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
from torch._inductor.runtime import triton_helpers
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_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 32000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 500
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 22400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 350
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_relu_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 12800
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 200
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_relu_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 8320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 130
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (500, 4), (4, 1))
    assert_size_stride(primals_3, (500,), (1,))
    assert_size_stride(primals_4, (350, 500), (500, 1))
    assert_size_stride(primals_5, (350,), (1,))
    assert_size_stride(primals_6, (200, 350), (350, 1))
    assert_size_stride(primals_7, (200,), (1,))
    assert_size_stride(primals_8, (130, 200), (200, 1))
    assert_size_stride(primals_9, (130,), (1,))
    assert_size_stride(primals_10, (4, 130), (130, 1))
    assert_size_stride(primals_11, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 500), (500, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 500), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(32000)](buf1, primals_3, 32000, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((64, 350), (350, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (500, 350), (
            1, 500), 0), out=buf2)
        buf3 = buf2
        del buf2
        triton_poi_fused_relu_1[grid(22400)](buf3, primals_5, 22400, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((64, 200), (200, 1), torch.float32)
        extern_kernels.mm(buf3, reinterpret_tensor(primals_6, (350, 200), (
            1, 350), 0), out=buf4)
        buf5 = buf4
        del buf4
        triton_poi_fused_relu_2[grid(12800)](buf5, primals_7, 12800, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_7
        buf6 = empty_strided_cuda((64, 130), (130, 1), torch.float32)
        extern_kernels.mm(buf5, reinterpret_tensor(primals_8, (200, 130), (
            1, 200), 0), out=buf6)
        buf7 = buf6
        del buf6
        triton_poi_fused_relu_3[grid(8320)](buf7, primals_9, 8320, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_9
        buf8 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_11, buf7, reinterpret_tensor(
            primals_10, (130, 4), (1, 130), 0), alpha=1, beta=1, out=buf8)
        del primals_11
    return buf8, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
        ), buf1, buf3, buf5, buf7, primals_10, primals_8, primals_6, primals_4


class NetworkNew(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.config = config
        self.l1 = nn.Linear(self.config['in_feature'], 500)
        self.l2 = nn.Linear(500, 350)
        self.l3 = nn.Linear(350, 200)
        self.l4 = nn.Linear(200, 130)
        self.l5 = nn.Linear(130, self.config['out_feature'])

    def forward(self, input_0):
        primals_2 = self.l1.weight
        primals_3 = self.l1.bias
        primals_4 = self.l2.weight
        primals_5 = self.l2.bias
        primals_6 = self.l3.weight
        primals_7 = self.l3.bias
        primals_8 = self.l4.weight
        primals_9 = self.l4.bias
        primals_10 = self.l5.weight
        primals_11 = self.l5.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11])
        return output[0]


Measured latency_ms: 0.072002