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# This implementation was adapted from https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/embedding.py
# Commit id: f1a73d074002226c42ce65a1df170ecff9f022c0
# Copyright (c) 2022, Tri Dao.
import torch
import torch.nn as nn
from einops import rearrange
from torch import Tensor
from transformers.models.xlm_roberta.modeling_xlm_roberta import create_position_ids_from_input_ids
class XLMRobertaEmbeddings(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, task_type=None, adapter_mask=None):
"""
input_ids: (batch, seqlen)
position_ids: (batch, seqlen)
token_type_ids: (batch, seqlen)
"""
batch_size, seqlen = input_ids.shape
if isinstance(task_type, tuple):
assert input_ids.shape[0] % 9 == 0
split = int(input_ids.shape[0] / 9)
tensor1 = input_ids[:split, :]
tensor2 = input_ids[split:, :]
emb1 = self.word_embeddings(tensor1, task_type=task_type[0])
emb2 = self.word_embeddings(tensor2, task_type=task_type[1])
embeddings = torch.cat((emb1, emb2), dim=0)
unique_tasks = torch.unique(adapter_mask).tolist()
torch_dtype = next(self.word_embeddings.parameters()).dtype
embeddings = torch.empty(*input_ids.shape, self.word_embeddings.embedding_dim, dtype=torch_dtype).to(input_ids.device)
for task in unique_tasks:
indices = (adapter_mask == task).nonzero(as_tuple=True)[0]
inp = input_ids[indices]
lora_kwargs = {'task_type': task} if task is not None else {}
emb = self.word_embeddings(inp, **lora_kwargs)
embeddings[indices] = emb
exit(0)
else:
unique_task = torch.unique(adapter_mask)[0]
task1_indices = (adapter_mask == unique_task).nonzero(as_tuple=True)[0]
input1 = input_ids[task1_indices]
lora_kwargs = {'task_type': unique_task} if unique_task is not None else {}
embeddings = self.word_embeddings(input1, **lora_kwargs)
if self.max_position_embeddings > 0:
if position_ids is None:
position_ids = create_position_ids_from_input_ids(input_ids, padding_idx=self.word_embeddings.padding_idx).to(input_ids.device)
# 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)
if isinstance(task_type, tuple):
assert embeddings.shape[0] % 9 == 0
split = int(embeddings.shape[0] / 9)
emb1 = embeddings[:split, :, :]
emb2 = embeddings[split:, :, :]
token_type_embs1 = self.token_type_embeddings(token_type_ids, task_type=task_type[0])
token_type_embs2 = self.token_type_embeddings(token_type_ids, task_type=task_type[1])
emb1 = emb1 + token_type_embs1
emb2 = emb2 + token_type_embs2
embeddings = torch.cat((emb1, emb2), dim=0)
else:
unique_task = torch.unique(adapter_mask)[0]
lora_kwargs = {'task_type': unique_task} if unique_task is not None else {}
token_type_embeddings = self.token_type_embeddings(token_type_ids, **lora_kwargs)
embeddings = embeddings + token_type_embeddings
return embeddings
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