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!pip install torch==2.0.1 transformers==4.27.1 datasets==2.4.0 wget==3.2 huggingface-hub==0.14.1 beautifulsoup4==4.11.1 requests==2.28.1 matplotlib tqdm python-dotenv diffusers
import os
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
from torch.optim import AdamW
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import time
import threading
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModel, TrainingArguments, pipeline
from diffusers import DiffusionPipeline
from huggingface_hub import login, HfApi, Repository
from dotenv import load_dotenv
# Cargar variables de entorno
load_dotenv()
class UnifiedModel(nn.Module):
def __init__(self, models):
super(UnifiedModel, self).__init__()
self.models = nn.ModuleList(models)
self.classifier = nn.Linear(sum([model.config.hidden_size for model in models if hasattr(model, 'config')]), 2)
def forward(self, inputs):
hidden_states = []
for model in self.models:
if isinstance(model, nn.Module):
outputs = model(inputs)
hidden_states.append(outputs.last_hidden_state[:, 0, :])
elif isinstance(model, DiffusionPipeline) or isinstance(model, pipeline):
outputs = model(inputs)
hidden_states.append(torch.tensor(outputs))
concatenated_hidden_states = torch.cat(hidden_states, dim=-1)
logits = self.classifier(concatenated_hidden_states)
return logits
class SyntheticDataset(Dataset):
def __init__(self, tokenizers, size=100):
self.tokenizers = tokenizers
self.size = size
self.data = self._generate_data()
def _generate_data(self):
data = []
for _ in range(self.size):
text = "This is a sample sentence for testing purposes."
label = torch.tensor(0) # Sample label
item = {"text": text, "label": label}
for name, tokenizer in self.tokenizers.items():
tokenized = tokenizer(text, padding="max_length", truncation=True, max_length=128)
item[f"input_ids_{name}"] = torch.tensor(tokenized["input_ids"])
item[f"attention_mask_{name}"] = torch.tensor(tokenized["attention_mask"])
data.append(item)
return data
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
return self.data[idx]
def push_to_hub(local_dir, repo_name):
try:
repo_url = HfApi().create_repo(repo_name, exist_ok=True)
repo = Repository(local_dir, clone_from=repo_url)
if not os.path.exists(os.path.join(local_dir, ".git")):
os.system(f"cd {local_dir} && git init && git remote add origin {repo_url} && git pull origin main")
repo.git_add(auto_lfs_track=True)
repo.git_commit("Add model and tokenizer files")
json_files = ["config.json", "generation_config.json", "special_tokens_map.json", "tokenizer.json", "tokenizer.model", "tokenizer_config.json"]
for json_file in json_files:
json_file_path = os.path.join(local_dir, json_file)
if os.path.exists(json_file_path):
repo.git_add(json_file_path)
repo.git_push()
print(f"Pushed model and tokenizer to {repo_url}")
except Exception as e:
print(f"Error pushing to Hugging Face Hub: {e}")
def main():
while True:
try:
os.system("git config --global credential.helper store")
login(token=os.getenv("HUGGINGFACE_TOKEN"), add_to_git_credential=True)
# Definir los modelos que se van a utilizar
models_to_train = [
"openai-community/gpt2-xl",
"google/gemma-2-9b-it",
"google/gemma-2-9b",
"meta-llama/Meta-Llama-3.1-8B-Instruct",
"meta-llama/Meta-Llama-3.1-8B",
"openbmb/MiniCPM-V-2_6",
"bigcode/starcoder",
"WizardLMTeam/WizardCoder-Python-34B-V1.0",
"Qwen/Qwen2-72B-Instruct",
"google/gemma-2-2b-it",
"facebook/bart-large-cnn",
"Falconsai/text_summarization",
"microsoft/speecht5_tts",
"Groq/Llama-3-Groq-70B-Tool-Use",
"Groq/Llama-3-Groq-8B-Tool-Use"
]
# Inicializar los pipelines
pipelines_to_unify = [
pipeline("text-to-audio", model="facebook/musicgen-melody"),
pipeline("text-to-audio", model="facebook/musicgen-large"),
pipeline("text-to-audio", model="facebook/musicgen-small"),
DiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt-1-1"),
pipeline("automatic-speech-recognition", model="openai/whisper-small"),
DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev"),
DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1"),
DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell"),
pipeline("text-generation", model="meta-llama/Meta-Llama-3.1-8B"),
pipeline("text-generation", model="openbmb/MiniCPM-V-2_6"),
pipeline("text-generation", model="bigcode/starcoder"),
pipeline("text-to-speech", model="microsoft/speecht5_tts"),
pipeline("text-generation", model="WizardLMTeam/WizardCoder-Python-34B-V1.0"),
pipeline("text-generation", model="Qwen/Qwen2-72B-Instruct"),
pipeline("text-generation", model="google/gemma-2-2b-it"),
pipeline("summarization", model="facebook/bart-large-cnn"),
pipeline("summarization", model="Falconsai/text_summarization"),
DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev"),
pipeline("text-to-audio", model="facebook/musicgen-small"),
pipeline("text-generation", model="Groq/Llama-3-Groq-70B-Tool-Use"),
pipeline("text-generation", model="Groq/Llama-3-Groq-8B-Tool-Use")
]
tokenizers = {}
models = []
for model_name in models_to_train:
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
if tokenizer.pad_token is None:
tokenizer.add_special_tokens({'pad_token': tokenizer.eos_token})
model = AutoModel.from_pretrained(model_name)
tokenizers[model_name] = tokenizer
models.append(model)
# Agregar pipelines como modelos
models.extend(pipelines_to_unify)
# Crear un dataset sint茅tico para entrenamiento y evaluaci贸n
synthetic_dataset = SyntheticDataset(tokenizers, size=100)
# Dividir el dataset en entrenamiento y evaluaci贸n
train_size = int(0.8 * len(synthetic_dataset))
val_size = len(synthetic_dataset) - train_size
train_dataset, val_dataset = torch.utils.data.random_split(synthetic_dataset, [train_size, val_size])
# Crear DataLoaders para entrenamiento y evaluaci贸n
train_loader = DataLoader(train_dataset, batch_size=2, shuffle=True)
eval_loader = DataLoader(val_dataset, batch_size=16)
# Unificar los modelos y pipelines en uno solo
unified_model = UnifiedModel(models)
unified_model.to(torch.device("cpu"))
# Mostrar la cantidad de par谩metros totales a entrenar
total_params = sum(p.numel() for p in unified_model.parameters())
print(f"Total parameters to train: {total_params}")
# Definir los argumentos de entrenamiento
training_args = TrainingArguments(
output_dir="outputs/unified_model",
evaluation_strategy="epoch",
learning_rate=9e-4,
per_device_train_batch_size=2,
per_device_eval_batch_size=16,
num_train_epochs=1, # Reduced epochs for quick training
weight_decay=0.01,
logging_steps=10, # More frequent logging for quicker feedback
optim="adamw_hf"
)
# Definir el optimizador
optimizer = AdamW(unified_model.parameters(), lr=training_args.learning_rate)
train_losses = []
eval_losses = []
def train(model, train_loader, eval_loader, args):
model.train()
epoch = 0
total_steps = args.num_train_epochs * len(train_loader)
progress_bar = tqdm(total=total_steps, desc="Training")
while epoch < args.num_train_epochs:
start_time = time.time()
for step, batch in enumerate(train_loader):
input_ids = [batch[f"input_ids_{name}"].to("cpu") for name in tokenizers.keys()]
attention_mask = [batch[f"attention_mask_{name}"].to("cpu") for name in tokenizers.keys()]
labels = batch["label"].to("cpu")
optimizer.zero_grad()
outputs = model(input_ids)
loss = nn.CrossEntropyLoss()(outputs, labels)
loss.backward()
optimizer.step()
progress_bar.update(1)
elapsed_time = time.time() - start_time
estimated_total_time = total_steps * (elapsed_time / (step + 1))
estimated_remaining_time = estimated_total_time - elapsed_time
if step % args.logging_steps == 0:
train_losses.append(loss.item())
print(f"Step {step}/{total_steps}, Loss: {loss.item()}, Estimated remaining time: {estimated_remaining_time:.2f} seconds")
epoch += 1
model.eval()
eval_loss = 0
with torch.no_grad():
for batch in eval_loader:
input_ids = [batch[f"input_ids_{name}"].to("cpu") for name in tokenizers.keys()]
attention_mask = [batch[f"attention_mask_{name}"].to("cpu") for name in tokenizers.keys()]
labels = batch["label"].to("cpu")
outputs = model(input_ids)
loss = nn.CrossEntropyLoss()(outputs, labels)
eval_loss += loss.item()
eval_loss /= len(eval_loader)
eval_losses.append(eval_loss)
print(f"Epoch {epoch}/{args.num_train_epochs}, Evaluation Loss: {eval_loss}")
train(unified_model, train_loader, eval_loader, training_args)
# Visualizar p茅rdidas durante el entrenamiento
fig, ax = plt.subplots()
ax.set_xlabel("Epochs")
ax.set_ylabel("Loss")
ax.legend()
def animate(i):
ax.clear()
ax.plot(train_losses[:i], label="Train Loss")
ax.plot(eval_losses[:i], label="Eval Loss")
ax.legend()
ani = animation.FuncAnimation(fig, animate, frames=len(train_losses), blit=False)
plt.show()
# Subir el modelo unificado a Hugging Face Hub
local_dir = "./outputs/unified_model"
push_to_hub(local_dir, repo_name="Ffftdtd5dtft/my_model")
break
except Exception as e:
print(f"Error: {e}")
time.sleep(2)
if __name__ == "__main__":
main() |