# 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/q2/cq2gvq6rqezjkilykq2je6ghe4odwrozuthoq7pu753gygr3fxk5.py
# Topologically Sorted Source Nodes: [add, r, mul], Original ATen: [aten.add, aten.sigmoid, aten.mul, aten.sigmoid_backward]
# Source node to ATen node mapping:
#   add => add
#   mul => mul
#   r => sigmoid
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %view_3), kwargs = {})
#   %sigmoid : [num_users=3] = call_function[target=torch.ops.aten.sigmoid.default](args = (%add,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %primals_4), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {})
#   %mul_11 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %sub_4), kwargs = {})
triton_poi_fused_add_mul_sigmoid_sigmoid_backward_0 = async_compile.triton('triton_poi_fused_add_mul_sigmoid_sigmoid_backward_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=[256], 
    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_0', '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_add_mul_sigmoid_sigmoid_backward_0(in_ptr0, in_ptr1, in_ptr2, 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
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask)
    tmp4 = tl.load(in_ptr2 + (x0), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tmp5 = tmp3 * tmp4
    tmp6 = 1.0
    tmp7 = tmp6 - tmp3
    tmp8 = tmp3 * tmp7
    tl.store(out_ptr0 + (x0), tmp5, xmask)
    tl.store(out_ptr1 + (x0), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/mf/cmf7lo6oka7zywjll3ye3rrgznwvdpoc4xpumd4fso6mum3h6cat.py
# Topologically Sorted Source Nodes: [add_1, sub, z, add_2, h, sub_1, mul_1, mul_2, g], Original ATen: [aten.add, aten.sub, aten.sigmoid, aten.tanh, aten.rsub, aten.mul]
# Source node to ATen node mapping:
#   add_1 => add_1
#   add_2 => add_2
#   g => add_3
#   h => tanh
#   mul_1 => mul_1
#   mul_2 => mul_2
#   sub => sub
#   sub_1 => sub_1
#   z => sigmoid_1
# Graph fragment:
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_5, %view_7), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_1, %primals_7), kwargs = {})
#   %sigmoid_1 : [num_users=3] = call_function[target=torch.ops.aten.sigmoid.default](args = (%sub,), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_9, %view_11), kwargs = {})
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add_2,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid_1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %primals_4), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_1, %tanh), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %mul_2), kwargs = {})
triton_poi_fused_add_mul_rsub_sigmoid_sub_tanh_1 = async_compile.triton('triton_poi_fused_add_mul_rsub_sigmoid_sub_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=[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_rsub_sigmoid_sub_tanh_1', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 6, '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_rsub_sigmoid_sub_tanh_1(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x2), xmask)
    tmp3 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_out_ptr1 + (x2), xmask)
    tmp7 = tl.load(in_ptr2 + (x2), xmask)
    tmp12 = tl.load(in_ptr3 + (x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp5 = tl.sigmoid(tmp4)
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.tanh(tmp8)
    tmp10 = 1.0
    tmp11 = tmp10 - tmp5
    tmp13 = tmp11 * tmp12
    tmp14 = tmp5 * tmp9
    tmp15 = tmp13 + tmp14
    tl.store(in_out_ptr0 + (x2), tmp5, xmask)
    tl.store(in_out_ptr1 + (x2), tmp9, xmask)
    tl.store(out_ptr0 + (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, primals_6, primals_7, primals_8, primals_9 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_3], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf3)
        del primals_6
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_4], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf5)
        del primals_8
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add, r, mul], Original ATen: [aten.add, aten.sigmoid, aten.mul, aten.sigmoid_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mul_sigmoid_sigmoid_backward_0.run(buf0, buf1, primals_4, buf6, buf10, 256, grid=grid(256), stream=stream0)
        buf7 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [linear_5], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf6, (64, 4), (4, 1), 0), reinterpret_tensor(primals_9, (4, 4), (1, 4), 0), out=buf7)
        buf4 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf2  # reuse
        buf8 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf5  # reuse
        buf9 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [add_1, sub, z, add_2, h, sub_1, mul_1, mul_2, g], Original ATen: [aten.add, aten.sub, aten.sigmoid, aten.tanh, aten.rsub, aten.mul]
        triton_poi_fused_add_mul_rsub_sigmoid_sub_tanh_1.run(buf4, buf8, buf3, primals_7, buf7, primals_4, buf9, 256, grid=grid(256), stream=stream0)
        del buf3
        del buf7
        del primals_7
    return (buf9, primals_4, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), buf4, reinterpret_tensor(buf6, (64, 4), (4, 1), 0), buf8, primals_9, buf10, )


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, 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((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9])
    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.utils.data


class GRUGatingUnit(torch.nn.Module):
    """
    Overview:
        GRU Gating Unit used in GTrXL.
    """

    def __init__(self, input_dim: 'int', bg: 'float'=2.0):
        """
        Arguments:
            - input_dim: (:obj:`int`): dimension of input.
            - bg (:obj:`bg`): gate bias. By setting bg > 0 we can explicitly initialize the gating mechanism to
            be close to the identity map. This can greatly improve the learning speed and stability since it
            initializes the agent close to a Markovian policy (ignore attention at the beginning).
        """
        super(GRUGatingUnit, self).__init__()
        self.Wr = torch.nn.Linear(input_dim, input_dim, bias=False)
        self.Ur = torch.nn.Linear(input_dim, input_dim, bias=False)
        self.Wz = torch.nn.Linear(input_dim, input_dim, bias=False)
        self.Uz = torch.nn.Linear(input_dim, input_dim, bias=False)
        self.Wg = torch.nn.Linear(input_dim, input_dim, bias=False)
        self.Ug = torch.nn.Linear(input_dim, input_dim, bias=False)
        self.bg = nn.Parameter(torch.full([input_dim], bg))
        self.sigmoid = torch.nn.Sigmoid()
        self.tanh = torch.nn.Tanh()

    def forward(self, x: 'torch.Tensor', y: 'torch.Tensor'):
        """
        Overview:
            Compute output value with gating mechanism
        Arguments:
            - x: (:obj:`torch.Tensor`): first input.
            - y: (:obj:`torch.Tensor`): second input.
            x and y have same shape and last shape is input_dim.
        Returns:
            - g: (:obj:`torch.Tensor`): output of GRU. Same shape of x and y.
        """
        r = self.sigmoid(self.Wr(y) + self.Ur(x))
        z = self.sigmoid(self.Wz(y) + self.Uz(x) - self.bg)
        h = self.tanh(self.Wg(y) + self.Ug(torch.mul(r, x)))
        g = torch.mul(1 - z, x) + torch.mul(z, h)
        return g


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


def get_init_inputs():
    return [[], {'input_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
import torch.nn as nn
import torch.utils.data
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_0(in_ptr0, in_ptr1,
    in_ptr2, 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
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tmp5 = tmp3 * tmp4
    tmp6 = 1.0
    tmp7 = tmp6 - tmp3
    tmp8 = tmp3 * tmp7
    tl.store(out_ptr0 + x0, tmp5, xmask)
    tl.store(out_ptr1 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_mul_rsub_sigmoid_sub_tanh_1(in_out_ptr0,
    in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x2, xmask)
    tmp3 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_out_ptr1 + x2, xmask)
    tmp7 = tl.load(in_ptr2 + x2, xmask)
    tmp12 = tl.load(in_ptr3 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp5 = tl.sigmoid(tmp4)
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.tanh(tmp8)
    tmp10 = 1.0
    tmp11 = tmp10 - tmp5
    tmp13 = tmp11 * tmp12
    tmp14 = tmp5 * tmp9
    tmp15 = tmp13 + tmp14
    tl.store(in_out_ptr0 + x2, tmp5, xmask)
    tl.store(in_out_ptr1 + x2, tmp9, xmask)
    tl.store(out_ptr0 + x2, tmp15, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf3)
        del primals_6
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf5)
        del primals_8
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_sigmoid_sigmoid_backward_0[grid(256)](buf0,
            buf1, primals_4, buf6, buf10, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        buf7 = buf1
        del buf1
        extern_kernels.mm(reinterpret_tensor(buf6, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_9, (4, 4), (1, 4), 0), out=buf7)
        buf4 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf2
        buf8 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf5
        buf9 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        triton_poi_fused_add_mul_rsub_sigmoid_sub_tanh_1[grid(256)](buf4,
            buf8, buf3, primals_7, buf7, primals_4, buf9, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf3
        del buf7
        del primals_7
    return buf9, primals_4, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0
        ), buf4, reinterpret_tensor(buf6, (64, 4), (4, 1), 0
        ), buf8, primals_9, buf10


class GRUGatingUnitNew(torch.nn.Module):
    """
    Overview:
        GRU Gating Unit used in GTrXL.
    """

    def __init__(self, input_dim: 'int', bg: 'float'=2.0):
        """
        Arguments:
            - input_dim: (:obj:`int`): dimension of input.
            - bg (:obj:`bg`): gate bias. By setting bg > 0 we can explicitly initialize the gating mechanism to
            be close to the identity map. This can greatly improve the learning speed and stability since it
            initializes the agent close to a Markovian policy (ignore attention at the beginning).
        """
        super(GRUGatingUnitNew, self).__init__()
        self.Wr = torch.nn.Linear(input_dim, input_dim, bias=False)
        self.Ur = torch.nn.Linear(input_dim, input_dim, bias=False)
        self.Wz = torch.nn.Linear(input_dim, input_dim, bias=False)
        self.Uz = torch.nn.Linear(input_dim, input_dim, bias=False)
        self.Wg = torch.nn.Linear(input_dim, input_dim, bias=False)
        self.Ug = torch.nn.Linear(input_dim, input_dim, bias=False)
        self.bg = nn.Parameter(torch.full([input_dim], bg))
        self.sigmoid = torch.nn.Sigmoid()
        self.tanh = torch.nn.Tanh()

    def forward(self, input_0, input_1):
        primals_7 = self.bg
        primals_1 = self.Wr.weight
        primals_3 = self.Ur.weight
        primals_5 = self.Wz.weight
        primals_6 = self.Uz.weight
        primals_8 = self.Wg.weight
        primals_9 = self.Ug.weight
        primals_2 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]
