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 typing import Callable
from typing import Optional
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_clone_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 64 * y1), xmask & ymask)
    tl.store(out_ptr0 + (x2 + 16 * y3), tmp0, xmask & ymask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (16, 4), (4, 1))
    assert_size_stride(primals_4, (16,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_4, reinterpret_tensor(primals_1, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 16), (1, 4), 
            0), alpha=1, beta=1, out=buf0)
        del primals_3
        del primals_4
        buf1 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 16, 4), (64, 4, 1),
            0), reinterpret_tensor(primals_2, (4, 4, 4), (16, 1, 4), 0),
            out=buf1)
        buf2 = reinterpret_tensor(buf0, (4, 4, 16), (64, 16, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(16, 16)](buf1, buf2, 16, 16, XBLOCK=
            16, YBLOCK=16, num_warps=4, num_stages=1)
        del buf1
    return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), primals_2


class BiaffineNew(nn.Module):

    def __init__(self, in1_features: 'int', in2_features: 'int',
        out_features: 'int', init_func: 'Optional[Callable]'=None) ->None:
        super(BiaffineNew, self).__init__()
        self.in1_features = in1_features
        self.in2_features = in2_features
        self.out_features = out_features
        self.linear_in_features = in1_features
        self.linear_out_features = out_features * in2_features
        self._linear = nn.Linear(in_features=self.linear_in_features,
            out_features=self.linear_out_features)
        self.reset_parameters(init_func=init_func)

    def reset_parameters(self, init_func: 'Optional[Callable]'=None) ->None:
        if init_func:
            init_func(self._linear.weight)

    def forward(self, input_0, input_1):
        primals_3 = self._linear.weight
        primals_4 = self._linear.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: 0.031482