[Stablehlo] support lowering sinh & cosh to stablehlo (llvm#3213)

lib/Conversion/TorchToStablehlo/Basic.cpp

@@ -1981,7 +1981,9 @@ void mlir::torch::torch_to_stablehlo::populateBasicOpPatternsAndLegality(
   INSERT_UNARY_PROMOTE_TO_FP_PATTERN(AtenSinOp, stablehlo::SineOp);
   INSERT_UNARY_PROMOTE_TO_FP_PATTERN(AtenCosOp, stablehlo::CosineOp);
   INSERT_UNARY_PROMOTE_TO_FP_PATTERN(AtenAsinOp, chlo::AsinOp);
+  INSERT_UNARY_PROMOTE_TO_FP_PATTERN(AtenSinhOp, chlo::SinhOp);
   INSERT_UNARY_PROMOTE_TO_FP_PATTERN(AtenAcosOp, chlo::AcosOp);
+  INSERT_UNARY_PROMOTE_TO_FP_PATTERN(AtenCoshOp, chlo::CoshOp);
   INSERT_UNARY_PROMOTE_TO_FP_PATTERN(AtenAtanOp, chlo::AtanOp);
 #undef INSERT_UNARY_PROMOTE_TO_FP_PATTERN
 

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -6332,6 +6332,10 @@ 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.sinh\"(%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"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten.asin\"(%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"
@@ -9699,6 +9703,11 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %1 = call @__torch__._get_dtype_of_floating_point_op(%0#1) : (!torch.int) -> !torch.int\n"
 "    return %1 : !torch.int\n"
 "  }\n"
+"  func.func @\"__torch_mlir_dtype_fn.aten.sinh\"(%arg0: !torch.tuple<int, int>) -> !torch.int {\n"
+"    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
+"    %1 = call @__torch__._get_dtype_of_floating_point_op(%0#1) : (!torch.int) -> !torch.int\n"
+"    return %1 : !torch.int\n"
+"  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.asin\"(%arg0: !torch.tuple<int, int>) -> !torch.int {\n"
 "    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
 "    %1 = call @__torch__._get_dtype_of_floating_point_op(%0#1) : (!torch.int) -> !torch.int\n"

projects/pt1/e2e_testing/xfail_sets.py

@@ -636,8 +636,6 @@
     "ElementwiseBitwiseRightShiftInt32Module_basic",
     "ElementwiseBitwiseRightShiftInt64Module_basic",
     "ElementwiseBitwiseRightShiftInt8Module_basic",
-    "ElementwiseCoshIntModule_basic",
-    "ElementwiseCoshModule_basic",
     "ElementwiseDequantizePerChannelModule_basic",
     "ElementwiseDequantizePerTensorModule_basic",
     "ElementwiseErfIntModule_basic",
@@ -1465,6 +1463,10 @@
     "ElementwiseSinIntModule_basic",
     "ElementwiseSqrtIntModule_basic",
     "ElementwiseUnaryIntModule_basic",
+    "ElementwiseCoshIntModule_basic",
+    "ElementwiseCoshModule_basic",
+    "ElementwiseSinhIntModule_basic",
+    "ElementwiseSinhModule_basic",
 }
 
 STABLEHLO_CRASHING_SET =  {
@@ -2326,6 +2328,8 @@
     "ElementwiseBitwiseRightShiftInt8Module_basic",
     "ElementwiseBitwiseXorModule_basic",
     "ElementwiseBitwiseXorStaticShapeModule_basic",
+    "ElementwiseSinhIntModule_basic",
+    "ElementwiseSinhModule_basic",
     "ElementwiseCoshIntModule_basic",
     "ElementwiseCoshModule_basic",
     "ElementwiseDequantizePerChannelModule_basic",

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

@@ -121,6 +121,9 @@ def aten〇fake_quantize_per_tensor_affine〡shape(self: List[int], scale: float
 def aten〇sin〡shape(self: List[int]) -> List[int]:
     return upstream_shape_functions.unary(self)
 
+def aten〇sinh〡shape(self: List[int]) -> List[int]:
+    return upstream_shape_functions.unary(self)
+
 def aten〇asin〡shape(self: List[int]) -> List[int]:
     return upstream_shape_functions.unary(self)
 
@@ -2014,6 +2017,11 @@ def aten〇sin〡dtype(self_rank_dtype: Tuple[int, int]) -> int:
     self_rank, self_dtype = self_rank_dtype
     return _get_dtype_of_floating_point_op(self_dtype)
 
+@check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1))
+def aten〇sinh〡dtype(self_rank_dtype: Tuple[int, int]) -> int:
+    self_rank, self_dtype = self_rank_dtype
+    return _get_dtype_of_floating_point_op(self_dtype)
+
 @check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1))
 def aten〇asin〡dtype(self_rank_dtype: Tuple[int, int]) -> int:
     self_rank, self_dtype = self_rank_dtype

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

@@ -63,6 +63,50 @@ def ElementwiseUnaryIntModule_basic(module, tu: TestUtils):
 # ==============================================================================
 
 
+class ElementwiseSinhModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.float32, True),
+    ])
+    def forward(self, a):
+        return torch.sinh(a)
+
+
+@register_test_case(module_factory=lambda: ElementwiseSinhModule())
+def ElementwiseSinhModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3, 4))
+
+
+# ==============================================================================
+
+
+class ElementwiseSinhIntModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.int32, True),
+    ])
+    def forward(self, a):
+        return torch.sinh(a)
+
+
+@register_test_case(module_factory=lambda: ElementwiseSinhIntModule())
+def ElementwiseSinhIntModule_basic(module, tu: TestUtils):
+    module.forward(tu.randint(3, 4, low=1, high=10).to(torch.int32))
+
+
+# ==============================================================================
+
+
 class ElementwiseCoshModule(torch.nn.Module):
 
     def __init__(self):