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 math
from torch import Tensor
from torch.nn import Linear
from torch.nn import Parameter
import torch.utils.data
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 = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tmp4 = tmp2 * tmp3
    tl.store(out_ptr0 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_add_mul_sigmoid_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x3, xmask)
    tmp2 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tl.sigmoid(tmp3)
    tmp5 = tmp3 * tmp4
    tmp6 = tmp0 + tmp5
    tl.store(out_ptr0 + x3, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (1, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (1, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (4, 1), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_sigmoid_0[grid(256)](buf0, primals_3, buf1, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (4, 1), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_sigmoid_1[grid(256)](primals_2, buf2,
            primals_5, buf3, 256, XBLOCK=256, num_warps=4, num_stages=1)
    return buf3, primals_3, primals_5, buf0, buf2, reinterpret_tensor(buf1,
        (4, 64), (1, 4), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0
        ), reinterpret_tensor(primals_2, (4, 64), (1, 4), 0)


def uniform(size, tensor):
    bound = 1.0 / math.sqrt(size)
    if tensor is not None:
        tensor.data.uniform_(-bound, bound)


def kaiming_uniform(tensor, fan, a):
    if tensor is not None:
        bound = math.sqrt(6 / ((1 + a ** 2) * fan))
        tensor.data.uniform_(-bound, bound)


def swish(x):
    return x * x.sigmoid()


def glorot_orthogonal(tensor, scale):
    if tensor is not None:
        torch.nn.init.orthogonal_(tensor.data)
        scale /= (tensor.size(-2) + tensor.size(-1)) * tensor.var()
        tensor.data *= scale.sqrt()


class Linear(torch.nn.Module):

    def __init__(self, in_channels, out_channels, groups=1, bias=True):
        super(Linear, self).__init__()
        assert in_channels % groups == 0 and out_channels % groups == 0
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.groups = groups
        self.weight = Parameter(Tensor(groups, in_channels // groups, 
            out_channels // groups))
        if bias:
            self.bias = Parameter(torch.Tensor(out_channels))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        kaiming_uniform(self.weight, fan=self.weight.size(1), a=math.sqrt(5))
        uniform(self.weight.size(1), self.bias)

    def forward(self, src):
        if self.groups > 1:
            size = list(src.size())[:-1]
            src = src.view(-1, self.groups, self.in_channels // self.groups)
            src = src.transpose(0, 1).contiguous()
            out = torch.matmul(src, self.weight)
            out = out.transpose(1, 0).contiguous()
            out = out.view(*(size + [self.out_channels]))
        else:
            out = torch.matmul(src, self.weight.squeeze(0))
        if self.bias is not None:
            out += self.bias
        return out

    def __repr__(self):
        return '{}({}, {}, groups={}, bias={})'.format(self.__class__.
            __name__, self.in_channels, self.out_channels, self.groups, 
            self.bias is not None)


class ResidualLayerNew(torch.nn.Module):

    def __init__(self, hidden_channels, act=swish):
        super(ResidualLayerNew, self).__init__()
        self.act = act
        self.lin1 = Linear(hidden_channels, hidden_channels)
        self.lin2 = Linear(hidden_channels, hidden_channels)
        self.reset_parameters()

    def reset_parameters(self):
        glorot_orthogonal(self.lin1.weight, scale=2.0)
        self.lin1.bias.data.fill_(0)
        glorot_orthogonal(self.lin2.weight, scale=2.0)
        self.lin2.bias.data.fill_(0)

    def forward(self, input_0):
        primals_1 = self.lin1.weight
        primals_3 = self.lin1.bias
        primals_4 = self.lin2.weight
        primals_5 = self.lin2.bias
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.155016