This includes custom op matching for decomposed operations and fusing
dequantization into dense operations. As a validation we compare
to the dequant+mm torch implementation.
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>
Currently transposed convolution is not handled correctly by
`TorchToTosa`. This PR allows transposed convolutions to pass through
the conversion so that they can be handled by other conversion passes
later in a pipeline.
An example input which produces a compilation error is:
```
func.func @forward(%input: !torch.vtensor<[1,64,1,100],f32>) -> !torch.vtensor<[1,64,2,200],f32> {
%true = torch.constant.bool true
%int1 = torch.constant.int 1
%int2 = torch.constant.int 2
%weight = torch.vtensor.literal(dense<0.0> : tensor<64x64x3x3xf32>) : !torch.vtensor<[64,64,3,3],f32>
%bias = torch.vtensor.literal(dense<0.0> : tensor<64xf32>) : !torch.vtensor<[64],f32>
%stride = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
%int1x1 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
%output = torch.aten.convolution %input, %weight, %bias, %stride, %int1x1, %int1x1, %true, %int1x1, %int1 : !torch.vtensor<[1,64,1,100],f32>, !torch.vtensor<[64,64,3,3],f32>, !torch.vtensor<[64],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,64,2,200],f32>
return %output : !torch.vtensor<[1,64,2,200],f32>
}
```
This MLIR produces an error about a cast operation with a size mismatch
when passed through `torch-to-tosa`:
```
error: 'tensor.cast' op operand type 'tensor<1x64x1x50xf32>' and result type 'tensor<1x64x2x200xf32>' are cast incompatible
```
---------
Co-authored-by: Srinath Avadhanula <srinath.avadhanula@getcruise.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.
This PR updates the torch-to-tosa conversion with following changes:
- Support torch.none as min/max input argument for tosa.clamp op
- Support negative value as start index for tosa.slice op
- Add tosa.logical_or lowering support
e2e test:
python -m e2e_testing.main --config=tosa
LIT tests:
cmake --build build --target tools/torch-mlir/all
---------
Co-authored-by: Ze Zhang <ze.zhang@getcruise.com>
Changes made during upstreaming:
* Removed comments attributing some copied code back to torch-mlir
(since it is now repatriated).
* Re-organized imports.
* Inlined RefMapping/RefTracker and TypeSubclassMap from an external
utility module.
* Added FxImporter class comments.
* Updated stack trace extraction to be fail safe.
* Added an entry-point for `import_frozen_exported_program` which uses
the shiny new upstream `torch.export.export()` API (versus the
lower-level/older API that Turbine is presently using). This
necessitated a small FX rewrite to line external state management up
with current conventions.
* Adapted one of Turbine's importer tests to go with this initial
submission. Turbine unfortunately has a lot of more-integration-ey
tests, and I would like to extract those as more of unit tests of the
importer features and upstream them that way vs trying to copy directly.
For now, one overall test with the initial submission gets us moving.
I acknowledge that there are some code quality things that could be
improved in this submission: this was authored over the course of many
months (and often via some trial and error). I would like to keep it
relatively converged with the downstream for the next few steps while
getting the test suite upstreamed. And then it will be easier to take a
hygienic pass through the code.
Including co-authors for contributors in the git log of the original
repository.
Co-authored-by: Ean Garvey <87458719+monorimet@users.noreply.github.com>
Co-authored-by: Avinash Sharma <aviator1994@gmail.com>
Co-authored-by: Arham Khan <arhammkhan@gmail.com>
Co-authored-by: brucekimrokcmu <kwangkyk@alumni.cmu.edu>
Co-authored-by: saienduri <77521230+saienduri@users.noreply.github.com>
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.
This PR adds the `enable_ir_printing` option to `torch_mlir.compile`,
which can be used to print the IR for all intermediate passes.
When running the added test file via:
```shell
$ python test/python/compile.py 2> tiny.stderr
```
the file `tiny.stderr` is about 700 KB.
Rob Suderman
Vivek Khandelwal
Gaurav Shukla
Phaneesh Barwaria
Chi_Liu
James Newling
Srinath Avadhanula
Dave Liddell
John Wu
Andreas Falkenberg
Ze Zhang
kumardeepakamd
Han-Chung Wang
Xida Ren (Cedar)
aldesilv
saienduri
Stella Laurenzo
Rik Huijzer