# AOT ID: ['0_forward']
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_7/inductor_cache/lm/clmbg43tu4wuuy3igx7w2eyfh5b57nifgahbmiwrzi36yhqleuiz.py
# Topologically Sorted Source Nodes: [neg, precision, mul, loss, neg_1, precision_1, mul_1, add_1, loss_1, neg_2, precision_2, mul_2, add_2, loss_2, neg_3, precision_3, mul_3, add_3, loss_3], Original ATen: [aten.neg, aten.exp, aten.mul, aten.add]
# Source node to ATen node mapping:
#   add_1 => add_1
#   add_2 => add_3
#   add_3 => add_5
#   loss => add
#   loss_1 => add_2
#   loss_2 => add_4
#   loss_3 => add_6
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   neg => neg
#   neg_1 => neg_1
#   neg_2 => neg_2
#   neg_3 => neg_3
#   precision => exp
#   precision_1 => exp_1
#   precision_2 => exp_2
#   precision_3 => exp_3
# Graph fragment:
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%select,), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%exp, %select_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %select), kwargs = {})
#   %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%select_3,), kwargs = {})
#   %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg_1,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%exp_1, %select_4), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %select_3), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %add_1), kwargs = {})
#   %neg_2 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%select_6,), kwargs = {})
#   %exp_2 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg_2,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%exp_2, %select_7), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %select_6), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %add_3), kwargs = {})
#   %neg_3 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%select_9,), kwargs = {})
#   %exp_3 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg_3,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%exp_3, %select_10), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_3, %select_9), kwargs = {})
#   %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_4, %add_5), kwargs = {})
triton_poi_fused_add_exp_mul_neg_0 = async_compile.triton('triton_poi_fused_add_exp_mul_neg_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=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_exp_mul_neg_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, '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},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_exp_mul_neg_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 + (0))
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp4 = tl.load(in_ptr1 + (x0), xmask)
    tmp7 = tl.load(in_ptr0 + (1))
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK])
    tmp11 = tl.load(in_ptr1 + (64 + x0), xmask)
    tmp15 = tl.load(in_ptr0 + (2))
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
    tmp19 = tl.load(in_ptr1 + (128 + x0), xmask)
    tmp23 = tl.load(in_ptr0 + (3))
    tmp24 = tl.broadcast_to(tmp23, [XBLOCK])
    tmp27 = tl.load(in_ptr1 + (192 + x0), xmask)
    tmp2 = -tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp5 = tmp3 * tmp4
    tmp6 = tmp5 + tmp1
    tmp9 = -tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp12 = tmp10 * tmp11
    tmp13 = tmp12 + tmp8
    tmp14 = tmp6 + tmp13
    tmp17 = -tmp16
    tmp18 = tl_math.exp(tmp17)
    tmp20 = tmp18 * tmp19
    tmp21 = tmp20 + tmp16
    tmp22 = tmp14 + tmp21
    tmp25 = -tmp24
    tmp26 = tl_math.exp(tmp25)
    tmp28 = tmp26 * tmp27
    tmp29 = tmp28 + tmp24
    tmp30 = tmp22 + tmp29
    tl.store(out_ptr0 + (x0), tmp30, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (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: [neg, precision, mul, loss, neg_1, precision_1, mul_1, add_1, loss_1, neg_2, precision_2, mul_2, add_2, loss_2, neg_3, precision_3, mul_3, add_3, loss_3], Original ATen: [aten.neg, aten.exp, aten.mul, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_exp_mul_neg_0.run(primals_2, primals_1, buf0, 64, grid=grid(64), stream=stream0)
    return (buf0, primals_1, primals_2, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2])
    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 multiloss(nn.Module):

    def __init__(self, objective_num):
        super(multiloss, self).__init__()
        self.objective_num = objective_num
        self.log_var = nn.Parameter(torch.zeros(self.objective_num))

    def forward(self, losses):
        for i in range(len(losses)):
            precision = torch.exp(-self.log_var[i])
            if i == 0:
                loss = precision * losses[i] + self.log_var[i]
            else:
                loss += precision * losses[i] + self.log_var[i]
        return loss


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


def get_init_inputs():
    return [[], {'objective_num': 4}]

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 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_poi_fused_add_exp_mul_neg_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 + 0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp4 = tl.load(in_ptr1 + x0, xmask)
    tmp7 = tl.load(in_ptr0 + 1)
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK])
    tmp11 = tl.load(in_ptr1 + (64 + x0), xmask)
    tmp15 = tl.load(in_ptr0 + 2)
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
    tmp19 = tl.load(in_ptr1 + (128 + x0), xmask)
    tmp23 = tl.load(in_ptr0 + 3)
    tmp24 = tl.broadcast_to(tmp23, [XBLOCK])
    tmp27 = tl.load(in_ptr1 + (192 + x0), xmask)
    tmp2 = -tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp5 = tmp3 * tmp4
    tmp6 = tmp5 + tmp1
    tmp9 = -tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp12 = tmp10 * tmp11
    tmp13 = tmp12 + tmp8
    tmp14 = tmp6 + tmp13
    tmp17 = -tmp16
    tmp18 = tl_math.exp(tmp17)
    tmp20 = tmp18 * tmp19
    tmp21 = tmp20 + tmp16
    tmp22 = tmp14 + tmp21
    tmp25 = -tmp24
    tmp26 = tl_math.exp(tmp25)
    tmp28 = tmp26 * tmp27
    tmp29 = tmp28 + tmp24
    tmp30 = tmp22 + tmp29
    tl.store(out_ptr0 + x0, tmp30, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (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_add_exp_mul_neg_0[grid(64)](primals_2, primals_1,
            buf0, 64, XBLOCK=64, num_warps=1, num_stages=1)
    return buf0, primals_1, primals_2


class multilossNew(nn.Module):

    def __init__(self, objective_num):
        super(multilossNew, self).__init__()
        self.objective_num = objective_num
        self.log_var = nn.Parameter(torch.zeros(self.objective_num))

    def forward(self, input_0):
        primals_2 = self.log_var
        primals_1 = input_0
        output = call([primals_1, primals_2])
        return output[0]
