# 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_7/inductor_cache/bu/cbur6sxiwjkyczoyaejigcz7gusy73v6foklopv6bcnlgdhs64ym.py
# Topologically Sorted Source Nodes: [seq_class_log_prob], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
#   seq_class_log_prob => amax, exp, log, sub, sub_1, sum_1
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_1, [-1], True), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_1, %amax), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %log), kwargs = {})
triton_poi_fused__log_softmax_0 = async_compile.triton('triton_poi_fused__log_softmax_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=[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__log_softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__log_softmax_0(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
    x1 = (xindex // 2)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (2*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (2*x1)), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp4 = tmp0 - tmp3
    tmp5 = tmp1 - tmp3
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tmp2 - tmp3
    tmp8 = tl_math.exp(tmp7)
    tmp9 = tmp6 + tmp8
    tmp10 = tl_math.log(tmp9)
    tmp11 = tmp4 - tmp10
    tl.store(out_ptr0 + (x2), tmp11, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/zq/czqwvzissnz2gedfwhsozn4lthtnshxrffksqx3iu6baxxr4mlgs.py
# Topologically Sorted Source Nodes: [mul, truediv, erf, add, gelu, hidden_states], Original ATen: [aten.mul, aten.div, aten.erf, aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
#   add => add
#   erf => erf
#   gelu => mul_1
#   hidden_states => var_mean
#   mul => mul
#   truediv => div
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_3, 0.5), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%view_3, 1.4142135623730951), kwargs = {})
#   %erf : [num_users=1] = call_function[target=torch.ops.aten.erf.default](args = (%div,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%erf, 1.0), kwargs = {})
#   %mul_1 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %add), kwargs = {})
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%mul_1, [2]), kwargs = {correction: 0, keepdim: True})
triton_poi_fused_add_div_erf_mul_native_layer_norm_1 = async_compile.triton('triton_poi_fused_add_div_erf_mul_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=[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_add_div_erf_mul_native_layer_norm_1', '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_add_div_erf_mul_native_layer_norm_1(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')
    tmp9 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865475
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tmp10 = tmp9 * tmp1
    tmp11 = tmp9 * tmp3
    tmp12 = libdevice.erf(tmp11)
    tmp13 = tmp12 + tmp6
    tmp14 = tmp10 * tmp13
    tmp15 = tmp8 + tmp14
    tmp17 = tmp16 * tmp1
    tmp18 = tmp16 * tmp3
    tmp19 = libdevice.erf(tmp18)
    tmp20 = tmp19 + tmp6
    tmp21 = tmp17 * tmp20
    tmp22 = tmp15 + tmp21
    tmp24 = tmp23 * tmp1
    tmp25 = tmp23 * tmp3
    tmp26 = libdevice.erf(tmp25)
    tmp27 = tmp26 + tmp6
    tmp28 = tmp24 * tmp27
    tmp29 = tmp22 + tmp28
    tmp30 = 4.0
    tmp31 = tmp29 / tmp30
    tmp32 = tmp8 - tmp31
    tmp33 = tmp32 * tmp32
    tmp34 = tmp14 - tmp31
    tmp35 = tmp34 * tmp34
    tmp36 = tmp33 + tmp35
    tmp37 = tmp21 - tmp31
    tmp38 = tmp37 * tmp37
    tmp39 = tmp36 + tmp38
    tmp40 = tmp28 - tmp31
    tmp41 = tmp40 * tmp40
    tmp42 = tmp39 + tmp41
    tmp43 = tmp42 / tmp30
    tl.store(out_ptr0 + (x0), tmp31, xmask)
    tl.store(out_ptr1 + (x0), tmp43, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/m4/cm4x2n7fjcrc4ix456udvedcpdxco3glqohf4hlvpd5rtrfkbjze.py
# Topologically Sorted Source Nodes: [mul, truediv, erf, add, gelu, hidden_states], Original ATen: [aten.mul, aten.div, aten.erf, aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
#   add => add
#   erf => erf
#   gelu => mul_1
#   hidden_states => add_1, add_2, mul_2, mul_3, rsqrt, sub_2
#   mul => mul
#   truediv => div
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_3, 0.5), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%view_3, 1.4142135623730951), kwargs = {})
#   %erf : [num_users=1] = call_function[target=torch.ops.aten.erf.default](args = (%div,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%erf, 1.0), kwargs = {})
#   %mul_1 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %add), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-12), kwargs = {})
#   %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_1,), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_1, %getitem_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, %rsqrt), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, %primals_7), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_3, %primals_8), kwargs = {})
triton_poi_fused_add_div_erf_mul_native_layer_norm_2 = async_compile.triton('triton_poi_fused_add_div_erf_mul_native_layer_norm_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=[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_add_div_erf_mul_native_layer_norm_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_div_erf_mul_native_layer_norm_2(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)
    tmp9 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr3 + (x0), xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr4 + (x0), xmask, eviction_policy='evict_last')
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865475
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tmp10 = tmp8 - tmp9
    tmp12 = 1e-12
    tmp13 = tmp11 + tmp12
    tmp14 = libdevice.rsqrt(tmp13)
    tmp15 = tmp10 * tmp14
    tmp17 = tmp15 * tmp16
    tmp19 = tmp17 + tmp18
    tl.store(out_ptr0 + (x2), tmp19, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/he/chew65shr35o6j45todpl7hkiuhemqof2icbj26p5c7dzrdpmfzy.py
# Topologically Sorted Source Nodes: [prediction_scores, prediction_log_prob], Original ATen: [aten.add, aten._log_softmax]
# Source node to ATen node mapping:
#   prediction_log_prob => amax_1, exp_1, sub_3, sum_2
#   prediction_scores => add_3
# Graph fragment:
#   %add_3 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_5, %primals_10), kwargs = {})
#   %amax_1 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%add_3, [-1], True), kwargs = {})
#   %sub_3 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_3, %amax_1), kwargs = {})
#   %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub_3,), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp_1, [-1], True), kwargs = {})
triton_poi_fused__log_softmax_add_3 = async_compile.triton('triton_poi_fused__log_softmax_add_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], 
    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__log_softmax_add_3', '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__log_softmax_add_3(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 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (1))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp9 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (2))
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK])
    tmp14 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + (3))
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp7 = tmp4 + tmp6
    tmp8 = triton_helpers.maximum(tmp3, tmp7)
    tmp12 = tmp9 + tmp11
    tmp13 = triton_helpers.maximum(tmp8, tmp12)
    tmp17 = tmp14 + tmp16
    tmp18 = triton_helpers.maximum(tmp13, tmp17)
    tmp19 = tmp3 - tmp18
    tmp20 = tl_math.exp(tmp19)
    tmp21 = tmp7 - tmp18
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp20 + tmp22
    tmp24 = tmp12 - tmp18
    tmp25 = tl_math.exp(tmp24)
    tmp26 = tmp23 + tmp25
    tmp27 = tmp17 - tmp18
    tmp28 = tl_math.exp(tmp27)
    tmp29 = tmp26 + tmp28
    tl.store(out_ptr0 + (x0), tmp18, xmask)
    tl.store(out_ptr1 + (x0), tmp29, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/7a/c7agix66c4ygscid3opzpfmvqlnv3qbqbx6jblmvx63amecaonlz.py
# Topologically Sorted Source Nodes: [prediction_scores, prediction_log_prob], Original ATen: [aten.add, aten._log_softmax]
# Source node to ATen node mapping:
#   prediction_log_prob => amax_1, log_1, sub_3, sub_4
#   prediction_scores => add_3
# Graph fragment:
#   %add_3 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_5, %primals_10), kwargs = {})
#   %amax_1 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%add_3, [-1], True), kwargs = {})
#   %sub_3 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_3, %amax_1), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_2,), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_3, %log_1), kwargs = {})
triton_poi_fused__log_softmax_add_4 = async_compile.triton('triton_poi_fused__log_softmax_add_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=[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__log_softmax_add_4', '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__log_softmax_add_4(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
    x1 = (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 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = tl_math.log(tmp5)
    tmp7 = tmp4 - tmp6
    tl.store(in_out_ptr0 + (x2), tmp7, 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 = args
    args.clear()
    assert_size_stride(primals_1, (2, 4), (4, 1))
    assert_size_stride(primals_2, (2, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, ), (1, ))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, ), (1, ))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    assert_size_stride(primals_10, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 2), (2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [seq_relationship_score], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 2), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 2), (32, 8, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [seq_class_log_prob], Original ATen: [aten._log_softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__log_softmax_0.run(buf0, buf1, 128, grid=grid(128), stream=stream0)
        del buf0
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_6, reinterpret_tensor(primals_4, (16, 4), (4, 1), 192), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del primals_5
        del primals_6
        buf3 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        # Topologically Sorted Source Nodes: [mul, truediv, erf, add, gelu, hidden_states], Original ATen: [aten.mul, aten.div, aten.erf, aten.add, aten.native_layer_norm]
        triton_poi_fused_add_div_erf_mul_native_layer_norm_1.run(buf2, buf3, buf4, 16, grid=grid(16), stream=stream0)
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, truediv, erf, add, gelu, hidden_states], Original ATen: [aten.mul, aten.div, aten.erf, aten.add, aten.native_layer_norm]
        triton_poi_fused_add_div_erf_mul_native_layer_norm_2.run(buf2, buf3, buf4, primals_7, primals_8, buf5, 64, grid=grid(64), stream=stream0)
        del primals_8
        buf6 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf5, (16, 4), (4, 1), 0), reinterpret_tensor(primals_9, (4, 4), (1, 4), 0), out=buf6)
        buf7 = buf4; del buf4  # reuse
        buf8 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [prediction_scores, prediction_log_prob], Original ATen: [aten.add, aten._log_softmax]
        triton_poi_fused__log_softmax_add_3.run(buf6, primals_10, buf7, buf8, 16, grid=grid(16), stream=stream0)
        buf9 = reinterpret_tensor(buf6, (4, 4, 4), (16, 4, 1), 0); del buf6  # reuse
        # Topologically Sorted Source Nodes: [prediction_scores, prediction_log_prob], Original ATen: [aten.add, aten._log_softmax]
        triton_poi_fused__log_softmax_add_4.run(buf9, primals_10, buf7, buf8, 64, grid=grid(64), stream=stream0)
        del buf7
        del buf8
        del primals_10
    return (buf1, buf9, primals_7, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf1, reinterpret_tensor(primals_4, (16, 4), (4, 1), 192), buf2, reinterpret_tensor(buf5, (16, 4), (4, 1), 0), buf9, primals_9, )


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((2, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((2, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((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, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = 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])
    return print_performance(fn, times=times, repeat=repeat)


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

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import math
import torch.nn as nn
import torch.cuda
import torch.distributed
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__log_softmax_0(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
    x1 = xindex // 2
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 2 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 2 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp4 = tmp0 - tmp3
    tmp5 = tmp1 - tmp3
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tmp2 - tmp3
    tmp8 = tl_math.exp(tmp7)
    tmp9 = tmp6 + tmp8
    tmp10 = tl_math.log(tmp9)
    tmp11 = tmp4 - tmp10
    tl.store(out_ptr0 + x2, tmp11, xmask)


@triton.jit
def triton_poi_fused_add_div_erf_mul_native_layer_norm_1(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')
    tmp9 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp23 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865475
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tmp10 = tmp9 * tmp1
    tmp11 = tmp9 * tmp3
    tmp12 = libdevice.erf(tmp11)
    tmp13 = tmp12 + tmp6
    tmp14 = tmp10 * tmp13
    tmp15 = tmp8 + tmp14
    tmp17 = tmp16 * tmp1
    tmp18 = tmp16 * tmp3
    tmp19 = libdevice.erf(tmp18)
    tmp20 = tmp19 + tmp6
    tmp21 = tmp17 * tmp20
    tmp22 = tmp15 + tmp21
    tmp24 = tmp23 * tmp1
    tmp25 = tmp23 * tmp3
    tmp26 = libdevice.erf(tmp25)
    tmp27 = tmp26 + tmp6
    tmp28 = tmp24 * tmp27
    tmp29 = tmp22 + tmp28
    tmp30 = 4.0
    tmp31 = tmp29 / tmp30
    tmp32 = tmp8 - tmp31
    tmp33 = tmp32 * tmp32
    tmp34 = tmp14 - tmp31
    tmp35 = tmp34 * tmp34
    tmp36 = tmp33 + tmp35
    tmp37 = tmp21 - tmp31
    tmp38 = tmp37 * tmp37
    tmp39 = tmp36 + tmp38
    tmp40 = tmp28 - tmp31
    tmp41 = tmp40 * tmp40
    tmp42 = tmp39 + tmp41
    tmp43 = tmp42 / tmp30
    tl.store(out_ptr0 + x0, tmp31, xmask)
    tl.store(out_ptr1 + x0, tmp43, xmask)


@triton.jit
def triton_poi_fused_add_div_erf_mul_native_layer_norm_2(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)
    tmp9 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865475
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tmp10 = tmp8 - tmp9
    tmp12 = 1e-12
    tmp13 = tmp11 + tmp12
    tmp14 = libdevice.rsqrt(tmp13)
    tmp15 = tmp10 * tmp14
    tmp17 = tmp15 * tmp16
    tmp19 = tmp17 + tmp18
    tl.store(out_ptr0 + x2, tmp19, xmask)


@triton.jit
def triton_poi_fused__log_softmax_add_3(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 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + 1)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp9 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + 2)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK])
    tmp14 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr1 + 3)
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp7 = tmp4 + tmp6
    tmp8 = triton_helpers.maximum(tmp3, tmp7)
    tmp12 = tmp9 + tmp11
    tmp13 = triton_helpers.maximum(tmp8, tmp12)
    tmp17 = tmp14 + tmp16
    tmp18 = triton_helpers.maximum(tmp13, tmp17)
    tmp19 = tmp3 - tmp18
    tmp20 = tl_math.exp(tmp19)
    tmp21 = tmp7 - tmp18
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp20 + tmp22
    tmp24 = tmp12 - tmp18
    tmp25 = tl_math.exp(tmp24)
    tmp26 = tmp23 + tmp25
    tmp27 = tmp17 - tmp18
    tmp28 = tl_math.exp(tmp27)
    tmp29 = tmp26 + tmp28
    tl.store(out_ptr0 + x0, tmp18, xmask)
    tl.store(out_ptr1 + x0, tmp29, xmask)


@triton.jit
def triton_poi_fused__log_softmax_add_4(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
    x1 = 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 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = tl_math.log(tmp5)
    tmp7 = tmp4 - tmp6
    tl.store(in_out_ptr0 + x2, tmp7, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10) = args
    args.clear()
    assert_size_stride(primals_1, (2, 4), (4, 1))
    assert_size_stride(primals_2, (2,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    assert_size_stride(primals_10, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 2), (2, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 2), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 2), (32, 8, 2, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_0[grid(128)](buf0, buf1, 128, XBLOCK=
            128, num_warps=4, num_stages=1)
        del buf0
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_6, reinterpret_tensor(primals_4, (16, 
            4), (4, 1), 192), reinterpret_tensor(primals_5, (4, 4), (1, 4),
            0), alpha=1, beta=1, out=buf2)
        del primals_5
        del primals_6
        buf3 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused_add_div_erf_mul_native_layer_norm_1[grid(16)](buf2,
            buf3, buf4, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_div_erf_mul_native_layer_norm_2[grid(64)](buf2,
            buf3, buf4, primals_7, primals_8, buf5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_8
        buf6 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf5, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_9, (4, 4), (1, 4), 0), out=buf6)
        buf7 = buf4
        del buf4
        buf8 = buf3
        del buf3
        triton_poi_fused__log_softmax_add_3[grid(16)](buf6, primals_10,
            buf7, buf8, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf9 = reinterpret_tensor(buf6, (4, 4, 4), (16, 4, 1), 0)
        del buf6
        triton_poi_fused__log_softmax_add_4[grid(64)](buf9, primals_10,
            buf7, buf8, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf7
        del buf8
        del primals_10
    return buf1, buf9, primals_7, reinterpret_tensor(primals_3, (64, 4), (4,
        1), 0), buf1, reinterpret_tensor(primals_4, (16, 4), (4, 1), 192
        ), buf2, reinterpret_tensor(buf5, (16, 4), (4, 1), 0), buf9, primals_9


def get_activation_fn(activation):
    """Return an activation function Module according to its name."""
    if activation == 'gelu':
        fn = GELU()
    elif activation == 'relu':
        fn = nn.ReLU()
    elif activation == 'tanh':
        fn = nn.Tanh()
    else:
        raise ValueError(
            'Please pass a valid                           activation function'
            )
    return fn


class GELU(nn.Module):
    """ Implementation of the gelu activation function
        :cite:`DBLP:journals/corr/HendrycksG16`

        For information: OpenAI GPT's gelu is slightly different
        (and gives slightly different results):
        0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi)
                    * (x + 0.044715 * torch.pow(x, 3))))

        Examples::
        >>> m = GELU()
        >>> inputs = torch.randn(2)
        >>> outputs = m(inputs)
    """

    def forward(self, x):
        gelu = x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
        return gelu


class BertPredictionTransform(nn.Module):
    """{Linear(h,h), Activation, LN} block."""

    def __init__(self, hidden_size):
        """
        Args:
            hidden_size (int): BERT model hidden layer size.
        """
        super(BertPredictionTransform, self).__init__()
        self.dense = nn.Linear(hidden_size, hidden_size)
        self.activation = get_activation_fn('gelu')
        self.layer_norm = nn.LayerNorm(hidden_size, eps=1e-12)

    def forward(self, hidden_states):
        """
        Args:
            hidden_states (Tensor): BERT encoder output ``(B, S, H)``
        """
        hidden_states = self.layer_norm(self.activation(self.dense(
            hidden_states)))
        return hidden_states


class MaskedLanguageModel(nn.Module):
    """predicting origin token from masked input sequence
    n-class classification problem, n-class = vocab_size

    Args:
            hidden_size (int): output size of BERT model
            vocab_size (int): total vocab size
    """

    def __init__(self, hidden_size, vocab_size):
        super(MaskedLanguageModel, self).__init__()
        self.transform = BertPredictionTransform(hidden_size)
        self.decode = nn.Linear(hidden_size, vocab_size, bias=False)
        self.bias = nn.Parameter(torch.zeros(vocab_size))
        self.log_softmax = nn.LogSoftmax(dim=-1)

    def forward(self, x):
        """
        Args:
            x (Tensor): first output of bert encoder, ``(B, S, H)``
        Returns:
            prediction_log_prob (Tensor): shape ``(B, S, vocab)``
        """
        x = self.transform(x)
        prediction_scores = self.decode(x) + self.bias
        prediction_log_prob = self.log_softmax(prediction_scores)
        return prediction_log_prob


class NextSentencePrediction(nn.Module):
    """
    2-class classification model : is_next, is_random_next

    Args:
            hidden_size (int): BERT model output size
    """

    def __init__(self, hidden_size):
        super(NextSentencePrediction, self).__init__()
        self.linear = nn.Linear(hidden_size, 2)
        self.log_softmax = nn.LogSoftmax(dim=-1)

    def forward(self, x):
        """
        Args:
            x (Tensor): second output of bert encoder, ``(B, H)``
        Returns:
            seq_class_prob (Tensor): ``(B, 2)``
        """
        seq_relationship_score = self.linear(x)
        seq_class_log_prob = self.log_softmax(seq_relationship_score)
        return seq_class_log_prob


class BertPreTrainingHeadsNew(nn.Module):
    """
    Bert Pretraining Heads: Masked Language Models, Next Sentence Prediction

    Args:
            hidden_size (int): output size of BERT model
            vocab_size (int): total vocab size
    """

    def __init__(self, hidden_size, vocab_size):
        super(BertPreTrainingHeadsNew, self).__init__()
        self.next_sentence = NextSentencePrediction(hidden_size)
        self.mask_lm = MaskedLanguageModel(hidden_size, vocab_size)

    def forward(self, input_0, input_1):
        primals_1 = self.next_sentence.linear.weight
        primals_2 = self.next_sentence.linear.bias
        primals_6 = self.mask_lm.bias
        primals_5 = self.mask_lm.transform.dense.weight
        primals_7 = self.mask_lm.transform.dense.bias
        primals_8 = self.mask_lm.transform.layer_norm.weight
        primals_10 = self.mask_lm.transform.layer_norm.bias
        primals_9 = self.mask_lm.decode.weight
        primals_3 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9, primals_10])
        return output[0], output[1]
