Decompose AtenNormalFunctionalOp into AtenRandn* and other arithmetic. (#2737)

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -7655,6 +7655,9 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "  func.func @\"__torch_mlir_shape_fn.aten.randn.generator\"(%arg0: !torch.list<int>, %arg1: !torch.any, %arg2: !torch.optional<int>, %arg3: !torch.optional<int>, %arg4: !torch.optional<Device>, %arg5: !torch.optional<bool>) -> !torch.list<int> {\n"
 "    return %arg0 : !torch.list<int>\n"
 "  }\n"
+"  func.func @\"__torch_mlir_shape_fn.aten.normal_functional\"(%arg0: !torch.list<int>, %arg1: !torch.float, %arg2: !torch.float, %arg3: !torch.any) -> !torch.list<int> {\n"
+"    return %arg0 : !torch.list<int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten.arange.start_step\"(%arg0: !torch.float, %arg1: !torch.float, %arg2: !torch.float, %arg3: !torch.optional<int>, %arg4: !torch.optional<int>, %arg5: !torch.optional<Device>, %arg6: !torch.optional<bool>) -> !torch.list<int> {\n"
 "    %0 = torch.derefine %arg0 : !torch.float to !torch.union<float, int>\n"
 "    %1 = torch.derefine %arg1 : !torch.float to !torch.union<float, int>\n"
@@ -11557,6 +11560,20 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    }\n"
 "    return %1 : !torch.int\n"
 "  }\n"
+"  func.func @\"__torch_mlir_dtype_fn.aten.normal_functional\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.float, %arg2: !torch.float, %arg3: !torch.any) -> !torch.int {\n"
+"    %none = torch.constant.none\n"
+"    %str = torch.constant.str \"AssertionError: \"\n"
+"    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
+"    %1 = call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.is_integer_dtype(%0#1) : (!torch.int) -> !torch.bool\n"
+"    %2 = torch.aten.__not__ %1 : !torch.bool -> !torch.bool\n"
+"    torch.prim.If %2 -> () {\n"
+"      torch.prim.If.yield\n"
+"    } else {\n"
+"      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
+"      torch.prim.If.yield\n"
+"    }\n"
+"    return %0#1 : !torch.int\n"
+"  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.randn.generator\"(%arg0: !torch.list<int>, %arg1: !torch.any, %arg2: !torch.optional<int>, %arg3: !torch.optional<int>, %arg4: !torch.optional<Device>, %arg5: !torch.optional<bool>) -> !torch.int {\n"
 "    %str = torch.constant.str \"AssertionError: \"\n"
 "    %int6 = torch.constant.int 6\n"

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -3669,9 +3669,38 @@ public:
     return success();
   }
 };
-} // namespace
 
-namespace {
+// aten.normal_functional(mean, sigma) = randn() * sigma + mean.
+class DecomposeAtenNormalFunctionalOp
+    : public OpRewritePattern<AtenNormalFunctionalOp> {
+public:
+  using OpRewritePattern::OpRewritePattern;
+  LogicalResult matchAndRewrite(AtenNormalFunctionalOp op,
+                                PatternRewriter &rewriter) const override {
+    if (!op.getGenerator().getType().isa<Torch::NoneType>())
+      return rewriter.notifyMatchFailure(
+          op, "The generator has to be None because only global default "
+              "generator is supported");
+
+    Location loc = op.getLoc();
+    Type resultType = op.getType();
+    Value std = op.getStd();
+    Value mean = op.getMean();
+
+    Value none = rewriter.create<ConstantNoneOp>(loc);
+    Value one =
+        rewriter.create<ConstantFloatOp>(loc, rewriter.getF64FloatAttr(1.0));
+    Value randN = rewriter.create<AtenRandnLikeOp>(
+        loc, resultType, op.getSelf(), /*dtype=*/none, /*layout=*/none,
+        /*device=*/none, /*pin_memory=*/none, /*memory_format=*/none);
+    Value stdRandN =
+        rewriter.create<AtenMulScalarOp>(loc, resultType, randN, std);
+    rewriter.replaceOpWithNewOp<AtenAddScalarOp>(op, resultType, stdRandN,
+                                                 mean, /*alpha=*/one);
+    return success();
+  }
+};
+
 template <typename OpTy, typename T1T2Op>
 class DecomposeAtenAddCLikeOp : public OpRewritePattern<OpTy> {
   using OpRewritePattern<OpTy>::OpRewritePattern;
@@ -6591,6 +6620,7 @@ public:
     addPatternIfTargetOpIsIllegal<DecomposeAtenRandnOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenRandnGeneratorOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenRandnLikeOp>(patterns);
+    addPatternIfTargetOpIsIllegal<DecomposeAtenNormalFunctionalOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenVarMeanOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenEluOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenSeluOp>(patterns);

lib/Dialect/Torch/Transforms/LowerToBackendContract.cpp

@@ -494,6 +494,7 @@ static void markDecomposedOpsAsIllegal(MLIRContext *context,
   target.addIllegalOp<AtenRandnOp>();
   target.addIllegalOp<AtenRandnGeneratorOp>();
   target.addIllegalOp<AtenRandnLikeOp>();
+  target.addIllegalOp<AtenNormalFunctionalOp>();
   target.addIllegalOp<AtenVarMeanOp>();
   target.addIllegalOp<AtenCosineSimilarityOp>();
   target.addIllegalOp<AtenNewEmptyStridedOp>();

projects/pt1/e2e_testing/xfail_sets.py

@@ -1484,6 +1484,7 @@ LTC_XFAIL_SET = {
     "VarMeanUnbiasedModule_basic",
     "RandnLikeModule_basic",
     "RandnLikeDtypeModule_basic",
+    "NormalFunctionalModule_basic",
     "BernoulliFloatModule_basic",
     "BernoulliModule_basic",
     "BernoulliPModule_basic",

projects/pt1/python/torch_mlir/jit_ir_importer/build_tools/abstract_interp_lib_gen.py

@@ -902,6 +902,9 @@ def aten〇randn〡shape(size: List[int], dtype: Optional[int] = None, layout: O
 def aten〇randn〇generator〡shape(size: List[int], generator: Any, dtype: Optional[int] = None, layout: Optional[int] = None, device: Optional[device] = None, pin_memory: Optional[bool] = None) -> List[int]:
     return size
 
+def aten〇normal_functional〡shape(self: List[int], mean: float = 0., std: float = 1., generator: Any = None) -> List[int]:
+    return self
+
 def aten〇arange〇start_step〡shape(start: float, end: float, step: float = 1, dtype: Optional[int] = None, layout: Optional[int] = None, device: Optional[device] = None, pin_memory: Optional[bool] = None) -> List[int]:
     return upstream_shape_functions.arange_start_step(start, end, step, dtype, layout, device, pin_memory)
 
@@ -3822,6 +3825,16 @@ def aten〇randn〡dtype(size: List[int], dtype: Optional[int] = None, layout: O
     assert not is_integer_dtype(dtype)
     return dtype
 
+@check_dtype_function(_check_tensors_with_the_same_dtype(
+        num_of_tensors=1,
+        error_types={torch.bool, torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64}))
+def aten〇normal_functional〡dtype(self_rank_dtype: Tuple[int, int], mean: float = 0., std: float = 1., generator: Any = None) -> int:
+    self_rank, self_dtype = self_rank_dtype
+    if self_dtype is None:
+        return torch.float32
+    assert not is_integer_dtype(self_dtype)
+    return self_dtype
+
 @check_dtype_function([Invocation(size=[1], generator=None),
                        Invocation(size=[1], generator=None, dtype=torch.float32),
                        ErrorInvocation(size=[1], generator=None, dtype=torch.int32),

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

@@ -605,3 +605,24 @@ class RandnLikeDtypeModule(torch.nn.Module):
 @register_test_case(module_factory=lambda: RandnLikeDtypeModule())
 def RandnLikeDtypeModule_basic(module, tu: TestUtils):
     module.forward(tu.rand(256, 1024).double())
+# ==============================================================================
+
+class NormalFunctionalModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.float64, True),
+    ])
+    def forward(self, x):
+        a = torch.ops.aten.normal_functional(x, mean=-5.0, std=2.0)
+        mean = torch.mean(a)
+        std = torch.std(a)
+        return mean, std
+
+
+@register_test_case(module_factory=lambda: NormalFunctionalModule())
+def NormalFunctionalModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(2048, 4096).double())