# 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/xk/cxkrchc4zkrin5yn6rzwzqealdfyb3dyy5hynflatgdupvu22s7a.py
# Topologically Sorted Source Nodes: [attn_dense, attn_dense_1, attn_dense_2, zero_vec, gt, adj], Original ATen: [aten.add, aten.mul, aten.leaky_relu, aten.gt, aten.where]
# Source node to ATen node mapping:
#   adj => where_1
#   attn_dense => add
#   attn_dense_1 => mul
#   attn_dense_2 => gt, mul_1, where
#   gt => gt_1
#   zero_vec => full_default
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_1, %permute), kwargs = {})
#   %mul : [num_users=3] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, %primals_5), kwargs = {})
#   %gt : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%mul, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, 4), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %mul, %mul_1), kwargs = {})
#   %full_default : [num_users=2] = call_function[target=torch.ops.aten.full.default](args = ([4, 4], -8999999815811072.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %gt_1 : [num_users=3] = call_function[target=torch.ops.aten.gt.Scalar](args = (%primals_6, 0), kwargs = {})
#   %where_1 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %where, %full_default), kwargs = {})
triton_poi_fused_add_gt_leaky_relu_mul_where_0 = async_compile.triton('triton_poi_fused_add_gt_leaky_relu_mul_where_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: '*fp32', 3: '*fp32', 4: '*i1', 5: '*i1', 6: '*fp32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_gt_leaky_relu_mul_where_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_gt_leaky_relu_mul_where_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    x2 = (xindex // 4)
    x1 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp3 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr3 + (x0), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 * tmp6
    tmp8 = tmp7 > tmp1
    tmp9 = 4.0
    tmp10 = tmp7 * tmp9
    tmp11 = tl.where(tmp8, tmp7, tmp10)
    tmp12 = -8999999815811072.0
    tmp13 = tl.where(tmp2, tmp11, tmp12)
    tl.store(out_ptr0 + (x0), tmp2, xmask)
    tl.store(out_ptr1 + (x0), tmp8, xmask)
    tl.store(out_ptr2 + (x0), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ts/ctscnzvbagjv4t25zui245b3recij5udu7nvujnr5rixcyo7elc6.py
# Topologically Sorted Source Nodes: [attention], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   attention => amax, exp, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%where_1, [1], True), 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_1 = async_compile.triton('triton_poi_fused__softmax_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_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
    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 = 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 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/k6/ck6fz3qsfeqgn5jtm4ugikmu7cwvvlq3jpttijbb5kdniicwtyz6.py
# Topologically Sorted Source Nodes: [attention], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   attention => 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_2 = async_compile.triton('triton_poi_fused__softmax_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=[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_2', '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_2(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')


# kernel path: runs/run_shard_6/inductor_cache/pw/cpweyblhtqit4kfy7wt67jzr3ipxejla322emax4rwol3ziwz2ep.py
# Topologically Sorted Source Nodes: [h_1], Original ATen: [aten.elu]
# Source node to ATen node mapping:
#   h_1 => expm1, gt_2, mul_3, mul_5, where_2
# Graph fragment:
#   %gt_2 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%mm_3, 0), kwargs = {})
#   %mul_3 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_3, 1.0), kwargs = {})
#   %expm1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_3,), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1, 1.0), kwargs = {})
#   %where_2 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %mul_3, %mul_5), kwargs = {})
triton_poi_fused_elu_3 = async_compile.triton('triton_poi_fused_elu_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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_elu_3', '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_elu_3(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
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tl.store(out_ptr0 + (x0), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/cp/ccpysjbepqq5untebqpuwvwo5a3ofzbmyjkroebqstojzqgwm6v2.py
# Topologically Sorted Source Nodes: [zero_vec, attn_dense_3, attn_dense_4, attn_dense_5, adj_1], Original ATen: [aten.mul, aten.add, aten.leaky_relu, aten.where]
# Source node to ATen node mapping:
#   adj_1 => where_4
#   attn_dense_3 => add_1
#   attn_dense_4 => mul_6
#   attn_dense_5 => gt_3, mul_7, where_3
#   zero_vec => full_default
# Graph fragment:
#   %full_default : [num_users=2] = call_function[target=torch.ops.aten.full.default](args = ([4, 4], -8999999815811072.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_5, %permute_1), kwargs = {})
#   %mul_6 : [num_users=3] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, %primals_5), kwargs = {})
#   %gt_3 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%mul_6, 0), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_6, 4), kwargs = {})
#   %where_3 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_3, %mul_6, %mul_7), kwargs = {})
#   %where_4 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %where_3, %full_default), kwargs = {})
triton_poi_fused_add_leaky_relu_mul_where_4 = async_compile.triton('triton_poi_fused_add_leaky_relu_mul_where_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: '*fp32', 3: '*i1', 4: '*i1', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_leaky_relu_mul_where_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_leaky_relu_mul_where_4(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, 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')
    tmp3 = tl.load(in_ptr2 + (x2), xmask)
    tmp7 = tl.load(in_ptr3 + (x2), xmask).to(tl.int1)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 * tmp3
    tmp5 = 0.0
    tmp6 = tmp4 > tmp5
    tmp8 = 4.0
    tmp9 = tmp4 * tmp8
    tmp10 = tl.where(tmp6, tmp4, tmp9)
    tmp11 = -8999999815811072.0
    tmp12 = tl.where(tmp7, tmp10, tmp11)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
    tl.store(out_ptr1 + (x2), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/qb/cqbmbqzro65tgr5aurjot6fuwhxfrgwg6hepcr6pqop7qugzudmw.py
# Topologically Sorted Source Nodes: [h_3, z], Original ATen: [aten.elu, aten.linalg_vector_norm]
# Source node to ATen node mapping:
#   h_3 => expm1_1, gt_5, mul_11, mul_9, where_5
#   z => pow_1, sum_3
# Graph fragment:
#   %gt_5 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%mm_7, 0), kwargs = {})
#   %mul_9 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_7, 1.0), kwargs = {})
#   %expm1_1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_9,), kwargs = {})
#   %mul_11 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_1, 1.0), kwargs = {})
#   %where_5 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_5, %mul_9, %mul_11), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%where_5, 2), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [1], True), kwargs = {})
triton_poi_fused_elu_linalg_vector_norm_5 = async_compile.triton('triton_poi_fused_elu_linalg_vector_norm_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=[4], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_elu_linalg_vector_norm_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_elu_linalg_vector_norm_5(in_ptr0, 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')
    tmp9 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tmp8 = tmp7 * tmp7
    tmp10 = tmp9 > tmp1
    tmp11 = tmp9 * tmp3
    tmp12 = libdevice.expm1(tmp11)
    tmp13 = tmp12 * tmp3
    tmp14 = tl.where(tmp10, tmp11, tmp13)
    tmp15 = tmp14 * tmp14
    tmp16 = tmp8 + tmp15
    tmp18 = tmp17 > tmp1
    tmp19 = tmp17 * tmp3
    tmp20 = libdevice.expm1(tmp19)
    tmp21 = tmp20 * tmp3
    tmp22 = tl.where(tmp18, tmp19, tmp21)
    tmp23 = tmp22 * tmp22
    tmp24 = tmp16 + tmp23
    tmp26 = tmp25 > tmp1
    tmp27 = tmp25 * tmp3
    tmp28 = libdevice.expm1(tmp27)
    tmp29 = tmp28 * tmp3
    tmp30 = tl.where(tmp26, tmp27, tmp29)
    tmp31 = tmp30 * tmp30
    tmp32 = tmp24 + tmp31
    tl.store(out_ptr0 + (x0), tmp32, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/dz/cdzir4vbplaxstsukhd4kfwxgbauph4zk52qswlfm7lt7ibtsld6.py
# Topologically Sorted Source Nodes: [h_3, z], Original ATen: [aten.elu, aten.div]
# Source node to ATen node mapping:
#   h_3 => expm1_1, gt_5, mul_11, mul_9, where_5
#   z => div_2
# Graph fragment:
#   %gt_5 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%mm_7, 0), kwargs = {})
#   %mul_9 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_7, 1.0), kwargs = {})
#   %expm1_1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_9,), kwargs = {})
#   %mul_11 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_1, 1.0), kwargs = {})
#   %where_5 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_5, %mul_9, %mul_11), kwargs = {})
#   %div_2 : [num_users=3] = call_function[target=torch.ops.aten.div.Tensor](args = (%where_5, %expand), kwargs = {})
triton_poi_fused_div_elu_6 = async_compile.triton('triton_poi_fused_div_elu_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=[16], 
    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_div_elu_6', '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_div_elu_6(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
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp8 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tmp9 = libdevice.sqrt(tmp8)
    tmp10 = 1e-12
    tmp11 = triton_helpers.maximum(tmp9, tmp10)
    tmp12 = tmp7 / tmp11
    tl.store(out_ptr0 + (x2), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ql/cql4yda3dgxho4izqrhql6enjwpi46thrgvr3hu37ny5tadowl35.py
# Topologically Sorted Source Nodes: [A_pred], Original ATen: [aten.sigmoid]
# Source node to ATen node mapping:
#   A_pred => sigmoid
# Graph fragment:
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%mm_8,), kwargs = {})
triton_poi_fused_sigmoid_7 = async_compile.triton('triton_poi_fused_sigmoid_7', '''
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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sigmoid_7', 'mutated_arg_names': ['in_out_ptr0'], '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_sigmoid_7(in_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_out_ptr0 + (x0), xmask)
    tmp1 = tl.sigmoid(tmp0)
    tl.store(in_out_ptr0 + (x0), tmp1, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 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, 4), (4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4, 1), (1, 1))
    assert_size_stride(primals_9, (4, 1), (1, 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: [h], 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_neighs], Original ATen: [aten.mm]
        extern_kernels.mm(buf0, primals_4, out=buf2)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attn_dense, attn_dense_1, attn_dense_2, zero_vec, gt, adj], Original ATen: [aten.add, aten.mul, aten.leaky_relu, aten.gt, aten.where]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_gt_leaky_relu_mul_where_0.run(primals_6, buf1, buf2, primals_5, buf4, buf3, buf5, 16, grid=grid(16), stream=stream0)
        del primals_6
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attention], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_1.run(buf5, buf6, 16, grid=grid(16), stream=stream0)
        buf7 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [attention], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_2.run(buf6, buf7, 16, grid=grid(16), stream=stream0)
        buf8 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [h_prime], Original ATen: [aten.mm]
        extern_kernels.mm(buf7, buf0, out=buf8)
        buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_1], Original ATen: [aten.elu]
        triton_poi_fused_elu_3.run(buf8, buf9, 16, grid=grid(16), stream=stream0)
        buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_2], Original ATen: [aten.mm]
        extern_kernels.mm(buf9, primals_7, out=buf10)
        buf11 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [attn_for_self_1], Original ATen: [aten.mm]
        extern_kernels.mm(buf10, primals_8, out=buf11)
        buf12 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [attn_for_neighs_1], Original ATen: [aten.mm]
        extern_kernels.mm(buf10, primals_9, out=buf12)
        buf13 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        buf14 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [zero_vec, attn_dense_3, attn_dense_4, attn_dense_5, adj_1], Original ATen: [aten.mul, aten.add, aten.leaky_relu, aten.where]
        triton_poi_fused_add_leaky_relu_mul_where_4.run(buf11, buf12, primals_5, buf4, buf13, buf14, 16, grid=grid(16), stream=stream0)
        del buf11
        buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attention_1], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_1.run(buf14, buf15, 16, grid=grid(16), stream=stream0)
        buf16 = buf14; del buf14  # reuse
        # Topologically Sorted Source Nodes: [attention_1], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_2.run(buf15, buf16, 16, grid=grid(16), stream=stream0)
        buf17 = buf15; del buf15  # reuse
        # Topologically Sorted Source Nodes: [h_prime_1], Original ATen: [aten.mm]
        extern_kernels.mm(buf16, buf10, out=buf17)
        buf18 = reinterpret_tensor(buf12, (4, 1), (1, 4), 0); del buf12  # reuse
        # Topologically Sorted Source Nodes: [h_3, z], Original ATen: [aten.elu, aten.linalg_vector_norm]
        triton_poi_fused_elu_linalg_vector_norm_5.run(buf17, buf18, 4, grid=grid(4), stream=stream0)
        buf19 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_3, z], Original ATen: [aten.elu, aten.div]
        triton_poi_fused_div_elu_6.run(buf17, buf18, buf19, 16, grid=grid(16), stream=stream0)
        del buf18
        buf20 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul_2], Original ATen: [aten.mm]
        extern_kernels.mm(buf19, reinterpret_tensor(buf19, (4, 4), (1, 4), 0), out=buf20)
        buf21 = buf20; del buf20  # reuse
        # Topologically Sorted Source Nodes: [A_pred], Original ATen: [aten.sigmoid]
        triton_poi_fused_sigmoid_7.run(buf21, 16, grid=grid(16), stream=stream0)
    return (buf21, buf19, primals_5, buf3, buf4, buf7, buf8, buf13, buf16, buf17, buf19, buf21, reinterpret_tensor(buf10, (4, 4), (1, 4), 0), reinterpret_tensor(primals_9, (1, 4), (1, 1), 0), reinterpret_tensor(primals_8, (1, 4), (1, 1), 0), reinterpret_tensor(buf9, (4, 4), (1, 4), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), 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, 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, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9])
    return print_performance(fn, times=times, repeat=repeat)


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

import torch
import torch.nn as nn
import torch.nn.functional as F


class GATLayer(nn.Module):
    """
    Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
    """

    def __init__(self, in_features, out_features, alpha=0.2):
        super(GATLayer, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.alpha = alpha
        self.W = nn.Parameter(torch.zeros(size=(in_features, out_features)))
        nn.init.xavier_uniform_(self.W.data, gain=1.414)
        self.a_self = nn.Parameter(torch.zeros(size=(out_features, 1)))
        nn.init.xavier_uniform_(self.a_self.data, gain=1.414)
        self.a_neighs = nn.Parameter(torch.zeros(size=(out_features, 1)))
        nn.init.xavier_uniform_(self.a_neighs.data, gain=1.414)
        self.leakyrelu = nn.LeakyReLU(self.alpha)

    def forward(self, input, adj, M, concat=True):
        h = torch.mm(input, self.W)
        attn_for_self = torch.mm(h, self.a_self)
        attn_for_neighs = torch.mm(h, self.a_neighs)
        attn_dense = attn_for_self + torch.transpose(attn_for_neighs, 0, 1)
        attn_dense = torch.mul(attn_dense, M)
        attn_dense = self.leakyrelu(attn_dense)
        zero_vec = -9000000000000000.0 * torch.ones_like(adj)
        adj = torch.where(adj > 0, attn_dense, zero_vec)
        attention = F.softmax(adj, dim=1)
        h_prime = torch.matmul(attention, h)
        if concat:
            return F.elu(h_prime)
        else:
            return h_prime

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


class GAT(nn.Module):

    def __init__(self, num_features, hidden_size, embedding_size, alpha):
        super(GAT, self).__init__()
        self.hidden_size = hidden_size
        self.embedding_size = embedding_size
        self.alpha = alpha
        self.conv1 = GATLayer(num_features, hidden_size, alpha)
        self.conv2 = GATLayer(hidden_size, embedding_size, alpha)

    def forward(self, x, adj, M):
        h = self.conv1(x, adj, M)
        h = self.conv2(h, adj, M)
        z = F.normalize(h, p=2, dim=1)
        A_pred = self.dot_product_decode(z)
        return A_pred, z

    def dot_product_decode(self, Z):
        A_pred = torch.sigmoid(torch.matmul(Z, Z.t()))
        return A_pred


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


def get_init_inputs():
    return [[], {'num_features': 4, 'hidden_size': 4, 'embedding_size': 4,
        'alpha': 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 libdevice, math as tl_math
import torch.nn as nn
import torch.nn.functional as F
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_gt_leaky_relu_mul_where_0(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    x2 = xindex // 4
    x1 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp3 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr3 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 * tmp6
    tmp8 = tmp7 > tmp1
    tmp9 = 4.0
    tmp10 = tmp7 * tmp9
    tmp11 = tl.where(tmp8, tmp7, tmp10)
    tmp12 = -8999999815811072.0
    tmp13 = tl.where(tmp2, tmp11, tmp12)
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr1 + x0, tmp8, xmask)
    tl.store(out_ptr2 + x0, tmp13, xmask)


@triton.jit
def triton_poi_fused__softmax_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
    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 = 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 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_2(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)


@triton.jit
def triton_poi_fused_elu_3(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
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tl.store(out_ptr0 + x0, tmp7, xmask)


@triton.jit
def triton_poi_fused_add_leaky_relu_mul_where_4(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, out_ptr0, out_ptr1, 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')
    tmp3 = tl.load(in_ptr2 + x2, xmask)
    tmp7 = tl.load(in_ptr3 + x2, xmask).to(tl.int1)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 * tmp3
    tmp5 = 0.0
    tmp6 = tmp4 > tmp5
    tmp8 = 4.0
    tmp9 = tmp4 * tmp8
    tmp10 = tl.where(tmp6, tmp4, tmp9)
    tmp11 = -8999999815811072.0
    tmp12 = tl.where(tmp7, tmp10, tmp11)
    tl.store(out_ptr0 + x2, tmp6, xmask)
    tl.store(out_ptr1 + x2, tmp12, xmask)


@triton.jit
def triton_poi_fused_elu_linalg_vector_norm_5(in_ptr0, 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')
    tmp9 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp25 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tmp8 = tmp7 * tmp7
    tmp10 = tmp9 > tmp1
    tmp11 = tmp9 * tmp3
    tmp12 = libdevice.expm1(tmp11)
    tmp13 = tmp12 * tmp3
    tmp14 = tl.where(tmp10, tmp11, tmp13)
    tmp15 = tmp14 * tmp14
    tmp16 = tmp8 + tmp15
    tmp18 = tmp17 > tmp1
    tmp19 = tmp17 * tmp3
    tmp20 = libdevice.expm1(tmp19)
    tmp21 = tmp20 * tmp3
    tmp22 = tl.where(tmp18, tmp19, tmp21)
    tmp23 = tmp22 * tmp22
    tmp24 = tmp16 + tmp23
    tmp26 = tmp25 > tmp1
    tmp27 = tmp25 * tmp3
    tmp28 = libdevice.expm1(tmp27)
    tmp29 = tmp28 * tmp3
    tmp30 = tl.where(tmp26, tmp27, tmp29)
    tmp31 = tmp30 * tmp30
    tmp32 = tmp24 + tmp31
    tl.store(out_ptr0 + x0, tmp32, xmask)


@triton.jit
def triton_poi_fused_div_elu_6(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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp8 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tmp9 = libdevice.sqrt(tmp8)
    tmp10 = 1e-12
    tmp11 = triton_helpers.maximum(tmp9, tmp10)
    tmp12 = tmp7 / tmp11
    tl.store(out_ptr0 + x2, tmp12, xmask)


@triton.jit
def triton_poi_fused_sigmoid_7(in_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_out_ptr0 + x0, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tl.store(in_out_ptr0 + x0, tmp1, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 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, 4), (4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4, 1), (1, 1))
    assert_size_stride(primals_9, (4, 1), (1, 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)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_gt_leaky_relu_mul_where_0[grid(16)](primals_6,
            buf1, buf2, primals_5, buf4, buf3, buf5, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_6
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(16)](buf5, buf6, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf7 = buf5
        del buf5
        triton_poi_fused__softmax_2[grid(16)](buf6, buf7, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf8 = buf6
        del buf6
        extern_kernels.mm(buf7, buf0, out=buf8)
        buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_elu_3[grid(16)](buf8, buf9, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf9, primals_7, out=buf10)
        buf11 = buf2
        del buf2
        extern_kernels.mm(buf10, primals_8, out=buf11)
        buf12 = buf1
        del buf1
        extern_kernels.mm(buf10, primals_9, out=buf12)
        buf13 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        buf14 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_leaky_relu_mul_where_4[grid(16)](buf11, buf12,
            primals_5, buf4, buf13, buf14, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del buf11
        buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(16)](buf14, buf15, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf16 = buf14
        del buf14
        triton_poi_fused__softmax_2[grid(16)](buf15, buf16, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf17 = buf15
        del buf15
        extern_kernels.mm(buf16, buf10, out=buf17)
        buf18 = reinterpret_tensor(buf12, (4, 1), (1, 4), 0)
        del buf12
        triton_poi_fused_elu_linalg_vector_norm_5[grid(4)](buf17, buf18, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
        buf19 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_div_elu_6[grid(16)](buf17, buf18, buf19, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del buf18
        buf20 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf19, reinterpret_tensor(buf19, (4, 4), (1, 4), 
            0), out=buf20)
        buf21 = buf20
        del buf20
        triton_poi_fused_sigmoid_7[grid(16)](buf21, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
    return (buf21, buf19, primals_5, buf3, buf4, buf7, buf8, buf13, buf16,
        buf17, buf19, buf21, reinterpret_tensor(buf10, (4, 4), (1, 4), 0),
        reinterpret_tensor(primals_9, (1, 4), (1, 1), 0),
        reinterpret_tensor(primals_8, (1, 4), (1, 1), 0),
        reinterpret_tensor(buf9, (4, 4), (1, 4), 0), reinterpret_tensor(
        primals_7, (4, 4), (1, 4), 0), 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 GATLayer(nn.Module):
    """
    Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
    """

    def __init__(self, in_features, out_features, alpha=0.2):
        super(GATLayer, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.alpha = alpha
        self.W = nn.Parameter(torch.zeros(size=(in_features, out_features)))
        nn.init.xavier_uniform_(self.W.data, gain=1.414)
        self.a_self = nn.Parameter(torch.zeros(size=(out_features, 1)))
        nn.init.xavier_uniform_(self.a_self.data, gain=1.414)
        self.a_neighs = nn.Parameter(torch.zeros(size=(out_features, 1)))
        nn.init.xavier_uniform_(self.a_neighs.data, gain=1.414)
        self.leakyrelu = nn.LeakyReLU(self.alpha)

    def forward(self, input, adj, M, concat=True):
        h = torch.mm(input, self.W)
        attn_for_self = torch.mm(h, self.a_self)
        attn_for_neighs = torch.mm(h, self.a_neighs)
        attn_dense = attn_for_self + torch.transpose(attn_for_neighs, 0, 1)
        attn_dense = torch.mul(attn_dense, M)
        attn_dense = self.leakyrelu(attn_dense)
        zero_vec = -9000000000000000.0 * torch.ones_like(adj)
        adj = torch.where(adj > 0, attn_dense, zero_vec)
        attention = F.softmax(adj, dim=1)
        h_prime = torch.matmul(attention, h)
        if concat:
            return F.elu(h_prime)
        else:
            return h_prime

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


class GATNew(nn.Module):

    def __init__(self, num_features, hidden_size, embedding_size, alpha):
        super(GATNew, self).__init__()
        self.hidden_size = hidden_size
        self.embedding_size = embedding_size
        self.alpha = alpha
        self.conv1 = GATLayer(num_features, hidden_size, alpha)
        self.conv2 = GATLayer(hidden_size, embedding_size, alpha)

    def dot_product_decode(self, Z):
        A_pred = torch.sigmoid(torch.matmul(Z, Z.t()))
        return A_pred

    def forward(self, input_0, input_1, input_2):
        primals_1 = self.conv1.W
        primals_3 = self.conv1.a_self
        primals_4 = self.conv1.a_neighs
        primals_2 = self.conv2.W
        primals_8 = self.conv2.a_self
        primals_9 = self.conv2.a_neighs
        primals_5 = input_0
        primals_6 = input_1
        primals_7 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0], output[1]
