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
from torch import nn
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_native_layer_norm_0(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-05
    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_native_layer_norm_1(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)


@triton.jit
def triton_poi_fused_add_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 3
    x0 = xindex % 4
    x4 = xindex // 12
    x2 = xindex // 12 % 4
    x5 = xindex
    tmp5 = tl.load(in_ptr0 + (x0 + 12 * x4), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr1 + (x2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp8 = tl.load(in_ptr0 + (4 + x0 + 12 * x4), xmask, eviction_policy=
        'evict_last')
    tmp12 = tl.load(in_ptr0 + x5, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 1, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = tmp1 == tmp3
    tmp7 = tmp5 + tmp6
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp9 + tmp6
    tmp11 = tmp0 == tmp3
    tmp13 = tl.where(tmp11, tmp7, tmp12)
    tmp14 = tl.where(tmp2, tmp10, tmp13)
    tl.store(out_ptr0 + x5, tmp14, xmask)


@triton.jit
def triton_poi_fused_clone_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    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 % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 12 * x2 + 48 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    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 % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (4 + y0 + 12 * x2 + 48 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused__softmax_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
    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 = tmp14 * tmp1
    tmp16 = tl_math.exp(tmp15)
    tl.store(out_ptr0 + x2, tmp16, xmask)


@triton.jit
def triton_poi_fused__softmax_6(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_7(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    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 % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (8 + y0 + 12 * x2 + 48 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_8(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    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 % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_9(in_ptr0, in_ptr1, in_ptr2,
    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')
    tmp2 = tl.load(in_ptr2 + 0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + 1)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
    tmp13 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr2 + 2)
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
    tmp20 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp21 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp22 = tl.load(in_ptr2 + 3)
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK])
    tmp4 = tmp1 + tmp3
    tmp5 = tmp0 + tmp4
    tmp10 = tmp7 + tmp9
    tmp11 = tmp6 + tmp10
    tmp12 = tmp5 + tmp11
    tmp17 = tmp14 + tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = tmp12 + tmp18
    tmp24 = tmp21 + tmp23
    tmp25 = tmp20 + tmp24
    tmp26 = tmp19 + tmp25
    tmp27 = 4.0
    tmp28 = tmp26 / tmp27
    tmp29 = tmp5 - tmp28
    tmp30 = tmp29 * tmp29
    tmp31 = tmp11 - tmp28
    tmp32 = tmp31 * tmp31
    tmp33 = tmp30 + tmp32
    tmp34 = tmp18 - tmp28
    tmp35 = tmp34 * tmp34
    tmp36 = tmp33 + tmp35
    tmp37 = tmp25 - tmp28
    tmp38 = tmp37 * tmp37
    tmp39 = tmp36 + tmp38
    tmp40 = tmp39 / tmp27
    tl.store(out_ptr0 + x0, tmp28, xmask)
    tl.store(out_ptr1 + x0, tmp40, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_10(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, in_ptr6, 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
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr6 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp6 = tmp4 - tmp5
    tmp8 = 1e-05
    tmp9 = tmp7 + tmp8
    tmp10 = libdevice.rsqrt(tmp9)
    tmp11 = tmp6 * tmp10
    tmp13 = tmp11 * tmp12
    tmp15 = tmp13 + tmp14
    tl.store(out_ptr0 + x2, tmp15, xmask)


@triton.jit
def triton_poi_fused_clone_11(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    out_ptr2, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    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 % 4
    y1 = yindex // 4
    y3 = yindex
    tmp2 = tl.load(in_ptr0 + (y0 + 12 * x2 + 48 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x2 + 4 * y0), xmask & ymask, eviction_policy=
        'evict_last')
    tmp8 = tl.load(in_ptr0 + (4 + y0 + 12 * x2 + 48 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (8 + y0 + 12 * x2 + 48 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp0 = tl.full([1, 1], 0, tl.int32)
    tmp1 = tmp0 == tmp0
    tmp4 = tmp2 + tmp3
    tmp5 = tl.where(tmp1, tmp4, tmp2)
    tmp6 = tl.full([1, 1], 1, tl.int32)
    tmp7 = tmp6 == tmp0
    tmp9 = tl.where(tmp7, tmp4, tmp8)
    tmp10 = tl.full([1, 1], 2, tl.int32)
    tmp11 = tmp10 == tmp0
    tmp13 = tl.where(tmp11, tmp4, tmp12)
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp5, xmask & ymask)
    tl.store(out_ptr1 + (x2 + 4 * y3), tmp9, xmask & ymask)
    tl.store(out_ptr2 + (x2 + 4 * y3), tmp13, xmask & ymask)


@triton.jit
def triton_poi_fused_add_12(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    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)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_out_ptr0 + x2, xmask)
    tmp6 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp7 = tmp5 + tmp6
    tmp8 = tmp4 + tmp7
    tl.store(in_out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_gelu_13(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
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865476
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tl.store(out_ptr0 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_14(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
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_out_ptr0 + x2, xmask)
    tmp2 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tl.store(in_out_ptr0 + x2, tmp4, 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
        ) = args
    args.clear()
    assert_size_stride(primals_1, (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, (12, 4), (4, 1))
    assert_size_stride(primals_5, (1, 4, 4, 1), (16, 4, 1, 1))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (12, 4), (4, 1))
    assert_size_stride(primals_11, (4, 4), (4, 1))
    assert_size_stride(primals_12, (4,), (1,))
    assert_size_stride(primals_13, (4,), (1,))
    assert_size_stride(primals_14, (4,), (1,))
    assert_size_stride(primals_15, (16, 4), (4, 1))
    assert_size_stride(primals_16, (16,), (1,))
    assert_size_stride(primals_17, (4, 16), (16, 1))
    assert_size_stride(primals_18, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_native_layer_norm_0[grid(16)](primals_3, buf0,
            buf1, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_1[grid(64)](primals_3, buf0,
            buf1, primals_1, primals_2, buf2, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del primals_1
        del primals_2
        buf3 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 12), (1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((3, 4, 4, 4, 1), (4, 48, 1, 12, 192),
            torch.float32)
        triton_poi_fused_add_2[grid(192)](buf3, primals_5, buf4, 192,
            XBLOCK=128, num_warps=4, num_stages=1)
        del buf3
        buf5 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_3[grid(16, 4)](buf4, buf5, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32)
        triton_poi_fused_clone_4[grid(16, 4)](buf4, buf6, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf7 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 1), (4, 1, 0), 
            0), reinterpret_tensor(buf6, (16, 1, 4), (4, 0, 1), 0), out=buf7)
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_5[grid(256)](buf7, buf8, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf9 = reinterpret_tensor(buf7, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf7
        triton_poi_fused__softmax_6[grid(256)](buf8, buf9, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf10 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_7[grid(16, 4)](buf4, buf10, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf11 = empty_strided_cuda((16, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf9, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf10, (16, 4, 1), (4, 1, 0), 0), out=buf11)
        buf12 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_8[grid(16, 4)](buf11, buf12, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf13 = reinterpret_tensor(buf11, (16, 4), (4, 1), 0)
        del buf11
        extern_kernels.mm(reinterpret_tensor(buf12, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf13)
        buf14 = buf1
        del buf1
        buf15 = buf0
        del buf0
        triton_poi_fused_add_native_layer_norm_9[grid(16)](primals_3, buf13,
            primals_7, buf14, buf15, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf16 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_10[grid(64)](primals_3,
            buf13, primals_7, buf14, buf15, primals_8, primals_9, buf16, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_9
        buf17 = reinterpret_tensor(buf4, (16, 12), (12, 1), 0)
        del buf4
        extern_kernels.mm(reinterpret_tensor(buf16, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_10, (4, 12), (1, 4), 0), out=buf17)
        buf18 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        buf19 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32)
        buf23 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_11[grid(16, 4)](buf17, primals_5, buf18,
            buf19, buf23, 16, 4, XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1
            )
        del buf17
        del primals_5
        buf20 = reinterpret_tensor(buf8, (16, 4, 4), (16, 4, 1), 0)
        del buf8
        extern_kernels.bmm(reinterpret_tensor(buf18, (16, 4, 1), (4, 1, 0),
            0), reinterpret_tensor(buf19, (16, 1, 4), (4, 0, 1), 0), out=buf20)
        buf21 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_5[grid(256)](buf20, buf21, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        buf22 = reinterpret_tensor(buf20, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf20
        triton_poi_fused__softmax_6[grid(256)](buf21, buf22, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf24 = empty_strided_cuda((16, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf22, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf23, (16, 4, 1), (4, 1, 0), 0), out=buf24)
        buf25 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_8[grid(16, 4)](buf24, buf25, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf26 = reinterpret_tensor(buf24, (16, 4), (4, 1), 0)
        del buf24
        extern_kernels.mm(reinterpret_tensor(buf25, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_11, (4, 4), (1, 4), 0), out=buf26)
        buf27 = reinterpret_tensor(buf26, (4, 4, 4), (16, 4, 1), 0)
        del buf26
        triton_poi_fused_add_12[grid(64)](buf27, primals_3, buf13,
            primals_7, primals_12, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_12
        buf28 = buf15
        del buf15
        buf29 = buf14
        del buf14
        triton_poi_fused_native_layer_norm_0[grid(16)](buf27, buf28, buf29,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf30 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_1[grid(64)](buf27, buf28, buf29,
            primals_13, primals_14, buf30, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf28
        del buf29
        del primals_14
        buf31 = reinterpret_tensor(buf21, (16, 16), (16, 1), 0)
        del buf21
        extern_kernels.addmm(primals_16, reinterpret_tensor(buf30, (16, 4),
            (4, 1), 0), reinterpret_tensor(primals_15, (4, 16), (1, 4), 0),
            alpha=1, beta=1, out=buf31)
        del primals_16
        buf32 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        triton_poi_fused_gelu_13[grid(256)](buf31, buf32, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf33 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf32, (16, 16), (16, 1), 0),
            reinterpret_tensor(primals_17, (16, 4), (1, 16), 0), out=buf33)
        buf34 = reinterpret_tensor(buf33, (4, 4, 4), (16, 4, 1), 0)
        del buf33
        triton_poi_fused_add_14[grid(64)](buf34, buf27, primals_18, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_18
    return (buf34, primals_3, primals_7, primals_8, primals_13,
        reinterpret_tensor(buf2, (16, 4), (4, 1), 0), buf9,
        reinterpret_tensor(buf12, (16, 4), (4, 1), 0), buf13,
        reinterpret_tensor(buf16, (16, 4), (4, 1), 0), buf22,
        reinterpret_tensor(buf25, (16, 4), (4, 1), 0), buf27,
        reinterpret_tensor(buf30, (16, 4), (4, 1), 0), buf31,
        reinterpret_tensor(buf32, (16, 16), (16, 1), 0), primals_17,
        primals_15, primals_11, reinterpret_tensor(buf23, (16, 1, 4), (4, 1,
        1), 0), reinterpret_tensor(buf18, (16, 1, 4), (4, 1, 1), 0),
        reinterpret_tensor(buf19, (16, 4, 1), (4, 1, 4), 0), primals_10,
        primals_6, reinterpret_tensor(buf10, (16, 1, 4), (4, 1, 1), 0),
        reinterpret_tensor(buf5, (16, 1, 4), (4, 1, 1), 0),
        reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 4), 0), primals_4)


class Ffn(nn.Module):

    def __init__(self, in_features, hidden_features=None, out_features=None,
        act_layer=nn.GELU, drop=0.0):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class Attention(nn.Module):

    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None,
        attn_drop=0.0, proj_drop=0.0):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5
        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x, task_embed=None, level=0):
        N, L, D = x.shape
        qkv = self.qkv(x).reshape(N, L, 3, self.num_heads, D // self.num_heads
            ).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]
        if task_embed is not None:
            _N, _H, _L, _D = q.shape
            task_embed = task_embed.reshape(1, _H, _L, _D)
            if level == 1:
                q += task_embed
                k += task_embed
            if level == 2:
                q += task_embed
        attn = q @ k.transpose(-2, -1) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)
        x = (attn @ v).transpose(1, 2).reshape(N, L, D)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class DecoderLayerNew(nn.Module):

    def __init__(self, dim, num_heads, ffn_ratio=4.0, qkv_bias=False,
        qk_scale=None, drop=0.0, attn_drop=0.0, act_layer=nn.GELU,
        norm_layer=nn.LayerNorm):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn1 = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias,
            qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
        self.norm2 = norm_layer(dim)
        self.attn2 = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias,
            qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
        self.norm3 = norm_layer(dim)
        ffn_hidden_dim = int(dim * ffn_ratio)
        self.ffn = Ffn(in_features=dim, hidden_features=ffn_hidden_dim,
            act_layer=act_layer, drop=drop)

    def forward(self, input_0, input_1):
        primals_1 = self.norm1.weight
        primals_2 = self.norm1.bias
        primals_4 = self.attn1.qkv.weight
        primals_6 = self.attn1.proj.weight
        primals_7 = self.attn1.proj.bias
        primals_8 = self.norm2.weight
        primals_9 = self.norm2.bias
        primals_10 = self.attn2.qkv.weight
        primals_11 = self.attn2.proj.weight
        primals_12 = self.attn2.proj.bias
        primals_13 = self.norm3.weight
        primals_14 = self.norm3.bias
        primals_15 = self.ffn.fc1.weight
        primals_16 = self.ffn.fc1.bias
        primals_17 = self.ffn.fc2.weight
        primals_18 = self.ffn.fc2.bias
        primals_3 = input_0
        primals_5 = 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])
        return output[0]


Measured latency_ms: 0.07944