embed_count = self.config['encoder_ffn_dim'] glu_embed_count = self.config['encoder_ffn_dim']
) )
encoder_params = convert_dalle_bart_torch_from_flax_params( params = convert_dalle_bart_torch_from_flax_params(
self.model_params.pop('encoder'), self.model_params.pop('encoder'),
layer_count=self.config['encoder_layers'], layer_count=self.config['encoder_layers'],
is_encoder=True is_encoder=True
) )
self.encoder.load_state_dict(encoder_params, strict=False) self.encoder.load_state_dict(params, strict=False)
if torch.cuda.is_available(): self.encoder = self.encoder.cuda()
del params
print("loading decoder")
def init_decoder(self):
print("initializing DalleBartDecoderTorch")
self.decoder = DalleBartDecoderTorch( self.decoder = DalleBartDecoderTorch(
image_vocab_size = self.config['image_vocab_size'], image_vocab_size = self.config['image_vocab_size'],
image_token_count = self.config['image_length'], image_token_count = self.config['image_length'],
sample_token_count = sample_token_count, sample_token_count = self.token_count,
embed_count = self.config['d_model'], embed_count = self.config['d_model'],
attention_head_count = self.config['decoder_attention_heads'], attention_head_count = self.config['decoder_attention_heads'],
glu_embed_count = self.config['decoder_ffn_dim'], glu_embed_count = self.config['decoder_ffn_dim'],
@ -48,16 +65,19 @@ class MinDalleTorch(MinDalle):
start_token = self.config['decoder_start_token_id'], start_token = self.config['decoder_start_token_id'],
is_verbose = True is_verbose = True
) )
decoder_params = convert_dalle_bart_torch_from_flax_params( params = convert_dalle_bart_torch_from_flax_params(
self.model_params.pop('decoder'), self.model_params.pop('decoder'),
layer_count=self.config['decoder_layers'], layer_count=self.config['decoder_layers'],
is_encoder=False is_encoder=False
) )
self.decoder.load_state_dict(decoder_params, strict=False) self.decoder.load_state_dict(params, strict=False)
if torch.cuda.is_available(): self.decoder = self.decoder.cuda()
del params
def init_detokenizer(self):
super().init_detokenizer()
if torch.cuda.is_available(): if torch.cuda.is_available():
self.encoder = self.encoder.cuda()
self.decoder = self.decoder.cuda()
self.detokenizer = self.detokenizer.cuda() self.detokenizer = self.detokenizer.cuda()
@ -66,18 +86,24 @@ class MinDalleTorch(MinDalle):
text_tokens = torch.tensor(text_tokens).to(torch.long) text_tokens = torch.tensor(text_tokens).to(torch.long)
if torch.cuda.is_available(): text_tokens = text_tokens.cuda() if torch.cuda.is_available(): text_tokens = text_tokens.cuda()
if self.is_expendable: self.init_encoder()
print("encoding text tokens") print("encoding text tokens")
encoder_state = self.encoder.forward(text_tokens) encoder_state = self.encoder.forward(text_tokens)
if self.is_expendable: del self.encoder
if self.is_expendable: self.init_decoder()
print("sampling image tokens") print("sampling image tokens")
torch.manual_seed(seed) torch.manual_seed(seed)
image_tokens = self.decoder.forward(text_tokens, encoder_state) image_tokens = self.decoder.forward(text_tokens, encoder_state)
if self.is_expendable: del self.decoder
return image_tokens return image_tokens
def generate_image(self, text: str, seed: int) -> Image.Image: def generate_image(self, text: str, seed: int) -> Image.Image:
image_tokens = self.generate_image_tokens(text, seed) image_tokens = self.generate_image_tokens(text, seed)
if self.is_expendable: self.init_detokenizer()
print("detokenizing image") print("detokenizing image")
image = self.detokenizer.forward(image_tokens).to(torch.uint8) image = self.detokenizer.forward(image_tokens).to(torch.uint8)
if self.is_expendable: del self.detokenizer
image = Image.fromarray(image.to('cpu').detach().numpy()) image = Image.fromarray(image.to('cpu').detach().numpy())
return image return image