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.nn import Module
from torch.nn import functional as F
from torch.nn import MultiheadAttention
from torch.nn import Dropout
from torch.nn import Linear
from torch.nn import LayerNorm
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_mul_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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 = 1.0
    tmp4 = tmp2 * tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused__softmax_1(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
    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_2(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
    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_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 4
    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
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask)
    tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_4(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, 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 + 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_5(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp4 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tl.store(out_ptr0 + x2, tmp13, xmask)


@triton.jit
def triton_poi_fused_mean_6(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 = tl.load(in_ptr0 + (16 + x0), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x0), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x0), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr0 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_7(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    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)


@triton.jit
def triton_poi_fused_native_layer_norm_8(in_ptr0, out_ptr0, out_ptr1,
    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 + 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_9(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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_10(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 2048
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, None)


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) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (12, 4), (4, 1))
    assert_size_stride(primals_3, (12,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (12, 4), (4, 1))
    assert_size_stride(primals_10, (12,), (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, (2048, 4), (4, 1))
    assert_size_stride(primals_16, (2048,), (1,))
    assert_size_stride(primals_17, (4, 2048), (2048, 1))
    assert_size_stride(primals_18, (4,), (1,))
    assert_size_stride(primals_19, (4,), (1,))
    assert_size_stride(primals_20, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 4),
            (1, 4), 0), out=buf0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(reinterpret_tensor(primals_3, (4,), (1,), 4),
            primals_1, reinterpret_tensor(primals_2, (4, 4), (1, 4), 16),
            alpha=1, beta=1, out=buf1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(reinterpret_tensor(primals_3, (4,), (1,), 8),
            primals_1, reinterpret_tensor(primals_2, (4, 4), (1, 4), 32),
            alpha=1, beta=1, out=buf2)
        del primals_2
        buf3 = reinterpret_tensor(buf0, (4, 4, 1), (1, 4, 16), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(16)](buf3, primals_3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_3
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf3, reinterpret_tensor(buf1, (4, 1, 4), (1, 1,
            4), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(64)](buf4, buf5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf6 = buf4
        del buf4
        triton_poi_fused__softmax_2[grid(64)](buf5, buf6, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf7 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(buf6, reinterpret_tensor(buf2, (4, 4, 1), (1, 4,
            1), 0), out=buf7)
        buf8 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        triton_poi_fused_clone_3[grid(4, 4)](buf7, buf8, 4, 4, XBLOCK=4,
            YBLOCK=4, num_warps=1, num_stages=1)
        buf9 = reinterpret_tensor(buf7, (4, 4), (4, 1), 0)
        del buf7
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf8, (4, 4), (4,
            1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha
            =1, beta=1, out=buf9)
        del primals_5
        buf10 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf11 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        triton_poi_fused_add_native_layer_norm_4[grid(4)](primals_1, buf9,
            buf10, buf11, 4, XBLOCK=4, num_warps=1, num_stages=1)
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_5[grid(16)](primals_1, buf9,
            buf10, buf11, primals_6, primals_7, buf12, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_7
        buf13 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf12, reinterpret_tensor(primals_9, (4, 4), (1, 
            4), 0), out=buf13)
        buf14 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(reinterpret_tensor(primals_10, (4,), (1,), 4),
            primals_8, reinterpret_tensor(primals_9, (4, 4), (1, 4), 16),
            alpha=1, beta=1, out=buf14)
        buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(reinterpret_tensor(primals_10, (4,), (1,), 8),
            primals_8, reinterpret_tensor(primals_9, (4, 4), (1, 4), 32),
            alpha=1, beta=1, out=buf15)
        buf16 = reinterpret_tensor(buf13, (4, 4, 1), (1, 4, 16), 0)
        del buf13
        triton_poi_fused_mul_0[grid(16)](buf16, primals_10, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_10
        buf17 = buf5
        del buf5
        extern_kernels.bmm(buf16, reinterpret_tensor(buf14, (4, 1, 4), (1, 
            1, 4), 0), out=buf17)
        buf18 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(64)](buf17, buf18, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf19 = buf17
        del buf17
        triton_poi_fused__softmax_2[grid(64)](buf18, buf19, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf18
        buf20 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(buf19, reinterpret_tensor(buf15, (4, 4, 1), (1, 
            4, 1), 0), out=buf20)
        buf21 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        triton_poi_fused_clone_3[grid(4, 4)](buf20, buf21, 4, 4, XBLOCK=4,
            YBLOCK=4, num_warps=1, num_stages=1)
        buf22 = reinterpret_tensor(buf20, (4, 4), (4, 1), 0)
        del buf20
        extern_kernels.mm(reinterpret_tensor(buf21, (4, 4), (4, 1), 0),
            reinterpret_tensor(primals_11, (4, 4), (1, 4), 0), out=buf22)
        buf23 = empty_strided_cuda((1, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_mean_6[grid(16)](buf19, buf23, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf24 = buf22
        del buf22
        triton_poi_fused_add_7[grid(16)](buf24, buf12, primals_12, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_12
        buf25 = buf11
        del buf11
        buf26 = buf10
        del buf10
        triton_poi_fused_native_layer_norm_8[grid(4)](buf24, buf25, buf26, 
            4, XBLOCK=4, num_warps=1, num_stages=1)
        buf27 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_9[grid(16)](buf24, buf25, buf26,
            primals_13, primals_14, buf27, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del primals_14
        buf28 = empty_strided_cuda((4, 2048), (2048, 1), torch.float32)
        extern_kernels.mm(buf27, reinterpret_tensor(primals_15, (4, 2048),
            (1, 4), 0), out=buf28)
        buf29 = buf28
        del buf28
        triton_poi_fused_relu_10[grid(8192)](buf29, primals_16, 8192,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_16
        buf30 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf29, reinterpret_tensor(primals_17, (2048, 4),
            (1, 2048), 0), out=buf30)
        buf31 = buf30
        del buf30
        triton_poi_fused_add_7[grid(16)](buf31, buf27, primals_18, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_18
        buf32 = buf26
        del buf26
        buf33 = buf25
        del buf25
        triton_poi_fused_native_layer_norm_8[grid(4)](buf31, buf32, buf33, 
            4, XBLOCK=4, num_warps=1, num_stages=1)
        buf34 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_9[grid(16)](buf31, buf32, buf33,
            primals_19, primals_20, buf34, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del buf32
        del buf33
        del primals_20
    return (buf34, reinterpret_tensor(buf23, (4, 4), (4, 1), 0), primals_1,
        primals_6, primals_13, primals_19, buf6, reinterpret_tensor(buf8, (
        4, 4), (4, 1), 0), buf9, buf12, primals_8, buf19,
        reinterpret_tensor(buf21, (4, 4), (4, 1), 0), buf24, buf27, buf29,
        buf31, primals_17, primals_15, primals_11, reinterpret_tensor(buf15,
        (4, 1, 4), (1, 1, 4), 0), reinterpret_tensor(buf16, (4, 1, 4), (1, 
        1, 4), 0), reinterpret_tensor(buf14, (4, 4, 1), (1, 4, 1), 0),
        reinterpret_tensor(primals_9, (4, 4), (4, 1), 0), primals_4,
        reinterpret_tensor(buf2, (4, 1, 4), (1, 1, 4), 0),
        reinterpret_tensor(buf3, (4, 1, 4), (1, 1, 4), 0),
        reinterpret_tensor(buf1, (4, 4, 1), (1, 4, 1), 0))


def _get_activation_fn(activation):
    if activation == 'relu':
        return F.relu
    elif activation == 'gelu':
        return F.gelu
    raise RuntimeError('activation should be relu/gelu, not {}'.format(
        activation))


class TransformerDecoderLayerNew(Module):
    """TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network.
    This standard decoder layer is based on the paper "Attention Is All You Need".
    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
    Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in
    Neural Information Processing Systems, pages 6000-6010. Users may modify or implement
    in a different way during application.

    Args:
        d_model: the number of expected features in the input (required).
        nhead: the number of heads in the multiheadattention models (required).
        dim_feedforward: the dimension of the feedforward network model (default=2048).
        dropout: the dropout value (default=0.1).
        activation: the activation function of intermediate layer, relu or gelu (default=relu).

    Examples::
        >>> decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8)
        >>> memory = torch.rand(10, 32, 512)
        >>> tgt = torch.rand(20, 32, 512)
        >>> out = decoder_layer(tgt, memory)
    """

    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
        activation='relu'):
        super(TransformerDecoderLayerNew, self).__init__()
        self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout)
        self.multihead_attn = MultiheadAttention(d_model, nhead, dropout=
            dropout)
        self.linear1 = Linear(d_model, dim_feedforward)
        self.dropout = Dropout(dropout)
        self.linear2 = Linear(dim_feedforward, d_model)
        self.norm1 = LayerNorm(d_model)
        self.norm2 = LayerNorm(d_model)
        self.norm3 = LayerNorm(d_model)
        self.dropout1 = Dropout(dropout)
        self.dropout2 = Dropout(dropout)
        self.dropout3 = Dropout(dropout)
        self.activation = _get_activation_fn(activation)

    def __setstate__(self, state):
        if 'activation' not in state:
            state['activation'] = F.relu
        super(TransformerDecoderLayerNew, self).__setstate__(state)

    def forward(self, input_0, input_1):
        primals_2 = self.self_attn.in_proj_weight
        primals_3 = self.self_attn.in_proj_bias
        primals_1 = self.self_attn.out_proj.weight
        primals_5 = self.self_attn.out_proj.bias
        primals_9 = self.multihead_attn.in_proj_weight
        primals_10 = self.multihead_attn.in_proj_bias
        primals_4 = self.multihead_attn.out_proj.weight
        primals_6 = self.multihead_attn.out_proj.bias
        primals_15 = self.linear1.weight
        primals_16 = self.linear1.bias
        primals_17 = self.linear2.weight
        primals_7 = self.linear2.bias
        primals_12 = self.norm1.weight
        primals_13 = self.norm1.bias
        primals_14 = self.norm2.weight
        primals_18 = self.norm2.bias
        primals_19 = self.norm3.weight
        primals_20 = self.norm3.bias
        primals_8 = input_0
        primals_11 = 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])
        return output[0], output[1]


Measured latency_ms: 0.070152