[MLIR][TORCH] Add E2E support for aten.var_mean.dim op

This commit adds the decomposition for the aten.var_mean.dim op.

Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>

e2e_testing/xfail_sets.py

@@ -945,4 +945,6 @@ LTC_XFAIL_SET = {
     "PrimsViewOfModule_basic",
     "PrimsViewOfZeroRankModule_basic",
     "OneHotModule_basic",
+    "VarMeanDimModule_basic",
+    "VarMeanDimBiasedModule_basic",
 }

include/torch-mlir/Dialect/Torch/IR/GeneratedTorchOps.td

@@ -5385,6 +5385,33 @@ def Torch_AtenVarMeanOp : Torch_Op<"aten.var_mean", [
   }];
 }
 
+def Torch_AtenVarMeanDimOp : Torch_Op<"aten.var_mean.dim", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::var_mean.dim : (Tensor, int[]?, bool, bool) -> (Tensor, Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self,
+    AnyTorchOptionalListOfTorchIntType:$dim,
+    Torch_BoolType:$unbiased,
+    Torch_BoolType:$keepdim
+  );
+  let results = (outs
+    AnyTorchTensorType:$result0,
+    AnyTorchTensorType:$result1
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenVarMeanDimOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 4, 2);
+    }
+    void AtenVarMeanDimOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 4, 2);
+    }
+  }];
+}
+
 def Torch_AtenNllLoss2dForwardOp : Torch_Op<"aten.nll_loss2d_forward", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -6501,6 +6501,18 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %2 = torch.prim.TupleConstruct %1, %1 : !torch.list<int>, !torch.list<int> -> !torch.tuple<list<int>, list<int>>\n"
 "    return %2 : !torch.tuple<list<int>, list<int>>\n"
 "  }\n"
+"  func.func @\"__torch_mlir_shape_fn.aten.var_mean.dim\"(%arg0: !torch.list<int>, %arg1: !torch.optional<list<int>>, %arg2: !torch.bool, %arg3: !torch.bool) -> !torch.tuple<list<int>, list<int>> {\n"
+"    %none = torch.constant.none\n"
+"    %0 = torch.derefine %none : !torch.none to !torch.any\n"
+"    %1 = call @__torch__.torch.jit._shape_functions.sum_mean_dim(%arg0, %arg1, %arg3, %0) : (!torch.list<int>, !torch.optional<list<int>>, !torch.bool, !torch.any) -> !torch.list<int>\n"
+"    %2 = torch.prim.TupleConstruct %1, %1 : !torch.list<int>, !torch.list<int> -> !torch.tuple<list<int>, list<int>>\n"
+"    return %2 : !torch.tuple<list<int>, list<int>>\n"
+"  }\n"
+"  func.func @\"__torch_mlir_dtype_fn.aten.var_mean.dim\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.optional<list<int>>, %arg2: !torch.bool, %arg3: !torch.bool) -> !torch.tuple<int, int> {\n"
+"    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
+"    %1 = torch.prim.TupleConstruct %0#1, %0#1 : !torch.int, !torch.int -> !torch.tuple<int, int>\n"
+"    return %1 : !torch.tuple<int, int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten.var_mean\"(%arg0: !torch.list<int>, %arg1: !torch.bool) -> !torch.tuple<list<int>, list<int>> {\n"
 "    %0 = torch.prim.ListConstruct  : () -> !torch.list<int>\n"
 "    %1 = torch.prim.ListConstruct  : () -> !torch.list<int>\n"

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -4298,6 +4298,28 @@ class DecomposeAtenOneHotOp : public OpRewritePattern<AtenOneHotOp> {
 
 } // namespace
 
+namespace {
+// Decompose `aten.var_mean.dim` op into `aten.var.dim` and
+// `aten.mean.dim` op.
+class DecomposeAtenVarMeanDimOp : public OpRewritePattern<AtenVarMeanDimOp> {
+public:
+  using OpRewritePattern::OpRewritePattern;
+  LogicalResult matchAndRewrite(AtenVarMeanDimOp op,
+                                PatternRewriter &rewriter) const override {
+    Location loc = op.getLoc();
+    Value noneVal = rewriter.create<ConstantNoneOp>(loc);
+    Value var = rewriter.create<AtenVarDimOp>(loc, op.getType(0), op.getSelf(),
+                                              op.getDim(), op.getUnbiased(),
+                                              op.getKeepdim());
+    Value mean = rewriter.create<AtenMeanDimOp>(
+        loc, op.getType(0), op.getSelf(), op.getDim(), op.getKeepdim(),
+        /*dtype=*/noneVal);
+    rewriter.replaceOp(op, {var, mean});
+    return success();
+  }
+};
+} // namespace
+
 namespace {
 class DecomposeComplexOpsPass
     : public DecomposeComplexOpsBase<DecomposeComplexOpsPass> {
@@ -4460,6 +4482,7 @@ public:
     addPatternIfTargetOpIsIllegal<DecomposeAtenMovedimIntOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenOneHotOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenCrossEntropyLossOp>(patterns);
+    addPatternIfTargetOpIsIllegal<DecomposeAtenVarMeanDimOp>(patterns);
 
     GreedyRewriteConfig config;
     config.useTopDownTraversal = true;

lib/Dialect/Torch/Transforms/LowerToBackendContract.cpp

@@ -476,6 +476,7 @@ static void markDecomposedOpsAsIllegal(MLIRContext *context,
   target.addIllegalOp<AtenMovedimIntOp>();
   target.addIllegalOp<AtenOneHotOp>();
   target.addIllegalOp<AtenCrossEntropyLossOp>();
+  target.addIllegalOp<AtenVarMeanDimOp>();
   for (auto &opName : backendLegalOpsSet) {
     target.addLegalOp(
         OperationName(kTorchOpPrefix + opName.first().str(), context));

python/torch_mlir/dialects/torch/importer/jit_ir/build_tools/abstract_interp_lib_gen.py

@@ -332,6 +332,14 @@ def aten〇var_mean〇correction〡shape(self: List[int], dim: Optional[List[int
     out = upstream_shape_functions.sum_mean_dim(self, dim, keepdim, None)
     return out, out
 
+def aten〇var_mean〇dim〡shape(self: List[int], dim: Optional[List[int]], unbiased: bool = True, keepdim: bool = False) -> Tuple[List[int], List[int]]:
+    out = upstream_shape_functions.sum_mean_dim(self, dim, keepdim, None)
+    return out, out
+
+def aten〇var_mean〇dim〡dtype(self_rank_dtype: Tuple[int, int], dim: Optional[List[int]], unbiased: bool = True, keepdim: bool = False) -> Tuple[int, int]:
+    _, self_dtype = self_rank_dtype
+    return self_dtype, self_dtype
+
 def aten〇var_mean〡shape(self: List[int], unbiased: bool = True) -> Tuple[List[int], List[int]]:
     return [], []
 

python/torch_mlir/dialects/torch/importer/jit_ir/build_tools/torch_ods_gen.py

@@ -422,6 +422,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
     emit("aten::var.correction : (Tensor, int[]?, Scalar?, bool) -> (Tensor)")
     emit("aten::var_mean.correction : (Tensor, int[]?, Scalar?, bool) -> (Tensor, Tensor)")
     emit("aten::var_mean : (Tensor, bool) -> (Tensor, Tensor)")
+    emit("aten::var_mean.dim : (Tensor, int[]?, bool, bool) -> (Tensor, Tensor)")
     emit("aten::nll_loss2d_forward : (Tensor, Tensor, Tensor?, int, int) -> (Tensor, Tensor)")
     emit("aten::nll_loss2d_backward : (Tensor, Tensor, Tensor, Tensor?, int, int, Tensor) -> (Tensor)")
     emit("aten::nll_loss_forward : (Tensor, Tensor, Tensor?, int, int) -> (Tensor, Tensor)")

python/torch_mlir_e2e_test/test_suite/stats.py

@@ -1000,3 +1000,44 @@ class VarMeanBiasedModule(torch.nn.Module):
 @register_test_case(module_factory=lambda: VarMeanBiasedModule())
 def VarMeanBiasedModule_basic(module, tu: TestUtils):
     module.forward(tu.rand(3, 4, 7))
+
+
+# ==============================================================================
+
+
+class VarMeanDimModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1, -1], torch.float32, True),
+    ])
+    def forward(self, x):
+        return torch.ops.aten.var_mean(x, dim=[1])
+
+
+@register_test_case(module_factory=lambda: VarMeanDimModule())
+def VarMeanDimModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3, 4, 7))
+
+
+class VarMeanDimBiasedModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1, -1], torch.float32, True),
+    ])
+    def forward(self, x):
+        return torch.ops.aten.var_mean(x, dim=[1], unbiased=False, keepdim=True)
+
+
+@register_test_case(module_factory=lambda: VarMeanDimBiasedModule())
+def VarMeanDimBiasedModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3, 4, 7))