import math
from collections import OrderedDict
from typing import Callable
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from torch.nn import Parameter
from colossalai.communication import all_reduce, broadcast
from colossalai.constants import INPUT_GROUP_3D, INPUT_X_WEIGHT_3D, OUTPUT_GROUP_3D, OUTPUT_X_WEIGHT_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 ..utils import divide, set_tensor_parallel_attribute_by_partition, to_2tuple
from ._operation import (
all_gather_tensor_3d,
classifier_3d,
layernorm_3d,
linear_3d,
reduce_scatter_tensor_3d,
split_batch_3d,
split_tensor_3d,
vocab_parallel_classifier_3d,
)
from ._utils import get_depth_from_env, get_parallel_mode_from_env, register_async_grad_hook, 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_parallel_mode_from_env(OUTPUT_GROUP_3D)
self.input_x_weight_parallel_mode = get_parallel_mode_from_env(INPUT_X_WEIGHT_3D)
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.reset_parameters()
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)
register_async_grad_hook(self.weight)
if self.bias is not None:
init.zeros_()(self.bias)
register_async_grad_hook(self.bias)
def _load_from_global_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_global_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_global_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.output_parallel_mode,
self.input_x_weight_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,
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.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_parallel_mode_from_env(OUTPUT_GROUP_3D)
self.output_x_weight_parallel_mode = get_parallel_mode_from_env(OUTPUT_X_WEIGHT_3D)
self.depth = get_depth_from_env()
self.skip_bias_add = skip_bias_add
self.in_features_per_partition = divide(in_features, self.depth**2)
self.out_features_per_partition = divide(out_features, self.depth)
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 _sync_grad_hook(self, grad) -> Tensor:
grad = all_reduce(grad.clone(), self.output_x_weight_parallel_mode)
return grad
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)
register_async_grad_hook(self.weight)
if self.bias is not None:
bias_initializer(self.bias, fan_in=fan_in)
broadcast(self.bias,
gpc.get_ranks_in_group(self.output_x_weight_parallel_mode)[0],
self.output_x_weight_parallel_mode)
self.bias.register_hook(self._sync_grad_hook)
def _load_from_global_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: 0,
bias_key: 0
},
partition_states={
weight_key: True,
bias_key: False
},
)
super()._load_from_global_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_global_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: -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:
output = linear_3d(
input_,
self.weight,
self.input_parallel_mode,
self.weight_parallel_mode,
self.output_parallel_mode,
)
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 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_parallel_mode_from_env(OUTPUT_GROUP_3D)
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
if self.has_weight:
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
broadcast(self.weight, gpc.get_ranks_in_group(self.weight_parallel_mode)[0], self.weight_parallel_mode)
register_async_grad_hook(self.weight)
if self.bias is not None:
bias_initializer(self.bias, fan_in=fan_in)
broadcast(self.bias, gpc.get_ranks_in_group(ParallelMode.TENSOR)[0], ParallelMode.TENSOR)
register_async_grad_hook(self.bias)
def _load_from_global_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_global_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_global_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_parallel_mode_from_env(OUTPUT_GROUP_3D)
self.output_x_weight_parallel_mode = get_parallel_mode_from_env(OUTPUT_X_WEIGHT_3D)
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)
register_async_grad_hook(self.weight)
if self.bias is not None:
bias_initializer(self.bias, fan_in=fan_in)
broadcast(self.bias,
gpc.get_ranks_in_group(self.output_x_weight_parallel_mode)[0],
self.output_x_weight_parallel_mode)
register_async_grad_hook(self.bias)
def _load_from_global_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_global_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_global_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 vocab_parallel_classifier_3d(
input_,
self.weight,
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_parallel_mode_from_env(OUTPUT_GROUP_3D)
self.input_x_weight_parallel_mode = get_parallel_mode_from_env(INPUT_X_WEIGHT_3D)
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.embed_size = embed_size
embed_size_per_partition = 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, self.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_x_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)
src_rank = gpc.get_ranks_in_group(self.input_x_weight_parallel_mode)[0]
broadcast(self.weight, src_rank, self.input_x_weight_parallel_mode)
broadcast(self.bias, src_rank, self.input_x_weight_parallel_mode)
broadcast(self.pos_embed, src_rank, self.input_x_weight_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_global_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_global_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_global_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_batch_3d(input_,
input_parallel_mode=self.input_parallel_mode,
weight_parallel_mode=self.weight_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_parallel_mode_from_env(OUTPUT_GROUP_3D)
self.input_x_weight_parallel_mode = get_parallel_mode_from_env(INPUT_X_WEIGHT_3D)
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 _sync_grad_hook(self, grad) -> Tensor:
grad = all_reduce(grad.clone(), self.input_x_weight_parallel_mode)
return grad
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()
broadcast(self.weight,
gpc.get_ranks_in_group(self.input_x_weight_parallel_mode)[0], self.input_x_weight_parallel_mode)
self.weight.register_hook(self._sync_grad_hook)
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_global_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_global_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_global_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_batch_3d(input_,
input_parallel_mode=self.input_parallel_mode,
weight_parallel_mode=self.weight_parallel_mode)
output = F.embedding(input_, self.weight, self.padding_idx, *self.embed_args, **self.embed_kwargs)
return output
@LAYERS.register_module
class VocabParallelEmbedding3D(ParallelLayer):
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_parallel_mode_from_env(OUTPUT_GROUP_3D)
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_global_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_global_state_dict(local_state, prefix, *args, **kwargs)
def _save_to_global_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