# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_7/inductor_cache/td/ctdj5kazgiki6gdaadhqtp2x7tq2ee5ey5hqqdcoqmp54jyhf74f.py
# Topologically Sorted Source Nodes: [output], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
#   output => amax, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg1_1, [1], True), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %amax), kwargs = {})
triton_poi_fused__log_softmax_0 = async_compile.triton('triton_poi_fused__log_softmax_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__log_softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), 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 + (x3), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/wd/cwd6mg7etw6euphqmgfdaincwxibslbk7utkzwvyq7si75u4fdwb.py
# Topologically Sorted Source Nodes: [long, max_1, nll_loss], Original ATen: [aten._to_copy, aten.max, aten.nll_loss2d_forward]
# Source node to ATen node mapping:
#   long => convert_element_type
#   max_1 => max_1
#   nll_loss => convert_element_type_1, div, full_default_1, ne_1, ne_2, neg, sum_2, sum_3, where_1
# Graph fragment:
#   %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%arg0_1, torch.int64), kwargs = {})
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%convert_element_type, 1), kwargs = {})
#   %ne_1 : [num_users=1] = call_function[target=torch.ops.aten.ne.Scalar](args = (%getitem_1, -100), 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_1, %neg, %full_default_1), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%where_1,), kwargs = {})
#   %ne_2 : [num_users=1] = call_function[target=torch.ops.aten.ne.Scalar](args = (%getitem_1, -100), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%ne_2,), kwargs = {})
#   %convert_element_type_1 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%sum_2, torch.float32), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_3, %convert_element_type_1), kwargs = {})
triton_per_fused__to_copy_max_nll_loss2d_forward_1 = async_compile.triton('triton_per_fused__to_copy_max_nll_loss2d_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, 64],
    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_max_nll_loss2d_forward_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, '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__to_copy_max_nll_loss2d_forward_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    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 % 16
    r1 = (rindex // 16)
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + (r0 + (64*r1)), None)
    tmp2 = tl.load(in_ptr0 + (16 + r0 + (64*r1)), None)
    tmp13 = tl.load(in_ptr0 + (32 + r0 + (64*r1)), None)
    tmp23 = tl.load(in_ptr0 + (48 + r0 + (64*r1)), None)
    tmp42 = tl.load(in_ptr1 + (r0 + (64*r1)), None)
    tmp44 = tl.load(in_ptr1 + (16 + r0 + (64*r1)), None)
    tmp47 = tl.load(in_ptr1 + (32 + r0 + (64*r1)), None)
    tmp50 = tl.load(in_ptr1 + (48 + r0 + (64*r1)), None)
    tmp1 = tmp0.to(tl.int64)
    tmp3 = tmp2.to(tl.int64)
    tmp4 = tmp1 > tmp3
    tmp5 = tmp1 == tmp3
    tmp6 = tl.full([1, 1], 0, tl.int64)
    tmp7 = tl.full([1, 1], 1, tl.int64)
    tmp8 = tmp6 < tmp7
    tmp9 = tmp5 & tmp8
    tmp10 = tmp4 | tmp9
    tmp11 = tl.where(tmp10, tmp1, tmp3)
    tmp12 = tl.where(tmp10, tmp6, tmp7)
    tmp14 = tmp13.to(tl.int64)
    tmp15 = tmp11 > tmp14
    tmp16 = tmp11 == tmp14
    tmp17 = tl.full([1, 1], 2, tl.int64)
    tmp18 = tmp12 < tmp17
    tmp19 = tmp16 & tmp18
    tmp20 = tmp15 | tmp19
    tmp21 = tl.where(tmp20, tmp11, tmp14)
    tmp22 = tl.where(tmp20, tmp12, tmp17)
    tmp24 = tmp23.to(tl.int64)
    tmp25 = tmp21 > tmp24
    tmp26 = tmp21 == tmp24
    tmp27 = tl.full([1, 1], 3, tl.int64)
    tmp28 = tmp22 < tmp27
    tmp29 = tmp26 & tmp28
    tmp30 = tmp25 | tmp29
    tmp31 = tl.where(tmp30, tmp21, tmp24)
    tmp32 = tl.where(tmp30, tmp22, tmp27)
    tmp33 = tl.full([1, 1], -100, tl.int64)
    tmp34 = tmp32 != tmp33
    tmp35 = tl.where(tmp34, tmp32, tmp6)
    tmp36 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
    tmp37 = tmp35 + tmp36
    tmp38 = tmp35 < 0
    tmp39 = tl.where(tmp38, tmp37, tmp35)
    tl.device_assert((0 <= tmp39) & (tmp39 < 4), "index out of bounds: 0 <= tmp39 < 4")
    tmp41 = tl.load(in_ptr1 + (r0 + (16*tmp39) + (64*r1)), None)
    tmp43 = tl_math.exp(tmp42)
    tmp45 = tl_math.exp(tmp44)
    tmp46 = tmp43 + tmp45
    tmp48 = tl_math.exp(tmp47)
    tmp49 = tmp46 + tmp48
    tmp51 = tl_math.exp(tmp50)
    tmp52 = tmp49 + tmp51
    tmp53 = tl_math.log(tmp52)
    tmp54 = tmp41 - tmp53
    tmp55 = -tmp54
    tmp56 = 0.0
    tmp57 = tl.where(tmp34, tmp55, tmp56)
    tmp58 = tl.broadcast_to(tmp57, [XBLOCK, RBLOCK])
    tmp60 = tl.sum(tmp58, 1)[:, None]
    tmp61 = tmp34.to(tl.int64)
    tmp62 = tl.broadcast_to(tmp61, [XBLOCK, RBLOCK])
    tmp64 = tl.sum(tmp62, 1)[:, None]
    tmp65 = tmp64.to(tl.float32)
    tmp66 = tmp60 / tmp65
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp66, 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)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten._log_softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__log_softmax_0.run(arg1_1, buf1, 256, grid=grid(256), stream=stream0)
        del arg1_1
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [long, max_1, nll_loss], Original ATen: [aten._to_copy, aten.max, aten.nll_loss2d_forward]
        triton_per_fused__to_copy_max_nll_loss2d_forward_1.run(buf4, arg0_1, buf1, 1, 64, grid=grid(1), stream=stream0)
        del arg0_1
        del buf1
    return (buf4, )


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.utils.data
import torch.nn as nn


class CrossEntropyLoss(nn.Module):

    def __init__(self, label_nc):
        super(CrossEntropyLoss, self).__init__()
        self.softmax = nn.LogSoftmax(dim=1)
        self.criterion = nn.NLLLoss2d()

    def forward(self, output, label):
        label = label.long().max(1)[1]
        output = self.softmax(output)
        return self.criterion(output, label)


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'label_nc': 4}]

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.utils.data
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_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), 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 + x3, tmp8, xmask)


@triton.jit
def triton_per_fused__to_copy_max_nll_loss2d_forward_1(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    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 % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp2 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp13 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp23 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp42 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp44 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp47 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp50 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp1 = tmp0.to(tl.int64)
    tmp3 = tmp2.to(tl.int64)
    tmp4 = tmp1 > tmp3
    tmp5 = tmp1 == tmp3
    tmp6 = tl.full([1, 1], 0, tl.int64)
    tmp7 = tl.full([1, 1], 1, tl.int64)
    tmp8 = tmp6 < tmp7
    tmp9 = tmp5 & tmp8
    tmp10 = tmp4 | tmp9
    tmp11 = tl.where(tmp10, tmp1, tmp3)
    tmp12 = tl.where(tmp10, tmp6, tmp7)
    tmp14 = tmp13.to(tl.int64)
    tmp15 = tmp11 > tmp14
    tmp16 = tmp11 == tmp14
    tmp17 = tl.full([1, 1], 2, tl.int64)
    tmp18 = tmp12 < tmp17
    tmp19 = tmp16 & tmp18
    tmp20 = tmp15 | tmp19
    tmp21 = tl.where(tmp20, tmp11, tmp14)
    tmp22 = tl.where(tmp20, tmp12, tmp17)
    tmp24 = tmp23.to(tl.int64)
    tmp25 = tmp21 > tmp24
    tmp26 = tmp21 == tmp24
    tmp27 = tl.full([1, 1], 3, tl.int64)
    tmp28 = tmp22 < tmp27
    tmp29 = tmp26 & tmp28
    tmp30 = tmp25 | tmp29
    tl.where(tmp30, tmp21, tmp24)
    tmp32 = tl.where(tmp30, tmp22, tmp27)
    tmp33 = tl.full([1, 1], -100, tl.int64)
    tmp34 = tmp32 != tmp33
    tmp35 = tl.where(tmp34, tmp32, tmp6)
    tmp36 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
    tmp37 = tmp35 + tmp36
    tmp38 = tmp35 < 0
    tmp39 = tl.where(tmp38, tmp37, tmp35)
    tl.device_assert((0 <= tmp39) & (tmp39 < 4),
        'index out of bounds: 0 <= tmp39 < 4')
    tmp41 = tl.load(in_ptr1 + (r0 + 16 * tmp39 + 64 * r1), None)
    tmp43 = tl_math.exp(tmp42)
    tmp45 = tl_math.exp(tmp44)
    tmp46 = tmp43 + tmp45
    tmp48 = tl_math.exp(tmp47)
    tmp49 = tmp46 + tmp48
    tmp51 = tl_math.exp(tmp50)
    tmp52 = tmp49 + tmp51
    tmp53 = tl_math.log(tmp52)
    tmp54 = tmp41 - tmp53
    tmp55 = -tmp54
    tmp56 = 0.0
    tmp57 = tl.where(tmp34, tmp55, tmp56)
    tmp58 = tl.broadcast_to(tmp57, [XBLOCK, RBLOCK])
    tmp60 = tl.sum(tmp58, 1)[:, None]
    tmp61 = tmp34.to(tl.int64)
    tmp62 = tl.broadcast_to(tmp61, [XBLOCK, RBLOCK])
    tmp64 = tl.sum(tmp62, 1)[:, None]
    tmp65 = tmp64.to(tl.float32)
    tmp66 = tmp60 / tmp65
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp66, 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)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_0[grid(256)](arg1_1, buf1, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg1_1
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf2
        del buf2
        triton_per_fused__to_copy_max_nll_loss2d_forward_1[grid(1)](buf4,
            arg0_1, buf1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del buf1
    return buf4,


class CrossEntropyLossNew(nn.Module):

    def __init__(self, label_nc):
        super(CrossEntropyLossNew, self).__init__()
        self.softmax = nn.LogSoftmax(dim=1)
        self.criterion = nn.NLLLoss2d()

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