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.distributed
import torch
import torch.nn as nn
import torch.nn.functional
import torch.utils.data
import torch.optim
import torch.optim.lr_scheduler
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_add_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, 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
    x4 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex % 16
    tmp0 = tl.load(in_ptr0 + x4, 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')
    tmp9 = tl.load(in_ptr5 + x2, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 + tmp9
    tl.store(out_ptr0 + x4, tmp8, xmask)
    tl.store(out_ptr1 + x4, tmp10, xmask)


@triton.jit
def triton_poi_fused_clone_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 + 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__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)
    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_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    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_add_native_layer_norm_5(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_native_layer_norm_6(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_add_native_layer_norm_7(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, 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
    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')
    tmp9 = tl.load(in_ptr5 + x2, xmask)
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 + tmp9
    tl.store(out_ptr0 + x2, tmp8, xmask)
    tl.store(out_ptr1 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_8(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
    x3 = xindex
    x0 = xindex % 4
    x4 = xindex // 4
    x5 = xindex % 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x3, xmask)
    tmp2 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x4, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x4, 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')
    tmp16 = tl.load(in_ptr7 + x5, 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
    tmp17 = tmp15 + tmp16
    tl.store(out_ptr0 + x3, tmp17, xmask)


@triton.jit
def triton_poi_fused__softmax_9(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = tmp2 - tmp2
    tmp4 = tmp3 * tmp1
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp5 / tmp5
    tl.store(in_out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_10(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_native_layer_norm_11(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_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_13(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, primals_19, primals_20, primals_21, primals_22,
        primals_23, primals_24, primals_25, primals_26) = 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, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (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, 4), (4, 1))
    assert_size_stride(primals_15, (4, 4), (4, 1))
    assert_size_stride(primals_16, (4, 4), (4, 1))
    assert_size_stride(primals_17, (4, 4), (4, 1))
    assert_size_stride(primals_18, (4, 4), (4, 1))
    assert_size_stride(primals_19, (4, 4), (4, 1))
    assert_size_stride(primals_20, (4,), (1,))
    assert_size_stride(primals_21, (4,), (1,))
    assert_size_stride(primals_22, (4,), (1,))
    assert_size_stride(primals_23, (16, 4), (4, 1))
    assert_size_stride(primals_24, (16,), (1,))
    assert_size_stride(primals_25, (4, 16), (16, 1))
    assert_size_stride(primals_26, (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)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_1[grid(64)](primals_3, buf0,
            buf1, primals_1, primals_2, primals_4, buf2, buf3, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        del primals_1
        del primals_2
        buf4 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf4)
        buf5 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf5)
        buf6 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_2[grid(16, 4)](buf4, buf7, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf8 = reinterpret_tensor(buf4, (4, 4, 1, 4), (16, 4, 4, 1), 0)
        del buf4
        triton_poi_fused_clone_2[grid(16, 4)](buf5, buf8, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf9 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf7, (16, 4, 1), (4, 1, 0), 
            0), reinterpret_tensor(buf8, (16, 1, 4), (4, 0, 1), 0), out=buf9)
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_3[grid(256)](buf9, buf10, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf11 = reinterpret_tensor(buf9, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf9
        triton_poi_fused__softmax_4[grid(256)](buf10, buf11, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        buf12 = reinterpret_tensor(buf5, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf5
        triton_poi_fused_clone_2[grid(16, 4)](buf6, buf12, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf13 = reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 1), 0)
        del buf6
        extern_kernels.bmm(reinterpret_tensor(buf11, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf12, (16, 4, 1), (4, 1, 0), 0), out=buf13)
        buf14 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_2[grid(16, 4)](buf13, buf14, 16, 4, XBLOCK=4,
            YBLOCK=16, 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_8, (4, 4), (1, 4), 0), out=buf15)
        buf16 = buf1
        del buf1
        buf17 = buf0
        del buf0
        triton_poi_fused_add_native_layer_norm_5[grid(16)](primals_3, buf15,
            primals_9, buf16, buf17, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf18 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf19 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        triton_poi_fused_native_layer_norm_6[grid(4)](primals_14, buf18,
            buf19, 4, XBLOCK=4, num_warps=1, num_stages=1)
        buf20 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf23 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_7[grid(16)](primals_14,
            buf18, buf19, primals_12, primals_13, primals_16, buf20, buf23,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf18
        del buf19
        del primals_12
        del primals_13
        del primals_16
        buf21 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_8[grid(64)](primals_3, buf15,
            primals_9, buf16, buf17, primals_10, primals_11, primals_4,
            buf21, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_11
        del primals_4
        buf22 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf21, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_15, (4, 4), (1, 4), 0), out=buf22)
        buf24 = reinterpret_tensor(buf17, (4, 4), (4, 1), 0)
        del buf17
        extern_kernels.mm(buf23, reinterpret_tensor(primals_17, (4, 4), (1,
            4), 0), out=buf24)
        buf25 = reinterpret_tensor(buf16, (4, 4), (4, 1), 0)
        del buf16
        extern_kernels.mm(buf20, reinterpret_tensor(primals_18, (4, 4), (1,
            4), 0), out=buf25)
        buf26 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_2[grid(16, 4)](buf22, buf26, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf27 = reinterpret_tensor(buf22, (16, 4, 1), (4, 1, 1), 0)
        del buf22
        extern_kernels.bmm(reinterpret_tensor(buf26, (16, 4, 1), (4, 1, 0),
            0), reinterpret_tensor(buf24, (16, 1, 1), (1, 1, 1), 0), out=buf27)
        buf28 = reinterpret_tensor(buf27, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf27
        triton_poi_fused__softmax_9[grid(64)](buf28, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf29 = empty_strided_cuda((16, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf28, (16, 4, 1), (4, 1, 1),
            0), reinterpret_tensor(buf25, (16, 1, 1), (1, 1, 1), 0), out=buf29)
        buf30 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_2[grid(16, 4)](buf29, buf30, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf31 = reinterpret_tensor(buf29, (16, 4), (4, 1), 0)
        del buf29
        extern_kernels.mm(reinterpret_tensor(buf30, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_19, (4, 4), (1, 4), 0), out=buf31)
        buf32 = reinterpret_tensor(buf31, (4, 4, 4), (16, 4, 1), 0)
        del buf31
        triton_poi_fused_add_10[grid(64)](buf32, primals_3, buf15,
            primals_9, primals_20, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_20
        buf33 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf34 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused_native_layer_norm_0[grid(16)](buf32, buf33, buf34,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf35 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_11[grid(64)](buf32, buf33, buf34,
            primals_21, primals_22, buf35, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf33
        del buf34
        del primals_22
        buf36 = reinterpret_tensor(buf10, (16, 16), (16, 1), 0)
        del buf10
        extern_kernels.addmm(primals_24, reinterpret_tensor(buf35, (16, 4),
            (4, 1), 0), reinterpret_tensor(primals_23, (4, 16), (1, 4), 0),
            alpha=1, beta=1, out=buf36)
        del primals_24
        buf37 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        triton_poi_fused_gelu_12[grid(256)](buf36, buf37, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf38 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf37, (16, 16), (16, 1), 0),
            reinterpret_tensor(primals_25, (16, 4), (1, 16), 0), out=buf38)
        buf39 = reinterpret_tensor(buf38, (4, 4, 4), (16, 4, 1), 0)
        del buf38
        triton_poi_fused_add_13[grid(64)](buf39, buf32, primals_26, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_26
    return (buf39, primals_3, primals_9, primals_10, primals_14, primals_21,
        reinterpret_tensor(buf3, (16, 4), (4, 1), 0), reinterpret_tensor(
        buf2, (16, 4), (4, 1), 0), buf11, reinterpret_tensor(buf14, (16, 4),
        (4, 1), 0), buf15, buf20, reinterpret_tensor(buf21, (16, 4), (4, 1),
        0), buf23, buf28, reinterpret_tensor(buf30, (16, 4), (4, 1), 0),
        buf32, reinterpret_tensor(buf35, (16, 4), (4, 1), 0), buf36,
        reinterpret_tensor(buf37, (16, 16), (16, 1), 0), primals_25,
        primals_23, primals_19, reinterpret_tensor(buf25, (16, 1, 1), (1, 1,
        4), 0), reinterpret_tensor(buf26, (16, 1, 4), (4, 1, 1), 0),
        reinterpret_tensor(buf24, (16, 1, 1), (1, 4, 1), 0), primals_18,
        primals_17, primals_15, primals_8, reinterpret_tensor(buf12, (16, 1,
        4), (4, 1, 1), 0), reinterpret_tensor(buf7, (16, 1, 4), (4, 1, 1), 
        0), reinterpret_tensor(buf8, (16, 4, 1), (4, 1, 4), 0), primals_7,
        primals_6, primals_5)


class Mlp(nn.Module):
    """ Multilayer perceptron."""

    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):
        """
            Args:
                x (torch.Tensor): (B, L, C), input tensor
            Returns:
                torch.Tensor: (B, L, C), output tensor
        """
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class CrossAttention(nn.Module):

    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None,
        attn_drop=0.0, proj_drop=0.0, attn_pos_encoding_only=False):
        super(CrossAttention, self).__init__()
        assert dim % num_heads == 0, f'dim {dim} should be divided by num_heads {num_heads}.'
        self.dim = dim
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5
        if attn_pos_encoding_only:
            self.q = nn.Linear(dim, dim, bias=qkv_bias)
            self.kv = nn.Linear(dim, 2 * dim, bias=qkv_bias)
        else:
            self.q = nn.Linear(dim, dim, bias=qkv_bias)
            self.k = nn.Linear(dim, dim, bias=qkv_bias)
            self.v = nn.Linear(dim, dim, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)
        self.attn_pos_encoding_only = attn_pos_encoding_only

    def forward(self, q, kv, q_ape, k_ape, attn_pos):
        """
            Args:
                q (torch.Tensor): (B, L_q, C)
                kv (torch.Tensor): (B, L_kv, C)
                q_ape (torch.Tensor | None): (1 or B, L_q, C), absolute positional encoding for q
                k_ape (torch.Tensor | None): (1 or B, L_kv, C), absolute positional encoding for k
                attn_pos (torch.Tensor | None): (1 or B, num_heads, L_q, L_kv), untied positional encoding
            Returns:
                torch.Tensor: (B, L_q, C)
        """
        B, q_N, C = q.shape
        kv_N = kv.shape[1]
        if self.attn_pos_encoding_only:
            assert q_ape is None and k_ape is None
            q = self.q(q).reshape(B, q_N, self.num_heads, C // self.num_heads
                ).permute(0, 2, 1, 3)
            kv = self.kv(kv).reshape(B, kv_N, 2, self.num_heads, C // self.
                num_heads).permute(2, 0, 3, 1, 4)
            k, v = kv[0], kv[1]
        else:
            q = q + q_ape if q_ape is not None else q
            q = self.q(q).reshape(B, q_N, self.num_heads, C // self.num_heads
                ).permute(0, 2, 1, 3)
            k = kv + k_ape if k_ape is not None else kv
            k = self.k(k).reshape(B, -1, self.num_heads, C // self.num_heads
                ).permute(0, 2, 1, 3)
            v = self.v(kv).reshape(B, -1, self.num_heads, C // self.num_heads
                ).permute(0, 2, 1, 3)
        attn = q @ k.transpose(-2, -1)
        attn = attn * self.scale
        if attn_pos is not None:
            attn = attn + attn_pos
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)
        x = attn @ v
        x = x.transpose(1, 2).reshape(B, q_N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class SelfAttention(nn.Module):

    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None,
        attn_drop=0.0, proj_drop=0.0, attn_pos_encoding_only=False):
        super(SelfAttention, self).__init__()
        assert dim % num_heads == 0, f'dim {dim} should be divided by num_heads {num_heads}.'
        self.dim = dim
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5
        if attn_pos_encoding_only:
            self.qkv = nn.Linear(dim, 3 * dim, bias=qkv_bias)
        else:
            self.q = nn.Linear(dim, dim, bias=qkv_bias)
            self.k = nn.Linear(dim, dim, bias=qkv_bias)
            self.v = nn.Linear(dim, dim, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)
        self.attn_pos_encoding_only = attn_pos_encoding_only

    def forward(self, x, q_ape, k_ape, attn_pos):
        """
            Args:
                x (torch.Tensor): (B, L, C)
                q_ape (torch.Tensor | None): (1 or B, L, C), absolute positional encoding for q
                k_ape (torch.Tensor | None): (1 or B, L, C), absolute positional encoding for k
                attn_pos (torch.Tensor | None): (1 or B, num_heads, L, L), untied positional encoding
            Returns:
                torch.Tensor: (B, L, C)
        """
        B, N, C = x.shape
        if self.attn_pos_encoding_only:
            assert q_ape is None and k_ape is None
            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], qkv[1], qkv[2]
        else:
            q = x + q_ape if q_ape is not None else x
            q = self.q(q).reshape(B, N, self.num_heads, C // self.num_heads
                ).permute(0, 2, 1, 3)
            k = x + k_ape if k_ape is not None else x
            k = self.k(k).reshape(B, -1, self.num_heads, C // self.num_heads
                ).permute(0, 2, 1, 3)
            v = self.v(x).reshape(B, -1, self.num_heads, C // self.num_heads
                ).permute(0, 2, 1, 3)
        attn = q @ k.transpose(-2, -1)
        attn = attn * self.scale
        if attn_pos is not None:
            attn = attn + attn_pos
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)
        x = attn @ v
        x = x.transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class TargetQueryDecoderLayerNew(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=nn.Identity(),
        act_layer=nn.GELU, norm_layer=nn.LayerNorm):
        super(TargetQueryDecoderLayerNew, self).__init__()
        self.norm_1 = norm_layer(dim)
        self.self_attn = SelfAttention(dim, num_heads, qkv_bias, qk_scale,
            attn_drop, drop)
        self.norm_2_query = norm_layer(dim)
        self.norm_2_memory = norm_layer(dim)
        self.cross_attn = CrossAttention(dim, num_heads, qkv_bias, qk_scale,
            attn_drop, drop)
        self.norm_3 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(dim, hidden_features=mlp_hidden_dim, act_layer=
            act_layer, drop=drop)
        self.drop_path = drop_path

    def forward(self, input_0, input_1, input_2, input_3):
        primals_1 = self.norm_1.weight
        primals_2 = self.norm_1.bias
        primals_4 = self.self_attn.q.weight
        primals_5 = self.self_attn.k.weight
        primals_6 = self.self_attn.v.weight
        primals_7 = self.self_attn.proj.weight
        primals_9 = self.self_attn.proj.bias
        primals_10 = self.norm_2_query.weight
        primals_11 = self.norm_2_query.bias
        primals_12 = self.norm_2_memory.weight
        primals_13 = self.norm_2_memory.bias
        primals_8 = self.cross_attn.q.weight
        primals_14 = self.cross_attn.k.weight
        primals_15 = self.cross_attn.v.weight
        primals_16 = self.cross_attn.proj.weight
        primals_20 = self.cross_attn.proj.bias
        primals_21 = self.norm_3.weight
        primals_22 = self.norm_3.bias
        primals_23 = self.mlp.fc1.weight
        primals_24 = self.mlp.fc1.bias
        primals_25 = self.mlp.fc2.weight
        primals_26 = self.mlp.fc2.bias
        primals_3 = input_0
        primals_17 = input_1
        primals_18 = input_2
        primals_19 = input_3
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21, primals_22, primals_23, primals_24,
            primals_25, primals_26])
        return output[0]


Measured latency_ms: 0.015023