### Initialize and Use Mirasol Model

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

Instantiate the Mirasol model with specified dimensions and parameters. This example demonstrates how to pass audio, video, and text data to the model for training and how to generate text after training.

```python
import torch
from mirasol_pytorch import Mirasol

model = Mirasol(
    dim = 512,
    num_text_tokens = 256,
    video_image_size = 128,
    video_frames_per_timechunk = 2,
    audio_freq_dim = 64,
    audio_time_dim_per_timechunk = 32,
    audio_patch_size = (32, 16),
    video_patch_size = (64, 2),
    audio_encoder = dict(
        dim = 512,
        depth = 2
    ),
    video_encoder = dict(
        dim = 512,
        depth = 2
    )
)

audio = torch.randn(1, 64, 1024)
video = torch.randn(1, 3, 12, 128, 128)

text = torch.randint(0, 256, (1, 1024))

loss = model(
    audio = audio,
    video = video,
    text = text
)

loss.backward()

# after much training

sampled_text = model.generate(
    audio = audio,
    video = video,
    seq_len = 512
)
```

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

### Install Mirasol-Pytorch

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

Install the library using pip. This command is used to add the Mirasol-Pytorch package to your project.

```bash
pip install mirasol-pytorch
```

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

### Distributed Training Setup and Model Initialization

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

Configure distributed training using `torch.distributed` and initialize the Mirasol model on the appropriate device. The model can be wrapped with `DistributedDataParallel` if running in a distributed environment. Similarity regularization loss is enabled via `sim_reg_loss_weight`.

```python
import torch
import torch.distributed as dist
from mirasol_pytorch import Mirasol
from mirasol_pytorch.distributed import get_is_distributed, all_gather

# Initialize distributed training (example with torchrun)
# torchrun --nproc_per_node=4 train.py

def setup_distributed():
    dist.init_process_group(backend='nccl')
    local_rank = dist.get_rank()
    torch.cuda.set_device(local_rank)
    return local_rank

# Check if running in distributed mode
if get_is_distributed():
    local_rank = setup_distributed()
    device = torch.device(f'cuda:{local_rank}')
else:
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

model = Mirasol(
    dim=512,
    num_text_tokens=256,
    video_image_size=128,
    video_frames_per_timechunk=2,
    audio_freq_dim=64,
    audio_time_dim_per_timechunk=32,
    audio_patch_size=(32, 16),
    video_patch_size=(64, 2),
    audio_encoder=dict(dim=512, depth=2),
    video_encoder=dict(dim=512, depth=2),
    # Enable similarity regularization (uses all_gather in distributed)
    sim_reg_loss_weight=0.1
).to(device)

if get_is_distributed():
    model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[local_rank])

# Training loop with distributed data
audio = torch.randn(2, 64, 1024, device=device)
video = torch.randn(2, 3, 12, 128, 128, device=device)
text = torch.randint(0, 256, (2, 256), device=device)

loss = model(audio=audio, video=video, text=text)
loss.backward()

print(f"[Rank {dist.get_rank() if get_is_distributed() else 0}] Loss: {loss.item():.4f}")
```

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

### Initialize and Train Mirasol Model

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

Create a Mirasol model instance and perform a forward pass with audio, video, and text inputs.

```python
import torch
from mirasol_pytorch import Mirasol

# Initialize the Mirasol model with configuration
model = Mirasol(
    dim=512,                              # Model dimension
    num_text_tokens=256,                  # Size of text vocabulary
    video_image_size=128,                 # Video frame spatial size (height/width)
    video_frames_per_timechunk=2,         # Frames per time chunk
    audio_freq_dim=64,                    # Audio frequency dimension
    audio_time_dim_per_timechunk=32,      # Audio time steps per chunk
    audio_patch_size=(32, 16),            # Audio patch (freq, time)
    video_patch_size=(64, 2),             # Video patch (spatial, time)
    audio_encoder=dict(
        dim=512,
        depth=2
    ),
    video_encoder=dict(
        dim=512,
        depth=2
    ),
    encoder_depth=6,                      # A/V encoder transformer depth
    decoder_depth=6,                      # Text decoder depth
    combiner_depth=2,                     # Combiner transformer depth
    combiner_output_num_tokens=3,         # Output tokens per time chunk
    num_audio_video_register_tokens=8,    # Register tokens for attention
    audio_video_mask_prob=0.15,           # Masking probability for A/V tokens
    text_max_seq_len=2048,                # Maximum text sequence length
    text_forgetful_causal_mask_prob=0.1,  # Forgetful causal masking prob
    flash_attn=True,                      # Use Flash Attention
    av_autoregressive_loss_weight=1.,     # Weight for A/V autoregressive loss
    av_reconstruction_loss_weight=1.,     # Weight for reconstruction loss
    sim_reg_loss_weight=0.                # Similarity regularization weight
)

# Prepare input data
# Audio: (batch, frequency, time)
audio = torch.randn(1, 64, 1024)

# Video: (batch, channels, frames, height, width)
video = torch.randn(1, 3, 12, 128, 128)

# Text tokens: (batch, sequence_length)
text = torch.randint(0, 256, (1, 1024))

# Forward pass returns combined loss
loss = model(
    audio=audio,
    video=video,
    text=text
)

# Backpropagation
loss.backward()
print(f"Training loss: {loss.item()}")
```

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

### Defining Custom Encoders

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

Demonstrates how to instantiate custom encoder modules for use with the Mirasol architecture.

```python
import torch
from torch import nn
from mirasol_pytorch import Mirasol
from x_transformers import Encoder

# Create custom video encoder with specific configuration
custom_video_encoder = Encoder(
    dim=512,
    depth=4,
    heads=8,
    dim_head=64,
    ff_mult=4,
    flash_attn=True
)

# Create custom audio encoder
custom_audio_encoder = Encoder(
    dim=512,
    depth=3,
    heads=8,
    dim_head=64,
    ff_mult=4,
    flash_attn=True
)
```

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

### Create Custom Combiner and Model

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

Instantiate a custom combiner and then the Mirasol model, passing the custom audio, video, and combiner encoders. This allows for flexible model architecture customization.

```python
from mirasol_pytorch import Mirasol, Encoder

custom_combiner = Encoder(
    dim=512,
    depth=4,
    heads=8,
    dim_head=64,
    flash_attn=True
)

model = Mirasol(
    dim=512,
    num_text_tokens=256,
    video_image_size=128,
    video_frames_per_timechunk=2,
    audio_freq_dim=64,
    audio_time_dim_per_timechunk=32,
    audio_patch_size=(32, 16),
    video_patch_size=(64, 2),
    # Pass custom modules directly
    audio_encoder=custom_audio_encoder,
    video_encoder=custom_video_encoder,
    combiner=custom_combiner,
    combiner_output_num_tokens=4
)

audio = torch.randn(1, 64, 1024)
video = torch.randn(1, 3, 12, 128, 128)
text = torch.randint(0, 256, (1, 256))

loss = model(audio=audio, video=video, text=text)
print(f"Loss with custom encoders: {loss.item():.4f}")
```

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

### Retrieve Detailed Loss Breakdown

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

Enable return_loss_breakdown to monitor individual loss components during training.

```python
import torch
from mirasol_pytorch import Mirasol

model = Mirasol(
    dim=512,
    num_text_tokens=256,
    video_image_size=128,
    video_frames_per_timechunk=2,
    audio_freq_dim=64,
    audio_time_dim_per_timechunk=32,
    audio_patch_size=(32, 16),
    video_patch_size=(64, 2),
    audio_encoder=dict(dim=512, depth=2),
    video_encoder=dict(dim=512, depth=2),
    av_autoregressive_loss_weight=1.,
    av_reconstruction_loss_weight=1.,
    sim_reg_loss_weight=0.1  # Enable similarity regularization
)

audio = torch.randn(2, 64, 1024)
video = torch.randn(2, 3, 12, 128, 128)
text = torch.randint(0, 256, (2, 512))

# Get detailed loss breakdown
total_loss, loss_breakdown = model(
    audio=audio,
    video=video,
    text=text,
    return_loss_breakdown=True
)

# Access individual loss components
print(f"Total Loss: {total_loss.item():.4f}")
print(f"Text Autoregressive Loss: {loss_breakdown.text_autoregressive:.4f}")
print(f"A/V Autoregressive Loss: {loss_breakdown.av_autoregressive:.4f}")
print(f"A/V Reconstruction Loss: {loss_breakdown.av_recon:.4f}")
print(f"Text-A/V Similarity Reg Loss: {loss_breakdown.text_av_sim_reg:.4f}")

total_loss.backward()
```

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

### Video Reconstruction with Reduced Resolution

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

Configures the model to use a reconstruction loss with a smaller video resolution for improved training efficiency.

```python
import torch
from mirasol_pytorch import Mirasol

# Configure model with video reconstruction at reduced resolution
model = Mirasol(
    dim=512,
    num_text_tokens=256,
    video_image_size=128,
    video_frames_per_timechunk=2,
    audio_freq_dim=64,
    audio_time_dim_per_timechunk=32,
    audio_patch_size=(32, 16),
    video_patch_size=(64, 2),
    # Smaller patch size for reconstruction (must be <= video_patch_size)
    video_recon_patch_size=(32, 1),
    video_recon_interpolate_mode='bilinear',  # Interpolation mode
    audio_encoder=dict(dim=512, depth=2),
    video_encoder=dict(dim=512, depth=2),
    av_reconstruction_loss_weight=1.0
)

audio = torch.randn(1, 64, 1024)
video = torch.randn(1, 3, 12, 128, 128)
text = torch.randint(0, 256, (1, 256))

# Training with reconstruction loss at reduced resolution
total_loss, losses = model(
    audio=audio,
    video=video,
    text=text,
    return_loss_breakdown=True
)

print(f"Reconstruction loss (reduced res): {losses.av_recon:.4f}")
```

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

### Inference with Pre-encoded Audio and Video

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

Passes pre-computed features directly to the model to skip the encoding step during inference.

```python
import torch
from mirasol_pytorch import Mirasol

model = Mirasol(
    dim=512,
    num_text_tokens=256,
    video_image_size=128,
    video_frames_per_timechunk=2,
    audio_freq_dim=64,
    audio_time_dim_per_timechunk=32,
    audio_patch_size=(32, 16),
    video_patch_size=(64, 2),
    audio_encoder=dict(dim=512, depth=2),
    video_encoder=dict(dim=512, depth=2)
)

# Get expected shapes from model
audio_tokens_per_chunk = model.audio_timechunk_tokens
video_tokens_per_chunk = model.video_timechunk_tokens
dim = 512

print(f"Audio tokens per chunk: {audio_tokens_per_chunk}")
print(f"Video tokens per chunk: {video_tokens_per_chunk}")

# Pre-encode using external encoder or cached features
num_time_chunks = 32
batch_size = 2

# Pre-encoded audio: (batch, time_chunks, tokens_per_chunk, dim)
encoded_audio = torch.randn(batch_size, num_time_chunks, audio_tokens_per_chunk, dim)

# Pre-encoded video: (batch, time_chunks, tokens_per_chunk, dim)
encoded_video = torch.randn(batch_size, num_time_chunks, video_tokens_per_chunk, dim)

text = torch.randint(0, 256, (batch_size, 256))

# Forward pass with pre-encoded features (skips encoding step)
loss = model(
    encoded_audio=encoded_audio,
    encoded_video=encoded_video,
    text=text
)

print(f"Loss with pre-encoded inputs: {loss.item():.4f}")
```

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

### Inference Mode (No Loss Calculation)

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

Set `return_loss=False` during model instantiation for inference to directly obtain decoder outputs (logits) without computing losses. Ensure the model is in evaluation mode (`model.eval()`) and use `torch.no_grad()`.

```python
import torch
from mirasol_pytorch import Mirasol

model = Mirasol(
    dim=512,
    num_text_tokens=256,
    video_image_size=128,
    video_frames_per_timechunk=2,
    audio_freq_dim=64,
    audio_time_dim_per_timechunk=32,
    audio_patch_size=(32, 16),
    video_patch_size=(64, 2),
    audio_encoder=dict(dim=512, depth=2),
    video_encoder=dict(dim=512, depth=2)
)

model.eval()

audio = torch.randn(1, 64, 1024)
video = torch.randn(1, 3, 12, 128, 128)
text = torch.randint(0, 256, (1, 128))

with torch.no_grad():
    # Get decoder logits without loss computation
    logits = model(
        audio=audio,
        video=video,
        text=text,
        return_loss=False
    )

print(f"Output logits shape: {logits.shape}")
# Output: torch.Size([1, 129, 257]) - (batch, seq_len+1, vocab_size+1)

# Get predicted tokens
predicted_tokens = logits.argmax(dim=-1)
print(f"Predicted tokens shape: {predicted_tokens.shape}")
```

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

### Autoregressive Text Generation with Mirasol

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

Performs text generation conditioned on audio and video inputs. The model automatically switches to evaluation mode during generation.

```python
import torch
from mirasol_pytorch import Mirasol

model = Mirasol(
    dim=512,
    num_text_tokens=256,
    video_image_size=128,
    video_frames_per_timechunk=2,
    audio_freq_dim=64,
    audio_time_dim_per_timechunk=32,
    audio_patch_size=(32, 16),
    video_patch_size=(64, 2),
    audio_encoder=dict(dim=512, depth=2),
    video_encoder=dict(dim=512, depth=2),
    text_max_seq_len=2048
)

# Load trained weights (after training)
# model.load_state_dict(torch.load('mirasol_trained.pt'))

# Prepare audio and video inputs
audio = torch.randn(1, 64, 1024)
video = torch.randn(1, 3, 12, 128, 128)

# Generate text from audio/video context
# Model automatically switches to eval mode
generated_tokens = model.generate(
    audio=audio,
    video=video,
    seq_len=512  # Maximum tokens to generate
)

print(f"Generated token shape: {generated_tokens.shape}")
# Output: Generated token shape: torch.Size([1, 512])

# Generate with a text prompt for continuation
prompt = torch.randint(0, 256, (1, 32))  # Starting prompt tokens

continued_tokens = model.generate(
    audio=audio,
    video=video,
    prompt=prompt,
    seq_len=256
)

print(f"Continued token shape: {continued_tokens.shape}")
# Output: Continued token shape: torch.Size([1, 289]) (prompt + generated)
```

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