Torch does not have an equivalent matmul operation for integers. Instead
it sidechannels the information via its quantized types. For this
lowering we setup these sidechannels then invoke `torch.mm`.
We can plumb the linear matmul into pytorch using its quantized types
with side channel information. To handle the final int8 operation we
dequantize and requantize.
This commit adds mapping from `onnx.pad` op to `torch.pad` op. Currently
it does not support `axes` parameter of `onnx.pad` op.
Signed-off-by: Gaurav Shukla <gaurav.shukla@amd.com>
We can make the per-tensor version of the operation to the dequantize
operation via marking with the make quantized tensor component. This
introductions the `qint*` and `quint*` tensor type that can be lowered
to teh appropriate dequantization behavior during the torch-to-linalg
conversion.
We can map the per_tensor case to the `torch.aten.quantize_per_linear`
operation. In this case we extract the `scale` and `zeropoint` values
and directly invoke the quantization, then return the integer
representation value.
Implemented ONNX.Range. The spec says the data type for start, limit,
delta are 0-D can be double, float, int16, int32, int64, All int types
mapped to !torch.int and all float types mapped to !torch.float
---------
Co-authored-by: Kumar Deepak <kumar@xilinx.com>
Handles the multiple cases of `onnx` constant values and converts them
to `torch` literal tensors. This can include splats with a single
integer or floating point value, a set of explicit integer values, or
an elements array attr of values.
The expression for HardSigmoid in Onnx
(https://onnx.ai/onnx/operators/onnx__HardSigmoid.html): max(0, min(1,
alpha * x + beta))
is inherently different from HardSigmoid in Torch
(https://pytorch.org/docs/stable/generated/torch.nn.Hardsigmoid.html)
which is: if x < -3 -> 0
elif x > 3 -> 1
else x/6 + 1/2
That being said, it was just better to compute out the entire expression
when translating the Onnx expression to Torch mlir, which is done in
this PR. Some of the logic is shared from the files in
`DecomposeComplexOps`. Therefore, refactored some shared logic between
`DecomposeComplexOps` and `DefaultDomainGToP` and put it in a `Utils`
file.
The three remaining compare operations
onnx.Greater
onnx.Less
onnx.GreaterOrEqual
Are also added with this push request.
This concludes a set of basic tensor compare functions.
Lowerings for `transpose` from ONNX to `aten`. Implementation depends on
making multiple `aten.transpose` operations swapping pairs of dimensions.
As `onnx.transpose` can swap around any dimensions it may require
constructing multiple `aten.transpose`.
This commit adds the OnnxToTorch support for Reciprocal, Round,
ScatterElements, Sigmoid, Sin, Tanh, Sqrt, Sub, Sum, Where, Xor,
Squeeze, Unsqueeze ops.
For reviewers, the ops that weren't trivial and probably require extra
review are Sum, Squeeze, and Unsqueeze.
Lowerings for `selu` lowerings for ONNX to the corresponding torch
implementations. Torch's `selu` implementation has fewer features so
we use the a generalized `elu` with the input scale set to `1.0`.
This commit adds the OnnxToTorch support for BitwiseXor, BitwiseOr, Div, Equal, Cast,
Ceil, Floor, Cos, and Clip op.
This commit also adds the TorchToLinalg support for aten.clamp.Tensor and aten.clamp_min.Tensor op.
Signed-Off By: vivekkhandelwal1424@gmail.com
This commit adds the OnnxToTorch support for Atan, Bitshift, BitwiseAnd,
and BitwiseNot op.
This commit also adds the TorchToLinalg support for AtenBitwiseLeftShiftTensorOp.
Signed-Off By: vivekkhandelwal@nod-labs.com
Adds a pipeline to convert custom ops and metadata represented as
`torch.operator` custom ops to corresponding `torch` ops where possible.
This is part of a multi-part approach for building ONNX import in as a
regular feature of torch-mlir. It is focused on the conversions vs the
infra. We will end up maintaining a [pure-python
importer](https://github.com/nod-ai/SHARK-Turbine/blob/main/python/shark_turbine/importers/onnx_importer.py)
to go with this in torch-mlir, and we will also maintain test case
generation utilities derived from it.
I have left substantial documentation in the README of the conversion
directory, including the recommended approach that we will take to keep
building this out.
(note that this organizes the code to coincide with the refactoring in
#2442 versus the current flat arrangement)
Rob Suderman
Vivek Khandelwal
Phaneesh Barwaria
Gaurav Shukla
Chi_Liu
James Newling
Ramiro Leal-Cavazos
Dave Liddell
Andreas Falkenberg
kumardeepakamd
Xida Ren (Cedar)
Ben Vanik
Han-Chung Wang
John Wu
aldesilv
saienduri
Stella Laurenzo
Gaurav Shukla
Frederik Harwath