llvm-project/mlir/test/Conversion/AffineToStandard/lower-affine.mlir

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// RUN: mlir-opt -lower-affine %s | FileCheck %s
// CHECK-LABEL: func @empty() {
func @empty() {
return // CHECK: return
} // CHECK: }
Basic conversion of MLFunctions to CFGFunctions. Implement a pass converting a subset of MLFunctions to CFGFunctions. Currently supports arbitrarily complex imperfect loop nests with statically constant (i.e., not affine map) bounds filled with operations. Does NOT support branches and non-constant loop bounds. Conversion is performed per-function and the function names are preserved to avoid breaking any external references to the current module. In-memory IR is updated to point to the right functions in direct calls and constant loads. This behavior is tested via a really hidden flag that enables function renaming. Inside each function, the control flow conversion is based on single-entry single-exit regions, i.e. subgraphs of the CFG that have exactly one incoming and exactly one outgoing edge. Since an MLFunction must have a single "return" statement as per MLIR spec, it constitutes an SESE region. Individual operations are appended to this region. Control flow statements are recursively converted into such regions that are concatenated with the current region. Bodies of the compound statement also form SESE regions, which allows to nest control flow statements easily. Note that SESE regions are not materialized in the code. It is sufficent to keep track of the end of the region as the current instruction insertion point as long as all recursive calls update the insertion point in the end. The converter maintains a mapping between SSA values in ML functions and their CFG counterparts. The mapping is used to find the operands for each operation and is updated to contain the results of each operation as the conversion continues. PiperOrigin-RevId: 221162602
2018-11-13 06:58:36 +08:00
func @body(index) -> ()
Basic conversion of MLFunctions to CFGFunctions. Implement a pass converting a subset of MLFunctions to CFGFunctions. Currently supports arbitrarily complex imperfect loop nests with statically constant (i.e., not affine map) bounds filled with operations. Does NOT support branches and non-constant loop bounds. Conversion is performed per-function and the function names are preserved to avoid breaking any external references to the current module. In-memory IR is updated to point to the right functions in direct calls and constant loads. This behavior is tested via a really hidden flag that enables function renaming. Inside each function, the control flow conversion is based on single-entry single-exit regions, i.e. subgraphs of the CFG that have exactly one incoming and exactly one outgoing edge. Since an MLFunction must have a single "return" statement as per MLIR spec, it constitutes an SESE region. Individual operations are appended to this region. Control flow statements are recursively converted into such regions that are concatenated with the current region. Bodies of the compound statement also form SESE regions, which allows to nest control flow statements easily. Note that SESE regions are not materialized in the code. It is sufficent to keep track of the end of the region as the current instruction insertion point as long as all recursive calls update the insertion point in the end. The converter maintains a mapping between SSA values in ML functions and their CFG counterparts. The mapping is used to find the operands for each operation and is updated to contain the results of each operation as the conversion continues. PiperOrigin-RevId: 221162602
2018-11-13 06:58:36 +08:00
// Simple loops are properly converted.
// CHECK-LABEL: func @simple_loop
// CHECK-NEXT: %[[c1:.*]] = constant 1 : index
// CHECK-NEXT: %[[c42:.*]] = constant 42 : index
// CHECK-NEXT: %[[c1_0:.*]] = constant 1 : index
// CHECK-NEXT: for %{{.*}} = %[[c1]] to %[[c42]] step %[[c1_0]] {
// CHECK-NEXT: call @body(%{{.*}}) : (index) -> ()
// CHECK-NEXT: }
Basic conversion of MLFunctions to CFGFunctions. Implement a pass converting a subset of MLFunctions to CFGFunctions. Currently supports arbitrarily complex imperfect loop nests with statically constant (i.e., not affine map) bounds filled with operations. Does NOT support branches and non-constant loop bounds. Conversion is performed per-function and the function names are preserved to avoid breaking any external references to the current module. In-memory IR is updated to point to the right functions in direct calls and constant loads. This behavior is tested via a really hidden flag that enables function renaming. Inside each function, the control flow conversion is based on single-entry single-exit regions, i.e. subgraphs of the CFG that have exactly one incoming and exactly one outgoing edge. Since an MLFunction must have a single "return" statement as per MLIR spec, it constitutes an SESE region. Individual operations are appended to this region. Control flow statements are recursively converted into such regions that are concatenated with the current region. Bodies of the compound statement also form SESE regions, which allows to nest control flow statements easily. Note that SESE regions are not materialized in the code. It is sufficent to keep track of the end of the region as the current instruction insertion point as long as all recursive calls update the insertion point in the end. The converter maintains a mapping between SSA values in ML functions and their CFG counterparts. The mapping is used to find the operands for each operation and is updated to contain the results of each operation as the conversion continues. PiperOrigin-RevId: 221162602
2018-11-13 06:58:36 +08:00
// CHECK-NEXT: return
// CHECK-NEXT: }
func @simple_loop() {
affine.for %i = 1 to 42 {
Basic conversion of MLFunctions to CFGFunctions. Implement a pass converting a subset of MLFunctions to CFGFunctions. Currently supports arbitrarily complex imperfect loop nests with statically constant (i.e., not affine map) bounds filled with operations. Does NOT support branches and non-constant loop bounds. Conversion is performed per-function and the function names are preserved to avoid breaking any external references to the current module. In-memory IR is updated to point to the right functions in direct calls and constant loads. This behavior is tested via a really hidden flag that enables function renaming. Inside each function, the control flow conversion is based on single-entry single-exit regions, i.e. subgraphs of the CFG that have exactly one incoming and exactly one outgoing edge. Since an MLFunction must have a single "return" statement as per MLIR spec, it constitutes an SESE region. Individual operations are appended to this region. Control flow statements are recursively converted into such regions that are concatenated with the current region. Bodies of the compound statement also form SESE regions, which allows to nest control flow statements easily. Note that SESE regions are not materialized in the code. It is sufficent to keep track of the end of the region as the current instruction insertion point as long as all recursive calls update the insertion point in the end. The converter maintains a mapping between SSA values in ML functions and their CFG counterparts. The mapping is used to find the operands for each operation and is updated to contain the results of each operation as the conversion continues. PiperOrigin-RevId: 221162602
2018-11-13 06:58:36 +08:00
call @body(%i) : (index) -> ()
}
return
}
/////////////////////////////////////////////////////////////////////
func @pre(index) -> ()
func @body2(index, index) -> ()
func @post(index) -> ()
Basic conversion of MLFunctions to CFGFunctions. Implement a pass converting a subset of MLFunctions to CFGFunctions. Currently supports arbitrarily complex imperfect loop nests with statically constant (i.e., not affine map) bounds filled with operations. Does NOT support branches and non-constant loop bounds. Conversion is performed per-function and the function names are preserved to avoid breaking any external references to the current module. In-memory IR is updated to point to the right functions in direct calls and constant loads. This behavior is tested via a really hidden flag that enables function renaming. Inside each function, the control flow conversion is based on single-entry single-exit regions, i.e. subgraphs of the CFG that have exactly one incoming and exactly one outgoing edge. Since an MLFunction must have a single "return" statement as per MLIR spec, it constitutes an SESE region. Individual operations are appended to this region. Control flow statements are recursively converted into such regions that are concatenated with the current region. Bodies of the compound statement also form SESE regions, which allows to nest control flow statements easily. Note that SESE regions are not materialized in the code. It is sufficent to keep track of the end of the region as the current instruction insertion point as long as all recursive calls update the insertion point in the end. The converter maintains a mapping between SSA values in ML functions and their CFG counterparts. The mapping is used to find the operands for each operation and is updated to contain the results of each operation as the conversion continues. PiperOrigin-RevId: 221162602
2018-11-13 06:58:36 +08:00
// CHECK-LABEL: func @imperfectly_nested_loops
// CHECK-NEXT: %[[c0:.*]] = constant 0 : index
// CHECK-NEXT: %[[c42:.*]] = constant 42 : index
// CHECK-NEXT: %[[c1:.*]] = constant 1 : index
// CHECK-NEXT: for %{{.*}} = %[[c0]] to %[[c42]] step %[[c1]] {
// CHECK-NEXT: call @pre(%{{.*}}) : (index) -> ()
// CHECK-NEXT: %[[c7:.*]] = constant 7 : index
// CHECK-NEXT: %[[c56:.*]] = constant 56 : index
// CHECK-NEXT: %[[c2:.*]] = constant 2 : index
// CHECK-NEXT: for %{{.*}} = %[[c7]] to %[[c56]] step %[[c2]] {
// CHECK-NEXT: call @body2(%{{.*}}, %{{.*}}) : (index, index) -> ()
// CHECK-NEXT: }
// CHECK-NEXT: call @post(%{{.*}}) : (index) -> ()
// CHECK-NEXT: }
Basic conversion of MLFunctions to CFGFunctions. Implement a pass converting a subset of MLFunctions to CFGFunctions. Currently supports arbitrarily complex imperfect loop nests with statically constant (i.e., not affine map) bounds filled with operations. Does NOT support branches and non-constant loop bounds. Conversion is performed per-function and the function names are preserved to avoid breaking any external references to the current module. In-memory IR is updated to point to the right functions in direct calls and constant loads. This behavior is tested via a really hidden flag that enables function renaming. Inside each function, the control flow conversion is based on single-entry single-exit regions, i.e. subgraphs of the CFG that have exactly one incoming and exactly one outgoing edge. Since an MLFunction must have a single "return" statement as per MLIR spec, it constitutes an SESE region. Individual operations are appended to this region. Control flow statements are recursively converted into such regions that are concatenated with the current region. Bodies of the compound statement also form SESE regions, which allows to nest control flow statements easily. Note that SESE regions are not materialized in the code. It is sufficent to keep track of the end of the region as the current instruction insertion point as long as all recursive calls update the insertion point in the end. The converter maintains a mapping between SSA values in ML functions and their CFG counterparts. The mapping is used to find the operands for each operation and is updated to contain the results of each operation as the conversion continues. PiperOrigin-RevId: 221162602
2018-11-13 06:58:36 +08:00
// CHECK-NEXT: return
// CHECK-NEXT: }
func @imperfectly_nested_loops() {
affine.for %i = 0 to 42 {
Basic conversion of MLFunctions to CFGFunctions. Implement a pass converting a subset of MLFunctions to CFGFunctions. Currently supports arbitrarily complex imperfect loop nests with statically constant (i.e., not affine map) bounds filled with operations. Does NOT support branches and non-constant loop bounds. Conversion is performed per-function and the function names are preserved to avoid breaking any external references to the current module. In-memory IR is updated to point to the right functions in direct calls and constant loads. This behavior is tested via a really hidden flag that enables function renaming. Inside each function, the control flow conversion is based on single-entry single-exit regions, i.e. subgraphs of the CFG that have exactly one incoming and exactly one outgoing edge. Since an MLFunction must have a single "return" statement as per MLIR spec, it constitutes an SESE region. Individual operations are appended to this region. Control flow statements are recursively converted into such regions that are concatenated with the current region. Bodies of the compound statement also form SESE regions, which allows to nest control flow statements easily. Note that SESE regions are not materialized in the code. It is sufficent to keep track of the end of the region as the current instruction insertion point as long as all recursive calls update the insertion point in the end. The converter maintains a mapping between SSA values in ML functions and their CFG counterparts. The mapping is used to find the operands for each operation and is updated to contain the results of each operation as the conversion continues. PiperOrigin-RevId: 221162602
2018-11-13 06:58:36 +08:00
call @pre(%i) : (index) -> ()
affine.for %j = 7 to 56 step 2 {
Basic conversion of MLFunctions to CFGFunctions. Implement a pass converting a subset of MLFunctions to CFGFunctions. Currently supports arbitrarily complex imperfect loop nests with statically constant (i.e., not affine map) bounds filled with operations. Does NOT support branches and non-constant loop bounds. Conversion is performed per-function and the function names are preserved to avoid breaking any external references to the current module. In-memory IR is updated to point to the right functions in direct calls and constant loads. This behavior is tested via a really hidden flag that enables function renaming. Inside each function, the control flow conversion is based on single-entry single-exit regions, i.e. subgraphs of the CFG that have exactly one incoming and exactly one outgoing edge. Since an MLFunction must have a single "return" statement as per MLIR spec, it constitutes an SESE region. Individual operations are appended to this region. Control flow statements are recursively converted into such regions that are concatenated with the current region. Bodies of the compound statement also form SESE regions, which allows to nest control flow statements easily. Note that SESE regions are not materialized in the code. It is sufficent to keep track of the end of the region as the current instruction insertion point as long as all recursive calls update the insertion point in the end. The converter maintains a mapping between SSA values in ML functions and their CFG counterparts. The mapping is used to find the operands for each operation and is updated to contain the results of each operation as the conversion continues. PiperOrigin-RevId: 221162602
2018-11-13 06:58:36 +08:00
call @body2(%i, %j) : (index, index) -> ()
}
call @post(%i) : (index) -> ()
}
return
}
/////////////////////////////////////////////////////////////////////
func @mid(index) -> ()
func @body3(index, index) -> ()
Basic conversion of MLFunctions to CFGFunctions. Implement a pass converting a subset of MLFunctions to CFGFunctions. Currently supports arbitrarily complex imperfect loop nests with statically constant (i.e., not affine map) bounds filled with operations. Does NOT support branches and non-constant loop bounds. Conversion is performed per-function and the function names are preserved to avoid breaking any external references to the current module. In-memory IR is updated to point to the right functions in direct calls and constant loads. This behavior is tested via a really hidden flag that enables function renaming. Inside each function, the control flow conversion is based on single-entry single-exit regions, i.e. subgraphs of the CFG that have exactly one incoming and exactly one outgoing edge. Since an MLFunction must have a single "return" statement as per MLIR spec, it constitutes an SESE region. Individual operations are appended to this region. Control flow statements are recursively converted into such regions that are concatenated with the current region. Bodies of the compound statement also form SESE regions, which allows to nest control flow statements easily. Note that SESE regions are not materialized in the code. It is sufficent to keep track of the end of the region as the current instruction insertion point as long as all recursive calls update the insertion point in the end. The converter maintains a mapping between SSA values in ML functions and their CFG counterparts. The mapping is used to find the operands for each operation and is updated to contain the results of each operation as the conversion continues. PiperOrigin-RevId: 221162602
2018-11-13 06:58:36 +08:00
// CHECK-LABEL: func @more_imperfectly_nested_loops
// CHECK-NEXT: %[[c0:.*]] = constant 0 : index
// CHECK-NEXT: %[[c42:.*]] = constant 42 : index
// CHECK-NEXT: %[[c1:.*]] = constant 1 : index
// CHECK-NEXT: for %{{.*}} = %[[c0]] to %[[c42]] step %[[c1]] {
// CHECK-NEXT: call @pre(%{{.*}}) : (index) -> ()
// CHECK-NEXT: %[[c7:.*]] = constant 7 : index
// CHECK-NEXT: %[[c56:.*]] = constant 56 : index
// CHECK-NEXT: %[[c2:.*]] = constant 2 : index
// CHECK-NEXT: for %{{.*}} = %[[c7]] to %[[c56]] step %[[c2]] {
// CHECK-NEXT: call @body2(%{{.*}}, %{{.*}}) : (index, index) -> ()
// CHECK-NEXT: }
// CHECK-NEXT: call @mid(%{{.*}}) : (index) -> ()
// CHECK-NEXT: %[[c18:.*]] = constant 18 : index
// CHECK-NEXT: %[[c37:.*]] = constant 37 : index
// CHECK-NEXT: %[[c3:.*]] = constant 3 : index
// CHECK-NEXT: for %{{.*}} = %[[c18]] to %[[c37]] step %[[c3]] {
// CHECK-NEXT: call @body3(%{{.*}}, %{{.*}}) : (index, index) -> ()
// CHECK-NEXT: }
// CHECK-NEXT: call @post(%{{.*}}) : (index) -> ()
// CHECK-NEXT: }
Basic conversion of MLFunctions to CFGFunctions. Implement a pass converting a subset of MLFunctions to CFGFunctions. Currently supports arbitrarily complex imperfect loop nests with statically constant (i.e., not affine map) bounds filled with operations. Does NOT support branches and non-constant loop bounds. Conversion is performed per-function and the function names are preserved to avoid breaking any external references to the current module. In-memory IR is updated to point to the right functions in direct calls and constant loads. This behavior is tested via a really hidden flag that enables function renaming. Inside each function, the control flow conversion is based on single-entry single-exit regions, i.e. subgraphs of the CFG that have exactly one incoming and exactly one outgoing edge. Since an MLFunction must have a single "return" statement as per MLIR spec, it constitutes an SESE region. Individual operations are appended to this region. Control flow statements are recursively converted into such regions that are concatenated with the current region. Bodies of the compound statement also form SESE regions, which allows to nest control flow statements easily. Note that SESE regions are not materialized in the code. It is sufficent to keep track of the end of the region as the current instruction insertion point as long as all recursive calls update the insertion point in the end. The converter maintains a mapping between SSA values in ML functions and their CFG counterparts. The mapping is used to find the operands for each operation and is updated to contain the results of each operation as the conversion continues. PiperOrigin-RevId: 221162602
2018-11-13 06:58:36 +08:00
// CHECK-NEXT: return
// CHECK-NEXT: }
func @more_imperfectly_nested_loops() {
affine.for %i = 0 to 42 {
Basic conversion of MLFunctions to CFGFunctions. Implement a pass converting a subset of MLFunctions to CFGFunctions. Currently supports arbitrarily complex imperfect loop nests with statically constant (i.e., not affine map) bounds filled with operations. Does NOT support branches and non-constant loop bounds. Conversion is performed per-function and the function names are preserved to avoid breaking any external references to the current module. In-memory IR is updated to point to the right functions in direct calls and constant loads. This behavior is tested via a really hidden flag that enables function renaming. Inside each function, the control flow conversion is based on single-entry single-exit regions, i.e. subgraphs of the CFG that have exactly one incoming and exactly one outgoing edge. Since an MLFunction must have a single "return" statement as per MLIR spec, it constitutes an SESE region. Individual operations are appended to this region. Control flow statements are recursively converted into such regions that are concatenated with the current region. Bodies of the compound statement also form SESE regions, which allows to nest control flow statements easily. Note that SESE regions are not materialized in the code. It is sufficent to keep track of the end of the region as the current instruction insertion point as long as all recursive calls update the insertion point in the end. The converter maintains a mapping between SSA values in ML functions and their CFG counterparts. The mapping is used to find the operands for each operation and is updated to contain the results of each operation as the conversion continues. PiperOrigin-RevId: 221162602
2018-11-13 06:58:36 +08:00
call @pre(%i) : (index) -> ()
affine.for %j = 7 to 56 step 2 {
Basic conversion of MLFunctions to CFGFunctions. Implement a pass converting a subset of MLFunctions to CFGFunctions. Currently supports arbitrarily complex imperfect loop nests with statically constant (i.e., not affine map) bounds filled with operations. Does NOT support branches and non-constant loop bounds. Conversion is performed per-function and the function names are preserved to avoid breaking any external references to the current module. In-memory IR is updated to point to the right functions in direct calls and constant loads. This behavior is tested via a really hidden flag that enables function renaming. Inside each function, the control flow conversion is based on single-entry single-exit regions, i.e. subgraphs of the CFG that have exactly one incoming and exactly one outgoing edge. Since an MLFunction must have a single "return" statement as per MLIR spec, it constitutes an SESE region. Individual operations are appended to this region. Control flow statements are recursively converted into such regions that are concatenated with the current region. Bodies of the compound statement also form SESE regions, which allows to nest control flow statements easily. Note that SESE regions are not materialized in the code. It is sufficent to keep track of the end of the region as the current instruction insertion point as long as all recursive calls update the insertion point in the end. The converter maintains a mapping between SSA values in ML functions and their CFG counterparts. The mapping is used to find the operands for each operation and is updated to contain the results of each operation as the conversion continues. PiperOrigin-RevId: 221162602
2018-11-13 06:58:36 +08:00
call @body2(%i, %j) : (index, index) -> ()
}
call @mid(%i) : (index) -> ()
affine.for %k = 18 to 37 step 3 {
Basic conversion of MLFunctions to CFGFunctions. Implement a pass converting a subset of MLFunctions to CFGFunctions. Currently supports arbitrarily complex imperfect loop nests with statically constant (i.e., not affine map) bounds filled with operations. Does NOT support branches and non-constant loop bounds. Conversion is performed per-function and the function names are preserved to avoid breaking any external references to the current module. In-memory IR is updated to point to the right functions in direct calls and constant loads. This behavior is tested via a really hidden flag that enables function renaming. Inside each function, the control flow conversion is based on single-entry single-exit regions, i.e. subgraphs of the CFG that have exactly one incoming and exactly one outgoing edge. Since an MLFunction must have a single "return" statement as per MLIR spec, it constitutes an SESE region. Individual operations are appended to this region. Control flow statements are recursively converted into such regions that are concatenated with the current region. Bodies of the compound statement also form SESE regions, which allows to nest control flow statements easily. Note that SESE regions are not materialized in the code. It is sufficent to keep track of the end of the region as the current instruction insertion point as long as all recursive calls update the insertion point in the end. The converter maintains a mapping between SSA values in ML functions and their CFG counterparts. The mapping is used to find the operands for each operation and is updated to contain the results of each operation as the conversion continues. PiperOrigin-RevId: 221162602
2018-11-13 06:58:36 +08:00
call @body3(%i, %k) : (index, index) -> ()
}
call @post(%i) : (index) -> ()
}
return
}
// CHECK-LABEL: func @affine_apply_loops_shorthand
// CHECK-NEXT: %[[c0:.*]] = constant 0 : index
// CHECK-NEXT: %[[c1:.*]] = constant 1 : index
// CHECK-NEXT: for %{{.*}} = %[[c0]] to %{{.*}} step %[[c1]] {
// CHECK-NEXT: %[[c42:.*]] = constant 42 : index
// CHECK-NEXT: %[[c1_0:.*]] = constant 1 : index
// CHECK-NEXT: for %{{.*}} = %{{.*}} to %[[c42]] step %[[c1_0]] {
// CHECK-NEXT: call @body2(%{{.*}}, %{{.*}}) : (index, index) -> ()
// CHECK-NEXT: }
// CHECK-NEXT: }
// CHECK-NEXT: return
// CHECK-NEXT: }
func @affine_apply_loops_shorthand(%N : index) {
affine.for %i = 0 to %N {
affine.for %j = affine_map<(d0)[]->(d0)>(%i)[] to 42 {
call @body2(%i, %j) : (index, index) -> ()
}
}
return
}
ConvertToCFG: convert "if" statements. The condition of the "if" statement is an integer set, defined as a conjunction of affine constraints. An affine constraints consists of an affine expression and a flag indicating whether the expression is strictly equal to zero or is also allowed to be greater than zero. Affine maps, accepted by `affine_apply` are also formed from affine expressions. Leverage this fact to implement the checking of "if" conditions. Each affine expression from the integer set is converted into an affine map. This map is applied to the arguments of the "if" statement. The result of the application is compared with zero given the equality flag to obtain the final boolean value. The conjunction of conditions is tested sequentially with short-circuit branching to the "else" branch if any of the condition evaluates to false. Create an SESE region for the if statement (including its "then" and optional "else" statement blocks) and append it to the end of the current region. The conditional region consists of a sequence of condition-checking blocks that implement the short-circuit scheme, followed by a "then" SESE region and an "else" SESE region, and the continuation block that post-dominates all blocks of the "if" statement. The flow of blocks that correspond to the "then" and "else" clauses are constructed recursively, enabling easy nesting of "if" statements and if-then-else-if chains. Note that MLIR semantics does not require nor prohibit short-circuit evaluation. Since affine expressions do not have side effects, there is no observable difference in the program behavior. We may trade off extra operations for operation-level parallelism opportunity by first performing all `affine_apply` and comparison operations independently, and then performing a tree pattern reduction of the resulting boolean values with the `muli i1` operations (in absence of the dedicated bit operations). The pros and cons are not clear, and since MLIR does not include parallel semantics, we prefer to minimize the number of sequentially executed operations. PiperOrigin-RevId: 223970248
2018-12-04 23:04:44 +08:00
/////////////////////////////////////////////////////////////////////
func @get_idx() -> (index)
ConvertToCFG: convert "if" statements. The condition of the "if" statement is an integer set, defined as a conjunction of affine constraints. An affine constraints consists of an affine expression and a flag indicating whether the expression is strictly equal to zero or is also allowed to be greater than zero. Affine maps, accepted by `affine_apply` are also formed from affine expressions. Leverage this fact to implement the checking of "if" conditions. Each affine expression from the integer set is converted into an affine map. This map is applied to the arguments of the "if" statement. The result of the application is compared with zero given the equality flag to obtain the final boolean value. The conjunction of conditions is tested sequentially with short-circuit branching to the "else" branch if any of the condition evaluates to false. Create an SESE region for the if statement (including its "then" and optional "else" statement blocks) and append it to the end of the current region. The conditional region consists of a sequence of condition-checking blocks that implement the short-circuit scheme, followed by a "then" SESE region and an "else" SESE region, and the continuation block that post-dominates all blocks of the "if" statement. The flow of blocks that correspond to the "then" and "else" clauses are constructed recursively, enabling easy nesting of "if" statements and if-then-else-if chains. Note that MLIR semantics does not require nor prohibit short-circuit evaluation. Since affine expressions do not have side effects, there is no observable difference in the program behavior. We may trade off extra operations for operation-level parallelism opportunity by first performing all `affine_apply` and comparison operations independently, and then performing a tree pattern reduction of the resulting boolean values with the `muli i1` operations (in absence of the dedicated bit operations). The pros and cons are not clear, and since MLIR does not include parallel semantics, we prefer to minimize the number of sequentially executed operations. PiperOrigin-RevId: 223970248
2018-12-04 23:04:44 +08:00
#set1 = affine_set<(d0) : (20 - d0 >= 0)>
#set2 = affine_set<(d0) : (d0 - 10 >= 0)>
ConvertToCFG: convert "if" statements. The condition of the "if" statement is an integer set, defined as a conjunction of affine constraints. An affine constraints consists of an affine expression and a flag indicating whether the expression is strictly equal to zero or is also allowed to be greater than zero. Affine maps, accepted by `affine_apply` are also formed from affine expressions. Leverage this fact to implement the checking of "if" conditions. Each affine expression from the integer set is converted into an affine map. This map is applied to the arguments of the "if" statement. The result of the application is compared with zero given the equality flag to obtain the final boolean value. The conjunction of conditions is tested sequentially with short-circuit branching to the "else" branch if any of the condition evaluates to false. Create an SESE region for the if statement (including its "then" and optional "else" statement blocks) and append it to the end of the current region. The conditional region consists of a sequence of condition-checking blocks that implement the short-circuit scheme, followed by a "then" SESE region and an "else" SESE region, and the continuation block that post-dominates all blocks of the "if" statement. The flow of blocks that correspond to the "then" and "else" clauses are constructed recursively, enabling easy nesting of "if" statements and if-then-else-if chains. Note that MLIR semantics does not require nor prohibit short-circuit evaluation. Since affine expressions do not have side effects, there is no observable difference in the program behavior. We may trade off extra operations for operation-level parallelism opportunity by first performing all `affine_apply` and comparison operations independently, and then performing a tree pattern reduction of the resulting boolean values with the `muli i1` operations (in absence of the dedicated bit operations). The pros and cons are not clear, and since MLIR does not include parallel semantics, we prefer to minimize the number of sequentially executed operations. PiperOrigin-RevId: 223970248
2018-12-04 23:04:44 +08:00
// CHECK-LABEL: func @if_only
// CHECK-NEXT: %[[v0:.*]] = call @get_idx() : () -> index
// CHECK-NEXT: %[[c0:.*]] = constant 0 : index
// CHECK-NEXT: %[[cm1:.*]] = constant -1 : index
// CHECK-NEXT: %[[v1:.*]] = muli %[[v0]], %[[cm1]] : index
// CHECK-NEXT: %[[c20:.*]] = constant 20 : index
// CHECK-NEXT: %[[v2:.*]] = addi %[[v1]], %[[c20]] : index
// CHECK-NEXT: %[[v3:.*]] = cmpi "sge", %[[v2]], %[[c0]] : index
// CHECK-NEXT: if %[[v3]] {
// CHECK-NEXT: call @body(%[[v0:.*]]) : (index) -> ()
// CHECK-NEXT: }
ConvertToCFG: convert "if" statements. The condition of the "if" statement is an integer set, defined as a conjunction of affine constraints. An affine constraints consists of an affine expression and a flag indicating whether the expression is strictly equal to zero or is also allowed to be greater than zero. Affine maps, accepted by `affine_apply` are also formed from affine expressions. Leverage this fact to implement the checking of "if" conditions. Each affine expression from the integer set is converted into an affine map. This map is applied to the arguments of the "if" statement. The result of the application is compared with zero given the equality flag to obtain the final boolean value. The conjunction of conditions is tested sequentially with short-circuit branching to the "else" branch if any of the condition evaluates to false. Create an SESE region for the if statement (including its "then" and optional "else" statement blocks) and append it to the end of the current region. The conditional region consists of a sequence of condition-checking blocks that implement the short-circuit scheme, followed by a "then" SESE region and an "else" SESE region, and the continuation block that post-dominates all blocks of the "if" statement. The flow of blocks that correspond to the "then" and "else" clauses are constructed recursively, enabling easy nesting of "if" statements and if-then-else-if chains. Note that MLIR semantics does not require nor prohibit short-circuit evaluation. Since affine expressions do not have side effects, there is no observable difference in the program behavior. We may trade off extra operations for operation-level parallelism opportunity by first performing all `affine_apply` and comparison operations independently, and then performing a tree pattern reduction of the resulting boolean values with the `muli i1` operations (in absence of the dedicated bit operations). The pros and cons are not clear, and since MLIR does not include parallel semantics, we prefer to minimize the number of sequentially executed operations. PiperOrigin-RevId: 223970248
2018-12-04 23:04:44 +08:00
// CHECK-NEXT: return
// CHECK-NEXT: }
func @if_only() {
ConvertToCFG: convert "if" statements. The condition of the "if" statement is an integer set, defined as a conjunction of affine constraints. An affine constraints consists of an affine expression and a flag indicating whether the expression is strictly equal to zero or is also allowed to be greater than zero. Affine maps, accepted by `affine_apply` are also formed from affine expressions. Leverage this fact to implement the checking of "if" conditions. Each affine expression from the integer set is converted into an affine map. This map is applied to the arguments of the "if" statement. The result of the application is compared with zero given the equality flag to obtain the final boolean value. The conjunction of conditions is tested sequentially with short-circuit branching to the "else" branch if any of the condition evaluates to false. Create an SESE region for the if statement (including its "then" and optional "else" statement blocks) and append it to the end of the current region. The conditional region consists of a sequence of condition-checking blocks that implement the short-circuit scheme, followed by a "then" SESE region and an "else" SESE region, and the continuation block that post-dominates all blocks of the "if" statement. The flow of blocks that correspond to the "then" and "else" clauses are constructed recursively, enabling easy nesting of "if" statements and if-then-else-if chains. Note that MLIR semantics does not require nor prohibit short-circuit evaluation. Since affine expressions do not have side effects, there is no observable difference in the program behavior. We may trade off extra operations for operation-level parallelism opportunity by first performing all `affine_apply` and comparison operations independently, and then performing a tree pattern reduction of the resulting boolean values with the `muli i1` operations (in absence of the dedicated bit operations). The pros and cons are not clear, and since MLIR does not include parallel semantics, we prefer to minimize the number of sequentially executed operations. PiperOrigin-RevId: 223970248
2018-12-04 23:04:44 +08:00
%i = call @get_idx() : () -> (index)
affine.if #set1(%i) {
ConvertToCFG: convert "if" statements. The condition of the "if" statement is an integer set, defined as a conjunction of affine constraints. An affine constraints consists of an affine expression and a flag indicating whether the expression is strictly equal to zero or is also allowed to be greater than zero. Affine maps, accepted by `affine_apply` are also formed from affine expressions. Leverage this fact to implement the checking of "if" conditions. Each affine expression from the integer set is converted into an affine map. This map is applied to the arguments of the "if" statement. The result of the application is compared with zero given the equality flag to obtain the final boolean value. The conjunction of conditions is tested sequentially with short-circuit branching to the "else" branch if any of the condition evaluates to false. Create an SESE region for the if statement (including its "then" and optional "else" statement blocks) and append it to the end of the current region. The conditional region consists of a sequence of condition-checking blocks that implement the short-circuit scheme, followed by a "then" SESE region and an "else" SESE region, and the continuation block that post-dominates all blocks of the "if" statement. The flow of blocks that correspond to the "then" and "else" clauses are constructed recursively, enabling easy nesting of "if" statements and if-then-else-if chains. Note that MLIR semantics does not require nor prohibit short-circuit evaluation. Since affine expressions do not have side effects, there is no observable difference in the program behavior. We may trade off extra operations for operation-level parallelism opportunity by first performing all `affine_apply` and comparison operations independently, and then performing a tree pattern reduction of the resulting boolean values with the `muli i1` operations (in absence of the dedicated bit operations). The pros and cons are not clear, and since MLIR does not include parallel semantics, we prefer to minimize the number of sequentially executed operations. PiperOrigin-RevId: 223970248
2018-12-04 23:04:44 +08:00
call @body(%i) : (index) -> ()
}
return
}
// CHECK-LABEL: func @if_else
// CHECK-NEXT: %[[v0:.*]] = call @get_idx() : () -> index
// CHECK-NEXT: %[[c0:.*]] = constant 0 : index
// CHECK-NEXT: %[[cm1:.*]] = constant -1 : index
// CHECK-NEXT: %[[v1:.*]] = muli %[[v0]], %[[cm1]] : index
// CHECK-NEXT: %[[c20:.*]] = constant 20 : index
// CHECK-NEXT: %[[v2:.*]] = addi %[[v1]], %[[c20]] : index
// CHECK-NEXT: %[[v3:.*]] = cmpi "sge", %[[v2]], %[[c0]] : index
// CHECK-NEXT: if %[[v3]] {
// CHECK-NEXT: call @body(%[[v0:.*]]) : (index) -> ()
// CHECK-NEXT: } else {
// CHECK-NEXT: call @mid(%[[v0:.*]]) : (index) -> ()
// CHECK-NEXT: }
ConvertToCFG: convert "if" statements. The condition of the "if" statement is an integer set, defined as a conjunction of affine constraints. An affine constraints consists of an affine expression and a flag indicating whether the expression is strictly equal to zero or is also allowed to be greater than zero. Affine maps, accepted by `affine_apply` are also formed from affine expressions. Leverage this fact to implement the checking of "if" conditions. Each affine expression from the integer set is converted into an affine map. This map is applied to the arguments of the "if" statement. The result of the application is compared with zero given the equality flag to obtain the final boolean value. The conjunction of conditions is tested sequentially with short-circuit branching to the "else" branch if any of the condition evaluates to false. Create an SESE region for the if statement (including its "then" and optional "else" statement blocks) and append it to the end of the current region. The conditional region consists of a sequence of condition-checking blocks that implement the short-circuit scheme, followed by a "then" SESE region and an "else" SESE region, and the continuation block that post-dominates all blocks of the "if" statement. The flow of blocks that correspond to the "then" and "else" clauses are constructed recursively, enabling easy nesting of "if" statements and if-then-else-if chains. Note that MLIR semantics does not require nor prohibit short-circuit evaluation. Since affine expressions do not have side effects, there is no observable difference in the program behavior. We may trade off extra operations for operation-level parallelism opportunity by first performing all `affine_apply` and comparison operations independently, and then performing a tree pattern reduction of the resulting boolean values with the `muli i1` operations (in absence of the dedicated bit operations). The pros and cons are not clear, and since MLIR does not include parallel semantics, we prefer to minimize the number of sequentially executed operations. PiperOrigin-RevId: 223970248
2018-12-04 23:04:44 +08:00
// CHECK-NEXT: return
// CHECK-NEXT: }
func @if_else() {
ConvertToCFG: convert "if" statements. The condition of the "if" statement is an integer set, defined as a conjunction of affine constraints. An affine constraints consists of an affine expression and a flag indicating whether the expression is strictly equal to zero or is also allowed to be greater than zero. Affine maps, accepted by `affine_apply` are also formed from affine expressions. Leverage this fact to implement the checking of "if" conditions. Each affine expression from the integer set is converted into an affine map. This map is applied to the arguments of the "if" statement. The result of the application is compared with zero given the equality flag to obtain the final boolean value. The conjunction of conditions is tested sequentially with short-circuit branching to the "else" branch if any of the condition evaluates to false. Create an SESE region for the if statement (including its "then" and optional "else" statement blocks) and append it to the end of the current region. The conditional region consists of a sequence of condition-checking blocks that implement the short-circuit scheme, followed by a "then" SESE region and an "else" SESE region, and the continuation block that post-dominates all blocks of the "if" statement. The flow of blocks that correspond to the "then" and "else" clauses are constructed recursively, enabling easy nesting of "if" statements and if-then-else-if chains. Note that MLIR semantics does not require nor prohibit short-circuit evaluation. Since affine expressions do not have side effects, there is no observable difference in the program behavior. We may trade off extra operations for operation-level parallelism opportunity by first performing all `affine_apply` and comparison operations independently, and then performing a tree pattern reduction of the resulting boolean values with the `muli i1` operations (in absence of the dedicated bit operations). The pros and cons are not clear, and since MLIR does not include parallel semantics, we prefer to minimize the number of sequentially executed operations. PiperOrigin-RevId: 223970248
2018-12-04 23:04:44 +08:00
%i = call @get_idx() : () -> (index)
affine.if #set1(%i) {
ConvertToCFG: convert "if" statements. The condition of the "if" statement is an integer set, defined as a conjunction of affine constraints. An affine constraints consists of an affine expression and a flag indicating whether the expression is strictly equal to zero or is also allowed to be greater than zero. Affine maps, accepted by `affine_apply` are also formed from affine expressions. Leverage this fact to implement the checking of "if" conditions. Each affine expression from the integer set is converted into an affine map. This map is applied to the arguments of the "if" statement. The result of the application is compared with zero given the equality flag to obtain the final boolean value. The conjunction of conditions is tested sequentially with short-circuit branching to the "else" branch if any of the condition evaluates to false. Create an SESE region for the if statement (including its "then" and optional "else" statement blocks) and append it to the end of the current region. The conditional region consists of a sequence of condition-checking blocks that implement the short-circuit scheme, followed by a "then" SESE region and an "else" SESE region, and the continuation block that post-dominates all blocks of the "if" statement. The flow of blocks that correspond to the "then" and "else" clauses are constructed recursively, enabling easy nesting of "if" statements and if-then-else-if chains. Note that MLIR semantics does not require nor prohibit short-circuit evaluation. Since affine expressions do not have side effects, there is no observable difference in the program behavior. We may trade off extra operations for operation-level parallelism opportunity by first performing all `affine_apply` and comparison operations independently, and then performing a tree pattern reduction of the resulting boolean values with the `muli i1` operations (in absence of the dedicated bit operations). The pros and cons are not clear, and since MLIR does not include parallel semantics, we prefer to minimize the number of sequentially executed operations. PiperOrigin-RevId: 223970248
2018-12-04 23:04:44 +08:00
call @body(%i) : (index) -> ()
} else {
call @mid(%i) : (index) -> ()
}
return
}
// CHECK-LABEL: func @nested_ifs
// CHECK-NEXT: %[[v0:.*]] = call @get_idx() : () -> index
// CHECK-NEXT: %[[c0:.*]] = constant 0 : index
// CHECK-NEXT: %[[cm1:.*]] = constant -1 : index
// CHECK-NEXT: %[[v1:.*]] = muli %[[v0]], %[[cm1]] : index
// CHECK-NEXT: %[[c20:.*]] = constant 20 : index
// CHECK-NEXT: %[[v2:.*]] = addi %[[v1]], %[[c20]] : index
// CHECK-NEXT: %[[v3:.*]] = cmpi "sge", %[[v2]], %[[c0]] : index
// CHECK-NEXT: if %[[v3]] {
// CHECK-NEXT: %[[c0_0:.*]] = constant 0 : index
// CHECK-NEXT: %[[cm10:.*]] = constant -10 : index
// CHECK-NEXT: %[[v4:.*]] = addi %[[v0]], %[[cm10]] : index
// CHECK-NEXT: %[[v5:.*]] = cmpi "sge", %[[v4]], %[[c0_0]] : index
// CHECK-NEXT: if %[[v5]] {
// CHECK-NEXT: call @body(%[[v0:.*]]) : (index) -> ()
// CHECK-NEXT: }
// CHECK-NEXT: } else {
// CHECK-NEXT: %[[c0_0:.*]] = constant 0 : index
// CHECK-NEXT: %[[cm10:.*]] = constant -10 : index
// CHECK-NEXT: %{{.*}} = addi %[[v0]], %[[cm10]] : index
// CHECK-NEXT: %{{.*}} = cmpi "sge", %{{.*}}, %[[c0_0]] : index
// CHECK-NEXT: if %{{.*}} {
// CHECK-NEXT: call @mid(%[[v0:.*]]) : (index) -> ()
// CHECK-NEXT: }
// CHECK-NEXT: }
ConvertToCFG: convert "if" statements. The condition of the "if" statement is an integer set, defined as a conjunction of affine constraints. An affine constraints consists of an affine expression and a flag indicating whether the expression is strictly equal to zero or is also allowed to be greater than zero. Affine maps, accepted by `affine_apply` are also formed from affine expressions. Leverage this fact to implement the checking of "if" conditions. Each affine expression from the integer set is converted into an affine map. This map is applied to the arguments of the "if" statement. The result of the application is compared with zero given the equality flag to obtain the final boolean value. The conjunction of conditions is tested sequentially with short-circuit branching to the "else" branch if any of the condition evaluates to false. Create an SESE region for the if statement (including its "then" and optional "else" statement blocks) and append it to the end of the current region. The conditional region consists of a sequence of condition-checking blocks that implement the short-circuit scheme, followed by a "then" SESE region and an "else" SESE region, and the continuation block that post-dominates all blocks of the "if" statement. The flow of blocks that correspond to the "then" and "else" clauses are constructed recursively, enabling easy nesting of "if" statements and if-then-else-if chains. Note that MLIR semantics does not require nor prohibit short-circuit evaluation. Since affine expressions do not have side effects, there is no observable difference in the program behavior. We may trade off extra operations for operation-level parallelism opportunity by first performing all `affine_apply` and comparison operations independently, and then performing a tree pattern reduction of the resulting boolean values with the `muli i1` operations (in absence of the dedicated bit operations). The pros and cons are not clear, and since MLIR does not include parallel semantics, we prefer to minimize the number of sequentially executed operations. PiperOrigin-RevId: 223970248
2018-12-04 23:04:44 +08:00
// CHECK-NEXT: return
// CHECK-NEXT: }
func @nested_ifs() {
ConvertToCFG: convert "if" statements. The condition of the "if" statement is an integer set, defined as a conjunction of affine constraints. An affine constraints consists of an affine expression and a flag indicating whether the expression is strictly equal to zero or is also allowed to be greater than zero. Affine maps, accepted by `affine_apply` are also formed from affine expressions. Leverage this fact to implement the checking of "if" conditions. Each affine expression from the integer set is converted into an affine map. This map is applied to the arguments of the "if" statement. The result of the application is compared with zero given the equality flag to obtain the final boolean value. The conjunction of conditions is tested sequentially with short-circuit branching to the "else" branch if any of the condition evaluates to false. Create an SESE region for the if statement (including its "then" and optional "else" statement blocks) and append it to the end of the current region. The conditional region consists of a sequence of condition-checking blocks that implement the short-circuit scheme, followed by a "then" SESE region and an "else" SESE region, and the continuation block that post-dominates all blocks of the "if" statement. The flow of blocks that correspond to the "then" and "else" clauses are constructed recursively, enabling easy nesting of "if" statements and if-then-else-if chains. Note that MLIR semantics does not require nor prohibit short-circuit evaluation. Since affine expressions do not have side effects, there is no observable difference in the program behavior. We may trade off extra operations for operation-level parallelism opportunity by first performing all `affine_apply` and comparison operations independently, and then performing a tree pattern reduction of the resulting boolean values with the `muli i1` operations (in absence of the dedicated bit operations). The pros and cons are not clear, and since MLIR does not include parallel semantics, we prefer to minimize the number of sequentially executed operations. PiperOrigin-RevId: 223970248
2018-12-04 23:04:44 +08:00
%i = call @get_idx() : () -> (index)
affine.if #set1(%i) {
affine.if #set2(%i) {
ConvertToCFG: convert "if" statements. The condition of the "if" statement is an integer set, defined as a conjunction of affine constraints. An affine constraints consists of an affine expression and a flag indicating whether the expression is strictly equal to zero or is also allowed to be greater than zero. Affine maps, accepted by `affine_apply` are also formed from affine expressions. Leverage this fact to implement the checking of "if" conditions. Each affine expression from the integer set is converted into an affine map. This map is applied to the arguments of the "if" statement. The result of the application is compared with zero given the equality flag to obtain the final boolean value. The conjunction of conditions is tested sequentially with short-circuit branching to the "else" branch if any of the condition evaluates to false. Create an SESE region for the if statement (including its "then" and optional "else" statement blocks) and append it to the end of the current region. The conditional region consists of a sequence of condition-checking blocks that implement the short-circuit scheme, followed by a "then" SESE region and an "else" SESE region, and the continuation block that post-dominates all blocks of the "if" statement. The flow of blocks that correspond to the "then" and "else" clauses are constructed recursively, enabling easy nesting of "if" statements and if-then-else-if chains. Note that MLIR semantics does not require nor prohibit short-circuit evaluation. Since affine expressions do not have side effects, there is no observable difference in the program behavior. We may trade off extra operations for operation-level parallelism opportunity by first performing all `affine_apply` and comparison operations independently, and then performing a tree pattern reduction of the resulting boolean values with the `muli i1` operations (in absence of the dedicated bit operations). The pros and cons are not clear, and since MLIR does not include parallel semantics, we prefer to minimize the number of sequentially executed operations. PiperOrigin-RevId: 223970248
2018-12-04 23:04:44 +08:00
call @body(%i) : (index) -> ()
}
} else {
affine.if #set2(%i) {
ConvertToCFG: convert "if" statements. The condition of the "if" statement is an integer set, defined as a conjunction of affine constraints. An affine constraints consists of an affine expression and a flag indicating whether the expression is strictly equal to zero or is also allowed to be greater than zero. Affine maps, accepted by `affine_apply` are also formed from affine expressions. Leverage this fact to implement the checking of "if" conditions. Each affine expression from the integer set is converted into an affine map. This map is applied to the arguments of the "if" statement. The result of the application is compared with zero given the equality flag to obtain the final boolean value. The conjunction of conditions is tested sequentially with short-circuit branching to the "else" branch if any of the condition evaluates to false. Create an SESE region for the if statement (including its "then" and optional "else" statement blocks) and append it to the end of the current region. The conditional region consists of a sequence of condition-checking blocks that implement the short-circuit scheme, followed by a "then" SESE region and an "else" SESE region, and the continuation block that post-dominates all blocks of the "if" statement. The flow of blocks that correspond to the "then" and "else" clauses are constructed recursively, enabling easy nesting of "if" statements and if-then-else-if chains. Note that MLIR semantics does not require nor prohibit short-circuit evaluation. Since affine expressions do not have side effects, there is no observable difference in the program behavior. We may trade off extra operations for operation-level parallelism opportunity by first performing all `affine_apply` and comparison operations independently, and then performing a tree pattern reduction of the resulting boolean values with the `muli i1` operations (in absence of the dedicated bit operations). The pros and cons are not clear, and since MLIR does not include parallel semantics, we prefer to minimize the number of sequentially executed operations. PiperOrigin-RevId: 223970248
2018-12-04 23:04:44 +08:00
call @mid(%i) : (index) -> ()
}
}
return
}
#setN = affine_set<(d0)[N,M,K,L] : (N - d0 + 1 >= 0, N - 1 >= 0, M - 1 >= 0, K - 1 >= 0, L - 42 == 0)>
ConvertToCFG: convert "if" statements. The condition of the "if" statement is an integer set, defined as a conjunction of affine constraints. An affine constraints consists of an affine expression and a flag indicating whether the expression is strictly equal to zero or is also allowed to be greater than zero. Affine maps, accepted by `affine_apply` are also formed from affine expressions. Leverage this fact to implement the checking of "if" conditions. Each affine expression from the integer set is converted into an affine map. This map is applied to the arguments of the "if" statement. The result of the application is compared with zero given the equality flag to obtain the final boolean value. The conjunction of conditions is tested sequentially with short-circuit branching to the "else" branch if any of the condition evaluates to false. Create an SESE region for the if statement (including its "then" and optional "else" statement blocks) and append it to the end of the current region. The conditional region consists of a sequence of condition-checking blocks that implement the short-circuit scheme, followed by a "then" SESE region and an "else" SESE region, and the continuation block that post-dominates all blocks of the "if" statement. The flow of blocks that correspond to the "then" and "else" clauses are constructed recursively, enabling easy nesting of "if" statements and if-then-else-if chains. Note that MLIR semantics does not require nor prohibit short-circuit evaluation. Since affine expressions do not have side effects, there is no observable difference in the program behavior. We may trade off extra operations for operation-level parallelism opportunity by first performing all `affine_apply` and comparison operations independently, and then performing a tree pattern reduction of the resulting boolean values with the `muli i1` operations (in absence of the dedicated bit operations). The pros and cons are not clear, and since MLIR does not include parallel semantics, we prefer to minimize the number of sequentially executed operations. PiperOrigin-RevId: 223970248
2018-12-04 23:04:44 +08:00
// CHECK-LABEL: func @multi_cond
// CHECK-NEXT: %[[v0:.*]] = call @get_idx() : () -> index
// CHECK-NEXT: %[[c0:.*]] = constant 0 : index
// CHECK-NEXT: %[[cm1:.*]] = constant -1 : index
// CHECK-NEXT: %[[v1:.*]] = muli %[[v0]], %[[cm1]] : index
// CHECK-NEXT: %[[v2:.*]] = addi %[[v1]], %{{.*}} : index
// CHECK-NEXT: %[[c1:.*]] = constant 1 : index
// CHECK-NEXT: %[[v3:.*]] = addi %[[v2]], %[[c1]] : index
// CHECK-NEXT: %[[v4:.*]] = cmpi "sge", %[[v3]], %[[c0]] : index
// CHECK-NEXT: %[[cm1_0:.*]] = constant -1 : index
// CHECK-NEXT: %[[v5:.*]] = addi %{{.*}}, %[[cm1_0]] : index
// CHECK-NEXT: %[[v6:.*]] = cmpi "sge", %[[v5]], %[[c0]] : index
// CHECK-NEXT: %[[v7:.*]] = and %[[v4]], %[[v6]] : i1
// CHECK-NEXT: %[[cm1_1:.*]] = constant -1 : index
// CHECK-NEXT: %[[v8:.*]] = addi %{{.*}}, %[[cm1_1]] : index
// CHECK-NEXT: %[[v9:.*]] = cmpi "sge", %[[v8]], %[[c0]] : index
// CHECK-NEXT: %[[v10:.*]] = and %[[v7]], %[[v9]] : i1
// CHECK-NEXT: %[[cm1_2:.*]] = constant -1 : index
// CHECK-NEXT: %[[v11:.*]] = addi %{{.*}}, %[[cm1_2]] : index
// CHECK-NEXT: %[[v12:.*]] = cmpi "sge", %[[v11]], %[[c0]] : index
// CHECK-NEXT: %[[v13:.*]] = and %[[v10]], %[[v12]] : i1
// CHECK-NEXT: %[[cm42:.*]] = constant -42 : index
// CHECK-NEXT: %[[v14:.*]] = addi %{{.*}}, %[[cm42]] : index
// CHECK-NEXT: %[[v15:.*]] = cmpi "eq", %[[v14]], %[[c0]] : index
// CHECK-NEXT: %[[v16:.*]] = and %[[v13]], %[[v15]] : i1
// CHECK-NEXT: if %[[v16]] {
// CHECK-NEXT: call @body(%[[v0:.*]]) : (index) -> ()
// CHECK-NEXT: } else {
// CHECK-NEXT: call @mid(%[[v0:.*]]) : (index) -> ()
// CHECK-NEXT: }
ConvertToCFG: convert "if" statements. The condition of the "if" statement is an integer set, defined as a conjunction of affine constraints. An affine constraints consists of an affine expression and a flag indicating whether the expression is strictly equal to zero or is also allowed to be greater than zero. Affine maps, accepted by `affine_apply` are also formed from affine expressions. Leverage this fact to implement the checking of "if" conditions. Each affine expression from the integer set is converted into an affine map. This map is applied to the arguments of the "if" statement. The result of the application is compared with zero given the equality flag to obtain the final boolean value. The conjunction of conditions is tested sequentially with short-circuit branching to the "else" branch if any of the condition evaluates to false. Create an SESE region for the if statement (including its "then" and optional "else" statement blocks) and append it to the end of the current region. The conditional region consists of a sequence of condition-checking blocks that implement the short-circuit scheme, followed by a "then" SESE region and an "else" SESE region, and the continuation block that post-dominates all blocks of the "if" statement. The flow of blocks that correspond to the "then" and "else" clauses are constructed recursively, enabling easy nesting of "if" statements and if-then-else-if chains. Note that MLIR semantics does not require nor prohibit short-circuit evaluation. Since affine expressions do not have side effects, there is no observable difference in the program behavior. We may trade off extra operations for operation-level parallelism opportunity by first performing all `affine_apply` and comparison operations independently, and then performing a tree pattern reduction of the resulting boolean values with the `muli i1` operations (in absence of the dedicated bit operations). The pros and cons are not clear, and since MLIR does not include parallel semantics, we prefer to minimize the number of sequentially executed operations. PiperOrigin-RevId: 223970248
2018-12-04 23:04:44 +08:00
// CHECK-NEXT: return
// CHECK-NEXT: }
func @multi_cond(%N : index, %M : index, %K : index, %L : index) {
ConvertToCFG: convert "if" statements. The condition of the "if" statement is an integer set, defined as a conjunction of affine constraints. An affine constraints consists of an affine expression and a flag indicating whether the expression is strictly equal to zero or is also allowed to be greater than zero. Affine maps, accepted by `affine_apply` are also formed from affine expressions. Leverage this fact to implement the checking of "if" conditions. Each affine expression from the integer set is converted into an affine map. This map is applied to the arguments of the "if" statement. The result of the application is compared with zero given the equality flag to obtain the final boolean value. The conjunction of conditions is tested sequentially with short-circuit branching to the "else" branch if any of the condition evaluates to false. Create an SESE region for the if statement (including its "then" and optional "else" statement blocks) and append it to the end of the current region. The conditional region consists of a sequence of condition-checking blocks that implement the short-circuit scheme, followed by a "then" SESE region and an "else" SESE region, and the continuation block that post-dominates all blocks of the "if" statement. The flow of blocks that correspond to the "then" and "else" clauses are constructed recursively, enabling easy nesting of "if" statements and if-then-else-if chains. Note that MLIR semantics does not require nor prohibit short-circuit evaluation. Since affine expressions do not have side effects, there is no observable difference in the program behavior. We may trade off extra operations for operation-level parallelism opportunity by first performing all `affine_apply` and comparison operations independently, and then performing a tree pattern reduction of the resulting boolean values with the `muli i1` operations (in absence of the dedicated bit operations). The pros and cons are not clear, and since MLIR does not include parallel semantics, we prefer to minimize the number of sequentially executed operations. PiperOrigin-RevId: 223970248
2018-12-04 23:04:44 +08:00
%i = call @get_idx() : () -> (index)
affine.if #setN(%i)[%N,%M,%K,%L] {
ConvertToCFG: convert "if" statements. The condition of the "if" statement is an integer set, defined as a conjunction of affine constraints. An affine constraints consists of an affine expression and a flag indicating whether the expression is strictly equal to zero or is also allowed to be greater than zero. Affine maps, accepted by `affine_apply` are also formed from affine expressions. Leverage this fact to implement the checking of "if" conditions. Each affine expression from the integer set is converted into an affine map. This map is applied to the arguments of the "if" statement. The result of the application is compared with zero given the equality flag to obtain the final boolean value. The conjunction of conditions is tested sequentially with short-circuit branching to the "else" branch if any of the condition evaluates to false. Create an SESE region for the if statement (including its "then" and optional "else" statement blocks) and append it to the end of the current region. The conditional region consists of a sequence of condition-checking blocks that implement the short-circuit scheme, followed by a "then" SESE region and an "else" SESE region, and the continuation block that post-dominates all blocks of the "if" statement. The flow of blocks that correspond to the "then" and "else" clauses are constructed recursively, enabling easy nesting of "if" statements and if-then-else-if chains. Note that MLIR semantics does not require nor prohibit short-circuit evaluation. Since affine expressions do not have side effects, there is no observable difference in the program behavior. We may trade off extra operations for operation-level parallelism opportunity by first performing all `affine_apply` and comparison operations independently, and then performing a tree pattern reduction of the resulting boolean values with the `muli i1` operations (in absence of the dedicated bit operations). The pros and cons are not clear, and since MLIR does not include parallel semantics, we prefer to minimize the number of sequentially executed operations. PiperOrigin-RevId: 223970248
2018-12-04 23:04:44 +08:00
call @body(%i) : (index) -> ()
} else {
call @mid(%i) : (index) -> ()
}
return
}
// CHECK-LABEL: func @if_for
func @if_for() {
// CHECK-NEXT: %[[v0:.*]] = call @get_idx() : () -> index
ConvertToCFG: convert "if" statements. The condition of the "if" statement is an integer set, defined as a conjunction of affine constraints. An affine constraints consists of an affine expression and a flag indicating whether the expression is strictly equal to zero or is also allowed to be greater than zero. Affine maps, accepted by `affine_apply` are also formed from affine expressions. Leverage this fact to implement the checking of "if" conditions. Each affine expression from the integer set is converted into an affine map. This map is applied to the arguments of the "if" statement. The result of the application is compared with zero given the equality flag to obtain the final boolean value. The conjunction of conditions is tested sequentially with short-circuit branching to the "else" branch if any of the condition evaluates to false. Create an SESE region for the if statement (including its "then" and optional "else" statement blocks) and append it to the end of the current region. The conditional region consists of a sequence of condition-checking blocks that implement the short-circuit scheme, followed by a "then" SESE region and an "else" SESE region, and the continuation block that post-dominates all blocks of the "if" statement. The flow of blocks that correspond to the "then" and "else" clauses are constructed recursively, enabling easy nesting of "if" statements and if-then-else-if chains. Note that MLIR semantics does not require nor prohibit short-circuit evaluation. Since affine expressions do not have side effects, there is no observable difference in the program behavior. We may trade off extra operations for operation-level parallelism opportunity by first performing all `affine_apply` and comparison operations independently, and then performing a tree pattern reduction of the resulting boolean values with the `muli i1` operations (in absence of the dedicated bit operations). The pros and cons are not clear, and since MLIR does not include parallel semantics, we prefer to minimize the number of sequentially executed operations. PiperOrigin-RevId: 223970248
2018-12-04 23:04:44 +08:00
%i = call @get_idx() : () -> (index)
// CHECK-NEXT: %[[c0:.*]] = constant 0 : index
// CHECK-NEXT: %[[cm1:.*]] = constant -1 : index
// CHECK-NEXT: %[[v1:.*]] = muli %[[v0]], %[[cm1]] : index
// CHECK-NEXT: %[[c20:.*]] = constant 20 : index
// CHECK-NEXT: %[[v2:.*]] = addi %[[v1]], %[[c20]] : index
// CHECK-NEXT: %[[v3:.*]] = cmpi "sge", %[[v2]], %[[c0]] : index
// CHECK-NEXT: if %[[v3]] {
// CHECK-NEXT: %[[c0:.*]]{{.*}} = constant 0 : index
// CHECK-NEXT: %[[c42:.*]]{{.*}} = constant 42 : index
// CHECK-NEXT: %[[c1:.*]]{{.*}} = constant 1 : index
// CHECK-NEXT: for %{{.*}} = %[[c0:.*]]{{.*}} to %[[c42:.*]]{{.*}} step %[[c1:.*]]{{.*}} {
// CHECK-NEXT: %[[c0_:.*]]{{.*}} = constant 0 : index
// CHECK-NEXT: %[[cm10:.*]] = constant -10 : index
// CHECK-NEXT: %[[v4:.*]] = addi %{{.*}}, %[[cm10]] : index
// CHECK-NEXT: %[[v5:.*]] = cmpi "sge", %[[v4]], %[[c0_:.*]]{{.*}} : index
// CHECK-NEXT: if %[[v5]] {
// CHECK-NEXT: call @body2(%[[v0]], %{{.*}}) : (index, index) -> ()
affine.if #set1(%i) {
affine.for %j = 0 to 42 {
affine.if #set2(%j) {
ConvertToCFG: convert "if" statements. The condition of the "if" statement is an integer set, defined as a conjunction of affine constraints. An affine constraints consists of an affine expression and a flag indicating whether the expression is strictly equal to zero or is also allowed to be greater than zero. Affine maps, accepted by `affine_apply` are also formed from affine expressions. Leverage this fact to implement the checking of "if" conditions. Each affine expression from the integer set is converted into an affine map. This map is applied to the arguments of the "if" statement. The result of the application is compared with zero given the equality flag to obtain the final boolean value. The conjunction of conditions is tested sequentially with short-circuit branching to the "else" branch if any of the condition evaluates to false. Create an SESE region for the if statement (including its "then" and optional "else" statement blocks) and append it to the end of the current region. The conditional region consists of a sequence of condition-checking blocks that implement the short-circuit scheme, followed by a "then" SESE region and an "else" SESE region, and the continuation block that post-dominates all blocks of the "if" statement. The flow of blocks that correspond to the "then" and "else" clauses are constructed recursively, enabling easy nesting of "if" statements and if-then-else-if chains. Note that MLIR semantics does not require nor prohibit short-circuit evaluation. Since affine expressions do not have side effects, there is no observable difference in the program behavior. We may trade off extra operations for operation-level parallelism opportunity by first performing all `affine_apply` and comparison operations independently, and then performing a tree pattern reduction of the resulting boolean values with the `muli i1` operations (in absence of the dedicated bit operations). The pros and cons are not clear, and since MLIR does not include parallel semantics, we prefer to minimize the number of sequentially executed operations. PiperOrigin-RevId: 223970248
2018-12-04 23:04:44 +08:00
call @body2(%i, %j) : (index, index) -> ()
}
}
}
// CHECK: %[[c0:.*]]{{.*}} = constant 0 : index
// CHECK-NEXT: %[[c42:.*]]{{.*}} = constant 42 : index
// CHECK-NEXT: %[[c1:.*]]{{.*}} = constant 1 : index
// CHECK-NEXT: for %{{.*}} = %[[c0:.*]]{{.*}} to %[[c42:.*]]{{.*}} step %[[c1:.*]]{{.*}} {
// CHECK-NEXT: %[[c0:.*]]{{.*}} = constant 0 : index
// CHECK-NEXT: %[[cm10:.*]]{{.*}} = constant -10 : index
// CHECK-NEXT: %{{.*}} = addi %{{.*}}, %[[cm10:.*]]{{.*}} : index
// CHECK-NEXT: %{{.*}} = cmpi "sge", %{{.*}}, %[[c0:.*]]{{.*}} : index
// CHECK-NEXT: if %{{.*}} {
// CHECK-NEXT: %[[c0_:.*]]{{.*}} = constant 0 : index
// CHECK-NEXT: %[[c42_:.*]]{{.*}} = constant 42 : index
// CHECK-NEXT: %[[c1_:.*]]{{.*}} = constant 1 : index
// CHECK-NEXT: for %{{.*}} = %[[c0_:.*]]{{.*}} to %[[c42_:.*]]{{.*}} step %[[c1_:.*]]{{.*}} {
affine.for %k = 0 to 42 {
affine.if #set2(%k) {
affine.for %l = 0 to 42 {
ConvertToCFG: convert "if" statements. The condition of the "if" statement is an integer set, defined as a conjunction of affine constraints. An affine constraints consists of an affine expression and a flag indicating whether the expression is strictly equal to zero or is also allowed to be greater than zero. Affine maps, accepted by `affine_apply` are also formed from affine expressions. Leverage this fact to implement the checking of "if" conditions. Each affine expression from the integer set is converted into an affine map. This map is applied to the arguments of the "if" statement. The result of the application is compared with zero given the equality flag to obtain the final boolean value. The conjunction of conditions is tested sequentially with short-circuit branching to the "else" branch if any of the condition evaluates to false. Create an SESE region for the if statement (including its "then" and optional "else" statement blocks) and append it to the end of the current region. The conditional region consists of a sequence of condition-checking blocks that implement the short-circuit scheme, followed by a "then" SESE region and an "else" SESE region, and the continuation block that post-dominates all blocks of the "if" statement. The flow of blocks that correspond to the "then" and "else" clauses are constructed recursively, enabling easy nesting of "if" statements and if-then-else-if chains. Note that MLIR semantics does not require nor prohibit short-circuit evaluation. Since affine expressions do not have side effects, there is no observable difference in the program behavior. We may trade off extra operations for operation-level parallelism opportunity by first performing all `affine_apply` and comparison operations independently, and then performing a tree pattern reduction of the resulting boolean values with the `muli i1` operations (in absence of the dedicated bit operations). The pros and cons are not clear, and since MLIR does not include parallel semantics, we prefer to minimize the number of sequentially executed operations. PiperOrigin-RevId: 223970248
2018-12-04 23:04:44 +08:00
call @body3(%k, %l) : (index, index) -> ()
}
}
}
// CHECK: return
ConvertToCFG: convert "if" statements. The condition of the "if" statement is an integer set, defined as a conjunction of affine constraints. An affine constraints consists of an affine expression and a flag indicating whether the expression is strictly equal to zero or is also allowed to be greater than zero. Affine maps, accepted by `affine_apply` are also formed from affine expressions. Leverage this fact to implement the checking of "if" conditions. Each affine expression from the integer set is converted into an affine map. This map is applied to the arguments of the "if" statement. The result of the application is compared with zero given the equality flag to obtain the final boolean value. The conjunction of conditions is tested sequentially with short-circuit branching to the "else" branch if any of the condition evaluates to false. Create an SESE region for the if statement (including its "then" and optional "else" statement blocks) and append it to the end of the current region. The conditional region consists of a sequence of condition-checking blocks that implement the short-circuit scheme, followed by a "then" SESE region and an "else" SESE region, and the continuation block that post-dominates all blocks of the "if" statement. The flow of blocks that correspond to the "then" and "else" clauses are constructed recursively, enabling easy nesting of "if" statements and if-then-else-if chains. Note that MLIR semantics does not require nor prohibit short-circuit evaluation. Since affine expressions do not have side effects, there is no observable difference in the program behavior. We may trade off extra operations for operation-level parallelism opportunity by first performing all `affine_apply` and comparison operations independently, and then performing a tree pattern reduction of the resulting boolean values with the `muli i1` operations (in absence of the dedicated bit operations). The pros and cons are not clear, and since MLIR does not include parallel semantics, we prefer to minimize the number of sequentially executed operations. PiperOrigin-RevId: 223970248
2018-12-04 23:04:44 +08:00
return
}
#lbMultiMap = affine_map<(d0)[s0] -> (d0, s0 - d0)>
#ubMultiMap = affine_map<(d0)[s0] -> (s0, d0 + 10)>
// CHECK-LABEL: func @loop_min_max
// CHECK-NEXT: %[[c0:.*]] = constant 0 : index
// CHECK-NEXT: %[[c42:.*]] = constant 42 : index
// CHECK-NEXT: %[[c1:.*]] = constant 1 : index
// CHECK-NEXT: for %{{.*}} = %[[c0]] to %[[c42]] step %[[c1]] {
// CHECK-NEXT: %[[cm1:.*]] = constant -1 : index
// CHECK-NEXT: %[[a:.*]] = muli %{{.*}}, %[[cm1]] : index
// CHECK-NEXT: %[[b:.*]] = addi %[[a]], %{{.*}} : index
// CHECK-NEXT: %[[c:.*]] = cmpi "sgt", %{{.*}}, %[[b]] : index
// CHECK-NEXT: %[[d:.*]] = select %[[c]], %{{.*}}, %[[b]] : index
// CHECK-NEXT: %[[c10:.*]] = constant 10 : index
// CHECK-NEXT: %[[e:.*]] = addi %{{.*}}, %[[c10]] : index
// CHECK-NEXT: %[[f:.*]] = cmpi "slt", %{{.*}}, %[[e]] : index
// CHECK-NEXT: %[[g:.*]] = select %[[f]], %{{.*}}, %[[e]] : index
// CHECK-NEXT: %[[c1_0:.*]] = constant 1 : index
// CHECK-NEXT: for %{{.*}} = %[[v3]] to %[[v6]] step %[[c1_0]] {
// CHECK-NEXT: call @body2(%{{.*}}, %{{.*}}) : (index, index) -> ()
// CHECK-NEXT: }
// CHECK-NEXT: }
// CHECK-NEXT: return
// CHECK-NEXT: }
func @loop_min_max(%N : index) {
affine.for %i = 0 to 42 {
affine.for %j = max #lbMultiMap(%i)[%N] to min #ubMultiMap(%i)[%N] {
call @body2(%i, %j) : (index, index) -> ()
}
}
return
}
#map_7_values = affine_map<(i) -> (i, i, i, i, i, i, i)>
// Check that the "min" (cmpi "slt" + select) reduction sequence is emitted
// correctly for a an affine map with 7 results.
// CHECK-LABEL: func @min_reduction_tree
// CHECK-NEXT: %[[c0:.*]] = constant 0 : index
// CHECK-NEXT: %[[c01:.+]] = cmpi "slt", %{{.*}}, %{{.*}} : index
// CHECK-NEXT: %[[r01:.+]] = select %[[c01]], %{{.*}}, %{{.*}} : index
// CHECK-NEXT: %[[c012:.+]] = cmpi "slt", %[[r01]], %{{.*}} : index
// CHECK-NEXT: %[[r012:.+]] = select %[[c012]], %[[r01]], %{{.*}} : index
// CHECK-NEXT: %[[c0123:.+]] = cmpi "slt", %[[r012]], %{{.*}} : index
// CHECK-NEXT: %[[r0123:.+]] = select %[[c0123]], %[[r012]], %{{.*}} : index
// CHECK-NEXT: %[[c01234:.+]] = cmpi "slt", %[[r0123]], %{{.*}} : index
// CHECK-NEXT: %[[r01234:.+]] = select %[[c01234]], %[[r0123]], %{{.*}} : index
// CHECK-NEXT: %[[c012345:.+]] = cmpi "slt", %[[r01234]], %{{.*}} : index
// CHECK-NEXT: %[[r012345:.+]] = select %[[c012345]], %[[r01234]], %{{.*}} : index
// CHECK-NEXT: %[[c0123456:.+]] = cmpi "slt", %[[r012345]], %{{.*}} : index
// CHECK-NEXT: %[[r0123456:.+]] = select %[[c0123456]], %[[r012345]], %{{.*}} : index
// CHECK-NEXT: %[[c1:.*]] = constant 1 : index
// CHECK-NEXT: for %{{.*}} = %[[c0]] to %[[v11]] step %[[c1]] {
// CHECK-NEXT: call @body(%{{.*}}) : (index) -> ()
// CHECK-NEXT: }
// CHECK-NEXT: return
// CHECK-NEXT: }
func @min_reduction_tree(%v : index) {
affine.for %i = 0 to min #map_7_values(%v)[] {
call @body(%i) : (index) -> ()
}
return
}
/////////////////////////////////////////////////////////////////////
#map0 = affine_map<() -> (0)>
#map1 = affine_map<()[s0] -> (s0)>
#map2 = affine_map<(d0) -> (d0)>
#map3 = affine_map<(d0)[s0] -> (d0 + s0 + 1)>
#map4 = affine_map<(d0,d1,d2,d3)[s0,s1,s2] -> (d0 + 2*d1 + 3*d2 + 4*d3 + 5*s0 + 6*s1 + 7*s2)>
#map5 = affine_map<(d0,d1,d2) -> (d0,d1,d2)>
#map6 = affine_map<(d0,d1,d2) -> (d0 + d1 + d2)>
// CHECK-LABEL: func @affine_applies(
func @affine_applies(%arg0 : index) {
// CHECK: %[[c0:.*]] = constant 0 : index
%zero = affine.apply #map0()
// Identity maps are just discarded.
// CHECK-NEXT: %[[c101:.*]] = constant 101 : index
%101 = constant 101 : index
%symbZero = affine.apply #map1()[%zero]
// CHECK-NEXT: %[[c102:.*]] = constant 102 : index
%102 = constant 102 : index
%copy = affine.apply #map2(%zero)
// CHECK-NEXT: %[[v0:.*]] = addi %[[c0]], %[[c0]] : index
// CHECK-NEXT: %[[c1:.*]] = constant 1 : index
// CHECK-NEXT: %[[v1:.*]] = addi %[[v0]], %[[c1]] : index
%one = affine.apply #map3(%symbZero)[%zero]
// CHECK-NEXT: %[[c2:.*]] = constant 2 : index
// CHECK-NEXT: %[[v2:.*]] = muli %arg0, %[[c2]] : index
// CHECK-NEXT: %[[v3:.*]] = addi %arg0, %[[v2]] : index
// CHECK-NEXT: %[[c3:.*]] = constant 3 : index
// CHECK-NEXT: %[[v4:.*]] = muli %arg0, %[[c3]] : index
// CHECK-NEXT: %[[v5:.*]] = addi %[[v3]], %[[v4]] : index
// CHECK-NEXT: %[[c4:.*]] = constant 4 : index
// CHECK-NEXT: %[[v6:.*]] = muli %arg0, %[[c4]] : index
// CHECK-NEXT: %[[v7:.*]] = addi %[[v5]], %[[v6]] : index
// CHECK-NEXT: %[[c5:.*]] = constant 5 : index
// CHECK-NEXT: %[[v8:.*]] = muli %arg0, %[[c5]] : index
// CHECK-NEXT: %[[v9:.*]] = addi %[[v7]], %[[v8]] : index
// CHECK-NEXT: %[[c6:.*]] = constant 6 : index
// CHECK-NEXT: %[[v10:.*]] = muli %arg0, %[[c6]] : index
// CHECK-NEXT: %[[v11:.*]] = addi %[[v9]], %[[v10]] : index
// CHECK-NEXT: %[[c7:.*]] = constant 7 : index
// CHECK-NEXT: %[[v12:.*]] = muli %arg0, %[[c7]] : index
// CHECK-NEXT: %[[v13:.*]] = addi %[[v11]], %[[v12]] : index
%four = affine.apply #map4(%arg0, %arg0, %arg0, %arg0)[%arg0, %arg0, %arg0]
return
}
// CHECK-LABEL: func @args_ret_affine_apply(
func @args_ret_affine_apply(index, index) -> (index, index) {
^bb0(%0 : index, %1 : index):
// CHECK-NEXT: return %{{.*}}, %{{.*}} : index, index
%00 = affine.apply #map2 (%0)
%11 = affine.apply #map1 ()[%1]
return %00, %11 : index, index
}
//===---------------------------------------------------------------------===//
// Test lowering of Euclidean (floor) division, ceil division and modulo
// operation used in affine expressions. In addition to testing the
// operation-level output, check that the obtained results are correct by
// applying constant folding transformation after affine lowering.
//===---------------------------------------------------------------------===//
#mapmod = affine_map<(i) -> (i mod 42)>
// --------------------------------------------------------------------------//
// IMPORTANT NOTE: if you change this test, also change the @lowered_affine_mod
// test in the "constant-fold.mlir" test to reflect the expected output of
// affine.apply lowering.
// --------------------------------------------------------------------------//
// CHECK-LABEL: func @affine_apply_mod
func @affine_apply_mod(%arg0 : index) -> (index) {
// CHECK-NEXT: %[[c42:.*]] = constant 42 : index
// CHECK-NEXT: %[[v0:.*]] = remi_signed %{{.*}}, %[[c42]] : index
// CHECK-NEXT: %[[c0:.*]] = constant 0 : index
// CHECK-NEXT: %[[v1:.*]] = cmpi "slt", %[[v0]], %[[c0]] : index
// CHECK-NEXT: %[[v2:.*]] = addi %[[v0]], %[[c42]] : index
// CHECK-NEXT: %[[v3:.*]] = select %[[v1]], %[[v2]], %[[v0]] : index
%0 = affine.apply #mapmod (%arg0)
return %0 : index
}
#mapfloordiv = affine_map<(i) -> (i floordiv 42)>
// --------------------------------------------------------------------------//
// IMPORTANT NOTE: if you change this test, also change the @lowered_affine_mod
// test in the "constant-fold.mlir" test to reflect the expected output of
// affine.apply lowering.
// --------------------------------------------------------------------------//
// CHECK-LABEL: func @affine_apply_floordiv
func @affine_apply_floordiv(%arg0 : index) -> (index) {
// CHECK-NEXT: %[[c42:.*]] = constant 42 : index
// CHECK-NEXT: %[[c0:.*]] = constant 0 : index
// CHECK-NEXT: %[[cm1:.*]] = constant -1 : index
// CHECK-NEXT: %[[v0:.*]] = cmpi "slt", %{{.*}}, %[[c0]] : index
// CHECK-NEXT: %[[v1:.*]] = subi %[[cm1]], %{{.*}} : index
// CHECK-NEXT: %[[v2:.*]] = select %[[v0]], %[[v1]], %{{.*}} : index
// CHECK-NEXT: %[[v3:.*]] = divi_signed %[[v2]], %[[c42]] : index
// CHECK-NEXT: %[[v4:.*]] = subi %[[cm1]], %[[v3]] : index
// CHECK-NEXT: %[[v5:.*]] = select %[[v0]], %[[v4]], %[[v3]] : index
%0 = affine.apply #mapfloordiv (%arg0)
return %0 : index
}
#mapceildiv = affine_map<(i) -> (i ceildiv 42)>
// --------------------------------------------------------------------------//
// IMPORTANT NOTE: if you change this test, also change the @lowered_affine_mod
// test in the "constant-fold.mlir" test to reflect the expected output of
// affine.apply lowering.
// --------------------------------------------------------------------------//
// CHECK-LABEL: func @affine_apply_ceildiv
func @affine_apply_ceildiv(%arg0 : index) -> (index) {
// CHECK-NEXT: %[[c42:.*]] = constant 42 : index
// CHECK-NEXT: %[[c0:.*]] = constant 0 : index
// CHECK-NEXT: %[[c1:.*]] = constant 1 : index
// CHECK-NEXT: %[[v0:.*]] = cmpi "sle", %{{.*}}, %[[c0]] : index
// CHECK-NEXT: %[[v1:.*]] = subi %[[c0]], %{{.*}} : index
// CHECK-NEXT: %[[v2:.*]] = subi %{{.*}}, %[[c1]] : index
// CHECK-NEXT: %[[v3:.*]] = select %[[v0]], %[[v1]], %[[v2]] : index
// CHECK-NEXT: %[[v4:.*]] = divi_signed %[[v3]], %[[c42]] : index
// CHECK-NEXT: %[[v5:.*]] = subi %[[c0]], %[[v4]] : index
// CHECK-NEXT: %[[v6:.*]] = addi %[[v4]], %[[c1]] : index
// CHECK-NEXT: %[[v7:.*]] = select %[[v0]], %[[v5]], %[[v6]] : index
%0 = affine.apply #mapceildiv (%arg0)
return %0 : index
}
// CHECK-LABEL: func @affine_load
func @affine_load(%arg0 : index) {
%0 = alloc() : memref<10xf32>
affine.for %i0 = 0 to 10 {
%1 = affine.load %0[%i0 + symbol(%arg0) + 7] : memref<10xf32>
}
// CHECK: %[[a:.*]] = addi %{{.*}}, %{{.*}} : index
// CHECK-NEXT: %[[c7:.*]] = constant 7 : index
// CHECK-NEXT: %[[b:.*]] = addi %[[a]], %[[c7]] : index
// CHECK-NEXT: %{{.*}} = load %[[v0:.*]][%[[b]]] : memref<10xf32>
return
}
// CHECK-LABEL: func @affine_store
func @affine_store(%arg0 : index) {
%0 = alloc() : memref<10xf32>
%1 = constant 11.0 : f32
affine.for %i0 = 0 to 10 {
affine.store %1, %0[%i0 - symbol(%arg0) + 7] : memref<10xf32>
}
// CHECK: %c-1 = constant -1 : index
// CHECK-NEXT: %[[a:.*]] = muli %arg0, %c-1 : index
// CHECK-NEXT: %[[b:.*]] = addi %{{.*}}, %[[a]] : index
// CHECK-NEXT: %c7 = constant 7 : index
// CHECK-NEXT: %[[c:.*]] = addi %[[b]], %c7 : index
// CHECK-NEXT: store %cst, %0[%[[c]]] : memref<10xf32>
return
}
// CHECK-LABEL: func @affine_load_store_zero_dim
func @affine_load_store_zero_dim(%arg0 : memref<i32>, %arg1 : memref<i32>) {
%0 = affine.load %arg0[] : memref<i32>
affine.store %0, %arg1[] : memref<i32>
// CHECK: %[[x:.*]] = load %arg0[] : memref<i32>
// CHECK: store %[[x]], %arg1[] : memref<i32>
return
}
// CHECK-LABEL: func @affine_prefetch
func @affine_prefetch(%arg0 : index) {
%0 = alloc() : memref<10xf32>
affine.for %i0 = 0 to 10 {
affine.prefetch %0[%i0 + symbol(%arg0) + 7], read, locality<3>, data : memref<10xf32>
}
// CHECK: %[[a:.*]] = addi %{{.*}}, %{{.*}} : index
// CHECK-NEXT: %[[c7:.*]] = constant 7 : index
// CHECK-NEXT: %[[b:.*]] = addi %[[a]], %[[c7]] : index
// CHECK-NEXT: prefetch %[[v0:.*]][%[[b]]], read, locality<3>, data : memref<10xf32>
return
}
// CHECK-LABEL: func @affine_dma_start
func @affine_dma_start(%arg0 : index) {
%0 = alloc() : memref<100xf32>
%1 = alloc() : memref<100xf32, 2>
%2 = alloc() : memref<1xi32>
%c0 = constant 0 : index
%c64 = constant 64 : index
affine.for %i0 = 0 to 10 {
affine.dma_start %0[%i0 + 7], %1[%arg0 + 11], %2[%c0], %c64
: memref<100xf32>, memref<100xf32, 2>, memref<1xi32>
}
// CHECK: %c7 = constant 7 : index
// CHECK-NEXT: %[[a:.*]] = addi %{{.*}}, %c7 : index
// CHECK-NEXT: %c11 = constant 11 : index
// CHECK-NEXT: %[[b:.*]] = addi %arg0, %c11 : index
// CHECK-NEXT: dma_start %0[%[[a]]], %1[%[[b]]], %c64, %2[%c0] : memref<100xf32>, memref<100xf32, 2>, memref<1xi32>
return
}
// CHECK-LABEL: func @affine_dma_wait
func @affine_dma_wait(%arg0 : index) {
%2 = alloc() : memref<1xi32>
%c64 = constant 64 : index
affine.for %i0 = 0 to 10 {
affine.dma_wait %2[%i0 + %arg0 + 17], %c64 : memref<1xi32>
}
// CHECK: %[[a:.*]] = addi %{{.*}}, %arg0 : index
// CHECK-NEXT: %c17 = constant 17 : index
// CHECK-NEXT: %[[b:.*]] = addi %[[a]], %c17 : index
// CHECK-NEXT: dma_wait %0[%[[b]]], %c64 : memref<1xi32>
return
}
// CHECK-LABEL: func @affine_min
// CHECK-SAME: %[[ARG0:.*]]: index, %[[ARG1:.*]]: index
func @affine_min(%arg0: index, %arg1: index) -> index{
// CHECK: %[[Cm1:.*]] = constant -1
// CHECK: %[[neg1:.*]] = muli %[[ARG1]], %[[Cm1:.*]]
// CHECK: %[[first:.*]] = addi %[[ARG0]], %[[neg1]]
// CHECK: %[[Cm2:.*]] = constant -1
// CHECK: %[[neg2:.*]] = muli %[[ARG0]], %[[Cm2:.*]]
// CHECK: %[[second:.*]] = addi %[[ARG1]], %[[neg2]]
// CHECK: %[[cmp:.*]] = cmpi "slt", %[[first]], %[[second]]
// CHECK: select %[[cmp]], %[[first]], %[[second]]
%0 = affine.min affine_map<(d0,d1) -> (d0 - d1, d1 - d0)>(%arg0, %arg1)
return %0 : index
}
// CHECK-LABEL: func @affine_max
// CHECK-SAME: %[[ARG0:.*]]: index, %[[ARG1:.*]]: index
func @affine_max(%arg0: index, %arg1: index) -> index{
// CHECK: %[[Cm1:.*]] = constant -1
// CHECK: %[[neg1:.*]] = muli %[[ARG1]], %[[Cm1:.*]]
// CHECK: %[[first:.*]] = addi %[[ARG0]], %[[neg1]]
// CHECK: %[[Cm2:.*]] = constant -1
// CHECK: %[[neg2:.*]] = muli %[[ARG0]], %[[Cm2:.*]]
// CHECK: %[[second:.*]] = addi %[[ARG1]], %[[neg2]]
// CHECK: %[[cmp:.*]] = cmpi "sgt", %[[first]], %[[second]]
// CHECK: select %[[cmp]], %[[first]], %[[second]]
%0 = affine.max affine_map<(d0,d1) -> (d0 - d1, d1 - d0)>(%arg0, %arg1)
return %0 : index
}