auto dprops = at::cuda::getCurrentDeviceProperties(); return dprops->major >= 9; } // flash_attention V2 is universally faster than efficient_attention and Math std::array<SDPBackend, num_backends> priority_orde
torch.backends.cuda模块主要用于配置CUDA的行为,如设置是否使用CUDA、是否同步CUDA流等。 检查代码中的拼写错误或误用API: 请检查你的代码中是否有拼写错误。确认你是否想要访问的是其他存在的属性或方法,例如torch.backends.cudnn.enabled或torch.backends.cuda.matmul.allow_tf32。 如果sdp_kernel是你在其他地方看到...
🐛 Describe the bug import torch @torch.compile(fullgraph=True) def f(q, k, v): q = torch.cos(q) with torch.backends.cuda.sdp_kernel(enable_flash=True): return F.scaled_dot_product_attention(q, k, v) f(*[torch.randn(1,8,1024,64, dtype=tor...
H,N,D,dtype=dtype,device=device)k=torch.rand(B,H,N,D,dtype=dtype,device=device)v=torch.rand(B,H,N,D,dtype=dtype,device=device)withtorch.backends.cuda.sdp_kernel(enable_flash=False,enable_math=False,enable_mem_efficient=True):out=torch.nn.functional.scaled_dot_product...
57 + auto dprops = at::cuda::getCurrentDeviceProperties(); 58 + return dprops->major >= 9; 59 + #else 60 + return false; 61 + #endif 62 + } 63 + 47 64 // flash_attention V2 is universally faster than efficient_attention and Math 48 65 std::array<SDPBackend, num...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - `torch.backends.cuda.sdp_kernel(enable_flash=True)` causes graph breaks · pytorch/pytorch@ec660c3
Solve torch.backends.cuda.sdp_kernel() is deprecated. f74c2d1 Merge branch 'kvcache-ai:main' into main d3b45d5 Merge branch 'main' into main ca1dc1e Contributor Atream commented Mar 1, 2025 Thank you for your contribution! Atream merged commit 69382e5 into kvcache-ai:main Mar ...
Sign in to comment Reviewers kit1980 Assignees No one assigned Labels CLA Signed Projects None yet Milestone No milestone Development Successfully merging this pull request may close these issues. Rule for deprecated torch.backends.cuda.sdp_kernel() 3 participants ...
🐛 Describe the bug code: import torch from transformers import StaticCache NUM_TOKENS_TO_GENERATE = 40 torch_device = "cuda" from torch.nn.attention import SDPBackend, sdpa_kernel def decode_one_tokens(model, cur_token, input_pos, cache_...
backends.cuda.sdp_kernel(enable_flash=False, enable_math=True, enable_mem_efficient=False): res_sdpa = torch.nn.functional.scaled_dot_product_attention(query, key, value, is_causal=is_causal, attn_mask=None) res_eager_cast, softmax_inp = scaled_dot_product_attention(query, key, value, ...