# 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/jl/cjlk4xuqpzl2lat2kevec2h4nkczd6gpdaqkalpgipxvtafjlvtr.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%unsqueeze, %unsqueeze_1], -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=[32], 
    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_cat_0', '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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = (xindex // 2)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x1), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 2, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x1), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x2), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/lq/clqafcr737eg5vlzqjpwf4tfkm4vwuesyjawy3bpl3gd6luwsdtw.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_2 => convolution
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%permute, %primals_3, %primals_4, [1], [0], [1], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_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=[8, 4], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_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_convolution_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 8
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 2
    y1 = (yindex // 2)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (2*x2) + (8*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/tb/ctb246ju53t5owajaechkni5wyt2buny53njlfmj6mhtbvjee4ry.py
# Topologically Sorted Source Nodes: [x_2, y, y_2], Original ATen: [aten.convolution, aten.native_layer_norm, aten.clone]
# Source node to ATen node mapping:
#   x_2 => convolution
#   y => add, clone, rsqrt, var_mean
#   y_2 => clone_1
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%permute, %primals_3, %primals_4, [1], [0], [1], False, [0], 1), kwargs = {})
#   %clone : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%permute_1,), kwargs = {memory_format: torch.contiguous_format})
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%clone, [1, 2]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
#   %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_2,), kwargs = {memory_format: torch.contiguous_format})
triton_per_fused_clone_convolution_native_layer_norm_2 = async_compile.triton('triton_per_fused_clone_convolution_native_layer_norm_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.persistent_reduction(
    size_hints=[4, 128],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 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, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_clone_convolution_native_layer_norm_2', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 4, '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}
)
@triton.jit
def triton_per_fused_clone_convolution_native_layer_norm_2(in_out_ptr0, in_out_ptr1, in_ptr0, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 80
    RBLOCK: tl.constexpr = 128
    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 = rindex < rnumel
    r3 = rindex
    x0 = xindex
    r2 = (rindex // 4)
    tmp0 = tl.load(in_out_ptr0 + (r3 + (80*x0)), rmask & xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (r2), rmask, eviction_policy='evict_last', other=0.0)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(rmask & xmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp8 = tl.where(rmask & xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 80, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp3 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(rmask & xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 80.0
    tmp20 = tmp18 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tmp24 = tmp2 - tmp12
    tmp25 = tmp24 * tmp23
    tl.store(in_out_ptr0 + (r3 + (80*x0)), tmp2, rmask & xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (x0), tmp23, xmask)
    tl.store(out_ptr1 + (r3 + (80*x0)), tmp25, rmask & xmask)
    tl.store(out_ptr0 + (x0), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ea/ceamewukbzvd2qadnyd7yfsn36mfmt424lcinex6cru5tadkok3g.py
# Topologically Sorted Source Nodes: [y_2, y_3], Original ATen: [aten.add, aten.gelu]
# Source node to ATen node mapping:
#   y_2 => add_1
#   y_3 => add_2, erf, mul_1, mul_2, mul_3
# Graph fragment:
#   %add_1 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %primals_6), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, 0.5), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, 0.7071067811865476), kwargs = {})
#   %erf : [num_users=1] = call_function[target=torch.ops.aten.erf.default](args = (%mul_2,), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%erf, 1), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %add_2), kwargs = {})
triton_poi_fused_add_gelu_3 = async_compile.triton('triton_poi_fused_add_gelu_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=[16384], 
    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_gelu_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_gelu_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 200
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.5
    tmp4 = tmp2 * tmp3
    tmp5 = 0.7071067811865476
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.erf(tmp6)
    tmp8 = 1.0
    tmp9 = tmp7 + tmp8
    tmp10 = tmp4 * tmp9
    tl.store(out_ptr0 + (x2), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ju/cjuakxwc4qgqk4wps56rug3afprgmfg7lm66rgnjvkyysqoprjxx.py
# Topologically Sorted Source Nodes: [x_4, y_6], Original ATen: [aten.add, aten.native_layer_norm, aten.native_layer_norm_backward]
# Source node to ATen node mapping:
#   x_4 => add_3
#   y_6 => add_4, clone_2, rsqrt_1, var_mean_1
# Graph fragment:
#   %add_3 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%permute_1, %permute_5), kwargs = {})
#   %clone_2 : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%add_3,), kwargs = {memory_format: torch.contiguous_format})
#   %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%clone_2, [1, 2]), kwargs = {correction: 0, keepdim: True})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_2, 1e-05), kwargs = {})
#   %rsqrt_1 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_4,), kwargs = {})
#   %div_6 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%rsqrt_1, 80), kwargs = {})
triton_per_fused_add_native_layer_norm_native_layer_norm_backward_4 = async_compile.triton('triton_per_fused_add_native_layer_norm_native_layer_norm_backward_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.persistent_reduction(
    size_hints=[4, 128],
    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=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, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_native_layer_norm_native_layer_norm_backward_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 4, '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}
)
@triton.jit
def triton_per_fused_add_native_layer_norm_native_layer_norm_backward_4(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 80
    RBLOCK: tl.constexpr = 128
    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 = rindex < rnumel
    r3 = rindex
    x0 = xindex
    r1 = rindex % 4
    tmp0 = tl.load(in_ptr0 + (r3 + (80*x0)), rmask & xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r3 + (80*x0)), rmask & xmask, other=0.0)
    tmp2 = tl.load(in_ptr2 + (r1), rmask, eviction_policy='evict_last', other=0.0)
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp7 = tl.where(rmask & xmask, tmp5, 0)
    tmp8 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp10 = tl.where(rmask & xmask, tmp8, 0)
    tmp11 = tl.sum(tmp10, 1)[:, None]
    tmp12 = tl.full([XBLOCK, 1], 80, tl.int32)
    tmp13 = tmp12.to(tl.float32)
    tmp14 = tmp11 / tmp13
    tmp15 = tmp5 - tmp14
    tmp16 = tmp15 * tmp15
    tmp17 = tl.broadcast_to(tmp16, [XBLOCK, RBLOCK])
    tmp19 = tl.where(rmask & xmask, tmp17, 0)
    tmp20 = tl.sum(tmp19, 1)[:, None]
    tmp21 = 80.0
    tmp22 = tmp20 / tmp21
    tmp23 = 1e-05
    tmp24 = tmp22 + tmp23
    tmp25 = libdevice.rsqrt(tmp24)
    tmp26 = 0.0125
    tmp27 = tmp25 * tmp26
    tl.store(out_ptr2 + (x0), tmp27, xmask)
    tl.store(out_ptr0 + (x0), tmp14, xmask)
    tl.store(out_ptr1 + (x0), tmp20, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/d4/cd4jxzwkujub7r7x2kc2ptuhbzq2wknyzptylsmaqweblkuyo3gg.py
# Topologically Sorted Source Nodes: [x_4, y_6], Original ATen: [aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
#   x_4 => add_3
#   y_6 => add_4, clone_2, mul_4, rsqrt_1, sub_1, var_mean_1
# Graph fragment:
#   %add_3 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%permute_1, %permute_5), kwargs = {})
#   %clone_2 : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%add_3,), kwargs = {memory_format: torch.contiguous_format})
#   %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%clone_2, [1, 2]), kwargs = {correction: 0, keepdim: True})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_2, 1e-05), kwargs = {})
#   %rsqrt_1 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_4,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clone_2, %getitem_3), kwargs = {})
#   %mul_4 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %rsqrt_1), kwargs = {})
triton_poi_fused_add_native_layer_norm_5 = async_compile.triton('triton_poi_fused_add_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=[128, 4], tile_hint=TileHint.DEFAULT,
    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=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_add_native_layer_norm_5', '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_add_native_layer_norm_5(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 80
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y1 = (yindex // 20)
    y0 = yindex % 20
    tmp0 = tl.load(in_ptr0 + (x2 + (4*y3)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2 + (4*y3)), xmask & ymask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (y1), ymask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + (y1), ymask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp6 = tmp4 - tmp5
    tmp8 = 80.0
    tmp9 = tmp7 / tmp8
    tmp10 = 1e-05
    tmp11 = tmp9 + tmp10
    tmp12 = libdevice.rsqrt(tmp11)
    tmp13 = tmp6 * tmp12
    tl.store(out_ptr0 + (y0 + (20*x2) + (80*y1)), tmp13, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/rk/crklwmg5q7x7alpquawbito7ooohgdxx65vzxrffgnke22xk56fp.py
# Topologically Sorted Source Nodes: [y_8], Original ATen: [aten.gelu]
# Source node to ATen node mapping:
#   y_8 => add_5, erf_1, mul_5, mul_6, mul_7
# Graph fragment:
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_5, 0.5), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_5, 0.7071067811865476), kwargs = {})
#   %erf_1 : [num_users=1] = call_function[target=torch.ops.aten.erf.default](args = (%mul_6,), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%erf_1, 1), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_5, %add_5), kwargs = {})
triton_poi_fused_gelu_6 = async_compile.triton('triton_poi_fused_gelu_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=[64], 
    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_gelu_6', '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_gelu_6(in_ptr0, 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 + (x0), xmask)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865476
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tl.store(out_ptr0 + (x0), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ol/colw25ixmqldxap3vr7tcopgkkckz2p2p54yrgudg27wrk5t77kh.py
# Topologically Sorted Source Nodes: [x_4, x_5], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   x_4 => add_3
#   x_5 => add_6
# Graph fragment:
#   %add_3 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%permute_1, %permute_5), kwargs = {})
#   %add_6 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_3, %view_7), kwargs = {})
triton_poi_fused_add_7 = async_compile.triton('triton_poi_fused_add_7', '''
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=[128, 4], tile_hint=TileHint.DEFAULT,
    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=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_add_7', '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_add_7(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 80
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 20
    y1 = (yindex // 20)
    tmp0 = tl.load(in_ptr0 + (x2 + (4*y3)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2 + (4*y3)), xmask & ymask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (y0 + (20*x2) + (80*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr4 + (y0), ymask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp7 = tmp5 + tmp6
    tmp8 = tmp4 + tmp7
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp8, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/7o/c7oa6vu3xqqazriukmh2kbzldqeyz5n5v4nn26b7k2y3awodor3f.py
# Topologically Sorted Source Nodes: [y_9], Original ATen: [aten.native_layer_norm, aten.native_layer_norm_backward]
# Source node to ATen node mapping:
#   y_9 => add_7, clone_3, rsqrt_2, var_mean_2
# Graph fragment:
#   %clone_3 : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%add_6,), kwargs = {memory_format: torch.contiguous_format})
#   %var_mean_2 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%clone_3, [1, 2]), kwargs = {correction: 0, keepdim: True})
#   %add_7 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_4, 1e-05), kwargs = {})
#   %rsqrt_2 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_7,), kwargs = {})
#   %div_5 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%rsqrt_2, 80), kwargs = {})
triton_per_fused_native_layer_norm_native_layer_norm_backward_8 = async_compile.triton('triton_per_fused_native_layer_norm_native_layer_norm_backward_8', '''
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=[4, 128],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: '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, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_native_layer_norm_native_layer_norm_backward_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 4, '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}
)
@triton.jit
def triton_per_fused_native_layer_norm_native_layer_norm_backward_8(in_ptr0, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 80
    RBLOCK: tl.constexpr = 128
    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 = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (80*x0)), rmask & xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(rmask & xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(rmask & xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 80, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(rmask & xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = 80.0
    tmp18 = tmp16 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tmp22 = 0.0125
    tmp23 = tmp21 * tmp22
    tl.store(out_ptr2 + (x0), tmp23, xmask)
    tl.store(out_ptr0 + (x0), tmp10, xmask)
    tl.store(out_ptr1 + (x0), tmp16, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/pw/cpw5buzc33725qu7ogmpsrjxe7sbelsszbmrz2ex4yq64qzcwa6p.py
# Topologically Sorted Source Nodes: [y_9], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
#   y_9 => add_7, clone_3, mul_8, rsqrt_2, sub_2, var_mean_2
# Graph fragment:
#   %clone_3 : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%add_6,), kwargs = {memory_format: torch.contiguous_format})
#   %var_mean_2 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%clone_3, [1, 2]), kwargs = {correction: 0, keepdim: True})
#   %add_7 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_4, 1e-05), kwargs = {})
#   %rsqrt_2 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_7,), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clone_3, %getitem_5), kwargs = {})
#   %mul_8 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, %rsqrt_2), kwargs = {})
triton_poi_fused_native_layer_norm_9 = async_compile.triton('triton_poi_fused_native_layer_norm_9', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16, 32], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: '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_native_layer_norm_9', '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_native_layer_norm_9(in_ptr0, in_ptr1, in_ptr2, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 20
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (80*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y1), ymask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (y1), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 80.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tl.store(out_ptr0 + (x2 + (20*y3)), tmp9, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/26/c26dxisjciatxhvyg75cwsdktjacyunf673lp3isrh43clfzsrbq.py
# Topologically Sorted Source Nodes: [y_11], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   y_11 => clone_4
# Graph fragment:
#   %clone_4 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_8,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_10 = async_compile.triton('triton_poi_fused_clone_10', '''
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=[128, 4], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_10', '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_clone_10(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 80
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 20
    y1 = (yindex // 20)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (20*x2) + (80*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/tr/ctrzirgo5k7tqs3hquo3onguh75zqqge5q3h65lsscevadjqjcrg.py
# Topologically Sorted Source Nodes: [x_10, x_12], Original ATen: [aten.native_layer_norm, aten.mean]
# Source node to ATen node mapping:
#   x_10 => add_21, mul_24, rsqrt_6, sub_6, var_mean_6
#   x_12 => mean
# Graph fragment:
#   %var_mean_6 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%clone_9, [1, 2]), kwargs = {correction: 0, keepdim: True})
#   %add_21 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_12, 1e-05), kwargs = {})
#   %rsqrt_6 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_21,), kwargs = {})
#   %sub_6 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clone_9, %getitem_13), kwargs = {})
#   %mul_24 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_6, %rsqrt_6), kwargs = {})
#   %mean : [num_users=2] = call_function[target=torch.ops.aten.mean.dim](args = (%permute_20, [1]), kwargs = {})
triton_per_fused_mean_native_layer_norm_11 = async_compile.triton('triton_per_fused_mean_native_layer_norm_11', '''
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=[16, 32],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mean_native_layer_norm_11', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_mean_native_layer_norm_11(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 20
    RBLOCK: tl.constexpr = 32
    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 = rindex < rnumel
    r2 = rindex
    x0 = xindex % 4
    x1 = (xindex // 4)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*r2) + (80*x1)), rmask & xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 80.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp12 = tl.where(rmask & xmask, tmp10, 0)
    tmp13 = tl.sum(tmp12, 1)[:, None]
    tmp14 = 20.0
    tmp15 = tmp13 / tmp14
    tl.store(out_ptr0 + (r2 + (20*x3)), tmp9, rmask & xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x3), tmp15, 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, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (20, 2, 1), (2, 1, 1))
    assert_size_stride(primals_4, (20, ), (1, ))
    assert_size_stride(primals_5, (200, 4), (4, 1))
    assert_size_stride(primals_6, (200, ), (1, ))
    assert_size_stride(primals_7, (4, 200), (200, 1))
    assert_size_stride(primals_8, (4, ), (1, ))
    assert_size_stride(primals_9, (4, 20), (20, 1))
    assert_size_stride(primals_10, (4, ), (1, ))
    assert_size_stride(primals_11, (20, 4), (4, 1))
    assert_size_stride(primals_12, (20, ), (1, ))
    assert_size_stride(primals_13, (200, 4), (4, 1))
    assert_size_stride(primals_14, (200, ), (1, ))
    assert_size_stride(primals_15, (4, 200), (200, 1))
    assert_size_stride(primals_16, (4, ), (1, ))
    assert_size_stride(primals_17, (4, 20), (20, 1))
    assert_size_stride(primals_18, (4, ), (1, ))
    assert_size_stride(primals_19, (20, 4), (4, 1))
    assert_size_stride(primals_20, (20, ), (1, ))
    assert_size_stride(primals_21, (200, 4), (4, 1))
    assert_size_stride(primals_22, (200, ), (1, ))
    assert_size_stride(primals_23, (4, 200), (200, 1))
    assert_size_stride(primals_24, (4, ), (1, ))
    assert_size_stride(primals_25, (4, 20), (20, 1))
    assert_size_stride(primals_26, (4, ), (1, ))
    assert_size_stride(primals_27, (20, 4), (4, 1))
    assert_size_stride(primals_28, (20, ), (1, ))
    assert_size_stride(primals_29, (4, 4), (4, 1))
    assert_size_stride(primals_30, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 2), (8, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_1, primals_2, buf0, 32, grid=grid(32), stream=stream0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 2, 4), (8, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf0, buf1, 8, 4, grid=grid(8, 4), stream=stream0)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_3, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf2, (4, 20, 4), (80, 4, 1))
        del buf1
        buf3 = buf2; del buf2  # reuse
        buf4 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32)
        buf7 = reinterpret_tensor(buf5, (4, 1, 1), (1, 1, 1), 0); del buf5  # reuse
        buf8 = empty_strided_cuda((4, 20, 4), (80, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2, y, y_2], Original ATen: [aten.convolution, aten.native_layer_norm, aten.clone]
        triton_per_fused_clone_convolution_native_layer_norm_2.run(buf3, buf7, primals_4, buf4, buf8, 4, 80, grid=grid(4), stream=stream0)
        del primals_4
        buf9 = empty_strided_cuda((80, 200), (200, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y_2], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf8, (80, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 200), (1, 4), 0), out=buf9)
        buf10 = empty_strided_cuda((4, 20, 200), (4000, 200, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y_2, y_3], Original ATen: [aten.add, aten.gelu]
        triton_poi_fused_add_gelu_3.run(buf9, primals_6, buf10, 16000, grid=grid(16000), stream=stream0)
        buf11 = empty_strided_cuda((80, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf10, (80, 200), (200, 1), 0), reinterpret_tensor(primals_7, (200, 4), (1, 200), 0), out=buf11)
        buf12 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32)
        buf13 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32)
        buf64 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_4, y_6], Original ATen: [aten.add, aten.native_layer_norm, aten.native_layer_norm_backward]
        triton_per_fused_add_native_layer_norm_native_layer_norm_backward_4.run(buf3, buf11, primals_8, buf12, buf13, buf64, 4, 80, grid=grid(4), stream=stream0)
        buf15 = empty_strided_cuda((4, 4, 20), (80, 20, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_4, y_6], Original ATen: [aten.add, aten.native_layer_norm]
        triton_poi_fused_add_native_layer_norm_5.run(buf3, buf11, primals_8, buf12, buf13, buf15, 80, 4, grid=grid(80, 4), stream=stream0)
        buf16 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y_7], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_10, reinterpret_tensor(buf15, (16, 20), (20, 1), 0), reinterpret_tensor(primals_9, (20, 4), (1, 20), 0), alpha=1, beta=1, out=buf16)
        del primals_10
        buf17 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y_8], Original ATen: [aten.gelu]
        triton_poi_fused_gelu_6.run(buf16, buf17, 64, grid=grid(64), stream=stream0)
        buf18 = empty_strided_cuda((16, 20), (20, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf17, (16, 4), (4, 1), 0), reinterpret_tensor(primals_11, (4, 20), (1, 4), 0), out=buf18)
        buf19 = empty_strided_cuda((4, 4, 20), (80, 1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [x_4, x_5], Original ATen: [aten.add]
        triton_poi_fused_add_7.run(buf3, buf11, primals_8, buf18, primals_12, buf19, 80, 4, grid=grid(80, 4), stream=stream0)
        del primals_12
        del primals_8
        buf20 = buf13; del buf13  # reuse
        buf21 = buf12; del buf12  # reuse
        buf63 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y_9], Original ATen: [aten.native_layer_norm, aten.native_layer_norm_backward]
        triton_per_fused_native_layer_norm_native_layer_norm_backward_8.run(buf19, buf20, buf21, buf63, 4, 80, grid=grid(4), stream=stream0)
        buf23 = reinterpret_tensor(buf18, (4, 4, 20), (80, 20, 1), 0); del buf18  # reuse
        # Topologically Sorted Source Nodes: [y_9], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_9.run(buf19, buf20, buf21, buf23, 16, 20, grid=grid(16, 20), stream=stream0)
        buf24 = reinterpret_tensor(buf11, (4, 20, 4), (80, 4, 1), 0); del buf11  # reuse
        # Topologically Sorted Source Nodes: [y_11], Original ATen: [aten.clone]
        triton_poi_fused_clone_10.run(buf23, buf24, 80, 4, grid=grid(80, 4), stream=stream0)
        buf25 = empty_strided_cuda((80, 200), (200, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y_11], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf24, (80, 4), (4, 1), 0), reinterpret_tensor(primals_13, (4, 200), (1, 4), 0), out=buf25)
        buf26 = empty_strided_cuda((4, 20, 200), (4000, 200, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y_11, y_12], Original ATen: [aten.add, aten.gelu]
        triton_poi_fused_add_gelu_3.run(buf25, primals_14, buf26, 16000, grid=grid(16000), stream=stream0)
        buf27 = empty_strided_cuda((80, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf26, (80, 200), (200, 1), 0), reinterpret_tensor(primals_15, (200, 4), (1, 200), 0), out=buf27)
        buf28 = buf21; del buf21  # reuse
        buf29 = buf20; del buf20  # reuse
        buf62 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_6, y_15], Original ATen: [aten.add, aten.native_layer_norm, aten.native_layer_norm_backward]
        triton_per_fused_add_native_layer_norm_native_layer_norm_backward_4.run(buf19, buf27, primals_16, buf28, buf29, buf62, 4, 80, grid=grid(4), stream=stream0)
        buf31 = empty_strided_cuda((4, 4, 20), (80, 20, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_6, y_15], Original ATen: [aten.add, aten.native_layer_norm]
        triton_poi_fused_add_native_layer_norm_5.run(buf19, buf27, primals_16, buf28, buf29, buf31, 80, 4, grid=grid(80, 4), stream=stream0)
        buf32 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y_16], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_18, reinterpret_tensor(buf31, (16, 20), (20, 1), 0), reinterpret_tensor(primals_17, (20, 4), (1, 20), 0), alpha=1, beta=1, out=buf32)
        del primals_18
        buf33 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y_17], Original ATen: [aten.gelu]
        triton_poi_fused_gelu_6.run(buf32, buf33, 64, grid=grid(64), stream=stream0)
        buf34 = empty_strided_cuda((16, 20), (20, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf33, (16, 4), (4, 1), 0), reinterpret_tensor(primals_19, (4, 20), (1, 4), 0), out=buf34)
        buf35 = empty_strided_cuda((4, 4, 20), (80, 1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [x_6, x_7], Original ATen: [aten.add]
        triton_poi_fused_add_7.run(buf19, buf27, primals_16, buf34, primals_20, buf35, 80, 4, grid=grid(80, 4), stream=stream0)
        del primals_16
        del primals_20
        buf36 = buf29; del buf29  # reuse
        buf37 = buf28; del buf28  # reuse
        buf61 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y_18], Original ATen: [aten.native_layer_norm, aten.native_layer_norm_backward]
        triton_per_fused_native_layer_norm_native_layer_norm_backward_8.run(buf35, buf36, buf37, buf61, 4, 80, grid=grid(4), stream=stream0)
        buf39 = reinterpret_tensor(buf34, (4, 4, 20), (80, 20, 1), 0); del buf34  # reuse
        # Topologically Sorted Source Nodes: [y_18], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_9.run(buf35, buf36, buf37, buf39, 16, 20, grid=grid(16, 20), stream=stream0)
        buf40 = reinterpret_tensor(buf27, (4, 20, 4), (80, 4, 1), 0); del buf27  # reuse
        # Topologically Sorted Source Nodes: [y_20], Original ATen: [aten.clone]
        triton_poi_fused_clone_10.run(buf39, buf40, 80, 4, grid=grid(80, 4), stream=stream0)
        buf41 = empty_strided_cuda((80, 200), (200, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y_20], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf40, (80, 4), (4, 1), 0), reinterpret_tensor(primals_21, (4, 200), (1, 4), 0), out=buf41)
        buf42 = empty_strided_cuda((4, 20, 200), (4000, 200, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y_20, y_21], Original ATen: [aten.add, aten.gelu]
        triton_poi_fused_add_gelu_3.run(buf41, primals_22, buf42, 16000, grid=grid(16000), stream=stream0)
        buf43 = reinterpret_tensor(buf19, (80, 4), (4, 1), 0); del buf19  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf42, (80, 200), (200, 1), 0), reinterpret_tensor(primals_23, (200, 4), (1, 200), 0), out=buf43)
        buf44 = buf37; del buf37  # reuse
        buf45 = buf36; del buf36  # reuse
        buf60 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_8, y_24], Original ATen: [aten.add, aten.native_layer_norm, aten.native_layer_norm_backward]
        triton_per_fused_add_native_layer_norm_native_layer_norm_backward_4.run(buf35, buf43, primals_24, buf44, buf45, buf60, 4, 80, grid=grid(4), stream=stream0)
        buf47 = empty_strided_cuda((4, 4, 20), (80, 20, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_8, y_24], Original ATen: [aten.add, aten.native_layer_norm]
        triton_poi_fused_add_native_layer_norm_5.run(buf35, buf43, primals_24, buf44, buf45, buf47, 80, 4, grid=grid(80, 4), stream=stream0)
        buf48 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y_25], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_26, reinterpret_tensor(buf47, (16, 20), (20, 1), 0), reinterpret_tensor(primals_25, (20, 4), (1, 20), 0), alpha=1, beta=1, out=buf48)
        del primals_26
        buf49 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y_26], Original ATen: [aten.gelu]
        triton_poi_fused_gelu_6.run(buf48, buf49, 64, grid=grid(64), stream=stream0)
        buf50 = empty_strided_cuda((16, 20), (20, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf49, (16, 4), (4, 1), 0), reinterpret_tensor(primals_27, (4, 20), (1, 4), 0), out=buf50)
        buf51 = empty_strided_cuda((4, 4, 20), (80, 1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [x_8, x_9, x_10], Original ATen: [aten.add, aten.native_layer_norm]
        triton_poi_fused_add_7.run(buf35, buf43, primals_24, buf50, primals_28, buf51, 80, 4, grid=grid(80, 4), stream=stream0)
        del buf35
        del buf43
        del primals_24
        del primals_28
        buf52 = buf45; del buf45  # reuse
        buf53 = buf44; del buf44  # reuse
        buf59 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.native_layer_norm, aten.native_layer_norm_backward]
        triton_per_fused_native_layer_norm_native_layer_norm_backward_8.run(buf51, buf52, buf53, buf59, 4, 80, grid=grid(4), stream=stream0)
        buf55 = reinterpret_tensor(buf50, (4, 4, 20), (80, 20, 1), 0); del buf50  # reuse
        buf56 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf57 = buf56; del buf56  # reuse
        # Topologically Sorted Source Nodes: [x_10, x_12], Original ATen: [aten.native_layer_norm, aten.mean]
        triton_per_fused_mean_native_layer_norm_11.run(buf57, buf51, buf52, buf53, buf55, 16, 20, grid=grid(16), stream=stream0)
        del buf51
        del buf52
        del buf53
        buf58 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_13], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_30, buf57, reinterpret_tensor(primals_29, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf58)
        del primals_30
    return (buf58, primals_3, primals_6, primals_14, primals_22, reinterpret_tensor(buf0, (4, 2, 4), (8, 1, 2), 0), buf3, buf4, buf7, reinterpret_tensor(buf8, (80, 4), (4, 1), 0), buf9, reinterpret_tensor(buf10, (80, 200), (200, 1), 0), buf15, buf16, reinterpret_tensor(buf17, (16, 4), (4, 1), 0), buf23, reinterpret_tensor(buf24, (80, 4), (4, 1), 0), buf25, reinterpret_tensor(buf26, (80, 200), (200, 1), 0), buf31, buf32, reinterpret_tensor(buf33, (16, 4), (4, 1), 0), buf39, reinterpret_tensor(buf40, (80, 4), (4, 1), 0), buf41, reinterpret_tensor(buf42, (80, 200), (200, 1), 0), buf47, buf48, reinterpret_tensor(buf49, (16, 4), (4, 1), 0), buf55, buf57, primals_29, buf59, primals_27, primals_25, buf60, primals_23, primals_21, buf61, primals_19, primals_17, buf62, primals_15, primals_13, buf63, primals_11, primals_9, buf64, primals_7, 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, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((20, 2, 1), (2, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((20, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((200, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((200, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 200), (200, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, 20), (20, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((20, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((20, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((200, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((200, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((4, 200), (200, 1), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((4, 20), (20, 1), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((20, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((20, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((200, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((200, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_23 = rand_strided((4, 200), (200, 1), device='cuda:0', dtype=torch.float32)
    primals_24 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_25 = rand_strided((4, 20), (20, 1), device='cuda:0', dtype=torch.float32)
    primals_26 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_27 = rand_strided((20, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_28 = rand_strided((20, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_29 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_30 = 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, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30])
    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
import torch.nn.functional as F


class MlpBlock(nn.Module):

    def __init__(self, features, hidden_dim):
        super().__init__()
        self.hidden_dim = hidden_dim
        self.features = features
        self.fc1 = nn.Linear(self.features, self.hidden_dim)
        self.fc2 = nn.Linear(self.hidden_dim, int(self.features))

    def forward(self, x):
        y = self.fc1(x)
        y = F.gelu(y)
        return self.fc2(y)


class MixerBlock(nn.Module):
    """Mixer block layer"""

    def __init__(self, tokens_dim, tokens_mlp_dim, channels_dim,
        channels_mlp_dim):
        super().__init__()
        self.tokens_mlp_dim = tokens_mlp_dim
        self.tokens_dim = tokens_dim
        self.channels_mlp_dim = channels_mlp_dim
        self.channels_dim = channels_dim
        self.token_mixer = MlpBlock(self.tokens_dim, self.tokens_mlp_dim)
        self.channel_mixer = MlpBlock(self.channels_dim, self.channels_mlp_dim)

    def forward(self, x):
        y = F.layer_norm(x, x.shape[1:])
        y = torch.transpose(y, 1, 2)
        y = self.token_mixer(y)
        y = torch.transpose(y, 1, 2)
        x = x + y
        y = F.layer_norm(x, x.shape[1:])
        return x + self.channel_mixer(y)


class MlpMixer(nn.Module):
    """Mixer architecture"""

    def __init__(self, patches, feature_length, num_classes, num_blocks,
        hidden_dim, tokens_mlp_dim, channels_mlp_dim):
        super().__init__()
        self.patches = patches
        self.feature_length = feature_length
        self.num_classes = num_classes
        self.num_blocks = num_blocks
        self.hidden_dim = hidden_dim
        self.tokens_mlp_dim = tokens_mlp_dim
        self.channels_mlp_dim = channels_mlp_dim
        self.conv = nn.Conv1d(in_channels=self.patches, out_channels=self.
            hidden_dim, kernel_size=1)
        for nb in range(self.num_blocks):
            setattr(self, 'mixerBlock_{}'.format(nb), MixerBlock(self.
                feature_length, self.tokens_mlp_dim, self.hidden_dim, self.
                channels_mlp_dim))
        self.fc = nn.Linear(in_features=self.feature_length, out_features=
            self.num_classes)

    def forward(self, inputs):
        x = torch.transpose(inputs, 1, 2)
        x = self.conv(x)
        x = torch.transpose(x, 1, 2)
        for nb in range(self.num_blocks):
            x = getattr(self, 'mixerBlock_{}'.format(nb))(x)
        x = F.layer_norm(x, x.shape[1:])
        x = torch.transpose(x, 1, 2)
        x = torch.mean(x, dim=1)
        return self.fc(x)


class OrMixer(nn.Module):

    def __init__(self, n_layers, entity_dim):
        super(OrMixer, self).__init__()
        self.mlpMixer = MlpMixer(patches=2, feature_length=entity_dim,
            num_classes=entity_dim, num_blocks=3, hidden_dim=20,
            tokens_mlp_dim=200, channels_mlp_dim=4)

    def forward(self, x1, x2):
        x1 = x1.unsqueeze(-1)
        x2 = x2.unsqueeze(-1)
        x = torch.cat((x1, x2), dim=-1)
        x = self.mlpMixer(x)
        return x


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


def get_init_inputs():
    return [[], {'n_layers': 1, 'entity_dim': 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 torch.nn as nn
import torch.nn.functional as F
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, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = xindex // 2
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + x1, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 2, tl.int64)
    tmp9 = tl.load(in_ptr1 + x1, tmp6 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 8
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 2
    y1 = yindex // 2
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 2 * x2 + 8 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_per_fused_clone_convolution_native_layer_norm_2(in_out_ptr0,
    in_out_ptr1, in_ptr0, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK: tl.
    constexpr):
    xnumel = 4
    rnumel = 80
    RBLOCK: tl.constexpr = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r3 = rindex
    x0 = xindex
    r2 = rindex // 4
    tmp0 = tl.load(in_out_ptr0 + (r3 + 80 * x0), rmask & xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + r2, rmask, eviction_policy='evict_last', other=0.0
        )
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tl.where(rmask & xmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp8 = tl.where(rmask & xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 80, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp3 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(rmask & xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 80.0
    tmp20 = tmp18 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tmp24 = tmp2 - tmp12
    tmp25 = tmp24 * tmp23
    tl.store(in_out_ptr0 + (r3 + 80 * x0), tmp2, rmask & xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x0, tmp23, xmask)
    tl.store(out_ptr1 + (r3 + 80 * x0), tmp25, rmask & xmask)
    tl.store(out_ptr0 + x0, tmp12, xmask)


@triton.jit
def triton_poi_fused_add_gelu_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 200
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.5
    tmp4 = tmp2 * tmp3
    tmp5 = 0.7071067811865476
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.erf(tmp6)
    tmp8 = 1.0
    tmp9 = tmp7 + tmp8
    tmp10 = tmp4 * tmp9
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_per_fused_add_native_layer_norm_native_layer_norm_backward_4(in_ptr0
    , in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    rnumel = 80
    RBLOCK: tl.constexpr = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r3 = rindex
    x0 = xindex
    r1 = rindex % 4
    tmp0 = tl.load(in_ptr0 + (r3 + 80 * x0), rmask & xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r3 + 80 * x0), rmask & xmask, other=0.0)
    tmp2 = tl.load(in_ptr2 + r1, rmask, eviction_policy='evict_last', other=0.0
        )
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tl.where(rmask & xmask, tmp5, 0)
    tmp8 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp10 = tl.where(rmask & xmask, tmp8, 0)
    tmp11 = tl.sum(tmp10, 1)[:, None]
    tmp12 = tl.full([XBLOCK, 1], 80, tl.int32)
    tmp13 = tmp12.to(tl.float32)
    tmp14 = tmp11 / tmp13
    tmp15 = tmp5 - tmp14
    tmp16 = tmp15 * tmp15
    tmp17 = tl.broadcast_to(tmp16, [XBLOCK, RBLOCK])
    tmp19 = tl.where(rmask & xmask, tmp17, 0)
    tmp20 = tl.sum(tmp19, 1)[:, None]
    tmp21 = 80.0
    tmp22 = tmp20 / tmp21
    tmp23 = 1e-05
    tmp24 = tmp22 + tmp23
    tmp25 = libdevice.rsqrt(tmp24)
    tmp26 = 0.0125
    tmp27 = tmp25 * tmp26
    tl.store(out_ptr2 + x0, tmp27, xmask)
    tl.store(out_ptr0 + x0, tmp14, xmask)
    tl.store(out_ptr1 + x0, tmp20, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_5(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr,
    XBLOCK: tl.constexpr):
    ynumel = 80
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y1 = yindex // 20
    y0 = yindex % 20
    tmp0 = tl.load(in_ptr0 + (x2 + 4 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + (x2 + 4 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + y1, ymask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + y1, ymask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp6 = tmp4 - tmp5
    tmp8 = 80.0
    tmp9 = tmp7 / tmp8
    tmp10 = 1e-05
    tmp11 = tmp9 + tmp10
    tmp12 = libdevice.rsqrt(tmp11)
    tmp13 = tmp6 * tmp12
    tl.store(out_ptr0 + (y0 + 20 * x2 + 80 * y1), tmp13, xmask & ymask)


@triton.jit
def triton_poi_fused_gelu_6(in_ptr0, 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 + x0, xmask)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865476
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tl.store(out_ptr0 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_7(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 80
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 20
    y1 = yindex // 20
    tmp0 = tl.load(in_ptr0 + (x2 + 4 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + (x2 + 4 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (y0 + 20 * x2 + 80 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr4 + y0, ymask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp7 = tmp5 + tmp6
    tmp8 = tmp4 + tmp7
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp8, xmask & ymask)


@triton.jit
def triton_per_fused_native_layer_norm_native_layer_norm_backward_8(in_ptr0,
    out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    rnumel = 80
    RBLOCK: tl.constexpr = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 80 * x0), rmask & xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tl.where(rmask & xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(rmask & xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 80, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(rmask & xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = 80.0
    tmp18 = tmp16 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tmp22 = 0.0125
    tmp23 = tmp21 * tmp22
    tl.store(out_ptr2 + x0, tmp23, xmask)
    tl.store(out_ptr0 + x0, tmp10, xmask)
    tl.store(out_ptr1 + x0, tmp16, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_9(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 20
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 80 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y1, ymask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + y1, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 80.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tl.store(out_ptr0 + (x2 + 20 * y3), tmp9, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_10(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl
    .constexpr, XBLOCK: tl.constexpr):
    ynumel = 80
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 20
    y1 = yindex // 20
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 20 * x2 + 80 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_per_fused_mean_native_layer_norm_11(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    rnumel = 20
    RBLOCK: tl.constexpr = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r2 = rindex
    x0 = xindex % 4
    x1 = xindex // 4
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * r2 + 80 * x1), rmask & xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 80.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp12 = tl.where(rmask & xmask, tmp10, 0)
    tmp13 = tl.sum(tmp12, 1)[:, None]
    tmp14 = 20.0
    tmp15 = tmp13 / tmp14
    tl.store(out_ptr0 + (r2 + 20 * x3), tmp9, rmask & xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x3, tmp15, 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, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21, primals_22,
        primals_23, primals_24, primals_25, primals_26, primals_27,
        primals_28, primals_29, primals_30) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (20, 2, 1), (2, 1, 1))
    assert_size_stride(primals_4, (20,), (1,))
    assert_size_stride(primals_5, (200, 4), (4, 1))
    assert_size_stride(primals_6, (200,), (1,))
    assert_size_stride(primals_7, (4, 200), (200, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 20), (20, 1))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (20, 4), (4, 1))
    assert_size_stride(primals_12, (20,), (1,))
    assert_size_stride(primals_13, (200, 4), (4, 1))
    assert_size_stride(primals_14, (200,), (1,))
    assert_size_stride(primals_15, (4, 200), (200, 1))
    assert_size_stride(primals_16, (4,), (1,))
    assert_size_stride(primals_17, (4, 20), (20, 1))
    assert_size_stride(primals_18, (4,), (1,))
    assert_size_stride(primals_19, (20, 4), (4, 1))
    assert_size_stride(primals_20, (20,), (1,))
    assert_size_stride(primals_21, (200, 4), (4, 1))
    assert_size_stride(primals_22, (200,), (1,))
    assert_size_stride(primals_23, (4, 200), (200, 1))
    assert_size_stride(primals_24, (4,), (1,))
    assert_size_stride(primals_25, (4, 20), (20, 1))
    assert_size_stride(primals_26, (4,), (1,))
    assert_size_stride(primals_27, (20, 4), (4, 1))
    assert_size_stride(primals_28, (20,), (1,))
    assert_size_stride(primals_29, (4, 4), (4, 1))
    assert_size_stride(primals_30, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 2), (8, 2, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(32)](primals_1, primals_2, buf0, 32,
            XBLOCK=32, num_warps=1, num_stages=1)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 2, 4), (8, 4, 1), torch.float32)
        triton_poi_fused_convolution_1[grid(8, 4)](buf0, buf1, 8, 4, XBLOCK
            =4, YBLOCK=8, num_warps=1, num_stages=1)
        buf2 = extern_kernels.convolution(buf1, primals_3, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf2, (4, 20, 4), (80, 4, 1))
        del buf1
        buf3 = buf2
        del buf2
        buf4 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32)
        buf7 = reinterpret_tensor(buf5, (4, 1, 1), (1, 1, 1), 0)
        del buf5
        buf8 = empty_strided_cuda((4, 20, 4), (80, 4, 1), torch.float32)
        triton_per_fused_clone_convolution_native_layer_norm_2[grid(4)](buf3,
            buf7, primals_4, buf4, buf8, 4, 80, XBLOCK=1, num_warps=2,
            num_stages=1)
        del primals_4
        buf9 = empty_strided_cuda((80, 200), (200, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf8, (80, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 200), (1, 4), 0), out=buf9)
        buf10 = empty_strided_cuda((4, 20, 200), (4000, 200, 1), torch.float32)
        triton_poi_fused_add_gelu_3[grid(16000)](buf9, primals_6, buf10, 
            16000, XBLOCK=256, num_warps=4, num_stages=1)
        buf11 = empty_strided_cuda((80, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf10, (80, 200), (200, 1), 0),
            reinterpret_tensor(primals_7, (200, 4), (1, 200), 0), out=buf11)
        buf12 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32)
        buf13 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32)
        buf64 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        triton_per_fused_add_native_layer_norm_native_layer_norm_backward_4[
            grid(4)](buf3, buf11, primals_8, buf12, buf13, buf64, 4, 80,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf15 = empty_strided_cuda((4, 4, 20), (80, 20, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_5[grid(80, 4)](buf3, buf11,
            primals_8, buf12, buf13, buf15, 80, 4, XBLOCK=4, YBLOCK=32,
            num_warps=4, num_stages=1)
        buf16 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_10, reinterpret_tensor(buf15, (16, 20),
            (20, 1), 0), reinterpret_tensor(primals_9, (20, 4), (1, 20), 0),
            alpha=1, beta=1, out=buf16)
        del primals_10
        buf17 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_gelu_6[grid(64)](buf16, buf17, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf18 = empty_strided_cuda((16, 20), (20, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf17, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_11, (4, 20), (1, 4), 0), out=buf18)
        buf19 = empty_strided_cuda((4, 4, 20), (80, 1, 4), torch.float32)
        triton_poi_fused_add_7[grid(80, 4)](buf3, buf11, primals_8, buf18,
            primals_12, buf19, 80, 4, XBLOCK=4, YBLOCK=32, num_warps=4,
            num_stages=1)
        del primals_12
        del primals_8
        buf20 = buf13
        del buf13
        buf21 = buf12
        del buf12
        buf63 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        triton_per_fused_native_layer_norm_native_layer_norm_backward_8[grid(4)
            ](buf19, buf20, buf21, buf63, 4, 80, XBLOCK=1, num_warps=2,
            num_stages=1)
        buf23 = reinterpret_tensor(buf18, (4, 4, 20), (80, 20, 1), 0)
        del buf18
        triton_poi_fused_native_layer_norm_9[grid(16, 20)](buf19, buf20,
            buf21, buf23, 16, 20, XBLOCK=32, YBLOCK=16, num_warps=4,
            num_stages=1)
        buf24 = reinterpret_tensor(buf11, (4, 20, 4), (80, 4, 1), 0)
        del buf11
        triton_poi_fused_clone_10[grid(80, 4)](buf23, buf24, 80, 4, XBLOCK=
            4, YBLOCK=32, num_warps=4, num_stages=1)
        buf25 = empty_strided_cuda((80, 200), (200, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf24, (80, 4), (4, 1), 0),
            reinterpret_tensor(primals_13, (4, 200), (1, 4), 0), out=buf25)
        buf26 = empty_strided_cuda((4, 20, 200), (4000, 200, 1), torch.float32)
        triton_poi_fused_add_gelu_3[grid(16000)](buf25, primals_14, buf26, 
            16000, XBLOCK=256, num_warps=4, num_stages=1)
        buf27 = empty_strided_cuda((80, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf26, (80, 200), (200, 1), 0),
            reinterpret_tensor(primals_15, (200, 4), (1, 200), 0), out=buf27)
        buf28 = buf21
        del buf21
        buf29 = buf20
        del buf20
        buf62 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        triton_per_fused_add_native_layer_norm_native_layer_norm_backward_4[
            grid(4)](buf19, buf27, primals_16, buf28, buf29, buf62, 4, 80,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf31 = empty_strided_cuda((4, 4, 20), (80, 20, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_5[grid(80, 4)](buf19, buf27,
            primals_16, buf28, buf29, buf31, 80, 4, XBLOCK=4, YBLOCK=32,
            num_warps=4, num_stages=1)
        buf32 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_18, reinterpret_tensor(buf31, (16, 20),
            (20, 1), 0), reinterpret_tensor(primals_17, (20, 4), (1, 20), 0
            ), alpha=1, beta=1, out=buf32)
        del primals_18
        buf33 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_gelu_6[grid(64)](buf32, buf33, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf34 = empty_strided_cuda((16, 20), (20, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf33, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_19, (4, 20), (1, 4), 0), out=buf34)
        buf35 = empty_strided_cuda((4, 4, 20), (80, 1, 4), torch.float32)
        triton_poi_fused_add_7[grid(80, 4)](buf19, buf27, primals_16, buf34,
            primals_20, buf35, 80, 4, XBLOCK=4, YBLOCK=32, num_warps=4,
            num_stages=1)
        del primals_16
        del primals_20
        buf36 = buf29
        del buf29
        buf37 = buf28
        del buf28
        buf61 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        triton_per_fused_native_layer_norm_native_layer_norm_backward_8[grid(4)
            ](buf35, buf36, buf37, buf61, 4, 80, XBLOCK=1, num_warps=2,
            num_stages=1)
        buf39 = reinterpret_tensor(buf34, (4, 4, 20), (80, 20, 1), 0)
        del buf34
        triton_poi_fused_native_layer_norm_9[grid(16, 20)](buf35, buf36,
            buf37, buf39, 16, 20, XBLOCK=32, YBLOCK=16, num_warps=4,
            num_stages=1)
        buf40 = reinterpret_tensor(buf27, (4, 20, 4), (80, 4, 1), 0)
        del buf27
        triton_poi_fused_clone_10[grid(80, 4)](buf39, buf40, 80, 4, XBLOCK=
            4, YBLOCK=32, num_warps=4, num_stages=1)
        buf41 = empty_strided_cuda((80, 200), (200, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf40, (80, 4), (4, 1), 0),
            reinterpret_tensor(primals_21, (4, 200), (1, 4), 0), out=buf41)
        buf42 = empty_strided_cuda((4, 20, 200), (4000, 200, 1), torch.float32)
        triton_poi_fused_add_gelu_3[grid(16000)](buf41, primals_22, buf42, 
            16000, XBLOCK=256, num_warps=4, num_stages=1)
        buf43 = reinterpret_tensor(buf19, (80, 4), (4, 1), 0)
        del buf19
        extern_kernels.mm(reinterpret_tensor(buf42, (80, 200), (200, 1), 0),
            reinterpret_tensor(primals_23, (200, 4), (1, 200), 0), out=buf43)
        buf44 = buf37
        del buf37
        buf45 = buf36
        del buf36
        buf60 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        triton_per_fused_add_native_layer_norm_native_layer_norm_backward_4[
            grid(4)](buf35, buf43, primals_24, buf44, buf45, buf60, 4, 80,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf47 = empty_strided_cuda((4, 4, 20), (80, 20, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_5[grid(80, 4)](buf35, buf43,
            primals_24, buf44, buf45, buf47, 80, 4, XBLOCK=4, YBLOCK=32,
            num_warps=4, num_stages=1)
        buf48 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_26, reinterpret_tensor(buf47, (16, 20),
            (20, 1), 0), reinterpret_tensor(primals_25, (20, 4), (1, 20), 0
            ), alpha=1, beta=1, out=buf48)
        del primals_26
        buf49 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_gelu_6[grid(64)](buf48, buf49, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf50 = empty_strided_cuda((16, 20), (20, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf49, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_27, (4, 20), (1, 4), 0), out=buf50)
        buf51 = empty_strided_cuda((4, 4, 20), (80, 1, 4), torch.float32)
        triton_poi_fused_add_7[grid(80, 4)](buf35, buf43, primals_24, buf50,
            primals_28, buf51, 80, 4, XBLOCK=4, YBLOCK=32, num_warps=4,
            num_stages=1)
        del buf35
        del buf43
        del primals_24
        del primals_28
        buf52 = buf45
        del buf45
        buf53 = buf44
        del buf44
        buf59 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        triton_per_fused_native_layer_norm_native_layer_norm_backward_8[grid(4)
            ](buf51, buf52, buf53, buf59, 4, 80, XBLOCK=1, num_warps=2,
            num_stages=1)
        buf55 = reinterpret_tensor(buf50, (4, 4, 20), (80, 20, 1), 0)
        del buf50
        buf56 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf57 = buf56
        del buf56
        triton_per_fused_mean_native_layer_norm_11[grid(16)](buf57, buf51,
            buf52, buf53, buf55, 16, 20, XBLOCK=1, num_warps=2, num_stages=1)
        del buf51
        del buf52
        del buf53
        buf58 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_30, buf57, reinterpret_tensor(
            primals_29, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf58)
        del primals_30
    return (buf58, primals_3, primals_6, primals_14, primals_22,
        reinterpret_tensor(buf0, (4, 2, 4), (8, 1, 2), 0), buf3, buf4, buf7,
        reinterpret_tensor(buf8, (80, 4), (4, 1), 0), buf9,
        reinterpret_tensor(buf10, (80, 200), (200, 1), 0), buf15, buf16,
        reinterpret_tensor(buf17, (16, 4), (4, 1), 0), buf23,
        reinterpret_tensor(buf24, (80, 4), (4, 1), 0), buf25,
        reinterpret_tensor(buf26, (80, 200), (200, 1), 0), buf31, buf32,
        reinterpret_tensor(buf33, (16, 4), (4, 1), 0), buf39,
        reinterpret_tensor(buf40, (80, 4), (4, 1), 0), buf41,
        reinterpret_tensor(buf42, (80, 200), (200, 1), 0), buf47, buf48,
        reinterpret_tensor(buf49, (16, 4), (4, 1), 0), buf55, buf57,
        primals_29, buf59, primals_27, primals_25, buf60, primals_23,
        primals_21, buf61, primals_19, primals_17, buf62, primals_15,
        primals_13, buf63, primals_11, primals_9, buf64, primals_7, primals_5)


class MlpBlock(nn.Module):

    def __init__(self, features, hidden_dim):
        super().__init__()
        self.hidden_dim = hidden_dim
        self.features = features
        self.fc1 = nn.Linear(self.features, self.hidden_dim)
        self.fc2 = nn.Linear(self.hidden_dim, int(self.features))

    def forward(self, x):
        y = self.fc1(x)
        y = F.gelu(y)
        return self.fc2(y)


class MixerBlock(nn.Module):
    """Mixer block layer"""

    def __init__(self, tokens_dim, tokens_mlp_dim, channels_dim,
        channels_mlp_dim):
        super().__init__()
        self.tokens_mlp_dim = tokens_mlp_dim
        self.tokens_dim = tokens_dim
        self.channels_mlp_dim = channels_mlp_dim
        self.channels_dim = channels_dim
        self.token_mixer = MlpBlock(self.tokens_dim, self.tokens_mlp_dim)
        self.channel_mixer = MlpBlock(self.channels_dim, self.channels_mlp_dim)

    def forward(self, x):
        y = F.layer_norm(x, x.shape[1:])
        y = torch.transpose(y, 1, 2)
        y = self.token_mixer(y)
        y = torch.transpose(y, 1, 2)
        x = x + y
        y = F.layer_norm(x, x.shape[1:])
        return x + self.channel_mixer(y)


class MlpMixer(nn.Module):
    """Mixer architecture"""

    def __init__(self, patches, feature_length, num_classes, num_blocks,
        hidden_dim, tokens_mlp_dim, channels_mlp_dim):
        super().__init__()
        self.patches = patches
        self.feature_length = feature_length
        self.num_classes = num_classes
        self.num_blocks = num_blocks
        self.hidden_dim = hidden_dim
        self.tokens_mlp_dim = tokens_mlp_dim
        self.channels_mlp_dim = channels_mlp_dim
        self.conv = nn.Conv1d(in_channels=self.patches, out_channels=self.
            hidden_dim, kernel_size=1)
        for nb in range(self.num_blocks):
            setattr(self, 'mixerBlock_{}'.format(nb), MixerBlock(self.
                feature_length, self.tokens_mlp_dim, self.hidden_dim, self.
                channels_mlp_dim))
        self.fc = nn.Linear(in_features=self.feature_length, out_features=
            self.num_classes)

    def forward(self, inputs):
        x = torch.transpose(inputs, 1, 2)
        x = self.conv(x)
        x = torch.transpose(x, 1, 2)
        for nb in range(self.num_blocks):
            x = getattr(self, 'mixerBlock_{}'.format(nb))(x)
        x = F.layer_norm(x, x.shape[1:])
        x = torch.transpose(x, 1, 2)
        x = torch.mean(x, dim=1)
        return self.fc(x)


class OrMixerNew(nn.Module):

    def __init__(self, n_layers, entity_dim):
        super(OrMixerNew, self).__init__()
        self.mlpMixer = MlpMixer(patches=2, feature_length=entity_dim,
            num_classes=entity_dim, num_blocks=3, hidden_dim=20,
            tokens_mlp_dim=200, channels_mlp_dim=4)

    def forward(self, input_0, input_1):
        primals_3 = self.mlpMixer.conv.weight
        primals_4 = self.mlpMixer.conv.bias
        primals_5 = self.mlpMixer.mixerBlock_0.token_mixer.fc1.weight
        primals_6 = self.mlpMixer.mixerBlock_0.token_mixer.fc1.bias
        primals_7 = self.mlpMixer.mixerBlock_0.token_mixer.fc2.weight
        primals_8 = self.mlpMixer.mixerBlock_0.token_mixer.fc2.bias
        primals_9 = self.mlpMixer.mixerBlock_0.channel_mixer.fc1.weight
        primals_10 = self.mlpMixer.mixerBlock_0.channel_mixer.fc1.bias
        primals_11 = self.mlpMixer.mixerBlock_0.channel_mixer.fc2.weight
        primals_12 = self.mlpMixer.mixerBlock_0.channel_mixer.fc2.bias
        primals_13 = self.mlpMixer.mixerBlock_1.token_mixer.fc1.weight
        primals_14 = self.mlpMixer.mixerBlock_1.token_mixer.fc1.bias
        primals_15 = self.mlpMixer.mixerBlock_1.token_mixer.fc2.weight
        primals_16 = self.mlpMixer.mixerBlock_1.token_mixer.fc2.bias
        primals_17 = self.mlpMixer.mixerBlock_1.channel_mixer.fc1.weight
        primals_18 = self.mlpMixer.mixerBlock_1.channel_mixer.fc1.bias
        primals_19 = self.mlpMixer.mixerBlock_1.channel_mixer.fc2.weight
        primals_20 = self.mlpMixer.mixerBlock_1.channel_mixer.fc2.bias
        primals_21 = self.mlpMixer.mixerBlock_2.token_mixer.fc1.weight
        primals_22 = self.mlpMixer.mixerBlock_2.token_mixer.fc1.bias
        primals_23 = self.mlpMixer.mixerBlock_2.token_mixer.fc2.weight
        primals_24 = self.mlpMixer.mixerBlock_2.token_mixer.fc2.bias
        primals_25 = self.mlpMixer.mixerBlock_2.channel_mixer.fc1.weight
        primals_26 = self.mlpMixer.mixerBlock_2.channel_mixer.fc1.bias
        primals_27 = self.mlpMixer.mixerBlock_2.channel_mixer.fc2.weight
        primals_28 = self.mlpMixer.mixerBlock_2.channel_mixer.fc2.bias
        primals_1 = self.mlpMixer.fc.weight
        primals_30 = self.mlpMixer.fc.bias
        primals_2 = input_0
        primals_29 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21, primals_22, primals_23, primals_24,
            primals_25, primals_26, primals_27, primals_28, primals_29,
            primals_30])
        return output[0]
