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# Copyright (c) 2022, Tri Dao.
import torch
import torch.nn as nn
from einops import rearrange
from torch import Tensor
from flash_attn.utils.distributed import all_reduce, reduce_scatter
class GPT2Embeddings(nn.Module):
def __init__(
self,
embed_dim,
vocab_size,
max_position_embeddings,
padding_idx=None,
word_embed_proj_dim=None,
device=None,
dtype=None,
):
"""
If max_position_embeddings <= 0, there's no position embeddings
If word_embe_proj_dim is not None (e.g., OPT-350m), we embed to that dimension
the project up to embed_dim
"""
factory_kwargs = {"device": device, "dtype": dtype}
super().__init__()
if word_embed_proj_dim is None:
self.word_embeddings = nn.Embedding(
vocab_size, embed_dim, padding_idx=padding_idx, **factory_kwargs
)
self.project_in = None
else:
self.word_embeddings = nn.Embedding(
vocab_size, word_embed_proj_dim, padding_idx=padding_idx, **factory_kwargs
)
self.project_in = nn.Linear(
word_embed_proj_dim, embed_dim, bias=False, **factory_kwargs
)
self.max_position_embeddings = max_position_embeddings
if self.max_position_embeddings > 0:
self.position_embeddings = nn.Embedding(
max_position_embeddings, embed_dim, **factory_kwargs
)
def forward(self, input_ids, position_ids=None):
"""
input_ids: (batch, seqlen)
position_ids: (batch, seqlen)
"""
batch_size, seqlen = input_ids.shape
embeddings = self.word_embeddings(input_ids)
if self.project_in is not None:
embeddings = self.project_in(embeddings)
if self.max_position_embeddings > 0:
if position_ids is None:
position_ids = torch.arange(seqlen, dtype=torch.long, device=input_ids.device)
position_embeddings = self.position_embeddings(position_ids)
embeddings = embeddings + position_embeddings
return embeddings
class BertEmbeddings(nn.Module):
def __init__(
self,
embed_dim,
vocab_size,
max_position_embeddings,
type_vocab_size,
padding_idx=None,
device=None,
dtype=None,
):
"""
If max_position_embeddings <= 0, there's no position embeddings
If type_vocab_size <= 0, there's no token type embeddings
"""
factory_kwargs = {"device": device, "dtype": dtype}
super().__init__()
self.word_embeddings = nn.Embedding(
vocab_size, embed_dim, padding_idx=padding_idx, **factory_kwargs
)
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
if self.max_position_embeddings > 0:
self.position_embeddings = nn.Embedding(
max_position_embeddings, embed_dim, **factory_kwargs
)
if self.type_vocab_size > 0:
self.token_type_embeddings = nn.Embedding(type_vocab_size, embed_dim, **factory_kwargs)
def forward(self, input_ids, position_ids=None, token_type_ids=None):
"""
input_ids: (batch, seqlen)
position_ids: (batch, seqlen)
token_type_ids: (batch, seqlen)
"""
batch_size, seqlen = input_ids.shape
embeddings = self.word_embeddings(input_ids)
if self.max_position_embeddings > 0:
if position_ids is None:
position_ids = torch.arange(seqlen, dtype=torch.long, device=input_ids.device)
position_embeddings = self.position_embeddings(position_ids)
embeddings = embeddings + position_embeddings
if self.type_vocab_size > 0:
if token_type_ids is None:
token_type_ids = torch.zeros(seqlen, dtype=torch.long, device=input_ids.device)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = embeddings + token_type_embeddings
return embeddings
class VocabParallelEmbedding(nn.Embedding):
def __init__(self, num_embeddings, *args, process_group=None, padding_idx=None, **kwargs):
self.process_group = process_group
if process_group is not None:
world_size = torch.distributed.get_world_size(process_group)
if num_embeddings % world_size != 0:
raise ValueError(
f"num_embeddings ({num_embeddings}) must be divisible by "
f"world_size ({world_size})"
)
if world_size > 1 and padding_idx is not None:
raise RuntimeError("ParallelEmbedding does not support padding_idx")
else:
world_size = 1
super().__init__(num_embeddings // world_size, *args, padding_idx=padding_idx, **kwargs)
def forward(self, input: Tensor) -> Tensor:
if self.process_group is None:
return super().forward(input)
else:
rank = torch.distributed.get_rank(self.process_group)
vocab_size = self.num_embeddings
vocab_start_index, vocab_end_index = rank * vocab_size, (rank + 1) * vocab_size
# Create a mask of valid vocab ids (1 means it needs to be masked).
input_ids_mask = (input < vocab_start_index) | (input >= vocab_end_index)
input = input - vocab_start_index
input[input_ids_mask] = 0
embeddings = super().forward(input)
embeddings[input_ids_mask] = 0.0
return embeddings
class ColumnParallelEmbedding(nn.Embedding):
def __init__(self, num_embeddings, embedding_dim, *args, process_group=None, **kwargs):
self.process_group = process_group
if process_group is not None:
world_size = torch.distributed.get_world_size(process_group)
if embedding_dim % world_size != 0:
raise ValueError(
f"embedding_dim ({embedding_dim}) must be divisible by "
f"world_size ({world_size})"
)
else:
world_size = 1
super().__init__(num_embeddings, embedding_dim // world_size, *args, **kwargs)
class ParallelGPT2Embeddings(nn.Module):
def __init__(
self,
embed_dim,
vocab_size,
max_position_embeddings,
process_group,
padding_idx=None,
sequence_parallel=True,
device=None,
dtype=None,
):
"""
If max_position_embeddings <= 0, there's no position embeddings
"""
factory_kwargs = {"device": device, "dtype": dtype}
super().__init__()
self.process_group = process_group
self.sequence_parallel = sequence_parallel
self.word_embeddings = VocabParallelEmbedding(
vocab_size,
embed_dim,
padding_idx=padding_idx,
process_group=process_group,
**factory_kwargs,
)
self.max_position_embeddings = max_position_embeddings
if self.max_position_embeddings > 0:
self.position_embeddings = ColumnParallelEmbedding(
max_position_embeddings, embed_dim, process_group=process_group, **factory_kwargs
)
def forward(self, input_ids, position_ids=None, combine_batch_seqlen_dim=False):
"""
input_ids: (batch, seqlen)
position_ids: (batch, seqlen)
"""
batch_size, seqlen = input_ids.shape
world_size = torch.distributed.get_world_size(self.process_group)
embeddings = self.word_embeddings(input_ids)
if self.max_position_embeddings > 0:
if position_ids is None:
position_ids = torch.arange(seqlen, dtype=torch.long, device=input_ids.device)
position_embeddings = self.position_embeddings(position_ids)
if world_size <= 1:
embeddings = embeddings + position_embeddings
else:
partition_dim = self.position_embeddings.embedding_dim
rank = torch.distributed.get_rank(self.process_group)
embeddings[
..., rank * partition_dim : (rank + 1) * partition_dim
] += position_embeddings
if combine_batch_seqlen_dim:
embeddings = rearrange(embeddings, "b s d -> (b s) d")
reduce_fn = reduce_scatter if self.sequence_parallel else all_reduce
return embeddings if world_size <= 1 else reduce_fn(embeddings, self.process_group)
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