The motivation is to avoid having to negate `isDynamic*` checks, avoid
double negations, and allow for `ShapedType::isStaticDim` to be used in
ADT functions without having to wrap it in a lambda performing the
negation.
Also add the new functions to C and Python bindings.
After the introduction of `OpAsmAttrInterface`, it is favorable to
migrate code using `OpAsmDialectInterface` for ASM alias generation,
which lives in `Dialect.cpp`, to use `OpAsmAttrInterface`, which lives
in `Attrs.td`. In this way, attribute behavior is placed near its
tablegen definition and people won't need to go through other files to
know what other (unexpected) hooks comes into play.
Sparse tensors are always ranked tensors. Encodings cannot be attached
to unranked tensors. Change the type constraint to `RankedTensorOf`, so
that we generate `TypedValue<RankedTensorType>` instead of
`TypedValue<TensorType>`. This removes the need for type casting in some
cases.
Also improve the verifiers (missing `return` statements) and switch a
few other `AnyTensor` to `AnyRankedTensor`.
This commit is in preparation of a dialect conversion commit that
required fixes in the sparse dialect.
As specified in the docs,
1) raw_string_ostream is always unbuffered and
2) the underlying buffer may be used directly
( 65b13610a5226b84889b923bae884ba395ad084d for further reference )
* Don't call raw_string_ostream::flush(), which is essentially a no-op.
* Avoid unneeded calls to raw_string_ostream::str(), to avoid excess indirection.
When a type/attribute is defined in TableGen, a type constraint can be
used for parameters, but the type constraint verification was missing.
Example:
```
def TestTypeVerification : Test_Type<"TestTypeVerification"> {
let parameters = (ins AnyTypeOf<[I16, I32]>:$param);
// ...
}
```
No verification code was generated to ensure that `$param` is I16 or
I32.
When type constraints a present, a new method will generated for types
and attributes: `verifyInvariantsImpl`. (The naming is similar to op
verifiers.) The user-provided verifier is called `verify` (no change).
There is now a new entry point to type/attribute verification:
`verifyInvariants`. This function calls both `verifyInvariantsImpl` and
`verify`. If neither of those two verifications are present, the
`verifyInvariants` function is not generated.
When a type/attribute is not defined in TableGen, but a verifier is
needed, users can implement the `verifyInvariants` function. (This
function was previously called `verify`.)
Note for LLVM integration: If you have an attribute/type that is not
defined in TableGen (i.e., just C++), you have to rename the
verification function from `verify` to `verifyInvariants`. (Most
attributes/types have no verification, in which case there is nothing to
do.)
Depends on #102657.
A `sparse_tensor.iterate` iterates over a sparse iteration space
extracted from `sparse_tensor.extract_iteration_space` operation
introduced in https://github.com/llvm/llvm-project/pull/88554.
I'm planning to remove StringRef::equals in favor of
StringRef::operator==.
- StringRef::operator==/!= outnumber StringRef::equals by a factor of
10 under mlir/ in terms of their usage.
- The elimination of StringRef::equals brings StringRef closer to
std::string_view, which has operator== but not equals.
- S == "foo" is more readable than S.equals("foo"), especially for
!Long.Expression.equals("str") vs Long.Expression != "str".
1. Verify that the type of explicit/implicit values should be the same
as the tensor element type.
2. Verify that implicit value could only be zero.
3. Verify that explicit/implicit values should be numeric.
4. Fix the type change issue caused by SparseTensorType(enc).
1. Explicit value means the non-zero value in a sparse tensor. If
explicitVal is set, then all the non-zero values in the tensor have the
same explicit value. The default value Attribute() indicates that it is
not set.
2. Implicit value means the "zero" value in a sparse tensor. If
implicitVal is set, then the "zero" value in the tensor is equal to the
implicit value. For now, we only support `0` as the implicit value but
it could be extended in the future. The default value Attribute()
indicates that the implicit value is `0` (same type as the tensor
element type).
Example:
```
#CSR = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : dense, d1 : compressed),
posWidth = 64,
crdWidth = 64,
explicitVal = 1 : i64,
implicitVal = 0 : i64
}>
```
Note: this PR tests that implicitVal could be set to other values as
well. The following PR will add verifier and reject any value that's not
zero for implicitVal.
A `sparse_tensor.extract_space %tensor at %iterator` extracts a *sparse*
iteration space defined `%tensor`, the operation to traverse the
iteration space will be introduced in following PRs.
This PR adds promised interface declarations for all interfaces declared
in `InitAllDialects.h`.
Promised interfaces allow a dialect to declare that it will have an
implementation of a particular interface, crashing the program if one
isn't provided when the interface is used.
1. Add python test for n out of m
2. Add more methods for python binding
3. Add verification for n:m and invalid encoding tests
4. Add e2e test for n:m
Previous PRs for n:m #80501#79935
