# 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_1/inductor_cache/zz/czzblgh3vk6nfdnkaetw5pix5taebblmwm62khkbi32cm4a3m7ng.py
# Topologically Sorted Source Nodes: [i, f, mul, tanh, mul_1, c], Original ATen: [aten.sigmoid, aten.mul, aten.tanh, aten.add, aten.sigmoid_backward]
# Source node to ATen node mapping:
#   c => add_1
#   f => sigmoid_1
#   i => sigmoid
#   mul => mul
#   mul_1 => mul_1
#   tanh => tanh
# Graph fragment:
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%slice_2,), kwargs = {})
#   %sigmoid_1 : [num_users=3] = call_function[target=torch.ops.aten.sigmoid.default](args = (%slice_4,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_1, %primals_6), kwargs = {})
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%slice_6,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %tanh), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %sub_5 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid_1), kwargs = {})
#   %mul_20 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_1, %sub_5), kwargs = {})
triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_0 = async_compile.triton('triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_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: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 10, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_tanh_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, out_ptr3, 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 = tl.load(in_ptr0 + (x0 + (16*x1)), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x0 + (16*x1)), xmask)
    tmp6 = tl.load(in_ptr0 + (8 + x0 + (16*x1)), xmask)
    tmp7 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (8 + x0 + (16*x1)), xmask)
    tmp12 = tl.load(in_ptr0 + (4 + x0 + (16*x1)), xmask)
    tmp13 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr2 + (4 + x0 + (16*x1)), xmask)
    tmp18 = tl.load(in_ptr3 + (x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = tl.sigmoid(tmp4)
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 + tmp9
    tmp11 = libdevice.tanh(tmp10)
    tmp14 = tmp12 + tmp13
    tmp16 = tmp14 + tmp15
    tmp17 = tl.sigmoid(tmp16)
    tmp19 = tmp17 * tmp18
    tmp20 = tmp5 * tmp11
    tmp21 = tmp19 + tmp20
    tmp22 = 1.0
    tmp23 = tmp22 - tmp17
    tmp24 = tmp17 * tmp23
    tl.store(out_ptr0 + (x2), tmp5, xmask)
    tl.store(out_ptr1 + (x2), tmp11, xmask)
    tl.store(out_ptr2 + (x2), tmp21, xmask)
    tl.store(out_ptr3 + (x2), tmp24, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/kb/ckbwp5dfxnbqyhmfyfmyzfodee36tq5mri5eimtspciu3dv3tfda.py
# Topologically Sorted Source Nodes: [o], Original ATen: [aten.sigmoid]
# Source node to ATen node mapping:
#   o => sigmoid_2
# Graph fragment:
#   %sigmoid_2 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%slice_8,), kwargs = {})
triton_poi_fused_sigmoid_1 = async_compile.triton('triton_poi_fused_sigmoid_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sigmoid_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_sigmoid_1(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 % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (12 + x0 + (16*x1)), xmask)
    tmp1 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (12 + x0 + (16*x1)), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = tl.sigmoid(tmp4)
    tl.store(out_ptr0 + (x2), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/d3/cd3bshar637alg4lqiwqsscdkjad7rbx65xnonua2wu3jf4zc4k4.py
# Topologically Sorted Source Nodes: [f, mul, mul_1, c, mul_2, mul_3, c_1, tanh_1, h, mul_5, mul_6, h_1], Original ATen: [aten.sigmoid, aten.mul, aten.add, aten.tanh]
# Source node to ATen node mapping:
#   c => add_1
#   c_1 => add_2
#   f => sigmoid_1
#   h => mul_4
#   h_1 => add_3
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   mul_5 => mul_5
#   mul_6 => mul_6
#   tanh_1 => tanh_1
# Graph fragment:
#   %sigmoid_1 : [num_users=3] = call_function[target=torch.ops.aten.sigmoid.default](args = (%slice_4,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_1, %primals_6), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %tanh), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%unsqueeze, %add_1), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%unsqueeze_1, %primals_6), kwargs = {})
#   %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %mul_3), kwargs = {})
#   %tanh_1 : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add_2,), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_2, %tanh_1), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%unsqueeze, %mul_4), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%unsqueeze_1, %primals_5), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_5, %mul_6), kwargs = {})
triton_poi_fused_add_mul_sigmoid_tanh_2 = async_compile.triton('triton_poi_fused_add_mul_sigmoid_tanh_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=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_tanh_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_tanh_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, 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 % 4
    x2 = (xindex // 16)
    x3 = xindex % 16
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x3), 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')
    tmp12 = tl.load(in_ptr4 + (x3), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp0
    tmp6 = tmp4 * tmp5
    tmp7 = tmp2 + tmp6
    tmp9 = libdevice.tanh(tmp7)
    tmp10 = tmp8 * tmp9
    tmp11 = tmp0 * tmp10
    tmp13 = tmp4 * tmp12
    tmp14 = tmp11 + tmp13
    tl.store(out_ptr0 + (x4), tmp7, xmask)
    tl.store(out_ptr1 + (x4), tmp14, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args
    args.clear()
    assert_size_stride(primals_1, (16, 4), (4, 1))
    assert_size_stride(primals_2, (16, ), (1, ))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (16, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 16), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm]
        extern_kernels.mm(primals_5, reinterpret_tensor(primals_4, (4, 16), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [i, f, mul, tanh, mul_1, c], Original ATen: [aten.sigmoid, aten.mul, aten.tanh, aten.add, aten.sigmoid_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_0.run(buf0, primals_2, buf1, primals_6, buf2, buf4, buf5, buf8, 16, grid=grid(16), stream=stream0)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [o], Original ATen: [aten.sigmoid]
        triton_poi_fused_sigmoid_1.run(buf0, primals_2, buf1, buf3, 16, grid=grid(16), stream=stream0)
        del primals_2
        buf6 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0); del buf1  # reuse
        buf7 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [f, mul, mul_1, c, mul_2, mul_3, c_1, tanh_1, h, mul_5, mul_6, h_1], Original ATen: [aten.sigmoid, aten.mul, aten.add, aten.tanh]
        triton_poi_fused_add_mul_sigmoid_tanh_2.run(primals_7, buf5, primals_6, buf3, primals_5, buf6, buf7, 64, grid=grid(64), stream=stream0)
        del buf5
    return (buf7, buf6, primals_3, primals_5, primals_6, primals_7, buf2, buf3, buf4, buf6, buf8, )


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((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
    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)

from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn


class MaskLSTMCell(nn.Module):

    def __init__(self, options):
        super(MaskLSTMCell, self).__init__()
        self.n_in = options['n_in']
        self.n_out = options['n_out']
        self.input = nn.Linear(self.n_in, self.n_out * 4)
        self.hidden = nn.Linear(self.n_out, self.n_out * 4, bias=False)

    def forward(self, input, mask, h_prev, c_prev):
        activation = self.input(input) + self.hidden(h_prev)
        activation_i = activation[:, :self.n_out]
        activation_f = activation[:, self.n_out:self.n_out * 2]
        activation_c = activation[:, self.n_out * 2:self.n_out * 3]
        activation_o = activation[:, self.n_out * 3:self.n_out * 4]
        i = torch.sigmoid(activation_i)
        f = torch.sigmoid(activation_f)
        o = torch.sigmoid(activation_o)
        c = f * c_prev + i * torch.tanh(activation_c)
        c = mask[:, None] * c + (1 - mask)[:, None] * c_prev
        h = o * torch.tanh(c)
        h = mask[:, None] * h + (1 - mask)[:, None] * h_prev
        return h, c


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


def get_init_inputs():
    return [[], {'options': _mock_config(n_in=4, n_out=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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as 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_add_mul_sigmoid_sigmoid_backward_tanh_0(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, out_ptr3,
    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 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x0 + 16 * x1), xmask)
    tmp6 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
    tmp7 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (8 + x0 + 16 * x1), xmask)
    tmp12 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
    tmp13 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr2 + (4 + x0 + 16 * x1), xmask)
    tmp18 = tl.load(in_ptr3 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = tl.sigmoid(tmp4)
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 + tmp9
    tmp11 = libdevice.tanh(tmp10)
    tmp14 = tmp12 + tmp13
    tmp16 = tmp14 + tmp15
    tmp17 = tl.sigmoid(tmp16)
    tmp19 = tmp17 * tmp18
    tmp20 = tmp5 * tmp11
    tmp21 = tmp19 + tmp20
    tmp22 = 1.0
    tmp23 = tmp22 - tmp17
    tmp24 = tmp17 * tmp23
    tl.store(out_ptr0 + x2, tmp5, xmask)
    tl.store(out_ptr1 + x2, tmp11, xmask)
    tl.store(out_ptr2 + x2, tmp21, xmask)
    tl.store(out_ptr3 + x2, tmp24, xmask)


@triton.jit
def triton_poi_fused_sigmoid_1(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 % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (12 + x0 + 16 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = tl.sigmoid(tmp4)
    tl.store(out_ptr0 + x2, tmp5, xmask)


@triton.jit
def triton_poi_fused_add_mul_sigmoid_tanh_2(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, 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 % 4
    x2 = xindex // 16
    x3 = xindex % 16
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl.load(in_ptr1 + x3, 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')
    tmp12 = tl.load(in_ptr4 + x3, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp0
    tmp6 = tmp4 * tmp5
    tmp7 = tmp2 + tmp6
    tmp9 = libdevice.tanh(tmp7)
    tmp10 = tmp8 * tmp9
    tmp11 = tmp0 * tmp10
    tmp13 = tmp4 * tmp12
    tmp14 = tmp11 + tmp13
    tl.store(out_ptr0 + x4, tmp7, xmask)
    tl.store(out_ptr1 + x4, tmp14, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (16, 4), (4, 1))
    assert_size_stride(primals_2, (16,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (16, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 16),
            (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.mm(primals_5, reinterpret_tensor(primals_4, (4, 16),
            (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_0[grid(16)](buf0
            , primals_2, buf1, primals_6, buf2, buf4, buf5, buf8, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_sigmoid_1[grid(16)](buf0, primals_2, buf1, buf3, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_2
        buf6 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0)
        del buf1
        buf7 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0)
        del buf0
        triton_poi_fused_add_mul_sigmoid_tanh_2[grid(64)](primals_7, buf5,
            primals_6, buf3, primals_5, buf6, buf7, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf5
    return (buf7, buf6, primals_3, primals_5, primals_6, primals_7, buf2,
        buf3, buf4, buf6, buf8)


class MaskLSTMCellNew(nn.Module):

    def __init__(self, options):
        super(MaskLSTMCellNew, self).__init__()
        self.n_in = options['n_in']
        self.n_out = options['n_out']
        self.input = nn.Linear(self.n_in, self.n_out * 4)
        self.hidden = nn.Linear(self.n_out, self.n_out * 4, bias=False)

    def forward(self, input_0, input_1, input_2, input_3):
        primals_1 = self.input.weight
        primals_2 = self.input.bias
        primals_4 = self.hidden.weight
        primals_3 = input_0
        primals_5 = input_1
        primals_6 = input_2
        primals_7 = input_3
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0], output[1]
