# 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_8/inductor_cache/wd/cwdz7kqs3uwyg53zsyekt77eye7yjl6v7vulow2q6ni534mkf6zw.py
# Topologically Sorted Source Nodes: [layer_norm], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
#   layer_norm => add, rsqrt, var_mean
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%primals_3, [2]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
triton_poi_fused_native_layer_norm_0 = async_compile.triton('triton_poi_fused_native_layer_norm_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_native_layer_norm_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_native_layer_norm_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + (x0), tmp8, xmask)
    tl.store(out_ptr1 + (x0), tmp23, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/vs/cvsfvbs4wlaqvwxm3svg65dnhcq336ptudvn6xetnbnrtzj7xssn.py
# Topologically Sorted Source Nodes: [layer_norm], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
#   layer_norm => add, add_1, mul, mul_1, rsqrt, sub, var_mean
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%primals_3, [2]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_3, %getitem_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %primals_1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_2), kwargs = {})
triton_poi_fused_native_layer_norm_1 = async_compile.triton('triton_poi_fused_native_layer_norm_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_native_layer_norm_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/kv/ckvagf2u4zvqlt6iqkqbyyrbjch62j2c3uyovixzjraqunow377y.py
# Topologically Sorted Source Nodes: [cached_kv], Original ATen: [aten.stack]
# Source node to ATen node mapping:
#   cached_kv => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%permute_2, %permute_3],), kwargs = {})
triton_poi_fused_stack_2 = async_compile.triton('triton_poi_fused_stack_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=[128], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_stack_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_stack_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 16)
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x3 = xindex
    tmp0 = x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 + x0 + (12*x1) + (48*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr0 + (8 + x0 + (12*x1) + (48*((-4) + x2))), tmp6 & xmask, other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x3), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/av/cav43r7e5cwolxynapl5dr4nqus4ttv7rsw7l4ierw56oanc6x27.py
# Topologically Sorted Source Nodes: [attn], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   attn => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand,), 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, 4], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_3', '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_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (12*x2) + (48*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/mm/cmmu3ymlkl4lebtailau4uhz3akogdbdnttz7llt6secz2773ja2.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_4 = async_compile.triton('triton_poi_fused_clone_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16, 4], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_4', '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_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (4 + y0 + (12*x2) + (48*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/ns/cnskhjvk5vjbk4dvbr7d6khf6t7ktnlaam5hvan3xcu5ehiqxgem.py
# Topologically Sorted Source Nodes: [attention_mask], Original ATen: [aten.tril]
# Source node to ATen node mapping:
#   attention_mask => le, sub_1
# Graph fragment:
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%unsqueeze, %unsqueeze_1), kwargs = {})
#   %le : [num_users=2] = call_function[target=torch.ops.aten.le.Scalar](args = (%sub_1, 0), kwargs = {})
triton_poi_fused_tril_5 = async_compile.triton('triton_poi_fused_tril_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*i1', 1: '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_tril_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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_tril_5(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 % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = x0 + ((-1)*x1)
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 <= tmp1
    tl.store(out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/cf/ccfc25aigo5vsozbjqqljp22qpcrayfw3nevkyk4gfdntt5aajaa.py
# Topologically Sorted Source Nodes: [attn_1, mul, sub, mul_1, attn_2, attn_3], Original ATen: [aten.div, aten.mul, aten.rsub, aten.sub, aten._softmax]
# Source node to ATen node mapping:
#   attn_1 => div
#   attn_2 => sub_3
#   attn_3 => amax, exp, sub_4, sum_1
#   mul => mul_2
#   mul_1 => mul_3
#   sub => sub_2
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%view_8, 1.0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, %view_9), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %view_9), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, 10000.0), kwargs = {})
#   %sub_3 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_2, %mul_3), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%sub_3, [-1], True), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_3, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_4,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
triton_poi_fused__softmax_div_mul_rsub_sub_6 = async_compile.triton('triton_poi_fused__softmax_div_mul_rsub_sub_6', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*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__softmax_div_mul_rsub_sub_6', '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__softmax_div_mul_rsub_sub_6(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (4*x2), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (1 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (2 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr0 + (3 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = (-1)*x0
    tmp4 = tl.full([1], 0, tl.int64)
    tmp5 = tmp3 <= tmp4
    tmp6 = 0.0
    tmp7 = tl.where(tmp5, tmp1, tmp6)
    tmp8 = tmp2 * tmp7
    tmp9 = tmp1 - tmp7
    tmp10 = 10000.0
    tmp11 = tmp9 * tmp10
    tmp12 = tmp8 - tmp11
    tmp14 = tmp13 * tmp1
    tmp15 = 1 + ((-1)*x0)
    tmp16 = tmp15 <= tmp4
    tmp17 = tl.where(tmp16, tmp1, tmp6)
    tmp18 = tmp14 * tmp17
    tmp19 = tmp1 - tmp17
    tmp20 = tmp19 * tmp10
    tmp21 = tmp18 - tmp20
    tmp22 = triton_helpers.maximum(tmp12, tmp21)
    tmp24 = tmp23 * tmp1
    tmp25 = 2 + ((-1)*x0)
    tmp26 = tmp25 <= tmp4
    tmp27 = tl.where(tmp26, tmp1, tmp6)
    tmp28 = tmp24 * tmp27
    tmp29 = tmp1 - tmp27
    tmp30 = tmp29 * tmp10
    tmp31 = tmp28 - tmp30
    tmp32 = triton_helpers.maximum(tmp22, tmp31)
    tmp34 = tmp33 * tmp1
    tmp35 = 3 + ((-1)*x0)
    tmp36 = tmp35 <= tmp4
    tmp37 = tl.where(tmp36, tmp1, tmp6)
    tmp38 = tmp34 * tmp37
    tmp39 = tmp1 - tmp37
    tmp40 = tmp39 * tmp10
    tmp41 = tmp38 - tmp40
    tmp42 = triton_helpers.maximum(tmp32, tmp41)
    tmp43 = tmp12 - tmp42
    tmp44 = tl_math.exp(tmp43)
    tmp45 = tmp21 - tmp42
    tmp46 = tl_math.exp(tmp45)
    tmp47 = tmp44 + tmp46
    tmp48 = tmp31 - tmp42
    tmp49 = tl_math.exp(tmp48)
    tmp50 = tmp47 + tmp49
    tmp51 = tmp41 - tmp42
    tmp52 = tl_math.exp(tmp51)
    tmp53 = tmp50 + tmp52
    tl.store(out_ptr0 + (x2), tmp42, xmask)
    tl.store(out_ptr1 + (x2), tmp53, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/az/caz7vysyeguge346hx2uv4wfyoukiqa7fvghloxqwol634uuhklg.py
# Topologically Sorted Source Nodes: [attn_1, mul, sub, mul_1, attn_2, attn_3], Original ATen: [aten.div, aten.mul, aten.rsub, aten.sub, aten._softmax]
# Source node to ATen node mapping:
#   attn_1 => div
#   attn_2 => sub_3
#   attn_3 => div_1, exp, sub_4
#   mul => mul_2
#   mul_1 => mul_3
#   sub => sub_2
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%view_8, 1.0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, %view_9), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %view_9), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, 10000.0), kwargs = {})
#   %sub_3 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_2, %mul_3), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_3, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_4,), kwargs = {})
#   %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_div_mul_rsub_sub_7 = async_compile.triton('triton_poi_fused__softmax_div_mul_rsub_sub_7', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*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__softmax_div_mul_rsub_sub_7', '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__softmax_div_mul_rsub_sub_7(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x4 = (xindex // 4)
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp13 = tl.load(in_ptr0 + (x4), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (x4), xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = x0 + ((-1)*x1)
    tmp4 = tl.full([1], 0, tl.int64)
    tmp5 = tmp3 <= tmp4
    tmp6 = 0.0
    tmp7 = tl.where(tmp5, tmp1, tmp6)
    tmp8 = tmp2 * tmp7
    tmp9 = tmp1 - tmp7
    tmp10 = 10000.0
    tmp11 = tmp9 * tmp10
    tmp12 = tmp8 - tmp11
    tmp14 = tmp12 - tmp13
    tmp15 = tl_math.exp(tmp14)
    tmp17 = tmp15 / tmp16
    tl.store(in_out_ptr0 + (x3), tmp17, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/z5/cz5vsepa6xzt2kxyz2o5pev7avr6mhxpo7iklp72hpog6wgaos5u.py
# Topologically Sorted Source Nodes: [y], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   y => 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_8 = async_compile.triton('triton_poi_fused_clone_8', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16, 4], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_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_clone_8(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (8 + y0 + (12*x2) + (48*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/he/chelvagj4d5lscmfzmvqqdjzm5txfx45x7j7qh4gwkcdx2i3wvui.py
# Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   linear_1 => clone_4
# Graph fragment:
#   %clone_4 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%view_13,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_9 = async_compile.triton('triton_poi_fused_clone_9', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16, 4], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_9', '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_9(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/3l/c3lixqbd7uwwhffritbjnc3rwnwjtp437sgxhoambuih5s377bmw.py
# Topologically Sorted Source Nodes: [linear_1, x_4, z], Original ATen: [aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
#   linear_1 => add_2
#   x_4 => add_3
#   z => var_mean_1
# Graph fragment:
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_15, %primals_7), kwargs = {})
#   %add_3 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_3, %add_2), kwargs = {})
#   %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%add_3, [2]), kwargs = {correction: 0, keepdim: True})
triton_poi_fused_add_native_layer_norm_10 = async_compile.triton('triton_poi_fused_add_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: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_native_layer_norm_10', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 12, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_native_layer_norm_10(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (0))
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
    tmp6 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + (1))
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
    tmp13 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr2 + (2))
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
    tmp20 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr2 + (3))
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK])
    tmp4 = tmp1 + tmp3
    tmp5 = tmp0 + tmp4
    tmp10 = tmp7 + tmp9
    tmp11 = tmp6 + tmp10
    tmp12 = tmp5 + tmp11
    tmp17 = tmp14 + tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = tmp12 + tmp18
    tmp24 = tmp21 + tmp23
    tmp25 = tmp20 + tmp24
    tmp26 = tmp19 + tmp25
    tmp27 = 4.0
    tmp28 = tmp26 / tmp27
    tmp29 = tmp5 - tmp28
    tmp30 = tmp29 * tmp29
    tmp31 = tmp11 - tmp28
    tmp32 = tmp31 * tmp31
    tmp33 = tmp30 + tmp32
    tmp34 = tmp18 - tmp28
    tmp35 = tmp34 * tmp34
    tmp36 = tmp33 + tmp35
    tmp37 = tmp25 - tmp28
    tmp38 = tmp37 * tmp37
    tmp39 = tmp36 + tmp38
    tmp40 = tmp39 / tmp27
    tl.store(out_ptr0 + (x0), tmp28, xmask)
    tl.store(out_ptr1 + (x0), tmp40, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/7h/c7h7jgc5vfmlymkofbcivorbfofjclqbtxpl2ufejrpj7wygjxhn.py
# Topologically Sorted Source Nodes: [linear_1, x_4, z], Original ATen: [aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
#   linear_1 => add_2
#   x_4 => add_3
#   z => add_4, add_5, mul_4, mul_5, rsqrt_1, sub_5
# Graph fragment:
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_15, %primals_7), kwargs = {})
#   %add_3 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_3, %add_2), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_5, 1e-05), kwargs = {})
#   %rsqrt_1 : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_4,), kwargs = {})
#   %sub_5 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_3, %getitem_6), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_5, %rsqrt_1), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_4, %primals_8), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_5, %primals_9), kwargs = {})
triton_poi_fused_add_native_layer_norm_11 = async_compile.triton('triton_poi_fused_add_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: '*fp32', 7: '*fp32', 8: '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, 8), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_native_layer_norm_11', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_native_layer_norm_11(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, 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
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x2), xmask)
    tmp2 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + (x1), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + (x0), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr6 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp6 = tmp4 - tmp5
    tmp8 = 1e-05
    tmp9 = tmp7 + tmp8
    tmp10 = libdevice.rsqrt(tmp9)
    tmp11 = tmp6 * tmp10
    tmp13 = tmp11 * tmp12
    tmp15 = tmp13 + tmp14
    tl.store(out_ptr0 + (x2), tmp15, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/fx/cfxiyfr3c2gao54ay6g5yclydzi2o2ptpuogh7nkfkixpwhktjkb.py
# Topologically Sorted Source Nodes: [h], Original ATen: [aten.gelu]
# Source node to ATen node mapping:
#   h => add_6, erf, mul_6, mul_7, mul_8
# Graph fragment:
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_17, 0.5), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_17, 0.7071067811865476), kwargs = {})
#   %erf : [num_users=1] = call_function[target=torch.ops.aten.erf.default](args = (%mul_7,), kwargs = {})
#   %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%erf, 1), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_6, %add_6), kwargs = {})
triton_poi_fused_gelu_12 = async_compile.triton('triton_poi_fused_gelu_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_gelu_12', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_gelu_12(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
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865476
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tl.store(out_ptr0 + (x0), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/p2/cp2bqo2c2h75soeb2ntzsz45bxhookq2c7yf7n3y3zkeekmnhzmu.py
# Topologically Sorted Source Nodes: [linear_1, x_4, x_5], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   linear_1 => add_2
#   x_4 => add_3
#   x_5 => add_7
# Graph fragment:
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_15, %primals_7), kwargs = {})
#   %add_3 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_3, %add_2), kwargs = {})
#   %add_7 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_3, %view_19), 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: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), 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': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_13(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x2), xmask)
    tmp2 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp6 = tl.load(in_ptr3 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp7 = tmp5 + tmp6
    tmp8 = tmp4 + tmp7
    tl.store(in_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 = args
    args.clear()
    assert_size_stride(primals_1, (4, ), (1, ))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (12, 4), (4, 1))
    assert_size_stride(primals_5, (12, ), (1, ))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, ), (1, ))
    assert_size_stride(primals_9, (4, ), (1, ))
    assert_size_stride(primals_10, (16, 4), (4, 1))
    assert_size_stride(primals_11, (16, ), (1, ))
    assert_size_stride(primals_12, (4, 16), (16, 1))
    assert_size_stride(primals_13, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        # Topologically Sorted Source Nodes: [layer_norm], Original ATen: [aten.native_layer_norm]
        stream0 = get_raw_stream(0)
        triton_poi_fused_native_layer_norm_0.run(primals_3, buf0, buf1, 16, grid=grid(16), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [layer_norm], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_1.run(primals_3, buf0, buf1, primals_1, primals_2, buf2, 64, grid=grid(64), stream=stream0)
        del primals_1
        del primals_2
        buf3 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 12), (1, 4), 0), alpha=1, beta=1, out=buf3)
        del primals_5
        buf4 = empty_strided_cuda((8, 4, 4, 1), (16, 1, 4, 16), torch.float32)
        # Topologically Sorted Source Nodes: [cached_kv], Original ATen: [aten.stack]
        triton_poi_fused_stack_2.run(buf3, buf4, 128, grid=grid(128), stream=stream0)
        buf5 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attn], Original ATen: [aten.clone]
        triton_poi_fused_clone_3.run(buf3, buf5, 16, 4, grid=grid(16, 4), stream=stream0)
        buf6 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attn], Original ATen: [aten.clone]
        triton_poi_fused_clone_4.run(buf3, buf6, 16, 4, grid=grid(16, 4), stream=stream0)
        buf7 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attn], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 1), (4, 1, 0), 0), reinterpret_tensor(buf6, (16, 1, 4), (4, 0, 1), 0), out=buf7)
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [attention_mask], Original ATen: [aten.tril]
        triton_poi_fused_tril_5.run(buf8, 16, grid=grid(16), stream=stream0)
        buf9 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf10 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        # Topologically Sorted Source Nodes: [attn_1, mul, sub, mul_1, attn_2, attn_3], Original ATen: [aten.div, aten.mul, aten.rsub, aten.sub, aten._softmax]
        triton_poi_fused__softmax_div_mul_rsub_sub_6.run(buf7, buf9, buf10, 64, grid=grid(64), stream=stream0)
        buf11 = reinterpret_tensor(buf7, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf7  # reuse
        # Topologically Sorted Source Nodes: [attn_1, mul, sub, mul_1, attn_2, attn_3], Original ATen: [aten.div, aten.mul, aten.rsub, aten.sub, aten._softmax]
        triton_poi_fused__softmax_div_mul_rsub_sub_7.run(buf11, buf9, buf10, 256, grid=grid(256), stream=stream0)
        buf12 = reinterpret_tensor(buf9, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf9  # reuse
        # Topologically Sorted Source Nodes: [y], Original ATen: [aten.clone]
        triton_poi_fused_clone_8.run(buf3, buf12, 16, 4, grid=grid(16, 4), stream=stream0)
        del buf3
        buf13 = reinterpret_tensor(buf10, (16, 4, 1), (4, 1, 1), 0); del buf10  # reuse
        # Topologically Sorted Source Nodes: [y], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf11, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf12, (16, 4, 1), (4, 1, 0), 0), out=buf13)
        buf14 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.clone]
        triton_poi_fused_clone_9.run(buf13, buf14, 16, 4, grid=grid(16, 4), stream=stream0)
        buf15 = reinterpret_tensor(buf13, (16, 4), (4, 1), 0); del buf13  # reuse
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf14, (16, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf15)
        buf16 = buf1; del buf1  # reuse
        buf17 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [linear_1, x_4, z], Original ATen: [aten.add, aten.native_layer_norm]
        triton_poi_fused_add_native_layer_norm_10.run(primals_3, buf15, primals_7, buf16, buf17, 16, grid=grid(16), stream=stream0)
        buf18 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1, x_4, z], Original ATen: [aten.add, aten.native_layer_norm]
        triton_poi_fused_add_native_layer_norm_11.run(primals_3, buf15, primals_7, buf16, buf17, primals_8, primals_9, buf18, 64, grid=grid(64), stream=stream0)
        del buf16
        del buf17
        del primals_9
        buf19 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf18, (16, 4), (4, 1), 0), reinterpret_tensor(primals_10, (4, 16), (1, 4), 0), alpha=1, beta=1, out=buf19)
        del primals_11
        buf20 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h], Original ATen: [aten.gelu]
        triton_poi_fused_gelu_12.run(buf19, buf20, 256, grid=grid(256), stream=stream0)
        buf21 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf20, (16, 16), (16, 1), 0), reinterpret_tensor(primals_12, (16, 4), (1, 16), 0), out=buf21)
        buf22 = reinterpret_tensor(buf21, (4, 4, 4), (16, 4, 1), 0); del buf21  # reuse
        # Topologically Sorted Source Nodes: [linear_1, x_4, x_5], Original ATen: [aten.add]
        triton_poi_fused_add_13.run(buf22, primals_3, buf15, primals_7, primals_13, 64, grid=grid(64), stream=stream0)
        del primals_13
    return (buf22, reinterpret_tensor(buf4, (2, 4, 4, 4, 1), (64, 16, 1, 4, 4), 0), primals_3, primals_7, primals_8, reinterpret_tensor(buf2, (16, 4), (4, 1), 0), buf8, buf11, reinterpret_tensor(buf14, (16, 4), (4, 1), 0), buf15, reinterpret_tensor(buf18, (16, 4), (4, 1), 0), buf19, reinterpret_tensor(buf20, (16, 16), (16, 1), 0), primals_12, primals_10, primals_6, reinterpret_tensor(buf12, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf5, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 4), 0), primals_4, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((12, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((12, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = 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])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import math
import torch
import torch.nn as nn


class MLP(nn.Module):

    def __init__(self, embedding_size):
        super(MLP, self).__init__()
        self.dense_h_to_4h = nn.Linear(embedding_size, embedding_size * 4)
        self.dense_4h_to_h = nn.Linear(embedding_size * 4, embedding_size)
        self.act = nn.functional.gelu

    def forward(self, x):
        h = self.act(self.dense_h_to_4h(x))
        h2 = self.dense_4h_to_h(h)
        return h2


class Attention(nn.Module):

    def __init__(self, embedding_size, num_attention_heads,
        attention_dropout, residual_dropout):
        super(Attention, self).__init__()
        self.num_attention_heads = num_attention_heads
        self.size_per_head = embedding_size // num_attention_heads
        self.embedding_size = embedding_size
        self.query_key_value = nn.Linear(embedding_size, embedding_size * 3)
        self.attn_drop = nn.Dropout(attention_dropout)
        self.resid_drop = nn.Dropout(residual_dropout)
        self.dense = nn.Linear(embedding_size, embedding_size)

    def split_heads(self, x):
        x = x.reshape([-1, self.seq_len, self.num_attention_heads, self.
            size_per_head])
        return x.permute(0, 2, 1, 3)

    def forward(self, x, kv_cache=None):
        self.seq_len = x.shape[1]
        x = self.query_key_value(x)
        q, k, v = torch.split(x, split_size_or_sections=self.embedding_size,
            dim=2)
        q = self.split_heads(q)
        k = self.split_heads(k)
        v = self.split_heads(v)
        if kv_cache is not None:
            pk, pv = kv_cache[0], kv_cache[1]
            k = torch.cat([pk, k], dim=-2)
            v = torch.cat([pv, v], dim=-2)
        cached_kv = torch.stack([k, v])
        attn = torch.matmul(q, k.transpose(-1, -2))
        attn = attn / math.sqrt(self.size_per_head)
        attention_mask = torch.tril(torch.ones(self.seq_len, self.seq_len,
            dtype=torch.float32, device=x.device))
        attention_mask = attention_mask.reshape([1, 1, self.seq_len, self.
            seq_len])
        attn = attn * attention_mask - 10000.0 * (1.0 - attention_mask)
        attn = nn.Softmax(dim=-1)(attn)
        attn = self.attn_drop(attn)
        y = torch.matmul(attn, v)
        y = y.permute(0, 2, 1, 3)
        y = torch.reshape(y, [-1, self.seq_len, self.embedding_size])
        y = self.resid_drop(self.dense(y))
        return y, cached_kv


class Block(nn.Module):

    def __init__(self, embedding_size, num_attention_heads,
        attention_dropout, residual_dropout):
        super(Block, self).__init__()
        self.input_layernorm = nn.LayerNorm(embedding_size, eps=1e-05)
        self.attention = Attention(embedding_size, num_attention_heads,
            attention_dropout, residual_dropout)
        self.post_attention_layernorm = nn.LayerNorm(embedding_size, eps=1e-05)
        self.mlp = MLP(embedding_size)

    def forward(self, x, kv_cache=None):
        attn, cached_kv = self.attention(self.input_layernorm(x), kv_cache=
            kv_cache)
        x = x + attn
        z = self.post_attention_layernorm(x)
        z = self.mlp(z)
        x = x + z
        return x, cached_kv


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


def get_init_inputs():
    return [[], {'embedding_size': 4, 'num_attention_heads': 4,
        'attention_dropout': 0.5, 'residual_dropout': 0.5}]

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


@triton.jit
def triton_poi_fused_native_layer_norm_0(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_stack_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 16
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x3 = xindex
    tmp0 = x2
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 + x0 + 12 * x1 + 48 * x2), tmp4 & xmask,
        other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr0 + (8 + x0 + 12 * x1 + 48 * (-4 + x2)), tmp6 &
        xmask, other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_clone_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 12 * x2 + 48 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (4 + y0 + 12 * x2 + 48 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_tril_5(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 % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = x0 + -1 * x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 <= tmp1
    tl.store(out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused__softmax_div_mul_rsub_sub_6(in_ptr0, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (1 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp23 = tl.load(in_ptr0 + (2 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp33 = tl.load(in_ptr0 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = -1 * x0
    tmp4 = tl.full([1], 0, tl.int64)
    tmp5 = tmp3 <= tmp4
    tmp6 = 0.0
    tmp7 = tl.where(tmp5, tmp1, tmp6)
    tmp8 = tmp2 * tmp7
    tmp9 = tmp1 - tmp7
    tmp10 = 10000.0
    tmp11 = tmp9 * tmp10
    tmp12 = tmp8 - tmp11
    tmp14 = tmp13 * tmp1
    tmp15 = 1 + -1 * x0
    tmp16 = tmp15 <= tmp4
    tmp17 = tl.where(tmp16, tmp1, tmp6)
    tmp18 = tmp14 * tmp17
    tmp19 = tmp1 - tmp17
    tmp20 = tmp19 * tmp10
    tmp21 = tmp18 - tmp20
    tmp22 = triton_helpers.maximum(tmp12, tmp21)
    tmp24 = tmp23 * tmp1
    tmp25 = 2 + -1 * x0
    tmp26 = tmp25 <= tmp4
    tmp27 = tl.where(tmp26, tmp1, tmp6)
    tmp28 = tmp24 * tmp27
    tmp29 = tmp1 - tmp27
    tmp30 = tmp29 * tmp10
    tmp31 = tmp28 - tmp30
    tmp32 = triton_helpers.maximum(tmp22, tmp31)
    tmp34 = tmp33 * tmp1
    tmp35 = 3 + -1 * x0
    tmp36 = tmp35 <= tmp4
    tmp37 = tl.where(tmp36, tmp1, tmp6)
    tmp38 = tmp34 * tmp37
    tmp39 = tmp1 - tmp37
    tmp40 = tmp39 * tmp10
    tmp41 = tmp38 - tmp40
    tmp42 = triton_helpers.maximum(tmp32, tmp41)
    tmp43 = tmp12 - tmp42
    tmp44 = tl_math.exp(tmp43)
    tmp45 = tmp21 - tmp42
    tmp46 = tl_math.exp(tmp45)
    tmp47 = tmp44 + tmp46
    tmp48 = tmp31 - tmp42
    tmp49 = tl_math.exp(tmp48)
    tmp50 = tmp47 + tmp49
    tmp51 = tmp41 - tmp42
    tmp52 = tl_math.exp(tmp51)
    tmp53 = tmp50 + tmp52
    tl.store(out_ptr0 + x2, tmp42, xmask)
    tl.store(out_ptr1 + x2, tmp53, xmask)


@triton.jit
def triton_poi_fused__softmax_div_mul_rsub_sub_7(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x4 = xindex // 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp13 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = x0 + -1 * x1
    tmp4 = tl.full([1], 0, tl.int64)
    tmp5 = tmp3 <= tmp4
    tmp6 = 0.0
    tmp7 = tl.where(tmp5, tmp1, tmp6)
    tmp8 = tmp2 * tmp7
    tmp9 = tmp1 - tmp7
    tmp10 = 10000.0
    tmp11 = tmp9 * tmp10
    tmp12 = tmp8 - tmp11
    tmp14 = tmp12 - tmp13
    tmp15 = tl_math.exp(tmp14)
    tmp17 = tmp15 / tmp16
    tl.store(in_out_ptr0 + x3, tmp17, xmask)


@triton.jit
def triton_poi_fused_clone_8(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (8 + y0 + 12 * x2 + 48 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_9(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_10(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + 0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + 1)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
    tmp13 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr2 + 2)
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
    tmp20 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp21 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp22 = tl.load(in_ptr2 + 3)
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK])
    tmp4 = tmp1 + tmp3
    tmp5 = tmp0 + tmp4
    tmp10 = tmp7 + tmp9
    tmp11 = tmp6 + tmp10
    tmp12 = tmp5 + tmp11
    tmp17 = tmp14 + tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = tmp12 + tmp18
    tmp24 = tmp21 + tmp23
    tmp25 = tmp20 + tmp24
    tmp26 = tmp19 + tmp25
    tmp27 = 4.0
    tmp28 = tmp26 / tmp27
    tmp29 = tmp5 - tmp28
    tmp30 = tmp29 * tmp29
    tmp31 = tmp11 - tmp28
    tmp32 = tmp31 * tmp31
    tmp33 = tmp30 + tmp32
    tmp34 = tmp18 - tmp28
    tmp35 = tmp34 * tmp34
    tmp36 = tmp33 + tmp35
    tmp37 = tmp25 - tmp28
    tmp38 = tmp37 * tmp37
    tmp39 = tmp36 + tmp38
    tmp40 = tmp39 / tmp27
    tl.store(out_ptr0 + x0, tmp28, xmask)
    tl.store(out_ptr1 + x0, tmp40, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_11(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, in_ptr6, 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
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr6 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp6 = tmp4 - tmp5
    tmp8 = 1e-05
    tmp9 = tmp7 + tmp8
    tmp10 = libdevice.rsqrt(tmp9)
    tmp11 = tmp6 * tmp10
    tmp13 = tmp11 * tmp12
    tmp15 = tmp13 + tmp14
    tl.store(out_ptr0 + x2, tmp15, xmask)


@triton.jit
def triton_poi_fused_gelu_12(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
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865476
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tl.store(out_ptr0 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_13(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_out_ptr0 + x2, xmask)
    tmp6 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp7 = tmp5 + tmp6
    tmp8 = tmp4 + tmp7
    tl.store(in_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) = args
    args.clear()
    assert_size_stride(primals_1, (4,), (1,))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (12, 4), (4, 1))
    assert_size_stride(primals_5, (12,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (16, 4), (4, 1))
    assert_size_stride(primals_11, (16,), (1,))
    assert_size_stride(primals_12, (4, 16), (16, 1))
    assert_size_stride(primals_13, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_native_layer_norm_0[grid(16)](primals_3, buf0,
            buf1, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_1[grid(64)](primals_3, buf0,
            buf1, primals_1, primals_2, buf2, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del primals_1
        del primals_2
        buf3 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf2, (16, 4), (
            4, 1), 0), reinterpret_tensor(primals_4, (4, 12), (1, 4), 0),
            alpha=1, beta=1, out=buf3)
        del primals_5
        buf4 = empty_strided_cuda((8, 4, 4, 1), (16, 1, 4, 16), torch.float32)
        triton_poi_fused_stack_2[grid(128)](buf3, buf4, 128, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_3[grid(16, 4)](buf3, buf5, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32)
        triton_poi_fused_clone_4[grid(16, 4)](buf3, buf6, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf7 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 1), (4, 1, 0), 
            0), reinterpret_tensor(buf6, (16, 1, 4), (4, 0, 1), 0), out=buf7)
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_tril_5[grid(16)](buf8, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        buf9 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf10 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        triton_poi_fused__softmax_div_mul_rsub_sub_6[grid(64)](buf7, buf9,
            buf10, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf11 = reinterpret_tensor(buf7, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf7
        triton_poi_fused__softmax_div_mul_rsub_sub_7[grid(256)](buf11, buf9,
            buf10, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf12 = reinterpret_tensor(buf9, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf9
        triton_poi_fused_clone_8[grid(16, 4)](buf3, buf12, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        del buf3
        buf13 = reinterpret_tensor(buf10, (16, 4, 1), (4, 1, 1), 0)
        del buf10
        extern_kernels.bmm(reinterpret_tensor(buf11, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf12, (16, 4, 1), (4, 1, 0), 0), out=buf13)
        buf14 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_9[grid(16, 4)](buf13, buf14, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf15 = reinterpret_tensor(buf13, (16, 4), (4, 1), 0)
        del buf13
        extern_kernels.mm(reinterpret_tensor(buf14, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf15)
        buf16 = buf1
        del buf1
        buf17 = buf0
        del buf0
        triton_poi_fused_add_native_layer_norm_10[grid(16)](primals_3,
            buf15, primals_7, buf16, buf17, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        buf18 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_11[grid(64)](primals_3,
            buf15, primals_7, buf16, buf17, primals_8, primals_9, buf18, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del buf16
        del buf17
        del primals_9
        buf19 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf18, (16, 4),
            (4, 1), 0), reinterpret_tensor(primals_10, (4, 16), (1, 4), 0),
            alpha=1, beta=1, out=buf19)
        del primals_11
        buf20 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        triton_poi_fused_gelu_12[grid(256)](buf19, buf20, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf21 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf20, (16, 16), (16, 1), 0),
            reinterpret_tensor(primals_12, (16, 4), (1, 16), 0), out=buf21)
        buf22 = reinterpret_tensor(buf21, (4, 4, 4), (16, 4, 1), 0)
        del buf21
        triton_poi_fused_add_13[grid(64)](buf22, primals_3, buf15,
            primals_7, primals_13, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_13
    return buf22, reinterpret_tensor(buf4, (2, 4, 4, 4, 1), (64, 16, 1, 4, 
        4), 0), primals_3, primals_7, primals_8, reinterpret_tensor(buf2, (
        16, 4), (4, 1), 0), buf8, buf11, reinterpret_tensor(buf14, (16, 4),
        (4, 1), 0), buf15, reinterpret_tensor(buf18, (16, 4), (4, 1), 0
        ), buf19, reinterpret_tensor(buf20, (16, 16), (16, 1), 0
        ), primals_12, primals_10, primals_6, reinterpret_tensor(buf12, (16,
        1, 4), (4, 1, 1), 0), reinterpret_tensor(buf5, (16, 1, 4), (4, 1, 1), 0
        ), reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 4), 0), primals_4


class MLP(nn.Module):

    def __init__(self, embedding_size):
        super(MLP, self).__init__()
        self.dense_h_to_4h = nn.Linear(embedding_size, embedding_size * 4)
        self.dense_4h_to_h = nn.Linear(embedding_size * 4, embedding_size)
        self.act = nn.functional.gelu

    def forward(self, x):
        h = self.act(self.dense_h_to_4h(x))
        h2 = self.dense_4h_to_h(h)
        return h2


class Attention(nn.Module):

    def __init__(self, embedding_size, num_attention_heads,
        attention_dropout, residual_dropout):
        super(Attention, self).__init__()
        self.num_attention_heads = num_attention_heads
        self.size_per_head = embedding_size // num_attention_heads
        self.embedding_size = embedding_size
        self.query_key_value = nn.Linear(embedding_size, embedding_size * 3)
        self.attn_drop = nn.Dropout(attention_dropout)
        self.resid_drop = nn.Dropout(residual_dropout)
        self.dense = nn.Linear(embedding_size, embedding_size)

    def split_heads(self, x):
        x = x.reshape([-1, self.seq_len, self.num_attention_heads, self.
            size_per_head])
        return x.permute(0, 2, 1, 3)

    def forward(self, x, kv_cache=None):
        self.seq_len = x.shape[1]
        x = self.query_key_value(x)
        q, k, v = torch.split(x, split_size_or_sections=self.embedding_size,
            dim=2)
        q = self.split_heads(q)
        k = self.split_heads(k)
        v = self.split_heads(v)
        if kv_cache is not None:
            pk, pv = kv_cache[0], kv_cache[1]
            k = torch.cat([pk, k], dim=-2)
            v = torch.cat([pv, v], dim=-2)
        cached_kv = torch.stack([k, v])
        attn = torch.matmul(q, k.transpose(-1, -2))
        attn = attn / math.sqrt(self.size_per_head)
        attention_mask = torch.tril(torch.ones(self.seq_len, self.seq_len,
            dtype=torch.float32, device=x.device))
        attention_mask = attention_mask.reshape([1, 1, self.seq_len, self.
            seq_len])
        attn = attn * attention_mask - 10000.0 * (1.0 - attention_mask)
        attn = nn.Softmax(dim=-1)(attn)
        attn = self.attn_drop(attn)
        y = torch.matmul(attn, v)
        y = y.permute(0, 2, 1, 3)
        y = torch.reshape(y, [-1, self.seq_len, self.embedding_size])
        y = self.resid_drop(self.dense(y))
        return y, cached_kv


class BlockNew(nn.Module):

    def __init__(self, embedding_size, num_attention_heads,
        attention_dropout, residual_dropout):
        super(BlockNew, self).__init__()
        self.input_layernorm = nn.LayerNorm(embedding_size, eps=1e-05)
        self.attention = Attention(embedding_size, num_attention_heads,
            attention_dropout, residual_dropout)
        self.post_attention_layernorm = nn.LayerNorm(embedding_size, eps=1e-05)
        self.mlp = MLP(embedding_size)

    def forward(self, input_0):
        primals_1 = self.input_layernorm.weight
        primals_2 = self.input_layernorm.bias
        primals_4 = self.attention.query_key_value.weight
        primals_5 = self.attention.query_key_value.bias
        primals_6 = self.attention.dense.weight
        primals_7 = self.attention.dense.bias
        primals_8 = self.post_attention_layernorm.weight
        primals_9 = self.post_attention_layernorm.bias
        primals_10 = self.mlp.dense_h_to_4h.weight
        primals_11 = self.mlp.dense_h_to_4h.bias
        primals_12 = self.mlp.dense_4h_to_h.weight
        primals_13 = self.mlp.dense_4h_to_h.bias
        primals_3 = 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])
        return output[0], output[1]
