### Install CoCa Pytorch

Source: https://github.com/lucidrains/coca-pytorch/blob/main/README.md

Install the package via pip.

```bash
$ pip install coca-pytorch
```

--------------------------------

### Install CoCa PyTorch and Dependencies

Source: https://context7.com/lucidrains/coca-pytorch/llms.txt

Install the core library and the required vision transformer dependency.

```bash
pip install coca-pytorch
```

```bash
pip install vit-pytorch>=0.40.2
```

--------------------------------

### Install Vision Transformer Dependency

Source: https://github.com/lucidrains/coca-pytorch/blob/main/README.md

Install the required vit-pytorch package for the image encoder.

```bash
$ pip install vit-pytorch>=0.40.2
```

--------------------------------

### Initialize CoCa Model

Source: https://context7.com/lucidrains/coca-pytorch/llms.txt

Configure the CoCa model with a vision transformer encoder and specify architecture dimensions and loss weights.

```python
import torch
from vit_pytorch.simple_vit_with_patch_dropout import SimpleViT
from vit_pytorch.extractor import Extractor
from coca_pytorch import CoCa

# Create a Vision Transformer as the image encoder
vit = SimpleViT(
    image_size=256,
    patch_size=32,
    num_classes=1000,
    dim=1024,
    depth=6,
    heads=16,
    mlp_dim=2048,
    patch_dropout=0.5  # PatchDropout for regularization
)

# Wrap ViT with Extractor to return embeddings instead of class predictions
vit = Extractor(vit, return_embeddings_only=True, detach=False)

# Initialize CoCa model
coca = CoCa(
    dim=512,                      # Model dimension
    img_encoder=vit,              # Vision transformer image encoder
    image_dim=1024,               # Image embedding dimension (must match ViT output)
    num_tokens=20000,             # Vocabulary size for text tokens
    unimodal_depth=6,             # Number of unimodal transformer layers
    multimodal_depth=6,           # Number of multimodal transformer layers
    dim_head=64,                  # Dimension per attention head
    heads=8,                      # Number of attention heads
    num_img_queries=256,          # Number of image queries for attention pooling
    caption_loss_weight=1.0,      # Weight for autoregressive caption loss
    contrastive_loss_weight=1.0,  # Weight for contrastive loss
    pad_id=0                      # Padding token ID
).cuda()
```

--------------------------------

### Initialize and Train CoCa

Source: https://github.com/lucidrains/coca-pytorch/blob/main/README.md

Configure the vision transformer, instantiate the CoCa model, and perform training or inference steps.

```python
import torch

# import vision transformer

from vit_pytorch.simple_vit_with_patch_dropout import SimpleViT
from vit_pytorch.extractor import Extractor

vit = SimpleViT(
    image_size = 256,
    patch_size = 32,
    num_classes = 1000,
    dim = 1024,
    depth = 6,
    heads = 16,
    mlp_dim = 2048,
    patch_dropout = 0.5  # https://arxiv.org/abs/2212.00794
)

vit = Extractor(vit, return_embeddings_only = True, detach = False)

# extractor will enable it so the vision transformer returns its embeddings

# import CoCa and instantiate it

from coca_pytorch.coca_pytorch import CoCa

coca = CoCa(
    dim = 512,                     # model dimension
    img_encoder = vit,             # vision transformer - image encoder, returning image embeddings as (batch, seq, dim)
    image_dim = 1024,              # image embedding dimension, if not the same as model dimensions
    num_tokens = 20000,            # number of text tokens
    unimodal_depth = 6,            # depth of the unimodal transformer
    multimodal_depth = 6,          # depth of the multimodal transformer
    dim_head = 64,                 # dimension per attention head
    heads = 8,                     # number of attention heads
    caption_loss_weight = 1.,      # weight on the autoregressive caption loss
    contrastive_loss_weight = 1.,  # weight on the contrastive loss between image and text CLS embeddings
).cuda()

# mock text and images

text = torch.randint(0, 20000, (4, 512)).cuda()
images = torch.randn(4, 3, 256, 256).cuda()

# train by giving CoCa your text and images with `return_loss = True`

loss = coca(
    text = text,
    images = images,
    return_loss = True  # set this to True to get the full caption + contrastive loss
)

loss.backward()

# do the above for as much text and images...
# then you can get the caption logits as so

logits = coca(
    text = text,
    images = images
) # (4, 512, 20000)

# and the CLIP-like text and image embeddings as

text_embeds, image_embeds = coca(
    text = text,
    images = images,
    return_embeddings = True
) # (4, 512), (4, 512)
```

--------------------------------

### Initialize CoCa Model for Distributed Training

Source: https://context7.com/lucidrains/coca-pytorch/llms.txt

Initializes the CoCa model with distributed training support. The model automatically detects the distributed environment. Ensure `vit` is your pre-trained vision transformer encoder. The model is moved to CUDA, and `DistributedDataParallel` should be used for actual distributed training.

```python
import torch
import torch.distributed as dist
from coca_pytorch import CoCa

# Initialize distributed training (typically done in main script)
# dist.init_process_group(backend='nccl')

# CoCa automatically detects distributed environment
coca = CoCa(
    dim=512,
    img_encoder=vit,  # Your ViT encoder
    image_dim=1024,
    num_tokens=20000,
    unimodal_depth=6,
    multimodal_depth=6,
    dim_head=64,
    heads=8,
    caption_loss_weight=1.0,
    contrastive_loss_weight=1.0
).cuda()

# Wrap with DistributedDataParallel
# coca = torch.nn.parallel.DistributedDataParallel(coca, device_ids=[local_rank])

# Training loop - all-gather is handled automatically
# When is_distributed=True, text and image latents are gathered across
# all processes before computing contrastive loss
text = torch.randint(0, 20000, (4, 512)).cuda()
images = torch.randn(4, 3, 256, 256).cuda()

loss = coca(text=text, images=images, return_loss=True)
loss.backward()
```

--------------------------------

### Configure CrossAttention Module

Source: https://context7.com/lucidrains/coca-pytorch/llms.txt

Use the cross-attention module for multimodal fusion between text queries and image context.

```python
import torch
from coca_pytorch.coca_pytorch import CrossAttention

# Create cross-attention layer with parallel feedforward
cross_attn = CrossAttention(
    dim=512,              # Query dimension
    context_dim=1024,     # Context (key/value) dimension
    dim_head=64,          # Dimension per attention head
    heads=8,              # Number of attention heads
    parallel_ff=True,     # Enable parallel feedforward
    ff_mult=4,            # Feedforward multiplier
    norm_context=True     # Apply LayerNorm to context
).cuda()

# Text tokens as queries
text_tokens = torch.randn(4, 128, 512).cuda()

# Image tokens as context (keys and values)
image_tokens = torch.randn(4, 64, 1024).cuda()

# Cross-attend text to image
output = cross_attn(text_tokens, image_tokens)

print(f"Text input: {text_tokens.shape}")    # torch.Size([4, 128, 512])
print(f"Image context: {image_tokens.shape}") # torch.Size([4, 64, 1024])
print(f"Output shape: {output.shape}")        # torch.Size([4, 128, 512])
```

--------------------------------

### Implement ParallelTransformerBlock

Source: https://context7.com/lucidrains/coca-pytorch/llms.txt

Create a transformer block with parallel attention and feedforward layers using SwiGLU activation.

```python
import torch
from coca_pytorch.coca_pytorch import ParallelTransformerBlock

# Create a parallel transformer block
block = ParallelTransformerBlock(
    dim=512,        # Model dimension
    dim_head=64,    # Dimension per attention head
    heads=8,        # Number of attention heads
    ff_mult=4       # Feedforward multiplier (ff_dim = dim * ff_mult)
).cuda()

# Input tensor: (batch_size, sequence_length, dim)
x = torch.randn(4, 128, 512).cuda()

# Forward pass with causal masking
output = block(x)

print(f"Input shape: {x.shape}")      # torch.Size([4, 128, 512])
print(f"Output shape: {output.shape}") # torch.Size([4, 128, 512])

# With custom attention mask
attn_mask = torch.ones(4, 128, 128, dtype=torch.bool).cuda()
output_masked = block(x, attn_mask=attn_mask)
```

--------------------------------

### Train with Custom Labels

Source: https://context7.com/lucidrains/coca-pytorch/llms.txt

Provide explicit target labels separately from input text to control the training objective.

```python
import torch
from coca_pytorch import CoCa

# Assume coca model is initialized

# Separate input text and target labels
text_input = torch.randint(0, 20000, (4, 511)).cuda()  # Input sequence
labels = torch.randint(0, 20000, (4, 511)).cuda()      # Target sequence
images = torch.randn(4, 3, 256, 256).cuda()

# Train with explicit labels
loss = coca(
    text=text_input,
    images=images,
    labels=labels,        # Explicit labels for caption loss
    return_loss=True
)

loss.backward()
print(f"Loss with custom labels: {loss.item():.4f}")
```

--------------------------------

### Use Pre-computed Image Tokens

Source: https://context7.com/lucidrains/coca-pytorch/llms.txt

Pass pre-computed image tokens directly to the CoCa model to bypass the image encoder for efficiency.

```python
import torch
from coca_pytorch import CoCa

# Initialize CoCa without image encoder for pre-computed tokens
coca = CoCa(
    dim=512,
    img_encoder=None,             # No image encoder needed
    image_dim=1024,               # Must match pre-computed token dimension
    num_tokens=20000,
    unimodal_depth=6,
    multimodal_depth=6,
    dim_head=64,
    heads=8
).cuda()

# Pre-computed image tokens (e.g., from a separate ViT forward pass)
# Shape: (batch_size, num_patches, image_dim)
image_tokens = torch.randn(4, 64, 1024).cuda()
text = torch.randint(0, 20000, (4, 512)).cuda()

# Forward pass with pre-computed image tokens
loss = coca(
    text=text,
    image_tokens=image_tokens,  # Use image_tokens instead of images
    return_loss=True
)

loss.backward()
print(f"Loss with pre-computed tokens: {loss.item():.4f}")
```

--------------------------------

### Train CoCa with Combined Loss

Source: https://context7.com/lucidrains/coca-pytorch/llms.txt

Perform a forward pass with return_loss=True to compute the weighted sum of caption and contrastive losses.

```python
import torch
from coca_pytorch import CoCa

# Assume coca model is initialized as shown above

# Prepare training data
batch_size = 4
seq_length = 512
vocab_size = 20000

text = torch.randint(0, vocab_size, (batch_size, seq_length)).cuda()
images = torch.randn(batch_size, 3, 256, 256).cuda()

# Forward pass with loss computation
# When return_loss=True, text is automatically split into input and labels
loss = coca(
    text=text,
    images=images,
    return_loss=True  # Returns combined caption + contrastive loss
)

# Backward pass
loss.backward()

# Output: Single scalar tensor combining caption loss and contrastive loss
# The loss is weighted by caption_loss_weight and contrastive_loss_weight
print(f"Training loss: {loss.item():.4f}")
```

--------------------------------

### Calculate Similarity and Matches

Source: https://context7.com/lucidrains/coca-pytorch/llms.txt

Compute similarity between text and image embeddings and identify the best matching indices.

```python
# Embeddings are already L2-normalized
similarity = torch.matmul(text_embeds, image_embeds.t())
print(f"Similarity matrix: {similarity.shape}")  # torch.Size([4, 4])

# Find best matching images for each text query
best_matches = similarity.argmax(dim=-1)
print(f"Best matching image indices: {best_matches}")
```

--------------------------------

### Generate Caption Logits

Source: https://context7.com/lucidrains/coca-pytorch/llms.txt

Perform inference by setting return_loss=False to retrieve raw logits for token prediction.

```python
import torch
from coca_pytorch import CoCa

# Assume coca model is initialized

text = torch.randint(0, 20000, (4, 512)).cuda()
images = torch.randn(4, 3, 256, 256).cuda()

# Get caption logits (no loss computation)
logits = coca(
    text=text,
    images=images,
    return_loss=False  # Default behavior
)

# Output shape: (batch_size, sequence_length, vocab_size)
print(f"Logits shape: {logits.shape}")  # torch.Size([4, 512, 20000])

# Get predicted tokens
predicted_tokens = logits.argmax(dim=-1)
print(f"Predicted tokens shape: {predicted_tokens.shape}")  # torch.Size([4, 512])
```

--------------------------------

### Extract CLIP-like Embeddings

Source: https://context7.com/lucidrains/coca-pytorch/llms.txt

Retrieve normalized text and image embeddings for similarity matching by setting return_embeddings=True.

```python
import torch
import torch.nn.functional as F
from coca_pytorch import CoCa

# Assume coca model is initialized

text = torch.randint(0, 20000, (4, 512)).cuda()
images = torch.randn(4, 3, 256, 256).cuda()

# Get text and image embeddings
text_embeds, image_embeds = coca(
    text=text,
    images=images,
    return_embeddings=True  # Returns CLS token embeddings
)

# Output shapes: both (batch_size, dim_latents)
print(f"Text embeddings: {text_embeds.shape}")   # torch.Size([4, 512])
print(f"Image embeddings: {image_embeds.shape}") # torch.Size([4, 512])

# Compute similarity matrix for retrieval tasks
```

--------------------------------

### Apply Rotary Position Embeddings

Source: https://context7.com/lucidrains/coca-pytorch/llms.txt

Applies rotary position embeddings to queries and keys. Ensure `pos_emb` is pre-computed and compatible with `queries` and `keys` shapes.

```python
queries_rotated = apply_rotary_pos_emb(pos_emb, queries)
keys_rotated = apply_rotary_pos_emb(pos_emb, keys)
```

--------------------------------

### CoCa and PaLM Citations

Source: https://github.com/lucidrains/coca-pytorch/blob/main/README.md

BibTeX citations for the CoCa and PaLM papers.

```bibtex
@inproceedings{Yu2022CoCaCC,
  title   = {CoCa: Contrastive Captioners are Image-Text Foundation Models},
  author  = {Jiahui Yu and Zirui Wang and Vijay Vasudevan and Legg Yeung and Mojtaba Seyedhosseini and Yonghui Wu},
  year    = {2022}
}
```

```bibtex
@inproceedings{Chowdhery2022PaLMSL,
    title   = {PaLM: Scaling Language Modeling with Pathways},
    author  = {Aakanksha Chowdhery and Sharan Narang and Jacob Devlin and Maarten Bosma and Gaurav Mishra and Adam Roberts and Paul Barham and Hyung Won Chung and Charles Sutton and Sebastian Gehrmann and Parker Schuh and Kensen Shi and Sasha Tsvyashchenko and Joshua Maynez and Abhishek Rao and Parker Barnes and Yi Tay and Noam M. Shazeer and Vinodkumar Prabhakaran and Emily Reif and Nan Du and Benton C. Hutchinson and Reiner Pope and James Bradbury and Jacob Austin and Michael Isard and Guy Gur-Ari and Pengcheng Yin and Toju Duke and Anselm Levskaya and Sanjay Ghemawat and Sunipa Dev and Henryk Michalewski and Xavier Garc{'i}a and Vedant Misra and Kevin Robinson and Liam Fedus and Denny Zhou and Daphne Ippolito and David Luan and Hyeontaek Lim and Barret Zoph and Alexander Spiridonov and Ryan Sepassi and David Dohan and Shivani Agrawal and Mark Omernick and Andrew M. Dai and Thanumalayan Sankaranarayana Pillai and Marie Pellat and Aitor Lewkowycz and Erica Oliveira Moreira and Rewon Child and Oleksandr Polozov and Katherine Lee and Zongwei Zhou and Xuezhi Wang and Brennan Saeta and Mark Diaz and Orhan Firat and Michele Catasta and Jason Wei and Kathleen S. Meier-Hellstern and Douglas Eck and Jeff Dean and Slav Petrov and Noah Fiedel},
    year    = {2022}
}
```

--------------------------------

### Apply RotaryEmbedding

Source: https://context7.com/lucidrains/coca-pytorch/llms.txt

Generate and apply rotary positional embeddings to attention mechanisms.

```python
import torch
from coca_pytorch.coca_pytorch import RotaryEmbedding, apply_rotary_pos_emb

# Create rotary embedding layer
rotary_emb = RotaryEmbedding(dim=64).cuda()  # dim should match dim_head

# Generate positional frequencies for sequence length
max_seq_len = 512
pos_emb = rotary_emb(max_seq_len, device=torch.device('cuda'))

print(f"Positional embedding shape: {pos_emb.shape}")  # torch.Size([512, 64])

# Apply rotary embeddings to queries/keys
queries = torch.randn(4, 8, 512, 64).cuda()  # (batch, heads, seq, dim_head)
keys = torch.randn(4, 512, 64).cuda()        # (batch, seq, dim_head)
```

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