# AOT ID: ['0_forward']
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_1/inductor_cache/vu/cvucantlgh56apzu7xmoy3xdwz7ss3dj46ir2bgdit6cpuv3wzyk.py
# Topologically Sorted Source Nodes: [mul, truediv, erf, add, mul_1, u, sub, pow_1, s, add_1, sqrt, x, mul_2, add_2], Original ATen: [aten.mul, aten.div, aten.erf, aten.add, aten.mean, aten.sub, aten.pow, aten.sqrt]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   erf => erf
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   pow_1 => pow_1
#   s => mean_1
#   sqrt => sqrt
#   sub => sub
#   truediv => div
#   u => mean
#   x => div_1
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, 0.5), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%view_1, 1.4142135623730951), kwargs = {})
#   %erf : [num_users=1] = call_function[target=torch.ops.aten.erf.default](args = (%div,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%erf, 1.0), kwargs = {})
#   %mul_1 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %add), kwargs = {})
#   %mean : [num_users=2] = call_function[target=torch.ops.aten.mean.dim](args = (%mul_1, [-1], True), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_1, %mean), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%pow_1, [-1], True), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean_1, 1e-12), kwargs = {})
#   %sqrt : [num_users=2] = call_function[target=torch.ops.aten.sqrt.default](args = (%add_1,), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %sqrt), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_4, %div_1), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %primals_5), kwargs = {})
triton_per_fused_add_div_erf_mean_mul_pow_sqrt_sub_0 = async_compile.triton('triton_per_fused_add_div_erf_mean_mul_pow_sqrt_sub_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.persistent_reduction(
    size_hints=[64, 8],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_erf_mean_mul_pow_sqrt_sub_0', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 2, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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}
)
@triton.jit
def triton_per_fused_add_div_erf_mean_mul_pow_sqrt_sub_0(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    rnumel = 8
    RBLOCK: tl.constexpr = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (8*x0)), xmask, other=0.0)
    tmp25 = tl.load(in_ptr1 + (r1), None, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr2 + (r1), None, eviction_policy='evict_last')
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865475
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.where(xmask, tmp9, 0)
    tmp12 = tl.sum(tmp11, 1)[:, None]
    tmp13 = 8.0
    tmp14 = tmp12 / tmp13
    tmp15 = tmp8 - tmp14
    tmp16 = tmp15 * tmp15
    tmp17 = tl.broadcast_to(tmp16, [XBLOCK, RBLOCK])
    tmp19 = tl.where(xmask, tmp17, 0)
    tmp20 = tl.sum(tmp19, 1)[:, None]
    tmp21 = tmp20 / tmp13
    tmp22 = 1e-12
    tmp23 = tmp21 + tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp26 = tmp15 / tmp24
    tmp27 = tmp25 * tmp26
    tmp29 = tmp27 + tmp28
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp14, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (x0), tmp24, xmask)
    tl.store(out_ptr0 + (r1 + (8*x0)), tmp29, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args
    args.clear()
    assert_size_stride(primals_1, (8, 4), (4, 1))
    assert_size_stride(primals_2, (8, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (8, ), (1, ))
    assert_size_stride(primals_5, (8, ), (1, ))
    assert_size_stride(primals_6, (4, 8), (8, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 8), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf2 = reinterpret_tensor(buf1, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf1  # reuse
        buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf4 = reinterpret_tensor(buf3, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf3  # reuse
        buf5 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, truediv, erf, add, mul_1, u, sub, pow_1, s, add_1, sqrt, x, mul_2, add_2], Original ATen: [aten.mul, aten.div, aten.erf, aten.add, aten.mean, aten.sub, aten.pow, aten.sqrt]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_div_erf_mean_mul_pow_sqrt_sub_0.run(buf2, buf4, buf0, primals_4, primals_5, buf5, 64, 8, grid=grid(64), stream=stream0)
        del primals_5
        buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pred], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf5, (64, 8), (8, 1), 0), reinterpret_tensor(primals_6, (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf6)
        del primals_7
    return (reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0), primals_4, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0, buf2, buf4, reinterpret_tensor(buf5, (64, 8), (8, 1), 0), primals_6, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((8, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
    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)

from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn


def gelu(x):
    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))


class LayerNorm(nn.Module):

    def __init__(self, hidden_size, eps=1e-12):
        super(LayerNorm, self).__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.bias = nn.Parameter(torch.zeros(hidden_size))
        self.variance_epsilon = eps

    def forward(self, x):
        u = x.mean(-1, keepdim=True)
        s = (x - u).pow(2).mean(-1, keepdim=True)
        x = (x - u) / torch.sqrt(s + self.variance_epsilon)
        return self.weight * x + self.bias


class MmQAHead(nn.Module):

    def __init__(self, cfg, ans_size):
        super(MmQAHead, self).__init__()
        self.cfg = cfg
        self.dense0 = nn.Linear(cfg.HSIZE, cfg.HSIZE * 2)
        self.dense1 = nn.Linear(cfg.HSIZE * 2, ans_size)
        self.layer_norm = LayerNorm(cfg.HSIZE * 2, eps=1e-12)

    def forward(self, x_pooled):
        pred = self.dense1(self.layer_norm(gelu(self.dense0(x_pooled))))
        return pred


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


def get_init_inputs():
    return [[], {'cfg': _mock_config(HSIZE=4), 'ans_size': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
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
from torch._inductor.runtime.triton_helpers import libdevice
import math
import torch.nn as nn
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_per_fused_add_div_erf_mean_mul_pow_sqrt_sub_0(in_out_ptr0,
    in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    RBLOCK: tl.constexpr = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 8 * x0), xmask, other=0.0)
    tmp25 = tl.load(in_ptr1 + r1, None, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr2 + r1, None, eviction_policy='evict_last')
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865475
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.where(xmask, tmp9, 0)
    tmp12 = tl.sum(tmp11, 1)[:, None]
    tmp13 = 8.0
    tmp14 = tmp12 / tmp13
    tmp15 = tmp8 - tmp14
    tmp16 = tmp15 * tmp15
    tmp17 = tl.broadcast_to(tmp16, [XBLOCK, RBLOCK])
    tmp19 = tl.where(xmask, tmp17, 0)
    tmp20 = tl.sum(tmp19, 1)[:, None]
    tmp21 = tmp20 / tmp13
    tmp22 = 1e-12
    tmp23 = tmp21 + tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp26 = tmp15 / tmp24
    tmp27 = tmp25 * tmp26
    tmp29 = tmp27 + tmp28
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp14, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x0, tmp24, xmask)
    tl.store(out_ptr0 + (r1 + 8 * x0), tmp29, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (8, 4), (4, 1))
    assert_size_stride(primals_2, (8,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (8,), (1,))
    assert_size_stride(primals_5, (8,), (1,))
    assert_size_stride(primals_6, (4, 8), (8, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 8), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf2 = reinterpret_tensor(buf1, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf1
        buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf4 = reinterpret_tensor(buf3, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf3
        buf5 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_add_div_erf_mean_mul_pow_sqrt_sub_0[grid(64)](buf2,
            buf4, buf0, primals_4, primals_5, buf5, 64, 8, XBLOCK=8,
            num_warps=2, num_stages=1)
        del primals_5
        buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf5, (64, 8), (
            8, 1), 0), reinterpret_tensor(primals_6, (8, 4), (1, 8), 0),
            alpha=1, beta=1, out=buf6)
        del primals_7
    return reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), primals_4, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf0, buf2, buf4, reinterpret_tensor(buf5, (64, 8), (8, 1), 0
        ), primals_6


def gelu(x):
    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))


class LayerNorm(nn.Module):

    def __init__(self, hidden_size, eps=1e-12):
        super(LayerNorm, self).__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.bias = nn.Parameter(torch.zeros(hidden_size))
        self.variance_epsilon = eps

    def forward(self, x):
        u = x.mean(-1, keepdim=True)
        s = (x - u).pow(2).mean(-1, keepdim=True)
        x = (x - u) / torch.sqrt(s + self.variance_epsilon)
        return self.weight * x + self.bias


class MmQAHeadNew(nn.Module):

    def __init__(self, cfg, ans_size):
        super(MmQAHeadNew, self).__init__()
        self.cfg = cfg
        self.dense0 = nn.Linear(cfg.HSIZE, cfg.HSIZE * 2)
        self.dense1 = nn.Linear(cfg.HSIZE * 2, ans_size)
        self.layer_norm = LayerNorm(cfg.HSIZE * 2, eps=1e-12)

    def forward(self, input_0):
        primals_1 = self.dense0.weight
        primals_2 = self.dense0.bias
        primals_6 = self.dense1.weight
        primals_7 = self.dense1.bias
        primals_4 = self.layer_norm.weight
        primals_5 = self.layer_norm.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
