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Running
on
T4
Running
on
T4
| from threading import Thread | |
| import torch | |
| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer | |
| import time | |
| model_id = "EleutherAI/pythia-6.9b-deduped" | |
| assistant_id = "EleutherAI/pythia-70m-deduped" | |
| torch_device = "cuda" if torch.cuda.is_available() else "cpu" | |
| print("Running on device:", torch_device) | |
| print("CPU threads:", torch.get_num_threads()) | |
| if torch_device == "cuda": | |
| model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True, device_map="auto") | |
| else: | |
| model = AutoModelForCausalLM.from_pretrained(model_id) | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| assistant_model = AutoModelForCausalLM.from_pretrained(assistant_id).to(torch_device) | |
| def run_generation(user_text, use_assistant, temperature, max_new_tokens): | |
| if temperature < 0.1: | |
| do_sample = False | |
| else: | |
| do_sample = True | |
| # Get the model and tokenizer, and tokenize the user text. | |
| model_inputs = tokenizer([user_text], return_tensors="pt").to(torch_device) | |
| # 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(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True) | |
| generate_kwargs = dict( | |
| model_inputs, | |
| assistant_model=assistant_model if use_assistant else None, | |
| streamer=streamer, | |
| max_new_tokens=max_new_tokens, | |
| do_sample=do_sample, | |
| top_p=0.95, | |
| temperature=float(temperature), | |
| top_k=50, | |
| ) | |
| t = Thread(target=model.generate, kwargs=generate_kwargs) | |
| start = time.time() | |
| t.start() | |
| # Pull the generated text from the streamer, and update the model output. Return the model output and time | |
| # spent so far. | |
| model_output = "" | |
| for new_text in streamer: | |
| model_output += new_text | |
| yield [model_output, round(time.time() - start, 3)] | |
| return [model_output, round(time.time() - start, 3)] | |
| def reset_textbox(): | |
| return gr.update(value='') | |
| with gr.Blocks() as demo: | |
| gr.Markdown( | |
| "# 🤗 Assisted Generation Demo\n" | |
| f"- Model: {model_id} (using INT8)\n" | |
| f"- Assistant Model: {assistant_id}\n" | |
| "- Disclaimer: due to INT8 quantization and the use of causal masking in assisted generation, the output " | |
| "of greedy decoding may differ in rare occasions." | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=4): | |
| user_text = gr.Textbox( | |
| placeholder="A sequence: one, two, three, ", | |
| label="Prompt" | |
| ) | |
| model_output = gr.Textbox(label="Model output", lines=10, interactive=False) | |
| button_submit = gr.Button(value="Submit") | |
| with gr.Column(scale=1, min_width=200): | |
| gr.Markdown("### Generation Settings") | |
| use_assistant = gr.Checkbox(label="Use Assisted Generation", value=True) | |
| max_new_tokens = gr.Slider( | |
| minimum=1, maximum=500, value=100, step=1, interactive=True, label="Max New Tokens", | |
| ) | |
| temperature = gr.Slider( | |
| minimum=0.0, maximum=2.0, value=0.0, step=0.1, interactive=True, label="Temperature (0.0 = Greedy)", | |
| ) | |
| gr.Markdown("### Generation time (seconds)") | |
| generation_time = gr.Textbox(lines=1, interactive=False, show_label=False) | |
| generate_inputs = [user_text, use_assistant, temperature, max_new_tokens] | |
| generate_outputs = [model_output, generation_time] | |
| user_text.submit(run_generation, generate_inputs, generate_outputs) | |
| button_submit.click(run_generation, generate_inputs, generate_outputs) | |
| demo.queue(max_size=32).launch(enable_queue=True) | |