JEPA-AI:Core Technologies & Programming Stack
Technical Notes for My Article: LLMs Are Already Dead — The New AI Killed Them.
JEPA Key Technical Innovations
Representation Learning: Unlike pixel-level reconstruction (MAE), JEPA models predict in abstract representation space
Masking Strategy: Non-random, informative masking patterns
Target Encoder: Uses EMA updates instead of gradient updates
Multi-scale Features: Hierarchical representations at different abstraction levels
The models are typically trained on large GPU clusters (100s of A100/H100 GPUs) using frameworks like PyTorch with distributed training capabilities. The core innovation isn’t in the programming language but in the architectural choices and training objectives that enable learning useful representations without pixel-level reconstruction
JEPA Tech Flavors and Products
1. I-JEPA (Image-based JEPA)
Primary Language: Python Core Framework: PyTorch
Key Technologies:
Vision Transformer (ViT) architecture as the backbone
Self-supervised learning using masked image modeling
Joint embedding spaces for representation learning
Core Implementation Sample:
import torch
import torch.nn as nn
import torch.nn.functional as F
class IJEPA(nn.Module):
def __init__(self, encoder, predictor, target_encoder):
super().__init__()
self.encoder = encoder # Context encoder
self.predictor = predictor # Predicts target representations
self.target_encoder = target_encoder # Encodes target patches
def forward(self, context_patches, target_patches, context_masks, target_masks):
# Encode context (visible patches)
context_repr = self.encoder(context_patches * context_masks)
# Predict target representation in latent space
predicted_repr = self.predictor(context_repr)
# Get target representation (with stop gradient)
with torch.no_grad():
target_repr = self.target_encoder(target_patches * target_masks)
# Loss in representation space, not pixel space
loss = F.smooth_l1_loss(predicted_repr, target_repr)
return loss
# Vision Transformer backbone
class ViTEncoder(nn.Module):
def __init__(self, dim=768, depth=12, heads=12, patch_size=16):
super().__init__()
self.patch_embed = nn.Conv2d(3, dim, patch_size, patch_size)
self.pos_embed = nn.Parameter(torch.randn(1, 196, dim))
self.transformer = nn.TransformerEncoder(
nn.TransformerEncoderLayer(dim, heads),
depth
)
def forward(self, x):
x = self.patch_embed(x).flatten(2).transpose(1, 2)
x = x + self.pos_embed
return self.transformer(x)
2. V-JEPA (Video-based JEPA)
Core Additions to I-JEPA:
Temporal modeling through 3D convolutions or temporal transformers
Motion prediction in latent space
Spatiotemporal masking strategies
Key Implementation Features:
class VJEPA(nn.Module):
def __init__(self, spatial_encoder, temporal_encoder, predictor):
super().__init__()
self.spatial_encoder = spatial_encoder
self.temporal_encoder = temporal_encoder
self.predictor = predictor
def forward(self, video_frames, spatial_masks, temporal_masks):
B, T, C, H, W = video_frames.shape
# Spatial encoding per frame
spatial_features = []
for t in range(T):
feat = self.spatial_encoder(video_frames[:, t] * spatial_masks[:, t])
spatial_features.append(feat)
# Temporal encoding across frames
temporal_features = torch.stack(spatial_features, dim=1)
temporal_repr = self.temporal_encoder(temporal_features * temporal_masks)
# Predict future representations
predicted_future = self.predictor(temporal_repr)
return predicted_future
# Spatiotemporal transformer
class SpatioTemporalTransformer(nn.Module):
def __init__(self, dim=768, spatial_depth=12, temporal_depth=4):
super().__init__()
self.spatial_attn = nn.MultiheadAttention(dim, 12)
self.temporal_attn = nn.MultiheadAttention(dim, 12)
self.norm1 = nn.LayerNorm(dim)
self.norm2 = nn.LayerNorm(dim)
def forward(self, x):
# x shape: [batch, time, patches, dim]
B, T, P, D = x.shape
# Spatial attention within each frame
x_spatial = x.view(B*T, P, D)
x_spatial = self.norm1(x_spatial + self.spatial_attn(x_spatial, x_spatial, x_spatial)[0])
# Temporal attention across frames
x_temporal = x_spatial.view(B, T, P, D).transpose(1, 2) # [B, P, T, D]
x_temporal = x_temporal.reshape(B*P, T, D)
x_temporal = self.norm2(x_temporal + self.temporal_attn(x_temporal, x_temporal, x_temporal)[0])
return x_temporal.view(B, P, T, D).transpose(1, 2)3. Core Training Infrastructure
Distributed Training Setup (PyTorch):
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data.distributed import DistributedSampler
def setup_distributed_training():
dist.init_process_group(backend=’nccl’)
local_rank = int(os.environ[’LOCAL_RANK’])
torch.cuda.set_device(local_rank)
return local_rank
def create_model_ddp(model, local_rank):
model = model.cuda(local_rank)
model = DDP(model, device_ids=[local_rank])
return model
# Exponential Moving Average for target encoder
class EMA:
def __init__(self, model, decay=0.999):
self.model = model
self.decay = decay
def update(self, model):
with torch.no_grad():
for ema_param, param in zip(self.model.parameters(), model.parameters()):
ema_param.data.mul_(self.decay).add_(param.data, alpha=1 - self.decay)4. Key Libraries & Dependencies
# Core dependencies (requirements.txt style)
torch>=2.0.0
torchvision>=0.15.0
timm>=0.9.0 # For vision transformer architectures
einops>=0.6.0 # For tensor operations
opencv-python>=4.8.0 # Video processing
accelerate>=0.20.0 # Hugging Face training utilities
wandb>=0.15.0 # Experiment tracking5. Performance Optimizations
Mixed Precision Training:from torch.cuda.amp
from torch.cuda.amp import autocast, GradScaler
scaler = GradScaler()
def training_step(model, data, optimizer):
with autocast():
loss = model(data)
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()Flash Attention for Transformers:
# Using xformers or flash-attn libraries
from xformers.ops import memory_efficient_attention
def efficient_attention(q, k, v):
return memory_efficient_attention(q, k, v)