# 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/nr/cnrkptzsuv7qm3ss6i6xgoxkou23z76h2vmwqkwz2zkgpdbxhedc.py
# Topologically Sorted Source Nodes: [log_softmax], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
#   log_softmax => amax, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view, [1], True), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %amax), 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=[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__log_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__log_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
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/wh/cwhvriz6hxksjnf7xkq6bdjc3fmcw4ahhhtmiub5rzoghqztmqjv.py
# Topologically Sorted Source Nodes: [log_softmax, p, add, truediv, sub, add_1, log, rp, log_softmax_1, y, add_2, truediv_1, sub_1, add_3, log_1, ry, sub_2, mul, mul_1, sum_1, loss, loss_1], Original ATen: [aten._log_softmax, aten.exp, aten.add, aten.reciprocal, aten.mul, aten.sub, aten.log, aten.neg, aten.sum, aten.div]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   add_3 => add_3
#   log => log_2
#   log_1 => log_3
#   log_softmax => exp, log, sub_1, sum_1
#   log_softmax_1 => exp_2, log_1, sub_3, sum_2
#   loss => div
#   loss_1 => mul_4
#   mul => mul_2
#   mul_1 => mul_3
#   p => exp_1
#   rp => neg
#   ry => neg_1
#   sub => sub_4
#   sub_1 => sub_5
#   sub_2 => sub_6
#   sum_1 => sum_3
#   truediv => mul, reciprocal
#   truediv_1 => mul_1, reciprocal_1
#   y => exp_3
# 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 = {})
#   %exp_1 : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%exp_1, 1e-06), kwargs = {})
#   %reciprocal : [num_users=1] = call_function[target=torch.ops.aten.reciprocal.default](args = (%add,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%reciprocal, 1.0), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, 1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_4, 1e-06), kwargs = {})
#   %log_2 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add_1,), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%log_2,), kwargs = {})
#   %exp_2 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub_2,), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp_2, [1], True), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_2,), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_2, %log_1), kwargs = {})
#   %exp_3 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub_3,), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%exp_3, 1e-06), kwargs = {})
#   %reciprocal_1 : [num_users=1] = call_function[target=torch.ops.aten.reciprocal.default](args = (%add_2,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%reciprocal_1, 1.0), kwargs = {})
#   %sub_5 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_1, 1), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_5, 1e-06), kwargs = {})
#   %log_3 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add_3,), kwargs = {})
#   %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%log_3,), kwargs = {})
#   %sub_6 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%neg, %neg_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%exp_1, %sub_6), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, 2), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul_3,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_3, 64), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, 1.0), kwargs = {})
triton_red_fused__log_softmax_add_div_exp_log_mul_neg_reciprocal_sub_sum_1 = async_compile.triton('triton_red_fused__log_softmax_add_div_exp_log_mul_neg_reciprocal_sub_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__log_softmax_add_div_exp_log_mul_neg_reciprocal_sub_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__log_softmax_add_div_exp_log_mul_neg_reciprocal_sub_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, :]
    _tmp52 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        r1 = (rindex // 4)
        tmp0 = tl.load(in_ptr0 + (r2), rmask, eviction_policy='evict_first', other=0.0)
        tmp1 = tl.load(in_ptr0 + (4*r1), rmask, eviction_policy='evict_last', other=0.0)
        tmp3 = tl.load(in_ptr0 + (1 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp6 = tl.load(in_ptr0 + (2 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp9 = tl.load(in_ptr0 + (3 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp25 = tl.load(in_ptr1 + (r2), rmask, eviction_policy='evict_first', other=0.0)
        tmp26 = tl.load(in_ptr1 + (4*r1), rmask, eviction_policy='evict_last', other=0.0)
        tmp28 = tl.load(in_ptr1 + (1 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp31 = tl.load(in_ptr1 + (2 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp34 = tl.load(in_ptr1 + (3 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp2 = tl_math.exp(tmp1)
        tmp4 = tl_math.exp(tmp3)
        tmp5 = tmp2 + tmp4
        tmp7 = tl_math.exp(tmp6)
        tmp8 = tmp5 + tmp7
        tmp10 = tl_math.exp(tmp9)
        tmp11 = tmp8 + tmp10
        tmp12 = tl_math.log(tmp11)
        tmp13 = tmp0 - tmp12
        tmp14 = tl_math.exp(tmp13)
        tmp15 = 1e-06
        tmp16 = tmp14 + tmp15
        tmp17 = tl.full([1, 1], 1, tl.int32)
        tmp18 = tmp17 / tmp16
        tmp19 = 1.0
        tmp20 = tmp18 * tmp19
        tmp21 = tmp20 - tmp19
        tmp22 = tmp21 + tmp15
        tmp23 = tl_math.log(tmp22)
        tmp24 = -tmp23
        tmp27 = tl_math.exp(tmp26)
        tmp29 = tl_math.exp(tmp28)
        tmp30 = tmp27 + tmp29
        tmp32 = tl_math.exp(tmp31)
        tmp33 = tmp30 + tmp32
        tmp35 = tl_math.exp(tmp34)
        tmp36 = tmp33 + tmp35
        tmp37 = tl_math.log(tmp36)
        tmp38 = tmp25 - tmp37
        tmp39 = tl_math.exp(tmp38)
        tmp40 = tmp39 + tmp15
        tmp41 = tmp17 / tmp40
        tmp42 = tmp41 * tmp19
        tmp43 = tmp42 - tmp19
        tmp44 = tmp43 + tmp15
        tmp45 = tl_math.log(tmp44)
        tmp46 = -tmp45
        tmp47 = tmp24 - tmp46
        tmp48 = tmp14 * tmp47
        tmp49 = 2.0
        tmp50 = tmp48 * tmp49
        tmp51 = tl.broadcast_to(tmp50, [XBLOCK, RBLOCK])
        tmp53 = _tmp52 + tmp51
        _tmp52 = tl.where(rmask, tmp53, _tmp52)
    tmp52 = tl.sum(_tmp52, 1)[:, None]
    tmp54 = 0.015625
    tmp55 = tmp52 * tmp54
    tmp56 = 1.0
    tmp57 = tmp55 * tmp56
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp57, 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)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [log_softmax], Original ATen: [aten._log_softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__log_softmax_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0)
        del arg0_1
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [log_softmax_1], Original ATen: [aten._log_softmax]
        triton_poi_fused__log_softmax_0.run(arg1_1, buf2, 256, grid=grid(256), stream=stream0)
        del arg1_1
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [log_softmax, p, add, truediv, sub, add_1, log, rp, log_softmax_1, y, add_2, truediv_1, sub_1, add_3, log_1, ry, sub_2, mul, mul_1, sum_1, loss, loss_1], Original ATen: [aten._log_softmax, aten.exp, aten.add, aten.reciprocal, aten.mul, aten.sub, aten.log, aten.neg, aten.sum, aten.div]
        triton_red_fused__log_softmax_add_div_exp_log_mul_neg_reciprocal_sub_sum_1.run(buf5, buf0, buf2, 1, 256, grid=grid(1), stream=stream0)
        del buf0
        del buf2
    return (buf5, )


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
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *


def stable_kl(logit, target, epsilon=1e-06, reduce=True):
    logit = logit.view(-1, logit.size(-1)).float()
    target = target.view(-1, target.size(-1)).float()
    bs = logit.size(0)
    p = F.log_softmax(logit, 1).exp()
    y = F.log_softmax(target, 1).exp()
    rp = -(1.0 / (p + epsilon) - 1 + epsilon).detach().log()
    ry = -(1.0 / (y + epsilon) - 1 + epsilon).detach().log()
    if reduce:
        return (p * (rp - ry) * 2).sum() / bs
    else:
        return (p * (rp - ry) * 2).sum()


class Criterion(_Loss):

    def __init__(self, alpha=1.0, name='criterion'):
        super().__init__()
        """Alpha is used to weight each loss term
        """
        self.alpha = alpha
        self.name = name

    def forward(self, input, target, weight=None, ignore_index=-1):
        """weight: sample weight
        """
        return


class NsKlCriterion(Criterion):

    def __init__(self, alpha=1.0, name='KL Div Criterion'):
        """
        stable KL 损失
        :param alpha:
        :type alpha:
        :param name:
        :type name:
        """
        super().__init__()
        self.alpha = alpha
        self.name = name

    def forward(self, input, target, weight=None, ignore_index=-1):
        """input/target: logits
        """
        input = input.float()
        target = target.float()
        loss = stable_kl(input, target.detach())
        loss = loss * self.alpha
        return 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
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
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__log_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
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_red_fused__log_softmax_add_div_exp_log_mul_neg_reciprocal_sub_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, :]
    _tmp52 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        r1 = rindex // 4
        tmp0 = tl.load(in_ptr0 + r2, rmask, eviction_policy='evict_first',
            other=0.0)
        tmp1 = tl.load(in_ptr0 + 4 * r1, rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp3 = tl.load(in_ptr0 + (1 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp6 = tl.load(in_ptr0 + (2 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp9 = tl.load(in_ptr0 + (3 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp25 = tl.load(in_ptr1 + r2, rmask, eviction_policy='evict_first',
            other=0.0)
        tmp26 = tl.load(in_ptr1 + 4 * r1, rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp28 = tl.load(in_ptr1 + (1 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp31 = tl.load(in_ptr1 + (2 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp34 = tl.load(in_ptr1 + (3 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp2 = tl_math.exp(tmp1)
        tmp4 = tl_math.exp(tmp3)
        tmp5 = tmp2 + tmp4
        tmp7 = tl_math.exp(tmp6)
        tmp8 = tmp5 + tmp7
        tmp10 = tl_math.exp(tmp9)
        tmp11 = tmp8 + tmp10
        tmp12 = tl_math.log(tmp11)
        tmp13 = tmp0 - tmp12
        tmp14 = tl_math.exp(tmp13)
        tmp15 = 1e-06
        tmp16 = tmp14 + tmp15
        tmp17 = tl.full([1, 1], 1, tl.int32)
        tmp18 = tmp17 / tmp16
        tmp19 = 1.0
        tmp20 = tmp18 * tmp19
        tmp21 = tmp20 - tmp19
        tmp22 = tmp21 + tmp15
        tmp23 = tl_math.log(tmp22)
        tmp24 = -tmp23
        tmp27 = tl_math.exp(tmp26)
        tmp29 = tl_math.exp(tmp28)
        tmp30 = tmp27 + tmp29
        tmp32 = tl_math.exp(tmp31)
        tmp33 = tmp30 + tmp32
        tmp35 = tl_math.exp(tmp34)
        tmp36 = tmp33 + tmp35
        tmp37 = tl_math.log(tmp36)
        tmp38 = tmp25 - tmp37
        tmp39 = tl_math.exp(tmp38)
        tmp40 = tmp39 + tmp15
        tmp41 = tmp17 / tmp40
        tmp42 = tmp41 * tmp19
        tmp43 = tmp42 - tmp19
        tmp44 = tmp43 + tmp15
        tmp45 = tl_math.log(tmp44)
        tmp46 = -tmp45
        tmp47 = tmp24 - tmp46
        tmp48 = tmp14 * tmp47
        tmp49 = 2.0
        tmp50 = tmp48 * tmp49
        tmp51 = tl.broadcast_to(tmp50, [XBLOCK, RBLOCK])
        tmp53 = _tmp52 + tmp51
        _tmp52 = tl.where(rmask, tmp53, _tmp52)
    tmp52 = tl.sum(_tmp52, 1)[:, None]
    tmp54 = 0.015625
    tmp55 = tmp52 * tmp54
    tmp56 = 1.0
    tmp57 = tmp55 * tmp56
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp57, 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)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_0[grid(256)](arg0_1, buf0, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        triton_poi_fused__log_softmax_0[grid(256)](arg1_1, buf2, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg1_1
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf4
        del buf4
        triton_red_fused__log_softmax_add_div_exp_log_mul_neg_reciprocal_sub_sum_1[
            grid(1)](buf5, buf0, buf2, 1, 256, XBLOCK=1, RBLOCK=256,
            num_warps=8, num_stages=1)
        del buf0
        del buf2
    return buf5,


def stable_kl(logit, target, epsilon=1e-06, reduce=True):
    logit = logit.view(-1, logit.size(-1)).float()
    target = target.view(-1, target.size(-1)).float()
    bs = logit.size(0)
    p = F.log_softmax(logit, 1).exp()
    y = F.log_softmax(target, 1).exp()
    rp = -(1.0 / (p + epsilon) - 1 + epsilon).detach().log()
    ry = -(1.0 / (y + epsilon) - 1 + epsilon).detach().log()
    if reduce:
        return (p * (rp - ry) * 2).sum() / bs
    else:
        return (p * (rp - ry) * 2).sum()


class Criterion(_Loss):

    def __init__(self, alpha=1.0, name='criterion'):
        super().__init__()
        """Alpha is used to weight each loss term
        """
        self.alpha = alpha
        self.name = name

    def forward(self, input, target, weight=None, ignore_index=-1):
        """weight: sample weight
        """
        return


class NsKlCriterionNew(Criterion):

    def __init__(self, alpha=1.0, name='KL Div Criterion'):
        """
        stable KL 损失
        :param alpha:
        :type alpha:
        :param name:
        :type name:
        """
        super().__init__()
        self.alpha = alpha
        self.name = name

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