e2e support aten.linalg_norm to aten.linalg_vector_norm (#2953)

Add e2d support for `aten.linalg_norm` by decompose it to
`aten.linalg_vector_norm`.

Lowering to `aten.linalg_matrix_norm` is still unsupported.

To Test: 

`python -m e2e_testing.main -v`

---------

Co-authored-by: Ze Zhang <ze.zhang@getcruise.com>

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

@@ -7938,6 +7938,33 @@ def Torch_AtenLinalgVectorNormOp : Torch_Op<"aten.linalg_vector_norm", [
   }];
 }
 
+def Torch_AtenLinalgNormOp : Torch_Op<"aten.linalg_norm", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::linalg_norm : (Tensor, Scalar?, int[]?, bool, int?) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self,
+    AnyTorchOptionalScalarType:$ord,
+    AnyTorchOptionalListOfTorchIntType:$dim,
+    Torch_BoolType:$keepdim,
+    AnyTorchOptionalIntType:$dtype
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenLinalgNormOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 5, 1);
+    }
+    void AtenLinalgNormOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 5, 1);
+    }
+  }];
+}
+
 def Torch_AtenLinalgQrOp : Torch_Op<"aten.linalg_qr", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -9336,6 +9336,11 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %1 = call @__torch__.torch.jit._shape_functions.sum_mean_dim(%arg0, %arg2, %arg3, %0) : (!torch.list<int>, !torch.optional<list<int>>, !torch.bool, !torch.any) -> !torch.list<int>\n"
 "    return %1 : !torch.list<int>\n"
 "  }\n"
+"  func.func @\"__torch_mlir_shape_fn.aten.linalg_norm\"(%arg0: !torch.list<int>, %arg1: !torch.optional<float>, %arg2: !torch.optional<list<int>>, %arg3: !torch.bool, %arg4: !torch.optional<int>) -> !torch.list<int> {\n"
+"    %0 = torch.derefine %arg4 : !torch.optional<int> to !torch.any\n"
+"    %1 = call @__torch__.torch.jit._shape_functions.sum_mean_dim(%arg0, %arg2, %arg3, %0) : (!torch.list<int>, !torch.optional<list<int>>, !torch.bool, !torch.any) -> !torch.list<int>\n"
+"    return %1 : !torch.list<int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten.frobenius_norm.dim\"(%arg0: !torch.list<int>, %arg1: !torch.list<int>, %arg2: !torch.bool) -> !torch.list<int> {\n"
 "    %int0 = torch.constant.int 0\n"
 "    %0 = torch.derefine %arg1 : !torch.list<int> to !torch.optional<list<int>>\n"
@@ -12058,6 +12063,60 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    }\n"
 "    return %4 : !torch.int\n"
 "  }\n"
+"  func.func @\"__torch_mlir_dtype_fn.aten.linalg_norm\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.optional<number>, %arg2: !torch.optional<list<int>>, %arg3: !torch.bool, %arg4: !torch.optional<int>) -> !torch.int {\n"
+"    %true = torch.constant.bool true\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"
+"    %3 = torch.aten.__isnot__ %arg4, %none : !torch.optional<int>, !torch.none -> !torch.bool\n"
+"    %4 = torch.prim.If %3 -> (!torch.int) {\n"
+"      %5 = torch.prim.unchecked_cast %arg4 : !torch.optional<int> -> !torch.int\n"
+"      %6 = func.call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.is_integer_dtype(%5) : (!torch.int) -> !torch.bool\n"
+"      %7 = torch.aten.__not__ %6 : !torch.bool -> !torch.bool\n"
+"      torch.prim.If %7 -> () {\n"
+"        torch.prim.If.yield\n"
+"      } else {\n"
+"        torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
+"        torch.prim.If.yield\n"
+"      }\n"
+"      %8 = func.call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.is_complex_dtype(%0#1) : (!torch.int) -> !torch.bool\n"
+"      %9 = torch.prim.If %8 -> (!torch.int) {\n"
+"        %10 = func.call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.is_complex_dtype(%5) : (!torch.int) -> !torch.bool\n"
+"        torch.prim.If %10 -> () {\n"
+"          torch.prim.If.yield\n"
+"        } else {\n"
+"          torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
+"          torch.prim.If.yield\n"
+"        }\n"
+"        %11 = torch.prim.TupleConstruct %0#0, %5 : !torch.int, !torch.int -> !torch.tuple<int, int>\n"
+"        %12 = func.call @\"__torch_mlir_dtype_fn.aten.std\"(%11, %true) : (!torch.tuple<int, int>, !torch.bool) -> !torch.int\n"
+"        torch.prim.If.yield %12 : !torch.int\n"
+"      } else {\n"
+"        %10 = func.call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.is_complex_dtype(%5) : (!torch.int) -> !torch.bool\n"
+"        %11 = torch.aten.__not__ %10 : !torch.bool -> !torch.bool\n"
+"        torch.prim.If %11 -> () {\n"
+"          torch.prim.If.yield\n"
+"        } else {\n"
+"          torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
+"          torch.prim.If.yield\n"
+"        }\n"
+"        torch.prim.If.yield %5 : !torch.int\n"
+"      }\n"
+"      torch.prim.If.yield %9 : !torch.int\n"
+"    } else {\n"
+"      %5 = func.call @\"__torch_mlir_dtype_fn.aten.std\"(%arg0, %true) : (!torch.tuple<int, int>, !torch.bool) -> !torch.int\n"
+"      torch.prim.If.yield %5 : !torch.int\n"
+"    }\n"
+"    return %4 : !torch.int\n"
+"  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.norm.Scalar\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.number) -> !torch.int {\n"
 "    %true = torch.constant.bool true\n"
 "    %int5 = torch.constant.int 5\n"

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -6971,6 +6971,37 @@ public:
 };
 } // namespace
 
+namespace {
+// Decompose AtenLinalgNormOp to AtenLinalgVectorNormOp only
+class DecomposeAtenLinalgNormOp : public OpRewritePattern<AtenLinalgNormOp> {
+public:
+  using OpRewritePattern::OpRewritePattern;
+  LogicalResult matchAndRewrite(AtenLinalgNormOp op,
+                                PatternRewriter &rewriter) const override {
+    Location loc = op.getLoc();
+    SmallVector<Value> dimList;
+    if (!getListConstructElements(op.getDim(), dimList)) {
+      return rewriter.notifyMatchFailure(
+          op, "dim should comes from a PrimListConstructOp");
+    }
+    if (dimList.size() != 1) {
+      return rewriter.notifyMatchFailure(
+          op, "Unimplemented: only dim size of 1 is supported");
+    }
+
+    // default ord value is 2 for vector_norm
+    auto ord = op.getOrd();
+    if (ord.getType().isa<Torch::NoneType>()) {
+      ord = rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(2));
+    }
+    rewriter.replaceOpWithNewOp<Torch::AtenLinalgVectorNormOp>(
+        op, op.getType(), op.getSelf(), ord, op.getDim(), op.getKeepdim(),
+        op.getDtype());
+    return success();
+  }
+};
+} // namespace
+
 namespace {
 class DecomposeComplexOpsPass
     : public DecomposeComplexOpsBase<DecomposeComplexOpsPass> {
@@ -7177,6 +7208,7 @@ public:
     addPatternIfTargetOpIsIllegal<DecomposeAtenReshapeAsOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenIndexTensorOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenTriuOp>(patterns);
+    addPatternIfTargetOpIsIllegal<DecomposeAtenLinalgNormOp>(patterns);
     // More specific conv ops
     addPatternIfTargetOpIsIllegal<DecomposeAtenConvTbcOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenConv1dOp>(patterns);

lib/Dialect/Torch/Transforms/LowerToBackendContract.cpp

@@ -520,6 +520,7 @@ static void markDecomposedOpsAsIllegal(MLIRContext *context,
   target.addIllegalOp<AtenTileOp>();
   target.addIllegalOp<AtenReshapeAsOp>();
   target.addIllegalOp<AtenTriuOp>();
+  target.addIllegalOp<AtenLinalgNormOp>();
   for (auto &opName : backendLegalOpsSet) {
     target.addLegalOp(
         OperationName(kTorchOpPrefix + opName.first().str(), context));

projects/pt1/e2e_testing/xfail_sets.py

@@ -1112,6 +1112,7 @@ TOSA_PASS_SET = {
     "LiftFreshCopyModule_basic",
     "LinalgVectorNormKeepDimModule_basic",
     "LinalgVectorNormModule_basic",
+    "LinalgNormKeepDimModule_basic",
     "MaskedFillScalarDefaultModule_basic",
     "MaskedFillScalarIntValueModule_basic",
     "MaskedFillScalarIntValueStaticModule_basic",
@@ -1885,6 +1886,8 @@ ONNX_XFAIL_SET = {
     "ScatterReduceIntSumModuleIncludeSelf",
     "TileBigDimsSizeModule_basic",
     "TileSmallDimsSizeModule_basic",
+    "LinalgNormKeepDimModule_basic",
+    "LinalgNormModule_basic",
 
     # Failure - onnx_lowering: onnx.AveragePool
     "AdaptiveAvgPool1dNonUnitOutputSizeStaticModule_basic",

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

@@ -1722,6 +1722,9 @@ def aten〇nonzero_static〡shape(self: List[int], size: int, fill_value: int =
 def aten〇linalg_vector_norm〡shape(self: List[int], ord: float = 2, dim: Optional[List[int]] = None, keepdim: bool = False, dtype: Optional[int] = None) -> List[int]:
     return upstream_shape_functions.sum_mean_dim(self, dim, keepdim, dtype)
 
+def aten〇linalg_norm〡shape(self: List[int], ord: Optional[float] = None, dim: Optional[List[int]] = None, keepdim: bool = False, dtype: Optional[int] = None) -> List[int]:
+    return upstream_shape_functions.sum_mean_dim(self, dim, keepdim, dtype)
+
 def aten〇frobenius_norm〇dim〡shape(self: List[int], dim: List[int], keepdim: bool = False) -> List[int]:
     return upstream_shape_functions.sum_mean_dim(self, dim, keepdim, 0)
 
@@ -3938,6 +3941,29 @@ def aten〇linalg_vector_norm〡dtype(self_rank_dtype: Tuple[int, int], ord: Uni
         return dtype
     return aten〇std〡dtype(self_rank_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}) +
+    _check_tensors_with_the_same_dtype(
+        num_of_tensors=1,
+        error_types={torch.bool, torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64, torch.complex64, torch.complex128}, dtype=torch.float64) +
+    _check_tensors_with_the_same_dtype(
+        num_of_tensors=1,
+        error_types={torch.bool, torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64, torch.bfloat16, torch.float16, torch.float32, torch.float64}, dtype=torch.complex128) +
+    [ErrorInvocation(NonZeroDTensorWithDtype(torch.float32), dtype=torch.int32)])
+def aten〇linalg_norm〡dtype(self_rank_dtype: Tuple[int, int], ord: Optional[Union[int, float, complex]] = None, dim: Optional[List[int]] = None, keepdim: bool = False, dtype: Optional[int] = None) -> int:
+    self_rank, self_dtype = self_rank_dtype
+    assert not is_integer_dtype(self_dtype)
+    if dtype is not None:
+        assert not is_integer_dtype(dtype)
+        if is_complex_dtype(self_dtype):
+            assert is_complex_dtype(dtype)
+            return aten〇std〡dtype((self_rank, dtype))
+        assert not is_complex_dtype(dtype)
+        return dtype
+    return aten〇std〡dtype(self_rank_dtype)
+
 @check_dtype_function(
         _check_tensors_with_the_same_dtype(
             num_of_tensors=1,

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

@@ -542,6 +542,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
     emit("aten::nll_loss_backward : (Tensor, Tensor, Tensor, Tensor?, int, int, Tensor) -> (Tensor)")
     emit("aten::bincount : (Tensor, Tensor?, int) -> (Tensor)")
     emit("aten::linalg_vector_norm : (Tensor, Scalar, int[]?, bool, int?) -> (Tensor)")
+    emit("aten::linalg_norm : (Tensor, Scalar?, int[]?, bool, int?) -> (Tensor)")
     emit("aten::linalg_qr : (Tensor, str) -> (Tensor, Tensor)")
     emit("aten::frobenius_norm.dim : (Tensor, int[], bool) -> (Tensor)")
     emit("aten::mse_loss : (Tensor, Tensor, int) -> (Tensor)")

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

@@ -1228,6 +1228,42 @@ def LinalgVectorNormKeepDimModule_basic(module, tu: TestUtils):
 
 # ==============================================================================
 
+class LinalgNormModule(torch.nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1, -1], torch.float32, True),
+    ])
+    def forward(self, a):
+        return torch.ops.aten.linalg_norm(a, ord=None, dim=[0], keepdim=False)
+
+@register_test_case(module_factory=lambda: LinalgNormModule())
+def LinalgNormModule_basic(module, tu: TestUtils):
+    module.forward(torch.rand(3, 4, 5))
+
+# ==============================================================================
+
+class LinalgNormKeepDimModule(torch.nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1, -1], torch.float32, True),
+    ])
+    def forward(self, a):
+        return torch.ops.aten.linalg_norm(a, ord=None, dim=[0], keepdim=True)
+
+@register_test_case(module_factory=lambda: LinalgNormKeepDimModule())
+def LinalgNormKeepDimModule_basic(module, tu: TestUtils):
+    module.forward(torch.rand(3, 4, 5))
+
+# ==============================================================================
+
 class MseLossNoReductionModule(torch.nn.Module):
     def __init__(self):
         super().__init__()