Some productions in the LangRef were using undefined terminals and non-terminals, which have been added to the EBNF.
The dialect type and dialect attribute productions matched precisely the same structure and have been deduplicated.
The production for ssa-id was ambiguous but the fix is trivial (merging the leading '%') and has been applied.
Closestensorflow/mlir#265
PiperOrigin-RevId: 282470892
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
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
This commit add `dialect-attribute-entry` requirements on function arguments,
function results, and function attributes to the documentation.
PiperOrigin-RevId: 281227740
The toy language uses element-wise multiplication. Transposing and multiplying
two tensors with shape <2, 3> gives a tensor with shape <3, 2>.
Closestensorflow/mlir#227
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/227 from ombre5733:toy-ch1-docu-fix d79e5d3f9e3d5150a7ac8aac28b899df5a0d10a0
PiperOrigin-RevId: 281221671
Update LangRef to explicitly mention the type canonicalization rule applied to
MemRef types: identity maps do not contribute to type identification.
PiperOrigin-RevId: 280684904
Thus far DRR always invokes the separate-parameter builder (i.e., requiring
a separate parameter for each result-type/operand/attribute) for creating
ops, no matter whether we can auto-generate a builder with type-deduction
ability or not.
This CL changes the path for ops that we can auto-generate type-deduction
builders, i.e., with SameOperandsAndResultType/FirstAttrDerivedResultType
traits. Now they are going through a aggregate-parameter builder (i.e.,
requiring one parameter for all result-types/operands/attributes).
attributes.)
It is expected this approach will be more friendly for future shape inference
function autogen and calling those autogen'd shape inference function without
excessive packing and repacking operand/attribute lists.
Also, it would enable better support for creating ops with optional attributes
because we are not required to provide an Attribute() as placeholder for
an optional attribute anymore.
PiperOrigin-RevId: 280654800
This CL moves VectorOps to Tablegen and cleans up the implementation.
This is almost NFC but 2 changes occur:
1. an interface change occurs in the padding value specification in vector_transfer_read:
the value becomes non-optional. As a shortcut we currently use %f0 for all paddings.
This should become an OpInterface for vectorization in the future.
2. the return type of vector.type_cast is trivial and simplified to `memref<vector<...>>`
Relevant roundtrip and invalid tests that used to sit in core are moved to the vector dialect.
The op documentation is moved to the .td file.
PiperOrigin-RevId: 280430869
This causes the AsmPrinter to use a local value numbering when printing the IR, allowing for the printer to be used safely in a local context, e.g. to ensure thread-safety when printing the IR. This means that the IR printing instrumentation can also be used during multi-threading when module-scope is disabled. Operation::dump and DiagnosticArgument(Operation*) are also updated to always print local scope, as this is the most common use case when debugging.
PiperOrigin-RevId: 279988203
This CL adds an extra pointer to the memref descriptor to allow specifying alignment.
In a previous implementation, we used 2 types: `linalg.buffer` and `view` where the buffer type was the unit of allocation/deallocation/alignment and `view` was the unit of indexing.
After multiple discussions it was decided to use a single type, which conflates both, so the memref descriptor now needs to carry both pointers.
This is consistent with the [RFC-Proposed Changes to MemRef and Tensor MLIR Types](https://groups.google.com/a/tensorflow.org/forum/#!searchin/mlir/std.view%7Csort:date/mlir/-wKHANzDNTg/4K6nUAp8AAAJ).
PiperOrigin-RevId: 279959463
This change allows for adding additional nested references to a SymbolRefAttr to allow for further resolving a symbol if that symbol also defines a SymbolTable. If a referenced symbol also defines a symbol table, a nested reference can be used to refer to a symbol within that table. Nested references are printed after the main reference in the following form:
symbol-ref-attribute ::= symbol-ref-id (`::` symbol-ref-id)*
Example:
module @reference {
func @nested_reference()
}
my_reference_op @reference::@nested_reference
Given that SymbolRefAttr is now more general, the existing functionality centered around a single reference is moved to a derived class FlatSymbolRefAttr. Followup commits will add support to lookups, rauw, etc. for scoped references.
PiperOrigin-RevId: 279860501
Add a note to chapter 2 of the Toy tutorial stating how to invoke
mlir-tblgen to check the generated C++ code. IMHO this is incredibly
useful when getting acquainted with TableGen/ODS.
Closestensorflow/mlir#228
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/228 from ombre5733:toy-ch2-howto-mlir-tblgen a051a3734ca8bbf4f12027fe737aca07c64ca59d
PiperOrigin-RevId: 279518989
This chapter adds a new composite type to Toy, and shows the process of adding a new type to the IR, adding and updating operations to use it, and constant folding operations producing it.
PiperOrigin-RevId: 279107885
"sgt" and "ult" used twice
the second "slt" should be "sge" for signed greater than or equal
the second "ult" should be "ule" unsigned less than or equal
Closestensorflow/mlir#223
PiperOrigin-RevId: 278745410
This allows GlobalOp to either take a value attribute (for simple constants) or a region that can
contain IR instructions (that must be constant-foldable) to create a ConstantExpr initializer.
Example:
// A complex initializer is constructed with an initializer region.
llvm.mlir.global constant @int_gep() : !llvm<"i32*"> {
%0 = llvm.mlir.addressof @g2 : !llvm<"i32*">
%1 = llvm.mlir.constant(2 : i32) : !llvm.i32
%2 = llvm.getelementptr %0[%1] : (!llvm<"i32*">, !llvm.i32) -> !llvm<"i32*">
llvm.return %2 : !llvm<"i32*">
}
PiperOrigin-RevId: 278717836
BitEnumAttr is a mechanism for modelling attributes whose value is
a bitfield. It should not be scoped to the SPIR-V dialect and can
be used by other dialects too.
This CL is mostly shuffling code around and adding tests and docs.
Functionality changes are:
* Fixed to use `getZExtValue()` instead of `getSExtValue()` when
getting the value from the underlying IntegerAttr for a case.
* Changed to auto-detect whether there is a case whose value is
all bits unset (i.e., zero). If so handle it specially in all
helper methods.
PiperOrigin-RevId: 277964926
This CL adds another control flow instruction in SPIR-V: OpPhi.
It is modelled as block arguments to be idiomatic with MLIR.
See the rationale.md doc for "Block Arguments vs PHI nodes".
Serialization and deserialization is updated to convert between
block arguments and SPIR-V OpPhi instructions.
PiperOrigin-RevId: 277161545
In some cases, it may be desirable to mark entire regions of operations as legal. This provides an additional granularity of context to the concept of "legal". The `ConversionTarget` supports marking operations, that were previously added as `Legal` or `Dynamic`, as `recursively` legal. Recursive legality means that if an operation instance is legal, either statically or dynamically, all of the operations nested within are also considered legal. An operation can be marked via `markOpRecursivelyLegal<>`:
```c++
ConversionTarget &target = ...;
/// The operation must first be marked as `Legal` or `Dynamic`.
target.addLegalOp<MyOp>(...);
target.addDynamicallyLegalOp<MySecondOp>(...);
/// Mark the operation as always recursively legal.
target.markOpRecursivelyLegal<MyOp>();
/// Mark optionally with a callback to allow selective marking.
target.markOpRecursivelyLegal<MyOp, MySecondOp>([](Operation *op) { ... });
/// Mark optionally with a callback to allow selective marking.
target.markOpRecursivelyLegal<MyOp>([](MyOp op) { ... });
```
PiperOrigin-RevId: 277086382
This allows for them to be used on other non-function, or even other function-like, operations. The algorithms are already generic, so this is simply changing the derived pass type. The majority of this change is just ensuring that the nesting of these passes remains the same, as the pass manager won't auto-nest them anymore.
PiperOrigin-RevId: 276573038
This simplifies defining expected-* directives when there are multiple that apply to the next or previous line. @below applies the directive to the next non-designator line, i.e. the next line that does not contain an expected-* designator. @above applies to the previous non designator line.
Examples:
// Expect an error on the next line that does not contain a designator.
// expected-remark@below {{remark on function below}}
// expected-remark@below {{another remark on function below}}
func @bar(%a : f32)
// Expect an error on the previous line that does not contain a designator.
func @baz(%a : f32)
// expected-remark@above {{remark on function above}}
// expected-remark@above {{another remark on function above}}
PiperOrigin-RevId: 276369085
This change rewrites Ch-4.md to introduced interfaces in a detailed step-by-step manner, adds examples, and fixes some errors.
PiperOrigin-RevId: 275887017
This allows dialect-specific attributes to be attached to func results. (or more specifically, FunctionLike ops).
For example:
```
func @f() -> (i32 {my_dialect.some_attr = 3})
```
This attaches my_dialect.some_attr with value 3 to the first result of func @f.
Another more complex example:
```
func @g() -> (i32, f32 {my_dialect.some_attr = "foo", other_dialect.some_other_attr = [1,2,3]}, i1)
```
Here, the second result has two attributes attached.
PiperOrigin-RevId: 275564165
JKIsaacLee