Fix set mem_id for inference and refactor

scripts/finetune.py

@@ -78,6 +78,9 @@ def do_inference(cfg, model, tokenizer, prompter="AlpacaPrompter"):
         )
 
     if cfg.landmark_attention:
+        from axolotl.monkeypatch.llama_landmark_attn import set_model_mem_id
+
+        set_model_mem_id(model, tokenizer)
         model.set_mem_cache_args(
             max_seq_len=255, mem_freq=50, top_k=5, max_cache_size=None
         )

src/axolotl/monkeypatch/llama_landmark_attn.py

@@ -29,6 +29,7 @@ import torch
 import torch.utils.checkpoint
 from torch import nn
 from torch.nn import CrossEntropyLoss
+from transformers import LlamaTokenizer
 from transformers.modeling_outputs import (
     BaseModelOutputWithPast,
     CausalLMOutputWithPast,
@@ -1237,3 +1238,12 @@ def patch_llama_with_landmark_attn():
     transformers.models.llama.modeling_llama.LlamaAttention = LlamaAttention
     transformers.models.llama.modeling_llama.LlamaDecoderLayer = LlamaDecoderLayer
     transformers.models.llama.modeling_llama.apply_rotary_pos_emb = apply_rotary_pos_emb
+
+
+def set_model_mem_id(model: LlamaForCausalLM, tokenizer: LlamaTokenizer):
+    mem_id = tokenizer.convert_tokens_to_ids(MEM_TOKEN)
+    model.set_mem_id(mem_id)
+
+
+def get_mem_id(tokenizer: LlamaTokenizer):
+    return tokenizer.convert_tokens_to_ids(MEM_TOKEN)

src/axolotl/utils/trainer.py

@@ -239,16 +239,19 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
     if cfg.is_llama_derived_model and cfg.landmark_attention:
         from functools import partial
 
-        from axolotl.monkeypatch.llama_landmark_attn import MEM_TOKEN, add_mem_tokens
+        from axolotl.monkeypatch.llama_landmark_attn import (
+            add_mem_tokens,
+            get_mem_id,
+            set_model_mem_id,
+        )
 
-        mem_id = tokenizer.convert_tokens_to_ids(MEM_TOKEN)
+        set_model_mem_id(model, tokenizer)
-        model.set_mem_id(mem_id)
 
         logging.info("Adding landmark attention tokens to dataset")
 
         for dataset in [train_dataset, eval_dataset]:
             dataset = dataset.map(
-                partial(add_mem_tokens, mem_freq=50, mem_id=mem_id),
+                partial(add_mem_tokens, mem_freq=50, mem_id=get_mem_id(tokenizer)),
                 batched=False,
                 num_proc=32,
             )