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.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_clone_div_0(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)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x4, tmp2, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 16
    y1 = yindex // 16
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (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_2(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 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    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_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), xmask)
    tl.store(out_ptr0 + x4, tmp0, xmask)


@triton.jit
def triton_poi_fused_mean_5(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 % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp7 = tl.load(in_ptr0 + (4 + x0 + 64 * x1), xmask)
    tmp8 = tl.load(in_ptr0 + (20 + x0 + 64 * x1), xmask)
    tmp10 = tl.load(in_ptr0 + (36 + x0 + 64 * x1), xmask)
    tmp12 = tl.load(in_ptr0 + (52 + x0 + 64 * x1), xmask)
    tmp15 = tl.load(in_ptr0 + (8 + x0 + 64 * x1), xmask)
    tmp16 = tl.load(in_ptr0 + (24 + x0 + 64 * x1), xmask)
    tmp18 = tl.load(in_ptr0 + (40 + x0 + 64 * x1), xmask)
    tmp20 = tl.load(in_ptr0 + (56 + x0 + 64 * x1), xmask)
    tmp23 = tl.load(in_ptr0 + (12 + x0 + 64 * x1), xmask)
    tmp24 = tl.load(in_ptr0 + (28 + x0 + 64 * x1), xmask)
    tmp26 = tl.load(in_ptr0 + (44 + x0 + 64 * x1), xmask)
    tmp28 = tl.load(in_ptr0 + (60 + x0 + 64 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp9 = tmp7 + tmp8
    tmp11 = tmp9 + tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tmp6 + tmp13
    tmp17 = tmp15 + tmp16
    tmp19 = tmp17 + tmp18
    tmp21 = tmp19 + tmp20
    tmp22 = tmp14 + tmp21
    tmp25 = tmp23 + tmp24
    tmp27 = tmp25 + tmp26
    tmp29 = tmp27 + tmp28
    tmp30 = tmp22 + tmp29
    tmp31 = 4.0
    tmp32 = tmp30 / tmp31
    tl.store(out_ptr0 + x2, tmp32, xmask)


@triton.jit
def triton_poi_fused__softmax_6(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
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (12 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_7(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
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (12 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_poi_fused_mul_sum_8(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 // 16
    x3 = xindex % 16
    x0 = xindex % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + 64 * x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x3 + 64 * x2), xmask)
    tmp4 = tl.load(in_ptr1 + (4 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr0 + (32 + x3 + 64 * x2), xmask)
    tmp8 = tl.load(in_ptr1 + (8 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp11 = tl.load(in_ptr0 + (48 + x3 + 64 * x2), xmask)
    tmp12 = tl.load(in_ptr1 + (12 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tl.store(out_ptr0 + x4, tmp14, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_9(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 + tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = 4.0
    tmp16 = tmp14 / tmp15
    tmp17 = tmp2 - tmp16
    tmp18 = tmp17 * tmp17
    tmp19 = tmp5 - tmp16
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp9 - tmp16
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp16
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = tmp27 / tmp15
    tl.store(out_ptr0 + x0, tmp16, xmask)
    tl.store(out_ptr1 + x0, tmp28, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_10(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-06
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp4 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tl.store(out_ptr0 + x2, tmp13, xmask)


@triton.jit
def triton_poi_fused_add_11(in_out_ptr0, in_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_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_12(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_native_layer_norm_13(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_relu_threshold_backward_14(in_out_ptr0, 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
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_15(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_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + 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, primals_19, primals_20, primals_21, primals_22,
        primals_23, primals_24) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (16, 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, (16, 4), (4, 1))
    assert_size_stride(primals_6, (16,), (1,))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_11, (16, 4), (4, 1))
    assert_size_stride(primals_12, (16, 4), (4, 1))
    assert_size_stride(primals_13, (16, 4), (4, 1))
    assert_size_stride(primals_14, (16, 4), (4, 1))
    assert_size_stride(primals_15, (16,), (1,))
    assert_size_stride(primals_16, (4, 4), (4, 1))
    assert_size_stride(primals_17, (4,), (1,))
    assert_size_stride(primals_18, (4,), (1,))
    assert_size_stride(primals_19, (4, 4), (4, 1))
    assert_size_stride(primals_20, (4,), (1,))
    assert_size_stride(primals_21, (4, 4), (4, 1))
    assert_size_stride(primals_22, (4,), (1,))
    assert_size_stride(primals_23, (4,), (1,))
    assert_size_stride(primals_24, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 16), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 16), (1, 4), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 16), (1, 4), 0), out=buf2)
        del primals_4
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_div_0[grid(256)](buf0, buf3, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf4 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        triton_poi_fused_clone_1[grid(64, 4)](buf1, buf4, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        buf5 = reinterpret_tensor(buf1, (16, 4, 4), (16, 4, 1), 0)
        del buf1
        extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf4, (16, 4, 4), (16, 4, 1), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(256)](buf5, buf6, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf7 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf5
        triton_poi_fused__softmax_3[grid(256)](buf6, buf7, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf8 = buf6
        del buf6
        triton_poi_fused_clone_4[grid(256)](buf2, buf8, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf9 = reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1), 0)
        del buf2
        extern_kernels.bmm(reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf8, (16, 4, 4), (16, 4, 1), 0), out=buf9)
        buf10 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        triton_poi_fused_mean_5[grid(16)](buf9, buf10, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf11 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_6, reinterpret_tensor(buf10, (4, 4), (
            4, 1), 0), reinterpret_tensor(primals_5, (4, 16), (1, 4), 0),
            alpha=1, beta=1, out=buf11)
        del primals_6
        buf12 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_6[grid(64)](buf11, buf12, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf13 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_7[grid(64)](buf12, buf13, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf14 = reinterpret_tensor(buf12, (4, 1, 4, 4), (16, 1, 4, 1), 0)
        del buf12
        triton_poi_fused_mul_sum_8[grid(64)](buf9, buf13, buf14, 64, XBLOCK
            =64, num_warps=1, num_stages=1)
        buf15 = reinterpret_tensor(buf13, (16, 4), (4, 1), 0)
        del buf13
        extern_kernels.mm(reinterpret_tensor(buf14, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf15)
        buf16 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf17 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused_add_native_layer_norm_9[grid(16)](buf15, primals_1,
            buf16, buf17, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf18 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_10[grid(64)](buf15,
            primals_1, buf16, buf17, primals_8, primals_9, buf18, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_9
        buf19 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf18, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_11, (4, 16), (1, 4), 0), out=buf19)
        buf20 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_10, (16, 4), (4, 1), 0
            ), reinterpret_tensor(primals_12, (4, 16), (1, 4), 0), out=buf20)
        del primals_12
        buf21 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_10, (16, 4), (4, 1), 0
            ), reinterpret_tensor(primals_13, (4, 16), (1, 4), 0), out=buf21)
        del primals_13
        buf22 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_div_0[grid(256)](buf19, buf22, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf23 = reinterpret_tensor(buf19, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf19
        triton_poi_fused_clone_1[grid(64, 4)](buf20, buf23, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        buf24 = reinterpret_tensor(buf20, (16, 4, 4), (16, 4, 1), 0)
        del buf20
        extern_kernels.bmm(reinterpret_tensor(buf22, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf23, (16, 4, 4), (16, 4, 1), 0), out=buf24
            )
        buf25 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(256)](buf24, buf25, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        buf26 = reinterpret_tensor(buf24, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf24
        triton_poi_fused__softmax_3[grid(256)](buf25, buf26, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf27 = buf25
        del buf25
        triton_poi_fused_clone_4[grid(256)](buf21, buf27, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf28 = reinterpret_tensor(buf21, (16, 4, 4), (16, 4, 1), 0)
        del buf21
        extern_kernels.bmm(reinterpret_tensor(buf26, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf27, (16, 4, 4), (16, 4, 1), 0), out=buf28
            )
        buf29 = reinterpret_tensor(buf17, (4, 4, 1, 1), (4, 1, 1, 1), 0)
        del buf17
        triton_poi_fused_mean_5[grid(16)](buf28, buf29, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf30 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_15, reinterpret_tensor(buf29, (4, 4),
            (4, 1), 0), reinterpret_tensor(primals_14, (4, 16), (1, 4), 0),
            alpha=1, beta=1, out=buf30)
        del primals_15
        buf31 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_6[grid(64)](buf30, buf31, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf32 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_7[grid(64)](buf31, buf32, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf33 = reinterpret_tensor(buf31, (4, 1, 4, 4), (16, 1, 4, 1), 0)
        del buf31
        triton_poi_fused_mul_sum_8[grid(64)](buf28, buf32, buf33, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf34 = reinterpret_tensor(buf32, (16, 4), (4, 1), 0)
        del buf32
        extern_kernels.mm(reinterpret_tensor(buf33, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_16, (4, 4), (1, 4), 0), out=buf34)
        buf35 = reinterpret_tensor(buf34, (4, 4, 4), (16, 4, 1), 0)
        del buf34
        triton_poi_fused_add_11[grid(64)](buf35, buf18, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf36 = buf16
        del buf16
        buf37 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused_native_layer_norm_12[grid(16)](buf35, buf36, buf37,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf38 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_13[grid(64)](buf35, buf36, buf37,
            primals_17, primals_18, buf38, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del primals_18
        buf39 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf38, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_19, (4, 4), (1, 4), 0), out=buf39)
        buf40 = reinterpret_tensor(buf39, (4, 4, 4), (16, 4, 1), 0)
        del buf39
        buf46 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_14[grid(64)](buf40,
            primals_20, buf46, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_20
        buf41 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf40, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_21, (4, 4), (1, 4), 0), out=buf41)
        buf42 = reinterpret_tensor(buf41, (4, 4, 4), (16, 4, 1), 0)
        del buf41
        triton_poi_fused_add_15[grid(64)](buf42, primals_22, buf38, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_22
        buf43 = buf37
        del buf37
        buf44 = buf36
        del buf36
        triton_poi_fused_native_layer_norm_12[grid(16)](buf42, buf43, buf44,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf45 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_13[grid(64)](buf42, buf43, buf44,
            primals_23, primals_24, buf45, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf43
        del buf44
        del primals_24
    return (buf45, buf7, buf26, primals_1, primals_8, primals_17,
        primals_23, buf7, buf9, reinterpret_tensor(buf10, (4, 4), (4, 1), 0
        ), buf11, reinterpret_tensor(buf14, (16, 4), (4, 1), 0), buf15,
        reinterpret_tensor(buf18, (16, 4), (4, 1), 0), reinterpret_tensor(
        primals_10, (16, 4), (4, 1), 0), buf26, buf28, reinterpret_tensor(
        buf29, (4, 4), (4, 1), 0), buf30, reinterpret_tensor(buf33, (16, 4),
        (4, 1), 0), buf35, reinterpret_tensor(buf38, (16, 4), (4, 1), 0),
        reinterpret_tensor(buf40, (16, 4), (4, 1), 0), buf42, primals_21,
        buf46, primals_19, primals_16, primals_14, reinterpret_tensor(buf27,
        (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf22, (16, 4, 4), (
        16, 1, 4), 0), reinterpret_tensor(buf23, (16, 4, 4), (16, 1, 4), 0),
        primals_11, primals_7, primals_5, reinterpret_tensor(buf8, (16, 4, 
        4), (16, 1, 4), 0), reinterpret_tensor(buf3, (16, 4, 4), (16, 1, 4),
        0), reinterpret_tensor(buf4, (16, 4, 4), (16, 1, 4), 0))


class SPA(nn.Module):
    """ Selective parallel attention """

    def __init__(self, n_head: 'int'=8, d_v: 'int'=64):
        super().__init__()
        self.gap = nn.AdaptiveAvgPool1d(1)
        self.sk = nn.Linear(d_v, n_head * d_v)
        self.softmax = nn.Softmax(dim=1)

    def forward(self, x):
        bs, n_head, _lq, d_v = x.size()
        u = x.sum(dim=1)
        s = self.gap(u.transpose(1, 2)).view(bs, d_v)
        v = self.sk(s)
        v = v.view(bs, n_head, d_v)
        v = self.softmax(v)
        v = v.unsqueeze(2)
        f = x * v.expand_as(x)
        return f


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

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

    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, -1000000000.0)
        attn = self.dropout(F.softmax(attn, dim=-1))
        output = torch.matmul(attn, v)
        return output, attn

    def __repr__(self):
        tmpstr = self.__class__.__name__ + '('
        tmpstr += 'temperature=' + str(self.temperature)
        tmpstr += ', attn_dropout=' + str(self.attn_dropout)
        tmpstr += ')'
        return tmpstr


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

    def __init__(self, n_head: 'int'=8, d_model: 'int'=512, d_k: 'int'=64,
        d_v: 'int'=64, dropout: 'float'=0.1):
        super().__init__()
        self.n_head = n_head
        self.d_k = d_k
        self.d_v = d_v
        self.w_qs = nn.Linear(d_model, n_head * d_k, bias=False)
        self.w_ks = nn.Linear(d_model, n_head * d_k, bias=False)
        self.w_vs = nn.Linear(d_model, n_head * d_v, bias=False)
        if n_head > 1:
            self.spa = SPA(n_head=n_head, d_v=d_v)
            self.fc = nn.Linear(d_v, d_model, bias=False)
        else:
            self.fc = 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)

    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(q).view(sz_b, len_q, n_head, d_k)
        k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
        v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)
        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)
        if n_head > 1:
            q = self.spa(q)
            q = q.sum(dim=1, keepdim=True)
        q = q.transpose(1, 2).contiguous().view(sz_b, len_q, -1)
        q = self.dropout(self.fc(q))
        q += residual
        q = self.layer_norm(q)
        return q, attn


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

    def __init__(self, d_in, d_hid, dropout=0.1):
        super().__init__()
        self.w_1 = nn.Linear(d_in, d_hid)
        self.w_2 = nn.Linear(d_hid, d_in)
        self.layer_norm = nn.LayerNorm(d_in, eps=1e-06)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        residual = x
        x = self.w_2(F.relu(self.w_1(x)))
        x = self.dropout(x)
        x += residual
        x = self.layer_norm(x)
        return x


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

    def __init__(self, d_model, d_inner, n_head, d_k, d_v, dropout=0.1):
        super(DecoderLayerNew, self).__init__()
        self.slf_attn = MultiHeadAttention(n_head, d_model, d_k, d_v,
            dropout=dropout)
        self.enc_attn = MultiHeadAttention(n_head, d_model, d_k, d_v,
            dropout=dropout)
        self.pos_ffn = PositionwiseFeedForward(d_model, d_inner, dropout=
            dropout)

    def forward(self, input_0, input_1):
        primals_2 = self.slf_attn.w_qs.weight
        primals_3 = self.slf_attn.w_ks.weight
        primals_4 = self.slf_attn.w_vs.weight
        primals_5 = self.slf_attn.spa.sk.weight
        primals_6 = self.slf_attn.spa.sk.bias
        primals_7 = self.slf_attn.fc.weight
        primals_8 = self.slf_attn.layer_norm.weight
        primals_9 = self.slf_attn.layer_norm.bias
        primals_11 = self.enc_attn.w_qs.weight
        primals_12 = self.enc_attn.w_ks.weight
        primals_13 = self.enc_attn.w_vs.weight
        primals_14 = self.enc_attn.spa.sk.weight
        primals_15 = self.enc_attn.spa.sk.bias
        primals_16 = self.enc_attn.fc.weight
        primals_17 = self.enc_attn.layer_norm.weight
        primals_18 = self.enc_attn.layer_norm.bias
        primals_19 = self.pos_ffn.w_1.weight
        primals_20 = self.pos_ffn.w_1.bias
        primals_21 = self.pos_ffn.w_2.weight
        primals_22 = self.pos_ffn.w_2.bias
        primals_23 = self.pos_ffn.layer_norm.weight
        primals_24 = self.pos_ffn.layer_norm.bias
        primals_1 = input_0
        primals_10 = 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])
        return output[0], output[1], output[2]


Measured latency_ms: 0.279785