The static uneven divisible AdaptiveAvgPool2d means that although the
input size is not an integer multiple of ouput size, but the kernel and
stride size can also be fixed (not dynamic). The derivation logic of
kernel and stride size is consistent with
torch/_decomp/decomposations.py:adaptive_avg_pool2d as described in the
following:
1. Stride Size
Firstly , derive the start index in each reduce operation according to
the output size (`n`), `start_index = ([0, 1, ..., n - 1] * input_size)
// output_size`. For each index `k`, if `k * (input_size % output_size)
< output_size`, then the current and previous stride keeps the same as
`input_size // output_size`. So suppose `(n-1) * (input_size %
output_size) < output_size`, the stride in the whole AdaptiveAvgPool2d
process keeps static, as `input_size // output_size`.
2. Kernel Size
torch/_decomp/decomposations.py:adaptive_avg_pool2d calculates a static
kernel size when the input/output sizes satisfy either of the two
conditions, `input_size % output_size == 0` or `output_size %
(input_size % output_size) == 0`. Here if `input_size % output_size ==
0`, then the kernel size equals `input_size // output_size`, otherwise
`input_size // output_size + 1.`
This PR adds a conversion in the TorchOnnxToTorch pass for the ONNX
Multinomial operation. It also adds a TorchToLinalg lowering for the
`aten.Multinomial` op and does a light refactor of some repeated code
that generates random floating point numbers in
`TorchToLinalg/Random.cpp`.
This patch adds a few misc pad op related changes:
1. Addresses issue <https://github.com/llvm/torch-mlir/issues/3457>
2. Addresses issue <https://github.com/llvm/torch-mlir/issues/3442>
3. Fixes the padding order for asymmetrically padded onnx.Conv ops
4. Enables passing quantization through those onnx.Conv op pre-paddings
5. Modifies the torch-to-linalg lowering of AtenReplicationPad2d op to
enable support for input rank != 4
Unfortunately, even with all of these changes, the e2e tests for the
ReplicationPad2d still fail the onnx config, since the torch export
procedure for rearranging the pad order is complicated enough that the
padding ints end up not being able to fold back to constants.
Register `aten.fake_quantize_per_channel_affine` and
`aten.fake_quantize_per_tensor_affine.tensor_qparams` ops
---------
Co-authored-by: Ze Zhang <ze.zhang@getcruise.com>
This adds support for a few ops:
- torch.linalg_det
- torch._linalg_det (if the LU and pivot returns are unused)
- onnx.Det
An scf loop is used, since the row reduction algorithm applied here has
some loop-carried dependencies.
The current support being added here is very basic, and only works if no
permutations are required during row reduction, and assumes the matrices
are non-singular.
This adds a torchvision op to torch-mlir and a path from onnx.DeformConv
to torchvision.deform_conv2d.
I'm not implementing the torch->linalg lowering for the torchvision op
yet, but posting this PR to get feedback on some of the choices being
made here and to flesh out the onnx frontend a bit.
This adds an onnx->torch conversion for onnx.RoiAlign into
torchvision.roi_align or torchvision.roi_pool, and adds those two
torchvision ops to torch-mlir.
Add a new op with shape/dtypes and decompose into
`fake_quantize_per_tensor_affine` when the second result is unused.
The xfail_set change is on ONNX because torch cannot export this op to
ONNX.
There is currently no int16 quantization support in torch. This patch
adds a new mlir type to correspond to the missing "torch.qint16" type,
and enables lowering of quantization-related onnx ops using int16 types.
In follow-up patches, custom quantization logic for ops like
aten.matmul/aten.mm/aten.convolution may need to be revisited to allow
support for qint16. The passes in FuseQuantizedOps.cpp may also need
slight modifications.
This addresses 7 of the model failures I'm seeing in the test suite. See
[Shark-Turbine issue
#566](https://github.com/nod-ai/SHARK-Turbine/issues/566).
Need the op ```linalg.conv_2d_ngchw_gfchw_q``` to be added upstream
before merging this. See [llvm-project PR #92136
](https://github.com/llvm/llvm-project/pull/92136).
A small additional expansion to operand quantization is included in this
patch to address a model failure that occurs when unblocking the
quantized group convolutions in one of these onnx models.
This PR fixes the bugs for `Torch::AtenOneHotOp` by:
1) Using `Torch::kUnknownSize` as the default value for `numClasses` in
the pattern matching stage in `DecomposeAtenOneHotOp`
2) Adding `AtenIntScalarOp` to the patterns in `TorchToArith`
3) Handling both `int` and `float` types for `off` and `on` values in
`TorchOnnxToTorch` conversion
It also includes:
1) A new test in `TorchToArith/basic.mlir`, for `torch.aten.Int.Scalar`,
and
2) A new test in `decompose-complex-ops.mlir`, for `torch.aten.one_hot`
**Dependencies**
This PR is dependent on #3334.
I am trying to eliminate 'getWithLeastStaticInformation' in
DecomposeAtenTriuOp. Could you provide me with some suggestions?
@qingyunqu @zjgarvey
See issue https://github.com/llvm/torch-mlir/issues/3312
Discord Thread:
https://discord.com/channels/636084430946959380/1238330633328005243
## Context:
[This](https://github.com/llvm/torch-mlir/blob/main/python/torch_mlir/fx.py#L61)
was updated to support e2e tests for the TorchDynamo frontend in
Torch-MLIR, where we run FX decompositions and import the FX IR to
generate Torch dialect, followed by
`torch-function-to-torch-backend-pipeline`, skipping only the shape/type
refinement for now. However, we should be able to skip many of the torch
simplification passes, as depicted in the [frontend
roadmap](https://github.com/llvm/torch-mlir/blob/main/docs/images/roadmap_frontend.png).
Based on IREE's TorchDynamo
[pipeline](https://github.com/iree-org/iree/blob/main/compiler/plugins/input/Torch/InputConversion/Passes.cpp#L29),
the only two passes we seem to require are: `ReduceOpVariantsPass` and
`DecomposeComplexOpsPass`. This is inline with our findings as well
based on initial exploration.
This PR creates a dedicated frontend simplification pipeline for
TorchDynamo / FX Importer which calls only `ReduceOpVariantsPass` and
`DecomposeComplexOpsPass`. We rely on the e2e fx_importer tests to
ensure we're not regressing by removing many of the passes that were
historically needed for TorchScript.
One notable change here is that we do not call the
`LowerToBackendContractPass` anymore, which used to call
`TorchSimplificationPipeline` iteratively until VerifyBackendContract
was clean. Some of this was required for the shape/type refinement to
converge, which seems a non-issue for Dynamo frontend. Do we anticipate
this (the iterative invocation of TorchSimplificationPipeline followed
by VerifyBackendContract) to be worth retaining in the Dynamo frontend
pipeline? If so, I can make those changes, PLMK.
yyp0
Ivan Butygin
Vivek Khandelwal
Ze Zhang
Yuanqiang Liu
bosko-syrmia
Branko Trifkovic
pkapris-syrmia
Arham Khan
rohan-tan-bhowmik
Xinyu Yang
zjgarvey
Scott Todd
Jiawei Wu
Phaneesh Barwaria
Vinayak Dev
Matthias Gehre
ptrifunovic98
Lei Zhang
Rob Suderman
penguin_wwy
Angel Zhang
Sambhav Jain
