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from torch.optim.lr_scheduler import OneCycleLR as _OneCycleLR
from colossalai.registry import LR_SCHEDULERS
@LR_SCHEDULERS.register_module
class OneCycleLR(_OneCycleLR):
r"""Sets the learning rate of each parameter group according to the
1cycle learning rate policy. The 1cycle policy anneals the learning
rate from an initial learning rate to some maximum learning rate and then
from that maximum learning rate to some minimum learning rate much lower
than the initial learning rate.
This policy was initially described in the paper `Super-Convergence:
Very Fast Training of Neural Networks Using Large Learning Rates`_.
The 1cycle learning rate policy changes the learning rate after every batch.
`step` should be called after a batch has been used for training.
This scheduler is not chainable.
Note also that the total number of steps in the cycle can be determined in one
of two ways (listed in order of precedence):
* A value for total_steps is explicitly provided.
* A number of epochs (epochs) and a number of steps per epoch (steps_per_epoch) are provided.
In this case, the number of total steps is inferred by total_steps = epochs * steps_per_epoch
You must either provide a value for total_steps or provide a value for both
epochs and steps_per_epoch.
The default behaviour of this scheduler follows the fastai implementation of 1cycle, which
claims that "unpublished work has shown even better results by using only two phases". To
mimic the behaviour of the original paper instead, set ``three_phase=True``.
Args:
optimizer (:class:`torch.optim.Optimizer`): Wrapped optimizer.
total_steps (int): Number of total training steps.
pct_start (float, optional):
The percentage of the cycle (in number of steps) spent increasing the learning rate, defaults to 0.3.
anneal_strategy (str, optional): {'cos', 'linear'}, Specifies the annealing strategy:
"cos" for cosine annealing, "linear" for linear annealing, defaults to 'cos'.
cycle_momentum (bool, optional): If ``True``, momentum is cycled inversely
to learning rate between 'base_momentum' and 'max_momentum', defaults to True.
base_momentum (float, optional): Lower momentum boundaries in the cycle for each parameter group.
Note that momentum is cycled inversely to learning rate; at the peak of a cycle, momentum is
'base_momentum' and learning rate is 'max_lr', defaults to 0.85.
max_momentum (float, optional): Upper momentum boundaries in the cycle for each parameter group.
Functionally, it defines the cycle amplitude (max_momentum - base_momentum).
Note that momentum is cycled inversely to learning rate; at the start of a cycle, momentum is 'max_momentum'
and learning rate is 'base_lr', defaults to 0.95.
div_factor (float, optional): Determines the initial learning rate via
initial_lr = max_lr/div_factor, defaults to 25.0.
final_div_factor (float, optional): Determines the minimum learning rate via
min_lr = initial_lr/final_div_factor, defaults to 10000.0.
last_epoch (int, optional): The index of the last batch. This parameter is used when resuming a training job.
Since `step()` should be invoked after each batch instead of after each epoch, this number represents
the total number of *batches* computed, not the total number of epochs computed.
When last_epoch=-1, the schedule is started from the beginning, defaults to -1
The ``kwargs`` for initializing torch.optim.lr_scheduler.OneCycleLR should include parameters below:
::
epochs (int, optional, default=None)
steps_per_epoch (int, optional, default=None)
three_phase (bool, optional, default=False)
verbose (bool, optional, default=False)
More details about kwargs could be found in
`OneCycleLR <https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.OneCycleLR.html#torch.optim.lr_scheduler.OneCycleLR>`_.
.. _Super-Convergence\: Very Fast Training of Neural Networks Using Large Learning Rates:
https://arxiv.org/abs/1708.07120
"""
def __init__(self, optimizer, total_steps: int,
pct_start=0.3,
anneal_strategy='cos',
cycle_momentum=True,
base_momentum=0.85,
max_momentum=0.95,
div_factor=25.0,
final_div_factor=10000.0,
last_epoch=-1, **kwargs):
max_lrs = list(map(lambda group: group['lr'], optimizer.param_groups))
super().__init__(optimizer, max_lrs, total_steps=total_steps,
pct_start=pct_start,
anneal_strategy=anneal_strategy,
cycle_momentum=cycle_momentum,
base_momentum=base_momentum,
max_momentum=max_momentum,
div_factor=div_factor,
final_div_factor=final_div_factor,
last_epoch=last_epoch)
|
from typing import List
from torch.optim.lr_scheduler import MultiStepLR as _MultiStepLR
from colossalai.registry import LR_SCHEDULERS
from .delayed import WarmupScheduler
@LR_SCHEDULERS.register_module
class MultiStepLR(_MultiStepLR):
"""Decays the learning rate of each parameter group by gamma once the
number of epoch reaches one of the milestones. Notice that such decay can
happen simultaneously with other changes to the learning rate from outside
this scheduler. When last_epoch=-1, sets initial lr as lr.
Args:
optimizer (:class:`torch.optim.Optimizer`): Wrapped optimizer.
total_steps (int): Number of total training steps.
milestones (List[int], optional): List of epoch indices. Must be increasing, defaults to None.
gamma (float, optional): Multiplicative factor of learning rate decay, defaults to 0.1.
last_epoch (int, optional): The index of last epoch, defaults to -1. When last_epoch=-1,
the schedule is started from the beginning or When last_epoch=-1, sets initial lr as lr.
"""
def __init__(self, optimizer, total_steps: int, milestones: List[int] = None, gamma: float = 0.1, last_epoch: int = -1, **kwargs):
super().__init__(optimizer, milestones, gamma=gamma, last_epoch=last_epoch)
@LR_SCHEDULERS.register_module
class MultiStepWarmupLR(WarmupScheduler):
"""Multistep learning rate scheduler with warmup.
Args:
optimizer (:class:`torch.optim.Optimizer`): Wrapped optimizer.
total_steps (int): Number of total training steps.
warmup_steps (int, optional): Number of warmup steps, defaults to 0.
milestones (List[int], optional): List of epoch indices. Must be increasing, defaults to None.
gamma (float, optional): Multiplicative factor of learning rate decay, defaults to 0.1.
num_steps_per_epoch (int, optional): Number of steps per epoch, defaults to -1.
last_epoch (int, optional): The index of last epoch, defaults to -1. When last_epoch=-1,
the schedule is started from the beginning or When last_epoch=-1, sets initial lr as lr.
"""
def __init__(self, optimizer, total_steps: int, warmup_steps: int = 0, milestones: List[int] = None,
gamma: float = 0.1, last_epoch: int = -1, **kwargs):
if len(milestones) == 0:
raise ValueError('milestones cannot be empty')
milestones = [
v - warmup_steps for v in milestones if v >= warmup_steps]
base_scheduler = _MultiStepLR(optimizer, milestones=milestones,
gamma=gamma)
super().__init__(optimizer, warmup_steps, base_scheduler, last_epoch=last_epoch)
|
from .cosine import CosineAnnealingLR, CosineAnnealingWarmupLR, FlatAnnealingLR, FlatAnnealingWarmupLR
from .linear import LinearWarmupLR
from .multistep import MultiStepLR, MultiStepWarmupLR
from .onecycle import OneCycleLR
from .poly import PolynomialLR, PolynomialWarmupLR
from .torch import LambdaLR, MultiplicativeLR, StepLR, ExponentialLR
__all__ = [
'CosineAnnealingLR', 'CosineAnnealingWarmupLR', 'FlatAnnealingLR', 'FlatAnnealingWarmupLR', 'LinearWarmupLR',
'MultiStepLR', 'MultiStepWarmupLR', 'OneCycleLR', 'PolynomialLR', 'PolynomialWarmupLR', 'LambdaLR',
'MultiplicativeLR', 'StepLR',
'ExponentialLR'
]
|
from torch.optim.lr_scheduler import _LRScheduler
from colossalai.registry import LR_SCHEDULERS
from .delayed import WarmupScheduler
@LR_SCHEDULERS.register_module
class PolynomialLR(_LRScheduler):
"""Polynomial learning rate scheduler.
Args:
optimizer (:class:`torch.optim.Optimizer`): Wrapped optimizer.
total_steps (int): Number of total training steps.
end_lr (float, optional): Minimum learning rate, defaults to 0.0001.
power (float, optional): The power of polynomial, defaults to 1.0.
last_epoch (int, optional): The index of last epoch, defaults to -1. When last_epoch=-1,
the schedule is started from the beginning or When last_epoch=-1, sets initial lr as lr.
"""
def __init__(self, optimizer, total_steps: int, end_lr: float = 0.0001, power: float = 1.0, last_epoch: int = -1,
**kwargs):
if end_lr < 0:
raise ValueError(f'end_lr must >= 0, got {end_lr}')
self.total_steps = total_steps
self.end_lr = end_lr
self.power = power
super().__init__(optimizer, last_epoch=last_epoch)
def get_lr(self):
return self._get_closed_form_lr()
def _get_closed_form_lr(self):
return [
(base_lr - self.end_lr) * ((1 - min(self.last_epoch, self.total_steps) /
self.total_steps) ** self.power) + self.end_lr
for base_lr in self.base_lrs
]
@LR_SCHEDULERS.register_module
class PolynomialWarmupLR(WarmupScheduler):
"""Polynomial learning rate scheduler with warmup.
Args:
optimizer (:class:`torch.optim.Optimizer`): Wrapped optimizer.
total_steps (int): Number of total training steps.
warmup_steps (int, optional): Number of warmup steps, defaults to 0.
end_lr (float, optional): Minimum learning rate, defaults to 0.0001.
power (float, optional): The power of polynomial, defaults to 1.0.
last_epoch (int, optional): The index of last epoch, defaults to -1. When last_epoch=-1,
the schedule is started from the beginning or When last_epoch=-1, sets initial lr as lr.
"""
def __init__(self, optimizer, total_steps: int, warmup_steps: int = 0, end_lr: float = 0.0001, power: float = 1.0,
last_epoch: int = -1, **kwargs):
base_scheduler = PolynomialLR(
optimizer, total_steps - warmup_steps, end_lr=end_lr, power=power)
super().__init__(optimizer, warmup_steps, base_scheduler, last_epoch=last_epoch)
|
from torch.optim.lr_scheduler import _LRScheduler
class _enable_get_lr_call:
def __init__(self, o):
self.o = o
def __enter__(self):
self.o._get_lr_called_within_step = True
return self
def __exit__(self, type, value, traceback):
self.o._get_lr_called_within_step = False
class DelayerScheduler(_LRScheduler):
"""Starts with a flat lr schedule until it reaches N epochs then applies
the specific scheduler (For example: ReduceLROnPlateau)
Args:
optimizer (:class:`torch.optim.Optimizer`): Wrapped optimizer.
delay_epochs (int): Number of epochs to keep the initial lr until starting applying the scheduler.
after_scheduler (:class:`torch.optim.lr_scheduler`): After target_epoch, use this scheduler.
last_epoch (int, optional): The index of last epoch, defaults to -1. When last_epoch=-1,
the schedule is started from the beginning or When last_epoch=-1, sets initial lr as lr.
"""
def __init__(self, optimizer, delay_epochs, after_scheduler, last_epoch=-1):
if delay_epochs < 0:
raise ValueError(f'delay_epochs must >= 0, got {delay_epochs}')
self.delay_epochs = delay_epochs
self.after_scheduler = after_scheduler
self.finished = False
super().__init__(optimizer, last_epoch)
def get_lr(self):
if self.last_epoch >= self.delay_epochs:
if not self.finished:
self.after_scheduler.base_lrs = self.base_lrs
self.finished = True
with _enable_get_lr_call(self.after_scheduler):
return self.after_scheduler.get_lr()
return self.base_lrs
def step(self, epoch=None):
if self.finished:
if epoch is None:
self.after_scheduler.step(None)
self._last_lr = self.after_scheduler.get_last_lr()
else:
self.after_scheduler.step(epoch - self.delay_epochs)
self._last_lr = self.after_scheduler.get_last_lr()
else:
return super(DelayerScheduler, self).step(epoch)
class WarmupScheduler(_LRScheduler):
"""Starts with a linear warmup lr schedule until it reaches N epochs then applies
the specific scheduler (For example: ReduceLROnPlateau).
Args:
optimizer (:class:`torch.optim.Optimizer`): Wrapped optimizer.
warmup_epochs (int): Number of epochs to linearly warmup lr until starting applying the scheduler.
after_scheduler (:class:`torch.optim.lr_scheduler`): After target_epoch, use this scheduler.
last_epoch (int, optional): The index of last epoch, defaults to -1. When last_epoch=-1,
the schedule is started from the beginning or When last_epoch=-1, sets initial lr as lr.
"""
def __init__(self, optimizer, warmup_epochs, after_scheduler, last_epoch=-1):
self.warmup_epochs = int(warmup_epochs)
self.after_scheduler = after_scheduler
self.finished = False
super().__init__(optimizer, last_epoch)
def get_lr(self):
if self.last_epoch >= self.warmup_epochs:
if not self.finished:
self.after_scheduler.base_lrs = self.base_lrs
self.finished = True
return self.after_scheduler.get_lr()
return [(self.last_epoch + 1) / self.warmup_epochs * lr for lr in self.base_lrs]
def step(self, epoch=None):
if self.finished:
if epoch is None:
self.after_scheduler.step(None)
self._last_lr = self.after_scheduler.get_last_lr()
else:
self.after_scheduler.step(epoch - self.warmup_epochs)
self._last_lr = self.after_scheduler.get_last_lr()
else:
return super().step(epoch)
class WarmupDelayerScheduler(_LRScheduler):
"""Starts with a linear warmup lr schedule until it reaches N epochs and a flat lr schedule
until it reaches M epochs then applies the specific scheduler (For example: ReduceLROnPlateau).
Args:
optimizer (:class:`torch.optim.Optimizer`): Wrapped optimizer.
warmup_epochs (int): Number of epochs to linearly warmup lr until starting applying the scheduler.
delay_epochs (int): Number of epochs to keep the initial lr until starting applying the scheduler.
after_scheduler (:class:`torch.optim.lr_scheduler`): After target_epoch, use this scheduler.
last_epoch (int, optional): The index of last epoch, defaults to -1. When last_epoch=-1,
the schedule is started from the beginning or When last_epoch=-1, sets initial lr as lr.
"""
def __init__(self, optimizer, warmup_epochs, delay_epochs, after_scheduler, last_epoch=-1):
if delay_epochs < 0:
raise ValueError(f'delay_epochs must >= 0, got {delay_epochs}')
if warmup_epochs < 0:
raise ValueError(f'warmup_epochs must >= 0, got {warmup_epochs}')
self.warmup_epochs = warmup_epochs
self.delay_epochs = delay_epochs
self.after_scheduler = after_scheduler
self.finished = False
super().__init__(optimizer, last_epoch)
def get_lr(self):
if self.last_epoch >= self.warmup_epochs + self.delay_epochs:
if not self.finished:
self.after_scheduler.base_lrs = self.base_lrs
# reset lr to base_lr
for group, base_lr in zip(self.optimizer.param_groups, self.base_lrs):
group['lr'] = base_lr
self.finished = True
with _enable_get_lr_call(self.after_scheduler):
return self.after_scheduler.get_lr()
elif self.last_epoch >= self.warmup_epochs:
return self.base_lrs
return [(self.last_epoch + 1) / self.warmup_epochs * lr for lr in self.base_lrs]
def step(self, epoch=None):
if self.finished:
if epoch is None:
self.after_scheduler.step(None)
self._last_lr = self.after_scheduler.get_last_lr()
else:
self.after_scheduler.step(epoch - self.warmup_epochs)
self._last_lr = self.after_scheduler.get_last_lr()
else:
return super().step(epoch)
|
from torch.optim.lr_scheduler import LambdaLR as _LambdaLR
from torch.optim.lr_scheduler import MultiplicativeLR as _MultiplicativeLR
from torch.optim.lr_scheduler import StepLR as _StepLR
from torch.optim.lr_scheduler import ExponentialLR as _ExponentialLR
from colossalai.registry import LR_SCHEDULERS
@LR_SCHEDULERS.register_module
class LambdaLR(_LambdaLR):
"""Sets the learning rate of each parameter group to the initial lr
times a given function. When last_epoch=-1, sets initial lr as lr.
Args:
optimizer (:class:`torch.optim.Optimizer`): Wrapped optimizer.
total_steps (int): Number of total training steps.
lr_lambda (Union[``function``, ``list[function]``]): A function which computes a multiplicative
factor given an integer parameter epoch, or a list of such functions,
one for each group in optimizer.param_groups, defaults to None.
last_epoch (int, optional): The index of last epoch, defaults to -1.
"""
def __init__(self, optimizer, total_steps, lr_lambda=None, last_epoch: int = -1) -> None:
super().__init__(optimizer, lr_lambda, last_epoch=last_epoch)
@LR_SCHEDULERS.register_module
class MultiplicativeLR(_MultiplicativeLR):
"""Multiply the learning rate of each parameter group by the factor given
in the specified function. When last_epoch=-1, sets initial lr as lr.
Args:
optimizer (:class:`torch.optim.Optimizer`): Wrapped optimizer.
total_steps (int): Number of total training steps.
lr_lambda (Union[``function``, ``list[function]``]): A function which computes a multiplicative
factor given an integer parameter epoch, or a list of such functions,
one for each group in optimizer.param_groups, defaults to None.
last_epoch (int, optional): The index of last epoch, defaults to -1.
"""
def __init__(self, optimizer, total_steps, lr_lambda=None, last_epoch: int = -1) -> None:
super().__init__(optimizer, lr_lambda, last_epoch=last_epoch)
@LR_SCHEDULERS.register_module
class StepLR(_StepLR):
"""Decays the learning rate of each parameter group by gamma every
step_size epochs. Notice that such decay can happen simultaneously with
other changes to the learning rate from outside this scheduler. When
last_epoch=-1, sets initial lr as lr.
Args:
optimizer (:class:`torch.optim.Optimizer`): Wrapped optimizer.
total_steps (int): Number of total training steps.
step_size (int, optional): Period of learning rate decay, defaults to 1.
gamma (float, optional): Multiplicative factor of learning rate decay, defaults to 0.1.
last_epoch (int, optional): The index of last epoch, defaults to -1.
"""
def __init__(self, optimizer, total_steps, step_size: int = 1, gamma: float = 0.1, last_epoch: int = -1) -> None:
super().__init__(optimizer, step_size,
gamma=gamma, last_epoch=last_epoch)
@LR_SCHEDULERS.register_module
class ExponentialLR(_ExponentialLR):
"""Decays the learning rate of each parameter group by gamma every epoch.
When last_epoch=-1, sets initial lr as lr
Args:
optimizer (Union[:class:`torch.optim.Optimizer`, :class:`colossalai.nn.optimizer`]): Wrapped optimizer.
total_steps (int): Number of total training steps.
gamma (float, optional): Multiplicative factor of learning rate decay, defaults to 1.0.
last_epoch (int, optional): The index of last epoch, defaults to -1.
"""
def __init__(self, optimizer, total_steps, gamma: float = 1.0,
last_epoch: int = -1) -> None:
super().__init__(optimizer, gamma, last_epoch=last_epoch)
|
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import torch.nn as nn
from colossalai.context import ParallelMode
from colossalai.core import global_context as gpc
class ParallelLayer(nn.Module):
def __init__(self):
super().__init__()
self.data_parallel_rank = 0 if not gpc.is_initialized(ParallelMode.DATA) else gpc.get_local_rank(
ParallelMode.DATA)
self.data_parallel_size = 1 if not gpc.is_initialized(ParallelMode.DATA) else gpc.get_world_size(
ParallelMode.DATA)
self.tensor_parallel_rank = 0 if not gpc.is_initialized(ParallelMode.TENSOR) else gpc.get_local_rank(
ParallelMode.TENSOR)
self.tensor_parallel_size = 1 if not gpc.is_initialized(ParallelMode.TENSOR) else gpc.get_world_size(
ParallelMode.TENSOR)
self.pipeline_parallel_rank = 0 if not gpc.is_initialized(ParallelMode.PIPELINE) else gpc.get_local_rank(
ParallelMode.PIPELINE)
self.pipeline_parallel_size = 1 if not gpc.is_initialized(ParallelMode.PIPELINE) else gpc.get_world_size(
ParallelMode.PIPELINE)
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys,
error_msgs):
super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys,
error_msgs)
if gpc.get_local_rank(ParallelMode.TENSOR) != 0:
missing_keys.clear()
unexpected_keys.clear()
|
from .colossalai_layer import *
from .parallel_1d import *
from .parallel_2d import *
from .parallel_2p5d import *
from .parallel_3d import *
from .parallel_sequence import *
from .moe import *
from .utils import *
from .vanilla import *
from .wrapper import *
|
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import torch
from torch import distributed as dist
from colossalai.communication import ring_forward
from colossalai.context.parallel_mode import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.nn.layer.parallel_sequence._utils import _calc_incoming_device_range, _calc_current_device_range
from colossalai.utils import get_current_device
from torch.cuda.amp import custom_bwd, custom_fwd
class RingQK(torch.autograd.Function):
"""
Calculate QK in a ring-exchange style
"""
@staticmethod
@custom_fwd
def forward(ctx,
sub_q,
sub_k,
batch_size,
num_attention_heads,
sub_seq_length):
# save tensor for backward
ctx.save_for_backward(sub_q, sub_k)
ctx.sub_seq_length = sub_seq_length
# create local segment of attention score
attention_score = torch.empty(
batch_size * num_attention_heads,
sub_seq_length,
sub_seq_length * gpc.get_world_size(ParallelMode.SEQUENCE),
dtype=sub_q.dtype,
device=get_current_device()
)
# compute local QK^T
part_a = torch.matmul(sub_q, sub_k.transpose(2, 1))
local_rank = gpc.get_local_rank(ParallelMode.SEQUENCE)
local_world_size = gpc.get_world_size(ParallelMode.SEQUENCE)
start_idx = local_rank * sub_seq_length
end_idx = (local_rank + 1) * sub_seq_length
attention_score[:, :, start_idx: end_idx] = part_a
# compute QK^T in ring-all-reduce style
for i in range(local_world_size - 1):
sub_k = ring_forward(sub_k, ParallelMode.SEQUENCE)
start_idx, end_idx = _calc_incoming_device_range(i, local_rank, local_world_size, sub_seq_length)
part_a = torch.matmul(sub_q, sub_k.transpose(2, 1))
attention_score[:, :, start_idx:end_idx] = part_a
return attention_score
@staticmethod
@custom_bwd
def backward(ctx, grad_output):
sub_q, sub_k, = ctx.saved_tensors
local_rank = gpc.get_local_rank(ParallelMode.SEQUENCE)
local_world_size = gpc.get_world_size(ParallelMode.SEQUENCE)
# calculate gradient of sub_k
grad_k = torch.matmul(
grad_output.transpose(2, 1),
sub_q
)
dist.all_reduce(grad_k, group=gpc.get_group(ParallelMode.SEQUENCE))
grad_k = grad_k[:, local_rank * ctx.sub_seq_length: (local_rank + 1) * ctx.sub_seq_length]
grad_k /= local_world_size
# calculate gradient for sub_q
grad_q = torch.zeros_like(sub_q,
dtype=sub_q.dtype,
device=get_current_device(), )
# compute with local sub_k
start_idx, end_idx = _calc_current_device_range(local_rank, ctx.sub_seq_length)
grad_q += torch.matmul(grad_output[:, :, start_idx:end_idx], sub_k)
# compute QK^T in ring-all-reduce style
for i in range(local_world_size - 1):
sub_k = ring_forward(sub_k, ParallelMode.SEQUENCE)
start_idx, end_idx = _calc_incoming_device_range(i, local_rank, local_world_size, ctx.sub_seq_length)
grad_q += torch.matmul(grad_output[:, :, start_idx: end_idx], sub_k)
grad_q /= local_world_size
return grad_q, grad_k, None, None, None
class RingAV(torch.autograd.Function):
"""
Calculate AV in a ring-exchange style
"""
@staticmethod
@custom_fwd
def forward(ctx,
attention_score,
sub_v,
batch_size,
num_attention_heads,
attention_head_size,
sub_seq_length):
local_rank = gpc.get_local_rank(ParallelMode.SEQUENCE)
local_world_size = gpc.get_world_size(ParallelMode.SEQUENCE)
local_start_idx, local_end_idx = _calc_current_device_range(local_rank, sub_seq_length)
sub_attention_result = torch.zeros(
batch_size * num_attention_heads,
sub_seq_length,
attention_head_size,
device=get_current_device(),
dtype=attention_score.dtype)
# save tensors for backward
ctx.save_for_backward(attention_score, sub_v)
ctx.sub_seq_length = sub_seq_length
# compute local AV
part_av = torch.matmul(attention_score[:, :, local_start_idx:local_end_idx], sub_v)
sub_attention_result += part_av
# compute AV in ring - all - reduce style
for i in range(local_world_size - 1):
sub_v = ring_forward(sub_v, ParallelMode.SEQUENCE)
start_idx, end_idx = _calc_incoming_device_range(i, local_rank, local_world_size, sub_seq_length)
# compute QK^T
part_av = torch.matmul(attention_score[:, :, start_idx:end_idx], sub_v)
sub_attention_result += part_av
return sub_attention_result
@staticmethod
@custom_bwd
def backward(ctx, grad_output):
local_rank = gpc.get_local_rank(ParallelMode.SEQUENCE)
local_world_size = gpc.get_world_size(ParallelMode.SEQUENCE)
local_start_idx, local_end_idx = _calc_current_device_range(local_rank, ctx.sub_seq_length)
attention_scores, sub_v = ctx.saved_tensors
# calculate gradient of v
grad_v = torch.matmul(
attention_scores.transpose(2, 1),
grad_output
)
dist.all_reduce(grad_v, group=gpc.get_group(ParallelMode.SEQUENCE))
grad_v = grad_v[:, local_start_idx:local_end_idx]
grad_v /= local_world_size
# calculate gradient for attention score
grad_attention_score = torch.zeros_like(attention_scores,
dtype=grad_output.dtype,
device=get_current_device())
# compute with local sub_k
grad_attention_score[:, :, local_start_idx:local_end_idx] += torch.matmul(
grad_output,
sub_v.transpose(2, 1))
# compute QK^T in ring-all-reduce style
for i in range(local_world_size - 1):
sub_v = ring_forward(sub_v, ParallelMode.SEQUENCE)
start_idx, end_idx = _calc_incoming_device_range(i, local_rank, local_world_size, ctx.sub_seq_length)
# compute grad_q
grad_attention_score[:, :, start_idx:end_idx] += torch.matmul(
grad_output,
sub_v.transpose(2, 1))
return grad_attention_score, grad_v, None, None, None, None
|
from ._operation import RingQK, RingAV
from .layers import TransformerSelfAttentionRing
__all__ = ['TransformerSelfAttentionRing', 'RingAV', 'RingQK']
|
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import math
import colossalai
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
from colossalai.context.parallel_mode import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.nn.layer.parallel_sequence._operation import RingQK, RingAV
from colossalai.registry import LAYERS
from colossalai.kernel.cuda_native.scaled_softmax import AttnMaskType
from colossalai.kernel import FusedScaleMaskSoftmax
from colossalai.context import seed
@LAYERS.register_module
class TransformerSelfAttentionRing(nn.Module):
"""Parallel self-attention layer abstract class.
Self-attention layer takes input with size [b, s, h]
and returns output of the same size.
Args:
hidden_size (int): hidden size.
num_attention_heads (int): number of attention heads.
attention_dropout (float): dropout probability for attention layer.
attention_mask_func (:class:`typing.Callable`): Mask function to be applied.
layer_number (int): number of layers.
"""
def __init__(self,
hidden_size,
num_attention_heads,
attention_dropout,
attention_mask_func,
layer_number,
apply_query_key_layer_scaling: bool = False,
convert_fp16_to_fp32_in_softmax: bool = False,
attn_mask_type=AttnMaskType.padding,
masked_softmax_fusion=True,
fp16=False,
bf16=False
):
super().__init__()
self.convert_fp16_to_fp32_in_softmax = convert_fp16_to_fp32_in_softmax
self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
self.attention_mask_func = attention_mask_func
self.layer_number = layer_number
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
self.attn_mask_type = attn_mask_type
assert self.layer_number > 0
self.attention_dropout = attention_dropout
if self.apply_query_key_layer_scaling:
self.convert_fp16_to_fp32_in_softmax = True
assert self.hidden_size % self.num_attention_heads == 0, \
'hidden size is not divisible by the number of attention heads'
self.hidden_size_per_attention_head = self.hidden_size // num_attention_heads
self.world_size = gpc.get_world_size(ParallelMode.SEQUENCE)
# Strided linear layer.
self.query_key_value = _Linear(
hidden_size,
3 * self.hidden_size,
)
self.coeff = None
self.norm_factor = math.sqrt(self.hidden_size)
if self.apply_query_key_layer_scaling:
self.coeff = layer_number
self.norm_factor *= self.coeff
self.scale_mask_softmax = FusedScaleMaskSoftmax(
fp16, bf16,
self.attn_mask_type,
masked_softmax_fusion,
self.attention_mask_func,
self.convert_fp16_to_fp32_in_softmax,
self.coeff)
self.attention_dropout = nn.Dropout(attention_dropout)
# Output.
self.dense = _Linear(hidden_size,
hidden_size,
bias=True,
skip_bias_add=True)
def forward(self, hidden_states, attention_mask):
# hidden_states: [sub_seq_len, batch_size, hidden_size]
# attention_mask: [batch_size, 1, sub_seq_len, seq_len]
sub_seq_length, batch_size, hidden_size = hidden_states.size()
# =====================
# Query, Key, and Value
# =====================
# Attention heads shape change:
# [sub_seq_len, batch_size, hidden_size] --> [sub_seq_len, batch_size, (3 * head_size * num_heads)]
mixed_x_layer = self.query_key_value(hidden_states)
# [sub_seq_len, batch_size, num_heads, 3 * head_size] --> 3 [sub_seq_len, batch_size, num_heads, head_size]
new_tensor_shape = mixed_x_layer.size()[:-1] + (self.num_attention_heads,
3 * self.hidden_size_per_attention_head)
mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)
# split into query, key and value
last_dim = mixed_x_layer.dim() - 1
last_dim_value = mixed_x_layer.size(-1)
assert last_dim_value % 3 == 0, 'the last dimension is not a multiple of 3, ' \
'cannot be divided into query, key and value'
partition_size = last_dim_value // 3
(query_layer, key_layer, value_layer) = torch.split(
mixed_x_layer, partition_size, dim=last_dim)
# attention scores: [batch_size, num_heads, sub_seq_len, seq_len]
output_size = (query_layer.size(1),
query_layer.size(2),
query_layer.size(0),
key_layer.size(0) * self.world_size)
# [sub_seq_len, batch_size, num_heads, head_size] -> [sub_seq_len, batch_size * num_heads, head_size]
query_layer = query_layer.view(output_size[2],
output_size[0] * output_size[1], -1)
# [sub_seq_len, batch_size, num_heads, head_size] -> [sub_seq_len, batch_size * num_heads, head_size]
key_layer = key_layer.view(key_layer.size(0),
output_size[0] * output_size[1], -1)
# attention_scores: [batch_size * num_heads, sub_seq_len, seq_len]
attention_scores = RingQK.apply(
query_layer.transpose(0, 1).contiguous(), # [batch_size * num_heads, sub_seq_len, head_size]
key_layer.transpose(0, 1).contiguous(), # [batch_size * num_heads, sub_seq_len, head_size],
batch_size,
self.num_attention_heads,
sub_seq_length
)
attention_scores /= self.norm_factor
# change view to [batch_size, num_heads, sub_seq_len, seq_len]
attention_scores = attention_scores.view(*output_size)
# change shape to [batch_size, num_heads, sub_seq_len, seq_len]
attention_probs = self.scale_mask_softmax(attention_scores, attention_mask)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
with seed(ParallelMode.TENSOR):
attention_probs = self.attention_dropout(attention_probs)
# context layer shape: [batch_size, num_heads, sub_seq_len, head_size]
output_size = (value_layer.size(1),
value_layer.size(2),
query_layer.size(0),
value_layer.size(3))
# change view [sub_seq_len, batch_size * num_heads, head_size]
value_layer = value_layer.contiguous().view(value_layer.size(0),
output_size[0] * output_size[1], -1)
# # change view [b * num_heads, sub_seq_len, seq_len]
attention_probs = attention_probs.view(attention_probs.size(0) * attention_probs.size(1),
attention_probs.size(2),
attention_probs.size(3))
# matmul: [batch_size * num_heads, sub_seq_len, head_size]
context_layer = RingAV.apply(
attention_probs,
value_layer.transpose(0, 1).contiguous(),
batch_size,
self.num_attention_heads,
self.hidden_size_per_attention_head,
sub_seq_length
)
# change view [batch_size, num_heads, sub_seq_len, head_size]
context_layer = context_layer.view(*output_size)
# [batch_size, num_heads, sub_seq_len, head_size] -> [sub_seq_len, batch_size, num_heads, head_size]
context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
# [sub_seq_len, batch_size, num_heads, head_size] -> [sub_seq_len, batch_size, hidden_size]
new_context_layer_shape = context_layer.size()[:-2] + (
self.hidden_size_per_attention_head * self.num_attention_heads,)
context_layer = context_layer.view(*new_context_layer_shape)
output, bias = self.dense(context_layer)
return output, bias
def __repr__(self):
return f'TransformerSelfAttentionRing(apply_query_key_layer_scaling={self.apply_query_key_layer_scaling}, ' \
f'layer_number={self.layer_number}, hidden_size:{self.hidden_size}, attention_dropout={self.attention_dropout}, ' \
f'attn_mask_type={self.attn_mask_type}, num_attention_heads={self.num_attention_heads}, ' \
f'hidden_size_per_attention_head={self.hidden_size_per_attention_head}, coeff={self.coeff}, norm_factor={self.norm_factor}, ' \
f'convert_fp16_to_fp32_in_softmax={self.convert_fp16_to_fp32_in_softmax})'
class _Linear(nn.Module):
"""Linear layer with column parallelism.
The linear layer is defined as Y = XA + b. A is parallelized along
its second dimension as A = [A_1, ..., A_p].
Arguments:
input_size: first dimension of matrix A.
output_size: second dimension of matrix A.
bias: If true, add bias
init_method: method to initialize weights. Note that bias is always set
to zero.
stride: For the strided linear layers.
keep_master_weight_for_test: This was added for testing and should be
set to False. It returns the master weights
used for initialization.
skip_bias_add: This was added to enable performance optimations where bias
can be fused with other elementwise operations. we skip
adding bias but instead return it.
"""
def __init__(self,
input_size,
output_size,
bias=True,
skip_bias_add=False):
super(_Linear, self).__init__()
# Keep input parameters
self.input_size = input_size
self.output_size = output_size
self.skip_bias_add = skip_bias_add
self.weight = Parameter(torch.empty(self.output_size,
self.input_size,
))
nn.init.xavier_normal_(self.weight)
if bias:
self.bias = Parameter(torch.empty(self.output_size))
# Always initialize bias to zero.
with torch.no_grad():
self.bias.zero_()
else:
self.register_parameter('bias', None)
def forward(self, input_):
# Matrix multiply.
bias = self.bias if not self.skip_bias_add else None
output = F.linear(input_, self.weight, bias)
if self.skip_bias_add:
return output, self.bias
else:
return output
def __repr__(self):
return f'Linear(in_features={self.input_size}, out_features={self.output_size}, ' + \
f'bias={self.bias is not None}, skip_bias_add={self.skip_bias_add})'
|
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
def _calc_incoming_device_range(i, rank, world_size, sub_seq_length):
device_of_incoming_k = (rank - i - 1) % world_size
start_idx = sub_seq_length * device_of_incoming_k
end_idx = sub_seq_length * (device_of_incoming_k + 1)
return start_idx, end_idx
def _calc_current_device_range(rank, sub_seq_length):
start_idx = sub_seq_length * rank
end_idx = sub_seq_length * (rank + 1)
return start_idx, end_idx
|
import torch.nn as nn
import torch.distributed as dist
from typing import List, Tuple, Union
from colossalai.context import ParallelMode
from colossalai.core import global_context as gpc
class PipelineSharedModuleWrapper:
def __init__(self, pipeline_ranks: Union[List[int], Tuple[int]]) -> None:
assert len(pipeline_ranks) > 1, f'Expect len(pipeline_ranks) > 1, got {len(pipeline_ranks)}'
self.pipeline_ranks = pipeline_ranks
self.group = None
self.ranks_in_group = None
self._init_group()
def _init_group(self):
world_size = gpc.get_world_size(ParallelMode.GLOBAL)
dp_size = gpc.get_world_size(ParallelMode.DATA)
pp_size = gpc.get_world_size(ParallelMode.PIPELINE)
rank = gpc.get_global_rank()
num_dp_groups = world_size // dp_size
num_pp_stages = num_dp_groups // pp_size
for i in range(dp_size):
for j in range(num_pp_stages):
pipeline_ranks = list(
range(i * num_dp_groups + j,
(i + 1) * num_dp_groups,
num_pp_stages))
sub_ranks = [pipeline_ranks[idx] for idx in self.pipeline_ranks]
group = dist.new_group(sub_ranks)
if rank in sub_ranks:
self.group = group
self.ranks_in_group = sub_ranks
def register_module(self, module: nn.Module):
assert self.ranks_in_group is not None,\
f'Rank {gpc.get_local_rank(ParallelMode.PIPELINE)} is not in pipeline_ranks {self.pipeline_ranks}'
src = self.ranks_in_group[self.pipeline_ranks[0]]
for p in module.parameters():
setattr(p, 'pipeline_shared_module_pg', self.group)
dist.broadcast(p, src, group=self.group)
def register_parameter(self, param: nn.Parameter):
assert self.ranks_in_group is not None,\
f'Rank {gpc.get_local_rank(ParallelMode.PIPELINE)} is not in pipeline_ranks {self.pipeline_ranks}'
src = self.ranks_in_group[self.pipeline_ranks[0]]
setattr(param, 'pipeline_shared_module_pg', self.group)
dist.broadcast(param, src, group=self.group)
|
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import torch.nn as nn
from colossalai.builder import build_layer
from colossalai.registry import LAYERS
@LAYERS.register_module
class LambdaWrapper(nn.Module):
"""Wrap a function to nn.Module, which takes a config of layers and can fully access them.
Args:
func (``Callable``): User customed function.
layers_cfg (dict, optional): Config of layers, defaults to None.
"""
def __init__(self, func, layers_cfg: dict = None):
super().__init__()
self.func = func
self.layers = self._build_layers(layers_cfg)
def _build_layers(self, layers_cfg: dict):
if layers_cfg is None:
return None
else:
layers = []
for cfg in layers_cfg:
layer = build_layer(cfg)
layers.append(layer)
return layers
def forward(self, *args, **kwargs):
return self.func(self, *args, **kwargs)
|
from .lambda_wrapper import LambdaWrapper
from .pipeline_wrapper import PipelineSharedModuleWrapper
__all__ = ['LambdaWrapper', 'PipelineSharedModuleWrapper']
|
from typing import Any, Tuple
import torch
import torch.distributed as dist
from colossalai.communication.collective import (all_gather, all_reduce, reduce_scatter)
from colossalai.context.parallel_mode import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.utils import get_current_device
from torch import Tensor
from torch.cuda.amp import custom_bwd, custom_fwd
def get_parallel_group(parallel_mode: ParallelMode):
return gpc.get_group(parallel_mode)
def get_global_rank():
return gpc.get_global_rank()
def get_parallel_rank(parallel_mode: ParallelMode):
return gpc.get_local_rank(parallel_mode)
class _Classifier2p5D(torch.autograd.Function):
@staticmethod
@custom_fwd(cast_inputs=torch.float16)
def forward(
ctx: Any,
A: Tensor,
B: Tensor,
bias,
tesseract_dim: int,
out_shape: Tuple[int, ...],
row_rank: int,
col_rank: int,
row_parallel_mode: ParallelMode,
col_parallel_mode: ParallelMode,
data_parallel_rank: int,
pipeline_parallel_rank: int,
pipeline_parallel_size: int,
tensor_parallel_size: int,
) -> Tensor:
A = A.clone().detach()
A_shape = A.shape
A = A.reshape((-1, A_shape[-1]))
B_shape = B.shape
B = B.reshape((-1, B_shape[-1]))
B_temp = all_gather(B, -1, col_parallel_mode)
if ctx:
ctx.save_for_backward(A, B_temp)
C = torch.matmul(A, B_temp.transpose(0, 1))
C = all_reduce(C, row_parallel_mode)
ctx.use_bias = bias is not None
if bias is not None:
C = C + bias
out = C.reshape(out_shape)
if ctx:
ctx.tesseract_dim = tesseract_dim
ctx.row_rank = row_rank
ctx.col_rank = col_rank
ctx.row_parallel_mode = row_parallel_mode
ctx.col_parallel_mode = col_parallel_mode
ctx.A_shape = A_shape
ctx.B_shape = B_shape
ctx.data_parallel_rank = data_parallel_rank
ctx.pipeline_parallel_rank = pipeline_parallel_rank
ctx.pipeline_parallel_size = pipeline_parallel_size
ctx.tensor_parallel_size = tensor_parallel_size
return out
@staticmethod
@custom_bwd
def backward(ctx: Any, output_grad: Tensor) -> Tuple[Tensor, ...]:
A, B = ctx.saved_tensors
with torch.no_grad():
A_grad = torch.matmul(output_grad, B)
A_grad = A_grad.reshape(ctx.A_shape)
B_grad = torch.matmul(output_grad.reshape(-1, output_grad.shape[-1]).transpose(0, 1), A)
B_grad = reduce_scatter(B_grad, -1, ctx.col_parallel_mode)
B_grad = B_grad.reshape(ctx.B_shape)
if ctx.use_bias:
bias_grad = torch.sum(output_grad, dim=tuple(range(output_grad.ndim - 1)))
bias_grad = all_reduce(bias_grad, ctx.col_parallel_mode)
else:
bias_grad = None
return A_grad, B_grad, bias_grad, None, None, None, None, None, None, None, None, None, None
def classifier_2p5d(A: Tensor, B: Tensor, bias, tesseract_dim: int, out_shape: Tuple[int,
...], row_rank: int, col_rank: int,
row_parallel_mode: ParallelMode, col_parallel_mode: ParallelMode, data_parallel_rank: int,
pipeline_parallel_rank: int, pipeline_parallel_size: int, tensor_parallel_size: int) -> Tensor:
r"""Classifier.
Args:
A (:class:`torch.tensor`): matrix :math:`A`.
B (:class:`torch.tensor`): matrix :math:`B`.
bias (:class:`torch.tensor`): matrix of bias.
tesseract_dim (int): dimension of TESSERACT fo 2.5D parallelism.
out_shape (:class:`torch.size`): shape of output tensor.
row_rank (int): the rank of row.
col_rank (int): the rank of column.
row_parallel_mode (:class:`colossalai.context.ParallelMode`): row parallel mode.
col_parallel_mode (:class:`colossalai.context.ParallelMode`): column parallel mode.
data_parallel_rank (int): data parallel rank.
pipeline_parallel_rank (int): pipeline parallel rank
pipeline_parallel_size (int): pipeline parallel size.
tensor_parallel_size (int): tensor parallel size.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
return _Classifier2p5D.apply(A, B, bias, tesseract_dim, out_shape, row_rank, col_rank, row_parallel_mode,
col_parallel_mode, data_parallel_rank, pipeline_parallel_rank, pipeline_parallel_size,
tensor_parallel_size)
class Matmul_AB_2p5D(torch.autograd.Function):
r"""Matrix multiplication for :math:`C = AB`.
Args:
A (:class:`torch.tensor`): matrix :math:`A`.
B (:class:`torch.tensor`): matrix :math:`B`.
tesseract_dim (int): dimension of TESSERACT fo 2.5D parallelism.
out_shape (:class:`torch.size`): shape of output tensor.
row_rank (int): the rank of row.
col_rank (int): the rank of column.
dep_rank (int): the rank of depth.
row_parallel_mode (:class:`colossalai.context.ParallelMode`): row parallel mode.
col_parallel_mode (:class:`colossalai.context.ParallelMode`): column parallel mode.
data_parallel_rank (int): data parallel rank.
pipeline_parallel_rank (int): pipeline parallel rank
pipeline_parallel_size (int): pipeline parallel size.
tensor_parallel_size (int): tensor parallel size.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
@staticmethod
@custom_fwd(cast_inputs=torch.float16)
def forward(ctx: Any, A: Tensor, B: Tensor, tesseract_dim: int, out_shape: Tuple[int, ...], row_rank: int,
col_rank: int, dep_rank: int, row_parallel_mode: ParallelMode, col_parallel_mode: ParallelMode,
data_parallel_rank: int, pipeline_parallel_rank: int, pipeline_parallel_size: int,
tensor_parallel_size: int) -> Tensor:
# A: [b / dq, s, h / q] -> [(b * s) / dq, h / q]
# B: [h / dq, s / q]
# C: [b / dq, s, s / q] -> [(b * s) / dq, s / q]
assert A.shape[-1] == B.shape[-2], \
'Invalid shapes: A={}, B={} for AB.'.format(A.shape, B.shape)
if ctx:
ctx.save_for_backward(A, B)
A_shape = A.shape
A = A.reshape((-1, A_shape[-1]))
B_shape = B.shape
B = B.reshape((-1, B_shape[-1]))
C_shape = (A.shape[0], B.shape[-1])
C = torch.zeros(C_shape, dtype=A.dtype, device=get_current_device())
# use circular buffer to store the communication tensor
# 2 is enough for all cases
A_list = [torch.empty_like(A) for _ in range(2)]
B_list = [torch.empty_like(B) for _ in range(2)]
row_group = gpc.get_group(row_parallel_mode)
col_group = gpc.get_group(col_parallel_mode)
src_a = \
tesseract_dim * row_rank + tesseract_dim ** 2 * dep_rank + \
data_parallel_rank * pipeline_parallel_size * tensor_parallel_size + \
pipeline_parallel_rank * tensor_parallel_size
src_b = \
col_rank + tesseract_dim ** 2 * dep_rank + \
data_parallel_rank * pipeline_parallel_size * tensor_parallel_size + \
pipeline_parallel_rank * tensor_parallel_size
opa = [None] * 2
opb = [None] * 2
A_list[0].copy_(A)
B_list[0].copy_(B)
opa[0] = dist.broadcast(A_list[0], src=src_a, group=row_group, async_op=True)
opb[0] = dist.broadcast(B_list[0], src=src_b, group=col_group, async_op=True)
cur = 0
for i in range(tesseract_dim):
if i != tesseract_dim - 1:
A_list[1 - cur].copy_(A)
opa[1 - cur] = dist.broadcast(A_list[1 - cur], src=src_a + 1, group=row_group, async_op=True)
B_list[1 - cur].copy_(B)
opb[1 - cur] = dist.broadcast(B_list[1 - cur],
src=src_b + tesseract_dim,
group=col_group,
async_op=True)
if opa[cur] is not None:
opa[cur].wait()
if opb[cur] is not None:
opb[cur].wait()
torch.addmm(C, A_list[cur], B_list[cur], out=C)
cur = 1 - cur
src_a += 1
src_b += tesseract_dim
out = C.reshape(out_shape)
if ctx:
ctx.tesseract_dim = tesseract_dim
ctx.row_rank = row_rank
ctx.col_rank = col_rank
ctx.dep_rank = dep_rank
ctx.row_parallel_mode = row_parallel_mode
ctx.col_parallel_mode = col_parallel_mode
ctx.A_shape = A_shape
ctx.B_shape = B_shape
ctx.data_parallel_rank = data_parallel_rank
ctx.pipeline_parallel_rank = pipeline_parallel_rank
ctx.pipeline_parallel_size = pipeline_parallel_size
ctx.tensor_parallel_size = tensor_parallel_size
return out
@staticmethod
@custom_bwd
def backward(ctx: Any, output_grad: Tensor) -> Tuple[Tensor, ...]:
A, B = ctx.saved_tensors
with torch.no_grad():
A_grad = Matmul_ABT_2p5D.apply(output_grad, B, ctx.tesseract_dim, ctx.A_shape, ctx.row_rank, ctx.col_rank,
ctx.dep_rank, ctx.row_parallel_mode, ctx.col_parallel_mode,
ctx.data_parallel_rank, ctx.pipeline_parallel_rank,
ctx.pipeline_parallel_size, ctx.tensor_parallel_size)
B_grad = Matmul_ATB_2p5D.apply(A, output_grad, ctx.tesseract_dim, ctx.B_shape, ctx.row_rank, ctx.col_rank,
ctx.dep_rank, ctx.row_parallel_mode, ctx.col_parallel_mode,
ctx.data_parallel_rank, ctx.pipeline_parallel_rank,
ctx.pipeline_parallel_size, ctx.tensor_parallel_size)
return A_grad, B_grad, None, None, None, None, None, None, None, None, None, None, None, None, None
class Matmul_ABT_2p5D(torch.autograd.Function):
r"""Matrix multiplication for :math:`C = AB^T`.
Args:
A (:class:`torch.tensor`): matrix :math:`A`.
B (:class:`torch.tensor`): matrix :math:`B`.
tesseract_dim (int): dimension of TESSERACT fo 2.5D parallelism.
out_shape (:class:`torch.size`): shape of output tensor.
row_rank (int): the rank of row.
col_rank (int): the rank of column.
dep_rank (int): the rank of depth.
row_parallel_mode (:class:`colossalai.context.ParallelMode`): row parallel mode.
col_parallel_mode (:class:`colossalai.context.ParallelMode`): column parallel mode.
data_parallel_rank (int): data parallel rank.
pipeline_parallel_rank (int): pipeline parallel rank
pipeline_parallel_size (int): pipeline parallel size.
tensor_parallel_size (int): tensor parallel size.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
@staticmethod
@custom_fwd(cast_inputs=torch.float16)
def forward(ctx: Any, A: Tensor, B: Tensor, tesseract_dim: int, out_shape: Tuple[int, ...], row_rank: int,
col_rank: int, dep_rank: int, row_parallel_mode: ParallelMode, col_parallel_mode: ParallelMode,
data_parallel_rank: int, pipeline_parallel_rank: int, pipeline_parallel_size: int,
tensor_parallel_size: int) -> Tensor:
assert A.shape[-1] == B.shape[-1], \
'Invalid shapes: A={}, B={} for ABT.'.format(A.shape, B.shape)
if ctx:
ctx.save_for_backward(A, B)
A_shape = A.shape
A = A.reshape((-1, A_shape[-1]))
B_shape = B.shape
B = B.reshape((-1, B_shape[-1]))
C_shape = (A.shape[0], B.shape[0])
C = torch.empty(C_shape, dtype=A.dtype, device=get_current_device())
# use circular buffer to store the communication tensor
# 2 is enough for all cases
B_list = [torch.empty_like(B) for _ in range(2)]
C_list = [torch.empty_like(C) for _ in range(2)]
row_group = gpc.get_group(row_parallel_mode)
col_group = gpc.get_group(col_parallel_mode)
src_b = \
col_rank + tesseract_dim ** 2 * dep_rank + \
data_parallel_rank * pipeline_parallel_size * tensor_parallel_size + \
pipeline_parallel_rank * tensor_parallel_size
src_c = \
tesseract_dim * row_rank + tesseract_dim ** 2 * dep_rank + \
data_parallel_rank * pipeline_parallel_size * tensor_parallel_size + \
pipeline_parallel_rank * tensor_parallel_size
opb = [None] * 2
opr = [None] * 2
B_list[0].copy_(B)
opb[0] = dist.broadcast(B_list[0], src=src_b, group=col_group, async_op=True)
cur = 0
for i in range(tesseract_dim):
if i != tesseract_dim - 1:
B_list[1 - cur].copy_(B)
opb[1 - cur] = dist.broadcast(B_list[1 - cur],
src=src_b + tesseract_dim,
group=col_group,
async_op=True)
if opr[cur] is not None:
opr[cur].wait()
if i - 2 == col_rank:
C.copy_(C_list[cur])
if opb[cur] is not None:
opb[cur].wait()
torch.matmul(A, B_list[cur].transpose(0, 1), out=C_list[cur])
opr[cur] = dist.reduce(C_list[cur], dst=src_c, group=row_group, async_op=True)
cur = 1 - cur
src_b += tesseract_dim
src_c += 1
for op in opr:
op.wait()
if tesseract_dim - 2 == col_rank:
C.copy_(C_list[cur])
if tesseract_dim - 1 == col_rank:
C.copy_(C_list[1 - cur])
out = C.reshape(out_shape)
if ctx:
ctx.tesseract_dim = tesseract_dim
ctx.row_rank = row_rank
ctx.col_rank = col_rank
ctx.dep_rank = dep_rank
ctx.row_parallel_mode = row_parallel_mode
ctx.col_parallel_mode = col_parallel_mode
ctx.A_shape = A_shape
ctx.B_shape = B_shape
ctx.data_parallel_rank = data_parallel_rank
ctx.pipeline_parallel_rank = pipeline_parallel_rank
ctx.pipeline_parallel_size = pipeline_parallel_size
ctx.tensor_parallel_size = tensor_parallel_size
return out
@staticmethod
@custom_bwd
def backward(ctx: Any, output_grad: Tensor) -> Tuple[Tensor, ...]:
A, B = ctx.saved_tensors
with torch.no_grad():
A_grad = Matmul_AB_2p5D.apply(output_grad, B, ctx.tesseract_dim, ctx.A_shape, ctx.row_rank, ctx.col_rank,
ctx.dep_rank, ctx.row_parallel_mode, ctx.col_parallel_mode,
ctx.data_parallel_rank, ctx.pipeline_parallel_rank,
ctx.pipeline_parallel_size, ctx.tensor_parallel_size)
B_grad = Matmul_ATB_2p5D.apply(output_grad, A, ctx.tesseract_dim, ctx.B_shape, ctx.row_rank, ctx.col_rank,
ctx.dep_rank, ctx.row_parallel_mode, ctx.col_parallel_mode,
ctx.data_parallel_rank, ctx.pipeline_parallel_rank,
ctx.pipeline_parallel_size, ctx.tensor_parallel_size)
return A_grad, B_grad, None, None, None, None, None, None, None, None, None, None, None, None, None
class Matmul_ATB_2p5D(torch.autograd.Function):
r"""Matrix multiplication for :math:`C = A^TB`
Args:
A (:class:`torch.tensor`): matrix :math:`A`.
B (:class:`torch.tensor`): matrix :math:`B`.
tesseract_dim (int): dimension of TESSERACT fo 2.5D parallelism.
out_shape (:class:`torch.size`): shape of output tensor.
row_rank (int): the rank of row.
col_rank (int): the rank of column.
dep_rank (int): the rank of depth.
row_parallel_mode (:class:`colossalai.context.ParallelMode`): row parallel mode.
col_parallel_mode (:class:`colossalai.context.ParallelMode`): column parallel mode.
data_parallel_rank (int): data parallel rank.
pipeline_parallel_rank (int): pipeline parallel rank
pipeline_parallel_size (int): pipeline parallel size.
tensor_parallel_size (int): tensor parallel size.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
@staticmethod
@custom_fwd(cast_inputs=torch.float16)
def forward(ctx: Any, A: Tensor, B: Tensor, tesseract_dim: int, out_shape: Tuple[int, ...], row_rank: int,
col_rank: int, dep_rank: int, row_parallel_mode: ParallelMode, col_parallel_mode: ParallelMode,
data_parallel_rank: int, pipeline_parallel_rank: int, pipeline_parallel_size: int,
tensor_parallel_size: int):
assert A.shape[-2] == B.shape[-2], \
'Invalid shapes: A={}, B={} for ATB.'.format(A.shape, B.shape)
if ctx:
ctx.save_for_backward(A, B)
A_shape = A.shape
A = A.reshape((-1, A_shape[-1]))
B_shape = B.shape
B = B.reshape((-1, B_shape[-1]))
C_shape = (A.shape[-1], B.shape[-1])
C = torch.empty(C_shape, dtype=A.dtype, device=get_current_device())
# use circular buffer to store the communication tensor
# 2 is enough for all cases
A_list = [torch.empty_like(A) for _ in range(2)]
C_list = [torch.empty_like(C) for _ in range(2)]
row_group = gpc.get_group(row_parallel_mode)
col_group = gpc.get_group(col_parallel_mode)
src_a = \
tesseract_dim * row_rank + tesseract_dim ** 2 * dep_rank + \
data_parallel_rank * pipeline_parallel_size * tensor_parallel_size + \
pipeline_parallel_rank * tensor_parallel_size
src_c = \
col_rank + tesseract_dim ** 2 * dep_rank + \
data_parallel_rank * pipeline_parallel_size * tensor_parallel_size + \
pipeline_parallel_rank * tensor_parallel_size
opa = [None] * 2
opr = [None] * 2
A_list[0].copy_(A)
opa[0] = dist.broadcast(A_list[0], src=src_a, group=row_group, async_op=True)
cur = 0
for i in range(tesseract_dim):
if i != tesseract_dim - 1:
A_list[1 - cur].copy_(A)
opa[1 - cur] = dist.broadcast(A_list[1 - cur], src=src_a + 1, group=row_group, async_op=True)
if opr[cur] is not None:
opr[cur].wait()
if i - 2 == row_rank:
C.copy_(C_list[cur])
if opa[cur] is not None:
opa[cur].wait()
torch.matmul(A_list[cur].transpose(0, 1), B, out=C_list[cur])
opr[cur] = dist.reduce(C_list[cur], dst=src_c, group=col_group, async_op=True)
cur = 1 - cur
src_a += 1
src_c += tesseract_dim
for op in opr:
op.wait()
if tesseract_dim - 2 == row_rank:
C.copy_(C_list[cur])
if tesseract_dim - 1 == row_rank:
C.copy_(C_list[1 - cur])
out = C.reshape(out_shape)
if ctx:
ctx.tesseract_dim = tesseract_dim
ctx.row_rank = row_rank
ctx.col_rank = col_rank
ctx.dep_rank = dep_rank
ctx.row_parallel_mode = row_parallel_mode
ctx.col_parallel_mode = col_parallel_mode
ctx.A_shape = A_shape
ctx.B_shape = B_shape
ctx.data_parallel_rank = data_parallel_rank
ctx.pipeline_parallel_rank = pipeline_parallel_rank
ctx.pipeline_parallel_size = pipeline_parallel_size
ctx.tensor_parallel_size = tensor_parallel_size
return out
@staticmethod
@custom_bwd
def backward(ctx: Any, output_grad: Tensor) -> Tuple[Tensor, ...]:
A, B = ctx.saved_tensors
with torch.no_grad():
A_grad = Matmul_ABT_2p5D.apply(B, output_grad, ctx.tesseract_dim, ctx.A_shape, ctx.row_rank, ctx.col_rank,
ctx.dep_rank, ctx.row_parallel_mode, ctx.col_parallel_mode,
ctx.data_parallel_rank, ctx.pipeline_parallel_rank,
ctx.pipeline_parallel_size, ctx.tensor_parallel_size)
B_grad = Matmul_AB_2p5D.apply(A, output_grad, ctx.tesseract_dim, ctx.B_shape, ctx.row_rank, ctx.col_rank,
ctx.dep_rank, ctx.row_parallel_mode, ctx.col_parallel_mode,
ctx.data_parallel_rank, ctx.pipeline_parallel_rank,
ctx.pipeline_parallel_size, ctx.tensor_parallel_size)
return A_grad, B_grad, None, None, None, None, None, None, None, None, None, None, None, None, None
class _Add_Bias_2p5D(torch.autograd.Function):
@staticmethod
@custom_fwd(cast_inputs=torch.float16)
def forward(ctx: Any, input: Tensor, bias: Tensor, output_size_per_partition: int, tesseract_dim: int,
row_rank: int, col_rank: int, dep_rank: int, col_parallel_mode: ParallelMode, skip_bias_add: bool,
data_parallel_rank: int, pipeline_parallel_rank: int, pipeline_parallel_size: int,
tensor_parallel_size: int) -> Tensor:
if row_rank == 0:
bias_temp = bias.clone()
else:
bias_temp = torch.zeros(output_size_per_partition, dtype=bias.dtype, device=get_current_device())
src_rank = \
col_rank + dep_rank * tesseract_dim ** 2 + \
data_parallel_rank * pipeline_parallel_size * tensor_parallel_size + \
pipeline_parallel_rank * tensor_parallel_size
dist.broadcast(bias_temp, src=src_rank, group=get_parallel_group(col_parallel_mode))
ctx.row_rank = row_rank
ctx.col_rank = col_rank
ctx.dep_rank = dep_rank
ctx.tesseract_dim = tesseract_dim
ctx.col_parallel_mode = col_parallel_mode
ctx.bias = skip_bias_add
ctx.data_parallel_rank = data_parallel_rank
ctx.pipeline_parallel_rank = pipeline_parallel_rank
ctx.pipeline_parallel_size = pipeline_parallel_size
ctx.tensor_parallel_size = tensor_parallel_size
if skip_bias_add:
return bias_temp
else:
output = input + bias_temp
return output
@staticmethod
@custom_bwd
def backward(ctx: Any, output_grad: Tensor) -> Tuple[Tensor, ...]:
row_rank = ctx.row_rank
col_rank = ctx.col_rank
dep_rank = ctx.dep_rank
tesseract_dim = ctx.tesseract_dim
col_parallel_mode = ctx.col_parallel_mode
data_parallel_rank = ctx.data_parallel_rank
pipeline_parallel_rank = ctx.pipeline_parallel_rank
pipeline_parallel_size = ctx.pipeline_parallel_size
tensor_parallel_size = ctx.tensor_parallel_size
if ctx.bias:
dst_rank = \
col_rank + dep_rank * (tesseract_dim ** 2) + \
data_parallel_rank * pipeline_parallel_size * tensor_parallel_size + \
pipeline_parallel_rank * tensor_parallel_size
dist.reduce(output_grad, dst=dst_rank, group=get_parallel_group(col_parallel_mode))
if row_rank == 0:
return \
None, output_grad, None, None, None, None, None, None, \
None, None, None, None, None, None, None, None
else:
grad_tmp = torch.zeros_like(output_grad)
return \
None, grad_tmp, None, None, None, None, None, None, \
None, None, None, None, None, None, None, None
else:
reduce_dim = tuple(range(output_grad.ndim - 1))
reduce = torch.sum(output_grad, dim=reduce_dim)
dst_rank = \
col_rank + dep_rank * (tesseract_dim ** 2) + \
data_parallel_rank * pipeline_parallel_size * tensor_parallel_size + \
pipeline_parallel_rank * tensor_parallel_size
dist.reduce(reduce, dst=dst_rank, group=get_parallel_group(col_parallel_mode))
if row_rank == 0:
return \
output_grad, reduce, None, None, None, None, None, None, None, \
None, None, None, None, None, None, None, None
else:
reduce_tmp = torch.zeros_like(reduce)
return \
output_grad, reduce_tmp, None, None, None, None, None, None, \
None, None, None, None, None, None, None, None, None
def add_bias_2p5d(input: Tensor, bias: Tensor, output_size_per_partition: int, tesseract_dim: int, row_rank: int,
col_rank: int, dep_rank: int, col_parallel_mode: ParallelMode, skip_bias_add: bool,
data_parallel_rank: int, pipeline_parallel_rank: int, pipeline_parallel_size: int,
tensor_parallel_size: int) -> Tensor:
r"""Matrix add bias: :math:`C = A + b`.
Args:
input (:class:`torch.tensor`): matrix :math:`A`.
bias (:class:`torch.tensor`): matrix :math:`B`.
tesseract_dim (int): dimension of TESSERACT fo 2.5D parallelism.
output_size_per_partition (int): output size in each partition.
row_rank (int): the rank of row.
col_rank (int): the rank of column.
dep_rank (int): the rank of depth.
col_parallel_mode (:class:`colossalai.context.ParallelMode`): column parallel mode.
skip_bias_add (bool): If set to ``True``, it will skip bias add for linear layer,
which is preserved for kernel fusion.
data_parallel_rank (int): data parallel rank.
pipeline_parallel_rank (int): pipeline parallel rank
pipeline_parallel_size (int): pipeline parallel size.
tensor_parallel_size (int): tensor parallel size.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
return _Add_Bias_2p5D.apply(input, bias, output_size_per_partition, tesseract_dim, row_rank, col_rank, dep_rank,
col_parallel_mode, skip_bias_add, data_parallel_rank, pipeline_parallel_rank,
pipeline_parallel_size, tensor_parallel_size)
class _Layernorm2p5D(torch.autograd.Function):
r"""Layernorm.
Args:
input (:class:`torch.tensor`): input matrix.
E_x (:class:`torch.tensor`): mean.
Var_x (:class:`torch.tensor`): variance.
hidden_size (int): hidden size.
row_parallel_mode (:class:`colossalai.context.ParallelMode`): row parallel mode.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
@staticmethod
@custom_fwd(cast_inputs=torch.float32)
def forward(ctx: Any, input: Tensor, E_x: Tensor, Var_x: Tensor, hidden_size: int,
row_parallel_mode: ParallelMode) -> Tensor:
input = input - E_x
# in here, input = x - E[x], Var_x = 1 / sqrt(Var[x] + eps)
ctx.hidden_size = hidden_size
output = input * Var_x
ctx.save_for_backward(output, Var_x)
ctx.row_parallel_mode = row_parallel_mode
return output
@staticmethod
@custom_bwd
def backward(ctx, output_grad):
row_parallel_mode = ctx.row_parallel_mode
x, Var_x = ctx.saved_tensors
# in here, Var_x = 1 / sqrt(Var[x] + eps), x = (x - E[x]) * Var_x
with torch.no_grad():
output_grad_sum = torch.sum(output_grad, dim=-1, keepdim=True)
torch.distributed.all_reduce(output_grad_sum, group=get_parallel_group(row_parallel_mode))
output_grad_sum /= ctx.hidden_size
output_grad_mul_x_sum = torch.sum(output_grad * x, dim=-1, keepdim=True)
torch.distributed.all_reduce(output_grad_mul_x_sum, group=get_parallel_group(row_parallel_mode))
output_grad_mul_x_sum /= ctx.hidden_size
input_grad = output_grad.clone()
input_grad -= x * output_grad_mul_x_sum
input_grad -= output_grad_sum
input_grad *= Var_x
return input_grad, None, None, None, None, None, None
def layernorm_2p5d(input: Tensor, E_x: Tensor, Var_x: Tensor, hidden_size: int,
row_parallel_mode: ParallelMode) -> Tensor:
r"""Layernorm.
Args:
input (:class:`torch.tensor`): input matrix.
E_x (:class:`torch.tensor`): mean.
Var_x (:class:`torch.tensor`): variance.
hidden_size (int): hidden size.
row_parallel_mode (:class:`colossalai.context.ParallelMode`): row parallel mode.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_.
"""
return _Layernorm2p5D.apply(input, E_x, Var_x, hidden_size, row_parallel_mode)
class _AllGatherTensor2p5D(torch.autograd.Function):
@staticmethod
@custom_fwd(cast_inputs=torch.float16)
def forward(ctx: Any, inputs: Tensor, dim: int, col_parallel_mode: ParallelMode) -> Tensor:
ctx.dim = dim
ctx.col_parallel_mode = col_parallel_mode
outputs = all_gather(inputs, dim, col_parallel_mode)
return outputs
@staticmethod
@custom_bwd
def backward(ctx: Any, output_grad: Tensor) -> Tuple[Tensor, ...]:
grad = reduce_scatter(output_grad, ctx.dim, ctx.col_parallel_mode)
return grad.contiguous(), None, None
def all_gather_tensor_2p5d(inputs: Tensor, dim: int, col_parallel_mode: ParallelMode) -> Tensor:
r"""all gather the weight of 2.5D parallelism.
Args:
inputs (:class:`torch.tensor`): input tensor.
dim (int): dimension of all-gather.
col_parallel_mode (:class:`colossalai.context.ParallelMode`): column parallel mode.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_.
"""
return _AllGatherTensor2p5D.apply(inputs, dim, col_parallel_mode)
class SplitFirst(torch.autograd.Function):
r"""
Args:
inputs (:class:`torch.tensor`): input tensor.
tesseract_dim (int): dimension of TESSERACT fo 2.5D parallelism
col_parallel_mode (:class:`colossalai.context.ParallelMode`): column parallel mode.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_.
"""
@staticmethod
@custom_fwd(cast_inputs=torch.float16)
def forward(ctx: Any, inputs: Tensor, tesseract_dim: int, col_parallel_mode: ParallelMode) -> Tensor:
ctx.tesseract_dim = tesseract_dim
ctx.batch_size = inputs.size(0)
ctx.para_mode = col_parallel_mode
row_rank = gpc.get_local_rank(col_parallel_mode)
outputs = inputs.chunk(tesseract_dim, dim=0)[row_rank]
return outputs
@staticmethod
@custom_bwd
def backward(ctx: Any, output_grad: Tensor) -> Tuple[Tensor, ...]:
grad_shape = (ctx.batch_size,) + output_grad.shape[1:]
grad = torch.empty(grad_shape, dtype=output_grad.dtype, device=get_current_device())
dist.all_gather(list(grad.chunk(ctx.tesseract_dim, dim=0)),
output_grad.contiguous(),
group=gpc.get_group(ctx.para_mode))
return grad, None, None
def split_batch_2p5d(input_: Tensor, dim: int = 0) -> Tensor:
"""Splits 2P5D tensor in specified dimension across cols.
Args:
input_ (:class:`torch.tensor`): Input tensor.
dim (int): Specified dimension in which to split.
Returns:
:class:`torch.tensor`: The tensor has been split.
"""
dim_size = input_.size(dim)
world_size = gpc.get_world_size(ParallelMode.PARALLEL_2P5D_COL)
if world_size <= 1:
return input_
assert dim_size % world_size == 0, \
f'The batch size ({dim_size}) is not a multiple of 2.5D size * depth ({world_size}).'
return torch.chunk(input_, gpc.get_world_size(ParallelMode.PARALLEL_2P5D_COL),
dim=dim)[gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL)].contiguous()
class _ReduceTensor2p5D(torch.autograd.Function):
@staticmethod
def forward(ctx, input_, parallel_mode):
return all_reduce(input_, parallel_mode)
@staticmethod
def backward(ctx, output_grad):
return output_grad, None
def reduce_tensor_2p5d(input_: Tensor, parallel_mode: ParallelMode) -> Tensor:
r"""All-reduce the input.
Args:
input_ (:class:`torch.tensor`): Input tensor.
parallel_mode (:class:`colossalai.context.ParallelMode`): The parallel mode tensor used.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
return _ReduceTensor2p5D.apply(input_, parallel_mode)
class _ReduceScatterTensor2p5D(torch.autograd.Function):
@staticmethod
def forward(ctx, input_, dim, parallel_mode):
ctx.dim = dim
ctx.parallel_mode = parallel_mode
return reduce_scatter(input_, dim, parallel_mode)
@staticmethod
def backward(ctx, output_grad):
return all_gather(output_grad, ctx.dim, ctx.parallel_mode), None, None
def reduce_scatter_tensor_2p5d(input_: Tensor, dim: int, parallel_mode: ParallelMode) -> Tensor:
r"""Reduce-scatter the input.
Args:
input_ (:class:`torch.tensor`): Input tensor.
dim (int): Dimension to reduce.
parallel_mode (:class:`colossalai.context.ParallelMode`): The parallel mode tensor used.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
dim_size = input_.size(dim)
world_size = gpc.get_world_size(parallel_mode)
assert dim_size % world_size == 0, \
f'The batch size ({dim_size}) is not a multiple of 2.5D size * depth ({world_size}).'
return _ReduceScatterTensor2p5D.apply(input_, dim, parallel_mode)
class _RreduceByBatch2p5D(torch.autograd.Function):
@staticmethod
def symbolic(graph, input_, reduce_mean: bool = False):
output = all_reduce(input_, ParallelMode.PARALLEL_2P5D_COL)
if reduce_mean:
reduce_size = gpc.get_world_size(ParallelMode.PARALLEL_2P5D_COL)
return output / reduce_size
return output
@staticmethod
@custom_fwd(cast_inputs=torch.float32)
def forward(ctx, input_, reduce_mean: bool = False):
output = all_reduce(input_, ParallelMode.PARALLEL_2P5D_COL)
ctx.reduce_mean = reduce_mean
if reduce_mean:
reduce_size = gpc.get_world_size(ParallelMode.PARALLEL_2P5D_COL)
ctx.reduce_size = reduce_size
return output.clone() / reduce_size
return output.clone()
@staticmethod
@custom_bwd
def backward(ctx, output_grad):
if ctx.reduce_mean:
return output_grad / ctx.reduce_size, None
else:
return output_grad, None
def reduce_by_batch_2p5d(input_, reduce_mean: bool = False) -> Tensor:
r"""All-reduce the input from the model parallel region.
Args:
input_ (:class:`torch.tensor`): input matrix.
reduce_mean (bool, optional):
If set to ``True``, it will divide the output by column parallel size, default to False.
"""
return _RreduceByBatch2p5D.apply(input_, reduce_mean)
|
from ._operation import reduce_by_batch_2p5d, split_batch_2p5d
from .layers import (Classifier2p5D, Embedding2p5D, LayerNorm2p5D, Linear2p5D, PatchEmbedding2p5D,
VocabParallelClassifier2p5D, VocabParallelEmbedding2p5D)
__all__ = [
'split_batch_2p5d', 'reduce_by_batch_2p5d', 'Linear2p5D', 'LayerNorm2p5D', 'Classifier2p5D', 'PatchEmbedding2p5D',
'Embedding2p5D', 'VocabParallelClassifier2p5D', 'VocabParallelEmbedding2p5D'
]
|
import math
from collections import OrderedDict
from typing import Callable
import torch
import torch.nn as nn
import torch.nn.functional as F
from colossalai.communication import broadcast
from colossalai.context import ParallelMode, seed
from colossalai.core import global_context as gpc
from colossalai.global_variables import tensor_parallel_env as env
from colossalai.nn import init as init
from colossalai.registry import LAYERS
from colossalai.utils.checkpointing import (broadcast_state_dict, gather_tensor_parallel_state_dict,
partition_tensor_parallel_state_dict)
from colossalai.utils.cuda import get_current_device
from torch import Tensor
from torch.nn import Parameter
from ..base_layer import ParallelLayer
from ..utils import divide, set_tensor_parallel_attribute_by_partition, to_2tuple
from ._operation import (Matmul_AB_2p5D, Matmul_ABT_2p5D, add_bias_2p5d, all_gather_tensor_2p5d, classifier_2p5d,
layernorm_2p5d, reduce_scatter_tensor_2p5d, split_batch_2p5d)
from ._utils import assert_tesseract_initialization, get_tesseract_dim_dep_from_env
@LAYERS.register_module
class Linear2p5D(ParallelLayer):
r"""Linear layer for 2.5D parallelism.
Args:
in_features (int): size of each input sample.
out_features (int): size of each output sample.
bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
skip_bias_add (bool, optional): If set to ``True``, it will skip bias add for linear layer,
which is preserved for kernel fusion, defaults to False.
weight_initializer (:class:`typing.Callable`, optional):
The initializer of weight, defaults to kaiming uniform initializer.
bias_initializer (:class:`typing.Callable`, optional):
The initializer of bias, defaults to xavier uniform initializer.
More details about ``initializer`` please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(self,
in_features: int,
out_features: int,
bias: bool = True,
dtype: torch.dtype = None,
skip_bias_add: bool = False,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.skip_bias_add = skip_bias_add
# parallel setting
assert_tesseract_initialization()
self.row_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL)
self.col_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_ROW)
self.dep_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_DEP)
self.tesseract_dim, _ = get_tesseract_dim_dep_from_env()
# partitioning dimension
self.input_size_per_partition = divide(in_features, self.tesseract_dim)
self.hidden_size_per_partition = divide(out_features, self.tesseract_dim)
# create weight, shape: [k/q, h/q]
factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
self.weight = Parameter(
torch.empty(self.input_size_per_partition, self.hidden_size_per_partition, **factory_kwargs))
# create bias, shape: [h/q]
if bias:
self.bias = Parameter(torch.empty(self.hidden_size_per_partition, **factory_kwargs))
else:
self.register_parameter('bias', None)
# initialize parameters
with seed(ParallelMode.TENSOR):
self.reset_parameters(weight_initializer, bias_initializer)
self._set_tensor_parallel_attributes()
def _set_tensor_parallel_attributes(self):
set_tensor_parallel_attribute_by_partition(self.weight, self.tesseract_dim**2)
if self.bias is not None:
set_tensor_parallel_attribute_by_partition(self.bias, self.tesseract_dim)
def reset_parameters(self, weight_initializer, bias_initializer) -> None:
fan_in, fan_out = self.in_features, self.out_features
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
if self.bias is not None:
bias_initializer(self.bias, fan_in=fan_in)
def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
local_state = OrderedDict()
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight.transpose(0, 1)
# bias
if self.bias is not None:
bias = state_dict.pop(bias_key, None)
if bias is not None:
local_state[bias_key] = bias
# broadcast in dep groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL) == 0 and \
gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_ROW) == 0:
broadcast_state_dict(local_state, ParallelMode.PARALLEL_2P5D_DEP)
# partition in column groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_ROW) == 0:
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_COL,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: False
},
)
# partition in row groups
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_ROW,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
)
super()._load_from_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_state_dict(self, destination, prefix, keep_vars):
if gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_DEP) == 0:
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
local_state = OrderedDict({weight_key: self.weight})
if self.bias is not None:
local_state[bias_key] = self.bias
# gather in row groups
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_ROW,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
keep_vars=keep_vars,
)
# gather in column groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_ROW) == 0:
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_COL,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: False
},
keep_vars=keep_vars,
)
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
local_state[weight_key] = local_state[weight_key].transpose(0, 1)
destination.update(local_state)
def forward(self, x: Tensor) -> Tensor:
# input: [m/dq, n/q, k/q]
# output: [m/dq, n/q, h/q]
out_shape = x.shape[:-1] + (self.hidden_size_per_partition, )
output = Matmul_AB_2p5D.apply(
x,
self.weight,
self.tesseract_dim,
out_shape,
self.row_rank,
self.col_rank,
self.dep_rank,
ParallelMode.PARALLEL_2P5D_ROW,
ParallelMode.PARALLEL_2P5D_COL,
self.data_parallel_rank,
self.pipeline_parallel_rank,
self.pipeline_parallel_size,
self.tensor_parallel_size,
)
if self.bias is not None:
if self.skip_bias_add:
bias = add_bias_2p5d(None, self.bias, self.hidden_size_per_partition, self.tesseract_dim, self.row_rank,
self.col_rank, self.dep_rank, ParallelMode.PARALLEL_2P5D_COL, True,
self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
self.tensor_parallel_size)
return output, bias
else:
output = add_bias_2p5d(output, self.bias, self.hidden_size_per_partition, self.tesseract_dim,
self.row_rank, self.col_rank, self.dep_rank, ParallelMode.PARALLEL_2P5D_COL,
False, self.data_parallel_rank, self.pipeline_parallel_rank,
self.pipeline_parallel_size, self.tensor_parallel_size)
return output
else:
return output
@LAYERS.register_module
class LayerNorm2p5D(ParallelLayer):
r"""Layer Normalization for 2.5D parallelism.
Args:
normalized_shape (int): input shape from an expected input of size.
:math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1]
\times \ldots \times \text{normalized_shape}[-1]]`
If a single integer is used, it is treated as a singleton list, and this module will
normalize over the last dimension which is expected to be of that specific size.
eps (float, optional): a value added to the denominator for numerical stability, defaults to 1e-05.
bias (bool, optional): Whether to add a bias, defaults to ``True``.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
"""
def __init__(self, normalized_shape: int, eps: float = 1e-05, bias=True, dtype=None):
super().__init__()
# layer norm config
self.normalized_shape = normalized_shape
self.variance_epsilon = eps
# parallel setting
assert_tesseract_initialization()
self.row_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL)
self.col_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_ROW)
self.dep_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_DEP)
self.tesseract_dim, _ = get_tesseract_dim_dep_from_env()
# partitioning dimension
self.partitioned_partition = divide(normalized_shape, self.tesseract_dim) # *
# create parameters
factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
self.weight = Parameter(torch.ones(self.partitioned_partition, **factory_kwargs))
if bias:
self.bias = Parameter(torch.zeros(self.partitioned_partition, **factory_kwargs))
else:
self.bias = None
self._set_tensor_parallel_attribute()
def _set_tensor_parallel_attribute(self):
set_tensor_parallel_attribute_by_partition(self.weight, self.tesseract_dim)
if self.bias is not None:
set_tensor_parallel_attribute_by_partition(self.bias, self.tesseract_dim)
def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
local_state = OrderedDict()
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight
# bias
bias = state_dict.pop(bias_key, None)
if bias is not None:
local_state[bias_key] = bias
# partition in row groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL) == 0:
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_ROW,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
)
# partition in column groups
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_COL,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
)
super()._load_from_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_state_dict(self, destination, prefix, keep_vars):
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
local_state = OrderedDict({weight_key: self.weight})
if self.bias is not None:
local_state[bias_key] = self.bias
# gather in column groups
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_COL,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
keep_vars=keep_vars,
)
# gather in row groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL) == 0:
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_ROW,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
keep_vars=keep_vars,
)
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
destination.update(local_state)
def forward(self, x: Tensor) -> Tensor:
with torch.no_grad():
E_x = torch.sum(x, dim=-1, keepdim=True) # [b/q, s, 1]
torch.distributed.all_reduce(E_x, group=gpc.get_group(ParallelMode.PARALLEL_2P5D_ROW))
E_x /= self.normalized_shape
# Var_x in the block below is the sum of input^2
Var_x = torch.sum(x * x, dim=-1, keepdim=True) # [b/q, s, 1]
torch.distributed.all_reduce(Var_x, group=gpc.get_group(ParallelMode.PARALLEL_2P5D_ROW))
Var_x /= self.normalized_shape
Var_x = Var_x - E_x * E_x # variance of x [b/q, s, 1]
# this time 1/sqrt(Var_x + epsilon)
Var_x = 1.0 / torch.sqrt(Var_x + self.variance_epsilon)
output = layernorm_2p5d(x, E_x, Var_x, self.normalized_shape, ParallelMode.PARALLEL_2P5D_ROW)
scale = add_bias_2p5d(None, self.weight, self.partitioned_partition, self.tesseract_dim, self.row_rank,
self.col_rank, self.dep_rank, ParallelMode.PARALLEL_2P5D_COL, True,
self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
self.tensor_parallel_size)
if self.bias is not None:
bias = add_bias_2p5d(None, self.bias, self.partitioned_partition, self.tesseract_dim, self.row_rank,
self.col_rank, self.dep_rank, ParallelMode.PARALLEL_2P5D_COL, True,
self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
self.tensor_parallel_size)
output = torch.addcmul(bias, scale, output)
else:
output = torch.mul(scale, output)
return output
@LAYERS.register_module
class PatchEmbedding2p5D(ParallelLayer):
r"""2D Image to Patch Embedding.
Args:
img_size (int): image size.
patch_size (int): patch size.
in_chans (int): number of channels of input image.
embed_size (int): size of embedding.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
flatten (bool, optional): whether to flatten output tensor, defaults to True.
weight_initializer (:class:`typing.Callable`, optional):
The initializer of weight, defaults to kaiming uniform initializer.
bias_initializer (:class:`typing.Callable`, optional):
The initializer of bias, defaults to xavier uniform initializer.
position_embed_initializer (:class:`typing.Callable`, optional):
The initializer of position embedding, defaults to zeros initializer.
More details about ``initializer`` please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(self,
img_size: int,
patch_size: int,
in_chans: int,
embed_size: int,
flatten: bool = True,
dtype: torch.dtype = None,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
position_embed_initializer: Callable = init.zeros_()):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
assert_tesseract_initialization()
self.tesseract_dim, self.tesseract_dep = get_tesseract_dim_dep_from_env()
self.img_size = img_size
self.patch_size = patch_size
self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
self.num_patches = self.grid_size[0] * self.grid_size[1]
self.flatten = flatten
self.embed_size = embed_size
self.embed_size_per_partition = embed_size // self.tesseract_dim**2
with seed(ParallelMode.TENSOR):
self.weight = Parameter(
torch.empty((self.embed_size_per_partition, in_chans, *self.patch_size),
device=get_current_device(),
dtype=dtype))
self.bias = Parameter(torch.empty(self.embed_size_per_partition, device=get_current_device(), dtype=dtype))
self.cls_token = Parameter(
torch.zeros((1, 1, self.embed_size_per_partition), device=get_current_device(), dtype=dtype))
self.pos_embed = Parameter(
torch.zeros((1, self.num_patches + 1, self.embed_size_per_partition),
device=get_current_device(),
dtype=dtype))
self.reset_parameters(weight_initializer, bias_initializer, position_embed_initializer)
self._set_tensor_parallel_attribute()
def _set_tensor_parallel_attribute(self):
set_tensor_parallel_attribute_by_partition(self.weight, self.tesseract_dim**2)
set_tensor_parallel_attribute_by_partition(self.bias, self.tesseract_dim**2)
set_tensor_parallel_attribute_by_partition(self.cls_token, self.tesseract_dim**2)
set_tensor_parallel_attribute_by_partition(self.pos_embed, self.tesseract_dim**2)
def reset_parameters(self, weight_initializer, bias_initializer, position_embed_initializer):
with seed(ParallelMode.TENSOR):
fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
fan_out = self.embed_size
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
bias_initializer(self.bias, fan_in=fan_in)
position_embed_initializer(self.pos_embed)
def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
local_state = OrderedDict()
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
cls_token_key = prefix + 'cls_token'
pos_embed_key = prefix + 'pos_embed'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight
# bias
bias = state_dict.pop(bias_key, None)
if bias is not None:
local_state[bias_key] = bias
# cls token
cls_token = state_dict.pop(cls_token_key, None)
if cls_token is not None:
local_state[cls_token_key] = cls_token
# pos embed
pos_embed = state_dict.pop(pos_embed_key, None)
if pos_embed is not None:
local_state[pos_embed_key] = pos_embed
# partition in row groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL) == 0:
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_ROW,
dims={
weight_key: 0,
bias_key: 0,
cls_token_key: -1,
pos_embed_key: -1
},
partition_states={
weight_key: True,
bias_key: True,
cls_token_key: True,
pos_embed_key: True
},
)
# partition in column groups
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_COL,
dims={
weight_key: 0,
bias_key: 0,
cls_token_key: -1,
pos_embed_key: -1
},
partition_states={
weight_key: True,
bias_key: True,
cls_token_key: True,
pos_embed_key: True
},
)
super()._load_from_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_state_dict(self, destination, prefix, keep_vars):
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
cls_token_key = prefix + 'cls_token'
pos_embed_key = prefix + 'pos_embed'
local_state = OrderedDict({
weight_key: self.weight,
bias_key: self.bias,
cls_token_key: self.cls_token,
pos_embed_key: self.pos_embed
})
# gather in column groups
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_COL,
dims={
weight_key: 0,
bias_key: 0,
cls_token_key: -1,
pos_embed_key: -1
},
partition_states={
weight_key: True,
bias_key: True,
cls_token_key: True,
pos_embed_key: True
},
keep_vars=keep_vars,
)
# gather in row groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL) == 0:
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_ROW,
dims={
weight_key: 0,
bias_key: 0,
cls_token_key: -1,
pos_embed_key: -1
},
partition_states={
weight_key: True,
bias_key: True,
cls_token_key: True,
pos_embed_key: True
},
keep_vars=keep_vars,
)
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
destination.update(local_state)
def forward(self, input_: Tensor) -> Tensor:
input_ = split_batch_2p5d(input_, 0)
B, C, H, W = input_.shape
assert H == self.img_size[0] and W == self.img_size[1], \
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
weight = all_gather_tensor_2p5d(self.weight, 0, ParallelMode.PARALLEL_2P5D_COL)
bias = all_gather_tensor_2p5d(self.bias, 0, ParallelMode.PARALLEL_2P5D_COL)
output = F.conv2d(input_, weight, bias, stride=self.patch_size)
if self.flatten:
output = output.flatten(2).transpose(1, 2) # BCHW -> BNC
cls_token = all_gather_tensor_2p5d(self.cls_token, -1, ParallelMode.PARALLEL_2P5D_COL)
pos_embed = all_gather_tensor_2p5d(self.pos_embed, -1, ParallelMode.PARALLEL_2P5D_COL)
cls_token = cls_token.expand(output.shape[0], -1, -1)
output = torch.cat((cls_token, output), dim=1)
output = output + pos_embed
return output
@LAYERS.register_module
class Embedding2p5D(ParallelLayer):
r"""Embedding for 2.5D parallelism.
Args:
num_embeddings (int): number of embeddings.
embedding_dim (int): dimension of embedding.
padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient;
therefore, the embedding vector at padding_idx is not updated during training,
i.e. it remains as a fixed “pad”, defaults to None.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
weight_initializer (:class:`typing.Callable`, optional):
he initializer of weight, defaults to normal initializer.
The ``args`` and ``kwargs`` used in :class:``torch.nn.functional.embedding`` should contain:
::
max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is
renormalized to have norm max_norm. Note: this will modify weight in-place.
norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2.
scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse
of frequency of the words in the mini-batch. Default False.
sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False.
More details about ``args`` and ``kwargs`` could be found in
`Embedding <https://pytorch.org/docs/stable/generated/torch.nn.functional.embedding.html#torch.nn.functional.embedding>`_.
More details about initializer please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_
"""
def __init__(self,
num_embeddings: int,
embedding_dim: int,
padding_idx: int = None,
dtype: torch.dtype = None,
weight_initializer: Callable = init.normal_(),
*args,
**kwargs):
super().__init__()
assert_tesseract_initialization()
self.tesseract_dim, self.tesseract_dep = get_tesseract_dim_dep_from_env()
self.num_embeddings = num_embeddings
self.embed_dim = embedding_dim
embed_dim_per_partition = embedding_dim // self.tesseract_dim**2
self.padding_idx = padding_idx
self.embed_args = args
self.embed_kwargs = kwargs
self.weight = Parameter(
torch.empty((num_embeddings, embed_dim_per_partition), device=get_current_device(), dtype=dtype))
self.reset_parameters(weight_initializer)
self._set_tensor_parallel_attributes()
def _set_tensor_parallel_attributes(self):
set_tensor_parallel_attribute_by_partition(self.weight, self.tesseract_dim**2)
def reset_parameters(self, weight_initializer) -> None:
with seed(ParallelMode.TENSOR):
fan_in, fan_out = self.num_embeddings, self.embed_dim
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
self._fill_padding_idx_with_zero()
def _fill_padding_idx_with_zero(self) -> None:
if self.padding_idx is not None:
with torch.no_grad():
self.weight[self.padding_idx].fill_(0)
def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
local_state = OrderedDict()
weight_key = prefix + 'weight'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight
# partition in row groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL) == 0:
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_ROW,
dims={weight_key: -1},
partition_states={weight_key: True},
)
# partition in column groups
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_COL,
dims={weight_key: -1},
partition_states={weight_key: True},
)
super()._load_from_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_state_dict(self, destination, prefix, keep_vars):
weight_key = prefix + 'weight'
local_state = OrderedDict({weight_key: self.weight})
# gather in column groups
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_COL,
dims={weight_key: -1},
partition_states={weight_key: True},
keep_vars=keep_vars,
)
# gather in row groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL) == 0:
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_ROW,
dims={weight_key: -1},
partition_states={weight_key: True},
keep_vars=keep_vars,
)
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
destination.update(local_state)
def forward(self, input_: Tensor) -> Tensor:
input_ = split_batch_2p5d(input_, 0)
weight = all_gather_tensor_2p5d(self.weight, -1, ParallelMode.PARALLEL_2P5D_COL)
output = F.embedding(input_, weight, self.padding_idx, *self.embed_args, **self.embed_kwargs)
return output
@LAYERS.register_module
class VocabParallelEmbedding2p5D(torch.nn.Module):
"""Embedding parallelized in the vocabulary dimension.
Args:
num_embeddings (int): number of embeddings.
embedding_dim (int): dimension of embedding.
padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient;
therefore, the embedding vector at padding_idx is not updated during training,
i.e. it remains as a fixed “pad”, defaults to None.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
weight_initializer (:class:`typing.Callable`, optional):
he initializer of weight, defaults to normal initializer.
The ``args`` and ``kwargs`` used in :class:``torch.nn.functional.embedding`` should contain:
::
max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is
renormalized to have norm max_norm. Note: this will modify weight in-place.
norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2.
scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse
of frequency of the words in the mini-batch. Default False.
sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False.
More details about ``args`` and ``kwargs`` could be found in
`Embedding <https://pytorch.org/docs/stable/generated/torch.nn.functional.embedding.html#torch.nn.functional.embedding>`_.
More details about initializer please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(self,
num_embeddings: int,
embedding_dim: int,
padding_idx: int = None,
dtype: torch.dtype = None,
weight_initializer: Callable = init.normal_(),
*args,
**kwargs):
super().__init__()
self.num_embeddings = num_embeddings
self.embed_dim = embedding_dim
self.padding_idx = padding_idx
self.embed_args = args
self.embed_kwargs = kwargs
assert_tesseract_initialization()
self.tesseract_dim, self.tesseract_dep = get_tesseract_dim_dep_from_env()
self.num_embeddings_per_partition = divide(self.num_embeddings, self.tesseract_dim)
self.embed_dim_per_partition = divide(self.embed_dim, self.tesseract_dim)
tensor_parallel_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL)
self.vocab_start_index = tensor_parallel_rank * self.num_embeddings_per_partition
self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition
self.weight = Parameter(
torch.empty((self.num_embeddings_per_partition, self.embed_dim_per_partition),
device=get_current_device(),
dtype=dtype))
self.reset_parameters(weight_initializer)
self._set_tensor_parallel_attributes()
env.vocab_parallel = True
def _set_tensor_parallel_attributes(self):
set_tensor_parallel_attribute_by_partition(self.weight, self.tesseract_dim**2)
def reset_parameters(self, weight_initializer) -> None:
with seed(ParallelMode.TENSOR):
fan_in, fan_out = self.num_embeddings, self.embed_dim
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
self._fill_padding_idx_with_zero()
def _fill_padding_idx_with_zero(self) -> None:
if self.padding_idx is not None and \
self.vocab_start_index <= self.padding_idx < self.vocab_end_index:
with torch.no_grad():
self.weight[self.padding_idx - self.vocab_start_index].fill_(0)
def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
local_state = OrderedDict()
weight_key = prefix + 'weight'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight
# partition in row groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL) == 0:
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_ROW,
dims={weight_key: -1},
partition_states={weight_key: True},
)
# partition in column groups
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_COL,
dims={weight_key: 0},
partition_states={weight_key: True},
)
super()._load_from_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_state_dict(self, destination, prefix, keep_vars):
weight_key = prefix + 'weight'
local_state = OrderedDict({weight_key: self.weight})
# gather in column groups
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_COL,
dims={weight_key: 0},
partition_states={weight_key: True},
keep_vars=keep_vars,
)
# gather in row groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL) == 0:
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_ROW,
dims={weight_key: -1},
partition_states={weight_key: True},
keep_vars=keep_vars,
)
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
destination.update(local_state)
def forward(self, input_: Tensor) -> Tensor:
# Build the mask.
input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index)
# Mask the input.
masked_input = input_.clone() - self.vocab_start_index
masked_input[input_mask] = 0
output_parallel = F.embedding(masked_input, self.weight, self.padding_idx, *self.embed_args,
**self.embed_kwargs)
# Mask the output embedding.
output_parallel[input_mask, :] = 0.
# Reduce across all the model parallel GPUs.
output = reduce_scatter_tensor_2p5d(output_parallel, 0, ParallelMode.PARALLEL_2P5D_COL)
return output
@LAYERS.register_module
class Classifier2p5D(ParallelLayer):
r"""Classifier for 2.5D parallelism.
Args:
in_features (int): size of each input sample.
num_classes (int): number of classes.
weight (:class:`torch.nn.Parameter`, optional): weight of the classifier, defaults to None.
bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
weight_initializer (:class:`typing.Callable`, optional):
The initializer of weight, defaults to kaiming uniform initializer.
bias_initializer (:class:`typing.Callable`, optional):
The initializer of bias, defaults to xavier uniform initializer.
More details about ``initializer`` please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(self,
in_features: int,
num_classes: int,
weight: Parameter = None,
bias: bool = True,
dtype: torch.dtype = None,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
super().__init__()
self.in_features = in_features
self.num_classes = num_classes
assert_tesseract_initialization()
self.row_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL)
self.col_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_ROW)
self.dep_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_DEP)
self.tesseract_dim, self.tesseract_dep = get_tesseract_dim_dep_from_env()
# partitioning dimension
self.input_size_per_partition = divide(self.in_features, self.tesseract_dim**2)
if weight is not None:
self.weight = weight
self.has_weight = False
else:
self.weight = Parameter(
torch.empty(self.num_classes, self.input_size_per_partition, device=get_current_device(), dtype=dtype))
self.has_weight = True
if bias:
self.bias = Parameter(torch.zeros(self.num_classes, device=get_current_device(), dtype=dtype))
else:
self.bias = None
self.reset_parameters(weight_initializer, bias_initializer)
self._set_tensor_parallel_attributes()
def _set_tensor_parallel_attributes(self):
if self.has_weight:
set_tensor_parallel_attribute_by_partition(self.weight, self.tesseract_dim**2)
def reset_parameters(self, weight_initializer, bias_initializer) -> None:
with seed(ParallelMode.TENSOR):
fan_in, fan_out = self.in_features, self.num_classes
col_src_rank = gpc.get_ranks_in_group(ParallelMode.PARALLEL_2P5D_COL)[0]
row_src_rank = gpc.get_ranks_in_group(ParallelMode.PARALLEL_2P5D_ROW)[0]
if self.has_weight:
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
if self.bias is not None:
bias_initializer(self.bias, fan_in=fan_in)
broadcast(self.bias, col_src_rank, ParallelMode.PARALLEL_2P5D_COL)
broadcast(self.bias, row_src_rank, ParallelMode.PARALLEL_2P5D_ROW)
def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
local_state = OrderedDict()
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
if self.has_weight:
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight
# bias
if self.bias is not None:
bias = state_dict.pop(bias_key, None)
if bias is not None:
local_state[bias_key] = bias
# partition in row groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL) == 0:
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_ROW,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: False
},
)
# partition in column groups
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_COL,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: False
},
)
super()._load_from_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_state_dict(self, destination, prefix, keep_vars):
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
local_state = OrderedDict()
if self.has_weight:
local_state[weight_key] = self.weight
if self.bias is not None:
local_state[bias_key] = self.bias
# gather in column groups
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_COL,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: False
},
keep_vars=keep_vars,
)
# gather in row groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL) == 0:
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_ROW,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: False
},
keep_vars=keep_vars,
)
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
destination.update(local_state)
def forward(self, input_: Tensor) -> Tensor:
out_shape = input_.shape[:-1] + (self.num_classes, )
return classifier_2p5d(input_, self.weight, self.bias, self.tesseract_dim, out_shape, self.row_rank,
self.col_rank, ParallelMode.PARALLEL_2P5D_ROW, ParallelMode.PARALLEL_2P5D_COL,
self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
self.tensor_parallel_size)
@LAYERS.register_module
class VocabParallelClassifier2p5D(ParallelLayer):
r"""Vocab parallel classifier layer for 2.5D parallelism.
Args:
in_features (int): size of each input sample.
num_classes (int): number of classes.
weight (:class:`torch.nn.Parameter`, optional): weight of the classifier, defaults to None.
bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
weight_initializer (:class:`typing.Callable`, optional):
The initializer of weight, defaults to kaiming uniform initializer.
bias_initializer (:class:`typing.Callable`, optional):
The initializer of bias, defaults to xavier uniform initializer.
More details about ``initializer`` please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(self,
in_features: int,
num_classes: int,
weight: Parameter = None,
bias: bool = True,
dtype: torch.dtype = None,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
super().__init__()
self.in_features = in_features
self.num_classes = num_classes
# parallel setting
assert_tesseract_initialization()
self.row_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL)
self.col_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_ROW)
self.dep_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_DEP)
self.tesseract_dim, _ = get_tesseract_dim_dep_from_env()
# partitioning dimension
self.input_size_per_partition = divide(in_features, self.tesseract_dim)
self.hidden_size_per_partition = divide(num_classes, self.tesseract_dim)
# create weight, shape: [k/q, h/q]
factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
if weight is not None:
self.weight = weight
self.has_weight = False
else:
self.weight = Parameter(
torch.empty(self.hidden_size_per_partition, self.input_size_per_partition, **factory_kwargs))
self.has_weight = True
# create bias, shape: [h/q]
if bias:
self.bias = Parameter(torch.empty(self.hidden_size_per_partition, **factory_kwargs))
else:
self.bias = None
# initialize parameters
with seed(ParallelMode.TENSOR):
self.reset_parameters(weight_initializer, bias_initializer)
self._set_tensor_parallel_attributes()
env.vocab_parallel = True
def _set_tensor_parallel_attributes(self):
if self.has_weight:
set_tensor_parallel_attribute_by_partition(self.weight, self.tesseract_dim**2)
if self.bias is not None:
set_tensor_parallel_attribute_by_partition(self.bias, self.tesseract_dim)
def reset_parameters(self, weight_initializer, bias_initializer) -> None:
fan_in, fan_out = self.in_features, self.num_classes
if self.has_weight:
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
if self.bias is not None:
bias_initializer(self.bias, fan_in=fan_in)
def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
local_state = OrderedDict()
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
if self.has_weight:
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight
# bias
if self.bias is not None:
bias = state_dict.pop(bias_key, None)
if bias is not None:
local_state[bias_key] = bias
# partition in row groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL) == 0:
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_ROW,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
)
# partition in column groups
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2P5D_COL,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
)
super()._load_from_state_dict(local_state, prefix, *args, **kwargs)
def forward(self, x: Tensor) -> Tensor:
# input: [m/dq, n/q, k/q]
# output: [m/dq, n/q, h/q]
out_shape = x.shape[:-1] + (self.hidden_size_per_partition, )
output = Matmul_ABT_2p5D.apply(
x,
self.weight,
self.tesseract_dim,
out_shape,
self.row_rank,
self.col_rank,
self.dep_rank,
ParallelMode.PARALLEL_2P5D_ROW,
ParallelMode.PARALLEL_2P5D_COL,
self.data_parallel_rank,
self.pipeline_parallel_rank,
self.pipeline_parallel_size,
self.tensor_parallel_size,
)
if self.bias is not None:
output = add_bias_2p5d(output, self.bias, self.hidden_size_per_partition, self.tesseract_dim, self.row_rank,
self.col_rank, self.dep_rank, ParallelMode.PARALLEL_2P5D_COL, False,
self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
self.tensor_parallel_size)
return output
|
from colossalai.context.parallel_mode import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.global_variables import tensor_parallel_env as env
def get_tesseract_dim_dep_from_env():
try:
tesseract_dim = env.tesseract_dim
tesseract_dep = env.tesseract_dep
assert tesseract_dim > 0, 'TESSERACT_DIM must be larger than zero'
assert tesseract_dep > 0, 'TESSERACT_DEP must be larger than zero'
return tesseract_dim, tesseract_dep
except KeyError as e:
raise EnvironmentError('TESSERACT_DIM or TESSERACT_DEP is not found in the current environment, '
'please make sure that you have used the correct process group initializer')
def assert_tesseract_initialization():
assert gpc.is_initialized(ParallelMode.PARALLEL_2P5D_COL) and \
gpc.is_initialized(ParallelMode.PARALLEL_2P5D_ROW) and \
gpc.is_initialized(ParallelMode.PARALLEL_2P5D_DEP) and \
gpc.is_initialized(ParallelMode.PARALLEL_2P5D_XZ), \
'Both PARALLEL_2P5D_COL, PARALLEL_2P5D_ROW, PARALLEL_2P5D_DEP and PARALLEL_2P5D_XZ ' \
'must be initialized by the process group initializer'
|
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
from typing import Optional, Tuple
import torch
from colossalai.communication import (all_gather, all_reduce, broadcast, reduce, reduce_scatter)
from colossalai.context.parallel_mode import ParallelMode
from colossalai.core import global_context as gpc
from torch import Tensor
from torch.cuda.amp import custom_bwd, custom_fwd
from ._utils import get_parallel_mode_from_env
from colossalai.constants import INPUT_GROUP_3D, WEIGHT_GROUP_3D
class _Linear3D(torch.autograd.Function):
@staticmethod
@custom_fwd(cast_inputs=torch.float16)
def forward(ctx,
input_: Tensor,
weight: Tensor,
bias: Optional[Tensor],
input_parallel_mode: ParallelMode,
weight_parallel_mode: ParallelMode,
output_parallel_mode: ParallelMode,
input_dim: int = 0,
weight_dim: int = -1,
output_dim: int = 0) -> Tensor:
ctx.use_bias = bias is not None
input_ = all_gather(input_, input_dim, input_parallel_mode)
weight = all_gather(weight, weight_dim, weight_parallel_mode)
ctx.save_for_backward(input_, weight)
output = torch.matmul(input_, weight)
output = reduce_scatter(output, output_dim, output_parallel_mode)
if bias is not None:
output += bias
ctx.input_parallel_mode = input_parallel_mode
ctx.weight_parallel_mode = weight_parallel_mode
ctx.output_parallel_mode = output_parallel_mode
ctx.input_dim = input_dim
ctx.weight_dim = weight_dim
ctx.output_dim = output_dim
return output
@staticmethod
@custom_bwd
def backward(ctx, output_grad: Tensor) -> Tuple[Tensor, ...]:
input_, weight = ctx.saved_tensors
with torch.no_grad():
output_grad = all_gather(output_grad, ctx.output_dim, ctx.output_parallel_mode)
async_ops = list()
input_grad = torch.matmul(output_grad, weight.transpose(0, 1))
input_grad, op = reduce_scatter(input_grad, ctx.input_dim, ctx.input_parallel_mode, async_op=True)
async_ops.append(op)
weight_grad = torch.matmul(
input_.reshape(-1, input_.shape[-1]).transpose(0, 1), output_grad.reshape(-1, output_grad.shape[-1]))
weight_grad, op = reduce_scatter(weight_grad, ctx.weight_dim, ctx.weight_parallel_mode, async_op=True)
async_ops.append(op)
if ctx.use_bias:
bias_grad = torch.sum(output_grad, dim=tuple(range(len(output_grad.shape))[:-1]))
bias_grad, op = all_reduce(bias_grad, ctx.weight_parallel_mode, async_op=True)
async_ops.append(op)
else:
bias_grad = None
for op in async_ops:
if op is not None:
op.wait()
return input_grad, weight_grad, bias_grad, None, None, None, None, None, None
def linear_3d(input_: Tensor,
weight: Tensor,
bias: Optional[Tensor],
input_parallel_mode: ParallelMode,
weight_parallel_mode: ParallelMode,
output_parallel_mode: ParallelMode,
input_dim: int = 0,
weight_dim: int = -1,
output_dim: int = 0) -> Tensor:
r"""Linear layer for 3D parallelism.
Args:
input_ (:class:`torch.tensor`): input matrix.
weight (:class:`torch.tensor`): matrix of weight.
bias (:class:`torch.tensor`): matrix of bias.
input_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): input parallel mode.
weight_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): weight parallel mode.
output_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): output parallel mode.
input_dim (int, optional): dimension of input, defaults to 0.
weight_dim (int, optional): dimension of weight, defaults to -1.
output_dim (int, optional): dimension of output, defaults to 0.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
return _Linear3D.apply(input_, weight, bias, input_parallel_mode, weight_parallel_mode, output_parallel_mode,
input_dim, weight_dim, output_dim)
class _Classifier3D(torch.autograd.Function):
@staticmethod
@custom_fwd(cast_inputs=torch.float16)
def forward(ctx, input_: Tensor, weight: Tensor, bias: Optional[Tensor], input_parallel_mode: ParallelMode,
weight_parallel_mode: ParallelMode, output_parallel_mode: ParallelMode) -> Tensor:
ctx.use_bias = bias is not None
ranks_in_group = gpc.get_ranks_in_group(input_parallel_mode)
src_rank = ranks_in_group[gpc.get_local_rank(output_parallel_mode)]
weight = broadcast(weight, src_rank, input_parallel_mode)
ctx.save_for_backward(input_, weight)
output = torch.matmul(input_, weight.transpose(0, 1))
output = all_reduce(output, output_parallel_mode)
if bias is not None:
output += bias
ctx.src_rank = src_rank
ctx.input_parallel_mode = input_parallel_mode
ctx.weight_parallel_mode = weight_parallel_mode
ctx.output_parallel_mode = output_parallel_mode
return output
@staticmethod
@custom_bwd
def backward(ctx, output_grad: Tensor) -> Tuple[Tensor, ...]:
input_, weight = ctx.saved_tensors
with torch.no_grad():
async_ops = list()
weight_grad = torch.matmul(
output_grad.reshape(-1, output_grad.shape[-1]).transpose(0, 1), input_.reshape(-1, input_.shape[-1]))
weight_grad = reduce(weight_grad, ctx.src_rank, ctx.input_parallel_mode)
if gpc.get_local_rank(ctx.input_parallel_mode) == gpc.get_local_rank(ctx.output_parallel_mode):
weight_grad, op = all_reduce(weight_grad, ctx.weight_parallel_mode, async_op=True)
async_ops.append(op)
else:
weight_grad = None
if ctx.use_bias:
bias_grad = torch.sum(output_grad, dim=tuple(range(len(output_grad.shape))[:-1]))
bias_grad = all_reduce(bias_grad, ctx.input_parallel_mode)
bias_grad, op = all_reduce(bias_grad, ctx.weight_parallel_mode, async_op=True)
async_ops.append(op)
else:
bias_grad = None
input_grad = torch.matmul(output_grad, weight)
for op in async_ops:
if op is not None:
op.wait()
return input_grad, weight_grad, bias_grad, None, None, None, None, None, None
def classifier_3d(input_: Tensor, weight: Tensor, bias: Optional[Tensor], input_parallel_mode: ParallelMode,
weight_parallel_mode: ParallelMode, output_parallel_mode: ParallelMode) -> Tensor:
r"""3D parallel classifier.
Args:
input_ (:class:`torch.tensor`): input matrix.
weight (:class:`torch.tensor`): matrix of weight.
bias (:class:`torch.tensor`): matrix of bias.
input_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): input parallel mode.
weight_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): weight parallel mode.
output_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): output parallel mode.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
return _Classifier3D.apply(input_, weight, bias, input_parallel_mode, weight_parallel_mode, output_parallel_mode)
class _Layernorm3D(torch.autograd.Function):
@staticmethod
@custom_fwd(cast_inputs=torch.float32)
def forward(ctx, input_: Tensor, weight: Tensor, bias: Optional[Tensor], normalized_shape: int, eps: float,
input_parallel_mode: ParallelMode, weight_parallel_mode: ParallelMode,
output_parallel_mode: ParallelMode) -> Tensor:
mean = all_reduce(torch.sum(input_, dim=-1, keepdim=True), output_parallel_mode) / normalized_shape
mu = input_ - mean
var = all_reduce(torch.sum(mu**2, dim=-1, keepdim=True), output_parallel_mode) / normalized_shape
sigma = torch.sqrt(var + eps)
ctx.save_for_backward(mu, sigma, weight)
z = mu / sigma
output = weight * z
if bias is not None:
output = output + bias
ctx.use_bias = bias is not None
ctx.normalized_shape = normalized_shape
ctx.input_parallel_mode = input_parallel_mode
ctx.weight_parallel_mode = weight_parallel_mode
ctx.output_parallel_mode = output_parallel_mode
return output
@staticmethod
@custom_bwd
def backward(ctx, output_grad: Tensor) -> Tuple[Tensor, ...]:
mu, sigma, weight = ctx.saved_tensors
with torch.no_grad():
weight_grad = output_grad * mu / sigma
if ctx.use_bias:
bias_grad = output_grad
weight_grad = torch.stack([bias_grad, weight_grad]).contiguous()
else:
bias_grad = None
weight_grad = torch.sum(weight_grad, dim=tuple(range(len(weight_grad.shape))[1:-1]))
weight_grad = all_reduce(weight_grad, ctx.weight_parallel_mode)
weight_grad = all_reduce(weight_grad, ctx.input_parallel_mode)
if ctx.use_bias:
bias_grad, weight_grad = weight_grad[0], weight_grad[1]
dz = output_grad * weight
dvar = dz * mu * (-0.5) * sigma**(-3)
dvar = all_reduce(torch.sum(dvar, dim=-1, keepdim=True), ctx.output_parallel_mode)
dmean = dz * (-1 / sigma) + dvar * -2 * mu / ctx.normalized_shape
dmean = all_reduce(torch.sum(dmean, dim=-1, keepdim=True), ctx.output_parallel_mode)
input_grad = dz / sigma + dvar * 2 * mu / \
ctx.normalized_shape + dmean / ctx.normalized_shape
return input_grad, weight_grad, bias_grad, None, None, None, None, None
def layernorm_3d(input_: Tensor, weight: Tensor, bias: Optional[Tensor], normalized_shape: int, eps: float,
input_parallel_mode: ParallelMode, weight_parallel_mode: ParallelMode,
output_parallel_mode: ParallelMode) -> Tensor:
r"""3D parallel Layernorm.
Args:
input_ (:class:`torch.tensor`): input matrix.
weight (:class:`torch.tensor`): matrix of weight.
bias (:class:`torch.tensor`): matrix of bias.
normalized_shape (int): input shape from an expected input of size.
:math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1]
\times \ldots \times \text{normalized_shape}[-1]]`
If a single integer is used, it is treated as a singleton list, and this module will
normalize over the last dimension which is expected to be of that specific size.
eps (float): a value added to the denominator for numerical stability
input_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): input parallel mode.
weight_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): weight parallel mode.
output_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): output parallel mode.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
return _Layernorm3D.apply(input_, weight, bias, normalized_shape, eps, input_parallel_mode, weight_parallel_mode,
output_parallel_mode)
def split_tensor_3d(tensor: Tensor, dim: int, parallel_mode: ParallelMode) -> Tensor:
r"""Splits 3D parallel tensor in specified dimension.
Args:
tensor (:class:`torch.tensor`): Input tensor.
dim (int): Specified dimension in which to split.
parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`, optional): Parallel mode.
Returns:
:class:`torch.tensor`: The tensor has been split.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_.
"""
dim_size = tensor.size(dim)
world_size = gpc.get_world_size(parallel_mode)
assert dim_size % world_size == 0, \
f'The dimension {dim} to split, size ({dim_size}) is not a multiple of world size ({world_size}), ' \
f'cannot split tensor evenly'
if tensor.size(dim) <= 1:
return tensor
output = torch.chunk(tensor, gpc.get_world_size(parallel_mode),
dim=dim)[gpc.get_local_rank(parallel_mode)].contiguous()
return output
def split_batch_3d(input_: Tensor,
dim: int = 0,
input_parallel_mode: ParallelMode = ParallelMode.PARALLEL_3D_INPUT,
weight_parallel_mode: ParallelMode = ParallelMode.PARALLEL_3D_WEIGHT) -> Tensor:
r"""Splits 3D tensor in batch.
Args:
input_ (:class:`torch.tensor`): Input tensor.
dim (int): Specified dimension in which to split.
input_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`, optional): input parallel mode.
weight_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`, optional): weight parallel mode.
Returns:
:class:`torch.tensor`: The tensor has been split.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_.
"""
dim_size = input_.size(dim)
weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
weight_world_size = gpc.get_world_size(weight_parallel_mode)
input_world_size = gpc.get_world_size(input_parallel_mode)
assert dim_size % (input_world_size*weight_world_size) == 0, \
f'The batch size ({dim_size}) is not a multiple of square of 3D depth ({input_world_size*weight_world_size}).'
if input_.size(dim) <= 1:
return input_
output = torch.chunk(input_, weight_world_size,
dim=dim)[gpc.get_local_rank(weight_parallel_mode)].contiguous()
output = torch.chunk(output, input_world_size,
dim=dim)[gpc.get_local_rank(input_parallel_mode)].contiguous()
return output
class _ReduceTensor3D(torch.autograd.Function):
@staticmethod
def forward(ctx, input_, parallel_mode):
return all_reduce(input_, parallel_mode)
@staticmethod
def backward(ctx, output_grad):
return output_grad, None
def reduce_tensor_3d(tensor: Tensor, parallel_mode: ParallelMode) -> Tensor:
r"""All-reduce the input
Args:
tensor (:class:`torch.tensor`): Input tensor.
parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): Parallel mode.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_.
"""
return _ReduceTensor3D.apply(tensor, parallel_mode)
class _AllGatherTensor3D(torch.autograd.Function):
@staticmethod
def forward(ctx, input_, dim, parallel_mode):
ctx.dim = dim
ctx.parallel_mode = parallel_mode
output = all_gather(input_, dim, parallel_mode)
return output
@staticmethod
def backward(ctx, output_grad):
input_grad = reduce_scatter(output_grad, ctx.dim, ctx.parallel_mode)
return input_grad, None, None
def all_gather_tensor_3d(tensor: Tensor, dim: int, parallel_mode: ParallelMode) -> Tensor:
r"""All-reduce the gradient in backward pass.
Args:
tensor (:class:`torch.tensor`): Input tensor.
dim (int): Dimension to gather.
parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): Parallel mode.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_.
"""
return _AllGatherTensor3D.apply(tensor, dim, parallel_mode)
class _ReduceScatterTensor3D(torch.autograd.Function):
@staticmethod
def forward(ctx, input_, dim, parallel_mode):
ctx.dim = dim
ctx.parallel_mode = parallel_mode
return reduce_scatter(input_, dim, parallel_mode)
@staticmethod
def backward(ctx, output_grad):
input_grad = all_gather(output_grad, ctx.dim, ctx.parallel_mode)
return input_grad, None, None
def reduce_scatter_tensor_3d(tensor: Tensor, dim: int, parallel_mode: ParallelMode) -> Tensor:
r"""Reduce-scatter the input.
Args:
tensor (:class:`torch.tensor`): Input tensor.
dim (int): Dimension to scatter.
parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): Parallel mode.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
dim_size = tensor.size(dim)
world_size = gpc.get_world_size(parallel_mode)
assert dim_size % world_size == 0, \
f'The batch size ({dim_size}) is not a multiple of square of 3D depth ({world_size}).'
return _ReduceScatterTensor3D.apply(tensor, dim, parallel_mode)
class _ReduceByBatch3D(torch.autograd.Function):
@staticmethod
@custom_fwd(cast_inputs=torch.float32)
def forward(ctx,
input_: Tensor,
input_parallel_mode: ParallelMode,
weight_parallel_mode: ParallelMode,
reduce_mean: bool = False) -> Tensor:
output = all_reduce(input_, input_parallel_mode)
output = all_reduce(output, weight_parallel_mode)
ctx.reduce_mean = reduce_mean
if reduce_mean:
reduce_size = gpc.get_world_size(input_parallel_mode) * gpc.get_world_size(weight_parallel_mode)
ctx.reduce_size = reduce_size
return output.clone() / reduce_size
return output.clone()
@staticmethod
@custom_bwd
def backward(ctx, output_grad: Tensor) -> Tuple[Tensor, ...]:
if ctx.reduce_mean:
return output_grad / ctx.reduce_size, None, None, None
else:
return output_grad, None, None, None
def reduce_by_batch_3d(tensor: Tensor,
input_parallel_mode: ParallelMode,
weight_parallel_mode: ParallelMode,
reduce_mean: bool = False) -> Tensor:
r"""All-reduce the input from the model parallel region.
Args:
input_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): input parallel mode.
weight_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): weight parallel mode.
reduce_mean (bool, optional): If set to ``True``, it will divide the output by
(input parallel size * weight parallel size), default to False.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
return _ReduceByBatch3D.apply(tensor, input_parallel_mode, weight_parallel_mode, reduce_mean)
class _BroadcastWeight3D_FromDiagonal(torch.autograd.Function):
r"""broadcast weight from diagonal.
Args:
input_ (:class:`torch.tensor`): input matrix.
input_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): input parallel mode.
weight_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): weight parallel mode.
output_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): output parallel mode.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
@staticmethod
@custom_fwd(cast_inputs=torch.float16)
def forward(ctx, input_: Tensor, input_parallel_mode: ParallelMode, weight_parallel_mode: ParallelMode,
output_parallel_mode: ParallelMode) -> Tensor:
ranks_in_group = gpc.get_ranks_in_group(input_parallel_mode)
src_rank = ranks_in_group[gpc.get_local_rank(output_parallel_mode)]
output = broadcast(input_, src_rank, input_parallel_mode)
ctx.src_rank = src_rank
ctx.input_parallel_mode = input_parallel_mode
ctx.weight_parallel_mode = weight_parallel_mode
ctx.output_parallel_mode = output_parallel_mode
return output
@staticmethod
@custom_bwd
def backward(ctx, output_grad: Tensor) -> Tuple[Tensor, ...]:
input_grad = reduce(output_grad, ctx.src_rank, ctx.input_parallel_mode)
if gpc.get_local_rank(ctx.input_parallel_mode) == gpc.get_local_rank(ctx.output_parallel_mode):
input_grad = all_reduce(input_grad, ctx.weight_parallel_mode)
else:
input_grad = None
return input_grad, None, None, None
def broadcast_weight_3d_from_diagonal(tensor: Tensor, input_parallel_mode: ParallelMode,
weight_parallel_mode: ParallelMode, output_parallel_mode: ParallelMode) -> Tensor:
return _BroadcastWeight3D_FromDiagonal.apply(tensor, input_parallel_mode, weight_parallel_mode,
output_parallel_mode)
|
from ._operation import reduce_by_batch_3d, split_batch_3d, split_tensor_3d
from .layers import (Classifier3D, Embedding3D, LayerNorm3D, Linear3D, PatchEmbedding3D, VocabParallelClassifier3D,
VocabParallelEmbedding3D)
__all__ = [
'reduce_by_batch_3d', 'split_tensor_3d', 'split_batch_3d', 'Linear3D', 'LayerNorm3D', 'PatchEmbedding3D',
'Classifier3D', 'Embedding3D', 'VocabParallelEmbedding3D', 'VocabParallelClassifier3D'
]
|
import math
from collections import OrderedDict
from typing import Callable
import torch
import torch.nn as nn
import torch.nn.functional as F
from colossalai.communication import all_reduce, broadcast
from colossalai.constants import INPUT_GROUP_3D, WEIGHT_GROUP_3D
from colossalai.context import ParallelMode, seed
from colossalai.core import global_context as gpc
from colossalai.global_variables import tensor_parallel_env as env
from colossalai.nn import init as init
from colossalai.nn.layer.base_layer import ParallelLayer
from colossalai.registry import LAYERS
from colossalai.utils.checkpointing import (broadcast_state_dict, gather_tensor_parallel_state_dict,
partition_tensor_parallel_state_dict)
from colossalai.utils.cuda import get_current_device
from torch import Tensor
from torch.nn import Parameter
from ..utils import divide, set_tensor_parallel_attribute_by_partition, to_2tuple
from ._operation import (all_gather_tensor_3d, broadcast_weight_3d_from_diagonal, classifier_3d, layernorm_3d,
linear_3d, reduce_scatter_tensor_3d, split_tensor_3d)
from ._utils import get_depth_from_env, get_last_group, get_parallel_mode_from_env, swap_in_out_group
@LAYERS.register_module
class LayerNorm3D(ParallelLayer):
r"""Layer Normalization for 3D parallelism.
Args:
normalized_shape (int): input shape from an expected input of size.
:math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1]
\times \ldots \times \text{normalized_shape}[-1]]`
If a single integer is used, it is treated as a singleton list, and this module will
normalize over the last dimension which is expected to be of that specific size.
eps (float, optional): a value added to the denominator for numerical stability, defaults to 1e-12.
bias (bool, optional): Whether to add a bias, defaults to ``True``.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
"""
def __init__(self, normalized_shape: int, eps: float = 1e-12, bias=True, dtype=None):
super().__init__()
self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
self.output_parallel_mode = get_last_group(self.input_parallel_mode, self.weight_parallel_mode)
self.depth = get_depth_from_env()
self.normalized_shape = normalized_shape
self.normalized_shape_per_partition = divide(normalized_shape, self.depth)
self.weight = Parameter(
torch.ones(self.normalized_shape_per_partition, device=get_current_device(), dtype=dtype))
if bias:
self.bias = Parameter(torch.zeros(self.normalized_shape_per_partition,
device=get_current_device(), dtype=dtype))
else:
self.bias = None
self.variance_epsilon = eps
self._set_tensor_parallel_attributes()
def _set_tensor_parallel_attributes(self) -> None:
set_tensor_parallel_attribute_by_partition(self.weight, self.depth)
if self.bias is not None:
set_tensor_parallel_attribute_by_partition(self.bias, self.depth)
def reset_parameters(self) -> None:
init.ones_()(self.weight)
if self.bias is not None:
init.zeros_()(self.bias)
def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
local_state = OrderedDict()
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight.transpose(0, 1)
# bias
bias = state_dict.pop(bias_key, None)
if bias is not None:
local_state[bias_key] = bias
# partition in output groups
if gpc.get_local_rank(self.input_parallel_mode) == 0 and \
gpc.get_local_rank(self.weight_parallel_mode) == 0:
local_state = partition_tensor_parallel_state_dict(
local_state,
self.output_parallel_mode,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True,
},
)
# broadcast in input groups
if gpc.get_local_rank(self.weight_parallel_mode) == 0:
local_state = broadcast_state_dict(local_state, self.input_parallel_mode)
# broadcast in weight groups
local_state = broadcast_state_dict(local_state, self.weight_parallel_mode)
super()._load_from_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_state_dict(self, destination, prefix, keep_vars):
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
local_state = OrderedDict({weight_key: self.weight})
if self.bias is not None:
local_state[bias_key] = self.bias
# gather in output groups
if gpc.get_local_rank(self.input_parallel_mode) == 0 and \
gpc.get_local_rank(self.weight_parallel_mode) == 0:
local_state = gather_tensor_parallel_state_dict(
local_state,
self.output_parallel_mode,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
keep_vars=keep_vars,
)
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
destination.update(local_state)
def forward(self, input_: Tensor) -> Tensor:
return layernorm_3d(input_, self.weight, self.bias, self.normalized_shape, self.variance_epsilon,
self.input_parallel_mode, self.weight_parallel_mode, self.output_parallel_mode)
@LAYERS.register_module
class Linear3D(ParallelLayer):
r"""Linear layer for 3D parallelism.
Args:
in_features (int): size of each input sample.
out_features (int): size of each output sample.
bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
weight_initializer (:class:`typing.Callable`, optional):
The initializer of weight, defaults to kaiming uniform initializer.
bias_initializer (:class:`typing.Callable`, optional):
The initializer of bias, defaults to xavier uniform initializer.
More details about ``initializer`` please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(self,
in_features: int,
out_features: int,
bias: bool = True,
dtype: torch.dtype = None,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
self.output_parallel_mode = get_last_group(self.input_parallel_mode, self.weight_parallel_mode)
self.depth = get_depth_from_env()
self.in_features_per_partition = divide(in_features, self.depth)
self.out_features_per_partition = divide(out_features, self.depth**2)
self.bias_features_per_partition = divide(out_features, self.depth)
self.weight = Parameter(
torch.empty(self.in_features_per_partition,
self.out_features_per_partition,
device=get_current_device(),
dtype=dtype))
if bias:
self.bias = Parameter(
torch.zeros(self.bias_features_per_partition, device=get_current_device(), dtype=dtype))
else:
self.bias = None
self.reset_parameters(weight_initializer, bias_initializer)
self._set_tensor_parallel_attributes()
swap_in_out_group()
def _set_tensor_parallel_attributes(self) -> None:
set_tensor_parallel_attribute_by_partition(self.weight, self.depth**3)
if self.bias is not None:
set_tensor_parallel_attribute_by_partition(self.bias, self.depth)
def reset_parameters(self, weight_initializer, bias_initializer) -> None:
with seed(ParallelMode.TENSOR):
fan_in, fan_out = self.in_features, self.out_features
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
if self.bias is not None:
bias_initializer(self.bias, fan_in=fan_in)
weight_src_rank = gpc.get_ranks_in_group(self.weight_parallel_mode)[0]
output_src_rank = gpc.get_ranks_in_group(self.output_parallel_mode)[0]
broadcast(self.bias, weight_src_rank, self.weight_parallel_mode)
broadcast(self.bias, output_src_rank, self.output_parallel_mode)
def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
local_state = OrderedDict()
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight.transpose(0, 1)
# bias
if self.bias is not None:
bias = state_dict.pop(bias_key, None)
if bias is not None:
local_state[bias_key] = bias
# partition in output groups
if gpc.get_local_rank(self.input_parallel_mode) == 0 and \
gpc.get_local_rank(self.weight_parallel_mode) == 0:
local_state = partition_tensor_parallel_state_dict(
local_state,
self.output_parallel_mode,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: False
},
)
# partition in input groups
if gpc.get_local_rank(self.weight_parallel_mode) == 0:
local_state = partition_tensor_parallel_state_dict(
local_state,
self.input_parallel_mode,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
)
# partition in weight groups
local_state = partition_tensor_parallel_state_dict(
local_state,
self.weight_parallel_mode,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: False
},
)
super()._load_from_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_state_dict(self, destination, prefix, keep_vars):
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
local_state = OrderedDict({weight_key: self.weight})
if self.bias is not None:
local_state[bias_key] = self.bias
# gather in weight groups
local_state = gather_tensor_parallel_state_dict(
local_state,
self.weight_parallel_mode,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: False
},
keep_vars=keep_vars,
)
# gather in input groups
if gpc.get_local_rank(self.weight_parallel_mode) == 0:
local_state = gather_tensor_parallel_state_dict(
local_state,
self.input_parallel_mode,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
keep_vars=keep_vars,
)
# gather in output groups
if gpc.get_local_rank(self.input_parallel_mode) == 0 and \
gpc.get_local_rank(self.weight_parallel_mode) == 0:
local_state = gather_tensor_parallel_state_dict(
local_state,
self.output_parallel_mode,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: False
},
keep_vars=keep_vars,
)
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
local_state[weight_key] = local_state[weight_key].transpose(0, 1)
destination.update(local_state)
def forward(self, input_: Tensor) -> Tensor:
return linear_3d(input_, self.weight, self.bias, self.input_parallel_mode, self.weight_parallel_mode,
self.output_parallel_mode)
@LAYERS.register_module
class Classifier3D(ParallelLayer):
r"""Classifier for 3D parallelism.
Args:
in_features (int): size of each input sample.
num_classes (int): number of classes.
weight (:class:`torch.nn.Parameter`, optional): weight of the classifier, defaults to None.
bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
weight_initializer (:class:`typing.Callable`, optional):
The initializer of weight, defaults to kaiming uniform initializer.
bias_initializer (:class:`typing.Callable`, optional):
The initializer of bias, defaults to xavier uniform initializer.
More details about ``initializer`` please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(self,
in_features: int,
num_classes: int,
weight: Parameter = None,
bias: bool = True,
dtype: torch.dtype = None,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
super().__init__()
self.in_features = in_features
self.num_classes = num_classes
self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
self.output_parallel_mode = get_last_group(self.input_parallel_mode, self.weight_parallel_mode)
self.depth = get_depth_from_env()
self.in_features_per_partition = divide(in_features, self.depth)
if weight is not None:
self.weight = weight
self.has_weight = False
else:
self.weight = Parameter(
torch.empty(self.num_classes, self.in_features_per_partition, device=get_current_device(), dtype=dtype))
self.has_weight = True
if bias:
self.bias = Parameter(torch.zeros(self.num_classes, device=get_current_device(), dtype=dtype))
else:
self.bias = None
self.reset_parameters(weight_initializer, bias_initializer)
self._set_tensor_parallel_attributes()
def _set_tensor_parallel_attributes(self) -> None:
if self.has_weight:
set_tensor_parallel_attribute_by_partition(self.weight, self.depth)
def reset_parameters(self, weight_initializer, bias_initializer) -> None:
with seed(ParallelMode.TENSOR):
fan_in, fan_out = self.in_features, self.num_classes
weight_src_rank = gpc.get_ranks_in_group(self.weight_parallel_mode)[0]
output_src_rank = gpc.get_ranks_in_group(self.output_parallel_mode)[0]
input_src_rank = gpc.get_ranks_in_group(self.input_parallel_mode)[0]
if self.has_weight:
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
broadcast(self.weight, weight_src_rank, self.weight_parallel_mode)
if self.bias is not None:
bias_initializer(self.bias, fan_in=fan_in)
broadcast(self.bias, weight_src_rank, self.weight_parallel_mode)
broadcast(self.bias, output_src_rank, self.output_parallel_mode)
broadcast(self.bias, input_src_rank, self.input_parallel_mode)
def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
local_state = OrderedDict()
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
if self.has_weight:
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight
# bias
if self.bias is not None:
bias = state_dict.pop(bias_key, None)
if bias is not None:
local_state[bias_key] = bias
# partition in output groups
if gpc.get_local_rank(self.input_parallel_mode) == 0 and \
gpc.get_local_rank(self.weight_parallel_mode) == 0:
local_state = partition_tensor_parallel_state_dict(
local_state,
self.output_parallel_mode,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: False
},
)
# broadcast in input groups
if gpc.get_local_rank(self.weight_parallel_mode) == 0:
local_state = broadcast_state_dict(local_state, self.input_parallel_mode)
# broadcast in weight groups
local_state = broadcast_state_dict(local_state, self.weight_parallel_mode)
super()._load_from_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_state_dict(self, destination, prefix, keep_vars):
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
local_state = OrderedDict()
if self.has_weight:
local_state[weight_key] = self.weight
if self.bias is not None:
local_state[bias_key] = self.bias
# gather in output groups
if gpc.get_local_rank(self.input_parallel_mode) == 0 and \
gpc.get_local_rank(self.weight_parallel_mode) == 0:
local_state = gather_tensor_parallel_state_dict(
local_state,
self.output_parallel_mode,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: False
},
keep_vars=keep_vars,
)
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
destination.update(local_state)
def forward(self, input_: Tensor) -> Tensor:
return classifier_3d(input_, self.weight, self.bias, self.input_parallel_mode, self.weight_parallel_mode,
self.output_parallel_mode)
@LAYERS.register_module
class VocabParallelClassifier3D(ParallelLayer):
r"""Vocab parallel classifier layer for 3D parallelism.
Args:
in_features (int): size of each input sample.
num_classes (int): number of classes.
weight (:class:`torch.nn.Parameter`, optional): weight of the classifier, defaults to None.
bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
weight_initializer (:class:`typing.Callable`, optional):
The initializer of weight, defaults to kaiming uniform initializer.
bias_initializer (:class:`typing.Callable`, optional):
The initializer of bias, defaults to xavier uniform initializer.
More details about ``initializer`` please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(self,
in_features: int,
num_classes: int,
weight: Parameter = None,
bias: bool = True,
dtype: torch.dtype = None,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
super().__init__()
self.in_features = in_features
self.num_classes = num_classes
self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
self.output_parallel_mode = get_last_group(self.input_parallel_mode, self.weight_parallel_mode)
self.depth = get_depth_from_env()
self.in_features_per_partition = divide(in_features, self.depth)
self.out_features_per_partition = divide(num_classes, self.depth**2)
self.bias_features_per_partition = divide(num_classes, self.depth)
if weight is not None:
self.weight = weight
self.has_weight = False
else:
self.weight = Parameter(
torch.empty(self.out_features_per_partition,
self.in_features_per_partition,
device=get_current_device(),
dtype=dtype))
self.has_weight = True
if bias:
self.bias = Parameter(
torch.zeros(self.bias_features_per_partition, device=get_current_device(), dtype=dtype))
else:
self.bias = None
self.reset_parameters(weight_initializer, bias_initializer)
self._set_tensor_parallel_attributes()
swap_in_out_group()
env.vocab_parallel = True
def _set_tensor_parallel_attributes(self) -> None:
if self.has_weight:
set_tensor_parallel_attribute_by_partition(self.weight, self.depth**3)
if self.bias is not None:
set_tensor_parallel_attribute_by_partition(self.bias, self.depth)
def reset_parameters(self, weight_initializer, bias_initializer) -> None:
with seed(ParallelMode.TENSOR):
fan_in, fan_out = self.in_features, self.num_classes
if self.has_weight:
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
if self.bias is not None:
bias_initializer(self.bias, fan_in=fan_in)
weight_src_rank = gpc.get_ranks_in_group(self.weight_parallel_mode)[0]
output_src_rank = gpc.get_ranks_in_group(self.output_parallel_mode)[0]
broadcast(self.bias, weight_src_rank, self.weight_parallel_mode)
broadcast(self.bias, output_src_rank, self.output_parallel_mode)
def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
local_state = OrderedDict()
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
if self.has_weight:
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight
# bias
if self.bias is not None:
bias = state_dict.pop(bias_key, None)
if bias is not None:
local_state[bias_key] = bias
# partition in output groups
if gpc.get_local_rank(self.input_parallel_mode) == 0 and \
gpc.get_local_rank(self.weight_parallel_mode) == 0:
local_state = partition_tensor_parallel_state_dict(
local_state,
self.output_parallel_mode,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: False
},
)
# partition in input groups
if gpc.get_local_rank(self.weight_parallel_mode) == 0:
local_state = partition_tensor_parallel_state_dict(
local_state,
self.input_parallel_mode,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
)
# partition in weight groups
local_state = partition_tensor_parallel_state_dict(
local_state,
self.weight_parallel_mode,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: False
},
)
super()._load_from_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_state_dict(self, destination, prefix, keep_vars):
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
local_state = OrderedDict({weight_key: self.weight})
if self.bias is not None:
local_state[bias_key] = self.bias
# gather in weight groups
local_state = gather_tensor_parallel_state_dict(
local_state,
self.weight_parallel_mode,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: False
},
keep_vars=keep_vars,
)
# gather in input groups
if gpc.get_local_rank(self.weight_parallel_mode) == 0:
local_state = gather_tensor_parallel_state_dict(
local_state,
self.input_parallel_mode,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
keep_vars=keep_vars,
)
# gather in output groups
if gpc.get_local_rank(self.input_parallel_mode) == 0 and \
gpc.get_local_rank(self.weight_parallel_mode) == 0:
local_state = gather_tensor_parallel_state_dict(
local_state,
self.output_parallel_mode,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: False
},
keep_vars=keep_vars,
)
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
destination.update(local_state)
def forward(self, input_: Tensor) -> Tensor:
return linear_3d(input_, self.weight.transpose(0, 1), self.bias, self.input_parallel_mode,
self.weight_parallel_mode, self.output_parallel_mode)
@LAYERS.register_module
class PatchEmbedding3D(ParallelLayer):
r"""2D Image to Patch Embedding.
Args:
img_size (int): image size.
patch_size (int): patch size.
in_chans (int): number of channels of input image.
embed_size (int): size of embedding.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
flatten (bool, optional): whether to flatten output tensor, defaults to True.
weight_initializer (:class:`typing.Callable`, optional):
The initializer of weight, defaults to kaiming uniform initializer.
bias_initializer (:class:`typing.Callable`, optional):
The initializer of bias, defaults to xavier uniform initializer.
position_embed_initializer (:class:`typing.Callable`, optional):
The initializer of position embedding, defaults to zeros initializer.
More details about ``initializer`` please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(self,
img_size: int,
patch_size: int,
in_chans: int,
embed_size: int,
flatten: bool = True,
dtype: torch.dtype = None,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
position_embed_initializer: Callable = init.zeros_()):
super().__init__()
self.depth = get_depth_from_env()
self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
self.output_parallel_mode = get_last_group(self.input_parallel_mode, self.weight_parallel_mode)
self.patch_size = to_2tuple(patch_size)
grid_size = to_2tuple(img_size // patch_size)
num_patches = grid_size[0] * grid_size[1]
self.embed_size = embed_size
embed_size_per_partition = divide(embed_size, self.depth)
self.flatten = flatten
self.weight = nn.Parameter(
torch.empty((embed_size_per_partition, in_chans, *self.patch_size),
device=get_current_device(),
dtype=dtype))
self.bias = nn.Parameter(torch.empty(embed_size_per_partition, device=get_current_device(), dtype=dtype))
self.cls_token = nn.Parameter(
torch.zeros((1, 1, embed_size_per_partition), device=get_current_device(), dtype=dtype))
self.pos_embed = nn.Parameter(
torch.zeros((1, num_patches + 1, embed_size_per_partition), device=get_current_device(), dtype=dtype))
self.reset_parameters(weight_initializer, bias_initializer, position_embed_initializer)
self._set_tensor_parallel_attributes()
def _set_tensor_parallel_attributes(self) -> None:
set_tensor_parallel_attribute_by_partition(self.weight, self.depth)
set_tensor_parallel_attribute_by_partition(self.bias, self.depth)
set_tensor_parallel_attribute_by_partition(self.cls_token, self.depth)
set_tensor_parallel_attribute_by_partition(self.pos_embed, self.depth)
def _sync_grad_hook(self, grad) -> Tensor:
grad = all_reduce(grad.clone(), self.input_parallel_mode)
grad = all_reduce(grad, self.weight_parallel_mode)
return grad
def reset_parameters(self, weight_initializer, bias_initializer, position_embed_initializer) -> None:
with seed(ParallelMode.TENSOR):
fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
fan_out = self.embed_size
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
bias_initializer(self.bias, fan_in=fan_in)
position_embed_initializer(self.pos_embed)
weight_src_rank = gpc.get_ranks_in_group(self.weight_parallel_mode)[0]
input_src_rank = gpc.get_ranks_in_group(self.input_parallel_mode)[0]
broadcast(self.weight, weight_src_rank, self.weight_parallel_mode)
broadcast(self.bias, weight_src_rank, self.weight_parallel_mode)
broadcast(self.pos_embed, weight_src_rank, self.weight_parallel_mode)
broadcast(self.weight, input_src_rank, self.input_parallel_mode)
broadcast(self.bias, input_src_rank, self.input_parallel_mode)
broadcast(self.pos_embed, input_src_rank, self.input_parallel_mode)
self.weight.register_hook(self._sync_grad_hook)
self.bias.register_hook(self._sync_grad_hook)
self.cls_token.register_hook(self._sync_grad_hook)
self.pos_embed.register_hook(self._sync_grad_hook)
def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
local_state = OrderedDict()
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
cls_token_key = prefix + 'cls_token'
pos_embed_key = prefix + 'pos_embed'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight
# bias
bias = state_dict.pop(bias_key, None)
if bias is not None:
local_state[bias_key] = bias
# cls token
cls_token = state_dict.pop(cls_token_key, None)
if cls_token is not None:
local_state[cls_token_key] = cls_token
# pos embed
pos_embed = state_dict.pop(pos_embed_key, None)
if pos_embed is not None:
local_state[pos_embed_key] = pos_embed
# partition in output groups
if gpc.get_local_rank(self.input_parallel_mode) == 0 and \
gpc.get_local_rank(self.weight_parallel_mode) == 0:
local_state = partition_tensor_parallel_state_dict(
local_state,
self.output_parallel_mode,
dims={
weight_key: 0,
bias_key: 0,
cls_token_key: -1,
pos_embed_key: -1
},
partition_states={
weight_key: True,
bias_key: True,
cls_token_key: True,
pos_embed_key: True
},
)
# broadcast in input groups
if gpc.get_local_rank(self.weight_parallel_mode) == 0:
local_state = broadcast_state_dict(local_state, self.input_parallel_mode)
# broadcast in weight groups
local_state = broadcast_state_dict(local_state, self.weight_parallel_mode)
super()._load_from_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_state_dict(self, destination, prefix, keep_vars):
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
cls_token_key = prefix + 'cls_token'
pos_embed_key = prefix + 'pos_embed'
local_state = OrderedDict({
weight_key: self.weight,
bias_key: self.bias,
cls_token_key: self.cls_token,
pos_embed_key: self.pos_embed
})
# gather in output groups
if gpc.get_local_rank(self.input_parallel_mode) == 0 and \
gpc.get_local_rank(self.weight_parallel_mode) == 0:
local_state = gather_tensor_parallel_state_dict(
local_state,
self.output_parallel_mode,
dims={
weight_key: 0,
bias_key: 0,
cls_token_key: -1,
pos_embed_key: -1
},
partition_states={
weight_key: True,
bias_key: True,
cls_token_key: True,
pos_embed_key: True
},
keep_vars=keep_vars,
)
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
destination.update(local_state)
def forward(self, input_: Tensor) -> Tensor:
input_ = split_tensor_3d(input_, 0, self.weight_parallel_mode)
input_ = split_tensor_3d(input_, 0, self.input_parallel_mode)
output = F.conv2d(input_, self.weight, self.bias, stride=self.patch_size)
if self.flatten:
output = output.flatten(2).transpose(1, 2) # BCHW -> BNC
cls_token = self.cls_token.expand(output.shape[0], -1, -1)
output = torch.cat((cls_token, output), dim=1)
output = output + self.pos_embed
return output
@LAYERS.register_module
class Embedding3D(ParallelLayer):
r"""Embedding for 3D parallelism.
Args:
num_embeddings (int): number of embeddings.
embedding_dim (int): dimension of embedding.
padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient;
therefore, the embedding vector at padding_idx is not updated during training,
i.e. it remains as a fixed “pad”, defaults to None.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
weight_initializer (:class:`typing.Callable`, optional):
he initializer of weight, defaults to normal initializer.
The ``args`` and ``kwargs`` used in :class:``torch.nn.functional.embedding`` should contain:
::
max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is
renormalized to have norm max_norm. Note: this will modify weight in-place.
norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2.
scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse
of frequency of the words in the mini-batch. Default False.
sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False.
More details about ``args`` and ``kwargs`` could be found in
`Embedding <https://pytorch.org/docs/stable/generated/torch.nn.functional.embedding.html#torch.nn.functional.embedding>`_.
More details about initializer please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_
"""
def __init__(self,
num_embeddings: int,
embedding_dim: int,
padding_idx: int = None,
dtype: torch.dtype = None,
weight_initializer: Callable = init.normal_(),
*args,
**kwargs):
super().__init__()
self.depth = get_depth_from_env()
self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
self.output_parallel_mode = get_last_group(self.input_parallel_mode, self.weight_parallel_mode)
self.num_embeddings = num_embeddings
self.embed_dim = embedding_dim
embed_dim_per_partition = divide(embedding_dim, self.depth)
self.padding_idx = padding_idx
self.embed_args = args
self.embed_kwargs = kwargs
self.weight = nn.Parameter(
torch.empty((num_embeddings, embed_dim_per_partition), device=get_current_device(), dtype=dtype))
self.reset_parameters(weight_initializer)
self._set_tensor_parallel_attributes()
def _set_tensor_parallel_attributes(self) -> None:
set_tensor_parallel_attribute_by_partition(self.weight, self.depth)
def reset_parameters(self, weight_initializer) -> None:
with seed(ParallelMode.TENSOR):
fan_in, fan_out = self.num_embeddings, self.embed_dim
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
self._fill_padding_idx_with_zero()
weight_src_rank = gpc.get_ranks_in_group(self.weight_parallel_mode)[0]
broadcast(self.weight, weight_src_rank, self.weight_parallel_mode)
def _fill_padding_idx_with_zero(self) -> None:
if self.padding_idx is not None:
with torch.no_grad():
self.weight[self.padding_idx].fill_(0)
def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
local_state = OrderedDict()
weight_key = prefix + 'weight'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight
# partition in output groups
if gpc.get_local_rank(self.input_parallel_mode) == 0 and \
gpc.get_local_rank(self.weight_parallel_mode) == 0:
local_state = partition_tensor_parallel_state_dict(
local_state,
self.output_parallel_mode,
dims={weight_key: 0},
partition_states={weight_key: True},
)
# broadcast in input groups
if gpc.get_local_rank(self.weight_parallel_mode) == 0:
local_state = broadcast_state_dict(local_state, self.input_parallel_mode)
# broadcast in weight groups
local_state = broadcast_state_dict(local_state, self.weight_parallel_mode)
super()._load_from_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_state_dict(self, destination, prefix, keep_vars):
weight_key = prefix + 'weight'
local_state = OrderedDict({weight_key: self.weight})
# gather in output groups
if gpc.get_local_rank(self.input_parallel_mode) == 0 and \
gpc.get_local_rank(self.weight_parallel_mode) == 0:
local_state = gather_tensor_parallel_state_dict(
local_state,
self.output_parallel_mode,
dims={weight_key: 0},
partition_states={weight_key: True},
keep_vars=keep_vars,
)
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
destination.update(local_state)
def forward(self, input_: Tensor) -> Tensor:
input_ = split_tensor_3d(input_, 0, self.weight_parallel_mode)
input_ = split_tensor_3d(input_, 0, self.input_parallel_mode)
weight = broadcast_weight_3d_from_diagonal(self.weight, self.input_parallel_mode, self.weight_parallel_mode,
self.output_parallel_mode)
output = F.embedding(input_, weight, self.padding_idx, *self.embed_args, **self.embed_kwargs)
return output
@LAYERS.register_module
class VocabParallelEmbedding3D(torch.nn.Module):
r"""Embedding parallelized in the vocabulary dimension.
Args:
num_embeddings (int): number of embeddings.
embedding_dim (int): dimension of embedding.
padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient;
therefore, the embedding vector at padding_idx is not updated during training,
i.e. it remains as a fixed “pad”, defaults to None.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
weight_initializer (:class:`typing.Callable`, optional):
he initializer of weight, defaults to normal initializer.
The ``args`` and ``kwargs`` used in :class:``torch.nn.functional.embedding`` should contain:
::
max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is
renormalized to have norm max_norm. Note: this will modify weight in-place.
norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2.
scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse
of frequency of the words in the mini-batch. Default False.
sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False.
More details about ``args`` and ``kwargs`` could be found in
`Embedding <https://pytorch.org/docs/stable/generated/torch.nn.functional.embedding.html#torch.nn.functional.embedding>`_.
More details about initializer please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(self,
num_embeddings: int,
embedding_dim: int,
padding_idx: int = None,
dtype: torch.dtype = None,
weight_initializer: Callable = init.normal_(),
*args,
**kwargs):
super().__init__()
self.num_embeddings = num_embeddings
self.embed_dim = embedding_dim
self.padding_idx = padding_idx
self.embed_args = args
self.embed_kwargs = kwargs
self.depth = get_depth_from_env()
self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
self.output_parallel_mode = get_last_group(self.input_parallel_mode, self.weight_parallel_mode)
self.num_embeddings_per_partition = divide(self.num_embeddings, self.depth**2)
self.embed_dim_per_partition = divide(self.embed_dim, self.depth)
vocab_parallel_rank = gpc.get_local_rank(self.input_parallel_mode)
self.vocab_start_index = vocab_parallel_rank * self.num_embeddings_per_partition * self.depth
self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition * self.depth
self.weight = Parameter(
torch.empty((self.num_embeddings_per_partition, self.embed_dim_per_partition),
device=get_current_device(),
dtype=dtype))
self.reset_parameters(weight_initializer)
self._set_tensor_parallel_attributes()
env.vocab_parallel = True
def _set_tensor_parallel_attributes(self):
set_tensor_parallel_attribute_by_partition(self.weight, self.depth**3)
def reset_parameters(self, weight_initializer) -> None:
with seed(ParallelMode.TENSOR):
fan_in, fan_out = self.num_embeddings, self.embed_dim
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
self._fill_padding_idx_with_zero()
def _fill_padding_idx_with_zero(self) -> None:
if self.padding_idx is not None and \
self.padding_idx >= self.vocab_start_index and self.padding_idx < self.vocab_end_index:
with torch.no_grad():
self.weight[self.padding_idx - self.vocab_start_index].fill_(0)
def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
local_state = OrderedDict()
weight_key = prefix + 'weight'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight
# partition in output groups
if gpc.get_local_rank(self.input_parallel_mode) == 0 and \
gpc.get_local_rank(self.weight_parallel_mode) == 0:
local_state = partition_tensor_parallel_state_dict(
local_state,
self.output_parallel_mode,
dims={weight_key: -1},
partition_states={weight_key: True},
)
# partition in input groups
if gpc.get_local_rank(self.weight_parallel_mode) == 0:
local_state = partition_tensor_parallel_state_dict(
local_state,
self.input_parallel_mode,
dims={weight_key: 0},
partition_states={weight_key: True},
)
# partition in weight groups
local_state = partition_tensor_parallel_state_dict(
local_state,
self.weight_parallel_mode,
dims={weight_key: 0},
partition_states={weight_key: True},
)
super()._load_from_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_state_dict(self, destination, prefix, keep_vars):
weight_key = prefix + 'weight'
local_state = OrderedDict({weight_key: self.weight})
# gather in weight groups
local_state = gather_tensor_parallel_state_dict(
local_state,
self.weight_parallel_mode,
dims={weight_key: 0},
partition_states={weight_key: True},
keep_vars=keep_vars,
)
# gather in input groups
if gpc.get_local_rank(self.weight_parallel_mode) == 0:
local_state = gather_tensor_parallel_state_dict(
local_state,
self.input_parallel_mode,
dims={weight_key: 0},
partition_states={weight_key: True},
keep_vars=keep_vars,
)
# gather in output groups
if gpc.get_local_rank(self.input_parallel_mode) == 0 and \
gpc.get_local_rank(self.weight_parallel_mode) == 0:
local_state = gather_tensor_parallel_state_dict(
local_state,
self.output_parallel_mode,
dims={weight_key: -1},
partition_states={weight_key: True},
keep_vars=keep_vars,
)
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
destination.update(local_state)
def forward(self, input_: Tensor) -> Tensor:
input_ = split_tensor_3d(input_, 0, self.weight_parallel_mode)
input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index)
masked_input = input_.clone() - self.vocab_start_index
masked_input[input_mask] = 0
weight = all_gather_tensor_3d(self.weight, 0, self.weight_parallel_mode)
output_parallel = F.embedding(masked_input, weight, self.padding_idx, *self.embed_args, **self.embed_kwargs)
output_parallel[input_mask, :] = 0.
output = reduce_scatter_tensor_3d(output_parallel, 0, self.input_parallel_mode)
return output
|
from colossalai.constants import INPUT_GROUP_3D, WEIGHT_GROUP_3D, OUTPUT_GROUP_3D
from colossalai.context.parallel_mode import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.global_variables import tensor_parallel_env as env
from torch import Tensor
def get_depth_from_env() -> int:
try:
depth = env.depth_3d
assert depth > 0, 'DEPTH must be greater than zero'
return depth
except KeyError as e:
raise EnvironmentError('DEPTH is not found in the current environment, '
'please make sure that you have used the correct process group initializer')
def get_parallel_mode_from_env(group):
assert group in [INPUT_GROUP_3D, WEIGHT_GROUP_3D, OUTPUT_GROUP_3D], \
f'{group} is not valid for 3D tensor parallelism.'
return getattr(env, group)
def get_last_group(a, b):
mapping = {
ParallelMode.PARALLEL_3D_INPUT: 'A',
ParallelMode.PARALLEL_3D_WEIGHT: 'B',
ParallelMode.PARALLEL_3D_OUTPUT: 'C',
}
res = chr(ord('A') + ord('B') + ord('C') - ord(mapping[a]) - ord(mapping[b]))
if res == 'A':
return ParallelMode.PARALLEL_3D_INPUT
elif res == 'B':
return ParallelMode.PARALLEL_3D_WEIGHT
elif res == 'C':
return ParallelMode.PARALLEL_3D_OUTPUT
def swap_in_out_group():
env.input_group_3d, env.output_group_3d = env.output_group_3d, env.input_group_3d
def dbg_check_shape(tensor: Tensor, shape: tuple):
rank = gpc.get_global_rank()
if rank == 0:
print(tensor.shape)
assert tensor.shape == shape, \
'{} does not match {}'.format(tensor.shape, shape)
|
from .common import (ACT2FN, CheckpointModule, _ntuple, divide, get_tensor_parallel_mode,
set_tensor_parallel_attribute_by_partition, set_tensor_parallel_attribute_by_size, to_2tuple)
__all__ = [
'CheckpointModule', 'divide', 'ACT2FN', 'set_tensor_parallel_attribute_by_size',
'set_tensor_parallel_attribute_by_partition', 'get_tensor_parallel_mode', '_ntuple', 'to_2tuple'
]
|
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import collections.abc
from itertools import repeat
import numpy as np
import torch
from colossalai.constants import IS_TENSOR_PARALLEL, NUM_PARTITIONS
from colossalai.global_variables import tensor_parallel_env as env
from colossalai.utils import checkpoint
from torch import Tensor, nn
class CheckpointModule(nn.Module):
def __init__(self, checkpoint: bool = True, offload : bool = False):
super().__init__()
self.checkpoint = checkpoint
self._use_checkpoint = checkpoint
self._offload = offload
def _forward(self, *args, **kwargs):
raise NotImplementedError('CheckpointModule should implement _forward method instead of origin forward')
def forward(self, *args, **kwargs):
if self._use_checkpoint:
return checkpoint(self._forward, self._offload, *args, **kwargs)
else:
return self._forward(*args, **kwargs)
def train(self, mode: bool = True):
self._use_checkpoint = self.checkpoint
return super().train(mode=mode)
def eval(self):
self._use_checkpoint = False
return super().eval()
def divide(numerator, denominator):
"""Only allow exact division.
Args:
numerator (int): Numerator of the division.
denominator (int): Denominator of the division.
Returns:
int: the result of exact division.
"""
assert denominator != 0, 'denominator can not be zero'
assert numerator % denominator == 0, \
'{} is not divisible by {}'.format(numerator, denominator)
return numerator // denominator
def swish(x: Tensor) -> Tensor:
return x * torch.sigmoid(x)
ACT2FN = {"gelu": torch.nn.functional.gelu, "relu": torch.nn.functional.relu, "swish": swish}
def set_tensor_parallel_attribute_by_size(param, size):
setattr(param, IS_TENSOR_PARALLEL, True)
setattr(param, NUM_PARTITIONS, size // np.prod(param.shape))
def set_tensor_parallel_attribute_by_partition(param, num_partitions):
setattr(param, IS_TENSOR_PARALLEL, True)
setattr(param, NUM_PARTITIONS, num_partitions)
def get_tensor_parallel_mode():
return env.mode
# From PyTorch internals
def _ntuple(n):
def parse(x):
if isinstance(x, collections.abc.Iterable):
return x
return tuple(repeat(x, n))
return parse
to_2tuple = _ntuple(2)
|
import torch
try:
import fused_mix_prec_layer_norm_cuda
except:
fused_mix_prec_layer_norm_cuda = None
class FusedLayerNormAffineFunction1D(torch.autograd.Function):
r"""Layernorm
Args:
input: input matrix.
weight: weight matrix.
bias: bias matrix.
normalized_shape: input shape from an expected input of size.
:math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1] \times \ldots \times \text{normalized_shape}[-1]]`
If a single integer is used, it is treated as a singleton list, and this module will
normalize over the last dimension which is expected to be of that specific size.
eps: a value added to the denominator for numerical stability
"""
@staticmethod
def forward(ctx, input, weight, bias, normalized_shape, eps):
ctx.normalized_shape = normalized_shape
ctx.eps = eps
input_ = input.contiguous()
weight_ = weight.contiguous()
bias_ = bias.contiguous()
output, mean, invvar = fused_mix_prec_layer_norm_cuda.forward_affine(input_, ctx.normalized_shape, weight_,
bias_, ctx.eps)
ctx.save_for_backward(input_, weight_, bias_, mean, invvar)
return output
@staticmethod
def backward(ctx, grad_output):
input_, weight_, bias_, mean, invvar = ctx.saved_tensors
grad_input = grad_weight = grad_bias = None
grad_input, grad_weight, grad_bias \
= fused_mix_prec_layer_norm_cuda.backward_affine(
grad_output.contiguous(), mean, invvar,
input_, ctx.normalized_shape,
weight_, bias_, ctx.eps)
return grad_input, grad_weight, grad_bias, None, None
|
from .layers import (Classifier1D, Dropout1D, Embedding1D, LayerNorm1D, Linear1D, Linear1D_Col, Linear1D_Row,
PatchEmbedding1D, VocabParallelClassifier1D, VocabParallelEmbedding1D)
__all__ = [
'Linear1D', 'Linear1D_Col', 'Linear1D_Row', 'Embedding1D', 'Dropout1D', 'Classifier1D', 'VocabParallelClassifier1D',
'VocabParallelEmbedding1D', 'LayerNorm1D', 'PatchEmbedding1D'
]
|
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import math
from collections import OrderedDict
from typing import Callable, Tuple
import torch
import torch.nn.functional as F
from colossalai.communication import broadcast
from colossalai.context import ParallelMode, seed
from colossalai.core import global_context as gpc
from colossalai.global_variables import tensor_parallel_env as env
from colossalai.kernel import LayerNorm
from colossalai.nn import init as init
from colossalai.registry import LAYERS
from colossalai.utils.checkpointing import (broadcast_state_dict, gather_tensor_parallel_state_dict,
partition_tensor_parallel_state_dict)
from colossalai.utils.cuda import get_current_device
from torch import Tensor
from torch.nn.parameter import Parameter
from ..vanilla import VanillaPatchEmbedding, VanillaLayerNorm
from ..base_layer import ParallelLayer
from ..colossalai_layer._utils import ColossalaiModule
from ..utils import divide, set_tensor_parallel_attribute_by_partition
from ._utils import (gather_forward_split_backward, get_parallel_input, reduce_grad, reduce_input, set_parallel_input,
split_forward_gather_backward)
@LAYERS.register_module
class Linear1D(ColossalaiModule):
r"""Linear layer for 1D parallelism.
Args:
in_features (int): size of each input sample.
out_features (int): size of each output sample.
bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
gather_output (bool, optional): Whether to call all-gather on output, defaults to False.
skip_bias_add (bool, optional): If set to ``True``, it will skip bias add for linear layer,
which is preserved for kernel fusion, defaults to False
weight_initializer (:class:`typing.Callable`, optional):
The initializer of weight, defaults to kaiming uniform initializer.
bias_initializer (:class:`typing.Callable`, optional):
The initializer of bias, defaults to xavier uniform initializer.
More details about ``initializer`` please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(self,
in_features: int,
out_features: int,
bias: bool = True,
dtype: torch.dtype = None,
gather_output: bool = False,
skip_bias_add: bool = False,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
parallel_input = get_parallel_input()
if not parallel_input:
layer = Linear1D_Col(in_features,
out_features,
bias=bias,
dtype=dtype,
gather_output=gather_output,
skip_bias_add=skip_bias_add,
weight_initializer=weight_initializer,
bias_initializer=bias_initializer)
else:
layer = Linear1D_Row(in_features,
out_features,
bias=bias,
dtype=dtype,
parallel_input=parallel_input,
skip_bias_add=skip_bias_add,
weight_initializer=weight_initializer,
bias_initializer=bias_initializer)
super().__init__(layer)
@LAYERS.register_module
class LayerNorm1D(ColossalaiModule):
r"""
Layer Normalization for colossalai
Args:
normalized_shape (int): input shape from an expected input of size.
:math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1]
\times \ldots \times \text{normalized_shape}[-1]]`
If a single integer is used, it is treated as a singleton list, and this module will
normalize over the last dimension which is expected to be of that specific size.
eps (float): a value added to the denominator for numerical stability, defaults to 1e-05.
bias (bool, optional): Whether to add a bias, defaults to ``True``.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
"""
def __init__(self, normalized_shape: int, eps=1e-05, bias=True, dtype=None):
norm = VanillaLayerNorm(normalized_shape, eps=eps, bias=bias, dtype=dtype)
super().__init__(norm)
def _load_from_state_dict(self, state_dict, prefix, *args):
local_state = OrderedDict()
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight
# bias
bias = state_dict.pop(bias_key, None)
if bias is not None:
local_state[bias_key] = bias
local_state = broadcast_state_dict(local_state, ParallelMode.PARALLEL_1D)
super()._load_from_state_dict(local_state, prefix, *args)
def _save_to_state_dict(self, destination, prefix, keep_vars):
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
super()._save_to_state_dict(destination, prefix, keep_vars)
@LAYERS.register_module
class Classifier1D(ParallelLayer):
r"""RowLinear with given weight. Classifier of 1D parallelism.
Args:
in_features (int): size of each input sample.
num_classes (int): number of classes.
weight (:class:`torch.nn.Parameter`, optional): weight of the classifier, defaults to None.
bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
weight_initializer (:class:`typing.Callable`, optional):
The initializer of weight, defaults to kaiming uniform initializer.
bias_initializer (:class:`typing.Callable`, optional):
The initializer of bias, defaults to xavier uniform initializer.
More details about ``initializer`` please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(self,
in_features: int,
num_classes: int,
weight: Parameter = None,
bias: bool = True,
dtype: torch.dtype = None,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
super().__init__()
self.in_features = in_features
self.num_classes = num_classes
self.parallel_input = get_parallel_input()
# Divide the weight matrix along the last dimension.
self.input_size_per_partition = divide(in_features, gpc.tensor_parallel_size)
# Parameters.
# Initialize weight.
factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
if weight is not None:
self.weight = weight
self.has_weight = False
else:
self.weight = Parameter(torch.empty(self.num_classes, self.input_size_per_partition, **factory_kwargs))
self.has_weight = True
if bias:
self.bias = Parameter(torch.empty(self.num_classes, **factory_kwargs))
else:
self.bias = None
with seed(ParallelMode.TENSOR):
self.reset_parameters(weight_initializer, bias_initializer)
self._set_tensor_parallel_attributes()
set_parallel_input(False)
env.vocab_parallel = False
def reset_parameters(self, weight_initializer, bias_initializer) -> None:
fan_in, fan_out = self.in_features, self.num_classes
if self.has_weight:
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
if self.bias is not None:
bias_initializer(self.bias, fan_in=fan_in)
broadcast(self.bias, gpc.get_ranks_in_group(ParallelMode.PARALLEL_1D)[0], ParallelMode.PARALLEL_1D)
def _set_tensor_parallel_attributes(self):
if self.has_weight:
num_partition = gpc.get_world_size(ParallelMode.TENSOR)
set_tensor_parallel_attribute_by_partition(self.weight, num_partition)
def _load_from_state_dict(self, state_dict, prefix, *args):
local_state = OrderedDict()
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
if self.has_weight:
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight
# bias
if self.bias is not None:
bias = state_dict.pop(bias_key, None)
if bias is not None:
local_state[bias_key] = bias
local_state = partition_tensor_parallel_state_dict(local_state,
ParallelMode.PARALLEL_1D,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: False
})
super()._load_from_state_dict(local_state, prefix, *args)
def _save_to_state_dict(self, destination, prefix, keep_vars):
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
local_state = OrderedDict()
if self.has_weight:
local_state[weight_key] = self.weight
if self.bias is not None:
local_state[bias_key] = self.bias
local_state = gather_tensor_parallel_state_dict(local_state,
ParallelMode.PARALLEL_1D,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: False
},
keep_vars=keep_vars)
destination.update(local_state)
def forward(self, input_: Tensor) -> Tensor:
# Set up backprop all-reduce.
if self.parallel_input:
assert input_.shape[-1] == self.weight.shape[-1], \
'Invalid shapes in Classifier1D forward: input={}, weight={}. Expected last dim of input {}.'.format(
input_.shape, self.weight.shape, self.weight.shape[-1])
input_ = input_
else:
assert divide(input_.shape[-1], gpc.tensor_parallel_size) == self.weight.shape[-1], \
'Invalid shapes in Classifier1D forward: input={}, weight={}. Expected last dim of input {}.'.format(
input_.shape, self.weight.shape, self.weight.shape[-1] * gpc.tensor_parallel_size)
input_ = split_forward_gather_backward(input_, ParallelMode.PARALLEL_1D, dim=-1)
output_parallel = F.linear(input_, self.weight)
output = reduce_input(output_parallel, ParallelMode.PARALLEL_1D)
if self.bias is not None:
output = output + self.bias
return output
@LAYERS.register_module
class VocabParallelClassifier1D(ParallelLayer):
r"""ColLinear with given weight. Classifier of 1D parallelism.
Args:
in_features (int): size of each input sample.
num_classes (int): number of classes.
weight (:class:`torch.nn.Parameter`, optional): weight of the classifier, defaults to None.
bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
weight_initializer (:class:`typing.Callable`, optional):
The initializer of weight, defaults to kaiming uniform initializer.
bias_initializer (:class:`typing.Callable`, optional):
The initializer of bias, defaults to xavier uniform initializer.
More details about ``initializer`` please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(self,
in_features: int,
num_classes: int,
weight: Parameter = None,
bias: bool = True,
dtype: torch.dtype = None,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
super().__init__()
self.in_features = in_features
self.num_classes = num_classes
self.parallel_input = get_parallel_input()
# Divide the weight matrix along the last dimension.
self.num_classes_per_partition = divide(num_classes, gpc.tensor_parallel_size)
# Parameters.
# Initialize weight.
factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
if weight is not None:
self.weight = weight
self.has_weight = False
else:
self.weight = Parameter(torch.empty(self.num_classes_per_partition, self.in_features, **factory_kwargs))
self.has_weight = True
if bias:
self.bias = Parameter(torch.empty(self.num_classes_per_partition, **factory_kwargs))
else:
self.bias = None
with seed(ParallelMode.TENSOR):
self.reset_parameters(weight_initializer, bias_initializer)
self._set_tensor_parallel_attributes()
set_parallel_input(False)
env.vocab_parallel = True
def reset_parameters(self, weight_initializer, bias_initializer) -> None:
fan_in, fan_out = self.in_features, self.num_classes
if self.has_weight:
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
if self.bias is not None:
bias_initializer(self.bias, fan_in=fan_in)
def _set_tensor_parallel_attributes(self):
num_partition = gpc.get_world_size(ParallelMode.TENSOR)
if self.has_weight:
set_tensor_parallel_attribute_by_partition(self.weight, num_partition)
if self.bias is not None:
set_tensor_parallel_attribute_by_partition(self.bias, num_partition)
def _load_from_state_dict(self, state_dict, prefix, *args):
local_state = OrderedDict()
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
if self.has_weight:
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight
# bias
if self.bias is not None:
bias = state_dict.pop(bias_key, None)
if bias is not None:
local_state[bias_key] = bias
local_state = partition_tensor_parallel_state_dict(local_state,
ParallelMode.PARALLEL_1D,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
})
super()._load_from_state_dict(local_state, prefix, *args)
def _save_to_state_dict(self, destination, prefix, keep_vars):
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
local_state = OrderedDict()
if self.has_weight:
local_state[weight_key] = self.weight
if self.bias is not None:
local_state[bias_key] = self.bias
local_state = gather_tensor_parallel_state_dict(local_state,
ParallelMode.PARALLEL_1D,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
keep_vars=keep_vars)
destination.update(local_state)
def forward(self, input_: Tensor) -> Tensor:
assert input_.shape[-1] == self.weight.shape[-1], \
'Invalid shapes in VocabParallelClassifier1D forward: input={}, weight={}. Expected last dim of input {}.'.format(
input_.shape, self.weight.shape, self.weight.shape[-1])
# Set up backprop all-reduce.
input_parallel = reduce_grad(input_, ParallelMode.PARALLEL_1D)
# Matrix multiply.
output = F.linear(input_parallel, self.weight, self.bias)
return output
@LAYERS.register_module
class Linear1D_Col(ParallelLayer):
r"""Linear layer with column parallelism.
The linear layer is defined as :math:`Y = XA + b`. A is parallelized along
its second dimension as :math:`A = [A_1, ..., A_p]`.
Args:
in_features (int): size of each input sample.
out_features (int): size of each output sample.
bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
gather_output (bool, optional): If true, call all-gather on output and make Y available
to all GPUs, otherwise, every GPU will have its output
which is :math:`Y_i = XA_i`, defaults to False
skip_bias_add (bool, optional): If set to ``True``, it will skip bias add for linear layer,
which is preserved for kernel fusion, defaults to Fals
weight_initializer (:class:`typing.Callable`, optional):
The initializer of weight, defaults to kaiming uniform initializer.
bias_initializer (:class:`typing.Callable`, optional):
The initializer of bias, defaults to xavier uniform initializer.
More details about ``initializer`` please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(self,
in_features: int,
out_features: int,
bias: bool = True,
dtype: torch.dtype = None,
gather_output: bool = False,
skip_bias_add: bool = False,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
super().__init__()
# Keep input parameters
self.in_features = in_features
self.out_features = out_features
self.gather_output = gather_output
self.skip_bias_add = skip_bias_add
if skip_bias_add and not bias:
raise ValueError('cannot skip bias addition if bias is None')
self.out_features_per_partition = divide(out_features, gpc.tensor_parallel_size)
# Parameters.
# Initialize weight.
factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
self.weight = Parameter(torch.empty(self.out_features_per_partition, self.in_features, **factory_kwargs))
if bias:
self.bias = Parameter(torch.empty(self.out_features_per_partition, **factory_kwargs))
else:
self.bias = None
with seed(ParallelMode.TENSOR):
self.reset_parameters(weight_initializer, bias_initializer)
self._set_tensor_parallel_attributes()
is_parallel_output = not self.gather_output
set_parallel_input(is_parallel_output)
def reset_parameters(self, weight_initializer, bias_initializer) -> None:
fan_in, fan_out = self.in_features, self.out_features
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
if self.bias is not None:
bias_initializer(self.bias, fan_in=fan_in)
def _set_tensor_parallel_attributes(self):
num_partition = gpc.get_world_size(ParallelMode.TENSOR)
set_tensor_parallel_attribute_by_partition(self.weight, num_partition)
if self.bias is not None:
set_tensor_parallel_attribute_by_partition(self.bias, num_partition)
def _load_from_state_dict(self, state_dict, prefix, *args):
local_state = OrderedDict()
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight
# bias
if self.bias is not None:
bias = state_dict.pop(bias_key, None)
if bias is not None:
local_state[bias_key] = bias
local_state = partition_tensor_parallel_state_dict(local_state,
ParallelMode.PARALLEL_1D,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
})
super()._load_from_state_dict(local_state, prefix, *args)
def _save_to_state_dict(self, destination, prefix, keep_vars):
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
local_state = OrderedDict({weight_key: self.weight})
if self.bias is not None:
local_state[bias_key] = self.bias
local_state = gather_tensor_parallel_state_dict(local_state,
ParallelMode.PARALLEL_1D,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
keep_vars=keep_vars)
destination.update(local_state)
def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]:
assert input_.shape[-1] == self.weight.shape[-1], \
'Invalid shapes in Linear1D_Col forward: input={}, weight={}. Expected last dim of input {}.'.format(
input_.shape, self.weight.shape, self.weight.shape[-1])
# Set up backprop all-reduce.
input_parallel = reduce_grad(input_, ParallelMode.PARALLEL_1D)
# Matrix multiply.
bias = self.bias if not self.skip_bias_add else None
output_parallel = F.linear(input_parallel, self.weight, bias)
if self.gather_output:
# All-gather across the partitions.
output = gather_forward_split_backward(output_parallel, ParallelMode.PARALLEL_1D, dim=-1)
else:
output = output_parallel
if self.skip_bias_add:
return output, self.bias
else:
return output
@LAYERS.register_module
class Linear1D_Row(ParallelLayer):
r""" Linear layer with row parallelism
Args:
in_features (int): size of each input sample.
out_features (int): size of each output sample.
bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
parallel_input (bool, optional): If set to ``True``, it's assumed that the input is split, defaults to False.
skip_bias_add (bool, optional): If set to ``True``, it will skip bias add for linear layer,
which is preserved for kernel fusion, defaults to Fals
weight_initializer (:class:`typing.Callable`, optional):
The initializer of weight, defaults to kaiming uniform initializer.
bias_initializer (:class:`typing.Callable`, optional):
The initializer of bias, defaults to xavier uniform initializer.
More details about ``initializer`` please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(self,
in_features: int,
out_features: int,
bias: bool = True,
dtype: torch.dtype = None,
parallel_input: bool = True,
skip_bias_add: bool = False,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
super().__init__()
# Keep input parameters
self.in_features = in_features
self.out_features = out_features
self.parallel_input = parallel_input
self.skip_bias_add = skip_bias_add
if skip_bias_add and not bias:
raise ValueError('cannot skip bias addition if bias is None')
# Divide the weight matrix along the last dimension.
self.input_size_per_partition = divide(in_features, gpc.tensor_parallel_size)
# Parameters.
# Initialize weight.
factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
self.weight = Parameter(torch.empty(self.out_features, self.input_size_per_partition, **factory_kwargs))
if bias:
self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs))
else:
self.bias = None
with seed(ParallelMode.TENSOR):
self.reset_parameters(weight_initializer, bias_initializer)
self._set_tensor_parallel_attributes()
set_parallel_input(False)
def reset_parameters(self, weight_initializer, bias_initializer) -> None:
fan_in, fan_out = self.in_features, self.out_features
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
if self.bias is not None:
bias_initializer(self.bias, fan_in=fan_in)
broadcast(self.bias, gpc.get_ranks_in_group(ParallelMode.PARALLEL_1D)[0], ParallelMode.PARALLEL_1D)
def _set_tensor_parallel_attributes(self):
num_partition = gpc.get_world_size(ParallelMode.TENSOR)
set_tensor_parallel_attribute_by_partition(self.weight, num_partition)
def _load_from_state_dict(self, state_dict, prefix, *args):
local_state = OrderedDict()
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight
# bias
if self.bias is not None:
bias = state_dict.pop(bias_key, None)
if bias is not None:
local_state[bias_key] = bias
local_state = partition_tensor_parallel_state_dict(local_state,
ParallelMode.PARALLEL_1D,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: False
})
super()._load_from_state_dict(local_state, prefix, *args)
def _save_to_state_dict(self, destination, prefix, keep_vars):
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
local_state = OrderedDict({weight_key: self.weight})
if self.bias is not None:
local_state[bias_key] = self.bias
local_state = gather_tensor_parallel_state_dict(local_state,
ParallelMode.PARALLEL_1D,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: False
},
keep_vars=keep_vars)
destination.update(local_state)
def forward(self, input_: Tensor) -> Tensor:
# Set up backprop all-reduce.
if self.parallel_input:
assert input_.shape[-1] == self.weight.shape[-1], \
'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format(
input_.shape, self.weight.shape, self.weight.shape[-1])
input_ = input_
else:
assert divide(input_.shape[-1], gpc.tensor_parallel_size) == self.weight.shape[-1], \
'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format(
input_.shape, self.weight.shape, self.weight.shape[-1] * gpc.tensor_parallel_size)
input_ = split_forward_gather_backward(input_, ParallelMode.PARALLEL_1D, dim=-1)
output_parallel = F.linear(input_, self.weight)
output = reduce_input(output_parallel, ParallelMode.PARALLEL_1D)
if not self.skip_bias_add:
if self.bias is not None:
output = output + self.bias
return output
else:
return output, self.bias
@LAYERS.register_module
class Embedding1D(ParallelLayer):
r"""Embedding for 1D parallelism.
Args:
num_embeddings (int): number of embeddings.
embedding_dim (int): dimension of embedding.
padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient;
therefore, the embedding vector at padding_idx is not updated during training,
i.e. it remains as a fixed “pad”, defaults to None.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
weight_initializer (:class:`typing.Callable`, optional):
he initializer of weight, defaults to normal initializer.
The ``args`` and ``kwargs`` used in :class:`torch.nn.functional.embedding` should contain:
::
max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is
renormalized to have norm max_norm. Note: this will modify weight in-place.
norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2.
scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse
of frequency of the words in the mini-batch. Default False.
sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False.
More details about ``args`` and ``kwargs`` could be found in
`Embedding <https://pytorch.org/docs/stable/generated/torch.nn.functional.embedding.html#torch.nn.functional.embedding>`_.
More details about ``initializer`` please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_
"""
def __init__(self,
num_embeddings: int,
embedding_dim: int,
padding_idx: int = None,
dtype: torch.dtype = None,
weight_initializer: Callable = init.normal_(),
*args,
**kwargs):
super().__init__()
self.num_embeddings = num_embeddings
self.embed_dim = embedding_dim
embed_dim_per_partition = divide(embedding_dim, gpc.tensor_parallel_size)
self.padding_idx = padding_idx
self.embed_args = args
self.embed_kwargs = kwargs
self.weight = Parameter(
torch.empty((num_embeddings, embed_dim_per_partition), device=get_current_device(), dtype=dtype))
self.reset_parameters(weight_initializer)
self._set_tensor_parallel_attributes()
set_parallel_input(False)
def _set_tensor_parallel_attributes(self):
set_tensor_parallel_attribute_by_partition(self.weight, gpc.tensor_parallel_size)
def reset_parameters(self, weight_initializer) -> None:
with seed(ParallelMode.TENSOR):
fan_in, fan_out = self.num_embeddings, self.embed_dim
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
self._fill_padding_idx_with_zero()
def _fill_padding_idx_with_zero(self) -> None:
if self.padding_idx is not None:
with torch.no_grad():
self.weight[self.padding_idx].fill_(0)
def _load_from_state_dict(self, state_dict, prefix, *args):
local_state = OrderedDict()
weight_key = prefix + 'weight'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight
local_state = partition_tensor_parallel_state_dict(local_state,
ParallelMode.PARALLEL_1D,
dims={weight_key: -1},
partition_states={weight_key: True})
super()._load_from_state_dict(local_state, prefix, *args)
def _save_to_state_dict(self, destination, prefix, keep_vars):
weight_key = prefix + 'weight'
local_state = OrderedDict({weight_key: self.weight})
local_state = gather_tensor_parallel_state_dict(local_state,
ParallelMode.PARALLEL_1D,
dims={weight_key: -1},
partition_states={weight_key: True},
keep_vars=keep_vars)
destination.update(local_state)
def forward(self, input_: Tensor) -> Tensor:
output_parallel = F.embedding(input_, self.weight, self.padding_idx, *self.embed_args, **self.embed_kwargs)
output = gather_forward_split_backward(output_parallel, ParallelMode.PARALLEL_1D, dim=-1)
return output
@LAYERS.register_module
class VocabParallelEmbedding1D(torch.nn.Module):
r"""Embedding parallelized in the vocabulary dimension.
Args:
num_embeddings (int): number of embeddings.
embedding_dim (int): dimension of embedding.
padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient;
therefore, the embedding vector at padding_idx is not updated during training,
i.e. it remains as a fixed “pad”, defaults to None.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
weight_initializer (:class:`typing.Callable`, optional):
he initializer of weight, defaults to normal initializer.
The ``args`` and ``kwargs`` used in :class:``torch.nn.functional.embedding`` should contain:
::
max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is
renormalized to have norm max_norm. Note: this will modify weight in-place.
norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2.
scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse
of frequency of the words in the mini-batch. Default False.
sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False.
More details about ``args`` and ``kwargs`` could be found in
`Embedding <https://pytorch.org/docs/stable/generated/torch.nn.functional.embedding.html#torch.nn.functional.embedding>`_.
More details about initializer please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(self,
num_embeddings: int,
embedding_dim: int,
padding_idx: int = None,
dtype: torch.dtype = None,
weight_initializer: Callable = init.normal_(),
*args,
**kwargs):
super().__init__()
self.num_embeddings = num_embeddings
self.embed_dim = embedding_dim
self.padding_idx = padding_idx
self.embed_args = args
self.embed_kwargs = kwargs
tensor_parallel_size = gpc.get_world_size(ParallelMode.PARALLEL_1D)
tensor_parallel_rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
self.num_embeddings_per_partition = divide(num_embeddings, tensor_parallel_size)
self.vocab_start_index = tensor_parallel_rank * self.num_embeddings_per_partition
self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition
self.weight = Parameter(
torch.empty((self.num_embeddings_per_partition, self.embed_dim), device=get_current_device(), dtype=dtype))
self.reset_parameters(weight_initializer)
self._set_tensor_parallel_attributes()
set_parallel_input(False)
env.vocab_parallel = True
def _set_tensor_parallel_attributes(self):
set_tensor_parallel_attribute_by_partition(self.weight, gpc.tensor_parallel_size)
def reset_parameters(self, weight_initializer) -> None:
with seed(ParallelMode.TENSOR):
fan_in, fan_out = self.num_embeddings, self.embed_dim
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
self._fill_padding_idx_with_zero()
def _fill_padding_idx_with_zero(self) -> None:
if self.padding_idx is not None and \
self.padding_idx >= self.vocab_start_index and self.padding_idx < self.vocab_end_index:
with torch.no_grad():
self.weight[self.padding_idx - self.vocab_start_index].fill_(0)
def _load_from_state_dict(self, state_dict, prefix, *args):
local_state = OrderedDict()
weight_key = prefix + 'weight'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight
local_state = partition_tensor_parallel_state_dict(local_state,
ParallelMode.PARALLEL_1D,
dims={weight_key: 0},
partition_states={weight_key: True})
super()._load_from_state_dict(local_state, prefix, *args)
def _save_to_state_dict(self, destination, prefix, keep_vars):
weight_key = prefix + 'weight'
local_state = OrderedDict({weight_key: self.weight})
local_state = gather_tensor_parallel_state_dict(local_state,
ParallelMode.PARALLEL_1D,
dims={weight_key: 0},
partition_states={weight_key: True},
keep_vars=keep_vars)
destination.update(local_state)
def forward(self, input_: Tensor) -> Tensor:
# Build the mask.
input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index)
# Mask the input.
masked_input = input_.clone() - self.vocab_start_index
masked_input[input_mask] = 0
output_parallel = F.embedding(masked_input, self.weight, self.padding_idx, *self.embed_args,
**self.embed_kwargs)
# Mask the output embedding.
output_parallel[input_mask, :] = 0.
# Reduce across all the model parallel GPUs.
output = reduce_input(output_parallel, ParallelMode.PARALLEL_1D)
return output
@LAYERS.register_module
class Dropout1D(ParallelLayer):
"""Dropout layer of 1D parallelism.
Args:
p (float, optional): probability of an element to be zeroed, defaults 0.5.
inplace (bool, optional): whether to do dropout in-place, default to be False.
"""
def __init__(self, p: float = 0.5, inplace: bool = False):
super().__init__()
self.parallel_input = get_parallel_input()
self.p = p
self.inplace = inplace
def forward(self, input_: Tensor) -> Tensor:
if self.parallel_input:
with seed(ParallelMode.TENSOR):
output = F.dropout(input_, self.p, self.training, self.inplace)
else:
output = F.dropout(input_, self.p, self.training, self.inplace)
return output
@LAYERS.register_module
class PatchEmbedding1D(ColossalaiModule):
"""
2D Image to Patch Embedding
:param img_size: image size
:type img_size: int
:param patch_size: patch size
:type patch_size: int
:param in_chans: number of channels of input image
:type in_chans: int
:param embed_size: size of embedding
:type embed_size: int
:param dtype: The dtype of parameters, defaults to None
:type dtype: torch.dtype, optional
:param flatten: whether to flatten output tensor, defaults to True
:type flatten: bool, optional
:param weight_initializer: The intializer of weight, defaults to kaiming uniform initializer
:type weight_initializer: typing.Callable, optional
:param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
:type bias_initializer: typing.Callable, optional
:param position_embed_initializer: The intializer of position embedding, defaults to zero
:type position_embed_initializer: typing.Callable, optional
"""
def __init__(self,
img_size: int,
patch_size: int,
in_chans: int,
embed_size: int,
dtype: torch.dtype = None,
flatten: bool = True,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
position_embed_initializer: Callable = init.zeros_()):
embed = VanillaPatchEmbedding(img_size,
patch_size,
in_chans,
embed_size,
dtype=dtype,
flatten=flatten,
weight_initializer=weight_initializer,
bias_initializer=bias_initializer,
position_embed_initializer=position_embed_initializer)
super().__init__(embed)
def _load_from_state_dict(self, state_dict, prefix, *args):
local_state = OrderedDict()
param_keys = [prefix + 'weight', prefix + 'bias', prefix + 'cls_token', prefix + 'pos_embed']
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
for key in param_keys:
param = state_dict.pop(key, None)
if param is not None:
local_state[key] = param
local_state = broadcast_state_dict(local_state, ParallelMode.PARALLEL_1D)
super()._load_from_state_dict(local_state, prefix, *args)
def _save_to_state_dict(self, destination, prefix, keep_vars):
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
super()._save_to_state_dict(destination, prefix, keep_vars)
|
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import torch
import torch.distributed as dist
from colossalai.core import global_context as gpc
from colossalai.global_variables import tensor_parallel_env as env
from ..utils import divide
def set_parallel_input(input_parallel: bool):
env.parallel_input_1d = input_parallel
def get_parallel_input():
return env.parallel_input_1d
def vocab_range_from_per_partition_vocab_size(per_partition_vocab_size, rank):
index_f = rank * per_partition_vocab_size
index_l = index_f + per_partition_vocab_size
return index_f, index_l
def vocab_range_from_global_vocab_size(global_vocab_size, rank, world_size):
per_partition_vocab_size = divide(global_vocab_size, world_size)
return vocab_range_from_per_partition_vocab_size(per_partition_vocab_size, rank)
def _reduce(input_, parallel_mode):
# skip if only one rank involved
if gpc.get_world_size(parallel_mode) == 1:
return input_
dist.all_reduce(input_, group=gpc.get_group(parallel_mode))
return input_
def _split(input_, parallel_mode, dim=-1):
# skip if only one rank involved
world_size = gpc.get_world_size(parallel_mode)
if world_size == 1:
return input_
# Split along last dimension.
dim_size = input_.size(dim)
assert dim_size % world_size == 0, \
f'The dimension to split ({dim_size}) is not a multiple of world size ({world_size}), ' \
f'cannot split tensor evenly'
tensor_list = torch.split(input_, dim_size // world_size, dim=dim)
rank = gpc.get_local_rank(parallel_mode)
output = tensor_list[rank].contiguous()
return output
def _gather(input_, parallel_mode, dim=-1):
# skip if only one rank involved
world_size = gpc.get_world_size(parallel_mode)
if world_size == 1:
return input_
# all gather
rank = gpc.get_local_rank(parallel_mode)
tensor_list = [torch.empty_like(input_) for _ in range(world_size)]
tensor_list[rank] = input_
torch.distributed.all_gather(tensor_list, input_, group=gpc.get_group(parallel_mode))
# concat
output = torch.cat(tensor_list, dim=dim).contiguous()
return output
class _ReduceGrad(torch.autograd.Function):
"""
Pass the input to the model parallel region.
Args:
input_: input matrix.
parallel_mode: parallel mode.
"""
@staticmethod
def symbolic(graph, input_):
return input_
@staticmethod
def forward(ctx, input_, parallel_mode):
ctx.mode = parallel_mode
return input_
@staticmethod
def backward(ctx, grad_output):
return _reduce(grad_output, ctx.mode), None
class _ReduceInput(torch.autograd.Function):
"""
All-reduce the input from the model parallel region.
Args:
input_: input matrix.
parallel_mode: parallel mode.
"""
@staticmethod
def symbolic(graph, input_):
return _reduce(input_)
@staticmethod
def forward(ctx, input_, parallel_mode):
return _reduce(input_, parallel_mode)
@staticmethod
def backward(ctx, grad_output):
return grad_output, None
class _SplitForwardGatherBackward(torch.autograd.Function):
"""
Split the input and keep only the corresponding chuck to the rank.
Args:
input_: input matrix.
parallel_mode: parallel mode.
dim: dimension
"""
@staticmethod
def symbolic(graph, input_):
return _split(input_)
@staticmethod
def forward(ctx, input_, parallel_mode, dim):
ctx.mode = parallel_mode
ctx.dim = dim
return _split(input_, parallel_mode, dim)
@staticmethod
def backward(ctx, grad_output):
return _gather(grad_output, ctx.mode, ctx.dim), None, None
class _GatherForwardSplitBackward(torch.autograd.Function):
"""Gather the input from model parallel region and concatenate.
Args:
input_: input matrix.
parallel_mode: parallel mode.
dim: dimension
"""
@staticmethod
def symbolic(graph, input_):
return _gather(input_)
@staticmethod
def forward(ctx, input_, parallel_mode, dim):
ctx.mode = parallel_mode
ctx.dim = dim
return _gather(input_, parallel_mode, dim)
@staticmethod
def backward(ctx, grad_output):
return _split(grad_output, ctx.mode, ctx.dim), None, None
def reduce_grad(input_, parallel_mode):
return _ReduceGrad.apply(input_, parallel_mode)
def reduce_input(input_, parallel_mode):
return _ReduceInput.apply(input_, parallel_mode)
def split_forward_gather_backward(input_, parallel_mode, dim):
return _SplitForwardGatherBackward.apply(input_, parallel_mode, dim)
def gather_forward_split_backward(input_, parallel_mode, dim):
return _GatherForwardSplitBackward.apply(input_, parallel_mode, dim)
|
from typing import Any, Optional, Tuple
import torch
import torch.distributed as dist
from colossalai.communication.collective import (all_gather, all_reduce, reduce, reduce_scatter)
from colossalai.context.parallel_mode import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.utils import get_current_device
from torch import Tensor
from torch.cuda.amp import custom_bwd, custom_fwd
from colossalai.global_variables import tensor_parallel_env as env
def matmul_2d(
a,
b,
summa_dim,
out_shape,
row_rank=None,
col_rank=None,
row_parallel_mode=ParallelMode.PARALLEL_2D_ROW,
col_parallel_mode=ParallelMode.PARALLEL_2D_COL,
):
r"""Matrix multiplication for 2D parallelism.
Args:
a (:class:`torch.tensor`): matrix :math:`A`.
b (:class:`torch.tensor`): matrix :math:`B`.
summa_dim (int): dimension of SUMMA fo 2D parallelism.
out_shape (:class:`torch.size`): shape of output tensor.
row_rank (int, optional): the rank of row, defaults to None.
col_rank (int, optional): the rank of column, defaults to None.
row_parallel_mode (:class:`colossalai.context.ParallelMode`, optional):
row parallel mode, defaults to ParallelMode.PARALLEL_2D_ROW.
col_parallel_mode (:class:`colossalai.context.ParallelMode`, optional):
column parallel mode, defaults to ParallelMode.PARALLEL_2D_COL.
Returns:
:class:`torch.tensor`: :math:`C = AB`.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
if row_rank is None:
row_rank = gpc.get_local_rank(col_parallel_mode)
if col_rank is None:
col_rank = gpc.get_local_rank(row_parallel_mode)
data_parallel_rank = 0 if not gpc.is_initialized(ParallelMode.DATA) else gpc.get_local_rank(ParallelMode.DATA)
pipeline_parallel_rank = 0 if not gpc.is_initialized(ParallelMode.PIPELINE) else gpc.get_local_rank(
ParallelMode.PIPELINE)
pipeline_parallel_size = 1 if not gpc.is_initialized(ParallelMode.PIPELINE) else gpc.get_world_size(
ParallelMode.PIPELINE)
tensor_parallel_size = summa_dim**2
return Matmul_AB_2D(a, b, summa_dim, out_shape, row_rank, col_rank, row_parallel_mode, col_parallel_mode,
data_parallel_rank, pipeline_parallel_rank, pipeline_parallel_size, tensor_parallel_size)
class _Classifier2D(torch.autograd.Function):
@staticmethod
@custom_fwd(cast_inputs=torch.float16)
def forward(
ctx: Any,
A: Tensor,
B: Tensor,
bias: Optional[Tensor],
summa_dim: int,
out_shape: Tuple[int, ...],
row_rank: int,
col_rank: int,
row_parallel_mode: ParallelMode,
col_parallel_mode: ParallelMode,
data_parallel_rank: int,
pipeline_parallel_rank: int,
pipeline_parallel_size: int,
tensor_parallel_size: int,
) -> Tensor:
A = A.clone().detach()
A_shape = A.shape
A = A.reshape((-1, A_shape[-1]))
B_shape = B.shape
B = B.reshape((-1, B_shape[-1]))
B_temp = all_gather(B, -1, col_parallel_mode)
if ctx:
ctx.save_for_backward(A, B_temp)
C = torch.matmul(A, B_temp.transpose(0, 1))
C = all_reduce(C, row_parallel_mode)
ctx.use_bias = bias is not None
if bias is not None:
C = C + bias
out = C.reshape(out_shape)
if ctx:
ctx.summa_dim = summa_dim
ctx.row_rank = row_rank
ctx.col_rank = col_rank
ctx.row_parallel_mode = row_parallel_mode
ctx.col_parallel_mode = col_parallel_mode
ctx.A_shape = A_shape
ctx.B_shape = B_shape
ctx.data_parallel_rank = data_parallel_rank
ctx.pipeline_parallel_rank = pipeline_parallel_rank
ctx.pipeline_parallel_size = pipeline_parallel_size
ctx.tensor_parallel_size = tensor_parallel_size
return out
@staticmethod
@custom_bwd
def backward(ctx: Any, output_grad: Tensor) -> Tuple[Tensor, ...]:
A, B = ctx.saved_tensors
with torch.no_grad():
A_grad = torch.matmul(output_grad, B)
A_grad = A_grad.reshape(ctx.A_shape)
B_grad = torch.matmul(output_grad.reshape(-1, output_grad.shape[-1]).transpose(0, 1), A)
B_grad = reduce_scatter(B_grad, -1, ctx.col_parallel_mode)
B_grad = B_grad.reshape(ctx.B_shape)
if ctx.use_bias:
bias_grad = torch.sum(output_grad, dim=tuple(range(output_grad.ndim - 1)))
bias_grad = all_reduce(bias_grad, ctx.col_parallel_mode)
else:
bias_grad = None
return A_grad, B_grad, bias_grad, None, None, None, None, None, None, None, None, None, None
def classifier_2d(A: Tensor, B: Tensor, bias: Optional[Tensor], summa_dim: int, out_shape: Tuple[int, ...],
row_rank: int, col_rank: int, row_parallel_mode: ParallelMode, col_parallel_mode: ParallelMode,
data_parallel_rank: int, pipeline_parallel_rank: int, pipeline_parallel_size: int,
tensor_parallel_size: int) -> Tensor:
r"""2D parallel classifier.
Args:
A (:class:`torch.tensor`): matrix :math:`A`.
B (:class:`torch.tensor`): matrix :math:`B`.
bias (:class:`torch.tensor`, optional): matrix of bias.
summa_dim (int): dimension of SUMMA fo 2D parallelism.
out_shape (:class:`torch.size`): shape of output tensor.
row_rank (int, optional): the rank of row, defaults to None.
col_rank (int, optional): the rank of column, defaults to None.
row_parallel_mode (:class:`colossalai.context.ParallelMode`): row parallel mode.
col_parallel_mode (:class:`colossalai.context.ParallelMode`): column parallel mode.
data_parallel_rank (int): data parallel rank.
pipeline_parallel_rank (int): pipeline parallel rank
pipeline_parallel_size (int): pipeline parallel size.
tensor_parallel_size (int): tensor parallel size.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
return _Classifier2D.apply(A, B, bias, summa_dim, out_shape, row_rank, col_rank, row_parallel_mode,
col_parallel_mode, data_parallel_rank, pipeline_parallel_rank, pipeline_parallel_size,
tensor_parallel_size)
class Matmul_AB_2D(torch.autograd.Function):
r"""Matrix multiplication for :math:`C = AB`.
Args:
A (:class:`torch.tensor`): matrix :math:`A`.
B (:class:`torch.tensor`): matrix :math:`B`.
summa_dim (int): dimension of SUMMA fo 2D parallelism.
out_shape (:class:`torch.size`): shape of output tensor.
row_rank (int, optional): the rank of row, defaults to None.
col_rank (int, optional): the rank of column, defaults to None.
row_parallel_mode (:class:`colossalai.context.ParallelMode`): row parallel mode.
col_parallel_mode (:class:`colossalai.context.ParallelMode`): column parallel mode.
data_parallel_rank (int): data parallel rank.
pipeline_parallel_rank (int): pipeline parallel rank
pipeline_parallel_size (int): pipeline parallel size.
tensor_parallel_size (int): tensor parallel size.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
@staticmethod
@custom_fwd(cast_inputs=torch.float16)
def forward(
ctx: Any,
A: Tensor,
B: Tensor,
summa_dim: int,
out_shape: Tuple[int, ...],
row_rank: int,
col_rank: int,
row_parallel_mode: ParallelMode,
col_parallel_mode: ParallelMode,
data_parallel_rank: int,
pipeline_parallel_rank: int,
pipeline_parallel_size: int,
tensor_parallel_size: int,
) -> Tensor:
# A: [b / q, s, h / q] -> [(b * s) / q, h / q]
# B: [h / q, s / q]
# C: [b / q, s, s / q] -> [(b * s) / q, s / q]
assert A.shape[-1] == B.shape[-2], \
'Invalid shapes: A={}, B={} for AB.'.format(A.shape, B.shape)
if ctx:
ctx.save_for_backward(A, B)
A_shape = A.shape
A = A.reshape((-1, A_shape[-1]))
B_shape = B.shape
B = B.reshape((-1, B_shape[-1]))
C_shape = (A.shape[0], B.shape[-1])
C = torch.zeros(C_shape, dtype=A.dtype, device=get_current_device())
# use circular buffer to store the communication tensor
# 2 is enough for all cases
A_list = [torch.empty_like(A) for _ in range(2)]
B_list = [torch.empty_like(B) for _ in range(2)]
row_group = gpc.get_group(row_parallel_mode)
col_group = gpc.get_group(col_parallel_mode)
src_a = summa_dim * row_rank + data_parallel_rank * pipeline_parallel_size * tensor_parallel_size + \
pipeline_parallel_rank * tensor_parallel_size
src_b = col_rank + data_parallel_rank * pipeline_parallel_size * tensor_parallel_size + \
pipeline_parallel_rank * tensor_parallel_size
opa = [None] * 2
opb = [None] * 2
A_list[0].copy_(A)
B_list[0].copy_(B)
opa[0] = dist.broadcast(A_list[0], src=src_a, group=row_group, async_op=True)
opb[0] = dist.broadcast(B_list[0], src=src_b, group=col_group, async_op=True)
cur = 0
for i in range(summa_dim):
if i != summa_dim - 1:
A_list[1 - cur].copy_(A)
opa[1 - cur] = dist.broadcast(A_list[1 - cur], src=src_a + 1, group=row_group, async_op=True)
B_list[1 - cur].copy_(B)
opb[1 - cur] = dist.broadcast(B_list[1 - cur], src=src_b + summa_dim, group=col_group, async_op=True)
if opa[cur] is not None:
opa[cur].wait()
if opb[cur] is not None:
opb[cur].wait()
torch.addmm(C, A_list[cur], B_list[cur], out=C)
cur = 1 - cur
src_a += 1
src_b += summa_dim
out = C.reshape(out_shape)
if ctx:
ctx.summa_dim = summa_dim
ctx.row_rank = row_rank
ctx.col_rank = col_rank
ctx.row_parallel_mode = row_parallel_mode
ctx.col_parallel_mode = col_parallel_mode
ctx.A_shape = A_shape
ctx.B_shape = B_shape
ctx.data_parallel_rank = data_parallel_rank
ctx.pipeline_parallel_rank = pipeline_parallel_rank
ctx.pipeline_parallel_size = pipeline_parallel_size
ctx.tensor_parallel_size = tensor_parallel_size
return out
@staticmethod
@custom_bwd
def backward(ctx: Any, output_grad: Tensor) -> Tuple[Tensor, ...]:
A, B = ctx.saved_tensors
with torch.no_grad():
A_grad = Matmul_ABT_2D.apply(output_grad, B, ctx.summa_dim, ctx.A_shape, ctx.row_rank, ctx.col_rank,
ctx.row_parallel_mode, ctx.col_parallel_mode, ctx.data_parallel_rank,
ctx.pipeline_parallel_rank, ctx.pipeline_parallel_size,
ctx.tensor_parallel_size)
B_grad = Matmul_ATB_2D.apply(A, output_grad, ctx.summa_dim, ctx.B_shape, ctx.row_rank, ctx.col_rank,
ctx.row_parallel_mode, ctx.col_parallel_mode, ctx.data_parallel_rank,
ctx.pipeline_parallel_rank, ctx.pipeline_parallel_size,
ctx.tensor_parallel_size)
return A_grad, B_grad, None, None, None, None, None, None, None, None, None, None
class Matmul_ABT_2D(torch.autograd.Function):
r"""Matrix multiplication for :math:`C = AB^T`
Args:
A (:class:`torch.tensor`): matrix :math:`A`.
B (:class:`torch.tensor`): matrix :math:`B`.
summa_dim (int): dimension of SUMMA fo 2D parallelism.
out_shape (:class:`torch.size`): shape of output tensor.
row_rank (int, optional): the rank of row, defaults to None.
col_rank (int, optional): the rank of column, defaults to None.
row_parallel_mode (:class:`colossalai.context.ParallelMode`): row parallel mode.
col_parallel_mode (:class:`colossalai.context.ParallelMode`): column parallel mode.
column parallel mode, defaults to ParallelMode.PARALLEL_2D_COL.
data_parallel_rank (int): data parallel rank.
pipeline_parallel_rank (int): pipeline parallel rank
pipeline_parallel_size (int): pipeline parallel size.
tensor_parallel_size (int): tensor parallel size.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_.
"""
@staticmethod
@custom_fwd(cast_inputs=torch.float16)
def forward(
ctx: Any,
A: Tensor,
B: Tensor,
summa_dim: int,
out_shape: Tuple[int, ...],
row_rank: int,
col_rank: int,
row_parallel_mode: ParallelMode,
col_parallel_mode: ParallelMode,
data_parallel_rank: int,
pipeline_parallel_rank: int,
pipeline_parallel_size: int,
tensor_parallel_size: int,
) -> Tensor:
assert A.shape[-1] == B.shape[-1], \
'Invalid shapes: A={}, B={} for ABT.'.format(A.shape, B.shape)
if ctx:
ctx.save_for_backward(A, B)
A_shape = A.shape
A = A.reshape((-1, A_shape[-1]))
B_shape = B.shape
B = B.reshape((-1, B_shape[-1]))
C_shape = (A.shape[0], B.shape[0])
C = torch.empty(C_shape, dtype=A.dtype, device=get_current_device())
# use circular buffer to store the communication tensor
# 2 is enough for all cases
B_list = [torch.empty_like(B) for _ in range(2)]
C_list = [torch.empty_like(C) for _ in range(2)]
row_group = gpc.get_group(row_parallel_mode)
col_group = gpc.get_group(col_parallel_mode)
src_b = col_rank + data_parallel_rank * pipeline_parallel_size * tensor_parallel_size + \
pipeline_parallel_rank * tensor_parallel_size
src_c = summa_dim * row_rank + data_parallel_rank * pipeline_parallel_size * tensor_parallel_size + \
pipeline_parallel_rank * tensor_parallel_size
opb = [None] * 2
opr = [None] * 2
B_list[0].copy_(B)
opb[0] = dist.broadcast(B_list[0], src=src_b, group=col_group, async_op=True)
cur = 0
for i in range(summa_dim):
if i != summa_dim - 1:
B_list[1 - cur].copy_(B)
opb[1 - cur] = dist.broadcast(B_list[1 - cur], src=src_b + summa_dim, group=col_group, async_op=True)
if opr[cur] is not None:
opr[cur].wait()
if i - 2 == col_rank:
C.copy_(C_list[cur])
if opb[cur] is not None:
opb[cur].wait()
torch.matmul(A, B_list[cur].transpose(0, 1), out=C_list[cur])
opr[cur] = dist.reduce(C_list[cur], dst=src_c, group=row_group, async_op=True)
cur = 1 - cur
src_b += summa_dim
src_c += 1
for op in opr:
op.wait()
if summa_dim - 2 == col_rank:
C.copy_(C_list[cur])
if summa_dim - 1 == col_rank:
C.copy_(C_list[1 - cur])
out = C.reshape(out_shape)
if ctx:
ctx.summa_dim = summa_dim
ctx.row_rank = row_rank
ctx.col_rank = col_rank
ctx.row_parallel_mode = row_parallel_mode
ctx.col_parallel_mode = col_parallel_mode
ctx.A_shape = A_shape
ctx.B_shape = B_shape
ctx.data_parallel_rank = data_parallel_rank
ctx.pipeline_parallel_rank = pipeline_parallel_rank
ctx.pipeline_parallel_size = pipeline_parallel_size
ctx.tensor_parallel_size = tensor_parallel_size
return out
@staticmethod
@custom_bwd
def backward(ctx: Any, output_grad: Tensor) -> Tuple[Tensor, ...]:
A, B = ctx.saved_tensors
with torch.no_grad():
A_grad = Matmul_AB_2D.apply(output_grad, B, ctx.summa_dim, ctx.A_shape, ctx.row_rank, ctx.col_rank,
ctx.row_parallel_mode, ctx.col_parallel_mode, ctx.data_parallel_rank,
ctx.pipeline_parallel_rank, ctx.pipeline_parallel_size,
ctx.tensor_parallel_size)
B_grad = Matmul_ATB_2D.apply(output_grad, A, ctx.summa_dim, ctx.B_shape, ctx.row_rank, ctx.col_rank,
ctx.row_parallel_mode, ctx.col_parallel_mode, ctx.data_parallel_rank,
ctx.pipeline_parallel_rank, ctx.pipeline_parallel_size,
ctx.tensor_parallel_size)
return A_grad, B_grad, None, None, None, None, None, None, None, None, None, None
class Matmul_ATB_2D(torch.autograd.Function):
r"""Matrix multiplication for :math:`C = A^TB`.
Args:
A (:class:`torch.tensor`): matrix :math:`A`.
B (:class:`torch.tensor`): matrix :math:`B`.
summa_dim (int): dimension of SUMMA fo 2D parallelism.
out_shape (:class:`torch.size`): shape of output tensor.
row_rank (int, optional): the rank of row, defaults to None.
col_rank (int, optional): the rank of column, defaults to None.
row_parallel_mode (:class:`colossalai.context.ParallelMode`): row parallel mode.
col_parallel_mode (:class:`colossalai.context.ParallelMode`): column parallel mode.
data_parallel_rank (int): data parallel rank.
pipeline_parallel_rank (int): pipeline parallel rank
pipeline_parallel_size (int): pipeline parallel size.
tensor_parallel_size (int): tensor parallel size.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_.
"""
@staticmethod
@custom_fwd(cast_inputs=torch.float16)
def forward(
ctx: Any,
A: Tensor,
B: Tensor,
summa_dim: int,
out_shape: Tuple[int, ...],
row_rank: int,
col_rank: int,
row_parallel_mode: ParallelMode,
col_parallel_mode: ParallelMode,
data_parallel_rank: int,
pipeline_parallel_rank: int,
pipeline_parallel_size: int,
tensor_parallel_size: int,
) -> Tensor:
assert A.shape[-2] == B.shape[-2], \
'Invalid shapes: A={}, B={} for ATB.'.format(A.shape, B.shape)
if ctx:
ctx.save_for_backward(A, B)
A_shape = A.shape
A = A.reshape((-1, A_shape[-1]))
B_shape = B.shape
B = B.reshape((-1, B_shape[-1]))
C_shape = (A.shape[-1], B.shape[-1])
C = torch.empty(C_shape, dtype=A.dtype, device=get_current_device())
# use circular buffer to store the communication tensor
# 2 is enough for all cases
A_list = [torch.empty_like(A) for _ in range(2)]
C_list = [torch.empty_like(C) for _ in range(2)]
row_group = gpc.get_group(row_parallel_mode)
col_group = gpc.get_group(col_parallel_mode)
src_a = summa_dim * row_rank + data_parallel_rank * pipeline_parallel_size * tensor_parallel_size + \
pipeline_parallel_rank * tensor_parallel_size
src_c = col_rank + data_parallel_rank * pipeline_parallel_size * tensor_parallel_size + \
pipeline_parallel_rank * tensor_parallel_size
opa = [None] * 2
opr = [None] * 2
A_list[0].copy_(A)
opa[0] = dist.broadcast(A_list[0], src=src_a, group=row_group, async_op=True)
cur = 0
for i in range(summa_dim):
if i != summa_dim - 1:
A_list[1 - cur].copy_(A)
opa[1 - cur] = dist.broadcast(A_list[1 - cur], src=src_a + 1, group=row_group, async_op=True)
if opr[cur] is not None:
opr[cur].wait()
if i - 2 == row_rank:
C.copy_(C_list[cur])
if opa[cur] is not None:
opa[cur].wait()
torch.matmul(A_list[cur].transpose(0, 1), B, out=C_list[cur])
opr[cur] = dist.reduce(C_list[cur], dst=src_c, group=col_group, async_op=True)
cur = 1 - cur
src_a += 1
src_c += summa_dim
for op in opr:
op.wait()
if summa_dim - 2 == row_rank:
C.copy_(C_list[cur])
if summa_dim - 1 == row_rank:
C.copy_(C_list[1 - cur])
out = C.reshape(out_shape)
if ctx:
ctx.summa_dim = summa_dim
ctx.row_rank = row_rank
ctx.col_rank = col_rank
ctx.row_parallel_mode = row_parallel_mode
ctx.col_parallel_mode = col_parallel_mode
ctx.A_shape = A_shape
ctx.B_shape = B_shape
ctx.data_parallel_rank = data_parallel_rank
ctx.pipeline_parallel_rank = pipeline_parallel_rank
ctx.pipeline_parallel_size = pipeline_parallel_size
ctx.tensor_parallel_size = tensor_parallel_size
return out
@staticmethod
@custom_bwd
def backward(ctx: Any, output_grad: Tensor) -> Tuple[Tensor, ...]:
A, B = ctx.saved_tensors
with torch.no_grad():
A_grad = Matmul_ABT_2D.apply(B, output_grad, ctx.summa_dim, ctx.A_shape, ctx.row_rank, ctx.col_rank,
ctx.row_parallel_mode, ctx.col_parallel_mode, ctx.data_parallel_rank,
ctx.pipeline_parallel_rank, ctx.pipeline_parallel_size,
ctx.tensor_parallel_size)
B_grad = Matmul_AB_2D.apply(A, output_grad, ctx.summa_dim, ctx.B_shape, ctx.row_rank, ctx.col_rank,
ctx.row_parallel_mode, ctx.col_parallel_mode, ctx.data_parallel_rank,
ctx.pipeline_parallel_rank, ctx.pipeline_parallel_size,
ctx.tensor_parallel_size)
return A_grad, B_grad, None, None, None, None, None, None, None, None, None, None
class _Add_Bias_2D(torch.autograd.Function):
@staticmethod
@custom_fwd(cast_inputs=torch.float16)
def forward(
ctx: Any,
input_: Tensor,
bias: Tensor,
output_size_per_partition: int,
row_rank: int,
col_rank: int,
row_parallel_mode: ParallelMode,
col_parallel_mode: ParallelMode,
skip_bias_add: bool,
data_parallel_rank: int,
pipeline_parallel_rank: int,
pipeline_parallel_size: int,
tensor_parallel_size: int,
) -> Tensor:
bias_temp = all_gather(bias, -1, col_parallel_mode)
ctx.row_rank = row_rank
ctx.col_rank = col_rank
ctx.row_parallel_mode = row_parallel_mode
ctx.col_parallel_mode = col_parallel_mode
ctx.bias = skip_bias_add
ctx.data_parallel_rank = data_parallel_rank
ctx.pipeline_parallel_rank = pipeline_parallel_rank
ctx.pipeline_parallel_size = pipeline_parallel_size
ctx.tensor_parallel_size = tensor_parallel_size
if skip_bias_add:
return bias_temp
else:
output = input_ + bias_temp
return output
@staticmethod
@custom_bwd
def backward(ctx: Any, output_grad: Tensor) -> Tuple[Tensor, ...]:
col_parallel_mode = ctx.col_parallel_mode
if ctx.bias:
grad = reduce_scatter(output_grad, -1, col_parallel_mode)
return None, grad, None, None, None, None, None, None, None, None, None, None
else:
reduce_dim = tuple(range(output_grad.ndim - 1))
reduce = torch.sum(output_grad, dim=reduce_dim)
grad = reduce_scatter(reduce, -1, col_parallel_mode)
return output_grad, grad, None, None, None, None, None, None, None, None, None, None
def add_bias_2d(input_: Tensor, bias: Tensor, output_size_per_partition: int, row_rank: int, col_rank: int,
row_parallel_mode: ParallelMode, col_parallel_mode: ParallelMode, skip_bias_add: bool,
data_parallel_rank: int, pipeline_parallel_rank: int, pipeline_parallel_size: int,
tensor_parallel_size: int) -> Tensor:
r"""Matrix add bias: :math:`C = A + b`.
Args:
input_ (:class:`torch.tensor`): matrix :math:`A`.
bias (:class:`torch.tensor`): matrix :math:`B`.
output_size_per_partition (int): size of output per partition.
row_rank (int, optional): the rank of row, defaults to None.
col_rank (int, optional): the rank of column, defaults to None.
row_parallel_mode (:class:`colossalai.context.ParallelMode`): row parallel mode.
col_parallel_mode (:class:`colossalai.context.ParallelMode`): column parallel mode.
skip_bias_add (bool):
If set to ``True``, it will skip bias add for linear layer, which is preserved for kernel fusion.
data_parallel_rank (int): data parallel rank.
pipeline_parallel_rank (int): pipeline parallel rank
pipeline_parallel_size (int): pipeline parallel size.
tensor_parallel_size (int): tensor parallel size.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
return _Add_Bias_2D.apply(input_, bias, output_size_per_partition, row_rank, col_rank, row_parallel_mode,
col_parallel_mode, skip_bias_add, data_parallel_rank, pipeline_parallel_rank,
pipeline_parallel_size, tensor_parallel_size)
class _Layernorm_2D(torch.autograd.Function):
@staticmethod
@custom_fwd(cast_inputs=torch.float32)
def forward(ctx: Any, input_: Tensor, E_x: Tensor, Var_x: Tensor, hidden_size: int, row_parallel_mode: ParallelMode,
col_parallel_mode: ParallelMode) -> Tensor:
input_ = input_ - E_x
# in here, input = x - E[x], Var_x = 1 / sqrt(Var[x] + eps)
ctx.normalized_shape = hidden_size
output = input_ * Var_x
ctx.save_for_backward(output, Var_x)
ctx.row_parallel_mode = row_parallel_mode
ctx.col_parallel_mode = col_parallel_mode
return output
@staticmethod
@custom_bwd
def backward(ctx: Any, output_grad: Tensor) -> Tuple[Tensor, ...]:
row_parallel_mode = ctx.row_parallel_mode
col_parallel_mode = ctx.col_parallel_mode
x, Var_x = ctx.saved_tensors
# in here, Var_x = 1 / sqrt(Var[x] + eps), x = (x - E[x]) * Var_x
output_grad_sum = torch.sum(output_grad, dim=-1, keepdim=True)
torch.distributed.all_reduce(output_grad_sum, group=gpc.get_group(row_parallel_mode))
output_grad_sum /= ctx.normalized_shape
output_grad_mul_x_sum = torch.sum(output_grad * x, dim=-1, keepdim=True)
torch.distributed.all_reduce(output_grad_mul_x_sum, group=gpc.get_group(row_parallel_mode))
output_grad_mul_x_sum /= ctx.normalized_shape
input_grad = output_grad.clone()
input_grad -= x * output_grad_mul_x_sum
input_grad -= output_grad_sum
input_grad *= Var_x
return input_grad, None, None, None, None, None
def layernorm_2d(input_: Tensor, E_x: Tensor, Var_x: Tensor, hidden_size: int, row_parallel_mode: ParallelMode,
col_parallel_mode: ParallelMode) -> Tensor:
r"""Layernorm.
Args:
input_ (:class:`torch.tensor`): input matrix.
E_x (:class:`torch.tensor`): mean.
Var_x (:class:`torch.tensor`): variance.
hidden_size (int): hidden size.
row_parallel_mode (:class:`colossalai.context.ParallelMode`): row parallel mode.
col_parallel_mode (:class:`colossalai.context.ParallelMode`): column parallel mode.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
return _Layernorm_2D.apply(input_, E_x, Var_x, hidden_size, row_parallel_mode, col_parallel_mode)
class _AllGatherTensor2D(torch.autograd.Function):
@staticmethod
@custom_fwd(cast_inputs=torch.float16)
def forward(ctx: Any, inputs: Tensor, dim: int, parallel_mode: ParallelMode) -> Tensor:
ctx.dim = dim
ctx.parallel_mode = parallel_mode
outputs = all_gather(inputs, dim, parallel_mode)
return outputs
@staticmethod
@custom_bwd
def backward(ctx: Any, output_grad: Tensor) -> Tuple[Tensor, ...]:
grad = reduce_scatter(output_grad, ctx.dim, ctx.parallel_mode)
return grad.contiguous(), None, None
def all_gather_tensor_2d(tensor: Tensor, dim: int, parallel_mode: ParallelMode) -> Tensor:
r"""All gather the tensor of 2D parallelism.
Args:
tensor (:class:`torch.tensor`): Input tensor.
dim (int): Dimension to gather.
parallel_mode (:class:`colossalai.context.ParallelMode`): The parallel mode tensor used.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
return _AllGatherTensor2D.apply(tensor, dim, parallel_mode)
def split_batch_2d(input_: Tensor, dim: int = 0) -> Tensor:
"""Splits 2D tensor in specified dimension across cols.
Args:
input_ (:class:`torch.tensor`): Input tensor.
dim (int): Specified dimension in which to split.
Returns:
:class:`torch.tensor`: The tensor has been split.
"""
dim_size = input_.size(dim)
world_size = gpc.get_world_size(ParallelMode.PARALLEL_2D_COL)
if world_size <= 1:
return input_
assert dim_size % world_size == 0, \
f'The batch size ({dim_size}) is not a multiple of 2D size ({world_size}).'
return torch.chunk(input_, gpc.get_world_size(ParallelMode.PARALLEL_2D_COL),
dim=dim)[gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)].contiguous()
class _ReduceTensor2D(torch.autograd.Function):
@staticmethod
def forward(ctx, input_, parallel_mode):
return all_reduce(input_, parallel_mode)
@staticmethod
def backward(ctx, output_grad):
return output_grad, None
def reduce_tensor_2d(input_: Tensor, parallel_mode: ParallelMode) -> Tensor:
r"""All-reduce the input.
Args:
input_ (:class:`torch.tensor`): Input tensor.
parallel_mode (:class:`colossalai.context.ParallelMode`): The parallel mode tensor used.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
return _ReduceTensor2D.apply(input_, parallel_mode)
class _ReduceScatterTensor2D(torch.autograd.Function):
@staticmethod
def forward(ctx, input_, dim, parallel_mode):
ctx.dim = dim
ctx.parallel_mode = parallel_mode
return reduce_scatter(input_, dim, parallel_mode)
@staticmethod
def backward(ctx, output_grad):
return all_gather(output_grad, ctx.dim, ctx.parallel_mode), None, None
def reduce_scatter_tensor_2d(tensor: Tensor, dim: int, parallel_mode: ParallelMode) -> Tensor:
r"""Reduce-scatter the input.
Args:
tensor (:class:`torch.tensor`): Input tensor.
dim (int): Dimension to reduce.
parallel_mode (:class:`colossalai.context.ParallelMode`): The parallel mode tensor used.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
dim_size = tensor.size(dim)
world_size = gpc.get_world_size(parallel_mode)
assert dim_size % world_size == 0, \
f'The batch size ({dim_size}) is not a multiple of 2D size ({world_size}).'
return _ReduceScatterTensor2D.apply(tensor, dim, parallel_mode)
class _ReduceByBatch2D(torch.autograd.Function):
@staticmethod
def symbolic(graph, input_, reduce_mean: bool = False):
output = all_reduce(input_, ParallelMode.PARALLEL_2D_COL)
if reduce_mean:
reduce_size = gpc.get_world_size(ParallelMode.PARALLEL_2D_COL)
return output / reduce_size
return output
@staticmethod
@custom_fwd(cast_inputs=torch.float32)
def forward(ctx, input_, reduce_mean: bool = False):
output = all_reduce(input_, ParallelMode.PARALLEL_2D_COL)
ctx.reduce_mean = reduce_mean
if reduce_mean:
reduce_size = gpc.get_world_size(ParallelMode.PARALLEL_2D_COL)
ctx.reduce_size = reduce_size
return output.clone() / reduce_size
return output.clone()
@staticmethod
@custom_bwd
def backward(ctx, output_grad):
if ctx.reduce_mean:
return output_grad / ctx.reduce_size, None
else:
return output_grad, None
def reduce_by_batch_2d(input_, reduce_mean: bool = False) -> Tensor:
r"""All-reduce the input from the model parallel region.
Args:
input_ (:class:`torch.tensor`): input matrix.
reduce_mean (bool, optional):
If set to ``True``, it will divide the output by column parallel size, default to False.
"""
return _ReduceByBatch2D.apply(input_, reduce_mean)
|
from ._operation import reduce_by_batch_2d, split_batch_2d
from .layers import (Classifier2D, Embedding2D, LayerNorm2D, Linear2D, PatchEmbedding2D, VocabParallelClassifier2D,
VocabParallelEmbedding2D)
__all__ = [
'split_batch_2d', 'reduce_by_batch_2d', 'Linear2D', 'LayerNorm2D', 'Classifier2D', 'PatchEmbedding2D',
'Embedding2D', 'VocabParallelEmbedding2D', 'VocabParallelClassifier2D'
]
|
import math
from collections import OrderedDict
from typing import Callable
import torch
import torch.nn as nn
import torch.nn.functional as F
from colossalai.communication import broadcast
from colossalai.context import ParallelMode, seed
from colossalai.core import global_context as gpc
from colossalai.global_variables import tensor_parallel_env as env
from colossalai.nn import init as init
from colossalai.registry import LAYERS
from colossalai.utils.checkpointing import gather_tensor_parallel_state_dict, partition_tensor_parallel_state_dict
from colossalai.utils.cuda import get_current_device
from torch import Tensor
from torch.nn import Parameter
from ..base_layer import ParallelLayer
from ..utils import divide, set_tensor_parallel_attribute_by_partition, to_2tuple
from ._operation import (Matmul_AB_2D, Matmul_ABT_2D, add_bias_2d, all_gather_tensor_2d, classifier_2d, layernorm_2d,
reduce_scatter_tensor_2d, split_batch_2d)
from ._utils import assert_summa_initialization, get_summa_dim_from_env
@LAYERS.register_module
class Linear2D(ParallelLayer):
r"""Linear layer for 2D parallelism
Args:
in_features (int): size of each input sample.
out_features (int): size of each output sample.
bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
skip_bias_add (bool, optional): If set to ``True``, it will skip bias add for linear layer,
which is preserved for kernel fusion, defaults to False.
weight_initializer (:class:`typing.Callable`, optional):
The initializer of weight, defaults to kaiming uniform initializer.
bias_initializer (:class:`typing.Callable`, optional):
The initializer of bias, defaults to xavier uniform initializer.
More details about ``initializer`` please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(self,
in_features: int,
out_features: int,
bias: bool = True,
dtype: torch.dtype = None,
skip_bias_add: bool = False,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.skip_bias_add = skip_bias_add
# parallel settings
assert_summa_initialization()
self.row_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)
self.col_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)
self.summa_dim = get_summa_dim_from_env()
# partitioning dimension
self.input_size_per_partition = divide(self.in_features, self.summa_dim)
self.hidden_size_per_partition = divide(self.out_features, self.summa_dim)
# create weight, shape: [k/q, h/q]
factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
self.weight = Parameter(
torch.empty(self.input_size_per_partition, self.hidden_size_per_partition, **factory_kwargs))
# create bias, shape: [h/q]
if bias:
self.bias = Parameter(torch.empty(divide(self.out_features, self.summa_dim**2), **factory_kwargs))
else:
self.register_parameter('bias', None)
# initialize parameters
with seed(ParallelMode.TENSOR):
self.reset_parameters(weight_initializer, bias_initializer)
self._set_tensor_parallel_attributes()
def _set_tensor_parallel_attributes(self):
set_tensor_parallel_attribute_by_partition(self.weight, self.summa_dim**2)
if self.bias is not None:
set_tensor_parallel_attribute_by_partition(self.bias, self.summa_dim**2)
def reset_parameters(self, weight_initializer, bias_initializer) -> None:
fan_in, fan_out = self.in_features, self.out_features
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
if self.bias is not None:
bias_initializer(self.bias, fan_in=fan_in)
def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
local_state = OrderedDict()
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight.transpose(0, 1)
# bias
if self.bias is not None:
bias = state_dict.pop(bias_key, None)
if bias is not None:
local_state[bias_key] = bias
# partition in row groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_ROW,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
)
# partition in column groups
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_COL,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
)
super()._load_from_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_state_dict(self, destination, prefix, keep_vars):
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
local_state = OrderedDict({weight_key: self.weight})
if self.bias is not None:
local_state[bias_key] = self.bias
# gather in column groups
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_COL,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
keep_vars=keep_vars,
)
# gather in row groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_ROW,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
keep_vars=keep_vars,
)
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
local_state[weight_key] = local_state[weight_key].transpose(0, 1)
destination.update(local_state)
def forward(self, x: Tensor) -> Tensor:
# input: [m/q, n/q, k/q]
# output: [m/q, n/q, h/q]
out_shape = x.shape[:-1] + (self.hidden_size_per_partition, )
output = Matmul_AB_2D.apply(x, self.weight, self.summa_dim, out_shape, self.row_rank, self.col_rank,
ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL, self.data_parallel_rank,
self.pipeline_parallel_rank, self.pipeline_parallel_size, self.tensor_parallel_size)
if self.bias is not None:
if self.skip_bias_add:
bias = add_bias_2d(None, self.bias, self.hidden_size_per_partition, self.row_rank, self.col_rank,
ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL, True,
self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
self.tensor_parallel_size)
return output, bias
else:
output = add_bias_2d(output, self.bias, self.hidden_size_per_partition, self.row_rank, self.col_rank,
ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL, False,
self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
self.tensor_parallel_size)
return output
else:
return output
@LAYERS.register_module
class LayerNorm2D(ParallelLayer):
r"""Layer Normalization for 2D parallelism.
Args:
normalized_shape (int): input shape from an expected input of size.
:math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1]
\times \ldots \times \text{normalized_shape}[-1]]`
If a single integer is used, it is treated as a singleton list, and this module will
normalize over the last dimension which is expected to be of that specific size.
eps (float, optional): a value added to the denominator for numerical stability, defaults to 1e-05.
bias (bool, optional): Whether to add a bias, defaults to ``True``.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
"""
def __init__(self, normalized_shape: int, eps: float = 1e-05, bias=True, dtype=None):
super().__init__()
# layer norm config
self.normalized_shape = normalized_shape
self.variance_epsilon = eps
# parallel setting
assert_summa_initialization()
self.row_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)
self.col_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)
self.summa_dim = get_summa_dim_from_env()
# partitioning dimension
self.partitioned_partition = divide(normalized_shape, self.summa_dim**2)
# create parameters
factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
self.weight = Parameter(torch.ones(self.partitioned_partition, **factory_kwargs))
if bias:
self.bias = Parameter(torch.zeros(self.partitioned_partition, **factory_kwargs))
else:
self.bias = None
self._set_tensor_parallel_attributes()
def _set_tensor_parallel_attributes(self):
set_tensor_parallel_attribute_by_partition(self.weight, self.summa_dim**2)
if self.bias is not None:
set_tensor_parallel_attribute_by_partition(self.bias, self.summa_dim**2)
def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
local_state = OrderedDict()
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight
# bias
bias = state_dict.pop(bias_key, None)
if bias is not None:
local_state[bias_key] = bias
# partition in row groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_ROW,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
)
# partition in column groups
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_COL,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
)
super()._load_from_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_state_dict(self, destination, prefix, keep_vars):
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
local_state = OrderedDict({weight_key: self.weight})
if self.bias is not None:
local_state[bias_key] = self.bias
# gather in column groups
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_COL,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
keep_vars=keep_vars,
)
# gather in row groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_ROW,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
keep_vars=keep_vars,
)
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
destination.update(local_state)
def forward(self, x: Tensor) -> Tensor:
with torch.no_grad():
E_x = torch.sum(x, dim=-1, keepdim=True) # [b/q, s, 1]
torch.distributed.all_reduce(E_x, group=gpc.get_group(ParallelMode.PARALLEL_2D_ROW))
E_x /= self.normalized_shape
# Var_x in the block below is the sum of input^2
Var_x = torch.sum(x * x, dim=-1, keepdim=True) # [b/q, s, 1]
torch.distributed.all_reduce(Var_x, group=gpc.get_group(ParallelMode.PARALLEL_2D_ROW))
Var_x /= self.normalized_shape
Var_x = Var_x - E_x * E_x # variance of x [b/q, s, 1]
# this time 1/sqrt(Var_x + epsilon)
Var_x = 1.0 / torch.sqrt(Var_x + self.variance_epsilon)
output = layernorm_2d(x, E_x, Var_x, self.normalized_shape, ParallelMode.PARALLEL_2D_ROW,
ParallelMode.PARALLEL_2D_COL)
scale = add_bias_2d(None, self.weight, self.partitioned_partition, self.row_rank, self.col_rank,
ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL, True, self.data_parallel_rank,
self.pipeline_parallel_rank, self.pipeline_parallel_size, self.tensor_parallel_size)
if self.bias is not None:
bias = add_bias_2d(None, self.bias, self.partitioned_partition, self.row_rank, self.col_rank,
ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL, True,
self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
self.tensor_parallel_size)
output = torch.addcmul(bias, scale, output)
else:
output = torch.mul(scale, output)
return output
@LAYERS.register_module
class PatchEmbedding2D(ParallelLayer):
r"""2D Image to Patch Embedding.
Args:
img_size (int): image size.
patch_size (int): patch size.
in_chans (int): number of channels of input image.
embed_size (int): size of embedding.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
flatten (bool, optional): whether to flatten output tensor, defaults to True.
weight_initializer (:class:`typing.Callable`, optional):
The initializer of weight, defaults to kaiming uniform initializer.
bias_initializer (:class:`typing.Callable`, optional):
The initializer of bias, defaults to xavier uniform initializer.
position_embed_initializer (:class:`typing.Callable`, optional):
The initializer of position embedding, defaults to zeros initializer.
More details about ``initializer`` please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(self,
img_size: int,
patch_size: int,
in_chans: int,
embed_size: int,
flatten: bool = True,
dtype: torch.dtype = None,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
position_embed_initializer: Callable = init.zeros_()):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
assert_summa_initialization()
self.summa_dim = get_summa_dim_from_env()
self.img_size = img_size
self.patch_size = patch_size
self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
self.num_patches = self.grid_size[0] * self.grid_size[1]
self.flatten = flatten
self.embed_size = embed_size
self.embed_size_per_partition = embed_size // (self.summa_dim**2)
with seed(ParallelMode.TENSOR):
self.weight = Parameter(
torch.empty((self.embed_size_per_partition, in_chans, *self.patch_size),
device=get_current_device(),
dtype=dtype))
self.bias = Parameter(torch.empty(self.embed_size_per_partition, device=get_current_device(), dtype=dtype))
self.cls_token = Parameter(
torch.zeros((1, 1, self.embed_size_per_partition), device=get_current_device(), dtype=dtype))
self.pos_embed = Parameter(
torch.zeros((1, self.num_patches + 1, self.embed_size_per_partition),
device=get_current_device(),
dtype=dtype))
self.reset_parameters(weight_initializer, bias_initializer, position_embed_initializer)
self._set_tensor_parallel_attribute()
def _set_tensor_parallel_attribute(self):
set_tensor_parallel_attribute_by_partition(self.weight, self.summa_dim**2)
set_tensor_parallel_attribute_by_partition(self.bias, self.summa_dim**2)
set_tensor_parallel_attribute_by_partition(self.cls_token, self.summa_dim**2)
set_tensor_parallel_attribute_by_partition(self.pos_embed, self.summa_dim**2)
def reset_parameters(self, weight_initializer, bias_initializer, position_embed_initializer):
with seed(ParallelMode.TENSOR):
fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
fan_out = self.embed_size
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
bias_initializer(self.bias, fan_in=fan_in)
position_embed_initializer(self.pos_embed)
def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
local_state = OrderedDict()
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
cls_token_key = prefix + 'cls_token'
pos_embed_key = prefix + 'pos_embed'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight
# bias
bias = state_dict.pop(bias_key, None)
if bias is not None:
local_state[bias_key] = bias
# cls token
cls_token = state_dict.pop(cls_token_key, None)
if cls_token is not None:
local_state[cls_token_key] = cls_token
# pos embed
pos_embed = state_dict.pop(pos_embed_key, None)
if pos_embed is not None:
local_state[pos_embed_key] = pos_embed
# partition in row groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_ROW,
dims={
weight_key: 0,
bias_key: 0,
cls_token_key: -1,
pos_embed_key: -1
},
partition_states={
weight_key: True,
bias_key: True,
cls_token_key: True,
pos_embed_key: True
},
)
# partition in column groups
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_COL,
dims={
weight_key: 0,
bias_key: 0,
cls_token_key: -1,
pos_embed_key: -1
},
partition_states={
weight_key: True,
bias_key: True,
cls_token_key: True,
pos_embed_key: True
},
)
super()._load_from_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_state_dict(self, destination, prefix, keep_vars):
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
cls_token_key = prefix + 'cls_token'
pos_embed_key = prefix + 'pos_embed'
local_state = OrderedDict({
weight_key: self.weight,
bias_key: self.bias,
cls_token_key: self.cls_token,
pos_embed_key: self.pos_embed
})
# gather in column groups
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_COL,
dims={
weight_key: 0,
bias_key: 0,
cls_token_key: -1,
pos_embed_key: -1
},
partition_states={
weight_key: True,
bias_key: True,
cls_token_key: True,
pos_embed_key: True
},
keep_vars=keep_vars,
)
# gather in row groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_ROW,
dims={
weight_key: 0,
bias_key: 0,
cls_token_key: -1,
pos_embed_key: -1
},
partition_states={
weight_key: True,
bias_key: True,
cls_token_key: True,
pos_embed_key: True
},
keep_vars=keep_vars,
)
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
destination.update(local_state)
def forward(self, input_: Tensor) -> Tensor:
input_ = split_batch_2d(input_)
B, C, H, W = input_.shape
assert H == self.img_size[0] and W == self.img_size[1], \
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
weight = all_gather_tensor_2d(self.weight, 0, ParallelMode.PARALLEL_2D_COL)
bias = all_gather_tensor_2d(self.bias, 0, ParallelMode.PARALLEL_2D_COL)
output = F.conv2d(input_, weight, bias, stride=self.patch_size)
if self.flatten:
output = output.flatten(2).transpose(1, 2) # BCHW -> BNC
cls_token = all_gather_tensor_2d(self.cls_token, -1, ParallelMode.PARALLEL_2D_COL)
pos_embed = all_gather_tensor_2d(self.pos_embed, -1, ParallelMode.PARALLEL_2D_COL)
cls_token = cls_token.expand(output.shape[0], -1, -1)
output = torch.cat((cls_token, output), dim=1)
output = output + pos_embed
return output
@LAYERS.register_module
class Embedding2D(ParallelLayer):
r"""Embedding for 2D parallelism.
Args:
num_embeddings (int): number of embeddings.
embedding_dim (int): dimension of embedding.
padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient;
therefore, the embedding vector at padding_idx is not updated during training,
i.e. it remains as a fixed “pad”, defaults to None.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
weight_initializer (:class:`typing.Callable`, optional):
he initializer of weight, defaults to normal initializer.
The ``args`` and ``kwargs`` used in :class:``torch.nn.functional.embedding`` should contain:
::
max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is
renormalized to have norm max_norm. Note: this will modify weight in-place.
norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2.
scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse
of frequency of the words in the mini-batch. Default False.
sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False.
More details about ``args`` and ``kwargs`` could be found in
`Embedding <https://pytorch.org/docs/stable/generated/torch.nn.functional.embedding.html#torch.nn.functional.embedding>`_.
More details about initializer please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_
"""
def __init__(self,
num_embeddings: int,
embedding_dim: int,
padding_idx: int = None,
dtype: torch.dtype = None,
weight_initializer: Callable = init.normal_(),
*args,
**kwargs):
super().__init__()
assert_summa_initialization()
self.summa_dim = get_summa_dim_from_env()
self.num_embeddings = num_embeddings
self.embed_dim = embedding_dim
embed_dim_per_partition = divide(embedding_dim, self.summa_dim**2)
self.padding_idx = padding_idx
self.embed_args = args
self.embed_kwargs = kwargs
self.weight = Parameter(
torch.empty((num_embeddings, embed_dim_per_partition), device=get_current_device(), dtype=dtype))
self.reset_parameters(weight_initializer)
self._set_tensor_parallel_attributes()
def _set_tensor_parallel_attributes(self):
set_tensor_parallel_attribute_by_partition(self.weight, self.summa_dim**2)
def reset_parameters(self, weight_initializer) -> None:
with seed(ParallelMode.TENSOR):
fan_in, fan_out = self.num_embeddings, self.embed_dim
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
self._fill_padding_idx_with_zero()
def _fill_padding_idx_with_zero(self) -> None:
if self.padding_idx is not None:
with torch.no_grad():
self.weight[self.padding_idx].fill_(0)
def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
local_state = OrderedDict()
weight_key = prefix + 'weight'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight
# partition in row groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_ROW,
dims={weight_key: -1},
partition_states={weight_key: True},
)
# partition in column groups
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_COL,
dims={weight_key: -1},
partition_states={weight_key: True},
)
super()._load_from_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_state_dict(self, destination, prefix, keep_vars):
weight_key = prefix + 'weight'
local_state = OrderedDict({weight_key: self.weight})
# gather in column groups
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_COL,
dims={weight_key: -1},
partition_states={weight_key: True},
keep_vars=keep_vars,
)
# gather in row groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_ROW,
dims={weight_key: -1},
partition_states={weight_key: True},
keep_vars=keep_vars,
)
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
destination.update(local_state)
def forward(self, input_: Tensor) -> Tensor:
input_ = split_batch_2d(input_)
weight = all_gather_tensor_2d(self.weight, -1, ParallelMode.PARALLEL_2D_COL)
output = F.embedding(input_, weight, self.padding_idx, *self.embed_args, **self.embed_kwargs)
return output
@LAYERS.register_module
class VocabParallelEmbedding2D(torch.nn.Module):
r"""Embedding parallelized in the vocabulary dimension.
Args:
num_embeddings (int): number of embeddings.
embedding_dim (int): dimension of embedding.
padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient;
therefore, the embedding vector at padding_idx is not updated during training,
i.e. it remains as a fixed “pad”, defaults to None.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
weight_initializer (:class:`typing.Callable`, optional):
he initializer of weight, defaults to normal initializer.
The ``args`` and ``kwargs`` used in :class:``torch.nn.functional.embedding`` should contain:
::
max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is
renormalized to have norm max_norm. Note: this will modify weight in-place.
norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2.
scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse
of frequency of the words in the mini-batch. Default False.
sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False.
More details about ``args`` and ``kwargs`` could be found in
`Embedding <https://pytorch.org/docs/stable/generated/torch.nn.functional.embedding.html#torch.nn.functional.embedding>`_.
More details about initializer please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(self,
num_embeddings: int,
embedding_dim: int,
padding_idx: int = None,
dtype: torch.dtype = None,
weight_initializer: Callable = init.normal_(),
*args,
**kwargs):
super().__init__()
self.num_embeddings = num_embeddings
self.embed_dim = embedding_dim
self.padding_idx = padding_idx
self.embed_args = args
self.embed_kwargs = kwargs
assert_summa_initialization()
self.summa_dim = get_summa_dim_from_env()
self.num_embeddings_per_partition = divide(self.num_embeddings, self.summa_dim)
self.embed_dim_per_partition = divide(self.embed_dim, self.summa_dim)
tensor_parallel_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)
self.vocab_start_index = tensor_parallel_rank * self.num_embeddings_per_partition
self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition
self.weight = Parameter(
torch.empty((self.num_embeddings_per_partition, self.embed_dim_per_partition),
device=get_current_device(),
dtype=dtype))
self.reset_parameters(weight_initializer)
self._set_tensor_parallel_attributes()
env.vocab_parallel = True
def _set_tensor_parallel_attributes(self):
set_tensor_parallel_attribute_by_partition(self.weight, self.summa_dim**2)
def reset_parameters(self, weight_initializer) -> None:
with seed(ParallelMode.TENSOR):
fan_in, fan_out = self.num_embeddings, self.embed_dim
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
self._fill_padding_idx_with_zero()
def _fill_padding_idx_with_zero(self) -> None:
if self.padding_idx is not None and \
self.padding_idx >= self.vocab_start_index and self.padding_idx < self.vocab_end_index:
with torch.no_grad():
self.weight[self.padding_idx - self.vocab_start_index].fill_(0)
def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
local_state = OrderedDict()
weight_key = prefix + 'weight'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight
# partition in row groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_ROW,
dims={weight_key: -1},
partition_states={weight_key: True},
)
# partition in column groups
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_COL,
dims={weight_key: 0},
partition_states={weight_key: True},
)
super()._load_from_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_state_dict(self, destination, prefix, keep_vars):
weight_key = prefix + 'weight'
local_state = OrderedDict({weight_key: self.weight})
# gather in column groups
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_COL,
dims={weight_key: 0},
partition_states={weight_key: True},
keep_vars=keep_vars,
)
# gather in row groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_ROW,
dims={weight_key: -1},
partition_states={weight_key: True},
keep_vars=keep_vars,
)
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
destination.update(local_state)
def forward(self, input_: Tensor) -> Tensor:
input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index)
masked_input = input_.clone() - self.vocab_start_index
masked_input[input_mask] = 0
output_parallel = F.embedding(masked_input, self.weight, self.padding_idx, *self.embed_args,
**self.embed_kwargs)
output_parallel[input_mask, :] = 0.
output = reduce_scatter_tensor_2d(output_parallel, 0, ParallelMode.PARALLEL_2D_COL)
return output
@LAYERS.register_module
class Classifier2D(ParallelLayer):
r"""Classifier for 2D parallelism.
Args:
in_features (int): size of each input sample.
num_classes (int): number of classes.
weight (:class:`torch.nn.Parameter`, optional): weight of the classifier, defaults to None.
bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
weight_initializer (:class:`typing.Callable`, optional):
The initializer of weight, defaults to kaiming uniform initializer.
bias_initializer (:class:`typing.Callable`, optional):
The initializer of bias, defaults to xavier uniform initializer.
More details about ``initializer`` please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(self,
in_features: int,
num_classes: int,
weight: Parameter = None,
bias: bool = True,
dtype: torch.dtype = None,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
super().__init__()
self.in_features = in_features
self.num_classes = num_classes
assert_summa_initialization()
self.row_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)
self.col_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)
self.summa_dim = get_summa_dim_from_env()
# partitioning dimension
self.input_size_per_partition = divide(self.in_features, self.summa_dim**2)
if weight is not None:
self.weight = weight
self.has_weight = False
else:
self.weight = Parameter(
torch.empty(self.num_classes, self.input_size_per_partition, device=get_current_device(), dtype=dtype))
self.has_weight = True
if bias:
self.bias = Parameter(torch.zeros(self.num_classes, device=get_current_device(), dtype=dtype))
else:
self.bias = None
self.reset_parameters(weight_initializer, bias_initializer)
self._set_tensor_parallel_attributes()
def _set_tensor_parallel_attributes(self):
if self.has_weight:
set_tensor_parallel_attribute_by_partition(self.weight, self.summa_dim**2)
def reset_parameters(self, weight_initializer, bias_initializer) -> None:
with seed(ParallelMode.TENSOR):
fan_in, fan_out = self.in_features, self.num_classes
col_src_rank = gpc.get_ranks_in_group(ParallelMode.PARALLEL_2D_COL)[0]
row_src_rank = gpc.get_ranks_in_group(ParallelMode.PARALLEL_2D_ROW)[0]
if self.has_weight:
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
if self.bias is not None:
bias_initializer(self.bias, fan_in=fan_in)
broadcast(self.bias, col_src_rank, ParallelMode.PARALLEL_2D_COL)
broadcast(self.bias, row_src_rank, ParallelMode.PARALLEL_2D_ROW)
def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
local_state = OrderedDict()
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
if self.has_weight:
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight
# bias
if self.bias is not None:
bias = state_dict.pop(bias_key, None)
if bias is not None:
local_state[bias_key] = bias
# partition in row groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_ROW,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: False
},
)
# partition in column groups
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_COL,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: False
},
)
super()._load_from_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_state_dict(self, destination, prefix, keep_vars):
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
local_state = OrderedDict()
if self.has_weight:
local_state[weight_key] = self.weight
if self.bias is not None:
local_state[bias_key] = self.bias
# gather in column groups
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_COL,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: False
},
keep_vars=keep_vars,
)
# gather in row groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_ROW,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: False
},
keep_vars=keep_vars,
)
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
destination.update(local_state)
def forward(self, input_: Tensor) -> Tensor:
out_shape = input_.shape[:-1] + (self.num_classes, )
return classifier_2d(input_, self.weight, self.bias, self.summa_dim, out_shape, self.row_rank, self.col_rank,
ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL, self.data_parallel_rank,
self.pipeline_parallel_rank, self.pipeline_parallel_size, self.tensor_parallel_size)
@LAYERS.register_module
class VocabParallelClassifier2D(ParallelLayer):
r"""Vocab parallel classifier layer for 2D parallelism.
Args:
in_features (int): size of each input sample.
num_classes (int): number of classes.
weight (:class:`torch.nn.Parameter`, optional): weight of the classifier, defaults to None.
bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
weight_initializer (:class:`typing.Callable`, optional):
The initializer of weight, defaults to kaiming uniform initializer.
bias_initializer (:class:`typing.Callable`, optional):
The initializer of bias, defaults to xavier uniform initializer.
More details about ``initializer`` please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(self,
in_features: int,
num_classes: int,
weight: Parameter = None,
bias: bool = True,
dtype: torch.dtype = None,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
super().__init__()
self.in_features = in_features
self.num_classes = num_classes
# parallel setting
assert_summa_initialization()
self.row_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL)
self.col_rank = gpc.get_local_rank(ParallelMode.PARALLEL_2D_ROW)
self.summa_dim = get_summa_dim_from_env()
# partitioning dimension
self.input_size_per_partition = divide(in_features, self.summa_dim)
self.output_size_per_partition = divide(num_classes, self.summa_dim)
# create weight, shape: [k/q, h/q]
factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
if weight is not None:
self.weight = weight
self.has_weight = False
else:
self.weight = Parameter(
torch.empty(self.output_size_per_partition, self.input_size_per_partition, **factory_kwargs))
self.has_weight = True
# create bias, shape: [h/q]
if bias:
self.bias = Parameter(torch.empty(divide(self.num_classes, self.summa_dim**2), **factory_kwargs))
else:
self.bias = None
# initialize parameters
with seed(ParallelMode.TENSOR):
self.reset_parameters(weight_initializer, bias_initializer)
self._set_tensor_parallel_attributes()
env.vocab_parallel = True
def _set_tensor_parallel_attributes(self):
if self.has_weight:
set_tensor_parallel_attribute_by_partition(self.weight, self.summa_dim**2)
if self.bias is not None:
set_tensor_parallel_attribute_by_partition(self.bias, self.summa_dim**2)
def reset_parameters(self, weight_initializer, bias_initializer) -> None:
fan_in, fan_out = self.in_features, self.num_classes
if self.has_weight:
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
if self.bias is not None:
bias_initializer(self.bias, fan_in=fan_in)
def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
local_state = OrderedDict()
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# weight
if self.has_weight:
weight = state_dict.pop(weight_key, None)
if weight is not None:
local_state[weight_key] = weight
# bias
if self.bias is not None:
bias = state_dict.pop(bias_key, None)
if bias is not None:
local_state[bias_key] = bias
# partition in row groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_ROW,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
)
# partition in column groups
local_state = partition_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_COL,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
)
super()._load_from_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_state_dict(self, destination, prefix, keep_vars):
weight_key = prefix + 'weight'
bias_key = prefix + 'bias'
local_state = OrderedDict()
if self.has_weight:
local_state[weight_key] = self.weight
if self.bias is not None:
local_state[bias_key] = self.bias
# gather in column groups
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_COL,
dims={
weight_key: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
keep_vars=keep_vars,
)
# gather in row groups
if gpc.get_local_rank(ParallelMode.PARALLEL_2D_COL) == 0:
local_state = gather_tensor_parallel_state_dict(
local_state,
ParallelMode.PARALLEL_2D_ROW,
dims={
weight_key: -1,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: True
},
keep_vars=keep_vars,
)
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
local_state[weight_key] = local_state[weight_key].transpose(0, 1)
destination.update(local_state)
def forward(self, x: Tensor) -> Tensor:
# input: [m/q, n/q, k/q]
# output: [m/q, n/q, h/q]
out_shape = x.shape[:-1] + (self.output_size_per_partition, )
output = Matmul_ABT_2D.apply(x, self.weight, self.summa_dim, out_shape, self.row_rank, self.col_rank,
ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL,
self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
self.tensor_parallel_size)
if self.bias is not None:
output = add_bias_2d(output, self.bias, self.output_size_per_partition, self.row_rank, self.col_rank,
ParallelMode.PARALLEL_2D_ROW, ParallelMode.PARALLEL_2D_COL, False,
self.data_parallel_rank, self.pipeline_parallel_rank, self.pipeline_parallel_size,
self.tensor_parallel_size)
return output
|
from colossalai.context.parallel_mode import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.global_variables import tensor_parallel_env as env
def get_summa_dim_from_env() -> int:
try:
summa_dim = env.summa_dim
assert summa_dim > 0, 'SUMMA_DIM must be larger than zero'
return summa_dim
except KeyError as e:
raise EnvironmentError('SUMMA_DIM is not found in the current environment, '
'please make sure that you have used the correct process group initializer')
def assert_summa_initialization():
assert gpc.is_initialized(ParallelMode.PARALLEL_2D_COL) and \
gpc.is_initialized(ParallelMode.PARALLEL_2D_ROW), \
'Both TWO_DIMENSION_COL and TWO_DIMENSION_ROW must be initialized by the process group initializer'
|
import math
from typing import Callable
from colossalai.utils import get_current_device
from torch import dtype, nn
from ... import init as init
from ..parallel_1d import Embedding1D, PatchEmbedding1D, VocabParallelEmbedding1D
from ..parallel_2d import Embedding2D, PatchEmbedding2D, VocabParallelEmbedding2D
from ..parallel_2p5d import Embedding2p5D, PatchEmbedding2p5D, VocabParallelEmbedding2p5D
from ..parallel_3d import Embedding3D, PatchEmbedding3D, VocabParallelEmbedding3D
from ..utils import get_tensor_parallel_mode
from ..vanilla import VanillaPatchEmbedding
from ._utils import ColossalaiModule
_parallel_embedding = {
'1d': Embedding1D,
'2d': Embedding2D,
'2.5d': Embedding2p5D,
'3d': Embedding3D,
}
_vocab_parallel_embedding = {
'1d': VocabParallelEmbedding1D,
'2d': VocabParallelEmbedding2D,
'2.5d': VocabParallelEmbedding2p5D,
'3d': VocabParallelEmbedding3D
}
_parallel_patchembedding = {
None: VanillaPatchEmbedding,
'1d': PatchEmbedding1D,
'2d': PatchEmbedding2D,
'2.5d': PatchEmbedding2p5D,
'3d': PatchEmbedding3D
}
class Embedding(ColossalaiModule):
r"""Embedding for colossalai.
Args:
num_embeddings (int): number of embeddings.
embedding_dim (int): dimension of embedding.
padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient;
therefore, the embedding vector at padding_idx is not updated during training,
i.e. it remains as a fixed “pad”, defaults to None.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
weight_initializer (:class:`typing.Callable`, optional):
he initializer of weight, defaults to normal initializer.
The ``args`` and ``kwargs`` used in :class:`torch.nn.functional.embedding` should contain:
::
max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is
renormalized to have norm max_norm. Note: this will modify weight in-place.
norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2.
scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse
of frequency of the words in the mini-batch. Default False.
sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False.
More details about ``args`` and ``kwargs`` could be found in
`Embedding <https://pytorch.org/docs/stable/generated/torch.nn.functional.embedding.html#torch.nn.functional.embedding>`_.
More details about ``initializer`` please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_
"""
def __init__(self,
num_embeddings: int,
embedding_dim: int,
padding_idx: int = None,
dtype: dtype = None,
weight_initializer: Callable = init.normal_(),
vocab_parallel_limit: int = 2048,
*args,
**kwargs) -> None:
tensor_parallel = get_tensor_parallel_mode()
if tensor_parallel is None:
embed = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx, *args,
**kwargs).to(dtype).to(get_current_device())
weight_initializer(embed.weight, fan_in=num_embeddings, fan_out=embedding_dim)
elif num_embeddings <= vocab_parallel_limit:
embed = _parallel_embedding[tensor_parallel](
num_embeddings,
embedding_dim,
padding_idx=padding_idx,
dtype=dtype,
weight_initializer=weight_initializer,
*args,
**kwargs,
)
else:
embed = _vocab_parallel_embedding[tensor_parallel](
num_embeddings,
embedding_dim,
padding_idx=padding_idx,
dtype=dtype,
weight_initializer=weight_initializer,
*args,
**kwargs,
)
super().__init__(embed)
class PatchEmbedding(ColossalaiModule):
"""2D Image to Patch Embedding.
Args:
img_size (int): image size.
patch_size (int): patch size.
in_chans (int): number of channels of input image.
embed_size (int): size of embedding.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
flatten (bool, optional): whether to flatten output tensor, defaults to True.
weight_initializer (:class:`typing.Callable`, optional):
The initializer of weight, defaults to kaiming uniform initializer.
bias_initializer (:class:`typing.Callable`, optional):
The initializer of bias, defaults to xavier uniform initializer.
position_embed_initializer (:class:`typing.Callable`, optional):
The initializer of position embedding, defaults to zeros initializer.
More details about ``initializer`` please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(
self,
img_size: int,
patch_size: int,
in_chans: int,
embed_size: int,
dtype: dtype = None,
flatten: bool = True,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
position_embed_initializer: Callable = init.zeros_()
) -> None:
tensor_parallel = get_tensor_parallel_mode()
embed = _parallel_patchembedding[tensor_parallel](
img_size,
patch_size,
in_chans,
embed_size,
dtype=dtype,
flatten=flatten,
weight_initializer=weight_initializer,
bias_initializer=bias_initializer,
position_embed_initializer=position_embed_initializer,
)
super().__init__(embed)
|
import math
import inspect
from typing import Callable
from colossalai.utils import get_current_device
from torch import dtype, nn
from ... import init as init
from ..parallel_1d import *
from ..parallel_2d import *
from ..parallel_2p5d import *
from ..parallel_3d import *
from ..utils import get_tensor_parallel_mode
from ..vanilla import *
from ._utils import ColossalaiModule
_parallel_linear = {'1d': Linear1D, '2d': Linear2D, '2.5d': Linear2p5D, '3d': Linear3D}
_parallel_classifier = {
None: VanillaClassifier,
'1d': Classifier1D,
'2d': Classifier2D,
'2.5d': Classifier2p5D,
'3d': Classifier3D
}
_vocab_parallel_classifier = {
'1d': VocabParallelClassifier1D,
'2d': VocabParallelClassifier2D,
'2.5d': VocabParallelClassifier2p5D,
'3d': VocabParallelClassifier3D
}
class Linear(ColossalaiModule):
"""Linear layer of colossalai.
Args:
in_features (int): size of each input sample.
out_features (int): size of each output sample.
bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
weight_initializer (:class:`typing.Callable`, optional):
The initializer of weight, defaults to kaiming uniform initializer.
bias_initializer (:class:`typing.Callable`, optional):
The initializer of bias, defaults to xavier uniform initializer.
Note: ``kwargs`` would contain different parameters when you use different parallelisms.
The ``kwargs`` should contain parameters below:
::
Linear1D:
gather_output: bool (optional, default to be false)
skip_bias_add: bool (optional, default to be false)
Linear2D:
skip_bias_add: bool (optional, default to be false)
Linear2p5D:
skip_bias_add: bool (optional, default to be false)
Linear3D:
None
More details about ``initializer`` please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(self,
in_features: int,
out_features: int,
bias: bool = True,
dtype: dtype = None,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
**kwargs) -> None:
tensor_parallel = get_tensor_parallel_mode()
if tensor_parallel is None:
layer = nn.Linear(in_features, out_features, bias=bias).to(dtype).to(get_current_device())
weight_initializer(layer.weight, fan_in=in_features, fan_out=out_features)
if layer.bias is not None:
bias_initializer(layer.bias, fan_in=in_features)
else:
linear_cls = _parallel_linear[tensor_parallel]
gather_output = kwargs.pop('gather_output', None)
if 'gather_output' in inspect.signature(linear_cls.__init__).parameters.keys(): # gather_out arg is available
kwargs['gather_output'] = gather_output
layer = linear_cls(
in_features,
out_features,
bias=bias,
dtype=dtype,
weight_initializer=weight_initializer,
bias_initializer=bias_initializer,
**kwargs,
)
super().__init__(layer)
class Classifier(ColossalaiModule):
"""Classifier layer of colossalai.
Args:
in_features (int): size of each input sample.
num_classes (int): number of classes.
weight (:class:`torch.nn.Parameter`, optional): weight of the classifier, defaults to None.
bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
weight_initializer (:class:`typing.Callable`, optional):
The initializer of weight, defaults to kaiming uniform initializer.
bias_initializer (:class:`typing.Callable`, optional):
The initializer of bias, defaults to xavier uniform initializer.
More details about ``initializer`` please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(self,
in_features: int,
num_classes: int,
weight: nn.Parameter = None,
bias: bool = True,
dtype: dtype = None,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
vocab_parallel_limit: int = 2048) -> None:
tensor_parallel = get_tensor_parallel_mode()
if num_classes <= vocab_parallel_limit or tensor_parallel is None:
layer = _parallel_classifier[tensor_parallel](
in_features,
num_classes,
weight=weight,
bias=bias,
dtype=dtype,
weight_initializer=weight_initializer,
bias_initializer=bias_initializer,
)
else:
layer = _vocab_parallel_classifier[tensor_parallel](
in_features,
num_classes,
weight=weight,
bias=bias,
dtype=dtype,
weight_initializer=weight_initializer,
bias_initializer=bias_initializer,
)
super().__init__(layer)
|
from ._utils import partition_batch
from .dropout import Dropout
from .embedding import Embedding, PatchEmbedding
from .linear import Classifier, Linear
from .normalization import LayerNorm
__all__ = ['Linear', 'Classifier', 'Embedding', 'PatchEmbedding', 'LayerNorm', 'Dropout', 'partition_batch']
|
import torch.nn as nn
from colossalai.context import ParallelMode, seed
from ..parallel_1d import *
from ..utils import get_tensor_parallel_mode
from ._utils import ColossalaiModule
class Dropout(ColossalaiModule):
"""Dropout layer of colossalai.
Args:
p (float, optional): probability of an element to be zeroed, defaults 0.5.
inplace (bool, optional): whether to do dropout in-place, default to be False.
"""
def __init__(self, p: float = 0.5, inplace: bool = False) -> None:
tensor_parallel = get_tensor_parallel_mode()
if tensor_parallel == "1d":
drop = Dropout1D(p, inplace)
else:
drop = nn.Dropout(p, inplace)
super().__init__(drop, tensor_parallel=tensor_parallel)
def forward(self, *args):
if self.tensor_parallel in [None, '1d']:
return self._forward_func(*args)
else:
with seed(ParallelMode.TENSOR):
return self._forward_func(*args)
|
from colossalai.utils import get_current_device
from torch import nn
from ..parallel_1d import LayerNorm1D
from ..parallel_2d import LayerNorm2D
from ..parallel_2p5d import LayerNorm2p5D
from ..parallel_3d import LayerNorm3D
from ..utils import get_tensor_parallel_mode
from ..vanilla import VanillaLayerNorm
from ._utils import ColossalaiModule
_parallel_layernorm = {
None: VanillaLayerNorm,
"1d": LayerNorm1D,
"2d": LayerNorm2D,
"2.5d": LayerNorm2p5D,
"3d": LayerNorm3D,
}
class LayerNorm(ColossalaiModule):
r"""Layer Normalization for colossalai.
Args:
normalized_shape (int): input shape from an expected input of size.
:math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1]
\times \ldots \times \text{normalized_shape}[-1]]`
If a single integer is used, it is treated as a singleton list, and this module will
normalize over the last dimension which is expected to be of that specific size.
eps (float): a value added to the denominator for numerical stability, defaults to 1e-05.
bias (bool, optional): Whether to add a bias, defaults to ``True``.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
"""
def __init__(self, normalized_shape: int, eps=1e-05, bias=True, dtype=None) -> None:
tensor_parallel = get_tensor_parallel_mode()
if tensor_parallel is None:
norm = nn.LayerNorm(normalized_shape, eps=eps).to(dtype).to(get_current_device())
else:
norm = _parallel_layernorm[tensor_parallel](normalized_shape, eps=eps, dtype=dtype)
super().__init__(norm)
|
import torch.nn as nn
from torch import Tensor
from ..parallel_2d._operation import split_batch_2d
from ..parallel_2p5d._operation import split_batch_2p5d
from ..parallel_3d._operation import split_batch_3d
from ..utils import get_tensor_parallel_mode
_parallel_split_batch = {'2d': split_batch_2d, '2.5d': split_batch_2p5d, '3d': split_batch_3d}
def partition_batch(input_) -> Tensor:
tensor_parallel_mode = get_tensor_parallel_mode()
if tensor_parallel_mode in _parallel_split_batch:
if isinstance(input_, dict):
return {k: _parallel_split_batch[tensor_parallel_mode](v) for k, v in input_.items()}
else:
return _parallel_split_batch[tensor_parallel_mode](input_)
else:
return input_
class ColossalaiModule(nn.Module):
def __init__(self, module: nn.Module, **kwargs):
super().__init__()
# copy values
self.__dict__ = module.__dict__.copy()
# copy methods
for name, attr in module.__class__.__dict__.items():
if name not in ['__init__', 'forward'] and callable(attr):
setattr(self, name, getattr(module, name))
self._forward_func = module.forward
for k, v in kwargs.items():
setattr(self, k, v)
def forward(self, *args):
return self._forward_func(*args)
|
from .layers import (DropPath, VanillaClassifier, VanillaLayerNorm,
VanillaPatchEmbedding, WrappedDropout, WrappedDropPath)
__all__ = [
"VanillaLayerNorm", "VanillaPatchEmbedding", "VanillaClassifier",
"DropPath", "WrappedDropout", "WrappedDropPath"
]
|
import math
from typing import Callable
import torch
import torch.nn.functional as F
from colossalai.context import seed
from colossalai.nn import init as init
from colossalai.registry import LAYERS
from colossalai.utils.cuda import get_current_device
from torch import Tensor
from torch import nn as nn
from ..utils import to_2tuple
def drop_path(x, drop_prob: float = 0., training: bool = False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
'survival rate' as the argument.
Args:
drop_prob (float, optional): probability of dropping path, defaults 0.0.
training (bool, optional): whether in training progress, defaults False.
"""
if drop_prob == 0. or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0], ) + (1, ) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
random_tensor.floor_() # binarize
output = x.div(keep_prob) * random_tensor
return output
class DropPath(nn.Module):
"""
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
Adapted from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py
Args:
drop_prob (float, optional): probability of dropping path, defaults None.
"""
def __init__(self, drop_prob=None):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
def forward(self, x):
return drop_path(x, self.drop_prob, self.training)
class WrappedDropout(nn.Module):
r"""Same as torch.nn.Dropout. But it is wrapped with the context of seed manager. During training, randomly zeroes
some elements of the input tensor with probability p using samples from a Bernoulli distribution. Each
channel will be zeroed out independently on every forward call. Furthermore, the outputs are scaled by a factor of
1/(1-p) during training. This means that during evaluation the module simply computes an identity function.
Args:
p (float, optional): probability of an element to be zeroed, defaults 0.5.
inplace (bool, optional): whether to do dropout in-place, default to be False.
mode (:class:`colossalai.context.ParallelMode`): The chosen parallel mode.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
def __init__(self, p: float = 0.5, inplace: bool = False, mode=None):
super().__init__()
if p < 0 or p > 1:
raise ValueError("dropout probability has to be between 0 and 1, "
"but got {}".format(p))
self.p = p
self.inplace = inplace
if mode is None:
self.func = self.nonefunc
else:
self.func = self.normalfunc
self.mode = mode
def nonefunc(self, inputs):
return F.dropout(inputs, self.p, self.training, self.inplace)
def normalfunc(self, inputs):
with seed(self.mode):
return F.dropout(inputs, self.p, self.training, self.inplace)
def forward(self, inputs):
return self.func(inputs)
class WrappedDropPath(nn.Module):
r"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
Here, it is wrapped with the context of seed manager.
Args:
p (float, optional): probability of dropping path, defaults 0.0.
mode (:class:`colossalai.context.ParallelMode`): The chosen parallel mode.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
def __init__(self, p: float = 0., mode=None):
super().__init__()
self.p = p
self.mode = mode
if self.mode is None:
self.func = self.nonefunc
else:
self.func = self.normalfunc
self.mode = mode
def nonefunc(self, inputs):
return drop_path(inputs, self.p, self.training)
def normalfunc(self, inputs):
with seed(self.mode):
return drop_path(inputs, self.p, self.training)
def forward(self, inputs):
return self.func(inputs)
@LAYERS.register_module
class VanillaPatchEmbedding(nn.Module):
r"""
2D Image to Patch Embedding
Args:
img_size (int): image size.
patch_size (int): patch size.
in_chans (int): number of channels of input image.
embed_size (int): size of embedding.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
flatten (bool, optional): whether to flatten output tensor, defaults to True.
weight_initializer (:class:`typing.Callable`, optional):
The initializer of weight, defaults to kaiming uniform initializer.
bias_initializer (:class:`typing.Callable`, optional):
The initializer of bias, defaults to xavier uniform initializer.
position_embed_initializer (:class:`typing.Callable`, optional):
The initializer of position embedding, defaults to zeros initializer.
More details about initializer please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(self,
img_size: int,
patch_size: int,
in_chans: int,
embed_size: int,
flatten: bool = True,
dtype: torch.dtype = None,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
position_embed_initializer: Callable = init.zeros_()):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
self.img_size = img_size
self.patch_size = patch_size
self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
self.num_patches = self.grid_size[0] * self.grid_size[1]
self.flatten = flatten
self.weight = nn.Parameter(
torch.empty((embed_size, in_chans, *self.patch_size), device=get_current_device(), dtype=dtype))
self.bias = nn.Parameter(torch.empty(embed_size, device=get_current_device(), dtype=dtype))
self.cls_token = nn.Parameter(torch.zeros((1, 1, embed_size), device=get_current_device(), dtype=dtype))
self.pos_embed = nn.Parameter(
torch.zeros((1, self.num_patches + 1, embed_size), device=get_current_device(), dtype=dtype))
self.reset_parameters(weight_initializer, bias_initializer, position_embed_initializer)
def reset_parameters(self, weight_initializer, bias_initializer, position_embed_initializer):
fan_in, fan_out = nn.init._calculate_fan_in_and_fan_out(self.weight)
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
bias_initializer(self.bias, fan_in=fan_in)
position_embed_initializer(self.pos_embed)
def forward(self, input_: Tensor) -> Tensor:
B, C, H, W = input_.shape
assert H == self.img_size[0] and W == self.img_size[1], \
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
output = F.conv2d(input_, self.weight, self.bias, stride=self.patch_size)
if self.flatten:
output = output.flatten(2).transpose(1, 2) # BCHW -> BNC
cls_token = self.cls_token.expand(output.shape[0], -1, -1)
output = torch.cat((cls_token, output), dim=1)
output = output + self.pos_embed
return output
@LAYERS.register_module
class VanillaClassifier(nn.Module):
r"""Dense linear classifier.
Args:
in_features (int): size of each input sample.
num_classes (int): number of classes.
weight (:class:`torch.nn.Parameter`, optional): weight of the classifier, defaults to None.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
flatten (bool, optional): whether to flatten output tensor, defaults to True.
weight_initializer (:class:`typing.Callable`, optional):
The initializer of weight, defaults to kaiming uniform initializer.
bias_initializer (:class:`typing.Callable`, optional):
The initializer of bias, defaults to xavier uniform initializer.
More details about initializer please refer to
`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
"""
def __init__(self,
in_features: int,
num_classes: int,
weight: nn.Parameter = None,
bias: bool = True,
dtype: torch.dtype = None,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
super().__init__()
self.in_features = in_features
self.num_classes = num_classes
if weight is not None:
self.weight = weight
self.has_weight = False
else:
self.weight = nn.Parameter(
torch.empty(self.num_classes, self.in_features, device=get_current_device(), dtype=dtype))
self.has_weight = True
if bias:
self.bias = nn.Parameter(torch.zeros(self.num_classes, device=get_current_device(), dtype=dtype))
else:
self.bias = None
self.reset_parameters(weight_initializer, bias_initializer)
def reset_parameters(self, weight_initializer, bias_initializer):
fan_in, fan_out = self.in_features, self.num_classes
if self.has_weight:
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
if self.bias is not None:
bias_initializer(self.bias, fan_in=fan_in)
def forward(self, input_: Tensor) -> Tensor:
return F.linear(input_, self.weight, self.bias)
@LAYERS.register_module
class VanillaLayerNorm(nn.Module):
r"""
Layer Normalization for colossalai
Args:
normalized_shape (int): input shape from an expected input of size.
:math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1]
\times \ldots \times \text{normalized_shape}[-1]]`
If a single integer is used, it is treated as a singleton list, and this module will
normalize over the last dimension which is expected to be of that specific size.
eps (float): a value added to the denominator for numerical stability, defaults to 1e-05.
bias (bool, optional): Whether to add a bias, defaults to ``True``.
dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
"""
def __init__(self, normalized_shape: int, eps=1e-05, bias=True, dtype=None):
super().__init__()
self.normalized_shape = (normalized_shape,)
self.variance_epsilon = eps
factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
self.weight = nn.Parameter(torch.ones(normalized_shape, **factory_kwargs))
if bias:
self.bias = nn.Parameter(torch.zeros(normalized_shape, **factory_kwargs))
else:
self.bias = None
def forward(self, x: Tensor) -> Tensor:
return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.variance_epsilon)
|
import torch
import torch.distributed as dist
from torch import Tensor
from typing import Any, Tuple, Optional
from torch.distributed import ProcessGroup
COL_MOE_KERNEL_FLAG = False
try:
import colossal_moe_cuda
COL_MOE_KERNEL_FLAG = True
except ImportError:
print("If you want to activate cuda mode for MoE, please install with cuda_ext!")
class AllGather(torch.autograd.Function):
@staticmethod
def forward(ctx: Any, inputs: Tensor, group: Optional[ProcessGroup] = None) -> Tensor:
if ctx is not None:
ctx.comm_grp = group
comm_size = dist.get_world_size(group)
if comm_size == 1:
return inputs.unsqueeze(0)
buffer_shape = (comm_size,) + inputs.shape
outputs = torch.empty(buffer_shape, dtype=inputs.dtype, device=inputs.device)
buffer_list = list(torch.chunk(outputs, comm_size, dim=0))
dist.all_gather(buffer_list, inputs, group=group)
return outputs
@staticmethod
def backward(ctx: Any, grad_outputs: Tensor) -> Tuple[Tensor, None]:
return ReduceScatter.forward(None, grad_outputs, ctx.comm_grp), None
class ReduceScatter(torch.autograd.Function):
@staticmethod
def forward(ctx: Any, inputs: Tensor, group: Optional[ProcessGroup] = None) -> Tensor:
if ctx is not None:
ctx.comm_grp = group
comm_size = dist.get_world_size(group)
if comm_size == 1:
return inputs.squeeze(0)
if not inputs.is_contiguous():
inputs = inputs.contiguous()
output_shape = inputs.shape[1:]
outputs = torch.empty(output_shape, dtype=inputs.dtype, device=inputs.device)
buffer_list = list(torch.chunk(inputs, comm_size, dim=0))
dist.reduce_scatter(outputs, buffer_list, group=group)
return outputs
@staticmethod
def backward(ctx: Any, grad_outputs: Tensor) -> Tuple[Tensor, None]:
return AllGather.forward(None, grad_outputs, ctx.comm_grp), None
class AllToAll(torch.autograd.Function):
"""Dispatches input tensor [e, c, h] to all experts by all_to_all_single
operation in torch.distributed.
"""
@staticmethod
def forward(ctx: Any, inputs: Tensor, group: Optional[ProcessGroup] = None) -> Tensor:
if ctx is not None:
ctx.comm_grp = group
if not inputs.is_contiguous():
inputs = inputs.contiguous()
if dist.get_world_size(group) == 1:
return inputs
output = torch.empty_like(inputs)
dist.all_to_all_single(output, inputs, group=group)
return output
@staticmethod
def backward(ctx: Any, *grad_outputs: Tensor) -> Tuple[Tensor, None]:
return AllToAll.forward(None, *grad_outputs, ctx.comm_grp), None
class MoeDispatch(torch.autograd.Function):
@staticmethod
def forward(ctx, tokens, mask, dest_idx, ec):
s = tokens.size(0)
h = tokens.size(1)
expert_input = colossal_moe_cuda.dispatch_forward(s, ec, h, tokens, mask, dest_idx)
ctx.save_for_backward(mask, dest_idx)
ctx.s = s
ctx.h = h
ctx.ec = ec
return expert_input
@staticmethod
def backward(ctx, output_grad):
mask, dest_idx = ctx.saved_tensors
d_tokens = colossal_moe_cuda.dispatch_backward(ctx.s, ctx.ec, ctx.h, output_grad, mask, dest_idx)
return d_tokens, None, None, None
class MoeCombine(torch.autograd.Function):
@staticmethod
def forward(ctx, expert_tokens, logits, mask, dest_idx, ec):
assert logits.dtype == torch.float32
s = logits.size(0)
e = logits.size(1)
c = ec // e
h = expert_tokens.size(-1)
fp16_flag = (expert_tokens.dtype == torch.float16)
cb_input = expert_tokens.to(torch.float32) if fp16_flag else expert_tokens
ctokens = colossal_moe_cuda.combine_forward(s, e, c, h, cb_input, logits, mask, dest_idx)
output = ctokens.to(torch.float16) if fp16_flag else ctokens
ctx.save_for_backward(expert_tokens, logits, mask, dest_idx)
ctx.s = s
ctx.e = e
ctx.c = c
ctx.h = h
ctx.fp16_flag = fp16_flag
return output
@staticmethod
def backward(ctx, tokens_grad):
expert_tokens, logits, mask, dest_idx = ctx.saved_tensors
cb_grad = tokens_grad.to(torch.float32) if tokens_grad.dtype is torch.float16 \
else tokens_grad
cb_input = expert_tokens.to(torch.float32) if ctx.fp16_flag else expert_tokens
d_expert, d_logits = colossal_moe_cuda.combine_backward(ctx.s, ctx.e, ctx.c, ctx.h, cb_grad, cb_input, logits,
mask, dest_idx)
d_expert = d_expert.to(torch.float16) if ctx.fp16_flag else d_expert
return d_expert, d_logits, None, None, None
def moe_cumsum(inputs: Tensor):
dim0 = inputs.size(0)
flag = (dim0 <= 1024) or (dim0 <= 2048 and dim0 % 2 == 0) or (dim0 % 4 == 0)
if flag and COL_MOE_KERNEL_FLAG:
return colossal_moe_cuda.cumsum_sub_one(inputs)
else:
return torch.cumsum(inputs, dim=0) - 1
|
from .experts import Experts, FFNExperts, TPExperts
from .layers import MoeLayer, Top1Router, Top2Router, MoeModule
from .utils import NormalNoiseGenerator, UniformNoiseGenerator, build_ffn_experts
__all__ = [
'Experts', 'FFNExperts', 'TPExperts', 'Top1Router', 'Top2Router', 'MoeLayer', 'NormalNoiseGenerator',
'UniformNoiseGenerator', 'build_ffn_experts', 'MoeModule'
]
|
import torch
import torch.nn.functional as F
from colossalai.utils import get_current_device
from colossalai.context.moe_context import MOE_CONTEXT
from .experts import FFNExperts, TPExperts
class ForceFP32Parameter(torch.nn.Parameter):
def half(self, memory_format=None):
return self.data
class NormalNoiseGenerator:
"""Generates a random noisy mask for logtis tensor.
All noise is generated from a normal distribution :math:`(0, 1 / E^2)`, where
`E = the number of experts`.
Args:
num_experts (int): The number of experts.
"""
def __init__(self, num_experts: int):
self.normal = torch.distributions.normal.Normal(loc=torch.tensor(0.0, device=get_current_device()),
scale=torch.tensor(1.0 / num_experts**2,
device=get_current_device())).rsample
def __call__(self, inputs: torch.Tensor):
noisy = self.normal(inputs.shape)
return inputs + noisy
class UniformNoiseGenerator:
"""Generates a random noisy mask for logtis tensor.
copied from mesh tensorflow:
Multiply values by a random number between :math:`1-epsilon` and :math:`1+epsilon`.
Makes models more resilient to rounding errors introduced by bfloat16.
This seems particularly important for logits.
Args:
eps (float, optional): Epsilon in generator, defaults 1e-2.
"""
def __init__(self, eps: float = 1e-2):
self.uniform = torch.distributions.uniform.Uniform(low=torch.tensor(1.0 - eps, device=get_current_device()),
high=torch.tensor(1.0 + eps,
device=get_current_device())).rsample
def __call__(self, inputs: torch.Tensor):
noisy = self.uniform(inputs.shape)
return inputs * noisy
def autocast_softmax(logit: torch.Tensor, dim: int):
if logit.dtype != torch.float32:
logit = logit.float()
return F.softmax(logit, dim=dim)
def build_ffn_experts(num_experts: int, d_model: int, d_ff: int, activation=None, drop_rate: float = 0):
mep_size = MOE_CONTEXT.max_ep_size
if num_experts % mep_size == 0 or mep_size % num_experts == 0:
return FFNExperts(num_experts, d_model, d_ff, activation, drop_rate)
elif d_ff % mep_size == 0:
return TPExperts(num_experts, d_model, d_ff, activation, drop_rate)
else:
raise NotImplementedError(f"Can not build {num_experts} experts in {mep_size} GPUS.")
|
import math
import torch
import torch.nn as nn
from colossalai.context import ParallelMode, seed
from colossalai.utils import get_current_device
from colossalai.context.moe_context import MOE_CONTEXT
from colossalai.zero.init_ctx import no_shard_zero_decrator
from typing import Type
class MoeExperts(nn.Module):
"""Basic class for experts in MoE. It stores what kind of communication expersts use
to exchange tokens, how many experts in a single GPU and parallel information such as
expert parallel size, data parallel size and their distributed communication groups.
"""
def __init__(self, comm_name: str, num_experts: int):
super().__init__()
assert comm_name in {"all_to_all", "all_gather"}, \
"This kind of communication has not been implemented yet.\n Please use Experts build function."
self.comm_name = comm_name
# Get the configuration of experts' deployment and parallel information from moe contex
self.num_local_experts, self.dist_info = MOE_CONTEXT.get_info(num_experts)
class Experts(MoeExperts):
"""A wrapper class to create experts. It will create E experts across the
moe model parallel group, where E is the number of experts. Every expert
is a instence of the class, 'expert' in initialization parameters.
Args:
expert_cls (:class:`torch.nn.Module`): The class of all experts
num_experts (int): The number of experts
expert_args: Args used to initialize experts, the args could be found in corresponding expert class
"""
@no_shard_zero_decrator(is_replicated=False)
def __init__(self, expert_cls: Type[nn.Module], num_experts: int, **expert_args):
super().__init__("all_to_all", num_experts)
# Use seed to make every expert different from others
with seed(ParallelMode.TENSOR):
self.experts = nn.ModuleList([expert_cls(**expert_args) for _ in range(self.num_local_experts)])
# Attach parallel information for all parameters in Experts
for exp in self.experts:
for param in exp.parameters():
param.__setattr__('moe_info', self.dist_info)
def forward(self, inputs: torch.Tensor):
# Split inputs for each expert
expert_input = torch.chunk(inputs, self.num_local_experts, dim=1)
expert_output = []
# Get outputs from each expert
for i in range(self.num_local_experts):
expert_output.append(self.experts[i](expert_input[i]))
# Concatenate all outputs together
output = torch.cat(expert_output, dim=1).contiguous()
return output
class FFNExperts(MoeExperts):
"""Use torch.bmm to speed up for multiple experts.
"""
def __init__(self, num_experts: int, d_model: int, d_ff: int, activation=None, drop_rate: float = 0):
super().__init__("all_to_all", num_experts)
self.w1 = nn.Parameter(torch.empty(self.num_local_experts, d_model, d_ff, device=get_current_device()))
self.b1 = nn.Parameter(torch.empty(self.num_local_experts, 1, d_ff, device=get_current_device()))
self.w2 = nn.Parameter(torch.empty(self.num_local_experts, d_ff, d_model, device=get_current_device()))
self.b2 = nn.Parameter(torch.empty(self.num_local_experts, 1, d_model, device=get_current_device()))
s1 = math.sqrt(0.1 / d_model)
s2 = math.sqrt(0.1 / d_ff)
with seed(ParallelMode.TENSOR):
nn.init.trunc_normal_(self.w1, std=s1)
nn.init.trunc_normal_(self.b1, std=s1)
nn.init.trunc_normal_(self.w2, std=s2)
nn.init.trunc_normal_(self.b2, std=s2)
self.act = nn.GELU() if activation is None else activation
self.drop = nn.Dropout(p=drop_rate)
for param in self.parameters():
param.__setattr__('moe_info', self.dist_info)
def forward(self, inputs): # inputs [g, el, c, h]
el = inputs.size(1)
h = inputs.size(-1)
inputs = inputs.transpose(0, 1)
inshape = inputs.shape
inputs = inputs.reshape(el, -1, h)
out_ff = torch.baddbmm(self.b1, inputs, self.w1)
out_act = self.act(out_ff)
with seed(ParallelMode.TENSOR):
out_inter = self.drop(out_act)
out_model = torch.baddbmm(self.b2, out_inter, self.w2)
with seed(ParallelMode.TENSOR):
outputs = self.drop(out_model) # outputs [el, gc, h]
outputs = outputs.reshape(inshape)
outputs = outputs.transpose(0, 1).contiguous()
return outputs
class TPExperts(MoeExperts):
"""Use tensor parallelism to split each expert evenly, which can deploy experts in
case that the number of experts can't be divied by maximum expert parallel size or
maximum expert parallel size can't be divied by the number of experts.
"""
def __init__(self, num_experts: int, d_model: int, d_ff: int, activation=None, drop_rate: float = 0):
super().__init__("all_gather", MOE_CONTEXT.max_ep_size)
assert d_ff % MOE_CONTEXT.max_ep_size == 0, \
"d_ff should be divied by maximum expert parallel size"
p_ff = d_ff // MOE_CONTEXT.max_ep_size
self.w1 = nn.Parameter(torch.empty(num_experts, d_model, p_ff, device=get_current_device()))
self.b1 = nn.Parameter(torch.empty(num_experts, 1, p_ff, device=get_current_device()))
self.w2 = nn.Parameter(torch.empty(num_experts, p_ff, d_model, device=get_current_device()))
self.b2 = nn.Parameter(torch.empty(num_experts, 1, d_model, device=get_current_device()))
s1 = math.sqrt(0.1 / d_model)
s2 = math.sqrt(0.1 / d_ff)
with seed(ParallelMode.TENSOR):
nn.init.trunc_normal_(self.w1, std=s1)
nn.init.trunc_normal_(self.b1, std=s1)
nn.init.trunc_normal_(self.w2, std=s2)
nn.init.trunc_normal_(self.b2, std=s2)
self.act = nn.GELU() if activation is None else activation
self.drop = nn.Dropout(p=drop_rate)
self.w1.__setattr__('moe_info', self.dist_info)
self.w2.__setattr__('moe_info', self.dist_info)
self.b1.__setattr__('moe_info', self.dist_info)
def forward(self, inputs): # inputs [g, e, c, h]
e = inputs.size(1)
h = inputs.size(-1)
inputs = inputs.transpose(0, 1)
inshape = inputs.shape
inputs = inputs.reshape(e, -1, h)
out_ff = torch.baddbmm(self.b1, inputs, self.w1)
out_act = self.act(out_ff)
with seed(ParallelMode.TENSOR):
out_inter = self.drop(out_act)
out_model = torch.baddbmm(self.b2, out_inter, self.w2)
outputs = self.drop(out_model) # outputs [e, gc, h]
outputs = outputs.reshape(inshape)
outputs = outputs.transpose(0, 1).contiguous()
return outputs # outputs [g, e, c, h]
|
import functools
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed as dist
from colossalai.context.moe_context import MOE_CONTEXT
from colossalai.utils import get_current_device
from ._operation import COL_MOE_KERNEL_FLAG, AllToAll, AllGather, ReduceScatter, MoeDispatch, MoeCombine, moe_cumsum
from .experts import MoeExperts, Experts
from .utils import ForceFP32Parameter, UniformNoiseGenerator, NormalNoiseGenerator, autocast_softmax
from colossalai.zero.init_ctx import no_shard_zero_context, no_shard_zero_decrator
from typing import Callable, Optional, Type
from torch.distributed import ProcessGroup
class Top1Router(nn.Module):
"""Top1 router that returns the dispatch mask [s, e, c] and combine weight [s, e, c]
for routing usage. More deailted function can be found in the paper about Switch Transformer
of Google.
Args:
capacity_factor_train (float, optional): Capacity factor in routing of training.
capacity_factor_eval (float, optional): Capacity factor in routing of evaluation.
min_capacity (int, optional): The minimum number of the capacity of each expert.
select_policy (str, optional): The policy about tokens selection.
noisy_func (:class:`typing.Callable`, optional): Noisy function used in logits.
drop_tks (bool, optional): Whether drops tokens in evaluation
"""
def __init__(self,
capacity_factor_train: float = 1.25,
capacity_factor_eval: float = 2.0,
min_capacity: int = 4,
select_policy: str = "first",
noisy_func: Callable = None,
drop_tks: bool = True):
super().__init__()
self.capacity_factor_train = capacity_factor_train
self.capacity_factor_eval = capacity_factor_eval
self.min_capacity = min_capacity
self.select_policy = select_policy
self.noisy_func = noisy_func
self.drop_tks = drop_tks
assert select_policy in {"first", "random"}
if select_policy == "random":
self.uniform = torch.distributions.uniform.Uniform(low=torch.tensor(0.0, device=get_current_device()),
high=torch.tensor(1.0,
device=get_current_device())).rsample
def get_capacity(
self,
logits_shape,
):
capacity_factor = self.capacity_factor_train if self.training else self.capacity_factor_eval
capacity = math.floor(capacity_factor * logits_shape[-2] / logits_shape[-1])
capacity += capacity % 2
capacity = max(capacity, self.min_capacity)
assert capacity > 0
return capacity
def forward(self, inputs: torch.Tensor, use_kernel: bool = False, ep_group: Optional[ProcessGroup] = None):
if self.noisy_func is not None and self.training:
inputs = self.noisy_func(inputs)
logits = autocast_softmax(inputs, dim=-1)
num_experts = logits.size(-1)
capacity = self.get_capacity(logits.shape)
top1_idx = torch.argmax(inputs, dim=-1)
mask = F.one_hot(top1_idx, num_classes=num_experts).to(torch.int32)
if self.training:
me = torch.mean(logits, dim=0)
ce = torch.mean(mask.float(), dim=0)
l_aux = num_experts * torch.sum(me * ce)
MOE_CONTEXT.add_loss(l_aux)
elif not self.drop_tks:
max_num = torch.max(torch.sum(mask, dim=0))
dist.all_reduce(max_num, op=dist.ReduceOp.MAX, group=ep_group)
capacity = max_num.item()
else:
pass
if self.select_policy == "random":
rand_mask = mask * self.uniform(mask.shape)
_, dispatch_idx = torch.topk(rand_mask, k=capacity, dim=0)
mask = mask * torch.zeros_like(mask).scatter_(0, dispatch_idx, 1)
ranks = moe_cumsum(mask)
elif self.select_policy == "first":
ranks = moe_cumsum(mask)
mask = mask * torch.lt(ranks, capacity)
else:
raise NotImplementedError("Not support such select policy yet.")
ranks = torch.sum(mask * ranks, dim=-1)
if use_kernel:
mask = torch.sum(mask, dim=-1)
mask = torch.stack([mask], dim=0).to(torch.int32)
dest_idx = torch.stack([top1_idx * capacity + ranks], dim=0).to(torch.int32)
return logits, mask, dest_idx, num_experts * capacity
else:
ranks = F.one_hot(ranks, num_classes=capacity)
weight = mask * logits.type_as(inputs)
combine_weights = weight.unsqueeze(2) * ranks.unsqueeze(1)
sec_mask = combine_weights.bool()
return combine_weights, sec_mask
class Top2Router(nn.Module):
"""Top2 router that returns the dispatch mask [s, e, c] and combine weight [s, e, c]
for routing usage. More deailted function can be found in the paper about ViT-MoE.
Args:
capacity_factor_train (float, optional): Capacity factor in routing of training.
capacity_factor_eval (float, optional): Capacity factor in routing of evaluation.
min_capacity (int, optional): The minimum number of the capacity of each expert
noisy_func (:class:`typing.Callable`, optional): Noisy function used in logits.
drop_tks (bool, optional): Whether drops tokens in evaluation.
"""
def __init__(self,
capacity_factor_train: float = 1.25,
capacity_factor_eval: float = 2.0,
min_capacity: int = 4,
noisy_func: Callable = None,
drop_tks: bool = True):
super().__init__()
self.capacity_factor_train = capacity_factor_train
self.capacity_factor_eval = capacity_factor_eval
self.min_capacity = min_capacity
self.noisy_func = noisy_func
self.drop_tks = drop_tks
def get_capacity(
self,
logits_shape,
):
capacity_factor = self.capacity_factor_train if self.training else self.capacity_factor_eval
capacity = math.floor(capacity_factor * logits_shape[-2] / logits_shape[-1])
capacity += capacity % 2
capacity = max(capacity, self.min_capacity)
assert capacity > 0
return capacity
def forward(self, inputs: torch.Tensor, use_kernel: bool = False, ep_group: Optional[ProcessGroup] = None):
# inputs: [s, h]
if self.noisy_func is not None and self.training:
inputs = self.noisy_func(inputs)
logits = autocast_softmax(inputs, dim=-1) # logits: [s, e]
num_experts = logits.size(-1)
capacity = self.get_capacity(logits.shape)
top1_idx = torch.argmax(logits, dim=-1)
mask1 = F.one_hot(top1_idx, num_classes=num_experts).to(torch.int32)
logits_except1 = logits.masked_fill(mask1.bool(), float("-inf"))
top2_idx = torch.argmax(logits_except1, dim=-1)
mask2 = F.one_hot(top2_idx, num_classes=num_experts).to(torch.int32)
cmask = (mask1 + mask2) # loss: [s, e]
if self.training:
me = torch.mean(logits, dim=0)
ce = torch.mean(cmask.float(), dim=0)
l_aux = num_experts * torch.sum(me * ce) / 2.0 # div 2 to normalize it to 1
MOE_CONTEXT.add_loss(l_aux)
elif not self.drop_tks:
max_num = torch.max(torch.sum(cmask, dim=0))
dist.all_reduce(max_num, op=dist.ReduceOp.MAX, group=ep_group)
capacity = max_num.item()
else:
pass
rank1 = moe_cumsum(mask1) # rank1: [s, e]
rank2 = moe_cumsum(mask2)
rank2 += torch.sum(mask1, dim=-2, keepdim=True)
mask1 *= torch.lt(rank1, capacity)
mask2 *= torch.lt(rank2, capacity)
rank1 = torch.sum(mask1 * rank1, dim=-1)
rank2 = torch.sum(mask2 * rank2, dim=-1)
if use_kernel:
mask1 = torch.sum(mask1, dim=-1)
mask2 = torch.sum(mask2, dim=-1)
mask = torch.stack([mask1, mask2], dim=0).to(torch.int32)
dest_idx = torch.stack([top1_idx * capacity + rank1, top2_idx * capacity + rank2], dim=0).to(torch.int32)
return logits, mask, dest_idx, num_experts * capacity
else:
weight1 = mask1 * logits.type_as(inputs)
weight2 = mask2 * logits.type_as(inputs)
rank1_sc = F.one_hot(rank1, num_classes=capacity)
rank2_sc = F.one_hot(rank2, num_classes=capacity)
cb_weight1 = weight1.unsqueeze(2) * rank1_sc.unsqueeze(1)
cb_weight2 = weight2.unsqueeze(2) * rank2_sc.unsqueeze(1)
cb_weight = cb_weight1 + cb_weight2
sec_mask = cb_weight.bool()
return cb_weight, sec_mask
class FP32LinearGate(nn.Module):
"""Gate module used in MOE layer. Just a linear function without bias.
But it should be kept as fp32 forever.
Args:
d_model (int): Hidden dimension of training model
num_experts (int): The number experts
Attributes:
weight (ForceFP32Parameter): The weight of linear gate
"""
def __init__(self, d_model: int, num_experts: int, scale: float = 0.1):
super().__init__()
self.weight = ForceFP32Parameter(torch.empty(num_experts, d_model, device=get_current_device()))
nn.init.trunc_normal_(self.weight, std=math.sqrt(scale / d_model))
def forward(self, x: torch.Tensor):
return F.linear(x, self.weight)
class MoeLayer(nn.Module):
"""A MoE layer, that puts its input tensor to its gate and uses the output logits
to router all tokens, is mainly used to exchange all tokens for every expert across
the moe tensor group by all to all comunication. Then it will get the output of all
experts and exchange the output. At last returns the output of the moe system.
Args:
dim_model (int): Dimension of model.
num_experts (int): The number of experts.
router (:class:`torch.nn.Module`): Instance of router used in routing.
experts (:class:`torch.nn.Module`): Instance of experts generated by Expert.
"""
@no_shard_zero_decrator(is_replicated=True)
def __init__(self, dim_model: int, num_experts: int, router: nn.Module, experts: MoeExperts):
super().__init__()
self.d_model = dim_model
self.num_experts = num_experts
self.gate = FP32LinearGate(dim_model, num_experts)
self.router = router
self.experts = experts
self.use_kernel = True if COL_MOE_KERNEL_FLAG and MOE_CONTEXT.use_kernel_optim else False
self.ep_group = experts.dist_info.ep_group
self.ep_size = experts.dist_info.ep_size
self.num_local_experts = experts.num_local_experts
def a2a_process(self, dispatch_data: torch.Tensor):
expert_input = AllToAll.apply(dispatch_data, self.ep_group)
input_shape = expert_input.shape
expert_input = expert_input.reshape(self.ep_size, self.num_local_experts, -1, self.d_model)
expert_output = self.experts(expert_input)
expert_output = expert_output.reshape(input_shape)
expert_output = AllToAll.apply(expert_output, self.ep_group)
return expert_output
def tp_process(self, dispatch_data: torch.Tensor):
expert_in = AllGather.apply(dispatch_data, self.ep_group)
expert_out = self.experts(expert_in)
expert_out = ReduceScatter.apply(expert_out, self.ep_group)
return expert_out
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
tokens = inputs.reshape(-1, self.d_model)
fp32_input = tokens.to(torch.float32) if inputs.dtype != torch.float32 else tokens
gate_output = self.gate(fp32_input)
router_res = self.router(inputs=gate_output, use_kernel=self.use_kernel, ep_group=self.ep_group)
if self.use_kernel:
dispatch_data = MoeDispatch.apply(tokens, *router_res[1:])
dispatch_data = dispatch_data.reshape(self.num_experts, -1, self.d_model)
else:
sec_mask_f = router_res[1].type_as(inputs)
dispatch_data = torch.matmul(sec_mask_f.permute(1, 2, 0), tokens)
# dispatch_data [e, c, h]
if self.experts.comm_name == "all_to_all":
expert_output = self.a2a_process(dispatch_data)
elif self.experts.comm_name == "all_gather":
expert_output = self.tp_process(dispatch_data)
else:
raise NotImplementedError("This kind of communication has not been implemented yet.\n Please use Experts "
"build function.")
# expert_output [e, c, h]
if self.use_kernel:
expert_output = expert_output.reshape(-1, self.d_model)
ans = MoeCombine.apply(expert_output, *router_res)
else:
combine_weights = router_res[0].type_as(inputs)
combine_weights = combine_weights.view(combine_weights.shape[0], -1)
expert_output = expert_output.view(-1, expert_output.shape[-1])
ans = torch.matmul(combine_weights, expert_output)
ans = ans.reshape(inputs.shape)
return ans
class MoeModule(nn.Module):
"""A class for users to create MoE modules in their models.
Args:
dim_model (int): Hidden dimension of training model
num_experts (int): The number experts
top_k (int, optional): The number of experts for dispatchment of each token
capacity_factor_train (float, optional): Capacity factor in routing during training
capacity_factor_eval (float, optional): Capacity factor in routing during evaluation
min_capacity (int, optional): The minimum number of the capacity of each expert
noisy_policy (str, optional): The policy of noisy function. Now we have 'Jitter' and 'Gaussian'.
'Jitter' can be found in `Switch Transformer paper`_.
'Gaussian' can be found in `ViT-MoE paper`_.
drop_tks (bool, optional): Whether drops tokens in evaluation
use_residual (bool, optional): Makes this MoE layer a Residual MoE.
More information can be found in `Microsoft paper`_.
residual_instance (nn.Module, optional): The instance of residual module in Resiual MoE
expert_instance (MoeExperts, optional): The instance of experts module in MoeLayer
expert_cls (Type[nn.Module], optional): The class of each expert when no instance is given
expert_args (optional): The args of expert when no instance is given
.. _Switch Transformer paper:
https://arxiv.org/abs/2101.03961
.. _ViT-MoE paper:
https://arxiv.org/abs/2106.05974
.. _Microsoft paper:
https://arxiv.org/abs/2201.05596
"""
def __init__(self,
dim_model: int,
num_experts: int,
top_k: int = 1,
capacity_factor_train: float = 1.25,
capacity_factor_eval: float = 2.0,
min_capacity: int = 4,
noisy_policy: Optional[str] = None,
drop_tks: bool = True,
use_residual: bool = False,
residual_instance: Optional[nn.Module] = None,
expert_instance: Optional[MoeExperts] = None,
expert_cls: Optional[Type[nn.Module]] = None,
**expert_args):
super().__init__()
noisy_func = None
if noisy_policy is not None:
if noisy_policy == 'Jitter':
noisy_func = UniformNoiseGenerator()
elif noisy_policy == 'Gaussian':
noisy_func = NormalNoiseGenerator(num_experts)
else:
raise NotImplementedError("Unsupported input noisy policy")
if top_k == 1:
moe_router_cls = Top1Router
elif top_k == 2:
moe_router_cls = Top2Router
else:
raise NotImplementedError("top_k > 2 is not supported yet")
self.moe_router = moe_router_cls(capacity_factor_train=capacity_factor_train,
capacity_factor_eval=capacity_factor_eval,
min_capacity=min_capacity,
noisy_func=noisy_func,
drop_tks=drop_tks)
self.use_residual = use_residual
if use_residual:
if residual_instance is not None:
self.residual_module = residual_instance
else:
assert expert_cls is not None, \
"Expert class can't be None when residual instance is not given"
self.residual_module = expert_cls(**expert_args)
with no_shard_zero_context():
self.residual_combine = nn.Linear(dim_model, 2, device=get_current_device())
if expert_instance is not None:
self.experts = expert_instance
else:
assert expert_cls is not None, \
"Expert class can't be None when experts instance is not given"
self.experts = Experts(expert_cls, num_experts, **expert_args)
self.moe_layer = MoeLayer(dim_model=dim_model,
num_experts=num_experts,
router=self.moe_router,
experts=self.experts)
def forward(self, inputs: torch.Tensor):
moe_output = self.moe_layer(inputs)
if self.use_residual:
residual_output = self.residual_module(inputs)
combine_coef = self.residual_combine(inputs)
combine_coef = F.softmax(combine_coef, dim=-1)
output = moe_output * combine_coef[..., 0:1] + residual_output * combine_coef[..., 1:]
else:
output = moe_output
return output
|
from .model_from_config import ModelFromConfig
__all__ = ['ModelFromConfig']
|
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
from abc import ABC, abstractmethod
import torch.nn as nn
from colossalai.builder import build_layer
class ModelFromConfig(nn.Module, ABC):
def __init__(self):
super(ModelFromConfig, self).__init__()
self.layers = nn.ModuleList()
self.layers_cfg = []
def build_from_cfg(self, start=None, end=None):
assert hasattr(self, 'layers_cfg'), 'Cannot find attribute layers_cfg from the module, please check the ' \
'spelling and if you have initialized this variable'
if start is None:
start = 0
if end is None:
end = len(self.layers_cfg)
for cfg in self.layers_cfg[start: end]:
layer = build_layer(cfg)
self.layers.append(layer)
@abstractmethod
def init_weights(self):
pass
def state_dict_for_save_checkpoint(self, destination=None, prefix='',
keep_vars=False):
"""Use this function to override the state dict for
saving checkpoints."""
return self.state_dict(destination, prefix, keep_vars)
|
from torch import nn
from ._utils import calc_acc
from .accuracy_2d import Accuracy2D
from .accuracy_2p5d import Accuracy2p5D
from .accuracy_3d import Accuracy3D
from colossalai.nn.layer.utils import get_tensor_parallel_mode
_parallel_accuracy = {
'2d': Accuracy2D,
'2.5d': Accuracy2p5D,
'3d': Accuracy3D,
}
class Accuracy(nn.Module):
def __init__(self):
super().__init__()
tensor_parallel = get_tensor_parallel_mode()
if tensor_parallel not in _parallel_accuracy:
self.acc = calc_acc
else:
self.acc = _parallel_accuracy[tensor_parallel]()
def forward(self, *args):
return self.acc(*args)
|
import torch
from colossalai.nn.layer.parallel_2d import reduce_by_batch_2d, split_batch_2d
from torch import nn
from ._utils import calc_acc
class Accuracy2D(nn.Module):
"""Accuracy for 2D parallelism
"""
def __init__(self):
super().__init__()
def forward(self, logits, targets):
"""Calculate the accuracy of predicted labels.
Args:
logits (:class:`torch.tensor`): Predicted labels.
targets (:class:`torch.tensor`): True labels from data.
Returns:
float: the accuracy of prediction.
"""
with torch.no_grad():
targets = split_batch_2d(targets)
correct = calc_acc(logits, targets)
correct = reduce_by_batch_2d(correct)
return correct
|
import torch
from colossalai.constants import INPUT_GROUP_3D, WEIGHT_GROUP_3D
from colossalai.nn.layer.parallel_3d import reduce_by_batch_3d, split_tensor_3d
from colossalai.nn.layer.parallel_3d._utils import get_parallel_mode_from_env
from torch import nn
from ._utils import calc_acc
class Accuracy3D(nn.Module):
"""Accuracy for 3D parallelism
"""
def __init__(self):
super().__init__()
self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
def forward(self, logits, targets):
"""Calculate the accuracy of predicted labels.
Args:
logits (:class:`torch.tensor`): Predicted labels.
targets (:class:`torch.tensor`): True labels from data.
Returns:
float: the accuracy of prediction.
"""
with torch.no_grad():
targets = split_tensor_3d(targets, 0, self.weight_parallel_mode)
targets = split_tensor_3d(targets, 0, self.input_parallel_mode)
correct = calc_acc(logits, targets)
correct = reduce_by_batch_3d(correct, self.input_parallel_mode, self.weight_parallel_mode)
return correct
|
import torch
from colossalai.nn.layer.parallel_2p5d import reduce_by_batch_2p5d, split_batch_2p5d
from torch import nn
from ._utils import calc_acc
class Accuracy2p5D(nn.Module):
"""Accuracy for 2p5D parallelism
"""
def __init__(self):
super().__init__()
def forward(self, logits, targets):
"""Calculate the accuracy of predicted labels.
Args:
logits (:class:`torch.tensor`): Predicted labels.
targets (:class:`torch.tensor`): True labels from data.
Returns:
float: the accuracy of prediction.
"""
with torch.no_grad():
targets = split_batch_2p5d(targets)
correct = calc_acc(logits, targets)
correct = reduce_by_batch_2p5d(correct)
return correct
|
import torch
def calc_acc(logits, targets):
preds = torch.argmax(logits, dim=-1)
correct = torch.sum(targets == preds)
return correct
|
import torch
import gc
import psutil
from collections import namedtuple
from colossalai.context.parallel_mode import ParallelMode
from colossalai.utils import get_current_device
from colossalai.core import global_context as gpc
from colossalai.context.parallel_mode import ParallelMode
from colossalai.logging import get_dist_logger
from packaging import version
_GLOBAL_CUDA_MEM_FRACTION = 1.0
def _bytes_to_MB(val, decimal=2):
"""A byte-to-Megabyte converter, default using binary notation.
:param val: X bytes to convert
:return: X' MB
"""
return round(val / (1024 * 1024), decimal)
# copy from PatrickStar
def _get_cpu_memory_info():
ps_mem_info = namedtuple("ps_mem_info", ["total", "free", "cached", "buffers", "used"])
try:
# psutil reads the memory info from /proc/memory_info,
# which results in returning the host memory instead of
# that of container.
# Here we try to read the container memory with method in:
# https://stackoverflow.com/a/46213331/5163915
mems = {}
with open("/sys/fs/cgroup/memory/memory.meminfo", "rb") as f:
for line in f:
fields = line.split()
mems[fields[0]] = int(fields[1]) * 1024
total = mems[b"MemTotal:"]
free = mems[b"MemFree:"]
cached = mems[b"Cached:"]
buffers = mems[b"Buffers:"]
used = total - free - cached - buffers
if used < 0:
used = total - free
mem_info = ps_mem_info(total=total, free=free, cached=cached, buffers=buffers, used=used)
except FileNotFoundError:
mems = psutil.virtual_memory()
mem_info = ps_mem_info(
total=mems.total,
free=mems.free,
cached=mems.cached,
buffers=mems.buffers,
used=mems.used,
)
return mem_info
def report_memory_usage(message, logger=None, report_cpu=False):
"""Calculate and print RAM usage (in GB)
Args:
message (str): A prefix message to add in the log.
logger (:class:`colossalai.logging.DistributedLogger`): The logger used to record memory information.
report_cpu (bool, optional): Whether to report CPU memory.
Raises:
EnvironmentError: Raise error if no distributed environment has been initialized.
"""
if not gpc.is_initialized(ParallelMode.GLOBAL):
raise EnvironmentError("No distributed environment is initialized")
gpu_allocated = _bytes_to_MB(torch.cuda.memory_allocated())
gpu_max_allocated = _bytes_to_MB(torch.cuda.max_memory_allocated())
gpu_cached = _bytes_to_MB(torch.cuda.memory_reserved())
gpu_max_cached = _bytes_to_MB(torch.cuda.max_memory_reserved())
full_log = f"{message}: GPU: allocated {gpu_allocated} MB, max allocated {gpu_max_allocated} MB, " \
+ f"cached: {gpu_cached} MB, max cached: {gpu_max_cached} MB"
if report_cpu:
# python doesn't do real-time garbage collection so do it explicitly to get the correct RAM reports
gc.collect()
vm_stats = psutil.virtual_memory()
vm_used = _bytes_to_MB(vm_stats.total - vm_stats.available)
full_log += f", CPU Virtual Memory: used = {vm_used} MB, percent = {vm_stats.percent}%"
if logger is None:
logger = get_dist_logger()
logger.info(full_log)
# get the peak memory to report correct data, so reset the counter for the next call
if hasattr(torch.cuda, "reset_peak_memory_stats"): # pytorch 1.4+
torch.cuda.reset_peak_memory_stats()
def colo_device_memory_capacity(device: torch.device) -> int:
"""
Get the capacity of the memory of the device
Args:
device (torch.device): a device
Returns:
int: size in byte
"""
assert isinstance(device, torch.device)
if device.type == 'cpu':
mem_info = _get_cpu_memory_info()
# In the context of 1-CPU-N-GPU, the memory capacity of the current process is 1/N overall CPU memory.
return mem_info.total / gpc.num_processes_on_current_node
if device.type == 'cuda':
return torch.cuda.get_device_properties(get_current_device()).total_memory * _GLOBAL_CUDA_MEM_FRACTION
def colo_device_memory_used(device: torch.device) -> int:
"""
Get the device memory on device belonging to the current process.
Args:
device (torch.device): a device
Returns:
int: memory size in bytes
"""
if device.type == 'cpu':
mem_info = _get_cpu_memory_info()
# In the context of 1-CPU-N-GPU, the memory usage of the current process is 1/N CPU memory used.
# Each process consumes the same amount of memory.
ret = mem_info.used / gpc.num_processes_on_current_node
return ret
elif device.type == 'cuda':
ret: int = torch.cuda.memory_allocated(device)
# get the peak memory to report correct data, so reset the counter for the next call
if hasattr(torch.cuda, "reset_peak_memory_stats"): # pytorch 1.4+
torch.cuda.reset_peak_memory_stats(device)
return ret
def colo_set_process_memory_fraction(ratio: float) -> None:
"""colo_set_process_memory_fraction
set how much cuda memory used on the gpu belonging to the current process.
Args:
ratio (float): a ratio between 0. ~ 1.
"""
if version.parse(torch.__version__) < version.parse('1.8'):
logger = get_dist_logger('colo_set_process_memory_fraction')
logger.warning('colo_set_process_memory_fraction failed because torch version is less than 1.8')
return
global _GLOBAL_CUDA_MEM_FRACTION
_GLOBAL_CUDA_MEM_FRACTION = ratio
torch.cuda.set_per_process_memory_fraction(_GLOBAL_CUDA_MEM_FRACTION, get_current_device())
|
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import time
from typing import Tuple
from .cuda import synchronize
class Timer:
"""A timer object which helps to log the execution times, and provides different tools to assess the times.
"""
def __init__(self):
self._started = False
self._start_time = time.time()
self._elapsed = 0
self._history = []
@property
def has_history(self):
return len(self._history) != 0
@property
def current_time(self) -> float:
synchronize()
return time.time()
def start(self):
"""Firstly synchronize cuda, reset the clock and then start the timer.
"""
self._elapsed = 0
synchronize()
self._start_time = time.time()
self._started = True
def lap(self):
"""lap time and return elapsed time
"""
return self.current_time - self._start_time
def stop(self, keep_in_history: bool = False):
"""Stop the timer and record the start-stop time interval.
Args:
keep_in_history (bool, optional): Whether does it record into history
each start-stop interval, defaults to False.
Returns:
int: Start-stop interval.
"""
synchronize()
end_time = time.time()
elapsed = end_time - self._start_time
if keep_in_history:
self._history.append(elapsed)
self._elapsed = elapsed
self._started = False
return elapsed
def get_history_mean(self):
"""Mean of all history start-stop time intervals.
Returns:
int: Mean of time intervals
"""
return sum(self._history) / len(self._history)
def get_history_sum(self):
"""Add up all the start-stop time intervals.
Returns:
int: Sum of time intervals.
"""
return sum(self._history)
def get_elapsed_time(self):
"""Return the last start-stop time interval.
Returns:
int: The last time interval.
Note:
Use it only when timer is not in progress
"""
assert not self._started, 'Timer is still in progress'
return self._elapsed
def reset(self):
"""Clear up the timer and its history
"""
self._history = []
self._started = False
self._elapsed = 0
class MultiTimer:
"""An object contains multiple timers.
Args:
on (bool, optional): Whether the timer is enabled. Default is True.
"""
def __init__(self, on: bool = True):
self._on = on
self._timers = dict()
def start(self, name: str):
"""Start namely one of the timers.
Args:
name (str): Timer's key.
"""
if self._on:
if name not in self._timers:
self._timers[name] = Timer()
return self._timers[name].start()
def stop(self, name: str, keep_in_history: bool):
"""Stop namely one of the timers.
Args:
name (str): Timer's key.
keep_in_history (bool): Whether does it record into history each start-stop interval.
"""
if self._on:
return self._timers[name].stop(keep_in_history)
else:
return None
def get_timer(self, name):
"""Get timer by its name (from multitimer)
Args:
name (str): Timer's key.
Returns:
:class:`colossalai.utils.Timer`: Timer with the name you give correctly.
"""
return self._timers[name]
def reset(self, name=None):
"""Reset timers.
Args:
name (str, optional): If name is designated, the named timer will be reset
and others will not, defaults to None.
"""
if self._on:
if name is not None:
self._timers[name].reset()
else:
for timer in self._timers:
timer.reset()
def is_on(self):
return self._on
def set_status(self, mode: bool):
self._on = mode
def __iter__(self) -> Tuple[str, Timer]:
for name, timer in self._timers.items():
yield name, timer
|
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import torch
from torch.utils.checkpoint import check_backward_validity, detach_variable
from colossalai.context.random import get_states, get_current_mode, set_seed_states, set_mode, sync_states
from .cuda import get_current_device
def copy_to_device(obj, device):
if torch.is_tensor(obj):
# Notice:
# When in no_grad context, requires_gard is False after movement
ret = obj.to(device).detach()
ret.requires_grad = obj.requires_grad
return ret
elif isinstance(obj, list):
return [copy_to_device(i, device) for i in obj]
elif isinstance(obj, tuple):
return tuple([copy_to_device(v, device) for v in obj])
elif isinstance(obj, dict):
return {k: copy_to_device(v, device) for k, v in obj.items()}
else:
return obj
class CheckpointFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, run_function, activation_offload=False, *args):
check_backward_validity(args)
ctx.run_function = run_function
ctx.activation_offload = activation_offload
ctx.device = get_current_device()
# preserve rng states
ctx.fwd_cpu_rng_state = torch.get_rng_state()
sync_states()
ctx.fwd_seed_states = get_states(copy=True)
ctx.fwd_current_mode = get_current_mode()
if hasattr(torch, 'is_autocast_enabled'):
ctx.had_autocast_in_fwd = torch.is_autocast_enabled()
else:
ctx.had_autocast_in_fwd = False
if activation_offload:
inputs_cuda = copy_to_device(args, ctx.device)
else:
inputs_cuda = args
with torch.no_grad():
outputs = run_function(*inputs_cuda)
# Save non-tensor inputs in ctx, keep a placeholder None for tensors
# to be filled out during the backward.
ctx.inputs = []
ctx.tensor_indices = []
tensor_inputs = []
for i, arg in enumerate(args):
if torch.is_tensor(arg):
if activation_offload:
tensor_inputs.append(copy_to_device(arg, 'cpu'))
else:
tensor_inputs.append(arg)
ctx.tensor_indices.append(i)
ctx.inputs.append(None)
else:
ctx.inputs.append(arg)
if activation_offload:
ctx.tensor_inputs = tensor_inputs
else:
ctx.save_for_backward(*tensor_inputs)
return outputs
@staticmethod
def backward(ctx, *args):
if not torch.autograd._is_checkpoint_valid():
raise RuntimeError("Checkpointing is not compatible with .grad() or when an `inputs` parameter is "
"passed to .backward(). Please use .backward() and do not pass its `inputs` argument.")
# Copy the list to avoid modifying original list.
inputs = list(ctx.inputs)
tensor_indices = ctx.tensor_indices
if ctx.activation_offload:
tensors = ctx.tensor_inputs
else:
tensors = ctx.saved_tensors
# store the current states
bwd_cpu_rng_state = torch.get_rng_state()
sync_states()
bwd_seed_states = get_states(copy=True)
bwd_current_mode = get_current_mode()
# set the states to what it used to be
torch.set_rng_state(ctx.fwd_cpu_rng_state)
for parallel_mode, state in ctx.fwd_seed_states.items():
set_seed_states(parallel_mode, state)
set_mode(ctx.fwd_current_mode)
if ctx.activation_offload:
tensors = copy_to_device(tensors, ctx.device)
# Fill in inputs with appropriate saved tensors.
for i, idx in enumerate(tensor_indices):
inputs[idx] = tensors[i]
detached_inputs = detach_variable(tuple(inputs))
if ctx.had_autocast_in_fwd:
with torch.enable_grad(), torch.cuda.amp.autocast():
outputs = ctx.run_function(*detached_inputs)
else:
with torch.enable_grad():
outputs = ctx.run_function(*detached_inputs)
if isinstance(outputs, torch.Tensor):
outputs = (outputs,)
# recover the rng states
torch.set_rng_state(bwd_cpu_rng_state)
for parallel_mode, state in bwd_seed_states.items():
set_seed_states(parallel_mode, state)
set_mode(bwd_current_mode)
# run backward() with only tensor that requires grad
outputs_with_grad = []
args_with_grad = []
for i in range(len(outputs)):
if torch.is_tensor(outputs[i]) and outputs[i].requires_grad:
outputs_with_grad.append(outputs[i])
args_with_grad.append(args[i])
if len(outputs_with_grad) == 0:
raise RuntimeError("none of output has requires_grad=True,"
" this checkpoint() is not necessary")
torch.autograd.backward(outputs_with_grad, args_with_grad)
grads = tuple(inp.grad if isinstance(inp, torch.Tensor) else None for inp in detached_inputs)
return (None, None) + grads
def checkpoint(function, activation_offload, *args):
"""Checkpoint the computation while preserve the rng states, modified from Pytorch torch.utils.checkpoint.
Args:
function: Describe the forward pass function. It should know how to handle the input tuples.
args (list): Tuple containing the parameters of the function
Returns:
Output of running function with provided args.
"""
return CheckpointFunction.apply(function, activation_offload, *args)
|
from .cuda import empty_cache, get_current_device, set_to_cuda, synchronize
from .activation_checkpoint import checkpoint
from .checkpointing import load_checkpoint, save_checkpoint
from .common import (clip_grad_norm_fp32, conditional_context, copy_tensor_parallel_attributes, count_zeros_fp32,
ensure_path_exists, free_port, is_dp_rank_0, is_model_parallel_parameter, is_no_pp_or_last_stage,
is_tp_rank_0, is_using_ddp, is_using_pp, is_using_sequence, multi_tensor_applier,
param_is_not_tensor_parallel_duplicate, print_rank_0, switch_virtual_pipeline_parallel_rank,
sync_model_param, disposable)
from .data_sampler import DataParallelSampler, get_dataloader
from .gradient_accumulation import accumulate_gradient
from .memory import report_memory_usage, colo_device_memory_used, colo_set_process_memory_fraction, colo_device_memory_capacity
from .timer import MultiTimer, Timer
from .tensor_detector import TensorDetector
__all__ = [
'checkpoint', 'free_port', 'print_rank_0', 'sync_model_param', 'is_dp_rank_0', 'is_tp_rank_0',
'is_no_pp_or_last_stage', 'is_using_ddp', 'is_using_pp', 'is_using_sequence', 'conditional_context',
'is_model_parallel_parameter', 'clip_grad_norm_fp32', 'count_zeros_fp32', 'copy_tensor_parallel_attributes',
'param_is_not_tensor_parallel_duplicate', 'get_current_device', 'synchronize', 'empty_cache', 'set_to_cuda',
'report_memory_usage', 'colo_device_memory_capacity', 'colo_device_memory_used', 'colo_set_process_memory_fraction',
'Timer', 'MultiTimer', 'multi_tensor_applier', 'accumulate_gradient', 'DataParallelSampler', 'get_dataloader',
'switch_virtual_pipeline_parallel_rank', 'TensorDetector', 'load_checkpoint', 'save_checkpoint',
'ensure_path_exists', 'disposable'
]
|
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import os
import random
import socket
from pathlib import Path
from typing import Callable, List, Union
import functools
import torch
from torch._six import inf
from torch.nn.parameter import Parameter
try:
import colossal_C
except:
pass
from contextlib import contextmanager
import torch.distributed as dist
from colossalai.constants import (IS_TENSOR_PARALLEL, NUM_PARTITIONS, TENSOR_PARALLEL_ATTRIBUTES)
from colossalai.context.parallel_mode import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.global_variables import tensor_parallel_env as env
from .multi_tensor_apply import multi_tensor_applier
def print_rank_0(msg: str, logger=None):
"""Print messages and save logs(optional). This is executed only if you are the rank-0 gpu.
Args:
msg (str): A string message to output.
logger (:class:`colossalai.logging.DistributedLogger`, optional):
The logger to record the message, defaults to None.
"""
if gpc.get_global_rank() == 0:
if logger is None:
print(msg, flush=True)
else:
logger.info(msg)
def ensure_path_exists(filename: str):
# ensure the path exists
dirpath = os.path.dirname(filename)
if not os.path.exists(dirpath):
Path(dirpath).mkdir(parents=True, exist_ok=True)
def free_port():
while True:
try:
sock = socket.socket()
sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
port = random.randint(20000, 65000)
sock.bind(('localhost', port))
sock.close()
return port
except Exception:
continue
def sync_model_param(model, parallel_mode):
r"""Make sure data parameters are consistent during Data Parallel Mode.
Args:
model (:class:`torch.nn.Module`): A pyTorch model on whose parameters you check the consistency.
parallel_mode (:class:`colossalai.context.ParallelMode`): Parallel mode to be checked.
Note:
The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
"""
if gpc.is_initialized(parallel_mode) and gpc.get_world_size(parallel_mode) > 1:
for param in model.parameters():
ranks = gpc.get_ranks_in_group(parallel_mode)
dist.broadcast(param, src=ranks[0], group=gpc.get_group(parallel_mode))
def is_dp_rank_0():
return not gpc.is_initialized(ParallelMode.DATA) or gpc.is_first_rank(ParallelMode.DATA)
def is_tp_rank_0():
return not gpc.is_initialized(ParallelMode.TENSOR) or gpc.is_first_rank(ParallelMode.TENSOR)
def is_no_pp_or_last_stage():
return not gpc.is_initialized(ParallelMode.PIPELINE) or gpc.is_last_rank(ParallelMode.PIPELINE)
def is_using_ddp():
return gpc.is_initialized(ParallelMode.DATA) and gpc.get_world_size(ParallelMode.DATA) > 1
def is_using_pp():
return gpc.is_initialized(ParallelMode.PIPELINE) and gpc.get_world_size(ParallelMode.PIPELINE) > 1
def is_using_sequence():
return gpc.is_initialized(ParallelMode.SEQUENCE) and gpc.get_world_size(ParallelMode.SEQUENCE) > 1
@contextmanager
def conditional_context(context_manager, enable=True):
if enable:
with context_manager:
yield
else:
yield
class model_branch_context(object):
def __enter__(self):
self.env_status = env.save()
def __exit__(self, *exc_info):
env.load(**self.env_status)
def is_model_parallel_parameter(p):
return hasattr(p, IS_TENSOR_PARALLEL) and getattr(p, IS_TENSOR_PARALLEL)
def _calc_l2_norm(grads):
norm = 0.0
if len(grads) > 0:
dummy_overflow_buf = torch.cuda.IntTensor([0])
norm, _ = multi_tensor_applier(
colossal_C.multi_tensor_l2norm,
dummy_overflow_buf,
[grads],
False # no per-parameter norm
)
return norm
def _calc_lp(grads, norm_type):
norm = 0.0
for grad in grads:
grad_norm = torch.norm(grad, norm_type)
norm += grad_norm**norm_type
return norm
def _move_norm_to_cuda(norm: Union[float, torch.Tensor]) -> Union[float, torch.Tensor]:
if torch.is_tensor(norm) and norm.device.type != 'cuda':
norm = norm.to(torch.cuda.current_device())
return norm
# ======== Gradient Clipping =========
def clip_grad_norm_fp32(parameters, max_norm, norm_type=2):
"""Clips gradient norm of an iterable of parameters whose gradients are in fp32.
This is adapted from :func:`torch.nn.utils.clip_grad.clip_grad_norm_` and
added functionality to handle model parallel parameters.
Note:
the gradients are modified in place.
Args:
parameters (Iterable[:class:`torch.tensor`] or :class:`torch.tensor`):
An iterable of Tensors or a single Tensor that will have gradients normalized.
max_norm (Union[float, int]): Max norm of the gradients.
norm_type (Union[float, int, 'inf']): Type of the used p-norm. Can be ``'inf'`` for infinity norm.
Returns:
float: Total norm of the parameters.
"""
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
# Filter parameters based on:
# - grad should not be none
# - parameter should not be shared
# - should not be a replica due to tensor model parallelism
params: List[Parameter] = []
has_zero_shared_param: bool = False
for param in parameters:
if param.grad is not None:
# Make sure the grads are in fp32
assert param.grad.dtype == torch.float, \
f'expected gradient to be dtype torch.float, but got {param.grad.type()}'
if hasattr(param, 'zero_is_sharded'):
has_zero_shared_param = True
params.append(param)
if len(params) == 0:
return 0.0
# Norm parameters.
max_norm = float(max_norm)
norm_type = float(norm_type)
# Parameters can be on CPU or CUDA
# If parameters are on CPU, disable CUDA kernerls
enable_cuda_kernels = params[0].grad.device.type == 'cuda'
# Calculate norm.
if norm_type == inf:
total_norm = max(p.grad.data.abs().max() for p in params)
total_norm_cuda = torch.cuda.FloatTensor([float(total_norm)])
# Take max across all model-parallel GPUs.
if gpc.is_initialized(ParallelMode.MODEL) and gpc.get_world_size(ParallelMode.MODEL) > 1:
dist.all_reduce(total_norm_cuda,
op=dist.ReduceOp.MAX,
group=gpc.get_group(ParallelMode.MODEL),
async_op=False)
if has_zero_shared_param:
dist.all_reduce(total_norm_cuda,
op=dist.ReduceOp.MAX,
group=gpc.get_group(ParallelMode.DATA),
async_op=False)
total_norm = total_norm_cuda[0].item()
else:
tensor_parallel_grads = []
no_tensor_parallel_grads = []
zero_sharded_grads = []
for p in params:
if is_model_parallel_parameter(p):
reductor = (gpc.get_world_size(ParallelMode.TENSOR) / getattr(p, NUM_PARTITIONS))**(1 / norm_type)
tensor_parallel_grads.append(p.grad.data / reductor)
elif hasattr(p, 'zero_is_sharded'):
zero_sharded_grads.append(p.grad.data)
else:
no_tensor_parallel_grads.append(p.grad.data)
if norm_type == 2.0 and enable_cuda_kernels:
tensor_parallel_norm = _calc_l2_norm(tensor_parallel_grads)**norm_type
no_tensor_parallel_norm = _calc_l2_norm(no_tensor_parallel_grads)**norm_type
zero_sharded_norm = _calc_l2_norm(zero_sharded_grads)**norm_type
else:
tensor_parallel_norm = _calc_lp(tensor_parallel_grads, norm_type)
no_tensor_parallel_norm = _calc_lp(no_tensor_parallel_grads, norm_type)
zero_sharded_norm = _calc_lp(zero_sharded_grads, norm_type)
# If grads are on CPU, the norms is also on CPU. Cast them to CUDA tensors
if not enable_cuda_kernels:
tensor_parallel_norm = _move_norm_to_cuda(tensor_parallel_norm)
no_tensor_parallel_norm = _move_norm_to_cuda(no_tensor_parallel_norm)
zero_sharded_norm = _move_norm_to_cuda(zero_sharded_norm)
# Sum across all model-parallel GPUs.
if gpc.is_initialized(ParallelMode.TENSOR) and len(tensor_parallel_grads) > 0:
dist.all_reduce(tensor_parallel_norm, op=dist.ReduceOp.SUM, group=gpc.get_group(ParallelMode.TENSOR))
# Sum across all zero sharded GPUs
if len(zero_sharded_grads) > 0:
dist.all_reduce(zero_sharded_norm, group=gpc.get_group(ParallelMode.DATA))
no_tensor_parallel_norm += zero_sharded_norm
total_norm = tensor_parallel_norm + no_tensor_parallel_norm
if gpc.is_initialized(ParallelMode.PIPELINE) and gpc.get_world_size(ParallelMode.PIPELINE) > 1:
dist.all_reduce(total_norm, op=dist.ReduceOp.SUM, group=gpc.get_group(ParallelMode.PIPELINE))
total_norm = total_norm**(1.0 / norm_type)
if torch.is_tensor(total_norm):
total_norm = total_norm.item()
# Scale.
clip_coeff = max_norm / (total_norm + 1.0e-6)
if clip_coeff < 1.0:
if enable_cuda_kernels:
grads = [p.grad.detach() for p in params]
dummy_overflow_buf = torch.cuda.IntTensor([0])
multi_tensor_applier(colossal_C.multi_tensor_scale, dummy_overflow_buf, [grads, grads], clip_coeff)
else:
for p in params:
p.grad.detach().mul_(clip_coeff)
return total_norm
def count_zeros_fp32(parameters):
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
# Filter parameters based on:
# - grad should not be none
# - parameter should not be shared
# - should not be a replica due to tensor model parallelism
total_num_zeros = 0.0
for param in parameters:
grad_not_none = param.grad is not None
is_not_tp_duplicate = param_is_not_tensor_parallel_duplicate(param)
if grad_not_none and is_not_tp_duplicate:
grad = param.grad.detach()
num_zeros = grad.numel() - torch.count_nonzero(grad)
total_num_zeros = num_zeros + total_num_zeros
total_num_zeros = torch.IntTensor([int(total_num_zeros)]).cuda()
# Sum across all model-parallel GPUs.
ops = []
ops.append(
dist.all_reduce(total_num_zeros, op=dist.ReduceOp.SUM, group=gpc.get_group(ParallelMode.TENSOR), async_op=True))
if gpc.is_initialized(ParallelMode.PIPELINE):
ops.append(
dist.all_reduce(total_num_zeros,
op=dist.ReduceOp.SUM,
group=gpc.get_group(ParallelMode.PIPELINE),
async_op=True))
for req in ops:
req.wait()
total_num_zeros = total_num_zeros.item()
return total_num_zeros
def copy_tensor_parallel_attributes(src_tensor, dst_tensor):
for attr in TENSOR_PARALLEL_ATTRIBUTES:
if hasattr(src_tensor, attr):
val = getattr(src_tensor, attr)
setattr(dst_tensor, attr, val)
def param_is_not_tensor_parallel_duplicate(param):
return (hasattr(param, IS_TENSOR_PARALLEL) and getattr(param, IS_TENSOR_PARALLEL)) or (gpc.get_local_rank(
ParallelMode.TENSOR) == 0)
@contextmanager
def switch_virtual_pipeline_parallel_rank(rank):
prev_rank = gpc.virtual_pipeline_parallel_rank
try:
gpc.set_virtual_pipeline_parallel_rank(rank)
yield
finally:
gpc.set_virtual_pipeline_parallel_rank(prev_rank)
def disposable(func: Callable) -> Callable:
executed = False
@functools.wraps(func)
def wrapper(*args, **kwargs):
nonlocal executed
if not executed:
executed = True
return func(*args, **kwargs)
return wrapper
|
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import torch
def set_to_cuda(models):
"""Send model to gpu.
:param models: nn.module or a list of module
"""
if isinstance(models, list) and len(models) > 1:
ret = []
for model in models:
ret.append(model.to(get_current_device()))
return ret
elif isinstance(models, list):
return models[0].to(get_current_device())
else:
return models.to(get_current_device())
def get_current_device() -> torch.device:
"""
Returns currently selected device (gpu/cpu).
If cuda available, return gpu, otherwise return cpu.
"""
if torch.cuda.is_available():
return torch.device(f'cuda:{torch.cuda.current_device()}')
else:
return torch.device('cpu')
def synchronize():
"""Similar to cuda.synchronize().
Waits for all kernels in all streams on a CUDA device to complete.
"""
if torch.cuda.is_available():
torch.cuda.synchronize()
def empty_cache():
"""Similar to cuda.empty_cache()
Releases all unoccupied cached memory currently held by the caching allocator.
"""
if torch.cuda.is_available():
torch.cuda.empty_cache()
|
import torch.nn as nn
import torch.distributed as dist
from colossalai.core import global_context as gpc
from colossalai.context.moe_context import MOE_CONTEXT
from colossalai.context import ParallelMode
from .common import is_using_ddp
from typing import Dict, List
def get_moe_epsize_param_dict(model: nn.Module) -> Dict[int, List[nn.Parameter]]:
"""Returns a parameter dictionary, the key of which is the expert parallel
size of every parameter. Since the parameters in data parallelism is replicated
in each GPU, we set their ep_size to 1.
Args:
model (:class:`torch.nn.Module`): A pyTorch `nn.Module` from which we get dict.
"""
epsize_param_dict = dict()
for param in model.parameters():
if not hasattr(param, 'moe_info'):
ep_size = 1 # set ep_size to 1 for dp parameters
else:
ep_size = param.moe_info.ep_size
if ep_size not in epsize_param_dict:
epsize_param_dict[ep_size] = []
epsize_param_dict[ep_size].append(param)
return epsize_param_dict
def sync_moe_model_param(model: nn.Module):
"""Make sure model parameters are consistent in MoE parallel context.
Args:
model (:class:`torch.nn.Module`): A pyTorch model on whose parameters you check the consistency.
"""
if is_using_ddp():
param_dict = get_moe_epsize_param_dict(model)
# synchrosize the parameters whose dp_group is the whole world
if 1 in param_dict:
src_rank = gpc.get_ranks_in_group(ParallelMode.DATA)[0]
for param in param_dict[1]:
dist.broadcast(param, src=src_rank, group=gpc.get_group(ParallelMode.DATA))
for ep_size in param_dict:
# When ep_size = world_size, communication is not needed
if ep_size != 1 and ep_size != MOE_CONTEXT.world_size:
src_rank = dist.get_rank(MOE_CONTEXT.parallel_info_dict[ep_size].ep_group)
for param in param_dict[ep_size]:
dist.broadcast(param, src=src_rank, group=param.moe_info.dp_group)
|
from collections import OrderedDict
from itertools import chain
import torch
import torch.distributed as dist
from colossalai.communication.collective import scatter_object_list
from colossalai.context.parallel_mode import ParallelMode
from colossalai.core import global_context as gpc
try:
from torch.nn.modules.module import _EXTRA_STATE_KEY_SUFFIX
except ImportError:
_EXTRA_STATE_KEY_SUFFIX = '_extra_state'
from .common import is_using_pp
__all__ = ["save_checkpoint", "load_checkpoint"]
def broadcast_state_dict(state_dict, parallel_mode):
state_dict = [state_dict.copy() if isinstance(state_dict, dict) else state_dict]
src_rank = gpc.get_ranks_in_group(parallel_mode)[0]
dist.broadcast_object_list(state_dict, src=src_rank, group=gpc.get_cpu_group(parallel_mode))
return state_dict[0]
def partition_tensor_parallel_state_dict(state_dict: OrderedDict,
parallel_mode: ParallelMode,
dims: dict = dict(),
partition_states: dict = dict()):
src_rank = gpc.get_ranks_in_group(parallel_mode)[0]
depth = gpc.get_world_size(parallel_mode)
if gpc.get_local_rank(parallel_mode) == 0:
partitioned_state_list = [dict() for _ in range(depth)]
for key in list(state_dict.keys()):
param = state_dict.pop(key)
dim = dims.get(key, 0)
do_partition = partition_states.get(key, True)
if do_partition:
param = torch.chunk(param, depth, dim=dim)
for i, p in enumerate(partitioned_state_list):
p[key] = param[i] if do_partition else param
else:
partitioned_state_list = [None for _ in range(depth)]
partitioned_state = [None]
scatter_object_list(partitioned_state, partitioned_state_list, src=src_rank, group=gpc.get_cpu_group(parallel_mode))
return partitioned_state[0]
def gather_tensor_parallel_state_dict(
state_dict: OrderedDict,
parallel_mode: ParallelMode,
dims: dict = dict(),
partition_states: dict = dict(),
keep_vars: bool = False,
):
dst_rank = gpc.get_ranks_in_group(parallel_mode)[0]
depth = gpc.get_world_size(parallel_mode)
for key in list(state_dict.keys()):
param = state_dict.pop(key)
param = param if keep_vars else param.detach()
dim = dims.get(key, 0)
do_partition = partition_states.get(key, True)
if do_partition:
temp = param.transpose(0, dim).contiguous()
gather_list = None
if gpc.get_local_rank(parallel_mode) == 0:
shape = list(param.shape)
shape[0], shape[dim] = shape[dim], shape[0]
shape[0] *= depth
param = torch.empty(shape, dtype=param.dtype, device=param.device)
gather_list = list(torch.chunk(param, depth, dim=0))
dist.gather(temp, gather_list, dst=dst_rank, group=gpc.get_cpu_group(parallel_mode))
param = torch.transpose(param, 0, dim)
# update params in state_dict only on local rank 0
if gpc.get_local_rank(parallel_mode) == 0:
state_dict[key] = param
return state_dict
def _send_state_dict(state_dict, dst, parallel_mode):
state_tensor, state_size = dist.distributed_c10d._object_to_tensor(state_dict)
dist.send(state_size, dst, group=gpc.get_cpu_group(parallel_mode))
dist.send(state_tensor, dst, group=gpc.get_cpu_group(parallel_mode))
def _recv_state_dict(src, parallel_mode):
state_size = torch.tensor([0], dtype=torch.long)
dist.recv(state_size, src, group=gpc.get_cpu_group(parallel_mode))
state_tensor = torch.empty(state_size.item(), dtype=torch.uint8)
dist.recv(state_tensor, src, group=gpc.get_cpu_group(parallel_mode))
state_dict = dist.distributed_c10d._tensor_to_object(state_tensor, state_size)
return state_dict
def partition_pipeline_parallel_state_dict(model, state_dict):
pipeline_state = OrderedDict()
if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
# receive all states from prev stage
if not gpc.is_first_rank(ParallelMode.PIPELINE):
state_dict = _recv_state_dict(gpc.get_prev_global_rank(ParallelMode.PIPELINE), ParallelMode.PIPELINE)
# move states to output
for name, _ in model.named_parameters(recurse=True):
if name in state_dict:
pipeline_state[name] = state_dict.pop(name)
for name, _ in model.named_buffers(recurse=True):
if name in state_dict:
pipeline_state[name] = state_dict.pop(name)
for name, _ in model.named_modules():
extra_state_key = name + "." + _EXTRA_STATE_KEY_SUFFIX
if extra_state_key in state_dict:
pipeline_state[extra_state_key] = state_dict.pop(extra_state_key)
# send rest states to next stage
if not gpc.is_last_rank(ParallelMode.PIPELINE):
_send_state_dict(state_dict, gpc.get_next_global_rank(ParallelMode.PIPELINE), ParallelMode.PIPELINE)
return pipeline_state
def gather_pipeline_parallel_state_dict(state_dict):
gathered_states = ([None for _ in range(gpc.get_world_size(ParallelMode.PIPELINE))]
if gpc.get_local_rank(ParallelMode.PIPELINE) == 0 else None)
dist.gather_object(
state_dict,
gathered_states,
dst=gpc.get_ranks_in_group(ParallelMode.PIPELINE)[0],
group=gpc.get_cpu_group(ParallelMode.PIPELINE),
)
state_dict = (OrderedDict(chain.from_iterable(state.items() for state in gathered_states))
if gpc.get_local_rank(ParallelMode.PIPELINE) == 0 else OrderedDict())
return state_dict
def save_checkpoint(file,
epoch: int,
model: torch.nn.Module,
optimizer: torch.optim.Optimizer = None,
lr_scheduler: torch.optim.lr_scheduler._LRScheduler = None,
**kwargs):
"""Stores the checkpoint to disk. Saves all the training components' parameters or buffers, such as model, optimizer,
lr_scheduler etc. into a checkpoint dictionary.
Args:
file: a file-like object (has to implement write and flush) or a string or os.PathLike object containing a
file name.
epoch (int): Epoch number (indicates how many epochs have you trained this model).
model (:class:`torch.nn.Module`): Model to be saved.
optimizer (Union[:class:`torch.optim.Optimizer`, :class:`colossalai.nn.optimizer`]): Optimizer to be saved.
lr_scheduler (Union[:class:`torch.optim.lr_scheduler`, :class:`colossalai.nn.lr_scheduler`], optional):
lr_scheduler to be saved, defaults to None.
pickle_module: module used for pickling metadata and objects
pickle_protocol: can be specified to override the default protocol
"""
# ckpt container
checkpoint = {"epoch": epoch}
model_state = model.state_dict()
if is_using_pp() and gpc.get_local_rank(ParallelMode.TENSOR) == 0:
model_state = gather_pipeline_parallel_state_dict(model_state)
if gpc.get_global_rank() == 0:
checkpoint["model"] = model_state
# if optimizer is not None:
# checkpoint['optimizer'] = optimizer.state_dict()
# if lr_scheduler is not None:
# checkpoint['lr_scheduler'] = lr_scheduler.state_dict()
torch.save(checkpoint, file, **kwargs)
def load_checkpoint(
file,
model: torch.nn.Module,
optimizer: torch.optim.Optimizer = None,
lr_scheduler: torch.optim.lr_scheduler._LRScheduler = None,
strict: bool = True,
):
"""Loads training states from a checkpoint file.
Args:
file: a file-like object (has to implement read(), readline(), tell(), and seek()), or a string or os.PathLike
object containing a file name.
model (:class:`torch.nn.Module`): Model to load saved weights and buffers.
optimizer (Union[:class:`torch.optim.Optimizer`, :class:`colossalai.nn.optimizer`]): Optimizer to recuperate.
lr_scheduler (:class:`torch.optim.lr_scheduler._LRScheduler`, optional):
lr_scheduler to recuperate, defaults to None.
strict (bool, optional): Whether to strictly enforce that the keys in :attr:`state_dict`
of the checkpoint match the names of parameters and buffers in model, defaults to True.
Returns:
int: The saved epoch number.
Raises:
RuntimeError: Raise error if the model/optimizer cannot successfully be recuperated
"""
state_dict = (torch.load(file, map_location=torch.device("cpu"))
if gpc.get_local_rank(ParallelMode.MODEL) == 0 else None)
# model states
model_state = state_dict.pop("model") if state_dict is not None else dict()
# pipeline
if is_using_pp():
model_state = partition_pipeline_parallel_state_dict(model, model_state)
try:
model.load_state_dict(model_state, strict=strict)
except RuntimeError as e:
error_msgs = str(e)
if error_msgs.startswith("Error(s) in loading state_dict for "):
error_msgs = error_msgs.split("\n\t")[1:]
dst_rank = gpc.get_ranks_in_group(ParallelMode.MODEL)[0]
all_error_msgs = [None for _ in range(gpc.get_world_size(ParallelMode.MODEL))]
dist.gather_object(error_msgs, all_error_msgs, dst=dst_rank, group=gpc.get_cpu_group(ParallelMode.MODEL))
if gpc.get_global_rank() == 0:
all_error_msgs = list(chain.from_iterable(all_error_msgs))
raise RuntimeError("Error(s) in loading state_dict for {}:\n\t{}".format(
model.__class__.__name__, "\n\t".join(all_error_msgs)))
else:
raise e
# broadcast the rest states
state_dict = broadcast_state_dict(state_dict, ParallelMode.MODEL)
# # optimizer states
# if optimizer is not None and 'optimizer' in state_dict:
# optimizer.load_state_dict(state_dict['optimizer'])
# # lr scheduler states
# if lr_scheduler is not None and 'lr_scheduler' in state_dict:
# lr_scheduler.load_state_dict(state_dict['lr_scheduler'])
# last epoch
last_epoch = state_dict.pop("epoch", -1)
return last_epoch
|
from colossalai.context.singleton_meta import SingletonMeta
import torch
from typing import Tuple, Optional
from colossalai.logging import DistributedLogger
def colo_model_optimizer_usage(optim) -> Tuple[int, int]:
"""Trace the optimizer memory usage
Args:
optim (ShardedOptimV2): an instance of ShardedOptimver
Returns:
Tuple[int, int]: cuda/cpu memory usage in Byte
"""
if optim is None:
return 0, 0
assert hasattr(optim, 'get_memory_usage'), f"{type(optim)} has no attr get_memory_usage()"
return optim.get_memory_usage()
def colo_model_mem_usage(model: torch.nn.Module) -> Tuple[int, int]:
"""
Trace the model memory usage.
Args:
model (torch.nn.Module): a torch model
Returns:
Tuple[int, int]: cuda memory usage in Byte, cpu memory usage in Byte
"""
if model is None:
return 0, 0
def _get_tensor_mem_use(t: Optional[torch.Tensor]):
if t is None:
return 0, 0
assert isinstance(t, torch.Tensor)
_cpu_mem_usage, _cuda_mem_usage = 0, 0
if t.device.type == 'cpu':
_cpu_mem_usage += t.numel() * t.element_size()
elif t.device.type == 'cuda':
_cuda_mem_usage += t.numel() * t.element_size()
return _cuda_mem_usage, _cpu_mem_usage
cuda_mem_usage = 0
cpu_mem_usage = 0
for param in model.parameters():
if hasattr(param, 'colo_attr'):
t_cuda, t_cpu = param.colo_attr.get_memory_usage()
cuda_mem_usage += t_cuda
cpu_mem_usage += t_cpu
else:
t_cuda, t_cpu = _get_tensor_mem_use(param.data)
cuda_mem_usage += t_cuda
cpu_mem_usage += t_cpu
t_cuda, t_cpu = _get_tensor_mem_use(param.grad)
cuda_mem_usage += t_cuda
cpu_mem_usage += t_cpu
return cuda_mem_usage, cpu_mem_usage
class ModelDataTracer(metaclass=SingletonMeta):
"""
A tracer singleton to trace model data usage during runtime.
You have to register a model on the singleton first.
"""
def __init__(self) -> None:
self._logger = DistributedLogger("ModelDataTracer")
self._model = None
self._opitimizer = None
def _get_mem_usage(self) -> Tuple[int, int]:
"""
get the memory usage of the model registered.
Returns:
Tuple[int, int]: cuda, cpu mem usage
"""
cuda_use_opt, cpu_use_opt = colo_model_optimizer_usage(self._opitimizer)
cuda_use_model, cpu_use_model = colo_model_mem_usage(self._model)
return cuda_use_opt + cuda_use_model, cpu_use_opt + cpu_use_model
def register_model(self, model) -> None:
if self._model is not None:
self._logger.warning("ModelDataTracer has already registered a model")
self._model = model
def register_optimizer(self, optimizer) -> None:
if self._opitimizer is not None:
self._logger.warning("ModelDataTracer has already registered an optimizer")
self._opitimizer = optimizer
@property
def cpu_usage(self):
_, cpu_usage = self._get_mem_usage()
return cpu_usage
@property
def cuda_usage(self):
cuda_usage, _ = self._get_mem_usage()
return cuda_usage
@property
def both_mem_usage(self):
return self._get_mem_usage()
GLOBAL_MODEL_DATA_TRACER = ModelDataTracer()
|
from .memory_monitor import AsyncMemoryMonitor, SyncCudaMemoryMonitor
from .memstats_collector import MemStatsCollector
__all__ = ['AsyncMemoryMonitor', 'SyncCudaMemoryMonitor', 'MemStatsCollector']
|
from abc import abstractmethod
from concurrent.futures import ThreadPoolExecutor
from time import sleep, time
import json
import torch
from colossalai.utils.memory import colo_device_memory_used
from colossalai.utils import get_current_device
class MemoryMonitor:
"""Base class for all types of memory monitor.
All monitors should have a list called `time_stamps` and a list called `mem_stats`.
"""
def __init__(self):
self.time_stamps = []
self.mem_stats = []
def __len__(self):
return len(self.mem_stats)
@abstractmethod
def start(self):
pass
@abstractmethod
def finish(self):
pass
def state_dict(self):
return {
"time_stamps": self.time_stamps,
"mem_stats": self.mem_stats,
}
def save(self, filename):
with open(filename, "w") as f:
json.dump(self.state_dict(), f)
def clear(self):
self.mem_stats.clear()
self.time_stamps.clear()
class AsyncMemoryMonitor(MemoryMonitor):
"""
An Async Memory Monitor runing during computing. Sampling memory usage of the current GPU
at interval of `1/(10**power)` sec.
The idea comes from Runtime Memory Tracer of PatrickStar
`PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management`_
Usage::
async_mem_monitor = AsyncMemoryMonitor()
input = torch.randn(2, 20).cuda()
OP1 = torch.nn.Linear(20, 30).cuda()
OP2 = torch.nn.Linear(30, 40).cuda()
async_mem_monitor.start()
output = OP1(input)
async_mem_monitor.finish()
async_mem_monitor.start()
output = OP2(output)
async_mem_monitor.finish()
async_mem_monitor.save('log.pkl')
Args:
power (int, optional): the power of time interva. Defaults to 10.
.. _PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management:
https://arxiv.org/abs/2108.05818
"""
def __init__(self, power: int = 10):
super().__init__()
self.keep_measuring = False
current_device = get_current_device()
def _set_cuda_device():
torch.cuda.set_device(current_device)
self.executor = ThreadPoolExecutor(max_workers=1, initializer=_set_cuda_device)
self.monitor_thread = None
self.interval = 1 / (10**power)
def set_interval(self, power: int):
self.clear()
self.interval = 1 / (10**power)
def is_measuring(self):
return self.keep_measuring
def start(self):
self.keep_measuring = True
self.monitor_thread = self.executor.submit(self._measure_usage)
def finish(self):
if self.keep_measuring is False:
return 0
self.keep_measuring = False
max_usage = self.monitor_thread.result()
self.monitor_thread = None
self.time_stamps.append(time())
self.mem_stats.append(max_usage)
return max_usage
def _measure_usage(self):
max_usage = 0
while self.keep_measuring:
max_usage = max(
max_usage,
colo_device_memory_used(get_current_device()),
)
sleep(self.interval)
return max_usage
class SyncCudaMemoryMonitor(MemoryMonitor):
"""
A synchronized cuda memory monitor.
It only record the maximum allocated cuda memory from start point to finish point.
"""
def __init__(self, power: int = 10):
super().__init__()
def start(self):
torch.cuda.synchronize()
torch.cuda.reset_peak_memory_stats()
def finish(self):
torch.cuda.synchronize()
self.time_stamps.append(time())
max_usage = torch.cuda.max_memory_allocated()
self.mem_stats.append(max_usage)
return max_usage
|
from colossalai.utils.memory_tracer.model_data_memtracer import GLOBAL_MODEL_DATA_TRACER
from colossalai.utils.memory import colo_device_memory_used
from colossalai.utils.memory_tracer import SyncCudaMemoryMonitor
import torch
import time
from typing import List
class MemStatsCollector:
"""
A Memory statistic collector.
It works in two phases.
Phase 1. Collection Phase: collect memory usage statistics of CPU and GPU.
The first iteration of DNN training.
Phase 2. Runtime Phase: use the read-only collected stats
The rest iterations of DNN training.
It has a Sampling counter which is reset after DNN training iteration.
"""
def __init__(self) -> None:
self._mem_monitor = SyncCudaMemoryMonitor()
self._model_data_cuda_list = []
self._overall_cuda_list = []
self._model_data_cpu_list = []
self._overall_cpu_list = []
self._non_model_data_cuda_list = []
self._non_model_data_cpu_list = []
self._sampling_time = []
self._start_flag = False
self._step_idx = 0
self._step_total = 0
def overall_mem_stats(self, device_type: str) -> List[int]:
if device_type == 'cuda':
return self._overall_cuda_list
elif device_type == 'cpu':
return self._overall_cpu_list
else:
raise TypeError
def model_data_list(self, device_type: str) -> List[int]:
if device_type == 'cuda':
return self._model_data_cuda_list
elif device_type == 'cpu':
return self._model_data_cpu_list
else:
raise TypeError
def non_model_data_list(self, device_type: str) -> List[int]:
if device_type == 'cuda':
return self._non_model_data_cuda_list
elif device_type == 'cpu':
return self._non_model_data_cpu_list
else:
raise TypeError
def next_period_non_model_data_usage(self, device_type: str) -> int:
"""Get max non model data memory usage of current sampling period
Args:
device_type (str): device type, can be 'cpu' or 'cuda'.
Returns:
int: max non model data memory usage of current sampling period
"""
assert not self._start_flag, 'Cannot get mem stats info during collection phase.'
assert self._step_total > 0, 'Cannot get mem stats info before collection phase.'
next_non_model_data = self.non_model_data_list(device_type)[self._step_idx]
self._step_idx = (self._step_idx + 1) % self._step_total
return next_non_model_data
@property
def sampling_time(self):
return [t - self._sampling_time[0] for t in self._sampling_time]
def start_collection(self):
self._start_flag = True
self._mem_monitor.start()
def finish_collection(self):
self.sample_overall_data()
self._step_total = len(self._sampling_time)
self._start_flag = False
self._mem_monitor.finish()
def sample_model_data(self) -> None:
"""Sampling model data statistics.
"""
if self._start_flag:
cuda_mem, cpu_mem = GLOBAL_MODEL_DATA_TRACER.both_mem_usage
self._model_data_cuda_list.append(cuda_mem)
self._model_data_cpu_list.append(cpu_mem)
def sample_overall_data(self) -> None:
"""Sampling non model data statistics.
"""
if self._start_flag:
# overall data recording is after model data recording
if len(self._model_data_cuda_list) == 0:
return
self._overall_cuda_list.append(self._mem_monitor.finish())
self._overall_cpu_list.append(colo_device_memory_used(torch.device('cpu')))
assert len(self._model_data_cuda_list) == len(self._overall_cuda_list)
self._non_model_data_cuda_list.append(self._overall_cuda_list[-1] - self._model_data_cuda_list[-1])
self._non_model_data_cpu_list.append(self._overall_cpu_list[-1] - self._model_data_cpu_list[-1])
self._sampling_time.append(time.time())
self._mem_monitor.start()
def sample_memstats(self) -> None:
"""
Sampling memory statistics.
Record the current model data CUDA memory usage as well as system CUDA memory usage.
Advance the sampling cnter.
"""
if self._start_flag:
self._model_data_cuda_list.append(GLOBAL_MODEL_DATA_TRACER.cuda_usage)
self._overall_cuda_list.append(self._mem_monitor.finish())
self._non_model_data_cuda_list.append(self._overall_cuda_list[-1] - self._model_data_cuda_list[-1])
self._model_data_cpu_list.append(GLOBAL_MODEL_DATA_TRACER.cpu_usage)
# FIXME(jiaruifang) cpu sys used should also return from self._mem_monitor()
self._overall_cpu_list.append(colo_device_memory_used(torch.device(f'cpu')))
self._non_model_data_cpu_list.append(self._overall_cpu_list[-1] - self._model_data_cpu_list[-1])
self._sampling_time.append(time.time())
self._mem_monitor.start()
def clear(self) -> None:
self._model_data_cuda_list = []
self._overall_cuda_list = []
self._model_data_cpu_list = []
self._overall_cpu_list = []
self._start_flag = False
self._step_idx = 0
self._step_total = 0
|
from .multi_tensor_apply import MultiTensorApply
multi_tensor_applier = MultiTensorApply(2048 * 32)
|
# modified from https://github.com/NVIDIA/apex/blob/master/apex/multi_tensor_apply/multi_tensor_apply.py
class MultiTensorApply(object):
"""
Apply an operation to a list of tensors efficiently.
Args:
chunk_size (int): Size of a chunk.
"""
available = False
warned = False
def __init__(self, chunk_size):
try:
import colossal_C
MultiTensorApply.available = True
self.chunk_size = chunk_size
except ImportError as err:
MultiTensorApply.available = False
MultiTensorApply.import_err = err
def check_avail(self):
if not MultiTensorApply.available:
raise RuntimeError(
"Attempted to call MultiTensorApply method, but MultiTensorApply "
"is not available, possibly because Apex was installed without "
"--cpp_ext --cuda_ext. Original import error message:",
MultiTensorApply.import_err)
def __call__(self, op, noop_flag_buffer, tensor_lists, *args):
self.check_avail()
return op(self.chunk_size,
noop_flag_buffer,
tensor_lists,
*args)
|
import torch.nn as nn
from typing import List
from colossalai.engine import BaseGradientHandler
from typing import Iterable
from torch.optim import Optimizer
from torch.optim.lr_scheduler import _LRScheduler
from ._gradient_accumulation import GradAccumDataloader, GradAccumOptimizer, GradAccumLrSchedulerByStep, GradAccumGradientHandler
def accumulate_gradient(model: nn.Module,
optimizer: Optimizer,
dataloader: Iterable,
accumulate_size: int,
gradient_handlers: List[BaseGradientHandler] = None,
lr_scheduler: _LRScheduler = None):
r"""Turning model, optimizer, dataloader into corresponding object for gradient accumulation.
Args:
model (:class:`torch.nn.Module`): your model object for gradient accumulation.
optimizer (:class:`torch.optim.Optimizer`): your optimizer object for gradient accumulation.
dataloader (:class:`torch.utils.data.DataLoader` or iterable objects):
your dataloader object, would be called like iter(dataloader)
accumulate_size (int): the number of steps to accumulate gradients
gradient_handlers (List[:class:`colossalai.engine.BaseGradientHandler`]):
list of gradient handler objects. Default is None.
lr_scheduler (`torch.optim.lr_scheduler` or `colossalai.nn.lr_scheduler`):
your ``lr_scheduler`` object for gradient accumulation. Defaults to None.
More details about `gradient_handlers` could be found in
`Gradient_handler <https://github.com/hpcaitech/ColossalAI/tree/main/colossalai/engine/gradient_handler>`_.
More details about `lr_scheduler` could be found
`lr_scheduler <https://github.com/hpcaitech/ColossalAI/tree/main/colossalai/nn/lr_scheduler>`_. and
`how to adjust learning rate <https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate>`_.
"""
optimizer = GradAccumOptimizer(optimizer, accumulate_size=accumulate_size, model=model)
dataloader = GradAccumDataloader(dataloader, accumulate_size=accumulate_size)
if gradient_handlers is not None:
gradient_handlers = [GradAccumGradientHandler(handler, accumulate_size) for handler in gradient_handlers]
if lr_scheduler is not None:
lr_scheduler = GradAccumLrSchedulerByStep(lr_scheduler, accumulate_size=accumulate_size)
return optimizer, dataloader, gradient_handlers, lr_scheduler
__all__ = ['accumulate_gradient', 'GradAccumDataloader', 'GradAccumOptimizer',
'GradAccumLrSchedulerByStep', 'GradAccumGradientHandler']
|
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import torch.nn as nn
from torch import Tensor
from typing import Iterable, Any
from colossalai.nn.optimizer import ColossalaiOptimizer
from torch.nn.parallel.distributed import DistributedDataParallel
from torch.optim import Optimizer
from torch.optim.lr_scheduler import _LRScheduler
from torch.utils.data import DataLoader
from colossalai.utils import conditional_context
from colossalai.engine import BaseGradientHandler
class GradAccumOptimizer(ColossalaiOptimizer):
"""A wrapper for the optimizer to enable gradient accumulation by skipping the steps
before accumulation size is reached.
Args:
optim (:class:`torch.optim.Optimizer`): Your optimizer object for gradient accumulation.
accumulate_size (int): The number of steps to accumulate gradients.
model (:class:`torch.nn.Module`):
Your model object to check if it is DistributedDataParallel for special handling of no_sync() context.
"""
def __init__(self, optim: Optimizer, accumulate_size: int, model: nn.Module = None):
super().__init__(optim)
self.accumulate_size = accumulate_size
self.accumulate_step = 0
# handle pytorch ddp auto all reduce
self.model = model
self.is_torch_ddp = isinstance(self.model, DistributedDataParallel)
def zero_grad(self, *args, **kwargs):
if self.accumulate_step == 0:
self.optim.zero_grad(*args, **kwargs)
def step(self, *args, **kwargs):
if self.accumulate_step < self.accumulate_size:
return None
else:
self.accumulate_step = 0
return self.optim.step(*args, **kwargs)
def clip_grad_norm(self, model: nn.Module, max_norm: float):
if self.accumulate_step < self.accumulate_size:
pass
else:
self.optim.clip_grad_norm(model, max_norm)
def backward(self, loss: Tensor):
self.accumulate_step += 1
if self.is_torch_ddp:
no_sync = self.accumulate_step < self.accumulate_size
with conditional_context(self.model.no_sync(), enable=no_sync):
scaled_loss = loss / self.accumulate_size
self.optim.backward(scaled_loss)
else:
scaled_loss = loss / self.accumulate_size
self.optim.backward(scaled_loss)
def backward_by_grad(self, tensor: Tensor, grad: Tensor):
self.accumulate_step += 1
no_sync = self.is_torch_ddp and self.accumulate_step < self.accumulate_size
if no_sync:
with self.model.no_sync():
self.optim.backward_by_grad(tensor, grad)
else:
self.optim.backward_by_grad(tensor, grad)
class GradAccumDataloader:
"""A wrapper for dataloader to enable gradient accumulation by dropping the last incomplete steps.
Note:
The dataloader would drop the last incomplete steps for gradient accumulation.
For example, if a dataloader has 10 batches of data and accumulate size is 4. The model parameters will
be updated only twice at step 4 and step 8. The last two batches of data do not form a complete 4-step cycle.
Thus, they will be automatically skipped by this class. If the dataloader is not standard PyTorch dataloader,
(e.g. Dali dataloader), this class will automatically consume (load data for nothing) the remaining 2 batches.
Args:
optim (``Iterable``): Your dataloader object for gradient accumulation.
accumulate_size (int): The number of steps to accumulate gradients.
"""
def __init__(self, dataloader: Iterable, accumulate_size: int) -> None:
self.dataloader = dataloader
self.consume_remain_data = not isinstance(dataloader, DataLoader)
self.steps_per_epoch = len(dataloader) - len(dataloader) % accumulate_size
def __getattr__(self, __name: str) -> Any:
return getattr(self.dataloader, __name)
def __len__(self):
return self.steps_per_epoch
def __iter__(self):
self._cur_step = 0
self._dataiter = iter(self.dataloader)
return self
def __next__(self) -> Any:
if self._cur_step < self.steps_per_epoch:
self._cur_step += 1
if self._cur_step == self.steps_per_epoch and self.consume_remain_data:
# this is to handle non standard pytorch dataloader
# such as dali dataloader
while True:
try:
_ = next(self._dataiter)
except StopIteration:
break
return next(self._dataiter)
else:
raise StopIteration
class GradAccumLrSchedulerByStep(_LRScheduler):
"""A wrapper for the LR scheduler to enable gradient accumulation by skipping the steps
before accumulation size is reached.
Args:
lr_scheduler (:class:`torch.optim.lr_scheduler._LRScheduler`):
Your ``lr_scheduler`` object for gradient accumulation.
accumulate_size (int): The number of steps to accumulate gradients.
"""
def __init__(self, lr_scheduler: _LRScheduler, accumulate_size: int) -> None:
self.lr_scheduler = lr_scheduler
self.accumulate_size = accumulate_size
self.accumulate_step = 0
@staticmethod
def compute_effective_steps_per_epoch(dataloader: Iterable, accumulate_size: int):
return len(dataloader) // accumulate_size
def __getattr__(self, __name: str) -> Any:
return getattr(self.lr_scheduler, __name)
def step(self, *args, **kwargs):
self.accumulate_step += 1
if self.accumulate_step < self.accumulate_size:
pass
else:
self.accumulate_step = 0
self.lr_scheduler.step(*args, **kwargs)
def get_lr(self):
return self.lr_scheduler.get_lr()
def get_last_lr(self):
return self.lr_scheduler.get_last_lr()
def print_lr(self, *args, **kwargs):
self.lr_scheduler.print_lr(*args, **kwargs)
def state_dict(self) -> dict:
return self.lr_scheduler.state_dict()
def load_state_dict(self, state_dict: dict) -> None:
self.lr_scheduler.load_state_dict(state_dict)
class GradAccumGradientHandler:
r"""A wrapper for the gradient handler to enable gradient accumulation by skipping the steps
before accumulation size is reached.
Args:
grad_handler (:class:`colossalai.engine.BaseGradientHandler`):
Your ``gradient_handler`` object for gradient accumulation, would be called when achieving `accumulate_size`.
accumulate_size (int): The number of steps to accumulate gradients.
More details about ``gradient_handlers`` could be found in
`Gradient_handler <https://github.com/hpcaitech/ColossalAI/tree/main/colossalai/engine/gradient_handler>`_.
"""
def __init__(self, grad_handler: BaseGradientHandler, accumulate_size: int) -> None:
assert isinstance(grad_handler, BaseGradientHandler), \
f'expected grad_handler to be type BaseGradientHandler, but got {type(grad_handler)}'
self.grad_handler = grad_handler
self.accumulate_size = accumulate_size
self.accumulate_step = 0
def handle_gradient(self):
self.accumulate_step += 1
if self.accumulate_step < self.accumulate_size:
pass
else:
self.accumulate_step = 0
self.grad_handler.handle_gradient()
|
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
from abc import ABC, abstractmethod
class BaseSampler(ABC):
def __init__(self, dataset, batch_size):
self.dataset = dataset
self.batch_size = batch_size
@abstractmethod
def __len__(self):
pass
@abstractmethod
def __iter__(self):
pass
|
from .base_sampler import BaseSampler
from .data_parallel_sampler import DataParallelSampler, get_dataloader
__all__ = ['BaseSampler', 'DataParallelSampler', 'get_dataloader']
|
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
# adpated from torch.utils.data.DistributedSampler
import math
import random
import numpy as np
from typing import TypeVar, Iterator
import torch
from torch.utils.data import Sampler, Dataset, DataLoader
from colossalai.context.parallel_mode import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.registry import DATA_SAMPLERS
T_co = TypeVar('T_co', covariant=True)
@DATA_SAMPLERS.register_module
class DataParallelSampler(Sampler):
"""A data sampler for distributed data parallelism.
Args:
dataset (:class:`torch.utils.data.Dataset`): The Dataset for sampling.
shuffle (bool, optional): Whether to shuffle data, defaults to False.
seed (int, optional): The random seed used for sampling, defaults to 0.
drop_last (bool, optional): Set to True to drop the last incomplete batch, if the dataset size
is not divisible by the batch size. If False and the size of dataset is not divisible by
the batch size, then the last batch will be smaller, defaults to False.
"""
def __init__(self,
dataset: Dataset,
shuffle: bool = False,
seed: int = 0,
drop_last: bool = False) -> None:
self.dataset = dataset
self.num_replicas = gpc.get_world_size(ParallelMode.DATA)
self.rank = gpc.get_local_rank(ParallelMode.DATA)
self.epoch = 0
self.drop_last = drop_last
# If the dataset length is evenly divisible by # of replicas, then there
# is no need to drop any data, since the dataset will be split equally.
# type: ignore[arg-type]
if self.drop_last and len(self.dataset) % self.num_replicas != 0:
# Split to nearest available length that is evenly divisible.
# This is to ensure each rank receives the same amount of data when
# using this Sampler.
self.num_samples = math.ceil(
# `type:ignore` is required because Dataset cannot provide a default __len__
# see NOTE in pytorch/torch/utils/data/sampler.py
(len(self.dataset) - self.num_replicas) / \
self.num_replicas # type: ignore[arg-type]
)
else:
self.num_samples = math.ceil(
len(self.dataset) / self.num_replicas) # type: ignore[arg-type]
self.total_size = self.num_samples * self.num_replicas
self.shuffle = shuffle
self.seed = seed
def __iter__(self) -> Iterator[T_co]:
if self.shuffle:
# deterministically shuffle based on epoch and seed
g = torch.Generator()
g.manual_seed(self.seed + self.epoch)
# type: ignore[arg-type]
indices = torch.randperm(len(self.dataset), generator=g).tolist()
# update for next epoch so that there is no need to call
# set_epoch manually
self.epoch += 1
else:
indices = list(range(len(self.dataset))) # type: ignore[arg-type]
if not self.drop_last:
# add extra samples to make it evenly divisible
padding_size = self.total_size - len(indices)
if padding_size <= len(indices):
indices += indices[:padding_size]
else:
indices += (indices * math.ceil(padding_size /
len(indices)))[:padding_size]
else:
# remove tail of data to make it evenly divisible.
indices = indices[:self.total_size]
assert len(indices) == self.total_size
# subsample
indices = indices[self.rank:self.total_size:self.num_replicas]
assert len(indices) == self.num_samples
return iter(indices)
def __len__(self) -> int:
return self.num_samples
def set_epoch(self, epoch: int) -> None:
r"""Sets the epoch for this sampler. When :attr:`shuffle=True`, this ensures all replicas
use a different random ordering for each epoch. Otherwise, the next iteration of this
sampler will yield the same ordering.
Args:
epoch (int): Epoch number.
"""
self.epoch = epoch
def get_dataloader(dataset,
shuffle=False,
seed=1024,
add_sampler=True,
drop_last=False,
pin_memory=False,
num_workers=0,
**kwargs):
r"""Set up a deterministic dataloader (also configure seed workers, samplers and whether shuffle or not)
Note:
When pipeline parallel is enabled, shuffle cannot be True as it will result in mismatch between input data
on the 1st stage and label on the last stage.
Args:
dataset (:class:`torch.utils.data.Dataset`): The dataset to be loaded.
shuffle (bool, optional): Whether to shuffle the dataset. Defaults to False.
seed (int, optional): Random worker seed for sampling, defaults to 1024.
add_sampler: Whether to add ``DistributedDataParallelSampler`` to the dataset. Defaults to True.
drop_last (bool, optional): Set to True to drop the last incomplete batch, if the dataset size
is not divisible by the batch size. If False and the size of dataset is not divisible by
the batch size, then the last batch will be smaller, defaults to False.
pin_memory (bool, optional): Whether to pin memory address in CPU memory. Defaults to False.
num_workers (int, optional): Number of worker threads for this dataloader. Defaults to 0.
kwargs (dict): optional parameters for ``torch.utils.data.DataLoader``, more details could be found in
`DataLoader <https://pytorch.org/docs/stable/_modules/torch/utils/data/dataloader.html#DataLoader>`_.
Returns:
:class:`torch.utils.data.DataLoader`: A DataLoader used for training or testing.
"""
_kwargs = kwargs.copy()
if add_sampler and gpc.is_initialized(ParallelMode.DATA) and gpc.get_world_size(ParallelMode.DATA) > 1:
sampler = DataParallelSampler(dataset, shuffle=shuffle)
else:
sampler = None
# Deterministic dataloader
def seed_worker(worker_id):
worker_seed = seed
np.random.seed(worker_seed)
torch.manual_seed(worker_seed)
random.seed(worker_seed)
if sampler is None:
return DataLoader(dataset,
worker_init_fn=seed_worker,
shuffle=shuffle,
drop_last=drop_last,
pin_memory=pin_memory,
num_workers=num_workers,
**_kwargs)
else:
return DataLoader(dataset,
sampler=sampler,
worker_init_fn=seed_worker,
drop_last=drop_last,
pin_memory=pin_memory,
num_workers=num_workers,
**_kwargs)
|
import gc
import inspect
import torch
import torch.nn as nn
from typing import Optional
from collections import defaultdict
LINE_WIDTH = 108
LINE = '-' * LINE_WIDTH + '\n'
class TensorDetector():
def __init__(self,
show_info: bool = True,
log: str = None,
include_cpu: bool = False,
module: Optional[nn.Module] = None
):
"""This class is a detector to detect tensor on different devices.
Args:
show_info (bool, optional): whether to print the info on screen, default True.
log (str, optional): the file name to save the log. Defaults to None.
include_cpu (bool, optional): whether to detect tensor on cpu, default False.
module (Optional[:class:`nn.Module`]): when sending an ``nn.Module`` object,
the detector can name the tensors detected better.
"""
self.show_info = show_info
self.log = log
self.include_cpu = include_cpu
self.tensor_info = defaultdict(list)
self.saved_tensor_info = defaultdict(list)
self.order = []
self.detected = []
self.devices = []
self.info = ""
self.module = module
if isinstance(module, nn.Module):
# if module is an instance of nn.Module, we can name the parameter with its real name
for name, param in module.named_parameters():
self.tensor_info[id(param)].append(name)
self.tensor_info[id(param)].append(param.device)
self.tensor_info[id(param)].append(param.shape)
self.tensor_info[id(param)].append(param.requires_grad)
self.tensor_info[id(param)].append(param.dtype)
self.tensor_info[id(param)].append(self.get_tensor_mem(param))
def get_tensor_mem(self, tensor):
# calculate the memory occupied by a tensor
memory_size = tensor.element_size() * tensor.storage().size()
if (tensor.is_leaf or tensor.retains_grad) and tensor.grad is not None:
grad_memory_size = tensor.grad.element_size() * tensor.grad.storage().size()
memory_size += grad_memory_size
return self.mem_format(memory_size)
def mem_format(self, real_memory_size):
# format the tensor memory into a reasonal magnitude
if real_memory_size >= 2 ** 30:
return str(real_memory_size / (2 ** 30)) + ' GB'
if real_memory_size >= 2 ** 20:
return str(real_memory_size / (2 ** 20)) + ' MB'
if real_memory_size >= 2 ** 10:
return str(real_memory_size / (2 ** 10)) + ' KB'
return str(real_memory_size) + ' B'
def collect_tensors_state(self):
for obj in gc.get_objects():
if torch.is_tensor(obj):
# skip cpu tensor when include_cpu is false and the tensor we have collected before
if (not self.include_cpu) and obj.device == torch.device('cpu'):
continue
self.detected.append(id(obj))
# skip paramters we had added in __init__ when module is an instance of nn.Module for the first epoch
if id(obj) not in self.tensor_info:
name = type(obj).__name__
# after backward, we want to update the records, to show you the change
if isinstance(self.module, nn.Module) and name == 'Parameter':
if obj.grad is not None:
# with grad attached
for par_name, param in self.module.named_parameters():
if param.requires_grad and param.grad.equal(obj.grad):
name = par_name + ' (with grad)'
else:
# with no grad attached
# there will be no new paramters created during running
# so it must be in saved_tensor_info
continue
# we can also marked common tensors as tensor(with grad)
if name == 'Tensor' and (obj.is_leaf or obj.retains_grad):
if obj.grad is not None:
name = name + ' (with grad)'
# in fact, common tensor have no grad
# unless you set retain_grad()
if id(obj) in self.saved_tensor_info.keys() and name == self.saved_tensor_info[id(obj)][0]:
continue
self.tensor_info[id(obj)].append(name)
self.tensor_info[id(obj)].append(obj.device)
self.tensor_info[id(obj)].append(obj.shape)
self.tensor_info[id(obj)].append(obj.requires_grad)
self.tensor_info[id(obj)].append(obj.dtype)
self.tensor_info[id(obj)].append(self.get_tensor_mem(obj))
# recorded the order we got the tensor
# by this we can guess the tensor easily
# it will record every tensor updated this turn
self.order.append(id(obj))
# recorded all different devices
if obj.device not in self.devices:
self.devices.append(obj.device)
def print_tensors_state(self):
template_format = '{:3s}{:<30s}{:>10s}{:>20s}{:>10s}{:>20s}{:>15s}'
self.info += LINE
self.info += template_format.format(' ', 'Tensor', 'device', 'shape', 'grad', 'dtype', 'Mem')
self.info += '\n'
self.info += LINE
# if a tensor updates this turn, and was recorded before
# it should be updated in the saved_tensor_info as well
outdated = [x for x in self.saved_tensor_info.keys() if x in self.order]
minus = [x for x in self.saved_tensor_info.keys() if x not in self.detected]
minus = outdated + minus
if len(self.order) > 0:
for tensor_id in self.order:
self.info += template_format.format('+',
str(self.tensor_info[tensor_id][0]),
str(self.tensor_info[tensor_id][1]),
str(tuple(self.tensor_info[tensor_id][2])),
str(self.tensor_info[tensor_id][3]),
str(self.tensor_info[tensor_id][4]),
str(self.tensor_info[tensor_id][5]))
self.info += '\n'
if len(self.order) > 0 and len(minus) > 0:
self.info += '\n'
if len(minus) > 0:
for tensor_id in minus:
self.info += template_format.format('-',
str(self.saved_tensor_info[tensor_id][0]),
str(self.saved_tensor_info[tensor_id][1]),
str(tuple(self.saved_tensor_info[tensor_id][2])),
str(self.saved_tensor_info[tensor_id][3]),
str(self.saved_tensor_info[tensor_id][4]),
str(self.saved_tensor_info[tensor_id][5]))
self.info += '\n'
# deleted the updated tensor
self.saved_tensor_info.pop(tensor_id)
# trace where is the detect()
locate_info = inspect.stack()[2]
locate_msg = '"' + locate_info.filename + '" line ' + str(locate_info.lineno)
self.info += LINE
self.info += f"Detect Location: {locate_msg}\n"
for device in self.devices:
if device == torch.device('cpu'):
continue
gpu_mem_alloc = self.mem_format(torch.cuda.memory_allocated(device))
self.info += f"Totle GPU Memery Allocated on {device} is {gpu_mem_alloc}\n"
self.info += LINE
self.info += '\n\n'
if self.show_info:
print(self.info)
if self.log is not None:
with open(self.log + '.log', 'a') as f:
f.write(self.info)
def detect(self, include_cpu = False):
self.include_cpu = include_cpu
self.collect_tensors_state()
self.print_tensors_state()
self.saved_tensor_info.update(self.tensor_info)
self.tensor_info.clear()
self.order = []
self.detected = []
self.info = ""
def close(self):
self.saved_tensor_info.clear()
self.module = None
|
from .tensor_detector import TensorDetector
|
from pathlib import Path
from torch.autograd.profiler import profile
from .prof_utils import BaseProfiler, _format_time, _format_memory, _format_bandwidth
from typing import List
def _get_size(dtype: str):
if dtype == "fp16":
return 2
elif dtype == "fp32":
return 4
else:
raise NotImplementedError
def _get_numel(my_list: List[int]) -> int:
from functools import reduce
from operator import mul
return reduce(mul, my_list)
def _reduce_location(locations: List[str]) -> str:
ret = []
for lo in locations:
ret.append(lo)
ret.append("\n")
ret = ret[:-1]
return ''.join(ret)
class PcieEvent(object):
"""Pcie Event.
"""
def __init__(self, count: int = 0, pcie_vol: int = 0, cuda_time: int = 0):
self.count = count
self.pcie_vol = pcie_vol
self.cuda_time = cuda_time
def add(self, rhs):
self.count += rhs.count
self.pcie_vol += rhs.pcie_vol
self.cuda_time += rhs.cuda_time
class PcieProfiler(BaseProfiler):
"""Pcie profiler. Records all data transmission between CPU and GPU.
TODO: Merge pcie profiler into communication profiler
"""
def __init__(self, dtype: str = "fp32", depth: int = 1):
super().__init__(profiler_name="Pcie", priority=10)
self.depth = depth
self.data_size = _get_size(dtype)
self.h2d_count = 0
self.h2d_time = 0
self.d2h_count = 0
self.d2h_time = 0
self.ops_record = dict()
self.profiler = None
def reset(self):
self.h2d_count = 0
self.h2d_time = 0
self.d2h_count = 0
self.d2h_time = 0
self.ops_record = dict()
self.profiler = None
def enable(self):
self.profiler = profile(enabled=True,
use_cuda=True,
use_cpu=True,
use_kineto=True,
record_shapes=True,
with_stack=True)
self.profiler.__enter__()
def disable(self):
self.profiler.__exit__(None, None, None)
if self.profiler.enabled:
events = self.profiler.function_events
for event in events:
if event.name == "aten::copy_":
t_shape = event.input_shapes[0]
if len(t_shape) == 0 or event.cuda_time_total == 0 or len(event.stack) == 0:
continue
current_comm_event = PcieEvent(1, self.data_size * _get_numel(t_shape), event.cuda_time_total)
code_location = _reduce_location(event.stack[:self.depth])
if code_location in self.ops_record:
self.ops_record[code_location].add(current_comm_event)
else:
self.ops_record[code_location] = current_comm_event
elif 'Memcpy HtoD' in event.name:
self.h2d_count += 1
self.h2d_time += event.cuda_time_total
elif 'Memcpy DtoH' in event.name:
self.d2h_count += 1
self.d2h_time += event.cuda_time_total
self.profiler = None
def to_tensorboard(self, writer):
writer.add_text(tag="Data Transmission", text_string=self.result_str("\n\n"))
def to_file(self, filename: Path):
with open(filename, "w") as f:
f.write(self.result_str())
def show(self):
print(self.result_str())
def result_str(self, sep: str = "\n"):
res = []
def append(s: str = None):
if s is not None:
res.append(s)
res.append(sep)
append("Pcie profiling result:")
append("time of data transmission (CPU -> GPU): {}".format(_format_time(self.h2d_time)))
append("number of transmission (CPU -> GPU): {}".format(self.h2d_count))
append("time of data transmission (GPU -> CPU): {}".format(_format_time(self.d2h_time)))
append("number of transmission (GPU -> CPU): {}".format(self.d2h_count))
append("Possible data transmission events in PCIE:")
seperation = '-' * 62
row_format = '{:^10}' + '{:^12}' + '{:^16}' + '{:^12}' * 2
append(seperation)
append(row_format.format('Location', 'GPU time', 'Trans volume', 'Bandwidth', 'Num of calls'))
append(seperation)
show_list = sorted(self.ops_record.items(), key=lambda kv: -kv[1].cuda_time)
for location, event in show_list:
append(location)
append(
row_format.format('', _format_time(event.cuda_time), _format_memory(event.pcie_vol),
_format_bandwidth(event.pcie_vol, event.cuda_time), event.count))
append()
return ''.join(res)
|
import inspect
from pathlib import Path
from functools import partial
import torch
from torch.autograd.profiler import profile
import torch.distributed as dist
from torch.distributed import ReduceOp
from colossalai.utils import get_current_device
from .prof_utils import BaseProfiler, _format_time, _format_memory, _format_bandwidth
from typing import List, Optional
def _get_code_location(depth: int):
ret = []
length = min(len(inspect.stack()), depth + 1)
for i in range(3, length):
upper_frame = inspect.stack()[i]
function_name = inspect.stack()[i - 1].function
ret.append(upper_frame.filename)
ret.append('(')
ret.append(str(upper_frame.lineno))
ret.append('): ')
ret.append(function_name)
if i != length - 1:
ret.append('\n')
return ''.join(ret)
torch_all_reduce = dist.all_reduce
torch_all_gather = dist.all_gather
torch_reduce_scatter = dist.reduce_scatter
torch_broadcast = dist.broadcast
torch_reduce = dist.reduce
class CommEvent(object):
"""Communication Event. Used for communication time and communication
volume recording.
"""
def __init__(self, count: int = 0, comm_vol: float = 0., cuda_time: int = 0):
self.self_count = count
self.self_comm_vol = comm_vol
self.self_cuda_time = cuda_time
def add(self, rhs):
self.self_count += rhs.self_count
self.self_comm_vol += rhs.self_comm_vol
self.self_cuda_time += rhs.self_cuda_time
class CommProfiler(BaseProfiler):
"""Communication profiler. Records all communication events.
"""
def __init__(self, depth: int = 0, total_count: int = 0, total_comm_vol: float = 0, total_cuda_time: int = 0):
super().__init__(profiler_name="Collective_Communication", priority=0)
self.depth = 3 + depth
self.total_count = total_count
self.total_comm_vol = total_comm_vol
self.total_cuda_time = total_cuda_time
self.ops_record = dict()
self.profiler = None
self.pending_op = None
self.pending_metadata = None
self.warn_flag = False
def reset(self):
self.total_count = 0
self.total_comm_vol = 0
self.total_cuda_time = 0
self.ops_record = dict()
self.profiler = None
self.pending_op = None
self.pending_metadata = None
self.warn_flag = False
def enable(self):
dist.all_reduce = partial(all_reduce, profiler=self)
dist.all_gather = partial(all_gather, profiler=self)
dist.reduce_scatter = partial(reduce_scatter, profiler=self)
dist.broadcast = partial(broadcast, profiler=self)
dist.reduce = partial(reduce, profiler=self)
def disable(self):
dist.all_reduce = torch_all_reduce
dist.all_gather = torch_all_gather
dist.reduce_scatter = torch_reduce_scatter
dist.broadcast = torch_broadcast
dist.reduce = torch_reduce
def to_tensorboard(self, writer):
writer.add_text(tag="Collective Communication", text_string=self.result_str("\n\n"))
def to_file(self, filename: Path):
with open(filename, "w") as f:
f.write(self.result_str())
def show(self):
print(self.result_str())
def result_str(self, sep: str = "\n"):
res = []
def append(s: str = None):
if s is not None:
res.append(s)
res.append(sep)
if self.warn_flag:
append("Warnning: there exists multiple communication operations in the same time. As a result, "
"the profiling result is not accurate.")
if self.total_cuda_time == 0:
return "No collective communication has been called yet!"
append("Collective communication profiling result:")
append("total cuda time: {}".format(_format_time(self.total_cuda_time)))
append("average bandwidth: {}".format(_format_bandwidth(self.total_comm_vol, self.total_cuda_time)))
append("total number of calls: {}".format(self.total_count))
append("All events:")
seperation = '-' * 74
row_format = '{:^10}' + '{:^12}' * 2 + '{:^16}' + '{:^12}' * 2
append(seperation)
append(row_format.format('Location', 'GPU time', 'Percentage', 'Comm volume', 'Bandwidth', 'Num of calls'))
append(seperation)
show_list = sorted(self.ops_record.items(), key=lambda kv: -kv[1].self_cuda_time)
for location, event in show_list:
append(location)
append(
row_format.format('', _format_time(event.self_cuda_time),
'{:.1f}%'.format(event.self_cuda_time / self.total_cuda_time * 100.0),
_format_memory(event.self_comm_vol),
_format_bandwidth(event.self_comm_vol, event.self_cuda_time), event.self_count))
append()
return ''.join(res)
@property
def has_aync_op(self):
return self.pending_op is not None
def activate_profiler(self, kn: str, vol: float):
self.pending_metadata = (kn, _get_code_location(self.depth), vol)
self.profiler = profile(enabled=True, use_cuda=True, use_cpu=True, use_kineto=True)
self.profiler.__enter__()
def close_profiler(self, group=None):
assert self.profiler is not None, "There is no running dist op"
kernel_name, code_location, vol = self.pending_metadata
self.profiler.__exit__(None, None, None)
if self.profiler.enabled and dist.get_world_size(group) > 1:
assert_flag = 0
current_comm_event = None
events = self.profiler.function_events
for event in events:
if kernel_name in event.name:
assert assert_flag == 0, "Multiple dist ops has been called "
current_comm_event = CommEvent(1, vol, event.self_cuda_time_total)
assert_flag += 1
assert current_comm_event is not None, "dist op has not been found"
buffer = torch.tensor([current_comm_event.self_cuda_time], device=get_current_device())
torch_all_reduce(buffer, op=ReduceOp.MIN, group=group)
current_comm_event.self_cuda_time = buffer.item()
self.total_count += current_comm_event.self_count
self.total_comm_vol += current_comm_event.self_comm_vol
self.total_cuda_time += current_comm_event.self_cuda_time
if code_location in self.ops_record:
self.ops_record[code_location].add(current_comm_event)
else:
self.ops_record[code_location] = current_comm_event
self.profiler = None
self.pending_op = None
self.pending_metadata = None
def wait_async_op(self):
if self.pending_op is not None:
op = self.pending_op
op.wait()
self.close_profiler()
class CommHandler(object):
"""Communication handler. A dummy handler to wait aync operations.
"""
def __init__(self, profiler: CommProfiler):
super().__init__()
self.prof = profiler
def wait(self):
self.prof.wait_async_op()
def async_check(profiler: CommProfiler):
if profiler.pending_op is not None:
profiler.warn_flag = True
profiler.wait_async_op()
def all_reduce(tensor: torch.Tensor,
op: ReduceOp = ReduceOp.SUM,
group=None,
async_op: bool = False,
profiler: CommProfiler = None) -> Optional[CommHandler]:
async_check(profiler)
comm_size = dist.get_world_size(group)
correction = 2 * (comm_size - 1) / comm_size
comm_vol = correction * tensor.element_size() * tensor.numel()
profiler.activate_profiler("ncclKernel_AllReduce_", comm_vol)
profiler.pending_op = torch_all_reduce(tensor, op, group, async_op)
if async_op:
return CommHandler(profiler)
profiler.close_profiler(group)
def reduce_scatter(output: torch.Tensor,
input_list: List[torch.Tensor],
op: ReduceOp = ReduceOp.SUM,
group=None,
async_op: bool = False,
profiler: CommProfiler = None) -> Optional[CommHandler]:
async_check(profiler)
comm_size = dist.get_world_size(group)
correction = (comm_size - 1) / comm_size
comm_vol = 0
for tensor in input_list:
comm_vol += tensor.element_size() * tensor.numel()
comm_vol *= correction
profiler.activate_profiler("ncclKernel_ReduceScatter_", comm_vol)
profiler.pending_op = torch_reduce_scatter(output, input_list, op, group, async_op)
if async_op:
return CommHandler(profiler)
profiler.close_profiler(group)
def all_gather(tensor_list: List[torch.Tensor],
tensor: torch.Tensor,
group=None,
async_op: bool = False,
profiler: CommProfiler = None) -> Optional[CommHandler]:
async_check(profiler)
comm_size = dist.get_world_size(group)
correction = (comm_size - 1) / comm_size
comm_vol = 0
for ten in tensor_list:
comm_vol += ten.element_size() * ten.numel()
comm_vol *= correction
profiler.activate_profiler("ncclKernel_AllGather_", comm_vol)
profiler.pending_op = torch_all_gather(tensor_list, tensor, group, async_op)
if async_op:
return CommHandler(profiler)
profiler.close_profiler(group)
def broadcast(tensor: torch.Tensor,
src: int,
group=None,
async_op: bool = False,
profiler: CommProfiler = None) -> Optional[CommHandler]:
async_check(profiler)
comm_vol = 1.0 * tensor.element_size() * tensor.numel()
profiler.activate_profiler("ncclKernel_Broadcast_", comm_vol)
profiler.pending_op = torch_broadcast(tensor, src, group, async_op)
if async_op:
return CommHandler(profiler)
profiler.close_profiler(group)
def reduce(tensor: torch.Tensor,
dst: int,
op: ReduceOp = ReduceOp.SUM,
group=None,
async_op: bool = False,
profiler: CommProfiler = None) -> Optional[CommHandler]:
async_check(profiler)
comm_vol = 1.0 * tensor.element_size() * tensor.numel()
profiler.activate_profiler("ncclKernel_Reduce_", comm_vol)
profiler.pending_op = torch_reduce(tensor, dst, op, group, async_op)
if async_op:
return CommHandler(profiler)
profiler.close_profiler(group)
|
from .comm_profiler import CommProfiler
from .pcie_profiler import PcieProfiler
from .prof_utils import ProfilerContext, BaseProfiler
from .mem_profiler import MemProfiler
__all__ = ['BaseProfiler', 'CommProfiler', 'PcieProfiler', 'MemProfiler', 'ProfilerContext']
|
from pathlib import Path
from typing import Union
from colossalai.engine import Engine
from torch.utils.tensorboard import SummaryWriter
from colossalai.engine.ophooks import MemTracerOpHook
from colossalai.utils.profiler import BaseProfiler
class MemProfiler(BaseProfiler):
"""Wraper of MemOpHook, used to show GPU memory usage through each iteration
To use this profiler, you need to pass an `engine` instance. And the usage is same like
CommProfiler.
Usage::
mm_prof = MemProfiler(engine)
with ProfilerContext([mm_prof]) as prof:
writer = SummaryWriter("mem")
engine.train()
...
prof.to_file("./log")
prof.to_tensorboard(writer)
"""
def __init__(self, engine: Engine, warmup: int = 50, refreshrate: int = 10) -> None:
super().__init__(profiler_name="MemoryProfiler", priority=0)
self._mem_tracer = MemTracerOpHook(warmup=warmup, refreshrate=refreshrate)
self._engine = engine
def enable(self) -> None:
self._engine.add_hook(self._mem_tracer)
def disable(self) -> None:
self._engine.remove_hook(self._mem_tracer)
def to_tensorboard(self, writer: SummaryWriter) -> None:
stats = self._mem_tracer.async_mem_monitor.state_dict['mem_stats']
for info, i in enumerate(stats):
writer.add_scalar("memory_usage/GPU", info, i)
def to_file(self, data_file: Path) -> None:
self._mem_tracer.save_results(data_file)
def show(self) -> None:
stats = self._mem_tracer.async_mem_monitor.state_dict['mem_stats']
print(stats)
|
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Union, List
from colossalai.core import global_context as gpc
# copied from high version pytorch to support low version
def _format_time(time_us):
"""Defines how to format time in FunctionEvent"""
US_IN_SECOND = 1000.0 * 1000.0
US_IN_MS = 1000.0
if time_us >= US_IN_SECOND:
return '{:.3f}s'.format(time_us / US_IN_SECOND)
if time_us >= US_IN_MS:
return '{:.3f}ms'.format(time_us / US_IN_MS)
return '{:.3f}us'.format(time_us)
# copied from high version pytorch to support low version
def _format_memory(nbytes):
"""Returns a formatted memory size string"""
KB = 1024
MB = 1024 * KB
GB = 1024 * MB
if (abs(nbytes) >= GB):
return '{:.2f} GB'.format(nbytes * 1.0 / GB)
elif (abs(nbytes) >= MB):
return '{:.2f} MB'.format(nbytes * 1.0 / MB)
elif (abs(nbytes) >= KB):
return '{:.2f} KB'.format(nbytes * 1.0 / KB)
else:
return str(nbytes) + ' B'
def _format_bandwidth(volme: float or int, time_us: int):
sec_div_mb = (1000.0 / 1024.0)**2
mb_per_sec = volme / time_us * sec_div_mb
if mb_per_sec >= 1024.0:
return '{:.3f} GB/s'.format(mb_per_sec / 1024.0)
else:
return '{:.3f} MB/s'.format(mb_per_sec)
class BaseProfiler(ABC):
def __init__(self, profiler_name: str, priority: int):
self.name = profiler_name
self.priority = priority
@abstractmethod
def enable(self):
pass
@abstractmethod
def disable(self):
pass
@abstractmethod
def to_tensorboard(self, writer):
pass
@abstractmethod
def to_file(self, filename: Path):
pass
@abstractmethod
def show(self):
pass
class ProfilerContext(object):
"""Profiler context manager
Usage::
world_size = 4
inputs = torch.randn(10, 10, dtype=torch.float32, device=get_current_device())
outputs = torch.empty(world_size, 10, 10, dtype=torch.float32, device=get_current_device())
outputs_list = list(torch.chunk(outputs, chunks=world_size, dim=0))
cc_prof = CommProfiler()
with ProfilerContext([cc_prof]) as prof:
op = dist.all_reduce(inputs, async_op=True)
dist.all_gather(outputs_list, inputs)
op.wait()
dist.reduce_scatter(inputs, outputs_list)
dist.broadcast(inputs, 0)
dist.reduce(inputs, 0)
prof.show()
"""
def __init__(self, profilers: List[BaseProfiler] = None, enable: bool = True):
self.enable = enable
self.profilers = sorted(profilers, key=lambda prof: prof.priority)
def __enter__(self):
if self.enable:
for prof in self.profilers:
prof.enable()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
if self.enable:
for prof in self.profilers:
prof.disable()
def to_tensorboard(self, writer):
from torch.utils.tensorboard import SummaryWriter
assert isinstance(writer, SummaryWriter), \
f'torch.utils.tensorboard.SummaryWriter is required, but found {type(writer)}.'
for prof in self.profilers:
prof.to_tensorboard(writer)
def to_file(self, log_dir: Union[str, Path]):
if isinstance(log_dir, str):
log_dir = Path(log_dir)
if not log_dir.exists():
log_dir.mkdir(parents=True, exist_ok=True)
for prof in self.profilers:
log_file = log_dir.joinpath(f'{prof.name}_rank_{gpc.get_global_rank()}.log')
prof.to_file(log_file)
def show(self):
for prof in self.profilers:
prof.show()
|
from .comparison import assert_equal, assert_not_equal, assert_close, assert_close_loose, assert_equal_in_group
from .utils import parameterize, rerun_on_exception, rerun_if_address_is_in_use
__all__ = [
'assert_equal', 'assert_not_equal', 'assert_close', 'assert_close_loose', 'assert_equal_in_group', 'parameterize',
'rerun_on_exception', 'rerun_if_address_is_in_use'
]
|
import torch
import torch.distributed as dist
from torch import Tensor
from torch.distributed import ProcessGroup
def assert_equal(a: Tensor, b: Tensor):
assert torch.all(a == b), f'expected a and b to be equal but they are not, {a} vs {b}'
def assert_not_equal(a: Tensor, b: Tensor):
assert not torch.all(a == b), f'expected a and b to be not equal but they are, {a} vs {b}'
def assert_close(a: Tensor, b: Tensor, rtol: float = 1e-5, atol: float = 1e-8):
assert torch.allclose(a, b, rtol=rtol, atol=atol), f'expected a and b to be close but they are not, {a} vs {b}'
def assert_close_loose(a: Tensor, b: Tensor, rtol: float = 1e-3, atol: float = 1e-3):
assert_close(a, b, rtol, atol)
def assert_equal_in_group(tensor: Tensor, process_group: ProcessGroup = None):
# all gather tensors from different ranks
world_size = dist.get_world_size(process_group)
tensor_list = [torch.empty_like(tensor) for _ in range(world_size)]
dist.all_gather(tensor_list, tensor, group=process_group)
# check if they are equal one by one
for i in range(world_size - 1):
a = tensor_list[i]
b = tensor_list[i+1]
assert torch.all(a == b), f'expected tensors on rank {i} and {i+1} to be equal but they are not, {a} vs {b}'
|
import re
import torch
from typing import Callable, List, Any
from functools import partial
from inspect import signature
from packaging import version
def parameterize(argument: str, values: List[Any]) -> Callable:
"""
This function is to simulate the same behavior as pytest.mark.parameterize. As
we want to avoid the number of distributed network initialization, we need to have
this extra decorator on the function launched by torch.multiprocessing.
If a function is wrapped with this wrapper, non-paramterized arguments must be keyword arguments,
positioanl arguments are not allowed.
Usgae::
# Example 1:
@parameterize('person', ['xavier', 'davis'])
def say_something(person, msg):
print(f'{person}: {msg}')
say_something(msg='hello')
# This will generate output:
# > xavier: hello
# > davis: hello
# Exampel 2:
@parameterize('person', ['xavier', 'davis'])
@parameterize('msg', ['hello', 'bye', 'stop'])
def say_something(person, msg):
print(f'{person}: {msg}')
say_something()
# This will generate output:
# > xavier: hello
# > xavier: bye
# > xavier: stop
# > davis: hello
# > davis: bye
# > davis: stop
Args:
argument (str): the name of the argument to parameterize
values (List[Any]): a list of values to iterate for this argument
"""
def _wrapper(func):
def _execute_function_by_param(**kwargs):
for val in values:
arg_map = {argument: val}
partial_func = partial(func, **arg_map)
partial_func(**kwargs)
return _execute_function_by_param
return _wrapper
def rerun_on_exception(exception_type: Exception = Exception, pattern: str = None, max_try: int = 5) -> Callable:
"""
A decorator on a function to re-run when an exception occurs.
Usage::
# rerun for all kinds of exception
@rerun_on_exception()
def test_method():
print('hey')
raise RuntimeError('Address already in use')
# rerun for RuntimeError only
@rerun_on_exception(exception_type=RuntimeError)
def test_method():
print('hey')
raise RuntimeError('Address already in use')
# rerun for maximum 10 times if Runtime error occurs
@rerun_on_exception(exception_type=RuntimeError, max_try=10)
def test_method():
print('hey')
raise RuntimeError('Address already in use')
# rerun for infinite times if Runtime error occurs
@rerun_on_exception(exception_type=RuntimeError, max_try=None)
def test_method():
print('hey')
raise RuntimeError('Address already in use')
# rerun only the exception message is matched with pattern
# for infinite times if Runtime error occurs
@rerun_on_exception(exception_type=RuntimeError, pattern="^Address.*$")
def test_method():
print('hey')
raise RuntimeError('Address already in use')
Args:
exception_type (Exception, Optional): The type of exception to detect for rerun
pattern (str, Optional): The pattern to match the exception message.
If the pattern is not None and matches the exception message,
the exception will be detected for rerun
max_try (int, Optional): Maximum reruns for this function. The default value is 5.
If max_try is None, it will rerun foreven if exception keeps occurings
"""
def _match_lines(lines, pattern):
for line in lines:
if re.match(pattern, line):
return True
return False
def _wrapper(func):
def _run_until_success(*args, **kwargs):
try_count = 0
assert max_try is None or isinstance(max_try, int), \
f'Expected max_try to be None or int, but got {type(max_try)}'
while max_try is None or try_count < max_try:
try:
try_count += 1
ret = func(*args, **kwargs)
return ret
except exception_type as e:
error_lines = str(e).split('\n')
if try_count < max_try and (pattern is None or _match_lines(error_lines, pattern)):
print('Exception is caught, retrying...')
# when pattern is not specified, we always skip the exception
# when pattern is specified, we only skip when pattern is matched
continue
else:
print('Maximum number of attempts is reached or pattern is not matched, no more retrying...')
raise e
# Override signature
# otherwise pytest.mark.parameterize will raise the following error:
# function does not use argumetn xxx
sig = signature(func)
_run_until_success.__signature__ = sig
return _run_until_success
return _wrapper
def rerun_if_address_is_in_use():
"""
This function reruns a wrapped function if "address already in use" occurs
in testing spawned with torch.multiprocessing
Usage::
@rerun_if_address_is_in_use()
def test_something():
...
"""
# check version
torch_version = version.parse(torch.__version__)
assert torch_version.major == 1
# only torch >= 1.8 has ProcessRaisedException
if torch_version.minor >= 8:
exception = torch.multiprocessing.ProcessRaisedException
else:
exception = Exception
func_wrapper = rerun_on_exception(exception_type=exception, pattern=".*Address already in use.*")
return func_wrapper
|
from typing import Tuple
import torch
import torch.nn as nn
from colossalai.logging import get_dist_logger
from colossalai.zero.sharded_model.sharded_model_v2 import ShardedModelV2
from colossalai.zero.sharded_optim.sharded_optim_v2 import ShardedOptimizerV2
def convert_to_zero_v2(model: nn.Module, optimizer: torch.optim.Optimizer, model_config,
optimizer_config) -> Tuple[ShardedModelV2, ShardedOptimizerV2]:
"""
A helper function to integrate the model and optimizer with ZeRO optimizer and off-loading
:param model: Your model object
:type model: :class:`torch.nn.Module`
:param optimizer_config: Your optimizer object
:type optimizer_config: :class:`dict`
:return: (model, optimizer)
:rtype: Tuple
"""
logger = get_dist_logger('convert_to_zero_v2')
logger.info(f'optimizer_config is {optimizer_config}')
if optimizer_config is None:
optimizer_config = dict()
logger.info(f'model_config is {model_config}')
if model_config is None:
model_config = dict()
zero_model = ShardedModelV2(model, **model_config)
zero_optimizer = ShardedOptimizerV2(zero_model, optimizer, **optimizer_config)
return zero_model, zero_optimizer
__all__ = ['convert_to_zerov2', 'ShardedModelV2', 'ShardedOptimizerV2']
|
from .init_context import ZeroInitContext, no_shard_zero_context, no_shard_zero_decrator
__all__ = ['ZeroInitContext', 'no_shard_zero_context', 'no_shard_zero_decrator']
|
import contextlib
import functools
from typing import Optional
import torch
import torch.nn as nn
import torch.distributed as dist
from colossalai.context.parallel_mode import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.context.singleton_meta import SingletonMeta
from colossalai.logging import get_dist_logger
from colossalai.zero.shard_utils import BaseShardStrategy
from colossalai.zero.sharded_model._utils import cast_tensor_to_fp16
from colossalai.zero.sharded_param import ShardedParamV2
from contextlib import AbstractContextManager
def _substitute_init_recursively(cls, func):
for subcls in cls.__subclasses__():
_substitute_init_recursively(subcls, func)
func(subcls)
class InsertPostInitMethodToModuleSubClasses(object):
def __init__(self):
pass
def __enter__(self):
r"""
Enter the context scope.
"""
def preprocess_after(f):
@functools.wraps(f)
def wrapper(module: torch.nn.Module, *args, **kwargs):
f(module, *args, **kwargs)
self._post_init_method(module)
return wrapper
def _enable_class(cls):
cls._old_init = cls.__init__
cls.__init__ = preprocess_after(cls.__init__)
# The function is called during init subclass.
def _init_subclass(cls, **kwargs):
cls.__init__ = preprocess_after(cls.__init__)
# Replace .__init__() for all existing subclasses of torch.nn.Module
# Excution self._post_init_method after the default init function.
_substitute_init_recursively(torch.nn.modules.module.Module, _enable_class)
# holding on to the current __init__subclass__ for exit
torch.nn.modules.module.Module._old_init_subclass = (torch.nn.modules.module.Module.__init_subclass__)
# Replace .__init__() for future subclasses of torch.nn.Module
torch.nn.modules.module.Module.__init_subclass__ = classmethod(_init_subclass)
self._pre_context_exec()
def __exit__(self, exc_type, exc_value, traceback):
def _disable_class(cls):
cls.__init__ = cls._old_init
# Replace .__init__() for all existing subclasses of torch.nn.Module
_substitute_init_recursively(torch.nn.modules.module.Module, _disable_class)
# Replace .__init__() for future subclasses of torch.nn.Module
torch.nn.modules.module.Module.__init_subclass__ = (torch.nn.modules.module.Module._old_init_subclass)
self._post_context_exec()
# Now that we cleaned up the metaclass injection, raise the exception.
if exc_type is not None:
return False
# To be implemented by inheriting classes
def _post_init_method(self, module):
pass
def _pre_context_exec(self):
pass
def _post_context_exec(self):
pass
class ZeroContextConfig(object):
"""The configuration used to control zero context initialization.
Args:
target_device (torch.device): The device where param data are after exiting the context.
replicated (bool, optional): Whether the param is replicated across data parallel group.
Some parameters are not replicated, e.g. parameters in MOE experts.
shard_param (bool, optional): Is param sharded after exiting the context. Defaults to False.
"""
def __init__(self, target_device: torch.device, replicated: bool = True, shard_param: bool = False):
super().__init__()
if shard_param:
assert replicated, "Non-replicated parameters can't be sharded."
# replicated no-shard parameters should locate in cuda, since we will broadcast them soon
if replicated and not shard_param:
assert target_device.type == 'cuda', "Replicated no-shard paramters should locate in cuda."
self.target_device = target_device
self.is_replicated: bool = replicated
self.shard_param: bool = shard_param
class ZeroInitContext(InsertPostInitMethodToModuleSubClasses):
"""A context to initialize model.
1. Convert the model to fp16.
2. The paramaters of the module are adapted to type ShardedParameter.
3. Shard the param and grad according to flags.
Args:
target_device (torch.device): The device where param data are after exiting the context.
shard_strategy (BaseShardStrategy): Shard strategy instance.
seed (int, optional): Random seed for weight initialization
shard_param (bool, optional): Is param sharded after exiting the context. Defaults to False.
model_numel_tensor (torch.Tensor, optional): A tensor which will store the number of elements of model. Defaults to torch.zeros(1, dtype=torch.int).
"""
def __init__(self,
target_device: torch.device,
shard_strategy: BaseShardStrategy,
seed: int = 2**10 - 1,
shard_param: bool = False,
model_numel_tensor: torch.Tensor = torch.zeros(1, dtype=torch.long)):
super().__init__()
self.shard_strategy = shard_strategy
self.param_list = []
self.model_numel_tensor = model_numel_tensor
self.seed = seed
self.dp_process_group = gpc.get_group(ParallelMode.DATA)
self.config = ZeroContextConfig(target_device=target_device, replicated=True, shard_param=shard_param)
ZeroContextMgr().current_context = self
@property
def target_device(self):
return self.config.target_device
@property
def is_replicated(self):
return self.config.is_replicated
@property
def shard_param(self):
return self.config.shard_param
@staticmethod
def calc_fanin_fanout(tensor: torch.Tensor):
"""We use this function to substitute fan-in and fan-out calculation in torch.nn.init.
This can help us get correct fan-in and fan-out for sharded tensor.
"""
assert isinstance(tensor, nn.Parameter), "Sharded tensor initilization is only allowed for paramters"
# get correct shape of input tensor
if not hasattr(tensor, 'colo_attr') or not tensor.colo_attr.param_is_sharded:
tensor_shape = tensor.shape
else:
tensor_shape = tensor.colo_attr.sharded_data_tensor.origin_shape
dimensions = len(tensor_shape)
if dimensions < 2:
raise ValueError("Fan in and fan out can not be computed for tensor with fewer than 2 dimensions")
num_input_fmaps = tensor_shape[1]
num_output_fmaps = tensor_shape[0]
receptive_field_size = 1
if dimensions > 2:
# math.prod is not always available, accumulate the product manually
# we could use functools.reduce but that is not supported by TorchScript
for s in tensor_shape[2:]:
receptive_field_size *= s
fan_in = num_input_fmaps * receptive_field_size
fan_out = num_output_fmaps * receptive_field_size
return fan_in, fan_out
def _pre_context_exec(self):
"""
The Callback function when entering the context
"""
self.logger = get_dist_logger("ZeroInitContext")
# substitute fan-in and fan-out calculation
self.nn_fanin_fanout = nn.init._calculate_fan_in_and_fan_out
nn.init._calculate_fan_in_and_fan_out = self.calc_fanin_fanout
# reserve rng states
self.cpu_rng_state = torch.get_rng_state()
self.cuda_rng_state = torch.cuda.get_rng_state()
# set new seed for initialization, since we initialize sharded tensor separately
# we don't want all processes have the same seed
# otherwise all sharded tensors are same after init
offset = self.seed + 1 # we want to have more 1 in binary format seed
torch.manual_seed(self.seed + offset * dist.get_rank())
def _post_context_exec(self):
"""The callback function when exiting context.
"""
# broadcast replicated no-shard parameters
src_rank = gpc.get_ranks_in_group(ParallelMode.DATA)[0]
for param in self.param_list:
assert hasattr(param, 'colo_attr')
if not param.colo_attr.param_is_sharded and param.colo_attr.is_replicated:
dist.broadcast(tensor=param.data, src=src_rank, group=self.dp_process_group)
param.colo_attr.set_data_none()
del self.param_list
nn.init._calculate_fan_in_and_fan_out = self.nn_fanin_fanout
torch.set_rng_state(self.cpu_rng_state)
torch.cuda.set_rng_state(self.cuda_rng_state)
def _post_init_method(self, module: torch.nn.Module):
"""
The function to call at the end of the constructor of each module.
NOTE() The module may be passed to this function multiple times.
"""
def half_fn(t: torch.Tensor):
return t.half() if t.is_floating_point() else t
for param in module.parameters(recurse=False):
# avoid adapting a param to ShardedParam twice
if hasattr(param, 'colo_attr'):
continue
self.model_numel_tensor += param.numel()
# convert parameters to half
param_half = half_fn(param)
param.data = param_half
if param.grad is not None:
grad_half = half_fn(param.grad)
param.grad.data = grad_half
# move torch parameters to the target device
target_device = self.target_device
param.data = param.data.to(target_device)
if param.grad is not None:
param.grad = param.grad.to(target_device)
param.colo_attr = ShardedParamV2(param, set_data_none=False)
if self.shard_param:
self.shard_strategy.shard([param.colo_attr.sharded_data_tensor], self.dp_process_group)
param.data = param.colo_attr.data_payload # set param.data to payload
# mark whether the param is replicated
param.colo_attr.is_replicated = self.is_replicated
# mark whether the param should keep not sharded
# if True, the param is used as Zero stage 2
param.colo_attr.keep_not_shard = not self.shard_param
self.param_list.append(param)
# We must cast buffers
# If we use BN, buffers may be on CPU and Float
# We must cast them
for buffer in module.buffers(recurse=False):
buffer.data = buffer.data.to(device=torch.cuda.current_device())
buffer.data = cast_tensor_to_fp16(buffer.data)
class ZeroContextMgr(metaclass=SingletonMeta):
current_context: Optional[ZeroInitContext] = None
@contextlib.contextmanager
def hijack_context_config(self, **kwargs):
if self.current_context is None:
yield
else:
old_config = self.current_context.config
self.current_context.config = ZeroContextConfig(**kwargs)
yield
self.current_context.config = old_config
def no_shard_zero_context(is_replicated: bool = True) -> AbstractContextManager:
return ZeroContextMgr().hijack_context_config(target_device=torch.device('cuda', torch.cuda.current_device()),
replicated=is_replicated,
shard_param=False)
def no_shard_zero_decrator(is_replicated: bool = True):
def _wrapper(init_func):
def _no_shard(*args, **kwargs):
with no_shard_zero_context(is_replicated):
init_func(*args, **kwargs)
return _no_shard
return _wrapper
|
from .sharded_optim_v2 import ShardedOptimizerV2
__all__ = ['ShardedOptimizerV2']
|
from enum import Enum
from os import stat
from typing import Dict, Optional, Tuple
import torch
import torch.distributed as dist
import torch.nn as nn
from colossalai.amp.naive_amp.grad_scaler import DynamicGradScaler
from colossalai.context.parallel_mode import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.logging import get_dist_logger
from colossalai.nn.optimizer import ColossalaiOptimizer
from colossalai.utils.memory_tracer.model_data_memtracer import \
GLOBAL_MODEL_DATA_TRACER
from colossalai.zero.sharded_param.tensor_utils import (colo_model_data_tensor_move_inline, colo_model_tensor_clone,
colo_tensor_mem_usage)
from colossalai.zero.sharded_model import ShardedModelV2
from colossalai.zero.sharded_model._utils import cast_tensor_to_fp32
from colossalai.zero.sharded_param.tensorful_state import (StatefulTensor, TensorState)
from torch import Tensor
from torch.distributed import ProcessGroup
from torch.nn.parameter import Parameter
from torch.optim import Optimizer
from colossalai.gemini.tensor_placement_policy import AutoTensorPlacementPolicy
class OptimState(Enum):
SCALED = 1
UNSCALED = 2
class ShardedOptimizerV2(ColossalaiOptimizer):
"""A wrapper for optimizer. ``ShardedOptimizerV2`` and ``ShardedModelV2`` implement Zero Redundancy Optimizer (ZeRO).
By default the ZeRO optimizer stage 3 offload Optimizer States on CPU.
We apply the Device-aware Operator Placement technique for OS placement from the following paper.
`PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management`_
GPU margin space is the remaining space after removing peak non-model data from the overall GPU memory,
which is detected by a runtime memory tracer.
We place as many OS chunks in the margin space as possible.
The size of margin space can be controlled by ``gpu_margin_mem_ratio``.
If it is set as ``0.0``, it is the same as classical ZeRO optimizer.
Note:
You must use ``ShardedOptimizerV2`` with ``ShardedModelV2``.
Note:
Make sure you set ``tensor_placement_policy`` in ``ShardedModelV2`` to `"auto"`,
if you set ``gpu_margin_mem_ratio > 0``.
Args:
sharded_model (ShardedModelV2): A sharded model initialized by class ShardedModelV2. The optimizer will use the
shard strategy provided by sharded model to shard param fp32 tensors.
optimizer (Optimizer): An Optimizer instance.
gpu_margin_mem_ratio (float, optional): The ratio of GPU remaining memory (after the first forward-backward)
which will be used when using hybrid CPU optimizer.
This argument is meaningless when `tensor_placement_policy` of `ShardedModelV2` is not "auto".
Defaults to 0.0.
initial_scale (float, optional): Initial scale used by DynamicGradScaler. Defaults to 2**32.
min_scale (float, optional): Min scale used by DynamicGradScaler. Defaults to 1.
growth_factor (float, optional): growth_factor used by DynamicGradScaler. Defaults to 2.
backoff_factor (float, optional): backoff_factor used by DynamicGradScaler. Defaults to 0.5.
growth_interval (float, optional): growth_interval used by DynamicGradScaler. Defaults to 1000.
hysteresis (float, optional): hysteresis used by DynamicGradScaler. Defaults to 2.
max_scale (int, optional): max_scale used by DynamicGradScaler. Defaults to 2**32.
dp_process_group (Optional[ProcessGroup], optional): data paralle process group. Defaults to None.
mp_process_group (Optional[ProcessGroup], optional): model paralle process group. Defaults to None.
.. _PatrickStar\: Parallel Training of Pre-trained Models via Chunk-based Memory Management:
https://arxiv.org/abs/2108.05818
"""
def __init__(self,
sharded_model: ShardedModelV2,
optimizer: Optimizer,
gpu_margin_mem_ratio: float = 0.0,
initial_scale: float = 2**32,
min_scale: float = 1,
growth_factor: float = 2,
backoff_factor: float = 0.5,
growth_interval: int = 1000,
hysteresis: int = 2,
max_scale: float = 2**32,
dp_process_group: Optional[ProcessGroup] = None,
mp_process_group: Optional[ProcessGroup] = None,
verbose: bool = False) -> None:
assert isinstance(sharded_model, ShardedModelV2), 'model must be wrapped with ShardedModel'
super().__init__(optimizer)
self.shard_strategy = sharded_model.shard_strategy
self.model: ShardedModelV2 = sharded_model
self.gpu_margin_mem_ratio: float = float(gpu_margin_mem_ratio)
assert 0.0 <= self.gpu_margin_mem_ratio <= 1.0, f'gpu_margin_mem_ratio must >=0.0 and <=1.0'
# Only move fp32 shards from CPU to GPU when user allows and inner optimizer is valid
# Inner optimizer must support optimizing hybrid (CPU and CUDA) tensors,
# and it must set `num_fp32_shards_per_param` correctly
self._should_move_fp32_shards_h2d: bool = sharded_model.cpu_offload and self.gpu_margin_mem_ratio > 0.0 and getattr(
optimizer, 'num_fp32_shards_per_param', 0) >= 2
self.device = sharded_model._tensor_placement_policy.device or torch.device('cpu')
self.optim_state: OptimState = OptimState.UNSCALED
self.dp_process_group = dp_process_group or gpc.get_group(ParallelMode.DATA)
self.mp_process_group = mp_process_group or gpc.get_group(ParallelMode.MODEL)
# Grad scaler
self.grad_scaler = DynamicGradScaler(initial_scale=initial_scale,
min_scale=min_scale,
growth_factor=growth_factor,
backoff_factor=backoff_factor,
growth_interval=growth_interval,
hysteresis=hysteresis,
max_scale=max_scale)
self._found_overflow: Tensor = torch.IntTensor([0]).to(torch.cuda.current_device())
self._logger = get_dist_logger("ShardedOptimizerV2")
self._verbose = verbose
# Store fp32 param shards
self._register_master_weight()
if self.gpu_margin_mem_ratio != 0.0 and not isinstance(sharded_model._tensor_placement_policy,
AutoTensorPlacementPolicy):
self._logger.warning(f'gpu_margin_mem_ratio is meaningless when tensor_placement_policy is not "auto"')
if self._verbose:
self._logger.debug(
f"After init ShardedOptimizerV2 consumes {self.get_memory_usage()[0] / 1e6} MB CUDA Memory!", ranks=[0])
self._use_memory_tracer = self.model.use_memory_tracer
if self._use_memory_tracer:
GLOBAL_MODEL_DATA_TRACER.register_optimizer(self)
@property
def loss_scale(self):
return self.grad_scaler.scale.item()
def get_memory_usage(self) -> Tuple[int, int]:
""" Get the memory usage of the optimizer. Including master_params (param fp32),
momentum (``self.state[p]['exp_avg']``) variance (``self.state[p]['exp_avg_sq']``)
Returns:
Tuple[int, int]: cuda/cpu memory usage in Byte.
"""
cuda_use = 0
cpu_use = 0
def update_mem_use(t):
nonlocal cuda_use
nonlocal cpu_use
t_cuda_use, t_cpu_use = colo_tensor_mem_usage(t)
cuda_use += t_cuda_use
cpu_use += t_cpu_use
for _, p_fp32 in self.master_params.items():
update_mem_use(p_fp32)
for group in self.optim.param_groups:
for p in group['params']:
state = self.optim.state[p]
for k, v in state.items():
update_mem_use(v)
return cuda_use, cpu_use
def zero_grad(self, *args, **kwargs):
self._zero_grad()
def backward(self, loss: Tensor) -> None:
loss = self.loss_scale * loss
self.optim_state = OptimState.SCALED
self.model.backward(loss)
def backward_by_grad(self, tensor: Tensor, grad: Tensor) -> None:
self.model.backward_by_grad(tensor, grad)
def clip_grad_norm(self, model: nn.Module, max_norm: float):
if self.optim_state == OptimState.SCALED:
self._unscale_grads()
return super().clip_grad_norm(model, max_norm)
def step(self, *args, **kwargs):
self._prepare_grads()
self._maybe_move_fp32_shards()
# unscale grads if scaled
if self.optim_state == OptimState.SCALED:
self._unscale_grads()
found_inf = self._check_overflow()
self.grad_scaler.update(found_inf)
if found_inf:
self._logger.warning('found inf during ShardedOptimV2 step')
self._zero_grad(recover_data=True)
return
self._point_param_fp16_to_master_param()
if self._verbose:
gpu_mem, cpu_mem = self.get_memory_usage()
self._logger.debug(
f"Before step ShardedOptimizerV2 consumes {gpu_mem / 1e6} MB CUDA Memory, {cpu_mem / 1e6} MB CUDA Memory!",
ranks=[0])
ret = self.optim.step(*args, **kwargs)
if self._verbose:
gpu_mem, cpu_mem = self.get_memory_usage()
self._logger.debug(
f"After step ShardedOptimizerV2 consumes {gpu_mem / 1e6} MB CUDA Memory, {cpu_mem / 1e6} MB CUDA Memory!",
ranks=[0])
self._copy_master_model_to_model_fp16()
return ret
def _check_overflow(self):
# clear previous overflow record
self._found_overflow.fill_(self.model.overflow_counter)
# all-reduce across dp group
dist.all_reduce(self._found_overflow, group=self.dp_process_group)
# all-reduce over model parallel group
dist.all_reduce(self._found_overflow, group=self.mp_process_group)
return self._found_overflow.item() > 0
def _unscale_grads(self):
assert self.optim_state == OptimState.SCALED
for group in self.optim.param_groups:
for p in group['params']:
if p.grad is not None:
p.grad.data.div_(self.loss_scale)
self.optim_state = OptimState.UNSCALED
def _zero_grad(self, recover_data: bool = False):
"""zero grad and maybe recover fp16 params
When `reuse_fp16_shard` is enabled,
p.colo_attr.sharded_data_tensor stores grad here.
We have to recover them from fp32 params.
Args:
recover_data (bool, optional): Whether to recover fp16 param from fp32 param. Defaults to False.
"""
# We must set grad to None
# Because grad here is sharded
# But next backward pass will create a full grad first
# Which leads to wrong accumulation
self.optim.zero_grad(set_to_none=True)
for group in self.optim.param_groups:
for p in group['params']:
# p.colo_attr.sharded_data_tensor stores grad now
# we have to recover fp16 param
reuse_fp16_shard = p.colo_attr.saved_grad.data_ptr() == p.colo_attr.sharded_data_tensor.data_ptr()
if recover_data and reuse_fp16_shard:
self._copy_master_param_to_param_fp16(p)
else:
# release saved gradient
p.colo_attr.saved_grad.set_null()
self.model.overflow_counter = 0 # set overflow counter to zero
def sync_grad(self):
pass
def _register_master_weight(self):
self.master_params: Dict[Parameter, StatefulTensor] = {}
for group in self.optim.param_groups:
for p in group['params']:
assert hasattr(p, 'colo_attr'), 'The parameter must be wrapped with ShardedParam'
shard_flag = not p.colo_attr.sharded_data_tensor.is_sharded and p.colo_attr.is_replicated
if shard_flag:
# we always shard replicated paramters
self.shard_strategy.shard([p.colo_attr.sharded_data_tensor], self.dp_process_group)
self.master_params[p] = StatefulTensor(cast_tensor_to_fp32(p.colo_attr.data_payload.to(self.device)))
if shard_flag:
# In this branch, there's no need to shard param
# So we gather here
self.shard_strategy.gather([p.colo_attr.sharded_data_tensor], self.dp_process_group)
def _maybe_move_fp32_shards(self):
if self._should_move_fp32_shards_h2d:
self._should_move_fp32_shards_h2d = False
available_cuda_margin_mem = self.model.cuda_margin_space * self.gpu_margin_mem_ratio
fp32_shards_available_cuda_margin_mem = available_cuda_margin_mem / self.optim.num_fp32_shards_per_param
fp32_shards_used_cuda_margin_mem = 0
for group in self.optim.param_groups:
for p in group['params']:
shard_mem = self.master_params[p].payload.numel() * self.master_params[p].payload.element_size()
if fp32_shards_used_cuda_margin_mem + shard_mem < fp32_shards_available_cuda_margin_mem:
colo_model_data_tensor_move_inline(self.master_params[p], torch.cuda.current_device())
p.grad.data = p.grad.data.to(torch.cuda.current_device())
p.colo_attr.offload_grad = False
fp32_shards_used_cuda_margin_mem += shard_mem
def _prepare_grads(self):
for group in self.optim.param_groups:
for p in group['params']:
if p.colo_attr.saved_grad.is_null():
continue
p.colo_attr.saved_grad.trans_state(TensorState.COMPUTE)
# If reuse_fp16_shard, grad fp16 which wasn't be offloaded may be evicted to CPU
if not p.colo_attr.offload_grad:
colo_model_data_tensor_move_inline(p.colo_attr.grad_payload, torch.cuda.current_device())
# FIXME(ver217): p.data here is an empty tensor on CUDA and has no useful infomation
# If we change p.grad directly
# it may raise error because of different shape/dtype/device of p.data and p.grad
# We just set p.data = p.colo_attr.saved_grad.payload here
p.data = p.colo_attr.grad_payload
p.grad = p.colo_attr.grad_payload
# Set p.data to empty tensor, in case of memory leaking
p.colo_attr.set_data_none()
def _point_param_fp16_to_master_param(self):
# assign master param pointers to p.data.
# We will not trigger data copy here.
for group in self.optim.param_groups:
for p in group['params']:
self.master_params[p].trans_state(TensorState.COMPUTE)
p.data = self.master_params[p].payload
# Now p.data is sharded
# So optimizer states are sharded naturally
def _copy_master_model_to_model_fp16(self):
# Copy master param data (fp32) to payload of colo_attr (fp16)
# TODO() improve efficiency by gathering tensors into a chunk and transfering
# a chunk.
for group in self.optim.param_groups:
for p in group['params']:
self._copy_master_param_to_param_fp16(p)
def _copy_master_param_to_param_fp16(self, p):
# flush gradient
p.colo_attr.saved_grad.set_null()
# TODO() optimize this line CPU (fp32) -> GPU (fp16)
p.data = self.master_params[p].payload
p.colo_attr.reset_data_payload(
colo_model_tensor_clone(p.half().detach(), p.colo_attr.sharded_data_tensor.device))
p.colo_attr.set_data_none()
if p.colo_attr.keep_not_shard and p.colo_attr.is_replicated:
# We gather full fp16 param here
p.colo_attr.sharded_data_tensor.is_sharded = True # since only gradient is sharded, we should set to True
self.shard_strategy.gather([p.colo_attr.sharded_data_tensor], self.dp_process_group)
self.master_params[p].trans_state(TensorState.HOLD)
|
import math
import torch
from torch._six import inf
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
from colossalai.core import global_context as gpc
from colossalai.context import ParallelMode
from colossalai.utils import is_model_parallel_parameter
import torch.distributed as dist
def flatten(input_):
return _flatten_dense_tensors(input_)
def unflatten(flat, tensors):
return _unflatten_dense_tensors(flat, tensors)
def count_numel(tensor_list):
res = 0
for tensor in tensor_list:
res += tensor.numel()
return res
def calculate_padding(numel, unit_size):
remainder = numel % unit_size
return unit_size - remainder if remainder else remainder
def shuffle_by_round_robin(tensor_list, num_partitions):
partitions = dict()
for tensor_idx, tensor in enumerate(tensor_list):
partition_to_go = tensor_idx % num_partitions
if partition_to_go not in partitions:
partitions[partition_to_go] = []
partitions[partition_to_go].append(dict(tensor=tensor, index=tensor_idx))
partitions_count = len(partitions)
new_tensor_list = []
tensor_index_mapping = dict()
for partition_id in range(partitions_count):
partition_tensors = partitions[partition_id]
for item in partition_tensors:
tensor_index_mapping[item['index']] = len(new_tensor_list)
new_tensor_list.append(item['tensor'])
return new_tensor_list, tensor_index_mapping
# create a flat tensor aligned at the alignment boundary
def flatten_dense_tensors_with_padding(tensor_list, unit_size):
num_elements = count_numel(tensor_list)
padding = calculate_padding(num_elements, unit_size=unit_size)
if padding > 0:
pad_tensor = torch.zeros(padding, device=tensor_list[0].device, dtype=tensor_list[0].dtype)
padded_tensor_list = tensor_list + [pad_tensor]
else:
padded_tensor_list = tensor_list
return flatten(padded_tensor_list)
def is_nccl_aligned(tensor):
return tensor.data_ptr() % 4 == 0
def get_grad_accumulate_object(tensor):
"""
Return the AccumulateGrad of the input tensor
"""
# grad_fn reference:
# https://discuss.pytorch.org/t/in-the-grad-fn-i-find-a-next-functions-but-i-dont-understand-the-meaning-of-the-attribute/24463
# expand_as reference: https://pytorch.org/docs/stable/generated/torch.Tensor.expand.html#torch.Tensor.expand
#
# `next_functions` will return the backward graph where
# the first element is the AccumulateGrad of the leaf nodes.
# we want to get the AccumulateGrad of the input tensor instead of the leaf
# node in the whole computation graph.
# Therefore, we call expand_as to create a dummy graph
# where tensor_tmp and tensor indeed point to the same object.
# You can check this by print(tensor.data_ptr() == tensor_tmp.data_ptr())
tensor_tmp = tensor.expand_as(tensor)
grad_acc_obj = tensor_tmp.grad_fn.next_functions[0][0]
return grad_acc_obj
def split_half_float_double(tensor_list):
dtypes = ["torch.cuda.HalfTensor", "torch.cuda.FloatTensor", "torch.cuda.DoubleTensor", "torch.cuda.BFloat16Tensor"]
buckets = []
for i, dtype in enumerate(dtypes):
bucket = [t for t in tensor_list if t.type() == dtype]
if bucket:
buckets.append(bucket)
return buckets
def reduce_tensor(tensor, dtype, dst_rank=None, parallel_mode=ParallelMode.DATA):
"""
Reduce the tensor in the data parallel process group
:param tensor: A tensor object to reduce/all-reduce
:param dtype: The data type used in communication
:param dst_rank: The source rank for reduce. If dst_rank is None,
all-reduce will be used instead of reduce. Default is None.
:type tensor: torch.Tensor
:type dtype: torch.dtype
:type dst_rank: int, optional
"""
# cast the data to specified dtype for reduce/all-reduce
if tensor.dtype != dtype:
tensor_to_reduce = tensor.to(dtype)
else:
tensor_to_reduce = tensor
world_size = gpc.get_world_size(parallel_mode)
group = gpc.get_group(parallel_mode)
tensor_to_reduce.div_(world_size)
# if rank is None, all reduce will be used
# else, reduce is used
use_all_reduce = dst_rank is None
if use_all_reduce:
dist.all_reduce(tensor_to_reduce, group=group)
else:
ranks_in_group = gpc.get_ranks_in_group(parallel_mode)
global_rank = ranks_in_group[dst_rank]
dist.reduce(tensor=tensor_to_reduce, dst=global_rank, group=group)
# recover the original dtype
if tensor.dtype != dtype and tensor is not tensor_to_reduce:
local_rank = gpc.get_local_rank(parallel_mode)
if use_all_reduce or dst_rank == local_rank:
tensor.copy_(tensor_to_reduce)
return tensor
def has_inf_or_nan(tensor):
try:
# if tensor is half, the .float() incurs an additional deep copy, but it's necessary if
# Pytorch's .sum() creates a one-element tensor of the same type as tensor
# (which is true for some recent version of pytorch).
tensor_sum = float(tensor.float().sum())
# More efficient version that can be used if .sum() returns a Python scalar
# tensor_sum = float(tensor.sum())
except RuntimeError as instance:
# We want to check if inst is actually an overflow exception.
# RuntimeError could come from a different error.
# If so, we still want the exception to propagate.
if "value cannot be converted" not in instance.args[0]:
raise
return True
else:
if tensor_sum == float('inf') or tensor_sum == -float('inf') or tensor_sum != tensor_sum:
return True
return False
def release_param_grad(tensor_list):
for tensor in tensor_list:
tensor.grad = None
def calculate_global_norm_from_list(norm_list):
""" Compute total from a list of norms
"""
total_norm = 0.0
for norm in norm_list:
total_norm += norm**2.0
return math.sqrt(total_norm)
def compute_norm(gradients, params, dp_group, mp_group, norm_type=2):
"""Clips gradient norm of an iterable of parameters.
This is adapted from torch.nn.utils.clip_grad.clip_grad_norm_ and
added functionality to handle model parallel parameters. Note that
the gradients are modified in place.
Arguments:
parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
single Tensor that will have gradients normalized
max_norm (float or int): max norm of the gradients
norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
infinity norm.
Returns:
Total norm of the parameters (viewed as a single vector).
"""
if mp_group is None:
mp_rank = 0
else:
mp_rank = dist.get_rank(mp_group)
norm_type = float(norm_type)
if norm_type == inf:
total_norm = max(g.data.abs().max() for g in gradients)
total_norm_cuda = torch.cuda.FloatTensor([float(total_norm)])
dist.all_reduce(total_norm_cuda, op=torch.distributed.ReduceOp.MAX, group=dp_group)
# Take max across all GPUs.
if mp_group is not None:
dist.all_reduce(tensor=total_norm_cuda, op=torch.distributed.ReduceOp.MAX)
total_norm = total_norm_cuda[0].item()
else:
total_norm = 0.0
# if dist.get_rank() == 0:
# logger.info(f"Total Norm beginning {total_norm}")
for g, p in zip(gradients, params):
# Pipeline parallelism may replicate parameters. Avoid multi-counting.
if is_model_parallel_parameter(p) or mp_rank == 0:
param_norm = g.data.double().norm(2)
total_norm += param_norm.item()**2
# Sum across all model parallel GPUs.
total_norm_cuda = torch.cuda.FloatTensor([float(total_norm)])
torch.distributed.all_reduce(total_norm_cuda, op=torch.distributed.ReduceOp.SUM, group=dp_group)
if mp_group is not None:
dist.all_reduce(tensor=total_norm_cuda, op=torch.distributed.ReduceOp.SUM)
total_norm = total_norm_cuda[0].item()**(1. / norm_type)
if total_norm == float('inf') or total_norm == -float('inf') or total_norm != total_norm:
total_norm = -1
return total_norm
def sync_param(flat_tensor, tensor_list):
"""
Synchronize the flattened tensor and unflattened tensor list. When
a list of tensor are flattened with `torch._utils._unflatten_dense_tensors`,
a new tensor is created. Thus, the flat tensor and original tensor list do not
share the same memory space. This function will update the tensor list so that
they point to the same value.
:param flat_tensor: A flat tensor obtained by calling `torch._utils._unflatten_dense_tensors` on a tensor lsit
:param tensor_list: A list of tensors corresponding to the flattened tensor
:type flat_tensor: torch.Tensor
:type tensor_list: List[torch.Tensor]
"""
updated_params = unflatten(flat_tensor, tensor_list)
# update the tensor data
for p, q in zip(tensor_list, updated_params):
p.data = q.data
|
from typing import Optional
import torch
import torch.distributed as dist
from colossalai.registry import OPHOOKS
from colossalai.utils import get_current_device
from colossalai.utils.memory_tracer.memstats_collector import MemStatsCollector
from colossalai.zero.shard_utils import BaseShardStrategy
from colossalai.zero.sharded_param.tensorful_state import TensorState
from colossalai.gemini.stateful_tensor_mgr import StatefulTensorMgr
from colossalai.engine.ophooks import BaseOpHook
@OPHOOKS.register_module
class ZeroHook(BaseOpHook):
"""
A hook to process sharded param for ZeRO method.
"""
def __init__(self,
shard_strategy: BaseShardStrategy,
memstarts_collector: Optional[MemStatsCollector] = None,
stateful_tensor_mgr: Optional[StatefulTensorMgr] = None,
process_group: Optional[dist.ProcessGroup] = None):
super().__init__()
self.shard_strategy = shard_strategy
self.process_group = process_group
# NOTE(jiaruifang) Now the computing device of FWD and BWD is always on GPU
self.computing_device = get_current_device()
self._memstarts_collector = memstarts_collector
self._stateful_tensor_mgr = stateful_tensor_mgr
def gather_parameters(self, module: torch.nn.Module):
# gather sharded parameters
if module.param_is_sharded:
tensor_list = []
for param in module.parameters(recurse=False):
assert hasattr(param, 'colo_attr')
tensor_list.append(param.colo_attr.sharded_data_tensor)
self.shard_strategy.gather(tensor_list, self.process_group)
def shard_parameters(self, module: torch.nn.Module):
# shard gathered parameters
if module.param_is_sharded:
tensor_list = []
for param in module.parameters(recurse=False):
assert hasattr(param, 'colo_attr')
tensor_list.append(param.colo_attr.sharded_data_tensor)
self.shard_strategy.shard(tensor_list, self.process_group)
def adjust_module_data(self, module: torch.nn.Module):
# record overall data statistics
if self._memstarts_collector:
self._memstarts_collector.sample_overall_data()
for param in module.parameters(recurse=False):
param.colo_attr.sharded_data_tensor.trans_state(TensorState.COMPUTE)
# adjust stateful tensor to get enough CUDA memory
self._stateful_tensor_mgr.adjust_layout()
# record model data statistics
if self._memstarts_collector:
self._memstarts_collector.sample_model_data()
def pre_fwd_exec(self, module: torch.nn.Module, *args):
self.adjust_module_data(module)
self.gather_parameters(module)
for param in module.parameters(recurse=False):
param.data = param.colo_attr.data_payload
assert param.data.device.type == 'cuda', f"PRE FWD param.data must be on CUDA"
def post_fwd_exec(self, module: torch.nn.Module, *args):
# change tensor state to HOLD_AFTER_FWD
for param in module.parameters(recurse=False):
param.colo_attr.sharded_data_tensor.trans_state(TensorState.HOLD_AFTER_FWD)
self.shard_parameters(module)
# remove torch payload
for param in module.parameters(recurse=False):
param.colo_attr.set_data_none()
def pre_bwd_exec(self, module: torch.nn.Module, input, output):
self.adjust_module_data(module)
self.gather_parameters(module)
for param in module.parameters(recurse=False):
param.data = param.colo_attr.data_payload
assert param.data.device.type == 'cuda', f"PRE BWD param.data must be on CUDA"
def post_bwd_exec(self, module: torch.nn.Module, input):
# change tensor state to HOLD_AFTER_BWD
for param in module.parameters(recurse=False):
param.colo_attr.sharded_data_tensor.trans_state(TensorState.HOLD_AFTER_BWD)
self.shard_parameters(module)
# remove torch payload
for param in module.parameters(recurse=False):
param.colo_attr.set_data_none()
def pre_iter(self):
pass
def post_iter(self):
if self._stateful_tensor_mgr:
self._stateful_tensor_mgr.reset()
|
from .zero_hook import ZeroHook
__all__ = ['ZeroHook']
|
import torch
from colossalai.zero.sharded_param.tensorful_state import StatefulTensor, TensorState
from typing import Optional
class ShardedTensor(StatefulTensor):
def __init__(self, tensor: torch.Tensor, state: TensorState = TensorState.HOLD) -> None:
r"""
A tensor sharded in multiple processes. Constructed from an existing torch.Tensor instance.
"""
assert tensor.requires_grad is False
super().__init__(tensor, state)
# kept the shape, numel and dtype of the init tensor.
self._origin_shape = tensor.shape
self._origin_numel = tensor.numel()
self._origin_dtype = tensor.dtype
self._is_sharded = False
@property
def dtype(self) -> torch.dtype:
assert self._payload.dtype == self._origin_dtype
return self._payload.dtype
@property
def origin_numel(self) -> int:
return self._origin_numel
@property
def origin_shape(self) -> int:
return self._origin_shape
@property
def is_sharded(self):
return self._is_sharded
@is_sharded.setter
def is_sharded(self, flag: bool):
self._is_sharded = flag
|
import torch
from colossalai.zero.sharded_param import ShardedTensor
from typing import Optional, Tuple
from colossalai.zero.sharded_param.tensor_utils import colo_tensor_mem_usage
from .tensorful_state import StatefulTensor, TensorState
from typing import List
EMPTY_TENSOR_DICT = {}
def get_empty_tensor(device: torch.device, dtype: torch.dtype):
key = (device, dtype)
if key not in EMPTY_TENSOR_DICT:
EMPTY_TENSOR_DICT[key] = torch.empty(0, dtype=dtype, device=device)
return EMPTY_TENSOR_DICT[key]
class ShardedParamV2(object):
def __init__(self, param: torch.nn.Parameter, set_data_none: bool = False) -> None:
self._sharded_data_tensor: ShardedTensor = ShardedTensor(param.data)
self.saved_grad: StatefulTensor = StatefulTensor(None, TensorState.FREE)
# This attribute must be initialized in ShardedModel
self.offload_grad: bool = False
# make sure the shared param is the only owner of payload
# The param.data maybe used to init the other part of the model.
# For example: File "resnet.py", line 190, in __init__
# nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
# So we can not empty the .data at this time
self.param = param
if set_data_none:
self.set_data_none()
def get_payload_tensors(self) -> List[StatefulTensor]:
"""returns stateful tensors kept by this class.
"""
return [self._sharded_data_tensor]
def set_data_none(self):
self.param.data = get_empty_tensor(self.sharded_data_tensor.device, self.sharded_data_tensor.dtype)
def set_grad_none(self):
self.saved_grad.set_null()
@property
def sharded_data_tensor(self):
return self._sharded_data_tensor
@property
def data_payload(self):
return self.sharded_data_tensor.payload
@property
def grad_payload(self):
assert not self.saved_grad.is_null()
return self.saved_grad.payload
@property
def param_is_sharded(self):
return self.sharded_data_tensor.is_sharded
def reset_data_payload(self, tensor: torch.Tensor):
assert type(tensor) is torch.Tensor
assert tensor.requires_grad is False
self.sharded_data_tensor.reset_payload(tensor)
def reset_grad_payload(self, tensor: torch.Tensor):
assert type(tensor) is torch.Tensor
assert tensor.requires_grad is False
self.saved_grad.reset_payload(tensor)
def get_memory_usage(self) -> Tuple[int, int]:
"""
get the memory usage of the param, including data and grad
Returns:
Tuple[int, int]: cuda mem usage in Byte, cpu memory usage in Byte
"""
cuda_mem_use, cpu_mem_use = 0, 0
def _update_mem_use(t: Optional[torch.Tensor]):
if t is None:
return
assert isinstance(t, torch.Tensor)
nonlocal cuda_mem_use
nonlocal cpu_mem_use
t_cuda, t_cpu = colo_tensor_mem_usage(t)
cuda_mem_use += t_cuda
cpu_mem_use += t_cpu
address_set = set()
_update_mem_use(self.data_payload)
address_set.add(self.data_payload.data_ptr())
if not self.saved_grad.is_null() and self.saved_grad.data_ptr() not in address_set:
_update_mem_use(self.grad_payload)
address_set.add(self.saved_grad.data_ptr())
if self.param.data is not None and self.param.data.data_ptr() not in address_set:
_update_mem_use(self.param.data)
address_set.add(self.param.data.data_ptr())
if self.param.grad is not None and self.param.grad.data_ptr() not in address_set:
_update_mem_use(self.param.grad)
return cuda_mem_use, cpu_mem_use
|
from colossalai.zero.sharded_param.sharded_tensor import ShardedTensor
from colossalai.zero.sharded_param.sharded_param import ShardedParamV2
from colossalai.zero.sharded_param.tensor_utils import (colo_model_data_tensor_move, colo_model_data_tensor_move_inline,
colo_model_data_move_to_cpu, colo_model_tensor_clone,
colo_tensor_mem_usage)
from colossalai.zero.sharded_param.tensorful_state import TensorState, StatefulTensor
__all__ = [
'ShardedTensor', 'ShardedParamV2', 'colo_model_data_tensor_move', 'colo_model_data_tensor_move_inline',
'colo_model_data_move_to_cpu', 'colo_model_tensor_clone', 'colo_tensor_mem_usage', 'TensorState', 'StatefulTensor'
]
|
import torch
from colossalai.zero.sharded_param.tensorful_state import StatefulTensor
from typing import Union, Tuple
def colo_tensor_mem_usage(tensor: Union[torch.Tensor, StatefulTensor]) -> Tuple[int, int]:
if issubclass(type(tensor), StatefulTensor):
t = tensor.payload
elif isinstance(tensor, torch.Tensor):
t = tensor
else:
return 0, 0
cuda_use, cpu_use = 0, 0
mem_use = t.storage().size() * t.element_size()
if t.device.type == 'cuda':
cuda_use += mem_use
elif t.device.type == 'cpu':
cpu_use += mem_use
return cuda_use, cpu_use
def colo_model_data_tensor_move(src_t: Union[StatefulTensor, torch.Tensor], tgt_t: Union[StatefulTensor,
torch.Tensor]) -> None:
"""
A colossal API for model data tensor move.
The src and target tensors could be resident on both CPU and GPU.
NOTE() The source tensor payload will be removed after this function.
The function will record the communication volume between CPU and GPU.
Args:
t_src (Union[StatefulTensor, torch.Tensor]): source tensor
tgt_t (Union[StatefulTensor, torch.Tensor]): target tensor
"""
if issubclass(type(src_t), StatefulTensor):
src_t_payload = src_t.payload
else:
src_t_payload = src_t.data
src_dev = src_t_payload.device
if issubclass(type(tgt_t), StatefulTensor):
tgt_t_payload = tgt_t.payload
else:
tgt_t_payload = tgt_t.data
tgt_t_payload.copy_(src_t_payload)
# remove payload of src_t
if issubclass(type(src_t), StatefulTensor):
src_t.reset_payload(torch.tensor([], device=src_dev, dtype=src_t_payload.dtype))
else:
src_t.data = torch.tensor([], device=src_dev, dtype=src_t_payload.dtype)
def colo_model_data_tensor_move_inline(t: Union[StatefulTensor, torch.Tensor], target_device: Union[torch.device,
int]) -> None:
"""
move a tensor to the target_device
Args:
t (Union[StatefulTensor, torch.Tensor]): the tensor be moved
target_device: a traget device, if type is int, it the index of cuda card.
"""
if isinstance(t, torch.Tensor):
t_payload = t
elif issubclass(type(t), StatefulTensor):
t_payload = t.payload
else:
raise TypeError('colo_model_data_move_to_cpu dose not accept type {type(t)}')
if not isinstance(target_device, torch.device):
target_device = torch.device(f'cuda:{target_device}')
# deal with torch.device('cpu') and torch.device('cpu:0)
if t_payload.device.type == target_device.type:
return
t_payload.data = t_payload.data.to(target_device)
def colo_model_data_move_to_cpu(t: Union[StatefulTensor, torch.Tensor]) -> None:
"""colo_model_data_move_to_cpu
move a model data tensor from gpu to cpu
Args:
t (Union[StatefulTensor, torch.Tensor]): _description_
"""
if issubclass(type(t), StatefulTensor):
t_payload = t.payload
elif isinstance(t, torch.Tensor):
t_payload = t
else:
raise TypeError('colo_model_data_move_to_cpu dose not accept type {type(t)}')
if t_payload.device.type == 'cpu':
return
# TODO() optimize the tensor moving with non-blocking
t_payload.data = t_payload.data.cpu()
def colo_model_tensor_clone(t: Union[StatefulTensor, torch.Tensor], target_device: torch.device) -> torch.Tensor:
"""
Clone a model data tensor
Args:
t (Union[StatefulTensor, torch.Tensor]): a model data tensor
target_device (torch.device): the target device
Returns:
torch.Tensor: a cloned torch tensor
"""
t_payload = t.payload if issubclass(type(t), StatefulTensor) else t
ret = t_payload.to(target_device)
return ret
|
from enum import Enum
from typing import Optional
import torch
class TensorState(Enum):
FREE = 0
HOLD = 1
HOLD_AFTER_FWD = 2
HOLD_AFTER_BWD = 3
COMPUTE = 4
class StatefulTensor(object):
"""A Structure stores a Torch Tensor and labeled states.
Inspired from the paper:
PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management
https://arxiv.org/abs/2108.05818
"""
def __init__(self, tensor: Optional[torch.Tensor], state: Optional[TensorState] = TensorState.HOLD) -> None:
self._state = state
self._payload = tensor
if self._state == TensorState.FREE:
assert self._payload is None, f"payload has to None if state is {self._state}"
def data_ptr(self):
if self._payload is None:
return None
return self._payload.data_ptr()
@property
def state(self) -> TensorState:
return self._state
def set_null(self) -> None:
self._state = TensorState.FREE
self._payload = None
def is_null(self) -> bool:
if self._state == TensorState.FREE:
assert self._payload is None
return True
return False
def trans_state(self, state: TensorState) -> None:
self._state = state
if state == TensorState.FREE:
self._payload = None
@property
def payload(self) -> Optional[torch.Tensor]:
return self._payload
def copy_payload(self, tensor) -> None:
self._payload.view(-1).copy_(tensor.view(-1))
def reset_payload(self, tensor) -> None:
del self._payload
self._payload = tensor
self.trans_state(TensorState.HOLD)
@property
def device(self) -> torch.device:
return self._payload.device
@property
def dtype(self) -> torch.dtype:
return self._payload.dtype
@property
def shape(self):
return self._payload.shape
def to(self, device: torch.device):
raise RuntimeError("Use colo_model_tensor_move install of call .to() on ShardedTensor")
def to_(self, device: torch.device):
raise RuntimeError("Use colo_model_tensor_move install of call .to_() on ShardedTensor")
|
from typing import List, Optional
import torch
import torch.distributed as dist
from colossalai.utils import get_current_device
from colossalai.zero.sharded_param.sharded_tensor import ShardedTensor
from torch._utils import _flatten_dense_tensors as flatten
from .tensor_shard_strategy import TensorShardStrategy
class BucketTensorShardStrategy(TensorShardStrategy):
"""Use the same shard scheme as `TensorShardStrategy`'s, but it gathers tensors of a sub-module together,
which will fully utilize network bandwidth.
It is especially useful when sub-module contains bias,
since we cannot utilize network bandwidth well if we only gather a bias tensor (bias is usaully small).
"""
def gather(self, tensor_list: List[ShardedTensor], process_group: Optional[dist.ProcessGroup] = None):
tensor_list: List[ShardedTensor] = [t for t in tensor_list if t.is_sharded]
if len(tensor_list) == 0:
return
target_device = tensor_list[0].device
dtype = tensor_list[0].dtype
buffer_list: List[torch.Tensor] = []
tensor_numels = [t.payload.numel() for t in tensor_list]
buffer_size = sum(tensor_numels)
world_size = dist.get_world_size(process_group)
rank = dist.get_rank(process_group)
for i in range(world_size):
if i == rank:
buffer_list.append(flatten([t.payload for t in tensor_list]).cuda(get_current_device()))
# Release payload here, to decrease peak memory usage
for t in tensor_list:
t.reset_payload(None)
else:
buffer_list.append(torch.zeros(buffer_size, dtype=dtype, device=get_current_device()))
dist.all_gather(buffer_list, buffer_list[rank], group=process_group)
# Move to target device before splitting buffer
# Ensure we utilize maximum PCIE bandwidth
buffer_list = [buffer.to(target_device) for buffer in buffer_list]
offset = 0
for i, t in enumerate(tensor_list):
gathered_payload = [buffer[offset:offset + tensor_numels[i]] for buffer in buffer_list]
gathered_payload = torch.cat(gathered_payload)[:t.origin_numel].view(t.origin_shape)
t.reset_payload(gathered_payload)
t.is_sharded = False
offset += tensor_numels[i]
|
from .base_shard_strategy import BaseShardStrategy
from .bucket_tensor_shard_strategy import BucketTensorShardStrategy
from .tensor_shard_strategy import TensorShardStrategy
__all__ = ['BaseShardStrategy', 'TensorShardStrategy', 'BucketTensorShardStrategy']
|
from abc import ABC, abstractmethod
from typing import List, Optional
import torch.distributed as dist
from colossalai.zero.sharded_param.sharded_tensor import ShardedTensor
class BaseShardStrategy(ABC):
def __init__(self) -> None:
"""Abstract Shard Strategy. Use to shard a tensors on multiple GPUs.
"""
super().__init__()
@abstractmethod
def shard(self, tensor_list: List[ShardedTensor], process_group: Optional[dist.ProcessGroup] = None):
pass
@abstractmethod
def gather(self, tensor_list: List[ShardedTensor], process_group: Optional[dist.ProcessGroup] = None):
pass
|
import torch
import torch.nn.functional as F
from typing import Tuple
def get_shard(tensor: torch.Tensor, rank: int, world_size: int) -> Tuple[torch.Tensor, int]:
"""Return the local shard of a full tensor."""
# Shard using torch.chunk to match all-gather/reduce-scatter.
chunks = list(torch.flatten(tensor).chunk(world_size))
while len(chunks) < world_size:
chunks.append(chunks[0].new_empty(0))
# Determine number of padding elements.
num_to_pad = chunks[0].numel() - chunks[rank].numel()
assert num_to_pad >= 0, num_to_pad
shard = torch.zeros_like(chunks[0])
length = chunks[rank].size(0)
shard_temp = shard[:length]
shard_temp.copy_(chunks[rank])
return shard, num_to_pad
|
from typing import List, Optional
import torch
import torch.distributed as dist
from colossalai.utils import get_current_device
from colossalai.zero.sharded_param.tensor_utils import colo_model_data_tensor_move_inline
from colossalai.zero.shard_utils import BaseShardStrategy
from colossalai.zero.shard_utils.commons import get_shard
from colossalai.zero.sharded_param.sharded_tensor import ShardedTensor
class TensorShardStrategy(BaseShardStrategy):
"""
A naive implementation which shard each tensor evenly over all ranks
"""
def shard(self, tensor_list: List[ShardedTensor], process_group: Optional[dist.ProcessGroup] = None):
for t in tensor_list:
self._shard_tensor(t, process_group)
def gather(self, tensor_list: List[ShardedTensor], process_group: Optional[dist.ProcessGroup] = None):
for t in tensor_list:
self._gather_tensor(t, process_group)
def _shard_tensor(self, t: ShardedTensor, process_group: Optional[dist.ProcessGroup] = None):
""" Shard tensor among processes.
Args:
t (ShardedTensor): a tensor to be sharded.
process_group (Optional[dist.ProcessGroup], optional): the process group among which tensor shards.
Defaults to None.
"""
if t.is_sharded:
return
if t.payload.device.type == 'cuda':
assert t.payload.device == get_current_device(), f"shard tensor on cuda device index {t.payload.device.index},"\
f" but current cuda device is {get_current_device()}"
sharded_payload, _ = get_shard(t.payload, dist.get_rank(process_group), dist.get_world_size(process_group))
t.reset_payload(sharded_payload)
t.is_sharded = True
def _gather_tensor(self, t: ShardedTensor, process_group: Optional[dist.ProcessGroup] = None):
if not t.is_sharded:
return
target_device = t.device
payload_numel = t.payload.numel()
world_size = dist.get_world_size(process_group)
rank = dist.get_rank(process_group)
buffer = torch.empty(payload_numel * world_size, dtype=t.payload.dtype, device=get_current_device())
buffer_list = list(torch.chunk(buffer, chunks=world_size, dim=0))
buffer_list[rank].copy_(t.payload)
dist.all_gather(buffer_list, buffer_list[rank], group=process_group, async_op=False)
gathered_payload = torch.narrow(buffer, 0, 0, t.origin_numel).reshape(t.origin_shape)
t.reset_payload(gathered_payload)
colo_model_data_tensor_move_inline(t, target_device)
t.is_sharded = False
|
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