Hoonkyung Cho
Hoonkyung Cho
# Description New feature to support aten._cdist_forward converter converter. I have added test cases ensuring compatibility with both matching and broadcasting input shapes. Fixes # ([issue](https://github.com/pytorch/TensorRT/issues/2725)) ## Type of change...
# Description A converter for the torch.ops.aten._local_scalar_dense operation, a low-level operation in PyTorch used internally to extract a scalar value from a tensor containing a single element. To reviewer, I...
# Description New feature to support aten.as_strided converter converter. Our implementation focuses on accurately calculating the indices required Fixes # ([issue](https://github.com/pytorch/TensorRT/issues/2734)) ## Example Usage Given a tensor `x`: ```python x...
Implement `aten.empty_strided` converter https://pytorch.org/docs/stable/generated/torch.empty_strided.html#torch-empty-strided
aten op converter for `sym_storage_offset` https://github.com/pytorch/pytorch/blob/d0211e207c78fafac2edaf2e14954f668e898b4a/aten/src/ATen/TensorGeometry.h#L88 https://github.com/pytorch/pytorch/blob/d0211e207c78fafac2edaf2e14954f668e898b4a/aten/src/ATen/TensorGeometry.h#L139
The `torch.ops.aten._local_scalar_dense` operation is a low-level operation in PyTorch used internally to extract a scalar value from a tensor containing a single element.
Implement a converter for aten.as_strided https://pytorch.org/docs/stable/generated/torch.as_strided.html#torch-as-strided
Implement a converter for aten._cdist_forward https://pytorch.org/docs/stable/generated/torch.cdist.html#torch.cdist https://github.com/pytorch/pytorch/blob/f00ece024bd70e6a5f3aa763715a1f353cfdacdf/aten/src/ATen/native/Distance.cpp#L81
# Description A converter for the torch.ops.aten.log1p operation (elementwise log(1 + x)) Fixes # ([issue](https://github.com/pytorch/TensorRT/issues/2760)) ## Type of change - New feature (non-breaking change which adds functionality) # Checklist: -...
# Description The `aten.atan2.out` operation calculates the element-wise arctangent of two tensors and stores the results in a specified output tensor, `out`. This does not alter the input tensors, meaning...