submit the llvm#3902 to local repo (#5)

* Decompose lstm and gru.

* Add tests and update xfail_sets.py

* Rebase main

* Fix casting for arith.cmpi operands to be of same type.

Author: sahas3

lib/Conversion/TorchToLinalg/IndirectDataMovement.cpp

@@ -417,6 +417,21 @@ class ConvertAtenEmbeddingBagPaddingIdxOp
 };
 } // namespace
 
+static Value wrapIndicesAroundMax(OpBuilder &b, Location loc, Value index,
+                                  Value input, int64_t dim) {
+  // performs the operation : index = index % maxIndex to wrap index around
+  // maxIndex
+  Value maxIndexValue = getDimOp(b, loc, input, dim);
+  maxIndexValue =
+      b.createOrFold<arith::IndexCastOp>(loc, index.getType(), maxIndexValue);
+  Value isBeyondMaxIndices = b.createOrFold<arith::CmpIOp>(
+      loc, arith::CmpIPredicate::sge, index, maxIndexValue);
+  Value wrappedIndices =
+      b.createOrFold<arith::RemSIOp>(loc, index, maxIndexValue);
+  return b.createOrFold<arith::SelectOp>(loc, isBeyondMaxIndices,
+                                         wrappedIndices, index);
+}
+
 namespace {
 // Let's say we have an input tensor: initialized with some random values of
 // size [4, 5, 6]. An index tensor (always 1-d): [0, 2] of size [2], and an
@@ -478,16 +493,17 @@ class ConvertAtenIndexSelectOp : public OpConversionPattern<AtenIndexSelectOp> {
 
     auto indexingMaps = AffineMap::inferFromExprList({indicesExpr, resultExpr},
                                                      rewriter.getContext());
-
     Value finalRes =
         rewriter
             .create<linalg::GenericOp>(
                 loc, initTensor.getType(), ValueRange{indices}, initTensor,
                 /*indexingMaps=*/indexingMaps,
                 /*iteratorTypes=*/iteratorTypes,
                 [&](OpBuilder &b, Location loc, ValueRange args) {
-                  Value index = rewriter.create<arith::IndexCastOp>(
-                      loc, rewriter.getIndexType(), args[0]);
+                  Value index =
+                      wrapIndicesAroundMax(b, loc, args[0], input, dimInt);
+                  index = rewriter.create<arith::IndexCastOp>(
+                      loc, rewriter.getIndexType(), index);
                   SmallVector<Value> indexTarget;
                   for (unsigned i = 0; i < inputRank; i++)
                     indexTarget.push_back(b.create<linalg::IndexOp>(loc, i));

lib/Conversion/TorchToTosa/TorchToTosa.cpp

@@ -4023,6 +4023,41 @@ LogicalResult ConvertAtenOp<AtenGatherOp>::matchAndRewrite(
   return success();
 }
 
+Value wrapIndicesAroundMax(Value index, int maxIndex, Operation *op,
+                           ConversionPatternRewriter &rewriter) {
+  // performs the operation : index = index % maxIndex to wrap index around
+  // maxIndex
+
+  auto maxIndexValue =
+      tosa::getConstTensor<int32_t>(rewriter, op, maxIndex, {}).value();
+  auto maxIndexValueMinusOne =
+      tosa::getConstTensor<int32_t>(rewriter, op, maxIndex - 1, {}).value();
+
+  auto indexType = dyn_cast<RankedTensorType>(index.getType());
+  auto boolType = indexType.clone(rewriter.getIntegerType(1));
+
+  auto isBeyondMaxIndices = tosa::CreateOpAndInfer<tosa::GreaterOp>(
+      rewriter, op->getLoc(), boolType, index, maxIndexValueMinusOne);
+  auto wrappedBeyondMaxIndicesQuotient =
+      tosa::CreateOpAndInfer<tosa::IntDivOp>(rewriter, op->getLoc(), indexType,
+                                             index, maxIndexValue)
+          .getResult();
+  auto wrappedBeyondMaxIndicesQuotientTimesIndices =
+      tosa::CreateOpAndInfer<tosa::MulOp>(rewriter, op->getLoc(), indexType,
+                                          wrappedBeyondMaxIndicesQuotient,
+                                          maxIndexValue, /*shift=*/0)
+          .getResult();
+  auto wrappedBeyondMaxIndices =
+      tosa::CreateOpAndInfer<tosa::SubOp>(
+          rewriter, op->getLoc(), indexType, index,
+          wrappedBeyondMaxIndicesQuotientTimesIndices)
+          .getResult();
+
+  return tosa::CreateOpAndInfer<tosa::SelectOp>(rewriter, op->getLoc(),
+                                                indexType, isBeyondMaxIndices,
+                                                wrappedBeyondMaxIndices, index);
+}
+
 template <>
 LogicalResult ConvertAtenOp<AtenIndexSelectOp>::matchAndRewrite(
     AtenIndexSelectOp op, OpAdaptor adaptor,
@@ -4066,6 +4101,10 @@ LogicalResult ConvertAtenOp<AtenIndexSelectOp>::matchAndRewrite(
         RankedTensorType::get(indexShape, rewriter.getIntegerType(32)), index);
   }
 
+  int64_t selfNumElems = std::accumulate(inputShape.begin(), inputShape.end(),
+                                         1, std::multiplies<int64_t>());
+  index = wrapIndicesAroundMax(index, selfNumElems, op, rewriter);
+
   // Get positive dim
   int64_t dim;
   if (!matchPattern(op.getDim(), m_TorchConstantInt(&dim)))
@@ -7266,10 +7305,12 @@ LogicalResult ConvertAtenOp<AtenAsStridedOp>::matchAndRewrite(
     //              coord_i_n * stride[n]
     int32_t index = offset;
     int64_t coordFinder = i;
+
     for (int64_t dim = 0; dim < outputRank; dim++) {
       int64_t indexCoord = coordFinder % outputSize[outputRank - dim - 1];
       index += indexCoord * stride[outputRank - dim - 1];
       coordFinder /= outputSize[outputRank - dim - 1];
+      index = (index % selfNumElems);
     }
     targetIndicesVec.push_back(index);
   }

projects/pt1/e2e_testing/xfail_sets.py

@@ -484,6 +484,7 @@
     "SplitTensorNegativeDimModule_basic",
     "SplitWithSizesListUnpackModule_basic",
     "SplitWithSizes_Module_basic",
+    "AsStridedWithOffsetModule_basic",
     "AdaptiveAvgPool1dGeneralDynamic_basic",
     "AdaptiveAvgPool1dStaticEvenMultiple_basic",
     "AdaptiveAvgPool1dStaticLargerOutput_basic",
@@ -895,6 +896,7 @@
     "SplitTensorNegativeDimModule_basic",
     "SplitWithSizesListUnpackModule_basic",
     "SplitWithSizes_Module_basic",
+    "AsStridedWithOffsetModule_basic",
     "Unfold_Module_basic",
     "Unfold_Module_Rank_4",
     "Unfold_Module_Rank_Zero_basic",
@@ -1784,6 +1786,7 @@
     "AdaptiveAvgPool2dNonUnitOutputSizeStaticModule_basic",
     "AdaptiveAvgPool2dOutputSizeDivisibleByInputStaticModule_basic",
     "AdaptiveAvgPool2dUnitOutputSizeStaticModule_basic",
+    "AsStridedWithOffsetModule_basic",
     "ElementwiseAtenLogicalNotOpPromoteModule_basic",
     "ElementwiseCosIntModule_basic",
     "ElementwiseReciprocalIntModule_basic",

projects/pt1/python/torch_mlir_e2e_test/test_suite/slice_like.py

@@ -1144,3 +1144,32 @@ def forward(self, x):
 @register_test_case(module_factory=lambda: TensorSplitSections_ListUnpackModule())
 def TensorSplitSections_ListUnpackModule_basic(module, tu: TestUtils):
     module.forward(tu.rand(2, 5))
+
+
+# ==============================================================================
+
+
+class AsStridedWithOffsetModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([2, 6, 60], torch.float32, True),
+        ]
+    )
+    def forward(self, x):
+        output_size = [6, 20]
+        stride = [60, 1]
+        slice = torch.ops.aten.slice.Tensor(x, 0, 1, 2)
+        squeeze = torch.ops.aten.squeeze.dim(slice, 0)
+        return torch.ops.aten.as_strided(
+            squeeze, size=output_size, stride=stride, storage_offset=360
+        )
+
+
+@register_test_case(module_factory=lambda: AsStridedWithOffsetModule())
+def AsStridedWithOffsetModule_basic(module, tu: TestUtils):
+    module.forward(torch.rand(2, 6, 60))

test/Conversion/TorchToTosa/basic.mlir

@@ -1918,22 +1918,29 @@ func.func @torch.aten.diagonal$basic(%arg0: !torch.vtensor<[3,4,5,6], si32>) ->
 // CHECK:           %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,5,6],f32> -> tensor<4x5x6xf32>
 // CHECK:           %[[VAL_4:.*]] = torch.constant.int 2
 // CHECK:           %[[VAL_5:.*]] = tosa.cast %[[VAL_2]] : (tensor<2xi64>) -> tensor<2xi32>
-// CHECK:           %[[VAL_6:.*]] = tosa.reshape %[[VAL_5]] {new_shape = array<i64: 1, 1, 2>} : (tensor<2xi32>) -> tensor<1x1x2xi32>
-// CHECK:           %[[VAL_7:.*]] = tosa.tile %[[VAL_6]] {multiples = array<i64: 4, 5, 1>} : (tensor<1x1x2xi32>) -> tensor<4x5x2xi32>
-// CHECK:           %[[VAL_8:.*]] = tosa.reshape %[[VAL_7]] {new_shape = array<i64: 4, 5, 2, 1>} : (tensor<4x5x2xi32>) -> tensor<4x5x2x1xi32>
-// CHECK:           %[[VAL_9:.*]] = "tosa.const"() <{value = dense<{{\[\[}}{{\[\[}}0], [0]], {{\[\[}}0], [0]], {{\[\[}}0], [0]], {{\[\[}}0], [0]], {{\[\[}}0], [0]]], {{\[\[}}[1], [1]], {{\[\[}}1], [1]], {{\[\[}}1], [1]], {{\[\[}}1], [1]], {{\[\[}}1], [1]]], {{\[\[}}[2], [2]], {{\[\[}}2], [2]], {{\[\[}}2], [2]], {{\[\[}}2], [2]], {{\[\[}}2], [2]]], {{\[\[}}[3], [3]], {{\[\[}}3], [3]], {{\[\[}}3], [3]], {{\[\[}}3], [3]], {{\[\[}}3], [3]]]]> : tensor<4x5x2x1xi32>}> : () -> tensor<4x5x2x1xi32>
-// CHECK:           %[[VAL_10:.*]] = "tosa.const"() <{value = dense<{{\[\[}}{{\[\[}}0], [0]], {{\[\[}}1], [1]], {{\[\[}}2], [2]], {{\[\[}}3], [3]], {{\[\[}}4], [4]]], {{\[\[}}[0], [0]], {{\[\[}}1], [1]], {{\[\[}}2], [2]], {{\[\[}}3], [3]], {{\[\[}}4], [4]]], {{\[\[}}[0], [0]], {{\[\[}}1], [1]], {{\[\[}}2], [2]], {{\[\[}}3], [3]], {{\[\[}}4], [4]]], {{\[\[}}[0], [0]], {{\[\[}}1], [1]], {{\[\[}}2], [2]], {{\[\[}}3], [3]], {{\[\[}}4], [4]]]]> : tensor<4x5x2x1xi32>}> : () -> tensor<4x5x2x1xi32>
-// CHECK:           %[[VAL_11:.*]] = tosa.concat %[[VAL_9]], %[[VAL_10]], %[[VAL_8]] {axis = 3 : i32} : (tensor<4x5x2x1xi32>, tensor<4x5x2x1xi32>, tensor<4x5x2x1xi32>) -> tensor<4x5x2x3xi32>
-// CHECK:           %[[VAL_12:.*]] = tosa.reshape %[[VAL_3]] {new_shape = array<i64: 1, 120, 1>} : (tensor<4x5x6xf32>) -> tensor<1x120x1xf32>
-// CHECK:           %[[VAL_13:.*]] = tosa.reshape %[[VAL_11]] {new_shape = array<i64: 40, 3>} : (tensor<4x5x2x3xi32>) -> tensor<40x3xi32>
-// CHECK:           %[[VAL_14:.*]] = "tosa.const"() <{value = dense<[30, 6, 1]> : tensor<3xi32>}> : () -> tensor<3xi32>
-// CHECK:           %[[VAL_15:.*]] = tosa.mul %[[VAL_13]], %[[VAL_14]] {shift = 0 : i8} : (tensor<40x3xi32>, tensor<3xi32>) -> tensor<40x3xi32>
-// CHECK:           %[[VAL_16:.*]] = tosa.reduce_sum %[[VAL_15]] {axis = 1 : i32} : (tensor<40x3xi32>) -> tensor<40x1xi32>
-// CHECK:           %[[VAL_17:.*]] = tosa.reshape %[[VAL_16]] {new_shape = array<i64: 1, 40>} : (tensor<40x1xi32>) -> tensor<1x40xi32>
-// CHECK:           %[[VAL_18:.*]] = tosa.gather %[[VAL_12]], %[[VAL_17]] : (tensor<1x120x1xf32>, tensor<1x40xi32>) -> tensor<1x40x1xf32>
-// CHECK:           %[[VAL_19:.*]] = tosa.reshape %[[VAL_18]] {new_shape = array<i64: 4, 5, 2>} : (tensor<1x40x1xf32>) -> tensor<4x5x2xf32>
-// CHECK:           %[[VAL_20:.*]] = torch_c.from_builtin_tensor %[[VAL_19]] : tensor<4x5x2xf32> -> !torch.vtensor<[4,5,2],f32>
-// CHECK:           return %[[VAL_20]] : !torch.vtensor<[4,5,2],f32>
+// CHECK:           %[[VAL_6:.*]] = "tosa.const"() <{value = dense<120> : tensor<i32>}> : () -> tensor<i32>
+// CHECK:           %[[VAL_7:.*]] = "tosa.const"() <{value = dense<119> : tensor<i32>}> : () -> tensor<i32>
+// CHECK:           %[[VAL_8:.*]] = tosa.greater %[[VAL_5]], %[[VAL_7]] : (tensor<2xi32>, tensor<i32>) -> tensor<2xi1>
+// CHECK:           %[[VAL_9:.*]] = tosa.int_div %[[VAL_5]], %[[VAL_6]] : (tensor<2xi32>, tensor<i32>) -> tensor<2xi32>
+// CHECK:           %[[VAL_10:.*]] = tosa.mul %[[VAL_9]], %[[VAL_6]] {shift = 0 : i8} : (tensor<2xi32>, tensor<i32>) -> tensor<2xi32>
+// CHECK:           %[[VAL_11:.*]] = tosa.sub %[[VAL_5]], %[[VAL_10]] : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
+// CHECK:           %[[VAL_12:.*]] = tosa.select %[[VAL_8]], %[[VAL_11]], %[[VAL_5]] : (tensor<2xi1>, tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
+// CHECK:           %[[VAL_13:.*]] = tosa.reshape %[[VAL_12]] {new_shape = array<i64: 1, 1, 2>} : (tensor<2xi32>) -> tensor<1x1x2xi32>
+// CHECK:           %[[VAL_14:.*]] = tosa.tile %[[VAL_13]] {multiples = array<i64: 4, 5, 1>} : (tensor<1x1x2xi32>) -> tensor<4x5x2xi32>
+// CHECK:           %[[VAL_15:.*]] = tosa.reshape %[[VAL_14]] {new_shape = array<i64: 4, 5, 2, 1>} : (tensor<4x5x2xi32>) -> tensor<4x5x2x1xi32>
+// CHECK:           %[[VAL_16:.*]] = "tosa.const"() <{value = dense<{{\[\[}}{{\[\[}}0], [0]], {{\[\[}}0], [0]], {{\[\[}}0], [0]], {{\[\[}}0], [0]], {{\[\[}}0], [0]]], {{\[\[}}[1], [1]], {{\[\[}}1], [1]], {{\[\[}}1], [1]], {{\[\[}}1], [1]], {{\[\[}}1], [1]]], {{\[\[}}[2], [2]], {{\[\[}}2], [2]], {{\[\[}}2], [2]], {{\[\[}}2], [2]], {{\[\[}}2], [2]]], {{\[\[}}[3], [3]], {{\[\[}}3], [3]], {{\[\[}}3], [3]], {{\[\[}}3], [3]], {{\[\[}}3], [3]]]]> : tensor<4x5x2x1xi32>}> : () -> tensor<4x5x2x1xi32>
+// CHECK:           %[[VAL_17:.*]] = "tosa.const"() <{value = dense<{{\[\[}}{{\[\[}}0], [0]], {{\[\[}}1], [1]], {{\[\[}}2], [2]], {{\[\[}}3], [3]], {{\[\[}}4], [4]]], {{\[\[}}[0], [0]], {{\[\[}}1], [1]], {{\[\[}}2], [2]], {{\[\[}}3], [3]], {{\[\[}}4], [4]]], {{\[\[}}[0], [0]], {{\[\[}}1], [1]], {{\[\[}}2], [2]], {{\[\[}}3], [3]], {{\[\[}}4], [4]]], {{\[\[}}[0], [0]], {{\[\[}}1], [1]], {{\[\[}}2], [2]], {{\[\[}}3], [3]], {{\[\[}}4], [4]]]]> : tensor<4x5x2x1xi32>}> : () -> tensor<4x5x2x1xi32>
+// CHECK:           %[[VAL_18:.*]] = tosa.concat %[[VAL_16]], %[[VAL_17]], %[[VAL_15]] {axis = 3 : i32} : (tensor<4x5x2x1xi32>, tensor<4x5x2x1xi32>, tensor<4x5x2x1xi32>) -> tensor<4x5x2x3xi32>
+// CHECK:           %[[VAL_19:.*]] = tosa.reshape %[[VAL_3]] {new_shape = array<i64: 1, 120, 1>} : (tensor<4x5x6xf32>) -> tensor<1x120x1xf32>
+// CHECK:           %[[VAL_20:.*]] = tosa.reshape %[[VAL_18]] {new_shape = array<i64: 40, 3>} : (tensor<4x5x2x3xi32>) -> tensor<40x3xi32>
+// CHECK:           %[[VAL_21:.*]] = "tosa.const"() <{value = dense<[30, 6, 1]> : tensor<3xi32>}> : () -> tensor<3xi32>
+// CHECK:           %[[VAL_22:.*]] = tosa.mul %[[VAL_20]], %[[VAL_21]] {shift = 0 : i8} : (tensor<40x3xi32>, tensor<3xi32>) -> tensor<40x3xi32>
+// CHECK:           %[[VAL_23:.*]] = tosa.reduce_sum %[[VAL_22]] {axis = 1 : i32} : (tensor<40x3xi32>) -> tensor<40x1xi32>
+// CHECK:           %[[VAL_24:.*]] = tosa.reshape %[[VAL_23]] {new_shape = array<i64: 1, 40>} : (tensor<40x1xi32>) -> tensor<1x40xi32>
+// CHECK:           %[[VAL_25:.*]] = tosa.gather %[[VAL_19]], %[[VAL_24]] : (tensor<1x120x1xf32>, tensor<1x40xi32>) -> tensor<1x40x1xf32>
+// CHECK:           %[[VAL_26:.*]] = tosa.reshape %[[VAL_25]] {new_shape = array<i64: 4, 5, 2>} : (tensor<1x40x1xf32>) -> tensor<4x5x2xf32>
+// CHECK:           %[[VAL_27:.*]] = torch_c.from_builtin_tensor %[[VAL_26]] : tensor<4x5x2xf32> -> !torch.vtensor<[4,5,2],f32>
+// CHECK:           return %[[VAL_27]] : !torch.vtensor<[4,5,2],f32>
 // CHECK:         }
 func.func @torch.aten.index_select(%arg0: !torch.vtensor<[4,5,6],f32>, %arg1: !torch.vtensor<[2],si64>) -> !torch.vtensor<[4,5,2],f32> {
   %int2 = torch.constant.int 2
@@ -2331,6 +2338,42 @@ func.func @torch.aten.as_strided$basic(%arg0: !torch.vtensor<[5,5],f32>) -> !tor
   return %2 : !torch.vtensor<[3,3],f32>
 }
 
+// -----
+// CHECK-LABEL:   func.func @torch.aten.as_strided$offset(
+// CHECK-SAME:                                           %[[VAL_0:.*]]: !torch.vtensor<[5,5],f32>) -> !torch.vtensor<[3,3],f32> {
+// CHECK:           %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[5,5],f32> -> tensor<5x5xf32>
+// CHECK:           %[[VAL_2:.*]] = torch.constant.int 30
+// CHECK:           %[[VAL_3:.*]] = torch.constant.int 1
+// CHECK:           %[[VAL_4:.*]] = torch.constant.int 2
+// CHECK:           %[[VAL_5:.*]] = torch.constant.int 3
+// CHECK:           %[[VAL_6:.*]] = torch.prim.ListConstruct %[[VAL_5]], %[[VAL_5]] : (!torch.int, !torch.int) -> !torch.list<int>
+// CHECK:           %[[VAL_7:.*]] = torch.prim.ListConstruct %[[VAL_4]], %[[VAL_3]] : (!torch.int, !torch.int) -> !torch.list<int>
+// CHECK:           %[[VAL_8:.*]] = tosa.reshape %[[VAL_1]] {new_shape = array<i64: 25>} : (tensor<5x5xf32>) -> tensor<25xf32>
+// CHECK:           %[[VAL_9:.*]] = "tosa.const"() <{value = dense<[5, 6, 7, 7, 8, 9, 9, 10, 11]> : tensor<9xi32>}> : () -> tensor<9xi32>
+// CHECK:           %[[VAL_10:.*]] = tosa.reshape %[[VAL_9]] {new_shape = array<i64: 9, 1>} : (tensor<9xi32>) -> tensor<9x1xi32>
+// CHECK:           %[[VAL_11:.*]] = tosa.concat %[[VAL_10]] {axis = 1 : i32} : (tensor<9x1xi32>) -> tensor<9x1xi32>
+// CHECK:           %[[VAL_12:.*]] = tosa.reshape %[[VAL_8]] {new_shape = array<i64: 1, 25, 1>} : (tensor<25xf32>) -> tensor<1x25x1xf32>
+// CHECK:           %[[VAL_13:.*]] = tosa.reshape %[[VAL_11]] {new_shape = array<i64: 9, 1>} : (tensor<9x1xi32>) -> tensor<9x1xi32>
+// CHECK:           %[[VAL_14:.*]] = "tosa.const"() <{value = dense<1> : tensor<1xi32>}> : () -> tensor<1xi32>
+// CHECK:           %[[VAL_15:.*]] = tosa.mul %[[VAL_13]], %[[VAL_14]] {shift = 0 : i8} : (tensor<9x1xi32>, tensor<1xi32>) -> tensor<9x1xi32>
+// CHECK:           %[[VAL_16:.*]] = tosa.reduce_sum %[[VAL_15]] {axis = 1 : i32} : (tensor<9x1xi32>) -> tensor<9x1xi32>
+// CHECK:           %[[VAL_17:.*]] = tosa.reshape %[[VAL_16]] {new_shape = array<i64: 1, 9>} : (tensor<9x1xi32>) -> tensor<1x9xi32>
+// CHECK:           %[[VAL_18:.*]] = tosa.gather %[[VAL_12]], %[[VAL_17]] : (tensor<1x25x1xf32>, tensor<1x9xi32>) -> tensor<1x9x1xf32>
+// CHECK:           %[[VAL_19:.*]] = tosa.reshape %[[VAL_18]] {new_shape = array<i64: 9>} : (tensor<1x9x1xf32>) -> tensor<9xf32>
+// CHECK:           %[[VAL_20:.*]] = tosa.reshape %[[VAL_19]] {new_shape = array<i64: 3, 3>} : (tensor<9xf32>) -> tensor<3x3xf32>
+// CHECK:           %[[VAL_21:.*]] = torch_c.from_builtin_tensor %[[VAL_20]] : tensor<3x3xf32> -> !torch.vtensor<[3,3],f32>
+// CHECK:           return %[[VAL_21]] : !torch.vtensor<[3,3],f32>
+func.func @torch.aten.as_strided$offset(%arg0: !torch.vtensor<[5,5],f32>) -> !torch.vtensor<[3,3],f32> {
+  %int30 = torch.constant.int 30
+  %int1 = torch.constant.int 1
+  %int2 = torch.constant.int 2
+  %int3 = torch.constant.int 3
+  %0 = torch.prim.ListConstruct %int3, %int3 : (!torch.int, !torch.int) -> !torch.list<int>
+  %1 = torch.prim.ListConstruct %int2, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
+  %2 = torch.aten.as_strided %arg0, %0, %1, %int30 : !torch.vtensor<[5,5],f32>, !torch.list<int>, !torch.list<int>, !torch.int -> !torch.vtensor<[3,3],f32>
+  return %2 : !torch.vtensor<[3,3],f32>
+}
+
 // -----
 
 // CHECK-LABEL:   func.func @torch.aten.max_pool1d$basic(