This PR only performs a lit test. In lieu of an e2e test, https://github.com/nod-ai/SHARK-TestSuite/pull/142 makede sure that the lowering works & the numbers check out.
Co-authored-by: Xida Ren <xida.ren.dev@gmail.com>
This commit also cleans up the OnnxToTorch lowering for the ReduceMean
op and adds the support for handling edge cases.
Signed-Off By: Vivek Khandelwal vivekkhandelwal1424@gmail.com
This commit adds the OnnxToTorch lowering for the Mish, Softplus,
HardSwish, Trilu, ThresholdedRelu op
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
This adds support for converting DynamicQuantizeLinear from torch-onnx
to torch.
I could not get an e2e test to pass, since there seems to be some issues
with uint8 casting somewhere lower in the pipeline. For example
compiling with IREE for llvm-cpu, I would get either the correct zero
point (if zp < 128) or the correct zero-point minus 256 (if zp >= 128).
The output tensor seems to always return a tensor of zeros, which also
occurs when running uint8 examples through QuantizeLinear.
Edit: the first problem can be resolved by casting the output back to
uint8 on output, the second problem is resolved with PR #3018
Reduce mean lowerings did not succesfully lower to `linalg` via torched.
There were two separate paths that could be consolidated to a single
simpler pass. This resulted in a significant improvement in test
coverage.
If the broadcast shape is length-1 at a dim while `?` in the input dim
then we need to broadcast to the dynamic dim. This is equivalent to
taking a max of two dimensions.
This folds small version of the tensor-scalar comparison operators as
they are commonly used for shape computations. This includes le, lt, ge,
gt, eq, and ne.
The current padding operation was not functional for dynamic shapes.
Updated and enabled tests so that onnx.pad tests pass.
Work TBD for reflection padding.
We can support `onnx.Size` by requesing the size of each dimensions and
taking the product of the results, then packing it into a tensor.
---------
Co-authored-by: Scott Todd <scott.todd0@gmail.com>
This mostly copy-pastes the reduce minimum implementation to reduce max
to improve test coverage. We also improve the aten lowering for min/max
dim for unsigned types.
The addition of an e2e test is actually provided in the Shark-Testsuite.
This adds 2 test cases for the gridsampler e2e test.
Also as intended there were some items found which needed correction, so
the Gridsampler op has also a change.
`getRawBuffer` expects a densely packed vector of `i1` values however
`onnx` does not densely pack the values. Include code to handle the
packing / unpacking.
This is the lowering of gridsampler from onnx to torch using our prior
implementation of AtenGridSamplerOp.
Here are several checks for cornercases implemented. We may decide to
have part of these checks in AtenGridSamplerOp instead of the onnx
lowering portion.
Torch lowering only supported the most recent version. Refactored the
lowering so more easily handle default values and optional operands /
attributes.
There is no reason to treat `ConstantOfShape` as a specialized import
any as there exists a onnx-to-torch equivalent. Dropping the import
coding and adding support for resource conversion substantially
increases test coverage for dynamically shaped tests.
Onnx slice lowering used arange needlessly instead of directly
constructing the constant dimension values. This makes lowerings to
linalg struggle as multiple folders are required to get what is a
constant index value.
This commit adds the OnnxToTorch lowering for cosh, acosh, asin, asinh,
and atanh op.
This commit also adds the TorchToLinalg lowering for acosh, asin, asinh,
and atanh op.
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
This commit adds the OnnxToTorch support for Mean, IsInf, IsNaN, and
PRelu ops. All high priority ops were taken so went with these. The non
trivial ones are Mean and IsInf which might require extra review
---------
Co-authored-by: MaheshRavishankar <mravisha@amd.com>
This PR contains three commits to update the validation checks in the
ONNX -> Torch conversion pass for the AveragePool, Pad, and Slice operators:
> onnx: fix preconditions for lowering AveragePool ops
>
> The `pads` attribute of the AveragePool operator specifies the value to
> pad at both the beginning as well as the end of the axis (see
> https://onnx.ai/onnx/operators/onnx__AveragePool.html#attributes), so
> the size of this attribute should be twice the rank of the input tensor.
> However, our TorchOnnxToTorch bails out early since it incorrectly
> compares the pads attribute with the rank (not twice the rank) of the
> input tensor.
>
> This patch fixes the code to match the spec and adds a lit test.
> onnx: allow optional constant value for Pad operator
>
> The `constant_value` input of the onnx.Pad operator is optional (see
> https://onnx.ai/onnx/operators/onnx__Pad.html#inputs), but the
existing
> logic for lowering the operator into the Torch dialect assumes that it
> is mandatory.
>
> This patch makes the attribute optional and constructs a default value
> (a list of zeros the size of the input tensor) if the attribute was not
> specified.
> onnx: fix checks for axes and steps inputs of Slice operator
>
> The ONNX Spec for the Slice operator allows the `starts` and `ends`
> inputs to have fewer indices that the dimensions of the `data` tensor
> (see https://onnx.ai/onnx/operators/onnx__Slice.html), but our code
> expects these inputs to be as many as the `data` tensor's dimensions.
>
> More precisely, the spec requires that the `starts` and `ends` inputs
> are only as long as the `axes` input, but since the `axes` input is
> optional, the default type for the `axes` input has to match the type
> for the `starts` and `ends` inputs. Moreover, the number of indices in
> the `steps` input also has to match those in the `axes` inputs (instad
> of matching the dimensions of the `data` input).
>
> This patch fixes the checks in the TorchOnnxToTorch conversion so that
> they match the ONNX spec.
This commit modifies the OnnxToTorch lowering of Onnx.Reshape op by
creating the result shape list for the aten.reshape using the result
shape values inferred from the op's result shape.
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
Leaning on the QDQ functionality in torch we can support the QLinearConv
operation by piggybacking through `torch.Convolution`. This includes
some changes such as allowing the `onnx` rewriter to run recursively.
Doing so allows `QLinearConv` to decopmose to `onnx.Convolution` which
is then lowered to `torch`.
The existing `flatten` lowering did not define what the intermediate
shape was. This could result in failures to lower further to linalg as
the intermediate shape was unknown. Added a shape refinement section.
`torch` requires that padding be symmetric for pooling operations. To
support non-symmetric pad we need to separately materialize out the
padding operation.
---------
Co-authored-by: James Newling <james.newling@gmail.com>
`onnx` explicitly specifies that `raw_data` is stored in `little-endian`
layout. While converting
to `torch` we need to convert from a known endian format to an internal
format of consistent
layout. This means endianness must be correct during the import of
`onnx.Constant`.
---------
Co-authored-by: Xida Ren (Cedar) <cedar.ren@gmail.com>
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.
Xida Ren (Cedar)
Vivek Khandelwal
Gaurav Shukla
zjgarvey
jinchen
Pavani Chowdary
aldesilv
Rob Suderman
Andreas Falkenberg
Scott Todd
Chi_Liu
saienduri
Ashay Rane
Stella Laurenzo
Phaneesh Barwaria
Aart Bik