picotron/src/parallel/data_parallel/data_parallel.py

import contextlib
import torch
import torch.distributed as dist
from torch import nn
import src.distributed.process_group_manager as pgm

class DataParallel(nn.Module):
    def __init__(self, module):
        """
        Initializes the DataParallel wrapper for a given module.

        Args:
            module (nn.Module): The model to be wrapped for data parallelism.
            process_group (torch.distributed.ProcessGroup): The process group used for gradient synchronization. 
                                                            It could be a data parallel or context parallel group.
        """
        super().__init__()
        self.module = module
        self.process_group = pgm.process_group_manager.dp_group # process group for gradient synchronization. could be data parallel group and context parallel group
        self.dp_world_size = dist.get_world_size(group=self.process_group)
        self.require_backward_grad_sync = True # whether to synchronize gradients during backward pass. Set to False when using gradient accumulation
        self.register_backward_hook(self._allreduce_grads)
    
    def forward(self, *inputs, **kwargs):
        return self.module(*inputs, **kwargs)
    
    def register_backward_hook(self, hook):
        """
        Registers a backward hook for all parameters of the model that require gradients.    
        """
        for p in self.module.parameters():
            if p.requires_grad is True:
                p.register_hook(hook)
                
    def _allreduce_grads(self, grad):
        """
        Performs an all-reduce operation to synchronize gradients across multiple processes.    
        """
        # No synchronization needed during gradient accumulation, except at the final accumulation step.
        # 324K tokens/s/gpu -> 334K tokens/s/gpu
        if self.require_backward_grad_sync:
            dist.all_reduce(grad, op=dist.ReduceOp.SUM, group=self.process_group)
            grad /= self.dp_world_size
        return grad 
    
    @contextlib.contextmanager
    def no_sync(self):
        """
        A context manager to temporarily disable gradient synchronization. 
        This is useful for performing multiple backward passes during gradient accumulation without synchronizing 
        gradients in between.
        """
        self.require_backward_grad_sync = False
        yield
        self.require_backward_grad_sync = True