from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
import torch
import torch.cuda
import torch.distributed
from torch.nn import Module, ModuleList
from torch.utils._pytree import tree_flatten, tree_map
from colossalai.accelerator import get_accelerator
from colossalai.interface import OptimizerWrapper
from colossalai.pipeline.p2p import PipelineP2PCommunication, create_send_metadata
from colossalai.pipeline.schedule.v_schedule import ScheduledNode
from colossalai.pipeline.stage_manager import PipelineStageManager
from colossalai.pipeline.weight_grad_store import WeightGradStore
from ._utils import (
clone,
detach,
get_batch_size,
get_micro_batch,
merge_batch,
model_forward,
release_tensor_data,
require_grad,
retain_grad,
to_device,
)
from .base import PipelineSchedule
AUTO_SCHEDULE_COMMUNICATION_TYPES = {"RECV_FORWARD", "RECV_BACKWARD", "SEND_FORWARD", "SEND_BACKWARD"}
def _wait_p2p(wait_handles: List[torch.cuda.Event]) -> None:
if wait_handles is not None:
for req in wait_handles:
req.wait()
class ZeroBubbleVPipeScheduler(PipelineSchedule):
def __init__(
self,
stage_manager: PipelineStageManager,
schedule: List[ScheduledNode],
num_model_chunks: int,
num_microbatch: Optional[int] = None,
microbatch_size: Optional[int] = None,
enable_metadata_cache: bool = True,
overlap_p2p: bool = True,
):
super().__init__(stage_manager)
# batch info
self.num_microbatch = num_microbatch
self.microbatch_size = microbatch_size
self.num_model_chunks = num_model_chunks
self.batch: Any
self.batch_size: int
self.last_batch_size: Optional[int] = None
self.microbatch_offset: List[int]
self.schedules = schedule
# TODO: optim post valid
self.do_post_validation = False
# 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
# P2P communication
self.comm = PipelineP2PCommunication(stage_manager, overlap_p2p=overlap_p2p)
# init communication map
self.communication_map = {
"SEND_FORWARD": self.send_forward,
"RECV_FORWARD": self.recv_forward,
"SEND_BACKWARD": self.send_backward,
"RECV_BACKWARD": self.recv_backward,
}
# init buffer
self._free_buffers()
def _free_buffers(self):
# free local buffer
# two dim array, first dim is the model chunk, second dim is the microbatch queue
# x & y buffer for schedule b
self.input_tensors = [[], []]
self.output_tensors = [[], []]
# y & dy buffer for schedule w
self.output_tensors_dw = [[], []]
self.output_tensors_grad_dw = [[], []]
# buffer for communication
self.send_forward_buffer = [[], []]
self.recv_forward_buffer = [[], []]
self.send_backward_buffer = [[], []]
self.recv_backward_buffer = [[], []]
# y buffer for local send fwd
self.local_send_forward_buffer = []
# dy buffer for local send bwd
self.local_send_backward_buffer = []
# wait pp buffer
self.send_handles = []
def assert_buffer_empty(self):
# assert buffer is empty at end
assert len(self.input_tensors[0]) == 0
assert len(self.input_tensors[1]) == 0
assert len(self.output_tensors[0]) == 0
assert len(self.output_tensors[1]) == 0
assert len(self.output_tensors_dw[0]) == 0
assert len(self.output_tensors_dw[1]) == 0
assert len(self.output_tensors_grad_dw[0]) == 0
assert len(self.output_tensors_grad_dw[1]) == 0
assert len(self.send_forward_buffer[0]) == 0
assert len(self.send_forward_buffer[1]) == 0
assert len(self.recv_forward_buffer[0]) == 0
assert len(self.recv_forward_buffer[1]) == 0
assert len(self.send_backward_buffer[0]) == 0
assert len(self.send_backward_buffer[1]) == 0
assert len(self.recv_backward_buffer[0]) == 0
assert len(self.recv_backward_buffer[1]) == 0
assert len(self.local_send_forward_buffer) == 0
assert len(self.local_send_backward_buffer) == 0
# assert len(self.send_handles) == 0
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 for _ in range(self.num_model_chunks)]
self.batch = batch
self.batch_size = get_batch_size(batch)
if self.microbatch_size is None:
assert self.batch_size % self.num_microbatch == 0, "Batch size should divided by the number of microbatch"
self.microbatch_size = self.batch_size // self.num_microbatch
if self.num_microbatch is None:
assert self.batch_size % self.microbatch_size == 0, "Batch size should divided by the microbatch size"
self.num_microbatch = 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_microbatch
assert (
self.num_microbatch % self.stage_manager.num_stages == 0
), "Number of microbatch should be an integer multiple of number of pipeline parallel devices"
if self.forward_only:
self.num_microbatch = (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, 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.
"""
assert self.microbatch_offset[model_chunk_id] <= self.batch_size, "Microbatches exhausted"
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_accelerator().get_current_device()), micro_batch)
def get_model_chunk_id(self, microbatch_id: int, is_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
"""
assert (
microbatch_id < self.num_microbatch * self.num_model_chunks
), f"microbatch_id {microbatch_id} is out of range ({self.num_microbatch * self.num_model_chunks})"
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 is_forward:
# Reverse order
model_chunk_id = self.num_model_chunks - model_chunk_id - 1
return model_chunk_id
def recv_forward(self, model_chunk_id: int, prev_rank: int = None) -> Tuple[Any, List]:
"""Copy the forward output from the previous stage in pipeline as the input tensor of this stage.
For ZBV.
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.
Any: The wait handles for the communication.
"""
with self.stage_manager.switch_model_chunk_id(model_chunk_id):
if model_chunk_id == 0:
################
# chunk = 0 & is_first_stage
# do nothing; cause u are chunk 0 in first rank, u have no prev rank;
#################
if self.stage_manager.is_first_stage(ignore_chunk=True):
# return None, []
return []
################
# chunk = 0 & not is_first_stage
# Recv y from PREV_rank as input
#################
else:
prev_rank = self.stage_manager.get_prev_rank()
input_tensor, wait_handles = self.comm.recv_forward(
prev_rank=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)
self.recv_forward_buffer[model_chunk_id].append(input_tensor)
# return input_tensor, wait_handles
return wait_handles
else:
################
# chunk = 1 & is_last_stage
# do nothing; cause u get y from local_send_forward_buffer in schedule f
################
if self.stage_manager.is_last_stage(ignore_chunk=True):
# return None, []
return []
################
# chunk = 1 & not is_last_stage
# recv y from NEXT_rank as input
################
else:
next_rank = self.stage_manager.get_next_rank()
input_tensor, wait_handles = self.comm.recv_forward(
next_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)
self.recv_forward_buffer[model_chunk_id].append(input_tensor)
# return input_tensor, wait_handles
return wait_handles
def recv_backward(self, model_chunk_id: int, next_rank: int = None) -> Tuple[Any, List]:
"""Copy the gradient tensor from the next stage in pipeline as the input gradient of this stage.
For ZBV.
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.
Any: The wait handles for the communication.
"""
with self.stage_manager.switch_model_chunk_id(model_chunk_id):
if model_chunk_id == 0:
# bwd chunk0 is right V;
################
# chunk = 0 & is_last_stage
# do nothing; Already get dy from local_send_backward_buffer in schedule b
################
if self.stage_manager.is_last_stage(ignore_chunk=True):
# return None, []
return []
################
# chunk = 0 & not is_last_stage
# Recv bwd from next stage;
################
else:
next_rank = self.stage_manager.get_next_rank()
output_tensor_grad, wait_handles = self.comm.recv_backward(
next_rank, metadata_recv=self.grad_metadata_recv
)
if self.enable_metadata_cache and self.grad_metadata_recv is None:
self.grad_metadata_recv = create_send_metadata(output_tensor_grad)
self.recv_backward_buffer[model_chunk_id].append(output_tensor_grad)
# return output_tensor_grad, wait_handles
return wait_handles
else:
# bwd chunk1 is left V;
################
# chunk = 1 & is_first_stage
# do nothing; get loss from local
################
if self.stage_manager.is_first_stage(ignore_chunk=True):
# return None, []
return []
################
# chunk = 1 & not first stage
# recv_backward recv bwd from prev stage;
################
else:
prev_rank = self.stage_manager.get_prev_rank()
output_tensor_grad, wait_handles = self.comm.recv_backward(
next_rank=prev_rank, metadata_recv=self.grad_metadata_recv
)
if self.enable_metadata_cache and self.grad_metadata_recv is None:
self.grad_metadata_recv = create_send_metadata(output_tensor_grad)
self.recv_backward_buffer[model_chunk_id].append(output_tensor_grad)
# return output_tensor_grad, wait_handles
return wait_handles
def send_forward(self, model_chunk_id: int, next_rank: int = None) -> List:
"""Sends the input tensor to the next stage in pipeline.
For ZBV.
Args:
model_chunk_id (int): The current model chunk idx.
next_rank (int, optional): The rank of the recipient of the tensor.
Returns:
Any: The wait handles for the communication.
"""
with self.stage_manager.switch_model_chunk_id(model_chunk_id):
if model_chunk_id == 0:
################
# chunk = 0 && is_last_stage
# do nothing; hold y on local_send_forward_buffer
################
if self.stage_manager.is_last_stage(ignore_chunk=True):
return []
################
# chunk = 0 && not is_last_stage
# self.comm.send_forward send y to NEXT stage
################
else:
next_rank = self.stage_manager.get_next_rank()
output_tensor = self.send_forward_buffer[model_chunk_id].pop(0)
send_handles = self.comm.send_forward(
output_object=output_tensor, next_rank=next_rank, send_metadata=self.send_tensor_metadata
)
self.send_tensor_metadata = not self.enable_metadata_cache
return send_handles
else:
################
# chunk = 1 && is_first_stage
# do nothing; Already send LOSS to local_send_backward_buffer in schedule f send part
################
if self.stage_manager.is_first_stage(ignore_chunk=True):
return []
################
# chunk = 1 && not is_first_stage
# self.comm.send_forward send y to PREV stage
################
else:
prev_rank = self.stage_manager.get_prev_rank()
output_tensor = self.send_forward_buffer[model_chunk_id].pop(0)
send_handles = self.comm.send_forward(
output_tensor, prev_rank, send_metadata=self.send_tensor_metadata
)
self.send_tensor_metadata = not self.enable_metadata_cache
return send_handles
def send_backward(self, model_chunk_id: int, prev_rank: int = None) -> List:
"""Sends the gradient tensor to the previous stage in pipeline.
For ZBV.
Args:
model_chunk_id (int): The current model chunk idx.
prev_rank (int, optional): The rank of the recipient of the tensor
Returns:
Any: The wait handles for the communication.
"""
with self.stage_manager.switch_model_chunk_id(model_chunk_id):
if model_chunk_id == 0:
# bwd chunk0 is right V;
################
# chunk = 0 && is_first_stage
# do nothing; cause u are the first chunk in first stage; bwd end
################
if self.stage_manager.is_first_stage(ignore_chunk=True):
return []
################
# chunk = 0 && not is_first_stage
# Send dx to PREV stage;
################
else:
prev_rank = self.stage_manager.get_prev_rank()
input_tensor_grad = self.send_backward_buffer[model_chunk_id].pop(0)
send_handles = self.comm.send_backward(
input_tensor_grad, prev_rank, send_metadata=self.send_grad_metadata
)
self.send_grad_metadata = not self.enable_metadata_cache
return send_handles
# bwd chunk1 is left V;
else:
################
# chunk = 1 && is_last_stage
# do nothing; Already send input_tensor_grad to local_send_bwd_buffer in schedule b;
################
if self.stage_manager.is_last_stage(ignore_chunk=True):
return []
################
# chunk = 1 && not is_last_stage
# Send dx to NEXT stage;
################
else:
next_rank = self.stage_manager.get_next_rank()
input_tensor_grad = self.send_backward_buffer[model_chunk_id].pop(0)
send_handles = self.comm.send_backward(
input_tensor_grad, next_rank, send_metadata=self.send_grad_metadata
)
self.send_grad_metadata = not self.enable_metadata_cache
return send_handles
def forward_step(
self,
model_chunk: Union[ModuleList, Module],
model_chunk_id: int,
micro_batch: Optional[dict],
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_chunk (ModuleList or Module): Model Chunk to be run;
model_chunk_id (int): The current model chunk idx;
input_obj (Optional[dict]): x;
criterion (Callable): loss function;
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).
"""
# Load input ids, attention mask and labels
# for the first stage, input_obj is None; So,we use micro_batch as input_obj
# for other stages, input_obj is the output of the previous/next stage containing hidden_states etc.
# Only attention_mask from micro_batch is used
with self.stage_manager.switch_model_chunk_id(model_chunk_id):
# fwd calculate
internal_inputs = {} if input_obj is None else input_obj
internal_inputs["stage_index"] = self.stage_manager.stage_indices[model_chunk_id]
output_obj = model_forward(model_chunk, micro_batch, internal_inputs)
# last layer in model
if model_chunk_id == 1 and self.stage_manager.is_first_stage(ignore_chunk=True):
loss = criterion(output_obj, micro_batch) / self.num_microbatch
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_b_step(
self,
model_chunk: Union[ModuleList, Module],
model_chunk_id: int,
optimizer: OptimizerWrapper,
# micro_batch: Optional[dict],
input_obj: Optional[dict],
output_obj: Union[dict, torch.Tensor],
output_obj_grad: Optional[dict],
) -> Optional[dict]:
"""Backward dx step of the pipeline; we calculate "dx = w*dy" here;
Args:
model_chunk (ModuleList or Module): Model Chunk to be run;
model_chunk_id (int): The current model chunk idx;
optimizer (OptimizerWrapper): Optimizer to update the model
input_obj (Optional[Tuple(dict)]): x. (microbatch, input_obj)
output_obj (Union[dict, torch.Tensor]): y.
output_obj_grad (dict): dy.
Returns:
Optional[dict]: dx.
"""
# calculate bwd b step ; only dx = w*dy;
# Retain the grad on the input_obj. No need retain_grad microbatch
if input_obj is not None:
tree_map(retain_grad, input_obj)
# x, y, dy list for backward_by_grad; Type: list[tensor];
input_obj_ = []
output_obj_ = []
output_obj_grad_ = []
# For chunk 0 stage 0, use micro_batch as input_obj_; and we don't have to cal microbatch dx.
# if model_chunk_id == 0 and self.stage_manager.is_first_stage(ignore_chunk=True):
# return None
# For loss backward; output_obj is loss; output_obj_grad should be None
if model_chunk_id == 1 and self.stage_manager.is_first_stage(ignore_chunk=True):
assert output_obj_grad is None
input_obj_, _ = tree_flatten(input_obj)
output_obj_.append(output_obj) # LOSS
output_obj_grad_.append(output_obj_grad) # None
# For other chunk stage, use input_obj as input_obj_;
else:
input_obj_, _ = tree_flatten(input_obj)
output_obj_, _ = tree_flatten(output_obj) # y
output_obj_grad_, _ = tree_flatten(output_obj_grad) # dy
# filter item which is not torch.Tensor
input_obj_ = [v for v in input_obj_ if isinstance(v, torch.Tensor) or v is None]
output_obj_ = [v for v in output_obj_ if isinstance(v, torch.Tensor) or v is None]
output_obj_grad_ = [v for v in output_obj_grad_ if isinstance(v, torch.Tensor) or v is None]
try:
ctx = optimizer.no_sync()
except AttributeError:
ctx = model_chunk.no_sync()
with ctx:
optimizer.backward_by_grad(
tensor=output_obj_,
grad=output_obj_grad_,
# inputs=input_obj_,
retain_graph=False,
)
# Format output_obj_grad
input_obj_grad = dict()
if model_chunk_id == 0 and self.stage_manager.is_first_stage(ignore_chunk=True):
pass
else:
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 backward_w_step(
self,
model_chunk: Union[ModuleList, Module],
model_chunk_id: int,
optimizer: OptimizerWrapper,
output_obj: Union[dict, torch.Tensor],
output_obj_grad: Optional[dict],
):
"""Backward dw step of the pipeline; we calculate "dw = x*dy" here;
Args:
model_chunk (ModuleList or Module): Model Chunk to be run;
model_chunk_id (int): The current model chunk idx;
optimizer (OptimizerWrapper): Optimizer to update the model
output_obj (Union[dict, torch.Tensor]): y.
output_obj_grad (dict): dy.
Returns:
Nothing need to return; we only calculate dw then update w;
"""
# calculate bwd w step ; only dw = x*dy;
# y, dy list for w backward_by_grad; Type: list[tensor];
output_obj_ = []
output_obj_grad_ = []
if model_chunk_id == 1 and self.stage_manager.is_first_stage(ignore_chunk=True):
# loss backward; output_obj is loss;
output_obj_.append(output_obj) # LOSS
output_obj_grad_.append(None) # None
else:
output_obj_, _ = tree_flatten(output_obj) # y
output_obj_grad_, _ = tree_flatten(output_obj_grad) # dy
# filter item which is not torch.Tensor
output_obj_ = [v for v in output_obj_ if isinstance(v, torch.Tensor) or v is None]
output_obj_grad_ = [v for v in output_obj_grad_ if isinstance(v, torch.Tensor) or v is None]
optimizer.backward_by_grad(
tensor=output_obj_,
grad=output_obj_grad_,
inputs=list(model_chunk.parameters()),
retain_graph=False,
)
def schedule_f(
self,
scheduled_node,
model_chunk: torch.nn.ModuleList,
model_chunk_id: int,
criterion: Callable,
accum_loss: Optional[torch.Tensor] = None,
outputs: Optional[List[Any]] = None,
):
"""A complete forward schedule; Include recv fwd --> cal fwd --> send fwd;
Args:
scheduled_node:
model_chunk (ModuleList or Module): Model Chunk to be run;
model_chunk_id (int): The current model chunk idx;
criterion (Callable): loss function;
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:
Nothing.
"""
micro_batch = self.load_micro_batch(model_chunk_id=model_chunk_id)
# Step1: recv fwd
if model_chunk_id == 0:
# is first stage; get input from microbatch
if self.stage_manager.is_first_stage(ignore_chunk=True):
input_obj = None
else:
input_obj = self.recv_forward_buffer[model_chunk_id].pop(0)
else:
# is last stage; recv from local
if self.stage_manager.is_last_stage(ignore_chunk=True):
input_obj = self.local_send_forward_buffer.pop(0)
# not last stage; recv from next
else:
input_obj = self.recv_forward_buffer[model_chunk_id].pop(0)
# Here, let input_obj.requires_grad_()
# if input_obj is not None:
if not isinstance(input_obj, torch.Tensor):
tree_map(require_grad, input_obj)
# Also requires_grad_ for micro_batch in stage 0 chunk 0 fwd,
# tree_map(torch.Tensor.requires_grad_, micro_batch)
# Step2: fwd step
output_obj = self.forward_step(
model_chunk=model_chunk,
model_chunk_id=model_chunk_id,
micro_batch=micro_batch,
input_obj=input_obj,
criterion=criterion,
accum_loss=accum_loss,
outputs=outputs,
)
# Step3:
# 3-1:detach output; detach output for send fwd;
if model_chunk_id == 1 and self.stage_manager.is_first_stage(ignore_chunk=True):
# We should not detach bwd LOSS
pass
else:
# detach output
detached_output_obj = tree_map(detach, output_obj)
# 3-2 clone detached_output_obj
detached_output_obj = tree_map(clone, detached_output_obj)
# 3-3 release cloned output.data; release_tensor_data output for bwd b & w; (do not detach output)
if model_chunk_id == 1 and self.stage_manager.is_first_stage(ignore_chunk=True):
# We should not release_tensor_data bwd LOSS
pass
else:
# release_tensor_data output
tree_map(release_tensor_data, output_obj)
# add input and output object for backward b
self.input_tensors[model_chunk_id].append(input_obj)
# for bwd b&w, we only need the graph(grad_fn) of output_obj
# Do not release_tensor_data loss, release_tensor_data other output_obj;
if model_chunk_id == 1 and self.stage_manager.is_first_stage(ignore_chunk=True):
self.output_tensors[model_chunk_id].append(output_obj)
# self.output_tensors_dw[model_chunk_id].append(output_obj)
else:
self.output_tensors[model_chunk_id].append(output_obj)
# self.output_tensors_dw[model_chunk_id].append(output_obj)
# add output to send_fwd_buffer
if model_chunk_id == 0: # chunk 0
# is last stage; send to local_send_forward_buffer
if self.stage_manager.is_last_stage(ignore_chunk=True):
self.local_send_forward_buffer.append(detached_output_obj)
else:
self.send_forward_buffer[model_chunk_id].append(detached_output_obj)
else: # chunk 1
# is first stage; end of fwd; do nothing
if self.stage_manager.is_first_stage(ignore_chunk=True):
pass
else:
self.send_forward_buffer[model_chunk_id].append(detached_output_obj)
def schedule_b(
self,
scheduled_node,
model_chunk: Union[ModuleList, Module],
model_chunk_id: int,
optimizer: OptimizerWrapper,
):
"""A complete backward b schedule; Include recv bwd --> cal bwd step --> send bwd;
Args:
scheduled_node:
model_chunk (ModuleList or Module): Model Chunk to be run;
model_chunk_id (int): The current model chunk idx;
Returns:
Nothing.
"""
# Step1: recv bwd
if model_chunk_id == 0:
# chunk0 is last stage; recv output_grad from local_send_backward_buffer
if self.stage_manager.is_last_stage(ignore_chunk=True):
output_tensor_grad = self.local_send_backward_buffer.pop(0)
# chunk0 not last stage; recv output_grad from recv_backward_buffer
else:
output_tensor_grad = self.recv_backward_buffer[model_chunk_id].pop(0)
else:
# chunk1, is first stage; recv LOSS from local send bwd buffer
if self.stage_manager.is_first_stage(ignore_chunk=True):
output_tensor_grad = None
# chunk1, not first stage; recv output_grad from recv_backward_buffer
else:
output_tensor_grad = self.recv_backward_buffer[model_chunk_id].pop(0)
# get input and output object from buffer;
input_obj = self.input_tensors[model_chunk_id].pop(0)
output_obj = self.output_tensors[model_chunk_id].pop(0)
# # save output_tensor_grad for dw
# if model_chunk_id == 1 and self.stage_manager.is_first_stage(ignore_chunk=True):
# # we save loss here
# self.output_tensors_grad_dw[model_chunk_id].append(output_obj)
# else:
# # we save output_tensor_grad here
# self.output_tensors_grad_dw[model_chunk_id].append(output_tensor_grad)
# the_output_obj_grad = []
# if isinstance(output_obj, dict):
# for (k, v) in output_obj.items():
# the_output_obj_grad.append(v.requires_grad)
# else:
# the_output_obj_grad.append(output_obj.requires_grad)
input_object_grad = self.backward_b_step(
model_chunk=model_chunk,
model_chunk_id=model_chunk_id,
optimizer=optimizer,
input_obj=input_obj,
output_obj=output_obj,
output_obj_grad=output_tensor_grad,
)
# Step3: send bwd
if model_chunk_id == 0:
# do nothing; end of bwd;
if self.stage_manager.is_first_stage(ignore_chunk=True):
pass
# save input_object_grad to send_backward_buffer
else:
self.send_backward_buffer[model_chunk_id].append(input_object_grad)
else:
# send to local_send_backward_buffer
if self.stage_manager.is_last_stage(ignore_chunk=True):
self.local_send_backward_buffer.append(input_object_grad)
# send to next
else:
self.send_backward_buffer[model_chunk_id].append(input_object_grad)
WeightGradStore.flush(chunk=model_chunk_id)
def schedule_w(
self,
scheduled_node,
model_chunk: Union[ModuleList, Module],
model_chunk_id: int,
optimizer: OptimizerWrapper,
):
"""A complete backward w schedule; Include get y & dy from buffer --> cal bwd w step(cal dw & update w);
Args:
scheduled_node:
model_chunk (ModuleList or Module): Model Chunk to be run;
model_chunk_id (int): The current model chunk idx;
Returns:
Nothing.
"""
# get y & dy from buffer
# output_obj = self.output_tensors_dw[model_chunk_id].pop(0)
# output_obj_grad = self.output_tensors_grad_dw[model_chunk_id].pop(0)
WeightGradStore.pop(chunk=model_chunk_id)
# self.backward_w_step(
# model_chunk=model_chunk,
# model_chunk_id=model_chunk_id,
# optimizer=optimizer,
# output_obj=output_obj,
# output_obj_grad=output_obj_grad,
# )
def run_forward_only(
self,
model_chunk: Union[ModuleList, Module],
data_iter: Iterable,
criterion: Callable[..., Any],
return_loss: bool = False,
return_outputs: bool = False,
) -> Dict:
assert self.forward_only
# prepare batch
self.load_batch(data_iter)
# prepare accum loss & output
accum_loss = None
# reset accum loss at fwd end;
if return_loss and self.stage_manager.is_first_stage(ignore_chunk=True):
accum_loss = torch.scalar_tensor(0, device=get_accelerator().get_current_device())
outputs = [] if return_outputs and self.stage_manager.is_first_stage(ignore_chunk=True) else None
# while we still have schedules_node in self.schedules
for it in range(len(self.schedules)):
scheduled_node = self.schedules[it]
if scheduled_node.type in {"RECV_FORWARD", "SEND_FORWARD"}:
# communication
communication_func = self.communication_map[scheduled_node.type]
communication_func(scheduled_node.chunk)
if scheduled_node.type == "F":
self.schedule_f(
scheduled_node=scheduled_node,
model_chunk=model_chunk,
model_chunk_id=scheduled_node.chunk,
criterion=criterion,
accum_loss=accum_loss,
outputs=outputs,
)
# return loss & output
if outputs is not None:
outputs = merge_batch(outputs)
return {"loss": accum_loss, "outputs": outputs}
def run_forward_backward(
self,
model_chunk: Union[ModuleList, Module],
data_iter: Iterable,
criterion: Callable[..., Any],
optimizer: Optional[OptimizerWrapper] = None,
return_loss: bool = False,
return_outputs: bool = False,
) -> Dict:
"""
Runs Zerobubble schedule, with communication between pipeline stages.
"""
# prepare batch
self.load_batch(data_iter)
# prepare accum loss & output
accum_loss = None
# reset accum loss at fwd end;
if return_loss and self.stage_manager.is_first_stage(ignore_chunk=True):
accum_loss = torch.scalar_tensor(0, device=get_accelerator().get_current_device())
outputs = [] if return_outputs and self.stage_manager.is_first_stage(ignore_chunk=True) else None
# while we still have schedules_node in self.schedules
schedule = self.schedules[self.stage_manager.stage] # get schedule by stage (rank)
for it in range(len(schedule)):
scheduled_node = schedule[it]
if scheduled_node.type in AUTO_SCHEDULE_COMMUNICATION_TYPES:
# communication
communication_func = self.communication_map[scheduled_node.type]
wait_handle = communication_func(scheduled_node.chunk)
self.send_handles.append(wait_handle)
elif scheduled_node.type == "F":
self.schedule_f(
scheduled_node=scheduled_node,
model_chunk=model_chunk,
model_chunk_id=scheduled_node.chunk,
criterion=criterion,
accum_loss=accum_loss,
outputs=outputs,
)
elif scheduled_node.type == "B":
self.schedule_b(
scheduled_node=scheduled_node,
model_chunk=model_chunk,
model_chunk_id=scheduled_node.chunk,
optimizer=optimizer,
)
elif scheduled_node.type == "W":
self.schedule_w(
scheduled_node=scheduled_node,
model_chunk=model_chunk,
model_chunk_id=scheduled_node.chunk,
optimizer=optimizer,
)
for h in self.send_handles:
for hh in h:
hh.wait()
# return loss & output
if outputs is not None:
outputs = merge_batch(outputs)
return {"loss": accum_loss, "outputs": outputs}
def forward_backward_step(
self,
model_chunk: Union[ModuleList, Module],
data_iter: Iterable,
criterion: Callable[..., Any],
optimizer: Optional[OptimizerWrapper] = None,
return_loss: bool = False,
return_outputs: bool = False,
) -> dict:
"""
Args:
model_chunk (ModuleList or Module): Model Chunk to be trained. Original interleaved uses a module list whereas shardformer uses entire model + layer specification
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'.
"""
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_chunk, data_iter, criterion, return_loss, return_outputs)
else:
result = self.run_forward_backward(
model_chunk, data_iter, criterion, optimizer, return_loss, return_outputs
)
self.assert_buffer_empty()
return result