Generative AI
Starting the depth of organized transformers: advanced training with gradient and similar
In this advanced Deedpeed The lesson, we provide the submission of the submarines of cutting opplesization strategies to train large-language models. By combining zero performance, mixed mixed training, gradient training, and deep-deep, teaching how you can train TPU models, and enable transformer models in the affected workplace, such as Colob. Next to the creation and training that is right, and includes monitoring performance, efficiency, to look at the different zero phases, to provide artists with both notices and active code to accelerate the development of model. Look Full codes here.
import subprocess
import sys
import os
import json
import time
from pathlib import Path
def install_dependencies():
"""Install required packages for DeepSpeed in Colab"""
print("🚀 Installing DeepSpeed and dependencies...")
subprocess.check_call([
sys.executable, "-m", "pip", "install",
"torch", "torchvision", "torchaudio", "--index-url",
"
])
subprocess.check_call([sys.executable, "-m", "pip", "install", "deepspeed"])
subprocess.check_call([
sys.executable, "-m", "pip", "install",
"transformers", "datasets", "accelerate", "wandb"
])
print("✅ Installation complete!")
install_dependencies()
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
import deepspeed
from transformers import GPT2Config, GPT2LMHeadModel, GPT2Tokenizer
import numpy as np
from typing import Dict, Any
import argparseWe put our Colob Nature by installing pytro support, Deeppeed, and important libraries such as transformers, datasets, acceleration, and metals. We make sure that everything is ready and we can build well and train models with the remuneration. Look Full codes here.
class SyntheticTextDataset(Dataset):
"""Synthetic dataset for demonstration purposes"""
def __init__(self, size: int = 1000, seq_length: int = 512, vocab_size: int = 50257):
self.size = size
self.seq_length = seq_length
self.vocab_size = vocab_size
self.data = torch.randint(0, vocab_size, (size, seq_length))
def __len__(self):
return self.size
def __getitem__(self, idx):
return {
'input_ids': self.data[idx],
'labels': self.data[idx].clone()
}We create syntheticitatsovasususized where we produce random sequeline to set the actual text data. We use these consecutive material as both input and labels, allow us to test the deeper training without depending on the external data. Look Full codes here.
class AdvancedDeepSpeedTrainer:
"""Advanced DeepSpeed trainer with multiple optimization techniques"""
def __init__(self, model_config: Dict[str, Any], ds_config: Dict[str, Any]):
self.model_config = model_config
self.ds_config = ds_config
self.model = None
self.engine = None
self.tokenizer = None
def create_model(self):
"""Create a GPT-2 style model for demonstration"""
print("🧠 Creating model...")
config = GPT2Config(
vocab_size=self.model_config['vocab_size'],
n_positions=self.model_config['seq_length'],
n_embd=self.model_config['hidden_size'],
n_layer=self.model_config['num_layers'],
n_head=self.model_config['num_heads'],
resid_pdrop=0.1,
embd_pdrop=0.1,
attn_pdrop=0.1,
)
self.model = GPT2LMHeadModel(config)
self.tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
self.tokenizer.pad_token = self.tokenizer.eos_token
print(f"📊 Model parameters: {sum(p.numel() for p in self.model.parameters()):,}")
return self.model
def create_deepspeed_config(self):
"""Create comprehensive DeepSpeed configuration"""
return {
"train_batch_size": self.ds_config['train_batch_size'],
"train_micro_batch_size_per_gpu": self.ds_config['micro_batch_size'],
"gradient_accumulation_steps": self.ds_config['gradient_accumulation_steps'],
"zero_optimization": {
"stage": self.ds_config['zero_stage'],
"allgather_partitions": True,
"allgather_bucket_size": 5e8,
"overlap_comm": True,
"reduce_scatter": True,
"reduce_bucket_size": 5e8,
"contiguous_gradients": True,
"cpu_offload": self.ds_config.get('cpu_offload', False)
},
"fp16": {
"enabled": True,
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": self.ds_config['learning_rate'],
"betas": [0.9, 0.999],
"eps": 1e-8,
"weight_decay": 0.01
}
},
"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": 0,
"warmup_max_lr": self.ds_config['learning_rate'],
"warmup_num_steps": 100
}
},
"gradient_clipping": 1.0,
"wall_clock_breakdown": True,
"memory_breakdown": True,
"tensorboard": {
"enabled": True,
"output_path": "./logs/",
"job_name": "deepspeed_advanced_tutorial"
}
}
def initialize_deepspeed(self):
"""Initialize DeepSpeed engine"""
print("⚡ Initializing DeepSpeed...")
parser = argparse.ArgumentParser()
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args([])
self.engine, optimizer, _, lr_scheduler = deepspeed.initialize(
args=args,
model=self.model,
config=self.create_deepspeed_config()
)
print(f"🎯 DeepSpeed engine initialized with ZeRO stage {self.ds_config['zero_stage']}")
return self.engine
def train_step(self, batch: Dict[str, torch.Tensor]) -> Dict[str, float]:
"""Perform a single training step with DeepSpeed optimizations"""
input_ids = batch['input_ids'].to(self.engine.device)
labels = batch['labels'].to(self.engine.device)
outputs = self.engine(input_ids=input_ids, labels=labels)
loss = outputs.loss
self.engine.backward(loss)
self.engine.step()
return {
'loss': loss.item(),
'lr': self.engine.lr_scheduler.get_last_lr()[0] if self.engine.lr_scheduler else 0
}
def train(self, dataloader: DataLoader, num_epochs: int = 2):
"""Complete training loop with monitoring"""
print(f"🏋️ Starting training for {num_epochs} epochs...")
self.engine.train()
total_steps = 0
for epoch in range(num_epochs):
epoch_loss = 0.0
epoch_steps = 0
print(f"n📈 Epoch {epoch + 1}/{num_epochs}")
for step, batch in enumerate(dataloader):
start_time = time.time()
metrics = self.train_step(batch)
epoch_loss += metrics['loss']
epoch_steps += 1
total_steps += 1
if step % 10 == 0:
step_time = time.time() - start_time
print(f" Step {step:4d} | Loss: {metrics['loss']:.4f} | "
f"LR: {metrics['lr']:.2e} | Time: {step_time:.3f}s")
if step % 20 == 0 and hasattr(self.engine, 'monitor'):
self.log_memory_stats()
if step >= 50:
break
avg_loss = epoch_loss / epoch_steps
print(f"📊 Epoch {epoch + 1} completed | Average Loss: {avg_loss:.4f}")
print("🎉 Training completed!")
def log_memory_stats(self):
"""Log GPU memory statistics"""
if torch.cuda.is_available():
allocated = torch.cuda.memory_allocated() / 1024**3
reserved = torch.cuda.memory_reserved() / 1024**3
print(f" 💾 GPU Memory - Allocated: {allocated:.2f}GB | Reserved: {reserved:.2f}GB")
def save_checkpoint(self, path: str):
"""Save model checkpoint using DeepSpeed"""
print(f"💾 Saving checkpoint to {path}")
self.engine.save_checkpoint(path)
def demonstrate_inference(self, text: str = "The future of AI is"):
"""Demonstrate optimized inference with DeepSpeed"""
print(f"n🔮 Running inference with prompt: '{text}'")
inputs = self.tokenizer.encode(text, return_tensors="pt").to(self.engine.device)
self.engine.eval()
with torch.no_grad():
outputs = self.engine.module.generate(
inputs,
max_length=inputs.shape[1] + 50,
num_return_sequences=1,
temperature=0.8,
do_sample=True,
pad_token_id=self.tokenizer.eos_token_id
)
generated_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
print(f"📝 Generated text: {generated_text}")
self.engine.train()We create an END-END coach who built a GPT-2 model, sets Deeppeed Config (zero, FP16, Adamw, warmthe warmth, tensorboard), and starts the engine. We then activate the working training measures with mathematical and memory statistics, keep checkpoints, and to show the discovery of verification and generation in one place. Look Full codes here.
def run_advanced_tutorial():
"""Main function to run the advanced DeepSpeed tutorial"""
print("🌟 Advanced DeepSpeed Tutorial Starting...")
print("=" * 60)
model_config = {
'vocab_size': 50257,
'seq_length': 512,
'hidden_size': 768,
'num_layers': 6,
'num_heads': 12
}
ds_config = {
'train_batch_size': 16,
'micro_batch_size': 4,
'gradient_accumulation_steps': 4,
'zero_stage': 2,
'learning_rate': 1e-4,
'cpu_offload': False
}
print("📋 Configuration:")
print(f" Model size: ~{sum(np.prod(shape) for shape in [[model_config['vocab_size'], model_config['hidden_size']], [model_config['hidden_size'], model_config['hidden_size']] * model_config['num_layers']]) / 1e6:.1f}M parameters")
print(f" ZeRO Stage: {ds_config['zero_stage']}")
print(f" Batch size: {ds_config['train_batch_size']}")
trainer = AdvancedDeepSpeedTrainer(model_config, ds_config)
model = trainer.create_model()
engine = trainer.initialize_deepspeed()
print("n📚 Creating synthetic dataset...")
dataset = SyntheticTextDataset(
size=200,
seq_length=model_config['seq_length'],
vocab_size=model_config['vocab_size']
)
dataloader = DataLoader(
dataset,
batch_size=ds_config['micro_batch_size'],
shuffle=True
)
print("n📊 Pre-training memory stats:")
trainer.log_memory_stats()
trainer.train(dataloader, num_epochs=2)
print("n📊 Post-training memory stats:")
trainer.log_memory_stats()
trainer.demonstrate_inference("DeepSpeed enables efficient training of")
checkpoint_path = "./deepspeed_checkpoint"
trainer.save_checkpoint(checkpoint_path)
demonstrate_zero_stages()
demonstrate_memory_optimization()
print("n🎯 Tutorial completed successfully!")
print("Key DeepSpeed features demonstrated:")
print(" ✅ ZeRO optimization for memory efficiency")
print(" ✅ Mixed precision training (FP16)")
print(" ✅ Gradient accumulation")
print(" ✅ Learning rate scheduling")
print(" ✅ Checkpoint saving/loading")
print(" ✅ Memory monitoring")
def demonstrate_zero_stages():
"""Demonstrate different ZeRO optimization stages"""
print("n🔧 ZeRO Optimization Stages Explained:")
print(" Stage 0: Disabled (baseline)")
print(" Stage 1: Optimizer state partitioning (~4x memory reduction)")
print(" Stage 2: Gradient partitioning (~8x memory reduction)")
print(" Stage 3: Parameter partitioning (~Nx memory reduction)")
zero_configs = {
1: {"stage": 1, "reduce_bucket_size": 5e8},
2: {"stage": 2, "allgather_partitions": True, "reduce_scatter": True},
3: {"stage": 3, "stage3_prefetch_bucket_size": 5e8, "stage3_param_persistence_threshold": 1e6}
}
for stage, config in zero_configs.items():
estimated_memory_reduction = [1, 4, 8, "Nx"][stage]
print(f" 📉 Stage {stage}: ~{estimated_memory_reduction}x memory reduction")
def demonstrate_memory_optimization():
"""Show memory optimization techniques"""
print("n🧠 Memory Optimization Techniques:")
print(" 🔄 Gradient Checkpointing: Trade compute for memory")
print(" 📤 CPU Offloading: Move optimizer states to CPU")
print(" 🗜️ Compression: Reduce communication overhead")
print(" ⚡ Mixed Precision: Use FP16 for faster training")We include the full training orchestrase which describes Zero categories and highlights memory remembers, such as the Gradient Cowerchpoint and Uploading CPU. Look Full codes here.
class DeepSpeedConfigGenerator:
"""Utility class to generate DeepSpeed configurations"""
@staticmethod
def generate_config(
batch_size: int = 16,
zero_stage: int = 2,
use_cpu_offload: bool = False,
learning_rate: float = 1e-4
) -> Dict[str, Any]:
"""Generate a complete DeepSpeed configuration"""
config = {
"train_batch_size": batch_size,
"train_micro_batch_size_per_gpu": max(1, batch_size // 4),
"gradient_accumulation_steps": max(1, batch_size // max(1, batch_size // 4)),
"zero_optimization": {
"stage": zero_stage,
"allgather_partitions": True,
"allgather_bucket_size": 5e8,
"overlap_comm": True,
"reduce_scatter": True,
"reduce_bucket_size": 5e8,
"contiguous_gradients": True
},
"fp16": {
"enabled": True,
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": learning_rate,
"betas": [0.9, 0.999],
"eps": 1e-8,
"weight_decay": 0.01
}
},
"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": 0,
"warmup_max_lr": learning_rate,
"warmup_num_steps": 100
}
},
"gradient_clipping": 1.0,
"wall_clock_breakdown": True
}
if use_cpu_offload:
config["zero_optimization"]["cpu_offload"] = True
config["zero_optimization"]["pin_memory"] = True
if zero_stage == 3:
config["zero_optimization"].update({
"stage3_prefetch_bucket_size": 5e8,
"stage3_param_persistence_threshold": 1e6,
"stage3_gather_16bit_weights_on_model_save": True
})
return config
def benchmark_zero_stages():
"""Benchmark different ZeRO stages"""
print("n🏁 Benchmarking ZeRO Stages...")
model_config = {
'vocab_size': 50257,
'seq_length': 256,
'hidden_size': 512,
'num_layers': 4,
'num_heads': 8
}
results = {}
for stage in [1, 2]:
print(f"n🔬 Testing ZeRO Stage {stage}...")
ds_config = {
'train_batch_size': 8,
'micro_batch_size': 2,
'gradient_accumulation_steps': 4,
'zero_stage': stage,
'learning_rate': 1e-4
}
try:
trainer = AdvancedDeepSpeedTrainer(model_config, ds_config)
model = trainer.create_model()
engine = trainer.initialize_deepspeed()
if torch.cuda.is_available():
torch.cuda.reset_peak_memory_stats()
dataset = SyntheticTextDataset(size=20, seq_length=model_config['seq_length'])
dataloader = DataLoader(dataset, batch_size=ds_config['micro_batch_size'])
start_time = time.time()
for i, batch in enumerate(dataloader):
if i >= 5:
break
trainer.train_step(batch)
end_time = time.time()
peak_memory = torch.cuda.max_memory_allocated() / 1024**3
results[stage] = {
'peak_memory_gb': peak_memory,
'time_per_step': (end_time - start_time) / 5
}
print(f" 📊 Peak Memory: {peak_memory:.2f}GB")
print(f" ⏱️ Time per step: {results[stage]['time_per_step']:.3f}s")
del trainer, model, engine
torch.cuda.empty_cache()
except Exception as e:
print(f" ❌ Error with stage {stage}: {str(e)}")
if len(results) > 1:
print(f"n📈 Comparison:")
stage_1_memory = results.get(1, {}).get('peak_memory_gb', 0)
stage_2_memory = results.get(2, {}).get('peak_memory_gb', 0)
if stage_1_memory > 0 and stage_2_memory > 0:
memory_reduction = (stage_1_memory - stage_2_memory) / stage_1_memory * 100
print(f" 🎯 Memory reduction from Stage 1 to 2: {memory_reduction:.1f}%")
def demonstrate_advanced_features():
"""Demonstrate additional advanced DeepSpeed features"""
print("n🚀 Advanced DeepSpeed Features:")
print(" 🎚️ Dynamic Loss Scaling: Automatically adjusts FP16 loss scaling")
print(" 🗜️ Gradient Compression: Reduces communication overhead")
print(" 🔄 Pipeline Parallelism: Splits model across devices")
print(" 🧑🎓 Expert Parallelism: Efficient Mixture-of-Experts training")
print(" 📚 Curriculum Learning: Progressive training strategies")
if __name__ == "__main__":
print(f"🖥️ CUDA Available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
print(f" GPU: {torch.cuda.get_device_name()}")
print(f" Memory: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.1f}GB")
try:
run_advanced_tutorial()
benchmark_zero_stages()
demonstrate_advanced_features()
except Exception as e:
print(f"❌ Error during tutorial: {str(e)}")
print("💡 Tips for troubleshooting:")
print(" - Ensure you have GPU runtime enabled in Colab")
print(" - Try reducing batch_size or model size if facing memory issues")
print(" - Enable CPU offloading in ds_config if needed")It produces restored configuration, sectors to spread zero categories to compare memory and speed, and display advanced features such as measurement of losses and pipeline / moe Paragraphs. We also see Cuda, run the full-end end, and give clear tips to solve problems, allow us to give us confidently to Calob.
In conclusion, we get a complete understanding of how Deeppeed promotes the proper functioning of the model by beating balance between working and trading memory. From the finding of Zero Zero categories to reduce memory reduction in order to use a mixed accuracy of FP16 and loading CPU, it is displayed by the powerful strategic techniques to make the hardware training. Finally, the students will be trained and prepared for a GPT-style model, sealing, GPU resources, and examines advanced features such as Pipeline Pipelinelism.
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