import torch
import torch.cuda
from torch.nn import functional as F
from torch import nn
import torch.distributed
import torch.utils.data
import torch.optim


class MultiHeadAttn(nn.Module):

    def __init__(self, n_head, d_model, d_head, dropout, dropatt=0.1,
        pre_lnorm=False):
        super(MultiHeadAttn, self).__init__()
        self.n_head = n_head
        self.d_model = d_model
        self.d_head = d_head
        self.scale = 1 / d_head ** 0.5
        self.pre_lnorm = pre_lnorm
        self.qkv_net = nn.Linear(d_model, 3 * n_head * d_head)
        self.drop = nn.Dropout(dropout)
        self.dropatt = nn.Dropout(dropatt)
        self.o_net = nn.Linear(n_head * d_head, d_model, bias=False)
        self.layer_norm = nn.LayerNorm(d_model)

    def forward(self, inp, attn_mask=None):
        return self._forward(inp, attn_mask)

    def _forward(self, inp, attn_mask=None):
        residual = inp
        if self.pre_lnorm:
            inp = self.layer_norm(inp)
        n_head, d_head = self.n_head, self.d_head
        head_q, head_k, head_v = torch.chunk(self.qkv_net(inp), 3, dim=2)
        head_q = head_q.view(inp.size(0), inp.size(1), n_head, d_head)
        head_k = head_k.view(inp.size(0), inp.size(1), n_head, d_head)
        head_v = head_v.view(inp.size(0), inp.size(1), n_head, d_head)
        q = head_q.permute(0, 2, 1, 3).reshape(-1, inp.size(1), d_head)
        k = head_k.permute(0, 2, 1, 3).reshape(-1, inp.size(1), d_head)
        v = head_v.permute(0, 2, 1, 3).reshape(-1, inp.size(1), d_head)
        attn_score = torch.bmm(q, k.transpose(1, 2))
        attn_score.mul_(self.scale)
        if attn_mask is not None:
            attn_mask = attn_mask.unsqueeze(1)
            attn_mask = attn_mask.repeat(n_head, attn_mask.size(2), 1)
            attn_score.masked_fill_(attn_mask, -float('inf'))
        attn_prob = F.softmax(attn_score, dim=2)
        attn_prob = self.dropatt(attn_prob)
        attn_vec = torch.bmm(attn_prob, v)
        attn_vec = attn_vec.view(n_head, inp.size(0), inp.size(1), d_head)
        attn_vec = attn_vec.permute(1, 2, 0, 3).contiguous().view(inp.size(
            0), inp.size(1), n_head * d_head)
        attn_out = self.o_net(attn_vec)
        attn_out = self.drop(attn_out)
        if self.pre_lnorm:
            output = residual + attn_out
        else:
            output = self.layer_norm(residual + attn_out)
        return output


def get_inputs():
    return [torch.rand([4, 4, 4])]


def get_init_inputs():
    return [[], {'n_head': 4, 'd_model': 4, 'd_head': 4, 'dropout': 0.5}]

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.cuda
from torch.nn import functional as F
from torch import nn
import torch.distributed
import torch.utils.data
import torch.optim
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, in_ptr1, 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 + 48 * x1 + 192 * x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x4, tmp2, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, in_ptr1, 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 + (32 + x0 + 4 * x2 + 48 * x1 + 192 * x3), xmask)
    tmp1 = tl.load(in_ptr1 + (32 + x0 + 4 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x4, tmp2, xmask)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, in_ptr1, 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 + (16 + x0 + 4 * x2 + 48 * x1 + 192 * x3), xmask)
    tmp1 = tl.load(in_ptr1 + (16 + x0 + 4 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x4, tmp2, xmask)


@triton.jit
def triton_poi_fused__softmax_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)
    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_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)


@triton.jit
def triton_poi_fused_clone_5(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
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 64 * x1), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_6(in_ptr0, in_ptr1, 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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), 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
    tmp15 = 4.0
    tmp16 = tmp14 / tmp15
    tmp17 = tmp2 - tmp16
    tmp18 = tmp17 * tmp17
    tmp19 = tmp5 - tmp16
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp9 - tmp16
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp16
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = tmp27 / tmp15
    tl.store(out_ptr0 + x0, tmp16, xmask)
    tl.store(out_ptr1 + x0, tmp28, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_7(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, 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
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp4 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tl.store(out_ptr0 + x2, tmp13, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (48, 4), (4, 1))
    assert_size_stride(primals_3, (48,), (1,))
    assert_size_stride(primals_4, (4, 16), (16, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 48), (48, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 48), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(256)](buf0, primals_3, buf1, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_1[grid(256)](buf0, primals_3, buf2, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_2[grid(256)](buf0, primals_3, buf3, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del primals_3
        buf4 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf1, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf3, (16, 4, 4), (16, 1, 4), 0), out=buf4)
        buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_3[grid(256)](buf4, buf5, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf6 = buf4
        del buf4
        triton_poi_fused__softmax_4[grid(256)](buf5, buf6, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf7 = buf5
        del buf5
        extern_kernels.bmm(buf6, reinterpret_tensor(buf2, (16, 4, 4), (16, 
            4, 1), 0), out=buf7)
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_5[grid(256)](buf7, buf8, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf7
        buf9 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf8, (16, 16), (16, 1), 0),
            reinterpret_tensor(primals_4, (16, 4), (1, 16), 0), out=buf9)
        buf10 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf11 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused_add_native_layer_norm_6[grid(16)](primals_1, buf9,
            buf10, buf11, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf12 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_7[grid(64)](primals_1, buf9,
            buf10, buf11, primals_5, primals_6, buf12, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf10
        del buf11
        del primals_6
    return buf12, primals_1, primals_5, buf6, reinterpret_tensor(buf8, (16,
        16), (16, 1), 0), buf9, primals_4, reinterpret_tensor(buf2, (16, 4,
        4), (16, 1, 4), 0), reinterpret_tensor(buf1, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1), 0)


class MultiHeadAttnNew(nn.Module):

    def __init__(self, n_head, d_model, d_head, dropout, dropatt=0.1,
        pre_lnorm=False):
        super(MultiHeadAttnNew, self).__init__()
        self.n_head = n_head
        self.d_model = d_model
        self.d_head = d_head
        self.scale = 1 / d_head ** 0.5
        self.pre_lnorm = pre_lnorm
        self.qkv_net = nn.Linear(d_model, 3 * n_head * d_head)
        self.drop = nn.Dropout(dropout)
        self.dropatt = nn.Dropout(dropatt)
        self.o_net = nn.Linear(n_head * d_head, d_model, bias=False)
        self.layer_norm = nn.LayerNorm(d_model)

    def _forward(self, inp, attn_mask=None):
        residual = inp
        if self.pre_lnorm:
            inp = self.layer_norm(inp)
        n_head, d_head = self.n_head, self.d_head
        head_q, head_k, head_v = torch.chunk(self.qkv_net(inp), 3, dim=2)
        head_q = head_q.view(inp.size(0), inp.size(1), n_head, d_head)
        head_k = head_k.view(inp.size(0), inp.size(1), n_head, d_head)
        head_v = head_v.view(inp.size(0), inp.size(1), n_head, d_head)
        q = head_q.permute(0, 2, 1, 3).reshape(-1, inp.size(1), d_head)
        k = head_k.permute(0, 2, 1, 3).reshape(-1, inp.size(1), d_head)
        v = head_v.permute(0, 2, 1, 3).reshape(-1, inp.size(1), d_head)
        attn_score = torch.bmm(q, k.transpose(1, 2))
        attn_score.mul_(self.scale)
        if attn_mask is not None:
            attn_mask = attn_mask.unsqueeze(1)
            attn_mask = attn_mask.repeat(n_head, attn_mask.size(2), 1)
            attn_score.masked_fill_(attn_mask, -float('inf'))
        attn_prob = F.softmax(attn_score, dim=2)
        attn_prob = self.dropatt(attn_prob)
        attn_vec = torch.bmm(attn_prob, v)
        attn_vec = attn_vec.view(n_head, inp.size(0), inp.size(1), d_head)
        attn_vec = attn_vec.permute(1, 2, 0, 3).contiguous().view(inp.size(
            0), inp.size(1), n_head * d_head)
        attn_out = self.o_net(attn_vec)
        attn_out = self.drop(attn_out)
        if self.pre_lnorm:
            output = residual + attn_out
        else:
            output = self.layer_norm(residual + attn_out)
        return output

    def forward(self, input_0):
        primals_2 = self.qkv_net.weight
        primals_3 = self.qkv_net.bias
        primals_4 = self.o_net.weight
        primals_5 = self.layer_norm.weight
        primals_6 = self.layer_norm.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
