# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_6/inductor_cache/rn/crnvqrppxb3ocltksxzamskmx3mpvscqlqbanvhmgkmj5b53kfuf.py
# Topologically Sorted Source Nodes: [attn, attn_1], Original ATen: [aten.add, aten.leaky_relu]
# Source node to ATen node mapping:
#   attn => add
#   attn_1 => gt
# Graph fragment:
#   %add : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_1, %permute), kwargs = {})
#   %gt : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%add, 0), kwargs = {})
triton_poi_fused_add_leaky_relu_0 = async_compile.triton('triton_poi_fused_add_leaky_relu_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_leaky_relu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_leaky_relu_0(in_ptr0, in_ptr1, 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)
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tl.store(out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/k2/ck2qvrsga6qzh3n4zrcqhgn3gcw55gg5hx55rqebdhhoptcty66e.py
# Topologically Sorted Source Nodes: [gt], Original ATen: [aten.gt]
# Source node to ATen node mapping:
#   gt => gt_1
# Graph fragment:
#   %gt_1 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%primals_5, 0), kwargs = {})
triton_poi_fused_gt_1 = async_compile.triton('triton_poi_fused_gt_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: '*i1', 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_gt_1', '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_gt_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tl.store(out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ip/cipj6puxrifqofswgl2afqxxhkod6xgj5lswwkb4j2i3nu5mzghn.py
# Topologically Sorted Source Nodes: [attn, attn_1, mask, attn_2, attn_3], Original ATen: [aten.add, aten.leaky_relu, aten.mul, aten.where, aten._softmax]
# Source node to ATen node mapping:
#   attn => add
#   attn_1 => mul, where
#   attn_2 => where_1
#   attn_3 => amax
#   mask => full_default
# Graph fragment:
#   %add : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_1, %permute), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 0.2), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %add, %mul), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 4], -10000000000.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where_1 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %where, %full_default), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%where_1, [-1], True), kwargs = {})
triton_poi_fused__softmax_add_leaky_relu_mul_where_2 = async_compile.triton('triton_poi_fused__softmax_add_leaky_relu_mul_where_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: '*i1', 1: '*i1', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_add_leaky_relu_mul_where_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 13, '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__softmax_add_leaky_relu_mul_where_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, 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_ptr0 + (4*x0), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp1 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp2 = tl.load(in_ptr2 + (x0), xmask)
    tmp3 = tl.load(in_ptr3 + (0))
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
    tmp11 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp12 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp13 = tl.load(in_ptr3 + (1))
    tmp14 = tl.broadcast_to(tmp13, [XBLOCK])
    tmp20 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp21 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp22 = tl.load(in_ptr3 + (2))
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK])
    tmp29 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp30 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last').to(tl.int1)
    tmp31 = tl.load(in_ptr3 + (3))
    tmp32 = tl.broadcast_to(tmp31, [XBLOCK])
    tmp5 = tmp2 + tmp4
    tmp6 = 0.2
    tmp7 = tmp5 * tmp6
    tmp8 = tl.where(tmp1, tmp5, tmp7)
    tmp9 = -10000000000.0
    tmp10 = tl.where(tmp0, tmp8, tmp9)
    tmp15 = tmp2 + tmp14
    tmp16 = tmp15 * tmp6
    tmp17 = tl.where(tmp12, tmp15, tmp16)
    tmp18 = tl.where(tmp11, tmp17, tmp9)
    tmp19 = triton_helpers.maximum(tmp10, tmp18)
    tmp24 = tmp2 + tmp23
    tmp25 = tmp24 * tmp6
    tmp26 = tl.where(tmp21, tmp24, tmp25)
    tmp27 = tl.where(tmp20, tmp26, tmp9)
    tmp28 = triton_helpers.maximum(tmp19, tmp27)
    tmp33 = tmp2 + tmp32
    tmp34 = tmp33 * tmp6
    tmp35 = tl.where(tmp30, tmp33, tmp34)
    tmp36 = tl.where(tmp29, tmp35, tmp9)
    tmp37 = triton_helpers.maximum(tmp28, tmp36)
    tl.store(out_ptr0 + (x0), tmp37, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/4j/c4jns64k5hrxwhu2va4lcdafbf7obziv7s3m5z66dtfi2uvbgbt4.py
# Topologically Sorted Source Nodes: [attn, attn_1, mask, attn_2, attn_3], Original ATen: [aten.add, aten.leaky_relu, aten.mul, aten.where, aten._softmax]
# Source node to ATen node mapping:
#   attn => add
#   attn_1 => mul, where
#   attn_2 => where_1
#   attn_3 => exp, sub
#   mask => full_default
# Graph fragment:
#   %add : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_1, %permute), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 0.2), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %add, %mul), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 4], -10000000000.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where_1 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %where, %full_default), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where_1, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
triton_poi_fused__softmax_add_leaky_relu_mul_where_3 = async_compile.triton('triton_poi_fused__softmax_add_leaky_relu_mul_where_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: '*i1', 1: '*i1', 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__softmax_add_leaky_relu_mul_where_3', '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__softmax_add_leaky_relu_mul_where_3(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)
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x2), xmask).to(tl.int1)
    tmp1 = tl.load(in_ptr1 + (x2), xmask).to(tl.int1)
    tmp2 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr3 + (x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr4 + (x1), xmask, eviction_policy='evict_last')
    tmp4 = tmp2 + tmp3
    tmp5 = 0.2
    tmp6 = tmp4 * tmp5
    tmp7 = tl.where(tmp1, tmp4, tmp6)
    tmp8 = -10000000000.0
    tmp9 = tl.where(tmp0, tmp7, tmp8)
    tmp11 = tmp9 - tmp10
    tmp12 = tl_math.exp(tmp11)
    tl.store(out_ptr0 + (x2), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/rr/crrmj7r54x5uk325xkhuskxp4m5prz3fpx53yc2st4o5pwbhq32p.py
# Topologically Sorted Source Nodes: [attn_3], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   attn_3 => 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=[16], 
    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__softmax_4', '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__softmax_4(in_ptr0, 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_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x2), tmp8, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 1), (1, 1))
    assert_size_stride(primals_4, (4, 1), (1, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [features], Original ATen: [aten.mm]
        extern_kernels.mm(primals_2, primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attn_for_self], Original ATen: [aten.mm]
        extern_kernels.mm(buf0, primals_3, out=buf1)
        buf2 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attn_for_neigh], Original ATen: [aten.mm]
        extern_kernels.mm(buf0, primals_4, out=buf2)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [attn, attn_1], Original ATen: [aten.add, aten.leaky_relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_leaky_relu_0.run(buf1, buf2, buf3, 16, grid=grid(16), stream=stream0)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [gt], Original ATen: [aten.gt]
        triton_poi_fused_gt_1.run(primals_5, buf4, 16, grid=grid(16), stream=stream0)
        del primals_5
        buf5 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [attn, attn_1, mask, attn_2, attn_3], Original ATen: [aten.add, aten.leaky_relu, aten.mul, aten.where, aten._softmax]
        triton_poi_fused__softmax_add_leaky_relu_mul_where_2.run(buf4, buf3, buf1, buf2, buf5, 4, grid=grid(4), stream=stream0)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attn, attn_1, mask, attn_2, attn_3], Original ATen: [aten.add, aten.leaky_relu, aten.mul, aten.where, aten._softmax]
        triton_poi_fused__softmax_add_leaky_relu_mul_where_3.run(buf4, buf3, buf1, buf2, buf5, buf6, 16, grid=grid(16), stream=stream0)
        del buf1
        del buf2
        del buf5
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attn_3], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_4.run(buf6, buf7, 16, grid=grid(16), stream=stream0)
        buf8 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.addmm(primals_6, buf7, buf0, alpha=1, beta=1, out=buf8)
        del primals_6
    return (buf8, buf3, buf4, buf7, reinterpret_tensor(buf0, (4, 4), (1, 4), 0), reinterpret_tensor(primals_4, (1, 4), (1, 1), 0), reinterpret_tensor(primals_3, (1, 4), (1, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
    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 torch
import torch.nn.functional as F
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter


class GATLayer(Module):

    def __init__(self, input_channel, output_channel, use_bias=True):
        super(GATLayer, self).__init__()
        self.use_bias = use_bias
        self.input_channel = input_channel
        self.output_channel = output_channel
        self.kernel = Parameter(torch.FloatTensor(input_channel,
            output_channel))
        torch.nn.init.xavier_uniform_(self.kernel, gain=1.414)
        if self.use_bias:
            self.bias = Parameter(torch.FloatTensor(output_channel))
            torch.nn.init.constant_(self.bias, 0)
        self.attn_kernel_self = Parameter(torch.FloatTensor(output_channel, 1))
        torch.nn.init.xavier_uniform_(self.attn_kernel_self, gain=1.414)
        self.attn_kernel_neighs = Parameter(torch.FloatTensor(
            output_channel, 1))
        torch.nn.init.xavier_uniform_(self.attn_kernel_neighs, gain=1.414)

    def forward(self, X, adj):
        features = torch.mm(X, self.kernel)
        attn_for_self = torch.mm(features, self.attn_kernel_self)
        attn_for_neigh = torch.mm(features, self.attn_kernel_neighs)
        attn = attn_for_self + attn_for_neigh.t()
        attn = F.leaky_relu(attn, negative_slope=0.2)
        mask = -10000000000.0 * torch.ones_like(attn)
        attn = torch.where(adj > 0, attn, mask)
        attn = torch.softmax(attn, dim=-1)
        attn = F.dropout(attn, p=0.5, training=self.training)
        features = F.dropout(features, p=0.5, training=self.training)
        node_features = torch.mm(attn, features)
        if self.use_bias:
            node_features = node_features + self.bias
        return node_features


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


def get_init_inputs():
    return [[], {'input_channel': 4, 'output_channel': 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn import Module
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter
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_leaky_relu_0(in_ptr0, in_ptr1, 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
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tl.store(out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_gt_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused__softmax_add_leaky_relu_mul_where_2(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, out_ptr0, 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_ptr0 + 4 * x0, xmask, eviction_policy='evict_last').to(tl
        .int1)
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last').to(tl
        .int1)
    tmp2 = tl.load(in_ptr2 + x0, xmask)
    tmp3 = tl.load(in_ptr3 + 0)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
    tmp11 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp12 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp13 = tl.load(in_ptr3 + 1)
    tmp14 = tl.broadcast_to(tmp13, [XBLOCK])
    tmp20 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp21 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp22 = tl.load(in_ptr3 + 2)
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK])
    tmp29 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp30 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp31 = tl.load(in_ptr3 + 3)
    tmp32 = tl.broadcast_to(tmp31, [XBLOCK])
    tmp5 = tmp2 + tmp4
    tmp6 = 0.2
    tmp7 = tmp5 * tmp6
    tmp8 = tl.where(tmp1, tmp5, tmp7)
    tmp9 = -10000000000.0
    tmp10 = tl.where(tmp0, tmp8, tmp9)
    tmp15 = tmp2 + tmp14
    tmp16 = tmp15 * tmp6
    tmp17 = tl.where(tmp12, tmp15, tmp16)
    tmp18 = tl.where(tmp11, tmp17, tmp9)
    tmp19 = triton_helpers.maximum(tmp10, tmp18)
    tmp24 = tmp2 + tmp23
    tmp25 = tmp24 * tmp6
    tmp26 = tl.where(tmp21, tmp24, tmp25)
    tmp27 = tl.where(tmp20, tmp26, tmp9)
    tmp28 = triton_helpers.maximum(tmp19, tmp27)
    tmp33 = tmp2 + tmp32
    tmp34 = tmp33 * tmp6
    tmp35 = tl.where(tmp30, tmp33, tmp34)
    tmp36 = tl.where(tmp29, tmp35, tmp9)
    tmp37 = triton_helpers.maximum(tmp28, tmp36)
    tl.store(out_ptr0 + x0, tmp37, xmask)


@triton.jit
def triton_poi_fused__softmax_add_leaky_relu_mul_where_3(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
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask).to(tl.int1)
    tmp1 = tl.load(in_ptr1 + x2, xmask).to(tl.int1)
    tmp2 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp4 = tmp2 + tmp3
    tmp5 = 0.2
    tmp6 = tmp4 * tmp5
    tmp7 = tl.where(tmp1, tmp4, tmp6)
    tmp8 = -10000000000.0
    tmp9 = tl.where(tmp0, tmp7, tmp8)
    tmp11 = tmp9 - tmp10
    tmp12 = tl_math.exp(tmp11)
    tl.store(out_ptr0 + x2, tmp12, xmask)


@triton.jit
def triton_poi_fused__softmax_4(in_ptr0, 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_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 1), (1, 1))
    assert_size_stride(primals_4, (4, 1), (1, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_2, primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf0, primals_3, out=buf1)
        buf2 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf0, primals_4, out=buf2)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_add_leaky_relu_0[grid(16)](buf1, buf2, buf3, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_gt_1[grid(16)](primals_5, buf4, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_5
        buf5 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        triton_poi_fused__softmax_add_leaky_relu_mul_where_2[grid(4)](buf4,
            buf3, buf1, buf2, buf5, 4, XBLOCK=4, num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__softmax_add_leaky_relu_mul_where_3[grid(16)](buf4,
            buf3, buf1, buf2, buf5, buf6, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del buf1
        del buf2
        del buf5
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__softmax_4[grid(16)](buf6, buf7, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf8 = buf6
        del buf6
        extern_kernels.addmm(primals_6, buf7, buf0, alpha=1, beta=1, out=buf8)
        del primals_6
    return buf8, buf3, buf4, buf7, reinterpret_tensor(buf0, (4, 4), (1, 4), 0
        ), reinterpret_tensor(primals_4, (1, 4), (1, 1), 0
        ), reinterpret_tensor(primals_3, (1, 4), (1, 1), 0
        ), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0)


class GATLayerNew(Module):

    def __init__(self, input_channel, output_channel, use_bias=True):
        super(GATLayerNew, self).__init__()
        self.use_bias = use_bias
        self.input_channel = input_channel
        self.output_channel = output_channel
        self.kernel = Parameter(torch.FloatTensor(input_channel,
            output_channel))
        torch.nn.init.xavier_uniform_(self.kernel, gain=1.414)
        if self.use_bias:
            self.bias = Parameter(torch.FloatTensor(output_channel))
            torch.nn.init.constant_(self.bias, 0)
        self.attn_kernel_self = Parameter(torch.FloatTensor(output_channel, 1))
        torch.nn.init.xavier_uniform_(self.attn_kernel_self, gain=1.414)
        self.attn_kernel_neighs = Parameter(torch.FloatTensor(
            output_channel, 1))
        torch.nn.init.xavier_uniform_(self.attn_kernel_neighs, gain=1.414)

    def forward(self, input_0, input_1):
        primals_1 = self.kernel
        primals_6 = self.bias
        primals_3 = self.attn_kernel_self
        primals_4 = self.attn_kernel_neighs
        primals_2 = input_0
        primals_5 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
