torch.nn.TransformerEncoder(encoder_layer, num_layers, norm=None) nn.TransformerEncoder是堆叠num_layers个自编码器层数的模块 2.函数参数 encoder_layer:nn.TransformerEncoderLayer的实例对象,必需参数 num_layers:编码器中子编码器层数,必需参数 norm:层规范化组件,可选参数 3.2 nn.TransformerEncoder使用 1.函数...
self.device = device#self.u2gnn_layers = torch.nn.ModuleList()for_inrange(self.num_U2GNN_layers): encoder_layers =TransformerEncoderLayer(d_model=self.feature_dim_size, nhead=1, dim_feedforward=self.ff_hidden_size, dropout=0.5)# embed_dim must be divisible by num_headsself.u2gnn_layers....
def __init__(self, vocab_size, feature_dim_size, ff_hidden_size, sampled_num, num_self_att_layers, num_U2GNN_layers, dropout, device): super(TransformerU2GNN, self).__init__() self.feature_dim_size = feature_dim_size self.ff_hidden_size = ff_hidden_size self.num_self_att_layers...
add('embedding.weight') # actual output layer name, projection layer is using weight-tying with embedding layer for i in range(num_layers): optimizer_denoiser.wqk_names.add(f'transformer_encoder.layers.{i}.self_attn.in_proj_weight') # For query, key, and value combined optimizer_denoiser....
(self.encoder_layer, num_layers) def generate_src_mask(self, seq_len, sep_idx, end_idx, is_same, device): if is_same: mask = torch.arange(seq_len, device=device)[None, :] >= torch.broadcast_to(end_idx, (seq_len,1)) mask[:,sep_idx] = True mask[sep_idx,:] = True mask...
但是，我们日常所说的“时间”这个词实际上包含了多个相似却有微妙差异的概念。如果不能分清它们，会给...
BertSelfAttention是通过extended_attention_mask/attention_mask和embedding_output/hidden_states计算得到context_layer,这个context_layer的shape为[batch_size, bert_seq_length, all_head_size = num_attention_heads*attention_head_size],它就是batch_size个句子每个token的词向量,这个词向量是综合了上下文得到的,注...
class Seq2SeqTransformer(nn.Module): def __init__(self, num_encoder_layers: int, num_decoder_layers: int, emb_size: int, nhead: int, src_vocab_size: int, tgt_vocab_size: int, dim_feedforward: int = 512, dropout: float = 0.1): super(Seq2SeqTransformer, self).__init__() self...
... File "/home/allgeuer/anaconda3/envs/ovod/lib/python3.10/site-packages/torch/nn/modules/transformer.py", line 387, in forward output = mod(output, src_mask=mask, is_causal=is_causal, src_key_padding_mask=src_key_padding_mask_for_layers) File "/home/allgeuer/anaconda3/envs/ovod...
Other LLaVA models can still use num_hidden_layers_override to determine whether enable post_layer_norm layer. However, I believe that consistently using this parameter for judgment would be better. Member DarkLight1337 commented Oct 24, 2024 Let's update the other models then, since from a...