# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_0/inductor_cache/f4/cf4wzaoib6zlvrqqjktwzisb46krxyk3mdts3jt54cj43kfq3q7d.py
# Topologically Sorted Source Nodes: [inp, tgt, mse_loss], Original ATen: [aten.div, aten.mse_loss]
# Source node to ATen node mapping:
#   inp => div
#   mse_loss => mean, pow_5, sub
#   tgt => div_1
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg0_1, %expand), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg1_1, %expand_1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div, %div_1), kwargs = {})
#   %pow_5 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_5,), kwargs = {})
triton_red_fused_div_mse_loss_0 = async_compile.triton('triton_red_fused_div_mse_loss_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.reduction(
    size_hints=[1, 256],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused_div_mse_loss_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 10, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_red_fused_div_mse_loss_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    _tmp34 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        r1 = (rindex // 4)
        tmp0 = tl.load(in_ptr0 + (r2), rmask, eviction_policy='evict_first', other=0.0)
        tmp1 = tl.load(in_ptr0 + (4*r1), rmask, eviction_policy='evict_last', other=0.0)
        tmp3 = tl.load(in_ptr0 + (1 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp6 = tl.load(in_ptr0 + (2 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp9 = tl.load(in_ptr0 + (3 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp16 = tl.load(in_ptr1 + (r2), rmask, eviction_policy='evict_first', other=0.0)
        tmp17 = tl.load(in_ptr1 + (4*r1), rmask, eviction_policy='evict_last', other=0.0)
        tmp19 = tl.load(in_ptr1 + (1 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp22 = tl.load(in_ptr1 + (2 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp25 = tl.load(in_ptr1 + (3 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
        tmp2 = tmp1 * tmp1
        tmp4 = tmp3 * tmp3
        tmp5 = tmp2 + tmp4
        tmp7 = tmp6 * tmp6
        tmp8 = tmp5 + tmp7
        tmp10 = tmp9 * tmp9
        tmp11 = tmp8 + tmp10
        tmp12 = libdevice.sqrt(tmp11)
        tmp13 = 1e-12
        tmp14 = triton_helpers.maximum(tmp12, tmp13)
        tmp15 = tmp0 / tmp14
        tmp18 = tmp17 * tmp17
        tmp20 = tmp19 * tmp19
        tmp21 = tmp18 + tmp20
        tmp23 = tmp22 * tmp22
        tmp24 = tmp21 + tmp23
        tmp26 = tmp25 * tmp25
        tmp27 = tmp24 + tmp26
        tmp28 = libdevice.sqrt(tmp27)
        tmp29 = triton_helpers.maximum(tmp28, tmp13)
        tmp30 = tmp16 / tmp29
        tmp31 = tmp15 - tmp30
        tmp32 = tmp31 * tmp31
        tmp33 = tl.broadcast_to(tmp32, [XBLOCK, RBLOCK])
        tmp35 = _tmp34 + tmp33
        _tmp34 = tl.where(rmask, tmp35, _tmp34)
    tmp34 = tl.sum(_tmp34, 1)[:, None]
    tmp36 = 256.0
    tmp37 = tmp34 / tmp36
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp37, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [inp, tgt, mse_loss], Original ATen: [aten.div, aten.mse_loss]
        stream0 = get_raw_stream(0)
        triton_red_fused_div_mse_loss_0.run(buf2, arg0_1, arg1_1, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf2, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

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 libdevice
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_red_fused_div_mse_loss_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
    rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    rnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    _tmp34 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        r1 = rindex // 4
        tmp0 = tl.load(in_ptr0 + r2, rmask, eviction_policy='evict_first',
            other=0.0)
        tmp1 = tl.load(in_ptr0 + 4 * r1, rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp3 = tl.load(in_ptr0 + (1 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp6 = tl.load(in_ptr0 + (2 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp9 = tl.load(in_ptr0 + (3 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp16 = tl.load(in_ptr1 + r2, rmask, eviction_policy='evict_first',
            other=0.0)
        tmp17 = tl.load(in_ptr1 + 4 * r1, rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp19 = tl.load(in_ptr1 + (1 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp22 = tl.load(in_ptr1 + (2 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp25 = tl.load(in_ptr1 + (3 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp2 = tmp1 * tmp1
        tmp4 = tmp3 * tmp3
        tmp5 = tmp2 + tmp4
        tmp7 = tmp6 * tmp6
        tmp8 = tmp5 + tmp7
        tmp10 = tmp9 * tmp9
        tmp11 = tmp8 + tmp10
        tmp12 = libdevice.sqrt(tmp11)
        tmp13 = 1e-12
        tmp14 = triton_helpers.maximum(tmp12, tmp13)
        tmp15 = tmp0 / tmp14
        tmp18 = tmp17 * tmp17
        tmp20 = tmp19 * tmp19
        tmp21 = tmp18 + tmp20
        tmp23 = tmp22 * tmp22
        tmp24 = tmp21 + tmp23
        tmp26 = tmp25 * tmp25
        tmp27 = tmp24 + tmp26
        tmp28 = libdevice.sqrt(tmp27)
        tmp29 = triton_helpers.maximum(tmp28, tmp13)
        tmp30 = tmp16 / tmp29
        tmp31 = tmp15 - tmp30
        tmp32 = tmp31 * tmp31
        tmp33 = tl.broadcast_to(tmp32, [XBLOCK, RBLOCK])
        tmp35 = _tmp34 + tmp33
        _tmp34 = tl.where(rmask, tmp35, _tmp34)
    tmp34 = tl.sum(_tmp34, 1)[:, None]
    tmp36 = 256.0
    tmp37 = tmp34 / tmp36
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp37, 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)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_red_fused_div_mse_loss_0[grid(1)](buf2, arg0_1, arg1_1, 1, 
            256, XBLOCK=1, RBLOCK=256, num_warps=8, num_stages=1)
        del arg0_1
        del arg1_1
    return buf2,


class NormedMSENew(nn.MSELoss):

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