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import torch |
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import torch.nn as nn |
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from einops import rearrange |
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from torch import Tensor |
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from transformers.models.xlm_roberta.modeling_xlm_roberta import create_position_ids_from_input_ids |
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class XLMRobertaEmbeddings(nn.Module): |
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def __init__( |
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self, |
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embed_dim, |
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vocab_size, |
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max_position_embeddings, |
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type_vocab_size, |
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padding_idx=None, |
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device=None, |
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dtype=None, |
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): |
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""" |
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If max_position_embeddings <= 0, there's no position embeddings |
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If type_vocab_size <= 0, there's no token type embeddings |
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""" |
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factory_kwargs = {"device": device, "dtype": dtype} |
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super().__init__() |
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self.word_embeddings = nn.Embedding( |
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vocab_size, embed_dim, padding_idx=padding_idx, **factory_kwargs |
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) |
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self.max_position_embeddings = max_position_embeddings |
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self.type_vocab_size = type_vocab_size |
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if self.max_position_embeddings > 0: |
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self.position_embeddings = nn.Embedding( |
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max_position_embeddings, embed_dim, **factory_kwargs |
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) |
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if self.type_vocab_size > 0: |
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self.token_type_embeddings = nn.Embedding(type_vocab_size, embed_dim, **factory_kwargs) |
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def forward(self, input_ids, position_ids=None, token_type_ids=None, adapter_mask=None): |
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""" |
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input_ids: (batch, seqlen) |
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position_ids: (batch, seqlen) |
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token_type_ids: (batch, seqlen) |
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""" |
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batch_size, seqlen = input_ids.shape |
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if adapter_mask is not None: |
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unique_tasks = torch.unique(adapter_mask).tolist() |
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embedding_dtype = next(self.word_embeddings.parameters()).dtype |
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embeddings = torch.empty(*input_ids.shape, self.word_embeddings.embedding_dim, |
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dtype=embedding_dtype).to(input_ids.device) |
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for task_id in unique_tasks: |
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task_indices = (adapter_mask == task_id).nonzero(as_tuple=True)[0] |
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task_input_ids = input_ids[task_indices] |
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task_embeddings = self.word_embeddings(task_input_ids, task_id=task_id) |
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embeddings[task_indices] = task_embeddings |
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else: |
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embeddings = self.word_embeddings(input_ids) |
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if self.max_position_embeddings > 0: |
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if position_ids is None: |
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position_ids = create_position_ids_from_input_ids(input_ids, padding_idx=self.word_embeddings.padding_idx).to(input_ids.device) |
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position_embeddings = self.position_embeddings(position_ids) |
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embeddings = embeddings + position_embeddings |
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if self.type_vocab_size > 0: |
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if token_type_ids is None: |
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token_type_ids = torch.zeros(seqlen, dtype=torch.long, device=input_ids.device) |
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if adapter_mask is not None: |
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unique_tasks = torch.unique(adapter_mask).tolist() |
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for task_id in unique_tasks: |
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task_token_type_embeddings = self.token_type_embeddings(token_type_ids, task_id=task_id) |
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task_indices = (adapter_mask == task_id).nonzero(as_tuple=True)[0] |
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embeddings[task_indices] = embeddings[task_indices] + task_token_type_embeddings |
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else: |
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token_type_embeddings = self.token_type_embeddings(token_type_ids) |
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embeddings = embeddings + token_type_embeddings |
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return embeddings |
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