# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_6/inductor_cache/wd/cwdz7kqs3uwyg53zsyekt77eye7yjl6v7vulow2q6ni534mkf6zw.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
#   out => add, rsqrt, var_mean
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%primals_3, [2]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
triton_poi_fused_native_layer_norm_0 = async_compile.triton('triton_poi_fused_native_layer_norm_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

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


# kernel path: runs/run_shard_6/inductor_cache/vs/cvsfvbs4wlaqvwxm3svg65dnhcq336ptudvn6xetnbnrtzj7xssn.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
#   out => add, add_1, mul, mul_1, rsqrt, sub, var_mean
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%primals_3, [2]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_3, %getitem_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %primals_1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_2), kwargs = {})
triton_poi_fused_native_layer_norm_1 = async_compile.triton('triton_poi_fused_native_layer_norm_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

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


# kernel path: runs/run_shard_6/inductor_cache/se/csebvp3bmolgy5c7lrzs2aipfus76vmgeh3xk3enrbbgzf5oq3nn.py
# Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.native_layer_norm, aten.repeat]
# Source node to ATen node mapping:
#   out => add, add_1, mul, mul_1, rsqrt, sub, var_mean
#   out_1 => repeat
# 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 = {})
#   %repeat : [num_users=1] = call_function[target=torch.ops.aten.repeat.default](args = (%add_1, [1, 1, 4]), kwargs = {})
triton_poi_fused_native_layer_norm_repeat_2 = async_compile.triton('triton_poi_fused_native_layer_norm_repeat_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_native_layer_norm_repeat_2', '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_native_layer_norm_repeat_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((4*x1) + (x0 % 4)), xmask)
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/3q/c3qlo5h2a7hopa2crhkn6mzwhkfjo6dvlrxttnheh77ukhu2soye.py
# Topologically Sorted Source Nodes: [q_1], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   q_1 => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_3,), 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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_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, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16) % 4
    x3 = (xindex // 64)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x2) + (16*x1) + (64*x3)), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x4), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/2g/c2gbluyeyqil45hjfgkpafvl2dgu3xy73v7dkyzz3d2ktbsbjcnx.py
# Topologically Sorted Source Nodes: [attention], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   attention => exp
# Graph fragment:
#   %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%bmm, 1), kwargs = {})
#   %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor, [-1], True), kwargs = {})
#   %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {})
#   %div_tensor : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_tensor, 2.0), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%div_tensor,), kwargs = {})
triton_poi_fused__softmax_4 = async_compile.triton('triton_poi_fused__softmax_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_4(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp3 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp6 = tmp5 * tmp1
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tmp8 * tmp1
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = tmp11 * tmp1
    tmp13 = triton_helpers.maximum(tmp10, tmp12)
    tmp14 = tmp2 - tmp13
    tmp15 = 0.5
    tmp16 = tmp14 * tmp15
    tmp17 = tl_math.exp(tmp16)
    tl.store(out_ptr0 + (x2), tmp17, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/a4/ca4u6hbohfqkgchihihlu5hrf3vuqm27r2ncsg7xb6g4ikttl2at.py
# Topologically Sorted Source Nodes: [attention], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   attention => div_1, sum_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_5 = async_compile.triton('triton_poi_fused__softmax_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_5(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/4p/c4p7nmmaeg3do3k4aqmp6mh43rupubtod4kype3ux64cxp3b3uub.py
# Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   y_1 => clone_3
# Graph fragment:
#   %clone_3 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_7,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_6 = async_compile.triton('triton_poi_fused_clone_6', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clone_6(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16) % 4
    x3 = (xindex // 64)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x2) + (16*x1) + (64*x3)), xmask)
    tl.store(out_ptr0 + (x4), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/pw/cpw6jeu3vr4erot3bahfr3gqwa2vqailcc5xbtuiztd5l6w2zwjm.py
# Topologically Sorted Source Nodes: [attention_1, out2], Original ATen: [aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
#   attention_1 => add_2
#   out2 => var_mean_1
# Graph fragment:
#   %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_21, %primals_3), kwargs = {})
#   %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%add_2, [2]), kwargs = {correction: 0, keepdim: True})
triton_poi_fused_add_native_layer_norm_7 = async_compile.triton('triton_poi_fused_add_native_layer_norm_7', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_native_layer_norm_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_native_layer_norm_7(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 + tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = 4.0
    tmp16 = tmp14 / tmp15
    tmp17 = tmp2 - tmp16
    tmp18 = tmp17 * tmp17
    tmp19 = tmp5 - tmp16
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp9 - tmp16
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp16
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = tmp27 / tmp15
    tl.store(out_ptr0 + (x0), tmp16, xmask)
    tl.store(out_ptr1 + (x0), tmp28, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/hy/chy77uudjf33jqcobrq24clniixoyn2vucadggy6c6n33mq4favb.py
# Topologically Sorted Source Nodes: [attention_1, out2], Original ATen: [aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
#   attention_1 => add_2
#   out2 => add_3, add_4, mul_2, mul_3, rsqrt_1, sub_2
# Graph fragment:
#   %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_21, %primals_3), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_2, 1e-05), kwargs = {})
#   %rsqrt_1 : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_3,), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_2, %getitem_3), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, %rsqrt_1), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, %primals_12), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_3, %primals_13), kwargs = {})
triton_poi_fused_add_native_layer_norm_8 = async_compile.triton('triton_poi_fused_add_native_layer_norm_8', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_native_layer_norm_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_native_layer_norm_8(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x2), xmask)
    tmp3 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr4 + (x0), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp4 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tl.store(out_ptr0 + (x2), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/bx/cbxzdkbxnjj4ysq7xrysng3xgzgpomqcvqe5mdc477yiqcwvtclw.py
# Topologically Sorted Source Nodes: [attention_1, out3_1], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   attention_1 => add_2
#   out3_1 => add_5
# Graph fragment:
#   %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_21, %primals_3), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_23, %add_2), 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: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_9', '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_9(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_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x2), xmask)
    tmp4 = tl.load(in_ptr2 + (x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tl.store(in_out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15 = args
    args.clear()
    assert_size_stride(primals_1, (4, ), (1, ))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (16, 16), (16, 1))
    assert_size_stride(primals_5, (16, ), (1, ))
    assert_size_stride(primals_6, (16, 16), (16, 1))
    assert_size_stride(primals_7, (16, ), (1, ))
    assert_size_stride(primals_8, (16, 16), (16, 1))
    assert_size_stride(primals_9, (16, ), (1, ))
    assert_size_stride(primals_10, (4, 16), (16, 1))
    assert_size_stride(primals_11, (4, ), (1, ))
    assert_size_stride(primals_12, (4, ), (1, ))
    assert_size_stride(primals_13, (4, ), (1, ))
    assert_size_stride(primals_14, (4, 4), (4, 1))
    assert_size_stride(primals_15, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.native_layer_norm]
        stream0 = get_raw_stream(0)
        triton_poi_fused_native_layer_norm_0.run(primals_3, buf0, buf1, 16, grid=grid(16), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_1.run(primals_3, buf0, buf1, primals_1, primals_2, buf2, 64, grid=grid(64), stream=stream0)
        del primals_1
        del primals_2
        buf3 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.native_layer_norm, aten.repeat]
        triton_poi_fused_native_layer_norm_repeat_2.run(buf2, buf3, 256, grid=grid(256), stream=stream0)
        buf4 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf3, (16, 16), (16, 1), 0), reinterpret_tensor(primals_4, (16, 16), (1, 16), 0), out=buf4)
        buf5 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf3, (16, 16), (16, 1), 0), reinterpret_tensor(primals_6, (16, 16), (1, 16), 0), out=buf5)
        buf6 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf3, (16, 16), (16, 1), 0), reinterpret_tensor(primals_8, (16, 16), (1, 16), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [q_1], Original ATen: [aten.clone]
        triton_poi_fused_clone_3.run(buf4, primals_5, buf7, 256, grid=grid(256), stream=stream0)
        del primals_5
        buf8 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf4  # reuse
        # Topologically Sorted Source Nodes: [k_1], Original ATen: [aten.clone]
        triton_poi_fused_clone_3.run(buf5, primals_7, buf8, 256, grid=grid(256), stream=stream0)
        del primals_7
        buf9 = reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0); del buf5  # reuse
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf8, (16, 4, 4), (16, 1, 4), 0), out=buf9)
        buf10 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attention], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_4.run(buf9, buf10, 256, grid=grid(256), stream=stream0)
        buf11 = buf9; del buf9  # reuse
        # Topologically Sorted Source Nodes: [attention], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_5.run(buf10, buf11, 256, grid=grid(256), stream=stream0)
        buf12 = reinterpret_tensor(buf10, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf10  # reuse
        # Topologically Sorted Source Nodes: [v_1], Original ATen: [aten.clone]
        triton_poi_fused_clone_3.run(buf6, primals_9, buf12, 256, grid=grid(256), stream=stream0)
        del primals_9
        buf13 = reinterpret_tensor(buf6, (16, 4, 4), (16, 4, 1), 0); del buf6  # reuse
        # Topologically Sorted Source Nodes: [y], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf11, reinterpret_tensor(buf12, (16, 4, 4), (16, 4, 1), 0), out=buf13)
        buf14 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.clone]
        triton_poi_fused_clone_6.run(buf13, buf14, 256, grid=grid(256), stream=stream0)
        del buf13
        buf15 = reinterpret_tensor(buf2, (16, 4), (4, 1), 0); del buf2  # reuse
        # Topologically Sorted Source Nodes: [y_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf14, (16, 16), (16, 1), 0), reinterpret_tensor(primals_10, (16, 4), (1, 16), 0), alpha=1, beta=1, out=buf15)
        del primals_11
        buf16 = buf1; del buf1  # reuse
        buf17 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [attention_1, out2], Original ATen: [aten.add, aten.native_layer_norm]
        triton_poi_fused_add_native_layer_norm_7.run(buf15, primals_3, buf16, buf17, 16, grid=grid(16), stream=stream0)
        buf18 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attention_1, out2], Original ATen: [aten.add, aten.native_layer_norm]
        triton_poi_fused_add_native_layer_norm_8.run(buf15, primals_3, buf16, buf17, primals_12, primals_13, buf18, 64, grid=grid(64), stream=stream0)
        del buf16
        del buf17
        del primals_13
        buf19 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf18, (16, 4), (4, 1), 0), reinterpret_tensor(primals_14, (4, 4), (1, 4), 0), out=buf19)
        buf20 = reinterpret_tensor(buf19, (4, 4, 4), (16, 4, 1), 0); del buf19  # reuse
        # Topologically Sorted Source Nodes: [attention_1, out3_1], Original ATen: [aten.add]
        triton_poi_fused_add_9.run(buf20, primals_15, buf15, primals_3, 64, grid=grid(64), stream=stream0)
        del primals_15
    return (buf20, primals_3, primals_12, reinterpret_tensor(buf3, (16, 16), (16, 1), 0), buf11, reinterpret_tensor(buf14, (16, 16), (16, 1), 0), buf15, reinterpret_tensor(buf18, (16, 4), (4, 1), 0), primals_14, primals_10, reinterpret_tensor(buf12, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf7, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf8, (16, 4, 4), (16, 4, 1), 0), primals_8, primals_6, primals_4, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((16, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((16, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((16, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15])
    return print_performance(fn, times=times, repeat=repeat)


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

import math
import torch
import torch.nn as nn
import torch.nn.functional as F


class ScaledDotProductAttention(nn.Module):

    def forward(self, query, key, value, mask=None):
        dk = query.size()[-1]
        scores = query.matmul(key.transpose(-2, -1)) / math.sqrt(dk)
        if mask is not None:
            scores = scores.masked_fill(mask == 0, -1000000000.0)
        attention = F.softmax(scores, dim=-1)
        return attention.matmul(value)


class MultiHeadAttention(nn.Module):

    def __init__(self, in_features, head_num, bias=True, activation=None):
        super(MultiHeadAttention, self).__init__()
        if in_features % head_num != 0:
            raise ValueError(
                '`in_features`({}) should be divisible by `head_num`({})'.
                format(in_features, head_num))
        self.in_features = in_features
        self.head_num = head_num
        self.activation = activation
        self.bias = bias
        self.linear_q = nn.Linear(in_features, in_features, bias)
        self.linear_k = nn.Linear(in_features, in_features, bias)
        self.linear_v = nn.Linear(in_features, in_features, bias)
        self.linear_o = nn.Linear(in_features, in_features // self.head_num,
            bias)

    def forward(self, q, k, v, mask=None):
        q, k, v = self.linear_q(q), self.linear_k(k), self.linear_v(v)
        if self.activation is not None:
            q = self.activation(q)
            k = self.activation(k)
            v = self.activation(v)
        q = self._reshape_to_batches(q)
        k = self._reshape_to_batches(k)
        v = self._reshape_to_batches(v)
        if mask is not None:
            mask = mask.repeat(self.head_num, 1, 1)
        y = ScaledDotProductAttention()(q, k, v, mask)
        y = self._reshape_from_batches(y)
        y = self.linear_o(y)
        if self.activation is not None:
            y = self.activation(y)
        return y

    @staticmethod
    def gen_history_mask(x):
        """Generate the mask that only uses history data.
        :param x: Input tensor.
        :return: The mask.
        """
        batch_size, seq_len, _ = x.size()
        return torch.tril(torch.ones(seq_len, seq_len)).view(1, seq_len,
            seq_len).repeat(batch_size, 1, 1)

    def _reshape_to_batches(self, x):
        batch_size, seq_len, in_feature = x.size()
        sub_dim = in_feature // self.head_num
        return x.reshape(batch_size, seq_len, self.head_num, sub_dim).permute(
            0, 2, 1, 3).reshape(batch_size * self.head_num, seq_len, sub_dim)

    def _reshape_from_batches(self, x):
        batch_size, seq_len, in_feature = x.size()
        batch_size //= self.head_num
        out_dim = in_feature * self.head_num
        return x.reshape(batch_size, self.head_num, seq_len, in_feature
            ).permute(0, 2, 1, 3).reshape(batch_size, seq_len, out_dim)

    def extra_repr(self):
        return 'in_features={}, head_num={}, bias={}, activation={}'.format(
            self.in_features, self.head_num, self.bias, self.activation)


class EncoderBlock(nn.Module):

    def __init__(self, embed_size, heads):
        super(EncoderBlock, self).__init__()
        self.heads = heads
        self.norm1 = nn.LayerNorm(embed_size)
        self.attention = MultiHeadAttention(embed_size * heads, head_num=
            heads, bias=True, activation=None)
        self.norm2 = nn.LayerNorm(embed_size)
        self.mlp = nn.Linear(embed_size, embed_size)

    def forward(self, x):
        out = self.norm1(x)
        out = out.repeat(1, 1, self.heads)
        attention = self.attention(out, out, out, mask=None)
        attention = attention + x
        out2 = self.norm2(attention)
        out3 = self.mlp(out2)
        out3 = out3 + attention
        return out3


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


def get_init_inputs():
    return [[], {'embed_size': 4, 'heads': 4}]

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


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


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


@triton.jit
def triton_poi_fused_native_layer_norm_repeat_2(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * x1 + x0 % 4), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x4, tmp2, xmask)


@triton.jit
def triton_poi_fused__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)
    tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp6 = tmp5 * tmp1
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tmp8 * tmp1
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = tmp11 * tmp1
    tmp13 = triton_helpers.maximum(tmp10, tmp12)
    tmp14 = tmp2 - tmp13
    tmp15 = 0.5
    tmp16 = tmp14 * tmp15
    tmp17 = tl_math.exp(tmp16)
    tl.store(out_ptr0 + x2, tmp17, xmask)


@triton.jit
def triton_poi_fused__softmax_5(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_clone_6(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), xmask)
    tl.store(out_ptr0 + x4, tmp0, xmask)


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


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


@triton.jit
def triton_poi_fused_add_9(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_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp4 = tl.load(in_ptr2 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tl.store(in_out_ptr0 + x2, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15) = args
    args.clear()
    assert_size_stride(primals_1, (4,), (1,))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (16, 16), (16, 1))
    assert_size_stride(primals_5, (16,), (1,))
    assert_size_stride(primals_6, (16, 16), (16, 1))
    assert_size_stride(primals_7, (16,), (1,))
    assert_size_stride(primals_8, (16, 16), (16, 1))
    assert_size_stride(primals_9, (16,), (1,))
    assert_size_stride(primals_10, (4, 16), (16, 1))
    assert_size_stride(primals_11, (4,), (1,))
    assert_size_stride(primals_12, (4,), (1,))
    assert_size_stride(primals_13, (4,), (1,))
    assert_size_stride(primals_14, (4, 4), (4, 1))
    assert_size_stride(primals_15, (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((4, 4, 16), (64, 16, 1), torch.float32)
        triton_poi_fused_native_layer_norm_repeat_2[grid(256)](buf2, buf3, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (16, 16), (16, 1), 0),
            reinterpret_tensor(primals_4, (16, 16), (1, 16), 0), out=buf4)
        buf5 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (16, 16), (16, 1), 0),
            reinterpret_tensor(primals_6, (16, 16), (1, 16), 0), out=buf5)
        buf6 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (16, 16), (16, 1), 0),
            reinterpret_tensor(primals_8, (16, 16), (1, 16), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_3[grid(256)](buf4, primals_5, buf7, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf8 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf4
        triton_poi_fused_clone_3[grid(256)](buf5, primals_7, buf8, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
        buf9 = reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0)
        del buf5
        extern_kernels.bmm(reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf8, (16, 4, 4), (16, 1, 4), 0), out=buf9)
        buf10 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_4[grid(256)](buf9, buf10, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf11 = buf9
        del buf9
        triton_poi_fused__softmax_5[grid(256)](buf10, buf11, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        buf12 = reinterpret_tensor(buf10, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf10
        triton_poi_fused_clone_3[grid(256)](buf6, primals_9, buf12, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_9
        buf13 = reinterpret_tensor(buf6, (16, 4, 4), (16, 4, 1), 0)
        del buf6
        extern_kernels.bmm(buf11, reinterpret_tensor(buf12, (16, 4, 4), (16,
            4, 1), 0), out=buf13)
        buf14 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_6[grid(256)](buf13, buf14, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf13
        buf15 = reinterpret_tensor(buf2, (16, 4), (4, 1), 0)
        del buf2
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf14, (16, 16),
            (16, 1), 0), reinterpret_tensor(primals_10, (16, 4), (1, 16), 0
            ), alpha=1, beta=1, out=buf15)
        del primals_11
        buf16 = buf1
        del buf1
        buf17 = buf0
        del buf0
        triton_poi_fused_add_native_layer_norm_7[grid(16)](buf15, primals_3,
            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_8[grid(64)](buf15, primals_3,
            buf16, buf17, primals_12, primals_13, buf18, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf16
        del buf17
        del primals_13
        buf19 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf18, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_14, (4, 4), (1, 4), 0), out=buf19)
        buf20 = reinterpret_tensor(buf19, (4, 4, 4), (16, 4, 1), 0)
        del buf19
        triton_poi_fused_add_9[grid(64)](buf20, primals_15, buf15,
            primals_3, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_15
    return buf20, primals_3, primals_12, reinterpret_tensor(buf3, (16, 16),
        (16, 1), 0), buf11, reinterpret_tensor(buf14, (16, 16), (16, 1), 0
        ), buf15, reinterpret_tensor(buf18, (16, 4), (4, 1), 0
        ), primals_14, primals_10, reinterpret_tensor(buf12, (16, 4, 4), (
        16, 1, 4), 0), reinterpret_tensor(buf7, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf8, (16, 4, 4), (16, 4, 1), 0
        ), primals_8, primals_6, primals_4


class ScaledDotProductAttention(nn.Module):

    def forward(self, query, key, value, mask=None):
        dk = query.size()[-1]
        scores = query.matmul(key.transpose(-2, -1)) / math.sqrt(dk)
        if mask is not None:
            scores = scores.masked_fill(mask == 0, -1000000000.0)
        attention = F.softmax(scores, dim=-1)
        return attention.matmul(value)


class MultiHeadAttention(nn.Module):

    def __init__(self, in_features, head_num, bias=True, activation=None):
        super(MultiHeadAttention, self).__init__()
        if in_features % head_num != 0:
            raise ValueError(
                '`in_features`({}) should be divisible by `head_num`({})'.
                format(in_features, head_num))
        self.in_features = in_features
        self.head_num = head_num
        self.activation = activation
        self.bias = bias
        self.linear_q = nn.Linear(in_features, in_features, bias)
        self.linear_k = nn.Linear(in_features, in_features, bias)
        self.linear_v = nn.Linear(in_features, in_features, bias)
        self.linear_o = nn.Linear(in_features, in_features // self.head_num,
            bias)

    def forward(self, q, k, v, mask=None):
        q, k, v = self.linear_q(q), self.linear_k(k), self.linear_v(v)
        if self.activation is not None:
            q = self.activation(q)
            k = self.activation(k)
            v = self.activation(v)
        q = self._reshape_to_batches(q)
        k = self._reshape_to_batches(k)
        v = self._reshape_to_batches(v)
        if mask is not None:
            mask = mask.repeat(self.head_num, 1, 1)
        y = ScaledDotProductAttention()(q, k, v, mask)
        y = self._reshape_from_batches(y)
        y = self.linear_o(y)
        if self.activation is not None:
            y = self.activation(y)
        return y

    @staticmethod
    def gen_history_mask(x):
        """Generate the mask that only uses history data.
        :param x: Input tensor.
        :return: The mask.
        """
        batch_size, seq_len, _ = x.size()
        return torch.tril(torch.ones(seq_len, seq_len)).view(1, seq_len,
            seq_len).repeat(batch_size, 1, 1)

    def _reshape_to_batches(self, x):
        batch_size, seq_len, in_feature = x.size()
        sub_dim = in_feature // self.head_num
        return x.reshape(batch_size, seq_len, self.head_num, sub_dim).permute(
            0, 2, 1, 3).reshape(batch_size * self.head_num, seq_len, sub_dim)

    def _reshape_from_batches(self, x):
        batch_size, seq_len, in_feature = x.size()
        batch_size //= self.head_num
        out_dim = in_feature * self.head_num
        return x.reshape(batch_size, self.head_num, seq_len, in_feature
            ).permute(0, 2, 1, 3).reshape(batch_size, seq_len, out_dim)

    def extra_repr(self):
        return 'in_features={}, head_num={}, bias={}, activation={}'.format(
            self.in_features, self.head_num, self.bias, self.activation)


class EncoderBlockNew(nn.Module):

    def __init__(self, embed_size, heads):
        super(EncoderBlockNew, self).__init__()
        self.heads = heads
        self.norm1 = nn.LayerNorm(embed_size)
        self.attention = MultiHeadAttention(embed_size * heads, head_num=
            heads, bias=True, activation=None)
        self.norm2 = nn.LayerNorm(embed_size)
        self.mlp = nn.Linear(embed_size, embed_size)

    def forward(self, input_0):
        primals_1 = self.norm1.weight
        primals_2 = self.norm1.bias
        primals_4 = self.attention.linear_q.weight
        primals_5 = self.attention.linear_q.bias
        primals_6 = self.attention.linear_k.weight
        primals_7 = self.attention.linear_k.bias
        primals_8 = self.attention.linear_v.weight
        primals_9 = self.attention.linear_v.bias
        primals_10 = self.attention.linear_o.weight
        primals_11 = self.attention.linear_o.bias
        primals_12 = self.norm2.weight
        primals_13 = self.norm2.bias
        primals_14 = self.mlp.weight
        primals_15 = self.mlp.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, primals_14,
            primals_15])
        return output[0]
