# 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_4/inductor_cache/wv/cwvti54lsojjpkh6f73xsvv55wjtno2rrmtjgaznlsjst37yn74a.py
# Topologically Sorted Source Nodes: [softmax, log_softmax_1], Original ATen: [aten._softmax, aten._log_softmax]
# Source node to ATen node mapping:
#   log_softmax_1 => amax_3, sub_5
#   softmax => amax, exp, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg1_1, [-1], True), kwargs = {})
#   %sub : [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,), kwargs = {})
#   %amax_3 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg1_1, [-1], True), kwargs = {})
#   %sub_5 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %amax_3), kwargs = {})
triton_poi_fused__log_softmax__softmax_0 = async_compile.triton('triton_poi_fused__log_softmax__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: '*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__log_softmax__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__softmax_0(in_ptr0, out_ptr0, out_ptr1, 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)
    tl.store(out_ptr1 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ew/cewejal4rpvwi6jnzulqnyb3ftv6rl774lzryrdhbo5q76xi6iai.py
# Topologically Sorted Source Nodes: [softmax, softmax_1, m, m_1, kl_div, log_softmax, kl_div_1, log_softmax_1, loss, loss_1], Original ATen: [aten._softmax, aten.add, aten.mul, aten.xlogy, aten._log_softmax, aten.sub, aten.sum, aten.div]
# Source node to ATen node mapping:
#   kl_div => div_2, eq, full_default, full_default_1, isnan, log_1, mul_1, mul_2, sub_4, sum_4, where, where_1
#   kl_div_1 => div_3, eq_1, full_default_2, full_default_3, isnan_1, log_3, mul_3, mul_4, sub_7, sum_6, where_2, where_3
#   log_softmax => exp_2, log, sub_3, sum_3
#   log_softmax_1 => exp_3, log_2, sub_6, sum_5
#   loss => add_1
#   loss_1 => mul_5
#   m => add
#   m_1 => mul
#   softmax => div, sum_1
#   softmax_1 => div_1, sum_2
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp_1, [-1], True), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp_1, %sum_2), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, %div_1), kwargs = {})
#   %mul : [num_users=10] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 0.5), kwargs = {})
#   %isnan : [num_users=1] = call_function[target=torch.ops.aten.isnan.default](args = (%mul,), kwargs = {})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], nan), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%mul, 0), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%mul,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %log_1), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%eq, %full_default, %mul_2), kwargs = {})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%isnan, %full_default_1, %where), kwargs = {})
#   %exp_2 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub_2,), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp_2, [-1], True), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_3,), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_2, %log), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %sub_3), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where_1, %mul_1), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%sub_4,), kwargs = {})
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_4, 4), kwargs = {})
#   %isnan_1 : [num_users=1] = call_function[target=torch.ops.aten.isnan.default](args = (%mul,), kwargs = {})
#   %full_default_3 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], nan), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %eq_1 : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%mul, 0), kwargs = {})
#   %full_default_2 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %log_3 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%mul,), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %log_3), kwargs = {})
#   %where_2 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%eq_1, %full_default_2, %mul_4), kwargs = {})
#   %where_3 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%isnan_1, %full_default_3, %where_2), kwargs = {})
#   %exp_3 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub_5,), kwargs = {})
#   %sum_5 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp_3, [-1], True), kwargs = {})
#   %log_2 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_5,), kwargs = {})
#   %sub_6 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_5, %log_2), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %sub_6), kwargs = {})
#   %sub_7 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where_3, %mul_3), kwargs = {})
#   %sum_6 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%sub_7,), kwargs = {})
#   %div_3 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_6, 4), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div_2, %div_3), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, 1.0), kwargs = {})
triton_red_fused__log_softmax__softmax_add_div_mul_sub_sum_xlogy_1 = async_compile.triton('triton_red_fused__log_softmax__softmax_add_div_mul_sub_sum_xlogy_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: '*fp32', 4: '*fp32', 5: 'i32', 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': {5: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 6), equal_to_1=(5,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused__log_softmax__softmax_add_div_mul_sub_sum_xlogy_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 20, 'num_reduction': 2, '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__softmax_add_div_mul_sub_sum_xlogy_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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, :]
    _tmp46 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    _tmp65 = 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)
        tmp2 = tl.load(in_ptr0 + (1 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp4 = tl.load(in_ptr0 + (2 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp6 = tl.load(in_ptr0 + (3 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp9 = tl.load(in_ptr1 + (r2), rmask, eviction_policy='evict_first', other=0.0)
        tmp10 = tl.load(in_ptr1 + (4*r1), rmask, eviction_policy='evict_last', other=0.0)
        tmp11 = tl.load(in_ptr1 + (1 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp13 = tl.load(in_ptr1 + (2 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp15 = tl.load(in_ptr1 + (3 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp29 = tl.load(in_ptr2 + (r2), rmask, eviction_policy='evict_first', other=0.0)
        tmp30 = tl.load(in_ptr2 + (4*r1), rmask, eviction_policy='evict_last', other=0.0)
        tmp32 = tl.load(in_ptr2 + (1 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp35 = tl.load(in_ptr2 + (2 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp38 = tl.load(in_ptr2 + (3 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp48 = tl.load(in_ptr3 + (r2), rmask, eviction_policy='evict_first', other=0.0)
        tmp49 = tl.load(in_ptr3 + (4*r1), rmask, eviction_policy='evict_last', other=0.0)
        tmp51 = tl.load(in_ptr3 + (1 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp54 = tl.load(in_ptr3 + (2 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp57 = tl.load(in_ptr3 + (3 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp3 = tmp1 + tmp2
        tmp5 = tmp3 + tmp4
        tmp7 = tmp5 + tmp6
        tmp8 = tmp0 / tmp7
        tmp12 = tmp10 + tmp11
        tmp14 = tmp12 + tmp13
        tmp16 = tmp14 + tmp15
        tmp17 = tmp9 / tmp16
        tmp18 = tmp8 + tmp17
        tmp19 = 0.5
        tmp20 = tmp18 * tmp19
        tmp21 = libdevice.isnan(tmp20).to(tl.int1)
        tmp22 = 0.0
        tmp23 = tmp20 == tmp22
        tmp24 = tl_math.log(tmp20)
        tmp25 = tmp20 * tmp24
        tmp26 = tl.where(tmp23, tmp22, tmp25)
        tmp27 = float("nan")
        tmp28 = tl.where(tmp21, tmp27, tmp26)
        tmp31 = tl_math.exp(tmp30)
        tmp33 = tl_math.exp(tmp32)
        tmp34 = tmp31 + tmp33
        tmp36 = tl_math.exp(tmp35)
        tmp37 = tmp34 + tmp36
        tmp39 = tl_math.exp(tmp38)
        tmp40 = tmp37 + tmp39
        tmp41 = tl_math.log(tmp40)
        tmp42 = tmp29 - tmp41
        tmp43 = tmp20 * tmp42
        tmp44 = tmp28 - tmp43
        tmp45 = tl.broadcast_to(tmp44, [XBLOCK, RBLOCK])
        tmp47 = _tmp46 + tmp45
        _tmp46 = tl.where(rmask, tmp47, _tmp46)
        tmp50 = tl_math.exp(tmp49)
        tmp52 = tl_math.exp(tmp51)
        tmp53 = tmp50 + tmp52
        tmp55 = tl_math.exp(tmp54)
        tmp56 = tmp53 + tmp55
        tmp58 = tl_math.exp(tmp57)
        tmp59 = tmp56 + tmp58
        tmp60 = tl_math.log(tmp59)
        tmp61 = tmp48 - tmp60
        tmp62 = tmp20 * tmp61
        tmp63 = tmp28 - tmp62
        tmp64 = tl.broadcast_to(tmp63, [XBLOCK, RBLOCK])
        tmp66 = _tmp65 + tmp64
        _tmp65 = tl.where(rmask, tmp66, _tmp65)
    tmp46 = tl.sum(_tmp46, 1)[:, None]
    tmp65 = tl.sum(_tmp65, 1)[:, None]
    tmp67 = 0.25
    tmp68 = tmp46 * tmp67
    tmp69 = tmp65 * tmp67
    tmp70 = tmp68 + tmp69
    tmp71 = 1.0
    tmp72 = tmp70 * tmp71
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp72, 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((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [softmax, log_softmax_1], Original ATen: [aten._softmax, aten._log_softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__log_softmax__softmax_0.run(arg1_1, buf0, buf5, 256, grid=grid(256), stream=stream0)
        del arg1_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [softmax_1, log_softmax], Original ATen: [aten._softmax, aten._log_softmax]
        triton_poi_fused__log_softmax__softmax_0.run(arg0_1, buf1, buf3, 256, grid=grid(256), stream=stream0)
        del arg0_1
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf7 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [softmax, softmax_1, m, m_1, kl_div, log_softmax, kl_div_1, log_softmax_1, loss, loss_1], Original ATen: [aten._softmax, aten.add, aten.mul, aten.xlogy, aten._log_softmax, aten.sub, aten.sum, aten.div]
        triton_red_fused__log_softmax__softmax_add_div_mul_sub_sum_xlogy_1.run(buf7, buf0, buf1, buf3, buf5, 1, 256, grid=grid(1), stream=stream0)
        del buf0
        del buf1
        del buf3
        del buf5
    return (buf7, )


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 *


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 JSCriterion(Criterion):

    def __init__(self, alpha=1.0, name='JS Div Criterion'):
        super().__init__()
        self.alpha = alpha
        self.name = name

    def forward(self, input, target, weight=None, ignore_index=-1,
        reduction='batchmean'):
        """input/target: logits
        """
        input = input.float()
        target = target.float()
        m = F.softmax(target.detach(), dim=-1, dtype=torch.float32
            ) + F.softmax(input.detach(), dim=-1, dtype=torch.float32)
        m = 0.5 * m
        loss = F.kl_div(F.log_softmax(input, dim=-1, dtype=torch.float32),
            m, reduction=reduction) + F.kl_div(F.log_softmax(target, dim=-1,
            dtype=torch.float32), m, reduction=reduction)
        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 libdevice, math as tl_math
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__softmax_0(in_ptr0, out_ptr0, out_ptr1,
    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)
    tl.store(out_ptr1 + x2, tmp8, xmask)


@triton.jit
def triton_red_fused__log_softmax__softmax_add_div_mul_sub_sum_xlogy_1(
    in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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, :]
    _tmp46 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    _tmp65 = 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)
        tmp2 = tl.load(in_ptr0 + (1 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp4 = tl.load(in_ptr0 + (2 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp6 = tl.load(in_ptr0 + (3 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp9 = tl.load(in_ptr1 + r2, rmask, eviction_policy='evict_first',
            other=0.0)
        tmp10 = tl.load(in_ptr1 + 4 * r1, rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp11 = tl.load(in_ptr1 + (1 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp13 = tl.load(in_ptr1 + (2 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp15 = tl.load(in_ptr1 + (3 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp29 = tl.load(in_ptr2 + r2, rmask, eviction_policy='evict_first',
            other=0.0)
        tmp30 = tl.load(in_ptr2 + 4 * r1, rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp32 = tl.load(in_ptr2 + (1 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp35 = tl.load(in_ptr2 + (2 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp38 = tl.load(in_ptr2 + (3 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp48 = tl.load(in_ptr3 + r2, rmask, eviction_policy='evict_first',
            other=0.0)
        tmp49 = tl.load(in_ptr3 + 4 * r1, rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp51 = tl.load(in_ptr3 + (1 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp54 = tl.load(in_ptr3 + (2 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp57 = tl.load(in_ptr3 + (3 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp3 = tmp1 + tmp2
        tmp5 = tmp3 + tmp4
        tmp7 = tmp5 + tmp6
        tmp8 = tmp0 / tmp7
        tmp12 = tmp10 + tmp11
        tmp14 = tmp12 + tmp13
        tmp16 = tmp14 + tmp15
        tmp17 = tmp9 / tmp16
        tmp18 = tmp8 + tmp17
        tmp19 = 0.5
        tmp20 = tmp18 * tmp19
        tmp21 = libdevice.isnan(tmp20).to(tl.int1)
        tmp22 = 0.0
        tmp23 = tmp20 == tmp22
        tmp24 = tl_math.log(tmp20)
        tmp25 = tmp20 * tmp24
        tmp26 = tl.where(tmp23, tmp22, tmp25)
        tmp27 = float('nan')
        tmp28 = tl.where(tmp21, tmp27, tmp26)
        tmp31 = tl_math.exp(tmp30)
        tmp33 = tl_math.exp(tmp32)
        tmp34 = tmp31 + tmp33
        tmp36 = tl_math.exp(tmp35)
        tmp37 = tmp34 + tmp36
        tmp39 = tl_math.exp(tmp38)
        tmp40 = tmp37 + tmp39
        tmp41 = tl_math.log(tmp40)
        tmp42 = tmp29 - tmp41
        tmp43 = tmp20 * tmp42
        tmp44 = tmp28 - tmp43
        tmp45 = tl.broadcast_to(tmp44, [XBLOCK, RBLOCK])
        tmp47 = _tmp46 + tmp45
        _tmp46 = tl.where(rmask, tmp47, _tmp46)
        tmp50 = tl_math.exp(tmp49)
        tmp52 = tl_math.exp(tmp51)
        tmp53 = tmp50 + tmp52
        tmp55 = tl_math.exp(tmp54)
        tmp56 = tmp53 + tmp55
        tmp58 = tl_math.exp(tmp57)
        tmp59 = tmp56 + tmp58
        tmp60 = tl_math.log(tmp59)
        tmp61 = tmp48 - tmp60
        tmp62 = tmp20 * tmp61
        tmp63 = tmp28 - tmp62
        tmp64 = tl.broadcast_to(tmp63, [XBLOCK, RBLOCK])
        tmp66 = _tmp65 + tmp64
        _tmp65 = tl.where(rmask, tmp66, _tmp65)
    tmp46 = tl.sum(_tmp46, 1)[:, None]
    tmp65 = tl.sum(_tmp65, 1)[:, None]
    tmp67 = 0.25
    tmp68 = tmp46 * tmp67
    tmp69 = tmp65 * tmp67
    tmp70 = tmp68 + tmp69
    tmp71 = 1.0
    tmp72 = tmp70 * tmp71
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp72, 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((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax__softmax_0[grid(256)](arg1_1, buf0,
            buf5, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del arg1_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__log_softmax__softmax_0[grid(256)](arg0_1, buf1,
            buf3, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf7 = buf4
        del buf4
        triton_red_fused__log_softmax__softmax_add_div_mul_sub_sum_xlogy_1[grid
            (1)](buf7, buf0, buf1, buf3, buf5, 1, 256, XBLOCK=1, RBLOCK=256,
            num_warps=8, num_stages=1)
        del buf0
        del buf1
        del buf3
        del buf5
    return buf7,


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 JSCriterionNew(Criterion):

    def __init__(self, alpha=1.0, name='JS Div Criterion'):
        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]
