tanner/min-dalle-test
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

is_expendable argument reduces memory usage for command line script

Browse Source
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
Download Patch File Download Diff File Expand all files Collapse all files
min_dalle/min_dalle_torch.py
+39 -13
View File
@@ -9,17 +9,30 @@ torch.set_grad_enabled(False)
torch.set_num_threads(os.cpu_count())
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_decoder_torch import DalleBartDecoderTorch
class MinDalleTorch(MinDalle):
def __init__(self, is_mega: bool, sample_token_count: int = 256):
class MinDalleTorch(MinDalleBase):
def __init__(
self,
is_mega: bool,
is_expendable: bool = False,
token_count: int = 256
):
super().__init__(is_mega)
self.is_expendable = is_expendable
self.token_count = token_count
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(
layer_count = self.config['encoder_layers'],
embed_count = self.config['d_model'],
@@ -28,18 +41,22 @@ class MinDalleTorch(MinDalle):
text_token_count = self.config['max_text_length'],
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'),
layer_count=self.config['encoder_layers'],
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(
image_vocab_size = self.config['image_vocab_size'],
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'],
attention_head_count = self.config['decoder_attention_heads'],
glu_embed_count = self.config['decoder_ffn_dim'],
@@ -48,36 +65,45 @@ class MinDalleTorch(MinDalle):
start_token = self.config['decoder_start_token_id'],
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'),
layer_count=self.config['decoder_layers'],
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():
self.encoder = self.encoder.cuda()
self.decoder = self.decoder.cuda()
self.detokenizer = self.detokenizer.cuda()
def generate_image_tokens(self, text: str, seed: int) -> LongTensor:
text_tokens = self.tokenize_text(text)
text_tokens = torch.tensor(text_tokens).to(torch.long)
if torch.cuda.is_available(): text_tokens = text_tokens.cuda()
if self.is_expendable: self.init_encoder()
print("encoding 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")
torch.manual_seed(seed)
image_tokens = self.decoder.forward(text_tokens, encoder_state)
if self.is_expendable: del self.decoder
return image_tokens
def generate_image(self, text: str, seed: int) -> Image.Image:
image_tokens = self.generate_image_tokens(text, seed)
if self.is_expendable: self.init_detokenizer()
print("detokenizing image")
image = self.detokenizer.forward(image_tokens).to(torch.uint8)
if self.is_expendable: del self.detokenizer
image = Image.fromarray(image.to('cpu').detach().numpy())
return image