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_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 800
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 200
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 784), (784, 1))
    assert_size_stride(primals_2, (200, 784), (784, 1))
    assert_size_stride(primals_3, (200,), (1,))
    assert_size_stride(primals_4, (200, 200), (200, 1))
    assert_size_stride(primals_5, (200,), (1,))
    assert_size_stride(primals_6, (10, 200), (200, 1))
    assert_size_stride(primals_7, (10,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 200), (200, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (784, 
            200), (1, 784), 0), out=buf0)
        del primals_2
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(800)](buf1, primals_3, 800, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 200), (200, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (200, 200), (
            1, 200), 0), out=buf2)
        buf3 = buf2
        del buf2
        triton_poi_fused_relu_0[grid(800)](buf3, primals_5, 800, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf3, reinterpret_tensor(primals_6,
            (200, 10), (1, 200), 0), alpha=1, beta=1, out=buf4)
        del primals_7
    return buf4, primals_1, buf1, buf3, primals_6, primals_4


class MLPNew(nn.Module):
    """
    Simple MLP to demonstrate Jacobian regularization.
    """

    def __init__(self, in_channel=1, im_size=28, num_classes=10,
        fc_channel1=200, fc_channel2=200):
        super(MLPNew, self).__init__()
        compression = in_channel * im_size * im_size
        self.compression = compression
        self.fc1 = nn.Linear(compression, fc_channel1)
        self.fc2 = nn.Linear(fc_channel1, fc_channel2)
        self.fc3 = nn.Linear(fc_channel2, num_classes)
        nn.init.xavier_uniform_(self.fc1.weight)
        nn.init.xavier_uniform_(self.fc2.weight)
        nn.init.xavier_uniform_(self.fc3.weight)

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_6 = self.fc3.weight
        primals_7 = self.fc3.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.046196