import torch
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
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_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 5
    x1 = xindex // 5
    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
    tl.full([1], 5, tl.int64)
    tmp9 = 1.0
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp6, tmp9, tmp10)
    tmp12 = tl.where(tmp4, tmp5, tmp11)
    tl.store(out_ptr0 + x2, tmp12, xmask)


@triton.jit
def triton_poi_fused_add_1(in_out_ptr0, in_ptr0, 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')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 5, 5), (25, 5, 1))
    assert_size_stride(primals_4, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 5), (80, 20, 5, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(320)](primals_1, buf0, 320, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 5), (80, 20, 5, 1), torch.float32)
        triton_poi_fused_cat_0[grid(320)](primals_2, buf1, 320, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_2
        buf2 = torch.ops.aten._trilinear.default(reinterpret_tensor(buf0, (
            64, 5), (5, 1), 0), primals_3, reinterpret_tensor(buf1, (64, 5),
            (5, 1), 0), [1, 3], [0], [1, 2], [2, 3])
        del primals_3
        buf3 = buf2
        del buf2
        buf4 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf3
        triton_poi_fused_add_1[grid(256)](buf4, primals_4, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_4
    return buf4, reinterpret_tensor(buf0, (64, 5), (5, 1), 0
        ), reinterpret_tensor(buf1, (64, 5), (5, 1), 0)


class BiaffineScorerNew(nn.Module):

    def __init__(self, input1_size, input2_size, output_size):
        super().__init__()
        self.W_bilin = nn.Bilinear(input1_size + 1, input2_size + 1,
            output_size)
        self.W_bilin.weight.data.zero_()
        self.W_bilin.bias.data.zero_()

    def forward(self, input_0, input_1):
        primals_3 = self.W_bilin.weight
        primals_4 = self.W_bilin.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.135221