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...
E RuntimeError: (*bias): last dimension must be contiguous keras/src/backend/torch/nn.py:959: RuntimeError
This PR adds support for the efficient attention from torch2.0. Flash and memory efficient attention (from xformers) is now built into n the latest torch nightlies and can be used without any extr...
attn_output = torch.nn.functional.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask=causal_mask, dropout_p=0.0, is_causal=False, scale=scale) 如果不是自定义的mask,则直…
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...
序列维度必须位于维度-2（请参见documentation）。因此，在您的情况下，必须将维度1与维度2转置：
在PyTorch的官方文档中,torch.nn.functional 模块确实不包含名为 scaled_dot_product_attention 的函数。这个函数名称通常与Transformer模型中的缩放点积注意力机制相关联,但它并不是PyTorch标准库的一部分。 查找'scaled_dot_product_attention'函数的正确来源或实现方式: scaled_dot_product_attention 函数通常是在实现...
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