build: manually update PyTorch version (#4415)

This commit sets the PyTorch and TorchVision versions to nightly release
2025-12-22.

Signed-off-by: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>

Author: vivekkhandelwal1

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -12299,9 +12299,19 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    return %2 : !torch.int\n"
 "  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.softshrink\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.number) -> !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__._get_dtype_of_floating_point_op(%0#1) : (!torch.int) -> !torch.int\n"
-"    return %1 : !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 = call @__torch__._get_dtype_of_floating_point_op(%0#1) : (!torch.int) -> !torch.int\n"
+"    return %3 : !torch.int\n"
 "  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.polar\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.tuple<int, int>) -> !torch.int {\n"
 "    %int9 = torch.constant.int 9\n"
@@ -16854,11 +16864,19 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %int6 = torch.constant.int 6\n"
 "    %none = torch.constant.none\n"
 "    %str = torch.constant.str \"AssertionError: \"\n"
+"    %int15 = torch.constant.int 15\n"
+"    %true = torch.constant.bool true\n"
 "    %int5 = torch.constant.int 5\n"
 "    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
 "    %1 = torch.prim.If %arg2 -> (!torch.int) {\n"
 "      %2 = torch.aten.eq.int %0#1, %int5 : !torch.int, !torch.int -> !torch.bool\n"
-"      torch.prim.If %2 -> () {\n"
+"      %3 = torch.prim.If %2 -> (!torch.bool) {\n"
+"        torch.prim.If.yield %true : !torch.bool\n"
+"      } else {\n"
+"        %4 = torch.aten.eq.int %0#1, %int15 : !torch.int, !torch.int -> !torch.bool\n"
+"        torch.prim.If.yield %4 : !torch.bool\n"
+"      }\n"
+"      torch.prim.If %3 -> () {\n"
 "        torch.prim.If.yield\n"
 "      } else {\n"
 "        torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -5238,7 +5238,6 @@ class DecomposeAtenUpsampleNearestVecOp
   using OpRewritePattern<UpsampleVecOp>::OpRewritePattern;
   LogicalResult matchAndRewrite(UpsampleVecOp op,
                                 PatternRewriter &rewriter) const override {
-    Value scales = op.getScaleFactors();
     static_assert(std::is_same_v<UpsampleVecOp, AtenUpsampleNearest1dVecOp> ||
                   std::is_same_v<UpsampleVecOp, AtenUpsampleNearest2dVecOp>);
     Value cstMode = Torch::ConstantStrOp::create(

projects/pt1/e2e_testing/xfail_sets.py

@@ -3509,6 +3509,12 @@
         "MaxPool2dStaticCeilModeTrueReduceOutputModule_basic",
     }
 
+if torch_version_for_comparison() > version.parse("2.10.0.dev"):
+    ONNX_XFAIL_SET = ONNX_XFAIL_SET | {
+        "Aten_CastLongModule_basic",
+        "Aten_CastFloatModule_basic",
+    }
+
 if torch_version_for_comparison() < version.parse("2.4.0.dev"):
     STABLEHLO_PASS_SET = STABLEHLO_PASS_SET - {
         "AtenIntMM_basic",

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

@@ -3013,9 +3013,10 @@ def aten〇hardshrink〡dtype(self_rank_dtype: Tuple[int, int], lambd: Union[int
         return torch.int64
     return self_dtype
 
-@check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1, lambd=0.5))
+@check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1, lambd=0.5, error_types={*all_integer_dtypes()}))
 def aten〇softshrink〡dtype(self_rank_dtype: Tuple[int, int], lambd: Union[int, float, complex] = 0.5) -> int:
     self_rank, self_dtype = self_rank_dtype
+    assert not is_integer_dtype(self_dtype)
     return _get_dtype_of_floating_point_op(self_dtype)
 
 
@@ -6085,12 +6086,12 @@ def aten〇softmax〇int〡dtype(self_rank_dtype: Tuple[int, int], dim: int, dty
     # _check_tensors_with_the_same_dtype(num_of_tensors=1, dim=0, half_to_float=False) +
     _check_tensors_with_the_same_dtype(
         num_of_tensors=1,
-        error_types=(all_integer_dtypes() + all_complex_dtypes() + [torch.bfloat16, torch.float32, torch.float64]),
+        error_types=(all_integer_dtypes() + all_complex_dtypes() + [torch.float32, torch.float64]),
         dim=0, half_to_float=True))
 def aten〇_softmax〡dtype(self_rank_dtype: Tuple[int, int], dim: int, half_to_float: bool) -> int:
     self_rank, self_dtype = self_rank_dtype
     if half_to_float:
-        assert self_dtype == torch.float16
+        assert self_dtype == torch.float16 or self_dtype == torch.bfloat16
         return torch.float32
     return self_dtype
 

pytorch-requirements.txt

@@ -1,3 +1,3 @@
 -f https://download.pytorch.org/whl/nightly/cpu/torch/
 --pre
-torch==2.10.0.dev20251016
+torch==2.11.0.dev20251222

torchvision-requirements.txt

@@ -1,3 +1,3 @@
 -f https://download.pytorch.org/whl/nightly/cpu/torchvision/
 --pre
-torchvision==0.25.0.dev20251016
+torchvision==0.25.0.dev20251222