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
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_add_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr0 + x2, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = tl.full([1], 0, tl.int32)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp9 = 0.0
    tmp10 = tmp8 <= tmp9
    tl.store(in_out_ptr0 + x2, tmp8, xmask)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex % 64
    x0 = xindex % 4
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr0 + x3, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = tl.full([1], 0, tl.int32)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp9 = 0.0
    tmp10 = tmp8 <= tmp9
    tl.store(in_out_ptr0 + x3, tmp8, xmask)
    tl.store(out_ptr0 + x3, tmp10, 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, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (1, 4), (4, 1))
    assert_size_stride(primals_7, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 64
            ), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
        buf2 = empty_strided_cuda((32, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (32, 4), (4, 1), 
            128), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2)
        del primals_4
        buf3 = reinterpret_tensor(buf1, (1, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf1
        buf10 = empty_strided_cuda((1, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_add_relu_threshold_backward_0[grid(64)](buf3, buf0,
            primals_3, primals_5, buf10, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        buf5 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (16, 4), (
            4, 1), 0), reinterpret_tensor(primals_6, (4, 1), (1, 4), 0),
            alpha=1, beta=1, out=buf5)
        buf6 = reinterpret_tensor(buf2, (2, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf2
        buf9 = empty_strided_cuda((2, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_add_relu_threshold_backward_1[grid(128)](buf6,
            buf0, primals_3, primals_5, buf9, 128, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf0
        del primals_3
        del primals_5
        buf8 = empty_strided_cuda((32, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf6, (32, 4), (
            4, 1), 0), reinterpret_tensor(primals_6, (4, 1), (1, 4), 0),
            alpha=1, beta=1, out=buf8)
        del primals_7
    return reinterpret_tensor(buf5, (1, 4, 4, 1), (16, 4, 1, 1), 0
        ), reinterpret_tensor(buf8, (2, 4, 4, 1), (16, 4, 1, 1), 0
        ), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
        ), reinterpret_tensor(primals_1, (16, 4), (4, 1), 64
        ), reinterpret_tensor(primals_1, (32, 4), (4, 1), 128
        ), reinterpret_tensor(buf3, (16, 4), (4, 1), 0), reinterpret_tensor(
        buf6, (32, 4), (4, 1), 0), primals_6, buf9, buf10


class EdgePredictorNew(torch.nn.Module):

    def __init__(self, dim_in):
        super(EdgePredictorNew, self).__init__()
        self.dim_in = dim_in
        self.src_fc = torch.nn.Linear(dim_in, dim_in)
        self.dst_fc = torch.nn.Linear(dim_in, dim_in)
        self.out_fc = torch.nn.Linear(dim_in, 1)

    def forward(self, input_0):
        primals_2 = self.src_fc.weight
        primals_3 = self.src_fc.bias
        primals_4 = self.dst_fc.weight
        primals_5 = self.dst_fc.bias
        primals_6 = self.out_fc.weight
        primals_7 = self.out_fc.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0], output[1]


Measured latency_ms: 0.081023