Comments (3)
@bdhirsh you had a pr around this - not sure if exactly same one bug assigning to you, feel free to unassign
from pytorch.
cc @bdhirsh who root caused this
from pytorch.
@bdhirsh you had a pr around this - not sure if exactly same one bug assigning to you, feel free to unassign
Yeah it's the same bug
from pytorch.
Related Issues (20)
- DISABLED test_flash_attention_vs_math_ref_grads_batch_size_1_seq_len_q_1024_seq_len_k_1024_head_dim_64_is_causal_True_dropout_p_0_48_bfloat16_scale0_cuda_bfloat16 (__main__.TestSDPACudaOnlyCUDA) HOT 1
- DISABLED test_flash_attention_vs_math_ref_grads_batch_size_1_seq_len_q_1024_seq_len_k_1024_head_dim_16_is_causal_True_dropout_p_0_48_bfloat16_scale0_cuda_bfloat16 (__main__.TestSDPACudaOnlyCUDA) HOT 1
- The empty 1D or more D tensor for `mode()` with the deepest `dim` doesn't work HOT 2
- Masked Attention has no effect in ``TransformerEncoderLayer``
- DISABLED test_sdp_math_gradcheck_contiguous_inputs_True_cuda (__main__.TestSDPACUDA) HOT 1
- torch.nonzero with known count to avoid host-device synchronization HOT 2
- FlashAttention IMA HOT 1
- An API to re-enable dynamo-disabled functions
- [torch.compile][HF] torch.compile issue tracker for torch.compile on forward method of Meta Llama model HOT 2
- ONNX: wrong operator for ceil_mode Pooling in case of skip the last window HOT 1
- The empty 0D or more D tensor with exceeding `dim`(`dim=100` or `dim=-100`) for `cummin()` works
- The empty 0D or more D tensor with exceeding `dim`(`dim=100` or `dim=-100`) for `cummax()` works
- Failing to export ONNX when `GroupNorm` input shape = 2
- The shape of weights in lstm seems to be wrong.
- `input` argument of `nan_to_num()` works with `complex` type but `nan`, `posinf` and `neginf` argument don't work with `complex` type
- torch cuda-121 fails to load .so library HOT 1
- MPS and cpu method results are drastically different HOT 6
- [OpenMP] Unlimited number of threads are spawned.
- Poor scaling of `torch.masked.mean` HOT 1
- compiling outer function uses much more memory than compiling inner function when there is bit-packing
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from pytorch.