Comments (3)
This should be fixed by #125080
from pytorch.
- Test name:
test_matmul_small_brute_force_1d_Nd_cuda_float32 (__main__.TestLinalgCUDA)
- Platforms for which to skip the test: win
- Disabled by
malfet
Within ~15 minutes, test_matmul_small_brute_force_1d_Nd_cuda_float32 (__main__.TestLinalgCUDA)
will be disabled in PyTorch CI for these platforms: win. Please verify that your test name looks correct, e.g., test_cuda_assert_async (__main__.TestCuda)
.
To modify the platforms list, please include a line in the issue body, like below. The default action will disable the test for all platforms if no platforms list is specified.
Platforms: case-insensitive, list, of, platforms
We currently support the following platforms: asan, dynamo, inductor, linux, mac, macos, rocm, slow, win, windows.
from pytorch.
First time failed when https://hud.pytorch.org/pytorch/pytorch/commit/6ede882c0b1d5ccc95b0c82ca5e206eb2dfb2911 was landed. cc: @jeffdaily
from pytorch.
Related Issues (20)
- [inductor] TorchInductor does not correctly recognize the grad status of model code HOT 3
- [dynamo] nn module parameterization can cause excessive restarts HOT 3
- got Unexpected error from cudaGetDeviceCount() after calling torch.cuda.is_available() HOT 3
- [Compiled autograd] Support user-defined triton kernel for which speculate_subgraph on bw failed
- torch_randn_like function potentially giving different results with torch.manual_seed()? HOT 1
- Error calling to_sparse_coo() on any subclass of Tensor
- C++ libtorch x86_64 Darwin deprecation note missing HOT 5
- [NestedTensor] chunk fails under DEBUG=1 builds HOT 4
- DISABLED test_ring_attention_compile_attention_fn0 (__main__.RingAttentionTest) HOT 1
- DISABLED test_full_symbolic_value_cuda (__main__.TestInductorDynamicCUDA) HOT 2
- Eager/FX backend sometimes uses more memory than eager HOT 2
- scheduler.step() HOT 1
- Dynamic shapes: Cannot determine contiguity of split on inner dimension HOT 2
- Inductor generated Triton kernel spends double time from Llama2 to Llama 3 HOT 3
- `Dockerfile` should set the `syntax` directive to v1 HOT 2
- [DCP] DCP load for non-tensor values HOT 3
- Support loading with map_location on xpu backend HOT 4
- `LRScheduler` Refactoring HOT 6
- Why I can't differentiate the output of ODE solver(Scipy) by autograd.grad HOT 2
- ❓Different results between normal batching and `vmap` while using lower precision (e.g., bfloat16) HOT 1
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from pytorch.