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ColossalAI/colossalai/nn/layer/parallel_3d/layers.py

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import math
from typing import Callable
import torch
import torch.nn as nn
import torch.nn.functional as F
from colossalai.communication import all_reduce, broadcast
from colossalai.constants import INPUT_GROUP_3D, WEIGHT_GROUP_3D
from colossalai.context import ParallelMode, seed
from colossalai.core import global_context as gpc
from colossalai.global_variables import tensor_parallel_env as env
from colossalai.nn import init as init
from colossalai.nn.layer.base_layer import ParallelLayer
from colossalai.registry import LAYERS
from colossalai.utils.cuda import get_current_device
from torch import Tensor
from torch.nn import Parameter
from ..utils import divide, set_tensor_parallel_attribute_by_partition, to_2tuple
from ._operation import layernorm_3d, linear_3d, classifier_3d, split_tensor_3d
from ._operation import all_gather_tensor_3d, reduce_scatter_tensor_3d, broadcast_weight_3d_from_diagonal
from ._utils import get_depth_from_env, get_last_group, get_parallel_mode_from_env, swap_in_out_group
@LAYERS.register_module
class LayerNorm3D(ParallelLayer):
r"""
Layer Normalization for 3D parallelism
: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-12
: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-12, dtype=None):
super().__init__()
self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
self.output_parallel_mode = get_last_group(self.input_parallel_mode, self.weight_parallel_mode)
self.depth = get_depth_from_env()
self.normalized_shape = normalized_shape
self.normalized_shape_per_partition = divide(normalized_shape, self.depth)
self.weight = Parameter(
torch.ones(self.normalized_shape_per_partition, device=get_current_device(), dtype=dtype))
self.bias = Parameter(torch.zeros(self.normalized_shape_per_partition, device=get_current_device(),
dtype=dtype))
self.variance_epsilon = eps
self._set_tensor_parallel_attributes()
def _set_tensor_parallel_attributes(self) -> None:
set_tensor_parallel_attribute_by_partition(self.weight, self.depth)
set_tensor_parallel_attribute_by_partition(self.bias, self.depth)
def reset_parameters(self) -> None:
init.zeros_()(self.bias)
init.ones_()(self.weight)
def forward(self, input_: Tensor) -> Tensor:
return layernorm_3d(input_, self.weight, self.bias, self.normalized_shape, self.variance_epsilon,
self.input_parallel_mode, self.weight_parallel_mode, self.output_parallel_mode)
@LAYERS.register_module
class Linear3D(ParallelLayer):
"""
Linear layer for 3D 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
:param weight_initializer: The intializer of weight, defaults to kaiming uniform initializer
:type weight_initializer: typing.Callable, optional
:param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
:type bias_initializer: typing.Callable, optional
"""
def __init__(self,
in_features: int,
out_features: int,
bias: bool = True,
dtype: torch.dtype = None,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
self.output_parallel_mode = get_last_group(self.input_parallel_mode, self.weight_parallel_mode)
self.depth = get_depth_from_env()
self.in_features_per_partition = divide(in_features, self.depth)
self.out_features_per_partition = divide(out_features, self.depth**2)
self.bias_features_per_partition = divide(out_features, self.depth)
self.weight = Parameter(
torch.empty(self.in_features_per_partition,
self.out_features_per_partition,
device=get_current_device(),
dtype=dtype))
if bias:
self.bias = Parameter(
torch.zeros(self.bias_features_per_partition, device=get_current_device(), dtype=dtype))
else:
self.bias = None
self.reset_parameters(weight_initializer, bias_initializer)
self._set_tensor_parallel_attributes()
swap_in_out_group()
def _set_tensor_parallel_attributes(self) -> None:
set_tensor_parallel_attribute_by_partition(self.weight, self.depth**3)
if self.bias is not None:
set_tensor_parallel_attribute_by_partition(self.bias, self.depth)
def reset_parameters(self, weight_initializer, bias_initializer) -> None:
with seed(ParallelMode.TENSOR):
fan_in, fan_out = self.in_features, self.out_features
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
if self.bias is not None:
bias_initializer(self.bias, fan_in=fan_in)
weight_src_rank = gpc.get_ranks_in_group(self.weight_parallel_mode)[0]
output_src_rank = gpc.get_ranks_in_group(self.output_parallel_mode)[0]
broadcast(self.bias, weight_src_rank, self.weight_parallel_mode)
broadcast(self.bias, output_src_rank, self.output_parallel_mode)
def forward(self, input_: Tensor) -> Tensor:
return linear_3d(input_, self.weight, self.bias, self.input_parallel_mode, self.weight_parallel_mode,
self.output_parallel_mode)
@LAYERS.register_module
class Classifier3D(ParallelLayer):
"""
Classifier for 3D parallelism
:param in_features: size of each input sample
:type in_features: int
:param num_classes: number of classes
:type num_classes: int
:param weight: weight of the classifier, defaults to True
:type weight: torch.nn.Parameter, optional
: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
:param weight_initializer: The intializer of weight, defaults to kaiming uniform initializer
:type weight_initializer: typing.Callable, optional
:param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
:type bias_initializer: typing.Callable, optional
"""
def __init__(self,
in_features: int,
num_classes: int,
weight: Parameter = None,
bias: bool = True,
dtype: torch.dtype = None,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
super().__init__()
self.in_features = in_features
self.num_classes = num_classes
self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
self.output_parallel_mode = get_last_group(self.input_parallel_mode, self.weight_parallel_mode)
self.depth = get_depth_from_env()
self.in_features_per_partition = divide(in_features, self.depth)
if weight is not None:
self.weight = weight
self.has_weight = False
else:
self.weight = Parameter(
torch.empty(self.num_classes, self.in_features_per_partition, device=get_current_device(), dtype=dtype))
self.has_weight = True
if bias:
self.bias = Parameter(torch.zeros(self.num_classes, device=get_current_device(), dtype=dtype))
else:
self.bias = None
self.reset_parameters(weight_initializer, bias_initializer)
self._set_tensor_parallel_attributes()
def _set_tensor_parallel_attributes(self) -> None:
if self.has_weight:
set_tensor_parallel_attribute_by_partition(self.weight, self.depth)
def reset_parameters(self, weight_initializer, bias_initializer) -> None:
with seed(ParallelMode.TENSOR):
fan_in, fan_out = self.in_features, self.num_classes
weight_src_rank = gpc.get_ranks_in_group(self.weight_parallel_mode)[0]
output_src_rank = gpc.get_ranks_in_group(self.output_parallel_mode)[0]
input_src_rank = gpc.get_ranks_in_group(self.input_parallel_mode)[0]
if self.has_weight:
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
broadcast(self.weight, weight_src_rank, self.weight_parallel_mode)
if self.bias is not None:
bias_initializer(self.bias, fan_in=fan_in)
broadcast(self.bias, weight_src_rank, self.weight_parallel_mode)
broadcast(self.bias, output_src_rank, self.output_parallel_mode)
broadcast(self.bias, input_src_rank, self.input_parallel_mode)
def 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):
"""
Vocab parallel classifier layer for 2D parallelism
:param in_features: size of each input sample
:type in_features: int
:param num_classes: number of classes
:type num_classes: int
:param weight: weight of the classifier, defaults to True
:type weight: torch.nn.Parameter, optional
: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
:param weight_initializer: The intializer of weight, defaults to kaiming uniform initializer
:type weight_initializer: typing.Callable, optional
:param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
:type bias_initializer: typing.Callable, optional
"""
def __init__(self,
in_features: int,
num_classes: int,
weight: Parameter = None,
bias: bool = True,
dtype: torch.dtype = None,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
super().__init__()
self.in_features = in_features
self.num_classes = num_classes
self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
self.output_parallel_mode = get_last_group(self.input_parallel_mode, self.weight_parallel_mode)
self.depth = get_depth_from_env()
self.in_features_per_partition = divide(in_features, self.depth)
self.out_features_per_partition = divide(num_classes, self.depth**2)
self.bias_features_per_partition = divide(num_classes, self.depth)
if weight is not None:
self.weight = weight
self.has_weight = False
else:
self.weight = Parameter(
torch.empty(self.out_features_per_partition,
self.in_features_per_partition,
device=get_current_device(),
dtype=dtype))
self.has_weight = True
if bias:
self.bias = Parameter(
torch.zeros(self.bias_features_per_partition, device=get_current_device(), dtype=dtype))
else:
self.bias = None
self.reset_parameters(weight_initializer, bias_initializer)
self._set_tensor_parallel_attributes()
swap_in_out_group()
env.vocab_parallel = True
def _set_tensor_parallel_attributes(self) -> None:
if self.has_weight:
set_tensor_parallel_attribute_by_partition(self.weight, self.depth**2)
if self.bias is not None:
set_tensor_parallel_attribute_by_partition(self.bias, self.depth)
def reset_parameters(self, weight_initializer, bias_initializer) -> None:
with seed(ParallelMode.TENSOR):
fan_in, fan_out = self.in_features, self.num_classes
if self.has_weight:
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
if self.bias is not None:
bias_initializer(self.bias, fan_in=fan_in)
weight_src_rank = gpc.get_ranks_in_group(self.weight_parallel_mode)[0]
output_src_rank = gpc.get_ranks_in_group(self.output_parallel_mode)[0]
broadcast(self.bias, weight_src_rank, self.weight_parallel_mode)
broadcast(self.bias, output_src_rank, self.output_parallel_mode)
def forward(self, input_: Tensor) -> Tensor:
return linear_3d(input_, self.weight.transpose(0, 1), self.bias, self.input_parallel_mode,
self.weight_parallel_mode, self.output_parallel_mode)
@LAYERS.register_module
class PatchEmbedding3D(ParallelLayer):
"""
2D Image to Patch Embedding
:param img_size: image size
:type img_size: int
:param patch_size: patch size
:type patch_size: int
:param in_chans: number of channels of input image
:type in_chans: int
:param embed_size: size of embedding
:type embed_size: int
:param dtype: The dtype of parameters, defaults to None
:type dtype: torch.dtype, optional
:param flatten: whether to flatten output tensor, defaults to True
:type flatten: bool, optional
:param weight_initializer: The intializer of weight, defaults to kaiming uniform initializer
:type weight_initializer: typing.Callable, optional
:param bias_initializer: The intializer of bias, defaults to xavier uniform initializer
:type bias_initializer: typing.Callable, optional
:param position_embed_initializer: The intializer of position embedding, defaults to zero
:type position_embed_initializer: typing.Callable, optional
"""
def __init__(self,
img_size: int,
patch_size: int,
in_chans: int,
embed_size: int,
flatten: bool = True,
dtype: torch.dtype = None,
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
position_embed_initializer: Callable = init.zeros_()):
super().__init__()
self.depth = get_depth_from_env()
self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
self.output_parallel_mode = get_last_group(self.input_parallel_mode, self.weight_parallel_mode)
self.patch_size = to_2tuple(patch_size)
grid_size = to_2tuple(img_size // patch_size)
num_patches = grid_size[0] * grid_size[1]
self.embed_size = embed_size
embed_size_per_partition = divide(embed_size, self.depth)
self.flatten = flatten
self.weight = nn.Parameter(
torch.empty((embed_size_per_partition, in_chans, *self.patch_size),
device=get_current_device(),
dtype=dtype))
self.bias = nn.Parameter(torch.empty(embed_size_per_partition, device=get_current_device(), dtype=dtype))
self.cls_token = nn.Parameter(
torch.zeros((1, 1, embed_size_per_partition), device=get_current_device(), dtype=dtype))
self.pos_embed = nn.Parameter(
torch.zeros((1, num_patches + 1, embed_size_per_partition), device=get_current_device(), dtype=dtype))
self.reset_parameters(weight_initializer, bias_initializer, position_embed_initializer)
self._set_tensor_parallel_attributes()
def _set_tensor_parallel_attributes(self) -> None:
set_tensor_parallel_attribute_by_partition(self.weight, self.depth)
set_tensor_parallel_attribute_by_partition(self.bias, self.depth)
set_tensor_parallel_attribute_by_partition(self.cls_token, self.depth)
set_tensor_parallel_attribute_by_partition(self.pos_embed, self.depth)
def _sync_grad_hook(self, grad) -> Tensor:
grad = all_reduce(grad.clone(), self.input_parallel_mode)
grad = all_reduce(grad, self.weight_parallel_mode)
return grad
def reset_parameters(self, weight_initializer, bias_initializer, position_embed_initializer) -> None:
with seed(ParallelMode.TENSOR):
fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
fan_out = self.embed_size
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
bias_initializer(self.bias, fan_in=fan_in)
position_embed_initializer(self.pos_embed)
weight_src_rank = gpc.get_ranks_in_group(self.weight_parallel_mode)[0]
input_src_rank = gpc.get_ranks_in_group(self.input_parallel_mode)[0]
broadcast(self.weight, weight_src_rank, self.weight_parallel_mode)
broadcast(self.bias, weight_src_rank, self.weight_parallel_mode)
broadcast(self.pos_embed, weight_src_rank, self.weight_parallel_mode)
broadcast(self.weight, input_src_rank, self.input_parallel_mode)
broadcast(self.bias, input_src_rank, self.input_parallel_mode)
broadcast(self.pos_embed, input_src_rank, self.input_parallel_mode)
self.weight.register_hook(self._sync_grad_hook)
self.bias.register_hook(self._sync_grad_hook)
self.cls_token.register_hook(self._sync_grad_hook)
self.pos_embed.register_hook(self._sync_grad_hook)
def forward(self, input_: Tensor) -> Tensor:
input_ = split_tensor_3d(input_, 0, self.weight_parallel_mode)
input_ = split_tensor_3d(input_, 0, self.input_parallel_mode)
output = F.conv2d(input_, self.weight, self.bias, stride=self.patch_size)
if self.flatten:
output = output.flatten(2).transpose(1, 2) # BCHW -> BNC
cls_token = self.cls_token.expand(output.shape[0], -1, -1)
output = torch.cat((cls_token, output), dim=1)
output = output + self.pos_embed
return output
@LAYERS.register_module
class Embedding3D(ParallelLayer):
"""
Embedding for 3D parallelism
:param num_embeddings: number of embeddings
:type num_embeddings: int
:param embedding_dim: dimension of embedding
:type embedding_dim: int
:param padding_idx: index of padding, defaults to None
:type padding_idx: int, optional
:param dtype: The dtype of parameters, defaults to None
:type dtype: torch.dtype, optional
:param weight_initializer: The intializer of weight, defaults to normal initializer
:type weight_initializer: typing.Callable, optional
:param args: Args used in F.embedding
:param kwargs: Kwargs used in F.embedding
"""
def __init__(self,
num_embeddings: int,
embedding_dim: int,
padding_idx: int = None,
dtype: torch.dtype = None,
weight_initializer: Callable = init.normal_(),
*args,
**kwargs):
super().__init__()
self.depth = get_depth_from_env()
self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
self.output_parallel_mode = get_last_group(self.input_parallel_mode, self.weight_parallel_mode)
self.num_embeddings = num_embeddings
self.embed_dim = embedding_dim
embed_dim_per_partition = divide(embedding_dim, self.depth)
self.padding_idx = padding_idx
self.embed_args = args
self.embed_kwargs = kwargs
self.weight = nn.Parameter(
torch.empty((num_embeddings, embed_dim_per_partition), device=get_current_device(), dtype=dtype))
self.reset_parameters(weight_initializer)
self._set_tensor_parallel_attributes()
def _set_tensor_parallel_attributes(self) -> None:
set_tensor_parallel_attribute_by_partition(self.weight, self.depth)
def reset_parameters(self, weight_initializer) -> None:
with seed(ParallelMode.TENSOR):
fan_in, fan_out = self.num_embeddings, self.embed_dim
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
self._fill_padding_idx_with_zero()
weight_src_rank = gpc.get_ranks_in_group(self.weight_parallel_mode)[0]
broadcast(self.weight, weight_src_rank, self.weight_parallel_mode)
def _fill_padding_idx_with_zero(self) -> None:
if self.padding_idx is not None:
with torch.no_grad():
self.weight[self.padding_idx].fill_(0)
def forward(self, input_: Tensor) -> Tensor:
input_ = split_tensor_3d(input_, 0, self.weight_parallel_mode)
input_ = split_tensor_3d(input_, 0, self.input_parallel_mode)
weight = broadcast_weight_3d_from_diagonal(self.weight, self.input_parallel_mode, self.weight_parallel_mode,
self.output_parallel_mode)
output = F.embedding(input_, weight, self.padding_idx, *self.embed_args, **self.embed_kwargs)
return output
@LAYERS.register_module
class VocabParallelEmbedding3D(torch.nn.Module):
"""Embedding parallelized in the vocabulary dimension.
:param num_embeddings: number of embeddings
:type num_embeddings: int
:param embedding_dim: dimension of embedding
:type embedding_dim: int
:param padding_idx: index of padding, defaults to None
:type padding_idx: int, optional
:param dtype: The dtype of parameters, defaults to None
:type dtype: torch.dtype, optional
:param weight_initializer: The intializer of weight, defaults to normal initializer
:type weight_initializer: typing.Callable, optional
:param args: Args used in F.embedding
:param kwargs: Kwargs used in F.embedding
"""
def __init__(self,
num_embeddings: int,
embedding_dim: int,
padding_idx: int = None,
dtype: torch.dtype = None,
weight_initializer: Callable = init.normal_(),
*args,
**kwargs):
super().__init__()
self.num_embeddings = num_embeddings
self.embed_dim = embedding_dim
self.padding_idx = padding_idx
self.embed_args = args
self.embed_kwargs = kwargs
self.depth = get_depth_from_env()
self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
self.output_parallel_mode = get_last_group(self.input_parallel_mode, self.weight_parallel_mode)
self.num_embeddings_per_partition = divide(self.num_embeddings, self.depth**2)
self.embed_dim_per_partition = divide(self.embed_dim, self.depth)
vocab_parallel_rank = gpc.get_local_rank(self.input_parallel_mode)
self.vocab_start_index = vocab_parallel_rank * self.num_embeddings_per_partition * self.depth
self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition * self.depth
self.weight = Parameter(
torch.empty((self.num_embeddings_per_partition, self.embed_dim_per_partition),
device=get_current_device(),
dtype=dtype))
self.reset_parameters(weight_initializer)
self._set_tensor_parallel_attributes()
env.vocab_parallel = True
def _set_tensor_parallel_attributes(self):
set_tensor_parallel_attribute_by_partition(self.weight, self.depth**2)
def reset_parameters(self, weight_initializer) -> None:
with seed(ParallelMode.TENSOR):
fan_in, fan_out = self.num_embeddings, self.embed_dim
weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
self._fill_padding_idx_with_zero()
def _fill_padding_idx_with_zero(self) -> None:
if self.padding_idx is not None and \
self.padding_idx >= self.vocab_start_index and self.padding_idx < self.vocab_end_index:
with torch.no_grad():
self.weight[self.padding_idx - self.vocab_start_index].fill_(0)
def 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
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