# 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_6/inductor_cache/65/c65frvogjvzvcjnoj7n72ziopkhhgusygsvovz7h4ukukiilkzeo.py
# Topologically Sorted Source Nodes: [cross_entropy], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
#   cross_entropy => 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=[64], 
    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 = 64
    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_6/inductor_cache/ig/cigklgjvndpfvc7rkzbq2ej3kvwcxbbuyizweiydfjy7unf2pgnl.py
# Topologically Sorted Source Nodes: [label_batch, cross_entropy], Original ATen: [aten._to_copy, aten.nll_loss_forward]
# Source node to ATen node mapping:
#   cross_entropy => full_default_1, ne_2, neg, sum_4, where_1
#   label_batch => convert_element_type_1
# Graph fragment:
#   %convert_element_type_1 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%arg2_1, torch.int64), kwargs = {})
#   %ne_2 : [num_users=1] = call_function[target=torch.ops.aten.ne.Scalar](args = (%convert_element_type_1, -1), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%squeeze,), kwargs = {})
#   %full_default_1 : [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})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%ne_2, %neg, %full_default_1), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%where_1,), kwargs = {})
triton_per_fused__to_copy_nll_loss_forward_1 = async_compile.triton('triton_per_fused__to_copy_nll_loss_forward_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, 16],
    reduction_hint=ReductionHint.INNER,
    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_per_fused__to_copy_nll_loss_forward_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_per_fused__to_copy_nll_loss_forward_1(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    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)
    tmp12 = tl.load(in_ptr1 + (4*r0), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr1 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr1 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp1 = tmp0.to(tl.int64)
    tmp2 = tl.full([1, 1], -1, tl.int64)
    tmp3 = tmp1 != tmp2
    tmp4 = tl.full([1, 1], 0, tl.int64)
    tmp5 = tl.where(tmp3, tmp1, tmp4)
    tmp6 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
    tmp7 = tmp5 + tmp6
    tmp8 = tmp5 < 0
    tmp9 = tl.where(tmp8, tmp7, tmp5)
    tl.device_assert((0 <= tmp9) & (tmp9 < 4), "index out of bounds: 0 <= tmp9 < 4")
    tmp11 = tl.load(in_ptr1 + (tmp9 + (4*r0)), None, eviction_policy='evict_last')
    tmp13 = tl_math.exp(tmp12)
    tmp15 = tl_math.exp(tmp14)
    tmp16 = tmp13 + tmp15
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp16 + tmp18
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp19 + tmp21
    tmp23 = tl_math.log(tmp22)
    tmp24 = tmp11 - tmp23
    tmp25 = -tmp24
    tmp26 = 0.0
    tmp27 = tl.where(tmp3, tmp25, tmp26)
    tmp28 = tl.broadcast_to(tmp27, [XBLOCK, RBLOCK])
    tmp30 = tl.sum(tmp28, 1)[:, None]
    tl.store(out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp30, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/kd/ckd7oru5le43u4lcqhsj4wqfgosdbs3zxvplwfqb4rmznyztvnrb.py
# Topologically Sorted Source Nodes: [ne, sum_1, total_sents, cross_entropy_loss], Original ATen: [aten.ne, aten.sum, aten._to_copy, aten.div]
# Source node to ATen node mapping:
#   cross_entropy_loss => div
#   ne => ne
#   sum_1 => sum_1
#   total_sents => convert_element_type
# Graph fragment:
#   %ne : [num_users=1] = call_function[target=torch.ops.aten.ne.Scalar](args = (%arg1_1, 0), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%ne,), kwargs = {})
#   %convert_element_type : [num_users=2] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%sum_1, torch.float32), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_4, %convert_element_type), kwargs = {})
triton_per_fused__to_copy_div_ne_sum_2 = async_compile.triton('triton_per_fused__to_copy_div_ne_sum_2', '''
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, 256],
    reduction_hint=ReductionHint.INNER,
    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_per_fused__to_copy_div_ne_sum_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, '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_per_fused__to_copy_div_ne_sum_2(in_out_ptr0, in_ptr0, out_ptr1, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp8 = tl.load(in_out_ptr0 + (0))
    tmp9 = tl.broadcast_to(tmp8, [1])
    tmp1 = 0.0
    tmp2 = tmp0 != tmp1
    tmp3 = tmp2.to(tl.int64)
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
    tmp7 = tmp6.to(tl.float32)
    tmp10 = tmp9 / tmp7
    tl.store(out_ptr1 + (tl.full([1], 0, tl.int32)), tmp7, None)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp10, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (16, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cross_entropy], Original ATen: [aten._log_softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__log_softmax_0.run(arg0_1, buf0, 64, grid=grid(64), stream=stream0)
        del arg0_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [label_batch, cross_entropy], Original ATen: [aten._to_copy, aten.nll_loss_forward]
        triton_per_fused__to_copy_nll_loss_forward_1.run(arg2_1, buf0, buf1, 1, 16, grid=grid(1), stream=stream0)
        del arg2_1
        del buf0
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [ne, sum_1, total_sents, cross_entropy_loss], Original ATen: [aten.ne, aten.sum, aten._to_copy, aten.div]
        triton_per_fused__to_copy_div_ne_sum_2.run(buf4, arg1_1, buf3, 1, 256, grid=grid(1), stream=stream0)
        del arg1_1
    return (buf4, buf3, )


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), (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)
    arg2_1 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_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
import torch.utils.data.dataloader


class CrossEntropyLoss(nn.Module):
    """Custom cross-entropy loss"""

    def __init__(self):
        super(CrossEntropyLoss, self).__init__()
        self.pytorch_ce_loss = torch.nn.CrossEntropyLoss(ignore_index=-1,
            reduction='sum')

    def forward(self, predictions, label_batch, length_batch):
        """
      Computes and returns CrossEntropyLoss.
      Ignores all entries where label_batch=-1
      Noralizes by the number of sentences in the batch.
      Args: 
        predictions: A pytorch batch of logits
        label_batch: A pytorch batch of label indices
        length_batch: A pytorch batch of sentence lengths
      Returns:
        A tuple of:
          cross_entropy_loss: average loss in the batch
          total_sents: number of sentences in the batch
      """
        batchlen, seqlen, class_count = predictions.size()
        total_sents = torch.sum(length_batch != 0).float()
        predictions = predictions.view(batchlen * seqlen, class_count)
        label_batch = label_batch.view(batchlen * seqlen).long()
        cross_entropy_loss = self.pytorch_ce_loss(predictions, label_batch
            ) / total_sents
        return cross_entropy_loss, total_sents


def get_inputs():
    return [torch.rand([4, 4, 4]), torch.rand([16]), 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 as nn
import torch.utils.data.dataloader
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 = 64
    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_per_fused__to_copy_nll_loss_forward_1(in_ptr0, in_ptr1, out_ptr0,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 16
    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)
    tmp12 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp1 = tmp0.to(tl.int64)
    tmp2 = tl.full([1, 1], -1, tl.int64)
    tmp3 = tmp1 != tmp2
    tmp4 = tl.full([1, 1], 0, tl.int64)
    tmp5 = tl.where(tmp3, tmp1, tmp4)
    tmp6 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
    tmp7 = tmp5 + tmp6
    tmp8 = tmp5 < 0
    tmp9 = tl.where(tmp8, tmp7, tmp5)
    tl.device_assert((0 <= tmp9) & (tmp9 < 4),
        'index out of bounds: 0 <= tmp9 < 4')
    tmp11 = tl.load(in_ptr1 + (tmp9 + 4 * r0), None, eviction_policy=
        'evict_last')
    tmp13 = tl_math.exp(tmp12)
    tmp15 = tl_math.exp(tmp14)
    tmp16 = tmp13 + tmp15
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp16 + tmp18
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp19 + tmp21
    tmp23 = tl_math.log(tmp22)
    tmp24 = tmp11 - tmp23
    tmp25 = -tmp24
    tmp26 = 0.0
    tmp27 = tl.where(tmp3, tmp25, tmp26)
    tmp28 = tl.broadcast_to(tmp27, [XBLOCK, RBLOCK])
    tmp30 = tl.sum(tmp28, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp30, None)


@triton.jit
def triton_per_fused__to_copy_div_ne_sum_2(in_out_ptr0, in_ptr0, out_ptr1,
    xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp8 = tl.load(in_out_ptr0 + 0)
    tmp9 = tl.broadcast_to(tmp8, [1])
    tmp1 = 0.0
    tmp2 = tmp0 != tmp1
    tmp3 = tmp2.to(tl.int64)
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
    tmp7 = tmp6.to(tl.float32)
    tmp10 = tmp9 / tmp7
    tl.store(out_ptr1 + tl.full([1], 0, tl.int32), tmp7, None)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp10, None)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (16,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_0[grid(64)](arg0_1, buf0, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused__to_copy_nll_loss_forward_1[grid(1)](arg2_1, buf0,
            buf1, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del arg2_1
        del buf0
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf1
        del buf1
        triton_per_fused__to_copy_div_ne_sum_2[grid(1)](buf4, arg1_1, buf3,
            1, 256, num_warps=2, num_stages=1)
        del arg1_1
    return buf4, buf3


class CrossEntropyLossNew(nn.Module):
    """Custom cross-entropy loss"""

    def __init__(self):
        super(CrossEntropyLossNew, self).__init__()
        self.pytorch_ce_loss = torch.nn.CrossEntropyLoss(ignore_index=-1,
            reduction='sum')

    def forward(self, input_0, input_1, input_2):
        arg0_1 = input_0
        arg2_1 = input_1
        arg1_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0], output[1]
