intermediate_size=compute_intermediate_size(dim, ffn_dim_multiplier, multiple_of), num_attention_heads=params["n_heads"], num_hidden_layers=params["n_layers"], rms_norm_eps=params["norm_eps"], num_key_value_heads=num_key_value_heads, vocab_size=vocab_size, rope_theta=base, max_position...
1.embedding size的修改比较简单,只需要在config配置文件run_llama2_7b.yaml里将vocab_size修改为32768即可,如下图中标识部分 congfig文件修改位置 2.llama2中的MLP前馈神经网络定义在llama_layer.py的类LlamaFeedForward中。建议直接在LlamaFeedForward中,无视外部传参,直接对self.hidden_dim这个参数赋值为14336 前馈...
以llama7B模型为例,hidden_size为4096,也就是每个K、V有4096个数据,假设半精度浮点数数据float16,一个Transformer Block中就有409622=16KB的单序列KV缓存空间,而llama2一共32个Transformer Block,所以单序列整个模型需要16*32=512KB的缓存空间,那多序列呢?如果此时句子长度为1024,那就得512MB的缓存空间了。而现在...
hidden_dropout ... 0.1 hidden_size ... 5120 hysteresis ... 2 ict_head_size ... None ict_load ... None img_h ...
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[k1, k2], dim=(k1.ndim - 1)) else: position_ids = position_ids.transpose(0, 1) cos, sin = self.rotary_emb(value_layer, seq_len=position_ids.max() + 1) # [seq_len, batch, num_attention_heads, hidden_size_per_attention_head] query_layer, key_layer = ...
"emb_dim": 2048, # NEW: Half the embedding dimension "n_heads": 32, # Number of attention heads "n_layers": 16, # NEW: Half the number of layers "hidden_dim": 8192, # NEW: Almopst half the size of the intermediate dimension in FeedForward ...
'vocab_size': tokenizer.vocab_size, 'n_layers': 1, 'embed_dim': 2048, 'n_heads': 32, 'n_kv_heads': 8, 'multiple_of': 64, 'ffn_dim_multiplier': None, 'norm_eps': 1e-5, 'max_batch_size': 16, 'max_seq_len': 64, 'device': 'cuda', } dataset = load_dataset('glue'...
batch_size: 1 # add for increase predict seq_length: 2048 hidden_size: 4096 num_layers: 32 num_heads: 32 vocab_size: 32000 multiple_of: 256 rms_norm_eps: 1.0e-5 bos_token_id: 1 eos_token_id: 2 pad_token_id: 0 ignore_token_id: -100 ...
You'll notice that the 110M model is equivalent to GPT-1 in size. Alternatively, this is also the smallest model in the GPT-2 series (GPT-2 small), except the max context length is only 1024 instead of 2048. The only notable changes from GPT-1/2 architecture is that Llama uses RoPE...