🐛 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...
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
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...
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 ...
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_order(sdp_params const& params) { constexpr std::array<SDPBackend, num_backends>...
47 64 // flash_attention V2 is universally faster than efficient_attention and Math 48 65 std::array<SDPBackend, num_backends> priority_order(sdp_params const& params) { 49 66 constexpr std::array<SDPBackend, num_backends> default_order{ 67 + SDPBackend::flash_attention, 68 + SDP...
🐛 Describe the bug Using nested tensors generated with torch.narrow as inputs to torch.nn.functional.scaled_dot_product_attention works fine in the forward pass of the model. However, both the math and Flash backends crash when training ...
AttentionBackwardKernel::half_t, true, false, true, 64, 64, 64, false>, void (*)(PyTorchMemEffAttention::AttentionBackwardKernel::half_t::Params)) const [clone .constprop.0] [0x31ce8de] === in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_cuda.so === Host Frame:void...
importtorchtorch.manual_seed(42)device="cuda:0"dtype=torch.float32B=4H=12N=2**12D=1024q=torch.rand(B,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=Fals...