mirror of https://github.com/hpcaitech/ColossalAI
404 lines
18 KiB
Python
404 lines
18 KiB
Python
import math
|
|
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.nn import init as init
|
|
from colossalai.registry import LAYERS
|
|
from colossalai.utils import get_current_device
|
|
from torch import Tensor, dtype
|
|
from torch.nn import Parameter
|
|
|
|
from .._common_utils import (divide, set_tensor_parallel_attribute_by_partition, to_2tuple)
|
|
from ..base_layer import ParallelLayer
|
|
from ._operation import (Add_Bias_2p5D, Matmul_AB_2p5D, all_gather_weight_2p5d, classifier_2p5d, layernorm_2p5d,
|
|
split_batch_2p5d)
|
|
from ._utils import (assert_tesseract_initialization, get_tesseract_dim_dep_from_env)
|
|
|
|
|
|
@LAYERS.register_module
|
|
class Linear2p5D(ParallelLayer):
|
|
"""Linear layer for 2.5D parallelism
|
|
|
|
:param in_features: size of each input sample
|
|
:type in_features: int
|
|
:param out_features: size of each output sample
|
|
:type out_features: int
|
|
:param bias: If set to ``False``, the layer will not learn an additive bias, defaults to True
|
|
:type bias: bool, optional
|
|
:param dtype: The dtype of parameters, defaults to None
|
|
:type dtype: torch.dtype, optional
|
|
"""
|
|
def __init__(self,
|
|
in_features: int,
|
|
out_features: int,
|
|
bias: bool = True,
|
|
dtype: 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 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.apply(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.apply(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
|
|
|
|
:param 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.
|
|
:type normalized_shape: int
|
|
:param eps: a value added to the denominator for numerical stability, defaults to 1e-05
|
|
:type eps: float, optional
|
|
:param dtype: The dtype of parameters, defaults to None
|
|
:type dtype: torch.dtype, optional
|
|
"""
|
|
def __init__(self, normalized_shape: int, eps: float = 1e-05, 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.gamma = Parameter(torch.ones(self.partitioned_partition, **factory_kwargs))
|
|
self.beta = Parameter(torch.zeros(self.partitioned_partition, **factory_kwargs))
|
|
|
|
self._set_tensor_parallel_attribute()
|
|
|
|
def _set_tensor_parallel_attribute(self):
|
|
set_tensor_parallel_attribute_by_partition(self.gamma, self.tesseract_dim)
|
|
set_tensor_parallel_attribute_by_partition(self.beta, self.tesseract_dim)
|
|
|
|
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.apply(x, E_x, Var_x, self.normalized_shape, ParallelMode.PARALLEL_2P5D_ROW)
|
|
bias = Add_Bias_2p5D.apply(None, self.beta, 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)
|
|
scale = Add_Bias_2p5D.apply(None, self.gamma, 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)
|
|
return output
|
|
|
|
|
|
@LAYERS.register_module
|
|
class PatchEmbedding2p5D(ParallelLayer):
|
|
""" 2D Image to Patch Embedding
|
|
:param img_size: iamge size
|
|
:type img_size: int
|
|
:param patch_size: patch size
|
|
:type patch_size: int
|
|
:param embed_dim: dimension of embedding
|
|
:type embed_dim: int
|
|
:param in_chans: number of channels of input image, defaults to 3
|
|
:type in_chans: int, optional
|
|
:param flatten: whether to flatten output tensor, defaults to True
|
|
:type flatten: bool, optional
|
|
"""
|
|
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_()):
|
|
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_dep * 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_dep * self.tesseract_dim**2)
|
|
set_tensor_parallel_attribute_by_partition(self.bias, self.tesseract_dep * self.tesseract_dim**2)
|
|
set_tensor_parallel_attribute_by_partition(self.cls_token, self.tesseract_dep * self.tesseract_dim**2)
|
|
set_tensor_parallel_attribute_by_partition(self.pos_embed, self.tesseract_dep * 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 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]})."
|
|
|
|
input_ = split_batch_2p5d(input_)
|
|
|
|
weight = all_gather_weight_2p5d.apply(self.weight, 0, self.tesseract_dim, ParallelMode.PARALLEL_2P5D_COL)
|
|
bias = all_gather_weight_2p5d.apply(self.bias, 0, self.tesseract_dim, 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_weight_2p5d.apply(self.cls_token, -1, self.tesseract_dim, ParallelMode.PARALLEL_2P5D_COL)
|
|
pos_embed = all_gather_weight_2p5d.apply(self.pos_embed, -1, self.tesseract_dim, 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):
|
|
def __init__(self,
|
|
num_embeddings: int,
|
|
embedding_dim: int,
|
|
padding_idx: int = None,
|
|
dtype: 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_dep * 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_dep * 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 forward(self, input_: Tensor) -> Tensor:
|
|
input_ = split_batch_2p5d(input_)
|
|
|
|
weight = all_gather_weight_2p5d.apply(self.weight, -1, self.tesseract_dim, ParallelMode.PARALLEL_2P5D_COL)
|
|
|
|
output = F.embedding(input_, weight, self.padding_idx, *self.embed_args, **self.embed_kwargs)
|
|
|
|
return output
|
|
|
|
|
|
@LAYERS.register_module
|
|
class Classifier2p5D(ParallelLayer):
|
|
def __init__(self,
|
|
in_features: int,
|
|
num_classes: int,
|
|
weight: 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)):
|
|
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_dep * 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_dep * 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 forward(self, input_: Tensor) -> Tensor:
|
|
out_shape = input_.shape[:-1] + (self.num_classes, )
|
|
|
|
return classifier_2p5d.apply(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)
|