import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch import nn
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_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_2(in_ptr0, in_ptr1, 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')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp4, xmask & ymask)


@triton.jit
def triton_poi_fused_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 = 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_4(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp18 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp25 = tl.load(in_ptr1 + x2, xmask)
    tmp26 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp29 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp31 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = float('-inf')
    tmp2 = tmp0 == tmp1
    tmp3 = tmp2 == 0
    tmp4 = tmp3.to(tl.int64)
    tmp5 = tmp4 != 0
    tmp7 = tmp6 == tmp1
    tmp8 = tmp7 == 0
    tmp9 = tmp8.to(tl.int64)
    tmp10 = tmp9 != 0
    tmp11 = tmp5 | tmp10
    tmp13 = tmp12 == tmp1
    tmp14 = tmp13 == 0
    tmp15 = tmp14.to(tl.int64)
    tmp16 = tmp15 != 0
    tmp17 = tmp11 | tmp16
    tmp19 = tmp18 == tmp1
    tmp20 = tmp19 == 0
    tmp21 = tmp20.to(tl.int64)
    tmp22 = tmp21 != 0
    tmp23 = tmp17 | tmp22
    tmp24 = tmp23 == 0
    tmp28 = tmp26 + tmp27
    tmp30 = tmp28 + tmp29
    tmp32 = tmp30 + tmp31
    tmp33 = tmp25 / tmp32
    tmp34 = 0.0
    tmp35 = tl.where(tmp24, tmp34, tmp33)
    tl.store(out_ptr0 + x2, tmp35, xmask)


@triton.jit
def triton_poi_fused_5(in_ptr0, in_ptr1, 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')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)


@triton.jit
def triton_poi_fused_view_6(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
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (4 * x1 + 16 * (y0 // 4) + y0 % 4), xmask &
        ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_7(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 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr2 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + 1)
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK])
    tmp10 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp13 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr1 + 2)
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK])
    tmp17 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp21 = tl.load(in_ptr1 + 3)
    tmp22 = tl.broadcast_to(tmp21, [XBLOCK])
    tmp24 = tl.load(in_ptr2 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp3 = tmp0 + tmp2
    tmp5 = tmp3 + tmp4
    tmp9 = tmp6 + tmp8
    tmp11 = tmp9 + tmp10
    tmp12 = tmp5 + tmp11
    tmp16 = tmp13 + tmp15
    tmp18 = tmp16 + tmp17
    tmp19 = tmp12 + tmp18
    tmp23 = tmp20 + tmp22
    tmp25 = tmp23 + tmp24
    tmp26 = tmp19 + tmp25
    tmp27 = 4.0
    tmp28 = tmp26 / tmp27
    tmp29 = tmp5 - tmp28
    tmp30 = tmp29 * tmp29
    tmp31 = tmp11 - tmp28
    tmp32 = tmp31 * tmp31
    tmp33 = tmp30 + tmp32
    tmp34 = tmp18 - tmp28
    tmp35 = tmp34 * tmp34
    tmp36 = tmp33 + tmp35
    tmp37 = tmp25 - tmp28
    tmp38 = tmp37 * tmp37
    tmp39 = tmp36 + tmp38
    tmp40 = tmp39 / tmp27
    tl.store(out_ptr0 + x0, tmp28, xmask)
    tl.store(out_ptr1 + x0, tmp40, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_8(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, in_ptr6, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x2, xmask)
    tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr6 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 - tmp5
    tmp8 = 1e-05
    tmp9 = tmp7 + tmp8
    tmp10 = libdevice.rsqrt(tmp9)
    tmp11 = tmp6 * tmp10
    tmp13 = tmp11 * tmp12
    tmp15 = tmp13 + tmp14
    tl.store(out_ptr0 + x2, tmp15, xmask)


@triton.jit
def triton_poi_fused_gelu_9(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
    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_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_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr3 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 + tmp7
    tl.store(in_out_ptr0 + x2, tmp8, 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) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_5, (4, 1), (1, 1))
    assert_size_stride(primals_6, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_7, (4, 1), (1, 1))
    assert_size_stride(primals_8, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_9, (4, 1), (1, 1))
    assert_size_stride(primals_10, (4, 1, 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, 4), (4, 1))
    assert_size_stride(primals_15, (4,), (1,))
    assert_size_stride(primals_16, (4, 4), (4, 1))
    assert_size_stride(primals_17, (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_1, 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_1, 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, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (4, 1), 0), out=buf3)
        buf4 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (4, 1), 0), out=buf4)
        buf5 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 4), (4, 1), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_2[grid(16, 4)](buf3, primals_5, buf6, 16, 4,
            XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1)
        del primals_5
        buf7 = reinterpret_tensor(buf3, (4, 4, 1, 4), (16, 4, 4, 1), 0)
        del buf3
        triton_poi_fused_2[grid(16, 4)](buf4, primals_7, buf7, 16, 4,
            XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1)
        del primals_7
        buf8 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 0), 
            0), reinterpret_tensor(buf7, (16, 1, 4), (4, 0, 1), 0), out=buf8)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_3[grid(256)](buf8, buf9, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_4[grid(256)](buf8, buf9, buf10, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf8
        del buf9
        buf11 = reinterpret_tensor(buf4, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf4
        triton_poi_fused_5[grid(16, 4)](buf5, primals_9, buf11, 16, 4,
            XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1)
        del primals_9
        buf12 = reinterpret_tensor(buf5, (16, 4, 1), (4, 1, 1), 0)
        del buf5
        extern_kernels.bmm(reinterpret_tensor(buf10, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf11, (16, 4, 1), (4, 1, 0), 0), out=buf12)
        buf13 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        triton_poi_fused_view_6[grid(16, 4)](buf12, buf13, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf14 = reinterpret_tensor(buf12, (16, 4), (4, 1), 0)
        del buf12
        extern_kernels.mm(buf13, reinterpret_tensor(primals_10, (4, 4), (4,
            1), 0), out=buf14)
        buf15 = buf1
        del buf1
        buf16 = buf0
        del buf0
        triton_poi_fused_add_native_layer_norm_7[grid(16)](buf14,
            primals_11, primals_1, buf15, buf16, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        buf17 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_8[grid(64)](buf14,
            primals_11, primals_1, buf15, buf16, primals_12, primals_13,
            buf17, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf15
        del buf16
        del primals_13
        buf18 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_15, reinterpret_tensor(buf17, (16, 4),
            (4, 1), 0), reinterpret_tensor(primals_14, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf18)
        del primals_15
        buf19 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_gelu_9[grid(64)](buf18, buf19, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf20 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf19, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_16, (4, 4), (1, 4), 0), out=buf20)
        buf21 = reinterpret_tensor(buf20, (4, 4, 4), (16, 4, 1), 0)
        del buf20
        triton_poi_fused_add_10[grid(64)](buf21, primals_17, buf14,
            primals_11, primals_1, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_17
    return buf21, primals_1, primals_11, primals_12, buf10, reinterpret_tensor(
        buf11, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf6, (16, 1, 
        4), (4, 1, 1), 0), reinterpret_tensor(buf7, (16, 4, 1), (4, 1, 4), 0
        ), buf14, reinterpret_tensor(buf17, (16, 4), (4, 1), 0
        ), buf18, reinterpret_tensor(buf19, (16, 4), (4, 1), 0
        ), primals_16, primals_14, reinterpret_tensor(buf13, (4, 16), (1, 4), 0
        ), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0
        ), reinterpret_tensor(buf2, (4, 16), (1, 4), 0), reinterpret_tensor(
        primals_8, (4, 4), (1, 4), 0), reinterpret_tensor(primals_6, (4, 4),
        (1, 4), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0)


class MlpBlock(nn.Module):

    def __init__(self, in_dim, mlp_dim, out_dim, dropout_rate=0.1):
        super(MlpBlock, self).__init__()
        self.fc1 = nn.Linear(in_dim, mlp_dim)
        self.fc2 = nn.Linear(mlp_dim, out_dim)
        self.act = nn.GELU()
        if dropout_rate > 0.0:
            self.dropout1 = nn.Dropout(dropout_rate)
            self.dropout2 = nn.Dropout(dropout_rate)
        else:
            self.dropout1 = None
            self.dropout2 = None

    def forward(self, x):
        out = self.fc1(x)
        out = self.act(out)
        if self.dropout1:
            out = self.dropout1(out)
        out = self.fc2(out)
        if self.dropout2:
            out = self.dropout2(out)
        return out


class LinearGeneral(nn.Module):

    def __init__(self, in_dim=(768,), feat_dim=(12, 64)):
        super(LinearGeneral, self).__init__()
        self.weight = nn.Parameter(torch.randn(*in_dim, *feat_dim))
        self.bias = nn.Parameter(torch.zeros(*feat_dim))

    def forward(self, x, dims):
        a = torch.tensordot(x, self.weight, dims=dims) + self.bias
        return a


class SelfAttention(nn.Module):

    def __init__(self, in_dim, heads=8, dropout_rate=0.1):
        super(SelfAttention, self).__init__()
        self.heads = heads
        self.head_dim = in_dim // heads
        self.scale = self.head_dim ** 0.5
        self.query = LinearGeneral((in_dim,), (self.heads, self.head_dim))
        self.key = LinearGeneral((in_dim,), (self.heads, self.head_dim))
        self.value = LinearGeneral((in_dim,), (self.heads, self.head_dim))
        self.out = LinearGeneral((self.heads, self.head_dim), (in_dim,))
        if dropout_rate > 0:
            self.dropout = nn.Dropout(dropout_rate)
        else:
            self.dropout = None

    def forward(self, x):
        _b, _n, _ = x.shape
        q = self.query(x, dims=([2], [0]))
        k = self.key(x, dims=([2], [0]))
        v = self.value(x, dims=([2], [0]))
        q = q.permute(0, 2, 1, 3)
        k = k.permute(0, 2, 1, 3)
        v = v.permute(0, 2, 1, 3)
        attn_weights = torch.matmul(q, k.transpose(-2, -1)) / self.scale
        attn_weights = F.softmax(attn_weights, dim=-1)
        out = torch.matmul(attn_weights, v)
        out = out.permute(0, 2, 1, 3)
        out = self.out(out, dims=([2, 3], [0, 1]))
        return out


class EncoderBlockNew(nn.Module):

    def __init__(self, in_dim, mlp_dim, num_heads, dropout_rate=0.1,
        attn_dropout_rate=0.1):
        super(EncoderBlockNew, self).__init__()
        self.norm1 = nn.LayerNorm(in_dim)
        self.attn = SelfAttention(in_dim, heads=num_heads, dropout_rate=
            attn_dropout_rate)
        if dropout_rate > 0:
            self.dropout = nn.Dropout(dropout_rate)
        else:
            self.dropout = None
        self.norm2 = nn.LayerNorm(in_dim)
        self.mlp = MlpBlock(in_dim, mlp_dim, in_dim, dropout_rate)

    def forward(self, input_0):
        primals_2 = self.norm1.weight
        primals_3 = self.norm1.bias
        primals_4 = self.attn.query.weight
        primals_5 = self.attn.query.bias
        primals_6 = self.attn.key.weight
        primals_7 = self.attn.key.bias
        primals_8 = self.attn.value.weight
        primals_9 = self.attn.value.bias
        primals_10 = self.attn.out.weight
        primals_11 = self.attn.out.bias
        primals_12 = self.norm2.weight
        primals_13 = self.norm2.bias
        primals_14 = self.mlp.fc1.weight
        primals_15 = self.mlp.fc1.bias
        primals_16 = self.mlp.fc2.weight
        primals_17 = self.mlp.fc2.bias
        primals_1 = 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])
        return output[0]


Measured latency_ms: 0.015005