distributed/debug: add an HTTP server for debugging running jobs

This PR adds a basic spin configuration to allow for linting. It is designed as a drop-in replacement for the current Makefile based solution, i.e. it sets up and updates lintrunner based on the hashes of certain configuration files.

Lintrunner is called via Uv's `uvx` command, separating its environment from the general development environment in an effort to reduce instances of competing requirements breaking environments.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/167226
Approved by: https://github.com/atalman, https://github.com/albanD

.ci/docker/requirements-ci.txt

@@ -402,3 +402,6 @@ scikit-build==0.18.1
 pyre-extensions==0.0.32
 tabulate==0.9.0
 #Description: These package are needed to build FBGEMM and torchrec on PyTorch CI
+
+Flask==3.1.2
+#Description: required for torch.distributed.debug

.spin/cmds.py

@@ -0,0 +1,330 @@
+import hashlib
+import subprocess
+import sys
+from pathlib import Path
+
+import click
+import spin
+
+
+def file_digest(file, algorithm: str):
+    try:
+        return hashlib.file_digest(file, algorithm)
+    except AttributeError:
+        pass  # Fallback to manual implementation below
+    hash = hashlib.new(algorithm)
+    while chunk := file.read(8192):
+        hash.update(chunk)
+    return hash
+
+
+def _hash_file(file):
+    with open(file, "rb") as f:
+        hash = file_digest(f, "sha256")
+    return hash.hexdigest()
+
+
+def _hash_files(files):
+    hashes = {file: _hash_file(file) for file in files}
+    return hashes
+
+
+def _read_hashes(hash_file: Path):
+    if not hash_file.exists():
+        return {}
+    with hash_file.open("r") as f:
+        lines = f.readlines()
+    hashes = {}
+    for line in lines:
+        hash = line[:64]
+        file = line[66:].strip()
+        hashes[file] = hash
+    return hashes
+
+
+def _updated_hashes(hash_file, files_to_hash):
+    old_hashes = _read_hashes(hash_file)
+    new_hashes = _hash_files(files_to_hash)
+    if new_hashes != old_hashes:
+        return new_hashes
+    return None
+
+
+@click.command()
+def regenerate_version():
+    """Regenerate version.py."""
+    cmd = [
+        sys.executable,
+        "-m",
+        "tools.generate_torch_version",
+        "--is-debug=false",
+    ]
+    spin.util.run(cmd)
+
+
+TYPE_STUBS = [
+    (
+        "Pytorch type stubs",
+        Path(".lintbin/.pytorch-type-stubs.sha256"),
+        [
+            "aten/src/ATen/native/native_functions.yaml",
+            "aten/src/ATen/native/tags.yaml",
+            "tools/autograd/deprecated.yaml",
+        ],
+        [
+            sys.executable,
+            "-m",
+            "tools.pyi.gen_pyi",
+            "--native-functions-path",
+            "aten/src/ATen/native/native_functions.yaml",
+            "--tags-path",
+            "aten/src/ATen/native/tags.yaml",
+            "--deprecated-functions-path",
+            "tools/autograd/deprecated.yaml",
+        ],
+    ),
+    (
+        "Datapipes type stubs",
+        None,
+        [],
+        [
+            sys.executable,
+            "torch/utils/data/datapipes/gen_pyi.py",
+        ],
+    ),
+]
+
+
+@click.command()
+def regenerate_type_stubs():
+    """Regenerate type stubs."""
+    for name, hash_file, files_to_hash, cmd in TYPE_STUBS:
+        if hash_file:
+            if hashes := _updated_hashes(hash_file, files_to_hash):
+                click.echo(
+                    f"Changes detected in type stub files for {name}. Regenerating..."
+                )
+                spin.util.run(cmd)
+                hash_file.parent.mkdir(parents=True, exist_ok=True)
+                with hash_file.open("w") as f:
+                    for file, hash in hashes.items():
+                        f.write(f"{hash}  {file}\n")
+                click.echo("Type stubs and hashes updated.")
+            else:
+                click.echo(f"No changes detected in type stub files for {name}.")
+        else:
+            click.echo(f"No hash file for {name}. Regenerating...")
+            spin.util.run(cmd)
+            click.echo("Type stubs regenerated.")
+
+
+@click.command()
+def regenerate_clangtidy_files():
+    """Regenerate clang-tidy files."""
+    cmd = [
+        sys.executable,
+        "-m",
+        "tools.linter.clang_tidy.generate_build_files",
+    ]
+    spin.util.run(cmd)
+
+
+#: These linters are expected to need less than 3s cpu time total
+VERY_FAST_LINTERS = {
+    "ATEN_CPU_GPU_AGNOSTIC",
+    "BAZEL_LINTER",
+    "C10_NODISCARD",
+    "C10_UNUSED",
+    "CALL_ONCE",
+    "CMAKE_MINIMUM_REQUIRED",
+    "CONTEXT_DECORATOR",
+    "COPYRIGHT",
+    "CUBINCLUDE",
+    "DEPLOY_DETECTION",
+    "ERROR_PRONE_ISINSTANCE",
+    "EXEC",
+    "HEADER_ONLY_LINTER",
+    "IMPORT_LINTER",
+    "INCLUDE",
+    "LINTRUNNER_VERSION",
+    "MERGE_CONFLICTLESS_CSV",
+    "META_NO_CREATE_UNBACKED",
+    "NEWLINE",
+    "NOQA",
+    "NO_WORKFLOWS_ON_FORK",
+    "ONCE_FLAG",
+    "PYBIND11_INCLUDE",
+    "PYBIND11_SPECIALIZATION",
+    "PYPIDEP",
+    "PYPROJECT",
+    "RAWCUDA",
+    "RAWCUDADEVICE",
+    "ROOT_LOGGING",
+    "TABS",
+    "TESTOWNERS",
+    "TYPEIGNORE",
+    "TYPENOSKIP",
+    "WORKFLOWSYNC",
+}
+
+
+#: These linters are expected to take a few seconds, but less than 10s cpu time total
+FAST_LINTERS = {
+    "CMAKE",
+    "DOCSTRING_LINTER",
+    "GHA",
+    "NATIVEFUNCTIONS",
+    "RUFF",
+    "SET_LINTER",
+    "SHELLCHECK",
+    "SPACES",
+}
+
+
+#: These linters are expected to take more than 10s cpu time total;
+#: some need more than 1 hour.
+SLOW_LINTERS = {
+    "ACTIONLINT",
+    "CLANGFORMAT",
+    "CLANGTIDY",
+    "CODESPELL",
+    "FLAKE8",
+    "GB_REGISTRY",
+    "PYFMT",
+    "PYREFLY",
+    "TEST_DEVICE_BIAS",
+    "TEST_HAS_MAIN",
+}
+
+
+ALL_LINTERS = VERY_FAST_LINTERS | FAST_LINTERS | SLOW_LINTERS
+
+
+LINTRUNNER_CACHE_INFO = (
+    Path(".lintbin/.lintrunner.sha256"),
+    [
+        "requirements.txt",
+        "pyproject.toml",
+        ".lintrunner.toml",
+    ],
+)
+
+
+LINTRUNNER_BASE_CMD = [
+    "uvx",
+    "--python",
+    "3.10",
+    "lintrunner@0.12.7",
+]
+
+
+@click.command()
+def setup_lint():
+    """Set up lintrunner with current CI version."""
+    cmd = LINTRUNNER_BASE_CMD + ["init"]
+    subprocess.run(cmd, check=True, capture_output=True, text=True)
+
+
+def _check_linters():
+    cmd = LINTRUNNER_BASE_CMD + ["list"]
+    ret = spin.util.run(cmd, output=False, stderr=subprocess.PIPE)
+    linters = {l.strip() for l in ret.stdout.decode().strip().split("\n")[1:]}
+    unknown_linters = linters - ALL_LINTERS
+    missing_linters = ALL_LINTERS - linters
+    if unknown_linters:
+        click.secho(
+            f"Unknown linters found; please add them to the correct category "
+            f"in .spin/cmds.py: {', '.join(unknown_linters)}",
+            fg="yellow",
+        )
+    if missing_linters:
+        click.secho(
+            f"Missing linters found; please update the corresponding category "
+            f"in .spin/cmds.py: {', '.join(missing_linters)}",
+            fg="yellow",
+        )
+    return unknown_linters, missing_linters
+
+
+@spin.util.extend_command(
+    setup_lint,
+    doc=f"""
+        If configuration has changed, update lintrunner.
+
+        Compares the stored old hashes of configuration files with new ones and
+        performs setup via setup-lint if the hashes have changed.
+        Hashes are stored in {LINTRUNNER_CACHE_INFO[0]}; the following files are
+        considered: {", ".join(LINTRUNNER_CACHE_INFO[1])}.
+        """,
+)
+@click.pass_context
+def lazy_setup_lint(ctx, parent_callback, **kwargs):
+    if hashes := _updated_hashes(*LINTRUNNER_CACHE_INFO):
+        click.echo(
+            "Changes detected in lint configuration files. Setting up linting tools..."
+        )
+        parent_callback(**kwargs)
+        hash_file = LINTRUNNER_CACHE_INFO[0]
+        hash_file.parent.mkdir(parents=True, exist_ok=True)
+        with hash_file.open("w") as f:
+            for file, hash in hashes.items():
+                f.write(f"{hash}  {file}\n")
+        click.echo("Linting tools set up and hashes updated.")
+    else:
+        click.echo("No changes detected in lint configuration files. Skipping setup.")
+    click.echo("Regenerating version...")
+    ctx.invoke(regenerate_version)
+    click.echo("Regenerating type stubs...")
+    ctx.invoke(regenerate_type_stubs)
+    click.echo("Done.")
+    _check_linters()
+
+
+@click.command()
+@click.option("-a", "--apply-patches", is_flag=True)
+@click.pass_context
+def lint(ctx, apply_patches, **kwargs):
+    """Lint all files."""
+    ctx.invoke(lazy_setup_lint)
+    all_files_linters = VERY_FAST_LINTERS | FAST_LINTERS
+    changed_files_linters = SLOW_LINTERS
+    cmd = LINTRUNNER_BASE_CMD
+    if apply_patches:
+        cmd += ["--apply-patches"]
+    all_files_cmd = cmd + [
+        "--take",
+        ",".join(all_files_linters),
+        "--all-files",
+    ]
+    spin.util.run(all_files_cmd)
+    changed_files_cmd = cmd + [
+        "--take",
+        ",".join(changed_files_linters),
+    ]
+    spin.util.run(changed_files_cmd)
+
+
+@click.command()
+@click.pass_context
+def fixlint(ctx, **kwargs):
+    """Autofix all files."""
+    ctx.invoke(lint, apply_patches=True)
+
+
+@click.command()
+@click.option("-a", "--apply-patches", is_flag=True)
+@click.pass_context
+def quicklint(ctx, apply_patches, **kwargs):
+    """Lint changed files."""
+    ctx.invoke(lazy_setup_lint)
+    cmd = LINTRUNNER_BASE_CMD
+    if apply_patches:
+        cmd += ["--apply-patches"]
+    spin.util.run(cmd)
+
+
+@click.command()
+@click.pass_context
+def quickfix(ctx, **kwargs):
+    """Autofix changed files."""
+    ctx.invoke(quicklint, apply_patches=True)

cmake/Codegen.cmake

@@ -118,6 +118,11 @@ if(INTERN_BUILD_ATEN_OPS)
             list(APPEND _file_compile_flags "-gencode;arch=compute_120a,code=sm_120a")
           endif()
         endif()
+        if("${_arch}" STREQUAL "121a")
+          if(_existing_arch_flags MATCHES ".*compute_120.*")
+            list(APPEND _file_compile_flags "-gencode;arch=compute_121a,code=sm_121a")
+          endif()
+        endif()
       endforeach()
       list(JOIN _file_compile_flags " " _file_compile_flags)
 
@@ -126,7 +131,7 @@ if(INTERN_BUILD_ATEN_OPS)
 
     _BUILD_FOR_ADDITIONAL_ARCHS(
       "${CMAKE_CURRENT_LIST_DIR}/../aten/src/ATen/native/cuda/RowwiseScaledMM.cu"
-      "89;90a;100a;103a;120a")
+      "89;90a;100a;103a;120a;121a")
     _BUILD_FOR_ADDITIONAL_ARCHS(
       "${CMAKE_CURRENT_LIST_DIR}/../aten/src/ATen/native/cuda/ScaledGroupMM.cu"
       "90a")

pyproject.toml

@@ -376,3 +376,19 @@ keep-runtime-typing = true
 
 [tool.codespell]
 ignore-words = "tools/linter/dictionary.txt"
+
+[tool.spin]
+package = 'torch'
+
+[tool.spin.commands]
+"Build" = [
+  ".spin/cmds.py:lint",
+  ".spin/cmds.py:fixlint",
+  ".spin/cmds.py:quicklint",
+  ".spin/cmds.py:quickfix",
+]
+"Regenerate" = [
+  ".spin/cmds.py:regenerate_version",
+  ".spin/cmds.py:regenerate_type_stubs",
+  ".spin/cmds.py:regenerate_clangtidy_files",
+]

requirements.txt

@@ -14,6 +14,7 @@ lintrunner ; platform_machine != "s390x" and platform_machine != "riscv64"
 networkx>=2.5.1
 optree>=0.13.0
 psutil
+spin
 sympy>=1.13.3
 typing-extensions>=4.13.2
 wheel

test/distributed/tensor/test_pointwise_ops.py

@@ -331,6 +331,25 @@ class DistElementwiseOpsTest(DTensorOpTestBase):
         self.assertEqual(z.placements, (Replicate(),))
         self.assertEqual(z.to_local(), input)
 
+    def test_inplace_op_partial_to_replicate(self):
+        # test that in-place operations that require redistribution raise an error
+        # to preserve aliasing semantics (issue #163374)
+        device_mesh = self.build_device_mesh()
+
+        input_tensor = torch.tensor(64.0, device=self.device_type)
+        partial_dt = DTensor.from_local(
+            input_tensor, device_mesh, placements=(Partial(),)
+        )
+
+        self.assertTrue(partial_dt.placements[0].is_partial())
+
+        # Inplace ops that require placement changes (Partial -> Replicate) should error
+        with self.assertRaisesRegex(
+            RuntimeError,
+            "in-place operations that require placement changes are not supported",
+        ):
+            partial_dt.clamp_(max=10)
+
 
 if __name__ == "__main__":
     run_tests()

test/distributed/test_debug.py

@@ -0,0 +1,53 @@
+# Owner(s): ["oncall: distributed"]
+
+import os
+
+import requests
+from requests.adapters import HTTPAdapter
+from urllib3.util.retry import Retry
+
+import torch
+import torch.distributed as dist
+from torch.distributed.debug import start_debug_server, stop_debug_server
+from torch.testing._internal.common_utils import run_tests, TestCase
+
+
+session = requests.Session()
+retry_strategy = Retry(total=5, backoff_factor=0.5)
+adapter = HTTPAdapter(max_retries=retry_strategy)
+session.mount("http://", adapter)
+session.mount("https://", adapter)
+
+
+class TestDebug(TestCase):
+    def test_basics(self) -> None:
+        store = dist.TCPStore("localhost", 0, 1, is_master=True, wait_for_workers=False)
+        os.environ["MASTER_ADDR"] = "localhost"
+        os.environ["MASTER_PORT"] = str(store.port)
+        os.environ["RANK"] = "0"
+        os.environ["WORLD_SIZE"] = "1"
+
+        port = 25999
+
+        def fetch(path: str) -> str:
+            resp = session.get(f"http://localhost:{port}{path}")
+            resp.raise_for_status()
+            return resp.text
+
+        print("starting!")
+
+        start_debug_server(port=port)
+
+        self.assertIn("torch profiler", fetch("/"))
+        self.assertIn("View 0", fetch("/profile?duration=0.01"))
+        self.assertIn("test_basics", fetch("/stacks"))
+        self.assertIn("pg_status", fetch("/fr_trace"))
+
+        if torch.cuda.is_available():
+            self.assertIn("pg_status", fetch("/fr_trace_nccl"))
+
+        stop_debug_server()
+
+
+if __name__ == "__main__":
+    run_tests()

test/dynamo/test_aot_compile.py

@@ -1,9 +1,11 @@
 # Owner(s): ["module: dynamo"]
 
+import copy
 import functools
 import inspect
 import os
 import pickle
+import unittest
 from contextlib import contextmanager
 from unittest.mock import patch
 
@@ -13,13 +15,16 @@ import torch._inductor.config
 import torch._inductor.test_case
 import torch.onnx.operators
 import torch.utils.cpp_extension
-from torch._dynamo.aot_compile import ModelInput, SerializableCallable
+from torch._dynamo.aot_compile import AOTCompiledModel, ModelInput, SerializableCallable
 from torch._dynamo.exc import PackageError, Unsupported
 from torch._dynamo.package import DynamoCache
 from torch._dynamo.precompile_context import PrecompileContext
 from torch._inductor.runtime.runtime_utils import cache_dir
 from torch.fx._graph_pickler import GraphPickler
-from torch.testing._internal.common_utils import instantiate_parametrized_tests
+from torch.testing._internal.common_utils import (
+    instantiate_parametrized_tests,
+    TEST_CUDA,
+)
 
 
 MY_LAMBDA = lambda x: x + 1  # noqa: E731
@@ -599,6 +604,92 @@ from user code:
         actual = compiled_fn(*inputs)
         self.assertEqual(expected, actual)
 
+    @unittest.skipIf(not TEST_CUDA, "requires cuda")
+    def test_aot_compile_with_aoti(self):
+        with torch.device("cuda"):
+            from torch._dynamo.hooks import Hooks
+
+            def fn(x, y):
+                return x + y
+
+            def make_inputs():
+                return (torch.randn(3, 4), torch.randn(3, 4))
+
+            compiled_fn = torch._dynamo.aot_compile.aot_compile_fullgraph(
+                fn,
+                (make_inputs(), {}),
+                Hooks(),
+                torch._TorchCompileAOTInductorWrapper(None, None, None),
+            )
+
+            test_inputs = make_inputs()
+            expected = fn(*test_inputs)
+            actual = compiled_fn(*test_inputs)
+            self.assertEqual(expected, actual)
+            compiled_fn.save_compiled_function(self.path())
+            with open(self.path(), "rb") as f:
+                compiled_fn = torch.compiler.load_compiled_function(f)
+            actual = compiled_fn(*test_inputs)
+            self.assertEqual(expected, actual)
+
+    @unittest.skipIf(not TEST_CUDA, "requires cuda")
+    def test_aot_compile_with_aoti_module(self):
+        with torch.device("cuda"):
+            from torch._dynamo.hooks import Hooks
+
+            mod = SimpleLinearModule()
+
+            def make_inputs():
+                return (torch.randn(4, 3),)
+
+            compiled_mod = torch._dynamo.aot_compile.aot_compile_module(
+                mod,
+                [ModelInput(make_inputs(), {}, [])],
+                Hooks(),
+                torch._TorchCompileAOTInductorWrapper(None, None, None),
+            )
+
+            def get_grads(m: torch.nn.Module):
+                return {name: p.grad for name, p in m.named_parameters()}
+
+            original_mod = copy.deepcopy(mod)
+            test_inputs = make_inputs()
+            expected = mod(*test_inputs)
+            expected.sum().backward()
+            expected_grads = get_grads(mod)
+
+            actual = compiled_mod(*test_inputs)
+            self.assertEqual(expected, actual)
+            serialized = compiled_mod.serialize()
+            compiled_fn = AOTCompiledModel.deserialize(original_mod, serialized)
+            actual = compiled_fn(*test_inputs)
+            actual.sum().backward()
+            self.assertEqual(get_grads(original_mod), expected_grads)
+
+    @unittest.skipIf(not TEST_CUDA, "requires cuda")
+    def test_aot_compile_with_aoti_torch_compile(self):
+        with torch.device("cuda"):
+
+            def fn(x, y):
+                return x + y
+
+            def make_inputs():
+                return (torch.randn(3, 4), torch.randn(3, 4))
+
+            compiled_fn = torch.compile(
+                fn, fullgraph=True, options={"use_aoti": True}
+            ).aot_compile((make_inputs(), {}))
+            test_inputs = make_inputs()
+            expected = fn(*test_inputs)
+            actual = compiled_fn(*test_inputs)
+            self.assertEqual(expected, actual)
+            compiled_fn.save_compiled_function(self.path())
+            with open(self.path(), "rb") as f:
+                compiled_fn = torch.compiler.load_compiled_function(f)
+            actual = compiled_fn(*test_inputs)
+            self.assertEqual(compiled_fn._artifacts.backend_name, "aotinductor")
+            self.assertEqual(expected, actual)
+
 
 if __name__ == "__main__":
     from torch._dynamo.test_case import run_tests

torch/_C/_distributed_c10d.pyi

@@ -100,7 +100,9 @@ class Logger:
     def _set_static_graph(self) -> None: ...
 
 class _WorkerServer:
-    def __init__(self, socket_path: str) -> None: ...
+    port: int
+
+    def __init__(self, host_or_file: str, port: int = ...) -> None: ...
     def shutdown(self) -> None: ...
 
 def get_debug_level(): ...
@@ -206,6 +208,7 @@ class Store:
         desired_value: str,
     ) -> bytes: ...
     def delete_key(self, key: str) -> bool: ...
+    def multi_get(self, keys: list[str]) -> list[bytes]: ...
     def num_keys(self) -> int: ...
     def set_timeout(self, timeout: timedelta): ...
     @overload
@@ -871,3 +874,15 @@ class ProcessGroupXCCL(Backend):
 
 def _set_process_group(pg: ProcessGroup) -> None: ...
 def _current_process_group() -> ProcessGroup: ...
+
+class _Request:
+    def body(self) -> bytes: ...
+    def get_param(self, str) -> str: ...
+
+class _Response:
+    def set_content(self, content: str | bytes, content_type: str) -> None: ...
+    def set_status(self, status: int) -> None: ...
+
+def _register_handler(
+    name: str, handler: Callable[[_Request, _Response], None]
+) -> None: ...

torch/_C/_profiler.pyi

@@ -60,6 +60,7 @@ class _ExperimentalConfig:
         verbose: bool = ...,
         performance_events: list[str] = ...,
         enable_cuda_sync_events: bool = ...,
+        profile_all_threads: bool = ...,
     ) -> None: ...
 
 class ProfilerConfig:

torch/__init__.py

@@ -2439,6 +2439,35 @@ class _TorchCompileInductorWrapper:
                 reset_cudagraph_trees()
 
 
+class _TorchCompileAOTInductorWrapper(_TorchCompileInductorWrapper):
+    compiler_name = "aotinductor"
+
+    def __init__(self, mode, options, dynamic):
+        super().__init__(mode, options, dynamic)
+        self.apply_options({"cpp_wrapper": True})
+        self.apply_options({"aot_inductor.package": True})
+
+    def __call__(self, model_, inputs_):
+        from contextlib import nullcontext
+        from unittest import mock
+
+        from torch._guards import detect_fake_mode
+        from torch._inductor.virtualized import V
+
+        fake_mode = detect_fake_mode(inputs_)
+        ctx = (
+            mock.patch.object(fake_mode, "allow_non_fake_inputs", True)
+            if fake_mode
+            else nullcontext()
+        )
+        with (
+            V.set_aot_compilation(True),
+            ctx,
+            torch._inductor.config.patch("enable_autograd_for_aot", True),
+        ):
+            return super().__call__(model_, inputs_)
+
+
 class _TorchCompileWrapper:
     def __init__(self, backend, mode, options, dynamic):
         from torch._dynamo.backends.registry import lookup_backend
@@ -2672,8 +2701,10 @@ def compile(
             backend = bisect_backend
 
     guard_filter_fn = None
+    use_aoti = False
     if options and isinstance(options, dict):
         guard_filter_fn = options.pop("guard_filter_fn", None)
+        use_aoti = options.pop("use_aoti", False)
 
     if torch.compiler.is_exporting():
         warnings.warn(
@@ -2700,7 +2731,10 @@ def compile(
         return export_wrapped_fn
 
     if backend == "inductor":
-        backend = _TorchCompileInductorWrapper(mode, options, dynamic)
+        if use_aoti:
+            backend = _TorchCompileAOTInductorWrapper(mode, options, dynamic)
+        else:
+            backend = _TorchCompileInductorWrapper(mode, options, dynamic)
     else:
         backend = _TorchCompileWrapper(backend, mode, options, dynamic)
 

torch/_dynamo/aot_compile.py

@@ -53,6 +53,7 @@ class CompileArtifacts:
     argdefs: Optional[tuple[Any, ...]]
     source_info: "SourceInfo"
     device_type: str
+    backend_name: str
     system_info: SystemInfo = dataclasses.field(default_factory=SystemInfo.current)
 
     def check_compatibility(self) -> None:
@@ -166,7 +167,8 @@ class AOTCompiledFunction:
         state = pickle.loads(data)
         state["bytecode"] = SerializedCode.to_code_object(state["bytecode"])
         deserializer, compiled_fn_state = state["compiled_fn"]
-        state["compiled_fn"] = deserializer(compiled_fn_state)
+        with torch._inductor.config.patch(enable_autograd_for_aot=True):
+            state["compiled_fn"] = deserializer(compiled_fn_state)
         state["original_code"] = SerializedCode.to_code_object(state["original_code"])
 
         artifacts = CompileArtifacts(**state)
@@ -273,6 +275,7 @@ def aot_compile_fullgraph(
             argdefs=fn.__defaults__,
             source_info=source_info,
             device_type=device_type,
+            backend_name=getattr(backend, "compiler_name", "unknown"),
         )
         aot_compiled_fn = AOTCompiledFunction(_artifacts=artifacts)
 

torch/_functorch/_aot_autograd/aot_autograd_result.py

@@ -511,6 +511,7 @@ class GenericAOTAutogradResult(Generic[TForward, TBackward]):
         ).post_compile(
             compiled_fw_func, aot_config, runtime_metadata=self.runtime_metadata
         )
+        compiled_fw_func._boxed_call = True
         disable_amp = torch._C._is_any_autocast_enabled()
 
         if needs_autograd:

torch/_inductor/compile_fx.py

@@ -1640,7 +1640,9 @@ class _InProcessFxCompile(FxCompile):
                             # pyrefly: ignore [unbound-name]
                             (str, list, torch.fx.GraphModule),
                         ), type(compiled_fn)
-                        return CompiledAOTI(compiled_fn)
+                        return CompiledAOTI(
+                            filename=compiled_fn, device_type=graph.device_type
+                        )
 
                     # TODO: Hoist this above V.aot_compilation
                     # pyrefly: ignore [unbound-name]
@@ -2713,7 +2715,7 @@ def _compile_fx_main(
             or torch._guards.TracingContext(fake_mode)
         )
 
-        if V.aot_compilation:
+        if V.aot_compilation and not config.enable_autograd_for_aot:
             from .utils import is_valid_aoti_model_name
 
             is_valid_aoti_model_name()

torch/_inductor/config.py

@@ -1190,6 +1190,8 @@ autotune_lookup_table: dict[str, dict[str, Any]] = {}
 
 file_lock_timeout: int = int(os.environ.get("TORCHINDUCTOR_FILE_LOCK_TIMEOUT", "600"))
 
+enable_autograd_for_aot: bool = False
+
 
 def get_worker_log_path() -> Optional[str]:
     log_loc = None

torch/_inductor/output_code.py

@@ -773,9 +773,86 @@ class CompiledAOTI(OutputCode):
     """
 
     filename: Union[str, list[Union[str, Weights]], torch.fx.GraphModule]
+    device_type: str
+    current_callable: Optional[Callable[..., Any]] = None
+    _cached_files: dict[str, bytes] = dataclasses.field(default_factory=dict)
+
+    def __post_init__(self):
+        if not config.aot_inductor.link_libtorch:
+            return
+
+        if (
+            torch._inductor.cpp_builder._IS_MACOS
+            or torch._inductor.cpp_builder._IS_WINDOWS
+        ):
+            return
+
+        if config.aot_inductor.cross_target_platform == "windows":
+            return
+
+        if config.aot_inductor.package_cpp_only:
+            return
+
+        if not config.enable_autograd_for_aot:
+            return
+
+        if isinstance(self.filename, list):
+            current_callable = next(
+                fn for fn in self.filename if isinstance(fn, str) and fn.endswith(".so")
+            )
+        else:
+            current_callable = self.filename
+
+        if isinstance(current_callable, torch.fx.GraphModule):
+            self.current_callable = current_callable
+            return
+
+        if self.device_type.startswith("cuda"):
+            current_callable = (
+                torch._C._aoti.AOTIModelContainerRunnerCuda(  # type: ignore[call-arg]
+                    current_callable,
+                    1,
+                    self.device_type,
+                    "",
+                    True,
+                ).run  # type: ignore[attr-defined]
+            )  # type: ignore[attr-defined]
+        elif self.device_type == "cpu":
+            current_callable = (
+                torch._C._aoti.AOTIModelContainerRunnerCpu(  # type: ignore[call-arg]
+                    current_callable, 1
+                ).run  # type: ignore[attr-defined]
+            )  # type: ignore[attr-defined]
+        else:
+            raise RuntimeError(f"unsupported device type {self.device_type}")
+        self.current_callable = current_callable
+        self._boxed_call = True
+        for file in self._cached_files:
+            if not os.path.exists(file):
+                with open(file, "wb") as f:
+                    f.write(self._cached_files[file])
 
     def __call__(self, inputs: Sequence[Any]) -> Any:
-        raise NotImplementedError("NYI")
+        if self.current_callable is None:
+            raise RuntimeError("AOTInductor compiled so is not loaded")
+        return self.current_callable(inputs)
+
+    def prepare_for_serialization(self) -> None:
+        self.current_callable = None
+        self._cached_files = {}
+        filenames: list[str] = []
+        if isinstance(self.filename, list):
+            filenames = self.filename  # type: ignore[assignment]
+        elif isinstance(self.filename, str):
+            filenames = [self.filename]
+        for name in filenames:
+            with open(name, "rb") as f:
+                self._cached_files[name] = f.read()
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["current_callable"] = None
+        return state
 
     def post_compile(
         self,
@@ -783,10 +860,8 @@ class CompiledAOTI(OutputCode):
         constants: CompiledFxGraphConstants,
         graph_kwargs: _CompileFxKwargs,
     ) -> None:
-        pass
-
-    def prepare_for_serialization(self) -> None:
-        pass
+        if self.current_callable is None:
+            self.__post_init__()
 
     def set_triton_bundle(self, triton_bundle: Any) -> None:
         pass

torch/csrc/distributed/c10d/control_plane/Handlers.cpp

@@ -1,5 +1,7 @@
 #include <torch/csrc/distributed/c10d/control_plane/Handlers.hpp>
 
+#include <torch/csrc/distributed/c10d/FlightRecorder.hpp>
+
 #include <fmt/format.h>
 #include <mutex>
 #include <shared_mutex>
@@ -63,6 +65,14 @@ RegisterHandler pingHandler{"ping", [](const Request&, Response& res) {
                               res.setStatus(200);
                             }};
 
+RegisterHandler frTracehandler(
+    "fr_trace_json",
+    [](const Request&, Response& res) {
+      auto trace = ::c10d::dump_fr_trace_json(true, true);
+      res.setContent(std::move(trace), "application/json");
+      res.setStatus(200);
+    });
+
 } // namespace
 
 void registerHandler(const std::string& name, HandlerFunc f) {

torch/csrc/distributed/c10d/control_plane/Handlers.hpp

@@ -18,6 +18,14 @@ class TORCH_API Request {
   virtual const std::string& body() const = 0;
 
   virtual const std::multimap<std::string, std::string>& params() const = 0;
+
+  std::string getParam(const std::string& key) const {
+    auto it = params().find(key);
+    if (it != params().end()) {
+      return it->second;
+    }
+    return "";
+  }
 };
 
 // Response represents a response to the handler. This conceptually maps to an

torch/csrc/distributed/c10d/control_plane/WorkerServer.cpp

@@ -152,11 +152,17 @@ WorkerServer::WorkerServer(const std::string& hostOrFile, int port) {
     TORCH_CHECK(
         server_.bind_to_port(hostOrFile, 80),
         fmt::format("Error binding to {}", hostOrFile));
+  } else if (port == 0) {
+    C10D_WARNING("Server listening to TCP {}:{}", hostOrFile, port);
+    port_ = server_.bind_to_any_port(hostOrFile);
+    TORCH_CHECK(
+        port_ >= 0, fmt::format("Error binding to {}:{}", hostOrFile, port));
   } else {
     C10D_WARNING("Server listening to TCP {}:{}", hostOrFile, port);
     TORCH_CHECK(
         server_.bind_to_port(hostOrFile, port),
         fmt::format("Error binding to {}:{}", hostOrFile, port));
+    port_ = port;
   }
 
   serverThread_ = std::thread([this]() {

torch/csrc/distributed/c10d/control_plane/WorkerServer.hpp

@@ -19,9 +19,14 @@ class TORCH_API WorkerServer : public c10::intrusive_ptr_target {
 
   void shutdown();
 
+  int port() {
+    return port_;
+  }
+
  private:
   httplib::Server server_;
   std::thread serverThread_;
+  int port_;
 };
 
 } // namespace c10d::control_plane

torch/csrc/distributed/c10d/init.cpp

@@ -46,6 +46,7 @@
 
 #include <fmt/format.h>
 #include <pybind11/chrono.h>
+#include <pybind11/functional.h>
 #include <torch/csrc/distributed/c10d/PrefixStore.hpp>
 #include <torch/csrc/distributed/c10d/symm_mem/DMAConnectivity.hpp>
 #include <torch/csrc/distributed/c10d/symm_mem/SymmetricMemory.hpp>
@@ -4203,7 +4204,9 @@ such as `dist.all_reduce(tensor, async_op=True)`.
           }),
           py::arg("host_or_file"),
           py::arg("port") = -1)
-      .def("shutdown", &::c10d::control_plane::WorkerServer::shutdown);
+      .def("shutdown", &::c10d::control_plane::WorkerServer::shutdown)
+      .def_property_readonly(
+          "port", &::c10d::control_plane::WorkerServer::port);
 
   module.def(
       "_get_handler",
@@ -4219,6 +4222,25 @@ such as `dist.all_reduce(tensor, async_op=True)`.
       Returns the handler with the specified name.
     )");
 
+  module.def(
+      "_register_handler",
+      [](const std::string& name, const py::function& handler) {
+        ::c10d::control_plane::registerHandler(
+            name,
+            [handler](
+                const ::c10d::control_plane::Request& req,
+                ::c10d::control_plane::Response& res) {
+              py::gil_scoped_acquire acquire;
+              handler(std::ref(req), std::ref(res));
+            });
+      },
+
+      py::arg("name"),
+      py::arg("handler"),
+      R"(
+    Registers a handler by name.
+  )");
+
   module.def(
       "_get_handler_names",
       &::c10d::control_plane::getHandlerNames,
@@ -4236,12 +4258,9 @@ such as `dist.all_reduce(tensor, async_op=True)`.
       // Default constructor.
       .def(py::init<>())
       .def("body", &::c10d::control_plane::Request::body)
-      .def("params", &::c10d::control_plane::Request::params);
+      .def("get_param", &::c10d::control_plane::Request::getParam);
 
-  py::class_<
-      ::c10d::control_plane::Response,
-      std::shared_ptr<::c10d::control_plane::Response>,
-      PythonResponse>(
+  py::class_<::c10d::control_plane::Response, PythonResponse>(
       module,
       "_Response",
       R"(

torch/csrc/inductor/aoti_runner/pybind.cpp

@@ -66,6 +66,12 @@ void initAOTIRunnerBindings(PyObject* module) {
            int,
            const std::string&,
            const std::string&>())
+      .def(py::init<
+           const std::string&,
+           int,
+           const std::string&,
+           const std::string&,
+           const bool>())
       .def(
           "run",
           &AOTIModelContainerRunnerCuda::run,

torch/distributed/debug/__init__.py

@@ -0,0 +1,59 @@
+import logging
+import multiprocessing
+import socket
+
+# import for registration side effect
+import torch.distributed.debug._handlers  # noqa: F401
+from torch._C._distributed_c10d import _WorkerServer
+from torch.distributed.debug._store import get_rank, tcpstore_client
+
+
+__all__ = [
+    "start_debug_server",
+    "stop_debug_server",
+]
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+_WORKER_SERVER: _WorkerServer | None = None
+_DEBUG_SERVER_PROC: multiprocessing.Process | None = None
+
+
+def start_debug_server(port: int = 25999, worker_port: int = 0) -> None:
+    global _WORKER_SERVER, _DEBUG_SERVER_PROC
+
+    assert _WORKER_SERVER is None, "debug server already started"
+    assert _DEBUG_SERVER_PROC is None, "debug server already started"
+
+    logger.info("Starting debug server on port %d", port)
+
+    store = tcpstore_client()
+
+    _WORKER_SERVER = _WorkerServer("::", worker_port)
+
+    RANK = get_rank()
+    store.set(f"rank{RANK}", f"http://{socket.gethostname()}:{_WORKER_SERVER.port}")
+
+    from torch.distributed.debug._flask import main
+
+    if RANK == 0:
+        _DEBUG_SERVER_PROC = multiprocessing.Process(
+            target=main, args=(port,), daemon=True
+        )
+        _DEBUG_SERVER_PROC.start()
+
+
+def stop_debug_server() -> None:
+    global _WORKER_SERVER, _DEBUG_SERVER_PROC
+
+    assert _DEBUG_SERVER_PROC is not None
+    assert _WORKER_SERVER is not None
+
+    logger.info("Stopping debug server")
+
+    _DEBUG_SERVER_PROC.terminate()
+    _WORKER_SERVER.shutdown()
+    _DEBUG_SERVER_PROC.join()
+
+    _WORKER_SERVER = None
+    _DEBUG_SERVER_PROC = None

torch/distributed/debug/_flask.py

@@ -0,0 +1,265 @@
+import json
+from collections.abc import Iterator
+from concurrent.futures import ThreadPoolExecutor
+
+import requests
+from flask import Flask, render_template, request
+from jinja2 import DictLoader
+
+from torch.distributed.debug._store import get_world_size, tcpstore_client
+
+
+def fetch_all(
+    endpoint: str, args: str = ""
+) -> tuple[list[str], Iterator[requests.Response]]:
+    store = tcpstore_client()
+    keys = [f"rank{r}" for r in range(get_world_size())]
+    addrs = store.multi_get(keys)
+    addrs = [f"{addr.decode()}/handler/{endpoint}?{args}" for addr in addrs]
+
+    with ThreadPoolExecutor(max_workers=10) as executor:
+        resps = executor.map(requests.post, addrs)
+
+    return addrs, resps
+
+
+def format_json(blob: str):
+    parsed = json.loads(blob)
+    return json.dumps(parsed, indent=2)
+
+
+templates = {
+    "base.html": """
+<!doctype html>
+<head>
+    <title>{% block title %}{% endblock %} - PyTorch Distributed</title>
+    <link rel="shortcut icon" type="image/x-icon" href="https://pytorch.org/favicon.ico?">
+
+    <style>
+        body {
+            margin: 0;
+            font-family:
+                -apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,
+                "Helvetica Neue",Arial,"Noto Sans",sans-serif,"Apple Color Emoji",
+                "Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";
+            font-size: 1rem;
+            font-weight: 400;
+            line-height: 1.5;
+            color: #212529;
+            text-align: left;
+            background-color: #fff;
+        }
+        h1, h2, h2, h4, h5, h6, .h1, .h2, .h2, .h4, .h5, .h6 {
+            margin-bottom: .5rem;
+            font-weight: 500;
+            line-height: 1.2;
+        }
+        nav {
+            background-color: rgba(0, 0, 0, 0.17);
+            padding: 10px;
+            display: flex;
+            align-items: center;
+            padding: 16px;
+            justify-content: flex-start;
+        }
+        nav h1 {
+            display: inline-block;
+            margin: 0;
+        }
+        nav a {
+           margin: 0 8px;
+        }
+        section {
+            max-width: 1280px;
+            padding: 16px;
+            margin: 0 auto;
+        }
+        pre {
+            white-space: pre-wrap;
+            max-width: 100%;
+        }
+    </style>
+</head>
+
+<nav>
+    <h1>Torch Distributed Debug Server</h1>
+
+    <a href="/">Home</a> <!--@lint-ignore-->
+    <a href="/stacks">Python Stack Traces</a> <!--@lint-ignore-->
+    <a href="/fr_trace">FlightRecorder</a> <!--@lint-ignore-->
+    <a href="/fr_trace_nccl">FlightRecorder NCCL</a> <!--@lint-ignore-->
+    <a href="/profile">torch profiler</a> <!--@lint-ignore-->
+</nav>
+
+<section class="content">
+  {% block header %}{% endblock %}
+  {% for message in get_flashed_messages() %}
+    <div class="flash">{{ message }}</div>
+  {% endfor %}
+  {% block content %}{% endblock %}
+</section>
+    """,
+    "index.html": """
+{% extends "base.html" %}
+{% block header %}
+  <h1>{% block title %}Index{% endblock %}</h1>
+{% endblock %}
+{% block content %}
+Hi
+{% endblock %}
+    """,
+    "raw_resp.html": """
+{% extends "base.html" %}
+{% block header %}
+    <h1>{% block title %}{{title}}{% endblock %}</h1>
+{% endblock %}
+{% block content %}
+    {% for i, (addr, resp) in enumerate(zip(addrs, resps)) %}
+        <h2>Rank {{ i }}: {{ addr }}</h2>
+        {% if resp.status_code != 200 %}
+            <p>Failed to fetch: status={{ resp.status_code }}</p>
+            <pre>{{ resp.text }}</pre>
+        {% else %}
+            <pre>{{ resp.text }}</pre>
+        {% endif %}
+    {% endfor %}
+{% endblock %}
+    """,
+    "json_resp.html": """
+{% extends "base.html" %}
+{% block header %}
+    <h1>{% block title %}{{ title }}{% endblock %}</h1>
+{% endblock %}
+{% block content %}
+    {% for i, (addr, resp) in enumerate(zip(addrs, resps)) %}
+        <h2>Rank {{ i }}: {{ addr }}</h2>
+        {% if resp.status_code != 200 %}
+            <p>Failed to fetch: status={{ resp.status_code }}</p>
+            <pre>{{ resp.text }}</pre>
+        {% else %}
+            <pre>{{ format_json(resp.text) }}</pre>
+        {% endif %}
+    {% endfor %}
+{% endblock %}
+    """,
+    "profile.html": """
+{% extends "base.html" %}
+{% block header %}
+    <h1>{% block title %}torch.profiler{% endblock %}</h1>
+{% endblock %}
+
+{% block content %}
+    <form action="/profile" method="get">
+        <label for="duration">Duration (seconds):</label>
+        <input type="number" id="duration" name="duration" value="{{ duration }}" min="1" max="60">
+        <input type="submit" value="Submit">
+    </form>
+
+    <script>
+    function stringToArrayBuffer(str) {
+        const encoder = new TextEncoder();
+        return encoder.encode(str).buffer;
+    }
+    async function openPerfetto(data) {
+        const ui = window.open('https://ui.perfetto.dev/#!/');
+        if (!ui) { alert('Popup blocked. Allow popups for this page and click again.'); return; }
+
+        // Perfetto readiness handshake: PING until we receive PONG
+        await new Promise((resolve, reject) => {
+        const onMsg = (e) => {
+            if (e.source === ui && e.data === 'PONG') {
+            window.removeEventListener('message', onMsg);
+            clearInterval(pinger);
+            resolve();
+            }
+        };
+        window.addEventListener('message', onMsg);
+        const pinger = setInterval(() => { try { ui.postMessage('PING', '*'); } catch (_e) {} }, 250);
+        setTimeout(() => { clearInterval(pinger); window.removeEventListener('message', onMsg); reject(); }, 20000);
+        }).catch(() => { alert('Perfetto UI did not respond. Try again.'); return; });
+
+        ui.postMessage({
+        perfetto: {
+            buffer: stringToArrayBuffer(JSON.stringify(data)),
+            title: "torch profiler",
+            fileName: "trace.json",
+        }
+        }, '*');
+    }
+    </script>
+
+    {% for i, (addr, resp) in enumerate(zip(addrs, resps)) %}
+        <h2>Rank {{ i }}: {{ addr }}</h2>
+        {% if resp.status_code != 200 %}
+            <p>Failed to fetch: status={{ resp.status_code }}</p>
+            <pre>{{ resp.text }}</pre>
+        {% else %}
+            <script>
+            function run{{ i }}() {
+                var data = {{ resp.text | safe }};
+                openPerfetto(data);
+            }
+            </script>
+
+            <button onclick="run{{ i }}()">View {{ i }}</button>
+        {% endif %}
+    {% endfor %}
+{% endblock %}
+    """,
+}
+
+app = Flask(__name__)
+app.jinja_loader = DictLoader(templates)
+app.jinja_env.globals.update(
+    zip=zip,
+    format_json=format_json,
+    enumerate=enumerate,
+)
+
+
+@app.route("/")
+def _index_handler():
+    return render_template("index.html")
+
+
+@app.route("/stacks")
+def _stacks_handler():
+    addrs, resps = fetch_all("dump_traceback")
+    return render_template("raw_resp.html", title="Stacks", addrs=addrs, resps=resps)
+
+
+@app.route("/fr_trace")
+def _fr_trace_handler():
+    addrs, resps = fetch_all("fr_trace_json")
+
+    return render_template(
+        "json_resp.html",
+        title="FlightRecorder",
+        addrs=addrs,
+        resps=resps,
+    )
+
+
+@app.route("/fr_trace_nccl")
+def _fr_trace_nccl_handler():
+    addrs, resps = fetch_all("dump_nccl_trace_json", "onlyactive=true")
+
+    return render_template(
+        "json_resp.html",
+        title="FlightRecorder NCCL",
+        addrs=addrs,
+        resps=resps,
+    )
+
+
+@app.route("/profile")
+def _profiler_handler():
+    duration = request.args.get("duration", default=1.0, type=float)
+
+    addrs, resps = fetch_all("torch_profile", f"duration={duration}")
+
+    return render_template("profile.html", addrs=addrs, resps=resps)
+
+
+def main(port: int) -> None:
+    app.run(host="::", port=port)

torch/distributed/debug/_handlers.py

@@ -0,0 +1,22 @@
+import tempfile
+import time
+
+from torch._C._distributed_c10d import _register_handler, _Request, _Response
+from torch.profiler import _ExperimentalConfig, profile
+
+
+def _torch_profile(req: _Request, resp: _Response) -> None:
+    experimental_config = _ExperimentalConfig(
+        profile_all_threads=True,
+    )
+    duration = float(req.get_param("duration"))
+    with profile(record_shapes=True, experimental_config=experimental_config) as prof:
+        time.sleep(duration)
+
+    with tempfile.NamedTemporaryFile(prefix="torch_debug", suffix=".json") as f:
+        prof.export_chrome_trace(f.name)
+        resp.set_content(open(f.name, "rb").read(), "application/json")
+        resp.set_status(200)
+
+
+_register_handler("torch_profile", _torch_profile)

torch/distributed/debug/_store.py

@@ -0,0 +1,24 @@
+import os
+
+import torch.distributed as dist
+
+
+def get_rank() -> int:
+    return int(os.environ["RANK"])
+
+
+def get_world_size() -> int:
+    return int(os.environ["WORLD_SIZE"])
+
+
+def tcpstore_client() -> dist.Store:
+    MASTER_ADDR = os.environ["MASTER_ADDR"]
+    MASTER_PORT = int(os.environ["MASTER_PORT"])
+
+    store = dist.TCPStore(
+        host_name=MASTER_ADDR,
+        port=MASTER_PORT,
+        is_master=False,
+    )
+    store = dist.PrefixStore("debug_server", store)
+    return store

torch/distributed/tensor/_dispatch.py

@@ -337,19 +337,34 @@ class OpDispatcher:
         if is_inplace_op:
             # inplace op should return self instead of re-wrapping
             if output_sharding.output_spec is not None:
+                output_spec = output_sharding.output_spec
+                assert isinstance(output_spec, DTensorSpec)
+                assert isinstance(args[0], dtensor.DTensor)
+
                 # NOTE: aten.squeeze_.dim is an inplace op but it also may change
                 # the inplace argument's tensor meta. Here we choose to special case
                 # this op because as far as I know this is the only inplace op that
                 # has such as behavior. We can extend this special case if necessary.
                 if op_call == aten.squeeze_.dim:
-                    output_spec = output_sharding.output_spec
-                    assert isinstance(output_spec, DTensorSpec)
-                    assert isinstance(args[0], dtensor.DTensor)
+                    # update the spec to handle tensor meta changes
                     args[0]._spec = output_spec
                     # use return_and_correct_aliasing to match the outer and the inner
                     # aliasing. See https://github.com/pytorch/pytorch/pull/158954
                     return return_and_correct_aliasing(op_call, args, kwargs, args[0])
                 else:
+                    # For all other inplace ops, check if placement changes are required
+                    # Inplace operations that change placement are not supported because
+                    # they would require redistribution, which breaks aliasing semantics.
+                    # If there are views into the tensor, the views would not be updated.
+                    if args[0]._spec.placements != output_spec.placements:
+                        raise RuntimeError(
+                            f"{op_call}: in-place operations that require placement changes "
+                            f"are not supported. The operation would change placement from "
+                            f"{args[0]._spec.placements} to {output_spec.placements}, "
+                            f"which requires redistribution and breaks aliasing semantics. "
+                            f"Please use the out-of-place version of this operation instead."
+                        )
+                    # Most inplace ops don't change tensor meta, so no spec update needed
                     return args[0]
             else:
                 return None