# 调用如下历程创建一个BERT网络结构,如果按照默认值,即创建的是BASE版BERT # 我们只对创建BERT结构的核心代码做注释 def transformer_model(input_tensor, attention_mask=None, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, intermediate_act_fn=gelu, hidden_dropout...
class TransformerIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) self.intermediate_act_fn = F.relu def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = s...
我们需要注意的是,在计算 FFN 的时候,参数量为 3*H*H_inter,这里的 H_inter 就是参数表中的 intermediate_size,即 11008。当然这里我们也忽略了一些其他的参数,这里的计算仅供参考。 其次是训练和推理的计算量,我们这里忽略 Attention,那么推理的计算量约为 2N,即 14 TFLOPS,训练的计算量约为 6N,即 42 TFLO...
self.hidden_size= config.hidden_sizeself.self_attn=LlamaAttention(config=config) self.mlp=LlamaMLP( hidden_size=self.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, ) self.input_layernorm=LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post...
intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, ) self.input_layernorm= LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm= LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) ...
"intermediate_size": 3072, "layer_norm_eps": 1e-12, "max_position_embeddings": 512, "model_type": "bert", "num_attention_heads": 12, "num_hidden_layers": 3, "output_past": true, "pad_token_id": 0, "pooler_fc_size": 768, "pooler_num_attention_heads": 12, "pooler_num_fc...
{"architectures":["BertForMaskedLM"],"attention_probs_dropout_prob":0.1,"gradient_checkpointing":false,"hidden_act":"gelu","hidden_dropout_prob":0.1,"hidden_size":768,"initializer_range":0.02,"intermediate_size":3072,"layer_norm_eps":1e-12,"max_position_embeddings":512,"model_type":"...
self.self_attn=LlamaAttention(config=config)self.mlp=LlamaMLP(hidden_size=self.hidden_size,intermediate_size=config.intermediate_size,hidden_act=config.hidden_act,)self.input_layernorm=LlamaRMSNorm(config.hidden_size,eps=config.rms_norm_eps)self.post_attention_layernorm=LlamaRMSNorm(config.hidden_si...
=Intermediate(hidden_size,intermediate_size)self.output=Output(intermediate_size,hidden_size,hidden_dropout_prob)defforward(self,hidden_states,attention_mask):attention_output=self.attention(hidden_states,attention_mask)intermediate_output=self.intermediate(attention_output)layer_output=self.output(intermediate...
() # 词表大小 self.vocab_size = vocab_size # Transformer的编码器的数目 self.n_block = n_block # 每个词映射成稠密向量的维度 self.hidden_size = hidden_size # 多头注意力的个数 self.heads_num = heads_num # 逐位前馈层的的维度 self.intermediate_size = intermediate_size # Embedding层的 ...