| Commit message (Collapse) | Author | Age | Files | Lines |
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This commit defines a new SPIR-V dialect attribute for specifying
a SPIR-V target environment. It is a dictionary attribute containing
the SPIR-V version, supported extension list, and allowed capability
list. A SPIRVConversionTarget subclass is created to take in the
target environment and sets proper dynmaically legal ops by querying
the op availability interface of SPIR-V ops to make sure they are
available in the specified target environment. All existing conversions
targeting SPIR-V is changed to use this SPIRVConversionTarget. It
probes whether the input IR has a `spv.target_env` attribute,
otherwise, it uses the default target environment: SPIR-V 1.0 with
Shader capability and no extra extensions.
Differential Revision: https://reviews.llvm.org/D72256
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conflicts with function types.
Summary: The current syntax for AffineMapAttr and IntegerSetAttr conflict with function types, making it currently impossible to round-trip function types(and e.g. FuncOp) in the IR. This revision changes the syntax for the attributes by wrapping them in a keyword. AffineMapAttr is wrapped with `affine_map<>` and IntegerSetAttr is wrapped with `affine_set<>`.
Reviewed By: nicolasvasilache, ftynse
Differential Revision: https://reviews.llvm.org/D72429
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Differential Revision: https://reviews.llvm.org/D72296
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Differential Revision: https://reviews.llvm.org/D72205
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This commit fixes shader ABI attributes to use `spv.` as the prefix
so that they match the dialect's namespace. This enables us to add
verification hooks in the SPIR-V dialect to verify them.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D72062
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This commit updates gen_spirv_dialect.py to query the grammar and
generate availability spec for various enum attribute definitions
and all defined ops.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D72095
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Lots of SPIR-V ops take enum attributes and certain enum cases
need extra capabilities or extensions to be available. This commit
extends to allow specifying availability spec on enum cases.
Extra utility functions are generated for the corresponding enum
classes to return the availability requirement. The availability
interface implemention for a SPIR-V op now goes over all enum
attributes to collect the availability requirements.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D71947
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SPIR-V has a few mechanisms to control op availability: version,
extension, and capabilities. These mechanisms are considered as
different availability classes.
This commit introduces basic definitions for modelling SPIR-V
availability classes. Specifically, an `Availability` class is
added to SPIRVBase.td, along with two subclasses: MinVersion
and MaxVersion for versioning. SPV_Op is extended to take a
list of `Availability`. Each `Availability` instance carries
information for generating op interfaces for the corresponding
availability class and also the concrete availability
requirements.
With the availability spec on ops, we can now auto-generate the
op interfaces of all SPIR-V availability classes and also
synthesize the op's implementations of these interfaces. The
interface generation is done via new TableGen backends
-gen-avail-interface-{decls|defs}. The op's implementation is
done via -gen-spirv-avail-impls.
Differential Revision: https://reviews.llvm.org/D71930
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The inline interface uses two methods to check legality of inling:
1) Can a region be inlined into another.
2) Can an operation be inlined into another.
Setting the former to true, allows the inliner to use the second for
legality checks. Add this method to the SPIR-V dialect inlining
interface.
PiperOrigin-RevId: 286041734
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Some changes to the dialect generation script to allow specification
of different base class to derive from in ODS.
PiperOrigin-RevId: 285859230
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PiperOrigin-RevId: 285849308
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Closes tensorflow/mlir#308
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/308 from denis0x0D:sandbox/composite_construct 9ef7180f77f9374bcd05afc4f9e6c1d2d72d02b7
PiperOrigin-RevId: 284613617
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The patterns to be folded away can be commonly generated
during lowering to SPIR-V.
PiperOrigin-RevId: 284604855
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Closes tensorflow/mlir#304
PiperOrigin-RevId: 284568358
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A CompositeInsertOp operation make a copy of a composite object,
while modifying one part of it.
Closes tensorflow/mlir#292
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/292 from denis0x0D:sandbox/composite_insert 2200962b9057bda53cd2f2866b461e2797196380
PiperOrigin-RevId: 284036551
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PiperOrigin-RevId: 283997917
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For serialization, when we have nested ops, the inner loop will create multiple
SPIR-V blocks. If the outer loop has block arguments (which corresponds to
OpPhi instructions), we defer the handling of OpPhi's parent block handling
until we serialized all blocks and then fix it up with the result <id>. These two
cases happening together was generating invalid SPIR-V blob because we
previously assume the parent block to be the block containing the terminator.
That is not true anymore when the block contains structured control flow ops.
If that happens, it should be fixed to use the structured control flow op's
merge block.
For deserialization, we record a map from header blocks to their corresponding
merge and continue blocks during the initial deserialization and then use the
info to construct spv.selection/spv.loop. The existing implementation will also
fall apart when we have nested loops. If so, we clone all blocks for the outer
loop, including the ones for the inner loop, to the spv.loop's region. So the map
for header blocks' merge info need to be updated; otherwise we are operating
on already deleted blocks.
PiperOrigin-RevId: 283949230
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PiperOrigin-RevId: 283769736
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This CL also did the following cleanup:
- Moved the test for spv.SubgroupBallotKHR to its own file
- Wrapped generated canonicalization patterns in anonymous namespace
- Updated header comments in SPVOps.td
PiperOrigin-RevId: 283650091
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PiperOrigin-RevId: 283522284
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folding.
Closes tensorflow/mlir#281
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/281 from denis0x0D:sandbox/composite_ex_fold d02d73658bd1b9eaa515eb4e0aee34bc41d4252b
PiperOrigin-RevId: 282971563
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Adding zero and multiplying one can be common when generating code
for index calculation.
This CL also sorted canonicalize.mlir to alphabetical order.
PiperOrigin-RevId: 282828055
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These changes to SPIR-V lowering while adding support for lowering
SUbViewOp, but are not directly related.
- Change the lowering of MemRefType to
!spv.ptr<!spv.struct<!spv.array<...>[offset]>, ..>
This is consistent with the Vulkan spec.
- To enable testing a simple pattern of lowering functions is added to
ConvertStandardToSPIRVPass. This is just used to convert the type of
the arguments of the function. The added function lowering itself is
not meant to be the way functions are eventually lowered into SPIR-V
dialect.
PiperOrigin-RevId: 282589644
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To simplify the lowering into SPIR-V, while still respecting the ABI
requirements of SPIR-V/Vulkan, split the process into two
1) While lowering a function to SPIR-V (when the function is an entry
point function), allow specifying attributes on arguments and
function itself that describe the ABI of the function.
2) Add a pass that materializes the ABI described in the function.
Two attributes are needed.
1) Attribute on arguments of the entry point function that describe
the descriptor_set, binding, storage class, etc, of the
spv.globalVariable this argument will be replaced by
2) Attribute on function that specifies workgroup size, etc. (for now
only workgroup size).
Add the pass -spirv-lower-abi-attrs to materialize the ABI described
by the attributes.
This change makes the SPIRVBasicTypeConverter class unnecessary and is
removed, further simplifying the SPIR-V lowering path.
PiperOrigin-RevId: 282387587
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Add a canonicalizer for `spirv::LogicalNotOp`.
Converts:
* spv.LogicalNot(spv.IEqual(...)) -> spv.INotEqual(...)
* spv.LogicalNot(spv.INotEqual(...)) -> spv.IEqual(...)
* spv.LogicalNot(spv.LogicalEqual(...)) -> spv.LogicalNotEqual(...)
* spv.LogicalNot(spv.LogicalNotEqual(...)) -> spv.LogicalEqual(...)
Also moved the test for spv.IMul to arithemtic tests.
Closes tensorflow/mlir#256
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/256 from denis0x0D:sandbox/canon_logical_not 76ab5787b2c777f948c8978db061d99e76453d44
PiperOrigin-RevId: 282012356
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Iterates each element to build the array. This includes a little refactor to
combine bool/int/float into a function, since they are similar. The only
difference is calling different function in the end.
PiperOrigin-RevId: 281210288
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The variant that accepts a type will check that the parsed attribute is a valid instance of AttrType. The non-type variant would silently fail in this case, leading to garbage attribute values.
PiperOrigin-RevId: 281136528
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Convert chained `spirv::BitcastOp` operations into
one `spirv::BitcastOp` operation.
Closes tensorflow/mlir#238
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/238 from denis0x0D:sandbox/canon_bitcast 4352ed4f81b959ec92f849c599e733b62a99c010
PiperOrigin-RevId: 281129234
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This CL added op definitions for a few bit operations:
* OpBitFieldInsert
* OpBitFieldSExtract
* OpBitFieldUExtract
Closes tensorflow/mlir#233
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/233 from denis0x0D:sandbox/bit_field_ops e7fd85b00d72d483d7992dc42b9cc4d673903455
PiperOrigin-RevId: 280691816
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Since VariableOp is serialized during processBlock, we add two more fields,
`functionHeader` and `functionBody`, to collect instructions for a function.
After all the blocks have been processed, we append them to the `functions`.
Also, fix a bug in processGlobalVariableOp. The global variables should be
encoded into `typesGlobalValues`.
PiperOrigin-RevId: 280105366
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During deserialization, the loop header block will be moved into the
spv.loop's region. If the loop header block has block arguments,
we need to make sure it is correctly carried over to the block where
the new spv.loop resides.
During serialization, we need to make sure block arguments from the
spv.loop's entry block are not silently dropped.
PiperOrigin-RevId: 280021777
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This CL added op definitions for a few bit operations:
* OpShiftLeftLogical
* OpShiftRightArithmetic
* OpShiftRightLogical
* OpBitCount
* OpBitReverse
* OpNot
Also moved the definition of spv.BitwiseAnd to follow the
lexicographical order.
Closes tensorflow/mlir#215
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/215 from denis0x0D:sandbox/bit_ops d9b0852b689ac6c4879a9740b1740a2357f44d24
PiperOrigin-RevId: 279350470
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directly.
This simplifies the implementation quite a bit, and removes the need for explicit string munging. One change is made to some of the enum elements of SPV_DimAttr to ensure that they are proper identifiers; The string form is now prefixed with 'Dim'.
PiperOrigin-RevId: 278027132
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This CL added op definitions for a few cast operations:
* OpConvertFToU
* OpConvertFToS
* OpConvertSToF
* OpConvertUToF
* OpUConvert
* OpSConvert
* OpFConvert
Also moved the definition of spv.Bitcast to the new file.
Closes tensorflow/mlir#208 and tensorflow/mlir#174
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/208 from denis0x0D:sandbox/cast_ops 79bc9b37398aafddee6cf6beb301807988fe67f9
PiperOrigin-RevId: 277587891
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This CL fixed gen_spirv_dialect.py to support nested delimiters when
chunking existing ODS entries in .td files and to allow ops without
correspondence in the spec. This is needed to pull in the definition
of OpUnreachable.
PiperOrigin-RevId: 277486465
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This CL adds another control flow instruction in SPIR-V: OpPhi.
It is modelled as block arguments to be idiomatic with MLIR.
See the rationale.md doc for "Block Arguments vs PHI nodes".
Serialization and deserialization is updated to convert between
block arguments and SPIR-V OpPhi instructions.
PiperOrigin-RevId: 277161545
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Combine chained `spirv::AccessChainOp` operations into one
`spirv::AccessChainOp` operation.
Closes tensorflow/mlir#198
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/198 from denis0x0D:sandbox/canon_access_chain 0cb87955a85511071143d62637ff939d0dabc2bd
PiperOrigin-RevId: 276609345
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This allows for them to be used on other non-function, or even other function-like, operations. The algorithms are already generic, so this is simply changing the derived pass type. The majority of this change is just ensuring that the nesting of these passes remains the same, as the pass manager won't auto-nest them anymore.
PiperOrigin-RevId: 276573038
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We will use block arguments as the way to model SPIR-V OpPhi in
the SPIR-V dialect.
This CL also adds a few useful helper methods to both ops to
get the block arguments.
Also added tests for branch weight (de)serialization.
PiperOrigin-RevId: 275960797
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Closes tensorflow/mlir#175
PiperOrigin-RevId: 275726876
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Add a canonicalization pattern for spv.selection operation.
Convert spv.selection operation to spv.Select based on
simple pattern.
Closes tensorflow/mlir#183
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/183 from denis0x0D:sandbox/canon_select 43d04d923272dd60b9da39f70bdbc51a5168db62
PiperOrigin-RevId: 275312748
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We just need to implement a few interface hooks to DialectInlinerInterface
and CallOpInterface to gain the benefits of an inliner. :)
Right now only supports some trivial cases:
* Inlining single block with spv.Return/spv.ReturnValue
* Inlining multi block with spv.Return
* Inlining spv.selection/spv.loop without return ops
More advanced cases will require block argument and Phi support.
PiperOrigin-RevId: 275151132
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PiperOrigin-RevId: 274935374
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The SpecId decoration is the handle for providing external specialization.
Similar to descriptor set and binding on global variables, we directly
bake it into assembly parsing and printing.
PiperOrigin-RevId: 274893879
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The lowering is specified as a pattern and is done only if the result
is a SPIR-V scalar type or vector type.
Handling ConstantOp with index return type needs special handling
since SPIR-V dialect does not have index types. Based on the bitwidth
of the attribute value, either i32 or i64 is chosen.
Other constant lowerings are left as a TODO.
PiperOrigin-RevId: 274056805
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Add a pass to decorate the composite types used by
composite objects in the StorageBuffer, PhysicalStorageBuffer,
Uniform, and PushConstant storage classes with layout information.
Closes tensorflow/mlir#156
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/156 from denis0x0D:sandbox/layout_info_decoration 7c50840fd38ca169a2da7ce9886b52b50c868b84
PiperOrigin-RevId: 273634140
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PiperOrigin-RevId: 273379318
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The SPIR-V spec recommends all OpUndef instructions be generated at
module level. For the SPIR-V dialect its better for UndefOp to produce
an SSA value for use with other instructions. If UndefOp is to be used
at module level, it cannot produce an SSA value (use of this SSA value
within FuncOp would need implicit capture). To satisfy needs of the
SPIR-V spec while making it simpler to represent UndefOp in the SPIR-V
dialect, the serialization is updated to create OpUndef instruction
at module scope.
PiperOrigin-RevId: 273355526
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The structured selection/loop's entry block does not have arguments.
If the function's header block is also part of the structured control
flow, we cannot just simply erase it because it may contain arguments
matching the function signature and used by the cloned blocks. Instead,
turn it into a block only containing a spv.Branch op.
Also, we can directly emit instructions for the spv.selection header
block to the block containing the spv.selection op. This eliminates
unnecessary branches in the SPIR-V blob.
Added a test for nested spv.loop.
PiperOrigin-RevId: 273351424
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Use `getParentOfType<FunctionOp>()` instead of `cast<FuncOp>(getParentOp())`
to avoid crash when return ops are used inside spv.selection/spv.loop.
PiperOrigin-RevId: 273006041
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