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 torch._inductor.runtime.triton_helpers import libdevice, math as tl_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__softmax_0(in_ptr0, 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
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tmp0 - tmp0
    tmp2 = tl_math.exp(tmp1)
    tmp3 = tmp2 / tmp2
    tl.store(out_ptr0 + x0, tmp3, xmask)


@triton.jit
def triton_poi_fused_tanh_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    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
    tmp3 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (1, 4), (4, 1))
    assert_size_stride(primals_2, (1,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 1), (1, 4), 0
            ), alpha=1, beta=1, out=buf1)
        del primals_1
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(16)](buf1, buf2, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_3, (4, 4, 4), (16, 1,
            4), 0), buf2, out=buf3)
        buf4 = reinterpret_tensor(buf2, (4, 4), (4, 1), 0)
        del buf2
        extern_kernels.mm(reinterpret_tensor(buf3, (4, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf4)
        buf5 = buf4
        del buf4
        triton_poi_fused_tanh_1[grid(16)](buf5, primals_5, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_5
    return buf5, primals_3, buf1, reinterpret_tensor(buf3, (4, 4), (4, 1), 0
        ), buf5, primals_4


class ProteinResNetPoolerNew(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.attention_weights = nn.Linear(config.hidden_size, 1)
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.activation = nn.Tanh()

    def forward(self, input_0):
        primals_1 = self.attention_weights.weight
        primals_2 = self.attention_weights.bias
        primals_4 = self.dense.weight
        primals_5 = self.dense.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.026819