# 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/gx/cgxrgl276zm6ljhhctu6squhup4zr27w4n7zpzvgml45usdwim2s.py
# Topologically Sorted Source Nodes: [add_1, i, sub, f, mul, mul_1, add_2, h], Original ATen: [aten.add, aten.sigmoid, aten.sub, aten.mul, aten.tanh, aten.sigmoid_backward]
# Source node to ATen node mapping:
#   add_1 => add_1
#   add_2 => add_2
#   f => sigmoid_1
#   h => tanh
#   i => sigmoid
#   mul => mul
#   mul_1 => mul_1
#   sub => sub
# Graph fragment:
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, %primals_4), kwargs = {})
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%add_1,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%getitem_1, %primals_4), kwargs = {})
#   %sigmoid_1 : [num_users=3] = call_function[target=torch.ops.aten.sigmoid.default](args = (%sub,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %getitem_2), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_1, %primals_4), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add_2,), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid_1), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_1, %sub_2), kwargs = {})
triton_poi_fused_add_mul_sigmoid_sigmoid_backward_sub_tanh_0 = async_compile.triton('triton_poi_fused_add_mul_sigmoid_sigmoid_backward_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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_sigmoid_sigmoid_backward_sub_tanh_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_sub_tanh_0(in_ptr0, in_ptr1, 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 + (12*x1)), xmask)
    tmp1 = tl.load(in_ptr1 + (x2), xmask)
    tmp4 = tl.load(in_ptr0 + (8 + x0 + (12*x1)), xmask)
    tmp6 = tl.load(in_ptr0 + (4 + x0 + (12*x1)), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tmp5 = tmp3 * tmp4
    tmp7 = tmp6 - tmp1
    tmp8 = tl.sigmoid(tmp7)
    tmp9 = tmp8 * tmp1
    tmp10 = tmp5 + tmp9
    tmp11 = libdevice.tanh(tmp10)
    tmp12 = 1.0
    tmp13 = tmp12 - tmp8
    tmp14 = tmp8 * tmp13
    tl.store(out_ptr0 + (x2), tmp3, xmask)
    tl.store(out_ptr1 + (x2), tmp11, xmask)
    tl.store(out_ptr2 + (x2), tmp14, 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, 12), (12, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (12, ), (1, ))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 12), (12, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.addmm(primals_3, primals_2, primals_1, alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_3
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add_1, i, sub, f, mul, mul_1, add_2, h], Original ATen: [aten.add, aten.sigmoid, aten.sub, aten.mul, aten.tanh, aten.sigmoid_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mul_sigmoid_sigmoid_backward_sub_tanh_0.run(buf0, primals_4, buf1, buf2, buf3, 16, grid=grid(16), stream=stream0)
    return (buf2, primals_4, reinterpret_tensor(buf0, (4, 4), (12, 1), 8), buf1, buf2, buf3, reinterpret_tensor(primals_2, (4, 4), (1, 4), 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, 12), (12, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((12, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 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
import torch.nn as nn


class LRNCell(nn.Module):

    def __init__(self, input_size, hidden_size):
        super(LRNCell, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self._W = nn.Parameter(torch.FloatTensor(input_size, hidden_size * 3))
        self._W_b = nn.Parameter(torch.FloatTensor(hidden_size * 3))
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.xavier_uniform_(self._W.data)
        nn.init.constant_(self._W_b.data, 0)

    def forward(self, x, h_):
        p, q, r = (torch.mm(x, self._W) + self._W_b).split(self.hidden_size, -1
            )
        i = (p + h_).sigmoid()
        f = (q - h_).sigmoid()
        h = (i * r + f * h_).tanh()
        return h


def get_inputs():
    return [torch.rand([4, 4]), torch.rand([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_add_mul_sigmoid_sigmoid_backward_sub_tanh_0(in_ptr0,
    in_ptr1, 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 + 12 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp4 = tl.load(in_ptr0 + (8 + x0 + 12 * x1), xmask)
    tmp6 = tl.load(in_ptr0 + (4 + x0 + 12 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tmp5 = tmp3 * tmp4
    tmp7 = tmp6 - tmp1
    tmp8 = tl.sigmoid(tmp7)
    tmp9 = tmp8 * tmp1
    tmp10 = tmp5 + tmp9
    tmp11 = libdevice.tanh(tmp10)
    tmp12 = 1.0
    tmp13 = tmp12 - tmp8
    tmp14 = tmp8 * tmp13
    tl.store(out_ptr0 + x2, tmp3, xmask)
    tl.store(out_ptr1 + x2, tmp11, xmask)
    tl.store(out_ptr2 + x2, tmp14, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 12), (12, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (12,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 12), (12, 1), torch.float32)
        extern_kernels.addmm(primals_3, primals_2, primals_1, alpha=1, beta
            =1, out=buf0)
        del primals_1
        del primals_3
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_sigmoid_sigmoid_backward_sub_tanh_0[grid(16)](
            buf0, primals_4, buf1, buf2, buf3, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
    return buf2, primals_4, reinterpret_tensor(buf0, (4, 4), (12, 1), 8
        ), buf1, buf2, buf3, reinterpret_tensor(primals_2, (4, 4), (1, 4), 0)


class LRNCellNew(nn.Module):

    def __init__(self, input_size, hidden_size):
        super(LRNCellNew, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self._W = nn.Parameter(torch.FloatTensor(input_size, hidden_size * 3))
        self._W_b = nn.Parameter(torch.FloatTensor(hidden_size * 3))
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.xavier_uniform_(self._W.data)
        nn.init.constant_(self._W_b.data, 0)

    def forward(self, input_0, input_1):
        primals_1 = self._W
        primals_3 = self._W_b
        primals_2 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
