Implement lowering of torch.aten.hstack (#3563)

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

@@ -14437,6 +14437,29 @@ def Torch_AtenStackOp : Torch_Op<"aten.stack", [
   }];
 }
 
+def Torch_AtenHstackOp : Torch_Op<"aten.hstack", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::hstack : (Tensor[]) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchListOfTensorType:$tensors
+  );
+  let results = (outs
+    AnyTorchOptionalTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenHstackOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 1, 1);
+    }
+    void AtenHstackOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 1, 1);
+    }
+  }];
+}
+
 def Torch_AtenAppendTOp : Torch_Op<"aten.append.t", [
     AllowsTypeRefinement
   ]> {

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -10608,6 +10608,31 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %0 = call @__torch__.torch.jit._shape_functions.stack(%arg0, %arg1) : (!torch.list<list<int>>, !torch.int) -> !torch.list<int>\n"
 "    return %0 : !torch.list<int>\n"
 "  }\n"
+"  func.func @\"__torch_mlir_shape_fn.aten.hstack\"(%arg0: !torch.list<list<int>>) -> !torch.list<int> {\n"
+"    %true = torch.constant.bool true\n"
+"    %int0 = torch.constant.int 0\n"
+"    %int1 = torch.constant.int 1\n"
+"    %0 = torch.prim.ListConstruct  : () -> !torch.list<list<int>>\n"
+"    %1 = torch.aten.len.t %arg0 : !torch.list<list<int>> -> !torch.int\n"
+"    torch.prim.Loop %1, %true, init() {\n"
+"    ^bb0(%arg1: !torch.int):\n"
+"      %6 = torch.aten.__getitem__.t %arg0, %arg1 : !torch.list<list<int>>, !torch.int -> !torch.list<int>\n"
+"      %7 = func.call @\"__torch_mlir_shape_fn.aten.atleast_1d\"(%6) : (!torch.list<int>) -> !torch.list<int>\n"
+"      %8 = torch.aten.append.t %0, %7 : !torch.list<list<int>>, !torch.list<int> -> !torch.list<list<int>>\n"
+"      torch.prim.Loop.condition %true, iter()\n"
+"    } : (!torch.int, !torch.bool) -> ()\n"
+"    %2 = torch.aten.__getitem__.t %0, %int0 : !torch.list<list<int>>, !torch.int -> !torch.list<int>\n"
+"    %3 = torch.aten.len.t %2 : !torch.list<int> -> !torch.int\n"
+"    %4 = torch.aten.eq.int %3, %int1 : !torch.int, !torch.int -> !torch.bool\n"
+"    %5 = torch.prim.If %4 -> (!torch.list<int>) {\n"
+"      %6 = func.call @__torch__.torch.jit._shape_functions.cat(%0, %int0) : (!torch.list<list<int>>, !torch.int) -> !torch.list<int>\n"
+"      torch.prim.If.yield %6 : !torch.list<int>\n"
+"    } else {\n"
+"      %6 = func.call @__torch__.torch.jit._shape_functions.cat(%0, %int1) : (!torch.list<list<int>>, !torch.int) -> !torch.list<int>\n"
+"      torch.prim.If.yield %6 : !torch.list<int>\n"
+"    }\n"
+"    return %5 : !torch.list<int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten.fft_fft\"(%arg0: !torch.list<int>, %arg1: !torch.optional<int>, %arg2: !torch.int, %arg3: !torch.optional<str>) -> !torch.list<int> {\n"
 "    return %arg0 : !torch.list<int>\n"
 "  }\n"
@@ -15232,6 +15257,33 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
 "    return %0#1 : !torch.int\n"
 "  }\n"
+"  func.func @\"__torch_mlir_dtype_fn.aten.hstack\"(%arg0: !torch.list<tuple<int, int>>) -> !torch.int {\n"
+"    %true = torch.constant.bool true\n"
+"    %none = torch.constant.none\n"
+"    %str = torch.constant.str \"AssertionError: \"\n"
+"    %int0 = torch.constant.int 0\n"
+"    %0 = torch.prim.ListConstruct  : () -> !torch.list<optional<int>>\n"
+"    %1 = torch.prim.ListConstruct  : () -> !torch.list<int>\n"
+"    %2 = torch.aten.len.t %arg0 : !torch.list<tuple<int, int>> -> !torch.int\n"
+"    %3 = torch.aten.ne.int %2, %int0 : !torch.int, !torch.int -> !torch.bool\n"
+"    torch.prim.If %3 -> () {\n"
+"      torch.prim.If.yield\n"
+"    } else {\n"
+"      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
+"      torch.prim.If.yield\n"
+"    }\n"
+"    %4 = torch.aten.len.t %arg0 : !torch.list<tuple<int, int>> -> !torch.int\n"
+"    torch.prim.Loop %4, %true, init() {\n"
+"    ^bb0(%arg1: !torch.int):\n"
+"      %6 = torch.aten.__getitem__.t %arg0, %arg1 : !torch.list<tuple<int, int>>, !torch.int -> !torch.tuple<int, int>\n"
+"      %7:2 = torch.prim.TupleUnpack %6 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
+"      %8 = torch.aten.append.t %0, %7#0 : !torch.list<optional<int>>, !torch.int -> !torch.list<optional<int>>\n"
+"      %9 = torch.aten.append.t %1, %7#1 : !torch.list<int>, !torch.int -> !torch.list<int>\n"
+"      torch.prim.Loop.condition %true, iter()\n"
+"    } : (!torch.int, !torch.bool) -> ()\n"
+"    %5 = call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.promote_dtypes(%0, %1) : (!torch.list<optional<int>>, !torch.list<int>) -> !torch.int\n"
+"    return %5 : !torch.int\n"
+"  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.einsum\"(%arg0: !torch.str, %arg1: !torch.list<tuple<int, int>>, %arg2: !torch.optional<list<int>>) -> !torch.int {\n"
 "    %true = torch.constant.bool true\n"
 "    %none = torch.constant.none\n"

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -3779,6 +3779,58 @@ class DecomposeAtenStackOp : public OpRewritePattern<AtenStackOp> {
 };
 } // namespace
 
+// Decompose `aten.hstack` into `aten.at_least1d` and `aten.cat`.
+// https://github.com/pytorch/pytorch/blob/207564bab1c4fe42750931765734ee604032fb69/torch/_refs/__init__.py#L3908
+namespace {
+class DecomposeAtenHstackOp : public OpRewritePattern<AtenHstackOp> {
+public:
+  using OpRewritePattern::OpRewritePattern;
+  LogicalResult matchAndRewrite(AtenHstackOp op,
+                                PatternRewriter &rewriter) const override {
+    Location loc = op.getLoc();
+
+    // Get SmallVector<Value> from Value.
+    SmallVector<Value> tensors;
+    if (!getListConstructElements(op.getTensors(), tensors))
+      return rewriter.notifyMatchFailure(
+          op, "unimplemented: the tensor list is not from list construct");
+
+    // Execute AtenAtleast1dOp on every tensor inside tensors.
+    SmallVector<Value> atleast1dTensors;
+    for (auto tensor : tensors) {
+      std::optional<unsigned> tensorRank = getTensorRank(tensor);
+
+      // Check if the tensor is already of rank >= 1.
+      if (*tensorRank < 1) {
+        auto atleast1dTensor =
+            rewriter.create<AtenAtleast1dOp>(loc, tensor.getType(), tensor);
+        atleast1dTensors.push_back(atleast1dTensor);
+      } else {
+        atleast1dTensors.push_back(tensor);
+      }
+    }
+
+    // Make Value list from atleast1dTensors variable.
+    auto elemType = cast<BaseTensorType>(atleast1dTensors[0].getType())
+                        .getWithSizesAndDtype(std::nullopt, nullptr);
+    Value atleast1dTensorList = rewriter.create<PrimListConstructOp>(
+        loc, Torch::ListType::get(elemType), atleast1dTensors);
+
+    // Replace hstack with cat operator.
+    if (getTensorRank(atleast1dTensors[0]) == 1)
+      rewriter.replaceOpWithNewOp<AtenCatOp>(
+          op, op.getType(), atleast1dTensorList,
+          rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(0)));
+    else
+      rewriter.replaceOpWithNewOp<AtenCatOp>(
+          op, op.getType(), atleast1dTensorList,
+          rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(1)));
+
+    return success();
+  }
+};
+} // namespace
+
 // Decompose aten.roll into aten.slice and aten.cat ops.
 // https://pytorch.org/docs/stable/generated/torch.roll.html
 namespace {
@@ -10024,6 +10076,7 @@ class DecomposeComplexOpsPass
     addPatternIfTargetOpIsIllegal<
         DecomposeConstantTensorAllocLikeOp<AtenZerosLikeOp, 0>>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenStackOp>(patterns);
+    addPatternIfTargetOpIsIllegal<DecomposeAtenHstackOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenRollOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenRepeatOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenRepeatInterleaveSelfIntOp>(

lib/Dialect/Torch/Transforms/LowerToBackendContract.cpp

@@ -380,6 +380,7 @@ static void markDecomposedOpsAsIllegal(MLIRContext *context,
   target.addIllegalOp<AtenOnesLikeOp>();
   target.addIllegalOp<AtenZerosLikeOp>();
   target.addIllegalOp<AtenStackOp>();
+  target.addIllegalOp<AtenHstackOp>();
   target.addIllegalOp<AtenRollOp>();
   target.addIllegalOp<AtenRepeatOp>();
   target.addIllegalOp<AtenRepeatInterleaveSelfIntOp>();

projects/pt1/e2e_testing/xfail_sets.py

@@ -1196,6 +1196,10 @@
     "GridSamplerBasic4_basic",
     "GtFloatIntModule_basic",
     "GtIntModule_basic",
+    "HstackBasicComplexModule_basic",
+    "HstackBasicFloatModule_basic",
+    "HstackBasicIntFloatModule_basic",
+    "HstackBasicIntModule_basic",
     "IndexTensorMultiIndexStaticModule_basic",
     "IndexTensorStaticModule_basic",
     "IntFloatModule_basic",
@@ -2175,6 +2179,11 @@
     # failed to legalize operation 'torch.aten.rrelu_with_noise'
     "ElementwiseRreluEvalModule_basic",
     "ElementwiseRreluEvalStaticModule_basic",
+    # incompatible return type failure for tosa.concat.
+    "HstackBasicComplexModule_basic",
+    "HstackBasicFloatModule_basic",
+    "HstackBasicIntFloatModule_basic",
+    "HstackBasicIntModule_basic",
     # Shape Related failures
     "PrimListUnpackNumMismatchModule_basic",
     "ReshapeExpandModule_basic",
@@ -2573,6 +2582,10 @@
     "GtFloatIntModule_basic",
     "GtIntModule_basic",
     "HardtanhBackward_basic",
+    "HstackBasicComplexModule_basic",
+    "HstackBasicFloatModule_basic",
+    "HstackBasicIntFloatModule_basic",
+    "HstackBasicIntModule_basic",
     "IndexPutImpl1DFloatAccumulateModule_basic",
     "IndexPutImpl1DFloatNonAccumulateModule_basic",
     "IndexPutImpl1DIntAccumulateModule_basic",

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

@@ -2162,6 +2162,19 @@ def aten〇atleast_2d〡shape(self: List[int]) -> List[int]:
 def aten〇stack〡shape(tensors: List[List[int]], dim: int = 0) -> List[int]:
     return upstream_shape_functions.stack(tensors, dim)
 
+
+@check_shape_function([
+    Invocation([LongTensorOfShape(2, 4, 3), LongTensorOfShape(2, 5, 3)]), # Basic case.
+])
+def aten〇hstack〡shape(tensors: List[List[int]]) -> List[int]:
+
+    tensors_atleast1d = [aten〇atleast_1d〡shape(tensor) for tensor in tensors]
+
+    if len(tensors_atleast1d[0]) == 1:
+        return upstream_shape_functions.cat(tensors_atleast1d, dim=0)    
+
+    return upstream_shape_functions.cat(tensors_atleast1d, dim=1)
+
 def aten〇fft_fft〡shape(self: List[int], n: Optional[int] = None, dim: int = -1, norm: Optional[str] = None) -> List[int]:
     return self
 
@@ -5384,6 +5397,23 @@ def aten〇atleast_2d〡dtype(self_rank_dtype: Tuple[int, int]) -> int:
     self_rank, self_dtype = self_rank_dtype
     return self_dtype
 
+@check_dtype_function(
+    [Invocation([NonZeroDTensorWithDtype(torch.bool), NonZeroDTensorWithDtype(torch.int32), NonZeroDTensorWithDtype(torch.int64)]),
+     Invocation([NonZeroDTensorWithDtype(torch.float32), NonZeroDTensorWithDtype(torch.int32)]),
+     Invocation([NonZeroDTensorWithDtype(torch.float16), NonZeroDTensorWithDtype(torch.float64)]),
+     Invocation([NonZeroDTensorWithDtype(torch.float32), NonZeroDTensorWithDtype(torch.int32),
+                 NonZeroDTensorWithDtype(torch.complex64)])])
+def aten〇hstack〡dtype(tensors_rank_dtype: List[Tuple[int, int]]) -> int:
+    ranks: List[Optional[int]] = []
+    dtypes: List[int] = []
+    assert len(tensors_rank_dtype) != 0
+    for tensor_rank_dtype in tensors_rank_dtype:
+        tensor_rank, tensor_dtype = tensor_rank_dtype
+        ranks.append(tensor_rank)
+        dtypes.append(tensor_dtype)
+
+    return promote_dtypes(ranks, dtypes)
+
 @check_dtype_function(
     [Invocation("i,j->ij", [TensorOfShape(1, dtype=torch.float32),
                             TensorOfShape(1, dtype=torch.int32)]),])

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

@@ -1035,6 +1035,7 @@ def emit_with_mutating_variants(key, **kwargs):
         has_folder=True,
     )
     emit("aten::stack : (Tensor[], int) -> (Tensor)")
+    emit("aten::hstack : (Tensor[]) -> (Tensor)")
     emit("aten::append.t : (t[], t) -> (t[])")
     emit("aten::add.t : (t[], t[]) -> (t[])", has_canonicalizer=True)
     emit("aten::eq.int_list : (int[], int[]) -> (bool)", has_folder=True)

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

@@ -1308,6 +1308,107 @@ def TensorsStackPromoteDTypeModule_basic(module, tu: TestUtils):
 # ==============================================================================
 
 
+class HstackBasicIntModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([2, 3, 4], torch.bool, True),
+            ([2, 3, 4], torch.int32, True),
+            ([2, 3, 4], torch.int64, True),
+        ]
+    )
+    def forward(self, x, y, z):
+        return torch.ops.aten.hstack([x, y, z])
+
+
+@register_test_case(module_factory=lambda: HstackBasicIntModule())
+def HstackBasicIntModule_basic(module, tu: TestUtils):
+    module.forward(
+        tu.randint(2, 3, 4, low=0, high=2).bool(),
+        tu.randint(2, 3, 4, low=0, high=100).int(),
+        tu.randint(2, 3, 4, low=0, high=100).long(),
+    )
+
+
+class HstackBasicFloatModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([2, 6, 4], torch.int32, True),
+            ([2, 3, 4], torch.float64, True),
+        ]
+    )
+    def forward(self, x, y):
+        return torch.ops.aten.hstack([x, y])
+
+
+@register_test_case(module_factory=lambda: HstackBasicFloatModule())
+def HstackBasicFloatModule_basic(module, tu: TestUtils):
+    module.forward(
+        tu.rand(2, 6, 4).int(),
+        tu.rand(2, 3, 4).double(),
+    )
+
+
+class HstackBasicIntFloatModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([-1, -1, -1, -1], torch.int32, True),
+            ([-1, -1, -1, -1], torch.float32, True),
+        ]
+    )
+    def forward(self, x, y):
+        return torch.ops.aten.hstack([x, y])
+
+
+@register_test_case(module_factory=lambda: HstackBasicIntFloatModule())
+def HstackBasicIntFloatModule_basic(module, tu: TestUtils):
+    module.forward(
+        tu.randint(4, 6, 4, 2, low=1, high=50).int(),
+        tu.rand(4, 3, 4, 2),
+    )
+
+
+class HstackBasicComplexModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([-1, -1, -1, -1], torch.complex64, True),
+            ([-1, -1, -1, -1], torch.complex128, True),
+        ]
+    )
+    def forward(self, x, y):
+        return torch.ops.aten.hstack([x, y])
+
+
+@register_test_case(module_factory=lambda: HstackBasicComplexModule())
+def HstackBasicComplexModule_basic(module, tu: TestUtils):
+    module.forward(
+        tu.rand(4, 6, 4, 2).type(torch.complex64),
+        tu.rand(4, 3, 4, 2).type(torch.complex128),
+    )
+
+
+# ==============================================================================
+
+
 class GatherModule(torch.nn.Module):
     def __init__(self):
         super().__init__()