# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/75/c75w3rgnfmm4c7hp5div65urlkb5kzh2656pt75swmio7vzn3vp3.py
# Topologically Sorted Source Nodes: [ones, triangle, mask], Original ATen: [aten.ones, aten.tril, aten.mul]
# Source node to ATen node mapping:
#   mask => mul_1
#   ones => full_default
#   triangle => full_default_1, le, sub, where
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 4], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%unsqueeze, %unsqueeze_1), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%sub, 0), kwargs = {})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%le, %full_default, %full_default_1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where, %permute), kwargs = {})
triton_poi_fused_mul_ones_tril_0 = async_compile.triton('triton_poi_fused_mul_ones_tril_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_ones_tril_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_mul_ones_tril_0(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 % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = x0 + ((-1)*x1)
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 <= tmp1
    tmp3 = 1.0
    tmp4 = 0.0
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = 1 + x1
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp3 / tmp7
    tmp9 = tmp5 * tmp8
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ha/chavpwdtejkyqus2olvrr56v6fhdolpm5dx6l26ahmwfvz664fnv.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %bmm], -1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[128], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = (xindex // 8)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x2), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/bj/cbjkk5x2yiy67l3q4l7ooe5u7plvwkualpweocfe25rsydr62zek.py
# Topologically Sorted Source Nodes: [sigmoid, mul_1, sigmoid_1, mul_2, gating_outputs_1], Original ATen: [aten.sigmoid, aten.mul, aten.add, aten.sigmoid_backward]
# Source node to ATen node mapping:
#   gating_outputs_1 => add_1
#   mul_1 => mul_2
#   mul_2 => mul_3
#   sigmoid => sigmoid
#   sigmoid_1 => sigmoid_1
# Graph fragment:
#   %sigmoid : [num_users=3] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %primals_1), kwargs = {})
#   %sigmoid_1 : [num_users=3] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_1,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_1, %bmm), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %mul_3), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid_1), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_1, %sub_1), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %sub_2), kwargs = {})
triton_poi_fused_add_mul_sigmoid_sigmoid_backward_2 = async_compile.triton('triton_poi_fused_add_mul_sigmoid_sigmoid_backward_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_sigmoid_sigmoid_backward_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_mul_sigmoid_sigmoid_backward_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (8*x1)), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + (x2), xmask)
    tmp6 = tl.load(in_ptr0 + (4 + x0 + (8*x1)), xmask)
    tmp7 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tmp5 = tmp3 * tmp4
    tmp8 = tmp6 + tmp7
    tmp9 = tl.sigmoid(tmp8)
    tmp11 = tmp9 * tmp10
    tmp12 = tmp5 + tmp11
    tmp13 = 1.0
    tmp14 = tmp13 - tmp9
    tmp15 = tmp9 * tmp14
    tmp16 = tmp13 - tmp3
    tmp17 = tmp3 * tmp16
    tl.store(out_ptr0 + (x2), tmp12, xmask)
    tl.store(out_ptr1 + (x2), tmp15, xmask)
    tl.store(out_ptr2 + (x2), tmp17, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (8, 8), (8, 1))
    assert_size_stride(primals_3, (8, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [ones, triangle, mask], Original ATen: [aten.ones, aten.tril, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_ones_tril_0.run(buf0, 16, grid=grid(16), stream=stream0)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [average_outputs], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 4), (0, 4, 1), 0), primals_1, out=buf1)
        del buf0
        buf2 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(primals_1, buf1, buf2, 128, grid=grid(128), stream=stream0)
        buf3 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 8), (8, 1), 0), reinterpret_tensor(primals_2, (8, 8), (1, 8), 0), out=buf3)
        del primals_2
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sigmoid, mul_1, sigmoid_1, mul_2, gating_outputs_1], Original ATen: [aten.sigmoid, aten.mul, aten.add, aten.sigmoid_backward]
        triton_poi_fused_add_mul_sigmoid_sigmoid_backward_2.run(buf3, primals_3, primals_1, buf1, buf4, buf5, buf6, 64, grid=grid(64), stream=stream0)
        del buf3
        del primals_3
    return (buf4, buf1, primals_1, buf1, reinterpret_tensor(buf2, (16, 8), (8, 1), 0), buf5, buf6, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((8, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn as nn
import torch.cuda
import torch.distributed


class ActivationFunction(object):
    relu = 'relu'
    gelu = 'gelu'


class PositionwiseFeedForward(nn.Module):
    """ A two-layer Feed-Forward-Network with residual layer norm.

    Args:
        d_model (int): the size of input for the first-layer of the FFN.
        d_ff (int): the hidden layer size of the second-layer
            of the FNN.
        dropout (float): dropout probability in :math:`[0, 1)`.
        activation_fn (ActivationFunction): activation function used.
    """

    def __init__(self, d_model, d_ff, dropout=0.1, activation_fn=
        ActivationFunction.relu):
        super(PositionwiseFeedForward, self).__init__()
        self.w_1 = nn.Linear(d_model, d_ff)
        self.w_2 = nn.Linear(d_ff, d_model)
        self.layer_norm = nn.LayerNorm(d_model, eps=1e-06)
        self.dropout_1 = nn.Dropout(dropout)
        self.activation = ACTIVATION_FUNCTIONS[activation_fn]
        self.dropout_2 = nn.Dropout(dropout)

    def forward(self, x):
        """Layer definition.

        Args:
            x: ``(batch_size, input_len, model_dim)``

        Returns:
            (FloatTensor): Output ``(batch_size, input_len, model_dim)``.
        """
        inter = self.dropout_1(self.activation(self.w_1(self.layer_norm(x))))
        output = self.dropout_2(self.w_2(inter))
        return output + x

    def update_dropout(self, dropout):
        self.dropout_1.p = dropout
        self.dropout_2.p = dropout


class AverageAttention(nn.Module):
    """
    Average Attention module from
    "Accelerating Neural Transformer via an Average Attention Network"
    :cite:`DBLP:journals/corr/abs-1805-00631`.

    Args:
       model_dim (int): the dimension of keys/values/queries,
           must be divisible by head_count
       dropout (float): dropout parameter
       pos_ffn_activation_fn (ActivationFunction):
           activation function choice for PositionwiseFeedForward layer
    """

    def __init__(self, model_dim, dropout=0.1, aan_useffn=False,
        pos_ffn_activation_fn=ActivationFunction.relu):
        self.model_dim = model_dim
        self.aan_useffn = aan_useffn
        super(AverageAttention, self).__init__()
        if aan_useffn:
            self.average_layer = PositionwiseFeedForward(model_dim,
                model_dim, dropout, pos_ffn_activation_fn)
        self.gating_layer = nn.Linear(model_dim * 2, model_dim * 2)

    def cumulative_average_mask(self, batch_size, inputs_len, device):
        """
        Builds the mask to compute the cumulative average as described in
        :cite:`DBLP:journals/corr/abs-1805-00631` -- Figure 3

        Args:
            batch_size (int): batch size
            inputs_len (int): length of the inputs

        Returns:
            (FloatTensor):

            * A Tensor of shape ``(batch_size, input_len, input_len)``
        """
        triangle = torch.tril(torch.ones(inputs_len, inputs_len, dtype=
            torch.float, device=device))
        weights = torch.ones(1, inputs_len, dtype=torch.float, device=device
            ) / torch.arange(1, inputs_len + 1, dtype=torch.float, device=
            device)
        mask = triangle * weights.transpose(0, 1)
        return mask.unsqueeze(0).expand(batch_size, inputs_len, inputs_len)

    def cumulative_average(self, inputs, mask_or_step, layer_cache=None,
        step=None):
        """
        Computes the cumulative average as described in
        :cite:`DBLP:journals/corr/abs-1805-00631` -- Equations (1) (5) (6)

        Args:
            inputs (FloatTensor): sequence to average
                ``(batch_size, input_len, dimension)``
            mask_or_step: if cache is set, this is assumed
                to be the current step of the
                dynamic decoding. Otherwise, it is the mask matrix
                used to compute the cumulative average.
            layer_cache: a dictionary containing the cumulative average
                of the previous step.

        Returns:
            a tensor of the same shape and type as ``inputs``.
        """
        if layer_cache is not None:
            step = mask_or_step
            average_attention = (inputs + step * layer_cache['prev_g']) / (step
                 + 1)
            layer_cache['prev_g'] = average_attention
            return average_attention
        else:
            mask = mask_or_step
            return torch.matmul(mask, inputs)

    def forward(self, inputs, mask=None, layer_cache=None, step=None):
        """
        Args:
            inputs (FloatTensor): ``(batch_size, input_len, model_dim)``

        Returns:
            (FloatTensor, FloatTensor):

            * gating_outputs ``(batch_size, input_len, model_dim)``
            * average_outputs average attention
                ``(batch_size, input_len, model_dim)``
        """
        batch_size = inputs.size(0)
        inputs_len = inputs.size(1)
        average_outputs = self.cumulative_average(inputs, self.
            cumulative_average_mask(batch_size, inputs_len, inputs.device) if
            layer_cache is None else step, layer_cache=layer_cache)
        if self.aan_useffn:
            average_outputs = self.average_layer(average_outputs)
        gating_outputs = self.gating_layer(torch.cat((inputs,
            average_outputs), -1))
        input_gate, forget_gate = torch.chunk(gating_outputs, 2, dim=2)
        gating_outputs = torch.sigmoid(input_gate) * inputs + torch.sigmoid(
            forget_gate) * average_outputs
        return gating_outputs, average_outputs


def get_inputs():
    return [torch.rand([4, 4, 4])]


def get_init_inputs():
    return [[], {'model_dim': 4}]

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
import torch.nn as nn
import torch.cuda
import torch.distributed
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_mul_ones_tril_0(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 % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = x0 + -1 * x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 <= tmp1
    tmp3 = 1.0
    tmp4 = 0.0
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = 1 + x1
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp3 / tmp7
    tmp9 = tmp5 * tmp8
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_mul_sigmoid_sigmoid_backward_2(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, 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
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 8 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + x2, xmask)
    tmp6 = tl.load(in_ptr0 + (4 + x0 + 8 * x1), xmask)
    tmp7 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tmp5 = tmp3 * tmp4
    tmp8 = tmp6 + tmp7
    tmp9 = tl.sigmoid(tmp8)
    tmp11 = tmp9 * tmp10
    tmp12 = tmp5 + tmp11
    tmp13 = 1.0
    tmp14 = tmp13 - tmp9
    tmp15 = tmp9 * tmp14
    tmp16 = tmp13 - tmp3
    tmp17 = tmp3 * tmp16
    tl.store(out_ptr0 + x2, tmp12, xmask)
    tl.store(out_ptr1 + x2, tmp15, xmask)
    tl.store(out_ptr2 + x2, tmp17, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (8, 8), (8, 1))
    assert_size_stride(primals_3, (8,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_ones_tril_0[grid(16)](buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 4), (0, 4, 1), 0
            ), primals_1, out=buf1)
        del buf0
        buf2 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        triton_poi_fused_cat_1[grid(128)](primals_1, buf1, buf2, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 8), (8, 1), 0),
            reinterpret_tensor(primals_2, (8, 8), (1, 8), 0), out=buf3)
        del primals_2
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_sigmoid_sigmoid_backward_2[grid(64)](buf3,
            primals_3, primals_1, buf1, buf4, buf5, buf6, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf3
        del primals_3
    return buf4, buf1, primals_1, buf1, reinterpret_tensor(buf2, (16, 8), (
        8, 1), 0), buf5, buf6


class ActivationFunction(object):
    relu = 'relu'
    gelu = 'gelu'


class PositionwiseFeedForward(nn.Module):
    """ A two-layer Feed-Forward-Network with residual layer norm.

    Args:
        d_model (int): the size of input for the first-layer of the FFN.
        d_ff (int): the hidden layer size of the second-layer
            of the FNN.
        dropout (float): dropout probability in :math:`[0, 1)`.
        activation_fn (ActivationFunction): activation function used.
    """

    def __init__(self, d_model, d_ff, dropout=0.1, activation_fn=
        ActivationFunction.relu):
        super(PositionwiseFeedForward, self).__init__()
        self.w_1 = nn.Linear(d_model, d_ff)
        self.w_2 = nn.Linear(d_ff, d_model)
        self.layer_norm = nn.LayerNorm(d_model, eps=1e-06)
        self.dropout_1 = nn.Dropout(dropout)
        self.activation = ACTIVATION_FUNCTIONS[activation_fn]
        self.dropout_2 = nn.Dropout(dropout)

    def forward(self, x):
        """Layer definition.

        Args:
            x: ``(batch_size, input_len, model_dim)``

        Returns:
            (FloatTensor): Output ``(batch_size, input_len, model_dim)``.
        """
        inter = self.dropout_1(self.activation(self.w_1(self.layer_norm(x))))
        output = self.dropout_2(self.w_2(inter))
        return output + x

    def update_dropout(self, dropout):
        self.dropout_1.p = dropout
        self.dropout_2.p = dropout


class AverageAttentionNew(nn.Module):
    """
    Average Attention module from
    "Accelerating Neural Transformer via an Average Attention Network"
    :cite:`DBLP:journals/corr/abs-1805-00631`.

    Args:
       model_dim (int): the dimension of keys/values/queries,
           must be divisible by head_count
       dropout (float): dropout parameter
       pos_ffn_activation_fn (ActivationFunction):
           activation function choice for PositionwiseFeedForward layer
    """

    def __init__(self, model_dim, dropout=0.1, aan_useffn=False,
        pos_ffn_activation_fn=ActivationFunction.relu):
        self.model_dim = model_dim
        self.aan_useffn = aan_useffn
        super(AverageAttentionNew, self).__init__()
        if aan_useffn:
            self.average_layer = PositionwiseFeedForward(model_dim,
                model_dim, dropout, pos_ffn_activation_fn)
        self.gating_layer = nn.Linear(model_dim * 2, model_dim * 2)

    def cumulative_average_mask(self, batch_size, inputs_len, device):
        """
        Builds the mask to compute the cumulative average as described in
        :cite:`DBLP:journals/corr/abs-1805-00631` -- Figure 3

        Args:
            batch_size (int): batch size
            inputs_len (int): length of the inputs

        Returns:
            (FloatTensor):

            * A Tensor of shape ``(batch_size, input_len, input_len)``
        """
        triangle = torch.tril(torch.ones(inputs_len, inputs_len, dtype=
            torch.float, device=device))
        weights = torch.ones(1, inputs_len, dtype=torch.float, device=device
            ) / torch.arange(1, inputs_len + 1, dtype=torch.float, device=
            device)
        mask = triangle * weights.transpose(0, 1)
        return mask.unsqueeze(0).expand(batch_size, inputs_len, inputs_len)

    def cumulative_average(self, inputs, mask_or_step, layer_cache=None,
        step=None):
        """
        Computes the cumulative average as described in
        :cite:`DBLP:journals/corr/abs-1805-00631` -- Equations (1) (5) (6)

        Args:
            inputs (FloatTensor): sequence to average
                ``(batch_size, input_len, dimension)``
            mask_or_step: if cache is set, this is assumed
                to be the current step of the
                dynamic decoding. Otherwise, it is the mask matrix
                used to compute the cumulative average.
            layer_cache: a dictionary containing the cumulative average
                of the previous step.

        Returns:
            a tensor of the same shape and type as ``inputs``.
        """
        if layer_cache is not None:
            step = mask_or_step
            average_attention = (inputs + step * layer_cache['prev_g']) / (step
                 + 1)
            layer_cache['prev_g'] = average_attention
            return average_attention
        else:
            mask = mask_or_step
            return torch.matmul(mask, inputs)

    def forward(self, input_0):
        primals_2 = self.gating_layer.weight
        primals_3 = self.gating_layer.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0], output[1]
