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, 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 + x0, xmask)
    tmp2 = tl.load(in_ptr1 + x0, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tl.store(out_ptr0 + x0, tmp3, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4), (4, 1))
    assert_size_stride(primals_3, (1,), (1,))
    assert_size_stride(primals_4, (1, 4), (4, 1))
    assert_size_stride(primals_5, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 1), (1, 4), 0
            ), alpha=1, beta=1, out=buf1)
        del primals_2
        del primals_3
        buf3 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 1), (1, 4), 0
            ), alpha=1, beta=1, out=buf3)
        del primals_4
        del primals_5
        buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_sigmoid_0[grid(64)](buf1, buf3, buf4, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
    return buf4, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), buf1, buf3


class GatedLinearUnitNew(nn.Module):

    def __init__(self, input_size, hidden_layer_size, dropout_rate,
        activation=None):
        super(GatedLinearUnitNew, self).__init__()
        self.input_size = input_size
        self.hidden_layer_size = hidden_layer_size
        self.dropout_rate = dropout_rate
        self.activation_name = activation
        if self.dropout_rate:
            self.dropout = nn.Dropout(p=self.dropout_rate)
        self.W4 = torch.nn.Linear(self.input_size, self.hidden_layer_size)
        self.W5 = torch.nn.Linear(self.input_size, self.hidden_layer_size)
        if self.activation_name:
            self.activation = getattr(nn, self.activation_name)()
        self.sigmoid = nn.Sigmoid()
        self.init_weights()

    def init_weights(self):
        for n, p in self.named_parameters():
            if 'bias' not in n:
                torch.nn.init.xavier_uniform_(p)
            elif 'bias' in n:
                torch.nn.init.zeros_(p)

    def forward(self, input_0):
        primals_2 = self.W4.weight
        primals_3 = self.W4.bias
        primals_4 = self.W5.weight
        primals_5 = self.W5.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.037944