self.prefix_projection = config.prefix_projection # 初始化embedding层 self.word_embeddings = init_method( torch.nn.Embedding, num_embeddings=self.vocab_size, embedding_dim=self.hidden_size, dtype=self.params_dtype ) self.gradient_checkpointing = False def get_layer(layer_id): return GL...
prefix_projection = config.prefix_projection if self.pre_seq_len is not None: # 如果启用了 PTuning,需要冻结除了前缀编码器的所有参数 for param in self.parameters(): param.requires_grad = False # 生成前缀 ID,是 0 - PreSeqLen 的数组 self.prefix_tokens = torch.arange(self.pre_seq_len)....
nn model to encode the prefix Input shape: (batch-size, prefix-length) Output shape: (batch-size, prefix-length, 2*layers*hidden) """ def __init__(self, config: ChatGLMConfig): super().__init__() # 控制是否开启前缀投影,即用两层 MLP 处理前缀嵌入 self.prefix_projection = config....
)# 输出之前做最后一次的层归一化self.final_layernorm = LayerNorm(self.hidden_size, eps=self.layernorm_epsilon)# 处理微调,pre_seq_len参数来自微调脚本train.sh的PRE_SEQ_LEN参数ifself.pre_seq_lenisnotNone:forparaminself.parameters(): param.requires_grad =Falseself.prefix_tokens = torch.arange(s...
prefix_projection # 初始化embedding层 self.word_embeddings = init_method( torch.nn.Embedding, num_embeddings=self.vocab_size, embedding_dim=self.hidden_size, dtype=self.params_dtype ) self.gradient_checkpointing = False def get_layer(layer_id): return GLMBlock( self.hidden_size, self....
The torch.nn model to encode the prefix Input shape: (batch-size, prefix-length) Output shape: (batch-size, prefix-length, 2*layers*hidden) """def__init__(self, config):super().__init__() self.prefix_projection = config.prefix_projectionifself.prefix_projection:# Use a two-layer ML...
(self, prefix: torch.Tensor): # 前缀 ID 尺寸为 [BatchSize, PreSeqLen] # 根据前缀 ID 获取嵌入,尺寸为 [BatchSize, PreSeqLen, KVSize] # 如果设定了需要投影,就用两层 MLP 处理嵌入 if self.prefix_projection: prefix_tokens = self.embedding(prefix) past_key_values = self.trans(prefix_tokens...
当prefix_projection为True时,就是P-Tuning-V2方法,会在每一层前都加上新的参数;为False时,就是P-Tuning方法,仅在大模型的Embedding上新的参数。 Lora Lora的方法稍微复杂一点,但效果也不错。它的核心思想是在大型语言模型上对指定参数增加额外的低秩矩阵,然后用新的数据训练这些额外参数。具体操作也是类似的,你...
"prefix_projection": false, "quantization_bit": 0, "recompute": false, "tensor_parallel_degree": 1, "use_cache": true, "vocab_size": 130528 } [2024-02-01 14:58:56,228] [ INFO] - Found /home/aistudio/.paddlenlp/models/THUDM/chatglm-6b/model_state.pdparams W0201 14:59:17.78513...
控制前缀编码器中是否启用投影变换self.prefix_projection = config.prefix_projectionif self.pre_seq_len is not None:# 如果启用了 PTuning,需要冻结除了前缀编码器的所有参数for param in self.parameters():param.requires_grad = False# 生成前缀 ID,是 0 - PreSeqLen 的数组self.prefix_tokens = torch....