# 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_7/inductor_cache/pc/cpcr3623cktnc2724ttepfbk25u5ng32io6u3pxmpjwbdpi5psaw.py
# Topologically Sorted Source Nodes: [h_0], Original ATen: [aten.zero]
# Source node to ATen node mapping:
#   h_0 => full
# Graph fragment:
#   %full : [num_users=3] = call_function[target=torch.ops.aten.full.default](args = ([4, 4], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
triton_poi_fused_zero_0 = async_compile.triton('triton_poi_fused_zero_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_zero_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_zero_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
    tmp0 = 0.0
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/zq/czqzkhlox35fklur6ihxj3macd26s7fcwmyoi3mjplrpxqb66tn4.py
# Topologically Sorted Source Nodes: [sigmoid, mul, sigmoid_1, tanh, mul_1, c_1, sigmoid_2, tanh_1, h_1], Original ATen: [aten.sigmoid, aten.mul, aten.tanh, aten.add, aten.sigmoid_backward]
# Source node to ATen node mapping:
#   c_1 => add_1
#   h_1 => mul_2
#   mul => mul
#   mul_1 => mul_1
#   sigmoid => sigmoid
#   sigmoid_1 => sigmoid_1
#   sigmoid_2 => sigmoid_2
#   tanh => tanh
#   tanh_1 => tanh_1
# Graph fragment:
#   %sigmoid : [num_users=3] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %full), kwargs = {})
#   %sigmoid_1 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_1,), kwargs = {})
#   %tanh : [num_users=2] = 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 = (%sigmoid_1, %tanh), kwargs = {})
#   %add_1 : [num_users=4] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %sigmoid_2 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_2,), kwargs = {})
#   %tanh_1 : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add_1,), kwargs = {})
#   %mul_2 : [num_users=3] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_2, %tanh_1), kwargs = {})
#   %sub_19 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {})
#   %mul_73 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %sub_19), kwargs = {})
triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1 = async_compile.triton('triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_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: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: '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, 8, 9), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 12, '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_tanh_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, out_ptr3, out_ptr4, out_ptr5, 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 + (4 + x0 + (16*x1)), xmask)
    tmp1 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (4 + x0 + (16*x1)), xmask)
    tmp6 = tl.load(in_ptr0 + (12 + x0 + (16*x1)), xmask)
    tmp7 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (12 + x0 + (16*x1)), xmask)
    tmp12 = tl.load(in_ptr0 + (x0 + (16*x1)), xmask)
    tmp13 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr2 + (x0 + (16*x1)), xmask)
    tmp25 = tl.load(in_ptr0 + (8 + x0 + (16*x1)), xmask)
    tmp26 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr2 + (8 + x0 + (16*x1)), 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)
    tmp18 = 0.0
    tmp19 = tmp17 * tmp18
    tmp20 = tmp5 * tmp11
    tmp21 = tmp19 + tmp20
    tmp22 = 1.0
    tmp23 = tmp22 - tmp17
    tmp24 = tmp17 * tmp23
    tmp27 = tmp25 + tmp26
    tmp29 = tmp27 + tmp28
    tmp30 = tl.sigmoid(tmp29)
    tmp31 = libdevice.tanh(tmp21)
    tmp32 = tmp30 * tmp31
    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)
    tl.store(out_ptr4 + (x2), tmp30, xmask)
    tl.store(out_ptr5 + (x2), tmp32, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/hw/chwjbojajdvf753zel4valmd7qcz6snsa2bu3zhkovdyszf3rzes.py
# Topologically Sorted Source Nodes: [sigmoid_3, mul_3, sigmoid_4, tanh_2, mul_4, c_2, sigmoid_5, tanh_3, h_2], Original ATen: [aten.sigmoid, aten.mul, aten.tanh, aten.add]
# Source node to ATen node mapping:
#   c_2 => add_3
#   h_2 => mul_5
#   mul_3 => mul_3
#   mul_4 => mul_4
#   sigmoid_3 => sigmoid_3
#   sigmoid_4 => sigmoid_4
#   sigmoid_5 => sigmoid_5
#   tanh_2 => tanh_2
#   tanh_3 => tanh_3
# Graph fragment:
#   %sigmoid_3 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_4,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_3, %add_1), kwargs = {})
#   %sigmoid_4 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_5,), kwargs = {})
#   %tanh_2 : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%getitem_7,), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_4, %tanh_2), kwargs = {})
#   %add_3 : [num_users=4] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_3, %mul_4), kwargs = {})
#   %sigmoid_5 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_6,), kwargs = {})
#   %tanh_3 : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add_3,), kwargs = {})
#   %mul_5 : [num_users=3] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_5, %tanh_3), 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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: '*fp32', 10: '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, 8, 9, 10), 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': 13, '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_tanh_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, out_ptr3, out_ptr4, out_ptr5, 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 + (4 + x0 + (16*x1)), xmask)
    tmp7 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (4 + x0 + (16*x1)), xmask)
    tmp12 = tl.load(in_ptr0 + (12 + x0 + (16*x1)), xmask)
    tmp13 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr2 + (12 + x0 + (16*x1)), xmask)
    tmp18 = tl.load(in_ptr3 + (x2), xmask)
    tmp22 = tl.load(in_ptr0 + (8 + x0 + (16*x1)), xmask)
    tmp23 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr2 + (8 + x0 + (16*x1)), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = tl.sigmoid(tmp4)
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 + tmp9
    tmp11 = tl.sigmoid(tmp10)
    tmp14 = tmp12 + tmp13
    tmp16 = tmp14 + tmp15
    tmp17 = libdevice.tanh(tmp16)
    tmp19 = tmp5 * tmp18
    tmp20 = tmp11 * tmp17
    tmp21 = tmp19 + tmp20
    tmp24 = tmp22 + tmp23
    tmp26 = tmp24 + tmp25
    tmp27 = tl.sigmoid(tmp26)
    tmp28 = libdevice.tanh(tmp21)
    tmp29 = tmp27 * tmp28
    tl.store(out_ptr0 + (x2), tmp5, xmask)
    tl.store(out_ptr1 + (x2), tmp11, xmask)
    tl.store(out_ptr2 + (x2), tmp17, xmask)
    tl.store(out_ptr3 + (x2), tmp21, xmask)
    tl.store(out_ptr4 + (x2), tmp27, xmask)
    tl.store(out_ptr5 + (x2), tmp29, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/jl/cjluwms2whl2lvm4jykyhs4ioawu2sx57fya6l5f2y4gvg36hs5h.py
# Topologically Sorted Source Nodes: [sigmoid_9, mul_9, sigmoid_10, tanh_6, mul_10, c_4, tanh_7], Original ATen: [aten.sigmoid, aten.mul, aten.tanh, aten.add]
# Source node to ATen node mapping:
#   c_4 => add_7
#   mul_10 => mul_10
#   mul_9 => mul_9
#   sigmoid_10 => sigmoid_10
#   sigmoid_9 => sigmoid_9
#   tanh_6 => tanh_6
#   tanh_7 => tanh_7
# Graph fragment:
#   %sigmoid_9 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_12,), kwargs = {})
#   %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_9, %add_5), kwargs = {})
#   %sigmoid_10 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_13,), kwargs = {})
#   %tanh_6 : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%getitem_15,), kwargs = {})
#   %mul_10 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_10, %tanh_6), kwargs = {})
#   %add_7 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_9, %mul_10), kwargs = {})
#   %tanh_7 : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add_7,), kwargs = {})
triton_poi_fused_add_mul_sigmoid_tanh_3 = async_compile.triton('triton_poi_fused_add_mul_sigmoid_tanh_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=[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=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, 8), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_sigmoid_tanh_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 10, '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_tanh_3(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 + (4 + x0 + (16*x1)), xmask)
    tmp7 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (4 + x0 + (16*x1)), xmask)
    tmp12 = tl.load(in_ptr0 + (12 + x0 + (16*x1)), xmask)
    tmp13 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr2 + (12 + 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 = tl.sigmoid(tmp10)
    tmp14 = tmp12 + tmp13
    tmp16 = tmp14 + tmp15
    tmp17 = libdevice.tanh(tmp16)
    tmp19 = tmp5 * tmp18
    tmp20 = tmp11 * tmp17
    tmp21 = tmp19 + tmp20
    tmp22 = libdevice.tanh(tmp21)
    tl.store(out_ptr0 + (x2), tmp5, xmask)
    tl.store(out_ptr1 + (x2), tmp11, xmask)
    tl.store(out_ptr2 + (x2), tmp17, xmask)
    tl.store(out_ptr3 + (x2), tmp22, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/tn/ctnk3tco2xzy2lj4un5qf4sk4vennxmerbmibgw3yiebiljtterf.py
# Topologically Sorted Source Nodes: [sigmoid_11], Original ATen: [aten.sigmoid]
# Source node to ATen node mapping:
#   sigmoid_11 => sigmoid_11
# Graph fragment:
#   %sigmoid_11 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_14,), kwargs = {})
triton_poi_fused_sigmoid_4 = async_compile.triton('triton_poi_fused_sigmoid_4', '''
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=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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sigmoid_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_4(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 + (8 + x0 + (16*x1)), xmask)
    tmp1 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (8 + 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_7/inductor_cache/mc/cmcgwy75mchsexfjy4ga5bobksauuj2ou2ekxspblctnukxiexex.py
# Topologically Sorted Source Nodes: [c_n], Original ATen: [aten.stack]
# Source node to ATen node mapping:
#   c_n => cat_1
# Graph fragment:
#   %cat_1 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%add_1, %add_3, %add_5, %add_7],), kwargs = {})
triton_poi_fused_stack_5 = async_compile.triton('triton_poi_fused_stack_5', '''
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_stack_5', '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_stack_5(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
    x1 = (xindex // 4)
    x0 = xindex % 4
    x2 = xindex
    tmp0 = x1
    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 + (x0 + (4*x1)), 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_ptr1 + (x0 + (4*((-4) + x1))), 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_ptr2 + (x0 + (4*((-8) + x1))), tmp14 & xmask, other=0.0)
    tmp16 = tmp0 >= tmp12
    tmp17 = tl.full([1], 16, tl.int64)
    tmp18 = tmp0 < tmp17
    tmp19 = tl.load(in_ptr3 + (x0 + (4*((-12) + x1))), tmp16 & xmask, other=0.0)
    tmp20 = tl.load(in_ptr2 + (x0 + (4*((-12) + x1))), tmp16 & xmask, other=0.0)
    tmp21 = tmp19 * tmp20
    tmp22 = tl.load(in_ptr4 + (x0 + (4*((-12) + x1))), tmp16 & xmask, other=0.0)
    tmp23 = tl.load(in_ptr5 + (x0 + (4*((-12) + x1))), tmp16 & xmask, other=0.0)
    tmp24 = tmp22 * tmp23
    tmp25 = tmp21 + tmp24
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp16, tmp25, tmp26)
    tmp28 = tl.where(tmp14, tmp15, tmp27)
    tmp29 = tl.where(tmp9, tmp10, tmp28)
    tmp30 = tl.where(tmp4, tmp5, tmp29)
    tl.store(out_ptr0 + (x2), tmp30, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/7v/c7vzfzfugsasaoi62cwx45au5eeuftkul3wi2lpzxsy6petydqdo.py
# Topologically Sorted Source Nodes: [h_n], Original ATen: [aten.stack]
# Source node to ATen node mapping:
#   h_n => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%mul_2, %mul_5, %mul_8, %mul_11],), kwargs = {})
triton_poi_fused_stack_6 = async_compile.triton('triton_poi_fused_stack_6', '''
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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_stack_6', 'mutated_arg_names': [], '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_stack_6(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4)
    x0 = xindex % 4
    x2 = xindex
    tmp0 = x1
    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 + (x0 + (4*x1)), 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_ptr1 + (x0 + (4*((-4) + x1))), 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_ptr2 + (x0 + (4*((-8) + x1))), tmp14 & xmask, other=0.0)
    tmp16 = tmp0 >= tmp12
    tmp17 = tl.full([1], 16, tl.int64)
    tmp18 = tmp0 < tmp17
    tmp19 = tl.load(in_ptr3 + (x0 + (4*((-12) + x1))), tmp16 & xmask, other=0.0)
    tmp20 = tl.load(in_ptr4 + (x0 + (4*((-12) + x1))), tmp16 & xmask, other=0.0)
    tmp21 = tmp19 * tmp20
    tmp22 = tl.full(tmp21.shape, 0.0, tmp21.dtype)
    tmp23 = tl.where(tmp16, tmp21, tmp22)
    tmp24 = tl.where(tmp14, tmp15, tmp23)
    tmp25 = tl.where(tmp9, tmp10, tmp24)
    tmp26 = tl.where(tmp4, tmp5, tmp25)
    tl.store(out_ptr0 + (x2), tmp26, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (16, ), (1, ))
    assert_size_stride(primals_3, (4, 16), (16, 1))
    assert_size_stride(primals_4, (4, 16), (16, 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: [h_0], Original ATen: [aten.zero]
        stream0 = get_raw_stream(0)
        triton_poi_fused_zero_0.run(buf0, 16, grid=grid(16), stream=stream0)
        buf1 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf0, primals_3, out=buf1)
        buf2 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mm], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 0), primals_4, out=buf2)
        buf3 = 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)
        buf33 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sigmoid, mul, sigmoid_1, tanh, mul_1, c_1, sigmoid_2, tanh_1, h_1], Original ATen: [aten.sigmoid, aten.mul, aten.tanh, aten.add, aten.sigmoid_backward]
        triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1.run(buf1, primals_2, buf2, buf3, buf4, buf5, buf33, buf6, buf7, 16, grid=grid(16), stream=stream0)
        buf8 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf7, primals_3, out=buf8)
        buf9 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [mm_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 4), primals_4, out=buf9)
        buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf13 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf14 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sigmoid_3, mul_3, sigmoid_4, tanh_2, mul_4, c_2, sigmoid_5, tanh_3, h_2], Original ATen: [aten.sigmoid, aten.mul, aten.tanh, aten.add]
        triton_poi_fused_add_mul_sigmoid_tanh_2.run(buf8, primals_2, buf9, buf5, buf10, buf11, buf12, buf13, buf14, buf15, 16, grid=grid(16), stream=stream0)
        buf16 = buf9; del buf9  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf15, primals_3, out=buf16)
        buf17 = buf8; del buf8  # reuse
        # Topologically Sorted Source Nodes: [mm_2], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 8), primals_4, out=buf17)
        buf18 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf19 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf20 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf21 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf22 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf23 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sigmoid_6, mul_6, sigmoid_7, tanh_4, mul_7, c_3, sigmoid_8, tanh_5, h_3], Original ATen: [aten.sigmoid, aten.mul, aten.tanh, aten.add]
        triton_poi_fused_add_mul_sigmoid_tanh_2.run(buf16, primals_2, buf17, buf13, buf18, buf19, buf20, buf21, buf22, buf23, 16, grid=grid(16), stream=stream0)
        buf24 = buf17; del buf17  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf23, primals_3, out=buf24)
        buf25 = buf16; del buf16  # reuse
        # Topologically Sorted Source Nodes: [mm_3], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 12), primals_4, out=buf25)
        del primals_4
        buf26 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf27 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf28 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf30 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sigmoid_9, mul_9, sigmoid_10, tanh_6, mul_10, c_4, tanh_7], Original ATen: [aten.sigmoid, aten.mul, aten.tanh, aten.add]
        triton_poi_fused_add_mul_sigmoid_tanh_3.run(buf24, primals_2, buf25, buf21, buf26, buf27, buf28, buf30, 16, grid=grid(16), stream=stream0)
        buf29 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sigmoid_11], Original ATen: [aten.sigmoid]
        triton_poi_fused_sigmoid_4.run(buf24, primals_2, buf25, buf29, 16, grid=grid(16), stream=stream0)
        del primals_2
        buf31 = reinterpret_tensor(buf25, (16, 4), (4, 1), 0); del buf25  # reuse
        # Topologically Sorted Source Nodes: [c_n], Original ATen: [aten.stack]
        triton_poi_fused_stack_5.run(buf5, buf13, buf21, buf26, buf27, buf28, buf31, 64, grid=grid(64), stream=stream0)
        buf32 = reinterpret_tensor(buf24, (16, 4), (4, 1), 0); del buf24  # reuse
        # Topologically Sorted Source Nodes: [h_n], Original ATen: [aten.stack]
        triton_poi_fused_stack_6.run(buf7, buf15, buf23, buf29, buf30, buf32, 64, grid=grid(64), stream=stream0)
    return (reinterpret_tensor(buf32, (4, 4, 4), (4, 16, 1), 0), reinterpret_tensor(buf31, (4, 4, 4), (4, 16, 1), 0), buf0, buf3, buf4, buf5, buf6, buf10, buf11, buf12, buf13, buf14, buf18, buf19, buf20, buf21, buf22, buf26, buf27, buf28, buf29, buf30, reinterpret_tensor(primals_1, (4, 4), (1, 16), 12), reinterpret_tensor(primals_3, (16, 4), (1, 16), 0), reinterpret_tensor(buf23, (4, 4), (1, 4), 0), reinterpret_tensor(primals_1, (4, 4), (1, 16), 8), reinterpret_tensor(buf15, (4, 4), (1, 4), 0), reinterpret_tensor(primals_1, (4, 4), (1, 16), 4), reinterpret_tensor(buf7, (4, 4), (1, 4), 0), buf33, reinterpret_tensor(primals_1, (4, 4), (1, 16), 0), )


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((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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
from typing import Tuple
import torch.nn as nn


class LSTM(nn.Module):
    """Implementation of the standard LSTM.


    Parameters
    ----------
    input_size : int
        Number of input features
    hidden_size : int
        Number of hidden/memory cells.
    batch_first : bool, optional
        If True, expects the batch inputs to be of shape [batch, seq, features] otherwise, the
        shape has to be [seq, batch, features], by default True.
    initial_forget_bias : int, optional
        Value of the initial forget gate bias, by default 0
    """

    def __init__(self, input_size: 'int', hidden_size: 'int', batch_first:
        'bool'=True, initial_forget_bias: 'int'=0):
        super(LSTM, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.batch_first = batch_first
        self.initial_forget_bias = initial_forget_bias
        self.weight_ih = nn.Parameter(torch.FloatTensor(input_size, 4 *
            hidden_size))
        self.weight_hh = nn.Parameter(torch.FloatTensor(hidden_size, 4 *
            hidden_size))
        self.bias = nn.Parameter(torch.FloatTensor(4 * hidden_size))
        self.reset_parameters()

    def reset_parameters(self):
        """Initialize all learnable parameters of the LSTM"""
        nn.init.orthogonal_(self.weight_ih.data)
        weight_hh_data = torch.eye(self.hidden_size)
        weight_hh_data = weight_hh_data.repeat(1, 4)
        self.weight_hh.data = weight_hh_data
        nn.init.constant_(self.bias.data, val=0)
        if self.initial_forget_bias != 0:
            self.bias.data[:self.hidden_size] = self.initial_forget_bias

    def forward(self, x: 'torch.Tensor') ->Tuple[torch.Tensor, torch.Tensor]:
        """[summary]
        
        Parameters
        ----------
        x : torch.Tensor
            Tensor, containing a batch of input sequences. Format must match the specified format,
            defined by the batch_first agrument.

        Returns
        -------
        h_n : torch.Tensor
            The hidden states of each time step of each sample in the batch.
        c_n : torch.Tensor]
            The cell states of each time step of each sample in the batch.
        """
        if self.batch_first:
            x = x.transpose(0, 1)
        seq_len, batch_size, _ = x.size()
        h_0 = x.data.new(batch_size, self.hidden_size).zero_()
        c_0 = x.data.new(batch_size, self.hidden_size).zero_()
        h_x = h_0, c_0
        h_n, c_n = [], []
        bias_batch = self.bias.unsqueeze(0).expand(batch_size, *self.bias.
            size())
        for t in range(seq_len):
            h_0, c_0 = h_x
            gates = torch.addmm(bias_batch, h_0, self.weight_hh) + torch.mm(x
                [t], self.weight_ih)
            f, i, o, g = gates.chunk(4, 1)
            c_1 = torch.sigmoid(f) * c_0 + torch.sigmoid(i) * torch.tanh(g)
            h_1 = torch.sigmoid(o) * torch.tanh(c_1)
            h_n.append(h_1)
            c_n.append(c_1)
            h_x = h_1, c_1
        h_n = torch.stack(h_n, 0)
        c_n = torch.stack(c_n, 0)
        if self.batch_first:
            h_n = h_n.transpose(0, 1)
            c_n = c_n.transpose(0, 1)
        return h_n, c_n


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


def get_init_inputs():
    return [[], {'input_size': 4, 'hidden_size': 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_zero_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
    tmp0 = 0.0
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1(in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, out_ptr3, out_ptr4,
    out_ptr5, 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 + (4 + x0 + 16 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (4 + x0 + 16 * x1), xmask)
    tmp6 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
    tmp7 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (12 + x0 + 16 * x1), xmask)
    tmp12 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
    tmp13 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr2 + (x0 + 16 * x1), xmask)
    tmp25 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
    tmp26 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr2 + (8 + x0 + 16 * x1), 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)
    tmp18 = 0.0
    tmp19 = tmp17 * tmp18
    tmp20 = tmp5 * tmp11
    tmp21 = tmp19 + tmp20
    tmp22 = 1.0
    tmp23 = tmp22 - tmp17
    tmp24 = tmp17 * tmp23
    tmp27 = tmp25 + tmp26
    tmp29 = tmp27 + tmp28
    tmp30 = tl.sigmoid(tmp29)
    tmp31 = libdevice.tanh(tmp21)
    tmp32 = tmp30 * tmp31
    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)
    tl.store(out_ptr4 + x2, tmp30, xmask)
    tl.store(out_ptr5 + x2, tmp32, xmask)


@triton.jit
def triton_poi_fused_add_mul_sigmoid_tanh_2(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, out_ptr0, out_ptr1, out_ptr2, out_ptr3, out_ptr4, out_ptr5,
    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 + (4 + x0 + 16 * x1), xmask)
    tmp7 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (4 + x0 + 16 * x1), xmask)
    tmp12 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
    tmp13 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr2 + (12 + x0 + 16 * x1), xmask)
    tmp18 = tl.load(in_ptr3 + x2, xmask)
    tmp22 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
    tmp23 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr2 + (8 + x0 + 16 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = tl.sigmoid(tmp4)
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 + tmp9
    tmp11 = tl.sigmoid(tmp10)
    tmp14 = tmp12 + tmp13
    tmp16 = tmp14 + tmp15
    tmp17 = libdevice.tanh(tmp16)
    tmp19 = tmp5 * tmp18
    tmp20 = tmp11 * tmp17
    tmp21 = tmp19 + tmp20
    tmp24 = tmp22 + tmp23
    tmp26 = tmp24 + tmp25
    tmp27 = tl.sigmoid(tmp26)
    tmp28 = libdevice.tanh(tmp21)
    tmp29 = tmp27 * tmp28
    tl.store(out_ptr0 + x2, tmp5, xmask)
    tl.store(out_ptr1 + x2, tmp11, xmask)
    tl.store(out_ptr2 + x2, tmp17, xmask)
    tl.store(out_ptr3 + x2, tmp21, xmask)
    tl.store(out_ptr4 + x2, tmp27, xmask)
    tl.store(out_ptr5 + x2, tmp29, xmask)


@triton.jit
def triton_poi_fused_add_mul_sigmoid_tanh_3(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 + (4 + x0 + 16 * x1), xmask)
    tmp7 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (4 + x0 + 16 * x1), xmask)
    tmp12 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
    tmp13 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr2 + (12 + 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 = tl.sigmoid(tmp10)
    tmp14 = tmp12 + tmp13
    tmp16 = tmp14 + tmp15
    tmp17 = libdevice.tanh(tmp16)
    tmp19 = tmp5 * tmp18
    tmp20 = tmp11 * tmp17
    tmp21 = tmp19 + tmp20
    tmp22 = libdevice.tanh(tmp21)
    tl.store(out_ptr0 + x2, tmp5, xmask)
    tl.store(out_ptr1 + x2, tmp11, xmask)
    tl.store(out_ptr2 + x2, tmp17, xmask)
    tl.store(out_ptr3 + x2, tmp22, xmask)


@triton.jit
def triton_poi_fused_sigmoid_4(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 + (8 + x0 + 16 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (8 + 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_stack_5(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
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4 * x1), 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_ptr1 + (x0 + 4 * (-4 + x1)), 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_ptr2 + (x0 + 4 * (-8 + x1)), tmp14 & xmask, other=0.0)
    tmp16 = tmp0 >= tmp12
    tl.full([1], 16, tl.int64)
    tmp19 = tl.load(in_ptr3 + (x0 + 4 * (-12 + x1)), tmp16 & xmask, other=0.0)
    tmp20 = tl.load(in_ptr2 + (x0 + 4 * (-12 + x1)), tmp16 & xmask, other=0.0)
    tmp21 = tmp19 * tmp20
    tmp22 = tl.load(in_ptr4 + (x0 + 4 * (-12 + x1)), tmp16 & xmask, other=0.0)
    tmp23 = tl.load(in_ptr5 + (x0 + 4 * (-12 + x1)), tmp16 & xmask, other=0.0)
    tmp24 = tmp22 * tmp23
    tmp25 = tmp21 + tmp24
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp16, tmp25, tmp26)
    tmp28 = tl.where(tmp14, tmp15, tmp27)
    tmp29 = tl.where(tmp9, tmp10, tmp28)
    tmp30 = tl.where(tmp4, tmp5, tmp29)
    tl.store(out_ptr0 + x2, tmp30, xmask)


@triton.jit
def triton_poi_fused_stack_6(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4 * x1), 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_ptr1 + (x0 + 4 * (-4 + x1)), 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_ptr2 + (x0 + 4 * (-8 + x1)), tmp14 & xmask, other=0.0)
    tmp16 = tmp0 >= tmp12
    tl.full([1], 16, tl.int64)
    tmp19 = tl.load(in_ptr3 + (x0 + 4 * (-12 + x1)), tmp16 & xmask, other=0.0)
    tmp20 = tl.load(in_ptr4 + (x0 + 4 * (-12 + x1)), tmp16 & xmask, other=0.0)
    tmp21 = tmp19 * tmp20
    tmp22 = tl.full(tmp21.shape, 0.0, tmp21.dtype)
    tmp23 = tl.where(tmp16, tmp21, tmp22)
    tmp24 = tl.where(tmp14, tmp15, tmp23)
    tmp25 = tl.where(tmp9, tmp10, tmp24)
    tmp26 = tl.where(tmp4, tmp5, tmp25)
    tl.store(out_ptr0 + x2, tmp26, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (16,), (1,))
    assert_size_stride(primals_3, (4, 16), (16, 1))
    assert_size_stride(primals_4, (4, 16), (16, 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_zero_0[grid(16)](buf0, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        buf1 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.mm(buf0, primals_3, out=buf1)
        buf2 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 0),
            primals_4, out=buf2)
        buf3 = 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)
        buf33 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_1[grid(16)](buf1
            , primals_2, buf2, buf3, buf4, buf5, buf33, buf6, buf7, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf8 = buf2
        del buf2
        extern_kernels.mm(buf7, primals_3, out=buf8)
        buf9 = buf1
        del buf1
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 4),
            primals_4, out=buf9)
        buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf13 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf14 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_mul_sigmoid_tanh_2[grid(16)](buf8, primals_2,
            buf9, buf5, buf10, buf11, buf12, buf13, buf14, buf15, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf16 = buf9
        del buf9
        extern_kernels.mm(buf15, primals_3, out=buf16)
        buf17 = buf8
        del buf8
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 8),
            primals_4, out=buf17)
        buf18 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf19 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf20 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf21 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf22 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf23 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_mul_sigmoid_tanh_2[grid(16)](buf16, primals_2,
            buf17, buf13, buf18, buf19, buf20, buf21, buf22, buf23, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf24 = buf17
        del buf17
        extern_kernels.mm(buf23, primals_3, out=buf24)
        buf25 = buf16
        del buf16
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (16, 1), 12
            ), primals_4, out=buf25)
        del primals_4
        buf26 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf27 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf28 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf30 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_mul_sigmoid_tanh_3[grid(16)](buf24, primals_2,
            buf25, buf21, buf26, buf27, buf28, buf30, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf29 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_sigmoid_4[grid(16)](buf24, primals_2, buf25, buf29,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_2
        buf31 = reinterpret_tensor(buf25, (16, 4), (4, 1), 0)
        del buf25
        triton_poi_fused_stack_5[grid(64)](buf5, buf13, buf21, buf26, buf27,
            buf28, buf31, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf32 = reinterpret_tensor(buf24, (16, 4), (4, 1), 0)
        del buf24
        triton_poi_fused_stack_6[grid(64)](buf7, buf15, buf23, buf29, buf30,
            buf32, 64, XBLOCK=64, num_warps=1, num_stages=1)
    return (reinterpret_tensor(buf32, (4, 4, 4), (4, 16, 1), 0),
        reinterpret_tensor(buf31, (4, 4, 4), (4, 16, 1), 0), buf0, buf3,
        buf4, buf5, buf6, buf10, buf11, buf12, buf13, buf14, buf18, buf19,
        buf20, buf21, buf22, buf26, buf27, buf28, buf29, buf30,
        reinterpret_tensor(primals_1, (4, 4), (1, 16), 12),
        reinterpret_tensor(primals_3, (16, 4), (1, 16), 0),
        reinterpret_tensor(buf23, (4, 4), (1, 4), 0), reinterpret_tensor(
        primals_1, (4, 4), (1, 16), 8), reinterpret_tensor(buf15, (4, 4), (
        1, 4), 0), reinterpret_tensor(primals_1, (4, 4), (1, 16), 4),
        reinterpret_tensor(buf7, (4, 4), (1, 4), 0), buf33,
        reinterpret_tensor(primals_1, (4, 4), (1, 16), 0))


class LSTMNew(nn.Module):
    """Implementation of the standard LSTM.


    Parameters
    ----------
    input_size : int
        Number of input features
    hidden_size : int
        Number of hidden/memory cells.
    batch_first : bool, optional
        If True, expects the batch inputs to be of shape [batch, seq, features] otherwise, the
        shape has to be [seq, batch, features], by default True.
    initial_forget_bias : int, optional
        Value of the initial forget gate bias, by default 0
    """

    def __init__(self, input_size: 'int', hidden_size: 'int', batch_first:
        'bool'=True, initial_forget_bias: 'int'=0):
        super(LSTMNew, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.batch_first = batch_first
        self.initial_forget_bias = initial_forget_bias
        self.weight_ih = nn.Parameter(torch.FloatTensor(input_size, 4 *
            hidden_size))
        self.weight_hh = nn.Parameter(torch.FloatTensor(hidden_size, 4 *
            hidden_size))
        self.bias = nn.Parameter(torch.FloatTensor(4 * hidden_size))
        self.reset_parameters()

    def reset_parameters(self):
        """Initialize all learnable parameters of the LSTM"""
        nn.init.orthogonal_(self.weight_ih.data)
        weight_hh_data = torch.eye(self.hidden_size)
        weight_hh_data = weight_hh_data.repeat(1, 4)
        self.weight_hh.data = weight_hh_data
        nn.init.constant_(self.bias.data, val=0)
        if self.initial_forget_bias != 0:
            self.bias.data[:self.hidden_size] = self.initial_forget_bias

    def forward(self, input_0):
        primals_3 = self.weight_ih
        primals_4 = self.weight_hh
        primals_2 = self.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0], output[1]
