# 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/wd/cwdz7kqs3uwyg53zsyekt77eye7yjl6v7vulow2q6ni534mkf6zw.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
#   out => add, rsqrt, var_mean
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%primals_3, [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=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
triton_poi_fused_native_layer_norm_0 = async_compile.triton('triton_poi_fused_native_layer_norm_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], 
    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_0', '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_0(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_4/inductor_cache/vs/cvsfvbs4wlaqvwxm3svg65dnhcq336ptudvn6xetnbnrtzj7xssn.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
#   out => add, add_1, mul, mul_1, rsqrt, sub, var_mean
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%primals_3, [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=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_3, %getitem_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %primals_1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_2), kwargs = {})
triton_poi_fused_native_layer_norm_1 = async_compile.triton('triton_poi_fused_native_layer_norm_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=[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_1', '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_1(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_4/inductor_cache/up/cuprnpwtkpmhqrfwovo4e6wbteu2nkhiczaysue7ywns7rx4rsy2.py
# Topologically Sorted Source Nodes: [wrapped_sqrt, truediv, att], Original ATen: [aten.sqrt, aten.div, aten.clone]
# Source node to ATen node mapping:
#   att => clone
#   truediv => div
#   wrapped_sqrt => full_default
# Graph fragment:
#   %full_default : [num_users=2] = call_function[target=torch.ops.aten.full.default](args = ([], 2.0), kwargs = {dtype: torch.float64, layout: torch.strided, device: cpu, pin_memory: False})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%permute_1, %full_default), kwargs = {})
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_div_sqrt_2 = async_compile.triton('triton_poi_fused_clone_div_sqrt_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '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_div_sqrt_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clone_div_sqrt_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16) % 4
    x3 = (xindex // 64)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x2) + (16*x1) + (64*x3)), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 2.0
    tmp4 = tmp2 / tmp3
    tl.store(out_ptr0 + (x4), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/fw/cfw2fw4tf46lsmfrewrtpmeuv7vjl4zehj4rnftx2aaimrz2ades.py
# Topologically Sorted Source Nodes: [att], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   att => clone_1
# Graph fragment:
#   %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_1,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_3 = async_compile.triton('triton_poi_fused_clone_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=[64, 4], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_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_clone_3(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 64
    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 % 16
    y1 = (yindex // 16)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (16*x2) + (64*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp2, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/iv/civdgwzpphyda4rs4fr3g6w25bprv7bn4anqgivrgzavi7xr5pdl.py
# Topologically Sorted Source Nodes: [att_1], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   att_1 => amax, exp, sub_1
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_11, [-1], True), kwargs = {})
#   %sub_1 : [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_1,), kwargs = {})
triton_poi_fused__softmax_4 = async_compile.triton('triton_poi_fused__softmax_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: '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__softmax_4', '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__softmax_4(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_4/inductor_cache/a4/ca4u6hbohfqkgchihihlu5hrf3vuqm27r2ncsg7xb6g4ikttl2at.py
# Topologically Sorted Source Nodes: [att_1], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   att_1 => div_1, sum_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_5 = async_compile.triton('triton_poi_fused__softmax_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=[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__softmax_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__softmax_5(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 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/bx/cbxvklkwdhdzrx77dsihwkjap5v3erlkbyhbbws4cmn2fi5fsag5.py
# Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   out_1 => clone_2
# Graph fragment:
#   %clone_2 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_3,), 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=[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_6', '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_6(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
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16) % 4
    x3 = (xindex // 64)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x2) + (16*x1) + (64*x3)), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x4), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/kg/ckgg6be44dehi2hnoyd5smc7md67gknojtbkrkhaztdptl5bvzrm.py
# Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   contiguous => clone_3
# Graph fragment:
#   %clone_3 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_7,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_7 = async_compile.triton('triton_poi_fused_clone_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=[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_clone_7', '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_7(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
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16) % 4
    x3 = (xindex // 64)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x2) + (16*x1) + (64*x3)), xmask)
    tl.store(out_ptr0 + (x4), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/xw/cxwztfhiso3uego7k7zbqdm4uahezg4u5zy7ri26p3g76e2lbxyf.py
# Topologically Sorted Source Nodes: [out_5, out_6], Original ATen: [aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
#   out_5 => add_2
#   out_6 => var_mean_1
# Graph fragment:
#   %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_3, %view_17), kwargs = {})
#   %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%add_2, [2]), kwargs = {correction: 0, keepdim: True})
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=[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_8', '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_8(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_4/inductor_cache/so/csozgpoz3hmv3hrlzcentzlzvi4jqhlqmcwqydcltpnthhqeurgb.py
# Topologically Sorted Source Nodes: [out_5, out_6], Original ATen: [aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
#   out_5 => add_2
#   out_6 => add_3, add_4, mul_2, mul_3, rsqrt_1, sub_2
# Graph fragment:
#   %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_3, %view_17), kwargs = {})
#   %add_3 : [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_3,), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_2, %getitem_3), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, %rsqrt_1), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, %primals_12), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_3, %primals_13), kwargs = {})
triton_poi_fused_add_native_layer_norm_9 = async_compile.triton('triton_poi_fused_add_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=[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_9', '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_9(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_4/inductor_cache/5o/c5oq2dd4gv3qfn7dbi4gn37m4q5cqp3syup6457zs5qf2qor5xrv.py
# Topologically Sorted Source Nodes: [out_5, out_11], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   out_11 => add_5
#   out_5 => add_2
# Graph fragment:
#   %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_3, %view_17), kwargs = {})
#   %add_5 : [num_users=4] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %view_35), 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: '*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_10', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_10(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, 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_ptr1 + (x2), xmask)
    tmp3 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp4 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tl.store(in_out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/x4/cx4dy7nub5axbgswcjd7xtsxm4hlex3zmjlfcyucivazkjm63wl6.py
# Topologically Sorted Source Nodes: [out_14], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   out_14 => relu
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_37,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_11 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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=[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_11', '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_11(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_4/inductor_cache/hz/chzzhlll7wy342csqrojpmln2mteqejrojpbbjel66xnknr7oawa.py
# Topologically Sorted Source Nodes: [out_17], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   out_17 => add_8
# Graph fragment:
#   %add_8 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_5, %view_39), kwargs = {})
triton_poi_fused_add_12 = async_compile.triton('triton_poi_fused_add_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: '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_12', '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_12(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')


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, ), (1, ))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (16, 4), (4, 1))
    assert_size_stride(primals_5, (16, ), (1, ))
    assert_size_stride(primals_6, (16, 4), (4, 1))
    assert_size_stride(primals_7, (16, ), (1, ))
    assert_size_stride(primals_8, (16, 4), (4, 1))
    assert_size_stride(primals_9, (16, ), (1, ))
    assert_size_stride(primals_10, (4, 16), (16, 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, (16, 4), (4, 1))
    assert_size_stride(primals_16, (16, ), (1, ))
    assert_size_stride(primals_17, (16, 4), (4, 1))
    assert_size_stride(primals_18, (16, ), (1, ))
    assert_size_stride(primals_19, (16, 4), (4, 1))
    assert_size_stride(primals_20, (16, ), (1, ))
    assert_size_stride(primals_21, (4, 16), (16, 1))
    assert_size_stride(primals_22, (4, ), (1, ))
    assert_size_stride(primals_23, (4, ), (1, ))
    assert_size_stride(primals_24, (4, ), (1, ))
    assert_size_stride(primals_25, (4, 4), (4, 1))
    assert_size_stride(primals_26, (4, ), (1, ))
    assert_size_stride(primals_27, (4, 4), (4, 1))
    assert_size_stride(primals_28, (4, ), (1, ))
    assert_size_stride(primals_29, (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, 1), (4, 1, 16), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.native_layer_norm]
        stream0 = get_raw_stream(0)
        triton_poi_fused_native_layer_norm_0.run(primals_3, buf0, buf1, 16, grid=grid(16), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_1.run(primals_3, buf0, buf1, primals_1, primals_2, buf2, 64, grid=grid(64), stream=stream0)
        del primals_1
        del primals_2
        buf3 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 16), (1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 16), (1, 4), 0), out=buf4)
        buf5 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 16), (1, 4), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [wrapped_sqrt, truediv, att], Original ATen: [aten.sqrt, aten.div, aten.clone]
        triton_poi_fused_clone_div_sqrt_2.run(buf3, primals_5, buf6, 256, grid=grid(256), stream=stream0)
        del primals_5
        buf7 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [att], Original ATen: [aten.clone]
        triton_poi_fused_clone_3.run(buf4, primals_7, buf7, 64, 4, grid=grid(64, 4), stream=stream0)
        del primals_7
        buf8 = reinterpret_tensor(buf4, (16, 4, 4), (16, 4, 1), 0); del buf4  # reuse
        # Topologically Sorted Source Nodes: [att], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf6, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1), 0), out=buf8)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [att_1], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_4.run(buf8, buf9, 256, grid=grid(256), stream=stream0)
        buf10 = reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf8  # reuse
        # Topologically Sorted Source Nodes: [att_1], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_5.run(buf9, buf10, 256, grid=grid(256), stream=stream0)
        buf11 = buf9; del buf9  # reuse
        # Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.clone]
        triton_poi_fused_clone_6.run(buf5, primals_9, buf11, 256, grid=grid(256), stream=stream0)
        del primals_9
        buf12 = reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0); del buf5  # reuse
        # Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf10, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf11, (16, 4, 4), (16, 4, 1), 0), out=buf12)
        buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone]
        triton_poi_fused_clone_7.run(buf12, buf13, 256, grid=grid(256), stream=stream0)
        buf14 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf13, (16, 16), (16, 1), 0), reinterpret_tensor(primals_10, (16, 4), (1, 16), 0), alpha=1, beta=1, out=buf14)
        del primals_11
        buf15 = buf1; del buf1  # reuse
        buf16 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [out_5, out_6], Original ATen: [aten.add, aten.native_layer_norm]
        triton_poi_fused_add_native_layer_norm_8.run(primals_3, buf14, buf15, buf16, 16, grid=grid(16), stream=stream0)
        buf17 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_5, out_6], Original ATen: [aten.add, aten.native_layer_norm]
        triton_poi_fused_add_native_layer_norm_9.run(primals_3, buf14, buf15, buf16, primals_12, primals_13, buf17, 64, grid=grid(64), stream=stream0)
        del primals_13
        buf18 = reinterpret_tensor(buf12, (16, 16), (16, 1), 0); del buf12  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf17, (16, 4), (4, 1), 0), reinterpret_tensor(primals_15, (4, 16), (1, 4), 0), out=buf18)
        buf19 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_14, (16, 4), (4, 1), 0), reinterpret_tensor(primals_17, (4, 16), (1, 4), 0), out=buf19)
        del primals_17
        buf20 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_14, (16, 4), (4, 1), 0), reinterpret_tensor(primals_19, (4, 16), (1, 4), 0), out=buf20)
        del primals_19
        buf21 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [wrapped_sqrt, truediv_1, att_2], Original ATen: [aten.sqrt, aten.div, aten.clone]
        triton_poi_fused_clone_div_sqrt_2.run(buf18, primals_16, buf21, 256, grid=grid(256), stream=stream0)
        del primals_16
        buf22 = reinterpret_tensor(buf18, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf18  # reuse
        # Topologically Sorted Source Nodes: [att_2], Original ATen: [aten.clone]
        triton_poi_fused_clone_3.run(buf19, primals_18, buf22, 64, 4, grid=grid(64, 4), stream=stream0)
        del primals_18
        buf23 = reinterpret_tensor(buf19, (16, 4, 4), (16, 4, 1), 0); del buf19  # reuse
        # Topologically Sorted Source Nodes: [att_2], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf21, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf22, (16, 4, 4), (16, 4, 1), 0), out=buf23)
        buf24 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [att_3], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_4.run(buf23, buf24, 256, grid=grid(256), stream=stream0)
        buf25 = reinterpret_tensor(buf23, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf23  # reuse
        # Topologically Sorted Source Nodes: [att_3], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_5.run(buf24, buf25, 256, grid=grid(256), stream=stream0)
        buf26 = buf24; del buf24  # reuse
        # Topologically Sorted Source Nodes: [out_7], Original ATen: [aten.clone]
        triton_poi_fused_clone_6.run(buf20, primals_20, buf26, 256, grid=grid(256), stream=stream0)
        del primals_20
        buf27 = reinterpret_tensor(buf20, (16, 4, 4), (16, 4, 1), 0); del buf20  # reuse
        # Topologically Sorted Source Nodes: [out_7], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf25, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf26, (16, 4, 4), (16, 4, 1), 0), out=buf27)
        buf28 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [contiguous_1], Original ATen: [aten.clone]
        triton_poi_fused_clone_7.run(buf27, buf28, 256, grid=grid(256), stream=stream0)
        del buf27
        buf29 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf28, (16, 16), (16, 1), 0), reinterpret_tensor(primals_21, (16, 4), (1, 16), 0), out=buf29)
        buf30 = reinterpret_tensor(buf29, (4, 4, 4), (16, 4, 1), 0); del buf29  # reuse
        # Topologically Sorted Source Nodes: [out_5, out_11], Original ATen: [aten.add]
        triton_poi_fused_add_10.run(buf30, primals_3, buf14, primals_22, 64, grid=grid(64), stream=stream0)
        del primals_22
        buf31 = buf16; del buf16  # reuse
        buf32 = buf15; del buf15  # reuse
        # Topologically Sorted Source Nodes: [out_12], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_0.run(buf30, buf31, buf32, 16, grid=grid(16), stream=stream0)
        buf33 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_12], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_1.run(buf30, buf31, buf32, primals_23, primals_24, buf33, 64, grid=grid(64), stream=stream0)
        del primals_24
        buf34 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf33, (16, 4), (4, 1), 0), reinterpret_tensor(primals_25, (4, 4), (1, 4), 0), out=buf34)
        buf35 = reinterpret_tensor(buf34, (4, 4, 4), (16, 4, 1), 0); del buf34  # reuse
        buf41 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [out_14], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_11.run(buf35, primals_26, buf41, 64, grid=grid(64), stream=stream0)
        del primals_26
        buf36 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf35, (16, 4), (4, 1), 0), reinterpret_tensor(primals_27, (4, 4), (1, 4), 0), out=buf36)
        buf37 = reinterpret_tensor(buf36, (4, 4, 4), (16, 4, 1), 0); del buf36  # reuse
        # Topologically Sorted Source Nodes: [out_17], Original ATen: [aten.add]
        triton_poi_fused_add_12.run(buf37, buf30, primals_28, 64, grid=grid(64), stream=stream0)
        del primals_28
        buf38 = buf32; del buf32  # reuse
        buf39 = buf31; del buf31  # reuse
        # Topologically Sorted Source Nodes: [out_18], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_0.run(buf37, buf38, buf39, 16, grid=grid(16), stream=stream0)
        buf40 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_18], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_1.run(buf37, buf38, buf39, primals_29, primals_30, buf40, 64, grid=grid(64), stream=stream0)
        del buf38
        del buf39
        del primals_30
    return (buf40, primals_3, primals_12, primals_23, primals_29, reinterpret_tensor(buf2, (16, 4), (4, 1), 0), buf10, reinterpret_tensor(buf13, (16, 16), (16, 1), 0), buf14, reinterpret_tensor(buf17, (16, 4), (4, 1), 0), reinterpret_tensor(primals_14, (16, 4), (4, 1), 0), buf25, reinterpret_tensor(buf28, (16, 16), (16, 1), 0), buf30, reinterpret_tensor(buf33, (16, 4), (4, 1), 0), reinterpret_tensor(buf35, (16, 4), (4, 1), 0), buf37, primals_27, buf41, primals_25, primals_21, reinterpret_tensor(buf26, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf21, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf22, (16, 4, 4), (16, 1, 4), 0), primals_15, primals_10, reinterpret_tensor(buf11, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf6, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf7, (16, 4, 4), (16, 1, 4), 0), primals_8, primals_6, primals_4, )


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, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, 16), (16, 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((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_23 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_24 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_25 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_26 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_27 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_28 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_29 = rand_strided((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 numpy as np
import torch.nn as nn
import torch.nn.functional as F


class ResidualConnectionLayer(nn.Module):

    def __init__(self, dim_model, prob_dropout=0.1, add_sublayer=True):
        super(ResidualConnectionLayer, self).__init__()
        self.add_sublayer = add_sublayer
        self.norm = nn.LayerNorm(dim_model)
        self.dropout = nn.Dropout(prob_dropout)

    def forward(self, x, sublayer):
        out = self.norm(x)
        out = sublayer(out)
        out = self.dropout(out)
        if self.add_sublayer:
            return x + out
        else:
            return out


class BaseLayer(nn.Module):

    def __init__(self, dim_model, dim_k, dim_v, h, dim_ff, prob_dropout):
        super(BaseLayer, self).__init__()
        self._dim_model = dim_model
        self._dim_k = dim_k
        self._dim_v = dim_v
        self._h = h
        self._dim_ff = dim_ff
        self._prob_dropout = prob_dropout


class MultiHeadedAttentionLayer(nn.Module):

    def __init__(self, dim_model, dim_k, dim_v, h):
        super(MultiHeadedAttentionLayer, self).__init__()
        self.dim_model = dim_model
        self.dim_k = dim_k
        self.dim_v = dim_v
        self.h = h
        self.Q_linear = nn.Linear(dim_model, dim_k * h)
        self.K_linear = nn.Linear(dim_model, dim_k * h)
        self.V_linear = nn.Linear(dim_model, dim_v * h)
        self.out_linear = nn.Linear(self.h * dim_v, dim_model)

    def forward(self, Q, K, V, mask=None):
        b, len_q, len_k, len_v = Q.size(0), Q.size(1), K.size(1), V.size(1)
        Q_ = self.Q_linear(Q).view(b, len_q, self.h, self.dim_k).transpose(1, 2
            )
        K_ = self.K_linear(K).view(b, len_k, self.h, self.dim_k).transpose(1, 2
            )
        V_ = self.V_linear(V).view(b, len_v, self.h, self.dim_v).transpose(1, 2
            )
        if mask is not None:
            mask = mask.unsqueeze(1)
        out = self.__attention(Q_, K_, V_, mask)
        out = out.transpose(1, 2).contiguous().view(b, len_q, -1)
        out = self.out_linear(out)
        return out

    @staticmethod
    def __attention(Q, K, V, mask=None):
        d_k = K.shape[0]
        att = (Q / np.sqrt(d_k)).matmul(K.transpose(-1, -2))
        if mask is not None:
            att = att.masked_fill(mask == 0, -float('inf'))
        att = F.softmax(att, dim=-1)
        out = att.matmul(V)
        return out


class PositionWiseFeedForwardLayer(nn.Module):

    def __init__(self, dim_in, dim_ff, prob_dropout=0.1):
        super(PositionWiseFeedForwardLayer, self).__init__()
        self.fc1 = nn.Linear(dim_in, dim_ff)
        self.fc2 = nn.Linear(dim_ff, dim_in)
        self.dropout = nn.Dropout(prob_dropout)

    def forward(self, x):
        out = self.fc1(x)
        out = F.relu(out)
        out = self.fc2(out)
        return out


class ForwardCrossAttentionLayer(BaseLayer):

    def __init__(self, dim_model, dim_k, dim_v, h, dim_ff, prob_dropout):
        super(ForwardCrossAttentionLayer, self).__init__(dim_model, dim_k,
            dim_v, h, dim_ff, prob_dropout)
        self.self_att = MultiHeadedAttentionLayer(dim_model, dim_k, dim_v, h)
        self.rc1 = ResidualConnectionLayer(dim_model, prob_dropout)
        self.cross_att = MultiHeadedAttentionLayer(dim_model, dim_k, dim_v, h)
        self.rc2 = ResidualConnectionLayer(dim_model, prob_dropout)
        self.ff = PositionWiseFeedForwardLayer(dim_model, dim_ff)
        self.rc3 = ResidualConnectionLayer(dim_model, prob_dropout)
        self.norm = nn.LayerNorm(dim_model)

    def forward(self, x, memory_x, mask=None, memory_mask=None):
        out = self.rc1(x, lambda item: self.self_att(item, item, item, mask))
        out = self.rc2(out, lambda item: self.cross_att(item, memory_x,
            memory_x, memory_mask))
        out = self.rc3(out, self.ff)
        out = self.norm(out)
        return out


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


def get_init_inputs():
    return [[], {'dim_model': 4, 'dim_k': 4, 'dim_v': 4, 'h': 4, 'dim_ff': 
        4, 'prob_dropout': 0.5}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import numpy as np
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_native_layer_norm_0(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_1(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_clone_div_sqrt_2(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp3 = 2.0
    tmp4 = tmp2 / tmp3
    tl.store(out_ptr0 + x4, tmp4, xmask)


@triton.jit
def triton_poi_fused_clone_3(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    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 % 16
    y1 = yindex // 16
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 64 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)


@triton.jit
def triton_poi_fused__softmax_4(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__softmax_5(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 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_clone_6(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
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x4, tmp2, xmask)


@triton.jit
def triton_poi_fused_clone_7(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
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), xmask)
    tl.store(out_ptr0 + x4, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_8(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_9(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_add_10(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, 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_ptr1 + x2, xmask)
    tmp3 = tl.load(in_out_ptr0 + x2, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tl.store(in_out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_11(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_12(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)


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,), (1,))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (16, 4), (4, 1))
    assert_size_stride(primals_5, (16,), (1,))
    assert_size_stride(primals_6, (16, 4), (4, 1))
    assert_size_stride(primals_7, (16,), (1,))
    assert_size_stride(primals_8, (16, 4), (4, 1))
    assert_size_stride(primals_9, (16,), (1,))
    assert_size_stride(primals_10, (4, 16), (16, 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, (16, 4), (4, 1))
    assert_size_stride(primals_16, (16,), (1,))
    assert_size_stride(primals_17, (16, 4), (4, 1))
    assert_size_stride(primals_18, (16,), (1,))
    assert_size_stride(primals_19, (16, 4), (4, 1))
    assert_size_stride(primals_20, (16,), (1,))
    assert_size_stride(primals_21, (4, 16), (16, 1))
    assert_size_stride(primals_22, (4,), (1,))
    assert_size_stride(primals_23, (4,), (1,))
    assert_size_stride(primals_24, (4,), (1,))
    assert_size_stride(primals_25, (4, 4), (4, 1))
    assert_size_stride(primals_26, (4,), (1,))
    assert_size_stride(primals_27, (4, 4), (4, 1))
    assert_size_stride(primals_28, (4,), (1,))
    assert_size_stride(primals_29, (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, 1), (4, 1, 16), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_native_layer_norm_0[grid(16)](primals_3, buf0,
            buf1, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_1[grid(64)](primals_3, buf0,
            buf1, primals_1, primals_2, buf2, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del primals_1
        del primals_2
        buf3 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 16), (1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 16), (1, 4), 0), out=buf4)
        buf5 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 16), (1, 4), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_div_sqrt_2[grid(256)](buf3, primals_5, buf6,
            256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf7 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf3
        triton_poi_fused_clone_3[grid(64, 4)](buf4, primals_7, buf7, 64, 4,
            XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_7
        buf8 = reinterpret_tensor(buf4, (16, 4, 4), (16, 4, 1), 0)
        del buf4
        extern_kernels.bmm(reinterpret_tensor(buf6, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1), 0), out=buf8)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_4[grid(256)](buf8, buf9, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf10 = reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf8
        triton_poi_fused__softmax_5[grid(256)](buf9, buf10, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf11 = buf9
        del buf9
        triton_poi_fused_clone_6[grid(256)](buf5, primals_9, buf11, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_9
        buf12 = reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0)
        del buf5
        extern_kernels.bmm(reinterpret_tensor(buf10, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf11, (16, 4, 4), (16, 4, 1), 0), out=buf12
            )
        buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_7[grid(256)](buf12, buf13, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf14 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf13, (16, 16),
            (16, 1), 0), reinterpret_tensor(primals_10, (16, 4), (1, 16), 0
            ), alpha=1, beta=1, out=buf14)
        del primals_11
        buf15 = buf1
        del buf1
        buf16 = buf0
        del buf0
        triton_poi_fused_add_native_layer_norm_8[grid(16)](primals_3, buf14,
            buf15, buf16, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf17 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_9[grid(64)](primals_3, buf14,
            buf15, buf16, primals_12, primals_13, buf17, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_13
        buf18 = reinterpret_tensor(buf12, (16, 16), (16, 1), 0)
        del buf12
        extern_kernels.mm(reinterpret_tensor(buf17, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_15, (4, 16), (1, 4), 0), out=buf18)
        buf19 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_14, (16, 4), (4, 1), 0
            ), reinterpret_tensor(primals_17, (4, 16), (1, 4), 0), out=buf19)
        del primals_17
        buf20 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_14, (16, 4), (4, 1), 0
            ), reinterpret_tensor(primals_19, (4, 16), (1, 4), 0), out=buf20)
        del primals_19
        buf21 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_div_sqrt_2[grid(256)](buf18, primals_16,
            buf21, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_16
        buf22 = reinterpret_tensor(buf18, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf18
        triton_poi_fused_clone_3[grid(64, 4)](buf19, primals_18, buf22, 64,
            4, XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_18
        buf23 = reinterpret_tensor(buf19, (16, 4, 4), (16, 4, 1), 0)
        del buf19
        extern_kernels.bmm(reinterpret_tensor(buf21, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf22, (16, 4, 4), (16, 4, 1), 0), out=buf23
            )
        buf24 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_4[grid(256)](buf23, buf24, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf25 = reinterpret_tensor(buf23, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf23
        triton_poi_fused__softmax_5[grid(256)](buf24, buf25, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        buf26 = buf24
        del buf24
        triton_poi_fused_clone_6[grid(256)](buf20, primals_20, buf26, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_20
        buf27 = reinterpret_tensor(buf20, (16, 4, 4), (16, 4, 1), 0)
        del buf20
        extern_kernels.bmm(reinterpret_tensor(buf25, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf26, (16, 4, 4), (16, 4, 1), 0), out=buf27
            )
        buf28 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_7[grid(256)](buf27, buf28, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf27
        buf29 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf28, (16, 16), (16, 1), 0),
            reinterpret_tensor(primals_21, (16, 4), (1, 16), 0), out=buf29)
        buf30 = reinterpret_tensor(buf29, (4, 4, 4), (16, 4, 1), 0)
        del buf29
        triton_poi_fused_add_10[grid(64)](buf30, primals_3, buf14,
            primals_22, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_22
        buf31 = buf16
        del buf16
        buf32 = buf15
        del buf15
        triton_poi_fused_native_layer_norm_0[grid(16)](buf30, buf31, buf32,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf33 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_1[grid(64)](buf30, buf31, buf32,
            primals_23, primals_24, buf33, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del primals_24
        buf34 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf33, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_25, (4, 4), (1, 4), 0), out=buf34)
        buf35 = reinterpret_tensor(buf34, (4, 4, 4), (16, 4, 1), 0)
        del buf34
        buf41 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_11[grid(64)](buf35,
            primals_26, buf41, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_26
        buf36 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf35, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_27, (4, 4), (1, 4), 0), out=buf36)
        buf37 = reinterpret_tensor(buf36, (4, 4, 4), (16, 4, 1), 0)
        del buf36
        triton_poi_fused_add_12[grid(64)](buf37, buf30, primals_28, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_28
        buf38 = buf32
        del buf32
        buf39 = buf31
        del buf31
        triton_poi_fused_native_layer_norm_0[grid(16)](buf37, buf38, buf39,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf40 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_1[grid(64)](buf37, buf38, buf39,
            primals_29, primals_30, buf40, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf38
        del buf39
        del primals_30
    return (buf40, primals_3, primals_12, primals_23, primals_29,
        reinterpret_tensor(buf2, (16, 4), (4, 1), 0), buf10,
        reinterpret_tensor(buf13, (16, 16), (16, 1), 0), buf14,
        reinterpret_tensor(buf17, (16, 4), (4, 1), 0), reinterpret_tensor(
        primals_14, (16, 4), (4, 1), 0), buf25, reinterpret_tensor(buf28, (
        16, 16), (16, 1), 0), buf30, reinterpret_tensor(buf33, (16, 4), (4,
        1), 0), reinterpret_tensor(buf35, (16, 4), (4, 1), 0), buf37,
        primals_27, buf41, primals_25, primals_21, reinterpret_tensor(buf26,
        (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf21, (16, 4, 4), (
        16, 1, 4), 0), reinterpret_tensor(buf22, (16, 4, 4), (16, 1, 4), 0),
        primals_15, primals_10, reinterpret_tensor(buf11, (16, 4, 4), (16, 
        1, 4), 0), reinterpret_tensor(buf6, (16, 4, 4), (16, 1, 4), 0),
        reinterpret_tensor(buf7, (16, 4, 4), (16, 1, 4), 0), primals_8,
        primals_6, primals_4)


class ResidualConnectionLayer(nn.Module):

    def __init__(self, dim_model, prob_dropout=0.1, add_sublayer=True):
        super(ResidualConnectionLayer, self).__init__()
        self.add_sublayer = add_sublayer
        self.norm = nn.LayerNorm(dim_model)
        self.dropout = nn.Dropout(prob_dropout)

    def forward(self, x, sublayer):
        out = self.norm(x)
        out = sublayer(out)
        out = self.dropout(out)
        if self.add_sublayer:
            return x + out
        else:
            return out


class BaseLayer(nn.Module):

    def __init__(self, dim_model, dim_k, dim_v, h, dim_ff, prob_dropout):
        super(BaseLayer, self).__init__()
        self._dim_model = dim_model
        self._dim_k = dim_k
        self._dim_v = dim_v
        self._h = h
        self._dim_ff = dim_ff
        self._prob_dropout = prob_dropout


class MultiHeadedAttentionLayer(nn.Module):

    def __init__(self, dim_model, dim_k, dim_v, h):
        super(MultiHeadedAttentionLayer, self).__init__()
        self.dim_model = dim_model
        self.dim_k = dim_k
        self.dim_v = dim_v
        self.h = h
        self.Q_linear = nn.Linear(dim_model, dim_k * h)
        self.K_linear = nn.Linear(dim_model, dim_k * h)
        self.V_linear = nn.Linear(dim_model, dim_v * h)
        self.out_linear = nn.Linear(self.h * dim_v, dim_model)

    def forward(self, Q, K, V, mask=None):
        b, len_q, len_k, len_v = Q.size(0), Q.size(1), K.size(1), V.size(1)
        Q_ = self.Q_linear(Q).view(b, len_q, self.h, self.dim_k).transpose(1, 2
            )
        K_ = self.K_linear(K).view(b, len_k, self.h, self.dim_k).transpose(1, 2
            )
        V_ = self.V_linear(V).view(b, len_v, self.h, self.dim_v).transpose(1, 2
            )
        if mask is not None:
            mask = mask.unsqueeze(1)
        out = self.__attention(Q_, K_, V_, mask)
        out = out.transpose(1, 2).contiguous().view(b, len_q, -1)
        out = self.out_linear(out)
        return out

    @staticmethod
    def __attention(Q, K, V, mask=None):
        d_k = K.shape[0]
        att = (Q / np.sqrt(d_k)).matmul(K.transpose(-1, -2))
        if mask is not None:
            att = att.masked_fill(mask == 0, -float('inf'))
        att = F.softmax(att, dim=-1)
        out = att.matmul(V)
        return out


class PositionWiseFeedForwardLayer(nn.Module):

    def __init__(self, dim_in, dim_ff, prob_dropout=0.1):
        super(PositionWiseFeedForwardLayer, self).__init__()
        self.fc1 = nn.Linear(dim_in, dim_ff)
        self.fc2 = nn.Linear(dim_ff, dim_in)
        self.dropout = nn.Dropout(prob_dropout)

    def forward(self, x):
        out = self.fc1(x)
        out = F.relu(out)
        out = self.fc2(out)
        return out


class ForwardCrossAttentionLayerNew(BaseLayer):

    def __init__(self, dim_model, dim_k, dim_v, h, dim_ff, prob_dropout):
        super(ForwardCrossAttentionLayerNew, self).__init__(dim_model,
            dim_k, dim_v, h, dim_ff, prob_dropout)
        self.self_att = MultiHeadedAttentionLayer(dim_model, dim_k, dim_v, h)
        self.rc1 = ResidualConnectionLayer(dim_model, prob_dropout)
        self.cross_att = MultiHeadedAttentionLayer(dim_model, dim_k, dim_v, h)
        self.rc2 = ResidualConnectionLayer(dim_model, prob_dropout)
        self.ff = PositionWiseFeedForwardLayer(dim_model, dim_ff)
        self.rc3 = ResidualConnectionLayer(dim_model, prob_dropout)
        self.norm = nn.LayerNorm(dim_model)

    def forward(self, input_0, input_1):
        primals_4 = self.self_att.Q_linear.weight
        primals_5 = self.self_att.Q_linear.bias
        primals_6 = self.self_att.K_linear.weight
        primals_7 = self.self_att.K_linear.bias
        primals_8 = self.self_att.V_linear.weight
        primals_9 = self.self_att.V_linear.bias
        primals_10 = self.self_att.out_linear.weight
        primals_1 = self.self_att.out_linear.bias
        primals_2 = self.rc1.norm.weight
        primals_11 = self.rc1.norm.bias
        primals_15 = self.cross_att.Q_linear.weight
        primals_16 = self.cross_att.Q_linear.bias
        primals_17 = self.cross_att.K_linear.weight
        primals_18 = self.cross_att.K_linear.bias
        primals_19 = self.cross_att.V_linear.weight
        primals_20 = self.cross_att.V_linear.bias
        primals_21 = self.cross_att.out_linear.weight
        primals_12 = self.cross_att.out_linear.bias
        primals_13 = self.rc2.norm.weight
        primals_22 = self.rc2.norm.bias
        primals_25 = self.ff.fc1.weight
        primals_23 = self.ff.fc1.bias
        primals_27 = self.ff.fc2.weight
        primals_24 = self.ff.fc2.bias
        primals_26 = self.rc3.norm.weight
        primals_28 = self.rc3.norm.bias
        primals_29 = self.norm.weight
        primals_30 = self.norm.bias
        primals_3 = input_0
        primals_14 = 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]
