# 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_1/inductor_cache/qm/cqmqejojpdhiutcecl4uiqkwznpqhn353cvazvgjyo3qp6end7wo.py
# Topologically Sorted Source Nodes: [linear], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   linear => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%select,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (64*x1)), xmask)
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/26/c26winupcyxobfqstbc26s7cdod45g5vl6rvqgqwcu4qeritg7no.py
# Topologically Sorted Source Nodes: [linear, log_softmax], Original ATen: [aten.add, aten._log_softmax]
# Source node to ATen node mapping:
#   linear => add
#   log_softmax => amax, sub
# Graph fragment:
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %primals_3), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%add, [-1], True), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %amax), kwargs = {})
triton_poi_fused__log_softmax_add_1 = async_compile.triton('triton_poi_fused__log_softmax_add_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=[32], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax_add_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 2
    x1 = (xindex // 2)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2*x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (0))
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK])
    tmp7 = tl.load(in_ptr0 + (1 + (2*x1)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (1))
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp6 = tmp3 + tmp5
    tmp10 = tmp7 + tmp9
    tmp11 = triton_helpers.maximum(tmp6, tmp10)
    tmp12 = tmp2 - tmp11
    tl.store(out_ptr0 + (x2), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/3g/c3gu4q24itp4dqvxgetbf7jng3fr6zeo7rqh5dsmxtfjda33eyws.py
# Topologically Sorted Source Nodes: [log_softmax], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
#   log_softmax => exp, log, sub_1, sum_1
# Graph fragment:
#   %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_2 = async_compile.triton('triton_poi_fused__log_softmax_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=[32], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    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')
    tmp3 = tl.load(in_ptr0 + (1 + (2*x1)), xmask, eviction_policy='evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp6 = tl_math.log(tmp5)
    tmp7 = tmp0 - tmp6
    tl.store(out_ptr0 + (x2), tmp7, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (2, 4), (4, 1))
    assert_size_stride(primals_3, (2, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_0.run(primals_1, buf0, 64, grid=grid(64), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((16, 2), (2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 2), (1, 4), 0), out=buf1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 2), (8, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear, log_softmax], Original ATen: [aten.add, aten._log_softmax]
        triton_poi_fused__log_softmax_add_1.run(buf1, primals_3, buf2, 32, grid=grid(32), stream=stream0)
        del primals_3
        buf3 = reinterpret_tensor(buf1, (4, 4, 2), (8, 2, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [log_softmax], Original ATen: [aten._log_softmax]
        triton_poi_fused__log_softmax_2.run(buf2, buf3, 32, grid=grid(32), stream=stream0)
        del buf2
    return (buf3, reinterpret_tensor(buf0, (16, 4), (4, 1), 0), buf3, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((2, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((2, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    return print_performance(fn, times=times, repeat=repeat)


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

import torch
import torch.optim.lr_scheduler
import torch.nn as nn
import torch.optim
import torch.onnx.operators


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

    def __init__(self, hidden):
        """
        :param hidden: BERT model output size
        """
        super().__init__()
        self.linear = nn.Linear(hidden, 2)
        self.softmax = nn.LogSoftmax(dim=-1)

    def forward(self, x):
        return self.softmax(self.linear(x[:, 0]))


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


def get_init_inputs():
    return [[], {'hidden': 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 math as tl_math
import torch.optim.lr_scheduler
import torch.nn as nn
import torch.optim
import torch.onnx.operators
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_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused__log_softmax_add_1(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 2
    x1 = xindex // 2
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + 2 * x1, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + 0)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK])
    tmp7 = tl.load(in_ptr0 + (1 + 2 * x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + 1)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp6 = tmp3 + tmp5
    tmp10 = tmp7 + tmp9
    tmp11 = triton_helpers.maximum(tmp6, tmp10)
    tmp12 = tmp2 - tmp11
    tl.store(out_ptr0 + x2, tmp12, xmask)


@triton.jit
def triton_poi_fused__log_softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 32
    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')
    tmp3 = tl.load(in_ptr0 + (1 + 2 * x1), xmask, eviction_policy='evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp6 = tl_math.log(tmp5)
    tmp7 = tmp0 - tmp6
    tl.store(out_ptr0 + x2, tmp7, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (2, 4), (4, 1))
    assert_size_stride(primals_3, (2,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64)](primals_1, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((16, 2), (2, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 2), (1, 4), 0), out=buf1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 2), (8, 2, 1), torch.float32)
        triton_poi_fused__log_softmax_add_1[grid(32)](buf1, primals_3, buf2,
            32, XBLOCK=32, num_warps=1, num_stages=1)
        del primals_3
        buf3 = reinterpret_tensor(buf1, (4, 4, 2), (8, 2, 1), 0)
        del buf1
        triton_poi_fused__log_softmax_2[grid(32)](buf2, buf3, 32, XBLOCK=32,
            num_warps=1, num_stages=1)
        del buf2
    return buf3, reinterpret_tensor(buf0, (16, 4), (4, 1), 0), buf3


class NextSentencePredictionNew(nn.Module):
    """
    2-class classification model : is_next, is_not_next
    """

    def __init__(self, hidden):
        """
        :param hidden: BERT model output size
        """
        super().__init__()
        self.linear = nn.Linear(hidden, 2)
        self.softmax = nn.LogSoftmax(dim=-1)

    def forward(self, input_0):
        primals_2 = self.linear.weight
        primals_3 = self.linear.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
