LoopFusion
- getConstDifference in LoopFusion is pending a refactoring to handle bounds
with min's and max's; it currently asserts on some useful test cases that we
want to experiment with. This CL changes getSliceBounds to be more
conservative so as to not trigger the assertion. Filed b/126426796 to track this.
PiperOrigin-RevId: 235826538
- clean up loop fusion CL options for promoting local buffers to fast memory
space
- add parameters to loop fusion pass instantiation
PiperOrigin-RevId: 235813419
When lowering to MLIR(LLVMDialect) we unbox the structs that result
from converting static memrefs, that is, singleton structs
that just contain a raw pointer. This allows us to get rid of all
"extractvalue" instructions in the common case where shapes are fully
known.
PiperOrigin-RevId: 235706021
Since the goal of the LLVM IR dialect is to reflect LLVM IR in MLIR, the
dialect and the conversion procedure must account for the differences betweeen
block arguments and LLVM IR PHI nodes. In particular, LLVM IR disallows PHI
nodes with different values coming from the same source. Therefore, the LLVM IR
dialect now disallows `cond_br` operations that have identical successors
accepting arguments, which would lead to invalid PHI nodes. The conversion
process resolves the potential PHI source ambiguity by injecting dummy blocks
if the same block is used more than once as a successor in an instruction.
These dummy blocks branch unconditionally to the original successors, pass them
the original operands (available in the dummy block because it is dominated by
the original block) and are used instead of them in the original terminator
operation.
PiperOrigin-RevId: 235682798
Addressing post-submit comments. The `getelementptr` operation now supports
non-constant indexes, similarly to LLVM, and this functionality is exercised by
the lowering to the dialect. Update the documentation accordingly.
List the values of integer comparison predicates, which currently correspond to
those of CmpIOp in MLIR. Ideally, we would use strings instead, but it
requires additional support for argument conversion in both the dialect
lowering pass and the LLVM translator.
PiperOrigin-RevId: 235678877
This CL adds a primitive to perform stripmining of a loop by a given factor and
sinking it under multiple target loops.
In turn this is used to implement imperfectly nested loop tiling (with interchange) by repeatedly calling the stripmineSink primitive.
The API returns the point loops and allows repeated invocations of tiling to achieve declarative, multi-level, imperfectly-nested tiling.
Note that this CL is only concerned with the mechanical aspects and does not worry about analysis and legality.
The API is demonstrated in an example which creates an EDSC block, emits the corresponding MLIR and applies imperfectly-nested tiling:
```cpp
auto block = edsc::block({
For(ArrayRef<edsc::Expr>{i, j}, {zero, zero}, {M, N}, {one, one}, {
For(k1, zero, O, one, {
C({i, j, k1}) = A({i, j, k1}) + B({i, j, k1})
}),
For(k2, zero, O, one, {
C({i, j, k2}) = A({i, j, k2}) + B({i, j, k2})
}),
}),
});
// clang-format on
emitter.emitStmts(block.getBody());
auto l_i = emitter.getAffineForOp(i), l_j = emitter.getAffineForOp(j),
l_k1 = emitter.getAffineForOp(k1), l_k2 = emitter.getAffineForOp(k2);
auto indicesL1 = mlir::tile({l_i, l_j}, {512, 1024}, {l_k1, l_k2});
auto l_ii1 = indicesL1[0][0], l_jj1 = indicesL1[1][0];
mlir::tile({l_jj1, l_ii1}, {32, 16}, l_jj1);
```
The edsc::Expr for the induction variables (i, j, k_1, k_2) provide the programmatic hooks from which tiling can be applied declaratively.
PiperOrigin-RevId: 235548228
Leverage the recently introduced support for multiple argument groups and
multiple destination blocks in EDSC Expressions to implement conditional
branches in EDSC. Conditional branches have two successors and three argument
groups. The first group contains a single expression of i1 type that
corresponds to the condition of the branch. The two following groups contain
arguments of the two successors of the conditional branch instruction, in the
same order as the successors. Expose this instruction to the C API and Python
bindings.
PiperOrigin-RevId: 235542768
The new implementation of blocks was designed to support blocks with arguments.
More specifically, StmtBlock can be constructed with a list of Bindables that
will be bound to block aguments upon construction. Leverage this functionality
to implement branch instructions with arguments.
This additionally requires the statement storage to have a list of successors,
similarly to core IR operations.
Becauase successor chains can form loops, we need a possibility to decouple
block declaration, after which it becomes usable by branch instructions, from
block body definition. This is achieved by creating an empty block and by
resetting its body with a new list of instructions. Note that assigning a
block from another block will not affect any instructions that may have
designated this block as their successor (this behavior is necessary to make
value-type semantics of EDSC types consistent). Combined, one can now write
generators like
EDSCContext context;
Type indexType = ...;
Bindable i(indexType), ii(indexType), zero(indexType), one(indexType);
StmtBlock loopBlock({i}, {});
loopBlock.set({ii = i + one,
Branch(loopBlock, {ii})});
MLIREmitter(&builder)
.bindConstant<ConstantIndexOp>(zero, 0)
.bindConstant<ConstantIndexOp>(one, 1)
.emitStmt(Branch(loopBlock, {zero}));
where the emitter will emit the statement and its successors, if present.
PiperOrigin-RevId: 235541892
This came up in post-submit review. Use LLVM's support for outputting APInt
values directly instead of obtaining a 64-bit integer value from APInt, which
will not work for wider integers.
PiperOrigin-RevId: 235531574
Previously we have `auto pos = std::string::find(...) != std::string::npos` as
if condition to control substring substitution. Instead of the position for the
found substring, `pos` will be a boolean value indicating found nor not. Then
used as the replace start position, we were always replacing starting from 0 or
1. If the replaced substring also has the pattern to be matched, we'll see
an infinite loop.
PiperOrigin-RevId: 235504681
Analysis - NFC
- refactor AffineExprFlattener (-> SimpleAffineExprFlattener) so that it
doesn't depend on FlatAffineConstraints, and so that FlatAffineConstraints
could be moved out of IR/; the simplification that the IR needs for
AffineExpr's doesn't depend on FlatAffineConstraints
- have AffineExprFlattener derive from SimpleAffineExprFlattener to use for
all Analysis/Transforms purposes; override addLocalFloorDivId in the derived
class
- turn addAffineForOpDomain into a method on FlatAffineConstraints
- turn AffineForOp::getAsValueMap into an AffineValueMap ctor
PiperOrigin-RevId: 235283610
The only reason in starting with a fixedpoint add is that it is the absolute simplest variant and illustrates the level of abstraction I'm aiming for.
The overall flow would be:
1. Determine quantization parameters (out of scope of this cl).
2. Source dialect rules to lower supported math ops to the quantization dialect (out of scope of this cl).
3. Quantization passes: [-quant-convert-const, -quant-lower-uniform-real-math, -quant-lower-unsupported-to-float] (the last one not implemented yet)
4. Target specific lowering of the integral arithmetic ops (roughly at the level of gemmlowp) to more fundamental operations (i.e. calls to gemmlowp, simd instructions, DSP instructions, etc).
How I'm doing this should facilitate implementation of just about any kind of backend except TFLite, which has a very course, adhoc surface area for its quantized kernels. Options there include (I'm not taking an opinion on this - just trying to provide options):
a) Not using any of this: just match q/dbarrier + tf math ops to the supported TFLite quantized op set.
b) Implement the more fundamental integer math ops on TFLite and convert to those instead of the current op set.
Note that I've hand-waved over the process of choosing appropriate quantization parameters. Getting to that next. As you can see, different implementations will likely have different magic combinations of specific math support, and we will need the target system that has been discussed for some of the esoteric cases (i.e. many DSPs only support POT fixedpoint).
Two unrelated changes to the overall goal of this CL and can be broken out of desired:
- Adding optional attribute support to TabelGen
- Allowing TableGen native rewrite hooks to return nullptr, signalling that no rewrite has been done.
PiperOrigin-RevId: 235267229
Add a documentation page on the key points of the conversion to LLVM IR. This
focuses on the aspects of conversion that are relevant for integration of the
LLVM IR dialect (and produced LLVM IR that is mostly a one-to-one translation)
into other projects. In particular, it describes the type conversion rules and
the memref model supporting dynamic sizes.
PiperOrigin-RevId: 235190772
The LLVM IR pass was bootstrapped without user documentation, following LLVM's
language reference and existing conversions between MLIR standard operations
and LLVM IR instructions. Provide concise documentation of the LLVM IR dialect
operations. This documentation does not describe the semantics of the
operations, which should match that of LLVM IR, but highlights the structural
differences in operation definitions, in particular using attributes instead of
constant-only values. It also describes pseudo-operations that exist only to
make the LLVM IR dialect self-contained within MLIR.
While it could have been possible to generate operation description from
TableGen, this opts for a more concise format where groups of related
operations are described together.
PiperOrigin-RevId: 235149136
Add support for lowering DivF and RemF to LLVM::FDiv and LLMV::FRem
respectively. The lowering is a trivial one-to-one transformation.
The corresponding operations already existed in the LLVM IR dialect and can be
lowered to the LLVM IR proper. Add the necessary tests for scalar and vector
forms.
PiperOrigin-RevId: 234984608
This change introduces three new operators in EDSC: Div (also exposed
via Expr.__div__ aka /) -- floating-point division, FloorDiv and CeilDiv
for flooring/ceiling index division.
The lowering to LLVM will be implemented in b/124872679.
PiperOrigin-RevId: 234963217
Introduce support for binding MLIR functions as constant expressions. Standard
constant operation supports functions as possible constant values.
Provide C APIs to look up existing named functions in an MLIR module and expose
them to the Python bindings. Provide Python bindings to declare a function in
an MLIR module without defining it and to add a definition given a function
declaration. These declarations are useful when attempting to link MLIR
modules with, e.g., the standard library.
Introduce EDSC support for direct and indirect calls to other MLIR functions.
Internally, an indirect call is always emitted to leverage existing support for
delayed construction of MLIR Values using EDSC Exprs. If the expression is
bound to a constant function (looked up or declared beforehand), MLIR constant
folding will be able to replace an indirect call by a direct call. Currently,
only zero- and one-result functions are supported since we don't have support
for multi-valued expressions in EDSC.
Expose function calling interface to Python bindings on expressions by defining
a `__call__` function accepting a variable number of arguments.
PiperOrigin-RevId: 234959444
* Introduce a OpTrait class in C++ to wrap the TableGen definition;
* Introduce PredOpTrait and rename previous usage of OpTrait to NativeOpTrait;
* PredOpTrait allows specifying a trait of the operation by way of predicate on the operation. This will be used in future to create reusable set of trait building blocks in the definition of operations. E.g., indicating whether to operands have the same type and allowing locally documenting op requirements by trait composition.
- Some of these building blocks could later evolve into known fixed set as LLVMs backends do, but that can be considered with more data.
* Use the modelling to address one verify TODO in a very local manner.
This subsumes the current custom verify specification which will be removed in a separate mechanical CL.
PiperOrigin-RevId: 234827169
A recent change made ConstantOp::build accept a NumericAttr or assert that a
generic Attribute is in fact a NumericAttr. The rationale behind the change
was that NumericAttrs have a type that can be used as the result type of the
constant operation. FunctionAttr also has a type, and it is valid to construct
function-typed constants as exercised by the parser.mlir test. Relax
ConstantOp::build back to take a generic Attribute. In the overload that only
takes an attribute, assert that the Attribute is either a NumericAttr or a
FunctionAttr, because it is necessary to extract the type. In the overload
that takes both type type and the attribute, delegate the attribute type
checking to ConstantOp::verify to prevent non-Builder-based Op construction
mechanisms from creating invalid IR.
PiperOrigin-RevId: 234798569
The recent rework of MLIREmitter switched to using the generic call to
`builder.createOperation` from OperationState instead of individual customized
calls to `builder.create<>`. As a result, regular non-composed affine apply
operations where emitted. Introduce a special case in Expr::build to always
create composed affine maps instead, as it used to be the case before the
rework.
Such special-casing goes against the idea of EDSC generality and extensibility.
Instead, we should consider declaring the composed form canonical for
affine.apply operations and using the builder support for creating operations
and canonicalizing them immediately (ongoing effort).
PiperOrigin-RevId: 234790129
Introduce a type-safe way of building a 'for' loop with max/min bounds in EDSC.
Define new types MaxExpr and MinExpr in C++ EDSC API and expose them to Python
bindings. Use values of these type to construct 'for' loops with max/min in
newly introduced overloads of the `edsc::For` factory function. Note that in C
APIs, we still must expose MaxMinFor as a different function because C has no
overloads. Also note that MaxExpr and MinExpr do _not_ derive from Expr
because they are not allowed to be used in a regular Expr context (which may
produce `affine.apply` instructions not expecting `min` or `max`).
Factory functions `Min` and `Max` in Python can be further overloaded to
produce chains of comparisons and selects on non-index types. This is not
trivial in C++ since overloaded functions cannot differ by the return type only
(`MaxExpr` or `Expr`) and making `MaxExpr` derive from `Expr` defies the
purpose of type-safe construction.
PiperOrigin-RevId: 234786131
MLIR supports 'for' loops with lower(upper) bound defined by taking a
maximum(minimum) of a list of expressions, but does not have first-class affine
constructs for the maximum(minimum). All these expressions must have affine
provenance, similarly to a single-expression bound. Add support for
constructing such loops using EDSC. The expression factory function is called
`edsc::MaxMinFor` to (1) highlight that the maximum(minimum) operation is
applied to the lower(upper) bound expressions and (2) differentiate it from a
`edsc::For` that creates multiple perfectly nested loops (and should arguably
be called `edsc::ForNest`).
PiperOrigin-RevId: 234785996
Introduce a functionality to create EDSC expressions from typed constants.
This complements the current functionality that uses "unbound" expressions and
binds them to a specific constant before emission. It comes in handy in cases
where we want to check if something is a constant early during construciton
rather than late during emission, for example multiplications and divisions in
affine expressions. This is also consistent with MLIR vision of constants
being defined by an operation (rather than being special kinds of values in the
IR) by exposing this operation as EDSC expression.
PiperOrigin-RevId: 234758020
This CL fixes 2 recent issues with EDSCs:
1. the type of the LHS in Stmt::operator=(Expr rhs) should be the same as the (asserted unique) return type;
2. symbols coming from DimOp should be admissible as lower / upper bounds in For
The relevant tests are added.
PiperOrigin-RevId: 234750249
- compute slices precisely where the destination iteration depends on multiple source
iterations (instead of over-approximating to the whole source loop extent)
- update unionBoundingBox to deal with input with non-matching symbols
- reenable disabled backend test case
PiperOrigin-RevId: 234714069
- hoist DMAs past all loops immediately surrounding the region that the latter
is invariant on - do this at DMA generation time itself
PiperOrigin-RevId: 234628447
Originally, edsc::Expr had a long enum edsc::ExprKind with all supported types
of operations. Recent Expr extensibility support removed the need to specify
supported types in advance. Replace the no-longer-used blocks of enum values
reserved for unary/binary/ternary/variadic expressions with simple values (it
is still useful to know if an expression is, e.g., binary to access it through
a simpler API).
Furthermore, wrap string-comparison now used to identify specific ops into an
`Expr::is_op<>` function template, that acts similarly to `Instruction::isa<>`.
Introduce `{Unary,Binary,Ternary,Variadic}Expr::make<> ` function template that
creates a Expression emitting the MLIR Op specified as template argument.
PiperOrigin-RevId: 234612916
Expose the result types of edsc::Expr, which are now stored for all types of
Exprs and not only for the variadic ones. Require return types when an Expr is
constructed, if it will ever have some. An empty return type list is
interpreted as an Expr that does not create a value (e.g. `return` or `store`).
Conceptually, all edss::Exprs are now typed, with the type being a (potentially
empty) tuple of return types. Unbound expressions and Bindables must now be
constructed with a specific type they will take. This makes EDSC less
evidently type-polymorphic, but we can still write generic code such as
Expr sumOfSquares(Expr lhs, Expr rhs) { return lhs * lhs + rhs * rhs; }
and use it to construct different typed expressions as
sumOfSquares(Bindable(IndexType::get(ctx)), Bindable(IndexType::get(ctx)));
sumOfSquares(Bindable(FloatType::getF32(ctx)),
Bindable(FloatType::getF32(ctx)));
On the positive side, we get the following.
1. We can now perform type checking when constructing Exprs rather than during
MLIR emission. Nevertheless, this is still duplicates the Op::verify()
until we can factor out type checking from that.
2. MLIREmitter is significantly simplified.
3. ExprKind enum is only used for actual kinds of expressions. Data structures
are converging with AbstractOperation, and the users can now create a
VariadicExpr("canonical_op_name", {types}, {exprs}) for any operation, even
an unregistered one without having to extend the enum and make pervasive
changes to EDSCs.
On the negative side, we get the following.
1. Typed bindables are more verbose, even in Python.
2. We lose the ability to do print debugging for higher-level EDSC abstractions
that are implemented as multiple MLIR Ops, for example logical disjunction.
This is the step 2/n towards making EDSC extensible.
***
Move MLIR Op construction from MLIREmitter::emitExpr to Expr::build since Expr
now has sufficient information to build itself.
This is the step 3/n towards making EDSC extensible.
Both of these strive to minimize the amount of irrelevant changes. In
particular, this introduces more complex pretty-printing for affine and binary
expression to make sure tests continue to pass. It also relies on string
comparison to identify specific operations that an Expr produces.
PiperOrigin-RevId: 234609882
This CL extended TableGen Operator class to provide accessors for information on op
results.
In OpDefinitionGen, added checks to make sure only the last result can be variadic,
and adjusted traits and builders generation to consider variadic results.
PiperOrigin-RevId: 234596124
EDSC currently implement a block as a statement that is itself a list of
statements. This suffers from two modeling problems: (1) these blocks are not
addressable, i.e. one cannot create an instruction where thus constructed block
is a successor; (2) they support block nesting, which is not supported by MLIR
blocks. Furthermore, emitting such "compound statement" (misleadingly named
`Block` in Python bindings) does not actually produce a new Block in the IR.
Implement support for creating actual IR Blocks in EDSC. In particular, define
a new StmtBlock EDSC class that is neither an Expr nor a Stmt but contains a
list of Stmts. Additionally, StmtBlock may have (early-) typed arguments.
These arguments are Bindable expressions that can be used inside the block.
Provide two calls in the MLIREmitter, `emitBlock` that actually emits a new
block and `emitBlockBody` that only emits the instructions contained in the
block without creating a new block. In the latter case, the instructions must
not use block arguments.
Update Python bindings to make it clear when instruction emission happens
without creating a new block.
PiperOrigin-RevId: 234556474
The parameter to emitStandaloneParamBuilder() was renamed from hasResultType to
isAllSameType, which is the opposite boolean value. The logic should be changed
to make them consistent.
Also re-ordered some methods in Operator. And few other tiny improvements.
PiperOrigin-RevId: 234478316
generation pass to make it drop certain assumptions, complete TODOs.
- multiple fixes for getMemoryFootprintBytes
- pass loopDepth correctly from getMemoryFootprintBytes()
- use union while computing memory footprints
- bug fixes for addAffineForOpDomain
- take into account loop step
- add domains of other loop IVs in turn that might have been used in the bounds
- dma-generate: drop assumption of "non-unit stride loops being tile space loops
and skipping those and recursing to inner depths"; DMA generation is now purely
based on available fast mem capacity and memory footprint's calculated
- handle memory region compute failures/bailouts correctly from dma-generate
- loop tiling cleanup/NFC
- update some debug and error messages to use emitNote/emitError in
pipeline-data-transfer pass - NFC
PiperOrigin-RevId: 234245969
The existing implementation of makeFunctionType in EDSC contains a bug: the
array of input types is overwritten using output types passed as arguments and
the array of output types is never filled in. This leads to all sorts of
incorrect memory behavior. Fill in the array of output types using the proper
argument.
PiperOrigin-RevId: 234177221
A recent change introduced a possibility to run LLVM IR transformation during
JIT-compilation in the ExecutionEngine. Provide helper functions that
construct IR transformers given either clang-style optimization levels or a
list passes to run. The latter wraps the LLVM command line option parser to
parse strings rather than actual command line arguments. As a result, we can
run either of
mlir-cpu-runner -O3 input.mlir
mlir-cpu-runner -some-mlir-pass -llvm-opts="-llvm-pass -other-llvm-pass"
to combine different transformations. The transformer builder functions are
provided as a separate library that depends on LLVM pass libraries unlike the
main execution engine library. The library can be used for integrating MLIR
execution engine into external frameworks.
PiperOrigin-RevId: 234173493
*) Adds utility to LoopUtils to perform loop interchange of two AffineForOps.
*) Adds utility to LoopUtils to sink a loop to a specified depth within a loop nest, using a series of loop interchanges.
*) Computes dependences between all loads and stores in the loop nest, and classifies each loop as parallel or sequential.
*) Computes loop interchange permutation required to sink sequential loops (and raise parallel loop nests) while preserving relative order among them.
*) Checks each dependence against the permutation to make sure that dependences would not be violated by the loop interchange transformation.
*) Calls loop interchange in LoopFusion pass on consumer loop nests before fusing in producers, sinking loops with loop carried dependences deeper into the consumer loop nest.
*) Adds and updates related unit tests.
PiperOrigin-RevId: 234158370
We specify op operands and results in TableGen op definition using the same syntax.
They should be modelled similarly in TableGen driver wrapper classes.
PiperOrigin-RevId: 234153332
Add support for converting MLIR `call_indirect` instructions to the LLVM IR
dialect. In LLVM IR, the same instruction is used for direct and indirect
calls. In the dialect, we have `llvm.call` and `llvm.call0` to work around the
absence of the void type in MLIR. For direct calls, the callee is stored as
instruction attribute. Use the same pair of instructions for indirect calls by
omitting the callee attribute. In the MLIR to LLVM IR translator, check the
presence of attribute to decide whether to construct a direct or an indirect
call using different LLVM IR Builder functions.
Add support for converting constants of function type to the LLVM IR dialect
and for translating them to the LLVM IR proper. The `llvm.constant` operation
works similarly to other types: its attribute has MLIR function type but the
value it produces has LLVM IR function type wrapped in the dialect type. While
lowering, look up the pointer to the converted function in the corresponding
mapping.
PiperOrigin-RevId: 234132351
Function types are built-in in MLIR and affect the validity of the IR itself.
However, advanced target dialects such as the LLVM IR dialect may include
custom function types. Until now, dialect conversion was expecting function
types not to be converted to the custom type: although the signatures was
allowed to change, the outer type must have been an mlir::FunctionType. This
effectively prevented dialect conversion from creating instructions that
operate on values of the custom function type.
Dissociate function signature conversion from general type conversion.
Function signature conversion must still produce an mlir::FunctionType and is
used in places where built-in types are required to make IR valid. General
type conversion is used for SSA values, including function and block arguments
and function results.
Exercise this behavior in the LLVM IR dialect conversion by converting function
types to LLVM IR function pointer types. The pointer to a function is chosen
to provide consistent lowering of higher-order functions: while it is possible
to have a value of function type, it is not possible to create a function type
accepting a returning another function type.
PiperOrigin-RevId: 234124494
Update FlatAffineConstraints::getLower/UpperBounds to project to the identifier for which bounds are being computed. This change enables computing bounds on an identifier which were previously dependent on the bounds of another identifier.
PiperOrigin-RevId: 234017514
for dma-generate, loop-unroll.
- add -tile-sizes command line option for loop tiling to specify different tile
sizes for loops in a band
- clean up command line options for loop-unroll, dma-generate (remove
cl::hidden)
PiperOrigin-RevId: 234006232
If we see an add op adding a constant value to a convolution op with constant
bias, we can fuse the add into the convolution op by constant folding the
bias and the add op's constant operand.
This CL also removes dangling RewriterGen check that prevents us from using
nested DAG nodes in result patterns, which is already supported.
PiperOrigin-RevId: 233989654
For ops with the SameOperandsAndResultType trait, we know that all result types
should be the same as the first operand's type. So we can generate a build()
method without requiring result types as parameters and also invoke this method
when constructing such ops during expanding rewrite patterns.
Similarly for ops have broadcast behavior, we can define build() method to use
the deduced type as the result type. So we can also calling into this build()
method when constructing ops in RewriterGen.
PiperOrigin-RevId: 233988307
In LowerEDSCTestPass, there are two range-for loops that only do assertions on
the loop variable. With assertions disabled, the variable becomes unused and
triggers a warning promoted to error. Cast it to void in the loop to supress
the warning.
PiperOrigin-RevId: 233936171
Original implementation of the translation from MLIR to LLVM IR operated on the
Standard+BuiltIn dialect, with a later addition of the SuperVector dialect.
This required the translation to be aware of a potetially large number of other
dialects as the infrastructure extended. With the recent introduction of the
LLVM IR dialect into MLIR, the translation can be switched to only translate
the LLVM IR dialect, and the translation of the operations becomes largely
mechanical.
The reimplementation of the translator follows the lines of the original
translator in function and basic block conversion. In particular, block
arguments are converted to LLVM IR PHI nodes, which are connected to their
sources after all blocks of a function had been converted. Thanks to LLVM IR
types being wrapped in the MLIR LLVM dialect type, type conversion is
simplified to only convert function types, all other types are simply
unwrapped. Individual instructions are constructed using the LLVM IRBuilder,
which has a great potential for being table-generated from the LLVM IR dialect
operation definitions.
The input of the test/Target/llvmir.mlir is updated to use the MLIR LLVM IR
dialect. While it is now redundant with the dialect conversion test, the point
of the exercise is to guarantee exactly the same LLVM IR is emitted. (Only the
name of the allocation function is changed from `__mlir_alloc` to `alloc` in
the CHECK lines.) It will be simplified in a follow-up commit.
PiperOrigin-RevId: 233842306
EDSC expressions evolved to have different types of underlying storage.
Separate classes are used for unary, binary, ternary and variadic expressions.
The latter covers all the needs of the three special cases. Remove these
special cases and use a single ExprStorage class everywhere while maintaining
the same APIs at the Expr level (ExprStorage is an internal implementation
class).
This is step 1/n to converging EDSC expressions and Ops and making EDSCs
support custom operations.
PiperOrigin-RevId: 233704912
DimOp is converted to a constant LLVM IR dialect operation for static
dimensions and to an access to the dynamic size info stored in the memref
descriptor for the dynamic dimensions. This behavior is consistent with the
existing mlir-translator.
This completes the porting of MLIR -> LLVM lowering to the dialect conversion
infrastructure.
PiperOrigin-RevId: 233665634
- for the DMA transfers being pipelined through double buffering, generate
deallocs for the double buffers being alloc'ed
This change is along the lines of cl/233502632. We initially wanted to experiment with
scoped allocation - so the deallocation's were usually not necessary; however, they are
needed even with scoped allocations in some situations - for eg. when the enclosing loop
gets unrolled. The dealloc serves as an end of lifetime marker.
PiperOrigin-RevId: 233653463
In the current state, edsc::Expr and edsc::Stmt overload operators to construct
other Exprs and Stmts. This includes some unconventional overloads of the
`operator==` to create a comparison expression and of the `operator!` to create
a negation expression. This situation could lead to unpleasant surprises where
the code does not behave like expected. Make all Expr and Stmt construction
operators free functions and move them to the `edsc::op` namespace. Callers
willing to use these operators must explicitly include them with the `using`
declaration. This can be done in some local scope.
Additionally, we currently emit signed comparisons for order-comparison
operators. With namespaces, we can later introduce two sets of operators in
different namespace, e.g. `edsc::op::sign` and `edsc::op::unsign` to clearly
state which kind of comparison is implied.
PiperOrigin-RevId: 233578674
The existing implementation in EDSC of 'for' loops in MLIREmitter is
unnecessarily restricted to constant bounds. The underlying AffineForOp can be
constructed from (a list of) Values and AffineMaps instead of constants. Its
verifier will check that the "affine provenance" conditions, i.e. that the
values used in the loop conditions are defined in such a way that they can be
analyzed by affine passes, are respected. One can use non-constant values in
affine loop bounds in conjunction with a single-dimensional identity affine
map. Implement this in MLIREmitter while maintaining the special case for
constant bounds that leads to significantly simpler generated IR when
applicable.
Test this change using the EDSC lowering test pass to inject code emitted from
EDSC into functions with predefined names.
PiperOrigin-RevId: 233578220
Associates opaque constants with a particular dialect. Adds general mechanism to register dialect-specific hooks defined in external components. Adds hooks to decode opaque tensor constant and extract an element of an opaque tensor constant.
This CL does not change the existing mechanism for registering constant folding hook yet. One thing at a time.
PiperOrigin-RevId: 233544757
- for the DMA buffers being allocated (and their tags), generate corresponding deallocs
- minor related update to replaceAllMemRefUsesWith and PipelineDataTransfer pass
Code generation for DMA transfers was being done with the initial simplifying
assumption that the alloc's would map to scoped allocations, and so no
deallocations would be necessary. Drop this assumption to generalize. Note that
even with scoped allocations, unrolling loops that have scoped allocations
could create a series of allocations and exhaustion of fast memory. Having a
end of lifetime marker like a dealloc in fact allows creating new scopes if
necessary when lowering to a backend and still utilize scoped allocation.
DMA buffers created by -dma-generate are guaranteed to have either
non-overlapping lifetimes or nested lifetimes.
PiperOrigin-RevId: 233502632
- determine symbols for the memref region correctly
- this wasn't exposed earlier since we didn't have any test cases where the
portion of the nest being DMAed for was non-hyperrectangular (i.e., bounds of
one IV depending on other IVs within that part)
PiperOrigin-RevId: 233493872
* Add common broadcastable binary adder in TF ops and use for a few ops;
- Adding Sub, Mul here
* Change the prepare lowering to use TF variants;
* Add some more legalization patterns;
PiperOrigin-RevId: 233310952
* Fixed tfl.conv_2d and tfl.depthwise_conv_2d to have fused activation
function attribute
* Fixed RewriterGen crash: trying to get attribute match template when
the matcher is unspecified (UnsetInit)
PiperOrigin-RevId: 233241755
This CL allowed developers to write result ops having nested DAG nodes as their
arguments. Now we can write
```
def : Pat<(...), (AOp (BOp, ...), AOperand)>
```
PiperOrigin-RevId: 233207225
Previously we were using PatternRewrite::replaceOpWithNewOp() to both create the new op
inline and rewrite the matched op. That does not work well if we want to generate multiple
ops in a sequence. To support that, this CL changed to assign each newly created op to a
separate variable.
This CL also refactors how PatternEmitter performs the directive dispatch logic.
PiperOrigin-RevId: 233206819
Add support for converting `memref_cast` operations into the LLVM IR dialect.
This goes beyond want is currently implemented in the MLIR standard ops to LLVM
IR translation, but follows the general principles of the memref descriptors.
A memref cast creates a new descriptor containing the same buffer pointer but a
potentially different number of dynamic sizes (as many as dynamic dimensions in
the target memref type). The lowering copies the buffer pointer to the new
descriptor and inserts dynamic sizes to it. If the size is static in the
source type, a constant value is inserted as the dynamic size, otherwise a
dynamic value is copied from the source descriptor, taking into account the
difference in dynamic size positions in the descriptor.
PiperOrigin-RevId: 233082035
Make sure the module is always passed to the optimization layer.
Drop unused default argument for the IR transformation and remove the function
that was only used in this default argument. The transformation wrapper
constructor already checks for the null function, so the caller can just pass
`{}` if they don't want any transformation (no callers currently need this).
PiperOrigin-RevId: 233068817
Implement the lowering of memref load and store standard operations into the
LLVM IR dialect. This largely follows the existing mechanism in
MLIR-to-LLVM-IR translation for the sake of compatibility. A memref value is
transformed into a memref descriptor value which holds the pointer to the
underlying data buffer and the dynamic memref sizes. The data buffer is
contiguous. Accesses to multidimensional memrefs are linearized in row-major
form. In linear address computation, statically known sizes are used as
constants while dynamic sizes are extracted from the memref descriptor.
PiperOrigin-RevId: 233043846
That allows TensorFlow Add and Div ops to use Broadcastable op trait instead of
more restrictive SameValueType op trait.
That in turn allows TensorFlow ops to be registered by defining GET_OP_LIST and
including the generated ops file. Currently, tf-raise-control-flow pass tests
are using dynamic shapes in tf.Add op and AddOp can't be registered without
supporting the dynamic shapes.
TESTED with unit tests
PiperOrigin-RevId: 232927998
* Add tf.LeakyRelu op definition + folders (well one is really canonicalizer)
* Change generated error message to use attribute description instead;
* Change the return type of F32Attr to be APFloat - internally it is already
stored as APFloat so let the caller decides if they want to convert it or
not. I could see varying opinions here though :) (did not change i32attr
similarly)
PiperOrigin-RevId: 232923358
Aggregate types where at least one dimension is zero do not fully make sense as
they cannot contain any values (their total size is zero). However, TensorFlow
and XLA support tensors with zero sizes, so we must support those too. This is
relatively safe since, unlike vectors and memrefs, we don't have first-class
element accessors for MLIR tensors.
To support sparse element attributes of vector types that have no non-zero
elements, make sure that index and value element attributes have tensor type so
that we never need to create a zero vector type internally. Note that this is
already consistent with the inline documentation of the sparse elements
attribute. Users of the sparse elements attribute should not rely on the
storage schema anyway.
PiperOrigin-RevId: 232896707
Existing IR syntax is ambiguous in type declarations in presence of zero sizes.
In particular, `0x1` in the type size can be interpreted as either a
hexadecimal literal corresponding to 1, or as two distinct decimal literals
separated by an `x` for sizes. Furthermore, the shape `<0xi32>` fails lexing
because it is expected to be an integer literal.
Fix the lexer to treat `0xi32` as an integer literal `0` followed by a bare
identifier `xi32` (look one character ahead and early return instead of
erroring out).
Disallow hexadecimal literals in type declarations and forcibly split the token
into multiple parts while parsing the type. Note that the splitting trick has
been already present to separate the element type from the preceding `x`
character.
PiperOrigin-RevId: 232880373
The current ExecutionEngine flow generates the LLVM IR from MLIR and
JIT-compiles it as is without any transformation. It thus misses the
opportunity to perform optimizations supported by LLVM or collect statistics
about the module. Modify the Orc JITter to perform transformations on the LLVM
IR. Accept an optional LLVM module transformation function when constructing
the ExecutionEngine and use it while JIT-compiling. This prevents MLIR
ExecutionEngine from depending on LLVM passes; its clients should depend on the
passes they require.
PiperOrigin-RevId: 232877060
Instead, we deduce the result type from the given attribute.
This is in preparation for generating constant ops with TableGen.
PiperOrigin-RevId: 232723467
Implement the lowering of memref allocation and deallocation standard
operations into the LLVM IR dialect. This largely follows the existing
mechanism in MLIR-to-LLVM-IR translation for the sake of compatibility.
A memref value is transformed into a memref descriptor value which holds the
pointer to the underlying data buffer and the dynamic memref sizes. The buffer
is allocated using `malloc` and freed using `free`. The lowering inserts
declarations of these functions if necessary. Memref descriptors are values of
the LLVM IR structure type wrapped into an MLIR LLVM dialect type. The pointer
to the buffer and the individual sizes are accessed using `extractvalue` and
`insertvalue` LLVM IR instructions.
PiperOrigin-RevId: 232719419
*) Adds parameter to public API of MemRefRegion::compute for passing in the slice loop bounds to compute the memref region of the loop nest slice.
*) Exposes public method MemRefRegion::getRegionSize for computing the size of the memref region in bytes.
PiperOrigin-RevId: 232706165
Previously, we were using the trait mechanism to specify that an op has variadic operands.
That led a discrepancy between how we handle ops with deterministic number of operands.
Besides, we have no way to specify the constraints and match against the variadic operands.
This CL introduced Variadic<Type> as a way to solve the above issues.
PiperOrigin-RevId: 232656104
* AffineStructures has moved to IR.
* simplifyAffineExpr/simplifyAffineMap/getFlattenedAffineExpr have moved to IR.
* makeComposedAffineApply/fullyComposeAffineMapAndOperands have moved to AffineOps.
* ComposeAffineMaps is replaced by AffineApplyOp::canonicalize and deleted.
PiperOrigin-RevId: 232586468
Motivation for this change is to remove redundant TF type attributes for
TensorFlow ops. For example, tf$T: "tfdtype$DT_FLOAT". Type attributes can be derived using the MLIR operand or result MLIR types, attribute names and their mapping. This will also allow constant folding of instructions generated within MLIR (and not imported from TensorFlow) without adding type attributes for the instruction.
Derived attributes are populated while exporting MLIR to TF GraphDef using
auto-generated populators. Populators are only available for the ops that are generated by the TableGen.
Also, fixed Operator::getNumArgs method to exclude derived attributes as they are not
part of the arguments.
TESTED with unit test
PiperOrigin-RevId: 232531561
Existing type syntax contains the following productions:
function-type ::= type-list-parens `->` type-list
type-list ::= type | type-list-parens
type ::= <..> | function-type
Due to these rules, when the parser sees `->` followed by `(`, it cannot
disambiguate if `(` starts a parenthesized list of function result types, or a
parenthesized list of operands of another function type, returned from the
current function. We would need an unknown amount of lookahead to try to find
the `->` at the right level of function nesting to differentiate between type
lists and singular function types.
Instead, require the result type of the function that is a function type itself
to be always parenthesized, at the syntax level. Update the spec and the
parser to correspond to the production rule names used in the spec (although it
would have worked without modifications). Fix the function type parsing bug in
the process, as it used to accept the non-parenthesized list of types for
arguments, disallowed by the spec.
PiperOrigin-RevId: 232528361
In optional attribute dictionary used, among others, in the generic form of the
ops, attribute types for integers and floats are omitted. This could lead to
inconsistencies when round-tripping the IR, in particular the attributes are
created with incorrect types after parsing (integers default to i64, floats
default to f64). Provide API to emit a trailing type after the attribute for
integers and floats. Use it while printing the optional attribute dictionary.
Omitting types for i64 and f64 is a pragmatic decision that minimizes changes
in tests. We may want to reconsider in the future and always print types of
attributes in the generic form.
PiperOrigin-RevId: 232480116
*) After a private memref buffer is created for a fused loop nest, dependences on the old memref are reduced, which can open up fusion opportunities. In these cases, users of the old memref are added back to the worklist to be reconsidered for fusion.
*) Fixed a bug in fusion insertion point dependence check where the memref being privatized was being skipped from the check.
PiperOrigin-RevId: 232477853
- use getAccessMap() instead of repeating it
- fold getMemRefRegion into MemRefRegion ctor (more natural, avoid heap
allocation and unique_ptr where possible)
- change extractForInductionVars - MutableArrayRef -> ArrayRef for the
arguments. Since the method is just returning copies of 'Value *', the client
can't mutate the pointers themselves; it's fine to mutate the 'Value''s
themselves, but that doesn't mutate the pointers to those.
- change the way extractForInductionVars returns (see b/123437690)
PiperOrigin-RevId: 232359277
- with this we won't see duplicate / unused operands when getting access maps,
or when constructing FlatAffineConstraints based on such maps
- we can probably change fullyComposeAffineMapAndOperands to ensure this
TODO(b/123879896).
PiperOrigin-RevId: 232356600
The generic form may be more desirable even when there is a custom form
specified so add option to enable emitting it. This also exposes a current bug
when round tripping constant with function attribute.
PiperOrigin-RevId: 232350712
loops), (2) take into account fast memory space capacity and lower 'dmaDepth'
to fit, (3) add location information for debug info / errors
- change dma-generate pass to work on blocks of instructions (start/end
iterators) instead of 'for' loops; complete TODOs - allows DMA generation for
straightline blocks of operation instructions interspersed b/w loops
- take into account fast memory capacity: check whether memory footprint fits
in fastMemoryCapacity parameter, and recurse/lower the depth at which DMA
generation is performed until it does fit in the provided memory
- add location information to MemRefRegion; any insufficient fast memory
capacity errors or debug info w.r.t dma generation shows location information
- allow DMA generation pass to be instantiated with a fast memory capacity
option (besides command line flag)
- change getMemRefRegion to return unique_ptr's
- change getMemRefFootprintBytes to work on a 'Block' instead of 'ForInst'
- other helper methods; add postDomInstFilter option for
replaceAllMemRefUsesWith; drop forInst->walkOps, add Block::walkOps methods
Eg. output
$ mlir-opt -dma-generate -dma-fast-mem-capacity=1 /tmp/single.mlir
/tmp/single.mlir:9:13: error: Total size of all DMA buffers' for this block exceeds fast memory capacity
for %i3 = (d0) -> (d0)(%i1) to (d0) -> (d0 + 32)(%i1) {
^
$ mlir-opt -debug-only=dma-generate -dma-generate -dma-fast-mem-capacity=400 /tmp/single.mlir
/tmp/single.mlir:9:13: note: 8 KiB of DMA buffers in fast memory space for this block
for %i3 = (d0) -> (d0)(%i1) to (d0) -> (d0 + 32)(%i1) {
PiperOrigin-RevId: 232297044
They are essentially both modelling MLIR OpTrait; the former achieves the
purpose via introducing corresponding symbols in TableGen, while the latter
just uses plain strings.
Unify them to provide a single mechanism to avoid confusion and to better
reflect the definitions on MLIR C++ side.
Ideally we should be able to deduce lots of these traits automatically via
other bits of op definitions instead of manually specifying them; but not
for now though.
PiperOrigin-RevId: 232191401
These attribute kinds are different from the rest in the sense that their types are defined
in MLIR's type hierarchy and we can build constant op out of them.
By defining this middle-level base class, we have a unified way to test and query the type
of these attributes, which will be useful when constructing constant ops of various dialects.
This CL also added asserts to reject non-NumericAttr in constant op's build() method.
PiperOrigin-RevId: 232188178
- fusion already includes the necessary analysis to create small/local buffers
post fusion; allocate these buffers in a higher memory space if the necessary
pass parameters are provided (threshold size, memory space id)
- although there will be a separate utility at some point to directly detect
and promote small local buffers to higher memory spaces, doing it while fusion
when possible is much less expensive, comes free with fusion analysis, and covers
a key common case.
PiperOrigin-RevId: 232063894
Nothing in the loop can (legally) cause curPtr -> nullptr. And if it did, we
would null dereference right below anyway.
This loop still reads funny to me but doesn't make me stare at it and wonder
what I am missing anymore.
--
PiperOrigin-RevId: 232062076
This CL added a tblgen::DagLeaf wrapper class with several helper methods for handling
DAG arguments. It helps to refactor the rewriter generation logic to be more higher
level.
This CL also added a tblgen::ConstantAttr wrapper class for constant attributes.
PiperOrigin-RevId: 232050683
- getTerminator() on a block can return nullptr; moreover, blocks that are improperly
constructed/transformed by utilities/passes may not have terminators even for the
top-level blocks
PiperOrigin-RevId: 232025963
This CL applies the following simplifications to EDSCs:
1. Rename Block to StmtList because an MLIR Block is a different, not yet
supported, notion;
2. Rework Bindable to drop specific storage and just use it as a simple wrapper
around Expr. The only value of Bindable is to force a static cast when used by
the user to bind into the emitter. For all intended purposes, Bindable is just
a lightweight check that an Expr is Unbound. This simplifies usage and reduces
the API footprint. After playing with it for some time, it wasn't worth the API
cognition overhead;
3. Replace makeExprs and makeBindables by makeNewExprs and copyExprs which is
more explicit and less easy to misuse;
4. Add generally useful functionality to MLIREmitter:
a. expose zero and one for the ubiquitous common lower bounds and step;
b. add support to create already bound Exprs for all function arguments as
well as shapes and views for Exprs bound to memrefs.
5. Delete Stmt::operator= and replace by a `Stmt::set` method which is more
explicit.
6. Make Stmt::operator Expr() explicit.
7. Indexed.indices assertions are removed to pave the way for expressing slices
and views as well as to work with 0-D memrefs.
The CL plugs those simplifications with TableGen and allows emitting a full MLIR function for
pointwise add.
This "x.add" op is both type and rank-agnostic (by allowing ArrayRef of Expr
passed to For loops) and opens the door to spinning up a composable library of
existing and custom ops that should automate a lot of the tedious work in
TF/XLA -> MLIR.
Testing needs to be significantly improved but can be done in a separate CL.
PiperOrigin-RevId: 231982325
This allow for arbitrarily complex builder patterns which is meant to cover initial cases while the modelling is improved and long tail cases/cases for which expanding the DSL would result in worst overall system.
NFC just sorting the emit replace methods alphabetical in the class and file body.
PiperOrigin-RevId: 231890352
This CL introduces a hotfix post refactoring of NestedMatchers:
- fix uninitialized read to skip
- avoid bumpptr allocating with 0 elements
Interestingly the latter issue only surfaced in fastbuild mode with no-san and
manifested itself by a SIGILL. All other combinations that were tried failed to
reproduce the issue (dbg, opt, fastbuild with asan)
PiperOrigin-RevId: 231787642
A performance issue was reported due to the usage of NestedMatcher in
ComposeAffineMaps. The main culprit was the ubiquitous copies that were
occuring when appending even a single element in `matchOne`.
This CL generally simplifies the implementation and removes one level of indirection by getting rid of
auxiliary storage as well as simplifying the API.
The users of the API are updated accordingly.
The implementation was tested on a heavily unrolled example with
ComposeAffineMaps and is now close in performance with an implementation based
on stateless InstWalker.
As a reminder, the whole ComposeAffineMaps pass is slated to disappear but the bug report was very useful as a stress test for NestedMatchers.
Lastly, the following cleanups reported by @aminim were addressed:
1. make NestedPatternContext scoped within runFunction rather than at the Pass level. This was caused by a previous misunderstanding of Pass lifetime;
2. use defensive assertions in the constructor of NestedPatternContext to make it clear a unique such locally scoped context is allowed to exist.
PiperOrigin-RevId: 231781279
This CL addresses some cleanups that were leftover after an incorrect rebase:
1. use StringSwitch
2. use // NOLINTNEXTLINE
3. remove a dead line of code
PiperOrigin-RevId: 231726640
a trivial inst walker :-) (reduces pass time from several minutes non-terminating to 120ms) - (fixes b/123541184)
- use a simple 7-line inst walker to collect affine_apply op's instead of the nested
matcher; -compose-affine-maps pass runs in 120ms now instead of 5 minutes + (non-
terminating / out of memory) - on a realistic test case that is 20,000 lines 12-d
loop nest
- this CL is also pushing for simple existing/standard patterns unless there
is a real efficiency issue (OTOH, fixing nested matcher to address this issue requires
cl/231400521)
- the improvement is from swapping out the nested walker as opposed to from a bug
or anything else that this CL changes
- update stale comment
PiperOrigin-RevId: 231623619
This CL mandated TypeConstraint and Type to provide descriptions and fixed
various subclasses and definitions to provide so. The purpose is to enforce
good documentation; using empty string as the default just invites oversight.
PiperOrigin-RevId: 231579629
* Emitted result lists for ops.
* Changed to allow empty summary and description for ops.
* Avoided indenting description to allow proper MarkDown rendering of
formatting markers inside description content.
* Used fixed width font for operand/attribute names.
* Massaged TensorFlow op docs and generated dialect op doc.
PiperOrigin-RevId: 231427574
Similar to op operands and attributes, use DAG to specify operation's results.
This will allow us to provide names and matchers for outputs.
Also Defined `outs` as a marker to indicate the start of op result list.
PiperOrigin-RevId: 231422455
This CL also introduces a set of python bindings using pybind11. The bindings
are exercised using a `test_py2andpy3.py` test suite that works for both
python 2 and 3.
`test_py3.py` on the other hand uses the more idiomatic,
python 3 only "PEP 3132 -- Extended Iterable Unpacking" to implement a rank
and type-agnostic copy with transposition.
Because python assignment is by reference, we cannot easily make the
assignment operator use the same type of sugaring as in C++; i.e. the
following:
```cpp
Stmt block = edsc::Block({
For(ivs, zeros, shapeA, ones, {
C[ivs] = IA[ivs] + IB[ivs]
})});
```
has no equivalent in the native Python EDSCs at this time.
However, the sugaring can be built as a simple DSL in python and is left as
future work.
PiperOrigin-RevId: 231337667
Python modules cannot be defined under a directory that has a `-` character in its name inside of Google code.
Rename to `google_mlir` which circumvents this limitation.
PiperOrigin-RevId: 231329321
Update to allow constant attribute values to be used to match or as result in rewrite rule. Define variable ctx in the matcher to allow matchers to refer to the context of the operation being matched.
PiperOrigin-RevId: 231322019
This CL adds support for calling EDSCs from other languages than C++.
Following the LLVM convention this CL:
1. declares simple opaque types and a C API in mlir-c/Core.h;
2. defines the implementation directly in lib/EDSC/Types.cpp and
lib/EDSC/MLIREmitter.cpp.
Unlike LLVM however the nomenclature for these types and API functions is not
well-defined, naming suggestions are most welcome.
To avoid the need for conversion functions, Types.h and MLIREmitter.h include
mlir-c/Core.h and provide constructors and conversion operators between the
mlir::edsc type and the corresponding C type.
In this first commit, mlir-c/Core.h only contains the types for the C API
to allow EDSCs to work from Python. This includes both a minimal set of core
MLIR
types (mlir_context_t, mlir_type_t, mlir_func_t) as well as the EDSC types
(edsc_mlir_emitter_t, edsc_expr_t, edsc_stmt_t, edsc_indexed_t). This can be
restructured in the future as concrete needs arise.
For now, the API only supports:
1. scalar types;
2. memrefs of scalar types with static or symbolic shapes;
3. functions with input and output of these types.
The C API is not complete wrt ownership semantics. This is in large part due
to the fact that python bindings are written with Pybind11 which allows very
idiomatic C++ bindings. An effort is made to write a large chunk of these
bindings using the C API but some C++isms are used where the design benefits
from this simplication. A fully isolated C API will make more sense once we
also integrate with another language like Swift and have enough use cases to
drive the design.
Lastly, this CL also fixes a bug in mlir::ExecutionEngine were the order of
declaration of llvmContext and the JIT result in an improper order of
destructors (which used to crash before the fix).
PiperOrigin-RevId: 231290250
Similar to other tblgen:: abstractions, tblgen::Pattern hides the native TableGen
API and provides a nicer API that is more coherent with the TableGen definitions.
PiperOrigin-RevId: 231285143
* Matching an attribute and specifying a attribute constraint is the same thing executionally, so represent it such.
* Extract AttrConstraint helper to match TypeConstraint and use that where mAttr was previously used in RewriterGen.
PiperOrigin-RevId: 231213580
Cleanup a usage of functional::map that is deemed too obscure in
`reindexAffineIndices`. Also fix a stale comment in `reindexAffineIndices`.
PiperOrigin-RevId: 231211184
Addresses b/122486036
This CL addresses some leftover crumbs in AffineMap and IntegerSet by removing
the Null method and cleaning up the constructors.
As the ::Null uses were tracked down, opportunities appeared to untangle some
of the Parsing logic and make it explicit where AffineMap/IntegerSet have
ambiguous syntax. Previously, ambiguous cases were hidden behind the implicit
pointer values of AffineMap* and IntegerSet* that were passed as function
parameters. Depending the values of those pointers one of 3 behaviors could
occur.
This parsing logic convolution is one of the rare cases where I would advocate
for code duplication. The more proper fix would be to make the syntax
unambiguous or to allow some lookahead.
PiperOrigin-RevId: 231058512
This CL follows up on a memory leak issue related to SmallVector growth that
escapes the BumpPtrAllocator.
The fix is to properly use ArrayRef and placement new to define away the
issue.
The following renaming is also applied:
1. MLFunctionMatcher -> NestedPattern
2. MLFunctionMatches -> NestedMatch
As a consequence all allocations are now guaranteed to live on the BumpPtrAllocator.
PiperOrigin-RevId: 231047766
Example:
dma-generate options:
-dma-fast-mem-capacity - Set fast memory space ...
-dma-fast-mem-space=<uint> - Set fast memory space ...
loop-fusion options:
-fusion-compute-tolerance=<number> - Fractional increase in ...
-fusion-maximal - Enables maximal loop fusion
loop-tile options:
-tile-size=<uint> - Use this tile size for ...
loop-unroll options:
-unroll-factor=<uint> - Use this unroll factor ...
-unroll-full - Fully unroll loops
-unroll-full-threshold=<uint> - Unroll all loops with ...
-unroll-num-reps=<uint> - Unroll innermost loops ...
loop-unroll-jam options:
-unroll-jam-factor=<uint> - Use this unroll jam factor ...
PiperOrigin-RevId: 231019363
Use `-mlir-pretty-debuginfo` if the user wants line breaks between different callsite lines.
The print results before and after this CL are shown in the tests.
PiperOrigin-RevId: 231013812
index remapping
- generate a sequence of single result affine_apply's for the index remapping
(instead of one multi result affine_apply)
- update dma-generate and loop-fusion test cases; while on this, change test cases
to use single result affine apply ops
- some fusion comment fix/cleanup
PiperOrigin-RevId: 230985830
Ben Vanik