* [MHLO] Support for dynamic shape in basic op conversion by introducing CHLO dialect
Co-authored-by: Bairen Yi <yibairen.byron@bytedance.com>
Co-authored-by: Jiawei Wu <xremold@gmail.com>
Co-authored-by: Tianyou Guo <tianyou.gty@alibaba-inc.com>
Co-authored-by: Xu Yan <yancey.yx@alibaba-inc.com>
Co-authored-by: Ziheng Jiang <ziheng.jiang@bytedance.com>
* [MHLO] Support I32 as shape tensor dtype
* [NFC] Add a 'TODO' annotation
This commit fixes the shape calculation for:
1.) aten.mean.dim
2.) aten.var.dim
3.) aten.sum.dim_IntList op
Also, it fixes the lowering of `aten.mean.dim` and
`aten.sum.dim_IntList` for handling the cases of empty dim list.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com
- Includes a canonicalizer for `aten.add.t`needed for successfully lowering the shape function
- Only offers support for statically sized index tensors when there is more than one
- Dynamic shape support remains for single indexing tensors
This commit adds verifiers to the ops `ToBuiltinTensorOp` and
`FromBuiltinTensorOp` that make sure that the input and output have
the same shape and data type.
An upstream MLIR bug (that was recently fixed) caused the result to be
ignored for Region- and Block-visitor functions. Now that the bug is
fixed, we don't need an auxiliary boolean to track whether the visitor
function has succeeded.
The biggest change here is to upgrade RefineTypes to the new sparse
dataflow framework.
Smaller changes:
- minor changes to type parsing
- suppress warnings in e2e tests
This commit adds the decomposition for `aten.var.dim` op.
This commit also make changes in the decomposition for `aten.var` op.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
This patch adds a new pass `torch-verify-conversion-to-value-semantics`,
which looks for non-value semantics tensors to catch such tensors early
during compilation.
This pass requires `torch-refine-public-return` pass to ensure that
return operations are updated to use value tensors, followed by the
canonicalize pass to remove any dead ops that may use or produce
non-value tensors.
Prior to this patch, the canonicalizers for `AtenSizeOp` and
`AtenSizeIntOp` succeeded only if the tensor operand's type information
included the size of the requested dimension(s). We can extend the set
of optimizable cases by propagating types across operations whose result
type matches the input tensor type.
Specifically, this patch enables the canonicalizers for `AtenSizeOp` and
`AtenSizeIntOp` to see past `tensor_static_info_cast`,
`copy.to_vtensor`, and `copy.to_tensor` ops until it reaches the first
op whose result type contains size information for the requested
dimensions, with a maximum bound of 6 parent lookups to avoid indefinite
compilation times. All other encountered ops cause the canonicalizer to
give up.
Prior to this patch, the code in the `torch-simplify-shape-calculations`
pass iterated on the uses of an op's result while also modifying the
value. This caused the iterator to get invalidated, thus terminating
the loop early and producing incorrect IR. This patch makes use of
`llvm::make_early_inc_range()` to ensure that the iterator is not
invalidated while executing the loop body.
This commit does three things:
1. Reverts some of the shape lib changes merged in
https://github.com/llvm/torch-mlir/pull/844
2. Updates the signature of `aten.sum_dim_IntList` that was recently
updated in
23bdb570cf
3. Replaces `aten.zero.functional` with `aten.zero`, updated in 960758b0b7
`aten.select_scatter` op.
This commit adds:
1. Lowering of `aten.slice_scatter` op into `tensor.insert_slice`
op.
2. Decomposes the `aten.select_scatter` op into `aten.slice_scater`
op.
Signed-Off-By: Prateek Gupta <gprateek93@gmail.com>
The canonicalizer converts `torch.prim.dtype` ops into integer constants
for valid types, but the type may not be known until type refinement is
complete. However, type refinement cannot make progress until
`torch.prim.dtype` ops have been resolved to their corresponding integer
constants, thus creating a circular dependency.
This patch creates a tight coupling between type refinement and the
lowering of `torch.prim.dtype` ops by handling such ops as they are
encountered during type refinement. The unit test in this patch aims to
check whether the type refinement pass can now handle chains of
operations that alternate between type construction and type refinement.
This patch replaces the use of raw integers like 6, 4, etc. (that
represent PyTorch's scalar types) with named values from the ScalarType
enum (e.g. `ScalarType::Float`, `ScalarType::Long`, etc.) in code for
folding `prim.dtype` ops into numeric constants.
This patch isn't strictly a non-functional change, since its use of
`Torch::getScalarTypeForType()` implies that the input type has to be
one among the supported types, otherwise compilation will abort, whereas
previously, compilation proceeded without folding the unsupported data
type into a numeric constant.
A prior patch (63538de2) that added support for bfloat16 type did not
add the canonicalization pattern to fold `torch.prim.dtype` operations
on bfloat16 tensors into the integer constant 15. This patch fixes the
problem.
In the `pyhpc_turbulent_kinetic_energy` TorchBench benchmark, the shape
calculation occurs inside loops, but because `DropShapeCalculationsPass`
does not explicitly mark the Torch dialect as legal, the pass execution
fails.
This patch adds Torch to the list of legal dialects, and adds a test to
validate the translation.
This commit fixes the shape function for `index.Tensor`, adding
support for multiple index tensors and `None`s in the indices
list. This commit also adds support for input tensors of rank greater
than 1. The lowering for `index.Tensor` still has the the limitation
that only a single index tensor along the first dimension of the input
tensor is supported.
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.
* [MLIR][TORCH] Add folder for torch_c.from_i64 & torch_c.to_i64
* add unit tests for each individual fold
* fix failure of NumelZeroRankModule & TestMultipleTensorAndPrimitiveTypesReturn
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.
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.
Now that upstream exposes them nicely, we can use them.
I noticed that we had added stuff into the upstream_shape_helpers.py
file (which was supposed to stay pristine), so some more shape functions
need to be upstreamed.
Going forward, all shape functions should be upstreamed similar to
https://github.com/pytorch/pytorch/pull/76889 instead of added in this
file.
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 decomposes `aten.baddbmm` op into `aten.bmm`,
`aten.mul.Scalar`, and `aten.add.Tensor` op.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
Ramiro Leal-Cavazos
Quinn Dawkins
武家伟
Vidush Singhal
Alec
PhaneeshB
Vivek Khandelwal
Kevin Kiningham
Sean Silva
Ziheng Jiang
Ashay Rane
Prateek Gupta
George Petterson
Quinn Dawkins
Tanyo Kwok
erman-gurses
Maksim Levental
Bob Adolf