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