[ghstack-poisoned]

…m fbgemm_gpu"


PyTorch can't depend on `fbgemm_gpu` as a dependency because `fbgemm_gpu` already has a dependency on PyTorch. So this PR copy / pastes kernels from `fbgemm_gpu`:
* `jagged_to_padded_dense_forward()`
* `jagged_to_padded_dense_backward()`

[ghstack-poisoned]

…m fbgemm_gpu"


PyTorch can't depend on `fbgemm_gpu` as a dependency because `fbgemm_gpu` already has a dependency on PyTorch. So this PR copy / pastes kernels from `fbgemm_gpu`:
* `dense_to_jagged_forward()`
* `jagged_to_padded_dense_forward()`
* `jagged_to_padded_dense_backward()`

[ghstack-poisoned]

…m fbgemm_gpu"


PyTorch can't depend on `fbgemm_gpu` as a dependency because `fbgemm_gpu` already has a dependency on PyTorch. So this PR copy / pastes kernels from `fbgemm_gpu`:
* `dense_to_jagged_forward()`
* `jagged_to_padded_dense_forward()`
* `jagged_to_padded_dense_backward()`

[ghstack-poisoned]

…m fbgemm_gpu"


PyTorch can't depend on `fbgemm_gpu` as a dependency because `fbgemm_gpu` already has a dependency on PyTorch. So this PR copy / pastes kernels from `fbgemm_gpu`:
* `dense_to_jagged_forward()`
* `jagged_to_padded_dense_forward()`
* `jagged_to_padded_dense_backward()`

[ghstack-poisoned]

…m fbgemm_gpu"


PyTorch can't depend on `fbgemm_gpu` as a dependency because `fbgemm_gpu` already has a dependency on PyTorch. So this PR copy / pastes kernels from `fbgemm_gpu`:
* `dense_to_jagged_forward()`
* `jagged_to_padded_dense_forward()`
* `jagged_to_padded_dense_backward()`

[ghstack-poisoned]

…m fbgemm_gpu"


PyTorch can't depend on `fbgemm_gpu` as a dependency because `fbgemm_gpu` already has a dependency on PyTorch. So this PR copy / pastes kernels from `fbgemm_gpu`:
* `dense_to_jagged_forward()`
* `jagged_to_padded_dense_forward()`
* `jagged_to_padded_dense_backward()`

[ghstack-poisoned]

…m fbgemm_gpu"


PyTorch can't depend on `fbgemm_gpu` as a dependency because `fbgemm_gpu` already has a dependency on PyTorch. So this PR copy / pastes kernels from `fbgemm_gpu`:
* `dense_to_jagged_forward()`
* `jagged_to_padded_dense_forward()`
* `jagged_to_padded_dense_backward()`

[ghstack-poisoned]

…m fbgemm_gpu"


PyTorch can't depend on `fbgemm_gpu` as a dependency because `fbgemm_gpu` already has a dependency on PyTorch. So this PR copy / pastes kernels from `fbgemm_gpu`:
* `dense_to_jagged_forward()`
* `jagged_to_padded_dense_forward()`
* `jagged_to_padded_dense_backward()`

[ghstack-poisoned]

…m fbgemm_gpu"


PyTorch can't depend on `fbgemm_gpu` as a dependency because `fbgemm_gpu` already has a dependency on PyTorch. So this PR copy / pastes kernels from `fbgemm_gpu`:
* `dense_to_jagged_forward()`
* `jagged_to_padded_dense_forward()`
* `jagged_to_padded_dense_backward()`

[ghstack-poisoned]

…m fbgemm_gpu"


PyTorch can't depend on `fbgemm_gpu` as a dependency because `fbgemm_gpu` already has a dependency on PyTorch. So this PR copy / pastes kernels from `fbgemm_gpu`:
* `dense_to_jagged_forward()`
* `jagged_to_padded_dense_forward()`
* `jagged_to_padded_dense_backward()`

[ghstack-poisoned]

…m fbgemm_gpu"


PyTorch can't depend on `fbgemm_gpu` as a dependency because `fbgemm_gpu` already has a dependency on PyTorch. So this PR copy / pastes kernels from `fbgemm_gpu`:
* `dense_to_jagged_forward()`
* `jagged_to_padded_dense_forward()`
* `jagged_to_padded_dense_backward()`

[ghstack-poisoned]

…m fbgemm_gpu"


PyTorch can't depend on `fbgemm_gpu` as a dependency because `fbgemm_gpu` already has a dependency on PyTorch. So this PR copy / pastes kernels from `fbgemm_gpu`:
* `dense_to_jagged_forward()`
* `jagged_to_padded_dense_forward()`
* `jagged_to_padded_dense_backward()`

[ghstack-poisoned]

…m fbgemm_gpu"


PyTorch can't depend on `fbgemm_gpu` as a dependency because `fbgemm_gpu` already has a dependency on PyTorch. So this PR copy / pastes kernels from `fbgemm_gpu`:
* `dense_to_jagged_forward()`
* `jagged_to_padded_dense_forward()`
* `jagged_to_padded_dense_backward()`

[ghstack-poisoned]

…m fbgemm_gpu"


PyTorch can't depend on `fbgemm_gpu` as a dependency because `fbgemm_gpu` already has a dependency on PyTorch. So this PR copy / pastes kernels from `fbgemm_gpu`:
* `dense_to_jagged_forward()` as CUDA registration for new ATen op `_padded_dense_to_jagged_forward()`
* `jagged_to_padded_dense_forward()` as CUDA registration for new ATen op `_jagged_to_padded_dense_forward()`

CPU impls for these new ATen ops will be added in a follow-up PR.

[ghstack-poisoned]

…m fbgemm_gpu"


PyTorch can't depend on `fbgemm_gpu` as a dependency because `fbgemm_gpu` already has a dependency on PyTorch. So this PR copy / pastes kernels from `fbgemm_gpu`:
* `dense_to_jagged_forward()` as CUDA registration for new ATen op `_padded_dense_to_jagged_forward()`
* `jagged_to_padded_dense_forward()` as CUDA registration for new ATen op `_jagged_to_padded_dense_forward()`

CPU impls for these new ATen ops will be added in a follow-up PR.

[ghstack-poisoned]

…m fbgemm_gpu"


PyTorch can't depend on `fbgemm_gpu` as a dependency because `fbgemm_gpu` already has a dependency on PyTorch. So this PR copy / pastes kernels from `fbgemm_gpu`:
* `dense_to_jagged_forward()` as CUDA registration for new ATen op `_padded_dense_to_jagged_forward()`
* `jagged_to_padded_dense_forward()` as CUDA registration for new ATen op `_jagged_to_padded_dense_forward()`

CPU impls for these new ATen ops will be added in a follow-up PR.

[ghstack-poisoned]

…m fbgemm_gpu"


PyTorch can't depend on `fbgemm_gpu` as a dependency because `fbgemm_gpu` already has a dependency on PyTorch. So this PR copy / pastes kernels from `fbgemm_gpu`:
* `dense_to_jagged_forward()` as CUDA registration for new ATen op `_padded_dense_to_jagged_forward()`
* `jagged_to_padded_dense_forward()` as CUDA registration for new ATen op `_jagged_to_padded_dense_forward()`

CPU impls for these new ATen ops will be added in a follow-up PR.

[ghstack-poisoned]

…m fbgemm_gpu"


PyTorch can't depend on `fbgemm_gpu` as a dependency because `fbgemm_gpu` already has a dependency on PyTorch. So this PR copy / pastes kernels from `fbgemm_gpu`:
* `dense_to_jagged_forward()` as CUDA registration for new ATen op `_padded_dense_to_jagged_forward()`
* `jagged_to_padded_dense_forward()` as CUDA registration for new ATen op `_jagged_to_padded_dense_forward()`

CPU impls for these new ATen ops will be added in a follow-up PR.

[ghstack-poisoned]

…m fbgemm_gpu"


PyTorch can't depend on `fbgemm_gpu` as a dependency because `fbgemm_gpu` already has a dependency on PyTorch. So this PR copy / pastes kernels from `fbgemm_gpu`:
* `dense_to_jagged_forward()` as CUDA registration for new ATen op `_padded_dense_to_jagged_forward()`
* `jagged_to_padded_dense_forward()` as CUDA registration for new ATen op `_jagged_to_padded_dense_forward()`

CPU impls for these new ATen ops will be added in a follow-up PR.

[ghstack-poisoned]

…m fbgemm_gpu"


PyTorch can't depend on `fbgemm_gpu` as a dependency because `fbgemm_gpu` already has a dependency on PyTorch. So this PR copy / pastes kernels from `fbgemm_gpu`:
* `dense_to_jagged_forward()` as CUDA registration for new ATen op `_padded_dense_to_jagged_forward()`
* `jagged_to_padded_dense_forward()` as CUDA registration for new ATen op `_jagged_to_padded_dense_forward()`

CPU impls for these new ATen ops will be added in a follow-up PR.

[ghstack-poisoned]

…m fbgemm_gpu"


PyTorch can't depend on `fbgemm_gpu` as a dependency because `fbgemm_gpu` already has a dependency on PyTorch. So this PR copy / pastes kernels from `fbgemm_gpu`:
* `dense_to_jagged_forward()` as CUDA registration for new ATen op `_padded_dense_to_jagged_forward()`
* `jagged_to_padded_dense_forward()` as CUDA registration for new ATen op `_jagged_to_padded_dense_forward()`

CPU impls for these new ATen ops will be added in a follow-up PR.

[ghstack-poisoned]