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
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_per_fused_mean_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.
    constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_per_fused_mean_pow_sub_1(in_out_ptr0, 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 + 4 * r0, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp1 = 16.0
    tmp2 = tmp0 / tmp1
    tmp4 = tmp3 / tmp1
    tmp5 = tmp2 - tmp4
    tmp6 = tmp5 * tmp5
    tmp8 = tmp7 / tmp1
    tmp10 = tmp9 / tmp1
    tmp11 = tmp8 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp6 + tmp12
    tmp15 = tmp14 / tmp1
    tmp17 = tmp16 / tmp1
    tmp18 = tmp15 - tmp17
    tmp19 = tmp18 * tmp18
    tmp20 = tmp13 + tmp19
    tmp22 = tmp21 / tmp1
    tmp24 = tmp23 / tmp1
    tmp25 = tmp22 - tmp24
    tmp26 = tmp25 * tmp25
    tmp27 = tmp20 + tmp26
    tmp28 = 4.0
    tmp29 = tmp27 / tmp28
    tmp30 = tl.broadcast_to(tmp29, [XBLOCK, RBLOCK])
    tmp32 = tl.sum(tmp30, 1)[:, None]
    tmp33 = tmp32 / tmp28
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp33, 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, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_mean_0[grid(16)](arg0_1, buf0, 16, 16, XBLOCK=8,
            num_warps=2, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_per_fused_mean_0[grid(16)](arg1_1, buf1, 16, 16, XBLOCK=8,
            num_warps=2, num_stages=1)
        del arg1_1
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2
        del buf2
        triton_per_fused_mean_pow_sub_1[grid(1)](buf3, buf0, buf1, 1, 4,
            XBLOCK=1, num_warps=2, num_stages=1)
        del buf0
        del buf1
    return buf3,


class statm_lossNew(nn.Module):

    def __init__(self, eps=2):
        super(statm_lossNew, self).__init__()
        self.eps = eps

    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: 0.040475