### Install Coconut PyTorch

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

Install the package via pip.

```bash
$ pip install coconut-pytorch
```

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

### Configure and Train with Begin-of-Thought Learning

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

Instantiate the Coconut model with learn_begin_of_thought enabled and inspect the resulting loss components during training.

```python
import torch
from coconut_pytorch import Coconut

model = Coconut(
    num_reasoning_steps=3,
    learn_begin_of_thought=True,     # Enable learning when to start thinking
    begin_thought_loss_weight=0.5,   # Weight for this auxiliary loss
    transformer=dict(
        num_tokens=256,
        dim=512,
        depth=6
    )
)

prompt = torch.randint(0, 256, (2, 512))
answer = torch.randint(0, 256, (2, 64))

# Training includes begin-of-thought prediction loss
loss = model(prompt, answer)
loss.backward()

# Inspect individual loss components
loss, intermediates = model(prompt, answer, return_intermediates=True)
loss_breakdown = intermediates[0]
answer_loss, bot_loss = loss_breakdown
print(f"Answer loss: {answer_loss.item():.4f}")
print(f"Begin-of-thought loss: {bot_loss.item():.4f}")
```

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

### Initialize and Use Transformer

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

Configure the core Transformer architecture and perform forward passes with various input types including tokens, embeddings, and mixed inputs.

```python
import torch
from coconut_pytorch import Transformer

# Initialize standalone transformer
transformer = Transformer(
    num_tokens=256,           # Vocabulary size
    dim=512,                  # Model dimension
    depth=6,                  # Number of layers
    dim_head=64,              # Dimension per head
    heads=8,                  # Number of attention heads
    ff_mult=4,                # Feedforward multiplier
    num_residual_streams=4,   # Hyper-connections streams (1 to disable)
    attend_kwargs=dict()      # Additional args for attention
)

# Forward with token IDs
tokens = torch.randint(0, 256, (2, 512))
logits = transformer(tokens)
# logits.shape: (2, 512, 256)

# Forward with embeddings
embeddings = torch.randn(2, 512, 512)
logits = transformer(embeddings)

# Mixed input: tokens followed by embeddings
mixed_input = [tokens, embeddings]  # List of tensors
logits = transformer(mixed_input)

# Get intermediates for latent reasoning
logits, embeds, cached_kv = transformer(tokens, return_intermediates=True)

# Incremental generation with cached key-values
next_logits = transformer(tokens[:, -1:], cached_kv=cached_kv)

# Get embeddings and cache for reasoning steps
embeds, cached_kv = transformer(tokens, return_embed_with_cache_kv=True)
```

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

### Initialize and Train Coconut Model

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

Configure the Coconut model with reasoning steps and transformer parameters, then perform a forward pass and backward propagation.

```python
import torch
from coconut_pytorch import Coconut

model = Coconut(
    num_reasoning_steps = 3,
    num_latents_per_step = 1,
    transformer = dict(
        num_tokens = 256,
        dim = 512,
        depth = 6
    )
)

prompt = torch.randint(0, 256, (2, 1024))
answer = torch.randint(0, 256, (2, 64))

loss = model(prompt, answer)
loss.backward()

# after much training

answer = model.generate(prompt, max_length = 64) # (2, 64)
```

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

### Enable Gradient Checkpointing

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

Configure models to use gradient checkpointing for reduced memory consumption during training.

```python
import torch
from coconut_pytorch import Coconut

# Enable checkpointing for memory-efficient training
model = Coconut(
    num_reasoning_steps=8,     # Many reasoning steps
    num_latents_per_step=2,    # Multiple latents per step
    checkpoint=True,           # Enable gradient checkpointing
    transformer=dict(
        num_tokens=50000,
        dim=1024,
        depth=12,
        heads=16
    )
)

# Training with checkpointing active
prompt = torch.randint(0, 50000, (4, 2048))
answer = torch.randint(0, 50000, (4, 256))

# Checkpointing only activates during training with gradients
model.train()
loss = model(prompt, answer)
loss.backward()  # Memory-efficient backward pass

# Checkpointing disabled during inference
model.eval()
with torch.no_grad():
    generated = model.generate(prompt, max_length=128)
```

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

### Coconut Forward Method Usage

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

Demonstrates standard forward passes, handling variable-length prompts, inference mode, and runtime reasoning step overrides.

```python
import torch
from coconut_pytorch import Coconut

model = Coconut(
	num_reasoning_steps=3,
	transformer=dict(num_tokens=256, dim=512, depth=4)
)

# Standard forward with fixed-size tensors
prompt = torch.randint(0, 256, (2, 512))
answer = torch.randint(0, 256, (2, 32))
loss = model(prompt, answer)

# Variable-length prompts using list input (padded automatically)
variable_prompts = [
	torch.randint(0, 256, (128,)),  # Short prompt
	torch.randint(0, 256, (256,)),  # Longer prompt
]
variable_answers = [
	torch.randint(0, 256, (16,)),
	torch.randint(0, 256, (32,)),
]
loss = model(variable_prompts, variable_answers)

# Inference mode - get intermediates without loss computation
prompt_logits, latent_tokens, answer_logits, cached_kv = model(
	prompt,
	answer=None,           # No answer for inference
	return_loss=False      # Don't compute loss
)

# Override reasoning steps at runtime
loss = model(prompt, answer, num_reasoning_steps=5)
```

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

### Configure Multi-Stream Coconut

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

Set up a multi-stream model to explore parallel reasoning paths and handle training losses and generation.

```python
import torch
from coconut_pytorch.multi_stream_coconut import Coconut as MultistreamCoconut

# Initialize multi-stream model
model = MultistreamCoconut(
    num_reasoning_steps=3,
    num_latents_per_step=1,
    num_hypothesis=4,                    # Number of parallel reasoning streams
    synthesize_hypothesis_per_step=True, # Allow cross-hypothesis communication
    checkpoint=False,                    # Gradient checkpointing
    begin_thought_loss_weight=1.0,
    hyp_diff_loss_weight=1.0,            # Encourage hypothesis diversity
    transformer=dict(
        num_tokens=256,
        dim=512,
        depth=4,
        num_residual_streams=4
    )
)

# Training
prompt = torch.randint(0, 256, (2, 1024))
answer = torch.randint(0, 256, (2, 64))

loss = model(prompt, answer)
loss.backward()

# Get detailed loss breakdown
loss, intermediates = model(prompt, answer, return_intermediates=True)
loss_breakdown, prompt_logits, latent_tokens, answer_logits, cached_kv = intermediates
answer_loss, bot_loss, hyp_diff_loss = loss_breakdown  # Includes hypothesis diversity loss

# Generation
generated = model.generate(prompt, max_length=64)
assert generated.shape == (2, 64)
```

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

### Initialize and Train Coconut Model

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

Initialize the Coconut model with a transformer configuration and perform a training forward pass.

```python
import torch
from coconut_pytorch import Coconut

# Initialize Coconut model with embedded transformer
model = Coconut(
	num_reasoning_steps=3,           # Number of latent reasoning iterations
	num_latents_per_step=1,          # Latent tokens per reasoning step
	learn_begin_of_thought=False,    # Whether to learn when to start thinking
	begin_thought_loss_weight=1.0,   # Weight for begin-of-thought loss
	checkpoint=False,                # Enable gradient checkpointing for memory efficiency
	transformer=dict(
		num_tokens=256,              # Vocabulary size
		dim=512,                     # Model dimension
		depth=6,                     # Number of transformer layers
		dim_head=64,                 # Dimension per attention head
		heads=8,                     # Number of attention heads
		ff_mult=4,                   # Feedforward multiplier
		num_residual_streams=4       # Hyper-connections streams
	)
)

# Prepare training data
prompt = torch.randint(0, 256, (2, 1024))  # Batch of 2, sequence length 1024
answer = torch.randint(0, 256, (2, 64))    # Target answers

# Training forward pass - returns loss
loss = model(prompt, answer)
loss.backward()

# Forward pass with intermediates for analysis
loss, intermediates = model(prompt, answer, return_intermediates=True)
loss_breakdown, prompt_logits, latent_tokens, answer_logits, cached_kv = intermediates
answer_loss, bot_loss = loss_breakdown
```

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

### Reasoning by Superposition Citation

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

BibTeX citation for Reasoning by Superposition.

```bibtex
@inproceedings{Zhu2025ReasoningBS,
    title     = {Reasoning by Superposition: A Theoretical Perspective on Chain of Continuous Thought},
    author    = {Hanlin Zhu and Shibo Hao and Zhiting Hu and Jiantao Jiao and Stuart Russell and Yuandong Tian},
    year      = {2025},
    url       = {https://api.semanticscholar.org/CorpusID:278740606}
}
```

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

### Multi-Stream Transformers Citation

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

BibTeX citation for Multi-Stream Transformers.

```bibtex
@article{Burtsev2021MultiStreamT,
    title   = {Multi-Stream Transformers},
    author  = {Mikhail S. Burtsev and Anna Rumshisky},
    journal = {ArXiv},
    year    = {2021},
    volume  = {abs/2107.10342},
    url     = {https://api.semanticscholar.org/CorpusID:236171087}
}
```

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

### Coconut Generate Method Usage

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

Perform autoregressive text generation with support for temperature sampling and min-p filtering.

```python
import torch
from coconut_pytorch import Coconut

model = Coconut(
	num_reasoning_steps=3,
	transformer=dict(num_tokens=256, dim=512, depth=6)
)

prompt = torch.randint(0, 256, (2, 1024))

# Basic generation
generated = model.generate(
	prompt,
	max_length=64  # Generate 64 tokens
)
# generated.shape: (2, 64)

# Generation with custom parameters
generated = model.generate(
	prompt,
	max_length=128,
	temperature=0.8,           # Sampling temperature
	filter_kwargs=dict(min_p=0.05),  # Min-p filtering threshold
	num_reasoning_steps=5      # Override reasoning steps
)
```

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

### Hyper-Connections Citation

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

BibTeX citation for Hyper-Connections.

```bibtex
@article{Zhu2024HyperConnections,
    title   = {Hyper-Connections},
    author  = {Defa Zhu and Hongzhi Huang and Zihao Huang and Yutao Zeng and Yunyao Mao and Banggu Wu and Qiyang Min and Xun Zhou},
    journal = {ArXiv},
    year    = {2024},
    volume  = {abs/2409.19606},
    url     = {https://api.semanticscholar.org/CorpusID:272987528}
}
```

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

### Implement Custom Logit Filtering

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

Define a custom filter function to manipulate logits during generation, such as applying top-k sampling.

```python
def top_k_filter(logits, k=50):
    values, indices = torch.topk(logits, k)
    mask = torch.ones_like(logits) * float('-inf')
    mask.scatter_(-1, indices, values)
    return mask

generated = model.generate(
    prompt,
    max_length=64,
    filter_fn=top_k_filter,
    filter_kwargs=dict(k=40)
)
```

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

### Value Residual Learning Citation

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

BibTeX citation for Value Residual Learning.

```bibtex
@inproceedings{Zhou2024ValueRL,
    title   = {Value Residual Learning For Alleviating Attention Concentration In Transformers},
    author  = {Zhanchao Zhou and Tianyi Wu and Zhiyun Jiang and Zhenzhong Lan},
    year    = {2024},
    url     = {https://api.semanticscholar.org/CorpusID:273532030}
}
```

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

### Coconut Citation BibTeX

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

BibTeX citation for the Coconut paper.

```bibtex
@inproceedings{Hao2024TrainingLL,
    title   = {Training Large Language Models to Reason in a Continuous Latent Space},
    author  = {Shibo Hao and Sainbayar Sukhbaatar and DiJia Su and Xian Li and Zhiting Hu and Jason Weston and Yuandong Tian},
    year    = {2024},
    url     = {https://api.semanticscholar.org/CorpusID:274610816}
}
```

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