# 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_6/inductor_cache/yr/cyrvpoicx542o7l5zk6rjoea35aunxmpz2sijrdm6ao2qoj4bvgj.py
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.mul, aten.transpose]
# Source node to ATen node mapping:
#   multi_head_attention_forward => mul
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Scalar](args = (%view_6, 1.0), kwargs = {})
#   %permute_29 : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%view_9, [0, 2, 1]), kwargs = {})
triton_poi_fused_mul_transpose_0 = async_compile.triton('triton_poi_fused_mul_transpose_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=[16, 4], 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_mul_transpose_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_mul_transpose_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    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 % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (12*y1) + (48*x2)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp4, xmask & ymask)
    tl.store(out_ptr1 + (y3 + (16*x2)), tmp4, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/of/cofu6zktaqykiqb54lgneepflg47mm7m74y2g7tiq4m3dv42pnvl.py
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.mul, aten.transpose]
# Source node to ATen node mapping:
#   multi_head_attention_forward => mul_1
# Graph fragment:
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Scalar](args = (%permute_5, 1.0), kwargs = {})
#   %permute_30 : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%view_10, [0, 2, 1]), kwargs = {})
triton_poi_fused_mul_transpose_1 = async_compile.triton('triton_poi_fused_mul_transpose_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=[16, 4], 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_mul_transpose_1', '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_mul_transpose_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    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 % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (4 + y0 + (12*y1) + (48*x2)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4 + y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp4, xmask & ymask)
    tl.store(out_ptr1 + (y3 + (16*x2)), tmp4, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/rg/crg7nqye2hlyq5u53fojts2unoebbb6m3p6j53spexhzok6ontmj.py
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten._safe_softmax]
# Source node to ATen node mapping:
#   multi_head_attention_forward => amax, exp, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_11, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_11, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
triton_poi_fused__safe_softmax_2 = async_compile.triton('triton_poi_fused__safe_softmax_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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__safe_softmax_2', '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__safe_softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/uy/cuyqionbrqc3htd4pbnwfftu6m5ijmeoinj4crusviajhktak5nn.py
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten._safe_softmax]
# Source node to ATen node mapping:
#   multi_head_attention_forward => any_1, div, eq, full_default, logical_not, logical_not_1, sum_1, where
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%view_11, -inf), kwargs = {})
#   %logical_not : [num_users=1] = call_function[target=torch.ops.aten.logical_not.default](args = (%eq,), kwargs = {})
#   %any_1 : [num_users=1] = call_function[target=torch.ops.aten.any.dim](args = (%logical_not, -1, True), kwargs = {})
#   %logical_not_1 : [num_users=1] = call_function[target=torch.ops.aten.logical_not.default](args = (%any_1,), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 4, 4, 4], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%logical_not_1, %full_default, %div), kwargs = {})
triton_poi_fused__safe_softmax_3 = async_compile.triton('triton_poi_fused__safe_softmax_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__safe_softmax_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, '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__safe_softmax_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr1 + (x2), xmask)
    tmp26 = tl.load(in_ptr1 + (4*x1), xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr1 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr1 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr1 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp1 = float("-inf")
    tmp2 = tmp0 == tmp1
    tmp3 = tmp2 == 0
    tmp4 = tmp3.to(tl.int64)
    tmp5 = (tmp4 != 0)
    tmp7 = tmp6 == tmp1
    tmp8 = tmp7 == 0
    tmp9 = tmp8.to(tl.int64)
    tmp10 = (tmp9 != 0)
    tmp11 = tmp5 | tmp10
    tmp13 = tmp12 == tmp1
    tmp14 = tmp13 == 0
    tmp15 = tmp14.to(tl.int64)
    tmp16 = (tmp15 != 0)
    tmp17 = tmp11 | tmp16
    tmp19 = tmp18 == tmp1
    tmp20 = tmp19 == 0
    tmp21 = tmp20.to(tl.int64)
    tmp22 = (tmp21 != 0)
    tmp23 = tmp17 | tmp22
    tmp24 = tmp23 == 0
    tmp28 = tmp26 + tmp27
    tmp30 = tmp28 + tmp29
    tmp32 = tmp30 + tmp31
    tmp33 = tmp25 / tmp32
    tmp34 = 0.0
    tmp35 = tl.where(tmp24, tmp34, tmp33)
    tl.store(out_ptr0 + (x2), tmp35, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ry/crymxush4rzdmsz62q7wp64whddb4k672pfna3mrkhtvuk3xdnt6.py
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   multi_head_attention_forward => clone
# Graph fragment:
#   %clone : [num_users=3] = call_function[target=torch.ops.aten.clone.default](args = (%squeeze,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_4 = async_compile.triton('triton_poi_fused_clone_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clone_4(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4) % 16
    x2 = (xindex // 64)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x2) + (12*x1)), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/me/cmesjmsjhrj4ceejs7ktpkfmid74uo3wl5rm3dyvhuegj6bbj75o.py
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.bmm, aten.transpose]
# Source node to ATen node mapping:
#   multi_head_attention_forward => bmm_1
# Graph fragment:
#   %bmm_1 : [num_users=1] = call_function[target=torch.ops.aten.bmm.default](args = (%view_12, %view_13), kwargs = {})
#   %permute_28 : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%view_13, [0, 2, 1]), kwargs = {})
triton_poi_fused_bmm_transpose_5 = async_compile.triton('triton_poi_fused_bmm_transpose_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_bmm_transpose_5', '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_bmm_transpose_5(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (128 + x0 + (4*((x0 % 4) // 4)) + (16*x1)), xmask)
    tl.store(out_ptr0 + (x2), tmp0, xmask)
    tl.store(out_ptr1 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/q3/cq3oorhy57xbcyalz2y6g76c5pd6yqgdh2qlqjwbjqxf4bqyazbf.py
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   multi_head_attention_forward => clone_1
# Graph fragment:
#   %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_6,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_6 = async_compile.triton('triton_poi_fused_clone_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=[4, 16], 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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_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_clone_6(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 4
    xnumel = 16
    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
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x1 + (16*y0)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/pw/cpw6jeu3vr4erot3bahfr3gqwa2vqailcc5xbtuiztd5l6w2zwjm.py
# Topologically Sorted Source Nodes: [add, x], Original ATen: [aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
#   add => add
#   x => var_mean
# Graph fragment:
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %view_16), kwargs = {})
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%add, [2]), kwargs = {correction: 0, keepdim: True})
triton_poi_fused_add_native_layer_norm_7 = async_compile.triton('triton_poi_fused_add_native_layer_norm_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_native_layer_norm_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_7(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 + tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = 4.0
    tmp16 = tmp14 / tmp15
    tmp17 = tmp2 - tmp16
    tmp18 = tmp17 * tmp17
    tmp19 = tmp5 - tmp16
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp9 - tmp16
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp16
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = tmp27 / tmp15
    tl.store(out_ptr0 + (x0), tmp16, xmask)
    tl.store(out_ptr1 + (x0), tmp28, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/hy/chy77uudjf33jqcobrq24clniixoyn2vucadggy6c6n33mq4favb.py
# Topologically Sorted Source Nodes: [add, x], Original ATen: [aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
#   add => add
#   x => add_1, add_2, mul_2, mul_3, rsqrt, sub_1
# Graph fragment:
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %view_16), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_1,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %getitem_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %rsqrt), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, %primals_6), kwargs = {})
#   %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_3, %primals_7), kwargs = {})
triton_poi_fused_add_native_layer_norm_8 = async_compile.triton('triton_poi_fused_add_native_layer_norm_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.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 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, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_native_layer_norm_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, '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_8(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x2), xmask)
    tmp3 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr4 + (x0), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp4 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tl.store(out_ptr0 + (x2), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/5s/c5sz7jq4tmcun7rcsmmfeqrom6cicyxjx2pclothphxo54ygxlxu.py
# Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   relu => relu
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_18,), kwargs = {})
#   %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_9 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_relu_threshold_backward_9', 'mutated_arg_names': ['in_out_ptr0'], '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_relu_threshold_backward_9(in_out_ptr0, 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
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/cn/ccn3ztml5qupb2u6trhxfni5siq2chr5f7il63gpx775u3hi6iz5.py
# Topologically Sorted Source Nodes: [add_1], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   add_1 => add_3
# Graph fragment:
#   %add_3 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %view_20), kwargs = {})
triton_poi_fused_add_10 = async_compile.triton('triton_poi_fused_add_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_10', 'mutated_arg_names': ['in_out_ptr0'], '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_add_10(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp2 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/s5/cs5qfpz5dtrtoopym4yheekk2kuetbhcvypaqjhns7stzzfhkjha.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
#   x_2 => add_4, rsqrt_1, var_mean_1
# Graph fragment:
#   %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%add_3, [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=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_4,), kwargs = {})
triton_poi_fused_native_layer_norm_11 = async_compile.triton('triton_poi_fused_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.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_native_layer_norm_11', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_native_layer_norm_11(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + (x0), tmp8, xmask)
    tl.store(out_ptr1 + (x0), tmp23, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/dr/cdrqch7dkzd6wgbo46meh7vw2wmmm5avruav7pzwc4s4i4zleekn.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
#   x_2 => add_4, add_5, mul_4, mul_5, rsqrt_1, sub_2, var_mean_1
# Graph fragment:
#   %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%add_3, [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=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_4,), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_3, %getitem_3), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, %rsqrt_1), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_4, %primals_12), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_5, %primals_13), kwargs = {})
triton_poi_fused_native_layer_norm_12 = async_compile.triton('triton_poi_fused_native_layer_norm_12', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_native_layer_norm_12', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_native_layer_norm_12(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/2w/c2w6xjm2yhfcxhyb5y5wobuyg3db26y62couv4w75feaxismungq.py
# Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv1d => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%permute_10, %primals_14, %primals_15, [1], [1], [1], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_13 = async_compile.triton('triton_poi_fused_convolution_13', '''
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, 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_convolution_13', '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_13(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    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
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (16*x1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x1 + (4*y0)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/h6/ch6c32hfwunyggngpke5ontevy2xjxnd7kqnam5djemzxd2q272i.py
# Topologically Sorted Source Nodes: [conv1d, output], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv1d => convolution
#   output => relu_1
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%permute_10, %primals_14, %primals_15, [1], [1], [1], False, [0], 1), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_14 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_14', '''
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: '*i1', 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_convolution_relu_threshold_backward_14', 'mutated_arg_names': ['in_out_ptr0'], '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_convolution_relu_threshold_backward_14(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 3) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x3), tmp4, xmask)
    tl.store(out_ptr0 + (x3), tmp6, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (12, ), (1, ))
    assert_size_stride(primals_3, (12, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, ), (1, ))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    assert_size_stride(primals_10, (4, 4), (4, 1))
    assert_size_stride(primals_11, (4, ), (1, ))
    assert_size_stride(primals_12, (4, ), (1, ))
    assert_size_stride(primals_13, (4, ), (1, ))
    assert_size_stride(primals_14, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_15, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 12), (1, 4), 0), out=buf0)
        del primals_3
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        buf27 = empty_strided_cuda((16, 1, 4), (1, 1, 16), torch.float32)
        # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.mul, aten.transpose]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_transpose_0.run(buf0, primals_2, buf1, buf27, 16, 4, grid=grid(16, 4), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32)
        buf28 = empty_strided_cuda((16, 4, 1), (1, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.mul, aten.transpose]
        triton_poi_fused_mul_transpose_1.run(buf0, primals_2, buf2, buf28, 16, 4, grid=grid(16, 4), stream=stream0)
        buf3 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf1, (16, 4, 1), (4, 1, 0), 0), reinterpret_tensor(buf2, (16, 1, 4), (4, 0, 1), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten._safe_softmax]
        triton_poi_fused__safe_softmax_2.run(buf3, buf4, 256, grid=grid(256), stream=stream0)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten._safe_softmax]
        triton_poi_fused__safe_softmax_3.run(buf3, buf4, buf5, 256, grid=grid(256), stream=stream0)
        del buf3
        del buf4
        buf6 = empty_strided_cuda((3, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.clone]
        triton_poi_fused_clone_4.run(buf0, primals_2, buf6, 192, grid=grid(192), stream=stream0)
        del buf0
        del primals_2
        buf7 = reinterpret_tensor(buf2, (16, 4, 1), (1, 16, 64), 0); del buf2  # reuse
        buf26 = reinterpret_tensor(buf1, (16, 1, 4), (1, 1, 16), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.bmm, aten.transpose]
        triton_poi_fused_bmm_transpose_5.run(buf6, buf7, buf26, 64, grid=grid(64), stream=stream0)
        del buf6
        buf8 = empty_strided_cuda((16, 4, 1), (4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0), buf7, out=buf8)
        buf9 = reinterpret_tensor(buf7, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf7  # reuse
        # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.clone]
        triton_poi_fused_clone_6.run(buf8, buf9, 4, 16, grid=grid(4, 16), stream=stream0)
        buf10 = reinterpret_tensor(buf8, (16, 4), (4, 1), 0); del buf8  # reuse
        # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf9, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf10)
        del primals_5
        buf11 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf12 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        # Topologically Sorted Source Nodes: [add, x], Original ATen: [aten.add, aten.native_layer_norm]
        triton_poi_fused_add_native_layer_norm_7.run(primals_1, buf10, buf11, buf12, 16, grid=grid(16), stream=stream0)
        buf13 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add, x], Original ATen: [aten.add, aten.native_layer_norm]
        triton_poi_fused_add_native_layer_norm_8.run(primals_1, buf10, buf11, buf12, primals_6, primals_7, buf13, 64, grid=grid(64), stream=stream0)
        del primals_7
        buf14 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf13, (16, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf14)
        buf15 = reinterpret_tensor(buf14, (4, 4, 4), (16, 4, 1), 0); del buf14  # reuse
        buf25 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_9.run(buf15, primals_9, buf25, 64, grid=grid(64), stream=stream0)
        del primals_9
        buf16 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf15, (16, 4), (4, 1), 0), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), out=buf16)
        buf17 = reinterpret_tensor(buf16, (4, 4, 4), (16, 4, 1), 0); del buf16  # reuse
        # Topologically Sorted Source Nodes: [add_1], Original ATen: [aten.add]
        triton_poi_fused_add_10.run(buf17, buf13, primals_11, 64, grid=grid(64), stream=stream0)
        del primals_11
        buf18 = buf12; del buf12  # reuse
        buf19 = buf11; del buf11  # reuse
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_11.run(buf17, buf18, buf19, 16, grid=grid(16), stream=stream0)
        buf20 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_12.run(buf17, buf18, buf19, primals_12, primals_13, buf20, 64, grid=grid(64), stream=stream0)
        del buf18
        del buf19
        del primals_13
        buf21 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_13.run(buf20, buf21, 16, 4, grid=grid(16, 4), stream=stream0)
        # Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
        buf22 = extern_kernels.convolution(buf21, primals_14, stride=(1,), padding=(1,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf22, (4, 4, 3), (12, 3, 1))
        del buf21
        buf23 = buf22; del buf22  # reuse
        buf24 = empty_strided_cuda((4, 4, 3), (12, 3, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv1d, output], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_14.run(buf23, primals_15, buf24, 48, grid=grid(48), stream=stream0)
        del primals_15
    return (reinterpret_tensor(buf23, (3, 4, 4), (1, 12, 3), 0), primals_1, primals_6, primals_12, primals_14, buf5, reinterpret_tensor(buf9, (16, 4), (4, 1), 0), buf10, reinterpret_tensor(buf13, (16, 4), (4, 1), 0), reinterpret_tensor(buf15, (16, 4), (4, 1), 0), buf17, reinterpret_tensor(buf20, (4, 4, 4), (4, 1, 16), 0), buf24, primals_10, buf25, primals_8, primals_4, buf26, buf27, buf28, )


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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((12, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((12, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = 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])
    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 TransformerEncoderLayerWithConv1d(nn.Module):
    """
      Input and output shape: seqlen x batch_size x dim
    """

    def __init__(self, dim_model, nheads, dim_feedforward, dropout,
        kernel_size, stride):
        super(TransformerEncoderLayerWithConv1d, self).__init__()
        self.encoder_layer = nn.TransformerEncoderLayer(dim_model, nheads,
            dim_feedforward, dropout)
        self.conv1d = nn.Conv1d(dim_model, dim_model, kernel_size, stride=
            stride, padding=1)

    def forward(self, src, src_mask=None, src_key_padding_mask=None):
        output = self.encoder_layer(src, src_mask, src_key_padding_mask)
        output = F.relu(self.conv1d(output.permute(1, 2, 0)))
        return output.permute(2, 0, 1)


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


def get_init_inputs():
    return [[], {'dim_model': 4, 'nheads': 4, 'dim_feedforward': 4,
        'dropout': 0.5, 'kernel_size': 4, 'stride': 1}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_mul_transpose_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    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 % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 12 * y1 + 48 * x2), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp4, xmask & ymask)
    tl.store(out_ptr1 + (y3 + 16 * x2), tmp4, xmask & ymask)


@triton.jit
def triton_poi_fused_mul_transpose_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    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 % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (4 + y0 + 12 * y1 + 48 * x2), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4 + y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp4, xmask & ymask)
    tl.store(out_ptr1 + (y3 + 16 * x2), tmp4, xmask & ymask)


@triton.jit
def triton_poi_fused__safe_softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__safe_softmax_3(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp18 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp25 = tl.load(in_ptr1 + x2, xmask)
    tmp26 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp29 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp31 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = float('-inf')
    tmp2 = tmp0 == tmp1
    tmp3 = tmp2 == 0
    tmp4 = tmp3.to(tl.int64)
    tmp5 = tmp4 != 0
    tmp7 = tmp6 == tmp1
    tmp8 = tmp7 == 0
    tmp9 = tmp8.to(tl.int64)
    tmp10 = tmp9 != 0
    tmp11 = tmp5 | tmp10
    tmp13 = tmp12 == tmp1
    tmp14 = tmp13 == 0
    tmp15 = tmp14.to(tl.int64)
    tmp16 = tmp15 != 0
    tmp17 = tmp11 | tmp16
    tmp19 = tmp18 == tmp1
    tmp20 = tmp19 == 0
    tmp21 = tmp20.to(tl.int64)
    tmp22 = tmp21 != 0
    tmp23 = tmp17 | tmp22
    tmp24 = tmp23 == 0
    tmp28 = tmp26 + tmp27
    tmp30 = tmp28 + tmp29
    tmp32 = tmp30 + tmp31
    tmp33 = tmp25 / tmp32
    tmp34 = 0.0
    tmp35 = tl.where(tmp24, tmp34, tmp33)
    tl.store(out_ptr0 + x2, tmp35, xmask)


@triton.jit
def triton_poi_fused_clone_4(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 16
    x2 = xindex // 64
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 12 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_bmm_transpose_5(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (128 + x0 + 4 * (x0 % 4 // 4) + 16 * x1), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)
    tl.store(out_ptr1 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_6(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 4
    xnumel = 16
    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
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (x1 + 16 * y0), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_7(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 + tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = 4.0
    tmp16 = tmp14 / tmp15
    tmp17 = tmp2 - tmp16
    tmp18 = tmp17 * tmp17
    tmp19 = tmp5 - tmp16
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp9 - tmp16
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp16
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = tmp27 / tmp15
    tl.store(out_ptr0 + x0, tmp16, xmask)
    tl.store(out_ptr1 + x0, tmp28, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_8(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp4 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tl.store(out_ptr0 + x2, tmp13, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_9(in_out_ptr0, 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
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_10(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_out_ptr0 + x2, xmask)
    tmp2 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_11(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_12(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_convolution_13(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    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
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 16 * x1), xmask & ymask, eviction_policy
        ='evict_last')
    tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_14(in_out_ptr0,
    in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x3, tmp4, xmask)
    tl.store(out_ptr0 + x3, tmp6, 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) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (12,), (1,))
    assert_size_stride(primals_3, (12, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4, 4), (4, 1))
    assert_size_stride(primals_11, (4,), (1,))
    assert_size_stride(primals_12, (4,), (1,))
    assert_size_stride(primals_13, (4,), (1,))
    assert_size_stride(primals_14, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_15, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 12), (1, 4), 0), out=buf0)
        del primals_3
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        buf27 = empty_strided_cuda((16, 1, 4), (1, 1, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_transpose_0[grid(16, 4)](buf0, primals_2, buf1,
            buf27, 16, 4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32)
        buf28 = empty_strided_cuda((16, 4, 1), (1, 16, 1), torch.float32)
        triton_poi_fused_mul_transpose_1[grid(16, 4)](buf0, primals_2, buf2,
            buf28, 16, 4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf1, (16, 4, 1), (4, 1, 0), 
            0), reinterpret_tensor(buf2, (16, 1, 4), (4, 0, 1), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__safe_softmax_2[grid(256)](buf3, buf4, 256, XBLOCK
            =256, num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__safe_softmax_3[grid(256)](buf3, buf4, buf5, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del buf3
        del buf4
        buf6 = empty_strided_cuda((3, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_4[grid(192)](buf0, primals_2, buf6, 192,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del primals_2
        buf7 = reinterpret_tensor(buf2, (16, 4, 1), (1, 16, 64), 0)
        del buf2
        buf26 = reinterpret_tensor(buf1, (16, 1, 4), (1, 1, 16), 0)
        del buf1
        triton_poi_fused_bmm_transpose_5[grid(64)](buf6, buf7, buf26, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del buf6
        buf8 = empty_strided_cuda((16, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1),
            0), buf7, out=buf8)
        buf9 = reinterpret_tensor(buf7, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf7
        triton_poi_fused_clone_6[grid(4, 16)](buf8, buf9, 4, 16, XBLOCK=16,
            YBLOCK=4, num_warps=1, num_stages=1)
        buf10 = reinterpret_tensor(buf8, (16, 4), (4, 1), 0)
        del buf8
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf9, (16, 4), (
            4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf10)
        del primals_5
        buf11 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf12 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused_add_native_layer_norm_7[grid(16)](primals_1, buf10,
            buf11, buf12, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf13 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_8[grid(64)](primals_1, buf10,
            buf11, buf12, primals_6, primals_7, buf13, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_7
        buf14 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf13, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf14)
        buf15 = reinterpret_tensor(buf14, (4, 4, 4), (16, 4, 1), 0)
        del buf14
        buf25 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_9[grid(64)](buf15,
            primals_9, buf25, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_9
        buf16 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf15, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), out=buf16)
        buf17 = reinterpret_tensor(buf16, (4, 4, 4), (16, 4, 1), 0)
        del buf16
        triton_poi_fused_add_10[grid(64)](buf17, buf13, primals_11, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_11
        buf18 = buf12
        del buf12
        buf19 = buf11
        del buf11
        triton_poi_fused_native_layer_norm_11[grid(16)](buf17, buf18, buf19,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf20 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_12[grid(64)](buf17, buf18, buf19,
            primals_12, primals_13, buf20, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf18
        del buf19
        del primals_13
        buf21 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_convolution_13[grid(16, 4)](buf20, buf21, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf22 = extern_kernels.convolution(buf21, primals_14, stride=(1,),
            padding=(1,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf22, (4, 4, 3), (12, 3, 1))
        del buf21
        buf23 = buf22
        del buf22
        buf24 = empty_strided_cuda((4, 4, 3), (12, 3, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_14[grid(48)](buf23
            , primals_15, buf24, 48, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_15
    return reinterpret_tensor(buf23, (3, 4, 4), (1, 12, 3), 0
        ), primals_1, primals_6, primals_12, primals_14, buf5, reinterpret_tensor(
        buf9, (16, 4), (4, 1), 0), buf10, reinterpret_tensor(buf13, (16, 4),
        (4, 1), 0), reinterpret_tensor(buf15, (16, 4), (4, 1), 0
        ), buf17, reinterpret_tensor(buf20, (4, 4, 4), (4, 1, 16), 0
        ), buf24, primals_10, buf25, primals_8, primals_4, buf26, buf27, buf28


class TransformerEncoderLayerWithConv1dNew(nn.Module):
    """
      Input and output shape: seqlen x batch_size x dim
    """

    def __init__(self, dim_model, nheads, dim_feedforward, dropout,
        kernel_size, stride):
        super(TransformerEncoderLayerWithConv1dNew, self).__init__()
        self.encoder_layer = nn.TransformerEncoderLayer(dim_model, nheads,
            dim_feedforward, dropout)
        self.conv1d = nn.Conv1d(dim_model, dim_model, kernel_size, stride=
            stride, padding=1)

    def forward(self, input_0):
        primals_3 = self.encoder_layer.self_attn.in_proj_weight
        primals_2 = self.encoder_layer.self_attn.in_proj_bias
        primals_4 = self.encoder_layer.self_attn.out_proj.weight
        primals_5 = self.encoder_layer.self_attn.out_proj.bias
        primals_8 = self.encoder_layer.linear1.weight
        primals_6 = self.encoder_layer.linear1.bias
        primals_10 = self.encoder_layer.linear2.weight
        primals_7 = self.encoder_layer.linear2.bias
        primals_9 = self.encoder_layer.norm1.weight
        primals_11 = self.encoder_layer.norm1.bias
        primals_12 = self.encoder_layer.norm2.weight
        primals_13 = self.encoder_layer.norm2.bias
        primals_1 = self.conv1d.weight
        primals_15 = self.conv1d.bias
        primals_14 = input_0
        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])
        return output[0]
