The current design uses a unique entry for each argument/result attribute, with the name of the entry being something like "arg0". This provides for a somewhat sparse design, but ends up being much more expensive (from a runtime perspective) in-practice. The design requires building a string every time we lookup the dictionary for a specific arg/result, and also requires N attribute lookups when collecting all of the arg/result attribute dictionaries.
This revision restructures the design to instead have an ArrayAttr that contains all of the attribute dictionaries for arguments and another for results. This design reduces the number of attribute name lookups to 1, and allows for O(1) lookup for individual element dictionaries. The major downside is that we can end up with larger memory usage, as the ArrayAttr contains an entry for each element even if that element has no attributes. If the memory usage becomes too problematic, we can experiment with a more sparse structure that still provides a lot of the wins in this revision.
This dropped the compilation time of a somewhat large TensorFlow model from ~650 seconds to ~400 seconds.
Differential Revision: https://reviews.llvm.org/D102035
This patch extends the control-flow cost-model for detensoring by
implementing a forward-looking pass on block arguments that should be
detensored. This makes sure that if a (to-be-detensored) block argument
"escapes" its block through the terminator, then the successor arguments
are also detensored.
Reviewed By: silvas
Differential Revision: https://reviews.llvm.org/D100457
This patch introduces the neccessary infrastructure changes to implement
cost-modelling for detensoring. In particular, it introduces the
following changes:
- An extension to the dialect conversion framework to selectively
convert sub-set of non-entry BB arguments.
- An extension to branch conversion pattern to selectively convert
sub-set of a branche's operands.
- An interface for detensoring cost-modelling.
- 2 simple implementations of 2 different cost models.
This sets the stage to explose cost-modelling for detessoring in an
easier way. We still need to come up with better cost models.
Reviewed By: silvas
Differential Revision: https://reviews.llvm.org/D99945
To match an interface or trait, users currently have to use the `MatchAny` tag. This tag can be quite problematic for compile time for things like the canonicalizer, as the `MatchAny` patterns may get applied to *every* operation. This revision adds better support by bucketing interface/trait patterns based on which registered operations have them registered. This means that moving forward we will only attempt to match these patterns to operations that have this interface registered. Two simplify defining patterns that match traits and interfaces, two new utility classes have been added: OpTraitRewritePattern and OpInterfaceRewritePattern.
Differential Revision: https://reviews.llvm.org/D98986
This doesn't change APIs, this just cleans up the many in-tree uses of these
names to use the new preferred names. We'll keep the old names around for a
couple weeks to help transitions.
Differential Revision: https://reviews.llvm.org/D99127
This updates the codebase to pass the context when creating an instance of
OwningRewritePatternList, and starts removing extraneous MLIRContext
parameters. There are many many more to be removed.
Differential Revision: https://reviews.llvm.org/D99028
This patch continues detensorizing implementation by detensoring
internal control flow in functions.
In order to detensorize functions, all the non-entry block's arguments
are detensored and branches between such blocks are properly updated to
reflect the detensored types as well. Function entry block (signature)
is left intact.
This continues work towards handling github/google/iree#1159.
Reviewed By: silvas
Differential Revision: https://reviews.llvm.org/D97148
This commit is the first baby step towards detensoring in
linalg-on-tensors.
Detensoring is the process through which a tensor value is convereted to one
or potentially more primitive value(s). During this process, operations with
such detensored operands are also converted to an equivalen form that works
on primitives.
The detensoring process is driven by linalg-on-tensor ops. In particular, a
linalg-on-tensor op is checked to see whether *all* its operands can be
detensored. If so, those operands are converted to thier primitive
counterparts and the linalg op is replaced by an equivalent op that takes
those new primitive values as operands.
This works towards handling github/google/iree#1159.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D96271
KareemErgawy-TomTom