Add jagged_sum operator for padded nested tensors to TritonBench #2305
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…orch#2305) Summary: Pull Request resolved: pytorch#2305 Add a `jagged_sum` reduction operator for padded nested tensors, based on the PyTorch `sum` operator, to TritonBench. This diff uses the PyTorch function [`torch.ops.aten._jagged_to_padded_dense_forward`](https://www.internalfb.com/code/fbsource/[92c2a067ab04e3eebc999254fed4ae2fbea6def3]/fbcode/deeplearning/fbgemm/fbgemm_gpu/fb/inductor_lowerings/elementwise_ops.py?lines=26), hosted at this [GitHub pull request](pytorch/pytorch#125968), to pad each 2-dimensional tensor in a nested tensor of shape `(B, *, M)`, then reduce across the `N`-th dimension (`dim == 1`) to a `(B, M)` output tensor. Measure accuracy of padded implementation against unpadded baseline implementation via `accuracy` TritonBench metric. Reviewed By: davidberard98 Differential Revision: D58423489
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…orch#2305) Summary: Pull Request resolved: pytorch#2305 Add a `jagged_sum` reduction operator for padded nested tensors, based on the PyTorch `sum` operator, to TritonBench. This diff uses the PyTorch function [`torch.ops.aten._jagged_to_padded_dense_forward`](https://www.internalfb.com/code/fbsource/[92c2a067ab04e3eebc999254fed4ae2fbea6def3]/fbcode/deeplearning/fbgemm/fbgemm_gpu/fb/inductor_lowerings/elementwise_ops.py?lines=26), hosted at this [GitHub pull request](pytorch/pytorch#125968), to pad each 2-dimensional tensor in a nested tensor of shape `(B, *, M)`, then reduce across the `N`-th dimension (`dim == 1`) to a `(B, M)` output tensor. Measure accuracy of padded implementation against unpadded baseline implementation via `accuracy` TritonBench metric. Reviewed By: davidberard98 Differential Revision: D58423489
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…orch#2305) Summary: Pull Request resolved: pytorch#2305 Add a `jagged_sum` reduction operator for padded nested tensors, based on the PyTorch `sum` operator, to TritonBench. This diff uses the PyTorch function [`torch.ops.aten._jagged_to_padded_dense_forward`](https://www.internalfb.com/code/fbsource/[92c2a067ab04e3eebc999254fed4ae2fbea6def3]/fbcode/deeplearning/fbgemm/fbgemm_gpu/fb/inductor_lowerings/elementwise_ops.py?lines=26), hosted at this [GitHub pull request](pytorch/pytorch#125968), to pad each 2-dimensional tensor in a nested tensor of shape `(B, *, M)`, then reduce across the `N`-th dimension (`dim == 1`) to a `(B, M)` output tensor. Measure accuracy of padded implementation against unpadded baseline implementation via `accuracy` TritonBench metric. Reviewed By: davidberard98 Differential Revision: D58423489
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…orch#2305) Summary: Pull Request resolved: pytorch#2305 Add a `jagged_sum` reduction operator for padded nested tensors, based on the PyTorch `sum` operator, to TritonBench. This diff uses the PyTorch function [`torch.ops.aten._jagged_to_padded_dense_forward`](https://www.internalfb.com/code/fbsource/[92c2a067ab04e3eebc999254fed4ae2fbea6def3]/fbcode/deeplearning/fbgemm/fbgemm_gpu/fb/inductor_lowerings/elementwise_ops.py?lines=26), hosted at this [GitHub pull request](pytorch/pytorch#125968), to pad each 2-dimensional tensor in a nested tensor of shape `(B, *, M)`, then reduce across the `N`-th dimension (`dim == 1`) to a `(B, M)` output tensor. Measure accuracy of padded implementation against unpadded baseline implementation via `accuracy` TritonBench metric. Reviewed By: davidberard98 Differential Revision: D58423489
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Summary:
Add a
jagged_sumreduction operator for padded nested tensors, based on the PyTorchsumoperator, to TritonBench. This diff uses the PyTorch functiontorch.ops.aten._jagged_to_padded_dense_forward, hosted at this GitHub pull request, to pad each 2-dimensional tensor in a nested tensor of shape(B, *, M), then reduce across theN-th dimension (dim == 1) to a(B, M)output tensor.Measure accuracy of padded implementation against unpadded baseline implementation via
accuracyTritonBench metric.Reviewed By: davidberard98
Differential Revision: D58423489