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| from transformers import TextIteratorStreamer | |
| from threading import Thread | |
| from .tree_utils import get_docstrings, grab_before_comments | |
| def combine_generation_kwargs(temperature=2.0, max_new_tokens=512, top_p=0.95, repetition_penalty=1.2): | |
| """ | |
| Combines the generation kwargs into a single dict. | |
| """ | |
| gen_kwargs = {} | |
| gen_kwargs["temperature"] = temperature | |
| gen_kwargs["max_new_tokens"] = max_new_tokens | |
| gen_kwargs["top_p"] = top_p | |
| gen_kwargs["repetition_penalty"] = repetition_penalty | |
| return gen_kwargs | |
| def stream_generation(prompt:str, pipe, gen_kwargs:dict): | |
| """ | |
| Text generation function | |
| Args: | |
| prompt (str): The context to start generation from. | |
| pipe (Pipeline): The pipeline to use for generation. | |
| gen_kwargs (dict): The generation kwargs. | |
| Returns: | |
| str: The generated text. (it iterates over time) | |
| """ | |
| # Tokenize the model_context | |
| model_inputs = pipe.tokenizer(prompt, return_tensors="pt") | |
| # Start generation on a separate thread, so that we don't block the UI. The text is pulled from the streamer | |
| # in the main thread. Adds timeout to the streamer to handle exceptions in the generation thread. | |
| streamer = TextIteratorStreamer(pipe.tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=15.0) | |
| generate_kwargs = dict(model_inputs, streamer=streamer, **gen_kwargs) | |
| t = Thread(target=pipe.model.generate, kwargs=generate_kwargs) | |
| t.start() | |
| # Pull the generated text from the streamer, and update the model output. | |
| model_output = "" | |
| for new_text in streamer: | |
| # print("step", end="") | |
| model_output += new_text | |
| yield model_output | |
| streamer.on_finalized_text("stream reached the end.") | |
| return model_output #is this ever reached? | |
| def construct_model_context(func_node, prompt="") -> str: | |
| """ | |
| Constructs the model context from a function node. | |
| """ | |
| model_context = func_node.child_by_field_name("type").text.decode() + " " + func_node.child_by_field_name("declarator").text.decode() #func_start_idx:body_start_idx? | |
| docstring = get_docstrings(func_node) #might be empty? | |
| if docstring: | |
| model_context = model_context + "\n" + docstring | |
| model_context = grab_before_comments(func_node) + model_context #prepend comments | |
| if prompt != "": | |
| model_context = "//Title: " + prompt + "\n" + model_context #prepend user prompt/title | |
| model_context = "//Language: Shadertoy GLSL fragment shader\n" + model_context #prepend system prompt, language hint | |
| return model_context |