# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/5v/c5vqsotjiyyydjenxs3ttprtusysjuigcmpcuavw4a5cbsh4movc.py
# Topologically Sorted Source Nodes: [sum_1, square_of_sum, pow_2, sum_of_square, ix, ix_1, mul], Original ATen: [aten.sum, aten.pow, aten.sub, aten.mul]
# Source node to ATen node mapping:
#   ix => sub
#   ix_1 => sum_3
#   mul => mul
#   pow_2 => pow_2
#   square_of_sum => pow_1
#   sum_1 => sum_1
#   sum_of_square => sum_2
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%arg0_1, [1]), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_1, 2), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%arg0_1, 2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_2, [1]), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%pow_1, %sum_2), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sub, [1], True), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_3, 0.5), kwargs = {})
triton_poi_fused_mul_pow_sub_sum_0 = async_compile.triton('triton_poi_fused_mul_pow_sub_sum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_pow_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 16, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_mul_pow_sub_sum_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (64*x1)), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x0 + (64*x1)), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x0 + (64*x1)), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x0 + (64*x1)), xmask)
    tmp16 = tl.load(in_ptr0 + (4 + x0 + (64*x1)), xmask)
    tmp17 = tl.load(in_ptr0 + (20 + x0 + (64*x1)), xmask)
    tmp19 = tl.load(in_ptr0 + (36 + x0 + (64*x1)), xmask)
    tmp21 = tl.load(in_ptr0 + (52 + x0 + (64*x1)), xmask)
    tmp33 = tl.load(in_ptr0 + (8 + x0 + (64*x1)), xmask)
    tmp34 = tl.load(in_ptr0 + (24 + x0 + (64*x1)), xmask)
    tmp36 = tl.load(in_ptr0 + (40 + x0 + (64*x1)), xmask)
    tmp38 = tl.load(in_ptr0 + (56 + x0 + (64*x1)), xmask)
    tmp50 = tl.load(in_ptr0 + (12 + x0 + (64*x1)), xmask)
    tmp51 = tl.load(in_ptr0 + (28 + x0 + (64*x1)), xmask)
    tmp53 = tl.load(in_ptr0 + (44 + x0 + (64*x1)), xmask)
    tmp55 = tl.load(in_ptr0 + (60 + x0 + (64*x1)), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tmp0 * tmp0
    tmp9 = tmp1 * tmp1
    tmp10 = tmp8 + tmp9
    tmp11 = tmp3 * tmp3
    tmp12 = tmp10 + tmp11
    tmp13 = tmp5 * tmp5
    tmp14 = tmp12 + tmp13
    tmp15 = tmp7 - tmp14
    tmp18 = tmp16 + tmp17
    tmp20 = tmp18 + tmp19
    tmp22 = tmp20 + tmp21
    tmp23 = tmp22 * tmp22
    tmp24 = tmp16 * tmp16
    tmp25 = tmp17 * tmp17
    tmp26 = tmp24 + tmp25
    tmp27 = tmp19 * tmp19
    tmp28 = tmp26 + tmp27
    tmp29 = tmp21 * tmp21
    tmp30 = tmp28 + tmp29
    tmp31 = tmp23 - tmp30
    tmp32 = tmp15 + tmp31
    tmp35 = tmp33 + tmp34
    tmp37 = tmp35 + tmp36
    tmp39 = tmp37 + tmp38
    tmp40 = tmp39 * tmp39
    tmp41 = tmp33 * tmp33
    tmp42 = tmp34 * tmp34
    tmp43 = tmp41 + tmp42
    tmp44 = tmp36 * tmp36
    tmp45 = tmp43 + tmp44
    tmp46 = tmp38 * tmp38
    tmp47 = tmp45 + tmp46
    tmp48 = tmp40 - tmp47
    tmp49 = tmp32 + tmp48
    tmp52 = tmp50 + tmp51
    tmp54 = tmp52 + tmp53
    tmp56 = tmp54 + tmp55
    tmp57 = tmp56 * tmp56
    tmp58 = tmp50 * tmp50
    tmp59 = tmp51 * tmp51
    tmp60 = tmp58 + tmp59
    tmp61 = tmp53 * tmp53
    tmp62 = tmp60 + tmp61
    tmp63 = tmp55 * tmp55
    tmp64 = tmp62 + tmp63
    tmp65 = tmp57 - tmp64
    tmp66 = tmp49 + tmp65
    tmp67 = 0.5
    tmp68 = tmp66 * tmp67
    tl.store(in_out_ptr0 + (x2), tmp68, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4), (4, 16, 1), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 1, 4), (4, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [sum_1, square_of_sum, pow_2, sum_of_square, ix, ix_1, mul], Original ATen: [aten.sum, aten.pow, aten.sub, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_pow_sub_sum_0.run(buf1, arg0_1, 16, grid=grid(16), stream=stream0)
        del arg0_1
    return (buf1, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.utils.data


class FactorizationMachine(torch.nn.Module):

    def __init__(self, reduce_sum=True):
        super().__init__()
        self.reduce_sum = reduce_sum

    def forward(self, x):
        """
        :param x: Float tensor of size ``(batch_size, num_fields, embed_dim)``
        """
        square_of_sum = torch.sum(x, dim=1) ** 2
        sum_of_square = torch.sum(x ** 2, dim=1)
        ix = square_of_sum - sum_of_square
        if self.reduce_sum:
            ix = torch.sum(ix, dim=1, keepdim=True)
        return 0.5 * ix


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {}]

import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_mul_pow_sub_sum_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp16 = tl.load(in_ptr0 + (4 + x0 + 64 * x1), xmask)
    tmp17 = tl.load(in_ptr0 + (20 + x0 + 64 * x1), xmask)
    tmp19 = tl.load(in_ptr0 + (36 + x0 + 64 * x1), xmask)
    tmp21 = tl.load(in_ptr0 + (52 + x0 + 64 * x1), xmask)
    tmp33 = tl.load(in_ptr0 + (8 + x0 + 64 * x1), xmask)
    tmp34 = tl.load(in_ptr0 + (24 + x0 + 64 * x1), xmask)
    tmp36 = tl.load(in_ptr0 + (40 + x0 + 64 * x1), xmask)
    tmp38 = tl.load(in_ptr0 + (56 + x0 + 64 * x1), xmask)
    tmp50 = tl.load(in_ptr0 + (12 + x0 + 64 * x1), xmask)
    tmp51 = tl.load(in_ptr0 + (28 + x0 + 64 * x1), xmask)
    tmp53 = tl.load(in_ptr0 + (44 + x0 + 64 * x1), xmask)
    tmp55 = tl.load(in_ptr0 + (60 + x0 + 64 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tmp0 * tmp0
    tmp9 = tmp1 * tmp1
    tmp10 = tmp8 + tmp9
    tmp11 = tmp3 * tmp3
    tmp12 = tmp10 + tmp11
    tmp13 = tmp5 * tmp5
    tmp14 = tmp12 + tmp13
    tmp15 = tmp7 - tmp14
    tmp18 = tmp16 + tmp17
    tmp20 = tmp18 + tmp19
    tmp22 = tmp20 + tmp21
    tmp23 = tmp22 * tmp22
    tmp24 = tmp16 * tmp16
    tmp25 = tmp17 * tmp17
    tmp26 = tmp24 + tmp25
    tmp27 = tmp19 * tmp19
    tmp28 = tmp26 + tmp27
    tmp29 = tmp21 * tmp21
    tmp30 = tmp28 + tmp29
    tmp31 = tmp23 - tmp30
    tmp32 = tmp15 + tmp31
    tmp35 = tmp33 + tmp34
    tmp37 = tmp35 + tmp36
    tmp39 = tmp37 + tmp38
    tmp40 = tmp39 * tmp39
    tmp41 = tmp33 * tmp33
    tmp42 = tmp34 * tmp34
    tmp43 = tmp41 + tmp42
    tmp44 = tmp36 * tmp36
    tmp45 = tmp43 + tmp44
    tmp46 = tmp38 * tmp38
    tmp47 = tmp45 + tmp46
    tmp48 = tmp40 - tmp47
    tmp49 = tmp32 + tmp48
    tmp52 = tmp50 + tmp51
    tmp54 = tmp52 + tmp53
    tmp56 = tmp54 + tmp55
    tmp57 = tmp56 * tmp56
    tmp58 = tmp50 * tmp50
    tmp59 = tmp51 * tmp51
    tmp60 = tmp58 + tmp59
    tmp61 = tmp53 * tmp53
    tmp62 = tmp60 + tmp61
    tmp63 = tmp55 * tmp55
    tmp64 = tmp62 + tmp63
    tmp65 = tmp57 - tmp64
    tmp66 = tmp49 + tmp65
    tmp67 = 0.5
    tmp68 = tmp66 * tmp67
    tl.store(in_out_ptr0 + x2, tmp68, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4), (4, 16, 1), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 1, 4), (4, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_mul_pow_sub_sum_0[grid(16)](buf1, arg0_1, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del arg0_1
    return buf1,


class FactorizationMachineNew(torch.nn.Module):

    def __init__(self, reduce_sum=True):
        super().__init__()
        self.reduce_sum = reduce_sum

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]
