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

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


# kernel path: runs/run_shard_4/inductor_cache/6e/c6e2eebeysgmbnym7fnc4cp55gfpuvahjqnwm6b6ib5ddqju4j6y.py
# Topologically Sorted Source Nodes: [add, input_gate, add_1, new_gate, sub, mul, add_2], Original ATen: [aten.add, aten.sigmoid, aten.tanh, aten.sub, aten.mul]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   input_gate => sigmoid
#   mul => mul
#   new_gate => tanh
#   sub => sub
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, %getitem_2), kwargs = {})
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%add,), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_1, %getitem_3), kwargs = {})
#   %tanh : [num_users=3] = call_function[target=torch.ops.aten.tanh.default](args = (%add_1,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_5, %tanh), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %sub), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%tanh, %mul), kwargs = {})
triton_poi_fused_add_mul_sigmoid_sub_tanh_0 = async_compile.triton('triton_poi_fused_add_mul_sigmoid_sub_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: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_sigmoid_sub_tanh_0', '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_sigmoid_sub_tanh_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, 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 + (8*x1)), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x0 + (8*x1)), xmask)
    tmp6 = tl.load(in_ptr0 + (4 + x0 + (8*x1)), xmask)
    tmp7 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (4 + x0 + (8*x1)), xmask)
    tmp12 = 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)
    tmp13 = tmp12 - tmp11
    tmp14 = tmp5 * tmp13
    tmp15 = tmp11 + tmp14
    tl.store(out_ptr0 + (x2), tmp5, xmask)
    tl.store(out_ptr1 + (x2), tmp11, xmask)
    tl.store(out_ptr2 + (x2), tmp15, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (8, 4), (4, 1))
    assert_size_stride(primals_2, (8, ), (1, ))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (8, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 8), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm]
        extern_kernels.mm(primals_5, reinterpret_tensor(primals_4, (4, 8), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add, input_gate, add_1, new_gate, sub, mul, add_2], Original ATen: [aten.add, aten.sigmoid, aten.tanh, aten.sub, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mul_sigmoid_sub_tanh_0.run(buf0, primals_2, buf1, primals_5, buf2, buf3, buf4, 16, grid=grid(16), stream=stream0)
        del buf0
        del buf1
        del primals_2
    return (buf4, primals_3, primals_5, buf2, buf3, )


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((8, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((8, ), (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((8, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = 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])
    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 torch import nn
import torch as th


class UpdateCell(nn.Module):

    def __init__(self, input_dim, output_dim):
        super().__init__()
        self.x2i = nn.Linear(input_dim, 2 * output_dim, bias=True)
        self.h2h = nn.Linear(output_dim, 2 * output_dim, bias=False)

    def forward(self, x, hidden):
        i_i, i_n = self.x2i(x).chunk(2, 1)
        h_i, h_n = self.h2h(hidden).chunk(2, 1)
        input_gate = th.sigmoid(i_i + h_i)
        new_gate = th.tanh(i_n + h_n)
        return new_gate + input_gate * (hidden - new_gate)


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


def get_init_inputs():
    return [[], {'input_dim': 4, 'output_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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_add_mul_sigmoid_sub_tanh_0(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, out_ptr0, out_ptr1, out_ptr2, 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 + 8 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x0 + 8 * x1), xmask)
    tmp6 = tl.load(in_ptr0 + (4 + x0 + 8 * x1), xmask)
    tmp7 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (4 + x0 + 8 * x1), xmask)
    tmp12 = 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)
    tmp13 = tmp12 - tmp11
    tmp14 = tmp5 * tmp13
    tmp15 = tmp11 + tmp14
    tl.store(out_ptr0 + x2, tmp5, xmask)
    tl.store(out_ptr1 + x2, tmp11, xmask)
    tl.store(out_ptr2 + x2, tmp15, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (8, 4), (4, 1))
    assert_size_stride(primals_2, (8,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (8, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 8),
            (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        extern_kernels.mm(primals_5, reinterpret_tensor(primals_4, (4, 8),
            (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_sigmoid_sub_tanh_0[grid(16)](buf0,
            primals_2, buf1, primals_5, buf2, buf3, buf4, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf0
        del buf1
        del primals_2
    return buf4, primals_3, primals_5, buf2, buf3


class UpdateCellNew(nn.Module):

    def __init__(self, input_dim, output_dim):
        super().__init__()
        self.x2i = nn.Linear(input_dim, 2 * output_dim, bias=True)
        self.h2h = nn.Linear(output_dim, 2 * output_dim, bias=False)

    def forward(self, input_0, input_1):
        primals_1 = self.x2i.weight
        primals_2 = self.x2i.bias
        primals_4 = self.h2h.weight
        primals_3 = input_0
        primals_5 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
