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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
from numpy import sqrt
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_repeat_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 % 4
    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__softmax_relu_threshold_backward_1(in_ptr0, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp1, tmp3)
    tmp6 = triton_helpers.maximum(tmp1, tmp5)
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = triton_helpers.maximum(tmp1, tmp8)
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = triton_helpers.maximum(tmp1, tmp11)
    tmp13 = triton_helpers.maximum(tmp10, tmp12)
    tmp14 = tmp2 - tmp13
    tmp15 = tl_math.exp(tmp14)
    tmp16 = 0.0
    tmp17 = tmp2 <= tmp16
    tl.store(out_ptr0 + x2, tmp15, xmask)
    tl.store(out_ptr1 + x2, tmp17, xmask)


@triton.jit
def triton_poi_fused__softmax_2(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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_mul_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x2, tmp2, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 1), (1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_repeat_0[grid(16)](primals_2, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(primals_1, buf0, out=buf1)
        buf2 = reinterpret_tensor(buf0, (4, 4), (4, 1), 0)
        del buf0
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused__softmax_relu_threshold_backward_1[grid(16)](buf1,
            buf2, buf5, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf3 = reinterpret_tensor(buf1, (4, 4), (4, 1), 0)
        del buf1
        triton_poi_fused__softmax_2[grid(16)](buf2, buf3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf2
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_mul_3[grid(64)](primals_1, buf3, buf4, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
    return buf4, buf3, primals_1, buf3, buf5


class AttnNew(nn.Module):

    def __init__(self, hidden_size, batch_first=True):
        super(AttnNew, self).__init__()
        self.hidden_size = hidden_size
        self.batch_first = batch_first
        self.weights = nn.Parameter(torch.Tensor(hidden_size, 1))
        stdv = 1.0 / sqrt(self.hidden_size)
        for weight in self.weights:
            nn.init.uniform_(weight, -stdv, stdv)

    def forward(self, input_0):
        primals_2 = self.weights
        primals_1 = input_0
        output = call([primals_1, primals_2])
        return output[0], output[1]


Measured latency_ms: 0.059605