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.utils.checkpoint
import torch.nn.functional as F
from torch.cuda.amp import autocast
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__to_copy_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
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tmp0.to(tl.float32)
    tl.store(out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_poi_fused__to_copy_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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tmp0.to(tl.float32)
    tl.store(out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_poi_fused_clone_div_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
    x4 = xindex
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x4, xmask).to(tl.float32)
    tmp1 = tl.load(in_ptr1 + x4, xmask).to(tl.float32)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.5
    tmp4 = tmp2 * tmp3
    tl.store(out_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), tmp4, xmask)


@triton.jit
def triton_poi_fused_clone_3(in_ptr0, in_ptr1, 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 + (x2 + 16 * y3), xmask & ymask).to(tl.float32)
    tmp1 = tl.load(in_ptr1 + (x2 + 16 * y3), xmask & ymask).to(tl.float32)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (y0 + 4 * x2 + 64 * y1), tmp2, xmask & ymask)


@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).to(tl.float32)
    tmp2 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last').to(tl
        .float32)
    tmp4 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        ).to(tl.float32)
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        ).to(tl.float32)
    tmp10 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        ).to(tl.float32)
    tmp1 = tmp0.to(tl.float32)
    tmp3 = tmp2.to(tl.float32)
    tmp5 = tmp4.to(tl.float32)
    tmp6 = triton_helpers.maximum(tmp3, tmp5)
    tmp8 = tmp7.to(tl.float32)
    tmp9 = triton_helpers.maximum(tmp6, tmp8)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = triton_helpers.maximum(tmp9, tmp11)
    tmp13 = tmp1 - tmp12
    tmp14 = tl_math.exp(tmp13)
    tl.store(out_ptr0 + x2, tmp14, xmask)


@triton.jit
def triton_poi_fused__softmax__to_copy_5(in_ptr0, 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
    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
    tmp9 = tmp8.to(tl.float32)
    tl.store(out_ptr0 + x2, tmp8, xmask)
    tl.store(out_ptr1 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_clone_6(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
    x4 = xindex
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x4, xmask).to(tl.float32)
    tmp1 = tl.load(in_ptr1 + x4, xmask).to(tl.float32)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), tmp2, xmask)


@triton.jit
def triton_poi_fused_clone_7(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).to(tl
        .float32)
    tl.store(out_ptr0 + x4, tmp0, xmask)


@triton.jit
def triton_poi_fused__to_copy_t_8(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tmp0.to(tl.float32)
    tl.store(out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_poi_fused_add_9(in_ptr0, in_ptr1, in_ptr2, 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
    tmp0 = tl.load(in_ptr0 + x0, xmask).to(tl.float32)
    tmp1 = tl.load(in_ptr1 + x0, xmask).to(tl.float32)
    tmp4 = tl.load(in_ptr2 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp2.to(tl.float32)
    tmp5 = tmp3 + tmp4
    tl.store(out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_10(in_ptr0, 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_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-06
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused__to_copy_native_layer_norm_11(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, in_ptr4, 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
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = tmp8.to(tl.float32)
    tl.store(out_ptr0 + x2, tmp8, xmask)
    tl.store(out_ptr1 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_add_12(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask).to(tl.float32)
    tmp1 = tl.load(in_ptr1 + x0, xmask).to(tl.float32)
    tmp4 = tl.load(in_out_ptr0 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp2.to(tl.float32)
    tmp5 = tmp3 + tmp4
    tl.store(in_out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_add_relu_threshold_backward_13(in_out_ptr0, in_ptr0,
    in_ptr1, 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
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask).to(tl.float32)
    tmp1 = tl.load(in_ptr0 + x2, xmask).to(tl.float32)
    tmp2 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp2.to(tl.float32)
    tmp4 = tmp1 + tmp3
    tmp5 = tmp0 + tmp4
    tmp6 = tl.full([1], 0, tl.int32)
    tmp7 = triton_helpers.maximum(tmp6, tmp5)
    tmp8 = 0.0
    tmp9 = tmp7 <= tmp8
    tl.store(in_out_ptr0 + x2, tmp7, xmask)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_add_14(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, 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
    tmp0 = tl.load(in_ptr0 + x2, xmask).to(tl.float32)
    tmp1 = tl.load(in_ptr1 + x2, xmask).to(tl.float32)
    tmp2 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_out_ptr0 + x2, xmask)
    tmp3 = tmp2.to(tl.float32)
    tmp4 = tmp1 + tmp3
    tmp5 = tmp0 + tmp4
    tmp6 = tmp5.to(tl.float32)
    tmp8 = tmp6 + tmp7
    tl.store(in_out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_15(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, 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 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + 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, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21, primals_22,
        primals_23, primals_24, primals_25, primals_26, primals_27,
        primals_28, primals_29, primals_30, primals_31, primals_32,
        primals_33, primals_34, primals_35, primals_36, primals_37,
        primals_38, primals_39, primals_40) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (16, 4), (4, 1))
    assert_size_stride(primals_4, (16, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (16, 4), (4, 1))
    assert_size_stride(primals_7, (16, 4), (4, 1))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (16, 4), (4, 1))
    assert_size_stride(primals_10, (16, 4), (4, 1))
    assert_size_stride(primals_11, (1, 16), (16, 1))
    assert_size_stride(primals_12, (4, 1), (1, 1))
    assert_size_stride(primals_13, (4, 16), (16, 1))
    assert_size_stride(primals_14, (4,), (1,))
    assert_size_stride(primals_15, (4,), (1,))
    assert_size_stride(primals_16, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_17, (4, 4), (4, 1))
    assert_size_stride(primals_18, (16, 4), (4, 1))
    assert_size_stride(primals_19, (16, 4), (4, 1))
    assert_size_stride(primals_20, (4, 4), (4, 1))
    assert_size_stride(primals_21, (16, 4), (4, 1))
    assert_size_stride(primals_22, (16, 4), (4, 1))
    assert_size_stride(primals_23, (4, 4), (4, 1))
    assert_size_stride(primals_24, (16, 4), (4, 1))
    assert_size_stride(primals_25, (16, 4), (4, 1))
    assert_size_stride(primals_26, (1, 16), (16, 1))
    assert_size_stride(primals_27, (4, 1), (1, 1))
    assert_size_stride(primals_28, (4, 16), (16, 1))
    assert_size_stride(primals_29, (4,), (1,))
    assert_size_stride(primals_30, (4,), (1,))
    assert_size_stride(primals_31, (1, 4), (4, 1))
    assert_size_stride(primals_32, (4, 1), (1, 1))
    assert_size_stride(primals_33, (4, 4), (4, 1))
    assert_size_stride(primals_34, (4,), (1,))
    assert_size_stride(primals_35, (1, 4), (4, 1))
    assert_size_stride(primals_36, (4, 1), (1, 1))
    assert_size_stride(primals_37, (4, 4), (4, 1))
    assert_size_stride(primals_38, (4,), (1,))
    assert_size_stride(primals_39, (4,), (1,))
    assert_size_stride(primals_40, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float16)
        get_raw_stream(0)
        triton_poi_fused__to_copy_0[grid(64)](primals_1, buf0, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_1[grid(16)](primals_2, buf1, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(buf1, (4, 4), (1, 4), 0), out=buf2)
        buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_0[grid(64)](primals_3, buf3, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        del primals_3
        buf4 = empty_strided_cuda((16, 16), (16, 1), torch.float16)
        extern_kernels.mm(buf2, reinterpret_tensor(buf3, (4, 16), (1, 4), 0
            ), out=buf4)
        buf5 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_0[grid(64)](primals_4, buf5, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        del primals_4
        buf6 = empty_strided_cuda((16, 16), (16, 1), torch.float16)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(buf5, (4, 16), (1, 4), 0), out=buf6)
        buf7 = buf1
        del buf1
        triton_poi_fused__to_copy_1[grid(16)](primals_5, buf7, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        del primals_5
        buf8 = buf5
        del buf5
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(buf7, (4, 4), (1, 4), 0), out=buf8)
        buf9 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_0[grid(64)](primals_6, buf9, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        del primals_6
        buf10 = empty_strided_cuda((16, 16), (16, 1), torch.float16)
        extern_kernels.mm(buf8, reinterpret_tensor(buf9, (4, 16), (1, 4), 0
            ), out=buf10)
        buf11 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_0[grid(64)](primals_7, buf11, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        del primals_7
        buf12 = empty_strided_cuda((16, 16), (16, 1), torch.float16)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(buf11, (4, 16), (1, 4), 0), out=buf12)
        buf13 = buf7
        del buf7
        triton_poi_fused__to_copy_1[grid(16)](primals_8, buf13, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        del primals_8
        buf14 = buf11
        del buf11
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(buf13, (4, 4), (1, 4), 0), out=buf14)
        buf15 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_0[grid(64)](primals_9, buf15, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        del primals_9
        buf16 = empty_strided_cuda((16, 16), (16, 1), torch.float16)
        extern_kernels.mm(buf14, reinterpret_tensor(buf15, (4, 16), (1, 4),
            0), out=buf16)
        buf17 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_0[grid(64)](primals_10, buf17, 64, XBLOCK
            =64, num_warps=1, num_stages=1)
        del primals_10
        buf18 = empty_strided_cuda((16, 16), (16, 1), torch.float16)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(buf17, (4, 16), (1, 4), 0), out=buf18)
        buf19 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float16)
        triton_poi_fused_clone_div_2[grid(256)](buf4, buf6, buf19, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf20 = reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf6
        triton_poi_fused_clone_3[grid(16, 16)](buf10, buf12, buf20, 16, 16,
            XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        buf21 = reinterpret_tensor(buf12, (16, 4, 4), (16, 4, 1), 0)
        del buf12
        extern_kernels.bmm(reinterpret_tensor(buf19, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf20, (16, 4, 4), (16, 4, 1), 0), out=buf21
            )
        buf22 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_4[grid(256)](buf21, buf22, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf23 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf25 = reinterpret_tensor(buf21, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf21
        triton_poi_fused__softmax__to_copy_5[grid(256)](buf22, buf23, buf25,
            256, XBLOCK=256, num_warps=4, num_stages=1)
        buf24 = reinterpret_tensor(buf10, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf10
        triton_poi_fused_clone_6[grid(256)](buf16, buf18, buf24, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf26 = reinterpret_tensor(buf18, (16, 4, 4), (16, 4, 1), 0)
        del buf18
        extern_kernels.bmm(reinterpret_tensor(buf25, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf24, (16, 4, 4), (16, 4, 1), 0), out=buf26
            )
        buf27 = reinterpret_tensor(buf13, (16, 1), (1, 16), 0)
        del buf13
        triton_poi_fused__to_copy_1[grid(16)](primals_11, buf27, 16, XBLOCK
            =16, num_warps=1, num_stages=1)
        del primals_11
        buf28 = reinterpret_tensor(buf16, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf16
        triton_poi_fused_clone_7[grid(256)](buf26, buf28, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf29 = empty_strided_cuda((16, 1), (1, 1), torch.float16)
        extern_kernels.mm(reinterpret_tensor(buf28, (16, 16), (16, 1), 0),
            buf27, out=buf29)
        buf30 = empty_strided_cuda((1, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_t_8[grid(4)](primals_12, buf30, 4, XBLOCK
            =4, num_warps=1, num_stages=1)
        del primals_12
        buf31 = buf17
        del buf17
        extern_kernels.mm(buf29, buf30, out=buf31)
        buf32 = empty_strided_cuda((16, 4), (1, 16), torch.float16)
        triton_poi_fused__to_copy_0[grid(64)](primals_13, buf32, 64, XBLOCK
            =64, num_warps=1, num_stages=1)
        del primals_13
        buf33 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        extern_kernels.mm(reinterpret_tensor(buf28, (16, 16), (16, 1), 0),
            buf32, out=buf33)
        buf34 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_9[grid(64)](buf31, buf33, primals_1, buf34, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_1
        buf35 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf36 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused_native_layer_norm_10[grid(16)](buf34, buf35, buf36,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf37 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf39 = reinterpret_tensor(buf33, (4, 4, 4), (16, 4, 1), 0)
        del buf33
        triton_poi_fused__to_copy_native_layer_norm_11[grid(64)](buf34,
            buf35, buf36, primals_14, primals_15, buf37, buf39, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        del primals_15
        buf38 = empty_strided_cuda((4, 4), (1, 4), torch.float16)
        triton_poi_fused__to_copy_1[grid(16)](primals_17, buf38, 16, XBLOCK
            =16, num_warps=1, num_stages=1)
        del primals_17
        buf40 = buf31
        del buf31
        extern_kernels.mm(reinterpret_tensor(buf39, (16, 4), (4, 1), 0),
            buf38, out=buf40)
        buf41 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_0[grid(64)](primals_18, buf41, 64, XBLOCK
            =64, num_warps=1, num_stages=1)
        del primals_18
        buf42 = reinterpret_tensor(buf26, (16, 16), (16, 1), 0)
        del buf26
        extern_kernels.mm(buf40, reinterpret_tensor(buf41, (4, 16), (1, 4),
            0), out=buf42)
        buf43 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_0[grid(64)](primals_19, buf43, 64, XBLOCK
            =64, num_warps=1, num_stages=1)
        del primals_19
        buf44 = buf4
        del buf4
        extern_kernels.mm(reinterpret_tensor(buf39, (16, 4), (4, 1), 0),
            reinterpret_tensor(buf43, (4, 16), (1, 4), 0), out=buf44)
        buf45 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float16)
        triton_poi_fused__to_copy_0[grid(64)](primals_16, buf45, 64, XBLOCK
            =64, num_warps=1, num_stages=1)
        del primals_16
        buf46 = empty_strided_cuda((4, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_1[grid(16)](primals_20, buf46, 16, XBLOCK
            =16, num_warps=1, num_stages=1)
        del primals_20
        buf47 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        extern_kernels.mm(reinterpret_tensor(buf45, (16, 4), (4, 1), 0),
            reinterpret_tensor(buf46, (4, 4), (1, 4), 0), out=buf47)
        buf48 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_0[grid(64)](primals_21, buf48, 64, XBLOCK
            =64, num_warps=1, num_stages=1)
        del primals_21
        buf49 = empty_strided_cuda((16, 16), (16, 1), torch.float16)
        extern_kernels.mm(buf47, reinterpret_tensor(buf48, (4, 16), (1, 4),
            0), out=buf49)
        buf50 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_0[grid(64)](primals_22, buf50, 64, XBLOCK
            =64, num_warps=1, num_stages=1)
        del primals_22
        buf51 = empty_strided_cuda((16, 16), (16, 1), torch.float16)
        extern_kernels.mm(reinterpret_tensor(buf45, (16, 4), (4, 1), 0),
            reinterpret_tensor(buf50, (4, 16), (1, 4), 0), out=buf51)
        buf52 = buf46
        del buf46
        triton_poi_fused__to_copy_1[grid(16)](primals_23, buf52, 16, XBLOCK
            =16, num_warps=1, num_stages=1)
        del primals_23
        buf53 = buf50
        del buf50
        extern_kernels.mm(reinterpret_tensor(buf45, (16, 4), (4, 1), 0),
            reinterpret_tensor(buf52, (4, 4), (1, 4), 0), out=buf53)
        buf54 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_0[grid(64)](primals_24, buf54, 64, XBLOCK
            =64, num_warps=1, num_stages=1)
        del primals_24
        buf55 = empty_strided_cuda((16, 16), (16, 1), torch.float16)
        extern_kernels.mm(buf53, reinterpret_tensor(buf54, (4, 16), (1, 4),
            0), out=buf55)
        buf56 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_0[grid(64)](primals_25, buf56, 64, XBLOCK
            =64, num_warps=1, num_stages=1)
        del primals_25
        buf57 = empty_strided_cuda((16, 16), (16, 1), torch.float16)
        extern_kernels.mm(reinterpret_tensor(buf45, (16, 4), (4, 1), 0),
            reinterpret_tensor(buf56, (4, 16), (1, 4), 0), out=buf57)
        buf58 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float16)
        triton_poi_fused_clone_div_2[grid(256)](buf42, buf44, buf58, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf42
        buf59 = reinterpret_tensor(buf44, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf44
        triton_poi_fused_clone_3[grid(16, 16)](buf49, buf51, buf59, 16, 16,
            XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        buf60 = reinterpret_tensor(buf51, (16, 4, 4), (16, 4, 1), 0)
        del buf51
        extern_kernels.bmm(reinterpret_tensor(buf58, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf59, (16, 4, 4), (16, 4, 1), 0), out=buf60
            )
        buf61 = buf22
        del buf22
        triton_poi_fused__softmax_4[grid(256)](buf60, buf61, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf62 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf64 = reinterpret_tensor(buf60, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf60
        triton_poi_fused__softmax__to_copy_5[grid(256)](buf61, buf62, buf64,
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf61
        buf63 = reinterpret_tensor(buf49, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf49
        triton_poi_fused_clone_6[grid(256)](buf55, buf57, buf63, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf65 = reinterpret_tensor(buf57, (16, 4, 4), (16, 4, 1), 0)
        del buf57
        extern_kernels.bmm(reinterpret_tensor(buf64, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf63, (16, 4, 4), (16, 4, 1), 0), out=buf65
            )
        buf66 = reinterpret_tensor(buf52, (16, 1), (1, 16), 0)
        del buf52
        triton_poi_fused__to_copy_1[grid(16)](primals_26, buf66, 16, XBLOCK
            =16, num_warps=1, num_stages=1)
        del primals_26
        buf67 = reinterpret_tensor(buf55, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf55
        triton_poi_fused_clone_7[grid(256)](buf65, buf67, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf65
        buf68 = empty_strided_cuda((16, 1), (1, 1), torch.float16)
        extern_kernels.mm(reinterpret_tensor(buf67, (16, 16), (16, 1), 0),
            buf66, out=buf68)
        buf69 = empty_strided_cuda((1, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_t_8[grid(4)](primals_27, buf69, 4, XBLOCK
            =4, num_warps=1, num_stages=1)
        del primals_27
        buf70 = buf56
        del buf56
        extern_kernels.mm(buf68, buf69, out=buf70)
        buf71 = empty_strided_cuda((16, 4), (1, 16), torch.float16)
        triton_poi_fused__to_copy_0[grid(64)](primals_28, buf71, 64, XBLOCK
            =64, num_warps=1, num_stages=1)
        del primals_28
        buf72 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        extern_kernels.mm(reinterpret_tensor(buf67, (16, 16), (16, 1), 0),
            buf71, out=buf72)
        buf73 = buf37
        del buf37
        triton_poi_fused_add_12[grid(64)](buf73, buf70, buf72, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        buf74 = buf36
        del buf36
        buf75 = buf35
        del buf35
        triton_poi_fused_native_layer_norm_10[grid(16)](buf73, buf74, buf75,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf76 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf78 = reinterpret_tensor(buf72, (4, 4, 4), (16, 4, 1), 0)
        del buf72
        triton_poi_fused__to_copy_native_layer_norm_11[grid(64)](buf73,
            buf74, buf75, primals_29, primals_30, buf76, buf78, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        del primals_30
        buf77 = empty_strided_cuda((4, 1), (1, 4), torch.float16)
        triton_poi_fused__to_copy_t_8[grid(4)](primals_31, buf77, 4, XBLOCK
            =4, num_warps=1, num_stages=1)
        del primals_31
        buf79 = empty_strided_cuda((16, 1), (1, 1), torch.float16)
        extern_kernels.mm(reinterpret_tensor(buf78, (16, 4), (4, 1), 0),
            buf77, out=buf79)
        buf80 = empty_strided_cuda((1, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_t_8[grid(4)](primals_32, buf80, 4, XBLOCK
            =4, num_warps=1, num_stages=1)
        del primals_32
        buf81 = buf70
        del buf70
        extern_kernels.mm(buf79, buf80, out=buf81)
        buf82 = empty_strided_cuda((4, 4), (1, 4), torch.float16)
        triton_poi_fused__to_copy_1[grid(16)](primals_33, buf82, 16, XBLOCK
            =16, num_warps=1, num_stages=1)
        del primals_33
        buf83 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        extern_kernels.mm(reinterpret_tensor(buf78, (16, 4), (4, 1), 0),
            buf82, out=buf83)
        buf84 = empty_strided_cuda((4, 1), (1, 4), torch.float16)
        triton_poi_fused__to_copy_t_8[grid(4)](primals_35, buf84, 4, XBLOCK
            =4, num_warps=1, num_stages=1)
        del primals_35
        buf85 = reinterpret_tensor(buf81, (4, 4, 4), (16, 4, 1), 0)
        del buf81
        buf95 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        triton_poi_fused_add_relu_threshold_backward_13[grid(64)](buf85,
            buf83, primals_34, buf95, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_34
        buf86 = empty_strided_cuda((16, 1), (1, 1), torch.float16)
        extern_kernels.mm(reinterpret_tensor(buf85, (16, 4), (4, 1), 0),
            buf84, out=buf86)
        buf87 = empty_strided_cuda((1, 4), (4, 1), torch.float16)
        triton_poi_fused__to_copy_t_8[grid(4)](primals_36, buf87, 4, XBLOCK
            =4, num_warps=1, num_stages=1)
        del primals_36
        buf88 = buf83
        del buf83
        extern_kernels.mm(buf86, buf87, out=buf88)
        buf89 = empty_strided_cuda((4, 4), (1, 4), torch.float16)
        triton_poi_fused__to_copy_1[grid(16)](primals_37, buf89, 16, XBLOCK
            =16, num_warps=1, num_stages=1)
        del primals_37
        buf90 = empty_strided_cuda((16, 4), (4, 1), torch.float16)
        extern_kernels.mm(reinterpret_tensor(buf85, (16, 4), (4, 1), 0),
            buf89, out=buf90)
        buf91 = buf76
        del buf76
        triton_poi_fused_add_14[grid(64)](buf91, buf88, buf90, primals_38, 
            64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf88
        del buf90
        del primals_38
        buf92 = buf75
        del buf75
        buf93 = buf74
        del buf74
        triton_poi_fused_native_layer_norm_10[grid(16)](buf91, buf92, buf93,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf94 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_15[grid(64)](buf91, buf92, buf93,
            primals_39, primals_40, buf94, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf92
        del buf93
        del primals_40
    return (buf94, buf23, buf62, primals_14, primals_29, primals_39,
        reinterpret_tensor(buf0, (16, 4), (4, 1), 0), reinterpret_tensor(
        buf3, (4, 16), (1, 4), 0), buf2, reinterpret_tensor(buf9, (4, 16),
        (1, 4), 0), buf8, reinterpret_tensor(buf15, (4, 16), (1, 4), 0),
        buf14, buf23, reinterpret_tensor(buf28, (16, 16), (16, 1), 0),
        buf29, buf34, reinterpret_tensor(buf39, (16, 4), (4, 1), 0),
        reinterpret_tensor(buf41, (4, 16), (1, 4), 0), buf40,
        reinterpret_tensor(buf43, (4, 16), (1, 4), 0), reinterpret_tensor(
        buf45, (16, 4), (4, 1), 0), reinterpret_tensor(buf48, (4, 16), (1, 
        4), 0), buf47, reinterpret_tensor(buf54, (4, 16), (1, 4), 0), buf53,
        buf62, reinterpret_tensor(buf67, (16, 16), (16, 1), 0), buf68,
        buf73, reinterpret_tensor(buf78, (16, 4), (4, 1), 0), buf79,
        reinterpret_tensor(buf85, (16, 4), (4, 1), 0), buf86, buf91,
        reinterpret_tensor(buf89, (4, 4), (4, 1), 0), reinterpret_tensor(
        buf87, (4, 1), (1, 1), 0), reinterpret_tensor(buf84, (1, 4), (4, 1),
        0), buf95, reinterpret_tensor(buf82, (4, 4), (4, 1), 0),
        reinterpret_tensor(buf80, (4, 1), (1, 1), 0), reinterpret_tensor(
        buf77, (1, 4), (4, 1), 0), reinterpret_tensor(buf71, (4, 16), (16, 
        1), 0), reinterpret_tensor(buf69, (4, 1), (1, 1), 0),
        reinterpret_tensor(buf66, (1, 16), (16, 1), 0), reinterpret_tensor(
        buf64, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf63, (16, 4,
        4), (16, 1, 4), 0), reinterpret_tensor(buf58, (16, 4, 4), (16, 1, 4
        ), 0), reinterpret_tensor(buf59, (16, 4, 4), (16, 1, 4), 0),
        reinterpret_tensor(buf38, (4, 4), (4, 1), 0), reinterpret_tensor(
        buf32, (4, 16), (16, 1), 0), reinterpret_tensor(buf30, (4, 1), (1, 
        1), 0), reinterpret_tensor(buf27, (1, 16), (16, 1), 0),
        reinterpret_tensor(buf25, (16, 4, 4), (16, 1, 4), 0),
        reinterpret_tensor(buf24, (16, 4, 4), (16, 1, 4), 0),
        reinterpret_tensor(buf19, (16, 4, 4), (16, 1, 4), 0),
        reinterpret_tensor(buf20, (16, 4, 4), (16, 1, 4), 0))


class ScaledDotProductAttention(nn.Module):
    """ Scaled Dot-Product Attention """

    def __init__(self, temperature, attn_dropout=0.1):
        super().__init__()
        self.temperature = temperature
        self.dropout = nn.Dropout(attn_dropout)

    @autocast()
    def forward(self, q, k, v, mask=None):
        attn = torch.matmul(q / self.temperature, k.transpose(2, 3))
        if mask is not None:
            attn = attn.masked_fill(mask == 0, -2 ** 15)
        attn = self.dropout(F.softmax(attn, dim=-1))
        output = torch.matmul(attn, v)
        return output, attn


class LowRankResidualMultiHeadAttention(nn.Module):
    """ Multi-Head Attention module """

    def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
        super().__init__()
        self.n_head = n_head
        self.d_k = d_k
        self.d_v = d_v
        self.w_qs_u = nn.Linear(int(n_head * d_k / 4), n_head * d_k, bias=False
            )
        self.w_qs_v = nn.Linear(d_model, int(n_head * d_k / 4), bias=False)
        self.w_qs_res = nn.Linear(d_model, n_head * d_k, bias=False)
        self.w_ks_u = nn.Linear(int(n_head * d_k / 4), n_head * d_k, bias=False
            )
        self.w_ks_v = nn.Linear(d_model, int(n_head * d_k / 4), bias=False)
        self.w_ks_res = nn.Linear(d_model, n_head * d_k, bias=False)
        self.w_vs_u = nn.Linear(int(n_head * d_k / 4), n_head * d_k, bias=False
            )
        self.w_vs_v = nn.Linear(d_model, int(n_head * d_k / 4), bias=False)
        self.w_vs_res = nn.Linear(d_model, n_head * d_v, bias=False)
        self.fc_u = nn.Linear(int(d_model / 4), d_model, bias=False)
        self.fc_v = nn.Linear(n_head * d_v, int(d_model / 4), bias=False)
        self.fc_res = nn.Linear(n_head * d_v, d_model, bias=False)
        self.attention = ScaledDotProductAttention(temperature=d_k ** 0.5)
        self.dropout = nn.Dropout(dropout)
        self.layer_norm = nn.LayerNorm(d_model, eps=1e-06)

    @autocast()
    def forward(self, q, k, v, mask=None):
        d_k, _d_v, n_head = self.d_k, self.d_v, self.n_head
        sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1)
        residual = q
        q = (self.w_qs_u(self.w_qs_v(q)) + self.w_qs_res(q)).view(sz_b,
            len_q, n_head, d_k)
        k = (self.w_ks_u(self.w_ks_v(k)) + self.w_ks_res(k)).view(sz_b,
            len_k, n_head, d_k)
        v = (self.w_vs_u(self.w_vs_v(v)) + self.w_vs_res(v)).view(sz_b,
            len_v, n_head, d_k)
        q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
        if mask is not None:
            mask = mask.unsqueeze(1)
        q, attn = self.attention(q, k, v, mask=mask)
        q = q.transpose(1, 2).contiguous().view(sz_b, len_q, -1)
        q = self.dropout(self.fc_u(self.fc_v(q)) + self.fc_res(q))
        q += residual
        q = self.layer_norm(q)
        return q, attn


class LowRankResidualPositionwiseFeedForward(nn.Module):
    """ A two-feed-forward-layer module """

    def __init__(self, d_in, d_hid, dropout=0.1):
        super().__init__()
        self.w_1_u = nn.Linear(int(d_in / 4), d_hid, bias=False)
        self.w_1_v = nn.Linear(d_in, int(d_in / 4), bias=False)
        self.w_1_res = nn.Linear(d_in, d_hid)
        self.w_2_u = nn.Linear(int(d_in / 4), d_in, bias=False)
        self.w_2_v = nn.Linear(d_hid, int(d_in / 4), bias=False)
        self.w_2_res = nn.Linear(d_hid, d_in)
        self.layer_norm = nn.LayerNorm(d_in, eps=1e-06)
        self.dropout = nn.Dropout(dropout)

    @autocast()
    def forward(self, x):
        residual = x
        x = F.relu(self.w_1_u(self.w_1_v(x)) + self.w_1_res(x))
        x = self.w_2_u(self.w_2_v(x)) + self.w_2_res(x)
        x = self.dropout(x)
        x += residual
        x = self.layer_norm(x)
        return x


class LowRankResidualDecoderLayerNew(nn.Module):
    """ Compose with three layers """

    def __init__(self, d_model, d_inner, n_head, d_k, d_v, dropout=0.1):
        super(LowRankResidualDecoderLayerNew, self).__init__()
        self.slf_attn = LowRankResidualMultiHeadAttention(n_head, d_model,
            d_k, d_v, dropout=dropout)
        self.enc_attn = LowRankResidualMultiHeadAttention(n_head, d_model,
            d_k, d_v, dropout=dropout)
        self.pos_ffn = LowRankResidualPositionwiseFeedForward(d_model,
            d_inner, dropout=dropout)

    def forward(self, input_0, input_1):
        primals_3 = self.slf_attn.w_qs_u.weight
        primals_2 = self.slf_attn.w_qs_v.weight
        primals_4 = self.slf_attn.w_qs_res.weight
        primals_6 = self.slf_attn.w_ks_u.weight
        primals_5 = self.slf_attn.w_ks_v.weight
        primals_7 = self.slf_attn.w_ks_res.weight
        primals_9 = self.slf_attn.w_vs_u.weight
        primals_8 = self.slf_attn.w_vs_v.weight
        primals_10 = self.slf_attn.w_vs_res.weight
        primals_12 = self.slf_attn.fc_u.weight
        primals_11 = self.slf_attn.fc_v.weight
        primals_13 = self.slf_attn.fc_res.weight
        primals_14 = self.slf_attn.layer_norm.weight
        primals_15 = self.slf_attn.layer_norm.bias
        primals_18 = self.enc_attn.w_qs_u.weight
        primals_17 = self.enc_attn.w_qs_v.weight
        primals_19 = self.enc_attn.w_qs_res.weight
        primals_21 = self.enc_attn.w_ks_u.weight
        primals_20 = self.enc_attn.w_ks_v.weight
        primals_22 = self.enc_attn.w_ks_res.weight
        primals_24 = self.enc_attn.w_vs_u.weight
        primals_23 = self.enc_attn.w_vs_v.weight
        primals_25 = self.enc_attn.w_vs_res.weight
        primals_27 = self.enc_attn.fc_u.weight
        primals_26 = self.enc_attn.fc_v.weight
        primals_28 = self.enc_attn.fc_res.weight
        primals_29 = self.enc_attn.layer_norm.weight
        primals_30 = self.enc_attn.layer_norm.bias
        primals_32 = self.pos_ffn.w_1_u.weight
        primals_31 = self.pos_ffn.w_1_v.weight
        primals_33 = self.pos_ffn.w_1_res.weight
        primals_34 = self.pos_ffn.w_1_res.bias
        primals_36 = self.pos_ffn.w_2_u.weight
        primals_35 = self.pos_ffn.w_2_v.weight
        primals_37 = self.pos_ffn.w_2_res.weight
        primals_38 = self.pos_ffn.w_2_res.bias
        primals_39 = self.pos_ffn.layer_norm.weight
        primals_40 = self.pos_ffn.layer_norm.bias
        primals_1 = input_0
        primals_16 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21, primals_22, primals_23, primals_24,
            primals_25, primals_26, primals_27, primals_28, primals_29,
            primals_30, primals_31, primals_32, primals_33, primals_34,
            primals_35, primals_36, primals_37, primals_38, primals_39,
            primals_40])
        return output[0], output[1], output[2]


Measured latency_ms: 0.257248