inherited constructors, which is cleaner and means you can now use DimOp()
to get a null op, instead of having to use Instruction::getNull<DimOp>().
This removes another 200 lines of code.
PiperOrigin-RevId: 240068113
This should probably be changed to instead use the negated form (e.g., get predicate + negate it + get resulting template), but this fixes it locally.
PiperOrigin-RevId: 240067116
tblgen be non-const. This requires introducing some const_cast's at the
moment, but those (and lots more stuff) will disappear in subsequent patches.
This significantly simplifies those patches because the various tblgen op emitters
get adjusted.
PiperOrigin-RevId: 239954566
Previously we emit both op declaration and definition into one file and include it
in *Ops.h. That pulls in lots of implementation details in the header file and we
cannot hide symbols local to implementation. This CL splits them to provide a cleaner
interface.
The way how we define custom builders in TableGen is changed accordingly because now
we need to distinguish signatures and implementation logic. Some custom builders with
complicated logic now can be moved to be implemented in .cpp entirely.
PiperOrigin-RevId: 239509594
Previously Value was a pair of name & Type, but for operands/result a TypeConstraint rather then a Type is specified. Update C++ side to match declarative side.
PiperOrigin-RevId: 238984799
* print-ir-before=(comma-separated-pass-list)
- Print the IR before each of the passes provided within the pass list.
* print-ir-before-all
- Print the IR before every pass in the pipeline.
* print-ir-after=(comma-separated-pass-list)
- Print the IR after each of the passes provided within the pass list.
* print-ir-after-all
- Print the IR after every pass in the pipeline.
* print-ir-module-scope
- Always print the Module IR, even for non module passes.
PiperOrigin-RevId: 238523649
Add support to create a new attribute from multiple attributes. It extended the
DagNode class to represent attribute creation dag. It also changed the
RewriterGen::emitOpCreate method to support this nested dag emit.
An unit test is added.
PiperOrigin-RevId: 238090229
Below shows the output for an example mlir-opt command line.
mlir-opt foo.mlir -verify-each=false -cse -canonicalize -cse -cse -pass-timing
list view (-pass-timing-display=list):
* In this mode the results are displayed in a list sorted by total time; with each pass/analysis instance aggregated into one unique result. This mode is similar to the output of 'time-passes' in llvm-opt.
===-------------------------------------------------------------------------===
... Pass execution timing report ...
===-------------------------------------------------------------------------===
Total Execution Time: 0.0097 seconds (0.0096 wall clock)
---User Time--- --System Time-- --User+System-- ---Wall Time--- --- Name ---
0.0051 ( 58.3%) 0.0001 ( 12.2%) 0.0052 ( 53.8%) 0.0052 ( 53.8%) Canonicalizer
0.0025 ( 29.1%) 0.0005 ( 58.2%) 0.0031 ( 31.9%) 0.0031 ( 32.0%) CSE
0.0011 ( 12.6%) 0.0003 ( 29.7%) 0.0014 ( 14.3%) 0.0014 ( 14.2%) DominanceInfo
0.0087 (100.0%) 0.0009 (100.0%) 0.0097 (100.0%) 0.0096 (100.0%) Total
pipeline view (-pass-timing-display=pipeline):
* In this mode the results are displayed in a nested pipeline view that mirrors the internal pass pipeline that is being executed in the pass manager. This view is useful for understanding specifically which parts of the pipeline are taking the most time, and can also be used to identify when analyses are being invalidated and recomputed.
===-------------------------------------------------------------------------===
... Pass execution timing report ...
===-------------------------------------------------------------------------===
Total Execution Time: 0.0082 seconds (0.0081 wall clock)
---User Time--- --System Time-- --User+System-- ---Wall Time--- --- Name ---
0.0042 (100.0%) 0.0039 (100.0%) 0.0082 (100.0%) 0.0081 (100.0%) Function Pipeline
0.0005 ( 11.6%) 0.0008 ( 21.1%) 0.0013 ( 16.1%) 0.0013 ( 16.2%) CSE
0.0002 ( 5.0%) 0.0004 ( 9.3%) 0.0006 ( 7.0%) 0.0006 ( 7.0%) (A) DominanceInfo
0.0026 ( 61.8%) 0.0018 ( 45.6%) 0.0044 ( 54.0%) 0.0044 ( 54.1%) Canonicalizer
0.0005 ( 11.7%) 0.0005 ( 13.0%) 0.0010 ( 12.3%) 0.0010 ( 12.4%) CSE
0.0003 ( 6.1%) 0.0003 ( 8.3%) 0.0006 ( 7.2%) 0.0006 ( 7.1%) (A) DominanceInfo
0.0002 ( 3.8%) 0.0001 ( 2.8%) 0.0003 ( 3.3%) 0.0003 ( 3.3%) CSE
0.0042 (100.0%) 0.0039 (100.0%) 0.0082 (100.0%) 0.0081 (100.0%) Total
PiperOrigin-RevId: 237825367
There are two ways that we can attach a name to a DAG node:
1) (Op:$name ...)
2) (Op ...):$name
The problem with 2) is that we cannot do it on the outmost DAG node in a tree.
Switch from 2) to 1).
PiperOrigin-RevId: 237513962
This CL added the ability to generate multiple ops using multiple result
patterns, with each of them replacing one result of the matched source op.
Specifically, the syntax is
```
def : Pattern<(SourceOp ...),
[(ResultOp1 ...), (ResultOp2 ...), (ResultOp3 ...)]>;
```
Assuming `SourceOp` has three results.
Currently we require that each result op must generate one result, which
can be lifted later when use cases arise.
To help with cases that certain output is unused and we don't care about it,
this CL also introduces a new directive: `verifyUnusedValue`. Checks will
be emitted in the `match()` method to make sure if the corresponding output
is not unused, `match()` returns with `matchFailure()`.
PiperOrigin-RevId: 237513904
The LLVM IR Dialect strives to be close to the original LLVM IR instructions.
The conversion from the LLVM IR Dialect to LLVM IR proper is mostly mechanical
and can be automated. Implement TableGen support for generating conversions
from a concise pattern form in the TableGen definition of the LLVM IR Dialect
operations. It is used for all operations except calls and branches. These
operations need access to function and block remapping tables and would require
significantly more code to generate the conversions from TableGen definitions
than the current manually written conversions.
This implementation is accompanied by various necessary changes to the TableGen
operation definition infrastructure. In particular, operation definitions now
contain named accessors to results as well as named accessors to the variadic
operand (returning a vector of operands). The base operation support TableGen
file now contains a FunctionAttr definition. The TableGen now allows to query
the names of the operation results.
PiperOrigin-RevId: 237203077
The existing implementation of the Op definition generator assumes and relies
on the fact that native Op Attributes appear after its value-based operands in
the Arguments list. Furthermore, the same order is used in the generated
`build` function for the operation. This is not desirable for some operations
with mandatory attributes that would want the attribute to appear upfront for
better consistency with their textual representation, for example `cmpi` would
prefer the `predicate` attribute to be foremost in the argument list.
Introduce support for using attributes and operands in the Arguments DAG in no
particular order. This is achieved by maintaining a list of Arguments that
point to either the value or the attribute and are used to generate the `build`
method.
PiperOrigin-RevId: 237002921
The recently introduced support for generating MLIR Operations with optional
attributes did not handle the formatted string emission properly, in particular
it did not escape `{` and `}` in calls to `formatv` leading to assertions
during TableGen op definition generation. Fix this by splitting out the
unncessary braces from the format string. Additionally, fix the emission of
the builder argument comment to correctly indicate which attributes are indeed
optional and which are not.
PiperOrigin-RevId: 236832230
Original implementation of OutUtils provided two different LLVM IR module
transformers to be used with the MLIR ExecutionEngine: OptimizingTransformer
parameterized by the optimization levels (similar to -O3 flags) and
LLVMPassesTransformer parameterized by the string formatted similarly to
command line options of LLVM's "opt" tool without support for -O* flags.
Introduce such support by declaring the flags inside the parser and by
populating the pass managers similarly to what "opt" does. Remove the
additional flags from mlir-cpu-runner as they can now be wrapped into
`-llvm-opts` together with other LLVM-related flags.
PiperOrigin-RevId: 236107292
The only reason in starting with a fixedpoint add is that it is the absolute simplest variant and illustrates the level of abstraction I'm aiming for.
The overall flow would be:
1. Determine quantization parameters (out of scope of this cl).
2. Source dialect rules to lower supported math ops to the quantization dialect (out of scope of this cl).
3. Quantization passes: [-quant-convert-const, -quant-lower-uniform-real-math, -quant-lower-unsupported-to-float] (the last one not implemented yet)
4. Target specific lowering of the integral arithmetic ops (roughly at the level of gemmlowp) to more fundamental operations (i.e. calls to gemmlowp, simd instructions, DSP instructions, etc).
How I'm doing this should facilitate implementation of just about any kind of backend except TFLite, which has a very course, adhoc surface area for its quantized kernels. Options there include (I'm not taking an opinion on this - just trying to provide options):
a) Not using any of this: just match q/dbarrier + tf math ops to the supported TFLite quantized op set.
b) Implement the more fundamental integer math ops on TFLite and convert to those instead of the current op set.
Note that I've hand-waved over the process of choosing appropriate quantization parameters. Getting to that next. As you can see, different implementations will likely have different magic combinations of specific math support, and we will need the target system that has been discussed for some of the esoteric cases (i.e. many DSPs only support POT fixedpoint).
Two unrelated changes to the overall goal of this CL and can be broken out of desired:
- Adding optional attribute support to TabelGen
- Allowing TableGen native rewrite hooks to return nullptr, signalling that no rewrite has been done.
PiperOrigin-RevId: 235267229
* Introduce a OpTrait class in C++ to wrap the TableGen definition;
* Introduce PredOpTrait and rename previous usage of OpTrait to NativeOpTrait;
* PredOpTrait allows specifying a trait of the operation by way of predicate on the operation. This will be used in future to create reusable set of trait building blocks in the definition of operations. E.g., indicating whether to operands have the same type and allowing locally documenting op requirements by trait composition.
- Some of these building blocks could later evolve into known fixed set as LLVMs backends do, but that can be considered with more data.
* Use the modelling to address one verify TODO in a very local manner.
This subsumes the current custom verify specification which will be removed in a separate mechanical CL.
PiperOrigin-RevId: 234827169
This CL extended TableGen Operator class to provide accessors for information on op
results.
In OpDefinitionGen, added checks to make sure only the last result can be variadic,
and adjusted traits and builders generation to consider variadic results.
PiperOrigin-RevId: 234596124
The parameter to emitStandaloneParamBuilder() was renamed from hasResultType to
isAllSameType, which is the opposite boolean value. The logic should be changed
to make them consistent.
Also re-ordered some methods in Operator. And few other tiny improvements.
PiperOrigin-RevId: 234478316
A recent change introduced a possibility to run LLVM IR transformation during
JIT-compilation in the ExecutionEngine. Provide helper functions that
construct IR transformers given either clang-style optimization levels or a
list passes to run. The latter wraps the LLVM command line option parser to
parse strings rather than actual command line arguments. As a result, we can
run either of
mlir-cpu-runner -O3 input.mlir
mlir-cpu-runner -some-mlir-pass -llvm-opts="-llvm-pass -other-llvm-pass"
to combine different transformations. The transformer builder functions are
provided as a separate library that depends on LLVM pass libraries unlike the
main execution engine library. The library can be used for integrating MLIR
execution engine into external frameworks.
PiperOrigin-RevId: 234173493
We specify op operands and results in TableGen op definition using the same syntax.
They should be modelled similarly in TableGen driver wrapper classes.
PiperOrigin-RevId: 234153332
If we see an add op adding a constant value to a convolution op with constant
bias, we can fuse the add into the convolution op by constant folding the
bias and the add op's constant operand.
This CL also removes dangling RewriterGen check that prevents us from using
nested DAG nodes in result patterns, which is already supported.
PiperOrigin-RevId: 233989654
For ops with the SameOperandsAndResultType trait, we know that all result types
should be the same as the first operand's type. So we can generate a build()
method without requiring result types as parameters and also invoke this method
when constructing such ops during expanding rewrite patterns.
Similarly for ops have broadcast behavior, we can define build() method to use
the deduced type as the result type. So we can also calling into this build()
method when constructing ops in RewriterGen.
PiperOrigin-RevId: 233988307
Original implementation of the translation from MLIR to LLVM IR operated on the
Standard+BuiltIn dialect, with a later addition of the SuperVector dialect.
This required the translation to be aware of a potetially large number of other
dialects as the infrastructure extended. With the recent introduction of the
LLVM IR dialect into MLIR, the translation can be switched to only translate
the LLVM IR dialect, and the translation of the operations becomes largely
mechanical.
The reimplementation of the translator follows the lines of the original
translator in function and basic block conversion. In particular, block
arguments are converted to LLVM IR PHI nodes, which are connected to their
sources after all blocks of a function had been converted. Thanks to LLVM IR
types being wrapped in the MLIR LLVM dialect type, type conversion is
simplified to only convert function types, all other types are simply
unwrapped. Individual instructions are constructed using the LLVM IRBuilder,
which has a great potential for being table-generated from the LLVM IR dialect
operation definitions.
The input of the test/Target/llvmir.mlir is updated to use the MLIR LLVM IR
dialect. While it is now redundant with the dialect conversion test, the point
of the exercise is to guarantee exactly the same LLVM IR is emitted. (Only the
name of the allocation function is changed from `__mlir_alloc` to `alloc` in
the CHECK lines.) It will be simplified in a follow-up commit.
PiperOrigin-RevId: 233842306
* Fixed tfl.conv_2d and tfl.depthwise_conv_2d to have fused activation
function attribute
* Fixed RewriterGen crash: trying to get attribute match template when
the matcher is unspecified (UnsetInit)
PiperOrigin-RevId: 233241755
This CL allowed developers to write result ops having nested DAG nodes as their
arguments. Now we can write
```
def : Pat<(...), (AOp (BOp, ...), AOperand)>
```
PiperOrigin-RevId: 233207225
Previously we were using PatternRewrite::replaceOpWithNewOp() to both create the new op
inline and rewrite the matched op. That does not work well if we want to generate multiple
ops in a sequence. To support that, this CL changed to assign each newly created op to a
separate variable.
This CL also refactors how PatternEmitter performs the directive dispatch logic.
PiperOrigin-RevId: 233206819
* Add tf.LeakyRelu op definition + folders (well one is really canonicalizer)
* Change generated error message to use attribute description instead;
* Change the return type of F32Attr to be APFloat - internally it is already
stored as APFloat so let the caller decides if they want to convert it or
not. I could see varying opinions here though :) (did not change i32attr
similarly)
PiperOrigin-RevId: 232923358
Previously, we were using the trait mechanism to specify that an op has variadic operands.
That led a discrepancy between how we handle ops with deterministic number of operands.
Besides, we have no way to specify the constraints and match against the variadic operands.
This CL introduced Variadic<Type> as a way to solve the above issues.
PiperOrigin-RevId: 232656104
They are essentially both modelling MLIR OpTrait; the former achieves the
purpose via introducing corresponding symbols in TableGen, while the latter
just uses plain strings.
Unify them to provide a single mechanism to avoid confusion and to better
reflect the definitions on MLIR C++ side.
Ideally we should be able to deduce lots of these traits automatically via
other bits of op definitions instead of manually specifying them; but not
for now though.
PiperOrigin-RevId: 232191401
This CL added a tblgen::DagLeaf wrapper class with several helper methods for handling
DAG arguments. It helps to refactor the rewriter generation logic to be more higher
level.
This CL also added a tblgen::ConstantAttr wrapper class for constant attributes.
PiperOrigin-RevId: 232050683
This CL applies the following simplifications to EDSCs:
1. Rename Block to StmtList because an MLIR Block is a different, not yet
supported, notion;
2. Rework Bindable to drop specific storage and just use it as a simple wrapper
around Expr. The only value of Bindable is to force a static cast when used by
the user to bind into the emitter. For all intended purposes, Bindable is just
a lightweight check that an Expr is Unbound. This simplifies usage and reduces
the API footprint. After playing with it for some time, it wasn't worth the API
cognition overhead;
3. Replace makeExprs and makeBindables by makeNewExprs and copyExprs which is
more explicit and less easy to misuse;
4. Add generally useful functionality to MLIREmitter:
a. expose zero and one for the ubiquitous common lower bounds and step;
b. add support to create already bound Exprs for all function arguments as
well as shapes and views for Exprs bound to memrefs.
5. Delete Stmt::operator= and replace by a `Stmt::set` method which is more
explicit.
6. Make Stmt::operator Expr() explicit.
7. Indexed.indices assertions are removed to pave the way for expressing slices
and views as well as to work with 0-D memrefs.
The CL plugs those simplifications with TableGen and allows emitting a full MLIR function for
pointwise add.
This "x.add" op is both type and rank-agnostic (by allowing ArrayRef of Expr
passed to For loops) and opens the door to spinning up a composable library of
existing and custom ops that should automate a lot of the tedious work in
TF/XLA -> MLIR.
Testing needs to be significantly improved but can be done in a separate CL.
PiperOrigin-RevId: 231982325
This allow for arbitrarily complex builder patterns which is meant to cover initial cases while the modelling is improved and long tail cases/cases for which expanding the DSL would result in worst overall system.
NFC just sorting the emit replace methods alphabetical in the class and file body.
PiperOrigin-RevId: 231890352
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
* Emitted result lists for ops.
* Changed to allow empty summary and description for ops.
* Avoided indenting description to allow proper MarkDown rendering of
formatting markers inside description content.
* Used fixed width font for operand/attribute names.
* Massaged TensorFlow op docs and generated dialect op doc.
PiperOrigin-RevId: 231427574
Similar to op operands and attributes, use DAG to specify operation's results.
This will allow us to provide names and matchers for outputs.
Also Defined `outs` as a marker to indicate the start of op result list.
PiperOrigin-RevId: 231422455
Python modules cannot be defined under a directory that has a `-` character in its name inside of Google code.
Rename to `google_mlir` which circumvents this limitation.
PiperOrigin-RevId: 231329321
Update to allow constant attribute values to be used to match or as result in rewrite rule. Define variable ctx in the matcher to allow matchers to refer to the context of the operation being matched.
PiperOrigin-RevId: 231322019
Similar to other tblgen:: abstractions, tblgen::Pattern hides the native TableGen
API and provides a nicer API that is more coherent with the TableGen definitions.
PiperOrigin-RevId: 231285143
* Matching an attribute and specifying a attribute constraint is the same thing executionally, so represent it such.
* Extract AttrConstraint helper to match TypeConstraint and use that where mAttr was previously used in RewriterGen.
PiperOrigin-RevId: 231213580
This CL adds a new marker, replaceWithValue, to indicate that no new result
op is generated by applying a pattern. Instead, the matched DAG is replaced
by an existing SSA value.
Converted the tf.Identity converter to use the pattern.
PiperOrigin-RevId: 230922323
This implements a simple CPU runner based on LLVM Orc JIT. The base
functionality is provided by the ExecutionEngine class that compiles and links
the module, and provides an interface for obtaining function pointers to the
JIT-compiled MLIR functions and for invoking those functions directly. Since
function pointers need to be casted to the correct pointer type, the
ExecutionEngine wraps LLVM IR functions obtained from MLIR into a helper
function with the common signature `void (void **)` where the single argument
is interpreted as a list of pointers to the actual arguments passed to the
function, eventually followed by a pointer to the result of the function.
Additionally, the ExecutionEngine is set up to resolve library functions to
those available in the current process, enabling support for, e.g., simple C
library calls.
For integration purposes, this also provides a simplistic runtime for memref
descriptors as expected by the LLVM IR code produced by MLIR translation. In
particular, memrefs are transformed into LLVM structs (can be mapped to C
structs) with a pointer to the data, followed by dynamic sizes. This
implementation only supports statically-shaped memrefs of type float, but can
be extened if necessary.
Provide a binary for the runner and a test that exercises it.
PiperOrigin-RevId: 230876363
canonicalizations of operations. The ultimate important user of this is
going to be a funcBuilder->foldOrCreate<YourOp>(...) API, but for now it
is just a more convenient way to write certain classes of canonicalizations
(see the change in StandardOps.cpp).
NFC.
PiperOrigin-RevId: 230770021
Add default values to attributes, to allow attribute being left unspecified. The attr getter will always return an attribute so callers need not check for it, if the attribute is not set then the default will be returned (at present the default will be constructed upon query but this will be changed).
Add op definition for tf.AvgPool in ops.td, rewrite matcher using pattern using attribute matching & transforms. Adding some helper functions to make it simpler.
Handle attributes with dialect prefix and map them to getter without dialect prefix.
Note: VerifyAvgPoolOp could probably be autogenerated by know given the predicate specification on attributes, but deferring that to a follow up.
PiperOrigin-RevId: 230364857
Start doc generation pass that generates simple markdown output. The output is formatted simply[1] in markdown, but this allows seeing what info we have, where we can refine the op description (e.g., the inputs is probably redundant), what info is missing (e.g., the attributes could probably have a description).
The formatting of the description is still left up to whatever was in the op definition (which luckily, due to the uniformity in the .td file, turned out well but relying on the indentation there is fragile). The mechanism to autogenerate these post changes has not been added yet either. The output file could be run through a markdown formatter too to remove extra spaces.
[1]. This is not proposal for final style :) There could also be a discussion around single doc vs multiple (per dialect, per op), whether we want a TOC, whether operands/attributes should be headings or just formatted differently ...
PiperOrigin-RevId: 230354538
Change MinMaxAttr to match hasValidMinMaxAttribute behavior. Post rewriting the other users of that function it could be removed too. The currently generated error message is:
error: 'tfl.fake_quant' op attribute 'minmax' failed to satisfy constraint of MinMaxAttr
PiperOrigin-RevId: 229775631
A recent change in TableGen definitions allowed arbitrary AND/OR predicate
compositions at the cost of removing known-true predicate simplification.
Introduce a more advanced simplification mechanism instead.
In particular, instead of folding predicate C++ expressions directly in
TableGen, keep them as is and build a predicate tree in TableGen C++ library.
The predicate expression-substitution mechanism, necessary to implement complex
predicates for nested classes such as `ContainerType`, is replaced by a
dedicated predicate. This predicate appears in the predicate tree and can be
used for tree matching and separation. More specifically, subtrees defined
below such predicate may be subject to different transformations than those
that appear above. For example, a subtree known to be true above the
substitution predicate is not necessarily true below it.
Use the predicate tree structure to eliminate known-true and known-false
predicates before code emission, as well as to collapse AND and OR predicates
if their value can be deduced based on the value of one child.
PiperOrigin-RevId: 229605997
Start simple with single predicate match & transform rules for attributes.
* Its unclear whether modelling Attr predicates will be needed so start with allowing matching attributes with a single predicate.
* The input and output attr type often differs and so add ability to specify a transform between the input and output format.
PiperOrigin-RevId: 229580879
This is mostly plumbing to start allowing testing EDSC lowering. Prototype specifying reference implementation using verbose format without any generation/binding support. Add test pass that dumps the constructed EDSC (of which there can only be one). The idea is to enable iterating from multiple sides, this is wrong on many dimensions at the moment.
PiperOrigin-RevId: 229570535
In TableGen definitions, the "Type" class has been used for types of things
that can be stored in Attributes, but not necessarily present in the MLIR type
system. As a consequence, records like "String" or "DerviedAttrBody" were of
class "Type", which can be confusing. Furthermore, the "builderCall" field of
the "Type" class serves only for attribute construction. Some TableGen "Type"
subclasses that correspond to MLIR kinds of types do not have a canonical way
of construction only from the data available in TableGen, e.g. MemRefType would
require the list of affine maps. This leads to a conclusion that the entities
that describe types of objects appearing in Attributes should be independent of
"Type": they have some properties "Type"s don't and vice versa.
Do not parameterize Tablegen "Attr" class by an instance of "Type". Instead,
provide a "constBuilderCall" field that can be used to build an attribute from
a constant value stored in TableGen instead of indirectly going through
Attribute.Type.builderCall. Some attributes still don't have a
"constBuilderCall" because they used to depend on types without a
"builderCall".
Drop definitions of class "Type" that don't correspond to MLIR Types. Provide
infrastructure to define type-dependent attributes and string-backed attributes
for convenience.
PiperOrigin-RevId: 229570087
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
Expand type matcher template generator to consider a set of predicates that are known to
hold. This avoids inserting redundant checking for trivially true predicates
(for example predicate that hold according to the op definition). This only targets predicates that trivially holds and does not attempt any logic equivalence proof.
PiperOrigin-RevId: 228880468
Multiple binaries have the needs to open input files. Use this function
to de-duplicate the code.
Also changed openOutputFile() to return errors using std::string since
it is a library call and accessing I/O in library call is not friendly.
PiperOrigin-RevId: 228878221
* Check was returning success instead of failure when reporting illegal type;
* TFL ops only support tensor types so update tests with corrected logic.
- Removed some checks in broadcasting that don't work with tensor input requirement;
PiperOrigin-RevId: 228770184
This CL added a tblgen::Attribute class to wrap around raw TableGen
Record getValue*() calls on Attr defs, which will provide a nicer
API for handling TableGen Record.
PiperOrigin-RevId: 228581107
This CL added a tblgen::Type class to wrap around raw TableGen
Record getValue*() calls on Type defs, which will provide a
nicer API for handling TableGen Record.
The PredCNF class is also updated to work together with
tblgen::Type.
PiperOrigin-RevId: 228429090
Bind attributes similar to operands. Use to rewrite leakyreulo and const rewrite pattern. The attribute type/attributes are not currently checked so should only be used where the attributes match due to the construction of the op.
To support current attribute namespacing, convert __ in attribute name to "$" for matching purposes ('$' is not valid character in variable in TableGen).
Some simplification to make it simpler to specify indented ostream and avoid so many spaces. The goal is not to have perfectly formatted code generated but good enough so that its still easy to read for a user.
PiperOrigin-RevId: 228183639
Expand type to include matcher predicates. Use CNF form to allow specifying combinations of constraints for type. The matching call for the type is used to verify the construction of the operation as well as in rewrite pattern generation.
The matching initially includes redundant checks (e.g., even if the operand of the op is guaranteed to satisfy some requirement, it is still checked during matcher generation for now). As well as some of the traits specified now check what the generated code already checks. Some of the traits can be removed in future as the verify method will include the relevant checks based on the op definition already.
More work is needed for variadic operands.
CNF form is used so that in the follow up redundant checks in the rewrite patterns could be omitted (e.g., when matching a F32Tensor, one does not need to verify that op X's operand 0 is a Tensor if that is guaranteed by op X's definition). The alternative was to have single matcher function specified, but this would not allow for reasoning about what attributes already hold (at the level of PredAtoms).
Use this new operand type restrictions to rewrite BiasAdd with floating point operands as declarative pattern.
PiperOrigin-RevId: 227991412
Use tablegen to generate definitions of the standard binary arithmetic
operations. These operations share a lot of boilerplate that is better off
generated by a tool.
Using tablegen for standard binary arithmetic operations requires the following
modifications.
1. Add a bit field `hasConstantFolder` to the base Op tablegen class; generate
the `constantFold` method signature if the bit is set. Differentiate between
single-result and zero/multi-result functions that use different signatures.
The implementation of the method remains in C++, similarly to canonicalization
patterns, since it may be large and non-trivial.
2. Define the `AnyType` record of class `Type` since `BinaryOp` currently
provided in op_base.td is supposed to operate on tensors and other tablegen
users may rely on this behavior.
Note that this drops the inline documentation on the operation classes that was
copy-pasted around anyway. Since we don't generate g3doc from tablegen yet,
keep LangRef.md as it is. Eventually, the user documentation can move to the
tablegen definition file as well.
PiperOrigin-RevId: 227820815
* Match using isa
- This limits the rewrite pattern to ops defined in op registry but that is probably better end state (esp. for additional verification).
PiperOrigin-RevId: 227598946
Add ops.td for TF dialect and start by adding tf.Add (op used in legalizer pattern). This CL does not add TensorOp trait (that's not part of OpTrait namespace).
Remove OpCode emission that is not currently used and can be added back later if needed.
PiperOrigin-RevId: 227590973
With the builder to construct the type on the Type, the appropriate mlir::Type can be constructed where needed. Also add a constant attr class that has the attribute and value as members.
PiperOrigin-RevId: 227564789
* Allow multi input node patterns in the rewrite;
* Use number of nodes matched as benefit;
* Rewrite relu(add(...)) matching using the new pattern;
To allow for undefined ops, do string compare - will address soon!
PiperOrigin-RevId: 227225425
Add convenience wrapper to make it easier to iterate over attributes and operands of operator defined in TableGen file. Use this class in RewriterGen (not used in the op generator yet, will do shortly). Change the RewriterGen to pass the bound arguments explicitly, this is in preparation for multi-op matching.
PiperOrigin-RevId: 227156748
StmtResult -> InstResult, StmtOperand -> InstOperand, and remove the old names.
This is step 17/n towards merging instructions and statements, NFC.
PiperOrigin-RevId: 227121537
StmtSuccessorIterator/StmtSuccessorIterator, and rename and move the
CFGFunctionViewGraph pass to ViewFunctionGraph.
This is step 13/n towards merging instructions and statements, NFC.
PiperOrigin-RevId: 227069438
is the new base of the SSA value hierarchy. This CL also standardizes all the
nomenclature and comments to use 'Value' where appropriate. This also eliminates a large number of cast<MLValue>(x)'s, which is very soothing.
This is step 11/n towards merging instructions and statements, NFC.
PiperOrigin-RevId: 227064624
This *only* changes the internal data structures, it does not affect the user visible syntax or structure of MLIR code. Function gets new "isCFG()" sorts of predicates as a transitional measure.
This patch is gross in a number of ways, largely in an effort to reduce the amount of mechanical churn in one go. It introduces a bunch of using decls to keep the old names alive for now, and a bunch of stuff needs to be renamed.
This is step 10/n towards merging instructions and statements, NFC.
PiperOrigin-RevId: 227044402
Move PrintOpStatsPass out of tools and to other passes (moved to Analysis as it
doesn't modify the program but it is different than the other analysis passes
as it is only consumer at present is the user).
PiperOrigin-RevId: 227018996
* Extend to handle rewrite patterns with output attributes;
- Constant attributes are defined with a value and a type;
- The type of the value is mapped to the corresponding attribute type (string -> StringAttr);
* Verifies the type of operands in the resultant matches the defined op's operands;
PiperOrigin-RevId: 226468908
Change operands to arguments in Op and use it for both operands and arguments. This unifies the way that operands and attributes are specified and the intended way that matching/creating ops with attributes will look. Both can now be represented using the same dag structure (and also makes the ordering more explicit). Derived attributes are not considered as part of the arguments (as they are inferred from the created op, not something needed to created it).
* Generate named operand accessors;
* Simplified the way of specifying Attr and use ElementAttr for TFL_Const instead.
* Fix a incorrect assertion generated;
The input parsing can be made more robust, I'll address that in a follow up.
PiperOrigin-RevId: 226307424
Renamed the name field in Op to opName since it is the opcode's name.
Renamed the name parameters in TFLite op templates to opSummary since
they are meant as a summary of the op's functionality.
We will use the name symbol later for the name given by users via TF.
PiperOrigin-RevId: 225807135
Named operands allow generating builders with more meaningful names + lay the groundwork for allowing the specification of attributes as part of the inputs pattern of an op (which allows the declarative pattern rewrite generator to define ops with attributs). This is a minimal change that just changes how input operands are represented, changes to attributes in follow up and returnTypes later.
PiperOrigin-RevId: 225509805
Besides the ops.td file changes to define both ops, this CL also changes the
mlir-op-gen to allow more flexible traits definition for "optional" operation
inputs.
Unit tests are added.
One TODO for the mlir-op-gen is to make attribute optional in the ops.
PiperOrigin-RevId: 225408349
* Start very basic (about as basic as possible) with the pattern rewrite generation by only
- Matching single node dags,
- Single output, single result,
- No constraints on inputs/outputs.
- No attributes (only operands)
* The matcher generates C++ code akin to what is currently manually written.
- This is very much not the final end state, and only intended for the short term;
* Always generate the default builder method to make it easier to generate calls;
- Also add additional builder method for TFL::Add as attributes are not yet supported;
* Replace TF Add -> TFL Add matching using this generation;
* Introduce a conceptual textual namespace in the op registry
- Will allow importing multiple dialect's op registry
- Avoids needing to do anything special with tablegen or define a custom DSL;
= I really want to do a custom DSL but this urge could just be as its fun :) So defer for now. From this structure we can dump out another structured form if needed;
- Add a mapping from <namespace>_<op> in the op_gen and pattern rewrite gen
= This allows placing ops in different namespaces from the same op registry which is convenient, esp. if we want to consider subnamespaces in future;
* Update tfl namespace to TFL to match TF and XLA;
PiperOrigin-RevId: 225155164
For each op, generate another builder with the following signature:
static void build(Builder* builder, OperationState* result,
ArrayRef<Type> resultTypes,
ArrayRef<SSAValue*> args,
ArrayRef<NamedAttribute> attributes);
PiperOrigin-RevId: 225066007
If no custom builder is supplied for an op, TableGen now generates
a default builder for it with the following signature:
static void build(Builder *builder, OperationState* result,
<list-of-all-result-types>,
<list-of-all-operands>,
<list-of-all-attributes>);
PiperOrigin-RevId: 224382473
Remove tfl.reshape for the following two cases:
1. A tfl.reshape's input is from another tfl.reshape.
Then these two tfl.reshape ops can be merged.
2. A tfl.reshape's result type is the same as its input type.
This tfl.reshape op does nothing, which can be removed.
These transformations are put in a new source file, Canonicalizer.cpp,
because they are TFLite op to TFLite op transformations, and aiming
to making TFLite ops more canonicalized.
Also added a hasCanonicalizationPatterns marker in TableGen Op class
to indicate whether an op has custom getCanonicalizationPatterns().
PiperOrigin-RevId: 223806921
Derived attributes are attributes that are derived from other properties of the operation (e.g., the shape returned from the type). DerivedAttr is parameterized on the return type and function body.
PiperOrigin-RevId: 223180315
This has been a long-standing TODO in the build system. Now that we need to
share the non-inlined implementation of file utilities for translators, create
a separate library for support functionality. Move Support/* headers to the
new library in the build system.
PiperOrigin-RevId: 222398880
Translations performed by mlir-translate only have MLIR on one end.
MLIR-to-MLIR conversions (including dialect changes) should be treated as
passes and run by mlir-opt. Individual translations should not care about
reading or writing MLIR and should work on in-memory representation of MLIR
modules instead. Split the TranslateFunction interface and the translate
registry into two parts: "from MLIR" and "to MLIR".
Update mlir-translate to handle both registries together by wrapping
translation functions into source-to-source convresions. Remove MLIR parsing
and writing from individual translations and make them operate on Modules
instead. This removes the need for individual translators to include
tools/mlir-translate/mlir-translate.h, which can now be safely removed.
Remove mlir-to-mlir translation that only existed as a registration example and
use mlir-opt instead for tests.
PiperOrigin-RevId: 222398707
The mlir-translate tool is expected to discover individual translations at link
time. These translations must register themselves and may need the utilities
that are currently defined in mlir-translate.cpp for their entry point
functions. Since mlir-translate is linking against individual translations,
the translations cannot link against mlir-translate themselves. Extract out
the utilities into a separate "Translation" library to avoid the potential
dependency cycle. Individual translations link to that library to access
TranslateRegistration. The mlir-translate tool links to individual translations
and to the "Translation" library because it needs the utilities as well.
The main header of the new library is located in include/mlir/Translation.h to
make it easily accessible by translators. The rationale for putting it to
include/mlir rather than to one of its subdirectories is that its purpose is
similar to that of include/mlir/Pass.h so it makes sense to put them at the
same level.
PiperOrigin-RevId: 222398617
op-stats pass currently returns the number of occurrences of different operations in a Module. Useful for verifying transformation properties (e.g., 3 ops of specific dialect, 0 of another), but probably not useful outside of that so keeping it local to mlir-opt. This does not consider op attributes when counting.
PiperOrigin-RevId: 222259727
This is to allow usage of comment blocks along with splits in test cases.
For example, "Function Control Flow Lowering" comment block in
raise-control-flow.mlir
TESTED with existing unit tests
PiperOrigin-RevId: 221214451
Value type abstraction for locations differ from others in that a Location can NOT be null. NOTE: dyn_cast returns an Optional<T>.
PiperOrigin-RevId: 220682078
Add static pass registration and change mlir-opt to use it. Future work is needed to refactor the registration for PassManager usage.
Change build targets to alwayslink to enforce registration.
PiperOrigin-RevId: 220390178
- simple perfectly nested band tiling with fixed tile sizes.
- only the hyper-rectangular case is handled, with other limitations of
getIndexSet applying (constant loop bounds, etc.); once
the latter utility is extended, tiled code generation should become more
general.
- Add FlatAffineConstraints::isHyperRectangular()
PiperOrigin-RevId: 220324933
Start of TFLite legalizer pass. Currently focussed on macro expanding ops, limited to what is registered directly in a separate pass (this should instead be a general pass), no querying of what gets produced, the matching is string based instead of using the ops proper (the matching TF ops should be defined) etc. This is a step to enable prototyping. In addition to the above shortcomings, the legalizer is very verbose in this form and should instead be driven by autogenerated patterns (same is true for the op builders too). But this starts from the explicit form and extracting out commonality in follow up.
Add definition for tfl.relu for basic selection of fused relu add.
PiperOrigin-RevId: 220287087
Adds equality constraints to dependence constraint system for accesses using dims/symbols where the defining operation of the dim/symbol is a constant.
PiperOrigin-RevId: 219814740
- Builds access functions and iterations domains for each access.
- Builds dependence polyhedron constraint system which has equality constraints for equated access functions and inequality constraints for iteration domain loop bounds.
- Runs elimination on the dependence polyhedron to test if no dependence exists between the accesses.
- Adds a trivial LoopFusion transformation pass with a simple test policy to test dependence between accesses to the same memref in adjacent loops.
- The LoopFusion pass will be extended in subsequent CLs.
PiperOrigin-RevId: 219630898
Introduce analysis to check memref accesses (in MLFunctions) for out of bound
ones. It works as follows:
$ mlir-opt -memref-bound-check test/Transforms/memref-bound-check.mlir
/tmp/single.mlir:10:12: error: 'load' op memref out of upper bound access along dimension tensorflow/mlir#1
%x = load %A[%idxtensorflow/mlir#0, %idxtensorflow/mlir#1] : memref<9 x 9 x i32>
^
/tmp/single.mlir:10:12: error: 'load' op memref out of lower bound access along dimension tensorflow/mlir#1
%x = load %A[%idxtensorflow/mlir#0, %idxtensorflow/mlir#1] : memref<9 x 9 x i32>
^
/tmp/single.mlir:10:12: error: 'load' op memref out of upper bound access along dimension tensorflow/mlir#2
%x = load %A[%idxtensorflow/mlir#0, %idxtensorflow/mlir#1] : memref<9 x 9 x i32>
^
/tmp/single.mlir:10:12: error: 'load' op memref out of lower bound access along dimension tensorflow/mlir#2
%x = load %A[%idxtensorflow/mlir#0, %idxtensorflow/mlir#1] : memref<9 x 9 x i32>
^
/tmp/single.mlir:12:12: error: 'load' op memref out of upper bound access along dimension tensorflow/mlir#1
%y = load %B[%idy] : memref<128 x i32>
^
/tmp/single.mlir:12:12: error: 'load' op memref out of lower bound access along dimension tensorflow/mlir#1
%y = load %B[%idy] : memref<128 x i32>
^
#map0 = (d0, d1) -> (d0, d1)
#map1 = (d0, d1) -> (d0 * 128 - d1)
mlfunc @test() {
%0 = alloc() : memref<9x9xi32>
%1 = alloc() : memref<128xi32>
for %i0 = -1 to 9 {
for %i1 = -1 to 9 {
%2 = affine_apply #map0(%i0, %i1)
%3 = load %0[%2tensorflow/mlir#0, %2tensorflow/mlir#1] : memref<9x9xi32>
%4 = affine_apply #map1(%i0, %i1)
%5 = load %1[%4] : memref<128xi32>
}
}
return
}
- Improves productivity while manually / semi-automatically developing MLIR for
testing / prototyping; also provides an indirect way to catch errors in
transformations.
- This pass is an easy way to test the underlying affine analysis
machinery including low level routines.
Some code (in getMemoryRegion()) borrowed from @andydavis cl/218263256.
While on this:
- create mlir/Analysis/Passes.h; move Pass.h up from mlir/Transforms/ to mlir/
- fix a bug in AffineAnalysis.cpp::toAffineExpr
TODO: extend to non-constant loop bounds (straightforward). Will transparently
work for all accesses once floordiv, mod, ceildiv are supported in the
AffineMap -> FlatAffineConstraints conversion.
PiperOrigin-RevId: 219397961
Separate the storage and return type more explicitly. This is in preparation for, among others, allowing supporting enum attributes where the return type is a enum class. NFC.
PiperOrigin-RevId: 219368487
This is done by changing Attribute to be a POD interface around an underlying pointer storage and adding in-class support for isa/dyn_cast/cast.
PiperOrigin-RevId: 218764173
- Introduce Fourier-Motzkin variable elimination to eliminate a dimension from
a system of linear equalities/inequalities. Update isEmpty to use this.
Since FM is only exact on rational/real spaces, an emptiness check based on
this is guaranteed to be exact whenever it says the underlying set is empty;
if it says, it's not empty, there may still be no integer points in it.
Also, supports a version that computes "dark shadows".
- Test this by checking for "always false" conditionals in if statements.
- Unique IntegerSet's that are small (few constraints, few variables). This
basically means the canonical empty set and other small sets that are
likely commonly used get uniqued; allows checking for the canonical empty set
by pointer. IntegerSet::kUniquingThreshold gives the threshold constraint size
for uniqui'ing.
- rename simplify-affine-expr -> simplify-affine-structures
Other cleanup
- IntegerSet::numConstraints, AffineMap::numResults are no longer needed;
remove them.
- add copy assignment operators for AffineMap, IntegerSet.
- rename Invalid() -> Null() on AffineExpr, AffineMap, IntegerSet
- Misc cleanup for FlatAffineConstraints API
PiperOrigin-RevId: 218690456
This CL implements a very simple loop vectorization **test** and the basic
infrastructure to support it.
The test simply consists in:
1. matching the loops in the MLFunction and all the Load/Store operations
nested under the loop;
2. testing whether all the Load/Store are contiguous along the innermost
memory dimension along that particular loop. If any reference is
non-contiguous (i.e. the ForStmt SSAValue appears in the expression), then
the loop is not-vectorizable.
The simple test above can gradually be extended with more interesting
behaviors to account for the fact that a layout permutation may exist that
enables contiguity etc. All these will come in due time but it is worthwhile
noting that the test already supports detection of outer-vetorizable loops.
In implementing this test, I also added a recursive MLFunctionMatcher and some
sugar that can capture patterns
such as `auto gemmLike = Doall(Doall(Red(LoadStore())))` and allows iterating
on the matched IR structures. For now it just uses in order traversal but
post-order DFS will be useful in the future once IR rewrites start occuring.
One may note that the memory management design decision follows a different
pattern from MLIR. After evaluating different designs and how they quickly
increase cognitive overhead, I decided to opt for the simplest solution in my
view: a class-wide (threadsafe) RAII context.
This way, a pass that needs MLFunctionMatcher can just have its own locally
scoped BumpPtrAllocator and everything is cleaned up when the pass is destroyed.
If passes are expected to have a longer lifetime, then the contexts can easily
be scoped inside the runOnMLFunction call and storage lifetime reduced.
Lastly, whatever the scope of threading (module, function, pass), this is
expected to also be future-proof wrt concurrency (but this is a detail atm).
PiperOrigin-RevId: 217622889
Change how attributes can be added to an Op to make the syntax in the td file a bit cleaner. Also avoid unnecessarily emitting verify method (trivial return false one that's already in base) and use custom syntax in test.
PiperOrigin-RevId: 217330036
Create tblgen based tool to generate the C++ Op definitions. The modelling is
currently simple (ops, attributes, properties) with the printer/parser/verifier
the bodies of those functions and builders being very explicit.
PiperOrigin-RevId: 217150213
out canonicalization pass to drive it, and a simple (x-x) === 0 pattern match
as a test case.
There is a tremendous number of improvements that need to land, and the
matcher/rewriter and patterns will be split out of this file, but this is a
starting point.
PiperOrigin-RevId: 216788604
Add target independent standard DMA ops: dma.start, dma.wait. Update pipeline
data transfer to use these to detect DMA ops.
While on this
- return failure from mlir-opt::performActions if a pass generates invalid output
- improve error message for verify 'n' operand traits
PiperOrigin-RevId: 216429885
mode. We even diagnose mistakes nicely (aside from the a/an vowel confusion
which isn't worth worrying about):
test/IR/invalid.mlir split at line tensorflow/mlir#399:8:34: error: 'note' diagnostic emitted when expecting a 'error'
%x = "bar"() : () -> i32 // expected-error {{operand defined here}}
^
PiperOrigin-RevId: 214773208
Super thin slice that can convert a MLIR program (with addfs) to MLIR HLO dialect. Add this as translations to mlir-translate. Also add hlo::AddOp op and HLO op registration.
PiperOrigin-RevId: 214480409
Instead of linking in different initializeMLIRContext functions, add a registry mechanism and function to initialize all registered ops in a given MLIRContext. Initialize all registered ops along with the StandardOps when constructing a MLIRContext.
PiperOrigin-RevId: 214073842
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
mlir-translate is a tool to translate from/to MLIR. The translations are registered at link time and intended for use in tests. An identity transformation (mlir-to-mlir) is registered by default as example and used in the parser test where simply parsing & printing required.
The TranslateFunctions take filenames (instead of MemoryBuffer) to allow translations special write behavior (e.g., writing to uncommon filesystems).
PiperOrigin-RevId: 213370448
- Compress the identifier/kind of a Function into a single word.
- Eliminate otherFailure from verifier now that we always have a location
- Eliminate the error string from the verifier now that we always have
locations.
- Simplify the parser's handling of fn forward references, using the location
tracked by the function.
PiperOrigin-RevId: 211985101
terminators. Improve mlir-opt to print better location info in the split-files
case.
Before:
error: unexpected error: branch has 2 operands, but target block has 1
br bb1(%0tensorflow/mlir#1, %0tensorflow/mlir#0 : i17, i1)
^
after:
invalid.mlir split at line tensorflow/mlir#305:6:3: error: unexpected error: branch has 2 operands, but target block has 1
br bb1(%0tensorflow/mlir#1, %0tensorflow/mlir#0 : i17, i1)
^
It still isn't optimal (it would be better to have just the original file and
line number but is a step forward, and doing the optimal thing would be a lot
more complicated.
PiperOrigin-RevId: 211917067
inserting shape_casts as necessary.
Along the way:
- Add some missing accessors to the AtLeastNOperands trait.
- Implement shape_cast / ShapeCastOp standard op.
- Improve handling of errors in mlir-opt, making it easier to understand
errors when invalid IR is rejected by the verifier.
PiperOrigin-RevId: 211897877
- Make the tf-lower-control flow handle error cases better. Add a testcase
that (currently) fails due to type mismatches.
- Factor more code in the verifier for basic block argument checking, and
check more invariants.
- Fix a crasher in the asmprinter on null instructions (which only occurs on
invalid code).
- Fix a bug handling conditional branches with no block operands, it would
access &operands[0] instead of using operands.data().
- Enhance the mlir-opt driver to use the verifier() in a non-crashing mode,
allowing issues to be reported as diagnostics.
PiperOrigin-RevId: 211818291
Enable using GraphWriter to dump graphviz in debug mode (kept to debug builds completely as this is only for debugging). Add option to mlir-opt to print CFGFunction after every transform in debug mode.
PiperOrigin-RevId: 211578699
- 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
Outside of IR/
- simplify a MutableAffineMap by flattening the affine expressions
- add a simplify affine expression pass that uses this analysis
- update the FlatAffineConstraints API (to be used in the next CL)
In IR:
- add isMultipleOf and getKnownGCD for AffineExpr, and make the in-IR
simplication of simplifyMod simpler and more powerful.
- rename the AffineExpr visitor methods to distinguish b/w visiting and
walking, and to simplify API names based on context.
The next CL will use some of these for the loop unrolling/unroll-jam to make
the detection for the need of cleanup loop powerful/non-trivial.
A future CL will finally move this simplification to FlatAffineConstraints to
make it more powerful. For eg., currently, even if a mod expr appearing in a
part of the expression tree can't be simplified, the whole thing won't be
simplified.
PiperOrigin-RevId: 211012256
- for test purposes, the unroll-jam pass unroll jams the first outermost loop.
While on this:
- fix StmtVisitor to allow overriding of function to iterate walk over children
of a stmt.
PiperOrigin-RevId: 210644813
- Implement support for the TensorFlow 'If' op, the first TF op definition.
- Fill in some missing basic infra, including the ability to split a basic block, the ability to create a branch with operands, etc.
- Implement basic lowering for some simple forms of If, where the condition is a zero-D bool tensor and when all the types line up. Future patches will generalize this.
There is still much to be done here. I'd like to get some example graphs coming from the converter to play with to direct this work.
PiperOrigin-RevId: 210198760
- 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
Previously mlir-opt had initializeMLIRContext function that added certain ops to the OperationSet of the context. But for different tests we'd want to register different ops. Make initializeMLIRContext an extern function so that the context initialization/set of ops to register can be determined at link time. This allows out-of-tree operations to easily expand the custom parsing/printing while still using mlir-opt.
PiperOrigin-RevId: 209078315
- fix/complete forStmt cloning for unrolling to work for outer loops
- create IV const's only when needed
- test outer loop unrolling by creating a short trip count unroll pass for
loops with trip counts <= <parameter>
- add unrolling test cases for multiple op results, outer loop unrolling
- fix/clean up StmtWalker class while on this
- switch unroll loop iterator values from i32 to affineint
PiperOrigin-RevId: 207645967
- Implement a diagnostic hook in one of the paths in mlir-opt which
captures and reports the diagnostics nicely.
- Have the parser capture simple location information from the parser
indicating where each op came from in the source .mlir file.
- Add a verifyDominance() method to MLFuncVerifier to demo this, resolving b/112086163
- Add some PrettyStackTrace handlers to make crashes in the testsuite easier
to track down.
PiperOrigin-RevId: 207488548
- Sketch out a TensorFlow/IR directory that will hold op definitions and common TF support logic. We will eventually have TensorFlow/TF2HLO, TensorFlow/Grappler, TensorFlow/TFLite, etc.
- Add sketches of a Switch/Merge op definition, including some missing stuff like the TwoResults trait. Add a skeleton of a pass to raise this form.
- Beef up the Pass/FunctionPass definitions slightly, moving the common code out of LoopUnroll.cpp into a new IR/Pass.cpp file.
- Switch ConvertToCFG.cpp to be a ModulePass.
- Allow _ to start bare identifiers, since this is important for TF attributes.
PiperOrigin-RevId: 206502517
- Implement a full loop unroll for innermost loops.
- Use it to implement a pass that unroll all the innermost loops of all
mlfunction's in a module. ForStmt's parsed currently have constant trip
counts (and constant loop bounds).
- Implement StmtVisitor based (Visitor pattern)
Loop IVs aren't currently parsed and represented as SSA values. Replacing uses
of loop IVs in unrolled bodies is thus a TODO. Class comments are sparse at some places - will add them after one round of comments.
A cmd-line flag triggers this for now.
Original:
mlfunc @loops() {
for x = 1 to 100 step 2 {
for x = 1 to 4 {
"Const"(){value: 1} : () -> ()
}
}
return
}
After unrolling:
mlfunc @loops() {
for x = 1 to 100 step 2 {
"Const"(){value: 1} : () -> ()
"Const"(){value: 1} : () -> ()
"Const"(){value: 1} : () -> ()
"Const"(){value: 1} : () -> ()
}
return
}
PiperOrigin-RevId: 205933235
This patch adds support for basic block arguments including parsing and printing.
In doing so noticed that `ssa-id-and-type` is undefined in the MLIR spec; suggested an implementation in the spec doc.
PiperOrigin-RevId: 205593369
Add a default error reporter for the parser that uses the SourceManager to print the error. Also and OptResult enum (mirroring ParseResult) to make the behavior self-documenting.
PiperOrigin-RevId: 203173647
For checking parse errors, the input file is split and failures reported per memory buffer. Simply reporting the errors loses the mapping back to the original file. Change the reporting to instead relate the error reported back to the original file.
Use SourceMgr's PrintMessage consistently for errors and relates back to file being parsed.
PiperOrigin-RevId: 202136152
Add diagnostic reporter function to lexer/parser and use that from mlir-opt to report errors instead of having the lexer/parser print the errors.
PiperOrigin-RevId: 201892004
Add parsing tests with errors. Follows direct path of splitting file into test groups (using a marker) and parsing each section individually. The expected errors are checked using FileCheck and parser error does not result in terminating parsing the rest of the file if check-parser-error.
This is an interim approach until refactoring lexer/parser.
PiperOrigin-RevId: 201867941
Chris Lattner