Fix torchToTosa lowering for avgpool2d to handle unsupported parameters (#3822)

The existing TorchToTosa lowering logic for `torch.aten.avg_pool2d`
doesn't handle some unsupported properties well, leading to a silent
wrong answer(SWA) when we go through
`torch-backend-to-tosa-backend-pipeline.` For instance, with the
existing TOSA avgpool2d specification, we can not represent
`count_include_pad` and `divisor_override,` so during TorchToTosa
lowering, we silently ignore these properties which leads to SWA in some
cases—the fix captured in this change errors for unsupported scenarios.

For details on `count_include_pad` and `divisor_override,` please see
the below link.

https://pytorch.org/docs/stable/generated/torch.nn.AvgPool2d.html

---------

Co-authored-by: Hanumanth Hanumantharayappa <hhanuman@ah-hhanuman-l.dhcp.mathworks.com>

lib/Conversion/TorchToTosa/TorchToTosa.cpp

@@ -5466,6 +5466,28 @@ public:
                               DenseI64ArrayAttr &kernel,
                               DenseI64ArrayAttr &stride, DenseI64ArrayAttr &pad,
                               Type &outputTy) const override {
+
+    // Currently, we can not represent `count_include_pad` with the existing
+    // TOSA AvgPool2d specification. Without the below check, we produce silent
+    // wrong answers (SWA) when the `count_include_pad` value is `true.`
+    bool countIncludePad;
+    if (!matchPattern(op.getCountIncludePad(),
+                      m_TorchConstantBool(&countIncludePad)) ||
+        countIncludePad) {
+      return rewriter.notifyMatchFailure(
+          op, "Unsupported `count_include_pad` value, for tosa AvgPool2dOp "
+              "`count_include_pad` value should be `False`.");
+    }
+
+    // Currently, we can not represent `divisor_override` with the existing TOSA
+    // AvgPool2d specification. Without the below check, we produce silent wrong
+    // answers (SWA) when the `divisor_override` value is other than `None.`
+    if (!isa<Torch::NoneType>(op.getDivisorOverride().getType())) {
+      return rewriter.notifyMatchFailure(
+          op, "Unsupported `divisor_override` value, for tosa AvgPool2dOp "
+              "`divisor_override` value should be `None`.");
+    }
+
     SmallVector<int64_t, 2> dilationArray{1, 1};
     if (failed(getOutputTypeAndPoolingParameters<AtenAvgPool2dOp,
                                                  tosa::AvgPool2dOp>(

test/Conversion/TorchToTosa/basic.mlir

@@ -852,37 +852,35 @@ func.func @torch.aten.dropout$basic(%arg0: !torch.vtensor<[?,?],f32> ) -> !torch
 // -----
 
 // CHECK-LABEL:   func.func @torch.aten.avg_pool2d$basic(
-// CHECK-SAME:                                   %[[VAL_0:.*]]: !torch.vtensor<[1,512,7,7],f32>) -> !torch.vtensor<[1,512,1,1],f32> {
+// CHECK-SAME:                                           %[[VAL_0:.*]]: !torch.vtensor<[1,512,7,7],f32>) -> !torch.vtensor<[1,512,1,1],f32> {
 // CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[1,512,7,7],f32> -> tensor<1x512x7x7xf32>
 // CHECK:           %[[VAL_2:.*]] = torch.constant.int 7
 // CHECK:           %[[VAL_3:.*]] = torch.constant.int 1
 // CHECK:           %[[VAL_4:.*]] = torch.constant.int 0
 // CHECK:           %[[VAL_5:.*]] = torch.constant.bool false
-// CHECK:           %[[VAL_6:.*]] = torch.constant.bool true
+// CHECK:           %[[VAL_6:.*]] = torch.constant.none
-// CHECK:           %[[VAL_7:.*]] = torch.constant.none
+// CHECK:           %[[VAL_7:.*]] = torch.prim.ListConstruct %[[VAL_2]], %[[VAL_2]] : (!torch.int, !torch.int) -> !torch.list<int>
-// CHECK:           %[[VAL_8:.*]] = torch.prim.ListConstruct %[[VAL_2]], %[[VAL_2]] : (!torch.int, !torch.int) -> !torch.list<int>
+// CHECK:           %[[VAL_8:.*]] = torch.prim.ListConstruct %[[VAL_3]], %[[VAL_3]] : (!torch.int, !torch.int) -> !torch.list<int>
-// CHECK:           %[[VAL_9:.*]] = torch.prim.ListConstruct %[[VAL_3]], %[[VAL_3]] : (!torch.int, !torch.int) -> !torch.list<int>
+// CHECK:           %[[VAL_9:.*]] = torch.prim.ListConstruct %[[VAL_4]], %[[VAL_4]] : (!torch.int, !torch.int) -> !torch.list<int>
-// CHECK:           %[[VAL_10:.*]] = torch.prim.ListConstruct %[[VAL_4]], %[[VAL_4]] : (!torch.int, !torch.int) -> !torch.list<int>
+// CHECK:           %[[VAL_10:.*]] = "tosa.const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi32>}> : () -> tensor<4xi32>
-// CHECK:           %[[VAL_11:.*]] = "tosa.const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi32>}> : () -> tensor<4xi32>
+// CHECK:           %[[VAL_11:.*]] = tosa.transpose %[[VAL_1]], %[[VAL_10]] : (tensor<1x512x7x7xf32>, tensor<4xi32>) -> tensor<1x7x7x512xf32>
-// CHECK:           %[[VAL_12:.*]] = tosa.transpose %[[VAL_1]], %[[VAL_11]] : (tensor<1x512x7x7xf32>, tensor<4xi32>) -> tensor<1x7x7x512xf32>
+// CHECK:           %[[VAL_12:.*]] = tosa.avg_pool2d %[[VAL_11]] {acc_type = f32, kernel = array<i64: 7, 7>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x7x7x512xf32>) -> tensor<1x1x1x512xf32>
-// CHECK:           %[[VAL_13:.*]] = tosa.avg_pool2d %[[VAL_12]] {acc_type = f32, kernel = array<i64: 7, 7>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x7x7x512xf32>) -> tensor<1x1x1x512xf32>
+// CHECK:           %[[VAL_13:.*]] = "tosa.const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}> : () -> tensor<4xi32>
-// CHECK:           %[[VAL_14:.*]] = "tosa.const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}> : () -> tensor<4xi32>
+// CHECK:           %[[VAL_14:.*]] = tosa.transpose %[[VAL_12]], %[[VAL_13]] : (tensor<1x1x1x512xf32>, tensor<4xi32>) -> tensor<1x512x1x1xf32>
-// CHECK:           %[[VAL_15:.*]] = tosa.transpose %[[VAL_13]], %[[VAL_14]] : (tensor<1x1x1x512xf32>, tensor<4xi32>) -> tensor<1x512x1x1xf32>
+// CHECK:           %[[VAL_15:.*]] = tensor.cast %[[VAL_14]] : tensor<1x512x1x1xf32> to tensor<1x512x1x1xf32>
-// CHECK:           %[[VAL_16:.*]] = tensor.cast %[[VAL_15]] : tensor<1x512x1x1xf32> to tensor<1x512x1x1xf32>
+// CHECK:           %[[VAL_16:.*]] = torch_c.from_builtin_tensor %[[VAL_15]] : tensor<1x512x1x1xf32> -> !torch.vtensor<[1,512,1,1],f32>
-// CHECK:           %[[VAL_17:.*]] = torch_c.from_builtin_tensor %[[VAL_16]] : tensor<1x512x1x1xf32> -> !torch.vtensor<[1,512,1,1],f32>
+// CHECK:           return %[[VAL_16]] : !torch.vtensor<[1,512,1,1],f32>
-// CHECK:           return %[[VAL_17]] : !torch.vtensor<[1,512,1,1],f32>
 // CHECK:         }
 func.func @torch.aten.avg_pool2d$basic(%arg0: !torch.vtensor<[1,512,7,7],f32> ) -> !torch.vtensor<[1,512,1,1],f32> {
   %int7 = torch.constant.int 7
   %int1 = torch.constant.int 1
   %int0 = torch.constant.int 0
   %false = torch.constant.bool false
-  %true = torch.constant.bool true
   %none = torch.constant.none
   %kernel = torch.prim.ListConstruct %int7, %int7 : (!torch.int, !torch.int) -> !torch.list<int>
   %stride = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
   %padding = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
-  %0 = torch.aten.avg_pool2d %arg0, %kernel, %stride, %padding, %false, %true, %none : !torch.vtensor<[1,512,7,7],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[1,512,1,1],f32>
+  %0 = torch.aten.avg_pool2d %arg0, %kernel, %stride, %padding, %false, %false, %none : !torch.vtensor<[1,512,7,7],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[1,512,1,1],f32>
   return %0 : !torch.vtensor<[1,512,1,1],f32>
 }
 
@@ -2001,6 +1999,42 @@ func.func @torch.aten.round(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtenso
 
 // -----
 
+func.func @torch.aten.avg_pool2d.count_include_pad_unsupported_value(%arg0: !torch.vtensor<[1,192,35,35],f32>) -> !torch.vtensor<[1,192,35,35],f32> {
+  %int0 = torch.constant.int 0
+  %int1 = torch.constant.int 1
+  %int3 = torch.constant.int 3
+  %false= torch.constant.bool false
+  %count_include_pad = torch.constant.bool true
+  %divisor_override = torch.constant.none
+
+  %0 = torch.prim.ListConstruct %int3, %int3 : (!torch.int, !torch.int) -> !torch.list<int>
+  %1 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
+  %2 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
+  // expected-error @+1 {{failed to legalize operation 'torch.aten.avg_pool2d' that was explicitly marked illegal}}
+  %3 = torch.aten.avg_pool2d %arg0, %0, %1, %2, %false, %count_include_pad, %divisor_override : !torch.vtensor<[1,192,35,35],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[1,192,35,35],f32>
+  return %3 : !torch.vtensor<[1,192,35,35],f32>
+}
+
+// -----
+
+func.func @torch.aten.avg_pool2d.divisor_override_unsupported_value(%arg0: !torch.vtensor<[1,192,35,35],f32>) -> !torch.vtensor<[1,192,35,35],f32> {
+  %int0 = torch.constant.int 0
+  %int1 = torch.constant.int 1
+  %int3 = torch.constant.int 3
+  %false= torch.constant.bool false
+  %count_include_pad = torch.constant.bool false
+  %divisor_override = torch.constant.int 9
+
+  %0 = torch.prim.ListConstruct %int3, %int3 : (!torch.int, !torch.int) -> !torch.list<int>
+  %1 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
+  %2 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
+  // expected-error @+1 {{failed to legalize operation 'torch.aten.avg_pool2d' that was explicitly marked illegal}}
+  %3 = torch.aten.avg_pool2d %arg0, %0, %1, %2, %false, %count_include_pad, %divisor_override : !torch.vtensor<[1,192,35,35],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.bool, !torch.int -> !torch.vtensor<[1,192,35,35],f32>
+  return %3 : !torch.vtensor<[1,192,35,35],f32>
+}
+
+// -----
+
 // CHECK-LABEL:   func.func @torch.aten.empty.memory_format$basic() -> !torch.vtensor<[3,4],si64> {
 // CHECK:           %[[VAL_0:.*]] = torch.constant.int 0
 // CHECK:           %[[VAL_1:.*]] = torch.constant.bool false