from functools import partial from typing import Any, Callable, Iterable, List, Optional, Union import torch import torch.cuda from torch.nn import Module from torch.utils._pytree import tree_map from colossalai.interface import OptimizerWrapper from colossalai.pipeline.p2p import PipelineP2PCommunication from colossalai.pipeline.stage_manager import PipelineStageManager from colossalai.utils.cuda import get_current_device from ._utils import detach, get_batch_size, get_micro_batch, merge_batch, model_forward, retain_grad, to_device from .base import PipelineSchedule class InterleavedSchedule(PipelineSchedule): def __init__(self, num_microbatches: int, num_model_chunks: int, stage_manager: PipelineStageManager) -> None: self.num_model_chunks = num_model_chunks assert num_microbatches % self.num_model_chunks == 0, \ "Number of microbatches should be an integer multiple of number of model chunks" super().__init__(stage_manager) self.comm = PipelineP2PCommunication(stage_manager) self.num_microbatches = num_microbatches self.batch: Optional[Any] = None self.batch_size: Optional[int] = None self.microbatch_offset: Optional[int] = None self.microbatch_size: Optional[int] = None def load_batch(self, data_iter: Iterable, device: Optional[torch.device] = None) -> None: """Load a batch from data iterator. Args: data_iter (Iterable): Data iterator. device (Optional[torch.device], optional): Target device. Defaults to None. """ batch = next(data_iter) if device is not None: batch = tree_map(partial(to_device, device=device), batch) self.batch = batch self.batch_size = get_batch_size(batch) self.microbatch_offset = [0 for _ in range(self.num_model_chunks)] assert self.batch_size % self.num_microbatches == 0, \ "Batch size should divided by the number of microbatches" self.microbatch_size = self.batch_size // self.num_microbatches def load_micro_batch(self, model_chunk_id: int) -> Any: """Load a micro batch from the current batch. Args: microbatch_id (int): the current model chunk idx. Returns: Any: Micro batch. """ micro_batch = get_micro_batch(self.batch, self.microbatch_offset[model_chunk_id], self.microbatch_size) self.microbatch_offset[model_chunk_id] += self.microbatch_size return tree_map(partial(to_device, device=get_current_device()), micro_batch) def get_model_chunk_id(self, microbatch_id: int, forward: bool) -> int: """Helper method to get the model chunk ID given the iteration number. Args: microbatch_id (int): the current microbatch idx forward (bool): if is the forward process Returns: int: The model chunk idx of the input microbatch_id """ microbatch_id_in_group = (microbatch_id) % (self.stage_manager.num_stages * self.num_model_chunks) model_chunk_id = microbatch_id_in_group // self.stage_manager.num_stages if not forward: model_chunk_id = (self.num_model_chunks - model_chunk_id - 1) return model_chunk_id def is_first_stage(self, model_chunk_id: int) -> bool: """Is the current virtual stage the first stage Args: model_chunk_id (int): The current model chunk idx. Returns: bool: Whether the current virtual stage is the first stage. """ if self.stage_manager.is_first_stage() and model_chunk_id == 0: return True return False def is_last_stage(self, model_chunk_id: int) -> bool: """Is the current virtual stage the last stage Args: model_chunk_id (int): The current model chunk idx. Returns: bool: Whether the current virtual stage is the last stage. """ if self.stage_manager.is_last_stage() and model_chunk_id == self.num_model_chunks - 1: return True return False def recv_forward(self, model_chunk_id: int, prev_rank: int = None) -> Any: """Copy the forward output from the previous stage in pipeline as the input tensor of this stage. For interleaved 1F1B. Args: model_chunk_id (int): The current model chunk idx. prev_rank (int, optional): The rank of the source of the tensor. Returns: Any: The input tensor or input tensor list. """ if self.is_first_stage(model_chunk_id): input_tensor = None else: input_tensor = self.comm.recv_forward(prev_rank) return input_tensor def recv_backward(self, model_chunk_id: int, next_rank: int = None) -> Any: """Copy the gradient tensor from the next stage in pipeline as the input gradient of this stage. For interleaved 1F1B. Args: model_chunk_id (int): The current model chunk idx. next_rank (int, optional): The rank of the source of the tensor. Returns: Any: The input gradient tensor or gradient tensor list. """ if self.is_last_stage(model_chunk_id): output_tensor_grad = None else: output_tensor_grad = self.comm.recv_backward(next_rank) return output_tensor_grad def send_forward(self, model_chunk_id, output_object: Any, next_rank: int = None) -> None: """Sends the input tensor to the next stage in pipeline. For interleaved 1F1B. Args: model_chunk_id (int): The current model chunk idx. output_object (Any): Object to be sent. next_rank (int, optional): The rank of the recipient of the tensor. """ if not self.is_last_stage(model_chunk_id): self.comm.send_forward(output_object, next_rank) def send_backward(self, model_chunk_id, input_object: Any, prev_rank: int = None) -> None: """Sends the gradient tensor to the previous stage in pipeline. For interleaved 1F1B. Args: model_chunk_id (int): The current model chunk idx. input_object (Any): Object to be sent. prev_rank (int, optional): The rank of the recipient of the tensor """ if not self.is_first_stage(model_chunk_id): self.comm.send_backward(input_object, prev_rank) def forward_step(self, model_chunk: Module, model_chunk_id: int, input_obj: Optional[dict], criterion: Callable, accum_loss: Optional[torch.Tensor] = None, outputs: Optional[List[Any]] = None) -> Union[torch.Tensor, dict]: """Forward one step of the pipeline Args: model (Module): Model Chunk to be run input_obj (Optional[dict]): The output from the previous stage. If it is the first stage, the `input_obj` is None. criterion (Callable): Criterion to calculate loss. accum_loss (Optional[torch.Tensor], optional): Accumulated loss. Defaults to None. outputs (Optional[List[Any]], optional): List to store the output of the last stage (final output). Defaults to None. Returns: Union[torch.Tensor, dict]: The intermediate output (dict) of the current stage. If it is the last stage, the output is the loss (Tensor). """ micro_batch = self.load_micro_batch(model_chunk_id=model_chunk_id) # for the first stage, input_obj is None # for the non-first stage, input_obj is the output of the previous stage and it's must be a dict output_obj = model_forward(model_chunk[model_chunk_id], micro_batch, input_obj) if self.is_last_stage(model_chunk_id): loss = criterion(output_obj, micro_batch) / self.num_microbatches if accum_loss is not None: accum_loss.add_(loss.detach()) if outputs is not None: outputs.append(tree_map(detach, output_obj)) return loss else: return output_obj def backward_step(self, optimizer: OptimizerWrapper, input_obj: Optional[dict], output_obj: Union[dict, torch.Tensor], output_obj_grad: Optional[dict]) -> Optional[dict]: """Backward one step of the pipeline Args: optimizer (OptimizerWrapper): Optimizer to update the model input_obj (Optional[dict]): Output of the previous stage. If it is the first stage, the `input_obj` is None. output_obj (Union[dict, torch.Tensor]): Output of the current stage. If it is the last stage, the output is the loss (Tensor). output_obj_grad (dict): Gradient of the `output_obj`. If it is the last stage, the `output_obj_grad` is None. Returns: Optional[dict]: Gradient of the `input_obj`. If it is the first stage, the `input_obj_grad` is None. """ # Retain the grad on the input_obj. tree_map(retain_grad, input_obj) # Backward pass. if output_obj_grad is None: optimizer.backward(output_obj) else: if "backward_tensor_keys" not in output_obj: for k, grad in output_obj_grad.items(): optimizer.backward_by_grad(output_obj[k], grad) else: for k, grad in output_obj_grad.items(): output_obj[k].grad = grad for k in output_obj["backward_tensor_keys"]: tensor_to_backward = output_obj[k] optimizer.backward_by_grad(tensor_to_backward, tensor_to_backward.grad) # Collect the grad of the input_obj. input_obj_grad = None if input_obj is not None: input_obj_grad = {} for k, v in input_obj.items(): if isinstance(v, torch.Tensor) and v.grad is not None: input_obj_grad[k] = v.grad return input_obj_grad def forward_backward_step(self, model_chunk: Module, data_iter: Iterable, criterion: Callable[..., Any], optimizer: Optional[OptimizerWrapper] = None, return_loss: bool = False, return_outputs: bool = False) -> dict: """Runs interleaved 1F1B schedule, with communication between pipeline stages. Args: model_chunk (List[Module]): Model Chunk to be trained. data_iter (Iterable): Data iterator. criterion (Callable[[Any, Any], Tensor]): Criterion to be used. It should take two arguments: model outputs and inputs, and returns loss tensor. optimizer (OptimizerWrapper, optional): Optimizer to be used. Can be None when only forward is executed. Defaults to None. return_loss (bool, optional): Whether to return loss. Defaults to False. Whether to return loss. return_outputs (bool, optional): Whether to return model outputs. Defaults to False. Whether to return model outputs. Returns: dict: A dict with keys: 'loss' and 'outputs'. """ forward_only = not torch.is_grad_enabled() if optimizer is None: assert forward_only, "Optimizer should be passed when doing backward." self.load_batch(data_iter) num_model_chunks = len(model_chunk) # num_warmup_microbatches is the step when not all the processes are working num_microbatches = self.num_microbatches * num_model_chunks if forward_only: num_warmup_microbatches = num_microbatches else: num_warmup_microbatches = (self.stage_manager.num_stages - self.stage_manager.stage - 1) * 2 num_warmup_microbatches += (num_model_chunks - 1) * self.stage_manager.num_stages num_warmup_microbatches = min(num_warmup_microbatches, num_microbatches) num_microbatches_remaining = num_microbatches - num_warmup_microbatches # Input, output tensors only need to be saved when doing backward passes input_objs = None output_objs = None if not forward_only: input_objs = [[] for _ in range(num_model_chunks)] output_objs = [[] for _ in range(num_model_chunks)] outputs = [] if return_outputs and self.stage_manager.is_last_stage() else None if return_loss and self.stage_manager.is_last_stage(): accum_loss = torch.zeros(1, device=get_current_device()) else: accum_loss = None # for ranks except the first one, get into recv state # print(self.stage_manager.stage,num_microbatches, num_warmup_microbatches, num_microbatches_remaining) input_obj = self.recv_forward(0) input_objs[0].append(input_obj) # Run warmup forward passes. for i in range(num_warmup_microbatches): model_chunk_id = self.get_model_chunk_id(i, forward=True) # recv first on first rank to avoid sending or recving at the same time if self.stage_manager.is_first_stage(): input_obj = self.recv_forward(model_chunk_id) output_obj = self.forward_step(model_chunk, model_chunk_id, input_obj, criterion, accum_loss, outputs) self.send_forward(model_chunk_id, output_obj) if not forward_only: input_objs[model_chunk_id].append(input_obj) output_objs[model_chunk_id].append(output_obj) else: output_obj = self.forward_step(model_chunk, model_chunk_id, input_obj, criterion, accum_loss, outputs) if not forward_only: output_objs[model_chunk_id].append(output_obj) self.send_forward(model_chunk_id, output_obj) if num_microbatches_remaining == 0 and i + 1 == num_warmup_microbatches: break else: model_chunk_id = self.get_model_chunk_id(i + 1, forward=True) input_obj = self.recv_forward(model_chunk_id) if not forward_only: input_objs[model_chunk_id].append(input_obj) # Run 1F1B in steady state. for i in range(num_microbatches_remaining): model_chunk_id = self.get_model_chunk_id(i + num_warmup_microbatches, forward=True) last_iteration = (i == (num_microbatches_remaining - 1)) output_obj = self.forward_step(model_chunk, model_chunk_id, input_obj, criterion, accum_loss, outputs) if forward_only: self.send_forward(model_chunk_id, output_obj) if not last_iteration: input_obj = self.recv_forward(model_chunk_id) else: self.send_forward(model_chunk_id, output_obj) # Add input_obj and output_obj to end of list. input_objs[model_chunk_id].append(input_obj) output_objs[model_chunk_id].append(output_obj) model_chunk_id = self.get_model_chunk_id(i, forward=False) output_obj_grad = self.recv_backward(model_chunk_id) # Pop output_obj and output_obj from the start of the list for # the backward pass. input_obj = input_objs[model_chunk_id].pop(0) output_obj = output_objs[model_chunk_id].pop(0) # backward input_obj_grad = self.backward_step(optimizer, input_obj, output_obj, output_obj_grad) if last_iteration: input_obj = None else: model_chunk_id = self.get_model_chunk_id(i + num_warmup_microbatches + 1, forward=True) input_obj = self.recv_forward(model_chunk_id) model_chunk_id = self.get_model_chunk_id(i, forward=False) self.send_backward(model_chunk_id, input_obj_grad) # Run cooldown backward passes. if not forward_only: for i in range(num_microbatches_remaining, num_microbatches): model_chunk_id = self.get_model_chunk_id(i, forward=False) # print(f"{self.stage_manager.stage}/{model_chunk_id}: {len(input_objs[model_chunk_id])} {len(output_objs[model_chunk_id])} {i}") input_obj = input_objs[model_chunk_id].pop(0) output_obj = output_objs[model_chunk_id].pop(0) output_obj_grad = self.recv_backward(model_chunk_id) input_obj_grad = self.backward_step(optimizer, input_obj, output_obj, output_obj_grad) self.send_backward(model_chunk_id, input_obj_grad) if outputs is not None: outputs = merge_batch(outputs) return {'loss': accum_loss, 'outputs': outputs}