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.
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.
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
1. A single dimension can either be blocked (with floordiv and mod pair)
or non-blocked. Mixing them would be invalid.
2. Block size should be non-zero value.
For BSR and convolutions, we encounter
(d0, d1, d2, d3) -> ((d0 + d2) floordiv 2, (d1 + d3) floordiv 2, (d0 +
d2) mod 2, (d1 + d3) mod 2)
which crashed the current test. Note that an actual test and working
code is still to follow (since we need to fix a few other things first)
This centralizes all COO methods, and provides a cleaner API. Note that
the "enc" only constructor is a temporary workaround the need for COO
methods inside the "enc" only storage specifier.
This is a minor step towards moving ALL COO related tests into the
SparseTensorType class rather than
having it all over the place (with risk of becoming inconsistent). Next
revision will move ALL COO related methods into this class.
Migrates dangling convenience method into proper SparseTensorType class.
Also cleans up some details (picking right dim2lvl/lvl2dim). Removes
more dead code.
The "Dim" prefix is a legacy left-over that no longer makes sense, since
we have a very strict "Dimension" vs. "Level" definition for sparse
tensor types and their storage.
