[TORCH] Add support for aten.rms_norm Op

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

@@ -7454,6 +7454,32 @@ def Torch_AtenLayerNormOp : Torch_Op<"aten.layer_norm", [
   }];
 }
 
+def Torch_AtenRmsNormOp : Torch_Op<"aten.rms_norm", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::rms_norm : (Tensor, int[], Tensor?, float?) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$input,
+    AnyTorchListOfTorchIntType:$normalized_shape,
+    AnyTorchOptionalTensorType:$weight,
+    AnyTorchOptionalFloatType:$eps
+  );
+  let results = (outs
+    AnyTorchOptionalTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenRmsNormOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 4, 1);
+    }
+    void AtenRmsNormOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 4, 1);
+    }
+  }];
+}
+
 def Torch_AtenRenormOp : Torch_Op<"aten.renorm", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -7326,6 +7326,10 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
 "    return %0 : !torch.list<int>\n"
 "  }\n"
+"  func.func @\"__torch_mlir_shape_fn.aten.rms_norm\"(%arg0: !torch.list<int>, %arg1: !torch.list<int>, %arg2: !torch.optional<list<int>>, %arg3: !torch.optional<float>) -> !torch.list<int> {\n"
+"    %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
+"    return %0 : !torch.list<int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten._softmax_backward_data\"(%arg0: !torch.list<int>, %arg1: !torch.list<int>, %arg2: !torch.int, %arg3: !torch.int) -> !torch.list<int> {\n"
 "    %0 = call @__torch__.torch.jit._shape_functions.unary(%arg1) : (!torch.list<int>) -> !torch.list<int>\n"
 "    return %0 : !torch.list<int>\n"
@@ -12732,6 +12736,20 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    }\n"
 "    return %0#1 : !torch.int\n"
 "  }\n"
+"  func.func @\"__torch_mlir_dtype_fn.aten.rms_norm\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.list<int>, %arg2: !torch.optional<tuple<int, int>>, %arg3: !torch.optional<float>) -> !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.leaky_relu_backward\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.tuple<int, int>, %arg2: !torch.number, %arg3: !torch.bool) -> !torch.int {\n"
 "    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
 "    %1:2 = torch.prim.TupleUnpack %arg1 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -7485,6 +7485,85 @@ class DecomposeAtenNativeLayerNormOp
 };
 } // namespace
 
+// RMS normalization:
+//     rms(x) = sqrt(eps + mean(x^2))
+//     output = (x / rms(x)) * weight
+namespace {
+class DecomposeAtenRMSLayerNormOp : public OpRewritePattern<AtenRmsNormOp> {
+  using OpRewritePattern<AtenRmsNormOp>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(AtenRmsNormOp op,
+                                PatternRewriter &rewriter) const override {
+    Location loc = op.getLoc();
+    auto context = op.getContext();
+    auto input = op.getInput();
+    auto inputTy = dyn_cast<ValueTensorType>(input.getType());
+    if (!inputTy || !inputTy.hasSizes() || !inputTy.hasDtype())
+      return rewriter.notifyMatchFailure(
+          op, "Expected input to be a tensor with sizes and a dtype");
+
+    auto outputTy = dyn_cast<ValueTensorType>(op.getType());
+    if (!outputTy.hasDtype())
+      return rewriter.notifyMatchFailure(op, "output should have a dtype.");
+
+    int64_t inputRank = inputTy.getSizes().size();
+    Value normalizedShape = op.getNormalizedShape();
+    SmallVector<Value> normalizedShapeSizesTorchInt;
+    if (!getListConstructElements(normalizedShape,
+                                  normalizedShapeSizesTorchInt))
+      return rewriter.notifyMatchFailure(op,
+                                         "should have constant shape values.");
+
+    int64_t normalize_from_idx =
+        inputRank - normalizedShapeSizesTorchInt.size();
+    auto reduceDimInts =
+        llvm::to_vector<4>(llvm::seq<int64_t>(normalize_from_idx, inputRank));
+    auto sizeListType = ListType::get(IntType::get(context));
+
+    SmallVector<Value> reduceDimVals;
+    for (int64_t dim : reduceDimInts)
+      reduceDimVals.push_back(rewriter.create<Torch::ConstantIntOp>(
+          loc, rewriter.getI64IntegerAttr(dim)));
+    Value reduceDimList =
+        rewriter.create<PrimListConstructOp>(loc, sizeListType, reduceDimVals);
+
+    auto inputShape = inputTy.getSizes();
+    SmallVector<int64_t> reducedShape(inputShape.begin(), inputShape.end());
+    for (int64_t i : reduceDimInts)
+      reducedShape[i] = 1;
+    auto reducedTy =
+        ValueTensorType::get(context, reducedShape, inputTy.getDtype());
+    // x^2
+    Value inputSquared = rewriter.create<AtenSquareOp>(loc, inputTy, input);
+    Value cstTrue = rewriter.create<Torch::ConstantBoolOp>(loc, true);
+    Value none = rewriter.create<Torch::ConstantNoneOp>(loc);
+    // mean(x^2)
+    Value mean = rewriter.create<AtenMeanDimOp>(loc, reducedTy, inputSquared,
+                                                reduceDimList, cstTrue, none);
+    //  mean(x^2) + eps: Add eps if provided
+    if (!isa<Torch::NoneType>(op.getEps().getType())) {
+      Value one = rewriter.create<Torch::ConstantIntOp>(
+          loc, rewriter.getI64IntegerAttr(1));
+      mean = rewriter.create<AtenAddScalarOp>(loc, reducedTy, mean, op.getEps(),
+                                              one);
+    }
+    // rsqrt(mean(x^2) + eps)
+    Value invRMS = rewriter.create<AtenRsqrtOp>(loc, reducedTy, mean);
+    // rsqrt(mean(x^2) + eps) * x
+    Value normalized =
+        rewriter.create<AtenMulTensorOp>(loc, inputTy, input, invRMS);
+    // Optionally multiply by weight if provided
+    Value weight = op.getWeight();
+    if (!isa<Torch::NoneType>(weight.getType())) {
+      normalized =
+          rewriter.create<AtenMulTensorOp>(loc, outputTy, normalized, weight);
+    }
+    rewriter.replaceOp(op, normalized);
+    return success();
+  }
+};
+} // namespace
+
 namespace {
 // Decompose `aten.emptyLike` op into `aten.size` and `aten.empty` ops.
 class DecomposeAtenEmptyLikeOp : public OpRewritePattern<AtenEmptyLikeOp> {
@@ -12070,6 +12149,7 @@ class DecomposeComplexOpsPass
     addPatternIfTargetOpIsIllegal<DecomposeAtenInstanceNormOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenLayerNormOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenNativeLayerNormOp>(patterns);
+    addPatternIfTargetOpIsIllegal<DecomposeAtenRMSLayerNormOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenGroupNormOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenNativeGroupNormOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenNativeBatchNormOp>(patterns);

lib/Dialect/Torch/Transforms/LowerToBackendContract.cpp

@@ -435,6 +435,7 @@ static void markDecomposedOpsAsIllegal(MLIRContext *context,
   target.addIllegalOp<AtenInstanceNormOp>();
   target.addIllegalOp<AtenLayerNormOp>();
   target.addIllegalOp<AtenNativeLayerNormOp>();
+  target.addIllegalOp<AtenRmsNormOp>();
   target.addIllegalOp<AtenGroupNormOp>();
   target.addIllegalOp<AtenNativeGroupNormOp>();
   target.addIllegalOp<AtenNativeBatchNormOp>();

projects/pt1/e2e_testing/xfail_sets.py

@@ -1473,6 +1473,10 @@
     "Rot90MultipleRotationsModule_basic",
     "Rot90NegativeEvenRotationsModule_basic",
     "Rot90NegativeOddRotationsModule_basic",
+    "RMSNormModule_basic",
+    "RMSNormWithoutEpsModule_basic",
+    "RMSNormWithoutWeightModule_basic",
+    "RMSNormAllNormalizeModule_basic",
     "RsubInt0d_NumToTensor_Module_basic",
     "ScalarConstantTupleModule_basic",
     "ScalarImplicitFloatModule_basic",
@@ -2331,6 +2335,10 @@
     "IscloseStaticModuleTrue_basic",
     "IscloseStaticModule_basic",
     "LayerNormNormalizeOverAllDimsModule_basic",
+    "RMSNormModule_basic",
+    "RMSNormWithoutEpsModule_basic",
+    "RMSNormWithoutWeightModule_basic",
+    "RMSNormAllNormalizeModule_basic",
     "LeakyReluBackwardModule_basic",
     "LeakyReluBackwardStaticModule_basic",
     "LiftFreshCopyModule_basic",
@@ -3037,6 +3045,11 @@
     "NativeGroupNormBackwardModule_basic",
     "NativeGroupNormModule_basic",
     "NativeLayerNormDynamicModule_basic",
+    "RMSNormModule_basic",
+    "RMSNormWithoutEpsModule_basic",
+    "RMSNormWithoutWeightModule_basic",
+    "RMSNormAllNormalizeModule_basic",
+    "RMSNormDynamicModule_basic",
     "NeFloatIntModule_basic",
     "NeIntModule_basic",
     "NewEmptyStridedModuleDefaultDtype_basic",
@@ -4725,6 +4738,11 @@
     "ReshapeCollapseModule_basic",
     "ReshapeDynamicModule_basic",
     "ReshapeExpandModule_basic",
+    "RMSNormModule_basic",
+    "RMSNormWithoutEpsModule_basic",
+    "RMSNormWithoutWeightModule_basic",
+    "RMSNormAllNormalizeModule_basic",
+    "RMSNormDynamicModule_basic",
     "RollModule_basic",
     "RsubIntModule_noalpha_basic",
     "ScalarConstantTupleModule_basic",

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

@@ -664,6 +664,9 @@ def aten〇gather〡shape(self: List[int], dim: int, index: List[int], sparse_gr
 def aten〇layer_norm〡shape(input: List[int], normalized_shape: List[int], weight: Optional[List[int]] = None, bias: Optional[List[int]] = None, eps: float = 1.0000000000000001e-05, cudnn_enable: bool = True) -> List[int]:
     return upstream_shape_functions.unary(input)
 
+def aten〇rms_norm〡shape(input: List[int], normalized_shape: List[int], weight: Optional[List[int]] = None, eps: Optional[float] = None) -> List[int]:
+    return upstream_shape_functions.unary(input)
+
 def aten〇_softmax_backward_data〡shape(grad_output: List[int], output: List[int], dim: int, input_dtype: int) -> List[int]:
     return upstream_shape_functions.unary(output)
 
@@ -3420,6 +3423,13 @@ def aten〇layer_norm〡dtype(input_rank_dtype: Tuple[int, int], normalized_shap
     assert not is_integer_dtype(input_dtype)
     return input_dtype
 
+@check_dtype_function(_check_tensors_with_the_same_dtype(
+    num_of_tensors=1, error_types={*all_integer_dtypes()}, normalized_shape=[1]))
+def aten〇rms_norm〡dtype(input_rank_dtype: Tuple[int, int], normalized_shape: List[int], weight_rank_dtype: Optional[Tuple[int, int]] = None, eps: Optional[float] = None) -> int:
+    input_rank, input_dtype = input_rank_dtype
+    assert not is_integer_dtype(input_dtype)
+    return input_dtype
+
 @check_dtype_function(_check_two_tensor_op(negative_slope=0.1, self_is_result=False))
 def aten〇leaky_relu_backward〡dtype(grad_output_rank_dtype: Tuple[int, int], self_rank_dtype: Tuple[int, int], negative_slope: Union[int, float, complex], self_is_result: bool) -> int:
     grad_output_rank, grad_output_dtype = grad_output_rank_dtype

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

@@ -640,6 +640,7 @@ def emit_with_mutating_variants(key, **kwargs):
     emit(
         "aten::layer_norm : (Tensor, int[], Tensor?, Tensor?, float, bool) -> (Tensor)"
     )
+    emit("aten::rms_norm : (Tensor, int[], Tensor?, float?) -> (Tensor)")
     emit("aten::renorm : (Tensor, Scalar, int, Scalar) -> (Tensor)", has_verifier=True)
     emit("aten::norm.Scalar : (Tensor, Scalar) -> (Tensor)", has_verifier=True)
     emit("aten::norm.ScalarOpt_dim : (Tensor, Scalar?, int[], bool) -> (Tensor)")

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

@@ -635,6 +635,112 @@ def AtenInstanceNormModule_basic(module, tu: TestUtils):
     module.forward(tu.rand(1, 2, 1, 3), tu.rand(2), tu.rand(2))
 
 
+# ==============================================================================
+class RMSNormModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([8, 9, 1, 2, 4], torch.float32, True),
+            ([1, 2, 4], torch.float32, True),
+        ]
+    )
+    def forward(self, x, weight):
+        list = [1, 2, 4]
+        return torch.ops.aten.rms_norm(x, list, weight, eps=0.5)
+
+
+@register_test_case(module_factory=lambda: RMSNormModule())
+def RMSNormModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(8, 9, 1, 2, 4), tu.rand(1, 2, 4))
+
+
+class RMSNormWithoutEpsModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([2, 5, 2, 2, 3], torch.float32, True),
+            ([2, 2, 3], torch.float32, True),
+        ]
+    )
+    def forward(self, x, weight):
+        list = [2, 2, 3]
+        return torch.ops.aten.rms_norm(x, list, weight)
+
+
+@register_test_case(module_factory=lambda: RMSNormWithoutEpsModule())
+def RMSNormWithoutEpsModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(2, 5, 2, 2, 3), tu.rand(2, 2, 3))
+
+
+class RMSNormWithoutWeightModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([1, 2, 3, 4], torch.float32, True),
+        ]
+    )
+    def forward(self, x):
+        list = [4]
+        return torch.ops.aten.rms_norm(x, list, eps=0.5)
+
+
+@register_test_case(module_factory=lambda: RMSNormWithoutWeightModule())
+def RMSNormWithoutWeightModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(1, 2, 3, 4))
+
+
+class RMSNormAllNormalizeModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [None, ([5, 6, 3], torch.float32, True), ([5, 6, 3], torch.float32, True)]
+    )
+    def forward(self, x, weight):
+        list = [5, 6, 3]
+        return torch.ops.aten.rms_norm(x, list, weight, eps=0.7)
+
+
+@register_test_case(module_factory=lambda: RMSNormAllNormalizeModule())
+def RMSNormAllNormalizeModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(5, 6, 3), tu.rand(5, 6, 3))
+
+
+class RMSNormDynamicModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([-1, -1, -1, -1], torch.float32, True),
+            ([-1, -1, -1], torch.float32, True),
+        ]
+    )
+    def forward(self, x, weight):
+        list = [2, 3, 4]
+        return torch.ops.aten.rms_norm(x, list, weight, eps=0.8)
+
+
+@register_test_case(module_factory=lambda: RMSNormDynamicModule())
+def RMSNormDynamicModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(1, 2, 3, 4), tu.rand(2, 3, 4))
+
+
 # ==============================================================================
 class RenormModuleFloat32(torch.nn.Module):
     def __init__(self):