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 libdevice, math as tl_math
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_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    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)


@triton.jit
def triton_poi_fused__softmax_1(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
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_2(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
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0), 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_sum_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')
    tmp3 = tl.load(in_ptr0 + (64 + x2), xmask)
    tmp4 = tl.load(in_ptr1 + (16 + x1), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (128 + x2), xmask)
    tmp8 = tl.load(in_ptr1 + (32 + x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (192 + x2), xmask)
    tmp12 = tl.load(in_ptr1 + (48 + x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tl.store(out_ptr0 + x2, tmp14, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 1), (1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_tanh_0[grid(256)](buf1, primals_2, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
            primals_4, out=buf2)
        buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        triton_poi_fused__softmax_1[grid(64)](buf2, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf4 = reinterpret_tensor(buf2, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf2
        triton_poi_fused__softmax_2[grid(64)](buf3, buf4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf5 = reinterpret_tensor(buf3, (4, 4, 4), (16, 4, 1), 0)
        del buf3
        triton_poi_fused_mul_sum_3[grid(64)](primals_3, buf4, buf5, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
    return buf5, reinterpret_tensor(buf4, (4, 4, 4), (16, 4, 1), 0
        ), primals_3, buf1, buf4, reinterpret_tensor(primals_4, (1, 4), (1,
        1), 0)


class SelfAttnNew(nn.Module):
    """
    Self attention layer: aggreagating a sequence into a single vector.
    This implementation uses the attention formula proposed by  Sukhbaatar etal. 2015
    https://papers.nips.cc/paper/5846-end-to-end-memory-networks.pdf

    Usage:
    seq_len=10; bsz=16; in_dim=128
    attn = SelfAtnn(in_dim)
    x = torch.rand(seq_len, bsz, in_dim)  # 10x16x128
    y, a = attn(x)  # output y 16x128, attention weight a 10x16
    """

    def __init__(self, d_input, units=None):
        """
        :param d_input: input feature dimension
        :param units: dimension of internal projection, if None it will be set to d_input
        """
        super(SelfAttnNew, self).__init__()
        self.d_input = d_input
        self.units = units if units else d_input
        self.projection = nn.Linear(self.d_input, self.units)
        self.V = nn.Parameter(torch.Tensor(self.units, 1))
        self.init_weights()

    def init_weights(self):
        initrange = 0.1
        self.projection.bias.data.zero_()
        self.projection.weight.data.uniform_(-initrange, initrange)
        self.V.data.uniform_(-initrange, initrange)

    def extra_repr(self):
        return 'Sx?x%d -> ?x%d' % (self.d_input, self.d_input)

    def forward(self, input_0):
        primals_4 = self.V
        primals_1 = self.projection.weight
        primals_2 = self.projection.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0], output[1]


Measured latency_ms: 0.040829