'attn_mask': cu_seqlens } # 配置FlashAttention变长序列处理函数 fromflash_attnimportflash_attn_varlen_func # 标准版本:用于评估模式 fa_varlen=lambdaq, k, v, attn_mask: flash_attn_varlen_func( q.squeeze(0), k.squeeze(0), v.squeeze(0), cu_seqlens_q=attn_mask, cu_seqlens_k=attn...
FlashAttention2优化实现 前面的文章我们已经探讨了FlashAttention对Transformer模型性能的影响。本节将重点介绍flash-attn 2.7.0版本中的flash_attn_varlen_func,这是一个专门为处理可变长度输入设计的API。这个优化方案的核心思想是将批次中的所有序列连接成一个连续序列,同时使用一个特殊的索引张量(cu_seqlens)来追踪...
bias:bool=False, dropout:float=0.0):super().__init__()assertembed_dimension % num_heads ==0# key, query, value projections for all heads, but in a batchself.c_attn = nn.Linear(embed_dimension,3* embed_dimension, bias=bias)# output projectionself.c_proj = nn.Linear(embed_dimension...
attn_scores / self.d_out_kq**0.5, dim=-1) context_vec = attn_weights @ values_2 returncontext_vec 使用这个交叉注意力模块: torch.manual_seed(123) d_in, d_out_kq, d_out_v =3,2,4 crossattn = CrossAttention...
train_batch_size 1 \ --gradient_accumulation_steps 1 \ --dataset_text_field "content" \ --use_gradient_checkpointing True \ --learning_rate 5e-5 \ --lr_scheduler_type "cosine" \ --weight_decay 0.01 \ --warmup_ratio 0.03 \ --use_flash_attn True 整个微调...
llama-chat-asst" \ --per_device_train_batch_size 1 \ --gradient_accumulation_steps 1 \ --dataset_text_field "content" \ --use_gradient_checkpointing True \ --learning_rate 5e-5 \ --lr_scheduler_type "cosine" \ --weight_decay 0.01 \ --warmup_ratio 0.03 \ --use_flash_attn True...
--use_flash_attn True 整个微调过程需要约 13.5 小时,下图给出了训练损失曲线。 下例给出了使用上述模型完成的一段对话: System Prompt: You are a helpful, respectful and honest assistant. Always answer as helpfully \ as possible, while being safe. Your answers should not include any harmful, \ ...
The 3.7.0 update documentation states that the PyTorch backend is optionally invoked. I now want to call the BERT model from keras_hub. How do I start Flash Attn? Hi@pass-lin- Flash attention is used to speed up the GPU by minimizing memory read/writes and accelerate Transformer training...
context_vec = attn_weights @ values returncontext_vec 这个类封装了以下步骤: 将输入投影到键、查询和值空间 计算注意力分数 缩放和归一化注意力权重 生成最终的上下文向量 关键组件说明: 在__init__中,我们将权重矩阵初始化为 nn.Parameter对象,使PyTorch能够在训练过程中自动跟踪和更新它们。
self.flash_attn=hasattr(torch.nn.functional, "scaled_dot_product_attention") self.q_lora_rank=model_args.q_lora_rank self.qk_rope_head_dim=model_args.qk_rope_head_dim self.kv_lora_rank=model_args.kv_lora_rank self.v_head_dim=model_args.v_head_dim ...