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skynet
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update txt2txt utils, create pipeline, update cli

add-txt2txt-models
Konstantine Tsafatinos 2023-06-08 23:29:05 -04:00
parent f66550b3b9
commit 82a7a3e076
3 changed files with 74 additions and 28 deletions
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11
skynet/cli.py
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@ -80,6 +80,9 @@ def img2img(model, prompt, input, output, strength, guidance, steps, seed):
@click.option('--output', '-o', default='output.txt')
@click.option('--temperature', '-t', default=1.0)
@click.option('--max-length', '-ml', default=256)
@click.option('--num-return-sequences', '-rs', defautl=1)
@click.option('--no-repeat-ngram', '-nr', default=2)
@click.option('--top-p', '-tp', default=0.95)
def txt2txt(*args, **kwargs):
from . import utils
_, hf_token, _, cfg = init_env_from_config()
@ -151,8 +154,9 @@ def enqueue(
binary = ''
ec, out = cleos.push_action(
'telos.gpu', 'enqueue', [account, req,
binary, reward], f'{account}@{permission}'
'telos.gpu', 'enqueue',
[account, req, binary, reward],
f'{account}@{permission}'
)
print(collect_stdout(out))
@ -293,7 +297,8 @@ def config(
'user', node_url, None, None)
with open_cleos(node_url, key=key) as cleos:
ec, out = cleos.push_action(
'telos.gpu', 'config', [token_contract, token_symbol], f'{account}@{permission}'
'telos.gpu', 'config', [token_contract,
token_symbol], f'{account}@{permission}'
)
print(collect_stdout(out))

16
skynet/dgpu/compute.py
View File

@ -12,7 +12,7 @@ import torch
from skynet.constants import DEFAULT_INITAL_MODELS, MODELS
from skynet.dgpu.errors import DGPUComputeError
from skynet.utils import convert_from_bytes_and_crop, convert_from_cv2_to_image, convert_from_image_to_cv2, convert_from_img_to_bytes, init_upscaler, pipeline_for
from skynet.utils import convert_from_bytes_and_crop, convert_from_cv2_to_image, convert_from_image_to_cv2, convert_from_img_to_bytes, init_upscaler, pipeline_for_image
def prepare_params_for_diffuse(
@ -62,7 +62,7 @@ class SkynetMM:
def is_model_loaded(self, model_name: str, image: bool):
for model_key, model_data in self._models.items():
if (model_key == model_name and
model_data['image'] == image):
model_data['image'] == image):
return True
return False
@ -75,7 +75,7 @@ class SkynetMM:
):
logging.info(f'loading model {model_name}...')
if force or len(self._models.keys()) == 0:
pipe = pipeline_for(model_name, image=image)
pipe = pipeline_for_image(model_name, image=image)
self._models[model_name] = {
'pipe': pipe,
'generated': 0,
@ -87,7 +87,7 @@ class SkynetMM:
for model in self._models:
if self._models[
least_used]['generated'] > self._models[model]['generated']:
least_used]['generated'] > self._models[model]['generated']:
least_used = model
del self._models[least_used]
@ -97,7 +97,7 @@ class SkynetMM:
gc.collect()
torch.cuda.empty_cache()
pipe = pipeline_for(model_name, image=image)
pipe = pipeline_for_image(model_name, image=image)
self._models[model_name] = {
'pipe': pipe,
@ -133,7 +133,8 @@ class SkynetMM:
arguments = prepare_params_for_diffuse(params, binary)
prompt, guidance, step, seed, upscaler, extra_params = arguments
model = self.get_model(params['model'], 'image' in extra_params)
model = self.get_model(
params['model'], 'image' in extra_params)
image = model(
prompt,
@ -155,6 +156,9 @@ class SkynetMM:
return img_sha, img_raw
case 'transformer':
# TODO: Understand dpgu code and figure out what to put here
pass
case _:
raise DGPUComputeError('Unsupported compute method')

75
skynet/utils.py
View File

@ -19,7 +19,7 @@ from diffusers import (
StableDiffusionImg2ImgPipeline,
EulerAncestralDiscreteScheduler
)
from transformers import pipeline, Conversation
from transformers import AutoTokenizer, AutoModelForCausalLM
from realesrgan import RealESRGANer
from huggingface_hub import login
@ -59,7 +59,7 @@ def convert_from_bytes_and_crop(raw: bytes, max_w: int, max_h: int) -> Image:
return image.convert('RGB')
def pipeline_for(model: str, mem_fraction: float = 1.0, image=False) -> DiffusionPipeline:
def pipeline_for_image(model: str, mem_fraction: float = 1.0, image=False) -> DiffusionPipeline:
assert torch.cuda.is_available()
torch.cuda.empty_cache()
torch.cuda.set_per_process_memory_fraction(mem_fraction)
@ -98,6 +98,41 @@ def pipeline_for(model: str, mem_fraction: float = 1.0, image=False) -> Diffusio
return pipe.to('cuda')
def pipeline_for_text(model: str, mem_fraction: float = 1.0, image=False) -> DiffusionPipeline:
assert torch.cuda.is_available()
torch.cuda.empty_cache()
torch.cuda.set_per_process_memory_fraction(mem_fraction)
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
# NOTE: image could be used for image to text
# NOTE: note sure if this is necessary or what it does exactly
# full determinism
# https://huggingface.co/docs/diffusers/using-diffusers/reproducibility#deterministic-algorithms
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8"
torch.backends.cudnn.benchmark = False
torch.use_deterministic_algorithms(True)
params = {
'torch_dtype': torch.float16,
'safety_checker': None
}
pipe = AutoModelForCausalLM.from_pretrained(
model, **params
)
# TODO: look if scheduler is necessary and what does this code do
# pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(
# pipe.scheduler.config)
#
# if not image:
# pipe.enable_vae_slicing()
#
return pipe.to('cuda')
def txt2img(
hf_token: str,
model: str = 'prompthero/openjourney',
@ -115,7 +150,7 @@ def txt2img(
torch.backends.cudnn.allow_tf32 = True
login(token=hf_token)
pipe = pipeline_for(model)
pipe = pipeline_for_image(model)
seed = seed if seed else random.randint(0, 2 ** 64)
prompt = prompt
@ -148,7 +183,7 @@ def img2img(
torch.backends.cudnn.allow_tf32 = True
login(token=hf_token)
pipe = pipeline_for(model, image=True)
pipe = pipeline_for_image(model, image=True)
with open(img_path, 'rb') as img_file:
input_img = convert_from_bytes_and_crop(img_file.read(), 512, 512)
@ -168,11 +203,12 @@ def img2img(
def txt2txt(
hf_token: str,
# TODO: change this to actual model ref
# add more granular control of models
model: str = 'microsoft/DialoGPT-small',
model: str = 'tiiuae/falcon-40b-instruct',
prompt: str = 'a red old tractor in a sunny wheat field',
output: str = 'output.txt',
num_return_sequences: int = 1,
no_repeat_ngram_size: int = 2,
top_p: float = 0.95,
temperature: float = 1.0,
max_length: int = 256,
):
@ -183,24 +219,25 @@ def txt2txt(
torch.backends.cudnn.allow_tf32 = True
login(token=hf_token)
chatbot = pipeline('text-generation', model=model, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained(model)
pipe = pipeline_for_text(model)
prompt = prompt
conversation = Conversation(prompt)
conversation = chatbot(
conversation,
# TODO: learn more about return tensors and model params
tokenized_input = tokenizer.encode(prompt, return_tensors='pt')
tokenized_output = pipe.generate(
tokenized_input,
max_length=max_length,
do_sample=True,
num_return_sequences=num_return_sequences,
no_repeat_ngram_size=2,
top_p=0.95,
temperature=temperature
)
response = conversation.generated_responses[-1]
response = tokenizer.decode(tokenized_output, skip_special_tokens=True)
with open(output, 'w', encoding='utf-8') as f:
f.write(response)
# This if for continued conversatin, need to figure out how to store convo
# conversation.add_user_input("Is it an action movie?")
# conversation = chatbot(conversation)
# conversation.generated_responses[-1]
# TODO: figure out continued conversation, store data on frontend?
def init_upscaler(model_path: str = 'weights/RealESRGAN_x4plus.pth'):
@ -249,6 +286,6 @@ def download_all_models(hf_token: str):
login(token=hf_token)
for model in MODELS:
print(f'DOWNLOADING {model.upper()}')
pipeline_for(model)
pipeline_for_image(model)
print(f'DOWNLOADING IMAGE {model.upper()}')
pipeline_for(model, image=True)
pipeline_for_image(model, image=True)