# 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_9/inductor_cache/7j/c7jycjp5htd6f5jdvv2i4z3gzdi3nf2c4tjg2ydcvoi5symiidqg.py
# Topologically Sorted Source Nodes: [linear], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   linear => convert_element_type_1
# Graph fragment:
#   %convert_element_type_1 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%primals_1, torch.float16), kwargs = {})
triton_poi_fused__to_copy_0 = async_compile.triton('triton_poi_fused__to_copy_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp16', 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__to_copy_0', '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__to_copy_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tmp0.to(tl.float32)
    tl.store(out_ptr0 + (x0), tmp1, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_9/inductor_cache/mp/cmpsbcrgyc56gvohxoei4nkltnxe3xirinqdxwxqfej56pgtfkar.py
# Topologically Sorted Source Nodes: [linear], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   linear => convert_element_type
# Graph fragment:
#   %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%primals_2, torch.float16), kwargs = {})
triton_poi_fused__to_copy_1 = async_compile.triton('triton_poi_fused__to_copy_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], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp16', 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__to_copy_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_1(in_ptr0, out_ptr0, 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 + (x0), xmask)
    tmp1 = tmp0.to(tl.float32)
    tl.store(out_ptr0 + (x0), tmp1, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_9/inductor_cache/6r/c6rxu6byphv4un7ktkqpdpp2mtv6jdosbhgxup6c76zy3yqtvxek.py
# Topologically Sorted Source Nodes: [truediv, attn], Original ATen: [aten.div, aten.clone]
# Source node to ATen node mapping:
#   attn => clone
#   truediv => div
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%permute_9, 2.0), 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_2 = async_compile.triton('triton_poi_fused_clone_div_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: '*fp16', 1: '*fp16', 2: '*fp16', 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_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_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
    x4 = xindex
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16) % 4
    x3 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x4), xmask).to(tl.float32)
    tmp1 = tl.load(in_ptr1 + (x4), xmask).to(tl.float32)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.5
    tmp4 = tmp2 * tmp3
    tl.store(out_ptr0 + (x0 + (4*x2) + (16*x1) + (64*x3)), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_9/inductor_cache/5z/c5zdit3b3gncrtrhksz3wugrwokfwhxiktdrpfc4lyxhd765qkvw.py
# Topologically Sorted Source Nodes: [attn], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   attn => 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=[16, 16], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp16', 1: '*fp16', 2: '*fp16', 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, 4), 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 = 16
    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
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2 + (16*y3)), xmask & ymask).to(tl.float32)
    tmp1 = tl.load(in_ptr1 + (x2 + (16*y3)), xmask & ymask).to(tl.float32)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (y0 + (4*x2) + (64*y1)), tmp2, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_9/inductor_cache/jx/cjxv75hajxx5elwieb4njn6zfg5rafhoeq5rerbjaunnsbndxs4d.py
# Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   softmax => amax, convert_element_type_35, exp, sub
# Graph fragment:
#   %convert_element_type_35 : [num_users=2] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%view_23, torch.float32), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%convert_element_type_35, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convert_element_type_35, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), 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: '*fp16', 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).to(tl.float32)
    tmp2 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last').to(tl.float32)
    tmp4 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last').to(tl.float32)
    tmp7 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last').to(tl.float32)
    tmp10 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last').to(tl.float32)
    tmp1 = tmp0.to(tl.float32)
    tmp3 = tmp2.to(tl.float32)
    tmp5 = tmp4.to(tl.float32)
    tmp6 = triton_helpers.maximum(tmp3, tmp5)
    tmp8 = tmp7.to(tl.float32)
    tmp9 = triton_helpers.maximum(tmp6, tmp8)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = triton_helpers.maximum(tmp9, tmp11)
    tmp13 = tmp1 - tmp12
    tmp14 = tl_math.exp(tmp13)
    tl.store(out_ptr0 + (x2), tmp14, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_9/inductor_cache/og/cogep24zfncw4nygiwn2xcmmqm7rt7wgmgue4s4uba3a22dqrr3i.py
# Topologically Sorted Source Nodes: [softmax, output], Original ATen: [aten._softmax, aten._to_copy]
# Source node to ATen node mapping:
#   output => convert_element_type_36
#   softmax => 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 = {})
#   %convert_element_type_36 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%div_1, torch.float16), kwargs = {})
triton_poi_fused__softmax__to_copy_5 = async_compile.triton('triton_poi_fused__softmax__to_copy_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: '*fp16', 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__softmax__to_copy_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__to_copy_5(in_ptr0, out_ptr0, out_ptr1, 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
    tmp9 = tmp8.to(tl.float32)
    tl.store(out_ptr0 + (x2), tmp8, xmask)
    tl.store(out_ptr1 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_9/inductor_cache/i5/ci5jnti4ulig4bvrenra5rcts24yk5uwrlszn2zg6dssexq53tnh.py
# Topologically Sorted Source Nodes: [output], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   output => clone_3
# Graph fragment:
#   %clone_3 : [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: '*fp16', 1: '*fp16', 2: '*fp16', 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
    x4 = xindex
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16) % 4
    x3 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x4), xmask).to(tl.float32)
    tmp1 = tl.load(in_ptr1 + (x4), xmask).to(tl.float32)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x0 + (4*x2) + (16*x1) + (64*x3)), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_9/inductor_cache/eq/ceqs6zn4xlihole7trexitgzzhcvid5u455d54zikujqk26j5mxb.py
# Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   contiguous => clone_4
# Graph fragment:
#   %clone_4 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_13,), 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: '*fp16', 1: '*fp16', 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).to(tl.float32)
    tl.store(out_ptr0 + (x4), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_9/inductor_cache/rh/crho5uemcsgmoawdcwbuvpityj4ij5dq3my3ikoy5nffsgeu43hy.py
# Topologically Sorted Source Nodes: [linear_10], Original ATen: [aten._to_copy, aten.t]
# Source node to ATen node mapping:
#   linear_10 => convert_element_type_42, permute_15
# Graph fragment:
#   %convert_element_type_42 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%primals_12, torch.float16), kwargs = {})
#   %permute_15 : [num_users=2] = call_function[target=torch.ops.aten.permute.default](args = (%convert_element_type_42, [1, 0]), kwargs = {})
triton_poi_fused__to_copy_t_8 = async_compile.triton('triton_poi_fused__to_copy_t_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=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp16', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_t_8', '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__to_copy_t_8(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tmp0.to(tl.float32)
    tl.store(out_ptr0 + (x0), tmp1, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_9/inductor_cache/dd/cdd44gygumakzbi7zyhnhbcrkskfgrf3ltivpidu4dadautrbrgl.py
# Topologically Sorted Source Nodes: [add_3, q_4], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   add_3 => add_3
#   q_4 => add_4
# Graph fragment:
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_31, %view_33), kwargs = {})
#   %add_4 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_3, %primals_1), kwargs = {})
triton_poi_fused_add_9 = async_compile.triton('triton_poi_fused_add_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: '*fp16', 1: '*fp16', 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_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_9(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask).to(tl.float32)
    tmp1 = tl.load(in_ptr1 + (x0), xmask).to(tl.float32)
    tmp4 = tl.load(in_ptr2 + (x0), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp2.to(tl.float32)
    tmp5 = tmp3 + tmp4
    tl.store(out_ptr0 + (x0), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_9/inductor_cache/in/cinndmzytbittomxphtrtjvi2ocrspv6cyphmacpq5bjpvueh5mk.py
# Topologically Sorted Source Nodes: [q_5], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
#   q_5 => add_5, rsqrt, var_mean
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%add_4, [2]), kwargs = {correction: 0, keepdim: True})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-06), kwargs = {})
#   %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_5,), kwargs = {})
triton_poi_fused_native_layer_norm_10 = async_compile.triton('triton_poi_fused_native_layer_norm_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=[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_10', '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_10(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-06
    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_9/inductor_cache/d2/cd2v4afwnnqdrgticluj3xnb55jjthl3xmxanzaiyi5j6flkwydd.py
# Topologically Sorted Source Nodes: [q_5, linear_12], Original ATen: [aten.native_layer_norm, aten._to_copy]
# Source node to ATen node mapping:
#   linear_12 => convert_element_type_51
#   q_5 => add_5, add_6, mul, mul_1, rsqrt, sub_1, var_mean
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%add_4, [2]), kwargs = {correction: 0, keepdim: True})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-06), kwargs = {})
#   %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_5,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_4, %getitem_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %rsqrt), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %primals_14), kwargs = {})
#   %add_6 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_15), kwargs = {})
#   %convert_element_type_51 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_6, torch.float16), kwargs = {})
triton_poi_fused__to_copy_native_layer_norm_11 = async_compile.triton('triton_poi_fused__to_copy_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp16', 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__to_copy_native_layer_norm_11', '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__to_copy_native_layer_norm_11(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, 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
    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
    tmp9 = tmp8.to(tl.float32)
    tl.store(out_ptr0 + (x2), tmp8, xmask)
    tl.store(out_ptr1 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_9/inductor_cache/2c/c2cbnljs6ol2q3yqs5uxoqsshx2k4yd4dz23rwiscc6ejdfq6dir.py
# Topologically Sorted Source Nodes: [add_4, x], Original ATen: [aten.add, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   add_4 => add_7
#   x => relu
# Graph fragment:
#   %add_7 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_37, %view_39), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_7,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_add_relu_threshold_backward_12 = async_compile.triton('triton_poi_fused_add_relu_threshold_backward_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: '*fp16', 1: '*fp16', 2: '*fp32', 3: '*i1', 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_relu_threshold_backward_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_relu_threshold_backward_12(in_out_ptr0, in_ptr0, in_ptr1, 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).to(tl.float32)
    tmp1 = tl.load(in_ptr0 + (x2), xmask).to(tl.float32)
    tmp2 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tmp2.to(tl.float32)
    tmp4 = tmp1 + tmp3
    tmp5 = tmp0 + tmp4
    tmp6 = tl.full([1], 0, tl.int32)
    tmp7 = triton_helpers.maximum(tmp6, tmp5)
    tmp8 = 0.0
    tmp9 = tmp7 <= tmp8
    tl.store(in_out_ptr0 + (x2), tmp7, xmask)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_9/inductor_cache/3h/c3hl74l6wm7asc63zmyasgv2zlcdgartz5haybyp2cihpcs2wboz.py
# Topologically Sorted Source Nodes: [x_1, x_3], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   x_1 => add_8
#   x_3 => add_9
# Graph fragment:
#   %add_8 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_43, %view_45), kwargs = {})
#   %add_9 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_8, %add_6), kwargs = {})
triton_poi_fused_add_13 = async_compile.triton('triton_poi_fused_add_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp16', 2: '*fp16', 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_13', '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_13(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).to(tl.float32)
    tmp1 = tl.load(in_ptr1 + (x2), xmask).to(tl.float32)
    tmp2 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp3 = tmp2.to(tl.float32)
    tmp4 = tmp1 + tmp3
    tmp5 = tmp0 + tmp4
    tmp6 = tmp5.to(tl.float32)
    tmp8 = tmp6 + tmp7
    tl.store(in_out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_9/inductor_cache/dr/cdrly2m2ajw4z7nsxvc4tnu6ks6b7s65jkv7p2qo565cfa7ogsb3.py
# Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
#   x_4 => add_10, add_11, mul_2, mul_3, 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_9, [2]), kwargs = {correction: 0, keepdim: True})
#   %add_10 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_2, 1e-06), kwargs = {})
#   %rsqrt_1 : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_10,), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_9, %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_24), kwargs = {})
#   %add_11 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_3, %primals_25), kwargs = {})
triton_poi_fused_native_layer_norm_14 = async_compile.triton('triton_poi_fused_native_layer_norm_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: '*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_14', '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_14(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')


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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (16, 4), (4, 1))
    assert_size_stride(primals_4, (16, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (16, 4), (4, 1))
    assert_size_stride(primals_7, (16, 4), (4, 1))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (16, 4), (4, 1))
    assert_size_stride(primals_10, (16, 4), (4, 1))
    assert_size_stride(primals_11, (1, 16), (16, 1))
    assert_size_stride(primals_12, (4, 1), (1, 1))
    assert_size_stride(primals_13, (4, 16), (16, 1))
    assert_size_stride(primals_14, (4, ), (1, ))
    assert_size_stride(primals_15, (4, ), (1, ))
    assert_size_stride(primals_16, (1, 4), (4, 1))
    assert_size_stride(primals_17, (4, 1), (1, 1))
    assert_size_stride(primals_18, (4, 4), (4, 1))
    assert_size_stride(primals_19, (4, ), (1, ))
    assert_size_stride(primals_20, (1, 4), (4, 1))
    assert_size_stride(primals_21, (4, 1), (1, 1))
    assert_size_stride(primals_22, (4, 4), (4, 1))
    assert_size_stride(primals_23, (4, ), (1, ))
    assert_size_stride(primals_24, (4, ), (1, ))
    assert_size_stride(primals_25, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float16)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten._to_copy]
        stream0 = get_raw_stream(0)
        triton_poi_fused__to_copy_0.run(primals_1, buf0, 64, grid=grid(64), stream=stream0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float16)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_1.run(primals_2, buf1, 16, grid=grid(16), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0), reinterpret_tensor(buf1, (4, 4), (1, 4), 0), out=buf2)
        buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_0.run(primals_3, buf3, 64, grid=grid(64), stream=stream0)
        del primals_3
        buf4 = empty_strided_cuda((16, 16), (16, 1), torch.float16)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm]
        extern_kernels.mm(buf2, reinterpret_tensor(buf3, (4, 16), (1, 4), 0), out=buf4)
        buf5 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_0.run(primals_4, buf5, 64, grid=grid(64), stream=stream0)
        del primals_4
        buf6 = empty_strided_cuda((16, 16), (16, 1), torch.float16)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0), reinterpret_tensor(buf5, (4, 16), (1, 4), 0), out=buf6)
        buf7 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [linear_3], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_1.run(primals_5, buf7, 16, grid=grid(16), stream=stream0)
        del primals_5
        buf8 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [linear_3], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0), reinterpret_tensor(buf7, (4, 4), (1, 4), 0), out=buf8)
        buf9 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        # Topologically Sorted Source Nodes: [linear_4], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_0.run(primals_6, buf9, 64, grid=grid(64), stream=stream0)
        del primals_6
        buf10 = empty_strided_cuda((16, 16), (16, 1), torch.float16)
        # Topologically Sorted Source Nodes: [linear_4], Original ATen: [aten.mm]
        extern_kernels.mm(buf8, reinterpret_tensor(buf9, (4, 16), (1, 4), 0), out=buf10)
        buf11 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        # Topologically Sorted Source Nodes: [linear_5], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_0.run(primals_7, buf11, 64, grid=grid(64), stream=stream0)
        del primals_7
        buf12 = empty_strided_cuda((16, 16), (16, 1), torch.float16)
        # Topologically Sorted Source Nodes: [linear_5], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0), reinterpret_tensor(buf11, (4, 16), (1, 4), 0), out=buf12)
        buf13 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [linear_6], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_1.run(primals_8, buf13, 16, grid=grid(16), stream=stream0)
        del primals_8
        buf14 = buf11; del buf11  # reuse
        # Topologically Sorted Source Nodes: [linear_6], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0), reinterpret_tensor(buf13, (4, 4), (1, 4), 0), out=buf14)
        buf15 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        # Topologically Sorted Source Nodes: [linear_7], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_0.run(primals_9, buf15, 64, grid=grid(64), stream=stream0)
        del primals_9
        buf16 = empty_strided_cuda((16, 16), (16, 1), torch.float16)
        # Topologically Sorted Source Nodes: [linear_7], Original ATen: [aten.mm]
        extern_kernels.mm(buf14, reinterpret_tensor(buf15, (4, 16), (1, 4), 0), out=buf16)
        buf17 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        # Topologically Sorted Source Nodes: [linear_8], Original ATen: [aten._to_copy]
        triton_poi_fused__to_copy_0.run(primals_10, buf17, 64, grid=grid(64), stream=stream0)
        del primals_10
        buf18 = empty_strided_cuda((16, 16), (16, 1), torch.float16)
        # Topologically Sorted Source Nodes: [linear_8], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0), reinterpret_tensor(buf17, (4, 16), (1, 4), 0), out=buf18)
        buf19 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float16)
        # Topologically Sorted Source Nodes: [truediv, attn], Original ATen: [aten.div, aten.clone]
        triton_poi_fused_clone_div_2.run(buf4, buf6, buf19, 256, grid=grid(256), stream=stream0)
        del buf4
        buf20 = reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf6  # reuse
        # Topologically Sorted Source Nodes: [attn], Original ATen: [aten.clone]
        triton_poi_fused_clone_3.run(buf10, buf12, buf20, 16, 16, grid=grid(16, 16), stream=stream0)
        buf21 = reinterpret_tensor(buf12, (16, 4, 4), (16, 4, 1), 0); del buf12  # reuse
        # Topologically Sorted Source Nodes: [attn], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf19, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf20, (16, 4, 4), (16, 4, 1), 0), out=buf21)
        buf22 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_4.run(buf21, buf22, 256, grid=grid(256), stream=stream0)
        buf23 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf25 = reinterpret_tensor(buf21, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf21  # reuse
        # Topologically Sorted Source Nodes: [softmax, output], Original ATen: [aten._softmax, aten._to_copy]
        triton_poi_fused__softmax__to_copy_5.run(buf22, buf23, buf25, 256, grid=grid(256), stream=stream0)
        del buf22
        buf24 = reinterpret_tensor(buf10, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf10  # reuse
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten.clone]
        triton_poi_fused_clone_6.run(buf16, buf18, buf24, 256, grid=grid(256), stream=stream0)
        buf26 = reinterpret_tensor(buf18, (16, 4, 4), (16, 4, 1), 0); del buf18  # reuse
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf25, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf24, (16, 4, 4), (16, 4, 1), 0), out=buf26)
        buf27 = reinterpret_tensor(buf13, (16, 1), (1, 16), 0); del buf13  # reuse
        # Topologically Sorted Source Nodes: [linear_9], Original ATen: [aten._to_copy, aten.t]
        triton_poi_fused__to_copy_1.run(primals_11, buf27, 16, grid=grid(16), stream=stream0)
        del primals_11
        buf28 = reinterpret_tensor(buf16, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf16  # reuse
        # Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone]
        triton_poi_fused_clone_7.run(buf26, buf28, 256, grid=grid(256), stream=stream0)
        del buf26
        buf29 = empty_strided_cuda((16, 1), (1, 1), torch.float16)
        # Topologically Sorted Source Nodes: [linear_9], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf28, (16, 16), (16, 1), 0), buf27, out=buf29)
        buf30 = empty_strided_cuda((1, 4), (4, 1), torch.float16)
        # Topologically Sorted Source Nodes: [linear_10], Original ATen: [aten._to_copy, aten.t]
        triton_poi_fused__to_copy_t_8.run(primals_12, buf30, 4, grid=grid(4), stream=stream0)
        del primals_12
        buf31 = buf17; del buf17  # reuse
        # Topologically Sorted Source Nodes: [linear_10], Original ATen: [aten.mm]
        extern_kernels.mm(buf29, buf30, out=buf31)
        buf32 = empty_strided_cuda((16, 4), (1, 16), torch.float16)
        # Topologically Sorted Source Nodes: [linear_11], Original ATen: [aten._to_copy, aten.t]
        triton_poi_fused__to_copy_0.run(primals_13, buf32, 64, grid=grid(64), stream=stream0)
        del primals_13
        buf33 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        # Topologically Sorted Source Nodes: [linear_11], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf28, (16, 16), (16, 1), 0), buf32, out=buf33)
        buf34 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add_3, q_4], Original ATen: [aten.add]
        triton_poi_fused_add_9.run(buf31, buf33, primals_1, buf34, 64, grid=grid(64), stream=stream0)
        del primals_1
        buf35 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf36 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        # Topologically Sorted Source Nodes: [q_5], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_10.run(buf34, buf35, buf36, 16, grid=grid(16), stream=stream0)
        buf37 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf39 = reinterpret_tensor(buf33, (4, 4, 4), (16, 4, 1), 0); del buf33  # reuse
        # Topologically Sorted Source Nodes: [q_5, linear_12], Original ATen: [aten.native_layer_norm, aten._to_copy]
        triton_poi_fused__to_copy_native_layer_norm_11.run(buf34, buf35, buf36, primals_14, primals_15, buf37, buf39, 64, grid=grid(64), stream=stream0)
        del primals_15
        buf38 = empty_strided_cuda((4, 1), (1, 4), torch.float16)
        # Topologically Sorted Source Nodes: [linear_12], Original ATen: [aten._to_copy, aten.t]
        triton_poi_fused__to_copy_t_8.run(primals_16, buf38, 4, grid=grid(4), stream=stream0)
        del primals_16
        buf40 = empty_strided_cuda((16, 1), (1, 1), torch.float16)
        # Topologically Sorted Source Nodes: [linear_12], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf39, (16, 4), (4, 1), 0), buf38, out=buf40)
        buf41 = empty_strided_cuda((1, 4), (4, 1), torch.float16)
        # Topologically Sorted Source Nodes: [linear_13], Original ATen: [aten._to_copy, aten.t]
        triton_poi_fused__to_copy_t_8.run(primals_17, buf41, 4, grid=grid(4), stream=stream0)
        del primals_17
        buf42 = buf31; del buf31  # reuse
        # Topologically Sorted Source Nodes: [linear_13], Original ATen: [aten.mm]
        extern_kernels.mm(buf40, buf41, out=buf42)
        buf43 = empty_strided_cuda((4, 4), (1, 4), torch.float16)
        # Topologically Sorted Source Nodes: [linear_14], Original ATen: [aten._to_copy, aten.t]
        triton_poi_fused__to_copy_1.run(primals_18, buf43, 16, grid=grid(16), stream=stream0)
        del primals_18
        buf44 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf39, (16, 4), (4, 1), 0), buf43, out=buf44)
        buf45 = empty_strided_cuda((4, 1), (1, 4), torch.float16)
        # Topologically Sorted Source Nodes: [linear_15], Original ATen: [aten._to_copy, aten.t]
        triton_poi_fused__to_copy_t_8.run(primals_20, buf45, 4, grid=grid(4), stream=stream0)
        del primals_20
        buf46 = reinterpret_tensor(buf42, (4, 4, 4), (16, 4, 1), 0); del buf42  # reuse
        buf56 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [add_4, x], Original ATen: [aten.add, aten.relu, aten.threshold_backward]
        triton_poi_fused_add_relu_threshold_backward_12.run(buf46, buf44, primals_19, buf56, 64, grid=grid(64), stream=stream0)
        del primals_19
        buf47 = empty_strided_cuda((16, 1), (1, 1), torch.float16)
        # Topologically Sorted Source Nodes: [linear_15], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf46, (16, 4), (4, 1), 0), buf45, out=buf47)
        buf48 = empty_strided_cuda((1, 4), (4, 1), torch.float16)
        # Topologically Sorted Source Nodes: [linear_16], Original ATen: [aten._to_copy, aten.t]
        triton_poi_fused__to_copy_t_8.run(primals_21, buf48, 4, grid=grid(4), stream=stream0)
        del primals_21
        buf49 = buf44; del buf44  # reuse
        # Topologically Sorted Source Nodes: [linear_16], Original ATen: [aten.mm]
        extern_kernels.mm(buf47, buf48, out=buf49)
        buf50 = empty_strided_cuda((4, 4), (1, 4), torch.float16)
        # Topologically Sorted Source Nodes: [linear_17], Original ATen: [aten._to_copy, aten.t]
        triton_poi_fused__to_copy_1.run(primals_22, buf50, 16, grid=grid(16), stream=stream0)
        del primals_22
        buf51 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf46, (16, 4), (4, 1), 0), buf50, out=buf51)
        buf52 = buf37; del buf37  # reuse
        # Topologically Sorted Source Nodes: [x_1, x_3], Original ATen: [aten.add]
        triton_poi_fused_add_13.run(buf52, buf49, buf51, primals_23, 64, grid=grid(64), stream=stream0)
        del buf49
        del buf51
        del primals_23
        buf53 = buf36; del buf36  # reuse
        buf54 = buf35; del buf35  # reuse
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_10.run(buf52, buf53, buf54, 16, grid=grid(16), stream=stream0)
        buf55 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_14.run(buf52, buf53, buf54, primals_24, primals_25, buf55, 64, grid=grid(64), stream=stream0)
        del buf53
        del buf54
        del primals_25
    return (buf55, buf23, primals_14, primals_24, reinterpret_tensor(buf0, (16, 4), (4, 1), 0), reinterpret_tensor(buf3, (4, 16), (1, 4), 0), buf2, reinterpret_tensor(buf9, (4, 16), (1, 4), 0), buf8, reinterpret_tensor(buf15, (4, 16), (1, 4), 0), buf14, buf23, reinterpret_tensor(buf28, (16, 16), (16, 1), 0), buf29, buf34, reinterpret_tensor(buf39, (16, 4), (4, 1), 0), buf40, reinterpret_tensor(buf46, (16, 4), (4, 1), 0), buf47, buf52, reinterpret_tensor(buf50, (4, 4), (4, 1), 0), reinterpret_tensor(buf48, (4, 1), (1, 1), 0), reinterpret_tensor(buf45, (1, 4), (4, 1), 0), buf56, reinterpret_tensor(buf43, (4, 4), (4, 1), 0), reinterpret_tensor(buf41, (4, 1), (1, 1), 0), reinterpret_tensor(buf38, (1, 4), (4, 1), 0), reinterpret_tensor(buf32, (4, 16), (16, 1), 0), reinterpret_tensor(buf30, (4, 1), (1, 1), 0), reinterpret_tensor(buf27, (1, 16), (16, 1), 0), reinterpret_tensor(buf25, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf24, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf19, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf20, (16, 4, 4), (16, 1, 4), 0), )


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((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((16, 4), (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((4, 4), (4, 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, 4), (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((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((1, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((4, 4), (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, ), (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])
    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
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
import torch.utils.checkpoint
import torch.nn.functional as F
from torch.cuda.amp import autocast
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__to_copy_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tmp0.to(tl.float32)
    tl.store(out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_poi_fused__to_copy_1(in_ptr0, out_ptr0, 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 + x0, xmask)
    tmp1 = tmp0.to(tl.float32)
    tl.store(out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_poi_fused_clone_div_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
    x4 = xindex
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x4, xmask).to(tl.float32)
    tmp1 = tl.load(in_ptr1 + x4, xmask).to(tl.float32)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.5
    tmp4 = tmp2 * tmp3
    tl.store(out_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), 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 = 16
    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
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask & ymask).to(tl.float32)
    tmp1 = tl.load(in_ptr1 + (x2 + 16 * y3), xmask & ymask).to(tl.float32)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (y0 + 4 * x2 + 64 * y1), 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).to(tl.float32)
    tmp2 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last').to(tl
        .float32)
    tmp4 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        ).to(tl.float32)
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        ).to(tl.float32)
    tmp10 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        ).to(tl.float32)
    tmp1 = tmp0.to(tl.float32)
    tmp3 = tmp2.to(tl.float32)
    tmp5 = tmp4.to(tl.float32)
    tmp6 = triton_helpers.maximum(tmp3, tmp5)
    tmp8 = tmp7.to(tl.float32)
    tmp9 = triton_helpers.maximum(tmp6, tmp8)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = triton_helpers.maximum(tmp9, tmp11)
    tmp13 = tmp1 - tmp12
    tmp14 = tl_math.exp(tmp13)
    tl.store(out_ptr0 + x2, tmp14, xmask)


@triton.jit
def triton_poi_fused__softmax__to_copy_5(in_ptr0, out_ptr0, out_ptr1,
    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
    tmp9 = tmp8.to(tl.float32)
    tl.store(out_ptr0 + x2, tmp8, xmask)
    tl.store(out_ptr1 + x2, tmp9, 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
    x4 = xindex
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x4, xmask).to(tl.float32)
    tmp1 = tl.load(in_ptr1 + x4, xmask).to(tl.float32)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), 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).to(tl
        .float32)
    tl.store(out_ptr0 + x4, tmp0, xmask)


@triton.jit
def triton_poi_fused__to_copy_t_8(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tmp0.to(tl.float32)
    tl.store(out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_poi_fused_add_9(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask).to(tl.float32)
    tmp1 = tl.load(in_ptr1 + x0, xmask).to(tl.float32)
    tmp4 = tl.load(in_ptr2 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp2.to(tl.float32)
    tmp5 = tmp3 + tmp4
    tl.store(out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_10(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-06
    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__to_copy_native_layer_norm_11(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, in_ptr4, 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
    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
    tmp9 = tmp8.to(tl.float32)
    tl.store(out_ptr0 + x2, tmp8, xmask)
    tl.store(out_ptr1 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_add_relu_threshold_backward_12(in_out_ptr0, in_ptr0,
    in_ptr1, 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).to(tl.float32)
    tmp1 = tl.load(in_ptr0 + x2, xmask).to(tl.float32)
    tmp2 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp2.to(tl.float32)
    tmp4 = tmp1 + tmp3
    tmp5 = tmp0 + tmp4
    tmp6 = tl.full([1], 0, tl.int32)
    tmp7 = triton_helpers.maximum(tmp6, tmp5)
    tmp8 = 0.0
    tmp9 = tmp7 <= tmp8
    tl.store(in_out_ptr0 + x2, tmp7, xmask)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_add_13(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).to(tl.float32)
    tmp1 = tl.load(in_ptr1 + x2, xmask).to(tl.float32)
    tmp2 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_out_ptr0 + x2, xmask)
    tmp3 = tmp2.to(tl.float32)
    tmp4 = tmp1 + tmp3
    tmp5 = tmp0 + tmp4
    tmp6 = tmp5.to(tl.float32)
    tmp8 = tmp6 + tmp7
    tl.store(in_out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_14(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)


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) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (16, 4), (4, 1))
    assert_size_stride(primals_4, (16, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (16, 4), (4, 1))
    assert_size_stride(primals_7, (16, 4), (4, 1))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (16, 4), (4, 1))
    assert_size_stride(primals_10, (16, 4), (4, 1))
    assert_size_stride(primals_11, (1, 16), (16, 1))
    assert_size_stride(primals_12, (4, 1), (1, 1))
    assert_size_stride(primals_13, (4, 16), (16, 1))
    assert_size_stride(primals_14, (4,), (1,))
    assert_size_stride(primals_15, (4,), (1,))
    assert_size_stride(primals_16, (1, 4), (4, 1))
    assert_size_stride(primals_17, (4, 1), (1, 1))
    assert_size_stride(primals_18, (4, 4), (4, 1))
    assert_size_stride(primals_19, (4,), (1,))
    assert_size_stride(primals_20, (1, 4), (4, 1))
    assert_size_stride(primals_21, (4, 1), (1, 1))
    assert_size_stride(primals_22, (4, 4), (4, 1))
    assert_size_stride(primals_23, (4,), (1,))
    assert_size_stride(primals_24, (4,), (1,))
    assert_size_stride(primals_25, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float16)
        get_raw_stream(0)
        triton_poi_fused__to_copy_0[grid(64)](primals_1, buf0, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_1[grid(16)](primals_2, buf1, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(buf1, (4, 4), (1, 4), 0), out=buf2)
        buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_0[grid(64)](primals_3, buf3, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        del primals_3
        buf4 = empty_strided_cuda((16, 16), (16, 1), torch.float16)
        extern_kernels.mm(buf2, reinterpret_tensor(buf3, (4, 16), (1, 4), 0
            ), out=buf4)
        buf5 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_0[grid(64)](primals_4, buf5, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        del primals_4
        buf6 = empty_strided_cuda((16, 16), (16, 1), torch.float16)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(buf5, (4, 16), (1, 4), 0), out=buf6)
        buf7 = buf1
        del buf1
        triton_poi_fused__to_copy_1[grid(16)](primals_5, buf7, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        del primals_5
        buf8 = buf5
        del buf5
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(buf7, (4, 4), (1, 4), 0), out=buf8)
        buf9 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_0[grid(64)](primals_6, buf9, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        del primals_6
        buf10 = empty_strided_cuda((16, 16), (16, 1), torch.float16)
        extern_kernels.mm(buf8, reinterpret_tensor(buf9, (4, 16), (1, 4), 0
            ), out=buf10)
        buf11 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_0[grid(64)](primals_7, buf11, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        del primals_7
        buf12 = empty_strided_cuda((16, 16), (16, 1), torch.float16)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(buf11, (4, 16), (1, 4), 0), out=buf12)
        buf13 = buf7
        del buf7
        triton_poi_fused__to_copy_1[grid(16)](primals_8, buf13, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        del primals_8
        buf14 = buf11
        del buf11
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(buf13, (4, 4), (1, 4), 0), out=buf14)
        buf15 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_0[grid(64)](primals_9, buf15, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        del primals_9
        buf16 = empty_strided_cuda((16, 16), (16, 1), torch.float16)
        extern_kernels.mm(buf14, reinterpret_tensor(buf15, (4, 16), (1, 4),
            0), out=buf16)
        buf17 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_0[grid(64)](primals_10, buf17, 64, XBLOCK
            =64, num_warps=1, num_stages=1)
        del primals_10
        buf18 = empty_strided_cuda((16, 16), (16, 1), torch.float16)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(buf17, (4, 16), (1, 4), 0), out=buf18)
        buf19 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float16)
        triton_poi_fused_clone_div_2[grid(256)](buf4, buf6, buf19, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf4
        buf20 = reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf6
        triton_poi_fused_clone_3[grid(16, 16)](buf10, buf12, buf20, 16, 16,
            XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        buf21 = reinterpret_tensor(buf12, (16, 4, 4), (16, 4, 1), 0)
        del buf12
        extern_kernels.bmm(reinterpret_tensor(buf19, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf20, (16, 4, 4), (16, 4, 1), 0), out=buf21
            )
        buf22 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_4[grid(256)](buf21, buf22, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf23 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf25 = reinterpret_tensor(buf21, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf21
        triton_poi_fused__softmax__to_copy_5[grid(256)](buf22, buf23, buf25,
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf22
        buf24 = reinterpret_tensor(buf10, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf10
        triton_poi_fused_clone_6[grid(256)](buf16, buf18, buf24, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf26 = reinterpret_tensor(buf18, (16, 4, 4), (16, 4, 1), 0)
        del buf18
        extern_kernels.bmm(reinterpret_tensor(buf25, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf24, (16, 4, 4), (16, 4, 1), 0), out=buf26
            )
        buf27 = reinterpret_tensor(buf13, (16, 1), (1, 16), 0)
        del buf13
        triton_poi_fused__to_copy_1[grid(16)](primals_11, buf27, 16, XBLOCK
            =16, num_warps=1, num_stages=1)
        del primals_11
        buf28 = reinterpret_tensor(buf16, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf16
        triton_poi_fused_clone_7[grid(256)](buf26, buf28, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf26
        buf29 = empty_strided_cuda((16, 1), (1, 1), torch.float16)
        extern_kernels.mm(reinterpret_tensor(buf28, (16, 16), (16, 1), 0),
            buf27, out=buf29)
        buf30 = empty_strided_cuda((1, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_t_8[grid(4)](primals_12, buf30, 4, XBLOCK
            =4, num_warps=1, num_stages=1)
        del primals_12
        buf31 = buf17
        del buf17
        extern_kernels.mm(buf29, buf30, out=buf31)
        buf32 = empty_strided_cuda((16, 4), (1, 16), torch.float16)
        triton_poi_fused__to_copy_0[grid(64)](primals_13, buf32, 64, XBLOCK
            =64, num_warps=1, num_stages=1)
        del primals_13
        buf33 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        extern_kernels.mm(reinterpret_tensor(buf28, (16, 16), (16, 1), 0),
            buf32, out=buf33)
        buf34 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_9[grid(64)](buf31, buf33, primals_1, buf34, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_1
        buf35 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf36 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused_native_layer_norm_10[grid(16)](buf34, buf35, buf36,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf37 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf39 = reinterpret_tensor(buf33, (4, 4, 4), (16, 4, 1), 0)
        del buf33
        triton_poi_fused__to_copy_native_layer_norm_11[grid(64)](buf34,
            buf35, buf36, primals_14, primals_15, buf37, buf39, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        del primals_15
        buf38 = empty_strided_cuda((4, 1), (1, 4), torch.float16)
        triton_poi_fused__to_copy_t_8[grid(4)](primals_16, buf38, 4, XBLOCK
            =4, num_warps=1, num_stages=1)
        del primals_16
        buf40 = empty_strided_cuda((16, 1), (1, 1), torch.float16)
        extern_kernels.mm(reinterpret_tensor(buf39, (16, 4), (4, 1), 0),
            buf38, out=buf40)
        buf41 = empty_strided_cuda((1, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_t_8[grid(4)](primals_17, buf41, 4, XBLOCK
            =4, num_warps=1, num_stages=1)
        del primals_17
        buf42 = buf31
        del buf31
        extern_kernels.mm(buf40, buf41, out=buf42)
        buf43 = empty_strided_cuda((4, 4), (1, 4), torch.float16)
        triton_poi_fused__to_copy_1[grid(16)](primals_18, buf43, 16, XBLOCK
            =16, num_warps=1, num_stages=1)
        del primals_18
        buf44 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        extern_kernels.mm(reinterpret_tensor(buf39, (16, 4), (4, 1), 0),
            buf43, out=buf44)
        buf45 = empty_strided_cuda((4, 1), (1, 4), torch.float16)
        triton_poi_fused__to_copy_t_8[grid(4)](primals_20, buf45, 4, XBLOCK
            =4, num_warps=1, num_stages=1)
        del primals_20
        buf46 = reinterpret_tensor(buf42, (4, 4, 4), (16, 4, 1), 0)
        del buf42
        buf56 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        triton_poi_fused_add_relu_threshold_backward_12[grid(64)](buf46,
            buf44, primals_19, buf56, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_19
        buf47 = empty_strided_cuda((16, 1), (1, 1), torch.float16)
        extern_kernels.mm(reinterpret_tensor(buf46, (16, 4), (4, 1), 0),
            buf45, out=buf47)
        buf48 = empty_strided_cuda((1, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_t_8[grid(4)](primals_21, buf48, 4, XBLOCK
            =4, num_warps=1, num_stages=1)
        del primals_21
        buf49 = buf44
        del buf44
        extern_kernels.mm(buf47, buf48, out=buf49)
        buf50 = empty_strided_cuda((4, 4), (1, 4), torch.float16)
        triton_poi_fused__to_copy_1[grid(16)](primals_22, buf50, 16, XBLOCK
            =16, num_warps=1, num_stages=1)
        del primals_22
        buf51 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        extern_kernels.mm(reinterpret_tensor(buf46, (16, 4), (4, 1), 0),
            buf50, out=buf51)
        buf52 = buf37
        del buf37
        triton_poi_fused_add_13[grid(64)](buf52, buf49, buf51, primals_23, 
            64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf49
        del buf51
        del primals_23
        buf53 = buf36
        del buf36
        buf54 = buf35
        del buf35
        triton_poi_fused_native_layer_norm_10[grid(16)](buf52, buf53, buf54,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf55 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_14[grid(64)](buf52, buf53, buf54,
            primals_24, primals_25, buf55, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf53
        del buf54
        del primals_25
    return buf55, buf23, primals_14, primals_24, reinterpret_tensor(buf0, (
        16, 4), (4, 1), 0), reinterpret_tensor(buf3, (4, 16), (1, 4), 0
        ), buf2, reinterpret_tensor(buf9, (4, 16), (1, 4), 0
        ), buf8, reinterpret_tensor(buf15, (4, 16), (1, 4), 0
        ), buf14, buf23, reinterpret_tensor(buf28, (16, 16), (16, 1), 0
        ), buf29, buf34, reinterpret_tensor(buf39, (16, 4), (4, 1), 0
        ), buf40, reinterpret_tensor(buf46, (16, 4), (4, 1), 0
        ), buf47, buf52, reinterpret_tensor(buf50, (4, 4), (4, 1), 0
        ), reinterpret_tensor(buf48, (4, 1), (1, 1), 0), reinterpret_tensor(
        buf45, (1, 4), (4, 1), 0), buf56, reinterpret_tensor(buf43, (4, 4),
        (4, 1), 0), reinterpret_tensor(buf41, (4, 1), (1, 1), 0
        ), reinterpret_tensor(buf38, (1, 4), (4, 1), 0), reinterpret_tensor(
        buf32, (4, 16), (16, 1), 0), reinterpret_tensor(buf30, (4, 1), (1, 
        1), 0), reinterpret_tensor(buf27, (1, 16), (16, 1), 0
        ), reinterpret_tensor(buf25, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf24, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf19, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf20, (16, 4, 4), (16, 1, 4), 0)


class ScaledDotProductAttention(nn.Module):
    """ Scaled Dot-Product Attention """

    def __init__(self, temperature, attn_dropout=0.1):
        super().__init__()
        self.temperature = temperature
        self.dropout = nn.Dropout(attn_dropout)

    @autocast()
    def forward(self, q, k, v, mask=None):
        attn = torch.matmul(q / self.temperature, k.transpose(2, 3))
        if mask is not None:
            attn = attn.masked_fill(mask == 0, -2 ** 15)
        attn = self.dropout(F.softmax(attn, dim=-1))
        output = torch.matmul(attn, v)
        return output, attn


class LowRankResidualMultiHeadAttention(nn.Module):
    """ Multi-Head Attention module """

    def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
        super().__init__()
        self.n_head = n_head
        self.d_k = d_k
        self.d_v = d_v
        self.w_qs_u = nn.Linear(int(n_head * d_k / 4), n_head * d_k, bias=False
            )
        self.w_qs_v = nn.Linear(d_model, int(n_head * d_k / 4), bias=False)
        self.w_qs_res = nn.Linear(d_model, n_head * d_k, bias=False)
        self.w_ks_u = nn.Linear(int(n_head * d_k / 4), n_head * d_k, bias=False
            )
        self.w_ks_v = nn.Linear(d_model, int(n_head * d_k / 4), bias=False)
        self.w_ks_res = nn.Linear(d_model, n_head * d_k, bias=False)
        self.w_vs_u = nn.Linear(int(n_head * d_k / 4), n_head * d_k, bias=False
            )
        self.w_vs_v = nn.Linear(d_model, int(n_head * d_k / 4), bias=False)
        self.w_vs_res = nn.Linear(d_model, n_head * d_v, bias=False)
        self.fc_u = nn.Linear(int(d_model / 4), d_model, bias=False)
        self.fc_v = nn.Linear(n_head * d_v, int(d_model / 4), bias=False)
        self.fc_res = nn.Linear(n_head * d_v, d_model, bias=False)
        self.attention = ScaledDotProductAttention(temperature=d_k ** 0.5)
        self.dropout = nn.Dropout(dropout)
        self.layer_norm = nn.LayerNorm(d_model, eps=1e-06)

    @autocast()
    def forward(self, q, k, v, mask=None):
        d_k, _d_v, n_head = self.d_k, self.d_v, self.n_head
        sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1)
        residual = q
        q = (self.w_qs_u(self.w_qs_v(q)) + self.w_qs_res(q)).view(sz_b,
            len_q, n_head, d_k)
        k = (self.w_ks_u(self.w_ks_v(k)) + self.w_ks_res(k)).view(sz_b,
            len_k, n_head, d_k)
        v = (self.w_vs_u(self.w_vs_v(v)) + self.w_vs_res(v)).view(sz_b,
            len_v, n_head, d_k)
        q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
        if mask is not None:
            mask = mask.unsqueeze(1)
        q, attn = self.attention(q, k, v, mask=mask)
        q = q.transpose(1, 2).contiguous().view(sz_b, len_q, -1)
        q = self.dropout(self.fc_u(self.fc_v(q)) + self.fc_res(q))
        q += residual
        q = self.layer_norm(q)
        return q, attn


class LowRankResidualPositionwiseFeedForward(nn.Module):
    """ A two-feed-forward-layer module """

    def __init__(self, d_in, d_hid, dropout=0.1):
        super().__init__()
        self.w_1_u = nn.Linear(int(d_in / 4), d_hid, bias=False)
        self.w_1_v = nn.Linear(d_in, int(d_in / 4), bias=False)
        self.w_1_res = nn.Linear(d_in, d_hid)
        self.w_2_u = nn.Linear(int(d_in / 4), d_in, bias=False)
        self.w_2_v = nn.Linear(d_hid, int(d_in / 4), bias=False)
        self.w_2_res = nn.Linear(d_hid, d_in)
        self.layer_norm = nn.LayerNorm(d_in, eps=1e-06)
        self.dropout = nn.Dropout(dropout)

    @autocast()
    def forward(self, x):
        residual = x
        x = F.relu(self.w_1_u(self.w_1_v(x)) + self.w_1_res(x))
        x = self.w_2_u(self.w_2_v(x)) + self.w_2_res(x)
        x = self.dropout(x)
        x += residual
        x = self.layer_norm(x)
        return x


class LowRankResidualEncoderLayerNew(nn.Module):
    """ Compose with two layers """

    def __init__(self, d_model, d_inner, n_head, d_k, d_v, dropout=0.1):
        super(LowRankResidualEncoderLayerNew, self).__init__()
        self.slf_attn = LowRankResidualMultiHeadAttention(n_head, d_model,
            d_k, d_v, dropout=dropout)
        self.pos_ffn = LowRankResidualPositionwiseFeedForward(d_model,
            d_inner, dropout=dropout)

    def forward(self, input_0):
        primals_3 = self.slf_attn.w_qs_u.weight
        primals_2 = self.slf_attn.w_qs_v.weight
        primals_4 = self.slf_attn.w_qs_res.weight
        primals_6 = self.slf_attn.w_ks_u.weight
        primals_5 = self.slf_attn.w_ks_v.weight
        primals_7 = self.slf_attn.w_ks_res.weight
        primals_9 = self.slf_attn.w_vs_u.weight
        primals_8 = self.slf_attn.w_vs_v.weight
        primals_10 = self.slf_attn.w_vs_res.weight
        primals_12 = self.slf_attn.fc_u.weight
        primals_11 = self.slf_attn.fc_v.weight
        primals_13 = self.slf_attn.fc_res.weight
        primals_14 = self.slf_attn.layer_norm.weight
        primals_15 = self.slf_attn.layer_norm.bias
        primals_17 = self.pos_ffn.w_1_u.weight
        primals_16 = self.pos_ffn.w_1_v.weight
        primals_18 = self.pos_ffn.w_1_res.weight
        primals_19 = self.pos_ffn.w_1_res.bias
        primals_21 = self.pos_ffn.w_2_u.weight
        primals_20 = self.pos_ffn.w_2_v.weight
        primals_22 = self.pos_ffn.w_2_res.weight
        primals_23 = self.pos_ffn.w_2_res.bias
        primals_24 = self.pos_ffn.layer_norm.weight
        primals_25 = self.pos_ffn.layer_norm.bias
        primals_1 = 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, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21, primals_22, primals_23, primals_24,
            primals_25])
        return output[0], output[1]
