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 import triton_helpers
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_relu_threshold_backward_0(in_out_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    x1 = xindex % 4000
    x2 = xindex // 4000
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = 0.0
    tmp4 = tmp2 <= tmp3
    tl.store(in_out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr0 + (x1 + 4096 * x2), tmp4, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 32000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 2000
    x1 = xindex // 2000
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = 0.0
    tmp4 = tmp2 <= tmp3
    tl.store(out_ptr0 + (x0 + 2016 * x1), tmp2, xmask)
    tl.store(out_ptr1 + (x0 + 2048 * x1), tmp4, xmask)


@triton.jit
def triton_poi_fused_relu_view_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 32000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 500
    x1 = xindex // 500
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 500 * (x1 % 4) + 2016 * (x1 // 4)), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_3(in_out_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 12800
    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 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = 0.0
    tmp4 = tmp2 <= tmp3
    tl.store(in_out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_4(in_out_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    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 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = 0.0
    tmp4 = tmp2 <= tmp3
    tl.store(in_out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr0 + x0, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (1000, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (1000, 1000), (1000, 1))
    assert_size_stride(primals_4, (500, 1000), (1000, 1))
    assert_size_stride(primals_5, (200, 500), (500, 1))
    assert_size_stride(primals_6, (4, 200), (200, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 1000), (1000, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 1000), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 1000), (16000, 4000, 1000,
            1), 0)
        del buf0
        buf15 = empty_strided_cuda((4, 4, 4, 1000), (16384, 4096, 1000, 1),
            torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(64000)](buf1, buf15,
            64000, XBLOCK=256, num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((64, 1000), (1000, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 1000), (1000, 1), 0
            ), reinterpret_tensor(primals_3, (1000, 1000), (1, 1000), 0),
            out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 1000), (16000, 4000, 1000,
            1), 0)
        del buf2
        buf14 = empty_strided_cuda((4, 4, 4, 1000), (16384, 4096, 1000, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(64000)](buf3, buf14,
            64000, XBLOCK=256, num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((64, 500), (500, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 1000), (1000, 1), 0
            ), reinterpret_tensor(primals_4, (1000, 500), (1, 1000), 0),
            out=buf4)
        buf5 = empty_strided_cuda((4, 4, 4, 500), (8064, 2016, 500, 1),
            torch.float32)
        buf13 = empty_strided_cuda((4, 4, 4, 500), (8192, 2048, 500, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(32000)](buf4, buf5,
            buf13, 32000, XBLOCK=256, num_warps=4, num_stages=1)
        buf6 = buf4
        del buf4
        triton_poi_fused_relu_view_2[grid(32000)](buf5, buf6, 32000, XBLOCK
            =128, num_warps=4, num_stages=1)
        del buf5
        buf7 = empty_strided_cuda((64, 200), (200, 1), torch.float32)
        extern_kernels.mm(buf6, reinterpret_tensor(primals_5, (500, 200), (
            1, 500), 0), out=buf7)
        buf8 = reinterpret_tensor(buf7, (4, 4, 4, 200), (3200, 800, 200, 1), 0)
        del buf7
        buf12 = empty_strided_cuda((4, 4, 4, 200), (3200, 800, 200, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_3[grid(12800)](buf8, buf12,
            12800, XBLOCK=256, num_warps=4, num_stages=1)
        buf9 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf8, (64, 200), (200, 1), 0),
            reinterpret_tensor(primals_6, (200, 4), (1, 200), 0), out=buf9)
        buf10 = reinterpret_tensor(buf9, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf9
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_4[grid(256)](buf10, buf11,
            256, XBLOCK=128, num_warps=4, num_stages=1)
    return (buf10, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
        reinterpret_tensor(buf1, (64, 1000), (1000, 1), 0),
        reinterpret_tensor(buf3, (64, 1000), (1000, 1), 0), buf6,
        reinterpret_tensor(buf8, (64, 200), (200, 1), 0), buf11, primals_6,
        buf12, primals_5, buf13, primals_4, buf14, primals_3, buf15)


class NeuralNerworkNew(nn.Module):
    """ Construct a ReLU-activated NN, set Bias to False
        Four hidden layers with sizes [1000, 1000, 500, 200]
        Features = 784, Targets = 10 classes
    """

    def __init__(self, features, targets):
        super(NeuralNerworkNew, self).__init__()
        self.fc1 = nn.Linear(features, 1000, bias=False)
        self.fc2 = nn.Linear(1000, 1000, bias=False)
        self.fc3 = nn.Linear(1000, 500, bias=False)
        self.fc4 = nn.Linear(500, 200, bias=False)
        self.fc5 = nn.Linear(200, targets, bias=False)

    def forward(self, input_0):
        primals_1 = self.fc1.weight
        primals_3 = self.fc2.weight
        primals_4 = self.fc3.weight
        primals_5 = self.fc4.weight
        primals_6 = self.fc5.weight
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]


Measured latency_ms: 0.126721