Adpter finetune (#528)

* support adapter finetune

* refine

* reformat

projects/Llama/adapter/adapter_config.py

@@ -0,0 +1,63 @@
+from omegaconf import DictConfig, OmegaConf
+
+from configs.common.train import train  # noqa
+from libai.config import LazyCall
+from projects.Llama.adapter.adapter_model import LlamaForCausalLM
+from projects.Llama.tokenizer import LlamaTokenizer
+
+cfg = dict(
+    # Model
+    hidden_act="silu",
+    hidden_size=4096,
+    initializer_range=0.02,
+    intermediate_size=11008,
+    max_position_embeddings=4096,
+    num_attention_heads=32,
+    hidden_layers=32,
+    pretraining_tp=1,
+    rms_norm_eps=1e-05,
+    rope_scaling=None,
+    tie_word_embeddings=False,
+    vocab_size=32000,
+    use_scaled_init_for_output_weights=False,
+    scale_mask_softmax_fusion=False,
+    amp_enabled=True,
+    # Inference
+    is_encoder_decoder=False,
+    max_length=256,
+    min_length=0,
+    do_sample=False,
+    early_stopping=False,
+    num_beams=1,
+    num_beam_groups=1,
+    diversity_penalty=0.0,
+    temperature=0.9,
+    top_k=50,
+    top_p=0.6,
+    typical_p=1.0,
+    repetition_penalty=1.0,
+    length_penalty=1.0,
+    no_repeat_ngram_size=0,
+    encoder_no_repeat_ngram_size=0,
+    num_return_sequences=1,
+    chunk_size_feed_forward=0,
+    output_scores=False,
+    use_cache=True,
+    bos_token_id=1,
+    eos_token_id=2,
+    pad_token_id=0,
+    # adapter
+    adapter_len=10,
+    adapter_layer=30,
+    # train
+    pretrained_model_path="meta-llama/Llama-2-7b-hf/",
+)
+
+cfg = DictConfig(cfg)
+
+model = LazyCall(LlamaForCausalLM)(cfg=cfg)
+tokenization = OmegaConf.create()
+tokenization.make_vocab_size_divisible_by = 1
+tokenization.tokenizer = LazyCall(LlamaTokenizer)(
+    pretrained_model_path="Llama-2-7b-hf/tokenizer.model"
+)