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.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.onnx
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__log_softmax_mean_mul_neg_1(in_out_ptr0, in_ptr0,
    in_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)
    r3 = rindex
    r0 = rindex % 16
    r2 = rindex // 64
    tmp0 = tl.load(in_ptr0 + r3, None)
    tmp2 = tl.load(in_ptr1 + r3, None)
    tmp3 = tl.load(in_ptr1 + (r0 + 64 * r2), None, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr1 + (16 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp8 = tl.load(in_ptr1 + (32 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp11 = tl.load(in_ptr1 + (48 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp1 = -tmp0
    tmp4 = tl_math.exp(tmp3)
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tmp4 + tmp6
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp7 + tmp9
    tmp12 = tl_math.exp(tmp11)
    tmp13 = tmp10 + tmp12
    tmp14 = tl_math.log(tmp13)
    tmp15 = tmp2 - tmp14
    tmp16 = tmp1 * tmp15
    tmp17 = tl.broadcast_to(tmp16, [RBLOCK])
    tmp19 = triton_helpers.promote_to_tensor(tl.sum(tmp17, 0))
    tmp20 = 256.0
    tmp21 = tmp19 / tmp20
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp21, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_0[grid(256)](arg1_1, buf0, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg1_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        triton_per_fused__log_softmax_mean_mul_neg_1[grid(1)](buf2, arg0_1,
            buf0, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del buf0
    return buf2,


def _reduce(x, reduction='elementwise_mean'):
    if reduction == 'none':
        return x
    elif reduction == 'elementwise_mean':
        return x.mean()
    elif reduction == 'sum':
        return x.sum()
    else:
        raise ValueError('No such reduction {} defined'.format(reduction))


class MultinomialNLLLossNew(nn.Module):
    """
  Computes the negative log-likelihood of the multinomial distribution.

  .. math::
    \\ell(x, y) = L = - y \\cdot \\log(softmax(x))

  Args:
    reduction (string, optional): Specifies the reduction to apply to the output:
        'none' | 'elementwise_mean' | 'sum'. 'none': no reduction will be applied,
        'elementwise_mean': the sum of the output will be divided by the number of
        elements in the output, 'sum': the output will be summed. Default: 'elementwise_mean'
  """

    def __init__(self, reduction='elementwise_mean'):
        super(MultinomialNLLLossNew, self).__init__()
        self.reduction = reduction

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


Measured latency_ms: 0.015129