MLPY/Lib/site-packages/torch/_dynamo/debug_utils.py

# mypy: disable-error-code="method-assign"

import copy
import functools
import getpass
import inspect
import itertools
import logging
import os
import re
import subprocess
import tempfile
import textwrap
from collections import Counter
from importlib import import_module
from typing import Any, Callable, Dict, List, Optional, TypeVar

import torch
import torch._prims_common as utils
import torch._subclasses.meta_utils
from torch import Tensor

from torch._dynamo.testing import rand_strided
from torch._prims_common import is_float_dtype
from torch.multiprocessing.reductions import StorageWeakRef
from torch.utils._content_store import ContentStoreReader, ContentStoreWriter

from . import config
from .utils import clone_inputs, get_debug_dir

log = logging.getLogger(__name__)

T = TypeVar("T")


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

if use_buck:
    import libfb.py.build_info


extra_deps = []
extra_imports = ""
if use_buck:
    extra_deps = [
        "//caffe2/torch/fb/sparsenn:sparsenn_operators_gpu",
        "//caffe2/torch/fb/sparsenn:sparsenn_operators",
        "//deeplearning/fbgemm/fbgemm_gpu:sparse_ops_cpu",
        "//deeplearning/fbgemm/fbgemm_gpu:sparse_ops",
    ]
    cur_target = libfb.py.build_info.BuildInfo.get_build_rule().replace("fbcode:", "//")  # type: ignore[possibly-undefined]
    extra_imports = "\n".join([f'torch.ops.load_library("{x}")' for x in extra_deps])


BUCK_CMD_PREFIX = ["buck2", "run", "@mode/dev-nosan"]


class BuckTargetWriter:
    def __init__(self, filename):
        self.subdir, self.py_file = os.path.split(os.path.abspath(filename))
        self.target = self.py_file.replace(".py", "")

        # Get main_module path from fbcode
        self.path = f'{self.subdir.replace("/", ".")}.{self.target}'
        self.path = self.path[self.path.find("fbcode.") :]
        self.path = self.path[7:]

        # Get cmd line path
        tmp = self.subdir
        tmp = tmp[tmp.find("fbcode/") :][7:]
        self.cmd_line_path = f"//{tmp}:{self.target}"

    def build(self):
        extra_cpp_deps = "\n".join([f'        "{x}",' for x in extra_deps])
        return textwrap.dedent(
            f"""

load("@fbcode_macros//build_defs:python_binary.bzl", "python_binary")



python_binary(

    name="{self.target}",

    srcs = ["{self.py_file}"],

    compile = False,

    deps = [

        "//caffe2:torch",

        "//caffe2/functorch:functorch",

        "//triton:triton",

        "{cur_target}",

    ],

    cpp_deps = [

{extra_cpp_deps}

    ],

    main_module = "{self.path}",

    par_style = "xar",

)

"""
        )

    def write(self, print_msg=True):
        target_file = os.path.join(self.subdir, "TARGETS")
        with open(target_file, "w") as fd:
            fd.write(self.build())
        # log.warning("Wrote isolation TARGETS file at %s", target_file)
        cmd_split = BUCK_CMD_PREFIX + [self.cmd_line_path]
        if print_msg:
            log.warning(
                "Found an example that reproduces the error. Run this cmd to repro - %s",
                " ".join(cmd_split),
            )
        return cmd_split


def minifier_dir():
    path = os.path.join(get_debug_dir(), "minifier")
    if path is None:
        path = f"{tempfile.gettempdir()}/minifier_{getpass.getuser()}"
    if not os.path.exists(path):
        os.makedirs(path, exist_ok=True)
    return path


MAX_CONSTANT_NUMEL_INLINE = 4


class NNModuleToString:
    safe_reprs = [
        torch.nn.Linear,
        torch.nn.Conv1d,
        torch.nn.Conv2d,
        torch.nn.Conv3d,
        torch.nn.BatchNorm1d,
        torch.nn.BatchNorm2d,
        torch.nn.BatchNorm3d,
        torch.nn.LayerNorm,
        torch.nn.Dropout,
        torch.nn.Softmax,
        torch.nn.ReLU,
        torch.nn.GELU,
        torch.nn.Identity,
        torch.nn.MaxPool2d,
        torch.nn.Embedding,
        torch.nn.Tanh,
        torch.nn.ConvTranspose1d,
        torch.nn.GLU,
        torch.nn.LSTM,
        torch.nn.Flatten,
        torch.nn.AdaptiveAvgPool2d,
    ]

    @staticmethod
    def can_convert_to_string(gm):
        cant_convert = set()
        for _, module in gm.named_children():
            if type(module) not in NNModuleToString.safe_reprs:
                cant_convert.add(module)

        if len(cant_convert) > 0:
            log.warning("We have not tested reprs of some modules - %s", cant_convert)
        # TODO - Assuming that all modules can be safely repr'd. Check if that assumption is correct.
        return True

    @staticmethod
    def convert(gm):
        from torch.nn.modules.module import _addindent

        tab = " " * 4

        model_str = textwrap.dedent(
            """

            from torch.nn import *

            class Repro(torch.nn.Module):

                def __init__(self):

                    super().__init__()

            """
        )

        for module_name, module in gm.named_children():
            module_str = f"{module.__repr__()}"
            # module should be a core torch.nn.Module, so all parameters
            # should be on the same device.
            example_param = next(module.parameters(), None)
            if example_param is not None and example_param.is_cuda:
                module_str = f"{module_str}.cuda()"
            model_str += f"{tab*2}self.{module_name} = {module_str}\n"

        for buffer_name, buffer in gm._buffers.items():
            if buffer is None:
                continue
            # Serialize full data for small buffers
            if buffer.numel() <= MAX_CONSTANT_NUMEL_INLINE:
                from torch._tensor_str import PRINT_OPTS

                assert PRINT_OPTS.threshold >= MAX_CONSTANT_NUMEL_INLINE
                tensor_str = repr(buffer)
            elif torch.is_floating_point(buffer):
                tensor_str = f"torch.randn({list(buffer.shape)}, dtype={buffer.dtype})"
            else:
                tensor_str = (
                    f"torch.randint(1, size={list(buffer.shape)}, dtype={buffer.dtype})"
                )
            if buffer.is_cuda:
                tensor_str = f"{tensor_str}.cuda()"
            model_str += f"{tab*2}self.register_buffer('{buffer_name}', {tensor_str})\n"

        for param_name, param in gm._parameters.items():
            if param is None:
                continue
            maybe_device = ""
            if param.is_cuda:
                maybe_device = ', device="cuda"'
            tensor_str = f"torch.nn.Parameter(torch.randn({list(param.shape)}, dtype={param.dtype}{maybe_device}))"
            model_str += f"{tab*2}self.{param_name} = {tensor_str}\n"

        # TODO - Keep this code for now. But, I don't think we will need this.
        # attrs = dir(gm)
        # for attr in attrs:
        #     if "_tensor_constant" in attr:
        #         val = getattr(gm, attr)
        #         model_str += f"    {attr} = {val!r}\n"

        model_str += f"{_addindent(gm.code, 4)}\n"
        return model_str


@functools.lru_cache(None)  # subprocess is expensive
def _cuda_system_info_comment():
    if not torch.cuda.is_available():
        return "# torch.cuda.is_available()==False, no GPU info collected\n"

    model_str = "# CUDA Info: \n"
    try:
        cuda_version_out = subprocess.check_output(["nvcc", "--version"])
        cuda_version_lines = cuda_version_out.decode().split("\n")
        comment = "".join([f"# {s} \n" for s in cuda_version_lines if s not in [""]])
        model_str += f"{comment}\n"
    except (FileNotFoundError, subprocess.CalledProcessError):
        model_str += "# nvcc not found\n"

    gpu_names = Counter(
        torch.cuda.get_device_name(i) for i in range(torch.cuda.device_count())
    )

    model_str += "# GPU Hardware Info: \n"
    for name, count in gpu_names.items():
        model_str += f"# {name} : {count} \n"
    model_str += "\n"
    return model_str


def generate_config_string(*, stable_output=False):
    import torch._functorch.config
    import torch._inductor.config

    if stable_output:
        return "# config omitted due to stable_output=True"

    experimental_config = torch.fx.experimental._config.codegen_config()  # type: ignore[attr-defined]
    return f"""\

import torch._dynamo.config

import torch._inductor.config

import torch._functorch.config

import torch.fx.experimental._config

{torch._dynamo.config.codegen_config()}

{torch._inductor.config.codegen_config()}

{torch._functorch.config.codegen_config()}

{experimental_config}

"""


def get_minifier_repro_path():
    return os.path.join(minifier_dir(), "minifier_launcher.py")


def helper_for_dump_minify(contents):
    minified_repro_path = get_minifier_repro_path()
    log.warning("Writing minified repro to:\n%s", minified_repro_path)

    if use_buck:
        BuckTargetWriter(minified_repro_path).write()
    try:
        with open(minified_repro_path, "w") as fd:
            fd.write(contents)

    except OSError as e:
        log.exception(e)
        raise NotImplementedError("Could not write to {minified_repro_path}") from e


class AccuracyError(Exception):
    pass


def clone_inputs_retaining_gradness(example_inputs):
    """

    This clone inputs is different from utils clone_input. In case of minifier,

    all the tensors are leaf tensors while creating a new graph. So, we set the

    requires_grad field w/o checking the leafness of the tensor.

    """
    cloned_inputs = clone_inputs(example_inputs)
    for idx in range(len(example_inputs)):
        if isinstance(cloned_inputs[idx], torch.Tensor):
            cloned_inputs[idx].requires_grad_(example_inputs[idx].requires_grad)
    return cloned_inputs


def run_fwd_maybe_bwd(gm, args, only_fwd=False, disable_clone=False):
    """

    Runs a forward and possibly backward iteration for a given mod and args.



    When disable_clone is True, we will use args as-is without cloning.

    This is higher fidelity but we may destroy the args in the process.

    """
    from torch._functorch.aot_autograd import make_boxed_func

    from .testing import collect_results, reduce_to_scalar_loss, requires_bwd_pass

    gm = copy.deepcopy(gm)
    if not disable_clone:
        args = clone_inputs_retaining_gradness(args)

    if hasattr(gm, "zero_grad"):
        gm.zero_grad(True)

    # TorchInductor returned callable expects lists. So, boxing the call.
    orig_named_parameters = getattr(gm, "named_parameters", None)
    orig_named_buffers = getattr(gm, "named_buffers", None)
    if not hasattr(gm, "_boxed_call") and (
        orig_named_parameters is not None or orig_named_buffers is not None
    ):
        gm = make_boxed_func(gm)
        if orig_named_parameters is not None:
            gm.named_parameters = orig_named_parameters
        if orig_named_buffers is not None:
            gm.named_buffers = orig_named_buffers

    out = gm(args)
    if only_fwd:
        return out
    if requires_bwd_pass(out):
        loss = reduce_to_scalar_loss(out)
        loss.backward()
    return collect_results(gm, out, None, args)


def same_two_models(

    gm,

    opt_gm,

    example_inputs,

    only_fwd=False,

    *,

    require_fp64=False,

    ignore_non_fp=False,

):
    """

    Check two models have same accuracy.



    require_fp64: if True, raise an error if we unable to calculate the fp64 reference

    ignore_non_fp: if True, do not compare outputs which are not floating point.  This

        is mostly useful for the minifier (which wants to avoid quantizing floating point

        error into integer/boolean error)

    """
    from .eval_frame import OptimizedModule
    from .testing import (
        named_buffers_for_optimized_module,
        named_parameters_for_optimized_module,
    )
    from .utils import same

    if isinstance(gm, OptimizedModule):
        gm.named_parameters = named_parameters_for_optimized_module(gm)
        gm.named_buffers = named_buffers_for_optimized_module(gm)

    if isinstance(opt_gm, OptimizedModule):
        opt_gm.named_parameters = named_parameters_for_optimized_module(opt_gm)
        opt_gm.named_buffers = named_buffers_for_optimized_module(opt_gm)

    ref = run_fwd_maybe_bwd(gm, example_inputs, only_fwd)

    fp64_ref = None
    if config.same_two_models_use_fp64:
        try:
            fp64_model, fp64_examples = cast_to_fp64(
                copy.deepcopy(gm), clone_inputs_retaining_gradness(example_inputs)
            )
            fp64_ref = run_fwd_maybe_bwd(fp64_model, fp64_examples, only_fwd)
        except Exception:
            if require_fp64:
                raise RuntimeError("Could not generate fp64 outputs")  # noqa: TRY200
            log.warning("Could not generate fp64 outputs")

    try:
        res = run_fwd_maybe_bwd(opt_gm, example_inputs, only_fwd)
    except Exception as e:
        # This means that the minified graph is bad/exposes a different problem.
        # As we are checking accuracy here, lets log the exception and return True.
        log.exception(
            "While minifying the program in accuracy minification mode, "
            "ran into a runtime exception which is likely an unrelated issue."
            " Skipping this graph."
        )
        return True

    passing = same(
        ref,
        res,
        fp64_ref,
        tol=config.repro_tolerance,
        equal_nan=True,
        ignore_non_fp=ignore_non_fp,
    )
    return passing


def cast_dtype_args_to_fp64(model):
    for node in model.graph.nodes:
        if (
            node.op == "call_function"
            and node.target == torch.ops.prims.convert_element_type.default
        ):
            assert len(node.args) == 2
            if is_float_dtype(node.args[1]) and node.args[1] != torch.float64:
                node.args = (node.args[0], torch.float64)
        if node.op == "call_function":
            dtype = node.kwargs.get("dtype")
            if dtype is not None and is_float_dtype(dtype):
                new_kwargs = dict(node.kwargs)
                new_kwargs["dtype"] = torch.float64
                node.kwargs = new_kwargs

    model.graph.lint()
    model.recompile()
    return model


def cast_to(dtype, model, inputs):
    from torch.utils._pytree import tree_map

    model = model.to(dtype)
    if dtype == torch.float64:
        # If casting to fp64 for accuracy comparison, we need to
        # replace dtype arguments embedded in the graph with fp64
        model = cast_dtype_args_to_fp64(model)

    inputs = tree_map(
        lambda x: x.to(dtype)
        if isinstance(x, torch.Tensor) and x.is_floating_point()
        else x,
        inputs,
    )
    return model, inputs


def cast_to_fp64(model, inputs):
    return cast_to(torch.float64, model, inputs)


def backend_accuracy_fails(

    gm,

    example_inputs,

    compiler_fn,

    only_fwd=False,

    *,

    require_fp64=False,

    ignore_non_fp=False,

):
    try:
        compiled_gm = compiler_fn(
            copy.deepcopy(gm), clone_inputs_retaining_gradness(example_inputs)
        )
        return not same_two_models(
            gm,
            compiled_gm,
            example_inputs,
            only_fwd,
            require_fp64=require_fp64,
            ignore_non_fp=ignore_non_fp,
        )
    except Exception as e:
        # This means that the minified graph is bad/exposes a different problem.
        # As we are checking accuracy here, lets log the exception and return False.
        log.exception(
            "While minifying the program in accuracy minification mode, "
            "ran into a runtime exception which is likely an unrelated issue."
            " Skipping this graph"
        )
        return False


# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
#                       REPRO SUPPORT CODE
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #


# Helper functions for computing what the default values of tensor
# values should be.  These all coincide with factory functions, e.g., torch.empty


def _stride_or_default(

    stride: Optional["torch._prims_common.StrideType"],

    *,

    shape: "torch._prims_common.ShapeType",

) -> "torch._prims_common.StrideType":
    return stride if stride is not None else utils.make_contiguous_strides_for(shape)


def _mk_defaulter(d: T) -> Callable[[Optional[T]], T]:
    return lambda x: x if x is not None else d


_dtype_or_default = _mk_defaulter(torch.float32)
_device_or_default = _mk_defaulter(torch.device("cpu"))
_storage_offset_or_default = _mk_defaulter(0)
_requires_grad_or_default = _mk_defaulter(False)
_is_leaf_or_default = _mk_defaulter(False)


class NopInputReader:
    def __init__(self):
        self.total = 0

    def storage(self, storage_hash, nbytes, *, device=None, dtype_hint=None):
        self.total += 1

    def tensor(self, *args, **kwargs):
        pass

    def symint(self, *args, **kwargs):
        pass


# TODO: Support bundling the entire repro into a zip file for ease of
# transferring around
class InputReader:
    def __init__(self, save_dir=None, *, pbar=None):
        # If None, we will generate random data instead.  It's important
        # to natively support this use case as it will allow people to
        # share repros without including the real data, if the problem
        # reproduces even on random data.
        if save_dir is None:
            log.warning("no save_dir specified, will generate random data")
        self.store = ContentStoreReader(save_dir) if save_dir is not None else None
        self.args = []
        self.pbar = pbar

    def storage(self, storage_hash, nbytes, *, device=None, dtype_hint=None):
        if self.pbar is not None:
            self.pbar.update(1)
        device = _device_or_default(device)
        dtype_hint = _dtype_or_default(dtype_hint)
        if self.store is not None and storage_hash is not None:
            try:
                storage = self.store.read_storage(storage_hash)
            except FileNotFoundError:
                pass
            else:
                if device != storage.device:
                    log.warning("device mismatch: %s != %s", device, storage.device)
                    # TODO: transfer it to the right device?  But failing this
                    # way would be very mysterious!  Would have been better
                    # not to store device in the serialized format...
                return storage
        log.warning("could not load %s, generating random data instead", storage_hash)
        shape = (nbytes // dtype_hint.itemsize,)
        stride = _stride_or_default(None, shape=shape)
        return rand_strided(shape, stride, dtype_hint, device).untyped_storage()

    def tensor(

        self,

        storage,

        shape,

        stride=None,

        *,

        storage_offset=None,

        dtype=None,

        requires_grad=None,

        is_leaf=None,

        **metadata,

    ):
        stride = _stride_or_default(stride, shape=shape)
        storage_offset = _storage_offset_or_default(storage_offset)
        dtype = _dtype_or_default(dtype)
        is_leaf = _is_leaf_or_default(is_leaf)
        requires_grad = _requires_grad_or_default(requires_grad)
        t = torch.tensor(
            [], dtype=dtype, device=storage.device, requires_grad=requires_grad
        )
        with torch.no_grad():
            t.set_(storage, storage_offset, shape, stride)
        if not is_leaf:
            # Fake up some autograd history in a very naughty way
            with torch.enable_grad():
                t = t.clone(memory_format=torch.preserve_format)
            with torch.no_grad():
                t.set_(storage, storage_offset, shape, stride)
        assert torch._subclasses.meta_utils.safe_is_leaf(t) == is_leaf
        torch._utils.set_tensor_metadata(t, metadata)
        self.args.append(t)
        return t  # for BC

    def symint(self, val):
        self.args.append(val)
        return val  # for BC


# Here is our writer strategy:
#  1. We will stream all of the inputs to disk
#  2. You can now deterministically randomize the inputs, or reload
#     the inputs from disk
#  3. You can YOLO run the script without the inputs, in which case
#     we'll fill the inputs with random data and pray.  This is the
#     legacy behavior, but it's also useful if you want to find out
#     if we're so broken even random inputs trigger it
#  4. We could offer an in process "check if the randomized thing
#     works too" but this is delicate so we don't do it


class InputWriter:
    def __init__(self, save_dir, *, stable_hash=False):
        self._lines = []
        # TODO: consider ensuring tensor and storage counters line up?
        self.storage_counter = itertools.count()
        self.save_dir = save_dir
        self.store = (
            ContentStoreWriter(save_dir, stable_hash=stable_hash)
            if save_dir is not None
            else None
        )
        self.seen_storages = {}

    def lines(self):
        r = [
            "def load_args(reader):",
        ]
        r.extend(f"    {l}" for l in self._lines)
        # In case we need to change the internal format of load_args
        # in an FC-breaking way
        r.append("load_args._version = 0")
        return r

    # Storages are untyped, but we need to initialize them with data if
    # we don't have the real data, so we give a hint saying what kind
    # of initialization may be appropriate
    #
    # If we had a FakeTensor, device_hint tells us what device should be
    def storage(self, untyped_storage, *, dtype_hint=None, device_hint=None) -> str:
        ws = StorageWeakRef(untyped_storage)
        v = self.seen_storages.get(ws)
        if v is not None:
            return v
        v = f"buf{next(self.storage_counter)}"
        maybe_dtype_hint = ""
        if _dtype_or_default(None) != _dtype_or_default(dtype_hint):
            maybe_dtype_hint = f", dtype_hint={dtype_hint!r}"
        # TODO: being optional on device is kind of pointless as the default
        # is CPU but most repros we care about are CUDA
        maybe_device = ""
        device = untyped_storage.device
        if device.type == "meta":
            assert device_hint is not None
            device = device_hint
        if _device_or_default(None) != device:
            maybe_device = f", device={device!r}"
        nbytes = untyped_storage.nbytes()
        storage_hash = None
        if self.store is not None and untyped_storage.device.type != "meta":
            storage_hash = self.store.write_storage(untyped_storage)
        self._lines.append(
            f"{v} = reader.storage({storage_hash!r}, {nbytes!r}{maybe_device}{maybe_dtype_hint})"
        )
        self.seen_storages[ws] = v
        return v

    def tensor(self, name, t) -> None:
        storage = self.storage(
            t.untyped_storage(), dtype_hint=t.dtype, device_hint=t.device
        )
        args = []
        # NB: this is positional, must come first
        if _stride_or_default(None, shape=t.shape) != t.stride():
            args.append(str(tuple(t.stride())))
        if _dtype_or_default(None) != t.dtype:
            args.append(f"dtype={t.dtype!r}")
        if _storage_offset_or_default(None) != t.storage_offset():
            args.append(f"storage_offset={t.storage_offset()!r}")
        tensor_metadata = torch._utils.get_tensor_metadata(t)
        if tensor_metadata:
            args.extend(f"{k}={v!r}" for k, v in tensor_metadata.items())
        if _requires_grad_or_default(None) != t.requires_grad:
            args.append(f"requires_grad={t.requires_grad!r}")
        is_leaf = torch._subclasses.meta_utils.safe_is_leaf(t)
        if _is_leaf_or_default(None) != is_leaf:
            args.append(f"is_leaf={is_leaf!r}")
        self._lines.append(
            "reader.tensor("
            + ", ".join([storage, str(tuple(t.shape)), *args])
            + f")  # {name}"
        )

    # TODO: this doesn't actually symint atm
    def symint(self, name, val) -> None:
        if isinstance(val, torch.SymInt):
            val = val.node.hint
        self._lines.append(f"reader.symint({val!r})  # {name}")


def aot_graph_input_parser(

    func: Callable[[List[Tensor]], List[Tensor]],

    device: str = "cuda",

    sym_shapes: Optional[Dict[str, int]] = None,

    default_sym_shape: Optional[int] = None,

) -> Dict[str, Any]:
    """

    Takes in a function which has been printed with print_readable() and constructs kwargs to run it.



    Handles Tensor inputs, Symints, and a graph module which might have tensor constants.



    Consider a function `forward` defined as follows:



    def forward(self, primals_1: "f32[1001, 6]", primals_2: "f32[s0]", primals_3: "Sym(s0)",):

        _tensor_constant0: "i64[4190]" = self._tensor_constant0

        # Further implementation



    kwargs = aot_graph_input_parser(forward)

    forward(**kwargs)

    """

    from torch.fx.graph import dtype_abbrs

    dtype_map = {value: key for key, value in dtype_abbrs.items()}
    dtype_pattern = "|".join(dtype_abbrs.values())

    # Extracting the source code from the function
    source = inspect.getsource(func)

    # Regular expressions
    tensor_assignment_regex = rf"(_tensor_constant\d+): \"({dtype_pattern})\[\s*(.*?)\s*\]\" = self\.(_tensor_constant\d+)"
    tensor_regex = rf"({dtype_pattern})\[\s*(.*?)\s*\]"
    sym_shape_regex = r"Sym\((s\d+)\)"

    class TensorContainer:
        "Container for tensors as attributes"
        pass

    # Dictionary for tensors from annotations
    kwargs: Dict[str, Any] = {}

    sym_shapes = sym_shapes or {}

    def get_sym_int(symint):
        torch._check(
            symint in sym_shapes or default_sym_shape is not None,
            lambda: f"{symint} not in symbolic_shapes and default sym shape not passed in",
        )
        return sym_shapes.get(symint, default_sym_shape)

    def gen_tensor(shape, dtype) -> Tensor:
        # Resolve symbolic shapes to concrete values
        resolved_shape = []
        dynamic_dims = []
        for i, dim in enumerate(shape):
            dim = dim.strip()
            if "s" in dim:
                s = get_sym_int(dim)
                resolved_shape.append(s)
                dynamic_dims.append(i)
            else:
                resolved_shape.append(int(dim))

        constructor = torch.randn if dtype.is_floating_point else torch.zeros
        out = constructor(resolved_shape, dtype=dtype, device=device)  # type: ignore[call-arg]
        for d in dynamic_dims:
            torch._dynamo.mark_dynamic(out, d)
        return out

    # Parse function annotations for tensor generation
    annotations = func.__annotations__
    for param, annotation in annotations.items():
        # Skip 'return' annotation
        if param == "return":
            continue

        match = re.search(tensor_regex, annotation)
        if match:
            data_type, shape_str = match.groups()
            shape = tuple(shape_str.split(","))
            dtype = dtype_map[data_type]
            kwargs[param] = gen_tensor(shape, dtype)

        match = re.search(sym_shape_regex, annotation)
        if match:
            kwargs[param] = get_sym_int(match.group(1))

    if "self" in inspect.signature(func).parameters:
        container = TensorContainer()
        kwargs["self"] = container
        for match in re.finditer(tensor_assignment_regex, source):
            attr_name, data_type, shape_str, _ = match.groups()
            shape = tuple(shape_str.split(","))
            dtype = dtype_map[data_type]
            setattr(container, attr_name, gen_tensor(shape, dtype))

    return kwargs