在Pytorch 1.9版本中,更新了torch.inference_mode()方法,该方法与torch.no_grad()类似,都不会记录梯度。 然而不同的是torch.inference_mode()相比torch.no_grad()有更好的性能,并且会强制关闭梯度记录。并且不能在中途设置梯度。 下例来自于官方论坛的提问 importtorchwithtorch.no_grad(): x = torch.randn(1...
在PyTorch 中,torch.no_grad和torch.inference_mode都用于在推理(inference)过程中禁用梯度计算,以提高性能和减少内存消耗。然而,它们之间有一些关键的区别和使用场景。 torch.no_grad torch.no_grad是一个上下文管理器,用于临时禁用梯度计算。它通常用于推理阶段,以确保在前向传播过程中不计算梯度,从而节省内存和...
除了设置requires_grad之外,Pytorch 还提供了三种可以影响 autograd 内部梯度计算的模式:默认模式/梯度模式(Grad Mode)、无梯度模式(No-grad Mode)和推理模式(Inference Mode),所有这些模式都可以通过python语法中的上下文管理器和装饰器进行切换 2.2.1 梯度模式 (Grad Mode) 这是Pytorch 工作的默认模式,是我们在没有...
inplace=True)# int_model.eval()# 以上代码可以将模型转换为int8模型进行测试acc=0.0# accumulate ...
@torch.inference_mode()def p_sample_loop(self, shape: tuple, return_all_timesteps: bool = False) -> torch.Tensor:batch, device = shape[0], "mps" img = torch.randn(shape, device=device)# This cause me a RunTimeError on MPS device due to MPS ...
PyTorch has an inference_mode (https://pytorch.org/docs/stable/generated/torch.inference_mode.html) that it recommends as a replacement for no_grad. But I don't think lightning is currently using it. Should it? cc @Borda @awaelchli @rohitgr7 @akihironitta...
📚 The doc issue https://pytorch.org/docs/stable/generated/torch.inference_mode.html At the end of the example code, we have this: >>> @torch.inference_mode ... def doubler(x): ... return x * 2 >>> out = doubler(x) >>> out.requires_grad F...
with torch.inference_mode(): outputs = model(input_ids) next_token_logits = outputs.logits[:, -1, :] top_k_logits, top_k_indices = torch.topk(next_token_logits, top_k) top_k_probs = F.softmax(top_k_logits / temperature, dim=-1) ...
with torch.inference_mode(): for _ in range(max_tokens): outputs = model(input_ids) next_token_logits = outputs.logits[:, -1, :] sorted_logits, sorted_indices = torch.sort(next_token_logits, descending=True) sorted_probabilities = F.softmax(sorted_logits, dim=-1) ...
withtorch.inference_mode(): for_inrange(max_tokens):outputs=model(input_ids)next_token_logits=outputs.logits[:, -1, :]next_token=torch.argmax(next_token_logits,dim=-1)ifnext_token==tokenizer.eos_token_id: breakinput_ids=torch.cat([input_ids, rearrange(next_token,'c -> 1 c')],dim...