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| import torch | |
| from transformers import is_torch_npu_available, is_torch_xpu_available | |
| from modules import sampler_hijack, shared | |
| from modules.logging_colors import logger | |
| from modules.text_generation import generate_reply | |
| global_scores = None | |
| 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)): | |
| output[row[1]] = 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 | |