mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-30 14:10:14 +01:00
226 lines
9.5 KiB
Python
226 lines
9.5 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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import modules.shared as shared
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from modules.callbacks import (Iteratorize, Stream,
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_SentinelTokenStoppingCriteria)
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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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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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if shared.is_RWKV:
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input_ids = shared.tokenizer.encode(str(prompt))
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input_ids = np.array(input_ids).reshape(1, len(input_ids))
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return input_ids
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else:
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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:
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return input_ids
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elif shared.args.flexgen:
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return input_ids.numpy()
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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 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 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 clear_torch_cache():
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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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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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clear_torch_cache()
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t0 = time.time()
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# These models are not part of Hugging Face, so we handle them
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# separately and terminate the function call earlier
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if shared.is_RWKV:
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if shared.args.no_stream:
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reply = shared.model.generate(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k)
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yield formatted_outputs(reply, shared.model_name)
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else:
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yield formatted_outputs(question, shared.model_name)
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# RWKV has proper streaming, which is very nice.
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# No need to generate 8 tokens at a time.
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for reply in shared.model.generate_with_streaming(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k):
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yield formatted_outputs(reply, shared.model_name)
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t1 = time.time()
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output = encode(reply)[0]
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input_ids = encode(question)
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print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(input_ids[0]))/(t1-t0):.2f} tokens/s, {len(output)-len(input_ids[0])} tokens)")
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return
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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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original_input_ids = input_ids
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output = input_ids[0]
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cuda = "" if (shared.args.cpu or shared.args.deepspeed or shared.args.flexgen) else ".cuda()"
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n = shared.tokenizer.eos_token_id if eos_token is None else int(encode(eos_token)[0][-1])
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stopping_criteria_list = transformers.StoppingCriteriaList()
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if stopping_string is not None:
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# Copied from 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.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=t, starting_idx=len(input_ids[0])))
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if not shared.args.flexgen:
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generate_params = [
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f"max_new_tokens=max_new_tokens",
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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"max_new_tokens={max_new_tokens if shared.args.no_stream else 8}",
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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.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, "inputs=filler_input_ids")
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else:
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generate_params.insert(0, "inputs=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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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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# Stream the reply 1 token at a time.
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# This is based on the trick of using 'stopping_criteria' to create an iterator.
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elif not shared.args.flexgen:
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def generate_with_callback(callback=None, **kwargs):
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kwargs['stopping_criteria'].append(Stream(callback_func=callback))
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clear_torch_cache()
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with torch.no_grad():
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shared.model.generate(**kwargs)
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def generate_with_streaming(**kwargs):
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return Iteratorize(generate_with_callback, kwargs, callback=None)
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yield formatted_outputs(original_question, shared.model_name)
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for output in eval(f"generate_with_streaming({', '.join(generate_params)})"):
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print(print('Used vram in gib:', torch.cuda.memory_allocated() / 1024**3))
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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 output[-1] == n:
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break
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# Stream the output naively for FlexGen since it doesn't support 'stopping_criteria'
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else:
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for i in range(max_new_tokens//8+1):
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clear_torch_cache()
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with torch.no_grad():
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output = eval(f"shared.model.generate({', '.join(generate_params)})")[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 np.count_nonzero(input_ids[0] == n) < np.count_nonzero(output == n):
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break
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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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t1 = time.time()
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print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(original_input_ids[0]))/(t1-t0):.2f} tokens/s, {len(output)-len(original_input_ids[0])} tokens)")
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return
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