fix: inference not using chat_template (#2019) [skip ci]
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@@ -190,18 +190,15 @@ def do_inference(
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):
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model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
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prompter = cli_args.prompter
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default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
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for token, symbol in default_tokens.items():
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# If the token isn't already specified in the config, add it
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if not (cfg.special_tokens and token in cfg.special_tokens):
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tokenizer.add_special_tokens({token: symbol})
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prompter_module = None
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chat_template_str = None
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if prompter:
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prompter_module = getattr(
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importlib.import_module("axolotl.prompters"), prompter
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)
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elif cfg.chat_template:
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chat_template_str = get_chat_template(cfg.chat_template)
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model = model.to(cfg.device, dtype=cfg.torch_dtype)
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@@ -211,13 +208,31 @@ def do_inference(
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instruction = get_multi_line_input()
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if not instruction:
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return
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if prompter_module:
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prompt: str = next(
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prompter_module().build_prompt(instruction=instruction.strip("\n"))
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)
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else:
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prompt = instruction.strip()
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batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
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if chat_template_str:
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batch = tokenizer.apply_chat_template(
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[
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{
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"role": "user",
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"content": prompt,
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}
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],
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return_tensors="pt",
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add_special_tokens=True,
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add_generation_prompt=True,
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chat_template=chat_template_str,
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tokenize=True,
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return_dict=True,
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)
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else:
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batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
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print("=" * 40)
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model.eval()
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@@ -257,13 +272,6 @@ def do_inference_gradio(
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model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
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prompter = cli_args.prompter
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# default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
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default_tokens: Dict[str, str] = {}
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for token, symbol in default_tokens.items():
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# If the token isn't already specified in the config, add it
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if not (cfg.special_tokens and token in cfg.special_tokens):
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tokenizer.add_special_tokens({token: symbol})
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prompter_module = None
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chat_template_str = None
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