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
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
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
- 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
Call llvm::outs().flush() to make sure we don't mix streams.
Remove CHECK-LABEL to avoid assuming the relative order
between the additional info and the output IR.
PiperOrigin-RevId: 271131100
A base class is added to implement all GLSL Binary operations and is
used to implement the FMax operation. The existing framework already
generates all the necessary (de)serialization code.
PiperOrigin-RevId: 271037166
- 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
This CL uses the newly added -split-input-file CLI option to
mlir-translate to combine certain (de)serialization tests.
It also renames certain test filenames.
PiperOrigin-RevId: 270816324
According to SPIR-V spec, spirv::CompositeType includes
spirv::RuntimeArrayType. This allows using objects of
spirv::RuntimeArrayType with spirv::AccessChainOp.
PiperOrigin-RevId: 270809492
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
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
The CL adds a rounding mode flag to the class and changes the default to rmNearestTiesToAway from rmNearestTiesToEven because 1) Tensorflow QuantizeV2 ops uses rmNearestTiesToAway; 2) the specialization only implements rmNearestTiesToAway.
PiperOrigin-RevId: 270600739
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
This CL adds a new FloatElementsAttr definition to ODS for float
elements attributes of a certain type.
Tests are added to show both verification and how to use it in patterns.
PiperOrigin-RevId: 270455487
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
Lei Zhang