See the documentation in `docs/shape_lib.md` and
`docs/adding_a_shape_function.md` for an overview of the system.
This completely overhauls how we represent shape functions. In
particular, RefineTypes does not infer shapes anymore (only dtypes).
Shape functions are now written in (TorchScript'able) Python.
Recommended review order:
1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`.
1. Code and tests for ReifyShapeCalculations, DropShapeCalculations.
1. Code and tests for SimplifyShapeCalculations.
1. shape_lib_gen.py
1. Code and tests for new RefineTypes pass.
1. Random folders/canonicalizers in TorchOps.cpp and associated test in
`canonicalize.mlir`.
1. New ReadOnly trait inferred from the registry.
1. Any miscellaneous remaining stuff.
Example `-print-ir-after-all` for ElementwiseUnaryModule:
[IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212).
Example `-print-ir-after-all` for ElementwiseBinaryModule:
[IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
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 commit replaces the two rewrite patterns of
maximize-value-semantics with a single pattern that captures the
behavior of both as well as other edge cases previously not
supported. The new pattern works by first performing alias analysis on
a subgraph to see if pattern is applicable, then rewriting all
non-value tensors to value tensors in a single go.
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>
- This commit adds E2E support for `aten.rand_like` and
`aten.bernoulli_.Tensor` ops.
- The `aten.bernoulli(x)` was implemented as:
`aten.bernoulli(x) = rand_like(x) < 0.5`, assuming 0.5 as default
probability, whereas according to the pytorch documentation:
https://pytorch.org/docs/stable/generated/torch.bernoulli.html#torch.bernoulli
the input x in `aten.bernoulli(x)` is itself a tensor containing
probabilities to be used for drawing the binary random number.
- So this commit fixes the `aten.bernoulli(x)` implementation as:
`aten.bernoulli(x) = rand_like(x) < x`.
- It also fixes the case where the input to `aten.bernoulli_.float` is
an integer tensor. In this case the input must be casted to float type
before passing it as operand to `aten.rand_like` op.
`aten.bernoulli_.float(x, p) = rand_like(float(x)) < p`.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
The pass is mostly borrowed from the BufferizeAnyLinalgOp pass in mlir
upstream with some minor changes. At a high level, it's a naive partial
bufferization pass which allocate new buffers for all the output
tensors. The initial value of an output buffer is copied from the
original buffer if there are uses of the original value.
One difference from linalg bufferization pass is the way to tell if
the loop body uses the init value of output operand. For TMTensor ops,
it differs from op to op because the payload region doesn't represent
the entire loop body.
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.
Its unclear to me what the right layering is here: Are you expecting
torch-mlir-dialects to always get built with LLVM? This is pretty
breaking for us, if so.
This should be set elsewhere depending on the build configuration.
In particular, we need to be careful when cross-compiling to pick
up the host mlir-tblgen.
This commit adds the invariant to the op `torch.overwrite.tensor.contents` that
both of its operands have the same shape and size. In order to
maintain the invariant, special handling of this op is added to the
`RefineTypes` pass.
There are cases where the op may update the same indices multiple
times. In this context, we can not parallelize updates. Instead,
we have to execute them sequentially. Adding a boolean attribute
to control the behavior.
Also adding test cases for invalid IR.
This commit adds handling to the `maximize-value-semantics` pass for
the case where a view-like op depends on a tensor that has been
overwritten by a value tensor. The approach for removing the
dependency is to change the input to the view-like op to be a copy of
the value tensor that is being used to overwrite.
This commit also removes `AtenFill_ScalarOp` and
`AtenBernoulli_FloatOp` from the list of view-like ops, since these
ops now have a corresponding op with value semantics into which they
get converted in the `reduce-op-variants` pass.
- This commit decomposes the `aten.batch_norm` op into the
`aten.native_batch_norm` op, instead of lowering it to the
`linalg.generic` op.
- It also adds run-time asserts in the `aten.native_batch_norm` lowering
to make sure that the shape of the weight, bias, running_mean, and
running_var must match the num of features.
- Since the `aten.native_batch_norm` op is not supported at TOSA backend,
all the modules that are dependent on the `aten.native_batch_norm` op
will fail and therefore they should be removed from the TOSA `passing`
set.
- It also moves `checkNotNone` to utility.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
This is intended to explore support for non-structured ops that can't
be modeled by Linalg dialect. `tm_tensor.scan` and `tm_tensor.scatter`
are added as the first such ops. The dialect should aim to be
upstreamed in the future.
This commit adds the op `PseudoAtenFillScalarOp` that represents
`AtenFill_ScalarOp` without the underscore. The approach is the same
as in commit dd998fa4d4.
Adding this op allows for a simpler and more consistent version of the
`empty` and `empty_like` op e2e tests.
- This commit adds lowering of `aten.le.Scalar` and `aten.ge.Scalar` ops
as a part of `convert-torch-to-linalg` pass.
- It also creates a new test script `elementwise_comparison.py` for all
element-wise comparison ops.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
This commit adds the op `PseudoAtenBernoulliFloatOp` that represents
`AtenBernoulli_FloatOp` 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 adds lowering of `aten.eq.int` op as a part of
`convert-torch-to-std` pass.
- It also refactors the code for binary comparison ops lowering.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
- This commit adds lowering of `aten.Bool.Tensor` and
`aten.Float.Tensor` op as a part of `convert-torch-to-linalg` pass.
- It also adds support for returning bool types.
- It also fixes lowering of the `aten.Int.Tensor` op for non-zero rank
input tensors.
- If a scalar number is converted to a 0-d tensor and passed on to the
`aten.Float.Tensor` op, it folds to the scalar number.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
- The `self` name is being used as a keyword argument to the
`torch.ops.aten.nll_loss_backward` function call, which produces
name-conflict error with the python keyword `self` which is pointer to
the current object.
- This commit fixes this issue by replacing the keyword argument by
positional argument.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
Some of the lowerings use the result type obtained from the op itself
to tell the `linalg::GenericOp` what the type of the result should be
rather than using the type of the result tensor given to the
`linalg::GenericOp`. This becomes a problem when the result type of
the op has static size information and the result tensor used in
`linalg::GenericOp` has dynamic dimensions, for `linalg::GenericOp`
expects the result type to be equal to the type of the output tensor.
This commit replaces the use of the result type from the op itself
with the type of the result tensor passed to `linalg::GenericOp`.
In order to not create too many dynamic/static versions of the same
e2e test, e2e tests have only been added to the ops that currently
fail when used with static sizes.
Prior to this commit, importing a `prim::Constant` node with list type would result in an error since it was not supported. `ivalue_importer::importIValue` was modified to return the MlirValue corresponding to the root so its parent operation could be extracted.
Sean Silva
Prashant Kumar
Ramiro Leal-Cavazos
Yi Zhang
Prateek Gupta
Vivek Khandelwal
Gaurav Shukla
Stephen Neuendorffer
Jae Hoon (Antonio) Kim
Nirvedh
Anup Gangwar
Henry Tu