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
import torch.nn as nn
import torch.nn.functional as F
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_mv_0(in_ptr0, in_ptr1, 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 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr1 + 0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
    tmp5 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + 1)
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK])
    tmp11 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp13 = tl.load(in_ptr1 + 2)
    tmp14 = tl.broadcast_to(tmp13, [XBLOCK])
    tmp17 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp19 = tl.load(in_ptr1 + 3)
    tmp20 = tl.broadcast_to(tmp19, [XBLOCK])
    tmp1 = libdevice.tanh(tmp0)
    tmp4 = tmp1 * tmp3
    tmp6 = libdevice.tanh(tmp5)
    tmp9 = tmp6 * tmp8
    tmp10 = tmp4 + tmp9
    tmp12 = libdevice.tanh(tmp11)
    tmp15 = tmp12 * tmp14
    tmp16 = tmp10 + tmp15
    tmp18 = libdevice.tanh(tmp17)
    tmp21 = tmp18 * tmp20
    tmp22 = tmp16 + tmp21
    tl.store(out_ptr0 + x0, tmp22, xmask)


@triton.jit
def triton_poi_fused__softmax_1(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 = 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 = 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)


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), (16, 4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((16,), (1,), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mv_0[grid(16)](buf0, primals_4, buf1, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(16)](buf1, buf2, 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)
        buf4 = reinterpret_tensor(buf2, (4, 1, 4), (4, 4, 1), 0)
        del buf2
        extern_kernels.bmm(reinterpret_tensor(buf3, (4, 1, 4), (4, 0, 1), 0
            ), primals_3, out=buf4)
        del buf3
    return reinterpret_tensor(buf4, (4, 4), (4, 1), 0
        ), primals_3, primals_4, buf0


class AdditiveAttention(torch.nn.Module):
    """
    A general additive attention module.
    Originally for NAML.
    """

    def __init__(self, query_vector_dim, candidate_vector_dim, writer=None,
        tag=None, names=None):
        super(AdditiveAttention, self).__init__()
        self.linear = nn.Linear(candidate_vector_dim, query_vector_dim)
        self.attention_query_vector = nn.Parameter(torch.empty(
            query_vector_dim).uniform_(-0.1, 0.1))
        self.writer = writer
        self.tag = tag
        self.names = names
        self.local_step = 1

    def forward(self, candidate_vector):
        """
        Args:
            candidate_vector: batch_size, candidate_size, candidate_vector_dim
        Returns:
            (shape) batch_size, candidate_vector_dim
        """
        temp = torch.tanh(self.linear(candidate_vector))
        candidate_weights = F.softmax(torch.matmul(temp, self.
            attention_query_vector), dim=1)
        if self.writer is not None:
            assert candidate_weights.size(1) == len(self.names)
            if self.local_step % 10 == 0:
                self.writer.add_scalars(self.tag, {x: y for x, y in zip(
                    self.names, candidate_weights.mean(dim=0))}, self.
                    local_step)
            self.local_step += 1
        target = torch.bmm(candidate_weights.unsqueeze(dim=1), candidate_vector
            ).squeeze(dim=1)
        return target


class UserEncoderNew(torch.nn.Module):

    def __init__(self, config):
        super(UserEncoderNew, self).__init__()
        self.additive_attention = AdditiveAttention(config.query_vector_dim,
            config.num_filters)

    def forward(self, input_0):
        primals_2 = self.additive_attention.attention_query_vector
        primals_1 = self.additive_attention.linear.weight
        primals_4 = self.additive_attention.linear.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.028612