Merge branch 'main' into patch-1

scripts/finetune.py

@@ -12,7 +12,7 @@ from typing import Any, Dict, List, Optional, Union
 import fire
 import torch
 import yaml
-from transformers import GenerationConfig
+from transformers import GenerationConfig, TextStreamer
 
 from axolotl.utils.data import load_prepare_datasets
 from axolotl.utils.dict import DictDefault
@@ -64,13 +64,17 @@ def get_multi_line_input() -> Optional[str]:
 
 
 def do_inference(cfg, model, tokenizer, prompter="AlpacaPrompter"):
-    tokenizer.add_special_tokens({"unk_token": "<unk>"})
-    tokenizer.add_special_tokens({"bos_token": "<s>"})
-    tokenizer.add_special_tokens({"eos_token": "</s>"})
+    default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
+
+    for token, symbol in default_tokens.items():
+        # If the token isn't already specified in the config, add it
+        if not (cfg.special_tokens and token in cfg.special_tokens):
+            tokenizer.add_special_tokens({token: symbol})
 
     prompter_module = getattr(importlib.import_module("axolotl.prompters"), prompter)
 
     while True:
+        print("=" * 80)
         # support for multiline inputs
         instruction = get_multi_line_input()
         if not instruction:
@@ -79,7 +83,7 @@ def do_inference(cfg, model, tokenizer, prompter="AlpacaPrompter"):
             prompter_module().build_prompt(instruction=instruction.strip("\n"))
         )
         batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
-
+        print("=" * 40)
         model.eval()
         with torch.no_grad():
             generation_config = GenerationConfig(
@@ -98,10 +102,13 @@ def do_inference(cfg, model, tokenizer, prompter="AlpacaPrompter"):
                 output_hidden_states=False,
                 output_scores=False,
             )
+            streamer = TextStreamer(tokenizer)
             generated = model.generate(
                 inputs=batch["input_ids"].to(cfg.device),
                 generation_config=generation_config,
+                streamer=streamer,
             )
+        print("=" * 40)
         print(tokenizer.decode(generated["sequences"].cpu().tolist()[0]))
 
 
@@ -183,6 +190,9 @@ def train(
             cfg.fp16 = True
         cfg.bf16 = False
 
+    if cfg.tf32:
+        torch.backends.cuda.matmul.allow_tf32 = True
+
     # load the tokenizer first
     tokenizer_config = cfg.tokenizer_config or cfg.base_model_config
     logging.info(f"loading tokenizer... {tokenizer_config}")