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


@triton.jit
def triton_poi_fused_clone_0(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_repeat_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
    x0 = xindex % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tl.store(out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused__softmax_masked_fill_2(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last').to(tl
        .int1)
    tmp1 = tl.load(in_ptr1 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp7 = tl.load(in_ptr1 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp12 = tl.load(in_ptr1 + (2 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp16 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp17 = tl.load(in_ptr1 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = -1.0000000200408773e+20
    tmp3 = tl.where(tmp0, tmp2, tmp1)
    tmp4 = 1.0
    tmp5 = tmp3 * tmp4
    tmp8 = tl.where(tmp6, tmp2, tmp7)
    tmp9 = tmp8 * tmp4
    tmp10 = triton_helpers.maximum(tmp5, tmp9)
    tmp13 = tl.where(tmp11, tmp2, tmp12)
    tmp14 = tmp13 * tmp4
    tmp15 = triton_helpers.maximum(tmp10, tmp14)
    tmp18 = tl.where(tmp16, tmp2, tmp17)
    tmp19 = tmp18 * tmp4
    tmp20 = triton_helpers.maximum(tmp15, tmp19)
    tmp21 = tmp5 - tmp20
    tmp22 = tmp21 * tmp4
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp9 - tmp20
    tmp25 = tmp24 * tmp4
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp23 + tmp26
    tmp28 = tmp14 - tmp20
    tmp29 = tmp28 * tmp4
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp27 + tmp30
    tmp32 = tmp19 - tmp20
    tmp33 = tmp32 * tmp4
    tmp34 = tl_math.exp(tmp33)
    tmp35 = tmp31 + tmp34
    tl.store(out_ptr0 + x2, tmp20, xmask)
    tl.store(out_ptr1 + x2, tmp35, xmask)


@triton.jit
def triton_poi_fused__softmax_masked_fill_3(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex // 16
    x3 = xindex
    x4 = xindex // 4
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp1 = tl.load(in_out_ptr0 + x3, xmask)
    tmp6 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp2 = -1.0000000200408773e+20
    tmp3 = tl.where(tmp0, tmp2, tmp1)
    tmp4 = 1.0
    tmp5 = tmp3 * tmp4
    tmp7 = tmp5 - tmp6
    tmp8 = tmp7 * tmp4
    tmp9 = tl_math.exp(tmp8)
    tmp11 = tmp9 / tmp10
    tl.store(in_out_ptr0 + x3, tmp11, xmask)


@triton.jit
def triton_poi_fused_clone_4(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_native_layer_norm_5(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_native_layer_norm_6(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-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_relu_threshold_backward_7(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_8(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)


@triton.jit
def triton_poi_fused_native_layer_norm_9(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_mul_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 + 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 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 * tmp9
    tl.store(out_ptr0 + x2, tmp10, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17, primals_18
        ) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 4), (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, 4), (4, 1))
    assert_size_stride(primals_14, (4,), (1,))
    assert_size_stride(primals_15, (4, 4), (4, 1))
    assert_size_stride(primals_16, (4,), (1,))
    assert_size_stride(primals_17, (4,), (1,))
    assert_size_stride(primals_18, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf0)
        del primals_3
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(16, 4)](buf0, primals_4, buf1, 16, 4,
            XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
        del primals_4
        buf2 = buf0
        del buf0
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf3)
        del primals_7
        buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_0[grid(16, 4)](buf3, primals_8, buf4, 16, 4,
            XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
        del primals_8
        buf5 = reinterpret_tensor(buf3, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf3
        triton_poi_fused_clone_0[grid(16, 4)](buf2, primals_6, buf5, 16, 4,
            XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
        del primals_6
        buf6 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf1, (16, 4, 1), (4, 1, 0), 
            0), reinterpret_tensor(buf5, (16, 1, 4), (4, 0, 1), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.bool)
        triton_poi_fused_repeat_1[grid(64)](primals_2, buf7, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf8 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 64), 0)
        del buf2
        buf9 = empty_strided_cuda((16, 4, 1), (4, 1, 64), torch.float32)
        triton_poi_fused__softmax_masked_fill_2[grid(64)](buf7, buf6, buf8,
            buf9, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf10 = buf6
        del buf6
        triton_poi_fused__softmax_masked_fill_3[grid(256)](buf10, buf7,
            buf8, buf9, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf11 = reinterpret_tensor(buf9, (16, 4, 1), (4, 1, 1), 0)
        del buf9
        extern_kernels.bmm(buf10, reinterpret_tensor(buf4, (16, 4, 1), (4, 
            1, 0), 0), out=buf11)
        buf12 = reinterpret_tensor(buf8, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf8
        triton_poi_fused_clone_4[grid(16, 4)](buf11, buf12, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf13 = reinterpret_tensor(buf11, (16, 4), (4, 1), 0)
        del buf11
        extern_kernels.addmm(primals_10, reinterpret_tensor(buf12, (16, 4),
            (4, 1), 0), reinterpret_tensor(primals_9, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf13)
        del primals_10
        buf14 = empty_strided_cuda((16, 1), (1, 16), torch.float32)
        buf15 = empty_strided_cuda((16, 1), (1, 16), torch.float32)
        triton_poi_fused_native_layer_norm_5[grid(16)](primals_1, buf13,
            buf14, buf15, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf16 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_6[grid(64)](primals_1, buf13,
            buf14, buf15, primals_11, primals_12, buf16, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_12
        buf17 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf16, reinterpret_tensor(primals_13, (4, 4), (1,
            4), 0), out=buf17)
        buf18 = reinterpret_tensor(buf17, (4, 4, 4), (16, 4, 1), 0)
        del buf17
        buf24 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_7[grid(64)](buf18,
            primals_14, buf24, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_14
        buf19 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf18, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_15, (4, 4), (1, 4), 0), out=buf19)
        buf20 = reinterpret_tensor(buf19, (4, 4, 4), (16, 4, 1), 0)
        del buf19
        triton_poi_fused_add_8[grid(64)](buf20, buf16, primals_16, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_16
        buf21 = buf15
        del buf15
        buf22 = buf14
        del buf14
        triton_poi_fused_native_layer_norm_9[grid(16)](buf20, buf21, buf22,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf23 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_mul_10[grid(64)](buf20, buf21, buf22, primals_17,
            primals_18, primals_2, buf23, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf21
        del buf22
        del primals_18
    return (buf23, primals_1, primals_2, primals_11, primals_17, buf7,
        buf10, reinterpret_tensor(buf12, (16, 4), (4, 1), 0), buf13, buf16,
        reinterpret_tensor(buf18, (16, 4), (4, 1), 0), reinterpret_tensor(
        buf20, (16, 4), (4, 1), 0), primals_15, buf24, primals_13,
        primals_9, reinterpret_tensor(buf4, (16, 1, 4), (4, 1, 1), 0),
        reinterpret_tensor(buf1, (16, 1, 4), (4, 1, 1), 0),
        reinterpret_tensor(buf5, (16, 4, 1), (4, 1, 1), 0))


def _normalize(tensor, norm_layer):
    """
    Broadcast layer norm
    """
    size = tensor.size()
    return norm_layer(tensor.view(-1, size[-1])).view(size)


class MultiHeadAttention(nn.Module):

    def __init__(self, n_heads, dim, dropout=0):
        super(MultiHeadAttention, self).__init__()
        self.n_heads = n_heads
        self.dim = dim
        self.attn_dropout = nn.Dropout(p=dropout)
        self.q_lin = nn.Linear(dim, dim)
        self.k_lin = nn.Linear(dim, dim)
        self.v_lin = nn.Linear(dim, dim)
        nn.init.xavier_normal_(self.q_lin.weight)
        nn.init.xavier_normal_(self.k_lin.weight)
        nn.init.xavier_normal_(self.v_lin.weight)
        self.out_lin = nn.Linear(dim, dim)
        nn.init.xavier_normal_(self.out_lin.weight)

    def forward(self, query, key=None, value=None, mask=None):
        batch_size, query_len, dim = query.size()
        assert dim == self.dim, f'Dimensions do not match: {dim} query vs {self.dim} configured'
        assert mask is not None, 'Mask is None, please specify a mask'
        n_heads = self.n_heads
        dim_per_head = dim // n_heads
        scale = math.sqrt(dim_per_head)

        def prepare_head(tensor):
            _bsz, seq_len, _ = tensor.size()
            tensor = tensor.view(batch_size, tensor.size(1), n_heads,
                dim_per_head)
            tensor = tensor.transpose(1, 2).contiguous().view(batch_size *
                n_heads, seq_len, dim_per_head)
            return tensor
        if key is None and value is None:
            key = value = query
        elif value is None:
            value = key
        _, key_len, dim = key.size()
        q = prepare_head(self.q_lin(query))
        k = prepare_head(self.k_lin(key))
        v = prepare_head(self.v_lin(value))
        dot_prod = q.bmm(k.transpose(1, 2))
        attn_mask = (mask == 0).view(batch_size, 1, -1, key_len).repeat(1,
            n_heads, 1, 1).expand(batch_size, n_heads, query_len, key_len
            ).view(batch_size * n_heads, query_len, key_len)
        assert attn_mask.shape == dot_prod.shape
        dot_prod.masked_fill_(attn_mask, -float(1e+20))
        attn_weights = F.softmax(dot_prod / scale, dim=-1)
        attn_weights = self.attn_dropout(attn_weights)
        attentioned = attn_weights.bmm(v)
        attentioned = attentioned.view(batch_size, n_heads, query_len,
            dim_per_head).transpose(1, 2).contiguous().view(batch_size,
            query_len, dim)
        out = self.out_lin(attentioned)
        return out


class TransformerFFN(nn.Module):

    def __init__(self, dim, dim_hidden, relu_dropout=0):
        super(TransformerFFN, self).__init__()
        self.relu_dropout = nn.Dropout(p=relu_dropout)
        self.lin1 = nn.Linear(dim, dim_hidden)
        self.lin2 = nn.Linear(dim_hidden, dim)
        nn.init.xavier_uniform_(self.lin1.weight)
        nn.init.xavier_uniform_(self.lin2.weight)

    def forward(self, x):
        x = F.relu(self.lin1(x))
        x = self.relu_dropout(x)
        x = self.lin2(x)
        return x


class TransformerEncoderLayerNew(nn.Module):

    def __init__(self, n_heads, embedding_size, ffn_size, attention_dropout
        =0.0, relu_dropout=0.0, dropout=0.0):
        super().__init__()
        self.dim = embedding_size
        self.ffn_dim = ffn_size
        self.attention = MultiHeadAttention(n_heads, embedding_size,
            dropout=attention_dropout)
        self.norm1 = nn.LayerNorm(embedding_size)
        self.ffn = TransformerFFN(embedding_size, ffn_size, relu_dropout=
            relu_dropout)
        self.norm2 = nn.LayerNorm(embedding_size)
        self.dropout = nn.Dropout(p=dropout)

    def forward(self, input_0, input_1):
        primals_2 = self.attention.q_lin.weight
        primals_4 = self.attention.q_lin.bias
        primals_3 = self.attention.k_lin.weight
        primals_6 = self.attention.k_lin.bias
        primals_5 = self.attention.v_lin.weight
        primals_8 = self.attention.v_lin.bias
        primals_7 = self.attention.out_lin.weight
        primals_10 = self.attention.out_lin.bias
        primals_11 = self.norm1.weight
        primals_12 = self.norm1.bias
        primals_9 = self.ffn.lin1.weight
        primals_14 = self.ffn.lin1.bias
        primals_13 = self.ffn.lin2.weight
        primals_16 = self.ffn.lin2.bias
        primals_17 = self.norm2.weight
        primals_18 = self.norm2.bias
        primals_1 = input_0
        primals_15 = 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])
        return output[0]


Measured latency_ms: 0.278145