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 numpy as np
import torch.nn.functional as F
import torch.nn as nn
import torch.distributed
import torch.distributions
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_clone_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
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), xmask)
    tl.store(out_ptr0 + x4, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 16
    y1 = yindex // 16
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 64 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused__softmax_sqrt_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    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)
    tmp8 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp13 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp16 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl.full([1], 2.0, tl.float64)
    tmp2 = tl.full([1], 0.0, tl.float64)
    tmp3 = tmp1 >= tmp2
    tmp4 = 1.0
    tmp5 = -1.0
    tmp6 = tl.where(tmp3, tmp4, tmp5)
    tmp7 = tmp0 * tmp6
    tmp9 = tmp8 * tmp6
    tmp11 = tmp10 * tmp6
    tmp12 = triton_helpers.maximum(tmp9, tmp11)
    tmp14 = tmp13 * tmp6
    tmp15 = triton_helpers.maximum(tmp12, tmp14)
    tmp17 = tmp16 * tmp6
    tmp18 = triton_helpers.maximum(tmp15, tmp17)
    tmp19 = tmp7 - tmp18
    tmp20 = tmp6.to(tl.float64)
    tmp21 = tmp20 * tmp1
    tmp22 = tmp21.to(tl.float32)
    tmp23 = tmp19 / tmp22
    tmp24 = tl_math.exp(tmp23)
    tl.store(out_ptr0 + x2, tmp24, xmask)


@triton.jit
def triton_poi_fused__softmax_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 1, 1), (16, 4, 1, 1, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64)](primals_1, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((1, 16, 16), (256, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_2, (1, 16, 4), (64, 4,
            1), 0), reinterpret_tensor(buf0, (1, 4, 16), (0, 16, 1), 0),
            out=buf1)
        buf2 = buf0
        del buf0
        triton_poi_fused_clone_0[grid(64)](primals_3, buf2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((1, 16, 16), (256, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_2, (1, 16, 4), (64, 4,
            1), 0), reinterpret_tensor(buf2, (1, 4, 16), (0, 16, 1), 0),
            out=buf3)
        buf4 = buf2
        del buf2
        triton_poi_fused_clone_0[grid(64)](primals_4, buf4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_4
        buf5 = empty_strided_cuda((1, 16, 16), (256, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_2, (1, 16, 4), (64, 4,
            1), 0), reinterpret_tensor(buf4, (1, 4, 16), (0, 16, 1), 0),
            out=buf5)
        buf6 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch
            .float32)
        triton_poi_fused_clone_1[grid(256)](buf1, buf6, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf7 = reinterpret_tensor(buf1, (4, 4, 4, 4, 1), (64, 16, 4, 1, 1), 0)
        del buf1
        triton_poi_fused_clone_2[grid(64, 4)](buf3, buf7, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        buf8 = reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1), 0)
        del buf3
        extern_kernels.bmm(reinterpret_tensor(buf6, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1), 0), out=buf8)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_sqrt_3[grid(256)](buf8, buf9, 256, XBLOCK
            =256, num_warps=4, num_stages=1)
        buf10 = reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf8
        triton_poi_fused__softmax_4[grid(256)](buf9, buf10, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf11 = reinterpret_tensor(buf9, (4, 4, 4, 4, 1), (64, 16, 4, 1, 1), 0)
        del buf9
        triton_poi_fused_clone_1[grid(256)](buf5, buf11, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf12 = reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0)
        del buf5
        extern_kernels.bmm(reinterpret_tensor(buf10, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf11, (16, 4, 4), (16, 4, 1), 0), out=buf12
            )
        buf13 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1),
            torch.float32)
        triton_poi_fused_clone_2[grid(64, 4)](buf12, buf13, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        del buf12
        buf14 = buf4
        del buf4
        triton_poi_fused_clone_0[grid(64)](primals_5, buf14, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_5
        buf15 = empty_strided_cuda((1, 16, 4), (64, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf13, (1, 16, 16), (0, 16, 1
            ), 0), reinterpret_tensor(buf14, (1, 16, 4), (0, 4, 1), 0), out
            =buf15)
    return reinterpret_tensor(buf15, (4, 4, 4), (16, 4, 1), 0
        ), buf10, reinterpret_tensor(buf13, (1, 16, 16), (256, 1, 16), 0
        ), reinterpret_tensor(buf14, (1, 4, 16), (64, 1, 4), 0
        ), reinterpret_tensor(buf11, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf6, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf7, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(primals_2, (1, 4, 16), (64, 1, 4), 0)


def compute_attention(q, k, v, dropout=None, mask=None):
    """
    :param q: Query [B, NH, NQ, EL] or [NH, 1, EL] (in this case NQ=1)
    :param k: Key [B, NH, NK, EL]
    :param v: Value [B, NH, NK, EL]
    :param mask: [B, NQ, NK]
    :param dropout:
    :return:
    """
    if q.ndim + 1 == k.ndim:
        score = torch.einsum('nij,bnkj->bnik', q, k)
    elif q.ndim == k.ndim:
        score = torch.einsum('bnij,bnkj->bnik', q, k)
    score = score / np.sqrt(q.shape[-1])
    if mask is not None:
        mask = mask[:, None]
        score = score * mask + -100000000.0 * (1 - mask)
    score = F.softmax(score, dim=-1)
    if dropout is not None:
        score = dropout(score)
    return torch.einsum('bnij,bnjk->bnik', score, v)


class MultiHeadedAttentionBase(nn.Module):

    def __init__(self, embed_dim, num_heads, latent_dim, dropout=None):
        """
        :param embed_dim: The dimension of feature in each entity.
        :param num_heads: The number of attention heads.
        :param latent_dim:
        :param dropout:
        """
        super().__init__()
        self.w_k = nn.Parameter(torch.empty(num_heads, embed_dim, latent_dim))
        self.w_v = nn.Parameter(torch.empty(num_heads, embed_dim, latent_dim))
        self.w_o = nn.Parameter(torch.empty(num_heads, latent_dim, embed_dim))
        self.dropout = nn.Dropout(dropout) if dropout else nn.Identity()

    def _reset_parameters(self):
        nn.init.xavier_normal_(self.w_k)
        nn.init.xavier_normal_(self.w_v)
        nn.init.xavier_normal_(self.w_o)
        if hasattr(self, 'q'):
            nn.init.xavier_normal_(self.q)
        if hasattr(self, 'w_q'):
            nn.init.xavier_normal_(self.w_q)


class MultiHeadSelfAttentionNew(MultiHeadedAttentionBase):

    def __init__(self, embed_dim, num_heads, latent_dim, dropout=None):
        super().__init__(embed_dim, num_heads, latent_dim, dropout)
        self.w_q = nn.Parameter(torch.empty(num_heads, embed_dim, latent_dim))
        self._reset_parameters()

    def forward(self, input_0):
        primals_1 = self.w_k
        primals_2 = self.w_v
        primals_3 = self.w_o
        primals_4 = self.w_q
        primals_5 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.013879