Comments (1)
This is the expected behavior for torch.Tensor
in general it is recommended to use the lower case torch.tensor
when possible
from pytorch.
Related Issues (20)
- DISABLED test_register_fsdp_forward_method (__main__.TestFullyShardCustomForwardMethod) HOT 1
- DISABLED test_register_fsdp_forward_method (__main__.TestFullyShardCustomForwardMethod) HOT 1
- DISABLED test_dtensor_op_db_inner_cpu_float32 (__main__.TestDTensorOpsCPU) HOT 1
- DISABLED test_vertical_pointwise_reduction_fusion_cuda (__main__.TestUnbackedSymintsCUDA) HOT 1
- LambdaLR has incorrect multiplicative behavior when using torch.tensor LR HOT 6
- TorchDynamo ONNX Export does not work as expected with masking (ScatterElements)
- [inductor][cpu]Background_Matting and pytorch_CycleGAN_and_pix2pix AMP multiple thread static/dynamic shape CPP/default wrapper performance regression HOT 1
- worse results by using the MPS backend, compared to the CPU HOT 2
- Compile with non-default mode + triton kernel fails
- DISABLED test__int_mm_k_16_n_32_use_transpose_a_False_use_transpose_b_False_cuda (__main__.TestLinalgCUDA) HOT 1
- DISABLED test_non_contiguous_input_mm_plus_mm (__main__.TestMaxAutotune) HOT 2
- DISABLED test_dtensor_op_db_vstack_cpu_float32 (__main__.TestDTensorOpsCPU) HOT 2
- opcheck has dependency on expecttest, which is not a pytorch runtime dependency, leading to "module not found" error message
- running opcheck leads to `Fail to import hypothesis in common_utils, tests are not derandomized` print
- [docs] scaled_dot_product_attention is_causal description is misleading HOT 4
- Add warning messages to provide info about expected performance improvement using cuda for a specific model
- UserWarning:Plan failed with a cudnnException HOT 1
- [DCP] DCP does not support objects which are lazy initialized. HOT 3
- Bug: `torch.func.jacrev` fails with backend=`aot_eager` HOT 1
- UNSTABLE inductor / cuda12.1-py3.10-gcc9-sm86 / test (inductor_timm) HOT 1
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from pytorch.