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.triton_helpers import libdevice
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused_clone_native_layer_norm_0(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, out_ptr1, 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)
    r2 = rindex % 4
    r3 = rindex // 4
    x0 = xindex % 4
    x1 = xindex // 4
    x4 = xindex
    r5 = rindex
    tmp0 = tl.load(in_ptr0 + (r2 + 4 * x0 + 16 * r3 + 64 * x1), xmask,
        other=0.0)
    tmp24 = tl.load(in_ptr1 + r5, None, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr2 + r5, None, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = 16.0
    tmp18 = tmp16 / tmp17
    tmp19 = 1e-12
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tmp22 = tmp0 - tmp10
    tmp23 = tmp22 * tmp21
    tmp25 = tmp23 * tmp24
    tmp27 = tmp25 + tmp26
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x4, tmp21, xmask)
    tl.store(out_ptr1 + (r5 + 16 * x4), tmp27, xmask)
    tl.store(out_ptr0 + x4, tmp10, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf3 = reinterpret_tensor(buf1, (4, 4, 1, 1), (4, 1, 1, 1), 0)
        del buf1
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_clone_native_layer_norm_0[grid(16)](buf3,
            primals_1, primals_2, primals_3, buf0, buf4, 16, 16, XBLOCK=8,
            num_warps=2, num_stages=1)
        del primals_2
        del primals_3
    return reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 4, 16, 1), 0
        ), primals_1, buf0, buf3


class LayerNorm2DNew(nn.Module):
    """Layer normalization for CNN outputs."""

    def __init__(self, channel, idim, eps=1e-12):
        super(LayerNorm2DNew, self).__init__()
        self.norm = nn.LayerNorm([channel, idim], eps=eps)

    def forward(self, input_0):
        primals_2 = self.norm.weight
        primals_3 = self.norm.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.021088