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
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_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 64
    x2 = xindex // 256
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x2 = xindex // 64
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 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, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(1024)](buf0, buf1, 1024, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf0
        buf2 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        triton_poi_fused_clone_1[grid(1024)](primals_3, buf2, 1024, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((64, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf1, (64, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf2, (64, 4, 4), (16, 4, 1), 0), out=buf3)
        del buf1
    return reinterpret_tensor(buf3, (1024, 1), (1, 1), 0), reinterpret_tensor(
        buf2, (64, 4, 4), (16, 1, 4), 0), reinterpret_tensor(primals_2, (4,
        64), (1, 4), 0)


class BilinearNew(nn.Module):

    def __init__(self, size):
        super(BilinearNew, self).__init__()
        self.size = size
        self.mat = nn.Parameter(torch.FloatTensor(self.size, self.size))
        self.reset_parameters()

    def reset_parameters(self):
        params = [p for p in self.parameters() if p.requires_grad]
        for i, param in enumerate(params):
            param.data.normal_()

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


Measured latency_ms: 0.044304