"""
Utilities for reproducing and debugging issues in PyTorch's Dynamo AOT compilation.

This module provides tools and infrastructure for:
1. Generating minimal reproducible test cases ("repros") from failing compilations
2. Analyzing accuracy issues between eager and compiled execution
3. Minifying large models/inputs to isolate problematic patterns
4. Debugging compiler errors and accuracy divergences

The main components include:
- Repro generation: Creates standalone Python files that reproduce compiler issues
- Minification: Reduces large graphs to minimal failing examples
- Accuracy analysis: Compares compiled vs eager execution, with fp64 reference
- Debug tools: Dumps graph state, tracks intermediates, analyzes divergences

This is primarily used by PyTorch developers and researchers to debug issues in
the Dynamo AOT compilation pipeline, particularly for the Inductor backend.
"""

from __future__ import annotations

import argparse
import copy
import functools
import io
import logging
import os
import shutil
import subprocess
import sys
import textwrap
import uuid
from importlib import import_module
from tempfile import TemporaryFile
from typing import Any, IO, Optional, TYPE_CHECKING, Union
from typing_extensions import Unpack

import sympy


try:
    from triton.runtime.autotuner import Autotuner, Heuristics
    from triton.runtime.jit import JITFunction
except ImportError:

    class Autotuner:  # type: ignore[no-redef]
        pass

    class JITFunction:  # type: ignore[no-redef]
        pass

    class Heuristics:  # type: ignore[no-redef]
        pass


import torch
import torch.fx as fx
import torch.nn as nn
from torch._dynamo.debug_utils import (
    _cuda_system_info_comment,
    AccuracyError,
    backend_accuracy_fails,
    BuckTargetWriter,
    cast_to_fp64,
    extra_deps,
    extra_imports,
    generate_config_string,
    generate_env_vars_string,
    helper_for_dump_minify,
    InputReader,
    InputWriter,
    MAX_CONSTANT_NUMEL_INLINE,
    minifier_dir,
    NNModuleToString,
    NopInputReader,
    same_two_models,
)
from torch._dynamo.utils import clone_inputs, counters, same
from torch._environment import is_fbcode
from torch._higher_order_ops.triton_kernel_wrap import kernel_side_table
from torch._inductor.cpp_builder import normalize_path_separator
from torch._library.fake_class_registry import FakeScriptObject
from torch._ops import OpOverload
from torch.fx.experimental.proxy_tensor import make_fx
from torch.fx.experimental.symbolic_shapes import (
    fx_placeholder_targets,
    has_free_symbols,
)
from torch.hub import tqdm

from .. import config


if TYPE_CHECKING:
    from collections.abc import Callable, Sequence

    from torch._inductor.compile_fx import _CompileFxCallable, _CompileFxKwargs
    from torch._inductor.output_code import OutputCode
    from torch._inductor.utils import InputType


log = logging.getLogger(__name__)


inductor_config = import_module("torch._inductor.config")
use_buck = is_fbcode()

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
#                           MAIN ENTRY POINT
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #


def wrap_compiler_debug(
    unconfigured_compiler_fn: _CompileFxCallable,
    compiler_name: str,
) -> _CompileFxCallable:
    """
    Minifier for Fx Graph modules after Aot Autograd has finished. We wrap both
    forward and backward call separately with the backend compiler_fn - like
    inductor or nvfuser. Intercepting after Aot Autograd presents neat
    abstraction, where all the params are lifted as graph inputs, making it easy
    to save the graph as a string.
    """

    @functools.wraps(unconfigured_compiler_fn)
    def debug_wrapper(
        gm: torch.fx.GraphModule,
        example_inputs: Sequence[InputType],
        **kwargs: Unpack[_CompileFxKwargs],
    ) -> OutputCode:
        from torch._subclasses import FakeTensorMode

        compiler_fn = functools.partial(unconfigured_compiler_fn, **kwargs)

        from torch._functorch.aot_autograd import get_aot_graph_name

        graph_name = get_aot_graph_name()

        # TODO: why do we need to deepcopy the original graph?
        orig_graph = copy.deepcopy(gm.graph)
        assert config.repro_after in ("dynamo", "aot", None)

        try:
            # Call the compiler_fn - which is either aot_autograd or inductor
            # with fake inputs
            inner_compiled_fn = compiler_fn(gm, example_inputs)
        except Exception:
            # TODO: Failures here are troublesome because no real inputs,
            # need a different serialization strategy
            if config.repro_after == "aot":
                if config.repro_level == 1:
                    dump_compiler_graph_state(
                        fx.GraphModule(gm, orig_graph),
                        example_inputs,
                        compiler_name,
                    )
                elif config.repro_level == 2:
                    dump_to_minify(
                        fx.GraphModule(gm, orig_graph),
                        example_inputs,
                        compiler_name,
                    )
                log.error("CompilerError")
            raise

        # We may run regular PyTorch compute that may trigger Dynamo, do NOT
        # recursively attempt to accuracy minify in that case!
        def deferred_for_real_inputs(
            real_inputs: Sequence[InputType], **_kwargs: object
        ) -> Any:
            # This is a bit obscure: if we recursively try to accuracy minify
            # the SAME function, this would trigger.  But most of the time
            # we should never hit this branch
            assert not _kwargs
            if config.repro_after != "aot":
                assert not isinstance(inner_compiled_fn, str)
                return inner_compiled_fn(real_inputs)
            with config.patch(repro_after=None):
                return inner_debug_fn(real_inputs)

        def inner_debug_fn(real_inputs: Sequence[InputType]) -> Any:
            """
            Aot Autograd fw_compiler and bw_compiler can have fake tensors. So,
            example_inputs can be fake tensors. We can call compiler_fn (which is
            inductor or nvfuser) with fake tensors but the actually compiled_fn
            should be called with real tensors. Therefore, the actual invocation
            is deferred.
            """
            # Copy the tensor attrs like shape, stride etc by converting to Fake Tensor
            # because inductor clears the tensor list in its codegen. And example_inputs
            # are available only for the first invocation.
            fake_mode = FakeTensorMode()
            copy_tensor_attrs = [
                fake_mode.from_tensor(x) if isinstance(x, torch.Tensor) else x
                for x in real_inputs
            ]
            if config.repro_level == 3:
                # Always dump the original module in case we have segfaults
                dump_to_minify(
                    fx.GraphModule(gm, orig_graph), real_inputs, compiler_name
                )

            if config.repro_level == 4:
                if compiler_name != "inductor":
                    raise NotImplementedError(
                        "Accuracy minification is supported for inductor only"
                    )
                failed = not same_two_models(
                    gm,
                    inner_compiled_fn,  # type: ignore[arg-type]
                    real_inputs,
                    only_fwd=True,
                    ignore_non_fp=config.repro_ignore_non_fp,
                )

                if failed:
                    log.warning(
                        "Accuracy failed for the AOT Autograd graph %s", graph_name
                    )
                    dump_compiler_graph_state(
                        fx.GraphModule(gm, orig_graph),
                        real_inputs,
                        f"{compiler_name}_accuracy",
                    )
                    dump_to_minify(
                        fx.GraphModule(gm, orig_graph),
                        real_inputs,
                        f"{compiler_name}_accuracy",
                    )
                    raise AccuracyError("Bad accuracy detected")
                else:
                    # Call the compiled function with real inputs
                    return inner_compiled_fn(real_inputs)  # type: ignore[operator]
            else:
                try:
                    # Call the compiled function with real inputs
                    out = inner_compiled_fn(real_inputs)  # type: ignore[operator]
                    # sync cuda kernels to ensure IMA detection
                    for arg in example_inputs:
                        if isinstance(arg, torch.Tensor) and arg.is_cuda:
                            torch.cuda.synchronize()
                            break
                    return out
                except Exception:
                    if config.repro_level == 1:
                        dump_compiler_graph_state(
                            fx.GraphModule(gm, orig_graph),
                            copy_tensor_attrs,
                            compiler_name,
                        )
                    elif config.repro_level == 2:
                        dump_to_minify(
                            fx.GraphModule(gm, orig_graph),
                            copy_tensor_attrs,
                            compiler_name,
                        )
                    raise

        if config.repro_after == "aot":
            compiled_fn = deferred_for_real_inputs
            compiled_fn._boxed_call = True  # type: ignore[attr-defined]
            return compiled_fn  # type: ignore[return-value]
        else:
            return inner_compiled_fn

    return debug_wrapper


# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
#                           DUMP REPROS
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #


def maybe_fbcode_instructions() -> str:
    if is_fbcode():
        extra_deps_formatted = "\n".join([f'        "{dep}",' for dep in extra_deps])
        if len(extra_deps_formatted) > 0:
            extra_deps_formatted = "\n" + extra_deps_formatted
        return f"""\
\"\"\"
To run this script in fbcode:
- Create a directory (//scripts/{{your_unixname}}/repro)
- Put this file in scripts/{{your_unixname}}/repro/fx_graph_runnable.py
- Add a TARGETS file that looks like the following
- `buck2 run //scripts/{{your_unixname}}/repro:repro`

NOTE: you may need additional deps to actually be able to run the script.
```
# Contents of TARGETS file
load("@fbcode_macros//build_defs:python_binary.bzl", "python_binary")

python_binary(
    name = "repro",
    main_src = "fx_graph_runnable.py",
    deps = [
        "//caffe2:torch",{extra_deps_formatted}
    ],
)
```
\"\"\"
"""
    else:
        return ""


def generate_compiler_repro_string(
    gm: torch.fx.GraphModule,
    args: Sequence[Any],
    *,
    stable_output: bool = False,
    save_dir: Optional[str] = None,
    stable_hash: bool = False,
    has_distributed_ops: bool = False,
) -> str:
    if save_dir is not None:
        save_dir = normalize_path_separator(save_dir)
    # Add distributed imports if needed
    distributed_imports = ""
    if has_distributed_ops:
        distributed_imports = textwrap.dedent(
            """
import torch.distributed as dist
from torch.testing._internal.distributed.fake_pg import FakeStore
        """
        ).strip()

    triton_imports = ""

    if len(kernel_side_table.id_to_kernel) > 0:
        triton_imports = textwrap.dedent(
            """
import triton
import triton.language as tl
        """
        ).strip()

    model_str = textwrap.dedent(
        f"""
{generate_env_vars_string(stable_output=stable_output)}
import torch
from torch import tensor, device
import torch.fx as fx
from torch._dynamo.testing import rand_strided
from math import inf
import torch._inductor.inductor_prims
{distributed_imports}
{triton_imports}

{generate_config_string(stable_output=stable_output)}

isolate_fails_code_str = None

{extra_imports}

{maybe_fbcode_instructions()}
     """
    )
    if not stable_output:
        model_str += f"# torch version: {torch.version.__version__}\n"
        if hasattr(torch.version, "cuda"):
            model_str += f"# torch cuda version: {torch.version.cuda}\n"
        if hasattr(torch.version, "git_version"):
            model_str += f"# torch git version: {torch.version.git_version}\n\n\n"
        model_str += _cuda_system_info_comment()

    kernel_side_table_prefix = (
        "torch._higher_order_ops.triton_kernel_wrap.kernel_side_table"
    )
    # Track which grid entry corresponds to the best config
    for id in kernel_side_table.id_to_kernel:
        kernel = kernel_side_table.get_kernel(id)

        try:
            if isinstance(kernel, Autotuner):
                # pyrefly: ignore [missing-attribute]
                if isinstance(kernel.fn, Heuristics):
                    model_str += "ERROR: Repro will not work as intended, "
                    model_str += "triton.runtime.autotuner.Heuristics is not currently supported\n"
                    break

                config_strs = []
                # pyrefly: ignore [missing-attribute]
                for kernel_config in kernel.configs:
                    # pyrefly: ignore [bad-argument-type]
                    config_strs.append(f"""triton.Config(
                            {str(kernel_config.kwargs)},
                            num_warps={kernel_config.num_warps},
                            num_stages={kernel_config.num_stages},
                        )""")

                config_str = ",".join(config_strs)
                model_str += textwrap.dedent(f"""
                @triton.autotune(
                    configs=[
                        {config_str}
                    ],
                    key=[]
                )
                """).strip()

            model_str += "\n@triton.jit\n"
            # pyrefly: ignore [missing-attribute]
            src_code = kernel.src if isinstance(kernel, JITFunction) else kernel.fn.src
            fn_name = (
                # pyrefly: ignore [missing-attribute]
                kernel._fn_name
                if isinstance(kernel, JITFunction)
                else kernel.fn._fn_name
            )
            fn_name = fn_name.split(".")[-1]

            model_str += src_code
            model_str += "\n"
            model_str += f"{kernel_side_table_prefix}.add_kernel({fn_name})\n"
        except AttributeError as e:
            model_str += "ERROR: Repro will not work as intended, "
            model_str += f"User defined triton kernel exception: {e}\n"

    # pyrefly: ignore [unbound-name]
    if len(kernel_side_table.constant_args) > 0:
        # pyrefly: ignore [unbound-name]
        model_str += f"{kernel_side_table_prefix}.constant_args={kernel_side_table.constant_args}\n"

    model_str += NNModuleToString.convert(gm)

    writer = InputWriter(save_dir, stable_hash=stable_hash)
    used_syms = {}

    # Extract from graph placeholders and their corresponding arguments
    placeholder_targets = fx_placeholder_targets(gm)
    for placeholder, arg in zip(placeholder_targets, args):
        # pyrefly: ignore [unbound-name]
        if isinstance(arg, (int, torch.SymInt)):
            writer.symint(placeholder, arg)
        # pyrefly: ignore [unbound-name]
        elif isinstance(arg, torch.Tensor):
            # TODO: improve these names with FQN
            writer.tensor(placeholder, arg)
        elif arg is None:
            writer.const(placeholder)
        else:
            writer.unsupported(placeholder, arg)

        # Extract symbolic variables from the same arguments
        # pyrefly: ignore [unbound-name]
        if (
            isinstance(arg, torch.SymInt)
            # By checking sympy.Symbol, we are excluding any symbolic expressions.
            # TODO: we may need to solve expressions to extract symbol definitions.
            and isinstance(arg.node.expr, sympy.Symbol)
            and arg.node.hint is not None
        ):
            used_syms[str(arg.node)] = arg.node.hint
        # pyrefly: ignore [unbound-name]
        elif isinstance(arg, torch.Tensor):
            # Extract symbolic variables from tensor shapes and strides
            for dim in arg.shape:
                # pyrefly: ignore [unbound-name]
                if (
                    isinstance(dim, torch.SymInt)
                    and isinstance(dim.node.expr, sympy.Symbol)
                    and dim.node.hint is not None
                ):
                    used_syms[str(dim.node)] = dim.node.hint
            for stride in arg.stride():
                # pyrefly: ignore [unbound-name]
                if (
                    isinstance(stride, torch.SymInt)
                    and isinstance(stride.node.expr, sympy.Symbol)
                    and stride.node.hint is not None
                ):
                    used_syms[str(stride.node)] = stride.node.hint
    # Add symbolic variable definitions to the top of the generated code
    if used_syms:
        hint_lines = "\n".join(
            f"{name} = {hint}" for name, hint in sorted(used_syms.items())
        )
        model_str = f"{hint_lines}\n\n{model_str}"

    load_args_lines = writer.lines()
    load_args_code = "\n".join(load_args_lines)
    model_str += load_args_code + "\n"

    model_str += "mod = Repro()\n"
    return model_str


def save_graph_repro(
    fd: IO[Any],
    gm: torch.fx.GraphModule,
    args: Sequence[Any],
    compiler_name: str,
    *,
    stable_output: bool = False,
    save_dir: Optional[str] = None,
    command: str = "run",
    accuracy: Optional[Union[str, bool]] = None,
    tracing_mode: Optional[str] = None,
    check_str: Optional[str] = None,
    stable_hash: bool = False,
) -> None:
    if any(
        isinstance(arg, torch.fx.experimental._backward_state.BackwardState)
        for arg in args
    ):
        fd.write(
            "Repro is not generated due to existence of BackwardState in graph input"
        )
        return

    if save_dir is not None:
        save_dir = normalize_path_separator(save_dir)

    # Check if the graph contains distributed operations
    has_distributed_ops = any(
        node.op == "call_function"
        and isinstance(node.target, OpOverload)
        and node.target.namespace in {"_c10d_functional", "c10d_functional"}
        for node in gm.graph.nodes
    )

    fd.write(
        generate_compiler_repro_string(
            gm,
            args,
            stable_output=stable_output,
            save_dir=save_dir,
            stable_hash=stable_hash,
            has_distributed_ops=has_distributed_ops,
        )
    )
    if accuracy is None:
        accuracy = "_accuracy" in compiler_name
    if tracing_mode is None:
        tracing_mode = "real"
        if any(
            has_free_symbols(a) for a in args if not isinstance(a, FakeScriptObject)
        ):
            tracing_mode = "symbolic"
    fd.write("if __name__ == '__main__':\n")
    fd.write("    from torch._dynamo.repro.after_aot import run_repro\n")

    # Add distributed initialization before run_repro if needed
    if has_distributed_ops:
        fd.write(
            "    # Initialize FakeProcessGroup for distributed operations\n"
            "    store = FakeStore()\n"
            "    dist.init_process_group(\n"
            '        backend="fake",\n'
            "        rank=0,\n"
            "        world_size=2,\n"
            "        store=store\n"
            "    )\n"
        )

    fd.write(
        f"    with torch.no_grad():\n"
        f"        run_repro(mod, load_args, accuracy={accuracy!r}, command={command!r}, "
        f"save_dir={save_dir!r}, tracing_mode={tracing_mode!r}, check_str={check_str!r})\n"
        f"        # To run it separately, do \n"
        f"        # mod, args = run_repro(mod, load_args, accuracy={accuracy!r}, command='get_args', "
        f"save_dir={save_dir!r}, tracing_mode={tracing_mode!r}, check_str={check_str!r})\n"
        f"        # mod(*args)"
    )

    # Add distributed cleanup after run_repro
    if has_distributed_ops:
        fd.write("\n    dist.destroy_process_group()\n")


def dump_compiler_graph_state(
    gm: torch.fx.GraphModule,
    args: Sequence[Any],
    compiler_name: str,
    *,
    accuracy: Optional[Union[str, bool]] = None,
) -> None:
    subdir = os.path.join(minifier_dir(), "checkpoints")
    if not os.path.exists(subdir):
        os.makedirs(subdir, exist_ok=True)
    file_name = os.path.join(subdir, f"{len(gm.graph.nodes)}.py")
    log.warning(
        "Writing checkpoint with %s nodes to %s", len(gm.graph.nodes), file_name
    )
    with open(file_name, "w") as fd:
        save_graph_repro(
            fd, gm, args, compiler_name, save_dir=subdir, accuracy=accuracy
        )
    curdir = os.getcwd()
    repro_path = os.path.join(curdir, "repro.py")
    try:
        shutil.copyfile(file_name, repro_path)
        log.warning("Copying repro file for convenience to %s", repro_path)
        if use_buck:
            BuckTargetWriter(file_name).write()
    except OSError:
        log.warning("No write permissions for %s", repro_path)


# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
#                           DUMP MINIFIER
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #


def dump_to_minify(
    gm: torch.fx.GraphModule, args: Sequence[Any], compiler_name: str
) -> None:
    out = io.StringIO()
    # TODO: factor this out
    subdir = os.path.join(minifier_dir(), "checkpoints")
    if not os.path.exists(subdir):
        os.makedirs(subdir, exist_ok=True)
    save_graph_repro(out, gm, args, compiler_name, save_dir=subdir, command="minify")
    return helper_for_dump_minify(out.getvalue())


def isolate_fails(
    fx_g: torch.fx.GraphModule,
    args: Sequence[Any],
    compiler_name: str,
    env: Optional[dict[str, Any]] = None,
    save_dir: Optional[str] = None,
    accuracy: Optional[Union[bool, str]] = None,
    tracing_mode: Optional[str] = None,
    check_str: Optional[str] = None,
) -> bool:
    if env is None:
        env = {}
    subdir = os.path.join(os.getcwd(), "isolate")
    if not os.path.exists(subdir):
        os.makedirs(subdir, exist_ok=True)
    file_name = os.path.join(subdir, f"{str(uuid.uuid4())[:5]}.py")
    with open(file_name, "w") as fd:
        save_graph_repro(
            fd,
            fx_g,
            args,
            compiler_name,
            save_dir=save_dir,
            command="minifier-query",
            accuracy=accuracy,
            tracing_mode=tracing_mode,
            check_str=check_str,
        )
    # with open(file_name, "r") as fd:
    #     print(fd.read())
    new_env = os.environ.copy()
    new_env = {**new_env, **env}
    stdout, stderr = TemporaryFile(), TemporaryFile()

    if use_buck:
        cmd = BuckTargetWriter(file_name).write(print_msg=False)
    else:
        cmd = [sys.executable, file_name]

    p = subprocess.Popen(
        cmd,
        cwd=subdir,
        stdout=stdout,
        stderr=stderr,
        env=new_env,
    )
    p.wait()

    stdout.seek(0)
    stderr.seek(0)
    print(
        textwrap.indent(stdout.read().decode("utf-8"), prefix=">>  "), file=sys.stdout
    )
    print(
        textwrap.indent(stderr.read().decode("utf-8"), prefix=">>  "), file=sys.stderr
    )
    # print(f"Isolated test failed - {file_name}")
    return p.returncode != 0


# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
#                       MINIFIER TOOLS
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #


def inductor_fails(
    fx_g: torch.fx.GraphModule, args: Sequence[Any], check_str: Optional[str] = None
) -> bool:
    has_cuda = False
    for arg in args:
        if isinstance(arg, torch.Tensor) and arg.is_cuda:
            has_cuda = True
            break

    def sync() -> None:
        if has_cuda:
            # Ensures that segfaults are surfaced
            torch.cuda.synchronize()

    from torch._inductor.compile_fx import compile_fx_inner

    try:
        result = fx_g(*args)
        assert isinstance(result, (tuple, list))
        assert not any(isinstance(x, (tuple, list)) for x in result)
    except Exception:
        return False

    sync()

    try:
        compile_mod = compile_fx_inner(fx_g, args)
        assert not isinstance(compile_mod, str)
        compile_mod(args)
        sync()
    except Exception as e:
        if check_str is not None and check_str not in repr(e):
            return False
        print(repr(e))
        return True
    return False


def inductor_accuracy_fails(
    fx_g: torch.fx.GraphModule,
    args: Sequence[Any],
    check_str: Optional[str] = None,
    *,
    require_fp64: bool = False,
    ignore_non_fp: bool = False,
) -> bool:
    from torch._inductor.compile_fx import compile_fx_inner

    return backend_aot_accuracy_fails(
        fx_g,
        args,  # type: ignore[arg-type]
        compile_fx_inner,  # type: ignore[arg-type]
        require_fp64=require_fp64,
        ignore_non_fp=ignore_non_fp,
    )


backend_aot_accuracy_fails = functools.partial(backend_accuracy_fails, only_fwd=True)


# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
#                           REPRO MAIN
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #


def repro_common(
    options: Any, mod: nn.Module, load_args: Any
) -> tuple[torch.fx.GraphModule, Sequence[Any]]:
    # Invariant for graphs we generate with the repro script
    assert not any(mod.named_parameters())
    for n, b in mod.named_buffers():
        if b.numel() > MAX_CONSTANT_NUMEL_INLINE:
            log.warning(
                "Constant %s was not serialized, generated random data instead. "
                "If you think this is affecting you, please comment on "
                "https://github.com/pytorch/pytorch/issues/100468",
                n,
            )

    if not hasattr(load_args, "_version"):
        log.warning(
            "load_args does not have a _version attribute, please file a bug to PyTorch "
            "and describe how you generate this repro script"
        )
    else:
        if load_args._version > 0:
            log.warning(
                "load_args is version %s, but this version of PyTorch only supports "
                "version 0.  We will try to run it anyway but there may be an incompatibility; "
                "if so, try upgrading your version of PyTorch.",
                load_args._version,
            )

    nop_reader = NopInputReader()
    load_args(nop_reader)

    with tqdm(desc="Loading inputs", total=nop_reader.total) as pbar:
        input_reader = InputReader(save_dir=options.save_dir, pbar=pbar)
        load_args(input_reader)
        args = input_reader.args

    # Turn mod into a GraphModule the slow way
    # TODO: speed this up
    mod = make_fx(mod, tracing_mode=options.tracing_mode)(*args)

    # pyrefly: ignore [bad-assignment]
    torch._inductor.config.generate_intermediate_hooks = True

    return mod, args


ACCURACY_FAILS: dict[str, Callable[[torch.fx.GraphModule, Any], bool]] = {
    "": inductor_fails,
    # This might look inverted but it's not.  strict_accuracy means "we will
    # minify any time we see anything that diverges", whereas accuracy is more
    # conservative, and will only minify if there is a meaningful fp64
    # divergence
    "accuracy": functools.partial(
        inductor_accuracy_fails, require_fp64=True, ignore_non_fp=True
    ),
    "strict_accuracy": inductor_accuracy_fails,
}


def repro_minifier_query(options: Any, mod: nn.Module, load_args: Any) -> None:
    mod, args = repro_common(options, mod, load_args)
    fail_fn = functools.partial(
        ACCURACY_FAILS[options.accuracy],
        check_str=options.check_str,  # type: ignore[call-arg]
    )
    if fail_fn(mod, args):
        sys.exit(1)
    else:
        sys.exit(0)


def repro_minify(options: Any, mod: nn.Module, load_args: Any) -> None:
    from functorch.compile import minifier

    mod, args = repro_common(options, mod, load_args)
    compiler_name = "inductor_accuracy" if options.accuracy != "" else "inductor"

    favored_device = 1 if torch.cuda.device_count() >= 2 else 0
    env_variables = {"CUDA_VISIBLE_DEVICES": str(favored_device)}

    module_fails: Any
    if options.isolate:
        module_fails = functools.partial(
            isolate_fails,
            env=env_variables,
            compiler_name=compiler_name,
            save_dir=options.save_dir,
            accuracy=options.accuracy,
            tracing_mode=options.tracing_mode,
        )
    else:
        module_fails = ACCURACY_FAILS[options.accuracy]

    minifier(
        mod,
        args,
        module_fails=functools.partial(module_fails, check_str=options.check_str),
        dump_state=functools.partial(
            dump_compiler_graph_state, compiler_name=compiler_name
        ),
        save_dir=options.save_dir,
        offload_to_disk=options.offload_to_disk,
        skip_offload=options.skip_saving_eager_intermediates,
        skip_sanity=options.skip_sanity,
        max_granularity=options.max_granularity,
    )


def repro_analyze(options: Any, mod: nn.Module, load_args: Any) -> None:
    from torch._inductor.compile_fx import compile_fx_inner
    from torch._inductor.hooks import intermediate_hook

    mod, args = repro_common(options, mod, load_args)

    # TODO: The logic for cloning inputs/models here is intentionally
    # modeled off of run_fwd_maybe_bwd, but arguably it is better not to
    # clone inputs (as you are doubling your effective GPU memory usage).
    # It is certainly faster though!  It probably makes sense to let the
    # user specify the offload strategy.

    with tqdm(desc="Compiling"):
        compiled = compile_fx_inner(mod, args)
    total = counters["inductor"]["intermediate_hooks"]

    known_names = set()

    def save_hook(name: str, val: Any) -> None:
        known_names.add(name)
        if not options.skip_saving_inductor_intermediates:
            writer.write_tensor(os.path.join("inductor", name), val)
        pbar.update(1)  # type: ignore[has-type]

    writer = torch.utils._content_store.ContentStoreWriter(
        options.save_dir, stable_hash=options.stable_hash
    )
    reader = torch.utils._content_store.ContentStoreReader(options.save_dir)

    new_args = clone_inputs(args)
    with (
        intermediate_hook(save_hook),
        tqdm(desc="Saving inductor intermediates", total=total) as pbar,
    ):
        assert not isinstance(compiled, str)
        compiled(new_args)  # type: ignore[arg-type]
        assert not new_args

    def compare_tuples(tuple1: tuple[Any], tuple2: tuple[Any]) -> Optional[str]:
        diff_indices = [i for i in range(len(tuple1)) if tuple1[i] != tuple2[i]]
        diff_values = [(tuple1[i], tuple2[i]) for i in diff_indices]

        if not diff_values:
            return None
        else:
            return " and ".join(f"{a} != {b}" for a, b in diff_values)

    def check_hook(name: str, val: Any) -> None:
        meta = writer.compute_tensor_metadata(val)
        meta2 = reader.read_tensor_metadata(os.path.join("inductor", name))
        reason = compare_tuples(meta, meta2)
        if reason is not None:
            pbar.write(f"NONDETERMINISTIC INDUCTOR at {name} ({reason})")
        pbar.update(1)

    if not options.skip_check_deterministic:
        new_args = clone_inputs(args)
        with (
            intermediate_hook(check_hook),
            tqdm(desc="Checking inductor determinism", total=total) as pbar,
        ):
            compiled(new_args)  # type: ignore[arg-type]
            assert not new_args

    class WriterInterp(fx.Interpreter):
        def __init__(self, mod: torch.nn.Module, subdir: str) -> None:
            super().__init__(mod)
            self.subdir = subdir

        def run_node(self, n: torch.fx.Node) -> Any:
            r = super().run_node(n)
            name = n.name
            if name in known_names:
                pbar.update(1)
                writer.write_tensor(os.path.join(self.subdir, name), r)
            return r

    # NB: the module cast doesn't actually do anything, since there are no
    # parameters/buffers on the module
    if not options.skip_saving_float64_intermediates:
        new_mod, new_args = cast_to_fp64(copy.deepcopy(mod), clone_inputs(args))  # type: ignore[arg-type]
        with tqdm(desc="Saving float64 intermediates", total=total) as pbar:
            WriterInterp(new_mod, "float64").boxed_run(new_args)
        assert not new_args

    class ExactReaderInterp(fx.Interpreter):
        def run_node(self, n: torch.fx.Node) -> Any:
            r = super().run_node(n)
            name = n.name
            if name in known_names:
                meta = writer.compute_tensor_metadata(r)
                meta2 = reader.read_tensor_metadata(os.path.join("float64", name))
                reason = compare_tuples(meta, meta2)
                if reason is not None:
                    pbar.write(f"NONDETERMINISTIC FLOAT64 at {name} ({reason})")
                pbar.update(1)
            return r

    # TODO: check eager determinism

    if not options.skip_check_deterministic:
        new_mod, new_args = cast_to_fp64(copy.deepcopy(mod), clone_inputs(args))  # type: ignore[arg-type]
        with tqdm(desc="Checking float64 determinism", total=total) as pbar:
            ExactReaderInterp(new_mod).boxed_run(new_args)
            assert not new_args

    # Now that we've saved everything, interp through the eager graph
    # and do comparisons
    class ReaderInterp(fx.Interpreter):
        def run_node(self, n: torch.fx.Node) -> Any:
            r = super().run_node(n)
            name = n.name
            if name in known_names:
                inductor = reader.read_tensor(os.path.join("inductor", name))
                float64 = reader.read_tensor(os.path.join("float64", name))
                logged = False

                def log_error(msg: str, *args: Any) -> None:
                    nonlocal logged
                    logged = True
                    pbar.write(f"DIVERGED at {name}: {msg % args}")

                if not same(
                    r,
                    inductor,
                    float64,
                    tol=torch._dynamo.config.repro_tolerance,
                    equal_nan=True,
                    log_error=log_error,
                ):
                    assert logged
                pbar.update(1)
            return r

    with tqdm(desc="Checking divergence", total=total) as pbar:
        ReaderInterp(mod).boxed_run(args)
    assert not args


def repro_get_args(
    options: Any, mod: nn.Module, load_args: Any
) -> tuple[torch.fx.GraphModule, list[Any]]:
    mod, args = repro_common(options, mod, load_args)
    return mod, args  # type: ignore[return-value]


def repro_run(options: Any, mod: nn.Module, load_args: Any) -> None:
    from torch._inductor.compile_fx import compile_fx_inner

    mod, args = repro_common(options, mod, load_args)

    from torch.cuda import synchronize

    compiled = compile_fx_inner(mod, args)
    assert not isinstance(compiled, str)

    if options.accuracy != "":
        # We don't really respect --accuracy vs --strict-accuracy here, it
        # seems counterintuitive
        if not same_two_models(
            mod,
            compiled,  # type: ignore[arg-type]
            args,
            only_fwd=True,
            ignore_non_fp=config.repro_ignore_non_fp,
        ):
            raise AccuracyError("Bad accuracy detected")
    else:
        need_sync = False

        for arg in args:
            if isinstance(arg, torch.Tensor) and arg.is_cuda:
                need_sync = True
                break

        compiled(list(args))

        if need_sync:
            synchronize()  # ensure segfaults are surfaced


# TODO: lazily load the inputs or something, rather than cloning them
def run_repro(
    mod: nn.Module,
    load_args: Any,
    *,
    command: str = "run",
    accuracy: Union[bool, str] = "",
    save_dir: Optional[str] = None,
    tracing_mode: Optional[str] = None,
    patch_code: Optional[str] = None,
    check_str: Optional[str] = None,
    **kwargs: Any,
) -> Any:
    for k in kwargs:
        log.warning(
            "Unrecognized kwarg %s; perhaps this repro was made on a newer version of PyTorch",
            k,
        )

    if accuracy is True:
        accuracy = "accuracy"
    elif accuracy is False:
        accuracy = ""

    if patch_code is not None:
        log.warning(
            "patch_code no longer works on this version of PyTorch, silently ignoring"
        )

    parser = argparse.ArgumentParser(
        description=f"""\
An after_aot repro script, typically triggering a bug in PyTorch Inductor.
When run with no arguments, this script defaults to running '{command}'.
Extra flags may be available; to find out more, try '{command} --help'.
There are also alternate subcommands available, see below.

default settings on this script:
  {accuracy=}
  {tracing_mode=}
  {save_dir=}
  {check_str=}
""",
        formatter_class=argparse.RawTextHelpFormatter,
    )

    def common_flags(parser: argparse.ArgumentParser) -> None:
        accuracy_group = parser.add_mutually_exclusive_group()
        accuracy_group.add_argument(
            "--no-accuracy",
            dest="accuracy",
            action="store_const",
            const="",
            default=accuracy,
            help="do not test accuracy, just run the module and see if it errors",
        )
        accuracy_group.add_argument(
            "--accuracy",
            action="store_const",
            const="accuracy",
            default=accuracy,
            help="""\
test if the RMSE between the compiled module and the fp64 reference is greater
than eager and the fp64 reference. This is usually more reliable than the
standard allclose test, as we expect numeric differences from compiling, often
improving accuracy over eager.  RMSE test allows for compiled module to
diverge greatly from eager, as long as this divergence moves it closer to the
'true' mathematical value of the network.  Caveats: (1) double precision can
still suffer from rounding error, so it is not a perfect reference (see for
example 'Herbie: Automatically Improving Floating Point Accuracy') for
approaches that detect the necessary working precision and compute it in
arbitrary precision floating point; unfortunately, this is not practical for
tensor computation; (2) if there are not enough samples in the output being
compared, we may get unlucky and have an unlucky greater RMSE than eager; this
could be overcome by applying a more rigorous statistical test at some
p-value, which we leave for future work.
""",
        )
        accuracy_group.add_argument(
            "--strict-accuracy",
            dest="accuracy",
            action="store_const",
            const="strict_accuracy",
            default=accuracy,
            help="""\
by default, when doing accuracy minification we will reject reductions which
change the divergence from a floating point divergence to a integral/boolean
divergence.  This is because some operations like ReLU involve temporarily
sharp boundaries that smooth out again afterwards; without requiring
divergence on floating point, the minifier will often fixate on divergent
boolean tensor even though this is not the true source of the divergence.
However, rejecting these reductions makes it more difficult for the minifier
to make process.  Using this option will let the minifier progress for ALL
divergences--you just might not end up with a useful repro in the end.""",
        )

        parser.add_argument(
            "--save-dir",
            type=str,
            default=save_dir,
            metavar="DIR",
            help="directory where saved inputs live",
        )
        parser.add_argument(
            "--no-save-dir",
            dest="save_dir",
            action="store_const",
            const=None,
            help="don't use any directory for saved inputs",
        )
        parser.add_argument(
            "--tracing-mode",
            type=str,
            metavar="{real,fake,symbolic}",
            default=tracing_mode,
            help="how to trace the repro module into a GraphModule with metadata",
        )

    subparsers = parser.add_subparsers(
        dest="command", metavar="{run,minify,analyze}", required=True
    )

    parser_run = subparsers.add_parser(
        "run",
        help="just run the repro",
    )
    common_flags(parser_run)

    parser_minify = subparsers.add_parser(
        "minify", help="run the minifier on the repro"
    )
    common_flags(parser_minify)
    parser_get_args = subparsers.add_parser("get_args", help="get the args")
    common_flags(parser_get_args)
    parser_minify_isolate = parser_minify.add_mutually_exclusive_group()
    parser_minify_isolate.add_argument(
        "--isolate",
        action="store_true",
        default=True,
        help="run in separate processes to avoid interference (default)",
    )
    parser_minify_isolate.add_argument(
        "--no-isolate",
        dest="isolate",
        action="store_false",
        help="speed up by running all compilation in same process",
    )
    parser_minify.add_argument(
        "--skip-saving-eager-intermediates",
        action="store_true",
        help="skip saving eager intermediates on --minify",
    )
    # TODO: make this an option for --analyze too
    parser_minify.add_argument(
        "--offload-to-disk",
        action="store_true",
        help="during minification, offload delta debugging intermediates to disk.  Use if you're OOMing",
    )
    parser_minify.add_argument(
        "--skip-sanity",
        action="store_true",
        help="skip sanity check at beginning of minification on original graph",
    )
    parser_minify.add_argument(
        "--max-granularity",
        type=int,
        default=None,
        help="start at this granularity and work down; must be power of 2",
    )
    parser_minify.add_argument(
        "--check-str",
        type=str,
        default=check_str,
        help="require minified program to fail with error containing this string",
    )

    parser_analyze = subparsers.add_parser(
        "analyze", help="run the accuracy analyzer on the repro"
    )
    common_flags(parser_analyze)
    parser_analyze.add_argument(
        "--skip-saving-inductor-intermediates",
        action="store_true",
        help="skip saving inductor intermediates on --analyze",
    )
    parser_analyze.add_argument(
        "--skip-saving-float64-intermediates",
        action="store_true",
        help="skip saving float64 intermediates",
    )
    parser_analyze.add_argument(
        "--skip-check-deterministic",
        action="store_true",
        help="skip checking that the network is deterministic",
    )
    parser_analyze.add_argument(
        "--stable-hash",
        action="store_true",
        help="use SHA-1 checksum instead of fast (but possibly unsound) hash",
    )

    # Run the repro in the context of minification, inverting exit code meaning
    parser_minifier_query = subparsers.add_parser(
        "minifier-query",
    )
    common_flags(parser_minifier_query)
    parser_minifier_query.add_argument(
        "--check-str",
        type=str,
        default=check_str,
        help="require minified program to fail with error containing this string",
    )

    args = None
    if len(sys.argv) <= 1:
        args = [command, *sys.argv[1:]]

    options = parser.parse_args(args)
    COMMAND_FNS = {
        "minify": repro_minify,
        "analyze": repro_analyze,
        "minifier-query": repro_minifier_query,
        "run": repro_run,
        "get_args": repro_get_args,
    }
    return COMMAND_FNS[options.command](options, mod, load_args)