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 math
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
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x2, 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 % 4
    x1 = xindex // 4 % 4
    x3 = xindex // 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x1 + 4 * x0 + 16 * x3), xmask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + x4, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_2(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)


@triton.jit
def triton_poi_fused_add_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 4
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_out_ptr0 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, 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, 4, 4), (16, 4, 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, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(1024)](primals_3, buf0, 1024, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_3
        buf1 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        triton_poi_fused_clone_1[grid(1024)](primals_2, buf1, 1024, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (64, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf1, (64, 4, 4), (16, 4, 1), 0), out=buf2)
        buf3 = buf0
        del buf0
        triton_poi_fused_clone_2[grid(1024)](primals_1, buf3, 1024, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_1
        buf4 = empty_strided_cuda((64, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf3, (64, 4, 4), (16, 4, 1),
            0), buf2, out=buf4)
        del buf2
        buf5 = reinterpret_tensor(buf4, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0
            )
        del buf4
        triton_poi_fused_add_3[grid(1024)](buf5, primals_4, 1024, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_4
    return buf5, reinterpret_tensor(buf3, (64, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf1, (64, 4, 4), (16, 1, 4), 0)


class FastBilinerNew(nn.Module):

    def __init__(self, in1_features, in2_features, out_features):
        super(FastBilinerNew, self).__init__()
        weight = torch.randn(out_features, in1_features, in2_features
            ) * math.sqrt(2 / (in1_features + in2_features))
        bias = torch.ones(out_features) * math.sqrt(2 / (in1_features +
            in2_features))
        self.weight = nn.Parameter(weight)
        self.bias = nn.Parameter(bias)
        self.out_features = out_features
        self.in1_features = in1_features
        self.in2_features = in2_features

    def forward(self, input_0, input_1):
        primals_3 = self.weight
        primals_4 = self.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: 0.065575