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
import torch.nn.functional as F
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_out_ptr1,
    in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_out_ptr1 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tmp8 = tmp7 + tmp1
    tmp9 = triton_helpers.maximum(tmp3, tmp8)
    tmp10 = tmp9 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)
    tl.store(in_out_ptr1 + x2, tmp9, xmask)
    tl.store(out_ptr1 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_out_ptr1,
    in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_out_ptr1 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tmp8 = tmp7 + tmp1
    tmp9 = triton_helpers.maximum(tmp3, tmp8)
    tmp10 = tmp9 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)
    tl.store(in_out_ptr1 + x2, tmp9, xmask)
    tl.store(out_ptr1 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_sigmoid_2(in_out_ptr0, in_out_ptr1, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    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.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp5 = tl.load(in_out_ptr1 + x0, xmask)
    tmp3 = tmp0 + tmp2
    tmp4 = tl.sigmoid(tmp3)
    tmp6 = tmp5 + tmp2
    tmp7 = tl.sigmoid(tmp6)
    tl.store(in_out_ptr0 + x0, tmp4, xmask)
    tl.store(in_out_ptr1 + x0, tmp7, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10) = args
    args.clear()
    assert_size_stride(primals_1, (8, 4), (4, 1))
    assert_size_stride(primals_2, (8,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 8), (8, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (1, 4), (4, 1))
    assert_size_stride(primals_9, (1,), (1,))
    assert_size_stride(primals_10, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 8), (1, 4), 0), out=buf0)
        buf8 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_10, (64, 4), (4, 1), 0
            ), reinterpret_tensor(primals_1, (4, 8), (1, 4), 0), out=buf8)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 8), (128, 32, 8, 1), 0)
        del buf0
        buf21 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.bool)
        buf9 = reinterpret_tensor(buf8, (4, 4, 4, 8), (128, 32, 8, 1), 0)
        del buf8
        buf18 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(512)](buf1, buf9,
            primals_2, buf21, buf18, 512, XBLOCK=128, num_warps=4, num_stages=1
            )
        del primals_2
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 8), (8, 1), 0),
            reinterpret_tensor(primals_4, (8, 4), (1, 8), 0), out=buf2)
        buf10 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf9, (64, 8), (8, 1), 0),
            reinterpret_tensor(primals_4, (8, 4), (1, 8), 0), out=buf10)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf2
        buf20 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf11 = reinterpret_tensor(buf10, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf10
        buf17 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(256)](buf3, buf11,
            primals_5, buf20, buf17, 256, XBLOCK=256, num_warps=4, num_stages=1
            )
        del primals_5
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf4)
        buf12 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf11, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf12)
        buf5 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf4
        buf19 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf13 = reinterpret_tensor(buf12, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf12
        buf16 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(256)](buf5, buf13,
            primals_7, buf19, buf16, 256, XBLOCK=256, num_warps=4, num_stages=1
            )
        del primals_7
        buf6 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf5, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 1), (1, 4), 0), out=buf6)
        buf14 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf13, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 1), (1, 4), 0), out=buf14)
        buf7 = reinterpret_tensor(buf6, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf6
        buf15 = reinterpret_tensor(buf14, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf14
        triton_poi_fused_sigmoid_2[grid(64)](buf7, buf15, primals_9, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_9
    return (buf7, buf15, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
        reinterpret_tensor(buf1, (64, 8), (8, 1), 0), reinterpret_tensor(
        buf3, (64, 4), (4, 1), 0), reinterpret_tensor(buf5, (64, 4), (4, 1),
        0), buf7, reinterpret_tensor(primals_10, (64, 4), (4, 1), 0),
        reinterpret_tensor(buf9, (64, 8), (8, 1), 0), reinterpret_tensor(
        buf11, (64, 4), (4, 1), 0), reinterpret_tensor(buf13, (64, 4), (4, 
        1), 0), buf15, primals_8, buf16, primals_6, buf17, primals_4, buf18,
        buf19, buf20, buf21)


class PairwiseNetworkNew(nn.Module):

    def __init__(self, hidden_size):
        super().__init__()
        self.fc1 = nn.Linear(hidden_size, 2 * hidden_size)
        self.fc2 = nn.Linear(2 * hidden_size, hidden_size)
        self.fc3 = nn.Linear(hidden_size, hidden_size)
        self.fc4 = nn.Linear(hidden_size, 1)

    def predict(self, test_feat):
        x = F.relu(self.fc1(test_feat))
        x = F.relu(self.fc2(x))
        x = F.relu(self.fc3(x))
        test_out = torch.sigmoid(self.fc4(x))
        return test_out

    def forward(self, input_0, input_1):
        primals_1 = self.fc1.weight
        primals_2 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_6 = self.fc3.weight
        primals_7 = self.fc3.bias
        primals_8 = self.fc4.weight
        primals_9 = self.fc4.bias
        primals_3 = input_0
        primals_10 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9, primals_10])
        return output[0], output[1]


Measured latency_ms: 0.015049