implement lowering of torch.aten._linalg_slogdet (#3524)

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

@@ -8737,6 +8737,30 @@ def Torch_Aten_LinalgDetOp : Torch_Op<"aten._linalg_det", [
   }];
 }
 
+def Torch_AtenLinalgSlogdetOp : Torch_Op<"aten.linalg_slogdet", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::linalg_slogdet : (Tensor) -> (Tensor, Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$A
+  );
+  let results = (outs
+    AnyTorchOptionalTensorType:$sign,
+    AnyTorchOptionalTensorType:$logabsdet
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenLinalgSlogdetOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 1, 2);
+    }
+    void AtenLinalgSlogdetOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 1, 2);
+    }
+  }];
+}
+
 def Torch_AtenFrobeniusNormDimOp : Torch_Op<"aten.frobenius_norm.dim", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -6555,6 +6555,54 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %3 = torch.prim.TupleConstruct %0, %1, %2 : !torch.int, !torch.int, !torch.int -> !torch.tuple<int, int, int>\n"
 "    return %3 : !torch.tuple<int, int, int>\n"
 "  }\n"
+"  func.func @\"__torch_mlir_shape_fn.aten.linalg_slogdet\"(%arg0: !torch.list<int>) -> !torch.tuple<list<int>, list<int>> {\n"
+"    %int-2 = torch.constant.int -2\n"
+"    %int-1 = torch.constant.int -1\n"
+"    %none = torch.constant.none\n"
+"    %str = torch.constant.str \"AssertionError: \"\n"
+"    %true = torch.constant.bool true\n"
+"    %int2 = torch.constant.int 2\n"
+"    %int3 = torch.constant.int 3\n"
+"    %int1 = torch.constant.int 1\n"
+"    %0 = torch.aten.len.t %arg0 : !torch.list<int> -> !torch.int\n"
+"    %1 = torch.aten.eq.int %0, %int2 : !torch.int, !torch.int -> !torch.bool\n"
+"    %2 = torch.prim.If %1 -> (!torch.bool) {\n"
+"      torch.prim.If.yield %true : !torch.bool\n"
+"    } else {\n"
+"      %9 = torch.aten.len.t %arg0 : !torch.list<int> -> !torch.int\n"
+"      %10 = torch.aten.eq.int %9, %int3 : !torch.int, !torch.int -> !torch.bool\n"
+"      torch.prim.If.yield %10 : !torch.bool\n"
+"    }\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.__getitem__.t %arg0, %int-1 : !torch.list<int>, !torch.int -> !torch.int\n"
+"    %4 = torch.aten.__getitem__.t %arg0, %int-2 : !torch.list<int>, !torch.int -> !torch.int\n"
+"    %5 = torch.aten.eq.int %3, %4 : !torch.int, !torch.int -> !torch.bool\n"
+"    torch.prim.If %5 -> () {\n"
+"      torch.prim.If.yield\n"
+"    } else {\n"
+"      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
+"      torch.prim.If.yield\n"
+"    }\n"
+"    %6 = torch.aten.len.t %arg0 : !torch.list<int> -> !torch.int\n"
+"    %7 = torch.aten.eq.int %6, %int3 : !torch.int, !torch.int -> !torch.bool\n"
+"    %8 = torch.prim.If %7 -> (!torch.tuple<list<int>, list<int>>) {\n"
+"      %9 = torch.aten.slice.t %arg0, %none, %int1, %int1 : !torch.list<int>, !torch.none, !torch.int, !torch.int -> !torch.list<int>\n"
+"      %10 = torch.aten.slice.t %arg0, %none, %int1, %int1 : !torch.list<int>, !torch.none, !torch.int, !torch.int -> !torch.list<int>\n"
+"      %11 = torch.prim.TupleConstruct %9, %10 : !torch.list<int>, !torch.list<int> -> !torch.tuple<list<int>, list<int>>\n"
+"      torch.prim.If.yield %11 : !torch.tuple<list<int>, list<int>>\n"
+"    } else {\n"
+"      %9 = torch.derefine %arg0 : !torch.list<int> to !torch.any\n"
+"      %10 = func.call @__torch__.torch.jit._shape_functions.zero_dim_tensor(%9) : (!torch.any) -> !torch.list<int>\n"
+"      %11 = torch.prim.TupleConstruct %10, %10 : !torch.list<int>, !torch.list<int> -> !torch.tuple<list<int>, list<int>>\n"
+"      torch.prim.If.yield %11 : !torch.tuple<list<int>, list<int>>\n"
+"    }\n"
+"    return %8 : !torch.tuple<list<int>, list<int>>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten.detach\"(%arg0: !torch.list<int>) -> !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"
@@ -11770,6 +11818,61 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "  func.func @\"__torch_mlir_dtype_fn.aten._softmax_backward_data\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.tuple<int, int>, %arg2: !torch.int, %arg3: !torch.int) -> !torch.int {\n"
 "    return %arg3 : !torch.int\n"
 "  }\n"
+"  func.func @\"__torch_mlir_dtype_fn.aten.linalg_slogdet\"(%arg0: !torch.tuple<int, int>) -> !torch.tuple<int, int> {\n"
+"    %int7 = torch.constant.int 7\n"
+"    %int10 = torch.constant.int 10\n"
+"    %int6 = torch.constant.int 6\n"
+"    %int9 = torch.constant.int 9\n"
+"    %true = torch.constant.bool true\n"
+"    %int8 = torch.constant.int 8\n"
+"    %false = torch.constant.bool false\n"
+"    %int15 = torch.constant.int 15\n"
+"    %int5 = torch.constant.int 5\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.ne.int %0#1, %int5 : !torch.int, !torch.int -> !torch.bool\n"
+"    %4 = torch.prim.If %3 -> (!torch.bool) {\n"
+"      %11 = torch.aten.ne.int %0#1, %int15 : !torch.int, !torch.int -> !torch.bool\n"
+"      torch.prim.If.yield %11 : !torch.bool\n"
+"    } else {\n"
+"      torch.prim.If.yield %false : !torch.bool\n"
+"    }\n"
+"    torch.prim.If %4 -> () {\n"
+"      torch.prim.If.yield\n"
+"    } else {\n"
+"      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
+"      torch.prim.If.yield\n"
+"    }\n"
+"    %5 = torch.aten.eq.int %0#1, %int8 : !torch.int, !torch.int -> !torch.bool\n"
+"    %6 = torch.prim.If %5 -> (!torch.bool) {\n"
+"      torch.prim.If.yield %true : !torch.bool\n"
+"    } else {\n"
+"      %11 = torch.aten.eq.int %0#1, %int9 : !torch.int, !torch.int -> !torch.bool\n"
+"      torch.prim.If.yield %11 : !torch.bool\n"
+"    }\n"
+"    %7 = torch.prim.If %6 -> (!torch.int) {\n"
+"      torch.prim.If.yield %int6 : !torch.int\n"
+"    } else {\n"
+"      torch.prim.If.yield %0#1 : !torch.int\n"
+"    }\n"
+"    %8 = torch.aten.eq.int %0#1, %int10 : !torch.int, !torch.int -> !torch.bool\n"
+"    %9 = torch.prim.If %8 -> (!torch.int) {\n"
+"      torch.prim.If.yield %int7 : !torch.int\n"
+"    } else {\n"
+"      torch.prim.If.yield %7 : !torch.int\n"
+"    }\n"
+"    %10 = torch.prim.TupleConstruct %0#1, %9 : !torch.int, !torch.int -> !torch.tuple<int, int>\n"
+"    return %10 : !torch.tuple<int, int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.square\"(%arg0: !torch.tuple<int, int>) -> !torch.int {\n"
 "    %int4 = torch.constant.int 4\n"
 "    %int11 = torch.constant.int 11\n"

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -2904,6 +2904,35 @@ public:
 };
 } // namespace
 
+// decompose aten.linalg_slogdet into: aten.sgn, aten.log, aten.abs
+// aten.linalg_det
+namespace {
+
+class DecomposeAtenLinalgSlogdetOp
+    : public OpRewritePattern<AtenLinalgSlogdetOp> {
+public:
+  using OpRewritePattern::OpRewritePattern;
+  LogicalResult matchAndRewrite(AtenLinalgSlogdetOp op,
+                                PatternRewriter &rewriter) const override {
+    SmallVector<Value, 2> results = op.getResults();
+    Location loc = op.getLoc();
+    Value input = op.getA();
+    Value determinant = rewriter.create<Torch::AtenLinalgDetOp>(
+        loc, results[0].getType(), input);
+    Value sign =
+        rewriter.create<AtenSgnOp>(loc, determinant.getType(), determinant);
+    Value abs_det =
+        rewriter.create<AtenAbsOp>(loc, determinant.getType(), determinant);
+    Value ln_abs_det =
+        rewriter.create<AtenLogOp>(loc, abs_det.getType(), abs_det);
+    rewriter.replaceAllUsesWith(results[0], sign);
+    rewriter.replaceAllUsesWith(results[1], ln_abs_det);
+    rewriter.eraseOp(op);
+    return success();
+  }
+};
+} // namespace
+
 namespace {
 
 class DecomposeAten_LinalgDetOp : public OpRewritePattern<Aten_LinalgDetOp> {
@@ -9274,6 +9303,7 @@ public:
     addPatternIfTargetOpIsIllegal<DecomposeAtenTrilIndicesOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenLinalgNormOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAten_LinalgDetOp>(patterns);
+    addPatternIfTargetOpIsIllegal<DecomposeAtenLinalgSlogdetOp>(patterns);
     addPatternIfTargetOpIsIllegal<
         DecomposeAtenFakeQuantizePerTensorAffineCachemaskOp>(patterns);
     // More specific conv ops

lib/Dialect/Torch/Transforms/LowerToBackendContract.cpp

@@ -406,6 +406,7 @@ static void markDecomposedOpsAsIllegal(MLIRContext *context,
   target.addIllegalOp<AtenRenormOp>();
   target.addIllegalOp<AtenLinalgCrossOp>();
   target.addIllegalOp<Aten_LinalgDetOp>();
+  target.addIllegalOp<AtenLinalgSlogdetOp>();
   target.addIllegalOp<AtenPixelShuffleOp>();
   target.addIllegalOp<AtenTOp>();
   target.addIllegalOp<Aten_LogSoftmaxBackwardDataOp>();

projects/pt1/e2e_testing/xfail_sets.py

@@ -451,6 +451,9 @@ FX_IMPORTER_XFAIL_SET = {
     "RsubInt0d_NumToTensor_Module_basic",
     "ScalarConstantTupleModule_basic",
     "ScalarImplicitFloatModule_basic",
+    "SignAndLogarithmOfDeterminantModule_F32",
+    "SignAndLogarithmOfDeterminantBatchedModule_F32",
+    "SignAndLogarithmOfDeterminantDynamicModule_F32",
     "SortIntListReverse_basic",
     "SortIntList_basic",
     "SplitDimDynamicModule_basic",
@@ -2563,6 +2566,9 @@ ONNX_XFAIL_SET = {
     "ScatterReduceIntSumModule",
     "SelectScattertModule_basic",
     "SelectScattertStaticModule_basic",
+    "SignAndLogarithmOfDeterminantModule_F32",
+    "SignAndLogarithmOfDeterminantBatchedModule_F32",
+    "SignAndLogarithmOfDeterminantDynamicModule_F32",
     "SliceEndSleStartModule_basic",
     "SliceOutOfUpperBoundIndexModule_basic",
     "SliceScatterModule_basic",
@@ -3429,6 +3435,9 @@ FX_IMPORTER_TOSA_XFAIL_SET = {
     "ScatterValueIntModule_basic",
     "SelectScattertModule_basic",
     "SelectScattertStaticModule_basic",
+    "SignAndLogarithmOfDeterminantModule_F32",
+    "SignAndLogarithmOfDeterminantBatchedModule_F32",
+    "SignAndLogarithmOfDeterminantDynamicModule_F32",
     "SliceCopyEndGreaterThanDimSize_Module_basic",
     "SliceCopyNegative_Module_basic",
     "SliceCopyNonZeroDim_Module_basic",
@@ -4341,6 +4350,9 @@ ONNX_TOSA_XFAIL_SET = {
     "SelectIntNegativeDimAndIndexStaticModule_basic",
     "SelectScattertModule_basic",
     "SelectScattertStaticModule_basic",
+    "SignAndLogarithmOfDeterminantModule_F32",
+    "SignAndLogarithmOfDeterminantBatchedModule_F32",
+    "SignAndLogarithmOfDeterminantDynamicModule_F32",
     "SliceCopyEndGreaterThanDimSize_Module_basic",
     "SliceCopyNegative_Module_basic",
     "SliceCopyNonZeroDim_Module_basic",

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

@@ -242,6 +242,14 @@ def aten〇_linalg_det〡shape(A: List[int]) -> Tuple[List[int], List[int], List
 def aten〇_linalg_det〡dtype(A_rank_dtype: Tuple[int, int]) -> Tuple[int, int, int]:
     return (A_rank_dtype[1], A_rank_dtype[1], A_rank_dtype[1])
 
+def aten〇linalg_slogdet〡shape(A: List[int]) -> Tuple[List[int], List[int]]:
+    assert len(A) == 2 or len(A) == 3
+    assert A[-1] == A[-2]
+    if len(A) == 3:
+        return A[:1], A[:1]
+    shape = upstream_shape_functions.zero_dim_tensor(A)
+    return shape, shape
+
 def aten〇detach〡shape(self: List[int]) -> List[int]:
     return upstream_shape_functions.unary(self)
 
@@ -3224,6 +3232,18 @@ def aten〇slice〇Tensor〡dtype(self_rank_dtype: Tuple[int, int], dim: int = 0
 def aten〇_softmax_backward_data〡dtype(grad_output_rank_dtype: Tuple[int, int], output_rank_dtype: Tuple[int, int], dim: int, input_dtype: int) -> int:
     return input_dtype
 
+@check_dtype_function(_check_tensors_with_the_same_dtype(tensor_shapes=[(4,4),], error_types={*all_integer_dtypes(), torch.float16, torch.bfloat16}))
+def aten〇linalg_slogdet〡dtype(A_rank_dtype: Tuple[int, int]) -> Tuple[int, int]:
+    self_rank, self_dtype = A_rank_dtype
+    assert not is_integer_dtype(self_dtype)
+    assert self_dtype != torch.float16 and self_dtype != torch.bfloat16
+    det_type = self_dtype 
+    if self_dtype == torch.complex32 or self_dtype == torch.complex64:
+        det_type = torch.float32
+    if self_dtype == torch.complex128:
+        det_type = torch.float64
+    return self_dtype, det_type
+
 @check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1))
 def aten〇square〡dtype(self_rank_dtype: Tuple[int, int]) -> int:
     self_rank, self_dtype = self_rank_dtype

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

@@ -709,6 +709,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
     emit("aten::linalg_qr : (Tensor, str) -> (Tensor, Tensor)")
     emit("aten::linalg_det : (Tensor) -> (Tensor)")
     emit("aten::_linalg_det : (Tensor) -> (Tensor, Tensor, Tensor)")
+    emit("aten::linalg_slogdet : (Tensor) -> (Tensor, Tensor)")
     emit("aten::frobenius_norm.dim : (Tensor, int[], bool) -> (Tensor)")
     emit("aten::mse_loss : (Tensor, Tensor, int) -> (Tensor)")
     emit("aten::mse_loss_backward : (Tensor, Tensor, Tensor, int) -> (Tensor)")

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

@@ -49,3 +49,45 @@ class DeterminantDynamicModule(torch.nn.Module):
 def DeterminantDynamicModule_F32(module, tu: TestUtils):
     A = tu.rand(3, 4, 4).to(dtype=torch.float32)
     module.forward(A)
+
+
+# ==============================================================================
+
+
+class SignAndLogarithmOfDeterminantModule(torch.nn.Module):
+    @export
+    @annotate_args([None, [(4, 4), torch.float32, True]])
+    def forward(self, A):
+        return torch.linalg.slogdet(A)
+
+
+@register_test_case(module_factory=lambda: SignAndLogarithmOfDeterminantModule())
+def SignAndLogarithmOfDeterminantModule_F32(module, tu: TestUtils):
+    A = tu.rand(4, 4).to(dtype=torch.float32)
+    module.forward(A)
+
+
+class SignAndLogarithmOfDeterminantBatchedModule(torch.nn.Module):
+    @export
+    @annotate_args([None, [(3, 4, 4), torch.float32, True]])
+    def forward(self, A):
+        return torch.linalg.slogdet(A)
+
+
+@register_test_case(module_factory=lambda: SignAndLogarithmOfDeterminantBatchedModule())
+def SignAndLogarithmOfDeterminantBatchedModule_F32(module, tu: TestUtils):
+    A = tu.rand(3, 4, 4).to(dtype=torch.float32)
+    module.forward(A)
+
+
+class SignAndLogarithmOfDeterminantDynamicModule(torch.nn.Module):
+    @export
+    @annotate_args([None, [(-1, -1, -1), torch.float32, True]])
+    def forward(self, A):
+        return torch.linalg.slogdet(A)
+
+
+@register_test_case(module_factory=lambda: SignAndLogarithmOfDeterminantBatchedModule())
+def SignAndLogarithmOfDeterminantDynamicModule_F32(module, tu: TestUtils):
+    A = tu.rand(3, 4, 4).to(dtype=torch.float32)
+    module.forward(A)