# AOT ID: ['0_inference']
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/wx/cwxwvlntewdrqi2r4caciy5ht4jdvafnhtiqncr4lo4aegcb4imz.py
# Topologically Sorted Source Nodes: [y_hat], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   y_hat => amax, exp, sub_2
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg1_1, [-1], True), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_2,), kwargs = {})
triton_poi_fused__softmax_0 = async_compile.triton('triton_poi_fused__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=[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_0', '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_0(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 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/37/c37sougfz2czguicfonttai6lohceo4gz7f46geljh26dequ5b45.py
# Topologically Sorted Source Nodes: [n_plus, add, add_1, n_plus_rate, mul, sub, n_minus, add_2, n_minus_rate, sub_1, mul_1, y_cv, y_hat, log, mul_2, sub_2, sub_3, log_1, mul_3, add_4, mean, platt_loss], Original ATen: [aten.sum, aten.add, aten.div, aten.mul, aten.rsub, aten.reciprocal, aten._softmax, aten.log, aten.mean]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   add_4 => add_4
#   log => log
#   log_1 => log_1
#   mean => mean
#   mul => mul_1
#   mul_1 => mul_2
#   mul_2 => mul_3
#   mul_3 => mul_4
#   n_minus => sum_2
#   n_minus_rate => mul, reciprocal
#   n_plus => sum_1
#   n_plus_rate => div
#   platt_loss => mul_5
#   sub => sub
#   sub_1 => sub_1
#   sub_2 => sub_3
#   sub_3 => sub_4
#   y_cv => add_3
#   y_hat => div_1, sum_3
# Graph fragment:
#   %sum_1 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%arg0_1, [0]), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 1.0), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 2.0), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add, %add_1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, %arg0_1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %arg0_1), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sub, [0]), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_2, 2.0), kwargs = {})
#   %reciprocal : [num_users=1] = call_function[target=torch.ops.aten.reciprocal.default](args = (%add_2,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%reciprocal, 1.0), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg0_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %sub_1), kwargs = {})
#   %add_3 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %mul_2), kwargs = {})
#   %sum_3 : [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_3), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%div_1,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_3, %log), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %add_3), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %div_1), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sub_4,), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_3, %log_1), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_3, %mul_4), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%add_4,), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mean, -1), kwargs = {})
triton_red_fused__softmax_add_div_log_mean_mul_reciprocal_rsub_sum_1 = async_compile.triton('triton_red_fused__softmax_add_div_log_mean_mul_reciprocal_rsub_sum_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.reduction(
    size_hints=[1, 256],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused__softmax_add_div_log_mean_mul_reciprocal_rsub_sum_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 10, 'num_reduction': 1, '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}
)
@triton.jit
def triton_red_fused__softmax_add_div_log_mean_mul_reciprocal_rsub_sum_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    _tmp45 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r0 = rindex % 64
        r4 = rindex
        r3 = (rindex // 4)
        tmp0 = tl.load(in_ptr0 + (r0), rmask, eviction_policy='evict_last', other=0.0)
        tmp1 = tl.load(in_ptr0 + (64 + r0), rmask, eviction_policy='evict_last', other=0.0)
        tmp3 = tl.load(in_ptr0 + (128 + r0), rmask, eviction_policy='evict_last', other=0.0)
        tmp5 = tl.load(in_ptr0 + (192 + r0), rmask, eviction_policy='evict_last', other=0.0)
        tmp12 = tl.load(in_ptr0 + (r4), rmask, eviction_policy='evict_first', other=0.0)
        tmp28 = tl.load(in_ptr1 + (r4), rmask, eviction_policy='evict_first', other=0.0)
        tmp29 = tl.load(in_ptr1 + (4*r3), rmask, eviction_policy='evict_last', other=0.0)
        tmp30 = tl.load(in_ptr1 + (1 + (4*r3)), rmask, eviction_policy='evict_last', other=0.0)
        tmp32 = tl.load(in_ptr1 + (2 + (4*r3)), rmask, eviction_policy='evict_last', other=0.0)
        tmp34 = tl.load(in_ptr1 + (3 + (4*r3)), rmask, eviction_policy='evict_last', other=0.0)
        tmp2 = tmp0 + tmp1
        tmp4 = tmp2 + tmp3
        tmp6 = tmp4 + tmp5
        tmp7 = 1.0
        tmp8 = tmp6 + tmp7
        tmp9 = 2.0
        tmp10 = tmp6 + tmp9
        tmp11 = tmp8 / tmp10
        tmp13 = tmp11 * tmp12
        tmp14 = tmp7 - tmp0
        tmp15 = tmp7 - tmp1
        tmp16 = tmp14 + tmp15
        tmp17 = tmp7 - tmp3
        tmp18 = tmp16 + tmp17
        tmp19 = tmp7 - tmp5
        tmp20 = tmp18 + tmp19
        tmp21 = tmp20 + tmp9
        tmp22 = tl.full([1, 1], 1, tl.int32)
        tmp23 = tmp22 / tmp21
        tmp24 = tmp23 * tmp7
        tmp25 = tmp7 - tmp12
        tmp26 = tmp24 * tmp25
        tmp27 = tmp13 + tmp26
        tmp31 = tmp29 + tmp30
        tmp33 = tmp31 + tmp32
        tmp35 = tmp33 + tmp34
        tmp36 = tmp28 / tmp35
        tmp37 = tl_math.log(tmp36)
        tmp38 = tmp27 * tmp37
        tmp39 = tmp7 - tmp27
        tmp40 = tmp7 - tmp36
        tmp41 = tl_math.log(tmp40)
        tmp42 = tmp39 * tmp41
        tmp43 = tmp38 + tmp42
        tmp44 = tl.broadcast_to(tmp43, [XBLOCK, RBLOCK])
        tmp46 = _tmp45 + tmp44
        _tmp45 = tl.where(rmask, tmp46, _tmp45)
    tmp45 = tl.sum(_tmp45, 1)[:, None]
    tmp47 = 256.0
    tmp48 = tmp45 / tmp47
    tmp49 = -1.0
    tmp50 = tmp48 * tmp49
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp50, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y_hat], Original ATen: [aten._softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__softmax_0.run(arg1_1, buf1, 256, grid=grid(256), stream=stream0)
        del arg1_1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [n_plus, add, add_1, n_plus_rate, mul, sub, n_minus, add_2, n_minus_rate, sub_1, mul_1, y_cv, y_hat, log, mul_2, sub_2, sub_3, log_1, mul_3, add_4, mean, platt_loss], Original ATen: [aten.sum, aten.add, aten.div, aten.mul, aten.rsub, aten.reciprocal, aten._softmax, aten.log, aten.mean]
        triton_red_fused__softmax_add_div_log_mean_mul_reciprocal_rsub_sum_1.run(buf4, arg0_1, buf1, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del buf1
    return (buf4, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1])
    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


class LogisticLoss(torch.nn.Module):

    def __init__(self):
        super(LogisticLoss, self).__init__()

    def forward(self, logits, targets, multi_label=False):
        y = targets.float()
        n_plus = torch.sum(y, dim=0)
        n_minus = torch.sum(1.0 - y, dim=0)
        n_plus_rate = (n_plus + 1.0) / (n_plus + 2.0)
        n_minus_rate = 1.0 / (n_minus + 2.0)
        y_cv = n_plus_rate * y + n_minus_rate * (1 - y)
        y_hat = torch.sigmoid(logits) if multi_label else torch.softmax(logits,
            dim=-1)
        platt_loss = -1 * torch.mean(y_cv * torch.log(y_hat) + (1 - y_cv) *
            torch.log(1 - y_hat))
        return platt_loss


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


def get_init_inputs():
    return [[], {}]

import torch
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused__softmax_0(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 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_red_fused__softmax_add_div_log_mean_mul_reciprocal_rsub_sum_1(
    in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr,
    RBLOCK: tl.constexpr):
    rnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    _tmp45 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r0 = rindex % 64
        r4 = rindex
        r3 = rindex // 4
        tmp0 = tl.load(in_ptr0 + r0, rmask, eviction_policy='evict_last',
            other=0.0)
        tmp1 = tl.load(in_ptr0 + (64 + r0), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp3 = tl.load(in_ptr0 + (128 + r0), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp5 = tl.load(in_ptr0 + (192 + r0), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp12 = tl.load(in_ptr0 + r4, rmask, eviction_policy='evict_first',
            other=0.0)
        tmp28 = tl.load(in_ptr1 + r4, rmask, eviction_policy='evict_first',
            other=0.0)
        tmp29 = tl.load(in_ptr1 + 4 * r3, rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp30 = tl.load(in_ptr1 + (1 + 4 * r3), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp32 = tl.load(in_ptr1 + (2 + 4 * r3), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp34 = tl.load(in_ptr1 + (3 + 4 * r3), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp2 = tmp0 + tmp1
        tmp4 = tmp2 + tmp3
        tmp6 = tmp4 + tmp5
        tmp7 = 1.0
        tmp8 = tmp6 + tmp7
        tmp9 = 2.0
        tmp10 = tmp6 + tmp9
        tmp11 = tmp8 / tmp10
        tmp13 = tmp11 * tmp12
        tmp14 = tmp7 - tmp0
        tmp15 = tmp7 - tmp1
        tmp16 = tmp14 + tmp15
        tmp17 = tmp7 - tmp3
        tmp18 = tmp16 + tmp17
        tmp19 = tmp7 - tmp5
        tmp20 = tmp18 + tmp19
        tmp21 = tmp20 + tmp9
        tmp22 = tl.full([1, 1], 1, tl.int32)
        tmp23 = tmp22 / tmp21
        tmp24 = tmp23 * tmp7
        tmp25 = tmp7 - tmp12
        tmp26 = tmp24 * tmp25
        tmp27 = tmp13 + tmp26
        tmp31 = tmp29 + tmp30
        tmp33 = tmp31 + tmp32
        tmp35 = tmp33 + tmp34
        tmp36 = tmp28 / tmp35
        tmp37 = tl_math.log(tmp36)
        tmp38 = tmp27 * tmp37
        tmp39 = tmp7 - tmp27
        tmp40 = tmp7 - tmp36
        tmp41 = tl_math.log(tmp40)
        tmp42 = tmp39 * tmp41
        tmp43 = tmp38 + tmp42
        tmp44 = tl.broadcast_to(tmp43, [XBLOCK, RBLOCK])
        tmp46 = _tmp45 + tmp44
        _tmp45 = tl.where(rmask, tmp46, _tmp45)
    tmp45 = tl.sum(_tmp45, 1)[:, None]
    tmp47 = 256.0
    tmp48 = tmp45 / tmp47
    tmp49 = -1.0
    tmp50 = tmp48 * tmp49
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp50, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(256)](arg1_1, buf1, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del arg1_1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3
        del buf3
        triton_red_fused__softmax_add_div_log_mean_mul_reciprocal_rsub_sum_1[
            grid(1)](buf4, arg0_1, buf1, 1, 256, XBLOCK=1, RBLOCK=256,
            num_warps=8, num_stages=1)
        del arg0_1
        del buf1
    return buf4,


class LogisticLossNew(torch.nn.Module):

    def __init__(self):
        super(LogisticLossNew, self).__init__()

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
