# 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_1/inductor_cache/s5/cs5cii52tc3hbclol2cj5szknqlzoteg2pw4i5j57dyaz6f54ybl.py
# Topologically Sorted Source Nodes: [sigmoid, mul_], Original ATen: [aten.sigmoid, aten.mul]
# Source node to ATen node mapping:
#   mul_ => mul
#   sigmoid => sigmoid
# Graph fragment:
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_1,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, %sigmoid), kwargs = {})
triton_poi_fused_mul_sigmoid_0 = async_compile.triton('triton_poi_fused_mul_sigmoid_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_sigmoid_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_mul_sigmoid_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ox/coxzkq4kueijjjccjhdtvzhfquura4nj5lgusydc343b4la3telx.py
# Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.view]
# Source node to ATen node mapping:
#   input_2 => view_7
# Graph fragment:
#   %view_7 : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%view_6, [64, 4]), kwargs = {})
triton_poi_fused_view_1 = async_compile.triton('triton_poi_fused_view_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.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_view_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_view_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x1) + (16*((x1 % 4) // 4)) + (64*(((4*((x1 // 4) % 4)) + (x1 % 4)) // 16))), xmask)
    tl.store(out_ptr0 + (x2), tmp0, 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, primals_9 = 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (3, 4), (4, 1))
    assert_size_stride(primals_9, (3, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), 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, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sigmoid, mul_], Original ATen: [aten.sigmoid, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_sigmoid_0.run(buf0, buf1, 256, grid=grid(256), stream=stream0)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.view]
        triton_poi_fused_view_1.run(buf1, buf2, 256, grid=grid(256), stream=stream0)
        buf3 = reinterpret_tensor(buf1, (64, 4), (4, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, buf2, reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf3)
        del primals_5
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sigmoid_1, mul__1], Original ATen: [aten.sigmoid, aten.mul]
        triton_poi_fused_mul_sigmoid_0.run(buf3, buf4, 256, grid=grid(256), stream=stream0)
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_3], Original ATen: [aten.view]
        triton_poi_fused_view_1.run(buf4, buf5, 256, grid=grid(256), stream=stream0)
        buf6 = reinterpret_tensor(buf4, (64, 4), (4, 1), 0); del buf4  # reuse
        # Topologically Sorted Source Nodes: [input_3], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, buf5, reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf6)
        del primals_7
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sigmoid_2, mul__2], Original ATen: [aten.sigmoid, aten.mul]
        triton_poi_fused_mul_sigmoid_0.run(buf6, buf7, 256, grid=grid(256), stream=stream0)
        buf8 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_4], Original ATen: [aten.view]
        triton_poi_fused_view_1.run(buf7, buf8, 256, grid=grid(256), stream=stream0)
        del buf7
        buf9 = empty_strided_cuda((64, 3), (3, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_4], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_9, buf8, reinterpret_tensor(primals_8, (4, 3), (1, 4), 0), alpha=1, beta=1, out=buf9)
        del primals_9
    return (reinterpret_tensor(buf9, (4, 4, 4, 3), (48, 12, 3, 1), 0), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0, buf2, buf3, buf5, buf6, buf8, primals_8, primals_6, primals_4, )


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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((3, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((3, ), (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, primals_9])
    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 Swish(nn.Module):

    def __init__(self, inplace=True):
        super(Swish, self).__init__()
        self.inplace = inplace

    def forward(self, x):
        if self.inplace:
            x.mul_(torch.sigmoid(x))
            return x
        else:
            return x * torch.sigmoid(x)


class Net(torch.nn.Module):

    def __init__(self, n_feature, n_hidden):
        super(Net, self).__init__()
        self.features = nn.Sequential()
        self.features.add_module('hidden', torch.nn.Linear(n_feature, n_hidden)
            )
        self.features.add_module('active1', Swish())
        self.features.add_module('hidden2', torch.nn.Linear(n_hidden, n_hidden)
            )
        self.features.add_module('active2', Swish())
        self.features.add_module('hidden3', torch.nn.Linear(n_hidden, n_hidden)
            )
        self.features.add_module('active3', Swish())
        self.features.add_module('predict', torch.nn.Linear(n_hidden, 3))

    def forward(self, x):
        return self.features(x)

    def reset_parameters(self, verbose=False):
        for module in self.modules():
            if isinstance(module, self.__class__):
                continue
        if 'reset_parameters' in dir(module):
            if callable(module.reset_parameters):
                module.reset_parameters()
            if verbose:
                None


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


def get_init_inputs():
    return [[], {'n_feature': 4, 'n_hidden': 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 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_mul_sigmoid_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_view_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * (x1 % 4 // 4) + 64 * ((4 *
        (x1 // 4 % 4) + x1 % 4) // 16)), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (3, 4), (4, 1))
    assert_size_stride(primals_9, (3,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), 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, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_sigmoid_0[grid(256)](buf0, buf1, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        triton_poi_fused_view_1[grid(256)](buf1, buf2, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf3 = reinterpret_tensor(buf1, (64, 4), (4, 1), 0)
        del buf1
        extern_kernels.addmm(primals_5, buf2, reinterpret_tensor(primals_4,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf3)
        del primals_5
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mul_sigmoid_0[grid(256)](buf3, buf4, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        triton_poi_fused_view_1[grid(256)](buf4, buf5, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf6 = reinterpret_tensor(buf4, (64, 4), (4, 1), 0)
        del buf4
        extern_kernels.addmm(primals_7, buf5, reinterpret_tensor(primals_6,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf6)
        del primals_7
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mul_sigmoid_0[grid(256)](buf6, buf7, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        buf8 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        triton_poi_fused_view_1[grid(256)](buf7, buf8, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf7
        buf9 = empty_strided_cuda((64, 3), (3, 1), torch.float32)
        extern_kernels.addmm(primals_9, buf8, reinterpret_tensor(primals_8,
            (4, 3), (1, 4), 0), alpha=1, beta=1, out=buf9)
        del primals_9
    return reinterpret_tensor(buf9, (4, 4, 4, 3), (48, 12, 3, 1), 0
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf0, buf2, buf3, buf5, buf6, buf8, primals_8, primals_6, primals_4


class Swish(nn.Module):

    def __init__(self, inplace=True):
        super(Swish, self).__init__()
        self.inplace = inplace

    def forward(self, x):
        if self.inplace:
            x.mul_(torch.sigmoid(x))
            return x
        else:
            return x * torch.sigmoid(x)


class NetNew(torch.nn.Module):

    def __init__(self, n_feature, n_hidden):
        super(NetNew, self).__init__()
        self.features = nn.Sequential()
        self.features.add_module('hidden', torch.nn.Linear(n_feature, n_hidden)
            )
        self.features.add_module('active1', Swish())
        self.features.add_module('hidden2', torch.nn.Linear(n_hidden, n_hidden)
            )
        self.features.add_module('active2', Swish())
        self.features.add_module('hidden3', torch.nn.Linear(n_hidden, n_hidden)
            )
        self.features.add_module('active3', Swish())
        self.features.add_module('predict', torch.nn.Linear(n_hidden, 3))

    def reset_parameters(self, verbose=False):
        for module in self.modules():
            if isinstance(module, self.__class__):
                continue
        if 'reset_parameters' in dir(module):
            if callable(module.reset_parameters):
                module.reset_parameters()
            if verbose:
                None

    def forward(self, input_0):
        primals_1 = self.features.hidden.weight
        primals_2 = self.features.hidden.bias
        primals_4 = self.features.hidden2.weight
        primals_5 = self.features.hidden2.bias
        primals_6 = self.features.hidden3.weight
        primals_7 = self.features.hidden3.bias
        primals_8 = self.features.predict.weight
        primals_9 = self.features.predict.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]
