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- Support Bidirectional LSTM (utilising the forward LSTM layer with flipped Inputs and Outputs) - Support layout 1 - Support default cases for attr `clip` and `input_forget` - Support returning partial outputs (1-3) - fixes for alt_e2e_tests lstm tests (1,2,3)
# Description Implementation of the op for `torch.aten.unfold`: [TorchToLinalg Op Support #347](nod-ai/SHARK-ModelDev#849) Documentation of op can be found here: [PyTorch Docs](https://pytorch.org/docs/stable/generated/torch.Tensor.unfold.html) For this op, we apply a sliding window of some `size` along a single `dimension`, with `step` in between iterations. `Declaration: aten::unfold(Tensor(a) self, int dimension, int size, int step) -> Tensor(a)` The resulting `unfolded` tensor modifies the shape of `dimension` to be equal to the number of blocks that the sliding windows extracts/inserts, with an additional dimension of `size` appended (the number of cols of the output tensor directly translates from the size of the sliding window). So if we had a tensor of rank 3 (A x B x C), with dimension = 1, size = 2 and step = 2: (A x B x C) |=> (A x (B - size) // step + 1 x C x size) After extracting the window from the input tensor, we insert the (1 x size) slice into the output tensor. We can make this simpler by mapping the output indices from the input indices, like they do in the official implementation: [PyTorch Code](https://github.com/pytorch/pytorch/blob/main/torch/_inductor/lowering.py#L1694)
…utility (llvm#3777) Signed-Off By: Vivek Khandelwal <[email protected]>
This patch adds two things: 1. support for folding scalar patterns like [1]---squeeze--->[] ---unsqueeze--->[1]. 2. a canonicalizer for aten.view that applies when we can statically or dynamically (through the scalarized view shapes) infer that it is a flatten or unflatten op in the last dim. I'm not sure if this is the right place to be adding such a view canonicalizer. Catastrophically, there is a decomposition from flatten and unflatten into aten.view. Until this gets deleted (and it definitely should be deleted), I felt like this would be an appropriate temporary home. We run scalarize shapes after lowering to the backend contract (i.e., decomposing), and scalarize shapes is required to be able to infer dynamic dims coming from size int ops.
This was preventing dynamic dims in an ONNX model from being reified (causing the generation of `tensor.cast`s and preventing fusion in iree): ```mlir %2 = torch.vtensor.literal(dense<[4, 256]> : tensor<2xsi64>) : !torch.vtensor<[2],si64>] %7 = torch.prim.ListConstruct %int2 : (!torch.int) -> !torch.list<int> %8 = torch.aten.reshape %2, %7 : !torch.vtensor<[2],si64>, !torch.list<int> -> !torch.vtensor<[2],si64> //... chain of foldable ops linking %2 to the `shape` operand of a `torch.aten.broadcast_to ... -> !torch.vtensor<[?,?],si64>` ```
cferry-AMD
approved these changes
Jan 10, 2025
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