# 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_8/inductor_cache/is/cisnph74fihxfyv555qtvs3tsuwatcz7maqbapauerikqfvcxsec.py
# Topologically Sorted Source Nodes: [multi_order_laplacian, eye], Original ATen: [aten.zeros, aten.eye]
# Source node to ATen node mapping:
#   eye => eq, full_default_1, full_default_2, iota, where
#   multi_order_laplacian => full_default
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([5, 4, 4], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %iota : [num_users=2] = call_function[target=torch.ops.prims.iota.default](args = (4,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%unsqueeze, %iota), kwargs = {})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([1], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %full_default_2 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%eq, %full_default_1, %full_default_2), kwargs = {})
#   %select_scatter_default : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%full_default, %where, 0, 0), kwargs = {})
#   %select_scatter_default_1 : [num_users=3] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default, %primals_1, 0, 1), kwargs = {})
triton_poi_fused_eye_zeros_0 = async_compile.triton('triton_poi_fused_eye_zeros_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=[128], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_eye_zeros_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_eye_zeros_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 16)
    x3 = xindex % 16
    x1 = (xindex // 4) % 4
    x0 = xindex % 4
    x4 = xindex
    tmp3 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last')
    tmp0 = x2
    tmp1 = tl.full([1], 1, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = tmp0 == tmp4
    tmp6 = x1
    tmp7 = x0
    tmp8 = tmp6 == tmp7
    tmp9 = 1.0
    tmp10 = 0.0
    tmp11 = tl.where(tmp8, tmp9, tmp10)
    tmp12 = tl.where(tmp5, tmp11, tmp10)
    tmp13 = tl.where(tmp2, tmp3, tmp12)
    tl.store(out_ptr0 + (x4), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/4q/c4qjswckfisnwcnwybhj3tnhck5cjqfhj5xmaed7vlckyegpflum.py
# Topologically Sorted Source Nodes: [mul, sub], Original ATen: [aten.mul, aten.sub]
# Source node to ATen node mapping:
#   mul => mul
#   sub => sub
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm, 2), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %select_8), kwargs = {})
#   %select_scatter_default_2 : [num_users=3] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_1, %sub, 0, 2), kwargs = {})
triton_poi_fused_mul_sub_1 = async_compile.triton('triton_poi_fused_mul_sub_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=[128], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '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_mul_sub_1', '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_mul_sub_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16)
    x0 = xindex % 16
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (x2), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 2, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = 2.0
    tmp5 = tmp3 * tmp4
    tmp7 = tmp5 - tmp6
    tmp9 = tl.where(tmp2, tmp7, tmp8)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/g5/cg5rgiibnfgjsex65ptr5zv3giz6ox4bpc4u2bnn3y3fq5wph7do.py
# Topologically Sorted Source Nodes: [mul_1, sub_1], Original ATen: [aten.mul, aten.sub]
# Source node to ATen node mapping:
#   mul_1 => mul_1
#   sub_1 => sub_1
# Graph fragment:
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_1, 2), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_1, %select_15), kwargs = {})
#   %select_scatter_default_3 : [num_users=3] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_2, %sub_1, 0, 3), kwargs = {})
triton_poi_fused_mul_sub_2 = async_compile.triton('triton_poi_fused_mul_sub_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=[128], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '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_mul_sub_2', '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_mul_sub_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16)
    x0 = xindex % 16
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (x2), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 3, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = 2.0
    tmp5 = tmp3 * tmp4
    tmp7 = tmp5 - tmp6
    tmp9 = tl.where(tmp2, tmp7, tmp8)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/ec/cecdj4hutg4bz2g5uj2vhf77axo326kwp3r2wkw2akxspq6sgs6g.py
# Topologically Sorted Source Nodes: [result], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   result => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_1,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_3 = async_compile.triton('triton_poi_fused_clone_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=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '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_clone_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_clone_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 64)
    x0 = xindex % 16
    x4 = xindex
    tmp3 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (32 + x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp0 = x2
    tmp1 = tl.full([1], 4, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = 2.0
    tmp5 = tmp3 * tmp4
    tmp7 = tmp5 - tmp6
    tmp9 = tl.where(tmp2, tmp7, tmp8)
    tl.store(out_ptr0 + (x4), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/jz/cjzxb5n54jlghigtif3aw4uu2opkucsyuypsqznsrplvbqkpcars.py
# Topologically Sorted Source Nodes: [result_1], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   result_1 => clone_1
# Graph fragment:
#   %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_4,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_4 = async_compile.triton('triton_poi_fused_clone_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=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_clone_4(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x2 = (xindex // 64)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/6v/c6vssrd7p7frgjmsgje6eqxm2tsopjqo37yom65ch7kgmuwuqmaj.py
# Topologically Sorted Source Nodes: [sum_1, result_2], Original ATen: [aten.sum, aten.add]
# Source node to ATen node mapping:
#   result_2 => add
#   sum_1 => sum_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%view_5, [0]), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, %primals_4), kwargs = {})
triton_poi_fused_add_sum_5 = async_compile.triton('triton_poi_fused_add_sum_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '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_sum_5', 'mutated_arg_names': [], '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_sum_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (64 + x2), xmask)
    tmp3 = tl.load(in_ptr0 + (128 + x2), xmask)
    tmp5 = tl.load(in_ptr0 + (192 + x2), xmask)
    tmp7 = tl.load(in_ptr0 + (256 + x2), xmask)
    tmp9 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 + tmp9
    tl.store(out_ptr0 + (x2), tmp10, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (5, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (5, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_4, (1, 1, 4), (4, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((5, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [multi_order_laplacian, eye], Original ATen: [aten.zeros, aten.eye]
        stream0 = get_raw_stream(0)
        triton_poi_fused_eye_zeros_0.run(primals_1, buf0, 80, grid=grid(80), stream=stream0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.mm]
        extern_kernels.mm(primals_1, reinterpret_tensor(buf0, (4, 4), (4, 1), 16), out=buf1)
        buf2 = empty_strided_cuda((5, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, sub], Original ATen: [aten.mul, aten.sub]
        triton_poi_fused_mul_sub_1.run(buf1, buf0, buf2, 80, grid=grid(80), stream=stream0)
        buf3 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [matmul_1], Original ATen: [aten.mm]
        extern_kernels.mm(primals_1, reinterpret_tensor(buf2, (4, 4), (4, 1), 32), out=buf3)
        buf4 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [mul_1, sub_1], Original ATen: [aten.mul, aten.sub]
        triton_poi_fused_mul_sub_2.run(buf3, buf2, buf4, 80, grid=grid(80), stream=stream0)
        del buf2
        buf5 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [matmul_2], Original ATen: [aten.mm]
        extern_kernels.mm(primals_1, reinterpret_tensor(buf4, (4, 4), (4, 1), 48), out=buf5)
        del primals_1
        buf6 = empty_strided_cuda((5, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [result], Original ATen: [aten.clone]
        triton_poi_fused_clone_3.run(buf5, buf4, buf6, 320, grid=grid(320), stream=stream0)
        del buf4
        del buf5
        buf7 = empty_strided_cuda((20, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [result], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf6, (20, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_2, (20, 4, 4), (16, 4, 1), 0), out=buf7)
        del primals_2
        buf8 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [result_1], Original ATen: [aten.clone]
        triton_poi_fused_clone_4.run(primals_3, buf8, 320, grid=grid(320), stream=stream0)
        del primals_3
        buf9 = empty_strided_cuda((20, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [result_1], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf7, reinterpret_tensor(buf8, (20, 4, 4), (16, 4, 1), 0), out=buf9)
        del buf8
        buf10 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sum_1, result_2], Original ATen: [aten.sum, aten.add]
        triton_poi_fused_add_sum_5.run(buf9, primals_4, buf10, 64, grid=grid(64), stream=stream0)
        del buf9
        del primals_4
    return (buf10, reinterpret_tensor(buf7, (20, 4, 4), (16, 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((5, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((5, 1, 4, 4), (16, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, 1, 4), (4, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn as nn


class ChebConv(nn.Module):
    """
    The ChebNet convolution operation.
    Laplacian is motified for direct-graph

    :param in_c: int, number of input channels.
    :param out_c: int, number of output channels.
    :param K: int, the order of Chebyshev Polynomial.
    """

    def __init__(self, in_c, out_c, K, bias=True, normalize=True):
        super(ChebConv, self).__init__()
        self.normalize = normalize
        self.weight = nn.Parameter(torch.FloatTensor(K + 1, 1, in_c, out_c))
        nn.init.xavier_normal_(self.weight)
        if bias:
            self.bias = nn.Parameter(torch.Tensor(1, 1, out_c))
            nn.init.zeros_(self.bias)
        else:
            self.register_parameter('bias', None)
        self.K = K + 1

    def forward(self, inputs, graphs):
        """
        :param inputs: the input data, [B, N, C]
        :param graph: the graph structure, [N, N]
        :return: convolution result, [B, N, D]
        """
        mul_L = self.cheb_polynomial(graphs).unsqueeze(1)
        result = torch.matmul(mul_L, inputs)
        result = torch.matmul(result, self.weight)
        result = torch.sum(result, dim=0) + self.bias
        return result

    def cheb_polynomial(self, laplacian):
        """
        Compute the Chebyshev Polynomial, according to the graph laplacian.

        :param laplacian: the graph laplacian, [B,N, N].
        :return: the multi order Chebyshev laplacian, [K,B, N, N].
        """
        N = laplacian.size(0)
        multi_order_laplacian = torch.zeros([self.K, N, N], device=
            laplacian.device, dtype=torch.float)
        multi_order_laplacian[0] = torch.eye(N, device=laplacian.device,
            dtype=torch.float)
        if self.K == 1:
            return multi_order_laplacian
        else:
            multi_order_laplacian[1] = laplacian
            if self.K == 2:
                return multi_order_laplacian
            else:
                for k in range(2, self.K):
                    multi_order_laplacian[k] = 2 * torch.matmul(laplacian,
                        multi_order_laplacian[k - 1]) - multi_order_laplacian[
                        k - 2]
        return multi_order_laplacian


def get_inputs():
    return [torch.rand([5, 4, 4, 4]), torch.rand([4, 4])]


def get_init_inputs():
    return [[], {'in_c': 4, 'out_c': 4, 'K': 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_eye_zeros_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 16
    x3 = xindex % 16
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x4 = xindex
    tmp3 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp0 = x2
    tmp1 = tl.full([1], 1, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = tmp0 == tmp4
    tmp6 = x1
    tmp7 = x0
    tmp8 = tmp6 == tmp7
    tmp9 = 1.0
    tmp10 = 0.0
    tmp11 = tl.where(tmp8, tmp9, tmp10)
    tmp12 = tl.where(tmp5, tmp11, tmp10)
    tmp13 = tl.where(tmp2, tmp3, tmp12)
    tl.store(out_ptr0 + x4, tmp13, xmask)


@triton.jit
def triton_poi_fused_mul_sub_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16
    x0 = xindex % 16
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + x2, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 2, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = 2.0
    tmp5 = tmp3 * tmp4
    tmp7 = tmp5 - tmp6
    tmp9 = tl.where(tmp2, tmp7, tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_mul_sub_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16
    x0 = xindex % 16
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + x2, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 3, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = 2.0
    tmp5 = tmp3 * tmp4
    tmp7 = tmp5 - tmp6
    tmp9 = tl.where(tmp2, tmp7, tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_clone_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 64
    x0 = xindex % 16
    x4 = xindex
    tmp3 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (32 + x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp0 = x2
    tmp1 = tl.full([1], 4, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = 2.0
    tmp5 = tmp3 * tmp4
    tmp7 = tmp5 - tmp6
    tmp9 = tl.where(tmp2, tmp7, tmp8)
    tl.store(out_ptr0 + x4, tmp9, xmask)


@triton.jit
def triton_poi_fused_clone_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x2 = xindex // 64
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_sum_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + (64 + x2), xmask)
    tmp3 = tl.load(in_ptr0 + (128 + x2), xmask)
    tmp5 = tl.load(in_ptr0 + (192 + x2), xmask)
    tmp7 = tl.load(in_ptr0 + (256 + x2), xmask)
    tmp9 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 + tmp9
    tl.store(out_ptr0 + x2, tmp10, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (5, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (5, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_4, (1, 1, 4), (4, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((5, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_eye_zeros_0[grid(80)](primals_1, buf0, 80, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(buf0, (4, 4), (4, 1
            ), 16), out=buf1)
        buf2 = empty_strided_cuda((5, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_mul_sub_1[grid(80)](buf1, buf0, buf2, 80, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf3 = buf1
        del buf1
        extern_kernels.mm(primals_1, reinterpret_tensor(buf2, (4, 4), (4, 1
            ), 32), out=buf3)
        buf4 = buf0
        del buf0
        triton_poi_fused_mul_sub_2[grid(80)](buf3, buf2, buf4, 80, XBLOCK=
            128, num_warps=4, num_stages=1)
        del buf2
        buf5 = buf3
        del buf3
        extern_kernels.mm(primals_1, reinterpret_tensor(buf4, (4, 4), (4, 1
            ), 48), out=buf5)
        del primals_1
        buf6 = empty_strided_cuda((5, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_3[grid(320)](buf5, buf4, buf6, 320, XBLOCK=
            256, num_warps=4, num_stages=1)
        del buf4
        del buf5
        buf7 = empty_strided_cuda((20, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf6, (20, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(primals_2, (20, 4, 4), (16, 4, 1), 0),
            out=buf7)
        del primals_2
        buf8 = buf6
        del buf6
        triton_poi_fused_clone_4[grid(320)](primals_3, buf8, 320, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_3
        buf9 = empty_strided_cuda((20, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf7, reinterpret_tensor(buf8, (20, 4, 4), (16, 
            4, 1), 0), out=buf9)
        del buf8
        buf10 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_sum_5[grid(64)](buf9, primals_4, buf10, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del buf9
        del primals_4
    return buf10, reinterpret_tensor(buf7, (20, 4, 4), (16, 1, 4), 0)


class ChebConvNew(nn.Module):
    """
    The ChebNet convolution operation.
    Laplacian is motified for direct-graph

    :param in_c: int, number of input channels.
    :param out_c: int, number of output channels.
    :param K: int, the order of Chebyshev Polynomial.
    """

    def __init__(self, in_c, out_c, K, bias=True, normalize=True):
        super(ChebConvNew, self).__init__()
        self.normalize = normalize
        self.weight = nn.Parameter(torch.FloatTensor(K + 1, 1, in_c, out_c))
        nn.init.xavier_normal_(self.weight)
        if bias:
            self.bias = nn.Parameter(torch.Tensor(1, 1, out_c))
            nn.init.zeros_(self.bias)
        else:
            self.register_parameter('bias', None)
        self.K = K + 1

    def cheb_polynomial(self, laplacian):
        """
        Compute the Chebyshev Polynomial, according to the graph laplacian.

        :param laplacian: the graph laplacian, [B,N, N].
        :return: the multi order Chebyshev laplacian, [K,B, N, N].
        """
        N = laplacian.size(0)
        multi_order_laplacian = torch.zeros([self.K, N, N], device=
            laplacian.device, dtype=torch.float)
        multi_order_laplacian[0] = torch.eye(N, device=laplacian.device,
            dtype=torch.float)
        if self.K == 1:
            return multi_order_laplacian
        else:
            multi_order_laplacian[1] = laplacian
            if self.K == 2:
                return multi_order_laplacian
            else:
                for k in range(2, self.K):
                    multi_order_laplacian[k] = 2 * torch.matmul(laplacian,
                        multi_order_laplacian[k - 1]) - multi_order_laplacian[
                        k - 2]
        return multi_order_laplacian

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