# 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/qd/cqd75lqwhilsyivr5ucpentcd2iugdkqpf3xy2kg2sefdgsokiex.py
# Topologically Sorted Source Nodes: [logits, logs], Original ATen: [aten._to_copy, aten._log_softmax]
# Source node to ATen node mapping:
#   logits => convert_element_type
#   logs => amax, sub
# Graph fragment:
#   %convert_element_type : [num_users=2] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%arg0_1, torch.float32), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%convert_element_type, [1], True), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convert_element_type, %amax), kwargs = {})
triton_poi_fused__log_softmax__to_copy_0 = async_compile.triton('triton_poi_fused__log_softmax__to_copy_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 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__to_copy_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__to_copy_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    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)
    tmp2 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp1 = tmp0.to(tl.float32)
    tmp3 = tmp2.to(tl.float32)
    tmp5 = tmp4.to(tl.float32)
    tmp6 = triton_helpers.maximum(tmp3, tmp5)
    tmp8 = tmp7.to(tl.float32)
    tmp9 = triton_helpers.maximum(tmp6, tmp8)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = triton_helpers.maximum(tmp9, tmp11)
    tmp13 = tmp1 - tmp12
    tl.store(out_ptr0 + (x2), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/x4/cx476jwtvkyupwmax7xadvmzeqrshphfbthofh7c3unq3fgedb4p.py
# Topologically Sorted Source Nodes: [logs, ignore, setitem, scatter_, mul, sum_2, loss, setitem_1, sum_3, eq_1, n_valid, loss_1], Original ATen: [aten._log_softmax, aten.eq, aten.lift_fresh, aten.index_put, aten.scatter, aten.mul, aten.sum, aten.neg, aten.div]
# Source node to ATen node mapping:
#   eq_1 => eq_1
#   ignore => eq
#   logs => exp, log, sub_1, sum_2
#   loss => neg
#   loss_1 => div
#   mul => mul
#   n_valid => sum_1
#   scatter_ => scatter_upon_const_tensor
#   setitem => full_default, index_put
#   setitem_1 => full_default_2, index_put_1
#   sum_2 => sum_3
#   sum_3 => sum_4
# Graph fragment:
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_2 : [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_2,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %log), kwargs = {})
#   %eq : [num_users=3] = call_function[target=torch.ops.aten.eq.Scalar](args = (%arg1_1, -100), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0), kwargs = {dtype: torch.int64, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put : [num_users=1] = call_function[target=torch.ops.aten.index_put.default](args = (%arg1_1, [%eq], %full_default), kwargs = {})
#   %scatter_upon_const_tensor : [num_users=1] = call_function[target=torch._inductor.fx_passes.post_grad.scatter_upon_const_tensor](args = (), kwargs = {shape: [4, 4], background_val: 0.025, dtype: torch.float32, dim: 1, selector: %unsqueeze_1, val: 0.9})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %scatter_upon_const_tensor), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1]), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%sum_3,), 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: cpu, pin_memory: False})
#   %index_put_1 : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%neg, [%eq], %full_default_2), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%index_put_1,), kwargs = {})
#   %eq_1 : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%eq, 0), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%eq_1,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_4, %sum_1), kwargs = {})
triton_per_fused__log_softmax_div_eq_index_put_lift_fresh_mul_neg_scatter_sum_1 = async_compile.triton('triton_per_fused__log_softmax_div_eq_index_put_lift_fresh_mul_neg_scatter_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.persistent_reduction(
    size_hints=[1, 4],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i64', 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), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__log_softmax_div_eq_index_put_lift_fresh_mul_neg_scatter_sum_1', 'mutated_arg_names': ['in_out_ptr1'], 'no_x_dim': False, 'num_load': 5, '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_per_fused__log_softmax_div_eq_index_put_lift_fresh_mul_neg_scatter_sum_1(in_out_ptr1, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 4
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp5 = tl.load(in_ptr1 + (4*r0), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp1 = tl.full([1, 1], -100, tl.int64)
    tmp2 = tmp0 == tmp1
    tmp3 = tl.full([1, 1], 0, tl.int64)
    tmp4 = tl.where(tmp2, tmp3, tmp0)
    tmp6 = tl_math.exp(tmp5)
    tmp8 = tl_math.exp(tmp7)
    tmp9 = tmp6 + tmp8
    tmp11 = tl_math.exp(tmp10)
    tmp12 = tmp9 + tmp11
    tmp14 = tl_math.exp(tmp13)
    tmp15 = tmp12 + tmp14
    tmp16 = tl_math.log(tmp15)
    tmp17 = tmp5 - tmp16
    tmp18 = tmp4 == tmp3
    tmp19 = 0.9
    tmp20 = 0.025
    tmp21 = tl.where(tmp18, tmp19, tmp20)
    tmp22 = tmp17 * tmp21
    tmp23 = tmp7 - tmp16
    tmp24 = tl.full([1, 1], 1, tl.int64)
    tmp25 = tmp4 == tmp24
    tmp26 = tl.where(tmp25, tmp19, tmp20)
    tmp27 = tmp23 * tmp26
    tmp28 = tmp22 + tmp27
    tmp29 = tmp10 - tmp16
    tmp30 = tl.full([1, 1], 2, tl.int64)
    tmp31 = tmp4 == tmp30
    tmp32 = tl.where(tmp31, tmp19, tmp20)
    tmp33 = tmp29 * tmp32
    tmp34 = tmp28 + tmp33
    tmp35 = tmp13 - tmp16
    tmp36 = tl.full([1, 1], 3, tl.int64)
    tmp37 = tmp4 == tmp36
    tmp38 = tl.where(tmp37, tmp19, tmp20)
    tmp39 = tmp35 * tmp38
    tmp40 = tmp34 + tmp39
    tmp41 = -tmp40
    tmp42 = 0.0
    tmp43 = tl.where(tmp2, tmp42, tmp41)
    tmp44 = tmp2.to(tl.int64)
    tmp45 = tmp44 == tmp3
    tmp46 = tmp45.to(tl.int64)
    tmp47 = tl.broadcast_to(tmp46, [XBLOCK, RBLOCK])
    tmp49 = tl.sum(tmp47, 1)[:, None]
    tmp50 = tl.broadcast_to(tmp43, [XBLOCK, RBLOCK])
    tmp52 = tl.sum(tmp50, 1)[:, None]
    tmp53 = tmp49.to(tl.float32)
    tmp54 = tmp52 / tmp53
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp54, 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, 1))
    assert_size_stride(arg1_1, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [logits, logs], Original ATen: [aten._to_copy, aten._log_softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__log_softmax__to_copy_0.run(arg0_1, buf0, 16, grid=grid(16), stream=stream0)
        del arg0_1
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf6 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [logs, ignore, setitem, scatter_, mul, sum_2, loss, setitem_1, sum_3, eq_1, n_valid, loss_1], Original ATen: [aten._log_softmax, aten.eq, aten.lift_fresh, aten.index_put, aten.scatter, aten.mul, aten.sum, aten.neg, aten.div]
        triton_per_fused__log_softmax_div_eq_index_put_lift_fresh_mul_neg_scatter_sum_1.run(buf6, arg1_1, buf0, 1, 4, grid=grid(1), stream=stream0)
        del arg1_1
        del buf0
    return (buf6, )


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, 1), device='cuda:0', dtype=torch.int64)
    arg1_1 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.int64)
    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 as nn


class LabelSmoothSoftmaxCEV1(nn.Module):
    """
    This is the autograd version, you can also try the LabelSmoothSoftmaxCEV2 that uses derived gradients
    """

    def __init__(self, lb_smooth=0.1, reduction='mean', ignore_index=-100):
        super(LabelSmoothSoftmaxCEV1, self).__init__()
        self.lb_smooth = lb_smooth
        self.reduction = reduction
        self.lb_ignore = ignore_index
        self.log_softmax = nn.LogSoftmax(dim=1)

    def forward(self, logits, label):
        """
        args: logits: tensor of shape (N, C, H, W)
        args: label: tensor of shape(N, H, W)
        """
        logits = logits.float()
        with torch.no_grad():
            num_classes = logits.size(1)
            label = label.clone().detach()
            ignore = label == self.lb_ignore
            n_valid = (ignore == 0).sum()
            label[ignore] = 0
            lb_pos, lb_neg = 1.0 - self.lb_smooth, self.lb_smooth / num_classes
            label = torch.empty_like(logits).fill_(lb_neg).scatter_(1,
                label.unsqueeze(1), lb_pos).detach()
        logs = self.log_softmax(logits)
        loss = -torch.sum(logs * label, dim=1)
        loss[ignore] = 0
        if self.reduction == 'mean':
            loss = loss.sum() / n_valid
        if self.reduction == 'sum':
            loss = loss.sum()
        return loss


def get_inputs():
    return [torch.ones([4, 4], dtype=torch.int64), torch.ones([4], dtype=
        torch.int64)]


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 as nn
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__to_copy_0(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    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)
    tmp2 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = tmp0.to(tl.float32)
    tmp3 = tmp2.to(tl.float32)
    tmp5 = tmp4.to(tl.float32)
    tmp6 = triton_helpers.maximum(tmp3, tmp5)
    tmp8 = tmp7.to(tl.float32)
    tmp9 = triton_helpers.maximum(tmp6, tmp8)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = triton_helpers.maximum(tmp9, tmp11)
    tmp13 = tmp1 - tmp12
    tl.store(out_ptr0 + x2, tmp13, xmask)


@triton.jit
def triton_per_fused__log_softmax_div_eq_index_put_lift_fresh_mul_neg_scatter_sum_1(
    in_out_ptr1, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp5 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp1 = tl.full([1, 1], -100, tl.int64)
    tmp2 = tmp0 == tmp1
    tmp3 = tl.full([1, 1], 0, tl.int64)
    tmp4 = tl.where(tmp2, tmp3, tmp0)
    tmp6 = tl_math.exp(tmp5)
    tmp8 = tl_math.exp(tmp7)
    tmp9 = tmp6 + tmp8
    tmp11 = tl_math.exp(tmp10)
    tmp12 = tmp9 + tmp11
    tmp14 = tl_math.exp(tmp13)
    tmp15 = tmp12 + tmp14
    tmp16 = tl_math.log(tmp15)
    tmp17 = tmp5 - tmp16
    tmp18 = tmp4 == tmp3
    tmp19 = 0.9
    tmp20 = 0.025
    tmp21 = tl.where(tmp18, tmp19, tmp20)
    tmp22 = tmp17 * tmp21
    tmp23 = tmp7 - tmp16
    tmp24 = tl.full([1, 1], 1, tl.int64)
    tmp25 = tmp4 == tmp24
    tmp26 = tl.where(tmp25, tmp19, tmp20)
    tmp27 = tmp23 * tmp26
    tmp28 = tmp22 + tmp27
    tmp29 = tmp10 - tmp16
    tmp30 = tl.full([1, 1], 2, tl.int64)
    tmp31 = tmp4 == tmp30
    tmp32 = tl.where(tmp31, tmp19, tmp20)
    tmp33 = tmp29 * tmp32
    tmp34 = tmp28 + tmp33
    tmp35 = tmp13 - tmp16
    tmp36 = tl.full([1, 1], 3, tl.int64)
    tmp37 = tmp4 == tmp36
    tmp38 = tl.where(tmp37, tmp19, tmp20)
    tmp39 = tmp35 * tmp38
    tmp40 = tmp34 + tmp39
    tmp41 = -tmp40
    tmp42 = 0.0
    tmp43 = tl.where(tmp2, tmp42, tmp41)
    tmp44 = tmp2.to(tl.int64)
    tmp45 = tmp44 == tmp3
    tmp46 = tmp45.to(tl.int64)
    tmp47 = tl.broadcast_to(tmp46, [XBLOCK, RBLOCK])
    tmp49 = tl.sum(tmp47, 1)[:, None]
    tmp50 = tl.broadcast_to(tmp43, [XBLOCK, RBLOCK])
    tmp52 = tl.sum(tmp50, 1)[:, None]
    tmp53 = tmp49.to(tl.float32)
    tmp54 = tmp52 / tmp53
    tl.debug_barrier()
    tl.store(in_out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp54, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax__to_copy_0[grid(16)](arg0_1, buf0, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del arg0_1
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf6 = buf4
        del buf4
        triton_per_fused__log_softmax_div_eq_index_put_lift_fresh_mul_neg_scatter_sum_1[
            grid(1)](buf6, arg1_1, buf0, 1, 4, XBLOCK=1, num_warps=2,
            num_stages=1)
        del arg1_1
        del buf0
    return buf6,


class LabelSmoothSoftmaxCEV1New(nn.Module):
    """
    This is the autograd version, you can also try the LabelSmoothSoftmaxCEV2 that uses derived gradients
    """

    def __init__(self, lb_smooth=0.1, reduction='mean', ignore_index=-100):
        super(LabelSmoothSoftmaxCEV1New, self).__init__()
        self.lb_smooth = lb_smooth
        self.reduction = reduction
        self.lb_ignore = ignore_index
        self.log_softmax = nn.LogSoftmax(dim=1)

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