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
from torch import 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 = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 128
    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_sigmoid_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.sigmoid(tmp3)
    tl.store(in_out_ptr0 + x0, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1400), (1400, 1))
    assert_size_stride(primals_2, (128, 1400), (1400, 1))
    assert_size_stride(primals_3, (128,), (1,))
    assert_size_stride(primals_4, (1, 128), (128, 1))
    assert_size_stride(primals_5, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = torch.ops.aten.native_dropout.default(primals_1, 0.5, True)
        del primals_1
        buf1 = buf0[0]
        del buf0
        buf3 = empty_strided_cuda((4, 128), (128, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_2, (1400, 128),
            (1, 1400), 0), out=buf3)
        del primals_2
        buf4 = buf3
        del buf3
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(512)](buf4, primals_3, 512, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_3
        buf5 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf4, reinterpret_tensor(primals_4, (128, 1), (1,
            128), 0), out=buf5)
        buf6 = buf5
        del buf5
        triton_poi_fused_sigmoid_1[grid(4)](buf6, primals_5, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del primals_5
    return buf6, buf1, buf4, buf6, primals_4


class MyNet2New(nn.Module):
    """Very simple network made with two fully connected layers"""

    def __init__(self):
        super(MyNet2New, self).__init__()
        self.fc1 = nn.Linear(28 * 50, 128)
        self.fc2 = nn.Linear(128, 1)

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.050514