pytorch/test/inductor/test_cpu_repro.py

# Owner(s): ["oncall: cpu inductor"]
import contextlib
import copy
import itertools
import math
import platform
import sys
import unittest
from typing import Callable
from unittest.mock import patch

import numpy as np
import sympy
import torch
from torch._C import FileCheck
from torch._dynamo.testing import rand_strided
from torch._dynamo.utils import same
from torch._inductor import codecache, config, metrics
from torch._inductor.codegen.common import OptimizationContext
from torch._inductor.codegen.cpp import (
    CppOverrides,
    CppVecKernelChecker,
    CppVecOverrides,
)
from torch._inductor.compile_fx import (
    compile_fx,
    compile_fx_inner,
    complex_memory_overlap,
)
from torch._inductor.graph import GraphLowering
from torch._inductor.ir import InterpreterShim
from torch._inductor.utils import timed
from torch._inductor.virtualized import V
from torch.fx.experimental.proxy_tensor import make_fx
from torch.nn import functional as F
from torch.testing._internal.common_utils import (
    instantiate_parametrized_tests,
    IS_MACOS,
    parametrize,
    slowTest,
)
from torch.utils._python_dispatch import TorchDispatchMode

try:
    try:
        from . import test_torchinductor
    except ImportError:
        import test_torchinductor
except unittest.SkipTest:
    if __name__ == "__main__":
        sys.exit(0)
    raise


vec_dtypes = test_torchinductor.vec_dtypes
_lowp_fp_dtypes = (
    torch.bfloat16,
    torch.float16,
)
run_and_get_cpp_code = test_torchinductor.run_and_get_cpp_code
TestCase = test_torchinductor.TestCase
aten = torch.ops.aten
check_model = test_torchinductor.check_model


class LstmModule(torch.nn.Module):
    def __init__(
        self,
        input_size,
        hidden_size,
        num_layers,
        bias=True,
        bidirectional=False,
        batch_first=False,
    ):
        super().__init__()
        self.lstm = torch.nn.LSTM(
            input_size=input_size,
            hidden_size=hidden_size,
            num_layers=num_layers,
            bias=bias,
            bidirectional=bidirectional,
            batch_first=batch_first,
        )

    def forward(self, x, h=None):
        x, h = self.lstm(x, h)
        return x, h


@instantiate_parametrized_tests
class CPUReproTests(TestCase):
    common = check_model

    def test_conv_stride_constraints(self):
        for fmt in [torch.contiguous_format, torch.channels_last]:
            # TorchDispatch doesn't work in our cuda invocation for some reason
            m = torch.nn.Conv2d(5, 6, [3, 3])

            def fn(inp, weight):
                return (
                    F.conv2d(
                        inp, weight, None, m.stride, m.padding, m.dilation, m.groups
                    ),
                )

            inp = torch.randn([2, 5, 16, 16])
            inps = [inp, m.weight.to(memory_format=fmt)]
            fn_fx = make_fx(fn)(*inps)
            fn_compiled = compile_fx_inner(fn_fx, inps)
            test_self = self
            conv_seen = False

            class RecordFunctions(TorchDispatchMode):
                def __torch_dispatch__(self, func, types, args=(), kwargs=None):
                    kwargs = kwargs if kwargs else {}
                    if func == torch.ops.aten.convolution.default:
                        # For CPU and mkldnn enable, we always using channles last
                        nonlocal fmt
                        if (
                            torch.backends.mkldnn.enabled
                            and torch.backends.mkldnn.is_available()
                        ):
                            fmt = torch.channels_last
                        test_self.assertTrue(args[0].is_contiguous(memory_format=fmt))
                        test_self.assertTrue(args[1].is_contiguous(memory_format=fmt))
                        nonlocal conv_seen
                        conv_seen = True

                    return func(*args, **kwargs)

            with RecordFunctions():
                out = fn_compiled(inps)

            self.assertTrue(conv_seen)

    @patch("torch.cuda.is_available", lambda: False)
    def test_conv2d_bn_mixed_dtype(self):
        class Model(torch.nn.Module):
            def __init__(self):
                super().__init__()
                self.conv = torch.nn.Conv2d(
                    3,
                    16,
                    kernel_size=3,
                    stride=1,
                    padding=1,
                    bias=False,
                    dtype=torch.bfloat16,
                )
                self.bn = torch.nn.BatchNorm2d(
                    16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True
                )

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

        v = torch.randn(1, 3, 64, 64, dtype=torch.bfloat16)
        mod = Model().eval()
        with torch.no_grad():
            self.common(
                mod,
                (v,),
            )

    @unittest.skipIf(not torch.backends.mkldnn.is_available(), "MKLDNN is not enabled")
    @patch("torch.cuda.is_available", lambda: False)
    def test_conv2d_packed(self):
        options = itertools.product([[3, 56, 56]], [True, False], [0, (0,)])
        for x_shape, mode_train, padding in options:
            mod = torch.nn.Sequential(
                torch.nn.Conv2d(3, 64, 3, 3, padding=padding)
            ).train(mode=mode_train)
            v = torch.randn(x_shape, dtype=torch.float32)

            with torch.no_grad():
                self.common(
                    mod,
                    (v,),
                )

    @patch("torch.cuda.is_available", lambda: False)
    def test_conv2d_autocast(self):
        v = torch.randn(1, 3, 28, 18, dtype=torch.float32)
        mod = torch.nn.Sequential(torch.nn.Conv2d(3, 64, 3, 3)).eval()
        with torch.no_grad(), torch.cpu.amp.autocast():
            self.common(
                mod,
                (v,),
            )

    @unittest.skipIf(not torch.backends.mkldnn.is_available(), "MKLDNN is not enabled")
    @patch("torch.cuda.is_available", lambda: False)
    def test_unsupported_conv_transpose(self):
        class Model(torch.nn.Module):
            def __init__(self):
                super().__init__()
                self.conv_transpose = torch.nn.ConvTranspose2d(
                    3, 6, 3, stride=1, padding=1, output_padding=1
                )

            def forward(self, input_tensor):
                x = self.conv_transpose(input_tensor)
                output = torch.tanh(x)
                return output

        input = torch.randn(1, 3, 28, 28)
        m = Model().eval()

        with torch.no_grad():
            compiled_m = torch.compile(m)
            with self.assertRaisesRegex(
                RuntimeError,
                "output padding must be smaller than either stride or dilation",
            ):
                compiled_m(input)

    @unittest.skipIf(not torch.backends.mkldnn.is_available(), "MKLDNN is not enabled")
    @patch("torch.cuda.is_available", lambda: False)
    def test_conv_used_from_multiple_places(self):
        class M(torch.nn.Module):
            def __init__(self, conv_in_channel, conv_out_channel) -> None:
                super().__init__()
                self.conv = torch.nn.Conv2d(conv_in_channel, conv_out_channel, (3, 3))

            def forward(self, x):
                res = self.conv(x)
                res = F.relu(res)
                res = self.conv(res)
                return res

        with torch.no_grad():
            mod = M(3, 3).eval()
            x = torch.randn(1, 3, 224, 224)
            self.common(
                mod,
                (x,),
            )

    @unittest.skipIf(not torch.backends.mkldnn.is_available(), "MKLDNN is not enabled")
    @patch("torch.cuda.is_available", lambda: False)
    def test_linear_used_from_multiple_places(self):
        class M(torch.nn.Module):
            def __init__(self, in_channel, out_channel) -> None:
                super().__init__()
                self.linear = torch.nn.Linear(in_channel, out_channel)

            def forward(self, x):
                res = self.linear(x)
                res = F.relu(res)
                res = self.linear(res)
                return res

        dtypes = []
        if torch.ops.mkldnn._is_mkldnn_bf16_supported():
            dtypes.append(torch.bfloat16)
        if torch.ops.mkldnn._is_mkldnn_fp16_supported():
            dtypes.append(torch.float16)
        for dtype in dtypes:
            with torch.no_grad():
                m = M(224, 224).to(dtype).eval()
                m_opt = torch.compile(m)
                x = torch.randn(224, 224, dtype=dtype)
                m_opt(x)
                self.assertEqual(m(x), m_opt(x))

    @config.patch(implicit_fallbacks=True)
    def test_multihead_attention_cpu(self):
        def fn(
            q,
            k,
            v,
            embed_dim,
            num_heads,
            qkv_weight,
            qkv_bias,
            proj_weight,
            proj_bias,
            mask,
            need_weights,
        ):
            return torch._native_multi_head_attention(
                q,
                k,
                v,
                embed_dim,
                num_heads,
                qkv_weight,
                qkv_bias,
                proj_weight,
                proj_bias,
                mask,
                need_weights,
            )

        B = 1
        T = 3
        embed_dim = 6
        num_heads = 2
        q = torch.randn([B, T, embed_dim])
        k = torch.randn([B, T, embed_dim])
        v = torch.randn([B, T, embed_dim])
        qkv_weight = torch.randn([3 * embed_dim, embed_dim])
        qkv_bias = torch.randn([3 * embed_dim])
        proj_weight = torch.randn([3 * embed_dim, embed_dim])
        proj_bias = torch.randn([3 * embed_dim])
        mask = None
        need_weights = False

        inps = [
            q,
            k,
            v,
            embed_dim,
            num_heads,
            qkv_weight,
            qkv_bias,
            proj_weight,
            proj_bias,
            mask,
            need_weights,
        ]
        self.common(fn, inps)

    @unittest.skipIf(not torch.backends.mkldnn.is_available(), "MKLDNN is not enabled")
    @patch("torch.cuda.is_available", lambda: False)
    def test_linear_packed(self):
        dtypes = []
        if torch.ops.mkldnn._is_mkldnn_bf16_supported():
            dtypes.append(torch.bfloat16)
        if torch.ops.mkldnn._is_mkldnn_fp16_supported():
            dtypes.append(torch.float16)
        options = itertools.product(
            [[2, 3, 10], [2, 10], [10], [2, 0]], [3, 0], [True, False], dtypes
        )
        for input_shape, out_dim, bias, dtype in options:
            mod = torch.nn.Sequential(
                torch.nn.Linear(input_shape[-1], out_dim, bias=bias)
            ).eval()

            v = torch.randn(input_shape)
            with torch.no_grad():
                self.common(
                    mod.to(dtype),
                    (v.to(dtype),),
                )

    @unittest.skipIf(not torch.backends.mkldnn.is_available(), "MKLDNN is not enabled")
    @patch("torch.cuda.is_available", lambda: False)
    def test_conv_transpose2d_packed_cpu(self):
        options = itertools.product([[1, 3, 28, 28], [3, 28, 28]], [0, (0,)])
        for x_shape, padding in options:
            mod = torch.nn.Sequential(
                torch.nn.ConvTranspose2d(3, 64, 3, 3, padding=padding)
            ).eval()
            v = torch.randn(x_shape, dtype=torch.float32)
            with torch.no_grad():
                self.common(
                    mod,
                    (v,),
                )

    @unittest.skipIf(not torch._C._has_mkldnn, "MKLDNN is not enabled")
    @patch("torch.cuda.is_available", lambda: False)
    @torch._dynamo.config.patch(dynamic_shapes=True)
    @torch._dynamo.config.patch(assume_static_by_default=False)
    @torch._dynamo.config.patch(allow_rnn=True)
    @config.patch(freezing=True)
    def _test_lstm_packed(self, params_dict, change_input_sizes=False):
        from torch._dynamo.utils import counters

        for (
            unbatched,
            input_size,
            hidden_size,
            num_layers,
            bidirectional,
            bias,
            empty_state,
            batch_first,
            batch_size,
            seq_len,
        ) in itertools.product(*list(params_dict.values())):
            dtypes = [torch.float]
            if torch.ops.mkldnn._is_mkldnn_bf16_supported():
                dtypes.append(torch.bfloat16)
            if torch.ops.mkldnn._is_mkldnn_fp16_supported():
                dtypes.append(torch.float16)
            for dtype in dtypes:
                counters.clear()
                num_directions = 2 if bidirectional else 1

                seq_len_var = seq_len + 3
                if unbatched:
                    v = torch.randn(seq_len, input_size)
                    v_var = torch.randn(seq_len_var, input_size)
                    h = torch.randn(num_layers * num_directions, hidden_size)
                    c = torch.randn(num_layers * num_directions, hidden_size)
                else:
                    if batch_first:
                        v = torch.randn(batch_size, seq_len, input_size)
                        v_var = torch.randn(batch_size, seq_len_var, input_size)
                    else:
                        v = torch.randn(seq_len, batch_size, input_size)
                        v_var = torch.randn(seq_len_var, batch_size, input_size)
                    h = torch.randn(
                        num_layers * num_directions, batch_size, hidden_size
                    )
                    c = torch.randn(
                        num_layers * num_directions, batch_size, hidden_size
                    )

                mod = LstmModule(
                    input_size,
                    hidden_size,
                    num_layers,
                    bias,
                    bidirectional,
                    batch_first,
                ).eval()
                maybe_autocast = (
                    torch.cpu.amp.autocast()
                    if dtype == torch.bfloat16
                    else contextlib.nullcontext()
                )

                with torch.no_grad(), maybe_autocast:
                    inps = [v]
                    if not empty_state:
                        inps.append((h, c))

                    fn_opt = torch._dynamo.optimize("inductor")(mod)
                    _, code = run_and_get_cpp_code(fn_opt, *inps)

                    # Check that _flat_weights are not functional_tensor, otherwise
                    # deepcopy will fail during recompilation.
                    fn_opt_copy = copy.deepcopy(fn_opt)
                    _flat_weights = fn_opt_copy.lstm._flat_weights
                    for _flat_weight in _flat_weights:
                        self.assertFalse(torch._is_functional_tensor(_flat_weight))

                    self.assertTrue("aten.mkldnn_rnn_layer" in code)
                    self.assertEqual(fn_opt(*inps), mod(*inps))
                    self.assertEqual(
                        counters["inductor"]["pattern_matcher_count"],
                        num_layers * num_directions
                        + 2,  # num of mkldnn_rnn_layer call + 2 view call on the concatenated hy, cy.
                    )

                    # Change input sizes
                    if change_input_sizes:
                        inps_var = [v_var]
                        self.assertEqual(fn_opt(*inps_var), mod(*inps_var))

    @slowTest
    def test_lstm_packed(self):
        params_dict = {
            "unbatched": [True, False],
            "input_size": [1, 2],
            "hidden_size": [2],
            "num_layers": [1, 2],
            "bidirectional": [False, True],
            "bias": [False, True],
            "empty_state": [False, True],
            "batch_first": [True, False],
            "batch_size": [1, 2],
            "seq_len": [1, 2],
        }
        self._test_lstm_packed(params_dict)

    def test_lstm_packed_change_input_sizes_cpu(self):
        params_dict = {
            "unbatched": [False],
            "input_size": [2],
            "hidden_size": [5],
            "num_layers": [3],
            "bidirectional": [True],
            "bias": [True],
            "empty_state": [False],
            "batch_first": [False],
            "batch_size": [2],
            "seq_len": [3],
        }
        self._test_lstm_packed(params_dict, change_input_sizes=True)

    @torch._dynamo.config.patch(dynamic_shapes=True)
    @torch._dynamo.config.patch(assume_static_by_default=False)
    @torch._dynamo.config.patch(allow_rnn=True)
    def test_pack_padded_sequence_lstm(self):
        embedding_dim = 12
        hidden_dim = 10
        batch_size = 24
        num_layers = 1
        bidirectional = True
        num_direc = 2
        max_lens = 96

        sent = torch.randn(batch_size, max_lens, embedding_dim)
        hid_0 = torch.rand(num_layers * num_direc, batch_size, hidden_dim)
        hid_1 = torch.randn(num_layers * num_direc, batch_size, hidden_dim)

        sent_lens = torch.Tensor(
            [1, 2, 3, 4, 5, 1, 3, 2, 96, 5, 3, 1, 1, 2, 1, 2, 3, 6, 1, 2, 4, 6, 2, 1]
        )

        assert sent_lens.shape[0] == batch_size
        assert sent_lens.max().item() == max_lens

        hidden_0 = hid_0.clone().requires_grad_(False)
        hidden_1 = hid_1.clone().requires_grad_(False)
        embeds = torch.nn.utils.rnn.pack_padded_sequence(
            sent, sent_lens, batch_first=True, enforce_sorted=False
        )

        mod = LstmModule(
            embedding_dim,
            hidden_dim,
            num_layers=num_layers,
            bias=True,
            bidirectional=bidirectional,
            batch_first=True,
        ).eval()

        with torch.no_grad():
            inps = [embeds, (hidden_0, hidden_1)]
            fn_opt = torch._dynamo.optimize("inductor")(mod)
            _, code = run_and_get_cpp_code(fn_opt, *inps)
            # This case is unsupported
            self.assertFalse("torch.ops.mkldnn._lstm" in code)
            self.assertEqual(fn_opt(*inps), mod(*inps))

    @patch("torch.cuda.is_available", lambda: False)
    def test_conv_transpose2d_has_output_size_input(self):
        # https://github.com/pytorch/pytorch/issues/100344.
        class M(torch.nn.Module):
            def __init__(self) -> None:
                super().__init__()
                self.conv_transpose = torch.nn.ConvTranspose2d(
                    in_channels=3, out_channels=1, kernel_size=3, stride=1, padding=1
                )

            def forward(self, x):
                return self.conv_transpose(x, output_size=(10, 10))

        mod = M().eval()
        v = torch.randn(1, 3, 10, 10, dtype=torch.float32)
        with torch.no_grad():
            self.common(
                mod,
                (v,),
            )

    def test_pad_with_nan_value(self):
        # https://github.com/pytorch/pytorch/issues/100988.
        class Model(torch.nn.Module):
            def forward(self, x):
                x = F.pad(x, (1, 1, 1, 1), value=float("nan"))
                return x

        mod = Model().eval()
        v = torch.randn(1, 3, 10, 10, dtype=torch.float32)
        with torch.no_grad():
            self.common(
                mod,
                (v,),
            )

    def test_masked_fill_with_inf_or_nan_value(self):
        def fn(value, mask):
            y1 = torch.masked_fill(value, mask, float("inf"))
            y2 = torch.masked_fill(value, mask, float("-inf"))
            y3 = torch.masked_fill(value, mask, float("nan"))
            return y1, y2, y3

        value = torch.randn((2, 17))
        mask = torch.randint(0, 1, size=(2, 17), dtype=torch.uint8).to(torch.bool)
        with torch.no_grad():
            self.common(
                fn,
                (value, mask),
            )

    def test_relu_with_inf_value(self):
        # https://github.com/pytorch/pytorch/issues/117544.

        def fn(out):
            out = torch.sinh(input=out)
            out = torch.relu(input=out)
            return out

        x = torch.Tensor([-572373.5000, 755109.1250, 330995.5625])
        with torch.no_grad():
            self.common(
                fn,
                (x,),
            )

    @config.patch(implicit_fallbacks=True)
    def test_repeat_interleave(self):
        def fn(y):
            return torch.repeat_interleave(y, 2, output_size=8)

        a = torch.tensor([[1, 2], [3, 4]])
        self.common(
            fn,
            (a,),
        )

    def test_inplace_squeeze_needed(self):
        mod = torch.nn.Sequential(
            torch.nn.Linear(10, 10),
            torch.nn.LayerNorm(10),
            torch.nn.ReLU(),
        ).eval()

        def fn(x):
            return mod(x)

        v = torch.randn(10)
        # TODO: OMP parallel reduction order is not deterministic.
        # Hence, the accurarcy might vary up and down. For short term,
        # we increase the tolerance and will fix it later by using
        # aten parallel.
        self.common(fn, (v,), atol=5e-1, rtol=5e-1)

    def test_cat_mul(self):
        # https://github.com/pytorch/pytorch/issues/93365
        def fn(p0, p1):
            y1 = torch.cat([p0, p1], dim=0)
            y2 = torch.mul(y1, y1)
            return y1, y2

        p0 = torch.randn(3, 4)
        p1 = torch.randn(3, 4)
        self.common(fn, (p0, p1))

    def test_pow_cos(self):
        # https://github.com/pytorch/pytorch/issues/98149
        def fn(x):
            t = x.pow(5)
            return torch.cos(t)

        x = torch.tensor([4], dtype=torch.uint8)
        self.common(fn, (x,))

    def test_reduce_with_masked(self):
        # https://github.com/pytorch/pytorch/issues/96484
        def fn(a, b):
            a = torch.nn.functional.pad(a, (0, -1))
            c = a + b
            return c.min(0).values

        a = torch.randn([2])
        b = torch.randn([2])
        self.common(fn, (a, b))

    def test_scalar_sign_with_min(self):
        # https://github.com/pytorch/pytorch/issues/101340
        def fn(a):
            t1 = torch.tanh(a)
            t2 = torch.sign(t1)
            return torch.min(t1, t2)

        a = torch.randn(1, 3)
        self.common(fn, (a,))

    def test_index_propagation_issue_102065(self):
        def fn(x):
            x = torch.arange(x.numel())
            return (x.unsqueeze(0) - x.unsqueeze(1)) ** 2

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

    def test_ModularIndexing_range_issue_103133(self):
        def fn(q, k):
            einsum = torch.einsum("bcxd,bcyd->bcxy", (q, k))
            constant_pad_nd = torch.ops.aten.constant_pad_nd.default(
                einsum, [0, 0, 0, 1], 0.0
            )
            view = torch.ops.aten.view.default(constant_pad_nd, [12, 1, 512, 513])
            y = view.new_zeros((12, 2, 256, 513))
            y[:, :-1, :, 256:] = view[:, :, :256, :257]
            return y

        self.common(
            fn,
            (
                torch.empty_strided((12, 1, 512, 64), (64, 196608, 768, 1)),
                torch.empty_strided((12, 1, 512, 64), (64, 196608, 768, 1)),
            ),
        )

    @patch("torch.cuda.is_available", lambda: False)
    def test_max_reduction_lowp_fp(self):
        def fn(x):
            return torch.ops.aten.max(x, 1, keepdim=True)[0].float()

        for dtype in _lowp_fp_dtypes:
            self.common(
                fn,
                (torch.randn(1, 32, 4, 4).to(dtype),),
            )

    @patch("torch.cuda.is_available", lambda: False)
    def test_vec_transpose_lowp_fp(self):
        for dtype in _lowp_fp_dtypes:

            def fn(x):
                return x.to(memory_format=torch.channels_last).to(dtype)

            self.common(
                fn,
                (torch.randn(2, 3, 4, 4),),
            )

    def test_load_inf_bf16(self):
        def fn1(x):
            return torch.where(x > 0, x, math.inf)

        def fn2(x):
            return torch.where(x > 0, x, -math.inf)

        for fn in [fn1, fn2]:
            self.common(
                fn,
                (torch.randn(1, 3, 16, 16),),
            )

    @patch("torch.cuda.is_available", lambda: False)
    def test_fp32_load_with_to_lowp_fp(self):
        # From llama model.
        class Model(torch.nn.Module):
            def __init__(self):
                super().__init__()
                self.cache_k = torch.zeros(8, 4, 2, 2)

            def forward(self, x, xk):
                bsz, seqlen, _ = x.shape
                self.cache_k = self.cache_k.to(x)
                self.cache_k[:bsz, 1 : 1 + seqlen] = xk
                return self.cache_k

        for dtype in _lowp_fp_dtypes:
            ref_model = Model().eval()
            opt_model = torch.compile()(Model().eval())
            x = torch.randn(4, 2, 2).to(dtype)
            xk = torch.randn(4, 2, 2, 2).to(dtype)
            self.assertEqual(opt_model(x, xk), ref_model(x, xk))

    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    @patch("torch.cuda.is_available", lambda: False)
    def test_sigmoid_with_reduction(self):
        def fn(x):
            x = torch.ops.aten.sigmoid.default(x)
            return torch.ops.aten.mean.dim(x, [-1, -2], True)

        x = torch.randn((1, 8, 8, 8))
        with config.patch({"cpp.simdlen": None}):
            torch._dynamo.reset()
            metrics.reset()
            self.common(fn, (x,))

    def test_slice_scatter_default_end_value(self):
        # From HF AllenaiLongformerBase.
        def fn(query, key, window_overlap):
            batch_size, seq_len, num_heads, head_dim = query.size()
            assert (
                seq_len % (window_overlap * 2) == 0
            ), f"Sequence length should be multiple of {window_overlap * 2}. Given {seq_len}"

            chunks_count = torch.div(seq_len, window_overlap, rounding_mode="trunc") - 1
            diagonal_chunked_attention_scores = key
            diagonal_attention_scores = diagonal_chunked_attention_scores.new_zeros(
                (
                    batch_size * num_heads,
                    chunks_count + 1,
                    window_overlap,
                    window_overlap * 2 + 1,
                )
            )
            diagonal_attention_scores[
                :, :3, :, window_overlap:
            ] = diagonal_chunked_attention_scores[
                :, :, :window_overlap, : window_overlap + 1
            ]
            return diagonal_attention_scores

        self.common(
            fn,
            (
                torch.randn(1, 1024, 12, 64),
                torch.randn(12, 3, 512, 513),
                256,
            ),
        )

    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    @patch("torch.cuda.is_available", lambda: False)
    def test_to_uint8_rounding_method(self):
        def fn(x):
            return x.to(torch.uint8)

        numerical_testsuit = [4.4, 4.5, 4.6, 5.5]
        for numerical_number in numerical_testsuit:
            x = torch.ones(17) * numerical_number
            with config.patch({"cpp.simdlen": None}):
                torch._dynamo.reset()
                metrics.reset()
                self.common(fn, (x,))
                assert metrics.generated_cpp_vec_kernel_count == 1

    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    @patch("torch.cuda.is_available", lambda: False)
    def test_decomposed_dequant_relu_quant(self):
        def fn(x, scale, zero_point, use_dequant, use_quant):
            # For quantized_decomposed.dequantize_per_tensor
            # Refer to torch/ao/quantization/fx/_decomposed.py
            if use_dequant:
                x = (x.to(torch.float32) - zero_point) * scale

            x = torch.relu(x)

            # For quantized_decomposed.quantize_per_tensor
            # Refer to torch/ao/quantization/fx/_decomposed.py
            if use_quant:
                inv_scale = 1.0 / scale
                x = torch.clamp(torch.round(x * inv_scale) + zero_point, 0, 255).to(
                    torch.uint8
                )
            return x

        use_dequant_list = [False, True]
        use_quant_list = [False, True]
        for use_dequant, use_quant in itertools.product(
            use_dequant_list, use_quant_list
        ):
            x = torch.clamp(
                torch.randn((1, 7, 7, 9), dtype=torch.float32) * 100, 0, 255
            )
            if use_dequant:
                x = x.to(torch.uint8)
            zero_point = 100
            scale = 0.01
            with config.patch({"cpp.simdlen": None}):
                torch._dynamo.reset()
                metrics.reset()
                self.common(fn, (x, scale, zero_point, use_dequant, use_quant))
                assert metrics.generated_cpp_vec_kernel_count == 1

    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    @patch("torch.cuda.is_available", lambda: False)
    def test_dequant_quant_lowering(self):
        def fn(x, scale, zero_point, use_dequant, use_quant):
            if use_dequant:
                x = torch.ops.quantized_decomposed.dequantize_per_tensor(
                    x, scale, zero_point, 0, 255, torch.uint8
                )

            x = torch.relu(x)

            if use_quant:
                x = torch.ops.quantized_decomposed.quantize_per_tensor(
                    x, scale, zero_point, 0, 255, torch.uint8
                )
            return x

        use_dequant_list = [False, True]
        use_quant_list = [False, True]
        use_tensor_overload_list = [False, True]
        for use_dequant, use_quant, use_tensor_overload in itertools.product(
            use_dequant_list, use_quant_list, use_tensor_overload_list
        ):
            x = torch.clamp(
                torch.randn((1, 7, 7, 9), dtype=torch.float32) * 100, 0, 255
            )
            if use_dequant:
                x = x.to(torch.uint8)
            zero_point = 100
            scale = 0.01
            if use_tensor_overload:
                zero_point = torch.tensor(zero_point, dtype=torch.int64)
                scale = torch.tensor(scale)
            with config.patch({"cpp.simdlen": None}):
                torch._dynamo.reset()
                metrics.reset()
                self.common(fn, (x, scale, zero_point, use_dequant, use_quant))
                assert metrics.generated_cpp_vec_kernel_count == 1

    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    @patch("torch.cuda.is_available", lambda: False)
    def test_dequant_maxpool2d_lowering(self):
        def fn(x, scale, zero_point):
            x = torch.ops.quantized_decomposed.dequantize_per_tensor(
                x, scale, zero_point, 0, 255, torch.uint8
            )
            max_pool2d_with_indices_default = (
                torch.ops.aten.max_pool2d_with_indices.default(
                    x, [2, 2], [2, 2], [1, 1]
                )[0]
            )
            return max_pool2d_with_indices_default

        use_tensor_overload_list = [False, True]
        for use_tensor_overload in use_tensor_overload_list:
            x = (
                torch.clamp(
                    torch.randn((3, 16, 8, 8), dtype=torch.float32) * 100, 0, 255
                )
                .to(torch.uint8)
                .contiguous(memory_format=torch.channels_last)
            )
            zero_point = 100
            scale = 0.01
            if use_tensor_overload:
                zero_point = torch.tensor(zero_point, dtype=torch.int64)
                scale = torch.tensor(scale)
            with config.patch({"cpp.simdlen": None}):
                torch._dynamo.reset()
                metrics.reset()
                self.common(fn, (x, scale, zero_point))
                assert metrics.generated_cpp_vec_kernel_count == 1

    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    @patch("torch.cuda.is_available", lambda: False)
    def test_tile2d_load_decomposed_dequant_add_relu_quant(self):
        def fn(
            x,
            scale,
            zero_point,
            x2,
            scale2,
            zero_point2,
            output_scale,
            output_zero_point,
            use_dequant,
            use_dequant2,
            use_quant,
        ):
            if use_dequant:
                x = torch.ops.quantized_decomposed.dequantize_per_tensor(
                    x, scale, zero_point, 0, 255, torch.uint8
                )
            if use_dequant2:
                x2 = torch.ops.quantized_decomposed.dequantize_per_tensor(
                    x2, scale2, zero_point2, 0, 255, torch.uint8
                )
            temp = x + x2
            y = torch.relu(temp)

            if use_quant:
                y = torch.ops.quantized_decomposed.quantize_per_tensor(
                    y, output_scale, output_zero_point, 0, 255, torch.uint8
                )
            return y.contiguous()

        use_dequant_list = [False, True]
        use_dequant_list2 = [False, True]
        use_quant_list = [False, True]
        for use_dequant, use_dequant2, use_quant in itertools.product(
            use_dequant_list, use_dequant_list2, use_quant_list
        ):
            x = torch.clamp(
                torch.randn((1, 1024, 14, 14), dtype=torch.float32) * 100, 0, 255
            ).contiguous(memory_format=torch.channels_last)
            x2 = torch.clamp(
                torch.randn((1, 1024, 14, 14), dtype=torch.float32) * 100, 0, 255
            ).contiguous(memory_format=torch.channels_last)
            if use_dequant:
                x = x.to(torch.uint8).contiguous(memory_format=torch.channels_last)
            if use_dequant2:
                x2 = x2.to(torch.uint8).contiguous(memory_format=torch.channels_last)
            zero_point = 1
            scale = 0.01
            zero_point2 = 2
            scale2 = 0.02
            output_zero_point = 3
            output_scale = 0.03
            with config.patch({"cpp.simdlen": None}):
                torch._dynamo.reset()
                metrics.reset()
                self.common(
                    fn,
                    (
                        x,
                        scale,
                        zero_point,
                        x2,
                        scale2,
                        zero_point2,
                        output_scale,
                        output_zero_point,
                        use_dequant,
                        use_dequant2,
                        use_quant,
                    ),
                )
                assert metrics.generated_cpp_vec_kernel_count == 2

    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    @patch("torch.cuda.is_available", lambda: False)
    def test_non_contiguous_load_buf_quant(self):
        def fn(
            x1,
            x2,
            groups,
        ):
            x = torch.cat((x1, x2), dim=1)
            batchsize, num_channels, height, width = x.size()
            channels_per_group = num_channels // groups
            x = torch.ops.quantized_decomposed.dequantize_per_tensor(
                x, 1.0, 0, 0, 255, torch.uint8
            )
            x = x.view(batchsize, groups, channels_per_group, height, width)
            x = torch.ops.quantized_decomposed.quantize_per_tensor(
                x, 1.0, 0, 0, 255, torch.uint8
            )
            x = torch.ops.quantized_decomposed.dequantize_per_tensor(
                x, 1.0, 0, 0, 255, torch.uint8
            )
            x = torch.transpose(x, 1, 2).contiguous()
            x = x.view(batchsize, num_channels, height, width)
            return x

        x = torch.randint(0, 8, (1, 116, 28, 28), dtype=torch.uint8).contiguous(
            memory_format=torch.channels_last
        )
        x2 = torch.randint(0, 8, (1, 116, 28, 28), dtype=torch.uint8).contiguous(
            memory_format=torch.channels_last
        )

        with config.patch({"cpp.simdlen": None}):
            torch._dynamo.reset()
            metrics.reset()
            self.common(
                fn,
                (
                    x,
                    x2,
                    2,
                ),
            )
            assert metrics.generated_cpp_vec_kernel_count == 2

    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    @patch("torch.cuda.is_available", lambda: False)
    def test_tile2d_store_channel_shuffle_cl_quant_output(self):
        def channel_shuffle(x, groups, output_scale, output_zero_point):
            batchsize, num_channels, height, width = x.size()
            channels_per_group = num_channels // groups
            x = x.view(batchsize, groups, channels_per_group, height, width)
            x = torch.transpose(x, 1, 2).contiguous()
            x = x.view(batchsize, -1, height, width)
            x = torch.ops.quantized_decomposed.quantize_per_tensor(
                x, output_scale, output_zero_point, 0, 255, torch.uint8
            )
            return x.contiguous(memory_format=torch.channels_last)

        with config.patch({"cpp.simdlen": None}):
            torch._dynamo.reset()
            metrics.reset()
            x = torch.randn(64, 58, 28, 28)
            output_zero_point = 3
            output_scale = 0.03
            self.common(channel_shuffle, (x, 2, output_scale, output_zero_point))
            assert metrics.generated_cpp_vec_kernel_count == 2

    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    @patch("torch.cuda.is_available", lambda: False)
    def test_dequant_relu_quant_dequant_relu_quant_lowering(self):
        def fn(x, scale, zero_point, scale2, zero_point2, scale3, zero_point3):
            x = torch.ops.quantized_decomposed.dequantize_per_tensor(
                x, scale, zero_point, 0, 255, torch.uint8
            )
            x = torch.relu(x)
            x = torch.ops.quantized_decomposed.quantize_per_tensor(
                x, scale2, zero_point2, 0, 255, torch.uint8
            )
            x = torch.ops.quantized_decomposed.dequantize_per_tensor(
                x, scale2, zero_point2, 0, 255, torch.uint8
            )
            x = torch.relu(x)
            x = torch.ops.quantized_decomposed.quantize_per_tensor(
                x, scale3, zero_point3, 0, 255, torch.uint8
            )
            return x

        for use_tensor_overload in [True, False]:
            x = torch.clamp(
                torch.randn((1, 7, 7, 9), dtype=torch.float32) * 100, 0, 255
            ).to(torch.uint8)
            zero_point_list = [100, 101, 102]
            scale_list = [0.01, 0.02, 0.03]
            if use_tensor_overload:
                for i in range(len(zero_point_list)):
                    zero_point_list[i] = torch.tensor(
                        zero_point_list[i], dtype=torch.int64
                    )
                    scale_list[i] = torch.tensor(scale_list[i])
            zero_point, zero_point2, zero_point3 = zero_point_list
            scale, scale2, scale3 = scale_list
            with config.patch({"cpp.simdlen": None}):
                torch._dynamo.reset()
                metrics.reset()
                self.common(
                    fn,
                    (x, scale, zero_point, scale2, zero_point2, scale3, zero_point3),
                    rtol=1e-2,
                    atol=1e-2,
                )
                assert metrics.generated_cpp_vec_kernel_count == 1

    def test_inplace_add_alpha(self):
        def fn(x, y):
            aten.add_.Tensor(x, y, alpha=0.55)
            return (x,)

        x1 = torch.zeros(10)
        x2 = torch.zeros(10)
        x3 = torch.zeros(10)
        y = torch.randn(10)
        fn_fx = make_fx(fn)(x1, y)
        fn_compiled = compile_fx_inner(fn_fx, [x1, y])
        fn(x2, y)
        fn_compiled([x3, y])
        assert same(x2, x3)

    def test_int_div(self):
        def fn(x, y):
            s3 = x.size(1)
            a = torch.zeros((1 + s3) // 2)
            a += y
            return a, s3

        p0 = torch.randint(5, (1, 8))
        p1 = torch.randn(1)
        self.common(fn, (p0, p1))

    def test_no_op_squeeze(self):
        @torch._dynamo.optimize("inductor")
        def forward(arg0_1):
            return torch.ops.aten.squeeze.dim(arg0_1, 1)

        x = torch.randn((10, 20))
        self.common(forward, (x,))

    def test_parallel_num_threads(self):
        @torch._dynamo.optimize("inductor")
        def fn(x1, x2):
            return x1 + x2

        @contextlib.contextmanager
        def set_num_threads(num_threads):
            orig_num_threads = torch.get_num_threads()
            torch.set_num_threads(num_threads)
            yield
            torch.set_num_threads(orig_num_threads)

        x1 = torch.randn((10, 20))
        x2 = torch.randn((10, 20))
        with set_num_threads(1):
            assert same(x1 + x2, fn(x1, x2))
        with set_num_threads(4):
            assert same(x1 + x2, fn(x1, x2))

    @patch("torch.cuda.is_available", lambda: False)
    def test_timed_cpu_only(self):
        timed(lambda: torch.randn(10), ())

    def test_complex_memory_overlap(self):
        dense = torch.zeros(64, 32)
        self.assertFalse(complex_memory_overlap(dense))
        self.assertFalse(complex_memory_overlap(dense.t()))

        strided = dense.split(4, dim=1)
        self.assertFalse(complex_memory_overlap(strided[0]))
        self.assertFalse(complex_memory_overlap(strided[0].t()))

        unsqueezed = dense.unsqueeze(1)
        self.assertFalse(complex_memory_overlap(unsqueezed))
        self.assertFalse(complex_memory_overlap(unsqueezed.permute(1, 2, 0)))

        gathered = dense.index_select(0, torch.IntTensor([1, 0, 1]))
        self.assertFalse(complex_memory_overlap(gathered))
        self.assertFalse(complex_memory_overlap(gathered.t()))

    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    def test_vec_dynamic_shapes(self):
        def fn(x):
            return torch.softmax(x, -1)

        value = torch.randn((2, 10))
        with config.patch({"cpp.simdlen": None}):
            torch._dynamo.reset()
            metrics.reset()
            self.common(fn, (value,))

    @unittest.skipIf(
        platform.machine() != "x86_64" or not codecache.valid_vec_isa_list(),
        "Does not support vectorization or not x86_64 machine",
    )
    @patch("torch.cuda.is_available", lambda: False)
    def test_auto_simd(self):
        vec_avx512 = codecache.supported_vec_isa_list[0]
        vec_avx2 = codecache.supported_vec_isa_list[1]
        self.assertTrue(vec_avx512.bit_width() == 512)
        self.assertTrue(vec_avx2.bit_width() == 256)
        self.assertTrue(vec_avx512.nelements() == 16)
        self.assertTrue(vec_avx2.nelements() == 8)
        self.assertTrue(vec_avx512.nelements(torch.bfloat16) == 32)
        self.assertTrue(vec_avx2.nelements(torch.bfloat16) == 16)

        with config.patch({"cpp.simdlen": None}):
            isa = codecache.pick_vec_isa()
            if vec_avx512 in codecache.valid_vec_isa_list():
                self.assertTrue(isa == vec_avx512)
            else:
                self.assertTrue(isa == vec_avx2)

        with config.patch({"cpp.simdlen": 0}):
            isa = codecache.pick_vec_isa()
            self.assertFalse(isa)

        with config.patch({"cpp.simdlen": 1}):
            isa = codecache.pick_vec_isa()
            self.assertFalse(isa)

        with config.patch({"cpp.simdlen": 257}):
            isa = codecache.pick_vec_isa()
            self.assertFalse(isa)

        with config.patch({"cpp.simdlen": 513}):
            isa_list = codecache.valid_vec_isa_list()
            if vec_avx512 in isa_list:
                self.assertFalse(isa)

        with config.patch({"cpp.simdlen": 512}):
            isa_list = codecache.valid_vec_isa_list()
            if vec_avx512 in isa_list:
                isa = codecache.pick_vec_isa()
                self.assertTrue(isa == vec_avx512)

        with config.patch({"cpp.simdlen": 256}):
            isa_list = codecache.valid_vec_isa_list()
            if vec_avx2 in isa_list:
                isa = codecache.pick_vec_isa()
                self.assertTrue(isa == vec_avx2)

    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    @patch("torch.cuda.is_available", lambda: False)
    def test_masked_fill_softmax(self):
        def fn(value, mask):
            mask = mask.to(torch.bool)
            x = torch.masked_fill(value, mask, -33.0)
            return torch.softmax(x, -1)

        for dtype in vec_dtypes:
            value = torch.randn((2, 17), dtype=dtype)
            mask = torch.randint(0, 1, size=(2, 17), dtype=torch.uint8)
            with config.patch({"cpp.simdlen": None}):
                for cpp_wrapper_flag in [True, False]:
                    with config.patch({"cpp_wrapper": cpp_wrapper_flag}):
                        torch._dynamo.reset()
                        metrics.reset()
                        self.common(fn, (value, mask))
                        assert metrics.generated_cpp_vec_kernel_count >= 1

    def test_load_same_bool_tensor_twice(self):
        @torch._dynamo.optimize("inductor")
        def fn(a, b):
            x = torch.masked_fill(a, b, -33.0)
            y = torch.masked_fill(a, b, -33.0)
            return x, y

        value = torch.randn((2, 17))
        mask = torch.randint(0, 1, size=(2, 17), dtype=torch.uint8).to(torch.bool)
        fn(value, mask)

    def test_cpu_vec_cosim(self):
        cpp_vec_op_list = []
        cpp_op_list = []

        for k, v in CppVecOverrides.__dict__.items():
            if isinstance(v, staticmethod):
                cpp_vec_op_list.append(k)
        for k, v in CppOverrides.__dict__.items():
            if isinstance(v, staticmethod):
                cpp_op_list.append(k)

        diff = [
            "constant",
            "index_expr",
            "signbit",
            "isinf",
            "mod",
            "masked",
            "randn",
            "isnan",
            "rand",
            "randint64",
            "logical_and",
            "logical_not",
            "logical_or",
            "logical_xor",
            "bitwise_and",
            "bitwise_left_shift",
            "bitwise_not",
            "bitwise_right_shift",
            "bitwise_or",
            "bitwise_xor",
            "to_dtype_bitcast",
        ]
        union = {*cpp_vec_op_list, *diff}
        self.assertTrue(
            set(cpp_op_list).issubset(union), f"unexpected: {set(cpp_op_list) - union}"
        )

    def test_atomic_add_lowp_fp(self):
        def fn(test_args):
            res = torch.gather(**test_args)
            return res

        for dtype in _lowp_fp_dtypes:
            input_tensor_for_ref = torch.tensor(
                [[3.0, -5.0]], dtype=dtype, requires_grad=True
            )
            input_tensor_for_opt = torch.tensor(
                [[3.0, -5.0]], dtype=dtype, requires_grad=True
            )

            test_args_for_ref = {
                "input": input_tensor_for_ref,
                "dim": 1,
                "index": torch.tensor([[1]]),
            }
            test_args_for_opt = {
                "input": input_tensor_for_opt,
                "dim": 1,
                "index": torch.tensor([[1]]),
            }

            opt_fn = torch.compile(fn)

            ref_fwd = fn(test_args_for_ref)
            res_fwd = opt_fn(test_args_for_opt)
            self.assertEqual(res_fwd, ref_fwd)

            torch.manual_seed(1)
            bwd_tensor_for_ref = torch.randn(ref_fwd.shape, dtype=dtype)
            torch.manual_seed(1)
            bwd_tensor_for_opt = torch.randn(res_fwd.shape, dtype=dtype)
            self.assertEqual(bwd_tensor_for_ref, bwd_tensor_for_opt)

            ref_fwd.backward(bwd_tensor_for_ref)
            res_fwd.backward(bwd_tensor_for_opt)

            ref_grad = test_args_for_ref["input"].grad
            res_grad = test_args_for_opt["input"].grad
            self.assertEqual(ref_grad, res_grad)

    @patch("torch.cuda.is_available", lambda: False)
    def test_scatter_using_atomic_add(self):
        def fn(a, dim, index, b):
            return aten.scatter(a, dim, index, b, reduce="add")

        inps = (
            torch.randn(5, 29, 13),
            2,
            torch.tensor([[[3, 5, 7, 9]]]),
            torch.randn(1, 1, 10),
        )

        fn_opt = torch.compile()(fn)
        with config.patch({"cpp.fallback_scatter_reduce_sum": False}):
            _, code = run_and_get_cpp_code(fn_opt, *inps)
            FileCheck().check("atomic_add").run(code)

            self.assertEqual(
                fn(*inps),
                fn_opt(*inps),
            )

    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    @patch("torch.cuda.is_available", lambda: False)
    def test_new_vec_op_cpu_only(self):
        def fn(x):
            return torch.log1p(torch.expm1(torch.erf(x)))

        for dtype in vec_dtypes:
            torch.manual_seed(0)
            x = torch.randn((2, 9), dtype=dtype)
            x[0, 0] = torch.nan
            x[1, -1] = torch.nan

            tol = 1e-2 if dtype == torch.bfloat16 else 1e-4

            with config.patch({"cpp.simdlen": None}):
                for cpp_wrapper_flag in [True, False]:
                    with config.patch({"cpp_wrapper": cpp_wrapper_flag}):
                        torch._dynamo.reset()
                        metrics.reset()
                        self.common(fn, (x,))
                        assert metrics.generated_cpp_vec_kernel_count == 1

    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    @patch("torch.cuda.is_available", lambda: False)
    def test_vec_cpu_only_for_all_available_isa(self):
        def fn(x):
            return torch.sin(torch.cos(torch.erf(x)))

        x = torch.randn((2, 9))
        x[0, 0] = torch.nan
        x[1, -1] = torch.nan

        bit_widths = [isa._bit_width for isa in codecache.valid_vec_isa_list()] + [None]
        for item in bit_widths:
            with config.patch({"cpp.simdlen": item}):
                torch._dynamo.reset()
                metrics.reset()
                self.common(fn, (x,))
                assert metrics.generated_cpp_vec_kernel_count == 1

    @slowTest
    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    @patch("torch.cuda.is_available", lambda: False)
    def test__adaptive_avg_pool2d(self):
        def wrap_fn(oh, ow):
            def fn(x):
                return torch._adaptive_avg_pool2d(x, (oh, ow))

            return fn

        bit_widths = [isa._bit_width for isa in codecache.valid_vec_isa_list()]
        ih = [16, 65]
        iw = ih
        oh = ih
        ow = ih
        for _ih, _iw, _oh, _ow, _simd_len, dtype in itertools.product(
            ih, iw, oh, ow, bit_widths, vec_dtypes
        ):
            x = torch.randn(2, 3, _ih, _iw, dtype=dtype).to(
                memory_format=torch.channels_last
            )
            _fn = wrap_fn(_oh, _ow)
            with config.patch({"cpp.simdlen": _simd_len}):
                torch._dynamo.reset()
                metrics.reset()
                self.common(_fn, (x,))
                assert metrics.generated_cpp_vec_kernel_count == 1

    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    @patch("torch.cuda.is_available", lambda: False)
    def test_vec_logical(self):
        def wrap_fn1(op: Callable):
            def fn(x: torch.Tensor):
                return torch.where(op(x), 1.0, 0.0)

            return fn

        def wrap_fn2(op: Callable):
            def fn(x: torch.Tensor, y: torch.Tensor):
                return torch.where(op(x, y), 1.0, 0.0)

            return fn

        for dtype in vec_dtypes:
            x = torch.randn(64, dtype=dtype)
            y = torch.randn(64, dtype=dtype)
            logical_fns = [
                torch.logical_and,
                torch.logical_not,
                torch.logical_or,
                torch.logical_xor,
            ]
            for logical_fn in logical_fns:
                torch._dynamo.reset()
                metrics.reset()
                if logical_fn == torch.logical_not:
                    _fn = wrap_fn1(logical_fn)
                    _args = (x,)
                else:
                    _fn = wrap_fn2(logical_fn)
                    _args = (x, y)
                self.common(_fn, _args)
                assert metrics.generated_cpp_vec_kernel_count == 1

    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    @patch("torch.cuda.is_available", lambda: False)
    def test_vec_compare_op_cpu_only(self):
        def fn(x):
            y1 = torch.eq(x, 1.0)
            x = torch.where(y1, x, -x)
            y2 = torch.ne(x, 0.0)
            x = torch.where(y2, x, -x)
            y3 = torch.lt(x, 5.0)
            x = torch.where(y3, x, x - 1.0)
            y4 = torch.gt(x, -2.0)
            x = torch.where(y4, x, x + 1.0)
            y5 = torch.le(x, 8.0)
            x = torch.where(y5, x, x - 1.0)
            y6 = torch.ge(x, -3.0)
            x = torch.where(y6, x, x + 1.0)
            y7 = x == 1.0
            x = torch.where(y7, x, -x)
            y8 = x != 0.0
            x = torch.where(y8, x, -x)
            y9 = x < 5.0
            x = torch.where(y9, x, x - 1.0)
            y10 = x > -2.0
            x = torch.where(y10, x, x + 1.0)
            y11 = x <= 8.0
            x = torch.where(y11, x, x - 1.0)
            y12 = x >= -3.0
            x = torch.where(y12, x, x + 1.0)
            return x

        for dtype in vec_dtypes:
            x = torch.randn((2, 9), dtype=dtype)

            with config.patch({"cpp.simdlen": None}):
                torch._dynamo.reset()
                metrics.reset()
                self.common(fn, (x,))
                assert metrics.generated_cpp_vec_kernel_count == 1
                assert (
                    metrics.generated_kernel_count
                    - metrics.generated_cpp_vec_kernel_count
                ) == 0

    def test_skip_cpp_codegen(self):
        with config.patch({"disable_cpp_codegen": True}):
            inps = torch.ones([20]), torch.rand([20])

            def f(x, y):
                return x + y + torch.tensor(1)

            f_opt = torch.compile()(f)

            _, code = run_and_get_cpp_code(f_opt, inps[0], inps[1])
            FileCheck().check_not("void kernel").run(code)

            self.assertEqual(
                f(*inps),
                f_opt(*inps),
            )

            # constant needs to be propagated on fallback
            def f(x):
                return x[torch.tensor(1) :] * 2

            f_opt = torch.compile()(f)
            _, code = run_and_get_cpp_code(f_opt, inps[0])
            FileCheck().check_not("void kernel").run(code)
            self.assertEqual(f_opt(inps[0]), f(inps[0]))

            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)
            model_f = torch.compile()(model)

            self.assertEqual(model(x), model_f(x))

    def test_redundant_to_node_elimination_lowp_fp(self):
        def fn(x, y):
            res = x + y
            res = torch.mean(res)
            return res

        for dtype in _lowp_fp_dtypes:
            x = torch.randn((2, 9), dtype=dtype)
            y = torch.randn((2, 9), dtype=dtype)

            for torch_compile_debug in [True, False]:
                with config.patch(
                    {"trace.enabled": torch_compile_debug, "cpp.simdlen": None}
                ):
                    torch._dynamo.reset()
                    metrics.reset()
                    self.common(fn, (x, y))
                    if codecache.valid_vec_isa_list():
                        assert metrics.generated_cpp_vec_kernel_count == 1

    def test_do_not_insert_to_dtype_for_memory_copy_only_kernel(self):
        def fn(x):
            res = x.clone()
            return res

        x = torch.randn((100, 100), dtype=torch.bfloat16)

        torch._dynamo.reset()
        metrics.reset()
        self.common(fn, (x,))
        assert metrics.cpp_to_dtype_count == 0
        if codecache.valid_vec_isa_list():
            assert metrics.generated_cpp_vec_kernel_count == 1

    def test_insert_to_dtype_count(self):
        def fn(x):
            res = x.relu()
            return res

        x = torch.randn((100, 100), dtype=torch.bfloat16)

        torch._dynamo.reset()
        metrics.reset()
        self.common(fn, (x,))
        assert metrics.cpp_to_dtype_count == 2
        if codecache.valid_vec_isa_list():
            assert metrics.generated_cpp_vec_kernel_count == 1

    def test_memory_copy_with_fusion(self):
        def fn(x):
            res = x.relu()
            x.copy_(res)
            return (res,)

        x = torch.randn((100, 100), dtype=torch.bfloat16)

        torch._dynamo.reset()
        metrics.reset()
        self.common(fn, (x,))
        assert metrics.cpp_to_dtype_count == 2
        if codecache.valid_vec_isa_list():
            assert metrics.generated_cpp_vec_kernel_count == 1

    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    @patch("torch.cuda.is_available", lambda: False)
    def test_cpp_vec_constant_checker(self):
        _graph: torch.fx.Graph = torch.fx.Graph()
        a: torch.fx.Node = _graph.create_node("placeholder", "ops")
        iv: torch.fx.Node = _graph.create_node("placeholder", "iv")
        fv: torch.fx.Node = _graph.create_node("placeholder", "fv")
        b: torch.fx.Node = _graph.create_node(
            "call_method",
            "constant",
            args=(
                a,
                iv,
                torch.int64,
            ),
        )
        c: torch.fx.Node = _graph.create_node(
            "call_method",
            "constant",
            args=(
                a,
                fv,
                torch.double,
            ),
        )
        d: torch.fx.Node = _graph.create_node(
            "call_method",
            "ge",
            args=(
                a,
                b,
                b,
            ),
        )
        _graph.output((d, c))

        def get_index():
            return ""

        submodules = {"get_index": get_index}

        graph_lowering = GraphLowering(
            torch.fx.GraphModule(submodules, _graph),
            shape_env=None,
            num_static_inputs=0,
        )

        def set_opt_dtype(graph):
            for node in graph.nodes:
                if node.target == "constant":
                    if OptimizationContext.key in node.meta:
                        opt_ctx = node.meta[OptimizationContext.key]
                    else:
                        opt_ctx = OptimizationContext()
                    opt_ctx.dtype = node.args[-1]
                    node.meta[OptimizationContext.key] = opt_ctx

        with patch.object(graph_lowering, "wrapper_code", ""), V.set_graph_handler(
            graph_lowering
        ):
            # The moset inner loop variable is used in the index_expr
            tiling_factor = codecache.pick_vec_isa().nelements(dtype=torch.float)
            with CppVecKernelChecker(
                args=None, num_threads=1, tiling_factor=tiling_factor
            ) as vec_checker:
                i32_iinfo = np.iinfo(np.int32)
                f32_iinfo = np.finfo(np.float32)
                set_opt_dtype(_graph)
                InterpreterShim(_graph, submodules).run(
                    V.get_ops_handler(), i32_iinfo.max, f32_iinfo.max
                )
                self.assertTrue(vec_checker.simd_vec)

                vec_checker.simd_vec = True
                set_opt_dtype(_graph)
                InterpreterShim(_graph, submodules).run(
                    V.get_ops_handler(), i32_iinfo.min, f32_iinfo.min
                )
                self.assertTrue(vec_checker.simd_vec)

                vec_checker.simd_vec = True
                set_opt_dtype(_graph)
                InterpreterShim(_graph, submodules).run(
                    V.get_ops_handler(), i32_iinfo.min, np.inf
                )
                self.assertTrue(vec_checker.simd_vec)

                vec_checker.simd_vec = True
                set_opt_dtype(_graph)
                InterpreterShim(_graph, submodules).run(
                    V.get_ops_handler(), i32_iinfo.min, -np.inf
                )
                self.assertTrue(vec_checker.simd_vec)

                vec_checker.simd_vec = True
                set_opt_dtype(_graph)
                InterpreterShim(_graph, submodules).run(
                    V.get_ops_handler(), i32_iinfo.min - 1, f32_iinfo.min
                )
                self.assertFalse(vec_checker.simd_vec)

                vec_checker.simd_vec = True
                set_opt_dtype(_graph)
                InterpreterShim(_graph, submodules).run(
                    V.get_ops_handler(), i32_iinfo.max + 1, f32_iinfo.max
                )
                self.assertFalse(vec_checker.simd_vec)

                vec_checker.simd_vec = True
                set_opt_dtype(_graph)
                InterpreterShim(_graph, submodules).run(
                    V.get_ops_handler(), i32_iinfo.min, f32_iinfo.min * (1 + 1e-5)
                )
                self.assertFalse(vec_checker.simd_vec)

                vec_checker.simd_vec = True
                set_opt_dtype(_graph)
                InterpreterShim(_graph, submodules).run(
                    V.get_ops_handler(), i32_iinfo.max, f32_iinfo.max * (1 + 1e-5)
                )
                self.assertFalse(vec_checker.simd_vec)

    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    @patch("torch.cuda.is_available", lambda: False)
    def test_cpp_vec_index_expr_checker(self):
        _graph: torch.fx.Graph = torch.fx.Graph()
        a: torch.fx.Node = _graph.create_node("placeholder", "ops")
        b: torch.fx.Node = _graph.create_node("call_module", "get_index", args=())
        c: torch.fx.Node = _graph.create_node(
            "call_method",
            "index_expr",
            args=(
                a,
                b,
                torch.int64,
            ),
        )
        d: torch.fx.Node = _graph.create_node(
            "call_method",
            "ge",
            args=(
                a,
                c,
                c,
            ),
        )
        _graph.output(d)

        def get_index():
            return ""

        submodules = {"get_index": get_index}
        graph_lowering = GraphLowering(
            torch.fx.GraphModule(submodules, _graph),
            shape_env=None,
            num_static_inputs=0,
        )
        with patch.object(graph_lowering, "wrapper_code", ""), V.set_graph_handler(
            graph_lowering
        ):
            itervars = [sympy.Symbol("i"), sympy.Symbol("j"), sympy.Symbol("k")]

            tiling_factor = codecache.pick_vec_isa().nelements(dtype=torch.float)
            # The most inner loop variable is used in the index_expr
            with CppVecKernelChecker(
                args=None, num_threads=1, tiling_factor=tiling_factor
            ) as vec_checker:

                def get_index():
                    return -itervars[0] ** 2 + 2 * itervars[0] + itervars[1]

                ranges = [0, 100, 200]
                vec_checker.itervars = itervars[:2]
                vec_checker.ranges = ranges[:2]
                submodules = {"get_index": get_index}
                InterpreterShim(_graph, submodules).run(V.get_ops_handler())
                self.assertTrue(vec_checker.simd_vec)

            # Most inner loop variable irrevalant
            with CppVecKernelChecker(
                args=None, num_threads=1, tiling_factor=tiling_factor
            ) as vec_checker:

                def get_index():
                    return -itervars[0] ** 2 + 2 * itervars[0] + itervars[1]

                ranges = [0, 100, 200]
                vec_checker.itervars = itervars
                vec_checker.ranges = ranges
                submodules = {"get_index": get_index}
                InterpreterShim(_graph, submodules).run(V.get_ops_handler())
                self.assertTrue(vec_checker.simd_vec)

            i32_iinfo = np.iinfo(np.int32)
            _max_value = i32_iinfo.max + 1
            ranges = [_max_value, _max_value, _max_value]
            # Most inner loop variable irrevalant but max value is greater than
            # the max value of INT32
            with CppVecKernelChecker(
                args=None, num_threads=1, tiling_factor=tiling_factor
            ) as vec_checker:

                def get_index():
                    return itervars[0]

                submodules = {"get_index": get_index}
                vec_checker.itervars = itervars
                vec_checker.ranges = ranges
                InterpreterShim(_graph, submodules).run(V.get_ops_handler())
                self.assertFalse(vec_checker.simd_vec)

            # Most inner loop variable irrevalant but min value is greater than
            # the min value of INT32
            with CppVecKernelChecker(
                args=None, num_threads=1, tiling_factor=tiling_factor
            ) as vec_checker:

                def get_index():
                    return -itervars[0] - 2

                submodules = {"get_index": get_index}
                vec_checker.itervars = itervars
                vec_checker.ranges = ranges
                InterpreterShim(_graph, submodules).run(V.get_ops_handler())
                self.assertFalse(vec_checker.simd_vec)

    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    @patch("torch.cuda.is_available", lambda: False)
    def test_maxpool2d_cpu_only(self):
        for dtype in vec_dtypes:
            input = torch.randn(26, 32, 112, 112, dtype=dtype).to(
                memory_format=torch.channels_last
            )
            maxpool = torch.nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

            def func(x):
                return maxpool(x)

            with patch.object(config.cpp, "simdlen", None):
                torch._dynamo.reset()
                metrics.reset()
                self.common(func, (input,))
                assert metrics.generated_cpp_vec_kernel_count == 1

    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    @patch("torch.cuda.is_available", lambda: False)
    def test_maxpool2d_with_pre_loop_collapse_cpu_only(self):
        x1 = torch.randn(2, 3, 20, 20).to(memory_format=torch.channels_last)
        x2 = torch.randn(2, 3, 20, 20).to(memory_format=torch.channels_last)
        maxpool = torch.nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True)

        def func(x1, x2):
            y = x1 + x2
            return maxpool(y)

        with patch.object(config.cpp, "simdlen", None):
            torch._dynamo.reset()
            metrics.reset()
            self.common(func, (x1, x2))
            assert metrics.generated_cpp_vec_kernel_count == 2

    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    @patch("torch.cuda.is_available", lambda: False)
    def test_sign_cpu_only(self):
        def fn(x):
            return torch.sign(x)

        for dtype in vec_dtypes:
            x = torch.randn((2, 9), dtype=dtype)
            x[0, 0] = torch.nan
            x[1, -1] = torch.nan

            with config.patch({"cpp.simdlen": None}):
                torch._dynamo.reset()
                metrics.reset()
                self.common(fn, (x,))
                assert metrics.generated_cpp_vec_kernel_count == 1

    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    @patch("torch.cuda.is_available", lambda: False)
    def test_reduction_cpu_only(self):
        def fn(x):
            return torch.argmax(x, -1)

        for dtype in vec_dtypes:
            x = torch.randn((10, 10), dtype=dtype)

            with config.patch({"cpp.simdlen": None}):
                torch._dynamo.reset()
                metrics.reset()
                self.common(fn, (x,))
                assert metrics.generated_cpp_vec_kernel_count == 0

    # Currently, we enabled AVX2 and AVX512 for vectorization. If the platform is not
    # supported, the vectorization will not work and skip this test case. For ARM or
    # other platforms support, we just need to add the ISA info to the supported_vector_isa
    # and include proper aten vectorization head file.
    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    @patch("torch.cuda.is_available", lambda: False)
    def test_vec_kernel_cpu_only(self):
        def fn(x1, x2):
            # Current, there are some limitations as follows.
            #   rsqrt:
            #     assert [both a fallback and a decomp for same kernel: aten.rsqrt.default]
            #   round:
            #     couldn't find symbolic meta function/decomposition
            #   fmod/logical_and/logic_or:
            #     vec kernel has not support to_type
            x = torch.abs(x1)
            x = torch.sin(x)
            x = torch.neg(x)
            x = torch.square(x)
            x = torch.sigmoid(x)
            x = torch.relu(x)
            x = torch.cos(x)
            x = torch.exp(x)
            x = torch.sqrt(x)
            x = torch.add(x, x1)
            x = torch.sub(x, x2)
            x = torch.mul(x, x1)
            x = torch.div(x, x1)
            x = torch.pow(x, 10)
            x = torch.log(x)
            x = torch.floor(x)
            x = torch.ceil(x)
            x = torch.trunc(x)
            x = torch.lgamma(x)
            x = torch.fmod(x, x2)
            x = torch.sign(x)
            res = x + x2
            return res

        for dtype in vec_dtypes:
            torch.manual_seed(0)
            x1 = torch.randn((5, 20), dtype=dtype)
            x2 = torch.randn((5, 20), dtype=dtype)

            tol = 1e-2 if dtype == torch.bfloat16 else 1e-4
            with config.patch({"cpp.simdlen": 1}):
                torch._dynamo.reset()
                metrics.reset()
                self.common(fn, (x1, x2))
                assert metrics.generated_cpp_vec_kernel_count == 0

            with config.patch({"cpp.simdlen": None}):
                torch._dynamo.reset()
                metrics.reset()
                self.common(fn, (x1, x2))
                assert metrics.generated_cpp_vec_kernel_count == 1

        with config.patch({"cpp.simdlen": None}):
            torch._dynamo.reset()
            metrics.reset()
            x1 = torch.randn(10, 20).permute(1, 0)
            x2 = torch.randn((20, 10))
            self.common(fn, (x1, x2))
            assert metrics.generated_cpp_vec_kernel_count == 2

            torch._dynamo.reset()
            metrics.reset()
            x1 = torch.randn((10, 7))
            x2 = torch.randn((10, 7))
            self.common(fn, (x1, x2))
            assert metrics.generated_cpp_vec_kernel_count == 1

    @unittest.skipIf(
        sys.platform != "linux", "cpp kernel profile only support linux now"
    )
    @patch("torch.cuda.is_available", lambda: False)
    @config.patch({"cpp.enable_kernel_profile": True})
    @config.patch({"cpp.descriptive_names": "original_aten"})
    def test_cpp_kernel_profile(self):
        from torch.profiler import profile

        @torch._dynamo.optimize("inductor", nopython=True)
        def fn(a, b):
            return a + b

        a = torch.rand((100,))
        b = torch.rand((100,))
        with profile() as prof:
            fn(a, b)

        kernel_profile_events = []
        for e in prof.profiler.function_events:
            if "cpp_fused_add_0" in e.name:
                kernel_profile_events.append(e.name)
        assert len(kernel_profile_events) > 0

    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    def test_channel_shuffle_cl_output(self):
        """code and shape extracted from shufflenet_v2_x1_0"""

        def channel_shuffle(x, groups):
            batchsize, num_channels, height, width = x.size()
            channels_per_group = num_channels // groups
            x = x.view(batchsize, groups, channels_per_group, height, width)
            x = torch.transpose(x, 1, 2).contiguous()
            x = x.view(batchsize, -1, height, width)
            return x.contiguous(memory_format=torch.channels_last)

        for simdlen in (None, 256, 1):
            with config.patch({"cpp.simdlen": simdlen}):
                torch._dynamo.reset()
                metrics.reset()
                x = torch.randn(64, 58, 28, 28)
                self.common(channel_shuffle, (x, 2))
                if simdlen != 1:
                    assert metrics.generated_cpp_vec_kernel_count == 2

    @slowTest
    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    def test_transpose_with_norm(self):
        """a sub-module from TIMM gmlp_s16_224"""

        class Model(torch.nn.Module):
            def __init__(self):
                super().__init__()
                self.linear = torch.nn.Linear(
                    in_features=256, out_features=1536, bias=True
                )
                self.act = torch.nn.GELU()
                self.norm = torch.nn.LayerNorm(768)
                self.proj = torch.nn.Linear(196, 196)
                self.fc = torch.nn.Linear(in_features=768, out_features=256, bias=True)

            def forward(self, x):
                x = self.linear(x)
                x = self.act(x)
                u, v = x.chunk(2, dim=-1)
                v = self.norm(v)
                v = self.proj(v.transpose(-1, -2))
                y = u * v.transpose(-1, -2)
                return self.fc(y)

        x = torch.randn(128, 196, 256)
        for simdlen in (None, 256, 1):
            with config.patch({"cpp.simdlen": simdlen}):
                for eval_mode in [True, False]:
                    torch._dynamo.reset()
                    metrics.reset()
                    m = Model().eval() if eval_mode else Model()
                    self.common(m, (x,))
                    if simdlen != 1:
                        assert metrics.generated_cpp_vec_kernel_count == 8

    @unittest.skipIf(
        not codecache.valid_vec_isa_list(), "Does not support vectorization"
    )
    def test_transpose_copy(self):
        def fn(a):
            return a.t().contiguous()

        for simdlen in (None, 256, 1):
            with config.patch({"cpp.simdlen": simdlen}):
                for dtype in (torch.float, torch.bfloat16):
                    for shape in (
                        (7, 7),
                        (8, 8),
                        (9, 9),
                        (16, 16),
                        (17, 17),
                        (32, 32),
                        (33, 33),
                    ):
                        torch._dynamo.reset()
                        metrics.reset()
                        x = torch.randn(shape, dtype=dtype)
                        self.common(fn, (x,))
                        if simdlen != 1:
                            assert metrics.generated_cpp_vec_kernel_count == 2

    def test_horizontal_fusion(self):
        def fn(a, b, c, idx):
            _a = torch.index_select(a, dim=0, index=idx)
            _b = torch.index_select(b, dim=0, index=idx)
            _c = torch.index_select(c, dim=0, index=idx)
            return _a, _b, _c

        with config.patch({"cpp.max_horizontal_fusion_size": 0}):
            metrics.reset()
            torch._dynamo.reset()
            a = torch.randn(size=(4, 16), dtype=torch.bfloat16)
            b = torch.randn(size=(4, 16), dtype=torch.bfloat16)
            c = torch.randn(size=(4, 16), dtype=torch.bfloat16)
            idx = torch.zeros(size=[4], dtype=torch.int64)
            opt_fn = torch._dynamo.optimize("inductor")(fn)
            opt_fn(a, b, c, idx)
            self.assertEqual(metrics.generated_kernel_count, 3)
            self.assertTrue(same(fn(a, b, c, idx), opt_fn(a, b, c, idx)))

        with config.patch({"cpp.max_horizontal_fusion_size": 1}):
            metrics.reset()
            torch._dynamo.reset()
            a = torch.randn(size=(4, 32), dtype=torch.bfloat16)
            b = torch.randn(size=(4, 32), dtype=torch.bfloat16)
            c = torch.randn(size=(4, 32), dtype=torch.bfloat16)
            idx = torch.zeros(size=[4], dtype=torch.int64)
            opt_fn = torch._dynamo.optimize("inductor")(fn)
            opt_fn(a, b, c, idx)
            self.assertEqual(metrics.generated_kernel_count, 3)
            self.assertTrue(same(fn(a, b, c, idx), opt_fn(a, b, c, idx)))

        with config.patch({"cpp.max_horizontal_fusion_size": 2}):
            metrics.reset()
            torch._dynamo.reset()
            a = torch.randn(size=(4, 64), dtype=torch.bfloat16)
            b = torch.randn(size=(4, 64), dtype=torch.bfloat16)
            c = torch.randn(size=(4, 64), dtype=torch.bfloat16)
            idx = torch.zeros(size=[4], dtype=torch.int64)
            opt_fn = torch._dynamo.optimize("inductor")(fn)
            opt_fn(a, b, c, idx)
            print(metrics.generated_kernel_count)
            self.assertEqual(metrics.generated_kernel_count, 2)
            self.assertTrue(same(fn(a, b, c, idx), opt_fn(a, b, c, idx)))

        with config.patch({"cpp.max_horizontal_fusion_size": 3}):
            metrics.reset()
            torch._dynamo.reset()
            a = torch.randn(size=(4, 128), dtype=torch.bfloat16)
            b = torch.randn(size=(4, 128), dtype=torch.bfloat16)
            c = torch.randn(size=(4, 128), dtype=torch.bfloat16)
            idx = torch.zeros(size=[4], dtype=torch.int64)
            opt_fn = torch._dynamo.optimize("inductor")(fn)
            opt_fn(a, b, c, idx)
            self.assertEqual(metrics.generated_kernel_count, 1)
            self.assertTrue(same(fn(a, b, c, idx), opt_fn(a, b, c, idx)))

    def test_lowp_fp_neg_abs(self):
        def fn(x):
            return x.neg().abs()

        for dtype in _lowp_fp_dtypes:
            metrics.reset()
            x = torch.randn(100, 100).to(dtype)
            opt_fn = torch._dynamo.optimize("inductor")(fn)
            self.assertTrue(same(fn(x), opt_fn(x)))
            assert metrics.cpp_to_dtype_count == 0
            assert metrics.generated_cpp_vec_kernel_count == 1

    def test_transpose_non_contiguous(self):
        def fn(a):
            # From part of timm HaloAttn:
            # (https://github.com/rwightman/pytorch-image-models/blob/main/timm/layers/halo_attn.py#L97).
            # Fixed https://github.com/pytorch/pytorch/issues/94269 accuracy issue.
            as_strided = torch.ops.aten.as_strided.default(
                a, [1, 384, 2, 20, 12], [153600, 1, 61440, 384, 7680]
            )
            as_strided_1 = torch.ops.aten.as_strided.default(
                as_strided,
                [1, 384, 2, 2, 12, 12],
                [153600, 1, 61440, 3072, 7680, 384],
            )
            clone_1 = torch.ops.aten.clone.default(
                as_strided_1, memory_format=torch.contiguous_format
            )
            _unsafe_view_1 = torch.ops.aten._unsafe_view.default(
                clone_1, [8, 48, 4, 144]
            )
            permute_2 = torch.ops.aten.permute.default(_unsafe_view_1, [0, 2, 3, 1])
            split_with_sizes = torch.ops.aten.split_with_sizes.default(
                permute_2, [16, 32], -1
            )
            getitem = split_with_sizes[0]
            getitem_1 = split_with_sizes[1]
            permute_3 = torch.ops.aten.permute.default(getitem, [0, 1, 3, 2])
            expand_1 = torch.ops.aten.expand.default(permute_3, [8, 4, 16, 144])
            clone_3 = torch.ops.aten.clone.default(
                expand_1, memory_format=torch.contiguous_format
            )
            return clone_3

        metrics.reset()
        x = torch.randn(1, 384, 20, 20).to(memory_format=torch.channels_last)
        self.common(fn, (x,))
        assert metrics.generated_cpp_vec_kernel_count == 1

    def test_non_contiguous_index_with_constant_stride(self):
        def fn(x):
            x1 = x[:, :, :, ::2]
            x2 = x[:, :, :, 1::2]
            x = torch.stack((-x2, x1), dim=-1)
            return x.flatten(-2)

        metrics.reset()
        x = torch.randn(1, 32, 16, 68)
        opt_fn = torch._dynamo.optimize("inductor")(fn)
        _, code = run_and_get_cpp_code(opt_fn, x)
        self.assertTrue(same(fn(x), opt_fn(x)))
        # def and use
        FileCheck().check_count("cpp_fused", 2, exactly=True).run(code)

    def test_invalid_index_of_empty_tensor(self):
        def fn(a):
            b = a[[0]]
            return b

        a = torch.tensor([])
        with self.assertRaises(RuntimeError):
            torch.compile(fn)(a)

    def test_ir_node_str(self):
        @torch.compile
        def fn(x: torch.Tensor) -> torch.Tensor:
            return x.sin(), torch.nn.Softmax(dim=1)(x.cos())

        def run_node_alt(*args, **kwargs):
            rv = run_node(*args, **kwargs)
            strings.append(str(rv))
            return rv

        strings = []
        run_node = GraphLowering.run_node
        with patch.object(GraphLowering, "run_node", run_node_alt):
            fn(torch.randn([8, 128]))
        self.assertGreater(len(strings), 3)

    def test_vertical_sum_cpu_only(self):
        def fn1(a):
            return a.sum(dim=0)

        def fn2(a):
            return a.sum(dim=1)

        metrics.reset()
        x = torch.randn(100, 100)
        self.common(fn1, (x,))
        assert metrics.generated_cpp_vec_kernel_count == 1

        metrics.reset()
        x = torch.randn(100, 100, 100)
        self.common(fn2, (x,))
        assert metrics.generated_cpp_vec_kernel_count == 1

    def test_transpose_vertical_sum_cpu_only(self):
        def fn(a, b):
            c = a * b
            return c.sum(dim=1)

        metrics.reset()
        x = torch.randn(100, 50, 50)
        y = torch.randn(100, 50, 50).transpose(1, 2)
        self.common(fn, (x, y))
        assert metrics.generated_cpp_vec_kernel_count == 2

    def test_transpose_sum2d_cpu_only(self):
        def fn(a, b):
            c = a * b
            return c.sum()

        metrics.reset()
        x = torch.randn(50, 50)
        y = torch.randn(50, 50).transpose(0, 1)
        self.common(fn, (x, y))
        assert metrics.generated_cpp_vec_kernel_count == 2

    def test_transpose_sum_outer(self):
        # https://github.com/pytorch/pytorch/issues/98573
        def fn(a):
            return a.transpose(2, 3).sum(dim=1).contiguous()

        metrics.reset()
        x = torch.randn(10, 50, 50, 50)
        self.common(fn, (x,))
        assert metrics.generated_cpp_vec_kernel_count == 1

    def test_to_dtype_bool_float(self):
        # https://github.com/pytorch/pytorch/issues/100800
        def f(a):
            return torch.where(
                torch.ones_like(a).to(torch.bool),
                torch.zeros_like(a),
                torch.ones_like(a) * 2,
            )

        self.common(f, (torch.ones(16),))

    def test_to_dtype_float_bool(self):
        # https://github.com/pytorch/pytorch/issues/100466
        def f(a):
            a = a * torch.tensor(a >= 0, dtype=torch.float32)
            return a

        x = torch.rand(16)
        self.common(f, (x,))

    def test_constant_store(self):
        # https://github.com/pytorch/pytorch/issues/104515
        def f(a):
            a[0, [3, 3]] = -float("inf")
            return a

        x = torch.rand(4, 5)
        self.common(f, (x,))

    def test_to_channels_last_lowp_fp(self):
        def f(a):
            return a.to(memory_format=torch.channels_last)

        for dtype in _lowp_fp_dtypes:
            x = torch.rand(2, 3, 14, 14).to(dtype)
            self.common(f, (x,))

    def test_broadcast_mul_lowp_fp(self):
        def f(a, b):
            return a * b

        for dtype in _lowp_fp_dtypes:
            a = torch.randn(2, 16, 16).to(dtype)
            b = torch.randn(2, 1, 1).to(dtype)
            self.common(f, (a, b))

    def test_linear_buffer_reuse(self):
        class M(torch.nn.Module):
            def __init__(self):
                super().__init__()
                self.linear1 = torch.nn.Linear(16, 16)
                self.tanh = torch.nn.Tanh()
                self.linear2 = torch.nn.Linear(16, 16)

            def forward(self, x):
                x = self.linear1(x)
                x = self.tanh(x)
                x = self.linear2(x)
                return x

        mod = M().eval()
        v = torch.randn(1, 16)

        with torch.no_grad():

            def compile_fx_wrapper(model_, example_inputs_):
                return compile_fx(model_, example_inputs_)

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

            run = torch._dynamo.optimize(compile_fx_wrapper)(run)
            _, code = run_and_get_cpp_code(run, v)
            self.assertFalse("= as_strided(" in code)
            self.assertEqual(run(*v), mod(*v))

    def test_invalid_dropout_args(self):
        class MyModel(torch.nn.Module):
            def forward(self, x):
                x = x * 2
                x = torch.nn.functional.dropout(x, p=0.5)
                x = torch.relu(x)
                return x

        example_inputs = torch.tensor([[1, 2, 3], [4, 5, 6]])

        func = MyModel()
        jit_func = torch.compile(func)
        self.assertRaises(RuntimeError, lambda: func(example_inputs))
        self.assertRaises(RuntimeError, lambda: jit_func(example_inputs))

    @config.patch(inplace_buffers=True)
    def test_in_out_buffer(self):
        def fn(x, y):
            z = torch.matmul(x, y.transpose(-1, -2)) / 8.0
            return z

        inps = [torch.randn(1, 2, 8, 4), torch.randn(1, 2, 8, 4)]
        fn_opt = torch._dynamo.optimize("inductor")(fn)
        _, code = run_and_get_cpp_code(fn_opt, *inps)
        self.assertTrue("in_out_ptr" in code)
        self.assertEqual(fn_opt(*inps), fn(*inps))

    def test_eliminate_meaningless_copy(self):
        def fn(x1, x2):
            permute = torch.ops.aten.permute.default(x2, [0, 2, 1, 3])
            clone = torch.ops.aten.clone.default(
                permute, memory_format=torch.contiguous_format
            )
            view = torch.ops.aten.view.default(clone, [1024, -1, 32])
            bmm = torch.ops.aten.bmm.default(view, x1)
            permute = torch.ops.aten.permute.default(view, [0, 2, 1])
            return (bmm, permute)

        metrics.reset()
        self.common(
            fn,
            [
                rand_strided(
                    (1024, 32, 128), (4096, 1, 32), device="cpu", dtype=torch.float32
                ),
                rand_strided(
                    (64, 128, 16, 32),
                    (65536, 512, 32, 1),
                    device="cpu",
                    dtype=torch.float32,
                ),
            ],
        )
        self.assertEqual(metrics.generated_kernel_count, 1)

    def test_attention_size_mismatch(self):
        class Attention(torch.nn.Module):
            def __init__(self, hidden_size, num_heads):
                super().__init__()
                self.hidden_size = hidden_size
                self.num_heads = num_heads
                self.head_size = hidden_size // num_heads
                self.query = torch.nn.Linear(hidden_size, hidden_size)
                self.key = torch.nn.Linear(hidden_size, hidden_size)
                self.value = torch.nn.Linear(hidden_size, hidden_size)
                self.inv_scale = torch.nn.Parameter(
                    torch.Tensor([1 / self.head_size**0.5]), requires_grad=False
                )

            def forward(self, x):
                query = self.query(x)
                key = self.key(x)
                value = self.value(x)
                (batch_size, seq_len, hidden_size) = query.size()
                query = query.view(
                    batch_size, seq_len, self.num_heads, self.head_size
                ).permute(0, 2, 1, 3)
                key = key.view(
                    batch_size, seq_len, self.num_heads, self.head_size
                ).permute(0, 2, 3, 1)
                value = value.view(
                    batch_size, seq_len, self.num_heads, self.head_size
                ).permute(0, 2, 1, 3)
                attention_weights = (
                    torch.matmul(query, key).div(self.inv_scale).softmax(dim=-1)
                )
                output = torch.matmul(attention_weights, value)
                return output

        torch.manual_seed(123)
        hidden_size = 16
        num_heads = 1
        seq_len = 4
        batch_size = 1
        x = torch.randn(batch_size, seq_len, hidden_size)

        func = Attention(hidden_size, num_heads).to("cpu")

        with torch.no_grad():
            res1 = func(x)
            jit_func = torch.compile(func)
            res2 = jit_func(x)
        self.assertEqual(res1, res2)

    def test_scalar_mul_bfloat16(self):
        def f(x):
            return torch.ops.aten.mul.Tensor(x, 1.7015043497085571)

        metrics.reset()
        x = torch.randn(4, 5, dtype=torch.bfloat16)
        self.common(f, (x,))
        assert metrics.generated_cpp_vec_kernel_count == 1

    def test_bf16_zeros(self):
        def fn():
            x = torch.zeros(1, 1, 32, dtype=torch.bfloat16)
            return x

        self.common(fn, ())

    def test_select_tiliing_with_index_expr(self):
        def fn(x, y):
            x = torch.ops.aten.view.default(x, [8, 8, 8, 3136])
            x = torch.ops.aten.permute.default(x, [0, 1, 3, 2])
            y = torch.ops.aten.mul.Tensor(y, x)
            return torch.ops.aten.constant_pad_nd.default(y, [0, 0, 1, 0, 0, 0], 0.0)

        x = torch.randn(8, 64, 56, 56)
        y = torch.randn(8, 8, 3136, 8)
        self.common(fn, (x, y))

    @unittest.skipIf(not torch.backends.mkldnn.is_available(), "MKLDNN is not enabled")
    @patch("torch.cuda.is_available", lambda: False)
    @config.patch(freezing=True)
    def test_linear_with_no_default_contiguous_input(self):
        dtypes = [
            torch.float32,
        ]
        if torch.ops.mkldnn._is_mkldnn_bf16_supported():
            dtypes.append(torch.bfloat16)
        if torch.ops.mkldnn._is_mkldnn_fp16_supported():
            dtypes.append(torch.float16)
        mod = torch.nn.Sequential(torch.nn.Linear(16, 16)).eval()
        temp = torch.randn(1, 16, 1, 1)
        v = torch.as_strided(temp, [1, 16], [0, 1], 0)
        self.assertTrue(v.is_contiguous())
        for dtype in dtypes:
            with torch.no_grad():
                self.common(
                    mod.to(dtype),
                    (v.to(dtype),),
                )

    @patch("torch.cuda.is_available", lambda: False)
    @config.patch(freezing=True)
    def test_linear_with_reshape(self):
        class M(torch.nn.Module):
            def __init__(self):
                super().__init__()
                self.linear = torch.nn.Linear(16, 16, bias=False)

            def forward(self, x):
                x = self.linear(x)
                return x.view(4, 4, 4)

        mod = M().eval()
        v = torch.randn(4, 16)
        with torch.no_grad():
            torch._dynamo.reset()
            metrics.reset()
            self.common(
                mod,
                (v,),
            )
            assert metrics.generated_kernel_count == 0

    @config.patch(implicit_fallbacks=True)
    def test_aten_normal_dtype(self):
        for dtype in [torch.float64, torch.float16, None]:

            def fn():
                return torch.normal(2, 3, (10, 10), dtype=dtype, device="cpu")

            self.assertEqual(
                torch.compile(fn, backend="aot_eager_decomp_partition")().dtype,
                dtype if dtype else torch.float32,
            )
            self.assertEqual(
                torch.compile(fn, backend="inductor")().dtype,
                dtype if dtype else torch.float32,
            )

    def test_group_norm_vec(self):
        class M(torch.nn.Module):
            def __init__(self):
                super().__init__()
                self.group_norm = torch.nn.GroupNorm(32, 32)

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

        metrics.reset()
        mod = M().eval()
        x = torch.randn(2, 32, 32, 32)
        with torch.no_grad():
            self.common(mod, (x,))
            # 2 generated kernels (one for var_mean, the other for result)
            assert metrics.generated_cpp_vec_kernel_count == 2

    def test_int_div_vec(self):
        def fn(x, y, mode):
            return torch.div(x, y, rounding_mode=mode)

        x = torch.randint(1, 100, (32, 32))
        y = torch.randint(1, 100, (32, 32))
        for mode in [None, "trunc", "floor"]:
            with torch.no_grad():
                metrics.reset()
                self.common(fn, (x, y, mode))
                # TODO: support vectorization for int div
                assert metrics.generated_cpp_vec_kernel_count == 0

    def test_uint8_add(self):
        # https://github.com/pytorch/pytorch/issues/113016
        def fn(x, y):
            return torch.add(x, y).neg().to(torch.int32)

        x = torch.randint(0, 255, (3, 3), dtype=torch.uint8)
        y = torch.randint(0, 255, (3, 3), dtype=torch.uint8)
        self.common(fn, (x, y))

    def test_uint8_sub(self):
        # https://github.com/pytorch/pytorch/issues/113016
        def fn(x, y):
            return torch.sub(x, y).neg().to(torch.int32)

        x = torch.randint(0, 255, (3, 3), dtype=torch.uint8)
        y = torch.randint(0, 255, (3, 3), dtype=torch.uint8)
        self.common(fn, (x, y))

    def test_non_contiguous_reduction_store(self):
        # https://github.com/pytorch/pytorch/issues/113018
        class M(torch.nn.Module):
            def __init__(self):
                super().__init__()
                self.conv = torch.nn.Conv2d(39, 1, kernel_size=(1, 17), stride=(2, 2))

            def forward(self, x):
                return self.conv(x.max(3).values)

        m = M()
        x = torch.randn(1, 39, 1, 18, 17)
        self.common(m, (x,))

    def test_embedding_vec(self):
        class M(torch.nn.Module):
            def __init__(self):
                super().__init__()
                self.emb = torch.nn.Embedding(64, 128)

            def forward(self, idx, x):
                return self.emb(idx) + x

        idx = torch.randint(0, 64, (4, 32))
        x = torch.randn(4, 32, 128)
        m = M().eval()
        with torch.no_grad():
            metrics.reset()
            self.common(m, (idx, x))
            assert metrics.generated_cpp_vec_kernel_count == 1

    def test_embedding_vec_bf16(self):
        class M(torch.nn.Module):
            def __init__(self):
                super().__init__()
                self.emb = torch.nn.Embedding(64, 128)

            def forward(self, idx, x):
                return self.emb(idx)

        idx = torch.randint(0, 64, (4, 32))
        x = torch.randn(4, 32, 128).to(torch.bfloat16)
        m = M().eval()
        with torch.no_grad():
            metrics.reset()
            self.common(m, (idx, x))
            assert metrics.generated_cpp_vec_kernel_count == 1

        # we are doing direct load/store, make sure we do not generate
        # redundant type casts
        m_opt = torch.compile(m)
        _, code = run_and_get_cpp_code(m_opt, idx, x)
        self.assertTrue("Vectorized" in code)
        self.assertTrue("cvt_lowp_fp_to_fp32" not in code)
        self.assertTrue("cvt_fp32_to_lowp_fp" not in code)

    def test_concat_inner_vec(self):
        def fn(x, y):
            return F.relu(torch.cat([x, y], dim=1))

        x = torch.randn(32, 35)
        y = torch.randn(32, 120)
        metrics.reset()
        self.common(fn, (x, y))
        assert metrics.generated_cpp_vec_kernel_count == 3

    def test_expr_vec_non_contiguous(self):
        def fn(x):
            # the pattern from sebotnet33ts_256
            y = torch.nn.functional.pad(x, (0, 31)).reshape(-1, 33, 63)
            y = y[:, :32, 31:].reshape(4, 32, 1, 32, 32).expand(-1, -1, 32, -1, -1)
            y = y.permute(0, 3, 1, 4, 2).clone(memory_format=torch.contiguous_format)
            y = y.view(4, 1024, 1024)
            return y.softmax(dim=-1)

        x = torch.randn(128, 2048)
        opt_fn = torch.compile(fn)
        metrics.reset()
        _, code = run_and_get_cpp_code(opt_fn, x)
        self.assertTrue(same(fn(x), opt_fn(x)))
        # 4 kernels for max, exp, sum and div
        assert metrics.generated_cpp_vec_kernel_count == 4
        FileCheck().check_count(
            "Vectorized<int>::loadu(tmpbuf.data())", 0, exactly=True
        ).run(code)

    def test_vec_contiguous_ModularIndexing(self):
        # https://github.com/pytorch/pytorch/issues/114488
        class M(torch.nn.Module):
            def __init__(self, dim):
                super().__init__()
                self.norm = torch.nn.LayerNorm(dim * 4)

            def forward(self, x):
                # the pattern from swin_base_patch4_window7_224
                B, H, W, C = x.shape
                x = (
                    x.reshape(B, H // 2, 2, W // 2, 2, C)
                    .permute(0, 1, 3, 4, 2, 5)
                    .flatten(3)
                )
                x = self.norm(x)
                return x

        x = torch.randn(1, 56, 56, 128)
        m = M(128)
        opt_m = torch.compile(m)
        with torch.no_grad():
            metrics.reset()
            _, code = run_and_get_cpp_code(opt_m, x)
            self.assertTrue(same(m(x), opt_m(x)))
            # Two kernels: one for reduction, one pointwises
            assert metrics.generated_cpp_vec_kernel_count == 2
            FileCheck().check_count(
                "Vectorized<float>::loadu(tmpbuf.data())", 0, exactly=True
            ).run(code)

    @parametrize("dtype", (torch.float16, torch.bfloat16, torch.float))
    @parametrize("shape", ("15,3,13", "4,2048,4096"))
    def test_fp8_cast(self, dtype: torch.dtype, shape: str):
        def fp8_cast(x):
            y0 = x.to(dtype=torch.float8_e4m3fn).to(dtype)
            y1 = x.to(dtype=torch.float8_e5m2).to(dtype)
            return y0, y1

        shape = [int(dim) for dim in shape.split(",")]
        x = torch.rand(*shape, device="cpu", dtype=dtype)
        self.common(fp8_cast, (x,))


if __name__ == "__main__":
    from torch._dynamo.test_case import run_tests
    from torch.testing._internal.inductor_utils import HAS_CPU

    if HAS_CPU and not IS_MACOS:
        run_tests(needs="filelock")