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]


Measured latency_ms: 0.021345