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
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_cat_1(in_ptr0, in_ptr1, in_ptr2, 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 = tl.sigmoid(tmp5)
    tmp7 = tl.load(in_ptr1 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp8 = tmp6 * tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp4, tmp8, tmp9)
    tmp11 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp14 = tl.load(in_ptr2 + (4 * x1 + (-4 + x0)), tmp11 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp15 = tl.where(tmp4, tmp10, tmp14)
    tl.store(out_ptr0 + x2, tmp15, xmask)


@triton.jit
def triton_poi_fused_add_mul_rsub_sigmoid_tanh_2(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, 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)
    tmp4 = tl.load(in_ptr1 + x0, xmask)
    tmp6 = tl.load(in_ptr2 + x0, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp2 = 1.0
    tmp3 = tmp2 - tmp1
    tmp5 = tmp3 * tmp4
    tmp7 = libdevice.tanh(tmp6)
    tmp8 = tmp1 * tmp7
    tmp9 = tmp5 + tmp8
    tl.store(out_ptr0 + x0, tmp9, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = 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,))
    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)
        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, 8), (8, 1), torch.float32)
        triton_poi_fused_cat_1[grid(32)](buf2, primals_1, primals_2, buf3, 
            32, XBLOCK=32, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_8, buf3, reinterpret_tensor(primals_7,
            (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf4)
        del primals_8
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_mul_rsub_sigmoid_tanh_2[grid(16)](buf1,
            primals_2, buf4, buf5, 16, XBLOCK=16, num_warps=1, num_stages=1)
    return buf5, primals_1, primals_2, buf0, buf1, buf2, buf3, buf4, primals_7


class GateNew(nn.Module):

    def __init__(self, dhid, dfeature, init_range=0.1, init_dist='uniform',
        dropout=0.5):
        super(GateNew, self).__init__()
        self.dhid = dhid
        self.dfeature = dfeature
        self.linear_z = nn.Linear(self.dhid + self.dfeature, self.dhid)
        self.linear_r = nn.Linear(self.dhid + self.dfeature, self.dfeature)
        self.linear_h_tilde = nn.Linear(self.dhid + self.dfeature, self.dhid)
        self.drop = nn.Dropout(dropout)
        self.sigmoid = nn.Sigmoid()
        self.tanh = nn.Tanh()
        self.init_weights(init_range, init_dist)

    def init_weights(self, init_range, init_dist):

        def init_w(data, init_dist):
            if init_dist == 'uniform':
                return data.uniform_(-init_range, init_range)
            elif init_dist == 'xavier':
                return nn.init.xavier_uniform(data)
        init_w(self.linear_z.weight.data, init_dist)
        init_w(self.linear_r.weight.data, init_dist)
        init_w(self.linear_h_tilde.weight.data, init_dist)
        self.linear_z.bias.data.fill_(0)
        self.linear_r.bias.data.fill_(0)
        self.linear_h_tilde.bias.data.fill_(0)

    def forward(self, input_0, input_1):
        primals_3 = self.linear_z.weight
        primals_4 = self.linear_z.bias
        primals_5 = self.linear_r.weight
        primals_6 = self.linear_r.bias
        primals_7 = self.linear_h_tilde.weight
        primals_8 = self.linear_h_tilde.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]


Measured latency_ms: 0.066773