Handle pytorch pybind11 changes and bump nightly pins

build_tools/cmake/TorchMLIRPyTorch.cmake

@@ -39,7 +39,10 @@ endfunction()
 # Separately, pybind11 keeps an internal variable which records its ABI info
 # (PYBIND11_INTERNALS_ID in include/pybind11/detail/internals.h). Differences
 # in this variable between torch-mlir and PyTorch will cause type errors.
-# Thus, our best option is to:
+# Note: as of version 2.9.0.dev20250826, torch has updated to pybind11 ver 3.0.
+# This simplifies compatibility considerably. For reference, see
+# https://github.com/pybind/pybind11/pull/5439
+# For pre-version 3.0 pybind11, our best option is to:
 #   a) Identify which ABI version PyTorch was compiled with
 #   b) Tell gcc to use that version
 #     or
@@ -70,23 +73,27 @@ function(TorchMLIRConfigurePyTorch)
     # Check ABI compatibility version
     execute_process(
       COMMAND ${Python3_EXECUTABLE}
-      -c "import torch; import sys; abi=torch._C._PYBIND11_BUILD_ABI; abi.startswith('_cxxabi10') or sys.exit(1); sys.stdout.write(str(abi[-2:]))"
+      -c "import torch; import sys; abi=getattr(torch._C, '_PYBIND11_BUILD_ABI', '-1'); abi=='-1' or abi.startswith('_cxxabi10') or sys.exit(1); sys.stdout.write(str(abi[-2:]))"
       RESULT_VARIABLE _result
       WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
       OUTPUT_VARIABLE _cxx_abi_version)
     if(_result)
       message(FATAL_ERROR "Failed to determine C++ ABI version")
-    endif()
-    message(STATUS "PyTorch C++ ABI version: \"${_cxx_abi_version}\"")
-
-    # Specialize compile flags for compiler
-    if(${CMAKE_CXX_COMPILER_ID} STREQUAL "GNU")
-      set(TORCH_CXXFLAGS "-D_GLIBCXX_USE_CXX11_ABI=${_use_cxx11_abi} -fabi-version=${_cxx_abi_version}")
-    elseif(${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang")
-      set(TORCH_CXXFLAGS "-D_GLIBCXX_USE_CXX11_ABI=${_use_cxx11_abi} -U__GXX_ABI_VERSION -D__GXX_ABI_VERSION=10${_cxx_abi_version} '-DPYBIND11_COMPILER_TYPE=\"_gcc\"'")
+    elseif(${_cxx_abi_version} STREQUAL "-1")
+      message(STATUS "Could not find `torch._C._PYBIND_BUILD_ABI`. This was removed in torch 2.9.0 (as of nightly release: dev20250826), and the TORCH_CXX_FLAGS manipulation is no longer required.")
+      # Everyone involved should be using cxx11 abi by default, but specify this just in case.
+      set(TORCH_CXXFLAGS "-D_GLIBCXX_USE_CXX11_ABI=${_use_cxx11_abi}")
     else()
-      message(WARNING "Unrecognized compiler. Cannot determine ABI flags.")
-      return()
+      message(STATUS "PyTorch C++ ABI version: \"${_cxx_abi_version}\"")
+      # Specialize compile flags for compiler
+      if(${CMAKE_CXX_COMPILER_ID} STREQUAL "GNU")
+        set(TORCH_CXXFLAGS "-D_GLIBCXX_USE_CXX11_ABI=${_use_cxx11_abi} -fabi-version=${_cxx_abi_version}")
+      elseif(${CMAKE_CXX_COMPILER_ID} STREQUAL "Clang")
+        set(TORCH_CXXFLAGS "-D_GLIBCXX_USE_CXX11_ABI=${_use_cxx11_abi} -U__GXX_ABI_VERSION -D__GXX_ABI_VERSION=10${_cxx_abi_version} '-DPYBIND11_COMPILER_TYPE=\"_gcc\"'")
+      else()
+        message(WARNING "Unrecognized compiler. Cannot determine ABI flags.")
+        return()
+      endif()
     endif()
     set(TORCH_CXXFLAGS "${TORCH_CXXFLAGS}" PARENT_SCOPE)
   endif()

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -15960,9 +15960,6 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    return %0 : !torch.int\n"
 "  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.linalg_vector_norm\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.number, %arg2: !torch.optional<list<int>>, %arg3: !torch.bool, %arg4: !torch.optional<int>) -> !torch.int {\n"
-"    %int6 = torch.constant.int 6\n"
-"    %int15 = torch.constant.int 15\n"
-"    %int5 = torch.constant.int 5\n"
 "    %true = torch.constant.bool true\n"
 "    %none = torch.constant.none\n"
 "    %str = torch.constant.str \"AssertionError: \"\n"
@@ -16011,22 +16008,12 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "      }\n"
 "      torch.prim.If.yield %9 : !torch.int\n"
 "    } else {\n"
-"      %5 = torch.prim.ListConstruct %int5, %int15 : (!torch.int, !torch.int) -> !torch.list<int>\n"
-"      %6 = torch.aten.__contains__.int_list %5, %0#1 : !torch.list<int>, !torch.int -> !torch.bool\n"
-"      %7 = torch.prim.If %6 -> (!torch.int) {\n"
-"        torch.prim.If.yield %int6 : !torch.int\n"
-"      } else {\n"
-"        %8 = func.call @\"__torch_mlir_dtype_fn.aten.std\"(%arg0, %true) : (!torch.tuple<int, int>, !torch.bool) -> !torch.int\n"
-"        torch.prim.If.yield %8 : !torch.int\n"
-"      }\n"
-"      torch.prim.If.yield %7 : !torch.int\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.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"
-"    %int6 = torch.constant.int 6\n"
-"    %int15 = torch.constant.int 15\n"
-"    %int5 = torch.constant.int 5\n"
 "    %true = torch.constant.bool true\n"
 "    %none = torch.constant.none\n"
 "    %str = torch.constant.str \"AssertionError: \"\n"
@@ -16075,15 +16062,8 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "      }\n"
 "      torch.prim.If.yield %9 : !torch.int\n"
 "    } else {\n"
-"      %5 = torch.prim.ListConstruct %int5, %int15 : (!torch.int, !torch.int) -> !torch.list<int>\n"
-"      %6 = torch.aten.__contains__.int_list %5, %0#1 : !torch.list<int>, !torch.int -> !torch.bool\n"
-"      %7 = torch.prim.If %6 -> (!torch.int) {\n"
-"        torch.prim.If.yield %int6 : !torch.int\n"
-"      } else {\n"
-"        %8 = func.call @\"__torch_mlir_dtype_fn.aten.std\"(%arg0, %true) : (!torch.tuple<int, int>, !torch.bool) -> !torch.int\n"
-"        torch.prim.If.yield %8 : !torch.int\n"
-"      }\n"
-"      torch.prim.If.yield %7 : !torch.int\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"
@@ -16107,8 +16087,6 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "  }\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"
-"    %int6 = torch.constant.int 6\n"
-"    %int15 = torch.constant.int 15\n"
 "    %int5 = torch.constant.int 5\n"
 "    %int8 = torch.constant.int 8\n"
 "    %none = torch.constant.none\n"
@@ -16126,15 +16104,8 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %4 = torch.prim.If %3 -> (!torch.int) {\n"
 "      torch.prim.If.yield %int5 : !torch.int\n"
 "    } else {\n"
-"      %5 = torch.prim.ListConstruct %int5, %int15 : (!torch.int, !torch.int) -> !torch.list<int>\n"
-"      %6 = torch.aten.__contains__.int_list %5, %0#1 : !torch.list<int>, !torch.int -> !torch.bool\n"
-"      %7 = torch.prim.If %6 -> (!torch.int) {\n"
-"        torch.prim.If.yield %int6 : !torch.int\n"
-"      } else {\n"
-"        %8 = func.call @\"__torch_mlir_dtype_fn.aten.std\"(%arg0, %true) : (!torch.tuple<int, int>, !torch.bool) -> !torch.int\n"
-"        torch.prim.If.yield %8 : !torch.int\n"
-"      }\n"
-"      torch.prim.If.yield %7 : !torch.int\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"

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

@@ -5544,8 +5544,6 @@ def aten〇linalg_vector_norm〡dtype(self_rank_dtype: Tuple[int, int], ord: Uni
             return aten〇std〡dtype((self_rank, dtype))
         assert not is_complex_dtype(dtype)
         return dtype
-    if self_dtype in [torch.float16, torch.bfloat16]:
-        return torch.float32
     return aten〇std〡dtype(self_rank_dtype)
 
 @check_dtype_function(
@@ -5569,8 +5567,6 @@ def aten〇linalg_norm〡dtype(self_rank_dtype: Tuple[int, int], ord: Optional[U
             return aten〇std〡dtype((self_rank, dtype))
         assert not is_complex_dtype(dtype)
         return dtype
-    if self_dtype in [torch.float16, torch.bfloat16]:
-        return torch.float32
     return aten〇std〡dtype(self_rank_dtype)
 
 def aten〇binary_cross_entropy_with_logits〡dtype(self_rank_dtype: Tuple[int, int], target_rank_dtype: Tuple[int, int], weight_rank_dtype: Optional[Tuple[int, int]] = None, pos_weight_rank_dtype: Optional[Tuple[int, int]] = None, reduction: int = 1) -> int:
@@ -5604,8 +5600,6 @@ def aten〇norm〇Scalar〡dtype(self_rank_dtype: Tuple[int, int], p: Union[int,
     # Should possibly be added to aten〇std〡dtype.
     if self_dtype == torch.complex32:
         return torch.half
-    if self_dtype in [torch.float16, torch.bfloat16]:
-        return torch.float32
     return aten〇std〡dtype(self_rank_dtype)
 
 @check_dtype_function([Invocation(0.0),

projects/pt1/python/torch_mlir_e2e_test/configs/fx_importer_backend.py

@@ -149,9 +149,12 @@ def _export_run(self, artifact: torch.nn.Module, trace: Trace) -> Trace:
             )
             module = self._backend.compile(module)
             backend_module = self._backend.load(module)
+            input_buffers = prog.graph_signature.inputs_to_buffers.values()
             params = {
                 # **dict(artifact.named_parameters(remove_duplicate=False)),
-                **dict(artifact.named_buffers(remove_duplicate=False)),
+                name: value
+                for (name, value) in artifact.named_buffers(remove_duplicate=False)
+                if name in input_buffers
             }
             params_flat, params_spec = pytree.tree_flatten(params)
             params_flat = list(params_flat)

projects/pt1/python/torch_mlir_e2e_test/configs/onnx_backend.py

@@ -85,6 +85,7 @@ def convert_onnx(model, inputs):
         input_names=input_names,
         dynamic_axes=dynamic_tensors,
         opset_version=max_opset_ver,
+        dynamo=False,
     )
     buffer = buffer.getvalue()
     return import_onnx(buffer)

pytorch-hash.txt

@@ -1 +1 @@
-7956a1d1d0dc7cdaaaa42d0863eebb1b1e75eb65
+0dfcb1a118dd45c544a156e1d86566368e528e69

pytorch-requirements.txt

@@ -1,3 +1,3 @@
 -f https://download.pytorch.org/whl/nightly/cpu/torch/
 --pre
-torch==2.9.0.dev20250820
+torch==2.10.0.dev20251016

torchvision-requirements.txt

@@ -1,3 +1,3 @@
 -f https://download.pytorch.org/whl/nightly/cpu/torchvision/
 --pre
-torchvision==0.24.0.dev20250820
+torchvision==0.25.0.dev20251016