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 sklearn.metrics import *
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 = 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)
    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 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp6 = tmp5 * tmp1
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tmp8 * tmp1
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = tmp11 * tmp1
    tmp13 = triton_helpers.maximum(tmp10, tmp12)
    tmp14 = tmp2 - tmp13
    tmp15 = 0.5
    tmp16 = tmp14 * tmp15
    tmp17 = tl_math.exp(tmp16)
    tl.store(out_ptr0 + x2, tmp17, 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
    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, primals_5, primals_6,
        primals_7) = 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, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (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, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_3, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(primals_3, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf2)
        del primals_6
        del primals_7
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 
            0), reinterpret_tensor(buf1, (4, 4, 4), (16, 1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(64)](buf3, buf4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf5 = reinterpret_tensor(buf3, (4, 1, 4, 4), (16, 16, 4, 1), 0)
        del buf3
        triton_poi_fused__softmax_1[grid(64)](buf4, buf5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf6 = reinterpret_tensor(buf4, (4, 4, 4), (16, 4, 1), 0)
        del buf4
        extern_kernels.bmm(reinterpret_tensor(buf5, (4, 4, 4), (16, 4, 1), 
            0), reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0), out=buf6)
    return reinterpret_tensor(buf6, (4, 4), (16, 1), 0
        ), buf5, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0
        ), buf5, reinterpret_tensor(buf2, (4, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf0, (4, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0)


def sequence_mask(lengths, max_len=None):
    """
    Creates a boolean mask from sequence lengths.
    """
    batch_size = lengths.numel()
    max_len = max_len or lengths.max()
    return torch.arange(0, max_len).type_as(lengths).repeat(batch_size, 1).lt(
        lengths.unsqueeze(1))


class BertSelfAttentionNew(nn.Module):
    """
    Extracted from
    """

    def __init__(self, hidden_size):
        super(BertSelfAttentionNew, self).__init__()
        self.num_attention_heads = 1
        self.attention_head_size = int(hidden_size / self.num_attention_heads)
        self.all_head_size = (self.num_attention_heads * self.
            attention_head_size)
        self.query = nn.Linear(hidden_size, self.all_head_size)
        self.key = nn.Linear(hidden_size, self.all_head_size)
        self.value = nn.Linear(hidden_size, self.all_head_size)
        self.dropout = nn.Dropout(0.2)

    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.
            attention_head_size)
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)

    def forward(self, input_0):
        primals_1 = self.query.weight
        primals_2 = self.query.bias
        primals_4 = self.key.weight
        primals_5 = self.key.bias
        primals_6 = self.value.weight
        primals_7 = self.value.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0], output[1]


Measured latency_ms: 0.05936