Distributed Operations

def broadcast(tensor: torch.Tensor, from_process: int = 0):
    """
    Broadcast tensor from one process to all other processes.
    
    Parameters:
    - tensor: Tensor to broadcast (modified in-place on receiving processes)
    - from_process: Source process rank (default: 0)
    """

def gather(tensor: torch.Tensor):
    """
    Gather tensors from all processes to the main process.
    
    Parameters:
    - tensor: Tensor to gather from current process
    
    Returns:
    Concatenated tensor from all processes (only on main process, None elsewhere)
    """

def reduce(tensor: torch.Tensor, reduction: str = "mean"):
    """
    Reduce tensor across all processes using specified operation.
    
    Parameters:
    - tensor: Tensor to reduce (modified in-place)
    - reduction: Reduction operation ("mean", "sum")
    
    Returns:
    Reduced tensor (same shape as input)
    """

def pad_across_processes(
    tensor: torch.Tensor,
    dim: int = 0,
    pad_index: int = 0,
    pad_first: bool = False
):
    """
    Pad tensor to same size across all processes.
    
    Useful for gathering tensors of different sizes by padding
    smaller tensors to match the largest tensor size.
    
    Parameters:
    - tensor: Tensor to pad
    - dim: Dimension along which to pad
    - pad_index: Value to use for padding
    - pad_first: Whether to pad at beginning or end
    
    Returns:
    Padded tensor with same size across all processes
    """

def broadcast_object_list(
    objects: list,
    from_process: int = 0
):
    """
    Broadcast list of Python objects from one process to all others.
    
    Parameters:
    - objects: List of objects to broadcast (modified in-place on receiving processes)
    - from_process: Source process rank
    """

def gather_object(obj):
    """
    Gather Python objects from all processes.
    
    Parameters:
    - obj: Object to gather from current process
    
    Returns:
    List of objects from all processes (only on main process, None elsewhere)
    """

def concatenate(data, dim: int = 0):
    """
    Concatenate tensors or nested data structures along specified dimension.
    
    Handles complex nested structures including lists, tuples, and dictionaries
    containing tensors or other concatenatable objects.
    
    Parameters:
    - data: Data structure containing tensors to concatenate
    - dim: Dimension along which to concatenate
    
    Returns:
    Concatenated data structure with same nesting as input
    """

def slice_tensors(data, tensor_slice: slice | int):
    """
    Slice tensors in nested data structures.
    
    Applies the same slice operation to all tensors found in nested
    lists, tuples, and dictionaries.
    
    Parameters:
    - data: Nested data structure containing tensors
    - tensor_slice: Slice object or integer index to apply
    
    Returns:
    Sliced data structure maintaining original nesting
    """

def send_to_device(
    tensor: torch.Tensor,
    device: torch.device | str,
    non_blocking: bool = False,
    skip_keys: list[str] | str | None = None
):
    """
    Move tensor or nested data structure to specified device.
    
    Recursively moves all tensors in nested structures while preserving
    the original data organization.
    
    Parameters:
    - tensor: Tensor or nested structure to move
    - device: Target device
    - non_blocking: Whether to use non-blocking transfer
    - skip_keys: Keys to skip when moving nested dictionaries
    
    Returns:
    Data moved to target device
    """

def find_batch_size(data):
    """
    Find batch size from tensor or nested data structure.
    
    Searches through nested structures to find the first tensor
    and returns its size along dimension 0 (batch dimension).
    
    Parameters:
    - data: Tensor or nested structure containing tensors
    
    Returns:
    Batch size (int) or None if no tensors found
    """

def find_device(*args):
    """
    Find device from tensor arguments.
    
    Searches through arguments to find the first tensor and
    returns its device.
    
    Parameters:
    - *args: Arguments that may contain tensors
    
    Returns:
    torch.device of first tensor found, or None
    """

def get_data_structure(data):
    """
    Analyze nested data structure containing tensors.
    
    Returns metadata about the structure including tensor shapes,
    devices, and nesting patterns.
    
    Parameters:
    - data: Nested data structure to analyze
    
    Returns:
    DataStructure object describing the input
    """

def is_torch_tensor(data):
    """
    Check if data is a PyTorch tensor.
    
    Parameters:
    - data: Object to check
    
    Returns:
    Boolean indicating if data is a torch.Tensor
    """

def is_tensor_information(data):
    """
    Check if data contains tensor metadata information.
    
    Parameters:
    - data: Object to check
    
    Returns:
    Boolean indicating if data is TensorInformation
    """

def wait_for_everyone():
    """
    Synchronization barrier - all processes wait until everyone reaches this point.
    
    Ensures all processes are synchronized before continuing execution.
    Essential for coordinating distributed operations.
    """

def synchronize_rng_states(rng_types: list[str] | None = None):
    """
    Synchronize random number generator states across all processes.
    
    Ensures reproducible results in distributed training by making
    all processes use the same random state.
    
    Parameters:
    - rng_types: Types of RNG to synchronize ("torch", "cuda", "xla")
                 If None, synchronizes all available types
    """

def set_seed(seed: int, device_specific: bool = False):
    """
    Set random seed across all processes and libraries.
    
    Sets seeds for PyTorch, NumPy, Python random, and other libraries
    to ensure reproducible results.
    
    Parameters:
    - seed: Random seed value
    - device_specific: Whether to use device-specific seeding
    """

class GatheredParameters:
    """
    Context manager for gathering distributed parameters.
    
    Temporarily gathers sharded parameters from all processes,
    enabling operations that require the full parameter tensor.
    """
    
    def __init__(self, *models, modifier_rank: int | None = None):
        """
        Initialize parameter gathering context.
        
        Parameters:
        - *models: Models with parameters to gather
        - modifier_rank: Process rank that can modify parameters
        """

def convert_to_fp32(tensor: torch.Tensor):
    """
    Convert tensor to FP32 precision.
    
    Parameters:
    - tensor: Tensor to convert
    
    Returns:
    Tensor converted to torch.float32
    """

def convert_outputs_to_fp32(data):
    """
    Convert nested data structure outputs to FP32.
    
    Recursively converts all tensors in nested structures to FP32,
    useful for metric computation and logging.
    
    Parameters:
    - data: Nested structure containing tensors
    
    Returns:
    Data structure with all tensors converted to FP32
    """

def honor_type(obj, generator):
    """
    Ensure generated object maintains same type hierarchy as original.
    
    Parameters:
    - obj: Original object to match type of
    - generator: Generator producing new values
    
    Returns:
    Object of same type as obj with values from generator
    """

from accelerate import broadcast, gather, reduce
import torch

# Initialize distributed training first
accelerator = Accelerator()

# Broadcast tensor from main process to all processes
if accelerator.is_main_process:
    data = torch.randn(10, 20)
else:
    data = torch.zeros(10, 20)

broadcast(data, from_process=0)  # Now all processes have the same data

# Gather results from all processes
local_result = model(local_batch)
all_results = gather(local_result)  # Only main process gets concatenated results

# Reduce loss across processes
loss = compute_loss(outputs, targets)
average_loss = reduce(loss, reduction="mean")

from accelerate import pad_across_processes, gather

# When batch sizes differ across processes
predictions = model(batch)  # Different sizes on each process

# Pad to same size before gathering
padded_predictions = pad_across_processes(predictions, dim=0, pad_index=-100)
all_predictions = gather(padded_predictions)

# Remove padding after gathering (on main process)
if accelerator.is_main_process:
    # Remove padded values
    valid_predictions = all_predictions[all_predictions != -100]

from accelerate import broadcast_object_list, gather_object

# Broadcast complex configuration
if accelerator.is_main_process:
    config = {
        "model_settings": {"layers": 12, "hidden_size": 768},
        "training_params": [0.001, 0.9, 0.999],
        "metadata": {"experiment_name": "test_run", "version": "1.0"}
    }
else:
    config = None

broadcast_object_list([config])
config = config[0]  # Extract from list

# Gather evaluation results
eval_metrics = {"accuracy": 0.95, "f1": 0.93}
all_metrics = gather_object(eval_metrics)

if accelerator.is_main_process:
    # all_metrics is list of metrics from each process
    avg_accuracy = sum(m["accuracy"] for m in all_metrics) / len(all_metrics)

from accelerate import concatenate, slice_tensors, send_to_device

# Work with nested data structures
batch = {
    "input_ids": torch.tensor([[1, 2, 3], [4, 5, 6]]),
    "attention_mask": torch.tensor([[1, 1, 1], [1, 1, 0]]),
    "labels": torch.tensor([0, 1])
}

# Move entire structure to GPU
batch_gpu = send_to_device(batch, "cuda:0")

# Slice first sample from nested structure
first_sample = slice_tensors(batch, 0)

# Concatenate batches from multiple sources
batches = [batch1, batch2, batch3]
combined_batch = concatenate(batches, dim=0)

from accelerate import wait_for_everyone, set_seed, synchronize_rng_states

# Set reproducible seeds
set_seed(42, device_specific=True)

# Synchronize RNG states across processes
synchronize_rng_states(["torch", "cuda"])

# Coordinate processes for sequential operations
if accelerator.is_main_process:
    # Download and prepare dataset
    dataset = download_and_preprocess()

wait_for_everyone()  # Wait for main process to finish

# Now all processes can safely access the dataset
dataloader = DataLoader(dataset, batch_size=32)