mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-09-20 00:07:06 +00:00
106 lines
4.0 KiB
Python
106 lines
4.0 KiB
Python
import time
|
|
import traceback
|
|
|
|
import torch
|
|
from transformers import is_torch_npu_available, is_torch_xpu_available
|
|
|
|
from modules import models, sampler_hijack, shared
|
|
from modules.logging_colors import logger
|
|
from modules.models import load_model
|
|
from modules.text_generation import generate_reply
|
|
|
|
global_scores = None
|
|
|
|
|
|
def get_next_logits(*args, **kwargs):
|
|
if shared.args.idle_timeout > 0 and shared.model is None and shared.previous_model_name not in [None, 'None']:
|
|
shared.model, shared.tokenizer = load_model(shared.previous_model_name)
|
|
|
|
shared.generation_lock.acquire()
|
|
try:
|
|
result = _get_next_logits(*args, **kwargs)
|
|
except Exception:
|
|
traceback.print_exc()
|
|
result = None
|
|
|
|
models.last_generation_time = time.time()
|
|
shared.generation_lock.release()
|
|
return result
|
|
|
|
|
|
def _get_next_logits(prompt, state, use_samplers, previous, top_logits=25, return_dict=False):
|
|
if shared.model is None:
|
|
logger.error("No model is loaded! Select one in the Model tab.")
|
|
return 'Error: No model is loaded1 Select one in the Model tab.', previous
|
|
|
|
is_non_hf_exllamav2 = shared.model.__class__.__name__ == 'Exllamav2Model'
|
|
is_non_hf_llamacpp = shared.model.__class__.__name__ == 'LlamaCppModel'
|
|
|
|
if use_samplers:
|
|
if any([is_non_hf_exllamav2, is_non_hf_llamacpp]):
|
|
logger.error("Sampler hijacking is not supported non-Huggingface loaders.")
|
|
# sampling is all done in c for exllama, so it is really hard to hijack
|
|
# it should be possible to hijack llamacpp sampler by hijacking all their sampling methods,
|
|
# but it is not implemented yet
|
|
return 'Error: Sampler hijacking is not supported non-Huggingface loaders. Please disable the "Use samplers" option.', previous
|
|
|
|
state['max_new_tokens'] = 1
|
|
state['auto_max_new_tokens'] = False
|
|
for _ in generate_reply(prompt, state):
|
|
pass
|
|
|
|
scores = sampler_hijack.global_scores[-1]
|
|
else:
|
|
if is_non_hf_exllamav2:
|
|
if is_torch_xpu_available():
|
|
tokens = shared.tokenizer.encode(prompt).to("xpu:0")
|
|
elif is_torch_npu_available():
|
|
tokens = shared.tokenizer.encode(prompt).to("npu:0")
|
|
else:
|
|
tokens = shared.tokenizer.encode(prompt).cuda()
|
|
scores = shared.model.get_logits(tokens)[-1][-1]
|
|
elif is_non_hf_llamacpp:
|
|
tokens = shared.tokenizer.encode(prompt)
|
|
scores = shared.model.get_logits(tokens)[-1][-1]
|
|
else:
|
|
if is_torch_xpu_available():
|
|
tokens = shared.tokenizer.encode(prompt, return_tensors='pt').to("xpu:0")
|
|
elif is_torch_npu_available():
|
|
tokens = shared.tokenizer.encode(prompt, return_tensors='pt').to("npu:0")
|
|
else:
|
|
tokens = shared.tokenizer.encode(prompt, return_tensors='pt').cuda()
|
|
output = shared.model(input_ids=tokens)
|
|
scores = output['logits'][-1][-1]
|
|
|
|
probs = torch.softmax(scores, dim=-1, dtype=torch.float)
|
|
topk_values, topk_indices = torch.topk(probs, k=top_logits, largest=True, sorted=True)
|
|
if is_non_hf_llamacpp:
|
|
topk_indices = [i.expand((1, 1)) for i in topk_indices]
|
|
|
|
if hasattr(shared.tokenizer, 'convert_ids_to_tokens'):
|
|
tokens = [shared.tokenizer.convert_ids_to_tokens(int(i)) for i in topk_indices]
|
|
else:
|
|
tokens = [shared.tokenizer.decode(i) for i in topk_indices]
|
|
|
|
if return_dict:
|
|
topk_values = [float(i) for i in topk_values]
|
|
output = {}
|
|
for row in list(zip(topk_values, tokens)):
|
|
key = row[1]
|
|
if isinstance(key, bytes):
|
|
try:
|
|
key = key.decode()
|
|
except:
|
|
key = key.decode('latin')
|
|
|
|
output[key] = row[0]
|
|
|
|
return output
|
|
else:
|
|
topk_values = [f"{float(i):.5f}" for i in topk_values]
|
|
output = ''
|
|
for row in list(zip(topk_values, tokens)):
|
|
output += f"{row[0]} - {repr(row[1])}\n"
|
|
|
|
return output, previous
|