# 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/yp/cypgmqbvz4ya4p7m3uq3dkc4d5nmsy5yjfitish6ab67lwyfv6c6.py
# Topologically Sorted Source Nodes: [smooth_l1_loss, l1_loss, sum_1], Original ATen: [aten.smooth_l1_loss, aten.mul, aten.sum]
# Source node to ATen node mapping:
#   l1_loss => mul_1
#   smooth_l1_loss => abs_1, div, lt, mul, pow_1, sub, sub_1, where
#   sum_1 => sum_1
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %slice_5), kwargs = {})
#   %abs_1 : [num_users=3] = call_function[target=torch.ops.aten.abs.default](args = (%sub,), 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_1 : [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_1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%unsqueeze, %where), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul_1,), kwargs = {})
triton_per_fused_mul_smooth_l1_loss_sum_0 = async_compile.triton('triton_per_fused_mul_smooth_l1_loss_sum_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, 128],
    reduction_hint=ReductionHint.INNER,
    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_per_fused_mul_smooth_l1_loss_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_mul_smooth_l1_loss_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 128
    RBLOCK: tl.constexpr = 128
    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 = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex % 16
    r2 = (rindex // 32)
    r3 = rindex
    r4 = rindex % 32
    tmp0 = tl.load(in_ptr0 + (32 + r0 + (64*r2)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (r3), None)
    tmp2 = tl.load(in_ptr0 + (r4 + (64*r2)), None)
    tmp3 = tmp1 - tmp2
    tmp4 = tl_math.abs(tmp3)
    tmp5 = 1.0
    tmp6 = tmp4 < tmp5
    tmp7 = tmp4 * tmp4
    tmp8 = 0.5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp9 * tmp5
    tmp11 = tmp4 - tmp8
    tmp12 = tl.where(tmp6, tmp10, tmp11)
    tmp13 = tmp0 * tmp12
    tmp14 = tl.broadcast_to(tmp13, [XBLOCK, RBLOCK])
    tmp16 = tl.sum(tmp14, 1)[:, None]
    tl.store(out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/hv/chvegbdiw6s22yn5hq4mfdg47ja3rm7jboxh2fkdfpgpfnbwqus4.py
# Topologically Sorted Source Nodes: [sum_2, clamp, off_loss], Original ATen: [aten.sum, aten.clamp, aten.div]
# Source node to ATen node mapping:
#   clamp => clamp_min
#   off_loss => div_1
#   sum_2 => sum_2
# Graph fragment:
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%select_1,), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sum_2, 1.0), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, %clamp_min), kwargs = {})
triton_per_fused_clamp_div_sum_1 = async_compile.triton('triton_per_fused_clamp_div_sum_1', '''
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, 64],
    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_clamp_div_sum_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_clamp_div_sum_1(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    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 = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex % 16
    r1 = (rindex // 16)
    tmp0 = tl.load(in_ptr0 + (32 + r0 + (64*r1)), None)
    tmp4 = tl.load(in_out_ptr0 + (0))
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, 1])
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tmp6 = 1.0
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tmp8 = tmp5 / tmp7
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp8, 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, 2, 4, 4), (32, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [smooth_l1_loss, l1_loss, sum_1], Original ATen: [aten.smooth_l1_loss, aten.mul, aten.sum]
        stream0 = get_raw_stream(0)
        triton_per_fused_mul_smooth_l1_loss_sum_0.run(arg0_1, arg1_1, buf0, 1, 128, grid=grid(1), stream=stream0)
        del arg1_1
        buf2 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [sum_2, clamp, off_loss], Original ATen: [aten.sum, aten.clamp, aten.div]
        triton_per_fused_clamp_div_sum_1.run(buf2, arg0_1, 1, 64, 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)
    arg1_1 = rand_strided((4, 2, 4, 4), (32, 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 torch.nn as nn


class offset_pos(nn.Module):

    def __init__(self):
        super(offset_pos, self).__init__()
        self.smoothl1 = nn.SmoothL1Loss(reduction='none')

    def forward(self, offset_pred, offset_label):
        l1_loss = offset_label[:, 2, :, :].unsqueeze(dim=1) * self.smoothl1(
            offset_pred, offset_label[:, :2, :, :])
        off_loss = torch.sum(l1_loss) / max(1.0, torch.sum(offset_label[:, 
            2, :, :]))
        return off_loss


def get_inputs():
    return [torch.rand([4, 2, 4, 4]), 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
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_mul_smooth_l1_loss_sum_0(in_ptr0, in_ptr1, out_ptr0,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 128
    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 % 16
    r2 = rindex // 32
    r3 = rindex
    r4 = rindex % 32
    tmp0 = tl.load(in_ptr0 + (32 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + r3, None)
    tmp2 = tl.load(in_ptr0 + (r4 + 64 * r2), None)
    tmp3 = tmp1 - tmp2
    tmp4 = tl_math.abs(tmp3)
    tmp5 = 1.0
    tmp6 = tmp4 < tmp5
    tmp7 = tmp4 * tmp4
    tmp8 = 0.5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp9 * tmp5
    tmp11 = tmp4 - tmp8
    tmp12 = tl.where(tmp6, tmp10, tmp11)
    tmp13 = tmp0 * tmp12
    tmp14 = tl.broadcast_to(tmp13, [XBLOCK, RBLOCK])
    tmp16 = tl.sum(tmp14, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp16, None)


@triton.jit
def triton_per_fused_clamp_div_sum_1(in_out_ptr0, in_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    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 % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp4 = tl.load(in_out_ptr0 + 0)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, 1])
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tmp6 = 1.0
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tmp8 = tmp5 / tmp7
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp8, 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, 2, 4, 4), (32, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_mul_smooth_l1_loss_sum_0[grid(1)](arg0_1, arg1_1,
            buf0, 1, 128, XBLOCK=1, num_warps=2, num_stages=1)
        del arg1_1
        buf2 = buf0
        del buf0
        triton_per_fused_clamp_div_sum_1[grid(1)](buf2, arg0_1, 1, 64,
            XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
    return buf2,


class offset_posNew(nn.Module):

    def __init__(self):
        super(offset_posNew, self).__init__()
        self.smoothl1 = nn.SmoothL1Loss(reduction='none')

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