# 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_4/inductor_cache/pz/cpznhaxldshs2hscttm7w2cepb2cey5fcd4xk3t34jxmbu44geyy.py
# Topologically Sorted Source Nodes: [output_1, hnet_out, mul_1, hnet_out_1, output_2], Original ATen: [aten.mul, aten.add]
# Source node to ATen node mapping:
#   hnet_out => mul_1
#   hnet_out_1 => add
#   mul_1 => mul_2
#   output_1 => mul
#   output_2 => add_1
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%addmm, %primals_4), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%slice_2, %mm_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%slice_4, %primals_8), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %mul_2), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %add), kwargs = {})
triton_poi_fused_add_mul_0 = async_compile.triton('triton_poi_fused_add_mul_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '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, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, '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_mul_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr2 + (x0 + (8*x1)), xmask)
    tmp5 = tl.load(in_ptr3 + (x2), xmask)
    tmp7 = tl.load(in_ptr2 + (4 + x0 + (8*x1)), xmask)
    tmp8 = tl.load(in_ptr4 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tmp0 * tmp2
    tmp6 = tmp4 * tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp11 = tmp3 + tmp10
    tl.store(out_ptr0 + (x2), tmp11, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (1, ), (1, ))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (8, 4), (4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_2, primals_3, reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [hnet_scalars], Original ATen: [aten.mm]
        extern_kernels.mm(primals_5, reinterpret_tensor(primals_6, (4, 8), (1, 4), 0), out=buf1)
        del primals_6
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.mm]
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf2)
        del primals_7
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output_1, hnet_out, mul_1, hnet_out_1, output_2], Original ATen: [aten.mul, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mul_0.run(buf0, primals_4, buf1, buf2, primals_8, buf3, 16, grid=grid(16), stream=stream0)
    return (buf3, primals_3, primals_4, primals_5, primals_8, buf0, buf1, buf2, )


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, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((8, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8])
    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 math
import torch
import torch.nn.functional as F
import torch.nn as nn


class HyperLinear(nn.Module):

    def __init__(self, in_features, out_features, num_hparams, bias=True):
        super(HyperLinear, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.num_hparams = num_hparams
        self.elem_weight = nn.Parameter(torch.Tensor(out_features, in_features)
            )
        self.hnet_weight = nn.Parameter(torch.Tensor(out_features, in_features)
            )
        if bias:
            self.elem_bias = nn.Parameter(torch.Tensor(out_features))
            self.hnet_bias = nn.Parameter(torch.Tensor(out_features))
        else:
            self.register_parameter('elem_bias', None)
            self.register_parameter('hnet_bias', None)
        self.htensor_to_scalars = nn.Linear(num_hparams, 2 * out_features,
            bias=False)
        self.elem_scalar = nn.Parameter(torch.ones(1))
        self.init_params()

    def forward(self, input, htensor):
        """
        Arguments:
            input (tensor): size should be (B, D)
            htensor (tensor): size should be (B, num_hparams)
        """
        output = F.linear(input, self.elem_weight, self.elem_bias)
        output *= self.elem_scalar
        if htensor is not None:
            hnet_scalars = self.htensor_to_scalars(htensor)
            hnet_wscalars = hnet_scalars[:, :self.out_features]
            hnet_bscalars = hnet_scalars[:, self.out_features:]
            hnet_out = hnet_wscalars * F.linear(input, self.hnet_weight)
            if self.hnet_bias is not None:
                hnet_out += hnet_bscalars * self.hnet_bias
            output += hnet_out
        return output

    def init_params(self):
        stdv = 1.0 / math.sqrt(self.in_features)
        self.elem_weight.data.uniform_(-stdv, stdv)
        self.hnet_weight.data.uniform_(-stdv, stdv)
        if self.elem_bias is not None:
            self.elem_bias.data.uniform_(-stdv, stdv)
            self.hnet_bias.data.uniform_(-stdv, stdv)
        self.htensor_to_scalars.weight.data.normal_(std=0.01)


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


def get_init_inputs():
    return [[], {'in_features': 4, 'out_features': 4, 'num_hparams': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
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
import 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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_add_mul_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr2 + (x0 + 8 * x1), xmask)
    tmp5 = tl.load(in_ptr3 + x2, xmask)
    tmp7 = tl.load(in_ptr2 + (4 + x0 + 8 * x1), xmask)
    tmp8 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp0 * tmp2
    tmp6 = tmp4 * tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp11 = tmp3 + tmp10
    tl.store(out_ptr0 + x2, tmp11, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (1,), (1,))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (8, 4), (4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, primals_3, reinterpret_tensor(
            primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        extern_kernels.mm(primals_5, reinterpret_tensor(primals_6, (4, 8),
            (1, 4), 0), out=buf1)
        del primals_6
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_7, (4, 4),
            (1, 4), 0), out=buf2)
        del primals_7
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_0[grid(16)](buf0, primals_4, buf1, buf2,
            primals_8, buf3, 16, XBLOCK=16, num_warps=1, num_stages=1)
    return buf3, primals_3, primals_4, primals_5, primals_8, buf0, buf1, buf2


class HyperLinearNew(nn.Module):

    def __init__(self, in_features, out_features, num_hparams, bias=True):
        super(HyperLinearNew, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.num_hparams = num_hparams
        self.elem_weight = nn.Parameter(torch.Tensor(out_features, in_features)
            )
        self.hnet_weight = nn.Parameter(torch.Tensor(out_features, in_features)
            )
        if bias:
            self.elem_bias = nn.Parameter(torch.Tensor(out_features))
            self.hnet_bias = nn.Parameter(torch.Tensor(out_features))
        else:
            self.register_parameter('elem_bias', None)
            self.register_parameter('hnet_bias', None)
        self.htensor_to_scalars = nn.Linear(num_hparams, 2 * out_features,
            bias=False)
        self.elem_scalar = nn.Parameter(torch.ones(1))
        self.init_params()

    def init_params(self):
        stdv = 1.0 / math.sqrt(self.in_features)
        self.elem_weight.data.uniform_(-stdv, stdv)
        self.hnet_weight.data.uniform_(-stdv, stdv)
        if self.elem_bias is not None:
            self.elem_bias.data.uniform_(-stdv, stdv)
            self.hnet_bias.data.uniform_(-stdv, stdv)
        self.htensor_to_scalars.weight.data.normal_(std=0.01)

    def forward(self, input_0, input_1):
        primals_1 = self.elem_weight
        primals_3 = self.hnet_weight
        primals_2 = self.elem_bias
        primals_8 = self.hnet_bias
        primals_4 = self.elem_scalar
        primals_6 = self.htensor_to_scalars.weight
        primals_5 = input_0
        primals_7 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]
