# 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_8/inductor_cache/yn/cynzv2zq4i632ctk73absljoktzm4ic5o2m43q3jt4cm2svgovn7.py
# Topologically Sorted Source Nodes: [smooth_l1_loss], Original ATen: [aten.smooth_l1_loss]
# Source node to ATen node mapping:
#   smooth_l1_loss => abs_1, cat, div, lt, mean, mul, pow_1, sub_4, where
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%sub, %sub_1, %sub_2],), kwargs = {})
#   %abs_1 : [num_users=3] = call_function[target=torch.ops.aten.abs.default](args = (%cat,), kwargs = {})
#   %lt : [num_users=1] = call_function[target=torch.ops.aten.lt.Scalar](args = (%abs_1, 1.0), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%abs_1, 2), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, 0.5), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, 1.0), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%abs_1, 0.5), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%lt, %div, %sub_4), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%where,), kwargs = {})
triton_per_fused_smooth_l1_loss_0 = async_compile.triton('triton_per_fused_smooth_l1_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.persistent_reduction(
    size_hints=[1, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_smooth_l1_loss_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 6, '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_per_fused_smooth_l1_loss_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 192
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = rindex < rnumel
    r2 = (rindex // 16)
    r0 = rindex % 4
    r1 = (rindex // 4) % 4
    r3 = rindex
    tmp0 = r2
    tmp1 = tl.full([1, 1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1, 1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (tl.broadcast_to(r0 + (16*r1) + (64*r2), [XBLOCK, RBLOCK])), rmask & tmp4, other=0.0)
    tmp6 = tl.load(in_ptr0 + (tl.broadcast_to(4 + r0 + (16*r1) + (64*r2), [XBLOCK, RBLOCK])), rmask & tmp4, other=0.0)
    tmp7 = tmp5 - tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1, 1], 8, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tmp10 & tmp12
    tmp14 = tl.load(in_ptr0 + (tl.broadcast_to(4 + r0 + (16*r1) + (64*((-4) + r2)), [XBLOCK, RBLOCK])), rmask & tmp13, other=0.0)
    tmp15 = tl.load(in_ptr0 + (tl.broadcast_to(8 + r0 + (16*r1) + (64*((-4) + r2)), [XBLOCK, RBLOCK])), rmask & tmp13, other=0.0)
    tmp16 = tmp14 - tmp15
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp13, tmp16, tmp17)
    tmp19 = tmp0 >= tmp11
    tmp20 = tl.full([1, 1], 12, tl.int64)
    tmp21 = tmp0 < tmp20
    tmp22 = tl.load(in_ptr0 + (tl.broadcast_to(8 + r0 + (16*r1) + (64*((-8) + r2)), [XBLOCK, RBLOCK])), rmask & tmp19, other=0.0)
    tmp23 = tl.load(in_ptr0 + (tl.broadcast_to(12 + r0 + (16*r1) + (64*((-8) + r2)), [XBLOCK, RBLOCK])), rmask & tmp19, other=0.0)
    tmp24 = tmp22 - tmp23
    tmp25 = tl.full(tmp24.shape, 0.0, tmp24.dtype)
    tmp26 = tl.where(tmp19, tmp24, tmp25)
    tmp27 = tl.where(tmp13, tmp18, tmp26)
    tmp28 = tl.where(tmp4, tmp9, tmp27)
    tmp29 = tl_math.abs(tmp28)
    tmp30 = 1.0
    tmp31 = tmp29 < tmp30
    tmp32 = tmp29 * tmp29
    tmp33 = 0.5
    tmp34 = tmp32 * tmp33
    tmp35 = tmp34 * tmp30
    tmp36 = tmp29 - tmp33
    tmp37 = tl.where(tmp31, tmp35, tmp36)
    tmp38 = tl.broadcast_to(tmp37, [XBLOCK, RBLOCK])
    tmp40 = tl.where(rmask, tmp38, 0)
    tmp41 = tl.sum(tmp40, 1)[:, None]
    tmp42 = 192.0
    tmp43 = tmp41 / tmp42
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp43, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_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: [smooth_l1_loss], Original ATen: [aten.smooth_l1_loss]
        stream0 = get_raw_stream(0)
        triton_per_fused_smooth_l1_loss_0.run(buf2, arg0_1, 1, 192, grid=grid(1), stream=stream0)
        del arg0_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)
    fn = lambda: call([arg0_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 torch.nn.modules
import torch.nn as nn


class ParsingRelationLoss(nn.Module):

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

    def forward(self, logits):
        _n, _c, h, _w = logits.shape
        loss_all = []
        for i in range(0, h - 1):
            loss_all.append(logits[:, :, i, :] - logits[:, :, i + 1, :])
        loss = torch.cat(loss_all)
        return torch.nn.functional.smooth_l1_loss(loss, torch.zeros_like(loss))


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {}]

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 math as tl_math
import torch.nn.modules
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_smooth_l1_loss_0(in_out_ptr0, in_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    rnumel = 192
    RBLOCK: tl.constexpr = 256
    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, :]
    rmask = rindex < rnumel
    r2 = rindex // 16
    r0 = rindex % 4
    r1 = rindex // 4 % 4
    tmp0 = r2
    tl.full([1, 1], 0, tl.int64)
    tmp3 = tl.full([1, 1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + tl.broadcast_to(r0 + 16 * r1 + 64 * r2, [
        XBLOCK, RBLOCK]), rmask & tmp4, other=0.0)
    tmp6 = tl.load(in_ptr0 + tl.broadcast_to(4 + r0 + 16 * r1 + 64 * r2, [
        XBLOCK, RBLOCK]), rmask & tmp4, other=0.0)
    tmp7 = tmp5 - tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1, 1], 8, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tmp10 & tmp12
    tmp14 = tl.load(in_ptr0 + tl.broadcast_to(4 + r0 + 16 * r1 + 64 * (-4 +
        r2), [XBLOCK, RBLOCK]), rmask & tmp13, other=0.0)
    tmp15 = tl.load(in_ptr0 + tl.broadcast_to(8 + r0 + 16 * r1 + 64 * (-4 +
        r2), [XBLOCK, RBLOCK]), rmask & tmp13, other=0.0)
    tmp16 = tmp14 - tmp15
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp13, tmp16, tmp17)
    tmp19 = tmp0 >= tmp11
    tl.full([1, 1], 12, tl.int64)
    tmp22 = tl.load(in_ptr0 + tl.broadcast_to(8 + r0 + 16 * r1 + 64 * (-8 +
        r2), [XBLOCK, RBLOCK]), rmask & tmp19, other=0.0)
    tmp23 = tl.load(in_ptr0 + tl.broadcast_to(12 + r0 + 16 * r1 + 64 * (-8 +
        r2), [XBLOCK, RBLOCK]), rmask & tmp19, other=0.0)
    tmp24 = tmp22 - tmp23
    tmp25 = tl.full(tmp24.shape, 0.0, tmp24.dtype)
    tmp26 = tl.where(tmp19, tmp24, tmp25)
    tmp27 = tl.where(tmp13, tmp18, tmp26)
    tmp28 = tl.where(tmp4, tmp9, tmp27)
    tmp29 = tl_math.abs(tmp28)
    tmp30 = 1.0
    tmp31 = tmp29 < tmp30
    tmp32 = tmp29 * tmp29
    tmp33 = 0.5
    tmp34 = tmp32 * tmp33
    tmp35 = tmp34 * tmp30
    tmp36 = tmp29 - tmp33
    tmp37 = tl.where(tmp31, tmp35, tmp36)
    tmp38 = tl.broadcast_to(tmp37, [XBLOCK, RBLOCK])
    tmp40 = tl.where(rmask, tmp38, 0)
    tmp41 = tl.sum(tmp40, 1)[:, None]
    tmp42 = 192.0
    tmp43 = tmp41 / tmp42
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp43, None)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_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_per_fused_smooth_l1_loss_0[grid(1)](buf2, arg0_1, 1, 192,
            XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
    return buf2,


class ParsingRelationLossNew(nn.Module):

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

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