Now that linalg.view and strided memrefs are unified, there is no reason to
disallow AllocOp in alias analysis. This CLs adds support for AllocOp which allows writing shorter tests that do not require explicitly creating a view for
each operation.
PiperOrigin-RevId: 273303060
Add new `typeDescription` (description was already used by base constraint class) field to type to allow writing longer descriptions about a type being defined. This allows for providing additional information/rationale for a defined type. This currently uses `description` as the heading/name for the type in the generated documentation.
PiperOrigin-RevId: 273299332
See RFC: https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/xE2IzfhE3Wg.
Opaque location stores two pointers, one of them points to some data structure that is external to MLIR, and the other one is unique for each type and represents type id of that data structure. OpaqueLoc also stores an optional location that can be used if the first one is not suitable.
OpaqueLoc is managed similar to FileLineColLoc. It is passed around by MLIR transformations and can be used in compound locations like CallSiteLoc.
PiperOrigin-RevId: 273266510
This allows confirming that a scalar argument has the same element type as a shaped one. It's easy to validate a type is shaped on its own if that's desirable, so this shouldn't make that use case harder. This matches the behavior of other traits that operate on element type (e.g. AllElementTypesMatch). Also this makes the code simpler because now we just use getElementTypeOrSelf.
Verified that all uses in core already check the type is shaped in another way.
PiperOrigin-RevId: 273068507
Use `getParentOfType<FunctionOp>()` instead of `cast<FuncOp>(getParentOp())`
to avoid crash when return ops are used inside spv.selection/spv.loop.
PiperOrigin-RevId: 273006041
Adding support for OpUndef instruction. Updating the dialect
generation script to fix a few bugs in the instruction spec
generation.
PiperOrigin-RevId: 272975685
Add builder functions for spv._address_of, spv.EntryPoint,
spv.ExecutionMode and spv.Load to make it easier to create these
operations.
Fix a minor bug in printing of spv.EntryPoint
Add a utility function to get the attribute name associated with a
decoration.
PiperOrigin-RevId: 272952846
Certain lowering patterns were reported as [missing](https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/dkdmHa77sSQ).
This CL adds them and allows Linalg/roundtrip.mlir and Linalg/loops.mlir to lower to LLVM directly. Those 2 tests are updated to additionally check that the direct lowering to LLVM does not crash.
The following points, left as TODOs still need to be addressed for correct end-to-end execution:
1. the lowering for ConvOp needs to pass attributes such as strides and dilations; the external library call needs to support it.
2. the lowering for GenericOp needs to support lowering to loops as a DialectConversion pattern. This is blocked on the DialectConversion infrastructure accepting an OperationFolder.
PiperOrigin-RevId: 272878131
The GPUIndexIntrinsicOpLowering template is currently used by the code in both the GPUToNVVM and GPUToROCDL dirs.
Moving it to a common location to remove code duplication.
Closestensorflow/mlir#163
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/163 from deven-amd:deven-refactor-gpu-index-ops-lowering b8dc2a5f5353df196039b6ff2ad42106028693ed
PiperOrigin-RevId: 272863297
Some dialects have implicit conversions inherent in their modeling, meaning that a call may have a different type that the type that the callable expects. To support this, a hook is added to the dialect interface that allows for materializing conversion operations during inlining when there is a mismatch. A hook is also added to the callable interface to allow for introspecting the expected result types.
PiperOrigin-RevId: 272814379
This allows for the inliner to work on arbitrary call operations. The updated inliner will also work bottom-up through the callgraph enabling support for multiple levels of inlining.
PiperOrigin-RevId: 272813876
The first dim length of the axisStats attribute should equals to the slice size
of the input argument when splitted by the axis dimension.
PiperOrigin-RevId: 272798042
This CL implements the last remaining bit of the [strided memref proposal](https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/MaL8m2nXuio).
The syntax is a bit more explicit than what was originally proposed and resembles:
`memref<?x?xf32, offset: 0 strides: [?, 1]>`
Nonnegative strides and offsets are currently supported. Future extensions will include negative strides.
This also gives a concrete example of syntactic sugar for the ([RFC] Proposed Changes to MemRef and Tensor MLIR Types)[https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/-wKHANzDNTg].
The underlying implementation still uses AffineMap layout.
PiperOrigin-RevId: 272717437
Module names are optional so it makes more sense to take and return an optional
any time the name is involved. Also update the language reference to reflect
the module names.
PiperOrigin-RevId: 272684698
Modules are now Ops and, as such, can be nested. They do not produce an SSA
value so there is no possibility to refer to them in the IR. Introduce support
for symbol names attached to the module Op so that it can be referred to using
SymbolRefAttrs. The name is optional, for example the implicit top-level module
does not have a name.
PiperOrigin-RevId: 272671600
This makes the name of the conversion pass more consistent with the naming
scheme, since it actually converts from the Loop dialect to the Standard
dialect rather than working with arbitrary control flow operations.
PiperOrigin-RevId: 272612112
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
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
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
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
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
Support the OpBitcast instruction of SPIR-V using the spv.Bitcast
operation. The semantics implemented in the dialect differ from the
SPIR-V spec in that the dialect does not allow conversion to/from
pointer types from/to non-pointer types.
PiperOrigin-RevId: 271255957
1) Process and ignore the following debug instructions: OpSource,
OpSourceContinued, OpSourceExtension, OpString, OpModuleProcessed.
2) While processing OpTypeInt instruction, ignore the signedness
specification. Currently MLIR doesnt make a distinction between signed
and unsigned integer types.
3) Process and ignore BufferBlock decoration (similar to Buffer
decoration). StructType needs to be enhanced to track this attribute
since its needed for proper validation checks.
4) Report better error for unhandled instruction during
deserialization.
PiperOrigin-RevId: 271057060
- introduce splat op in standard dialect (currently for int/float/index input
type, output type can be vector or statically shaped tensor)
- implement LLVM lowering (when result type is 1-d vector)
- add constant folding hook for it
- while on Ops.cpp, fix some stale names
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#141
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/141 from bondhugula:splat 48976a6aa0a75be6d91187db6418de989e03eb51
PiperOrigin-RevId: 270965304
The RFC for unifying Linalg and Affine compilation passes into an end-to-end flow with a predictable ABI and linkage to external function calls raised the question of why we have variable sized descriptors for memrefs depending on whether they have static or dynamic dimensions (https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/MaL8m2nXuio).
This CL standardizes the ABI on the rank of the memrefs.
The LLVM struct for a memref becomes equivalent to:
```
template <typename Elem, size_t Rank>
struct {
Elem *ptr;
int64_t sizes[Rank];
};
```
PiperOrigin-RevId: 270947276
According to SPIR-V spec, spirv::CompositeType includes
spirv::RuntimeArrayType. This allows using objects of
spirv::RuntimeArrayType with spirv::AccessChainOp.
PiperOrigin-RevId: 270809492
Similar to mlir-opt, having a -split-input-file mode is quite useful
in mlir-translate. It allows to put logically related tests in the
same test file for better organization.
PiperOrigin-RevId: 270805467
Sdd support in deserializer for OpMemberName instruction. For now
the name is just processed and not associated with the
spirv::StructType being built. That needs an enhancement to
spirv::StructTypes itself.
Add tests to check for errors reported during deserialization with
some refactoring to common out some utility functions.
PiperOrigin-RevId: 270794524
The existing logic to parse spirv::StructTypes is very brittle. This
change simplifies the parsing logic a lot. The simplification also
allows for memberdecorations to be separated by commas instead of
spaces (which was an artifact of the existing parsing logic). The
change also needs a modification to mlir::parseType to return the
number of chars parsed. Adding a new parseType method to do so.
Also allow specification of spirv::StructType with no members.
PiperOrigin-RevId: 270739672
Using the two call interfaces, CallOpInterface and CallableOpInterface, this change adds support for an initial multi-level CallGraph. This call graph builds a set of nodes for each callable region, and connects them via edges. An edge may be any of the following types:
* Abstract
- An edge not produced by a call operation, used for connecting to internal nodes from external nodes.
* Call
- A call edge is an edge defined via a call-like operation.
* Child
- This is an artificial edge connecting nested callgraph nodes.
This callgraph will be used, and improved upon, to begin supporting more interesting interprocedural analyses and transformation. In a followup, this callgraph will be used to support more complex inlining support.
PiperOrigin-RevId: 270724968
These two operation interfaces will be used in a followup to support building a callgraph:
* CallOpInterface
- Operations providing this interface are call-like, and have a "call" target. A call target may be a symbol reference, via SymbolRefAttr, or a SSA value.
* CallableOpInterface
- Operations providing this interfaces define destinations to call-like operations, e.g. FuncOp. These operations may define any number of callable regions.
PiperOrigin-RevId: 270723300
This fixes a problem with current save-restore pattern of diagnostics handlers, as there may be a thread race between when the previous handler is destroyed. For example, this occurs when using multiple ParallelDiagnosticHandlers asynchronously:
Handler A
Handler B | - LifeTime - | Restore A here.
Handler C | --- LifeTime ---| Restore B after it has been destroyed.
The new design allows for multiple handlers to be registered in a stack like fashion. Handlers can return success() to signal that they have fully processed a diagnostic, or failure to propagate otherwise.
PiperOrigin-RevId: 270720625
Roll forward of commit 5684a12.
When outlining GPU kernels, put the kernel function inside a nested module. Then use a nested pipeline to generate the cubins, independently per kernel. In a final pass, move the cubins back to the parent module.
PiperOrigin-RevId: 270639748
This adds sign- and zero-extension and truncation of integer types to the
standard dialects. This allows to perform integer type conversions without
having to go to the LLVM dialect and introduce custom type casts (between
standard and LLVM integer types).
Closestensorflow/mlir#134
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/134 from ombre5733:sext-zext-trunc-in-std c7657bc84c0ca66b304e53ec03797e09152e4d31
PiperOrigin-RevId: 270479722
- fix store to load forwarding for a certain set of cases (where
forwarding shouldn't have happened); use AffineValueMap difference
based MemRefAccess equality checking; utility logic is also greatly
simplified
- add missing equality/inequality operators for AffineExpr ==/!= ints
- add == != operators on MemRefAccess
Closestensorflow/mlir#136
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/136 from bondhugula:store-load-forwarding d79fd1add8bcfbd9fa71d841a6a9905340dcd792
PiperOrigin-RevId: 270457011
Make GlobalOp's value attribute an OptionalAttr. Change code that uses the value to handle 'nullopt'. Translate an unitialized value attribute to llvm::UndefValue.
PiperOrigin-RevId: 270423646
computeDepth calls itself recursively, which may insert into minPatternDepth. minPatternDepth is a DenseMap, which invalidates iterators on insertion, so this may lead to asan failures.
PiperOrigin-RevId: 270374203
The RFC for unifying Linalg and Affine compilation passes into an end-to-end flow discusses the notion of a strided MemRef (https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/MaL8m2nXuio).
This CL adds helper functions to extract strides from the layout map which in turn will allow converting between a strided form of the type and a layout map.
For now strides are only computed on a single affine map with a single result (i.e. the closed subset of linearization maps that are compatible with striding semantics). This restriction will be reevaluated / lifted in the future based on concrete use cases.
PiperOrigin-RevId: 270284686
Allow specification of decorators on SPIR-V StructType members. If the
struct has layout information, these decorations are to be specified
after the offset specification of the member. These decorations are
emitted as OpMemberDecorate instructions on the struct <id>. Update
(de)serialization to handle these decorations.
PiperOrigin-RevId: 270130136
A new converter with per axis quantization parameters is added to quantize a
dense elements attribute. For each slice along the quantization axis, it
creates an uniform quantized value converter, with different scale and zero
point, and quantizes the values in the slice.
The current implementation doesn't handle sparse elements attributes.
PiperOrigin-RevId: 270121986
When outlining GPU kernels, put the kernel function inside a nested module. Then use a nested pipeline to generate the cubins, independently per kernel. In a final pass, move the cubins back to the parent module.
PiperOrigin-RevId: 269987720
This modifies DominanceInfo::properlyDominates(Value *value, Operation *op) to return false if the value is defined by a parent operation of 'op'. This prevents using values defined by the parent operation from within any child regions.
PiperOrigin-RevId: 269934920
- allow symbols in index remapping provided for memref replacement
- fix memref normalize crash on cases with layout maps with symbols
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Reported by: Alex Zinenko
Closestensorflow/mlir#139
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/139 from bondhugula:memref-rep-symbols 2f48c1fdb5d4c58915bbddbd9f07b18541819233
PiperOrigin-RevId: 269851182
Introduce support for applying the stripe operator to sum expressions, as in
(x + A) # B = x + A - (x + A) mod B.
This is required to represent a combination of tiling and padding in the SDBM
framework, and is a valid SDBM construct that was not originally supported.
PiperOrigin-RevId: 269758807
Extend SDBM simplification patterns to support more cases where the addition of
two expressions each involving one or two variables would result in a sum
expression that only contains one variable and thus remains in the SDBM domain.
This is made possible by the new canonical structure of SDBM where the constant
term appears once. This simplification will be necessary to support
round-tripping of stripe expressions containing constant terms on the LHS
through affine expressions.
PiperOrigin-RevId: 269757732
This is useful in several cases, for example a user may want to sugar the syntax of a string(as we do with custom operation syntax), or avoid many nested ifs for parsing a set of known keywords.
PiperOrigin-RevId: 269695451
This CL registers a new mlir-translate hook, -test-spirv-roundtrip,
for testing SPIR-V serialization and deserialization round-trip.
This CL also moves the existing -serialize-spirv and
-deserialize-spirv hooks to one source file.
PiperOrigin-RevId: 269659528
Existing translations are either from MLIR or to MLIR. To support
cases like round-tripping some external format via MLIR, one must
chain two mlir-translate invocations together using pipes. This
can be problematic to support -split-input-file in mlir-translate
given that it won't work across pipes.
Motivated by the above, this CL adds another translation category
that allows file to file. This gives users more freedom.
PiperOrigin-RevId: 269636438
This CL changes translation functions to take MemoryBuffer
as input and raw_ostream as output. It is generally better to
avoid handling files directly in a library (unless the library
is specifically for file manipulation) and we can unify all
file handling to the mlir-translate binary itself.
PiperOrigin-RevId: 269625911
- add canonicalization pattern to compose maps into affine loads/stores;
templatize the pattern and reuse it for affine.apply as well
- rename getIndices -> getMapOperands() (getIndices is confusing since
these are no longer the indices themselves but operands to the map
whose results are the indices). This also makes the accessor uniform
across affine.apply/load/store. Change arg names on the affine
load/store builder to avoid confusion. Drop an unused confusing build
method on AffineStoreOp.
- update incomplete doc comment for canonicalizeMapAndOperands (this was
missed from a previous update).
Addresses issue tensorflow/mlir#121
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#122
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/122 from bondhugula:compose-load-store e71de1771e56a85c4282c10cb43f30cef0701c4f
PiperOrigin-RevId: 269619540
A generic mechanism for (de)serialization of extended instruction sets
is added with this CL. To facilitate this, a new class
"SPV_ExtendedInstSetOp" is added which is a base class for all
operations corresponding to extended instruction sets. The methods to
(de)serialization such ops as well as its dispatch is generated
automatically.
The behavior controlled by autogenSerialization and hasOpcode is also
slightly modified to enable this. They are now decoupled.
1) Setting hasOpcode=1 means the operation has a corresponding
opcode in SPIR-V binary format, and its dispatch for
(de)serialization is automatically generated.
2) Setting autogenSerialization=1 generates the function for
(de)serialization automatically.
So now it is possible to have hasOpcode=0 and autogenSerialization=1
(for example SPV_ExtendedInstSetOp).
Since the dispatch functions is also auto-generated, the input file
needs to contain all operations. To this effect, SPIRVGLSLOps.td is
included into SPIRVOps.td. This makes the previously added
SPIRVGLSLOps.h and SPIRVGLSLOps.cpp unnecessary, and are deleted.
The SPIRVUtilsGen.cpp is also changed to make better use of
formatv,making the code more readable.
PiperOrigin-RevId: 269456263
When performing A->B->C conversion, an operation may still refer to an operand of A. This makes it necessary to unmap through multiple levels of replacement for a specific value.
PiperOrigin-RevId: 269367859
Certain enum classes in SPIR-V, like function/loop control and memory
access, are bitmasks. This CL introduces a BitEnumAttr to properly
model this and drive auto-generation of verification code and utility
functions. We still store the attribute using an 32-bit IntegerAttr
for minimal memory footprint and easy (de)serialization. But utility
conversion functions are adjusted to inspect each bit and generate
"|"-concatenated strings for the bits; vice versa.
Each such enum class has a "None" case that means no bit is set. We
need special handling for "None". Because of this, the logic is not
general anymore. So right now the definition is placed in the SPIR-V
dialect. If later this turns out to be useful for other dialects,
then we can see how to properly adjust it and move to OpBase.td.
Added tests for SPV_MemoryAccess to check and demonstrate.
PiperOrigin-RevId: 269350620
Swap the allowed nesting of sum and diff expressions: now a diff expression can
contain a sum expression, but only on the left hand side. A difference of two
expressions sum must be canonicalized by grouping their constant terms in a
single expression. This change of sturcture became possible thanks to the
introduction of the "direct" super-kind. It is necessary to enable support of
sum expressions on the left hand side of the stripe expression.
SDBM expressions are now grouped into the following structure
- expression
- varying
- direct
- sum <- (term, constant)
- term
- symbol
- dimension
- stripe <- (term, constant)
- negation <- (direct)
- difference <- (direct, term)
- constant
The notation <- (...) denotes the types of subexpressions a compound
expression can combine.
PiperOrigin-RevId: 269337222
The helper functions makePositionAttr() and positionAttr() were originally
introduced in the lowering-to-LLVM-dialect pass to construct integer array
attributes that are used for static positions in extract/insertelement.
Constructing an integer array attribute being fairly common, a utility function
Builder::getI64ArrayAttr was later introduced into the Builder API. Drop
makePositionAttr and similar homegrown functions and use that API instead.
PiperOrigin-RevId: 269295836
Add support for specifying extended instructions sets. The operations
in SPIR-V dialect are named as 'spv.<extension-name>.<op-name>'. Use
this mechanism to define a 'Exp' operation from GLSL(450)
instructions.
Later CLs will add support for (de)serialization of these operations,
and update the dialect generation scripts to auto-generate the
specification using the spec directly.
Additional changes:
Add a Type Constraint to OpBase.td to check for vector of specified
lengths. This is used to check that the vector type used in SPIR-V
dialect are of lengths 2, 3 or 4.
Update SPIRVBase.td to use this Type constraints for vectors.
PiperOrigin-RevId: 269234377
OperationPass' are defined exactly the same way as they are now:
class DerivedPass : public OperationPass<DerivedPass>;
OpPass' are now defined as OperationPass, but with an additional template parameter for the operation type:
class DerivedPass : public OperationPass<DerivedPass, FuncOp>;
PiperOrigin-RevId: 269122410
- turn copy/dma generation method into a utility in LoopUtils, allowing
it to be reused elsewhere.
- no functional/logic change to the pass/utility
- trim down header includes in files affected
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#124
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/124 from bondhugula:datacopy 9f346e62e5bd9dd1986720a30a35f302eb4d3252
PiperOrigin-RevId: 269106088
- take care of symbolic operands with alloc
- add missing check for compose map failure and a test case
- add test cases on strides
- drop incorrect check for one-to-one'ness
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#132
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/132 from bondhugula:normalize-memrefs 8aebf285fb0d7c19269d85255aed644657e327b7
PiperOrigin-RevId: 269105947
- NFC - on any pass/utility logic/output.
- Resolve TODO; the method building loop trip count maps was
creating and deleting affine.apply ops (transforming IR from under
analysis!, strictly speaking). Introduce AffineValueMap::difference to
do this correctly (without the need to create any IR).
- Move AffineApplyNormalizer out so that its methods are reusable from
AffineStructures.cpp; add a helper method 'normalize' to it. Fix
AffineApplyNormalize::renumberOneDim (Issue tensorflow/mlir#89).
- Trim includes on files touched.
- add test case on a scenario previously not covered
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#133
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/133 from bondhugula:trip-count-build 7fc34d857f7788f98b641792cafad6f5bd50e47b
PiperOrigin-RevId: 269101118
- add missing canonicalization pattern to fold memref_cast + dim to
dim (needed to propagate constant when folding a dynamic shape to
a static one)
- also fix an outdated/inconsistent comment in StandardOps/Ops.td
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#126
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/126 from bondhugula:quickfix 4566e75e49685c532faffff91d64c5d83d4da524
PiperOrigin-RevId: 269020058
This allows for users other than those on the command line to apply a textual description of a pipeline to a given pass manager.
PiperOrigin-RevId: 269017028
SPIR-V recently publishes v1.5, which brings a bunch of symbols
into core. So the suffix "KHR"/"EXT"/etc. is removed from the
symbols. We use a script to pull information from the spec
directly.
Also changed conversion and tests to use GLSL450 instead of
VulkanKHR memory model. GLSL450 is still the main memory model
supported by Vulkan shaders and it does not require extra
capability to enable.
PiperOrigin-RevId: 268992661
This allows for explicitly specifying the pipeline to add to the pass manager. This includes the nesting structure, as well as the passes/pipelines to run. A textual pipeline string is defined as a series of names, each of which may in itself recursively contain a nested pipeline description. A name is either the name of a registered pass, or pass pipeline, (e.g. "cse") or the name of an operation type (e.g. "func").
For example, the following pipeline:
$ mlir-opt foo.mlir -cse -canonicalize -lower-to-llvm
Could now be specified as:
$ mlir-opt foo.mlir -pass-pipeline='func(cse, canonicalize), lower-to-llvm'
This will allow for running pipelines on nested operations, like say spirv modules. This does not remove any of the current functionality, and in fact can be used in unison. The new option is available via 'pass-pipeline'.
PiperOrigin-RevId: 268954279
This CL adds support for serializing and deserializing spv.loop ops.
This adds support for spv.Branch and spv.BranchConditional op
(de)serialization, too, because they are needed for spv.loop.
PiperOrigin-RevId: 268536962
This better reflects how this kind of expressions is used and avoids the
potential confusion since the expression can take negative values. Term
expressions comprise dimensions, symbols and stripe expressions. In an SDBM
domain, a stripe expression always corresponds to a variable, input or
temporary. This expression can appear anywhere an input variable can,
including on the LHS of other stripe expressions.
PiperOrigin-RevId: 268486066
If the composite is a constant, we can fold it away. This only
supports vector and array constants for now, given that struct
constant is not supported in spv.constant yet.
PiperOrigin-RevId: 268350340
Since we apply nudging for the zero point to make sure the nudged zerop points
can be in the range of [qmin, qmax], the constraint that rmin / rmax should
stride zero isn't necessary.
This also matches the documentation of tensorflow's FakeQuantWithMinMaxArgs op,
where min and max don't need to stride zero:
https://www.tensorflow.org/api_docs/python/tf/quantization/fake_quant_with_min_max_args
PiperOrigin-RevId: 268296285
* Add GraphTraits that treat a block as a graph, Operation* as node and use-relationship for edges;
- Just basic graph output;
* Add use iterator to iterate over all uses of an Operation;
* Add testing pass to generate op graph;
This does not support arbitrary operations other than function nor nested regions yet.
PiperOrigin-RevId: 268121782
For per channel fake quant attributes, the returned type should be
UniformQuantizedPerAxisType. Currently, this method isn't under test because we
haven't added the quant_ConstFakeQuantPerAxis op and the convert method.
PiperOrigin-RevId: 268084017
Some compilers will try to auto-generate the destructor, instead of using the user provided destructor, when creating a default move constructor.
PiperOrigin-RevId: 268067367
This allows for parallelizing across pipelines of multiple operation types. AdaptorPasses can now hold pass managers for multiple operation types and will dispatch based upon the operation being operated on.
PiperOrigin-RevId: 268017344
This method parses an operation in its generic form, from the current parser
state. This is the symmetric of OpAsmPrinter::printGenericOp(). An immediate
use case is illustrated in the test dialect, where an operation wraps another
one in its region and makes use of a single-line pretty-print form.
PiperOrigin-RevId: 267930869
This is done via a new set of instrumentation hooks runBeforePipeline/runAfterPipeline, that signal the lifetime of a pass pipeline on a specific operation type. These hooks also provide the parent thread of the pipeline, allowing for accurate merging of timers running on different threads.
PiperOrigin-RevId: 267909193
This will allow clients to implement a different collection strategy on these
values, including collecting each uses within the region for example.
PiperOrigin-RevId: 267803978
- the JIT codegen was being run at the default -O0 level; instead,
propagate the opt level from the cmd line.
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#123
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/123 from bondhugula:jit-runner 3b055e47f94c9a48bf487f6400787478738cda02
PiperOrigin-RevId: 267778586
The current restrictions on dim/symbols require a top-level symbol for the conservative case of a non-affine region. This should be relaxed in the future.
PiperOrigin-RevId: 267641838
View descriptors are converted to *pointer to* LLVM struct to avoid ABI issues related to C struct packing. This creates unnecessary complexity and hampers unification with memrefs.
Instead, this CL makes view descriptors convert to LLVM struct (as it was originally) and promotes all structs to pointers right before calling an external function.
PiperOrigin-RevId: 267602693
- turn canonicalizeMapAndOperands into a template that works on both
sets and maps, and use it to introduce a utility to canonicalize an
affine integer set and its operands
- add pattern to canonicalize affine if op's.
- rename IntegerSet::getNumOperands -> IntegerSet::getNumInputs to be
consistent with AffineMap
- add missing accessors for IntegerSet
Doesn't need extensive testing since canonicalizeSetAndOperands just
reuses canonicalizeMapAndOperands' logic, and the latter is tested on
affine.apply map + operands; the new method works the same way on an
integer set + operands of an affine if op for example.
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#112
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/112 from bondhugula:set-canonicalize eff72f23250b96fa7d9f5caff3877440f5de2cec
PiperOrigin-RevId: 267532876
This commit defines an initial implementation of the DialectInlinerInterface for the AffineOps dialect. This change allows for affine operations to be inlined into any region that is not an affine region. Inlining into affine regions requires special handling for dimension/symbol identifiers that will be added in followups.
PiperOrigin-RevId: 267467078
SPIR-V can explicitly declare structured control-flow constructs using merge
instructions. These explicitly declare a header block before the control
flow diverges and a merge block where control flow subsequently converges.
These blocks delimit constructs that must nest, and can only be entered
and exited in structured ways.
Instead of having a `spv.LoopMerge` op to directly model loop merge
instruction for indicating the merge and continue target, we use regions
to delimit the boundary of the loop: the merge target is the next op
following the `spv.loop` op and the continue target is the block that
has a back-edge pointing to the entry block inside the `spv.loop`'s region.
This way it's easier to discover all blocks belonging to a construct and
it plays nicer with the MLIR system.
Updated the SPIR-V.md doc.
PiperOrigin-RevId: 267431010
This defines a set of initial utilities for inlining a region(or a FuncOp), and defines a simple inliner pass for testing purposes.
A new dialect interface is defined, DialectInlinerInterface, that allows for dialects to override hooks controlling inlining legality. The interface currently provides the following hooks, but these are just premilinary and should be changed/added to/modified as necessary:
* isLegalToInline
- Determine if a region can be inlined into one of this dialect, *or* if an operation of this dialect can be inlined into a given region.
* shouldAnalyzeRecursively
- Determine if an operation with regions should be analyzed recursively for legality. This allows for child operations to be closed off from the legality checks for operations like lambdas.
* handleTerminator
- Process a terminator that has been inlined.
This cl adds support for inlining StandardOps, but other dialects will be added in followups as necessary.
PiperOrigin-RevId: 267426759
The refactoring of ExecutionEngine dropped the usage of the irTransform function used to pass -O3 and other options to LLVM. As a consequence, the proper optimizations do not kick in in LLMV-land.
This CL makes use of the transform function and allows producing avx512 instructions, on an internal example, when using:
`mlir-cpu-runner -dump-object-file=1 -object-filename=foo.o` combined with `objdump -D foo.o`.
Assembly produced resembles:
```
2b2e: 62 72 7d 48 18 04 0e vbroadcastss (%rsi,%rcx,1),%zmm8
2b35: 62 71 7c 48 28 ce vmovaps %zmm6,%zmm9
2b3b: 62 72 3d 48 a8 c9 vfmadd213ps %zmm1,%zmm8,%zmm9
2b41: 62 f1 7c 48 28 cf vmovaps %zmm7,%zmm1
2b47: 62 f2 3d 48 a8 c8 vfmadd213ps %zmm0,%zmm8,%zmm1
2b4d: 62 f2 7d 48 18 44 0e vbroadcastss 0x4(%rsi,%rcx,1),%zmm0
2b54: 01
2b55: 62 71 7c 48 28 c6 vmovaps %zmm6,%zmm8
2b5b: 62 72 7d 48 a8 c3 vfmadd213ps %zmm3,%zmm0,%zmm8
2b61: 62 f1 7c 48 28 df vmovaps %zmm7,%zmm3
2b67: 62 f2 7d 48 a8 da vfmadd213ps %zmm2,%zmm0,%zmm3
2b6d: 62 f2 7d 48 18 44 0e vbroadcastss 0x8(%rsi,%rcx,1),%zmm0
2b74: 02
2b75: 62 f2 7d 48 a8 f5 vfmadd213ps %zmm5,%zmm0,%zmm6
2b7b: 62 f2 7d 48 a8 fc vfmadd213ps %zmm4,%zmm0,%zmm7
```
etc.
Fixestensorflow/mlir#120
PiperOrigin-RevId: 267281097
- address remaining comments from PR tensorflow/mlir#87 for better test coverage for
pipeline-data-transfer/replaceAllMemRefUsesWith
- remove dead tag allocs the same way they are removed for the replaced buffers
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#106
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/106 from bondhugula:followup 9e868666d047e8d43e5f82f43e4093b838c710fa
PiperOrigin-RevId: 267144774
This CL adds support for proper cloning of Linalg ops that have regions (i.e. the generic linalg op). This is used to properly implement tiling and fusion for such ops. Adequate tests are added.
PiperOrigin-RevId: 267027176
- introduce utility to convert memrefs with non-identity layout maps to
ones with identity layout maps: convert the type and rewrite/remap all
its uses
- add this utility to -simplify-affine-structures pass for testing
purposes
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#104
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/104 from bondhugula:memref-normalize f2c914aa1890e8860326c9e33f9aa160b3d65e6d
PiperOrigin-RevId: 266985317
This will allow us to use MLIR's folding infrastructure to deduplicate
SPIR-V constants.
This CL also changed isValidSPIRVType in SPIRVDialect to a static method.
PiperOrigin-RevId: 266984403
- the [begin, end) range identified for copying could end in between the
block, which makes hoisting invalid in some cases. Change the range
identification to always end with end of block.
- add test case to exercise these (with fast mem capacity set to minimal so
that single element memref buffers are generated at the innermost loop)
- the location of begin/end of the block range for data copying was
being confused with the insert points for copy in and copy out code.
In cases, where we choose to hoist transfers, these are separate.
- when copy loops are single iteration ones, promote their bodies at
the end of the pass.
- change default fast mem space to 1 (setting it to zero made it
generate DMA op's that won't verify in the default case - since the
DMA ops have a check for src/dest memref spaces being different).
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Co-Authored-By: Mehdi Amini <joker.eph@gmail.com>
Closestensorflow/mlir#88
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/88 from bondhugula:datacopy 88697267c45e850c3ced87671e16e4a930c02a42
PiperOrigin-RevId: 266980911
Remove unused variables and attributes from BaseViewConversionHelper
on mlir/lib/Dialect/Linalg/Transforms/LowerToLLVMDialect.cpp
Closestensorflow/mlir#116
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/116 from alexst07:fix-warnings 5f638e4677492cf71a9cc040eeb6b57427d32e06
PiperOrigin-RevId: 266972082
Some of the operations in the LLVM dialect are required to model the LLVM IR in
MLIR, for example "constant" operations are needed to declare a constant value
since MLIR, unlike LLVM, does not support immediate values as operands. To
avoid confusion with actual LLVM operations, we prefix such axuiliary
operations with "mlir.".
PiperOrigin-RevId: 266942838
This change generalizes the structure of the pass manager to allow arbitrary nesting pass managers for other operations, at any level. The only user visible change to existing code is the fact that a PassManager must now provide an MLIRContext on construction. A new class `OpPassManager` has been added that represents a pass manager on a specific operation type. `PassManager` will remain the top-level entry point into the pipeline, with OpPassManagers being nested underneath. OpPassManagers will still be implicitly nested if the operation type on the pass differs from the pass manager. To explicitly build a pipeline, the 'nest' methods on OpPassManager may be used:
// Pass manager for the top-level module.
PassManager pm(ctx);
// Nest a pipeline operating on FuncOp.
OpPassManager &fpm = pm.nest<FuncOp>();
fpm.addPass(...);
// Nest a pipeline under the FuncOp pipeline that operates on spirv::ModuleOp
OpPassManager &spvModulePM = pm.nest<spirv::ModuleOp>();
// Nest a pipeline on FuncOps inside of the spirv::ModuleOp.
OpPassManager &spvFuncPM = spvModulePM.nest<FuncOp>();
To help accomplish this a new general OperationPass is added that operates on opaque Operations. This pass can be inserted in a pass manager of any type to operate on any operation opaquely. An example of this opaque OperationPass is a VerifierPass, that simply runs the verifier opaquely on the current operation.
/// Pass to verify an operation and signal failure if necessary.
class VerifierPass : public OperationPass<VerifierPass> {
void runOnOperation() override {
Operation *op = getOperation();
if (failed(verify(op)))
signalPassFailure();
markAllAnalysesPreserved();
}
};
PiperOrigin-RevId: 266840344
This interface will allow for providing hooks to interrop with operation folding. The first hook, 'shouldMaterializeInto', will allow for controlling which region to insert materialized constants into. The folder will generally materialize constants into the top-level isolated region, this allows for materializing into a lower level ancestor region if it is more profitable/correct.
PiperOrigin-RevId: 266702972
- the list of passes run by mlir-cpu-runner included -lower-affine and
-lower-to-llvm but was missing -lower-to-cfg (because -lower-affine at
some point used to lower straight to CFG); add -lower-to-cfg in
between. IR with affine ops can now be run by mlir-cpu-runner.
- update -lower-to-cfg to be consistent with other passes (create*Pass methods
were changed to return unique ptrs, but -lower-to-cfg appears to have been
missed).
- mlir-cpu-runner was unable to parse custom form of affine op's - fix
link options
- drop unnecessary run options from test/mlir-cpu-runner/simple.mlir
(none of the test cases had loops)
- -convert-to-llvmir was changed to -lower-to-llvm at some point, but the
create pass method name wasn't updated (this pass converts/lowers to LLVM
dialect as opposed to LLVM IR). Fix this.
(If we prefer "convert", the cmd-line options could be changed to
"-convert-to-llvm/cfg" then.)
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#115
PiperOrigin-RevId: 266666909
This pass class generalizes the current functionality between FunctionPass and ModulePass, and allows for operating on any operation type. The pass manager currently only supports OpPasses operating on FuncOp and ModuleOp, but this restriction will be relaxed in follow-up changes. A utility class OpPassBase<OpT> allows for generically referring to operation specific passes: e.g. FunctionPassBase == OpPassBase<FuncOp>.
PiperOrigin-RevId: 266442239
This commit introduces the bits to be able to dump JIT-compile
objects to external files by passing an object cache to OrcJit.
The new functionality is tested in mlir-cpu-runner under the flag
`dump-object-file`.
Closestensorflow/mlir#95
PiperOrigin-RevId: 266439265
Similar to enum, added a generator for structured data. This provide Dictionary that stores a fixed set of values and guarantees the values are valid. It is intended to store a fixed number of values by a given name.
PiperOrigin-RevId: 266437460
This is done by providing a walk callback that returns a WalkResult. This result is either `advance` or `interrupt`. `advance` means that the walk should continue, whereas `interrupt` signals that the walk should stop immediately. An example is shown below:
auto result = op->walk([](Operation *op) {
if (some_invariant)
return WalkResult::interrupt();
return WalkResult::advance();
});
if (result.wasInterrupted())
...;
PiperOrigin-RevId: 266436700
This CL just covers the op definition, its parsing, printing,
and verification. (De)serialization is to be implemented
in a subsequent CL.
PiperOrigin-RevId: 266431077
This change refactors and cleans up the implementation of the operation walk methods. After this refactoring is that the explicit template parameter for the operation type is no longer needed for the explicit op walks. For example:
op->walk<AffineForOp>([](AffineForOp op) { ... });
is now accomplished via:
op->walk([](AffineForOp op) { ... });
PiperOrigin-RevId: 266209552
- extend canonicalizeMapAndOperands to propagate constant operands into
the map's expressions (and thus drop those operands).
- canonicalizeMapAndOperands previously only dropped duplicate and
unused operands; however, operands that were constants were
retained.
This change makes IR maps/expressions generated by various
utilities/passes even simpler; also makes some of the test checks more
accurate and simpler -- for eg., 0' instead of symbol(%{{.*}}).
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#107
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/107 from bondhugula:canonicalize-maps c889a51486d14fbf7db489f224f881e7e1ff7d72
PiperOrigin-RevId: 266085289
The pass manager is moving towards being able to run on operations at arbitrary nesting. An operation may have both parent and child operations, and the AnalysisManager must be able to handle this generalization. The AnalysisManager class now contains generic 'getCachedParentAnalysis' and 'getChildAnalysis/getCachedChildAnalysis' functions to query analyses on parent/child operations. This removes the hard coded nesting relationship between Module/Function.
PiperOrigin-RevId: 266003636
Tweak to the pretty type parser to recognize that `->` is a special token that
shouldn't be split into two characters. This change allows dialect
types to wrap function types as in `!my.ptr_type<(i32) -> i32>`.
Closestensorflow/mlir#105
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/105 from schweitzpgi:parse-arrow 8b2d768053f419daae5a1a864121a44c4319acbe
PiperOrigin-RevId: 265986240
Instead of lowering the program in two steps (Standard->LLVM followed
by GPU->NVVM), leading to invalid IR inbetween, the runner now uses
one pattern based rewrite step to go directly from Standard+GPU to
LLVM+NVVM.
PiperOrigin-RevId: 265861934
Refactor replaceAllMemRefUsesWith to split it into two methods: the new
method does the replacement on a single op, and is used by the existing
one.
- make the methods return LogicalResult instead of bool
- Earlier, when replacement failed (due to non-deferencing uses of the
memref), the set of ops that had already been processed would have
been replaced leaving the IR in an inconsistent state. Now, a
pass is made over all ops to first check for non-deferencing
uses, and then replacement is performed. No test cases were affected
because all clients of this method were first checking for
non-deferencing uses before calling this method (for other reasons).
This isn't true for a use case in another upcoming PR (scalar
replacement); clients can now bail out with consistent IR on failure
of replaceAllMemRefUsesWith. Add test case.
- multiple deferencing uses of the same memref in a single op is
possible (we have no such use cases/scenarios), and this has always
remained unsupported. Add an assertion for this.
- minor fix to another test pipeline-data-transfer case.
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#87
PiperOrigin-RevId: 265808183
Each basic block in SPIR-V must start with an OpLabel instruction.
We don't support control flow yet, so this CL just makes sure that
the entry block follows this rule and is valid.
PiperOrigin-RevId: 265718841
To support a conversion of a simple load-compute-store kernel from GPU
dialect to SPIR-V dialect, the conversion of operations like
"gpu.block_dim", "gpu.thread_id" which allow threads to get the launch
conversion is needed. In SPIR-V these are specified as global
variables with builin attributes. This CL adds support to specify
builtin variables in SPIR-V conversion framework. This is used to
convert the relevant operations from GPU dialect to SPIR-V dialect.
Also add support for conversion of load/store operation in Standard
dialect to SPIR-V dialect.
To simplify the conversion add a method to build a spv.AccessChain
operation that automatically determines the return type based on the
base pointer type and the indices provided.
PiperOrigin-RevId: 265718525
Add an extra RewritePattern that does not convert types to rewrite a CopyOp that has non-identity permutations into a sequence of TransposeOp followed by a CopyOp without such permutations.
This RewitePattern is made to fail in the non-permutation case so that the conversion pattern can kick in to lower to LLVM.
This is an instance of A->A->B lowering where A->A is done by a RewritePattern in case_1 and A->B is done by a ConversionPatternRewriter when not(case_1).
PiperOrigin-RevId: 265171380
Add a conversion pattern that transforms a linalg.transpose op into:
1. A function entry `alloca` operation to allocate a ViewDescriptor.
2. A load of the ViewDescriptor from the pointer allocated in 1.
3. Updates to the ViewDescriptor to introduce the data ptr, offset, size
and stride. Size and stride are permutations of the original values.
4. A store of the resulting ViewDescriptor to the alloca'ed pointer.
The linalg.transpose op is replaced by the alloca'ed pointer.
PiperOrigin-RevId: 265169112
A linalg.transpose op is a pure metadata operation that takes a view + permutation map and produces
another view of the same underlying data, with a different reindexing. This is a
pure metadata operation that does not touch the underlying data.
Example:
```
%t = linalg.transpose %v (i, j) -> (j, i) : !linalg.view<?x?xf32>
```
PiperOrigin-RevId: 265139429
This CL extends support for lowering of linalg to external C++ libraries with CopyOp. Currently this can only work when the permutation maps in the copies are identity. Future support for permutations will be added later.
PiperOrigin-RevId: 265093025
This will allow iterating the values of a non-opaque ElementsAttr, with all of the types currently supported by DenseElementsAttr. This should help reduce the amount of specialization on DenseElementsAttr.
PiperOrigin-RevId: 264968151
linalg.subview used to lower to a slice with a bounded range resulting in correct bounded accesses. However linalg.slice could still index out of bounds. This CL moves the bounding to linalg.slice.
LLVM select and cmp ops gain a more idiomatic builder.
PiperOrigin-RevId: 264897125
Split out method into specialized instances + add an early exit. Should be NFC, but simplifies reading the logic slightly IMHO.
PiperOrigin-RevId: 264855529
Operation interfaces generally require a bit of boilerplate code to connect all of the pieces together. This cl introduces mechanisms in the ODS to allow for generating operation interfaces via the 'OpInterface' class.
Providing a definition of the `OpInterface` class will auto-generate the c++
classes for the interface. An `OpInterface` includes a name, for the c++ class,
along with a list of interface methods. There are two types of methods that can be used with an interface, `InterfaceMethod` and `StaticInterfaceMethod`. They are both comprised of the same core components, with the distinction that `StaticInterfaceMethod` models a static method on the derived operation.
An `InterfaceMethod` is comprised of the following components:
* ReturnType
- A string corresponding to the c++ return type of the method.
* MethodName
- A string corresponding to the desired name of the method.
* Arguments
- A dag of strings that correspond to a c++ type and variable name
respectively.
* MethodBody (Optional)
- An optional explicit implementation of the interface method.
def MyInterface : OpInterface<"MyInterface"> {
let methods = [
// A simple non-static method with no inputs.
InterfaceMethod<"unsigned", "foo">,
// A new non-static method accepting an input argument.
InterfaceMethod<"Value *", "bar", (ins "unsigned":$i)>,
// Query a static property of the derived operation.
StaticInterfaceMethod<"unsigned", "fooStatic">,
// Provide the definition of a static interface method.
// Note: `ConcreteOp` corresponds to the derived operation typename.
StaticInterfaceMethod<"Operation *", "create",
(ins "OpBuilder &":$builder, "Location":$loc), [{
return builder.create<ConcreteOp>(loc);
}]>,
// Provide a definition of the non-static method.
// Note: `op` corresponds to the derived operation variable.
InterfaceMethod<"unsigned", "getNumInputsAndOutputs", (ins), [{
return op.getNumInputs() + op.getNumOutputs();
}]>,
];
PiperOrigin-RevId: 264754898
This CL makes use of the standard LLVM LLJIT and removes the need for a custom JIT implementation within MLIR.
To achieve this, one needs to clone (i.e. serde) the produced llvm::Module into a new LLVMContext. This is currently necessary because the llvm::LLVMContext is owned by the LLVMDialect, somewhat deep in the call hierarchy.
In the future we should remove the reliance of serding the llvm::Module by allowing the injection of an LLVMContext from the top-level. Unfortunately this will require deeper API changes and impact multiple places. It is therefore left for future work.
PiperOrigin-RevId: 264737459
Previously Module and Function are builtinn constructs in MLIR.
Due to the structural requirements we must wrap the SPIR-V
module inside a Function inside a Module. Now the requirement
is lifted and we can remove the wrapping function! :)
PiperOrigin-RevId: 264736051
This will allow iterating the values of a non-opaque ElementsAttr, with all of the types currently supported by DenseElementsAttr. This should help reduce the amount of specialization on DenseElementsAttr.
PiperOrigin-RevId: 264637293
This will allow for adding more hooks for controlling parser behavior without bloating Dialect in the common case. This cl also adds iteration support to the DialectInterfaceCollection.
PiperOrigin-RevId: 264627846
This CL extends declarative rewrite rules to support matching and
generating ops with variadic operands/results. For this, the
generated `matchAndRewrite()` method for each pattern now are
changed to
* Use "range" types for the local variables used to store captured
values (`operand_range` for operands, `ArrayRef<Value *>` for
values, *Op for results). This allows us to have a unified way
of handling both single values and value ranges.
* Create local variables for each operand for op creation. If the
operand is variadic, then a `SmallVector<Value*>` will be created
to collect all values for that operand; otherwise a `Value*` will
be created.
* Use a collective result type builder. All result types are
specified via a single parameter to the builder.
We can use one result pattern to replace multiple results of the
matched root op. When that happens, it will require specifying
types for multiple results. Add a new collective-type builder.
PiperOrigin-RevId: 264588559
In SPIR-V binary format, constants are placed at the module level
and referenced by instructions inside functions using their result
<id>s. To model this natively (using SSA values for result <id>s),
it means we need to have implicit capturing functions. We will
lose the ability to have function passes if going down that path.
Instead, this CL changes to materialize constants at their use
sites in deserialization. It's cheap to copy constants in MLIR
given that attributes is uniqued to MLIRContext. By localizing
constants into functions, we can preserve isolated functions.
PiperOrigin-RevId: 264582532
Most dialects are initialized statically, which does not have a guaranteed initialization order. By keeping the dialect list sorted, we can guarantee a deterministic iteration order of dialects.
PiperOrigin-RevId: 264522875
Similar to global variables, specialization constants also live
in the module scope and can be referenced by instructions in
functions in native SPIR-V. A direct modelling would be to allow
functions in the SPIR-V dialect to implicit capture, but it means
we are losing the ability to write passes for Functions. While
in SPIR-V normally we want to process the module as a whole,
it's not common to see multiple functions get used so we'd like
to leave the door open for those cases. Therefore, similar to
global variables, we introduce spv.specConstant to model three
SPIR-V instructions: OpSpecConstantTrue, OpSpecConstantFalse,
and OpSpecConstant. They do not return SSA value results;
instead they have symbols and can only be referenced by the
symbols. To use it in a function, we need to have another op
spv._reference_of to turn the symbol into an SSA value. This
breaks the tie and makes functions still explicit capture.
Previously specialization constants were handled similarly as
normal constants. That is incorrect given that specialization
constant actually acts more like variable (without need to
load and store). E.g., they cannot be de-duplicated like normal
constants.
This CL also refines various documents and comments.
PiperOrigin-RevId: 264455172
tensorflow/mlir#58 fixed and exercised
verification of load/store ops using empty affine maps. Unfortunately,
it didn't exercise the creation of them. This PR addresses that aspect.
It removes the assumption of AffineMap having at least one result and
stores a pointer to MLIRContext as member of AffineMap.
* Add empty map support to affine.store + test
* Move MLIRContext to AffineMapStorage
Closestensorflow/mlir#74
PiperOrigin-RevId: 264416260
This conversion has been using a stack-allocated array of i8 to store the
null-terminated kernel name in order to pass it to the CUDA wrappers expecting
a C string because the LLVM dialect was missing support for globals. Now that
the suport is introduced, use a global instead.
Refactor global string construction from GenerateCubinAccessors into a common
utility function living in the LLVM namespace.
PiperOrigin-RevId: 264382489
JitRunner can use as entry points functions that produce either a single
'!llvm.f32' value or a list of memrefs. Memref support is legacy and was
introduced before MLIR could lower memref allocation and deallocation to
malloc/free calls so as to allocate the memory externally, and is likely to be
dropped in the future since it unconditionally runs affine+standard-to-llvm
lowering on the module instead of accepting the LLVM dialect. CUDA runner
relies on memref-based flow in the runner without actually returning anything.
Introduce a runner flow to use functions that return void as entry points.
PiperOrigin-RevId: 264381686
This CL allows binary operations on n-D vector types to be lowered to LLVMIR by performing an (n-1)-D extractvalue, 1-D vector operation and an (n-1)-D insertvalue.
PiperOrigin-RevId: 264339118
- fix missing check while simplifying an expression with floordiv to a
mod
- fixes issue tensorflow/mlir#82
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#84
PiperOrigin-RevId: 264338353
This will allow for naming values the same as existing SSA values for regions attached to operations that are isolated from above. This fits in with how the system already allows separate name scopes for sibling regions. This name shadowing can be enabled in the custom parser of operations by setting the 'enableNameShadowing' flag to true when calling 'parseRegion'.
%arg = constant 10 : i32
foo.op {
%arg = constant 10 : i32
}
PiperOrigin-RevId: 264255999
This CL adds an integer attribute to linalg.buffer_alloc and lowering to LLVM.
The alignment is constrained to be a positive power of 2.
Lowering to LLVM produces the pattern:
```
%[[alloc:.*]] = llvm.call @malloc(%[[s]]) : (!llvm.i64) -> !llvm<"i8*">
%[[cast:.*]] = llvm.bitcast %[[alloc]] : !llvm<"i8*"> to !llvm.i64
%[[rem:.*]] = llvm.urem %[[cast]], %[[c16]] : !llvm.i64
%[[drem:.*]] = llvm.sub %[[c16]], %[[rem]] : !llvm.i64
%[[off:.*]] = llvm.urem %[[drem]], %[[c16]] : !llvm.i64
llvm.getelementptr %{{.*}}[%[[off]]] : (!llvm<"i8*">, !llvm.i64) -> !llvm<"i8*">
```
where `ptr` is aligned on `align` by computing the address
`ptr + (align - ptr % align) % align`.
To allow dealloc op to still be able to free memory, additional information is needed in
the buffer type. The buffer type is thus extended with an extra i8* for the base allocation address.
PiperOrigin-RevId: 264244455
Change the prining/parsing of spv.globalVariable to print the type of
the variable after the ':' to be consistent with MLIR convention.
The spv._address_of should print the variable type after the ':'. It was
mistakenly printing the address of the return value. Add a (missing)
test that should have caught that.
Also move spv.globalVariable and spv._address_of tests to
structure-ops.mlir.
PiperOrigin-RevId: 264204686
Christian Sigg