6. 展望 Transformer虽然强大,但是它的复杂度为O(n2),这让它在处理长序列时十分吃力。为了改进Transformer,研究者们提出了FlashAttention,各种机制的sparse attention,以及可以长度外推的RoPE等等。我们可以期待,在不久的将来能处理Billion级别序列的高效Transformer的出现。发布...
这也解释了,为什么FlashAttention的实现是提前算好sin/cos,而TransformerEngine是每次计算sin/cos,反而是后者的性能更好,答案是因为计算根本就是free的。我看了FlashAttention的代码,他是用Triton实现的,那确实性能比CUDA差是符合预期的。 #pragma unroll for (int d_id = threadIdx.x; d_id < d2; d_id +=...
RoPE的旋转操作可通过逐元素复数乘法实现,计算复杂度为O ( d ) O(d)O(d),远低于传统位置编码的矩阵乘法(O ( d 2 ) O(d^2)O(d2))。这一特性使其与FlashAttention等优化库无缝集成。 与混合编码的适配 RoPE可与偏置项(Bias)结合,增强局部注意力效应。例如,在注意力矩阵中加入可学习的Bias项,进一步提升...
The installation of flash attention is a bit complex. Below is an example of how we install it. ```shell WORK_DIR=flash_attn_repro mkdir ${WORK_DIR} && cd ${WORK_DIR} python -m venv venv/flash_attn_repro source venv/flash_attn_repro/bin/activate pip install packaging # install trito...
self.flash_attn = hasattr(torch.nn.functional, "scaled_dot_product_attention") def forward(self, x: torch.Tensor, mask: torch.Tensor, freqs_cis) -> torch.Tensor: batch, seq_len, d_model = x.shape k: torch.Tensor q: torch.Tensor ...
self.flash_attn=hasattr(torch.nn.functional,"scaled_dot_product_attention") defforward(self, x: torch.Tensor, mask: torch.Tensor, freqs_cis) ->torch.Tensor: batch, seq_len,d_model=x.shape k: torch.Tensor q: torch.Tensor v: torch.Tensork=self.key(x)q=self.query(x)v=self.value(x...
从上一篇的评测结果来看,作为一种免训练的外推方案,ReRoPE 和 Leaky ReRoPE 的效果都是相当让人满意的,既没有损失训练长度内的效果,又实现了 “Longer Context, Lower Loss”。唯一美中不足的是,它们的推理速度相比原本的 Attention 来说是变慢的,并且目前尚不兼容 Flash Attention 等加速技术。
self.flash_attn=hasattr(torch.nn.functional, "scaled_dot_product_attention") defforward(self, x: torch.Tensor, mask: torch.Tensor, freqs_cis) ->torch.Tensor: batch, seq_len, d_model=x.shape k: torch.Tensor q: torch.Tensor v: torch.Tensor ...
self.flash_attn=hasattr(torch.nn.functional, "scaled_dot_product_attention") defforward(self, x: torch.Tensor, mask: torch.Tensor, freqs_cis) ->torch.Tensor: batch, seq_len, d_model=x.shape k: torch.Tensor q: torch.Tensor v: torch.Tensor ...
Can you add an example about Rope2d as in META Sam2https://github.com/facebookresearch/sam2/blob/main/sam2/modeling/sam/transformer.py#L289 Contributor Just to confirm, do you mean an example where rope is fused into FlashAttention as opposed to hows it done in SAM2 where q,k are ...