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 torch import 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_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 12
    x1 = xindex // 12
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tl.full([1], 12, tl.int64)
    tmp14 = tl.load(in_ptr2 + (4 * x1 + (-8 + x0)), tmp11 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp15 = tl.where(tmp9, tmp10, tmp14)
    tmp16 = tl.where(tmp4, tmp5, tmp15)
    tmp17 = tl.load(in_ptr3 + (4 * x1 + (-4 + x0)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp18 = tl.load(in_ptr4 + (4 * x1 + (-8 + x0)), tmp11 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp19 = tl.where(tmp9, tmp17, tmp18)
    tmp20 = tl.where(tmp4, tmp5, tmp19)
    tl.store(out_ptr0 + x2, tmp16, xmask)
    tl.store(out_ptr1 + x2, tmp20, xmask)


@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, in_out_ptr1, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    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')
    tmp5 = tl.load(in_out_ptr1 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = tmp5 + tmp1
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(in_out_ptr1 + x2, tmp7, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, 12), (12, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (1, 4), (4, 1))
    assert_size_stride(primals_9, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 12), (12, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 12), (12, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(48)](primals_3, primals_4, primals_1,
            primals_5, primals_2, buf0, buf1, 48, XBLOCK=64, num_warps=1,
            num_stages=1)
        del primals_1
        del primals_2
        del primals_3
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(primals_6, (12, 4), (1, 
            12), 0), out=buf2)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_6, (12, 4), (1, 
            12), 0), out=buf6)
        del primals_6
        buf3 = buf2
        del buf2
        buf7 = buf6
        del buf6
        triton_poi_fused_relu_1[grid(16)](buf3, buf7, primals_7, 16, XBLOCK
            =16, num_warps=1, num_stages=1)
        del primals_7
        buf5 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_9, buf3, reinterpret_tensor(primals_8,
            (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf5)
        buf9 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_9, buf7, reinterpret_tensor(primals_8,
            (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf9)
        del primals_9
    return reinterpret_tensor(buf5, (4,), (1,), 0), reinterpret_tensor(buf9,
        (4,), (1,), 0), buf0, buf1, buf3, buf7, primals_8


class StepRankerMarginNew(nn.Module):

    def __init__(self, parent_dim, child_short_dim, child_full_dim, hidden_dim
        ):
        super(StepRankerMarginNew, self).__init__()
        if child_full_dim is not None:
            self.hidden = nn.Linear(parent_dim + child_short_dim +
                child_full_dim, hidden_dim)
        else:
            self.hidden = nn.Linear(parent_dim + child_short_dim, hidden_dim)
        self.relu = nn.ReLU()
        self.out = nn.Linear(hidden_dim, 1)

    def score(self, parent_encodings, child_encodings):
        """return the score of multiple child encodings each with respective parent encoding"""
        encodings = torch.cat([parent_encodings, child_encodings], dim=1)
        scores = self.out(self.relu(self.hidden(encodings)))
        return scores.view(scores.numel())

    def forward(self, input_0, input_1, input_2, input_3, input_4):
        primals_6 = self.hidden.weight
        primals_7 = self.hidden.bias
        primals_8 = self.out.weight
        primals_9 = self.out.bias
        primals_1 = input_0
        primals_2 = input_1
        primals_3 = input_2
        primals_4 = input_3
        primals_5 = input_4
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0], output[1]


Measured latency_ms: 0.057658