feat: update inference code for pythia/cai data-based models
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3bfb623f26
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@ -61,7 +61,10 @@ def _build_blacklist_for(bot_name: str) -> list[str]:
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# out to the fine-tuned models as well.
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bad_opt_generations = ["___", "____", "_____"]
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return [pdm_prefix, *bad_opt_generations]
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# And Pythia likes to do this.
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bad_pythia_generations = ["...."]
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return [pdm_prefix, *bad_opt_generations, *bad_pythia_generations]
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def _build_model_and_tokenizer_for(args: argparse.Namespace,
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@ -80,9 +83,12 @@ def _build_model_and_tokenizer_for(args: argparse.Namespace,
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state_dict = torch.load(args.checkpoint,
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map_location="cuda").pop("model")
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tokenizer_kwargs = {"add_special_tokens": False}
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if "facebook/opt-" in args.model_name:
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tokenizer_kwargs["add_prefix_space"] = True
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bad_words_ids = [
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tokenizer(bad_word, add_prefix_space=True,
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add_special_tokens=False).input_ids
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tokenizer(bad_word, **tokenizer_kwargs).input_ids
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for bad_word in _build_blacklist_for(bot_name)
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]
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@ -104,7 +110,7 @@ def _run_raw_inference(model: t.Any, tokenizer: t.Any, prompt: str,
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logits = model.generate(
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input_ids,
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do_sample=True,
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max_new_tokens=3,
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max_new_tokens=32,
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top_k=50,
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top_p=0.90,
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)
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@ -113,39 +119,14 @@ def _run_raw_inference(model: t.Any, tokenizer: t.Any, prompt: str,
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# Then, contrastive search.
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input_ids = tokenizer(output, return_tensors="pt").input_ids.to("cuda")
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logits = model.generate(input_ids,
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max_new_tokens=128,
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max_new_tokens=96,
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penalty_alpha=0.6,
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top_k=6)
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# FIXME(11b): All of these break in different ways. Write a more robust
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# solution.
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USE_DUMB_TRIMMING_ALGORITHM = False
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if USE_DUMB_TRIMMING_ALGORITHM:
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output = tokenizer.decode(logits[0], skip_special_tokens=True)
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trimmed_output = output.replace(prompt, "").strip()
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# Set a breakpoint for when trimming goes wrong, so we can investigate.
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if len(trimmed_output) >= len(output):
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import pdb
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pdb.set_trace()
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return trimmed_output
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USE_SLICING_TRIMMING_ALGORITHM = False
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if USE_SLICING_TRIMMING_ALGORITHM:
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logger.debug("Untrimmed inference output is: `%s`",
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tokenizer.decode(logits[0], skip_special_tokens=True))
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# Slicing logic taken from:
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# https://github.com/huggingface/transformers/issues/17117#issuecomment-1124497554
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logits_without_input_prompt = logits[:, input_ids.shape[1]:]
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output = tokenizer.decode(logits_without_input_prompt[0],
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skip_special_tokens=True)
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return output
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# Then, we trim out the input prompt from the generated output.
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output = tokenizer.decode(logits[0], skip_special_tokens=True)
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if (idx := prompt.rfind(user_message)) != -1:
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trimmed_output = output[idx + len(user_message):]
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trimmed_output = output[idx + len(user_message):].strip()
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return trimmed_output
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else:
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raise ValueError("Couldn't find user message in the prompt. What?")
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@ -175,9 +156,9 @@ def _parse_messages_from_str(string: str, names: list[str]) -> list[str]:
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for match in speaker_regex.finditer(string):
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message_start_indexes.append(match.start())
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if len(message_start_indexes) == 0:
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# Single message in the string, so no message separators to match.
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return [string]
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if len(message_start_indexes) < 2:
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# Single message in the string.
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return [string.strip()]
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prev_start_idx = message_start_indexes[0]
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messages = []
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@ -208,25 +189,15 @@ def _gr_run_inference(model: t.Any, tokenizer: t.Any, context: str,
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prompt = "\n".join(
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[context, "", *history, f"You: {user_message}", f"{character_name}: "])
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raw_output = _run_raw_inference(model, tokenizer, prompt, user_message)
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logger.debug("After inference, `raw_output` is: `%s`", raw_output)
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# So there's a bit of a shitty bug here. The tensor slicing logic inside of
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# `_run_raw_inference` doesn't always slice off the input prompt cleanly,
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# sometimes it leaves a little bit of it in the beginning of the output. To
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# work around that, we look for a ":" close to the beginning of the output
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# string, and if we find it, we trim out everything that came before it.
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STOP_SEARCHING_AT_IDX = 8
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if (idx := raw_output.find(":", 0, STOP_SEARCHING_AT_IDX)) != -1:
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raw_output = raw_output[idx + 1:]
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output = f"{character_name}: {raw_output.strip()}"
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output = _run_raw_inference(model, tokenizer, prompt, user_message).strip()
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logger.debug("_run_raw_inference returned `%s` after .strip()", output)
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# If there's enough space, the model will likely generate more than just its
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# own message, so we need to trim that out and just remove the first
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# generated message.
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generated_messages = _parse_messages_from_str(output,
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["You", character_name])
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logger.debug("Generated messages is `%s`", generated_messages)
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bot_message = generated_messages[0]
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logger.info("Generated message: `%s`", bot_message)
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@ -287,8 +258,8 @@ def _build_gradio_ui_for(model: t.Any, tokenizer: t.Any) -> t.Any:
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with gr.Row():
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submit_btn = gr.Button("Submit input")
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submit_fn = lambda context, history, character_name, user_message: _gr_run_inference(
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model, tokenizer, context, history, character_name,
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user_message)
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model, tokenizer, context, history, character_name, user_message
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)
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submit_btn.click(
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fn=submit_fn,
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inputs=[context, history, character_name, user_message],
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@ -296,8 +267,8 @@ def _build_gradio_ui_for(model: t.Any, tokenizer: t.Any) -> t.Any:
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regenerate_btn = gr.Button("Regenerate last output")
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regenerate_fn = lambda context, history, character_name, user_message: _gr_regenerate_last_output(
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model, tokenizer, context, history, character_name,
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user_message)
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model, tokenizer, context, history, character_name, user_message
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)
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regenerate_btn.click(
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fn=regenerate_fn,
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inputs=[context, history, character_name, user_message],
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@ -305,8 +276,8 @@ def _build_gradio_ui_for(model: t.Any, tokenizer: t.Any) -> t.Any:
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undo_btn = gr.Button("Undo last exchange")
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undo_btn.click(fn=_gr_undo,
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inputs=[history],
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outputs=[history, history_text])
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inputs=[history],
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outputs=[history, history_text])
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return interface
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