Prior to this patch, the torch dialect included `AtenTriuOp` for
computing the upper triangular part of the input matrix, but there was
no code for lowering the op to the linalg dialect.
This patch adds code to generate a `linalg.generic` operation that
compares indices (computed using `linalg.index`) to choose between zero
or the original value (using `arith.select`). The lowering fails if the
number of dimensions are less than two. This patch also adds a few
end-to-end tests.
The MacOS builders are having linking trouble with the extension library.
Until it's fixed, all support for op extensions is disabled. It should be
easy to restore once the issue is resolved.
The function `AffineMap::inferFromExprList` does not work if the first
vector of expressions is empty, because it uses these expressions to
obtain the context. This prevented `aten.permute` from working for
inputs of 0-rank. This commit adds support for 0-rank inputs.
PyTorch allows new operators to be registered dynamically in modules.
Torch-mlir already makes it fairly straightforward to add support for
new operators, and this commit just extends that support to allow new
PyTorch ops to come from a external module.
This does *not* allow ops to be dynamically loaded into torch-mlir.
Torch-mlir must still be compiled with support built-in.
Add a `_torch_mlir_custom_op_example` subpackage to `torch_mlir` which
registers an demonstration op. It will not be imported by default when
importing torch_mlir. It's strictly for testing and documentation.
Adds an end-to-end test for the `torch_mlir_custom_op_example::identity` op.
With all these changes, we should now be actively testing PyTorch extension
support with all future patches.
This commit adds lowering of `aten.div.Tensor_mode` op.
This commit also fixes formatting for the test file elementwise.py.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
This commit adds the decomposition of `aten.adaptive_avg_pool2d` op into
`aten.avg_pool2d` op. The current decomposition only supports cases where
input size is equal to the output size.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
When compiling without assertions (i.e. in `NDEBUG` mode), a handful of
statements turn to NOPs, which results in warnings such as missing
return statement or unused variables and function. This patch replaces
such statements with `llvm_unreachable()`, which informs the compiler
about program termination regardless of the `NDEBUG` mode. This also
enables torch-mlir to be compiled using the flags `-Wall`, `-Wextra`,
`-Wpedantic`, and `-Werror`.
This patch adds support for the torch.linalg.vector_norm op to the torch
dialect, including the necessary shape function. It also extends the
conversion of reduction operators to support lowering of
AtenLinalgVectorNormOp, in addition to adding a handful of end-to-end
tests to validate the lowering.
There exist several opportunities to make this lowering optimal and
robust. For instance, in its current form, the translation does not
support ord = 0, +inf, or -inf. For L1 norms, we don't need to raise
each element to the power 1.0. Similarly, L2 norms could benefit from
strength reduction. Since the canonicalization pass is not able to
apply these optimizations, we should consider applying them during the
linalg lowering itself.
This commit adds support for aten.max_pool2d, aten.max_pool2d_with_indices,
and aten.avg_pool2d op for the cases where ceil_mode = true.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
The preserve memory specifies that `If any of the input tensors is in channels_last format,
operator output should be in channels_last format` and hence can be
added as is in aten_empty_like op.
This commit adds lowering of `aten.masked_fill.Scalar` op.
This commit also fixes the formatting of the file constant_alloc.py.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
The updated LLVM code includes a patch to create bfloat16 array
attributes, thus enabling a different patch to torch-mlir to flesh out
support for the bfloat16 type.
* shape: add shape transfer function for aten.neg
Prior to this patch, the list of shape transfer functions did not
include `aten.neg`, which resulted in errors like below.
```
error: unsupported by backend lowering: tensor with unknown rank or dtype
note: see current operation: %0 = "torch.aten.neg"(%arg0) :
(!torch.vtensor<[256,256],f32>) -> !torch.vtensor<*,f32>
note: this is likely due to a missing shape transfer function in shape_lib_gen.py
```
This patch fixes the problem by adding a shape transfer function to
reflect the point-wise nature of this operation.
* linalg: add translation of aten.neg operation
This patch adds a translation rule to lower `aten.neg` operations on
tensors to an `arith.negf` operation wrapped inside a `linalg.generic`
operation. This patch also adds a rudimentary test.
This commit adds the following support to the op `nll_loss_backward`:
- `input` tensor can be rank-1
- `weight` parameter
- `reduction` parameter
- `target`, `grad_output`, `total_weight` can be rank-0
- Checks that input tensors are of the expected type
This commit decomposes `aten.new_empty` op into `aten.empty.memory_format` op.
This commit also made a dtype fix to the constant tensor allocation like ops.
Earlier the dtype for the result was inferred from the result type; now, it's
being evaluated as per the original definition of the op.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
This commit adds support for the cases of view op where the rank and
the shapes of the input and result are equal.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
In order to make sure that the TorchToLinalg conversions leave the
graph in a valid state, the final result of the conversion has to be
casted to the result type of the op. This commit adds this cast to ops
that did not have it.
This commit adds the op `ValsemVariantAtenCopyOp` that represents
`AtenCopy_Op` without the underscore. This is needed to make sure
that the `ReduceOpVariants` pass turns the in-place op into an op
that takes value tensors as inputs, otherwise the
`MaximizeValueSemantics` pass will not be able to add value
semantics correctly.
This commit also adds the lowering of `ValsemVariantAtenCopyOp`.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
This commit fixes the 2nd and 3rd return types of the `aten.native_layer_norm`.
Previously the mean and rSTD were returned with reduction dims removed.
This commit fixes this and keeps the reduction dims of the results.
Signed-Off-By: Prateek Gupta <prateek@nord-labs.com>
The term "pseudo" is very vague and was getting confusing (I felt I had
to explain it in every comment referencing it). Instead, rework the
"pseudo" ops to instead be named:
- MLIR Syntax: `torch.valsem.*`
- C++ / ODS: `ValsemVariant*Op`
This makes it clear what the concept is, and avoids confusion with other
things that might be called "pseudo", since these are very specific and
should be 100% consistently named w.r.t. the non-valsem-variant ops that
they correspond to.
This helps keep things organized and also exposes more parallelism to
the build system. It seems though that most of the compile time is
actually spent in the headers though, so the wall time doesn't decrease
as much as I had hoped (and now that the headers are being included
multiple times, the cpu time actually increases a lot, sadly -- will try
to dig into this).
This pass is added to lower ops, which can not be lowered
via the TorchToLinalg pass, such as `torch.bincount` op.
This pass also uses torch-mlir's TMTensor Dialect to lower the
complex ops.
Also add torch.bincount op lowering with the help of TMTensor dialect
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
This commit moves the helper function which are common across
different torch-mlir conversion passes into a common directory
Utils.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
This commit adds support for integer type inputs for
`AtenMaxOp`, `AtenSumOp`, `AtenSumDimIntListOp`.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
The view op allows for the new shape argument to have a -1 value for
one of the dimensions, and the op is expected to deduce the size of
that dimension by looking at the sizes of the other dimensions and
comparing it to the total number of elements in the original
tensor. This commit adds this functionality.
This commit does a couple of things. First, it fixes a bug in the
`linalg.generic` body of the `nll_loss_forward` lowering where the
`ignoreIndex` was being compared with the loop index rather than the
current element of the `target` tensor. This was not being caught by
the tests because they were not testing the case where `ingnoreIndex`
actually corresponds to a value in `target`. This has been fixed.
Second, this commit adds support for the `reduction` argument in
`torch.nll_loss_forward` as well as support for 1-D inputs. In order
to simplify the lowering code, I've refactored the code that creates
the `linalg.generic` ops for elementwise and reduction ops into static
functions, to avoid having boilerplate code for indexing maps, etc
that can be very error prone.
Note: The function `convertScalarToDtype` was moved to before all the
conversion patterns, but nothing in it was modified.
Ashay Rane
Maksim Levental
Ramiro Leal-Cavazos
Vivek Khandelwal
Bob Adolf
Vidush Singhal
Prashant Kumar
gpetters94
Sean Silva
Qiang Fu
Vigilans
Prateek Gupta
Yi Zhang
Gaurav Shukla