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
from abc import *
from torch.optim import *
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, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, None)
    tl.store(out_ptr0 + x2, tmp6, None)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (512, 4), (4, 1))
    assert_size_stride(primals_2, (512,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 512), (512, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 512), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 512), (8192, 2048, 512, 1), 0
            )
        del buf0
        buf2 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1),
            torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(32768)](buf1,
            primals_2, buf2, 32768, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
    return buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf2


def orthogonal_init(layer, nonlinearity='relu'):
    if isinstance(nonlinearity, str):
        if nonlinearity == 'policy':
            gain = 0.01
        else:
            gain = torch.nn.init.calculate_gain(nonlinearity)
    else:
        gain = nonlinearity
    if isinstance(layer, list):
        for l in layer:
            torch.nn.init.orthogonal_(l.weight.data, gain)
            torch.nn.init.zeros_(l.bias.data)
    else:
        torch.nn.init.orthogonal_(layer.weight.data, gain)
        torch.nn.init.zeros_(layer.bias.data)


class MLPNew(torch.nn.Module):

    def __init__(self, D_in, D_hidden=512):
        super(MLPNew, self).__init__()
        self.l = torch.nn.Linear(D_in, D_hidden)
        self.D_head_out = D_hidden
        for layer in self.__dict__['_modules'].values():
            orthogonal_init(layer)

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


Measured latency_ms: 0.024028