This is the standard syntax for types on operations, and is also already used by IntegerAttr and FloatAttr.
Example:
dense<5> : tensor<i32>
dense<[3]> : tensor<1xi32>
PiperOrigin-RevId: 255069157
This name has caused some confusion because it suggests that it's running op verification (and that this verification isn't getting run by default).
PiperOrigin-RevId: 254035268
Index types integers of platform-specific bit width. They are used to index
memrefs and as loop induction variables, however they could not be obtained
from an integer until now, making it virtually impossible to express indirect
accesses (given that memrefs of indices are not allowed) or data-dependent
loops. Introduce `std.index_cast` to transform indices into integers and vice
versa. The semantics of this cast is to sign-extend when casting to a wider
integer, and to truncate when casting to a narrower integer. It belongs to
StandardOps because both types it operates on are standard types, and because
its results are likely to be used in std.load and std.store.
Introduce llvm.sext, llvm.zext and llvm.trunc operations to the LLVM dialect.
Provide the conversion of `std.index_cast` to llvm.sext or llvm.trunc,
depending on the actual bitwidth of `index` known during the conversion.
PiperOrigin-RevId: 253624100
This CL enables verification code generation for variadic operands and results.
In verify(), we use fallback getter methods to access all the dynamic values
belonging to one static variadic operand/result to reuse the value range
calculation there.
PiperOrigin-RevId: 252288219
* There is no longer a need to explicitly remap function attrs.
- This removes a potentially expensive call from the destructor of Function.
- This will enable some interprocedural transformations to now run intraprocedurally.
- This wasn't scalable and forces dialect defined attributes to override
a virtual function.
* Replacing a function is now a trivial operation.
* This is a necessary first step to representing functions as operations.
--
PiperOrigin-RevId: 249510802
Make it clear that it cares about the aggregate type being a vector or tensor and not just that it has a shape.
Remove redundant validation from the custom method that is now covered by the tablegen'ed verification
This is related to making MemRefs a ShapedType as well.
--
PiperOrigin-RevId: 248610443
This closely mirrors the llvm fcmp instruction, defining 16 different predicates
Constant folding is unsupported for NaN and Inf because there's no way to represent those as constants at the moment
--
PiperOrigin-RevId: 246932358
Example:
func @unknown_std_op() {
%0 = "std.foo_bar_op"() : () -> index
return
}
Will result in:
error: unregistered operation 'std.foo_bar_op' found in dialect ('std') that does not allow unknown operations
--
PiperOrigin-RevId: 241266009
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
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
MLIR has support for type-polymorphic instructions, i.e. instructions that may
take arguments of different types. For example, standard arithmetic operands
take scalars, vectors or tensors. In order to express such instructions in
TableGen, we need to be able to verify that a type object satisfies certain
constraints, but we don't need to construct an instance of this type. The
existing TableGen definition of Type requires both. Extract out a
TypeConstraint TableGen class to define restrictions on types. Define the Type
TableGen class as a subclass of TypeConstraint for consistency. Accept records
of the TypeConstraint class instead of the Type class as values in the
Arguments class when defining operators.
Replace the predicate logic TableGen class based on conjunctive normal form
with the predicate logic classes allowing for abitrary combinations of
predicates using Boolean operators (AND/OR/NOT). The combination is
implemented using simple string rewriting of C++ expressions and, therefore,
respects the short-circuit evaluation order. No logic simplification is
performed at the TableGen level so all expressions must be valid C++.
Maintaining CNF using TableGen only would have been complicated when one needed
to introduce top-level disjunction. It is also unclear if it could lead to a
significantly simpler emitted C++ code. In the future, we may replace inplace
predicate string combination with a tree structure that can be simplified in
TableGen's C++ driver.
Combined, these changes allow one to express traits like ArgumentsAreFloatLike
directly in TableGen instead of relying on C++ trait classes.
PiperOrigin-RevId: 229398247
The entire compiler now looks at structural properties of the function (e.g.
does it have one block, does it contain an if/for stmt, etc) so the only thing
holding up this difference is round tripping through the parser/printer syntax.
Removing this shrinks the compile by ~140LOC.
This is step 31/n towards merging instructions and statements. The last step
is updating the docs, which I will do as a separate patch in order to split it
from this mostly mechanical patch.
PiperOrigin-RevId: 227540453
printing the entry block in a CFG function's argument line. Since I'm touching
all of the testcases anyway, change the argument list from printing as
"%arg : type" to "%arg: type" which is more consistent with bb arguments.
In addition to being more consistent, this is a much nicer look for cfg functions.
PiperOrigin-RevId: 227240069
This CL hooks up and uses permutation_map in vector_transfer ops.
In particular, when going into the nuts and bolts of the implementation, it
became clear that cases arose that required supporting broadcast semantics.
Broadcast semantics are thus added to the general permutation_map.
The verify methods and tests are updated accordingly.
Examples of interest include.
Example 1:
The following MLIR snippet:
```mlir
for %i3 = 0 to %M {
for %i4 = 0 to %N {
for %i5 = 0 to %P {
%a5 = load %A[%i4, %i5, %i3] : memref<?x?x?xf32>
}}}
```
may vectorize with {permutation_map: (d0, d1, d2) -> (d2, d1)} into:
```mlir
for %i3 = 0 to %0 step 32 {
for %i4 = 0 to %1 {
for %i5 = 0 to %2 step 256 {
%4 = vector_transfer_read %arg0, %i4, %i5, %i3
{permutation_map: (d0, d1, d2) -> (d2, d1)} :
(memref<?x?x?xf32>, index, index) -> vector<32x256xf32>
}}}
````
Meaning that vector_transfer_read will be responsible for reading the 2-D slice:
`%arg0[%i4, %i5:%15+256, %i3:%i3+32]` into vector<32x256xf32>. This will
require a transposition when vector_transfer_read is further lowered.
Example 2:
The following MLIR snippet:
```mlir
%cst0 = constant 0 : index
for %i0 = 0 to %M {
%a0 = load %A[%cst0, %cst0] : memref<?x?xf32>
}
```
may vectorize with {permutation_map: (d0) -> (0)} into:
```mlir
for %i0 = 0 to %0 step 128 {
%3 = vector_transfer_read %arg0, %c0_0, %c0_0
{permutation_map: (d0, d1) -> (0)} :
(memref<?x?xf32>, index, index) -> vector<128xf32>
}
````
Meaning that vector_transfer_read will be responsible of reading the 0-D slice
`%arg0[%c0, %c0]` into vector<128xf32>. This will require a 1-D vector
broadcast when vector_transfer_read is further lowered.
Additionally, some minor cleanups and refactorings are performed.
One notable thing missing here is the composition with a projection map during
materialization. This is because I could not find an AffineMap composition
that operates on AffineMap directly: everything related to composition seems
to require going through SSAValue and only operates on AffinMap at a distance
via AffineValueMap. I have raised this concern a bunch of times already, the
followup CL will actually do something about it.
In the meantime, the projection is hacked at a minimum to pass verification
and materialiation tests are temporarily incorrect.
PiperOrigin-RevId: 224376828
This CL implements and uses VectorTransferOps in lieu of the former custom
call op. Tests are updated accordingly.
VectorTransferOps come in 2 flavors: VectorTransferReadOp and
VectorTransferWriteOp.
VectorTransferOps can be thought of as a backend-independent
pseudo op/library call that needs to be legalized to MLIR (whiteboxed) before
it can be lowered to backend-dependent IR.
Note that the current implementation does not yet support a real permutation
map. Proper support will come in a followup CL.
VectorTransferReadOp
====================
VectorTransferReadOp performs a blocking read from a scalar memref
location into a super-vector of the same elemental type. This operation is
called 'read' by opposition to 'load' because the super-vector granularity
is generally not representable with a single hardware register. As a
consequence, memory transfers will generally be required when lowering
VectorTransferReadOp. A VectorTransferReadOp is thus a mid-level abstraction
that supports super-vectorization with non-effecting padding for full-tile
only code.
A vector transfer read has semantics similar to a vector load, with additional
support for:
1. an optional value of the elemental type of the MemRef. This value
supports non-effecting padding and is inserted in places where the
vector read exceeds the MemRef bounds. If the value is not specified,
the access is statically guaranteed to be within bounds;
2. an attribute of type AffineMap to specify a slice of the original
MemRef access and its transposition into the super-vector shape. The
permutation_map is an unbounded AffineMap that must represent a
permutation from the MemRef dim space projected onto the vector dim
space.
Example:
```mlir
%A = alloc(%size1, %size2, %size3, %size4) : memref<?x?x?x?xf32>
...
%val = `ssa-value` : f32
// let %i, %j, %k, %l be ssa-values of type index
%v0 = vector_transfer_read %src, %i, %j, %k, %l
{permutation_map: (d0, d1, d2, d3) -> (d3, d1, d2)} :
(memref<?x?x?x?xf32>, index, index, index, index) ->
vector<16x32x64xf32>
%v1 = vector_transfer_read %src, %i, %j, %k, %l, %val
{permutation_map: (d0, d1, d2, d3) -> (d3, d1, d2)} :
(memref<?x?x?x?xf32>, index, index, index, index, f32) ->
vector<16x32x64xf32>
```
VectorTransferWriteOp
=====================
VectorTransferWriteOp performs a blocking write from a super-vector to
a scalar memref of the same elemental type. This operation is
called 'write' by opposition to 'store' because the super-vector
granularity is generally not representable with a single hardware register. As
a consequence, memory transfers will generally be required when lowering
VectorTransferWriteOp. A VectorTransferWriteOp is thus a mid-level
abstraction that supports super-vectorization with non-effecting padding
for full-tile only code.
A vector transfer write has semantics similar to a vector store, with
additional support for handling out-of-bounds situations.
Example:
```mlir
%A = alloc(%size1, %size2, %size3, %size4) : memref<?x?x?x?xf32>.
%val = `ssa-value` : vector<16x32x64xf32>
// let %i, %j, %k, %l be ssa-values of type index
vector_transfer_write %val, %src, %i, %j, %k, %l
{permutation_map: (d0, d1, d2, d3) -> (d3, d1, d2)} :
(vector<16x32x64xf32>, memref<?x?x?x?xf32>, index, index, index, index)
```
PiperOrigin-RevId: 223873234
This CL added two new traits, SameOperandsAndResultShape and
ResultsAreBoolLike, and changed CmpIOp to embody these two
traits. As a consequence, CmpIOp's result type now is verified
to be bool-like.
PiperOrigin-RevId: 223208438
The semantics of 'select' is conventional: return the second operand if the
first operand is true (1 : i1) and the third operand otherwise. It is
applicable to vectors and tensors element-wise, similarly to LLVM instruction.
This operation is necessary to implement min/max to lower 'for' loops with
complex bounds to CFG functions and to support ternary operations in ML
functions. It is preferred to first-class min/max because of its simplicity,
e.g. it is not concered with signedness.
PiperOrigin-RevId: 223160860
Arithmetic and comparison instructions are necessary to implement, e.g.,
control flow when lowering MLFunctions to CFGFunctions. (While it is possible
to replace some of the arithmetics by affine_apply instructions for loop
bounds, it is still necessary for loop bounds checking, steps, if-conditions,
non-trivial memref subscripts, etc.) Furthermore, working with indirect
accesses in, e.g., lookup tables for large embeddings, may require operating on
tensors of indexes. For example, the equivalents to C code "LUT[Index[i]]" or
"ResultIndex[i] = i + j" where i, j are loop induction variables require the
arithmetics on indices as well as the possibility to operate on tensors
thereof. Allow arithmetic and comparison operations to apply to index types by
declaring them integer-like. Allow tensors whose element type is index for
indirection purposes.
The absence of vectors with "index" element type is explicitly tested, but the
only justification for this restriction in the CL introducing the test is
"because we don't need them". Do NOT enable vectors of index types, although
it makes vector and tensor types inconsistent with respect to allowed element
types.
PiperOrigin-RevId: 220614055
This binary operation is applicable to integers, vectors and tensors thereof
similarly to binary arithmetic operations. The operand types must match
exactly, and the shape of the result type is the same as that of the operands.
The element type of the result is always i1. The kind of the comparison is
defined by the "predicate" integer attribute. This attribute requests one of:
- equals to;
- not equals to;
- signed less than;
- signed less than or equals;
- signed greater than;
- signed greater than or equals;
- unsigned less than;
- unsigned less than or equals;
- unsigned greater than;
- unsigned greater than or equals.
Since integer values themselves do not have a sign, the comparison operator
specifies whether to use signed or unsigned comparison logic, i.e. whether to
interpret values where the foremost bit is set as negatives expressed as two's
complements or as positive values. For non-scalar operands, pairwise
per-element comparison is performed. Comparison operators on scalars are
necessary to implement basic control flow with conditional branches.
PiperOrigin-RevId: 220613566
Introduce a new public static member function, MemRefType::getChecked, intended
for the users that want detailed error messages to be emitted during MemRefType
construction and can gracefully handle these errors. This function takes a
Location of the "MemRef" token if known. The parser is one user of getChecked
that has location information, it outputs errors as compiler diagnostics.
Other users may pass in an instance of UnknownLoc and still have error messages
emitted. Compiler-internal users not expecting the MemRefType construction to
fail should call MemRefType::get, which now aborts on failure with a generic
message.
Both "getChecked" and "get" call to a static free function that does actual
construction with well-formedness checks, optionally emits errors and returns
nullptr on failure.
The location information passed to getChecked has voluntarily coarse precision.
The error messages are intended for compiler engineers and do not justify
heavier API than a single location. The text of the messages can be written so
that it pinpoints the actual location of the error within a MemRef declaration.
PiperOrigin-RevId: 219765902
1) affineint (as it is named) is not a type suitable for general computation (e.g. the multiply/adds in an integer matmul). It has undefined width and is undefined on overflow. They are used as the indices for forstmt because they are intended to be used as indexes inside the loop.
2) It can be used in both cfg and ml functions, and in cfg functions. As you mention, “symbols” are not affine, and we use affineint values for symbols.
3) Integers aren’t affine, the algorithms applied to them can be. :)
4) The only suitable use for affineint in MLIR is for indexes and dimension sizes (i.e. the bounds of those indexes).
PiperOrigin-RevId: 216057974
optimization pass:
- Give the ability for operations to implement a constantFold hook (a simple
one for single-result ops as well as general support for multi-result ops).
- Implement folding support for constant and addf.
- Implement support in AbstractOperation and Operation to make this usable by
clients.
- Implement a very simple constant folding pass that does top down folding on
CFG and ML functions, with a testcase that exercises all the above stuff.
Random cleanups:
- Improve the build APIs for ConstantOp.
- Stop passing "-o -" to mlir-opt in the testsuite, since that is the default.
PiperOrigin-RevId: 213749809
Previously the error could mislead into thinking it was a parser bug instead of the input being erroneous. Update to make it clearer.
PiperOrigin-RevId: 212271145
Ensure delimiters are absent where not expected. This is only checked in the case where operand count is known. This allows for the currently accepted case where there is a operand list with no delimiter and variable number of operands (which could be empty), followed by a delimited operand list.
PiperOrigin-RevId: 212202064
- Add a new -verify mode to the mlir-opt tool that allows writing test cases
for optimization and other passes that produce diagnostics.
- Refactor existing the -check-parser-errors flag to mlir-opt into a new
-split-input-file option which is orthogonal to -verify.
- Eliminate the special error hook the parser maintained and use the standard
MLIRContext's one instead.
- Enhance the default MLIRContext error reporter to print file/line/col of
errors when it is available.
- Add new createChecked() methods to the builder that create ops and invoke
the verify hook on them, use this to detected unhandled code in the
RaiseControlFlow pass.
- Teach mlir-opt about expected-error @+, it previously only worked with @-
PiperOrigin-RevId: 211305770
This commit creates a static constexpr limit for the IntegerType
bitwidth and uses it. The check had to be moved because Token is
not aware of IR/Type and it was a sign the abstraction leaked:
bitwidth limit is not a property of the Token but of the IntegerType.
Added a positive and a negative test at the limit.
PiperOrigin-RevId: 210388192
- Have the parser rewrite forward references to their resolved values at the
end of parsing.
- Implement verifier support for detecting malformed function attrs.
- Add efficient query for (in general, recursive) attributes to tell if they
contain a function.
As part of this, improve other general infrastructure:
- Implement support for verifying OperationStmt's in ml functions, refactoring
and generalizing support for operations in the verifier.
- Refactor location handling code in mlir-opt to have the non-error expecting
form of mlir-opt invocations to report error locations precisely.
- Fix parser to detect verifier failures and report them through errorReporter
instead of printing the error and crashing.
This regresses the location info for verifier errors in the parser that were
previously ascribed to the function. This will get resolved in future patches
by adding support for function attributes, which we can use to manage location
information.
PiperOrigin-RevId: 209600980
encapsulates an operation that is yet to be created. This is a patch towards
custom ops providing create methods that don't need to be templated, allowing
them to move out of line in the future.
PiperOrigin-RevId: 207725557
This regresses parser error recovery in some cases (in invalid.mlir) which I'll
consider in a follow-up patch. The important thing in this patch is that the
parse methods in StandardOps.cpp are nice and simple.
PiperOrigin-RevId: 206023308
- Op classes can now provide customized matchers, allowing specializations
beyond just a name match.
- We now provide default implementations of verify/print hooks, so Op classes
only need to implement them if they're doing custom stuff, and only have to
implement the ones they're interested in.
- "Base" now takes a variadic list of template template arguments, allowing
concrete Op types to avoid passing the Concrete type multiple times.
- Add new ZeroOperands trait.
- Add verification hooks to Zero/One/Two operands and OneResult to check that
ops using them are correctly formed.
- Implement getOperand hooks to zero/one/two operand traits, and
getResult/getType hook to OneResult trait.
- Add a new "constant" op to show some of this off, with a specialization for
the constant case.
This patch also splits op validity checks out to a new test/IR/invalid-ops.mlir
file.
This stubs out support for default asmprinter support. My next planned patch
building on top of this will make asmprinter hooks real and will revise this.
PiperOrigin-RevId: 205833214
River Riddle