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_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
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp4, xmask & ymask)


@triton.jit
def triton_poi_fused_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2,
    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 % 16
    tmp0 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x2), 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 * x2), 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 * x2), 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 = triton_helpers.maximum(tmp2, tmp5)
    tmp9 = tmp7 + tmp8
    tmp10 = triton_helpers.maximum(tmp6, tmp9)
    tmp13 = tmp11 + tmp12
    tmp14 = triton_helpers.maximum(tmp10, tmp13)
    tmp15 = tmp2 - tmp14
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp5 - tmp14
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp16 + tmp18
    tmp20 = tmp9 - tmp14
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp19 + tmp21
    tmp23 = tmp13 - tmp14
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp22 + tmp24
    tmp26 = float('-inf')
    tmp27 = tmp2 == tmp26
    tmp28 = tmp27 == 0
    tmp29 = tmp28.to(tl.int64)
    tmp30 = tmp29 != 0
    tmp31 = tmp5 == tmp26
    tmp32 = tmp31 == 0
    tmp33 = tmp32.to(tl.int64)
    tmp34 = tmp33 != 0
    tmp35 = tmp30 | tmp34
    tmp36 = tmp9 == tmp26
    tmp37 = tmp36 == 0
    tmp38 = tmp37.to(tl.int64)
    tmp39 = tmp38 != 0
    tmp40 = tmp35 | tmp39
    tmp41 = tmp13 == tmp26
    tmp42 = tmp41 == 0
    tmp43 = tmp42.to(tl.int64)
    tmp44 = tmp43 != 0
    tmp45 = tmp40 | tmp44
    tl.store(out_ptr0 + x2, tmp14, xmask)
    tl.store(out_ptr1 + x2, tmp25, xmask)
    tl.store(out_ptr2 + x2, tmp45, xmask)


@triton.jit
def triton_poi_fused_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex // 4
    x4 = xindex
    x5 = xindex % 64
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last').to(tl
        .int1)
    tmp2 = tl.load(in_out_ptr0 + x4, xmask)
    tmp3 = tl.load(in_ptr1 + x5, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + x3, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr3 + x3, xmask, eviction_policy='evict_last')
    tmp1 = tmp0 == 0
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 - tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp9 = tmp7 / tmp8
    tmp10 = 0.0
    tmp11 = tl.where(tmp1, tmp10, tmp9)
    tl.store(in_out_ptr0 + x4, tmp11, xmask)


@triton.jit
def triton_poi_fused_3(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_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_add_mean_pow_sub_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_add_div_mean_mul_sqrt_sub_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
    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 = 1e-12
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.sqrt(tmp8)
    tmp10 = tmp5 / tmp9
    tmp11 = tmp0 * tmp10
    tmp13 = tmp11 + tmp12
    tl.store(out_ptr0 + x2, tmp13, xmask)


@triton.jit
def triton_poi_fused_add_mul_pow_tanh_7(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 = tmp0 * tmp0
    tmp4 = tmp3 * tmp0
    tmp5 = 0.044715
    tmp6 = tmp4 * tmp5
    tmp7 = tmp0 + tmp6
    tmp8 = 0.7978845608028654
    tmp9 = tmp7 * tmp8
    tmp10 = libdevice.tanh(tmp9)
    tmp11 = 1.0
    tmp12 = tmp10 + tmp11
    tmp13 = tmp2 * tmp12
    tl.store(out_ptr0 + x0, tmp13, xmask)


@triton.jit
def triton_poi_fused_add_mean_8(in_ptr0, in_ptr1, in_ptr2, 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 + 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
    tl.store(out_ptr0 + x0, tmp28, xmask)


@triton.jit
def triton_poi_fused_add_sub_9(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
    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_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp5 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 - tmp5
    tl.store(in_out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_div_mean_mul_pow_sqrt_10(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')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp2 * tmp2
    tmp5 = tmp4 * tmp4
    tmp6 = tmp3 + tmp5
    tmp8 = tmp7 * tmp7
    tmp9 = tmp6 + tmp8
    tmp11 = tmp10 * tmp10
    tmp12 = tmp9 + tmp11
    tmp13 = 4.0
    tmp14 = tmp12 / tmp13
    tmp15 = 1e-12
    tmp16 = tmp14 + tmp15
    tmp17 = libdevice.sqrt(tmp16)
    tmp18 = tmp1 / tmp17
    tmp19 = tmp0 * tmp18
    tmp21 = tmp19 + tmp20
    tl.store(out_ptr0 + x2, tmp21, 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, 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,), (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), (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, (16, 4), (4, 1))
    assert_size_stride(primals_14, (16,), (1,))
    assert_size_stride(primals_15, (4, 16), (16, 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_3, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf2)
        del primals_6
        buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(16, 4)](buf0, primals_2, buf3, 16, 4,
            XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1)
        del primals_2
        buf4 = reinterpret_tensor(buf0, (4, 4, 1, 4), (16, 4, 4, 1), 0)
        del buf0
        triton_poi_fused_0[grid(16, 4)](buf1, primals_5, buf4, 16, 4,
            XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1)
        del primals_5
        buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 0), 
            0), reinterpret_tensor(buf4, (16, 1, 4), (4, 0, 1), 0), out=buf5)
        buf6 = reinterpret_tensor(buf1, (4, 4, 4, 1), (16, 4, 1, 64), 0)
        del buf1
        buf7 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf8 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.bool)
        triton_poi_fused_1[grid(64)](buf5, primals_8, buf6, buf7, buf8, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf9 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf5
        triton_poi_fused_2[grid(256)](buf9, buf8, primals_8, buf6, buf7, 
            256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf8
        del primals_8
        buf10 = reinterpret_tensor(buf7, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf7
        triton_poi_fused_3[grid(16, 4)](buf2, primals_7, buf10, 16, 4,
            XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1)
        del primals_7
        buf11 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0)
        del buf2
        extern_kernels.bmm(reinterpret_tensor(buf9, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf10, (16, 4, 1), (4, 1, 0), 0), out=buf11)
        buf12 = reinterpret_tensor(buf6, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf6
        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((4, 4, 1), (4, 1, 16), torch.float32)
        buf15 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused_add_mean_pow_sub_5[grid(16)](buf13, primals_3,
            buf14, buf15, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf16 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_div_mean_mul_sqrt_sub_6[grid(64)](primals_11,
            buf13, primals_3, buf14, buf15, primals_12, buf16, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        del buf14
        del primals_12
        buf17 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_14, reinterpret_tensor(buf16, (16, 4),
            (4, 1), 0), reinterpret_tensor(primals_13, (4, 16), (1, 4), 0),
            alpha=1, beta=1, out=buf17)
        del primals_14
        buf18 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        triton_poi_fused_add_mul_pow_tanh_7[grid(256)](buf17, buf18, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf19 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf18, (16, 16), (16, 1), 0),
            reinterpret_tensor(primals_15, (16, 4), (1, 16), 0), out=buf19)
        buf20 = buf15
        del buf15
        triton_poi_fused_add_mean_8[grid(16)](buf19, primals_16, buf16,
            buf20, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf21 = reinterpret_tensor(buf19, (4, 4, 4), (16, 4, 1), 0)
        del buf19
        triton_poi_fused_add_sub_9[grid(64)](buf21, primals_16, buf16,
            buf20, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf20
        del primals_16
        buf22 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_div_mean_mul_pow_sqrt_10[grid(64)](primals_17,
            buf21, primals_18, buf22, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_18
    return buf22, primals_3, primals_11, primals_17, buf9, reinterpret_tensor(
        buf10, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf3, (16, 1, 
        4), (4, 1, 1), 0), reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 4), 0
        ), reinterpret_tensor(buf12, (16, 4), (4, 1), 0
        ), buf13, reinterpret_tensor(buf16, (16, 4), (4, 1), 0
        ), buf17, reinterpret_tensor(buf18, (16, 16), (16, 1), 0
        ), buf21, primals_15, primals_13, primals_9


class NormLayer(nn.Module):
    """
		Implementation of Layer Normalization (https://arxiv.org/abs/1607.06450)
		It consists of Batch Normalization Transform to speed up learning with mean and std computed according to the above paper

		normWeights:
			weights for this normalization layer which will be learnt during training
		normBias:
			bias for this normalization layer which will be learnt during training
		epsilon:
			numerical stability parameter to avoid division by zero
	"""

    def __init__(self, hiddenSize, epsilon=1e-12):
        super(NormLayer, self).__init__()
        self.weight = nn.Parameter(torch.ones(hiddenSize))
        self.bias = nn.Parameter(torch.zeros(hiddenSize))
        self.epsilon = epsilon

    def forward(self, input):
        mu = input.mean(-1, keepdim=True)
        stdArg = (input - mu).pow(2).mean(-1, keepdim=True) + self.epsilon
        std = torch.sqrt(stdArg)
        input = (input - mu) / std
        return self.weight * input + self.bias


class GELU(nn.Module):

    def __init__(self):
        super(GELU, self).__init__()

    def forward(self, tensor):
        geluPow = tensor + 0.044715 * torch.pow(tensor, 3)
        geluTanh = torch.tanh(math.sqrt(2 / math.pi) * geluPow)
        geluResult = 1 + geluTanh
        return 0.5 * tensor * geluResult


class FeedForward(nn.Module):

    def __init__(self, hiddenSize, innerLayerDimension, dropOutProb=0.1):
        super(FeedForward, self).__init__()
        self.activationFuncion = GELU()
        self.dropout = nn.Dropout(dropOutProb)
        self.w1 = nn.Linear(hiddenSize, innerLayerDimension)
        self.w2 = nn.Linear(innerLayerDimension, hiddenSize)

    def forward(self, tensor):
        intermediate = self.activationFuncion(self.w1(tensor))
        linearOut = self.w2(intermediate)
        return self.dropout(linearOut)


class ScaledDotProduct(nn.Module):

    def __init__(self, attentionHeadSize, dropOutProb=0.1):
        super(ScaledDotProduct, self).__init__()
        self.attentionHeadSize = attentionHeadSize
        self.dropout = nn.Dropout(dropOutProb)

    def forward(self, Q, K, V, attentionMask):
        aScores = torch.matmul(Q, K.transpose(-1, -2)) / math.sqrt(self.
            attentionHeadSize)
        aScores = aScores + attentionMask
        aProbs = self.dropout(nn.Softmax(dim=-1)(aScores))
        return torch.matmul(aProbs, V)


class MultiHeadAttention(nn.Module):

    def __init__(self, hiddenSize, numAttentionHeads, dropoutProb):
        super(MultiHeadAttention, self).__init__()
        if hiddenSize % numAttentionHeads != 0:
            raise ValueError(
                'The hidden size ({}) is not a multiple of the numeber of attention heads ({})'
                .format(hiddenSize, numAttentionHeads))
        self.numAttentionHeads = numAttentionHeads
        self.attentionHeadSize = hiddenSize // self.numAttentionHeads
        self.queriesLinear = nn.Linear(hiddenSize, hiddenSize)
        self.keysLinear = nn.Linear(hiddenSize, hiddenSize)
        self.valuesLinear = nn.Linear(hiddenSize, hiddenSize)
        self.sdp = ScaledDotProduct(self.attentionHeadSize, dropoutProb)
        self.outputLinear = nn.Linear(hiddenSize, hiddenSize)

    def prepareForScores(self, input):
        newShape = input.size()[:-1] + (self.numAttentionHeads, self.
            attentionHeadSize)
        input = input.view(*newShape)
        return input.permute(0, 2, 1, 3)

    def forward(self, hiddenStates, attentionMask):
        projQ = self.queriesLinear(hiddenStates)
        projK = self.keysLinear(hiddenStates)
        projV = self.valuesLinear(hiddenStates)
        queries = self.prepareForScores(projQ)
        keys = self.prepareForScores(projK)
        values = self.prepareForScores(projV)
        attentionScores = self.sdp(queries, keys, values, attentionMask)
        attentionScores = attentionScores.permute(0, 2, 1, 3).contiguous()
        newShape = attentionScores.size()[:-2] + (self.numAttentionHeads *
            self.attentionHeadSize,)
        attentionScores = attentionScores.view(*newShape)
        return self.outputLinear(attentionScores)


class EncoderNew(nn.Module):

    def __init__(self, hiddenSize, numAttentionHeads, normEpsilon=1e-12,
        dropoutProb=0.1):
        super(EncoderNew, self).__init__()
        self.multiHeadAtt = MultiHeadAttention(hiddenSize,
            numAttentionHeads, dropoutProb)
        self.feedForward = FeedForward(hiddenSize, 4 * hiddenSize, dropoutProb)
        self.attNorm = NormLayer(hiddenSize, normEpsilon)
        self.outputNorm = NormLayer(hiddenSize, normEpsilon)
        self.dropout = nn.Dropout(dropoutProb)

    def forward(self, input_0, input_1):
        primals_1 = self.multiHeadAtt.queriesLinear.weight
        primals_2 = self.multiHeadAtt.queriesLinear.bias
        primals_4 = self.multiHeadAtt.keysLinear.weight
        primals_5 = self.multiHeadAtt.keysLinear.bias
        primals_6 = self.multiHeadAtt.valuesLinear.weight
        primals_7 = self.multiHeadAtt.valuesLinear.bias
        primals_9 = self.multiHeadAtt.outputLinear.weight
        primals_10 = self.multiHeadAtt.outputLinear.bias
        primals_13 = self.feedForward.w1.weight
        primals_14 = self.feedForward.w1.bias
        primals_15 = self.feedForward.w2.weight
        primals_11 = self.feedForward.w2.bias
        primals_12 = self.attNorm.weight
        primals_16 = self.attNorm.bias
        primals_17 = self.outputNorm.weight
        primals_18 = self.outputNorm.bias
        primals_3 = input_0
        primals_8 = 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: 0.025061