# 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/z5/cz5cv4otoqqg7gisuc6bx7rlau6zkmkldsfxasbc2l2hvtqjgjou.py
# Topologically Sorted Source Nodes: [cated_vectors], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cated_vectors => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %primals_2, %primals_3], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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=[1024], 
    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_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 12
    x0 = xindex % 16
    x2 = (xindex // 192)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x1) + (64*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + (16*((-4) + x1)) + (64*x2)), tmp9 & xmask, other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tl.load(in_ptr2 + (x0 + (16*((-8) + x1)) + (64*x2)), tmp11 & xmask, other=0.0)
    tmp15 = tl.where(tmp9, tmp10, tmp14)
    tmp16 = tl.where(tmp4, tmp5, tmp15)
    tl.store(out_ptr0 + (x3), tmp16, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/lk/clkvwvqcst6t4cucaxdngnnuhangq7kuawiiub5xqxykvb3eih2u.py
# Topologically Sorted Source Nodes: [mul, pow_1, mul_1, add, mul_2, tanh, add_1, mul_3], Original ATen: [aten.mul, aten.pow, aten.add, aten.tanh]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   pow_1 => pow_1
#   tanh => tanh
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, 0.5), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%view_1, 3), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, 0.044715), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %mul_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 0.7978845608028654), kwargs = {})
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%mul_2,), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%tanh, 1), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %add_1), kwargs = {})
triton_poi_fused_add_mul_pow_tanh_1 = async_compile.triton('triton_poi_fused_add_mul_pow_tanh_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=[1024], 
    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_add_mul_pow_tanh_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_mul_pow_tanh_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = tmp0 * tmp0
    tmp4 = tmp3 * tmp0
    tmp5 = 0.044715
    tmp6 = tmp4 * tmp5
    tmp7 = tmp0 + tmp6
    tmp8 = 0.7978845608028654
    tmp9 = tmp7 * tmp8
    tmp10 = libdevice.tanh(tmp9)
    tmp11 = 1.0
    tmp12 = tmp10 + tmp11
    tmp13 = tmp2 * tmp12
    tl.store(out_ptr0 + (x0), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/qk/cqk4fc6sh5nxra5g2ze56qihubkj2kgf6kgs6cvrl7zew3sie6gm.py
# Topologically Sorted Source Nodes: [low_vectors_1], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   low_vectors_1 => add_2
# Graph fragment:
#   %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %getitem), kwargs = {})
triton_poi_fused_add_2 = async_compile.triton('triton_poi_fused_add_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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_2(in_ptr0, in_ptr1, out_ptr0, 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 % 64
    x1 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + (192*x1)), xmask)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/3o/c3ohg5u3kgv4nzwhohtfisgwxfppk7dvczwpyrw3fsxa3lspzcrm.py
# Topologically Sorted Source Nodes: [mid_vectors_1], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   mid_vectors_1 => add_3
# Graph fragment:
#   %add_3 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_2, %getitem_1), kwargs = {})
triton_poi_fused_add_3 = async_compile.triton('triton_poi_fused_add_3', '''
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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_3(in_ptr0, in_ptr1, out_ptr0, 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 % 64
    x1 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (64 + x0 + (192*x1)), xmask)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ci/ccicvllvu6lhrojla6mefablakbyu4tzhjfeosnnd47v7p7yde2j.py
# Topologically Sorted Source Nodes: [hig_vectors_1], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   hig_vectors_1 => add_4
# Graph fragment:
#   %add_4 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_3, %getitem_2), kwargs = {})
triton_poi_fused_add_4 = async_compile.triton('triton_poi_fused_add_4', '''
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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_4(in_ptr0, in_ptr1, out_ptr0, 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 % 64
    x1 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (128 + x0 + (192*x1)), xmask)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/we/cwehzh3z7s3nstmx7epm33gsgvy4mbg6xttmec2livc3ylvoisbm.py
# Topologically Sorted Source Nodes: [low_vectors_2], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
#   low_vectors_2 => add_5, rsqrt, var_mean
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%add_2, [3]), kwargs = {correction: 0, keepdim: True})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_3, 1e-08), kwargs = {})
#   %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_5,), kwargs = {})
triton_poi_fused_native_layer_norm_5 = async_compile.triton('triton_poi_fused_native_layer_norm_5', '''
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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_native_layer_norm_5', 'mutated_arg_names': [], '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_native_layer_norm_5(in_ptr0, out_ptr0, out_ptr1, 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_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-08
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + (x0), tmp8, xmask)
    tl.store(out_ptr1 + (x0), tmp23, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/gm/cgmzdl6z4u4iu5bafof4nvofri54twnq7gifufkslpwxjmyguzb3.py
# Topologically Sorted Source Nodes: [low_vectors_2], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
#   low_vectors_2 => add_5, add_6, mul_4, mul_5, rsqrt, sub, var_mean
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%add_2, [3]), kwargs = {correction: 0, keepdim: True})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_3, 1e-08), kwargs = {})
#   %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_5,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_2, %getitem_4), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_4, %primals_6), kwargs = {})
#   %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_5, %primals_7), kwargs = {})
triton_poi_fused_native_layer_norm_6 = async_compile.triton('triton_poi_fused_native_layer_norm_6', '''
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: '*fp32', 5: '*fp32', 6: '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, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_native_layer_norm_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_native_layer_norm_6(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, 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)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + (x2), tmp8, 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, primals_8, primals_9, primals_10, primals_11 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, ), (1, ))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, ), (1, ))
    assert_size_stride(primals_9, (4, ), (1, ))
    assert_size_stride(primals_10, (4, ), (1, ))
    assert_size_stride(primals_11, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 12, 4, 4), (192, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cated_vectors], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_1, primals_2, primals_3, buf0, 768, grid=grid(768), stream=stream0)
        buf1 = empty_strided_cuda((192, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf0, (192, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((4, 12, 4, 4), (192, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, pow_1, mul_1, add, mul_2, tanh, add_1, mul_3], Original ATen: [aten.mul, aten.pow, aten.add, aten.tanh]
        triton_poi_fused_add_mul_pow_tanh_1.run(buf1, buf2, 768, grid=grid(768), stream=stream0)
        buf3 = empty_strided_cuda((192, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf2, (192, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [low_vectors_1], Original ATen: [aten.add]
        triton_poi_fused_add_2.run(primals_1, buf3, buf4, 256, grid=grid(256), stream=stream0)
        del primals_1
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mid_vectors_1], Original ATen: [aten.add]
        triton_poi_fused_add_3.run(primals_2, buf3, buf5, 256, grid=grid(256), stream=stream0)
        del primals_2
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [hig_vectors_1], Original ATen: [aten.add]
        triton_poi_fused_add_4.run(primals_3, buf3, buf6, 256, grid=grid(256), stream=stream0)
        del buf3
        del primals_3
        buf7 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf8 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        # Topologically Sorted Source Nodes: [low_vectors_2], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_5.run(buf4, buf7, buf8, 64, grid=grid(64), stream=stream0)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [low_vectors_2], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_6.run(buf4, buf7, buf8, primals_6, primals_7, buf9, 256, grid=grid(256), stream=stream0)
        del primals_7
        buf10 = buf8; del buf8  # reuse
        buf11 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [mid_vectors_2], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_5.run(buf5, buf10, buf11, 64, grid=grid(64), stream=stream0)
        buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mid_vectors_2], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_6.run(buf5, buf10, buf11, primals_8, primals_9, buf12, 256, grid=grid(256), stream=stream0)
        del primals_9
        buf13 = buf11; del buf11  # reuse
        buf14 = buf10; del buf10  # reuse
        # Topologically Sorted Source Nodes: [hig_vectors_2], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_5.run(buf6, buf13, buf14, 64, grid=grid(64), stream=stream0)
        buf15 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [hig_vectors_2], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_6.run(buf6, buf13, buf14, primals_10, primals_11, buf15, 256, grid=grid(256), stream=stream0)
        del buf13
        del buf14
        del primals_11
    return (buf9, buf12, buf15, primals_6, primals_8, primals_10, reinterpret_tensor(buf0, (192, 4), (4, 1), 0), buf1, reinterpret_tensor(buf2, (192, 4), (4, 1), 0), buf4, buf5, buf6, primals_5, )


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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 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, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = 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, primals_8, primals_9, primals_10, primals_11])
    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 math
import torch
import torch.nn as nn


class GELU(nn.Module):
    """
    Paper Section 3.4, last paragraph notice that BERT used the GELU instead of RELU
    came from : https://github.com/codertimo/BERT-pytorch/blob/master/bert_pytorch/model/utils/gelu.py
    """

    def __init__(self):
        super(GELU, self).__init__()

    def forward(self, x):
        return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 
            0.044715 * torch.pow(x, 3))))


class FFN(nn.Module):

    def __init__(self, embedding_dim, hidden_unit, dropout=0.0, eps=1e-08):
        super(FFN, self).__init__()
        self.fc1 = nn.Linear(embedding_dim, hidden_unit, bias=False)
        self.fc2 = nn.Linear(hidden_unit, embedding_dim, bias=False)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)
        self.low_ln = nn.LayerNorm(embedding_dim, eps=eps)
        self.mid_ln = nn.LayerNorm(embedding_dim, eps=eps)
        self.hig_ln = nn.LayerNorm(embedding_dim, eps=eps)
        self.act = GELU()

    def forward(self, low_vectors, mid_vectors, hig_vectors):
        low_num, mid_num, hig_num = low_vectors.size()[1], mid_vectors.size()[1
            ], hig_vectors.size()[1]
        low_residual = low_vectors
        mid_residual = mid_vectors
        hig_residual = hig_vectors
        cated_vectors = torch.cat((low_vectors, mid_vectors, hig_vectors),
            dim=1)
        output = self.dropout2(self.fc2(self.dropout1(self.act(self.fc1(
            cated_vectors)))))
        low_vectors, mid_vectors, hig_vectors = torch.split(output, [
            low_num, mid_num, hig_num], dim=1)
        low_vectors = low_residual + low_vectors
        mid_vectors = mid_residual + mid_vectors
        hig_vectors = hig_residual + hig_vectors
        low_vectors = self.low_ln(low_vectors)
        mid_vectors = self.mid_ln(mid_vectors)
        hig_vectors = self.hig_ln(hig_vectors)
        return low_vectors, mid_vectors, hig_vectors


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


def get_init_inputs():
    return [[], {'embedding_dim': 4, 'hidden_unit': 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_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 12
    x0 = xindex % 16
    x2 = xindex // 192
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp9 & xmask,
        other=0.0)
    tmp11 = tmp0 >= tmp7
    tl.full([1], 12, tl.int64)
    tmp14 = tl.load(in_ptr2 + (x0 + 16 * (-8 + x1) + 64 * x2), tmp11 &
        xmask, other=0.0)
    tmp15 = tl.where(tmp9, tmp10, tmp14)
    tmp16 = tl.where(tmp4, tmp5, tmp15)
    tl.store(out_ptr0 + x3, tmp16, xmask)


@triton.jit
def triton_poi_fused_add_mul_pow_tanh_1(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = tmp0 * tmp0
    tmp4 = tmp3 * tmp0
    tmp5 = 0.044715
    tmp6 = tmp4 * tmp5
    tmp7 = tmp0 + tmp6
    tmp8 = 0.7978845608028654
    tmp9 = tmp7 * tmp8
    tmp10 = libdevice.tanh(tmp9)
    tmp11 = 1.0
    tmp12 = tmp10 + tmp11
    tmp13 = tmp2 * tmp12
    tl.store(out_ptr0 + x0, tmp13, xmask)


@triton.jit
def triton_poi_fused_add_2(in_ptr0, in_ptr1, out_ptr0, 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 % 64
    x1 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 192 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_3(in_ptr0, in_ptr1, out_ptr0, 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 % 64
    x1 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + (64 + x0 + 192 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_4(in_ptr0, in_ptr1, out_ptr0, 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 % 64
    x1 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + (128 + x0 + 192 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_5(in_ptr0, out_ptr0, out_ptr1,
    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_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-08
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_6(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, out_ptr0, 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)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 12, 4, 4), (192, 16, 4, 1), torch.float32
            )
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(768)](primals_1, primals_2, primals_3,
            buf0, 768, XBLOCK=128, num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((192, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (192, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((4, 12, 4, 4), (192, 16, 4, 1), torch.float32
            )
        triton_poi_fused_add_mul_pow_tanh_1[grid(768)](buf1, buf2, 768,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((192, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (192, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_2[grid(256)](primals_1, buf3, buf4, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_3[grid(256)](primals_2, buf3, buf5, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_4[grid(256)](primals_3, buf3, buf6, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf3
        del primals_3
        buf7 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf8 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        triton_poi_fused_native_layer_norm_5[grid(64)](buf4, buf7, buf8, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_6[grid(256)](buf4, buf7, buf8,
            primals_6, primals_7, buf9, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_7
        buf10 = buf8
        del buf8
        buf11 = buf7
        del buf7
        triton_poi_fused_native_layer_norm_5[grid(64)](buf5, buf10, buf11, 
            64, XBLOCK=64, num_warps=1, num_stages=1)
        buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_6[grid(256)](buf5, buf10, buf11,
            primals_8, primals_9, buf12, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_9
        buf13 = buf11
        del buf11
        buf14 = buf10
        del buf10
        triton_poi_fused_native_layer_norm_5[grid(64)](buf6, buf13, buf14, 
            64, XBLOCK=64, num_warps=1, num_stages=1)
        buf15 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_6[grid(256)](buf6, buf13, buf14,
            primals_10, primals_11, buf15, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf13
        del buf14
        del primals_11
    return (buf9, buf12, buf15, primals_6, primals_8, primals_10,
        reinterpret_tensor(buf0, (192, 4), (4, 1), 0), buf1,
        reinterpret_tensor(buf2, (192, 4), (4, 1), 0), buf4, buf5, buf6,
        primals_5)


class GELU(nn.Module):
    """
    Paper Section 3.4, last paragraph notice that BERT used the GELU instead of RELU
    came from : https://github.com/codertimo/BERT-pytorch/blob/master/bert_pytorch/model/utils/gelu.py
    """

    def __init__(self):
        super(GELU, self).__init__()

    def forward(self, x):
        return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 
            0.044715 * torch.pow(x, 3))))


class FFNNew(nn.Module):

    def __init__(self, embedding_dim, hidden_unit, dropout=0.0, eps=1e-08):
        super(FFNNew, self).__init__()
        self.fc1 = nn.Linear(embedding_dim, hidden_unit, bias=False)
        self.fc2 = nn.Linear(hidden_unit, embedding_dim, bias=False)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)
        self.low_ln = nn.LayerNorm(embedding_dim, eps=eps)
        self.mid_ln = nn.LayerNorm(embedding_dim, eps=eps)
        self.hig_ln = nn.LayerNorm(embedding_dim, eps=eps)
        self.act = GELU()

    def forward(self, input_0, input_1, input_2):
        primals_4 = self.fc1.weight
        primals_5 = self.fc2.weight
        primals_6 = self.low_ln.weight
        primals_7 = self.low_ln.bias
        primals_8 = self.mid_ln.weight
        primals_9 = self.mid_ln.bias
        primals_10 = self.hig_ln.weight
        primals_11 = self.hig_ln.bias
        primals_1 = input_0
        primals_2 = input_1
        primals_3 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11])
        return output[0], output[1], output[2]
