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的,请确保你理解其上下文,并确认它是否适用于你的PyTorch版本和配置。 检查自定义扩展或第三方库: 如果sdp_kernel是某个自定义扩展或第三方库添加的功能,请...
🐛 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
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 ...
(PyTorchMemEffAttention::AttentionBackwardKernel<cutlass::arch::Sm80, cutlass::half_t, true, false, true, 64, 64, 64, false>::Params&) [0x32fdef1] === in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_cuda.so === Host Frame:fmha_cutlassB_f16_aligned_64x64_k64_sm80...
(B,H,N,D,dtype=dtype,device=device)m=torch.ones((N,N),dtype=torch.bool,device=device).triu(N-N+1)m=m.float().masked_fill(m,float("-inf"))withtorch.backends.cuda.sdp_kernel(enable_flash=False,enable_math=False,enable_mem_efficient=True):out=torch.nn.functional.scaled_dot_product_...
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>...
🐛 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_...
def test_attention(backend: SDPBackend): config = Config() Attention = CausalSelfAttention(config).to("cuda", dtype=torch.float16) sample_input = torch.randn(1, 2048, config.n_embd, device="cuda", dtype = torch.float16) with sdpa_kernel(backend): ...