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 = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_mul_sum_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x0 + 64 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
    tmp7 = tl.load(in_ptr2 + (4 + x0 + 64 * x1), xmask)
    tmp10 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
    tmp12 = tl.load(in_ptr2 + (8 + x0 + 64 * x1), xmask)
    tmp15 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
    tmp17 = tl.load(in_ptr2 + (12 + x0 + 64 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp5 + tmp1
    tmp8 = tmp6 * tmp7
    tmp9 = tmp4 + tmp8
    tmp11 = tmp10 + tmp1
    tmp13 = tmp11 * tmp12
    tmp14 = tmp9 + tmp13
    tmp16 = tmp15 + tmp1
    tmp18 = tmp16 * tmp17
    tmp19 = tmp14 + tmp18
    tl.store(out_ptr0 + x2, tmp19, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64)](primals_1, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf1)
        del primals_2
        buf2 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        triton_poi_fused_add_mul_sum_1[grid(16)](buf1, primals_3, primals_1,
            buf2, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf1
        del primals_3
    return buf2, reinterpret_tensor(buf0, (16, 4), (4, 1), 0
        ), reinterpret_tensor(primals_1, (4, 4, 4), (64, 4, 1), 0)


class LearnedKernelNew(nn.Module):

    def __init__(self, args: 'Namespace'):
        super(LearnedKernelNew, self).__init__()
        self.A = nn.Linear(args.ffn_hidden_size, args.ffn_hidden_size)

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


Measured latency_ms: 0.013861