# Owner(s): ["module: inductor"]
# ruff: noqa: F841
import contextlib
import contextvars
import copy
import dataclasses
import functools
import gc
import importlib
import itertools
import math
import operator
import os
import random
import re
import subprocess
import sys
import threading
import time
import unittest
import unittest.mock
import weakref
from collections.abc import Callable
from pathlib import Path
from typing import TypeVar
from typing_extensions import ParamSpec
from unittest.mock import patch

import numpy as np

import torch
import torch._dynamo.config as dynamo_config
import torch._inductor.aoti_eager
import torch.fx.traceback as fx_traceback
import torch.nn as nn
from torch._C._dynamo.guards import assert_alignment, assert_size_stride
from torch._dispatch.python import enable_python_dispatcher
from torch._dynamo.debug_utils import aot_graph_input_parser
from torch._dynamo.device_interface import get_interface_for_device
from torch._dynamo.testing import (
    CompileCounterWithBackend,
    expectedFailureCodegenDynamic,
    rand_strided,
    reset_rng_state,
    same,
    skipIfPy312,
)
from torch._dynamo.utils import ifdynstaticdefault
from torch._guards import CompileContext, CompileId
from torch._inductor import lowering
from torch._inductor.aoti_eager import (
    aoti_compile_with_persistent_cache,
    aoti_eager_cache_dir,
    load_aoti_eager_cache,
)
from torch._inductor.codegen.common import DataTypePropagation, OptimizationContext
from torch._inductor.test_case import TestCase as InductorTestCase
from torch._inductor.utils import (
    add_scheduler_init_hook,
    run_and_get_code,
    run_and_get_cpp_code,
    run_and_get_kernels,
    run_and_get_triton_code,
    run_fw_bw_and_get_code,
    triton_version_uses_attrs_dict,
)
from torch._inductor.virtualized import V
from torch._prims_common import is_integer_dtype
from torch.fx.experimental.proxy_tensor import make_fx
from torch.library import _scoped_library
from torch.nn import functional as F
from torch.testing import FileCheck, make_tensor
from torch.testing._internal.common_cuda import (
    IS_SM90,
    PLATFORM_SUPPORTS_FLASH_ATTENTION,
    PLATFORM_SUPPORTS_MEM_EFF_ATTENTION,
    SM80OrLater,
    SM90OrLater,
    TEST_CUDNN,
    tf32_on_and_off,
    with_tf32_off,
)
from torch.testing._internal.common_device_type import (
    expectedFailureXPU,
    largeTensorTest,
)
from torch.testing._internal.common_dtype import (
    all_types,
    get_all_dtypes,
    highest_precision_float,
)
from torch.testing._internal.common_quantization import (
    _dynamically_quantize_per_channel,
    _group_quantize_tensor_symmetric,
)
from torch.testing._internal.common_utils import (
    DeterministicGuard,
    instantiate_parametrized_tests,
    IS_ARM64,
    IS_CPU_EXT_SVE_SUPPORTED,
    IS_FBCODE,
    IS_MACOS,
    IS_X86,
    MACOS_VERSION,
    MI200_ARCH,
    NAVI_ARCH,
    parametrize,
    serialTest,
    skipIfRocm,
    skipIfRocmArch,
    skipIfWindows,
    skipIfXpu,
    subtest,
    TEST_WITH_ASAN,
    TEST_WITH_ROCM,
    xfailIf,
    xfailIfS390X,
)
from torch.testing._internal.logging_utils import logs_to_string
from torch.utils import _pytree as pytree
from torch.utils._python_dispatch import TorchDispatchMode
from torch.utils._pytree import tree_flatten, tree_unflatten
from torch.utils.weak import WeakTensorKeyDictionary


DO_PERF_TEST = os.environ.get("DO_PERF_TEST") == "1"

importlib.import_module("functorch")
importlib.import_module("filelock")

from torch._inductor import config, cpu_vec_isa, test_operators
from torch._inductor.compile_fx import (
    compile_fx,
    compile_fx_inner,
    complex_memory_overlap,
)
from torch._inductor.utils import has_torchvision_roi_align
from torch.testing._internal.common_utils import slowTest
from torch.testing._internal.inductor_utils import (  # noqa: F401
    clone_preserve_strides_offset,
    GPU_TYPE,
    HAS_CPU,
    HAS_GPU,
    HAS_MPS,
    HAS_MULTIGPU,
    HAS_TPU,
    IS_BIG_GPU,
    requires_gpu,
    RUN_CPU,
    RUN_GPU,
    RUN_TPU,
    skipCPUIf,
    skipCUDAIf,
)
from torch.testing._internal.triton_utils import (
    requires_cuda_and_triton,
    requires_gpu_and_triton,
)


_T = TypeVar("_T")
_P = ParamSpec("_P")


HAS_AVX2 = "fbgemm" in torch.backends.quantized.supported_engines

if TEST_WITH_ROCM:
    torch._inductor.config.force_layout_optimization = 1
    os.environ["PYTORCH_MIOPEN_SUGGEST_NHWC"] = "1"

aten = torch.ops.aten

requires_multigpu = functools.partial(
    unittest.skipIf, not HAS_MULTIGPU, f"requires multiple {GPU_TYPE} devices"
)
requires_cuda = unittest.skipUnless(torch.cuda.is_available(), "requires cuda")
skip_if_x86_mac = functools.partial(
    unittest.skipIf, IS_MACOS and IS_X86, "Does not work on x86 Mac"
)
vec_dtypes = [torch.float, torch.bfloat16, torch.float16]

libtest = torch.library.Library("test", "FRAGMENT")  # noqa: TOR901
ids = set()

f32 = torch.float32
i64 = torch.int64
i32 = torch.int32

test_dtypes = [
    torch.float32,
    torch.float64,
    torch.float16,
    torch.uint8,
    torch.int8,
    torch.int16,
    torch.int32,
    torch.int64,
]

test_int_dtypes = [
    torch.uint8,
    torch.int8,
    torch.int16,
    torch.int32,
    torch.int64,
]

if SM80OrLater or MACOS_VERSION >= 14.0 or GPU_TYPE == "xpu":
    test_dtypes.append(torch.bfloat16)


def _large_cumprod_input(shape, dim, dtype, device):
    # Construct a cumprod input which guarantees not to overflow or underflow
    if is_integer_dtype(dtype):
        # Large products don't fit in integers, the best we can do
        # is random +/-1 values to test the sign of the result
        x = torch.randint(0, 1, shape, dtype=dtype, device=device)
        return x * 2 - 1

    comp_dtype = torch._prims_common.get_computation_dtype(dtype)
    batch_size = 256
    if comp_dtype != dtype:
        batch_size = math.floor(math.log2(torch.finfo(dtype).max) / 3)

    # Create random values with a uniform magnitude and uniform exponent
    num_batches = (shape[dim] + 2 * batch_size - 1) // (2 * batch_size)
    batch_shape = (
        shape[:dim]
        + (
            num_batches,
            batch_size,
        )
        + shape[dim + 1 :]
    )
    magnitude = 1 + torch.rand(batch_shape, dtype=comp_dtype, device=device)
    exponent = torch.randint(-1, 1, batch_shape, device=device).to(comp_dtype)
    batch = magnitude * exponent.exp2()

    # Alternate each batch of values with their reciprocals so the product
    # never gets too far away from 1
    t = torch.cat((batch, batch.reciprocal()), dim=dim + 1)
    t = t.flatten(dim, dim + 1)
    t = aten.slice(t, dim=dim, start=0, end=shape[dim])

    # Randomize sign
    sign = torch.randint(0, 1, shape, device=device) * 2 - 1
    return (t * sign).to(dtype)


def define_custom_op_for_test(id_, fn, fn_meta, tags=()):
    global libtest
    global ids
    if id_ not in ids:
        libtest.define(f"{id_}(Tensor self) -> Tensor", tags=tags)
        libtest.impl(id_, fn, "CPU")
        libtest.impl(id_, fn, "CUDA")
        libtest.impl(id_, fn, "XPU")
        libtest.impl(id_, fn, "MPS")
        libtest.impl(id_, fn_meta, "Meta")
        ids.add(id_)


def define_custom_op_2_for_test(id_, fn, fn_meta, tags=()):
    global libtest
    global ids
    if id_ not in ids:
        libtest.define(
            f"{id_}(Tensor self, float scale) -> (Tensor, Tensor)", tags=tags
        )
        libtest.impl(id_, fn, "CPU")
        libtest.impl(id_, fn, "CUDA")
        libtest.impl(id_, fn, "XPU")
        libtest.impl(id_, fn, "MPS")
        libtest.impl(id_, fn_meta, "Meta")
        ids.add(id_)


def define_custom_op_3_for_test(id_, fn, fn_meta, tags=()):
    global libtest
    global ids
    if id_ not in ids:
        libtest.define(f"{id_}(Tensor[] x) -> Tensor", tags=tags)
        libtest.impl(id_, fn, "CPU")
        libtest.impl(id_, fn, "CUDA")
        libtest.impl(id_, fn, "XPU")
        libtest.impl(id_, fn, "MPS")
        libtest.impl(id_, fn_meta, "Meta")
        ids.add(id_)


f32 = torch.float32


def register_ops_with_aoti_compile(ns, op_set, dispatch_key, torch_compile_op_lib_impl):
    for _op_name in op_set:
        qualified_op_name = f"{ns}::{_op_name}"
        _, overload_names = torch._C._jit_get_operation(qualified_op_name)
        for overload_name in overload_names:
            try:
                reg_op_name = qualified_op_name
                schema = torch._C._get_schema(qualified_op_name, overload_name)
                if schema.overload_name:
                    reg_op_name = f"{qualified_op_name}.{schema.overload_name}"
                torch_compile_op_lib_impl._impl_with_aoti_compile(  # noqa: F821
                    reg_op_name, dispatch_key
                )
            except Exception as e:
                continue


def get_divisible_by_16(cfg):
    # attribute was renamed between triton versions, from "divisible_by_16" to "divisibility_16"
    if hasattr(cfg, "divisibility_16"):
        return cfg.divisibility_16
    elif hasattr(cfg, "divisible_by_16"):
        return cfg.divisible_by_16
    # `cfg` example:
    # {(0,): [['tt.divisibility', 16]], (1,): [['tt.divisibility', 16]], (3,): [['tt.divisibility', 16]]}
    return [
        key[0]
        for key, value in cfg.items()
        if len(key) == 1 and value[0] == ["tt.divisibility", 16]
    ]


def get_post_grad_graph(f, inputs):
    log_stream, ctx = logs_to_string("torch._inductor.compile_fx", "post_grad_graphs")
    with ctx():
        f(*inputs)
    post_grad_graph = "\n".join(log_stream.getvalue().strip().split("\n")[3:]).strip()
    return post_grad_graph


class TestCase(InductorTestCase):
    @classmethod
    def setUpClass(cls):
        super().setUpClass()
        cls._stack = contextlib.ExitStack()
        cls._stack.enter_context(
            config.patch(
                {
                    "debug": True,
                    "debug_index_asserts": True,
                    "cpp.min_chunk_size": 1,
                    "triton.autotune_pointwise": False,  # too slow
                    "implicit_fallbacks": False,
                    "generate_intermediate_hooks": True,
                }
            )
        )

    @classmethod
    def tearDownClass(cls):
        cls._stack.close()
        super().tearDownClass()

    def setUp(self):
        torch._dynamo.reset()
        torch._inductor.metrics.reset()
        super().setUp()
        self._start = time.perf_counter()

    def tearDown(self):
        super().tearDown()
        torch._dynamo.reset()
        if os.environ.get("ERROR_ON_SLOW") == "1":
            elapsed = time.perf_counter() - self._start
            if elapsed >= 120:
                raise AssertionError(f"Test took too long: {elapsed:.1f}s >= 120s")


class ToTuple(torch.nn.Module):
    def forward(self, x):
        return (x,)


@dataclasses.dataclass
class InputGen:
    n: int
    device: str

    def dense(self):
        return torch.randn((self.n, self.n), device=self.device)

    def transposed(self):
        return self.dense().transpose(0, 1)

    def strided(self):
        return torch.randn((self.n * 2, self.n * 3), device=self.device)[
            self.n :, self.n :: 2
        ]

    def broadcast1(self):
        return torch.randn((self.n,), device=self.device)

    def broadcast2(self):
        return torch.randn((1, self.n, 1), device=self.device)

    def broadcast3(self):
        return torch.randn((1,), device=self.device)

    def double(self):
        if self.device == "mps":
            raise unittest.SkipTest("MPS does not support torch.float64")
        return torch.randn((self.n, self.n), device=self.device, dtype=torch.double)

    def int(self):
        return torch.arange(self.n, device=self.device, dtype=torch.int32)


def compute_grads(args, kwrags, results, grads):
    def gather_leaf_tensors(args, kwargs):
        args = pytree.arg_tree_leaves(*args, **kwargs)
        leaf_tensors = [
            arg for arg in args if isinstance(arg, torch.Tensor) and arg.requires_grad
        ]
        return leaf_tensors

    flat_results = pytree.tree_leaves(results)
    flat_diff_results = [
        r for r in flat_results if isinstance(r, torch.Tensor) and r.requires_grad
    ]
    if len(flat_diff_results) <= 0:
        raise AssertionError(
            f"Expected len(flat_diff_results) > 0, got {len(flat_diff_results)}"
        )

    leaf_tensors = gather_leaf_tensors(args, kwrags)
    if len(leaf_tensors) <= 0:
        raise AssertionError(f"Expected len(leaf_tensors) > 0, got {len(leaf_tensors)}")
    return torch.autograd.grad(
        flat_diff_results,
        leaf_tensors,
        grads,
        allow_unused=True,
        retain_graph=True,
    )


def check_model(
    self: TestCase,
    model,
    example_inputs,
    kwargs=None,
    *,
    atol=None,
    rtol=None,
    grad_atol=None,
    grad_rtol=None,
    check_lowp=True,
    exact_dtype=True,
    nopython=True,
    copy_to_gpu=True,
    reference_in_float=True,
    assert_equal=True,
    check_gradient=False,
    check_has_compiled=True,
    output_process_fn_grad=lambda x: x,
    # TODO: enable this for all tests
    exact_stride=False,
):
    kwargs = kwargs or {}
    torch._dynamo.reset()

    ref_inputs = [clone_preserve_strides_offset(x) for x in example_inputs]
    ref_kwargs = kwargs
    has_lowp_args = False

    if reference_in_float and exact_dtype:
        # Store expected dtypes so we can check actual result gives the correct types
        torch.manual_seed(0)
        try:
            eager_result = model(*ref_inputs, **ref_kwargs)
        except RuntimeError:
            # Eager model may fail if the dtype is not supported
            eager_result = None

        ref_inputs = [clone_preserve_strides_offset(x) for x in example_inputs]
        expect_dtypes = [
            x.dtype if isinstance(x, torch.Tensor) else None
            for x in pytree.tree_leaves(eager_result)
        ]
        del eager_result

    ref_model = model
    if reference_in_float:
        # check_lowp is ignored here, it's kept just to be able to call `common` with extra arg
        def upcast_fn(x):
            nonlocal has_lowp_args
            if isinstance(x, torch.Tensor) and (
                x.dtype == torch.float16 or x.dtype == torch.bfloat16
            ):
                has_lowp_args = True
                # Preserve strides when casting
                result = torch.empty_strided(
                    x.size(), x.stride(), device=x.device, dtype=torch.float
                )
                result.copy_(x)
                return result
            else:
                return x

        # We previously call upcast_fn on example_inputs. It's incorrect
        # if example_inputs is already fp32 and get inplace updated in the model.
        # Call on the cloned tensors instead
        ref_inputs = list(map(upcast_fn, ref_inputs))
        ref_kwargs = {k: upcast_fn(v) for k, v in kwargs.items()}
        if has_lowp_args and hasattr(model, "to"):
            ref_model = copy.deepcopy(model).to(torch.float)

    torch.manual_seed(0)

    correct = ref_model(*ref_inputs, **ref_kwargs)

    torch._inductor.metrics.reset()

    called = False

    def compile_fx_wrapper(model_, example_inputs_):
        nonlocal called
        called = True
        return compile_fx(model_, example_inputs_)

    def run(*ex, **kwargs):
        return model(*ex, **kwargs)

    run = torch.compile(run, backend=compile_fx_wrapper, fullgraph=nopython)

    torch.manual_seed(0)
    actual = run(*example_inputs, **kwargs)
    # if not called:
    #     exp = torch._dynamo.explain(run)(*example_inputs)
    #     print("Explain:", exp[0])
    #     for graph in exp[2]:
    #         print("Graph", graph)
    if check_has_compiled:
        if not called:
            raise AssertionError("Ran graph without calling compile_fx")
    if type(actual) is not type(correct):
        raise AssertionError(f"Expected type {type(correct)}, got {type(actual)}")
    if isinstance(actual, (tuple, list)):
        if len(actual) != len(correct):
            raise AssertionError(f"Expected length {len(correct)}, got {len(actual)}")
        if not all(
            type(actual_item) is type(correct_item)
            for actual_item, correct_item in zip(actual, correct)
        ):
            raise AssertionError(
                f"Item type mismatch: expected types {[type(c) for c in correct]}, "
                f"got {[type(a) for a in actual]}"
            )

    correct_flat, correct_spec = tree_flatten(correct)
    actual_flat = pytree.tree_leaves(actual)

    def to_dtype_preserve_strides(src, dtype):
        if any(s == 0 for s in src.stride()):
            return src.to(dtype)
        # Preserve strides when casting.
        result = torch.empty_strided(
            src.size(), src.stride(), device=src.device, dtype=dtype
        )
        result.copy_(src)
        return result

    def reference_to_expect(actual_flat, correct_flat):
        return tuple(
            (
                to_dtype_preserve_strides(y, x.dtype)
                if isinstance(y, torch.Tensor) and y.dtype.is_floating_point
                else y
            )
            for x, y in zip(actual_flat, correct_flat)
        )

    if reference_in_float and exact_dtype:
        for expect_dtype, actual_result in zip(expect_dtypes, actual_flat):
            if expect_dtype is not None:
                if actual_result.dtype != expect_dtype:
                    raise AssertionError(
                        f"dtype mismatch, expected {expect_dtype} but got {actual_result.dtype}"
                    )

    if reference_in_float:
        correct_flat = reference_to_expect(actual_flat, correct_flat)
        correct = tree_unflatten(correct_flat, correct_spec)

    def has_zero_dim(x):
        if not isinstance(x, tuple):
            x = (x,)
        return any(isinstance(t, torch.Tensor) and 0 in t.size() for t in x)

    # Allow assert_equal to be a custom function, instead of True or False, for
    # cases where differences may not indicate incorrectness.
    if assert_equal:
        if callable(assert_equal):

            def custom_assert_with_self(*args, **kwargs):
                assert_equal(self, *args, **kwargs)

            assert_equal_fn = custom_assert_with_self
        else:
            assert_equal_fn = self.assertEqual

        check_exact_stride = exact_stride and not has_zero_dim(correct)
        assert_equal_fn(
            actual,
            correct,
            atol=atol,
            rtol=rtol,
            equal_nan=True,
            exact_dtype=exact_dtype,
            exact_stride=check_exact_stride,
        )
        # In case of input mutations, check that inputs are the same
        # (This never uses a custom assert_equal fn.)
        self.assertEqual(
            ref_inputs,
            example_inputs,
            atol=atol,
            rtol=rtol,
            equal_nan=True,
            # our testing sometimes uses higher precision inputs for the reference
            exact_dtype=False,
            exact_stride=exact_stride,
        )
    else:
        for correct_val, actual_val in zip(correct_flat, actual_flat):
            if isinstance(correct_val, torch.Tensor):
                if correct_val.device != actual_val.device:
                    raise AssertionError(
                        f"Expected device {correct_val.device}, got {actual_val.device}"
                    )
                if correct_val.size() != actual_val.size():
                    raise AssertionError(
                        f"Expected size {correct_val.size()}, got {actual_val.size()}"
                    )
                strides_equal, _ = torch._prims_common.check_significant_strides(
                    correct_val, actual_val
                )
                if not strides_equal:
                    raise AssertionError(
                        f"Significant strides mismatch: expected {correct_val.stride()}, "
                        f"got {actual_val.stride()}"
                    )
                if correct_val.layout != actual_val.layout:
                    raise AssertionError(
                        f"Expected layout {correct_val.layout}, got {actual_val.layout}"
                    )
                if exact_dtype:
                    if correct_val.dtype != actual_val.dtype:
                        raise AssertionError(
                            f"Expected dtype {correct_val.dtype}, got {actual_val.dtype}"
                        )
                check_exact_stride = exact_stride and not has_zero_dim(correct_val)
                if check_exact_stride:
                    if correct_val.stride() != actual_val.stride():
                        raise AssertionError(
                            f"Expected stride {correct_val.stride()}, got {actual_val.stride()}"
                        )

    if check_gradient:
        actual = output_process_fn_grad(actual)
        correct = output_process_fn_grad(correct)
        actual_flat = pytree.tree_leaves(actual)
        correct_flat = pytree.tree_leaves(correct)

        # generate random unit norm gradients
        grads = [
            torch.randn_like(r)
            for r in correct_flat
            if isinstance(r, torch.Tensor) and r.requires_grad
        ]
        for g in grads:
            g /= g.norm()

        correct_grad = compute_grads(ref_inputs, ref_kwargs, correct, grads)
        all_none_grads = all(x is None for x in correct_grad)
        tensor_args = [
            x
            for x in pytree.tree_flatten(example_inputs)[0]
            if isinstance(x, torch.Tensor)
        ]
        any_non_leaves = any(x.grad_fn is not None for x in tensor_args)
        if all_none_grads and any_non_leaves:
            # See Note [Detaching inputs that never need gradients]
            # There are a handful of ops that can return None gradients, into of zero gradients.
            # If all inputs to an AOTAutograd graph are supposed to get None gradients,
            # AOTAutograd will end up forcing all of the outputs of the forward to not require grad.
            # There's no easy fix to this (see the note above), although one option is to
            # force any derivative formulas in core to return tensors of zeros instead of None.
            flat_results = pytree.tree_leaves(actual)
            results_that_require_grad = [
                x
                for x in flat_results
                if isinstance(x, torch.Tensor) and x.requires_grad
            ]
            self.assertEqual(len(results_that_require_grad), 0)
        else:
            actual_grad = compute_grads(example_inputs, kwargs, actual, grads)

            if reference_in_float:
                expect_grad = reference_to_expect(actual_grad, correct_grad)
            else:
                expect_grad = correct_grad

            for actual_g, expect_g in zip(actual_grad, expect_grad):
                check_exact_stride = exact_stride and not has_zero_dim(expect_g)
                self.assertEqual(
                    actual_g,
                    expect_g,
                    atol=grad_atol or atol,
                    rtol=grad_rtol or rtol,
                    equal_nan=True,
                    exact_dtype=exact_dtype,
                    exact_stride=check_exact_stride,
                )

    torch._dynamo.reset()


@torch._inductor.config.patch("triton.cudagraphs", False)
def check_model_gpu(
    self: TestCase,
    model,
    example_inputs,
    kwargs=None,
    *,
    atol=None,
    rtol=None,
    grad_atol=None,
    grad_rtol=None,
    check_lowp=True,
    exact_dtype=True,
    nopython=True,
    copy_to_gpu=True,
    reference_in_float=True,
    assert_equal=True,
    check_gradient=False,
    check_has_compiled=True,
    output_process_fn_grad=lambda x: x,
    # TODO: enable this for all tests
    exact_stride=False,
):
    kwargs = kwargs or {}
    if hasattr(model, "to"):
        model = model.to(device=GPU_TYPE)

    if copy_to_gpu:
        example_inputs = tuple(
            clone_preserve_strides_offset(x, device=GPU_TYPE) for x in example_inputs
        )

    check_model(
        self,
        model,
        example_inputs,
        kwargs,
        atol=atol,
        rtol=rtol,
        grad_atol=grad_atol,
        grad_rtol=grad_rtol,
        exact_dtype=exact_dtype,
        nopython=nopython,
        reference_in_float=reference_in_float,
        assert_equal=assert_equal,
        check_gradient=check_gradient,
        check_has_compiled=check_has_compiled,
        output_process_fn_grad=output_process_fn_grad,
        exact_stride=exact_stride,
    )

    if check_lowp:

        def downcast_fn(x):
            if not isinstance(x, torch.Tensor) or x.dtype != torch.float:
                return x
            return torch.empty_strided(
                x.size(), x.stride(), device=GPU_TYPE, dtype=torch.half
            ).copy_(x)

        example_inputs = list(map(downcast_fn, example_inputs))
        if hasattr(model, "to"):
            model = model.to(torch.half)
        if rtol is not None:
            rtol = max(2e-3, rtol)
        check_model(
            self,
            model,
            example_inputs,
            kwargs,
            atol=atol,
            rtol=rtol,
            grad_atol=grad_atol,
            grad_rtol=grad_rtol,
            exact_dtype=exact_dtype,
            nopython=nopython,
            reference_in_float=reference_in_float,
            assert_equal=assert_equal,
            check_gradient=check_gradient,
            check_has_compiled=check_has_compiled,
            output_process_fn_grad=output_process_fn_grad,
            exact_stride=exact_stride,
        )


check_model_cuda = check_model_gpu


def _run_and_assert_no_indirect_indexing(
    test_case, func, *args, has_wrapping=None, has_assert=False, **kwargs
):
    result, source_codes = run_and_get_code(func, *args, **kwargs)

    for code in source_codes:
        for line in code.split("\n"):
            stmt = None
            # Find indexing expressions
            if ".load(" in line:
                stmt = line.split(".load")[-1]
            elif "tl.store" in line:
                stmt = line.split(".store")[-1]
                stmt = ",".join(stmt.split(",")[:-2])  # Remove store value and mask
            elif ".store" in line:
                stmt = line.split(".store")[-1]
            elif "[" in line:
                stmt = line.split("[")[-1].split("]")[0]
            # Block descriptors do not support indirect indexing
            if any(
                block_descriptor_syntax in line
                for block_descriptor_syntax in (
                    "block_ptr",
                    "tma_descriptor",
                    "tl.make_block_ptr",
                    "tl.make_tensor_descriptor",
                )
            ):
                continue

            if stmt is None:
                continue

            # indirect indexing involves a `tmp` variable
            test_case.assertTrue(
                "tmp" not in stmt,
                msg=f"Found indirect indexing in statement '{stmt}' from code:\n{code}",
            )
        if has_wrapping is not None:
            test_case.assertTrue(
                ("where" in code or ") ? (" in code) is has_wrapping,
                msg=f"Wanted {has_wrapping=} but got\n{code}",
            )

    def has_assert_in_code(code):
        # Check for device_assert, or TORCH_CHECK that isn't AOTI_TORCH_CHECK
        # AOTI_TORCH_CHECK is for lazy Triton compile infrastructure, not bounds checking
        if "device_assert" in code:
            return True
        for line in code.split("\n"):
            if "TORCH_CHECK" in line and "AOTI_TORCH_CHECK" not in line:
                return True
        return False

    test_case.assertTrue(
        any(has_assert_in_code(code) is has_assert for code in source_codes)
    )
    return result


def assertGeneratedKernelCountEqual(self: TestCase, expected: int):
    if config.triton.multi_kernel:
        # when multi_kernel is enabled, we generated both persistent reduction
        # and non-persistent reduction kernels for the same node schedule.
        # That will mess up with the kernel count. Just don't check it.
        return
    self.assertEqual(torch._inductor.metrics.generated_kernel_count, expected)


class SweepInputs2:
    input_gen_types1 = [
        "dense",
        "transposed",
        "strided",
        "broadcast1",
        "broadcast2",
        "broadcast3",
        "double",
        "int",
    ]
    input_gen_types2 = input_gen_types1
    gen = None

    @staticmethod
    def kernel(a, b):
        return (a + b,)

    # Input gen types unsupported by specific backends. Subclasses or wrapper
    # test classes can set this to skip tests that use these input types.
    _unsupported_input_gen_types: set[str] = set()

    @classmethod
    def gen_template(cls, name1, name2):
        def test(self):
            unsupported = type(self)._unsupported_input_gen_types
            for name in (name1, name2):
                if name in unsupported:
                    self.skipTest(f"{name} inputs not supported by this backend")
            check_model(
                self,
                cls.kernel,
                (
                    getattr(cls.gen, name1)(),
                    getattr(cls.gen, name2)(),
                ),
            )

        test.__name__ = f"test_{cls.gen.device}_{name1}_{name2}"
        setattr(cls, test.__name__, test)

    @classmethod
    def populate(cls):
        for name1 in cls.input_gen_types1:
            for name2 in cls.input_gen_types2:
                cls.gen_template(name1, name2)


def is_cpp_backend(device):
    return getattr(device, "type", device) == "cpu" and config.cpu_backend == "cpp"


def skip_if_cpu(fn):
    @functools.wraps(fn)
    def wrapper(self, *args, **kwargs):
        if self.device == "cpu":
            raise unittest.SkipTest("cpu not supported")
        return fn(self, *args, **kwargs)

    return wrapper


def skip_if_halide(fn):
    @functools.wraps(fn)
    def wrapper(self, *args, **kwargs):
        if is_halide_backend(self.device):
            raise unittest.SkipTest("halide not supported")
        return fn(self, *args, **kwargs)

    return wrapper


def skip_if_pallas(fn):
    @functools.wraps(fn)
    def wrapper(self, *args, **kwargs):
        if is_pallas_backend(self.device):
            raise unittest.SkipTest("pallas not supported")
        return fn(self, *args, **kwargs)

    return wrapper


def xfail_if_mps(fn):
    @functools.wraps(fn)
    def wrapper(self, *args, **kwargs):
        if not is_mps_backend(self.device):
            return fn(self, *args, **kwargs)
        with self.assertRaises(Exception):
            return fn(self, *args, **kwargs)

    return wrapper


# Just an alias to track failures due to the missing eager ops
xfail_if_mps_unimplemented = xfail_if_mps


def skip_if_triton(fn):
    @functools.wraps(fn)
    def wrapper(self, *args, **kwargs):
        if is_triton_backend(self.device):
            raise unittest.SkipTest("triton not supported")
        return fn(self, *args, **kwargs)

    return wrapper


def skip_if_not_triton(fn):
    @functools.wraps(fn)
    def wrapper(self, *args, **kwargs):
        if not is_triton_backend(self.device):
            raise unittest.SkipTest(f"triton backend is required for {self.device}")
        return fn(self, *args, **kwargs)

    return wrapper


def skip_if_dynamic(fn):
    @functools.wraps(fn)
    def wrapper(self, *args, **kwargs):
        if ifdynstaticdefault(True, False):
            raise unittest.SkipTest("associtaive_scan doesn's support lifted SymInts.")
        return fn(self, *args, **kwargs)

    return wrapper


def is_halide_backend(device):
    if getattr(device, "type", device) == "cpu":
        return config.cpu_backend == "halide"
    return config.cuda_backend == "halide"


def is_mps_backend(device):
    return getattr(device, "type", device) == "mps"


def is_triton_backend(device):
    device_type = getattr(device, "type", device)
    if device_type == "cpu":
        return config.cpu_backend == "triton"
    if device_type == "mps":
        return False
    return config.cuda_backend == "triton"


def is_triton_cpu_backend(device):
    return getattr(device, "type", device) == "cpu" and config.cpu_backend == "triton"


def is_pallas_backend(device):
    if getattr(device, "type", device) == "cpu":
        return config.cpu_backend == "pallas"
    return config.cuda_backend == "pallas"


def skip_if_triton_cpu(fn):
    import types

    reason = "Triton CPU not supported"

    def decorator(fn):
        @functools.wraps(fn)
        def wrapper(self, *args, **kwargs):
            if is_triton_cpu_backend(self.device):
                raise unittest.SkipTest(reason)
            return fn(self, *args, **kwargs)

        return wrapper

    if isinstance(fn, types.FunctionType):
        return decorator(fn)
    else:
        reason = fn
        return decorator


def xfail_if_triton_cpu(fn):
    fn._expected_failure_triton_cpu = True
    return fn


def xfail_if_pallas(fn):
    fn._expected_failure_pallas = True
    return fn


def skip_if_gpu_halide(fn):
    @functools.wraps(fn)
    def wrapper(self, *args, **kwargs):
        if (
            is_halide_backend(self.device)
            and getattr(self.device, "type", self.device) == "cuda"
        ):
            raise unittest.SkipTest("halide not supported")
        return fn(self, *args, **kwargs)

    return wrapper


class skip_if_cpp_wrapper:
    def __init__(self, reason: str = "") -> None:
        self.reason = reason

    def __call__(self, fn, *args, **kwargs):
        @functools.wraps(fn)
        def wrapper(test_self):
            if config.cpp_wrapper:
                raise unittest.SkipTest(f"cpp wrapper bug to be fixed: {self.reason}")
            return fn(test_self, *args, **kwargs)

        return wrapper


def is_dynamic_shape_enabled():
    # What's the best way to decide this?
    return not torch._dynamo.config.assume_static_by_default


@instantiate_parametrized_tests
class CommonTemplate:
    def is_dtype_supported(self, dtype: torch.dtype) -> bool:
        device_interface = get_interface_for_device(self.device)
        return device_interface.is_dtype_supported(dtype)

    def test_bool(self):
        def fn(a, b):
            return (
                a + b,
                a * b,
                a & b,
                a | b,
                a ^ b,
                torch.logical_and(a, b),
                torch.logical_or(a, b),
                torch.logical_not(a),
                torch.sign(b),
            )

        self.common(
            fn,
            (
                torch.tensor([True, False, True, False]),
                torch.tensor([False, False, True, True]),
            ),
        )

    @skipCUDAIf(not SM80OrLater, "Requires sm80")
    @skip_if_halide  # aoti
    @skip_if_pallas  # aoti
    @skip_if_triton_cpu  # aoti
    @skipIfWindows(msg="aoti not support on Windows")
    def test_aoti_eager_dtype_device_layout(self):
        ns = "aten"
        op_name = "tril_indices"
        dispatch_key = "CPU"
        device = "cpu"
        if self.device.lower() == "cuda":
            dispatch_key = "CUDA"
            device = "cuda"

        with _scoped_library("aten", "IMPL") as torch_compile_op_lib_impl:
            row = 128
            col = 256
            offset = 1
            dtype = torch.int32
            layout = torch.strided
            pin_memory = False
            ref = torch.tril_indices(
                row=row,
                col=col,
                offset=offset,
                dtype=dtype,
                layout=layout,
                pin_memory=pin_memory,
                device=device,
            )
            register_ops_with_aoti_compile(
                ns, [op_name], dispatch_key, torch_compile_op_lib_impl
            )
            res = torch.tril_indices(
                row=row,
                col=col,
                offset=offset,
                dtype=dtype,
                layout=layout,
                pin_memory=pin_memory,
                device=device,
            )
            self.assertEqual(ref, res)

    @skipCUDAIf(not SM80OrLater, "Requires sm80")
    @skip_if_halide  # aoti
    @skip_if_pallas  # aoti
    @skip_if_triton_cpu  # aoti
    @skipIfWindows(msg="aoti not support on Windows")
    def test_aoti_eager_support_out(self):
        ns = "aten"
        op_name = "clamp"
        dispatch_key = "CPU"
        device = "cpu"
        if self.device.lower() == "cuda":
            dispatch_key = "CUDA"
            device = "cuda"

        inp_tensor = torch.randn(128, dtype=torch.float, device=device).fill_(1.0)
        min_tensor = inp_tensor - 0.05
        max_tensor = inp_tensor + 0.05
        with _scoped_library("aten", "IMPL") as torch_compile_op_lib_impl:
            ref_out_tensor = torch.randn(128, dtype=torch.float, device=device).fill_(
                -1
            )
            ref_tensor = torch.clamp(
                max=max_tensor, min=min_tensor, input=inp_tensor, out=ref_out_tensor
            )

            ref_out_tensor1 = torch.randn(128, dtype=torch.float, device=device).fill_(
                -1
            )
            ref_tensor1 = torch.clamp(
                max=max_tensor, out=ref_out_tensor1, min=min_tensor, input=inp_tensor
            )

            register_ops_with_aoti_compile(
                ns, [op_name], dispatch_key, torch_compile_op_lib_impl
            )

            res_out_tensor = torch.randn(128, dtype=torch.float, device=device).fill_(
                -1
            )
            res_tensor = torch.clamp(
                max=max_tensor, min=min_tensor, input=inp_tensor, out=res_out_tensor
            )

            self.assertEqual(ref_tensor, res_tensor)
            self.assertEqual(ref_out_tensor, res_out_tensor)

            res_out_tensor1 = torch.randn(128, dtype=torch.float, device=device).fill_(
                -1
            )
            res_tensor1 = torch.clamp(
                max=max_tensor, out=res_out_tensor1, min=min_tensor, input=inp_tensor
            )

            self.assertEqual(ref_tensor1, res_tensor1)
            self.assertEqual(ref_out_tensor1, res_out_tensor1)

    @skipCUDAIf(not SM80OrLater, "Requires sm80")
    @skip_if_halide  # aoti
    @skip_if_pallas  # aoti
    @skip_if_triton_cpu  # aoti
    @skipIfWindows(msg="aoti not support on Windows")
    def test_aoti_eager_support_str(self):
        ns = "aten"
        op_name = "div"
        dispatch_key = "CPU"
        device = "cpu"
        if self.device.lower() == "cuda":
            dispatch_key = "CUDA"
            device = "cuda"

        a = torch.randn(128, dtype=torch.float, device=device)
        b = torch.randn(128, dtype=torch.float, device=device)
        rounding_mode_list = ["trunc", "floor"]
        with _scoped_library("aten", "IMPL") as torch_compile_op_lib_impl:
            # Get ref result from eager
            ref_value_list = []
            for rounding_mode in rounding_mode_list:
                ref_value = getattr(torch.ops.aten, op_name)(
                    a, b, rounding_mode=rounding_mode
                )
                ref_value_list.append(ref_value)

            register_ops_with_aoti_compile(
                ns, [op_name], dispatch_key, torch_compile_op_lib_impl
            )

            # Invoke the pre-compiled kernel and get result.
            res_value_list = []
            for rounding_mode in rounding_mode_list:
                res_value = getattr(torch.ops.aten, op_name)(
                    a, b, rounding_mode=rounding_mode
                )
                res_value_list.append(res_value)

            for ref_value, res_value in zip(ref_value_list, res_value_list):
                self.assertEqual(ref_value, res_value)

    @skipCUDAIf(not SM80OrLater, "Requires sm80")
    @skip_if_halide  # aoti
    @skip_if_pallas  # aoti
    @skip_if_triton_cpu  # aoti
    @skipIfWindows(msg="aoti not support on Windows")
    def test_aoti_eager_cache_hit(self):
        ns = "aten"
        op_name = "abs"
        dispatch_key = "CPU"
        device = "cpu"
        if self.device.lower() == "cuda":
            dispatch_key = "CUDA"
            device = "cuda"

        input_tensor = torch.randn(128, dtype=torch.float, device=device)
        kernel_lib_path = aoti_compile_with_persistent_cache(
            ns,
            op_name,
            device,
            False,
            getattr(torch.ops.aten, op_name),
            (input_tensor,),
            {},
        )
        self.assertTrue(Path(kernel_lib_path).exists())

        from unittest import mock

        # Patch the aoti_compile_with_persistent_cache as None to ensure no new kernel is generated
        with mock.patch(
            "torch._inductor.aoti_eager.aoti_compile_with_persistent_cache", None
        ):
            with _scoped_library("aten", "IMPL") as torch_compile_op_lib_impl:
                # Get ref result from eager
                ref_value = getattr(torch.ops.aten, op_name)(input_tensor)

                register_ops_with_aoti_compile(
                    ns, [op_name], dispatch_key, torch_compile_op_lib_impl
                )

                # Invoke the pre-compiled kernel and get result.
                res_value = getattr(torch.ops.aten, op_name)(input_tensor)

                self.assertEqual(ref_value, res_value)

    @skipCUDAIf(not SM80OrLater, "Requires sm80")
    @skip_if_halide  # aoti
    @skip_if_pallas  # aoti
    @skip_if_triton_cpu  # aoti
    @skipIfWindows(msg="aoti not support on Windows")
    def test_aoti_eager_with_persistent_cache(self):
        def fn(a):
            return torch.abs(a)

        ns = "aten"
        op_name = "abs"

        device = "cpu"
        if self.device.lower() == "cuda":
            device = "cuda"

        input_tensor = torch.randn(128, dtype=torch.float, device=device)
        kernel_lib_path = aoti_compile_with_persistent_cache(
            ns,
            op_name,
            input_tensor.device.type,
            False,
            fn,
            args=(input_tensor,),
            kwargs={},
        )
        self.assertTrue(len(kernel_lib_path) > 0)

        device_kernel_cache = aoti_eager_cache_dir(ns, device)
        kernel_conf = device_kernel_cache / f"{op_name}.json"
        self.assertTrue(kernel_conf.exists())

        json_data = load_aoti_eager_cache("aten", "abs", input_tensor.device.type)
        self.assertTrue(json_data is not None)
        self.assertTrue(isinstance(json_data, list))
        self.assertTrue(len(json_data) > 0)

        op_info = json_data[0]
        self.assertTrue(isinstance(op_info, dict))
        self.assertTrue("meta_info" in op_info)
        self.assertTrue("kernel_path" in op_info)
        kernel_libs_abs_path = []
        for item in json_data:
            kernel_path = device_kernel_cache / item["kernel_path"]
            kernel_libs_abs_path.append(kernel_path.as_posix())

        self.assertTrue(kernel_lib_path in kernel_libs_abs_path)

    @skipCUDAIf(not SM80OrLater, "Requires sm80")
    @skip_if_halide  # aoti
    @skip_if_pallas  # aoti
    @skip_if_triton_cpu  # aoti
    @skipIfWindows(msg="aoti not support on Windows")
    def test_aoti_eager_with_scalar(self):
        namespace_name = "aten"
        op_name = "add"
        op_overload_name = "Tensor"
        op_name_with_overload = f"{op_name}.{op_overload_name}"

        dispatch_key = "CPU"
        device = torch.device("cpu")
        if self.device.lower() == "cuda":
            dispatch_key = "CUDA"
            device = torch.device("cuda")

        # Test the difference between scalar tensor and scalar
        a = torch.scalar_tensor(1.0, device=device)
        b = torch.scalar_tensor(2.0, device=device)

        kernel_lib_path = aoti_compile_with_persistent_cache(
            namespace_name,
            op_name_with_overload,
            a.device.type,
            False,
            torch.ops.aten.add,
            args=(a, b),
            kwargs={"alpha": 3.0},
        )
        self.assertTrue(Path(kernel_lib_path).exists())
        device_kernel_cache = aoti_eager_cache_dir(namespace_name, device.type)
        kernel_conf = device_kernel_cache / f"{op_name_with_overload}.json"
        self.assertTrue(kernel_conf.exists())
        json_data = load_aoti_eager_cache(
            namespace_name, op_name_with_overload, a.device.type
        )
        op_info = json_data[0]
        self.assertTrue(isinstance(op_info, dict))
        self.assertTrue("meta_info" in op_info)
        self.assertTrue(len(op_info["meta_info"]) == 3)
        # Scalar Tensor
        self.assertTrue("scalar_value" not in op_info["meta_info"][0])
        self.assertTrue(op_info["meta_info"][0]["sizes"] == [])
        self.assertTrue(op_info["meta_info"][0]["strides"] == [])
        # Scalar Tensor
        self.assertTrue("scalar_value" not in op_info["meta_info"][1])
        self.assertTrue(op_info["meta_info"][1]["sizes"] == [])
        self.assertTrue(op_info["meta_info"][1]["strides"] == [])
        # Scalar
        self.assertTrue("scalar_value" in op_info["meta_info"][2])
        self.assertTrue("sizes" not in op_info["meta_info"][2])
        self.assertTrue("strides" not in op_info["meta_info"][2])

        with _scoped_library("aten", "IMPL") as torch_compile_op_lib_impl:
            a = torch.randn(128, device=device)
            b = torch.randn(128, device=device)

            scalar_values = [1.0, 2.0, 3.0]
            ref_values = []
            for scalar_value in scalar_values:
                ref_values.append(torch.add(a, b, alpha=scalar_value))

            register_ops_with_aoti_compile(
                namespace_name, [op_name], dispatch_key, torch_compile_op_lib_impl
            )

            res_values = []
            for scalar_value in scalar_values:
                res_values.append(torch.add(a, b, alpha=scalar_value))

            self.assertEqual(len(ref_values), len(res_values))
            self.assertEqual(ref_values, res_values)

    @skipCUDAIf(not SM80OrLater, "Requires sm80")
    @skip_if_halide  # aoti
    @skip_if_pallas  # aoti
    @skip_if_triton_cpu  # aoti
    @skipIfWindows(msg="aoti not support on Windows")
    def test_aoti_eager_override_registration(self):
        namespace_name = "aten"
        dispatch_key = "CPU"
        device = torch.device("cpu")
        if self.device.lower() == "cuda":
            dispatch_key = "CUDA"
            device = torch.device("cuda")

        unary_op_set = ["abs", "acos"]

        def fn(x, op_name=""):
            return getattr(torch, op_name)(x)

        # Invoke torch.compile directly to get referent results
        x = torch.randn(3, 4, device=device)

        ref_array = []
        for unary_op_name in unary_op_set:
            opt_fn = torch.compile(functools.partial(fn, op_name=unary_op_name))
            ref = opt_fn(x)
            ref_array.append(ref)

        with _scoped_library("aten", "IMPL") as torch_compile_op_lib_impl:
            register_ops_with_aoti_compile(
                namespace_name, unary_op_set, dispatch_key, torch_compile_op_lib_impl
            )

            res_array = []
            for unary_op_name in unary_op_set:
                res_array.append(getattr(torch, unary_op_name)(x))

            for ref, res in zip(ref_array, res_array):
                self.assertEqual(ref, res)

        a = torch.randn(128, device=device)
        min_tensor = torch.randn(128, device=device)
        max_tensor = min_tensor + 0.5

        ref_with_min = torch.ops.aten.clamp(a, min_tensor)
        ref_with_min_max = torch.ops.aten.clamp(a, min_tensor, max_tensor)

        with _scoped_library("aten", "IMPL") as torch_compile_op_lib_impl:
            register_ops_with_aoti_compile(
                namespace_name, ["clamp"], dispatch_key, torch_compile_op_lib_impl
            )
            res_with_min = torch.ops.aten.clamp(a, min_tensor)
            res_with_min_max = torch.ops.aten.clamp(a, min_tensor, max_tensor)
            self.assertEqual(ref_with_min, res_with_min)
            self.assertEqual(ref_with_min_max, res_with_min_max)

    def test_add_const_int(self):
        def fn(a):
            return (a + 1, torch.add(a, 1, alpha=2))

        for dtype in [torch.float32, torch.int32, torch.int64]:
            self.common(fn, (torch.arange(32, dtype=dtype),))

    def test_add_const_float(self):
        def fn(a):
            return (a + 1.5,)

        self.common(fn, (torch.randn(32),))

    def test_add_inplace_permuted(self):
        if config.cpu_backend == "halide":
            raise unittest.SkipTest(
                "Halide cpu backend does not work for this test case: https://github.com/pytorch/pytorch/issues/140344"
            )

        def fn(x, y):
            return x.add_(y)

        x = torch.ones([2, 12, 13, 17]).transpose(1, 2)
        y = torch.randn([2, 13, 1, 17])

        self.common(fn, (x, y))

    def test_add_complex(self):
        def fn(a, b, alpha):
            return torch.add(a, b, alpha=alpha)

        x = torch.tensor([1 + 1j, -1 + 1j, -2 + 2j, 3 - 3j, 0, 1j, 1, -1])
        y = torch.tensor([1 + 1j, -1 + 1j, -2 + 2j, 3 - 3j, 0, 1j, 1, -1])

        self.common(fn, (x, y, 2))

    def test_add_complex3(self):
        # fix https://github.com/pytorch/pytorch/issues/115071
        @torch.compile
        def fn(*args):
            a = torch.neg(args[0])
            b = torch.add(args[0], args[0])
            return (a, b)

        x = torch.randn(41, dtype=torch.complex64, device=self.device)
        y = x.clone()
        # should not inplace write to the input
        fn(x)
        self.assertEqual(x, y)

    def test_add_complex4(self):
        @torch.compile
        def fn(a, b):
            c = a + b
            d = a + b
            return c + d

        for dtype in [torch.complex32, torch.complex64, torch.complex128]:
            if not self.is_dtype_supported(dtype):
                continue
            x = torch.tensor(
                [1 + 1j, -1 + 1j, -2 + 2j, 3 - 3j, 0, 1j, 1, -1],
                dtype=dtype,
                device=self.device,
            )
            y = torch.tensor(
                [1 + 1j, -1 + 1j, -2 + 2j, 3 - 3j, 0, 1j, 1, -1],
                dtype=dtype,
                device=self.device,
            )
            _, code = run_and_get_code(fn, x, y)
            code = " ".join(code)
            assert_keywords = ["assert_size_stride", "assert_alignment"]
            filtered_lines = [
                line
                for line in code.splitlines()
                if not any(assert_key in line for assert_key in assert_keywords)
            ]
            code = "\n".join(filtered_lines)
            self.assertGreaterEqual(
                code.count("view_dtype" if config.cpp_wrapper else "aten.view"), 3
            )

    def test_add_complex_strided_fallback(self):
        @torch.compile
        def fn(a, b):
            return a + b

        if not self.is_dtype_supported(torch.complex64):
            raise unittest.SkipTest("complex64 not supported on device")

        base = torch.randn(3, 4, dtype=torch.complex64, device=self.device)
        x = base.transpose(0, 1)
        y = base.transpose(0, 1)

        torch._inductor.metrics.reset()
        _, code = run_and_get_code(fn, x, y)
        self.assertEqual(torch._inductor.metrics.generated_kernel_count, 0)

        code = " ".join(code)
        fallback_markers = [
            "extern_kernels.add",
            "torch.ops.aten.add.Tensor",
        ]
        if config.cpp_wrapper:
            fallback_markers.extend(
                [
                    "aoti_torch_cuda_add_Tensor",
                    "aoti_torch_cpu_add_Tensor",
                ]
            )
        self.assertTrue(
            any(code.count(marker) >= 1 for marker in fallback_markers),
            msg=f"Expected complex add with strided inputs to fall back to extern kernels, got:\n{code}",
        )

    def test_add_complex5(self):
        def fn(a, b, alpha):
            return torch.add(a, b, alpha=alpha)

        x = torch.tensor([[1 + 1j, -1 + 1j], [-2 + 2j, 3 - 3j]])
        y = torch.tensor([[1 + 1j, -1 + 1j], [-2 + 2j, 3 - 3j]])

        self.common(fn, (x, y, 2))

    def test_add_complex6(self):
        # Fix https://github.com/pytorch/pytorch/issues/125745.
        # Add complex tensors with broadcasting.
        def fn(a, b, alpha):
            return torch.add(a, b, alpha=alpha)

        x = torch.tensor([[1 + 1j, -1 + 1j, -2 + 2j, 3 - 3j]])
        y = torch.tensor([[1 + 1j]])

        self.common(fn, (x, y, 2))

    def test_add_complex7(self):
        # Fix https://github.com/pytorch/pytorch/issues/160495
        # Test scalar (0-dimensional) complex tensor addition: 0D + 0D
        def fn(a, b, alpha):
            return torch.add(a, b, alpha=alpha)

        x = torch.rand((), dtype=torch.complex64, device=self.device)
        y = torch.rand((), dtype=torch.complex64, device=self.device)

        self.common(fn, (x, y, 2))

    def test_add_complex8(self):
        # Fix https://github.com/pytorch/pytorch/issues/160495
        # Test scalar complex addition: 1D + 0D
        def fn(a, b, alpha):
            return torch.add(a, b, alpha=alpha)

        x = torch.rand(1, dtype=torch.complex64, device=self.device)
        y = torch.rand((), dtype=torch.complex64, device=self.device)

        self.common(fn, (x, y, 2))

    def test_add_complex9(self):
        # Fix https://github.com/pytorch/pytorch/issues/160495
        # Test scalar complex addition: 0D + 1D
        def fn(a, b, alpha):
            return torch.add(a, b, alpha=alpha)

        x = torch.rand((), dtype=torch.complex64, device=self.device)
        y = torch.rand(1, dtype=torch.complex64, device=self.device)

        self.common(fn, (x, y, 2))

    def test_add_complex10(self):
        # Fix https://github.com/pytorch/pytorch/issues/160495
        # Test scalar complex broadcasting
        def fn(a, b, alpha):
            return torch.add(a, b, alpha=alpha)

        x = torch.randn(2, 3, dtype=torch.complex64, device=self.device)
        y = torch.rand((), dtype=torch.complex64, device=self.device)
        self.common(fn, (x, y, 2))

    def test_concat_add_inplace(self):
        def fn(x, y, z):
            return torch.cat([x, y], dim=1).add_(z)

        x = torch.randn([2, 12, 14, 14])
        y = torch.randn([2, 12, 14, 14])
        z = torch.randn([2, 24, 14, 14])

        self.common(fn, (x, y, z))

    def test_abs(self):
        def fn(a):
            return (a / (torch.abs(a) + 1),)

        self.common(fn, (torch.randn(17),))

    @xfail_if_triton_cpu
    def test_angle(self):
        def fn(a, b, c):
            return torch.angle(a), torch.angle(b), torch.angle(c)

        complex_input = torch.tensor(
            [1 + 1j, -1 + 1j, -2 + 2j, 3 - 3j, 0, 1j, 1, -1, float("nan")]
        )
        real_input = torch.tensor([-1.0, 0.0, 1.0, float("nan")])
        interger_real_input = torch.tensor([-1, 0, 1])
        self.common(fn, (complex_input, real_input, interger_real_input))

    def test_sgn(self):
        def fn(a):
            return torch.sgn(a), torch.sgn(a + 1) - 1

        self.common(fn, [torch.linspace(-10, 10, 41)])

    @skipCUDAIf(not SM80OrLater, "uses bfloat16 which requires SM >= 80")
    def test_scatter_bf16(self):
        def fn(inp, src, index):
            return inp.scatter_add(0, index, src)

        for dtype in [torch.int64, torch.bool, torch.bfloat16]:
            if not self.is_dtype_supported(dtype):
                continue
            self.common(
                fn,
                [
                    torch.zeros(3, 5, dtype=dtype),
                    torch.ones((2, 5), dtype=dtype),
                    torch.tensor([[0, 1, 2, 0, 0]]),
                ],
            )

    def test_randn_generator(self):
        def fn(a, generator):
            return torch.randn([20, 20], generator=generator, device=a.device)

        self.common(fn, (torch.linspace(-10, 10, 41), None), assert_equal=False)

        # generator not yet supported in dynamo
        with self.assertRaisesRegex(torch._dynamo.exc.Unsupported, "Generator"):
            self.common(fn, (torch.linspace(-10, 10, 41), torch.Generator(self.device)))

    def test_sgn_extremal(self):
        def fn(a):
            return (torch.sgn(a),)

        self.common(fn, [torch.tensor([np.nan, np.inf, -np.inf, 0])])

    def test_max_min(self):
        def fn(a, b):
            return (torch.maximum(a, b), torch.minimum(a, b))

        self.common(fn, (torch.randn(8), torch.randn(8)))
        t1 = torch.randn(8)
        t1[0] = float("nan")
        t2 = torch.randn(8)
        t2[1] = float("nan")
        self.common(fn, (t1, t2))

    def test_neg_max_uint8(self):
        # https://github.com/pytorch/pytorch/issues/93380
        def fn(a, b):
            c = torch.neg(a)
            return torch.maximum(b, c)

        a = torch.randint(256, (1,), dtype=torch.uint8)
        b = torch.randint(256, (8390,), dtype=torch.uint8)
        self.common(fn, (a, b))

    def test_compar(self):
        def fn(x):
            return x.gt(3.5), x.ge(3.5), x.eq(3.5), x.le(2.5), x.lt(3.5), x.ne(3.5)

        a = torch.tensor([3])
        self.common(fn, (a,))

    def test_horizonal_fusion1(self):
        def fn(a, b, c):
            return (a + b, a - c, b * c)

        self.common(
            fn, (torch.randn(8, 16, 16), torch.randn(8, 16, 16), torch.randn(1, 16, 1))
        )

    def test_horizonal_fusion2(self):
        def fn(a, b, c):
            return a + 1, b + 2, c + 3

        self.common(fn, (torch.randn(8, 16, 8), torch.randn(8, 16), torch.randn(16, 8)))

    def test_vertical_fusion1(self):
        def fn(sa, ct, p):
            # From torchbench.pyhpc_equation_of_state
            v17 = -3.087032500374211e-7
            v18 = -1.988366587925593e-8
            v19 = -1.061519070296458e-11
            v20 = 1.550932729220080e-10
            t15 = v19 * ct
            t19 = v17 + ct * (v18 + t15) + v20 * sa
            t20 = 1.0 / t19
            t128 = t19 * p
            return t20 + t128

        self.common(
            fn,
            (
                torch.randn(204, 204, 26),
                torch.randn(204, 204, 26),
                torch.randn(26),
            ),
        )
        assertGeneratedKernelCountEqual(self, 1)

    @config.patch({"fx_graph_cache": False})
    @skipIfWindows(msg="torch._dynamo.exc.Unsupported")
    def test_forced_buffer_realize(self):
        # Test torch._test_inductor_realize forces a buffer to be realized
        def fn(a):
            b = test_operators.realize(a * 2)
            return (b * 2,)

        self.common(fn, (torch.randn(10),))
        self.assertEqual(torch._inductor.metrics.ir_nodes_pre_fusion, 2)

    @config.patch({"fx_graph_cache": False})
    @skipIfWindows(msg="torch._dynamo.exc.Unsupported")
    def test_scheduler_vertical_fusion1(self):
        realize = test_operators.realize

        def fn(sa, ct, p):
            # From torchbench.pyhpc_equation_of_state
            v17 = -3.087032500374211e-7
            v18 = -1.988366587925593e-8
            v19 = -1.061519070296458e-11
            v20 = 1.550932729220080e-10
            t15 = realize(v19 * ct)
            t19 = realize(v17 + ct * (v18 + t15) + v20 * sa)
            t20 = realize(1.0 / t19)
            t128 = realize(t19 * p)
            return t20 + t128

        self.common(
            fn,
            (
                torch.randn(204, 204, 26),
                torch.randn(204, 204, 26),
                torch.randn(26),
            ),
        )
        self.assertEqual(torch._inductor.metrics.ir_nodes_pre_fusion, 5)
        assertGeneratedKernelCountEqual(
            self, 1 if not is_cpp_backend(self.device) else 2
        )

    def test_index_propagation(self):
        def copy(x):
            i = torch.arange(x.size(0), device=x.device)
            return x[i]

        x = torch.randn(8, device=self.device)
        copy_opt = torch.compile(copy, backend="inductor")

        expect = copy(x)
        actual = _run_and_assert_no_indirect_indexing(self, copy_opt, x)
        self.assertEqual(expect, actual)

    @dynamo_config.patch("capture_dynamic_output_shape_ops", True)
    # https://github.com/halide/Halide/issues/8308
    @config.patch("halide.scheduler_cpu", "Mullapudi2016")
    @config.patch("halide.scheduler_cuda", "Li2018")
    @config.patch(implicit_fallbacks=True)
    def test_index_propagation_nested_indirect_indexing(self):
        def nested(x, repeats):
            rank = torch.arange(repeats.numel(), device=x.device)
            index = rank.repeat_interleave(repeats, dim=0)
            return torch.index_select(x, index=index, dim=0)

        example_inputs = (
            torch.randn((32, 64), device=self.device),
            repeats := torch.tensor([5, 10, 15], device=self.device),
        )
        torch._dynamo.mark_dynamic(repeats, 0)  # create backed symint

        nested_opt = torch.compile(nested, backend="inductor")

        expect = nested(*example_inputs)
        actual = nested_opt(*example_inputs)
        self.assertEqual(expect, actual)

    def test_index_propagation_flip(self):
        def flip(x):
            i = torch.arange(x.size(0) - 1, -1, -1, device=x.device)
            return x[i]

        x = torch.randn(8, device=self.device)
        flip_opt = torch.compile(flip, backend="inductor")

        expect = flip(x)
        actual = _run_and_assert_no_indirect_indexing(self, flip_opt, x)
        self.assertEqual(expect, actual)

    def test_index_propagation_floordiv(self):
        def repeat_interleave(x, n):
            # e.g. x=[1, 2, 3], n=2 => returns [1, 1, 2, 2, 3, 3]
            i = torch.arange(x.shape[0] * n, device=x.device)
            return x[i // n]

        x = torch.randn(8, 16, device=self.device)
        repeat_interleave_opt = torch.compile(repeat_interleave, backend="inductor")
        # With static shapes we can prove the bound, our dynamic shapes reasoning is not good enough
        has_assert = ifdynstaticdefault(False, True)
        # this should be collapsed to direct indexing
        actual = _run_and_assert_no_indirect_indexing(
            self, repeat_interleave_opt, x, 3, has_assert=has_assert
        )
        expect = torch.repeat_interleave(x, 3, dim=0)
        self.assertEqual(expect, actual)
        self.assertEqual(actual, repeat_interleave(x, 3))

    def test_index_propagation_remainder(self):
        def repeat(x, n):
            # e.g. x=[1, 2, 3], n=2 => returns [1, 2, 3, 1, 2, 3]
            i = torch.arange(x.shape[0] * n, device=x.device)
            return x[i % x.shape[0]]

        x = torch.randn(8, 16, device=self.device)
        repeat_opt = torch.compile(repeat, backend="inductor")

        # With static shapes we can prove the bound, our dynamic shapes reasoning is not good enough
        has_assert = ifdynstaticdefault(False, True)
        # this should be collapsed to direct indexing
        actual = _run_and_assert_no_indirect_indexing(
            self, repeat_opt, x, 3, has_wrapping=False, has_assert=has_assert
        )
        expect = x.repeat(3, 1)
        self.assertEqual(expect, actual)
        self.assertEqual(actual, repeat(x, 3))

    def test_index_propagation_abs(self):
        def reflection_pad_left(x, n):
            # e.g. x=[1, 2, 3], n=2 => returns [3, 2, 1, 2, 3]
            i = torch.arange(x.shape[0] + n, device=x.device)
            return x[(i - n).abs()]

        x = torch.randn(8, device=self.device)
        opt_fn = torch.compile(reflection_pad_left, backend="inductor")

        # With static shapes we can prove the bound, our dynamic shapes reasoning is not good enough
        has_assert = ifdynstaticdefault(False, True)
        # this should be collapsed to direct indexing
        actual = _run_and_assert_no_indirect_indexing(
            self, opt_fn, x, 3, has_wrapping=False, has_assert=has_assert
        )
        expect = reflection_pad_left(x, 3)
        self.assertEqual(expect, actual)

    def test_index_propagation_device_assert_masked(self):
        def fn(a):
            idx = torch.arange(a.size(0), device=a.device)
            padded_idx = torch.constant_pad_nd(idx, (1050, 0))
            padded_idx = torch.where(padded_idx >= 0, padded_idx, padded_idx)
            return a[padded_idx]

        self.common(fn, (torch.randn(1024),))

    def test_index_remainder(self):
        def fn(x, y):
            return x[y % 12]

        self.common(fn, (torch.rand(1024), torch.randint(50, (50,))))

    @xfailIfS390X
    @config.patch(debug_index_asserts=False)
    @config.patch("cpp.enable_tiling_heuristics", False)
    def test_neg_index(self):
        def test(
            fn, inps, has_assert: bool, has_wrapping: bool, vectorize: bool = True
        ):
            fn_opt = torch.compile(fn)
            if is_halide_backend(self.device):
                pass  # no device asserts in halide
            elif is_mps_backend(self.device):
                _, codes = run_and_get_code(fn_opt, *inps)
                # MPS generates Metal shader code
                code = "\n".join(codes)
                # Check for error reporting in MPS kernels
                self.assertTrue(("TORCH_REPORT_ERROR" in code) is has_assert)
                # Check for wrapping (ternary operator for negative index handling)
                self.assertTrue((" ? " in code) is has_wrapping)
            elif is_pallas_backend(self.device):
                pass  # Pallas generates Python/JAX code, not C++/Triton
            elif self.device == "cpu" and not is_triton_cpu_backend(self.device):
                _, code = run_and_get_cpp_code(fn_opt, *inps)
                self.assertTrue(("TORCH_CHECK" in code) is has_assert)
                if (
                    cpu_vec_isa.valid_vec_isa_list()
                    and os.getenv("ATEN_CPU_CAPABILITY") != "default"
                ):
                    self.assertTrue(
                        (") ? (" in code or "blendv" in code) is has_wrapping
                    )
                    # Assert that we always vectorize the kernel regardless of wrapping / checks
                    self.assertTrue(("loadu" in code) is vectorize)
            else:
                code = run_and_get_triton_code(fn_opt, *inps)
                self.assertTrue(("tl.where" in code) is has_wrapping)
                self.assertTrue(("device_assert" in code) is has_assert)

        def indirect(a, b):
            return a[b - 1]

        a = torch.rand(1024, device=self.device)
        b = torch.zeros(256, dtype=torch.long, device=self.device)
        test(indirect, (a, b), has_assert=True, has_wrapping=True)

        def direct(x):
            return x[:, -1]

        a = torch.rand(1, 64, 32, device=self.device)
        # Does not even generate a kernel as it's a view
        test(direct, (a,), has_assert=False, has_wrapping=False, vectorize=False)

        def flip(a, b):
            return a[b]

        a = torch.rand(1024, device=self.device)
        b = torch.arange(start=-1, end=-a.numel() - 1, step=-1, device=self.device)
        test(flip, (a, b), has_assert=True, has_wrapping=True)

        # Constant propagate a constant that's negative
        def flip_with_index_constant(a):
            b = torch.arange(start=-1, end=-a.numel() - 1, step=-1, device=a.device)
            return a[b]

        # Wrapping is constant-folded
        test(flip_with_index_constant, (a,), has_assert=False, has_wrapping=False)

        # Operation where we can't prove that the index is always positive or negative
        def pos_and_neg(a):
            b = torch.arange(start=1, end=-a.numel() - 1, step=-1, device=a.device)
            return a[b]

        # It has wrapping but no assert
        test(pos_and_neg, (a,), has_assert=False, has_wrapping=True)

        # We currently don't do constant propagation with float constants
        # We cannot prove this kind of asserts just with bounds. We would need
        # to lift IndexPropagation.shape_env to be accessible in all of Inductor
        def flip_with_index(a):
            b = 1.0 * torch.arange(
                start=-1, end=-a.numel() - 1, step=-1, device=a.device
            )
            b = b.int()
            return a[b]

        test(
            flip_with_index,
            (a,),
            has_assert=ifdynstaticdefault(False, True),
            has_wrapping=False,
            vectorize=True,
        )

        def unsafe_index(a, b):
            return aten._unsafe_index(a, (b,))

        test(unsafe_index, (a, b), has_assert=False, has_wrapping=True)

        def constant_propagation(a):
            b = torch.tensor([2], device=a.device)
            return a[b]

        test(
            constant_propagation,
            (a,),
            has_assert=ifdynstaticdefault(False, True),
            has_wrapping=False,
            vectorize=False,  # There's no loop to vectorize!
        )

        def constant_propagation_neg(a):
            b = torch.tensor([-2], device=a.device)
            return a[b]

        # In symbolic shapes, we know that we can access -2, so no assert is necessary!
        test(
            constant_propagation_neg,
            (a,),
            has_assert=False,
            has_wrapping=False,
            vectorize=False,  # There's no loop to vectorize!
        )

    def test_computed_buffer_inlining(self):
        def flip(x):
            idx = torch.arange(x.size(0) - 1, -1, -1, device=x.device)
            return x[idx], idx

        flip_opt = torch.compile(flip, backend="inductor")
        x = torch.randn(8, device=self.device)

        expect = flip(x)
        actual = _run_and_assert_no_indirect_indexing(self, flip_opt, x)
        self.assertEqual(expect, actual)

    def test__unsafe_masked_index(self):
        def fn(a, mask, idx):
            return aten._unsafe_masked_index(a, mask, idx, 1)

        self.common(
            fn,
            (
                torch.randn(8, device=self.device),
                torch.tensor([True, False, True], device=self.device),
                [torch.tensor([3, 9, 2], device=self.device)],
            ),
        )

    def test__unsafe_masked_index_put_accumulate(self):
        def fn(a, mask, idx, values):
            return aten._unsafe_masked_index_put_accumulate(a, mask, idx, values)

        self.common(
            fn,
            (
                torch.randn(8, device=self.device),
                torch.tensor([True, False, True], device=self.device),
                [torch.tensor([3, 9, 2], device=self.device)],
                torch.randn(3, device=self.device),
            ),
        )

    def test_sum1(self):
        def fn(a, b):
            return ((a + b).sum(-1),)

        self.common(fn, (torch.randn(8, 8), torch.randn(8, 8)))

    def test_sum2(self):
        def fn(a, b):
            return ((a + b).sum([1, 2]), (a + b).sum(-1))

        self.common(fn, (torch.randn(8, 9, 3, 21), torch.randn(8, 9, 3, 21)))

    def test_sum3(self):
        def fn(a, b):
            r1 = a + b
            r2 = r1.sum(-1)
            r3 = torch.squeeze(b) + 10
            return (r1, r2, r3)

        # Mismatched elements: 2 / 10 (20.0%)
        # Greatest absolute difference: 0.0029296875 at index (8,) (up to 1e-05 allowed)
        # Greatest relative difference: 0.0017482517482517483 at index (6,) (up to 0.001 allowed)
        self.common(fn, (torch.randn(10, 10), torch.randn(1, 10)), atol=1e-5, rtol=2e-3)

    def test_sum4(self):
        def fn(a):
            b = a + 1
            c = b.sum(-1)
            d = c + 3
            e = d.sum(-1)
            f = e + 5
            return (f, e, d, c, b)

        self.common(fn, (torch.randn(1, 16, 8, 8),))

    def test_sum5(self):
        def fn(a):
            b = a + 1
            c = b.sum(-1)
            d = c + 3
            e = d.sum(-1)
            f = e + 5
            return (f,)

        self.common(fn, (torch.randn(1, 17, 8, 9),))

    def test_reduction1(self):
        def fn(a):
            return (a.sum(), a.max(), a.min(), a.argmax(), a.argmin())

        self.common(fn, (torch.tensor([float("-inf"), 0.0, float("inf")]),))

    @skip_if_x86_mac()
    def test_reduction2(self):
        def fn(a):
            # FIXME: a.argmax
            return (a.sum(), a.max(), a.min(), a.argmin())

        self.common(fn, (torch.full((4,), float("inf")),))

    @skip_if_x86_mac()
    def test_reduction3(self):
        def fn(a):
            # FIXME: a.argmin
            return (a.sum(), a.max(), a.min(), a.argmax())

        self.common(fn, (torch.full((4,), float("-inf")),))

    def test_reduction4(self):
        if self.device == "cpu":
            raise unittest.SkipTest("Non-deterministic CPU results")

        def fn(a):
            return (a.argmax(-1), a.argmin(-1))

        inputs = (torch.ones(128), torch.ones(4, 4, 1))
        for i in inputs:
            self.common(fn, (i,), check_lowp=not is_halide_backend(self.device))

    @config.patch(unroll_reductions_threshold=1)
    def test_reduction5(self):
        if self.device == "cpu":
            raise unittest.SkipTest("Non-deterministic CPU results")

        def fn(a):
            return (a.sum(), a.max(), a.min(), a.argmax())

        self.common(fn, (torch.full((4,), float("-inf")),))

    @skip_if_not_triton
    def test_reduction_config_limit(self):
        """
        This unit-test tests whether we exceed cudaDeviceProperties.maxGridSize in
        triton reduction configs for large size hints. #128826 introduced a scaling XBLOCK
        feature to resolve the issue in reduction configs which may exceed the maxGridSize
        """
        from torch._inductor.runtime.runtime_utils import next_power_of_2
        from torch._inductor.runtime.triton_heuristics import triton_config_reduction

        size_hints = {"x": 67108864, "r0_": 8192}
        for _ in range(4):
            size_hints["x"] = next_power_of_2(size_hints["x"])
            triton_config_reduction(size_hints, 1, 2048, 1, 8)

    def test_prod(self):
        def fn(a):
            return a.prod(0), a.prod(1), a.prod()

        self.common(fn, (torch.rand((10, 10)),))
        self.common(fn, (torch.rand((1, 2050)),))

    def test_unroll_small_reduction(self):
        def fn(x):
            val1, index1 = x.min(-1)
            val2, index2 = x.max(-1)
            return (
                val1,
                index1,
                val2,
                index2,
                x.sum(-1),
                (x > 1).any(-1),
                (x > 0).all(-1),
                x.argmin(-1),
                x.argmax(-1),
                x.amin(-1),
                x.amax(-1),
                x.aminmax(),
            )

        with config.patch(unroll_reductions_threshold=8):
            # small sized reductions will get unrolled
            self.common(fn, (torch.randn(8, 3),))
        torch._dynamo.reset()
        with config.patch(unroll_reductions_threshold=1):
            # make sure things also work if they aren't unrolled
            self.common(fn, (torch.randn(8, 3),))

    def test_multilayer_sum_low_prec(self):
        # fp16 nyi for cpu
        if self.device == "cpu":
            raise unittest.SkipTest(f"requires {GPU_TYPE}")

        def fn(a):
            return torch.mean(a)

        self.common(fn, ((torch.rand((10, 3, 352, 352), dtype=torch.float16),)))

    def test_multilayer_prime_size(self):
        def fn(a):
            return torch.max(a), torch.sum(a)

        # Requires masked loading for the intermediate reduction
        sample = torch.full((3999971,), 0, dtype=torch.int64)
        sample[-1] = 1
        self.common(fn, (sample,))

    @skip_if_gpu_halide
    @skipCPUIf(IS_MACOS, "fails on macos")
    def test_multilayer_var(self):
        def fn(a):
            return torch.var(a)

        self.common(
            fn,
            ((torch.rand((10, 3, 352, 352), dtype=torch.float32),)),
            atol=1e-3,
            rtol=1e-3,
        )
        self.common(
            fn,
            ((torch.rand((14923), dtype=torch.float32),)),
            atol=1e-3,
            rtol=1e-3,
        )

    @skipCPUIf(IS_MACOS, "fails on macos")
    @skip_if_halide  # accuracy 4.7% off
    def test_multilayer_var_lowp(self):
        def fn(a):
            return torch.var(a)

        atol = None
        rtol = None
        if self.device == "cpu" and os.getenv("ATEN_CPU_CAPABILITY") == "default":
            atol = 1e-3
            rtol = 1e-3
        self.common(
            fn,
            (torch.rand((16, 16, 352, 352), dtype=torch.float16),),
            atol=atol,
            rtol=rtol,
        )
        self.common(
            fn, (torch.rand((14923), dtype=torch.float16),), atol=atol, rtol=rtol
        )

    def test_split_cumsum(self):
        def fn(a):
            return torch.cumsum(a, -1)

        for dtype in get_all_dtypes(
            include_bfloat16=False,
            include_bool=True,
            include_complex=False,
            include_half=False,
        ):
            if not self.is_dtype_supported(dtype):
                continue
            # Use low=0 since when the mean value is 0, cumsum at all points
            # tends towards zero which makes the relative error term blow up
            inp = make_tensor(10, 3, 352, 352, low=0, dtype=dtype, device=self.device)
            self.common(fn, (inp.view(-1),), rtol=1e-4, atol=1e-5, check_lowp=False)
            self.common(fn, (inp.view(10, -1),), rtol=1e-4, atol=1e-5, check_lowp=False)

    @skipCUDAIf(not SM80OrLater, "Requires sm80")
    @skip_if_gpu_halide  # accuracy issue
    def test_split_cumsum_low_prec(self):
        if is_cpp_backend(self.device):
            raise unittest.SkipTest("ir.Scan nyi on CPU")

        def fn(a):
            return torch.cumsum(a.view(-1), 0)

        self.common(
            fn,
            (torch.rand((10, 3, 352, 352), dtype=torch.float16),),
            reference_in_float=True,
            check_lowp=False,
        )

    def test_consecutive_split_cumsum(self):
        def fn(a, b):
            a = a.view(-1)
            b = b.view(-1)
            return torch.cumsum(a, 0) + torch.cumsum(b, 0)

        dtype_a = torch.float32
        dtype_b = torch.float64

        ctx = (
            contextlib.nullcontext()
            if self.is_dtype_supported(dtype_a) and self.is_dtype_supported(dtype_b)
            else self.assertRaises(TypeError)
        )

        with ctx:
            a = make_tensor(10, 3, 352, 352, low=0, dtype=dtype_a, device=self.device)
            b = make_tensor(10, 3, 352, 352, low=0, dtype=dtype_b, device=self.device)

            self.common(fn, (a, b), rtol=1e-4, atol=1e-5, check_lowp=False)

    @config.patch(max_autotune_pointwise=True)
    def test_split_cumsum_index(self):
        # Split scan uses a workspace that needs to be zeroed before use.
        # data[index] does indirect indexing that should catch issues if the
        # workspace is not zeroed.
        def fn(lengths, data):
            offsets = torch.cumsum(lengths, 0)
            return data[offsets]

        lengths = torch.full((2**14,), 2**2, dtype=torch.int64, device=self.device)
        lengths[-2] = 3
        lengths[-1] = 3
        data = make_tensor((2**16,), dtype=torch.float32, device=self.device)
        self.common(fn, (lengths, data))

    def test_split_cumprod(self):
        def fn(a):
            return torch.cumprod(a, -1)

        for dtype in [torch.float32, torch.float64, torch.int32, torch.int64]:
            if not self.is_dtype_supported(dtype):
                continue
            inp = _large_cumprod_input(
                (10, 10000), dim=1, dtype=dtype, device=self.device
            )
            self.common(fn, (inp,), atol=1e-5, rtol=1e-4, check_lowp=False)

    @skipCUDAIf(not SM80OrLater, "Requires sm80")
    @skip_if_gpu_halide  # accuracy issue
    def test_split_cumprod_low_prec(self):
        if is_cpp_backend(self.device):
            raise unittest.SkipTest("ir.Scan nyi on CPU")

        def fn(a):
            return torch.cumprod(a.view(-1), 0)

        for dtype in [torch.float16, torch.bfloat16]:
            if not self.is_dtype_supported(dtype):
                continue
            inp = _large_cumprod_input(
                (10, 10000), dim=1, dtype=dtype, device=self.device
            )
            self.common(
                fn,
                (inp,),
                reference_in_float=True,
                check_lowp=False,
            )

    def test_consecutive_split_cumprod(self):
        def fn(a, b):
            return torch.cumprod(a, 0) + torch.cumprod(b, 0)

        dtype_a = torch.float32
        dtype_b = torch.float64

        ctx = (
            contextlib.nullcontext()
            if self.is_dtype_supported(dtype_a) and self.is_dtype_supported(dtype_b)
            else self.assertRaises(TypeError)
        )

        with ctx:
            a = _large_cumprod_input((10000,), dim=0, dtype=dtype_a, device=self.device)
            b = _large_cumprod_input((10000,), dim=0, dtype=dtype_b, device=self.device)

            self.common(fn, (a, b), atol=1e-5, rtol=1e-5, check_lowp=False)

    @skip_if_halide  # scan ops
    # TODO: support lifted symints when dynamic
    @torch._dynamo.config.patch(
        {"dynamic_shapes": False, "assume_static_by_default": True}
    )
    def test_custom_scan_op(self):
        if self.device != "cuda" and self.device != "xpu":
            raise unittest.SkipTest("associative_scan only supported on GPU")

        def sum_combine(a, b):
            return a + b

        from torch._higher_order_ops.associative_scan import associative_scan

        a = torch.randn(100, 100, device=self.device)
        expect = torch.cumsum(a, 0)
        actual = associative_scan(sum_combine, a, 0)
        self.assertEqual(expect, actual)

        def logcumsum_combine(a, b):
            min_v = torch.minimum(a, b)
            max_v = torch.maximum(a, b)
            mask = (min_v != max_v) | ~min_v.isinf()
            return torch.where(mask, max_v + (min_v - max_v).exp().log1p(), a)

        expect = torch.logcumsumexp(a, 0)
        actual = associative_scan(logcumsum_combine, a, 0)
        self.assertEqual(expect, actual)

    @skip_if_halide  # scan ops
    # TODO: support lifted symints when dynamic
    @torch._dynamo.config.patch(
        {"dynamic_shapes": False, "assume_static_by_default": True}
    )
    def test_custom_scan_op_compiled(self):
        if self.device != "cuda" and self.device != "xpu":
            raise unittest.SkipTest("associative_scan only supported on GPU")

        from torch._higher_order_ops.associative_scan import associative_scan

        def sum_combine(a, b):
            return a + b

        def fn(a, b, dim):
            diff = (a - b).abs()
            sad = associative_scan(sum_combine, diff, dim)
            return sad.sum(dim)

        a = torch.randn(100, 100, device=self.device)
        b = torch.randn(100, 100, device=self.device)
        self.common(fn, (a, b, 0))
        cfn = torch.compile(fn)
        _, code = run_and_get_code(cfn, a, b, 0)

        # Check everything is fused into a single kernel
        if config.cpp_wrapper:
            FileCheck().check_regex(r"call_triton_.*\(arg[01]_1, arg[12]_1, buf1,").run(
                code[0]
            )
        else:
            FileCheck().check_not("run(").check_regex(
                r"triton_.*\.run\(arg[01]_1, arg[12]_1, buf1,"
            ).check_not("run(").run(code[0])

    @skip_if_halide  # scan ops
    # TODO: support lifted symints when dynamic
    @torch._dynamo.config.patch(
        {"dynamic_shapes": False, "assume_static_by_default": True}
    )
    def test_custom_scan_op_multi_input(self):
        if self.device != "cuda" and self.device != "xpu":
            raise unittest.SkipTest("associative_scan only supported on GPU")

        def argmax_combine(a, b):
            a_value, a_index = a
            b_value, b_index = b
            mask = (a_value > b_value) | ((a_value == b_value) & (a_index > b_index))
            return (
                torch.where(mask, a_value, b_value),
                torch.where(mask, a_index, b_index),
            )

        from torch._higher_order_ops.associative_scan import associative_scan

        a = torch.randn(100, 100, device=self.device)
        expect = torch.cummax(a, 0)

        idx = torch.arange(100, device=self.device).view(100, 1).expand(100, 100)
        actual = associative_scan(argmax_combine, (a, idx), 0)
        self.assertEqual(expect, actual)

    @skip_if_halide  # scan ops
    # TODO: support lifted symints when dynamic
    @torch._dynamo.config.patch(
        {"dynamic_shapes": False, "assume_static_by_default": True}
    )
    def test_custom_scan_would_split(self):
        if self.device != "cuda" and self.device != "xpu":
            raise unittest.SkipTest("associative_scan only supported on GPU")

        def combine_linear_recurrence(left, right):
            xl, fl = left
            xr, fr = right
            x = xl * fr + xr
            f = fl * fr
            return x, f

        def eager_scan(x, g):
            x, g = x.to(torch.float64), g.to(torch.float64)
            x_out = torch.empty_like(x)
            g_out = torch.empty_like(g)
            x_out[:, 0] = x[:, 0]
            g_out[:, 0] = g[:, 0]
            for i in range(1, x.shape[1]):
                x_out[:, i], g_out[:, i] = combine_linear_recurrence(
                    (x_out[:, i - 1], g_out[:, i - 1]),
                    (x[:, i], g[:, i]),
                )
            return x_out.float(), g_out.float()

        @torch.compile
        def compiled_scan(x, f):
            from torch._higher_order_ops.associative_scan import associative_scan

            x, f = associative_scan(combine_linear_recurrence, (x, f), dim=1)
            return x, f

        x = torch.randn(1, 129, 2, device=self.device)
        f = torch.randn(1, 129, 2, device=self.device)
        expect = eager_scan(x, f)
        actual = compiled_scan(x, f)
        self.assertEqual(expect, actual)

    def test_embedding_bag_byte_unpack(self):
        if self.device != "cpu":
            raise unittest.SkipTest(f"No {GPU_TYPE} implementation (it returns empty)")

        def fn(a):
            return torch.ops.quantized.embedding_bag_byte_unpack(a)

        M, N = 32, 64
        scales = torch.randn(M, 1).view(torch.uint8)
        offsets = torch.randn(M, 1).view(torch.uint8)
        data = torch.randint(0, 255, (M, N), dtype=torch.uint8)
        packed = torch.cat([data, scales, offsets], dim=-1)
        self.common(fn, [packed])

    @xfail_if_mps_unimplemented
    def test_int8_weight_only_quant(self):
        def convert_weight_to_int8pack(b):
            b_int8pack, b_scales, _ = _dynamically_quantize_per_channel(
                b, -128, 127, torch.int8
            )
            return b_int8pack, b_scales

        def fn(a, b_int8pack, b_scales, c):
            res = torch._weight_int8pack_mm(a, b_int8pack, b_scales)
            res = res + c
            return res

        m = 32
        k = 32
        n = 48
        a = torch.rand((m, k), dtype=torch.bfloat16)
        b = torch.rand((n, k), dtype=torch.bfloat16)
        c = torch.rand((m, n), dtype=torch.bfloat16)
        b_int8pack, b_scales = convert_weight_to_int8pack(b)
        self.common(fn, (a, b_int8pack, b_scales, c))

    @xfail_if_mps_unimplemented
    @xfail_if_triton_cpu
    @skipCUDAIf(True, "No _dyn_quant_pack_4bit_weight implementation on CUDA")
    @skipIfXpu(msg="No _dyn_quant_pack_4bit_weight implementation on XPU")
    # Pallas codegen doesn't handle reduction axis after FloorDiv(ModularIndexing) simplification
    @xfail_if_pallas
    def test__dyn_quant_pack_4bit_weight_fp32(self):
        q_group = 32
        k = 128
        n = 128

        torch.manual_seed(1)
        b = torch.rand((k, n), dtype=torch.float32)
        in_features = b.size(0)
        out_features = b.size(1)

        def dyn_quant_pack_4bit_weight(b, in_features, out_features):
            b_uint8, b_scales_and_zeros = _group_quantize_tensor_symmetric(
                b, n_bit=4, groupsize=q_group
            )

            if q_group == in_features:
                b_scales_and_zeros = b_scales_and_zeros.to(torch.float)
            else:
                b_scales_and_zeros = b_scales_and_zeros.to(torch.bfloat16)
            b_int4pack = torch._dyn_quant_pack_4bit_weight(
                b_uint8, b_scales_and_zeros, None, q_group, in_features, out_features
            )

            return b_int4pack, b_scales_and_zeros

        def fn(b, in_features, out_features):
            b_int4pack, _ = dyn_quant_pack_4bit_weight(b, in_features, out_features)
            return b_int4pack

        self.common(fn, (b, in_features, out_features))

    @xfail_if_mps_unimplemented
    @xfail_if_triton_cpu
    @skipCUDAIf(True, "No _dyn_quant_pack_4bit_weight implementation on CUDA")
    @skipIfXpu(msg="No _dyn_quant_pack_4bit_weight implementation on XPU")
    @skip_if_halide  # bf16
    # Pallas codegen doesn't handle reduction axis after FloorDiv(ModularIndexing) simplification
    @xfail_if_pallas
    def test__dyn_quant_pack_4bit_weight_bf16(self):
        k = 128
        n = 128
        q_group = 32

        if not self.is_dtype_supported(torch.bfloat16):
            raise unittest.SkipTest(
                f"torch.bfloat16 not supported for device {self.device}"
            )

        torch.manual_seed(1)
        b = torch.rand((k, n), dtype=torch.bfloat16)
        in_features = b.size(0)
        out_features = b.size(1)

        def dyn_quant_pack_4bit_weight(b, in_features, out_features):
            b_uint8, b_scales_and_zeros = _group_quantize_tensor_symmetric(
                b, n_bit=4, groupsize=q_group
            )

            if q_group == in_features:
                b_scales_and_zeros = b_scales_and_zeros.to(torch.float)
            else:
                b_scales_and_zeros = b_scales_and_zeros.to(torch.bfloat16)
            b_int4pack = torch._dyn_quant_pack_4bit_weight(
                b_uint8, b_scales_and_zeros, None, q_group, in_features, out_features
            )

            return b_int4pack, b_scales_and_zeros

        def fn(b, in_features, out_features):
            b_int4pack, _ = dyn_quant_pack_4bit_weight(b, in_features, out_features)
            return b_int4pack

        self.common(fn, (b, in_features, out_features))

    @xfail_if_mps_unimplemented
    @xfail_if_triton_cpu
    # Pallas codegen doesn't handle reduction axis after FloorDiv(ModularIndexing) simplification
    @xfail_if_pallas
    @skipCUDAIf(True, "No _dyn_quant_matmul_4bit implementation on CUDA")
    @skipIfXpu(msg="No _dyn_quant_matmul_4bit implementation on XPU")
    def test__dyn_quant_matmul_4bit_fp32_input(self):
        q_group = 32
        m = 32
        k = 128
        n = 128

        torch.manual_seed(1)
        a = torch.rand((m, k), dtype=torch.float32)
        b = torch.rand((k, n), dtype=torch.float32)
        in_features = b.size(0)
        out_features = b.size(1)

        def dyn_quant_pack_4bit_weight(b, in_features, out_features):
            b_uint8, b_scales_and_zeros = _group_quantize_tensor_symmetric(
                b, n_bit=4, groupsize=q_group
            )

            if q_group == in_features:
                b_scales_and_zeros = b_scales_and_zeros.to(torch.float)
            else:
                b_scales_and_zeros = b_scales_and_zeros.to(torch.bfloat16)
            b_int4pack = torch._dyn_quant_pack_4bit_weight(
                b_uint8, b_scales_and_zeros, None, q_group, in_features, out_features
            )

            return b_int4pack, b_scales_and_zeros

        def fn(a, q_group, in_features, out_features):
            b_int4pack, _ = dyn_quant_pack_4bit_weight(b, in_features, out_features)
            res = torch._dyn_quant_matmul_4bit(
                a,
                b_int4pack,
                q_group,
                in_features,
                out_features,
            )
            return res

        self.common(fn, (a, q_group, in_features, out_features))

    @skipCPUIf(IS_MACOS, "fails on M1, mismatch in bf16 support reporting")
    @xfail_if_triton_cpu
    @xfailIf(
        IS_ARM64 and not IS_CPU_EXT_SVE_SUPPORTED
    )  # see https://github.com/pytorch/pytorch/issues/170787
    @skipCUDAIf(True, "No _dyn_quant_matmul_4bit implementation on CUDA")
    @skipIfXpu(msg="No _dyn_quant_matmul_4bit implementation on XPU")
    @skip_if_halide  # bf16
    def test__dyn_quant_matmul_4bit_bf16_input(self):
        m = 32
        k = 128
        n = 128
        q_group = k

        if not self.is_dtype_supported(torch.bfloat16):
            raise unittest.SkipTest(
                f"torch.bfloat16 not supported for device {self.device}"
            )

        torch.manual_seed(1)
        a = torch.rand((m, k), dtype=torch.bfloat16)
        b = torch.rand((k, n), dtype=torch.bfloat16)

        # codegen_dynamic_shape test fails without explicitly marking these dynamic
        torch._dynamo.mark_dynamic(a, 0)
        torch._dynamo.mark_dynamic(b, 1)

        in_features = b.size(0)
        out_features = b.size(1)

        if not self.is_dtype_supported(torch.bfloat16):
            raise unittest.SkipTest(
                f"torch.bfloat16 not supported for device {self.device}"
            )

        def dyn_quant_pack_4bit_weight(b, in_features, out_features):
            b_uint8, b_scales_and_zeros = _group_quantize_tensor_symmetric(
                b, n_bit=4, groupsize=q_group
            )

            if q_group == in_features:
                b_scales_and_zeros = b_scales_and_zeros.to(torch.float)
            else:
                b_scales_and_zeros = b_scales_and_zeros.to(torch.bfloat16)
            b_int4pack = torch._dyn_quant_pack_4bit_weight(
                b_uint8, b_scales_and_zeros, None, q_group, in_features, out_features
            )

            return b_int4pack, b_scales_and_zeros

        def fn(a, q_group, in_features, out_features):
            b_int4pack, _ = dyn_quant_pack_4bit_weight(b, in_features, out_features)
            res = torch.ops.aten._dyn_quant_matmul_4bit(
                a,
                b_int4pack,
                q_group,
                in_features,
                out_features,
            )
            return res

        self.common(fn, (a, q_group, in_features, out_features), atol=1, rtol=0.5)

    def test_expanded_reduction(self):
        def fn(x, y):
            z = x * y
            return z.sum((0, 1))

        atol = 1e-3
        rtol = 1e-3
        self.common(
            fn, (torch.randn(2, 197, 256), torch.randn(2, 1, 256)), atol=atol, rtol=rtol
        )

    @skip_if_gpu_halide
    def test_min_max_reduction(self):
        def fn(a, b):
            return (
                (a + b).max(),
                (a + b).min(),
                torch.amax(a + 1, keepdim=True),
                torch.amin(b + 1, keepdim=True),
            )

        dtypes = [torch.float, torch.float16]
        if self.is_dtype_supported(torch.bfloat16):
            dtypes += [torch.bfloat16]
        for dtype in dtypes:
            self.common(fn, (torch.randn(8, 8).to(dtype), torch.randn(8, 8).to(dtype)))

    @skip_if_halide  # bug in nan handling
    def test_min_max_reduction_nan(self):
        def fn(a):
            return (torch.max(a), torch.min(a))

        t1 = torch.randn(32)
        t1[16] = float("nan")
        self.common(fn, (t1,))

    @skip_if_halide  # bug in nan handling
    def test_fmin_fmax(self):
        def fn(a, b):
            return (
                torch.fmin(a, b),
                torch.fmax(a, b),
                torch.fmax(a + 1, torch.tensor(0.0)),
            )

        self.common(
            fn,
            (
                torch.tensor(
                    [-10.0, 10.0, float("nan"), float("nan"), float("nan"), 3, 4]
                ),
                torch.tensor(
                    [float("nan"), float("nan"), -10.0, 10.0, float("nan"), 4, 3]
                ),
            ),
        )

    def test_sum_int(self):
        def fn(x):
            return 2 * x.sum(-1) + x.sum()

        dtypes = torch.bool, torch.uint8, torch.int
        inps = [torch.randint(2, (64,), dtype=dtype) for dtype in dtypes]

        for i in inps:
            self.common(fn, (i,), check_lowp=False)

    def test_sum_dtype(self):
        sum_dtype = torch.double if self.device != "mps" else torch.bfloat16

        def fn(x):
            return x * x.sum(-1, dtype=sum_dtype) + x.sum(dtype=sum_dtype)

        self.common(fn, (torch.ones(32, 32) * 70,))

    @skip_if_halide
    def test_cummin(self):
        def fn(x):
            return x.cummin(0)

        self.common(
            fn, (torch.rand(16, 32),), check_lowp=not is_halide_backend(self.device)
        )
        self.common(fn, (torch.rand(1),), check_lowp=not is_halide_backend(self.device))
        self.common(fn, (torch.rand(0),), check_lowp=not is_halide_backend(self.device))

    def test_cumsum(self):
        def fn(x):
            return x.cumsum(0), x.cumsum(1)

        # Persistent reductions
        self.common(
            fn, (torch.rand(16, 32),), check_lowp=not is_halide_backend(self.device)
        )
        self.common(
            fn, (torch.rand(20, 30),), check_lowp=not is_halide_backend(self.device)
        )

        # Non-persistent reduction
        self.common(
            fn,
            (torch.rand(100, 4000),),
            check_lowp=not is_halide_backend(self.device),
            atol=1e-5,
            rtol=1e-5,
        )

    def test_cumsum_zero_dim(self):
        def fn(x):
            return x.cumsum(0), x.cumsum(-1)

        a = torch.rand(())
        self.common(fn, (a,))

    def test_cumsum_no_mask(self):
        def fn(x):
            return x.cumsum(-1)

        # Persistent reduction
        a = torch.rand((1, 1024))
        self.common(
            fn, (a,), check_lowp=not (TEST_WITH_ROCM or is_halide_backend(self.device))
        )

        # Non-persistent reduction
        b = torch.rand((1, 8192))
        self.common(
            fn,
            (b,),
            check_lowp=not (TEST_WITH_ROCM or is_halide_backend(self.device)),
            atol=1e-5,
            rtol=1e-5,
        )

    def test_cumprod_zero_dim(self):
        def fn(x):
            return x.cumprod(0), x.cumprod(-1)

        a = torch.rand(())
        self.common(fn, (a,))

    def test_cumprod_backward(self):
        if self.device == "mps":
            raise unittest.SkipTest(
                "MPS inductor codegen bug with argmax: threadgroup_argmax"
            )

        # Regression test for https://github.com/pytorch/pytorch/issues/136263
        # torch.compile used O(n^2) algorithm for cumprod backward with tensor
        # subclasses (like FakeTensor), making it extremely slow.
        def fn(x):
            y = x.cumprod(dim=1)
            return y.sum()

        for dim_size in [8, 32]:
            x = torch.rand(4, dim_size, 64, device=self.device, requires_grad=True)
            x_ref = x.clone().detach().requires_grad_(True)

            # Eager forward + backward
            y_ref = fn(x_ref)
            y_ref.backward()

            # Compiled forward + backward
            compiled_fn = torch.compile(fn, fullgraph=True)
            y = compiled_fn(x)
            y.backward()

            # Check correctness
            self.assertEqual(y, y_ref, atol=1e-4, rtol=1e-4)
            self.assertEqual(x.grad, x_ref.grad, atol=1e-4, rtol=1e-4)

    def test_cumprod_backward_with_zeros(self):
        if self.device == "mps":
            raise unittest.SkipTest(
                "MPS inductor codegen bug with argmax: threadgroup_argmax"
            )

        # Test cumprod backward with zeros in the input
        # This exercises the more complex O(n) algorithm path
        def fn(x):
            y = x.cumprod(dim=1)
            return y.sum()

        for dim_size in [8, 16]:
            x = torch.rand(4, dim_size, 32, device=self.device, requires_grad=True)
            # Insert some zeros to exercise the zero-handling path
            x.data[:, dim_size // 2, :] = 0
            x_ref = x.clone().detach().requires_grad_(True)

            # Eager forward + backward
            y_ref = fn(x_ref)
            y_ref.backward()

            # Compiled forward + backward
            compiled_fn = torch.compile(fn, fullgraph=True)
            y = compiled_fn(x)
            y.backward()

            # Check correctness
            self.assertEqual(y, y_ref, atol=1e-4, rtol=1e-4)
            self.assertEqual(x.grad, x_ref.grad, atol=1e-4, rtol=1e-4)

    def test_view_dtype_bool(self):
        # Regression test for boolean dtype handling in view.dtype lowering
        # torch.iinfo doesn't support bool, so we need special handling
        def fn(x, mask):
            # Create a computation that involves boolean tensors and where
            result = torch.where(mask, x, torch.zeros_like(x))
            return result.sum()

        x = torch.rand(4, 8, device=self.device, requires_grad=True)
        mask = torch.rand(4, 8, device=self.device) > 0.5

        x_ref = x.clone().detach().requires_grad_(True)

        # Eager
        y_ref = fn(x_ref, mask)
        y_ref.backward()

        # Compiled
        compiled_fn = torch.compile(fn, fullgraph=True)
        y = compiled_fn(x, mask)
        y.backward()

        self.assertEqual(y, y_ref)
        self.assertEqual(x.grad, x_ref.grad)

    def test_cumsum_inf(self):
        def fn(x):
            return x.cumsum(-1)

        _dtype = torch.float64

        def make_tensor(shape):
            return torch.full(shape, float("inf"), device=self.device, dtype=_dtype)

        ctx = (
            contextlib.nullcontext()
            if self.is_dtype_supported(_dtype)
            else self.assertRaises(TypeError)
        )
        with ctx:
            cfn = torch.compile(fn)

            for n in [100, 10, 100]:
                inp = torch.full((2, n), float("inf"), device=self.device, dtype=_dtype)
                self.assertEqual(cfn(inp), fn(inp))

    @xfail_if_triton_cpu
    def test_logcumsumexp(self):
        def fn(x):
            return x.logcumsumexp(0), x.logcumsumexp(1)

        # Persistent reductions
        self.common(
            fn,
            (torch.rand(16, 32),),
            check_lowp=not (TEST_WITH_ROCM or is_halide_backend(self.device)),
        )
        self.common(
            fn,
            (torch.rand(20, 30),),
            check_lowp=not (TEST_WITH_ROCM or is_halide_backend(self.device)),
        )

        # Non-persistent reduction
        self.common(
            fn,
            (torch.rand(100, 4000),),
            check_lowp=not (TEST_WITH_ROCM or is_halide_backend(self.device)),
            atol=1e-5,
            rtol=1e-5,
        )

    def test_logcumsumexp_zero_dim(self):
        def fn(x):
            return x.logcumsumexp(0), x.logcumsumexp(-1)

        a = torch.rand(())
        self.common(fn, (a,))

    def test_clamp(self):
        def fn(a, b):
            return (a.clamp(-0.1, 0.1), b.clamp(0), torch.clamp(a + b, max=0))

        self.common(fn, (torch.randn(8, 8), torch.randn(8, 8)))

    def test_clamp_type_promotion(self):
        tgt_dtype = torch.double if self.device != "mps" else torch.half

        def fn(a):
            b = torch.tensor(1.0, dtype=tgt_dtype, device=self.device)
            c = torch.full((4,), 2, device=self.device)
            return a.clamp(min=b, max=c)

        self.common(fn, (torch.randint(4, (4,)),))

    def test_clamp_type_promotion_non_tensor(self):
        def fn(a):
            return a.clamp(min=1.5), a.clamp(min=2)

        self.common(fn, (torch.randint(4, (4,)),))

    def test_comparison_scalar_type_promotion_bf16(self):
        if self.device == "mps":
            raise unittest.SkipTest(
                "MPS codegen doesn't upcast bf16 constants to float32"
            )

        def fn(x):
            return x == 3.14

        x = torch.tensor(
            [3.14, 1.0, 3.140625, 0.0], dtype=torch.bfloat16, device=self.device
        )
        expected = fn(x)
        actual = torch.compile(fn)(x)
        self.assertEqual(actual, expected)

    @skip_if_gpu_halide
    @xfail_if_triton_cpu
    def test_dist(self):
        def fn(a, b):
            return (
                torch.dist(a, b),
                torch.dist(a, b, p=1.2),
            )

        self.common(fn, (torch.randn(4, 4), torch.randn(4, 4)))

    @skip_if_halide  # different pow accuracies
    @xfail_if_triton_cpu
    def test_norm_constant_overflow(self):
        def fn(a):
            return (
                torch.norm(a, p=-41.0, dim=1),
                torch.norm(a, p=-41.0, dim=0),
            )

        self.common(fn, (torch.randn(4, 1, 4),))

    @skipCUDAIf(not SM80OrLater, "Requires sm80")
    @skip_if_gpu_halide  # https://github.com/halide/Halide/issues/8311
    def test_dist_bf16(self):
        def fn(a, b):
            return torch.dist(a.to(torch.bfloat16), b.to(torch.bfloat16))

        if not self.is_dtype_supported(torch.bfloat16):
            raise unittest.SkipTest(
                f"torch.bfloat16 not supported for device {self.device}"
            )
        self.common(fn, (torch.randn(4, 4), torch.randn(4, 4)))

    def test_arange1(self):
        def fn(x):
            rng1 = torch.arange(8 * 8, dtype=torch.float32, device=x.device).view(8, 8)
            rng2 = torch.arange(10, 18, device=x.device)
            tmp = x * rng1
            return tmp, tmp + rng2

        self.common(fn, (torch.randn(8, 8),))

    def test_arange2(self):
        def fn(x):
            rng1 = torch.arange(8, device=x.device)
            return (x + rng1,)

        self.common(fn, (torch.randint(4, (8, 8)),), check_lowp=False)

    def test_arange3(self):
        def fn(x):
            return x + torch.ops.aten.arange.start_step(
                0, 53, 4, dtype=torch.int64, device=x.device
            )

        self.common(fn, (torch.randn(14),))

    def test_arange4(self):
        def fn(x):
            return x - torch.arange(512, -512, -1.0, device=x.device)

        self.common(fn, (torch.randn(1024),))

    def test_arange5(self):
        def fn(step, device):
            return torch.arange(512, -512, step, device=device)

        compiled_fn = torch.compile(fn)

        # NOTE: use assertEqual to check dtypes which self.common doesn't do
        for step in (-1, -1.0):
            expect = fn(step, self.device)
            actual = compiled_fn(step, self.device)
            self.assertEqual(expect, actual)
        self.assertEqual(expect, actual)

    def test_arange6(self):
        def fn(x):
            return torch.arange(0.1, 8.0001, 1, dtype=x.dtype, device=x.device)

        # Test that float arguments are truncated to int when dtype is set explicitly
        make_arg = functools.partial(
            make_tensor, device=self.device, requires_grad=False
        )
        self.common(fn, (make_arg(1, dtype=torch.float32),))
        self.common(fn, (make_arg(1, dtype=torch.int64),))

    def test_arange7(self):
        def fn(x):
            # Test aten.arange.start_step lowering with integer dtypes
            return x + torch.ops.aten.arange.start_step(
                0, 10, 2, dtype=torch.int64, device=x.device
            )

        self.common(fn, (torch.zeros(5, dtype=torch.int64),), check_lowp=False)

    def test_linspace1(self):
        def fn(x):
            return torch.linspace(0.125, 0.875, 7, device=x.device) + x

        self.common(fn, (torch.randn(1, 7),))

    def test_linspace2(self):
        def fn(x):
            return torch.linspace(0, 2, 1, device=x.device) + x

        self.common(fn, (torch.randn(1, 1),))

    def test_linspace3(self):
        def fn(x):
            return torch.linspace(0, 2, 0, device=x.device)

        self.common(fn, (torch.Tensor([]),))

    @requires_multigpu()
    def test_linspace4(self):
        def fn(x):
            return torch.linspace(0, 2, 0, device=f"{GPU_TYPE}:1")

        self.common(fn, (torch.Tensor([]),))

    def test_tensor1(self):
        def fn(x):
            return torch.tensor([1], device=x.device) + x, torch.tensor(
                5, device=x.device
            )

        self.common(fn, (torch.randn(10),))

    def test_tensor2(self):
        def fn(x):
            return torch.tensor(list(range(2, 40, 2)), device=x.device) + x

        self.common(fn, (torch.randn(1),))

    def test_tensor3(self):
        def fn(x):
            return (
                torch.tensor([], device=x.device),
                torch.tensor([1, 2], device=x.device) + 1,
                torch.tensor([1, 2, 3], device=x.device) + 2,
                torch.tensor([1, 2, 3, 4], device=x.device) + x,
            )

        self.common(fn, [torch.randn(4)])

    def test_views1(self):
        def fn1(x, y):
            return (x.view(size2) + y,)

        def fn2(x, y):
            return ((x + 1).view(size2) + y,)

        views = [
            ([5 * 7], [5, 7]),
            ([2 * 3 * 4 * 5 * 6 * 7], [2, 3, 4, 5, 6, 7]),
            ([2 * 3, 4, 5, 6 * 7], [2, 3, 4, 5, 6, 7]),
            ([10 * 5, 20], [10, 5, 20]),
            ([1, 10, 1], [10]),
            ([10, 1, 10, 1, 10], [10, 100]),
            ([2, 2, 2, 2], [4, 4]),
        ]
        for size1, size2 in views:
            self.common(fn1, (torch.randn(size1), torch.randn(size2)))
            self.common(fn2, (torch.randn(size1), torch.randn(size2)))

        for size2, size1 in views:
            self.common(fn1, (torch.randn(size1), torch.randn(size2)))
            self.common(fn2, (torch.randn(size1), torch.randn(size2)))

    def test_views2(self):
        for size1, size2 in [
            ([2, 2, 2, 2], [4, -1]),
            ([10, 1, 10, 1, 10], [-1, 100]),
            ([10 * 5, 20], [10, -1, 20]),
        ]:

            def fn1(x):
                return (x.view(size2) + 1,)

            def fn2(x):
                return ((x * 2).view(size2) + 1,)

            self.common(fn1, (torch.randn(size1),))
            self.common(fn2, (torch.randn(size1),))

    def test_views3(self):
        # example taken from hf_BigBird
        def forward(arg1, arg2):
            index = torch.ops.aten.index(arg1, [arg2])
            view_1 = torch.ops.aten.view(index, [1, 2232, 64])
            view_2 = torch.ops.aten.view(view_1, [1, 12, 62, 192])
            return view_2

        self.common(
            forward,
            (
                rand_strided((64, 64), (64, 1), torch.float32),
                rand_strided((2232,), (1,), torch.int64),
            ),
        )

    def test_views4(self):
        # example taken from hf_BigBird
        def forward(arg1, arg2):
            arg1 = arg1.index_select(0, arg2)
            arg1 = torch.ops.aten.view(arg1, [2, 3, 4, 5, 5])
            arg1 = torch.ops.aten.view(arg1, [2, 3, 2, 10, -1])
            return arg1

        self.common(
            forward,
            (
                torch.randn(12, 5, 5),
                torch.randint(0, 11, (24,)),
            ),
        )

    def test_views5(self):
        # tensor with shape 0 in any dimension
        def forward(x):
            y = x[:, 4:]
            return y.view(len(y), -1, 4)

        self.common(
            forward,
            (torch.randn(4, 4, 4, 4),),
        )

    def test_views6(self):
        def forward(x):
            x = torch.ops.aten.relu(x)
            s = torch.ops.aten.slice(x, 0, 0, 9223372036854775807)
            s = torch.ops.aten.slice(s, 1, 0, 9223372036854775807)
            s = torch.ops.aten.slice(s, 3, 0, 0)
            y = torch.ops.aten.view(s, [4, 2, -1])
            return y

        self.common(
            forward,
            (torch.randn(4, 2, 4, 4),),
        )

    def test_views7(self):
        # x.view(dtype)
        def forward(x, y):
            x = (x + 1).to(torch.float32)
            y = (y + 1).to(torch.int32)
            return x.view(torch.int32), y.view(torch.float32)

        self.common(
            forward,
            (
                torch.rand(2, 3, dtype=torch.float32),
                torch.randint(10, (2, 3), dtype=torch.int32),
            ),
        )

    def test_view_copy_dtype_change(self):
        def fn(x, dtype):
            return torch.ops.aten.view_copy(x, dtype=dtype)

        x = torch.randint(2, 32, (8, 16), dtype=torch.int32, device=self.device)
        self.common(fn, (x, torch.int64))

    def test_torch_device_split(self):
        def fn(x):
            return x.split(2)

        x = torch.rand(10)

        with x.device:
            out = torch.compile(fn, backend=lambda gm, _: gm)(x)
            ref = fn(x)
            for a, b in zip(out, ref):
                self.assertTrue(torch.allclose(a, b))

    def test_relu(self):
        def fn(a, b):
            return (torch.relu(a), torch.relu(a + b) / 10)

        self.common(fn, (torch.randn(8, 8), torch.randn(8, 8)))

    def test_exp(self):
        def fn(a, b):
            return (torch.exp(a), torch.exp(a + b))

        self.common(fn, (torch.randn(8, 8), torch.randn(8, 8)))

    def test_exp2(self):
        def fn(a, b):
            return (torch.exp2(a), torch.exp2(a + b), torch.pow(2, -torch.abs(a - b)))

        self.common(fn, (torch.randn(8, 8), torch.randn(8, 8)))

    @skipIfXpu(msg="logaddexp_xpu not implemented for ComplexFloat")
    @skipCUDAIf(True, "Not implemented for CUDA")
    def test_logaddexp(self):
        self.common(
            torch.logaddexp,
            (
                torch.randn(8, 8).to(dtype=torch.complex64),
                torch.randn(8, 8).to(dtype=torch.complex64),
            ),
        )

    def test_sigmoid(self):
        def fn(a, b):
            return (torch.sigmoid(a), torch.sigmoid(a + b))

        self.common(fn, (torch.randn(8, 8), torch.randn(8, 8)))

    @xfail_if_triton_cpu
    def test_round(self):
        def fn(a, b):
            return torch.round(a), torch.round(b + 1), torch.round(a, decimals=2)

        # without manual_seed, there is some chance this test fails due to:
        # https://github.com/triton-lang/triton/issues/530
        torch.manual_seed(0)

        # with *100 we are always getting a number exactly at .5 which we don't do right in half
        self.common(fn, (torch.randn(8, 8) * 100, torch.randn(8, 8) * 10))

    @xfail_if_triton_cpu
    def test_round_correctness(self):
        if self.device == "cuda":
            raise unittest.SkipTest("need to debug tl.libdevice on A100/V100")

        def fn(a):
            return torch.round(a)

        dtype = highest_precision_float(self.device)
        self.common(
            fn,
            [torch.arange(-10, 10, 0.1, dtype=dtype)],
            check_lowp=False,
        )

    @xfail_if_triton_cpu
    def test_builtins_round(self):
        def fn(x, i):
            return x[: round(i / 2 + 1)] + round(i / 2)

        cfn = torch.compile(fullgraph=True, dynamic=True)(fn)

        x = torch.zeros(5, dtype=torch.int, device=self.device)
        with torch.no_grad():
            for i in range(1, 6):
                self.assertEqual(cfn(x, i), fn(x, i))

    @xfail_if_triton_cpu
    def test_builtins_round_float_ndigits_pos(self):
        def fn(x, i):
            return x + round(i / 2 * 123.4567, 1)

        cfn = torch.compile(fullgraph=True, dynamic=True)(fn)

        x = torch.zeros(2, device=self.device)
        i = 2

        with torch.no_grad():
            self.assertEqual(cfn(x, i), fn(x, i))

    @xfail_if_triton_cpu
    def test_builtins_round_float_ndigits_zero(self):
        def fn(x, i):
            return x + round(i / 2 * 123.4567, 0)

        cfn = torch.compile(fullgraph=True, dynamic=True)(fn)

        x = torch.zeros(2, device=self.device)
        i = 2

        with torch.no_grad():
            self.assertEqual(cfn(x, i), fn(x, i))

    @xfail_if_triton_cpu
    def test_builtins_round_float_ndigits_neg(self):
        def fn(x, i):
            return x + round(i / 2 * 123.4567, -1)

        cfn = torch.compile(fullgraph=True, dynamic=True)(fn)

        x = torch.zeros(2, device=self.device)
        i = 2

        with torch.no_grad():
            self.assertEqual(cfn(x, i), fn(x, i))

    def test_builtins_round_int_ndigits_pos(self):
        def fn(x, i):
            return x + round(i, 1)

        cfn = torch.compile(fullgraph=True, dynamic=True)(fn)

        x = torch.zeros(2, device=self.device)
        i = 123

        with torch.no_grad():
            self.assertEqual(cfn(x, i), fn(x, i))

    def test_builtins_round_int_ndigits_zero(self):
        def fn(x, i):
            return x + round(i, 0)

        cfn = torch.compile(fullgraph=True, dynamic=True)(fn)

        x = torch.zeros(2, device=self.device)
        i = 123

        with torch.no_grad():
            self.assertEqual(cfn(x, i), fn(x, i))

    def test_silu(self):
        def fn(a):
            return (torch.nn.functional.silu(a),)

        self.common(fn, (torch.randn(8, 8),))

    @skip_if_halide  # halide has different precision characteristics
    def test_silu_exact_eager(self):
        """Test that compiled SiLU matches eager exactly."""

        def fn(a):
            return torch.nn.functional.silu(a)

        # Test with normal random values
        self.common(fn, (torch.randn(1024, device=self.device),))

        # Test with bfloat16 edge cases (all possible bf16 values)
        x_bf16 = (
            torch.arange(0, 2**16, dtype=torch.int32, device=self.device)
            .to(torch.int16)
            .view(torch.bfloat16)
        )
        eager_result_bf16 = fn(x_bf16)
        torch._dynamo.reset()
        compiled_fn = torch.compile(fn)
        compiled_result_bf16 = compiled_fn(x_bf16)
        self.assertEqual(eager_result_bf16, compiled_result_bf16, equal_nan=True)

    @skip_if_halide  # halide has buggy nan handling
    def test_nan_to_num(self):
        def fn(a):
            return (
                torch.nan_to_num(a),
                torch.nan_to_num(a, nan=3.0),
                torch.nan_to_num(a, nan=None),
                torch.nan_to_num(a, posinf=4.0),
                torch.nan_to_num(a, neginf=5.0),
                torch.nan_to_num(a, nan=3.0, posinf=4.0, neginf=5.0),
            )

        self.common(
            fn,
            (torch.tensor((float("nan"), float("inf"), float("-inf"), 1.0)),),
            check_lowp=False,  # a much more elaborate test is required to match finfo max's for float and half
        )

    def test_one_hot(self):
        def fn(a):
            return torch.nn.functional.one_hot(a, 8) + 1

        self.common(
            fn,
            (torch.arange(100).view(4, 5, 5) % 8,),
            check_lowp=False,
        )

    test_one_hot._expected_failure_halide = True

    def test_div1(self):
        def fn(a, b):
            return (
                aten.div(a, b, rounding_mode=None),
                aten.div(a, b, rounding_mode="floor"),
                aten.div(a, b, rounding_mode="trunc"),
                a / b,
                a // b,
            )

        self.common(fn, (torch.randn(8, 8) * 100, torch.randn(8, 8) * 100))

    def test_div2(self):
        def fn(a, b):
            return (
                aten.div(a, b, rounding_mode=None),
                aten.div(a, b, rounding_mode="floor"),
                aten.div(a, b, rounding_mode="trunc"),
                a / b,
                a // b,
            )

        self.common(fn, (torch.randint(-100, 100, [8, 8]), 100 * torch.randn(8, 8)))

    def test_div3(self):
        def fn(a, b):
            return (
                aten.div(a, b, rounding_mode=None),
                aten.div(a, b, rounding_mode="floor"),
                aten.div(a, b, rounding_mode="trunc"),
                a / b,
                a // b,
            )

        a = torch.randint(1, 100, [8, 8])
        self.common(fn, (a * 2, a))

    def test_div4(self):
        def fn(a, b):
            return (
                aten.div(a, b, rounding_mode=None),
                aten.div(a, b, rounding_mode="floor"),
                aten.div(a, b, rounding_mode="trunc"),
                a / b,
                a // b,
            )

        self.common(
            fn,
            (torch.randint(-100, 0, [8, 8]), torch.randint(1, 10, [8, 8])),
        )

    def test_div5(self):
        def fn(a, b):
            return (
                aten.div(a, b, rounding_mode=None),
                aten.div(a, b, rounding_mode="floor"),
                aten.div(a, b, rounding_mode="trunc"),
                a / b,
                a // b,
            )

        # divide a scalar
        self.common(fn, (torch.randint(-100, 0, [8, 8]), 16))

    def test_div6(self):
        def fn(a, b):
            return (
                aten.div(a, b, rounding_mode=None),
                aten.div(a, b, rounding_mode="floor"),
                aten.div(a, b, rounding_mode="trunc"),
                a / b,
                a // b,
            )

        # treat boolean as integer
        self.common(
            fn,
            (torch.ones([8, 8], dtype=torch.bool), torch.randint(-100, -1, [8, 8])),
        )

    @skip_if_triton_cpu  # divide by zero; cannot xfail because it crashes process
    @skipIfXpu(msg="https://github.com/intel/intel-xpu-backend-for-triton/issues/6401")
    def test_div7(self):
        def fn(a, b):
            return (
                aten.div(a, b, rounding_mode=None),
                aten.div(a, b, rounding_mode="floor"),
                aten.div(a, b, rounding_mode="trunc"),
                a / b,
                a // b,
            )

        self.common(
            fn,
            (
                torch.randint(2**32, 2**40, [100, 100]),
                torch.randint(-10, -1, [100, 100]),
            ),
        )

    def test_div8(self):
        def fn(a, b):
            return (
                aten.div(a, b, rounding_mode=None),
                aten.div(a * 0.5, b, rounding_mode=None),
                aten.div(a, b * 1.0, rounding_mode=None),
                aten.div(a, b, rounding_mode="floor"),
                aten.div(a, b, rounding_mode="trunc"),
                a / b,
                a // b,
            )

        self.common(fn, (1024, 100))

    def test_div9(self):
        def fn(x):
            return (torch.div(42, x), aten.true_divide(42, x), aten.div.Tensor(42, x))

        self.common(fn, (torch.randn(8),))

    @skip_if_triton_cpu  # divide by zero; cannot xfail because it crashes process
    def test_div_zero_dim(self):
        def fn(a, b):
            return (
                aten.div(a, b, rounding_mode=None),
                aten.div(a, b, rounding_mode="floor"),
                aten.div(a, b, rounding_mode="trunc"),
                a / b,
                a // b,
            )

        for dtype in (torch.float32, torch.int64):
            self.common(
                fn,
                (
                    make_tensor(10, device=self.device, dtype=dtype),
                    make_tensor((), device=self.device, dtype=dtype, exclude_zero=True),
                ),
            )
            self.common(
                fn,
                (
                    make_tensor((), device=self.device, dtype=dtype),
                    make_tensor(10, device=self.device, dtype=dtype, exclude_zero=True),
                ),
            )

    @skip_if_triton_cpu  # divide by zero; cannot xfail because it crashes process
    def test_div_prim(self):
        def fn(a, b):
            return (torch.ops.prims.div(a, b),)

        for dtype in (torch.float32, torch.int64):
            self.common(
                fn,
                (
                    make_tensor(100, device=self.device, dtype=dtype),
                    make_tensor(
                        100, device=self.device, dtype=dtype, exclude_zero=True
                    ),
                ),
            )

    def test_floordiv(self):
        def fn_floor_input(a, i):
            n = (i * 1.234) // 8.234
            return a + n

        self.common(
            fn_floor_input,
            (make_tensor(10, device=self.device, dtype=torch.float32), 33),
        )

        def fn_int_input(a, i):
            n = i // 8
            return a + n

        self.common(
            fn_int_input, (make_tensor(10, device=self.device, dtype=torch.float32), 33)
        )

    def test_floordiv_negative_int(self):
        # Regression test for https://github.com/pytorch/pytorch/issues/158212
        # Signed integer floor division must handle negative dividends correctly.
        # The key case: (-1) // 2 should be -1 (Python floor), not 0 (C truncation).
        def fn(a, b):
            return a // b

        for divisor in [2, 3, 7]:
            a = torch.arange(-32, 32, device=self.device, dtype=torch.int32)
            b = torch.full_like(a, divisor)
            self.common(fn, (a, b))
            b_neg = torch.full_like(a, -divisor)
            self.common(fn, (a, b_neg))

    @skip_if_cpu
    def test_floordiv_div_by_zero_int(self):
        # Integer floor division by zero is undefined behavior on CUDA/Triton.
        # On CPU, integer division by zero correctly raises ZeroDivisionError.
        # Eager (c10::div_floor_integer) and compiled (Triton floordiv) must
        # both return 0 for elements where the divisor is zero on GPU.
        def fn(a, b):
            return torch.floor_divide(a, b)

        for dtype in [torch.int32, torch.int64]:
            # All-zero divisor: every element should be 0
            for dividend in [0, 1, -1, 5, -5]:
                a = torch.full((8,), dividend, device=self.device, dtype=dtype)
                b = torch.full((8,), 0, device=self.device, dtype=dtype)
                self.common(fn, (a, b))

    def test_floordiv_float_accuracy(self):
        # Triton uses an approximate reciprocal for fp32 division, so a naive
        # floor(a / b) can be off by one when the true quotient is very close
        # to an integer.  Verify the corrected lowering matches eager.
        def fn(a, b):
            return torch.floor_divide(a, b)

        # The original repro: fp16 values whose exact quotient is 4.0, but
        # Triton's approximate fp32 division yields 3.9999997…
        a = torch.full((8,), 84.3125, dtype=torch.float16, device=self.device)
        b = torch.full((8,), 21.078125, dtype=torch.float16, device=self.device)
        self.common(fn, (a, b))

        # Negative operands / mixed signs
        for dtype in [torch.float16, torch.float32]:
            for a_val, b_val in [
                (7.0, 2.0),
                (7.0, -2.0),
                (-7.0, 2.0),
                (-7.0, -2.0),
                (6.0, 3.0),
                (0.0, 5.0),
                (0.0, -5.0),
            ]:
                a = torch.full((8,), a_val, dtype=dtype, device=self.device)
                b = torch.full((8,), b_val, dtype=dtype, device=self.device)
                self.common(fn, (a, b))

    def test_div_precision(self):
        # Reproducer for https://github.com/pytorch/pytorch/issues/101039

        def forward(x, y):
            z = x.div(y)
            return F.softmax(z, dim=-1)

        query = torch.randn(1, 10, 40)
        key = torch.randn(1, 2, 40)
        x = torch.matmul(query, key.transpose(-2, -1))
        self.common(forward, (x, 1e-6))

        x = torch.tensor(
            [
                [
                    [
                        [-16.1649, 5.6846, -5.1022, -9.1134],
                        [-11.5552, -2.2615, -12.8913, 10.6538],
                        [-7.1666, -5.3333, 2.0776, -9.7984],
                        [7.4469, -2.3948, 2.7371, 0.9201],
                    ],
                    [
                        [-8.0361, -16.3771, 22.7741, 4.4685],
                        [20.8047, -0.7771, -2.4355, -2.2299],
                        [3.8343, -2.0914, -2.4077, 2.2740],
                        [-15.8663, -2.7015, -12.5241, -3.0040],
                    ],
                    [
                        [-2.5139, 14.4393, -3.7186, 1.2255],
                        [5.6742, 14.1842, -8.5976, 16.8366],
                        [-9.7358, -3.0279, 11.8164, -4.0787],
                        [-9.0621, 8.2580, 29.9486, -2.4107],
                    ],
                    [
                        [7.3622, 12.5640, -20.5592, 13.6237],
                        [-11.5640, 0.8832, 16.7275, -2.5009],
                        [-2.0953, -12.2276, -26.2633, 4.5268],
                        [15.3329, -11.7492, 6.5650, -9.2483],
                    ],
                ],
                [
                    [
                        [7.9980, -4.9369, 3.1508, 5.2994],
                        [3.8052, 3.9514, 8.4987, -10.5045],
                        [-2.6827, -4.0010, -4.0611, 6.4091],
                        [-19.0318, 6.4073, 2.8923, 8.0250],
                    ],
                    [
                        [7.1650, -3.4585, 5.7720, -5.0305],
                        [-0.9765, -3.0086, 11.7114, 8.0555],
                        [-3.1027, -3.5514, 9.6182, -8.8526],
                        [-9.2348, -6.0239, 6.2528, -6.7221],
                    ],
                    [
                        [11.5936, 22.4139, -0.4089, -4.9889],
                        [14.8217, -2.3426, -17.6189, 3.7427],
                        [1.9546, -13.0902, 8.6293, -7.2457],
                        [-7.6900, -4.5796, 9.6332, -10.2631],
                    ],
                    [
                        [0.8027, -1.0955, 14.8404, -0.2673],
                        [3.2143, -1.8640, -2.9678, 6.5165],
                        [-3.9865, 6.5230, 6.3019, -0.4247],
                        [8.3185, -13.5076, 27.0986, -1.6792],
                    ],
                ],
            ]
        )
        x = torch.matmul(x, x)
        y = torch.tensor([[[0.6331]], [[1.6358]], [[-0.3459]], [[1.0196]]])
        self.common(forward, (x, y))

    def test_div_softmax_symfloat(self):
        def forward(x, y):
            z = x.div(y * x.shape[-1])
            return F.softmax(z, dim=-1)

        query = torch.randn(1, 10, 40)
        key = torch.randn(1, 2, 40)
        x = torch.matmul(query, key.transpose(-2, -1))

        cf = torch.compile(forward, dynamic=True)
        cf(x, 1e-5)
        cf(x, 1e-6)

    def test_div_presicion_accuracy(self):
        # fix https://github.com/pytorch/pytorch/issues/157959
        def forward(x, y):
            return (x / y).sum()

        x = torch.rand((5, 5))
        y = 101
        self.common(forward, (x, y))

    def test_mul_softmax_symfloat(self):
        def forward(x, y):
            z = x.mul(y * x.shape[-1])
            return F.softmax(z, dim=-1)

        query = torch.randn(1, 10, 40)
        key = torch.randn(1, 2, 40)
        x = torch.matmul(query, key.transpose(-2, -1))

        cf = torch.compile(forward, dynamic=True)
        cf(x, 1e-5)
        cf(x, 1e-6)

    def test_div_by_zero(self):
        def fn(x, runtime_zero, runtime_neg_zero):
            zero = torch.zeros_like(x)
            return (
                x / 0.0,
                x / -0.0,
                zero / 0.0,
                x / zero,
                x / -zero,
                zero / zero,
                x / runtime_zero,
                x / runtime_neg_zero,
                runtime_zero / runtime_neg_zero,
            )

        a = torch.randn(10)
        zero = torch.zeros(10)
        neg_zero = -zero
        self.common(fn, (a, zero, neg_zero))

    def test_both_scalars(self):
        def fn(a, b):
            return (
                aten.add(a, b),
                aten.add(b, a),
                aten.sub(a, b),
                aten.sub(b, a),
                aten.mul(a, b),
                aten.mul(b, a),
            )

        self.common(fn, (4, 3.3), reference_in_float=False)

    def test_sum_keepdims(self):
        def fn(a, b):
            return (torch.sum(a + b, -1, keepdim=True),)

        self.common(fn, (torch.randn(8, 8), torch.randn(8, 8)))

    @skip_if_cpu
    @skip_if_halide  # only 32-bit indexing
    @largeTensorTest("4GB", inductor=True)
    def test_large_tensor_reduction(self):
        # Test 64-bit indexing works correctly
        def fn(a):
            return torch.max(a)

        t = torch.ones(2**32, dtype=torch.int8, device=self.device)
        t[-1] = 2

        # self.common OOMs here because it copies inputs to check for mutations
        compiled_fn = torch.compile(fn)
        actual = compiled_fn(t)
        expect = torch.tensor(2, dtype=torch.int8, device=self.device)
        self.assertEqual(actual, expect)

    @skip_if_cpu
    @skip_if_gpu_halide  # only 32-bit indexing
    def test_large_broadcast_reduction(self):
        # Test 64-bit indexing works correctly when inputs are less than 32-bit
        # but intermediate tensors require 64-bit indexing
        def fn(a, b):
            return torch.max(a + b)

        t1 = torch.ones(1, 2**16, dtype=torch.int8, device=self.device)
        t2 = torch.ones(2**16, 1, dtype=torch.int8, device=self.device)

        t1[-1, -1] = 2
        t2[-1, -1] = 2

        # self.common OOMs here because it copies inputs to check for mutations
        compiled_fn = torch.compile(fn)
        actual = compiled_fn(t1, t2)
        expect = torch.tensor(4, dtype=torch.int8, device=self.device)
        self.assertEqual(actual, expect)

    @skip_if_halide  # only 32-bit indexing
    @skip_if_pallas  # only 32-bit indexing
    @largeTensorTest("4GB", inductor=True)
    def test_large_pointwise(self):
        def fn(a):
            return a + 1

        t = torch.ones(2**31 + 1, dtype=torch.int8, device=self.device)
        compiled_fn = torch.compile(fn)
        actual = compiled_fn(t)

        # Can't use assertEqual as it expands broadcasted inputs
        del t
        if torch.device(self.device).type == GPU_TYPE:
            getattr(torch, GPU_TYPE).empty_cache()

        self.assertTrue((actual == 2).all())

    @skip_if_halide  # only 32-bit indexing
    @skip_if_pallas  # only 32-bit indexing
    @largeTensorTest("3GB", inductor=True)
    def test_large_offset_pointwise(self):
        # Test 64-bit indexing is used when input views a tensor that can be
        # indexed with 32-bit strides but the storage offset pushes it over
        # INT_MAX
        def fn(a):
            return a + 4

        t = torch.ones(2**31 + 1, dtype=torch.int8, device=self.device)
        t[2**30 :] = 0
        compiled_fn = torch.compile(fn)
        actual = compiled_fn(t[2**30 :])
        self.assertTrue((actual == 4).all())

    @skip_if_halide  # only 32-bit indexing
    @largeTensorTest("2GB", inductor=True)
    def test_large_strided_reduction(self):
        # Test 64-bit indexing is used when input numel is less than INT_MAX
        # but stride calculations go above INT_MAX
        def fn(a):
            return torch.max(a)

        storage = torch.ones(2**31 + 1, dtype=torch.int8, device=self.device)
        view = storage[::32]
        view[-1] = 2

        compiled_fn = torch.compile(fn)
        actual = compiled_fn(view)
        expect = torch.tensor(2, dtype=torch.int8, device=self.device)
        self.assertEqual(actual, expect)

    def test_softmax(self):
        def fn(a, b):
            return (torch.softmax(a + b, -1), torch.softmax(a, 0), torch.softmax(b, 1))

        self.common(fn, (torch.randn(8, 8), torch.randn(8, 8)))

    def test_log_softmax(self):
        def fn(a, b):
            return (F.log_softmax(a + b, -1), F.log_softmax(a, 0), F.log_softmax(b, 1))

        self.common(fn, (torch.randn(8, 8), torch.randn(8, 8)))

    def test_transpose(self):
        def fn(a, b):
            return (
                torch.t(a) + b,
                torch.transpose(b * 2, 0, 1) + 10,
            )

        self.common(fn, (torch.randn(8, 8), torch.randn(8, 8)))

    def test_permute1(self):
        def fn(a):
            return (
                torch.permute(a + 1, [2, 1, 4, 0, 3]) + 2,
                torch.permute(a, [2, 1, 4, 0, 3]) + 2,
            )

        self.common(fn, (torch.randn(2, 2, 2, 2, 2),))

    def test_permute2(self):
        def fn(a):
            a = a.unfold(0, 2, 1)
            a = torch.unsqueeze(a, 1)
            a = torch.permute(a, [0, 2, 3, -3])
            return (a,)

        self.common(fn, (torch.randn(4, 4),))

    def test_expand(self):
        def fn(a):
            return (
                (a + 1).expand(3, 4, 2, 3, 2) + 2,
                a.expand(2, 1, 2, 3, 2) + 2,
            ), a.expand(2, -1, 5, -1)

        self.common(fn, (torch.randn(2, 1, 2),))

    def test_squeeze1(self):
        def fn(a):
            return ((a + 1).squeeze() + 2, a.squeeze() + 2)

        self.common(fn, (torch.randn(1, 2, 1, 2, 2, 1, 1),))

    def test_squeeze2(self):
        def fn(a):
            return ((a + 1).squeeze(-1).squeeze(2) + 2, a.squeeze(0) + 2)

        self.common(fn, (torch.randn(1, 2, 1, 2, 2, 2, 1),))

    def test_squeeze_varargs(self):
        def fn(x):
            return x.squeeze(1, 2).clone()

        a = torch.randn(1024, 1, 1)
        self.common(fn, (a,))

    def test_simplify_loops(self):
        def fn(a, b):
            return a + b

        self.common(
            fn,
            (
                torch.randn(2, 3, 4, 5, 6),
                torch.randn(4, 2, 3, 5, 6).permute(1, 2, 0, 3, 4),
            ),
        )

    def test_unsqueeze(self):
        def fn(a):
            return (
                torch.unsqueeze(a + 1, -1) + 2,
                torch.unsqueeze(a, 2) + 2,
                torch.unsqueeze(a + 1, 0) + 2,
                torch.unsqueeze(a, -2) + 2,
            )

        self.common(
            fn,
            (
                torch.randn(
                    2,
                    2,
                    2,
                    2,
                ),
            ),
        )

    def test_unsqueeze_inplace(self):
        def fn(a):
            tmp1 = a + 1
            aten.unsqueeze_(tmp1, 2)
            tmp2 = aten.unsqueeze_(a + 1, 0) + 2
            return (tmp1, tmp2)

        self.common(
            fn,
            (
                torch.randn(
                    2,
                    2,
                    2,
                    2,
                ),
            ),
        )

    def test_addmm(self):
        def fn(a, b, c):
            return (torch.addmm(a + 1, b + 2, c + 3) + 4,)

        self.common(
            fn,
            (
                torch.randn(8, 8),
                torch.randn(8, 8),
                torch.randn(8, 8),
            ),
        )

    def test_addmv(self):
        def fn(a, b, c):
            return torch.addmv(a, b, c)

        cfn = torch.compile(backend="inductor")(fn)
        input = torch.tensor([2], dtype=torch.int32)
        mat = torch.tensor(np.random.randn(0, 0), dtype=torch.int32)
        vec = torch.tensor([], dtype=torch.int32)
        with torch.no_grad():
            self.assertEqual(cfn(input, mat, vec), fn(input, mat, vec))

    # https://github.com/pytorch/pytorch/issues/98979
    @skipCUDAIf(True, "cuda failed for float64 linear")
    @skipIfXpu(msg="Double and complex datatype matmul is not supported in oneDNN")
    def test_linear_float64(self):
        _dtype = torch.float64
        ctx = (
            contextlib.nullcontext()
            if self.is_dtype_supported(_dtype)
            else self.assertRaises(TypeError)
        )
        with ctx:
            mod = torch.nn.Sequential(torch.nn.Linear(8, 16).to(_dtype)).eval()
            with torch.no_grad():
                self.common(mod, (torch.randn(2, 8).to(_dtype),))

    def test_linear1(self):
        mod = torch.nn.Sequential(
            torch.nn.Linear(8, 16),
            torch.nn.Sigmoid(),
            ToTuple(),
        )
        self.common(mod, (torch.randn(2, 8),))

    def test_linear2(self):
        mod = torch.nn.Sequential(
            torch.nn.Linear(8, 8),
            torch.nn.ReLU(),
            torch.nn.Linear(8, 8),
            torch.nn.ReLU(),
            torch.nn.Linear(8, 8),
            torch.nn.ReLU(),
            torch.nn.Linear(8, 8),
            torch.nn.ReLU(),
        )
        self.common(
            mod,
            (torch.randn(2, 8),),
            atol=1e-3,
            rtol=0.01,
        )

    def test_bmm1(self):
        def fn(a, b):
            return (
                torch.bmm(a, b),
                torch.bmm(a + 1, b + 2) + 3,
            )

        self.common(
            fn,
            (
                torch.randn(2, 8, 8),
                torch.randn(2, 8, 8),
            ),
            check_lowp=False,
        )
        self.common(
            fn,
            (
                torch.randn(1, 16, 8),
                torch.randn(1, 8, 10),
            ),
            check_lowp=False,
        )

    def test_bmm2(self):
        def fn(a, b):
            return torch.bmm(a.permute(0, 2, 1), b)

        self.common(
            fn,
            (
                torch.randn(1, 8, 8),
                torch.randn(1, 8, 8),
            ),
            check_lowp=False,
        )

    @skipIfPy312  # segfaults
    @skipCUDAIf(not SM80OrLater, "Requires sm80")
    def test_mixed_mm(self):
        def fn(a, b):
            return torch.mm(a, b.to(a.dtype))

        self.common(
            fn,
            (
                torch.randn(8, 8),
                torch.randint(-128, 127, (8, 8), dtype=torch.int8),
            ),
            check_lowp=True,
        )

    @skipIfPy312  # segfaults
    @skipCUDAIf(not SM80OrLater, "Requires sm80")
    def test_mixed_mm2(self):
        def fn(a, b, scale, bias):
            return torch.mm(a, b.to(a.dtype)) * scale + bias

        self.common(
            fn,
            (
                torch.randn(8, 8),
                torch.randint(-128, 127, (8, 8), dtype=torch.int8),
                torch.randn(8),
                torch.randn(8),
            ),
            check_lowp=True,
        )

    @skipIfPy312  # segfaults
    @skipCUDAIf(not SM80OrLater, "Requires sm80")
    def test_mixed_mm3(self):
        def fn(a, b):
            return torch.mm(a, b.to(a.dtype))

        # (256, 256) @ (256, 256) so different block sizes are tried out during autotuning
        self.common(
            fn,
            (
                torch.randn(256, 256),
                torch.randint(-128, 127, (256, 256), dtype=torch.int8),
            ),
            rtol=0.01,
            atol=0.1,
        )

    @with_tf32_off
    def test_uint4x2_mixed_mm(self):
        def fn(a, b):
            return torch.mm(
                a,
                torch.cat((b & 0xF, b >> 4), 1)
                .reshape(-1, b.shape[1])
                .to(a.dtype)
                .sub(8),
            )

        self.common(
            fn,
            (
                torch.randn(8, 8),
                torch.randint(0, 255, (4, 8), dtype=torch.uint8),
            ),
            check_lowp=True,
        )

    def test_mm_mixed_dtype(self):
        def fn(a, b):
            return torch.mm(a, b)

        t1 = torch.arange(6, dtype=torch.float, device=self.device).view(2, 3)
        t2 = torch.arange(9, dtype=torch.int64, device=self.device).view(3, 3)

        msg = "expected .* and .* to have the same dtype, but got: .* != .*"
        with self.assertRaisesRegex(RuntimeError, msg):
            fn(t1, t2)
        if config.cpp_wrapper:
            msg = "aoti_torch_.* API call failed at .*"
        with self.assertRaisesRegex(RuntimeError, msg):
            torch.compile(fn)(t1, t2)

    @xfail_if_mps_unimplemented  # linear for non-float inputs
    def test_linear_mixed_dtype(self):
        class Net(nn.Module):
            def __init__(self) -> None:
                super(Net, self).__init__()  # noqa: UP008
                self.fc1 = nn.Linear(3, 3)

            def forward(self, x):
                x = self.fc1(x.permute(1, 2, 0))
                return x

        fn = Net().to(self.device)
        t = torch.arange(27, device=self.device).view(3, 3, 3)

        msg = "expected .* and .* to have the same dtype, but got: .* != .*"
        with self.assertRaisesRegex(RuntimeError, msg):
            fn(t)
        if config.cpp_wrapper:
            msg = "aoti_torch_.* API call failed at .*"
        with self.assertRaisesRegex(RuntimeError, msg):
            with torch.no_grad():
                torch.compile(fn)(t)
        with self.assertRaisesRegex(RuntimeError, "Autograd not support dtype:.*"):
            torch.compile(fn)(t)

    @unittest.skipIf(
        not IS_BIG_GPU, "Skipping triton backend only since not big GPU (not enough SM)"
    )
    @config.patch(
        {
            "max_autotune": True,
            "max_autotune_gemm_backends": "TRITON",
        }
    )
    def test_linear_dynamic_maxautotune(self):
        if self.device == "cpu":
            raise unittest.SkipTest("using triton backend only is not supported on CPU")

        @torch.compile(dynamic=True)
        class Model(torch.nn.Module):
            def __init__(self) -> None:
                super().__init__()
                self.linear = torch.nn.Linear(1, 1)

            def forward(self, x):
                return self.linear(x)

        x = torch.randn(10, 1)
        torch._dynamo.mark_dynamic(x, 0)
        self.common(Model(), (x,))

    def test_scalar_input(self):
        def fn(x, y):
            a = torch.div(x, y, rounding_mode="floor")
            return a

        self.common(fn, [torch.randint(5, (1, 8)), 5400])

    @torch._dynamo.config.patch(dynamic_shapes=True)
    @torch._dynamo.config.patch(assume_static_by_default=False)
    def test_scalar_output(self):
        def fn(arg0_1, arg2_1):
            arg1_1 = arg2_1.size(1)
            view = torch.ops.aten.view.default(arg2_1, [-1, arg1_1])
            embedding = torch.ops.aten.embedding.default(arg0_1, view)
            full = torch.ops.aten.full.default([1, arg1_1], 1, dtype=torch.float32)
            return (full, arg1_1, embedding)

        arg0_1 = rand_strided((32128, 768), (768, 1), device="cpu", dtype=torch.float32)
        arg2_1 = rand_strided((1, 22), (22, 1), device="cpu", dtype=torch.int64)
        self.common(fn, [arg0_1, arg2_1])

    def test_shape_prop_torch_ones(self):
        class Model(torch.nn.Module):
            def forward(self, attention_scores):
                extended_attention_mask = torch.ones(
                    8, 1, 1, 512, device=attention_scores.device
                )
                attention_scores = attention_scores + extended_attention_mask

                return attention_scores

        mod = Model().eval()
        with torch.no_grad():
            self.common(
                mod,
                (torch.randn(8, 12, 512, 512),),
            )

    @slowTest
    @expectedFailureCodegenDynamic
    @config.patch({"freezing": True})
    def test_conv_bn_fuse(self):
        # For gpu path, there is an accuracy issue
        if self.device == GPU_TYPE:
            raise unittest.SkipTest("only support cpu conv bn test")

        # fails dynamic check which bn is fused, and there will not have loops vars.
        input_shapes = {1: (112,), 2: (112, 112), 3: (55, 55, 55)}
        conv_modules = {1: torch.nn.Conv1d, 2: torch.nn.Conv2d, 3: torch.nn.Conv3d}
        bn_modules = {
            1: torch.nn.BatchNorm1d,
            2: torch.nn.BatchNorm2d,
            3: torch.nn.BatchNorm3d,
        }
        options = itertools.product(
            [1, 2, 3],
            [True, False],
            [1, 3],
            [1, 2],
            [1, 4],
        )

        for (
            dim,
            bias,
            kernel_size,
            dilation,
            groups,
        ) in options:
            oC = 32 * groups
            iC = 3 * groups
            x_shape = (1, iC) + input_shapes[dim]
            mod = torch.nn.Sequential(
                conv_modules[dim](
                    iC,
                    oC,
                    kernel_size=kernel_size,
                    dilation=dilation,
                    groups=groups,
                    bias=bias,
                ),
                bn_modules[dim](oC),
            ).eval()
            test_memory_format = [torch.contiguous_format]
            # TODO: GPU path doesn't support channels_last now.
            if not HAS_GPU and dim > 1:
                channels_last = (
                    torch.channels_last if dim == 2 else torch.channels_last_3d
                )
                test_memory_format.append(channels_last)
            for memory_format in test_memory_format:
                v = torch.randn(x_shape, dtype=torch.float32).to(
                    memory_format=memory_format
                )
                with torch.no_grad():
                    self.common(
                        mod,
                        (v,),
                    )

    def test_conv_functional_bn_fuse(self):
        # For gpu path, there is an accuracy issue
        if self.device == GPU_TYPE:
            raise unittest.SkipTest("only support cpu conv bn test")

        # Define a BatchNorm using functional BN.
        class BatchNorm(torch.nn.BatchNorm2d):
            def __init__(
                self,
                num_features,
                eps=1e-5,
                momentum=0.1,
                affine=True,
                track_running_stats=True,
                device=None,
                dtype=None,
            ):
                factory_kwargs = {"device": device, "dtype": dtype}
                super().__init__(
                    num_features,
                    eps=eps,
                    momentum=momentum,
                    affine=affine,
                    track_running_stats=track_running_stats,
                    **factory_kwargs,
                )

            def forward(self, x):
                if self.momentum is None:
                    exponential_average_factor = 0.0
                else:
                    exponential_average_factor = self.momentum

                if self.training and self.track_running_stats:
                    # TODO: if statement only here to tell the jit to skip emitting this when it is None
                    if self.num_batches_tracked is not None:  # type: ignore[has-type]
                        self.num_batches_tracked = self.num_batches_tracked + 1  # type: ignore[has-type]
                        if self.momentum is None:  # use cumulative moving average
                            exponential_average_factor = 1.0 / float(
                                self.num_batches_tracked
                            )
                        else:  # use exponential moving average
                            exponential_average_factor = self.momentum
                if self.training:
                    bn_training = True
                else:
                    bn_training = (self.running_mean is None) and (
                        self.running_var is None
                    )
                x = F.batch_norm(
                    x,
                    # If buffers are not to be tracked, ensure that they won't be updated
                    (
                        self.running_mean
                        if not self.training or self.track_running_stats
                        else None
                    ),
                    (
                        self.running_var
                        if not self.training or self.track_running_stats
                        else None
                    ),
                    self.weight,
                    self.bias,
                    bn_training,
                    exponential_average_factor,
                    self.eps,
                )
                return x

        v = torch.randn(1, 3, 556, 56, dtype=torch.float32)
        mod = torch.nn.Sequential(
            torch.nn.Conv2d(
                3,
                64,
                kernel_size=3,
                dilation=1,
                groups=1,
                bias=True,
            ),
            BatchNorm(64),
        ).eval()
        with torch.no_grad():
            self.common(
                mod,
                (v,),
            )

    @xfail_if_mps  # Expected to find .run(
    def test_conv_inference_heuristics(self):
        if self.device != GPU_TYPE:
            raise unittest.SkipTest(f"{GPU_TYPE} only test")

        in_channels = 6
        out_channels = 6
        kernel_size = 3
        groups = 3

        grouped_conv = nn.Conv2d(
            in_channels, out_channels, kernel_size, groups=groups
        ).to(self.device)

        input_tensor = torch.randn(1, in_channels, 10, 10).to(self.device)

        # Perform the forward pass
        @torch.compile()
        def foo(m, inp):
            return m(inp)

        if self.device != "xpu":
            with torch.no_grad():
                _, code = run_and_get_code(foo, grouped_conv, input_tensor)
                # no to channels last permuting before kernel
                if config.cpp_wrapper:
                    FileCheck().check_not("  call_triton").check("_convolution(").run(
                        code[0]
                    )
                else:
                    FileCheck().check_not(".run(").check(".convolution(").run(code[0])

        # in out should do channels last in inference
        in_channels = 8
        out_channels = 4
        kernel_size = 3

        # Create the convolution layer
        conv_layer = nn.Conv2d(in_channels, out_channels, kernel_size).to(self.device)

        input_tensor = torch.randn(1, in_channels, 10, 10).to(self.device)

        with torch.no_grad():
            _, code = run_and_get_code(foo, conv_layer, input_tensor)
            # should be channels last permuting before kernel
            if is_halide_backend(self.device):
                FileCheck().check("halide_kernel_0(").check(".convolution(").run(
                    code[0]
                )
            else:
                if config.cpp_wrapper:
                    FileCheck().check("call_triton").check("convolution(").run(code[0])
                else:
                    # without cpp_wrapper, the pattern is .convolution( and then .run(
                    FileCheck().check("convolution(").check(".run(").run(code[0])

    def test_upsample_cat_conv(self):
        if self.device == GPU_TYPE:
            raise unittest.SkipTest("only support cpu upsample_cat_conv test")

        class M(torch.nn.Module):
            def __init__(
                self,
                **kwargs,
            ):
                super().__init__()
                self.upsample = torch.nn.UpsamplingNearest2d(scale_factor=2)
                self.conv = torch.nn.Conv2d(
                    8,
                    5,
                    kernel_size=1,
                    padding=0,
                    stride=1,
                    dilation=1,
                    **kwargs,
                )

            def forward(self, x, y):
                x = self.upsample(x)
                z = torch.cat([x, y], dim=1)
                z = self.conv(z)
                return z

        v1 = torch.randn([8, 2, 12, 26])
        v2 = torch.randn([8, 6, 24, 52])

        with torch.no_grad():
            self.common(
                M().eval(),
                (v1, v2),
            )

    def test_aliased_buffer_reuse(self):
        def fn(x, y):
            x = 2 * x
            y = 2 * y
            c = torch.cat([x, y], dim=-1)
            d = 1 + c
            m = torch.mm(d, d)
            return m[:, :2] + x

        self.common(fn, (torch.randn(4, 2), torch.randn(4, 2)), check_lowp=False)

    def test_slice_view_with_graph_break(self):
        def fn():
            a = torch.tensor([1], device=self.device)
            a = a[0:1]
            b = a.squeeze()
            a[0] = 0
            if a[0] < 1e5:
                pass
            a[0] = 2
            return b

        expect = fn()
        opt_fn = torch.compile(fn)
        actual = opt_fn()
        self.assertEqual(expect, actual)

    def test_view_detach(self):
        def fn(a):
            return a[0].detach()

        self.common(
            fn,
            (torch.randn([4, 4], requires_grad=True),),
        )

    def test_gather1(self):
        def fn(a, b):
            return (
                torch.gather(a.expand([4, 5, 10, 6]), 3, b + 1),
                torch.gather(a.expand([4, 5, 10, 6]), -1, b + 1),
            )

        self.common(
            fn,
            (
                torch.randn([1, 1, 10, 6]),
                torch.randint(5, [4, 5, 10, 1], dtype=torch.int64),
            ),
        )

    def test_gather2(self):
        # 0d tensor
        def fn(a, b):
            return torch.gather(a, 0, b) + torch.gather(a, -1, b)

        x = torch.tensor(123)
        y = torch.tensor(0)
        self.assertEqual(fn(x, y), x + x)

    @xfail_if_mps_unimplemented  # Sparse not supported
    def test_gather3(self):
        def fn(a, b):
            return torch.gather(a, 1, b, sparse_grad=True)

        self.common(
            fn,
            (
                torch.randn([4, 5, 10, 6], requires_grad=True),
                torch.randint(5, [4, 5, 10, 1], dtype=torch.int64),
            ),
        )

    def test_device_assert(self):
        def fn(x, y):
            x = torch.sum(x.view(int(x.shape[0] / 6), 6), dim=1)
            return torch.gather(x, 0, torch.trunc(y).to(torch.int64))

        x1 = torch.randn(30, device=self.device)
        x2 = torch.randn(36, device=self.device)
        dtype = highest_precision_float(self.device)
        y = torch.ones(1, dtype=dtype, device=self.device)

        self.assertEqual(torch.compile(fn)(x1, y), fn(x1, y))
        self.assertEqual(torch.compile(fn)(x2, y), fn(x2, y))

    def test_slice1(self):
        def fn(a):
            return (
                a[:, :10, 0] + a[:, 10:, 0],
                (a + 1)[:, :10, 0] + (a + 1)[:, 10:, 0],
                a[:, -30:, 0],  # negative index out of range
                a[:, :-30, 0],  # negative index out of range
            )

        self.common(
            fn,
            (torch.randn([2, 20, 2]),),
        )

    def test_slice2(self):
        def fn(a):
            return (
                a[:-1, ::2, -1] + a[-1:, 1::2, -2],
                (a + 1)[:-1, ::2, -1] + (a + 2)[-1:, 1::2, -2],
            )

        self.common(
            fn,
            (torch.randn([2, 20, 2]),),
        )

    # It's a view so it doesn't generate a kernel
    @expectedFailureCodegenDynamic
    def test_slice3(self):
        def fn(a, b):
            return torch.ops.aten.slice.Tensor(a, 0, 0, -b)

        x = torch.rand(48, 3, 512, 512)
        self.common(fn, (x, 2))

    @expectedFailureCodegenDynamic
    def test_slice4(self):
        # empty slices that require clamping the start or end
        def fn(a):
            return (
                aten.slice.Tensor(a, 0, 2, 0, 1),
                aten.slice.Tensor(a, 0, a.shape[0], a.shape[0] + 10, 1),
                aten.slice.Tensor(a, 0, -20, 0, 1),
                aten.slice.Tensor(a, 0, -20, -16, 1),
            )

        x = torch.rand(10)
        self.common(fn, (x,))

    def test_split_with_list(self):
        def fn(a, sizes):
            return [t + 1.0 for t in torch.split(a * 2.0, sizes, -1)]

        self.common(fn, (torch.randn(2, 2, 10), [3, 3, 4]))
        self.common(fn, (torch.randn(2, 2, 10), [4, 3, 3]))
        self.common(fn, (torch.randn(2, 2, 10), [1, 2, 3, 4]))

    def test_split_with_integer(self):
        # argument `split_size_or_sections` is integer
        @torch.compile(dynamic=True)
        def f(x, sizes):
            return torch.split(x, sizes, -1)

        # split into equally sized chunks, 10 = 5 + 5
        r1, r2 = f(torch.randn(2, 10), 5)
        self.assertTrue(r1.size() == (2, 5))
        self.assertTrue(r2.size() == (2, 5))

        # split into equally sized chunks, 12 = 4 + 4 + 4
        r1, r2, r3 = f(torch.randn(2, 12), 4)
        self.assertTrue(r1.size() == (2, 4))
        self.assertTrue(r2.size() == (2, 4))
        self.assertTrue(r3.size() == (2, 4))

        # split unevenly, 10 = 3 + 3 + 3 + 1
        r1, r2, r3, r4 = f(torch.randn(2, 10), 3)
        self.assertTrue(r1.size() == (2, 3))
        self.assertTrue(r2.size() == (2, 3))
        self.assertTrue(r3.size() == (2, 3))
        self.assertTrue(r4.size() == (2, 1))

    def test_split_failed(self):
        @torch.compile(backend="inductor")
        def fn(a):
            return torch.split(a, [2, 1, 1], dim=1)

        with self.assertRaisesRegex(RuntimeError, ""):
            fn(torch.randn(1, 5))

    def test_inductor_assert(self):
        @torch.compile(backend="inductor", dynamic=True)
        def fn(a):
            assert a.shape[0] >= 2 and a.shape[1] >= 4  # noqa: S101
            return a.cos()

        inp = torch.randn(2, 4, 6)
        torch._dynamo.mark_dynamic(inp, 0)
        torch._dynamo.mark_dynamic(inp, 1)
        self.assertEqual(fn(inp), inp.cos())

    def test_split(self):
        def fn(a):
            t = torch.split(a, 3, -1)
            return (t[0], t[1], t[2], t[3])

        def fn2(a):
            return fn(a + 1)

        self.common(
            fn,
            (torch.randn([2, 2, 10]),),
        )

        self.common(
            fn2,
            (torch.randn([2, 2, 10]),),
        )

    def test_unsafe_chunk_empty_tensor(self):
        def fn(x, chunks):
            return torch.unsafe_chunk(x, chunks=chunks, dim=0)

        x = torch.zeros(0)
        result = torch.compile(fn)(x, 4)
        self.assertEqual(len(result), 4)
        for chunk in result:
            self.assertEqual(chunk.shape, torch.Size([0]))

        self.common(fn, (torch.zeros(0), 4))
        self.common(fn, (torch.zeros(0), 2))
        self.common(fn, (torch.zeros(0, dtype=torch.float32), 3))

    @parametrize("dilation", (1, 2))
    @parametrize("dim", (subtest(2), subtest(3)))
    @parametrize(
        "use_block_ptr",
        [subtest(False), subtest(True, decorators=[skip_if_not_triton])],
    )
    def test_low_memory_max_pool(self, dilation: int, dim: int, use_block_ptr: bool):
        prims = torch.ops.prims

        def fn(x):
            kernel_size = [3, 3] if dim == 2 else [3, 3, 2]
            stride = [2] * dim
            padding = [1] * dim
            ceil_mode = False

            vals, offsets = prims._low_memory_max_pool_with_offsets(
                x,
                kernel_size,
                stride,
                padding,
                [dilation] * dim,
                ceil_mode,
            )
            indices = prims._low_memory_max_pool_offsets_to_indices(
                offsets,
                kernel_size,
                x.shape[-dim:],
                stride,
                padding,
                dilation=[dilation] * dim,
            )
            return vals, indices, offsets

        with config.patch({"triton.use_block_ptr": use_block_ptr}):
            self.common(fn, (torch.randn(1, 3, *[10] * dim),))

    @xfail_if_mps  # aten::full with zero-sized dim triggers AcceleratorError on MPS
    def test_max_unpool_empty_output(self):
        class Unpool1d(nn.Module):
            def __init__(self):
                super().__init__()
                self.unpool = nn.MaxUnpool1d(kernel_size=2, stride=2, padding=1)

            def forward(self, x, indices):
                return self.unpool(x, indices)

        x1d = torch.randn(1, 1, 1, device=self.device)
        self.common(Unpool1d().to(self.device), (x1d, x1d.long()))

        class Unpool2d(nn.Module):
            def __init__(self):
                super().__init__()
                self.unpool = nn.MaxUnpool2d(kernel_size=2, stride=2, padding=1)

            def forward(self, x, indices):
                return self.unpool(x, indices)

        x2d = torch.randn(1, 1, 1, 1, device=self.device)
        self.common(Unpool2d().to(self.device), (x2d, x2d.long()))

        class Unpool3d(nn.Module):
            def __init__(self):
                super().__init__()
                self.unpool = nn.MaxUnpool3d(kernel_size=2, stride=2, padding=1)

            def forward(self, x, indices):
                return self.unpool(x, indices)

        x3d = torch.randn(1, 1, 1, 1, 1, device=self.device)
        self.common(Unpool3d().to(self.device), (x3d, x3d.long()))

    def test_to_dtype(self):
        new_dtype = torch.float64 if self.device != "mps" else torch.bfloat16

        def fn(a, b):
            return (
                aten._to_copy(a, dtype=6),
                aten._to_copy(b + 1, dtype=6),
                aten.to(b, new_dtype),
                aten.to(b, torch.bool),
            )

        self.common(
            fn,
            (
                torch.randn([2, 2, 10]),
                torch.randn([2, 2, 10], dtype=new_dtype),
            ),
        )

    @requires_gpu()
    def test_to_device(self):
        def fn(a):
            if a.device.type == "cpu":
                return aten._to_copy(
                    a, device=torch.device(GPU_TYPE), dtype=6, layout=0
                )
            else:
                return aten._to_copy(a, device=torch.device("cpu"), dtype=6, layout=0)

        self.common(
            fn,
            (torch.randn([2, 2, 10]),),
        )

    def test_to_memory_format(self):
        def fn(a, memory_format):
            return a.to(memory_format=memory_format)

        self.common(
            fn,
            (torch.randn([2, 2, 10, 10]), torch.channels_last),
        )
        self.common(
            fn,
            (
                torch.randn([2, 2, 10, 10]).to(memory_format=torch.channels_last),
                torch.contiguous_format,
            ),
        )

    @requires_gpu()
    def test_to_device_constant(self):
        def fn(a):
            d1 = a.device.type
            if d1 == "cpu":
                d2 = GPU_TYPE
            else:
                d2 = "cpu"

            const1 = torch.as_tensor(list(range(64)), device=d2)
            return (
                torch.arange(10, device=d2).to(d1) + a,
                const1.to(d1),
                (const1 + 1).to(d1),
            )

        self.common(
            fn,
            (torch.randn([10]),),
        )

    @requires_gpu()
    @xfail_if_triton_cpu
    def test_multi_device(self):
        def fn(x):
            x = x + 1
            x = x + 2
            x = x.to(device=GPU_TYPE)
            x = x + 3
            x = x + 4
            x = x.cpu()
            x = x + 5
            x = x + 6
            x = x.to(device=GPU_TYPE)
            x = x + 7
            x = x + 8
            x = x.cpu()
            x = x + 9
            x = x + 10
            return x

        self.common(
            fn,
            (torch.randn([2, 2, 10]),),
            check_lowp=False,  # cpu doesn't understand fp16, and there are explicit .cpu() calls
        )

    @requires_multigpu()
    def test_multi_gpu_device(self):
        # TODO: https://github.com/pytorch/pytorch/issues/92627
        x = torch.rand([4], device=GPU_TYPE)

        def fn(x, y):
            r = torch.ops.aten.div(x, y)
            r = r.to(f"{GPU_TYPE}:1")
            return 2 * r

        self.common(fn, (torch.randn(4), torch.randn(4)), check_lowp=False)

    @requires_multigpu()
    def test_multi_gpu_recompile_on_index(self):
        torch.set_float32_matmul_precision("high")

        def gemm(x, y):
            return x @ y

        failed_guard = None

        def fail(guard):
            nonlocal failed_guard
            failed_guard = guard

        gemm_opt = torch._dynamo.optimize("inductor", guard_fail_fn=fail)(gemm)

        x0 = torch.randn(1024, 1024, device=f"{GPU_TYPE}:0")
        y0 = torch.randn(1024, 1024, device=f"{GPU_TYPE}:0")

        gemm_opt(x0, y0)

        x1 = torch.randn(1024, 1024, device=f"{GPU_TYPE}:1")
        y1 = torch.randn(1024, 1024, device=f"{GPU_TYPE}:1")

        gemm_opt(x1, y1)
        self.assertTrue(failed_guard is not None)
        self.assertTrue(
            "tensor 'x' Tensor device index mismatch. Expected device index to be"
            in failed_guard.reason
        )

    def test_unbind(self):
        def fn(a):
            return torch.unbind(a), torch.unbind(a, -1)

        self.common(
            fn,
            (torch.randn([4, 4, 4]),),
        )

    def test_conv1d_with_permute(self):
        # fix https://github.com/pytorch/pytorch/issues/159462
        class ConvModel(nn.Module):
            def __init__(self):
                super().__init__()
                self.conv = nn.Conv1d(1, 64, kernel_size=3, padding=1)

            def forward(self, x):
                x = x.permute(0, 2, 1)
                return self.conv(x)

        self.common(ConvModel(), (torch.randn([32, 100, 1]),), check_lowp=False)

    def test_conv1d_depthwise(self):
        class ConvModel(nn.Module):
            def __init__(self):
                super().__init__()
                self.conv = nn.Conv1d(
                    768,
                    768,
                    kernel_size=(9,),
                    stride=(1,),
                    padding=(4,),
                    groups=768,
                    bias=False,
                )

            def forward(self, x):
                return self.conv(x)

        input_tensor = torch.randn([1, 768, 512]).as_strided(
            (1, 768, 512), (393216, 1, 768)
        )
        self.common(ConvModel(), (input_tensor,), check_lowp=False)

    def test_convolution1(self):
        m = torch.nn.Sequential(
            torch.nn.Conv2d(5, 6, [3, 3]),
            torch.nn.ReLU(),
            ToTuple(),
        )

        self.common(
            m,
            (torch.randn([2, 5, 16, 16]),),
            # Mismatched elements: 10 / 2352 (0.4%)
            # Greatest absolute difference: 5.7220458984375e-05 at index (0, 3, 12, 12) (up to 1e-05 allowed)
            # Greatest relative difference: 0.06512477175897748 at index (0, 4, 11, 9) (up to 0.001 allowed)
            atol=6e-5,
            rtol=0.001,
            # Make sure we compute also with fp16 in the reference. Otherwise,
            # the reference will compute with fp32 and cast back to fp16, which
            # causes numeric differences beyond tolerance.
            reference_in_float=not torch.version.hip,
        )

    def test_convolution2(self):
        def fn(x, w, b):
            # transposed conv
            return (aten.convolution(x, w, b, [4], [0], [1], True, [0], 1),)

        self.common(
            fn,
            (
                torch.randn([2, 32, 90]),
                torch.randn([32, 16, 8]),
                torch.randn([16]),
            ),
            check_lowp=False,
        )

    def test_convolution3(self):
        # Test stride or padding or dilation is 1 element list.
        m = torch.nn.Sequential(
            torch.nn.Conv2d(5, 6, [3, 3], stride=[1], padding=[0], dilation=[1]),
            torch.nn.ReLU(),
            ToTuple(),
        )

        self.common(
            m,
            (torch.randn([2, 5, 16, 16]),),
            atol=6e-5,
            rtol=0.001,
            # Make sure we compute also with fp16 in the reference. Otherwise,
            # the reference will compute with fp32 and cast back to fp16, which
            # causes numeric differences beyond tolerance.
            reference_in_float=not torch.version.hip,
        )

    @skip_if_gpu_halide
    def test_convolution4(self):
        def fn(x, w):
            x = F.conv2d(x, w, groups=w.shape[0])
            return x.sum()

        self.common(
            fn,
            (
                torch.randn([2, 3, 16, 20]),
                torch.randn([3, 1, 5, 5]),
            ),
        )

    def test_convolution5(self):
        def fn(x, w):
            x = F.conv2d(x, w, dilation=[x.size(0)])
            return x.sum()

        x = torch.randn([2, 1, 16, 20])
        w = torch.randn([1, 1, 5, 5])
        # X get specialized ! dilation can't be dynamic.
        torch._dynamo.maybe_mark_dynamic(x, 0)

        atol = None
        rtol = None
        if self.device == "xpu":
            # set to float32 default tolerance,
            # check_model_gpu with update rotl to 2e-3 for fp16.
            # fix issue #129974
            atol = 1e-05
            rtol = 1.3e-06
        self.common(fn, (x, w), atol=atol, rtol=rtol)

    def test_conv3d(self):
        m = torch.nn.Sequential(
            torch.nn.Conv3d(3, 3, kernel_size=7),
            ToTuple(),
        )

        self.common(
            m,
            (torch.randn([1, 3, 8, 16, 32]),),
            atol=1e-3,
            rtol=0.001,
            # Make sure we compute also with fp16 in the reference. Otherwise,
            # the reference will compute with fp32 and cast back to fp16, which
            # causes numeric differences beyond tolerance.
            reference_in_float=not torch.version.hip,
        )

    def test_conv2d_channels_last(self):
        if self.device == GPU_TYPE:
            raise unittest.SkipTest("only support cpu conv2d channels_last")

        m = torch.nn.Sequential(
            torch.nn.Conv2d(3, 3, 1, 1),
            ToTuple(),
        )
        # only weight is channels_last
        self.common(
            m.to(memory_format=torch.channels_last),
            (torch.randn([2, 3, 16, 16]),),
            check_lowp=False,
        )
        # only activation is channels_last
        self.common(
            m,
            (torch.randn([2, 3, 16, 16]).to(memory_format=torch.channels_last),),
            check_lowp=False,
        )
        # activation and weight are all channels_last
        self.common(
            m.to(memory_format=torch.channels_last),
            (torch.randn([2, 3, 16, 16]).to(memory_format=torch.channels_last),),
            check_lowp=False,
        )

    def test_conv2d_backward_channels_last(self):
        def fn(grad_output, inp, weight):
            convolution_backward_8 = torch.ops.aten.convolution_backward.default(
                grad_output,
                inp,
                weight,
                [320],
                [1, 1],
                [0, 0],
                [1, 1],
                False,
                [0, 0],
                1,
                [True, True, True],
            )
            return convolution_backward_8

        # only weight is channels_last
        self.common(
            fn,
            (
                torch.randn([2, 320, 8, 8]),
                torch.randn([2, 2048, 8, 8]),
                torch.randn([320, 2048, 1, 1]).to(memory_format=torch.channels_last),
            ),
            check_lowp=False,
        )

    @parametrize(
        "use_block_ptr",
        [subtest(False), subtest(True, decorators=[skip_if_not_triton])],
    )
    def test_conv3d_channels_last(self, use_block_ptr: bool):
        if self.device == GPU_TYPE:
            raise unittest.SkipTest("only support cpu conv3d channels_last")

        m = torch.nn.Sequential(
            torch.nn.Conv3d(3, 3, 1, 1),
            ToTuple(),
        )
        with config.patch({"triton.use_block_ptr": use_block_ptr}):
            # only weight is channels_last
            self.common(
                m.to(memory_format=torch.channels_last_3d),
                (torch.randn([2, 3, 16, 16, 16]),),
            )
            # only activation is channels_last
            self.common(
                m,
                (
                    torch.randn([2, 3, 16, 16, 16]).to(
                        memory_format=torch.channels_last_3d
                    ),
                ),
            )
            # activation and weight are all channels_last
            self.common(
                m.to(memory_format=torch.channels_last_3d),
                (
                    torch.randn([2, 3, 16, 16, 16]).to(
                        memory_format=torch.channels_last_3d
                    ),
                ),
            )

    @skip_if_gpu_halide  # slow
    @xfail_if_mps  # Non-divisible input sizes are not implemented on MPS device
    @parametrize("combo_kernels", (False, True))
    def test_adaptive_avg_pool2d1(self, combo_kernels):
        with config.patch(combo_kernels=combo_kernels):

            def fn(x):
                return aten._adaptive_avg_pool2d(x, (6, 6)), aten._adaptive_avg_pool2d(
                    x + 1, (2, 5)
                )

            self.common(
                fn,
                (torch.randn(2, 4, 16, 16),),
                check_lowp=False,
            )

            # lowering to avg_pool2d case
            self.common(
                fn,
                (torch.randn(2, 4, 3, 3),),
            )

            # no-op case
            self.common(
                fn,
                (torch.randn(2, 4, 6, 6),),
            )

    @xfail_if_mps  # Non-divisible input sizes are not implemented on MPS device
    def test_adaptive_avg_pool2d2(self):
        # Big kernel size, use fallback
        def fn(x):
            return aten._adaptive_avg_pool2d(x, (4, 4))

        torch._inductor.metrics.generated_kernel_count = 0
        self.common(
            fn,
            (torch.randn(2, 4, 21, 21),),
            check_lowp=False,
        )
        assertGeneratedKernelCountEqual(self, 0)

    @xfail_if_mps
    @skip_if_gpu_halide  # slow
    def test_adaptive_max_pool2d1(self):
        def fn(x):
            return aten.adaptive_max_pool2d(x, (6, 6))

        self.common(
            fn,
            (torch.randn(2, 4, 16, 16),),
            check_lowp=False,
        )

        self.common(
            fn,
            (torch.randn(2, 4, 3, 3),),
        )

        # no-op case
        self.common(
            fn,
            (torch.randn(2, 4, 6, 6),),
        )

    @skip_if_gpu_halide  # slow
    def test_adaptive_max_pool2d2(self):
        # Big kernel size, use fallback
        def fn(x):
            return aten.adaptive_max_pool2d(x, (4, 4))

        torch._inductor.metrics.generated_kernel_count = 0
        self.common(
            fn,
            (torch.randn(2, 4, 21, 21),),
            check_lowp=False,
        )
        assertGeneratedKernelCountEqual(self, 0)

    @skip_if_gpu_halide  # slow
    def test_adaptive_max_pool2d3(self):
        # test when adaptive_max_pool2d fallbacks to max_pool2d
        def fn(x):
            return aten.adaptive_max_pool2d(x, (2, 2))

        # Big kernel (12 / 2 * 12 / 2 > 25)
        self.common(
            fn,
            (torch.randn(2, 4, 12, 12),),
        )

        # Small kernel
        self.common(
            fn,
            (torch.randn(2, 4, 4, 4),),
        )

    @xfail_if_mps_unimplemented
    def test_fractional_max_pool2d1(self):
        def fn(x, samples):
            return aten.fractional_max_pool2d(x, (3, 3), (2, 2), samples)

        self.common(
            fn, (torch.randn(1, 4, 16, 16), torch.rand(1, 4, 2)), check_lowp=False
        )

    @xfail_if_mps_unimplemented
    def test_fractional_max_pool2d2(self):
        # large kernel size without unrolling

        def fn(x, samples):
            return aten.fractional_max_pool2d(x, (6, 5), (3, 3), samples)

        self.common(
            fn,
            (torch.randn(2, 4, 36, 36), torch.rand(2, 4, 2)),
            check_lowp=False,
        )

    @xfail_if_mps_unimplemented
    def test_fractional_max_pool2d3(self):
        def fn(x, samples):
            return aten.fractional_max_pool2d(x, (1, 1), (16, 16), samples)

        self.common(
            fn, (torch.randn(2, 4, 16, 16), torch.rand(2, 4, 2)), check_lowp=False
        )

    @xfail_if_mps_unimplemented
    @config.patch(fallback_random=True)
    @skip_if_halide  # Can only unroll for loops over a constant extent
    def test_fractional_max_pool2d4(self):
        random.seed(1234)
        torch.manual_seed(1234)

        # check rectangular kernel/output size

        def fn(x):
            return torch.nn.functional.fractional_max_pool2d_with_indices(
                x, (4, 3), (3, 2)
            )

        self.common(fn, (torch.randn(1, 4, 16, 16),), check_lowp=False)

    @xfail_if_mps_unimplemented
    def test_fractional_max_pool2d5(self):
        def fn(x, samples):
            return aten.fractional_max_pool2d(x, (3, 3), (1, 1), samples)

        self.common(
            fn, (torch.randn(2, 4, 6, 6), torch.rand(2, 4, 2)), check_lowp=False
        )

    def test_multi_threading(self):
        model = torch.nn.Linear(2, 3).eval()
        inp = torch.randn(4, 2)

        num_run = 3

        def run_weights_sharing_model(m, inp):
            with torch.no_grad():
                for _ in range(num_run):
                    y = m(inp)

        numb_instance = 2
        threads = []
        compiled_m = torch.compile(model)
        for _ in range(1, numb_instance + 1):
            ctx = contextvars.copy_context()
            thread = threading.Thread(
                target=ctx.run, args=(run_weights_sharing_model, compiled_m, inp)
            )
            threads.append(thread)
            thread.start()
        for thread in threads:
            thread.join()

    @skip_if_triton_cpu("Flaky on Triton CPU")
    @skip_if_gpu_halide  # https://github.com/halide/Halide/issues/8311
    def test_adaptive_avg_pool2d_low_prec(self):
        class Model(torch.nn.Module):
            def __init__(self) -> None:
                super().__init__()
                self.avgpool = torch.nn.AdaptiveAvgPool2d((1, 1))

            def forward(self, x):
                x = self.avgpool(x)
                return x

        mod = Model().to(self.device)
        for dtype in [torch.half, torch.bfloat16]:
            # Skip bfloat16 on MacOS-13 for MPS tests
            if not self.is_dtype_supported(dtype):
                continue
            x = torch.randn(4, 3, 7, 7, device=self.device).to(dtype=dtype)
            opt_mod = torch.compile(mod)
            res = opt_mod(x)
            expected = mod(x)
            self.assertTrue(torch.allclose(res, expected))

    def test_buffer_copied_in_graph(self):
        class MyModel(torch.nn.Module):
            def __init__(self) -> None:
                super().__init__()
                self.buf = torch.nn.Buffer(torch.zeros(1))
                self.w1 = torch.nn.Parameter(torch.zeros(1))
                self.w2 = torch.nn.Parameter(torch.zeros(1))

            def forward(self, x):
                self.buf.add_(1)
                return (self.w1 * x * self.w2).sum() + self.buf.sum()

        model_for_eager = MyModel().to(self.device)
        model_for_compile = copy.deepcopy(model_for_eager)

        eager_version_counters = [
            buffer._version for _, buffer in model_for_eager.named_buffers()
        ]
        compile_version_counters = [
            buffer._version for _, buffer in model_for_compile.named_buffers()
        ]

        compiled_f = torch.compile(model_for_compile, backend="inductor")

        inp_ref = torch.ones(1, requires_grad=True, device=self.device)
        inp_test = torch.ones(1, requires_grad=True, device=self.device)

        out_ref = model_for_eager(inp_ref.clone())
        out_test = compiled_f(inp_test.clone())

        eager_version_counters_after = [
            buffer._version for _, buffer in model_for_eager.named_buffers()
        ]
        compile_version_counters_after = [
            buffer._version for _, buffer in model_for_compile.named_buffers()
        ]

        eager_delta = list(
            map(operator.sub, eager_version_counters_after, eager_version_counters)
        )
        compile_delta = list(
            map(operator.sub, compile_version_counters_after, compile_version_counters)
        )

        self.assertEqual(eager_delta, compile_delta)

    @skip_if_gpu_halide
    def test_buffer_copied_in_graph_with_different_shapes(self):
        class MyModel(torch.nn.Module):
            def __init__(self) -> None:
                super().__init__()
                self.buf = torch.nn.Buffer(torch.ones(4, 4))
                self.w = torch.nn.Parameter(
                    torch.Tensor([[4, 5], [1, 2], [6, 7], [8, 9]])
                )

            def forward(self, x):
                self.buf.add_(1)
                return (self.w @ x).sum() + self.buf.sum()

        model_for_eager = MyModel().to(self.device)
        model_for_compile = copy.deepcopy(model_for_eager)

        eager_version_counters = [
            buffer._version for _, buffer in model_for_eager.named_buffers()
        ]
        compile_version_counters = [
            buffer._version for _, buffer in model_for_compile.named_buffers()
        ]

        compiled_f = torch.compile(model_for_compile, backend="inductor")

        inp_ref = torch.ones(2, 4, requires_grad=True, device=self.device)
        inp_test = torch.ones(2, 4, requires_grad=True, device=self.device)

        out_ref = model_for_eager(inp_ref.clone())
        out_test = compiled_f(inp_test.clone())

        eager_version_counters_after = [
            buffer._version for _, buffer in model_for_eager.named_buffers()
        ]
        compile_version_counters_after = [
            buffer._version for _, buffer in model_for_compile.named_buffers()
        ]

        eager_delta = list(
            map(operator.sub, eager_version_counters_after, eager_version_counters)
        )
        compile_delta = list(
            map(operator.sub, compile_version_counters_after, compile_version_counters)
        )

        self.assertEqual(eager_delta, compile_delta)

    def test_buffer_batch_norm(self):
        class MyModel(torch.nn.Module):
            def __init__(self) -> None:
                super().__init__()
                self.m = torch.nn.BatchNorm1d(100)

            def forward(self, x):
                return self.m(x)

        model_for_eager = MyModel().to(self.device)
        model_for_compile = copy.deepcopy(model_for_eager)

        eager_version_counters = [
            buffer._version for _, buffer in model_for_eager.named_buffers()
        ]
        compile_version_counters = [
            buffer._version for _, buffer in model_for_compile.named_buffers()
        ]

        compiled_f = torch.compile(model_for_compile, backend="inductor")

        inp_ref = torch.ones(20, 100, requires_grad=True, device=self.device)
        inp_test = torch.ones(20, 100, requires_grad=True, device=self.device)

        out_ref = model_for_eager(inp_ref.clone())
        out_test = compiled_f(inp_test.clone())

        eager_version_counters_after = [
            # TODO: remove the + 1 after https://github.com/pytorch/pytorch/issues/120622 is fixed
            (
                buffer._version + 1
                if k in ["m.running_mean", "m.running_var"]
                else buffer._version
            )
            for k, buffer in model_for_eager.named_buffers()
        ]

        compile_version_counters_after = [
            buffer._version for _, buffer in model_for_compile.named_buffers()
        ]

        eager_delta = list(
            map(operator.sub, eager_version_counters_after, eager_version_counters)
        )
        compile_delta = list(
            map(operator.sub, compile_version_counters_after, compile_version_counters)
        )

        self.assertEqual(eager_delta, compile_delta)

    @xfail_if_mps  # Non-divisible input sizes are not implemented on MPS device
    def test_adaptive_avg_pool_with_output_size_0(self):
        m1 = nn.AdaptiveAvgPool1d(0)
        self.common(m1, (torch.randn(1, 2),))
        m2 = nn.AdaptiveAvgPool2d(0)
        self.common(m2, (torch.randn(1, 2, 3),))

    def test_max_pool2d1(self):
        def fn(x):
            return aten.max_pool2d_with_indices(x, [3, 3], [2, 2])

        self.common(
            fn,
            (torch.randn(2, 4, 16, 16),),
        )

    @skip_if_gpu_halide  # slow
    def test_max_pool2d2(self):
        def fn(x):
            return aten.max_pool2d_with_indices(x, [3, 3], [2, 2])

        self.common(
            fn,
            (torch.randn([16, 64, 55, 55]),),
        )

    @skip_if_gpu_halide  # slow
    def test_max_pool2d3(self):
        def fn(x):
            # with padding
            return (
                aten.max_pool2d_with_indices(x, [3, 3], [2, 2], [1, 1]),
                aten.max_pool2d_with_indices(
                    x,
                    [
                        3,
                    ],
                    [
                        2,
                    ],
                    [
                        1,
                    ],
                ),
            )

        self.common(
            fn,
            (-torch.arange(1 * 8 * 8, dtype=torch.float32).view(1, 1, 8, 8),),
        )

    @skip_if_halide  # Can only unroll for loops over a constant extent
    def test_max_pool2d4(self):
        def fn(x):
            # with padding
            return aten.max_pool2d_with_indices(x, [3, 3], [2, 2], [0, 0], [1, 1], True)

        self.common(
            fn,
            (torch.randn([2, 8, 111, 111]),),
        )

    @skip_if_gpu_halide  # slow
    def test_max_pool2d5(self):
        def fn(x):
            return aten.max_pool2d_with_indices(x, [3, 3], [])

        self.common(
            fn,
            (torch.randn([16, 64, 55, 55]),),
        )

    @skip_if_gpu_halide  # slow
    @parametrize("dilation", (1, 2))
    def test_max_pool2d6(self, dilation: int):
        # Big kernel size
        def fn(x):
            return aten.max_pool2d_with_indices(
                x, [13, 13], [], dilation=[dilation] * 2
            )

        self.common(
            fn,
            (torch.randn([16, 64, 55, 55]),),
        )

    # From https://github.com/pytorch/pytorch/issues/94775
    def test_max_pool2d7(self):
        # ceil mode turns on
        def fn(x):
            return torch.nn.functional.max_pool2d(
                x, 1, stride=(2, 2), padding=0, ceil_mode=True
            )

        self.common(
            fn,
            (torch.randn([1, 1, 6, 7]),),
        )

    # From https://github.com/pytorch/pytorch/issues/93384
    def test_max_pool2d8(self):
        # dilation is not 1
        def fn(x):
            return aten.max_pool2d_with_indices(x, [3, 2], [2, 1], [1, 1], [1, 2])

        self.common(
            fn,
            (torch.randn([2, 2, 3, 6]),),
        )

    def test_avg_pool2d1(self):
        def fn(x):
            return aten.avg_pool2d(x, [3, 3], [2, 2])

        self.common(
            fn,
            (torch.randn(2, 4, 16, 16),),
        )

    def test_avg_pool2d2(self):
        def fn(x):
            return aten.avg_pool2d(x, [3, 3], [2, 2])

        self.common(
            fn,
            (torch.randn([16, 64, 55, 55]),),
        )

    def test_avg_pool2d3(self):
        def fn(x):
            return (
                aten.avg_pool2d(x, [3, 3], [2, 2], [1, 1]),
                aten.avg_pool2d(
                    x,
                    [
                        3,
                    ],
                    [
                        2,
                    ],
                    [
                        1,
                    ],
                ),
            )

        self.common(
            fn,
            (-torch.arange(1 * 8 * 8, dtype=torch.float32).view(1, 1, 8, 8),),
            check_lowp=not is_halide_backend(self.device),  # misaligned addr fp16
        )

    def test_avg_pool2d4(self):
        def fn(x):
            return aten.avg_pool2d(x, [3, 3], [2, 2], [0, 0], True)

        self.common(
            fn,
            (torch.randn([2, 8, 111, 111]),),
        )

    def test_avg_pool2d5(self):
        def fn(x):
            return aten.avg_pool2d(x, [3, 3], [2, 2], [1, 1], count_include_pad=False)

        self.common(
            fn,
            (-torch.arange(1 * 8 * 8, dtype=torch.float32).view(1, 1, 8, 8),),
            check_lowp=not is_halide_backend(self.device),  # misaligned addr fp16
        )

    def test_avg_pool2d6(self):
        def fn(x):
            return aten.avg_pool2d(x, [3, 3], [2, 2], [1, 1], divisor_override=3)

        self.common(
            fn,
            (-torch.arange(1 * 8 * 8, dtype=torch.float32).view(1, 1, 8, 8),),
            check_lowp=not is_halide_backend(self.device),  # misaligned addr fp16
        )

    def test_avg_pool2d7(self):
        # Large kernel size with stride != size
        def fn(x):
            return aten.avg_pool2d(x, [13, 13], [1, 1], [0, 0])

        torch._inductor.metrics.generated_kernel_count = 0
        self.common(
            fn,
            (-torch.arange(1 * 24 * 24, dtype=torch.float32).view(1, 1, 24, 24),),
        )
        assertGeneratedKernelCountEqual(self, 0)

    def test_avg_pool2d8(self):
        # https://github.com/pytorch/pytorch/issues/100987
        def fn(x):
            return aten.avg_pool2d(
                x, kernel_size=3, stride=2, padding=1, ceil_mode=True
            )

        self.common(
            fn,
            (torch.randn(1, 3, 6, 6),),
            check_lowp=not is_halide_backend(self.device),  # misaligned addr fp16
        )

    @tf32_on_and_off(0.006)
    @skip_if_gpu_halide  # slow
    def test_alexnet_prefix(self):
        def forward(arg6, arg7, arg16):
            convolution = torch.ops.aten.convolution(
                arg16, arg7, arg6, [4, 4], [2, 2], [1, 1], False, [0, 0], 1
            )
            relu = torch.ops.aten.relu(convolution)
            max_pool2d_with_indices = torch.ops.aten.max_pool2d_with_indices(
                relu, [3, 3], [2, 2]
            )
            getitem = max_pool2d_with_indices[0]
            return (getitem,)

        self.common(
            forward,
            (
                rand_strided((64,), (1,), torch.float32, "cpu"),
                rand_strided((64, 3, 11, 11), (363, 121, 11, 1), torch.float32, "cpu"),
                rand_strided(
                    (16, 3, 224, 224), (150528, 50176, 224, 1), torch.float32, "cpu"
                ),
            ),
            # Mismatched elements: 127 / 746496 (0.0%)
            # Greatest absolute difference: 0.0009765625 at index (1, 62, 7, 16) (up to 1e-05 allowed)
            # Greatest relative difference: 0.05187467899332306 at index (14, 18, 11, 0) (up to 0.001 allowed)
            atol=3e-3,
            rtol=2,
        )

    def test_elu(self):
        def fn(x):
            return aten.elu(x, 1.6732632423543772, 1.0507009873554805) + 2, aten.elu(
                x + 1, 2, 3, 4
            )

        self.common(
            fn,
            (torch.randn([16, 16]),),
            rtol=1e-4,
            atol=1e-4,
        )

    def test_tan(self):
        def fn(x):
            return aten.tan(x) + 2, aten.tan(x + 1)

        self.common(
            fn,
            (torch.randn([16, 16]),),
        )

    def test_tanh(self):
        def fn(x):
            return aten.tanh(x) + 2, aten.tanh(x + 1)

        self.common(
            fn,
            (torch.randn([16, 16]),),
        )

    @skip_if_halide  # lgamma not implemented
    @xfail_if_triton_cpu
    def test_lgamma(self):
        def fn(x):
            return aten.lgamma(x) + 2, aten.cos(x + 1)

        self.common(
            fn,
            (torch.randn([16, 16]),),
        )

    def test_cos(self):
        def fn(x):
            return aten.cos(x) + 2, aten.cos(x + 1)

        self.common(
            fn,
            (torch.randn([16, 16]),),
        )

    def test_sin(self):
        def fn(x):
            return aten.sin(x) + 2, aten.sin(x + 1)

        self.common(
            fn,
            (torch.randn([16, 16]),),
        )

    def test_repeat(self):
        def fn(x):
            return (
                x.repeat(0, 1, 1, 1),
                x.repeat(2, 2, 3, 1),
                x.repeat(8, 1, 1, 1),
                x.repeat(2, 1, 1, 1, 1, 1),
            )

        self.common(
            fn,
            (torch.randn([1, 2, 4, 8]),),
        )

    def test_repeat_as_strided(self):
        # Reproducer for #127474

        def fn(x):
            view_size = (3, 2)
            full = x.repeat((3, 2))
            view = torch.as_strided(full, view_size, full.stride())
            result = view + view

            return result

        self.common(fn, (torch.randn(1, 1),))

    def test_as_strided_on_views(self):
        # https://github.com/pytorch/pytorch/issues/163286
        def fn(a):
            c = a.view(-1)
            # convert to float16
            d = c.view(torch.float16)
            e = d.as_strided((2, 5), (1, 1))
            # convert back to bfloat16
            f = e.view(torch.bfloat16)
            g = f.as_strided((10, 10), (1, 1))
            return g

        a = torch.randn(10, 10, dtype=torch.bfloat16)
        self.common(fn, (a,), reference_in_float=False)

        # test dtype separately
        out = fn(a)
        if out.dtype != torch.bfloat16:
            raise AssertionError(f"Expected dtype torch.bfloat16, got {out.dtype}")

        out = torch.compile(fn)(a)
        if out.dtype != torch.bfloat16:
            raise AssertionError(f"Expected dtype torch.bfloat16, got {out.dtype}")

    def test_repeat_interleave(self):
        def fn(x):
            return (
                x.repeat_interleave(2),
                x.repeat_interleave(3, dim=0),
                x.repeat_interleave(x.size(1), dim=1),
            )

        self.common(
            fn,
            (torch.randn([1, 2, 4, 8]),),
        )

    @config.patch(implicit_fallbacks=True)
    def test_repeat_interleave_2(self):
        def fn(x):
            return torch.ops.aten.repeat_interleave.Tensor(x, output_size=12)

        self.common(
            fn,
            (torch.tensor([2, 4, 6]),),
        )

    @config.patch(fallback_random=True)
    def test_randn_with_dtype_and_device(self):
        if self.device == GPU_TYPE:
            raise unittest.SkipTest("only support cpu randn_with_dtype_and_device test")

        def fn(vectors):
            rotations_shape = (12, vectors.shape[-1], 1, 64)
            random_rotations = torch.randn(
                rotations_shape, device=vectors.device, dtype=vectors.dtype
            )
            random_rotations += 1
            return random_rotations

        self.common(
            fn,
            (torch.randn([4, 12, 2, 64]),),
        )

    def test_embedding(self):
        m = torch.nn.Sequential(
            torch.nn.Embedding(10, 4, padding_idx=0),
            torch.nn.ReLU(),
            ToTuple(),
        )

        self.common(
            m,
            (torch.randint(10, [2, 8]),),
        )

    def test_embedding_sparse(self):
        # Fix https://github.com/pytorch/pytorch/issues/150656
        def fn(weight, indices):
            return F.embedding(indices, weight, sparse=True)

        indices = torch.randint(10, (2, 3))
        weight = torch.randn(10, 3, requires_grad=True)

        self.common(
            fn,
            (weight, indices),
        )

    @torch._inductor.config.patch("combo_kernels", True)
    def test_mean(self):
        def fn(x):
            return (
                x.mean(),
                x.mean(-1),
                torch.mean(x, -2, keepdim=True),
                x.mean([0, 1]),
            )

        self.common(
            fn,
            (torch.randn([1, 2, 4, 8]),),
        )

    @parametrize("tile_reduction", (False, True))
    def test_var_mean(self, tile_reduction: bool):
        def fn(x):
            return (
                *torch.var_mean(x, -1),
                *torch.var_mean(x, [1, 3]),
            )

        with config.patch(
            {
                "triton.prefer_nd_tiling": tile_reduction,
                "triton.tile_reductions": tile_reduction,
                "combo_kernels": True,
            }
        ):
            self.common(
                fn,
                (torch.randn([1, 2, 4, 8]),),
            )

    def test_var_mean_div_by(self):
        def fn(x):
            var, mean = torch.var_mean(x, dim=2, keepdim=True)
            return x / var, var, mean

        self.common(fn, (torch.rand([1, 17, 2048]),))

    def test_var_correction(self):
        def fn(x):
            dim = -1
            return (
                torch.var(x, dim=dim, correction=1.3),
                torch.var(x, dim=dim, correction=3),
                torch.var(x, dim=dim, correction=10),
            )

        self.common(fn, (torch.randn([2, 8]),))
        # Unrolled reduction
        self.common(fn, (torch.randn([2, 4]),))

    @config.patch(pick_loop_orders=True)
    def test_transposed_propagates(self):
        @torch.compile(backend="inductor", fullgraph=True)
        def fn(x, y):
            return x + y

        a = torch.randn(1, 4, 4, 4, device=self.device).permute(0, 2, 3, 1)
        b = torch.randn(4, 4, 4, device=self.device).permute(1, 2, 0)
        c = fn(a, b)
        self.assertEqual(a.stride(), c.stride())
        self.assertEqual(c.stride()[2], 1)

    @skip_if_gpu_halide
    def test_std(self):
        def fn(x):
            return (
                torch.var(x, True),
                torch.var(x, False),
                torch.var(x, -1, True),
                torch.var(x, -1, False),
                torch.std(x, False),
                torch.std(x, [0, 1], True),
                torch.std(x, [0, 1], False),
                torch.std(x, -2, True, keepdim=True),
            )

        self.common(
            fn,
            (torch.randn([2, 4, 4, 8]),),
        )

    def test_embedding_bag(self):
        def fn(w, i, o):
            return aten._embedding_bag(w, i, o, False, 0, False, None)

        self.common(
            fn,
            (torch.randn([10, 4]), torch.randint(10, [8]), torch.tensor([0, 2, 6])),
        )

    def test_batch_norm_2d(self):
        m = torch.nn.Sequential(
            torch.nn.BatchNorm2d(10),
            torch.nn.ReLU(),
        )
        m.eval()
        self.common(m, (torch.randn([2, 10, 8, 8]),), check_lowp=False)
        self.common(
            m,
            (torch.randn([3, 10, 16, 16]),),
            check_lowp=False,  # too painful to match types of bn model
        )

    # From yolov3
    @with_tf32_off
    def test_batch_norm_2d_2(self):
        if self.device == "cpu":
            raise unittest.SkipTest(f"requires {GPU_TYPE}")

        class Repro(torch.nn.Module):
            def __init__(self) -> None:
                super().__init__()
                self.self_0 = torch.nn.Conv2d(
                    64,
                    128,
                    kernel_size=(3, 3),
                    stride=(2, 2),
                    padding=(1, 1),
                    bias=False,
                )
                self.self_1 = torch.nn.BatchNorm2d(
                    128,
                    eps=0.0001,
                    momentum=0.03,
                    affine=True,
                    track_running_stats=True,
                )
                self.self_2 = torch.nn.LeakyReLU(negative_slope=0.1, inplace=True)

            def forward(self, l_input_: torch.Tensor):
                self_0 = self.self_0(l_input_)
                self_1 = self.self_1(self_0)
                self_2 = self.self_2(self_1)
                return (self_2,)

        inp = torch.randn((4, 64, 192, 256), dtype=torch.float32, device=GPU_TYPE)
        mod = Repro().to(device=GPU_TYPE)
        o1 = mod(inp)
        o2 = torch.compile(mod)(inp)
        self.assertEqual(o1, o2, rtol=1e-3, atol=1e-3)

    @patch.object(config.trace, "enabled", True)
    def test_layer_norm(self):
        m = torch.nn.Sequential(
            torch.nn.LayerNorm(32),
            torch.nn.ReLU(),
        )
        m.eval()
        with torch.no_grad():
            self.common(m, (torch.randn([16, 32]),), check_lowp=False)
        if self.device != "cpu":
            assertGeneratedKernelCountEqual(self, 1)

    def test_layer_norm_rejects_complex_inputs(self):
        if self.device not in ("cpu", "cuda"):
            raise unittest.SkipTest("Only validated on CPU/CUDA")

        m = torch.nn.LayerNorm(10).to(self.device)
        x = torch.randn(1, 1, 10, device=self.device, dtype=torch.complex64)

        with self.assertRaises(RuntimeError):
            m(x)

        with self.assertRaises(RuntimeError) as compiled_error:
            torch.compile(m)(x)

        self.assertIn(
            "native_layer_norm does not support complex inputs",
            str(compiled_error.exception),
        )

    @torch._functorch.config.patch("donated_buffer", True)
    def test_matmul_layer_norm(self):
        batch_size = 32
        seq_length = 50
        hidden_size = 256

        inp = torch.randn(
            batch_size,
            seq_length,
            hidden_size,
            requires_grad=True,
            device=self.device,
        )
        weight = torch.randn(
            hidden_size, hidden_size, requires_grad=True, device=self.device
        )

        layer_norm = torch.nn.LayerNorm(hidden_size, device=self.device)

        def foo(inp, weight):
            matmul_output = inp @ weight
            final_output = layer_norm(matmul_output)
            return final_output

        self.common(foo, (inp, weight), check_lowp=False)

    def test_transpose_add(self):
        def fn(a, b):
            return a.t() + b

        self.common(
            fn, (torch.randn([16, 32]), torch.randn([32, 16])), check_lowp=False
        )
        if self.device != "cpu":
            assertGeneratedKernelCountEqual(self, 1)

    @patch.object(config.triton, "persistent_reductions", True)
    def test_softmax_one_kernel_persist(self):
        def fn(x):
            dim = 1
            x_max = torch.amax(x, dim, keepdim=True)
            unnormalized = torch.exp(x - x_max)
            result = unnormalized / torch.sum(unnormalized, dim, keepdim=True)
            return result

        self.common(fn, (torch.randn([16, 32]),), check_lowp=False)
        if self.device != "cpu":
            assertGeneratedKernelCountEqual(self, 1)

    @patch.object(config.triton, "persistent_reductions", False)
    def test_softmax_one_kernel_loop(self):
        def fn(x):
            x_max = torch.amax(x, 1, keepdim=True)
            unnormalized = torch.exp(x - x_max)
            result = unnormalized / torch.sum(unnormalized, 1, keepdim=True)
            return result

        self.common(fn, (torch.randn([16, 32]),), check_lowp=False)
        if self.device != "cpu":
            assertGeneratedKernelCountEqual(self, 1)

    def test_complex_fallback(self):
        def fn(x):
            return x * x + 10

        self.common(
            fn,
            (torch.randn([1, 2, 4, 8]).to(dtype=torch.complex64),),
        )
        assertGeneratedKernelCountEqual(self, 0)

        class ToComplex(nn.Module):
            def forward(self, x):
                return (x + x + 12).to(torch.complex64)

        self.common(ToComplex(), (torch.rand([1, 2, 4, 8]),), check_lowp=False)

        if self.device != "cpu":
            assertGeneratedKernelCountEqual(self, 1)

    def test_complex_zero_dim_scalar(self):
        # Test that 0-d complex tensors can be compiled without crashing.
        # This exercises a fix in constant folding where view.dtype on 0-d
        # complex tensors would fail because view ops pass through unchanged.
        def fn():
            out = torch.ops.aten.scalar_tensor(
                s=2.5,
                dtype=torch.complex64,
                layout=torch.strided,
                device=self.device,
                pin_memory=False,
            )
            out = out + out
            return out

        expected = fn()
        compiled = torch.compile(fn, backend="inductor")
        actual = compiled()
        self.assertEqual(actual, expected)

    def test_view_dtype_0d_smaller_to_larger_element_size(self):
        # Test that constant folding handles view.dtype on 0-d tensors when
        # the input element size is SMALLER than the output dtype itemsize.
        # This is the opposite case of test_complex_zero_dim_scalar which
        # tests element_size > itemsize (complex64 -> float32).
        # This test exercises element_size < itemsize (float32 -> complex64).
        import torch.fx as fx
        from torch._inductor.fx_passes.joint_graph import UniformValueConstantFolder

        graph = fx.Graph()

        # scalar_tensor creates a 0-d float32 tensor (4 bytes per element)
        scalar = graph.call_function(
            torch.ops.aten.scalar_tensor.default,
            args=(2.5,),
            kwargs={
                "dtype": torch.float32,
                "layout": torch.strided,
                "device": self.device,
                "pin_memory": False,
            },
        )
        scalar.meta["val"] = torch.tensor(2.5, dtype=torch.float32, device=self.device)

        # view.dtype to complex64 (8 bytes): element_size (4) < itemsize (8)
        view_node = graph.call_function(
            torch.ops.aten.view.dtype, args=(scalar, torch.complex64)
        )
        view_node.meta["val"] = torch.tensor(
            2.5 + 0j, dtype=torch.complex64, device=self.device
        )

        graph.output(view_node)
        gm = fx.GraphModule(torch.nn.Module(), graph)

        # Running constant folder should not crash (it should return
        # unknown_value since view.dtype fails on 0-d tensors when
        # element sizes differ)
        folder = UniformValueConstantFolder(gm)
        folder.run()

        # The view node should NOT be constant-folded
        self.assertNotIn(view_node, folder.node_replacements)

    def test_complex_1d_constant_tensor(self):
        # Test that 1-d complex constant tensors can be compiled without crashing.
        # This exercises view.dtype on non-0-d tensors during constant folding
        # where element sizes differ (complex64 -> float32 or vice versa).
        def fn():
            out = torch.full([2], 2.5 + 1.0j, dtype=torch.complex64, device=self.device)
            out = out + out
            return out

        expected = fn()
        compiled = torch.compile(fn, backend="inductor")
        actual = compiled()
        self.assertEqual(actual, expected)

    def test_complex_uniform_constant_folding(self):
        # Fix https://github.com/pytorch/pytorch/issues/174891
        # view.dtype with mismatched element sizes changes element count,
        # so constant folding must not treat the result as uniform.
        def fn(x):
            mask = torch.ones(2, 1, dtype=torch.complex64, device=self.device)
            return x + mask

        x = torch.full((2, 2), 1.0, dtype=torch.complex64, device=self.device)
        expected = fn(x)
        compiled = torch.compile(fn, backend="inductor")
        actual = compiled(x)
        self.assertEqual(actual, expected)

    def test_view_dtype_non_0d_larger_to_smaller_element_size(self):
        # Non-0-d counterpart of test_view_dtype_0d_smaller_to_larger_element_size.
        # element_size (8) > itemsize (4): complex64 -> float32.
        import torch.fx as fx
        from torch._inductor.fx_passes.joint_graph import UniformValueConstantFolder

        graph = fx.Graph()

        full_node = graph.call_function(
            torch.ops.aten.full.default,
            args=([2], 1 + 0j),
            kwargs={
                "dtype": torch.complex64,
                "layout": torch.strided,
                "device": self.device,
                "pin_memory": False,
            },
        )
        full_node.meta["val"] = torch.full(
            [2], 1 + 0j, dtype=torch.complex64, device=self.device
        )

        view_node = graph.call_function(
            torch.ops.aten.view.dtype, args=(full_node, torch.float32)
        )
        view_node.meta["val"] = torch.full(
            [2], 1 + 0j, dtype=torch.complex64, device=self.device
        ).view(torch.float32)

        graph.output(view_node)
        gm = fx.GraphModule(torch.nn.Module(), graph)

        folder = UniformValueConstantFolder(gm)
        folder.run()

        self.assertNotIn(view_node, folder.node_replacements)

    def test_view_dtype_non_0d_smaller_to_larger_element_size(self):
        # Non-0-d counterpart of test_view_dtype_0d_smaller_to_larger_element_size.
        # element_size (4) < itemsize (8): float32 -> complex64.
        import torch.fx as fx
        from torch._inductor.fx_passes.joint_graph import UniformValueConstantFolder

        graph = fx.Graph()

        full_node = graph.call_function(
            torch.ops.aten.full.default,
            args=([2], 1.0),
            kwargs={
                "dtype": torch.float32,
                "layout": torch.strided,
                "device": self.device,
                "pin_memory": False,
            },
        )
        full_node.meta["val"] = torch.full(
            [2], 1.0, dtype=torch.float32, device=self.device
        )

        view_node = graph.call_function(
            torch.ops.aten.view.dtype, args=(full_node, torch.complex64)
        )
        view_node.meta["val"] = torch.full(
            [2], 1.0, dtype=torch.float32, device=self.device
        ).view(torch.complex64)

        graph.output(view_node)
        gm = fx.GraphModule(torch.nn.Module(), graph)

        folder = UniformValueConstantFolder(gm)
        folder.run()

        self.assertNotIn(view_node, folder.node_replacements)

    def test_uniform(self):
        def fn(x):
            return aten.uniform.default(x, 0, 1)

        a = torch.randn([5, 3]).permute(1, 0)

        self.common(
            fn,
            (a,),
            exact_stride=True,
            reference_in_float=False,
        )

    def test_complex_from_real_imag(self):
        def fn(x, y):
            return aten.complex.default(x, y)

        a = torch.randn([5, 3]).permute(1, 0)

        self.common(
            fn,
            (a, a),
            exact_stride=True,
            reference_in_float=False,
        )

    @unittest.skipIf(
        TEST_WITH_ROCM and not torch.cuda.has_magma,
        "ROCm hipsolver backend does not currently support eig",
    )
    @xfail_if_mps_unimplemented  # aten::linalg_eig not implemented for MPS
    def test_linalg_eig_stride_consistency(self):
        def fn(x):
            eigenvals, eigenvecs = torch.linalg.eig(x)
            return eigenvecs

        x = torch.randn(5, 5, device=self.device, dtype=torch.float32)

        self.common(
            fn,
            [x],
            exact_stride=True,
            exact_dtype=True,
            check_lowp=False,
        )

    def test_view_as_complex(self):
        class Repro(torch.nn.Module):
            def __init__(self) -> None:
                super().__init__()

            def forward(self, view_2):
                clone = torch.ops.aten.clone.default(
                    view_2, memory_format=torch.contiguous_format
                )
                view_2 = None
                view_as_complex = torch.ops.aten.view_as_complex.default(clone)
                clone = None
                return (view_as_complex,)

        inp = torch.empty_strided((128, 64, 12, 32, 2), (1, 98304, 8192, 256, 128)).to(
            self.device
        )
        mod = Repro()

        o1 = mod(inp)
        o2 = torch.compile(mod)(inp)

        self.assertEqual(o1, o2)

    def test_view_as_real(self):
        def fn(x):
            y = torch.view_as_real(x)
            return y + 1

        x = torch.randn(4, dtype=torch.complex64)

        self.common(fn, (x,))

    @xfail_if_mps
    def test_complex_real_imag_conj(self):
        # Regression test for https://github.com/pytorch/pytorch/issues/171665
        # Tests that extracting real/imag from conjugated tensors works when compiled.
        # The issue was that clone operations from resolve_conj() were incorrectly
        # removed by remove_noop_ops because same_meta didn't check is_conj().
        def fn(x):
            x = x.conj()
            return x.real + x.imag

        x = torch.randn(4, dtype=torch.complex64)

        self.common(fn, (x,))

    def test_complex_conv2d_conj(self):
        # Regression test for https://github.com/pytorch/pytorch/issues/171665
        # Tests that complex convolution works on conjugated inputs when compiled.

        class Model(torch.nn.Module):
            def __init__(self):
                super().__init__()
                self.conv = torch.nn.Conv2d(1, 1, 2, dtype=torch.complex64)

            def forward(self, x):
                x = x.conj()
                x = x.view(2, 1, 2, 2)
                return self.conv(x)

        model = Model().to(self.device)
        x = torch.randn(2, 4, dtype=torch.complex64, device=self.device)

        self.common(model, (x,), check_lowp=False)

    def test_polar(self):
        def fn(dist, angle):
            return torch.polar(dist, angle)

        dtype = highest_precision_float(self.device)
        inp = (
            torch.tensor([1, 2], dtype=dtype),
            torch.tensor([np.pi / 2, 5 * np.pi / 4], dtype=dtype),
        )
        self.common(fn, (*inp,), reference_in_float=self.device != "mps")

    @skip_if_gpu_halide  # incorrect result on CUDA
    def test_cauchy(self):
        def fn(x, y):
            return torch.sum(1 / (torch.unsqueeze(x, -1) - y))

        self.common(
            fn,
            (
                torch.randn(32),
                torch.randn(32),
            ),
            # Absolute difference: 0.0003662109375 (up to 0.0001 allowed)
            # Relative difference: 1.8804297408767818e-05 (up to 1e-05 allowed)
            atol=5 * 1e-4,
            rtol=5 * 1e-5,
            check_lowp=False,
        )
        if self.device != "cpu":
            assertGeneratedKernelCountEqual(self, 1)

    @skip_if_gpu_halide  # misaligned address error
    def test_fusing_write_into_disjoint_read(self):
        def test_flip(a):
            return a.copy_(torch.flip(a, (0,)))

        self.common(test_flip, (torch.rand([20]),))

        assertGeneratedKernelCountEqual(self, 2)

        # issue only manifests on cuda with large tensors
        if self.device != "cpu":

            def f(a):
                a[:, 20:40] = a[:, 20:40] + 1
                a[:, 2:900025] = a[:, 1:900024] + 2

            a = torch.rand((1, 1000000), device=self.device)
            self.common(f, (a,))

    def test_inplace_flip(self):
        def f(x, y):
            x.copy_(x.flip(1))
            y = y.sum(dim=1, keepdim=True) + y
            return x + y

        x = torch.randn(20, 1024 * 1024)
        y = torch.randn(20, 1024 * 1024)
        self.common(f, (x, y), atol=1e-3, rtol=1e-3)

    def test_gather_scatter(self):
        def fn(node_feat, edge_index):
            src_node_feat = node_feat[edge_index[0]]
            dst_node_feat = node_feat[edge_index[1]]
            edge_feat = src_node_feat - dst_node_feat + 1
            new_node_feat = torch.zeros_like(node_feat)
            new_node_feat.scatter_add_(
                0, edge_index[1].unsqueeze(-1).expand_as(edge_feat), edge_feat
            )
            return new_node_feat

        num_nodes = 16
        num_features = 32
        node_feat = torch.randn(num_nodes, num_features)
        edge_index = torch.randint(0, num_nodes, size=(2, num_nodes * 5))
        self.common(
            fn,
            (
                node_feat,
                edge_index,
            ),
            check_lowp=False,
        )
        if self.device != "cpu":
            assertGeneratedKernelCountEqual(self, 2)

    @config.patch(max_fusion_size=1)
    def test_no_mega_fusion_during_lowering(self):
        n = 50

        def fn(*args):
            x = args[0]
            for i in range(n):
                x = torch.add(x, args[i])
            return x

        self.common(
            fn,
            [torch.randn(64) for _ in range(n)],
            check_lowp=False,
        )
        print("-->", torch._inductor.metrics.generated_kernel_count)
        if self.device != "cpu":
            self.assertTrue(torch._inductor.metrics.generated_kernel_count > 1)

    def test_move_arange(self):
        def fn(x):
            return torch.arange(len(x), device="cpu").to(x.device) + x

        self.common(fn, (torch.randn([32]),), check_lowp=False)
        # if we have a copy there will be more than 1 kernel
        assertGeneratedKernelCountEqual(self, 1)

    def test_leaky_relu(self):
        def fn(x):
            return aten.leaky_relu(x, 0.2) + 2, aten.leaky_relu(x + 1)

        self.common(
            fn,
            (torch.randn([16, 16]),),
        )

    def test_gelu(self):
        def fn(x):
            return aten.gelu(x) + 2, aten.gelu(x + 1)

        self.common(
            fn,
            (torch.randn([16, 16]),),
        )

    def test_clone(self):
        def fn(x):
            return aten.clone(x) + 2, aten.clone(x + 1)

        self.common(
            fn,
            (torch.randn([16, 16]),),
        )

    def test_masked_fill(self):
        def fn(mask, value):
            return aten.masked_fill(value, mask, -10000.0) + 2, aten.masked_fill(
                value / 2.0, torch.logical_not(mask), 667
            )

        self.common(
            fn,
            (
                torch.randint(0, 1, [1, 16], dtype=torch.bool),
                torch.randn([16, 16]),
            ),
        )

    def test_masked_fill_promotion(self):
        def fn(mask, value):
            return aten.masked_fill(value, mask, torch.tensor(3.5))

        opt_fn = torch.compile(fn, backend="inductor")
        for inp in (
            torch.randn(
                [16, 16],
                dtype=torch.float16 if self.device == GPU_TYPE else torch.float32,
                device=self.device,
            ),
            torch.randint(16, (16, 16), device=self.device),
        ):
            inputs = (
                torch.randint(0, 1, [1, 16], dtype=torch.bool, device=self.device),
                inp,
            )
            self.assertEqual(fn(*inputs), opt_fn(*inputs))

    def test_masked_scatter(self):
        def fn(value, mask, source):
            return torch.masked_scatter(value, mask, source)

        value = make_tensor(10, 10, dtype=torch.float32, device=self.device)
        mask = make_tensor(10, 10, dtype=torch.bool, device=self.device)
        source = make_tensor(
            mask.count_nonzero(), dtype=torch.float32, device=self.device
        )

        self.common(fn, (value, mask, source))

    def test_fill1(self):
        def fn(x):
            tmp = torch.ones_like(x)
            return tmp, aten.fill.Scalar(tmp, 2)

        self.common(
            fn,
            (torch.randn([16, 16]),),
        )

    def test_fill2(self):
        def fn(x):
            tmp = torch.ones_like(x)
            return tmp, aten.fill.Tensor(tmp, torch.tensor(3.0))

        self.common(
            fn,
            (torch.randn([16, 16]),),
        )

    def test_pow1(self):
        def fn(x):
            return [aten.pow(x, e) for e in range(-8, 9)]

        self.common(
            fn,
            (torch.randn([16, 16]),),
        )

    @xfail_if_triton_cpu
    def test_pow2(self):
        def fn(x):
            return aten.pow(1000, x), aten.pow(x, 1000)

        self.common(
            fn,
            (
                torch.randn(
                    [16, 16],
                    dtype=torch.float32,
                ),
            ),
            # Mismatched elements: 9 / 256 (3.5%)
            # Greatest absolute difference: 2.491354329061828e+28 at index (6, 6) (up to 1e-05 allowed)
            # Greatest relative difference: 2.9793410720160818e-05 at index (4, 5) (up to 1.3e-06 allowed)
            atol=1e-5,
            rtol=3e-05,
        )

    @skip_if_gpu_halide  # https://github.com/halide/Halide/issues/8318
    @config.patch("halide.scheduler_cuda", "Li2018")
    def test_pow3(self):
        # power of 0.5 is special-cased, arbitrary power would still produce triton codegen error
        def fn(x):
            z = torch.tensor(0.123, device=self.device)
            w = z + x
            return torch.pow(w, 0.5)

        opt = torch.compile(fn, backend="inductor")
        input = torch.rand((), device=self.device)
        self.assertTrue(same(opt(input), fn(input)))

    def test_pow_int(self):
        def fn(x, y):
            return torch.pow(x, 0x57), torch.pow(x, y)

        for dtype in (torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64):
            intmax = torch.iinfo(dtype).max
            make_arg = functools.partial(
                make_tensor, dtype=dtype, device=self.device, requires_grad=False
            )
            self.common(
                fn,
                (
                    make_arg(16, 16),
                    make_arg(16, 16, high=intmax),
                ),
            )

    @xfail_if_triton_cpu
    def test_pow_symfloat(self):
        def fn(x):
            r = math.sqrt(x.size(0))
            r = r**10
            return x * r

        cfn = torch.compile(fullgraph=True, dynamic=True)(fn)
        x = torch.randn([16, 16], device=self.device)
        self.assertEqual(cfn(x), fn(x))

    def test_glu(self):
        def fn(x):
            return aten.glu(x, -1), aten.glu(x, 1), aten.glu(x, 2)

        self.common(
            fn,
            (torch.randn([8, 16, 8, 8]),),
        )

    def test_unsigned_constant_tensors(self):
        def fn(x):
            c = torch.tensor(7, dtype=torch.uint8)
            return c + x, torch.neg(c), torch.neg(c) + x

        self.common(
            fn,
            (torch.randn([16, 16]),),
        )

    # Disable size_asserts for this test due to https://github.com/pytorch/pytorch/issues/145963
    @config.patch(size_asserts=os.environ.get("TORCHINDUCTOR_SIZE_ASSERTS") == "1")
    @torch._dynamo.config.patch(capture_dynamic_output_shape_ops=True)
    def test_nonzero_unbacked_refinement(self):
        def fn(x):
            z = x.nonzero()
            torch._check(z.size(0) == 4)
            return z + 3

        self.common(
            fn,
            (torch.tensor([0, 1, 3, 4, 2, 0, 0]),),
        )

        with self.assertRaises(RuntimeError):
            torch.compile(fn)(torch.tensor([0, 0, 0, 0]))

    @torch._dynamo.config.patch(capture_scalar_outputs=True)
    def test_unbacked_floordiv_simplify(self):
        def fn(x, y):
            z = y.item()
            torch._check(z // 2 == 3)
            return x + x.new_ones(z)

        self.common(
            fn,
            (
                torch.randn(6),
                torch.tensor([6]),
            ),
        )

        self.common(
            fn,
            (
                torch.randn(7),
                torch.tensor([7]),
            ),
        )

    @torch._dynamo.config.patch(capture_scalar_outputs=True)
    def test_unbacked_floordiv_simplify_errors(self):
        def fn(x, y):
            z = y.item()
            torch._check(z // 2 == 3)
            return x + x.new_zeros(z)

        # This is a little suboptimal: we actually fail /in the compiler/ but
        # not in a way that causes Dynamo to graph break
        with self.assertRaises(RuntimeError):
            torch.compile(fn)(torch.randn(8), torch.tensor(8))

    def test_cat(self):
        tgt_dtype = torch.double if self.device != "mps" else torch.half

        def fn(a):
            tmp = a * 2
            return (
                torch.cat((a, a[:, :4] + 1, a + 2), -1),
                torch.cat((tmp, tmp), 0),
                torch.cat((tmp, tmp.to(dtype=tgt_dtype)), 0),
            )

        self.common(
            fn,
            (torch.randn([8, 16]),),
        )
        self.common(
            fn,
            (torch.randn([1, 3, 3, 16]).to(memory_format=torch.channels_last),),
        )

    def test_cat_uint8(self):
        def fn(x):
            batch_shape = x.shape[:1]
            out = torch.cat([x.new_zeros(1).expand(batch_shape + (1,)), x], dim=-1)
            return out

        self.common(
            fn,
            (torch.randint(0, 256, size=(3, 255), dtype=torch.uint8),),
        )

    def test_cat_empty(self):
        def fn_2(*tensors):
            return torch.cat(tensors)

        self.common(
            fn_2,
            (
                torch.randn([1, 3, 3, 16]),
                torch.ones([0]),
            ),
        )
        self.common(
            fn_2,
            (
                torch.randn([1, 3, 3, 16]),
                torch.ones([0]),
                torch.randn([1, 3, 3, 16]),
            ),
        )
        self.common(
            fn_2,
            (
                torch.ones([0]),
                torch.randn([1, 3, 3, 16]),
            ),
        )

    def test_cat_empty_index(self):
        def fn(out, x):
            return torch.cat([out[0], x], dim=0)

        self.common(fn, (torch.randn(1, 0, 64), torch.randn(128, 64)))

    @torch._dynamo.config.patch(capture_scalar_outputs=True)
    def test_cat_unbacked_legacy_empty(self):
        def fn(x, y):
            z = y.item()
            return torch.cat([x, x.new_ones(z)])

        with self.assertRaisesRegex(
            RuntimeError,
            "Expected 2-D tensors, but got 1-D for tensor number 1 in the list",
        ):
            self.common(
                fn,
                (
                    torch.randn([2, 3]),
                    torch.tensor([0]),
                ),
            )

    @torch._dynamo.config.patch(capture_scalar_outputs=True)
    def test_cat_unbacked_empty_1d(self):
        def fn(x, y):
            z = y.item()
            return torch.cat([x, x.new_ones(z)])

        self.common(
            fn,
            (
                torch.randn([2]),
                torch.tensor([0]),
            ),
        )

        self.common(
            fn,
            (
                torch.randn([2]),
                torch.tensor([3]),
            ),
        )

    @torch._dynamo.config.patch(capture_scalar_outputs=True)
    def test_cat_unbacked_2d(self):
        def fn(x, y):
            z = y.item()
            return torch.cat([x, x.new_ones(z, x.shape[1])])

        self.common(
            fn,
            (
                torch.randn([2, 3]),
                torch.tensor([0]),
            ),
        )

        self.common(
            fn,
            (
                torch.randn([2, 3]),
                torch.tensor([4]),
            ),
        )

    def test_cat_negative_dim(self):
        def fn(*tensors):
            return torch.cat(tensors, dim=-1)

        self.common(
            fn,
            (
                torch.randn([2, 3]),
                torch.randn([2, 4]),
            ),
        )

        self.common(
            fn,
            (
                torch.randn([2, 3]),
                torch.randn([0]),
                torch.randn([2, 4]),
            ),
        )

        self.common(
            fn,
            (
                torch.randn([0]),
                torch.randn([2, 3]),
                torch.randn([2, 4]),
            ),
        )

    @expectedFailureCodegenDynamic
    def test_cat_single_empty(self):
        # fails dynamic check for 'has a dynamic dimension'
        def fn_2(*tensors):
            return torch.cat(tensors)

        self.common(
            fn_2,
            (torch.ones([0]),),
        )

    def test_cat_upcasting(self):
        def fn(arg4_1, slice_7):
            cat_1 = aten.cat.default([arg4_1, slice_7], 1)
            return (cat_1,)

        self.common(
            fn,
            (
                torch.randn([8, 16], dtype=torch.float32),
                torch.randn([8, 20], dtype=torch.float16),
            ),
        )

    def test_cat_extern_kernel(self):
        def fn(x1, x2, x3, x4):
            x = torch.mm(x2, x3)
            s = torch.narrow(x, 1, 0, 100)
            x = torch.mm(s, x4)
            c = torch.cat((x, x1), 1)
            return (c,)

        if self.device == "xpu":
            atol = 3e-4
            rtol = 1e-4
        else:
            atol = 5e-4
            rtol = 3e-4

        # MPS has correctness problem before MacOS15
        with (
            contextlib.nullcontext()
            if self.device != "mps" or MACOS_VERSION >= 15.0
            else self.assertRaises(AssertionError)
        ):
            self.common(
                fn,
                (
                    torch.randn(256, 256),
                    torch.randn(256, 1024),
                    torch.randn(1024, 1600),
                    torch.randn(100, 256),
                ),
                atol=atol,
                rtol=rtol,
                check_lowp=False,  # accuracy issues with relatively large matmuls
            )

    @skip_if_gpu_halide
    # Constant folding was explicitly turned off due to issue #108388
    # Turn it back on for test
    @torch._inductor.config.patch(
        {
            "joint_graph_constant_folding": True,
            # Numerical accuracy failure for triton fp16
            "max_autotune_gemm_backends": "ATEN",
            "triton.native_matmul": False,
        }
    )
    def test_remove_no_ops(self):
        def matmul_with_op(x, y, fn):
            return fn(x @ y)

        foo_opt = torch.compile(matmul_with_op)

        # test no-op
        fns = (
            lambda x: x + torch.zeros([256, 256], dtype=torch.float32, device=x.device),  # noqa: E731
            lambda x: x - torch.zeros([256, 256], dtype=torch.float32, device=x.device),  # noqa: E731
            lambda x: x * torch.ones([256, 256], dtype=torch.float32, device=x.device),  # noqa: E731
            lambda x: x / torch.ones([256, 256], dtype=torch.float32, device=x.device),  # noqa: E731
        )

        inps = [torch.rand([256, 256], device=self.device) for _ in range(2)]

        for fn in fns:
            out, source_codes = run_and_get_code(foo_opt, inps[0], inps[1], fn)
            self.assertEqual(out, matmul_with_op(inps[0], inps[1], fn))

            atol, rtol = None, None
            if self.device == "cpu":
                FileCheck().check_not("cpp_fused").run(source_codes[0])
            else:
                FileCheck().check_not("triton.jit").run(source_codes[0])

        # test dtype conversion
        for lowp_dtype in [torch.float16, torch.bfloat16]:
            if not self.is_dtype_supported(lowp_dtype):
                continue
            inps = [
                torch.rand([256, 256], device=self.device, dtype=lowp_dtype)
                for _ in range(2)
            ]
            for fn in fns:
                out, source_codes = run_and_get_code(foo_opt, inps[0], inps[1], fn)
                self.assertEqual(
                    out, matmul_with_op(inps[0], inps[1], fn), atol=atol, rtol=rtol
                )

            # test broadcasted shape bail
            fn = lambda x: x + torch.zeros(  # noqa: E731
                [256, 256, 256], dtype=lowp_dtype, device=self.device
            )
            out, source_codes = run_and_get_code(foo_opt, inps[0], inps[1], fn)
            self.assertEqual(
                out, matmul_with_op(inps[0], inps[1], fn), atol=atol, rtol=rtol
            )

    def test_remove_noop_copy(self):
        def fn(x, y):
            x = x.cos()
            a = x.copy_(y)
            return a.sin()

        self.common(fn, (torch.randn(8, 8), torch.randn(8)))

        def fn2(a, b):
            abs_max = torch.abs(a).max()
            b[0] = abs_max.to(a.dtype)
            return b

        self.common(
            fn2,
            (
                torch.randn(8, 8, dtype=torch.float16),
                torch.randn(8, dtype=torch.float32),
            ),
        )

    def test_remove_noop_clone(self):
        def fn(x):
            y = x.clone().reshape(-1, 4)
            y[:, [2, 0]] = y[:, [0, 2]]
            return y + x

        self.common(fn, (torch.randn(2, 4),))

    def test_remove_noop_slice(self):
        def f(x):
            x = x + 1
            size = x.shape[-1]
            y = torch.ops.aten.slice(x, -1, 0, size)  # noop
            return y + 1

        f = torch.compile(f)

        x = torch.ones((2, 3, 2), device=self.device)
        torch._dynamo.mark_dynamic(x, 0)
        torch._dynamo.mark_dynamic(x, 1)
        torch._dynamo.mark_dynamic(x, 2)

        post_grad_graph = get_post_grad_graph(f, (x,))
        expected_graph = f"""\
def forward(self, arg0_1: "Sym(s77)", arg1_1: "Sym(s27)", arg2_1: "Sym(s53)", arg3_1: "f32[s77, s27, s53][s27*s53, s53, 1]{str(x.device)}"):
        add: "f32[s77, s27, s53][s27*s53, s53, 1]{str(x.device)}" = torch.ops.aten.add.Tensor(arg3_1, 1);  arg3_1 = None
        add_9: "f32[s77, s27, s53][s27*s53, s53, 1]{str(x.device)}" = torch.ops.aten.add.Tensor(add, 1);  add = None
        return (add_9,)"""  # noqa: B950
        self.assertExpectedInline(
            post_grad_graph,
            expected_graph,
            ignore_comments=True,
            ignore_empty_lines=True,
        )

    def test_remove_noop_slice1(self):
        def f(x):
            x = x + 1
            y = torch.ops.aten.slice(x, -1, 0, -1)  # not a noop
            return y + 1

        f = torch.compile(f)
        x = torch.ones((2, 3, 2), device=self.device)
        torch._dynamo.mark_dynamic(x, 0)
        torch._dynamo.mark_dynamic(x, 1)
        post_grad_graph = get_post_grad_graph(f, (x,))
        expected_graph = f"""\
def forward(self, arg0_1: "Sym(s77)", arg1_1: "Sym(s27)", arg2_1: "f32[s77, s27, 2][2*s27, 2, 1]{str(x.device)}"):
        add: "f32[s77, s27, 2][2*s27, 2, 1]{str(x.device)}" = torch.ops.aten.add.Tensor(arg2_1, 1);  arg2_1 = None
        slice_1: "f32[s77, s27, 1][2*s27, 2, 1]{str(x.device)}" = torch.ops.aten.slice.Tensor(add, -1, 0, -1);  add = None
        add_9: "f32[s77, s27, 1][s27, 1, 1]{str(x.device)}" = torch.ops.aten.add.Tensor(slice_1, 1);  slice_1 = None
        return (add_9,)"""  # noqa: B950
        self.assertExpectedInline(
            post_grad_graph,
            expected_graph,
            ignore_comments=True,
            ignore_empty_lines=True,
        )

    def test_remove_noop_slice_scatter(self):
        def f(x):
            x = x + 1
            y = torch.empty_like(x)
            size = x.shape[-1]
            out = torch.ops.aten.slice_scatter(y, x, -1, 0, size)  # noop
            return out + 1

        f = torch.compile(f)

        x = torch.ones((2, 3, 2), device=self.device)
        torch._dynamo.mark_dynamic(x, 0)
        torch._dynamo.mark_dynamic(x, 1)
        torch._dynamo.mark_dynamic(x, 2)

        post_grad_graph = get_post_grad_graph(f, (x,))
        expected_graph = f"""\
def forward(self, arg0_1: "Sym(s77)", arg1_1: "Sym(s27)", arg2_1: "Sym(s53)", arg3_1: "f32[s77, s27, s53][s27*s53, s53, 1]{str(x.device)}"):
        empty: "f32[s77, s27, s53][s27*s53, s53, 1]{str(x.device)}" = torch.ops.aten.empty.memory_format([arg0_1, arg1_1, arg2_1], dtype = torch.float32, layout = torch.strided, device = {repr(x.device)}, pin_memory = False);  arg0_1 = arg1_1 = arg2_1 = empty = None
        add: "f32[s77, s27, s53][s27*s53, s53, 1]{str(x.device)}" = torch.ops.aten.add.Tensor(arg3_1, 1);  arg3_1 = None
        add_13: "f32[s77, s27, s53][s27*s53, s53, 1]{str(x.device)}" = torch.ops.aten.add.Tensor(add, 1);  add = None
        return (add_13,)"""  # noqa: B950
        self.assertExpectedInline(
            post_grad_graph,
            expected_graph,
            ignore_comments=True,
            ignore_empty_lines=True,
        )

    def test_cat_of_loops_and_extern_kernel(self):
        class M(torch.nn.Module):
            def __init__(
                self,
                **kwargs,
            ):
                super().__init__()
                self.conv = torch.nn.Conv2d(
                    64,
                    5,
                    1,
                    **kwargs,
                )
                self.max_pool2d = torch.nn.MaxPool2d(2)

            def forward(self, x, y):
                x1 = self.conv(x)
                y1 = self.max_pool2d(y)
                return torch.cat([x1, y1], 1)

        mod = M()
        opt_mod = torch.compile(mod, backend="inductor")
        memory_format = torch.channels_last
        inputs = (
            torch.randn([1, 64, 16, 16]).to(memory_format=memory_format),
            torch.randn([1, 64, 32, 32]).to(memory_format=memory_format),
        )
        y = mod(*inputs)
        opt_y = opt_mod(*inputs)
        self.assertEqual(y, opt_y)
        self.assertEqual(y.stride(), opt_y.stride())

    def test_cat_inplace(self):
        def fn(x):
            rt = torch.cat([x])
            v = x.sin_()
            return rt

        # can't use self.common because input is modified inplace
        inp = torch.ones(2)
        opt_fn = torch.compile(fn)
        res = opt_fn(inp.clone())
        expected = fn(inp.clone())
        self.assertEqual(res, expected)

    def test_stack(self):
        def fn(a, b):
            return torch.stack(
                [
                    a.expand(12, 16),
                    b.expand(12, 16),
                ],
                2,
            )

        self.common(fn, (torch.randn([1, 16]), torch.randn([12, 1])))

    def test_hardtanh(self):
        def fn(x):
            return F.hardtanh(x), F.hardtanh(x + 1), F.hardtanh(x - 1)

        self.common(
            fn,
            (torch.randn([64]),),
        )

    def test_hardsigmoid(self):
        def fn(x):
            return F.hardsigmoid(x), F.hardsigmoid(x + 3), F.hardsigmoid(x - 3)

        self.common(
            fn,
            (torch.randn([64]),),
        )

    def test_hardswish(self):
        def fn(x):
            return F.hardswish(x), F.hardswish(x + 3), F.hardswish(x - 3)

        self.common(
            fn,
            (torch.randn([64]),),
        )

    def test_rsqrt(self):
        def fn(x):
            return torch.rsqrt(x), torch.rsqrt(x + 1) - 2

        self.common(
            fn,
            (torch.randn([64]),),
        )

    def test_expm1(self):
        def fn(x):
            return torch.expm1(x), torch.expm1(x) * 2

        for dtype in (torch.float16, torch.float, torch.double, torch.int, torch.int64):
            if not self.is_dtype_supported(dtype):
                continue

            self.common(
                fn,
                (torch.randn([64]).to(dtype=dtype),),
            )
            self.common(
                fn,
                (torch.arange(-1e-5, 1e-5, 1e-7).to(dtype=dtype),),
            )

    @xfail_if_mps_unimplemented
    def test_adaptive_pool_errors_with_long(self):
        class Model(torch.nn.Module):
            def __init__(self, pool_operator):
                super().__init__()
                self.pool = pool_operator

            def forward(self, x):
                x = torch.argmax(x, dim=1)
                x = self.pool(x)
                return x

        for dim in (1, 2, 3):
            op_inst = eval(f"torch.nn.AdaptiveMaxPool{dim}d(5)")
            model = Model(op_inst).to(self.device)
            x = torch.randn([1] * (dim + 2)).to(self.device)
            model = torch.compile(model, fullgraph=True)
            with self.assertRaisesRegex(
                RuntimeError, r".*(not implemented|aoti_torch_).*"
            ):
                model(x)

    @xfail_if_mps_unimplemented
    def test_adaptive_avg_pool_errors_with_long(self):
        class Model(torch.nn.Module):
            def __init__(self, pool_operator):
                super().__init__()
                self.pool = pool_operator

            def forward(self, x):
                x = torch.argmax(x, dim=1)
                x = self.pool(x)
                return x

        for dim in (1, 2, 3):
            op_inst = eval(f"torch.nn.AdaptiveAvgPool{dim}d(5)")
            model = Model(op_inst).to(self.device)
            x = torch.randn([1] * (dim + 2)).to(self.device)
            model = torch.compile(model, fullgraph=True)
            with self.assertRaisesRegex(
                RuntimeError, r".*(not implemented|aoti_torch_).*"
            ):
                model(x)

    @torch._dynamo.config.patch(recompile_limit=12)
    def test_avg_pool_errors_with_uint(self):
        for dim in (1, 2, 3):
            for dtype in (torch.uint8, torch.uint16, torch.uint32, torch.uint64):
                x = torch.randn([2] * (dim + 2)).to(dtype)
                op = eval(f"torch.nn.functional.avg_pool{dim}d")
                c_op = torch.compile(op)
                with self.assertRaisesRegex(
                    RuntimeError, r".*(not implemented|aoti_torch_).*"
                ):
                    c_op(x, kernel_size=2, stride=2)

    def test_replication_pad_errors_with_bool(self):
        for dim in (1, 2, 3):

            def fn(x):
                x = torch.signbit(x)
                x = eval(f"nn.ReplicationPad{dim}d(padding=1)")(x)
                return x

            c_fn = torch.compile(fn)
            x = torch.randn([1] * (dim + 2))
            with self.assertRaisesRegex(
                RuntimeError, r".*(not implemented|aoti_torch_).*"
            ):
                c_fn(x)

    def test_log1p(self):
        def fn(x):
            return torch.log1p(x), torch.log1p(x) * 2

        for dtype in (torch.float16, torch.float, torch.double, torch.int, torch.int64):
            if not self.is_dtype_supported(dtype):
                continue

            self.common(
                fn,
                (torch.randn([64]).to(dtype=dtype),),
            )
            self.common(
                fn,
                (torch.arange(-1e-5, 1e-5, 1e-7).to(dtype=dtype),),
            )

    @config.patch(force_disable_caches=True)
    def test_deterministic_codegen(self):
        if "cpu" in str(self.device) and config.cpp_wrapper:
            raise unittest.SkipTest(
                "run_and_get_kernels can't extract kernels from CPU cpp_wrapper code"
            )

        @torch.compile(fullgraph=True)
        def a(x):
            return x.cos().sin().softmax(-1)

        @torch.compile(fullgraph=True)
        def b(x):
            return x.sin().cos().softmax(-1)

        @torch.compile(fullgraph=True)
        def c(x):
            return x.cos().sin().softmax(-1)

        x = torch.randn(16, 256, device=self.device)
        _, (coda_a0,) = _run_and_get_stripped_kernels(a, x)
        _, (coda_b0,) = _run_and_get_stripped_kernels(b, x)
        _, (coda_c0,) = _run_and_get_stripped_kernels(c, x)
        self.assertEqual(coda_a0, coda_c0)

        # compile in a different order
        torch.compiler.reset()
        _, (coda_c1,) = _run_and_get_stripped_kernels(c, x)
        _, (coda_a1,) = _run_and_get_stripped_kernels(a, x)
        _, (coda_b1,) = _run_and_get_stripped_kernels(b, x)
        self.assertEqual(coda_a0, coda_a1)
        self.assertEqual(coda_b0, coda_b1)
        self.assertEqual(coda_c0, coda_c1)

        # force a different CompileId
        torch.compiler.reset()
        CompileContext_init = CompileContext.__init__
        with patch.object(
            CompileContext,
            "__init__",
            lambda self, _: CompileContext_init(
                self, CompileId(frame_id=999, frame_compile_id=999)
            ),
        ):
            _, (coda_a2,) = _run_and_get_stripped_kernels(a, x)
            _, (coda_c2,) = _run_and_get_stripped_kernels(c, x)
            _, (coda_b2,) = _run_and_get_stripped_kernels(b, x)
        self.assertEqual(coda_a0, coda_a2)
        self.assertEqual(coda_b0, coda_b2)
        self.assertEqual(coda_c0, coda_c2)

    @config.patch(force_disable_caches=True)
    def test_deterministic_codegen_on_graph_break(self):
        if "cpu" in str(self.device) and config.cpp_wrapper:
            raise unittest.SkipTest(
                "run_and_get_kernels can't extract kernels from CPU cpp_wrapper code"
            )

        def a(x):
            return x.cos().sin().softmax(-1)

        @torch.compile()
        def b(x):
            x = a(x)
            torch._dynamo.graph_break()
            x = a(x)
            return x

        x = torch.randn(16, 256, device=self.device)
        if config.cpp_wrapper and config.triton.autotune_at_compile_time is not True:
            # With lazy compile, both graph segments produce identical code
            # (no unique .cubin paths), so run_and_get_code deduplicates them
            # and only 1 kernel is returned.
            _, codes = _run_and_get_stripped_kernels(b, x)
            self.assertEqual(len(codes), 1)
        else:
            _, (code0, code1) = _run_and_get_stripped_kernels(b, x)
            self.assertEqual(code0, code1)

    @config.patch(
        {
            "force_disable_caches": True,
            # Test expects a single (fused) kernel to be generated
            "max_autotune_gemm_backends": "ATEN",
            # native matmul codegens the matrix multiplication
            "triton.native_matmul": False,
        }
    )
    def test_deterministic_codegen_with_suffix(self):
        if "cpu" in str(self.device) and config.cpp_wrapper:
            raise unittest.SkipTest(
                "run_and_get_kernels can't extract kernels from CPU cpp_wrapper code"
            )

        @torch.compile(fullgraph=True)
        def a(x):
            return x.cos().sin().softmax(-1)

        @torch.compile(fullgraph=True)
        def b(x, y):
            x = x.cos().sin().softmax(-1)
            x = torch.matmul(x, y)
            return x

        x = torch.randn(16, 256, device=self.device)
        y = torch.randn(256, 256, device=self.device)
        _, (code0,) = _run_and_get_stripped_kernels(a, x)
        _, (code1,) = _run_and_get_stripped_kernels(b, x, y)
        self.assertEqual(code0, code1)

    def test_flip(self):
        def fn(x):
            return torch.flip(x, (-1,)), torch.flip(x, (0, 2)) - 2

        self.common(
            fn,
            (torch.randn([1, 2, 6, 6]),),
        )

    def test_signbit(self):
        def fn(x):
            return torch.signbit(x), ~torch.signbit(-x) & 1

        self.common(
            fn,
            (torch.randn([1, 2, 6, 6]),),
        )

    def test_sign_dtype(self):
        def fn(x):
            y = torch.sign(x)
            return torch.tanh(y)

        self.common(fn, (torch.randn([1, 2, 6, 6]),))

    @xfail_if_triton_cpu
    def test_fmod(self):
        def fn(a, b):
            return torch.fmod(a, b), torch.fmod(3.0 * a, b) - 2.0

        shape = [1, 2, 6, 6]
        self.common(fn, (torch.randn(shape), torch.randn(shape)))

    @xfail_if_triton_cpu
    def test_fmod_zero_dim(self):
        def fn(a, b):
            return (torch.fmod(a, b),)

        self.common(
            fn,
            (
                make_tensor(10, device=self.device, dtype=torch.float32),
                make_tensor((), device=self.device, dtype=torch.float32),
            ),
        )
        self.common(
            fn,
            (
                make_tensor((), device=self.device, dtype=torch.float32),
                make_tensor(10, device=self.device, dtype=torch.float32),
            ),
        )

    @skip_if_halide  # log2 not implemented for halide
    def test_log2(self):
        def fn(x):
            return torch.log2(x), torch.log2(x + 1) - 2

        self.common(
            fn,
            (torch.randn([64]) + 10,),
        )

    def test_logsumexp(self):
        def fn(x):
            return torch.logsumexp(x, -1), torch.logsumexp(x, 0) - 2

        self.common(
            fn,
            (torch.randn([8, 8]) + 10,),
        )

    @skip_if_halide  # log2 not implemented for halide
    def test_log_fp64(self):
        def fn(x):
            return torch.log(x), torch.log2(x)

        _dtype = torch.float64
        ctx = (
            contextlib.nullcontext()
            if self.is_dtype_supported(_dtype)
            else self.assertRaises(TypeError)
        )
        with ctx:
            self.common(
                fn,
                (torch.randn([1024], dtype=_dtype) + 10,),
            )

    def test_bitwise(self):
        def fn(x, y):
            return (
                torch.bitwise_not(x),
                torch.bitwise_or(x, y),
                torch.bitwise_xor(x, y),
                torch.bitwise_and(x, y),
            )

        self.common(
            fn,
            (
                torch.randint(0, 2**30, [64], dtype=torch.int32),
                torch.randint(0, 2**30, [64], dtype=torch.int32),
            ),
        )

    def test_bitwise2(self):
        # again with bool types
        def fn(x, y):
            return (
                torch.bitwise_not(x),
                torch.bitwise_or(x, y),
                torch.bitwise_xor(x, y),
                torch.bitwise_and(x, y),
            )

        self.common(
            fn,
            (
                torch.randint(0, 2, (2, 20), dtype=torch.bool),
                torch.randint(0, 2, (2, 20), dtype=torch.bool),
            ),
        )

    def test_bitwise3(self):
        # Repro for https://github.com/pytorch/pytorch/issues/97968
        def fn(x, y):
            return (
                torch.max(torch.bitwise_and(x, y), y),
                torch.clamp_max(torch.bitwise_or(x, y), y),
                torch.clamp_min(torch.bitwise_xor(x, y), y),
            )

        self.common(
            fn,
            (
                torch.rand([5, 10, 1]).to(torch.int8),
                torch.rand([10, 1]).to(torch.int8),
            ),
        )

    def test_inf(self):
        def fn(a):
            return a + float("inf"), a + float("-inf"), a * -float("inf")

        self.common(fn, (torch.randn(8),))

    def test_remainder(self):
        def fn(a, b):
            return (
                torch.remainder(a, b),
                torch.remainder(a + 1, b - 1),
                torch.remainder(a - 1, b + 1),
            )

        self.common(fn, (torch.randn(64), torch.randn(64)))

    def test_zeros(self):
        def fn(a):
            return (
                a + 1,
                torch.zeros(
                    (1, 8, 64, 64),
                    dtype=torch.float32,
                    device=a.device,
                ),
                torch.zeros(
                    1,
                    8,
                    64,
                    64,
                    dtype=torch.float32,
                    device=a.device,
                ),
                torch.zeros(2, 3),
                a + torch.ones(8, device=a.device),
                torch.full((2, 3), 3.1416, device=a.device),
            )

        self.common(fn, (torch.randn(8),))

    def test_new_ones(self):
        def fn(a):
            return (
                aten.new_ones(
                    a, [], device=a.device, dtype=6, layout=0, pin_memory=False
                ),
                aten.new_zeros(
                    a, [], device=a.device, dtype=6, layout=0, pin_memory=False
                ),
            )

        self.common(fn, (torch.randn(8),))

    def test_full_like(self):
        def fn(a):
            return torch.full_like(a, 7.777) - 1

        self.common(fn, (torch.randn(8),))

    def test_full_like_transposed(self):
        def fn(a):
            return torch.full_like(a, 3)

        self.common(fn, (torch.randn(4, 5, 6).transpose(1, -1),), exact_stride=True)

    def test_full_like_sliced(self):
        def fn(a):
            return torch.full_like(a, 3)

        self.common(fn, (torch.rand(3, 4)[:, ::2],), exact_stride=True)

    def test_full_truncation(self):
        def fn(a):
            return a + torch.full_like(a, 7.777)

        for dtype in all_types():
            ctx = (
                contextlib.nullcontext()
                if self.is_dtype_supported(dtype)
                else self.assertRaises(TypeError)
            )
            with ctx:
                self.common(
                    fn,
                    (make_tensor(8, dtype=dtype, device=self.device),),
                    check_lowp=False,
                )

    def test_full_boolean(self):
        def fn(n):
            x = torch.full((1,), n >= 1024, device=self.device)
            return x, x + 1

        self.common(fn, (1024,))
        self.common(fn, (1023,))

    def test_index1(self):
        def fn(a, b, c):
            return aten.index(a, [b, c])

        self.common(
            fn,
            (
                torch.randn(8, 8, 12),
                torch.tensor([0, 0, 2, 2], dtype=torch.int64),
                torch.tensor([3, 4, 4, 3], dtype=torch.int64),
            ),
        )
        self.common(
            fn,
            (
                torch.randn(8, 8, 12),
                torch.tensor([[0, 0, 2, 2]], dtype=torch.int64),
                torch.tensor([[3], [4], [4], [3]], dtype=torch.int64),
            ),
        )

    def test_index2(self):
        def fn(a, b):
            return (
                aten.index(a, [b]),
                aten.index(a, [None, b]),
            )

        self.common(
            fn,
            (
                torch.randn(8, 8, 8),
                torch.tensor([[0, 0, 2, 2]], dtype=torch.int64),
            ),
        )

    def test_index3(self):
        def fn(x, ia, ib):
            return (x[:, ia, None, ib, 0],)

        self.common(
            fn,
            (
                torch.randn(3, 4, 4, 4, 3),
                torch.tensor([0, 2, 1], dtype=torch.int64),
                torch.tensor([0, 2, 1], dtype=torch.int64),
            ),
        )

    def test_output_strides(self):
        def fn(x):
            y = x.permute(0, 2, 3, 1).contiguous()
            torch._dynamo.graph_break()
            return y.view(-1, 4)

        inp = torch.rand([4, 4, 4, 4], device=self.device)
        fn_opt = torch.compile(fn, backend="inductor")

        self.assertEqual(fn(inp), fn_opt(inp))
        self.assertEqual(fn(inp).stride(), fn_opt(inp).stride())

        # no redundant copy
        def foo(x):
            return x[0:2:2].T[3:].squeeze(0)

        foo_opt = torch.compile(foo, backend="inductor")
        out = foo_opt(inp)
        self.assertEqual(inp.storage(), out.storage())

    def test_index_select(self):
        def fn(a, b):
            return (
                torch.index_select(a, 0, b),
                torch.index_select(a, 1, b),
                torch.index_select(torch.index_select(a, 2, b), 1, b),
            )

        for ind_dtype in (torch.int32, torch.int64):
            self.common(
                fn,
                (
                    torch.randn(8, 8, 8),
                    torch.tensor([0, 0, 2, 1], dtype=ind_dtype),
                ),
            )

    @xfail_if_mps_unimplemented
    @skipCUDAIf(not TEST_CUDNN, "CUDNN not available")
    @skipIfXpu
    def test_cudnn_rnn(self):
        if self.device == "cpu":
            raise unittest.SkipTest(f"requires {GPU_TYPE}")

        def fn(
            a0,
            b0,
            b1,
            b2,
            b3,
            b4,
            b5,
            b6,
            b7,
            b8,
            b9,
            b10,
            b11,
            b12,
            b13,
            b14,
            b15,
            a3,
            a4,
            a5,
        ):
            a1 = [
                b0,
                b1,
                b2,
                b3,
                b4,
                b5,
                b6,
                b7,
                b8,
                b9,
                b10,
                b11,
                b12,
                b13,
                b14,
                b15,
            ]

            if torch.version.cuda:
                return aten._cudnn_rnn(
                    a0,
                    a1,
                    4,
                    a3,
                    a4,
                    a5,
                    2,
                    2048,
                    0,
                    2,
                    False,
                    0.0,
                    False,
                    True,
                    [],
                    None,
                )
            else:
                return aten.miopen_rnn(
                    a0,
                    a1,
                    4,
                    # weight_buf is cudnn only
                    a4,
                    a5,
                    2,
                    2048,
                    # proj_size is cudnn only
                    2,
                    False,
                    0.0,
                    False,
                    True,
                    [],
                    None,
                )

        self.common(
            fn,
            (
                torch.randn([92, 8, 2048]),
                torch.randn([8192, 2048]),
                torch.randn([8192, 2048]),
                torch.randn([8192]),
                torch.randn([8192]),
                torch.randn([8192, 2048]),
                torch.randn([8192, 2048]),
                torch.randn([8192]),
                torch.randn([8192]),
                torch.randn([8192, 4096]),
                torch.randn([8192, 2048]),
                torch.randn([8192]),
                torch.randn([8192]),
                torch.randn([8192, 4096]),
                torch.randn([8192, 2048]),
                torch.randn([8192]),
                torch.randn([8192]),
                torch.randn([167837696]),
                torch.randn([4, 8, 2048]),
                torch.randn([4, 8, 2048]),
            ),
            check_lowp=False,  # difference in rnn is too large between half and float inputs
        )

    def test_upsample_nearest1d(self):
        def fn(a):
            return (
                aten.upsample_nearest1d(a, [74], None),
                aten.upsample_nearest1d(a, [70], None),
                aten.upsample_nearest1d(a, [45], None),
                aten.upsample_nearest1d(a, [36], None),
                aten.upsample_nearest1d(a, None, [2.0]),
            )

        self.common(fn, (torch.randn([2, 4, 37]),))

    def test_upsample_nearest2d(self):
        def fn(a):
            return (
                aten.upsample_nearest2d(a, [74, 76]),
                aten.upsample_nearest2d(a, [70, 75]),
                aten.upsample_nearest2d(a, [45, 74]),
                aten.upsample_nearest2d(a, [36, 39]),
                aten.upsample_nearest2d(a, None, [2.0, 2.0]),
            )

        self.common(fn, (torch.randn([2, 4, 37, 38]),))

    def test_upsample_nearest2d_neg(self):
        def fn(a):
            return (
                aten.upsample_nearest2d(
                    a, output_size=[37, 38], scales_h=-1, scales_w=-1
                ),
            )

        self.common(fn, (torch.randn([2, 4, 37, 38]),))

    def test_upsample_nearest3d(self):
        def fn(a):
            return (
                aten.upsample_nearest3d(a, [74, 76, 78], None),
                aten.upsample_nearest3d(a, [70, 75, 80], None),
                aten.upsample_nearest3d(a, [45, 74, 103], None),
                aten.upsample_nearest3d(a, [36, 39, 40], None),
                aten.upsample_nearest3d(a, None, [2.0, 2.0, 2.0]),
            )

        self.common(fn, (torch.randn([2, 4, 37, 38, 39]),))

    def test_upsample_nearest2d_backward(self):
        func = torch.ops.aten.upsample_nearest2d_backward

        def fn(a):
            return (
                func(a, output_size=[6, 12], input_size=[3, 3, 3, 6]),
                func(a, output_size=[6, 12], input_size=[3, 3, 4, 5]),
                func(a, output_size=[6, 12], input_size=[3, 3, 2, 8]),
                func(a, output_size=[6, 12], input_size=[3, 3, 2, 8]),
                func(a, output_size=[6, 12], input_size=[3, 3, 4, 7]),
            )

        self.common(fn, (torch.randn([3, 3, 6, 12]),))

    @skip_if_x86_mac()
    def test_upsample_bilinear2d_a(self):
        def fn(a):
            return (
                aten.upsample_bilinear2d(a, [45, 45], False, None),
                aten.upsample_bilinear2d(a, None, True, [2.0, 2.0]),
            )

        self.common(fn, (torch.randn([2, 4, 37, 38]),), atol=2.5e-5, rtol=1.3e-6)

    def test_upsample_bilinear2d_b(self):
        def fn(a):
            return aten.upsample_bilinear2d(a, None, True, [2.0, 2.0])

        self.common(
            fn,
            [
                torch.randn([1, 2, 40, 59]),
            ],
            atol=2.5e-5,
            rtol=1.3e-6,
        )

    @skip_if_gpu_halide  # accuracy issue
    def test_reflection_pad2d(self):
        def fn(a, pad):
            return (
                aten.reflection_pad2d(a, [1, 1, 1, 1]),
                aten.reflection_pad2d(a, pad),
            )

        self.common(
            fn,
            (
                torch.randint(0, 999, size=[1, 1, 8, 8], dtype=torch.float32),
                [5, 2, 3, 4],
            ),
        )

    def test_reflection_pad2d_backward(self):
        def template(size, padding):
            def fn(grad_output, x):
                return aten.reflection_pad2d_backward(grad_output, x, padding)

            x = torch.randint(0, 999, size=size, dtype=torch.float32)
            result = aten.reflection_pad2d(x, padding)
            grad_output = torch.randn_like(result)

            self.common(
                fn, (grad_output, x), check_lowp=not is_halide_backend(self.device)
            )

        template([1, 1, 8, 8], [0, 0, 0, 0])
        template([1, 1, 8, 8], [1, 1, 1, 1])
        template([1, 1, 8, 8], [1, 2, 3, 4])
        template([1, 1, 8, 8], [0, -1, 2, 2])
        template([1, 1, 8, 8], [-1, 0, 2, 2])
        template([1, 1, 8, 8], [2, 2, 0, -1])
        template([1, 1, 8, 8], [2, 2, -1, 0])

    def test_grid_sampler_2d(self):
        def fn(a, b):
            return (
                aten.grid_sampler_2d(a, b, 0, 0, True),
                aten.grid_sampler_2d(a, b, 0, 1, False),
            )

        self.common(
            fn,
            (
                torch.randn([4, 3, 352, 352], dtype=torch.float32),
                torch.rand([4, 352, 352, 2], dtype=torch.float32) * 2 - 1,
            ),
            check_lowp=False,
            # Mismatched elements: 154697 / 1486848 (10.4%)
            # Greatest absolute difference: 0.0001976490020751953 at index (0, 0, 101, 243) (up to 1e-05 allowed)
            # Greatest relative difference: 7.332530120481928 at index (1, 1, 258, 301) (up to 1.3e-06 allowed)
            atol=0.0002,
            rtol=1.3e-06,
        )

    @requires_gpu()
    def test_grid_sampler_expand_preserves_view(self):
        if not self.device.startswith("cuda") and not self.device.startswith("xpu"):
            self.skipTest("requires CUDA or XPU")

        torch.manual_seed(0)
        torch._dynamo.reset()

        repeats = 9000
        batch = 48
        channels = 3
        img = 224
        grid_size = 13
        device = self.device

        class ExpandGridSampler(torch.nn.Module):
            def __init__(self) -> None:
                super().__init__()
                self.grid = torch.nn.Parameter(
                    torch.randn(repeats, grid_size, grid_size, 2, device=device)
                )
                self.fc = torch.nn.Linear(grid_size * grid_size * channels, 16).to(
                    device
                )

            def forward(self, x: torch.Tensor) -> torch.Tensor:
                per_channel = []
                for i in range(channels):
                    channel = x[:, i, ...].expand(repeats, -1, -1, -1)
                    patch = torch.nn.functional.grid_sample(
                        channel,
                        self.grid,
                        mode="bilinear",
                        align_corners=False,
                        padding_mode="border",
                    )
                    patch = patch.transpose(0, 1).flatten(start_dim=2)
                    per_channel.append(patch)
                x = torch.cat(per_channel, dim=2)
                return self.fc(x)

        model = ExpandGridSampler().to(device)
        compiled = torch.compile(model, backend="inductor")
        inp = torch.randn(batch, channels, img, img, device=device)

        out = compiled(inp)
        out.sum().backward()

        self.assertIsNotNone(model.grid.grad)

    def test_upsample_bicubic2d(self):
        def fn(a):
            return (
                aten.upsample_bicubic2d(a, (128, 128), True),
                aten.upsample_bicubic2d(a, (128, 256), False),
            )

        # Mismatched elements: 10 / 196608 (0.0%)
        # Greatest absolute difference: 1.3869255781173706e-05 at index (2, 1, 88, 65) (up to 1e-05 allowed)
        # Greatest relative difference: 0.0033082996811011046 at index (3, 1, 88, 91) (up to 1.3e-06 allowed)
        self.common(
            fn,
            (torch.randn([4, 3, 64, 32], dtype=torch.float32),),
            atol=2e-5,
            rtol=1e-3,
        )

    def test_float_index_expression(self):
        # Test that index propagation doesn't generate bad index_expr calls like
        # ops.index_expr(0.5*x, dtype) where the expression is not integral
        def fn(x):
            return aten.upsample_bicubic2d(x, (256, 256), False)

        x = torch.randn(1, 1, 128, 128, dtype=torch.float32, device=self.device)
        _, source_codes = run_and_get_code(fn, x)

        pattern = r"0\.50*\*[ix][\d]"
        for code in source_codes:
            self.assertIsNone(
                re.search(pattern, code), msg="Found bad index_expr in code:\n" + code
            )

    def test_float_index_expression_type_promotion(self):
        # Test that float indexing expressions participate in type promotion
        def fn(x):
            return x + 1.0 / x.size(0)

        x = torch.arange(10)
        self.common(fn, (x,))

    def test_sort(self):
        def fn(a, descending):
            return torch.sort(a)

        inp = torch.randint(0, 999, size=[1, 1, 8, 8], dtype=torch.float32)
        self.common(fn, (inp, False))
        self.common(fn, (inp, True))

    @parametrize("stable", (True, False))
    @parametrize("descending", (True, False))
    def test_nan_sort(self, descending, stable):
        def test_sort(x, descending, stable):
            out = torch.sort(x, descending=descending, stable=stable)
            if stable:
                return out
            else:
                # non stable idx may not be equal
                return out[0]

        tensor = torch.tensor(
            [
                0.7308,
                0.7053,
                0.3349,
                -0.7158,
                torch.nan,
                0.1234,
                1.0284,
                torch.nan,
                -1.8767,
                -0.4369,
            ],
            device=self.device,
        )
        inps = (tensor, descending, stable)
        a = torch.compile(test_sort)(*inps)
        b = test_sort(*inps)
        self.assertEqual(a, b, equal_nan=True)

    def test_sort_stable(self):
        def fn(a, descending):
            return a.sort(dim=-1, stable=True, descending=descending)

        # Duplicates give deterministic indices when stable sorting
        inp = torch.rand(10, 128, dtype=torch.float32)
        inp[:, 10:20] = 1.0
        inp[:, 30:40] = 1.0
        self.common(fn, (inp, False))
        self.common(fn, (inp, True))

        # Non-power of two
        inp = inp[:, :120]
        self.common(fn, (inp, False))
        self.common(fn, (inp, True))

    def test_sort_bool(self):
        def fn(a, descending):
            return torch.sort(a.to(torch.int8), stable=True, descending=descending)

        inp = torch.randint(0, 2, size=[10, 128], dtype=torch.bool)
        self.common(fn, (inp, False))
        self.common(fn, (inp, True))

    @skipIfWindows(msg="Crash UT")
    def test_sort_transpose(self):
        def fn(a, descending):
            return torch.sort(a, stable=True, descending=descending)

        # MPS has correctness problem for transposed sort before MacOS15
        ctx = (
            contextlib.nullcontext()
            if self.device != "mps" or MACOS_VERSION >= 15.0
            else self.assertRaises(AssertionError)
        )
        inp = torch.randn(128, 10).transpose(0, 1)
        with ctx:
            self.common(fn, (inp, False))
            self.common(fn, (inp, True))

    def test_topk(self):
        def fn(a):
            return torch.topk(a, 2, -1)

        self.common(
            fn, (torch.randint(0, 999, size=[1, 1, 8, 8], dtype=torch.float32),)
        )

    def test_long_tensor(self):
        def fn(a):
            return (
                torch.LongTensor([294]).to(a.device) - a,
                torch.as_tensor([295]).to(a.device) + a,
            )

        self.common(fn, (torch.randint(0, 999, size=[8, 8]),))

    @skip_if_gpu_halide  # correctness issue
    def test_constant_pad_1d(self):
        def fn(a):
            return (
                aten.constant_pad_nd(a, [0, 1], 6.0),
                aten.constant_pad_nd(a, [2, 3], 99.0),
            )

        self.common(fn, (torch.randint(0, 999, size=[2, 16, 31], dtype=torch.float32),))

    def test_constant_pad_fill_dtype(self):
        def fn(a, b):
            return (
                aten.constant_pad_nd(a, (1, 1), 1.0) & b,
                aten.constant_pad_nd(a, (1, 1), 0.0) & b,
            )

        self.common(
            fn,
            (torch.randint(2, (4,), dtype=torch.bool), torch.ones(6, dtype=torch.bool)),
        )

    @skip_if_gpu_halide  # misaligned address
    def test_constant_pad_2d(self):
        def fn(a):
            return (
                aten.constant_pad_nd(a, [1, 1, 1, 1], 6.0),
                aten.constant_pad_nd(a, [1, 2, 3, 4], 99.0),
            )

        self.common(
            fn, (torch.randint(0, 999, size=[1, 1, 8, 8], dtype=torch.float32),)
        )

    def test_constant_pad_2d_strides_nonpositive(self):
        def fn(a):
            return torch.constant_pad_nd(a, [0, 0, 0, -2, 0, 0])

        self.common(
            fn, (torch.empty_strided((2, 4, 5), (20, 1, 4), dtype=torch.float32),)
        )

    @skip_if_gpu_halide  # misaligned address
    def test_constant_pad_3d(self):
        def fn(a):
            return (
                aten.constant_pad_nd(a, [1, 2, 3, 4, 5, 6], 6.0),
                aten.constant_pad_nd(a, [0, 0, 3, 4, 0, 0], 6.0),
            )

        self.common(
            fn, (torch.randint(0, 999, size=[2, 4, 4, 4], dtype=torch.float32),)
        )

    def test_constant_pad_float64(self):
        # Repro for https://github.com/pytorch/pytorch/issues/93351
        def fn(input):
            v1 = torch.nn.functional.pad(input, pad=(1, 0))
            return torch.gt(v1, input)

        _dtype = torch.float64

        ctx = (
            contextlib.nullcontext()
            if self.is_dtype_supported(_dtype)
            else self.assertRaises(TypeError)
        )
        x = torch.rand([1, 2, 2, 1], dtype=_dtype)
        with ctx:
            self.common(fn, (x,))

    def test_constant_pad_nd_inplace(self):
        def fn(a):
            return aten.constant_pad_nd(a, [0, 0])

        x = torch.randn([2], device=self.device)
        fn_compiled = torch.compile(fn)
        y = fn_compiled(x)
        self.assertTrue(y is not x)

    def test_l1_loss(self):
        def fn(a, b):
            return torch.nn.functional.l1_loss(a, b), torch.nn.functional.mse_loss(a, b)

        self.common(
            fn,
            (
                torch.randn([2, 3, 16, 16]),
                torch.randn([2, 3, 16, 16]),
            ),
            check_lowp=False,
        )

    def test_triu(self):
        def fn(a):
            return aten.triu(a, 1), aten.triu(a, 0), aten.triu(a, 2)

        self.common(fn, (torch.randn([2, 10, 10]),))

    def test_no_op_reduction(self):
        def fn(a):
            return a.sum(-1), torch.amax(a + 1, 1, keepdim=True)

        self.common(fn, (torch.randn([8, 1, 1]),))

    def test_inplace_add(self):
        @torch.compile(backend="inductor")
        def fn(x, y):
            return x.add_(y)

        inputs = (
            rand_strided((4, 4), (4, 1), device=self.device),
            rand_strided((4, 4), (4, 1), device=self.device),
        )
        inp_clone = inputs[0].clone()
        out = fn(*inputs)
        self.assertTrue(same(out, inp_clone + inputs[1]))
        self.assertTrue(out is inputs[0])

    # The following 2 tests are meant to check the logic that drops
    # xmask from triton load/store if xnumel = 1
    @requires_gpu()
    def test_single_elem(self):
        def fn(a):
            b = a + 1
            return (b,)

        self.common(fn, (torch.randn(1),))

    @requires_gpu()
    def test_single_elem_indirect(self):
        def fn(a, b):
            c = a[b] + 1
            return (c,)

        a = torch.randn(1)
        b = (torch.tensor([0], dtype=torch.int64),)

        self.common(fn, (a, b))

    # This test is meant to check for issues from the logic
    # that drops xmask from trito load/store if XBLOCK divides xnumel

    @requires_gpu()
    def test_xblock_divides_xnumel(self):
        def fn(a):
            b = a + 1
            return (b,)

        # assumption is that XBLOCK is always a divisor of 1024
        # so xmask will be dropped iff xnumel is multiple of 1024
        self.common(fn, (torch.randn(1024),))
        self.common(fn, (torch.randn(1025),))

    def test_inplace_mixed_dtype_ops(self):
        @torch.compile(backend="inductor")
        def fn(x, y):
            z = x + y.float()
            w = z.add_(y)
            return w.mul_(y)

        tgt_dtype = torch.double if self.device != "mps" else torch.half
        inputs = (
            rand_strided((4, 4), (4, 1), device=self.device, dtype=torch.float),
            rand_strided((4, 4), (4, 1), device=self.device, dtype=tgt_dtype),
        )
        out = fn(*inputs)
        out_eager = (inputs[0] + inputs[1].float()).add_(inputs[1]).mul_(inputs[1])
        self.assertTrue(same(out, out_eager))

    @config.patch(
        {"triton.unique_kernel_names": True, "triton.descriptive_names": False}
    )
    def test_kernel_names(self):
        @torch.compile(backend="inductor")
        def fn(x):
            return 2 * x

        inputs = (rand_strided((8,), (1,), device=self.device),)
        self.assertTrue(same(fn(*inputs), 2 * inputs[0]))

    @config.patch({"triton.cudagraphs": True})
    @dynamo_config.patch(automatic_dynamic_shapes=True)
    def test_strided_inputs(self):
        @torch.compile(backend="inductor")
        def fn(x, y):
            return x + y

        inputs = (
            rand_strided((8, 16), (32, 2), device=self.device),
            rand_strided((8, 16), (16, 1), device=self.device),
        )
        self.assertTrue(same(fn(*inputs), inputs[0] + inputs[1]))

    @config.patch({"triton.cudagraphs": True})
    @dynamo_config.patch(automatic_dynamic_shapes=True)
    def test_input_mutation1(self):
        def fn(a):
            b = a + 1
            a.copy_(b)
            c = a + 2
            return a * b / c

        arg1 = torch.randn(64, device=self.device)
        arg2 = arg1.clone()
        arg3 = torch.randn(64, device=self.device)
        arg4 = arg3.clone()
        correct1 = fn(arg1)
        correct2 = fn(arg3)
        opt_fn = torch._dynamo.optimize_assert(compile_fx)(fn)
        actual1 = opt_fn(arg2)
        actual2 = opt_fn(arg4)

        self.assertTrue(same(actual1, correct1))
        self.assertTrue(same(actual2, correct2))
        self.assertTrue(same(arg1, arg2))
        self.assertTrue(same(arg3, arg4))

    def test_input_mutation2(self):
        def fn(a):
            b = a + 1
            a.view(64).copy_(torch.tensor([66.0], device=a.device))
            c = a + 2
            return b, c

        # NOTE: this test fails when none of the inputs require grad.
        # That seems like an inductor bug.
        arg1 = torch.randn([1, 64], device=self.device).requires_grad_(True).add(1)
        arg2 = arg1.clone()
        correct1 = fn(arg1)
        opt_fn = torch._dynamo.optimize_assert(compile_fx)(fn)
        actual1 = opt_fn(arg2)

        self.assertTrue(same(actual1, correct1))
        self.assertTrue(same(arg1, arg2))

    def test_input_mutation3(self):
        def fn(a):
            a += 1
            a *= 2
            aten.sigmoid_(a)
            a = a.view(64)
            a += 3
            a *= 4
            aten.relu_(a)
            return a

        arg1 = torch.randn([1, 64], device=self.device)
        arg2 = arg1.clone()
        correct1 = fn(arg1)
        opt_fn = torch._dynamo.optimize_assert(compile_fx)(fn)
        actual1 = opt_fn(arg2)

        self.assertTrue(same(actual1, correct1))
        self.assertTrue(same(arg1, arg2))

    def test_input_mutation4(self):
        def fn(a):
            torch.relu_(a)
            return a

        arg1 = torch.randn([1, 64], device=self.device)
        arg2 = arg1.clone()
        correct1 = fn(arg1)
        opt_fn = torch._dynamo.optimize_assert(compile_fx)(fn)
        actual1 = opt_fn(arg2)

        self.assertTrue(same(actual1, correct1))
        self.assertTrue(same(arg1, arg2))

    def test_input_mutation5(self):
        def fn(x):
            tmp = x.ceil()
            x.add_(10)
            return tmp

        opt_fn = torch.compile(fn)

        a = torch.zeros((), dtype=torch.int64, device=self.device)
        a_expect = a.clone()
        expect = fn(a_expect)

        a_actual = a.clone()
        actual = opt_fn(a_actual)

        self.assertEqual(a_expect, a_actual)
        self.assertEqual(expect, actual)

    def test_slice_mutation1(self):
        def fn(a):
            x = torch.zeros_like(a)
            b = x + 1
            x[:, 3] = 3.0
            c = torch.clone(x)
            x[4, :] = 4.0
            d = x + 1
            return x, b, c, d

        self.common(fn, (torch.randn([8, 8]),))

    @skip_if_gpu_halide  # accuracy issue
    def test_slice_mutation2(self):
        def fn(a):
            a[:, 20:40] = a[:, 20:40] + 1
            a[:, 2:11] = a[:, 1:10] + 2

        arg1 = torch.randn([1, 64], device=self.device)
        arg2 = arg1.clone()
        fn(arg1)
        opt_fn = torch._dynamo.optimize_assert(compile_fx)(fn)
        opt_fn(arg2)
        self.assertTrue(same(arg1, arg2))

    def test_slice_mutation3(self):
        def fn(a):
            a[:2, :2].fill_(10)

        opt_fn = torch._dynamo.optimize_assert(compile_fx)(fn)

        x1 = torch.randn(8, 8, device=self.device)
        x2 = x1.clone()
        fn(x1)
        opt_fn(x2)
        self.assertEqual(x1, x2)

    def test_tensor_index_slice(self):
        def fn(a):
            x = torch.tensor([1, 2], device=self.device)
            y = torch.tensor([2, 3], device=self.device)
            xx = torch.tensor([1, 2], device=self.device).view(1, 2)
            yy = torch.tensor([1, 2, 3], device=self.device).view(3, 1)
            return [
                a[x, y],
                a[:, x, y],
                a[:, x, y, :],
                a[x, :, y],
                a[:, x, :, y, :],
                a[xx, yy],
                a[:, xx, yy],
                a[xx, :, yy],
                a[xx, yy, :],
                a[:, xx, :, yy],
            ]

        a = torch.arange(3 * 4 * 5 * 6 * 7, device=self.device).view(3, 4, 5, 6, 7)
        refs = fn(a)
        tests = torch.compile(fn)(a)
        for ref, test in zip(refs, tests):
            torch.testing.assert_close(ref, test)

    @torch._dynamo.config.patch(recompile_limit=10)
    def test_tensor_index_put_slice(self):
        def fn(a, version):
            x = torch.tensor([1, 2], device=self.device, dtype=torch.int32)
            y = torch.tensor([2, 3], device=self.device, dtype=torch.int32)

            xx = torch.tensor([1, 2], device=self.device).view(1, 2)
            yy = torch.tensor([1, 2, 3], device=self.device).view(3, 1)

            if version == 0:
                a[x, y] = torch.zeros_like(a[x, y])
            elif version == 1:
                a[:, x, y] = torch.zeros_like(a[:, x, y])
            elif version == 2:
                a[:, x, y, :] = torch.zeros_like(a[:, x, y, :])
            elif version == 3:
                a[x, :, y] = torch.zeros_like(a[x, :, y])
            elif version == 4:
                a[:, x, :, y, :] = torch.zeros_like(a[:, x, :, y, :])
            elif version == 5:
                a[xx, yy] = torch.zeros_like(a[xx, yy])
            elif version == 6:
                a[:, xx, yy] = torch.zeros_like(a[:, xx, yy])
            elif version == 7:
                a[xx, :, yy] = torch.zeros_like(a[xx, :, yy])
            elif version == 8:
                a[xx, yy, :] = torch.zeros_like(a[xx, yy, :])
            elif version == 9:
                a[:, xx, :, yy] = torch.zeros_like(a[:, xx, :, yy])

            return a

        a = torch.arange(3 * 4 * 5 * 6 * 7, device=self.device, dtype=torch.int32).view(
            3, 4, 5, 6, 7
        )
        for i in range(10):
            ref = fn(torch.clone(a), i)
            test = torch.compile(fn)(torch.clone(a), i)
            torch.testing.assert_close(ref, test)

    def test_indirect_load_broadcast(self):
        def fn(in_ptr0, in_ptr1, in_ptr2):
            return torch.gather(in_ptr1, 0, in_ptr2) + in_ptr0

        arg190 = rand_strided((32, 21), (1, 32), device=self.device, dtype=torch.int64)
        arg190.fill_(0)
        arg111 = rand_strided(
            (9521, 512), (512, 1), device=self.device, dtype=torch.float32
        )
        self.common(
            fn,
            (
                torch.randn(32, 1),
                arg111,
                arg190,
            ),
        )

    def test_roi_align(self):
        if not has_torchvision_roi_align():
            raise unittest.SkipTest("requires torchvision")

        def fn(a, b):
            return torch.ops.torchvision.roi_align(a, b, 0.25, 7, 7, 2, False)

        self.common(fn, (torch.zeros([4, 256, 296, 304]), torch.zeros([2292, 5])))

    # https://github.com/halide/Halide/issues/8256
    @config.patch("halide.scheduler_cuda", "Li2018")
    def test_nll_loss_forward(self):
        def fn(a, b):
            return aten.nll_loss_forward(a, b, None, 1, -100)

        labels = (
            torch.zeros([5], dtype=torch.int64),
            torch.tensor([-100, -100, 3, -100, -100], dtype=torch.int64),
        )
        inps = (torch.randn(5, 5), torch.randn(5, 5))
        for a, b in zip(inps, labels):
            self.common(
                fn,
                (a, b),
            )

    @xfail_if_mps  # dtypes mismatch
    def test_nll_loss_backward(self):
        def fn(a, b, c):
            return aten.nll_loss_backward(
                a, b, c, None, 1, -100, torch.tensor(1.0, device=self.device)
            )

        labels = (
            torch.zeros([5], dtype=torch.int64),
            torch.tensor([-100, -100, 3, -100, -100], dtype=torch.int64),
        )
        inps = (torch.randn(5, 5), torch.randn(5, 5))
        grad_outs = (torch.randn(()), torch.randn(()))
        for a, b, c in zip(grad_outs, inps, labels):
            self.common(
                fn,
                (a, b, c),
            )

    @xfail_if_mps  # dtypes mismatch
    def test_nll_loss_backward_1d_input(self):
        # 1D input with 1D target used to crash the decomposition because
        # target.unsqueeze(0) produced a 2D index for a 1D scatter.
        def fn(a, b, c):
            return aten.nll_loss_backward(
                a, b, c, None, 0, 10, torch.tensor(1.0, device=self.device)
            )

        # 1D target — validation requires target.size(0) == self.size(0)
        self.common(
            fn,
            (torch.randn(1), torch.randn(3), torch.tensor([2, 0, 1])),
        )
        # scalar target (the no_batch_dim path in the C++ kernel)
        self.common(
            fn,
            (torch.randn(1), torch.randn(3), torch.tensor(2)),
        )

    def test_isinf(self):
        def fn(x):
            return x.isinf(), x.isnan()

        values = [1, float("inf"), 2, float("-inf"), float("nan")]
        for dtype in [torch.float32, torch.float64, torch.half, torch.bfloat16]:
            ctx = (
                contextlib.nullcontext()
                if self.is_dtype_supported(dtype)
                else self.assertRaises(TypeError)
            )
            with ctx:
                self.common(fn, [torch.tensor(values, dtype=dtype)], check_lowp=False)

    @skip_if_halide  # different nan behavior in ==
    def test_isinf2(self):
        def fn(x):
            y = torch.tensor(
                [1, float("inf"), 2, float("-inf"), float("nan")], device=self.device
            )
            return x == y

        self.common(
            fn, (torch.tensor([1, float("inf"), 2, float("-inf"), float("nan")]),)
        )

    def test_any(self):
        def fn(x):
            return (
                x.any(-1),
                x.isinf().any(),
                torch.all(x.isinf(), dim=0),
                torch.all(torch.logical_not(x.isinf())),
            )

        self.common(fn, [-torch.rand(64)])
        tmp = torch.randn(16, 8)
        tmp[1, 1] = float("inf")
        self.common(fn, [tmp])

    @skip_if_gpu_halide
    def test_multilayer_any(self):
        def fn(x):
            return (x.isinf().any(), x.isfinite().all())

        sample = torch.rand(9, 3, 353, 353)
        self.common(fn, [sample])

        sample.view(-1)[-1] = float("inf")
        self.common(fn, [sample])

    def test_inplace_activations(self):
        def fn(x):
            a = aten.hardswish_(x + 1)
            b = aten.hardtanh_(x + 1)
            c = aten.leaky_relu_(x + 1)
            d = aten.silu_(x + 1)
            e = aten.log1p(x + 1)
            f = aten.masked_fill_(x + 1, torch.zeros_like(x, dtype=torch.bool), 99.0)
            h = aten.masked_fill_(x + 1, torch.ones_like(x, dtype=torch.bool), 99.0)
            return (a, b, c, d, e, f, h)

        self.common(fn, [torch.randn(64) * 10])

    def test_baddbmm(self):
        def fn(a, b, c, beta):
            return aten.baddbmm(a, b, c, beta=beta)

        b = torch.randn(6, 128, 64)
        c = torch.randn(6, 64, 100)
        options = itertools.product(
            [torch.randn(6, 1, 100), torch.randn(6, 1, 100).fill_(torch.nan)],
            [0.0, 1.0],
        )
        for a, beta in options:
            self.common(
                fn,
                [a, b, c, beta],
                # Mismatched elements: 1212 / 76800 (1.6%)
                # Greatest absolute difference: 0.001953125 at index (0, 0, 93) (up to 1e-05 allowed)
                # Greatest relative difference: 1.0 at index (3, 19, 4) (up to 0.001 allowed)
                atol=0.002,
                rtol=0.001,
            )

    @config.patch({"triton.max_tiles": 2})
    def test_fuse_tiled(self):
        def fn(a, b, c):
            return a + b, c + 1

        self.common(
            fn, [torch.randn(128, 1), torch.randn(1, 128), torch.randn(128, 128)]
        )

    def test_expand_as(self):
        def fn(a, b):
            return aten.expand_as(a, b), aten.expand_as(a + 1, b + 1) + 1

        self.common(
            fn,
            [
                torch.randn(6, 1, 100),
                torch.randn(6, 128, 100),
            ],
        )

    def test_index_put1(self):
        def fn(a, b, c):
            return (
                torch.index_put(a, [b], c),
                torch.index_put_(a + 1, [b + 1], c + 1) + 1,
            )

        self.common(
            fn,
            [
                torch.randn([800, 256, 7, 7]),
                torch.randperm(601),
                torch.randn([601, 256, 7, 7]),
            ],
        )
        self.common(
            fn, [torch.randn(1024, 4, 2), torch.arange(4), torch.randn(4, 1, 1)]
        )

    def test_index_put2(self):
        def fn(a, b, c):
            return torch.index_put(a, [b], c, True)

        self.common(
            fn,
            [
                torch.randn([100, 256, 7, 7]),
                torch.randint(0, 100, size=[600], dtype=torch.int64),
                torch.randn([600, 256, 7, 7]),
            ],
            # workaround for https://github.com/triton-lang/triton/issues/558
            check_lowp=False,
        )

    def test_index_put3(self):
        def fn(a, b, c):
            torch.ops.aten.index_put_(a, (None, b, None), c)
            a1 = a + 1
            torch.ops.aten.index_put_(a1, (None, b + 1, None), c + 1)
            return (a, a1)

        self.common(
            fn,
            [
                torch.randn([1024, 4, 2]),
                torch.arange(3),
                torch.randn([1024, 1, 2]),
            ],
        )

    def test_index_put4(self):
        # a, b[0] are not broadcastable
        # https://github.com/pytorch/pytorch/issues/97104
        def fn(a, b, c):
            return torch.index_put(a, [b], c)

        self.common(
            fn,
            [
                torch.rand([8, 2]),
                torch.rand([8]) > 0.5,
                torch.rand([]),
            ],
        )

    def test_index_put_as_masked_fill(self):
        def fn(a, b, c, d):
            a = a.clone()
            torch.ops.aten.index_put_(a, [b], c, d)
            return a

        self.common(
            fn,
            (
                torch.randn([1024, 4, 2]),
                torch.randn([1024, 4, 2]) > 0,
                torch.randn([]),
                False,
            ),
        )

        self.common(
            fn,
            (
                torch.randn([1024, 4, 2]),
                torch.randn([1024, 4, 2]) > 0,
                torch.randn([]),
                True,
            ),
        )

    def test_index_put_fallback1(self):
        def fn(a, b, c, d):
            a = a.clone()
            torch.ops.aten.index_put_(a, [b], c, d)
            return a

        self.common(
            fn,
            (
                torch.randn([3]),
                torch.as_tensor([True, True, False]),
                torch.randn([2]),
                False,
            ),
        )

        self.common(
            fn,
            (
                torch.randn([3]),
                torch.as_tensor([True, True, False]),
                torch.randn([2]),
                True,
            ),
        )

    def test_index_put_fallback2(self):
        def fn(a, b, c, d, e):
            a = a.clone()
            torch.ops.aten.index_put_(a, [None, b, c], d, e)
            return a

        self.common(
            fn,
            (
                torch.randn([1, 2, 3]),
                torch.as_tensor([0, 1]),
                torch.as_tensor([True, True, False]),
                torch.randn([]),
                False,
            ),
        )
        self.common(
            fn,
            (
                torch.randn([1, 2, 3]),
                torch.as_tensor([0, 1]),
                torch.as_tensor([True, True, False]),
                torch.randn([]),
                True,
            ),
        )

    def test_index_put_deterministic_fallback(self):
        with DeterministicGuard(True):

            def fn(a, b, c):
                return torch.index_put(a, [b], c, True)

            self.common(
                fn,
                [
                    torch.randn([100, 32]),
                    torch.randint(0, 100, size=[600], dtype=torch.int64),
                    torch.randn([600, 32]),
                ],
                check_lowp=False,
            )

    @skip_if_gpu_halide  # https://github.com/halide/Halide/issues/8312
    def test_index_put_index(self):
        def fn(ind, x, src):
            y = torch.ops.aten.index_put.default(x, [ind], src)
            return torch.ops.aten.index.Tensor(y, [ind])

        args = [torch.tensor([1], dtype=torch.int64), torch.randn(8, 4), torch.randn(4)]
        self.common(fn, args)

    def test_index_put_reinplace(self):
        def fn(x, idx):
            src = torch.ones(idx.size(0), device=x.device)
            x.index_put_((idx,), src)
            return x.expand((2, x.shape[0]))

        a = torch.randn(1024)
        idx = torch.arange(10)
        torch._inductor.metrics.generated_kernel_count = 0
        self.common(fn, (a, idx))
        assertGeneratedKernelCountEqual(self, 1)

    def test_index_put_failed_reinplace(self):
        def fn(x, idx):
            src = torch.ones(idx.size(0), device=x.device)
            y = x.index_put((idx,), src)
            return x, y

        a = torch.randn(1024)
        idx = torch.arange(10)
        torch._inductor.metrics.generated_kernel_count = 0
        self.common(fn, (a, idx))
        assertGeneratedKernelCountEqual(self, 2)

    @skip_if_halide
    @skip_if_pallas
    def test_index_put_duplicate_indices_no_accumulate(self):
        # https://github.com/pytorch/pytorch/issues/174074
        def fn(x):
            x[[0, 0]] += x[[0, 0]]
            return x

        # ncols=2048 forces multiple Triton blocks on GPU, exposing the
        # inter-block race that small tensors mask.
        x = torch.randn(1, 2048, dtype=torch.float32)
        self.common(fn, (x,))

    @skip_if_pallas
    def test_index_put_duplicate_indices_accumulate(self):
        # accumulate=True is well-defined with duplicate indices.
        def fn(x):
            idx = torch.tensor([0, 0], device=x.device)
            vals = x[[0, 0]] * 2
            x.index_put_((idx,), vals, accumulate=True)
            return x

        x = torch.randn(1, 2048, dtype=torch.float32)
        self.common(fn, (x,))

    def test_adding_tensor_offsets(self):
        @torch.compile(fullgraph=True)
        def fn(x):
            return x[16:32]

        with torch.no_grad():
            x = torch.randn(1024, device=self.device)
            self.assertEqual(fn(x[0:]), x[16:][:16])
            self.assertEqual(fn(x[128:]), x[128 + 16 :][:16])

    def test_index_float_zero(self):
        def fn(arg0, arg1, arg2):
            t1 = torch.tanh(arg0)
            t2 = t1.clone()
            t2.fill_(arg1.item())
            t3 = torch.clamp(t2, 0, arg2.size(0) - 1).to(torch.long)
            return torch.nn.functional.embedding(t3, arg2)

        arg0 = torch.randint(0, 1000, [47], dtype=torch.int64, device=self.device)
        arg1 = torch.randint(0, 1000, [], dtype=torch.int64, device=self.device)
        arg2 = torch.rand([256, 88], dtype=torch.float16, device=self.device)

        cfn = torch.compile(fullgraph=True, dynamic=True)(fn)

        self.assertEqual(fn(arg0, arg1, arg2), cfn(arg0, arg1, arg2))

    # from GPT2ForSequenceClassification
    @skip_if_gpu_halide
    def test_index_tensor(self):
        def fn(x, y):
            ne = torch.ops.aten.ne.Scalar(x, 0)
            sum = torch.ops.aten.sum.dim_IntList(ne, [-1])
            sub = torch.ops.aten.sub.Tensor(sum, 1)
            iota = torch.ops.prims.iota.default(
                1,
                start=0,
                step=1,
                dtype=torch.int64,
                device=x.device,
                requires_grad=False,
            )
            return torch.ops.aten.index.Tensor(y, [iota, sub])

        self.common(fn, [torch.randn(1, 1024), torch.randn(1, 1024, 2)])

    @parametrize("combo_kernels", (False, True))
    @config.patch(fallback_random=True)
    def test_bernoulli1(self, combo_kernels):
        with config.patch(combo_kernels=combo_kernels):

            def fn(a):
                b = a.clone()
                # aten.bernoulli_() uses aten.bernoulli.p() behind the scene, so it will be decomposed.
                return aten.bernoulli_(b).sum() / torch.prod(torch.tensor(a.size()))

            p = 0.3
            self.common(
                fn,
                [
                    torch.ones(200, 200) * p,
                ],
                atol=p * 0.06,
                rtol=0.06,
            )

    @skip_if_triton_cpu
    def test_bernoulli2(self):
        def fn(a):
            return aten.bernoulli(a).sum() / torch.prod(torch.tensor(a.size()))

        p = 0.3
        self.common(
            fn,
            [torch.ones(200, 200) * p],
            atol=p * 0.06,
            rtol=0.06,
        )

    def test_narrow(self):
        def fn(x):
            return (
                aten.narrow(x, 1, 10, 16),
                aten.narrow(x + 2, 0, 10, 16) + 1,
                aten.narrow_copy(x, 1, 10, 16),
            )

        self.common(fn, [torch.randn(64, 64)])

    def test_as_strided(self):
        def fn(x):
            return (
                aten.as_strided(x, (8, 8, 64), (8 * 64, 64, 1), 0),
                aten.as_strided(x + 1, (8, 8, 64), (8 * 64, 64, 1), 0) + 2,
            )

        def fn_channels_last(x):
            return (
                aten.as_strided(
                    x, (8, 384, 2, 20, 12), (153600, 1, 61440, 384, 7680), 0
                ),
                aten.as_strided(
                    x + 1, (8, 384, 2, 20, 12), (153600, 1, 61440, 384, 7680), 0
                )
                + 2,
            )

        self.common(fn, [torch.randn(64, 64)])
        self.common(
            fn_channels_last,
            [torch.randn(8, 384, 20, 20).to(memory_format=torch.channels_last)],
        )

    def test_exact_stride(self):
        full = torch.randn((16, 16), device=self.device)
        view = torch.as_strided(full, (16, 8), full.stride())

        def fn(x):
            result = x + x
            result_strided = torch.empty_strided(
                x.size(), x.stride(), device=self.device
            )
            result_strided[:] = result
            return result_strided

        self.common(fn, [view])
        reference_out = fn(view)
        compiled_fn = torch.compile(fn)
        actual_out = compiled_fn(view)
        self.assertEqual(reference_out.stride(), actual_out.stride())

    def test_nonzero_static_stride(self):
        from torch._subclasses import FakeTensorMode

        L = 8
        x = torch.eye(L, device=self.device)

        eager_result = torch.nonzero_static(x, size=L)

        fake_mode = FakeTensorMode()
        with fake_mode:
            fake_x = fake_mode.from_tensor(x)
            fake_result = torch.nonzero_static(fake_x, size=L)

        self.assertEqual(eager_result.stride(), fake_result.stride())

    def test_like_channels_last(self):
        def foo():
            randn = torch.randn((4, 3, 8, 8), device=self.device, dtype=torch.float32)
            xc = randn.contiguous(memory_format=torch.channels_last)
            clone = torch.zeros_like(xc, memory_format=torch.preserve_format)
            rand_like = torch.rand_like(randn)
            return (xc, clone, rand_like)

        out = foo()
        out_comp = torch.compile()(foo)()

        for t, t_comp in zip(out, out_comp):
            self.assertEqual(t.stride(), t_comp.stride())

    def test_as_strided_scatter(self):
        def fn(a, b):
            return aten.as_strided_scatter(
                a * 8 + 10,
                b * 2 - 4,
                size=(a.shape[0], a.shape[1] // 2),
                stride=(a.shape[1], 2),
                storage_offset=0,
            )

        self.common(fn, [torch.randn(10, 1024), torch.randn(10, 512)])

    def test_select_scatter(self):
        def fn(x, a, b):
            return (
                aten.select_scatter(x, a, 1, 0),
                aten.select_scatter(x, b, 0, 1),
            )

        self.common(
            fn,
            [
                torch.randn(8, 197, 38),
                torch.randn(8, 38),
                torch.randn(197, 38),
            ],
        )

    def test_select_scatter_dtype_consistency(self):
        def fn(x, a):
            return (torch.select_scatter(x, a, 1, 0),)

        for dtype in [
            torch.int64,
            torch.float64,
        ]:
            if not self.is_dtype_supported(dtype):
                continue
            self.common(
                fn,
                [
                    torch.randn((8, 197, 38)).to(dtype),
                    torch.randn((8, 38), dtype=torch.float32),
                ],
            )

    @skip_if_gpu_halide  # accuracy issue
    def test_slice_scatter(self):
        def fn(x, a):
            return (
                aten.slice_scatter(x, a, 2, 10, -10),
                aten.slice_scatter(x, a[:, :, :40], 2, 10, -10, 2),
            )

        self.common(
            fn,
            [
                torch.randn(4, 8, 100),
                torch.randn(4, 8, 80),
            ],
        )

    @skip_if_gpu_halide  # accuracy issue
    def test_slice_scatter_backward_with_overlapping_base(self):
        def fn(x, y):
            return torch.slice_scatter(x, y, dim=1, start=0, end=6).sum(dim=1)

        torch.manual_seed(0)
        x = torch.randn(4, 13, 33, device=self.device, requires_grad=True)
        y = torch.randn(4, 6, 33, device=self.device, requires_grad=True)
        grad = torch.randn(4, 33, device=self.device)

        x_ref = x.detach().clone().requires_grad_(True)
        y_ref = y.detach().clone().requires_grad_(True)

        out_ref = fn(x_ref, y_ref)
        out_ref.backward(grad)

        compiled_fn = torch.compile(fn, backend="inductor", fullgraph=True)
        out = compiled_fn(x, y)
        out.backward(grad)

        self.assertEqual(out, out_ref)
        self.assertEqual(x.grad, x_ref.grad)
        self.assertEqual(y.grad, y_ref.grad)

    def test_slice_scatter2(self):
        def fn(a, b):
            return aten.slice_scatter(a, b, 0, 0, 9223372036854775807)

        self.common(
            fn,
            [
                torch.randn([8, 197, 384]),
                torch.randn([8, 197, 384]),
            ],
        )

    def test_slice_scatter3(self):
        def fn(a, b):
            return aten.slice_scatter.default(a, b, 1, 1, 9223372036854775807, 2)

        self.common(
            fn,
            [
                torch.randn([1, 4]),
                torch.randn([1, 2]),
            ],
        )

    def test_slice_scatter4(self):
        def fn(a, b):
            return aten.slice_scatter.default(a, b, 1, 2, 9223372036854775807, 3)

        self.common(
            fn,
            [
                torch.randn([1, 9]),
                torch.randn([1, 3]),
            ],
        )

    def test_slice_scatter5(self):
        # empty slices that require clamping the start or end
        def fn(a, b):
            return (
                aten.slice_scatter.default(a, b, 0, 2, 0, 1),
                aten.slice_scatter.default(a, b, 0, a.shape[0], a.shape[0] + 10, 1),
                aten.slice_scatter.default(a, b, 0, -20, 0, 1),
                aten.slice_scatter.default(a, b, 0, -20, -16, 1),
            )

        a = torch.arange(10, dtype=torch.float)
        b = torch.empty(0)
        self.common(fn, [a, b])

    @with_tf32_off
    def test_slice_scatter_reinplace(self):
        class M(nn.Module):
            def __init__(self, device):
                super().__init__()
                self.linear1 = nn.Linear(64, 64, bias=False)
                self.cache_k = torch.zeros((56, 384, 8, 64), device=device)

            def forward(self, x, start_pos):
                bsz, seqlen, _, _ = x.shape
                xk = self.linear1(x)
                with torch.no_grad():
                    self.cache_k[:bsz, start_pos : start_pos + seqlen] = xk
                keys = self.cache_k[:bsz, : start_pos + seqlen]
                scores = torch.matmul(
                    xk.transpose(1, 2), keys.transpose(1, 2).transpose(2, 3)
                )
                return scores

        kv_cache_module = M(self.device)
        inp = torch.randn(1, 32, 8, 64)

        # Test that the cache update is reinplaced such that the cache is updated inplace
        # rather than copy-scatter-copy-back.

        torch._inductor.metrics.generated_kernel_count = 0
        with torch.no_grad():
            self.common(kv_cache_module, (inp, 1), check_lowp=False)

        if (
            config.triton.native_matmul
            and config.cuda_backend == "triton"
            and self.device == "cuda"
        ):
            assertGeneratedKernelCountEqual(self, 2)
        else:
            assertGeneratedKernelCountEqual(self, 1)

    def test_slice_scatter_dtype_consistency(self):
        # Test dtype consistency of slice_scatter
        def fn(x, y):
            return torch.slice_scatter(y, x, 0)

        for dtype in [
            torch.int64,
            torch.float64,
        ]:
            if not self.is_dtype_supported(dtype):
                continue
            self.common(
                fn,
                [
                    torch.tensor([0], dtype=dtype),
                    torch.tensor([0], dtype=torch.float32),
                ],
            )

    @skip_if_gpu_halide  # compile error on gpu
    def test_scatter1(self):
        def fn(a, dim, index, b):
            return aten.scatter(a, dim, index, b)

        self.common(
            fn,
            [
                torch.zeros(2, 3),
                -1,
                torch.tensor([[0]]),
                torch.ones(2, 3),
            ],
        )

    def test_scatter2(self):
        if self.device == "cuda":
            raise unittest.SkipTest("unstable on sm86")

        check_lowp = True
        if self.device == "xpu":
            check_lowp = False

        def fn(a, dim, index, b):
            return aten.scatter.reduce(a, dim, index, b, reduce="add")

        self.common(
            fn,
            [
                torch.zeros(64, 512),
                0,
                torch.zeros((64, 512), dtype=torch.int64),
                torch.ones(64, 512),
            ],
            check_lowp=check_lowp,
        )

    def test_scatter3(self):
        def fn(a, dim, index, b):
            return aten.scatter(a, dim, index, b, reduce="add")

        check_lowp = True
        if self.device == "xpu":
            check_lowp = False

        self.common(
            fn,
            [
                torch.randn(5, 29, 13),
                2,
                torch.tensor([[[3, 5, 7, 9]]]),
                0.8,  # src can be a scalar
            ],
            # Mismatched elements: 1 / 1885 (0.1%)
            # Greatest absolute difference: 0.00018310546875 at index (0, 0, 3) (up to 1e-05 allowed)
            # Greatest relative difference: 0.0022371364653243847 at index (0, 0, 3) (up to 0.001 allowed)
            atol=2e-4,
            rtol=1e-3,
            check_lowp=check_lowp,
        )

    def test_scatter4(self):
        def fn(x, ind, src):
            return torch.scatter(x, 0, ind, src)

        check_lowp = True
        if self.device == "xpu":
            check_lowp = False

        for deterministic in [False, True]:
            with DeterministicGuard(deterministic):
                self.common(
                    fn,
                    [
                        torch.randn(196, 992),
                        torch.randint(196, (1, 992)),
                        torch.randn(1, 992),
                    ],
                    check_lowp=check_lowp,
                )

    def test_scatter5(self):
        def fn(a, dim, index, b, reduce):
            a = a.clone()
            a.scatter_(dim, index, b, reduce=reduce)
            a1 = a + 1.0
            a1.scatter_(dim, index, b, reduce=reduce)
            return (a, a1)

        check_lowp = True
        if self.device == "xpu":
            check_lowp = False

        for reduce in ["add", "multiply"]:
            self.common(
                fn,
                [
                    torch.ones((4, 5)),
                    0,
                    torch.tensor([[1], [2], [3]], dtype=torch.int64),
                    torch.randn(4, 5),
                    reduce,
                ],
                check_lowp=check_lowp,
            )

    def test_scatter6(self):
        def fn(a, dim, index, b):
            return aten.scatter(a, dim, index, b)

        check_lowp = True
        if self.device == "xpu":
            check_lowp = False

        for deterministic in [False, True]:
            with DeterministicGuard(deterministic):
                self.common(
                    fn,
                    [
                        torch.randn(5, 8, 13),
                        2,
                        torch.tensor([[[3, 5, 7, 9]]]),
                        0.8,  # src can be a scalar
                    ],
                    check_lowp=check_lowp,
                )

    @unittest.skip("Flaky test, needs debugging")
    def test_scatter_add1(self):
        def fn(a, dim, index, b):
            return aten.scatter_add(a, dim, index, b)

        check_lowp = True
        if self.device == "xpu":
            check_lowp = False

        self.common(
            fn,
            [
                torch.randn(2, 3),
                0,
                torch.tensor([[0]]),
                torch.randn(2, 3),
            ],
            check_lowp=check_lowp,
        )

    def test_scatter_add2(self):
        def fn(a, dim, index, b):
            return aten.scatter_add(a, dim, index, b)

        check_lowp = True
        if self.device == "xpu":
            check_lowp = False

        self.common(
            fn,
            [
                torch.randn(2, 3),
                0,
                torch.tensor([[0, 0, 0], [1, 1, 1]]),
                torch.randn(2, 3),
            ],
            check_lowp=check_lowp,
        )

    def test_scatter_add3(self):
        def fn(a, dim, index, b):
            return aten.scatter_add(a, dim, index, b)

        check_lowp = True
        if self.device == "xpu":
            check_lowp = False

        for deterministic in [False, True]:
            if deterministic and self.device == "xpu":
                # There is no deterministic implementation for scatter_add on Intel GPU.
                continue
            with DeterministicGuard(deterministic):
                self.common(
                    fn,
                    [
                        torch.randn(5, 29, 13),
                        2,
                        torch.tensor([[[3, 5, 7, 9]]]),
                        torch.randn(1, 1, 10),
                    ],
                    check_lowp=check_lowp,
                )

    def test_scatter_reduce1(self):
        def fn(a, dim, index, b):
            return aten.scatter_reduce(a, dim, index, b, "sum")

        check_lowp = True
        if self.device == "xpu":
            check_lowp = False

        self.common(
            fn,
            [
                torch.randn(5, 29, 13),
                2,
                torch.tensor([[[3, 5, 7, 9]]]),
                torch.randn(1, 1, 10),
            ],
            check_lowp=check_lowp,
        )

    def test_scatter_reduce2(self):
        def fn(a, dim, index, b, reduce):
            return aten.scatter_reduce(a, dim, index, b, reduce, include_self=False)

        check_lowp = True
        if self.device == "xpu":
            check_lowp = False

        for reduce in ["sum", "amax"]:
            self.common(
                fn,
                [
                    torch.randn(2, 3),
                    0,
                    torch.zeros((2, 3), dtype=torch.int64),
                    torch.randn(2, 3),
                    reduce,
                ],
                check_lowp=check_lowp,
            )

    def test_scatter_reduce3(self):
        def fn(a, dim, index, b, reduce):
            a = a.clone()
            a.scatter_reduce_(dim, index, b, reduce=reduce)
            a1 = a + 1.0
            a1.scatter_reduce_(dim, index, b, reduce=reduce)
            return (a, a1)

        check_lowp = True
        if self.device == "xpu":
            check_lowp = False

        for reduce in ["sum", "prod"]:
            self.common(
                fn,
                [
                    torch.ones((4, 5)),
                    0,
                    torch.tensor([[1], [2], [3]], dtype=torch.int64),
                    torch.randn(4, 5),
                    reduce,
                ],
                check_lowp=check_lowp,
            )

    @skip_if_gpu_halide
    def test_dense_mask_index(self):
        r"""
        There will be a little difference for reduce order between aten and inductor
        https://github.com/pytorch/pytorch/pull/122289
        Absolute difference: 0.00067138671875 (up to 1e-05 allowed)
        Relative difference: 3.1747371732500974e-06 (up to 1.3e-06 allowed)
        """
        kwargs = {}
        if self.device == "cpu":
            kwargs["atol"] = 1e-4
            kwargs["rtol"] = 1.3e-5

        def fn(x, y):
            y = torch.ops.aten.select.int(y, 0, 2)
            z = x * y
            return z.sum()

        self.common(fn, [torch.randn(102400), torch.randn(3)], **kwargs)

    def test_empty1(self):
        def fn():
            return torch.empty((1, 128, 128))

        self.common(fn, [], assert_equal=False)

    def test_empty2(self):
        def fn():
            return aten.empty((1, 128, 128))

        self.common(fn, [], assert_equal=False)

    # `requires_gpu` otherwise
    # RuntimeError: pin_memory=True requires a CUDA or other accelerator backend;
    # no pinned memory allocator is available on this system.
    @requires_gpu()
    @unittest.skipIf(IS_MACOS, "fails on macos")
    @parametrize(
        "constructor",
        [torch.empty, torch.ones, torch.zeros, torch.rand, torch.randn],
        name_fn=lambda constructor: constructor.__name__,
    )
    def test_constructors_pin_memory(self, constructor):
        if self.device != "cpu":
            raise unittest.SkipTest("pin_memory is not supported on non-CPU devices")

        failing_constructors = [torch.rand, torch.ones, torch.zeros]

        def fn():
            return constructor([1, 128, 128], pin_memory=True, device=self.device)

        result = torch.compile(fn, backend="inductor")()
        if constructor in failing_constructors:
            # We will get signal when one of the constructor correctly supports `pin_memory=True`.
            self.assertFalse(result.is_pinned())
        else:
            self.assertTrue(result.is_pinned())
        self.assertEqual(result.shape, [1, 128, 128])

    def test_new_empty(self):
        def fn(a):
            return aten.new_empty(a, [1, 128, 128])

        self.common(fn, [torch.randn(55, device=self.device)], assert_equal=False)

    def test_empty_strided(self):
        def fn():
            return aten.empty_strided(
                [1, 128, 128], [16384, 128, 1], device=self.device
            )

        self.common(fn, [], assert_equal=False)

    def test_new_empty_strided(self):
        def fn(a):
            return aten.new_empty_strided(a, [1, 128, 128], [16384, 128, 1])

        self.common(fn, [torch.randn(55, device=self.device)], assert_equal=False)

    def test_dropout_trivial_0(self):
        def fn1(a):
            return torch.nn.functional.dropout(a, 0.0, True) + a

        self.common(fn1, [torch.randn(55)])

    def test_dropout_trivial_1(self):
        def fn2(a):
            return torch.nn.functional.dropout(a, 1.0, True) + a

        self.common(fn2, [torch.randn(55)])

    @config.patch({"triton.cudagraphs": True})
    @dynamo_config.patch(automatic_dynamic_shapes=True)
    def test_dropout(self):
        random.seed(1234)
        torch.manual_seed(1234)

        @torch.compile(backend="inductor")
        def fn1(a):
            return torch.nn.functional.dropout(a)

        x = torch.ones(1000, device=self.device, dtype=torch.float32)
        result1 = fn1(x)
        self.assertTrue(400 < result1.nonzero().shape[0] < 600)
        self.assertTrue(0.9 < result1.mean().item() < 1.1)

        random.seed(1234)
        torch.manual_seed(1234)

        @torch.compile(backend="inductor")
        def fn2(a):
            return torch.nn.functional.dropout(a, 0.5, True)

        result2 = fn2(x)
        self.assertTrue(400 < result2.nonzero().shape[0] < 600)
        self.assertTrue(0.9 < result2.mean().item() < 1.1)

    @dynamo_config.patch(automatic_dynamic_shapes=True)
    @parametrize("cudagraphs", (True, False))
    def test_dropout_deterministic(self, cudagraphs):
        @torch.compile(backend="inductor")
        def fn(a):
            return torch.nn.functional.dropout(a, 0.55, True)

        with patch.object(config.triton, "cudagraphs", cudagraphs):
            torch._dynamo.reset()

            x = torch.ones(1024, device=self.device, dtype=torch.float32)

            random.seed(1234)
            torch.manual_seed(1234)
            a0 = fn(x).clone()
            a1 = fn(x).clone()
            a2 = fn(x).clone()

            random.seed(1234)
            torch.manual_seed(1234)
            b0 = fn(x).clone()
            b1 = fn(x).clone()
            b2 = fn(x).clone()

            # same seed, same values
            self.assertTrue(torch.allclose(a0, b0))
            self.assertTrue(torch.allclose(a1, b1))
            self.assertTrue(torch.allclose(a2, b2))

            # different calls, different values
            self.assertFalse(torch.allclose(a0, a1))
            self.assertFalse(torch.allclose(a1, a2))

    @parametrize("combo_kernels", (False, True))
    def test_rand_like_deterministic(self, combo_kernels):
        with config.patch(combo_kernels=combo_kernels):

            @torch.compile(backend="inductor")
            def fn(a):
                return torch.rand_like(a), torch.rand_like(a)

            x = torch.ones(1024, device=self.device, dtype=torch.float32)

            random.seed(1234)
            torch.manual_seed(1234)
            a0 = fn(x)[0].clone()
            a1 = fn(x)[0].clone()
            a2 = fn(x)[0].clone()

            random.seed(1234)
            torch.manual_seed(1234)
            b0 = fn(x)[0].clone()
            b1 = fn(x)[0].clone()
            b2 = fn(x)[0].clone()

            # same seed, same values
            self.assertTrue(torch.allclose(a0, b0))
            self.assertTrue(torch.allclose(a1, b1))
            self.assertTrue(torch.allclose(a2, b2))

            # different calls, different values
            self.assertFalse(torch.allclose(a0, a1))
            self.assertFalse(torch.allclose(a1, a2))

            c, d = fn(x)
            self.assertFalse(torch.allclose(c, d))
            self.assertTrue((c >= 0).all())
            self.assertTrue((c < 1).all())
            self.assertTrue((d >= 0).all())
            self.assertTrue((d < 1).all())

    @config.patch(implicit_fallbacks=True)
    def test_needs_contiguous_strides(self):
        # Construct a custom op whose output strides are not contiguous
        @torch.library.custom_op("mylib::myop", mutates_args={})
        def myop(x: torch.Tensor) -> torch.Tensor:
            return torch.zeros(2, 2).t()

        @myop.register_fake
        def _(x):
            return torch.zeros(2, 2).t()

        # custom op that needs contiguous inputs
        @torch.library.custom_op(
            "mylib::second_op",
            mutates_args={},
            tags=[torch._C.Tag.needs_contiguous_strides],
        )
        def second_op(x: torch.Tensor) -> torch.Tensor:
            if not x.is_contiguous():
                raise AssertionError("Expected x to be contiguous")
            return torch.ones(2, 2)

        @second_op.register_fake
        def _(x):
            return torch.ones(2, 2)

        def f(x):
            y = myop(x)
            return second_op(y)

        # Check that the x.is_contiguous() assertion never gets triggered
        x = torch.randn(2, 2)
        _ = torch.compile(f, backend="inductor", fullgraph=True)(x)

    @config.patch(implicit_fallbacks=True)
    def test_fallback_mutable_op_basic(self):
        with torch.library._scoped_library("mylib", "FRAGMENT") as m:

            def impl(a, b, c, d, e=2):
                a.add_(b[0] * c * e)
                if d is not None:
                    d.add_(b[1])

            m.define(
                "inplace_(Tensor(a!) a, Tensor[] b, SymInt c, *, Tensor(b!)? d, SymInt e=2) -> ()"
            )
            m.impl("inplace_", impl, "CompositeExplicitAutograd")

            # We do some clones and copy_ to test that Inductor doesn't reorder
            # the copy_ w.r.t. inplace_.
            def f(a, b1, b2, c, d):
                a_ = a.clone()
                d_ = d if d is None else d.clone()
                torch.ops.mylib.inplace_(a_, (b1, b2), c, d=d_)
                a.copy_(a_)
                if d is not None:
                    d.copy_(d_)
                return ()

            a = torch.tensor([0.0, 1.0, 2])
            b = [torch.tensor([2.0, 3.0, 5.0]), torch.tensor([1.0, 4.0, 6.0])]
            c = 4
            d = torch.tensor([2.0, 1, 0])
            args = (a, b[0], b[1], c, d)
            cloned_args = pytree.tree_map_only(torch.Tensor, torch.clone, args)
            mod = make_fx(f)(*cloned_args)
            cloned_args = pytree.tree_map_only(torch.Tensor, torch.clone, args)
            compiled_f = compile_fx_inner(mod, cloned_args)

            cloned_args = pytree.tree_map_only(torch.Tensor, torch.clone, args)
            compiled_f(list(cloned_args))
            f(*args)
            self.assertEqual(cloned_args, args)

    @config.patch(implicit_fallbacks=True)
    def test_fallback_mutable_op_list_tensor(self):
        @torch.library.custom_op(
            "mylib::mysin",
            mutates_args=["out_list"],
            schema="(Tensor x, Tensor(a!)[]? out_list) -> Tensor",
        )
        def mysin(x, out_list) -> torch.Tensor:
            r = x.sin()
            if out_list is not None:
                out_list[0].copy_(r)
            return r

        @mysin.register_fake
        def _(x, out_list) -> torch.Tensor:
            return torch.empty_like(x)

        def fn(x):
            x = x * 3
            s = [torch.empty_like(x)]
            x = mysin(x, s)
            x = x / 3
            return x, s[0]

        x = torch.randn(3, requires_grad=False)
        expected = fn(x)
        result = torch.compile(fn, fullgraph=True)(x)
        self.assertEqual(result, expected)

    @config.patch(implicit_fallbacks=True)
    def test_fallback_mutable_op_with_return(self):
        with torch.library._scoped_library("mylib", "FRAGMENT") as m:

            def impl(a, b, c, d, e=2):
                a.add_(b[0] * c * e)
                if d is not None:
                    d.add_(b[1])
                return b[0] + b[1]

            m.define(
                "inplace_(Tensor(a!) a, Tensor[] b, SymInt c, *, Tensor(b!)? d, SymInt e=2) -> Tensor"
            )
            m.impl("inplace_", impl, "CompositeExplicitAutograd")

            # We do some clones and copy_ to test that Inductor doesn't reorder
            # the copy_ w.r.t. inplace_.
            def f(a, b0, b1, c, d):
                a_ = a.clone()
                d_ = d if d is None else d.clone()
                res = torch.ops.mylib.inplace_(a_, (b0, b1), c, d=d_)
                a.copy_(a_)
                if d is not None:
                    d.copy_(d_)
                return (res,)

            a = torch.tensor([0.0, 1.0, 2])
            b = [torch.tensor([2.0, 3.0, 5.0]), torch.tensor([1.0, 4.0, 6.0])]
            c = 4
            d = torch.tensor([2.0, 1, 0])
            args = (a, b[0], b[1], c, d)

            cloned_args = pytree.tree_map_only(torch.Tensor, torch.clone, args)
            mod = make_fx(f)(*cloned_args)
            cloned_args = pytree.tree_map_only(torch.Tensor, torch.clone, args)
            compiled_f = compile_fx_inner(mod, cloned_args)

            cloned_args = pytree.tree_map_only(torch.Tensor, torch.clone, args)
            compiled_out = compiled_f(list(cloned_args))
            out = f(*args)
            self.assertEqual(cloned_args, args)
            self.assertEqual(compiled_out, out)

    @config.patch(implicit_fallbacks=True)
    def test_fallback_mutable_op_no_mutated_tensors(self):
        with torch.library._scoped_library("mylib", "FRAGMENT") as m:

            def impl(a, b):
                if b is not None:
                    b.add_(1)

            m.define("inplace_(Tensor a, Tensor(b!)? b) -> ()")
            m.impl("inplace_", impl, "CompositeExplicitAutograd")

            def f(a):
                torch.ops.mylib.inplace_(a, None)
                return ()

            a = torch.tensor([0.0, 1.0, 2])
            args = (a,)
            cloned_args = pytree.tree_map_only(torch.Tensor, torch.clone, args)
            mod = make_fx(f)(*cloned_args)
            cloned_args = pytree.tree_map_only(torch.Tensor, torch.clone, args)
            compiled_f = compile_fx_inner(mod, cloned_args)

            cloned_args = pytree.tree_map_only(torch.Tensor, torch.clone, args)
            compiled_f(list(cloned_args))
            f(*args)
            self.assertEqual(cloned_args, args)

    @config.patch(implicit_fallbacks=True)
    def test_fallback_mutable_op_list(self):
        with torch.library._scoped_library("mylib", "FRAGMENT") as m:

            def impl(a, b):
                for bi in b:
                    bi.add_(a)

            m.define("inplace_(Tensor a, Tensor(a!)[] b) -> ()")
            m.impl("inplace_", impl, "CompositeExplicitAutograd")

            def f(a, b):
                torch.ops.mylib.inplace_(a, b)
                return None

            a = torch.tensor([0.0, 1.0, 2])
            b = [torch.tensor([2.0, 3.0, 5.0]), torch.tensor([1.0, 4.0, 6.0])]
            args = (a, b)
            cloned_args = pytree.tree_map_only(torch.Tensor, torch.clone, args)
            mod = make_fx(f)(*cloned_args)
            cloned_args = pytree.tree_map_only(torch.Tensor, torch.clone, args)

            compiled_f = compile_fx_inner(mod, cloned_args)

        @torch.library.custom_op("mylib::sin_out", mutates_args={"outs"})
        def sin_out(x: torch.Tensor, outs: list[torch.Tensor]) -> None:
            x_np = x.numpy()
            if len(outs) != 2:
                raise AssertionError(f"Expected len(outs) == 2, got {len(outs)}")
            out_np0 = out[0].numpy()
            out_np1 = out[1].numpy()
            np.sin(x_np, out=out_np0)
            np.sin(x_np, out=out_np1)

        @torch.compile
        def g(x):
            outs = [torch.empty_like(x) for _ in range(2)]
            sin_out(x, outs)
            return outs

        x = torch.randn(3)
        out = [torch.empty_like(x) for _ in range(2)]
        y = g(x)

    @xfail_if_mps_unimplemented  # rng_prims not supported for MPS
    def test_functionalize_rng_wrappers(self):
        # Ideally, we would like to use torch.compile for these operators. But
        # currently the plan is to introduce these operators at the partitioner
        # level, obviating the need to support them fully through the
        # torch.compile stack. To ensure that we have good enough debugging with
        # minifiers, we have ensure that they work with make_fx. This test uses
        # make_fx to do the testing. In future, we can move on torch.compile.
        def fn():
            rng_state1, a1 = torch._prims.rng_prims.run_and_save_rng_state(
                torch.ops.aten.rand.default,
                [4, 4],
                dtype=torch.float32,
                device=self.device,
            )
            rng_state2, a2 = torch._prims.rng_prims.run_and_save_rng_state(
                torch.ops.aten.rand.default,
                [4, 4],
                dtype=torch.float32,
                device=self.device,
            )

            b1 = torch._prims.rng_prims.run_with_rng_state(
                rng_state1,
                torch.ops.aten.rand.default,
                [4, 4],
                dtype=torch.float32,
                device=self.device,
            )
            b2 = torch._prims.rng_prims.run_with_rng_state(
                rng_state2,
                torch.ops.aten.rand.default,
                [4, 4],
                dtype=torch.float32,
                device=self.device,
            )

            return (a1, a2, b1, b2)

        mod = make_fx(fn)()
        compiled_f = compile_fx_inner(mod, ())
        a1, a2, b1, b2 = compiled_f(())
        self.assertEqual(a1, b1)
        self.assertEqual(a2, b2)

    @patch.object(torch._functorch.config, "functionalize_rng_ops", True)
    @expectedFailureXPU
    @skip_if_gpu_halide  # rand
    @xfail_if_mps
    def test_philox_rand(self):
        if self.device == "cpu":
            raise unittest.SkipTest(
                f"functionalization of rng ops supported only on {GPU_TYPE}"
            )

        @torch.compile(backend="inductor")
        def fn(x):
            a = torch.rand_like(x) * x
            a = torch.rand_like(x) * a
            return a

        def check(x):
            torch.manual_seed(123)
            a = fn(x)

            torch.manual_seed(1234)
            b = fn(x)

            torch.manual_seed(123)
            c = fn(x)

            # same seed, same values
            self.assertTrue(torch.allclose(a, c))

            # different calls, different values
            self.assertFalse(torch.allclose(a, b))

        check(torch.ones(1024, device=self.device, dtype=torch.float32))
        # Need comment: should we add "_get_rng_state_offset" to common device interface?
        self.assertEqual(getattr(torch, self.device)._get_rng_state_offset(), 2048)
        # Check non-multiple of 4 numel
        check(torch.ones(3, device=self.device, dtype=torch.float32))
        self.assertEqual(getattr(torch, self.device)._get_rng_state_offset(), 8)

    # Already on by default, just want to make sure
    @patch.object(torch._inductor.config, "allow_buffer_reuse", True)
    def test_reuse_buffers_with_aliasing(self):
        def f(x):
            z = x + 1
            z = torch.view_as_complex(z)
            a = torch.view_as_real(z)
            out = a + 1
            return out, torch.view_as_real(z + 1)

        self.common(f, (torch.zeros((4, 2)),))

        code = run_and_get_triton_code(torch.compile(f), torch.zeros((4, 2)))
        # Make sure that we haven't added complex support and made this test
        # invalid. If we've added complex support please update the test to use
        # a different set of view ops we don't lower
        self.assertTrue("aten.view_as_real" in code)

        def f2(x):
            z = x + 1
            z = torch.view_as_complex(z)
            z = torch.view_as_real(z)
            z = torch.view_as_complex(z)
            a = torch.view_as_real(z)
            out = a + 1
            return out, torch.view_as_real(z + 1)

        self.common(f, (torch.zeros((4, 2)),))

    def test_softmax_backward_data(self):
        def fn(a, b):
            return aten._softmax_backward_data(a, b, dim=1, input_dtype=torch.float32)

        self.common(
            fn,
            (
                torch.randn(10, 10),
                torch.randn(10, 10),
            ),
        )

    def test_randn_like_empty(self):
        class Model(torch.nn.Module):
            def __init__(
                self,
            ):
                super().__init__()

            def forward(self, v1: torch.Tensor):
                vx = v1.min(dim=1).values
                v2 = torch.randn_like(vx)
                return v2

        model = Model()
        x = torch.rand(10, 3, 0)

        self.common(model, (x,), exact_stride=True)

    def test_randint(self):
        @torch.compile(fullgraph=True)
        def fn(x):
            return (
                torch.randint(10, [1024], device=x.device),
                torch.randint(-4, 7, [1024], dtype=torch.int32, device=x.device),
                torch.randint_like(x, 2**50),
            )

        torch.manual_seed(12345)
        a0, b0, c0 = fn(torch.zeros([40, 40], device=self.device))
        self.assertEqual(a0.shape, [1024])
        self.assertEqual(b0.shape, [1024])
        self.assertEqual(c0.shape, [40, 40])
        torch.manual_seed(12345)
        a1, b1, c1 = fn(torch.zeros([40, 40], device=self.device))
        self.assertEqual(a0, a1)
        self.assertEqual(b0, b1)
        self.assertEqual(c0, c1)

        self.assertEqual(a0.min(), 0)
        self.assertEqual(a0.max(), 9)

        self.assertEqual(b0.min(), -4)
        self.assertEqual(b0.max(), 6)

        self.assertGreaterEqual(c0.min(), 0)
        self.assertGreater(c0.max(), 2**40)
        self.assertLess(c0.max(), 2**50)

    def test_randint_distribution(self):
        @torch.compile(fullgraph=True)
        def fn(n_argsmax, size):
            return torch.randint(n_max, (size,), device=self.device)

        def bin(index, max_size):
            return index // (max_size // n_bins)

        size = 1_000_000
        n_max = int(0.75 * 2**32)
        n_bins = 8

        res = fn(n_max, size)
        bins = bin(res, n_max).float().cpu()
        hist, _ = bins.histogram(8, range=(0, n_bins))
        expected_bin = res.shape[0] / 8
        expected_error = math.sqrt(expected_bin) / expected_bin * 3
        error = (hist - expected_bin).abs().max() / expected_bin
        self.assertTrue(error < expected_error)

    @config.patch(fallback_random=True)
    @xfail_if_mps  # 100% are not close
    def test_like_rands(self):
        def fn(x):
            return torch.rand_like(x), torch.randn_like(x), torch.randint_like(x, 1, 11)

        self.common(fn, [torch.zeros([20, 20])], exact_stride=True)

    @config.patch(fallback_random=True)
    def test_like_rands_sliced(self):
        def fn(x):
            return (
                torch.randn_like(x),
                torch.randn_like(x),
                torch.randint_like(x, 1, 11),
            )

        self.common(fn, (torch.zeros([3, 4])[:, ::2].permute(1, 0),), exact_stride=True)

    @config.patch(fallback_random=True)
    def test_like_randn_non_contiguous_rng_consistency(self):
        x = torch.zeros((3, 6), device=self.device).t()
        self.assertFalse(x.is_contiguous())

        randn_like_compiled = torch.compile(torch.randn_like, backend="inductor")

        torch.manual_seed(0)
        eager_out = torch.randn_like(x)
        torch.manual_seed(0)
        compiled_out = randn_like_compiled(x)

        self.assertEqual(eager_out, compiled_out)

    @config.patch(fallback_random=False)
    def test_fast_like_rands_decomps_use_non_eager_path(self):
        x = torch.zeros(3, 6, device=self.device)

        randn_like_compiled = torch.compile(torch.randn_like, backend="inductor")
        torch.manual_seed(0)
        eager_randn_out = torch.randn_like(x)
        torch.manual_seed(0)
        compiled_randn_out = randn_like_compiled(x)
        self.assertNotEqual(eager_randn_out, compiled_randn_out)

    @config.patch(check_stack_no_cycles_TESTING_ONLY=True)
    def test_check_stack_no_cycles(self):
        if config.cpp_wrapper and self.device != "cpu":
            raise unittest.SkipTest(
                "codegen() gets called twice in cpp_wrapper GPU compilation, which "
                "causes this test to fail.  This can be removed if GPU compilation is "
                "done in a single pass."
            )

        @torch.compile()
        def fn(x):
            return x * 3

        r = fn(torch.randn(2, device=self.device, requires_grad=True))
        # Backward compilation isn't hooked into cprofile, it probably
        # should...
        # r.sum().backward()

    def test_like_rands2(self):
        # rand_like with kwargs `device` of str type
        d = self.device
        if not isinstance(d, str):
            raise AssertionError(f"Expected d to be str, got {type(d)}")

        @torch.compile
        def fn(x):
            return torch.rand_like(x, device=d)

        x = torch.ones(10, device=self.device, dtype=torch.float32)
        a0 = fn(x).clone()
        a1 = fn(x).clone()
        self.assertFalse(torch.allclose(a0, a1))
        self.assertEqual(a0.shape, a1.shape)
        self.assertEqual(a0.stride(), a1.stride())

    @requires_gpu()
    @skip_if_triton_cpu("Flaky on Triton CPU")
    def test_like_rands3(self):
        # rand_like with `device` which is different from `x.device`
        def test_like_rands_on_different_device(device1, device2):
            @torch.compile
            def fn(x, device):
                return torch.rand_like(x, device=device)

            x = torch.ones(10, device=device1, dtype=torch.float32)
            return fn(x, device2).clone()

        a0 = test_like_rands_on_different_device("cpu", GPU_TYPE)
        a1 = test_like_rands_on_different_device(GPU_TYPE, "cpu")
        self.assertTrue(a0.device.type == GPU_TYPE)
        self.assertTrue(a1.device.type == "cpu")
        self.assertEqual(a0.shape, a1.shape)
        self.assertEqual(a0.stride(), a1.stride())

    def test_max_pool2d_with_indices_backward(self):
        def fn(a, b, c):
            return aten.max_pool2d_with_indices_backward(
                a, b, [2, 2], [2, 2], [0, 0], [1, 1], False, c
            )

        x = torch.randn([2, 4, 18, 14])
        result, indices = aten.max_pool2d_with_indices(
            x,
            [2, 2],
            [2, 2],
            [0, 0],
            [1, 1],
            False,
        )

        self.common(
            fn,
            [
                torch.randn_like(result),
                x,
                indices,
            ],
        )

    @xfail_if_mps  # Small tolerances bug
    @skip_if_gpu_halide  # slow
    def test_max_pool2d_with_indices_backward2(self):
        def fn(a, b, c):
            return aten.max_pool2d_with_indices_backward(
                a, b, [3, 3], [2, 2], [1, 1], [1, 1], True, c
            )

        x = torch.randn([2, 4, 40, 56])
        result, indices = aten.max_pool2d_with_indices(
            x,
            [3, 3],
            [2, 2],
            [1, 1],
            [1, 1],
            True,
        )

        self.common(
            fn,
            [
                torch.randn_like(result),
                x,
                indices,
            ],
        )

    # From https://github.com/pytorch/torchdynamo/issues/1200
    def test_max_pool2d_with_indices_backward3(self):
        def fn(a, b, c):
            return aten.max_pool2d_with_indices_backward(
                a, b, [1, 1], [2, 2], [0, 0], [1, 1], False, c
            )

        x = torch.randn([32, 256, 37, 38])
        result, indices = aten.max_pool2d_with_indices(
            x,
            [1, 1],
            [2, 2],
            0,
            1,
            False,
        )
        self.common(
            fn,
            [
                torch.randn_like(result),
                x,
                indices,
            ],
        )

    # From https://github.com/pytorch/torchdynamo/issues/1352
    @xfail_if_mps  # Small tolerances bug
    @skip_if_halide  # hangs forever
    def test_max_pool2d_with_indices_backward4(self):
        def fn(a, b, c):
            return aten.max_pool2d_with_indices_backward(
                a, b, [5, 5], [1, 1], [2, 2], [1, 1], False, c
            )

        torch._inductor.metrics.generated_kernel_count = 0
        x = torch.randn([2, 64, 3, 4])
        result, indices = aten.max_pool2d_with_indices(
            x,
            [5, 5],
            [1, 1],
            2,
            1,
            False,
        )
        self.common(
            fn,
            [
                torch.randn_like(result),
                x,
                indices,
            ],
        )
        # Note: Kernel count varies by backend (CUDA ~3, ROCm ~2) due to fusion.
        # Correctness is validated by self.common() above.
        self.assertGreater(torch._inductor.metrics.generated_kernel_count, 0)

    def test_max_pool2d_with_indices_backward5(self):
        # Large window size - decomposition handles via scatter_add
        def fn(a, b, c):
            return aten.max_pool2d_with_indices_backward(
                a, b, [13, 13], [1, 1], [2, 2], [1, 1], False, c
            )

        torch._inductor.metrics.generated_kernel_count = 0
        x = torch.randn([2, 64, 20, 20])
        result, indices = aten.max_pool2d_with_indices(
            x,
            [13, 13],
            [1, 1],
            2,
            1,
            False,
        )
        self.common(
            fn,
            [
                torch.randn_like(result),
                x,
                indices,
            ],
        )
        # Note: Kernel count varies by backend (CUDA ~3, ROCm ~2) due to fusion.
        # Correctness is validated by self.common() above.
        # MPS: decomposition falls back to native kernel, so no inductor kernels generated
        if self.device != "mps":
            self.assertGreater(torch._inductor.metrics.generated_kernel_count, 0)

    # From https://github.com/pytorch/pytorch/issues/93384
    def test_max_pool2d_with_indices_backward6(self):
        # dilation != 1 - decomposition handles all dilation cases
        def fn(a, b, c):
            return aten.max_pool2d_with_indices_backward(
                a, b, [3, 2], [2, 1], [1, 1], [1, 2], False, c
            )

        torch._inductor.metrics.generated_kernel_count = 0
        x = torch.randn([2, 2, 3, 6])
        result, indices = aten.max_pool2d_with_indices(
            x,
            [3, 2],
            [2, 1],
            [1, 1],
            [1, 2],
            False,
        )
        self.common(
            fn,
            [
                torch.randn_like(result),
                x,
                indices,
            ],
        )
        # Note: Kernel count varies by backend (CUDA ~3, ROCm ~2) due to fusion.
        # Correctness is validated by self.common() above.
        # MPS: decomposition falls back to native kernel, so no inductor kernels generated
        if self.device != "mps":
            self.assertGreater(torch._inductor.metrics.generated_kernel_count, 0)

    def test_issue102546(self):
        def fn(x):
            return x.mean(0)

        self.common(fn, [torch.rand(())])

    def test_avg_pool2d_backward(self):
        def fn(a, b):
            return aten.avg_pool2d_backward(
                a,
                b,
                [2, 2],
                [2, 2],
                [0, 0],
                True,
                False,
                None,
            )

        self.common(
            fn,
            [
                torch.randn([2, 4, 7, 7]),
                torch.randn([2, 4, 14, 14]),
            ],
        )

    @skip_if_gpu_halide  # slow
    def test_avg_pool2d_backward2(self):
        def fn(a, b):
            return aten.avg_pool2d_backward(
                a,
                b,
                [3, 3],
                [1, 1],
                [1, 1],
                True,
                False,
                None,
            )

        self.common(
            fn,
            [
                torch.randn([1, 1, 20, 15]),
                torch.randn([1, 1, 20, 15]),
            ],
        )

    def test_avg_pool2d_backward3(self):
        def fn(a, b):
            return aten.avg_pool2d_backward(
                a,
                b,
                [1, 1],
                [2, 2],
                [0, 0],
                False,
                False,
                None,
            )

        torch._inductor.metrics.generated_kernel_count = 0
        self.common(
            fn,
            [
                torch.randn([1, 2016, 11, 11]),
                torch.randn([1, 2016, 21, 21]),
            ],
        )
        assertGeneratedKernelCountEqual(self, 1)

    def test_avg_pool2d_backward4(self):
        def fn(a, b):
            return aten.avg_pool2d_backward(
                a,
                b,
                [13, 13],
                [1, 1],
                [0, 0],
                True,
                False,
                None,
            )

        torch._inductor.metrics.generated_kernel_count = 0
        self.common(
            fn,
            [
                torch.randn([1, 16, 12, 12]),
                torch.randn([1, 16, 24, 24]),
            ],
            check_lowp=False,
        )
        assertGeneratedKernelCountEqual(self, 0)

    def test_avg_pool3d_backward(self):
        def fn(a, b):
            return aten.avg_pool3d_backward(
                a,
                b,
                [2, 2, 2],
                [2, 2, 2],
                [0, 0, 0],
                True,
                False,
                None,
            )

        self.common(
            fn,
            [
                torch.randn([2, 4, 7, 7, 7]),
                torch.randn([2, 4, 14, 14, 14]),
            ],
        )

    @skip_if_halide  # compiles for 5+ minutes
    def test_avg_pool3d_backward2(self):
        def fn(a, b):
            return aten.avg_pool3d_backward(
                a,
                b,
                [3, 3, 3],
                [1, 1, 1],
                [1, 1, 1],
                True,
                False,
                None,
            )

        self.common(
            fn,
            [
                torch.randn([1, 1, 20, 20, 15]),
                torch.randn([1, 1, 20, 20, 15]),
            ],
        )

    def test_avg_pool3d_backward3(self):
        def fn(a, b):
            return aten.avg_pool3d_backward(
                a,
                b,
                [1, 1, 1],
                [2, 2, 2],
                [0, 0, 0],
                False,
                False,
                None,
            )

        torch._inductor.metrics.generated_kernel_count = 0
        self.common(
            fn,
            [
                torch.randn([1, 2016, 11, 11, 11]),
                torch.randn([1, 2016, 21, 21, 21]),
            ],
        )
        assertGeneratedKernelCountEqual(self, 1)

    def test_avg_pool3d_backward4(self):
        def fn(a, b):
            return aten.avg_pool3d_backward(
                a,
                b,
                [13, 13, 13],
                [1, 1, 1],
                [0, 0, 0],
                True,
                False,
                None,
            )

        torch._inductor.metrics.generated_kernel_count = 0
        self.common(
            fn,
            [
                torch.randn([1, 16, 12, 12, 12]),
                torch.randn([1, 16, 24, 24, 24]),
            ],
            check_lowp=False,
        )
        assertGeneratedKernelCountEqual(self, 0)

    @config.patch(search_autotune_cache=False)
    def test_mm_views(self):
        def fn(a, b):
            return torch.mm(a.view(32, 32), b.view(32, 32))

        self.common(
            fn,
            (
                torch.randn([32, 32]).transpose(0, 1),
                torch.randn([1, 32, 32]).transpose(0, 1),
            ),
            check_lowp=False,
        )

        if (
            config.triton.native_matmul
            and config.cuda_backend == "triton"
            and self.device == "cuda"
        ):
            self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)
        else:
            # codegen mm kernel from template
            self.assertEqual(torch._inductor.metrics.generated_kernel_count, 0)

    @torch._dynamo.config.patch(assume_static_by_default=False)
    def test_dtype_sympy_expr(self):
        @torch._dynamo.optimize_assert("inductor")
        def fn(a):
            y = a[..., :-1, :].contiguous()
            return y

        result = fn(torch.randn([1, 2, 16, 4]).requires_grad_())
        result.sum().backward()

    @xfail_if_mps
    def test_dropout2(self):
        if is_mps_backend(self.device) and torch._inductor.config.align_random_eager:
            raise AssertionError(
                "MPS + align_random_eager: will pass but force failure for xfail_if_mps"
            )
        n = 100000
        weight = torch.ones(
            n, device=self.device, dtype=torch.float32, requires_grad=True
        )
        ones = torch.ones(n, device=self.device, dtype=torch.float32)

        @torch._dynamo.optimize_assert("inductor")
        def run(x, train=True):
            return F.dropout(x * weight, 0.33, train)

        def check(r, g):
            rmean = r.mean().item()
            gmean = g.mean().item()
            rcount = len(r.nonzero())
            gcount = len(g.nonzero())

            # dropped elements should match
            self.assertTrue(same(r.nonzero(), g.nonzero()))
            self.assertEqual(rcount, gcount)

            # dropped should be close to 0.33
            self.assertGreater(rcount, 0.64 * n)
            self.assertGreater(0.68 * n, rcount)

            self.assertAlmostEqual(rmean, gmean)
            self.assertAlmostEqual(rmean, 1.0, places=2)

        r1 = run(ones, train=False)
        r1.sum().backward()
        g1 = weight.grad.clone()
        # eval mode should be all ones
        self.assertTrue(same(r1, torch.ones_like(r1)))
        self.assertTrue(same(g1, torch.ones_like(g1)))

        torch.manual_seed(1234)
        weight.grad.zero_()
        r2, (fw_code, bw_code) = run_fw_bw_and_get_code(lambda: run(ones))
        if (
            is_halide_backend(self.device)
            and not torch._inductor.config.align_random_eager
        ):
            self.assertEqual(fw_code.count("halide_helpers.rand"), 1)
            self.assertEqual(bw_code.count("halide_helpers.rand"), 0)
        elif self.device == GPU_TYPE and not torch._inductor.config.align_random_eager:
            self.assertEqual(fw_code.count("tl.rand"), 1)
            self.assertEqual(bw_code.count("tl.rand"), 0)
        g2 = weight.grad.clone()
        check(r2, g2)

        torch.manual_seed(1234)
        weight.grad.zero_()
        r3 = run(ones)
        r3.sum().backward()
        g3 = weight.grad.clone()
        check(r3, g3)

        # second run is same result as first
        self.assertTrue(same(r2, r3))
        self.assertTrue(same(g2, g3))

    @xfail_if_mps
    @config.patch(search_autotune_cache=False)
    def test_dropout3(self):
        if is_mps_backend(self.device) and torch._inductor.config.align_random_eager:
            raise AssertionError(
                "MPS + align_random_eager + dynamic shapes: will pass but force failure for xfail_if_mps"
            )
        m = torch.nn.Sequential(
            torch.nn.Linear(32, 32, bias=False),
            torch.nn.Dropout(),
            torch.nn.Linear(32, 32, bias=False),
            torch.nn.Dropout(),
        ).to(self.device)

        @torch._dynamo.optimize_assert("inductor")
        def run(x):
            return m(x)

        torch._inductor.metrics.generated_kernel_count = 0

        result, (fw_code, bw_code) = run_fw_bw_and_get_code(
            lambda: run(torch.randn([8, 32], device=self.device))
        )

        if (
            is_halide_backend(self.device)
            and not torch._inductor.config.align_random_eager
        ):
            self.assertEqual(fw_code.count("halide_helpers.rand"), 2)
            self.assertEqual(bw_code.count("halide_helpers.rand"), 0)
        elif self.device == GPU_TYPE and not torch._inductor.config.align_random_eager:
            # the load_seed_offset arg can be 1 or non-1; depending on whether
            # the triton signature specializes on 1 vs non-1, you might get 1
            # or 2 kernels. In newer versions of triton, there's no specialization
            # so we get only 1 kernel.
            self.assertEqual(fw_code.count("tl.rand"), 2)
            self.assertEqual(bw_code.count("tl.rand"), 0)
            self.assertEqual(
                torch._inductor.metrics.generated_kernel_count,
                4 if not config.triton.native_matmul else 6,
            )
        else:
            self.assertEqual(
                torch._inductor.metrics.generated_kernel_count,
                4,
            )

    @xfail_if_mps  # Only works for triton
    def test_randint_kernel_count(self):
        if self.device != GPU_TYPE:
            raise unittest.SkipTest("Only valid for GPU!")

        @torch._dynamo.optimize_assert("inductor")
        def fn1():
            random_tensor1 = torch.randint(10, [32], device=self.device)
            random_tensor2 = torch.randint(10, [32], device=self.device)
            random_tensor3 = torch.randint(10, [32], device=self.device)
            return random_tensor1, random_tensor2, random_tensor3

        _, source_codes = run_and_get_code(fn1)
        # cpp_wrapper does a 2-pass generation on GPU.
        self.assertEqual(len(source_codes), 1)

        # the load_seed_offset arg can be 1 or non-1; depending on whether
        # the triton signature specializes on 1 vs non-1, you might get 1
        # or 2 kernels. In newer versions of triton, there's no specialization
        # so we get only 1 kernel.
        self.assertEqual(source_codes[0].count("async_compile.triton"), 2)

    def test_roll(self):
        def fn(a):
            return (
                aten.roll(a, [-3, 10], [1, 2]),
                aten.roll(a, [5]),
            )

        self.common(
            fn,
            [
                torch.randn([2, 56, 56, 16]),
            ],
        )

    def test_argmax_min_int32(self):
        # https://github.com/pytorch/pytorch/issues/94055
        def fn(a, b):
            c = a.argmax(3)
            return torch.min(b, c)

        a = torch.rand(3, 4, 2, 1).int()
        b = torch.rand(2, 2, 1, 4, 1).int()
        self.common(fn, (a, b))

    def test_argmax_argmin1(self):
        def fn(x):
            return (aten.argmax(x), aten.argmin(x))

        self.common(
            fn,
            [
                torch.randn([8, 256, 256]),
            ],
        )

    def test_argmax_argmin2(self):
        def fn(x):
            return (
                aten.argmax(x, 0),
                aten.argmin(x, 0),
                aten.argmax(x, 1),
                aten.argmin(x, 1),
            )

        self.common(fn, (torch.randn([144, 144]),))

    def test_argmax_argmin_with_duplicates(self):
        def fn(x):
            return (
                aten.argmax(x, 0),
                aten.argmin(x, 0),
                aten.argmax(x, 1),
                aten.argmin(x, 1),
            )

        # Unrolled reduction
        t1 = torch.randint(2, size=(6, 6))
        self.common(fn, (t1,))

        # Persistent reduction
        t1 = torch.randint(8, size=(32, 32))
        self.common(fn, (t1,))

        # Non-persistent reduction
        t1 = torch.randint(8, size=(1028, 1028))
        self.common(fn, (t1,))

    @xfail_if_mps  # eager nan is wrong, see https://github.com/pytorch/pytorch/issues/130295
    @skip_if_halide  # nan behavior
    def test_argmax_argmin_with_nan(self):
        def fn(x):
            return (
                aten.argmax(x, 0),
                aten.argmin(x, 0),
                aten.argmax(x, 1),
                aten.argmin(x, 1),
            )

        # Unrolled reduction
        t1 = torch.randn((6, 6))
        t1[:, 1] = float("nan")
        t1[:, 3] = float("nan")
        self.common(fn, (t1,))

        # Persistent reduction
        t1 = torch.randn((32, 32))
        t1[:, 4] = float("nan")
        t1[:, 8] = float("nan")
        self.common(fn, (t1,))

        # Non-persistent reduction
        t1 = torch.randn((1028, 1028))
        t1[:, 40] = float("nan")
        t1[:, 100] = float("nan")
        self.common(fn, (t1,))

    @requires_cuda
    def test_max_min_bool(self):
        # Regression test for https://github.com/pytorch/pytorch/issues/174069
        # max/min on boolean tensors returned incorrect indices in Triton
        def fn(x):
            return (
                x.max(0),
                x.min(0),
                x.max(1),
                x.min(1),
            )

        # Unrolled reduction
        t1 = torch.randint(2, size=(6, 6), dtype=torch.bool)
        self.common(fn, (t1,))

        # Persistent reduction
        t1 = torch.randint(2, size=(32, 32), dtype=torch.bool)
        self.common(fn, (t1,))

        # Non-persistent reduction
        t1 = torch.randint(2, size=(1028, 1028), dtype=torch.bool)
        self.common(fn, (t1,))

    def test_conv_backward(self):
        def fn(rank4_inps, rank3_inps, rank5_inps):
            out1 = aten.convolution_backward(
                *rank4_inps,
                [C],
                [1, 1],
                [0, 0],
                [1, 1],
                False,
                [0, 0],
                1,
                [True, True, True],
            )
            out2 = aten.convolution_backward(
                *rank4_inps,
                [C],
                [1, 1],
                [0, 0],
                [1, 1],
                False,
                [0, 0],
                1,
                [True, False, False],
            )
            out3 = aten.convolution_backward(
                *rank3_inps,
                [C],
                [1],
                [0],
                [1],
                False,
                [0],
                1,
                [True, True, True],
            )
            out4 = aten.convolution_backward(
                *rank5_inps,
                [C],
                [1, 1, 1],
                [0, 0, 0],
                [1, 1, 1],
                False,
                [0, 0, 0],
                1,
                [True, True, True],
            )
            return (out1, out2, out3, out4)

        B = 3
        C = 4
        H = 5
        grad_out = torch.randn(B, C, H - 2, H - 2, H - 2)
        inp = torch.randn(B, C, H, H, H)
        weight = torch.randn(C, C, 3, 3, 3)

        def shrink_rank(x, rank):
            res = x
            while res.dim() > rank:
                res = torch.select(res, -1, 0)
            return res.contiguous()

        rank4_inps = [shrink_rank(x, 4) for x in [grad_out, inp, weight]]
        rank3_inps = [shrink_rank(x, 4) for x in [grad_out, inp, weight]]
        rank5_inps = [shrink_rank(x, 5) for x in [grad_out, inp, weight]]

        with torch.backends.cudnn.flags(enabled=True, allow_tf32=False):
            self.common(
                fn,
                [rank4_inps, rank3_inps, rank5_inps],
            )

    def test_argmax_argmin3(self):
        def fn(x):
            return (
                aten.argmax(x, 0),
                aten.argmin(x, 0),
                aten.argmax(x, -1),
                aten.argmin(x, -1),
            )

        self.common(
            fn,
            [torch.randint(0, 5, [64, 64])],
        )

    def test_vdd_clamp(self):
        def fn(x):
            return torch.clamp_min(x, 3)

        self.common(
            fn,
            [
                torch.randn([16], requires_grad=True) * 10,
            ],
        )

    @parametrize(
        "use_block_ptr",
        [
            subtest(True, decorators=[skip_if_not_triton]),
        ],
    )
    def test_tmp_not_defined_issue1(self, use_block_ptr):
        def forward(
            primals_3,
            primals_4,
            add_tensor,
            convert_element_type_default,
            div_default,
            reciprocal_default,
        ):
            var_default = torch.ops.aten.var(
                convert_element_type_default, [2], correction=0
            )
            sub_tensor = torch.ops.aten.sub.Tensor(add_tensor, div_default)
            mul_tensor_1 = torch.ops.aten.mul.Tensor(sub_tensor, reciprocal_default)
            mul_tensor_2 = torch.ops.aten.mul.Tensor(mul_tensor_1, primals_3)
            add_tensor_2 = torch.ops.aten.add.Tensor(mul_tensor_2, primals_4)
            convert_element_type_default_1 = add_tensor_2.to(dtype=torch.float32)
            convert_element_type_default_2 = convert_element_type_default_1.to(
                dtype=torch.float32
            )
            var_default_1 = torch.ops.aten.var(
                convert_element_type_default_2, [2], correction=0
            )
            broadcast_in_dim_default_2 = var_default_1.reshape(1, 512, 1)
            sum_default_1 = convert_element_type_default_2.sum(2)
            add_tensor_3 = torch.ops.aten.add.Tensor(broadcast_in_dim_default_2, 1e-05)
            return (var_default, sum_default_1, add_tensor_3)

        inps = [
            (torch.Size([1024]), torch.float32),
            (torch.Size([1024]), torch.float32),
            (torch.Size([1, 512, 1024]), torch.float32),
            (torch.Size([1, 512, 1024]), torch.float32),
            (torch.Size([1, 512, 1]), torch.float32),
            (torch.Size([1, 512, 1]), torch.float32),
        ]
        inps = [torch.randn(shape, dtype=dtype) for (shape, dtype) in inps]
        with config.patch("triton.use_block_ptr", use_block_ptr):
            self.common(forward, inps, atol=1e-05, rtol=2e-05)

    @unittest.skipIf(
        os.environ.get("BUILD_ENVIRONMENT", "").startswith("parallelnative"),
        "TODO: debug this with asan",
    )
    @skip_if_gpu_halide
    def test_tmp_not_defined_issue2(self):
        def forward(arg38_1, arg81_1, getitem_17, new_zeros_default_4):
            div_tensor_7 = torch.ops.aten.div.Tensor(getitem_17, arg81_1)
            mul_tensor_24 = torch.ops.aten.mul.Tensor(div_tensor_7, arg38_1)
            sum_default_7 = torch.ops.aten.sum.default(mul_tensor_24)
            return (new_zeros_default_4, sum_default_7)

        dtype = torch.float32
        args = [
            ((1, 88, 40, 40), (140800, 1600, 40, 1), dtype),
            ((), (), dtype),
            ((1, 88, 40, 40), (140800, 1600, 40, 1), dtype),
            ((3,), (1,), dtype),
        ]
        args = [
            rand_strided(shape, stride, dtype).requires_grad_(True).add(1)
            for shape, stride, dtype in args
        ]
        self.common(forward, args, atol=1e-5, rtol=1e-5)

    @xfail_if_mps_unimplemented  # embedding bag
    @requires_gpu()
    @skip_if_halide  # cascading accuracy issues due rsqrt fallback
    def test_tmp_not_defined_issue3(self):
        test_device = torch.device(type=self.device)
        test_device_0 = (
            torch.device(type=self.device, index=0)
            if self.device != "cpu"
            else test_device
        )

        def forward(
            self,
            primals_1: "f32[1001, 6]",
            primals_2: "f32[1001]",
            primals_3: "f32[1001, 64]",
            primals_4: "f32[4190]",
            primals_5: "f32[4190]",
            primals_6: "f32[1739, 4190]",
            primals_48: "f32[6144, 4191]",
        ):
            _tensor_constant0: "i64[4190]" = self._tensor_constant0
            lift_fresh_copy: "i64[4190]" = torch.ops.aten.lift_fresh_copy.default(
                _tensor_constant0
            )

            index: "f32[6144, 4190]" = torch.ops.aten.index.Tensor(
                primals_48, [None, lift_fresh_copy]
            )

            _tensor_constant1: "i64[6]" = self._tensor_constant1
            lift_fresh_copy_1: "i64[6]" = torch.ops.aten.lift_fresh_copy.default(
                _tensor_constant1
            )
            index_1: "f32[6144, 6]" = torch.ops.aten.index.Tensor(
                primals_48, [None, lift_fresh_copy_1]
            )
            primals_48 = lift_fresh_copy_1 = None
            permute: "f32[6, 1001]" = torch.ops.aten.permute.default(primals_1, [1, 0])
            addmm: "f32[6144, 1001]" = torch.ops.aten.addmm.default(
                primals_2, index_1, permute
            )
            amax: "f32[6144, 1]" = torch.ops.aten.amax.default(addmm, [-1], True)
            sub: "f32[6144, 1001]" = torch.ops.aten.sub.Tensor(addmm, amax)
            exp: "f32[6144, 1001]" = torch.ops.aten.exp.default(sub)
            sum_1: "f32[6144, 1]" = torch.ops.aten.sum.dim_IntList(exp, [-1], True)
            div: "f32[6144, 1001]" = torch.ops.aten.div.Tensor(exp, sum_1)

            full_default: "i32[6144, 1001]" = torch.ops.aten.full.default(
                [6144, 1001],
                1,
                dtype=torch.int32,
                layout=torch.strided,
                device=test_device_0,
                pin_memory=False,
            )

            iota: "i32[1001]" = torch.ops.prims.iota.default(
                1001,
                start=0,
                step=1,
                dtype=torch.int32,
                device=test_device,
                requires_grad=False,
            )

            mul: "i32[6144, 1001]" = torch.ops.aten.mul.Tensor(full_default, iota)
            iota_1: "i32[6144]" = torch.ops.prims.iota.default(
                6144,
                start=0,
                step=1001,
                dtype=torch.int32,
                device=test_device_0,
                requires_grad=False,
            )
            view: "i32[6150144]" = torch.ops.aten.reshape.default(mul, [-1])
            view_1: "f32[6150144]" = torch.ops.aten.reshape.default(div, [-1])
            _embedding_bag = torch.ops.aten._embedding_bag.default(
                primals_3, view, iota_1, False, 0, False, view_1
            )
            getitem: "f32[6144, 64]" = _embedding_bag[0]
            getitem_1: "i32[6150144]" = _embedding_bag[1]
            getitem_2: "i32[6144]" = _embedding_bag[2]
            getitem_3: "i32[0]" = _embedding_bag[3]
            unsqueeze: "f32[6144, 1, 64]" = torch.ops.aten.unsqueeze.default(getitem, 1)
            var_mean = torch.ops.aten.var_mean.correction(
                index, [1], correction=0, keepdim=True
            )
            getitem_4: "f32[6144, 1]" = var_mean[0]
            getitem_5: "f32[6144, 1]" = var_mean[1]
            add: "f32[6144, 1]" = torch.ops.aten.add.Tensor(getitem_4, 1e-05)
            rsqrt: "f32[6144, 1]" = torch.ops.aten.rsqrt.default(add)
            sub_1: "f32[6144, 4190]" = torch.ops.aten.sub.Tensor(index, getitem_5)
            mul_1: "f32[6144, 4190]" = torch.ops.aten.mul.Tensor(sub_1, rsqrt)
            mul_2: "f32[6144, 4190]" = torch.ops.aten.mul.Tensor(mul_1, primals_4)
            add_1: "f32[6144, 4190]" = torch.ops.aten.add.Tensor(mul_2, primals_5)
            permute_1: "f32[4190, 1739]" = torch.ops.aten.permute.default(
                primals_6, [1, 0]
            )

            return [
                index,
                index_1,
                addmm,
                amax,
                sum_1,
                iota_1,
                view,
                view_1,
                getitem_1,
                getitem_2,
                getitem_3,
                unsqueeze,
                getitem_5,
                rsqrt,
                add_1,
                permute_1,
            ]

        kwargs = aot_graph_input_parser(forward, device=self.device)
        self.common(forward, [], kwargs=kwargs)

    @skip_if_gpu_halide
    @config.patch("halide.scheduler_cpu", "Mullapudi2016")
    def test_misaligned_address_issue1(self):
        def forward(sub_tensor_1, unsqueeze_default):
            gather_default = torch.ops.aten.gather.default(
                sub_tensor_1, 1, unsqueeze_default
            )
            return gather_default

        args = [
            ((1, 1000), (1000, 1), torch.float32),
            ((1, 1), (1, 1), torch.int64),
        ]
        args = [rand_strided(shape, stride, dtype) for shape, stride, dtype in args]
        self.common(forward, args)

    def test_invalid_operand_issue1(self):
        def forward(arg0_1, arg1_1, arg3_1, squeeze, view_1, slice_1):
            slice_scatter = torch.ops.aten.slice_scatter.default(
                slice_1, arg3_1, 1, 1, 9223372036854775807
            )
            slice_scatter_1 = torch.ops.aten.slice_scatter.default(
                arg1_1, slice_scatter, 0, 0, 9223372036854775807
            )
            slice_2 = torch.ops.aten.slice.Tensor(
                slice_scatter_1, 0, 0, 9223372036854775807
            )
            select_scatter = torch.ops.aten.select_scatter.default(
                slice_2, squeeze, 1, 0
            )
            slice_scatter_2 = torch.ops.aten.slice_scatter.default(
                slice_scatter_1, select_scatter, 0, 0, 9223372036854775807
            )
            view = torch.ops.aten.view.default(slice_scatter_2, [-1, 128])
            embedding = torch.ops.aten.embedding.default(arg0_1, view, 1)
            return [embedding, view_1]

        args = [
            ((50005, 768), (768, 1), torch.float32),
            ((8, 128), (128, 1), torch.int64),
            ((8, 127), (127, 1), torch.int64),
            ((8,), (1,), torch.int64),
            ((1024,), (1,), torch.int64),
            ((8, 128), (128, 1), torch.int64),
        ]
        args = [rand_strided(shape, stride, dtype) for shape, stride, dtype in args]
        self.common(forward, args)

    def test_sizehint_issue1(self):
        def forward(x):
            return torch.nn.functional.unfold(
                x, kernel_size=[4, 4], dilation=1, padding=0, stride=[4, 4]
            )

        args = [((2, 24, 56, 56), (75264, 3136, 56, 1), torch.float32, False)]
        args = [
            rand_strided(sh, st, dt).requires_grad_(rg) for (sh, st, dt, rg) in args
        ]
        self.common(forward, args)

    def test_zero_dim_reductions(self):
        for kd in [True, False]:
            inps0 = (torch.zeros(2, 0, device=self.device, dtype=torch.float16), 1, kd)
            failed_ops = [aten.argmin, aten.argmax, aten.max, aten.min]
            for op in failed_ops:
                with self.assertRaisesRegex(
                    IndexError, "Expected reduction dim 1 to have non-zero size"
                ):
                    mod = make_fx(op)(*inps0)
                    _ = compile_fx_inner(mod, inps0)

            pass_ops = [
                lambda *x: fn(*x) for fn in [aten.sum, aten.prod, aten.any, aten.all]
            ]
            for op in pass_ops:
                compiled = torch.compile(op, backend="inductor")
                expected = op(*inps0)
                actual = compiled(*inps0)

            self.assertTrue(torch.allclose(actual, expected, atol=1e-3, rtol=1e-3))

    def test_unfold_zero_dimension_tensor(self):
        def forward(x):
            return torch.unfold_copy(dimension=1, input=x, size=0, step=7)

        x = torch.rand([1, 0], dtype=torch.float32)

        y = forward(x)
        compiled_y = torch.compile(forward, fullgraph=True)(x)

        self.assertEqual(y, compiled_y)

    def test_zero_element_mutation(self):
        class CustomModel(nn.Module):
            def __init__(self) -> None:
                super().__init__()
                self.layer1 = nn.LeakyReLU(negative_slope=5.2955089, inplace=True)

            def forward(self, inputs):
                return self.layer1(inputs)

        ip_size = [0]
        input_tensor = torch.randn(ip_size)

        mymodel = CustomModel()
        self.common(mymodel, (input_tensor,))

    def test_lerp(self):
        # non-contiguous inputs for lerp
        def fn0(i0, i1):
            x1 = i0.transpose(-2, -3)
            return torch.lerp(i1, x1, 70000)

        # contiguous inputs for lerp
        def fn1(i0, i1):
            return torch.lerp(i1, i0, 70000)

        self.common(fn0, [torch.rand(10, 3, 10), torch.rand(3, 10, 10)])
        self.common(fn1, [torch.rand(3, 10, 10), torch.rand(3, 10, 10)])

    @parametrize(
        "dtype",
        test_dtypes,
    )
    def test_unspec_inputs(self, dtype):
        if self.device == "cpu":
            raise unittest.SkipTest("Testing mixed devices")

        if (
            is_halide_backend(self.device)
            and getattr(self.device, "type", self.device) == "cuda"
        ):
            # https://github.com/halide/Halide/issues/8318
            raise unittest.SkipTest("halide not supported")

        if not self.is_dtype_supported(dtype):
            raise unittest.SkipTest(
                f"dtype {dtype} not supported for device {self.device}"
            )

        def fn(x, y):
            return x + y, x * y, x / y

        opt = torch.compile(fn, backend="inductor")
        inputs = (
            rand_strided((2, 3), (3, 1), dtype=torch.float32, device=GPU_TYPE),
            rand_strided((), (), dtype=dtype, device="cpu"),
        )
        self.assertTrue(same(opt(*inputs), fn(*inputs)))
        inputs = (inputs[1], inputs[0])
        self.assertTrue(same(opt(*inputs), fn(*inputs)))

    @dynamo_config.patch(automatic_dynamic_shapes=True)
    def test_list_clearing(self):
        if self.device == "cpu":
            contexts = [contextlib.nullcontext]
        else:
            contexts = [
                contextlib.nullcontext,
                lambda: config.patch({"triton.cudagraphs": True}),
            ]

        for context in contexts:
            with context():
                inps = [
                    torch.rand([5, 5]).to(self.device),
                    torch.rand([5, 5]).to(self.device),
                ]
                inp_refs = [weakref.ref(inp) for inp in inps]

                def fn(x, y):
                    a = x + y
                    return (a @ a,)

                fn_fx = make_fx(fn)(inps[0], inps[1])
                fn_compiled = compile_fx_inner(fn_fx, inps)

                test_self = self
                matmul_seen = False

                class TestRefMode(TorchDispatchMode):
                    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
                        kwargs = kwargs if kwargs else {}

                        nonlocal inps
                        nonlocal inp_refs
                        nonlocal test_self
                        nonlocal matmul_seen

                        # by matmul, inputs should be deallocated
                        # TODO: should not be necessary, ref-cycle ?
                        gc.collect()
                        if func is aten.mm.out:
                            matmul_seen = True
                            test_self.assertEqual(len(inps), 0)
                            test_self.assertIsNone(inp_refs[0]())
                            test_self.assertIsNone(inp_refs[1]())

                        return func(*args, **kwargs)

                with TestRefMode():
                    fn_compiled(inps)

                # do an extra run to make sure we are deallocating on warmup and record
                if self.device == GPU_TYPE:
                    inps.extend(
                        [
                            torch.rand([5, 5]).to(self.device),
                            torch.rand([5, 5]).to(self.device),
                        ]
                    )
                    inp_refs.extend([weakref.ref(inp) for inp in inps])
                    matmul_seen = False

                    with TestRefMode():
                        fn_compiled(inps)

                # for some reason, TorchDispatch doesn't capture the
                # cuda mm call (even without cudagraphs)
                if self.device == "cpu":
                    self.assertTrue(matmul_seen)
                else:
                    self.assertEqual(len(inps), 0)

    def test_dtype_mismatch_issue(self):
        def fn(x):
            attn = torch.nn.functional.pad(x, [0, 1])
            return attn.softmax(dim=-1)

        x = torch.rand(128, 32, 63)
        self.common(fn, (x,))

    def test_vectorized_ops_masked(self):
        def fn(x):
            index = torch.arange(64, device=x.device)
            mask = index.view(1, 1, 64) < 63
            indices = [None, None, index]
            return torch.ops.aten._unsafe_masked_index(x, mask, indices, 7)

        x = torch.rand(128, 32, 63)
        self.common(fn, (x,))

    def test_vectorized_ops_masked_var_novec(self):
        def fn(x):
            index = torch.arange(10, device=x.device)
            mask = (index < 5).view(1, 1, 1, 10)
            indices = [None, None, None, index]
            return torch.ops.aten._unsafe_masked_index(x, mask, indices, 7)

        x = torch.rand(1, 1, 8, 8)
        self.common(fn, (x,))

    def test_diagonal_copy(self):
        def fn(x):
            return torch.diagonal_copy(x)

        for x in (torch.randn(2, 3), torch.randn(2, 2), torch.randn(3, 2)):
            self.common(fn, (x,))

    def test_copy_with_scalar_src(self):
        def fn(x):
            buffer = torch.zeros_like(x)
            buffer.copy_(2)
            result = x + buffer
            return result

        x = torch.randn(64, 64, dtype=torch.float32, device=self.device)
        self.common(fn, (x,))

    def test_kwargs(self):
        if self.device == GPU_TYPE:
            raise unittest.SkipTest("histogramdd only supports cpu")

        def fn(x, y):
            return torch.histogramdd(
                x,
                bins=[3, 3],
                weight=y,
            )

        self.common(
            fn,
            [torch.randn((4, 2)), torch.randn(4)],
        )

    # Shape padding causes the inputs to all get specialized, so the codegen
    # test fails
    @expectedFailureCodegenDynamic
    @requires_gpu()
    @torch._inductor.config.patch("shape_padding", True)
    def test_shape_padding(self):
        dtypes = [
            torch.float16,
            torch.float32,
        ]

        b, m, n, k = 7, 11, 13, 15

        def gen(*shape, dtype=torch.float32):
            return torch.randn(*shape, device=GPU_TYPE, dtype=dtype) / k + 1.0

        for dtype in dtypes:
            x = gen(m, k, dtype=dtype)
            y = gen(k, n, dtype=dtype)
            z = gen(n, dtype=dtype)
            self.common(lambda x, y: torch.mm(x, y), (x, y))
            self.common(lambda x, y: torch.matmul(x, y), (x, y))
            self.common(lambda x, y, z: torch.addmm(z, x, y), (x, y, z))

        for dtype in dtypes:
            x = gen(b, m, k, dtype=dtype)
            y = gen(b, k, n, dtype=dtype)
            z = gen(n, dtype=dtype)
            self.common(lambda x, y: torch.bmm(x, y), (x, y))
            self.common(lambda x, y: torch.matmul(x, y), (x, y))
            self.common(lambda x, y, z: torch.baddbmm(z, x, y), (x, y, z))

    @requires_gpu()
    @torch._inductor.config.patch("layout_optimization", True)
    @tf32_on_and_off(0.005)
    def test_inductor_layout_optimization_input_mutations(self):
        # channel dim must be > 64 for inductor to do layout optimization and use NHWC
        mod = nn.Conv2d(3, 128, 1, stride=1, bias=False).to(self.device)

        def f(x):
            x.mul_(2)
            out = mod(x)
            return out

        f_compiled = torch.compile(f)
        x_ref = torch.rand(2, 3, 128, 128, device=self.device)
        x_test = x_ref.detach().clone()
        with torch.no_grad():
            out_ref = f(x_ref)
            out_test = f_compiled(x_test)
            self.assertEqual(out_ref, out_test)
            self.assertEqual(out_ref.shape, out_test.shape)
            # Importantly, since inductor._config.keep_output_stride is True,
            # the outputs should have matching strides here.
            self.assertEqual(out_ref.stride(), out_test.stride())
            self.assertEqual(x_ref, x_test)

    @requires_gpu()
    @skip_if_not_triton
    @unittest.skipIf(
        not IS_BIG_GPU, "Skipping triton backend only since not big GPU (not enough SM)"
    )
    def test_inductor_multiple_specializations(self):
        @torch.compile(
            options={
                "max_autotune": True,
                "max_autotune_gemm_backends": "TRITON",
            },
            dynamic=False,
        )
        def inductor_matmul(a, b):
            torch._check(a.shape[0] == b.shape[1])
            return (m, torch.mm(a, b))

        m = 16
        k = 1280
        dynamic_a = torch.randn(m, k, device=GPU_TYPE, dtype=torch.bfloat16)
        dynamic_specialized_a = dynamic_a.clone()
        b = torch.randn(k, m, device=GPU_TYPE, dtype=torch.bfloat16)
        torch._dynamo.decorators.mark_dynamic(
            dynamic_a,
            0,
        )
        torch._dynamo.decorators.mark_dynamic(
            dynamic_specialized_a,
            0,
            specialize_on=[lambda x0: x0 == 16],
        )
        torch._dynamo.decorators.mark_dynamic(
            b,
            1,
        )
        dynamic = inductor_matmul(dynamic_a, b)
        torch._dynamo.reset()
        dynamic_specialized = inductor_matmul(dynamic_specialized_a, b)
        self.assertEqual(dynamic, dynamic_specialized)

    @requires_gpu()
    @skip_if_not_triton
    @unittest.skipIf(
        not IS_BIG_GPU, "Skipping triton backend only since not big GPU (not enough SM)"
    )
    @config.patch({"force_disable_caches": True})
    def test_mark_dynamic_with_hint_override(self):
        @torch.compile
        def no_override(x):
            return x.sum(dim=0)

        @torch.compile
        def override(x):
            return x.sum(dim=0)

        x_small = torch.randn(4096, 512, device=GPU_TYPE)
        torch._dynamo.decorators.mark_dynamic(x_small, 0)
        code1 = run_and_get_triton_code(no_override, x_small)

        torch._dynamo.reset_code_caches()

        torch._dynamo.decorators.mark_dynamic(x_small, 0, hint_override=4096 * 10)
        code2 = run_and_get_triton_code(override, x_small)
        self.assertNotEqual(code1, code2)

        self.assertEqual(no_override(x_small), override(x_small))

    @requires_gpu()
    @skip_if_not_triton
    @unittest.skipIf(
        not IS_BIG_GPU, "Skipping triton backend only since not big GPU (not enough SM)"
    )
    @config.patch({"force_disable_caches": True})
    def test_mark_unbacked_with_hint_override(self):
        @torch.compile
        def no_override(x):
            return x.sum(dim=0)

        @torch.compile
        def override(x):
            return x.sum(dim=0)

        @torch.compile(fullgraph=True)
        def branching(x):
            if x.shape[0] > 4096:
                return 1
            return 2

        x_small = torch.randn(4096, 512, device=GPU_TYPE)
        torch._dynamo.decorators.mark_unbacked(x_small, 0)
        code1 = run_and_get_triton_code(no_override, x_small)

        torch._dynamo.reset_code_caches()

        torch._dynamo.decorators.mark_unbacked(x_small, 0, hint_override=4096 * 10)
        code2 = run_and_get_triton_code(override, x_small)
        self.assertNotEqual(code1, code2)

        # Different size hints can produce different reduction orderings,
        # so we need tolerance for floating-point associativity differences
        self.assertEqual(no_override(x_small), override(x_small), atol=1e-4, rtol=1e-4)

        with self.assertRaisesRegex(
            RuntimeError, "Could not guard on data-dependent expression"
        ):
            branching(x_small)

    @requires_gpu()
    @skip_if_not_triton
    @unittest.skipIf(
        not IS_BIG_GPU, "Skipping triton backend only since not big GPU (not enough SM)"
    )
    def test_hint_override_cache_invalidation(self):
        """Different hint_override values must not produce stale cache hits."""
        from torch._inductor.utils import fresh_inductor_cache, run_and_get_code

        HINT_A = 48
        HINT_B = 99

        def fn(x):
            return x.sum(dim=0)

        x = torch.randn(256, 32, device=GPU_TYPE)

        with fresh_inductor_cache():
            compiled_a = torch.compile(fn)
            torch._dynamo.mark_dynamic(x, 0, hint_override=HINT_A)
            _, codes_a = run_and_get_code(compiled_a, x)

            compiled_b = torch.compile(fn)
            torch._dynamo.mark_dynamic(x, 0, hint_override=HINT_B)
            _, codes_b = run_and_get_code(compiled_b, x)

        code_a = codes_a[0]
        code_b = codes_b[0]

        def has_hint(code, h):
            return f"rand_strided(({h}," in code or f"rand_strided(({h}, " in code

        self.assertTrue(has_hint(code_a, HINT_A), f"hint {HINT_A} not in first code")
        self.assertTrue(has_hint(code_b, HINT_B), f"hint {HINT_B} not in second code")
        self.assertFalse(
            has_hint(code_b, HINT_A),
            f"second compilation has hint {HINT_A}; stale cache hit",
        )

    @requires_gpu()
    def test_stride_preservation_with_stride_modifying_fx_pass(self):
        def f(x):
            return x + 1

        def custom_pass(g: torch.fx.Graph) -> None:
            """
            Applies `lambda x: x.t().contiguous().t()` to the output.
            """
            output_node = g.find_nodes(op="output")[0]
            if len(output_node.args) != 1:
                raise AssertionError(
                    f"Expected len(output_node.args) == 1, got {len(output_node.args)}"
                )
            output = output_node.args[0][0]

            with g.inserting_before(output_node):
                output = g.call_function(
                    torch.ops.aten.permute.default, args=(output, [1, 0])
                )
                output = g.call_function(
                    torch.ops.aten.clone.default,
                    args=(output,),
                    kwargs={"memory_format": torch.contiguous_format},
                )
                output = g.call_function(
                    torch.ops.aten.permute.default, args=(output, [1, 0])
                )
            output_node.args = ((output,),)
            return g

        with config.patch(
            post_grad_custom_post_pass=custom_pass,
        ):
            f_compiled = torch.compile(f)

            x = torch.rand(4, 4, device=GPU_TYPE)
            y = f(x)
            y_compiled = f_compiled(x)

            self.assertEqual(y, y_compiled)
            self.assertEqual(y.stride(), y_compiled.stride())

    def test_int_input_dynamic_shapes(self):
        @torch.compile(dynamic=True)
        def fn(x, i):
            y = x * i
            return y

        # Constant must not get matched as constant
        self.common(fn, [torch.randn(3, 1, 1, 1, 1), 9132])

    def test_float_repr_dynamic_shapes(self):
        @torch.compile(dynamic=True)
        def fn(x):
            return F.interpolate(x, scale_factor=1 / 300, mode="linear")

        self.common(fn, [torch.randn(1, 8, 396 * 300)])

    @torch._dynamo.config.patch("capture_scalar_outputs", True)
    def test_pattern_matcher_unbacked(self):
        @torch.compile(fullgraph=True)
        def get_mask(W: torch.Tensor, percentage_nonzeros: torch.Tensor):
            total_elements = W.numel()
            k = total_elements * percentage_nonzeros
            top_k_indices = torch.topk(torch.abs(W).flatten(), k.int())[1]
            mask = torch.zeros(total_elements, dtype=torch.bool, device=W.device)
            mask.scatter_(0, top_k_indices, True)
            mask = mask.view(W.shape)
            return mask

        x = torch.randn((128, 64), device=self.device)
        p = torch.tensor(0.50, device=self.device)
        get_mask(x, p)

    def test_flexible_layout_immutable_free_symbols(self):
        import sympy

        x = sympy.Symbol("x")
        y = sympy.Symbol("y")
        z = sympy.Symbol("z")

        layout = torch._inductor.ir.FlexibleLayout(
            self.device, torch.float32, size=(x, y)
        )

        # pad_strides works since it does not add new symints
        layout.pad_strides()

        # same symints and different order should work
        layout.size = (y, x)

        # adding new symints should fail
        with self.assertRaisesRegex(
            AssertionError, "Expected free symbols unchanged, but got"
        ):
            layout.size = (z,)

    def test_sqrt_dynamic_shapes(self):
        # TIMM convit_base model: https://github.com/pytorch/pytorch/issues/97877.
        # TODO: support cuda path.
        if self.device == GPU_TYPE:
            raise unittest.SkipTest("sqrt dynamic shapes only supports cpu")

        class Model(torch.nn.Module):
            def __init__(self) -> None:
                super().__init__()

            def forward(self, x):
                B, N, C = x.shape
                return self.get_rel_indices(N)

            def get_rel_indices(self, num_patches: int) -> torch.Tensor:
                img_size = int(num_patches**0.5)
                ind = torch.arange(img_size)
                return ind

        self.common(
            Model(),
            [
                torch.randn(8, 4, 4),
            ],
        )

    def test_rsqrt_dynamic_shapes(self):
        # From HF hf_BigBird model.
        @torch.compile(dynamic=True)
        def fn(a, b):
            r = 1 / math.sqrt(a.size(1))
            return torch.bmm(a, b) / r

        self.common(
            fn,
            [
                torch.randn(2, 4, 4),
                torch.randn(2, 4, 4),
            ],
        )

    @xfail_if_triton_cpu
    def test_index_dynamic_shapes(self):
        # Repro from vision_maskrcnn
        def fn(arg0_1):
            unsqueeze = arg0_1.unsqueeze(0)
            sym_size = arg0_1.size(1)
            ceil = math.ceil(sym_size * 1.8735363483428955)
            iota = torch.ops.prims.iota.default(
                ceil,
                start=0,
                step=1,
                dtype=torch.int64,
                device=arg0_1.device,
                requires_grad=False,
            )
            convert_element_type_1 = iota.to(torch.float32)
            sym_size_1 = arg0_1.size(2)
            floor_1 = math.floor(sym_size_1 * 1.8735363483428955)
            ceil_1 = math.ceil(floor_1)
            iota_1 = torch.ops.prims.iota.default(
                ceil_1,
                start=0,
                step=1,
                dtype=torch.int64,
                device=arg0_1.device,
                requires_grad=False,
            )
            convert_element_type_3 = iota_1.to(torch.float32)
            sub_2 = (convert_element_type_1 + 0.5) * (sym_size / ceil) - 0.5
            clamp_min = sub_2.clamp_min(0.0)
            sub_3 = (convert_element_type_3 + 0.5) * (sym_size_1 / floor_1) - 0.5
            clamp_min_1 = sub_3.clamp_min(0.0)
            convert_element_type_4 = clamp_min.to(torch.int64)
            sub_4 = sym_size - 1
            clamp_max = clamp_min.ceil().clamp_max(sub_4)
            convert_element_type_5 = clamp_max.to(torch.int64)
            convert_element_type_6 = clamp_min_1.to(torch.int64)
            unsqueeze_2 = convert_element_type_4.unsqueeze(1)
            index = torch.ops.aten.index.Tensor(
                unsqueeze, [None, None, unsqueeze_2, convert_element_type_6]
            )
            index_1 = torch.ops.aten.index.Tensor(
                unsqueeze,
                [
                    None,
                    None,
                    convert_element_type_5.unsqueeze(1),
                    convert_element_type_6,
                ],
            )
            sub_6 = clamp_min.unsqueeze(1) - unsqueeze_2
            mul_10 = (index * (1.0 - sub_6) + index_1 * (sub_6)) * (
                1.0 - (clamp_min_1 - convert_element_type_6)
            )
            select = torch.ops.aten.select.int(mul_10, 0, 0)
            return (select,)

        x = torch.randn(15, 20, 3)
        self.common(
            fn,
            [x],
        )

    @skip_if_halide  # log2 not yet implemented
    @skip_if_triton_cpu  # log2 implemented only in Dec 2024
    def test_pow_by_natural_log2_dynamic_shapes(self):
        @torch.compile(dynamic=True)
        def fn(x):
            return x + 2 ** (math.floor(math.log2(x.shape[0]) + 1))

        self.common(fn, [torch.randn(5)])

    def test_setitem_with_int_parameter(self):
        x = torch.zeros(7, device=self.device)

        def fn(n, a):
            a[n] = -1
            return a

        cnts = CompileCounterWithBackend("inductor")
        opt_fn = torch.compile(fn, backend=cnts, fullgraph=True)

        for n in range(2, x.shape[0]):
            opt_fn(n, x)
            self.assertEqual(x[n], -1)

        # If assume_static_by_default is set, the calls above will trigger
        # 3 function compilation:
        #   1. assuming 'n' is static (equals 2)
        #   2. making 'n' dynamic, but with the guard 'end <= x.shape[0]'
        #      (from: torch._inductor.ir.SliceView.create)
        frame_count = 2 if torch._dynamo.config.assume_static_by_default else 1
        self.assertEqual(cnts.frame_count, frame_count)

        # Negative index triggers new compilation.
        opt_fn(-x.shape[0], x)
        self.assertEqual(x[0], -1)
        self.assertEqual(cnts.frame_count, frame_count + 1)

    @config.patch(profiler_mark_wrapper_call=True)
    def test_profiler_mark_wrapper_call(self):
        from torch.profiler import profile

        @torch.compile(backend="inductor", fullgraph=True)
        def fn(a, b):
            return a + b

        a = torch.rand((100,), device=self.device)
        b = torch.rand((100,), device=self.device)
        with profile() as prof:
            fn(a, b)
        if not any(
            "inductor_wrapper_call" in e.name for e in prof.profiler.function_events
        ):
            raise AssertionError(
                "Expected 'inductor_wrapper_call' in profiler function events"
            )

    def test_insignificant_strides(self):
        def f(x):
            tmp = x + 1
            return tmp.view(-1, 1, 2)

        x = torch.arange(8, device=self.device, dtype=torch.float32)
        out = f(x)
        compiled_out = torch.compile(f)(x)

        self.assertEqual(out.stride(), compiled_out.stride())
        self.assertEqual(out, compiled_out)

    @unittest.skipIf(IS_X86 and not HAS_AVX2, "Requires AVX2")
    def test_pixel_shuffle_channels_last(self):
        def fn(x):
            x = torch.nn.functional.pixel_shuffle(x, 2)
            x = torch.nn.functional.relu(x)
            return x

        self.common(
            fn,
            (torch.randn(1, 16, 64, 72).to(memory_format=torch.channels_last),),
        )

    def test_where_broadcast(self):
        # https://github.com/pytorch/pytorch/issues/93374
        def fn(x, p1, p0):
            o = torch.where(x, p1, p0)
            return o

        # https://github.com/pytorch/pytorch/issues/94725
        class Repro(torch.nn.Module):
            def __init__(self) -> None:
                super().__init__()
                self._tensor_constant0 = nn.Buffer(torch.randn([], dtype=torch.float32))

            def forward(self, arg0_1, arg1_1):
                convert_element_type = torch.ops.prims.convert_element_type.default(
                    arg1_1, torch.bool
                )
                bitwise_not = torch.ops.aten.bitwise_not.default(convert_element_type)
                _tensor_constant0 = self._tensor_constant0
                lift_fresh_copy = torch.ops.aten.lift_fresh_copy.default(
                    _tensor_constant0
                )
                where = torch.ops.aten.where.self(bitwise_not, lift_fresh_copy, arg0_1)
                return (where, bitwise_not)

        self.common(
            fn,
            (torch.tensor([[True]]), torch.rand(13, 7, 3), torch.rand(1, 1)),
        )

        args = [
            torch.randn(1, 4, 64, 64),
            torch.zeros(1, 1, 64, 64, dtype=torch.uint8),
        ]
        args[1][:, :, :32, :32] = 1
        eager_args = [x.clone() for x in args]
        eager_mod = Repro()
        mod = make_fx(eager_mod, tracing_mode="real")(*args)
        compiled = compile_fx_inner(mod, args)
        inductor_out = compiled(args)
        eager_out = eager_mod(*eager_args)
        self.assertEqual(inductor_out, eager_out)

    def test_require_stride_expanded(self):
        def forward(arg6, arg7, arg16):
            convolution = torch.ops.aten.convolution(
                arg16.unsqueeze(0), arg7, arg6, [4, 4], [2, 2], [1, 1], False, [0, 0], 1
            )
            return (convolution,)

        self.common(
            forward,
            (
                None,
                rand_strided(
                    (64, 3, 11, 11),
                    (363, 121, 11, 1),
                    torch.float32,
                    device=self.device,
                ).to(memory_format=torch.channels_last),
                rand_strided(
                    (1, 3, 224, 224),
                    (150528, 50176, 224, 1),
                    torch.float32,
                    device=self.device,
                )
                .to(memory_format=torch.channels_last)
                .squeeze(0),
            ),
            atol=1e-3,
            rtol=0.001,
        )

        # expanded dim should not cause copy in require_stride_order
        assertGeneratedKernelCountEqual(self, 0)

    @requires_gpu()
    @parametrize("prefer_nd_tiling", (False, True))
    @parametrize("use_block_ptr", (False, True))
    @unittest.skipIf(
        not PLATFORM_SUPPORTS_FLASH_ATTENTION,
        "Does not support SDPA or pre-SM80 hardware",
    )
    def test_sdpa(self, use_block_ptr: bool, prefer_nd_tiling: bool):
        def foo(arg0_1, arg1_1, arg2_1, arg3_1, arg4_1):
            view = torch.ops.aten.view.default(arg3_1, [23760, 128])
            arg3_1 = None
            mm = torch.ops.aten.mm.default(view, arg4_1)
            view = arg4_1 = None
            view_1 = torch.ops.aten.view.default(mm, [3, 99, 80, 8])
            mm = None
            view_2 = torch.ops.aten.view.default(view_1, [3, 99, 80, 8])
            view_1 = None
            permute = torch.ops.aten.permute.default(view_2, [0, 3, 1, 2])
            view_2 = None
            view_3 = torch.ops.aten.view.default(permute, [3, 8, 99, 80])
            permute = None

            clone = torch.ops.aten.clone.default(
                view_3, memory_format=torch.contiguous_format
            )
            view_3 = None

            expand = torch.ops.aten.expand.default(clone, [3, 8, 99, 80])
            clone = None
            _scaled_dot_product_efficient_attention = (
                torch.ops.aten._scaled_dot_product_efficient_attention.default(
                    arg0_1, arg1_1, arg2_1, expand, False
                )
            )
            arg0_1 = arg1_1 = arg2_1 = expand = None
            getitem = _scaled_dot_product_efficient_attention[0]
            _scaled_dot_product_efficient_attention = None
            return (getitem,)

        if self.device == "cpu":
            raise unittest.SkipTest(f"requires {GPU_TYPE}")

        DEVICE = torch.device(f"{GPU_TYPE}:0")
        DTYPE = torch.float16
        B = 3
        H = 8
        Q = 99
        K = 80
        D = 32
        C_bias = 128

        # inputs
        query = torch.randn((B, H, Q, D), device=DEVICE, dtype=DTYPE)
        key = torch.randn((B, H, K, D), device=DEVICE, dtype=DTYPE)
        value = torch.randn((B, H, K, D), device=DEVICE, dtype=DTYPE)
        bias = torch.randn((B, Q, K, C_bias), device=DEVICE, dtype=DTYPE)
        weights = torch.randn((C_bias, H), device=DEVICE, dtype=DTYPE)
        inps = (query, key, value, bias, weights)

        with config.patch(
            {
                "triton.prefer_nd_tiling": prefer_nd_tiling,
                "triton.use_block_ptr": use_block_ptr,
                "triton.native_matmul": False,
            }
        ):
            # Check accuracy
            self.common(
                foo,
                inps,
                atol=0.02,
                rtol=1e4,
            )

            # Check code for block pointers
            foo_opt = torch.compile(foo, backend="inductor")
            code = run_and_get_triton_code(foo_opt, *inps)
            have_block_ptr = code.count("tl.make_block_ptr") > 0
            if not is_halide_backend(self.device):
                self.assertEqual(have_block_ptr, use_block_ptr)

    @requires_gpu()
    @unittest.skipIf(
        not PLATFORM_SUPPORTS_MEM_EFF_ATTENTION,
        "Does not support mem_eff_attention",
    )
    def test_sdpa_unaligned_mask(self):
        def foo(
            arg0_1: "f32[8, 8, 16, 16]",
            arg1_1: "f32[8, 8, 15, 16]",
            arg2_1: "f32[8, 8, 15, 16]",
            arg3_1: "f32[1, 1, 16, 15]",
        ):
            constant_pad_nd: "f32[1, 1, 16, 16]" = (
                torch.ops.aten.constant_pad_nd.default(arg3_1, [0, 1], 0.0)
            )
            arg3_1 = None
            slice_1: "f32[1, 1, 16, 15]" = torch.ops.aten.slice.Tensor(
                constant_pad_nd, -1, 0, 15
            )
            constant_pad_nd = None
            expand: "f32[8, 8, 16, 15]" = torch.ops.aten.expand.default(
                slice_1, [8, 8, 16, 15]
            )
            slice_1 = None
            _scaled_dot_product_efficient_attention = (
                torch.ops.aten._scaled_dot_product_efficient_attention.default(
                    arg0_1, arg1_1, arg2_1, expand, False
                )
            )
            arg0_1 = arg1_1 = arg2_1 = expand = None
            getitem: "f32[8, 8, 16, 16]" = _scaled_dot_product_efficient_attention[0]
            _scaled_dot_product_efficient_attention = None
            return (getitem,)

        query = torch.rand(8, 8, 16, 16, device=GPU_TYPE)
        key = torch.rand(8, 8, 15, 16, device=GPU_TYPE)
        value = torch.rand(8, 8, 15, 16, device=GPU_TYPE)
        bias = torch.rand(1, 1, 16, 15, device=GPU_TYPE)
        self.common(
            foo,
            (query, key, value, bias),
            atol=0.02,
            rtol=1e4,
        )

    @requires_gpu()
    @unittest.skipIf(
        not PLATFORM_SUPPORTS_MEM_EFF_ATTENTION,
        "Does not support mem_eff_attention",
    )
    @config.patch(freezing=True)
    def test_sdpa_unaligned_mask_freezing(self):
        class Mod(torch.nn.Module):
            def __init__(self) -> None:
                super().__init__()
                self.arg3_1 = torch.rand(1, 1, 16, 15, device=GPU_TYPE)

            def forward(
                self,
                arg0_1: "f32[8, 8, 16, 16]",
                arg1_1: "f32[8, 8, 15, 16]",
                arg2_1: "f32[8, 8, 15, 16]",
            ):
                arg3_1 = self.arg3_1
                constant_pad_nd: "f32[1, 1, 16, 16]" = (
                    torch.ops.aten.constant_pad_nd.default(arg3_1, [0, 1], 0.0)
                )
                arg3_1 = None
                slice_1: "f32[1, 1, 16, 15]" = torch.ops.aten.slice.Tensor(
                    constant_pad_nd, -1, 0, 15
                )
                constant_pad_nd = None
                expand: "f32[8, 8, 16, 15]" = torch.ops.aten.expand.default(
                    slice_1, [8, 8, 16, 15]
                )
                slice_1 = None
                _scaled_dot_product_efficient_attention = (
                    torch.ops.aten._scaled_dot_product_efficient_attention.default(
                        arg0_1, arg1_1, arg2_1, expand, False
                    )
                )
                arg0_1 = arg1_1 = arg2_1 = expand = None
                getitem: "f32[8, 8, 16, 16]" = _scaled_dot_product_efficient_attention[
                    0
                ]
                _scaled_dot_product_efficient_attention = None
                return (getitem,)

        query = torch.rand(8, 8, 16, 16, device=GPU_TYPE)
        key = torch.rand(8, 8, 15, 16, device=GPU_TYPE)
        value = torch.rand(8, 8, 15, 16, device=GPU_TYPE)

        mod = Mod()
        out_eager = mod(query, key, value)

        with torch.no_grad():
            out_compiled = torch.compile(mod)(query, key, value)
            self.assertEqual(out_eager, out_compiled, atol=0.02, rtol=1e4)

    def test_where_with_logical_op(self):
        def fn_and(x, y):
            return torch.where(torch.logical_and(x, y), 1.0, 0.0)

        def fn_or(x, y):
            return torch.where(torch.logical_or(x, y), 1.0, 0.0)

        self.common(
            fn_and,
            (torch.randn(32), torch.randn(32)),
        )
        self.common(
            fn_or,
            (torch.randn(32), torch.randn(32)),
        )

    @skipIfRocmArch(MI200_ARCH)
    def test_conv_with_as_strided(self):
        class Model(nn.Module):
            def __init__(self) -> None:
                super().__init__()
                self.kv = torch.nn.Conv2d(
                    256, 384, kernel_size=(1, 1), stride=(1, 1), bias=False
                )

            def forward(self, x):
                convolution = self.kv(x)
                constant_pad_nd = torch.ops.aten.constant_pad_nd.default(
                    convolution, [2, 2, 2, 2], 0.0
                )
                # as_strided inputs are depend on input's size and stide.
                as_strided = torch.ops.aten.as_strided.default(
                    constant_pad_nd, [8, 384, 2, 20, 12], [153600, 400, 160, 1, 20]
                )
                as_strided_1 = torch.ops.aten.as_strided.default(
                    as_strided, [8, 384, 2, 2, 12, 12], [153600, 400, 160, 8, 20, 1]
                )
                clone = torch.ops.aten.clone.default(
                    as_strided_1, memory_format=torch.contiguous_format
                )
                return clone

        self.common(
            Model(),
            (torch.randn(8, 256, 16, 16),),
            check_lowp=not is_halide_backend(self.device),
        )

    def test_inplace_where_pointwise(self):
        # https://github.com/pytorch/pytorch/issues/96446
        def fn(a, b):
            a[0] = 2
            return a * b

        self.common(fn, (torch.rand(1), torch.rand(2)))

    @xfail_if_triton_cpu
    def test_view_on_aliased(self):
        # https://github.com/pytorch/pytorch/issues/96728
        def fn1(a, b):
            a = a.max(0).values
            c = torch.cat((a, b))
            c = c.round()
            b >= a[0]  # noqa: B015
            return c

        some_const = torch.tensor(6324)

        def fn2():
            a = torch.tensor([[0.6324]])
            ret = torch.cat((a, a), dim=0)
            some_const >= a[0]  # noqa: B015
            return ret

        self.common(fn1, (torch.tensor([[4.0]]), torch.tensor([5.0])))
        self.common(fn2, ())

    def test_argmax_to_float(self):
        # https://github.com/pytorch/pytorch/issues/97127
        def fn():
            a = torch.zeros([2, 2])
            b = a.argmax(0)
            return b.float().mean()

        self.common(fn, ())

    def test_const_int32_to_float(self):
        # https://github.com/pytorch/pytorch/issues/97124
        def fn():
            a = torch.zeros([1, 2], dtype=torch.int32)
            a = a + a
            b = a.to(dtype=torch.float32)
            return b * 0.8

        self.common(fn, ())

    def test_getitem(self):
        out_features = ["p3", "p4", "p5", "p6", "p7"]
        in_feature = "p5"

        def fn(a):
            return a[out_features.index(in_feature)]

        x = [
            torch.rand([1, 256, 100, 152], device=self.device),
            torch.rand([1, 256, 50, 76], device=self.device),
            torch.rand([1, 256, 25, 38], device=self.device),
        ]
        opt_fn = torch.compile(fn, backend="inductor")
        same(fn(x), opt_fn(x))

    def test_pad_view(self):
        def fn(a):
            y = torch.nn.functional.pad(a, (0, 0, 0, 1))
            y = y.view(*y.size()[:-2], y.size(-1), y.size(-2))
            return y

        x = torch.rand(48, 3, 512, 512)
        self.common(fn, (x,))

    def test_pad_single(self):
        def fn(a):
            y = torch.nn.functional.pad(a, (10, 10))
            return y

        x = torch.rand(1, 1, 1)
        self.common(fn, (x,))

    def test_pad_cast(self):
        def fn(x):
            return torch.nn.functional.pad(x.to(torch.float32), (0, 3, 0, 0))

        for dtype in [torch.int32, torch.int64]:
            self.common(fn, (torch.ones(1, 1, 13, dtype=dtype),))

    @unittest.skipIf(not HAS_CPU, "requires C++ compiler")
    @skip_if_triton  # No inductor data type propagation pass on scheduler nodes
    @skip_if_halide  # bf16
    def test_data_type_propogation(self):
        from torch._dynamo.utils import detect_fake_mode
        from torch._inductor.codegen.common import boolean_ops
        from torch._inductor.compile_fx import shape_env_from_inputs
        from torch._inductor.debug import DebugContext
        from torch._inductor.decomposition import decompositions
        from torch._inductor.graph import GraphLowering
        from torch._inductor.virtualized import V
        from torch.fx.passes.fake_tensor_prop import FakeTensorProp

        def get_data_type(node: torch.fx.Node):
            if OptimizationContext.key in node.meta:
                return node.meta[OptimizationContext.key].dtype
            else:
                return None

        def func(arg0_1):
            max_pool2d_with_indices = torch.ops.aten.max_pool2d_with_indices.default(
                arg0_1, [3, 3], [2, 2], [1, 1]
            )
            arg0_1 = None
            getitem = max_pool2d_with_indices[0]
            max_pool2d_with_indices = None
            return (getitem,)

        example_inputs = [
            torch.randn(10, 32, 20, 20, dtype=torch.bfloat16).to(
                memory_format=torch.channels_last
            )
        ]

        gm = make_fx(func, decomposition_table=decompositions, tracing_mode="fake")(
            *example_inputs
        )

        shape_env = shape_env_from_inputs(example_inputs)

        fake_mode = detect_fake_mode(example_inputs)
        if not fake_mode:
            fake_mode = torch._subclasses.FakeTensorMode(allow_non_fake_inputs=True)
            FakeTensorProp(gm, mode=fake_mode).propagate(*example_inputs)
        else:
            FakeTensorProp(gm, mode=fake_mode).propagate_dont_convert_inputs(
                *example_inputs
            )
        with V.set_fake_mode(fake_mode):
            graph = GraphLowering(
                gm,
                shape_env=shape_env,
            )
            with V.set_graph_handler(graph), V.set_debug_handler(DebugContext()):
                graph.run(*example_inputs)
                graph.compile_to_module()
                scheduler_node = graph.scheduler.nodes[0]
                DataTypePropagation.propagate_scheduler_node(scheduler_node)
                root_graph = scheduler_node._body.root_block.graph
                for node in root_graph.nodes:
                    if node.op == "placeholder":
                        self.assertEqual(get_data_type(node), None)
                    elif node.target in boolean_ops():
                        self.assertEqual(get_data_type(node), torch.bool)
                    elif node.target in (
                        "constant",
                        "to_dtype",
                        "index_expr",
                    ):
                        self.assertEqual(get_data_type(node), node.args[-1])
                    elif node.target in (
                        "get_index",
                        "index_expr",
                    ):
                        self.assertEqual(get_data_type(node), torch.int64)
                    elif node.target in (
                        "load",
                        "store",
                    ):
                        self.assertEqual(
                            get_data_type(node), V.graph.get_dtype(node.args[1])
                        )
                    elif node.target == "reduction":
                        _, _, dtype, _, _, _, _ = node.args
                        self.assertEqual(get_data_type(node), dtype)
                    elif node.target.startswith("masked_subblock"):
                        """
                        masked_subblocks:
                        opcode       name       target     args                        kwargs
                        -----------  ---------  ---------  --------------------------  --------
                        placeholder  ops        ops        ()                          {}
                        call_module  get_index  get_index  ('index2',)                 {}
                        call_method  load       load       (ops, 'arg0_1', get_index)  {}
                        call_method  to_dtype   to_dtype   (ops, load, torch.float32)  {}
                        output       output     output     (to_dtype,)                 {}
                        """
                        self.assertEqual(get_data_type(node), torch.float)
                    elif node.target == "and_":
                        """
                        and_'s input is boolean_ops:
                        -----------  ---------  ---------  --------------------------  --------
                        call_method  and__22           and_              (ops, ge_15, lt_15)
                        -----------  ---------  ---------  --------------------------  --------
                        """
                        self.assertEqual(get_data_type(node), torch.bool)
                    elif node.target == "maximum":
                        """
                        maximum's input is maximum or masked_subblock:
                        -----------  ---------  ---------  --------------------------  --------
                        call_method  maximum_6         maximum           (ops, masked_subblock8, maximum_5)
                        -----------  ---------  ---------  --------------------------  --------
                        """
                        self.assertEqual(get_data_type(node), torch.float)
                    elif node.target == "output":
                        self.assertEqual(get_data_type(node), torch.bfloat16)

    # Calling div only torch.SymInt arguments is not yet supported.
    # To support this behavior, we need to allow const-propping tensors that store symint data.
    # For now, dynamo will explicitly graph break when it encounters user code with this behavior.
    @expectedFailureCodegenDynamic
    @xfailIfS390X
    @skip_if_gpu_halide  # accuracy error
    def test_AllenaiLongformerBase_repro(self):
        def fn(query, scores, window_overlap):
            batch_size, seq_len, num_heads, _ = query.size()
            chunks_count = torch.div(seq_len, window_overlap, rounding_mode="trunc") - 1
            diagonal_attention_scores = scores.new_zeros(
                (
                    batch_size * num_heads,
                    chunks_count + 1,
                    window_overlap,
                    window_overlap * 2 + 1,
                )
            )
            diagonal_attention_scores[:, :-1, :, window_overlap:] = scores[
                :, :, :window_overlap, : window_overlap + 1
            ]
            input_tensor = diagonal_attention_scores.view(
                batch_size, num_heads, seq_len, 2 * window_overlap + 1
            ).transpose(2, 1)
            beginning_input = input_tensor[:, :window_overlap, :, : window_overlap + 1]
            input_tensor[:, :window_overlap, :, : window_overlap + 1] = torch.full_like(
                beginning_input, -float("inf")
            )
            return input_tensor

        args = [
            ((4, 1024, 12, 64), (768, 3072, 64, 1)),
            ((48, 3, 512, 513), (787968, 262656, 513, 1)),
        ]
        args = [rand_strided(sh, st) for (sh, st) in args]
        args.append(256)

        if is_cpp_backend(self.device):
            opt_fn = torch.compile(fn, backend="inductor")
            _, code = run_and_get_cpp_code(opt_fn, *args)
            num = (
                2
                if cpu_vec_isa.valid_vec_isa_list()
                and os.getenv("ATEN_CPU_CAPABILITY") != "default"
                else 1
            )
            FileCheck().check_count(
                "static_cast<int64_t>(256)",
                num,
                exactly=True,
            ).run(code)

        self.common(fn, args)

    def test_cumsum_pattern_matcher_issue(self):
        def fn(input_ids) -> torch.Tensor:
            input_shape = input_ids.size()
            input_ids = input_ids.view(-1, input_shape[-1])
            batch_size, seq_length = input_shape
            past_key_values_length = 0
            mask_seq_length = past_key_values_length + seq_length
            attention_mask = torch.ones(
                batch_size, mask_seq_length, device=input_ids.device
            )
            attention_mask = attention_mask.long()
            return torch.cumsum(attention_mask, dim=1)

        x = torch.randn(2, 2)
        self.common(fn, (x,), atol=0, rtol=0)

    @staticmethod
    def _check_resize_common(
        self, fn, x, size_or_y, memory_format, inplace, deterministic
    ):
        x = x.to(self.device)
        x_ref_arg = x.clone()
        x_opt_arg = x.clone()
        x_numel = x.numel()
        torch._dynamo.reset_code_caches()
        opt_fn = torch._dynamo.optimize_assert(compile_fx)(fn)
        correct = fn(x_ref_arg, size_or_y, memory_format)
        actual = opt_fn(x_opt_arg, size_or_y, memory_format)

        def get_numel(size_or_y):
            if isinstance(size_or_y, torch.Tensor):
                return size_or_y.numel()
            else:
                # assume shape
                return functools.reduce(lambda x, y: x * y, size_or_y, 1)

        if deterministic:
            nele_check = correct.numel()
        else:
            nele_check = min(x_numel, get_numel(size_or_y))

        correct_values = correct.as_strided((nele_check,), (1,))
        actual_values = actual.as_strided((nele_check,), (1,))
        self.assertTrue(same(correct_values, actual_values, equal_nan=deterministic))
        correct_strides = correct.stride()
        actual_strides = actual.stride()
        self.assertEqual(correct_strides, actual_strides)

    @staticmethod
    def _cases_resize_common():
        sizes = [
            ((2,), (1, 3, 2, 3)),
            ((100,), (1, 3, 2, 3)),
            ((1, 3, 2, 3), (1, 3, 2, 3)),
            ((2,), (1, 3, 2, 3, 1)),
            ((100,), (1, 3, 2, 3, 1)),
            ((1, 3, 2, 3, 1), (1, 3, 2, 3, 1)),
            ((2, 0, 1), (2, 2)),
        ]
        for x_size, y_size in sizes:
            memory_formats = [torch.contiguous_format]
            if len(y_size) == 4:
                memory_formats.append(torch.channels_last)
            if len(y_size) == 5:
                memory_formats.append(torch.channels_last_3d)
            for memory_format in memory_formats:
                x = torch.randn(*x_size)
                yield x, y_size, memory_format
                # check some non-contiguous tensors
                if x.numel() == 100:
                    x_strided = x[::2].reshape(25, 2).transpose(0, 1)
                    yield x_strided, y_size, memory_format

    def test_resize_overlapping_strides(self):
        # Resize on a stride-0 view should read logical elements, not raw storage.
        def fn(x):
            view = torch.as_strided(x, (100,), (0,))
            return torch.ops.aten.resize(view, (50,))

        self.common(fn, (torch.ones(10),))

    def test_resize(self):
        def fn(x, size, memory_format):
            # NOTE: Tensor.resize() =/= aten::resize()
            return torch.ops.aten.resize(x, size, memory_format=memory_format)

        for deterministic in [True, False]:
            with DeterministicGuard(
                deterministic, fill_uninitialized_memory=deterministic
            ):
                for x, y_size, memory_format in CommonTemplate._cases_resize_common():
                    CommonTemplate._check_resize_common(
                        self,
                        fn,
                        x,
                        y_size,
                        memory_format,
                        inplace=False,
                        deterministic=deterministic,
                    )

    @staticmethod
    def _cases_resize_as_common():
        for x, y_size, memory_format in CommonTemplate._cases_resize_common():
            # each sizes /memory_format combination tested in 2 ways:
            # 1. y is contiguous fn gets memory_format kwargs
            # 2. y has memory_format contiguity and fn gets preserve kwarg
            # 3. y has some other strides (not contiguous or channels last) and fn gets preserve
            yield x, torch.randn(*y_size), memory_format
            yield (
                x,
                torch.randn(*y_size).contiguous(memory_format=memory_format),
                torch.preserve_format,
            )
            yield (
                x,
                torch.randn(*y_size).permute(tuple(reversed(range(len(y_size))))),
                torch.preserve_format,
            )

    def test_resize_as(self):
        def fn(x, y, memory_format):
            return torch.ops.aten.resize_as(x, y, memory_format=memory_format)

        for deterministic in [True, False]:
            with DeterministicGuard(
                deterministic, fill_uninitialized_memory=deterministic
            ):
                for x, y, memory_format in CommonTemplate._cases_resize_as_common():
                    CommonTemplate._check_resize_common(
                        self,
                        fn,
                        x,
                        y,
                        memory_format,
                        inplace=False,
                        deterministic=deterministic,
                    )

    def test_inplace_resize_as(self):
        def fn(x, y):
            x.resize_as_(y)
            return x

        x = torch.randn(2, 3)
        y = torch.randn(200, 300)
        x_clone = x.clone()
        opt_fn = torch.compile(fn, backend="inductor")
        same(fn(x, y), opt_fn(x_clone, y))

    @xfail_if_triton_cpu
    def test_erfc(self):
        def fn(x):
            return torch.erfc(x)

        self.common(fn, (torch.randn(8, 8),))

    @skip_if_halide  # erfinv not implemented
    @xfail_if_triton_cpu
    def test_erfinv(self):
        def fn(x):
            return torch.erfinv(x)

        # domain for erfinv is (-1, 1)
        x = torch.empty(8, 8).uniform_(-1, 1)
        self.common(fn, (x,))

    def test_uint(self):
        def fn(z):
            x = torch.tensor(5, device=z.device, dtype=torch.uint8)
            y = torch.neg(x)
            return x < y

        self.common(fn, (torch.randn(26),))

    def test_scaled_dot_product_attention(self):
        if self.device == "cuda" and not PLATFORM_SUPPORTS_FLASH_ATTENTION:
            raise unittest.SkipTest("Can't run flash attention on this platform")

        def fn(q, k, v):
            return torch.nn.functional.scaled_dot_product_attention(
                q.transpose(1, 2).contiguous(),
                k.transpose(1, 2),
                v.transpose(1, 2),
                scale=0.125,
            )[:2]

        self.common(
            fn,
            (
                torch.randn(4, 2, 4, 2),
                torch.randn(4, 2, 4, 2),
                torch.randn(4, 2, 4, 2),
            ),
            atol=2e-4,  # to pass lowp check on GPU
            rtol=1e-2,  # to pass lowp check on GPU
        )

    @xfail_if_mps_unimplemented
    @unittest.skipIf(
        not PLATFORM_SUPPORTS_MEM_EFF_ATTENTION, "Some archs don't support mem eff SDPA"
    )
    def test_scaled_dot_product_efficient_attention(self):
        if self.device == "cpu":
            raise unittest.SkipTest(f"requires {GPU_TYPE}")

        # The first two values should be the same, attention output
        # and logsumexp since dropout is not being set
        def fn(q, k, v, attn_bias, compute_log_sumexp):
            return aten._scaled_dot_product_efficient_attention(
                q, k, v, attn_bias, compute_log_sumexp
            )[:2]

        self.common(
            fn,
            (
                torch.randn(4, 4, 36, 36),
                torch.randn(4, 4, 36, 36),
                torch.randn(4, 4, 36, 36),
                torch.randn(4, 4, 36, 36),
                False,
            ),
            check_lowp=False,
        )

    def test_fft_real_input(self):
        def fn(x):
            return torch.fft.fftn(x)

        self.common(fn, (torch.randn((16, 16, 16)),), check_lowp=False)

    def test_fft_real_input_real_output(self):
        def fn(x):
            return torch.fft.fftn(x).real

        self.common(fn, (torch.randn((16, 16, 16)),), check_lowp=False)

    def test_searchsorted(self):
        def fn(sorted_sequence, values, out_int32, right, side, sorter):
            return torch.searchsorted(
                sorted_sequence,
                values,
                out_int32=out_int32,
                right=right,
                side=side,
                sorter=sorter,
            )

        shapes = (
            ((1,), (16, 16)),  # scalar sorted_sequence
            ((16,), ()),  # scalar values
            ((32,), (16, 16)),  # 1-D sorted_sequence
            ((16, 32), (16, 16)),  # N-D sorted_sequence
            ((3, 5), (3, 7)),  # prime dimensioned sequence, to flush out indexing bugs
        )
        booleans = (False, True)

        for (seq_shape, value_shape), out_int32, right in itertools.product(
            shapes, booleans, booleans
        ):
            unsorted_sequence = torch.rand(seq_shape)
            sorted_sequence, sorting_indices = torch.sort(unsorted_sequence)
            values = torch.rand(value_shape)

            side = "right" if right else "left"
            self.common(
                fn,
                (sorted_sequence, values, out_int32, right, side, None),
                check_lowp=False,
            )
            self.common(
                fn,
                (
                    unsorted_sequence,
                    values,
                    out_int32,
                    right,
                    side,
                    sorting_indices,
                ),
                check_lowp=False,
            )

    @requires_gpu()
    @skip_if_gpu_halide
    @skip_if_not_triton
    def test_searchsorted_broadcast(self):
        def fn(sorted_sequence, values):
            return (
                torch.searchsorted(
                    sorted_sequence,
                    values,
                )
                .unsqueeze(-1)
                .expand(-1, 64)
                .contiguous()
            )

        unsorted_sequence = torch.rand((32,))
        sorted_sequence, sorting_indices = torch.sort(unsorted_sequence)
        values = torch.rand((64,))

        self.common(fn, (sorted_sequence, values), check_lowp=False)
        cfn = torch.compile(fn)
        _, code = run_and_get_code(
            cfn, sorted_sequence.to(GPU_TYPE), values.to(GPU_TYPE)
        )

        # make sure that we did not fuse the broadcast and the bucketize,
        # because bucketize is computationally expensive.
        FileCheck().check("def triton").check("def triton").run(code[0])

    @parametrize("nd_tiling", (False, True))
    def test_bucketize(self, nd_tiling: bool):
        def fn(input, boundaries, out_int32, right):
            return torch.bucketize(input, boundaries, out_int32=out_int32, right=right)

        input = torch.rand((64, 64)) * 2 - 1
        boundaries = torch.tensor([-0.9, -0.8, 0.1, 0.2, 0.5, 0.9])

        for out_int32 in [True, False]:
            for right in [True, False]:
                out_int32 = True
                right = False
                with config.patch("triton.prefer_nd_tiling", nd_tiling):
                    self.common(
                        fn, (input, boundaries, out_int32, right), check_lowp=False
                    )

    def test_bucketize_default_kwargs(self):
        def fn(input, offsets):
            return torch.bucketize(input, offsets)

        input = torch.tensor(
            [-1.0, -0.9, -0.8, -0.5, 0.0, 0.1, 0.2, 0.4, 0.5, 0.6, 0.9, 0.91]
        )
        offsets = torch.tensor([-0.9, -0.8, 0.1, 0.2, 0.5, 0.9])

        self.common(fn, (input, offsets), check_lowp=False)

    @parametrize(
        "dtype_input, dtype_boundaries",
        list(itertools.product(test_int_dtypes, test_int_dtypes)),
    )
    def test_bucketize_int(
        self, dtype_input: torch.dtype, dtype_boundaries: torch.dtype
    ):
        def fn(input, offsets, out_int32, right):
            return torch.bucketize(input, offsets, out_int32=out_int32, right=right)

        input = torch.randint(-(2**10), 2**10, (64, 64)).to(dtype_input)
        offsets = (torch.arange(10, dtype=torch.int32) ** 2 - 512).to(dtype_boundaries)

        for out_int32 in [True, False]:
            for right in [True, False]:
                self.common(fn, (input, offsets, out_int32, right), check_lowp=False)

    @patch.object(config.triton, "autotune_pointwise", True)
    def test_bucketize_add_autotune(self):
        # Causes a @pointwise(size_hints) where size_hints is 2D

        def fn(input, offsets, add_value):
            return torch.bucketize(input, offsets) + add_value

        input = torch.rand((16, 16, 64, 64))
        boundaries = torch.tensor([-0.9, -0.8, 0.1, 0.2, 0.5, 0.9])
        add_value = torch.randint(0, 1024, (16, 16, 64, 64)).to(
            memory_format=torch.channels_last
        )

        self.common(fn, (input, boundaries, add_value), check_lowp=False)

        assertGeneratedKernelCountEqual(self, 1)

    def test_bucketize_computed_offsets(self):
        def fn(inp, offsets):
            return torch.bucketize(inp, offsets + 0.01)

        inp = torch.tensor(
            [-1.0, -0.9, -0.8, -0.5, 0.0, 0.1, 0.2, 0.4, 0.5, 0.6, 0.9, 0.91]
        )
        offsets = torch.tensor([-0.9, -0.8, 0.1, 0.2, 0.5, 0.9]) - 0.01

        self.common(fn, (inp, offsets), check_lowp=False)

    def test_bucketize_scalar_various_values(self):
        def fn(boundaries, scalar_val):
            return torch.bucketize(scalar_val, boundaries)

        boundaries = torch.tensor([-0.9, -0.8, 0.1, 0.2, 0.5, 0.9])

        for scalar_val in [-1.0, 0.0, 0.15, 0.5, 1.0]:
            self.common(fn, (boundaries, scalar_val), check_lowp=False)

    def test_bucketize_scalar_with_options(self):
        def fn(boundaries, out_int32, right):
            return torch.bucketize(0.5, boundaries, out_int32=out_int32, right=right)

        boundaries = torch.tensor([-0.9, -0.8, 0.1, 0.2, 0.5, 0.9])

        for out_int32 in [True, False]:
            for right in [True, False]:
                self.common(fn, (boundaries, out_int32, right), check_lowp=False)

    @requires_gpu()
    @skip_if_gpu_halide
    @skip_if_not_triton
    def test_bucketize_broadcast(self):
        def fn(input, boundaries):
            return (
                torch.bucketize(input, boundaries)
                .unsqueeze(-1)
                .expand(-1, -1, 64)
                .contiguous()
            )

        inp = torch.rand((64, 64)) * 2 - 1
        boundaries = torch.tensor([-0.9, -0.8, 0.1, 0.2, 0.5, 0.9])

        self.common(fn, (inp, boundaries), check_lowp=False)
        cfn = torch.compile(fn)
        _, code = run_and_get_code(cfn, inp.to(GPU_TYPE), boundaries.to(GPU_TYPE))

        # make sure that we did not fuse the broadcast and the bucketize,
        # because bucketize is computationally expensive.
        FileCheck().check("def triton").check("def triton").run(code[0])

    @requires_gpu()
    @skip_if_gpu_halide
    @skip_if_not_triton
    def test_bucketize_nan_consistency(self):
        # Regression test for https://github.com/pytorch/pytorch/issues/173133.
        def fn(x, boundaries, right):
            return torch.bucketize(torch.rsqrt(x), boundaries, right=right)

        x = torch.tensor([-1.0], device=self.device)
        boundaries = torch.tensor([0.2, 0.5, 0.8], device=self.device)
        expected = torch.tensor(
            [boundaries.numel()], device=self.device, dtype=torch.int64
        )

        for right in (False, True):
            self.assertEqual(fn(x, boundaries, right), expected)
            self.common(fn, (x, boundaries, right), check_lowp=False)

    @requires_gpu()
    @config.patch(assume_aligned_inputs=False)
    def test_config_option_dont_assume_alignment(self):
        def fn(x: torch.Tensor) -> torch.Tensor:
            return x.sin() + x.cos()

        # Inductor specializes on the (unguarded) alignment of the initial input.
        # Make sure that for different configurations, nothing breaks.
        for offset in (0, 1, 2, 3, 4):
            base = torch.randn(64 * 64 + 64, dtype=torch.float32, device=self.device)
            inp = torch.as_strided(base, (64, 64), (64, 1), offset)
            torch._dynamo.reset()
            fn_c = torch.compile(fn)

            ref = fn(inp)
            res = fn_c(inp)
            self.assertEqual(ref, res)

            for offset2 in (0, 1, 2, 3, 4):
                base2 = torch.randn(
                    64 * 64 + 64, dtype=torch.float32, device=self.device
                )
                inp2 = torch.as_strided(base2, (64, 64), (64, 1), offset2)
                ref2 = fn(inp2)
                res2 = fn_c(inp2)
                self.assertEqual(ref2, res2, atol=1e-5, rtol=1e-5)

    @requires_gpu()
    @config.patch(assume_aligned_inputs=False)
    def test_config_option_dont_assume_alignment_recompiles(self):
        # Inputs:
        #  1. (32, 32) shape
        #  2. (64, 64) shape -> causes a recompile
        #  3. (64, 64) shape with different storage offset -> should NOT cause a recompile
        failed_guards = []

        def fail(guard):
            nonlocal failed_guards
            failed_guards.append(guard)

        def fn(x: torch.Tensor) -> torch.Tensor:
            return x.sin() + x.cos()

        base = torch.randn(64 * 64 + 64, dtype=torch.float32, device=self.device)

        inp1 = torch.as_strided(base, (32, 32), (32, 1), 4)
        inp2 = torch.as_strided(base, (64, 64), (64, 1), 4)
        inp3 = torch.as_strided(base, (64, 64), (64, 1), 5)

        torch._dynamo.reset()

        fn_c = torch._dynamo.optimize("inductor", guard_fail_fn=fail)(fn)

        ref1 = fn(inp1)
        res1 = fn_c(inp1)
        self.assertEqual(ref1, res1)
        self.assertEqual(0, len(failed_guards))

        ref2 = fn(inp2)
        res2 = fn_c(inp2)
        self.assertEqual(ref2, res2)
        # if dynamic shapes isn't already turned on, we might have a guard failure as we turn
        # on dynamic shapes
        self.assertLessEqual(len(failed_guards), 1)
        failed_guard_count_iteration_2 = len(failed_guards)

        failed_guards = []
        ref3 = fn(inp3)
        res3 = fn_c(inp3)
        self.assertEqual(ref3, res3)
        # we might still have the dynamics shapes failure, but offset change shouldn't be guarded on
        # see Note: [Input Alignment handling in Inductor]
        self.assertLessEqual(len(failed_guards), failed_guard_count_iteration_2)

    @requires_gpu()
    @config.patch(assume_aligned_inputs=False)
    def test_config_option_dont_assume_alignment_cudagraphs(self):
        def fn(x):
            return x.cos() * x.sin()

        fn_c = torch.compile(fn, mode="reduce-overhead", dynamic=True)

        for size, stride, offset in (
            ((32, 32), (32, 1), 4),
            ((48, 48), (48, 1), 4),
            ((64, 64), (64, 1), 5),
        ):
            torch.manual_seed(42)
            base = torch.randn(64 * 64 + 64, dtype=torch.float32, device=self.device)
            torch.manual_seed(42)
            base_ref = torch.randn(
                64 * 64 + 64, dtype=torch.float32, device=self.device
            )

            inp = torch.as_strided(base, size, stride, offset)
            inp_ref = torch.as_strided(base_ref, size, stride, offset)

            inp.requires_grad_(True)
            inp_ref.requires_grad_(True)

            res = fn_c(inp)
            ref = fn(inp_ref)
            self.assertEqual(ref, res)

            res.sum().backward()
            ref.sum().backward()
            self.assertEqual(base.grad, base_ref.grad)

    @config.patch(implicit_fallbacks=True)
    def test_custom_op_1(self):
        import torch.library

        def foo(x):
            return 3 * x

        def foo_meta(x):
            return torch.empty_like(x)

        define_custom_op_for_test("foo", foo, foo_meta)

        def fn(x):
            a = torch.nn.functional.relu(x)
            b = torch.ops.test.foo(a)
            c = torch.cos(b)
            return c

        self.common(fn, (torch.randn((16, 32)),), check_lowp=False)

    @config.patch(implicit_fallbacks=True)
    def test_custom_op_2(self):
        import torch.library

        def foo(x, scale: float):
            return scale * x, torch.cos(x)

        def foo_meta(x, scale: float):
            return torch.empty_like(x), torch.empty_like(x)

        define_custom_op_2_for_test("foo2", foo, foo_meta)

        def fn(x, scale: float):
            a = torch.nn.functional.relu(x)
            return torch.ops.test.foo2(a, scale)

        self.common(fn, (torch.randn((16, 32)), 2.0), check_lowp=False)

    @config.patch(implicit_fallbacks=True)
    def test_custom_op_3(self):
        def foo(x):
            result = torch.zeros_like(x[0])
            for t in x:
                result += t
            return result

        def foo_meta(x):
            return torch.empty_like(x[0])

        define_custom_op_3_for_test("foo3", foo, foo_meta)

        def fn(x):
            return torch.ops.test.foo3(x)

        self.common(
            fn,
            ([torch.randn((16, 32)), torch.randn((16, 32)), torch.randn((16, 32))],),
            check_lowp=False,
        )

    @requires_gpu()
    @skip_if_not_triton
    @config.patch(implicit_fallbacks=True)
    def test_generated_code_has_size_stride_assert(self):
        def foo(x):
            return 3 * x

        def foo_meta(x):
            return torch.empty_like(x)

        define_custom_op_for_test("foo", foo, foo_meta)

        def fn(x):
            a = torch.nn.functional.relu(x)
            b = torch.ops.test.foo(a)
            return b

        a = torch.randn((16, 32), device=self.device)

        _, code = run_and_get_code(
            torch.compile(fn),
            a,
        )
        if not is_dynamic_shape_enabled():
            if code and len(code) > 0 and "assert_size_stride(" in code[0]:
                try:
                    FileCheck().check_regex(
                        r"assert_size_stride\s*\(\s*[^,]+,\s*\([^\)]*\),\s*\([^\)]*\),\s*'[^']+'\s*\)"
                    ).run(code[0])
                except Exception as e:
                    print(f"Failed regex match for assert_size_stride: {e}")
                    print(code[0])
                    raise e
            else:
                print("Skipping: No assert_size_stride found.")

    @requires_gpu()
    @skip_if_not_triton
    @config.patch(implicit_fallbacks=True)
    def test_generated_code_has_alignment_assert(self):
        def foo(x):
            return 3 * x

        def foo_meta(x):
            return torch.empty_like(x)

        define_custom_op_for_test("foo", foo, foo_meta)

        def fn(x):
            a = torch.nn.functional.relu(x)
            b = torch.ops.test.foo(a)
            return b

        a = torch.randn((16, 32), device=self.device)

        _, code = run_and_get_code(
            torch.compile(fn),
            a,
        )
        if not is_dynamic_shape_enabled():
            if code and len(code) > 0 and "assert_alignment(" in code[0]:
                try:
                    FileCheck().check_regex(
                        r"assert_alignment\s*\(\s*[^,]+,\s*[^,]+,\s*'[^']+'\s*\)"
                    ).run(code[0])
                except Exception as e:
                    print(f"Failed regex match for assert_alignment: {e}")
                    print(code[0])
                    raise e
            else:
                print("Skipping: No assert_alignment found.")

    def test_assert_size_stride_op_name_pass(self):
        tensor = torch.empty((16, 32))
        assert_size_stride(tensor, (16, 32), (32, 1), "torch.ops.dummy.op_name")

    def test_assert_size_stride_op_name_fail(self):
        tensor = torch.empty((16, 32))
        with self.assertRaisesRegex(AssertionError, "torch.ops.dummy.op_name"):
            assert_size_stride(tensor, (32, 64), (32, 1), "torch.ops.dummy.op_name")

    def test_assert_alignment_op_name_pass(self):
        tensor = torch.empty((16, 32))
        assert_alignment(tensor, 16, "torch.ops.dummy.op_name")

    def test_assert_alignment_op_name_fail(self):
        tensor = torch.empty((16, 32))
        with self.assertRaisesRegex(AssertionError, "torch.ops.dummy.op_name"):
            assert_alignment(tensor, 0, "torch.ops.dummy.op_name")

    @requires_gpu()
    @skip_if_not_triton
    @unittest.skipIf(
        config.cpp_wrapper,
        "Inductor does not generate size/stride asserts for cpp_wrapper",
    )
    def test_input_asserts_deferred_to_first_use(self):
        def fn(x, y, z):
            a = torch.mm(x, y)
            b = torch.mm(a, z)
            return b

        x = torch.randn(16, 32, device=self.device)
        y = torch.randn(32, 64, device=self.device)
        z = torch.randn(64, 8, device=self.device)

        _, code = run_and_get_code(torch.compile(fn), x, y, z)
        # z's assert should appear after the first mm, not at the top
        # with all the other asserts
        FileCheck().check("def call").check_count(
            "assert_size_stride", 2, exactly=True
        ).check("extern_kernels.mm(").run(code[0])

    @requires_gpu()
    @skip_if_not_triton
    @unittest.skipIf(
        config.cpp_wrapper,
        "Deferred alignment copies are not generated for cpp_wrapper",
    )
    def test_alignment_copy_deferred_to_first_use(self):
        def fn(x, y, z):
            a = torch.mm(x, y)
            b = torch.mm(a, z)
            return b

        x = torch.randn(16, 32, device=self.device)
        y = torch.randn(32, 64, device=self.device)

        z = torch.randn(64, 8, device=self.device)

        _, code = run_and_get_code(torch.compile(fn), x, y, z)
        # z's alignment check should appear between the two mm calls:
        # first mm (uses x, y) -> alignment clone (for z) -> second mm (uses z)
        FileCheck().check("extern_kernels.mm(").check("copy_misaligned").check(
            "extern_kernels.mm("
        ).run(code[0])

    @requires_gpu()
    @skip_if_not_triton
    @torch._inductor.config.patch(cpp_wrapper=True)
    def test_alignment_copy_not_emitted_for_cpp_wrapper(self):
        def fn(x, y):
            return torch.mm(x, y)

        x = torch.randn(16, 32, device=self.device)
        y = torch.randn(32, 64, device=self.device)

        _, code = run_and_get_code(torch.compile(fn), x, y)
        # cpp_wrapper should NOT contain Python-syntax alignment copies
        self.assertNotIn("copy_misaligned", code[0])

    def test_copy_misaligned_returns_same_tensor_when_aligned(self):
        import weakref

        from torch._C._dynamo.guards import copy_misaligned

        x = torch.randn(32, 32, device=self.device)
        ref = weakref.ref(x)
        result = copy_misaligned(x)
        self.assertIs(result, x)
        del x, result
        self.assertIsNone(ref(), "aligned tensor should be freed")

    def test_copy_misaligned_clones_when_misaligned(self):
        import weakref

        from torch._C._dynamo.guards import copy_misaligned

        big = torch.randn(32 * 32 + 1, device=self.device)
        x = big[1:].reshape(32, 32)
        self.assertNotEqual(x.data_ptr() % 16, 0)
        ref_orig = weakref.ref(x)
        result = copy_misaligned(x)
        self.assertIsNot(result, x)
        self.assertEqual(result.data_ptr() % 16, 0)
        self.assertEqual(result, x)
        self.assertEqual(result.size(), x.size())
        self.assertEqual(result.stride(), x.stride())
        ref_clone = weakref.ref(result)
        del x, result
        self.assertIsNone(ref_clone(), "cloned tensor should be freed")
        # orig kept alive by big's storage, that's fine
        del big
        self.assertIsNone(ref_orig(), "original tensor should be freed")

    def test_copy_misaligned_empty_tensor(self):
        from torch._C._dynamo.guards import copy_misaligned

        x = torch.randn(0, device=self.device)
        result = copy_misaligned(x)
        self.assertEqual(result.size(), x.size())

    @torch._dynamo.config.patch(capture_dynamic_output_shape_ops=True)
    @torch._inductor.config.patch(implicit_fallbacks=True)
    def test_custom_op_unbacked_symints(self):
        @torch.library.custom_op("test_unbacked_symints::foo", mutates_args={})
        def foo(x: torch.Tensor) -> torch.Tensor:
            return x.clone()

        @foo.register_fake
        def _(x):
            u0 = torch.library.get_ctx().new_dynamic_size()
            u1 = torch.library.get_ctx().new_dynamic_size()
            u2 = torch.library.get_ctx().new_dynamic_size()
            return x.new_empty(u0, u1, u2)

        @torch.library.custom_op("test_unbacked_symints::bar", mutates_args={})
        def bar(x: torch.Tensor) -> torch.Tensor:
            return x.clone()

        @bar.register_fake
        def _(x):
            return torch.empty_like(x)

        x = torch.randn(2, 3, 4)

        @torch.compile(fullgraph=True)
        def f(x):
            y = foo(x)
            z = bar(y)
            return z

        # No error
        f(x)

    @requires_gpu()
    @torch._inductor.config.patch("layout_optimization", True)
    @torch._inductor.config.patch("keep_output_stride", False)
    @config.patch(implicit_fallbacks=True)
    @tf32_on_and_off(0.005)
    def test_custom_op_fixed_layout_sequential(self):
        import torch.library

        mod = nn.Conv2d(3, 128, 1, stride=1, bias=False).to(device=self.device)
        inp = torch.rand(2, 3, 128, 128, device=self.device)
        expected_stride = mod(inp).stride()

        def bar(x):
            self.assertEqual(x.stride(), expected_stride)
            return x.clone()

        def bar_meta(x):
            return torch.empty_like(x)

        define_custom_op_for_test(
            "bar",
            bar,
            bar_meta,
            tags=[torch._C.Tag.needs_fixed_stride_order],
        )

        def fn(x):
            z = mod(x)
            output = torch.ops.test.bar(z)
            return output

        with torch.no_grad():
            # With keep_output_stride False, inductor would normally have different layout from eager execution
            # But because our custom op needs fixed layout, the assertions in the custom op will pass
            self.common(fn, (inp,), check_lowp=False)

    @requires_gpu()
    @config.patch(implicit_fallbacks=True)
    @tf32_on_and_off(0.005)
    def test_mutable_custom_op_fixed_layout2(self):
        with torch.library._scoped_library("mylib", "DEF") as lib:
            mod = nn.Conv2d(3, 128, 1, stride=1, bias=False).to(device=self.device)
            inp = torch.rand(2, 3, 128, 128, device=self.device)
            expected_stride = mod(inp).clone().stride()

            lib.define(
                "bar(Tensor x, bool is_compiling) -> Tensor",
                tags=torch.Tag.flexible_layout,
            )

            bar_strides = []

            @torch.library.impl(lib, "bar", "CompositeExplicitAutograd")
            def _(x, is_compiling):
                if is_compiling:
                    bar_strides.append(x.stride())
                result = x.clone()
                if x.stride() != result.stride():
                    raise AssertionError(
                        f"Expected stride {x.stride()}, got {result.stride()}"
                    )
                return result

            @torch.library.impl(lib, "bar", "Meta")
            def _(x, is_compiling):
                return x.clone()

            lib.define(
                "add_one(Tensor(a!) x) -> ()",
                tags=torch.Tag.needs_fixed_stride_order,
            )

            @torch.library.impl(lib, "add_one", "CompositeExplicitAutograd")
            def _(x):
                self.assertEqual(x.stride(), expected_stride)
                x.copy_(x + 1)

            def fn(x):
                # Inductor changes the conv to be channels-last
                z = mod(x)
                output = torch.ops.mylib.bar(z, torch._dynamo.is_compiling())
                torch.ops.mylib.add_one(output)
                return output**2

            with torch.no_grad():
                self.common(fn, (inp,), check_lowp=False)

            # Dynamic shapes and rocm invalidate this test case
            if torch._dynamo.config.assume_static_by_default and not TEST_WITH_ROCM:
                # For this test to be valid, Inductor must have changed the conv
                # to be channels-last. If this assertion ever fails then we need
                # a new test case.
                self.assertEqual(len(bar_strides), 1)
                self.assertNotEqual(bar_strides[0], expected_stride)

    @config.patch(implicit_fallbacks=True)
    def test_mutable_custom_op_fixed_layout(self):
        with torch.library._scoped_library("mylib", "DEF") as lib:
            lib.define(
                "copy_(Tensor(a!) dst, Tensor src) -> ()",
                tags=torch.Tag.needs_fixed_stride_order,
            )

            @torch.library.impl(lib, "copy_", "Meta")
            def _(dst, src):
                return None

            @torch.library.impl(lib, "copy_", "CompositeExplicitAutograd")
            def _(dst, src):
                dst.copy_(src)

            def f(x):
                full_default_3 = torch.full([3], 7.0, device="cpu")
                chunk_cat_default_1 = torch.ops.mylib.copy_.default(full_default_3, x)
                mul_out = torch.mul(full_default_3, full_default_3)
                return mul_out

            x = torch.arange(3, dtype=torch.float, device="cpu")
            eager_out = f(x)

            compiled_inductor_f = torch.compile(f, backend="inductor", fullgraph=True)
            compiled_inductor_out = compiled_inductor_f(x)
            self.assertEqual(compiled_inductor_out, eager_out)

    @requires_gpu()
    @config.patch(implicit_fallbacks=True)
    def test_custom_op_fixed_layout_channels_last(self):
        class Block(nn.Module):
            def __init__(
                self,
            ):
                super().__init__()

                self.in_layers = nn.Sequential(
                    nn.Dropout(p=0.1),
                )

            def helper(self, x):
                out = F.gelu(x)
                out = self.in_layers(out)
                return out

            def forward(self, x):
                out = self.helper(x)
                out = torch.ops.test.baz(out)
                return out

        model = Block()
        model = model.to(GPU_TYPE).to(memory_format=torch.channels_last)
        input_t = torch.randn([1, 320, 128, 128], dtype=torch.float32, device=GPU_TYPE)
        input_t = input_t.to(memory_format=torch.channels_last)
        expected_strides = model.helper(input_t).stride()

        def baz(x):
            self.assertEqual(expected_strides, x.stride())
            return x.clone()

        def baz_meta(x):
            return torch.empty_like(x)

        define_custom_op_for_test(
            "baz",
            baz,
            baz_meta,
            tags=[torch._C.Tag.needs_fixed_stride_order],
        )

        with torch.no_grad():
            net = torch.compile(model)
            out = net(input_t)

    @config.patch(implicit_fallbacks=True)
    def test_custom_op_default_layout_constraint(self):
        with torch.library._scoped_library("mylib", "DEF") as lib:
            lib.define(
                "copy_(Tensor(a!) dst, Tensor src) -> ()",
                # No need to pass in an explicit tag since the default
                # behavior for custom op works.
                # tags=torch.Tag.needs_fixed_stride_order,
            )

            @torch.library.impl(lib, "copy_", "Meta")
            def _(dst, src):
                return None

            @torch.library.impl(lib, "copy_", "CompositeExplicitAutograd")
            def _(dst, src):
                if src.is_contiguous():
                    dst.copy_(src + 1)
                else:
                    dst.copy_(src)

            def f(x):
                full_default_3 = torch.full([3, 3], 7.0, device=self.device)
                chunk_cat_default_1 = torch.ops.mylib.copy_.default(full_default_3, x)
                mul_out = torch.mul(full_default_3, full_default_3)
                return mul_out

            x = (
                torch.arange(9, dtype=torch.float, device=self.device)
                .view(3, 3)
                .t()
                .contiguous()
                .t()
            )
            eager_out = f(x)

            compiled_inductor_f = torch.compile(f, backend="inductor", fullgraph=True)
            compiled_inductor_out = compiled_inductor_f(x)

        self.assertTrue(torch.allclose(compiled_inductor_out, eager_out))

    @skip_if_gpu_halide  # cuda error
    def test_buffer_use_after_remove(self):
        # https://github.com/pytorch/pytorch/issues/102857

        def rotvec_to_rotmat(rotvec) -> torch.Tensor:
            """Simplified rotvec to rotmat code from RoMa
            (https://github.com/naver/roma/blob/06e4b0cdc1c802a60a012bb19c581d6600c63358/roma/mappings.py#L371)
            """
            theta = torch.norm(rotvec, dim=-1)
            axis = rotvec / theta[..., None]
            kx, ky, kz = axis[:, 0], axis[:, 1], axis[:, 2]
            sin_theta = torch.sin(theta)
            cos_theta = torch.cos(theta)
            one_minus_cos_theta = 1 - cos_theta
            xs = kx * sin_theta
            ys = ky * sin_theta
            zs = kz * sin_theta
            xyc = kx * ky * one_minus_cos_theta
            xzc = kx * kz * one_minus_cos_theta
            yzc = ky * kz * one_minus_cos_theta
            xxc = kx**2 * one_minus_cos_theta
            yyc = ky**2 * one_minus_cos_theta
            zzc = kz**2 * one_minus_cos_theta
            R_rodrigues = torch.stack(
                [
                    1 - yyc - zzc,
                    xyc - zs,
                    xzc + ys,
                    xyc + zs,
                    1 - xxc - zzc,
                    -xs + yzc,
                    xzc - ys,
                    xs + yzc,
                    1 - xxc - yyc,
                ],
                dim=-1,
            ).reshape(-1, 3, 3)
            R = R_rodrigues
            return R

        def f(coord, rot, trans):
            rot_mat = rotvec_to_rotmat(rot)
            coord = torch.einsum("...ij,...bj->...bi", rot_mat, coord) + trans
            return coord.sum()

        foo_c = torch.compile(f, dynamic=True)

        def run(fn):
            coord = torch.ones((2, 3), device=self.device)
            rot = nn.Parameter(torch.ones((2, 3), device=self.device))
            trans = nn.Parameter(torch.ones((2, 3), device=self.device))

            U = fn(coord, rot, trans)
            U.backward()

            return U, rot, trans

        U_e, rot_e, trans_e = run(f)
        U, rot, trans = run(foo_c)

        self.assertEqual(U, U_e)
        self.assertEqual(rot.grad, rot_e.grad)
        self.assertEqual(trans.grad, trans_e.grad)

    # If we serve from the cache, the init hook isn't called
    @config.patch({"fx_graph_cache": False, "fx_graph_remote_cache": False})
    @skipIfWindows(msg="torch._dynamo.exc.Unsupported")
    def test_inner_fn_str_and_stride(self):
        def f(x):
            x = x + 1
            x = test_operators.realize(x)
            x = x * 2
            x = test_operators.realize(x)
            return x

        x = torch.rand(3, 2, device=self.device).t()
        ref = f(x)
        called = False

        def hook_fn(scheduler, nodes):
            nonlocal called
            called = True

            if self.device != "cpu":
                self.assertEqual(len(nodes), 3)
                _, mul_buf, _ = nodes
                self.assertTrue(
                    all(
                        V.graph.sizevars.optimization_hints(buf.get_stride()) == (1, 2)
                        for buf in nodes
                    )
                )
                # before the fix, the wrong index expression
                # 'i1 + 3 * i0' is cached.
                self.assertTrue(
                    "i0 + 2 * i1" in mul_buf.data.inner_fn_str()
                    or "i0 + i1 * s64" in mul_buf.data.inner_fn_str()
                )

        with add_scheduler_init_hook(hook_fn):
            actual = torch.compile(f, fullgraph=True)(x)
        self.assertEqual(ref, actual)
        self.assertTrue(called)

    @skip_if_gpu_halide  # cuda error
    def test_mutations_loop_fusion(self):
        def fn(tensor, index, source):
            out = tensor.index_add(0, index, source, alpha=2.0) / 2
            return out

        device = "cpu"
        dtype = highest_precision_float(self.device)
        tensor = torch.rand((1,), dtype=dtype, device=device)
        index = torch.tensor([0], dtype=torch.long, device=device)
        source = torch.rand((1,), dtype=dtype, device=device)
        self.common(
            fn,
            (
                tensor,
                index,
                source,
            ),
        )

    @config.patch(
        "triton.autotune_pointwise", True
    )  # needed to introduce config that exceed max shared memory usage
    @serialTest()
    @largeTensorTest("13GB", inductor=True)
    def test_large_block_sizes(self):
        """
        Inductor will try triton configs like x = 64 and y = 1024 which will
        result in out of shared memory if dtype is fp32.

        Currently inductor will skip such bad configs and pick the best one
        from the remaining configs.
        """

        @torch.compile
        def fn(x, y):
            return x.t() + y

        # Use shape (2**24, 65) rather than (2**24, 128) potentially avoid OOM in
        # CI while still keep the same up-rounded size-hints.
        a = torch.randn(2**24, 65, device=self.device)
        b = torch.randn(65, 2**24, device=self.device)
        fn(a, b)

    # Skipped on ROCm until https://github.com/ROCm/triton/issues/443 resolved
    @slowTest
    def test_fuse_large_params(self):
        def pt2_optimizer_step(optimizer):
            @torch.compile()
            def f():
                optimizer.step()

            f()

        params = [
            torch.rand(10, 10, dtype=torch.float32, device=self.device)
            for _ in range(194)
        ]
        for p in params:
            p.grad = torch.rand_like(p)

        o = torch.optim.AdamW(params)
        pt2_optimizer_step(o)

    # Skipped on MPS because avgpool size is not divisible
    @xfail_if_mps
    @skip_if_gpu_halide
    def test_adaptive_avg_pool1d_argmax(self):
        # https://github.com/pytorch/pytorch/issues/113013
        def fn(x):
            x = torch.adaptive_avg_pool1d(input=x, output_size=2)
            x = torch.argmax(input=x)
            return x

        x = torch.rand([4, 4, 3], dtype=torch.float64)
        self.common(fn, (x,))

    @skipCUDAIf(not SM80OrLater, "uses bfloat16 which requires SM >= 80")
    @parametrize(
        "dtype_x, dtype_y",
        list(itertools.product(test_dtypes, test_dtypes)),
    )
    def test_dtypeview(self, dtype_x, dtype_y):
        if TEST_WITH_ASAN:
            return

        if is_triton_cpu_backend(self.device):
            raise unittest.SkipTest("Compile time crash in Triton CPU CI")

        # https://github.com/pytorch/pytorch/issues/126338
        def fn(x, y, x_dtype, x2):
            x = x.view(x_dtype)
            y = y.view(x_dtype) + 1
            x2 = x2.view(x_dtype) + 1
            return x @ y, x2 @ x

        # @ operation needs arguments to be the same dtype
        for view_dtype in test_dtypes:
            try:
                x = rand_strided((2, 2), (2, 1), device=self.device, dtype=dtype_x)
                y = rand_strided((2, 2), (2, 1), device=self.device, dtype=dtype_y)
                x2 = x.clone()
                fn(x, y, view_dtype, x2)
            except Exception as e:
                continue
            self.common(
                fn,
                (x, y, view_dtype, x2),
                reference_in_float=False,
                check_lowp=False,
            )

    def test_dtypeview_fusion(self):
        @torch.compile
        def fn(x):
            x = x + 1
            x = torch.ops.aten.view.dtype(x, torch.int16)
            x = x * 2
            return x

        torch._inductor.metrics.generated_kernel_count = 0
        x = torch.randn([1024], dtype=torch.float16, device=self.device)
        self.common(fn, (x,), reference_in_float=False)
        assertGeneratedKernelCountEqual(self, 1)

    @expectedFailureCodegenDynamic
    def test_reinterpret_dtypeview(self):
        @torch.compile
        def fn(x, x2):
            return x.view([10, 10]).view(torch.int32), x2.view(torch.int32).view(
                [10, 10]
            )

        x = torch.randn([100, 1], device=self.device)
        x2 = x.clone()
        self.common(fn, (x, x2), reference_in_float=False, check_lowp=False)

        # The cpp_wrapper code is significantly more complex, so skip checking for exact
        # code lines.
        if not config.cpp_wrapper:
            x = torch.randn([100, 1], device=self.device)
            x2 = x.clone()
            _, code = run_and_get_code(fn, x, x2)
            FileCheck().check("aten.view.dtype(reinterpret_tensor").run(code[0])

    @xfail_if_triton_cpu
    @requires_gpu()
    def test_scalar_cpu_tensor_arg(self):
        def fn(x, y):
            return x + y.sum()

        test_dtypes = [
            torch.float32,
            torch.float64,
            torch.float16,
            torch.bfloat16,
        ]
        for cpu_dtype in test_dtypes:
            if not self.is_dtype_supported(cpu_dtype):
                continue
            x = torch.rand([20], device=self.device)
            y = torch.rand([4], device="cpu", dtype=cpu_dtype)
            self.common(
                fn,
                (x, y),
                check_lowp=False,
                copy_to_gpu=False,
                reference_in_float=False,
            )

    def test_float16_to_int16(self):
        def fn(x):
            x_view = x.view(dtype=torch.int16)
            return x_view.mul(2) + x_view.bitwise_and(2)

        x = torch.ones(4, dtype=torch.float16, device=self.device)
        ref = fn(x)
        actual = torch.compile(fn)(x)
        self.assertEqual(ref, actual)

    @skipCUDAIf(not SM80OrLater, "uses bfloat16 which requires SM >= 80")
    @skip_if_gpu_halide  # https://github.com/halide/Halide/issues/8311
    def test_bfloat16_to_int16(self):
        def fn(a, b):
            x = a + b
            x_view = x.view(dtype=torch.int16)
            return x_view.mul(2) + x_view.bitwise_and(2)

        if not self.is_dtype_supported(torch.bfloat16):
            raise unittest.SkipTest("bfloat16 is not supported on {self.device}")
        a = torch.ones(4, dtype=torch.bfloat16, device=self.device)
        b = torch.ones(4, dtype=torch.bfloat16, device=self.device)
        ref = fn(a, b)
        actual = torch.compile(fn)(a, b)
        self.assertEqual(ref, actual)

    def test_float32_to_int32(self):
        def fn(a, b):
            x = a + b
            x_view = x.view(dtype=torch.int32)
            return x_view.mul(2) + x_view.bitwise_and(2)

        a = 0.5 * torch.ones(4, dtype=torch.float32, device=self.device)
        b = 0.5 * torch.ones(4, dtype=torch.float32, device=self.device)
        ref = fn(a, b)
        actual = torch.compile(fn)(a, b)
        self.assertEqual(ref, actual)

    def test_randint_int64_mod(self):
        # This used to not compile due to a wrong return type of randint64_cpu
        # See https://github.com/pytorch/pytorch/issues/117435
        def fn(n):
            return (
                torch.randint(
                    low=-5, high=5, size=(n,), dtype=torch.int64, device=self.device
                )
                % 10
            )

        res = torch.compile(fn)(20)
        self.assertTrue(torch.all((res >= 0) & (res < 10)).item())

    @parametrize(
        "name, op",
        [
            subtest((name, getattr(torch.special, name)), name=name)
            for name in torch.special.__all__
            if name not in {"softmax", "log_softmax", "logsumexp"}
        ],
    )
    def test_pointwise(self, name, op):
        dtype = torch.float32
        check_lowp = True
        if self.device == GPU_TYPE and name in {
            "airy_ai",
            "bessel_i0",
            "bessel_i1",
            "bessel_j0",
            "bessel_j1",
            "bessel_y0",
            "bessel_y1",
            "erfcx",
            "gammainc",
            "gammaincc",
            "i1",
            "i1e",
            "modified_bessel_i0",
            "modified_bessel_i1",
            "modified_bessel_k0",
            "modified_bessel_k1",
            "ndtri",
            "scaled_modified_bessel_k0",
            "scaled_modified_bessel_k1",
            "spherical_bessel_j0",
            "zeta",
            "chebyshev_polynomial_t",
            "chebyshev_polynomial_v",
            "chebyshev_polynomial_u",
            "chebyshev_polynomial_w",
            "legendre_polynomial_p",
            "shifted_chebyshev_polynomial_t",
            "shifted_chebyshev_polynomial_u",
            "shifted_chebyshev_polynomial_v",
            "shifted_chebyshev_polynomial_w",
            "hermite_polynomial_h",
            "hermite_polynomial_he",
            "laguerre_polynomial_l",
        }:
            # <func>_cuda not implemented for Half
            check_lowp = False

        if (
            is_halide_backend(self.device)
            or is_triton_cpu_backend(self.device)
            and name
            in (
                "erfinv",
                "airy_ai",
                "bessel_j0",
                "bessel_j1",
                "bessel_y0",
                "bessel_y1",
                "chebyshev_polynomial_t",
                "chebyshev_polynomial_u",
                "chebyshev_polynomial_v",
                "chebyshev_polynomial_w",
                "digamma",
                "gammainc",
                "gammaincc",
                "gammaln",
                "hermite_polynomial_h",
                "hermite_polynomial_he",
                "i0",
                "i0e",
                "i1",
                "i1e",
                "laguerre_polynomial_l",
                "legendre_polynomial_p",
                "modified_bessel_i0",
                "modified_bessel_i1",
                "modified_bessel_k0",
                "modified_bessel_k1",
                "multigammaln",
                "ndtri",
                "polygamma",
                "psi",
                "scaled_modified_bessel_k0",
                "scaled_modified_bessel_k1",
                "shifted_chebyshev_polynomial_t",
                "shifted_chebyshev_polynomial_u",
                "shifted_chebyshev_polynomial_v",
                "shifted_chebyshev_polynomial_w",
                "spherical_bessel_j0",
                "zeta",
            )
        ):
            raise unittest.SkipTest(f"Halide & Triton CPU do not support {name}")

        if is_triton_cpu_backend(self.device) and name in [
            "erfc",
            "erfcx",
            "round",
            "log_ndtr",
        ]:
            raise unittest.SkipTest(f"Triton CPU does not support {name}")

        if is_pallas_backend(self.device) and name in {
            "airy_ai",
            "bessel_y0",
            "bessel_y1",
            "modified_bessel_k0",
            "modified_bessel_k1",
            "ndtri",
            "scaled_modified_bessel_k0",
            "scaled_modified_bessel_k1",
        }:
            raise unittest.SkipTest(f"Pallas does not support {name}")

        if name in {"gammainc", "gammaincc"}:
            args = (
                torch.randn(8, 8, dtype=dtype, device=self.device),
                torch.empty(8, 8, dtype=dtype, device=self.device).uniform_(1, 2),
            )

            def fn(x, y):
                return op(x, y)

        elif name in {"xlog1py", "xlogy", "zeta"}:
            args = (
                torch.randn(8, 8, dtype=dtype, device=self.device),
                torch.empty(8, 8, dtype=dtype, device=self.device).uniform_(1, 2),
            )

            def fn(x, y):
                return op(x, y)

        elif name == "multigammaln":
            args = (
                torch.empty(8, 8, dtype=dtype, device=self.device).uniform_(1, 2),
                2,
            )

            def fn(x, p):
                return op(x, p)

        elif name == "polygamma":
            args = (
                1,
                torch.empty(8, 8, dtype=dtype, device=self.device).uniform_(1, 10),
            )

            def fn(n, x):
                return op(n, x)

        elif "_polynomial_" in name:
            args = (
                torch.randn(8, 8, dtype=dtype, device=self.device),
                2,
            )

            def fn(x, n):
                return op(x, n)

        else:
            args = (torch.randn(8, 8, dtype=dtype, device=self.device),)

            def fn(x):
                return op(x)

        ctx = (
            contextlib.nullcontext()
            if self.device != "mps"
            or name
            not in [
                "airy_ai",
                "laguerre_polynomial_l",
                "legendre_polynomial_p",
                "log_ndtr",
                "ndtri",
            ]
            else self.assertRaises(NotImplementedError)
        )
        with ctx:
            self.common(fn, args, check_lowp=check_lowp, atol=1e-4, rtol=1e-4)

    # codegen test fails with no dynamic for loop in dynamic shape tests
    @expectedFailureCodegenDynamic
    def test_view_uint8_through_differing_bitwidths(self):
        # https://github.com/pytorch/pytorch/issues/120998
        def fn(x, view_dtype):
            return x.view(view_dtype).view(torch.uint8)

        view_dtypes = [torch.int16, torch.int32, torch.int64]
        for dtype in view_dtypes:
            x = torch.randint(0, 2**4, [4096, 4096], dtype=torch.uint8)
            self.common(
                fn,
                (
                    x,
                    dtype,
                ),
            )

    @torch._dynamo.config.patch(capture_scalar_outputs=True)
    def test_split_with_sizes_with_unbacked_symints(self):
        @torch.compile()
        def f(sz, x):
            s0, s1 = sz.tolist()
            r0, r1 = torch.ops.aten.split_with_sizes.default(x, [s0, s1])
            return torch.ops.aten.sort.default(r1)

        N = 7312
        S0 = 420
        S1 = N - S0

        result = f(torch.tensor([S0, S1]), torch.randn(N))
        self.assertTrue(len(result) == 2)

        @torch.compile()
        def f2(x):
            y = torch.arange(x.item())
            return torch.ops.aten.split_with_sizes.default(y, [5, 5, 10])

        result = f2(torch.tensor([20]))
        self.assertTrue(len(result) == 3)

    @torch._dynamo.config.patch(capture_scalar_outputs=True)
    def test_split_with_unbacked_symints(self):
        # https://github.com/pytorch/pytorch/issues/122937
        @torch.compile()
        def f(x):
            y = torch.arange(x.item())
            return torch.split(y, [5, 5, 10])

        result = f(torch.tensor([20]))
        self.assertTrue(len(result) == 3)

    def test_complex_memory_overlap(self):
        t = rand_strided((8, 1500, 1), (1504, 1, 1), device=self.device)
        self.assertFalse(complex_memory_overlap(t))

    @xfail_if_mps
    def test_generate_rand_fp8(self):
        """
        PyTorch can not generate fp8 tensors with a normal distribution because of
        missing needed kernels.

        We work around that in rand_strided by generating an fp16 tensor first and
        then do casting.
        """
        t = rand_strided((2, 3), (3, 1), device=self.device, dtype=torch.float8_e4m3fn)
        self.assertTrue(t.dtype is torch.float8_e4m3fn)

    @largeTensorTest("1GB", inductor=True)
    @parametrize(
        "use_block_ptr",
        [subtest(False), subtest(True, decorators=[skip_if_not_triton])],
    )
    def test_large_grid(self, use_block_ptr):
        # https://github.com/pytorch/pytorch/issues/123210
        def fn(primals_5):
            view = torch.ops.aten.reshape.default(primals_5, [-1, 2, 4])
            primals_5 = None
            permute = torch.ops.aten.permute.default(view, [0, 2, 1])
            clone = torch.ops.aten.clone.default(
                permute, memory_format=torch.contiguous_format
            )
            return clone

        s0 = 16777472
        s1 = 8

        with config.patch({"triton.use_block_ptr": use_block_ptr}):
            compiled_fn = torch.compile(fn)
            actual = compiled_fn(torch.ones(s0, s1, device=self.device))
            self.assertTrue((actual == 1).all())

    @skip_if_gpu_halide
    def test_pattern_matcher_multi_user(self):
        # Reproducer for https://github.com/pytorch/pytorch/issues/129685

        def forward(float_1, view_1):
            logits = float_1 / 64.0
            loss = torch.nn.functional.cross_entropy(logits, view_1, ignore_index=5)
            logsumexp = logits.logsumexp(dim=-1)
            return [loss, logsumexp]

        a = torch.randn(512, 4096, requires_grad=True)
        b = torch.randint(size=(512,), low=0, high=4095)

        self.common(forward, (a, b))

    def test_isin_tensor_scalar(self):
        for invert in [True, False]:
            torch._dynamo.reset()
            elements = 1
            test_elements = torch.tensor([1, 2, 3, 4])
            self.common(torch.isin, (elements, test_elements), {"invert": invert})
            torch._dynamo.reset()
            elements = torch.tensor([1, 2, 3, 4])
            test_elements = 1
            self.common(torch.isin, (elements, test_elements), {"invert": invert})
            torch._dynamo.reset()
            elements = torch.tensor([1, 2, 3, 4], device=self.device)
            test_elements = torch.tensor(2, device=self.device)
            self.common(torch.isin, (elements, test_elements), {"invert": invert})

        elements = torch.tensor(3.0)
        test_elements = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0])
        self.common(torch.isin, (elements, test_elements), {"assume_unique": True})

    def test_mul_index_expr(self):
        # Minified repro from https://github.com/pytorch/pytorch/issues/111884
        def forward():
            iota = torch.ops.prims.iota.default(
                16,
                start=0,
                step=1,
                dtype=torch.int64,
                device=self.device,
                requires_grad=False,
            )
            unsqueeze = torch.ops.aten.unsqueeze.default(iota, -1)
            mul = torch.ops.aten.mul.Tensor(unsqueeze, iota)
            unsqueeze = iota = None
            neg = torch.ops.aten.neg.default(mul)
            mul = None
            div = torch.ops.aten.div.Tensor(neg, 16)
            neg = None
            return (div,)

        self.common(forward, ())

    def test_flip_cat(self):
        def forward(unsqueeze, unsqueeze_1):
            cat_1 = torch.ops.aten.cat.default([unsqueeze, unsqueeze_1], 1)
            view = torch.ops.aten.view.default(cat_1, [4])
            slice_5 = torch.ops.aten.slice.Tensor(view, 0, 0, 3)
            rev_1 = torch.ops.aten.flip.default(slice_5, [0])
            return (rev_1,)

        a = torch.randn(2, 1, requires_grad=True)
        b = torch.randn(2, 1, requires_grad=True)
        self.common(forward, (a, b))

    @config.patch(implicit_fallbacks=True)
    def test_weight_norm_bwd(self):
        """
        Weight norm backward eager kernel does not support non-contiguous
        inputs. Eager kernel silently produces incorrect results when
        inputs are non-contiguous. Inductor implicitly fallback to eager
        for weight norm backward. Fix that by requiring contiguous inputs
        for any implicit fallback kernels.
        Check: https://github.com/pytorch/pytorch/issues/140452
        """

        class Repro(nn.Module):
            def __init__(self, in_features):
                super().__init__()
                self.weight_normed_linear = nn.utils.parametrizations.weight_norm(
                    nn.Linear(in_features, out_features=2)
                )
                self.linear = nn.Linear(in_features=2, out_features=1)

            def forward(self, x):
                return self.linear(self.weight_normed_linear(x))

        def f(m, x):
            with torch.amp.autocast(device_type=self.device, dtype=torch.half):
                loss = m(x).sum()
                loss.backward()
            return loss

        # odd number on purpose to trigger comprehensive padding
        in_features = 1025
        x = torch.randn(2, in_features, dtype=torch.half, requires_grad=True).to(
            device=self.device
        )
        m = Repro(in_features)
        m = m.to(self.device)

        f(m, x)

        ref_grad_list = [p.grad for p in m.parameters()]

        for p in m.parameters():
            p.grad = None

        opt_f = torch.compile(f)
        opt_f(m, x)
        act_grad_list = [p.grad for p in m.parameters()]
        self.assertTrue(
            same(ref_grad_list, act_grad_list, tol=1e-3),
            f"Ref:\n{ref_grad_list}\nAct:\n{act_grad_list}",
        )

    def test_chunk_recompiles(self):
        def f(x):
            return x.chunk(4)

        # Runs f and its torch.compile-d version with a fresh 1D tensor
        # of a specific size, and checks that the result is correct.
        def run(size):
            input = torch.randn(size)
            expected_out = f(input)
            actual_out = optf(input)
            self.assertEqual(expected_out, actual_out)

        cnts = CompileCounterWithBackend("inductor")
        optf = torch.compile(f, backend=cnts, fullgraph=True)

        # The first run should compile once with static shapes.
        run(4)
        self.assertEqual(cnts.frame_count, 1)

        # Varying the input size should trigger a recompilation.
        # Since the input size is a multiple of 4 (i.e. all runs shall
        # generate 4 output tensors), there should be no further
        # recompilation.
        for i in range(2, 12):
            run(4 * i)
        self.assertEqual(cnts.frame_count, 2)

        # Input size: 11
        # Not a multiple of 4, but still generates 4 output tensors,
        # where the last one has size > 1.
        run(11)
        self.assertEqual(cnts.frame_count, 2)

        # Input size: 10
        # Even though it still generates 4 output tensors, the last
        # one has size 1, falling into our 0/1 specialization. Thus,
        # this one also triggers recompilation.
        run(10)
        self.assertEqual(cnts.frame_count, 3)

        # Input size: 9
        # Yields one less output tensor, which should trigger a
        # recompilation.
        run(9)
        self.assertEqual(cnts.frame_count, 4)

    @dynamo_config.patch(error_on_recompile=True)
    def test_no_specization_over_symbolic_value(self):
        def fn(x):
            s0 = x.shape[0]
            y = torch.full((1,), s0)
            return x + y

        arg1 = torch.ones(10)
        arg2 = torch.ones(11)
        ref1 = fn(arg1)
        ref2 = fn(arg2)

        opt_fn = torch.compile(fn, fullgraph=True, dynamic=True, backend="inductor")
        res1 = opt_fn(arg1)
        res2 = opt_fn(arg2)

        self.assertEqual(res1, ref1)
        self.assertEqual(res2, ref2)

    def test_conv_shape_check(self):
        # https://github.com/pytorch/pytorch/issues/144013
        class Model(torch.nn.Module):
            def __init__(self, dim):
                super().__init__()
                conv_t_cls = eval(f"torch.nn.ConvTranspose{dim}d")
                self.conv_t = conv_t_cls(
                    1, 1, kernel_size=(2,) * dim, padding=(1,) * dim
                )

            def forward(self, x):
                x = self.conv_t(x)
                x = torch.sigmoid(x)  # trigger condition
                return x

        for dim in (1, 2, 3):
            inputs = torch.randn((1,) * (dim + 2))
            model = Model(dim)

            with self.assertRaisesRegex(RuntimeError, "Output size is too small"):
                _ = model(inputs)

            with self.assertRaisesRegex(RuntimeError, "Output size is too small"):
                _ = torch.compile(model)(inputs)

    @skipIfRocm
    @requires_cuda
    def test_conv_transpose_zero_size_output(self):
        # Only CUDA (cuDNN) supports zero-sized spatial outputs for conv_transpose.
        # ROCm/miopen fails with miopenStatusBadParm, MPS fails with empty placeholder assert.
        # This test ensures compiled mode matches eager behavior on CUDA.
        class Model(torch.nn.Module):
            def __init__(self):
                super().__init__()
                # ConvTranspose2d: in=3, out=1, kernel=2, stride=2, padding=2
                # Output size = (input_size - 1) * stride - 2 * padding + kernel_size
                #             = (2 - 1) * 2 - 2 * 2 + 2 = 0
                self.conv_transpose = torch.nn.ConvTranspose2d(
                    3, 1, 2, 2, 2, bias=False
                )

            def forward(self, x):
                x = self.conv_transpose(x)
                x = torch.tanh(x)
                return x

        func = Model().to(GPU_TYPE)
        x = torch.randn(1, 3, 2, 2, device=GPU_TYPE)

        with torch.no_grad():
            eager_out = func(x.clone())
            compiled_out = torch.compile(func)(x.clone())

        self.assertEqual(eager_out.shape, torch.Size([1, 1, 0, 0]))
        self.assertEqual(compiled_out.shape, torch.Size([1, 1, 0, 0]))
        self.assertEqual(eager_out, compiled_out)

    @requires_gpu()
    @config.patch(fallback_random=True)
    @unittest.skipIf(
        config.cpp_wrapper,
        "cpp wrapper does not support sort properly: https://gist.github.com/shunting314/e58f637f9972f1ad1a033d73cee6e42a",
    )
    def test_mix_device_index(self):
        """
        A tiny repro for this meta internal issue: https://fb.workplace.com/groups/1075192433118967/posts/1567334737238065
        whose root cause is Inductor having wrong assumption of index.Tensor's output
        stride.
        """
        image_latent = (
            torch.randn((24, 16, 32, 32), device=GPU_TYPE)
            .to(memory_format=torch.channels_last)
            .view(2, 12, 16, 32, 32)
        )

        def f(image_latent):
            indices = torch.argsort(torch.rand(2, 12), dim=-1)

            tar_latent = image_latent[torch.arange(2).unsqueeze(-1), indices[:, :3]]

            # The original model uses einops. In this unit test, we use view op directly
            # to avoid importing einops
            #   tar_latent_rearranged = einops.rearrange(
            #     tar_latent, "b n c h w -> (b n) c h w"
            #   )
            tar_latent_rearranged = tar_latent.view(-1, *tar_latent.size()[2:])

            return tar_latent_rearranged

        reset_rng_state()
        ref = f(image_latent)
        opt_f = torch.compile(f)

        code = run_and_get_triton_code(opt_f, image_latent)
        reset_rng_state()
        act = opt_f(image_latent)

        torch.testing.assert_close(ref, act, atol=1e-3, rtol=1e-3)

        if is_dynamic_shape_enabled():
            size_assert_pattern = r"assert_size_stride.[a-z]+[0-9]+, .2, 3, s12, s80, s80., .3\*s12\*s80\*s80, s12\*s80\*s80, 1, s12\*s80, s1.."  # noqa: B950
        else:
            size_assert_pattern = r"assert_size_stride.[a-z]+[0-9]+, .2, 3, 16, 32, 32., .49152, 16384, 1, 512, 16.."
        FileCheck().check_regex(size_assert_pattern).run(code)

    def test_lite_mode_fallback(self):
        def f(x):
            z = x.sin()
            return z.cos()

        f = torch.compile(f, mode="lite")

        _, code = run_and_get_code(f, torch.randn(2, device=self.device))

        # Checks that aten ops are kept and run
        if config.cpp_wrapper:
            FileCheck().check("aoti_torch_call_dispatcher(").check("aten::sin").check(
                "aoti_torch_call_dispatcher("
            ).check("aten::cos").run(code[0])
        else:
            FileCheck().check("torch.ops.aten.sin.default(").check(
                "torch.ops.aten.cos.default("
            ).run(code[0])
            # Checks that no triton code run in the generated code
            self.assertFalse(".run(" in code[0])

    # skip cpu test since rms norm is always decomposed on cpu
    @skipIfXpu(msg="_fused_rms_norm is not implemented on XPU yet")
    def test_lite_mode_not_decompose(self):
        if self.device != GPU_TYPE or self.device == "mps":
            raise unittest.SkipTest("requires GPU")

        def f(x, shape):
            y = x + 1
            z = torch.ops.aten._fused_rms_norm(y, shape, None, None)
            return z[0] + z[1]

        f = torch.compile(f, mode="lite")

        x = torch.randn(2, 3, device=self.device)
        _, code = run_and_get_code(f, x, [2, 3])
        if config.cpp_wrapper:
            FileCheck().check(
                "AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_cuda__fused_rms_norm("
            ).run(code[0])
        else:
            FileCheck().check("torch.ops.aten._fused_rms_norm.default(").run(code[0])

        x = torch.randn(2, 3, device=self.device, requires_grad=True)
        _, codes = run_fw_bw_and_get_code(lambda: f(x, [2, 3]))
        self.assertEqual(len(codes), 2)
        if config.cpp_wrapper:
            FileCheck().check(
                "AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_cuda__fused_rms_norm("
            ).run(codes[0])
        else:
            FileCheck().check("torch.ops.aten._fused_rms_norm.default(").run(codes[0])

    def test_lite_regional_compile_flex_attention(self):
        if self.device != GPU_TYPE or self.device == "mps":
            raise unittest.SkipTest("requires GPU")

        from torch.nn.attention.flex_attention import create_block_mask, flex_attention

        def _squared(score, b, h, m, n):
            return score * score

        def mask_mod(b, h, q, k):
            return q >= 0

        a = 12
        b = 64
        block_mask = create_block_mask(
            mask_mod, None, None, a * b, a * b, device=self.device
        )

        def fn(x):
            x = torch.sin(x)
            with fx_traceback.annotate({"compile_with_inductor": 0}):
                x = flex_attention(x, x, x, block_mask=block_mask, score_mod=_squared)
            return torch.cos(x)

        x = torch.randn(
            1,
            1,
            a * b,
            b,
            dtype=torch.bfloat16,
            device=self.device,
            requires_grad=True,
        )

        opt_fn = torch.compile(
            fn,
            mode="lite",
            fullgraph=True,
        )

        # Check that inductor compilation is called twice
        _, codes = run_fw_bw_and_get_code(lambda: opt_fn(x))
        self.assertEqual(len(codes), 2)

    @unittest.skipIf(
        config.cpp_wrapper,
        "codegen invoke_subgraph is not implemented for cpp wrapper",
    )
    def test_lite_regional_compile_invoke_subgraph(self):
        # Checks that get_attr nodes custom metadata is propagated
        @torch.compiler.nested_compile_region
        def gn(x):
            return torch.sin(x)

        def fn(x):
            x = x + 1
            with fx_traceback.annotate({"compile_with_inductor": 0}):
                z = gn(x)
            return torch.sigmoid(z)

        opt_fn = torch.compile(fn, mode="lite", fullgraph=True)
        x = torch.randn(10, requires_grad=True)

        _, codes = run_fw_bw_and_get_code(lambda: opt_fn(x))
        self.assertEqual(len(codes), 2)

    @unittest.skipIf(
        config.cpp_wrapper,
        "codegen triton_kernel_wrapper_functional is not implemented for cpp wrapper",
    )
    def test_lite_triton_kernel_wrapper_functional(self):
        if self.device != GPU_TYPE or self.device == "mps":
            raise unittest.SkipTest("requires GPU")

        from torch._higher_order_ops.triton_kernel_wrap import (
            kernel_side_table,
            triton_kernel_wrapper_functional,
        )
        from torch.testing._internal.triton_utils import mul2_kernel

        kernel_side_table.reset_table()

        def f(x, output):
            out = triton_kernel_wrapper_functional(
                kernel_idx=kernel_side_table.add_kernel(mul2_kernel),
                constant_args_idx=kernel_side_table.add_constant_args(
                    {"n_elements": output.numel(), "BLOCK_SIZE": 16}
                ),
                grid=[(x.numel(),)],
                tma_descriptor_metadata={},
                kwargs={
                    "in_ptr0": x,
                    "out_ptr": output,
                },
                tensors_to_clone=["in_ptr0", "out_ptr"],
            )
            return out["out_ptr"]

        t1 = torch.rand(5, device=self.device)
        t2 = torch.rand(5, device=self.device)

        compiled_f = torch.compile(f, mode="lite")
        out = compiled_f(t1, t2)

        # Make sure t2 was not modified
        self.assertNotEqual(out, t2)

    def test_lite_regional_compile_repeated_blocks(self):
        def fn(x, y):
            sin = torch.sin(x)

            with fx_traceback.annotate({"compile_with_inductor": 0}):
                mul = sin * y
                add = mul + 1

            return torch.sin(add)

        class Mod(torch.nn.Module):
            def __init__(self):
                super().__init__()

            def forward(self, x, y):
                a = fn(x, y)
                return fn(a, y)

        mod = Mod()

        opt_mod = torch.compile(
            mod,
            mode="lite",
            fullgraph=True,
        )
        x = torch.randn(10, requires_grad=True)
        y = torch.randn(10, requires_grad=True)

        _, codes = run_fw_bw_and_get_code(lambda: opt_mod(x, y))
        self.assertEqual(len(codes), 2)

    def test_lite_dynamic_shape_assertion(self):
        class Model(torch.nn.Module):
            def forward(self, c):
                d = torch.concat([c, c], dim=0)
                with fx_traceback.annotate({"compile_with_inductor": "my_region"}):
                    d = d + 1
                return d

        model = Model()
        model = torch.compile(
            model,
            mode="lite",
            fullgraph=True,
        )

        c = torch.randn((64, 32), device=self.device)
        torch._dynamo.decorators.mark_unbacked(c, 0)

        _, code = run_and_get_code(model, c)
        # Checks that unbacked symint assertions are kept
        if config.cpp_wrapper:
            FileCheck().check_regex(r"if \(!\(u.* >= 0L\)\)").check_regex(
                "Expected u.* >= 0 but receive"
            ).run(code[0])
        else:
            FileCheck().check_regex(r"if not \(u.* >= 0\):").check_regex(
                r"raise RuntimeError\('u.* >= 0'\)"
            ).run(code[0])

    @unittest.skipIf(
        config.cpp_wrapper,
        "Inductor does not generate size/stride asserts for cpp_wrapper",
    )
    @lowering.force_fallback(aten.sort.default)
    def test_size_asserts_for_multi_output_fallback(self):
        @torch.compile
        def f(x):
            return x.sort()

        x = torch.randn(16, 32, device=self.device)
        code = run_and_get_triton_code(f, x)

        if is_dynamic_shape_enabled():
            FileCheck().check("assert_size_stride(buf1, (s77, s27), (s27, 1)").check(
                "assert_size_stride(buf2, (s77, s27), (s27, 1)"
            ).run(code)
        else:
            FileCheck().check("assert_size_stride(buf1, (16, 32), (32, 1)").check(
                "assert_size_stride(buf2, (16, 32), (32, 1)"
            ).run(code)

    @requires_gpu_and_triton
    @config.patch(use_fast_math=True)
    def test_prepare_softmax_with_fast_math(self):
        """
        Measure on a A100, perf is 3.487ms v.s. 3.358ms without or with flushing to zero. A 4% speedup.
        """
        if DO_PERF_TEST:
            M = 32768
            N = 50304
        else:
            # Use small shapes if not doing perf test
            M = 128
            N = 128
        x = torch.randn(M, N, dtype=torch.bfloat16, device=GPU_TYPE)

        def f(x):
            """
            Not calling softmax directly to generate kernel just for
            computation of max & sum.

            If we call softmax directly, the computation of the final
            result will double the membw usage. In that case saving
            computation does not matter much.

            In reality during training, since max & sum need to be saved
            for bwd and the computation of softmax result is fused with
            other kernels, we do see such prepare_softmax kernel appear
            in real models.
            """
            x_max = x.amax(dim=-1, keepdim=True)
            x_sum = (x - x_max).exp().sum(dim=-1, keepdim=True).log()
            return x_max, x_sum

        opt_f = torch.compile(f)
        ref = f(x)
        act = opt_f(x)
        self.assertTrue(same(ref, act, tol=1e-2), f"Ref:\n{ref}\nAct:\n{act}")

        if DO_PERF_TEST:
            from triton.testing import do_bench

            ms = do_bench(lambda: opt_f(x))
            print(f"{ms=:.3f}")

    @torch._inductor.config.patch("graph_partition", True)
    def test_graph_partition_no_inputs(self):
        def foo():
            torch.manual_seed(3)
            return torch.randint(0, 5, (5,))

        foo = torch.compile(foo)
        foo()

    @torch._inductor.config.patch("graph_partition", True)
    def test_graph_partition_mutation_real_name(self):
        def f(x, y, z, other):
            mul = x * y
            diag = torch.diagonal(mul)
            diag.copy_(other)
            # force grah partition by device copy
            u = diag.cpu().to(self.device)
            return torch.mm(mul, z) + u + diag

        inps = (
            torch.randn(3, 3, device=self.device),
            torch.randn(3, 3, device=self.device),
            torch.randn(3, 3, device=self.device),
            torch.randn(3, device=self.device),
        )

        eager_out = f(*inps)
        compiled_f = torch.compile(f)
        compiled_out = compiled_f(*inps)
        torch.testing.assert_close(eager_out, compiled_out)

    @torch._inductor.config.patch("graph_partition", True)
    def test_graph_partition_arange1(self):
        def fn(step, device):
            return torch.arange(512, -512, step, device=device)

        compiled_fn = torch.compile(fn)

        for step in (-1, -1.0):
            expect = fn(step, "cpu")
            actual = compiled_fn(step, "cpu")
            self.assertEqual(expect, actual)

        self.assertEqual(expect, actual)

    @torch._inductor.config.patch("graph_partition", True)
    def test_graph_partition_arange2(self):
        def fn(x):
            return torch.arange(0.1, 8.0001, 1, dtype=x.dtype, device=x.device)

        make_arg = functools.partial(
            make_tensor, device=self.device, requires_grad=False
        )

        compiled_fn = torch.compile(fn)

        x = make_arg(1, dtype=torch.float32)
        self.assertEqual(fn(x), compiled_fn(x))

        x = make_arg(1, dtype=torch.int64)
        self.assertEqual(fn(x), compiled_fn(x))

    @torch._inductor.config.patch("graph_partition", True)
    def test_graph_partition_argmax(self):
        def fn():
            a = torch.zeros([2, 2])
            b = a.argmax(0)
            return b.float().mean()

        compiled_fn = torch.compile(fn)
        self.assertEqual(fn(), compiled_fn())

    @torch._inductor.config.patch("graph_partition", True)
    def test_graph_partition_both_scalars(self):
        def fn(a, b):
            return (
                aten.add(a, b),
                aten.add(b, a),
                aten.sub(a, b),
                aten.sub(b, a),
                aten.mul(a, b),
                aten.mul(b, a),
            )

        compiled_fn = torch.compile(fn)

        self.assertEqual(fn(4, 3.3), compiled_fn(4, 3.3))

    @torch._inductor.config.patch("graph_partition", True)
    @config.patch(assume_aligned_inputs=False)
    def test_graph_partition_misaligned_input(self):
        def fn(x):
            return x.cos() * x.sin()

        fn_c = torch.compile(fn, mode="reduce-overhead", dynamic=True)

        for size, stride, offset in (
            ((32, 32), (32, 1), 4),
            ((48, 48), (48, 1), 4),
            ((64, 64), (64, 1), 5),
        ):
            torch.manual_seed(42)
            base = torch.randn(
                64 * 64 + 64,
                dtype=torch.float32,
                device=self.device,
                requires_grad=True,
            )
            torch.manual_seed(42)
            base_ref = torch.randn(
                64 * 64 + 64,
                dtype=torch.float32,
                device=self.device,
                requires_grad=True,
            )

            inp = torch.as_strided(base, size, stride, offset)
            inp_ref = torch.as_strided(base_ref, size, stride, offset)

            inp.requires_grad_(True)
            inp_ref.requires_grad_(True)

            res = fn_c(inp)
            ref = fn(inp_ref)
            self.assertEqual(ref, res)

            res.sum().backward()
            ref.sum().backward()
            self.assertEqual(base.grad, base_ref.grad)

    @torch._inductor.config.patch("graph_partition", True)
    def test_graph_partition_constant_tensor1(self):
        def fn():
            a = torch.zeros([1, 2], dtype=torch.int32)
            a = a + a
            b = a.to(dtype=torch.float32)
            return b * 0.8

        compiled_fn = torch.compile(fn)

        self.assertEqual(fn(), compiled_fn())

    @torch._inductor.config.patch("graph_partition", True)
    def test_graph_partition_constant_tensor2(self):
        def fn(x):
            return torch.tensor(list(range(2, 40, 2)), device=self.device) + x

        compiled_fn = torch.compile(fn)

        x = torch.randn(1, device=self.device)

        self.assertEqual(fn(x), compiled_fn(x))

    @torch._inductor.config.patch("graph_partition", True)
    def test_graph_partition_scalar_inputs(self):
        def fn(a, b):
            return (
                aten.div(a, b, rounding_mode=None),
                aten.div(a * 0.5, b, rounding_mode=None),
                aten.div(a, b * 1.0, rounding_mode=None),
                aten.div(a, b, rounding_mode="floor"),
                aten.div(a, b, rounding_mode="trunc"),
                a / b,
                a // b,
            )

        compiled_fn = torch.compile(fn)
        self.assertEqual(fn(1024, 100), compiled_fn(1024, 100))

    @torch._inductor.config.patch("graph_partition", True)
    def test_graph_partition_unbacked_symint_as_output(self):
        def nested(x, repeats):
            rank = torch.arange(repeats.numel(), device=x.device)
            index = rank.repeat_interleave(repeats, dim=0)
            return torch.index_select(x, index=index, dim=0)

        example_inputs = (
            torch.randn((32, 64), device=self.device),
            repeats := torch.tensor([5, 10, 15], device=self.device),
        )
        torch._dynamo.mark_dynamic(repeats, 0)

        nested_opt = torch.compile(nested, backend="inductor")

        expect = nested(*example_inputs)
        actual = nested_opt(*example_inputs)
        self.assertEqual(expect, actual)

    @torch._inductor.config.patch("graph_partition", True)
    def test_graph_partition_refcount(self):
        contexts = [
            contextlib.nullcontext,
            lambda: torch._inductor.config.patch({"triton.cudagraphs": True}),
        ]

        for context in contexts:
            with context():
                inps = [
                    torch.rand([5, 5]).to(self.device),
                    torch.rand([5, 5]).to(self.device),
                ]
                inp_refs = [weakref.ref(inp) for inp in inps]

                def fn(x, y):
                    a = x + y
                    return (a @ a,)

                fn_fx = make_fx(fn)(inps[0], inps[1])
                fn_compiled = compile_fx_inner(fn_fx, inps)

                matmul_seen = False

                class TestRefMode(TorchDispatchMode):
                    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
                        kwargs = kwargs if kwargs else {}

                        nonlocal inps
                        nonlocal inp_refs
                        nonlocal matmul_seen

                        gc.collect()
                        if func is aten.mm.out:
                            matmul_seen = True
                            if len(inps) != 0:
                                raise AssertionError(
                                    f"Expected len(inps) == 0, got {len(inps)}"
                                )
                            if inp_refs[0]() is not None:
                                raise AssertionError(
                                    "Expected inp_refs[0]() to be None"
                                )
                            if inp_refs[1]() is not None:
                                raise AssertionError(
                                    "Expected inp_refs[1]() to be None"
                                )

                        return func(*args, **kwargs)

                with TestRefMode():
                    fn_compiled(inps)

                # do an extra run to make sure we are deallocating on warmup and record
                inps.extend(
                    [
                        torch.rand([5, 5]).to(self.device),
                        torch.rand([5, 5]).to(self.device),
                    ]
                )
                inp_refs.extend([weakref.ref(inp) for inp in inps])
                matmul_seen = False

                with TestRefMode():
                    fn_compiled(inps)

                if len(inps) != 0:
                    raise AssertionError(f"Expected len(inps) == 0, got {len(inps)}")

    @torch._inductor.config.patch("graph_partition", True)
    def test_graph_partition_pad_dynamic(self):
        def get_same_padding(x: int, k: int, s: int, d: int):
            return max((math.ceil(x / s) - 1) * s + (k - 1) * d + 1 - x, 0)

        def pad_same(x, k, s, d=(1, 1), value=0):
            ih, iw = x.size()[-2:]
            pad_h, pad_w = (
                get_same_padding(ih, k[0], s[0], d[0]),
                get_same_padding(iw, k[1], s[1], d[1]),
            )
            if pad_h > 0 or pad_w > 0:
                x = torch.nn.functional.pad(
                    x,
                    [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2],
                    value=value,
                )
            return x

        x = torch.randn(2, 24, 110, 110, device=self.device)
        opt = torch.compile(pad_same, dynamic=True)
        res = opt(x, (5, 5), (2, 2))
        ref = pad_same(x, (5, 5), (2, 2))
        self.assertEqual(res, ref, atol=0, rtol=0)

    @skip_if_halide  # only 32-bit indexing
    @largeTensorTest("16GB", inductor=True)
    def test_split_reduction_with_int64_size(self):
        if torch._inductor.config.cpu_backend == "triton":
            raise unittest.SkipTest(
                "Fail for triton cpu backend with error: https://gist.github.com/shunting314/a873fb32b6b7b5a437f44280ae86839f"
            )

        if self.device == "cpu":
            raise unittest.SkipTest(
                "The test fails some times on CI: "
                "https://github.com/pytorch/pytorch/actions/runs/15333913377/job/43153170162. "
                "Skip for now."
            )

        size = (30000, 100000)

        # rand rather than randn since the mean for the latter is close to 0
        # which happens to be close to the value generated by the bug.
        t = torch.rand(size, dtype=torch.float, device=self.device)
        op = torch.mean
        expected = op(t)
        actual = torch.compile(op)(t)
        # self.common takes more GPU memory. Do the check directly
        self.assertTrue(
            torch.allclose(expected, actual, atol=1e-2, rtol=1e-2),
            f"{expected=} {actual=}",
        )

    def test_remove_noop_view_default(self):
        def f(x):
            batch_size = x.shape[0]
            x = x.transpose(1, 2)  # (batch_size, 2, 3)
            x = x.reshape(batch_size, 2, 3)  # noop
            return x

        f = torch.compile(f)

        x = torch.randn((2, 3, 2), device=self.device)
        expected_graph1 = f"""\
def forward(self, arg0_1: "f32[2, 3, 2][6, 2, 1]{str(x.device)}"):
        permute: "f32[2, 2, 3][6, 1, 2]{str(x.device)}" = torch.ops.aten.permute.default(arg0_1, [0, 2, 1]);  arg0_1 = None
        return (permute,)"""  # noqa: B950

        post_grad_graph = get_post_grad_graph(f, (x,))

        self.assertExpectedInline(
            post_grad_graph,
            expected_graph1,
            ignore_comments=True,
            ignore_empty_lines=True,
        )

        # dynamic shape
        x = torch.randn((4, 3, 2), device=self.device)
        expected_graph2 = f"""\
def forward(self, arg0_1: "Sym(s77)", arg1_1: "f32[s77, 3, 2][6, 2, 1]{str(x.device)}"):
        permute: "f32[s77, 2, 3][6, 1, 2]{str(x.device)}" = torch.ops.aten.permute.default(arg1_1, [0, 2, 1]);  arg1_1 = None
        return (permute,)"""  # noqa: B950
        post_grad_graph = get_post_grad_graph(f, (x,))
        self.assertExpectedInline(
            post_grad_graph,
            expected_graph2,
            ignore_comments=True,
            ignore_empty_lines=True,
        )

    def test_remove_noop_view_dtype(self):
        def f(x):
            x = x.transpose(1, 2)  # (batch_size, 2, 3)
            x = x.view(torch.uint8)  # noop
            return x

        f = torch.compile(f)

        x = torch.ones((2, 3, 2), device=self.device, dtype=torch.uint8)
        torch._dynamo.mark_dynamic(x, 0)
        torch._dynamo.mark_dynamic(x, 1)
        torch._dynamo.mark_dynamic(x, 2)

        post_grad_graph = get_post_grad_graph(f, (x,))
        expected_graph = f"""\
def forward(self, arg0_1: "Sym(s77)", arg1_1: "Sym(s27)", arg2_1: "Sym(s53)", arg3_1: "u8[s77, s27, s53][s27*s53, s53, 1]{str(x.device)}"):
        permute: "u8[s77, s53, s27][s27*s53, 1, s53]{str(x.device)}" = torch.ops.aten.permute.default(arg3_1, [0, 2, 1]);  arg3_1 = None
        return (permute,)"""  # noqa: B950
        self.assertExpectedInline(
            post_grad_graph,
            expected_graph,
            ignore_comments=True,
            ignore_empty_lines=True,
        )

    @config.patch("min_num_split", 256)
    def test_split_reduction_dynamic_shape(self):
        from torch._dynamo.decorators import mark_dynamic

        def f(x):
            # outer reduction
            return x.sum(dim=0)

        N = 512
        x_small = torch.randn(4096, N, device=self.device)

        mark_dynamic(x_small, 0)
        expect = f(x_small)
        opt_f = torch.compile(f, dynamic=True)
        actual = opt_f(x_small)
        self.assertTrue(torch.allclose(expect, actual, atol=1e-3, rtol=1e-3))

        if DO_PERF_TEST:
            from triton.testing import do_bench

            # benchmark for a much larger input
            x_large = torch.randn(4096 * 1000, N, device=self.device)
            ms = do_bench(lambda: opt_f(x_large))
            print(f"{ms=:.3f}")

    @skip_if_halide
    @expectedFailureCodegenDynamic
    def test_special_polygamma(self):
        fn = torch.special.polygamma
        x = torch.tensor(2, dtype=torch.float32)
        self.common(fn, (0, x))
        self.common(fn, (1, x))
        self.common(fn, (2, x))

    @skip_if_triton
    @skip_if_halide
    @config.patch({"freezing": True})
    def test_dont_constant_fold(self):
        from torch._inductor.constant_folding import (
            add_dont_constant_fold,
            clear_dont_constant_fold,
        )

        m = 5

        class M(torch.nn.Module):
            def __init__(self):
                super().__init__()
                self.w = torch.randn(m)
                self.s = torch.randn(m)

            def forward(self, x):
                return self.w * self.s + x

        x = torch.rand(m)
        mod = M()
        for dont_constant_fold in [True, False]:
            clear_dont_constant_fold()
            if dont_constant_fold:
                add_dont_constant_fold(torch.ops.aten.mul.Tensor)
            with torch.no_grad():
                refe_out = mod(x)
                mod = torch.compile(mod)
                test_out, (code,) = run_and_get_code(mod, x)
            if dont_constant_fold:
                FileCheck().check("cpp_fused_add_mul").run(code)
            else:
                FileCheck().check("cpp_fused_add_0").run(code)
            self.assertEqual(refe_out, test_out)

    def test_triton_kernel_bool_param(self):
        if self.device != GPU_TYPE or self.device == "mps":
            raise unittest.SkipTest("requires GPU")

        from torch.testing._internal.triton_utils import add_kernel_with_boolean_param

        class Model(torch.nn.Module):
            def forward(self, x):
                out = torch.zeros_like(x)
                add_kernel_with_boolean_param[1,](
                    in_ptr0=x,
                    in_ptr1=x,
                    out_ptr=out,
                    n_elements=x.numel(),
                    add_xy=True,
                    BLOCK_SIZE=1,
                )
                return out

        inputs = (torch.randn(4, device=self.device),)
        self.common(Model(), inputs)

    @requires_gpu_and_triton
    def test_triton_kernel_bool_tensor_arg(self):
        if self.device != GPU_TYPE or self.device == "mps":
            raise unittest.SkipTest("requires GPU")

        from torch.testing._internal.triton_utils import (
            masked_add_kernel_with_bool_tensor,
        )

        class Model(torch.nn.Module):
            def forward(self, x, y, mask):
                out = torch.zeros_like(x)
                n = x.numel()
                masked_add_kernel_with_bool_tensor[(n,)](
                    in_ptr0=x,
                    in_ptr1=y,
                    mask_ptr=mask,
                    out_ptr=out,
                    n_elements=n,
                    BLOCK_SIZE=1024,
                )
                return out

        n = 128
        x = torch.randn(n, device=self.device)
        y = torch.randn(n, device=self.device)
        mask = torch.arange(n, device=self.device) < n // 2
        inputs = (x, y, mask)
        self.common(Model(), inputs)

    @skipIfXpu(
        msg="Profile not enabled on XPU CI, "
        "https://github.com/intel/torch-xpu-ops/issues/2334"
    )
    @skipIfRocmArch(NAVI_ARCH)
    @requires_gpu_and_triton
    @parametrize("use_cat", [True, False])
    def test_copy_non_blocking_is_pinned(self, use_cat):
        def f(a_list):
            a_cpu_list = []
            a_to_cpu_event_list = []

            for a in a_list:
                a_cpu = a.to(device="cpu", non_blocking=True)
                a_to_cpu_event = torch.Event()
                a_to_cpu_event.record()
                a_cpu_list.append(a_cpu)
                a_to_cpu_event_list.append(a_to_cpu_event)

            for e in a_to_cpu_event_list:
                e.synchronize()

            if use_cat:
                return torch.cat(a_cpu_list)
            else:
                return a_cpu_list

        f_compiled = torch.compile(f)
        inputs = [
            torch.rand(1000, dtype=torch.float16, device=GPU_TYPE) for _ in range(100)
        ]
        outputs = f(inputs)

        warmup_compiled = f_compiled(inputs)
        with torch.profiler.profile(
            activities=[
                getattr(torch.profiler.ProfilerActivity, GPU_TYPE.upper()),
            ],
        ) as p:
            outputs_compiled = f_compiled(inputs)

        self.assertEqual(outputs, outputs_compiled)
        profile_output = str(p.key_averages())
        print(profile_output)
        self.assertFalse("Pageable" in profile_output)

    @unittest.skipIf(
        config.cpp_wrapper,
        "cpp_wrapper samples will lead to invalid indexing",
    )
    def test_inductor_triton_bucketize_respects_masking(self):
        def fn(inp, repeats, output_size):
            # return torch.repeat_interleave(inp, repeats, dim=0, output_size=output_size)
            idx = torch.searchsorted(
                repeats.cumsum(0),
                torch.arange(0, output_size, device=repeats.device),
                right=True,
            )
            return torch.index_select(inp, 0, idx)

        inp = torch.arange(0, 4, device=self.device)
        repeats = torch.tensor([1, 2, 3, 4], device=self.device)
        output_size = repeats.sum().item()
        args = (inp, repeats, output_size)
        self.assertEqual(fn(*args), torch.compile(fn)(*args))

    @unittest.skipIf(
        config.triton.autotune_at_compile_time is True,
        "autotune_at_compile_time doesn't work for test with indexing",
    )
    @parametrize("dtype", [torch.int32, torch.int64])
    @parametrize("nd", [1, 2])
    def test_repeat_interleave_Tensor_decomp(self, dtype, nd):
        # https://github.com/pytorch/pytorch/issues/147160
        def f(input, repeats):
            return torch.repeat_interleave(input, repeats, dim=0, output_size=3) + 1

        input = torch.tensor([[1, 2], [3, 4]], dtype=dtype, device=self.device)
        input = torch.arange(1, 2**nd + 1, dtype=dtype, device=self.device).reshape(
            [2] * nd
        )
        repeat = torch.tensor([1, 2], device=self.device)

        f_compiled = torch.compile(f)
        output, (code,) = run_and_get_code(f_compiled, input, repeat)
        reference = f(input, repeat)
        self.assertEqual(output, reference)
        can_lower = input.device.type != "mps"
        has_lowered = not re.search(r"repeat_interleave.Tensor", code)
        self.assertEqual(has_lowered, can_lower)

    @staticmethod
    def _is_triggering_buffer_reuse(fn, *inputs):
        with config.patch(allow_buffer_reuse=True):
            _, (code_allowed,) = run_and_get_code(fn, *inputs)
        with config.patch(allow_buffer_reuse=False):
            _, (code_disallowed,) = run_and_get_code(fn, *inputs)
        code_allowed = re.sub(r"AOT ID: .*", "AOT ID: ['test']", code_allowed)
        code_disallowed = re.sub(r"AOT ID: .*", "AOT ID: ['test']", code_disallowed)
        return code_allowed != code_disallowed

    # If matmul is implemented by triton there is more reuse
    @config.patch(
        {
            "max_autotune_gemm_backends": "ATEN",
            "triton.native_matmul": False,
        }
    )
    def test_allow_reuse_disable_if_exceed_peak(self):
        @torch.compile
        def fn(inp):  # 1*N^2
            a = inp.mean(-1)  # 1*N^2 + N
            b = (inp - a) ** 2  # 2*N^2 + N
            c = b @ b  # 3*N^2 (!!) since this is the peak, can not reuse across
            d = c.mean(-1)  # 2*N^2 + N
            return d  # 1*N^2 + N

        inp = torch.randn(100, 100, device=self.device)
        self.assertFalse(CommonTemplate._is_triggering_buffer_reuse(fn, inp))

    def test_allow_reuse_active_if_under_peak(self):
        def g(inp):
            return (inp - torch.logsumexp(inp, -1)) ** 2

        @torch.compile
        def fn(m, inp):
            inp = m @ g(inp)
            inp = m @ g(inp)
            inp = m @ g(inp)
            inp = m @ g(inp)
            inp = m @ g(inp)
            return inp

        m = torch.randn(100, 100, device=self.device)
        inp = torch.randn(100, 100, device=self.device)
        self.assertTrue(CommonTemplate._is_triggering_buffer_reuse(fn, m, inp))

    @requires_gpu_and_triton
    def test_cpu_scalar_with_gpu_tensor(self):
        def fn(a, b):
            return a + b[0]

        a = torch.rand(20, device=GPU_TYPE)
        b = torch.rand(4, device="cpu")

        torch._inductor.metrics.generated_kernel_count = 0
        eager = fn(a, b)
        compiled = torch.compile(fn, backend="inductor")(a, b)
        self.assertEqual(eager, compiled)
        self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)

    @requires_gpu_and_triton
    @torch._inductor.config.patch(cpp_wrapper=True)
    def test_cpu_scalar_with_gpu_tensor_cpp(self):
        def fn(a, b):
            return a + b[0]

        a = torch.rand(20, device=GPU_TYPE)
        b = torch.rand(4, device="cpu")

        eager = fn(a, b)
        compiled = torch.compile(fn, backend="inductor")(a, b)
        self.assertEqual(eager, compiled)

    @requires_gpu_and_triton
    def test_cpu_scalar_with_gpu_tensor_dynamic(self):
        def fn(a, b):
            return a + b[0]

        a = torch.rand(20, device=GPU_TYPE)
        b = torch.rand(4, device="cpu")

        eager = fn(a, b)
        compiled = torch.compile(fn, backend="inductor", dynamic=True)(a, b)
        self.assertEqual(eager, compiled)

    def test_cpu_scalar_with_cpu_tensor(self):
        def fn(a, b):
            return a + b[0]

        a = torch.rand(20, device="cpu")
        b = torch.rand(4, device="cpu")

        torch._inductor.metrics.generated_kernel_count = 0
        eager = fn(a, b)
        compiled = torch.compile(fn, backend="inductor")(a, b)
        self.assertEqual(eager, compiled)
        self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)

    @requires_gpu_and_triton
    def test_gpu_scalar_with_gpu_tensor(self):
        def fn(a, b):
            return a + b[0]

        a = torch.rand(20, device=GPU_TYPE)
        b = torch.rand(4, device=GPU_TYPE)

        torch._inductor.metrics.generated_kernel_count = 0
        eager = fn(a, b)
        compiled = torch.compile(fn, backend="inductor")(a, b)
        self.assertEqual(eager, compiled)
        self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)

    @requires_gpu_and_triton
    def test_cpu_tensor_with_gpu_tensor(self):
        def fn(a, b):
            return a + b

        a = torch.rand(20, device=GPU_TYPE)
        b = torch.rand(20, device="cpu")

        with self.assertRaises(RuntimeError):
            compiled = torch.compile(fn, backend="inductor")(a, b)

    def test_cpu_tensor_with_cpu_tensor(self):
        def fn(a, b):
            return a + b

        a = torch.rand(20, device="cpu")
        b = torch.rand(20, device="cpu")

        eager = fn(a, b)
        compiled = torch.compile(fn, backend="inductor")(a, b)
        self.assertEqual(eager, compiled)

    def test_cpu_scalar_with_cpu_scalar(self):
        def fn(a, b):
            return a[0] + b[0]

        a = torch.rand(20, device="cpu")
        b = torch.rand(20, device="cpu")

        eager = fn(a, b)
        compiled = torch.compile(fn, backend="inductor")(a, b)
        self.assertEqual(eager, compiled)

    @requires_gpu_and_triton
    def test_gpu_scalar_with_cpu_tensor(self):
        def fn(a, b):
            return a[0] + b

        a = torch.rand(20, device=GPU_TYPE)
        b = torch.rand(20, device="cpu")

        with self.assertRaises(RuntimeError):
            compiled = torch.compile(fn, backend="inductor")(a, b)

    @requires_gpu_and_triton
    @config.patch(emulate_precision_casts=True)
    def test_emulate_precision_triton_fp_fusion(self):
        def fn(a, b):
            return 2.001 * a + b

        a = torch.full([256], 0.5001, device=GPU_TYPE, dtype=torch.float16)
        b = torch.full([256], -1, device=GPU_TYPE, dtype=torch.float16)

        compiled = torch.compile(fn)
        out, (code,) = run_and_get_code(compiled, a, b)
        self.assertTrue("'enable_fp_fusion': False" in code)
        torch.testing.assert_close(out, fn(a, b), atol=0, rtol=0)

    @requires_gpu_and_triton
    @config.patch(runtime_triton_nan_asserts=True)
    def test_nan_assert_inside_triton_kernel(self):
        def fn(x):
            x = x - 1
            # Uncomment the following line can trigger the failure of
            # the device size assertion
            # x = torch.log(x)
            return torch.where(x.isnan(), 3.14, x)

        compiled = torch.compile(fn)
        x = torch.randn(4096, device=GPU_TYPE)
        out, (code,) = run_and_get_code(compiled, x)
        self.assertTrue("'NaN or Inf found'" in code)
        torch.testing.assert_close(out, fn(x))

    @unittest.skipIf(
        config.triton.autotune_at_compile_time is True,
        "autotune_at_compile_time doesn't work for test with indexing",
    )
    @requires_gpu_and_triton
    def test_repeat_interleave_decomposition_has_clamp(self):
        repeat = torch.ones(2560, dtype=torch.int64, device=GPU_TYPE)
        output_size = 505450
        data = torch.arange(2560, device=GPU_TYPE)

        if is_dynamic_shape_enabled():
            raise unittest.SkipTest(
                "repeat_interleave decomp doesn't support dynamic output size"
            )

        @torch.compile
        def fn(repeat, output_size, data):
            indices = torch.ops.aten.repeat_interleave.Tensor(
                repeat, output_size=output_size
            )
            return data[indices]

        result, code = run_and_get_code(fn, repeat, output_size, data)

        self.assertEqual(result.shape[0], output_size)
        self.assertTrue(torch.all(result >= 0).item())
        self.assertTrue(torch.all(result < 2560).item())

        code_str = "\n".join(code)
        if torch.version.hip:
            triton_str = "tl.minimum"
        else:
            triton_str = "triton_helpers.minimum"
        self.assertIn(
            triton_str,
            code_str,
            "Generated Triton code should use triton_helpers.minimum for clamping",
        )

    @config.patch(implicit_fallbacks=True)
    def test_custom_op_dce(self):
        with torch.library._scoped_library("mylib", "FRAGMENT") as m:
            # CASE 1: The op should get wrapped with auto_functionalized, and
            # FX's DCE should not remove it because this op is registered as
            # effectful

            log1 = []

            @torch.library.custom_op(
                "mylib::my_logger1",
                mutates_args="unknown",
            )
            def my_logger1(s: str, t: torch.Tensor) -> torch.Tensor:
                log1.append(s)
                return torch.zeros(1)

            @my_logger1.register_fake
            def my_logger1(s, t) -> torch.Tensor:
                return torch.zeros(1)

            def foo(x):
                b = torch.scalar_tensor(x.shape[0])
                torch.ops.mylib.my_logger1("moo", b)
                return x + x

            torch.fx.node.has_side_effect(torch.ops.mylib.my_logger1.default)
            torch.compile(foo, fullgraph=True)(torch.ones(3, 3))
            self.assertTrue(len(log1), 1)

            # CASE 2: The op should not get DCEd by TorchInductor

            log2 = []

            @torch.library.custom_op(
                "mylib::my_logger2",
                mutates_args=(),
            )
            def my_logger2(s: str, t: torch.Tensor) -> torch.Tensor:
                log2.append(s)
                return torch.zeros(1)

            @my_logger2.register_fake
            def my_logger2(s, t) -> torch.Tensor:
                return torch.zeros(1)

            def foo(x):
                b = torch.scalar_tensor(x.shape[0])
                torch.ops.mylib.my_logger2("moo", b)
                return x + x

            torch.fx.node.has_side_effect(torch.ops.mylib.my_logger2.default)
            torch.compile(foo, fullgraph=True)(torch.ones(3, 3))
            self.assertTrue(len(log2), 1)

    def test_weight_norm_conv2d(self):
        """
        Verify fix for https://github.com/pytorch/pytorch/issues/165749
        """
        from torch.nn.utils.parametrizations import weight_norm

        d = 65
        x = torch.randn((2, 2, 32, 32), device=self.device)
        conv = weight_norm(nn.Conv2d(2, d, 2)).to(device=self.device)
        ref = conv(x)
        grad_out = torch.randn_like(ref)
        ref_grad = torch.autograd.grad(ref, list(conv.parameters()), grad_out)

        compiled_conv = torch.compile(conv)
        act = compiled_conv(x)
        act_grad = torch.autograd.grad(act, list(compiled_conv.parameters()), grad_out)

        self.assertTrue(same((ref, ref_grad), (act, act_grad), tol=1e-3))

    @xfail_if_mps  # MPS codegen does not emit ReductionHint.OUTER
    def test_inner_reduction_detection(self):
        if self.device == "cpu":
            self.skipTest("Skip for CPU device")

        x = torch.randn(100000, 1, 256, device=self.device)

        @torch.compile
        def f(x):
            return x.sum(dim=(0, 1))

        code = run_and_get_triton_code(f, x)
        self.assertTrue("ReductionHint.OUTER" in code)
        self.assertFalse("ReductionHint.INNER" in code)

    @skip_if_halide
    @requires_gpu_and_triton
    def test_triton_argmin_argmax_transpose_logical_index(self):
        def fn(x):
            x.tan_()
            x = x.t()
            return x.argmin()

        self.common(fn, (torch.randn(6, 4, device=GPU_TYPE),))

        def fn(x):
            return (x.t().argmin(), x.t().argmax())

        self.common(fn, (torch.randn(6, 4, device=GPU_TYPE),))
        self.common(fn, (torch.randn(128, 64, device=GPU_TYPE),))
        self.common(fn, (torch.randn(8, 6, device=GPU_TYPE, dtype=torch.float16),))

        def fn(x):
            # Permute: (A, B, C) -> (C, A, B)
            permuted = x.permute(2, 0, 1)
            return (permuted.argmin(), permuted.argmax())

        self.common(fn, (torch.randn(4, 6, 8, device=GPU_TYPE),))

        def fn(x):
            # sliced tensor with gaps in memory
            sliced = x[:, :10]
            return (sliced.argmin(), sliced.argmax())

        self.common(fn, (torch.randn(10, 20, device=GPU_TYPE),))

        # Test column major passed as input
        def fn(x):
            return (x.argmin(), x.argmax())

        self.common(fn, (torch.randn(6, 4, device=GPU_TYPE).t().contiguous().t(),))

    @skip_if_halide
    @requires_gpu_and_triton
    def test_unbacked_float_item(self):
        def fn(x, max_val):
            return torch.clamp(x, 0, max_val.item())

        self.common(
            fn,
            (
                torch.randn(10, 20, 30, device=self.device),
                torch.tensor(5.0, device=self.device),
            ),
        )

    @config.patch(combo_kernels=True)
    def test_combo_kernel_filter_cpu(self):
        def fn(a, b, c, d):
            a = a * 4
            b = b + 8
            return a, b, c.min(-1), d.max(-1)

        inps = [
            torch.rand(20, 20, device=self.device),
            torch.rand(30, 30, device=self.device),
            torch.rand(256, 256, device=self.device),
            torch.rand(256, 256, device=self.device),
        ]
        torch._inductor.metrics.reset()
        compiled_fn = torch.compile(fn)
        result = compiled_fn(*inps)
        expected = fn(*inps)

        self.assertEqual(result, expected)
        # on cuda combo kernel fuses (a, b) into one kernel and (c, d) into another (total 2)
        # on cpu combo kernel is skipped (a), (b), (c), (d) each run as separate kernels (total 4)
        if self.device.lower() == "cpu":
            self.assertEqual(torch._inductor.metrics.generated_kernel_count, 4)

        if self.device.lower() == "cuda":
            self.assertEqual(torch._inductor.metrics.generated_kernel_count, 2)

    @requires_gpu_and_triton
    @config.patch(combo_kernels=True)
    @torch._dynamo.config.patch(assume_static_by_default=False)
    def test_combo_kernel_store_mask(self):
        def fn(x):
            return (
                x.mean(),
                torch.mean(x, -2, keepdim=True),
            )

        x = torch.randn([1, 2, 4, 8], device=GPU_TYPE)
        torch._inductor.metrics.reset()
        result, (code,) = run_and_get_code(torch.compile(fn), x)
        expected = fn(x)

        self.assertEqual(result, expected)
        self.assertEqual(torch._inductor.metrics.generated_kernel_count, 1)
        FileCheck().check_regex(r"tl\.store\(.*xmask\)").run(code)

    @xfail_if_triton_cpu
    @requires_cuda_and_triton
    @config.patch({"emulate_precision_casts": True})
    def test_addcmul_fma_bitwise_equal(self):
        """Test that addcmul with FMA lowering produces bitwise equal results to eager."""
        self_tensor = torch.randn(64, 64, device=GPU_TYPE)
        tensor1 = torch.randn(64, 64, device=GPU_TYPE)
        tensor2 = torch.randn(64, 64, device=GPU_TYPE)

        # ROCm may have small numerical differences
        # For some reason ROCm isn't bitwise equivalent between eager and compiled
        atol = 1e-5 if TEST_WITH_ROCM else 0
        rtol = 1e-5 if TEST_WITH_ROCM else 0

        # Test value=1
        eager_result = torch.addcmul(self_tensor, tensor1, tensor2)

        @torch.compile
        def fn(s, t1, t2):
            return torch.addcmul(s, t1, t2)

        compiled_result = fn(self_tensor, tensor1, tensor2)
        self.assertEqual(eager_result, compiled_result, atol=atol, rtol=rtol)

        # Test value != 1
        eager_result2 = torch.addcmul(self_tensor, tensor1, tensor2, value=2.5)

        @torch.compile
        def fn2(s, t1, t2):
            return torch.addcmul(s, t1, t2, value=2.5)

        compiled_result2 = fn2(self_tensor, tensor1, tensor2)
        self.assertEqual(eager_result2, compiled_result2, atol=atol, rtol=rtol)

    @xfail_if_triton_cpu
    @skipIfRocm
    @requires_cuda_and_triton
    @config.patch({"emulate_precision_casts": True})
    def test_addcmul_fma_uses_fma_instruction(self):
        """Test that addcmul generates code using FMA instruction."""
        self_tensor = torch.randn(64, 64, device=GPU_TYPE)
        tensor1 = torch.randn(64, 64, device=GPU_TYPE)
        tensor2 = torch.randn(64, 64, device=GPU_TYPE)

        @torch.compile
        def fn(s, t1, t2):
            return torch.addcmul(s, t1, t2, value=2.0)

        _, code = run_and_get_code(fn, self_tensor, tensor1, tensor2)
        code = " ".join(code)
        self.assertIn("tl.fma", code, "Expected FMA to be used in generated code")

    @requires_cuda_and_triton
    @config.patch({"emulate_precision_casts": True})
    def test_addcmul_type_promotion(self):
        """Test that addcmul correctly promotes types when inputs have different dtypes."""
        # Test int + float promotion
        self_int = torch.randint(0, 10, (32, 32), device=GPU_TYPE, dtype=torch.int32)
        tensor1_float = torch.randn(32, 32, device=GPU_TYPE, dtype=torch.float32)
        tensor2_float = torch.randn(32, 32, device=GPU_TYPE, dtype=torch.float32)

        eager_result = torch.addcmul(self_int, tensor1_float, tensor2_float)

        @torch.compile
        def fn(s, t1, t2):
            return torch.addcmul(s, t1, t2)

        compiled_result = fn(self_int, tensor1_float, tensor2_float)
        self.assertEqual(eager_result.dtype, compiled_result.dtype)
        self.assertEqual(eager_result, compiled_result)

        # Test float16 + float32 promotion
        self_fp16 = torch.randn(32, 32, device=GPU_TYPE, dtype=torch.float16)
        tensor1_fp32 = torch.randn(32, 32, device=GPU_TYPE, dtype=torch.float32)
        tensor2_fp32 = torch.randn(32, 32, device=GPU_TYPE, dtype=torch.float32)

        eager_result2 = torch.addcmul(self_fp16, tensor1_fp32, tensor2_fp32)

        @torch.compile
        def fn2(s, t1, t2):
            return torch.addcmul(s, t1, t2)

        compiled_result2 = fn2(self_fp16, tensor1_fp32, tensor2_fp32)
        self.assertEqual(eager_result2.dtype, compiled_result2.dtype)
        self.assertEqual(eager_result2, compiled_result2)

        # Test all float16 inputs
        self_fp16_2 = torch.randn(32, 32, device=GPU_TYPE, dtype=torch.float16)
        tensor1_fp16 = torch.randn(32, 32, device=GPU_TYPE, dtype=torch.float16)
        tensor2_fp16 = torch.randn(32, 32, device=GPU_TYPE, dtype=torch.float16)

        eager_result3 = torch.addcmul(
            self_fp16_2, tensor1_fp16, tensor2_fp16, value=2.0
        )

        @torch.compile
        def fn3(s, t1, t2):
            return torch.addcmul(s, t1, t2, value=2.0)

        compiled_result3 = fn3(self_fp16_2, tensor1_fp16, tensor2_fp16)
        self.assertEqual(eager_result3.dtype, compiled_result3.dtype)
        self.assertEqual(eager_result3, compiled_result3)

        # Test with scalar tensor (0-d tensor) broadcasting
        self_tensor = torch.randn(32, 32, device=GPU_TYPE, dtype=torch.float32)
        tensor1_scalar = torch.tensor(2.5, device=GPU_TYPE, dtype=torch.float32)
        tensor2_tensor = torch.randn(32, 32, device=GPU_TYPE, dtype=torch.float32)

        eager_result4 = torch.addcmul(self_tensor, tensor1_scalar, tensor2_tensor)

        @torch.compile
        def fn4(s, t1, t2):
            return torch.addcmul(s, t1, t2)

        compiled_result4 = fn4(self_tensor, tensor1_scalar, tensor2_tensor)
        self.assertEqual(eager_result4.dtype, compiled_result4.dtype)
        self.assertEqual(eager_result4, compiled_result4)

        # Test with scalar tensor and type promotion
        self_fp32 = torch.randn(32, 32, device=GPU_TYPE, dtype=torch.float32)
        tensor1_fp64_scalar = torch.tensor(1.5, device=GPU_TYPE, dtype=torch.float64)
        tensor2_fp32 = torch.randn(32, 32, device=GPU_TYPE, dtype=torch.float32)

        eager_result5 = torch.addcmul(
            self_fp32, tensor1_fp64_scalar, tensor2_fp32, value=0.5
        )

        @torch.compile
        def fn5(s, t1, t2):
            return torch.addcmul(s, t1, t2, value=0.5)

        compiled_result5 = fn5(self_fp32, tensor1_fp64_scalar, tensor2_fp32)
        self.assertEqual(eager_result5.dtype, compiled_result5.dtype)
        self.assertEqual(eager_result5, compiled_result5)

    @requires_cuda_and_triton
    @skipCUDAIf(not SM90OrLater or TEST_WITH_ROCM, "PDL requires NVIDIA sm90+")
    @config.patch({"triton.enable_pdl": True})
    def test_pdl_mutation(self):
        def fn(a, b, c):
            b.copy_(c)  # second kernel
            return a**2 + b  # first kernel

        a, b, c = [
            torch.randn(s, device=GPU_TYPE) for s in [(1024, 1024), (1024,), (1024,)]
        ]
        self.common(fn, (a, b, c))

        code = run_and_get_triton_code(torch.compile(fn), a, b, c)
        (
            FileCheck()
            # first kernel
            .check("'launch_pdl': True")
            .check("gdc_wait")
            .check("load")
            .check("load")
            .check("gdc_launch")
            .check("store")
            # second kernel, no need to wait before load
            .check("'launch_pdl': True")
            .check("load")
            .check("gdc_wait")
            .check("gdc_launch")
            .check("store")
        ).run(code)

    @requires_cuda_and_triton
    @skipCUDAIf(not SM90OrLater or TEST_WITH_ROCM, "PDL requires NVIDIA sm90+")
    @config.patch(
        {
            "triton.enable_pdl": True,
            "max_autotune": True,
            "max_autotune_gemm_backends": "TRITON",
            "coordinate_descent_tuning": True,
        }
    )
    @xfailIf(not IS_BIG_GPU)  # templates require big gpu
    def test_pdl_template_and_delay(self):
        def fn(a, b):
            a = (a / (a**2).sum(-1, keepdim=True)) ** 2  # first kernel
            b = (b / (b**2).sum(-1, keepdim=True)) ** 2  # second kernel
            c = a @ b  # fused-epilogue template
            c = c**2
            return c

        a, b = [torch.randn(s, device=GPU_TYPE) for s in [(1024, 512), (512, 1024)]]
        self.common(fn, (a, b))
        code = run_and_get_triton_code(torch.compile(fn, mode="max-autotune"), a, b)
        if is_dynamic_shape_enabled():
            # Dynamic shapes version: store comes before gdc_launch
            (
                FileCheck()
                # first kernel
                .check("'launch_pdl': True")
                .check("gdc_wait")
                .check("load")
                .check("store")
                .check("gdc_launch")
                # second kernel, no need to wait before load
                .check("'launch_pdl': True")
                .check("load")
                .check("gdc_wait")
                .check("store")
                .check("gdc_launch")
                # matmul template
                .check_not("'launch_pdl': True")
                .check_not("gdc_wait")
                .check_not("gdc_launch")
                .check("store")
            ).run(code)
        else:
            # Static shapes version: gdc_launch comes before store
            (
                FileCheck()
                # first kernel
                .check("'launch_pdl': True")
                .check("gdc_wait")
                .check("load")
                .check("gdc_launch")
                .check("store")
                # second kernel, no need to wait before load
                .check("'launch_pdl': True")
                .check("load")
                .check("gdc_wait")
                .check("gdc_launch")
                .check("store")
                # matmul template
                .check_not("'launch_pdl': True")
                .check_not("gdc_wait")
                .check_not("gdc_launch")
                .check("store")
            ).run(code)

    def test_use_deterministic_algorithms(self):
        @torch.compile(backend="inductor", fullgraph=True)
        def fn(src, index, base_tensor):
            src = src + 10
            torch.use_deterministic_algorithms(True)
            base_tensor.scatter_(0, index, src)
            return base_tensor.clone() + 1

        src = torch.tensor([[100.0], [200.0], [300.0]])
        index = torch.tensor([[0], [0], [0]])
        base_tensor = torch.zeros(2, 1)
        out = fn(src, index, base_tensor)
        expected = torch.tensor([[311.0], [1.0]])
        self.assertEqual(out, expected)

    @requires_gpu()
    def test_device_context_with_python_scalar(self):
        from torch.utils._device import DeviceContext

        class SimpleModel(torch.nn.Module):
            def __init__(self, x, y):
                super().__init__()
                self.conv = torch.nn.Conv2d(3, 16, kernel_size=3, padding=1)
                self.scale = (x // y) ** -0.5
                self.relu = torch.nn.ReLU()
                self.tao = torch.nn.Parameter(torch.tensor([1.0]))
                self.forward = torch.compile(self.forward)

            def forward(self, x):
                x = self.relu(self.conv(x)) * self.scale
                x = x / self.tao
                return x * x

        def run_session(x_param, y_param, size, device):
            model = SimpleModel(x_param, y_param).to(device)
            input_data = torch.randn(1, 3, size, size, device=device)
            return model(input_data)

        with DeviceContext(self.device):
            # First call
            out1 = run_session(100, 24, 32, self.device)
            self.assertEqual(out1.device.type, self.device)

            # Second call with different params triggers recompilation
            out2 = run_session(100, 16, 64, self.device)
            self.assertEqual(out2.device.type, self.device)

    def test_index_reduce_on_view_input(self):
        # Regression test for https://github.com/pytorch/pytorch/issues/144846
        def fn(x, index, source):
            return x.index_reduce_(2, index, source, "mean", include_self=False)

        x_base = torch.randn(4, 34, 64, device=self.device)
        index = torch.randint(0, 34, (64,), device=self.device)
        source = torch.randn(4, 32, 64, device=self.device)

        expected = fn(x_base.clone()[:, 2:, :], index, source)
        result = torch.compile(fn)(x_base.clone()[:, 2:, :], index, source)
        self.assertEqual(result, expected)

    def test_pad_after_gelu(self):
        # Regression test for Voxtral compilation on MPS
        for dtype in [torch.float32, torch.float16, torch.bfloat16]:
            if not self.is_dtype_supported(dtype):
                continue
            self.common(
                lambda x: torch.nn.functional.pad(torch.nn.functional.gelu(x), [1, 0]),
                (torch.randn(8, 16, dtype=dtype, device=self.device),),
                check_lowp=False,
            )

    # end of class CommonTemplate - add new tests here


@dataclasses.dataclass
class TestFailure:
    suffixes: tuple[str, ...]
    is_skip: bool = False
    __test__: bool = False


def copy_tests(my_cls, other_cls, suffix, test_failures=None, xfail_prop=None):  # noqa: B902
    for name, value in my_cls.__dict__.items():
        if name.startswith("test_"):
            # You cannot copy functions in Python, so we use closures here to
            # create objects with different ids. Otherwise, unittest.skip
            # would modify all methods sharing the same object id. Also, by
            # using a default argument, we create a copy instead of a
            # reference. Otherwise, we would lose access to the value.

            @functools.wraps(value)
            def new_test(self, value=value):
                return value(self)

            # Copy __dict__ which may contain test metadata
            new_test.__dict__ = copy.deepcopy(value.__dict__)

            if xfail_prop is not None and hasattr(value, xfail_prop):
                new_test = unittest.expectedFailure(new_test)

            tf = test_failures and test_failures.get(name)
            if tf and suffix in tf.suffixes:
                skip_func = (
                    unittest.skip("Skipped!")
                    if tf.is_skip
                    else unittest.expectedFailure
                )
                new_test = skip_func(new_test)

            setattr(other_cls, f"{name}_{suffix}", new_test)

    # Special case convenience routine
    if hasattr(my_cls, "is_dtype_supported"):
        other_cls.is_dtype_supported = my_cls.is_dtype_supported


def add_test_failures(
    test_failures: dict[str, TestFailure], added_test_failures: dict[str, TestFailure]
):
    """
    In-place modifies the given dictionary of `test_failures` to add the
    contents of `added_test_failures` by unioning the test_failure.suffixes, and
    or-ing the is_skip value.
    """
    for name, new_failure in added_test_failures.items():
        if name in test_failures:
            orig_failure = test_failures[name]
            orig_failure.suffixes = tuple(
                set(orig_failure.suffixes).union(set(new_failure.suffixes))
            )
            orig_failure.is_skip = orig_failure.is_skip or new_failure.is_skip
        else:
            test_failures[name] = new_failure


if RUN_CPU:

    class SweepInputsCpuTest(SweepInputs2, TestCase):
        gen = InputGen(10, "cpu")

    SweepInputsCpuTest.populate()

    class CpuTests(TestCase):
        common = check_model
        device = "cpu"

    copy_tests(CommonTemplate, CpuTests, "cpu")

if RUN_GPU or HAS_MPS:

    class SweepInputsGPUTest(SweepInputs2, TestCase):
        gen = InputGen(10, GPU_TYPE)

    SweepInputsGPUTest.populate()

    class GPUTests(TestCase):
        common = check_model_gpu
        device = GPU_TYPE

    copy_tests(CommonTemplate, GPUTests, GPU_TYPE)

if RUN_TPU:
    from torch_tpu import api as tpu_api  # type: ignore[import-not-found]

    tpu_api.tpu_device()  # initialize TPU runtime

    class SweepInputsTpuTest(SweepInputs2, TestCase):
        gen = InputGen(10, "tpu")

    SweepInputsTpuTest.populate()

if RUN_GPU:

    @instantiate_parametrized_tests
    class TritonCodeGenTests(TestCase):
        from torch._inductor.runtime.triton_heuristics import CachingAutotuner

        device_type = GPU_TYPE
        device = GPU_TYPE

        class NoOpCompilerBackend:
            def __init__(self) -> None:
                self.example_args = None
                self.model = None

            def noop_backend(
                self,
                model_: torch.fx.GraphModule,
                example_inputs_: list[torch.Tensor],
            ):
                """
                The Noop backend does not compile the fx graph it is given.
                Instead, it transforms the fx graph so that its functions are
                aten operations. It then saves this graph.
                """
                from torch._inductor.decomposition import select_decomp_table
                from torch._subclasses import FakeTensorMode
                from torch.fx import Interpreter

                fake_mode = FakeTensorMode()

                def interpret(*args, **kwargs):
                    return Interpreter(model_).run(*args[0:], **kwargs)

                fake_flat_tensor_args = [
                    fake_mode.from_tensor(x) for x in example_inputs_
                ]
                fw_module = make_fx(interpret, select_decomp_table())(
                    *fake_flat_tensor_args
                )
                self.model = fw_module
                self.example_args = fake_flat_tensor_args
                return lambda x: example_inputs_

        def get_kernels(self, fn, args) -> list[CachingAutotuner]:
            from torch._inductor.debug import DebugContext
            from torch._inductor.graph import GraphLowering
            from torch._inductor.virtualized import V

            cxt = TritonCodeGenTests.NoOpCompilerBackend()
            torch.compile(fn, backend=cxt.noop_backend)(*args)
            graph = GraphLowering(cxt.model)
            kernels = []
            with V.set_graph_handler(graph), V.set_debug_handler(DebugContext()):
                graph.run(*(cxt.example_args))
                mod = graph.compile_to_module()

                for val in mod.__dict__.values():
                    if isinstance(
                        val, torch._inductor.runtime.triton_heuristics.CachingAutotuner
                    ):
                        kernels.append(val)

            return kernels

        def test_divisible_by_16_covers_numel_args(self):
            torch._dynamo.reset()

            def fn(a: torch.Tensor) -> torch.Tensor:
                return torch.sum(a)

            kernels = self.get_kernels(fn, [torch.randn([256, 256], device=GPU_TYPE)])
            expected_divisible = {
                # kernel0 reduces from 256 to (xnumel=8, rnumel=8192), which means it reduces 256 by 256 into an array of
                # size 8 by accumulating 8192 elements at once note that rnumel is equal to 512 * 16, so rnumel which is
                # at slot 3 should be in the divisible by 16 descriptor
                0: (0, 1, 3),
                # kernel1 reduces from 8 elements to a single scalar.
                # Since multi-kernel generate 2 variants for each kernel. The second
                # persistent-reduction has index 2.
                1: (0, 1),
            }
            if config.triton.multi_kernel:
                self.assertEqual(len(kernels), 4)
                expected_divisible[2] = expected_divisible.pop(1)
            elif (
                config.triton.cooperative_reductions
                or config.triton.force_cooperative_reductions
            ):
                self.assertEqual(len(kernels), 1)
                expected_divisible = {
                    # one kernel, with extra workspace/semaphore args
                    0: (0, 1, 2, 3, 5),
                }
            else:
                self.assertEqual(len(kernels), 2)

            for kernel_id, expected in expected_divisible.items():
                divisible_by_16 = get_divisible_by_16(
                    kernels[kernel_id].triton_meta["configs"][0]
                )
                self.assertEqual(divisible_by_16, expected)

            torch._dynamo.reset()

        @config.patch(assume_aligned_inputs=False)
        def test_codegen_config_option_dont_assume_alignment(self):
            def fn(x: torch.Tensor) -> torch.Tensor:
                return x.sin() + x.cos()

            # We want code that assumes alignment if the initial input is 16-byte aligned
            for offset in (0, 1, 2, 3, 4):
                base = torch.randn(64 * 64 + 64, dtype=torch.float32, device=GPU_TYPE)
                inps = torch.as_strided(base, (64, 64), (64, 1), offset)
                torch._dynamo.reset()
                kernels = self.get_kernels(fn, [inps])
                arguments_that_are_divisible_by_16 = get_divisible_by_16(
                    kernels[0].triton_meta["configs"][0]
                )

                #             NO_ALIGN ALIGN     ALIGN
                # def triton_(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr)

                if offset % 4 == 0:
                    expected_aligned = (0, 1, 2)
                else:
                    expected_aligned = (1, 2)
                self.assertEqual(arguments_that_are_divisible_by_16, expected_aligned)

            # If input isn't a view, storage offset != , inductor will assume alignment.
            torch._dynamo.reset()
            inp = torch.randn((64, 64), device=GPU_TYPE)
            kernels = self.get_kernels(fn, [inp])
            arguments_that_are_divisible_by_16 = get_divisible_by_16(
                kernels[0].triton_meta["configs"][0]
            )
            self.assertEqual(arguments_that_are_divisible_by_16, (0, 1, 2))

        def test_optimize_indexing_dtype(self):
            def fn(x: torch.Tensor) -> torch.Tensor:
                return aten.upsample_bilinear2d.vec(x, None, True, [2.0, 2.0])

            fn_opt = torch.compile(fn, backend="inductor")
            inps = [torch.randn(2, 4, 16, 16, device=GPU_TYPE)]
            code = run_and_get_triton_code(fn_opt, *inps)
            self.assertTrue("to(tl.int32)" in code)
            self.assertFalse("to(tl.int64)" in code)

            self.assertEqual(fn_opt(*inps), fn(*inps))

        @config.patch({"fx_graph_remote_cache": False})
        def test_optimize_indexing_dtype_with_constraint(self):
            def fn1(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
                x = torch.arange(0, b.shape[0], device=GPU_TYPE)
                y = ((x + x) / 3).int()
                return a[y.to(torch.int64)]

            def fn2(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
                torch._check(b.shape[0] >= 2)
                torch._check(b.shape[0] <= 100)
                return fn1(a, b)

            fn1_opt = torch.compile(fn1, backend="inductor")
            fn2_opt = torch.compile(fn2, backend="inductor")

            a = torch.rand([100, 100], device=GPU_TYPE)
            b1 = torch.rand([102], device=GPU_TYPE)
            b2 = torch.rand([100], device=GPU_TYPE)
            torch._dynamo.mark_dynamic(b1, 0)
            torch._dynamo.mark_dynamic(b2, 0)
            inps1 = [a, b1]
            inps2 = [a, b2]

            # Run fn2 first since it has more restrictive bounds -- to avoid cache hit
            code2 = run_and_get_triton_code(fn2_opt, *inps2)
            code1 = run_and_get_triton_code(fn1_opt, *inps1)

            # The function with the constrained tensor should be optimized, but
            # the other should not:
            self.assertTrue("to(tl.int64)" in code1)
            self.assertTrue("to(tl.int32)" in code2)
            self.assertFalse("to(tl.int64)" in code2)

            self.assertEqual(fn1_opt(*inps1), fn1(*inps1))
            self.assertEqual(fn2_opt(*inps2), fn1(*inps2))

        def test_constant_folding_deallocation(self):
            import torch._inductor

            def fn():
                li = []
                for i in range(10):
                    x = torch.full([100], i)
                    x = x + 1
                    li.append(x)

                return li

            mod = make_fx(fn)()

            live_tensors = WeakTensorKeyDictionary()
            max_live_tensors = 0

            class LiveTensors(TorchDispatchMode):
                def __torch_dispatch__(self, func, types, args=(), kwargs=None):
                    nonlocal live_tensors
                    nonlocal max_live_tensors

                    kwargs = kwargs if kwargs else {}
                    for arg in pytree.arg_tree_leaves(*args, **kwargs):
                        if isinstance(arg, torch.Tensor):
                            live_tensors[arg] = True

                    out = func(*args, **kwargs)
                    if not isinstance(out, torch.Tensor):
                        return out

                    live_tensors[out] = True
                    max_live_tensors = max(max_live_tensors, len(live_tensors))
                    return out

            mode = LiveTensors()
            from torch._inductor.fx_passes.joint_graph import UniformValueConstantFolder

            with mode:
                UniformValueConstantFolder(mod).run()

            # there are a couple extra tensors created in `insertable_tensor_check`
            self.assertTrue(max_live_tensors == 3)

        # See https://github.com/pytorch/pytorch/issues/100348
        @parametrize("backend", ["aot_eager", "inductor"])
        def test_inductor_detach_view(self, backend):
            def fn(x: torch.Tensor) -> torch.Tensor:
                a = x * 2
                return a, a.detach()

            fn_opt = torch.compile(fn, backend=backend)
            inp = torch.ones(2, 2, requires_grad=True, device=GPU_TYPE)
            inp_ref = inp.detach().clone().requires_grad_(True)
            out_ref = fn(inp_ref)
            out_ref[0].sum().backward()
            out = fn_opt(inp)
            out[0].sum().backward()
            self.assertEqual(inp.grad, inp_ref.grad)

        @requires_gpu()
        @unittest.skipIf(
            not PLATFORM_SUPPORTS_MEM_EFF_ATTENTION,
            "Does not support mem_eff_attention",
        )
        def test_sdpa_inference_mode_aot_compile(self):
            class TestSDPA(torch.nn.Module):
                def __init__(self) -> None:
                    super().__init__()

                def forward(
                    self,
                    q: torch.Tensor,
                    k: torch.Tensor,
                    v: torch.Tensor,
                    attn_mask: torch.Tensor,
                ):
                    return torch.nn.functional.scaled_dot_product_attention(
                        q, k, v, attn_mask=attn_mask, dropout_p=0.0, is_causal=False
                    )

            q = torch.rand([10, 4, 128, 64], device=GPU_TYPE, dtype=torch.bfloat16)
            k = torch.rand([10, 4, 128, 64], device=GPU_TYPE, dtype=torch.bfloat16)
            v = torch.rand([10, 4, 128, 64], device=GPU_TYPE, dtype=torch.bfloat16)
            attn_mask = (
                torch.rand([10, 4, 128, 128], device=GPU_TYPE, dtype=torch.bfloat16)
                < 0.9
            )

            inputs = (q, k, v, attn_mask)

            import torch.export._trace as export_trace

            with torch.inference_mode():
                traced = export_trace._export_to_torch_ir(
                    TestSDPA(),
                    inputs,
                    disable_constraint_solver=True,
                    restore_fqn=False,
                )
                torch._inductor.aot_compile(traced, inputs)

        @skipCUDAIf(not SM90OrLater, "Requires sm90")
        @requires_cuda_and_triton
        @unittest.skipIf(TEST_WITH_ROCM, "no grouped_mm support")
        @config.patch(implicit_fallbacks=True)
        def test_grouped_mm(self):
            @torch.compile(fullgraph=True)
            def f(a, b, offs, out_dtype):
                return F.grouped_mm(
                    a, b.transpose(-2, -1), offs=offs, out_dtype=out_dtype
                )

            device = "cuda"
            dtype = torch.bfloat16

            m, n, k, n_groups = 16, 32, 16, 4
            a_ref = torch.randn(m * n_groups, k, device=device, dtype=dtype)[:, :k]

            b_ref = torch.randn(
                n_groups,
                n,
                k,
                device=device,
                dtype=dtype,
            )[::1, :, :k]

            offs = torch.arange(
                m, n_groups * m + 1, m, device=device, dtype=torch.int32
            )

            a_ref.requires_grad_(True)
            b_ref.requires_grad_(True)

            a_test = a_ref.clone().detach().requires_grad_()
            b_test = b_ref.clone().detach().requires_grad_()

            out_ref = f(a_ref, b_ref, offs, out_dtype=torch.bfloat16)
            out_ref.sum().backward()

            out_test = f(a_test, b_test, offs=offs, out_dtype=torch.bfloat16)
            out_test.sum().backward()

            self.assertEqual(out_ref, out_test)
            self.assertEqual(a_ref.grad, a_test.grad)
            self.assertEqual(b_ref.grad, b_test.grad)

        def test_optimize_indexing_assert(self):
            def has_indirect(code, tl_fn: str):
                self.assertTrue(
                    tl_fn in code,
                    msg=f"{tl_fn} not present:\n{code}",
                )
                for line in code.split("\n"):
                    if tl_fn in line:
                        stmt = line.split(tl_fn)[-1]
                        # indirect indexing involves a `tmp` variable
                        self.assertTrue(
                            "tmp" in stmt,
                            msg=f"Indirect indexing not present in code:\n{line}",
                        )

            def has_assert(code, lower: bool, upper: bool):
                self.assertIn(
                    "device_assert", code, msg=f"No device assert found:\n{code}"
                )
                for line in code.split("\n"):
                    if "device_assert" in line:
                        self.assertTrue(
                            ("0 <= " in line) is lower,
                            msg=f"Lower bound {'' if lower else 'not '}elided:{line}",
                        )
                        self.assertTrue(
                            (" < " in line) is upper,
                            msg=f"Upper bound {'' if upper else 'not '}elided:{line}",
                        )

            def fn(x: torch.Tensor) -> torch.Tensor:
                s = 1.0 * torch.arange(x.shape[0], device=x.device)
                return x[s.long()]

            # aten.index
            for dynamic in (False, True):
                fn_opt = torch.compile(fn, dynamic=dynamic)

                x = torch.randn(8, device=GPU_TYPE)
                code = run_and_get_triton_code(fn_opt, x)
                self.assertEqual(fn_opt(x), fn(x), msg=f"{dynamic=}")

                # Check that there's indirect indexing...
                has_indirect(code, tl_fn="tl.load")
                if not dynamic:
                    # We elide the assert for static shapes
                    self.assertNotIn("device_assert", code)
                else:
                    # ...but we generate an upper bound for dynamic shapes
                    has_assert(code, lower=False, upper=True)

            def fn(a, z, b, idx0, idx1):
                idx2 = torch.arange(a.shape[-1], device=a.device)
                a.index_put_((z, idx0, idx1, idx2), b, accumulate=True)
                return a

            # aten.index_put
            for dynamic in (False, True):
                fn_opt = torch.compile(fn, dynamic=dynamic)
                a = torch.randn(1, 32, 32, 4, device=GPU_TYPE)
                z = torch.zeros((), dtype=torch.int64, device=GPU_TYPE)
                b = torch.randn(33, 1, device=GPU_TYPE)
                idx0 = torch.randint(32, (33,), device=GPU_TYPE).view(33, 1, 1)
                idx1 = torch.randint(32, (33,), device=GPU_TYPE).view(33, 1)
                inps = (a.clone(), z, b, idx0, idx1)
                code = run_and_get_triton_code(fn_opt, *inps)

                # Correctness
                out_opt = fn_opt(a.clone(), z, b, idx0, idx1)
                out = fn(a.clone(), z, b, idx0, idx1)
                self.assertEqual(out_opt, out, msg=f"{dynamic=}")

                # We have an indirect store via atomic_add
                has_indirect(code, tl_fn="tl.atomic_add")
                # We cannot elide he assert in this case
                has_assert(code, lower=True, upper=True)

        def test_not_materialize_pointwise_reduction(self):
            def fn(a, b):
                return (a - b).sum(dim=-1).amax(dim=-1)

            N = 16
            K = 7
            fn_opt = torch.compile(fn, backend="inductor")
            inps = [
                torch.randn(N, 1, K, device=GPU_TYPE),
                torch.randn(1, N, K, device=GPU_TYPE),
            ]
            code = run_and_get_triton_code(fn_opt, *inps)
            self.assertEqual(
                code.count("tl.store"), 2 if config.triton.multi_kernel else 1
            )
            self.assertTrue("out_ptr1" in code)
            self.assertFalse("out_ptr0" in code)
            self.assertEqual(fn_opt(*inps), fn(*inps))

        def test_reduction_hint_inner_with_high_tiling_ratio(self):
            """Test inner reduction hint with high tiling score ratio."""

            def f(x, y):
                return x.sum(dim=-1, keepdim=True) + (x + y[..., None])

            x = torch.randn(2048, 128, 1024, device=GPU_TYPE)
            y = torch.randn(128, 2048, device=GPU_TYPE).t()

            code = run_and_get_triton_code(torch.compile(f), x, y)
            self.assertIn("ReductionHint.INNER", code)

        def test_numpy_on_gpu(self):
            x = np.arange(10, dtype=np.float32)

            @torch.compile
            def fn(x):
                return np.sin(x)

            def fn_gpu(x):
                with torch.device(GPU_TYPE):
                    return fn(x)

            r = fn_gpu(x)
            code = run_and_get_triton_code(fn_gpu, x)
            self.assertIn("tl_math.sin", code)
            self.assertEqual(type(r), np.ndarray)
            self.assertEqual(r, np.sin(x))

        @config.patch(expand_dimension_for_pointwise_nodes=True)
        def test_rope_fusion(self):
            batch_size, seq_length, hidden_dim = 8, 16, 128
            num_q_heads, num_kv_heads = 32, 8

            def prepare_input(batch_size, seq_length):
                q = torch.randn(
                    (batch_size, num_q_heads, seq_length, hidden_dim), device=GPU_TYPE
                )
                k = torch.randn(
                    (batch_size, num_kv_heads, seq_length, hidden_dim),
                    device=GPU_TYPE,
                )
                pos_ids = torch.arange(
                    seq_length, device=GPU_TYPE, dtype=torch.long
                ).unsqueeze(0)

                # dummy cos and sin
                cos, sin = (
                    torch.randn((1, seq_length, hidden_dim), device=GPU_TYPE),
                    torch.randn((1, seq_length, hidden_dim), device=GPU_TYPE),
                )
                return q, k, cos, sin, pos_ids

            def rotate_half(x):
                """Rotates half the hidden dims of the input."""
                x1 = x[..., : x.shape[-1] // 2]
                x2 = x[..., x.shape[-1] // 2 :]
                return torch.cat((-x2, x1), dim=-1)

            def apply_rotary_pos_emb(
                q, k, cos, sin, position_ids=None, unsqueeze_dim=1
            ):
                cos = cos.unsqueeze(unsqueeze_dim)
                sin = sin.unsqueeze(unsqueeze_dim)
                q_embed = (q * cos) + (rotate_half(q) * sin)
                k_embed = (k * cos) + (rotate_half(k) * sin)
                return q_embed, k_embed

            q, k, cos, sin, pos_ids = prepare_input(batch_size, seq_length)
            compiled_fn = torch.compile(apply_rotary_pos_emb)
            compiled_out = compiled_fn(q, k, cos, sin, pos_ids)
            eager_out = apply_rotary_pos_emb(q, k, cos, sin, pos_ids)
            self.assertEqual(compiled_out, eager_out)

            # make sure that rope is fused into 1 kernel
            code = run_and_get_triton_code(compiled_fn, q, k, cos, sin, pos_ids)
            if config.cpp_wrapper:
                # Count actual kernel invocations, not function definitions
                # Kernel invocations end with ');' and don't start with 'static'
                import re

                kernel_calls = len(
                    re.findall(r"^\s+call_triton_.*\);$", code, re.MULTILINE)
                )
                self.assertEqual(kernel_calls, 1)
            else:
                self.assertEqual(code.count(".run("), 1)

        def test_numpy_autograd(self):
            def my_torch(x):
                y = torch.cat([torch.sin(x) ** 2, torch.max(x)[None]])
                return y.sum()

            def my_np(x):
                y = np.concatenate([np.sin(x) ** 2, np.max(x)[None]])
                return np.sum(y)

            @torch.compile
            def wrapper(x):
                return torch.compiler.wrap_numpy(my_np)(x)

            @torch.compile
            def wrapper2(x):
                x = x.numpy()
                y = my_np(x)
                return torch.from_numpy(y)

            x_np = torch.arange(8, dtype=torch.float32, requires_grad=True)
            x = torch.arange(8, dtype=torch.float32, requires_grad=True)
            out_np = wrapper(x_np)
            out = my_torch(x)
            self.assertEqual(out, out_np)

            x2_np = torch.arange(8, dtype=torch.float32, requires_grad=True)
            out2_np = wrapper2(x2_np)
            self.assertEqual(out, out2_np)

            out_np.backward()
            out.backward()
            self.assertEqual(x.grad, x_np.grad)

            out2_np.backward()
            self.assertEqual(x.grad, x2_np.grad)

        # Disable constant propagation, so we isolate value range analysis
        @patch.object(config, "constant_and_index_propagation", False)
        @patch.object(config, "joint_graph_constant_folding", False)
        def test_cant_optimize_compute(self):
            def ones():
                return torch.ones([4], device=GPU_TYPE)

            def suffix(inp):
                return (inp.to(torch.int64) + 1).to(torch.float64)

            ten = torch.rand([4], device=GPU_TYPE)

            for foo in (
                lambda x: x + 2147483657,
                lambda x: torch.where(x < 0, ones(), ones() - 2) * (-(2 ** (40))),
                lambda x: x + ten,
                lambda x: x + ten.sum(),
            ):

                def fn():
                    return suffix(foo(ones()))

                fn_opt = torch.compile(fn, backend="inductor")
                code = run_and_get_triton_code(fn_opt)

                # this cannot be optimized away, value too large
                self.assertTrue("to(tl.int64)" in code)
                self.assertEqual(fn_opt(), fn())

        # Disable constant propagation, so we isolate value range analysis
        @patch.object(config, "constant_and_index_propagation", False)
        @patch.object(config, "joint_graph_constant_folding", False)
        def test_optimize_compute(self):
            def ones():
                return torch.ones([4], device=GPU_TYPE)

            def suffix(inp):
                return (inp.to(torch.int64) + 1).to(torch.float64)

            for foo in (
                lambda x: x + 500,
                lambda x: torch.where(x < 0, ones(), ones() - 2) * (-(2 ** (20))),
                lambda x: x / 30,
            ):

                def fn():
                    return suffix(foo(ones()))

                fn_opt = torch.compile(fn, backend="inductor")
                code = run_and_get_triton_code(fn_opt)

                # this can be optimized away, value too large
                self.assertTrue("to(tl.int64)" not in code)
                self.assertTrue("to(tl.int32)" in code)

                self.assertEqual(fn_opt(), fn())

        # https://github.com/pytorch/pytorch/issues/130335
        def test_ctr_not_moved_to_cuda_when_used_in_index_put(self):
            @torch.compile
            def f(x, mask):
                x[:, mask] = -math.inf
                return x

            x_tmp = torch.randn(512, 19, device=GPU_TYPE)
            x = x_tmp.permute(1, 0).view(-1, 128, 4)[:, :, 1:]

            mask_tmp = torch.ones(128, 3, dtype=torch.int32, device=GPU_TYPE)
            mask = mask_tmp == mask_tmp
            f(x, mask)
            code = run_and_get_triton_code(f, x, mask)
            # What we are testing here:
            # inductor has a pass to move tensor constructors on cpu to cuda
            # (the -math.inf will become a scalar-tensor input to index_put_())
            # we are asserting that when inductor allocates this tensor,
            # it does not move the tensor constructor to cuda and keeps it on CPU.
            self.assertFalse("empty_strided_cuda(()" in code)

        # only uncoalesced without this :)
        @config.patch("triton.coalesce_tiling_analysis", False)
        @config.patch("triton.use_block_ptr", False)
        def test_evict_last_non_coalesced_loads(self):
            @torch.compile
            def f(a, b):
                return (a * b).sum(dim=-1)

            N = 512
            inps = (
                torch.randn(N, N, N, device=GPU_TYPE).permute(2, 1, 0),
                torch.randn(N, N, N, device=GPU_TYPE).permute(1, 2, 0),
            )
            code = run_and_get_triton_code(f, *inps)
            lines = [line for line in code.split("\n") if "tl.load" in line]
            if config.triton.multi_kernel:
                # the first 2 lines are generated for the persistent reduction
                # variant.
                self.assertExpectedInline(
                    "\n".join(lines),
                    """\
    tmp0 = tl.load(in_ptr0 + (x1 + (512*x0) + (262144*r2)), rmask, eviction_policy='evict_last', other=0.0)
    tmp1 = tl.load(in_ptr1 + (x3 + (262144*r2)), rmask, other=0.0)
        tmp0 = tl.load(in_ptr0 + (x1 + (512*x0) + (262144*r2)), rmask, eviction_policy='evict_last', other=0.0)
        tmp1 = tl.load(in_ptr1 + (x3 + (262144*r2)), rmask, eviction_policy='evict_first', other=0.0)""",
                )
            else:
                self.assertExpectedInline(
                    "\n".join(lines),
                    """\
        tmp0 = tl.load(in_ptr0 + (x1 + 512*x0 + 262144*r0_2), r0_mask, eviction_policy='evict_last', other=0.0)
        tmp1 = tl.load(in_ptr1 + (x3 + 262144*r0_2), r0_mask, eviction_policy='evict_first', other=0.0)""",
                )

        @config.patch("triton.skip_l1_cache", True)
        def test_skip_l1_cache(self):
            @torch.compile
            def f(a, b):
                return a + b

            N = 512
            inps = (torch.randn(N, device=GPU_TYPE), torch.randn(N, device=GPU_TYPE))
            code = run_and_get_triton_code(f, *inps)
            lines = [line for line in code.split("\n") if "tl.load" in line]
            self.assertExpectedInline(
                "\n".join(lines),
                """\
    tmp0 = tl.load(in_ptr0 + (x0), xmask, cache_modifier='.cg')
    tmp1 = tl.load(in_ptr1 + (x0), xmask, cache_modifier='.cg')""",
            )

        @config.patch("triton.skip_l1_cache", True)
        def test_skip_l1_cache_buf_read_counts_guard(self):
            from torch._inductor.codegen import simd_kernel_features

            class M(nn.Module):
                def __init__(self, n: int):
                    super().__init__()
                    # nn.Parameter ensures we exercise the primals_* path.
                    # We move the module to GPU later, so we don't need to
                    # specify the device here.
                    self.weight = nn.Parameter(torch.randn(n))

                def forward(self, x: torch.Tensor) -> torch.Tensor:
                    return x * self.weight

            N = 512
            m = torch.compile(M(N).to(device=GPU_TYPE))
            x = torch.randn(N, device=GPU_TYPE)

            # Monkeypatch SIMDKernelFeatures.buffer_read_counts to
            # simulate missing entries for all buffers (including primals_*).
            orig = simd_kernel_features.SIMDKernelFeatures.buffer_read_counts

            def fake_buffer_read_counts(self_inner):
                return {}

            simd_kernel_features.SIMDKernelFeatures.buffer_read_counts = (
                fake_buffer_read_counts
            )
            try:
                # This used to raise InductorError(KeyError('primals_X'))
                # when load() unconditionally indexed buffer_read_counts[name].
                # With the fix, we should successfully generate Triton code.
                code = run_and_get_triton_code(m, x)
                lines = [line for line in code.split("\n") if "tl.load" in line]
                self.assertTrue(lines)
            finally:
                simd_kernel_features.SIMDKernelFeatures.buffer_read_counts = orig

        @config.patch("triton.use_block_ptr", True)
        @config.patch("triton.coalesce_tiling_analysis", False)
        def test_evict_last_non_coalesced_loads_block_ptr(self):
            @torch.compile
            def f(a, b):
                return (a * b).sum(dim=-1)

            N = 512
            inps = (
                torch.randn(N, N, N, device=GPU_TYPE).permute(2, 1, 0),
                torch.randn(N, N, N, device=GPU_TYPE).permute(1, 2, 0),
            )
            code = run_and_get_triton_code(f, *inps)
            lines = [line for line in code.split("\n") if "tl.load" in line]

            if config.triton.multi_kernel:
                # the first 2 lines are generated for the persistent reduction
                # variant.
                self.assertExpectedInline(
                    "\n".join(lines),
                    """\
    tmp0 = tl.load(in_ptr0 + (x1 + (512*x0) + (262144*r0_2)), rmask, eviction_policy='evict_last', other=0.0)
    tmp1 = tl.load(tl.make_block_ptr(in_ptr1, shape=[262144, 512], strides=[1, 262144], block_shape=[XBLOCK, R0_BLOCK], order=[0, 1], offsets=[xoffset, roffset]), boundary_check=[1], padding_option='zero')
        tmp0 = tl.load(in_ptr0 + (x1 + (512*x0) + (262144*r0_2)), rmask, eviction_policy='evict_last', other=0.0)
        tmp1 = tl.load(block_ptr0, boundary_check=[1], padding_option='zero', eviction_policy='evict_first')""",  # noqa: B950 line too long
                )
            else:
                self.assertExpectedInline(
                    "\n".join(lines),
                    """\
        tmp0 = tl.reshape(tl.load(block_ptr0, boundary_check=[2], padding_option='zero', eviction_policy='evict_last'), [XBLOCK, R0_BLOCK])
        tmp1 = tl.load(block_ptr1, boundary_check=[1], padding_option='zero', eviction_policy='evict_first')""",  # noqa: B950 line too long
                )

        # Disable index propagation, so the indirect indexing isn't optimized away
        @patch.object(config, "constant_and_index_propagation", False)
        def test_computed_indirect_mask(self):
            def fn(x, n):
                tmp = torch.arange(n, device=x.device)
                return x[tmp] + 1

            x = torch.randn(8, device=GPU_TYPE)
            fn_opt = torch.compile(fn)
            code = run_and_get_triton_code(fn_opt, x, 8)
            # load should be masked
            self.assertTrue(
                "tl.load(in_ptr0 + (tmp0), xmask" in code
                or "tl.load(in_ptr0 + (tmp0), (xmask).to(tl.int1)" in code
            )
            self.assertEqual(fn(x, 8), fn_opt(x, 8))

        @config.patch("triton.prefer_nd_tiling", True)
        @config.patch("triton.max_tiles", 3)
        @parametrize(
            "block_multiple, ynumel_exceed_ygrid_size",
            [
                # xdim has constant mask, ydim does not
                [True, True],
                # xdim, ydim both have a constant mask
                [True, False],
                # if numel not a block multiple, no constant mask
                [False, False],
                # TODO: test zdim too
            ],
        )
        def test_has_constant_mask(self, block_multiple, ynumel_exceed_ygrid_size):
            from torch._inductor.runtime.hints import TRITON_MAX_BLOCK
            from torch._inductor.runtime.runtime_utils import get_max_y_grid

            shape = [TRITON_MAX_BLOCK["Y"], TRITON_MAX_BLOCK["X"]]

            if not block_multiple:
                shape = [s + 1 for s in shape]

            if ynumel_exceed_ygrid_size:
                shape[0] = (
                    shape[0] * (math.ceil(get_max_y_grid() / shape[0])) + shape[0]
                )

            a = torch.zeros(shape, device=GPU_TYPE, dtype=torch.bool)
            b = torch.zeros((shape[0], 1), device=GPU_TYPE, dtype=torch.bool)

            opt_fn = torch.compile(torch.add)
            code = run_and_get_triton_code(opt_fn, a, b)

            if block_multiple:
                self.assertTrue("xmask = tl.full" in code)
                if ynumel_exceed_ygrid_size:
                    self.assertTrue("ymask = yindex < ynumel" in code)
                else:
                    self.assertTrue("ymask = tl.full" in code)
            else:
                self.assertTrue("ymask = yindex < ynumel" in code)
                self.assertTrue("xmask = xindex < xnumel" in code)

        @parametrize(
            "rnumel",
            [16, 32],
        )
        @config.patch("triton.persistent_reductions", True)
        def test_has_constant_mask_small_persistent_reduction(self, rnumel):
            from torch._inductor.runtime.hints import DeviceProperties

            def fn(x):
                return x.sum(dim=-1)

            x = torch.randn(1024, rnumel, device=GPU_TYPE)
            opt_fn = torch.compile(fn)
            code = run_and_get_triton_code(opt_fn, x)

            device = torch.device(GPU_TYPE, 0)
            warp_size = DeviceProperties.create(device).warp_size or 32

            rblock = 1
            while rblock < rnumel:
                rblock *= 2

            if rblock < warp_size:
                self.assertTrue(
                    "r0_index < r0_numel" in code or "rindex < rnumel" in code,
                    f"Expected dynamic reduction mask for RBLOCK={rblock} < warp_size={warp_size}",
                )
            else:
                self.assertTrue(
                    "r0_mask = tl.full" in code or "rmask = tl.full" in code,
                    f"Expected constant reduction mask for RBLOCK={rblock} >= warp_size={warp_size}",
                )

            self.assertEqual(fn(x), opt_fn(x))

        @config.patch("triton.native_matmul", False)
        def test_kernel_names_descriptive(self):
            @torch.compile(backend="inductor")
            def fn1(x):
                return x.cos().sin()

            @torch.compile(backend="inductor")
            def fn2(x):
                x = torch.mm(x, x)
                x = torch.softmax(x, dim=1)
                return x

            mod = nn.Sequential(
                nn.Linear(4, 4),
                nn.LayerNorm(4),
                nn.ReLU(),
            ).to(device=GPU_TYPE)

            @torch.compile(backend="inductor")
            def fn3(x):
                return mod(x)

            func_and_kernel_aten = [
                (fn1, "triton_poi_fused_cos_sin", (torch.randn(8, device=GPU_TYPE),)),
                (
                    fn2,
                    "triton_poi_fused__softmax",
                    (torch.randn(4, 4, device=GPU_TYPE),),
                ),
                (
                    fn3,
                    "triton_poi_fused_native_layer_norm_relu",
                    (torch.randn(4, 4, device=GPU_TYPE),),
                ),
            ]
            func_and_kernel_torch = [
                (fn1, "triton_poi_fused_cos_sin", (torch.randn(8, device=GPU_TYPE),)),
                (
                    fn2,
                    "triton_poi_fused_softmax",
                    (torch.randn(4, 4, device=GPU_TYPE),),
                ),
                (
                    fn3,
                    "triton_poi_fused_LayerNorm_ReLU",
                    (torch.randn(4, 4, device=GPU_TYPE),),
                ),
            ]

            def test_funcs(func_and_kernel):
                with torch.no_grad():
                    for fn, kernel_name, inps in func_and_kernel:
                        code = run_and_get_triton_code(fn, *inps)
                        if kernel_name not in code:
                            print(code)
                        self.assertTrue(kernel_name in code)

            test_funcs(func_and_kernel_aten)
            patch.object(config.triton, "descriptive_names", "torch")(test_funcs)(
                func_and_kernel_torch
            )

        @skip_if_cpp_wrapper(
            "profile_bandwidth is not supported with cpp_wrapper",
        )
        @patch.object(config, "profile_bandwidth", True)
        def test_bandwidth_profiler(self):
            @torch.compile(backend="inductor")
            def fn(x):
                x = x.cos()
                x = x.cos()
                x = torch.mm(x, x)
                x = x.sin()
                x = x.relu()
                return x

            inp = torch.randn(4, 4, device=GPU_TYPE)
            code = run_and_get_triton_code(fn, inp)
            fn(inp)
            self.assertTrue("start_graph" in code)
            self.assertTrue("end_graph" in code)

        def test_comment_graph_fragment(self):
            @torch.compile(backend="inductor")
            def fn(x):
                x = x.sin()
                x = x.relu()
                return x

            inp = torch.randn(4, 4, device=GPU_TYPE)
            code = run_and_get_triton_code(fn, inp)
            fn(inp)
            if config.cpp_wrapper:
                self.assertTrue("fused_relu_sin" in code)
            else:
                self.assertTrue("Graph fragment" in code)
                self.assertTrue(
                    f'%sin : Tensor "f32[4, 4][4, 1]{GPU_TYPE}:0"[num_users=1] = call_function[target=torch.ops.aten.sin.default]'
                    in code
                )
                self.assertTrue(
                    f'%relu : Tensor "f32[4, 4][4, 1]{GPU_TYPE}:0"[num_users=1] = call_function[target=torch.ops.aten.relu.default]'
                    in code
                )

        def test_split_op_with_sym(self):
            def fn(x: torch.Tensor) -> torch.Tensor:
                # split(tensor, sympy.Integer), split(tensor, sympy.Expr)
                return torch.split(x, x.shape[0]), torch.split(x, x.shape[0] // 2)

            for dynamic_shapes in [True, False]:
                with torch._dynamo.config.patch(dynamic_shapes=dynamic_shapes):
                    torch._dynamo.reset()
                    fn_opt = torch.compile(
                        fn, backend="inductor", dynamic=dynamic_shapes
                    )
                    inps = torch.randn([5, 5])
                    fn_opt(inps)

        @skipIfRocm
        @unittest.skipIf(IS_FBCODE, "fbcode system python does not provide torch")
        def test_indirect_device_assert(self):
            dir_path = os.path.dirname(os.path.realpath(__file__))
            test_path = os.path.join(dir_path, "indirect_assert_helper.py")
            fns = ("first_arg", "store", "second_arg", "same_pm_one", "same_pp_one")

            def test(fn, ndims, dyn_shape, one_size=False):
                proc = subprocess.Popen(
                    [
                        sys.executable,
                        test_path,
                        fn,
                        str(ndims),
                        str(dyn_shape),
                        str(one_size),
                    ],
                    stdout=subprocess.PIPE,
                    stderr=subprocess.PIPE,
                    env={**os.environ, "MKL_THREADING_LAYER": "GNU"},
                )
                stderr = proc.communicate()[1]
                self.assertTrue(
                    any(
                        "out of bounds" in err.decode("utf-8")
                        for err in stderr.splitlines()
                    ),
                    f"{fn}, {ndims}, {dyn_shape}, {one_size}",
                )

            for fn, ndims, dyn_shape in itertools.product(fns, (2, 3), (True, False)):
                test(fn, ndims, dyn_shape)

            test("first_arg", 2, False, True)

            for fn, dyn_shape in itertools.product(
                ("upper1", "upper2", "lower1", "lower2"), (True, False)
            ):
                test(fn, 2, dyn_shape)

        @patch("torch._inductor.config.comment_origin", True)
        @patch("torch._functorch.config.max_dist_from_bw", 0)
        def test_inductor_sequence_nr(self):
            class Model(torch.nn.Module):
                def __init__(self) -> None:
                    super().__init__()
                    self.conv1 = torch.nn.Conv2d(
                        in_channels=16,
                        out_channels=16,
                        kernel_size=(1, 1),
                        stride=1,
                        padding="same",
                        bias=True,
                    )
                    self.bn1 = torch.nn.BatchNorm2d(num_features=16)
                    self.relu1 = torch.nn.ReLU()
                    self.loss_fn = torch.nn.L1Loss()

                def forward(self, x, target):
                    y = x
                    x = self.conv1(x)
                    x = self.bn1(x)
                    x = self.relu1(x)
                    x = x + y
                    x = torch.flatten(x)
                    output = self.loss_fn(x, target)
                    return (output,)

            def get_triton_codegen(optimized_module, args):
                def run_with_backward():
                    result = optimized_module(*args)
                    result[0].backward()
                    return result

                res, (fwd_code, bwd_code) = run_and_get_code(run_with_backward)
                return fwd_code, bwd_code

            x = torch.rand(100, 16, 32, 32, requires_grad=True, device=GPU_TYPE)
            target = torch.rand(1, device=GPU_TYPE)
            args = [x, target]
            model = Model().to(device=GPU_TYPE)
            opt_model = torch.compile(model)
            fwd_code, bwd_code = get_triton_codegen(opt_model, args)

            bwd_seq_nr_set = set()
            fwd_seq_nr_set = set()
            for idx, code in enumerate([fwd_code, bwd_code]):
                seq_nr_set = bwd_seq_nr_set if idx > 0 else fwd_seq_nr_set
                prefix = "BWD" if idx > 0 else "FWD"
                for line in code.split("\n"):
                    if "seq_nr" in line:
                        res = re.search(r"seq_nr:(\d+)", line)
                        if res:
                            seq_nr_set.add(int(res.group(1)))
            self.assertTrue(bwd_seq_nr_set.issubset(fwd_seq_nr_set))

        @config.patch(
            {
                "coordinate_descent_tuning": True,
                "triton.unique_kernel_names": True,
                "benchmark_kernel": True,
            }
        )
        @skipIfRocm
        @expectedFailureXPU
        @unittest.skipIf(
            torch.cuda.is_available() and torch.cuda.get_device_capability() < (9, 0),
            "Triton does not support fp8 on A100",
        )
        def test_red_followed_by_transposed_pointwise(self):
            bs = 26624
            dim = 1024

            @torch.compile(dynamic=False)
            def f(in1, in2, a, b, scale_a, scale_b):
                out = torch.nn.functional.silu(in1) * in2
                out_row = (out / out.amax(dim=1, keepdim=True)).to(torch.float8_e4m3fn)
                out_col = (out / out.amax(dim=0, keepdim=True)).to(torch.float8_e4m3fn)

                # setup strides for _scaled_mm
                out_row = out_row.contiguous()
                out_col = out_col.t().contiguous().t()

                return (
                    torch._scaled_mm(
                        out_row, a, scale_a, scale_b, out_dtype=torch.bfloat16
                    ),
                    torch._scaled_mm(
                        b, out_col, scale_a, scale_b, out_dtype=torch.bfloat16
                    ),
                )

            in1 = torch.randn((bs, dim), dtype=torch.bfloat16, device=GPU_TYPE)
            in2 = torch.randn((bs, dim), dtype=torch.bfloat16, device=GPU_TYPE)
            a = (
                torch.randn((dim, dim), dtype=torch.bfloat16, device=GPU_TYPE)
                .t()
                .to(torch.float8_e4m3fn)
            )
            b = torch.randn((dim, bs), dtype=torch.bfloat16, device=GPU_TYPE).to(
                torch.float8_e4m3fn
            )
            # Scales
            scale_a = torch.tensor(1.0, device=GPU_TYPE)
            scale_b = torch.tensor(1.0, device=GPU_TYPE)

            # warmup
            _, (wrapper,) = run_and_get_code(f, in1, in2, a, b, scale_a, scale_b)

            # Previously indcutor decide reduction hint for a reduction kernel without considering
            # the pointwise nodes. That will cause the third reduction kernel in this wrapper to be a
            # persistent inner reduction and cause bad perf.
            #
            # We fix that by making the third reduction a non-persistent reduction
            # and improve the perf by 4.14x (451us -> 109us)
            self.assertEqual(3, wrapper.count("def triton_red_"))
            self.assertEqual(0, wrapper.count("def triton_per_"))

            if DO_PERF_TEST:
                with torch.profiler.profile(
                    activities=[torch.profiler.ProfilerActivity.CUDA]
                ) as p:
                    for _ in range(1000):
                        f(in1, in2, a, b, scale_a, scale_b)

                print(p.key_averages().table(max_name_column_width=200))

        def test_non_blocking_copy_codegen(self):
            # Checks non_blocking arg is present in codegen
            # (see https://github.com/pytorch/pytorch/issues/136260)
            def fn(x):
                return x.to(device=self.device, non_blocking=True)

            inp = torch.randn(3, 4)
            _, (code,) = run_and_get_code(torch.compile(fn), inp)

            if config.cpp_wrapper:
                # cpp_wrapper passes "True" as "1" in this case, so check it more
                # explicitly.
                FileCheck().check("aoti_torch_copy_").check_same("1)").run(code)
            else:
                FileCheck().check("copy_").check_same("True").run(code)

        def test_layer_norm_inplaces_after_matmul(self):
            # https://github.com/pytorch/pytorch/issues/132826
            batch_size = 32
            seq_length = 50
            hidden_size = 768

            layer_norm = torch.nn.LayerNorm(hidden_size, device=GPU_TYPE)

            def fn(inp, weight):
                matmul_output = inp @ weight
                final_output = layer_norm(matmul_output)
                return final_output

            inps = [
                torch.randn(batch_size, seq_length, hidden_size, device=GPU_TYPE),
                torch.randn(hidden_size, hidden_size, device=GPU_TYPE),
            ]
            fn_opt = torch.compile(fn)
            code = run_and_get_triton_code(fn_opt, *inps)
            self.assertTrue(len(re.findall(r"in_out_ptr\d+", code)) > 0)
            self.assertEqual(fn_opt(*inps), fn(*inps))

        @torch._functorch.config.patch("donated_buffer", True)
        # The inplace updating does not happen after we fused the
        # layernorm backward, or if we do cooperative reductions (due to the change in
        # optimal peak memory ordering).
        @torch._inductor.config.patch(
            {
                "triton.cooperative_reductions": False,
                "triton.force_cooperative_reductions": False,
                "triton.mix_order_reduction": False,
            }
        )
        def test_donated_buffer_inplace(self):
            batch_size = 32
            seq_length = 50
            hidden_size = 512 if torch.version.hip is not None else 256

            inp = torch.randn(
                batch_size,
                seq_length,
                hidden_size,
                requires_grad=True,
                device=self.device,
            )
            weight = torch.randn(
                hidden_size, hidden_size, requires_grad=True, device=self.device
            )

            layer_norm = torch.nn.LayerNorm(hidden_size, device=self.device)

            def fn(inp, weight):
                matmul_output = inp @ weight
                final_output = layer_norm(matmul_output)
                return final_output

            fn_opt = torch.compile(fn)

            def wrapper(inp, weight):
                return fn_opt(inp, weight).sum().backward()

            _, code = run_and_get_code(wrapper, inp, weight)
            self.assertTrue("in_out_ptr" in code[1])

        @torch._functorch.config.patch("donated_buffer", True)
        @torch._inductor.config.patch("force_shape_pad", True)
        def test_donated_buffer_inplace_gpt(self):
            # model implementation from llm.c:
            # https://github.com/karpathy/llm.c/blob/master/train_gpt2.py
            class NewGELU(nn.Module):
                def forward(self, input):
                    return (
                        0.5
                        * input
                        * (
                            1.0
                            + torch.tanh(
                                math.sqrt(2.0 / math.pi)
                                * (input + 0.044715 * torch.pow(input, 3.0))
                            )
                        )
                    )

            class CausalSelfAttention(nn.Module):
                def __init__(self, config):
                    super().__init__()
                    if config.n_embd % config.n_head != 0:
                        raise AssertionError(
                            f"n_embd ({config.n_embd}) must be divisible by n_head ({config.n_head})"
                        )
                    # key, query, value projections for all heads, but in a batch
                    self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
                    # output projection
                    self.c_proj = nn.Linear(config.n_embd, config.n_embd)
                    self.c_proj.LLMC_RESIDUAL_SCALE_FLAG = 1
                    # regularization
                    self.n_head = config.n_head
                    self.n_embd = config.n_embd
                    # not really a 'bias', more of a mask, but following the OpenAI/HF naming though
                    self.register_buffer(
                        "bias",
                        torch.tril(
                            torch.ones(config.block_size, config.block_size)
                        ).view(1, 1, config.block_size, config.block_size),
                    )

                def forward(self, x):
                    (
                        B,
                        T,
                        C,
                    ) = x.size()  # batch size, sequence length, embedding dimensionality (n_embd)
                    # calculate query, key, values for all heads in batch and move head forward to be the batch dim
                    qkv = self.c_attn(x)
                    q, k, v = qkv.split(self.n_embd, dim=2)
                    k = k.view(B, T, self.n_head, C // self.n_head).transpose(
                        1, 2
                    )  # (B, nh, T, hs)
                    q = q.view(B, T, self.n_head, C // self.n_head).transpose(
                        1, 2
                    )  # (B, nh, T, hs)
                    v = v.view(B, T, self.n_head, C // self.n_head).transpose(
                        1, 2
                    )  # (B, nh, T, hs)
                    y = F.scaled_dot_product_attention(q, k, v, is_causal=True)
                    y = (
                        y.transpose(1, 2).contiguous().view(B, T, C)
                    )  # re-assemble all head outputs side by side
                    # output projection
                    y = self.c_proj(y)
                    return y

            class MLP(nn.Module):
                def __init__(self, config):
                    super().__init__()
                    self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd)
                    self.gelu = NewGELU()
                    self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd)
                    self.c_proj.LLMC_RESIDUAL_SCALE_FLAG = 1

                def forward(self, x):
                    x = self.c_fc(x)
                    x = self.gelu(x)
                    x = self.c_proj(x)
                    return x

            class Block(nn.Module):
                def __init__(self, config):
                    super().__init__()
                    self.ln_1 = nn.LayerNorm(config.n_embd)
                    self.attn = CausalSelfAttention(config)
                    self.ln_2 = nn.LayerNorm(config.n_embd)
                    self.mlp = MLP(config)

                def forward(self, x):
                    x = x + self.attn(self.ln_1(x))
                    x = x + self.mlp(self.ln_2(x))
                    return x

            class GPTConfig:
                block_size: int = 1024
                vocab_size: int = 50257
                n_layer: int = 1
                n_head: int = 12
                n_embd: int = 768

            class GPT(nn.Module):
                def __init__(self, config):
                    super().__init__()
                    self.config = config

                    self.transformer = nn.ModuleDict(
                        dict(
                            wte=nn.Embedding(config.vocab_size, config.n_embd),
                            wpe=nn.Embedding(config.block_size, config.n_embd),
                            h=nn.ModuleList(
                                [Block(config) for _ in range(config.n_layer)]
                            ),
                            ln_f=nn.LayerNorm(config.n_embd),
                        )
                    )
                    self.lm_head = nn.Linear(
                        config.n_embd, config.vocab_size, bias=False
                    )
                    self.lm_head.LLMC_SKIP_INIT = (
                        1  # don't init this one, we will tie weights
                    )
                    self.transformer.wte.weight = (
                        self.lm_head.weight
                    )  # https://paperswithcode.com/method/weight-tying

                def forward(self, idx, targets):
                    device = idx.device
                    b, t = idx.size()
                    if t > self.config.block_size:
                        raise AssertionError(
                            f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
                        )
                    pos = torch.arange(
                        0, t, dtype=torch.long, device=device
                    )  # shape (t)

                    # forward the GPT model itself
                    tok_emb = self.transformer.wte(
                        idx
                    )  # token embeddings of shape (b, t, n_embd)
                    pos_emb = self.transformer.wpe(
                        pos
                    )  # position embeddings of shape (t, n_embd)
                    x = tok_emb + pos_emb

                    for block in self.transformer.h:
                        x = block(x)
                    x = self.transformer.ln_f(x)

                    logits = self.lm_head(x)
                    loss = F.cross_entropy(
                        logits.view(-1, logits.size(-1)),
                        targets.view(-1),
                        ignore_index=-1,
                    )

                    return loss

            B, T = 1, 1024
            ctx = torch.amp.autocast(device_type=GPU_TYPE, dtype=torch.bfloat16)

            model = GPT(GPTConfig())
            model.train()
            model.to(GPU_TYPE)
            model = torch.compile(model)

            x = torch.randint(0, 50257, (B, T), dtype=torch.int64, device=GPU_TYPE)
            y = torch.randint(0, 50257, (B, T), dtype=torch.int64, device=GPU_TYPE)

            def wrapper(x, y):
                with ctx:
                    loss = model(x, y)
                loss.backward()

            _, code = run_and_get_code(wrapper, x, y)

            # The cpp_wrapper code is significantly more complex, so skip checking for exact
            # code lines.
            if not config.cpp_wrapper:
                FileCheck().check_regex(
                    r"reinterpret_tensor\(.*, \(1024, 50257\).*# reuse"
                ).run(code[1])

        @unittest.skipIf(
            not triton_version_uses_attrs_dict(),
            "Test only applies to newer triton versions",
        )
        def test_triton_attrs_dict_constexpr_signature(self):
            def fn(x):
                return x.sin()

            fn_c = torch.compile(fn)
            x = torch.rand(16, device=GPU_TYPE)

            _, code = run_and_get_code(fn_c, x)

            FileCheck().check("triton_meta").check("'signature':").check(
                "'XBLOCK': 'constexpr'"
            ).run(code[0])

        @unittest.skipIf(TEST_WITH_ROCM or not IS_SM90, "no scaled_grouped_mm support")
        def test_respect_scaled_grouped_mm_layout_tag(self):
            # scaled_grouped_mm needs `mat2` to be column-major
            M, K, N = 128, 64, 32  # K and N must be divisible by 16
            num_groups = 2
            E = num_groups  # B_t batch size must match number of groups
            group_size = M // num_groups

            A = torch.randn(
                M, K, dtype=torch.bfloat16, device=GPU_TYPE
            )  # Row-major by default

            # Create B_t with proper column-major layout
            B_t_transposed = torch.randn(
                E, N, K, dtype=torch.bfloat16, device=GPU_TYPE
            ).contiguous()
            B_t = B_t_transposed.transpose(-2, -1)  # (E, K, N)
            B_t = B_t.transpose(-2, -1).contiguous().transpose(-2, -1)

            # Verify column-major layout
            def _is_column_major(x: torch.Tensor) -> bool:
                """Check if tensor is column-major (stride(-2) == 1 and stride(-1) > 1)"""
                if not (x.ndim == 2 or x.ndim == 3):
                    raise AssertionError("input tensor must be 2D or 3D")
                return x.stride(-2) == 1 and x.stride(-1) > 1

            self.assertTrue(_is_column_major(B_t))

            offs = torch.tensor([group_size, M], dtype=torch.int32, device=GPU_TYPE)
            out_dtype = torch.bfloat16

            @torch.compile
            def fn():
                A_scales = torch.ones(M, dtype=torch.float32, device=GPU_TYPE)
                A_scaled = A.to(torch.float32) * A_scales.unsqueeze(-1)
                A_fp8_row_major = A_scaled.to(torch.float8_e4m3fn)

                B_t_scales = torch.ones(E, N, dtype=torch.float32, device=GPU_TYPE)
                B_t_scaled = B_t.to(torch.float32) * B_t_scales.unsqueeze(1)
                B_t_fp8_col_major = B_t_scaled.to(torch.float8_e4m3fn)

                A_scales_reciprocal = A_scales.reciprocal()
                B_t_scales_reciprocal = B_t_scales.reciprocal()

                return torch.ops.aten._scaled_grouped_mm(
                    A_fp8_row_major,
                    B_t_fp8_col_major,
                    A_scales_reciprocal,
                    B_t_scales_reciprocal,
                    offs,
                    out_dtype=out_dtype,
                    use_fast_accum=True,
                )

            fn()

        @config.patch(implicit_fallbacks=True)
        @torch._inductor.config.patch("graph_partition", True)
        def test_graph_partition_default_device_context(self):
            @torch.library.custom_op(
                "mylib::cg_unsafe_op",
                mutates_args=[],
                schema="(Tensor x) -> Tensor",
                device_types=GPU_TYPE,
                tags=(torch._C.Tag.cudagraph_unsafe,),
            )
            def cg_unsafe_op(x) -> torch.Tensor:
                return x + 1

            @cg_unsafe_op.register_fake
            def _(x) -> torch.Tensor:
                return torch.empty_like(x)

            def f(x):
                x += 1
                y = cg_unsafe_op(x)
                y += 1
                return y

            f = torch.compile(f, mode="reduce-overhead")

            inp = torch.randn(2, device=GPU_TYPE)
            _, (code,) = run_and_get_code(f, inp)

            if config.cpp_wrapper:
                FileCheck().check_count(
                    "AOTICudaGuard device_guard(0)", 1, exactly=True
                ).run(code)
            else:
                FileCheck().check_count(
                    f"with torch.{GPU_TYPE}._DeviceGuard(0)", 1, exactly=True
                ).run(code)

        @skipCUDAIf(
            not SM90OrLater, "uses bfloat16 atomic add instrs which requires SM >= 90"
        )
        def test_bf16_atomic_add(self):
            def fn(output, indices, values):
                output.index_put_([indices], values, accumulate=True)
                return output

            indices = torch.tensor([0, 1], device=GPU_TYPE)
            values = torch.randn(2, 768, dtype=torch.bfloat16, device=GPU_TYPE)
            output = torch.zeros(512, 768, dtype=torch.bfloat16, device=GPU_TYPE)

            result, code = run_and_get_code(torch.compile(fn), output, indices, values)
            if output.device.type == "xpu":
                # xpu fallback bf16 atomic add for better performance
                self.assertFalse(
                    "tl.atomic_add" in code[0],
                    "bf16 should not generate tl.atomic_add on xpu",
                )
            else:
                self.assertTrue(
                    "tl.atomic_add" in code[0],
                    "bf16 should generate tl.atomic_add",
                )
            expected = torch.zeros(512, 768, dtype=torch.bfloat16, device=GPU_TYPE)
            torch.testing.assert_close(result, fn(expected, indices, values))

        @parametrize("dtype", (torch.float32, torch.float64))
        def test_copysign(self, dtype):
            def fn(x):
                out = torch.copysign(x, -0.0)
                return out

            inp = torch.randn([6], dtype=dtype, device=GPU_TYPE)

            result, code = run_and_get_code(torch.compile(fn), inp)
            self.assertIn("0x80000000", code[0])
            torch.testing.assert_close(result, fn(inp))

        def test_3d_reductions_with_max_tiles_3(self):
            # Inductor only supports at most two reduction iteration ranges, R0 and R1, which the
            # reduction component of the kernel can be tiled across.
            # When max_tiles>=3, SIMDScheduling.create_tiling would previously incorrectly allow the
            # tiling of the kernel in three dimensions, despite there being no pointwise component
            # of the kernel.

            @torch._inductor.config.patch(
                {
                    "triton.prefer_nd_tiling": True,
                    "triton.max_tiles": 3,
                    "triton.tile_reductions": True,
                    "triton.use_tensor_descriptor": True,
                }
            )
            @torch.compile
            def sum(x):
                return torch.sum(x, [0, 1, 2])

            shape = (2, 2, 4)
            strides = (32, 8, 1)

            arg0_1_orig = torch.arange(
                math.prod(shape), device=GPU_TYPE, dtype=torch.int32
            ).view(shape)
            arg0_1 = torch.empty_strided(
                shape, strides, device=GPU_TYPE, dtype=torch.int32
            )
            arg0_1.copy_(arg0_1_orig)

            actual, code = run_and_get_code(sum, arg0_1)
            expected = torch.sum(arg0_1, [0, 1, 2])

            torch.testing.assert_close(actual=actual, expected=expected)

            fc = FileCheck()
            # There's no pointwise work to do, so xnumel should be 1...
            fc.check("xnumel = 1")
            fc.run(code[0])

        @config.patch({"triton.decompose_sort_ops": True})
        def test_median_decompose_sort_ops(self):
            def fn_default(a):
                return torch.median(a)

            def fn_dim(a):
                return torch.median(a, dim=1)

            inp = torch.randn(8, 16, device=GPU_TYPE)
            for fn in (fn_default, fn_dim):
                torch._dynamo.reset()
                result, code = run_and_get_code(torch.compile(fn), inp)
                self.assertIn(
                    "sort_with_index",
                    " ".join(code),
                    "Expected Triton sort codegen for median",
                )
                torch.testing.assert_close(result, fn(inp))

        @config.patch({"triton.decompose_sort_ops": True})
        def test_mode_decompose_sort_ops(self):
            def fn(a):
                return torch.mode(a, dim=1)

            # Use integers so ties are common, exercising run-length logic
            inp = torch.randint(
                0, 5, size=[8, 16], dtype=torch.float32, device=GPU_TYPE
            )
            torch._dynamo.reset()
            result, code = run_and_get_code(torch.compile(fn), inp)
            self.assertIn(
                "sort_with_index",
                " ".join(code),
                "Expected Triton sort codegen for mode",
            )
            expected = fn(inp)
            torch.testing.assert_close(result[0], expected[0])
            torch.testing.assert_close(result[1], expected[1])

        @config.patch({"triton.decompose_sort_ops": True})
        def test_topk_decompose_sort_ops(self):
            def fn(a):
                return torch.topk(a, 3, dim=-1)

            def fn_largest_false(a):
                return torch.topk(a, 3, dim=-1, largest=False)

            inp = torch.randn(8, 16, device=GPU_TYPE)
            for test_fn in (fn, fn_largest_false):
                torch._dynamo.reset()
                result, code = run_and_get_code(torch.compile(test_fn), inp)
                self.assertIn(
                    "sort_with_index",
                    " ".join(code),
                    "Expected Triton sort codegen for topk",
                )
                expected = test_fn(inp)
                torch.testing.assert_close(result[0], expected[0])
                torch.testing.assert_close(result[1], expected[1])

        @config.patch({"triton.decompose_sort_ops": True})
        def test_kthvalue_decompose_sort_ops(self):
            def fn(a):
                return torch.kthvalue(a, 3, dim=-1)

            inp = torch.randn(8, 16, device=GPU_TYPE)
            torch._dynamo.reset()
            result, code = run_and_get_code(torch.compile(fn), inp)
            self.assertIn(
                "sort_with_index",
                " ".join(code),
                "Expected Triton sort codegen for kthvalue",
            )
            expected = fn(inp)
            torch.testing.assert_close(result[0], expected[0])
            torch.testing.assert_close(result[1], expected[1])

    class RNNTest(TestCase):
        device_type = GPU_TYPE

        class Model(torch.nn.Module):
            def __init__(self) -> None:
                super().__init__()
                self.gru = torch.nn.GRU(16, 16, batch_first=True)

            def forward(self, x):
                return self.gru(x)

        def test_rnn_compile_safe(self):
            device = torch.device(GPU_TYPE)
            model = RNNTest.Model().to(device)
            model = torch.compile(model, backend="inductor")
            x = torch.rand(1024, 20, 16).to(device)
            model(x)

    class NanCheckerTest(TestCase):
        @config.patch("nan_asserts", True)
        def test_nan_checker_pass(self):
            def f(x):
                return torch.softmax(x, dim=-1)

            x = torch.randn(2, 1024, device=GPU_TYPE)
            ref = f(x)
            actual, code = run_and_get_code(torch.compile(f), x)
            self.assertTrue(torch.allclose(ref, actual))

            code = code[0]
            if config.cpp_wrapper:
                self.assertIn("aoti_torch_check_inf_and_nan", code)
            else:
                self.assertIn("# make sure graph inputs are not nan/inf", code)
                self.assertRegex(code, r"return_vars = (.*)")
                self.assertIn("for var in return_vars:", code)
                self.assertIn("if isinstance(var, torch.Tensor):", code)
                self.assertRegex(code, r"assert not .*\.isnan\(\)\.any\(\).item\(\)")
                self.assertRegex(code, r"assert not .*\.isinf\(\)\.any\(\).item\(\)")

        @config.patch("nan_asserts", True)
        def test_nan_checker_fail(self):
            def f(x):
                return torch.softmax(x, dim=-1)

            x = torch.randn(2, 1024, device=GPU_TYPE)
            x[0, 0] = float("nan")
            with self.assertRaises(
                AssertionError if not config.cpp_wrapper else RuntimeError
            ):
                torch.compile(f)(x)


if RUN_CPU:

    class TestFull(TestCase):
        def test_full_dtype(self):
            pytypes = (
                bool,
                int,
                float,
                # TODO: Triton's JITFunction._type_of has no support for complex
                # complex,
            )

            dtypes = (
                torch.bool,
                torch.int32,
                torch.int64,
                torch.float32,
                torch.float64,
                None,
                # torch.complex64,
                # torch.complex128,
            )

            def fn(pytype, dtype):
                if pytype is bool:
                    fill_value = True
                elif pytype is int:
                    fill_value = 42
                elif pytype is float:
                    fill_value = 42.0
                else:
                    raise AssertionError(f"Unexpected Python type: {pytype}")

                return torch.full(
                    (4, 6), fill_value, dtype=dtype, device=torch.device("cpu")
                )

            fn_opt = torch.compile(fn, backend="inductor")

            for pytype, dtype in itertools.product(pytypes, dtypes):
                with enable_python_dispatcher():
                    with torch.no_grad():
                        ret_opt = fn_opt(pytype, dtype)

                self.assertEqual(ret_opt, fn(pytype, dtype))


def _strip_tmp_path(code: str) -> str:
    """
    Canonicalize things that look like a tmp path so they can be compared.
    """
    return re.sub('#include ".*?"', '#include "<tmppath>"', code)


def _run_and_get_stripped_kernels(
    fn: Callable[_P, _T], *args: _P.args, **kwargs: _P.kwargs
) -> tuple[_T, list[str]]:
    result, codes = run_and_get_kernels(fn, *args, **kwargs)
    return result, [_strip_tmp_path(code) for code in codes]


if __name__ == "__main__":
    from torch._inductor.test_case import run_tests

    if RUN_CPU or RUN_GPU or HAS_MPS:
        run_tests(needs="filelock")
