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.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_add_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x3 = xindex // 16
    x4 = xindex % 64
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 48 * x3), xmask)
    tmp1 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp0 + tmp3
    tl.store(out_ptr0 + x5, tmp2, xmask)
    tl.store(out_ptr1 + x5, tmp4, xmask)


@triton.jit
def triton_poi_fused_clone_1(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 + (16 + y0 + 48 * y1 + 192 * x2), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused__softmax_add_mul_2(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, 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 % 4
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (1 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp29 = tl.load(in_ptr0 + (2 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp41 = tl.load(in_ptr0 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp1 = (4 + 4 * x0) % 5
    tl.full([1], 0, tl.int64)
    tmp4 = tl.full([1], 1, tl.int64)
    tmp5 = tmp1 < tmp4
    tmp6 = 0.0
    tmp7 = tl.full(tmp6.shape, 0.0, tmp6.dtype)
    tmp8 = tl.where(tmp5, tmp6, tmp7)
    tmp9 = tmp1 >= tmp4
    tl.full([1], 5, tl.int64)
    tmp12 = tl.load(in_ptr1 + (4 * ((64 + x1 + 64 * x0) // 80) + 16 * x1 +
        (-1 + (4 + 4 * x0) % 5)), tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp13 = tl.where(tmp5, tmp8, tmp12)
    tmp14 = tmp0 + tmp13
    tmp15 = 0.5
    tmp16 = tmp14 * tmp15
    tmp18 = 4 * x0 % 5
    tmp20 = tmp18 < tmp4
    tmp21 = tl.where(tmp20, tmp6, tmp7)
    tmp22 = tmp18 >= tmp4
    tmp24 = tl.load(in_ptr1 + (4 + 4 * ((x1 + 64 * x0) // 80) + 16 * x1 + (
        -1 + 4 * x0 % 5)), tmp22 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp25 = tl.where(tmp20, tmp21, tmp24)
    tmp26 = tmp17 + tmp25
    tmp27 = tmp26 * tmp15
    tmp28 = triton_helpers.maximum(tmp16, tmp27)
    tmp30 = (6 + 4 * x0) % 5
    tmp32 = tmp30 < tmp4
    tmp33 = tl.where(tmp32, tmp6, tmp7)
    tmp34 = tmp30 >= tmp4
    tmp36 = tl.load(in_ptr1 + (4 * ((96 + x1 + 64 * x0) // 80) + 16 * x1 +
        (-1 + (6 + 4 * x0) % 5)), tmp34 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp37 = tl.where(tmp32, tmp33, tmp36)
    tmp38 = tmp29 + tmp37
    tmp39 = tmp38 * tmp15
    tmp40 = triton_helpers.maximum(tmp28, tmp39)
    tmp42 = (7 + 4 * x0) % 5
    tmp44 = tmp42 < tmp4
    tmp45 = tl.where(tmp44, tmp6, tmp7)
    tmp46 = tmp42 >= tmp4
    tmp48 = tl.load(in_ptr1 + (4 * ((112 + x1 + 64 * x0) // 80) + 16 * x1 +
        (-1 + (7 + 4 * x0) % 5)), tmp46 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp49 = tl.where(tmp44, tmp45, tmp48)
    tmp50 = tmp41 + tmp49
    tmp51 = tmp50 * tmp15
    tmp52 = triton_helpers.maximum(tmp40, tmp51)
    tmp53 = tmp16 - tmp52
    tmp54 = tl_math.exp(tmp53)
    tmp55 = tmp27 - tmp52
    tmp56 = tl_math.exp(tmp55)
    tmp57 = tmp54 + tmp56
    tmp58 = tmp39 - tmp52
    tmp59 = tl_math.exp(tmp58)
    tmp60 = tmp57 + tmp59
    tmp61 = tmp51 - tmp52
    tmp62 = tl_math.exp(tmp61)
    tmp63 = tmp60 + tmp62
    tl.store(out_ptr0 + x2, tmp52, xmask)
    tl.store(out_ptr1 + x2, tmp63, xmask)


@triton.jit
def triton_poi_fused__softmax_add_mul_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    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
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (y3 + 16 * x2), xmask & ymask)
    tmp17 = tl.load(in_ptr2 + (y1 + 4 * x2), xmask & ymask, eviction_policy
        ='evict_last')
    tmp20 = tl.load(in_ptr3 + (y1 + 4 * x2), xmask & ymask, eviction_policy
        ='evict_last')
    tmp1 = (4 + y0 + 4 * y1) % 5
    tl.full([1, 1], 0, tl.int64)
    tmp4 = tl.full([1, 1], 1, tl.int64)
    tmp5 = tmp1 < tmp4
    tmp6 = 0.0
    tmp7 = tl.full(tmp6.shape, 0.0, tmp6.dtype)
    tmp8 = tl.where(tmp5, tmp6, tmp7)
    tmp9 = tmp1 >= tmp4
    tl.full([1, 1], 5, tl.int64)
    tmp12 = tl.load(in_ptr1 + (4 * ((64 + x2 + 16 * y0 + 64 * y1) // 80) + 
        16 * x2 + (-1 + (4 + y0 + 4 * y1) % 5)), tmp9 & xmask & ymask,
        eviction_policy='evict_last', other=0.0)
    tmp13 = tl.where(tmp5, tmp8, tmp12)
    tmp14 = tmp0 + tmp13
    tmp15 = 0.5
    tmp16 = tmp14 * tmp15
    tmp18 = tmp16 - tmp17
    tmp19 = tl_math.exp(tmp18)
    tmp21 = tmp19 / tmp20
    tl.store(out_ptr0 + (x2 + 16 * y3), tmp21, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_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
    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 * x3 + 192 * x1), xmask)
    tl.store(out_ptr0 + x4, tmp0, 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 % 16
    x2 = xindex // 64
    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_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,
        primals_7, primals_8, primals_9) = 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, (48, 4), (4, 1))
    assert_size_stride(primals_4, (16, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_7, (4, 16), (16, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (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_3, (4, 48), (1, 4), 0), out=buf0)
        del primals_3
        buf1 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 16), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_0[grid(256)](buf0, primals_5, primals_6, buf2,
            buf5, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        del primals_6
        buf3 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch
            .float32)
        triton_poi_fused_clone_1[grid(64, 4)](buf0, buf3, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf2, (16, 4, 4), (4, 64, 1),
            0), reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1), 0), out=buf4)
        buf6 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 4), (4, 64, 1),
            0), reinterpret_tensor(buf1, (16, 4, 4), (4, 1, 64), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 1, 4, 4), (1, 64, 16, 4), torch.float32)
        buf8 = empty_strided_cuda((4, 1, 4, 4), (1, 64, 16, 4), torch.float32)
        triton_poi_fused__softmax_add_mul_2[grid(64)](buf4, buf6, buf7,
            buf8, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_add_mul_3[grid(16, 16)](buf4, buf6, buf7,
            buf8, buf9, 16, 16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1
            )
        buf10 = reinterpret_tensor(buf6, (4, 4, 4, 4, 1), (64, 16, 4, 1, 1), 0)
        del buf6
        triton_poi_fused_clone_4[grid(256)](buf0, buf10, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf0
        buf11 = buf4
        del buf4
        extern_kernels.bmm(reinterpret_tensor(buf9, (16, 4, 4), (1, 64, 16),
            0), reinterpret_tensor(buf10, (16, 4, 4), (16, 4, 1), 0), out=buf11
            )
        buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_5[grid(256)](buf11, buf12, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf11
        buf13 = reinterpret_tensor(buf8, (16, 4), (4, 1), 0)
        del buf8
        extern_kernels.mm(reinterpret_tensor(buf12, (16, 16), (16, 1), 0),
            reinterpret_tensor(primals_7, (16, 4), (1, 16), 0), out=buf13)
        buf14 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf15 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused_add_native_layer_norm_6[grid(16)](primals_1, buf13,
            buf14, buf15, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf16 = reinterpret_tensor(buf7, (4, 4, 4), (16, 4, 1), 0)
        del buf7
        triton_poi_fused_add_native_layer_norm_7[grid(64)](primals_1, buf13,
            buf14, buf15, primals_8, primals_9, buf16, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf14
        del buf15
        del primals_9
    return buf16, primals_1, primals_8, reinterpret_tensor(primals_2, (16, 
        4), (4, 1), 0), buf9, reinterpret_tensor(buf12, (16, 16), (16, 1), 0
        ), buf13, primals_7, reinterpret_tensor(buf10, (16, 4, 4), (16, 1, 
        4), 0), reinterpret_tensor(buf5, (16, 4, 4), (4, 1, 64), 0
        ), reinterpret_tensor(buf1, (16, 4, 4), (4, 64, 1), 0
        ), reinterpret_tensor(buf2, (16, 4, 4), (4, 1, 64), 0
        ), reinterpret_tensor(buf3, (16, 4, 4), (16, 1, 4), 0)


class RelMultiHeadAttn(nn.Module):

    def __init__(self, n_head, d_model, d_head, dropout, dropatt=0, tgt_len
        =None, ext_len=None, mem_len=None, pre_lnorm=False):
        super(RelMultiHeadAttn, self).__init__()
        self.n_head = n_head
        self.d_model = d_model
        self.d_head = d_head
        self.dropout = dropout
        self.qkv_net = nn.Linear(d_model, 3 * n_head * d_head, bias=False)
        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)
        self.scale = 1 / d_head ** 0.5
        self.pre_lnorm = pre_lnorm

    def _parallelogram_mask(self, h, w, left=False):
        mask = torch.ones((h, w)).byte()
        m = min(h, w)
        mask[:m, :m] = torch.triu(mask[:m, :m])
        mask[-m:, -m:] = torch.tril(mask[-m:, -m:])
        if left:
            return mask
        else:
            return mask.flip(0)

    def _shift(self, x, qlen, klen, mask, left=False):
        if qlen > 1:
            zero_pad = torch.zeros((x.size(0), qlen - 1, x.size(2), x.size(
                3)), device=x.device, dtype=x.dtype)
        else:
            zero_pad = torch.zeros(0, device=x.device, dtype=x.dtype)
        if left:
            mask = mask.flip(1)
            x_padded = torch.cat([zero_pad, x], dim=1).expand(qlen, -1, -1, -1)
        else:
            x_padded = torch.cat([x, zero_pad], dim=1).expand(qlen, -1, -1, -1)
        x = x_padded.masked_select(mask[:, :, None, None]).view(qlen, klen,
            x.size(2), x.size(3))
        return x

    def _rel_shift(self, x, zero_triu=False):
        zero_pad = torch.zeros((x.size(0), 1, *x.size()[2:]), device=x.
            device, dtype=x.dtype)
        x_padded = torch.cat([zero_pad, x], dim=1)
        x_padded = x_padded.view(x.size(1) + 1, x.size(0), *x.size()[2:])
        x = x_padded[1:].view_as(x)
        if zero_triu:
            ones = torch.ones((x.size(0), x.size(1)))
            x = x * torch.tril(ones, x.size(1) - x.size(0))[:, :, None, None]
        return x

    def forward(self, w, r, attn_mask=None, mems=None):
        raise NotImplementedError


class RelPartialLearnableMultiHeadAttnNew(RelMultiHeadAttn):

    def __init__(self, *args, **kwargs):
        super(RelPartialLearnableMultiHeadAttnNew, self).__init__(*args, **
            kwargs)
        self.r_net = nn.Linear(self.d_model, self.n_head * self.d_head,
            bias=False)

    def forward(self, input_0, input_1, input_2, input_3):
        primals_3 = self.qkv_net.weight
        primals_7 = self.o_net.weight
        primals_8 = self.layer_norm.weight
        primals_9 = self.layer_norm.bias
        primals_4 = self.r_net.weight
        primals_1 = input_0
        primals_2 = input_1
        primals_5 = input_2
        primals_6 = input_3
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]


Measured latency_ms: 0.040052