[NNPA] Not using either in tablegen (onnx#2607)

* Not using either Signed-off-by: Tung D. Le <[email protected]> * add lit tests Signed-off-by: Tung D. Le <[email protected]> * Undo changes in RunONNXModel.py Signed-off-by: Tung D. Le <[email protected]> --------- Signed-off-by: Tung D. Le <[email protected]>
chentong319 · Nov 8, 2023 · 70358dd · 70358dd
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commit 70358dd
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diff --git a/src/Accelerators/NNPA/Conversion/ONNXToZHigh/ONNXToZHigh.td b/src/Accelerators/NNPA/Conversion/ONNXToZHigh/ONNXToZHigh.td
@@ -217,8 +217,8 @@ def replaceONNXDivPattern : Pat<
   (addBenefit 0)
 >;
 
-def replaceONNXDivBroadcastPattern : Pat<
-  (ONNXDivOp (either $x, $y)),
+def replaceONNXDivBroadcastPattern1 : Pat<
+  (ONNXDivOp $x, $y),
   (ZHighUnstickOp
     (ZHighDivOp
       (ZHighStickOp:$s_x $x, (NoneLayoutAttr)),
@@ -231,6 +231,20 @@ def replaceONNXDivBroadcastPattern : Pat<
   (addBenefit 1)
 >;
 
+def replaceONNXDivBroadcastPattern2 : Pat<
+  (ONNXDivOp $x, $y),
+  (ZHighUnstickOp
+    (ZHighDivOp
+      (ZHighStickifiedConstantOfShapeOp
+        (GetDynShape $y),
+        (GetScalarF32AttrFromConstant $x),
+        (NoneLayoutAttr)),
+      (ZHighStickOp:$s_y $y, (NoneLayoutAttr)),
+      (returnType $s_y))),
+  [(IsF32ScalarConstantTensor $x)], [],
+  (addBenefit 1)
+>;
+
 //===----------------------------------------------------------------------===//
 // ONNXLogOp %X = ZHighUnstickOp (ZHighLogOp (ZHighStickOp %X))
 //===----------------------------------------------------------------------===//

diff --git a/test/mlir/accelerators/nnpa/conversion/onnx-to-zhigh/div.mlir b/test/mlir/accelerators/nnpa/conversion/onnx-to-zhigh/div.mlir
@@ -33,12 +33,12 @@ 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_scalar(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
+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_scalar
+// 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<?x10xf32, #zhigh.layout<{dataLayout = "2D"}>>
@@ -54,6 +54,28 @@ func.func @test_div_unknown_scalar(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
 
 // -----
 
+// 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?xf32, #zhigh.layout<{dataLayout = "2D"}>>
+// CHECK-DAG:       [[VAR_5_:%.+]] = "zhigh.Stick"([[PARAM_0_]]) {layout = "2D"} : (tensor<?x10xf32>) -> tensor<?x10xf32, #zhigh.layout<{dataLayout = "2D"}>>
+// CHECK:           [[VAR_6_:%.+]] = "zhigh.Div"([[VAR_4_]], [[VAR_5_]]) : (tensor<?x?xf32, #zhigh.layout<{dataLayout = "2D"}>>, tensor<?x10xf32, #zhigh.layout<{dataLayout = "2D"}>>) -> tensor<?x10xf32, #zhigh.layout<{dataLayout = "2D"}>>
+// CHECK:           [[VAR_7_:%.+]] = "zhigh.Unstick"([[VAR_6_]]) : (tensor<?x10xf32, #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>