Torch were installed by the following command: (llama) conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia But when I try install this library I am getting: (llama) C:\Users\alex4321>python -m pip install flash-attn Collecting flash-attn Using cached flash_at...
File "/tmp/pip-install-t51xid6r/flash-attn_f5a3e9f183ec423884f394ac30739e5f/setup.py", line 164, in raise RuntimeError( RuntimeError: FlashAttention is only supported on CUDA 11.7 and above. Note: make sure nvcc has a supported version by running nvcc -V. torch.__version__ = 2.4....
修改flash attn为torch_npu.npu_fusion_attention,但是推理llama时,发现使用torch_npu.npu_fusion_attention和没使用generate速度一样,没有显著提高wangchuanyi 帖子 82 回复 3013 您好,性能问题请查看性能优化方案,依次进行分析确认:https://www.hiascend.com/document/detail/zh/Pytorch/60RC1/ptmoddevg/trainingmig...
1.首先检查你的cuda版本,通过nvcc -V查看环境是否含有cuda以及版本是否在11.6及以上,如果没有需要自己安装,下载地址在这里:cuda-toolkit,具体的安装流程这里不再赘述了(先提前安装好gcc,否则安装cuda会失败:sudo apt install build-essential) 2. 安装完毕后检查自己的pytorch版本是否与安装的cuda版本匹配,注意不要自己...
importtorchfromtransformersimportAutoModelForCausalLM,AutoTokenizer,LlamaForCausalLMmodel_id="tiiuae/falcon-7b"tokenizer=AutoTokenizer.from_pretrained(model_id)model=AutoModelForCausalLM.from_pretrained(model_id,torch_dtype=torch.bfloat16,attn_implementation="flash_attention_2",) ...
flash_attn-2.6.3-cu124-torch2.5-cp311预编译 很多人在这个依赖遇到问题,github上提供的win版本只有cu123的,这又和torch不兼容。所以研究了一天,编译了cu124的版本。 系统:win10/11 python:3.11 torch:2.5.0 cuda:12.4
flash_attn-2.6.3-cu124-torch241-cp311预编译 钢铁锅含热泪喊修瓢锅 2024年10月24日 21:50 https://www.123684.com/s/5OovTd-fEIpA 分享至 投诉或建议
[start:end, 1]] .view(1, kv_length, num_heads, hidden_size) .permute(0, 2, 1, 3) ) attn_out = torch.softmax(q @ k, dim=-1) @ v res[i] = attn_out.permute(0, 2, 1, 3).view(1, 1, num_heads * hidden_size) start = end diff = torch.abs(out - res) print(f"...
训练脚本说明 Yaml配置文件参数配置说明 模型NPU卡数、梯度累积值取值表 各个模型训练前文件替换 NPU_Flash_Attn融合算子约束 BF16和FP16说明 录制Profiling 父主题: 主流开源大模型基于Lite Server适配LlamaFactory PyTorch 来自:帮助中心 查看更多 → 训练脚本说明 ...
use_flash_attn, **kwargs, ) return cls(config, roberta=roberta) def _register_lora(self, num_adaptations, rank, dropout_p, alpha): self.apply( partial( LoRAParametrization.add_to_layer, num_adaptations=num_adaptations, rank=rank, dropout_p=dropout_p, alpha=alpha, ) ...