# 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/m3/cm3oyfplxv4jfq4lzivy2ytiixh7r3jdutou2fi3p7bnl5g734sl.py
# Topologically Sorted Source Nodes: [log_softmax, log_softmax_3], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
#   log_softmax => amax, sub
#   log_softmax_3 => amax_3, sub_9
# 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 = {})
#   %amax_3 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_3, [1], True), kwargs = {})
#   %sub_9 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_3, %amax_3), 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: '*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_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, 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
    tl.store(out_ptr0 + (x2), tmp8, xmask)
    tl.store(out_ptr1 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/dc/cdcsseiicjcgc6sqlyyouett3fn2xszalsbnt3viybno45khl7l3.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, truediv_2, log_softmax_2, p_1, add_4, truediv_3, sub_3, add_5, log_2, rp_1, log_softmax_3, y_1, add_6, truediv_4, sub_4, add_7, log_3, ry_1, sub_5, mul_2, mul_3, sum_2, truediv_5, 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
#   add_4 => add_4
#   add_5 => add_5
#   add_6 => add_6
#   add_7 => add_7
#   log => log_2
#   log_1 => log_3
#   log_2 => log_6
#   log_3 => log_7
#   log_softmax => exp, log, sub_1, sum_1
#   log_softmax_1 => exp_2, log_1, sub_3, sum_2
#   log_softmax_2 => exp_4, log_4, sub_8, sum_4
#   log_softmax_3 => exp_6, log_5, sub_10, sum_5
#   loss => add_8
#   loss_1 => mul_8
#   mul => mul_2
#   mul_1 => mul_3
#   mul_2 => mul_6
#   mul_3 => mul_7
#   p => exp_1
#   p_1 => exp_5
#   rp => neg
#   rp_1 => neg_2
#   ry => neg_1
#   ry_1 => neg_3
#   sub => sub_4
#   sub_1 => sub_5
#   sub_2 => sub_6
#   sub_3 => sub_11
#   sub_4 => sub_12
#   sub_5 => sub_13
#   sum_1 => sum_3
#   sum_2 => sum_6
#   truediv => mul, reciprocal
#   truediv_1 => mul_1, reciprocal_1
#   truediv_2 => div
#   truediv_3 => mul_4, reciprocal_2
#   truediv_4 => mul_5, reciprocal_3
#   truediv_5 => div_1
#   y => exp_3
#   y_1 => exp_7
# 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 = {})
#   %exp_4 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub_7,), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp_4, [1], True), kwargs = {})
#   %log_4 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_4,), kwargs = {})
#   %sub_8 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_7, %log_4), kwargs = {})
#   %exp_5 : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_8,), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%exp_5, 1e-06), kwargs = {})
#   %reciprocal_2 : [num_users=1] = call_function[target=torch.ops.aten.reciprocal.default](args = (%add_4,), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%reciprocal_2, 1.0), kwargs = {})
#   %sub_11 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_4, 1), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_11, 1e-06), kwargs = {})
#   %log_6 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add_5,), kwargs = {})
#   %neg_2 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%log_6,), kwargs = {})
#   %exp_6 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub_9,), kwargs = {})
#   %sum_5 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp_6, [1], True), kwargs = {})
#   %log_5 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_5,), kwargs = {})
#   %sub_10 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_9, %log_5), kwargs = {})
#   %exp_7 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub_10,), kwargs = {})
#   %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%exp_7, 1e-06), kwargs = {})
#   %reciprocal_3 : [num_users=1] = call_function[target=torch.ops.aten.reciprocal.default](args = (%add_6,), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%reciprocal_3, 1.0), kwargs = {})
#   %sub_12 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_5, 1), kwargs = {})
#   %add_7 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_12, 1e-06), kwargs = {})
#   %log_7 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add_7,), kwargs = {})
#   %neg_3 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%log_7,), kwargs = {})
#   %sub_13 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%neg_2, %neg_3), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%exp_5, %sub_13), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_6, 2), kwargs = {})
#   %sum_6 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul_7,), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_6, 64), kwargs = {})
#   %add_8 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, %div_1), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_8, 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: '*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_add_div_exp_log_mul_neg_reciprocal_sub_sum_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_add_div_exp_log_mul_neg_reciprocal_sub_sum_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, :]
    _tmp52 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    _tmp102 = 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)
        tmp54 = tl.load(in_ptr2 + (r2), rmask, eviction_policy='evict_first', other=0.0)
        tmp55 = tl.load(in_ptr2 + (4*r1), rmask, eviction_policy='evict_last', other=0.0)
        tmp57 = tl.load(in_ptr2 + (1 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp60 = tl.load(in_ptr2 + (2 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp63 = tl.load(in_ptr2 + (3 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp76 = tl.load(in_ptr3 + (r2), rmask, eviction_policy='evict_first', other=0.0)
        tmp77 = tl.load(in_ptr3 + (4*r1), rmask, eviction_policy='evict_last', other=0.0)
        tmp79 = tl.load(in_ptr3 + (1 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp82 = tl.load(in_ptr3 + (2 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp85 = tl.load(in_ptr3 + (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)
        tmp56 = tl_math.exp(tmp55)
        tmp58 = tl_math.exp(tmp57)
        tmp59 = tmp56 + tmp58
        tmp61 = tl_math.exp(tmp60)
        tmp62 = tmp59 + tmp61
        tmp64 = tl_math.exp(tmp63)
        tmp65 = tmp62 + tmp64
        tmp66 = tl_math.log(tmp65)
        tmp67 = tmp54 - tmp66
        tmp68 = tl_math.exp(tmp67)
        tmp69 = tmp68 + tmp15
        tmp70 = tmp17 / tmp69
        tmp71 = tmp70 * tmp19
        tmp72 = tmp71 - tmp19
        tmp73 = tmp72 + tmp15
        tmp74 = tl_math.log(tmp73)
        tmp75 = -tmp74
        tmp78 = tl_math.exp(tmp77)
        tmp80 = tl_math.exp(tmp79)
        tmp81 = tmp78 + tmp80
        tmp83 = tl_math.exp(tmp82)
        tmp84 = tmp81 + tmp83
        tmp86 = tl_math.exp(tmp85)
        tmp87 = tmp84 + tmp86
        tmp88 = tl_math.log(tmp87)
        tmp89 = tmp76 - tmp88
        tmp90 = tl_math.exp(tmp89)
        tmp91 = tmp90 + tmp15
        tmp92 = tmp17 / tmp91
        tmp93 = tmp92 * tmp19
        tmp94 = tmp93 - tmp19
        tmp95 = tmp94 + tmp15
        tmp96 = tl_math.log(tmp95)
        tmp97 = -tmp96
        tmp98 = tmp75 - tmp97
        tmp99 = tmp68 * tmp98
        tmp100 = tmp99 * tmp49
        tmp101 = tl.broadcast_to(tmp100, [XBLOCK, RBLOCK])
        tmp103 = _tmp102 + tmp101
        _tmp102 = tl.where(rmask, tmp103, _tmp102)
    tmp52 = tl.sum(_tmp52, 1)[:, None]
    tmp102 = tl.sum(_tmp102, 1)[:, None]
    tmp104 = 0.015625
    tmp105 = tmp52 * tmp104
    tmp106 = tmp102 * tmp104
    tmp107 = tmp105 + tmp106
    tmp108 = 1.0
    tmp109 = tmp107 * tmp108
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp109, 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)
        buf7 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [log_softmax, log_softmax_3], Original ATen: [aten._log_softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__log_softmax_0.run(arg0_1, buf0, buf7, 256, grid=grid(256), stream=stream0)
        del arg0_1
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [log_softmax_1, log_softmax_2], Original ATen: [aten._log_softmax]
        triton_poi_fused__log_softmax_0.run(arg1_1, buf2, buf5, 256, grid=grid(256), stream=stream0)
        del arg1_1
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf10 = 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, truediv_2, log_softmax_2, p_1, add_4, truediv_3, sub_3, add_5, log_2, rp_1, log_softmax_3, y_1, add_6, truediv_4, sub_4, add_7, log_3, ry_1, sub_5, mul_2, mul_3, sum_2, truediv_5, 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(buf10, buf0, buf2, buf5, buf7, 1, 256, grid=grid(1), stream=stream0)
        del buf0
        del buf2
        del buf5
        del buf7
    return (buf10, )


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 NsSymKlCriterion(Criterion):

    def __init__(self, alpha=1.0, name='KL Div Criterion'):
        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()) + stable_kl(target, input.
            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, 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
    tl.store(out_ptr0 + x2, tmp8, xmask)
    tl.store(out_ptr1 + 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, 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, :]
    _tmp52 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    _tmp102 = 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)
        tmp54 = tl.load(in_ptr2 + r2, rmask, eviction_policy='evict_first',
            other=0.0)
        tmp55 = tl.load(in_ptr2 + 4 * r1, rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp57 = tl.load(in_ptr2 + (1 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp60 = tl.load(in_ptr2 + (2 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp63 = tl.load(in_ptr2 + (3 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp76 = tl.load(in_ptr3 + r2, rmask, eviction_policy='evict_first',
            other=0.0)
        tmp77 = tl.load(in_ptr3 + 4 * r1, rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp79 = tl.load(in_ptr3 + (1 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp82 = tl.load(in_ptr3 + (2 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp85 = tl.load(in_ptr3 + (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)
        tmp56 = tl_math.exp(tmp55)
        tmp58 = tl_math.exp(tmp57)
        tmp59 = tmp56 + tmp58
        tmp61 = tl_math.exp(tmp60)
        tmp62 = tmp59 + tmp61
        tmp64 = tl_math.exp(tmp63)
        tmp65 = tmp62 + tmp64
        tmp66 = tl_math.log(tmp65)
        tmp67 = tmp54 - tmp66
        tmp68 = tl_math.exp(tmp67)
        tmp69 = tmp68 + tmp15
        tmp70 = tmp17 / tmp69
        tmp71 = tmp70 * tmp19
        tmp72 = tmp71 - tmp19
        tmp73 = tmp72 + tmp15
        tmp74 = tl_math.log(tmp73)
        tmp75 = -tmp74
        tmp78 = tl_math.exp(tmp77)
        tmp80 = tl_math.exp(tmp79)
        tmp81 = tmp78 + tmp80
        tmp83 = tl_math.exp(tmp82)
        tmp84 = tmp81 + tmp83
        tmp86 = tl_math.exp(tmp85)
        tmp87 = tmp84 + tmp86
        tmp88 = tl_math.log(tmp87)
        tmp89 = tmp76 - tmp88
        tmp90 = tl_math.exp(tmp89)
        tmp91 = tmp90 + tmp15
        tmp92 = tmp17 / tmp91
        tmp93 = tmp92 * tmp19
        tmp94 = tmp93 - tmp19
        tmp95 = tmp94 + tmp15
        tmp96 = tl_math.log(tmp95)
        tmp97 = -tmp96
        tmp98 = tmp75 - tmp97
        tmp99 = tmp68 * tmp98
        tmp100 = tmp99 * tmp49
        tmp101 = tl.broadcast_to(tmp100, [XBLOCK, RBLOCK])
        tmp103 = _tmp102 + tmp101
        _tmp102 = tl.where(rmask, tmp103, _tmp102)
    tmp52 = tl.sum(_tmp52, 1)[:, None]
    tmp102 = tl.sum(_tmp102, 1)[:, None]
    tmp104 = 0.015625
    tmp105 = tmp52 * tmp104
    tmp106 = tmp102 * tmp104
    tmp107 = tmp105 + tmp106
    tmp108 = 1.0
    tmp109 = tmp107 * tmp108
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp109, 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)
        buf7 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_0[grid(256)](arg0_1, buf0, buf7, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        triton_poi_fused__log_softmax_0[grid(256)](arg1_1, buf2, buf5, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg1_1
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf10 = buf4
        del buf4
        triton_red_fused__log_softmax_add_div_exp_log_mul_neg_reciprocal_sub_sum_1[
            grid(1)](buf10, buf0, buf2, buf5, buf7, 1, 256, XBLOCK=1,
            RBLOCK=256, num_warps=8, num_stages=1)
        del buf0
        del buf2
        del buf5
        del buf7
    return buf10,


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 NsSymKlCriterionNew(Criterion):

    def __init__(self, alpha=1.0, name='KL 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]
