This CL adds more common information to StructuredOpsUtils.h
The n_view attribute is retired in favor of args_in + args_out but the CL is otherwise NFC.
PiperOrigin-RevId: 285000621
Add one more simplification for floordiv and mod affine expressions.
Examples:
(2*d0 + 1) floordiv 2 is simplified to d0
(8*d0 + 4*d1 + d2) floordiv 4 simplified to 4*d0 + d1 + d2 floordiv 4.
etc.
Similarly, (4*d1 + 1) mod 2 is simplified to 1,
(2*d0 + 8*d1) mod 8 simplified to 2*d0 mod 8.
Change getLargestKnownDivisor to return int64_t to be consistent and
to avoid casting at call sites (since the return value is used in expressions
of int64_t/index type).
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#202
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/202 from bondhugula:affine b13fcb2f1c00a39ca5434613a02408e085a80e77
PiperOrigin-RevId: 284866710
Move the definition of gpu.launch_func operation from hand-rolled C++
implementation to the ODS framework. Also move the documentation. This only
performs the move and remains a non-functional change, a follow-up will clean
up the custom functions that can be auto-generated using ODS.
PiperOrigin-RevId: 284842252
This has several benefits:
* The implementation is much cleaner and more efficient.
* The ranges now have support for many useful operations: operator[], slice, drop_front, size, etc.
* Value ranges can now directly query a range for their types via 'getTypes()': e.g:
void foo(Operation::operand_range operands) {
auto operandTypes = operands.getTypes();
}
PiperOrigin-RevId: 284834912
This patch closes issue tensorflow/mlir#272
We add a standalone iterator permutation transformation to Linalg.
This transformation composes a permutation map with the maps in the
"indexing_maps" attribute. It also permutes "iterator_types"
accordingly.
Change-Id: I7c1e693b8203aeecc595a7c012e738ca1100c857
Closestensorflow/mlir#307
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/307 from tetuante:issue272 f7908d58792f4111119721885e247045104f1131
PiperOrigin-RevId: 284824102
This reorganizes the vector transformations to be more easily testable as patterns and more easily composable into fused passes in the future.
PiperOrigin-RevId: 284817474
Add some convenience build methods to SPIR-V ops and update the
lowering to use these methods where possible.
For SPIRV::CompositeExtractOp move the method to deduce type of
element based on base and indices into a convenience function. Some
additional functionality needed to handle differences between parsing
and verification methods.
PiperOrigin-RevId: 284794404
These come from a non-standard extenion that is not available on Github, so it
only clutters the documentation source with {.mlir} or {.ebnf} tags.
PiperOrigin-RevId: 284733003
For example
%0 = vector.shuffle %x, %y [3 : i32, 2 : i32, 1 : i32, 0 : i32] : vector<2xf32>, vector<2xf32>
yields a vector<4xf32> result with a permutation of the elements of %x and %y
PiperOrigin-RevId: 284657191
Each of the support classes for Block are now moved into a new header BlockSupport.h. The successor iterator class is also reimplemented as an indexed_accessor_range. This makes the class more efficient, and expands on its available functionality.
PiperOrigin-RevId: 284646792
Many ranges want similar functionality from a range type(e.g. slice/drop_front/operator[]/etc.), so these classes provide a generic implementation that may be used by many different types of ranges. This removes some code duplication, and also empowers many of the existing range types in MLIR(e.g. result type ranges, operand ranges, ElementsAttr ranges, etc.). This change only updates RegionRange and ValueRange, more ranges will be updated in followup commits.
PiperOrigin-RevId: 284615679
This CL starts extracting commonalities between dialects that use the structured ops abstractions. Also fixes an OSS build issue where StringRef were incorrectly used with constexpr.
PiperOrigin-RevId: 284591114
The existing GPU to SPIR-V lowering created a spv.module for every
function with gpu.kernel attribute. A better approach is to lower the
module that the function lives in (which has the attribute
gpu.kernel_module) to a spv.module operation. This better captures the
host-device separation modeled by GPU dialect and simplifies the
lowering as well.
PiperOrigin-RevId: 284574688
Unifies vector op unrolling transformation, by using the same unrolling implementation for contraction and elementwise operations.
Removes fakefork/join operations which are non longer needed now that we have the InsertStridedSlice operation.
PiperOrigin-RevId: 284570784
This CL uses the newly expanded matcher support to easily detect when a linalg.generic has a multiply-accumulate body. A linalg.generic with such a body is rewritten as a vector contraction.
This CL additionally limits the rewrite to the case of matrix multiplication on contiguous and statically shaped memrefs for now.
Before expanding further, we should harden the infrastructure for expressing custom ops with the structured ops abstraction.
PiperOrigin-RevId: 284566659
Follows ValueRange in representing a generic abstraction over the different
ways to represent a range of Regions. This wrapper is not as ValueRange and only
considers the current cases of interest: MutableArrayRef<Region> and
ArrayRef<std::unique_ptr<Region>> as occurs during op construction vs op region
querying.
Note: ArrayRef<std::unique_ptr<Region>> allows for unset regions, so this range
returns a pointer to a Region instead of a Region.
PiperOrigin-RevId: 284563229
This allows for users to provide operand_range and result_range in builder.create<> calls, instead of requiring an explicit copy into a separate data structure like SmallVector/std::vector.
PiperOrigin-RevId: 284360710
This class represents a generic abstraction over the different ways to represent a range of Values: ArrayRef<Value *>, operand_range, result_range. This class will allow for removing the many instances of explicit SmallVector<Value *, N> construction. It has the same memory cost as ArrayRef, and only suffers cost from indexing(if+elsing the different underlying representations).
This change only updates a few of the existing usages, with more to be changed in followups; e.g. 'build' API.
PiperOrigin-RevId: 284307996
This adds an additional filtering mode for printing after a pass that checks to see if the pass actually changed the IR before printing it. This "change" detection is implemented using a SHA1 hash of the current operation and its children.
PiperOrigin-RevId: 284291089
- for the symbol rules, the code was updated but the doc wasn't.
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#284
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/284 from bondhugula:doc 9aad8b8a715559f7ce61265f3da3f8a3c11b45ea
PiperOrigin-RevId: 284283712
During lowering, spv.module might be within other modules (for example
gpu kernel module). Walk the module op to find spirv module to
serialize.
PiperOrigin-RevId: 284262550
Move the definition of the GPU launch opreation from hand-rolled C++ code to
ODS framework. This only does the moves, a follow-up is necessary to clean up
users of custom functions that could be auto-generated by ODS.
PiperOrigin-RevId: 284261856
Since these operations lower to [insert|extract][element|value] at LLVM
dialect level, neither element nor value would correctly reflect the meaning.
PiperOrigin-RevId: 284240727
Accept the address space of the global as a builder argument when constructing
an LLVM::GlobalOp instance. This decreases the reliance of LLVM::GlobalOp users
on the internal name of the attribute used for this purpose. Update several
uses of the address space in GPU to NVVM conversion.
PiperOrigin-RevId: 284233254
Move the definition of the GPU function opreation from hand-rolled C++ code to
ODS framework. This only does the moves, a follow-up is necessary to clean up
users of custom functions that could be auto-generated by ODS.
PiperOrigin-RevId: 284233245
GPU functions use memory attributions, a combination of Op attributes and
region arguments, to specify function-wide buffers placed in workgroup or
private memory spaces. Introduce a lowering pattern for GPU functions to be
converted to LLVM functions taking into account memory attributions. Workgroup
attributions get transformed into module-level globals with unique names
derived from function names. Private attributions get converted into
llvm.allocas inside the function body. In both cases, we inject at the
beginning of the function the IR that obtains the raw pointer to the data and
populates a MemRef descriptor based on the MemRef type of buffer, making
attributions compose with the rest of the MemRef lowering and transparent for
use with std.load and std.store. While using raw pointers instead of
descriptors might have been more efficient, it is better implemented as a
canonicalization or a separate transformation so that non-attribution memrefs
could also benefit from it.
PiperOrigin-RevId: 284208396
Updates vector ContractionOp to use proper vector masks (produced by CreateMaskOp/ConstantMaskOp).
Leverages the following canonicalizations in unrolling unit test: CreateMaskOp -> ConstantMaskOp, StridedSliceOp(ConstantMaskOp) -> ConstantMaskOp
Removes IndexTupleOp (no longer needed now that we have vector mask ops).
Updates all unit tests.
PiperOrigin-RevId: 284182168
The iterator should be erased before adding a new entry
into blockMergeInfo to avoid iterator invalidation.
Closestensorflow/mlir#299
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/299 from denis0x0D:sandbox/reoder_erase 983be565809aa0aadfc7e92962e4d4b282f63c66
PiperOrigin-RevId: 284173235
The AddressOf operation in the LLVM dialect return a pointer to a global
variable. The latter may be in a non-default address space as indicated by the
"addr_space" attribute. Check that the address space of the pointer returned by
AddressOfOp matches that of the referenced GlobalOp. Update the AddressOfOp
builder to respect this constraint.
PiperOrigin-RevId: 284138860
This patch closes issue tensorflow/mlir#271.
It adds an optional permutation map to declarative tiling transformations.
The map is expressed as a list of integers.
Closestensorflow/mlir#288
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/288 from tetuante:issue271 2df2938d6a1f01b3bc404ded08dea2dd1e10b588
PiperOrigin-RevId: 284064151
This allows for more interesting behavior from users, e.g. enabling the ability to dump the IR to a separate file for each pass invocation.
PiperOrigin-RevId: 284059447
A CompositeInsertOp operation make a copy of a composite object,
while modifying one part of it.
Closestensorflow/mlir#292
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/292 from denis0x0D:sandbox/composite_insert 2200962b9057bda53cd2f2866b461e2797196380
PiperOrigin-RevId: 284036551
Statistics are a way to keep track of what the compiler is doing and how effective various optimizations are. It is useful to see what optimizations are contributing to making a particular program run faster. Pass-instance specific statistics take this even further as you can see the effect of placing a particular pass at specific places within the pass pipeline, e.g. they could help answer questions like "what happens if I run CSE again here".
Statistics can be added to a pass by simply adding members of type 'Pass::Statistics'. This class takes as a constructor arguments: the parent pass pointer, a name, and a description. Statistics can be dumped by the pass manager in a similar manner to how pass timing information is dumped, i.e. via PassManager::enableStatistics programmatically; or -pass-statistics and -pass-statistics-display via the command line pass manager options.
Below is an example:
struct MyPass : public OperationPass<MyPass> {
Statistic testStat{this, "testStat", "A test statistic"};
void runOnOperation() {
...
++testStat;
...
}
};
$ mlir-opt -pass-pipeline='func(my-pass,my-pass)' foo.mlir -pass-statistics
Pipeline Display:
===-------------------------------------------------------------------------===
... Pass statistics report ...
===-------------------------------------------------------------------------===
'func' Pipeline
MyPass
(S) 15 testStat - A test statistic
MyPass
(S) 6 testStat - A test statistic
List Display:
===-------------------------------------------------------------------------===
... Pass statistics report ...
===-------------------------------------------------------------------------===
MyPass
(S) 21 testStat - A test statistic
PiperOrigin-RevId: 284022014
SPIR-V/Vulkan spec requires the workgroups size to be specified with
the spv.ExecutionMode operation. This was hard-wired to be set to a
particular value. It is now changed to be configurable by clients of
the pass or of the patterns that implement the lowering from GPU to
SPIRV.
PiperOrigin-RevId: 284017482
It is often desirable to know where within the program that a diagnostic was emitted, without reverting to assert/unreachable which crash the program. This change adds a flag `mlir-print-stacktrace-on-diagnostic` that attaches the current stack trace as a note to every diagnostic that gets emitted.
PiperOrigin-RevId: 283996373
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
This change adds support for non-congruent indices in the operation ordering within a basic block. This effect of this is that insertions are less likely to cause an invalidation of the ordering within a block. This has a big effect on modules that have very large basic blocks.
PiperOrigin-RevId: 283858136
In some situations a diagnostic may optionally be emitted by the presence of a location, e.g. attribute and type verification. These situations currently require extra 'if(loc) emitError(...); return failure()' wrappers that make verification clunky. These new overloads take an optional location and a list of arguments to the diagnostic, and return a LogicalResult. We take the arguments directly and return LogicalResult instead of returning InFlightDiagnostic because we cannot create a valid diagnostic with a null location. This creates an awkward situation where a user may try to treat the, potentially null, diagnostic as a valid one and encounter crashes when attaching notes/etc. Below is an example of how these methods simplify some existing usages:
Before:
if (loc)
emitError(*loc, "this is my diagnostic with argument: ") << 5;
return failure();
After:
return emitOptionalError(loc, "this is my diagnostic with argument: ", 5);
PiperOrigin-RevId: 283853599
In the future, a more configurable malloc and free interface should be used and exposed via
extra parameters to the `createLowerToLLVMPass`. Until requirements are gathered, a simple CL flag allows generating code that runs successfully on hardware that cannot use the stdlib.
PiperOrigin-RevId: 283833424
Adds a ConstantMaskOp to the vector ops dialect.
Adds the following canonicalization patterns:
CreateMaskOp -> ConstantMaskOp
StridedSliceOp(ConstantMaskOp) -> ConstantMaskOp
PiperOrigin-RevId: 283816752
Now that we have unrolling as a declarative pattern, we can drop a full pass that has gone stale. In the future we may want to add specific unrolling patterns for VectorTransferReadOp.
PiperOrigin-RevId: 283806880
I found that when running crash reproducers, the elided elementsattr's
would prevent parsing the IR repro. I found myself manually going and
replacing the "..." with some valid IR.
With this change, we now print elided attrs as `opaque<"", "0xDEADBEEF">`
to clearly delineate them as being elided while still being parseable.
PiperOrigin-RevId: 283781806
- the name was misleading; this is really checking if a Value being used
to index was loop IV invariant. Update comment.
- the method is only used locally; what can be exposed in the future is
isAccessInvariant(LoadOrStoreOp op, Value *iv)
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#285
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/285 from bondhugula:quickfix fe5837abe987980c4ab469a9aa7de8e4f0007d9f
PiperOrigin-RevId: 283771923
In the replaceAllUsesExcept utility function called from loop coalescing the
iteration over the use-chain is incorrect. The use list nodes (IROperands) have
next/prev links, and bluntly resetting the use would make the loop to continue
on uses of the value that was replaced instead of the original one. As a
result, it could miss the existing uses and update the wrong ones. Make sure we
increment the iterator before updating the use in the loop body.
Reported-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#291.
PiperOrigin-RevId: 283754195
This CL refactors some of the MLIR vector dependencies to allow decoupling VectorOps, vector analysis, vector transformations and vector conversions from each other.
This makes the system more modular and allows extracting VectorToVector into VectorTransforms that do not depend on vector conversions.
This refactoring exhibited a bunch of cyclic library dependencies that have been cleaned up.
PiperOrigin-RevId: 283660308
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
Not all StandardOps can be lowered to SPIR-V. For example, subview op
implementation requires use of pointer bitcasts which is not valid
according to SPIR-V spec (or at least is ambiguous about it). Such ops
need to be removed/transformed before lowering to SPIR-V. The
SPIRVLegalizationPass is added a place where such legalizations can be
added. Current implementation folds the subview ops with load/stores
so that the lowering itself does not have to convert a subview op.
PiperOrigin-RevId: 283642981
The SPIR-V lowering used nested !spv.arrays to represented
multi-dimensional arrays, with the hope that in-conjunction with the
layout annotations, the shape and layout of memref can be represented
directly. It is unclear though how portable this representation will
end up being. It will rely on driver compilers implementing complex
index computations faithfully. A more portable approach is to use
linearized arrays to represent memrefs and explicitly instantiate all
the index computation in SPIR-V. This gives added benefit that we can
further optimize the generated code in MLIR before generating the
SPIR-V binary.
PiperOrigin-RevId: 283571167
As described in the documentation, ViewOp is expected to take an optional
dynamic offset followed by a list of dynamic sizes. However, the ViewOp parser
did not include a check for the offset being a single value and accepeted a
list of values instead.
Furthermore, several tests have been exercising the wrong syntax of a ViewOp,
passing multiple values to the dyanmic stride list, which was not caught by the
parser. The trailing values could have been erronously interpreted as dynamic
sizes. This is likely due to resyntaxing of the ViewOp, with the previous
syntax taking the list of sizes before the offset. Update the tests to use the
syntax with the offset preceding the sizes.
Worse, the conversion of ViewOp to the LLVM dialect assumed the wrong order of
operands with offset in the trailing position, and erronously relied on the
permissive parsing that interpreted trailing dynamic offset values as leading
dynamic sizes. Fix the lowering to use the correct order of operands.
PiperOrigin-RevId: 283532506
tensorflow/mlir#162 introduced a bug that
incorrectly allowed fusion of producer loops with multiple outgoing
edges. This commit fixes that problem. It also introduces a new flag to
disable sibling loop fusion so that we can test producer-consumer fusion
in isolation.
Closestensorflow/mlir#259
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/259 from dcaballe:dcaballe/fix_multi_out_edge_producer_fusion 578d5661705fd5c56c555832d5e0528df88c5282
PiperOrigin-RevId: 283531105
are constant (i.e., there are no size and stride operands).
We recently added canonicalization that rewrites constant size and stride operands to
SubViewOp into static information in the type, so these patterns now occur during code
generation.
PiperOrigin-RevId: 283524688
A recent commit introduced the Linkage attribute to the LLVM dialect and used
it in the Global Op. Also use it in LLVMFuncOp. As per LLVM Language Reference,
if the linkage attribute is omitted, the function is assumed to have external
linkage.
PiperOrigin-RevId: 283493299
Existing builders generated by ODS require attributes to be passed
in as mlir::Attribute or its subclasses. This is okay foraggregate-
parameter builders, which is primarily to be used by programmatic
C++ code generation; it is inconvenient for separate-parameter
builders meant to be called in manually written C++ code because
it requires developers to wrap raw values into mlir::Attribute by
themselves.
This CL extends to generate additional builder methods that
take raw values for attributes and handles the wrapping in the
builder implementation. Additionally, if an attribute appears
late in the arguments list and has a default value, the default
value is supplied in the declaration if possible.
PiperOrigin-RevId: 283355919
Right now op argument matching in DRR is position-based, meaning we need to
specify N arguments for an op with N ODS-declared argument. This can be annoying
when we don't want to capture all the arguments. `$_` is to remedy the situation.
PiperOrigin-RevId: 283339992
LLVM IR supports linkage on global objects such as global variables and
functions. Introduce the Linkage attribute into the LLVM dialect, backed by an
integer storage. Use this attribute on LLVM::GlobalOp and make it mandatory.
Implement parsing/printing of the attribute and conversion to LLVM IR.
See tensorflow/mlir#277.
PiperOrigin-RevId: 283309328
* Had leftover call that would result in converting to dictionary attr before
being implicitedly converted back to NamedAttributeList;
* NamedAttributeList is value typed, so don't use const reference;
PiperOrigin-RevId: 283072576
Helper utilies for parsing and printing FunctionLike Ops are only relevant to
the implementation of the Op, not its definition. They depend on
OpImplementation.h and increase the inclusion footprint of FunctionSupport.h,
and do so only to provide some utilities in the "impl" namespace. Move them to
a separate files, similarly to OpDefinition/OpImplementation distinction, and
make only Op implementations use them while keeping headers cleaner. NFC.
PiperOrigin-RevId: 282964556
Updated comments and used static instead of anonymous namspace
to hide functions to be consistent with the existing codebase.
PiperOrigin-RevId: 282847784
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
This CL rewrites the linalg ops to loops transformations as patterns that can be targeted directly from Tablegen. Reliance on OpFolder is removed and to cope with it we introduce local folding patterns that are applied greedily.
PiperOrigin-RevId: 282765550
Since second argument is always fully overwritten and
shape is define in "to" clause, it is not needed.
Also renamed "into" to "to" now that arg is dropped.
PiperOrigin-RevId: 282686475
This method is close to creating an OperationState first and then unpacking it
but avoids creating the OperationState and takes a NamedAttributeList for
attributes rather than array of NamedAttribute (to enable reusing an already
created NamedAttributeList).
Reuse this new method via create that takes OperationState. I'll update inferReturnTypes in follow up to also take NamedAttributeList and so a build method that uses both inferReturnTypes and create can reuse the same list.
PiperOrigin-RevId: 282651642
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
The affine_apply operation is currently "doubly" affine and conflates two things:
1. it applies an affine map to a list of values of type `index` that are defined as either dim or symbol
2. it restricts (and propagates constraints on) the provenance of dims and symbols to a small subset of ops for which more restrictive polyhedral constraints apply.
Point 2. is related to the ability to form so-called static control parts and is related to dependence analysis and legality of transformations.
Point 1. however is completely independent, the only local implication of dims and symbol for affine_apply is that dims compose while symbols concatenate as well as the structural constraint that dims may not be multiplied.
The properties of composition and canonicalization in affine_apply are more generally useful. This CL relaxes the verifier on affine_apply so it can be used more generally.
The relevant affine.for/if/load/store op verifiers already implement the dim and symbol checking.
See this thread for the related discussion: https://groups.google.com/a/tensorflow.org/g/mlir/c/HkwCbV8D9N0/m/8srUNrX6CAAJ
PiperOrigin-RevId: 282562517
Certain operations can have multiple variadic operands and their size
relationship is not always known statically. For such cases, we need
a per-op-instance specification to divide the operands into logical
groups or segments. This can be modeled by attributes.
This CL introduces C++ trait AttrSizedOperandSegments for operands and
AttrSizedResultSegments for results. The C++ trait just guarantees
such size attribute has the correct type (1D vector) and values
(non-negative), etc. It serves as the basis for ODS sugaring that
with ODS argument declarations we can further verify the number of
elements match the number of ODS-declared operands and we can generate
handy getter methods.
PiperOrigin-RevId: 282467075
This CL uses the recently added op to finish the implementation of Vector -> Vector unrolling by replacing the "fake join op" by a series of InsertStridedSliceOp.
Test is updated accordingly
PiperOrigin-RevId: 282451126
This new op is the counterpart of vector.StridedSliceOp and will be used for in the pattern rewrites for vector unrolling.
PiperOrigin-RevId: 282447414
A mismatch in the function declaration and function definition,
prevented the implementation of the createGPUToSPIRVLoweringPass from
being exposed.
PiperOrigin-RevId: 282419815
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
Memref_cast supports cast from static shape to dynamic shape
memrefs. The same should be true for strides as well, i.e a memref
with static strides can be casted to a memref with dynamic strides.
PiperOrigin-RevId: 282381862
This is the counterpart of vector.extractelement op and has the same
limitations at the moment (static I64IntegerArrayAttr to express position).
This restriction will be filterd in the future.
LLVM lowering will be added in a subsequent commit.
PiperOrigin-RevId: 282365760
Introduce a new function-like operation to the GPU dialect to provide a
placeholder for the execution semantic description and to add support for GPU
memory hierarchy. This aligns with the overall goal of the dialect to expose
the common abstraction layer for GPU devices, in particular by providing an
MLIR unit of semantics (i.e. an operation) for memory modeling.
This proposal has been discussed in the mailing list:
https://groups.google.com/a/tensorflow.org/d/msg/mlir/RfXNP7Hklsc/MBNN7KhjAgAJ
As decided, the "convergence" aspect of the execution model will be factored
out into a new discussion and therefore is not included in this commit. This
commit only introduces the operation but does not hook it up with the remaining
flow. The intention is to develop the new flow while keeping the old flow
operational and do the switch in a simple, separately reversible commit.
PiperOrigin-RevId: 282357599
This CL added necessary files and settings for using DRR to
write SPIR-V canonicalization patterns and also converted the
patterns for spv.Bitcast and spv.LogicalNot.
PiperOrigin-RevId: 282132786
The check in isValidSymbol, as far as a DimOp result went, checked if
the dim op was on a top-level memref. However, any alloc'ed, view, or
subview memref would be fine as long as the corresponding dimension of
that memref is either a static one or was in turn created using a valid
symbol in the case of dynamic dimensions.
Reported-by: Jose Gomez
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#252
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/252 from bondhugula:symbol 7b57dc394df9375e651f497231c6e4525a32a662
PiperOrigin-RevId: 282097114
Support for including a file multiple times was added in tablegen, removing the need for these extra guards. This is because we already insert c/c++ style header guards within each of the specific .td files.
PiperOrigin-RevId: 282076728
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.
Closestensorflow/mlir#256
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/256 from denis0x0D:sandbox/canon_logical_not 76ab5787b2c777f948c8978db061d99e76453d44
PiperOrigin-RevId: 282012356
Depending on which of the offsets, sizes, or strides are constant, the
subview op can be canonicalized in different ways. Add such
canonicalizations, which generalize the existing approach of
canonicalizing subview op only if all of offsets, sizes and shapes are
constants.
PiperOrigin-RevId: 282010703
Change vector op names from VectorFooOp to Vector_FooOp and from
vector::VectorFooOp to vector::FooOp.
Closestensorflow/mlir#257
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/257 from Kayjukh:master dfc3a0e04114885aaec8740d5951d6984d6e1577
PiperOrigin-RevId: 281967461
This changes changes the OpDefinitionsGen to automatically add the OpAsmOpInterface for operations with multiple result groups using the provided ODS names. We currently just limit the generation to multi-result ops as most single result operations don't have an interesting name(result/output/etc.). An example is shown below:
// The following operation:
def MyOp : ... {
let results = (outs AnyType:$first, Variadic<AnyType>:$middle, AnyType);
}
// May now be printed as:
%first, %middle:2, %0 = "my.op" ...
PiperOrigin-RevId: 281834156
This will make it easier to scale out test patterns and build specific passes that do not interfere with independent testing.
PiperOrigin-RevId: 281736335
Due to legacy reasons, a newline character followed by two spaces was always
inserted before the attributes of the function Op in pretty form. This breaks
formatting when functions are nested in some other operations. Don't print the
newline and just put the attributes on the same line, which is also more
consistent with module Op. Line breaking aware of indentation can be introduced
separately into the parser if deemed useful.
PiperOrigin-RevId: 281721793
This moves the different canonicalizations of regions into one place and invokes them in the fixed-point iteration of the canonicalizer.
PiperOrigin-RevId: 281617072
This is a simple multi-level DCE pass that operates pretty generically on
the IR. Its key feature compared to the existing peephole dead op folding
that happens during canonicalization is being able to delete recursively
dead cycles of the use-def graph, including block arguments.
PiperOrigin-RevId: 281568202
The current SubViewOp specification allows for either all offsets,
shape and stride to be dynamic or all of them to be static. There are
opportunities for more fine-grained canonicalization based on which of
these are static. For example, if the sizes are static, the result
memref is of static shape. The specification of SubViewOp is modified
to allow on or more of offsets, shapes and strides to be statically
specified. The verification is updated to ensure that the result type
of the subview op is consistent with which of these are static and
which are dynamic.
PiperOrigin-RevId: 281560457
This CL uses the pattern rewrite infrastructure to implement a simple VectorOps -> VectorOps legalization strategy to unroll coarse-grained vector operations into finer grained ones.
The transformation is written using local pattern rewrites to allow composition with other rewrites. It proceeds by iteratively introducing fake cast ops and cleaning canonicalizing or lowering them away where appropriate.
This is an example of writing transformations as compositions of local pattern rewrites that should enable us to make them significantly more declarative.
PiperOrigin-RevId: 281555100
This interface provides more fine-grained hooks into the AsmPrinter than the dialect interface, allowing for operations to define the asm name to use for results directly on the operations themselves. The hook is also expanded to enable defining named result "groups". Get a special name to use when printing the results of this operation.
The given callback is invoked with a specific result value that starts a
result "pack", and the name to give this result pack. To signal that a
result pack should use the default naming scheme, a None can be passed
in instead of the name.
For example, if you have an operation that has four results and you want
to split these into three distinct groups you could do the following:
setNameFn(getResult(0), "first_result");
setNameFn(getResult(1), "middle_results");
setNameFn(getResult(3), ""); // use the default numbering.
This would print the operation as follows:
%first_result, %middle_results:2, %0 = "my.op" ...
PiperOrigin-RevId: 281546873
The `vector.strided_slice` takes an n-D vector, k-D `offsets` integer array attribute, a
k-D `sizes` integer array attribute, a k-D `strides` integer array attribute and extracts
the n-D subvector at the proper offset.
Returns an n-D vector where the first k-D dimensions match the `sizes` attribute.
The returned subvector contains the elements starting at offset `offsets` and ending at
`offsets + sizes`.
Example:
```
%1 = vector.strided_slice %0
{offsets : [0, 2], sizes : [2, 4], strides : [1, 1]}:
vector<4x8x16xf32> // returns a vector<2x4x16xf32>
```
This op will be useful for progressive lowering within the VectorOp dialect.
PiperOrigin-RevId: 281352749
This method is needed for N->1 conversion patterns to retrieve remapped
Values used in the original N operations.
Closestensorflow/mlir#237
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/237 from dcaballe:dcaballe/getRemappedValue 1f64fadcf2b203f7b336ff0c5838b116ae3625db
PiperOrigin-RevId: 281321881
The command-line flag name `lower-to-llvm` for the pass performing dialect
conversion from the Standard dialect to the LLVM dialect is misleading and
inconsistent with most of the conversion passses. It leads the user to believe
that there are no restrictions on what can be converted, while in fact only a
subset of the Standard dialect can be converted (with operations from other
dialects converted by separate passes). Use `convert-std-to-llvm` that better
reflects what the pass does and is consistent with most other conversions.
PiperOrigin-RevId: 281238797
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
Mahesh Ravishankar