# 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/ah/cahef3zaa3iwoq6gptzhqlwjgp2gpb6mou2gxpitnuacow2imr5p.py
# Topologically Sorted Source Nodes: [add_1, z], Original ATen: [aten.add, aten.sigmoid]
# Source node to ATen node mapping:
#   add_1 => add_1
#   z => sigmoid_1
# Graph fragment:
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%squeeze_2, %squeeze_3), kwargs = {})
#   %sigmoid_1 : [num_users=3] = call_function[target=torch.ops.aten.sigmoid.default](args = (%add_1,), kwargs = {})
triton_poi_fused_add_sigmoid_0 = async_compile.triton('triton_poi_fused_add_sigmoid_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=[4], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_sigmoid_0', 'mutated_arg_names': ['in_out_ptr0'], '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_sigmoid_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask)
    tmp3 = tl.load(in_ptr1 + (x0), xmask)
    tmp4 = tl.load(in_ptr2 + (x0), xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = tl.sigmoid(tmp6)
    tl.store(in_out_ptr0 + (x0), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/cp/ccpizqvxiszvyq4nmrwe6jmeipx6lgw2tvbkys7puxxdcqqemkut.py
# Topologically Sorted Source Nodes: [add, r, mul], Original ATen: [aten.add, aten.sigmoid, aten.mul]
# 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 = (%squeeze, %squeeze_1), 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 = (%primals_6, %sigmoid), kwargs = {})
triton_poi_fused_add_mul_sigmoid_1 = async_compile.triton('triton_poi_fused_add_mul_sigmoid_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: '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_add_mul_sigmoid_1', '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_add_mul_sigmoid_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr4 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp6 = tmp4 + tmp5
    tmp7 = tmp3 + tmp6
    tmp8 = tl.sigmoid(tmp7)
    tmp9 = tmp0 * tmp8
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/yk/cykvpc4z4sz3gdkjlaubqprucmz42d5efaeci6kttpjjxbdz74as.py
# Topologically Sorted Source Nodes: [add_2, n], Original ATen: [aten.add, aten.tanh]
# Source node to ATen node mapping:
#   add_2 => add_2
#   n => tanh
# Graph fragment:
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%squeeze_4, %squeeze_5), kwargs = {})
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add_2,), kwargs = {})
triton_poi_fused_add_tanh_2 = async_compile.triton('triton_poi_fused_add_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=[4], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_tanh_2', 'mutated_arg_names': ['in_out_ptr0'], '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_tanh_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask)
    tmp3 = tl.load(in_ptr1 + (x0), xmask)
    tmp4 = tl.load(in_ptr2 + (x0), xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = libdevice.tanh(tmp6)
    tl.store(in_out_ptr0 + (x0), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/co/ccoyc34v6akdlyqfew2scbljelwffisdbxiebjx4dgvpoevvdxtd.py
# Topologically Sorted Source Nodes: [mul_1, sub, mul_2, add_3], Original ATen: [aten.mul, aten.rsub, aten.add]
# Source node to ATen node mapping:
#   add_3 => add_3
#   mul_1 => mul_1
#   mul_2 => mul_2
#   sub => sub
# Graph fragment:
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_1, %primals_6), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %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_3 = async_compile.triton('triton_poi_fused_add_mul_rsub_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: '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_add_mul_rsub_3', '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_rsub_3(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
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2), xmask)
    tmp5 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp0
    tmp6 = tmp4 * tmp5
    tmp7 = tmp2 + tmp6
    tl.store(out_ptr0 + (x2), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/nx/cnxpp3bj4w6lzbvlidh2mu2z5tsvxuwb7pllhmt32xjcyung2ewg.py
# Topologically Sorted Source Nodes: [add, r], Original ATen: [aten.add, aten.sigmoid, aten.sigmoid_backward]
# Source node to ATen node mapping:
#   add => add
#   r => sigmoid
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%squeeze, %squeeze_1), kwargs = {})
#   %sigmoid : [num_users=3] = call_function[target=torch.ops.aten.sigmoid.default](args = (%add,), kwargs = {})
#   %sub_3 : [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_3), kwargs = {})
triton_poi_fused_add_sigmoid_sigmoid_backward_4 = async_compile.triton('triton_poi_fused_add_sigmoid_sigmoid_backward_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=[4], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_sigmoid_sigmoid_backward_4', 'mutated_arg_names': ['in_out_ptr0'], '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_sigmoid_sigmoid_backward_4(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask)
    tmp3 = tl.load(in_ptr1 + (x0), xmask)
    tmp4 = tl.load(in_ptr2 + (x0), xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = tl.sigmoid(tmp6)
    tmp8 = 1.0
    tmp9 = tmp8 - tmp7
    tmp10 = tmp7 * tmp9
    tl.store(in_out_ptr0 + (x0), tmp10, 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, primals_10, primals_11, primals_12, primals_13, primals_14 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_8, (4, ), (1, ))
    assert_size_stride(primals_9, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_10, (4, ), (1, ))
    assert_size_stride(primals_11, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_12, (4, ), (1, ))
    assert_size_stride(primals_13, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_14, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_3, (1, 4, 4), (16, 4, 1), 0), primals_1, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf0, (1, 4, 1), (4, 1, 1))
        # Topologically Sorted Source Nodes: [conv1d_1], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(reinterpret_tensor(primals_6, (1, 4, 4), (16, 4, 1), 0), primals_4, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf1, (1, 4, 1), (4, 1, 1))
        # Topologically Sorted Source Nodes: [conv1d_2], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(reinterpret_tensor(primals_3, (1, 4, 4), (16, 4, 1), 0), primals_7, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf2, (1, 4, 1), (4, 1, 1))
        # Topologically Sorted Source Nodes: [conv1d_3], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(reinterpret_tensor(primals_6, (1, 4, 4), (16, 4, 1), 0), primals_9, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf3, (1, 4, 1), (4, 1, 1))
        buf4 = reinterpret_tensor(buf2, (4, 1), (1, 1), 0); del buf2  # reuse
        # Topologically Sorted Source Nodes: [add_1, z], Original ATen: [aten.add, aten.sigmoid]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_sigmoid_0.run(buf4, primals_8, buf3, primals_10, 4, grid=grid(4), stream=stream0)
        del buf3
        del primals_10
        del primals_8
        # Topologically Sorted Source Nodes: [conv1d_4], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(reinterpret_tensor(primals_3, (1, 4, 4), (16, 4, 1), 0), primals_11, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf5, (1, 4, 1), (4, 1, 1))
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add, r, mul], Original ATen: [aten.add, aten.sigmoid, aten.mul]
        triton_poi_fused_add_mul_sigmoid_1.run(primals_6, buf0, primals_2, buf1, primals_5, buf6, 16, grid=grid(16), stream=stream0)
        # Topologically Sorted Source Nodes: [conv1d_5], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(reinterpret_tensor(buf6, (1, 4, 4), (0, 4, 1), 0), primals_13, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf7, (1, 4, 1), (4, 1, 1))
        buf8 = reinterpret_tensor(buf5, (4, 1), (1, 1), 0); del buf5  # reuse
        # Topologically Sorted Source Nodes: [add_2, n], Original ATen: [aten.add, aten.tanh]
        triton_poi_fused_add_tanh_2.run(buf8, primals_12, buf7, primals_14, 4, grid=grid(4), stream=stream0)
        del buf7
        del primals_12
        del primals_14
        buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul_1, sub, mul_2, add_3], Original ATen: [aten.mul, aten.rsub, aten.add]
        triton_poi_fused_add_mul_rsub_3.run(buf4, primals_6, buf8, buf9, 16, grid=grid(16), stream=stream0)
        buf10 = reinterpret_tensor(buf0, (4, 1), (1, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [add, r], Original ATen: [aten.add, aten.sigmoid, aten.sigmoid_backward]
        triton_poi_fused_add_sigmoid_sigmoid_backward_4.run(buf10, primals_2, buf1, primals_5, 4, grid=grid(4), stream=stream0)
        del buf1
        del primals_2
        del primals_5
    return (buf9, primals_1, primals_4, primals_6, primals_7, primals_9, primals_11, primals_13, reinterpret_tensor(primals_3, (1, 4, 4), (16, 4, 1), 0), buf4, reinterpret_tensor(buf6, (1, 4, 4), (16, 4, 1), 0), buf8, 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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((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, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((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, primals_10, primals_11, primals_12, primals_13, primals_14])
    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.jit
import torch.nn


class ConvGRUCellNd(nn.Module):

    def __init__(self, in_size, out_size, kernel_size, N=1, **kwargs):
        super(ConvGRUCellNd, self).__init__()
        conv = eval(f'nn.Conv{N}d')
        self.conv_ir = conv(in_size, out_size, kernel_size, **kwargs)
        self.conv_hr = conv(in_size, out_size, kernel_size, **kwargs)
        self.conv_iz = conv(in_size, out_size, kernel_size, **kwargs)
        self.conv_hz = conv(in_size, out_size, kernel_size, **kwargs)
        self.conv_in = conv(in_size, out_size, kernel_size, **kwargs)
        self.conv_hn = conv(in_size, out_size, kernel_size, **kwargs)

    def forward(self, inputs, state):
        r = torch.sigmoid(self.conv_ir(inputs) + self.conv_hr(state))
        z = torch.sigmoid(self.conv_iz(inputs) + self.conv_hz(state))
        n = torch.tanh(self.conv_in(inputs) + self.conv_hn(state * r))
        return z * state + (1 - z) * n


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


def get_init_inputs():
    return [[], {'in_size': 4, 'out_size': 4, 'kernel_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
import torch.jit
import torch.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_sigmoid_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask)
    tmp3 = tl.load(in_ptr1 + x0, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = tl.sigmoid(tmp6)
    tl.store(in_out_ptr0 + x0, tmp7, xmask)


@triton.jit
def triton_poi_fused_add_mul_sigmoid_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp6 = tmp4 + tmp5
    tmp7 = tmp3 + tmp6
    tmp8 = tl.sigmoid(tmp7)
    tmp9 = tmp0 * tmp8
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_add_tanh_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask)
    tmp3 = tl.load(in_ptr1 + x0, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = libdevice.tanh(tmp6)
    tl.store(in_out_ptr0 + x0, tmp7, xmask)


@triton.jit
def triton_poi_fused_add_mul_rsub_3(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
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp5 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp0
    tmp6 = tmp4 * tmp5
    tmp7 = tmp2 + tmp6
    tl.store(out_ptr0 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_add_sigmoid_sigmoid_backward_4(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask)
    tmp3 = tl.load(in_ptr1 + x0, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = tl.sigmoid(tmp6)
    tmp8 = 1.0
    tmp9 = tmp8 - tmp7
    tmp10 = tmp7 * tmp9
    tl.store(in_out_ptr0 + x0, tmp10, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_12, (4,), (1,))
    assert_size_stride(primals_13, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_14, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_3, (1,
            4, 4), (16, 4, 1), 0), primals_1, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf0, (1, 4, 1), (4, 1, 1))
        buf1 = extern_kernels.convolution(reinterpret_tensor(primals_6, (1,
            4, 4), (16, 4, 1), 0), primals_4, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf1, (1, 4, 1), (4, 1, 1))
        buf2 = extern_kernels.convolution(reinterpret_tensor(primals_3, (1,
            4, 4), (16, 4, 1), 0), primals_7, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf2, (1, 4, 1), (4, 1, 1))
        buf3 = extern_kernels.convolution(reinterpret_tensor(primals_6, (1,
            4, 4), (16, 4, 1), 0), primals_9, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf3, (1, 4, 1), (4, 1, 1))
        buf4 = reinterpret_tensor(buf2, (4, 1), (1, 1), 0)
        del buf2
        get_raw_stream(0)
        triton_poi_fused_add_sigmoid_0[grid(4)](buf4, primals_8, buf3,
            primals_10, 4, XBLOCK=4, num_warps=1, num_stages=1)
        del buf3
        del primals_10
        del primals_8
        buf5 = extern_kernels.convolution(reinterpret_tensor(primals_3, (1,
            4, 4), (16, 4, 1), 0), primals_11, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf5, (1, 4, 1), (4, 1, 1))
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_mul_sigmoid_1[grid(16)](primals_6, buf0,
            primals_2, buf1, primals_5, buf6, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        buf7 = extern_kernels.convolution(reinterpret_tensor(buf6, (1, 4, 4
            ), (0, 4, 1), 0), primals_13, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf7, (1, 4, 1), (4, 1, 1))
        buf8 = reinterpret_tensor(buf5, (4, 1), (1, 1), 0)
        del buf5
        triton_poi_fused_add_tanh_2[grid(4)](buf8, primals_12, buf7,
            primals_14, 4, XBLOCK=4, num_warps=1, num_stages=1)
        del buf7
        del primals_12
        del primals_14
        buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_mul_rsub_3[grid(16)](buf4, primals_6, buf8,
            buf9, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf10 = reinterpret_tensor(buf0, (4, 1), (1, 1), 0)
        del buf0
        triton_poi_fused_add_sigmoid_sigmoid_backward_4[grid(4)](buf10,
            primals_2, buf1, primals_5, 4, XBLOCK=4, num_warps=1, num_stages=1)
        del buf1
        del primals_2
        del primals_5
    return (buf9, primals_1, primals_4, primals_6, primals_7, primals_9,
        primals_11, primals_13, reinterpret_tensor(primals_3, (1, 4, 4), (
        16, 4, 1), 0), buf4, reinterpret_tensor(buf6, (1, 4, 4), (16, 4, 1),
        0), buf8, buf10)


class ConvGRUCellNdNew(nn.Module):

    def __init__(self, in_size, out_size, kernel_size, N=1, **kwargs):
        super(ConvGRUCellNdNew, self).__init__()
        conv = eval(f'nn.Conv{N}d')
        self.conv_ir = conv(in_size, out_size, kernel_size, **kwargs)
        self.conv_hr = conv(in_size, out_size, kernel_size, **kwargs)
        self.conv_iz = conv(in_size, out_size, kernel_size, **kwargs)
        self.conv_hz = conv(in_size, out_size, kernel_size, **kwargs)
        self.conv_in = conv(in_size, out_size, kernel_size, **kwargs)
        self.conv_hn = conv(in_size, out_size, kernel_size, **kwargs)

    def forward(self, input_0, input_1):
        primals_1 = self.conv_ir.weight
        primals_2 = self.conv_ir.bias
        primals_4 = self.conv_hr.weight
        primals_5 = self.conv_hr.bias
        primals_7 = self.conv_iz.weight
        primals_8 = self.conv_iz.bias
        primals_9 = self.conv_hz.weight
        primals_10 = self.conv_hz.bias
        primals_11 = self.conv_in.weight
        primals_12 = self.conv_in.bias
        primals_13 = self.conv_hn.weight
        primals_14 = self.conv_hn.bias
        primals_3 = input_0
        primals_6 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14])
        return output[0]
