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
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_clone_mul_2(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')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)


@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 + (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_clone_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 16
    y1 = yindex // 16
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 64 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused__softmax_5(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
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
    tmp6 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
    tmp9 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp3 + tmp1
    tmp5 = triton_helpers.maximum(tmp2, tmp4)
    tmp7 = tmp6 + tmp1
    tmp8 = triton_helpers.maximum(tmp5, tmp7)
    tmp10 = tmp9 + tmp1
    tmp11 = triton_helpers.maximum(tmp8, tmp10)
    tmp12 = tmp2 - tmp11
    tmp13 = tl_math.exp(tmp12)
    tmp14 = tmp4 - tmp11
    tmp15 = tl_math.exp(tmp14)
    tmp16 = tmp13 + tmp15
    tmp17 = tmp7 - tmp11
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp16 + tmp18
    tmp20 = tmp10 - tmp11
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp19 + tmp21
    tl.store(out_ptr0 + x2, tmp11, xmask)
    tl.store(out_ptr1 + x2, tmp22, xmask)


@triton.jit
def triton_poi_fused_clone_6(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp6 = tl.load(in_ptr3 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp7 = tmp5 / tmp6
    tl.store(out_ptr0 + x3, tmp7, xmask)


@triton.jit
def triton_poi_fused_clone_7(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
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 64 * y1), xmask & ymask)
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 16 * y3), tmp2, 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 + (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_9(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_mul_native_layer_norm_10(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, 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 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tl.load(in_ptr2 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr3 + 0)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK])
    tmp9 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + 1)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK])
    tmp12 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp13 = tl.load(in_ptr3 + 1)
    tmp14 = tl.broadcast_to(tmp13, [XBLOCK])
    tmp19 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr1 + 2)
    tmp21 = tl.broadcast_to(tmp20, [XBLOCK])
    tmp22 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp23 = tl.load(in_ptr3 + 2)
    tmp24 = tl.broadcast_to(tmp23, [XBLOCK])
    tmp29 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp30 = tl.load(in_ptr1 + 3)
    tmp31 = tl.broadcast_to(tmp30, [XBLOCK])
    tmp32 = tl.load(in_ptr2 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp33 = tl.load(in_ptr3 + 3)
    tmp34 = tl.broadcast_to(tmp33, [XBLOCK])
    tmp6 = tmp3 + tmp5
    tmp7 = tmp2 * tmp6
    tmp8 = tmp0 + tmp7
    tmp15 = tmp12 + tmp14
    tmp16 = tmp11 * tmp15
    tmp17 = tmp9 + tmp16
    tmp18 = tmp8 + tmp17
    tmp25 = tmp22 + tmp24
    tmp26 = tmp21 * tmp25
    tmp27 = tmp19 + tmp26
    tmp28 = tmp18 + tmp27
    tmp35 = tmp32 + tmp34
    tmp36 = tmp31 * tmp35
    tmp37 = tmp29 + tmp36
    tmp38 = tmp28 + tmp37
    tmp39 = 4.0
    tmp40 = tmp38 / tmp39
    tmp41 = tmp8 - tmp40
    tmp42 = tmp41 * tmp41
    tmp43 = tmp17 - tmp40
    tmp44 = tmp43 * tmp43
    tmp45 = tmp42 + tmp44
    tmp46 = tmp27 - tmp40
    tmp47 = tmp46 * tmp46
    tmp48 = tmp45 + tmp47
    tmp49 = tmp37 - tmp40
    tmp50 = tmp49 * tmp49
    tmp51 = tmp48 + tmp50
    tmp52 = tmp51 / tmp39
    tl.store(out_ptr0 + x0, tmp40, xmask)
    tl.store(out_ptr1 + x0, tmp52, xmask)


@triton.jit
def triton_poi_fused_add_mul_native_layer_norm_11(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, 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 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + x2, xmask)
    tmp3 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr5 + x1, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr6 + x0, xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr7 + x0, xmask, eviction_policy='evict_last')
    tmp4 = tmp2 + tmp3
    tmp5 = tmp1 * tmp4
    tmp6 = tmp0 + tmp5
    tmp8 = tmp6 - tmp7
    tmp10 = 1e-05
    tmp11 = tmp9 + tmp10
    tmp12 = libdevice.rsqrt(tmp11)
    tmp13 = tmp8 * tmp12
    tmp15 = tmp13 * tmp14
    tmp17 = tmp15 + tmp16
    tl.store(out_ptr0 + x2, tmp17, xmask)


@triton.jit
def triton_poi_fused_gelu_12(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_mul_13(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
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + x2, xmask)
    tmp3 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr5 + x2, xmask)
    tmp4 = tmp2 + tmp3
    tmp5 = tmp1 * tmp4
    tmp6 = tmp0 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tl.store(out_ptr0 + x2, tmp10, 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,), (1,))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (12, 4), (4, 1))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4, 4), (4, 1))
    assert_size_stride(primals_11, (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_4, 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_4, buf0,
            buf1, primals_2, primals_3, buf2, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del primals_2
        del primals_3
        buf3 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 12), (1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_mul_2[grid(16, 4)](buf3, buf4, 16, 4, XBLOCK
            =4, YBLOCK=16, num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32)
        triton_poi_fused_clone_3[grid(16, 4)](buf3, buf5, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 0), 
            0), reinterpret_tensor(buf5, (16, 1, 4), (4, 0, 1), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_4[grid(64, 4)](buf6, buf7, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        buf8 = reinterpret_tensor(buf6, (64, 4), (4, 1), 0)
        del buf6
        extern_kernels.mm(reinterpret_tensor(buf7, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf8)
        buf9 = empty_strided_cuda((4, 4, 4, 1), (16, 1, 4, 64), torch.float32)
        buf10 = empty_strided_cuda((4, 4, 4, 1), (16, 1, 4, 64), torch.float32)
        triton_poi_fused__softmax_5[grid(64)](buf8, primals_7, buf9, buf10,
            64, XBLOCK=64, num_warps=1, num_stages=1)
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_6[grid(256)](buf8, primals_7, buf9, buf10,
            buf11, 256, XBLOCK=256, num_warps=4, num_stages=1)
        buf12 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf11, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf12)
        buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_7[grid(16, 16)](buf12, primals_9, buf13, 16,
            16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_9
        buf14 = reinterpret_tensor(buf9, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf9
        triton_poi_fused_clone_8[grid(16, 4)](buf3, buf14, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        del buf3
        buf15 = reinterpret_tensor(buf10, (16, 4, 1), (4, 1, 1), 0)
        del buf10
        extern_kernels.bmm(reinterpret_tensor(buf13, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf14, (16, 4, 1), (4, 1, 0), 0), out=buf15)
        buf16 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_9[grid(16, 4)](buf15, buf16, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf17 = reinterpret_tensor(buf15, (16, 4), (4, 1), 0)
        del buf15
        extern_kernels.mm(reinterpret_tensor(buf16, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), out=buf17)
        buf18 = buf1
        del buf1
        buf19 = buf0
        del buf0
        triton_poi_fused_add_mul_native_layer_norm_10[grid(16)](primals_4,
            primals_1, buf17, primals_11, buf18, buf19, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf20 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_native_layer_norm_11[grid(64)](primals_4,
            primals_1, buf17, primals_11, buf18, buf19, primals_13,
            primals_14, buf20, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf18
        del buf19
        del primals_14
        buf21 = reinterpret_tensor(buf12, (16, 16), (16, 1), 0)
        del buf12
        extern_kernels.addmm(primals_16, reinterpret_tensor(buf20, (16, 4),
            (4, 1), 0), reinterpret_tensor(primals_15, (4, 16), (1, 4), 0),
            alpha=1, beta=1, out=buf21)
        del primals_16
        buf22 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        triton_poi_fused_gelu_12[grid(256)](buf21, buf22, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf23 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_18, reinterpret_tensor(buf22, (16, 16),
            (16, 1), 0), reinterpret_tensor(primals_17, (16, 4), (1, 16), 0
            ), alpha=1, beta=1, out=buf23)
        del primals_18
        buf24 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_13[grid(64)](primals_4, primals_1, buf17,
            primals_11, primals_12, buf23, buf24, 64, XBLOCK=64, num_warps=
            1, num_stages=1)
    return (buf24, primals_1, primals_4, primals_7, primals_11, primals_12,
        primals_13, reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
        reinterpret_tensor(buf7, (64, 4), (4, 1), 0), buf8,
        reinterpret_tensor(buf11, (64, 4), (4, 1), 0), reinterpret_tensor(
        buf16, (16, 4), (4, 1), 0), buf17, reinterpret_tensor(buf20, (16, 4
        ), (4, 1), 0), buf21, reinterpret_tensor(buf22, (16, 16), (16, 1), 
        0), buf23, primals_17, primals_15, primals_10, reinterpret_tensor(
        buf13, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf14, (16, 1,
        4), (4, 1, 1), 0), primals_8, primals_6, reinterpret_tensor(buf4, (
        16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf5, (16, 4, 1), (4, 
        1, 4), 0), primals_5)


def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
    if drop_prob == 0.0 or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0],) + (1,) * (x.ndim - 1)
    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.
        device)
    random_tensor.floor_()
    output = x.div(keep_prob) * random_tensor
    return output


class DropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
    """

    def __init__(self, drop_prob=None):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob

    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training)


class Mlp(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_talking_head(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_l = nn.Linear(num_heads, num_heads)
        self.proj_w = nn.Linear(num_heads, num_heads)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads
            ).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]
        attn = q @ k.transpose(-2, -1)
        attn = self.proj_l(attn.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
        attn = attn.softmax(dim=-1)
        attn = self.proj_w(attn.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
        attn = self.attn_drop(attn)
        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class LayerScale_BlockNew(nn.Module):

    def __init__(self, dim, num_heads, mlp_ratio=4.0, qkv_bias=False,
        qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=nn
        .GELU, norm_layer=nn.LayerNorm, Attention_block=
        Attention_talking_head, Mlp_block=Mlp, init_values=0.0001):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention_block(dim, num_heads=num_heads, qkv_bias=
            qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
        self.drop_path = DropPath(drop_path
            ) if drop_path > 0.0 else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp_block(in_features=dim, hidden_features=
            mlp_hidden_dim, act_layer=act_layer, drop=drop)
        self.gamma_1 = nn.Parameter(init_values * torch.ones(dim),
            requires_grad=True)
        self.gamma_2 = nn.Parameter(init_values * torch.ones(dim),
            requires_grad=True)

    def forward(self, input_0):
        primals_1 = self.gamma_1
        primals_2 = self.gamma_2
        primals_3 = self.norm1.weight
        primals_7 = self.norm1.bias
        primals_5 = self.attn.qkv.weight
        primals_6 = self.attn.proj.weight
        primals_9 = self.attn.proj.bias
        primals_8 = self.attn.proj_l.weight
        primals_11 = self.attn.proj_l.bias
        primals_10 = self.attn.proj_w.weight
        primals_12 = self.attn.proj_w.bias
        primals_13 = self.norm2.weight
        primals_14 = self.norm2.bias
        primals_15 = self.mlp.fc1.weight
        primals_16 = self.mlp.fc1.bias
        primals_17 = self.mlp.fc2.weight
        primals_18 = self.mlp.fc2.bias
        primals_4 = input_0
        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: 0.060108