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