As specified in the MLIR language reference and rationale documents, `memref`
types should not be allowed to have `index` as element types. As observed in
https://groups.google.com/a/tensorflow.org/forum/#!msg/mlir/P49hVWqTMNc/nW89a4i_AgAJ
this restriction was lifted when canonicalization unit tests for affine
operations were introduced, without sufficient motivation to lift the
restriction itself. The test in question can be trivially rewritten (return
the value from a function instead of storing it to prevent DCE from removing
the producer operation) and the restriction put back in place.
If `memref<...x index>` is relevant for some use cases, the relaxation of the
type system can be implemented separately with appropriate modifications to the
documentation.
PiperOrigin-RevId: 272607043
This also adds coverage with a missing test, which uncovered a bug in the conditional for testing whether an offset is dynamic or not.
PiperOrigin-RevId: 272505798
Similar to spv.loop, spv.selection is another op for modelling
SPIR-V structured control flow. It covers both OpBranchConditional
and OpSwitch with OpSelectionMerge.
Instead of having a `spv.SelectionMerge` op to directly model
selection merge instruction for indicating the merge target,
we use regions to delimit the boundary of the selection: the
merge target is the next op following the `spv.selection` op.
This way it's easier to discover all blocks belonging to
the selection and it plays nicer with the MLIR system.
PiperOrigin-RevId: 272475006
The concept-based polymorphism structure was missing an inheritance link
between the concept and the model. The interface class did not re-export the
base class constructor, which made it unusable with llvm::isa calls. Fix these
and reformat the code around.
PiperOrigin-RevId: 272452062
This is a follow-up to the PRtensorflow/mlir#146 which introduced the ROCDL Dialect. This PR introduces a pass to lower GPU Dialect to the ROCDL Dialect. As with the previous PR, this one builds on the work done by @whchung, and addresses most of the review comments in the original PR.
Closestensorflow/mlir#154
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/154 from deven-amd:deven-lower-gpu-to-rocdl 809893e08236da5ab6a38e3459692fa04247773d
PiperOrigin-RevId: 272390729
In SPIR-V we can have multiple symbols corresponding to the same
enum value. This is because when an extension is introduced into
the core spec, its suffix is typically removed, e.g., 'VulkanKHR'
memory model becomes 'Vulkan' memory model in SPIR-V 1.5.
Previously we just keep the first symbol for an enum value. That
symbol is not necessarily a better one. This CL changes to sort
symbols, grouped by enum values, alphabetically and then keep
the first one, which is typically shorter and without the extension
suffix. We also fix up certain ones like HlslSemanticGOOGLE.
PiperOrigin-RevId: 272290363
This exposes hooks for accessing internal dominance nodes, and updating the internal DFS numbers.
Closestensorflow/mlir#151
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/151 from schweitzpgi:dominance_hooks 69d14214a423b816cbd59feffcacdd02f3b5f921
PiperOrigin-RevId: 272287352
A recent ABI compatibility change affected the conversion from standard
CallOp/CallIndirectOp to LLVM::CallOp by changing its signature. In order to
analyze the signature, the code was looking up the callee symbol in the module.
This is incorrect since, during the conversion, the module may contain both the
original and the converted function op that have the same symbol name. There is
no strict guarantee on which of the two symbols will be found by the lookup.
The conversion was not failing because the type legalizer converts the LLVM
types to themselves making the original and the converted function signatures
ultimately produce the same type.
Instead of looking up the function signature to get the list of result types,
use the types of the CallOp/CallIndirectOp results which must match those of
the function in valid IR. These types are guaranteed to be the original,
unconverted types when converting the operation. Furthermore, this avoids the
need to perform a lookup of a symbol name in the module which may be expensive.
Finally, propagate attributes as-is from the original op to the converted op
since they share the attribute name for the callee of direct calls and the rest
of attributes are not affected by the conversion. This removes the need for
additional contorsions between direct and indirect calls to extract the name of
the optional callee attribute only to insert it back. This also prevents the
conversion from unintentionally dropping the other attributes of the op.
PiperOrigin-RevId: 272218871
This CL finishes the implementation of the Linalg + Affine type unification of the [strided memref RFC](https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/MaL8m2nXuio).
As a consequence, the !linalg.view type, linalg::DimOp, linalg::LoadOp and linalg::StoreOp can now disappear and Linalg can use standard types everywhere.
PiperOrigin-RevId: 272187165
Perform second reduce only with first warp. This requires an additional __sync_threads(), but doesn't need special handling when the last warp is small. This simplifies support for block sizes that are not multiple of 32.
Supporting partial warp reduce will be done in a separate CL.
PiperOrigin-RevId: 272168917
For the cases where there are multiple levels of nested pass managers, the parent thread ID is not enough to distinguish the parent of a given pass pipeline. Passing in the parent pass gives an exact anchor point.
PiperOrigin-RevId: 272105461
According to the SPIR-V spec:
"Length is the number of elements in the array. It must be at least 1."
Closestensorflow/mlir#160
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/160 from denis0x0D:sandbox/array_len 0840dc0986ad0088a3aa7d5d8d3e97d489377ed9
PiperOrigin-RevId: 272094669
Add DeclareOpInterfaceFunctions to enable specifying whether OpInterfaceMethods
for an OpInterface should be generated automatically. This avoids needing to
declare the extra methods, while also allowing adding function declaration by way of trait/inheritance.
Most of this change is mechanical/extracting classes to be reusable.
PiperOrigin-RevId: 272042739
The current implementation always uses string comparison, but if the operation is registered the AbstractOperation instance can be used to provide faster pointer comparison.
PiperOrigin-RevId: 272041333
This CL finishes the implementation of the lowering part of the [strided memref RFC](https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/MaL8m2nXuio).
Strided memrefs correspond conceptually to the following templated C++ struct:
```
template <typename Elem, size_t Rank>
struct {
Elem *ptr;
int64_t offset;
int64_t sizes[Rank];
int64_t strides[Rank];
};
```
The linearization procedure for address calculation for strided memrefs is the same as for linalg views:
`base_offset + SUM_i index_i * stride_i`.
The following CL will unify Linalg and Standard by removing !linalg.view in favor of strided memrefs.
PiperOrigin-RevId: 272033399
Add operations corresponding to OpLogicalAnd, OpLogicalNot,
OpLogicalEqual, OpLogicalNotEqual and OpLogicalOr instructions in
SPIR-V dialect. This needs changes to class hierarchy in SPIR-V
TableGen files to split SPIRVLogicalOp into SPIRVLogicalUnaryOp and
SPIRVLogicalBinaryOp. All derived classes of SPIRVLogicalOp are
updated accordingly.
Update the spirv dialect generation script to
1) Allow specifying base class to use for instruction spec generation
and file name to generate the specification in separately.
2) Use the existing descriptions for operations.
3) Update define_inst.sh to also invoke define_opcode.sh to also
define the corresponding SPIR-V instruction opcode enum.
PiperOrigin-RevId: 272014876
MemRefType::getStrides uses AffineExpr::walk which operates in post-order from the leaves. In order to compute strides properly, it needs to escape on terminal nodes and analyze binary ops only. This did not work for AffineExpr that consist of a single term (i.e. without a binary op).
This CL fixes the corner case and adds relevant tests.
PiperOrigin-RevId: 271975746
Use OpInterfaces to add an interface for ops defining a return type function.
This change does not use this trait in any meaningful way, I'll use it in a
follow up to generalize and unify some of the op type traits/constraints. Also,
currently the infer type function can only be manually specified in C++, that should rather be the fallback in future.
PiperOrigin-RevId: 271883746
- removing outdated/confusing info
- the affine dialect is missing documentation on
affine.load/affine.store; the references herein have to be updated
once that's updated.
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#159
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/159 from bondhugula:doc 86dd794f2d0d7fd097dde5764c62eb406ed4f910
PiperOrigin-RevId: 271876525
- fix missing return value syntax on call / splat ops
- reflow cond_br / store op syntax
Closestensorflow/mlir#161
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/161 from bondhugula:patch-1 2beb5bdcb387a5e7c52438985f79e2987d3b3ebe
PiperOrigin-RevId: 271876453
The generated build methods have result type before the arguments (operands and attributes, which are also now adjacent in the explicit create method). This also results in changing the create method's ordering to match most build method's ordering.
PiperOrigin-RevId: 271755054
This is more consistent with other dump methods. Otherwise successive Value dumps are concatenated in same line, hurting readability.
PiperOrigin-RevId: 271669846
- also remove stale terminology/references in docs
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#148
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/148 from bondhugula:cleanup e846b641a3c2936e874138aff480a23cdbf66591
PiperOrigin-RevId: 271618279
The strided MemRef RFC discusses a normalized descriptor and interaction with library calls (https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/MaL8m2nXuio).
Lowering of nested LLVM structs as value types does not play nicely with externally compiled C/C++ functions due to ABI issues.
Solving the ABI problem generally is a very complex problem and most likely involves taking
a dependence on clang that we do not want atm.
A simple workaround is to pass pointers to memref descriptors at function boundaries, which this CL implement.
PiperOrigin-RevId: 271591708
linalg_integration_test.mlir and simple.mlir were temporarily disabled due to an OSS-only failure.
The issue is that, once created, an llvm::Error must be explicitly checked before it can be discarded or overwritten.
This CL fixes the issue and reenable the test.
PiperOrigin-RevId: 271589651
This commit introduces the ROCDL Dialect (i.e. the ROCDL ops + the code to lower those ROCDL ops to LLWM intrinsics/functions). Think of ROCDL Dialect as analogous to the NVVM Dialect, but for AMD GPUs. This patch contains just the essentials needed to get a simple example up and running. We expect to make further additions to the ROCDL Dialect.
This is the first of 3 commits, the follow-up will be:
* add a pass that lowers GPU Dialect to ROCDL Dialect
* add a "mlir-rocm-runner" utility
Closestensorflow/mlir#146
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/146 from deven-amd:deven-rocdl-dialect e78e8005c75a78912631116c78dc844fcc4b0de9
PiperOrigin-RevId: 271511259
This CL modifies the linalg-fusion pass such that it does not tile anymore as part of the pass. Tiling is a separate concern that enables linalg fusion but should happen before.
This makes fusion more composable with other decisions.
In particular the fusion pass now becomes greedy and only applies the transformation on a best-effort basis.
This should also let fusion work in a multi-hop fashion with chains of producer/consumers.
Since the fusion pass does not perform tiling anymore, tests are rewritten to be in pretiled form and make the intent of the test clearer (albeit more verbose).
PiperOrigin-RevId: 271357741
The support for functions taking and returning memrefs of floats was introduced
in the first version of the runner, created before MLIR had reliable lowering
of allocation/deallocation to library calls. It forcibly runs MLIR
transformation convering affine, loop and standard dialects into the LLVM
dialect, unlike the other runner flows that accept the LLVM dialect directly.
Memref support leads to more complex layering and is generally fragile. Drop
it in favor of functions returning a scalar, or library-based function calls to
print memrefs and other data structures.
PiperOrigin-RevId: 271330839
The reduction operation is currently fixed to "add", and the scope is fixed to "workgroup".
The implementation is currently limited to sizes that are multiple 32 (warp size) and no larger than 1024.
PiperOrigin-RevId: 271290265
Alex Zinenko