# 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_7/inductor_cache/ph/cphwvhrpw6wgxo5c3ybswi5n2fvf37x7qpkmoepg2w5bovbrbcjy.py
# Topologically Sorted Source Nodes: [output, sub, add, ln_out, mul, ln_out_1], Original ATen: [aten.relu, aten.sub, aten.add, aten.div, aten.mul]
# Source node to ATen node mapping:
#   add => add
#   ln_out => div
#   ln_out_1 => add_1
#   mul => mul
#   output => relu
#   sub => sub
# Graph fragment:
#   %relu : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%relu, %expand), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%expand_1, 0.001), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %add), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, %expand_2), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %expand_3), kwargs = {})
triton_poi_fused_add_div_mul_relu_sub_0 = async_compile.triton('triton_poi_fused_add_div_mul_relu_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.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_mul_relu_sub_0', 'mutated_arg_names': ['in_out_ptr0'], '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_mul_relu_sub_0(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
    x1 = (xindex // 4)
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp3 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp36 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp1, tmp3)
    tmp6 = triton_helpers.maximum(tmp1, tmp5)
    tmp7 = tmp4 + tmp6
    tmp9 = triton_helpers.maximum(tmp1, tmp8)
    tmp10 = tmp7 + tmp9
    tmp12 = triton_helpers.maximum(tmp1, tmp11)
    tmp13 = tmp10 + tmp12
    tmp14 = 4.0
    tmp15 = tmp13 / tmp14
    tmp16 = tmp2 - tmp15
    tmp17 = tmp4 - tmp15
    tmp18 = tmp17 * tmp17
    tmp19 = tmp6 - tmp15
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp9 - tmp15
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tmp12 - tmp15
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = 3.0
    tmp29 = tmp27 / tmp28
    tmp30 = libdevice.sqrt(tmp29)
    tmp31 = 0.001
    tmp32 = tmp30 + tmp31
    tmp33 = tmp16 / tmp32
    tmp35 = tmp33 * tmp34
    tmp37 = tmp35 + tmp36
    tl.store(in_out_ptr0 + (x2), tmp37, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = 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, ), (1, ))
    assert_size_stride(primals_5, (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: [linear], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [output, sub, add, ln_out, mul, ln_out_1], Original ATen: [aten.relu, aten.sub, aten.add, aten.div, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_div_mul_relu_sub_0.run(buf2, buf0, primals_4, primals_5, 256, grid=grid(256), stream=stream0)
        del primals_5
    return (buf2, primals_4, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0, )


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, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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.nn as nn
from torch.autograd import *
import torch.nn.init as init


class LayerNormalization(nn.Module):
    """ Layer normalization module """

    def __init__(self, d_hid, eps=0.001):
        super(LayerNormalization, self).__init__()
        self.eps = eps
        self.a_2 = nn.Parameter(torch.ones(d_hid), requires_grad=True)
        self.b_2 = nn.Parameter(torch.zeros(d_hid), requires_grad=True)

    def forward(self, z):
        if z.size(1) == 1:
            return z
        mu = torch.mean(z, keepdim=True, dim=-1)
        sigma = torch.std(z, keepdim=True, dim=-1)
        ln_out = (z - mu.expand_as(z)) / (sigma.expand_as(z) + self.eps)
        ln_out = ln_out * self.a_2.expand_as(ln_out) + self.b_2.expand_as(
            ln_out)
        return ln_out


class LinearEncoder(nn.Module):
    """docstring for LinearEncoder"""

    def __init__(self, d_model, d_inner, dropout=0.1):
        super(LinearEncoder, self).__init__()
        self.proj1 = nn.Linear(d_model, d_inner)
        self.layer_norm = LayerNormalization(d_inner)
        init.xavier_normal_(self.proj1.weight)
        self.dropout = nn.Dropout(dropout)
        self.relu = nn.ReLU()

    def forward(self, x):
        output = self.relu(self.proj1(x))
        output = self.dropout(output)
        return self.layer_norm(output)


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


def get_init_inputs():
    return [[], {'d_model': 4, 'd_inner': 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from torch.autograd import *
import torch.nn.init as init
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_div_mul_relu_sub_0(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
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp34 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp36 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp1, tmp3)
    tmp6 = triton_helpers.maximum(tmp1, tmp5)
    tmp7 = tmp4 + tmp6
    tmp9 = triton_helpers.maximum(tmp1, tmp8)
    tmp10 = tmp7 + tmp9
    tmp12 = triton_helpers.maximum(tmp1, tmp11)
    tmp13 = tmp10 + tmp12
    tmp14 = 4.0
    tmp15 = tmp13 / tmp14
    tmp16 = tmp2 - tmp15
    tmp17 = tmp4 - tmp15
    tmp18 = tmp17 * tmp17
    tmp19 = tmp6 - tmp15
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp9 - tmp15
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tmp12 - tmp15
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = 3.0
    tmp29 = tmp27 / tmp28
    tmp30 = libdevice.sqrt(tmp29)
    tmp31 = 0.001
    tmp32 = tmp30 + tmp31
    tmp33 = tmp16 / tmp32
    tmp35 = tmp33 * tmp34
    tmp37 = tmp35 + tmp36
    tl.store(in_out_ptr0 + x2, tmp37, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = 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,), (1,))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_poi_fused_add_div_mul_relu_sub_0[grid(256)](buf2, buf0,
            primals_4, primals_5, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
    return buf2, primals_4, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf0


class LayerNormalization(nn.Module):
    """ Layer normalization module """

    def __init__(self, d_hid, eps=0.001):
        super(LayerNormalization, self).__init__()
        self.eps = eps
        self.a_2 = nn.Parameter(torch.ones(d_hid), requires_grad=True)
        self.b_2 = nn.Parameter(torch.zeros(d_hid), requires_grad=True)

    def forward(self, z):
        if z.size(1) == 1:
            return z
        mu = torch.mean(z, keepdim=True, dim=-1)
        sigma = torch.std(z, keepdim=True, dim=-1)
        ln_out = (z - mu.expand_as(z)) / (sigma.expand_as(z) + self.eps)
        ln_out = ln_out * self.a_2.expand_as(ln_out) + self.b_2.expand_as(
            ln_out)
        return ln_out


class LinearEncoderNew(nn.Module):
    """docstring for LinearEncoder"""

    def __init__(self, d_model, d_inner, dropout=0.1):
        super(LinearEncoderNew, self).__init__()
        self.proj1 = nn.Linear(d_model, d_inner)
        self.layer_norm = LayerNormalization(d_inner)
        init.xavier_normal_(self.proj1.weight)
        self.dropout = nn.Dropout(dropout)
        self.relu = nn.ReLU()

    def forward(self, input_0):
        primals_1 = self.proj1.weight
        primals_2 = self.proj1.bias
        primals_4 = self.layer_norm.a_2
        primals_5 = self.layer_norm.b_2
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
