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 functools
import torch.utils.data
import torch
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_leaky_relu_0(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr1 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_1(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x0, tmp2, None)
    tl.store(out_ptr1 + x0, tmp5, None)


@triton.jit
def triton_poi_fused_leaky_relu_2(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x0, tmp2, None)
    tl.store(out_ptr1 + x0, tmp5, None)


@triton.jit
def triton_poi_fused_tanh_tanh_backward_3(in_out_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 3136
    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 = libdevice.tanh(tmp0)
    tmp2 = tmp1 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp2
    tl.store(in_out_ptr0 + x0, tmp1, xmask)
    tl.store(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, 4), (4, 1))
    assert_size_stride(primals_2, (256, 4), (4, 1))
    assert_size_stride(primals_3, (512, 256), (256, 1))
    assert_size_stride(primals_4, (1024, 512), (512, 1))
    assert_size_stride(primals_5, (784, 1024), (1024, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 256),
            (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 256), (256, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_0[grid(1024)](buf0, buf1, buf2, 1024,
            XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        buf3 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_3, (256, 512), (
            1, 256), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 512), (512, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        triton_poi_fused_leaky_relu_1[grid(2048)](buf3, buf4, buf5, 2048,
            XBLOCK=128, num_warps=4, num_stages=1)
        del buf3
        buf6 = empty_strided_cuda((4, 1024), (1024, 1), torch.float32)
        extern_kernels.mm(buf5, reinterpret_tensor(primals_4, (512, 1024),
            (1, 512), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 1024), (1024, 1), torch.bool)
        buf8 = empty_strided_cuda((4, 1024), (1024, 1), torch.float32)
        triton_poi_fused_leaky_relu_2[grid(4096)](buf6, buf7, buf8, 4096,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf6
        buf9 = empty_strided_cuda((4, 784), (784, 1), torch.float32)
        extern_kernels.mm(buf8, reinterpret_tensor(primals_5, (1024, 784),
            (1, 1024), 0), out=buf9)
        buf10 = buf9
        del buf9
        buf11 = empty_strided_cuda((4, 784), (784, 1), torch.float32)
        triton_poi_fused_tanh_tanh_backward_3[grid(3136)](buf10, buf11, 
            3136, XBLOCK=256, num_warps=4, num_stages=1)
    return (reinterpret_tensor(buf10, (4, 1, 28, 28), (784, 784, 28, 1), 0),
        primals_1, buf1, buf2, buf4, buf5, buf7, buf8, buf11, primals_5,
        primals_4, primals_3)


class DCGANGenerator_mnistNew(nn.Module):

    def __init__(self, z_dim, ngf=64, output_nc=1, norm_layer=nn.BatchNorm2d):
        super(DCGANGenerator_mnistNew, self).__init__()
        self.z_dim = z_dim
        self.ngf = ngf
        self.img_size = 28 * 28 * output_nc
        if type(norm_layer) == functools.partial:
            use_bias = norm_layer.func != nn.BatchNorm2d
        else:
            use_bias = norm_layer != nn.BatchNorm2d
        self.fc1 = nn.Linear(self.z_dim, 256, bias=use_bias)
        self.fc2 = nn.Linear(self.fc1.out_features, self.fc1.out_features *
            2, bias=use_bias)
        self.fc3 = nn.Linear(self.fc2.out_features, self.fc2.out_features *
            2, bias=use_bias)
        self.fc4 = nn.Linear(self.fc3.out_features, self.img_size, bias=
            use_bias)
        self.model = nn.Sequential(self.fc1, nn.LeakyReLU(negative_slope=
            0.2), self.fc2, nn.LeakyReLU(negative_slope=0.2), self.fc3, nn.
            LeakyReLU(negative_slope=0.2), self.fc4, nn.Tanh())

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc2.weight
        primals_4 = self.fc3.weight
        primals_5 = self.fc4.weight
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.088665