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.triton_helpers import libdevice
import torch.nn as nn
from torch.autograd import Variable
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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_mul_sigmoid_tanh_1(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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)
    tmp4 = tl.load(in_ptr2 + x0, xmask)
    tmp6 = tl.load(in_ptr3 + x0, xmask)
    tmp10 = tl.load(in_ptr4 + x0, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp5 = tl.sigmoid(tmp4)
    tmp7 = libdevice.tanh(tmp6)
    tmp8 = tmp5 * tmp7
    tmp9 = tmp3 + tmp8
    tmp11 = tl.sigmoid(tmp10)
    tmp12 = libdevice.tanh(tmp9)
    tmp13 = tmp11 * tmp12
    tl.store(out_ptr0 + x0, tmp9, xmask)
    tl.store(out_ptr1 + x0, tmp13, 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, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 8), (8, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 8), (8, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4, 8), (8, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 8), (8, 1))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(32)](primals_1, primals_2, buf0, 32,
            XBLOCK=32, num_warps=1, num_stages=1)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_4, buf0, reinterpret_tensor(primals_3,
            (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf1)
        del primals_3
        del primals_4
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_6, buf0, reinterpret_tensor(primals_5,
            (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf2)
        del primals_5
        del primals_6
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_8, buf0, reinterpret_tensor(primals_7,
            (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf3)
        del primals_7
        del primals_8
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_10, buf0, reinterpret_tensor(primals_9,
            (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf4)
        del primals_10
        del primals_9
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_mul_sigmoid_tanh_1[grid(16)](buf1, primals_11,
            buf2, buf4, buf3, buf5, buf6, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
    return buf6, buf5, primals_11, buf0, buf1, buf2, buf3, buf4, buf5


class LSTMNew(nn.Module):

    def __init__(self, input_size, cell_size, hidden_size):
        """
        cell_size is the size of cell_state.
        hidden_size is the size of hidden_state, or say the output_state of each step
        """
        super(LSTMNew, self).__init__()
        self.cell_size = cell_size
        self.hidden_size = hidden_size
        self.fl = nn.Linear(input_size + hidden_size, hidden_size)
        self.il = nn.Linear(input_size + hidden_size, hidden_size)
        self.ol = nn.Linear(input_size + hidden_size, hidden_size)
        self.Cl = nn.Linear(input_size + hidden_size, hidden_size)

    def loop(self, inputs):
        batch_size = inputs.size(0)
        time_step = inputs.size(1)
        Hidden_State, Cell_State = self.initHidden(batch_size)
        for i in range(time_step):
            Hidden_State, Cell_State = self.forward(torch.squeeze(inputs[:,
                i:i + 1, :]), Hidden_State, Cell_State)
        return Hidden_State, Cell_State

    def initHidden(self, batch_size):
        use_gpu = torch.cuda.is_available()
        if use_gpu:
            Hidden_State = Variable(torch.zeros(batch_size, self.hidden_size))
            Cell_State = Variable(torch.zeros(batch_size, self.hidden_size))
            return Hidden_State, Cell_State
        else:
            Hidden_State = Variable(torch.zeros(batch_size, self.hidden_size))
            Cell_State = Variable(torch.zeros(batch_size, self.hidden_size))
            return Hidden_State, Cell_State

    def forward(self, input_0, input_1, input_2):
        primals_3 = self.fl.weight
        primals_4 = self.fl.bias
        primals_5 = self.il.weight
        primals_6 = self.il.bias
        primals_7 = self.ol.weight
        primals_8 = self.ol.bias
        primals_9 = self.Cl.weight
        primals_10 = self.Cl.bias
        primals_1 = input_0
        primals_2 = input_1
        primals_11 = input_2
        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], output[1]


Measured latency_ms: 0.030854