from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Union
import torch
from torch.nn import Module
from torch.utils._pytree import tree_map
from colossalai.accelerator import get_accelerator
from colossalai.interface import ModelWrapper, OptimizerWrapper
from colossalai.pipeline.p2p import PipelineP2PCommunication, create_send_metadata
from colossalai.pipeline.stage_manager import PipelineStageManager
from colossalai.quantization.fp8 import cast_from_fp8_pipeline, cast_to_fp8_pipeline
from colossalai.utils import get_current_device
from ._utils import (
detach,
get_batch_size,
get_micro_batch,
merge_batch,
model_forward,
retain_grad,
to_device,
tree_map_hf,
)
from .base import PipelineSchedule
class OneForwardOneBackwardSchedule(PipelineSchedule):
def __init__(
self,
stage_manager: PipelineStageManager,
num_microbatches: Optional[int] = None,
microbatch_size: Optional[int] = None,
enable_metadata_cache: bool = True,
fp8_communication: bool = False,
) -> None:
"""1F1B pipeline schedule.
Args:
stage_manager (PipelineStageManager): Pipeline stage manager
num_microbatches (Optional[int], optional): The number of microbatches. If not provided, it will be derived from microbatch size. Defaults to None.
microbatch_size (Optional[int], optional): Microbatch size. If num_microbatches is provided, this will be ignored. Defaults to None.
"""
super().__init__(stage_manager)
assert (
num_microbatches is not None or microbatch_size is not None
), "Either num_microbatches or microbatch_size should be provided"
self.comm = PipelineP2PCommunication(stage_manager, overlap_p2p=False)
self.num_microbatches = num_microbatches
self.microbatch_size = microbatch_size
self.batch: Optional[Any] = None
self.batch_size: Optional[int] = None
self.last_batch_size: Optional[int] = None
self.microbatch_offset: Optional[int] = None
# P2PMeta cache
self.enable_metadata_cache = enable_metadata_cache
self.send_tensor_metadata = True
self.send_grad_metadata = True
self.tensor_metadata_recv = None
self.grad_metadata_recv = None
self.fp8_communication = fp8_communication
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.microbatch_offset = 0
self.batch = batch
self.batch_size = get_batch_size(batch)
if self.microbatch_size is None:
assert self.batch_size % self.num_microbatches == 0, "Batch size should divided by # microbatches"
self.microbatch_size = self.batch_size // self.num_microbatches
if self.num_microbatches is None:
assert self.batch_size % self.microbatch_size == 0, "Batch size should divided by the microbatch size"
self.num_microbatches = self.batch_size // self.microbatch_size
if not self.forward_only:
assert self.last_batch_size is None or self.last_batch_size == self.batch_size
assert self.batch_size == self.microbatch_size * self.num_microbatches
assert (
self.num_microbatches >= self.stage_manager.num_stages
), "Number of microbatch should be larger than number of stages"
if self.forward_only:
self.num_microbatches = (self.batch_size - 1) // self.microbatch_size + 1
# NOTE: disable metadata cache when batch size changes (not valid anymore)
if self.batch_size != self.last_batch_size:
self.enable_metadata_cache = False
self.send_tensor_metadata = True
self.send_grad_metadata = True
self.tensor_metadata_recv = None
self.grad_metadata_recv = None
self.last_batch_size = self.batch_size
def load_micro_batch(self) -> Any:
"""Load a micro batch from the current batch.
Returns:
Any: Micro batch.
"""
assert self.microbatch_offset <= self.batch_size, "Microbatches exhausted"
micro_batch = get_micro_batch(self.batch, self.microbatch_offset, self.microbatch_size)
self.microbatch_offset += self.microbatch_size
return tree_map(partial(to_device, device=get_accelerator().get_current_device()), micro_batch)
def recv_forward(self, prev_rank: int = None) -> Any:
"""Copy the forward output from the previous stage in pipeline as the input tensor of this stage.
For 1F1B.
Args:
prev_rank (int, optional): The rank of the source of the tensor.
Returns:
Any: The input tensor or input tensor list.
"""
if not self.stage_manager.is_first_stage():
input_tensor, _ = self.comm.recv_forward(prev_rank, metadata_recv=self.tensor_metadata_recv)
if self.enable_metadata_cache and self.tensor_metadata_recv is None:
self.tensor_metadata_recv = create_send_metadata(input_tensor)
if self.fp8_communication:
cast_from_fp8_pipeline(input_tensor)
return input_tensor
def recv_backward(self, next_rank: int = None) -> Any:
"""Copy the gradient tensor from the next stage in pipeline as the input gradient of this stage.
For 1F1B.
Args:
next_rank (int, optional): The rank of the source of the tensor.
Returns:
Any: The input gradient tensor or gradient tensor list.
"""
if not self.stage_manager.is_last_stage():
output_tensor_grad, _ = self.comm.recv_backward(next_rank, metadata_recv=self.grad_metadata_recv)
if self.fp8_communication:
cast_from_fp8_pipeline(output_tensor_grad)
if self.enable_metadata_cache and self.grad_metadata_recv is None:
self.grad_metadata_recv = create_send_metadata(output_tensor_grad)
return output_tensor_grad
def send_forward(self, output_tensor: Any, next_rank: int = None) -> None:
"""Sends the input tensor to the next stage in pipeline.
For 1F1B.
Args:
output_object (Any): Object to be sent.
next_rank (int, optional): The rank of the recipient of the tensor.
"""
if not self.stage_manager.is_last_stage():
if self.fp8_communication:
cast_to_fp8_pipeline(output_tensor)
self.comm.send_forward(output_tensor, next_rank, send_metadata=self.send_tensor_metadata)
self.send_tensor_metadata = not self.enable_metadata_cache
if self.fp8_communication:
cast_from_fp8_pipeline(output_tensor, del_metadata=False)
def send_backward(self, input_tensor_grad: Any, prev_rank: int = None) -> None:
"""Sends the gradient tensor to the previous stage in pipeline.
For 1F1B.
Args:
input_object (Any): Object to be sent.
prev_rank (int, optional): The rank of the recipient of the tensor
"""
if not self.stage_manager.is_first_stage():
if self.fp8_communication:
cast_to_fp8_pipeline(input_tensor_grad)
self.comm.send_backward(input_tensor_grad, prev_rank, send_metadata=self.send_grad_metadata)
self.send_grad_metadata = not self.enable_metadata_cache
if self.fp8_communication:
cast_from_fp8_pipeline(input_tensor_grad, del_metadata=False)
def send_forward_recv_backward(self, output_tensor: Any, send_first: Optional[bool] = None) -> Any:
"""Sends the input tensor to the next stage and copy the gradient tensor from the next stage in pipeline.
For 1F1B.
Args:
output_object (Any): Object to be sent.
next_rank (int, optional): The rank of the recipient of the tensor.
"""
if not self.stage_manager.is_last_stage():
if not self.send_tensor_metadata and self.grad_metadata_recv is not None:
send_first = None
if self.fp8_communication:
cast_to_fp8_pipeline(output_tensor)
output_tensor_grad, _ = self.comm.send_forward_recv_backward(
output_tensor,
send_metadata=self.send_tensor_metadata,
metadata_recv=self.grad_metadata_recv,
send_first=send_first,
)
self.send_tensor_metadata = not self.enable_metadata_cache
if self.enable_metadata_cache and self.grad_metadata_recv is None:
self.grad_metadata_recv = create_send_metadata(output_tensor_grad)
if self.fp8_communication:
cast_from_fp8_pipeline(output_tensor, del_metadata=False)
cast_from_fp8_pipeline(output_tensor_grad)
return output_tensor_grad
def send_backward_recv_forward(self, input_tensor_grad: Any, send_first: Optional[bool] = None) -> Any:
"""Sends the gradient tensor to the previous stage and copy the input tensor from the previous stage in pipeline.
For 1F1B.
Args:
output_object (Any): Object to be sent.
prev_rank (int, optional): The rank of the recipient of the tensor.
"""
if not self.stage_manager.is_first_stage():
if not self.send_grad_metadata and self.tensor_metadata_recv is not None:
send_first = None # must not fallback
if self.fp8_communication:
cast_to_fp8_pipeline(input_tensor_grad)
input_tensor, _ = self.comm.send_backward_recv_forward(
input_tensor_grad,
send_metadata=self.send_grad_metadata,
metadata_recv=self.tensor_metadata_recv,
send_first=send_first,
)
self.send_grad_metadata = not self.enable_metadata_cache
if self.enable_metadata_cache and self.tensor_metadata_recv is None:
self.tensor_metadata_recv = create_send_metadata(input_tensor)
if self.fp8_communication:
cast_from_fp8_pipeline(input_tensor)
cast_from_fp8_pipeline(input_tensor_grad, del_metadata=False)
return input_tensor
def forward_step(
self,
model: Module,
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 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()
# 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, micro_batch, input_obj)
if self.stage_manager.is_last_stage():
loss = criterion(output_obj, micro_batch) / self.num_microbatches
if accum_loss is not None:
accum_loss.add_(loss.data)
if outputs is not None:
outputs.append(tree_map_hf(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:
keys = output_obj.get("backward_tensor_keys", output_obj_grad.keys())
tensors_to_backward = []
grads_to_backward = []
for k in keys:
tensors_to_backward.append(output_obj[k])
grads_to_backward.append(output_obj_grad[k])
if len(tensors_to_backward) == 1:
optimizer.backward_by_grad(tensors_to_backward[0], grads_to_backward[0])
else:
optimizer.backward_by_grad(tensors_to_backward, grads_to_backward)
# 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 run_forward_only(
self,
model: Module,
data_iter: Iterable,
criterion: Callable[..., Any],
return_loss: bool = False,
return_outputs: bool = False,
) -> Dict:
"""
Runs forward only schedule, with communication between pipeline stages.
"""
assert self.forward_only
self.load_batch(data_iter)
accum_loss = None
if return_loss and self.stage_manager.is_last_stage():
accum_loss = torch.scalar_tensor(0, device=get_accelerator().get_current_device())
outputs = [] if return_outputs and self.stage_manager.is_last_stage() else None
for _ in range(self.num_microbatches):
input_obj = self.recv_forward()
output_obj = self.forward_step(model, input_obj, criterion, accum_loss, outputs)
self.send_forward(output_obj)
if outputs is not None:
if isinstance(model, ModelWrapper):
model = model.unwrap()
batch_size_dim = getattr(model, "batch_size_dim", 0)
outputs = merge_batch(outputs, batch_size_dim)
return {"loss": accum_loss, "outputs": outputs}
def run_forward_backward(
self,
model: Module,
data_iter: Iterable,
criterion: Callable[..., Any],
optimizer: Optional[OptimizerWrapper] = None,
return_loss: bool = False,
return_outputs: bool = False,
) -> Dict:
"""
Runs non-interleaved 1F1B schedule, with communication between pipeline stages.
"""
assert not self.forward_only
self.load_batch(data_iter)
# num_warmup_microbatches is the step when not all the processes are working
num_warmup_microbatches = self.stage_manager.num_stages - self.stage_manager.stage - 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, output_objs = [], []
accum_loss = None
if return_loss and self.stage_manager.is_last_stage():
accum_loss = torch.scalar_tensor(0, device=get_current_device())
outputs = [] if return_outputs and self.stage_manager.is_last_stage() else None
# Run warmup forward passes.
for i in range(num_warmup_microbatches):
input_obj = self.recv_forward()
output_obj = self.forward_step(model, input_obj, criterion, accum_loss, outputs)
self.send_forward(output_obj)
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:
input_obj = self.recv_forward()
# Run 1F1B in steady state.
for i in range(num_microbatches_remaining):
last_iteration = i == (num_microbatches_remaining - 1)
output_obj = self.forward_step(model, input_obj, criterion, accum_loss, outputs)
output_obj_grad = self.send_forward_recv_backward(output_obj, send_first=self.stage_manager.stage % 2 == 0)
# 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(optimizer, input_obj, output_obj, output_obj_grad)
if last_iteration:
self.send_backward(input_obj_grad)
else:
input_obj = self.send_backward_recv_forward(
input_obj_grad, send_first=self.stage_manager.stage % 2 == 0
)
# Run cooldown backward passes.
for i in range(num_warmup_microbatches):
input_obj = input_objs.pop(0)
output_obj = output_objs.pop(0)
output_obj_grad = self.recv_backward()
input_obj_grad = self.backward_step(optimizer, input_obj, output_obj, output_obj_grad)
self.send_backward(input_obj_grad)
assert all(len(v) == 0 for v in input_objs) and all(len(v) == 0 for v in output_objs)
if outputs is not None:
if isinstance(model, ModelWrapper):
model = model.unwrap()
batch_size_dim = getattr(model, "batch_size_dim", 0)
outputs = merge_batch(outputs, batch_size_dim)
return {"loss": accum_loss, "outputs": outputs}
def forward_backward_step(
self,
model: Module,
data_iter: Iterable,
criterion: Callable[..., Any],
optimizer: Optional[OptimizerWrapper] = None,
return_loss: bool = False,
return_outputs: bool = False,
) -> dict:
"""
Args:
model (Module): Model 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: Dictionary containing loss and outputs.
"""
self.forward_only = not torch.is_grad_enabled()
if optimizer is None:
assert self.forward_only, "Optimizer should be passed when doing backward."
if self.forward_only:
result = self.run_forward_only(model, data_iter, criterion, return_loss, return_outputs)
else:
result = self.run_forward_backward(model, data_iter, criterion, optimizer, return_loss, return_outputs)
return result