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Support signed int divisor in onnx.Mod op #2471

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merged 10 commits into from
Sep 13, 2023
Merged

Support signed int divisor in onnx.Mod op #2471

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7db9fcd
Support onnx.Mod with signed int divisor.
negiyas Sep 1, 2023
7903ee8
Merge branch 'main' into pr_support_signed_int_divosor_in_onnx_mod_op
negiyas Sep 1, 2023
ba3c7f9
Merge branch 'main' into pr_support_signed_int_divosor_in_onnx_mod_op
negiyas Sep 6, 2023
d5c55f2
Fix backend-test isssues.
negiyas Sep 6, 2023
9b192e9
Merge branch 'main' into pr_support_signed_int_divosor_in_onnx_mod_op
negiyas Sep 8, 2023
816f76f
Merge branch 'main' into pr_support_signed_int_divosor_in_onnx_mod_op
negiyas Sep 9, 2023
5056b7a
Fix onnx.Mod lowering issues.
negiyas Sep 13, 2023
dea35d2
Merge branch 'main' into pr_support_signed_int_divosor_in_onnx_mod_op
negiyas Sep 13, 2023
367df4b
Fix lit errors for lowering onnx.mod op with signed int.
negiyas Sep 13, 2023
a34fa54
Merge branch 'main' into pr_support_signed_int_divosor_in_onnx_mod_op
negiyas Sep 13, 2023
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58 changes: 54 additions & 4 deletions src/Conversion/ONNXToKrnl/Math/Elementwise.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1119,7 +1119,7 @@ Value emitScalarOpFor<ONNXModOp>(ConversionPatternRewriter &rewriter,
CheckIfCustomScalarOpIsSupported<ONNXModOp>(elementType);
Value dividend = scalarOperands[0];
Value divisor = scalarOperands[1];
MultiDialectBuilder<MathBuilder> create(rewriter, loc);
MultiDialectBuilder<MathBuilder, KrnlBuilder> create(rewriter, loc);

// TODO: here we assume fmod=1, what should if that is not the case?
if (create.math.isFloatWithVector(elementType)) {
Expand All @@ -1136,9 +1136,59 @@ Value emitScalarOpFor<ONNXModOp>(ConversionPatternRewriter &rewriter,
#endif
}
if (create.math.isIntegerWithVector(elementType)) {
// TODO: implement
llvm_unreachable("not support integers at this moment since MLIR integers "
"are signless.");
// "math.rem" returns "minus" for minus dividend and "plus or zero" for plus
// dividend. We call the math.rem's return value "mathRemaider". However
// onnx.ModOp should return "minus" for minus divisor and "plus or zero" for
// plus divisor. we call the value that onnx.Mod op should return "onnxMod".
// The following table shows mathRemainder, onnxMod and their diference
// (=onnxMod-mathRemainder) for some inputs.
//
// dividend | 7 | 7 | -7 | -7 | 6 | 6 | -6 | -6 |
// divisor | 3 | -3 | 3 | -3 | 3 | -3 | 3 | -3 |
// ------------------------+-----+----+----+----+----+----+----+----+
// mathRemainder | 1 | 1 | -1 | -1 | 0 | 0 | 0 | 0 |
// onnxMod | 1 | -2 | 2 | -1 | 0 | 0 | 0 | 0 |
// onnxMod - mathRemainder | 0 | -3 | 3 | 0 | 0 | 0 | 0 | 0 |
//
// The following code shows logic to get onnxMod from mathRemainder
//
// int dividend, divisor;
// int mathRemainder = diviend % divisor;
// int adjustedRemainder = mathRemainder + divisor;
//
// if ((mathRemainder != 0) && ((dividend < 0) ^ (divisor < 0))) # c.f. "^"
// shows "exclusive or".
// return adjustedRemainder;
// return mathRemainder;

Value mathRemainder = create.math.rem(dividend, divisor);
Value adjustedRemainder = create.math.add(mathRemainder, divisor);
Value zero = create.math.constant(elementType, 0);
Value falseVal = create.math.constant(rewriter.getI1Type(), 0);
Value isMathRemainderNonZero =
create.math.eq(create.math.eq(mathRemainder, zero), falseVal);
Value isDividendMinus = create.math.slt(dividend, zero);
Value isDivisorMinus = create.math.slt(divisor, zero);
Value exclusiveOrOfIsDividendMinusAndIsDivisorMinus = create.math.eq(
create.math.eq(isDividendMinus, isDivisorMinus), falseVal);
Value needAdjust = create.math.andi(
isMathRemainderNonZero, exclusiveOrOfIsDividendMinusAndIsDivisorMinus);
Value answer =
create.math.select(needAdjust, adjustedRemainder, mathRemainder);

#ifdef DEBUG_ONNX_MOD
create.krnl.printf("XXXX emitScalarOpFor<ONNXModOp>: diviend=", dividend,
dividend.getType());
create.krnl.printf(", divisor=", divisor, divisor.getType());
create.krnl.printf(
", mathReminder=", mathRemainder, mathRemainder.getType());
create.krnl.printf(
", adjustedReminder=", adjustedRemainder, adjustedRemainder.getType());
create.krnl.printf(", Answer=", answer, answer.getType());
create.krnl.printf("\n");
#endif

return answer;
}
llvm_unreachable("unsupported element type");
}
Expand Down
4 changes: 2 additions & 2 deletions test/backend/inference_backend.py
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Original file line number Diff line number Diff line change
Expand Up @@ -658,8 +658,8 @@ def get_test_models():
# "test_mod_broadcast_cpu": {STATIC_SHAPE:{}, DYNAMIC_SHAPE:{-1:{-1}}, CONSTANT_INPUT:{-1}},
# "test_mod_int64_fmod_cpu": {STATIC_SHAPE:{}, DYNAMIC_SHAPE:{-1:{-1}}, CONSTANT_INPUT:{-1}},
# "test_mod_mixed_sign_int16_cpu": {STATIC_SHAPE:{}, DYNAMIC_SHAPE:{-1:{-1}}, CONSTANT_INPUT:{-1}},
# "test_mod_mixed_sign_int32_cpu": {STATIC_SHAPE:{}, DYNAMIC_SHAPE:{-1:{-1}}, CONSTANT_INPUT:{-1}},
# "test_mod_mixed_sign_int64_cpu": {STATIC_SHAPE:{}, DYNAMIC_SHAPE:{-1:{-1}}, CONSTANT_INPUT:{-1}},
"test_mod_mixed_sign_int32_cpu": {STATIC_SHAPE:{}, DYNAMIC_SHAPE:{-1:{-1}}, CONSTANT_INPUT:{-1}},
"test_mod_mixed_sign_int64_cpu": {STATIC_SHAPE:{}, DYNAMIC_SHAPE:{-1:{-1}}, CONSTANT_INPUT:{-1}},
# "test_mod_mixed_sign_int8_cpu": {STATIC_SHAPE:{}, DYNAMIC_SHAPE:{-1:{-1}}, CONSTANT_INPUT:{-1}},
# "test_mod_uint16_cpu": {STATIC_SHAPE:{}, DYNAMIC_SHAPE:{-1:{-1}}, CONSTANT_INPUT:{-1}},
# "test_mod_uint32_cpu": {STATIC_SHAPE:{}, DYNAMIC_SHAPE:{-1:{-1}}, CONSTANT_INPUT:{-1}},
Expand Down
46 changes: 46 additions & 0 deletions test/mlir/conversion/onnx_to_krnl/Math/Elementwise.mlir
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Expand Up @@ -115,6 +115,52 @@ func.func private @test_div(%arg0 : tensor<10x10xf32>, %arg1 : tensor<10x10xf32>

// -----

func.func private @test_signed_int_mod(%arg0 : tensor<10x10xi64>, %arg1 : tensor<10x10xi64>) -> tensor<*xi64> {
%0 = "onnx.Mod"(%arg0, %arg1) : (tensor<10x10xi64>, tensor<10x10xi64>) -> tensor<*xi64>
"func.return"(%0) : (tensor<*xi64>) -> ()
// mlir2FileCheck.py
// CHECK-LABEL: func.func private @test_signed_int_mod
// CHECK-SAME: ([[PARAM_0_:%.+]]: memref<10x10xi64>, [[PARAM_1_:%.+]]: memref<10x10xi64>) -> memref<10x10xi64> {
// CHECK-DAG: [[CST_1_:%.+]] = arith.constant 1 : index
// CHECK-DAG: [[CST_10_:%.+]] = arith.constant 10 : index
// CHECK-DAG: [[CST_10_1_:%.+]] = arith.constant 10 : index
// CHECK-DAG: [[CST_10_2_:%.+]] = arith.constant 10 : index
// CHECK-DAG: [[CST_10_3_:%.+]] = arith.constant 10 : index
// CHECK-DAG: [[CST_1_1_:%.+]] = arith.constant 1 : index
// CHECK-DAG: [[RES_:%.+]] = memref.alloc() {{.*}}: memref<10x10xi64>
// CHECK-DAG: [[LOOP_0_:%.+]]:2 = krnl.define_loops 2
// CHECK-DAG: [[CST_0_:%.+]] = arith.constant 0 : index
// CHECK-DAG: [[CST_10_4_:%.+]] = arith.constant 10 : index
// CHECK-DAG: [[CST_10_5_:%.+]] = arith.constant 10 : index
// CHECK: krnl.iterate([[LOOP_0_]]#0, [[LOOP_0_]]#1) with ([[LOOP_0_]]#0 -> [[I_0_:%.+]] = 0 to 10, [[LOOP_0_]]#1 -> [[I_1_:%.+]] = 0 to 10){
// CHECK-DAG: [[VAR_1_:%.+]]:2 = krnl.get_induction_var_value([[LOOP_0_]]#0, [[LOOP_0_]]#1) : (!krnl.loop, !krnl.loop) -> (index, index)
// CHECK-DAG: [[CST_10_6_:%.+]] = arith.constant 10 : index
// CHECK-DAG: [[CST_10_7_:%.+]] = arith.constant 10 : index
// CHECK-NOT: separator of consecutive DAGs
// CHECK-DAG: [[LOAD_PARAM_0_MEM_:%.+]] = krnl.load [[PARAM_0_]]{{.}}[[VAR_1_]]#0, [[VAR_1_]]#1] : memref<10x10xi64>
// CHECK-DAG: [[CST_10_8_:%.+]] = arith.constant 10 : index
// CHECK-DAG: [[CST_10_9_:%.+]] = arith.constant 10 : index
// CHECK-DAG: [[LOAD_PARAM_1_MEM_:%.+]] = krnl.load [[PARAM_1_]]{{.}}[[VAR_1_]]#0, [[VAR_1_]]#1] : memref<10x10xi64>
// CHECK: [[VAR_4_:%.+]] = arith.remsi [[LOAD_PARAM_0_MEM_]], [[LOAD_PARAM_1_MEM_]] : i64
// CHECK-DAG: [[VAR_5_:%.+]] = arith.addi [[VAR_4_]], [[LOAD_PARAM_1_MEM_]] : i64
// CHECK-DAG: [[CST_0_1_:%.+]] = arith.constant 0 : i64
// CHECK-DAG: [[VAR_false_:%.+]] = arith.constant false
// CHECK: [[VAR_6_:%.+]] = arith.cmpi eq, [[VAR_4_]], [[CST_0_1_]] : i64
// CHECK-DAG: [[VAR_7_:%.+]] = arith.cmpi eq, [[VAR_6_]], [[VAR_false_]] : i1
// CHECK-DAG: [[VAR_8_:%.+]] = arith.cmpi slt, [[LOAD_PARAM_0_MEM_]], [[CST_0_1_]] : i64
// CHECK-DAG: [[VAR_9_:%.+]] = arith.cmpi slt, [[LOAD_PARAM_1_MEM_]], [[CST_0_1_]] : i64
// CHECK: [[VAR_10_:%.+]] = arith.cmpi eq, [[VAR_8_]], [[VAR_9_]] : i1
// CHECK: [[VAR_11_:%.+]] = arith.cmpi eq, [[VAR_10_]], [[VAR_false_]] : i1
// CHECK: [[VAR_12_:%.+]] = arith.andi [[VAR_7_]], [[VAR_11_]] : i1
// CHECK: [[VAR_13_:%.+]] = arith.select [[VAR_12_]], [[VAR_5_]], [[VAR_4_]] : i64
// CHECK: krnl.store [[VAR_13_]], [[RES_]]{{.}}[[VAR_1_]]#0, [[VAR_1_]]#1] : memref<10x10xi64>
// CHECK: }
// CHECK: return [[RES_]] : memref<10x10xi64>
// CHECK: }
}

// -----

func.func private @test_sub(%arg0 : tensor<10x10xf32>, %arg1 : tensor<10x10xf32>) -> tensor<*xf32> {
%0 = "onnx.Sub"(%arg0, %arg1) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<*xf32>
"func.return"(%0) : (tensor<*xf32>) -> ()
Expand Down
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