#!/usr/bin/env python # -*- encoding: utf-8 -*- import inspect from typing import Callable, List, Tuple, Union import torch.cuda import colossalai.legacy.communication as comm from colossalai.amp.naive_amp import NaiveAMPModel from colossalai.context.parallel_mode import ParallelMode from colossalai.core import global_context as gpc from colossalai.logging import get_dist_logger from colossalai.utils import switch_virtual_pipeline_parallel_rank from colossalai.utils.cuda import get_current_device from ._base_schedule import BaseSchedule def get_tensor_shape(): if hasattr(gpc.config, 'TENSOR_SHAPE'): return gpc.config.TENSOR_SHAPE if not gpc.is_initialized(ParallelMode.PIPELINE): return None if hasattr(gpc.config, 'SEQ_LENGTH') and hasattr(gpc.config, 'GLOBAL_BATCH_SIZE') and hasattr( gpc.config, 'GLOBAL_BATCH_SIZE') and hasattr(gpc.config, 'HIDDEN_SIZE'): if gpc.is_initialized(ParallelMode.DATA): dp_size = gpc.get_world_size(ParallelMode.DATA) else: dp_size = 1 if gpc.is_initialized(ParallelMode.SEQUENCE): seq_size = gpc.get_world_size(ParallelMode.SEQUENCE) else: seq_size = 1 tensor_shape = (gpc.config.SEQ_LENGTH // seq_size, gpc.config.GLOBAL_BATCH_SIZE // dp_size // gpc.config.NUM_MICRO_BATCHES, gpc.config.HIDDEN_SIZE) return tensor_shape else: return None def pack_return_tensors(return_tensors): output, label = tuple(zip(*return_tensors)) if isinstance(output[0], torch.Tensor): output = torch.cat(output, dim=0) elif isinstance(output[0], (list, tuple)): output = tuple(torch.cat(tensors, dim=0) for tensors in zip(*output)) else: raise TypeError(f'Output of model must be tensor or list/tuple of tensors') if isinstance(label[0], torch.Tensor): label = torch.cat(label, dim=0) else: merged_label = {k: [] for k in label[0].keys()} for d in label: for k, v in d.items(): merged_label[k].append(v) label = {k: torch.cat(v, dim=0) for k, v in merged_label.items()} return output, label class PipelineSchedule(BaseSchedule): """A helper schedule class for pipeline parallelism running environment. It uses non-interleaved 1F1B strategy. Other properties are similar as :class:`NonPipelineSchedule`. Args: num_microbatches (int): The number of microbatches. data_process_func (Callable, optional): The preprocessing function which receives a batch of data, and it will be executed in `load_batch`. tensor_shape (torch.Size, optional): Specified shape in pipeline communication. scatter_gather_tensors (bool, optional): If set to `True`, communication will be reduced over pipeline when using 1D tensor parallelization. Example: # this shows an example of customized data_process_func def data_process_func(stage_output, dataloader_output): output1, output2 = stage_output item1, item2, item3 = dataloader_output # assume item2 is not needed data = (output1, output2, item1) label = item3 return data, label """ def __init__(self, num_microbatches, data_process_func: Callable = None, tensor_shape: Union[torch.Size, List[int], Tuple[int]] = None, scatter_gather_tensors: bool = False): # we need to make sure that the signature of the data_process_func is valid if data_process_func: sig = inspect.signature(data_process_func) assert len(sig.parameters) == 2, \ 'The data_process_func only takes in two parameters for NonPipelineSchedule, ' \ 'which is the tensors passed by the previous pipeline stage and the dataloader output from this stage, ' \ 'i.e. data_process_func(stage_output, dataloader_output).' super().__init__(data_process_func=data_process_func) assert num_microbatches > 0, f'expected num_microbatches to be larger then 1, but got {num_microbatches}' self.num_microbatches = num_microbatches self.dtype = torch.float assert not isinstance(tensor_shape, int), "tensor_shape type should be one of Union[torch.Size, List[int], Tuple[int]]." if tensor_shape is None: self.tensor_shape = tensor_shape elif isinstance(tensor_shape, torch.Size): self.tensor_shape = tensor_shape else: self.tensor_shape = torch.Size(tensor_shape) self.scatter_gather_tensors = False if gpc.is_initialized(ParallelMode.PARALLEL_1D) and gpc.get_world_size(ParallelMode.PARALLEL_1D) > 1: self.scatter_gather_tensors = scatter_gather_tensors self._logger = get_dist_logger() # cache for the batch data self.batch_data = None def load_batch(self, data_iter): # Pipeline schedule just puts data in memory batch_data = super().load_batch(data_iter, to_gpu=False) self.microbatch_offset = 0 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 self.batch_data = batch_data def _get_data_slice(self, data, offset): if isinstance(data, torch.Tensor): return data[offset:offset + self.microbatch_size] elif isinstance(data, (list, tuple)): data_dict = {} for element in data: if isinstance(element, dict): data_dict.update({k: v[offset:offset + self.microbatch_size] for k, v in element.items()}) elif data_dict: data_dict['label'] = element[offset:offset + self.microbatch_size] if data_dict: return data_dict return [val[offset:offset + self.microbatch_size] for val in data] elif isinstance(data, dict): return {k: v[offset:offset + self.microbatch_size] for k, v in data.items()} else: raise TypeError(f"Expected data to be of type torch.Tensor, list, tuple, or dict, but got {type(data)}") def load_micro_batch(self): micro_batch_data = self._get_data_slice(self.batch_data, self.microbatch_offset) self.microbatch_offset += self.microbatch_size return self._move_to_device(micro_batch_data) def pre_processing(self, engine): from colossalai.zero.legacy import ShardedModelV2 # TODO: remove this after testing new zero with pipeline parallelism model = engine.model if isinstance(model, NaiveAMPModel): self.dtype = torch.half model = model.model if isinstance(model, ShardedModelV2): self.dtype = torch.half model = model.module # sig = inspect.signature(model.forward) # for p in sig.parameters.values(): # assert p.kind != inspect.Parameter.VAR_POSITIONAL, '*args is not supported' @staticmethod def _call_engine(model, data): if data is not None: if isinstance(data, torch.Tensor): return model(data) elif isinstance(data, (list, tuple)): return model(*data) elif isinstance(data, dict): stage_output = None if 'stage_output' in data: stage_output = data.pop('stage_output') if stage_output is None: return model(**data) elif isinstance(stage_output, torch.Tensor): return model(stage_output, **data) elif isinstance(stage_output, (tuple, list)): return model(*stage_output, **data) else: raise TypeError( f"Expected stage_output to be of type torch.Tensor, list, or tuple, but got {type(stage_output)}" ) else: raise TypeError(f"Expected data to be of type torch.Tensor, list, tuple, or dict, but got {type(data)}") def _get_actual_forward_func(self, module): if isinstance(module, NaiveAMPModel): sig = inspect.signature(module.model.forward) elif hasattr(module, 'colo_attr'): sig = inspect.signature(module.module.forward) else: sig = inspect.signature(module.forward) return sig def _get_data_label_for_current_step(self, stage_output, micro_batch_data, criterion, model): if self.data_process_func: # use customized function to get data and label data, label = self.data_process_func(stage_output, micro_batch_data) else: if isinstance(micro_batch_data, (tuple, list)): if gpc.is_first_rank(ParallelMode.PIPELINE): # for the first stage, we use the data from the # dataloader output by default data, label = micro_batch_data else: # for non-first stage, we use the output passed # by the previous as the model input data = stage_output _, label = micro_batch_data elif isinstance(micro_batch_data, dict): data = {} data['stage_output'] = stage_output if 'label' in micro_batch_data: label = micro_batch_data.pop('label') else: label = None load_data = micro_batch_data data.update(load_data) return data, label def _forward_step(self, engine, input_obj, return_tensors, return_output_label=True, accum_loss=None): """Forward step for passed-in model. If it is the first stage, the input tensor is obtained from data_iterator, otherwise the passed-in input_obj is used. Returns output tensor. This is a helper function and can be ignored by users. Args: engine (colossalai.legacy.engine.Engine): Colossalai engine for training and inference. input_obj (Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]): Input tensor for this pipeline stage. return_tensors (List[:class:`torch.Tensor`]): A list of tensors to return. return_output_label (bool, optional): Whether returns output labels. accum_loss (optional): Where accumulated loss stores. Returns: Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]: output or the loss value of the current pipeline stage. """ micro_batch_data = self.load_micro_batch() data, label = self._get_data_label_for_current_step(input_obj, micro_batch_data, engine.criterion, engine.model) output_obj = self._call_engine(engine.model, data) if gpc.is_last_rank(ParallelMode.PIPELINE): if return_output_label: return_tensors.append((output_obj, label)) if accum_loss is not None: loss_reduced = self._call_engine_criterion(engine, output_obj, label) / self.num_microbatches accum_loss.add_(loss_reduced.detach()) return loss_reduced else: # forward only, it's useless since backward is not needed return output_obj else: if isinstance(output_obj, torch.Tensor): self._logger.debug( f'Global rank {gpc.get_global_rank()}, pipeline rank {gpc.get_local_rank(ParallelMode.PIPELINE)} forward output tensor {output_obj.shape}, dtype {output_obj.dtype}' ) return output_obj def _backward_step(self, engine, input_obj, output_obj, output_obj_grad): """Backward step through the passed-in output tensor. If it is the last stage, the output_obj_grad is None, otherwise it is the gradients with respect to stage's output tensor. Returns the gradients with respect to the input tensor (None if first stage). This is a helper function and can be ignored by users. Args: engine (colossalai.legacy.engine.Engine): Colossalai engine for training and inference. input_obj (Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]): input tensor for this pipeline stage. output_obj (Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]): output tensor for this pipeline stage. output_obj_grad (Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]): gradient of output tensor for this pipeline stage. Returns: Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]: gradient of input tensor. """ # Retain the grad on the input_obj. if input_obj is not None: if isinstance(input_obj, torch.Tensor): input_obj.retain_grad() else: for in_tensor in input_obj: if in_tensor is not None: in_tensor.retain_grad() # Backward pass. if output_obj_grad is None: engine.backward(output_obj) else: engine.backward_by_grad(output_obj, output_obj_grad) # Collect the grad of the input_obj. input_obj_grad = None if input_obj is not None: if isinstance(input_obj, torch.Tensor): input_obj_grad = input_obj.grad else: input_obj_grad = [] for in_tensor in input_obj: input_obj_grad.append(in_tensor.grad) return input_obj_grad def forward_backward_step(self, engine, data_iter, forward_only=False, return_loss=True, return_output_label=True): """Runs non-interleaved 1F1B schedule, with communication between pipeline stages. Returns a tuple with losses if the last stage, an empty tuple otherwise. Args: engine (colossalai.legacy.engine.Engine): Colossalai engine for training and inference. data_iter (Iterable): Dataloader as the form of an iterator, obtained by calling iter(dataloader). forward_only (bool, optional): Whether run forward step only. Default is false. If true, no backward will be run. return_loss (bool, optional): Whether returns the loss value. Default is true. return_output_label (bool, optional): If False, the output and label won't be returned. Returns: Tuple[:class:`torch.Tensor`]: A tuple of (output, label, loss), loss and label could be None. """ assert forward_only or return_loss, \ 'The argument \'return_loss\' has to be True when \'forward_only\' is False, but got False.' self.load_batch(data_iter) num_warmup_microbatches = \ (gpc.get_world_size(ParallelMode.PIPELINE) - gpc.get_local_rank(ParallelMode.PIPELINE) - 1) num_warmup_microbatches = min(num_warmup_microbatches, self.num_microbatches) num_microbatches_remaining = self.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 = [] output_objs = [] return_tensors = [] if return_loss and gpc.is_pipeline_last_stage(ignore_virtual=True): accum_loss = torch.zeros(1, device=get_current_device()) else: accum_loss = None # Used for tensor meta information communication ft_shapes = self.tensor_shape bt_shapes = None fs_checker = self.tensor_shape is None # Run warmup forward passes. for i in range(num_warmup_microbatches): if not gpc.is_first_rank(ParallelMode.PIPELINE): ft_shapes = comm.recv_obj_meta(ft_shapes) input_obj = comm.recv_forward(ft_shapes, dtype=self.dtype, scatter_gather_tensors=self.scatter_gather_tensors) output_obj = self._forward_step(engine, input_obj, return_tensors, return_output_label=return_output_label, accum_loss=accum_loss) if not gpc.is_last_rank(ParallelMode.PIPELINE): if isinstance(output_obj, torch.Tensor): bt_shapes = output_obj.shape else: bt_shapes = [] for out_tensor in output_obj: bt_shapes.append(out_tensor.shape) fs_checker = comm.send_obj_meta(output_obj, fs_checker) comm.send_forward(output_obj, scatter_gather_tensors=self.scatter_gather_tensors) if not forward_only: input_objs.append(input_obj) output_objs.append(output_obj) # Before running 1F1B, need to receive first forward tensor. # If all microbatches are run in warmup / cooldown phase, then no need to # receive this tensor here. if num_microbatches_remaining > 0: if not gpc.is_first_rank(ParallelMode.PIPELINE): ft_shapes = comm.recv_obj_meta(ft_shapes) input_obj = comm.recv_forward(ft_shapes, dtype=self.dtype, scatter_gather_tensors=self.scatter_gather_tensors) # Run 1F1B in steady state. for i in range(num_microbatches_remaining): last_iteration = (i == (num_microbatches_remaining - 1)) output_obj = self._forward_step(engine, input_obj, return_tensors, return_output_label=return_output_label, accum_loss=accum_loss) if forward_only: comm.send_forward(output_obj, scatter_gather_tensors=self.scatter_gather_tensors) if not last_iteration: input_obj = comm.recv_forward(ft_shapes, dtype=self.dtype, scatter_gather_tensors=self.scatter_gather_tensors) else: output_obj_grad = comm.send_forward_recv_backward(output_obj, bt_shapes, dtype=self.dtype, scatter_gather_tensors=self.scatter_gather_tensors) # Add input_obj and output_obj to end of list. input_objs.append(input_obj) output_objs.append(output_obj) # Pop output_obj and output_obj from the start of the list for # the backward pass. input_obj = input_objs.pop(0) output_obj = output_objs.pop(0) input_obj_grad = self._backward_step(engine, input_obj, output_obj, output_obj_grad) if last_iteration: input_obj = None comm.send_backward(input_obj_grad, scatter_gather_tensors=self.scatter_gather_tensors) else: input_obj = comm.send_backward_recv_forward(input_obj_grad, ft_shapes, dtype=self.dtype, scatter_gather_tensors=self.scatter_gather_tensors) # Run cooldown backward passes. if not forward_only: for i in range(num_warmup_microbatches): input_obj = input_objs.pop(0) output_obj = output_objs.pop(0) output_obj_grad = comm.recv_backward(bt_shapes, dtype=self.dtype, scatter_gather_tensors=self.scatter_gather_tensors) input_obj_grad = self._backward_step(engine, input_obj, output_obj, output_obj_grad) comm.send_backward(input_obj_grad, scatter_gather_tensors=self.scatter_gather_tensors) if len(return_tensors) > 0: output, label = pack_return_tensors(return_tensors) return output, label, accum_loss else: return None, None, accum_loss class InterleavedPipelineSchedule(PipelineSchedule): def __init__(self, num_microbatches: int, num_model_chunks: int, data_process_func: Callable = None, tensor_shape: Union[torch.Size, List[int], Tuple[int]] = None, scatter_gather_tensors: bool = False): """A helper schedule class for pipeline parallelism running environment. It uses interleaved 1F1B strategy. Other properties are similar as :class:`NonPipelineSchedule`. Args: num_microbatches (int): The number of microbatches. num_model_chunks (int): The number of model chunks. data_process_func (Callable, optional): The preprocessing function which receives a batch of data, and it will be executed in `load_batch`. tensor_shape (torch.Size, optional): Specified shape in pipeline communication. scatter_gather_tensors (bool, optional): If set to `True`, communication will be reduced over pipeline when using 1D tensor parallelization. """ assert num_microbatches % gpc.get_world_size(ParallelMode.PIPELINE) == 0, \ 'num_microbatches must be an integer multiple of pipeline parallel world size' assert isinstance(num_model_chunks, int) and num_model_chunks > 0, \ f'expected num_model_chunks to be an integer and larger than 0, but got {num_model_chunks}' super().__init__(num_microbatches, data_process_func=data_process_func, tensor_shape=tensor_shape, scatter_gather_tensors=scatter_gather_tensors) gpc.set_virtual_pipeline_parallel_size(num_model_chunks) gpc.set_virtual_pipeline_parallel_rank(0) self.num_model_chunks = num_model_chunks def pre_processing(self, engine): from colossalai.zero.sharded_model.sharded_model_v2 import ShardedModelV2 if isinstance(engine.model, ShardedModelV2): self.dtype = torch.half elif isinstance(engine.model[0], NaiveAMPModel): self.dtype = torch.half for model in engine.model: if isinstance(model, NaiveAMPModel): model = model.model sig = inspect.signature(model.forward) for p in sig.parameters.values(): assert p.kind != inspect.Parameter.VAR_POSITIONAL, '*args is not supported' def load_batch(self, data_iter): super().load_batch(data_iter) # overwrite microbatch_offset, since model chunks load the same microbatch, and should tract the offset self.microbatch_offset = [0 for _ in range(self.num_model_chunks)] def load_micro_batch(self, model_chunk_id): data = self._get_data_slice(self.batch_data, self.microbatch_offset[model_chunk_id]) self.microbatch_offset[model_chunk_id] += self.microbatch_size return self._move_to_device(data) def _forward_step(self, engine, model_chunk_id, input_obj, return_tensors, return_output_label=True, accum_loss=None): """Forward step for passed-in model. If it is the first stage, the input tensor is obtained from data_iterator, otherwise the passed-in input_obj is used. Returns output tensor. This is a helper function and can be ignored by users. Args: engine (colossalai.legacy.engine.Engine): Colossalai engine for training and inference. model_chunk_id (int): The id of model chunks. input_obj (Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]): Input tensor for this pipeline stage. return_tensors (List[:class:`torch.Tensor`]): A list of tensors to return. return_output_label (bool, optional): Whether returns output labels. accum_loss (optional): Where accumulated loss stores. Returns: Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]: output or the loss value of the current pipeline stage. """ micro_batch_data = self.load_micro_batch(model_chunk_id) data, label = self._get_data_label_for_current_step(input_obj, micro_batch_data, engine.criterion, engine.model[model_chunk_id]) output_obj = self._call_engine(engine.model[model_chunk_id], data) if gpc.is_pipeline_last_stage(): if return_output_label: return_tensors.append((output_obj, label)) if accum_loss is not None: loss_reduced = self._call_engine_criterion(engine, output_obj, label) / self.num_microbatches accum_loss.add_(loss_reduced.detach()) return loss_reduced else: # forward only, it's useless since backward is not needed return output_obj else: if isinstance(output_obj, torch.Tensor): self._logger.debug( f'Global rank {gpc.get_global_rank()}, pipeline rank {gpc.get_local_rank(ParallelMode.PIPELINE)} forward output tensor {output_obj.shape}, dtype {output_obj.dtype}' ) return output_obj def forward_backward_step(self, engine, data_iter, forward_only=False, return_loss=True, return_output_label=True): """Run interleaved 1F1B schedule (model split into model chunks), with communication between pipeline stages as needed. Args: engine (colossalai.legacy.engine.Engine): Colossalai engine for training and inference. data_iter (Iterable): Dataloader as the form of an iterator, obtained by calling iter(dataloader). forward_only (bool, optional): Whether run forward step only. Default is false. If true, no backward will be run. return_loss (bool, optional): Whether returns the loss value. Default is true. return_output_label (bool, optional): If False, the output and label won't be returned. Returns: Tuple[:class:`torch.Tensor`]: A tuple of (output, label, loss), loss and label could be None. The loss would be returned only in the last stage. """ assert forward_only or return_loss, \ 'The argument \'return_loss\' has to be True when \'forward_only\' is False, but got False.' self.load_batch(data_iter) model = engine.model input_objs = [[] for _ in range(len(model))] output_objs = [[] for _ in range(len(model))] return_tensors = [] if not forward_only: output_obj_grads = [[] for _ in range(len(model))] if return_loss and gpc.is_pipeline_last_stage(ignore_virtual=True): accum_loss = torch.zeros(1, device=get_current_device()) else: accum_loss = None # Used for obj meta information communication input_obj_shapes = [self.tensor_shape for _ in range(len(model))] output_obj_shapes = [None for _ in range(len(model))] send_tensor_shape_flags = [self.tensor_shape is None for _ in range(len(model))] pipeline_parallel_size = gpc.get_world_size(ParallelMode.PIPELINE) pipeline_parallel_rank = gpc.get_local_rank(ParallelMode.PIPELINE) # Compute number of warmup and remaining microbatches. num_model_chunks = len(model) num_microbatches = self.num_microbatches * num_model_chunks all_warmup_microbatches = False if forward_only: num_warmup_microbatches = num_microbatches else: # Run all forward passes and then all backward passes if number of # microbatches is just the number of pipeline stages. # Otherwise, perform (num_model_chunks-1)*pipeline_parallel_size on # all workers, followed by more microbatches after depending on # stage ID (more forward passes for earlier stages, later stages can # immediately start with 1F1B). if self.num_microbatches == pipeline_parallel_size: num_warmup_microbatches = num_microbatches all_warmup_microbatches = True else: num_warmup_microbatches = \ (pipeline_parallel_size - pipeline_parallel_rank - 1) * 2 num_warmup_microbatches += (num_model_chunks - 1) * pipeline_parallel_size num_warmup_microbatches = min(num_warmup_microbatches, num_microbatches) num_microbatches_remaining = \ num_microbatches - num_warmup_microbatches def get_model_chunk_id(microbatch_id, forward): """Helper method to get the model chunk ID given the iteration number.""" microbatch_id_in_group = microbatch_id % (pipeline_parallel_size * num_model_chunks) model_chunk_id = microbatch_id_in_group // pipeline_parallel_size if not forward: model_chunk_id = (num_model_chunks - model_chunk_id - 1) return model_chunk_id def _forward_step_helper(microbatch_id): """Helper method to run forward step with model split into chunks (run set_virtual_pipeline_model_parallel_rank() before calling forward_step()).""" model_chunk_id = get_model_chunk_id(microbatch_id, forward=True) gpc.set_virtual_pipeline_parallel_rank(model_chunk_id) # forward step if gpc.is_pipeline_first_stage(): if len(input_objs[model_chunk_id]) == \ len(output_objs[model_chunk_id]): input_objs[model_chunk_id].append(None) input_obj = input_objs[model_chunk_id][-1] output_obj = self._forward_step(engine, model_chunk_id, input_obj, return_tensors, return_output_label=return_output_label, accum_loss=accum_loss) output_objs[model_chunk_id].append(output_obj) # if forward-only, no need to save tensors for a backward pass if forward_only: input_objs[model_chunk_id].pop() output_objs[model_chunk_id].pop() return output_obj def _backward_step_helper(microbatch_id): """Helper method to run backward step with model split into chunks (run set_virtual_pipeline_model_parallel_rank() before calling backward_step()).""" model_chunk_id = get_model_chunk_id(microbatch_id, forward=False) gpc.set_virtual_pipeline_parallel_rank(model_chunk_id) if gpc.is_pipeline_last_stage(): if len(output_obj_grads[model_chunk_id]) == 0: output_obj_grads[model_chunk_id].append(None) input_obj = input_objs[model_chunk_id].pop(0) output_obj = output_objs[model_chunk_id].pop(0) output_obj_grad = output_obj_grads[model_chunk_id].pop(0) input_obj_grad = self._backward_step(engine, input_obj, output_obj, output_obj_grad) return input_obj_grad # Run warmup forward passes. gpc.set_virtual_pipeline_parallel_rank(0) if not gpc.is_pipeline_first_stage(): input_obj_shapes[0] = comm.recv_obj_meta(input_obj_shapes[0]) input_objs[0].append( comm.recv_forward(input_obj_shapes[0], dtype=self.dtype, scatter_gather_tensors=self.scatter_gather_tensors)) for k in range(num_warmup_microbatches): model_chunk_id = get_model_chunk_id(k, forward=True) output_obj = _forward_step_helper(k) if not gpc.is_pipeline_last_stage(): if isinstance(output_obj, torch.Tensor): output_obj_shapes[model_chunk_id] = output_obj.shape else: output_obj_shapes[model_chunk_id] = [] for out_tensor in output_obj: output_obj_shapes[model_chunk_id].append(out_tensor.shape) send_tensor_shape_flags[model_chunk_id] = comm.send_obj_meta(output_obj, send_tensor_shape_flags[model_chunk_id]) # Determine if tensor should be received from previous stage. next_forward_model_chunk_id = get_model_chunk_id(k + 1, forward=True) recv_prev = True if gpc.is_pipeline_first_stage(ignore_virtual=True): if next_forward_model_chunk_id == 0: recv_prev = False if k == (num_microbatches - 1): recv_prev = False # Don't send tensor downstream if on last stage. if gpc.is_pipeline_last_stage(): output_obj = None with switch_virtual_pipeline_parallel_rank(next_forward_model_chunk_id): if not gpc.is_pipeline_first_stage(): input_obj_shapes[next_forward_model_chunk_id] = comm.recv_obj_meta( input_obj_shapes[next_forward_model_chunk_id]) # Send and receive tensors as appropriate (send tensors computed # in this iteration; receive tensors for next iteration). input_shape = input_obj_shapes[next_forward_model_chunk_id] if recv_prev else None if k == (num_warmup_microbatches - 1) and not forward_only and \ not all_warmup_microbatches: input_obj_grad = None recv_next = True if gpc.is_pipeline_last_stage(ignore_virtual=True): recv_next = False output_shape = output_obj_shapes[num_model_chunks - 1] if recv_next else None input_obj, output_obj_grad = \ comm.send_forward_backward_recv_forward_backward( output_obj, input_obj_grad, input_shape, output_shape, recv_prev=recv_prev, recv_next=recv_next, dtype=self.dtype, scatter_gather_tensors=self.scatter_gather_tensors) output_obj_grads[num_model_chunks - 1].append(output_obj_grad) else: input_obj = \ comm.send_forward_recv_forward( output_obj, input_shape, recv_prev=recv_prev, dtype=self.dtype, scatter_gather_tensors=self.scatter_gather_tensors) input_objs[next_forward_model_chunk_id].append(input_obj) # Run 1F1B in steady state. for k in range(num_microbatches_remaining): # Forward pass. forward_k = k + num_warmup_microbatches output_obj = _forward_step_helper(forward_k) # Backward pass. backward_k = k input_obj_grad = _backward_step_helper(backward_k) # Send output_obj and input_obj_grad, receive input_obj # and output_obj_grad. # Determine if current stage has anything to send in either direction, # otherwise set obj to None. forward_model_chunk_id = get_model_chunk_id(forward_k, forward=True) gpc.set_virtual_pipeline_parallel_rank(forward_model_chunk_id) if gpc.is_pipeline_last_stage(): output_obj = None backward_model_chunk_id = get_model_chunk_id(backward_k, forward=False) gpc.set_virtual_pipeline_parallel_rank(backward_model_chunk_id) if gpc.is_pipeline_first_stage(): input_obj_grad = None # Determine if peers are sending, and where in data structure to put # received tensors. recv_prev = True if gpc.is_pipeline_first_stage(ignore_virtual=True): # First stage is ahead of last stage by (pipeline_parallel_size - 1). next_forward_model_chunk_id = get_model_chunk_id(forward_k - (pipeline_parallel_size - 1), forward=True) if next_forward_model_chunk_id == (num_model_chunks - 1): recv_prev = False next_forward_model_chunk_id += 1 else: next_forward_model_chunk_id = get_model_chunk_id(forward_k + 1, forward=True) recv_next = True if gpc.is_pipeline_last_stage(ignore_virtual=True): # Last stage is ahead of first stage by (pipeline_parallel_size - 1). next_backward_model_chunk_id = get_model_chunk_id(backward_k - (pipeline_parallel_size - 1), forward=False) if next_backward_model_chunk_id == 0: recv_next = False next_backward_model_chunk_id -= 1 else: next_backward_model_chunk_id = get_model_chunk_id(backward_k + 1, forward=False) # If last iteration, don't receive; we already received one extra # before the start of the for loop. if k == (num_microbatches_remaining - 1): recv_prev = False input_shape = input_obj_shapes[next_forward_model_chunk_id] if recv_prev else None output_shape = output_obj_shapes[next_backward_model_chunk_id] if recv_next else None # Communicate objs. input_obj, output_obj_grad = \ comm.send_forward_backward_recv_forward_backward( output_obj, input_obj_grad, input_shape, output_shape, recv_prev=recv_prev, recv_next=recv_next, dtype=self.dtype, scatter_gather_tensors=self.scatter_gather_tensors) # Put input_obj and output_obj_grad in data structures in the # right location. if recv_prev: input_objs[next_forward_model_chunk_id].append(input_obj) if recv_next: output_obj_grads[next_backward_model_chunk_id].append(output_obj_grad) # Run cooldown backward passes (flush out pipeline). if not forward_only: if all_warmup_microbatches: output_obj_grads[num_model_chunks - 1].append( comm.recv_backward(output_obj_shapes[num_model_chunks - 1], scatter_gather_tensors=self.scatter_gather_tensors)) for k in range(num_microbatches_remaining, num_microbatches): input_obj_grad = _backward_step_helper(k) next_backward_model_chunk_id = get_model_chunk_id(k + 1, forward=False) recv_next = True if gpc.is_pipeline_last_stage(ignore_virtual=True): if next_backward_model_chunk_id == (num_model_chunks - 1): recv_next = False if k == (num_microbatches - 1): recv_next = False output_shape = output_obj_shapes[next_backward_model_chunk_id] if recv_next else None output_obj_grads[next_backward_model_chunk_id].append( comm.send_backward_recv_backward(input_obj_grad, output_shape, recv_next=recv_next, dtype=self.dtype, scatter_gather_tensors=self.scatter_gather_tensors)) if len(return_tensors) > 0: output, label = pack_return_tensors(return_tensors) return output, label, accum_loss else: return None, None, accum_loss