import torch
from torch._inductor.select_algorithm import extern_kernels
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
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_add_div_linalg_vector_norm_0(in_ptr0, out_ptr1, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    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 + 64 * x0), xmask, other=0.0)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp6 = libdevice.sqrt(tmp5)
    tmp7 = 1e-06
    tmp8 = tmp6 + tmp7
    tmp9 = tmp0 / tmp8
    tl.store(out_ptr1 + (r1 + 64 * x0), tmp9, xmask)


@triton.jit
def triton_per_fused_mean_pow_1(in_out_ptr0, in_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)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.sum(tmp2, 1)[:, None]
    tmp5 = 16.0
    tmp6 = tmp4 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp6, 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)
        buf2 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_add_div_linalg_vector_norm_0[grid(4)](arg0_1, buf2,
            4, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        buf3 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        triton_per_fused_add_div_linalg_vector_norm_0[grid(4)](arg1_1, buf3,
            4, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg1_1
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(buf3, (64, 4), (1, 64), 
            0), out=buf4)
        del buf2
        del buf3
        buf5 = empty_strided_cuda((), (), torch.float32)
        buf6 = buf5
        del buf5
        triton_per_fused_mean_pow_1[grid(1)](buf6, buf4, 1, 16, XBLOCK=1,
            num_warps=2, num_stages=1)
        del buf4
    return buf6,


class DiffLossNew(torch.nn.Module):

    def __init__(self):
        super(DiffLossNew, self).__init__()

    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: 0.046034