[Mlir-commits] [mlir] 2310ced - [mlir][NFC] Update textual references of `func` to `func.func` in examples+python scripts
River Riddle
llvmlistbot at llvm.org
Wed Apr 20 22:24:15 PDT 2022
Author: River Riddle
Date: 2022-04-20T22:17:26-07:00
New Revision: 2310ced8745b28a79a4ff7f08d461605e52d153d
URL: https://github.com/llvm/llvm-project/commit/2310ced8745b28a79a4ff7f08d461605e52d153d
DIFF: https://github.com/llvm/llvm-project/commit/2310ced8745b28a79a4ff7f08d461605e52d153d.diff
LOG: [mlir][NFC] Update textual references of `func` to `func.func` in examples+python scripts
The special case parsing of `func` operations is being removed.
Added:
Modified:
mlir/docs/BufferDeallocationInternals.md
mlir/docs/Diagnostics.md
mlir/docs/Dialects/Linalg/_index.md
mlir/docs/Dialects/SPIR-V.md
mlir/docs/Dialects/ShapeDialect.md
mlir/docs/LangRef.md
mlir/docs/PDLL.md
mlir/docs/PassManagement.md
mlir/docs/Rationale/MLIRForGraphAlgorithms.md
mlir/docs/Rationale/Rationale.md
mlir/docs/SPIRVToLLVMDialectConversion.md
mlir/docs/SymbolsAndSymbolTables.md
mlir/docs/TargetLLVMIR.md
mlir/docs/Tutorials/QuickstartRewrites.md
mlir/docs/Tutorials/Toy/Ch-2.md
mlir/docs/Tutorials/Toy/Ch-5.md
mlir/examples/standalone/test/Standalone/dummy.mlir
mlir/examples/toy/Ch2/include/toy/Ops.td
mlir/examples/toy/Ch3/include/toy/Ops.td
mlir/examples/toy/Ch4/include/toy/Ops.td
mlir/examples/toy/Ch5/include/toy/Ops.td
mlir/examples/toy/Ch6/include/toy/Ops.td
mlir/examples/toy/Ch7/include/toy/Ops.td
mlir/test/Integration/Dialect/SparseTensor/python/test_SDDMM.py
mlir/test/Integration/Dialect/SparseTensor/python/test_SpMM.py
mlir/test/Integration/Dialect/SparseTensor/python/test_elementwise_add_sparse_output.py
mlir/test/Integration/Dialect/SparseTensor/python/test_output.py
mlir/test/Integration/Dialect/SparseTensor/taco/tools/mlir_pytaco_utils.py
mlir/test/python/dialects/memref.py
mlir/test/python/execution_engine.py
mlir/test/python/integration/dialects/linalg/opsrun.py
mlir/test/python/ir/insertion_point.py
mlir/test/python/ir/module.py
mlir/test/python/ir/operation.py
mlir/test/python/ir/symbol_table.py
mlir/test/python/ir/value.py
mlir/utils/jupyter/mlir_opt_kernel/kernel.py
Removed:
################################################################################
diff --git a/mlir/docs/BufferDeallocationInternals.md b/mlir/docs/BufferDeallocationInternals.md
index 131e527a54a0c..3d528afe1c3a0 100644
--- a/mlir/docs/BufferDeallocationInternals.md
+++ b/mlir/docs/BufferDeallocationInternals.md
@@ -39,7 +39,7 @@ writes needs to dominate all buffer reads.
Example for breaking the invariant:
```mlir
-func @condBranch(%arg0: i1, %arg1: memref<2xf32>) {
+func.func @condBranch(%arg0: i1, %arg1: memref<2xf32>) {
%0 = memref.alloc() : memref<2xf32>
cf.cond_br %arg0, ^bb1, ^bb2
^bb1:
@@ -71,7 +71,7 @@ BufferDeallocation is fully compatible with “hybrid” setups in which tracked
untracked allocations are mixed:
```mlir
-func @mixedAllocation(%arg0: i1) {
+func.func @mixedAllocation(%arg0: i1) {
%0 = memref.alloca() : memref<2xf32> // aliases: %2
%1 = memref.alloc() : memref<2xf32> // aliases: %2
cf.cond_br %arg0, ^bb1, ^bb2
@@ -128,7 +128,7 @@ BufferHoisting pass:
```mlir
-func @condBranch(%arg0: i1, %arg1: memref<2xf32>, %arg2: memref<2xf32>) {
+func.func @condBranch(%arg0: i1, %arg1: memref<2xf32>, %arg2: memref<2xf32>) {
cf.cond_br %arg0, ^bb1, ^bb2
^bb1:
cf.br ^bb3(%arg1 : memref<2xf32>)
@@ -148,7 +148,7 @@ of code:
```mlir
-func @condBranch(%arg0: i1, %arg1: memref<2xf32>, %arg2: memref<2xf32>) {
+func.func @condBranch(%arg0: i1, %arg1: memref<2xf32>, %arg2: memref<2xf32>) {
%0 = memref.alloc() : memref<2xf32> // moved to bb0
cf.cond_br %arg0, ^bb1, ^bb2
^bb1:
@@ -170,7 +170,7 @@ Due to the data dependency of the allocation to %0, we cannot move the
allocation out of bb2 in this case:
```mlir
-func @condBranchDynamicType(
+func.func @condBranchDynamicType(
%arg0: i1,
%arg1: memref<?xf32>,
%arg2: memref<?xf32>,
@@ -199,7 +199,7 @@ copies to eliminate them. Consider the following example in which the
allocations have already been placed:
```mlir
-func @branch(%arg0: i1) {
+func.func @branch(%arg0: i1) {
%0 = memref.alloc() : memref<2xf32> // aliases: %2
cf.cond_br %arg0, ^bb1, ^bb2
^bb1:
@@ -231,7 +231,7 @@ Applying the BufferDeallocation pass to the program above yields the following
result:
```mlir
-func @branch(%arg0: i1) {
+func.func @branch(%arg0: i1) {
%0 = memref.alloc() : memref<2xf32>
cf.cond_br %arg0, ^bb1, ^bb2
^bb1:
@@ -268,7 +268,7 @@ and non-critical aliases:
```mlir
-func @condBranchDynamicTypeNested(
+func.func @condBranchDynamicTypeNested(
%arg0: i1,
%arg1: memref<?xf32>, // aliases: %3, %4
%arg2: memref<?xf32>,
@@ -301,7 +301,7 @@ Applying BufferDeallocation yields the following output:
```mlir
-func @condBranchDynamicTypeNested(
+func.func @condBranchDynamicTypeNested(
%arg0: i1,
%arg1: memref<?xf32>,
%arg2: memref<?xf32>,
@@ -379,7 +379,7 @@ the `RegionBranchOpInterface` to determine predecessors in order to infer the
high-level control flow:
```mlir
-func @inner_region_control_flow(
+func.func @inner_region_control_flow(
%arg0 : index,
%arg1 : index) -> memref<?x?xf32> {
%0 = memref.alloc(%arg0, %arg0) : memref<?x?xf32>
@@ -403,7 +403,7 @@ dialect-specific operations. BufferDeallocation supports this behavior via the
operation to determine the value of %2 at runtime which creates an alias:
```mlir
-func @nested_region_control_flow(%arg0 : index, %arg1 : index) -> memref<?x?xf32> {
+func.func @nested_region_control_flow(%arg0 : index, %arg1 : index) -> memref<?x?xf32> {
%0 = arith.cmpi "eq", %arg0, %arg1 : index
%1 = memref.alloc(%arg0, %arg0) : memref<?x?xf32>
%2 = scf.if %0 -> (memref<?x?xf32>) {
@@ -424,7 +424,7 @@ block since it cannot be accessed by the remainder of the program. Accessing the
%1 which does not need to be tracked.
```mlir
-func @nested_region_control_flow(%arg0: index, %arg1: index) -> memref<?x?xf32> {
+func.func @nested_region_control_flow(%arg0: index, %arg1: index) -> memref<?x?xf32> {
%0 = arith.cmpi "eq", %arg0, %arg1 : index
%1 = memref.alloc(%arg0, %arg0) : memref<?x?xf32>
%2 = scf.if %0 -> (memref<?x?xf32>) {
@@ -448,7 +448,7 @@ Reconsider a slightly adapted version of the “custom.region_if” example from
above that uses a nested allocation:
```mlir
-func @inner_region_control_flow_div(
+func.func @inner_region_control_flow_div(
%arg0 : index,
%arg1 : index) -> memref<?x?xf32> {
%0 = memref.alloc(%arg0, %arg0) : memref<?x?xf32>
@@ -471,7 +471,7 @@ Furthermore, %arg4 is returned to its parent operation and has an alias %1. This
causes BufferDeallocation to introduce additional copies:
```mlir
-func @inner_region_control_flow_div(
+func.func @inner_region_control_flow_div(
%arg0 : index,
%arg1 : index) -> memref<?x?xf32> {
%0 = memref.alloc(%arg0, %arg0) : memref<?x?xf32>
@@ -509,7 +509,7 @@ Consider the following “scf.for” use case containing a nested structured
control-flow if:
```mlir
-func @loop_nested_if(
+func.func @loop_nested_if(
%lb: index,
%ub: index,
%step: index,
@@ -547,7 +547,7 @@ buffer, we have to free the buffer from the previous iteration to avoid memory
leaks:
```mlir
-func @loop_nested_if(
+func.func @loop_nested_if(
%lb: index,
%ub: index,
%step: index,
@@ -624,7 +624,7 @@ analysis of this sample reveals that the highlighted operations are redundant
and can be removed.
```mlir
-func @dynamic_allocation(%arg0: index, %arg1: index) -> memref<?x?xf32> {
+func.func @dynamic_allocation(%arg0: index, %arg1: index) -> memref<?x?xf32> {
%1 = memref.alloc(%arg0, %arg1) : memref<?x?xf32>
%2 = bufferization.clone %1 : (memref<?x?xf32>) -> (memref<?x?xf32>)
memref.dealloc %1 : memref<?x?xf32>
@@ -635,7 +635,7 @@ func @dynamic_allocation(%arg0: index, %arg1: index) -> memref<?x?xf32> {
Will be transformed to:
```mlir
-func @dynamic_allocation(%arg0: index, %arg1: index) -> memref<?x?xf32> {
+func.func @dynamic_allocation(%arg0: index, %arg1: index) -> memref<?x?xf32> {
%1 = memref.alloc(%arg0, %arg1) : memref<?x?xf32>
return %1 : memref<?x?xf32>
}
@@ -656,7 +656,7 @@ merged into a single step. Canonicalization removes the clone operation and
%temp, and replaces the uses of %temp with %result:
```mlir
-func @reuseTarget(%arg0: memref<2xf32>, %result: memref<2xf32>){
+func.func @reuseTarget(%arg0: memref<2xf32>, %result: memref<2xf32>){
%temp = memref.alloc() : memref<2xf32>
test.generic {
args_in = 1 : i64,
@@ -676,7 +676,7 @@ func @reuseTarget(%arg0: memref<2xf32>, %result: memref<2xf32>){
Will be transformed to:
```mlir
-func @reuseTarget(%arg0: memref<2xf32>, %result: memref<2xf32>){
+func.func @reuseTarget(%arg0: memref<2xf32>, %result: memref<2xf32>){
test.generic {
args_in = 1 : i64,
args_out = 1 : i64,
diff --git a/mlir/docs/Diagnostics.md b/mlir/docs/Diagnostics.md
index a41b98ebc2d30..f7e223d4f316f 100644
--- a/mlir/docs/Diagnostics.md
+++ b/mlir/docs/Diagnostics.md
@@ -243,7 +243,7 @@ diagnostic. Example usage of this handler can be seen in the `mlir-opt` tool.
$ mlir-opt foo.mlir
/tmp/test.mlir:6:24: error: expected non-function type
-func @foo() -> (index, ind) {
+func.func @foo() -> (index, ind) {
^
```
@@ -306,12 +306,12 @@ A few examples are shown below:
```mlir
// Expect an error on the same line.
-func @bad_branch() {
+func.func @bad_branch() {
cf.br ^missing // expected-error {{reference to an undefined block}}
}
// Expect an error on an adjacent line.
-func @foo(%a : f32) {
+func.func @foo(%a : f32) {
// expected-error at +1 {{unknown comparison predicate "foo"}}
%result = arith.cmpf "foo", %a, %a : f32
return
@@ -320,10 +320,10 @@ func @foo(%a : f32) {
// Expect an error on the next line that does not contain a designator.
// expected-remark at below {{remark on function below}}
// expected-remark at below {{another remark on function below}}
-func @bar(%a : f32)
+func.func @bar(%a : f32)
// Expect an error on the previous line that does not contain a designator.
-func @baz(%a : f32)
+func.func @baz(%a : f32)
// expected-remark at above {{remark on function above}}
// expected-remark at above {{another remark on function above}}
@@ -336,7 +336,7 @@ any expected diagnostics weren't.
$ mlir-opt foo.mlir
/tmp/test.mlir:6:24: error: unexpected error: expected non-function type
-func @foo() -> (index, ind) {
+func.func @foo() -> (index, ind) {
^
/tmp/test.mlir:15:4: error: expected remark "expected some remark" was not produced
diff --git a/mlir/docs/Dialects/Linalg/_index.md b/mlir/docs/Dialects/Linalg/_index.md
index 5b44a7bb4cf78..ab53aa8770098 100644
--- a/mlir/docs/Dialects/Linalg/_index.md
+++ b/mlir/docs/Dialects/Linalg/_index.md
@@ -102,7 +102,7 @@ layout, and the second one is a `memref` of 4-element vectors with a 2-strided,
// memory layouts
#identity = affine_map<(d0) -> (d0)>
-func @example(%A: memref<?xf32, #identity>,
+func.func @example(%A: memref<?xf32, #identity>,
%B: memref<?xvector<4xf32>, offset: 1, strides: [2]>) {
linalg.generic #attrs
ins(%A: memref<?xf32, #identity>)
@@ -124,7 +124,7 @@ materialized by a lowering into a form that will resemble:
// It's syntax can be found here: https://mlir.llvm.org/docs/Dialects/SCFDialect/
#map0 = affine_map<(d0) -> (d0 * 2 + 1)>
-func @example(%arg0: memref<?xf32>, %arg1: memref<?xvector<4xf32>, #map0>) {
+func.func @example(%arg0: memref<?xf32>, %arg1: memref<?xvector<4xf32>, #map0>) {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%0 = memref.dim %arg0, %c0 : memref<?xf32>
@@ -186,7 +186,7 @@ uses an identity layout.
iterator_types = ["parallel", "parallel"]
}
-func @example(%A: memref<8x?xf32, offset: 0, strides: [2, 2]>,
+func.func @example(%A: memref<8x?xf32, offset: 0, strides: [2, 2]>,
%B: memref<?xvector<4xf32>>) {
linalg.generic #attrs
ins(%A: memref<8x?xf32, offset: 0, strides: [2, 2]>)
@@ -206,7 +206,7 @@ materialized by a lowering into a form that will resemble:
// Run: mlir-opt example2.mlir -allow-unregistered-dialect -convert-linalg-to-loops
#map0 = affine_map<(d0, d1) -> (d0 * 2 + d1 * 2)>
-func @example(%arg0: memref<8x?xf32, #map0>, %arg1: memref<?xvector<4xf32>>) {
+func.func @example(%arg0: memref<8x?xf32, #map0>, %arg1: memref<?xvector<4xf32>>) {
%c8 = arith.constant 8 : index
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
@@ -309,7 +309,7 @@ be when using a concrete operation `addf`:
iterator_types = ["parallel", "parallel"]
}
-func @example(%A: memref<?x?xf32>, %B: memref<?x?xf32>, %C: memref<?x?xf32>) {
+func.func @example(%A: memref<?x?xf32>, %B: memref<?x?xf32>, %C: memref<?x?xf32>) {
linalg.generic #attrs
ins(%A, %B: memref<?x?xf32>, memref<?x?xf32>)
outs(%C: memref<?x?xf32>) {
@@ -329,7 +329,7 @@ The property "*The Compute Payload is Specified With a Region*" is materialized
by a lowering into a form that will resemble:
```mlir
-func @example(%arg0: memref<?x?xf32>, %arg1: memref<?x?xf32>, %arg2: memref<?x?xf32>) {
+func.func @example(%arg0: memref<?x?xf32>, %arg1: memref<?x?xf32>, %arg2: memref<?x?xf32>) {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%0 = memref.dim %arg0, %c0 : memref<?x?xf32>
@@ -382,7 +382,7 @@ call we intend to use:
library_call = "pointwise_add"
}
-func @example(%A: memref<?x?xf32>, %B: memref<?x?xf32>, %C: memref<?x?xf32>) {
+func.func @example(%A: memref<?x?xf32>, %B: memref<?x?xf32>, %C: memref<?x?xf32>) {
linalg.generic #attrs
ins(%A, %B: memref<?x?xf32>, memref<?x?xf32>)
outs(%C: memref<?x?xf32>) {
@@ -402,14 +402,14 @@ into a form that will resemble:
#map0 = affine_map<(d0, d1)[s0, s1, s2] -> (d0 * s1 + s0 + d1 * s2)>
-func @example(%arg0: memref<?x?xf32>, %arg1: memref<?x?xf32>, %arg2: memref<?x?xf32>) {
+func.func @example(%arg0: memref<?x?xf32>, %arg1: memref<?x?xf32>, %arg2: memref<?x?xf32>) {
%0 = memref.cast %arg0 : memref<?x?xf32> to memref<?x?xf32, #map0>
%1 = memref.cast %arg1 : memref<?x?xf32> to memref<?x?xf32, #map0>
%2 = memref.cast %arg2 : memref<?x?xf32> to memref<?x?xf32, #map0>
call @pointwise_add(%0, %1, %2) : (memref<?x?xf32, #map0>, memref<?x?xf32, #map0>, memref<?x?xf32, #map0>) -> ()
return
}
-func @pointwise_add(memref<?x?xf32, #map0>, memref<?x?xf32, #map0>, memref<?x?xf32, #map0>) attributes {llvm.emit_c_interface}
+func.func @pointwise_add(memref<?x?xf32, #map0>, memref<?x?xf32, #map0>, memref<?x?xf32, #map0>) attributes {llvm.emit_c_interface}
```
Which, after lowering to LLVM resembles:
@@ -417,7 +417,7 @@ Which, after lowering to LLVM resembles:
```mlir
// Run: mlir-opt example4.mlir -convert-linalg-to-std | mlir-opt -convert-func-to-llvm
// Some generated code are omitted here.
-func @example(%arg0: !llvm<"float*">, ...) {
+func.func @example(%arg0: !llvm<"float*">, ...) {
...
llvm.call @pointwise_add(...) : (!llvm<"float*">, ...) -> ()
return
diff --git a/mlir/docs/Dialects/SPIR-V.md b/mlir/docs/Dialects/SPIR-V.md
index 7d9c0fe1c1d30..32d39e82a7a74 100644
--- a/mlir/docs/Dialects/SPIR-V.md
+++ b/mlir/docs/Dialects/SPIR-V.md
@@ -422,7 +422,7 @@ the SPIR-V dialect. Instead, we reuse the builtin `func` op to express functions
more concisely:
```mlir
-func @f(%arg: i32) -> i32 {
+func.func @f(%arg: i32) -> i32 {
"spv.ReturnValue"(%arg) : (i32) -> (i32)
}
```
@@ -580,7 +580,7 @@ void loop(bool cond) {
It will be represented as
```mlir
-func @selection(%cond: i1) -> () {
+func.func @selection(%cond: i1) -> () {
%zero = spv.Constant 0: i32
%one = spv.Constant 1: i32
%two = spv.Constant 2: i32
@@ -668,7 +668,7 @@ void loop(int count) {
It will be represented as
```mlir
-func @loop(%count : i32) -> () {
+func.func @loop(%count : i32) -> () {
%zero = spv.Constant 0: i32
%one = spv.Constant 1: i32
%var = spv.Variable init(%zero) : !spv.ptr<i32, Function>
@@ -728,7 +728,7 @@ example, for the following SPIR-V function `foo`:
It will be represented as:
```mlir
-func @foo() -> () {
+func.func @foo() -> () {
%var = spv.Variable : !spv.ptr<i32, Function>
spv.mlir.selection {
diff --git a/mlir/docs/Dialects/ShapeDialect.md b/mlir/docs/Dialects/ShapeDialect.md
index a86988122bbcb..0e7350df9fa92 100644
--- a/mlir/docs/Dialects/ShapeDialect.md
+++ b/mlir/docs/Dialects/ShapeDialect.md
@@ -97,7 +97,7 @@ separate shape function library, while here we would normally reify it as part
of lowering, but for simplicity will show as a standalone shape function.
```mlir
-func @matmul_shape1(%lhs: tensor<*xf32>, %rhs: tensor<*xindex>) -> tensor<?xindex> {
+func.func @matmul_shape1(%lhs: tensor<*xf32>, %rhs: tensor<*xindex>) -> tensor<?xindex> {
%c1 = shape.const_size 1
%c2 = shape.const_size 2
// We allow `shape.shape_of` to return either a `!shape.shape` or
@@ -136,7 +136,7 @@ We can now hoist computations of constraint were possible (which in the case
below is not too many as we need to verify the rank before we can split)
```mlir
-func @matmul_shape2(%lhs: tensor<*xf32>, %lhs: tensor<*xf32>) -> tensor<?xindex> {
+func.func @matmul_shape2(%lhs: tensor<*xf32>, %lhs: tensor<*xf32>) -> tensor<?xindex> {
%c1 = shape.const_size 1
%c2 = shape.const_size 2
%lhs_shape = shape.shape_of %lhs : tensor<*xf32> -> tensor<?xindex>
@@ -167,7 +167,7 @@ The above form can now be lowered to the fully imperative form (see
for example).
```mlir
-func @matmul_shape3(%lhs: tensor<*xf32>, %lhs: tensor<*xf32>) -> tensor<?xindex> {
+func.func @matmul_shape3(%lhs: tensor<*xf32>, %lhs: tensor<*xf32>) -> tensor<?xindex> {
%c1 = arith.constant 1 : index
%c2 = arith.constant 2 : index
%lhs_shape = shape.shape_of %lhs : tensor<*xf32> -> tensor<?xindex>
diff --git a/mlir/docs/LangRef.md b/mlir/docs/LangRef.md
index fad5b58f54b5b..597612126af4b 100644
--- a/mlir/docs/LangRef.md
+++ b/mlir/docs/LangRef.md
@@ -71,7 +71,7 @@ Here's an example of an MLIR module:
// Compute A*B using an implementation of multiply kernel and print the
// result using a TensorFlow op. The dimensions of A and B are partially
// known. The shapes are assumed to match.
-func @mul(%A: tensor<100x?xf32>, %B: tensor<?x50xf32>) -> (tensor<100x50xf32>) {
+func.func @mul(%A: tensor<100x?xf32>, %B: tensor<?x50xf32>) -> (tensor<100x50xf32>) {
// Compute the inner dimension of %A using the dim operation.
%n = memref.dim %A, 1 : tensor<100x?xf32>
@@ -102,7 +102,7 @@ func @mul(%A: tensor<100x?xf32>, %B: tensor<?x50xf32>) -> (tensor<100x50xf32>) {
}
// A function that multiplies two memrefs and returns the result.
-func @multiply(%A: memref<100x?xf32>, %B: memref<?x50xf32>)
+func.func @multiply(%A: memref<100x?xf32>, %B: memref<?x50xf32>)
-> (memref<100x50xf32>) {
// Compute the inner dimension of %A.
%n = memref.dim %A, 1 : memref<100x?xf32>
@@ -389,7 +389,7 @@ Here is a simple example function showing branches, returns, and block
arguments:
```mlir
-func @simple(i64, i1) -> i64 {
+func.func @simple(i64, i1) -> i64 {
^bb0(%a: i64, %cond: i1): // Code dominated by ^bb0 may refer to %a
cf.cond_br %cond, ^bb1, ^bb2
@@ -529,7 +529,7 @@ region, for example if a function call does not return.
Example:
```mlir
-func @accelerator_compute(i64, i1) -> i64 { // An SSACFG region
+func.func @accelerator_compute(i64, i1) -> i64 { // An SSACFG region
^bb0(%a: i64, %cond: i1): // Code dominated by ^bb0 may refer to %a
cf.cond_br %cond, ^bb1, ^bb2
diff --git a/mlir/docs/PDLL.md b/mlir/docs/PDLL.md
index 41fed10a98c83..1727386b14887 100644
--- a/mlir/docs/PDLL.md
+++ b/mlir/docs/PDLL.md
@@ -406,7 +406,7 @@ the concept, let's take a look at a quick example. Consider the `.mlir` snippet
below:
```mlir
-func @baz(%arg: i32) {
+func.func @baz(%arg: i32) {
%result = my_dialect.foo %arg, %arg -> i32
}
```
diff --git a/mlir/docs/PassManagement.md b/mlir/docs/PassManagement.md
index 41a37f5aa7af4..617ea161ad472 100644
--- a/mlir/docs/PassManagement.md
+++ b/mlir/docs/PassManagement.md
@@ -1091,7 +1091,7 @@ this instrumentation:
$ mlir-opt foo.mlir -pass-pipeline='func.func(cse)' -mlir-print-ir-before=cse
*** IR Dump Before CSE ***
-func @simple_constant() -> (i32, i32) {
+func.func @simple_constant() -> (i32, i32) {
%c1_i32 = arith.constant 1 : i32
%c1_i32_0 = arith.constant 1 : i32
return %c1_i32, %c1_i32_0 : i32, i32
@@ -1107,7 +1107,7 @@ func @simple_constant() -> (i32, i32) {
$ mlir-opt foo.mlir -pass-pipeline='func.func(cse)' -mlir-print-ir-after=cse
*** IR Dump After CSE ***
-func @simple_constant() -> (i32, i32) {
+func.func @simple_constant() -> (i32, i32) {
%c1_i32 = arith.constant 1 : i32
return %c1_i32, %c1_i32 : i32, i32
}
@@ -1128,7 +1128,7 @@ func @simple_constant() -> (i32, i32) {
$ mlir-opt foo.mlir -pass-pipeline='func.func(cse,cse)' -mlir-print-ir-after=cse -mlir-print-ir-after-change
*** IR Dump After CSE ***
-func @simple_constant() -> (i32, i32) {
+func.func @simple_constant() -> (i32, i32) {
%c1_i32 = arith.constant 1 : i32
return %c1_i32, %c1_i32 : i32, i32
}
@@ -1143,7 +1143,7 @@ func @simple_constant() -> (i32, i32) {
$ mlir-opt foo.mlir -pass-pipeline='func.func(cse,bad-pass)' -mlir-print-ir-after-failure
*** IR Dump After BadPass Failed ***
-func @simple_constant() -> (i32, i32) {
+func.func @simple_constant() -> (i32, i32) {
%c1_i32 = arith.constant 1 : i32
return %c1_i32, %c1_i32 : i32, i32
}
@@ -1159,22 +1159,22 @@ func @simple_constant() -> (i32, i32) {
$ mlir-opt foo.mlir -mlir-disable-threading -pass-pipeline='func.func(cse)' -mlir-print-ir-after=cse -mlir-print-ir-module-scope
*** IR Dump After CSE *** ('func.func' operation: @bar)
-func @bar(%arg0: f32, %arg1: f32) -> f32 {
+func.func @bar(%arg0: f32, %arg1: f32) -> f32 {
...
}
-func @simple_constant() -> (i32, i32) {
+func.func @simple_constant() -> (i32, i32) {
%c1_i32 = arith.constant 1 : i32
%c1_i32_0 = arith.constant 1 : i32
return %c1_i32, %c1_i32_0 : i32, i32
}
*** IR Dump After CSE *** ('func.func' operation: @simple_constant)
-func @bar(%arg0: f32, %arg1: f32) -> f32 {
+func.func @bar(%arg0: f32, %arg1: f32) -> f32 {
...
}
-func @simple_constant() -> (i32, i32) {
+func.func @simple_constant() -> (i32, i32) {
%c1_i32 = arith.constant 1 : i32
return %c1_i32, %c1_i32 : i32, i32
}
@@ -1196,7 +1196,7 @@ reproducible may have the form:
// configuration: -pass-pipeline='func.func(cse,canonicalize),inline' -verify-each
module {
- func @foo() {
+ func.func @foo() {
...
}
}
@@ -1231,7 +1231,7 @@ the following reproducer will be generated:
// configuration: -pass-pipeline='func.func(canonicalize)' -verify-each -mlir-disable-threading
module {
- func @foo() {
+ func.func @foo() {
...
}
}
diff --git a/mlir/docs/Rationale/MLIRForGraphAlgorithms.md b/mlir/docs/Rationale/MLIRForGraphAlgorithms.md
index b6ea991d27334..6d4962ead4b7f 100644
--- a/mlir/docs/Rationale/MLIRForGraphAlgorithms.md
+++ b/mlir/docs/Rationale/MLIRForGraphAlgorithms.md
@@ -155,7 +155,7 @@ turned into zero:
```mlir
// RUN: mlir-opt %s -canonicalize | FileCheck %s
- func @test_subi_zero_cfg(%arg0: i32) -> i32 {
+ func.func @test_subi_zero_cfg(%arg0: i32) -> i32 {
%y = arith.subi %arg0, %arg0 : i32
return %y: i32
}
@@ -209,7 +209,7 @@ write tests like this:
```mlir
// RUN: mlir-opt %s -memref-dependence-check -verify-diagnostics
- func @
diff erent_memrefs() {
+ func.func @
diff erent_memrefs() {
%m.a = memref.alloc() : memref<100xf32>
%m.b = memref.alloc() : memref<100xf32>
%c0 = arith.constant 0 : index
diff --git a/mlir/docs/Rationale/Rationale.md b/mlir/docs/Rationale/Rationale.md
index 90c9199692925..a8e23eafa2d10 100644
--- a/mlir/docs/Rationale/Rationale.md
+++ b/mlir/docs/Rationale/Rationale.md
@@ -137,13 +137,13 @@ unknown dimension can be queried using the "dim" builtin as shown below.
Example:
```mlir
-func foo(...) {
+func.func foo(...) {
%A = memref.alloc <8x?xf32, #lmap> (%N)
...
call bar(%A) : (memref<8x?xf32, #lmap>)
}
-func bar(%A : memref<8x?xf32, #lmap>) {
+func.func bar(%A : memref<8x?xf32, #lmap>) {
// Type of %A indicates that %A has dynamic shape with 8 rows
// and unknown number of columns. The number of columns is queried
// dynamically using dim instruction.
@@ -549,7 +549,7 @@ The presence of dynamic control flow leads to an inner non-affine function
nested in an outer function that uses affine loops.
```mlir
-func @search(%A: memref<?x?xi32>, %S: <?xi32>, %key : i32) {
+func.func @search(%A: memref<?x?xi32>, %S: <?xi32>, %key : i32) {
%ni = memref.dim %A, 0 : memref<?x?xi32>
// This loop can be parallelized
affine.for %i = 0 to %ni {
@@ -558,7 +558,7 @@ func @search(%A: memref<?x?xi32>, %S: <?xi32>, %key : i32) {
return
}
-func @search_body(%A: memref<?x?xi32>, %S: memref<?xi32>, %key: i32, %i : i32) {
+func.func @search_body(%A: memref<?x?xi32>, %S: memref<?xi32>, %key: i32, %i : i32) {
%nj = memref.dim %A, 1 : memref<?x?xi32>
cf.br ^bb1(0)
@@ -607,7 +607,7 @@ for (i = 0; i < N; i++)
```
```mlir
-func @outer_nest(%n : index) {
+func.func @outer_nest(%n : index) {
affine.for %i = 0 to %n {
affine.for %j = 0 to %n {
%pow = call @pow(2, %j) : (index, index) -> index
@@ -617,7 +617,7 @@ func @outer_nest(%n : index) {
return
}
-func @inner_nest(%m : index, %n : index) {
+func.func @inner_nest(%m : index, %n : index) {
affine.for %k = 0 to %m {
affine.for %l = 0 to %n {
...
@@ -658,7 +658,7 @@ in a dilated convolution.
// input: [batch, input_height, input_width, input_feature]
// kernel: [kernel_height, kernel_width, input_feature, output_feature]
// output: [batch, output_height, output_width, output_feature]
-func @conv2d(%input: memref<16x1024x1024x3xf32, #lm0, /*scratchpad=*/1>,
+func.func @conv2d(%input: memref<16x1024x1024x3xf32, #lm0, /*scratchpad=*/1>,
%kernel: memref<5x5x3x32xf32, #lm0, /*scratchpad=*/1>,
%output: memref<16x512x512x32xf32, #lm0, /*scratchpad=*/1>) {
affine.for %b = 0 to %batch {
@@ -756,7 +756,7 @@ instruction that appears in that branch. Each leaf node is an ML Instruction.
#intset_ij = (i, j) [M, N, K] : i >= 0, -i + N - 1 >= 0, j >= 0, -j + N-1 >= 0
#intset_ijk = (i, j, k) [M, N, K] : i >= 0, -i + N - 1 >= 0, j >= 0,
-j + M-1 >= 0, k >= 0, -k + N - 1 >= 0)
-func @matmul(%A, %B, %C, %M, %N, %K) : (...) { // %M, N, K are symbols
+func.func @matmul(%A, %B, %C, %M, %N, %K) : (...) { // %M, N, K are symbols
// t1, t2, t3, t4, t5, t6 are abstract polyhedral loops
mldim %t1 : {S1,S2,S3,S4,S5} floordiv (i, 128) {
mldim %t2 : {S1,S2,S3,S4,S5} floordiv (j, 128) {
@@ -838,7 +838,7 @@ Example:
// read relation: two elements ( d0 <= r0 <= d0+1 )
##aff_rel9 = (d0) -> (r0) : r0 - d0 >= 0, d0 - r0 + 1 >= 0
-func @count (%A : memref<128xf32>, %pos : i32) -> f32
+func.func @count (%A : memref<128xf32>, %pos : i32) -> f32
reads: {%A ##aff_rel9 (%pos)}
writes: /* empty */
may_reads: /* empty */
@@ -913,7 +913,7 @@ Example:
```mlir
##rel9 ( ) [s0] -> (r0, r1) : 0 <= r0 <= 1023, 0 <= r1 <= s0 - 1
-func @cblas_reduce_ffi(%M: memref<1024 x ? x f32, #layout_map0, /*mem=*/0>)
+func.func @cblas_reduce_ffi(%M: memref<1024 x ? x f32, #layout_map0, /*mem=*/0>)
-> f32 [
reads: {%M, ##rel9() }
writes: /* empty */
@@ -921,7 +921,7 @@ func @cblas_reduce_ffi(%M: memref<1024 x ? x f32, #layout_map0, /*mem=*/0>)
may_writes: /* empty */
]
-func @dma_mem_to_scratchpad(%a : memref<1024 x f32, #layout_map0, /*mem=*/0>,
+func.func @dma_mem_to_scratchpad(%a : memref<1024 x f32, #layout_map0, /*mem=*/0>,
%b : memref<1024 x f32, #layout_map0, 1>, %c : memref<1024 x f32,
#layout_map0>) [
reads: {%M, ##rel9() }
@@ -986,7 +986,7 @@ Example:
```mlir
// Return sum of elements in 1-dimensional mref A
-func i32 @sum(%A : memref<?xi32>, %N : i32) -> (i32) {
+func.func i32 @sum(%A : memref<?xi32>, %N : i32) -> (i32) {
%init = 0
%result = affine.for %i = 0 to N with %tmp(%init) {
%value = affine.load %A[%i]
@@ -1016,7 +1016,7 @@ Example:
```mlir
// Compute sum of half of the array
-func i32 @sum_half(%A : memref<?xi32>, %N : i32) -> (i32) {
+func.func i32 @sum_half(%A : memref<?xi32>, %N : i32) -> (i32) {
%s0 = 0
%s1 = affine.for %i = 1 ... N step 1 with %s2 (%s0) {
%s3 = if (%i >= %N / 2) {
diff --git a/mlir/docs/SPIRVToLLVMDialectConversion.md b/mlir/docs/SPIRVToLLVMDialectConversion.md
index 7b03dbdcc0c2b..d139cb6625663 100644
--- a/mlir/docs/SPIRVToLLVMDialectConversion.md
+++ b/mlir/docs/SPIRVToLLVMDialectConversion.md
@@ -858,7 +858,7 @@ gpu.module @foo {
}
}
-func @main() {
+func.func @main() {
// Fill the buffer with some data
%buffer = memref.alloc : memref<8xi32>
%data = ...
@@ -881,7 +881,7 @@ spv.module @__spv__foo /*VCE triple and other metadata here*/ {
spv.EntryPoint @bar, ...
}
-func @main() {
+func.func @main() {
// Fill the buffer with some data.
%buffer = memref.alloc : memref<8xi32>
%data = ...
diff --git a/mlir/docs/SymbolsAndSymbolTables.md b/mlir/docs/SymbolsAndSymbolTables.md
index d9c30b8d735c5..4c9b3ae257e3b 100644
--- a/mlir/docs/SymbolsAndSymbolTables.md
+++ b/mlir/docs/SymbolsAndSymbolTables.md
@@ -78,7 +78,7 @@ Below is an example of how an operation can reference a symbol operation:
```mlir
// This `func.func` operation defines a symbol named `symbol`.
-func @symbol()
+func.func @symbol()
// Our `foo.user` operation contains a SymbolRefAttr with the name of the
// `symbol` func.
@@ -86,7 +86,7 @@ func @symbol()
// Symbol references resolve to the nearest parent operation that defines a
// symbol table, so we can have references with arbitrary nesting levels.
-func @other_symbol() {
+func.func @other_symbol() {
affine.for %i0 = 0 to 10 {
// Our `foo.user` operation resolves to the same `symbol` func as defined
// above.
@@ -107,7 +107,7 @@ module {
// a symbol.
module @module_symbol {
// This `func.func` operation defines a symbol named `nested_symbol`.
- func @nested_symbol()
+ func.func @nested_symbol()
}
// Our `foo.user` operation may refer to the nested symbol, by resolving through
@@ -207,17 +207,17 @@ quote. A few examples of what this looks like in the IR are shown below:
module @public_module {
// This function can be accessed by 'live.user', but cannot be referenced
// externally; all uses are known to reside within parent regions.
- func nested @nested_function()
+ func.func nested @nested_function()
// This function cannot be accessed outside of 'public_module'.
- func private @private_function()
+ func.func private @private_function()
}
// This function can only be accessed from within the top-level module.
-func private @private_function()
+func.func private @private_function()
// This function may be referenced externally.
-func @public_function()
+func.func @public_function()
"live.user"() {uses = [
@public_module::@nested_function,
diff --git a/mlir/docs/TargetLLVMIR.md b/mlir/docs/TargetLLVMIR.md
index b313af6671ed9..6ebe149d57a3e 100644
--- a/mlir/docs/TargetLLVMIR.md
+++ b/mlir/docs/TargetLLVMIR.md
@@ -301,10 +301,10 @@ defines and uses of the values being returned.
Example:
```mlir
-func @foo(%arg0: i32, %arg1: i64) -> (i32, i64) {
+func.func @foo(%arg0: i32, %arg1: i64) -> (i32, i64) {
return %arg0, %arg1 : i32, i64
}
-func @bar() {
+func.func @bar() {
%0 = arith.constant 42 : i32
%1 = arith.constant 17 : i64
%2:2 = call @foo(%0, %1) : (i32, i64) -> (i32, i64)
@@ -360,7 +360,7 @@ aliasing attributes on the raw pointers underpinning the memref.
Examples:
```mlir
-func @foo(%arg0: memref<?xf32>) -> () {
+func.func @foo(%arg0: memref<?xf32>) -> () {
"use"(%arg0) : (memref<?xf32>) -> ()
return
}
@@ -390,7 +390,7 @@ llvm.func @foo(%arg0: !llvm.ptr<f32>, // Allocated pointer.
```
```mlir
-func @bar() {
+func.func @bar() {
%0 = "get"() : () -> (memref<?xf32>)
call @foo(%0) : (memref<?xf32>) -> ()
return
@@ -503,9 +503,9 @@ to the following.
Examples:
```
-func @callee(memref<2x4xf32>) {
+func.func @callee(memref<2x4xf32>) {
-func @caller(%0 : memref<2x4xf32>) {
+func.func @caller(%0 : memref<2x4xf32>) {
call @callee(%0) : (memref<2x4xf32>) -> ()
}
@@ -615,7 +615,7 @@ Examples:
```mlir
-func @qux(%arg0: memref<?x?xf32>)
+func.func @qux(%arg0: memref<?x?xf32>)
// Gets converted into the following
// (using type alias for brevity):
@@ -659,7 +659,7 @@ llvm.func @_mlir_ciface_qux(!llvm.ptr<struct<(ptr<f32>, ptr<f32>, i64,
```
```mlir
-func @foo(%arg0: memref<?x?xf32>) {
+func.func @foo(%arg0: memref<?x?xf32>) {
return
}
@@ -698,7 +698,7 @@ llvm.func @_mlir_ciface_foo(%arg0: !llvm.memref_2d_ptr) {
```
```mlir
-func @foo(%arg0: memref<?x?xf32>) -> memref<?x?xf32> {
+func.func @foo(%arg0: memref<?x?xf32>) -> memref<?x?xf32> {
return %arg0 : memref<?x?xf32>
}
diff --git a/mlir/docs/Tutorials/QuickstartRewrites.md b/mlir/docs/Tutorials/QuickstartRewrites.md
index 2de24e8343bb5..69fe1ead41cb3 100644
--- a/mlir/docs/Tutorials/QuickstartRewrites.md
+++ b/mlir/docs/Tutorials/QuickstartRewrites.md
@@ -275,7 +275,7 @@ the legalization pass test in TensorFlow Lite) such as:
```mlir
// RUN: mlir-opt -tfl-legalize-tf %s | FileCheck %s
-func @LeakyRelu(%arg0: tensor<1xf32>) -> tensor<1xf32> {
+func.func @LeakyRelu(%arg0: tensor<1xf32>) -> tensor<1xf32> {
%2 = "tf.LeakyRelu"(%arg0) {alpha: 0.1} : (tensor<1xf32>) -> tensor<1xf32>
return %2: tensor<1xf32>
diff --git a/mlir/docs/Tutorials/Toy/Ch-2.md b/mlir/docs/Tutorials/Toy/Ch-2.md
index 99e6cfe070ffb..29fd0f5fc5625 100644
--- a/mlir/docs/Tutorials/Toy/Ch-2.md
+++ b/mlir/docs/Tutorials/Toy/Ch-2.md
@@ -124,7 +124,7 @@ operation. For example, we could place our Toy operation from above into an
related dialect:
```mlir
-func @toy_func(%tensor: tensor<2x3xf64>) -> tensor<3x2xf64> {
+func.func @toy_func(%tensor: tensor<2x3xf64>) -> tensor<3x2xf64> {
%t_tensor = "toy.transpose"(%tensor) { inplace = true } : (tensor<2x3xf64>) -> tensor<3x2xf64>
return %t_tensor : tensor<3x2xf64>
}
@@ -144,7 +144,7 @@ This handling can be observed by crafting what should be an invalid IR for Toy
and seeing it round-trip without tripping the verifier:
```mlir
-func @main() {
+func.func @main() {
%0 = "toy.print"() : () -> tensor<2x3xf64>
}
```
diff --git a/mlir/docs/Tutorials/Toy/Ch-5.md b/mlir/docs/Tutorials/Toy/Ch-5.md
index d8f626b410d37..a81af2affd927 100644
--- a/mlir/docs/Tutorials/Toy/Ch-5.md
+++ b/mlir/docs/Tutorials/Toy/Ch-5.md
@@ -243,7 +243,7 @@ toy.func @main() {
With affine lowering added to our pipeline, we can now generate:
```mlir
-func @main() {
+func.func @main() {
%cst = arith.constant 1.000000e+00 : f64
%cst_0 = arith.constant 2.000000e+00 : f64
%cst_1 = arith.constant 3.000000e+00 : f64
@@ -301,7 +301,7 @@ help clean this up. Adding the `LoopFusion` and `MemRefDataFlowOpt` passes to
the pipeline gives the following result:
```mlir
-func @main() {
+func.func @main() {
%cst = arith.constant 1.000000e+00 : f64
%cst_0 = arith.constant 2.000000e+00 : f64
%cst_1 = arith.constant 3.000000e+00 : f64
diff --git a/mlir/examples/standalone/test/Standalone/dummy.mlir b/mlir/examples/standalone/test/Standalone/dummy.mlir
index dc48912059c5c..cb688d2734433 100644
--- a/mlir/examples/standalone/test/Standalone/dummy.mlir
+++ b/mlir/examples/standalone/test/Standalone/dummy.mlir
@@ -2,7 +2,7 @@
module {
// CHECK-LABEL: func @bar()
- func @bar() {
+ func.func @bar() {
%0 = arith.constant 1 : i32
// CHECK: %{{.*}} = standalone.foo %{{.*}} : i32
%res = standalone.foo %0 : i32
diff --git a/mlir/examples/toy/Ch2/include/toy/Ops.td b/mlir/examples/toy/Ch2/include/toy/Ops.td
index 2b12a63753141..e3e920ef6b79f 100644
--- a/mlir/examples/toy/Ch2/include/toy/Ops.td
+++ b/mlir/examples/toy/Ch2/include/toy/Ops.td
@@ -276,7 +276,7 @@ def ReturnOp : Toy_Op<"return", [NoSideEffect, HasParent<"FuncOp">,
the operation. For example:
```mlir
- func @foo() -> tensor<2xf64> {
+ toy.func @foo() -> tensor<2xf64> {
...
toy.return %0 : tensor<2xf64>
}
diff --git a/mlir/examples/toy/Ch3/include/toy/Ops.td b/mlir/examples/toy/Ch3/include/toy/Ops.td
index 4d4ba0b047099..1646bb846441c 100644
--- a/mlir/examples/toy/Ch3/include/toy/Ops.td
+++ b/mlir/examples/toy/Ch3/include/toy/Ops.td
@@ -278,7 +278,7 @@ def ReturnOp : Toy_Op<"return", [NoSideEffect, HasParent<"FuncOp">,
the operation. For example:
```mlir
- func @foo() -> tensor<2xf64> {
+ toy.func @foo() -> tensor<2xf64> {
...
toy.return %0 : tensor<2xf64>
}
diff --git a/mlir/examples/toy/Ch4/include/toy/Ops.td b/mlir/examples/toy/Ch4/include/toy/Ops.td
index 1e33affe38a54..672876eea5d1d 100644
--- a/mlir/examples/toy/Ch4/include/toy/Ops.td
+++ b/mlir/examples/toy/Ch4/include/toy/Ops.td
@@ -310,7 +310,7 @@ def ReturnOp : Toy_Op<"return", [NoSideEffect, HasParent<"FuncOp">,
the operation. For example:
```mlir
- func @foo() -> tensor<2xf64> {
+ toy.func @foo() -> tensor<2xf64> {
...
toy.return %0 : tensor<2xf64>
}
diff --git a/mlir/examples/toy/Ch5/include/toy/Ops.td b/mlir/examples/toy/Ch5/include/toy/Ops.td
index 790cc9b04b273..714ddef18ebf1 100644
--- a/mlir/examples/toy/Ch5/include/toy/Ops.td
+++ b/mlir/examples/toy/Ch5/include/toy/Ops.td
@@ -311,7 +311,7 @@ def ReturnOp : Toy_Op<"return", [NoSideEffect, HasParent<"FuncOp">,
the operation. For example:
```mlir
- func @foo() -> tensor<2xf64> {
+ toy.func @foo() -> tensor<2xf64> {
...
toy.return %0 : tensor<2xf64>
}
diff --git a/mlir/examples/toy/Ch6/include/toy/Ops.td b/mlir/examples/toy/Ch6/include/toy/Ops.td
index 0e0390480b0cc..2e1945e5e64c0 100644
--- a/mlir/examples/toy/Ch6/include/toy/Ops.td
+++ b/mlir/examples/toy/Ch6/include/toy/Ops.td
@@ -311,7 +311,7 @@ def ReturnOp : Toy_Op<"return", [NoSideEffect, HasParent<"FuncOp">,
the operation. For example:
```mlir
- func @foo() -> tensor<2xf64> {
+ toy.func @foo() -> tensor<2xf64> {
...
toy.return %0 : tensor<2xf64>
}
diff --git a/mlir/examples/toy/Ch7/include/toy/Ops.td b/mlir/examples/toy/Ch7/include/toy/Ops.td
index 5f84d5f05e8bd..cb59b368d0274 100644
--- a/mlir/examples/toy/Ch7/include/toy/Ops.td
+++ b/mlir/examples/toy/Ch7/include/toy/Ops.td
@@ -331,7 +331,7 @@ def ReturnOp : Toy_Op<"return", [NoSideEffect, HasParent<"FuncOp">,
the operation. For example:
```mlir
- func @foo() -> tensor<2xf64> {
+ toy.func @foo() -> tensor<2xf64> {
...
toy.return %0 : tensor<2xf64>
}
diff --git a/mlir/test/Integration/Dialect/SparseTensor/python/test_SDDMM.py b/mlir/test/Integration/Dialect/SparseTensor/python/test_SDDMM.py
index d8dde93e96d95..4ed741ed3727e 100644
--- a/mlir/test/Integration/Dialect/SparseTensor/python/test_SDDMM.py
+++ b/mlir/test/Integration/Dialect/SparseTensor/python/test_SDDMM.py
@@ -24,37 +24,37 @@ def sddmm_dsl(
B=dsl.TensorDef(dsl.T, dsl.S.K, dsl.S.N),
S=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.N),
C=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.N, output=True)):
- C[dsl.D.m,
- dsl.D.n] += S[dsl.D.m, dsl.D.n] * A[dsl.D.m, dsl.D.k] * B[dsl.D.k, dsl.D.n]
+ C[dsl.D.m,
+ dsl.D.n] += S[dsl.D.m, dsl.D.n] * A[dsl.D.m, dsl.D.k] * B[dsl.D.k, dsl.D.n]
def build_SDDMM(attr: st.EncodingAttr):
- """Build SDDMM kernel.
+ """Build SDDMM kernel.
This method generates a linalg op with for matrix multiplication using
just the Python API. Effectively, a generic linalg op is constructed
that computes C(i,j) += S(i,j) SUM_k A(i,k) B(k,j) for sparse S.
"""
- module = ir.Module.create()
- f64 = ir.F64Type.get()
- a = ir.RankedTensorType.get([8, 8], f64)
- b = ir.RankedTensorType.get([8, 8], f64)
- c = ir.RankedTensorType.get([8, 8], f64)
- s = ir.RankedTensorType.get([8, 8], f64, attr)
- arguments = [a, b, s, c]
- with ir.InsertionPoint(module.body):
+ module = ir.Module.create()
+ f64 = ir.F64Type.get()
+ a = ir.RankedTensorType.get([8, 8], f64)
+ b = ir.RankedTensorType.get([8, 8], f64)
+ c = ir.RankedTensorType.get([8, 8], f64)
+ s = ir.RankedTensorType.get([8, 8], f64, attr)
+ arguments = [a, b, s, c]
+ with ir.InsertionPoint(module.body):
- @func.FuncOp.from_py_func(*arguments)
- def sddmm(*args):
- return sddmm_dsl(args[0], args[1], args[2], outs=[args[3]])
+ @func.FuncOp.from_py_func(*arguments)
+ def sddmm(*args):
+ return sddmm_dsl(args[0], args[1], args[2], outs=[args[3]])
- return module
+ return module
def boilerplate(attr: st.EncodingAttr):
- """Returns boilerplate code for main driver."""
- return f"""
-func @main(%a: tensor<8x8xf64>,
+ """Returns boilerplate code for main driver."""
+ return f"""
+func.func @main(%a: tensor<8x8xf64>,
%b: tensor<8x8xf64>,
%c: tensor<8x8xf64>) -> tensor<8x8xf64> attributes {{ llvm.emit_c_interface }} {{
%t = arith.constant sparse<[[0,0], [0,2], [4,1]], [1.0, 2.0, 3.0]> : tensor<8x8xf64>
@@ -69,96 +69,96 @@ def boilerplate(attr: st.EncodingAttr):
def build_compile_and_run_SDDMMM(attr: st.EncodingAttr, compiler):
- # Build.
- module = build_SDDMM(attr)
- func = str(module.operation.regions[0].blocks[0].operations[0].operation)
- module = ir.Module.parse(func + boilerplate(attr))
-
- # Compile.
- engine = compiler.compile_and_jit(module)
-
- # Set up numpy input and buffer for output.
- a = np.array([[1.1, 2.1, 3.1, 4.1, 5.1, 6.1, 7.1, 8.1],
- [1.2, 2.2, 3.2, 4.2, 5.2, 6.2, 7.2, 8.2],
- [1.3, 2.3, 3.3, 4.3, 5.3, 6.3, 7.3, 8.3],
- [1.4, 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 8.4],
- [1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5],
- [1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6],
- [1.7, 2.7, 3.7, 4.7, 5.7, 6.7, 7.7, 8.7],
- [1.8, 2.8, 3.8, 4.8, 5.8, 6.8, 7.8, 8.8]], np.float64)
- b = np.ones((8, 8), np.float64)
- c = np.zeros((8, 8), np.float64)
-
- mem_a = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(a)))
- mem_b = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(b)))
- mem_c = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(c)))
-
- # Allocate a MemRefDescriptor to receive the output tensor.
- # The buffer itself is allocated inside the MLIR code generation.
- ref_out = rt.make_nd_memref_descriptor(2, ctypes.c_double)()
- mem_out = ctypes.pointer(ctypes.pointer(ref_out))
-
- # Invoke the kernel and get numpy output.
- # Built-in bufferization uses in-out buffers.
- # TODO: replace with inplace comprehensive bufferization.
- engine.invoke('main', mem_out, mem_a, mem_b, mem_c)
-
- # Sanity check on computed result. Only a few elements
- # are sampled from the full dense matrix multiplication.
- full_matmul = np.matmul(a, b)
- expected = np.zeros((8, 8), np.float64)
- expected[0, 0] = 1.0 * full_matmul[0, 0]
- expected[0, 2] = 2.0 * full_matmul[0, 2]
- expected[4, 1] = 3.0 * full_matmul[4, 1]
- c = rt.ranked_memref_to_numpy(mem_out[0])
- if np.allclose(c, expected):
- pass
- else:
- quit(f'FAILURE')
+ # Build.
+ module = build_SDDMM(attr)
+ func = str(module.operation.regions[0].blocks[0].operations[0].operation)
+ module = ir.Module.parse(func + boilerplate(attr))
+
+ # Compile.
+ engine = compiler.compile_and_jit(module)
+
+ # Set up numpy input and buffer for output.
+ a = np.array([[1.1, 2.1, 3.1, 4.1, 5.1, 6.1, 7.1, 8.1],
+ [1.2, 2.2, 3.2, 4.2, 5.2, 6.2, 7.2, 8.2],
+ [1.3, 2.3, 3.3, 4.3, 5.3, 6.3, 7.3, 8.3],
+ [1.4, 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 8.4],
+ [1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5],
+ [1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6],
+ [1.7, 2.7, 3.7, 4.7, 5.7, 6.7, 7.7, 8.7],
+ [1.8, 2.8, 3.8, 4.8, 5.8, 6.8, 7.8, 8.8]], np.float64)
+ b = np.ones((8, 8), np.float64)
+ c = np.zeros((8, 8), np.float64)
+
+ mem_a = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(a)))
+ mem_b = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(b)))
+ mem_c = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(c)))
+
+ # Allocate a MemRefDescriptor to receive the output tensor.
+ # The buffer itself is allocated inside the MLIR code generation.
+ ref_out = rt.make_nd_memref_descriptor(2, ctypes.c_double)()
+ mem_out = ctypes.pointer(ctypes.pointer(ref_out))
+
+ # Invoke the kernel and get numpy output.
+ # Built-in bufferization uses in-out buffers.
+ # TODO: replace with inplace comprehensive bufferization.
+ engine.invoke('main', mem_out, mem_a, mem_b, mem_c)
+
+ # Sanity check on computed result. Only a few elements
+ # are sampled from the full dense matrix multiplication.
+ full_matmul = np.matmul(a, b)
+ expected = np.zeros((8, 8), np.float64)
+ expected[0, 0] = 1.0 * full_matmul[0, 0]
+ expected[0, 2] = 2.0 * full_matmul[0, 2]
+ expected[4, 1] = 3.0 * full_matmul[4, 1]
+ c = rt.ranked_memref_to_numpy(mem_out[0])
+ if np.allclose(c, expected):
+ pass
+ else:
+ quit(f'FAILURE')
def main():
- support_lib = os.getenv('SUPPORT_LIB')
- assert support_lib is not None, 'SUPPORT_LIB is undefined'
- if not os.path.exists(support_lib):
- raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT),
- support_lib)
-
- # CHECK-LABEL: TEST: testSDDMMM
- print('\nTEST: testSDDMMM')
- with ir.Context() as ctx, ir.Location.unknown():
- count = 0
- # Loop over various ways to compile and annotate the SDDMM kernel with
- # a *single* sparse tensor. Note that we deliberate do not exhaustively
- # search the full state space to reduce runtime of the test. It is
- # straightforward to adapt the code below to explore more combinations.
- levels = [[st.DimLevelType.dense, st.DimLevelType.dense],
- [st.DimLevelType.dense, st.DimLevelType.compressed],
- [st.DimLevelType.compressed, st.DimLevelType.dense],
- [st.DimLevelType.compressed, st.DimLevelType.compressed]]
- orderings = [
- ir.AffineMap.get_permutation([0, 1]),
- ir.AffineMap.get_permutation([1, 0])
- ]
- for level in levels:
- for ordering in orderings:
- for pwidth in [32]:
- for iwidth in [32]:
- for par in [0]:
- for vec in [0, 1]:
- for e in [True]:
- vl = 1 if vec == 0 else 16
- attr = st.EncodingAttr.get(level, ordering, pwidth, iwidth)
- opt = (f'parallelization-strategy={par} '
- f'vectorization-strategy={vec} '
- f'vl={vl} enable-simd-index32={e}')
- compiler = sparse_compiler.SparseCompiler(
- options=opt, opt_level=0, shared_libs=[support_lib])
- build_compile_and_run_SDDMMM(attr, compiler)
- count = count + 1
- # CHECK: Passed 16 tests
- print('Passed ', count, 'tests')
+ support_lib = os.getenv('SUPPORT_LIB')
+ assert support_lib is not None, 'SUPPORT_LIB is undefined'
+ if not os.path.exists(support_lib):
+ raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT),
+ support_lib)
+
+ # CHECK-LABEL: TEST: testSDDMMM
+ print('\nTEST: testSDDMMM')
+ with ir.Context() as ctx, ir.Location.unknown():
+ count = 0
+ # Loop over various ways to compile and annotate the SDDMM kernel with
+ # a *single* sparse tensor. Note that we deliberate do not exhaustively
+ # search the full state space to reduce runtime of the test. It is
+ # straightforward to adapt the code below to explore more combinations.
+ levels = [[st.DimLevelType.dense, st.DimLevelType.dense],
+ [st.DimLevelType.dense, st.DimLevelType.compressed],
+ [st.DimLevelType.compressed, st.DimLevelType.dense],
+ [st.DimLevelType.compressed, st.DimLevelType.compressed]]
+ orderings = [
+ ir.AffineMap.get_permutation([0, 1]),
+ ir.AffineMap.get_permutation([1, 0])
+ ]
+ for level in levels:
+ for ordering in orderings:
+ for pwidth in [32]:
+ for iwidth in [32]:
+ for par in [0]:
+ for vec in [0, 1]:
+ for e in [True]:
+ vl = 1 if vec == 0 else 16
+ attr = st.EncodingAttr.get(level, ordering, pwidth, iwidth)
+ opt = (f'parallelization-strategy={par} '
+ f'vectorization-strategy={vec} '
+ f'vl={vl} enable-simd-index32={e}')
+ compiler = sparse_compiler.SparseCompiler(
+ options=opt, opt_level=0, shared_libs=[support_lib])
+ build_compile_and_run_SDDMMM(attr, compiler)
+ count = count + 1
+ # CHECK: Passed 16 tests
+ print('Passed ', count, 'tests')
if __name__ == '__main__':
- main()
+ main()
diff --git a/mlir/test/Integration/Dialect/SparseTensor/python/test_SpMM.py b/mlir/test/Integration/Dialect/SparseTensor/python/test_SpMM.py
index 5fda4ee32e1f4..9712620a4a7a9 100644
--- a/mlir/test/Integration/Dialect/SparseTensor/python/test_SpMM.py
+++ b/mlir/test/Integration/Dialect/SparseTensor/python/test_SpMM.py
@@ -23,41 +23,41 @@ def matmul_dsl(
A=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.K),
B=dsl.TensorDef(dsl.T, dsl.S.K, dsl.S.N),
C=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.N, output=True)):
- C[dsl.D.m, dsl.D.n] += A[dsl.D.m, dsl.D.k] * B[dsl.D.k, dsl.D.n]
+ C[dsl.D.m, dsl.D.n] += A[dsl.D.m, dsl.D.k] * B[dsl.D.k, dsl.D.n]
def build_SpMM(attr: st.EncodingAttr):
- """Build SpMM kernel.
+ """Build SpMM kernel.
This method generates a linalg op with for matrix multiplication using
just the Python API. Effectively, a generic linalg op is constructed
that computes C(i,j) += A(i,k) * B(k,j) for annotated matrix A.
"""
- module = ir.Module.create()
- f64 = ir.F64Type.get()
- a = ir.RankedTensorType.get([3, 4], f64, attr)
- b = ir.RankedTensorType.get([4, 2], f64)
- c = ir.RankedTensorType.get([3, 2], f64)
- arguments = [a, b, c]
- with ir.InsertionPoint(module.body):
+ module = ir.Module.create()
+ f64 = ir.F64Type.get()
+ a = ir.RankedTensorType.get([3, 4], f64, attr)
+ b = ir.RankedTensorType.get([4, 2], f64)
+ c = ir.RankedTensorType.get([3, 2], f64)
+ arguments = [a, b, c]
+ with ir.InsertionPoint(module.body):
- @func.FuncOp.from_py_func(*arguments)
- def spMxM(*args):
- return matmul_dsl(args[0], args[1], outs=[args[2]])
+ @func.FuncOp.from_py_func(*arguments)
+ def spMxM(*args):
+ return matmul_dsl(args[0], args[1], outs=[args[2]])
- return module
+ return module
def boilerplate(attr: st.EncodingAttr):
- """Returns boilerplate main method.
+ """Returns boilerplate main method.
This method sets up a boilerplate main method that takes three tensors
(a, b, c), converts the first tensor a into s sparse tensor, and then
calls the sparse kernel for matrix multiplication. For convenience,
this part is purely done as string input.
"""
- return f"""
-func @main(%ad: tensor<3x4xf64>, %b: tensor<4x2xf64>, %c: tensor<3x2xf64>) -> tensor<3x2xf64>
+ return f"""
+func.func @main(%ad: tensor<3x4xf64>, %b: tensor<4x2xf64>, %c: tensor<3x2xf64>) -> tensor<3x2xf64>
attributes {{ llvm.emit_c_interface }} {{
%a = sparse_tensor.convert %ad : tensor<3x4xf64> to tensor<3x4xf64, {attr}>
%0 = call @spMxM(%a, %b, %c) : (tensor<3x4xf64, {attr}>,
@@ -69,85 +69,85 @@ def boilerplate(attr: st.EncodingAttr):
def build_compile_and_run_SpMM(attr: st.EncodingAttr, compiler):
- # Build.
- module = build_SpMM(attr)
- func = str(module.operation.regions[0].blocks[0].operations[0].operation)
- module = ir.Module.parse(func + boilerplate(attr))
-
- # Compile.
- engine = compiler.compile_and_jit(module)
-
- # Set up numpy input and buffer for output.
- a = np.array(
- [[1.1, 0.0, 0.0, 1.4], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 3.3, 0.0]],
- np.float64)
- b = np.array([[1.0, 2.0], [4.0, 3.0], [5.0, 6.0], [8.0, 7.0]], np.float64)
- c = np.zeros((3, 2), np.float64)
-
- mem_a = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(a)))
- mem_b = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(b)))
- mem_c = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(c)))
- # Allocate a MemRefDescriptor to receive the output tensor.
- # The buffer itself is allocated inside the MLIR code generation.
- ref_out = rt.make_nd_memref_descriptor(2, ctypes.c_double)()
- mem_out = ctypes.pointer(ctypes.pointer(ref_out))
-
- # Invoke the kernel and get numpy output.
- # Built-in bufferization uses in-out buffers.
- # TODO: replace with inplace comprehensive bufferization.
- engine.invoke('main', mem_out, mem_a, mem_b, mem_c)
-
- # Sanity check on computed result.
- expected = np.matmul(a, b);
- c = rt.ranked_memref_to_numpy(mem_out[0])
- if np.allclose(c, expected):
- pass
- else:
- quit(f'FAILURE')
+ # Build.
+ module = build_SpMM(attr)
+ func = str(module.operation.regions[0].blocks[0].operations[0].operation)
+ module = ir.Module.parse(func + boilerplate(attr))
+
+ # Compile.
+ engine = compiler.compile_and_jit(module)
+
+ # Set up numpy input and buffer for output.
+ a = np.array(
+ [[1.1, 0.0, 0.0, 1.4], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 3.3, 0.0]],
+ np.float64)
+ b = np.array([[1.0, 2.0], [4.0, 3.0], [5.0, 6.0], [8.0, 7.0]], np.float64)
+ c = np.zeros((3, 2), np.float64)
+
+ mem_a = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(a)))
+ mem_b = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(b)))
+ mem_c = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(c)))
+ # Allocate a MemRefDescriptor to receive the output tensor.
+ # The buffer itself is allocated inside the MLIR code generation.
+ ref_out = rt.make_nd_memref_descriptor(2, ctypes.c_double)()
+ mem_out = ctypes.pointer(ctypes.pointer(ref_out))
+
+ # Invoke the kernel and get numpy output.
+ # Built-in bufferization uses in-out buffers.
+ # TODO: replace with inplace comprehensive bufferization.
+ engine.invoke('main', mem_out, mem_a, mem_b, mem_c)
+
+ # Sanity check on computed result.
+ expected = np.matmul(a, b);
+ c = rt.ranked_memref_to_numpy(mem_out[0])
+ if np.allclose(c, expected):
+ pass
+ else:
+ quit(f'FAILURE')
def main():
- support_lib = os.getenv('SUPPORT_LIB')
- assert support_lib is not None, 'SUPPORT_LIB is undefined'
- if not os.path.exists(support_lib):
- raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), support_lib)
-
- # CHECK-LABEL: TEST: testSpMM
- print('\nTEST: testSpMM')
- with ir.Context() as ctx, ir.Location.unknown():
- count = 0
- # Loop over various ways to compile and annotate the SpMM kernel with
- # a *single* sparse tensor. Note that we deliberate do not exhaustively
- # search the full state space to reduce runtime of the test. It is
- # straightforward to adapt the code below to explore more combinations.
- par = 0
- vec = 0
- vl = 1
- e = False
- opt = (f'parallelization-strategy={par} '
- f'vectorization-strategy={vec} '
- f'vl={vl} enable-simd-index32={e}')
- levels = [[st.DimLevelType.dense, st.DimLevelType.dense],
- [st.DimLevelType.dense, st.DimLevelType.compressed],
- [st.DimLevelType.compressed, st.DimLevelType.dense],
- [st.DimLevelType.compressed, st.DimLevelType.compressed]]
- orderings = [
- ir.AffineMap.get_permutation([0, 1]),
- ir.AffineMap.get_permutation([1, 0])
- ]
- bitwidths = [0]
- compiler = sparse_compiler.SparseCompiler(
- options=opt, opt_level=0, shared_libs=[support_lib])
- for level in levels:
- for ordering in orderings:
- for pwidth in bitwidths:
- for iwidth in bitwidths:
- attr = st.EncodingAttr.get(level, ordering, pwidth, iwidth)
- build_compile_and_run_SpMM(attr, compiler)
- count = count + 1
- # CHECK: Passed 8 tests
- print('Passed ', count, 'tests')
+ support_lib = os.getenv('SUPPORT_LIB')
+ assert support_lib is not None, 'SUPPORT_LIB is undefined'
+ if not os.path.exists(support_lib):
+ raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), support_lib)
+
+ # CHECK-LABEL: TEST: testSpMM
+ print('\nTEST: testSpMM')
+ with ir.Context() as ctx, ir.Location.unknown():
+ count = 0
+ # Loop over various ways to compile and annotate the SpMM kernel with
+ # a *single* sparse tensor. Note that we deliberate do not exhaustively
+ # search the full state space to reduce runtime of the test. It is
+ # straightforward to adapt the code below to explore more combinations.
+ par = 0
+ vec = 0
+ vl = 1
+ e = False
+ opt = (f'parallelization-strategy={par} '
+ f'vectorization-strategy={vec} '
+ f'vl={vl} enable-simd-index32={e}')
+ levels = [[st.DimLevelType.dense, st.DimLevelType.dense],
+ [st.DimLevelType.dense, st.DimLevelType.compressed],
+ [st.DimLevelType.compressed, st.DimLevelType.dense],
+ [st.DimLevelType.compressed, st.DimLevelType.compressed]]
+ orderings = [
+ ir.AffineMap.get_permutation([0, 1]),
+ ir.AffineMap.get_permutation([1, 0])
+ ]
+ bitwidths = [0]
+ compiler = sparse_compiler.SparseCompiler(
+ options=opt, opt_level=0, shared_libs=[support_lib])
+ for level in levels:
+ for ordering in orderings:
+ for pwidth in bitwidths:
+ for iwidth in bitwidths:
+ attr = st.EncodingAttr.get(level, ordering, pwidth, iwidth)
+ build_compile_and_run_SpMM(attr, compiler)
+ count = count + 1
+ # CHECK: Passed 8 tests
+ print('Passed ', count, 'tests')
if __name__ == '__main__':
- main()
+ main()
diff --git a/mlir/test/Integration/Dialect/SparseTensor/python/test_elementwise_add_sparse_output.py b/mlir/test/Integration/Dialect/SparseTensor/python/test_elementwise_add_sparse_output.py
index 91de742a83cad..f647066a79eda 100644
--- a/mlir/test/Integration/Dialect/SparseTensor/python/test_elementwise_add_sparse_output.py
+++ b/mlir/test/Integration/Dialect/SparseTensor/python/test_elementwise_add_sparse_output.py
@@ -33,7 +33,7 @@
doc = "X(i,j) = A(i,j) + B(i,j)"
}
-func @sparse_add_elt(
+func.func @sparse_add_elt(
%arga: tensor<3x4xf64, #DCSR>, %argb: tensor<3x4xf64, #DCSR>) -> tensor<3x4xf64, #DCSR> {
%c3 = arith.constant 3 : index
%c4 = arith.constant 4 : index
@@ -48,7 +48,7 @@
return %0 : tensor<3x4xf64, #DCSR>
}
-func @main(%ad: tensor<3x4xf64>, %bd: tensor<3x4xf64>) -> tensor<3x4xf64, #DCSR>
+func.func @main(%ad: tensor<3x4xf64>, %bd: tensor<3x4xf64>) -> tensor<3x4xf64, #DCSR>
attributes { llvm.emit_c_interface } {
%a = sparse_tensor.convert %ad : tensor<3x4xf64> to tensor<3x4xf64, #DCSR>
%b = sparse_tensor.convert %bd : tensor<3x4xf64> to tensor<3x4xf64, #DCSR>
diff --git a/mlir/test/Integration/Dialect/SparseTensor/python/test_output.py b/mlir/test/Integration/Dialect/SparseTensor/python/test_output.py
index 324561cd725c9..9b8ee5b7abaa9 100644
--- a/mlir/test/Integration/Dialect/SparseTensor/python/test_output.py
+++ b/mlir/test/Integration/Dialect/SparseTensor/python/test_output.py
@@ -18,9 +18,9 @@
# TODO: move more into actual IR building.
def boilerplate(attr: st.EncodingAttr):
- """Returns boilerplate main method."""
- return f"""
-func @main(%p : !llvm.ptr<i8>) -> () attributes {{ llvm.emit_c_interface }} {{
+ """Returns boilerplate main method."""
+ return f"""
+func.func @main(%p : !llvm.ptr<i8>) -> () attributes {{ llvm.emit_c_interface }} {{
%d = arith.constant sparse<[[0, 0], [1, 1], [0, 9], [9, 0], [4, 4]],
[1.0, 2.0, 3.0, 4.0, 5.0]> : tensor<10x10xf64>
%a = sparse_tensor.convert %d : tensor<10x10xf64> to tensor<10x10xf64, {attr}>
@@ -31,13 +31,13 @@ def boilerplate(attr: st.EncodingAttr):
def expected():
- """Returns expected contents of output.
+ """Returns expected contents of output.
Regardless of the dimension ordering, compression, and bitwidths that are
used in the sparse tensor, the output is always lexicographically sorted
by natural index order.
"""
- return f"""; extended FROSTT format
+ return f"""; extended FROSTT format
2 5
10 10
1 1 1
@@ -49,53 +49,53 @@ def expected():
def build_compile_and_run_output(attr: st.EncodingAttr, compiler):
- # Build and Compile.
- module = ir.Module.parse(boilerplate(attr))
- engine = compiler.compile_and_jit(module)
+ # Build and Compile.
+ module = ir.Module.parse(boilerplate(attr))
+ engine = compiler.compile_and_jit(module)
- # Invoke the kernel and compare output.
- with tempfile.TemporaryDirectory() as test_dir:
- out = os.path.join(test_dir, 'out.tns')
- buf = out.encode('utf-8')
- mem_a = ctypes.pointer(ctypes.pointer(ctypes.create_string_buffer(buf)))
- engine.invoke('main', mem_a)
+ # Invoke the kernel and compare output.
+ with tempfile.TemporaryDirectory() as test_dir:
+ out = os.path.join(test_dir, 'out.tns')
+ buf = out.encode('utf-8')
+ mem_a = ctypes.pointer(ctypes.pointer(ctypes.create_string_buffer(buf)))
+ engine.invoke('main', mem_a)
- actual = open(out).read()
- if actual != expected():
- quit('FAILURE')
+ actual = open(out).read()
+ if actual != expected():
+ quit('FAILURE')
def main():
- support_lib = os.getenv('SUPPORT_LIB')
- assert support_lib is not None, 'SUPPORT_LIB is undefined'
- if not os.path.exists(support_lib):
- raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT),
- support_lib)
-
- # CHECK-LABEL: TEST: test_output
- print('\nTEST: test_output')
- count = 0
- with ir.Context() as ctx, ir.Location.unknown():
- # Loop over various sparse types: CSR, DCSR, CSC, DCSC.
- levels = [[st.DimLevelType.dense, st.DimLevelType.compressed],
- [st.DimLevelType.compressed, st.DimLevelType.compressed]]
- orderings = [
- ir.AffineMap.get_permutation([0, 1]),
- ir.AffineMap.get_permutation([1, 0])
- ]
- bitwidths = [8, 16, 32, 64]
- compiler = sparse_compiler.SparseCompiler(
- options='', opt_level=2, shared_libs=[support_lib])
- for level in levels:
- for ordering in orderings:
- for bwidth in bitwidths:
- attr = st.EncodingAttr.get(level, ordering, bwidth, bwidth)
- build_compile_and_run_output(attr, compiler)
- count = count + 1
-
- # CHECK: Passed 16 tests
- print('Passed', count, 'tests')
+ support_lib = os.getenv('SUPPORT_LIB')
+ assert support_lib is not None, 'SUPPORT_LIB is undefined'
+ if not os.path.exists(support_lib):
+ raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT),
+ support_lib)
+
+ # CHECK-LABEL: TEST: test_output
+ print('\nTEST: test_output')
+ count = 0
+ with ir.Context() as ctx, ir.Location.unknown():
+ # Loop over various sparse types: CSR, DCSR, CSC, DCSC.
+ levels = [[st.DimLevelType.dense, st.DimLevelType.compressed],
+ [st.DimLevelType.compressed, st.DimLevelType.compressed]]
+ orderings = [
+ ir.AffineMap.get_permutation([0, 1]),
+ ir.AffineMap.get_permutation([1, 0])
+ ]
+ bitwidths = [8, 16, 32, 64]
+ compiler = sparse_compiler.SparseCompiler(
+ options='', opt_level=2, shared_libs=[support_lib])
+ for level in levels:
+ for ordering in orderings:
+ for bwidth in bitwidths:
+ attr = st.EncodingAttr.get(level, ordering, bwidth, bwidth)
+ build_compile_and_run_output(attr, compiler)
+ count = count + 1
+
+ # CHECK: Passed 16 tests
+ print('Passed', count, 'tests')
if __name__ == '__main__':
- main()
+ main()
diff --git a/mlir/test/Integration/Dialect/SparseTensor/taco/tools/mlir_pytaco_utils.py b/mlir/test/Integration/Dialect/SparseTensor/taco/tools/mlir_pytaco_utils.py
index 909738a617600..50d570f63b5cc 100644
--- a/mlir/test/Integration/Dialect/SparseTensor/taco/tools/mlir_pytaco_utils.py
+++ b/mlir/test/Integration/Dialect/SparseTensor/taco/tools/mlir_pytaco_utils.py
@@ -37,29 +37,29 @@
@functools.lru_cache()
def _get_support_lib_name() -> str:
- """Gets the string name for the supporting C shared library."""
- return os.getenv(_SUPPORTLIB_ENV_VAR, _DEFAULT_SUPPORTLIB)
+ """Gets the string name for the supporting C shared library."""
+ return os.getenv(_SUPPORTLIB_ENV_VAR, _DEFAULT_SUPPORTLIB)
@functools.lru_cache()
def _get_sparse_compiler() -> mlir_sparse_compiler.SparseCompiler:
- """Gets the MLIR sparse compiler with default setting."""
- return mlir_sparse_compiler.SparseCompiler(
- options="", opt_level=_OPT_LEVEL, shared_libs=[_get_support_lib_name()])
+ """Gets the MLIR sparse compiler with default setting."""
+ return mlir_sparse_compiler.SparseCompiler(
+ options="", opt_level=_OPT_LEVEL, shared_libs=[_get_support_lib_name()])
def _record_support_funcs(
ty: np.dtype, to_func: _SupportFunc, from_func: _SupportFunc,
ty_to_funcs: Dict[np.dtype, Tuple[_SupportFunc, _SupportFunc]]) -> None:
- """Records the two supporting functions for a given data type."""
- to_func.restype = ctypes.c_void_p
- from_func.restype = ctypes.c_void_p
- ty_to_funcs[ty] = (to_func, from_func)
+ """Records the two supporting functions for a given data type."""
+ to_func.restype = ctypes.c_void_p
+ from_func.restype = ctypes.c_void_p
+ ty_to_funcs[ty] = (to_func, from_func)
@functools.lru_cache()
def _get_support_func_locator() -> _SupportFuncLocator:
- """Constructs a function to locate the supporting functions for a data type.
+ """Constructs a function to locate the supporting functions for a data type.
Loads the supporting C shared library with the needed routines. Constructs a
dictionary from the supported data types to the routines for the data types,
@@ -75,36 +75,36 @@ def _get_support_func_locator() -> _SupportFuncLocator:
OSError: If there is any problem in loading the shared library.
ValueError: If the shared library doesn't contain the needed routines.
"""
- # This raises OSError exception if there is any problem in loading the shared
- # library.
- c_lib = ctypes.CDLL(_get_support_lib_name())
+ # This raises OSError exception if there is any problem in loading the shared
+ # library.
+ c_lib = ctypes.CDLL(_get_support_lib_name())
- type_to_funcs = {}
- try:
- _record_support_funcs(np.float32, c_lib.convertToMLIRSparseTensorF32,
- c_lib.convertFromMLIRSparseTensorF32, type_to_funcs)
- except Exception as e:
- raise ValueError(f"Missing supporting function: {e}") from e
+ type_to_funcs = {}
+ try:
+ _record_support_funcs(np.float32, c_lib.convertToMLIRSparseTensorF32,
+ c_lib.convertFromMLIRSparseTensorF32, type_to_funcs)
+ except Exception as e:
+ raise ValueError(f"Missing supporting function: {e}") from e
- try:
- _record_support_funcs(np.float64, c_lib.convertToMLIRSparseTensorF64,
- c_lib.convertFromMLIRSparseTensorF64, type_to_funcs)
- except Exception as e:
- raise ValueError(f"Missing supporting function: {e}") from e
+ try:
+ _record_support_funcs(np.float64, c_lib.convertToMLIRSparseTensorF64,
+ c_lib.convertFromMLIRSparseTensorF64, type_to_funcs)
+ except Exception as e:
+ raise ValueError(f"Missing supporting function: {e}") from e
- def get_support_funcs(ty: np.dtype):
- funcs = type_to_funcs[ty]
- assert funcs is not None
- return funcs
+ def get_support_funcs(ty: np.dtype):
+ funcs = type_to_funcs[ty]
+ assert funcs is not None
+ return funcs
- return get_support_funcs
+ return get_support_funcs
def sparse_tensor_to_coo_tensor(
sparse_tensor: ctypes.c_void_p,
dtype: np.dtype,
) -> Tuple[int, int, np.ndarray, np.ndarray, np.ndarray]:
- """Converts an MLIR sparse tensor to a COO-flavored format tensor.
+ """Converts an MLIR sparse tensor to a COO-flavored format tensor.
Args:
sparse_tensor: A ctypes.c_void_p to the MLIR sparse tensor descriptor.
@@ -124,26 +124,26 @@ def sparse_tensor_to_coo_tensor(
OSError: If there is any problem in loading the shared library.
ValueError: If the shared library doesn't contain the needed routines.
"""
- convert_from = _get_support_func_locator()(dtype)[1]
- rank = ctypes.c_ulonglong(0)
- nse = ctypes.c_ulonglong(0)
- shape = ctypes.POINTER(ctypes.c_ulonglong)()
- values = ctypes.POINTER(np.ctypeslib.as_ctypes_type(dtype))()
- indices = ctypes.POINTER(ctypes.c_ulonglong)()
- convert_from(sparse_tensor, ctypes.byref(rank), ctypes.byref(nse),
- ctypes.byref(shape), ctypes.byref(values), ctypes.byref(indices))
-
- # Convert the returned values to the corresponding numpy types.
- shape = np.ctypeslib.as_array(shape, shape=[rank.value])
- values = np.ctypeslib.as_array(values, shape=[nse.value])
- indices = np.ctypeslib.as_array(indices, shape=[nse.value, rank.value])
- return rank.value, nse.value, shape, values, indices
+ convert_from = _get_support_func_locator()(dtype)[1]
+ rank = ctypes.c_ulonglong(0)
+ nse = ctypes.c_ulonglong(0)
+ shape = ctypes.POINTER(ctypes.c_ulonglong)()
+ values = ctypes.POINTER(np.ctypeslib.as_ctypes_type(dtype))()
+ indices = ctypes.POINTER(ctypes.c_ulonglong)()
+ convert_from(sparse_tensor, ctypes.byref(rank), ctypes.byref(nse),
+ ctypes.byref(shape), ctypes.byref(values), ctypes.byref(indices))
+
+ # Convert the returned values to the corresponding numpy types.
+ shape = np.ctypeslib.as_array(shape, shape=[rank.value])
+ values = np.ctypeslib.as_array(values, shape=[nse.value])
+ indices = np.ctypeslib.as_array(indices, shape=[nse.value, rank.value])
+ return rank.value, nse.value, shape, values, indices
def coo_tensor_to_sparse_tensor(np_shape: np.ndarray, np_values: np.ndarray,
np_indices: np.ndarray, np_perm: np.ndarray,
np_sparse: np.ndarray) -> int:
- """Converts a COO-flavored format sparse tensor to an MLIR sparse tensor.
+ """Converts a COO-flavored format sparse tensor to an MLIR sparse tensor.
Args:
np_shape: A 1D numpy array of integers, for the shape of the tensor.
@@ -164,26 +164,26 @@ def coo_tensor_to_sparse_tensor(np_shape: np.ndarray, np_values: np.ndarray,
ValueError: If the shared library doesn't contain the needed routines.
"""
- r = len(np_shape)
- rank = ctypes.c_ulonglong(r)
- nse = ctypes.c_ulonglong(len(np_values))
- shape = np_shape.ctypes.data_as(ctypes.POINTER(ctypes.c_ulonglong))
- values = np_values.ctypes.data_as(
- ctypes.POINTER(np.ctypeslib.as_ctypes_type(np_values.dtype)))
- indices = np_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_ulonglong))
+ r = len(np_shape)
+ rank = ctypes.c_ulonglong(r)
+ nse = ctypes.c_ulonglong(len(np_values))
+ shape = np_shape.ctypes.data_as(ctypes.POINTER(ctypes.c_ulonglong))
+ values = np_values.ctypes.data_as(
+ ctypes.POINTER(np.ctypeslib.as_ctypes_type(np_values.dtype)))
+ indices = np_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_ulonglong))
- perm = np_perm.ctypes.data_as(ctypes.POINTER(ctypes.c_ulonglong))
- sparse = np_sparse.ctypes.data_as(ctypes.POINTER(ctypes.c_uint8))
+ perm = np_perm.ctypes.data_as(ctypes.POINTER(ctypes.c_ulonglong))
+ sparse = np_sparse.ctypes.data_as(ctypes.POINTER(ctypes.c_uint8))
- convert_to = _get_support_func_locator()(np_values.dtype.type)[0]
- ptr = convert_to(rank, nse, shape, values, indices, perm, sparse)
- assert ptr is not None, "Problem with calling convertToMLIRSparseTensorF64"
- return ptr
+ convert_to = _get_support_func_locator()(np_values.dtype.type)[0]
+ ptr = convert_to(rank, nse, shape, values, indices, perm, sparse)
+ assert ptr is not None, "Problem with calling convertToMLIRSparseTensorF64"
+ return ptr
def compile_and_build_engine(
module: ir.Module) -> execution_engine.ExecutionEngine:
- """Compiles an MLIR module and builds a JIT execution engine.
+ """Compiles an MLIR module and builds a JIT execution engine.
Args:
module: The MLIR module.
@@ -192,22 +192,22 @@ def compile_and_build_engine(
A JIT execution engine for the MLIR module.
"""
- return _get_sparse_compiler().compile_and_jit(module)
+ return _get_sparse_compiler().compile_and_jit(module)
class _SparseTensorDescriptor(ctypes.Structure):
- """A C structure for an MLIR sparse tensor."""
- _fields_ = [
- # A pointer for the MLIR sparse tensor storage.
- ("storage", ctypes.POINTER(ctypes.c_ulonglong)),
- # An MLIR MemRef descriptor for the shape of the sparse tensor.
- ("shape", runtime.make_nd_memref_descriptor(1, ctypes.c_ulonglong)),
- ]
+ """A C structure for an MLIR sparse tensor."""
+ _fields_ = [
+ # A pointer for the MLIR sparse tensor storage.
+ ("storage", ctypes.POINTER(ctypes.c_ulonglong)),
+ # An MLIR MemRef descriptor for the shape of the sparse tensor.
+ ("shape", runtime.make_nd_memref_descriptor(1, ctypes.c_ulonglong)),
+ ]
def _output_one_dim(dim: int, rank: int, shape: str, type: str) -> str:
- """Produces the MLIR text code to output the size for the given dimension."""
- return f"""
+ """Produces the MLIR text code to output the size for the given dimension."""
+ return f"""
%c{dim} = arith.constant {dim} : index
%d{dim} = tensor.dim %t, %c{dim} : tensor<{shape}x{type}, #enc>
memref.store %d{dim}, %b[%c{dim}] : memref<{rank}xindex>
@@ -222,30 +222,30 @@ def _output_one_dim(dim: int, rank: int, shape: str, type: str) -> str:
# when tensor.dim supports non-constant dimension value.
def _get_create_sparse_tensor_kernel(
sparsity_codes: Sequence[sparse_tensor.DimLevelType], type: str) -> str:
- """Creates an MLIR text kernel to contruct a sparse tensor from a file.
+ """Creates an MLIR text kernel to contruct a sparse tensor from a file.
The kernel returns a _SparseTensorDescriptor structure.
"""
- rank = len(sparsity_codes)
+ rank = len(sparsity_codes)
- # Use ? to represent a dimension in the dynamic shape string representation.
- shape = "x".join(map(lambda d: "?", range(rank)))
+ # Use ? to represent a dimension in the dynamic shape string representation.
+ shape = "x".join(map(lambda d: "?", range(rank)))
- # Convert the encoded sparsity values to a string representation.
- sparsity = ", ".join(
- map(lambda s: '"compressed"' if s.value else '"dense"', sparsity_codes))
+ # Convert the encoded sparsity values to a string representation.
+ sparsity = ", ".join(
+ map(lambda s: '"compressed"' if s.value else '"dense"', sparsity_codes))
- # Get the MLIR text code to write the dimension sizes to the output buffer.
- output_dims = "\n".join(
- map(lambda d: _output_one_dim(d, rank, shape, type), range(rank)))
+ # Get the MLIR text code to write the dimension sizes to the output buffer.
+ output_dims = "\n".join(
+ map(lambda d: _output_one_dim(d, rank, shape, type), range(rank)))
- # Return the MLIR text kernel.
- return f"""
+ # Return the MLIR text kernel.
+ return f"""
!Ptr = type !llvm.ptr<i8>
#enc = #sparse_tensor.encoding<{{
dimLevelType = [ {sparsity} ]
}}>
-func @{_ENTRY_NAME}(%filename: !Ptr) -> (tensor<{shape}x{type}, #enc>, memref<{rank}xindex>)
+func.func @{_ENTRY_NAME}(%filename: !Ptr) -> (tensor<{shape}x{type}, #enc>, memref<{rank}xindex>)
attributes {{ llvm.emit_c_interface }} {{
%t = sparse_tensor.new %filename : !Ptr to tensor<{shape}x{type}, #enc>
%b = memref.alloc() : memref<{rank}xindex>
@@ -257,7 +257,7 @@ def _get_create_sparse_tensor_kernel(
def create_sparse_tensor(filename: str,
sparsity: Sequence[sparse_tensor.DimLevelType],
type: str) -> Tuple[ctypes.c_void_p, np.ndarray]:
- """Creates an MLIR sparse tensor from the input file.
+ """Creates an MLIR sparse tensor from the input file.
Args:
filename: A string for the name of the file that contains the tensor data in
@@ -274,52 +274,54 @@ def create_sparse_tensor(filename: str,
OSError: If there is any problem in loading the supporting C shared library.
ValueError: If the shared library doesn't contain the needed routine.
"""
- with ir.Context() as ctx, ir.Location.unknown():
- module = _get_create_sparse_tensor_kernel(sparsity, type)
- module = ir.Module.parse(module)
- engine = compile_and_build_engine(module)
+ with ir.Context() as ctx, ir.Location.unknown():
+ module = _get_create_sparse_tensor_kernel(sparsity, type)
+ module = ir.Module.parse(module)
+ engine = compile_and_build_engine(module)
- # A sparse tensor descriptor to receive the kernel result.
- c_tensor_desc = _SparseTensorDescriptor()
- # Convert the filename to a byte stream.
- c_filename = ctypes.c_char_p(bytes(filename, "utf-8"))
+ # A sparse tensor descriptor to receive the kernel result.
+ c_tensor_desc = _SparseTensorDescriptor()
+ # Convert the filename to a byte stream.
+ c_filename = ctypes.c_char_p(bytes(filename, "utf-8"))
- arg_pointers = [
- ctypes.byref(ctypes.pointer(c_tensor_desc)),
- ctypes.byref(c_filename)
- ]
+ arg_pointers = [
+ ctypes.byref(ctypes.pointer(c_tensor_desc)),
+ ctypes.byref(c_filename)
+ ]
- # Invoke the execution engine to run the module and return the result.
- engine.invoke(_ENTRY_NAME, *arg_pointers)
- shape = runtime.ranked_memref_to_numpy(ctypes.pointer(c_tensor_desc.shape))
- return c_tensor_desc.storage, shape
+ # Invoke the execution engine to run the module and return the result.
+ engine.invoke(_ENTRY_NAME, *arg_pointers)
+ shape = runtime.ranked_memref_to_numpy(ctypes.pointer(c_tensor_desc.shape))
+ return c_tensor_desc.storage, shape
# TODO: With better support from MLIR, we may improve the current implementation
# by using Python code to generate the kernel instead of doing MLIR text code
# stitching.
def _get_output_sparse_tensor_kernel(
- sparsity_codes: Sequence[sparse_tensor.DimLevelType], type: str) -> str:
- """Creates an MLIR text kernel to output a sparse tensor to a file.
+ sparsity_codes: Sequence[sparse_tensor.DimLevelType],
+ type: str) -> str:
+ """Creates an MLIR text kernel to output a sparse tensor to a file.
The kernel returns void.
"""
- rank = len(sparsity_codes)
+ rank = len(sparsity_codes)
- # Use ? to represent a dimension in the dynamic shape string representation.
- shape = "x".join(map(lambda d: "?", range(rank)))
+ # Use ? to represent a dimension in the dynamic shape string representation.
+ shape = "x".join(map(lambda d: "?", range(rank)))
- # Convert the encoded sparsity values to a string representation.
- sparsity = ", ".join(
- map(lambda s: '"compressed"' if s.value else '"dense"', sparsity_codes))
+ # Convert the encoded sparsity values to a string representation.
+ sparsity = ", ".join(
+ map(lambda s: '"compressed"'
+ if s.value else '"dense"', sparsity_codes))
- # Return the MLIR text kernel.
- return f"""
+ # Return the MLIR text kernel.
+ return f"""
!Ptr = type !llvm.ptr<i8>
#enc = #sparse_tensor.encoding<{{
dimLevelType = [ {sparsity} ]
}}>
-func @{_ENTRY_NAME}(%t: tensor<{shape}x{type}, #enc>, %filename: !Ptr)
+func.func @{_ENTRY_NAME}(%t: tensor<{shape}x{type}, #enc>, %filename: !Ptr)
attributes {{ llvm.emit_c_interface }} {{
sparse_tensor.out %t, %filename : tensor<{shape}x{type}, #enc>, !Ptr
func.return
@@ -329,7 +331,7 @@ def _get_output_sparse_tensor_kernel(
def output_sparse_tensor(tensor: ctypes.c_void_p, filename: str,
sparsity: Sequence[sparse_tensor.DimLevelType],
type: str) -> None:
- """Outputs an MLIR sparse tensor to the given file.
+ """Outputs an MLIR sparse tensor to the given file.
Args:
tensor: A C pointer to the MLIR sparse tensor.
@@ -343,18 +345,18 @@ def output_sparse_tensor(tensor: ctypes.c_void_p, filename: str,
OSError: If there is any problem in loading the supporting C shared library.
ValueError: If the shared library doesn't contain the needed routine.
"""
- with ir.Context() as ctx, ir.Location.unknown():
- module = _get_output_sparse_tensor_kernel(sparsity, type)
- module = ir.Module.parse(module)
- engine = compile_and_build_engine(module)
+ with ir.Context() as ctx, ir.Location.unknown():
+ module = _get_output_sparse_tensor_kernel(sparsity, type)
+ module = ir.Module.parse(module)
+ engine = compile_and_build_engine(module)
- # Convert the filename to a byte stream.
- c_filename = ctypes.c_char_p(bytes(filename, "utf-8"))
+ # Convert the filename to a byte stream.
+ c_filename = ctypes.c_char_p(bytes(filename, "utf-8"))
- arg_pointers = [
- ctypes.byref(ctypes.cast(tensor, ctypes.c_void_p)),
- ctypes.byref(c_filename)
- ]
+ arg_pointers = [
+ ctypes.byref(ctypes.cast(tensor, ctypes.c_void_p)),
+ ctypes.byref(c_filename)
+ ]
- # Invoke the execution engine to run the module and return the result.
- engine.invoke(_ENTRY_NAME, *arg_pointers)
+ # Invoke the execution engine to run the module and return the result.
+ engine.invoke(_ENTRY_NAME, *arg_pointers)
diff --git a/mlir/test/python/dialects/memref.py b/mlir/test/python/dialects/memref.py
index db917678ba18f..d3501d403b52a 100644
--- a/mlir/test/python/dialects/memref.py
+++ b/mlir/test/python/dialects/memref.py
@@ -17,7 +17,7 @@ def testSubViewAccessors():
ctx = Context()
module = Module.parse(
r"""
- func @f1(%arg0: memref<?x?xf32>) {
+ func.func @f1(%arg0: memref<?x?xf32>) {
%0 = arith.constant 0 : index
%1 = arith.constant 1 : index
%2 = arith.constant 2 : index
@@ -59,7 +59,7 @@ def testSubViewAccessors():
def testCustomBuidlers():
with Context() as ctx, Location.unknown(ctx):
module = Module.parse(r"""
- func @f1(%arg0: memref<?x?xf32>, %arg1: index, %arg2: index) {
+ func.func @f1(%arg0: memref<?x?xf32>, %arg1: index, %arg2: index) {
return
}
""")
diff --git a/mlir/test/python/execution_engine.py b/mlir/test/python/execution_engine.py
index 67128444e0174..ef24f1a1ba0b3 100644
--- a/mlir/test/python/execution_engine.py
+++ b/mlir/test/python/execution_engine.py
@@ -49,7 +49,7 @@ def testInvalidModule():
with Context():
# Builtin function
module = Module.parse(r"""
- func @foo() { return }
+ func.func @foo() { return }
""")
# CHECK: Got RuntimeError: Failure while creating the ExecutionEngine.
try:
@@ -74,7 +74,7 @@ def lowerToLLVM(module):
def testInvokeVoid():
with Context():
module = Module.parse(r"""
-func @void() attributes { llvm.emit_c_interface } {
+func.func @void() attributes { llvm.emit_c_interface } {
return
}
""")
@@ -91,7 +91,7 @@ def testInvokeVoid():
def testInvokeFloatAdd():
with Context():
module = Module.parse(r"""
-func @add(%arg0: f32, %arg1: f32) -> f32 attributes { llvm.emit_c_interface } {
+func.func @add(%arg0: f32, %arg1: f32) -> f32 attributes { llvm.emit_c_interface } {
%add = arith.addf %arg0, %arg1 : f32
return %add : f32
}
@@ -122,11 +122,11 @@ def callback(a, b):
with Context():
# The module just forwards to a runtime function known as "some_callback_into_python".
module = Module.parse(r"""
-func @add(%arg0: f32, %arg1: i32) -> f32 attributes { llvm.emit_c_interface } {
+func.func @add(%arg0: f32, %arg1: i32) -> f32 attributes { llvm.emit_c_interface } {
%resf = call @some_callback_into_python(%arg0, %arg1) : (f32, i32) -> (f32)
return %resf : f32
}
-func private @some_callback_into_python(f32, i32) -> f32 attributes { llvm.emit_c_interface }
+func.func private @some_callback_into_python(f32, i32) -> f32 attributes { llvm.emit_c_interface }
""")
execution_engine = ExecutionEngine(lowerToLLVM(module))
execution_engine.register_runtime("some_callback_into_python", callback)
@@ -159,11 +159,11 @@ def callback(a):
with Context():
# The module just forwards to a runtime function known as "some_callback_into_python".
module = Module.parse(r"""
-func @callback_memref(%arg0: memref<*xf32>) attributes { llvm.emit_c_interface } {
+func.func @callback_memref(%arg0: memref<*xf32>) attributes { llvm.emit_c_interface } {
call @some_callback_into_python(%arg0) : (memref<*xf32>) -> ()
return
}
-func private @some_callback_into_python(memref<*xf32>) -> () attributes { llvm.emit_c_interface }
+func.func private @some_callback_into_python(memref<*xf32>) -> () attributes { llvm.emit_c_interface }
""")
execution_engine = ExecutionEngine(lowerToLLVM(module))
execution_engine.register_runtime("some_callback_into_python", callback)
@@ -210,11 +210,11 @@ def callback(a):
with Context():
# The module just forwards to a runtime function known as "some_callback_into_python".
module = Module.parse(r"""
-func @callback_memref(%arg0: memref<2x2xf32>) attributes { llvm.emit_c_interface } {
+func.func @callback_memref(%arg0: memref<2x2xf32>) attributes { llvm.emit_c_interface } {
call @some_callback_into_python(%arg0) : (memref<2x2xf32>) -> ()
return
}
-func private @some_callback_into_python(memref<2x2xf32>) -> () attributes { llvm.emit_c_interface }
+func.func private @some_callback_into_python(memref<2x2xf32>) -> () attributes { llvm.emit_c_interface }
""")
execution_engine = ExecutionEngine(lowerToLLVM(module))
execution_engine.register_runtime("some_callback_into_python", callback)
@@ -235,8 +235,8 @@ def callback(a):
def testMemrefAdd():
with Context():
module = Module.parse("""
- module {
- func @main(%arg0: memref<1xf32>, %arg1: memref<f32>, %arg2: memref<1xf32>) attributes { llvm.emit_c_interface } {
+ module {
+ func.func @main(%arg0: memref<1xf32>, %arg1: memref<f32>, %arg2: memref<1xf32>) attributes { llvm.emit_c_interface } {
%0 = arith.constant 0 : index
%1 = memref.load %arg0[%0] : memref<1xf32>
%2 = memref.load %arg1[] : memref<f32>
@@ -244,7 +244,7 @@ def testMemrefAdd():
memref.store %3, %arg2[%0] : memref<1xf32>
return
}
- } """)
+ } """)
arg1 = np.array([32.5]).astype(np.float32)
arg2 = np.array(6).astype(np.float32)
res = np.array([0]).astype(np.float32)
@@ -272,7 +272,7 @@ def testDynamicMemrefAdd2D():
with Context():
module = Module.parse("""
module {
- func @memref_add_2d(%arg0: memref<2x2xf32>, %arg1: memref<?x?xf32>, %arg2: memref<2x2xf32>) attributes {llvm.emit_c_interface} {
+ func.func @memref_add_2d(%arg0: memref<2x2xf32>, %arg1: memref<?x?xf32>, %arg2: memref<2x2xf32>) attributes {llvm.emit_c_interface} {
%c0 = arith.constant 0 : index
%c2 = arith.constant 2 : index
%c1 = arith.constant 1 : index
@@ -330,7 +330,7 @@ def testSharedLibLoad():
with Context():
module = Module.parse("""
module {
- func @main(%arg0: memref<1xf32>) attributes { llvm.emit_c_interface } {
+ func.func @main(%arg0: memref<1xf32>) attributes { llvm.emit_c_interface } {
%c0 = arith.constant 0 : index
%cst42 = arith.constant 42.0 : f32
memref.store %cst42, %arg0[%c0] : memref<1xf32>
@@ -338,7 +338,7 @@ def testSharedLibLoad():
call @print_memref_f32(%u_memref) : (memref<*xf32>) -> ()
return
}
- func private @print_memref_f32(memref<*xf32>) attributes { llvm.emit_c_interface }
+ func.func private @print_memref_f32(memref<*xf32>) attributes { llvm.emit_c_interface }
} """)
arg0 = np.array([0.0]).astype(np.float32)
@@ -366,7 +366,7 @@ def testNanoTime():
with Context():
module = Module.parse("""
module {
- func @main() attributes { llvm.emit_c_interface } {
+ func.func @main() attributes { llvm.emit_c_interface } {
%now = call @nano_time() : () -> i64
%memref = memref.alloca() : memref<1xi64>
%c0 = arith.constant 0 : index
@@ -375,8 +375,8 @@ def testNanoTime():
call @print_memref_i64(%u_memref) : (memref<*xi64>) -> ()
return
}
- func private @nano_time() -> i64 attributes { llvm.emit_c_interface }
- func private @print_memref_i64(memref<*xi64>) attributes { llvm.emit_c_interface }
+ func.func private @nano_time() -> i64 attributes { llvm.emit_c_interface }
+ func.func private @print_memref_i64(memref<*xi64>) attributes { llvm.emit_c_interface }
}""")
execution_engine = ExecutionEngine(
diff --git a/mlir/test/python/integration/dialects/linalg/opsrun.py b/mlir/test/python/integration/dialects/linalg/opsrun.py
index 18c8c46617b04..899c355f89607 100644
--- a/mlir/test/python/integration/dialects/linalg/opsrun.py
+++ b/mlir/test/python/integration/dialects/linalg/opsrun.py
@@ -20,7 +20,7 @@ def log(*args):
elemwise_boiler = """
-func @main() -> f32 attributes {llvm.emit_c_interface} {
+func.func @main() -> f32 attributes {llvm.emit_c_interface} {
%v0 = arith.constant 0.0 : f32
%v1 = arith.constant 1.0 : f32
%v2 = arith.constant 2.0 : f32
@@ -51,7 +51,7 @@ def log(*args):
"""
matmul_boiler = """
-func @main() -> f32 attributes {llvm.emit_c_interface} {
+func.func @main() -> f32 attributes {llvm.emit_c_interface} {
%v0 = arith.constant 0.0 : f32
%v1 = arith.constant -1 : i8
%v2 = arith.constant 2.0 : f32
@@ -82,7 +82,7 @@ def log(*args):
"""
fill_boiler = """
-func @main() -> i32 attributes {llvm.emit_c_interface} {
+func.func @main() -> i32 attributes {llvm.emit_c_interface} {
%O0 = memref.alloc() : memref<i32>
%O1 = memref.alloc() : memref<16xi32>
%O2 = memref.alloc() : memref<4x16xi32>
@@ -111,7 +111,7 @@ def log(*args):
"""
fill_rng_boiler = """
-func @main() -> i32 attributes {llvm.emit_c_interface} {
+func.func @main() -> i32 attributes {llvm.emit_c_interface} {
%O = memref.alloc() : memref<4x16xi32>
%min = arith.constant -1000.0 : f64
%max = arith.constant 1000.0 : f64
@@ -129,7 +129,7 @@ def log(*args):
"""
conv_boiler = """
-func @main() -> i32 attributes {llvm.emit_c_interface} {
+func.func @main() -> i32 attributes {llvm.emit_c_interface} {
%v0 = arith.constant 0 : i32
%v1 = arith.constant 1.0 : f64
%v2 = arith.constant 2.0 : f64
@@ -153,7 +153,7 @@ def log(*args):
"""
pooling_boiler = """
-func @main() -> i32 attributes {llvm.emit_c_interface} {
+func.func @main() -> i32 attributes {llvm.emit_c_interface} {
%v0 = arith.constant 0 : i32
%v42 = arith.constant 42.0 : f64
%v77 = arith.constant 77.0 : f64
diff --git a/mlir/test/python/ir/insertion_point.py b/mlir/test/python/ir/insertion_point.py
index 2e53aa64b999f..81a6ec2984d54 100644
--- a/mlir/test/python/ir/insertion_point.py
+++ b/mlir/test/python/ir/insertion_point.py
@@ -18,7 +18,7 @@ def test_insert_at_block_end():
ctx.allow_unregistered_dialects = True
with Location.unknown(ctx):
module = Module.parse(r"""
- func @foo() -> () {
+ func.func @foo() -> () {
"custom.op1"() : () -> ()
}
""")
@@ -38,7 +38,7 @@ def test_insert_before_operation():
ctx.allow_unregistered_dialects = True
with Location.unknown(ctx):
module = Module.parse(r"""
- func @foo() -> () {
+ func.func @foo() -> () {
"custom.op1"() : () -> ()
"custom.op2"() : () -> ()
}
@@ -60,7 +60,7 @@ def test_insert_at_block_begin():
ctx.allow_unregistered_dialects = True
with Location.unknown(ctx):
module = Module.parse(r"""
- func @foo() -> () {
+ func.func @foo() -> () {
"custom.op2"() : () -> ()
}
""")
@@ -88,7 +88,7 @@ def test_insert_at_terminator():
ctx.allow_unregistered_dialects = True
with Location.unknown(ctx):
module = Module.parse(r"""
- func @foo() -> () {
+ func.func @foo() -> () {
"custom.op1"() : () -> ()
return
}
@@ -109,7 +109,7 @@ def test_insert_at_block_terminator_missing():
ctx.allow_unregistered_dialects = True
with ctx:
module = Module.parse(r"""
- func @foo() -> () {
+ func.func @foo() -> () {
"custom.op1"() : () -> ()
}
""")
@@ -130,7 +130,7 @@ def test_insert_at_end_with_terminator_errors():
with Context() as ctx, Location.unknown():
ctx.allow_unregistered_dialects = True
module = Module.parse(r"""
- func @foo() -> () {
+ func.func @foo() -> () {
return
}
""")
@@ -151,7 +151,7 @@ def test_insertion_point_context():
ctx.allow_unregistered_dialects = True
with Location.unknown(ctx):
module = Module.parse(r"""
- func @foo() -> () {
+ func.func @foo() -> () {
"custom.op1"() : () -> ()
}
""")
diff --git a/mlir/test/python/ir/module.py b/mlir/test/python/ir/module.py
index adc27a2879d22..f0b62435f514b 100644
--- a/mlir/test/python/ir/module.py
+++ b/mlir/test/python/ir/module.py
@@ -64,7 +64,7 @@ def testCreateEmpty():
def testRoundtripUnicode():
ctx = Context()
module = Module.parse(r"""
- func private @roundtripUnicode() attributes { foo = "😊" }
+ func.func private @roundtripUnicode() attributes { foo = "😊" }
""", ctx)
print(str(module))
@@ -79,7 +79,7 @@ def testRoundtripUnicode():
def testRoundtripBinary():
with Context():
module = Module.parse(r"""
- func private @roundtripUnicode() attributes { foo = "😊" }
+ func.func private @roundtripUnicode() attributes { foo = "😊" }
""")
binary_asm = module.operation.get_asm(binary=True)
assert isinstance(binary_asm, bytes)
diff --git a/mlir/test/python/ir/operation.py b/mlir/test/python/ir/operation.py
index 7e23268c2d8ae..36a97187aae46 100644
--- a/mlir/test/python/ir/operation.py
+++ b/mlir/test/python/ir/operation.py
@@ -30,7 +30,7 @@ def testTraverseOpRegionBlockIterators():
ctx.allow_unregistered_dialects = True
module = Module.parse(
r"""
- func @f1(%arg0: i32) -> i32 {
+ func.func @f1(%arg0: i32) -> i32 {
%1 = "custom.addi"(%arg0, %arg0) : (i32, i32) -> i32
return %1 : i32
}
@@ -87,7 +87,7 @@ def testTraverseOpRegionBlockIndices():
ctx.allow_unregistered_dialects = True
module = Module.parse(
r"""
- func @f1(%arg0: i32) -> i32 {
+ func.func @f1(%arg0: i32) -> i32 {
%1 = "custom.addi"(%arg0, %arg0) : (i32, i32) -> i32
return %1 : i32
}
@@ -147,7 +147,7 @@ def testBlockArgumentList():
with Context() as ctx:
module = Module.parse(
r"""
- func @f1(%arg0: i32, %arg1: f64, %arg2: index) {
+ func.func @f1(%arg0: i32, %arg1: f64, %arg2: index) {
return
}
""", ctx)
@@ -192,7 +192,7 @@ def testOperationOperands():
with Context() as ctx:
ctx.allow_unregistered_dialects = True
module = Module.parse(r"""
- func @f1(%arg0: i32) {
+ func.func @f1(%arg0: i32) {
%0 = "test.producer"() : () -> i64
"test.consumer"(%arg0, %0) : (i32, i64) -> ()
return
@@ -215,7 +215,7 @@ def testOperationOperandsSlice():
with Context() as ctx:
ctx.allow_unregistered_dialects = True
module = Module.parse(r"""
- func @f1() {
+ func.func @f1() {
%0 = "test.producer0"() : () -> i64
%1 = "test.producer1"() : () -> i64
%2 = "test.producer2"() : () -> i64
@@ -273,7 +273,7 @@ def testOperationOperandsSet():
with Context() as ctx, Location.unknown(ctx):
ctx.allow_unregistered_dialects = True
module = Module.parse(r"""
- func @f1() {
+ func.func @f1() {
%0 = "test.producer0"() : () -> i64
%1 = "test.producer1"() : () -> i64
%2 = "test.producer2"() : () -> i64
@@ -328,7 +328,7 @@ def testOperationInsertionPoint():
ctx.allow_unregistered_dialects = True
module = Module.parse(
r"""
- func @f1(%arg0: i32) -> i32 {
+ func.func @f1(%arg0: i32) -> i32 {
%1 = "custom.addi"(%arg0, %arg0) : (i32, i32) -> i32
return %1 : i32
}
@@ -383,7 +383,7 @@ def testOperationWithRegion():
# TODO: Also verify accessing the terminator once both parents are nulled
# out.
module = Module.parse(r"""
- func @f1(%arg0: i32) -> i32 {
+ func.func @f1(%arg0: i32) -> i32 {
%1 = "custom.addi"(%arg0, %arg0) : (i32, i32) -> i32
return %1 : i32
}
@@ -405,11 +405,11 @@ def testOperationResultList():
ctx = Context()
module = Module.parse(
r"""
- func @f1() {
+ func.func @f1() {
%0:3 = call @f2() : () -> (i32, f64, index)
return
}
- func private @f2() -> (i32, f64, index)
+ func.func private @f2() -> (i32, f64, index)
""", ctx)
caller = module.body.operations[0]
call = caller.regions[0].blocks[0].operations[0]
@@ -437,7 +437,7 @@ def testOperationResultListSlice():
with Context() as ctx:
ctx.allow_unregistered_dialects = True
module = Module.parse(r"""
- func @f1() {
+ func.func @f1() {
"some.op"() : () -> (i1, i2, i3, i4, i5)
return
}
@@ -534,7 +534,7 @@ def testOperationPrint():
ctx = Context()
module = Module.parse(
r"""
- func @f1(%arg0: i32) -> i32 {
+ func.func @f1(%arg0: i32) -> i32 {
%0 = arith.constant dense<[1, 2, 3, 4]> : tensor<4xi32>
return %arg0 : i32
}
@@ -805,8 +805,8 @@ def testModuleMerge():
with Context():
m1 = Module.parse("func private @foo()")
m2 = Module.parse("""
- func private @bar()
- func private @qux()
+ func.func private @bar()
+ func.func private @qux()
""")
foo = m1.body.operations[0]
bar = m2.body.operations[0]
diff --git a/mlir/test/python/ir/symbol_table.py b/mlir/test/python/ir/symbol_table.py
index af8eafb605279..9ce89591c5e12 100644
--- a/mlir/test/python/ir/symbol_table.py
+++ b/mlir/test/python/ir/symbol_table.py
@@ -20,11 +20,11 @@ def testSymbolTableInsert():
with Context() as ctx:
ctx.allow_unregistered_dialects = True
m1 = Module.parse("""
- func private @foo()
- func private @bar()""")
+ func.func private @foo()
+ func.func private @bar()""")
m2 = Module.parse("""
- func private @qux()
- func private @foo()
+ func.func private @qux()
+ func.func private @foo()
"foo.bar"() : () -> ()""")
symbol_table = SymbolTable(m1.operation)
@@ -92,11 +92,11 @@ def testSymbolTableInsert():
def testSymbolTableRAUW():
with Context() as ctx:
m = Module.parse("""
- func private @foo() {
+ func.func private @foo() {
call @bar() : () -> ()
return
}
- func private @bar()
+ func.func private @bar()
""")
foo, bar = list(m.operation.regions[0].blocks[0].operations)[0:2]
SymbolTable.set_symbol_name(bar, "bam")
@@ -117,7 +117,7 @@ def testSymbolTableRAUW():
def testSymbolTableVisibility():
with Context() as ctx:
m = Module.parse("""
- func private @foo() {
+ func.func private @foo() {
return
}
""")
diff --git a/mlir/test/python/ir/value.py b/mlir/test/python/ir/value.py
index cfbfdf35fcf6f..4eebc53ab0c93 100644
--- a/mlir/test/python/ir/value.py
+++ b/mlir/test/python/ir/value.py
@@ -44,7 +44,7 @@ def testValueIsInstance():
ctx.allow_unregistered_dialects = True
module = Module.parse(
r"""
- func @foo(%arg0: f32) {
+ func.func @foo(%arg0: f32) {
%0 = "some_dialect.some_op"() : () -> f64
return
}""", ctx)
@@ -64,7 +64,7 @@ def testValueHash():
ctx.allow_unregistered_dialects = True
module = Module.parse(
r"""
- func @foo(%arg0: f32) -> f32 {
+ func.func @foo(%arg0: f32) -> f32 {
%0 = "some_dialect.some_op"(%arg0) : (f32) -> f32
return %0 : f32
}""", ctx)
diff --git a/mlir/utils/jupyter/mlir_opt_kernel/kernel.py b/mlir/utils/jupyter/mlir_opt_kernel/kernel.py
index 89947aa80e098..48029012dbd97 100644
--- a/mlir/utils/jupyter/mlir_opt_kernel/kernel.py
+++ b/mlir/utils/jupyter/mlir_opt_kernel/kernel.py
@@ -42,7 +42,7 @@ class MlirOptKernel(Kernel):
```mlir
// configuration: --pass
- func @foo(%tensor: tensor<2x3xf64>) -> tensor<3x2xf64> { ... }
+ func.func @foo(%tensor: tensor<2x3xf64>) -> tensor<3x2xf64> { ... }
```
```mlir
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