# 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_4/inductor_cache/ss/cssn3ayzwsxbizosd6ieezxafjef3fxscx57lbnlxbdiuph3p2je.py
# Topologically Sorted Source Nodes: [add, u], Original ATen: [aten.add, aten.mean]
# Source node to ATen node mapping:
#   add => add
#   u => mean
# Graph fragment:
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %primals_4), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%add, [-1], True), kwargs = {})
triton_poi_fused_add_mean_0 = async_compile.triton('triton_poi_fused_add_mean_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mean_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 12, '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_add_mean_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr2 + (4*x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1))
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK])
    tmp10 = tl.load(in_ptr2 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + (2))
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK])
    tmp17 = tl.load(in_ptr2 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr1 + (3))
    tmp22 = tl.broadcast_to(tmp21, [XBLOCK])
    tmp24 = tl.load(in_ptr2 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tmp0 + tmp2
    tmp5 = tmp3 + tmp4
    tmp9 = tmp6 + tmp8
    tmp11 = tmp9 + tmp10
    tmp12 = tmp5 + tmp11
    tmp16 = tmp13 + tmp15
    tmp18 = tmp16 + tmp17
    tmp19 = tmp12 + tmp18
    tmp23 = tmp20 + tmp22
    tmp25 = tmp23 + tmp24
    tmp26 = tmp19 + tmp25
    tmp27 = 4.0
    tmp28 = tmp26 / tmp27
    tl.store(out_ptr0 + (x0), tmp28, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/l6/cl6vibrzoyykzmbhmvlsdaksh3k2diif7eg66z2ho46tjsy6emma.py
# Topologically Sorted Source Nodes: [add, sub], Original ATen: [aten.add, aten.sub]
# Source node to ATen node mapping:
#   add => add
#   sub => sub
# Graph fragment:
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %primals_4), kwargs = {})
#   %sub : [num_users=3] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %mean), kwargs = {})
triton_poi_fused_add_sub_1 = async_compile.triton('triton_poi_fused_add_sub_1', '''
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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_sub_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, '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_add_sub_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    x1 = (xindex // 4)
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x2), xmask)
    tmp5 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 - tmp5
    tl.store(in_out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/4p/c4pnuv3rymhg72qutbvx7mkzv6t7edcefa73bt3nl66b4qtouu4a.py
# Topologically Sorted Source Nodes: [pow_1, s, add_1, sqrt, x, mul, hidden_states_2], Original ATen: [aten.pow, aten.mean, aten.add, aten.sqrt, aten.div, aten.mul]
# Source node to ATen node mapping:
#   add_1 => add_1
#   hidden_states_2 => add_2
#   mul => mul
#   pow_1 => pow_1
#   s => mean_1
#   sqrt => sqrt
#   x => div
# Graph fragment:
#   %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, 1), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add_1,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %sqrt), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_5, %div), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %primals_6), kwargs = {})
triton_poi_fused_add_div_mean_mul_pow_sqrt_2 = async_compile.triton('triton_poi_fused_add_div_mean_mul_pow_sqrt_2', '''
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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_mean_mul_pow_sqrt_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, '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_add_div_mean_mul_pow_sqrt_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2), xmask)
    tmp2 = tl.load(in_ptr1 + (4*x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tmp2 * tmp2
    tmp5 = tmp4 * tmp4
    tmp6 = tmp3 + tmp5
    tmp8 = tmp7 * tmp7
    tmp9 = tmp6 + tmp8
    tmp11 = tmp10 * tmp10
    tmp12 = tmp9 + tmp11
    tmp13 = 4.0
    tmp14 = tmp12 / tmp13
    tmp15 = 1.0
    tmp16 = tmp14 + tmp15
    tmp17 = libdevice.sqrt(tmp16)
    tmp18 = tmp1 / tmp17
    tmp19 = tmp0 * tmp18
    tmp21 = tmp19 + tmp20
    tl.store(out_ptr0 + (x2), tmp21, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        # Topologically Sorted Source Nodes: [add, u], Original ATen: [aten.add, aten.mean]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mean_0.run(buf0, primals_2, primals_4, buf1, 64, grid=grid(64), stream=stream0)
        buf2 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [add, sub], Original ATen: [aten.add, aten.sub]
        triton_poi_fused_add_sub_1.run(buf2, primals_2, primals_4, buf1, 256, grid=grid(256), stream=stream0)
        del buf1
        del primals_2
        del primals_4
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pow_1, s, add_1, sqrt, x, mul, hidden_states_2], Original ATen: [aten.pow, aten.mean, aten.add, aten.sqrt, aten.div, aten.mul]
        triton_poi_fused_add_div_mean_mul_pow_sqrt_2.run(primals_5, buf2, primals_6, buf3, 256, grid=grid(256), stream=stream0)
        del primals_6
    return (buf3, primals_5, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf2, )


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((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (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((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = 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])
    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 torch
import torch.nn as nn
import torch.utils.checkpoint


class BertLayerNorm(nn.Module):
    """LayerNorm层, 见Transformer(一), 讲编码器(encoder)的第3部分"""

    def __init__(self, hidden_size, eps=1e-12, conditional=False):
        """Construct a layernorm module in the TF style (epsilon inside the square root).
        """
        super(BertLayerNorm, self).__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.bias = nn.Parameter(torch.zeros(hidden_size))
        self.variance_epsilon = eps
        self.conditional = conditional
        if conditional is True:
            self.weight_dense = nn.Linear(2 * hidden_size, hidden_size,
                bias=False)
            self.weight_dense.weight.data.uniform_(0, 0)
            self.bias_dense = nn.Linear(2 * hidden_size, hidden_size, bias=
                False)
            self.bias_dense.weight.data.uniform_(0, 0)

    def forward(self, x):
        if self.conditional is False:
            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
        else:
            inputs = x[0]
            cond = x[1]
            for _ in range(len(inputs.shape) - len(cond.shape)):
                cond = cond.unsqueeze(dim=1)
            weight = self.weight + self.weight_dense(cond)
            bias = self.bias + self.bias_dense(cond)
            u = inputs.mean(-1, keepdim=True)
            s = (inputs - u).pow(2).mean(-1, keepdim=True)
            x = (inputs - u) / torch.sqrt(s + self.variance_epsilon)
            return weight * x + bias


class BertOutput(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.
            layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


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


def get_init_inputs():
    return [[], {'config': _mock_config(intermediate_size=4, hidden_size=4,
        layer_norm_eps=1, hidden_dropout_prob=0.5)}]

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 torch.nn as nn
import torch.utils.checkpoint
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_add_mean_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr2 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + 1)
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK])
    tmp10 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp13 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr1 + 2)
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK])
    tmp17 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp21 = tl.load(in_ptr1 + 3)
    tmp22 = tl.broadcast_to(tmp21, [XBLOCK])
    tmp24 = tl.load(in_ptr2 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp3 = tmp0 + tmp2
    tmp5 = tmp3 + tmp4
    tmp9 = tmp6 + tmp8
    tmp11 = tmp9 + tmp10
    tmp12 = tmp5 + tmp11
    tmp16 = tmp13 + tmp15
    tmp18 = tmp16 + tmp17
    tmp19 = tmp12 + tmp18
    tmp23 = tmp20 + tmp22
    tmp25 = tmp23 + tmp24
    tmp26 = tmp19 + tmp25
    tmp27 = 4.0
    tmp28 = tmp26 / tmp27
    tl.store(out_ptr0 + x0, tmp28, xmask)


@triton.jit
def triton_poi_fused_add_sub_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    x1 = xindex // 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp5 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 - tmp5
    tl.store(in_out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_div_mean_mul_pow_sqrt_2(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp2 * tmp2
    tmp5 = tmp4 * tmp4
    tmp6 = tmp3 + tmp5
    tmp8 = tmp7 * tmp7
    tmp9 = tmp6 + tmp8
    tmp11 = tmp10 * tmp10
    tmp12 = tmp9 + tmp11
    tmp13 = 4.0
    tmp14 = tmp12 / tmp13
    tmp15 = 1.0
    tmp16 = tmp14 + tmp15
    tmp17 = libdevice.sqrt(tmp16)
    tmp18 = tmp1 / tmp17
    tmp19 = tmp0 * tmp18
    tmp21 = tmp19 + tmp20
    tl.store(out_ptr0 + x2, tmp21, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mean_0[grid(64)](buf0, primals_2, primals_4,
            buf1, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf2 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        triton_poi_fused_add_sub_1[grid(256)](buf2, primals_2, primals_4,
            buf1, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf1
        del primals_2
        del primals_4
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_div_mean_mul_pow_sqrt_2[grid(256)](primals_5,
            buf2, primals_6, buf3, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_6
    return buf3, primals_5, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf2


class BertLayerNorm(nn.Module):
    """LayerNorm层, 见Transformer(一), 讲编码器(encoder)的第3部分"""

    def __init__(self, hidden_size, eps=1e-12, conditional=False):
        """Construct a layernorm module in the TF style (epsilon inside the square root).
        """
        super(BertLayerNorm, self).__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.bias = nn.Parameter(torch.zeros(hidden_size))
        self.variance_epsilon = eps
        self.conditional = conditional
        if conditional is True:
            self.weight_dense = nn.Linear(2 * hidden_size, hidden_size,
                bias=False)
            self.weight_dense.weight.data.uniform_(0, 0)
            self.bias_dense = nn.Linear(2 * hidden_size, hidden_size, bias=
                False)
            self.bias_dense.weight.data.uniform_(0, 0)

    def forward(self, x):
        if self.conditional is False:
            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
        else:
            inputs = x[0]
            cond = x[1]
            for _ in range(len(inputs.shape) - len(cond.shape)):
                cond = cond.unsqueeze(dim=1)
            weight = self.weight + self.weight_dense(cond)
            bias = self.bias + self.bias_dense(cond)
            u = inputs.mean(-1, keepdim=True)
            s = (inputs - u).pow(2).mean(-1, keepdim=True)
            x = (inputs - u) / torch.sqrt(s + self.variance_epsilon)
            return weight * x + bias


class BertOutputNew(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.
            layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, input_0, input_1):
        primals_1 = self.dense.weight
        primals_2 = self.dense.bias
        primals_5 = self.LayerNorm.weight
        primals_6 = self.LayerNorm.bias
        primals_3 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
