# 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_6/inductor_cache/c4/cc4khg7fwbxxm2fufox7nnkf4gfybrmj5ir2tx3zuxfioc5b2dya.py
# Topologically Sorted Source Nodes: [input_i], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   input_i => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %primals_2], 3), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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=[512], 
    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_0', '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_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    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_6/inductor_cache/u2/cu2rv4ghyzwe2epuzsdtjzryf5ldcn6cabgjy7nvhvjh57w7t4ch.py
# Topologically Sorted Source Nodes: [stack, sigmoid_5], Original ATen: [aten.stack, aten.sigmoid]
# Source node to ATen node mapping:
#   sigmoid_5 => sigmoid
#   stack => cat_1
# Graph fragment:
#   %cat_1 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%getitem_2, %getitem_1, %getitem, %getitem_5, %getitem_4],), kwargs = {})
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_2,), kwargs = {})
triton_poi_fused_sigmoid_stack_1 = async_compile.triton('triton_poi_fused_sigmoid_stack_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=[2048], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sigmoid_stack_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, '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_sigmoid_stack_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1280
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 64)
    x0 = xindex % 4
    x1 = (xindex // 4) % 16
    x3 = xindex
    tmp0 = x2
    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 + (8 + x0 + (28*x1) + (448*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr0 + (4 + x0 + (28*x1) + (448*((-4) + x2))), tmp9 & xmask, other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr0 + (x0 + (28*x1) + (448*((-8) + x2))), tmp14 & xmask, other=0.0)
    tmp16 = tmp0 >= tmp12
    tmp17 = tl.full([1], 16, tl.int64)
    tmp18 = tmp0 < tmp17
    tmp19 = tmp16 & tmp18
    tmp20 = tl.load(in_ptr0 + (20 + x0 + (28*x1) + (448*((-12) + x2))), tmp19 & xmask, other=0.0)
    tmp21 = tmp0 >= tmp17
    tmp22 = tl.full([1], 20, tl.int64)
    tmp23 = tmp0 < tmp22
    tmp24 = tl.load(in_ptr0 + (16 + x0 + (28*x1) + (448*((-16) + x2))), tmp21 & xmask, other=0.0)
    tmp25 = tl.where(tmp19, tmp20, tmp24)
    tmp26 = tl.where(tmp14, tmp15, tmp25)
    tmp27 = tl.where(tmp9, tmp10, tmp26)
    tmp28 = tl.where(tmp4, tmp5, tmp27)
    tmp29 = tl.sigmoid(tmp28)
    tl.store(in_out_ptr0 + (x3), tmp29, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/mp/cmpiil2uqfldt5pstfofakup25fjqxm7omagpgtctwz6hjs3xaan.py
# Topologically Sorted Source Nodes: [gate_pre_c_1, mul, mul_1, cell_i, mul_2, mul_3, cell_bar_i], Original ATen: [aten.tanh, aten.mul, aten.add]
# Source node to ATen node mapping:
#   cell_bar_i => add_1
#   cell_i => add
#   gate_pre_c_1 => tanh
#   mul => mul_1
#   mul_1 => mul_2
#   mul_2 => mul_3
#   mul_3 => mul_4
# Graph fragment:
#   %tanh : [num_users=3] = call_function[target=torch.ops.aten.tanh.default](args = (%getitem_3,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%getitem_8, %primals_5), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%getitem_9, %tanh), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %mul_2), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%getitem_10, %primals_6), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%getitem_11, %tanh), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_3, %mul_4), kwargs = {})
triton_poi_fused_add_mul_tanh_2 = async_compile.triton('triton_poi_fused_add_mul_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=[256], 
    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_tanh_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, '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_tanh_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (12 + x0 + (28*x1)), xmask)
    tmp2 = tl.load(in_ptr1 + (256 + x2), xmask)
    tmp3 = tl.load(in_ptr2 + (x2), xmask)
    tmp5 = tl.load(in_ptr1 + (512 + x2), xmask)
    tmp8 = tl.load(in_ptr1 + (768 + x2), xmask)
    tmp9 = tl.load(in_ptr3 + (x2), xmask)
    tmp11 = tl.load(in_ptr1 + (1024 + x2), xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp4 = tmp2 * tmp3
    tmp6 = tmp5 * tmp1
    tmp7 = tmp4 + tmp6
    tmp10 = tmp8 * tmp9
    tmp12 = tmp11 * tmp1
    tmp13 = tmp10 + tmp12
    tl.store(out_ptr0 + (x2), tmp1, xmask)
    tl.store(out_ptr1 + (x2), tmp7, xmask)
    tl.store(out_ptr2 + (x2), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/je/cjefeevcneojkxn7jnnn5zwx7ykwr4s2pudqkt6r3vsjbtcj4pse.py
# Topologically Sorted Source Nodes: [gate_decay_1], Original ATen: [aten.softplus]
# Source node to ATen node mapping:
#   gate_decay_1 => div, exp, gt, log1p, mul, where
# Graph fragment:
#   %mul : [num_users=3] = call_function[target=torch.ops.aten.mul.Tensor](args = (%getitem_6, 1.0), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%mul,), kwargs = {})
#   %log1p : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%exp,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%log1p, 1.0), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%mul, 20), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %getitem_6, %div), kwargs = {})
triton_poi_fused_softplus_3 = async_compile.triton('triton_poi_fused_softplus_3', '''
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=[256], 
    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_softplus_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_softplus_3(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    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 + (24 + x0 + (28*x1)), xmask)
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = 20.0
    tmp4 = tmp2 > tmp3
    tmp5 = tl_math.exp(tmp2)
    tmp6 = libdevice.log1p(tmp5)
    tmp7 = tmp6 * tmp1
    tmp8 = tl.where(tmp4, tmp0, tmp7)
    tl.store(out_ptr0 + (x2), tmp2, xmask)
    tl.store(out_ptr1 + (x2), tmp8, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (28, 8), (8, 1))
    assert_size_stride(primals_4, (28, ), (1, ))
    assert_size_stride(primals_5, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_i], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_1, primals_2, buf0, 512, grid=grid(512), stream=stream0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((64, 28), (28, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output_i], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_4, reinterpret_tensor(buf0, (64, 8), (8, 1), 0), reinterpret_tensor(primals_3, (8, 28), (1, 8), 0), alpha=1, beta=1, out=buf1)
        del primals_3
        del primals_4
        buf2 = empty_strided_cuda((20, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf3 = reinterpret_tensor(buf2, (5, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0); del buf2  # reuse
        # Topologically Sorted Source Nodes: [stack, sigmoid_5], Original ATen: [aten.stack, aten.sigmoid]
        triton_poi_fused_sigmoid_stack_1.run(buf3, buf1, 1280, grid=grid(1280), stream=stream0)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [gate_pre_c_1, mul, mul_1, cell_i, mul_2, mul_3, cell_bar_i], Original ATen: [aten.tanh, aten.mul, aten.add]
        triton_poi_fused_add_mul_tanh_2.run(buf1, buf3, primals_5, primals_6, buf4, buf7, buf8, 256, grid=grid(256), stream=stream0)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [gate_decay_1], Original ATen: [aten.softplus]
        triton_poi_fused_softplus_3.run(buf1, buf5, buf6, 256, grid=grid(256), stream=stream0)
        del buf1
    return (buf7, buf8, buf6, reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0), primals_5, primals_6, reinterpret_tensor(buf0, (64, 8), (8, 1), 0), buf3, reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 512), reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 1024), buf4, buf5, )


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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((28, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((28, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
    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.nn.functional as F


class CTLSTMCell(nn.Module):

    def __init__(self, hidden_dim, beta=1.0, device=None):
        super(CTLSTMCell, self).__init__()
        device = device or 'cpu'
        self.device = torch.device(device)
        self.hidden_dim = hidden_dim
        self.linear = nn.Linear(hidden_dim * 2, hidden_dim * 7, bias=True)
        self.beta = beta

    def forward(self, rnn_input, hidden_t_i_minus, cell_t_i_minus, cell_bar_im1
        ):
        dim_of_hidden = rnn_input.dim() - 1
        input_i = torch.cat((rnn_input, hidden_t_i_minus), dim=dim_of_hidden)
        output_i = self.linear(input_i)
        (gate_input, gate_forget, gate_output, gate_pre_c, gate_input_bar,
            gate_forget_bar, gate_decay) = output_i.chunk(7, dim_of_hidden)
        gate_input = torch.sigmoid(gate_input)
        gate_forget = torch.sigmoid(gate_forget)
        gate_output = torch.sigmoid(gate_output)
        gate_pre_c = torch.tanh(gate_pre_c)
        gate_input_bar = torch.sigmoid(gate_input_bar)
        gate_forget_bar = torch.sigmoid(gate_forget_bar)
        gate_decay = F.softplus(gate_decay, beta=self.beta)
        cell_i = gate_forget * cell_t_i_minus + gate_input * gate_pre_c
        cell_bar_i = (gate_forget_bar * cell_bar_im1 + gate_input_bar *
            gate_pre_c)
        return cell_i, cell_bar_i, gate_decay, gate_output

    def decay(self, cell_i, cell_bar_i, gate_decay, gate_output, dtime):
        if dtime.dim() < cell_i.dim():
            dtime = dtime.unsqueeze(cell_i.dim() - 1).expand_as(cell_i)
        cell_t_ip1_minus = cell_bar_i + (cell_i - cell_bar_i) * torch.exp(-
            gate_decay * dtime)
        hidden_t_ip1_minus = gate_output * torch.tanh(cell_t_ip1_minus)
        return cell_t_ip1_minus, hidden_t_ip1_minus


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


def get_init_inputs():
    return [[], {'hidden_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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 512
    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_sigmoid_stack_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 1280
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 64
    x0 = xindex % 4
    x1 = xindex // 4 % 16
    x3 = xindex
    tmp0 = x2
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (8 + x0 + 28 * x1 + 448 * x2), tmp4 & xmask,
        other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr0 + (4 + x0 + 28 * x1 + 448 * (-4 + x2)), tmp9 &
        xmask, other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr0 + (x0 + 28 * x1 + 448 * (-8 + x2)), tmp14 &
        xmask, other=0.0)
    tmp16 = tmp0 >= tmp12
    tmp17 = tl.full([1], 16, tl.int64)
    tmp18 = tmp0 < tmp17
    tmp19 = tmp16 & tmp18
    tmp20 = tl.load(in_ptr0 + (20 + x0 + 28 * x1 + 448 * (-12 + x2)), tmp19 &
        xmask, other=0.0)
    tmp21 = tmp0 >= tmp17
    tl.full([1], 20, tl.int64)
    tmp24 = tl.load(in_ptr0 + (16 + x0 + 28 * x1 + 448 * (-16 + x2)), tmp21 &
        xmask, other=0.0)
    tmp25 = tl.where(tmp19, tmp20, tmp24)
    tmp26 = tl.where(tmp14, tmp15, tmp25)
    tmp27 = tl.where(tmp9, tmp10, tmp26)
    tmp28 = tl.where(tmp4, tmp5, tmp27)
    tmp29 = tl.sigmoid(tmp28)
    tl.store(in_out_ptr0 + x3, tmp29, xmask)


@triton.jit
def triton_poi_fused_add_mul_tanh_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (12 + x0 + 28 * x1), xmask)
    tmp2 = tl.load(in_ptr1 + (256 + x2), xmask)
    tmp3 = tl.load(in_ptr2 + x2, xmask)
    tmp5 = tl.load(in_ptr1 + (512 + x2), xmask)
    tmp8 = tl.load(in_ptr1 + (768 + x2), xmask)
    tmp9 = tl.load(in_ptr3 + x2, xmask)
    tmp11 = tl.load(in_ptr1 + (1024 + x2), xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp4 = tmp2 * tmp3
    tmp6 = tmp5 * tmp1
    tmp7 = tmp4 + tmp6
    tmp10 = tmp8 * tmp9
    tmp12 = tmp11 * tmp1
    tmp13 = tmp10 + tmp12
    tl.store(out_ptr0 + x2, tmp1, xmask)
    tl.store(out_ptr1 + x2, tmp7, xmask)
    tl.store(out_ptr2 + x2, tmp13, xmask)


@triton.jit
def triton_poi_fused_softplus_3(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 256
    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 + (24 + x0 + 28 * x1), xmask)
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = 20.0
    tmp4 = tmp2 > tmp3
    tmp5 = tl_math.exp(tmp2)
    tmp6 = libdevice.log1p(tmp5)
    tmp7 = tmp6 * tmp1
    tmp8 = tl.where(tmp4, tmp0, tmp7)
    tl.store(out_ptr0 + x2, tmp2, xmask)
    tl.store(out_ptr1 + x2, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (28, 8), (8, 1))
    assert_size_stride(primals_4, (28,), (1,))
    assert_size_stride(primals_5, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(512)](primals_1, primals_2, buf0, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((64, 28), (28, 1), torch.float32)
        extern_kernels.addmm(primals_4, reinterpret_tensor(buf0, (64, 8), (
            8, 1), 0), reinterpret_tensor(primals_3, (8, 28), (1, 8), 0),
            alpha=1, beta=1, out=buf1)
        del primals_3
        del primals_4
        buf2 = empty_strided_cuda((20, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf3 = reinterpret_tensor(buf2, (5, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0
            )
        del buf2
        triton_poi_fused_sigmoid_stack_1[grid(1280)](buf3, buf1, 1280,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_tanh_2[grid(256)](buf1, buf3, primals_5,
            primals_6, buf4, buf7, buf8, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_softplus_3[grid(256)](buf1, buf5, buf6, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del buf1
    return buf7, buf8, buf6, reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16,
        4, 1), 0), primals_5, primals_6, reinterpret_tensor(buf0, (64, 8),
        (8, 1), 0), buf3, reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4,
        1), 512), reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 1024
        ), buf4, buf5


class CTLSTMCellNew(nn.Module):

    def __init__(self, hidden_dim, beta=1.0, device=None):
        super(CTLSTMCellNew, self).__init__()
        device = device or 'cpu'
        self.device = torch.device(device)
        self.hidden_dim = hidden_dim
        self.linear = nn.Linear(hidden_dim * 2, hidden_dim * 7, bias=True)
        self.beta = beta

    def decay(self, cell_i, cell_bar_i, gate_decay, gate_output, dtime):
        if dtime.dim() < cell_i.dim():
            dtime = dtime.unsqueeze(cell_i.dim() - 1).expand_as(cell_i)
        cell_t_ip1_minus = cell_bar_i + (cell_i - cell_bar_i) * torch.exp(-
            gate_decay * dtime)
        hidden_t_ip1_minus = gate_output * torch.tanh(cell_t_ip1_minus)
        return cell_t_ip1_minus, hidden_t_ip1_minus

    def forward(self, input_0, input_1, input_2, input_3):
        primals_3 = self.linear.weight
        primals_4 = self.linear.bias
        primals_1 = input_0
        primals_2 = input_1
        primals_5 = input_2
        primals_6 = input_3
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0], output[1], output[2], output[3]
