outputs_cache = generate(model, tokenizer, inputs, use_cache=True, device=device) outputs_no_cache = generate(model, tokenizer, inputs, use_cache=False, device=device) outputs_cache_attentions = generate(model, tokenizer, inputs, use_cache=True, output_attentions=True, device=device) outputs_...
use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should use the past last key/values attentions (if applicable to the model) to speed up decoding. 这个注释是用于控制生成策略的参数。它包含了以下几个参数: do_sample(可选,默认为False):是否使用采样;否则使用贪婪...
output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, ) 返回的BaseModelOutputWithPast类主要为这些属性 return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states,...
Retrace threshold is handy if you find that the light cache is "leaking" (sometimes light cache samples can have a big radius and bleed through walls - this fixes it) and them I'm upping the amount of lc samples from 1000 to 2000. I wouldn't bother saving anything out and just ...
_generate_cache_key() from django.utils.cache module makes no use of the method parameter passed to the function. Instead, it uses the method attribute of the request parameter. Changing: {{{cache_key = 'views.decorators.cache.cache_page.%s.%s.%s.%s' % ( key_prefix, request.method, pa...
Creates a tiling scheme file based on the information from the source dataset. The tiling scheme file will then be used in theManage Tile Cachetool when creating cache tiles. This tool can be used to edit the properties of an existing tiling scheme, such as tile format, storage format,...
这就将next_word预测了出来,后面的流程就是将“hello”加到“say”后面变成“say hello”,迭代上述流程直到生成eos_token(终止词),整个预测也就完成了,这就是整个自回归的过程。上述就是不加任何参数和后处理的生成式模型的generate/inference全过程,这个过程也叫做greedy decoding贪心解码策略,下文会介绍。
("EleutherAI/gpt-neo-125M", pad_token_id=tokenizer.eos_token_id, gradient_checkpointing=gradient_ckpt, use_cache=not gradient_ckpt) def test_generate(input_str: str): input_ids = tokenizer.encode(input_str, add_special_tokens=False, return_tensors="pt") attention_mask = torch.where(...
(options => { options.AddBasePolicy(policy => policy.Expire(TimeSpan.FromMinutes(10))); }); builder.Services.AddOpenApi(); var app = builder.Build(); app.UseOutputCache(); if (app.Environment.IsDevelopment()) { app.MapOpenApi() .CacheOutput(); } app.MapGet("/", () => "Hello ...
(options => { options.AddBasePolicy(policy => policy.Expire(TimeSpan.FromMinutes(10))); }); builder.Services.AddOpenApi(); var app = builder.Build(); app.UseOutputCache(); if (app.Environment.IsDevelopment()) { app.MapOpenApi() .CacheOutput(); } app.MapGet("/", (...