# 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_1/inductor_cache/bq/cbqrq7g33gulc6q2mqt2bs2jd3alujljkdtqczdgav5xnuimh3ug.py
# Topologically Sorted Source Nodes: [loss, sum_1], Original ATen: [aten.smooth_l1_loss, aten.sum]
# Source node to ATen node mapping:
#   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 = (%arg0_1, %arg1_1), 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 = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%where, [-1]), kwargs = {})
triton_poi_fused_smooth_l1_loss_sum_0 = async_compile.triton('triton_poi_fused_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.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_smooth_l1_loss_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_smooth_l1_loss_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = 1.0
    tmp5 = tmp3 < tmp4
    tmp6 = tmp3 * tmp3
    tmp7 = 0.5
    tmp8 = tmp6 * tmp7
    tmp9 = tmp8 * tmp4
    tmp10 = tmp3 - tmp7
    tmp11 = tl.where(tmp5, tmp9, tmp10)
    tmp14 = tmp12 - tmp13
    tmp15 = tl_math.abs(tmp14)
    tmp16 = tmp15 < tmp4
    tmp17 = tmp15 * tmp15
    tmp18 = tmp17 * tmp7
    tmp19 = tmp18 * tmp4
    tmp20 = tmp15 - tmp7
    tmp21 = tl.where(tmp16, tmp19, tmp20)
    tmp22 = tmp11 + tmp21
    tmp25 = tmp23 - tmp24
    tmp26 = tl_math.abs(tmp25)
    tmp27 = tmp26 < tmp4
    tmp28 = tmp26 * tmp26
    tmp29 = tmp28 * tmp7
    tmp30 = tmp29 * tmp4
    tmp31 = tmp26 - tmp7
    tmp32 = tl.where(tmp27, tmp30, tmp31)
    tmp33 = tmp22 + tmp32
    tmp36 = tmp34 - tmp35
    tmp37 = tl_math.abs(tmp36)
    tmp38 = tmp37 < tmp4
    tmp39 = tmp37 * tmp37
    tmp40 = tmp39 * tmp7
    tmp41 = tmp40 * tmp4
    tmp42 = tmp37 - tmp7
    tmp43 = tl.where(tmp38, tmp41, tmp42)
    tmp44 = tmp33 + tmp43
    tl.store(out_ptr0 + (x0), tmp44, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/xh/cxhzht2rkwj44wawdeum2gcobajfvzsitdnbv6neguszkyz5gdzd.py
# Topologically Sorted Source Nodes: [loss_1, loss_2], Original ATen: [aten.mul, aten.sum]
# Source node to ATen node mapping:
#   loss_1 => mul_1
#   loss_2 => sum_2
# Graph fragment:
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_1, %arg2_1), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul_1,), kwargs = {})
triton_per_fused_mul_sum_1 = async_compile.triton('triton_per_fused_mul_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, 256],
    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=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_sum_1', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_sum_1(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r0 = rindex % 64
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (r2), None)
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tl.store(out_ptr0 + (tl.full([1], 0, tl.int32)), tmp5, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_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))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [loss, sum_1], Original ATen: [aten.smooth_l1_loss, aten.sum]
        stream0 = get_raw_stream(0)
        triton_poi_fused_smooth_l1_loss_sum_0.run(arg0_1, arg1_1, buf0, 64, grid=grid(64), stream=stream0)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [loss_1, loss_2], Original ATen: [aten.mul, aten.sum]
        triton_per_fused_mul_sum_1.run(buf0, arg2_1, buf1, 1, 256, grid=grid(1), stream=stream0)
        del arg2_1
        del buf0
    return (buf1, )


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)
    arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_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
from torch import nn


class SmoothL1loss_with_weight(nn.Module):

    def __init__(self):
        super(SmoothL1loss_with_weight, self).__init__()

    def forward(self, pred, targets, weights):
        assert pred.shape[0] == targets.shape[0] == weights.shape[0]
        loss = nn.SmoothL1Loss(reduction='none')(pred, targets)
        loss = loss.sum(dim=-1) * weights
        loss = loss.sum()
        return loss


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 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
from torch import 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_poi_fused_smooth_l1_loss_sum_0(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp13 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp23 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp24 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp34 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp35 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = 1.0
    tmp5 = tmp3 < tmp4
    tmp6 = tmp3 * tmp3
    tmp7 = 0.5
    tmp8 = tmp6 * tmp7
    tmp9 = tmp8 * tmp4
    tmp10 = tmp3 - tmp7
    tmp11 = tl.where(tmp5, tmp9, tmp10)
    tmp14 = tmp12 - tmp13
    tmp15 = tl_math.abs(tmp14)
    tmp16 = tmp15 < tmp4
    tmp17 = tmp15 * tmp15
    tmp18 = tmp17 * tmp7
    tmp19 = tmp18 * tmp4
    tmp20 = tmp15 - tmp7
    tmp21 = tl.where(tmp16, tmp19, tmp20)
    tmp22 = tmp11 + tmp21
    tmp25 = tmp23 - tmp24
    tmp26 = tl_math.abs(tmp25)
    tmp27 = tmp26 < tmp4
    tmp28 = tmp26 * tmp26
    tmp29 = tmp28 * tmp7
    tmp30 = tmp29 * tmp4
    tmp31 = tmp26 - tmp7
    tmp32 = tl.where(tmp27, tmp30, tmp31)
    tmp33 = tmp22 + tmp32
    tmp36 = tmp34 - tmp35
    tmp37 = tl_math.abs(tmp36)
    tmp38 = tmp37 < tmp4
    tmp39 = tmp37 * tmp37
    tmp40 = tmp39 * tmp7
    tmp41 = tmp40 * tmp4
    tmp42 = tmp37 - tmp7
    tmp43 = tl.where(tmp38, tmp41, tmp42)
    tmp44 = tmp33 + tmp43
    tl.store(out_ptr0 + x0, tmp44, xmask)


@triton.jit
def triton_per_fused_mul_sum_1(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex % 64
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + r2, None)
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp5, None)


def call(args):
    arg0_1, arg1_1, arg2_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))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_smooth_l1_loss_sum_0[grid(64)](arg0_1, arg1_1,
            buf0, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_mul_sum_1[grid(1)](buf0, arg2_1, buf1, 1, 256,
            num_warps=2, num_stages=1)
        del arg2_1
        del buf0
    return buf1,


class SmoothL1loss_with_weightNew(nn.Module):

    def __init__(self):
        super(SmoothL1loss_with_weightNew, self).__init__()

    def forward(self, input_0, input_1, input_2):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]
