🐛 Describe the bug Under specific inputs, torch._scaled_dot_product_attention_math triggered a crash. import torch query = torch.full((1,2,8,3,1,1,0,9,), 0, dtype=torch.float) key = torch.full((0,3,7,1,1,2), 0, dtype=torch.float) value =...
🐛 Describe the bug When running F.scaled_dot_product_attention with an input matrix that contains NaNs on CPU, with PyTorch 2.4, the output is a NaN matrix, but with PyTorch 2.5, it is a zeros matrix. import contextlib import torch impor...
这个错误通常发生在PyTorch中执行Scaled Dot-Product Attention操作时,梯度反向传播过程中出现了NaN值。 错误原因 数值不稳定:在计算过程中,由于数值过大或过小,导致在计算梯度时出现溢出或下溢,从而产生NaN值。 梯度爆炸或消失:在深度神经网络中,如果梯度过大或过小,可能会导致梯度爆炸或消失,这也可能引发NaN值。 输...
RuntimeError: Function 'ScaledDotProductEfficientAttentionBackward0' returned nan values in its 0th output. 经过搜索,使用 torch.autograd.set_detect_anomaly(True) ,将其放在代码开头,之后会在出现nan值时报错,具体为:(好像没什么用) Traceback (most recent call last): File "train.py", line 165, in...
all in llm 6 人赞同了该文章 目录 收起 1、xformers 2、Flash Attention 3、torch 2.0 scaled_dot_product_attention是一种统称,目前有三种实现方式: 1、xformers from xformers.ops import memory_efficient_attention memory_efficient_attention的重点就是节约显存。 2、Flash Attention from flash_...
如何在Pytorch中用scaled_dot_product_attention()替换这些简单的代码？序列维度必须位于维度-2（请参见...
classDotProductAttention(nn.Module):def__init__(self,dropout,**kwargs):super(DotProductAttention,self).__init__(**kwargs)self.dropout=nn.Dropout(dropout)defforward(self,queries,keys,values,valid_lens=None):d=queries.shape[-1]scores=torch.bmm(queries,keys.transpose(1,2))/math.sqrt(d)self...
如何在Pytorch中用scaled_dot_product_attention()替换这些简单的代码？序列维度必须位于维度-2（请参见...
#include <torch/types.h> #include <torch/extension.h> #define WARP_SIZE 32 #define INT4(value) (reinterpret_cast<int4*>(&(value))[0]) #define FLOAT4(value) (reinterpret_cast<float4*>(&(value))[0]) #define HALF2(value) (reinterpret_cast<half2*>(&(value))[0]) ...
🐛 Describe the bug There is an illegal memory access in torch.nn.functional.scaled_dot_product_attention during the backward pass when using a float attention mask that requires grad while q, k and v do not require grad. import torch q, ...