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
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_eq_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused__softmax_div_masked_fill_1(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
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask).to(tl.int1)
    tmp1 = tl.load(in_ptr1 + (x0 + 64 * x1), xmask)
    tmp6 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask).to(tl.int1)
    tmp7 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask)
    tmp11 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask).to(tl.int1)
    tmp12 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask)
    tmp16 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask).to(tl.int1)
    tmp17 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
    tmp2 = 1.0
    tmp3 = tmp1 * tmp2
    tmp4 = -9.999999960041972e-13
    tmp5 = tl.where(tmp0, tmp4, tmp3)
    tmp8 = tmp7 * tmp2
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tmp10 = triton_helpers.maximum(tmp5, tmp9)
    tmp13 = tmp12 * tmp2
    tmp14 = tl.where(tmp11, tmp4, tmp13)
    tmp15 = triton_helpers.maximum(tmp10, tmp14)
    tmp18 = tmp17 * tmp2
    tmp19 = tl.where(tmp16, tmp4, tmp18)
    tmp20 = triton_helpers.maximum(tmp15, tmp19)
    tmp21 = tmp5 - tmp20
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp9 - tmp20
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp22 + tmp24
    tmp26 = tmp14 - tmp20
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tmp25 + tmp27
    tmp29 = tmp19 - tmp20
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp28 + tmp30
    tl.store(out_ptr0 + x2, tmp20, xmask)
    tl.store(out_ptr1 + x2, tmp31, xmask)


@triton.jit
def triton_poi_fused__softmax_div_masked_fill_2(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
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask).to(tl.int1)
    tmp1 = tl.load(in_out_ptr0 + x3, xmask)
    tmp6 = tl.load(in_ptr1 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr2 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = 1.0
    tmp3 = tmp1 * tmp2
    tmp4 = -9.999999960041972e-13
    tmp5 = tl.where(tmp0, tmp4, tmp3)
    tmp7 = tmp5 - tmp6
    tmp8 = tl_math.exp(tmp7)
    tmp10 = tmp8 / tmp9
    tl.store(in_out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_mean_std_3(in_out_ptr0, in_ptr0, in_ptr1, 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 + 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 = 3.0
    tmp29 = tmp27 / tmp28
    tl.store(in_out_ptr0 + x0, tmp29, xmask)
    tl.store(out_ptr0 + x0, tmp16, xmask)


@triton.jit
def triton_poi_fused_add_div_mean_mul_std_sub_4(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
    x0 = xindex % 4
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tl.load(in_ptr2 + x2, xmask)
    tmp4 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 - tmp4
    tmp7 = libdevice.sqrt(tmp6)
    tmp8 = 1e-12
    tmp9 = tmp7 + tmp8
    tmp10 = tmp5 / tmp9
    tmp11 = tmp0 * tmp10
    tmp13 = tmp11 + tmp12
    tl.store(out_ptr0 + x2, tmp13, xmask)


@triton.jit
def triton_poi_fused_add_5(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_add_div_mean_mul_std_sub_6(in_ptr0, in_ptr1, in_ptr2,
    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
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = 4.0
    tmp10 = tmp8 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp2 - tmp10
    tmp13 = tmp12 * tmp12
    tmp14 = tmp3 - tmp10
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp10
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp7 - tmp10
    tmp21 = tmp20 * tmp20
    tmp22 = tmp19 + tmp21
    tmp23 = 3.0
    tmp24 = tmp22 / tmp23
    tmp25 = libdevice.sqrt(tmp24)
    tmp26 = 1e-12
    tmp27 = tmp25 + tmp26
    tmp28 = tmp11 / tmp27
    tmp29 = tmp0 * tmp28
    tmp31 = tmp29 + tmp30
    tl.store(out_ptr0 + x2, tmp31, 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)


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, primals_27,
        primals_28, primals_29, primals_30) = 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,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4, 4, 4), (64, 16, 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), (16, 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,))
    assert_size_stride(primals_18, (4, 4), (4, 1))
    assert_size_stride(primals_19, (4,), (1,))
    assert_size_stride(primals_20, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_21, (4, 4), (4, 1))
    assert_size_stride(primals_22, (4,), (1,))
    assert_size_stride(primals_23, (4,), (1,))
    assert_size_stride(primals_24, (4,), (1,))
    assert_size_stride(primals_25, (4, 4), (4, 1))
    assert_size_stride(primals_26, (4,), (1,))
    assert_size_stride(primals_27, (4, 4), (4, 1))
    assert_size_stride(primals_28, (4,), (1,))
    assert_size_stride(primals_29, (4,), (1,))
    assert_size_stride(primals_30, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(primals_1, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf2)
        del primals_6
        del primals_7
        buf3 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (16, 4, 1), (4, 1, 1), 
            0), reinterpret_tensor(buf1, (16, 1, 4), (4, 4, 1), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_eq_0[grid(256)](primals_8, buf4, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_8
        buf5 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        triton_poi_fused__softmax_div_masked_fill_1[grid(64)](buf4, buf3,
            buf5, buf6, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf7 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf3
        triton_poi_fused__softmax_div_masked_fill_2[grid(256)](buf7, buf4,
            buf5, buf6, 256, XBLOCK=256, num_warps=4, num_stages=1)
        buf8 = reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 1), 0)
        del buf6
        extern_kernels.bmm(reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0), out=buf8)
        buf9 = reinterpret_tensor(buf5, (16, 4), (4, 1), 0)
        del buf5
        extern_kernels.addmm(primals_10, reinterpret_tensor(buf8, (16, 4),
            (4, 1), 0), reinterpret_tensor(primals_9, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf9)
        del primals_10
        buf10 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf11 = buf10
        del buf10
        buf12 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused_add_mean_std_3[grid(16)](buf11, buf9, primals_1,
            buf12, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf13 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_div_mean_mul_std_sub_4[grid(64)](primals_11,
            buf9, primals_1, buf12, buf11, primals_12, buf13, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf11
        del buf12
        del primals_12
        buf14 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_15, reinterpret_tensor(buf13, (16, 4),
            (4, 1), 0), reinterpret_tensor(primals_14, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf14)
        del primals_15
        buf15 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_17, reinterpret_tensor(primals_13, (16,
            4), (4, 1), 0), reinterpret_tensor(primals_16, (4, 4), (1, 4), 
            0), alpha=1, beta=1, out=buf15)
        del primals_16
        del primals_17
        buf16 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_19, reinterpret_tensor(primals_13, (16,
            4), (4, 1), 0), reinterpret_tensor(primals_18, (4, 4), (1, 4), 
            0), alpha=1, beta=1, out=buf16)
        del primals_18
        del primals_19
        buf17 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf14, (16, 4, 1), (4, 1, 1),
            0), reinterpret_tensor(buf15, (16, 1, 4), (4, 4, 1), 0), out=buf17)
        buf18 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_eq_0[grid(256)](primals_20, buf18, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_20
        buf19 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        buf20 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        triton_poi_fused__softmax_div_masked_fill_1[grid(64)](buf18, buf17,
            buf19, buf20, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf21 = reinterpret_tensor(buf17, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf17
        triton_poi_fused__softmax_div_masked_fill_2[grid(256)](buf21, buf18,
            buf19, buf20, 256, XBLOCK=256, num_warps=4, num_stages=1)
        buf22 = reinterpret_tensor(buf20, (16, 4, 1), (4, 1, 1), 0)
        del buf20
        extern_kernels.bmm(reinterpret_tensor(buf21, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf16, (16, 4, 1), (4, 1, 1), 0), out=buf22)
        buf23 = reinterpret_tensor(buf19, (16, 4), (4, 1), 0)
        del buf19
        extern_kernels.mm(reinterpret_tensor(buf22, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_21, (4, 4), (1, 4), 0), out=buf23)
        buf24 = reinterpret_tensor(buf23, (4, 4, 4), (16, 4, 1), 0)
        del buf23
        triton_poi_fused_add_5[grid(64)](buf24, primals_22, buf13, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_22
        buf25 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_div_mean_mul_std_sub_6[grid(64)](primals_23,
            buf24, primals_24, buf25, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_24
        buf26 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf25, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_25, (4, 4), (1, 4), 0), out=buf26)
        buf27 = reinterpret_tensor(buf26, (4, 4, 4), (16, 4, 1), 0)
        del buf26
        buf31 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_7[grid(64)](buf27,
            primals_26, buf31, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_26
        buf28 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf27, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_27, (4, 4), (1, 4), 0), out=buf28)
        buf29 = reinterpret_tensor(buf28, (4, 4, 4), (16, 4, 1), 0)
        del buf28
        triton_poi_fused_add_5[grid(64)](buf29, primals_28, buf25, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_28
        buf30 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_div_mean_mul_std_sub_6[grid(64)](primals_29,
            buf29, primals_30, buf30, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_30
    return (buf30, primals_1, primals_11, primals_23, primals_29, buf4,
        buf7, reinterpret_tensor(buf8, (16, 4), (4, 1), 0), buf9,
        reinterpret_tensor(buf13, (16, 4), (4, 1), 0), reinterpret_tensor(
        primals_13, (16, 4), (4, 1), 0), buf18, buf21, reinterpret_tensor(
        buf22, (16, 4), (4, 1), 0), buf24, reinterpret_tensor(buf25, (16, 4
        ), (4, 1), 0), reinterpret_tensor(buf27, (16, 4), (4, 1), 0), buf29,
        primals_27, buf31, primals_25, primals_21, reinterpret_tensor(buf16,
        (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf14, (16, 1, 4), (4,
        1, 1), 0), reinterpret_tensor(buf15, (16, 4, 1), (4, 1, 4), 0),
        primals_14, primals_9, reinterpret_tensor(buf2, (16, 1, 4), (4, 1, 
        1), 0), reinterpret_tensor(buf0, (16, 1, 4), (4, 1, 1), 0),
        reinterpret_tensor(buf1, (16, 4, 1), (4, 1, 4), 0))


class LayerNorm(nn.Module):

    def __init__(self, d_model, eps=1e-12):
        super(LayerNorm, self).__init__()
        self.gamma = nn.Parameter(torch.ones(d_model))
        self.beta = nn.Parameter(torch.zeros(d_model))
        self.eps = eps

    def forward(self, x):
        mean = x.mean(-1, keepdim=True)
        std = x.std(-1, keepdim=True)
        out = (x - mean) / (std + self.eps)
        out = self.gamma * out + self.beta
        return out


class ScaleDotProductAttention(nn.Module):
    """
    compute scale dot product attention

    Query : given sentence that we focused on (decoder)
    Key : every sentence to check relationship with Qeury(encoder)
    Value : every sentence same with Key (encoder)
    """

    def __init__(self):
        super(ScaleDotProductAttention, self).__init__()
        self.softmax = nn.Softmax()

    def forward(self, q, k, v, mask=None, e=1e-12):
        batch_size, head, length, d_tensor = k.size()
        k_t = k.view(batch_size, head, d_tensor, length)
        score = q @ k_t / math.sqrt(d_tensor)
        if mask is not None:
            score = score.masked_fill(mask == 0, -e)
        score = self.softmax(score)
        v = score @ v
        return v, score


class MultiHeadAttention(nn.Module):

    def __init__(self, d_model, n_head):
        super(MultiHeadAttention, self).__init__()
        self.n_head = n_head
        self.attention = ScaleDotProductAttention()
        self.w_q = nn.Linear(d_model, d_model)
        self.w_k = nn.Linear(d_model, d_model)
        self.w_v = nn.Linear(d_model, d_model)
        self.w_concat = nn.Linear(d_model, d_model)

    def forward(self, q, k, v, mask=None):
        q, k, v = self.w_q(q), self.w_k(k), self.w_v(v)
        q, k, v = self.split(q), self.split(k), self.split(v)
        out, _attention = self.attention(q, k, v, mask=mask)
        out = self.concat(out)
        out = self.w_concat(out)
        return out

    def split(self, tensor):
        """
        split tensor by number of head

        :param tensor: [batch_size, length, d_model]
        :return: [batch_size, head, length, d_tensor]
        """
        batch_size, length, d_model = tensor.size()
        d_tensor = d_model // self.n_head
        tensor = tensor.view(batch_size, self.n_head, length, d_tensor)
        return tensor

    def concat(self, tensor):
        """
        inverse function of self.split(tensor : torch.Tensor)

        :param tensor: [batch_size, head, length, d_tensor]
        :return: [batch_size, length, d_model]
        """
        batch_size, head, length, d_tensor = tensor.size()
        d_model = head * d_tensor
        tensor = tensor.view(batch_size, length, d_model)
        return tensor


class PositionwiseFeedForward(nn.Module):

    def __init__(self, d_model, hidden, drop_prob=0.1):
        super(PositionwiseFeedForward, self).__init__()
        self.linear1 = nn.Linear(d_model, hidden)
        self.linear2 = nn.Linear(hidden, d_model)
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(p=drop_prob)

    def forward(self, x):
        x = self.linear1(x)
        x = self.relu(x)
        x = self.dropout(x)
        x = self.linear2(x)
        return x


class DecoderLayerNew(nn.Module):

    def __init__(self, d_model, ffn_hidden, n_head, drop_prob):
        super(DecoderLayerNew, self).__init__()
        self.self_attention = MultiHeadAttention(d_model=d_model, n_head=n_head
            )
        self.norm1 = LayerNorm(d_model=d_model)
        self.dropout1 = nn.Dropout(p=drop_prob)
        self.enc_dec_attention = MultiHeadAttention(d_model=d_model, n_head
            =n_head)
        self.norm2 = LayerNorm(d_model=d_model)
        self.dropout2 = nn.Dropout(p=drop_prob)
        self.ffn = PositionwiseFeedForward(d_model=d_model, hidden=
            ffn_hidden, drop_prob=drop_prob)
        self.norm3 = LayerNorm(d_model=d_model)
        self.dropout3 = nn.Dropout(p=drop_prob)

    def forward(self, input_0, input_1, input_2, input_3):
        primals_2 = self.self_attention.w_q.weight
        primals_3 = self.self_attention.w_q.bias
        primals_4 = self.self_attention.w_k.weight
        primals_5 = self.self_attention.w_k.bias
        primals_6 = self.self_attention.w_v.weight
        primals_7 = self.self_attention.w_v.bias
        primals_9 = self.self_attention.w_concat.weight
        primals_10 = self.self_attention.w_concat.bias
        primals_11 = self.norm1.gamma
        primals_12 = self.norm1.beta
        primals_14 = self.enc_dec_attention.w_q.weight
        primals_15 = self.enc_dec_attention.w_q.bias
        primals_16 = self.enc_dec_attention.w_k.weight
        primals_17 = self.enc_dec_attention.w_k.bias
        primals_18 = self.enc_dec_attention.w_v.weight
        primals_19 = self.enc_dec_attention.w_v.bias
        primals_21 = self.enc_dec_attention.w_concat.weight
        primals_22 = self.enc_dec_attention.w_concat.bias
        primals_23 = self.norm2.gamma
        primals_24 = self.norm2.beta
        primals_25 = self.ffn.linear1.weight
        primals_26 = self.ffn.linear1.bias
        primals_27 = self.ffn.linear2.weight
        primals_28 = self.ffn.linear2.bias
        primals_29 = self.norm3.gamma
        primals_30 = self.norm3.beta
        primals_1 = input_0
        primals_13 = input_1
        primals_8 = input_2
        primals_20 = 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, primals_27, primals_28, primals_29,
            primals_30])
        return output[0]


Measured latency_ms: 0.015872