# 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_1/inductor_cache/qc/cqcqu66rzoovcxsmkmjxebplxpgvohadqsar5nypc2zlenewwoww.py
# Topologically Sorted Source Nodes: [phi_one], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   phi_one => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_1,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_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=[64, 4], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_clone_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 16
    y1 = (yindex // 16)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (16*x2) + (64*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/pt/cptaddavhv2oytblnrd523iahobbaw5xwxblz3exzrxoz7xe6bfc.py
# Topologically Sorted Source Nodes: [adj_matrix], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   adj_matrix => clone_4
# Graph fragment:
#   %clone_4 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_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, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_clone_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (64*y1)), xmask & ymask)
    tl.store(out_ptr0 + (x2 + (16*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/b5/cb5rdxg6gst2cmlhrkvtyln6zruyybu2lghmebzu6s3vdejglkdh.py
# Topologically Sorted Source Nodes: [phi_two], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   phi_two => clone_3
# Graph fragment:
#   %clone_3 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_7,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_2 = async_compile.triton('triton_poi_fused_clone_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=[64, 4], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/a6/ca6icrspsovyxtesf6b7hsinzmrvie7fz2ogayvujz625qt4lz47.py
# Topologically Sorted Source Nodes: [adj_matrix_1], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   adj_matrix_1 => amax, exp, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_8, [1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_8, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
triton_poi_fused__softmax_3 = async_compile.triton('triton_poi_fused__softmax_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + (x3), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ko/cko7cpzrqxhed2sujj6idv2pzsd4wggmdjmrkai2eet5kvvjceod.py
# Topologically Sorted Source Nodes: [adj_matrix_1], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   adj_matrix_1 => div, sum_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_4 = async_compile.triton('triton_poi_fused__softmax_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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__softmax_4(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x3), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ye/cyernaervxbkrusch6pmdjv4kcbxtob4lbe2c6pul3sqcu2upz7y.py
# Topologically Sorted Source Nodes: [add, x], Original ATen: [aten.add, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   add => add
#   x => relu
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_11, %primals_5), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add,), kwargs = {})
#   %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_add_relu_threshold_backward_5 = async_compile.triton('triton_poi_fused_add_relu_threshold_backward_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_relu_threshold_backward_5', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_relu_threshold_backward_5(in_out_ptr0, in_ptr0, 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 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/qc/cqcca2j4bsrx2np425ywyhjjkkfmcxu3p3v5ndtzayougmrlllrq.py
# Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   x_4 => add_2
# Graph fragment:
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_35, %primals_13), kwargs = {})
triton_poi_fused_add_6 = async_compile.triton('triton_poi_fused_add_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_6', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_6(in_out_ptr0, in_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 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, 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 = 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, 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, 1))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    assert_size_stride(primals_10, (4, 4), (4, 1))
    assert_size_stride(primals_11, (4, 4), (4, 1))
    assert_size_stride(primals_12, (4, 4), (4, 1))
    assert_size_stride(primals_13, (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: [support], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [phi_one], Original ATen: [aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_0.run(primals_2, buf1, 64, 4, grid=grid(64, 4), stream=stream0)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [phi_one], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf2)
        del primals_3
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [phi_two], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf3)
        del primals_4
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [adj_matrix], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(buf2, buf4, 16, 16, grid=grid(16, 16), stream=stream0)
        buf5 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf2  # reuse
        # Topologically Sorted Source Nodes: [phi_two], Original ATen: [aten.clone]
        triton_poi_fused_clone_2.run(buf3, buf5, 64, 4, grid=grid(64, 4), stream=stream0)
        buf6 = reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [adj_matrix], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf4, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [adj_matrix_1], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_3.run(buf6, buf7, 256, grid=grid(256), stream=stream0)
        buf8 = reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf6  # reuse
        # Topologically Sorted Source Nodes: [adj_matrix_1], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_4.run(buf7, buf8, 256, grid=grid(256), stream=stream0)
        buf9 = reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1), 0); del buf7  # reuse
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf8, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf0, (16, 4, 4), (16, 4, 1), 0), out=buf9)
        buf10 = reinterpret_tensor(buf9, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf9  # reuse
        buf40 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [add, x], Original ATen: [aten.add, aten.relu, aten.threshold_backward]
        triton_poi_fused_add_relu_threshold_backward_5.run(buf10, primals_5, buf40, 256, grid=grid(256), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [add, x, x_1], Original ATen: [aten.add, aten.relu, aten.native_dropout]
        buf11 = torch.ops.aten.native_dropout.default(buf10, 0.5, True)
        buf12 = buf11[0]
        buf13 = buf11[1]
        del buf11
        buf14 = reinterpret_tensor(buf10, (64, 4), (4, 1), 0); del buf10  # reuse
        # Topologically Sorted Source Nodes: [support_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf12, (64, 4), (4, 1), 0), primals_6, out=buf14)
        buf15 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [phi_one_1], Original ATen: [aten.clone]
        triton_poi_fused_clone_0.run(buf12, buf15, 64, 4, grid=grid(64, 4), stream=stream0)
        buf16 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [phi_one_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf15, (64, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf16)
        buf17 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [phi_two_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf15, (64, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf17)
        buf18 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [adj_matrix_2], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(buf16, buf18, 16, 16, grid=grid(16, 16), stream=stream0)
        buf19 = reinterpret_tensor(buf16, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf16  # reuse
        # Topologically Sorted Source Nodes: [phi_two_1], Original ATen: [aten.clone]
        triton_poi_fused_clone_2.run(buf17, buf19, 64, 4, grid=grid(64, 4), stream=stream0)
        buf20 = reinterpret_tensor(buf17, (16, 4, 4), (16, 4, 1), 0); del buf17  # reuse
        # Topologically Sorted Source Nodes: [adj_matrix_2], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf18, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf19, (16, 4, 4), (16, 4, 1), 0), out=buf20)
        buf21 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [adj_matrix_3], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_3.run(buf20, buf21, 256, grid=grid(256), stream=stream0)
        buf22 = reinterpret_tensor(buf20, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf20  # reuse
        # Topologically Sorted Source Nodes: [adj_matrix_3], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_4.run(buf21, buf22, 256, grid=grid(256), stream=stream0)
        buf23 = reinterpret_tensor(buf21, (16, 4, 4), (16, 4, 1), 0); del buf21  # reuse
        # Topologically Sorted Source Nodes: [output_1], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf22, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf14, (16, 4, 4), (16, 4, 1), 0), out=buf23)
        buf24 = reinterpret_tensor(buf23, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf23  # reuse
        buf39 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [add_1, x_2], Original ATen: [aten.add, aten.relu, aten.threshold_backward]
        triton_poi_fused_add_relu_threshold_backward_5.run(buf24, primals_9, buf39, 256, grid=grid(256), stream=stream0)
        del primals_9
        # Topologically Sorted Source Nodes: [add_1, x_2, x_3], Original ATen: [aten.add, aten.relu, aten.native_dropout]
        buf25 = torch.ops.aten.native_dropout.default(buf24, 0.5, True)
        buf26 = buf25[0]
        buf27 = buf25[1]
        del buf25
        buf28 = reinterpret_tensor(buf24, (64, 4), (4, 1), 0); del buf24  # reuse
        # Topologically Sorted Source Nodes: [support_2], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf26, (64, 4), (4, 1), 0), primals_10, out=buf28)
        buf29 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [phi_one_2], Original ATen: [aten.clone]
        triton_poi_fused_clone_0.run(buf26, buf29, 64, 4, grid=grid(64, 4), stream=stream0)
        buf30 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [phi_one_2], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf29, (64, 4), (4, 1), 0), reinterpret_tensor(primals_11, (4, 4), (1, 4), 0), out=buf30)
        buf31 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [phi_two_2], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf29, (64, 4), (4, 1), 0), reinterpret_tensor(primals_12, (4, 4), (1, 4), 0), out=buf31)
        buf32 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [adj_matrix_4], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(buf30, buf32, 16, 16, grid=grid(16, 16), stream=stream0)
        buf33 = reinterpret_tensor(buf30, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf30  # reuse
        # Topologically Sorted Source Nodes: [phi_two_2], Original ATen: [aten.clone]
        triton_poi_fused_clone_2.run(buf31, buf33, 64, 4, grid=grid(64, 4), stream=stream0)
        buf34 = reinterpret_tensor(buf31, (16, 4, 4), (16, 4, 1), 0); del buf31  # reuse
        # Topologically Sorted Source Nodes: [adj_matrix_4], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf32, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf33, (16, 4, 4), (16, 4, 1), 0), out=buf34)
        buf35 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [adj_matrix_5], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_3.run(buf34, buf35, 256, grid=grid(256), stream=stream0)
        buf36 = reinterpret_tensor(buf34, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf34  # reuse
        # Topologically Sorted Source Nodes: [adj_matrix_5], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_4.run(buf35, buf36, 256, grid=grid(256), stream=stream0)
        buf37 = reinterpret_tensor(buf35, (16, 4, 4), (16, 4, 1), 0); del buf35  # reuse
        # Topologically Sorted Source Nodes: [output_2], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf36, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf28, (16, 4, 4), (16, 4, 1), 0), out=buf37)
        buf38 = reinterpret_tensor(buf37, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf37  # reuse
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.add]
        triton_poi_fused_add_6.run(buf38, primals_13, 256, grid=grid(256), stream=stream0)
        del primals_13
    return (buf38, reinterpret_tensor(buf1, (64, 4), (4, 1), 0), buf8, buf13, reinterpret_tensor(buf15, (64, 4), (4, 1), 0), buf22, buf27, reinterpret_tensor(buf29, (64, 4), (4, 1), 0), buf36, reinterpret_tensor(buf28, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf32, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf33, (16, 4, 4), (16, 1, 4), 0), primals_12, primals_11, reinterpret_tensor(buf26, (4, 64), (1, 4), 0), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), buf39, reinterpret_tensor(buf14, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf18, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf19, (16, 4, 4), (16, 1, 4), 0), primals_8, primals_7, reinterpret_tensor(buf12, (4, 64), (1, 4), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), buf40, reinterpret_tensor(buf0, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf4, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf5, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(primals_2, (4, 64), (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, 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, 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, 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, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = 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])
    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)

from torch.nn import Module
import math
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module


class GraphConvolution(Module):
    """
    Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
    """

    def __init__(self, in_features, out_features, bias=True):
        super(GraphConvolution, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = Parameter(torch.FloatTensor(in_features, out_features))
        if bias:
            self.bias = Parameter(torch.FloatTensor(out_features))
        else:
            self.register_parameter('bias', None)
        self.w_one = Parameter(torch.FloatTensor(in_features, in_features))
        self.w_two = Parameter(torch.FloatTensor(in_features, in_features))
        self.reset_parameters()

    def reset_parameters(self):
        stdv = 1.0 / math.sqrt(self.weight.size(1))
        self.weight.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-stdv, stdv)
        w_one_stdv = 1.0 / math.sqrt(self.w_one.size(0))
        self.w_one.data.uniform_(-w_one_stdv, w_one_stdv)
        self.w_two.data.uniform_(-w_one_stdv, w_one_stdv)

    def forward(self, input):
        support = torch.matmul(input, self.weight)
        phi_one = torch.matmul(self.w_one, torch.transpose(input, 1, 2))
        phi_two = torch.matmul(self.w_two, torch.transpose(input, 1, 2))
        adj_matrix = torch.matmul(torch.transpose(phi_one, 1, 2), phi_two)
        adj_matrix = F.softmax(adj_matrix, dim=1)
        output = torch.matmul(adj_matrix, support)
        if self.bias is not None:
            return output + self.bias
        else:
            return output

    def __repr__(self):
        return self.__class__.__name__ + ' (' + str(self.in_features
            ) + ' -> ' + str(self.out_features) + ')'


class GCN(nn.Module):

    def __init__(self, nfeat, nhid, nclass, dropout):
        super(GCN, self).__init__()
        self.gc1 = GraphConvolution(nfeat, nhid)
        self.gc2 = GraphConvolution(nhid, nhid)
        self.gc3 = GraphConvolution(nhid, nclass)
        self.dropout = dropout

    def forward(self, x):
        x = F.relu(self.gc1(x))
        x = F.dropout(x, self.dropout)
        x = F.relu(self.gc2(x))
        x = F.dropout(x, self.dropout)
        x = self.gc3(x)
        return x


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


def get_init_inputs():
    return [[], {'nfeat': 4, 'nhid': 4, 'nclass': 4, 'dropout': 0.5}]

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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn import Module
import math
from torch import nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
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_clone_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 16
    y1 = yindex // 16
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 64 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 64 * y1), xmask & ymask)
    tl.store(out_ptr0 + (x2 + 16 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_relu_threshold_backward_5(in_out_ptr0, in_ptr0,
    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 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_6(in_out_ptr0, in_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 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, 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) = 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, 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, 1))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4, 4), (4, 1))
    assert_size_stride(primals_11, (4, 4), (4, 1))
    assert_size_stride(primals_12, (4, 4), (4, 1))
    assert_size_stride(primals_13, (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),
            primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64, 4)](primals_2, buf1, 64, 4,
            XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf2)
        del primals_3
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf3)
        del primals_4
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_1[grid(16, 16)](buf2, buf4, 16, 16, XBLOCK=
            16, YBLOCK=16, num_warps=4, num_stages=1)
        buf5 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf2
        triton_poi_fused_clone_2[grid(64, 4)](buf3, buf5, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        buf6 = reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1), 0)
        del buf3
        extern_kernels.bmm(reinterpret_tensor(buf4, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_3[grid(256)](buf6, buf7, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf8 = reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf6
        triton_poi_fused__softmax_4[grid(256)](buf7, buf8, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf9 = reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1), 0)
        del buf7
        extern_kernels.bmm(reinterpret_tensor(buf8, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf0, (16, 4, 4), (16, 4, 1), 0), out=buf9)
        buf10 = reinterpret_tensor(buf9, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf9
        buf40 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_add_relu_threshold_backward_5[grid(256)](buf10,
            primals_5, buf40, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf11 = torch.ops.aten.native_dropout.default(buf10, 0.5, True)
        buf12 = buf11[0]
        buf13 = buf11[1]
        del buf11
        buf14 = reinterpret_tensor(buf10, (64, 4), (4, 1), 0)
        del buf10
        extern_kernels.mm(reinterpret_tensor(buf12, (64, 4), (4, 1), 0),
            primals_6, out=buf14)
        buf15 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_0[grid(64, 4)](buf12, buf15, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        buf16 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf15, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf16)
        buf17 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf15, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf17)
        buf18 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_1[grid(16, 16)](buf16, buf18, 16, 16, XBLOCK
            =16, YBLOCK=16, num_warps=4, num_stages=1)
        buf19 = reinterpret_tensor(buf16, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf16
        triton_poi_fused_clone_2[grid(64, 4)](buf17, buf19, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        buf20 = reinterpret_tensor(buf17, (16, 4, 4), (16, 4, 1), 0)
        del buf17
        extern_kernels.bmm(reinterpret_tensor(buf18, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf19, (16, 4, 4), (16, 4, 1), 0), out=buf20
            )
        buf21 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_3[grid(256)](buf20, buf21, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf22 = reinterpret_tensor(buf20, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf20
        triton_poi_fused__softmax_4[grid(256)](buf21, buf22, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        buf23 = reinterpret_tensor(buf21, (16, 4, 4), (16, 4, 1), 0)
        del buf21
        extern_kernels.bmm(reinterpret_tensor(buf22, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf14, (16, 4, 4), (16, 4, 1), 0), out=buf23
            )
        buf24 = reinterpret_tensor(buf23, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf23
        buf39 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_add_relu_threshold_backward_5[grid(256)](buf24,
            primals_9, buf39, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_9
        buf25 = torch.ops.aten.native_dropout.default(buf24, 0.5, True)
        buf26 = buf25[0]
        buf27 = buf25[1]
        del buf25
        buf28 = reinterpret_tensor(buf24, (64, 4), (4, 1), 0)
        del buf24
        extern_kernels.mm(reinterpret_tensor(buf26, (64, 4), (4, 1), 0),
            primals_10, out=buf28)
        buf29 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_0[grid(64, 4)](buf26, buf29, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        buf30 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf29, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_11, (4, 4), (1, 4), 0), out=buf30)
        buf31 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf29, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_12, (4, 4), (1, 4), 0), out=buf31)
        buf32 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_1[grid(16, 16)](buf30, buf32, 16, 16, XBLOCK
            =16, YBLOCK=16, num_warps=4, num_stages=1)
        buf33 = reinterpret_tensor(buf30, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf30
        triton_poi_fused_clone_2[grid(64, 4)](buf31, buf33, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        buf34 = reinterpret_tensor(buf31, (16, 4, 4), (16, 4, 1), 0)
        del buf31
        extern_kernels.bmm(reinterpret_tensor(buf32, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf33, (16, 4, 4), (16, 4, 1), 0), out=buf34
            )
        buf35 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_3[grid(256)](buf34, buf35, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf36 = reinterpret_tensor(buf34, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf34
        triton_poi_fused__softmax_4[grid(256)](buf35, buf36, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        buf37 = reinterpret_tensor(buf35, (16, 4, 4), (16, 4, 1), 0)
        del buf35
        extern_kernels.bmm(reinterpret_tensor(buf36, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf28, (16, 4, 4), (16, 4, 1), 0), out=buf37
            )
        buf38 = reinterpret_tensor(buf37, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf37
        triton_poi_fused_add_6[grid(256)](buf38, primals_13, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_13
    return buf38, reinterpret_tensor(buf1, (64, 4), (4, 1), 0
        ), buf8, buf13, reinterpret_tensor(buf15, (64, 4), (4, 1), 0
        ), buf22, buf27, reinterpret_tensor(buf29, (64, 4), (4, 1), 0
        ), buf36, reinterpret_tensor(buf28, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf32, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf33, (16, 4, 4), (16, 1, 4), 0
        ), primals_12, primals_11, reinterpret_tensor(buf26, (4, 64), (1, 4), 0
        ), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0
        ), buf39, reinterpret_tensor(buf14, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf18, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf19, (16, 4, 4), (16, 1, 4), 0
        ), primals_8, primals_7, reinterpret_tensor(buf12, (4, 64), (1, 4), 0
        ), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0
        ), buf40, reinterpret_tensor(buf0, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf4, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf5, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(primals_2, (4, 64), (1, 4), 0)


class GraphConvolution(Module):
    """
    Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
    """

    def __init__(self, in_features, out_features, bias=True):
        super(GraphConvolution, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = Parameter(torch.FloatTensor(in_features, out_features))
        if bias:
            self.bias = Parameter(torch.FloatTensor(out_features))
        else:
            self.register_parameter('bias', None)
        self.w_one = Parameter(torch.FloatTensor(in_features, in_features))
        self.w_two = Parameter(torch.FloatTensor(in_features, in_features))
        self.reset_parameters()

    def reset_parameters(self):
        stdv = 1.0 / math.sqrt(self.weight.size(1))
        self.weight.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-stdv, stdv)
        w_one_stdv = 1.0 / math.sqrt(self.w_one.size(0))
        self.w_one.data.uniform_(-w_one_stdv, w_one_stdv)
        self.w_two.data.uniform_(-w_one_stdv, w_one_stdv)

    def forward(self, input):
        support = torch.matmul(input, self.weight)
        phi_one = torch.matmul(self.w_one, torch.transpose(input, 1, 2))
        phi_two = torch.matmul(self.w_two, torch.transpose(input, 1, 2))
        adj_matrix = torch.matmul(torch.transpose(phi_one, 1, 2), phi_two)
        adj_matrix = F.softmax(adj_matrix, dim=1)
        output = torch.matmul(adj_matrix, support)
        if self.bias is not None:
            return output + self.bias
        else:
            return output

    def __repr__(self):
        return self.__class__.__name__ + ' (' + str(self.in_features
            ) + ' -> ' + str(self.out_features) + ')'


class GCNNew(nn.Module):

    def __init__(self, nfeat, nhid, nclass, dropout):
        super(GCNNew, self).__init__()
        self.gc1 = GraphConvolution(nfeat, nhid)
        self.gc2 = GraphConvolution(nhid, nhid)
        self.gc3 = GraphConvolution(nhid, nclass)
        self.dropout = dropout

    def forward(self, input_0):
        primals_1 = self.gc1.weight
        primals_5 = self.gc1.bias
        primals_3 = self.gc1.w_one
        primals_4 = self.gc1.w_two
        primals_6 = self.gc2.weight
        primals_9 = self.gc2.bias
        primals_7 = self.gc2.w_one
        primals_8 = self.gc2.w_two
        primals_10 = self.gc3.weight
        primals_13 = self.gc3.bias
        primals_11 = self.gc3.w_one
        primals_12 = self.gc3.w_two
        primals_2 = input_0
        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])
        return output[0]
