Add a flag to turn on/off the lowering of scalar broadcasting binary ops to NNPA

src/Accelerators/NNPA/Compiler/CMakeLists.txt

@@ -1,5 +1,3 @@
-get_property(OMLibs GLOBAL PROPERTY ONNX_MLIR_LIBS)
-
 add_onnx_mlir_library(OMNNPACompilerOptions
   NNPACompilerOptions.cpp
 
@@ -12,7 +10,6 @@ add_onnx_mlir_library(OMNNPACompilerOptions
   ${NNPA_ONNX_MLIR_BIN_ROOT}
 
   LINK_LIBS PUBLIC
-  ${OMLibs}
   OMCompilerOptions
 
   ACCEL_INCLUDE_DIRS PRIVATE
@@ -32,7 +29,6 @@ add_onnx_mlir_library(OMNNPACompilerUtils
   ${NNPA_ONNX_MLIR_BIN_ROOT}
 
   LINK_LIBS PUBLIC
-  ${OMLibs}
   OMNNPACompilerOptions
   OMCompilerPasses
 

src/Accelerators/NNPA/Compiler/NNPACompilerOptions.cpp

@@ -55,6 +55,13 @@ llvm::cl::opt<bool> nnpaEnableCompilerStickUnstick(
                    "stick/unstick code. Default is false."),
     llvm::cl::init(false), llvm::cl::cat(OnnxMlirOptions));
 
+llvm::cl::opt<bool> nnpaEnableScalarBcastBinary(
+    "nnpa-enable-scalar-bcast-binary",
+    llvm::cl::desc("Enable the lowering to NNPA the broadcasting binary ops "
+                   "whose one of the operands is scalar. Currently support "
+                   "ONNXDiv only. Default is false."),
+    llvm::cl::init(false), llvm::cl::cat(OnnxMlirCommonOptions));
+
 llvm::cl::opt<std::string> nnpaLoadDevicePlacementFile{
     "nnpa-load-device-placement-file",
     llvm::cl::desc(

src/Accelerators/NNPA/Compiler/NNPACompilerOptions.hpp

@@ -49,11 +49,13 @@ typedef enum {
 } NNPAPlacementHeuristic;
 
 extern llvm::cl::OptionCategory OnnxMlirOptions;
+extern llvm::cl::OptionCategory OnnxMlirCommonOptions;
 extern llvm::cl::opt<onnx_mlir::NNPAEmissionTargetType> nnpaEmissionTarget;
 extern llvm::cl::opt<bool> nnpaClipToDLFloatRange;
 extern llvm::cl::opt<bool> nnpaEnableZHighToOnnx;
 extern llvm::cl::opt<bool> nnpaEnableZHighDecomposeStickUnstick;
 extern llvm::cl::opt<bool> nnpaEnableCompilerStickUnstick;
+extern llvm::cl::opt<bool> nnpaEnableScalarBcastBinary;
 extern llvm::cl::opt<NNPAPlacementHeuristic> nnpaPlacementHeuristic;
 extern llvm::cl::opt<bool> profileZHighIR;
 extern llvm::cl::opt<std::string> nnpaLoadDevicePlacementFile;

src/Accelerators/NNPA/Conversion/ONNXToZHigh/CMakeLists.txt

@@ -11,7 +11,7 @@ add_onnx_mlir_library(OMONNXToZHigh
   libzdnn
 
   LINK_LIBS PUBLIC
-  OMCompilerOptions
+  OMNNPACompilerOptions
   OMONNXOps
   OMONNXToKrnl
   OMZHighOps
@@ -32,7 +32,7 @@ add_onnx_mlir_library(OMRewriteONNXForZHigh
   libzdnn
 
   LINK_LIBS PUBLIC
-  OMCompilerOptions
+  OMNNPACompilerOptions
   OMONNXOps
   OMONNXToKrnl
   OMZHighOps

src/Accelerators/NNPA/Conversion/ONNXToZHigh/ONNXLegalityCheck.cpp

@@ -324,12 +324,19 @@ bool isSuitableForZDNN<ONNXDivOp>(
   // Check NNPA level.
   if (!isCompatibleWithNNPALevel(NNPA_Z16))
     return false;
-  if (!isF32ScalarConstantTensor(A) && !isValidElementTypeAndRank(A))
+  // Broadcast with a scalar operand.
+  if (isEnableScalarBcastBinary()) {
+    if (isF32ScalarConstantTensor(A) && isValidElementTypeAndRank(B))
+      return true;
+    if (isF32ScalarConstantTensor(B) && isValidElementTypeAndRank(A))
+      return true;
+  }
+  // Non-broadcast cases.
+  if (!isValidElementTypeAndRank(A))
     return false;
-  if (!isF32ScalarConstantTensor(B) && !isValidElementTypeAndRank(B))
+  if (!isValidElementTypeAndRank(B))
     return false;
-  return isF32ScalarConstantTensor(A) || isF32ScalarConstantTensor(B) ||
-         dimAnalysis->sameShape(A, B);
+  return dimAnalysis->sameShape(A, B);
 }
 
 /// Check legality for ONNXSum.

src/Accelerators/NNPA/Conversion/ONNXToZHigh/ONNXToZHigh.td

@@ -29,6 +29,8 @@ include "src/Accelerators/NNPA/Conversion/ONNXToZHigh/ONNXToZHighCommon.td"
 ///    dag benefitsAdded = (addBenefit 0)
 /// >;
 
+def IsEnableScalarBcastBinary: Constraint<CPred<"isEnableScalarBcastBinary()">>;
+
 def IsNoneType : Constraint<CPred<"(($_self).getType().isa<NoneType>())">>;
 
 def IsNotNoneType : Constraint<CPred<"(!($_self).getType().isa<NoneType>())">>;
@@ -227,7 +229,7 @@ def replaceONNXDivBroadcastPattern1 : Pat<
         (GetScalarF32AttrFromConstant $y),
         (NoneLayoutAttr)),
       (returnType $s_x))),
-  [(IsF32ScalarConstantTensor $y)], [],
+  [(IsEnableScalarBcastBinary), (IsF32ScalarConstantTensor $y)], [],
   (addBenefit 1)
 >;
 
@@ -241,7 +243,7 @@ def replaceONNXDivBroadcastPattern2 : Pat<
         (NoneLayoutAttr)),
       (ZHighStickOp:$s_y $y, (NoneLayoutAttr)),
       (returnType $s_y))),
-  [(IsF32ScalarConstantTensor $x)], [],
+  [(IsEnableScalarBcastBinary), (IsF32ScalarConstantTensor $x)], [],
   (addBenefit 1)
 >;
 

src/Accelerators/NNPA/Conversion/ONNXToZHigh/ONNXToZHighCommon.cpp

@@ -14,11 +14,14 @@
 //===----------------------------------------------------------------------===//
 
 #include "src/Accelerators/NNPA/Conversion/ONNXToZHigh/ONNXToZHighCommon.hpp"
+#include "src/Accelerators/NNPA/Compiler/NNPACompilerOptions.hpp"
 #include "src/Dialect/ONNX/DialectBuilder.hpp"
 
 using namespace mlir;
 namespace onnx_mlir {
 
+bool isEnableScalarBcastBinary() { return nnpaEnableScalarBcastBinary; }
+
 /// Get transposed tensor by using a permutation array.
 Value emitONNXTranspose(
     Location loc, PatternRewriter &rewriter, Value x, ArrayRef<int64_t> perms) {

src/Accelerators/NNPA/Conversion/ONNXToZHigh/ONNXToZHighCommon.hpp

@@ -27,6 +27,8 @@ const std::string DEVICE_ATTRIBUTE = "device";
 const std::string CPU_DEVICE = "cpu";
 const std::string NNPA_DEVICE = "nnpa";
 
+bool isEnableScalarBcastBinary();
+
 template <typename OP_TYPE>
 void addDynamicallyLegalOpFor(mlir::ConversionTarget *target,
     const onnx_mlir::DimAnalysis *dimAnalysis,

src/Accelerators/NNPA/Pass/NNPAPasses.hpp

@@ -29,11 +29,9 @@ std::unique_ptr<mlir::Pass> createDevicePlacementPass(
 
 /// Add pass for lowering ONNX ops to ZHigh ops.
 std::unique_ptr<mlir::Pass> createONNXToZHighPass();
-std::unique_ptr<mlir::Pass> createONNXToZHighPass();
 
 /// Add pass for rewriting ONNX ops for ZHigh.
 std::unique_ptr<mlir::Pass> createRewriteONNXForZHighPass();
-std::unique_ptr<mlir::Pass> createRewriteONNXForZHighPass();
 
 /// Add pass for re-construct ONNX ops from ZHigh ops.
 std::unique_ptr<mlir::Pass> createZHighToONNXPass();

src/Conversion/KrnlToAffine/KrnlGetLinearOffsetIndex.cpp

@@ -53,7 +53,7 @@ class KrnlGetLinearOffsetIndexLowering : public ConversionPattern {
 
     auto memrefTy = llvm::dyn_cast<MemRefType>(memref.getType());
     int64_t rank = memrefTy.getRank();
-    assert(mapResults.value().size() == rank && "Invalid indices");
+    assert((int64_t)mapResults.value().size() == rank && "Invalid indices");
 
     // Only lower this op after the memref is normalized.
     if (!memrefTy.getLayout().isIdentity())

test/mlir/accelerators/nnpa/conversion/onnx-to-zhigh/div-bcast.mlir

@@ -0,0 +1,44 @@
+// RUN: onnx-mlir-opt --mcpu=z16 --maccel=NNPA --shape-inference --convert-onnx-to-zhigh --nnpa-enable-scalar-bcast-binary %s -split-input-file | FileCheck %s
+
+// COM: Division by a scalar in case of dynamic dimensions.
+func.func @test_div_unknown_scalar1(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
+  %0 = onnx.Constant dense<8.000000e+00> : tensor<f32>
+  %1 = "onnx.Div"(%arg0, %0) : (tensor<?x10xf32>, tensor<f32>) -> tensor<*xf32>
+  "func.return"(%1) : (tensor<*xf32>) -> ()
+
+// CHECK-LABEL:  func.func @test_div_unknown_scalar1
+// CHECK-SAME:   ([[PARAM_0_:%.+]]: tensor<?x10xf32>) -> tensor<?x10xf32> {
+// CHECK-DAG:       [[VAR_0_:%.+]] = onnx.Constant dense<8.000000e+00> : tensor<f32>
+// CHECK-DAG:       [[VAR_1_:%.+]] = "zhigh.Stick"([[PARAM_0_]]) {layout = "2D"} : (tensor<?x10xf32>) -> tensor<?x10xf16, #zhigh.layout<{dataLayout = "2D"}>>
+// CHECK-DAG:       [[VAR_2_:%.+]] = "onnx.Dim"([[PARAM_0_]]) {axis = 0 : si64} : (tensor<?x10xf32>) -> tensor<1xi64>
+// CHECK-DAG:       [[VAR_3_:%.+]] = "onnx.Dim"([[PARAM_0_]]) {axis = 1 : si64} : (tensor<?x10xf32>) -> tensor<1xi64>
+// CHECK:           [[VAR_4_:%.+]] = "onnx.Concat"([[VAR_2_]], [[VAR_3_]]) {axis = 0 : si64} : (tensor<1xi64>, tensor<1xi64>) -> tensor<2xi64>
+// CHECK:           [[VAR_5_:%.+]] = "zhigh.StickifiedConstantOfShape"([[VAR_4_]]) {layout = "2D", value = 8.000000e+00 : f32} : (tensor<2xi64>) -> tensor<?x?xf16, #zhigh.layout<{dataLayout = "2D"}>>
+// CHECK:           [[VAR_6_:%.+]] = "zhigh.Div"([[VAR_1_]], [[VAR_5_]]) : (tensor<?x10xf16, #zhigh.layout<{dataLayout = "2D"}>>, tensor<?x?xf16, #zhigh.layout<{dataLayout = "2D"}>>) -> tensor<?x10xf16, #zhigh.layout<{dataLayout = "2D"}>>
+// CHECK:           [[VAR_7_:%.+]] = "zhigh.Unstick"([[VAR_6_]]) : (tensor<?x10xf16, #zhigh.layout<{dataLayout = "2D"}>>) -> tensor<?x10xf32>
+// CHECK:           return [[VAR_7_]] : tensor<?x10xf32>
+// CHECK:         }
+}
+
+// -----
+
+// COM: Division by a scalar in case of dynamic dimensions.
+func.func @test_div_unknown_scalar2(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
+  %0 = onnx.Constant dense<8.000000e+00> : tensor<f32>
+  %1 = "onnx.Div"(%0, %arg0) : (tensor<f32>, tensor<?x10xf32>) -> tensor<*xf32>
+  "func.return"(%1) : (tensor<*xf32>) -> ()
+
+// CHECK-LABEL:  func.func @test_div_unknown_scalar2
+// CHECK-SAME:   ([[PARAM_0_:%.+]]: tensor<?x10xf32>) -> tensor<?x10xf32> {
+// CHECK-DAG:       [[VAR_0_:%.+]] = onnx.Constant dense<8.000000e+00> : tensor<f32>
+// CHECK-DAG:       [[VAR_1_:%.+]] = "onnx.Dim"([[PARAM_0_]]) {axis = 0 : si64} : (tensor<?x10xf32>) -> tensor<1xi64>
+// CHECK-DAG:       [[VAR_2_:%.+]] = "onnx.Dim"([[PARAM_0_]]) {axis = 1 : si64} : (tensor<?x10xf32>) -> tensor<1xi64>
+// CHECK:           [[VAR_3_:%.+]] = "onnx.Concat"([[VAR_1_]], [[VAR_2_]]) {axis = 0 : si64} : (tensor<1xi64>, tensor<1xi64>) -> tensor<2xi64>
+// CHECK-DAG:       [[VAR_4_:%.+]] = "zhigh.StickifiedConstantOfShape"([[VAR_3_]]) {layout = "2D", value = 8.000000e+00 : f32} : (tensor<2xi64>) -> tensor<?x?xf16, #zhigh.layout<{dataLayout = "2D"}>>
+// CHECK-DAG:       [[VAR_5_:%.+]] = "zhigh.Stick"([[PARAM_0_]]) {layout = "2D"} : (tensor<?x10xf32>) -> tensor<?x10xf16, #zhigh.layout<{dataLayout = "2D"}>>
+// CHECK:           [[VAR_6_:%.+]] = "zhigh.Div"([[VAR_4_]], [[VAR_5_]]) : (tensor<?x?xf16, #zhigh.layout<{dataLayout = "2D"}>>, tensor<?x10xf16, #zhigh.layout<{dataLayout = "2D"}>>) -> tensor<?x10xf16, #zhigh.layout<{dataLayout = "2D"}>>
+// CHECK:           [[VAR_7_:%.+]] = "zhigh.Unstick"([[VAR_6_]]) : (tensor<?x10xf16, #zhigh.layout<{dataLayout = "2D"}>>) -> tensor<?x10xf32>
+// CHECK:           return [[VAR_7_]] : tensor<?x10xf32>
+// CHECK:         }
+}
+

test/mlir/accelerators/nnpa/conversion/onnx-to-zhigh/div.mlir

@@ -32,50 +32,6 @@ func.func @test_div_3ds(%arg0 : tensor<10x10x10xf32>, %arg1 : tensor<10x10x10xf3
 
 // -----
 
-// COM: Division by a scalar in case of dynamic dimensions.
-func.func @test_div_unknown_scalar1(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
-  %0 = onnx.Constant dense<8.000000e+00> : tensor<f32>
-  %1 = "onnx.Div"(%arg0, %0) : (tensor<?x10xf32>, tensor<f32>) -> tensor<*xf32>
-  "func.return"(%1) : (tensor<*xf32>) -> ()
-
-// CHECK-LABEL:  func.func @test_div_unknown_scalar1
-// CHECK-SAME:   ([[PARAM_0_:%.+]]: tensor<?x10xf32>) -> tensor<?x10xf32> {
-// CHECK-DAG:       [[VAR_0_:%.+]] = onnx.Constant dense<8.000000e+00> : tensor<f32>
-// CHECK-DAG:       [[VAR_1_:%.+]] = "zhigh.Stick"([[PARAM_0_]]) {layout = "2D"} : (tensor<?x10xf32>) -> tensor<?x10xf16, #zhigh.layout<{dataLayout = "2D"}>>
-// CHECK-DAG:       [[VAR_2_:%.+]] = "onnx.Dim"([[PARAM_0_]]) {axis = 0 : si64} : (tensor<?x10xf32>) -> tensor<1xi64>
-// CHECK-DAG:       [[VAR_3_:%.+]] = "onnx.Dim"([[PARAM_0_]]) {axis = 1 : si64} : (tensor<?x10xf32>) -> tensor<1xi64>
-// CHECK:           [[VAR_4_:%.+]] = "onnx.Concat"([[VAR_2_]], [[VAR_3_]]) {axis = 0 : si64} : (tensor<1xi64>, tensor<1xi64>) -> tensor<2xi64>
-// CHECK:           [[VAR_5_:%.+]] = "zhigh.StickifiedConstantOfShape"([[VAR_4_]]) {layout = "2D", value = 8.000000e+00 : f32} : (tensor<2xi64>) -> tensor<?x?xf16, #zhigh.layout<{dataLayout = "2D"}>>
-// CHECK:           [[VAR_6_:%.+]] = "zhigh.Div"([[VAR_1_]], [[VAR_5_]]) : (tensor<?x10xf16, #zhigh.layout<{dataLayout = "2D"}>>, tensor<?x?xf16, #zhigh.layout<{dataLayout = "2D"}>>) -> tensor<?x10xf16, #zhigh.layout<{dataLayout = "2D"}>>
-// CHECK:           [[VAR_7_:%.+]] = "zhigh.Unstick"([[VAR_6_]]) : (tensor<?x10xf16, #zhigh.layout<{dataLayout = "2D"}>>) -> tensor<?x10xf32>
-// CHECK:           return [[VAR_7_]] : tensor<?x10xf32>
-// CHECK:         }
-}
-
-// -----
-
-// COM: Division by a scalar in case of dynamic dimensions.
-func.func @test_div_unknown_scalar2(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
-  %0 = onnx.Constant dense<8.000000e+00> : tensor<f32>
-  %1 = "onnx.Div"(%0, %arg0) : (tensor<f32>, tensor<?x10xf32>) -> tensor<*xf32>
-  "func.return"(%1) : (tensor<*xf32>) -> ()
-
-// CHECK-LABEL:  func.func @test_div_unknown_scalar2
-// CHECK-SAME:   ([[PARAM_0_:%.+]]: tensor<?x10xf32>) -> tensor<?x10xf32> {
-// CHECK-DAG:       [[VAR_0_:%.+]] = onnx.Constant dense<8.000000e+00> : tensor<f32>
-// CHECK-DAG:       [[VAR_1_:%.+]] = "onnx.Dim"([[PARAM_0_]]) {axis = 0 : si64} : (tensor<?x10xf32>) -> tensor<1xi64>
-// CHECK-DAG:       [[VAR_2_:%.+]] = "onnx.Dim"([[PARAM_0_]]) {axis = 1 : si64} : (tensor<?x10xf32>) -> tensor<1xi64>
-// CHECK:           [[VAR_3_:%.+]] = "onnx.Concat"([[VAR_1_]], [[VAR_2_]]) {axis = 0 : si64} : (tensor<1xi64>, tensor<1xi64>) -> tensor<2xi64>
-// CHECK-DAG:       [[VAR_4_:%.+]] = "zhigh.StickifiedConstantOfShape"([[VAR_3_]]) {layout = "2D", value = 8.000000e+00 : f32} : (tensor<2xi64>) -> tensor<?x?xf16, #zhigh.layout<{dataLayout = "2D"}>>
-// CHECK-DAG:       [[VAR_5_:%.+]] = "zhigh.Stick"([[PARAM_0_]]) {layout = "2D"} : (tensor<?x10xf32>) -> tensor<?x10xf16, #zhigh.layout<{dataLayout = "2D"}>>
-// CHECK:           [[VAR_6_:%.+]] = "zhigh.Div"([[VAR_4_]], [[VAR_5_]]) : (tensor<?x?xf16, #zhigh.layout<{dataLayout = "2D"}>>, tensor<?x10xf16, #zhigh.layout<{dataLayout = "2D"}>>) -> tensor<?x10xf16, #zhigh.layout<{dataLayout = "2D"}>>
-// CHECK:           [[VAR_7_:%.+]] = "zhigh.Unstick"([[VAR_6_]]) : (tensor<?x10xf16, #zhigh.layout<{dataLayout = "2D"}>>) -> tensor<?x10xf32>
-// CHECK:           return [[VAR_7_]] : tensor<?x10xf32>
-// CHECK:         }
-}
-
-// -----
-
 // COM:  Do not lower broadcasting onnx.Div to zHigh.
 func.func @test_div_not_lowered_diff_shape(%arg0 : tensor<10x10xf32>, %arg1 : tensor<10xf32>) -> tensor<*xf32> {
   %0 = "onnx.Div"(%arg0, %arg1) : (tensor<10x10xf32>, tensor<10xf32>) -> tensor<*xf32>

test/mlir/accelerators/nnpa/driver/matmul-div-in-attention-layer.mlir

@@ -1,4 +1,4 @@
-// RUN: onnx-mlir --mcpu=z16 --maccel=NNPA --EmitMLIR --printIR %s | FileCheck %s
+// RUN: onnx-mlir --mcpu=z16 --maccel=NNPA --EmitMLIR --nnpa-enable-scalar-bcast-binary --printIR %s | FileCheck %s
 
 // Check whether the compiler can remove unstick/stick so that the output of zdnn matmul is passed directly to zdnn div.
 func.func @matmul_div(%arg0: tensor<?x12x?x64xf32>) -> tensor<?x?x?x?xf32> {