mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-22 08:07:56 +01:00
184 lines
7.3 KiB
Python
184 lines
7.3 KiB
Python
import gc
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import re
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import time
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import numpy as np
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import torch
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import transformers
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from tqdm import tqdm
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import modules.shared as shared
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from modules.extensions import apply_extensions
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from modules.html_generator import generate_4chan_html, generate_basic_html
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from modules.models import local_rank
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from modules.stopping_criteria import _SentinelTokenStoppingCriteria
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def get_max_prompt_length(tokens):
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max_length = 2048-tokens
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if shared.soft_prompt:
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max_length -= shared.soft_prompt_tensor.shape[1]
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return max_length
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def encode(prompt, tokens_to_generate=0, add_special_tokens=True):
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input_ids = shared.tokenizer.encode(str(prompt), return_tensors='pt', truncation=True, max_length=get_max_prompt_length(tokens_to_generate), add_special_tokens=add_special_tokens)
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if shared.args.cpu or shared.args.flexgen:
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return input_ids
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elif shared.args.deepspeed:
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return input_ids.to(device=local_rank)
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else:
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return input_ids.cuda()
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def decode(output_ids):
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reply = shared.tokenizer.decode(output_ids, skip_special_tokens=True)
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reply = reply.replace(r'<|endoftext|>', '')
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return reply
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def generate_softprompt_input_tensors(input_ids):
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inputs_embeds = shared.model.transformer.wte(input_ids)
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inputs_embeds = torch.cat((shared.soft_prompt_tensor, inputs_embeds), dim=1)
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filler_input_ids = torch.zeros((1, inputs_embeds.shape[1]), dtype=input_ids.dtype).to(shared.model.device)
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#filler_input_ids += shared.model.config.bos_token_id # setting dummy input_ids to bos tokens
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return inputs_embeds, filler_input_ids
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# Removes empty replies from gpt4chan outputs
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def fix_gpt4chan(s):
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for i in range(10):
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s = re.sub("--- [0-9]*\n>>[0-9]*\n---", "---", s)
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s = re.sub("--- [0-9]*\n *\n---", "---", s)
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s = re.sub("--- [0-9]*\n\n\n---", "---", s)
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return s
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# Fix the LaTeX equations in galactica
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def fix_galactica(s):
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s = s.replace(r'\[', r'$')
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s = s.replace(r'\]', r'$')
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s = s.replace(r'\(', r'$')
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s = s.replace(r'\)', r'$')
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s = s.replace(r'$$', r'$')
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s = re.sub(r'\n', r'\n\n', s)
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s = re.sub(r"\n{3,}", "\n\n", s)
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return s
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def formatted_outputs(reply, model_name):
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if not (shared.args.chat or shared.args.cai_chat):
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if shared.model_name.lower().startswith('galactica'):
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reply = fix_galactica(reply)
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return reply, reply, generate_basic_html(reply)
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elif shared.model_name.lower().startswith(('gpt4chan', 'gpt-4chan', '4chan')):
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reply = fix_gpt4chan(reply)
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return reply, 'Only applicable for GALACTICA models.', generate_4chan_html(reply)
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else:
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return reply, 'Only applicable for GALACTICA models.', generate_basic_html(reply)
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else:
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return reply
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def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=None, stopping_string=None):
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gc.collect()
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if not shared.args.cpu:
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torch.cuda.empty_cache()
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original_question = question
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if not (shared.args.chat or shared.args.cai_chat):
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question = apply_extensions(question, "input")
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if shared.args.verbose:
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print(f"\n\n{question}\n--------------------\n")
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input_ids = encode(question, max_new_tokens)
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cuda = "" if (shared.args.cpu or shared.args.deepspeed or shared.args.flexgen) else ".cuda()"
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if not shared.args.flexgen:
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n = shared.tokenizer.eos_token_id if eos_token is None else shared.tokenizer.encode(eos_token, return_tensors='pt')[0][-1]
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else:
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n = shared.tokenizer(eos_token).input_ids[0] if eos_token else None
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if stopping_string is not None:
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# The stopping_criteria code below was copied from
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# https://github.com/PygmalionAI/gradio-ui/blob/master/src/model.py
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t = encode(stopping_string, 0, add_special_tokens=False)
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stopping_criteria_list = transformers.StoppingCriteriaList([
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_SentinelTokenStoppingCriteria(
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sentinel_token_ids=t,
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starting_idx=len(input_ids[0])
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)
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])
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else:
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stopping_criteria_list = None
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if not shared.args.flexgen:
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generate_params = [
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f"eos_token_id={n}",
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f"stopping_criteria=stopping_criteria_list",
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f"do_sample={do_sample}",
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f"temperature={temperature}",
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f"top_p={top_p}",
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f"typical_p={typical_p}",
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f"repetition_penalty={repetition_penalty}",
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f"top_k={top_k}",
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f"min_length={min_length if shared.args.no_stream else 0}",
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f"no_repeat_ngram_size={no_repeat_ngram_size}",
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f"num_beams={num_beams}",
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f"penalty_alpha={penalty_alpha}",
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f"length_penalty={length_penalty}",
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f"early_stopping={early_stopping}",
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]
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else:
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generate_params = [
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f"do_sample={do_sample}",
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f"temperature={temperature}",
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f"stop={n}",
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]
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if shared.args.deepspeed:
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generate_params.append("synced_gpus=True")
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if shared.args.no_stream:
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generate_params.append("max_new_tokens=max_new_tokens")
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else:
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generate_params.append("max_new_tokens=8")
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if shared.soft_prompt:
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inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
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generate_params.insert(0, "inputs_embeds=inputs_embeds")
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generate_params.insert(0, "filler_input_ids")
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else:
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generate_params.insert(0, "input_ids")
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# Generate the entire reply at once
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if shared.args.no_stream:
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t0 = time.time()
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with torch.no_grad():
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output = eval(f"shared.model.generate({', '.join(generate_params)}){cuda}")[0]
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if shared.soft_prompt:
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output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
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reply = decode(output)
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if not (shared.args.chat or shared.args.cai_chat):
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reply = original_question + apply_extensions(reply[len(question):], "output")
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yield formatted_outputs(reply, shared.model_name)
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t1 = time.time()
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print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(input_ids[0]))/(t1-t0)/8:.2f} it/s, {len(output)-len(input_ids[0])} tokens)")
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# Generate the reply 8 tokens at a time
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else:
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yield formatted_outputs(original_question, shared.model_name)
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for i in tqdm(range(max_new_tokens//8+1)):
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with torch.no_grad():
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output = eval(f"shared.model.generate({', '.join(generate_params)}){cuda}")[0]
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if shared.soft_prompt:
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output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
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reply = decode(output)
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if not (shared.args.chat or shared.args.cai_chat):
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reply = original_question + apply_extensions(reply[len(question):], "output")
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yield formatted_outputs(reply, shared.model_name)
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if not shared.args.flexgen:
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input_ids = torch.reshape(output, (1, output.shape[0]))
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else:
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input_ids = np.reshape(output, (1, output.shape[0]))
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if shared.soft_prompt:
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inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
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if output[-1] == n:
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break
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